# MBI Deep Dives > Investment Research Public Ghost content for AI and LLM tooling. This file includes a bounded export of public pages first, then recent public posts. Append `.md` to any post or page URL to get the content in Markdown (for example, `/example-post.md`). ## Pages ### Start Here URL: https://www.mbi-deepdives.com/home/ Last updated: 2025-07-15T03:08:26.000Z ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/wp-content/uploads/2020/09/pexels-photomix-company-106344-1-1024x684.jpg) Welcome to MBI Deep Dives. I publish one in-depth research on a publicly listed company every month. Subscribers can download the pdf of the deep dives as well as detailed financial models, all of which can be found [here](https://www.mbi-deepdives.com/models/). Here are some sample deep dives you can explore before [subscribing](#/portal/signup) to my research: [Uber](https://www.mbi-deepdives.com/deep-dive-on-uber/), [Etsy](https://www.mbi-deepdives.com/etsy-a-handmade-giant-in-the-passion-economy/), [Lululemon](https://www.mbi-deepdives.com/lulu/), [Roku](https://www.mbi-deepdives.com/roku/), [Meta](https://www.mbi-deepdives.com/meta/). The next deep dive will be published on July 20, 2022. You may also consider reading MBI's [research process](https://www.mbi-deepdives.com/my-research-process/), [approach to valuation](https://www.mbi-deepdives.com/my-valuation-approach/), and latest [annual letter](https://www.mbi-deepdives.com/2021/) to subscribers. To know more about MBI, click [here](https://www.mbi-deepdives.com/about-mbi/). You can contact MBI at rezwan@mbi-deepdives.com. Follow MBI on [twitter](https://twitter.com/borrowed%5Fideas?ref=mbi-deepdives.com) or [LinkedIn](https://www.linkedin.com/in/abdullah-al-rezwan/?ref=mbi-deepdives.com). Once [subscribed](#/portal/signup), you can access the deep dives of following companies: Uber, Etsy, Lululemon, Angi, Ansys, Autodesk, Copart, Shopify, Otis, CrowdStrike, Roku, Boeing, Square, Trupanion, RH, Spotify, Pinterest, Twilio, Constellation Software, Ethereum, and Adyen. You can download PDF of the deep dives as well as excel models for each [here](https://www.mbi-deepdives.com/models/). For the latest posts, go [here](https://www.mbi-deepdives.com/latest-posts/). You can find our privacy policy [here](https://www.mbi-deepdives.com/privacy-policy/). ### Deep Dives URL: https://www.mbi-deepdives.com/models/ Last updated: 2026-07-28T14:09:58.000Z *Each deep dive will be accompanied by a detailed excel model every month. You can find the models on this page. Download the model to play around with the assumptions to build your own narrative. Make sure you understand the narrative well before you get your hands on the excel file. Anyone can make a DCF sing and it can potentially become a tool for confirming your biases. I have kept the model relatively simple so that people with even basic understanding of Excel can find it easy to navigate.* **Link of the full Deep Dives (chronological order)** 1. Sep'20: [UBER](https://www.mbi-deepdives.com/deep-dive-on-uber/) 2. Oct'20: [ETSY](https://www.mbi-deepdives.com/etsy-a-handmade-giant-in-the-passion-economy/) (Update: [Here](https://www.mbi-deepdives.com/etsy/)) 3. Nov'20: [LULU](https://www.mbi-deepdives.com/lulu/) (Update: [Here](https://www.mbi-deepdives.com/lulu2/)) 4. Dec'20: [ANGI](https://www.mbi-deepdives.com/angi/) 5. Jan' 21: [ANSS](https://www.mbi-deepdives.com/anss/) 6. Feb'21: [CPRT](https://www.mbi-deepdives.com/cprt/) 7. Mar'21: [ADSK](https://www.mbi-deepdives.com/adsk/) 8. Apr'21: [SHOP](https://www.mbi-deepdives.com/shop/) 9. May'21: [OTIS](https://www.mbi-deepdives.com/otis/) 10. Jun'21: [CRWD](https://www.mbi-deepdives.com/crwd/) 11. Jul'21: [ROKU](https://www.mbi-deepdives.com/roku/) 12. Aug'21: [BA](https://www.mbi-deepdives.com/ba/) 13. Sep'21: [SQ](https://www.mbi-deepdives.com/sq/) 14. Oct'21: [TRUP](https://www.mbi-deepdives.com/trup/) 15. Nov'21: [RH](https://www.mbi-deepdives.com/rh/) 16. Dec'21: [SPOT](https://www.mbi-deepdives.com/spot/) 17. Jan'22: [PINS](https://www.mbi-deepdives.com/pins/) 18. Feb'22: [TWLO](https://www.mbi-deepdives.com/twlo/) 19. Mar'22: [CSU](https://www.mbi-deepdives.com/csu/) 20. Apr'22: [ETH](https://www.mbi-deepdives.com/eth/) 21. May'22: [ADYEY](https://www.mbi-deepdives.com/adyey/) 22. Jun'22: [PYPL](https://www.mbi-deepdives.com/pypl/) 23. Jul'22: [DHR](https://www.mbi-deepdives.com/dhr/) 24. Aug'22: [ADBE](https://www.mbi-deepdives.com/adbe/) (Update: [Here](https://www.mbi-deepdives.com/adbe2/), and [Here](https://www.mbi-deepdives.com/adbe2q23/)) 25. Sep'22: [NET](https://www.mbi-deepdives.com/net/) 26. Oct'22: [ABNB](https://www.mbi-deepdives.com/abnb/) 27. Nov'22: [DDOG](https://www.mbi-deepdives.com/ddog/) 28. Dec'22: [SHW](https://www.mbi-deepdives.com/shw/) 29. Jan'23: [HLT](https://www.mbi-deepdives.com/hlt/) 30. Feb'23: [FAST](https://www.mbi-deepdives.com/fast/) 31. Mar'23: [META](https://www.mbi-deepdives.com/meta2023/) (Update: [2024](https://www.mbi-deepdives.com/meta2024/), [2025](https://www.mbi-deepdives.com/meta2025/), [2026](https://www.mbi-deepdives.com/meta2026/)) 32. Mar'23: [GOOG](https://www.mbi-deepdives.com/goog/) 33. Mar'23: [AMZN](https://www.mbi-deepdives.com/amzn/) (Update: [Here](https://www.mbi-deepdives.com/amzn2023/), and [Here](https://www.mbi-deepdives.com/feedback%5Famzn/), [2024](https://www.mbi-deepdives.com/amzn2024/), [2025](https://www.mbi-deepdives.com/amzn2025/), [2026](https://www.mbi-deepdives.com/amazon-2026-update/)) 34. Apr'23: [MSFT](https://www.mbi-deepdives.com/msft/) 35. May'23: [TYL](https://www.mbi-deepdives.com/tyl/) 36. Jun'23: [BRO](https://www.mbi-deepdives.com/bro/) 37. Jul'23: [CSX](https://www.mbi-deepdives.com/csx/) 38. Aug'23: [DG](https://www.mbi-deepdives.com/dg/) 39. Sep'23: [TSLA](https://www.mbi-deepdives.com/tsla/) 40. Oct'23: [ENPH](https://www.mbi-deepdives.com/enph/) 41. Nov'23: [FLT/CPAY](https://www.mbi-deepdives.com/flt/) 42. Dec'23: [FND](https://www.mbi-deepdives.com/fnd/) 43. Jan'24: [CSGP](https://www.mbi-deepdives.com/csgp/) 44. Feb'24: [APPF](https://www.mbi-deepdives.com/appf/) (Update [here](https://www.mbi-deepdives.com/appf3/)) 45. Mar'24: Semiconductor [Primer](https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/) 46. Apr'24: [TXN](https://www.mbi-deepdives.com/txn/) 47. May'24: [TSM](https://www.mbi-deepdives.com/tsm/) 48. Jun'24: [IQV](https://www.mbi-deepdives.com/iqv/) 49. Jul'24: [DIM](https://www.mbi-deepdives.com/dim/) 50. Aug'24: [ESLOY](https://www.mbi-deepdives.com/esloy/) 51. Sep'24: [VEEV](https://www.mbi-deepdives.com/veev/) 52. Oct'24: [UMG](https://www.mbi-deepdives.com/umg/) 53. Nov'24: [ORCL](https://www.mbi-deepdives.com/orcl/) 54. Dec'24: [MRVI](https://www.mbi-deepdives.com/mrvi/) 55. Jan'25: [IDXX](https://www.mbi-deepdives.com/idxx/) 56. Feb'25: Update on [META](https://www.mbi-deepdives.com/meta2025/) and [AMZN](https://www.mbi-deepdives.com/amzn2025/) 57. Mar'25: [ILMN](https://www.mbi-deepdives.com/ilmn/) 58. Apr'25: [SNPS](https://www.mbi-deepdives.com/snps/) 59. May'25: [PRM](https://www.mbi-deepdives.com/prm/) 60. Jun'25: [APG](https://www.mbi-deepdives.com/apg/) 61. Jul'25: [CGNX](https://www.mbi-deepdives.com/cgnx/) 62. Aug'25: [UNP](https://www.mbi-deepdives.com/unp/) 63. Sep'25: [ASML](https://www.mbi-deepdives.com/asml/) 64. Oct'25: [CART](https://www.mbi-deepdives.com/cart/) 65. Nov'25: [BKNG](https://www.mbi-deepdives.com/bkng/) 66. Jan'26: [FIG](https://www.mbi-deepdives.com/fig/) 67. Mar'26: [RYAN](https://www.mbi-deepdives.com/ryan/) 68. Apr'26: [RBLX](https://www.mbi-deepdives.com/rblx/) 69. May'26: FICO (Part [1](https://www.mbi-deepdives.com/fico1/), [2](https://www.mbi-deepdives.com/fico2/), [3](https://www.mbi-deepdives.com/fico3/), [4](https://www.mbi-deepdives.com/fico4/)) 70. July'26: [DASH](https://www.mbi-deepdives.com/dash/) **If you want to share with a colleague/friend to encourage them to subscribe to my future deep dives, please feel free to do so. Sharing snippets of the deep dive on social media is also totally fine.** _This page is for paying subscribers only._ ### About MBI URL: https://www.mbi-deepdives.com/about-mbi/ Last updated: 2026-09-17T21:51:08.000Z MBI is the abbreviation for “Mostly Borrowed Ideas”, which is my pseudonym on [Twitter](https://twitter.com/borrowed%5Fideas?ref=mbi-deepdives.com). Why “Mostly Borrowed Ideas?” As a generalist, I do not have a background in any particular sector. I enjoy navigating across industries, businesses, and countries to learn, understand, and connect the dots. In any case, most ideas and innovations are [incremental](https://twitter.com/borrowed%5Fideas/status/1292262126246141958?ref=mbi-deepdives.com) in nature. Very few of us are smart enough to come up with truly original or groundbreaking ideas. Although I started writing on twitter under my pseudonym, I think the current and future subscribers deserve to know the person behind “Mostly Borrowed Ideas”. I am Abdullah Al Rezwan. I was born and brought up in Bangladesh. I went to University of Dhaka for my undergrad and majored in Finance. After working three and half years in Equity Research (Sell-side) covering Financial Sector in Bangladesh, I left Bangladesh to pursue my MBA in the US. I graduated within top 10% of the class from Cornell University's two-year MBA program and worked on the buy-side following my graduation. During my time at Cornell, I won two stock pitch competitions and also became finalists in four other competitions. I thoroughly enjoyed my stint as a generalist in the US large cap team at Madison Investments and covered a wide range of companies from UnitedHealth to Amazon and Boeing before US immigration reality paused my career in investment management. As my work authorization expired, I moved to Canada in January, 2021\. Then in early 2022, I was able to return to the US. I am also a CFA and FRM charter holder. Yes, people from South Asian background have this strange fascination with credentials and I humbly succumbed to that stereotype. So with a CFA, FRM, and MBA, I have my fair share of alphabet soup under my belt. Of course, the market does not care about anyone’s credentials. I could add two more certificates and could still be a terrible investor. As I started to think about my future plans following the expiry of work authorization in the US, I realized one of the biggest constraints in my life for last few years had been geography/location. After dealing with immigration related hassles, I wish I didn’t have to be in a particular location to do what I want to do. After some twitter fame, I came up with the idea of starting this website in September, 2020\. I can be anywhere in the world doing what I love to do: read and analyze different businesses. [Subscribe](#/portal/signup) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2026/09/image.png) **What can you expect from MBI Deep Dives?** You should expect one email **everyday** on companies I follow or/and content I find interesting. I also publish one Deep Dive on a publicly listed company every month. I will pick a company that I am curious about. I will spend hours on the company going through [my research process](https://www.mbi-deepdives.com/my-research-process/). I will build financial model to get a better sense of the sensitivity of variables and expectations embedded in the stock price. You can explore my approach to valuation [here](https://www.mbi-deepdives.com/my-valuation-approach/). Finally, I will write a detailed piece on the business. You can find all the past Deep Dives [here](https://www.mbi-deepdives.com/models/). I disclose my portfolio everyday on my daily emails. But if you want to subscribe to my website to receive just hot stock ideas, I will discourage you to subscribe to my work. My objective is to understand, analyze, and write about businesses. In some cases, after spending weeks on the business, I will conclude I am not comfortable with this business, and I want to write to explain why. Sometimes, the business can be great, but the valuation is perhaps too rich to my taste. I do believe errors of omission is far more expensive than errors of commission. One of the reasons I want to write on ideas I am not bullish about is to document my thought process in detail and then build a Bayesian mindset to track these companies. Perhaps I will change my mind at some point as new information comes along. I believe it is impossible to find one great stock every month. So it is possible that in the next 12 months, I may only be “long” in 3 out of 12 stocks I will write about. As an individual investor, I do not want to have a portfolio of 40 stocks. Ideally, my ceiling in terms of number of stocks is 20 and the floor can be as low as 10. If you subscribe, I promise you to provide in-depth research on companies without the typical sell-side biases (e.g. 80-90% “Buy” recommendations). You will also receive the downloadable detailed excel model in which you can change the assumptions to fit your narrative. As a generalist, I constantly feel I do not know enough. If you have expertise on a company I have already written or will write in future, please feel free to share your opinion. If you think I am wrong, you are probably right and don’t hesitate to contact me and leave me feedback. Although I have been investing since 2013, I am deeply aware of the fact that I have not experienced any prolonged recession. It takes years and perhaps decades to evaluate an analyst or investor just how good he/she actually is across the market cycle. I will try my absolute best to produce high quality research and ideas that survive the test of time. But only time will tell. ### Twitter Threads URL: https://www.mbi-deepdives.com/twitter-threads/ Last updated: 2024-06-16T23:02:24.000Z _This page is for paying subscribers only._ ### Latest Posts URL: https://www.mbi-deepdives.com/latest-posts/ Last updated: 2022-08-04T15:09:27.000Z Click **[here](https://www.mbi-deepdives.com/)** ### Thank You URL: https://www.mbi-deepdives.com/thank-you/ Last updated: 2020-11-14T13:29:09.000Z Your subscription has been set up successfully. ### Account URL: https://www.mbi-deepdives.com/account/ Last updated: 2020-11-14T13:29:09.000Z _No content available._ ### Log In URL: https://www.mbi-deepdives.com/login/ Last updated: 2022-08-04T15:07:10.000Z [Log in](https://www.mbi-deepdives.com/#/portal/signup) [New? Subscribe here](#/portal/signup) If you have any problem logging in, please contact rezwan@mbi-deepdives.com ### Privacy Policy URL: https://www.mbi-deepdives.com/privacy-policy-2/ Last updated: 2026-01-21T22:05:12.000Z By signing up, you are providing us with some personal data, including names, addresses, email addresses and payment details. We will not share them with any third party except as is necessary for processing payments. We use MailerLite as our marketing platform for managing your personal information. Learn more about MailerLite's privacy practices [here](https://www.mailerlite.com/legal/privacy-policy?ref=mbi-deepdives.com). We also use MemberPress as our Membership Plugin. Learn more about Memberpress’ privacy practices [here](https://memberpress.com/privacy/?ref=mbi-deepdives.com). To process payments, we use Stripe. Learn more about Stripe’s privacy practices [here](https://stripe.com/en-ca/privacy?ref=mbi-deepdives.com). We hold no right whatsoever to share either your name or your firm/fund’s name in any of our social media posts or promotions. We will ask for written permission before we ever use your or your firm/fund’s name in our promotion. ### Audio URL: https://www.mbi-deepdives.com/audio/ Last updated: 2026-07-28T13:49:14.000Z The audio version of Deep Dives will be uploaded on this page from August, 2022\. _This page is for paying subscribers only._ ### Portfolio URL: https://www.mbi-deepdives.com/portfolio/ Last updated: 2026-08-31T23:55:56.000Z Please note that these are **NOT** my recommendation to buy/sell these securities. The disclosure serves three purposes: a) MBI Deep Dives is essentially my paywalled investing journal. I try to keep track of how my portfolio has evolved over time and as paying subscribers, you can also observe this evolution as well; b) you can quickly see what I personally own in my portfolio so that you can assess my potential biases, and c) if you are reading an old post on the website about a company I used to own but don't own right now, you can now figure out which post to go to in order to understand my rationale for changing my mind. Let me give a couple of examples to help you understand this. You can see below that I used to own Google/Alphabet but the stock stopped appearing on my portfolio in December, 2023\. To understand why, you can go to my December, 2023 Deep Dive (you can easily navigate the monthly Deep Dives posts [**here**](https://www.mbi-deepdives.com/models/)) and read the last section in which I typically discuss and provide further context for why I changed certain portfolio allocations. Similarly, although I did a Deep Dive on Adyen in May 2022, you will notice the stock only started appearing on my portfolio in August, 2023\. You can, hence, go to my Deep Dive published in August'23 to see my rationale to start buying Adyen. Although I have been investing in the US since August 2018, I launched MBI Deep Dives in September 2020, and I only started disclosing my portfolio since December 2020\. You can see my portfolio on a monthly basis since January 2021\. The portfolio will be updated on the last day of every month. [Subscribe](#/portal/signup) --- _This page is for paying subscribers only._ ## Posts ### A Few Changes to My Publishing Schedule URL: https://www.mbi-deepdives.com/a-few-changes-to-my-publishing-schedule/ Last updated: 2026-09-20T14:23:25.000Z I would like to make a few changes to my schedule. I am currently working on my Uber Deep Dive but, frankly speaking, I am further behind than I would like to be. As a result, I plan to spend next week solely on the Deep Dive, and I am hoping to publish it by the end of next week. There won’t be any new posts until my work on Uber is out. Ironically, even though AI has accelerated some of my workflows (e.g. AI is going to build all my [**financial models**](https://www.mbi-deepdives.com/my-last-financial-model/) going forward, including the Uber one), it has been rather challenging to focus on Deep Dive work while also closely following the AI news flow. Given that this velocity of information is likely to be the new normal, I have decided to tweak my schedule. I will continue to publish my usual coverage daily from Monday to Friday, but I will stop publishing on weekends, which I plan to devote entirely to Deep Dives. I continue to believe Deep Dives are an integral part of my work; over time, they become the bedrock of my daily coverage. I used to publish personal musings on Sundays, and those consistently led to more interaction with readers than I expected. I don’t want to lose that, so I will still write personal musings every once in a while on Fridays. After Uber, I hope to study Intuitive Surgical for my next Deep Dive. It’s a business I studied a few years ago but never got around to publishing a Deep Dive on, so I would like to get back up to speed and hopefully publish next month. Thank you for your support. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***70***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- ### What Anthropic's 25 Gigawatts Can Earn in 2030 URL: https://www.mbi-deepdives.com/anthropic-2030/ Last updated: 2026-09-19T16:44:00.000Z On Wednesday I [**showed**](https://www.mbi-deepdives.com/oai-breakeven/) what OpenAI has to believe to break even in 2030 and said I would run the same exercise for Anthropic. However, when I started working on Anthropic, I realized the relevant questions are a bit different for the company. Indeed, it was reported just yesterday that OpenAI actually [increased](https://www.theinformation.com/briefings/openai-said-forecast-nearly-280-billion-cash-burn-end-2030?rc=4lgoj7&ref=mbi-deepdives.com) its cash burn estimates from $180 Billion to $278 Billion by the end of 2030\. If OpenAI indeed becomes public next year, I continue to believe public market may be less forgiving to such persistent and gargantuan cash burn which may necessitate OpenAI to show investors a more less chaotic path to profitability. It will be particularly challenging if Anthropic, their primary competitor at the frontier model race, shows much better economics which can lower the appetite from investors to fund such cash burn. Anyways, given media reports [suggest](https://finance.yahoo.com/technology/ai/articles/exclusive-anthropic-ipo-valuation-hinges-001158885.html?ref=mbi-deepdives.com) Anthropic already posted operating profit (ex SBC) in Q2 this year and expects to be profitable in Q3 as well, the more relevant and useful question for Anthropic is what GAAP operating margin the fleet Anthropic is building can support on assumptions one can defend, and how the margin behaves if pricing at the frontier goes the wrong way. So, that’s the key focus on my Anthropic exercise. Speaking of becoming public, Anthropic is now apparently going to IPO a month [later](https://www.wsj.com/tech/ai/anthropic-shifts-planned-ipo-to-november-8874dffc?mod=article%5Finline&ref=mbi-deepdives.com) than expected. All I want is the IPO to actually happen so that we can follow at least one frontier lab with audited financials. It will also be useful to stop the charade of crossover investors enjoying an unusual informational advantage from frontier labs ARR trajectory which remains a key driver to much of the broader AI trade. Given that IPO is hopefully a couple of months away, this exercise is timely to wrap my head around Anthropic’s economics. Just as I did with OpenAI, I will show you the math, and share the downloadable spreadsheet (which you can change to fit your narrative and point of view) behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***70***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Groceries, Aggregators, and Agents URL: https://www.mbi-deepdives.com/groceries-aggregators-and-agents/ Last updated: 2026-09-18T16:29:27.000Z Just over two and a half decades after Walmart was founded, the company entered the grocery industry in [**1988**](https://www.mbi-deepdives.com/groceries%5F1/) and then in just thirteen years after getting into this business, Walmart became the largest grocery player in the US. Grocery leadership was also a major unlock for Walmart since not only a good chunk of discretionary spending goes to groceries for US household, it also drives frequency of visits to the store. Such frequency also creates familiarity and loyalty to a specific retailer since nobody likes to try grocery shopping different chains every week and figure out the maze of grocery aisles. However, the nature of grocery shopping is going through a fundamental evolution ever since the pandemic has forced a lot of people to give online groceries a shot. This good [deep dive](https://open.spotify.com/episode/4X6lkleelOb4Hjqm9aeKgw?si=d6e45df8cb144e60&nd=1&dlsi=f2ec5642cf0f4644&ref=mbi-deepdives.com) on Amazon’s grocery ambition by “The Jason & Scott Show” (h/t [Scuttleblurb](https://scuttleblurb.substack.com/p/scuttleslops-91426)) really drove this point home to me. Let me quote from the podcast (emphasis mine and slightly edited for clarity): > “I have been saying for a long time, just talking about digital commerce in general, that we used to go shopping and now because of digital, we always are shopping, right? And that was a kind of fun throwaway line. But think about how that impacts grocery in a very unique specific way over everything else, right? > > When you go grocery shopping, you buy 60 to 100 items, right? And so traditionally in the U.S., grocery was a weekly shop, right? Because you can’t make it a monthly shop because you can’t predict exactly what you’re going to consume and there’s perishables and things that expire and milk only has a 21-day shelf life and all these things. So it turned out to be about weekly that you’d go and you’d do a big shop every week and if you screwed up and forgot something, you would do a fill-in shop between that weekly shop. **When you always are shopping, if you suddenly can add one item to a cart and have it show up in three hours, in one hour, in 30 minutes, and you just fill in as you go, like it literally replaces this big shop with all these ad-hoc little shops.** > > And in our current environment, if you’re a busy time-starved family and you maybe don’t know how many times you’re going to sit down for dinner this week, it’s more convenient to be able to buy the items you need for today and tomorrow than it is to plan a week in advance. And if you have economic uncertainty in your life, there’s even more reason than you might want to shop in smaller bites so that you have less waste, you have less breakage, you have less out of pocket at a time, you can manage your cash flow better. > > So for all of these reasons, **the advent of digital commerce and the speed of service has meant that we’re fundamentally re-teaching people how to shop for groceries**. And that big stock-up trip is largely going away for a lot of American families and it’s being replaced by these ad-hoc trips, which interestingly was more common originally in, for example, Europe, where you might have lived really close to a local market and you might have gone to that baker three times a week to get fresh bread or the butcher two times a week or whatever the case is. Yeah, so inside of that, we used to have kind of like your one grocery you would do your big stock-up at. And now industry research shows that **the average consumer has three to four trips, but more than half consumers don’t really have a primary grocery**.” I understand this may be obvious to many, but somehow I didn’t fully appreciate it until listening to this podcast. I am actually a bit surprised that it didn’t occur to me because my wife herself is a prime example of this changing grocery behavior. We used to go to Target for our weekly grocery shopping, but ever since our son was born, we mostly moved our grocery shopping online. Since then, I have noticed my wife ordering from Instacart almost every other day. The entire weekly grocery shopping has essentially been replaced by a series of fill-in deliveries in our household. This behavioral evolution also made me update a bit on DoorDash. There is one positive and one negative read for DoorDash compared to how [I used to think](https://www.mbi-deepdives.com/grocery-basket/) about their bet on groceries. The positive read is DoorDash seems to be on the right side of this trend as they are already the [market leader](https://www.mbi-deepdives.com/cart-vs-dash/) among the marketplaces in fill-in trips, so if fill-in trips are indeed the new dominant behavior among consumers, DoorDash will be able to ride that secular trend. On the other hand, the non-linear economics of the grocery basket makes the math less compelling for fill-in trips than the larger weekly baskets. But you cannot really fight the consumer behavior, so if companies such as DoorDash indeed dominate the fill-in trips, they may ask for better economics from the grocery chains. We are still somewhat in the early phase of grocery delivery and my guess is end state economics will be favorable to the demand aggregators (with one caveat I will discuss later). One interesting implication for such rising fill-in trips is that while it has expanded TAM, the competitive intensity is also commensurately rising. From the same “The Jason & Scott Show” episode (emphasis mine and slightly edited for clarity): > “…the thing is that everyone’s TAM has greatly expanded. If you’re Walmart, you used to just sell the 40% of calories people consumed at home. Now you can sell 100% of the calories. But at the same time, **you used to just be competing with three other grocers. Now you’re competing with all the fast casual restaurants and the QSRs**. And so it’s become the wild west. **Everything is fragmented. You have to win each food occasion as opposed to winning that aggregate food opportunity**. > > …So the company that exclusively focuses on groceries is only the third largest grocer in America, which goes to this **aggregation** story. **That people want to get as much, are favoring the people that can solve multiple problems as opposed to only solve one problem**.” Indeed, aggregation story is exactly why marketplace business models can have compelling economics at scale. Even Walmart’s economic drivers are changing in front of our eyes and in several sell-side sessions over the last couple of weeks, Walmart management really wanted to hammer this point home. Dave Guggina, President and CEO of Walmart US, highlighted that Walmart’s e-commerce business would be profitable even **excluding** advertising: > “…we have these three magical businesses that are helping reshape the P&L within e-commerce. That’s our membership, which is incredibly important. That’s our Walmart Marketplace, which I’ve already mentioned, and that is our advertising business. All three of those saw double-digit comps this past quarter and have a lot of momentum. > > We could not be more happy with the trajectory that we are seeing in profitability. But I would also mention two other things we have talked about, and that is our Marketplace and that is advertising, which is absolutely contributing to the profitability of e-commerce. However, even if you strip out advertising, our e-commerce business was profitable in Q2.” That is quite notable given that in another sell-side session, Ryan Mayward, Senior Vice President and General Manager of Walmart Connect US, quantified advertising margins to be 70%+ (emphasis mine): > “We have a great high-margin advertising business, over 70% margins compared to 5-ish percent for the core retail business. And **this profitability is incremental to Walmart**. I think some of the retailers out there are growing their advertising businesses through sort of a pocket shifting, giving a break in one sort of trade investment area in exchange for investment in ads. That’s not something that we’re favorable on, which we don’t do that at all. And so the profit from the business is purely incremental to Walmart. And we see a lot of headroom to grow this business.” Of course, the big question mark for such high margin advertising business is the rise of consumer agents. Interestingly, Guggina shared some compelling stats and use case around Sparky, which is Walmart’s own shopping agent for consumers. From Guggina at GS Global Consumer and Retail Conference (emphasis mine): > “We launched a new capability with visual shopping with Sparky, so you can now take a photo and Sparky will help you shop depending on what your mission is with that photo. **When customers utilize visual shopping, we see the conversion rate jump by 57% versus text-based shopping**. > > One of my coworkers had this great example that he did recently and shared with me. He went out to his backyard, and I guess he doesn't do a great job taking care of his grass. He takes a photo of his lawn and says, "I need some help." Sparky, just with that context, was able to recommend seeds, lawn growth fertilizer, and help him solve that mission, build a basket of unique items that he may not even thought of to help solve that mission. As a result of these enhancements, customers are responding. **We've seen just quarter-over-quarter weekly engagement with Sparky grow over 60%. When customers engage with Sparky, their average order value jumps 40%.** That's because of what I just spoke to. Maybe you are having friends over to grill for the summer, and you just say, "Sparky, I've got eight folks coming over. I want to grill beef, I want potatoes, and I want other things. I need food for eight people." Sparky can build that meal out for you.” I am very, very skeptical that Walmart’s Sparky will be able to do a better job than Muse (and Apple, OpenAI, and Google’s Muse-like agents). The more I use Muse, the more I realize broader horizontal-layer agents like Muse will have the best context of my query. As computer use gets much better and much faster than humans over time, it is very conceivable to me that Muse can give me the best suggestion from the entire corpus of alternatives out there, not just Walmart. ChatGPT was lot clunkier to use for these use cases which is why vertical agents still seemed compelling to me, but my skepticism around vertical agents has increased by an order-of-magnitude after using Muse over the last week. So, in that sense, it is good news for Walmart that their e-commerce is profitable even excluding advertising but the terminal margin question remains very much alive for Walmart and Amazon’s e-commerce businesses. Amazon will likely try to compensate for the lost margins through higher margins from their logistics and fulfillment operations. Walmart’s 3P business is still sub-scale compared to Amazon, but that may be a new hill to climb for Walmart in the coming decade. Of course, the same logic could apply to DoorDash, but I still think their food delivery business is more insulated than the broader retail. Since you don’t always know beforehand what you want to order and actually need to browse the app to choose your meal, the browsing flow and the ad inventory that comes with it still seem largely intact to me for the core food delivery business. Grocery and retail orders are more exposed to agents, but there DoorDash is mostly delivering for retailers that have nothing resembling Amazon or Walmart's logistics and fulfillment operations. Even if an agent ends up deciding where the order goes, those retailers will likely still rely on DoorDash's network to get it to the door. And if agents do erode DoorDash's ability to generate compelling economics via ads, my guess is DoorDash would simply ask for a greater rake from the retailers, just like Amazon leaning on logistics and fulfillment to potentially make up for lost ad margins. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***70***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### What You Have to Believe for OpenAI to Break Even in 2030 URL: https://www.mbi-deepdives.com/oai-breakeven/ Last updated: 2026-09-16T17:36:43.000Z ***Programming Note***: MBI Deep Dives will be off tomorrow, but I will be back on Friday. --- Yesterday I[ **mentioned**](https://www.mbi-deepdives.com/my-last-financial-model/) that I would publish my work on OpenAI today. Let me be clear that I have no idea what OpenAI will earn in 2030\. But I wanted to get a good sense around what we need to believe for OpenAI inference revenue per GW to be in 2030 for them to breakeven. Why breakeven? OpenAI is apparently [considering](https://www.wsj.com/tech/ai/openai-considers-pre-ipo-funding-round-at-more-than-1-2-trillion-valuation-54555295?ref=mbi-deepdives.com) another pre-IPO round at $1.2 Trillion valuation and if they come to IPO next year, I am skeptical that public market will be patient enough with such a large company to be incurring significant losses for years to come. Therefore, I want to see what the breakeven economics needs to look like in 2030. While nobody knows what OpenAI’s inference revenue per gigawatt will be in 2030, the cost side is, relatively speaking, more knowable. OpenAI [has disclosed its capacity](https://www.cnbc.com/2026/01/19/openai-to-focus-on-practical-adoption-in-2026-says-finance-chief-sarah-friar.html?ref=mbi-deepdives.com) every year since 2023, it has [told investors](https://taekim.substack.com/p/exclusive-openai-highlights-massive) it plans to reach 30 GW by 2030, its [audited 2024 and 2025 financials](https://finance.yahoo.com/technology/ai/articles/openai-spending-hit-34-billion-034119662.html?ref=mbi-deepdives.com) leaked in June, its cloud contracts carry implied prices, and the WSJ has reported both its [equity-comp trajectory](https://www.wsj.com/tech/ai/openai-is-paying-employees-more-than-any-major-tech-startup-in-history-23472527?ref=mbi-deepdives.com) and its own [compute budget through 2030](https://finance.yahoo.com/technology/ai/articles/openai-lifts-planned-compute-spending-144917731.html?ref=mbi-deepdives.com). If you take all of these and build the 2030 cost base bottom-up and force EBIT to be zero, the model spits out the inference revenue per gigawatt you have to believe for OpenAI to break even. Of course, if you think they can do much more than what the breakeven math suggests, OpenAI can be very profitable by 2030\. And if you think the inference revenue per GW is too high and losses would be persistent and growing, you should deeply worry about their spending commitments to rest of the value chain. I will show you how I derived this math and share the downloadable spreadsheet (which you can change to fit your narrative and point of view) behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***70***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### My Last Financial Model URL: https://www.mbi-deepdives.com/my-last-financial-model/ Last updated: 2026-09-15T16:07:51.000Z A couple of months ago, I [**wrote**](https://www.mbi-deepdives.com/proof/) about Terence Tao’s proclamation that mathematics is evolving from an “era of proof scarcity to an era of proof abundance”. The more I use the frontier models, the more I realize this may be the eventual fate for most knowledge work itself. Let me explain why I had that epiphany over the last few days. For every company Deep Dive, I build a financial model for the company from scratch. I am well aware there are many investors out there who simply do not see the point of building such models. I too strongly discourage anyone, including myself, from falling in love with their models. However, the primary point of the exercise, at least for me, is to let the model help me visualize the economics of the business and more importantly, make the assumptions for the future much more explicit so that I know what I need to underwrite to make decent return from an investment. The reality is whether you are building the models or not, you are always underwriting a lot of assumptions for the future. A spreadsheet just lays it out in a visual form rather than (often hand wavy) mental math. I do acknowledge that the more adept an investor is, the less he or she requires the spreadsheet to guide such an exercise; such adept investors are usually also quite disciplined enough to make the mental math a lot less hand-wavy. This is also why Warren Buffett didn’t ever need to touch a spreadsheet and yet probably understood the essence of the economics of most businesses and the implicit assumptions for such businesses in much greater depth than most people with their thousand line spreadsheet models. For mere mortals such as myself, I always thought spreadsheet can still be an excellent tool to guide your intuition and understanding around the assumptions of the drivers of different businesses. Since launching MBI Deep Dives back in 2020, I have personally built such models for [**70**](https://www.mbi-deepdives.com/models/) different companies so far. As the promise of AGI grew louder over the last four years, I tried using the frontier models to build my models. While they could build a decent model, it was often more frustrating and time-consuming than building one myself, as they usually struggled to build it the way I wanted. Even Opus wasn’t great at this, and I continued to build the models myself. However, after trying Claude Fable 5.1, I can say with confidence that I have already built my last financial model from scratch. While working on my current Deep Dive on Uber, I asked Claude to to look at my past financial models on companies such as Booking, Airbnb and DoorDash and use them as a guide for building a decent shell for a model on Uber. I was pleasantly surprised by the quality. My confidence in Claude’s ability grew even more a few days later when I started working on building a model on OpenAI. I started working on the OpenAI model with a broad sketch of what I was trying to accomplish. I hope to publish my work on OpenAI tomorrow, so I will skip the granular details here but I thought it would be useful to explain my thought process about using AI in more detail first. The frontier models are essentially so good at building models that it is a very poor use of time for me to build one from scratch. However, the job of an analyst or investor is likely changing. Once I asked Claude to build OpenAI’s compute capacity in three buckets (training, alignment & safety, and inference) and use those capacity assumptions to drive revenue and margins, Claude followed my broad sketch and built a very decent shell of an OpenAI financial model. I was essentially just reviewing Claude’s models line by line and asked for clarifications and debated the assumptions it made. It provided specific sources and rationales for the assumptions made in the model, but when I probed certain assumptions, it sometimes accepted that it needed to do more work to establish the rationale for those assumptions and sometimes I myself accepted its arguments and moved on. Through this back and forth, Claude ended up building the model half a dozen times already. I’m not done yet, so by the time I publish it tomorrow, Claude may have to generate the model dozen times from the initial shell it built for me. As you can imagine, Claude is already perhaps top 1 percentile or higher in excel proficiency. But building the model still requires a tight collaboration and feedback loop between the user and the tool. “Hey Claude, build me a financial model for OpenAI. Make no mistakes” will give you at best an okay-ish result, but unless the user has a good idea about what “good” looks like and in what ways Claude/ChatGPT need to be probed to elicit a better way to visualize the drivers and economics of the business, the frontier models won’t be able to take you there on their own. Using the same prompt for every company also doesn’t seem to give the best outcome. Knowing what the end product should look like is still a clear advantage…for now! Unfortunately, many people have a lot of preconceived notions about AI and its effectiveness. I have no qualms in admitting that these models are much more proficient at Excel than I ever was. Moreover, the speed and ease with which Claude can incorporate different scenarios and new information were simply inconceivable even a year ago. The OpenAI model that I will publish tomorrow will focus on a couple of specific things I am trying to understand, but I certainly do not expect everyone to agree with the assumptions made in the model. So, my suggestion for you would be to download the model, upload it to your favorite AI, and just ask it to change the assumptions to cater to your narrative or point of view. This is, of course, a suggestion for every model going forward. One of the recurring emails that I receive from subscribers is “when will you update the company XYZ model?” You no longer need to wait for me. Just download the model and ask the AI to update it. Of course, you will still need to steer the model by asking good questions. I should caution you that the model building exercise with Fable 5.1 (extra high) is a fairly token intensive process and can often eat up your entire token limit in a particular session. I will have more thoughts in tomorrow’s post related on OpenAI. Of course, if advanced mathematics is moving from “an era of proof scarcity to era of proof abundance”, financial modeling is understandably child’s play for the frontier models. Frankly speaking, I won’t miss building these models from scratch. While going line by line for the model Claude built for me, I actually found the job to be just as enjoyable. It’s hard to imagine anyone entered this industry because they like using excel. But the core job of understanding, and thinking through the economic drivers to help allocate capital will likely prove to be timeless unless you simply give up and ask the frontier models to manage your money. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***70***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Meta’s Trojan Horse for the Consumer Internet URL: https://www.mbi-deepdives.com/meta-muse/ Last updated: 2026-09-14T15:35:35.000Z When I wrote my “[**first impression of Muse**](https://www.mbi-deepdives.com/muse/)” piece last week, I promised that I would write a follow-up piece on how I am using it after a month. I may still do that, but after using Muse for the last five days, I feel **compelled** to share my updated thoughts right now. As I will discuss behind the paywall, my updated thoughts also led to some material changes in portfolio allocation. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Why “Affordable Luxury” Doesn't Compound URL: https://www.mbi-deepdives.com/affordable_luxury/ Last updated: 2026-09-11T16:04:02.000Z ***Programming Note***: MBI Deep Dives will be off during the weekend and I will resume the daily posting cadence from Monday next week. --- Even though I sold my Lululemon position last year, I have to admit I don’t quite feel much relief for avoiding the 50% drawdown experienced this year. If anything, Lulu’s dismal performance is a source of embarrassment for me. The stock today trades around $100 and I was quite bullish when it was trading above $300 a couple of years ago! It’s hard not to feel at least a tinge of embarrassment when you are off by such a large margin! And no, it’s not just stock market being wildly wayward on sentiment alone; there is perhaps no business I was ever more wrong about than Lululemon. While I don’t follow Lulu closely anymore, I was shocked when I opened their earnings press release a couple of weeks ago. I clearly didn’t imagine the business would experience negative growth in almost all possible segments. Even within international segment which was the sole positive contributor to revenue growth, revenue from China Mainland (which is the largest chunk of international revenue) actually also declined by 2% once you adjust for FX. ![](https://substackcdn.com/image/fetch/$s_!lcYp!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3f7299b5-1c66-48ec-8e4a-2ead0fdbc99f_516x991.png) Source: Lululemon In retrospect, I tried to fight the “retail is hard” consensus crowd, and I guess I have to concede that there was more wisdom in that consensus than I appreciated. The moats that I imagined Lulu had turned out to be quite a bit fragile. I thought instead of relying on global superstar athletes, Lulu’s strategy of using local ambassadors to promote their brand would prove to be quite a sustainable way to grow the business over time. Unfortunately, in the age of algorithmic social media, you can buy influence at a relatively cheap price which has only intensified competition over the last few years. Moreover, I have also started to internalize a more fundamental tension around compounding in “affordable luxury” segment. For example, I have spent probably around $1k last year on buying various Lulu products. It’s just hard to imagine that my own spending on Lulu products will compound over time. While there are certainly Lulu addicts out there, perhaps only a small segment of people actually want their wardrobe to be completely dominated by one brand. As a result, the key source of long-term compounding here needs to be driven by widening the customer base. But by its very nature, in fashion the more mainstream a brand becomes, the more likely it becomes that the brand loses its appeal among the OG addicts. These can still be very good businesses but multiple decades of compounding especially when the brand is well past the early part of the S-curve just seems very, very difficult. It’s possible, but such an outcome would be an anomaly. Of course, many investors probably already figured these out before I have learned the hard lessons after paying exorbitant tuition. What gives me bit of a pause amidst this self-flagellation is Lulu’s current woes seem to be much more widespread in branded apparel and footwear land. Both Lululemon and Nike are down 80% from their respective peak a couple of years ago. Even Adidas and Deckers experienced 50-60% drawdown. ![chart](https://substackcdn.com/image/fetch/$s_!W7q3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe378b330-2618-4607-97b6-5047dcba3dfc_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Even within this group, Nike still looks optically much more expensive despite the whopping 80% drawdown. Lulu, Adidas, and Deckers are all trading now at low double digit NTM P/E multiples whereas Nike is still trading at \~22x P/E. So even though it may not feel like it, but if anything, investors are likely exhibiting a lot of patience with Nike. Perhaps investors think Nike is already closer to trough earnings whereas the rest still have to travel a bit longer before reaching the trough. ![chart](https://substackcdn.com/image/fetch/$s_!MAJ7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7f398270-4425-4998-8034-8cbb6d3210f6_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I asked Claude to show me annual revenue of Lulu, Nike, Adidas, and Deckers in 2015, 2019, 2023, and on LTM basis. As you can see below, it is only Nike within this group that experienced revenue decline LTM compared to their respective revenue in FY2023\. It’s also quite revealing to observe just how anomalous the 2019-2023 period seems in hindsight. Lulu added only \~$2 Billion incremental revenue in 2019 vs 2015, but added almost $6 Billion incremental revenue during 2019-2023 period. Post-covid spending splurge on consumer goods propelled these businesses so much that it misled investors like me thinking much of this growth had more secular forces behind such as athleisure, and DTC. But as consumers moved more of their spending to experiences, and other macro forces such as inflation, interest rates, rise of dupes, and tariffs played their roles, these businesses now appear to be much shakier. It also doesn’t help that China, which is supposed to be growth driver, has been turning out to be a drag instead. ![](https://substackcdn.com/image/fetch/$s_!vBm4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e5f6aed-41db-41f4-ab0a-5e4be914d0c9_1620x633.png) Source: Claude Fable 5.1 While Lulu is trading at a cheap multiple today, I cannot say I feel enticed by it. I was actually wondering if consumer agents are the latest headwind for these businesses. These consumer agents will be exceptionally capable of finding discount codes, and may nudge consumers more to long-tail brands than mainstream ones especially if Shopify’s catalog becomes their first pass before deciding to spend compute for browsing the open web. “Too hard pile” is perhaps indeed where branded apparel belongs if you intend to invest for the long-term. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I have made a couple of changes to my portfolio yesterday. _This post is for paying subscribers only._ ### Muse Implications for Consumer Internet URL: https://www.mbi-deepdives.com/muse-marketplace/ Last updated: 2026-09-10T16:39:38.000Z Since I have received several emails and messages from readers after publishing my [**first impression**](https://www.mbi-deepdives.com/muse/) of Muse yesterday, I want to elaborate behind the paywall a bit more on how I think agents such as Muse can have an impact on some of the consumer internet companies. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### First Impression of Muse URL: https://www.mbi-deepdives.com/muse/ Last updated: 2026-09-09T17:03:55.000Z Meta launched [Muse](https://ai.meta.com/muse/?ref=mbi-deepdives.com) yesterday which is Meta’s attempt to be your personal agent to get things done for you. Meta’s launch blog [post](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/?ref=mbi-deepdives.com) as well as its approach to [security](https://research.meta.ai/blog/security-and-safety-for-ai-agents-our-approach-with-muse?ref=mbi-deepdives.com) are both worth reading. In my post, however, I will share my **personal** experience of using Muse since yesterday. Before Muse, I poked around a bit with Instinct, a startup that has created a bit of buzz in the personal agent space but still in private beta. My very first impression of using Muse was that it seemed a pretty good clone of Instinct. But once I spent more time on Muse, I thought labeling Muse as a clone of Instinct is a very uncharitable framing. Like Instinct, you can use Muse on WhatsApp (I didn’t see iMessage integration which you can do with Instinct). But Muse also has its own app which I installed to play around throughout the day. While the WhatsApp version seems just as barebone as Instinct, the app is much more feature rich. The app is available only in the US so far. Given \~40% of my subscribers are from outside the US, I wanted to give you a better feel about the app just by going through my own experience with ample screenshots along the way. Muse has a very generous free tier. Zuckerberg [mentioned](https://sources.news/p/mark-zuckerberg-meta-muse-ai-podcast-interview?ref=mbi-deepdives.com) in an interview yesterday that the free tier has up to a 100 million weekly token usage limit. While that sounded plenty to me at first, as of this writing, I have already used 81% of my free plan’s weekly limit. I have an option to upgrade to two plans: a) Power ($20/month with 500 million weekly Muse tokens), or b) Maximum ($100/month with 3 Billion weekly Muse tokens). ![](https://substackcdn.com/image/fetch/$s_!Uw3n!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc006546a-fef6-4cd5-8c33-a7f443c89d18_553x1186.png) First of all, the app is FAST! The fluidity of chatting and the snappy feeling of Muse’s response just kept the conversation flowing. The best use case that I have found so far is I can finally connect all my Gmail accounts with Muse. While using Claude or ChatGPT, one of my persistent problems has been that I can only connect one Gmail account (for what it’s worth, Instinct can do this too). Unfortunately, I have four separate Gmail accounts (one for personal, one for business, one from Cornell, and one that I randomly opened and very sporadically use). The problem is I have used three of these Gmail accounts in different settings and given Gmail’s own poor search functionality, I actually need an AI to go through all my Gmail accounts simultaneously to find a specific email I am looking for. For example, for tax purposes I needed to find the exact date we moved from Ithaca to Sacramento in 2023\. If I could find the flight ticket from my email, I can easily figure out the exact date we reached in Sacramento. But I don’t exactly remember which email exactly has that flight details. But Muse can look into all my Gmails simultaneously to let me know the exact date. I have also set up a recurring task with Muse that lets it triage all my emails every day at 5 am and surface the most important emails I need to respond to and nudge me again if I haven’t responded by 6 pm. I also liked Muse’s “Connectors” which let me connect a bunch of apps from my phone. Once connected, it’s a lot faster for Muse to do the work that needs the data from the app. As I will show later, even if you don’t connect the app, Muse can manually work in its own browser to try to complete a task. ![](https://substackcdn.com/image/fetch/$s_!G9g1!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbff37cbf-7ee8-43a8-84fe-4183f47cd0fc_616x1246.png) Such integration can also be handy if you’re trying to buy or sell something on Marketplace. I haven’t tried this myself, but it is one of the highlighted use cases by Muse itself. ![](https://substackcdn.com/image/fetch/$s_!A1Gg!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9c031589-6dd5-4f05-86fb-814e2f87f486_706x1257.png) While the whole conversation happens on a “main chat”, I could also create a “side chat” to talk about a specific thing there. I dropped a couple of podcasts on the “side chat” and asked key points discussed in the conversation. Muse initially resorted to browsing and the media coverage of the episode instead of actually going through the conversation itself. I asked it to actually go through the podcast and then find me the 10 most interesting direct quotes from the conversation. It didn’t do this right away, rather did the job overnight and surfaced the quotes when I opened the app next morning. I also could connect it to Quartr and ask it for summaries from the sell-side conferences that’s going on at Citi and Goldman Sachs. This isn’t, of course, unique to Muse as any chat bot can do the same. But since it learns from my queries, the “feed” continuously keeps surfacing the news flow I might be interested in. If I want to engage in a particular news item, I can chat specifically on that news item by clicking “discuss”. ![](https://substackcdn.com/image/fetch/$s_!DoNn!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F889e8327-3cdf-44fe-91ae-2c4b2c3683bd_1794x1444.png) Theoretically, Muse can do pretty much anything I can do on my phone. Okay, let’s go through some tasks then one by one. Let me start with booking accommodation, which has been pretty much every consumer chat bot’s highlighted use case, including Meta. So, I asked Muse to look for an Airbnb for a family trip (8 adults) near Mendocino from December 26 to 31 this year. I was just testing Muse’s capability, so an actual query would require me to share much more contexts than I did. Anyways, Muse used its browser functionality and basically approached it the way any human would: go to Airbnb, fill in the destination, dates, number of guests etc. and go through the search results. After it went through search results, it showed me a wall of texts with different options which you can see below. Brian Chesky has been quite adamant that text based interaction will not work very well for travel booking and a more visual search is required to bridge the gap between hype and actual consumer behavior. When I went through Muse’s suggestions, there are two ways I could go about it: a) just accept whatever Muse is suggesting, and b) actually click through each of the suggested options and carefully assess which one I actually prefer. I’m skeptical that most people will choose the former, and if you choose the latter, are you really saving much time here instead of just going directly to the Airbnb app itself? ![](https://substackcdn.com/image/fetch/$s_!29dr!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6eb8174e-6c80-4088-9cf0-09aefe9e6b7b_696x1054.png) Again, just to test out the capability, I played along with Muse and asked it to pick the first one. It then went ahead and worked to finish the booking until Airbnb asked for payment. I denied the payment since I wasn’t actually trying to book the Airbnb. ![](https://substackcdn.com/image/fetch/$s_!CxOE!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0e70823c-ca2d-44bd-bf28-71844554500e_931x736.png) Muse then sent me an interesting message. It explained even if I allowed the payment, it wouldn’t be able to finish the booking itself as Airbnb requires an account to complete the reservation. I could give it my Airbnb sign-on details after which it could finish the booking. So, it’s very much theoretically possible, but I don’t see how this is a better way to book an Airbnb than just going to the app directly…yet. ![](https://substackcdn.com/image/fetch/$s_!XkvA!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbbe635f8-fd2d-49a7-9377-4b3165958433_855x670.png) There is, however, a tangible risk for Airbnb or any OTA out there. I could ask Muse to find out whether the Airbnb it picked is available at a lower price anywhere else on the internet. Muse tried, saw two options, but eventually found those listings to be inactive on those platforms. Airbnb may look fine in this example, but obviously there are indeed plenty of inventory that is multi-listed on different platforms or their own websites. In fact, I don’t have to manually ask this to Muse every time I book an Airbnb. I can just ask Muse to look into any accommodation or flight I book and always first figure out whether there is a cheaper way to book it. Once it’s saved in Muse’s memory, Muse can automatically do this without ever being asked again. Going through this flow was a good reminder why Booking’s “connected trip” vision or Airbnb’s desire to broaden its core to experiences, and services (especially car rental, luggage storage etc.) could be of paramount importance for them to incentivize customers to go to the app directly instead of asking the agent to book something. Similarly, being the merchant of record also seems very important as it would allow Airbnb to offer Reserve-now-pay-later (RNPL), insurance etc. to give customers additional reasons to stick with the app experience. ![](https://substackcdn.com/image/fetch/$s_!iyGk!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F04d6c395-77fd-451c-87b4-a315f0088fb8_925x871.png) Let’s try some shopping, shall we? I asked Muse to do winter jacket shopping for me. It browsed for a couple of minutes and then showed a list of options to choose from. ![](https://substackcdn.com/image/fetch/$s_!_CHH!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb957f0a1-916d-4b1b-82ec-a17ab0ff3a49_742x985.png) I went with Muse’s suggestion and asked it to buy one from Patagonia. Then it let me know that it was showing me the wrong prices. The discount it showed me earlier is no longer available. Why did Muse get it wrong? Muse said it ran “a catalog search and wider web sweep” while looking for a winter jacket. A catalog search almost certainly means a merchant product feed, most likely Meta's own Commerce Manager catalogs that retailers upload for ads and Shops. Feeds are batch-updated, typically once a day, sometimes less, and merchants tune them for ad delivery rather than for real-time accuracy. So the shortlist was likely built from a snapshot, rather than real-time feed. ![](https://substackcdn.com/image/fetch/$s_!Tlkf!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F257adbae-6ce1-4308-8e00-72d65a854cc9_756x406.png) Later, I asked it to buy men’s deodorant from Amazon. It did everything fine and after sharing my Amazon credentials, I just needed to allow the payment. But I noticed it was showing me that the product will be delivered on September 15\. I quickly went to my Amazon app and searched for the same product on the app and I could see there are a couple of options of the same product that can be delivered in two days. So, I asked Muse to stop the transaction and I just ordered it from Amazon app directly. Then for dinner, I wanted to see if Muse is up to the task of ordering via DoorDash. I asked it to show me some healthy options from Chipotle which I wanted to pick up myself. You can see our interaction below. ![](https://substackcdn.com/image/fetch/$s_!Ae5L!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F05361f73-68a4-419e-9725-a7ae6fde1ce3_904x1086.png) After some back and forth, I picked the chicken+ black bean bowl. To be clear, at least half the time I typically don’t know what I would like to order from DoorDash. I usually browse the app and it is through the browsing experience, I typically end up deciding what to order. So, this is far from my typical way of ordering food. I was just trying to test what Muse can do with a relatively simple food ordering query. Anyways, after picking an option Muse offered, I was prompted to share my DoorDash login credentials. I asked it to use my Gmail account to login. It tried but it got stuck in log-in circular hell. It would try to login, Google would let me know someone’s trying to login to my DoorDash account, I would confirm “it’s me”, and the whole process repeated three times after which I gave up. For what it’s worth, I personally found these issues prevalent even while using Instinct. I tried the same Airbnb queries that I did with Muse. It took literally an hour, I kid you not, for Instinct to respond to my aforementioned Airbnb query. When I tried the DoorDash query on Instinct, it still took 8 minutes to respond. After experiencing Muse’s speed, the bar just became too high for a startup such as Instinct which probably cannot afford as much compute as Meta is throwing at these problems. As you can see, there are still plenty of issues Meta (or any consumer agent) needs to work on to make it a very seamless experience. While it’s hard for me to see myself using Muse at the expense of apps such as Airbnb, DoorDash, or Amazon, it’s simply too early to either write Muse off as another flailing attempt at owning the consumer agent layer or assume Muse’s victory given its ample compute budget. I do sense a lot of switching costs though once I connect all my accounts and share login credentials with one agent. I would be very reluctant to repeat this whole process with another agent without a materially compelling incremental benefit. As a result, Meta is doing the right thing by being agile and increasing such switching costs by onboarding people before other alternatives show up. Google will almost certainly try to replicate this, and the longer they wait, the better it is for Meta as it may lead to a noticeable first-mover advantage especially among power users which seem to be the most attractive user segment in AI. OpenAI is also a leading candidate to offer something similar, but I suspect even they would be reluctant to offer weekly 100 million free token usage given their ability to monetize Codex at a very attractive rate. Indeed, my day 1 impression after using Muse is that Google seems to be the key loser here. The choice increasingly appears to me between apps and a consumer agent, rather than search. Over time, it’s hard to imagine anyone else will have better context to my life than something like “Muse” if I keep using it everyday and it goes through all my emails and connector apps. I can change my mind later, but so far I don’t see myself using Muse to book a hotel, order a delivery, or do shopping on my behalf because the status quo feels to me a better and more seamless experience. A big question to me is whether the power of such a consumer agent will graduate from personal switching costs to a bit of network effect. As of now, there are no visible network effects, but Zuckerberg [indicated](https://sources.news/p/mark-zuckerberg-meta-muse-ai-podcast-interview?ref=mbi-deepdives.com) in an interview with Alex Heath yesterday that Meta is eyeing to eventually graduate from single player game. From the podcast: > Alex Heath: When you're introducing network effect learning for agents, which no one's really done, where basically the agents are learning anonymized insights from the rest of the fleet. I mean, you're like the king of network effects. I'm really interested in this idea because I don't think anyone's doing this. > > Mark Zuckerberg: I think most of the industry is thinking about agents as like a single player game. Where it's like you have your agent and you use it. And there are going to be all these interesting things that basically you can do by having the agents interact with each other, and we already have all these interesting examples internally where people have their agents interacting with each other. This isn't, for the most part, rolling out in this release, but it's going to be an important part of how I think this works over time. As more of the people who you know start using Muse, it just gets better forever. While using Muse, I kept wondering what exactly I need “Meta AI” app for. It appears even Zuckerberg isn’t entirely sure, and it seems these two apps can converge over time. From the same interview: > Alex Heath: …it coexists with Meta AI? Or do you see those as separate? > > Mark Zuckerberg: Yeah, I think so. We'll see over time. I mean, I think right now they have somewhat different flavors. I mean, I use both of them. I mean, Muse is, you know, it's more conversational and it kind of interprets the questions that you ask it more as trying to understand you and what you might want over the long term. So it's more likely if you ask it something for it to just go off and work on. I think that's more the type of thing that I use Meta AI for. But we'll see. Maybe they'll converge over time, but I'm not sure. I haven’t seen Meta send me a notification for Muse on the Facebook, Instagram, or WhatsApp apps. Given the massive compute requirement to serve a product such as Muse for a billion users, I suspect Meta wants to scale this gradually and prefers to learn from feedback from early users and focus on scaling afterward once the product is ready for the mass market. I will do another review of my personal use in a month just to share in case I see any evolution of how I use Muse. I suspect Meta (and other AI companies) focus too much on use cases in which existing apps already do a very good job. The real opportunity likely lies in tasks for which we don't have a good alternative or **any** alternative at all. I’ll look for such tasks going forward, and will share further thoughts in a month. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Pulte’s Bag of Tricks URL: https://www.mbi-deepdives.com/pulte/ Last updated: 2026-09-08T16:17:55.000Z While covering FICO’s FY 3Q’26 earnings, I [**wrote**](https://www.mbi-deepdives.com/fico3q26/): “…*there is no doubt that Pulte’s FHFA is overtly hostile to FICO and perhaps investors are concerned that Pulte may have more tricks in his bag to truly hurt FICO’s compounding machine*.” On Thursday evening last week, Pulte pulled a few of those tricks out of his bag. For a change, he actually first took aim at the credit bureaus and [posted](https://x.com/pulte/status/2095671962806689853?ref=mbi-deepdives.com) the following on X: > “Equifax, Experian, and TransUnion have been overcharging Americans for far too long. This will end soon. We are seriously considering bi-merge, and stronger solutions (SAFER and SOUNDER). We will not allow companies to take advantage of American consumers. No more.” In case FICO shareholders felt left out, four minutes later Pulte [posted](https://x.com/pulte/status/2095672962422562893?ref=mbi-deepdives.com) a separate rant about FICO: > Since 2020, FICO has increased the price per a person’s credit score by 1,800%. FICO has enjoyed a monopoly. No more. > > Fannie and Freddie’s initial rollout of VantageScore has been incredibly successful, with 50 LENDERS DELIVERING LOANS. So, EFFECTIVE IMMEDIATELY, I’m instructing Fannie and Freddie to approve ALL lenders to use VantageScore. Pulte wasn’t done yet. The next morning, he floated the possibility of just one credit report instead of three (the status quo) or even the two he suggested the night before. ![](https://substackcdn.com/image/fetch/$s_!LY03!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F16cc1239-2610-47a8-b34b-d54af892fc02_685x384.png) FICO stock cratered more than 20% following the series of tweets and despite regaining a bit by the close, the stock is still down \~45% YTD. I will elaborate more about the stock price reaction and some implications for Pulte’s bag of tricks behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!m6K8!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8470ec48-822d-49c2-bac0-4a6a3ca72a01_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### A quick update on Deep Dives URL: https://www.mbi-deepdives.com/a-quick-update-on-deep-dives/ Last updated: 2026-09-07T13:52:47.000Z I won’t be publishing anything new today, but I wanted to share a short update on the Deep Dives I’ve been working on. Last month, I started working on Micron to understand the memory complex a bit better. After a couple of weeks, I concluded that not only was it far more technical than I expected, but more importantly, the technology is moving at a pace that is quite difficult to keep up with. I wasn’t sure I was making much progress and decided to shift my attention to businesses that are easier to wrap my head around. One such candidate was S&P Global. However, last week it was [reported](https://finance.yahoo.com/markets/stocks/articles/p-global-shares-rise-report-184319702.html?ref=mbi-deepdives.com) that the company may spin off more assets even after its [Mobility Global](https://investor.spglobal.com/news-releases/news-details/2026/SP-GLOBAL-INC--COMPLETES-SEPARATION-OF-MOBILITY-GLOBAL-INC-/default.aspx?ref=mbi-deepdives.com) spin-off. So, I decided to wait a few months and study the company after it goes through these spin-offs and files a new 10-K next year. After further deliberation, I realized I needed to think about new Deep Dives more strategically. Perhaps the most common request from subscribers has been to update some of the earlier Deep Dives I published a few years ago. That’s what I have decided to focus on for the rest of this year; from 2027 onwards, I will keep a mix of new Deep Dives and updates on past ones. I haven’t decided yet which of the past Deep Dives I will update, but this month I will be studying Uber which also happens to be the very first Deep Dive I published six years ago. As a DoorDash shareholder, I think it’s imperative that I follow Uber much more closely. The company also seems to be at a point where there is a vociferous ongoing debate about its future in an AV world. Meanwhile, I still expect to publish daily while working on these Deep Dives and updates. Thank you for your support. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### What can I learn from my 20-month-old son? URL: https://www.mbi-deepdives.com/learning_from_son/ Last updated: 2026-09-06T15:03:05.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- A couple of months ago, I stumbled onto this [reel](https://www.instagram.com/reel/DXEXt2Zk7Dd/?stkn=YzAyMDM1MGJkZA%3D%3D&ref=mbi-deepdives.com) on Instagram. It was a clip from a conversation between Bollywood actress Alia Bhatt and an Indian guru who is widely known as “Sadhguru”. It went like this: > **Alia Bhatt:** I’m a mother to a three-and-a-half-year-old, and she’s the joy of my life. But I’m so worried at all times. What would your one advice to a parent who is worried if they’re being a good parent be? > > **Sadhguru:** A worried parent is not a good parent. \[Laughter\] See, when you have a child, first thing is, drop this idea that you have to teach something. I know you’re a wonderful woman, but between you and your three-and-a-half-year-old, what’s her name? > > **Alia Bhatt:** Raha. > > **Sadhguru:** Raha. Between you and her, who is more joyful? > > **Alia Bhatt:** She. > > **Sadhguru:** Who should be a consultant for life? > > **Alia Bhatt:** She. > > **Sadhguru:** Then what’s there for you to teach? You must listen. You must watch. You must see how a child is, because a child is far closer to life than you are. > > **Alia Bhatt:** So your advice to me would be, you don’t give her any advice, you take from her. > > **Sadhguru:** You must learn how to be life because she is just life. Since then, I have often thought about this reel, especially during my daily ritual of 10,000 steps. I have a 20-month-old son myself and the more I pondered it, the more I leaned toward Sadhguru’s point that there is indeed more to learn about life from observing your child. So, I kept thinking about my son and how he goes through the day. When I think about my son’s sources of joy, they are usually quite simple. He wants to be surrounded by family members and the people he sees every now and then. The warmth and affection of family and acquaintances are natural sources of joy for him. He also seems to be a big proponent of the “touch grass” movement as he’s quite insistent on going outside for a walk or to library. Every time he sees an airplane or a bird, he will let you know about it. When he’s inside the house, he’s always tinkering and poking around things that he comes across. If any inanimate object is lying around the house, he will be the first person to explore it and figure out what to do with it. Observing any child closely can help you appreciate what exactly the phrase “childlike curiosity” means and how profoundly almost every adult lacks it. It’s also easy to tell that my son is not looking to get anything out of such experimenting or poking around inanimate objects. He just wants to vaguely grasp “what is this thing”. Sometimes he finds the thing interesting enough to play with it or do something with it, and sometimes he just moves onto something else with no sign of disappointment. The whole point seems to be just exploring everything he can get his hands on. He also likes music. Sometimes he keeps playing while trying to hum along to whatever is on. He clearly observes us and pays attention to what we are doing. He at times comes to me and insists pressing keys on my laptop in a wayward manner, with a mischievous smile on his face. When I am drinking tea or coffee, he demands we offer the same to him and no, it cannot be in his bottle. He wants it in the same cup or mug as we do. It’s amazing how persistent and strong-willed a kid can be. If my son REALLY wants me to do something, he’s the clear favorite in that tussle. He knows how to get what he wants even with a handful of vocabulary at his disposal. A strong vocal cord is apparently all you need to bend the world to your will. Technology also sits much more naturally in a kid's world. He’s not surprised or shocked by what phones, laptops, TVs, airplanes, or trains can do. He seems much more interested in just understanding “what is this” and if the thing turns out to be very interesting, he wants to dig in further. He is currently in his train phase, and when he’s not playing with his “chu-chu” trains, he wants me to show him photos and videos we took while taking a couple of trains around California. He’s never just sitting around a sofa or idly thinking about something. He’s always moving…either playing with something, or just randomly running around the house and laughing about it. The sound of his laughter is truly a limitless source of joy for me. And sometimes, of course, he's crying his heart out because we are not doing what he wants. He gets tired after moving all the time and goes for a nap when the exhaustion sinks in. Perhaps it really is this simple. Wake up, poke around and tinker with things, laugh about it, be strong-willed when you really want something, go outside to get some fresh air, wonder and wander aimlessly, come back to a shelter that truly feels like home, and just keep moving until your eyes are about to give up. He is not trying to teach me any of this, of course. He is just living. Whatever there is to learn depends entirely on whether I am paying attention. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Bypassing RevenueBench to Buy the Feedback Loop URL: https://www.mbi-deepdives.com/revenuebench/ Last updated: 2026-09-05T14:45:28.000Z We have seen an accelerating pace of model release cadence over the last few weeks. Frankly speaking, this may be a new normal for the rest of this year instead of being an anomaly. If you haven’t got there already, my guess is by the end of this year most of us will feel like the below [tweet](https://x.com/itsnoahd/status/2095789044894421195?ref=mbi-deepdives.com) when it comes to opining on any new model. ![](https://substackcdn.com/image/fetch/$s_!Zpf-!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdb7233ef-6d62-42d5-9673-f105ea563b42_706x387.png) Indeed, even the benchmarks are starting to act a little [strange](https://x.com/Hesamation/status/2096045009082388919?ref=mbi-deepdives.com) and perhaps finding it difficult to keep up with the model release cadence as frontier models start to saturate most benchmarks out there. I increasingly find myself leaning towards Elon Musk’s philosophy around benchmark: “[RevenueBench](https://x.com/elonmusk/status/2089739241114816671?ref=mbi-deepdives.com)”. You got to think that almost nothing is as reliable as people voting with their wallet and if a new model succeeds in changing wallet share noticeably, that is a much stronger indication of a model’s performance than anything else. Of course, the biggest challenge here is that revenue is a lagging indicator whereas benchmarks could be more of a leading indicator. So, there will still be massive demand for benchmarks or evals to gauge such leading indicator but as I said, differentiating the signal from the noise here will become more challenging over time. The other challenge here is that there are companies such as Meta who is deliberately playing the catch up game to the frontier by avoiding the “RevenueBench” altogether. Given the importance of good coding data, Meta is effectively giving their latest models away for free if you allow them to train their future models on your data. ![](https://substackcdn.com/image/fetch/$s_!mYAV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff96df913-51ad-4098-be70-8adb5cd14b00_1122x583.png) Source: Meta But are the developers willing to make that trade-off? OpenCode’s [data](https://opencode.ai/data?ref=mbi-deepdives.com#market-share) suggests that such strategy is working very well for Meta. Following Muse Spark 1.3 release, Meta’s tokens processed on OpenCode exceeded 5 trillion yesterday! ![](https://substackcdn.com/image/fetch/$s_!yKow!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F02edca89-593f-4f47-8f2f-5b7d1b53cc3f_1882x1000.png) Source: OpenCode In fact, following Muse Spark 1.3 release, Meta’s token share on OpenCode went from 25% to 40% and is approaching half of all tokens on OpenCode today. Let me contextualize what 5 trillion tokens per day means. Back in June 2026 while proposing $80 Billion equity raise, Sundar Pichai [mentioned](https://blog.google/alphabet/investor-presentation-june-2026/?ref=mbi-deepdives.com) the following (emphasis mine): > “The Antigravity coding harness has accelerated how we build internally. Every few weeks, we’re doubling the number of tokens we’re processing across our developer tooling. **Recently, we reached more than three trillion tokens a day. This scale creates a powerful feedback loop to improve our current and future models**.” So, only on OpenCode Meta is essentially processing almost **twice** that of what Google was processing for their coding harness couple of months ago. Of course, Google’s antigravity is processing more tokens today; so I am just pointing it to help you contextualize that Meta’s aggressive pricing strategy has helped them gain Google-shaped coding data and if Pichai is right that such data creates “a powerful feedback loop”, Meta is doing the right thing to bypass the “RevenueBench” for now. ![](https://substackcdn.com/image/fetch/$s_!TIem!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fde08f463-8151-4c08-8498-495afdcf1edc_2257x622.png) Source: Claude Fable 5.1, OpenCode Meta also has other strong incentives to ensure their model is on par with frontier models at coding. Recent media reports suggest that Meta is expected to spend [$10 Billion](https://www.nytimes.com/2026/08/27/technology/meta-anthropic-frenemies.html?ref=mbi-deepdives.com) on Anthropic’s models and I assume the amount could be even higher when we include OpenAI’s models. So, even if Meta’s models are barely monetized externally via paying customers, Meta can save a sizable amount internally just by not paying the margins for frontier models. Moreover, it can be increasingly difficult to compete against frontier model developers if they decide to keep their best model internally for first-party products while the models via API only get to use older and less capable models. Zuckerberg has been paranoid about this possibility for a while and if OpenAI’s Tibo’s [tweet](https://x.com/thsottiaux/status/2096101429832552872?ref=mbi-deepdives.com) yesterday is any indication, he was correct to entertain such possibility. This lopsided risk-reward for not owning the frontier model is perhaps only tangibly felt if you are the founder which may partly explain why the [founder-led](https://www.mbi-deepdives.com/frontier/) companies seem to feel this risk most acutely while the manager-led companies are mostly focused on selling compute. ![](https://substackcdn.com/image/fetch/$s_!OoqC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5146a799-67e4-4d5b-a554-29faa9d44e35_697x295.png) Even before reaching the frontier, Meta has been radically changing how it approaches its own product development. Their pace of release has clearly accelerated even for their Family of Apps (FOA) business as they have launched Forum (a standalone app for Facebook Groups), Facebook Creator Studio (for Facebook creators), and Seller (for Facebook Marketplace sellers) just in the last three months! In a recent [podcast](https://theskip.substack.com/p/what-the-past-year-taught-meta), [Jagjit Chawla](https://www.linkedin.com/in/jagjitchawla/?ref=mbi-deepdives.com), Meta’s VP behind Facebook’s Feed and Reels, explained why such standalone apps are helping their FOA ecosystem. Some key excerpts from the podcast: > “we have somewhat of a two-sided or maybe I can say three-sided marketplace, right? We acquire the world’s best content from creators. That’s number one. And creator could be like a group producer or I’m talking about the production side. Then we distribute that content to people who find that useful as consumers, and then we take consumer time and trade it for advertiser dollars and we take those dollars and flow that back into production. Now, the question is that if you have a 2 billion app, which is largely focused on the consumers, people who are going to consume all of this content, there is always going to be a set of power users who are on the other side, which are much smaller always. Producers always are going to be small in size compared to consumers. Will you ever be able to justify a new app for the producer segment? > > And if they produce more and you can then take that content in terms of liquidity and distribute on both platforms, let’s say forum and Facebook app, by definition, you have more blood in the ecosystem, which in this case “content”. More blood means more oxygen is reaching many places. I’m using a poor body analogy. > > But I think in my mind that assumption has now been validated that if you give producers a powerful surface that is unconstrained, they will produce more. The more they will produce, the more consumption So that’s why it’s not cannibalistic. And that was the first proof point we were looking for. > > We got that proven with a bunch of work on forum. And then we basically said, why not Facebook sellers? Because they are also producers. There’s a bunch of features we could give them. > > If you were to try to do that in a 2 billion daily user app like Facebook, it’ll get cluttered for a lot of existing users. So it kind of liberates both the teams and unconstrains the product to go do different things. And I think it’s additive to the ecosystem. > > We already have this definition of what we call family of apps. Traditionally, the big apps are Facebook, Instagram, WhatsApp, and Messenger. And **now we have a bunch of these standalone constellation apps for each of the apps, which is actually helping the ecosystem overall**. > > One of the big challenges with working in a big company setup like this is that there are different people who own different services. So most of the time, even speaking as an executive or even as an IC, a lot of the time goes into solving what I would, what in your vocabulary is inside the building problems, which is I need to go convince these set of five people before they will let me, allow me to ship something on the surface that they own because there is going to be the right conversation on trade-offs. There is going to be a conversation on opportunity costs and so on and so forth. **All of those constraints actually slow you down.** And that is one of the big challenges of big tech is because there is a lot to lose, so to speak, when you have 2 billion users, hundreds of billions of revenue, you can’t really be messing things up. That like 0.1% change is a meaningful change to the business and to our users. So in that sense, there was a lot of constraints in terms of what is the right lane. And there was a lot of process to make sure that you’re not going to break things inadvertently and not cause effects. So there was a lot of this emphasis on ecosystem thinking end to end. > > There was a lot of careful judgment required to navigate that process. And as you can imagine, that is onerous because you need to be in the system for a while. You need to have tenure to understand the ecosystem. You need to have tenure to have the right relationships to be able to influence those set of people. > > Now, if you contrast it to now, so if I am a groups PM, I used this example before. I have a small app called Forum, which has tens of millions of users. **I am unconstrained. I don’t need any approvals from service owners like the feed team. I have my own ranking team, and it’s a small pod of people who sit next to each other, and I can move much, much faster. And as and when we prove the right features to happen in forum, it’s a much easier sell, much lighter process to port them over to the main application**.” Indeed, Zuckerberg was perhaps too enamored by the ability of small teams to outcompete larger teams as a recent Reuters [report](https://www.reuters.com/investigations/mark-zuckerberg-had-bold-plan-replace-meta-staff-with-ai-heres-how-it-imploded-2026-08-26/??ref=mbi-deepdives.com) suggests that he was actually thinking of a much larger lay-off than the [10%](https://www.cnbc.com/2026/04/23/meta-will-cut-10percent-of-workforce-as-it-pushes-more-into-ai.html?ref=mbi-deepdives.com) Meta actually ended up doing in May. Gergely Orosz had a very critical [piece](https://blog.pragmaticengineer.com/the-pulse-meta-wanted-to-reduce-teams-by-60-because-of-ai/?ref=mbi-deepdives.com) on Meta’s approach to managing their employees, especially engineers and he estimated the actual lay-off Zuckerberg was perhaps thinking of is closer to \~30-40%. Orosz did make an attempt to understand where Zuckerberg might be coming from, especially in light of OpenAI and Anthropic potentially building a similar sized market cap companies with a fraction of Meta’s employee base, but he ultimately made the case that Meta might end up destroying too much goodwill as an employer to become an AI-native company. As a shareholder, I actually do share Zuckerberg’s concern. It’s always going to be painful to adapt to the new world in the age of AI as a public company that is always under the scanner. I don’t discard Orosz’s concerns either; it’s hardly ever fun to work in a setting where you can’t quite trust if you’ll be around in a few months. But if having a larger company becomes a barrier to keep up with the productivity harnessed in smaller, AI-native companies, this is a choice that many public tech companies may face. That’s also going to be very politically fraught as few people expect companies to downsize when revenues and profits are soaring. Having said that, Chawla in the aforementioned podcast made the point that the restructuring led to fewer processes which resulted in higher productivity. From the conversation: > “I think now that it has been a few months, we are behind that and it's all publicly known in terms of what we did there in terms of team restructuring. But I do think it goes back to the point we were talking about earlier where fewer people means fewer processes. Fewer process means more time available to build. And the output is clear, right? We were able to launch three new apps in the last three months. There are a bunch of other apps in the work which are standalone. These are conversations we were not even willing to entertain, let's say, last half or let's say last year.” Moreover, for all the complaints around data labeling and criticism around MSL hiring spree last year, Meta’s model release cadence in the last couple of months is a vindication that Meta’s top management is perhaps treated too cynically by most observers. Zuckerberg has perhaps a longer time horizon than most of the shareholders and employees (which is a recurring source of tension among these stakeholders) and as the largest and controlling owner of the company, Zuckerberg will continue to optimize for the long-term and endure through the skepticism around his decisions in the short-term. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Spare a Thought for Broadcom Shareholders URL: https://www.mbi-deepdives.com/avgo3q26/ Last updated: 2026-09-04T15:36:22.000Z After Nvidia’s earnings, I was almost [**shocked**](https://www.mbi-deepdives.com/nvdafy2q27/) to see how the company’s estimates for next year surpassed far above consensus estimates and yet the stock has barely outperformed the S&P 500 (and actually underperformed both the QQQ and the semiconductor ETF). Well, spare a few thoughts for Broadcom shareholders then. At least, Nvidia stock is up \~20% YTD whereas Broadcom stock is essentially flat YTD during perhaps one of the most lopsided demand-supply compute markets ever! And yes, just like Nvidia, sell-side wasn’t even remotely close to the outlook Broadcom provided for 2028\. At the beginning of 2026, analysts were estimating $162 Billion revenue for Broadcom in FY2028\. After the 2028 outlook provided by Broadcom in 3Q’26 call, the estimate has shot up by more than $100 Billion over the course of this year to reach \~$270 Billion! ![chart](https://substackcdn.com/image/fetch/$s_!rdaw!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F47673b98-bc3a-4948-9acf-d00f297e3732_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) These aren’t just empty calories either; Broadcom started the year with 2028 EPS estimate of $17.6\. It’s now $30! ![chart](https://substackcdn.com/image/fetch/$s_!dn-Y!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe67efbd0-f9ef-41ab-a02d-a60ea770c1c8_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) So, 2028 EPS estimate rose by **\~70%** over the course of this year and the stock is down YTD in a market full of investors chasing accelerating topline and upward estimates revision? What!!?? ![](https://substackcdn.com/image/fetch/$s_!WKJ4!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7ef4f325-1598-4101-932a-0ba03858900d_160x160.gif) I know, I know I am supposed to say buy-side estimates were already up there and sell-side estimates here are obsolete. But can we please not pretend that buy-side was estimating $30 EPS for 2028 at the start of this year? Whether you’re buy-side or sell-side, I have little doubt that the estimates for Broadcom became higher over the course of this year. So the fact that Broadcom stock has lagged the benchmarks so badly must be due to something else. As the saying goes, the “meta” is always changing in the stock market. Just when you think you know how the market works in the short-term (“Just buy accelerating topline, bro”) and most people start to believe that mantra, it may start to stop working. As an intended long-term shareholder of every stock I own, I admittedly admire all the ways market can confound short-term investors as it makes me feel good to not need to waste my brain cells to explain tick-by-tick changes in the stock price in the short term. So, no, I’m not going to be able to explain why Broadcom stock hasn’t been “working” this year, but behind the paywall, I will share some interesting divergences between Nvidia and Broadcom based on their respective earnings calls. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Notes from a Dasher Shift, and the Drone Delivery Future URL: https://www.mbi-deepdives.com/dasher/ Last updated: 2026-09-02T17:34:06.000Z ***Programming Note***: I will take a day off tomorrow, and will be back with daily posting schedule on Friday. --- DoorDash has a program called “[WeDash](https://careersatdoordash.com/blog/wedash-doordash-employee-program-how-does-it-work/?%5Fga=2.51293807.770438897.1784224451-2123547498.1784224451&%5Fgl=1%2A98125g%2A%5Fgcl%5Fau%2ANTQyNzI3NDIyLjE3ODQyMjQ0NTA.%2A%5Fga%2AMjEyMzU0NzQ5OC4xNzg0MjI0NDUx%2A%5Fga%5F4J1MLKETLL%2AczE3ODQyMjQ0NTAkbzEkZzAkdDE3ODQyMjQ0NTAkajYwJGwwJGgw&ref=mbi-deepdives.com)” which requires its employees do delivery shifts every year. While they have no such requirement for shareholders, I decided to sign up as a Dasher to understand the Dasher side of experience more intimately. My wife wasn’t particularly thrilled about the idea, but she begrudgingly understood my dedication to “research”! However, while trying to sign up, I got to know that my area already has “too many Dashers”. I still went ahead and finished the sign up process just to see whether I can potentially do deliveries in some other nearby regions. ![](https://substackcdn.com/image/fetch/$s_!Q6Kw!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F175662cd-6452-4ab8-bea9-03b43a71eaac_567x1144.png) Indeed, I could. Last week, I drove for 20 minutes to go to a nearby shopping mall, parked my car there, and logged into the Dasher app to become available to dash. Even though it was showing to be “peak” hour, I had to wait for almost 10 minutes before getting a notification to accept an order. The restaurant was just three minute walking distance from my parking area, so I accepted the order and walked to the restaurant. Once I was there, I waited for a couple of minutes for the restaurant to finish the order. After receiving the order, I had to go to the app and let DoorDash know that the order includes all the items the customer ordered. Then I went back to my car and drove for 27 minutes to drop the order at customer’s doorstep. I then had to drive another 30 minutes to return home. I checked my app and I made just $13 for the delivery. Even though I was “active” on the Dasher app for \~40 minutes (\~10-minute wait time and \~30-minute drive to drop the order), once you include the “[deadhead miles](https://truckstop.com/blog/deadhead-miles/?ref=mbi-deepdives.com)”, I was making less than $9 per hour, even while ignoring gas and depreciation costs. However, a couple of days later, DoorDash let me know that I received $5.58 tip from the customer. It’s pretty remarkable that \~30% of my actual earnings from that order was funded directly by the customer. Without such generosity from the customer, the Dasher experience would be even less appealing. However, the whole experience also reminded me the kind of people who would find this job worthwhile. The dasher job is particularly appropriate for people who need flexibility in hours. If someone is available to work for longer hours, they should probably work more regular retail jobs (e.g. Walmart) to optimize for higher earnings (especially when you include gas and depreciation costs). But if you crave for flexibility, this can be pretty decent gig since the opportunity cost for flexible work hours is very different than regular job. A couple of days ago, I actually received an email from DoorDash outlining how many hours I need to work to be eligible to receive healthcare stipend from DoorDash. If a dasher utilized this benefit, this could add another \~$3 to \~$5 cost per hour of dasher to DoorDash. Assuming my dasher earnings is somewhat representative of an average order in my area, DoorDash is essentially spending almost **$20 per hour of dasher availability** in my area. So even if I quibble around lower earnings of dashers, I’m not sure I can say DoorDash is being too stingy here. ![](https://substackcdn.com/image/fetch/$s_!xN82!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F145b3035-9b20-40b3-81c3-9b9bfde9cfb0_705x604.png) Of course, the regulatory wind around the country (and the world) likely means the effective cost of a dasher hour for DoorDash may continue to rise. Since \~3% Americans worked as dashers for DoorDash last year, it’s also hard to imagine that there is an ample supply of inexpensive dashers available in the US if DoorDash wants to scale their GOV \~4-5x over the next decade. For anyone intending to be long-term shareholder of DoorDash, it seems increasingly important to understand their bet on autonomous delivery on which I will share more thoughts behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Bid Shading Threat to Amazon Advertising URL: https://www.mbi-deepdives.com/amzn_ads/ Last updated: 2026-09-01T16:13:56.000Z Almost six years ago, NYT published a critical [piece](https://www.nytimes.com/2019/12/19/technology/amazon-sellers.html?ref=mbi-deepdives.com) on Amazon’s business practices with 3P sellers. The reason I still remember that piece after all these years is that the article had an interesting background on how Amazon made its foray **into** advertising. From the piece: > “For years, the question of whether Amazon should push ads on its site generated fierce debate among senior managers and executives inside the company, according to eight current and former Amazon employees. In memos and fiery meetings, they disagreed on what was best for a company that preached obsession with serving customers. One camp believed that ads would erode customer trust, because shoppers expected Amazon to show them popular products with strong reviews and a good price. The other camp saw ads as a cash machine Amazon could tap to drive down prices and fund new innovations for customers. > > …Workers eventually got word that Mr. Bezos had settled the debate, according to two senior employees. Mr. Bezos said that Amazon had two options: Sell ads, and use the cash for investments. Or shun ads, and get beaten by competitors.” Mr. Bezos took the right decision. Indeed, advertising has almost certainly been Amazon’s largest money printer over the last few years and the company would likely be worth lot less if Amazon had to generate attractive return in its retail operations without relying on advertising. Since 2019, Amazon’s advertising revenue became almost \~6x to reach \~$69 Billion in 2025\. Just to put things in perspective, even AWS grew at \~24% CAGR during this period while advertising revenue grew at \~33% CAGR. ![](https://substackcdn.com/image/fetch/$s_!KXL9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3e9abb63-8537-4f35-9515-25f60502900c_865x582.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Given the importance of advertising revenue for Amazon’s business ex AWS, yesterday’s FTC [lawsuit](https://www.ftc.gov/system/files/ftc%5Fgov/pdf/AmazonAds-Complaint.pdf?ref=mbi-deepdives.com) against Amazon’s advertising practices deserve a closer look which I will cover behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Left Tail of AI URL: https://www.mbi-deepdives.com/the-left-tail-of-ai/ Last updated: 2026-08-31T16:30:45.000Z AI is obviously a transformational technology, and anyone still dismissing it as a 'con' job is, ironically, likely pulling a con themselves. It’s hard not to feel that AI needs a better group of bears if we crave for a more diversity of opinions around AI. Over the weekend, I have wondered whether I was actually looking at the wrong places to find the left side of the distribution for the massive investments around AI. Perhaps the more credible and damning bear cases are hidden inside the people who are indeed the most bullish about AI’s capabilities in the future. I will expand more on that behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### An Ode to Ben Thompson URL: https://www.mbi-deepdives.com/an-ode-to-ben-thompson/ Last updated: 2026-08-30T16:17:18.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- When Om Malik [passed away](https://om.co/2026/06/24/1966-2026/?ref=mbi-deepdives.com) a couple of months ago, there was an outpouring of remembrances and admiration for Malik’s work and how his words sketched Silicon Valley for many readers. I have come across Malik’s work and enjoyed reading his words (especially this [one](https://om.co/2015/04/27/brunello-cucinelli-2/?ref=mbi-deepdives.com)), but wasn’t quite a regular reader. But reading through the lofty praises by his regular readers helped me appreciate the role he played for many who were voracious readers of his work. A part of me lamented that Malik wouldn’t get to see all the ways he touched so many people which prompted me to write today’s Sunday piece about my own favorite analyst for whom my admiration and gratitude run deep. Ben Thompson has been writing [Stratechery](https://stratechery.com/?ref=mbi-deepdives.com) since 2013, but I came across his work in 2019 right after finishing my MBA at Cornell. One of my colleagues at work mentioned “[aggregation theory](https://stratechery.com/aggregation-theory/?ref=mbi-deepdives.com)” with the caveat that he’s not entirely sure if the theory makes sense. I looked up “aggregation theory” later and signed up as a free subscriber. I was forwarded a couple of emails from my colleagues back then which led to more water cooler talk later. I don’t exactly remember what prompted me to upgrade to be a paying subscriber, but I’m sure it was an easy decision. Some of you may not be aware of this, but even in 2019 the internet was not a great place to find compelling and engaging analysis on big tech. As someone who just moved to the US for my MBA in 2017 and found a seat in a buy-side role a couple of years later, I needed to get up to speed on companies whose revenues were threatening to eclipse the entire GDP of the country I emigrated from. After finding Stratechery, I remember some weekends I basically binged on Stratechery. A few weekends like that elevated my understanding of the world of big tech to a level which made me start to feel I can do a good job at my work. For someone who was trying to find his feet in a foreign land in covering the most analyzed companies in the world, you can imagine why I started to feel such a reservoir of gratitude to Ben Thompson. To my utter surprise, many of my peers didn’t quite appreciate the depth and rigor of Thompson’s analysis as many seem to have struggled to get beyond the fact that Stratechery is “[just a blog](https://www.youtube.com/watch?v=cTDKMxdV1w4&ref=mbi-deepdives.com)”! On the contrary, the more time I spent reading Stratechery, the more I realized how differentiated many of his point of views were, and as more and more people started to take him seriously, Thompson was increasingly driving the conversation around big tech. Over time, one of the things that really struck me about Ben Thompson is that he is perhaps among a handful of analysts in the world who has a very good grasp on both [the east coast and west coast school of investing](https://www.mbi-deepdives.com/my-valuation-approach/). To simplify, east coast philosophy is more grounded on the present state and married to numbers whereas west coast is far more imaginative and way more attuned to technical aspects of the technology itself. While most analysts from business school have a predilection towards east coast philosophy (including yours truly), Ben Thompson is one of those analysts who can swim both currents with an ease that I still find hard to find on the internet (and beyond). I subscribed to Ben Thompson to deepen my understanding on big tech, but he ended up educating me on a broad range of topics. From chips to geopolitics, Thompson’s work is a pedagogical session for me almost every time he writes on Stratechery. My friend Liberty has a very intriguing [hypothesis](https://www.libertyrpf.com/p/why-your-mentors-seem-less-impressive-6a5?ref=mbi-deepdives.com) why your mentors seem less impressive over time. Liberty points out that once we quickly consume the foundational concepts and "greatest hits" of our mentors, we have a tendency to create a false sense of having caught up with our mentors. However, true expertise relies on hard-to-transfer traits like judgment and execution, meaning a critical gap in actual ability remains even when their insights begin to feel familiar. Of course, since almost everyone reads Stratechery these days, one may be more incentivized to claim it is pointless to read anymore since there is not much alpha there. I have always held the belief that most investors underestimate how much work it requires for active investors to earn the beta (although almost free if you’re passive). Anyways, I do believe Liberty is onto something real with his hypothesis, and I too suffer from it once in a while. But there are two “mentors” for whom I can confidently articulate that I have gained more, not less, admiration over time. One is Warren Buffett, and the other is Ben Thompson. While there are plenty of people who want to harangue Buffett for “mid” performance vs the S&P 500 last two decades, I would be more than happy to be “mid” when I’m closer to be an octogenarian. God knows how hard it is to keep pace with the benchmark (just look up the [data](https://www.spglobal.com/spdji/en/research-insights/spiva/?ref=mbi-deepdives.com) how many managers outperform the index over any 5,10,15-year period), so I know most of our **base case** should be that we may not need to be so patient to reach the “mid” stage since we may be inherently ordinary to begin with! Ben Thompson doesn’t actively invest. So, we cannot quite compress his “work” to mere relative numbers, but as a reader, I know I don’t need some numbers to tell me the value of his work. His care and passion for the pursuit of truth is deeply inspiring and a constant reminder how lacking it is in most of other people’s work. When Thompson argued in [favor](https://stratechery.com/2020/the-tiktok-war/?ref=mbi-deepdives.com) of banning TikTok or came out [against](https://stratechery.com/2025/trump-allows-h200-sales-to-china-the-sliding-scale-a-good-decision/?ref=mbi-deepdives.com) the chip export control policies, you could sense he wrestles with his opinions and show his reasoning in front of the readers. Reasonable people can certainly disagree with his positions, but I have never come away thinking Thompson hasn’t considered opposing views. I know very well that in an 'opinion' business, it is absurd to claim or pretend that your or anyone else's opinions are always 'right', but that's hardly ever the point. What matters is whether the opinion you settle on has appropriately weighted opposing views, and whether you can lay out the reasoning that led you to your stance. The world is too infinitely complex to always have the correct point of view from any particular vantage point on a broad range of topics Thompson covers. I too have slight disagreements here and there, but I am not writing this piece intending to allude to a “but” lurking somewhere down the line. No, I merely intend to express my gratitude for the analyst I personally look up to. The other similarity between Buffett and Thompson that I personally feel is there is never going to be anyone like them in their respective field when you carefully consider what exactly they accomplished and what they offered to the world. People talk about Buffett’s performance against benchmark and while his total track record is so damn impressive, I am extremely confident there will never be another investor who will allow you to enjoy a track record as good as Buffett’s without basically any fees. Thompson also fits this bill perfectly. Sure, he bills me $150 per year and he would be the first person to know this is **nowhere close to value maximizing price**. Given what Buffett and Thompson offered to the world and the prices they charged for it, I think such prices are closer to charity than actual market clearing price. You know I sometimes wonder about this inherent “communist” tendency of capitalism. It’s hard to imagine two industries that are more capitalistic than finance and tech, and yet the best investor and perhaps the best tech analyst offered their services to the world at a fraction of their market-clearing price. Of course, for anyone unfortunate enough to be in a group chat with me, none of this is news to them since they know how aggressively I tend to reply to any message that insinuates anything remotely negative about Ben Thompson. That doesn’t make me a fanboy, it is only a mere expression of the deep gratitude I carry with myself for Ben Thompson. I take great inspiration from him for my own work, and I hope he continues to share his work for decades to come! --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Veeva 2Q’26: "SaaSpocalypse" Cancelled URL: https://www.mbi-deepdives.com/veev2q26/ Last updated: 2026-08-31T02:19:14.000Z Early this year when most investors were grappling with “SaaSpocalypse”, I started buying Veeva when the stock was trading at $220\. As the SaaSpocalypse mania spread like a virus in investors minds, Veeva stock went into a tailspin for a few months. As I averaged down a couple of times when the stock was in a free fall, my average cost came to be $198/share. Following its CY 2Q’26 (or FY 2Q’27) earnings, the shares ended last week at \~$277/share, \~40% higher than what I paid for the shares. This is a victory lap, right? Right?? ![](https://substackcdn.com/image/fetch/$s_!wmSG!,w_2400,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7fb55edd-1ce2-4cda-ab07-7f3cecdfeb3c_350x197.gif) Regular readers may [**remember**](https://www.mbi-deepdives.com/its-not-a-lie-if-you-believe-it/) that after going through a psychologically torturous process of moving my assets from Canada to the US, most of the stocks I owned went up so much that I didn’t have enough cash to buyback everything I used to own. As I had to make a difficult decision in terms of what not to buyback, I made the unfortunate decision not to buy back Veeva. So instead of making \~40% return on Veeva, I actually ended up taking a loss while selling my assets in Canada. Active investing is challenging as it is, but this year continues to be marred by this completely random event in my life. I promise this is the last time you’ll be hearing about this BS; I just wanted to share some pain to feel a little lighter instead of pretending deep stoicism that I haven’t reached. Okay, let’s move on from the self-pity and focus on Veeva’s quarter. Against a $905 Mn revenue guide on the high end, Veeva reported $928 Mn, up \~17.6% YoY which is their fastest topline growth in nine quarters. Subscription revenue growth accelerated to 16.3% (from 15.0% last quarter), and normalized billings were up 19%. If that weren’t impressive enough, such revenue growth acceleration happened **despite** opex growing only 5.8% YoY. As a result, GAAP EBIT margin came in at 29.6% for the quarter, and LTM GAAP EBIT margin reached 29.9%, yet another all-time high. I will discuss the the key takeaways from the call behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!xZeK!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F70f1d1c8-81c6-478a-848f-7f1e2244c1b1_1452x604.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta Buys Away its Distraction at Minimal Cost URL: https://www.mbi-deepdives.com/meta-settlement/ Last updated: 2026-08-28T15:44:26.000Z A couple of days ago, Meta reached an [agreement](https://about.fb.com/news/2026/08/agreement-with-state-attorneys-general-supporting-teens/?ref=mbi-deepdives.com) with 52 attorneys general across US states to settle their lawsuit alleging that it knowingly designed addictive platforms that harmed young people’s mental health. As part of the settlement, Meta will pay a cumulative $18 Billion over 10-year period, 30% ($5.3 Billion) of which is contingent on two specific conditions: a) both YouTube and TikTok also agree to implement similar measures as Meta, and b) YouTube and TikTok both match half of the $5.3 Billion payment each. When these lawsuits attracted attention in the media, I specifically [**highlighted**](https://www.mbi-deepdives.com/fat%5Ftail/) why Meta cannot solve the problem at hand alone in the reality of a competitive market environment: > “Even if a company such as Meta decides to avoid any engagement tricks for minors but their competitors relentlessly pursue it, it is very difficult for Meta to watch its competitors building competing networks of young users that can hurt its competitiveness in the long term. This is precisely why despite Meta generating only [1% of its revenue](https://www.japantimes.co.jp/business/2026/02/19/tech/zuckerberg-instagram-age-limits/?ref=mbi-deepdives.com#:~:text=Zuckerberg%20testified%20that%20while%20it's,disposable%20income%2C%E2%80%9D%20Zuckerberg%20said.) from teens, it cannot willingly cede this market to its competitors. To the extent the courts force all the companies to make the apps boring for minors, it may end up narrowing the path of future competitors for incumbent social networks such as Meta.” I am glad to see Meta insisting that its major competitors are all playing by the same rules since that is indeed the only way the measures to limit teen usage of these apps can be effective. I wouldn’t assume apps not named YouTube and TikTok are outside of the purview of these measures. Sure, other companies won’t have to chip in to pay for the liabilities for now, but Meta’s settlement will set a strong precedent that “excessive” usage of any app can be deemed as a long-term risk for any digital company. The company that popped on my mind that can face damaging consequences for such precedent is Roblox. The majority of Daily Active Users (DAU) are minors and as Roblox publicly reports, the **average** time spent per DAU on its platform is three hours per day! Since it’s only average, I suspect the top 20% DAU (who may drive majority of the bookings) may actually spend noticeably more than three hours per day. If concerned parents end up suing Roblox and demanding it implement the time limit Meta agreed to, the consequences could be far more damaging for a company like Roblox. ![](https://substackcdn.com/image/fetch/$s_!8-mD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97198a84-d46e-4f9a-ab6a-e8f409827bcd_952x616.png) Source: Roblox, MBI Deep Dives Meta is also trying to be a bit opportunistic in exerting pressure on competing apps and creating more awareness around it by putting full-page advertisement on national newspapers as well as on Facebook and Instagram to US users. ![Image](https://substackcdn.com/image/fetch/$s_!mbP3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F252a8257-dd2b-4885-ab9f-280367e9be47_572x1058.jpeg "Image") Source: Meta I have noticed some people found it rather distasteful that Meta used the settlement as a pressure tactic on competitors. I am, frankly speaking, quite bamboozled by such accusation; if you really care about the potential negative effect of these apps on teens, why wouldn’t you want these measures to be industry standard and how does it help if Meta is the only company subjected to onerous usage limit while teens can use other apps as much as they want? However, I do think Meta’s lawyers were able to extract a much better deal than even Meta bulls likely hoped. Every state except New Mexico and Florida joined this agreement which means possibility of the most draconian penalties is clearly off the table. Florida and New Mexico are absent because each pursued its own separate path against Meta. New Mexico had already taken Meta to trial in state court and won which Meta is currently appealing. Florida, meanwhile, simply refused to sign on because it [viewed](https://abcnews.com/Business/meta-settles-states-landmark-social-media-addiction-trial/story?id=135967095&ref=mbi-deepdives.com) the terms as too lenient. **If** you believe what these states were alleging Meta did to mental health of our teens, it’s indeed hard not to think this whole settlement was rather a slap on Meta’s wrist. Take, for example, what is **excluded** from the time limit, school mode, or night mode restrictions Meta agreed with: Direct Messaging (DM). Three years ago, Instagram Head Adam Mosseri [said](https://www.thetwentyminutevc.com/adam-mosseri?ref=mbi-deepdives.com) that if you look at how teens spend their time on Instagram, they spend more time in DMs than in Stories, and more time in Stories than in Feed. He added further in the same interview that all of Instagram’s sharing growth over the prior five years had been in Stories and DMs, and that Feed is at best the third or fourth most important surface. The fact that YouTube or TikTok don’t have a popular DM surface may mean a time restriction on those apps may lead teens to spend more time on Meta’s DM surfaces in IG, WhatsApp, and Messenger! Given that context, I am not confident that time spent by teens on Meta’s apps will even go down after these restrictions are implemented when you **include** the time spent on DMs. Since 30% of the full settlement is contingent on YouTube and TikTok joining the agreement, the guaranteed cash expense for this settlement is only \~$1.2 Billion per year which is less than 1% of Meta’s opex base for this year. Meta may have paid more to hire a handful of AI researchers last summer than settling a lawsuit with such damning allegations! Zuckerberg should consider setting aside some bonuses for his lawyers this year. Meta is, however, not quite out of the woods yet. Even after the AG settlement, Meta faces hundreds of pending suits from individuals, families, and school districts over youth harms (the California [JCCP](https://mdlupdate.com/mdl/3047-social-media-adolescent-addiction/jccp-5255/?ref=mbi-deepdives.com) and federal [MDL](https://mdlupdate.com/mdl/3047-social-media-adolescent-addiction/?ref=mbi-deepdives.com)). I will also be shocked if EU and perhaps India and Brazil also don’t sue Meta on the same teen mental health issue in the hope to receive at least similar settlement. So, my guess is the eventual recurring liability from this settlement may be close to double the amount once you consider the other pending and future lawsuits. Even if you assume $2 Billion recurring eventual liability from this settlement, it’s basically \~1.2% of Meta’s operating expense this year. Of course, given opex will keep increasing, the impact of this settlement will be de minimis in 5-10 years. Ultimately, the settlement may be worth more to Meta than \~1% of its opex because it allows them to move forward from this distraction and focus on what will dictate the company’s long-term future: AI! Meta’s lawyers may have done their job to keep the downside in check, but the upside needs to be unlocked by its AI talent! --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Nvidia Climbing the Wall of Worries URL: https://www.mbi-deepdives.com/nvdafy2q27/ Last updated: 2026-08-27T15:34:32.000Z A year ago, the average sell-side consensus estimates for Nvidia’s FY 2028 (which is essentially CY 2027) revenue was \~$310 Billion. But yesterday, Nvidia guided for \~70% revenue growth in FY’28 which translates to approaching \~$700 Billion revenue next year! That means Nvidia’s FY’28 revenue estimates was wrong by \~$400 Billion just a year ago! Even before the call, analysts were estimating \~$574 Billion revenue next year. So even though analysts kept updating the estimates throughout last year, they were still short by \~$125 Billion! If that wasn’t enough, Nvidia repeatedly mentioned during the call that this growth outlook is supply constrained and they could probably double the revenue next year if that weren’t the case. Given their tone during the call, I think it’s fair to say \~70% revenue growth may be a **floor** for next year. It’s a cliche to say it these days, but I have never seen anything like it! ![chart](https://substackcdn.com/image/fetch/$s_!-EtB!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5238b3ce-f862-4fd2-bac3-8e16d605e53d_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Perhaps equally perplexingly, just when Nvidia was making mockery of analyst estimates over the last year, the stock has barely outperformed S&P 500 and actually underperformed both QQQ and semiconductor ETF. In case you needed another reminder why investing is hard, I’m happy to help. ![chart](https://substackcdn.com/image/fetch/$s_!2qJF!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F22721d5d-f819-4ca6-98b3-9fec62394b33_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Such relatively muted stock performance compared to Nvidia’s operating performance is a confirmation that buy-side likely have a materially different estimates compared to sell-side. Even though sell-side estimates show Nvidia will keep growing revenue with operating margins at mid to high 60s, Nvidia’s current earnings multiple likely implies that buy-side is far more skeptical about that. This doesn’t mean buy-side is necessarily right, but an indication that Nvidia will have to climb such wall of worries in the coming years. Nvidia’s revenue mix also shows what they’re trying to execute to climb the wall of worries. While revenue from hyperscalers doubled YoY, revenue from AI Clouds, Industrial, & Enterprise (AICE) segment grew by \~140% YoY! Nvidia mentioned they now expect neoclouds will exit the year with 8 GW of installed capacity in 2026, up from only \~3 GW capacity in 2025. ![](https://substackcdn.com/image/fetch/$s_!vLRH!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F33b507d6-c1fc-408e-8a1a-f7a3ef0e17ef_1923x817.png) Source: Nvidia To contextualize the neoclouds capacity, let me mention that AWS, the largest hyperscaler in the world, only had an estimated \~15 GW capacity in 2025\. Since Amazon mentioned that they expect to double their capacity from 2025 to 2027, let’s assume they’ll add 7-8 GW capacity this year which implies neoclouds in aggregate will add \~60-70% of AWS incremental capacity this year. Since the neoclouds are almost all exclusively buying Nvidia GPUs, the rise of neoclouds is a massive boon for Nvidia. Of course, the rise of neoclouds is also very much direct consequence of Nvidia’s deep desire to commoditize the hyperscalers while hyperscalers are busy trying to commoditize the chip layer. At the same time, let me repeat what I [**mentioned**](https://www.mbi-deepdives.com/nvda-lt-margin/) yesterday, who exactly are in the backlog of these neoclouds? It’s the same hyperscalers! So the biggest question here is what will happen to these neoclouds if hyperscalers manage to bring capacity closer to the demand curve over time. Nikesh Arora, CEO of Palo Alto Network, captures the skepticism many investors hold about **most** neoclouds’ future: ![](https://substackcdn.com/image/fetch/$s_!T2rQ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe8073b45-8100-485f-939b-9e98e3809449_699x634.png) Source: X Beyond the hyperscaler vs neoclouds debate, the other vector of concern is whether much of the incremental compute demand is going to be dominated by just a couple of frontier labs such as OpenAI and Anthropic. Both the labs have made it abundantly clear so far that they intend to have heterogenous compute fleet and do not want to exclusively use Nvidia’s GPUs (I guess they can change their mind if Nvidia provides them the funding to buy the GPUs). Nvidia highlighted yesterday that OpenAI’s existing and planned commitments represent 12 GW of Nvidia compute. Interestingly, OpenAI already signed a [6 GW](https://openai.com/index/openai-amd-strategic-partnership/?ref=mbi-deepdives.com) compute deal with AMD, [10 GW](https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/?ref=mbi-deepdives.com) with Broadcom, and [2 GW](https://e-commerce.news/story/amazon-invests-usd-50-billion-in-major-openai-aws-deal?ref=mbi-deepdives.com) with Amazon. As a result, Nvidia’s share in OpenAI’s commitments is only \~40%. However, much of these commitments are likely to be frontloaded in Nvidia’s chips but that is expected to flip to non-Nvidia chips over the medium to long term. Anthropic is on the opposite spectrum. Their compute commitment so far is roughly half of OpenAI’s: [5 GW](https://www.anthropic.com/news/anthropic-amazon-compute?ref=mbi-deepdives.com) with Amazon, [2 GW](https://ir.amd.com/news-events/press-releases/detail/1292/amd-and-anthropic-announce-strategic-partnership-to-deploy-up-to-2-gigawatts-of-amd-instinct-mi450-series-gpus?ref=mbi-deepdives.com) with AMD, \~[1 GW](https://www.anthropic.com/news/expanding-our-use-of-google-cloud-tpus-and-services?ref=mbi-deepdives.com) with Google Cloud, \~[3.5 GW](https://techcrunch.com/2026/04/07/anthropic-compute-deal-google-broadcom-tpus/?ref=mbi-deepdives.com) with Broadcom+ Google, and likely 3 GW with Nvidia. So, Nvidia has only \~20% share in Anthropic’s commitments. Nvidia probably would love to grow their share in Anthropic in the future, especially when they may start losing share in OpenAI’s compute footprint, but you can imagine Amazon and Alphabet will be ready to sweeten the deal to sell incremental chips to Anthropic. You may argue at the rate Anthropic is growing, they may not need such circular funding sweeteners but in that case, who do you think hold the negotiating leverage if four different companies are trying to cajole one company to buy their silicon? This is why having any concentrated compute demand will likely prove to be quite destabilizing for the long-term economics for both the silicon layer and hyperscalers. Such dynamic is perhaps why buy-side is less comfortable in underwriting higher multiple in Nvidia’s earnings power despite eye-watering growth. To be clear, Jensen Huang is very, very clear-eyed about the risks here. Even when he’s providing backstop or financing to AI labs, he does not want to see massive concentration in compute demand in the medium to long term. That explains why Nvidia is going to [acquire](https://techcrunch.com/2026/08/26/nvidia-closes-in-on-hugging-face-acquisition/?ref=mbi-deepdives.com) Hugging Face. As “Modest Proposal” wittily [put](https://x.com/modestproposal1/status/2092791629899825612?ref=mbi-deepdives.com) it, Nvidia wants to commoditize both its competitors and customers so that Nvidia remains the King! Indeed, AI is increasingly the game of Kings and while everyone appears to be friendly with each other, they are also very much wary of defending their thrones! It’s hard to know how the battle lines will be ultimately drawn. Given that context, perhaps we should wonder less about Nvidia’s anemic earnings multiple and question more why other AI beneficiaries deserve lofty multiples. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Nvidia’s Long-Term Margin Question URL: https://www.mbi-deepdives.com/nvda-lt-margin/ Last updated: 2026-08-26T16:05:42.000Z Nvidia will report earnings later today, but I want to take a bit longer term view and discuss some concerns I harbor in my mind about their long-term margin questions behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Big Tech Off-Balance Sheet Concerns URL: https://www.mbi-deepdives.com/off-bs/ Last updated: 2026-08-25T16:06:20.000Z A couple of weeks ago, WSJ published a [piece](https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2?mod=djem10point?st=S8gd2Q&ref=mbi-deepdives.com) titled “Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems**”**. Some excerpt from the piece: > “Each quarter, big tech companies disclose their massive capital expenditures on artificial-intelligence infrastructure, from [data centers](https://www.wsj.com/topics/industry/data-centers?ref=mbi-deepdives.com) to chips. > > But those figures don’t come close to expressing the [full extent of future spending](https://www.wsj.com/tech/ai/will-someone-finally-blink-in-the-ai-spending-war-0f59aa60?mod=article%5Finline&ref=mbi-deepdives.com) to which Google parent [Alphabet](https://www.wsj.com/market-data/quotes/GOOGL?ref=mbi-deepdives.com), [Meta Platforms](https://www.wsj.com/market-data/quotes/META?ref=mbi-deepdives.com), [Oracle](https://www.wsj.com/market-data/quotes/ORCL?ref=mbi-deepdives.com) and many others have committed. That is because a huge swath of their [coming financial obligations](https://www.wsj.com/tech/ai/meta-stumbles-as-tech-investors-demand-better-answers-on-ai-spending-fc731909?mod=article%5Finline&ref=mbi-deepdives.com) aren’t reflected on their balance sheets. > > Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.” WSJ also included a more memorable “iceberg” that showed big tech’s on and off balance sheet items. It’s not just WSJ, Financial Times also have been covering big tech’s growing affinity towards off-balance sheet financing in recent weeks. This [podcast](https://open.spotify.com/episode/65NWZksipDRwMOeJPC8CDy?si=4b54e22e32384735&ref=mbi-deepdives.com) with FT’s Robin Wigglesworth is also a good listen if you want to get up to speed on this topic. ![](https://substackcdn.com/image/fetch/$s_!pDr1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffb01bc56-a246-40b4-a900-4109fd5cfdba_1123x883.png) Image Source: [WSJ](https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2?mod=djem10point?st=S8gd2Q&ref=mbi-deepdives.com) I actually wrote a [piece](https://www.mbi-deepdives.com/the-coming-debt-deluge/) titled “The coming debt deluge” last year which sort of foreshadowed some of these recent developments but admittedly I too am a bit surprised by the **scale** of these off-balance sheet items. Yesterday, Olga Usvyatsky wrote a good [piece](https://deepquarry.substack.com/p/behind-big-techs-ai-commitments) on her Substack about this topic. While she shares concerns that such large off-balance sheet items can be potential source of problems if AI fails to deliver, she also pointed out that there is nothing nefarious going on here in terms of disclosures. From her piece: > Off-balance-sheet does not mean that the accounting is wrong or violates GAAP. Many commitments are kept off the balance sheet because accounting rules do not require companies to record them as liabilities until certain conditions are met — for example, until a lease begins or goods or services are delivered. Nor are these obligations necessarily hidden. After all, the WSJ was able to compile the data from the companies’ own SEC filings, where the commitments are clearly disclosed in the footnotes or MD&A section. Moreover, WSJ’s aforementioned iceberg compares present-value lease liabilities on the balance sheet against undiscounted future payments for un-commenced leases. Those are different measurements, so the present value of such future payments is lower than what WSJ implied. Again, from Usvyatsky’s piece: > **“…the present value of existing lease obligations is about 20% below their undiscounted future payments.** Applying that same relationship *simply as an illustration* to the $904 billion of the off the balance sheet leases in the Journal’s analysis would produce a lease liability of roughly $723 billion — about $181 billion below the amount shown in the visualization. This is not an estimate of what the companies will ultimately record: the actual liability will depend on when individual leases commence, their payment schedules, lease terms and the discount rates at commencement. But it illustrates why ***undiscounted future payments and present-value lease liabilities should not be treated as equivalent amounts.”*** That is exactly right! But it is important to keep in mind she mentioned that the 20% discount factor for undiscounted future payments is “simply an illustration” since that 20% is derived from **commenced** leases, which include a lot of short-remaining-term stuff. A lot of **un-commenced** data center leases will start in 2028-29 and run 15-20 years, so the discount on those should be meaningfully larger. As a result, even Usvyatsky’s $723bn is probably still the high end of the eventual recorded liability. Let me drive this point home by going through Amazon’s total commitments. Amazon is currently the only company that actually discloses all its commitments in each of the next five years as well as the commitments beyond 2030\. Such disclosure used to be commonplace since until 2021, SEC rules required a standardized contractual obligations table in every 10-K. The SEC [eliminated](https://www.sec.gov/files/rules/final/2020/33-10890.pdf?ref=mbi-deepdives.com) it in the November 2020 MD&A “modernization”, replacing it with principles-based "material cash requirements" discussion. Amazon **voluntarily** kept reporting its purchase commitment table, Microsoft kept a stub, but Alphabet and Meta went fully narrative. So, what did I see when I dug into Amazon’s total commitments? Almost **\~60% of its total $650 Billion commitments are after 2030**! So, while the aggregate number looks a bit scary, it looks quite manageable when you look at their commitment on an annual basis in the next five years. Of course, we do not quite know what the annual number looks like beyond 2030 but given the long-term nature of much of these commitments, I won’t be surprised if such commitments run well into 2050s. ![](https://substackcdn.com/image/fetch/$s_!XaH6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F88fc41a6-0563-49b4-a033-100bb8eab23a_1729x303.png) Source: Amazon, MBI Deep Dives As alluded earlier, Microsoft’s disclosure around its total commitments is less comprehensive than Amazon’s. Microsoft just discloses its obligations due in FY 2027 and thereafter i.e. FY 2028 onwards. Microsoft’s obligations are more front loaded than Amazon’s since \~68% of its \~$502 Billion total commitments are beyond FY’27 (vs \~83% for Amazon). ![](https://substackcdn.com/image/fetch/$s_!xFf8!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F638dd194-1439-488f-910c-eaf564ec4433_1329x237.png) Source: Microsoft, MBI Deep Dives Like I said, Alphabet and Meta went fully narrative and neither of them provides disclosure such as Microsoft and Amazon. This is what Alphabet disclosed in their 2Q’26 filing: > As of June 30, 2026, we had material purchase commitments and other contractual obligations totaling $811.0 billion, of which $200.7 billion was short-term. These purchase commitments primarily relate to costs for technical infrastructure and inventory through long-term supply agreements and open purchase orders. As you can see, Alphabet’s commitments are the largest and while it is also a bit frontloaded i.e. \~25% of the commitments are due in the next 12 months, the rest of the commitments is beyond 12 months. In another part of their 10-Q, Alphabet mentioned the following: > As of June 30, 2026, expected future fixed or guaranteed commitments under these agreements were $707.0 billion, the significant majority of which related to long-term supply agreements. > > We expect contractual commitments under the long-term supply agreements and content licenses to generally be fulfilled through 2030\. The energy service agreements include terms ranging from two to 26 years, with obligations through 2054, and generally include take-or-pay provisions for minimum quantities of energy supply and substantive termination fees. The $811.0 Billion mentioned earlier is under the "material cash requirements" standard i.e. the kind of commitment table Amazon discloses. Everything in the $707bn **and** **open purchase orders** i.e. equipment already ordered but not delivered, which are commitments but not multi-year agreements and whatever else falls under "other contractual obligations." Alphabet provides no bridge between the two numbers. but since open purchase orders are inherently near-term, the gap between these two numbers is presumably concentrated in the $200.7bn short-term bucket. What does Meta report? Again, as mentioned earlier, Meta too only provides narrative instead of granular year-by-year details. From Meta’s 10-Q: > As of June 30, 2026, we had $349.31 billion of non-cancelable contractual commitments, comprising both short-term and long-term arrangements. These commitments mostly relate to third-party cloud capacity arrangements and investments in servers and network infrastructure, data centers, and consumer hardware products in Reality Labs, with approximately $53.52 billion and $81.65 billion due in 2026 and 2027, respectively. So, \~15% and \~23% of their total non-cancellable contractual commitments is due in 2H’26 and 2027\. Here too the commitments are a bit more frontloaded but majority of these commitments will be after 2027\. As mentioned earlier, we do not have visibility of the cadence of commitments beyond 2027, but my best guess is everyone’s cadence is more or less similar. As a result, significant portion of the overall commitments is well into 2030s (and possibly beyond). Given these contexts, I do not think big tech investors need to suffer from insomnia after looking at such gigantic off-balance sheet commitments. Presumably they own the stock because they’re broadly optimistic about their investments in AI and unless returns from such investments spectacularly disappoint, we probably do not need to worry about existential questions. One challenge, however, is that these numbers are only snapshot as of 1H’26 and if recent quarters are any indication, these numbers seem to be going in only one direction: up and to the right. There is almost bit of a “FOMO” approach to these commitments and if they keep doing it and obligations become larger and larger over time, I may need to re-assess to what extent I can exhibit a bit of nonchalance to off-balance sheet exposure. I’m not there yet, but it’s something I will keep track and update after every quarter. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Stripe’s “Singularity” and the Divergence from Adyen URL: https://www.mbi-deepdives.com/stripe-vs-adyen/ Last updated: 2026-08-24T16:15:45.000Z Last week, Stripe [announced](https://stripe.com/newsroom/news/stripe-agrees-to-acquire-openrouter?ref=mbi-deepdives.com) their acquisition of OpenRouter, the AI model gateway and routing platform. Ben Thompson had an interesting [take](https://stratechery.com/2026/stripe-acquiring-openrouter-aggregating-ai-flipping-the-business-model/?ref=mbi-deepdives.com) on Stripe’s OpenRouter deal, so I won’t repeat the potential strategic reasons to pursue such an acquisition. I do want to focus a bit more on the [letter](https://www.axios.com/2026/08/19/stripe-payments-openrouter-singularity?ref=mbi-deepdives.com) Stripe wrote to its investors. Thanks to being the payment partner of choice for much of Silicon Valley, Collison brothers are almost always in the tech zeitgeist and hence, the way they speak or write very much assumes a tech forward audience. But perhaps most people still raised their eyebrows when they read the following in Stripe’s investor letter: > “It’s a fuzzy and perhaps already overworked term, but we decided that January 1st marked the beginning of the singularity, and we have since been operating on that basis. The singularity is often invoked alongside millenarian forecasts, but, in our case, we simply saw a large inflection in long-run trends (for example, a huge increase in the rate of new firm creation), and we decided that we ought to take the phase change seriously.” If you take “singularity” literally, it hardly makes sense as [pointed](https://x.com/fchollet/status/2090177471962591625?ref=mbi-deepdives.com) out by François Chollet. ![](https://substackcdn.com/image/fetch/$s_!eiU9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8e8a43ec-9032-4a1e-950d-bc6a850d841c_681x577.png) But if you do not take such “singularity” literally, rather the spirit which Stripe was getting at, it still seems a tad bit presumptuous. I would imagine we should at least experience 5% real GDP growth while invoking “singularity” to explain a technological revolution. Perhaps Stripe should contact [CBO](https://www.cbo.gov/publication/62105?ref=mbi-deepdives.com) so that CBO updates their GDP projections which forecast GDP growth to stay below 2% as far as eyes can see. ![](https://substackcdn.com/image/fetch/$s_!SG4N!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F79f75a7a-eb1c-4cc1-a5c4-18b91c9bf719_1267x244.png) Source: CBO To be quite frank, if CBO is ballpark accurate about such uninspiring GDP growth in the next few years, we may have bigger things to worry about than haggling Stripe for invoking singularity. Stripe also encouraged everyone to dream the dream of a world economy that’s 10x larger than it is today and they showed their “math” how we could get there. From Stripe’s letter: > “We continue to believe that there is no ceiling on the size of the global economy (somewhat larger than $100T today). Implausible though it might sound on first blush, we think that it’s useful to contemplate the quadrillion-dollar world and to enumerate the relevant bottlenecks to bringing it about. (If global GDP per capita matched that of every Irish person—around $100,000—we’d be 80% of the way there.)” Some people understandably took issue with using Ireland as an example. The Irish Times [explained](https://www.irishtimes.com/business/economy/give-me-a-crash-course-in-irish-economic-growth-1.3429505?ref=mbi-deepdives.com) back in 2018 why Ireland’s GDP strains credulity: > “Nobody believes our GDP numbers any more, not after a 26 per cent jump in 2015, which was famously derided as “leprechaun economics”. Even the CSO cautions against viewing last year’s 7.8 per cent jump as a reflection of real economic activity. > > There are several reasons for this but perhaps the biggest issue stems from multinationals moving intellectual property assets such as copyrights, patents and trademarks here – a move that seems to have been prompted by a global clampdown on tax avoidance. > > The numbers involved are so large that they distort our national accounts, mangling our headline growth figures in the process.” Byrne Hobart, however, had a very [witty take](https://x.com/ByrneHobart/status/2090223167365161467?ref=mbi-deepdives.com) to justify why Ireland may actually a better representative of our collective AI future: > “I choose to read this as a subtle allusion to the version of the future where AI does most of the work, but you have humans in the loop specifically so there’s somebody to sue. In that world, we are all economically equivalent to the Irish, taking a small cut of a big number.” I do think Collison brothers are well aware of GDP being a very imperfect metric for Ireland and they were likely indeed alluding to such quixotic economy of the future without laying it out in full detail. One of the reasons Stripe is so willing to depict the future, especially its own in such rosy terms is they really need to create more and more distance from the European payment company named Adyen. From 2018 to 2024, Stripe and Adyen were essentially neck on neck in terms of total payment volume (TPV) which inherently attracts comparison between these two companies. However, 2025 was the first year Stripe truly threatened to run away from Adyen’s TPV trajectory. While the investor letter didn’t mention TPV growth, it highlighted that Stripe’s net revenue increased by **41% in 1H’26 (vs 21% for Adyen** FX-adjusted). So, instead of 2025 growth differential of these two companies being an anomaly, it is accelerating so far in 2026. ![](https://substackcdn.com/image/fetch/$s_!R9Yo!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd2e7daa-14ac-471b-8c68-19b7707d01a0_1234x712.png) Source: Based on Stripe’s annual letters and media reports, and Adyen’s filings, converted to USD Stripe is disproportionately exposed to the fastest-growing part of the internet economy right now i.e. AI-native companies and startups. Stripe [acquired](https://stripe.com/newsroom/news/stripe-completes-metronome-acquisition?ref=mbi-deepdives.com) Metronome in early 2026, which powers usage-based billing for OpenAI, Anthropic, Confluent, and Nvidia. They also sit at the company formation layer: Stripe Atlas now handles over a quarter of all Delaware incorporations, so the AI-driven surge in new company creation flows straight into their funnel. When AI application-layer revenue is inflecting, Stripe basically clips a coupon on it the day it’s earned. Adyen, by contrast, is structurally levered to established enterprise commerce which grows at GDP-plus-wallet-share, and their own land-and-expand math means new wins take relatively longer to show up in the P&L. Moreover, changes to US tariffs weighed on online retail, particularly large-volume merchants headquartered in APAC (Temu/Shein de minimis exposure) in 2025. What’s telling is that Adyen now also seems to be chasing the same pool. In the 1H’26 earnings call, Adyen management mentioned that they won OpenAI as both an Adyen Agentic partner and a payments customer, and [acquired](https://www.adyen.com/knowledge-hub/talon-one-orb-acquisitions?ref=mbi-deepdives.com) Orb, extending into usage-based billing, that expected to add 2 points to 2H’26 revenue growth. Orb is essentially their answer to Metronome. Adyen management believes Orb will let them onboard AI-native companies earlier in their journey by combining billing and payments. As Adyen is clearly playing bit of a catch up here and Orb’s 2-point contribution to revenue growth is hardly inspiring, Stripe’s investors may be feeling a little less discomfort about Stripe’s towering valuation compared to Adyen’s. While Adyen’s market cap is hovering around \~$40 Billion, Stripe’s valuation went from [$91.5 Billion](https://finance.yahoo.com/news/stripe-valued-91-5-billion-133135464.html?guccounter=1&ref=mbi-deepdives.com) in February 2025, [$106 Billion](https://www.pymnts.com/news/fintech-investments/2025/stripe-valuation-reaches-record-106-billion/?ref=mbi-deepdives.com) in September 2025, then [$159 Billion](https://www.cnbc.com/2026/02/24/stripe-value-stock-sale-tender-offer.html?ref=mbi-deepdives.com) in February 2026. ![chart](https://substackcdn.com/image/fetch/$s_!fjzj!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc889edc0-139a-4d49-9911-624cba1de6e9_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Given Stripe is a private company, it is always hard to pinpoint source of growth. Was the growth in 2025 and 2026 YTD largely organic or recent acquisitions such as Bridge, Privy, and Metronome are the major reasons for growth divergence between these two companies? While such questions can be quite relevant for investors of both Adyen and Stripe, Stripe is quite eager in pointing out their valuation based on private market has handily beaten S&P 500: > “Even while undertaking significant organizational investment and M&A, Stripe’s share count is lower today than three years ago. Stripe’s share price has compounded at 31% since our Series D fundraise 10 years ago, versus 14% for the S&P 500 and 18% for the Nasdaq over that same period.” For the privilege of being a private company, Stripe is blissfully unaware of any volatility that comes with being a public company. Of course, volatility doesn’t evaporate even if it doesn’t show up on a day to day basis. Every autocratic regime usually feels very stable up until the moment it gets toppled whereas most democratic regime feels lot more chaotic and unstable. But such instability is what is usually required to create a more stable system over the long term. While I hold this framework in my mind while looking at private vs public market valuation, admittedly I too have started to find it difficult to hold any grudge against a company such as Stripe for being private. Dev Ittycheria, former CEO of MongoDB, recently [posted](https://x.com/dittycheria/status/2086466000929603897?ref=mbi-deepdives.com) why he believes the case for being public is lot less appealing today than it once was. As a public market investor, I worry about such a trend but find it increasingly hard to argue against. ![](https://substackcdn.com/image/fetch/$s_!SQXV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F10bbb7d8-1afa-470e-98f8-e5fd97e431e7_685x705.png) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Core Memory URL: https://www.mbi-deepdives.com/core-memory/ Last updated: 2026-08-23T16:21:12.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- Over the last few weeks, my wife has been preparing for the “Principles and Practice of Engineering” or what is more colloquially known as “PE” exam. As she’s more busy with her job and can hardly study during the week, she wants to spend her weekends studying for the exam. I almost chuckle a bit thinking about how just a hundred miles away from our home hundreds of mid-20 year-old engineers are toiling through to build the machine god(s) while my wife is preparing to pass her PE exam. To be fair, unlike CFA program (which I finished almost a decade ago) that largely has a signaling value i.e. you can show your potential employer that you have put in real effort in gaining decent understanding on a wide range of topics related to investment management, PA exam has more tangible and direct impact on my wife’s prospect of getting a promotion. Of course, I find it a little absurd that regardless of how good she is in her job, she is almost automatically disqualified from applying for more senior positions or getting a promotion until she gets her PE license. I was also a bit surprised to [learn](https://marginalrevolution.com/marginalrevolution/2026/06/colorados-funeral-mistake.html?ref=mbi-deepdives.com) that almost a quarter of US workforce requires some sort of license to work in their chosen profession, a number that crept up from just [5%](https://www.journals.uchicago.edu/doi/10.1086/669060?ref=mbi-deepdives.com) in 1950. Anyways, with a 20-month old son at home, my wife was starting to find it just as difficult to study during the weekend. So we made a plan that I will take our son outside doing various activities during the weekend so that she can focus for her PE exam. I opened my Airbnb app to see if there are some “Airbnb Experiences” nearby that would be appropriate for some father-son weekend activity. I came across this [experience](https://www.airbnb.com/experiences/7051466?ref=mbi-deepdives.com) that lets you bottle feed baby goats and hand feed horses at the farm. I thought not only my son may find the experience intriguing, I too never actually hand fed goats or horses. The only thing that gave me pause was it was priced at only $15 per person which was significantly lower than most of the experiences I came across on Airbnb. However, after reading handful of positive reviews, I decided to book the experience. So, my son and I showed up at the experience last weekend. The host was there to greet us with two baby goats. My son seemed more amused to see me feed the goats and horses rather than doing it himself. See the video below to get a sense of our experience in the farm. 0:00 /1:00 1× As regular readers can perhaps imagine, I spent half the experience chatting with the host about her business and experience as a host on Airbnb. She seemed genuinely interested in sharing her thoughts, so I ended up learning quite a bit from the conversation. She mentioned this summer was her best year on Airbnb since 2021\. Following the pandemic boost, her bookings didn’t surpass the Covid high until this year. She has three Airbnb listings on her farm and anyone who books for Stays gets the “Experience” for free. However, she noticed that she’s getting more traction just for the experience itself. In fact, she started to wonder whether she priced the experience too low, especially since some guests (just like me) mentioned they were a bit nervous after seeing the prices that seem suspiciously low compared to any other experiences on Airbnb. For what it’s worth, I just checked the prices and the host already increased the price to $25 per person. The host and I both agreed that Airbnb Experience has a serious supply problem. As Airbnb is trying to hand pick supply and approve manually, it’s taking them ages to scale supply here. She was telling me that she noticed there was a local shop nearby which was promoting a sourdough workshop class on Facebook. Why aren’t they listing on Airbnb Experience? Well, the host said even she had to wait quite a bit before getting the approval for the Experience even though she has been a host on Airbnb for a decade. A part of it could also be lack of broad awareness. Part of it may be simpler: when your daily routine feels utterly ordinary to you, it's hard to imagine strangers paying to experience it. Indeed, there are actually quite a few farms near my area and almost none of them offer anything similar. Our host seems to have been emboldened by the initial success of her “Experience”, and she’s now plotting to add one or two more experiences within her ranch. Airbnb does seem to understand that such sluggish pace of adding incremental supply may not cut it if they want to scale this as they have recently [partnered](https://skift.com/2026/08/11/airbnb-partners-with-tripadvisor-experiences-drops-build-your-own-strategy/?ref=mbi-deepdives.com) with Viator (which is a leader in this space) to bring a “selection” of tours, activities, and attractions available on Airbnb. It’s not clear which Viator supplies will be available on Airbnb, but Airbnb really needs a lot more supplies to have any chance of scaling this. I can tell you there is no other experiences left on Airbnb besides this farm experience that my son and I can do together. Of course, adding experiences from Viator raises the question whether Airbnb’s advantage of unique supplies is getting very much diluted. Their decision to aggressively add more and more hotel supplies on Airbnb certainly adds more fuel to such concerns. if Airbnb becomes just another OTA without any unique supply, can they maintain their high organic traffic/demand in 5-10 years? My guess is Chesky does feel bit of tension around this dynamic which is why he resisted for so long to add hotel supplies on Airbnb. I do think in the age of AI, deep personalization can go a long way to assuage much of these concerns. Any guest who may be put off by hotel listings should see very few or none of them in his or her search results. At the same time, Brian Chesky seems quite serious about injecting Airbnb to some sort of real world “social network”. On this point, a recent [hiring role](https://careers.airbnb.com/positions/8112204/?ref=mbi-deepdives.com) at Airbnb piqued my interest. Airbnb appears to have a team called “People to Meet” that focuses on experiences related to run clubs, supper clubs, book clubs, game nights etc. Airbnb has not quite discussed such initiatives in public yet, and my guess is they will talk more about it during the winter product release this year. Success in such experiences will certainly go a long way in differentiating Airbnb from other OTAs, but of course success here is far from guaranteed. And as mentioned in my recent [Airbnb update](https://www.mbi-deepdives.com/abnb-2026/), I do not suggest investors assume much success from Experiences & Services in their base case. ![](https://substackcdn.com/image/fetch/$s_!whbg!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb8cbce32-1e7c-47d6-b431-8ab78e19a838_2170x1162.png) Source: [Airbnb](https://careers.airbnb.com/positions/8112204/?ref=mbi-deepdives.com) Chesky once [**mentioned**](https://www.mbi-deepdives.com/airbnbs-bet-on-anti-trend/) why he believes people will yearn for real experiences in the age of AI (emphasis mine): > “I like to say you want to **ride a trend or ride the opposite trend**. And so if we’re basically creating this fantasy digital realm that is highly artificial, I think in reaction to that, people want what’s real. > > This is not an anti-phone rant. This is not an anti-AI thing. It’s just about the fact that **we need to have a balance**. Do you ever notice that devices and screens aren’t usually in your dreams? There’s something about the digital realm that doesn’t quite stick in your memory the way physical experiences do. And I think increasingly if AI frees up more of more of our time, hopefully that time can be spent in the real world having meaningful experiences with people we care about. And to me, that’s what life is really going to be about. And I want to be a part of that.” While driving back from the Airbnb Experience, I was actually thinking about this quote. My son will certainly not remember the experience, but I suspect feeding the baby goats and horses with my 20-month old son will become part of my own core memory. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Non-linear Economics of Grocery Delivery Basket URL: https://www.mbi-deepdives.com/grocery-basket/ Last updated: 2026-08-22T14:47:36.000Z A couple of days ago, I [**discussed**](https://www.mbi-deepdives.com/cart-vs-dash/) about Instacart’s unit economics and mentioned “there is non-linearity in grocery economics in terms of basket size which is why owning half the basket size of Instacart certainly does not lead to half the profits.” Today’s post is about quantifying such non-linearity and I will show behind the paywall the massive disparity in grocery delivery economics between Instacart and DoorDash. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Walmart’s Evolution URL: https://www.mbi-deepdives.com/wmt2q26/ Last updated: 2026-08-22T12:55:46.000Z After posting 4%+ same store sales growth in the US for eight consecutive quarters, Walmart’s comp numbers last quarter surprised investors a bit as it came to be only 2.6% in Q2 FY27\. During the call, management flagged their health & wellness (H&W) segment as the primary culprit for the poor comp. One of the key reasons for the underperformance in H&W segment is MFP. What exactly is MFP? MFP is the “Maximum Fair Price” that comes out of the Medicare drug price negotiation program created by the Inflation Reduction Act in 2022\. For the first time, CMS was authorized to directly negotiate prices with manufacturers for a selected list of high-spend drugs covered by Medicare. The first cohort of drugs had prices announced in August 2024 that took effect January 1, 2026, which is why Walmart's fiscal 2027 (Feb 2026–Jan 2027) is “the first year of Maximum Fair Price regulation.” The negotiated prices were cut 40–80% below list depending on the drug. Moreover, **the list expands annually**: a second round of 15 drugs takes effect January 2027. When the government administratively cuts the price of the highest-dollar drugs that Medicare seniors fill, revenue per script drops even as script volumes keep growing. That's why management insisted the impact is top-line only. The economics of dispensing i.e. the spread and fees Walmart earns per script aren't damaged proportionally to the revenue decline, and generic conversion can actually improve margin rate. Meanwhile, the strategic value of the pharmacy is the traffic and attachment. Walmart management shared that the H&W customer spends \~3x the average Walmart customer, and it doubles again when those customers sign up for pharmacy delivery. So you get the odd optical situation of SSS drag on reported Walmart US comps while the profit contribution of the category is fine and prescription volumes keep growing with share gains. Walmart had quantified the MFP headwind early this year, but never the GLP-1 tailwind or the ex-health-&-wellness comp view. The 100bps MFP headwind first appeared on the Q4 FY26 call in February 2026 during Q&A, and by the Q1 FY27 call in May it had graduated to management’s prepared remarks. Yesterday’s call made these headwinds more clear than it was before: the MFP headwind estimate was raised to 125bps for both Q2 and the full year, the GLP-1 tailwind was quantified **retroactively for the first time** (\~100bps in each of FY25 and FY26, fading to roughly half that in FY27), and then explicitly showed core comps ex-H&W running a steady 3–4% for the last two and half years. ![](https://substackcdn.com/image/fetch/$s_!lu4I!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fefa61b85-be15-4135-87be-fa70c3f84619_1197x619.png) Source: Walmart Earnings presentation Look, I don’t have a problem with these adjustments especially given the impact seems to be largely confined to topline without commensurate impact on bottom line. But the asymmetry of the disclosure should be really annoying for the shareholders. Where was this comp view ex H&W segment when GLP-1 was a tailwind? Management seems very willing to have their cake and eat it too. To be quite frank, I’m also quite careful about any explanation coming out of Walmart’s CFO John Rainey who doesn’t really have the best reputation in my mind. For context, before becoming Walmart’s CFO in 2022, he used to be CFO at PayPal. He had this “bright” idea of sharing a medium term outlook for PayPal that in retrospect appears to be borderline absurd. As you can see below, during the 2021 PayPal Investor Day, Rainey outlined monthly active of 750 million, revenue of $50+ Billion, and FCF of $10+ Billion in 2025\. After providing such rosy outlook for PayPal, Rainey moved to Walmart. What did PayPal actually do in 2025? Their monthly active was 439 million, revenue was $33.2 Billion, and FCF was $5.6 Billion. You can argue that Rainey shouldn’t be held responsible since he wasn’t really there to execute on his plan, but given how far the company fell short on his outlook, I am not willing to be so charitable in this case. ![](https://substackcdn.com/image/fetch/$s_!5Ljb!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2be6ac6a-9cd6-4999-b812-60e53d3e751f_1579x675.png) Source: PayPal Investor Day 2021 Anyways, while the US comp was a negative surprise for the investors, yesterday’s call also really made it clear the extent of Walmart’s evolution from merely brick-and-mortar presence to increasingly perhaps the only truly omnichannel retailer in the US which I will discuss behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Instacart’s Unit Economics, and Implications for DoorDash URL: https://www.mbi-deepdives.com/cart-vs-dash/ Last updated: 2026-08-20T15:50:40.000Z While covering DoorDash’s [**2Q’26**](https://www.mbi-deepdives.com/dash2q26/) a couple of weeks ago, I showed the company’s LTM unit economics on a per order basis. I re-created this per order economics for Instacart. I will discuss the unit economics of Instacart as well as some implications for DoorDash behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!9HM_!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd118d4c3-ff10-4646-9b50-6d36a08945c1_1632x595.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb Model Update (2026) URL: https://www.mbi-deepdives.com/abnb-2026/ Last updated: 2026-08-19T16:30:55.000Z The last time I shared my updated Airbnb model was [**October 2025**](https://www.mbi-deepdives.com/airbnb-model-update/). As the stock has gone up \~50% since then, investors need to re-underwrite the assumptions today. As[ **promised**](https://www.mbi-deepdives.com/options/) last week, I am going to share my updated model and lay out what you need to believe to get to a reasonable IRR from current price behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Rethinking the Data Moat URL: https://www.mbi-deepdives.com/data-moat/ Last updated: 2026-08-18T16:36:31.000Z I want to highlight a couple of pieces which I found to be quite intriguing. The first is Dwarkesh Patel’s [conversation](https://www.dwarkesh.com/p/ryan-greenblatt?ref=mbi-deepdives.com) with Ryan Greenblatt, chief scientist at Redwood Research. Admittedly, while the conversation about whether automating AI research triggers recursive self-improvement was thought provoking, it was also quite spooky at times. The second piece that I would like to highlight is a [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) by Shuchao Bi, titled “Advancing the Frontier of Silicon Intelligence: Past, Open Problems, and the Future”. Bi co-founded YouTube Shorts at Google, ran multimodal post-training at OpenAI, and now works at Meta Superintelligence Labs. While Greenblatt’s conversation with Dwarkesh was published last week, Bi gave the presentation more than a year ago. Since I happened to stumble onto both of these during the weekend, I could notice a healthy dose of similarity in Greenblatt’s and Bi’s arguments. Last month, I wrote about the [**salience of data**](https://www.mbi-deepdives.com/data/)in the context of AI and Alphabet [bidding](https://www.axios.com/2026/08/17/google-spirit-airlines-bankruptcy?ref=mbi-deepdives.com) for bankruptcy auction for Spirit Airline’s data certainly corroborates to that. However, both Greenblatt and Bi made me re-think my position a bit on this topic. Greenblatt had an interesting thought experiment: if you could hold compute or data constant, how much the model would still improve? That delta of improvement could be labeled as “algorithmic progress” and he made the case that it is a very important driver of AI progress over the last few years (emphasis mine): > “GPT-3 was released in 2020, so it was trained about six and a half, seven years ago. It’s worth noting that GPT-3 is maybe a little too far in the past, but let’s go with this for a second. > > If we were to train a model with GPT-3-level compute today, how good would that model be? My understanding, based on how algorithmic progress works, is that we’d be able to train a model that’s as good as the best model we had perhaps around three years ago. **So I think that right now we’d be able to train a version of GPT-3 that’s probably somewhat better than GPT-4, a moderate amount better than GPT-4.** I think that’s about right. That roughly lines up with how algorithmic progress has worked.” Bi didn’t quite say “algorithmic progress”, but pointed out that the raw data is “unlikely to be the best data distribution”. He suggested that the incremental improvement in scaling law may come from changing the data distributions or to say it differently, “by equalizing intelligence per token”. My read is that they are essentially alluding to the same argument but using different words to explain their intuitions. ![](https://substackcdn.com/image/fetch/$s_!TTdu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc4a4499-be67-4507-a260-117c199a3b2d_1282x637.png) Source: Shuchao Bi’s [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) Later in the conversation, Greenblatt expanded why he is not a big believer of the role of “human expert data” in model improvements, rather the process improvement around data itself is the larger driver. From the podcast (emphasis mine): > “I think the vast majority of pre-training data improvements are from science on better understanding what data sets are good and schleppy labor on figuring out how to filter down. > > So my view is that improvements of the form of, like, [OpenWebText](https://github.com/Skylion007/openwebtext?ref=mbi-deepdives.com) to [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb?ref=mbi-deepdives.com), that improvement is better described as an algorithmic improvement of the sort that you can study with some [GPUs](https://en.wikipedia.org/wiki/Graphics%5Fprocessing%5Funit?ref=mbi-deepdives.com), and you don’t need human expert data to do that. Now, there’s a different effect which we could talk about, which is that maybe the internet in 2026 is more of a fertile ground for training data than the internet in 2018\. There’s also been an effect where there are just more humans posting on the internet, so there’s more data to harvest. My sense is that that effect is going to be quite a bit smaller than the effect of humans knowing better how to curate the data, having better scrapes, knowing how to process those scrapes better — this sort of thing.” Greenblatt’s arguments certainly gave me a pause because my prior was a bit different and likely much closer to Dwarkesh who also appears to think human expert data played a critical role in the model’s recent trajectory. Bi probably agrees with Greenblatt since he decomposed where human knowledge comes from: a loop of proposing tasks, learning existing knowledge, thinking, getting feedback from the environment, and distilling the findings back into knowledge and wondered aloud in his talk which steps AI can accelerate. His answer is basically nearly all of them, including proposing the tasks in the first place. Greenblatt essentially makes the same claim retrospectively: RL environments improved over the last two years mostly because labs learned **what** to build and used enormous amounts of AI labor to build it. ![](https://substackcdn.com/image/fetch/$s_!_m1V!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F89d96280-1baf-4f8a-aaa0-0677e6f87431_1321x723.png) Source: Shuchao Bi’s [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) ![](https://substackcdn.com/image/fetch/$s_!QaH4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7d030acf-0cac-4b2f-8271-2fad844723a9_1267x724.png) Source: Shuchao Bi’s [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) Another interesting observation by Greenblatt was that machine learning (ML) is a “shallow” domain compared to math and given that even in math we are transitioning “[**from an era of proof scarcity to an era of proof abundance**](https://www.mbi-deepdives.com/proof/)**”,** automating much of ML may prove to be lot more amenable. From Greenblatt: > I think ML is a very shallow domain relative to math. In math, there was much more of a thing where you find some true deep abstraction, and if you really understand that thing, which is hard to understand, then you get somewhere. Whereas I feel like the things that are the equivalent of that in ML are really dumb bullshit. Like with scaling laws, come on guys, we can explain scaling laws really quickly. I think the deepest and most important concepts in math, for example, don’t have the property that you can really understand the underlying thing and why it matters in a very short period of time. > > My sense is that some domains are structurally different in terms of how they operate and how much they depend on deep abstractions. Physics and math are much more on the side of being very far on the deep, hard-to-come-up-with-ideas side, whereas I think ML and most other domains are much more amenable to [hill climbing](https://en.wikipedia.org/wiki/Hill%5Fclimbing?ref=mbi-deepdives.com). That’s my sense of how this will go in the future. > > Even in cases where there has been some breakthrough in AI, oftentimes in retrospect it looks like a big bottleneck to making that breakthrough happen was getting all of the micro details and mungy intuition right. An example of this is training AIs to be good at reasoning and [chain of thought](https://research.google/blog/language-models-perform-reasoning-via-chain-of-thought/?ref=mbi-deepdives.com), doing RL on chain of thought. It looks like you probably could have done RL and chain of thought on GPT-3 and gotten kind of interesting results on math if you had really scaled it up and done a good job. Bi also made the point that learning from environment interaction is efficient wherever a perfect simulator exists (coding, math etc.) and fundamentally blocked where simulation is impossible or the sim-to-real gap is simply too large ( for example biology, and experimental physics). Given that context, automating AI research doesn’t seem nearly as outlandish. ![](https://substackcdn.com/image/fetch/$s_!Ki68!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F15eb3521-a444-4160-b147-580d1b1e940b_1312x697.png) Source: Shuchao Bi’s [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) Gavin Baker in a recent [reply ](https://x.com/GavinSBaker/status/2088684251441254666?ref=mbi-deepdives.com)to an Anthropic researcher mentioned, “I do think the computational efficiency of humans (I’m impressed that your brain runs on only 15-20 watts tbh) means that humans will be economically useful for the foreseeable future \*even\* in a fast takeoff, AGI maximalist scenario.” Indeed, the efficiency of humans was also highlighted by Bi during his presentation (see a bunch of related slides below). ![](https://substackcdn.com/image/fetch/$s_!Pn5E!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3722acae-cf38-4a01-94a8-b7eb04a55e6a_1303x688.png) Source: Shuchao Bi’s [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) ![](https://substackcdn.com/image/fetch/$s_!cFFD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea3d0a21-b31b-4d91-9497-0edcbb1b1f5b_1308x658.png) Source: Shuchao Bi’s [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) ![](https://substackcdn.com/image/fetch/$s_!ogr2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7a536f56-9b48-4523-b3e4-37c4a2cc056c_1306x678.png) Source: Shuchao Bi’s [talk](https://www.youtube.com/watch?v=E22AOHAEtu4&ref=mbi-deepdives.com) However, the amazing efficiency of homo sapiens is also a stark reminder that nature has already shipped a general intelligence that runs on less power than a dim lightbulb. Today’s models need a building full of GPUs and most of the written internet to often do less. Does that indicate something “special” about us or is it a measure of how inefficient our current approach still is? Bi’s bet is that the biggest waste sits in **how** models learn. If someone fixes that, the cost of a given level of intelligence can fall by orders of magnitude. Of course, I am not in a position to know or predict whether this is at all fixable or even if it is, when that may happen. A peer recently praised me to help him improve his understanding of the AI landscape through my work at MBI Deep Dives. I jokingly mentioned to him I’m glad that you feel that way, but ironically the more I study AI landscape, the more certain I become that I need to hold every opinion related to AI very loosely. Such a frame of mind doesn’t inspire a lot of confidence in my own mind that I can see too far ahead. Investors are trying to price AI landscape based on near-term trajectory of respective companies, but given how fast things can alter in the AI landscape, it is hard not to feel that betting **for or against** this trade carries a monumental risk either way. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://mbideepdives.substack.com/p/deep-dives) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Why DoorDash’s Autonomous Strategy Beats Uber’s Hodgepodge URL: https://www.mbi-deepdives.com/dash-vs-uber/ Last updated: 2026-08-17T16:13:02.000Z Uber’s decision to [divest](https://techcrunch.com/2026/08/11/uber-surprised-robotics-company-serve-by-selling-its-entire-stake/?ref=mbi-deepdives.com) its stake in Serve, the autonomous delivery robot company that was spun out of Uber in 2021, was more interesting than it may appear at first glance. When I looked into it, I came to realize that Serve is actually a publicly listed company now. It was founded inside Postmates as a division back in 2017 but Postmates itself was acquired by Uber in 2020\. While Uber was a very close partner (and shareholder) of Serve from the very beginning of Serve’s independent existence, that started to unravel in 2026\. While going through Serve’s earnings call, some historical contexts as well as DoorDash’s approach to autonomous delivery, I came away thinking that Uber is taking a somewhat risky approach long-term which I will expand behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The AI Compute Squeeze URL: https://www.mbi-deepdives.com/compute-squeeze/ Last updated: 2026-08-14T15:18:15.000Z ***Programming Note***: MBI Deep Dives will be off during the weekend and I will resume the daily posting cadence from Monday next week. --- If you followed hyperscalers earnings, you probably already knew that the neoclouds would repeat some version of “demand for compute is outstripping supply” in their earnings calls. And of course, they did. However, there were some useful nuggets in CoreWeave and Nebius’ earnings calls that seem to be further shaping the narrative around the compute price environment. The quote that was highlighted perhaps the most by investors who are bullish on the business of selling compute was CoreWeave management pointing out the ever extending useful life of previous generation of GPUs. From CoreWeave’s call (emphasis mine): > “What we are seeing today is that the upside of re-contracting is real as we remain largely sold out of prior generations of NVIDIA GPUs in addition to the current SKUs. So as our earlier generation fleets roll off their original contract, they offer the potential to deliver strong returns in the subsequent years. We are seeing this across our Ampere and Hopper fleet. > > As an example, we recently signed an A100 contract that extends into 2029 at an attractive price. As a reminder, **this SKU was introduced in 2020**. Clusters of prior generations of architecture offer installed, energized production-grade compute already running at scale. They come with a proven ROI for customers. In a market where new capacity is supply constrained and costs are rising, AI cloud infrastructure and production is a scarce, valuable asset. While we have built a business whose economics do not rely on re-contracting after initial customer term. Increasingly, **we are seeing longer utilization at higher prices, offering the potential for significant further upside**.” The bull case is rather obvious from that aforementioned quote. If GPUs of previous generation can be sold at higher prices and the useful lives are longer than assumed, the earnings are currently understated. I do, however, suspect there might be some real holes in such convenient takeaways. DaRazor [pointed](https://x.com/akramsrazor/status/2087317475776159855?ref=mbi-deepdives.com) out an alternative interpretation which makes more intuitive sense to me. (in case you’re wondering, “supreme intelligence” here is AI) ![](https://substackcdn.com/image/fetch/$s_!2v9l!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F53adc2f9-6687-4cca-919d-5fd5e8272534_691x387.png) Later, DaRazor had another intriguing [take](https://x.com/akramsrazor/status/2087647595745411545?ref=mbi-deepdives.com) by pointing out the possibility that the type of customer who would be interested in A100 chips today: ![](https://substackcdn.com/image/fetch/$s_!YdTj!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F03ec46ee-35aa-4778-8412-3cbd759242c9_676x973.png) As you can see, there is a real possibility that we may not actually infer much from that CoreWeave quote. Nonetheless, if you still want to assume the useful lives of GPUs are actually longer than assumed, you can make an argument that it is perhaps Amazon, Meta, and Nebius that are under-reporting their earnings much more than CoreWeave and Alphabet. I would, however, caution to make such leap as I suspect depreciation rate may not prove to be the same across the board. Even when they are buying the same generation of GPUs from Nvidia, the actual depreciation rate can depend on variety of factors such as utilization intensity, power scarcity, workload cascade depth (to what extent an operator with a deep bench of lower-intensity workloads can demote chips from training to inference to batch/internal workloads etc. ![](https://substackcdn.com/image/fetch/$s_!8I6i!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb29c5872-1788-4b9b-bcef-5d9a95fe6ec1_1120x951.png) Source: Claude Fable 5 Gavin Baker also made the argument in his recent podcast [appearance](https://colossus.com/episode/ai-market-jitters/?ref=mbi-deepdives.com) at “Invest Like the Best” that revenue estimates for hyperscalers are currently underestimated given the exceptionally strong demand environment. From Gavin Baker (emphasis mine): > If you model it out, if you look at the amount of gigawatts that are supposed to come on, and consensus estimates for hyperscalers, they’re effectively modeled — and these are gigawatts of Blackwell and Rubin. Rubin being Nvidia’s next chip, Blackwell being the current chip. **They are modeled to monetize roughly at the rate of Ampere, which is two generations behind — not at Hopper, but Ampere. So** there’s $1.3 to $1.4 trillion in hyperscaler operating cash flow. If you just assume, I think it’s very unlikely they monetize at the rate of Ampere. We could go into why. Some of it comes from seeing what is happening on the ground with demand here from real quantitative metrics. > > **But let’s say they monetize at a discount to current Blackwells — then it’s more like $2 trillion of operating cash flow. And that takes $700 billion of credit demand out. Ironically, as that improves all the credit ratios, as these installed bases of compute reprice, we’re going to continue accelerating. Consensus is modeling in a deceleration, which I think is unlikely.** Then the credit metrics look better, and then all of a sudden it gets easier to finance with credit. Now, whether they choose to do that or not, we’ll see. Indeed, listening to Nebius call made me think that in case demand remains just as strong when 3P hyperscalers are going to renew their contracts and bring new capacity online, they may end up generating materially higher revenue per MW than their older contracts. Let me highlight what Nebius management said on how they’re pricing the compute deals in current market (emphasis mine): > “We choose when to sell, to whom we sell and on what terms, and how we finance everything. This flexibility allows us to meet the needs of independent AI builders and to support an open, diverse and competitive market. We address customer needs with 3 types of deals. Each has a different duration, pricing and role in our business. > > First, for our core AI cloud business. We have midterm contracts of 1 to 3 years with the world’s most ambitious AI companies. This quarter alone, we closed 4 landmark deals, with Reflection, Cohere as well as with a scaled U.S. new lab and a large U.S.-based quant trading firm. These deals were for an average of more than $1 billion each. **They represent a yield of $20 million to $25 million per megawatt, with upfront payments that cover 50%, 60% of the associated CapEx. But most importantly, we could sell today our entire 2027 capacity on these terms if we wanted to. But we are not doing this. We see that we can achieve higher value by retaining some capacity to serve shorter-term and immediate client needs.** > > And here we come to the second type of deal, **this is shorter-duration capacity, typically for up to 6 months for customers with an immediate time-bounded needs with high-value requirements. For this, they are ready to pay a significant premium. We’re negotiating deals for $40 million to $50 million per megawatt range, and sometimes above, under this model**. One of such deals has been signed just recently by the way. > > The third deal type, as we noted many times in the past, is **our long-term contracts with investment-grade customers. They serve an important purpose. They help us to finance our build-out faster and more efficiently**. The secured debt facility we raised in July was on the back of one of these deals. And with $40 billion in contracted backlog, we will do more of this.” Just to put these numbers in perspective, Meta signed a [$12 Billion, 5-year deal](https://nebius.com/newsroom/nebius-signs-new-ai-infrastructure-agreement-with-meta?ref=mbi-deepdives.com) with Nebius in March 2026 which will come online early next year. While the company hasn’t explicitly disclosed this, it likely implies $10-13 million per MW pricing range. When Meta builds their own data centers, obviously their internal cost per MW is likely even lower than that. When Meta mentioned in their earnings call that they are getting offer for their compute that are “multiples” of what they paid for, you can sense that the multiple could potentially be \~5x of what it costs Meta! Of course, the math is likely similar for 3P hyperscalers as well. The demand for compute is truly at stratospheric level which is leading to somewhat obscene margins for compute sellers. One may wonder given how the number of neoclouds have mushroomed over the years and there seems to be more and more entrants to compute seller market, why on earth such obscene margins are being kept by this increasingly fragmented market? Or to say it differently, why on earth Nvidia is not capturing more of this economics? Indeed, I suspect the only reason Jensen Huang hasn’t pressed the pricing accelerator too much is capital has become the key bottleneck in AI. That perhaps explains at least partly why Nvidia is [partnering](https://x.com/JensenHuang/status/2086934705207959965?ref=mbi-deepdives.com) with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR for the $500 Billion AI infrastructure bonanza. Once capital is not the bottleneck anymore and the compute seller layer becomes more fragmented over time, Nvidia will be happy to raise price on their GPUs to capture lion’s share of “excess profits”. Nvidia may not need to explicitly raise price and as the recent media reports [suggest](https://www.theinformation.com/articles/nvidia-says-will-take-cut-customers-cloud-revenues?rc=4lgoj7&ref=mbi-deepdives.com), they will simply ask for revenue share if, for example, compute price per MW exceeds certain threshold. My friend Liberty had an interesting [take](https://www.libertyrpf.com/p/653-jensen-commoditizes-capital-constellations?ref=mbi-deepdives.com) on Nvidia’s plan to solve the capital bottleneck: > **“Nvidia has a large information advantage over the banks: Jensen knows the roadmap** (he’s the one writing it ✍️)**.** So they should also have a better idea of what today’s GPUs will still be worth a few years from now. Of course, it’s about as far from a neutral appraiser as you can get, but that doesn’t mean it can’t pull this off. > > The irony here is that nobody is working harder to make today’s Nvidia GPUs obsolete than Nvidia itself” I actually think Liberty may have inadvertently explained why Jensen’s “information advantage” is worth very little in determining the residual value of GPUs. Jensen Huang, as he likes to call himself “[Chief Revenue Destroyer](https://techcrunch.com/snippet/2982699/chief-revenue-destroyer/?ref=mbi-deepdives.com)”, is running so fast to launch newer generation of GPUs not out of mere whims. Huang knows it very well that several well-capitalized competitors are very much deeply invested in commoditizing the chip layer if Nvidia ever lags behind or stops leading the performance frontier. As a result, the value of GPUs is not necessarily solely dictated by Nvidia’s own trajectory, rather also driven by both its own plans as well as the competing alternatives. Nvidia may have an outsized share in the chip layer today, but it is certainly not the only game in town. Perhaps even more importantly, almost everything in the compute value chain will ultimately be decided by compute demand on which Jensen Huang or the banks have no control. The companies who are really leading and pushing the demand frontier are frontier AI labs. If AI labs revenue fails to meet expectation in the next couple of year, my best wishes to anyone selling compute if they’re hoping compute pricing won’t feel a jolt. The good news for compute sellers is that there is really no tangible indication that things are slowing down for frontier labs. Given how aggressively they are buying compute and how much they are willing to pay for compute, I actually suspect they might be closer to a new inflection point for which they suspect they will need abundant compute. Since both the frontier AI labs will likely be public by 2027, they may look pretty foolish for signing such expensive compute deals if revenue starts decelerating and costs start accelerating while reporting on a quarterly basis. But their behavior would seem quite rational if the labs are close to crack something substantial….maybe continual learning? --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Why I am going to utilize options more to manage my portfolio URL: https://www.mbi-deepdives.com/options/ Last updated: 2026-08-13T14:44:26.000Z Back in 2021, I once responded to someone on X (formerly twitter) why I didn’t want to engage in trading options. While that choice sounded “wise” to me, reality hit me in 2022\. As you can see [**here**](https://www.mbi-deepdives.com/portfolio/), I came to 2022 with a significant exposure to big tech companies (Meta, Amazon, and Alphabet) all of which got hammered throughout 2022\. To make things worse, I kept averaging down through much of 2022 and by Q4 of 2022, I was looking at the drawdown abyss with no money left to buy more. Since the stocks I owned continued to bleed, I had to do an about turn on my “wisdom” related to options trading. I decided to buy [**call options**](https://www.mbi-deepdives.com/ddog/)(see last section) on Meta, Alphabet, and Amazon. I know this sounds like a victory lap and indeed, those call options were by far the largest contributors to my performance since inception. At the same time, Ray’s tweet below was also perhaps prescient in how one would behave after such a home run. Indeed, I ended up buying LEAPs on Lululemon in 2024 which then led me to suffer \~80% loss in those options, by far the largest ever loss in my career. Since the Lulu debacle, I haven’t used options in my portfolio yet. That **changed** yesterday and since I expect myself to utilize various options strategies going forward to manage my portfolio’s risk, I wanted to explain my approach today and elaborate on my thinking. I will do so behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!xEkV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5726b7d1-5e02-4bc5-9124-e53c650ceb92_703x433.png) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Constellation Software 2Q'26: Soft Organic Growth, but Encouraging Deployment Pace URL: https://www.mbi-deepdives.com/csu2q26/ Last updated: 2026-08-12T17:16:37.000Z Constellation Software (CSU) delivered a quarter that can be considered bit of a Rorschach test. Organic growth printed the weakest number in nearly 6 years while capital deployment pace is running pretty hot. I cannot speak for other shareholders, but if you own it (as I do) for CSU’s ability to deploy ever increasing amounts of capital at attractive ROIC, I do not expect you will lose much sleep following this quarter. Let’s start with the “problem child”: organic growth. Maintenance and other recurring revenue, which is \~77% of total revenue, grew just +2% (FXN) organically in 2Q’26, decelerating from +4% in 1Q’26 and +6% in 4Q’25\. Excluding Altera, recurring organic growth (FXN) was +4%, down from +5% in 1Q’26 which still looks somewhat uninspiring. ![](https://substackcdn.com/image/fetch/$s_!fmpr!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2985410e-b59a-4baa-9c89-d78640486854_1380x684.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Management came armed with a bridge of one-offs. Altera actually had a strong 2Q’25 comp (+1% organic vs -19% this quarter) because IFRS forced upfront recognition on a couple of new-name contracts; the underlying business remains a managed “slow shrinker.” Dark Matter, a turnaround they are still fixing, posted +10% organic in 2Q’25 and -18% this quarter off that comp. Lumine’s organic growth was just +1% as they digest their own recent acquisitions. And a South American business lost one large customer, a departure they knew about at the time of acquisition which alone was a \~30 bps drag on CSU’s consolidated number. From the call (emphasis mine): > *“*If you back out those three, four things, you would normalize back down to that sort of 5% number that we have always trended at… Based on what we own today… I would expect these anomalies to sort of revert back. **I am not expecting another large customer leaving or anything based on what we have today** *.”* While one could argue that perhaps CSU management should have disclosed all these one-off benefits when it helped them in 2Q’25, I would point out that CSU only started organizing their earnings calls from 4Q’25\. They never really elaborated these one-offs in their MD&A, including this quarter’s. So, I don’t think they were “hiding” such good news when it benefitted them. I guess CSU needs to be larger and even more fragmented company for its consolidated numbers not be affected by these one-off events. And in case you are worried about 22% total revenue decline for Altera in 2Q’26, management mentioned that IRR on the deal is still tracking **ahead** of what they initially underwrote. Four years since the Altera acquisition, it has generated \~$400 million of cumulative free cash flow while revenue shrank from over $800 million to \~$646 million LTM. For context, CSU paid \~$700 million for Altera (half of which was financed by non-recourse debt which helped the deal IRR even more). On AI, the message was consistent with the last two calls: development productivity gains are there and spreading across the portfolio, but there is no revenue impact yet…**in either direction**. Mark Miller’s framing on why AI didn’t make a dent on revenue was actually a good reminder of the nature of vertical market software : “you can build products fast, but selling them is a whole other thing.” Then he almost jokingly mentioned that CSU is essentially “using a blowtorch to light a cigarette” which speaks to not only how powerful today’s AI models are but more importantly, the trivial nature of R&D for CSU’s customers. The codebase was never quite the difficult part for CSU, but identifying the customer problem, and solving such problem on an ongoing basis is why CSU gets to keep the recurring subscription revenue. Miller also floated an angle that AI can potentially make CSU a faster fast-follower. If a horizontal AI player validates demand in one of their verticals, a business unit with the customer relationships can now copy the functionality in months rather than years. EBITA margin came in at 25.2%, up sequentially from 24.0% (Q1 payroll-tax seasonality reversing, as usual) but \~130 bps below 2Q’25\. The driver for such margin contraction was that the recent large acquisitions are coming in at low margins even though still being marched up the curve over time. The CFO disclosed that the 2026 cohort swung from -16% margins in Q1 to +16% in Q2, the 2025 cohort has climbed from \~16-17% to \~20%, while the pre-2025 cohorts sit in the high-20s to 30s. This is basically the standard CSU playbook: buy it cheap and messy, and then fix it over time, but it is worth acknowledging that as large deals become a bigger share of deployment, reported margins may structurally carry such drag at any given time. ![](https://substackcdn.com/image/fetch/$s_!f9fJ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2b59ae0b-c5aa-4b57-9d23-60d0dc396574_1501x703.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As alluded earlier, acquisitions and deployment pace was the major positive from 2Q’26\. CSU deployed $893 million in Q2 ($732 million cash plus $160 million estimated deferred), and the filing discloses another $818 million closed or committed in the first six weeks of Q3\. Add Q1’s $809 million and CSU has put \~$2.5 billion to work in about seven and a half months which is **more than $1 billion above their entire 2025 deployment**, with more than four months of the year still to go. LTM acquisitions as a percentage of FCFA2S remains above 100%. Of course, today’s acquisitions will lead to next year’s growth and pace of capital deployment this year should bode well for next year’s topline growth. ![](https://substackcdn.com/image/fetch/$s_!-3sL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F243037f2-3f26-4860-9b13-5647a9820ba1_1204x622.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The texture of the deployment is somewhat evolving though. The quarter’s marquee deals came out of PE portfolios, and DerbySoft in particular was bought at \~4x revenue by one analyst’s math (\~C$400 million for \~C$100 million of annualized revenue), which management did not dispute during the call. That is a very different fish than the \~1-1.5x revenue CSU has historically paid. Management called DerbySoft a “very successful organization… growing nicely” and noted they used leverage on the deal while reiterating that “hurdle rates aren’t changing.” Management also sort of pushed back on the idea that PE sellers are capitulating as competition for VMS assets remains “very robust,” the copycats are all still out there, and only “at the high end” is he seeing some pricing weakness. ![](https://substackcdn.com/image/fetch/$s_!dRop!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4f7d83b9-352f-4caf-8906-4e64bf7c6fbd_1045x514.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Of course, most CSU shareholders care about their ability to deploy all these capital and more importantly the ROIC trajectory of such capital deployment. While LTM ROIC remains stable at high-teen, it did tick down by \~40 bps this quarter. (FYI, read this post to understand how I calculated CSU’s [ROIC](https://www.mbi-deepdives.com/what-exactly-is-csus-roic/). Also note that I have excluded equity investments from invested capital base since the associated return is not incorporated in numerator.) Given ROIC is backward looking, it cannot quite give you a forward signal about what may happen in the future. But it is nonetheless encouraging to see high-teen stable ROIC for the last four years even though invested capital base increased from $6.4 billion in 3Q’22 to $14.4 billion in 2Q’26! ![](https://substackcdn.com/image/fetch/$s_!Bg-2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1ef15450-d1d5-4719-9925-9653731c146b_1485x762.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will expand on CSU’s current valuation behind the paywall.. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### License to Chase the AI Frontier URL: https://www.mbi-deepdives.com/frontier/ Last updated: 2026-08-11T15:57:45.000Z While looking at Artificial Analysis Intelligence [Index](https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index?ref=mbi-deepdives.com#results), I was quite struck by what they all have in common. The top model developers except one all have one thing in common: they are all currently founder-led companies. Alphabet is the sole exception. But even in Alphabet’s case, the company is still founder controlled; given that Sergey Brin is [reportedly](https://www.ft.com/content/1453e9c2-4922-482f-8720-0bafd7e07df7?syn-25a6b1a6=1&ref=mbi-deepdives.com) back in assuming direct oversight of Gemini, you can argue that even Alphabet is not quite an exception here. ![](https://substackcdn.com/image/fetch/$s_!SPHb!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd223fc82-182a-46b9-bc2c-3e8db421cb4c_1753x853.png) Source: [Artificial Analysis Intelligence Index](https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index?ref=mbi-deepdives.com) Then I wondered about the big tech companies who are **NOT** in the list of top model developers but somewhat tentatively building models: Apple, Microsoft, and Amazon. Can you spot the commonality of these companies? We cannot run the counterfactuals, but I did wonder if Jobs, Gates, and Bezos were still at the helm of their respective companies, would they make a different choice? This isn’t necessarily criticism of the “manager class” and yet another ode to “founder mode” mantra. The reality is building, and (perhaps more importantly) staying at the frontier will be incredibly capital intensive endeavor with very uncertain payoff. The reason founders can pursue such endeavor is due to myriad of factors such as voting control, and cultural buy-in from broader stakeholders (e.g. investors and employees) due to their past successes. Managers, by definition, do not have access to such reservoir at their disposal, at least not to the extent founders typically do. I do not, however, believe the pursuit of the frontier by these founders comes down to mere whims, adventure, or voting control. I sense the primary motivation to be entirely different: relevance! While I have had somewhat skeptical tone in the past about the economics of frontier models, I have been gradually updating my view that owning the frontier will likely prove to be very, very useful even if the precise knowledge of long-term economics is somewhat unknowable today. I carefully chose the words “long term economics” because it is not enough to generate attractive economics today, rather the ability to sustain such economics for a very long period is what will drive valuation for these companies. My skepticism of the frontier primarily stemmed from my inability to spot a source of long-term competitive advantage for the frontier labs such as Anthropic and OpenAI. However, I have updated my view here as I have started to internalize the likely source of their long-term differentiation: **data**. It is quite revealing how Meta is willing to essentially give away its latest coding model “Muse Spark 1.2” for “free” if you are fine with sharing the data with them. ![](https://substackcdn.com/image/fetch/$s_!PNr3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcfd225f8-acfe-4567-865f-8046649bfdd8_1918x541.png) Going back to my point about relevance, I suspect frontier models will unlock newer products and revenue streams in the future which can only be accessed if you are at the frontier. These founders intuitively understand the long-term arc of this technology and are willing to swing for the fences to stay relevant despite the uncertain payoff. They also seem to understand that the fate of these foundational technologies can potentially be decided early in their lifecycle. Indeed, Android was released just one year after iPhone was launched. In some sense, owning the frontier may mean you are at a new branch of technology, and thanks to potentially recursive self improving (RSI) nature of these models, it may be lot easier for such company to jump to the next branch. Almost nothing may be off limits for these models in the long term (which admittedly may not be “long”) if the bottleneck to your problem is intelligence. From [chip designing](https://x.com/semidoped/status/2086859211431194634?ref=mbi-deepdives.com) to [Riemann hypothesis](https://www.anthropic.com/research/riemann-zeta?ref=mbi-deepdives.com), everything may be a fair game once you’re at the frontier. The reality is investors probably care less about Riemann hypothesis and want to know more about how much these companies will make money. Unless your estimates are going up and you can articulate clear ROI for your capex buildout, public market investors do not seem to share these founders’ fascination around pursuing the frontier. If anything, given the uncertain payoff of such pursuit, investors are much more willing to sell compute capacity to the lagging model developers. It is not hard to understand the appeal for selling capacity. Given that almost every buyer is repeating ad nauseum “demand is outstripping supply”, it just **seems** lot easier to underwrite a data center buildout for third-party customers. On the other hand, it is a lot more difficult exercise to underwrite the **curve of the pay-off structure** from the most leading frontier model company to the second to the third and so on. ![](https://substackcdn.com/image/fetch/$s_!POjE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F52d25306-3b51-418f-b5f9-26fa5f362502_684x423.png) Given that context, it is perhaps no surprise that “managers” feel a lot more comfortable in supplying the capacity to companies pursuing the frontier than chasing it themselves. Managers understandably do not feel they have the license to pursue the speculative frontier as vigorously as founders might. I was a bit hesitant to even include Apple in this list as they clearly are not in frontier model race. Amazon, on the other hand, still does not exhibit such clarity. After articulating why Amazon doesn’t need a frontier model to succeed, Andy Jassy went onto then explain why Amazon is still choosing to build “frontier” models: > “…we are pursuing our own Frontier model. And we’re doing it for a few reasons. First of which is it just gives us additional control over cost, cost for our own consumer applications, but also we’re trying to drive costs down for customers. And having a player like ourselves, it’s always focused on trying to take the price performance and the cost down for customers all the time, we think will help keep the models more cost effective for customers. > > I think also it allows us to have more control over prioritization of what models focus on. And we have, both from our own external customers as well as our internal customers inside the company, certain priorities that matter that we want the models trained especially well for. And then it gives us some control on speed.” Like [Ben Thompson](https://stratechery.com/2026/apple-earnings-more-on-amazons-earnings/?ref=mbi-deepdives.com), I also suspect Jassy may have a very different definition of “frontier” than what is typically understood. My best guess is what he’s essentially saying is Amazon will have a model on their own and for some reason, he’s just labeling it as one of the many “frontier” models the world will have. As I have mentioned before, the frontier has not been as dynamic this year as it was in 2025 when Gemini was the best model for a brief period. Oh Gemini! Last year, I have written quite a few pieces (see [**here**](https://www.mbi-deepdives.com/more-alphabet/), or [**here**](https://www.mbi-deepdives.com/why-i-bought-more-alphabet/)) articulating why I believe Alphabet has a deep, structural advantage in pursuing the frontier model race. Unfortunately, they have been mostly missing from the race this year. While Alphabet remains founder controlled company, it is led by a manager. I do have a more positive perception about Pichai than most Alphabet skeptics out there, but as I have alluded earlier, managers inherently likely will find it very challenging to navigate the race to frontier given the speculative pay-off vs more amenable spreadsheet math of building capacity for serving 3P customers. Brin only seems to show up intermittently, but given the competitive intensity here, I’m less confident than ever before that such intermittent appearances will be sufficient to lead the frontier. Larry Page is allegedly [willing](https://www.joincolossus.com/episode/baker-ai-semiconductors-and-the-robotic-frontier/?utm%5Fmedium=email&%5Fhsenc=p2ANqtz-%5Fpd%5Ft4w9r6fvEiTD%5FiaIwaDIbD10SVmOUKPX6MIP%5FELOiGaEF9Q274a0aR4db9LjDphu%5F0qjQni8tYzeawT6k6iHQSIP0ZNfmkzeiIW459yGNPIqQ&%5Fhsmi=2&utm%5Fcontent=2&utm%5Fsource=hs%5Femail) to go rather bankrupt to pursue the elixir of AI, but I cannot actually recall the last time we heard from him and I do not have a good sense how deeply he is still involved in the company’s strategy. While Alphabet’s deteriorating position in the model race is surprising to me, I am actually somewhat similarly surprised by the absence of any Microsoft model in the aforementioned Artificial Analysis Index. If someone asked me three years ago to estimate the probability of Microsoft staying in the vicinity of frontier models in 2026, I would actually give them a decent chance given they have access to all the IP of OpenAI. The fact that they are still nowhere to be found in that index should be a source of at least some annoyance from their shareholders. Given their near term estimates are going up, I guess such annoyances can be kept at bay for now. I haven’t followed Microsoft as closely as I have followed Meta, Amazon, or Alphabet over the years, but after covering Microsoft’s earnings for the last several quarters, I have noticed that management has a vexing propensity to decide on a narrative that they want investors to believe about Microsoft and then ONLY disclose objective-looking numbers to fit such narrative. As I have [covered](https://www.mbi-deepdives.com/hyperscalers-vs-labs/) before, Microsoft’s goalposts seem to change every quarter depending on whatever the “current” narrative they believe in. It was particularly amusing when Microsoft management mentioned that 90% their “cloud revenue” came outside of frontier model companies. Such disclosure can assuage unsuspecting shareholders about lack of concentration risk of frontier model companies for Microsoft’s incremental growth, but I do want to highlight what consists of “cloud revenue”: Microsoft 365 Commercial cloud, Azure and other cloud services, the commercial portion of LinkedIn, Dynamics 365, and other commercial cloud properties. Presumably, most Microsoft shareholders actually want to know how much of Azure’s revenue came from OpenAI. This clever disclosure (or lack of) actually prompted me to make this meme which I then sent to a group chat I am in! ![](https://substackcdn.com/image/fetch/$s_!VPNQ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2809a6b9-6442-4a44-9b96-79b210fb195e_999x1005.png) Thankfully, even though Microsoft management is coy about certain disclosures during the call, OpenAI is an equity method investee which qualifies as a related party under ASC 850\. That forced Microsoft to disclose in their SEC filing how much of their revenue came from OpenAI and that number is **$24.1 Billion in FY26**. Nearly all of these revenue likely flow through Azure which makes Azure revenue the more appropriate denominator to understand the concentration risk of the capex buildout. Microsoft, of course, doesn’t even disclose Azure revenue consistently (only the growth), but they did mention Azure revenue exceeded $100 Billion in FY’26\. I guess it doesn’t quite fit with the Microsoft’s current preferred narrative that nearly one-quarter of Azure’s revenue is coming from one customer! I do suspect that as long as OpenAI and Anthropic continues their meteoric and frankly incredible growth trajectory, shareholders need not worry about such concentration. But we will get much more clarity where the skeletons are hidden in the seemingly “easy” business of selling capacity to frontier labs if that trajectory ever stops. To be fair, as of today, there is nothing in the horizon that will make you think such ever increasing annual revenue run-rate trajectory is about to slow down. But these things are inherently unforecastable. SpaceX is talking about building up to 10 GW capacity by 2027, and both Microsoft and Amazon promised to double their capacity by 2027\. On top of this, Jensen Huang seems to be on a [mission](https://x.com/JensenHuang/status/2086934705207959965?ref=mbi-deepdives.com) to teach hyperscalers a lesson for building their ASICs and want to further fragment the broader compute market. There is an incredible amount of supply of compute that’s coming in the next 18-24 months and if there is any hangover at all from such spike in supply, this business won’t seem as “easy” as it seems today. One company that seems to be making a very difficult choice today is Meta Platforms. Like Amazon, they too want investors to believe that their core business can succeed without owning a frontier model which I do find to be credible. Unlike Amazon, Meta’s financials are [**visibly**](https://www.mbi-deepdives.com/meta-googl-10q/) saddled with the massive talent and compute requirement to stay at the frontier. In the latest call, Zuckerberg tried a scattershot approach to assuage investors that such massive investments will lead to new revenue streams in the coming years. From the call: > “The opportunity in front of us is massive. First, we are now at a point where our investments in AI are accelerating every major part of our core business. They’re improving the experience for people using our apps, driving better performance for advertisers and helping our teams build new experiences and ship faster. > > Second, we are developing new personal agents that will be the foundation for our next wave of products and revenue lines in the months and years ahead. And third, we see a large enterprise opportunity to sell to businesses, including APIs, business agents, potentially selling compute directly and other services that we’re building for large customers. > > …we also expect to grow a large business serving large customers as well. > > We’ve built our API. We’re rolling out business agents. We’re getting a lot of offers for compute at a significant premium over what we paid for it. And we have more coding and productivity tools on our road map as well.” The only problem is apart from the first one, the rest of these potential revenue streams are at best nascent and at worst mere ideation stage. In the most recent Sharp Tech [podcast](https://open.spotify.com/episode/7snH2nwnfXaZexYWiEWd79?si=460f1cee8f4240c6&ref=mbi-deepdives.com), Ben Thompson criticized such scattershot approach which he thinks ended up confusing (or worse, alarming) investors. However, he was actually quite constructive about all of the monetization opportunities mentioned by Meta except the large enterprise opportunity. He pointed out how long it took for Google Cloud to be where they are today and Meta doesn’t have any credible “go-to-market” strategy to make a dent. I believe Google Cloud is the wrong analogy here. A more appropriate analogy might be OpenAI or Anthropic. Why are OpenAI and Anthropic running circles in enterprise market today? The OpenAI case study is perhaps much more instructive. To borrow Thompson’s own words, OpenAI was the “[accidental consumer company](https://stratechery.com/2023/the-accidental-consumer-tech-company-chatgpt-meta-and-product-market-fit-aggregation-and-apis/?ref=mbi-deepdives.com)” company and despite being late to the enterprise opportunity, they are clearly putting up a fight against Anthropic. Observing OpenAI’s success closely, I believe the limiting factor here is actually having a frontier model, and **IF** you have it, you can take care rest of the details much more quickly than ever before. Imagine for a moment that Meta manages to build a credible frontier model with equally impressive harness. I suspect all the hyperscalers will be tripping over each other to host Meta’s closed models and sell the API to their customers. As much revenue as these hyperscalers are making today from the frontier labs, they also know they don’t want to live in a world where the labs have monopsony power over their suppliers. In this scenario, I don’t think Meta needs to build a GCP-sized platform to monetize their model appropriately; they just need to make their models available everywhere to at least recoup the training cost. They make plenty of money from other ways anyway. But…this is a hypothetical scenario! And as mentioned earlier, the pay-off is lot more speculative today than supplying capacity to frontier labs. But when I imagine myself what would I do if I owned 100% of these companies, it becomes more clear to me that indeed I too would pursue the frontier **despite** such uncertain pay-off. Zuckerberg is doing exactly that and I suspect every Meta shareholder who would make a different choice may want to leave the boat before it gets potentially messy. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I made a slight change to my portfolio yesterday. _This post is for paying subscribers only._ ### Figma 2Q'26: Near-Term Re-acceleration vs. Long-Term Durability URL: https://www.mbi-deepdives.com/fig2q26/ Last updated: 2026-08-10T16:22:57.000Z Following its IPO last year, Figma reported two consecutive quarters of revenue growth deceleration. Then it flipped and revenue growth re-accelerated in each of the last three quarters. Given investors ever growing obsession with “accelerating revenue” companies, at first glance you may be surprised that the stock has been flailing around despite such accelerating revenue growth trajectory. ![](https://substackcdn.com/image/fetch/$s_!GSJK!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6fe1a494-3413-438e-a82a-23c268802328_919x565.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Of course, part of the story here is terminal value concerns related to AI which is definitionally unfalsifiable regardless of near-term operating performance. But more importantly, the stock reacted really poorly after its earnings last week because of 3Q’26 guide which implies an anemic sequential revenue growth QoQ. ![](https://substackcdn.com/image/fetch/$s_!RVtR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F13530922-2866-4c2f-b1cd-607a6da3e27a_1015x529.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) For context, in February they guided to 30% growth (against a \~24% sell-side consensus for 2026), raised to 35% in May, and are now guiding for 39% revenue growth in 2026\. Back in my [**4Q’25 update**](https://www.mbi-deepdives.com/fig4q25/) I wrote it “won’t surprise me at all” if actual 2026 growth landed close to mid-30s; the guide itself has now blown past that. Despite such outlook, the sequential growth of \~1% next quarter has likely spooked investors. Moreover, while Figma raised the revenue outlook for the year, the non-GAAP operating income guide was held flat at $125-135 million despite the $40 million revenue raise. So investors may be concerned about the incremental revenue as “empty calories”. I will discuss other key metrics and some key takeaways from the quarter as well as some additional thoughts on Figma’s AI risk behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Digital Advertising Industry Snapshot 2Q'26 URL: https://www.mbi-deepdives.com/digital_ad_2q26/ Last updated: 2026-08-09T14:10:15.000Z As I do after every quarter, let me share the snapshot of the overall digital advertising industry. Let me first explain some changes for this quarter’s edition. First of all, digital advertising industry is defined as Alphabet’s advertising revenue+ Meta’s advertising revenue+ Snap’s ad revenue+ Pinterest revenue+ Microsoft Search advertising revenue+ Amazon advertising revenue+ Trade Desk revenue+ AppLovin revenue+ Reddit’s ad revenue. Since Snap started reporting ad revenue separately since 1Q’24, I started including only ad revenues now. However, before 1Q’24, I assumed Snap’s total revenue as their ad revenue which is perhaps closer to reality anyway. Similarly, Reddit’s number has only been included since 1Q’23 since we don’t have their ad revenue numbers in earlier years. Keep these in mind while looking at YoY comparisons in the past. Also note that for visual convenience, I have colored all the Q2 of the last five years. Obviously, the actual digital advertising industry is larger than this, but this snapshot helps me gauge the broader digital advertising industry’s big picture. TikTok and Walmart advertising are notably missing here. Well, TikTok is a private company and Walmart doesn’t disclose their advertising revenue consistently. **Some key takeaways from 2Q’26:** Digital advertising industry **decelerated** slightly from 22.2% in 1Q’26 to 20.3% in 2Q’26\. We will likely see **further deceleration** for the next couple of quarters given the tough comps (both 3Q’25 and 4Q’25 grew by 18.5% YoY which were the highest growth quarters last year). While Meta’s share gain continued, the pace of share gain has **moderated** a bit in 2Q’26\. Given its incremental share of 44.5% is still comfortably ahead of current share of 34.8%, Meta will likely to continue to gain share in the foreseeable future. One challenge with inferring too much based on quarterly snapshot is FX changes likely play a material role in the rate of changes here. Among these companies, Meta likely had the highest mix of revenue coming outside the US and any FX tailwind or headwind would affect Meta’s market share more than others. See more on how FX likely affected Meta’s 2Q’26 ad revenues [**here**](https://www.mbi-deepdives.com/meta-googl-10q/). Over the last five years, Google Network business’ market share has **halved** from \~8% in 2022 to \~4% in 2026\. While Google Search too **steadily loses market share**, incremental market share has actually improved in 2Q’26 YoY. I am curious to see if search can continue to stabilize around \~30% incremental share or drifts lower to mid to low 20s over the next 24 months. Search seems fine for now, but **as OpenAI is likely to ramp up their ad efforts and Meta is under tremendous pressure to grow their ad revenue** in order to gain permission from the investors for further capex increases in 2027 (and beyond), there is likely a decent probability that \~30% incremental market share for Google Search may not quite prove to be “steady-state” over the medium term. No strong opinion here, but gun to my head, I suspect we may see mid-20s incremental market share for Search in 2027-28. Among the sub-scaled ad players, Reddit and Applovin are punching above their weight; look at their current share vs incremental share. TradeDesk has been an unmitigated disaster as growth has fallen off the cliff. Given that the company guided for **\~12% revenue decline** in 3Q’26, the worst is not behind them. I guess it’s not a surprise that the stock is down \~90% from its December 2024 peak. I haven’t studied Trade Desk closely, so I have no opinion about their future. I should, however, note that some of the definitions of advertising here are not quite apple to apple as some companies report advertising dollar on a gross and some report on a net basis which is why I like to focus more on incremental share data. ![](https://substackcdn.com/image/fetch/$s_!3oY_!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7835b5e6-102b-42ed-9fa5-0e8ee0740b76_1249x1071.png) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Shopify 2Q'26: Sustained Miraculous Growth URL: https://www.mbi-deepdives.com/shop2q26/ Last updated: 2026-08-08T17:25:29.000Z Shopify’s Gross Merchandise Value (GMV) maintained its almost miraculous 30% growth (FXN) in 2Q’26\. This was actually their fifth consecutive quarters of 29-30% GMV (FXN) growth. I say miraculous because never in a million years I would have imagined five years ago that Shopify would grow at such a rate at this scale. Despite internalizing Brian Arthur’s seminal [paper](https://sites.santafe.edu/~wbarthur/Papers/HBR.pdf?ref=mbi-deepdives.com) “increasing returns to scale”, I am often reminded by tech companies such as Shopify that I have continued to underestimate such a phenomenon. Given such sustained GMV growth, Shopify's GMV now stands at \~44% of Amazon's estimated retail GMV, up from \~10% back in 2017\. In 1Q'26 [**update**](https://www.mbi-deepdives.com/shop1q26/)**,** I suggested Shopify could reach half of Amazon's GMV by the end of next year; after this quarter, that trajectory not only looks firmly intact, they may actually get there slightly ahead of that timeline. ![](https://substackcdn.com/image/fetch/$s_!fLX1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed97662b-7287-4311-8103-739bebd5ba2c_1498x826.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) On an incremental basis, the story moderated slightly but remains extraordinary. Shopify added \~80% as much GMV YoY as Amazon retail likely did in the quarter, down from the \~95-130% range of the prior few quarters. For context, Shopify CFO cited eMarketer data on the call highlighting Shopify merchants have captured nearly **half of all incremental US e-commerce dollars since the start of 2025** while being only \~14% of US e-commerce. ![](https://substackcdn.com/image/fetch/$s_!e1sG!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7918ffee-7142-4d58-b0a4-be9e8e19978a_1167x712.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The reason Shopify can match Amazon almost dollar-for-dollar remains the same one flagged in the [**3Q’25**](https://www.mbi-deepdives.com/shop3q25/) update: Shopify’s platform model taps growth reservoirs that are structurally harder for Amazon’s marketplace to reach. In 2Q’26, international GMV grew 37% (Europe +34% FXN vs. North America +28%), offline/point-of-sale GMV grew 32%, and B2B GMV grew 76%. B2B is decelerating as the base scales; it was doubling annually through 3Q’25, then +84% in 4Q’25, +80% in 1Q’26, and now +76% but it remains a meaningful tailwind. Payments penetration hit 68% of GMV (+3 points YoY) with European penetration up more than 350bps YoY. There were some interesting details around why Shopify’s growth has sustained over such a long period. I will discuss that and more about their margins as well as key takeaways from the earnings call behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb 2Q'26: Widening the Gap with Booking URL: https://www.mbi-deepdives.com/abnb2q26/ Last updated: 2026-08-07T15:05:21.000Z Airbnb reported an acceleration in nights and seats (N&S) booked, adding 14 million (+10%) N&S YoY which was the **highest since 3Q’23**. More importantly, Airbnb guided low double digit N&S growth even for 3Q’26 which indicates a likely **sustained acceleration** in the business. With such double digit N&S and MSD ADR growth, GBV and revenue growth continue to grow at mid-teen rate and are expected to maintain such momentum in 3Q’26. Since Booking reported its own 2Q’26 a couple of days earlier, I will first go through Airbnb’s quarter through the usual KPIs, then compare and contrast against Booking, and finally discuss some broader takeaways from the calls. ![](https://substackcdn.com/image/fetch/$s_!HFBN!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff222ed17-2d70-4f6a-a0c6-72b0d84b3a11_1560x349.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Airbnb’s performance exhibits a lot of what I have been expecting and writing over the last few months, but let me start with something that has been somewhat diverging from my expectations: take rates! LTM take rates actually went down by \~31 bps YoY. Moreover, even though during 1Q’26, management expected full-year take rate to be higher in 2026, they have now guided it to be flat. This is despite the fact that their recent single service fee model is a tailwind to overall take rates **AND** significant growth in insurance related revenues which was up +45% and +60% in 1Q’26 and 2Q’26 respectively. So, what explains this divergence? Management cited two primary reasons: a) Reserve-Now-Pay-Later (RNPL) which was \~20% of GBV, and b) higher customer incentives. Airbnb is actually expanding RNPL given the strong results they have seen and they like the fact that RNPL lets host lock in earlier calendar share (vs other OTAs). As a result, RNPL is likely to be a continued headwind to the reported take rates even though it is net positive for the overall business. Similarly, to promote new verticals such as experiences and services as well as hotels, Airbnb is providing “credit” to guests that they can use in the future on Airbnb. So, Airbnb is sacrificing some or lot of the take rates in such GBV to grow the overall marketplace. Both decisions are highly likely to be net positive for the business over the long term even if it lowers the reported take rates in the near term. Once RNPL is more widely rolled out and early investment phase in new verticals are behind us, I still think take rate will go up over the medium to long term. But the pace of such improvement may be more sluggish than I initially expected, especially given Airbnb’s ambition to launch new verticals almost every year which may lead to more sustained investment in customer incentives. Geographically, the reported revenue growth was quite balanced: North America +15.8%, EMEA +15.6%, LatAm +26.0%, APAC +16.9%. As usual, FX impacts these reported numbers a bit too much to derive useful comparisons, so N&S growth and FXN ADR remain my preferred lens. ![](https://substackcdn.com/image/fetch/$s_!sT2A!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5dcb1edc-1248-4718-a2de-3a802db1045a_1459x184.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The standout on this quarter is North America (NA): after six quarters stuck in LSD-MSD nights growth, NA reached HSD for the second consecutive quarter, with FXN ADR up 7%. EMEA also moved up from MSD to HSD. In other words, the **core** markets are accelerating. CFO attributed part of the ADR strength to the continued disproportionate popularity of larger homes; bedroom nights grew faster than nights booked and accelerated more on a YoY basis, which frames the ADR appreciation as **incremental value delivered rather than pure price inflation**. Meanwhile, expansion markets continue to grow about twice as fast as core, first-time bookers accelerated to +11% (**the best in four years**, with Gen Z the fastest-growing cohort), and app nights grew 23% YoY to 64% of total nights (vs 59% a year ago) which is quite encouraging to see. ![](https://substackcdn.com/image/fetch/$s_!Gtou!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd873848-7d6b-4f49-8540-c64a8e9990e6_1485x201.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will benchmark Airbnb’s quarter against Booking as well some other key takeaways from the call which will be behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://mbideepdives.substack.com/p/deep-dives) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### DoorDash 2Q'26: Growth Beyond the Restaurant URL: https://www.mbi-deepdives.com/dash2q26/ Last updated: 2026-08-06T16:00:51.000Z DoorDash had a fine quarter with total orders growing \~27% YoY (\~17% ex Deliveroo) and marketplace Gross Order Value (GOV) growing \~36% YoY (\~23% ex Deliveroo). More importantly, GOV in US restaurant category slightly accelerated in 2Q’26 primarily due to DashPass membership. Speaking of DashPass, management shared some interesting details about its trajectory. I will discuss more granular aspects of the quarter, including the per order economics as well as a portfolio change behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!wfrp!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8639e556-f92d-4b6d-ae97-f3293a6883cf_1741x613.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Spotify 2Q'26: Pressing the Monetization Lever URL: https://www.mbi-deepdives.com/spot2q26/ Last updated: 2026-08-05T16:08:06.000Z Spotify reached 300 Million Premium subscribers milestone in 2Q'26\. Getting from zero to 100 Million took over a decade, 100 to 200 Million took 15.5 quarters, and 200 to 300 Million took roughly 14 quarters. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2026/08/image.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While premium subscriber number was above management’ guide, Monthly Active Users (MAU) came out 1 million below the guide. As I have shown below, Spotify has experienced a noticeable acceleration in 2022-23 period in incremental MAU growth even though Premium subscriber trend was more steady which led to a decline in free to paying subscriber conversion. Spotify management indicated that now is the time to focus on conversion than merely growing the top of the funnel. From the call (emphasis mine): > “…We’ve been working on turning our outperformance in MAU into revenue growth. So to capitalize on this opportunity, **we are adjusting elements like product optimization and ad load, among other things in select emerging markets. Now this strategy carefully increases friction in our free service with a goal of driving higher user conversion and revenue growth down the line.** > > Yes, this will show itself in our Q3 MAU, but we believe it’s well worth it. And as we’ve shared previously, the free-to-paid conversion cycle in emerging markets grows differently than our established markets. So while a move like this one will take time to play out, the opportunity is vast. This will be additive to our potential over time." ![](https://substackcdn.com/image/fetch/$s_!Yeto!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0425a17d-ba5a-4c5c-b496-7b3d21e5cb26_1026x603.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss segment wise breakdown, product development as well as margins and valuation behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### FICO FY 3Q'26: Welcome to "Gaming" URL: https://www.mbi-deepdives.com/fico3q26/ Last updated: 2026-08-04T15:42:18.000Z FICO 3Q’26 revenue increased +26% YoY, but this was actually slightly below the consensus estimates. I don’t quite think a slight revenue miss was the reason for FICO stock being down \~24% since they reported earnings last week. I will expand on the primary culprit behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://mbideepdives.substack.com/p/deep-dives) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta's "True" ROIC, and Some Notes from Meta and Alphabet's 10-Qs URL: https://www.mbi-deepdives.com/meta-googl-10q/ Last updated: 2026-08-03T15:34:43.000Z Over the weekend, I realized that the way I was calculating Meta’s ROIC somehow masked its “true” ROIC. So, I updated my process around calculating its ROIC and wanted to share the updated number since it is noticeably different from the one I shared [**earlier**](https://www.mbi-deepdives.com/meta2q26/) and I think reflects closer to its economic ROIC. Let’s start with the **numerator** in ROIC: Net Operating Profit After Tax or NOPAT. To make this number more comparable over a longer time series, I have **added back** all the one-off expenses over the years e.g. FTC fine in 2019, layoff and restructuring related expenses in 2022, and the most recent legal and severance related costs in 2Q’26\. Then I used a normalized tax rate of 21% (instead of actual effective tax) to net out tax variability over time. The denominator is the **Invested Capital**. Before I explain how I calculated invested capital, let me first mention that invested capital **should** **include capital that's ACTUALLY deployed AND generating the NOPAT in your numerator**. Anything you add to the denominator needs a corresponding adjustment to the numerator, or you're just mechanically depressing ROIC without economic meaning. So, I started with total assets and subtracted cash and marketable securities, equity investments (this has no impact on NOPAT but it went from zero in 2Q’20 to \~$30 Billion now which would depress ROIC in recent quarters following ScaleAI stake last year if you don’t adjust for it), and non-interest bearing operating liabilities. I used to not net out operating lease liabilities which was an error on my part; if I leave the lease liabilities unsubtracted (keeping \~$24 Billion of Right-Of-Use assets in the capital base) while NOPAT **already** bears the full lease expense including its embedded financing cost, I would have understated ROIC. Similarly, I didn’t net out “long-term income taxes” before, but since I am already using normalized tax rate than the typically lower effective tax rate for Meta, my numerator already captures such impact and it should be netted out from my invested capital calculation. Perhaps most importantly, I have subtracted “Construction in Progress” or CIP from my invested capital base since this non-depreciable asset by definition is currently generating **zero return** for the business and hence have no impact on NOPAT. Once this asset becomes “active”, it will transition from CIP to depreciable asset and will have associated NOPAT impact. After making all these adjustments, I get to the below LTM ROIC chart for Meta Platforms. While 2Q’26 did see slight downtick in ROIC, it is quite remarkable that companies of this size are actually reporting mid-50s ROIC. But this chart is also why investors are perhaps wary about the capex spree. For context, Meta’s **average** invested capital in my methodology in 2Q’26 was just \~$127 Billion. CIP in 2Q’26 was a whopping $80 Billion, almost all of which is presumably going to come online in the next 18-24 months. So, the invested capital base is going to go up significantly in the coming years (and the depreciation expenses). As a result, Meta’s LTM ROIC in this methodology will almost certainly keep going down as well. Generally speaking, investors don’t really like it whenever the incremental ROIC is noticeably worse than the overall ROIC. While that is understandable, given the scale of capital deployment investors should be okay with \~20-30% incremental ROIC which would still be lot lower than current “true” ROIC but obviously much, much higher than cost of capital. You can quip that given the incremental ROIC is likely going to be lot worse than current ROIC, shouldn’t the multiple for companies such as Meta go down? That makes intuitive sense, but important to remember the broader context here. One of the big drivers for multiples is **reinvestment runway**. Even if your ROIC is sky-high and has limited reinvestment runway, your multiple might be lower than a company with respectable ROIC but an immense reinvestment opportunities. This is essentially why Amazon has consistently traded at a materially more premium multiples in the past than either Alphabet or Meta despite having much lower ROIC. Investors were just much more comfortable about Amazon’s reinvestment runway than Google Search or Meta’s FOA business both of which were already outright dominant companies in their lane and investors perennially worried about their limited reinvestment opportunities. Google Cloud was an early indication why such concern may have been premature, but AI is a potential lifeline for a massive reinvestment runway for both Meta and Alphabet, albeit with likely an inferior incremental ROIC than their current “monopolies”. Of course, there is a **vigorous and legitimate** debate around long-term incremental ROIC of the capex build out for these companies. You can be skeptical about the actual return on the capacity that is about to come online in the next 2-3 years and still acknowledge that so far, the reporting numbers have shown very little deterioration of these businesses’ ROIC (I am just showing Meta’s ROIC here, but the broader message/trend is also very much applicable for Alphabet. Since Alphabet didn’t report “asset not yet in service” line item before 2024, we cannot build a longer term time series chart for their ROIC). ![](https://substackcdn.com/image/fetch/$s_!4qOy!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2c6123b8-14e3-42c7-bf82-f2a30fbfeb7b_1396x706.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Beyond the persistent ROIC debate, I also want to share some thoughts about Meta and Alphabet after going through their 10-Q behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Floor & Decor 2Q'26: Tentative Green Shoots URL: https://www.mbi-deepdives.com/fnd2q26/ Last updated: 2026-08-02T13:48:33.000Z **Programming Note**: Even though every Sunday I tend to write my personal musings, I’m skipping that this week given I have some earnings coverage left to catch up. --- For the first time in more than a year, Floor & Decor (FND) reported a quarter and did not cut its annual guidance. FY25 was guided down at every revision i.e. three consecutive cuts across the four reports in 2025, and 2026 started with yet another cut in 1Q, so much so that I wrote [**last quarter**](https://www.mbi-deepdives.com/fnd1q26/) I was a bit surprised they hadn’t given up on providing annual guidance altogether. Well, this time they got to keep it. The comp outlook was maintained at flat to -4%, adjusted EBITDA was nudged up to $550-$585 million from $545-$580 million, and adjusted EPS was raised to $1.88-$2.13 on the back of the 2Q beat and a greater expected contribution from buybacks. Management went out of their way to say they have “high confidence” in hitting the midpoint of the comp guide. Holding a guide is admittedly a low bar to celebrate, but given the last two years, it’s not nothing. The headline comp itself was hardly inspiring: -2.1%, which makes it **13 negative quarters out of the last 14**. As I mentioned in my 1Q recap, this one was near certain to be negative since April was already tracking -4.5% when they reported. What was much more encouraging is the cadence within the quarter: **April ended at -5.1%, May improved to -1.3%, and June was almost flat at -0.3%**. Transactions remain the primary culprit, but even that improved to -2.9% from -5.5% in 1Q, and average ticket grew +0.8% despite lapping last year’s strongest quarterly ticket growth of +3.8%. ![](https://substackcdn.com/image/fetch/$s_!qEW0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9a848920-8466-47cd-b640-7bd1860aa212_1435x834.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Just as I was wondering things may finally be turning a bit, it turns out July was a bit of a head-scratcher. Management mentioned “some pretty ugly days” around the July 4th holiday that they hadn’t seen in a while, before the business returned to the May/June run rate in late July and into August. 3Q-to-date comp stood at -2.2%. Importantly, none of this sequential improvement can be attributed to macro since US existing home sales (EHS) were still hovering around \~4 million annualized units in June, near historically low levels. Despite such subdued macro, two of FND’s three regions (West and now East) posted positive comps excluding cannibalization, and 8 of their 16 districts were positive on that basis. Within different customer segments, the Pro market is the more attractive one, and thankfully, the Pro mix shift continued its steady march in 2Q’26\. Pro sales grew \~4% YoY against a company that grew 3% and now account for **\~55% of sales, up from half last quarter and only 35% in 2021**. The new pro app launching next year, which will pull purchasing, loyalty rewards, pricing, and project management into one place, suggests the mix shift may not have peaked. Alongside that, online penetration reached 20.3% of sales (from 18.6% a year ago), and the company kicked off an 18-24 month digital transformation. None of this will move the near-term numbers much, but these are the kinds of investments that will be far more visible in the P&L once EHS normalizes. The GAAP optics this quarter were almost comically good: 48.2% gross margin (+430 bps YoY) and $0.89 diluted EPS (+53%), but almost all of that is the tariff refunds. I do want to note that these tariff refunds will keep affecting the P&L through the back half of this year. Adjusting for the refund, the underlying quarter was okay rather than spectacular: adjusted gross margin of 43.7% was down 20 bps YoY. ![](https://substackcdn.com/image/fetch/$s_!hAb6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6c289c94-16e9-45c2-a0e2-5aa1015b2bc3_1251x703.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); 2Q’26 gross margin adjusts for tariff refunds What I found most interesting from this call is what management intends to do with the tariff refunds. They laid out three buckets: offset the inflationary pressures they were otherwise bracing for (oil earlier in the year, and now rising domestic trucking rates), selectively invest in price where they see elasticity to drive share gains, and then the usual capital allocation waterfall. The competitive kicker here is that most independent flooring retailers source through two-step distribution and are not the importer of record, which means they will likely never see a tariff refund check. From the call: > *“…*when you think about tariff refunds, because they’re not the importer of record, unlikely that they’re going to get those tariff refunds and be able to reinvest into their business, which because of that, **I think would prevent them from getting too aggressive around price or promotion in the second half of the year**. Everything that we have seen, generally speaking, has been prices going up and certainly not going down*.”* As I [**mentioned**](https://www.mbi-deepdives.com/fnd2/) in the past, one of the core parts of my FND thesis was that “FND may even look at this period a decade from now as a blessing in disguise since this severe softness in existing home sales may be contributing much more to eliminate competition than investors are appreciating”. The tariff episode looks like yet another asymmetric blow: FND absorbed the tariffs with scale, got the money back with interest, and can now reinvest it in price especially where independents cannot follow. The longer this cycle drags, the more lopsided the eventual competitive landscape becomes. Like gross margin, adjusted EBITDA margin also went down slightly. Please note while management’s adjusted EBITDA definition adds back SBC, I do not. In some sense, I do find it rather impressive that despite posting 13 of the last 14 quarters negative comp, the company has been largely able to maintain double digit EBITDA margin. If FND can navigate one of the worst EHS trends in decades while maintaining double digit EBITDA margin, it bodes well for the future margin once the cycle turns. ![](https://substackcdn.com/image/fetch/$s_!NJwu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe9a2102c-c2da-45eb-9f8a-ede94c01bb62_1434x784.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Inventory was up just 0.7% vs year-end. More importantly, in 1H’26, FND generated $278 million of operating cash flow (vs $155 million in 1H’25), and that was despite the \~$90 million tariff refund receivable that only converted to cash after quarter end. So 2H cash flow will get that boost too. FND also began executing the $400 million buyback announced last quarter: in 2Q’26, they repurchased \~1.3 million shares (\~1.2% of shares outstanding) at $49.36/share. I believe buyback at these prices will prove to be excellent capital allocation decision over the long-term. The full-year capex guide was also trimmed to $240-$275 million. ![](https://substackcdn.com/image/fetch/$s_!hFqA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3885768e-37f9-4979-bf57-2a32d9cd3bb0_1624x339.png) Source: FND 10-Q Stepping back, my thesis for FND never required 2026 to be a good year, but it does require FND to keep taking share while the cycle grinds down its competitors and to be positioned for the other side whenever EHS mean-reverts, the timing of which remains way above my paygrade. On that note, this quarter was a bit encouraging: positive comps ex-cannibalization in two of three regions with EHS still stuck at \~4 million units, and a tariff windfall that landed asymmetrically in FND’s favor. When an analyst pressed on whether this is finally the inflection, the new CEO Bradley Paulsen’s answer was, “I’m going to be really careful here because I don’t want to be the initiator of a false start.” Indeed, I have no idea whether the June exit rate marks the trough or just another head-fake in a cycle full of them, but the improving competitive positioning, Pro mix, and respectable margins even at cyclical lows keeps me comfortable that the downside is limited and the eventual upside skew is intact. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Hyperscalers vs AI Labs Debate, and the Moving AI Frontier URL: https://www.mbi-deepdives.com/hyperscalers-vs-labs/ Last updated: 2026-08-01T14:59:32.000Z I thought I would cover Microsoft’s earnings today, but let me first respond to a [tweet](https://x.com/SouthernValue95/status/2083267530559271363?ref=mbi-deepdives.com) by SouthernValue and then I will share some thoughts on the state of AI models. I recommend you read his pushback to my yesterday’s [**piece**](https://www.mbi-deepdives.com/amzn2q26/) before you read the rest. I will start with a clarification. When I said Google Cloud’s growth is more "broad based" than AWS, it was only an observation of 2Q’26 and not a prospective one. What the mix looks like going forward is a different question, and given GCP's smaller base plus upfront recognition on TPU hardware sales, “Southern” may well be right that Anthropic becomes a larger percentage of GCP's revenue than AWS's over the next year. But even if that proves to be true, I'd argue the risk remains more asymmetric for AWS. If lab revenue disappoints, Google can slow capex and absorb capacity into its massive 1P workloads i.e. Search, YouTube, Gemini, DeepMind can all soak up TPUs. AWS has no comparable internal sink; its infrastructure is built to be rented. Higher revenue exposure with a shock absorber can be a better position than lower exposure without one. On the question of labs being still rather insignificant part of overall cloud revenue, I think a snapshot of today's mix understates where this is heading. Every hyperscaler is reporting hundreds of billions of backlog and that backlog is indeed tomorrow’s revenue. So even if labs are \~10% of AWS run-rate today, the composition of what's contracted to convert into revenue over the next few years is far more lab-heavy than the current mix. Ultimately, a lot hinges on what AI labs’ revenue will be in 3-4 years. If its hundreds of billions, the **incremental revenue** will have much higher concentration from AI labs. Sothern himself mentioned “*Trn/GPU clusters serving labs are basically the lowest rev per GW part of MSFT/AWS business, being long-term wholesale contracts. Clouds charge much higher prices selling to the enterprise (where they also cross sell high margin cloud services), and serving inference tokens*.” Indeed, that’s also my concern. The incremental revenue from the capacity coming online in the next few years will be inferior to current and traditional cloud economics. And every year the concentration increases, the labs bargaining power over hyperscalers should increase. There are multiple moving pieces here though. If compute demand remains comfortably higher than supply for years, this increasing bargaining power simply may not materialize in hyperscalers’ financials anytime soon since AI labs will be more busy focusing to get the capacity they want/need. As an investor who cares about moats which plays a critical role in what multiple we should be paying for the earnings streams, I am still alarmed by the evolution of AI labs bargaining power here even if they do not necessarily materialize in the next 2-3 years in current compute constrained environment. Southern also says “*Ultimately the labs will become profitable and want to build their own GPU training and inference infrastructure, so I don’t expect them to get better terms from clouds, just to build their own infra in time*”. I’m not sure I understand how that is actually bullish for hyperscalers in the long-term. The possibility of insourcing **IS** labs’ bargaining power. Once a lab matures to build its own capacity, it will know its all-in self-build cost per token with precision. That number then becomes the ceiling on what it will pay any landlord, plus perhaps a convenience premium for speed, and flexibility. The landlord's margin will become that **convenience premium** and the premium should shrink as the lab matures, and capital markets get comfortable financing compute against lab offtakes directly. The sweetener of hyperscalers’ circular deals will no longer be relevant and labs will act like a “full adult” in the negotiating table. Admittedly, there are some real barriers for such in-sourcing dreams even if labs are fully capable of financing and operating such data centers. There are potentially multiple bottlenecks in the AI value chain. Power could be a real constraint for transitioning away from 3P hyperscalers to 1P insourcing. So the “convenience premium” could end up being more attractive than it sounds if the incumbent hyperscalers simply have all the pieces of the puzzle. Moreover, we may very well see more concerted political backlash for more and more data centers as the buildout continues. As you can see, I am much more ambivalent than offering any certainty how this long-term potential economic tussle between hyperscalers and frontier labs may settle in 5-10 years. Given the ambivalence in my mind, especially in current multiples for hyperscalers, I think it makes sense for me to largely be observant for a few quarters or years. I still do have some exposure to this debate via GOOGL, so I’m not quite entirely a distant observer yet. Frankly speaking, if you listen hyperscaler CEOs closely during this earnings call, they are sort of telling you they are not as nonchalant about labs increasing bargaining power. For example, during the prepared remarks, Satya Nadella started his commentary by mentioning two of the goals Microsoft has. One of those goals is “*empowering every organization to build their own continuous learning loop and ensuring that they don’t outsource their core IP*.” He later explained during Q&A: > “…at the end of the day, every firm is going to evaluate who are the providers who are helping them with their outcomes and their knowledge creation. I think that, that is now fairly clear, and it’s going to become clearer by the day. This is not going to be about come in and take all my knowledge and benefit yourself, whereas I am not getting anything out of it. > > So given that direction of travel, **we are very, very clear about the architectural sort of design of the platform, which is you’ve got to keep your harness separate from the model.** When the harness will ensure that your memory, your context, all of that is external. **That means any given model at any given time is swappable**. You should and you can use frontier models. There’s no reason not to. But you also can use multiple of them, right? So if you look at some of the stats I gave. It’s a great example of how to use the frontier models for what they deliver, how to use low-cost models for what they deliver, and in fact, train your own model when you don’t want to use any external model itself because after all, you have all the output, you have all the traces, you have all the context. That’s really the enterprise design architecture that we are going to evangelize.” I am confident that Microsoft will certainly use “swappable” models in their 1P software products/solutions, but not nearly as confident that enterprise customers are nimble enough to follow Microsoft’s footsteps. In my last Microsoft earnings [**update**](https://www.mbi-deepdives.com/msft3q26/), I mentioned while Microsoft now evangelizes a multi-model world, their own disclosures suggested \~90% of Foundry customers were still using models from just one company. This quarter, the disclosure goalposts moved again. Microsoft now says Foundry has 100k customers (up from the 80k disclosed in FY 1Q’26) with revenue more than doubling YoY, and that customers building with models from **multiple** providers are up 5x since the start of the year. Notice they did not repeat the “used more than one model” count from last quarter, nor the OpenAI+ Anthropic customer count from FY 2Q’26\. Each quarter we get a new numerator on a new base, which makes penetration very hard to track; my best triangulation is that the large majority of Foundry customers are still effectively single-model, even if the multi-model cohort is growing quickly off a small base. None of the disclosures (or omission of certain data) in these earnings call is accidental. If it were conducive to the narrative of multi-model or “swappable” model world Microsoft is trying to propagate, we would have consistent data about it. Maybe it’s just too early though; the reality is Anthropic and OpenAI have been on a different gear in 2026 than the rest of the pack. The fact that Gemini and other closed models are increasingly missing from the frontier conversation is a huge impediment to the future Nadella and Jassy hope to see. Of course, Chinese models made material progress, and the idea that Chinese open weight models are closing the gap only got momentum recently. So we may get more adoption of multi-models in a couple of quarters. However, OpenAI has already made it clear that they are very much willing to price [aggressively](https://x.com/sama/status/2082880720989532597?ref=mbi-deepdives.com) to compete vigorously against open weight models. Ultimately, it seems the best way to hurt frontier models is to actually be **at the frontier** and make the frontier more crowded space than it is today. Unfortunately, the frontier keeps moving. Andy Jassy mentioned the models keep leapfrogging each other at the frontier, but in 2026 such leapfrogging has been largely confined to Anthropic and OpenAI. While Jassy was talking about leapfrogging in earnings call, Amazon actually [completed](https://x.com/deredleritt3r/status/2083335855502921922?ref=mbi-deepdives.com) their $50 Billion investment on OpenAI yesterday that had some interesting initial conditions: > Amazon completes a $50B investment in OpenAI. > > The deal, announced in April, included an immediate $15B investment + a commitment to invest a further $35B upon the earlier of: (1) OpenAI IPO, or (2) OpenAI meeting "specified milestones" - which Reuters reported meant OpenAI… [https://t.co/ZMJB1ulLpw](https://t.co/ZMJB1ulLpw?ref=mbi-deepdives.com) > > — prinz (@deredleritt3r) [July 31, 2026](https://x.com/deredleritt3r/status/2083335855502921922?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) Indeed, the very moment it appeared Chinese open weight models closed the gap, we may be on the cusp of again further divergence between frontier and open weight models. OpenAI and Anthropic both seem to have much more capable models internally that may be released soon. Just today, OpenAI [mentioned](https://x.com/polynoamial/status/2083467194663571701?ref=mbi-deepdives.com) an internal version of Astra (OpenAI’s next major model family), “solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science.” If that didn’t raise your eyebrows, get this: “The total number of tokens needed to find solutions to these problems would cost roughly $2,000 at Sol API rates.” We really are likely entering “[an era of proof abundance](https://www.mbi-deepdives.com/proof/)”. As impressive as it, I don’t know whether we are on the verge of finding multiple new capabilities unlocking hundreds of billions of revenue for the labs, or the fact that even such complex mathematical problems require $2,000 to solve may mean we are perhaps already in the “intelligence is too cheap to meter” era. So, selling raw intelligence to generate hundreds of billions of revenue every year may prove to be a high bar. I know, I know…”Jevon’s Paradox”! Let me be very clear. The whole AI space moves so quickly these days that it honestly becomes difficult to have a rigid thought about the future for weeks, let alone months or years. The unfortunate reality for investors is that stocks do get priced for their long-term future and market demands a lot of certainty for the future that I still do not quite share for much of the AI value chain. I am hoping once the labs IPO in the coming months or year, we will have **slightly** more clarity. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Amazon 2Q'26: AWS Gets the Love URL: https://www.mbi-deepdives.com/amzn2q26/ Last updated: 2026-07-31T15:09:01.000Z In [**1Q’26**](https://www.mbi-deepdives.com/amzn1q26/) earnings coverage of Amazon, I mentioned AWS would likely grow by \~35% in 2Q’26\. AWS did slightly better than that as it grew by \~37%! For a $169 Billion run-rate business, AWS growth certainly seems mind boggling. I will talk more about AWS later, but as I typically do every quarter, I will first start with non-AWS segments of Amazon. ![](https://substackcdn.com/image/fetch/$s_!wwuP!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F508a007d-6927-49a2-9f9f-2f545f49addc_1542x264.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Ex-AWS, Amazon’s North America and International segments margin trajectory were a bit underwhelming. International segment’s operating margin was flat YoY and even though it may seem North America’s operating margin expanded YoY, it includes $600 million tariff related refund. Excluding such impact, North America’s operating margin declined by 18 bps YoY. ![](https://substackcdn.com/image/fetch/$s_!OTlC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F17194937-80c4-41a5-bc3d-8d9af2c8585f_1225x714.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q’22\. Since then, unit growth has largely been faster than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. In 2Q’26, however, these lines converged at 17%. However, during the call management mentioned excluding the impact of higher fuel and linehaul rates, shipping cost would grow more slowly than unit growth “at a pace that is relatively consistent with last quarter.” That likely means the operating leverage theme still has further legs here once or if fuel costs come down. ![](https://substackcdn.com/image/fetch/$s_!h1Tr!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2d0336f5-eecd-471e-b568-07b86c920d1b_1402x568.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) A big driver for retail profitability is advertising. Given Amazon ads are perhaps more of a competitor to Google than Meta, I pay close attention to Amazon’s incremental share in advertising compared to Google Advertising. Last quarter, I wondered if Amazon is giving some share back to Google. That concern has evaporated by 2Q’26 numbers as Amazon’s incremental revenue share as a percentage of Google Advertising share went back to the long-term trend of \~40% following a dip in 1Q’26. ![](https://substackcdn.com/image/fetch/$s_!W2ZC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fceb5905d-f883-40ad-b52a-5655d23a0f90_970x543.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Okay, enough about non-AWS business. Let’s get into AWS related discussion and my rationale to make some portfolio changes which will be behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* --- _This post is for paying subscribers only._ ### Meta 2Q'26: Not Amazing, Not Bad, and Still Compelling URL: https://www.mbi-deepdives.com/meta2q26/ Last updated: 2026-08-09T13:32:16.000Z 2Q’26 wasn’t the quarter that propelled Meta out of the penalty box. While revenue came ahead of estimates, operating earnings fell short due to one-off expenses such as legal and severance related costs. I believe market was smart enough to see through that, so it’s the somewhat soft guide (relative to expectations) that likely led the investors sour a bit on the earnings. More on that later; let’s take a closer look into 2Q’26 first. I will begin with the comparison of incremental revenue YoY between Meta’s Family of Apps (FOA) and Google Search. Just like in 1Q’26, FOA’s incremental revenue was \~41% higher than Google Search revenue. By 2Q’27, I think it’s likely that the LTM ad revenue of Meta’s FOA will exceed Google Search revenue. For context, back in 2Q’22 Meta’s FOA ad business was just \~72% of Google Search revenue. So to go from there to exceed Google Search revenue by 2Q’27 would be quite remarkable indeed. Of course, Google Search hasn’t been a sloth either which only highlights the rapid pace of growth in FOA despite the ever increasing larger base. ![](https://substackcdn.com/image/fetch/$s_!VR7b!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3292b8a3-fa24-4ce1-9385-6ab3b6668ebc_1449x799.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Daily Active People (DAP) on Meta increased by 40 million QoQ and 120 million YoY to reach 3.6 Billion. Instagram now has 2 billion Daily Active Users (DAU) and Threads has 500 million Monthly Active Users (MAU). The geographical revenue trend was quite interesting in 2Q’26\. I would specifically highlight that revenue in North America **accelerated and was above 30% YoY for the first time since 3Q’21\.** Of course, 3Q’21 was benefitted substantially due to pandemic and ZIRP era quirks; so it’s notable that Meta North America ads grew >30% despite revenue being double that of 2Q’21.Europe and APAC were the reasons why Meta’s overall ad revenue growth decelerated by \~544 bps despite North America’s acceleration. Ad impression growth was actually fairly consistent across the regions, so it was mostly the price per ad that drove the deceleration in Europe. A good chunk of that is likely explained by FX trends, but I suspect less personalized ads in Europe was also the reason that explains relatively soft ad prices in Europe. In fact, in the follow-up call Meta highlighted the latter aspect to explain somewhat softer (again, relative to expectations) guide for Q3 too (emphasis mine): > First, we are lapping a quarter of accelerated impression growth, which benefited from engagement-related ranking improvements, mainly on Instagram feed and reels, as well as some ad load optimization on Instagram feed and stories. Second, **we’re seeing the impact of less personalized ads offering in Europe now that it’s fully rolled out, and that may be an additional headwind. And we’re also expecting an impact from our continued integrity enforcement efforts**. ![](https://substackcdn.com/image/fetch/$s_!YB3-!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0e885084-87ed-4a48-a673-a55261d818b0_1492x805.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Worldwide average price per ad was flat QoQ while impression growth decelerated by 500 bps. However, Meta shared data points to substantiate that their ranking and algorithm improvements are still throwing decent payoffs in impression growth. Instagram time spent grew double digits globally, Facebook video time grew 9% globally (10%+ in the US & Canada) on top of Q1's gain, and over half of recommended content on Instagram Feed is now less than a day old, more than double a year ago. Meta also shipped what it called its largest single ranking release ever on Reels, worth 15 bps of incremental Instagram sessions. During the call, Meta shared more granular details to explain why they expect further improvements in their ranking and recommendation infrastructure. Some key excerpts from both the earnings call as well as the follow-up call (emphasis mine): > “This quarter, we introduced Meta Generative Recommender, a paradigm shift in how our ad system works. **Rather than scoring every possible ad individually, we are now using LLM to reason about ad content and user preferences together and predict the best ad for each person**. This makes our ad matching more intelligent and more precise, which compounds performance gains for advertisers. > > We deployed the first generative model into our ads retrieval system and saw notable improvements in ads performance. **Early pilots using LLM to better understand user preferences drove a 1% increase in app event conversions on Instagram**. > > In Q2, we also advanced our user understanding models to analyze ads and organic activity and simultaneously improve both user experience and advertiser performance. **Combined with our GEM model for ads ranking and sequence learning, these advancements generated an 8.3% increase in ad clicks and a 15.7% uplift in conversions on Facebook**. > > “**we certainly see further headroom to continue improving recommendations over the rest of the year and into 2027**. And we expect that will help us drive additional gains on both engagement on Facebook and Instagram.” > > eventually, we hope to get to a place where we can collapse our current multi-stage recommendation model to a simpler unified model that, similar to how LLMs do next-token prediction, **can directly produce a set of tokens to show users, which we can then match to either organic or ads content within our inventory**. So we’ve already begun early validation work on LLM-native recommendation systems, and we’ll look to ship components of that to complement parts of the current stack. While FOA’s ad business understandably gets all the attention, its “other revenue” is making some noticeable progress. For context, **Meta’s other revenue doubled between 1Q’23 and 1Q’25, and then nearly doubled again from 1Q’25 to 2Q’26**. It wouldn’t surprise me if it doubles again by 2Q’27\. While other revenue is still below 2% of FOA’s total revenue and hence doesn’t get much attention, that may start to change in coming years. They just launched Meta Business Agent platform which does have the potential to become a more material contributor over time. Meta management actually sounded more excited about this opportunity during the follow-up call (emphasis mine): > “we also launched the Business Agent Platform earlier this month, which includes a suite of APIs and tools that allows enterprises to customize their business agent, integrate it through their existing systems, so they can connect to systems like catalog, CRMs, inventory management, giving their business agents the ability to take action on behalf of the business and to deploy those conversations on WhatsApp. So we are just getting started here. > > So we’re very excited about this. **We think the product is getting ready to scale**. We’re investing in driving greater awareness of it, while also continuing to improve the discoverability and onboarding and user experiences. And we’ll be introducing more capabilities into the business agent over the rest of this year and into ‘27\. And we’ll also keep improving our models, which we think will drive increased performance and demand for Meta Business Agent going forward. So **overall, I think this is an opportunity that we think is pretty unique. We are building a turnkey solution that’s going to leverage existing social media posts, ad campaigns, web presence, making it really easy for businesses to set up**. And we want Meta Business Agents to work day one for each business. And **we think there will be a big opportunity here again, given the size and scale of advertisers on our platform.** > > Additionally, earlier this month, **we announced a volume-based pricing model for messages sent using Meta Business Agent. And effective August 1st, we’re planning to charge on a per token basis for Meta Business Agent messages with one token base charge that will encompass both AI agent processing and message delivery**” It’s probably a bit too early to get excited about this, but I would expect this may become a source for potential upside in estimates in 2027 (and beyond). In terms of margins, overall reported EBIT declined by 6%, but adjusted for one-off expenses mentioned earlier, **EBIT would have grown by 9% YoY**. Of course, in an era when capital is becoming more of a constraint, the ever persistent $4-5 Billion losses in Reality Labs is increasingly even a more pronounced source of embarrassment for Meta. LTM losses here may have peaked, but that is hardly much of a solace as Meta management should start to promise a more concrete path to much lower losses in the medium term. Eric Seufert [characterized](https://mobiledevmemo.com/meta-q2-2026-earnings-the-albatross-weighs-heavily/?ref=mbi-deepdives.com) “Meta’s metaverse initiative from a few years ago as an albatross around the company’s neck that has left a trust deficit with public markets related to large-scale infrastructure investments.” Well put! ![](https://substackcdn.com/image/fetch/$s_!Y55Q!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9954157c-2e66-4829-9568-e3e8e5a87672_1405x472.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Meta Compute and AI Ambitions** One encouraging data point that Meta shared is that once they integrated Muse Spark to their AI assistant, number of people interacting with the assistant daily increased by 60% and continues to grow week-over-week. Predictably, the call had a lot of tidbits around what Meta plans to do with the massive capacity they are building despite not having a 3P cloud business. Zuckerberg mentioned Meta is getting “a lot of offers for compute at a significant premium” over what Meta paid for it. In the follow-up call, Susan Li also reiterated that the offer they’re receiving is “multiples” of what they paid for it. However, they’re still focused much more on internal uses first. Let me quote the detailed explanation (long excerpt, but important to understand) Meta provided during the call to explain their philosophy here (emphasis mine): > “Our approach to building capacity is strongly influenced by several key elements. First, the broad environment for building infrastructure is dynamic and uncertain in both near-term and longer-term time horizons. The industry has underbuilt historically for the wave of AI adoption, making existing capacity, including our own extremely valuable. Longer term, the supply chains need to be built out to support the capacity that we anticipate we and others will need for AI-powered experiences. Second, we have high confidence in our ability to utilize capacity to scale and build on top of our existing experiences as well as continue to invest in foundational models that will create substantial new opportunities. > > Consequently, our current plans are geared towards maximizing 2026 and 2027 capacity. When we have had incremental capacity in the past, it has proven extremely valuable in scaling experiences like Reels. And we are confident that this will be true in this time frame as well. > > **Longer term, it’s harder to predict the exact usage scaling curves**, but we believe that our distribution advantages will give us the opportunity to serve AI products that are valuable for everyone, both our 3.6 billion users and millions of businesses. **This should be true regardless of whether our models are on the frontier, but we believe that being on the frontier will unlock new markets and opportunities for which we may need additional compute**. **Therefore, our longer-term capacity strategy aims to give us the flexibility to continue growing compute in 2028 and beyond by laying down data center and network foundations to accommodate future server decisions.** > > The long-lived nature of these assets inherently provides the flexibility that will make it possible to adjust our investment to the pace of AI adoption. In addition, we have been making strategic investments in areas like our internal custom silicon effort, **which will provide long-term strategic flexibility and supply chain leverage**. This will be helpful in driving better returns on those long-term investments. > > Finally, we believe that overall industry capacity is going to remain tight for the foreseeable future. As we’ve said earlier, we strongly believe that the models, consumer experiences and enterprise offerings that we are building will be the best and highest ROI use of our infrastructure. Those enterprise offerings have the potential to take multiple forms…agentic tools, our API or monetizing compute directly given outsized market demand. We expect that remaining nimble about these opportunities will help us fund our build-out more efficiently while preserving our **strategic flexibility to have the compute when we need it and provide us multiple pathways to generate returns on invested capital.** > > …we believe that there will continue to be **a significantly higher margin on selling intelligence rather than selling compute directly**. But we think that there is a big opportunity, obviously, to sell compute as well. > > Now in terms of running the business, obviously, a common trade-off that we need to make is around **how much do you monetize something today versus develop future assets for the future?** And I think that it’s always a portfolio, right? It’s not like you don’t want to only do long-term things and you know like and not kind of prove the markets out that exist in the near term. **But I also think it would be foolish to basically just sell all of the compute and take a short-term profit. But when you have the opportunity to build intelligence on top of it, which will be a kind of a multiple and that compounds the value of the compute on top of tha**t. > > So I think the answer is what we’re doing, which is to basically use a lot of our capital to build out compute, having confidence that we have the ability to **monetize the compute directly when that makes sense**, but also knowing that we have quite a number of different use cases to monetize the intelligence on top of the compute, including the enterprise cases that we talked about and including some of the consumer and cases that we talked about, and including just the core business, which is not even necessarily new products that we haven’t talked about, but just in terms of using that to be able to further add intelligence and improve the ranking and recommendations and ads in the core services.” My read from this long excerpt is that Meta will **mostly** sell compute when the offer is so good that they cannot quite refuse. If some of the frontier labs underestimate compute demand and need some capacity soon to serve the demand, they might want to pay an arm and a leg for a short period of time. It can make a lot of sense for Meta to serve that market. ![Godfather GIF](https://substackcdn.com/image/fetch/$s_!ktqm!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4d99a029-683f-4d06-acfe-f1ed2b6187bd_480x480.gif "Godfather GIF") The other possibility is market may simply be too impatient to see Meta’s non-advertising monetization and force management’s hand to sell some capacity. In either of the cases, I don’t think consensus estimates still embed much revenue from such deals. This perhaps explains why the stock rallied so hard when market thought such deals may happen lot sooner than they realized. Now that Meta management somewhat downplayed the immediacy of such deal coming online, the stock gave up all such gains. Nonetheless it’s perhaps a useful message to Meta’s management what can reverse the flailing stock price which can be a competitive disadvantage in a hot AI talent market and potential capital infusion requirement from secondary equity offerings. While investors remain fixated on Meta’s ROIC, I am yet to be concerned. I will expand on my relative nonchalance about such concerns behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### DoorDash: The Infrastructure of Local Commerce URL: https://www.mbi-deepdives.com/dash/ Last updated: 2026-07-28T13:51:19.000Z ***Programming Note***: MBI Deep Dives will be off tomorrow, and the regular cadence of daily posts will resume on Thursday when I expect to cover Meta’s 2Q’26 earnings. --- *You can listen to the Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) The very first Deep Dive I ever wrote for MBI Deep Dives back in 2020 was on [**Uber**](https://www.mbi-deepdives.com/deep-dive-on-uber/). While discussing Uber Eats in that Deep Dive, I made the cardinal mistake of doubting the American eaters! I thought the pandemic likely pulled forward consumer propensity of ordering food online and once things normalize, people would likely go back to dine in restaurants and food delivery companies would face noticeable headwind afterwards. Six years later, as I was studying DoorDash over the last few weeks, I can now confirm that I was, in no uncertain terms, wrong! My mistake was viewing food delivery purely through the lens of pandemic-era consumer convenience, rather than as a fundamental infrastructure bottleneck for local commerce which was unleashed during Covid to become increasingly integral part of everyday life for millions of Americans. To understand why the habit stuck, let’s go back to when that bottleneck was first identified. In the fall of 2012, four Stanford students: Tony Xu, Stanley Tang, Andy Fang, and Evan Moore were working on a class project in the GSB’s “Startup Garage” course, interviewing small business owners around Palo Alto to figure out what technology they could build for them. One of those interviews was with a one-person macaron shop. During the interview, the shop owner pulled out a thick booklet pages and pages of delivery orders she had been forced to refuse because she had nobody to deliver them. As Xu recalled [in Sequoia’s “Crucible Moments” podcast](https://sequoiacap.com/podcast/crucible-moments-doordash/?ref=mbi-deepdives.com), small businesses running on 17-18 days of cash don’t have the luxury of turning away business and yet, the owner was forced to do it since there was not quite any viable solution. The four of them spent the following weeks interviewing another \~150-200 small businesses and kept hearing the same complaint: everyone wanted delivery, but nobody could afford to staff it. So, they thought they might be onto something here. In an afternoon, they stood up a landing page called PaloAltoDelivery.com with PDF menus of local restaurants and a Google Voice number that rang their own phones. DoorDash’s [S-1](https://www.sec.gov/Archives/edgar/data/1792789/000119312520292381/d752207ds1.htm?ref=mbi-deepdives.com) mentioned what happened next: on January 12, 2013, “…the first DoorDash consumer ordered prawn pad thai and spring rolls”, and one of the founders drove it over himself. For months, the founders were the dispatchers, the customer support, and the delivery fleet. Xu himself was among the company’s very first Dashers, a tradition that survives today in the form of “[WeDash](https://careersatdoordash.com/blog/wedash-doordash-employee-program-how-does-it-work/?%5Fga=2.51293807.770438897.1784224451-2123547498.1784224451&%5Fgl=1%2A98125g%2A%5Fgcl%5Fau%2ANTQyNzI3NDIyLjE3ODQyMjQ0NTA.%2A%5Fga%2AMjEyMzU0NzQ5OC4xNzg0MjI0NDUx%2A%5Fga%5F4J1MLKETLL%2AczE3ODQyMjQ0NTAkbzEkZzAkdDE3ODQyMjQ0NTAkajYwJGwwJGgw&ref=mbi-deepdives.com),” a program under which every salaried US employee must do delivery shifts each year. The company went through Y-Combinator in the summer of 2013 and renamed itself DoorDash. In some ways, restaurant food is perhaps the worst thing to deliver as they can be perishable within minutes, ordered in unforgiving lunch and dinner spikes, with customers who expect the whole thing done in under 45 minutes for a minimal fee. Indeed, Xu [wrote for Sequoia](https://articles.sequoiacap.com/2018-10-17-tony-xu?ref=mbi-deepdives.com) that when they started, they had exactly three questions to answer: “Would customers be willing to pay $5 for this service?” “Would restaurants be willing to pay us a certain percentage?” “Would drivers be willing to work for this wage?” If you can build a logistics network that can reliably move hot scrambled eggs across a suburb in 30 minutes, you can probably move basically anything else in local commerce later i.e. groceries, convenience, flowers, the tube of toothpaste...you name it! Restaurants also happened to be the largest category of local commerce with essentially no delivery infrastructure of its own; outside of pizza and Chinese food, most American restaurants simply didn’t deliver. Today, we take food delivery for granted, but it’s actually not that long ago it was far from the norm. Given that context, even though DoorDash’s ambition is to build last-mile logistics for every merchant on Main Street, restaurants were probably the hardest, AND the largest beachhead which means it made a lot of sense to solve that problem first! While Grubhub, Seamless, and Postmates fought over Manhattan and San Francisco, DoorDash deliberately went to the suburbs. When DoorDash finally launched in San Francisco in 2015 (two years after founding), Xu [told TechCrunch](https://techcrunch.com/2015/01/27/doordash-sf/amp/?ref=mbi-deepdives.com) that they had wanted to first learn in cities that looked like the **average** American city rather than the largest market next door. Suburbs had less competition, higher car ownership, easier parking, larger family order sizes, and more importantly, households for whom delivery was a genuinely new capability rather than a substitute for walking a block. Even though suburbs may have looked like a niche strategy at the time, it turned out to be one of the most important pieces of the puzzle to solve the broader problem. However, for a long time investors were highly skeptical. When Xu and his co-founders were raising money, [more than 100 investors said no](https://blog.southparkcommons.com/p/we-asked-doordashs-ceo-if-ai-competitors?ref=mbi-deepdives.com) since on-demand delivery still carried the stench of the dot-com graveyard (Webvan, Kozmo etc), and the margins looked impossibly thin. Sequoia’s Alfred Lin, who would later lead a round and take a board seat, [initially called Xu just to pass on the seed](https://techcrunch.com/2019/05/23/doordash-now-valued-at-12-6b-shoots-for-the-moon?ref=mbi-deepdives.com). Xu later admitted that solvency worried him for three years as he recalled in this [podcast](https://sequoiacap.com/podcast/crucible-moments-doordash/?ref=mbi-deepdives.com): > I always was nervous, and I think a lot of people involved with DoorDash were very nervous about, you know, our solvency. We were always worried about running out of cash and it was three years of being worried. > > …Besides just the constant financial stress, we had people lose confidence in the company, right? Even internally—about maybe a quarter or 20 to 25 percent of the company—voluntarily left over those three years. Xu and DoorDash went through those dog years to eventually become the dominant player in food delivery market. On December 9, 2020, DoorDash IPO’d at $102 per share. Then the pandemic-era market did exactly what it did to every 2020 IPOs: shares popped \~80% on the first day of trading, briefly pushing the market cap toward $60 billion. Enterprise Value (EV) reached \~$80 Billion during the 2021 peak and then crashed to **just \~$15 Billion** in 2022\. Since then, the company has recovered in value but EV is still hovering around $80 Billion. ![chart](https://substackcdn.com/image/fetch/$s_!N1Oa!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8cd214ae-d13b-4a0f-9079-47442088d8eb_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In 2025, DoorDash processed \~$102 Billion of Marketplace Gross Order Value (GOV) across 3.2 billion orders, generated $13.7 Billion of revenue, posted its first meaningfully profitable GAAP year, and after acquiring Wolt in 2022 and Deliveroo in 2025, it now operates across more than 40 countries with over 56 million monthly active users. Not bad for a company that started with a PDF menu website with a Google Voice number. In the rest of this Deep Dive, I will first walk through how the three-sided machine actually makes money and dissect the unit economics of an order. I will then discuss the competitive dynamics: whether food delivery is recession proof, whether the model is actually net beneficial to all three sides, why DoorDash beat Grubhub and the competition it faces against Uber, its chances in grocery against Amazon, Walmart, and Instacart, and whether AI is an opportunity or a disintermediation threat. Finally, I will cover capital allocation and management incentives before getting into valuation. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- # Business Overview _This post is for paying subscribers only._ ### The Fleet Owners' Push for Aggregating Demand URL: https://www.mbi-deepdives.com/uber-vs-waymo/ Last updated: 2026-07-27T20:53:28.000Z Last week, Financial Times [reported](https://www.ft.com/content/9dcb5d72-13aa-4f9c-ac6d-e022860df5ea?syn-25a6b1a6=1&ref=mbi-deepdives.com) that Waymo notified Uber that it intends to launch its own app in Austin and Atlanta in January 2028\. These were the two cities where Waymo’s robotaxis have been exclusive to Uber’s platform after the two already [unwound](https://www.thestreet.com/investing/stocks/uber-waymo-end-phoenix-robotaxi-pilot?ref=mbi-deepdives.com) their Phoenix pilot earlier this year. The divorce has been sort of telegraphed for months. Waymo hasn’t added a new Uber city since Atlanta in June 2025, and the two sides have traded accusations over safety incidents and “unsustainable” economics. Uber has actually been lobbying for “hybrid network” rules which means if you call an Uber in a market with AVs, you can get matched with either an AV or a human driver. If such a regulation were enacted, this all but guarantees Uber’s primacy in ridesharing for years (decades?) to come. TechCrunch [laid](https://techcrunch.com/2026/07/13/ubers-robotaxi-lobbying-effort-has-put-it-on-a-collision-course-with-waymo/?ref=mbi-deepdives.com) out plainly: > If Uber is successful and its hybrid network idea is adopted in D.C. — or elsewhere — it would leave AV developers like Waymo with two choices: put their robotaxis on ride-hailing apps like Uber’s, or employ human drivers who provide ride-hailing services alongside the robot cars that have taken years and hundreds of millions of dollars to develop. Uber’s “[AV transition white paper](https://drive.google.com/file/d/1xiCod8fr1ryLiTXnG21nsu9LfeUsgFb2/view?ref=mbi-deepdives.com)” is very eager to make the case that they are on the same side as labor which is a bit ironic given the company has for years on the opposite side of this labor and tech platform owner debate. Some interesting excerpts from their white paper (emphasis mine): > We owe people the truth. Autonomous vehicles may expand mobility overall, but they could also mean less work for many drivers over the long run, including people who rely on platform work as a flexible safety net. **We believe a phased transition to a hybrid model where drivers and autonomous vehicles work side-by-side offers the most promise for workers and greater reliability for consumers and cities**. A too-rapid shift to driverless fleets risks exposing gaps in today’s labor and benefit systems, pushing the burden onto the workers and communities to absorb the disruption. If we want an autonomous future that holds together socially, we have to plan for any workforce disruption with the same seriousness we bring to the underlying hardware and software. > > **In San Francisco and Los Angeles, where drivers compete against AV-only networks, driver utilization and hourly earnings declined last year. Given that the vast majority of drivers work part time, and an AV is online most of the day, we see that in California, one AV does the work of about four drivers.** > > The right path is phased and hybrid, moving fast but not all at once. This reduces real risks: worker displacement, uneven access, and pressure on city infrastructure and safety systems. While Uber is making the argument for hybrid network to the regulators, it’s simultaneously making the case to investors that AV-only competitor is simply bad business. For example, during 4Q’25 call, Uber pointed out that any AV-only competitor must either hold significant underutilized supply to match Uber’s reliability and prices, or deliver a worse consumer experience and leave demand on the table. ![](https://substackcdn.com/image/fetch/$s_!uzLM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F98626e27-f430-4b17-b221-08df96d4b970_1870x1027.png) Source: Uber 4Q’25 earnings presentation There’s an interesting academic paper titled “[Sharing economy in the era of full automation,](https://arxiv.org/pdf/2607.08610v1?ref=mbi-deepdives.com)” that was published earlier this month. The paper never mentions Uber vs Waymo debate and actually touches mostly on Elon Musk’s dream of your privately owned autonomous car moonlighting as a robotaxi while you sit in meetings at work. But if you look past that framing, it is a general model of the exact problem underneath the Uber-Waymo debate: how a platform facing spiky, spatially lopsided demand should combine always-on, capital-heavy base-load supply with flexible third-party supply. In the paper, the flexible layer is private AVs rented by the hour instead of being owned by the platform itself. The paper sides with Uber’s case that a rational fleet owner sizes to base load, which structurally leaves the peaks, the bad-weather surges, and the tail geographies to a flexible layer. Whoever aggregates that layer pays it marginal opportunity cost and keeps the spread. However, the paper models exactly one platform, and its result shows that whoever owns demand and dispatch captures essentially all the economics, while flexible suppliers (human or robotic) get paid opportunity cost. That is wonderful for Uber only so long as Uber is the one doing the dispatching, but as the news flow suggests, Waymo obviously understands the reality of the long-term economics here if they don’t own the demand directly. In fact, an analyst actually asked Elon Musk during last week’s Tesla’s 2Q’26 earnings call if Tesla would like to consider third-party distribution partnerships, such as with rideshare providers, to increase utilization. Elon Musk doesn’t appear to be interested as he said: “We expect to be vertically integrated with robotaxi as we are in the rest of our business.” The truth is both Elon Musk and Waymo deeply understand the underwhelming economics of being at the mercy of the demand aggregator. It would make long-term strategic sense only if they see no path at all to control the demand aggregation layer. So, I would expect both Tesla and Waymo to give their best shot at being at the demand aggregation and only succumb to partnership with incumbent ridesharing companies if they come to the conclusion that there is no viable path to aggregate demand themselves. We may not have to wait years to get our answers how the debates will be settled here. Driverless Digest Substack [pointed](https://www.thedriverlessdigest.com/p/waymos-lead-is-real-its-biggest-test?ref=mbi-deepdives.com) out that while AVs are being launched in more and more cities, in the next 12 months Waymo will likely transition from mere footprints to building density in many of these cities. From “Driverless Digest”: > Waymo has already entered or announced many of the largest markets. Based on my demand-weighted view of U.S. ride-hail, it is currently live in metros representing roughly 27% of estimated demand and has announced or signaled markets representing another 43%. In other words, Waymo now has a presence—or a stated intention to establish one—across close to 70% of estimated U.S. ride-hail demand. > > That does not mean Waymo serves 70% of the market today. Nor does it mean its service areas cover entire metros. This is a metro-level demand proxy, not a measure of actual coverage. I count Los Angeles as a live market because Waymo operates there, for example, even though its service remains concentrated within a limited portion of the broader metro area. > > But that still matters. Waymo is already in, or heading to, many of the markets that drive U.S. rideshare demand. Now it needs to make the service meaningfully available in each one: more vehicles, shorter wait times, broader coverage, airport access, and more consistent reliability. > > In robotaxis, availability is the product. A service that works great but comes with long wait times or is not available in your area is still not a true substitute for Uber or Lyft. > > The key point is simple: Waymo has established the market footprint. The next 12 months are about building density within it. > > That is both the opportunity and the risk. Waymo’s next chapter is less about proving the Driver and more about industrializing the fleet. ![](https://substackcdn.com/image/fetch/$s_!6BY0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd12848e0-f9b2-4e9c-baf1-4e67a34bd584_1774x887.png) Source: [Driverless Digest Substack](https://www.thedriverlessdigest.com/p/waymos-lead-is-real-its-biggest-test?ref=mbi-deepdives.com) Ultimately, the only question that may matter is whether Uber will continue to be the aggregator with dominant market share in ridesharing. If regulators demand hybrid network in all platforms, it pretty much ensures Uber’s prime position in ridesharing. Without such regulatory blessing, it appears the intensity to be at the demand aggregation layer will intensify in the near term. Uber competed tooth and nail over the last decade to dominate ridesharing today, and this decade is supposed to be the harvest decade for all the cash it deployed last decade or so. Such harvesting period may need to wait a bit if Waymo and Tesla start a new war in the demand aggregation layer in the next couple of years. I don’t have any strong point of view yet how such battle will be settled, but I intend to follow it more closely going forward to see if the picture is getting any clearer over time. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### "From an era of proof scarcity to an era of proof abundance" URL: https://www.mbi-deepdives.com/proof/ Last updated: 2026-07-26T15:04:40.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- Back in 2012, I visited Harvard for the first time to attend a conference. During that conference, I attended a social get together where I met a bunch of undergrads at Harvard (I myself was a Junior in college at that time back in Bangladesh). It was still socially acceptable to ask for each other’s Facebook account and stay connected through Facebook. So, after the event, I became “friends” with a bunch of Harvard undergrads. A couple of weeks ago, one such friend (who once won Gold at International Mathematical Olympiad) posted the below status update on Facebook: > “As International Mathematical Olympiad is being held in Shanghai right now, I’m having strange feelings while experimenting with AI model performance at the most well known global academic competition in the world. Our conclusion is that AI models are now practically better than almost all humans, even at IMO. Day 1 perfect score is confirmed, and likely to be the same on day 2 as well. This surpasses our previous expectation which was merely a gold medal level performance. > > If I was 15 year old right now, I know I wouldn’t be able to motivate myself to get better at math as I did back then, this makes me sad. At that age, I felt real romantic love towards mathematics, and AI is like a much stronger bully stealing my love. It may sound absurd, but that’s how I feel!” Then a couple days later, he confirmed the AI models’ supremacy in another status update on Facebook: > “GPT 5.6 Pro solved all 6 problems from IMO 2026 on the first attempt without any human help or steering…The problems are considered incredibly hard, usually a performance at this level is only accomplished by < 5 contestants from the whole world.” While reading these status updates, I was reminded of another interaction I had with a girl when I was visiting Bangladesh a couple of months ago. She was just in her 9th grade, and it was pretty evident to me that she was perhaps one of the smartest 9th graders I have ever interacted with. Her father used to teach my wife Mathematics when my wife was in High School, and he wanted to visit us with his daughter when he learned that we are in town. The daughter clearly got the Mathematics bug from her father and she was telling me how she spends much of her day solving Math problems and rummaging through different problem sets and discussions in different internet forums. it was a good reminder that despite being born in a second-tier city of a third world country, a gifted kid can still keep pace with the world through the blessings of the internet. She wanted to represent Bangladesh at IMO and was curious to learn more about college admission process in the US. The reality is most US colleges would be lucky to have her in their class, so I encouraged her to not fret over such mundane things. However, I did have one suggestion for her: “keep your identity small…don’t make mathematical proficiency your primary identity, rather just internalize that it is one of your gifts that the world may or may not value in a decade or two. Nurture the inherent joy of Mathematics that got you hooked in the first place so that even if the world becomes less appreciative of your gifts, you still have your reservoir of curiosity and joy to remain infatuated with Mathematics.” AI’s rapid progress may make these questions more urgent than I may have internalized even a couple of months ago, but while this discomforting reality may be a relatively new phenomenon for today’s mathematically gifted kids, many other gifted kids indeed grappled with such reality for a long time. Imagine a kid in India who is the national Champion in some random Olympic sport but fail to make the Olympic podium. Can you imagine how challenging it is to become national champion in anything in a country of a billion and half population? And yet, there are perhaps numerous such gifted kids who do not find much of an economic value for their gifts. Of course, Mathematics is no equivalent of a random Olympic sport as it is far more instrumental and fundamental in our understanding of the world. But I do often wonder Ilya Sutskever’s October 2023 [tweet](http://if you value intelligence above all other human qualities, you’re gonna have a bad time): “**if you value intelligence above all other human qualities, you’re gonna have a bad time**” Indeed, if intelligence becomes too cheap to meter in not-so-distant future, that may fundamentally reshape how we approach mathematics (and mathematicians) too. Terence Tao, one of the greatest living Mathematicians, actually gave a profound (and very accessible) [presentation](https://teorth.github.io/tao-web/slides/age-of-ai-icm-2026.pdf?ref=mbi-deepdives.com) titled “Mathematics in the age of AI” just two days ago at “International Congress of Mathematicians” (ICM). He didn’t mince the soul searching that may be required in the field of mathematics sooner rather than later (emphasis mine): > “…in the early twentieth century, discoveries such as Russell’s paradox (1901) or the Gödel incompleteness theorems (1931) forced practicing mathematicians to critically re-examine their implicit assumptions about the foundations of mathematics. > > This crisis in foundations (∼ 1900–1930) was a turbulent period for mathematics. But the end product was extremely valuable: an explicit, rigorous, and standardized foundational framework. There is scope for further improvement. But our current foundations have survived strenuous testing and are a trusted environment for mathematics. > > **I believe we are entering a similarly turbulent period —a crisis in the foundations of mathematical values and practices**.” Since there are plenty of people out there who remain deeply skeptical of AI’s actual capability (especially given how jagged it can be), Tao was careful in explaining his “AI Capability Conjecture”: > At **some** point in the near future, **some** AI tools will, at **some** expense, and with **some** level of human supervision, be able to correctly accomplish **some** research-level mathematical tasks in **some** fields of mathematics, with **some** non-trivial success rate, and at **some** level of correctness and quality. He then cited some independent assessment that led him to formulate his “working hypothesis” for AI’s capability: > AI tools will, **reasonably** **soon**, become capable of performing a **reasonable fraction** of research-level mathematical tasks, with **reasonable** levels of success, quality, supervision, and cost. In such a world, what should be the “Goals” of today’s and tomorrow’s Mathematicians? Tao goes through different iterations of the goals in his presentation and decided on the following in his fifth attempt: > Solve unsolved problems, verify them to be correct, ensure they are clearly communicated, and have them digested, accepted, and incorporated into the definitive theory of the field. The last bit may not seem super prestigious today, but Tao reminded a wonderful quote by William Thurston in ““On proof and progress in mathematics”: > "We are not trying to meet some abstract production quota of definitions, theorems and proofs. The measure of our success is whether what we do enables people to understand and think more clearly and effectively about math." Of course, digestion and acceptance may be the actual bottleneck relative to the generation of novel proofs, but that’s exactly where humans can leave their mark. From Tao: > Community acceptance of a result, by its nature, is slow and human. It can be **encouraged** with good exposition and careful writing. But it is ultimately an external process that **cannot be optimized purely by the authors and their AI tools** > > Our current publication infrastructure relies on human editors and referees to voluntarily provide this community acceptance as a service. This work is often regarded as less prestigious than that of generating proofs in the first place. But it is an essential component of our profession. It is also how we convert the individual achievements of mathematicians into collective progress and understanding. Near the end of the presentation, Tao acknowledged as we are likely moving from an “era of proof scarcity to an era of proof abundance”, the mathematics community needs to evolve with it: > In short, we will transition **from an era of proof scarcity to an era of proof abundance** > > we need to decrease the emphasis on proof generation, and of being the “first” to solve a problem; and increase the emphasis on “proof digestion”: exposition, publication, and canonicalization. > > my suggested rule of thumb: if the authors cannot convincingly demonstrate that they can give a clear, expert-level talk on their results, that is correct and properly attributed, then the result should not be published. The whole [presentation](https://teorth.github.io/tao-web/slides/age-of-ai-icm-2026.pdf?ref=mbi-deepdives.com) is worth going through; you can sense the internal struggle due to the rapidly progressing AI models, and the wisdom that came out of it from one of the greatest living Mathematicians. As the foundations of mathematical values shift from generation to digestion, the best path forward might just be the one I suggested to that 9th grader in Bangladesh. When the novelty of simply finding the answer fades, it is the inherent joy of the process and our ability to share that clarity with others that will truly endure. Ultimately, our greatest enduring moat may simply be our curiosity and our shared human experience of understanding. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Texas Instruments 2Q'26: Not Another Head Fake URL: https://www.mbi-deepdives.com/txn2q26/ Last updated: 2026-07-25T14:18:16.000Z While covering Texas Instruments (TI)’s [**1Q’26**](https://www.mbi-deepdives.com/txn1q26/) earnings, I wrote that TI had reached an inflection point, and that the fact management was even willing to talk about raising prices made me think this one might not be another "head fake." Indeed, revenue grew by 23% YoY and 13% sequentially in 2Q’26. ![](https://substackcdn.com/image/fetch/$s_!qc9p!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9f2c3498-1a93-4ce2-aa02-f917712b4f23_2007x229.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let me dig into 2Q’26 earnings of TI behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Tesla and SpaceX's Likely Merger, Portfolio Change URL: https://www.mbi-deepdives.com/tesla-and-spacexs-likely-merger-portfolio-change/ Last updated: 2026-07-24T14:41:14.000Z During Tesla’s earnings call on Wednesday this week, there was a very interesting discussion on the topic of whether SpaceX and Tesla will eventually be merged. When the analyst asked the question, I’m not even sure he was expecting an answer that almost confirms that such a merger is perhaps a question of when, not if. In response to the question about potential synergy between Tesla and SpaceX, this is what Elon Musk and Brandon Ehrhart (Tesla’s General Counsel) said: > **Elon Musk** > > as you can tell from the many collaborations on so many fronts with SpaceX and there’s a lot -- there’s more and more overlap, especially with Terafab, that’s really going to be a gigantic project. > > So -- but obviously, we can’t talk about combining companies and that kind of thing on an earnings call. It’s got to be done with the appropriate process. And with that, I’ll turn it over to Brandon, our General Counsel. > > **Brandon Ehrhart** > > that’s exactly right. We continue to benefit from our relationship with SpaceX, and we’ve -- they’ve been a great partner, and we have numerous beneficial transactions with them. And earlier this year, we deepened our relationship through an investment and a framework agreement. This will allow us to continue to work with them on projects that Elon mentioned like Terafab and Digital Optimus. > > **Elon Musk** > > Yes. And there’s many other things, too. Obviously, you’ve got Grok in the car. So -- and Grok helping drive Digital Optimus. You also got Starlink being integrated into the Cybercab and Starlink will be integrated into all of our vehicles, at least for markets that Starlink is active. Because for a robotaxi situation, you need to have coverage everywhere. And there are many places even in Silicon Valley where the cellular coverage is terrible or sometimes nonexistent, which is surprising for Silicon Valley. But I know when I want to drive to work with the first 10, 15 minutes, I can’t actually do any calls because the cellular connectivity is so bad. > > So we can’t have robotaxis getting stuck in these like Bermuda triangles of lack of cellular connectivity. So Starlink with its ability to do connectivity anywhere is actually quite important. So we don’t have robotaxis missing in action. And then obviously, if people are sitting in the car that they’re going to want to do high productivity stuff or entertainment. And with Starlink, you can watch 4K live sports in the car and with very low cost per gigabyte of data that’s really not feasible via the cellular system. And there’s many other situations. That's perhaps about as loud as a CEO can wink without formally announcing a merger. If I were either SpaceX or Tesla shareholder, I think I would actually welcome the merger. The reality is Neither of these companies is best understood or valued on their current core operating businesses. Both companies are essentially a portfolios of deep out-of-the-money call options attached to a cash-generating core. Given the cash generating core today doesn’t really come anywhere close to explain their current valuation, investors are likely paying hefty premium for the deep-out-of-the-money call options. To realize the full potential of such call options, both Tesla and SpaceX may need to deploy gargantuan amount of capex in the coming years. Tesla’s unsupervised FSD or robotaxis, Optimus, SpaceX’s orbital data centers, and frontier model ambition, or their joint Terrafab project…all of these call options seem pretty capital intensive. And it’s really hard to know which of them will take off at what timeframe. Given such uncertainty, it may indeed make sense to have all the call options under the same roof and then add fuel to the ones that actually start to pan out. I have no idea about the probability of success of any one of such call options. In some ways, Elon Musk’s companies increasingly seem to be “Berkshire inverted”. Buffett assembled a portfolio of short-vol cash streams and used float to buy more of them; following the very likely merger of Tesla and SpaceX, MuskCo mostly appears be a portfolio of deep-out-of-the-money calls funded partly by an industrial float but perhaps increasingly more and more be the generosity of the strangers by their willingness to pay such a hefty premium for a portfolio of deep-out-of-the-money call options. I wouldn’t touch it anytime soon, but I would also not bet against the possibility that if a couple of the call options come to fruition, that may “justify” the valuation of MuskCo in the coming years. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. After going through Alphabet’s earnings closely, I have decided to add another \~120 bps to my existing Meta position. I will expand on my rationale below. _This post is for paying subscribers only._ ### Alphabet 2Q'26: Google Cloud is On Cloud Nine URL: https://www.mbi-deepdives.com/goog2q26/ Last updated: 2026-07-23T16:15:21.000Z For the first time as a public company, Alphabet reported negative Free Cash Flow (FCF) in 2Q’26\. That sounds [scary](https://x.com/conorsen/status/2080037909516734536?ref=mbi-deepdives.com) (only if you have very rudimentary understanding of investing); you need to juxtapose it with the fact that the same company has now accelerated its revenue for six consecutive quarters. Revenue grew 24% (23% FX-adjusted) to $119.8 Billion in 2Q’26, and Google Cloud grew \~82%. Yep, no typos here: a business knocking on a \~$100 Billion annualized run-rate grew \~82%! ![](https://substackcdn.com/image/fetch/$s_!NSLR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc675a58e-fb9a-48a6-95a8-628bb1aa4d6b_1924x360.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Here’s a more granular segment wise growth trajectory since 1Q’23. ![](https://substackcdn.com/image/fetch/$s_!y5AU!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F47c082c9-30a1-4246-9a05-5013ebba86f9_1849x313.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) And these revenues weren’t really empty calories either. Operating income grew \~25% to $40.8 Billion for a 34% consolidated margin. Note that net income and EPS grew much faster than that, but it was primarily unrealized gains in the equity portfolio (SpaceX and Anthropic). As mentioned earlier, the showstopper was Google Cloud: operating income more than tripled YoY to $8.8 Billion, with margin expanding from 20.7% to 35.6% while still absorbing the Wiz integration drag i.e. margins would be even higher if we adjust for the Wiz integration. However, let me repeat the caveat I mentioned [last quarter](https://www.mbi-deepdives.com/goog1q26/): > In both these segments, I should highlight that there is a **slight** wrinkle in comparing current segment margins to historical segment margins. In [2Q’24](https://www.sec.gov/Archives/edgar/data/1652044/000165204424000076/googexhibit991q22024.htm?ref=mbi-deepdives.com), Alphabet disclosed that “*AI model development teams previously under Google Research in our Google Services segment are included as part of Google DeepMind, reported within Alphabet-level activities, prospectively beginning in the second quarter of 2024”*. > > As you can see below, there has been a noticeable step up in “Corporate costs” which includes the “Alphabet-level activities”. Given that context, it is better to focus on consolidated operating margins than any segment level margins. So revenues are accelerating and profits are growing at healthy margins. What are investors worried about then? I don’t want to engage in the day-to-day price action in current market environment, but there are indeed some legitimate concerns despite such operating performance which I will discuss more behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!j4_4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F49f3d5b3-6459-4499-80e7-9dead7e1508a_1930x316.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- Let me quickly go through the key segments of Alphabet. _This post is for paying subscribers only._ ### Danaher 2Q'26: Yet Another "Meh" Quarter URL: https://www.mbi-deepdives.com/dhr2q26/ Last updated: 2026-07-22T15:25:05.000Z Danaher is one of those compounders that has largely lost its luster in current market. However, Danaher shareholders cannot quite blame Mr. Market since 2Q’26 earnings didn’t provide much of an evidence of Danaher getting out of their growth slump that they have been under for multiple years. The company reported yet another anemic core revenue growth of 3.0% in 2Q’26\. It has been now **FOURTEEN** long quarters Danaher reported **BELOW** MSD core revenue growth every single quarter. Management even invented a new “Danaher ex respiratory” metric to show that the business will return to MSD core revenue growth (ex respiratory) in the next couple of quarters. I wonder why they didn’t show such adjustments when respiratory was a tailwind to overall core revenue growth. Or to say it differently, I’m not sure Danaher management will adjust core revenue growth downward in future years if respiratory again becomes a tailwind to organic growth. It is perhaps telling that despite such adjustments, the company still has hard time posting even MSD organic growth these days. ![](https://substackcdn.com/image/fetch/$s_!2AXA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc516362-f480-4eca-8931-b5098cd7daa6_1731x433.png) Source: Danaher If anything, Danaher shareholders perhaps should thank Mr. Market that investors have been so patient given that the company still trades at not-so-cheap multiple. To assess Danaher’s earnings power, I typically look at their LTM adjusted EBITA figure which was $7.1 Billion in 2Q’26\. For context, two years ago the LTM EBITA in 2Q’24 was $7.0 Billion. So, the earnings power is essentially flat over the last couple of years. Given Danaher’s \~$150 Billion Enterprise Value (EV) today, the company still trades at mid-20s multiple of this earnings power (after taking taxes into account). As you can see, you could perhaps legitimately still argue that such multiple is generous for a company that has found it very, very difficult to compound their earnings power for the last three years. ![](https://substackcdn.com/image/fetch/$s_!WrIK!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc196309c-6fdd-4a77-84ad-c1f307a3a455_1588x363.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will dissect the quarter segment by segment behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://mbideepdives.substack.com/p/deep-dives) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Geopolitics of Open Weights URL: https://www.mbi-deepdives.com/open-weights/ Last updated: 2026-07-21T14:56:11.000Z Ever since Kimi K3 was [released](https://www.kimi.com/blog/kimi-k3?ref=mbi-deepdives.com), it really captured the attention of the broader AI and investor community. As open weight closes the gap with closed, frontier models, many are understandably worried about the implications across the value chain. As far as I can tell, while you can legitimately argue about the potential margin erosion in the model layer if open models gain broad adoption, the **long-term** profitability of other parts of the AI value chain should not be affected even if open models become popular. Gavin Baker eloquently made this argument on X after Kimi’s release. Some excerpt from his [post](https://x.com/GavinSBaker/status/2078110934740980193?ref=mbi-deepdives.com): > “Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. > > A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers. > > Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software. > > An open-source model requires the \*exact\* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3\. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.” The reason I said the “long term” profitability should not be affected even if model layer margin proves to be thin is that the transition from OpenAI and Anthropic’s aggregate value from \~$2 Trillion to “just” a few hundreds billion can potentially be rather challenging for other parts of the value chain in the “short term”. After all, hyperscalers such as Microsoft, Amazon, and Alphabet all have massive backlogs from OpenAI and Anthropic. If frontier models cannot follow through their commitments made to hyperscalers, we may see a **temporary** moment where demand-supply gap evaporates. It might be only temporary given a more broad adoption of AI seems pretty secular and demand will eventually broaden out even if frontier models’ economics falter. Of course, even a short-term overcapacity can create some pressure on the economics in other parts of the AI value chain and their respective stock prices. To be clear, I’m not quite ready to announce “game over” for model companies; it may still prove to be the case that as Baker pointed out “Claude and ChatGPT products and harnesses may be more important than their models today”. It may be unsatisfying to not be able to infer anything definitively, but it is perhaps more dangerous to conclude more than we currently can. Observing closely but not being able to infer any long-term outcome confidently will likely be the default state for much of the AI value chain for quite some time. One reason open models can be a real concern for frontier models is not only the overall capability gap between closed and open models is diminishing, they may simply be more performant due to their lack of guardrails compared to closed models, especially in certain high value work such as cybersecurity. I would highlight the following [post](https://x.com/rauchg/status/2078647648307880209?ref=mbi-deepdives.com) from Guillermo Rauch, CEO of Vercel (emphasis mine): > “Based on internal evals: > > Kimi K3 is top-tier at cybersecurity > There is chatter on X that Moonshot benchmark-overfit. These are stealth evals. Model has raw IQ. > > Sol is a leap ahead in cyber capability > At a significantly higher cost, but quite remarkable still. > > **Fable refuses everything** > **We couldn’t get it to complete the run at all.** What’s interesting is that Sol in comparison was much more open to helping with defensive cyber hardening > > TL;DR: frontier, open-weight cybersecurity capability is here. > > Incidentally, I’m very bullish on cybersecurity as one of the best benchmarks for superintelligence. The “IQ test” of software engineering. > > The best engineers I’ve worked with in my career have usually had a deep background or interest in security. > > It’s actually easy for a model to “one-shot an XYZ clone” and impress people on X. But that’s not a good test. > > Finding, patching, reversing, and exploiting require a cognitive skill that transcends programming languages, runtimes, frameworks… It demands true reasoning power from the model and “corner thinking”. Very, very few humans excel at this, let alone in ordinary day-to-day software writing. > > Seeing Kimi K3 do so well here bodes well for open models.” China is clearly emboldened by the success of their open models. And in case if there was any doubt at all, Chinese President Xi Jinping in his [speech](http://english.scio.gov.cn/topnews/2026-07/18/content%5F118605932.html?ref=mbi-deepdives.com) at World AI Conference (WAIC) made it clear that they intend to offer a counter position to closed, frontier models by American companies. Some key excerpts from Xi’s speech: > “we should adhere to the principle of openness and win-win and boost innovation-driven development. As a new engine of world economic growth and an accelerator for the shift of growth drivers, AI is moving from the digital world into the physical world. We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing. We should facilitate technological innovation, industrial development and scenario-based application of AI. We should make coordinated advances in the transformation and upgrade of traditional industries, the cultivation and growth of emerging industries and forward-looking planning for future industries, so that all sectors and businesses can benefit from AI. > > We often say in China, "A single string cannot make music, and a single tree does not make a forest." AI development should not be a solo performance by a single country, but a symphony of international cooperation. > > China is ready to be more open, take more practical actions, and assume a more visionary perspective. We are ready to work with all parties to seize the opportunities of AI development and meet the challenges, and join hands to create a brighter future for humanity.” Interestingly, even though China has built a reputation of releasing open weight models, not all Chinese companies were actually following the same approach. For example, Alibaba’s Qwen models are closed models. But following Xi’s ardent defense of the “openness” at WAIC, it appears every single Chinese company will pivot away from closed models. It is a bit amusing that Alibaba’s verified twitter page actually [retweeted ](https://x.com/natolambert/status/2078822507684249811?ref=mbi-deepdives.com)a post that insinuated that the strategic direction came directly from Xi. Both in the US and in China, governments are clearly becoming integral players in the AI puzzle. ![](https://substackcdn.com/image/fetch/$s_!A6bw!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5f1676c8-bd10-4afa-9bcb-e37114895306_688x946.png) China’s government is perhaps much more comfortable in calling the shots about what the strategic direction for their AI companies should be, especially in light of their own national interest. I wonder if running similar pre-training run by four different Chinese companies is of the best interest given advanced chips remains their primary constraint. If three/four Chinese labs each possess a cluster too small or fragmented to conduct the best possible training run, aggregating those chips into a nationally scheduled cluster could permit a substantially larger and more reliable training run. Centralized purchasing, networking, utilization and power allocation could also remove genuine waste. A shared open-weight base model would then turn frontier pretraining into national infrastructure. A big assumption I am implying here is US AI companies running different, expensive training runs as pure duplication. However, OpenAI, Anthropic, Google, xAI and Meta do not merely take an identical recipe and repeat the same run. They make different bets on architecture, data curation, native multimodality, mixture-of-experts design, long-context attention, reinforcement learning, synthetic data, safety and inference-time reasoning. Nobody knows beforehand which combination will work best. Four independent training programs produce four shots at discovering a new capability or efficiency improvement. Nonetheless, it wouldn’t surprise me if China takes a more concerted approach in aggregating their limited resources in the pre-training stage. Alibaba, Tencent, Moonshot and thousands of startups could begin from the same strong checkpoint and spend their resources on continual training, reinforcement learning, inference optimization, tool use, memory, retrieval, agents and applications. The fixed cost of pretraining would be amortized across the entire Chinese economy. China could treat the base model as a subsidized public good and intentionally drive the market price of comparable intelligence toward inference cost. This speculation, of course, rests on the assumption that advanced chips remains the long-term bottleneck for Chinese AI companies. There are indication that we may want to hold even that opinion a bit loosely. See this excerpt Bloomberg [piece](https://www.bloomberg.com/news/articles/2026-07-20/z-ai-completes-giant-data-center-with-chinese-chips-to-train-ai?ref=mbi-deepdives.com): > “Z.AI, the Chinese artificial intelligence company formerly known as Zhipu and focused on developing its GLM model platform, has [completed](https://www.bloomberg.com/news/articles/2026-07-20/z-ai-completes-giant-data-center-with-chinese-chips-to-train-ai?ref=mbi-deepdives.com) construction of a massive 1-gigawatt data center powered entirely by Chinese-made chips. The facility has started partial operations and is designed to provide the computing capacity needed to develop Z.AI's most advanced GLM systems. > > Investors may view the facility as a major test of whether China's domestic chip industry can support increasingly advanced AI models over the longer term. Huawei Technologies, China's leading designer of AI accelerators, is competing with Cambricon Technologies, a Chinese chip company, and Alibaba Group Holding, a major Chinese technology and cloud-computing company, as local suppliers work to narrow the performance gap with NVIDIA. **The scale of Z.AI's new facility would place it among the largest data centers developed by a Chinese AI laboratory**, although Alibaba and China Telecom, a major Chinese telecommunications operator, remain among the country's largest builders of computing infrastructure.” I suspect Zhipu is not the only Chinese company to build a gigawatt scale data centers. As the training cluster for the next models become bigger and bigger, you can bet that other Chinese AI companies will also want to undertake similar projects. And if the Chinese government wants to pursue a more centralized training run in some future date, perhaps China may even go for the largest training run in the world! As you can see, the AI race is not only far from over among the companies involved, it may also be very much alive on the geopolitical front. The dominance of US AI companies may be far from certain even if they appear to be better positioned today. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### TSMC 2Q'26: Stratospheric Margins Amidst the AI Super Cycle URL: https://www.mbi-deepdives.com/tsm2q26/ Last updated: 2026-07-17T15:16:56.000Z **Programming Note**: MBI Deep Dives will be off for the next three days. The regular cadence of daily posts will resume on Tuesday next week. --- TSMC, which is perhaps the most [mission-critical](https://www.mbi-deepdives.com/tsm/) company on earth, is dealing with almost embarrassing amount of riches as their customers (and customers’ customers) cannot seem to get enough of their advanced chips. In the last semi up cycle, TSMC peaked at low 60s gross margin. In 2Q’26, their OPERATING margin crossed the 60% threshold! ![](https://substackcdn.com/image/fetch/$s_!GNCU!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffd68539d-2af1-4714-b835-ab2771902406_1554x895.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The rest of this piece will be behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### ASML 2Q'26: Time to Flex Pricing Power URL: https://www.mbi-deepdives.com/asml2q26/ Last updated: 2026-07-16T14:38:39.000Z Back in 1Q’26 call, ASML management was asked whether they are willing to flex their pricing power given how demand for chips seems to materially outweigh the supply. ASML management indicated to prefer more measured approach across different set of customers throughout the cycle. From 1Q’26 call (emphasis mine): > “Now in our model of pricing, as you know, **our model of pricing is not based on the squeeze that our customers find ourselves in**. That’s not the way we do business. **The way we do business is that we look at the value that we provide to our customers, generation on generation, tool on tool, and we take our fair share in that**. And you might say in the current climate, can’t you squeeze out a little bit more? I understand that. But it’s also true that when the market is good, it goes down a little bit and the customers are going through more difficult times that it also pays these fees. > > So fundamentally, we believe that the model that we have is a fair model. **It’s also a model that is fair to all the players because I would find it difficult to explain why we’re charging more in, let’s say, the memory environment versus the logic environment.** That’s just not the way we do business. So we’re very, very happy with the business model we have, which is based on the value of our tools, and we would gladly continue with that approach.” Just a quarter later, there is a noticeable shift in tone which I will discuss behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb’s Cost of Market Creation and Regulatory Survival URL: https://www.mbi-deepdives.com/abnb_ban/ Last updated: 2026-07-15T14:34:33.000Z Airbnb is one of those rare marketplaces that derive supermajority of its demand organically and hence, they have the privilege of mostly not needing to pay Google et al for traffic. However, Airbnb appears to have a different “recurring” expense to keep their business growing: field operations and policy expenses which is reported under Sales & Marketing (S&M). To underscore the significance of these costs, let me point out that **\~40%** of Airbnb’s last year’s S&M was actually field operations and policy expenses. What exactly are these expenses? I [**explained**](https://www.mbi-deepdives.com/abnb4q25/) in a piece early this year: > Field operations basically account for the employees who manage specific local markets, recruit new property Hosts, run host-community events, and manually build localized housing supply to meet traveler demand. As Airbnb started focusing beyond the five core markets, I suspect such field operations cost had a noticeable bump. > > Moreover, because Airbnb’s business model can potentially impact local housing markets, it faces intense regulatory scrutiny. This likely necessitates armies of lobbyists, lawyers, and policy experts who negotiate with city councils, mayors, and national governments regarding short-term rental bans, zoning laws etc. As a market leader of alternative accommodation, Airbnb must tackle these issues. This can actually be good news for its competitors such as Booking who can largely coattail on Airbnb’s spending on such regulatory efforts. As you can see, since there are a lot of different things bucketed in this reporting cost line item, it’s hard to pinpoint what exactly is driving the expenses. Even though I would expect to see operating leverage in this cost line item over time, that’s not what we have seen so far. For example, back in 2021, Airbnb spent 1.0% of its Gross Booking Value (GBV) on field operations and policy expenses. Since then, Airbnb’s GBV almost doubled by 2025\. And yet, Airbnb’s field operations and policy expenses was actually 1.1% of its GBV in 2025, implying not much of an operating leverage at all. ![](https://substackcdn.com/image/fetch/$s_!Qt1O!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F31b06cbe-a541-4a75-adf5-5bbd852642b7_1516x855.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The reality is over the last few years, the regulatory headaches around the impact of short-term rentals on housing has been intensifying. The poster child for such headache is New York City. Almost three years ago, NYC flipped the switch on [Local Law 18](https://www.nyc.gov/site/specialenforcement/registration-law/registration.page?ref=mbi-deepdives.com), its short-term-rental registration and platform-verification regime. Local Law 18 required hosts to register with the city and effectively banned unhosted stays under 30 days which predictably led to a collapse of Airbnb’s supply in NYC. The [Office of Special Enforcement](https://criminaljustice.cityofnewyork.us/press-release/ll18-report-sheds-light-on-eliminated-illegal-rentals-in-nyc/?ref=mbi-deepdives.com) says there were more than 38,000 active listings at the start of 2023; by 2025, New York had only 3,000 active short-term-rental registrations. The promise from the law's backers seemed quite simple and intuitive: those units would come back to the long-term market, supply would loosen, and rents would ease. Jay F. at [The Data Stream](https://datastream.substack.com/p/what-did-banning-airbnbs-in-nyc-accomplish) recently tried to answer whether the reality matches the promises three years after the de-facto ban on Airbnb. He compared rent trends in neighborhoods with high and low concentrations of Airbnb listings, using asking-rent data from both Zillow and StreetEasy. This is what he found (emphasis mine): > “I ran an event-study difference-in-differences: comparing rent trajectories in high-density versus low-density areas, within the same borough and controlling for new construction. To summarize, there was no notable effect size. **The best estimate is that the ban saved the typical renter about $52 a year but with a confidence interval of -$71 to +$175** .” ![](https://substackcdn.com/image/fetch/$s_!vkyl!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb1754a08-b9f6-4912-b344-c6629ea5139a_2280x1197.png) Image Source: The Data Stream As you can see above, asking rents in the most Airbnb-heavy neighborhoods did not clearly break away from rents in the least Airbnb-heavy group after enforcement. If anything happened, it is difficult to distinguish from ordinary neighborhood-level noise. This should not be particularly surprising. The Data Stream estimates short-term rentals were only 0.59% of New York’s rental stock before the crackdown. Even if every banned listing became a conventional rental, the supply shock would still be small relative to the city. In reality, only 42% of the affected listings had adopted a 30-night minimum by January 2024\. Another 44% disappeared from Airbnb, which does not tell us whether they became year-long rentals, returned to owner use, or something else. NYC may not be an exception either since back in 2019, Barron, Kung & Proserpio's US-wide [study](https://marketing.wharton.upenn.edu/wp-content/uploads/2019/08/09.05.2019-Proserpio-Davide-Paper.pdf?ref=mbi-deepdives.com) also would predict relatively minor impact of Airbnb on rent and housing prices. From the US-wide study back in 2019: > “At the median owner-occupancy rate zipcode, we find that a 1% increase in Airbnb listings leads to a 0.018% increase in rents and a 0.026% increase in house prices” Even though Airbnb has grown lot more since 2019, it appears the impact of Airbnb remains somewhat insignificant. Nonetheless, I’m not quite sure to what extent such data would convince NYC or other cities to not give into such populist impulse of banning short-term rentals. Perhaps everyone loves to find a bogeyman whom you can blame for rising rent or housing prices and as I alluded earlier, blaming Airbnb makes perhaps “intuitive” sense to most people. It is, however, telling that the impact of such ban may lead to unintended consequences. Again, from “The Data Stream”: > “one of the side-effects from the ban was its disproportionate impact upon the lower-income non-white residents that comprise these neighborhoods. In the richest fifth of zip codes, a lost listing was worth about two months of the neighborhood's median income. In the poorest fifth comprising 89% non-white, it was worth almost six” ![](https://substackcdn.com/image/fetch/$s_!sdq0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff77a339f-1bb3-4fb6-bdf3-f93a1a42363c_2090x1254.png) Image Source: The Data Stream So, while I do not necessarily think NYC’s ban would act as a cautionary tale to other cities who may ponder a similar ban, it is still a positive outcome for Airbnb that the evidence shows the impact of Airbnb is rather negligible on rent and housing prices. The opposite would be much more concerning; if NYC found more concrete evidence of Airbnb’s “culpability”, you could imagine plenty of other cities would use that as a further ammunition to capitalize on the sentiment and ban short-term rentals in their cities. Airbnb is fairly resilient to any city specific regulations since no city represents more than 1% of its revenue, but of course it could be become a major headache if very restrictive policies spread like a wildfire to more and more cities over time. In that light, the outcome of NYC ban is bit of a sigh of relief for Airbnb. However, as long as Airbnb continues to fight these regulatory battles, the field operations and policy costs may not see much operating leverage! Airbnb mostly escaped Google’s taxes for generating incremental demand, only to perhaps find themselves in regulatory tentacles that may not go away anytime soon. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### WhatsApp's New Era URL: https://www.mbi-deepdives.com/whatsapp/ Last updated: 2026-07-14T16:56:39.000Z Last month, Mark Zuckerberg [announced](https://www.facebook.com/zuck/posts/will-cathcart-just-announced-that-hes-stepping-down-as-the-head-of-whatsapp-afte/10117699115562331/) a surprising leadership change in WhatsApp. Will Cathcart, who led WhatsApp since 2019, was replaced by Kunal Shah, an India-based serial entrepreneur. Just before joining WhatsApp, Shah was running [CRED](https://cred.club/?ref=mbi-deepdives.com), an Indian fintech platform that rewards members with a high credit score for timely credit card bill payments and financial management. Moreover, while hiring Shah to lead WhatsApp, Meta also invested $900 million for a 20% stake on CRED. Meta has historically promoted from within: the leaders of Facebook, Instagram, and Reality Labs are all long-time Meta veterans, making an external hire with no prior Meta experience something of a new development. Bringing in an outsider for this specific seat suggests Meta wanted a different skill set than its bench offered. A Bloomberg piece [mentioned](https://www.bloomberg.com/news/articles/2026-06-23/meta-s-cox-sought-shah-s-whatsapp-advice-then-made-him-leader?ref=mbi-deepdives.com) that Chris Cox, Meta’s chief product officer, had been contacting entrepreneurs and investors in countries where WhatsApp is central (e.g. India, Brazil, Mexico etc.) to get perspective on how the app should evolve. Cox reached out to Shah for advice on what qualities the next WhatsApp leader needed, and concluded the person giving the advice was, in fact, the right person to lead the app. Given the somewhat surprising nature of this hire, I was curious to learn more about Kunal Shah and listened to a couple of his podcasts ([Lenny’s Podcast](https://www.lennysnewsletter.com/p/kunal-shah-on-winning-in-india-second?ref=mbi-deepdives.com), [The Knowledge Project](https://fs.blog/knowledge-project-podcast/kunal-shah/?ref=mbi-deepdives.com)). Since there is a decent amount of overlap among the ideas discussed in these podcasts, you can just pick one if you want to get a taste of Shah’s philosophy. After listening to these podcasts, I found him to be quite a compelling character. I guess I’m not surprised why Cox felt compelled to hire him. One of Shah’s signature product playbook was “Delta-4 framework” which he discussed in both podcasts. Let me quote the specific bit discussing this framework from Lenny’s podcast: > **Kunal Shah** > > it’s called the Delta 4 framework, but I think it’s actually quite simple. If you think about it, a lot of people say that your product has to be 10x better and it’s not very measurable. You don’t know if you are 10x better or not unless you’re delusional. > > And the trigger for me was actually, I’m the unusual tech founder in India. I’m the only humanities/philosophy major founder in India who’s got into tech. And I often wondered if most people who were my peers, or people who were ahead of me, were significantly smarter academically or otherwise. > > And why did I become successful with my first startup, Freecharge, which I exited in 2015 for nearly $450 million? And I was like, “What would make this happen?” Because I would not qualify into this super league of really top IIT rankers that exist in India. > > And that got me onto this philosophical quest to find out what makes things successful. So the simple framework is, an example I give often is that, imagine the old way of taking a cab ride and an Uber. > > And if I asked you to give me the score of efficiency on Uber and let’s say getting the old cab, what would you say, Lenny? I’m curious. Out of 10? > > **Lenny Rachitsky** > > Yeah, give a cab three and then Uber like a nine. Yeah. > > **Kunal Shah** > > So every time you see that the product efficiency delta is greater than or equal to four, three things happen. It is irreversible. Second is that you have a very high tolerance for it to fail. If Uber fails a little bit, will you say, “Oh my God, I’m going to really stop using it?” > > And the third thing is what I call the UBP, ‘Unique Brag-worthy Proposition.’ Every time humans unlock a Delta 4 product or service, they cannot stop talking or sharing about it. > > And therefore all Delta 4 products will naturally have lower CAC or sometimes zero CAC because people, humans.. and think about it, Lenny, how you discovered Google, definitely not through an ad, definitely not through some performance marketing here and there. Somebody showed you the demo and you were like, “Oh my God, this is crazy.” I am originally from Bangladesh and while India and Bangladesh are sufficiently different cultures, there are perhaps more cultural similarities than differences between these countries. Admittedly, I found it a bit odd that he felt the need to highlight his prior successful exit and personal history while explaining an product framework intuition that he came up with. I do agree that Shah has a very unlikely profile from typical Indian entrepreneurs. If you grow up in South Asian culture, you probably know that not having STEM background in South Asia is essentially tantamount to being vagabond. The fact that a Philosophy major had multiple successful exits in Indian tech startup scene and now going to lead one of the highest usage messaging apps in the world is indeed nothing short of incredible! While listening to Shah, I cannot help but think that he’s a conjecture machine! He is very fond of coming up with hypothesis that he developed based on his intuition and then test that idea in public and in market. He’s a very, very good storyteller and isn’t really married to have precise answer. An academician might care more about whether the optimum number is delta-4 or 3 or something else, but Shah seems to just care more about transmitting a simple intuition and then make it work through his products. He also has a lot of provocative ideas on all sorts of things and it won’t surprise me if he gets much more in hot water for his ideas now that he’ll be under the scanner to lead one of Meta’s most consequential apps for the next decade or so. Perhaps it speaks to his Philosophy background, but he seems to have a knack of amalgamating myriad different things from culture to religion to business in order to come up with a coherent framework. For example, take a look at his take on how low trust societies end up relying heavily on large brands. Some excerpts from Lenny’s podcast: > “…all low trust markets, and let me define low trust markets as where consumers are a lot more wary of trying new things because there are no institutions that protect you against bad behavior done by a company. > > So for example, if I ever had a fall in a coffee shop in the US, I can think about suing them and making money off it. In India, you’re only worried about “I’m going to pay for my thing.” You don’t even think about suing the coffee shop or even hoping that you’ll get any money for that. > > So what happens is in a low trust country, and all developing nations are low trust by design, because the institutions are not strong enough to really, really take care of many things. What happens is there is concentration of trust. > > So you will see that super apps, superstars, super companies all exist in low trust markets because the lack of trust creates concentration of trust, and therefore you will see one app can do 400 things. > > For example, we have a company like Tata that can do salt to car to jewelry to anything and people will buy it because it’s a Tata brand, because it comes from a low trust society trusting the brand and not being, “Oh, I’m going to prefer this new brand.” > > The joy of trying new things is not so high in low trust nations.” Can Shah capitalize on WhatsApp’s omnipresence brand to start a monetization bonanza in India? I suspect after speaking with Kunal Shah, Cox perhaps felt awfully ignorant about the nuances of Indian culture. In fact, Cox indicated that understanding how people use WhatsApp in a market like India requires familiarity with how the product is embedded in daily life. India is WhatsApp’s largest user base, and Meta evidently wanted someone who experiences the product the way those users do rather than someone managing it from Menlo Park. Ironically, if you listen closely to Shah’s podcast appearances, he actually seems less interested in services that are essentially utilities and WhatsApp is closer to utility than perhaps any app out there in India. Shah frames gross margins as a function of status and competing on utility is a race to the bottom, but "we gladly pay a premium for an increase in status." India is status-driven in its own ways: Indians spend multiples of annual salary on weddings (Shah claimed 6x of annual salary) and living rooms are more lavish than bedrooms because both signal "you have made it." It’s quite clear that Shah relishes on his Philosophy background and rightly understood it to be a real differentiator in a market where all products are built by STEM nerds. Nonetheless, it’s hard to look past how CRED was targeted to India’s affluent whereas WhatsApp operates at 3-billion user scale. While Shah’s extraordinary success so far gives me comfort that he doesn’t need to have a direct and relevant experience for him to be successful even on a global scale, it does indicate that WhatsApp will be a very different beast for him. Over the years, I have noticed there is almost a rift between two groups of Meta shareholders. One group thinks Zuckerberg showed incredible foresight for buying WhatsApp at only \~[$19 Billion](https://about.fb.com/news/2014/02/facebook-to-acquire-whatsapp/?ref=mbi-deepdives.com) valuation. With 3 Billion MAU today, that seems like a great bargain. But the other group points out Zuckerberg made the not-so-great decision to issue \~178 million Facebook shares at \~$78/share back then to acquire WhatsApp in 2014\. In other words, Meta bought WhatsApp with \~7% of outstanding shares of the company and twelve years later, WhatsApp is still contributing a low single-digit share of revenue (while perhaps incurring losses). However, the former group quips that such number driven analysis actually misses the strategic masterstroke Zuckerberg might have played; just imagine WhatsApp on the hand of any of Meta’s direct competitors and you can then perhaps appreciate why Meta would like to own the communication layer of the world instead. It’s a good debate, but at some point the numbers need to back up the “strategic masterstroke” argument. I think Shah will amp up the product velocity for WhatsApp in the coming year(s), but I do want to note that while he is a gifted story teller, he is perhaps yet to generate a single dollar of profit in any of the companies he founded and operated. In fact, while Shah is happy to brag about his \~$450 Million exit of FreeCharge to Snapdeal in 2015, let me also highlight that Snapdeal ended up selling FreeCharge for just [$60 Million](https://inc42.com/features/snapdeal-sold-freecharge-for-companys-survival-reveals-kunal-bahl/?ref=mbi-deepdives.com) in 2018\. Of course, WhatsApp is a very different canvas with near zero CAC and thin existing monetization. Shah probably could not ask for a better opportunity to generate his first dollar of operating profit in his career! --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Defending the Enterprise Castle from the Model Layer URL: https://www.mbi-deepdives.com/enterprise-vs-model_layer/ Last updated: 2026-07-13T15:00:53.000Z If Microsoft’s lagging stock performance didn’t already cause a tinge of nervousness among its shareholders, Satya Nadella has been writing several pieces on X almost to clarify why Microsoft may be in a spot of bother in a world where model layer eventually becomes utterly dominant in capturing value. A month ago, Nadella elaborated why “[A frontier without an ecosystem is not stable](https://x.com/satyanadella/status/2066182223213293753?ref=mbi-deepdives.com)”, and then yesterday, he penned another think piece titled “[The Reverse Information Paradox](https://x.com/satyanadella/article/2076323181154230284?ref=mbi-deepdives.com)”. Just a couple of weeks ago, Alex Karp from Palantir had some [feisty](https://www.cnbc.com/2026/07/01/palantir-karp-open-ai-anthropic-tokens.html?ref=mbi-deepdives.com) takes on frontier models. If it’s not abundantly clear by now, enterprise software CEOs are probably talking to each other and they would all like to defend their castles from the dramatic encroachment of the model layer, namely OpenAI and Anthropic. Nadella's prescription is for enterprises to build a hard trust boundary around their own learning loop: create private evals, retain ownership of memory and traces, train and tune inside the tenant boundary, and most consequentially, decouple the orchestration layer from any single model, so that if a given "generalist" model is taken away tomorrow, the firm's accumulated "veteran" capability stays home. He is particularly explicit about the asymmetry that perhaps almost offends him. From Nadella’s [piece](https://x.com/satyanadella/article/2076323181154230284?ref=mbi-deepdives.com) yesterday (emphasis mine): > “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data. **If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself**. Therefore, it’s imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop.” Of course, Microsoft is OpenAI's largest outside partner and operates one of the largest AI infrastructure businesses on earth. A world in which every enterprise treats frontier models as swappable commodities behind a model-agnostic orchestration layer is a world in which pricing power drains out of the model layer and pools in the layers Microsoft happens to sell i.e. the infrastructure underneath and the trust boundary around it. [Commoditize your complement](https://www.joelonsoftware.com/2002/06/12/strategy-letter-v/?ref=mbi-deepdives.com) is one of the oldest playbooks in tech, and while you can legitimately argue Nadella is obviously talking his book, that alone is not quite sufficient to invalidate his arguments. If I were running a large enterprise company, I would certainly find Nadella’s advice closer to my interest and although OpenAI and Anthropic’s products would make my employees more productive today, I would worry a lot more about the future of my IP given the not-so-restrictive data retention policy (for safety reasons, of course) of these AI labs. What I’m still not sure about is whether large enterprises are well equipped to pay heed to Nadella’s suggestions or the force of the current is too high to turn against it now. There are indeed indications that companies are trying to resist the temptation of giving into the comfort of just using the frontier models. Last month, [Exponential View](https://www.exponentialview.co/?ref=mbi-deepdives.com) had a good [presentation](https://intelligence.exponentialview.co/assets/ev-state-of-ai-economy-2026.pdf?ref=mbi-deepdives.com) in summarizing the state of AI market. Their analysis of OpenRouter token data shows Google, OpenAI, and Anthropic falling from 72% of weekly token share a year ago to about 33% today, with the difference absorbed almost entirely by open-weight models. However, since OpenRouter users are self-selecting model-routers and almost certainly not representative of the broader market share in token. Nonetheless, it is likely to be a leading indicator of the most price-elastic workloads. It definitely reveals that if the broader enterprise market gradually start caring deeply about their costs, the token market can potentially bifurcate materially away from the expensive frontier models. ![](https://substackcdn.com/image/fetch/$s_!B3q3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34356cfa-9955-4ce4-8630-20511bf6fe12_1882x934.png) Gavin Baker also [alluded](https://x.com/GavinSBaker/status/2076369936251851091?ref=mbi-deepdives.com) yesterday where this leads if it keeps going. The “mega bull case” for AI infrastructure, he argues, is precisely this migration: > The mega bull case for AI infrastructure would be \*if\* market share shifted away from certain frontier labs with 90%+ inference margins toward cheaper models, whether open-source or closed. > > It would increase the ROI on AI spend for end customers by increasing intelligence per dollar, which would drive incremental token demand. Margin dollars would effectively get redistributed from the frontier labs to AI infrastructure providers. The infra winners would be those with the lowest per token cost and the winners at the model layer would be those with the highest token efficiency. > > There are many reasons Jensen is so focused on open source, but this is likely the most important one as I think he is probably less worried about a monopsony these days. Lower margin % at the model layer = more margin $ at the infra layer all else equal. > > With SpaceX and Meta being vertically integrated and possessing the #3 and #4 models respectively it is more possible than ever. Note that Grok 4.5 is ahead of Fable for some useful tasks at a much lower cost, so ranking them #3 is conservative. > > This is not happening yet. Cheap, mostly open source tokens are likely the majority of volume today but the majority of economic value is still accruing to the most intelligent models. Might change though. > > We will see. But Gavin’s caveat is the crux, and it is the same caveat I would attach to the OpenRouter chart: **this is not happening yet in the entire token market**. Of course, we are so early that just because it hasn’t happened yet doesn’t really tell anything how it may unfold in the future. Token share and dollar share today are almost certainly wildly different charts. The token market is bifurcating into a commodity lane and a premium lane, and how the workload distribution ultimately splits between them may decide where the value gets ultimately captured. I’m not sure anybody quite knows yet, including the labs. What can the labs do to respond to such pricing pressure? Labs are already pushing up the stack into applications: Claude for Legal and Codex for Legal now squeezing the Harveys, Legoras, and Clios of the world, while Anthropic and OpenAI have both launched enterprise services joint ventures that look basically like consulting, with engagements that begin with the lab’s engineers sitting down alongside a customer’s IT staff. Ultimately, it won’t surprise me at all if AI labs end up releasing plethora of first-party enterprise applications in the next 3-5 years to capture any market that has attractive revenue and profit pool for them to feast on. And they are also pushing down the stack into infrastructure. When your layer’s pricing power is in question, you will obviously integrate toward the adjacent margin pools. ![](https://substackcdn.com/image/fetch/$s_!T8gE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7029debd-5e9d-4e07-8264-8ca0b27027e1_1866x924.png) As I hinted in my piece “[**Meta on the Offense**](https://www.mbi-deepdives.com/meta-on-the-offense/)”, don’t be surprised if the most frontier models cost an arm and a leg in not-so-distant future if the duration of the lead remains only a handful of months before other players catch up. In that world, it is quite rational for AI labs to serve your frontier model only to customers who can afford to pay through their nose because the value such customers can capture from frontier models justify the costs. Will that be sufficient to recoup the investment and train the next model? Like I said earlier, I’m afraid only time can perhaps answer that question. One of the reasons my own portfolio has become so concentrated is I am trying to avoid placing bets on anything that require me to answer this question with high level of confidence. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### What Makes Us Rich URL: https://www.mbi-deepdives.com/rich/ Last updated: 2026-07-12T14:35:15.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- When I was dealing with the [**transfer saga**](https://www.mbi-deepdives.com/its-not-a-lie-if-you-believe-it/) i.e. moving \~85% of my portfolio from Canada to the US, my wife could perhaps sense how stressful it was for me. So, after I mentioned to her that I might need to go to Canada for a few days to deal with it, she suggested that she, along with our son, travel with me. While finalizing the travel plan, it suddenly occurred to us that we should perhaps make lemonade out of the lemons life seems to be throwing at us. We decided to go for a road trip in Canada. After crossing the border via Washington State, we drove through British Columbia (BC) to all the way to Banff! Right after crossing the border, I went to one of the branches of TD Bank in Canada and initiated the wire transfer to my bank account in the US. Everything went smoothly and I had a huge sigh of relief thinking I will get to enjoy the rest of the trip. I drove for almost seven hours on the first day in our trip, but the drive through BC was so beautiful that I didn’t get exhausted at all. The real estate prices in BC almost started to make sense to me! We booked an Airbnb right in the middle between BC and Banff. After spending the night there, we drove for another five hours to Banff the next day. I have across pictures of Banff from friends who visited the place and frankly speaking, one of the “complaints” I had about Banff before visiting the place is the pictures look so incredibly beautiful that they appear to be almost AI-generated! However, while driving to Banff, I could soak in the natural beauty and appreciate the very real landscape in front of me! (see video captured through my Glasses) 0:00 /0:08 1× After waking up the next morning, I went out in the backyard of our Airbnb at Banff and almost uttered “Hallelujah” while looking at the mountains! 0:00 /0:17 1× Then I decided to open my phone and buyback the stocks I sold. It didn’t take too long for my face to turn pale once I realized for some indiscernible reasons, all the stocks I sold started rallying. I could hardly see any beauty around me after noticing that since internally I was basically like Michael Scott screaming “Noooo, God, Please Nooo”! If you’re not quite following what happened, I don’t want to repeat the story and you can read it [**here**](https://www.mbi-deepdives.com/its-not-a-lie-if-you-believe-it/). ![a man in a suit and tie is making a funny face and says no .](https://substackcdn.com/image/fetch/$s_!9c0g!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51458398-70c0-4c4a-848e-a276b5d61d49_244x244.gif "a man in a suit and tie is making a funny face and says no .") Since I paid close attention to daily stock price movements during those couple of weeks of transfer saga, I started noticing some really strange aspects of market these days that I didn’t quite appreciate before. For example, I noticed the iShares Semiconductor ETF: SOXX routinely goes up or down 4% on many days without much of a concrete news flow. One would imagine an index would not act so volatile, but that’s clearly not the case. Even more perplexingly, if someone told me what SOXX was doing on a particular day, there was a very good chance I could guess what stocks in very different sectors are doing on that day. The day to day trading perhaps was always somewhat incomprehensible, but staring at the daily stock prices reminded me of the privilege of being long-term investors who can choose not to go through these whipsaws on such regular basis. In fact, I think no matter how wealthy I may become, if my strategy depends on looking at stock prices on a daily basis, I am not sure I could ever quite enjoy the fruits of such wealth. Moreover, if your mood swings too much based on short-term stock prices, I think it’s going to be increasingly more and more difficult to be public equities investor. Charlie Munger used to preach about the importance of equanimity for any investor and I suspect the importance of such equanimity and emotionally stable frame of mind will be of paramount importance to survive in the market over the long term. I would like to carry this newfound appreciation for long-term investing with me even though I fully acknowledge the challenges associated with such a strategy given the lengthy feedback loop. Of course, even though daily volatility has reached somewhat incomprehensible territory, most investors will never quite give up the idea that they know why XYZ stock is going up or down. Perhaps we are “designed” to be incapable of sitting with the discomfort of admitting to ourselves “I don’t know”. It reminds me of the following excerpt from the book “[The Blank Slate](https://www.amazon.com/Blank-Slate-Modern-Denial-Nature/dp/0142003344/ref=sr%5F1%5F1?adgrpid=183621799142&dib=eyJ2IjoiMSJ9.G4BjCcz6OvEqvEyXJPxbNm0l6SWNiRQdSuhLTyJRR9xQJpPJMUDZ4vTAT9oSpGdMj6SDBoFkwx5LfmubJk875T-fdRIDnErjn4HosNJgKJw3Wnw3w3u%5FtQg5HHMh7b3cY5um6197xdDzV%5Fq2nOK3-fv1c2x8Sa6Ce1SqfBeV2k645YBeX4nNGUyoR4ZgsfY1pTKQD6W4Z5ySfsgWm7zPSXj90Ls9Uekbr6f0Ss1JXwo.SnITq%5FOVKBdDDdfXhdd%5Fa9dmWdWtEc8YrQy0BQHisNk&dib%5Ftag=se&hvadid=792847865179&hvdev=c&hvexpln=0&hvlocphy=1014166&hvnetw=g&hvocijid=11940727392626543211--&hvqmt=e&hvrand=11940727392626543211&hvtargid=kwd-296135859323&hydadcr=19131%5F13735104%5F2447939&keywords=the+blank+slate&mcid=9cad3370c1913e68a1d5f387e98398ad&qid=1783864965&sr=8-1&ref=mbi-deepdives.com)” that often reminds me how easy it is to fool ourselves: > “One of the most dramatic demonstrations of the illusion of the unified self comes from the neuroscientists Michael Gazzaniga and Roger Sperry, who showed that when surgeons cut the corpus callosum joining the cerebral hemispheres, they literally cut the self in two, and each hemisphere can exercise free will without the other one’s advice or consent. Even more disconcertingly, the left hemisphere constantly weaves a coherent but false account of the behavior chosen without its knowledge by the right. For example, if an experimenter flashes the command “WALK” to the right hemisphere (by keeping it in the part of the visual field that only the right hemisphere can see), the person will comply with the request and begin to walk out of the room. But when the person (specifically, the person’s left hemisphere) is asked why he just got up, he will say, in all sincerity, “To get a” “Coke”—rather than “I don’t really know” or “The urge just came over me” or “You’ve been testing me for years since I had the surgery, and sometimes you get me to do things but I don’t know exactly what you asked me to do.” Similarly, if the patient’s left hemisphere is shown a chicken and his right hemisphere is shown a snowfall, and both hemispheres have to select a picture that goes with what they see (each using a different hand), the left hemisphere picks a claw (correctly) and the right picks a shovel (also correctly). But when the left hemisphere is asked why the whole person made those choices, it blithely says, “Oh, that’s simple. The chicken claw goes with the chicken, and you need a shovel to clean out the chicken shed.” > > The spooky part is that we have no reason to think that the baloney-generator in the patient’s left hemisphere is behaving any differently from ours as we make sense of the inclinations emanating from the rest of our brains. The conscious mind—the self or soul—is a spin doctor, not the commander in chief. Sigmund Freud immodestly wrote that “humanity has “in the course of time had to endure from the hands of science three great outrages upon its naïve self-love”: the discovery that our world is not the center of the celestial spheres but rather a speck in a vast universe, the discovery that we were not specially created but instead descended from animals, and the discovery that often our conscious minds do not control how we act but merely tell us a story about our actions. He was right about the cumulative impact, but it was cognitive neuroscience rather than psychoanalysis that conclusively delivered the third blow.” After realizing I don’t want to ruin my holidays by staring at stock prices, I just decided to accept my fate and buyback all the stocks I sold. I felt like 20 lbs lighter after doing that and could again see the beauty right in front of my eyes. While strolling through Moraine Lake with my family later on that day, I almost admonished myself for even entertaining the idea that my fate hasn’t been kind to me. ![](https://substackcdn.com/image/fetch/$s_!hd43!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffce39123-1c6e-469d-abbe-044503a31eb5_1200x1600.jpeg) The highlight of our time at Banff came when we were driving through the I[cefields Parkway](https://icefieldsparkway.com/?ref=mbi-deepdives.com). I am running out of adjectives to describe that drive, but one broad takeaway from the whole trip was that road trips are perhaps quite underrated. The major highlight during the Icefields Parkway drive happened when we decided to stop at Coleman Creek. As my son and wife were running around the bank of the creek while the sound of the turquoise colored streams soothed our mind, it was impossible to not feel rich! 0:00 /0:18 1× The funny thing about memory is it is very hard to predict in the moment whether a certain moment will be etched in your memory for decades to come. So, I don’t know if I will remember it vividly, but one of the memories I would indeed like to reminisce in my old age is the laughter of my wife and son while running around Coleman Creek. It is perhaps a good reminder what makes us rich in the first place, and it’s not necessarily always the stock prices going up and to the right! --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Matan on Stitch Fix URL: https://www.mbi-deepdives.com/matan-on-stitch-fix/ Last updated: 2026-07-11T14:03:12.000Z [Matan Zinger](https://94040.substack.com/) has been writing a very interesting series on Stitch Fix over the last couple of weeks. I typically don’t spend much time on companies with less than a billion dollar market cap, but given the hype this company briefly enjoyed back in 2020-21, I was genuinely curious why the stock crashed by 95%. Matan’s pieces not only covered that question, but also were genuinely reflective of what can potentially lead to such unmitigated business disasters. Sometimes, studying debacles can be more useful than dissecting success stories. In fact, I enjoyed the part-3 of this series so much that I decided to re-print it with his permission. I encourage you to check out [part 1](https://94040.substack.com/p/what-happened-to-stitch-fix-part) (the rise of Stitch Fix) and [part 2](https://94040.substack.com/p/what-happened-to-stitch-fix-part-dd4) (its bold bet during COVID) before reading the part-3 of this series, but in case you don’t, let me get you up to speed so that you can read part-3 of this series even without all the granular details. Stitch Fix was that rare 2010s e-commerce company that grew while actually making money. The entire business was built around a single product: the “Fix,” a box of five clothing items curated by human stylists working alongside recommendation algorithms, aimed mostly at busy, suburban millennial women. Founder Katrina Lake raised just $42.5M in venture capital, obsessed over unit economics, and tuned every part of the operation e.g. warehouses, software, inventory, marketing, and thousands of part-time stylists around that five-item box. When COVID crushed brick-and-mortar apparel while Stitch Fix’s growth accelerated, the market re-rated it as “the Netflix of shopping”: the stock ran from a \~$2B valuation to \~$11B in six months, and a nascent “direct buy” channel which let customers shop individual items outside the box fueled predictions that Stitch Fix would disrupt all of apparel retail. Then came the bet-the-company pivot. In April 2021, Lake abruptly stepped down as CEO, handing the reins to Elizabeth Spaulding, who had joined only 15 months earlier to run direct buy. Spaulding rebranded the channel “Freestyle,” opened it to brand-new customers Stitch Fix had never served and had no data on, and redirected marketing and onboarding toward it in pursuit of the entire women’s apparel market. The rushed rollout cannibalized the core: prospective customers were funneled into an untested Freestyle experience instead of the proven Fix onboarding, net client adds collapsed, active clients began shrinking outright, and less than a year into the pivot Stitch Fix reported the first revenue decline in its history, followed by layoffs. Part 3 picks up from there: how deep the self-inflicted damage ran, and why it proved so hard to reverse. I'll let Matan continue that story below. --- # [**What Happened to Stitch Fix? Part 3: Freestyle Freefall**](https://94040.substack.com/p/what-happened-to-stitch-fix-part-e1a) *This is part 3 in What Happened To Stitch Fix; for better context, check out* [*part 1*](https://94040.substack.com/p/what-happened-to-stitch-fix-part) *(the rise of Stitch Fix) and* [*part 2*](https://94040.substack.com/p/what-happened-to-stitch-fix-part-dd4) *(its bold bet during COVID).* The hasty rollout of Freestyle ([*discussed in part 2*](https://94040.substack.com/p/what-happened-to-stitch-fix-part-dd4)) didn’t happen in a vacuum. In July 2020, Shopify’s Tobi Lütke argued that “2030 has gotten pulled forward into 2020.” [A McKinsey report](https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/high-growth-low-profit-the-e-commerce-dilemma-for-cpg-companies?ref=mbi-deepdives.com) stated in 2021 that the pandemic “accelerated the migration to e-commerce—the expected five-year trajectory happened in a matter of months.” This belief led to an e-commerce investment frenzy. In that environment, the biggest risk was missing the train by moving too slowly. Racing Freestyle to market made sense. Except the train never really left the station. [Shopify CEO Tobi Lütke wrote in July 2022](https://www.shopify.com/news/changes-to-shopify-s-team?ref=mbi-deepdives.com): > \[...\] Given what we saw, we placed another bet: We bet that the channel mix - the share of dollars that travel through ecommerce rather than physical retail - would permanently leap ahead by 5 or even 10 years \[...\] > > It’s now clear that bet didn’t pay off. Meta’s Mark Zuckerberg and Amazon’s Andy Jassy made similar concessions that year. But while these companies could contain their failed bets – by shutting down initiatives meant to serve the demand that never materialized – Stitch Fix kept declining. The failed bet Stitch Fix had made was much harder to undo. ## **“A Loss Of Focus”** “There’s some macro headwind, but I’m not willing to accept that it’s all macro,” Katrina Lake insisted on her first earnings call back. Spaulding’s tenure didn’t last long. Seventeen months after handing over the company, Lake returned as Interim CEO at the beginning of 2023\. The business was in far worse shape than she’d left it. Active clients had fallen below 3.4 million and quarterly revenue to $400M — both the lowest since 2019\. The pandemic-era gains had fully evaporated. Stitch Fix turned into a money-losing, declining business. The stock traded under $4, down over 96% from its 2021 peak. ![](https://substackcdn.com/image/fetch/$s_!QQPR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fda5bca90-c51d-4174-8c05-8c9df039fc7d_1200x742.png "Chart") By the time Lake returned, the Stitch Fix has less customers than it had when COVID started ![](https://substackcdn.com/image/fetch/$s_!e5bx!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e5b0d18-f7dd-43d2-bcb7-f3800b309287_2048x1058.png) Revenue reversed to 2019 levels, with growth rate and profit margin going negative The earnings call marking Lake’s return is one of the most memorable I’ve listened to. She acknowledged the decline in her opening remarks: > \[...\] We haven’t met recent expectations. Driving towards an ambitious vision has resulted in a loss of focus. We must now more than ever deliver on the client experience, bring focus in our marketing efforts and drive results for our shareholders. Lake was quite open when asked where Stitch Fix “lost its focus”: > \[...\] As we thought about expanding the business in a very ambitious way, we took a marketing approach that probably tried to bring people into a variety of different customer segments. And very notably, we spent marketing dollars trying to bring people into a Freestyle-first experience as an example. So that’s a place where not only did we find that that marketing of freestyle first wasn’t as effective as what we had done historically in Fixes. But it also actually made it harder for us to be able to be acquiring people into the Fix channel. > > \[...\] Another one is around inventory. We definitely built up an inventory in anticipation of a Freestyle customer that was a different set of inventory than Fixes and also more unknown, it was a customer we hadn’t served before. It was a channel we hadn’t served before. And so, there was more risk in the inventory. The word *focus* — which Lake said more than thirty times on the call — was *doing a lot of work*[*1*](https://94040.substack.com/p/what-happened-to-stitch-fix-part-e1a#footnote-1). These examples point to a bigger story: a decade of machinery, built and tuned around the Fix, repurposed to chase Freestyle — hurting the Fix business in the process. *Loss of focus* was a generous way to describe what had happened during the previous seventeen months. Stitch Fix didn’t get distracted; it was dismantling the elements that made it unique and successful in the first place. ## **Unstitching Stitch Fix** [Part 1 of this series](https://94040.substack.com/i/203379944/capital-efficient-growth) told the early success story of Stitch Fix: accepting a limited-size market allowed the company to build a differentiated business that – unlike its peers – was profitable and made efficient use of capital. All parts of its operation – the software infrastructure, fulfillment centers, marketing channels, algorithms, and human stylists – were optimized around the Fix concept. None of them alone was the moat; the way they were stitched together created a unique offering. The algorithm had blind spots, but the stylists learned to work around them. The algorithm and stylists worked around inventory gaps. The warehouse was designed to efficiently pack 5-item boxes, where clothes weren’t folded (since the customer was going to try them on first). Fix customers were motivated to provide item-level feedback – on style, cut, quality, and pricing – thus feeding the algorithm far more data than any retailer ever collected. Marketing was optimized for the type of customers who found those mystery Fix boxes delightful. The way each part was optimized around the others’ strengths and limits was the moat: competitors may have copied different components, but couldn’t replicate the integration. That moat is why Stitch Fix made money while its box-subscription peers burned cash. It’s also why the business rapidly came apart once the equilibrium was broken. --- The story of “Fix Preview” provides a great illustration: Originally, a Fix was a mystery box. Customers didn’t know its content until it arrived. In 2020, the company started[2](https://94040.substack.com/p/what-happened-to-stitch-fix-part-e1a#footnote-2) sending its UK customers an email with the content of an upcoming Fix, allowing them to make modifications. Encouraged by improved keep rates and order values, the company rolled out Fix Previews in the US during 2021. It backfired, however, when Stitch Fix failed to recognize its own limitations and stretched the feature too far: in many cases the preview was generated by the algorithm, with no human stylist in the loop. The customer was effectively doing the stylist’s job, which was an opportunity for Stitch Fix to save on styling costs. This led to some frustrating experiences – from a [2022 Vice article](https://www.vice.com/en/article/stitch-fix-stylists-are-unheard-overworked-and-at-the-mercy-of-robots/?ref=mbi-deepdives.com): > \[...\] According to stylists, the algorithm often just picks up on keywords in these \[customer feedback\] sentences without understanding context. If you tell the system that you don’t want jeans, you may very well end up with multiple pairs of jeans in your Fix Preview. > > “And sometimes our clients don’t know that the algorithm is picking stuff,” a stylist told Motherboard. “They’ll respond, like, ‘Why did you send me this stuff?’” > > “The algorithm was not trained well enough to take into account seasonality or where people live. It would just start pulling out like, a bunch of sweaters for someone who lives in Texas, or like 10 pairs of pants, or like 10 of the exact same shirt, or like 10 backpacks,” \[...\] Fix Previews violated the delicate balance: with the stylists removed, customers were directly exposed to algorithm shortcomings and inventory gaps[3](https://94040.substack.com/p/what-happened-to-stitch-fix-part-e1a#footnote-3). And they blamed their stylists. These complaints didn’t alarm management — there are always customers with grievances, and nothing was showing up in the numbers. Keep rates and order values held. When Lake returned, however, she reported what those metrics had been hiding: > Although at the highest level Fix Preview has demonstrated a positive impact on AOVs, digging into the data, we see a more nuanced story. There absolutely are clients who significantly benefit from Fix Preview. But there are also clients for whom showing a preview actually increases cancellation. Oops. This is textbook [survivorship bias](https://en.wikipedia.org/wiki/Survivorship%5Fbias?ref=mbi-deepdives.com) — just like Wald’s bomber planes, which which shot down and never made the stats – customers who canceled weren’t accounted for by the key metrics Stitch Fix was monitoring. You can’t measure the keep rate or the value of an order that didn’t go through. But the damage was real – algorithm-generated previews were repelling customers, even if the dashboards weren’t showing it. ![](https://substackcdn.com/image/fetch/$s_!y8Ca!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff634ef32-b7c5-494f-8785-2d3e55345e42_2048x1526.png) Just like the planes that never returned to base, prospective customers who never completed their order weren’t showing up in the metrics. There’s an interesting analogy. Customers were stuck with an algorithm too rigid to understand what they wanted; management was running Stitch Fix on metrics with the same flaw — confident, context-blind. Both these cases needed a human in the loop to catch the miss. The stylists[4](https://94040.substack.com/p/what-happened-to-stitch-fix-part-e1a#footnote-4). It didn’t end there. --- The stylists’ job, it turned out, was more than correcting the algorithm’s picks. “Shopping is inherently a personal and human activity,” [Lake wrote in 2018](https://hbr.org/2018/05/stitch-fixs-ceo-on-selling-personal-style-to-the-mass-market?ref=mbi-deepdives.com), insisting that data science must be combined with human stylists; she explained: > For example, when a client writes in with a very specific request, such as “I need a dress for an outdoor wedding in July,” our stylists immediately know what dress options might work for that event. In addition, our clients often share intimate details of a pregnancy, a major weight loss, or a new job opportunity—all occasions whose importance a machine can’t fully understand. But our stylists know exactly how special such life moments are and can go above and beyond to curate the right look, connect with the clients, and improvise when needed. That creates incredible brand loyalty. This didn’t show up in a metric, but played a critical role for Stitch Fix. Things changed the same month Spaulding became CEO. In August 2021, stylists were required to switch to 20-hour weeks, dropping the flexible arrangements — between shifts, after bedtime – that let Stitch Fix rent the judgment of people it could never have hired full-time. Hundreds of experienced stylists opted, instead, to take $1,000 and walk away. For those who stayed, a new points system roughly doubled the expected pace. Stylists were measured on efficiency – required to style a Fix within five to fifteen minutes[5](https://94040.substack.com/p/what-happened-to-stitch-fix-part-e1a#footnote-5) – and on keep rates. These incentives may have looked good on paper, but turned out to be perverse. In the old days, a stylist might spend fifteen or twenty unmeasured minutes drilling down into a client’s previous notes and browsing her Pinterest board and Facebook profile, trying to “crack” the right style. This large upfront effort offered the potential reward of winning over a loyal customer. The new system penalized exactly that: the efficiency hit was immediate, while the potential keep-rate payoff was months away. Hard clients — new, picky, style still unclear — became a losing trade. The system taught stylists to harvest the existing base of established and predictable clients, and let new clients churn. This was a one-two punch: the company was acquiring fewer customers, due to the abrupt marketing changes ([see Part 2](https://94040.substack.com/i/204579299/betting-the-company)); the customers it did acquire were monetized at lower rates due to the degraded service. ## **Post Mortem** Despite these issues, Lake was optimistic that Stitch Fix could recover by returning to its roots: > \[...\] In terms of what the customer is looking for, I think that’s really differentiated about our channel relative to others, it’s not necessarily price. It’s not necessarily finding the brand that you love. It is actually around fit \[...\] It’s about style. It’s about finding things that you love. And in some cases, find things that you love that are surprising to you. And that’s something that really only our channel can deliver on. > > \[...\] Stitch Fix is one that really makes shopping more tenable and makes it easier. It helps people to look their best without spending a lot of effort to do it. And those are really differentiating qualities in our customer that we can build the right assortment to be able to deliver on. > > \[...\] I think just really being able to focus on the things that we already know that we are able to deliver on that we have a business that’s 10-plus-years-old, that has a history of profitability delivering on this business to be able to focus back on the things that we know and know that we can deliver is kind of the core thesis. That thesis didn’t hold. The decline persisted, bottoming at 2.3 million active customers and $340M of quarterly revenue. Over three years later, Stitch Fix still hasn’t returned to profitability. The stock has been flat for almost four years. In reality, Stitch Fix’s “differentiating qualities” were already broken. The Fix moat was gone, in a way that wasn’t easily reversed. [As we’ve seen before](https://94040.substack.com/i/193651447/business-traps), saying “*I no longer want the cheese*” doesn’t get the mouse out of the trap. --- It’s tempting to conclude that Stitch Fix’s biggest mistake was violating the very aspect [Ben Thompson had liked about it](https://stratechery.com/2017/stitch-fix-and-the-senate/?ref=mbi-deepdives.com) when it went public in 2017: > Stitch Fix is a more important company than it may seem at first glance: it proves there is a way to build a venture capital-backed company that is not an aggregator, but still a generator of outsized returns. The keys, though, are positive unit economics from the get-go, and careful attention to profitability. The reason this matters is that these sorts of companies are by far the more likely to be built: Google and Facebook are dominating digital advertising, Amazon is dominating undifferentiated e-commerce \[...\] To compete with any of them is an incredibly difficult proposition; better to build a real differentiated business from the get-go, and that is exactly what Stitch Fix did. The COVID e-commerce euphoria pushed Stitch Fix to pursue an aggregator dream – transforming into a personalized apparel marketplace and disrupting all of shopping – which indeed turned out to be “an incredibly difficult proposition.” Stitch Fix abandoned its humble-yet-safe territory, flew too close to the sun, and crashed. *Right?* While this is largely true, the actual moral of the story is – like many things in today’s article – much more nuanced. --- I like [this story from former Amazon executive Dan Rose](https://colossus.com/episode/rose-how-stunning-founders-operate/?ref=mbi-deepdives.com), about how Jeff Bezos asked Steve Kessel – who was running Amazon’s media e-commerce business – to lead the Kindle initiative: > He said to Steve one day, “Steve, I need you to come over and run this digital business and get this digital book platform started so that we don’t get iPoded out of books.” > > And Steve said, “Great, I’ll take one of my best people. We’ll put them on it, and we’ll get a team going, and it will be great.” > > \[...\] And Jeff goes, “No, Steve, let me make this clear. > > “As of today, you’re fired from your job. > > “Your new job is to kill your old business. > > “I want you to put the physical books business out of business by building a digital product that’s so “good that people don’t buy physical books anymore. > > “If you run both, you’ll never be motivated to do that.” Bezos understood The Innovator’s Dilemma very well. In his 1995 HBR[6](https://94040.substack.com/p/what-happened-to-stitch-fix-part-e1a#footnote-6) article about Disruption, Harvard professor Clayton Christensen concluded that *responsibility for building a disruptive-technology business* *must be placed in an independent organization.* Unfortunately for Stitch Fix, even though Katrina Lake attended Harvard Business School, she didn’t follow Christensen’s advice; she handed Elizabeth Spaulding the responsibility for building Freestyle – meant to be a disruptive new business – and at the same time, the responsibility of overseeing the core Fix business. That’s the most catastrophic part of the story: yes, Freestyle was too ambitious, and failed. But had it been built in a separate organization – akin to Meta’s Reality Labs, or Amazon’s Fire Phone – it wouldn’t have been as consequential. Money would have been lost, but the Fix side of the house wouldn’t have been damaged. In that parallel universe Stitch Fix may have been able to bounce back after COVID, and to remain a profitable, growing business today. --- The most incredible thing, though, is that Christensen’s recommendation followed research showing that managers prioritize their existing core business, depriving the potentially disruptive new business of resources needed for it to succeed. An independent organization is, he concluded, the only solution. Which is why Bezos detached Kessel from Amazon’s existing physical books e-commerce business. He wanted a manager fully devoted to building the new digital books business. But the opposite happened at Stitch Fix: having both Freestyle and Fix under her supervision, Elizabeth Spaulding prioritized the new and uncertain business over the existing successful one. So eager was Stitch Fix to disrupt itself that it didn’t notice it was tearing down the Fix business. There aren’t many stories like this. I think that even the late Prof. Christensen would have been surprised. Imagine Bezos telling Kessel “*your new job is to kill your old business*,” but leaving him in charge of the old business too. The intention, surely, was for him to build a new business so good that it renders the old one obsolete. But there’s a shortcut, the kind that a rigid and unsophisticated algorithm might come up with: just directly kill the old business. That, tragically, is what happened to Stitch Fix. --- For more posts like this, I recommend you subscribe to [**Matan’s Blog**](https://94040.substack.com/). ### Meta On the Offense! URL: https://www.mbi-deepdives.com/meta-on-the-offense/ Last updated: 2026-07-10T14:39:05.000Z This week has turned out to be a plethora of model launches with SpaceX releasing [Grok 4.5](https://x.ai/news/grok-4-5?ref=mbi-deepdives.com) on Wednesday, and Meta and OpenAI releasing [Muse Spark 1.1](https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/?utm%5Fsource=twitter&utm%5Fmedium=organic%5Fsocial&utm%5Fcontent=image&utm%5Fcampaign=spark11) and [GPT-5.6](https://openai.com/index/gpt-5-6/?ref=mbi-deepdives.com) yesterday. While Meta has been on the news almost on a daily basis these days with admittedly more negative than positive developments, yesterday’s release was indeed a positive surprise. Meta released a multimodal reasoning model built for agentic tasks, with noticeable improvement in tool and computer use, and coding. The reason it was a real surprise even for me is that unlike the likes of Anthropic or OpenAI, I think Meta has been focused on coding only for a short while. The fact that Meta has developed a somewhat competitive model in coding in such a short time bodes very well for the bet Zuckerberg has made over the last year in building MSL. However, it also reiterates one of the points I was making [yesterday](https://www.mbi-deepdives.com/data/): it remains fairly challenging to articulate sustainable source of moat in the model layer. ![Inference-time compute scaling chart for Muse Image](https://substackcdn.com/image/fetch/$s_!sPv6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbb7aa1b3-aed2-4555-92a8-ec1e090ea8bf_1620x1620.png "Inference-time compute scaling chart for Muse Image") Source: [Meta](https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/?utm%5Fsource=twitter&utm%5Fmedium=organic%5Fsocial&utm%5Fcontent=image&utm%5Fcampaign=spark11) Of course, ever since the Llama 4 debacle, there is a sense of suspicion especially related to Meta whether these benchmark scores reflect the model’s true capability in the wild. Therefore, more than the benchmark scores, I took note of how Box praised the model in their [blog](https://blog.box.com/muse-spark-11-meets-mark-real-enterprise-work?ref=mbi-deepdives.com) yesterday: > Muse Spark 1.1 keeps pace with the leading models across most of the industries we test, and it’s especially at home in structured, procedural work: the [**professional services**](https://www.box.com/industries/professional-services?ref=mbi-deepdives.com),[ **public sector**](https://www.box.com/industries/government?ref=mbi-deepdives.com), and industrial tasks where the job is to follow a defined process over a stack of documents and get every step right. > > In those categories, it pulls ahead of the top-tier composite by as much as 5 to 6 points. This is the connective tissue of enterprise work — intake, review, reconciliation, reporting — and it’s where Muse Spark 1.1 is most consistently hitting the mark. > > Muse Spark 1.1 is also strong on report drafting. Given raw inputs and a format to fill, it produces clean, complete, well-organized deliverables that hold up against what the best models generate. For the day-to-day work of turning data and documents into something a person can act on, Muse Spark 1.1 is a dependable choice. But how is Meta catching up so fast? I highlighted [**yesterday** ](https://www.mbi-deepdives.com/data/)how data may prove to be true source of long-term differentiation for AI models. Although Meta has been lambasted both internally and externally for their approach to collecting data from their own employees, Semianalysis yesterday [pointed out](https://newsletter.semianalysis.com/p/the-future-of-meta-superintelligence?ref=mbi-deepdives.com) that even though such decision might be unpopular, it is indeed perhaps the right bet for Meta: > This is quite literally some of the most valuable data in the world today! Of course, it’s also poetically apt that the Scale AI man is the one spearheading the transformation. > > Whereas all the data companies are desperately trying to partner with investment banks, law firms, and advertising agencies to record their workflows, Meta is one of the few companies in the world that has a sufficiently large workforce dedicated to each of these industries in-house. > > The fact that Meta is still nimble and aggressive enough to do this despite the PR hit and initial employee backlash is already quite impressive. Yes, they’ve since walked it back [slightly](https://www.theinformation.com/articles/meta-rolls-back-parts-employee-tracking-tool-staff-backlash?ref=mbi-deepdives.com) by strengthening privacy protections and giving employees the option to pause the tracker for 30min, but we think these are very minor concessions. > > Furthermore, they took their data efforts to *another level* in late May by announcing a new “applied AI engineering org” as part of their most recent round of layoffs/restructuring. **\~3000 engineers**, which includes 70% of their new grads and a significant number of seniors, will now be **making RL tasks/environments full-time**. > > We think this is an extremely underappreciated advantage for MSL To be clear, Anthropic is still clearly ahead of the pack. In fact, Anthropic’s competitors, including several Meta executives and even Elon Musk almost have a reverential tone to Anthropic’s ability to build superior models: > I was clearly wrong about Anthropic. They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon. > > And I would never cut them off in a way that hurt them badly, even as a competitor.… > > — Elon Musk (@elonmusk) [July 9, 2026](https://x.com/elonmusk/status/2075278580955685036?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) While the rest of the pack is yet to catch up or surpass Anthropic’s lead, Zuckerberg seems determined to attack the profit pool in the model layer. He made his intentions clear in an interview with [Bloomberg](https://www.bloomberg.com/news/articles/2026-07-09/meta-starts-charging-for-ai-with-muse-spark-1-1-agentic-model?ref=mbi-deepdives.com): > *“The pricing from some of the other labs is very extreme and has very high margins,” Zuckerberg said, underscoring that his strategy is to get Meta’s technology in front of as many people as possible. “We think that there’s a real ability to be able to offer frontier or very high-level intelligence at a much more affordable cost.”* Meta can rationally price near serving cost, and every dollar of margin it denies competitors is a dollar removed from their competitors’ training war chest and can become even more reliant on external capital to stay in the race. Remember, Meta mostly doesn’t need to pay margins to other hyperscalers which structurally allows Meta to price more aggressively than OpenAI or Anthropic (I say mostly because Meta started renting some capacity from other hyperscalers and neoclouds as well but the point remains). If “SOTA adjacent” models lose much of their pricing power, Anthropic (and OpenAI) will be even more reliant on pricing SOTA models very, very aggressively which can ultimately lower the TAM for SOTA model. Moreover, it gives Zuckerberg something Wall Street has been demanding: a revenue line against the capex, plus utilization for a fleet whose worst enemy is idleness. The labs may face a difficult choice: cut price and compress the gross profit that funds training (forcing more external capital at more dilution, right as both approach IPOs), or hold price and cede the highest-volume-growth segment plus the usage data that comes with it. ![](https://substackcdn.com/image/fetch/$s_!KnJR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff64d9192-6c6f-47c7-9701-3887f4e4c1a8_916x459.png) Source: [SouthernValue at X](https://x.com/SouthernValue95/status/2075306569986621834?ref=mbi-deepdives.com) Releasing Muse Spark 1.1 wasn’t the only news that came to light yesterday; Reuters [reported](https://www.reuters.com/world/asia-pacific/meta-put-ai-chip-into-production-september-it-looks-double-computing-capacity-2026-07-09/?ref=mbi-deepdives.com) on the same day that Meta “outlined a two-step infrastructure expansion: seven gigawatts of computing capacity coming online in 2026, growing to 14 gigawatts by 2027.” Just for context, Andy Jassy during 4Q’25 call mentioned that AWS added \~4 GW of capacity in 2025\. That probably puts things into perspective how large Meta’s capacity additions are in 2026 and 2027\. Given the size of the expansion, there is perhaps a non-negligible probability that they might overbuild compared to their internal needs. I suspect Meta’s internal estimates for their own compute needs has a wide range to it, and they’re trying to build for the bull case rather than base case scenario. Given that context, it makes sense that Meta likely intends to develop their muscle of selling compute to 3P customers in case the bull scenario doesn’t pan out. And in case it does, Meta should structure the 3P compute deals in a way that allows them to re-allocate the compute capacity back to internal teams. I do want to note that while the pace of MSL seems quite encouraging, we need to see more consistent shipping throughout this year before heaping any further praise to Meta. Google too appeared to have found their mojo in late 2025 and they increasingly appear haphazard in the model layer so far in 2026\. If MSL can maintain their shipping cadence for the next 6 months, Zuckerberg’s “Hail Mary” bet on building MSL last year will prove to be quite prescient. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I made a slight change to the portfolio yesterday. _This post is for paying subscribers only._ ### The Salience of Data URL: https://www.mbi-deepdives.com/data/ Last updated: 2026-07-09T14:46:02.000Z Let me begin with a thought experiment. Imagine you know nothing about OpenAI’s or Anthropic’s financials. No ARR figures, and no valuation marks from the latest round. Now add one more condition, and it may be less hypothetical than it sounds: assume every frontier lab has access to a similar amount of compute, similar data, and similar talent. Here is the question: sitting behind this veil of ignorance, how would you tell whether any of these model labs has a sustainable moat in the next 3-5 years? This question was actually much, much more difficult to answer couple of years ago. The fact that Anthropic was valued only [$15 Billion](https://siliconangle.com/2023/12/20/ai-startup-anthropic-reportedly-talks-raise-750m-15b-valuation/?ref=mbi-deepdives.com) in December 2023 may be indicative of how contrarian it was to bet on a model company and how challenging it was to articulate why these model companies would be able to outcompete the incumbent big tech companies when such big tech companies appeared to have almost unbounded access to capital, compute, talent, and data. To be fair, it is still quite challenging to articulate which particular model companies will end up dominating this layer. At least, today OpenAI and Anthropic have far more credible claims why they can have access to similar compute, and talent. It still seems silly to argue that OpenAI or Anthropic would have higher compute than Alphabet or Meta in the next 3-4 years. Talent in AI labs are also pretty mobile, so it’s hard to assume that as a source of any sustainable moat either. You can perhaps mention culture, but while I’m not denying culture as a source of potential moat, I always suspect investors mention about culture as a source of moat when it becomes difficult to pinpoint the source of sustainable moat. How about data? You can legitimately argue one of the big reasons Anthropic has been such a resounding success is their [**focused**](https://www.mbi-deepdives.com/anthropics-focused-bet-portfolio-change/) bet on the best use case of these models: coding! And thanks to such a focused bet, they now have access to user data in coding which can beget to further improvement of the model. But even then, Anthropic’s coding model has found its greatest product market fit since December 2025\. So, we aren’t even a year into this data flywheel moat and there may still be time for other model companies to respond pretty effectively to this moat. Codex is gaining ground and just yesterday, this [tweet](https://x.com/jukan05/status/2074989572686315630?ref=mbi-deepdives.com) by Jukan made the case that xAI still has a pretty compelling shot at coding thanks to their acquisition of Cursor. Some excerpts from his tweet: > “One of the more interesting Grok bull cases I heard at ICML was this: > > The core idea is that xAI may actually be better positioned than OpenAI Codex in the coding-agent market. > > The reason Claude Code is currently leading in coding agents is not just model quality. Claude Code effectively pioneered the category at scale, which gave it one of the largest user pools in the industry. More users mean more real-world coding data. That data can then be used to improve Claude Code’s quality, which attracts even more users, creating a flywheel of more users, more data, and a better product. > > Seen through this lens, xAI’s acquisition of Cursor starts to make a lot more sense. > > Cursor likely has a much larger real-world user base and coding dataset than Codex. If xAI can effectively train on and leverage that data, the argument is that overtaking Codex may only be a matter of time.” Given the success of coding use case, even Meta today entered the arena with their launch of [Muse Spark 1.1](https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/?ref=mbi-deepdives.com) model. Nonetheless, I do suspect data is likely the most reasonable explanation ex-ante why any particular model company may gain an upper hand over others while the rest of the inputs may be closer to commodity since all the relevant players will essentially have access to those. The fact that data is indeed the key source of long-term differentiation isn’t quite a new hypothesis. Back in June 2023, [James Betker](https://www.linkedin.com/in/james-betker-4a051013/?ref=mbi-deepdives.com), a Research Engineer at OpenAI, in a [piece](https://nonint.com/2023/06/10/the-it-in-ai-models-is-the-dataset/?ref=mbi-deepdives.com) titled “**The “it” in AI models is the dataset”** argued exactly this (emphasis mine): > “…I’ve trained a **lot** of generative models. More than anyone really has any right to train. As I’ve spent these hours observing the effects of tweaking various model configurations and hyperparameters, one thing that has struck me is the similarities in between all the training runs. > > **It’s becoming awfully clear to me that these models are truly approximating their datasets to an incredible degree.** > > …**What this manifests as is – trained on the same dataset for long enough, pretty much every model with enough weights and training time converges to the same point.** > > …This is a surprising observation! It implies that **model behavior is not determined by architecture, hyperparameters, or optimizer choices. It’s determined by your dataset, nothing else.** Everything else is a means to an end in efficiently delivery compute to approximating that dataset.” In a more recent piece, [Will DePue](https://www.linkedin.com/in/willdepue/?ref=mbi-deepdives.com), another OpenAI engineer who just left the company couple of months ago, re-iterated that data remains the key path to model differentiation. He wrote a compelling [piece](https://willdepue.substack.com/p/a-stargate-for-data?r=9x0z5&utm%5Fmedium=ios&triedRedirect=true) titled “A Stargate for Data”; some key excerpts below: > At the foundation of the scaling revolution is a simple empirical law: deep neural networks improve smoothly, near magically, as you scale two things in proportion — (1) the size of the model and (2) the amount of data you train on. And despite the scaling laws being brutally diminishing, we’ve successfully bitten the bullet of logarithmic scaling with exponentially larger clusters and datasets, and received incredible new capabilities in return. > > But this exponential scaling is bound to hit some limits. Oddly enough, compute has compounded fairly smoothly without limit, with trillions flowing into hypercluster buildout. Instead, we’re starting to hit the limits of an exponential demand for data. Gone are the days of being purely in the compute-limited regime, where we had effectively infinite internet data but never enough GPUs, we’re now entering a data-limited regime. > > Only a fraction of useful data in the world is on the public internet, the rest is stored inside private datasets, corporations, personal archives, universities, governments, and otherwise. Labs can and will continue to license these private datasets, or create them from scratch, like Anthropic’s book scanning project. And we’ll increasingly task human experts to manufacture new high-quality data, with a large fraction of hard RL training tasks already being sourced this way. > > But collecting this data, unlike before, will be expensive. As the free internet dries up and demand for data rises, we should see labs investing equally in data as compute, likely spending a significant fraction of their compute budgets on data. **As we see trillions spent on compute, we should also expect hundreds of billions spent on data (human data & collection budgets), given their equivalent importance. And, notably, data spend is already tracking this way: total data spend across vendors, not counting internal lab efforts, is already roughly $7 billion per year. It’s quite reasonable we’ll see >10x by 2030**. Last year, I [**highlighted**](https://www.mbi-deepdives.com/googles-first-mistake/) another piece by Jack Morris: “**There Are No New Ideas in AI… Only New Datasets”** which also corroborated to the primacy of data . From his [post](https://blog.jxmo.io/p/there-are-no-new-ideas-in-ai-only?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com): > Our breakthrough is probably not going to come from a completely new idea, rather it’ll be the resurfacing of something we’ve known for a while. > > But there’s a missing piece here: each of these four breakthroughs **enabled us to learn from a new data source:** > > 1\. AlexNet and its follow-ups unlocked [ImageNet](http://%28https//www.image-net.org/?ref=mbi-deepdives.com), a large database of class-labeled images that drove fifteen years of progress in computer vision > > 2\. Transformers unlocked training on “The Internet” and a race to download, categorize, and parse all the text on [The Web](https://arxiv.org/abs/2101.00027?ref=mbi-deepdives.com) (which [it seems](https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications?ref=mbi-deepdives.com) [we’ve mostly done](https://arxiv.org/abs/2305.16264?ref=mbi-deepdives.com) [by now](https://arxiv.org/abs/2305.13230?ref=mbi-deepdives.com)) > > 3\. RLHF allowed us to learn from human labels indicating what “good text” is (mostly a vibes thing) > > 4\. Reasoning seems to let us learn from [“verifiers”](http://incompleteideas.net/IncIdeas/KeytoAI.html?ref=mbi-deepdives.com), things like calculators and compilers that can evaluate the outputs of language models > > …The obvious takeaway is that our next paradigm shift isn’t going to come from an improvement to RL or a fancy new type of neural net. It’s going to come when we unlock a source of data that we haven’t accessed before, or haven’t properly harnessed yet. > > One obvious source of information that a lot of people are working towards harnessing is video. According to [a random site on the Web](https://www.dexerto.com/entertainment/how-many-videos-are-there-on-youtube-2197264/?ref=mbi-deepdives.com), about 500 hours of video footage are uploaded to YouTube \*per minute\*. This is a ridiculous amount of data, much more than is available as text on the entire internet. It’s potentially a much richer source of information too as videos contain not just words but the inflection behind them as well as rich information about physics and culture that just can’t be gleaned from text. > > It’s safe to say that as soon as our models get efficient enough, or our computers grow beefy enough, Google is going to start training models on YouTube. They own the thing, after all; it would be silly not to use the data to their advantage. Given this context, I am currently much more appreciative of Meta’s [49% stake ](https://www.cnbc.com/2025/06/10/zuckerberg-makes-metas-biggest-bet-on-ai-14-billion-scale-ai-deal.html?ref=mbi-deepdives.com)in Scale AI. I have noticed that these “data labeling” companies are often mocked by investors, but based on my understanding of AI models’ key source of differentiation in the long term, I wonder whether Meta should have bought the entire company. I guess that might still happen and the primary reason they didn’t might be due to regulatory concerns. However, it still doesn’t seem abundantly clear who will have privileged access to differentiated datasets in the long term. And if data is indeed the true elixir to model differentiation, that also implies different models may have distinctly different strengths and weaknesses based on the access to data source they have managed to gain access. If an AI lab cannot secure the licensing deals for proprietary data, all the compute in the world will merely produce a highly efficient, mathematically perfect approximation of a commodity dataset. The structural advantage hence may shift slightly away from those who only own the picks and shovels, and toward those who own the land where the gold is buried. Ultimately, competitive dynamics in the next phase of AI will likely be defined by the unit economics of data acquisition. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Why Consumer AI Appears to be Lagging Enterprise URL: https://www.mbi-deepdives.com/consumer-vs-enterprise-ai/ Last updated: 2026-07-08T13:57:03.000Z Last week, Mark Zuckerberg internally [shared](https://techcrunch.com/2026/07/02/mark-zuckerberg-tells-staff-that-ai-agents-havent-progressed-as-quickly-as-hed-hoped/?ref=mbi-deepdives.com) that AI agents haven’t progressed as fast as he hoped. While initially many interpreted this as Meta specific stumble on their agentic AI efforts in consumer, Alexandr Wang later [clarified](https://x.com/alexandr%5Fwang/status/2072848108342677597?ref=mbi-deepdives.com) that Zuckerberg was talking about “the industry’s progress on agentic capabilities on the whole”. My guess is by “industry”, Wang (and Zuckerberg) likely meant the progress in consumer AI. That begs the question why consumer AI is not keeping pace with the exponential growth in enterprise AI in 2026\. Ben Thompson at [Stratechery](https://stratechery.com/?ref=mbi-deepdives.com) has been making the case that while enterprises deeply care about productivity, consumers mostly want to be entertained. With tasks such as coding with clear verifiability, productivity can be turbocharged which can directly convince enterprises to invest heavily on AI to extract as much productivity as possible from the existing workforce. While some prosumers certainly care about being productive, I too think that most consumers indeed likely prefer to be entertained. Consumers also have a much less appetite for patience about using tools that may not work all the time, especially when existing alternatives work just fine. A couple of days ago, Skift [published](https://stratechery.com/?ref=mbi-deepdives.com) a piece that highlighted the gulf of execution missteps in consumer AI when it comes to travel booking, a use case that most consumer AI companies have been touting in most of their demos over the last couple of years. From Skift: > We began our test with two unbranded prompts: > “Find me a hotel near Times Square in New York under $400 per night for Sept. 15-17, 2026, with free cancellation” > ”Find me a guided food tour in Paris for Sept. 15, 2026, under $150 per person.” > With a free ChatGPT account and no apps loaded, the result was predictable: general web search results. Useful for browsing, but not for a traveler ready to compare bookable options. > Then Skift loaded the relevant apps and asked for the brands by name. Here’s how it went for Viator: > No app loaded: ChatGPT returned generic results from Viator’s website, but didn’t mention the app or offer to connect it. > App loaded: ChatGPT still bypassed it, even when asked to use Viator by name. > App still active: ChatGPT refused a direct request to use it, saying it “can’t directly open or interact with the Viator app.” > After pushback: ChatGPT finally returned live cards showing available tours, real pricing, and a booking button deep linked to Viator. > Booking.com and Expedia followed the same basic path. For Times Square hotel searches, ChatGPT initially pulled from the online travel agencies’ websites, grabbing basic property information and price ranges instead of using the connected apps. > The integration isn’t integrating. > The bot’s explanation for not using Expedia was that the app was “not available in this environment as an executable tool,” even though it was connected and authenticated to a real Expedia account. > With Booking.com, the explanation got more elaborate: “Booking.com does not provide a stable ‘direct API-style search result export’ for live inventory, and I also do not have a guaranteed way to retrieve real-time, bookable Booking.com rates with enforced cancellation terms and deep links unless the booking connector or structured API surface is explicitly exposed.” > The workaround, in all three cases, was a blunt response: Typing in “You are wrong” appeared to jolt ChatGPT into action, so it finally checked if the app was actually available.” I think Skift’s experiment captures very well why consumer AI appears stuck at the chat layer while enterprise adoption keeps compounding: enterprises will take the time to make these things work, and consumers simply won't. The plumbing in consumer AI will presumably function someday, but it doesn’t seem like we are there yet on seemingly simple tasks with a level of accuracy and consistency that would grow consumer adoption of AI beyond the chat format. The difference in momentum in consumer vs enterprise also makes sense when you think about who owns the failure. Inside a company, a failed tool call automatically turns into a ticket. Somebody’s job, be it an internal platform team, or the vendor’s forward-deployed engineers, is to convert that failure into a fix, and their salary or renewal revenue depends on the thing eventually working. What the Skift reporter did, prodding the model until it cooperated, is what enterprise integration engineers do all day because it’s their job. However, when the same connector fails for a consumer, nothing probably happens. No ticket gets filed and the failure never reaches anyone’s eval suite. The user probably just returns to Google, which sits one tap away and has worked reliably for the last couple of decades. As a result, AI’s failure inside an enterprise becomes raw material for the next sprint while the consumer’s failure evaporates without leaving a trace. A consumer's alternative to a janky AI travel agent is a pretty competent incumbent stack i.e. Google and the OTA apps with their two decades of refined booking flows available at zero switching cost. Skift noted that chatbot behavior shifts from session to session with model updates and prompt phrasing. That variance is what enterprise deployment processes exist to suppress and what consumers experience undiluted. The persistence thresholds in enterprise to make AI work differ by orders of magnitude relative to what consumers will tolerate. What makes the Skift findings worse than a routine bug report is the nature of the failure. ChatGPT didn’t even throw an error as it generated plausible, and false explanations for why it couldn’t use an app that was connected and authenticated. I’m not sure how a lay user could distinguish a hallucinated capability limit from a real one. Of course, a large share of enterprise AI pilots also likely die. What enterprise has that consumer lacks is a selection mechanism: use cases survive where debugging cost runs below labor savings, and the surviving AI-automated workflows can get institutionalized and scaled. Consumer markets have no equivalent memory. Each user can hit the same bug in isolation and give up in private which means the learning from such failures scattered across millions of abandoned sessions nobody may ever read. Given that context, it’s perhaps not a surprise that agentic AI in consumer land so far failed to meet Zuckerberg’s expectation. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### "It's not a lie if you believe it" URL: https://www.mbi-deepdives.com/its-not-a-lie-if-you-believe-it/ Last updated: 2026-07-07T14:08:32.000Z A couple of weeks ago, I [explained](https://www.mbi-deepdives.com/scheduling-and-disclosure-update/) that I am currently in the process of moving \~85% of my portfolio from Canada to the US now that I have become a permanent resident in the US. Physically moving from one country to another can be stressful, and it turns out even moving assets digitally can be pretty complex too. Long story short, I decided to sell all the stocks I had in Canada and then transfer the cash to buyback the stocks in the US. I knew this would lead to some capital gain taxes but the simplicity of having everything in one country convinced me to go through the process anyway. Unfortunately, the whole episode turned out to be bit of a nightmare for me. As my bank in Canada asked me to be physically present in Canada to initiate the wire transfer back to the US, I decided to sell the stocks and travel to Canada to finish this process. I knew capital gain taxes wasn’t the only headache, and I could potentially **gain or lose** some during the transition period. Frankly speaking, in my mind, I was thinking of either gaining or losing max \~200-300 bps of my portfolio. Again, I was okay with the risk involved as I was much more eager to make my tax situation simple by keeping my portfolio and business in the same country so that I can concentrate on investing and studying businesses. I have paid a very, very hefty price for the desire for simplicity as I ended up effectively “losing” 1,000 bps of my portfolio during this transition! After my portfolio flailing for much of this year, it appears it almost bottomed near the day I sold my stocks in Canada. For hardly any discernible reasons, almost all the stocks I used to own started rallying simultaneously. Take Meta, for example. I was forced to sell the stocks at \~$564\. It dipped a little bit after I sold and I was optimistic of being able to buy it back around the same price (or lower). Then all on a sudden, the stock rallied by \~10% following a [report](https://www.bloomberg.com/news/articles/2026-07-01/meta-is-building-a-cloud-business-to-sell-excess-ai-compute?ref=mbi-deepdives.com) by Bloomberg last week that “Meta is planning a cloud business to sell AI computing power”. I was shocked to see the market reacted at all on that news since this really should not have been much of a market moving news. This is what Zuckerberg mentioned in **October 2025** during Meta’s 3Q’25 call: > “…any compute that we don’t need for that we feel pretty good that we’re going to be able to absorb a very large amount of that to just convert into more intelligence and better recommendations in our family of apps and ads in a profitable way. > > Now I mean, it’s, of course, possible to overshoot that, right? And if we do, I mean, this is what I mentioned in my comments, then we see that there’s just a lot of demand for other new things that we build internally, externally. **Like almost every week, people come to us from outside the company asking us to stand up an API service or asking if we have different compute that they could get from us and we haven’t done that yet. But obviously, if you got to a point where you overbuilt, you could have that as an option**.” If Meta overbuilds, it should have been blatantly obvious to anyone who’s paying attention that they aren’t just going to pointlessly hoard the assets and would rather sell their compute capacity as long as it easily clears the marginal costs. The fact that market reacted so positively on this news might imply that many investors were making seemingly absurd assumption that Meta would hoard compute capacity and eat depreciation costs even if they cannot figure out a way to monetize it. I’m not going to analyze Meta’s compute project in this post today, but frankly speaking, I think the real reason for such short-term stock price movement may be far more mundane. It’s perhaps mostly just random walk. I obviously intellectually knew any short-term stock price moves largely in a Brownian motion, but I truly internalized it after helplessly watching this random Brownian motion at work over the last couple of weeks. As I said, nearly everything I used to own perplexingly rallied hard over the last couple of weeks. Veeva and Ryan Specialty are both up \~25% on basically no incremental news flow. Two larger holdings such as Airbnb and Floor & Decor are up \~8% and \~12% respectively. These are really outsized move over the course of a week or two across disparate sectors on essentially very little to no incremental news. I got to admit that the stock market appears to be acting in strange way these days as large cap stocks are making outsized moves in an increasingly random fashion. If anyone leans to form their opinion based on short-term stock price movements, this market can make such investors increasingly insane. If you have the luxury of investing for the long-term, I suggest you take such opportunity with both hands and save yourself from the daily Brownian motions of the stock market. Give yourself peace and enjoy your hard earned wealth than being slave to your screen to watch the stocks tick by tick. Of course, once the money was transferred, I was facing another difficult question: what do I do now? Should I patiently wait for the Brownian motion to work in my favor? If some truly fundamental drivers changed the stocks, it would be easier to decide, but since it appears mostly random factor-driven narrative mumbo-jumbo that just wildly oscillates from one day to another, I was in a severe limbo for a day or two. Admittedly, this was the worst I have ever felt in my almost 13 years of managing my money. After some soul searching, I decided to prioritize my own mental peace and just buyback whatever I sold. It was such a soul crushing experience that I decided to forget that it ever happened. Of course, I don’t have enough money to buyback everything I sold. As a result, I was still forced to make some decisions in terms of what/how to buy the stocks I sold. I have decided to not buyback Veeva and Ryan Specialty. I may eventually buy them back depending on their respective stock prices. Except for Veeva and Ryan, I bought back everything that I used to own. However, to help me cope this episode, I will treat Veeva and Ryan Specialty as something I sold today for disclosure purposes. I will also not penalize my portfolio for the “lost” performance and will assume I have held the stocks I owned all along. I know that’s a lie, but please allow me to believe this lie to psychologically deal with the eyewatering money I have lost in the ether. I know I’m pulling a Costanza here. I myself dislike the “woe is me” tone in today’s piece, but I didn’t want to hide how terrible I was feeling during this process. Once I got this whole saga behind me, I started feeling a lot better and came back to my usual baseline of optimism and gratitude. ![It's not a lie if you believe it."-George Costanza #seinfeld #georgecostanza #funnyquotes](https://substackcdn.com/image/fetch/$s_!mf9q!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F640ad305-3d76-47cf-b4d7-29eac8faa3d2_596x335.jpeg "It's not a lie if you believe it."-George Costanza #seinfeld #georgecostanza #funnyquotes") --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### "Never Sell" Episode: Scuttleslops, OpenAI Valuation, Ryan Specialty URL: https://www.mbi-deepdives.com/never-sell-16/ Last updated: 2026-06-29T13:41:38.000Z **Programming Note**: As [mentioned](https://www.mbi-deepdives.com/scheduling-and-disclosure-update/) earlier, MBI Deep Dives will be off for the next week and I will resume the cadence of posting daily on Tuesday next week (July 07, 2026). --- For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode yesterday. Scuttleblurb recently introduced a new feature “Scuttleslops” and we took this opportunity to discuss the initial reactions as well as AI’s growing role in content creation. We also discussed what you need to underwrite to achieve a \~10% IRR if you were to take a stake in OpenAI at their latest funding round valuation. Then we took a hard turn from AI related discussion to…insurance brokers, especially Ryan Specialty. You can listen to the conversation here: [Spotify](https://open.spotify.com/episode/2nk9ViDeBz0nbp79438EIW?si=s7q2RexwTkewbJb1my8C6g&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/scuttleslops-openai-valuation-ryan-specialty/id1786912203?i=1000774614060&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=AqZCnS9oyrg&ref=mbi-deepdives.com), [RSS feed](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com) As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Recommended Read URL: https://www.mbi-deepdives.com/recommended-read/ Last updated: 2026-06-28T14:50:04.000Z Instead of writing my personal musings on this Sunday, I would actually suggest you read these two pieces that expanded my understanding of the world: “[**How funerals keep Africa poor**](https://davidoks.blog/p/how-funerals-keep-africa-poor?ref=mbi-deepdives.com)**”,** and “[**Why kinship societies kill their old**](https://davidoks.blog/p/why-kinship-societies-kill-their?ref=mbi-deepdives.com)**”.** They are both quite thought provoking and illuminating in a way that is hard to appreciate about the reality of living in a scarcity society. I highly recommend these pieces! ### The Shape of Compute Curve URL: https://www.mbi-deepdives.com/the-shape-of-compute-curve/ Last updated: 2026-06-27T14:57:56.000Z In a recent podcast, Lambda (one of the neoclouds) CTO Stephen Balaban expressed almost bit of a disdain for the critics who wondered whether the hyperscalers or neoclouds are “gaming” the GPU accounting by assuming higher useful lives of the GPUs than warranted. From the [podcast](https://www.youtube.com/watch?v=0NttU4CbyVs&ref=mbi-deepdives.com): > Matt Turck: You’re running an H100 at a higher rate because why? Because the demand for compute is so rabid that people will take any? Or the technical depreciation of the product is slower than people thought? What drives that? > > Stephen Balaban: Well, what’s driving it, I mean, certainly it’s the demand being high increases the price that you’re able to get in the market. There’s no question about that fundamental law. Again, going back to what people didn’t understand about this market. There were people who were saying, “Oh, well, there’s a five-year lifetime, or three-year lifetime,” I even heard some people say three-year lifetime for these GPUs. Completely false. You know, we have GPUs that we commissioned, and we’re one of the earliest neoclouds, in fact, we’re probably the only neocloud that actually has GPUs in our fleet that are fully depreciated from an accounting perspective. Most people are adapting around a six-year accounting depreciation schedule. But that’s not the usable life. The usable life is longer than the accounting depreciation schedule, and what really matters is the economic usable life. And so what we’re starting to see is that the people who were the naysayers—”oh, this is going to be, you’re going to throw these GPUs out in five years”—are completely wrong. They’re completely wrong and they’ve been wrong the entire time. Given the rental rate even for the older GPUs stayed much higher than most people expected a couple of years ago, I can understand why Balaban is so dismissive of the critics. Nonetheless, I do wonder whether he’s giving too much credit to themselves. Ultimately, whether GPUs have longer useful life or not may depend a lot on whether the demand side of the equation can keep finding compelling and economically useful things to do with such GPUs. If Claude Code or Codex were not a thing in 2026, the demand for GPUs would be lot more sober and the rental rate for older GPUs would be lot lower than it is today. Do the compute sellers have clear idea whether such new capabilities or use cases will keep showing up in a couple of years? Or how confident can you be that the current use cases can carry the day even if novel use cases don’t show up in a year or two? You see this whole depreciation debate is much more of a technological question than a boring accounting question and I suspect it’s nearly impossible to have a very high conviction **long-term** view on this topic from the outside. If compute buyers keep finding use cases that far exceed the cost for compute, demand will outweigh supply and compute prices can remain high to the extent that hyperscalers depreciating the GPUs at a 4-5 year timeframe may end up underreporting their earnings power in the next few years. Of course, the opposite could also happen if compute customers struggle to find valuable use cases with their compute. To be precise, not only the investors, but I think even compute sellers are not in a great position to have high conviction view about long-term depreciation schedule for the GPUs. Compute buyers, on the other hand, probably have a marginally better idea given they’re the ones who will have to extract value from these compute but even in this case, I don’t think they quite know the shape of the demand curve with high conviction in 3-5 years. One additional complexity here is that the depreciation of these GPUs may vary a lot in different hyperscalers/neoclouds. While traditional CPU is much more standardized and likely have less variability in depreciation curve among different providers, the use cases in GPUs are still somewhat nascent and different providers can end up with vastly different actual useful lives of these GPUs. For example, this [blog](https://www.aravolta.com/blog/gpu-depreciation-curve?ref=mbi-deepdives.com) points out several factors that can swing the depreciation rate of such GPUs and based on telemetry data, they found the following: “The fleet's effective depreciation curve **varied by 30–45%** across different end-customers, *even though the GPUs were identical model*” ![](https://substackcdn.com/image/fetch/$s_!u_GO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff9da176d-c6de-4275-9820-5f3181a268cc_1287x517.png) Source:[ Aravolta Blog](https://www.aravolta.com/blog/gpu-depreciation-curve?ref=mbi-deepdives.com) Speaking of the shape of compute curve, John Arnold, who sits at Meta’s board, had a bit of a provocative tweet a couple of weeks ago: > Most of the SpaceX neocloud analysis changes dramatically if you understand that there's a backwardated curve for compute today. > > — John Arnold (@johnarnold) [June 15, 2026](https://x.com/johnarnold/status/2066500154564452391?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) For the uninitiated, a market is backwardated when the spot price sits **above** forward prices i.e. the thing is scarce and expensive right now, and the curve slopes down because everyone expects it cheaper later. Saying compute is backwardated means a unit of GPU capacity rents at a steep premium today because of the power/chip/packaging crunch, while the forward curve is lower because the market expects supply to catch up and performance-per-dollar to keep improving. But wait a minute…if that’s what one of the board of directors thinks the shape of the curve for compute to be, why is Mark Zuckerberg buying compute hand over fist **today**? While that may seem contradictory at first glance, I think there may be less tension between these positions than one may think. Backwardation is a statement about the price of **renting** compute, and about the risk to a compute **merchant**, but not about the value of compute to a compute **consumer** who turns it into something else. Meta isn’t obviously earning the rental rate on its GPUs and it earns ad dollars, engagement, and model capability with the compute capacity it is building/renting. So, while the curve tells you merchant economics may deteriorate especially if the price falls faster than expected; it says almost nothing about whether a compute consumer should build for its own use. Again, one customer’s failure to utilize compute may not also mean disaster for the entire compute market either. When xAI failed to utilize their compute capacity, they could sell it to Anthropic and Google because those compute buyers can get presumably value from the compute capacity higher than what they’re paying for such capacity. Meta may be forced to do the same if their ambition in staying closer to the frontier model falls apart. Of course, the true disaster in compute prices will happen only when other potential buyers (OpenAI/Anthropic/Google) either run out of ideas to utilize such capacity or already have enough capacity to serve their users or build the next model. While these scenarios feel very unlikely today, I am not sure you can be **VERY** confident about the shape of the curve in a 3-5 year timeframe. Arnold made his money in trading energy and at one point, he was actually the youngest Billionaire in the US. In Arnold's own native language: “convenience yield**”** can also play a huge role in determining whether a compute buyer such as Meta should buy compute today or wait for prices to come down. A curve inverts into backwardation precisely when holding the physical thing **now** commands a premium. The only question is whether **your** private convenience yield beats that premium. "Wait and accumulate later" only works if the returns to compute aren't time-sensitive. AI, especially in consumer land, is potentially a winner-take-most race, and ceding two years of ad gains, model quality, and engagement to Google and OpenAI to save on input cost is likely a catastrophic trade. The savings can be completely dwarfed by the returns forgone. Of course, it also doesn’t help that power, land, interconnects, and GPU allocations have multi-year lead times. If you wait, you will just start the queue late. It is also worth [recalling](https://mebfaber.com/2022/01/26/e386-john-arnold/?ref=mbi-deepdives.com) how Arnold made his name. In 2006, Amaranth’s star trader Brian Hunter sat on a massive, highly leveraged position in natural gas [calendar spreads](https://en.wikipedia.org/wiki/Calendar%5Fspread?ref=mbi-deepdives.com). The 2005 hurricanes had made him a hero, and by 2006 he was running a book so large that Amaranth reportedly controlled something like 70% of the open interest in certain contracts. Arnold’s read was different. I dredge this up because it is perhaps the same muscle at work when he’s looking at the compute market today. Hunter bet a scarcity spread would persist; Arnold bet that supply and seasonality would reassert themselves and the curve would mean-revert and indeed, it did. I guess it’s the same instinct sitting behind a throwaway line about compute being backwardated: a man who got rich fading one-sided positioning in an energy curve, now staring at a market where the entire crowd leans the same way on “demand outstrips supply forever.” The only problem here is I think the question about shape of demand-supply curve here is much more of a technological one than it perhaps ever was for natural gas in 2000s and answering that question may be above Arnold’s (or anyone’s) paygrade sitting in mid-2026! --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### The ASIC-GPU Standoff URL: https://www.mbi-deepdives.com/the-asic-gpu-standoff/ Last updated: 2026-06-26T15:10:17.000Z My friend Liberty recently [highlighted](https://www.libertyrpf.com/p/645-how-chinas-ai-punches-above-its?ref=mbi-deepdives.com) an interesting [paper](https://arxiv.org/pdf/2606.15870?ref=mbi-deepdives.com) from Google that goes through the evolution of TPUs. The objection to domain-specific silicon which might sound quite persuasive is the one about obsolescence: a chip may take two to three years to design, fabricate, and deploy, and AI moves fast enough that whatever you tuned it for has usually moved on by the time it lands in a data center; you may end up optimizing for a workload that no longer exists while a GPU runs whatever the labs may cook up next quarter. So, Google’s bet on TPU was somewhat contrarian but thankfully, that bet has been on the right side of history so far. From the paper (emphasis mine): > Skeptics initially warned that an ASIC might be too tailored to existing DNN (Deep Neural Network) models, quickly becoming outdated given AI’s rapid pace. That proved not to be the case. **The founding principles of TPU v2 demonstrated remarkable longevity, with later generations increasing component speed and size by riding technological breakthroughs without altering the underlying design. Not all accelerators can make this claim**. ![](https://substackcdn.com/image/fetch/$s_!Vmx1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F469ea9f1-5b3b-4e9f-8b34-a49360ca0d2a_1119x1009.png) The paper quipped that TPU’s success has “launched a thousand chips”. Indeed, the latest ASIC that grabbed a lot of attention is OpenAI’s [Jalapeño chip,](https://openai.com/index/openai-broadcom-jalapeno-inference-chip/?ref=mbi-deepdives.com) its first in-house inference chip, built with Broadcom and Celestica. Unlike most press releases, this one is worth reading carefully. Here are some key excerpts (emphasis mine): > OpenAI designed the chip **from scratch** around its deep understanding of LLM fundamentals, informed by its roadmap of models, kernels, serving systems, and product needs, with partners Broadcom and Celestica, helping industrialize the platform through chip implementation, board, rack system integration, high-performance networking, and scalable production systems. **Jalapeño is designed with flexibility to work with all LLMs guided by OpenAI’s insights into the inference needs of current and future AI models across the industry.** Engineering samples of the Jalapeño chip are running ML workloads in the lab at production target frequency and power, including GPT‑5.3‑Codex‑Spark. > > While OpenAI is still measuring final performance, early testing shows that **Jalapeño will deliver performance per watt substantially better than current state-of-the-art**. A detailed technical report on performance will be presented in the coming months. The architecture reduces data movement and balances compute, memory, and networking resources to achieve **realized utilization much closer to theoretical peak performance**. > > Jalapeño was co-developed **from initial design to manufacturing tape-out in just nine months**, and the custom AI accelerator program represents what we believe to be the fastest ASIC development cycle ever achieved in high-performance advanced semiconductors. That speed reflects deep software-hardware co-development with OpenAI’s engineering teams, Broadcom’s silicon implementation expertise, and the use of OpenAI models to accelerate parts of the design and optimization process. > > Jalapeño is **the first step in a multi-generation compute platform designed for initial deployment by the end of 2026 and expanding in the years ahead**, combining OpenAI-designed accelerators with Broadcom silicon implementation, networking, and connectivity technologies; and Celestica’s board, rack, and system expertise. Going through that press release is a good reminder how damn useful owning the model layer can be. There may be capability overhang as the models are likely to be far more capable than what most people use it for, but the very researchers developing these models should be in the best seat to extract the highest capabilities out of these frontier models. Of course, such fast paced LLM-optimized chip design does raise question around the depth of moat around Nvidia’s CUDA. I myself wondered [**last year**](https://www.mbi-deepdives.com/how-would-we-know-if-market-were-agi-pilled/) that this question will inevitably be raised: > A good chunk of Nvidia’s moat comes from CUDA (Compute Unified Device Architecture) which is a parallel computing platform and programming model that has created immense developer lock-in. CUDA exists to bridge the gap between human programmers and the complex architecture of the GPU. As AI systems become capable over time, AI should not require human-friendly abstraction layers, SDKs, or documentation. It could theoretically look at any piece of hardware i.e. an Nvidia GPU, a Google TPU, or a novel architecture it just designed and write perfectly optimized machine code for it. If humans are increasingly out of the loop, shouldn’t the friction that keeps developers locked into CUDA also materially diminish over time? Jensen Huang was actually directly asked about this a couple of months ago during his [podcast](https://www.dwarkesh.com/p/jensen-huang?ref=mbi-deepdives.com) with Dwarkesh: > **Dwarkesh:** > > Can all the hyperscalers write these custom kernels for themselves? Nvidia still has great price performance, so they might still prefer to use Nvidia. But then the question is, does it just become a question of who is offering the best specs, the best flops and memory bandwidth for a given dollar. Whereas historically Nvidia has just had, and still has, the best margins in all of AI across hardware and software, +70%, because of this CUDA moat. And the question is, can you sustain those margins if for most of your customers, they can actually afford to build, instead of the CUDA moat? > > **Jensen Huang** > > The number of engineers we have assigned to these AI labs is insane, working with them, optimizing their stack. The reason for that is because nobody knows our architecture better than we do. These architectures are not as general purpose as a CPU. A CPU is kind of like a Cadillac. It’s a nice cruiser. It never goes too fast. Everybody drives it pretty well. It’s got cruise control, and everything’s easy. But in a lot of ways, Nvidia’s GPUs, accelerators, are like F1 racers. I could imagine everybody’s able to drive it at a hundred miles an hour, but it takes quite a bit of expertise to be able to push it to the limit. We use a ton of AI to create the kernels that we have. Huang likes to call Nvidia an “extreme co-design company,” and that’s where much of the magic comes from, but it stops being a uniquely Nvidia trick once a customer controls most layers and points its own models at optimizing across them. Even though CUDA moat may be diminishing in the age of increasingly more and more capable frontier models, Nvidia’s moat is likely to be fine in the near term given their control [across the supply chain](https://www.mbi-deepdives.com/nvda-moat-pichai-comp/), but long-term questions cannot quite be resolved. Speaking of supply chain, Rihard Jarc recently shared an interesting interview of a Google employee who has hinted at the possibility of hyperscalers building a more direct relationship with TSMC. Maybe TSMC’s CEO won’t need to utter “[customers’ customers](https://www.mbi-deepdives.com/tsm1q26/)” for too long in their earnings calls! Some key points from Rihard’s [post](https://x.com/RihardJarc/status/2069781596362719533?ref=mbi-deepdives.com): > Interview with a Google employee explaining that the value when it comes to ASIC design from companies like AVGO, Mediatek, and MRVL is in their TSMC allocation, not the co-design anymore: > > 1\. When it comes to co-design of chips, he thinks the real value of companies like AVGO, Mediatek, and MRVL lies in their TSMC allocation and memory allocation, which they got sooner than everyone else. In the whole process, he sees platform verification and then manufacturing as key. > > 2\. If you could flip a switch and completely reset the TSM allocation, he thinks the hyperscalers would move 100% to internal co-design and skip the co-designers for that part. There is still value in AVGO‘s IP for memory, or in MRVL’s interconnects, but for co-design specifically, he thinks it comes down to TSMC and the memory supply chain allocation. > > 3\. In the future, he thinks hyperscalers will move to direct TSMC relationships. Huang is, of course, acutely aware of how his top customers are all deeply incentivized to design their own chips and it’s hard to bet on such well-capitalized customers **NEVER** getting there. This is perhaps why Nvidia itself is also on their own journey of building open-weight models. Any time there is a monopoly formed in some parts of the AI value chain is not good news for the rest of the value chain. If Anthropic or OpenAI ends up with a monopoly or stable duopoly without a viable open-weight alternative (assuming China open-weight models get eventually banned in the US), you can see the pressure the likes of Nvidia and 3P hyperscalers might feel eventually to extract margin from OpenAI/Anthropic. With Meta sending not-so-reassuring signals about their approach to open-weight AI models, Nvidia is trying to [lead](https://www.wired.com/story/nvidia-investing-26-billion-open-source-models/?ref=mbi-deepdives.com) the open-weight models in the West. The equation is simple: everyone wants fragmentated customer base for whatever they’re selling and the moment your customer base becomes too concentrated, it’s hard to sit idle and pray/hope that such customers will forever pay your margins. In some sense, much of the AI value chain increasingly feels like this below meme from “The Office” and it’s not easy to know how all of these debates will largely settle in 3-5 years. ![](https://substackcdn.com/image/fetch/$s_!_0wx!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe5479aa9-9ac0-48da-8695-da4301966744_480x400.gif) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Scale Without Sluggishness URL: https://www.mbi-deepdives.com/scale-without-sluggishness/ Last updated: 2026-06-25T14:36:50.000Z _This post is for paying subscribers only._ ### Scheduling and Disclosure Update URL: https://www.mbi-deepdives.com/scheduling-and-disclosure-update/ Last updated: 2026-06-24T13:51:31.000Z A few things I wanted to share: what’s coming up, a break I’m taking next week, and a change to my portfolio disclosure. **What I’m working on** I’m currently working on DoorDash, which I expect to publish sometime next month. After that, I plan to turn to Pool Corp, and then spend some time on memory companies, Micron in particular. A quick word on cadence. Between the Deep Dives and the daily pieces, I expect each Deep Dive to take a little longer than a month to finish from here on. Some of that is simply the daily workload. But some of it reflects a view I’ve been forming for a while: six years in, the library of Deep Dives has grown large enough that I increasingly think the daily pieces are becoming the core of what MBI Deep Dives offers. In the early years, every new Deep Dive was almost entirely net-new for readers; today, an incremental one may not carry the same weight it once did. That said, there’s no shortage of businesses I still want to understand more deeply, so Deep Dives will remain an integral part of the work for the foreseeable future. **A break next week** I’m taking a week off, starting Tuesday, June 30\. I’ll publish daily as usual through Monday, June 29, and I’ll be back to the regular daily cadence on July 7. **A change to my portfolio disclosure** This one needs a bit of context. I started MBI Deep Dives almost six years ago, after my work authorization in the US expired. I moved to Canada in early 2021 and incorporated the business there. I later moved back to the US but kept the company in Canada. This year, after receiving my Green Card, I spoke with a couple of tax professionals about moving the business to the US to simplify my tax reporting now that I’m a permanent resident here. I’ve since begun that process, mainly moving over the investments I’ve held through the Canadian entity. It has turned out to be more complicated than I expected, and I’ll need to make a trip back to Canada to sort out some of the details. As part of this, I sold my entire Canadian-held portfolio yesterday. Because that was roughly 85% of my total holdings (the rest is in the US), I think the right thing to do is to pause the portfolio disclosure for a few weeks, until I’m able to move the assets over. Once I can, I intend to largely buy back the positions I sold though where I land will, of course, depend on where those stocks are trading by then. Frankly speaking, this whole process has been a real headache. But I’d rather deal with the distraction now, over the coming week, so I can get back to focusing fully on investing afterward. Thank you, as always, for your understanding and for reading. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Spotify Model Update URL: https://www.mbi-deepdives.com/spot-model-update/ Last updated: 2026-06-23T15:03:58.000Z As mentioned [**yesterday**](https://www.mbi-deepdives.com/spot-2026/), I am uploading my detailed Spotify model behind the paywall. The objective of this exercise is to gauge what metrics you need to underwrite to believe management’s long-term outlook provided for the business during the 2026 Investor Day. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- You can download the model and play with the assumptions to fit your narrative. _This post is for paying subscribers only._ ### Some Notes from Spotify's 2026 Investor Day URL: https://www.mbi-deepdives.com/spot-2026/ Last updated: 2026-06-22T14:49:32.000Z Spotify held its third Investor Day in May, eight years after the 2018 debut that asked whether it could win against the big tech walled gardens with their own music streaming offerings, and four years after the 2022 edition that asked whether it could ever be a real business with real margins. The answer to both, with the benefit of hindsight, is "yes, and then some." Spotify’s number looks pretty impressive: 761 million monthly users across 184 markets, nearly 300 million of them paying subscribers, and more than half a billion people now streaming audiobooks or podcasts on top of music! Management had an interesting framing that it kept returning to during the investor day: **there is no such thing as an average user**. Engagement and willingness-to-pay follow a power law, and Spotify has historically monetized only two slices of that curve i.e. the ad-supported long tail and a flat-ARPU Premium tier. Spotify now thinks that the head of the curve, the super-fans, is finally about to get monetized too. ![](https://substackcdn.com/image/fetch/$s_!vGdR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2fa05ce8-cd0b-4f52-a5d9-0a3f3f23f537_1863x1030.png) Source: Spotify 2026 Investor Day The proof of concept is Audiobooks. When audiobooks rolled into Premium across the first 22 markets, a cohort of heavy listeners kept hitting their monthly hour caps which is a pretty strong demand signal. Spotify sold them more hours as an add-on, and in under a year more than a million users are paying for Audiobooks+ on top of their subscription. Their lifetime value (LTV) runs at multiples of Premium-only subscribers, and the add-on should cross EUR 100 million in annualized recurring revenue this July. For two decades, you could argue one of the concerning bear cases for Spotify was that it has a capped ARPU since there is a limit how much they can raise price, especially when there are competing products available with pretty much the same content as Spotify. What management now describes is a portfolio of higher-ARPU products…each with a smaller TAM but far higher revenue per user, layered on top of 300 million premium subscribers. Audiobooks got there first, but it sounds like they will keep releasing new products/features to monetize the head of the curve. What gives Spotify’s ambition credibility is the country-by-country evidence Spotify walked through. In Sweden, the oldest market, paid penetration is now approaching **50% of the entire population** which gives you an indication where a mature Spotify market can eventually land. In the US and Canada, the share of users paying for Premium has gone **from 32% a decade ago to 60% today**; MIDiA data has Spotify gaining US premium share every single year for six years “without exception.” Brazil doubled its conversion rate from 22% in 2016 to 44% today, a 27x increase in subscribers and revenue there is set to grow more than 30% this year. India is the long tail of the long tail: the subscriber count is now seven times what it was at the last Investor Day, net adds in 2025 ran three times the 2022 pace, and fewer than 10% of Indian users are on Premium. Across different geographies, Spotify’s playbook is the same: attract, engage, convert, retain, then grow ARPU. ![](https://substackcdn.com/image/fetch/$s_!CPzA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97650abf-f11d-4448-b62d-c7067649aea4_1933x1122.png) Source: Spotify 2026 Investor Day On the supply side, the marketplace story still hasn’t lost steam: the gross-profit contribution from Spotify’s artist promotional tools has grown 4x since 2021, and it is a meaningful part of why music gross margin keeps grinding higher. ![](https://substackcdn.com/image/fetch/$s_!hMM2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5eec3c01-3af9-4d51-8936-1885e59cdc9c_1771x892.png) Source: Spotify 2026 Investor Day Speaking of supply side, the highlight was a landmark licensing agreement with Universal Music Group and Universal Music Publishing Group. Fans will be able to legally create covers and remixes from participating artists’ catalogs, with both the artist and the songwriter sharing in the value created. Most AI music to date has been net-new content that competes with the catalog; this instead monetizes the catalog by letting fans riff on it. Spotify management mentioned that a song that today might inspire three remixes and five covers could become 10,000 or 100,000 fan creations “paying tribute” to the original. Creation will be a Premium add-on but consumption stays free for everyone. How about the economics? This is what Spotify management said (emphasis mine): > “We don't want to go into the economics, but **you should expect this to be at least margin neutral to accretive for us**. We just don't do deals that are bad for any of us, which is one of the reasons why we spent so much time on getting these deals right.” I would also note that Spotify did not need every label to launch this product which bodes well for their bargaining power here. I would actually be surprised if other labels didn’t follow suit soon. Podcasts are in their second year of gross profitability with engagement doubled since 2022, sponsorships up over 100% YoY, and a potential path to 40% long-term gross margins. Audiobooks, only two years old, grew listening hours 60% from 2024 to 2025, expanded the indie catalog 50%, and now reach a meaningfully younger, roughly 50/50 male-female audience. Page Match, which lets users flip between a print book and the audiobook, is driving up to 55% more listening. ![](https://substackcdn.com/image/fetch/$s_!D6yj!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9db2fad7-6694-47b7-9a90-e1aafcfa2dd1_1905x1033.png) Source: Spotify 2026 Investor Day Of course, AI was a recurring topic. Spotify highlighted again that it is building a “Large Taste Model” trained on 3.4 trillion daily data points (behavior, metadata, creator and cultural signals) that no one else can replicate, because taste requires an insanely active user base for years. The payoff is twofold: better personalization and entirely new products. Let’s talk about the outlook now. I thought it would be useful to take a closer look at what Spotify promised in the 2018 and 2022 Investor Day and what they actually delivered. In 2018, Spotify guided to a 25-35% long-term revenue CAGR and a 30-35% gross margin. It largely hit the growth target but missed gross margin as podcast losses dragged the blended number down. In 2022, the bar moved to 20%+ revenue growth, an eventual \~20% operating margin, a $100 billion revenue North Star and a billion users; by 2025 it had delivered an 18% currency-neutral CAGR to EUR 17 billion, 32% gross margin (versus the 30% goal), 12.8% operating margin (versus the \~10% intermediate goal), and \~EUR 3 billion of free cash flow off a 2022 base of roughly zero. As alluded earlier, during the 2022 Investor Day, the dominant concern from investors was that Spotify may be a great product but may be a terrible business given the company was beholden to its suppliers and hardly ever made money. Even the bulls (including me) got deeply annoyed that Daniel Ek mentioned the fables of 100 Billion revenue “over the next decade”. Thankfully, the new co-CEOs seem to be more self-aware and dropped the “over the next decade” framing when it comes to 100 Billion revenue. For 2030, the new frame is a mid-teens revenue CAGR, a 35-40% gross margin, an operating margin above 20%, and strong FCF growth, with the $100 billion revenue and 1 billion subscriber North Stars reaffirmed. I have asked Claude to make a table that includes 2018 and 2022 targets and actual results as well as 2026 investor day outlook by different metrics which is shown below: ![](https://substackcdn.com/image/fetch/$s_!4fMQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9175a9d2-02f6-4943-b3b8-095ab64d1a7a_889x1189.png) Source: Spotify Disclosures, Claude So what are you paying for it? Take revenue from EUR 17.2 billion to EUR 34.6-37.7 billion by 2030 at a 15-17% CAGR (vs consensus at EUR 31.4 Billion in 2030); hold gross margin at the guided 35-40% and operating margin at 20-22%, and you get 2030 operating profit somewhere between EUR 6.9 billion and EUR 8.3 billion (vs consensus of 6.2 Billion for 2030) which is 26-30% CAGR over five-year period. Against the current enterprise value, the stock trades at about \~35x 2025 EBIT, but looks far more palatable if they can deliver in the next five years what they have guided during the Investor Day. ![](https://substackcdn.com/image/fetch/$s_!BKyv!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F94d97d36-c04e-49c1-b680-7ef08e96a489_460x403.png) Source: Spotify Disclosures, MBI Deep Dives The numbers looks interesting enough that I will take the time today to update my full Spotify model. I will share the model and some further thoughts on Spotify’s valuation tomorrow. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### On Father’s Day URL: https://www.mbi-deepdives.com/fathers-day-26/ Last updated: 2026-06-21T15:18:19.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- I actually forgot about today being Father’s day until my wife reminded me last night. While growing up in Bangladesh, Father’s day or Mother’s day just wasn’t a thing we used to celebrate. However, that started to change the moment Facebook became popular in Bangladesh and even all of our parents started using Facebook by mid to late 2010s. Every Father’s and Mother’s day, my Facebook feed is typically flooded with my friends uploading photos with their respective parent and caption it with emotions that you hardly ever get to express to your parent. In Bangladesh, you don’t quite say to your parent how much you love them (and vice versa). Of course, once it became a trend, your parents **almost** expect you to upload a photo and explain with captions how much they mean to you. Nonetheless, I still quite enjoy scrolling through my Facebook feed on these special days. I don’t have the data, but I do sense lot more people post on Mother’s day than they do on Father’s day. Even the static pictures often say a lot more than we think. The photos on Mother’s day imbues a sense of warmth and love whereas father’s day photos hint more towards a sense of admiration and respect. While admiration or respect have positive connotations, I cannot help but feeling a sense of distance many kids may feel about their fathers. Who knows maybe it’s just me as I do have a difficult relationship with my father. I will spare my father from being litigated in front of thousands of people on Father’s day and I am indeed still appreciative of the toil it must have taken to provide for the family. Now that I myself am a father of a 18-month old son, I do wonder a lot about the far more prevalence of kids having difficult relationships with their father than their mother. It’s hard to generalize the source of such tension in these relationships, but if I am forced to generalize, I think fathers tend to be lot more prescriptive about the world of tomorrow and can be particularly prone to want to engineer their children’s lives. Mothers, on the other hand, tend to be **relatively** more comfortable in playing the [role of shepherd](https://www.caryacademy.org/news-stories/letting-our-children-be-who-they-are-meant-to-be/?ref=mbi-deepdives.com). I have always thought one of the mistakes of parenting is the desire to be a precise director of their children’s lives. For myriad reasons, fathers can suffer from the illusion of knowledge that they know where the world is going and how the children should steer themselves to fit in that world. Of course, the truth is few of us have any clue whatsoever what the world will look like when our kids will go out there to make a dent in the world in a couple of decades (or sooner/later depending on your context). One thing that is perhaps far more useful than many of us appreciate is simply stories of our ancestors. I don’t need my father to impart his illusion of knowledge in how to navigate the world of tomorrow, rather I would love to know the stories of the past how he or my forefathers responded to the challenges in their own times. My sneaky suspicion is that the way I may be prone to responding to life’s challenges can be eerily similar to how my ancestors did in their own times even though details around such circumstances are likely to be completely different to each other. If your ancestors had a gambling problem, you should think twice before taking margin loans or loading up on options today. If my forefathers had difficulty in forming fruitful partnership in running a business, I should think long and hard before partnering with someone else. Unfortunately, as they say, “past is a foreign country”. In most cases, the stories we tell our children end up being too embellished to be useful for them. Perhaps you don’t want your children to picture their great grandparents or grandparents as alcoholic or someone with gambling habit. People perhaps would rather want to propagate the stories of greatness in their lineage even if they’re largely apocryphal in nature. On this father’s day, I am promising myself to tell my son in the years ahead mostly stories about what happened in my life **so far** and restrict myself as much as possible from prognosticating where my life, his life, or the world at large is heading. As a father, I want the past to be more familiar to my son and would like him to know that the future is perhaps **more foreign to me than it is to him**. Perhaps everyone reading this piece has already read Kahlil Gibran’s seminal [poem](https://poets.org/poem/children-1?ref=mbi-deepdives.com) on Children. I first read it when I was a freshman in college and I promised myself to remember these words when I become a father one day: > Your children are not your children. > They are the sons and daughters of Life’s longing for itself. > They come through you but not from you, > And though they are with you yet they belong not to you. > > You may give them your love but not your thoughts, > For they have their own thoughts. > You may house their bodies but not their souls, > For their souls dwell in the house of tomorrow, which you cannot visit, not even in your dreams. > You may strive to be like them, but seek not to make them like you. > For life goes not backward nor tarries with yesterday. There is a profound saying that *“we do not inherit the earth from our ancestors; we borrow it from our children”*. To borrow implies a return…a passing of the torch. My son doesn't need me to pave a road over the grass or tell him exactly where the trail ends. He just needs to know where he came from. Perhaps my only job is to hand him the keys with a steady hand, and the confidence that he is hopefully equipped to navigate the foreign country of tomorrow on his own terms. Happy Father’s Day! ![](https://substackcdn.com/image/fetch/$s_!hB58!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb4baa0fc-3a7a-4236-97e6-3d607b1f70d2_1542x2047.jpeg) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Airbnb's Fintech Opportunity URL: https://www.mbi-deepdives.com/abnb-fintech/ Last updated: 2026-06-20T15:24:30.000Z Airbnb first launched its guest travel insurance product in [2022](https://news.airbnb.com/covid-support-program/?ref=mbi-deepdives.com). Then in 4Q’22 call, Brian Chesky mentioned that the guest insurance product was available in 8 countries and the product had been “really, really successful”. However, Airbnb didn’t quite provide much detail about the product’s trajectory until 3Q’25 call when they mentioned guest insurance availability expanded to 12 countries and revenue from guest insurance was growing “over 25% year-over-year”. In fact, revenue from this product increased by 40% in full year 2025\. Then in the most recent earnings, Airbnb disclosed that growth has accelerated further to 45% in 1Q’26\. More interestingly, Airbnb explicitly called out their insurance program for **take rate expansion** for full-year 2026. In the recent shareholder letter, Airbnb also clearly hinted that they’re planning to launch more insurance related products (emphasis mine): > “We are excited by the performance of this offering over the past few years and have begun **piloting additional forms of insurance products to support both guests and hosts.**” We are starting to see Airbnb graduating some of these pilot programs to public release. For example, early this month Airbnb announced “[earnings protection](https://news.airbnb.com/protecting-earnings-for-hosts-when-the-unexpected-occurs/?ref=mbi-deepdives.com)” for hosts. Earnings Protection is an optional, paid insurance product offered in partnership with MIC Global that provides eligible US hosts with supplemental payouts based on historical averages to cover income loss when unexpected external events, like natural disasters or severe property damage, prevent them from hosting. ![Host Earnings Protection image](https://substackcdn.com/image/fetch/$s_!pTf9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdf6d4b80-67c6-4cf0-befa-ef0afae3e287_1920x1080.png "Host Earnings Protection image") Source: Airbnb Then yesterday Skift [mentioned](https://skift.com/2026/06/18/airbnb-moves-into-fintech-with-a-hopper-like-cancel-for-any-reason-feature/?ref=mbi-deepdives.com) that Airbnb has started offering “extended cancellation option” for guests recently even though they didn’t publicly announce such product release yet. From [Skift](https://skift.com/2026/06/18/airbnb-moves-into-fintech-with-a-hopper-like-cancel-for-any-reason-feature/?ref=mbi-deepdives.com): > Airbnb has introduced its first foray into travel fintech with a feature that lets guests pay for the option to cancel a stay for a full refund up to 24 hours before check-in. > > The feature, called [the extended cancellation option](https://www.airbnb.com/help/article/4061?ref=mbi-deepdives.com), is currently available in 12 countries including the U.S., Canada, Ireland, the Netherlands, and several others. Eligible hosts need to opt out rather than opt in and most listings with moderate, limited, firm, or strict cancellation policies get automatically enrolled. Airbnb didn’t disclose the amount of the fee, which guests can pay at booking. > > Airbnb is working with a third party in powering the feature but wouldn’t identify the partner**.** As it turns out, I was actually booking for an Airbnb yesterday for my family’s next month’s trip to Yosemite National Park. I was shown the extended cancellation option which would let me cancel the booking 24 hours before the stay for \~11% of the cost of my entire booking (including tax). Of course, this premium is a contingent liability, so we cannot really think it as a commission on a transaction. The economics are an insurer’s: the fee, collected on every policy, minus the refunds Airbnb must fund when guests do cancel. We also need to consider the servicing and payment costs, and the cut owed to the undisclosed third party “powering” the feature. ![](https://substackcdn.com/image/fetch/$s_!ZF0j!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6d8ada27-d9c5-4070-9f70-86ea449c4ef0_424x633.png) Source: Airbnb If a guest opts for extended cancellation and later indeed cancels the booking, the host is still paid according to their original policy, with Airbnb covering the gap. As you can imagine, the cost of such claim can be lumpy i.e. nothing when a guest cancels early enough that the host was owed nothing, but a large slice of the booking when the cancellation lands deep inside a strict policy’s penalty window. Of course, this is too early to know what the adoption would be for such a feature and since I don’t know how the economics is going to be divided between Airbnb and the unnamed insurance partner, it’s difficult to estimate the size of the revenue opportunity from Airbnb’s perspective. However, I was a bit surprised how such fintech products seemed to have quite the appeal among travelers when I came to know [\~40%](https://skift.com/2022/09/21/hopper-rides-rise-of-fintech-projects-to-major-growth/?utm%5Fsource=ask%5Fskift) of [Hopper](https://hopper.com/?ref=mbi-deepdives.com)’s annual revenue in 2022 came from various fintech products. Hopper, a travel booking app, is a private company, so we don’t have detailed updated financials about their business. But I came across this memo from Stack Capital, one of the investors of Hopper, from early 2022\. The investment memo also highlighted the range of fintech products Hopper offers to travelers and how that improved their economics. Some excerpt from the [memo](https://www.stackcapitalgroup.com/hopper-investment-overview?ref=mbi-deepdives.com) (emphasis mine): > One of the most successful new initiatives for Hopper has been the introduction of attractive fintech offerings such as “Cancel for any Reason”, “Price Prediction”, “Price Drop Guarantee”, “Price Freeze”, amongst other travel insurance options. These offerings have become increasingly important given the uncertainty surrounding travel during the pandemic, with more consumers now demanding increased flexibility when planning trips. > > The Company has also launched its loyalty program, designed to increase engagement through rewards for each booking in the form of “Carrot Cash”, which can net travelers between 1%-5%cash back on every booking, and can instantly be applied to any future flight, hotel, home rental and car rental…The ability for any travel provider to integrate and seamlessly distribute Hopper’s fintech solutions, which have been shown to **increase average order value, improve margins, and drive customer satisfaction, represents a significant growth opportunity for the business**. Estimated projections indicate that if all travel distribution channels offered travel fintech solutions, **it could potentially increase the total consumer spend within the sector by $200 billion annually**. It’s not just Airbnb or Hopper that appreciates the size of the fintech opportunity, I think it’s also one of the reasons Booking Holdings also has been very focused on migrating from agency model (where the hotel collects the guest’s money and Booking merely invoices a commission) to a merchant model, in which Booking takes the payment itself and becomes the merchant of record. While “connected trip” is often mentioned as the primary motivation behind such migration, you can obviously only offer these fintech products if you have full control on the payment. Even in 2019, merchant gross booking was only about a quarter of Booking’s overall gross booking, but it became \~70% of their gross booking by 2025\. My guess is a lot of these insurance products will also show up on Booking in not-so-distant future. Of course, Airbnb always was the merchant of record in payment which means they can potentially capitalize on these revenue streams faster than Booking. ![](https://substackcdn.com/image/fetch/$s_!5ZfU!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa32afe02-cb26-404d-8c7b-f1edef2a2d4b_945x532.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While we don’t exactly know how much take rate expansion is possible in the long-term, it seems quite likely that just these plethora of insurance products alone can perhaps expand Airbnb’s take rate by \~50 bps in three to five years. Given Airbnb is approaching $100 Billion Gross Booking Value today, it implies a \~$500 Million very high margin revenue that’s not currently captured in financials today but are likely to show up over time in the next few years. As I [highlighted](https://www.mbi-deepdives.com/airbnbs-incentives/) recently, Airbnb management is highly incentivized to accelerate their revenue growth. So perhaps there are lot more potential ancillary revenue streams that will be announced over the next few years. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Shopify at the E-com OS Layer URL: https://www.mbi-deepdives.com/shopify-os/ Last updated: 2026-06-19T15:11:44.000Z Shopify shipped its Spring ’26 Edition this week with [150+ updates](https://www.shopify.com/editions/spring2026?ref=mbi-deepdives.com). Given the plethora of updates, thankfully Eric Seufert had Venkat Prabhu (Director of Product for Shop Campaigns at Shopify) on the Mobile Dev Memo podcast to discuss the key highlights of Shopify’s recent product updates. One such highlight is [Campaign Autopilot](https://www.shopify.com/blog/introducing-campaign-autopilot?ref=mbi-deepdives.com) which is essentially a virtual marketing agency built into the admin. You set a monthly budget and some guardrails; the system recommends, launches, and optimizes campaigns across channels. The product seemed quite compelling when Prabhu explained how sellers who never ran paid advertising could find it very useful based on real data that only Shopify has access to (emphasis mine): > “I think the cross-channel optimization is a component of the benefit of Campaign Autopilot. The foremost benefit from our perspective is just the ability to access. This is an experience that sits right within the admin. I will give you simple examples of how this experience is different from a merchant going and running campaigns on their own. If I am a new merchant just getting started with my ads on Meta, I have to make a bunch of decisions. I need to make decisions like what should my budget be or what should my ROAS targets be. These are not easy decisions for merchants to figure out. One of the ways that we add value as Shopify through Campaign Autopilot is by helping merchants come up with out-of-the-box suggestions. We as Shopify sit on tons of transactions that happen across the Shopify network. To date, we have processed about 1.7 trillion of GMV through the Shopify platform. A**ll of that has given us intelligence into really understanding what are the right campaign settings that actually help a merchant get started with the maximum probability of being successful**. I would say that is one of the foremost things in terms of just getting started, because the reality is **the majority of merchants on Shopify never run paid advertising.** Just the ability for them to get started with paid advertising in a very easy manner is one of the most foremost benefits of Campaign Autopilot.” Autopilot sits **above** the channels. If Shopify thinks it can win back a lapsed customer through an email flow, it routes there before spending on paid advertising i.e. organic vs paid arbitrage no channel-specific optimizer would ever do for you, because obviously Meta, for example, has no reason to tell you not to spend on Meta. Moreover, as Gavin Baker once [said](https://x.com/gavinsbaker/status/1451540765314797577?ref=mbi-deepdives.com), “in advertising, amateurs discuss targeting while professionals discuss attribution.” It seems important to note that Shopify **owns** the conversion event once you have campaign autopilots on. From the podcast (emphasis mine): > “Imagine somebody who has now gotten started and they have activated a range of different channels. That is where the cross-channel coordination piece becomes important. For example, **if we feel we have a high probability of getting an existing user re-engaged through an email marketing program, then that would be a more preferable path than actually paying money on paid advertising for that specific user**. That is an example of an organic versus paid channel coordination piece. In the same way, if you think about attribution, today we end up passing basically all signals back to all platforms. **For the same order, we may have three different platforms that might be claiming credit for that same order. By virtue of Shopify being the operating system for the merchant, we can very easily figure out which channel should get that attribution.** In a future state, we may realize that there were three different transits that happened from three channels that resulted in an order, and so we could do weighted attribution as well. All of these things will help merchants figure out what is the best next place to put their next hundred dollars of marketing spend. There is a targeting component, there is an attribution and then a signals component, and Campaign Autopilot and the cross-channel coordination piece helps across all of that.” Shop Campaigns, which was made [available](https://changelog.shopify.com/posts/shop-campaigns-is-now-available-on-all-shopify-plans?ref=mbi-deepdives.com) a year and half ago, also continues to expand its tentacles by adding more and more channels where Shopify’s sellers can run their ads. Since Shopify only makes money if the seller makes an additional revenue and doesn’t need to care about whether the incremental revenue came from Meta advertising or an email flow, there is no such conflict of interest. Every incremental order is incremental GMV, and Shopify monetizes GMV through the rest of the stack (payments, Merchant Solutions etc). Again, from the podcast (emphasis mine): > “Shop Campaigns continues the promise of the premise of being a risk-free advertising product, and all of the expansion is a way for us to help merchants drive more incremental orders, drive more incremental GMV through Shop as the surface area. As a part of this expansion, all of these surfaces for the most part that we have expanded onto, most of our merchants don’t advertise on these surfaces. If you think about Microsoft, the integration through Microsoft Monetize, advertising on the open web, you spoke about D2C advertisers as really being at the forefront as it pertains to digital advertising, but still the number of D2C advertisers that are reaching the inventory that is on the open web through some sort of an SSP is going to be very few. This is effectively a capability that is now going to make every Shop Campaigns advertiser be able to surface their products in the form of ads across the open web. Our launch encompasses a lot of premium websites and apps, different news outlets, lifestyle blogs, review articles, places where shoppers already are browsing for relevant recommendations. > > In fact, **on a lot of these surfaces, the costs for us to reach net new customers is actually much lower**. One of the things with all of the third-party expansion of Shop Campaigns, Shopify ends up taking the risk from an advertising perspective. The way all of our third-party advertising programs work is Shopify is acting as the uber advertiser and Shopify is putting its ad dollars on each of these channels with the goal of driving these outcomes for merchants. Shopify ends up taking this risk to drive outcomes for merchants, and our whole goal here is we sort of operate in some ways like an infrastructure. Our goal here is with most DSPs and so on there is a markup. **Our goal here is to not make any money in this whole third-party advertising business. We want to scale this program in a way that can drive the highest amount of incremental GMV for merchants**.” Once Shopify can see the full funnel, own the conversion, and strike deals at a scale no merchant can replicate, all these product updates/features end up elevating Shopify to the “operating system” level for the sellers which you cannot really vibe code away to build your own system even if the models keep improving. Michael Morton, senior research analyst at MoffettNathanson, also made the same point even more emphatically in yesterday’s Stratechery [interview](https://stratechery.com/2026/an-interview-with-michael-morton-about-e-commerce-in-the-age-of-ai/?ref=mbi-deepdives.com): > “Today we actually did a call with a former Shopify employee who managed these large relationships, the infrastructure behind an e-commerce stack is so complicated that, for starters, it’s effectively impossible to recreate the Shopify stack you’re offered for less than you’re paying for it. Even if I had unlimited resources, an army of engineers, and a blank check from Anthropic, my ability to do that at a lower cost is almost impossible, due to the negotiating leverage Shopify brings to the table with all of their partnerships. They have a better fee structure with Stripe than I could ever get, they have CAPI \[[**Conversions API**](https://www.facebook.com/business/help/2041148702652965?id=818859032317965)\] integration with Meta, because \[Shopify CEO\] Tobi \[Lütke\] and \[Meta CEO\] Mark Zuckerberg are personally acquainted and worked on this deal together, so Shopify merchants get one-click CAPI integration. It’s a long list of reasons why you’re not going to vibe-code your way to a Shopify stack.” Anyways, just because Shopify stock is down \~33% YTD, I don’t quite subscribe to the idea that it has been discarded to the “AI loser” basket. I mean it’s possible that the stock has suffered because it’s situated around the bad neighborhood of enterprise software in 2026, but most software companies would love to trade at Shopify’s gross profit multiple today. If there is any slowdown in growth due to macro or whatever reason, Shopify’s multiple may have lot more room to fall. Shopify does seem to be positioned very well and is indeed a terrific business, but investors don’t seem to be oblivious to that fact. ![chart](https://substackcdn.com/image/fetch/$s_!lSBq!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F722ccd4a-05ea-4b9a-931b-e0cbd2092f9b_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### What's Embedded in OpenAI's Stock Price? URL: https://www.mbi-deepdives.com/openai/ Last updated: 2026-06-18T15:17:45.000Z OpenAI is still a private company. They have recently submitted a “[confidential S-1](https://openai.com/index/openai-submits-confidential-s-1/?ref=mbi-deepdives.com)”, so we don’t know their financials for certain. However, [Financial Times](https://www.ft.com/content/e15b0d7e-ff6b-4f16-ba7a-4068feddb828?syn-25a6b1a6=1&ref=mbi-deepdives.com) (and [Ed Zitron](https://www.wheresyoured.at/exclusive-openai-financials/?ref=mbi-deepdives.com)) both recently claimed that they have come across OpenAI’s actual financials. Based on this report, I did an exercise to assess what OpenAI investors may be underwriting to own the stock. I will show my work behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb's Incentives URL: https://www.mbi-deepdives.com/airbnbs-incentives/ Last updated: 2026-06-17T14:37:19.000Z On X, I often come across some version of a stock performance chart showing how Airbnb has performed since its IPO which has been largely flat for more than five years now! It looks particularly unflattering when you compare and contrast to its closest peer: Booking Holdings. ![chart](https://substackcdn.com/image/fetch/$s_!9bmV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd62ba917-baa0-47bd-9c3d-52d24b27903c_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Generally speaking, these comparisons lead to a barrage of pile on from people complaining about cleaning fees, or pointing out how the originator of “founder mode” has ironically flailing around to drive his own company forward. It appears Chesky himself is acutely aware of this dynamic as he felt the need to respond to one such tweet a couple of days ago. > Our stock price went up 5x in the run up to the IPO. This is not an excuse for the last 5 years, but we’ve rebuilt the company from the ground up in that time, and we anticipate better times ahead. The story isn’t over. > > — Brian Chesky (@bchesky) [June 15, 2026](https://x.com/bchesky/status/2066622384401174731?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) Perhaps one interpretation of Chesky’s tweet is that Airbnb timed the IPO almost perfectly to raise money from the market. However, many were quick to point out that despite Chesky’s message of optimism about Airbnb’s future, he has never ever purchased a single share in the open market and been consistently selling shares since the IPO. If anything, his pace of selling has increased as he sold \~$135 million Airbnb stock in just last three weeks which is more than what he sold in all of 2025\. Maybe he’s busy selling Airbnb stocks for [funding](https://www.bloomberg.com/news/articles/2026-06-04/airbnb-ceo-brian-chesky-plans-to-start-a-new-ai-company?ref=mbi-deepdives.com) a new AI company. In any case, such data points may not inspire a lot of confidence for current and prospective shareholders of Airbnb. ![](https://substackcdn.com/image/fetch/$s_!XWYp!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a0b050f-c07c-486f-9aec-9b4db29a04b6_1312x1431.png) Source: OpenInsider While these data points can look far from ideal in isolation, it doesn’t quite give you the full picture. I actually think Airbnb has one of the better CEO incentives I have come across while covering 65+ companies over the last six years. One of the reasons Chesky is a repeat seller of the stock is that he receives $1 salary and zero cash bonus from Airbnb. Even though Mark Zuckerberg also receives $1 salary from Meta, the expenses related to cover his personal security lead to cost the company \~$25 million per year. Chesky’s personal security related costs are far more banal and his total compensation from Airbnb was below $300k in each of the last three years. ![](https://substackcdn.com/image/fetch/$s_!wVbG!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa9c83ef6-73ee-4a56-b14e-c051ee76ca4c_1123x123.png) Source: Airbnb Proxy Statement More importantly, Chesky was given a highly ambitious RSU program exactly one month before the IPO which would only fully vest if the stock price became 7x the IPO price. The award was designed in 10 different tranches, and so far only two of the ten tranches have been vested. To the board’s credit, not only did they not revise the ambitious targets down once meeting the stock price targets appears to be increasingly less likely (unfortunately a far more common practice), they also did not award any new options/RSU program for Chesky since the 2020 program. Let me put it this way, if Airbnb stock is at $204 in November 2030 (slightly less than 10% CAGR from today) and Chesky remains CEO of Airbnb then, he will be one of the lowest paying executives in the S&P 500 over the 10-year period. Of course, these RSUs are hardly his primary incentives since he still owns \~$9.3 Billion worth of Airbnb shares. Chesky doesn’t really need any extra incentives for him to want the stock price to go up since he is still the largest shareholder of the company. ![](https://substackcdn.com/image/fetch/$s_!UrO4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F49e15e7b-3c04-4927-9693-d02ee9d9ecd4_1864x487.png) Source: Airbnb Proxy Statement Perhaps a more relevant question is how Airbnb’s other Named Executive Officers (NEO) are compensated. When I did my Airbnb Deep Dive back in [**October 2022**](https://www.mbi-deepdives.com/abnb/), I was slightly put off by the plethora of qualitative metrics to determine management’s annual incentives. ![](https://substackcdn.com/image/fetch/$s_!hf6Z!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fca8c4093-fa4a-4826-92e4-3d1989bcac20_1125x355.png) Source: Airbnb 2021 Proxy Statement This is what I wrote back then about the annual incentive structure for the rest of the management: > “While the metrics the board look at are revelatory to Airbnb's priorities such as direct and unpaid traffic to platform, new hosts and listings etc, there are also quite a few elements in the incentive structure that appears to be subjective in nature. For example, in the "technology" category, the board looks at "improved platform reliability for guests and hosts" in which the management received 15 out of 15\. I am not sure what that means and they don't disclose how they are scoring that. Moreover, while this qualitative-heavy metric system gave the management 96% payout in 2021, the board later decided to award them 100% in 2021 anyway because of *"the strength of the organization’s performance and acknowledgement that the performance is capped at target for each priority even where attainment exceeded target".* That sounds like compensation committee didn't actually do a good job in devising a sound incentive structure.” While the metrics related to annual incentive changed over the years, there were still plenty of qualitative factors even in the 2025 annual incentives. ![](https://substackcdn.com/image/fetch/$s_!39rR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F729f7173-b9d3-4ffc-b427-464c5880038f_1309x349.png) Source: Airbnb 2025 Proxy Statement **Thankfully, things are changing in 2026**. I took note of the following in Airbnb’s 2025 proxy statement (emphasis mine): > “Beginning in 2026, the people and compensation committee determined to simplify the Bonus Plan structure to focus on key financial metrics that are aligned with Airbnb’s business priorities for the year. **Payouts will incentivize revenue growth, which accounts for 75% of the target performance weighting, with the remaining 25% based on Adjusted EBITDA Margin performance**.” I am heartened to see the board has eliminated all the qualitative factors which are much easier to game, as evidenced by the fact that management has consistently received full scores in them. For a company that is battling the “mature” label in their core business, revenue growth is the primary weapon to dispel such concern. This incentive system makes it abundantly clear that management too is acutely focused on this metric. The fact that adjusted EBITDA margin still had 25% weight assuages the concern that management won’t necessarily pursue revenue growth with little concern for margin. Of course, you can (rightly) scoff at the idea of “adjusted EBITDA margin” which conveniently adds back stock based compensation. We are looking at a “Silicon Valley” company after all which are increasingly quite “innovative” in financial reporting; so I guess we cannot expect to cure them of all the diseases at once, but Airbnb is certainly on the right track with their incentive tweaks in 2026. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Owning the Hill URL: https://www.mbi-deepdives.com/owning-the-hill/ Last updated: 2026-06-16T14:59:09.000Z Satya Nadella spent part of last weekend writing a think piece on X titled “[**A frontier without an ecosystem is not stable**](https://x.com/satyanadella/status/2066182223213293753?ref=mbi-deepdives.com)**”.** I mostly nodded along while reading the piece, but there was also a lot of cynicism from many corners about his prescription of not mortgaging your IP to frontier AI labs as Nadella is clearly talking his book. I do believe Nadella’s posture is defensive, but he’s putting his fingers on the right areas even if he’s making points that are self-serving. What really stood out to me is Nadella’s point about creating a compounding loop that can be largely immune from ever improving frontier model capabilities. From Nadella’s post: > Companies need to turn their workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use. Private evals should capture whether a model is actually improving against outcomes that matter to the business (not just external benchmarks!). Private reinforcement learning environments should let models grow stronger on real traces from inside the organization. Its knowledge base makes institutional memory queryable and use of tokens more efficient. > > This loop becomes the new IP of the firm. I think of it as a hill climbing machine. And unlike most assets, it compounds. Every improved workflow generates better training signal, which accelerates the accumulation of tacit knowledge unique to the firm. The companies that build this early will have an advantage that is hard to replicate, regardless of any new individual model capability. Indeed, a couple of months ago in my piece “[**The Pendulum Between Intelligence and Knowledge**](https://www.mbi-deepdives.com/pendulum/)**”,** I highlighted Fin (formerly known as Intercom) which was able to climb the hill in a way Nadella would find gratifying. From Fin’s blog [post](https://www.intercom.com/blog/announcing-fin-apex-the-age-of-vertical-models-is-here/?ref=mbi-deepdives.com) in March 2026: > “As of last week, \~100% of all (English language, chat and email) customer conversations are now running on Apex. Since day 1, **the Fin engine has comprised a system of models, and last year we started replacing the off-the-shelf models with our own, custom trained on our proprietary data**. But the core answering model was always a frontier labs offering—initially versions of GPT and recently Sonnet 4.0\. But now that core answering model is Apex 1.0. > > This model resolves customer issues at a materially higher rate than any other model available. One of our largest customers in the gaming space saw their resolution rate improve overnight from 68% to 75% (i.e. a reduction in unresolved conversations of 22%). We’ve never seen a jump this large from a single improvement since we started Fin. > > But **importantly it’s also dramatically faster, has fewer hallucinations, and is far cheaper than all other available models—all factors that weigh significantly in the consideration of companies deploying these agents to their service operations**.” ![](https://substackcdn.com/image/fetch/$s_!L0wm!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F444aa701-2885-4e05-afda-e5b5a7173073_2102x912.png) Source: Fin Blog Perhaps it’s telling that Fin just got [acquired](https://investor.salesforce.com/news/news-details/2026/Salesforce-Signs-Definitive-Agreement-to-Acquire-Fin/default.aspx?ref=mbi-deepdives.com) by Salesforce yesterday. Speaking of acquisition, SpaceX just decided to acquire Cursor for [$60 Billion](https://www.cnbc.com/2026/06/16/spacex-spcx-cursor-acquisition-ipo.html?ref=mbi-deepdives.com). Cursor was perhaps one of the first companies to realize the conundrum of not owning the hill. Business Insider yesterday [reported](https://www.businessinsider.com/cursor-ceo-michael-truell-spacex-elon-musk-anthropic-2026-6?ref=mbi-deepdives.com) that Anthropic initially told Cursor that Claude Code was just a 'research effort'. I suspect this might be a recurring theme from the frontier labs; anytime there is a compelling product market fit that has potentially large addressable market, labs won’t be able to help themselves from building a first-party product to gobble that market. Cursor has been trying to respond to that risk the way Nadella would certainly approve. They built a private benchmark, "[CursorBench](https://cursor.com/cursorbench?ref=mbi-deepdives.com)," made of real requests from real users, and trained its Composer models with RL against it, grading not just correctness but adherence to a codebase's existing abstractions and software engineering practices. You can see the importance of having such private benchmark below. Please note that the x-axis runs backwards i.e. cost per task falls from $20 on the left to $0 on the right, so the model you want lives in the top-right corner (high score, low cost), not the top-left. Each colored line is a single model dialed across its effort settings, from Low up to Max; as you crank the reasoning effort, the dot climbs but slides left, buying a few more points of score for a lot more tokens, steps, and dollars. The curves are concave, and that concavity is the point: diminishing returns on compute. Fable 5 Medium scores 69.8% at $8.27, while pushing the same model to Max buys 72.9% at $18.02 which is roughly double the spend for three points. Composer 2.5, Cursor's own in-house model, manages 63.2% for just 55 cents and \~15,000 tokens, while Opus 4.8 at full effort barely beats Composer 2.5 but spends \~5x tokens and costs \~14x more for the same task! ![](https://substackcdn.com/image/fetch/$s_!Ig5D!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3908aba4-d3e1-4ff2-9d5e-c192e33a2935_1207x1344.png) Source: [Cursor](https://cursor.com/cursorbench?ref=mbi-deepdives.com) Once you have your private eval, the loop can feed itself: a better model ships, serves more users, generates more and better traces and surfaces new failure modes, which expand the eval and the training set, which yields a better model. A recent paper titled “[Test-Time Compute Games](https://arxiv.org/pdf/2601.21839?ref=mbi-deepdives.com)” also drove the point home to me why not having such private eval can be potentially quite damaging for companies running blind as there is a huge incentive misalignment between model providers and their customers. From the paper: > “…providers have the flexibility to (dynamically) adjust the amount of test-time compute an LLM uses to respond to a user’s query. However, in a competitive market, this flexibility creates a new strategic dimension beyond how providers price their services. In particular, providers can strategically increase the amount of test-time compute allocated to a user’s query to maximize profit, even if the additional test-time compute contributes little to the quality of the response. Consider a simple illustrative example where, for a given set of queries with verifiable ground truth (e.g., diagnosing patients based on their medical records), two different providers can run their LLMs in either a low-TTC (test time compute) mode (e.g., standard generation) or a high-TTC mode (e.g., chain-of-thought), with average accuracies and generation costs for the providers given by: ![](https://substackcdn.com/image/fetch/$s_!DDtw!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F99f0f644-a278-479c-ac0c-8ee76d395e53_979x139.png) > If both providers price their models with a 25% profit margin over their generation costs, it is easy to see that a user who values each percentage point of accuracy as $0.02 would always select the first provider, who offers them higher value (i.e., $0.02 × accuracy − price) independently of the TTC mode chosen by the second provider. However, to increase their profit, the first provider is financially incentivized to choose the high-TTC mode, even though the low-TTC mode would maximize the sum of the user value and provider profit, and would therefore be socially optimal.” As you can imagine, if you rent intelligence, you cannot inherently control the meter; the counterparty does, and its incentives are structurally misaligned with yours as it profits from selling you a longer, costlier path up the same hill. Without your own evals, you cannot tell whether the compute you’re paying for bought you anything more useful worth paying for. The provider, of course, knows but you’ll be just guessing. Despite this compelling logic, the reality is most tech companies don’t seem to have woken up to this conundrum yet. Perhaps Nadella’s post will finally jolt them out of such inertia and push them to give their best shot at owning their destiny instead of being just another wrapper without any compounding loop. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Meta's Myriad Questions URL: https://www.mbi-deepdives.com/metas-myriad-questions/ Last updated: 2026-06-15T16:12:17.000Z A couple of days ago, “SouthernValue” tagged me in the below tweet: > Can we get a [@borrowed\_ideas](https://x.com/borrowed%5Fideas?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) and [@evrgn11112231](https://x.com/evrgn11112231?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) podcast or spaces to discuss [$META](https://x.com/search?q=%24META&src=ctag&ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) state of affairs? New regulation, AI model delays, internal chaos, tokenmaxxing then unmaxxing, agentic business prospects, Neocloud prospects (and willingness / timing) … lots to discuss. > > — SouthernValue (@SouthernValue95) [June 14, 2026](https://x.com/SouthernValue95/status/2065970521938821320?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) Since I also received a handful of emails asking about some of the topics raised by the tweet, I decided to share some brief and crisp thoughts on them behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- Let’s start with **regulation**. _This post is for paying subscribers only._ ### The Geography of Time URL: https://www.mbi-deepdives.com/the-geography-of-time/ Last updated: 2026-06-14T16:30:05.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- After three and half years, I went to my home country: Bangladesh with my wife and son Nile. We were concerned how one and half year old Nile would cope with the grueling 20-hour flight from SFO to Dhaka (with six hour transit in Dubai), but thankfully it was uneventful. He was curious and tad bit excited at the first sight of Bangladesh. ![](https://substackcdn.com/image/fetch/$s_!blY3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F633bd6fb-2fea-43c1-b3a5-c962b8d15018_768x1024.jpeg) While I spent my first two and half decades in Bangladesh, it’s been almost a decade since I visited Bangladesh during the summer. Most people living abroad tend to visit the country during winter as the summer heat can be quite punishing. We still decided to go for it because we wanted to celebrate Eid with our family. However, right after landing, the humid, musty Bangladesh summer got us all drenched in sweat while rummaging for our luggage. I wasn’t bothered by the heat; if anything, I was almost nostalgic. The scent of hot, humid Bangladesh summer can be unpleasant to the unfamiliar but to someone who grew up there, it was a return to the familiar milieu that got lost for a decade. I was a little worried how Nile would handle the heat, but even he appeared to be nonchalant about it and was busy watching people. For the uninitiated, Bangladesh is the size of the New York state but has half the population of the US. So, if you are into peoplewatching, Bangladesh has plenty to offer! As we headed from the airport to my brother’s place, I was pleasantly surprised by the elevated expressways which likely cut down the time to our destination by at least half (if not more). I often joke with my friends that perhaps half the people who leave Bangladesh decide to do so while being stuck in traffic as they wonder there must be more to life than spending two-three hours everyday in traffic. I only learned to drive after moving to the US as we never had a car growing up in Bangladesh. But now that I know how to drive, it was quite a nerve wracking experience just sitting in the car in Bangladesh. There is traffic everywhere and hardly anyone is following any traffic rule. Frankly speaking, I felt if you are commuting in Bangladesh, you should consider everyday a little miracle that you made it back in one piece. After spending just one night in Dhaka, we traveled the next day to [Bogura](https://en.wikipedia.org/wiki/Bogra?ref=mbi-deepdives.com), the small city in Northern Bangladesh where both my wife and I grew up. Nile received quite the reception from both sets of grandparents. Thankfully, since both grandparents’ place is literally two-minute walking distance from each other, Nile was busy hopping from one grandparent to another everyday. My romanticism about Bangladesh started to fade the more time I spent in Bogura. The reality is even though I migrated to the Northern Hemisphere back in 2017, it was a long road of struggle to make it as a first generation of immigrant. But as things started to fall into places in the last couple of years, I discovered that I have become surprisingly used to certain amenities in life. It was almost impossible to maintain my daily 10k steps habit as the summer heat and dusty air reminded me that I was giving myself too much credit for being able to build a good habit. Perhaps California weather is simply nice enough to make it easier to stick to such habit. Moreover, as I was gaining weight after consuming copious amount of unhealthy Bangladeshi food every meal, I started googling about GLP-1\. The worst was, of course, that my sleep schedule got completely destroyed and by the time I could get back to some normalcy, I had to hop onto my return flight to California. It also didn’t help that I probably wasn’t in the greatest frame of mind while in Bangladesh. I was in the middle of two big work related stressors after discovering the myriad tax related complexities for owning a company incorporated in Canada but living in the US. I decided to move my business from Canada to the US which forced me to migrate all the subscriptions from Stripe Canada to Stripe US. This proved to be significantly more challenging than I expected going in and I was coordinating among Ghost, Substack, Stripe, and a developer I hired for the project. I know the payment stocks are getting killed in the market, but the whole experience reminded me that the last thing I want to change again is my payments infrastructure. Anyways, I was particularly curious about how people in Bangladesh are using AI. The fact that AI is a big deal is not news to anyone I interacted in Bangladesh. Everyone is familiar with ChatGPT, but Gemini was also frequently mentioned. While almost everyone uses the free versions, there are a handful of people who mentioned they pay for the subscription. However, they don’t buy the subscription directly from ChatGPT. Apparently, there are some “Facebook Groups” which sell ChatGPT subscriptions. They will give you a specific email address and password with access to ChatGPT subscription for \~$3-5/month. Presumably, if they give the same email address to ten people, you can see these resellers are making handsome margin (ironically almost certainly far higher than OpenAI itself). Of course, the main downside for such “subscribers” is they have no privacy as their queries can be seen by ten other people who have the access to the same account. If large part of the world needs to make a trade-off between privacy and intelligence, it’s not particularly a difficult choice for many people. However, almost everyone I talked to seems to be still confined to the “chatbot” era of AI. I haven’t heard anyone mentioning “agent” once while discussing AI in Bangladesh. Speaking of Facebook, I came across another story from a friend that is less than ideal for…Meta. My friend ordered some fish via a Facebook page. The payment went through but the products were never delivered. When he dug into see why he fell for a scam, it turns out someone cloned the Facebook page he was looking for and the scammer even managed to get thousands of likes (almost similar to the original page) presumably by promoting ads (or who knows maybe via “click farms”). What surprised my friend was that he actually spoke with the seller over the phone and confirmed the order only after speaking directly to the seller. You probably guessed where it’s going…the scammer used AI to clone the voice of the original seller! You can only imagine how much money will be lost to such sophisticated scams in the age of AI. Unfortunately, AI must be a godsend to the scammers all over the world and it’s going to be a long, difficult fight among the platforms, scammers, and users. Even in the age of AI, there are some things that never seem to change. I visited my paternal grandparents village in Bogura. I still marvel thinking about the fact that my paternal grandparent was born in 1920 and died in 2000 in the same village and yet the very land beneath his feet belonged to three different nations in his lifetime. He was born at the time the British still ruled the subcontinent, then the village was part of Pakistan after the British left in 1947, and then it changed again to be part of Bangladesh when the country became independent in 1971\. Whenever I want to imagine how dynamic the world around you can be, I just remind this personal anecdote to me. Anyways, this village got access to electricity only a decade ago and every time I go there, I am struck by how unevenly distributed time really is. If California represents the vanguard of 2026, walking through this village feels like stepping through a portal into a different century entirely. I captured the below with my Meta Glasses while strolling through the village: 0:00 /0:38 1× Our summer trip to Bangladesh was sweaty, and logistically chaotic, but seeing Nile bounce between his grandparents made the expensive 20-hour flight worth it. The romanticism of my youth might have faded into the practical realities of adulthood, but the roots remain. As we settled back into our routine under the beautiful California sun, I realized time moves differently depending on where you stand and I’m grateful for the chance to have stood in both places this summer. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Adobe's Continued Deceleration URL: https://www.mbi-deepdives.com/adbe2q26/ Last updated: 2026-06-13T14:58:47.000Z If you’re on X (formerly known as “twitter”), you may be under the impression that Adobe’s growth has accelerated in FY 2Q’26 as I noticed quite a few posts mentioning how total ARR has finally accelerated last quarter. Unfortunately, Adobe’s ARR included $480 million from the recent Semrush acquisition. Once you adjust for the acquisition, the organic ARR growth was 10.5% which implies a **ten consecutive quarters of deceleration in revenue growth.** That doesn’t quite inspire a lot of confidence! ![](https://substackcdn.com/image/fetch/$s_!tYbV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9ac1d9f9-3a5d-427c-9d26-1f367f7eb0a6_1975x853.png) Source: Adobe What also doesn’t inspire a lot of confidence is when you notice opex growth of \~17% surpassing revenue growth of \~13% last quarter. However, this opex growth was affected by $70 million non-cash goodwill impairment on the publishing & advertising unit, $30 million loss contingency tied to a litigation settlement, and $5 million Semrush acquisition related expenses. Once you adjust for all these, opex growth would be \~14% YoY which is still a touch above revenue growth last quarter. ![](https://substackcdn.com/image/fetch/$s_!N-lf!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff202a56a-d763-4226-8574-d5f641436cb0_1441x337.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); yellow colored cells adjust for Adobe’s penalty paid for canceling Figma acquisition Perhaps the more pressing concern was that management maintained its FY’26 Total ARR growth target of 10.2%, but only by folding in the \~$480 million of ARR that came with Semrush which closed in April. Remember, Adobe originally guided for 10.2% ARR growth for FY’26 in their FY 4Q’25 call. So, the fact that they didn’t change the guide despite Semrush acquisition implies they have effectively guided ARR growth down in 2Q’26 call. Adjusting for Semrush acquisition, now their FY’26 growth came to only 8.3% which is almost \~200 bps below the original guide. The high single digit organic growth outcome I called “quite conceivable” in my last [**Adobe update**](https://www.mbi-deepdives.com/editing-adobe/) is now embedded in the guide itself. Management has tried to make the case that they’re intentionally making a short-term trade-off to go after a more compelling longer term opportunity. From the call (emphasis mine): > Our FY '26 total Adobe ARR growth target of 10.2% now reflects both the addition of the Semrush book of business as well as the **strategic choice to accelerate MAU freemium growth and defer previously planned Creative Cloud line optimizations**. We believe this is the right long-term strategy to expand our customer base and strengthen the foundation for durable growth. > > …We believe now is the time to aggressively acquire the next generation of Adobe loyalists. The strategic shift to acquire more freemium customers through Adobe and Firefly lowers our second half ARR growth expectations from individual subscribers. We believe these changes do make Adobe even stronger. Unfortunately, the messengers are leaving**.** Narayen announced his retirement in March and remains CEO only until the board finds a successor. Adobe also disclosed that CFO Dan Durn is departing. Management explicitly said the next CEO will own FY’27 planning, and Narayen said the payback from this pivot “will play out, I think, over 2027.” In other words, the team making this promise will largely not be the team delivering on it. Charitably, an outgoing CEO has nothing left to prove and is clearing the deck so his successor starts from a cleaner base. We will have to wait and see if that is indeed the case. Adobe stock has, of course, been continually punished over the last couple of years and now trades at HSD LTM EV/EBIT multiple. ![chart](https://substackcdn.com/image/fetch/$s_!5FSz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa6624fcc-bbe5-43d1-b9c8-d219175f5e87_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Many investors understandably wonder whether much of the risk is priced in, perhaps more so than deserved. Software investors who are typically not accustomed to looking at HSD multiples on actual (not fake) operating earnings can be prone to thinking that the stock is priced for death in not-so-distant future. If you feel too tempted, I encourage you to re-read my piece “[**How elephants may die**](https://www.mbi-deepdives.com/how-elephants-may-die/)”: > Serious investors may scoff at the idea of narrative driving the valuation for many companies, but the reality is when it becomes very, very hard to articulate why a company will become more relevant over time and be able to protect its profit pool, the spreadsheet may not be able to rescue such a company. **If anything, the spreadsheet may be a lethal tool to show that the company can grow topline for years and still fail to be attractive for the long-term shareholders.** --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### FICO: Capital Allocation, Management Incentives, and Valuation URL: https://www.mbi-deepdives.com/fico4/ Last updated: 2026-06-12T15:21:09.000Z **Programming Note:** This is the final part of my FICO Series. Last month, I announced that I would start publishing my Deep Dives as a series of posts rather than one single post. Having now tried that format with FICO, I’ll be reverting back to single posts for future Deep Dives. Several paying subscribers emailed me saying they prefer reading a Deep Dive in one place, and frankly, I’ve come to the same conclusion myself: unless you’re already familiar with the company, it’s hard to keep track of the thread when the analysis is spread across multiple posts. It’s simply easier to read (or listen to) the whole thing at once. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **FICO's Capital Allocation and Management Incentives** _This post is for paying subscribers only._ ### Automation's Asymptote: Part 2 URL: https://www.mbi-deepdives.com/automations-asymptote-part-2/ Last updated: 2026-06-11T15:01:52.000Z Yesterday's [**piece**](https://www.mbi-deepdives.com/automations-asymptote/) ended on a question I could not quite resolve: it can be simultaneously true that humans remain in the loop and that the economics of being in the loop deteriorate. Shipper's frame-and-framer [**argument**](https://every.to/p/after-automation?ref=mbi-deepdives.com) gave me a philosophical reason to doubt the automation doom, but philosophy can be a cold comfort if your paycheck is tied to a frame the models are about to climb. You can go to [Mercor](https://work.mercor.com/explore?ref=mbi-deepdives.com) and see for yourself which skillsets are currently in the process of being RL-ed away. ![](https://substackcdn.com/image/fetch/$s_!gQYm!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F12fb4b89-855b-48d7-96b4-994a39f650d8_2023x1371.png) Source: Mercor How such automation affects different industries and companies is going to be a key question to ponder going forward. As I said yesterday, trillion dollars are being deployed on an annual basis to automate a good chunk of **current** knowledge work. Tom Reed wrote a very good [piece](https://meagreprotestanthistory.substack.com/p/the-goodhart-singularity?r=9x0z5&utm%5Fmedium=ios&triedRedirect=true) last month arguing that we may be pursuing what he calls “Goodhart Singularity”. Reed’s counter to automation doom is disarmingly simple: you cannot get good at solving problems without access to a source of problems, and the only source of most problems is slow, expensive interaction with the real world. Without that contact, the recursive loop produces something far less impressive than advertised. From Reed’s piece: > “The output of the R&D produced by an isolated datacenter of geniuses would be a mere ***Goodhart Singularity***.[4](https://meagreprotestanthistory.substack.com/p/the-goodhart-singularity?r=9x0z5&utm%5Fmedium=ios&triedRedirect=true#footnote-4) An isolated AI improving itself against benchmarks would only appear to be approaching superintelligence, while actually optimising for eval performance that fails to generalise beyond the lab.” Why would self-improvement stall outside the lab? Because models get good at what they practice, and for most economically valuable work, there is nothing to practice on. Reed’s most clarifying observation is about what kind of data exists at all: > “For most tasks in the economy, the pretraining corpus contains writing *about* the task, but not a record of the task itself. This is of course one of many reasons coding has progressed faster than other domains - code is one of the neat cases for which the task itself is almost entirely reducible to its token trace.” The internet contains commentary and advice in abundance, but the actual steps of closing an M&A deal or deciding which drone prototype to ship were never serialized into tokens. The natural rebuttal is that a sufficiently smart system can simulate whatever data it lacks. Reed is skeptical that simulation is a viable path: > “Consider that [almost half](https://metr.org/notes/2026-03-10-many-swe-bench-passing-prs-would-not-be-merged-into-main/?ref=mbi-deepdives.com) of SWE-Bench submissions accepted by AI auto-graders would be rejected by the actual human maintainers of the relevant repositories. The fact that you can pump SWE-bench scores without increasing actual merge rates is, to me, suggestive of the situation the datacenter-genius will find itself in. > > The great Zhengdong [makes this point](https://zhengdongwang.com/2024/12/29/2024-letter.html?ref=mbi-deepdives.com) about the progress of AI research itself. Not only are “evals” the only things that models are capable of getting good at, but “the researchers \[themselves\], they just wanna optimise… they just want an important problem to solve, a clear evaluation that measures progress towards it, and then they just wanna optimise it.” I suggest that AI companies *need* real-world deployment as a source of problems, or else they will have no good targets for optimisation.” The signal that something is good is generated by millions of market actors revealing their preferences through behavior, and it does not exist anywhere before deployment generates it. If Reed is right, diffusion itself is an input to capability. The benchmark frame is climbable precisely because it is frozen, but does or can the economy ever stop generating new “frames”? The standard objection here is “sample efficiency”: sure, the data does not exist today, but what if the models become more efficient learners, and the automated researchers will crack the learning problem itself. Dwarkesh recently wrote a [piece](https://www.dwarkesh.com/p/the-sample-efficiency-black-hole?ref=mbi-deepdives.com) in which he mentioned sample efficiency hasn’t been a key source for model improvement: > “One definition of intelligence is sample efficiency - that is to say, how much data do you need to see in a given domain in order to operate fluently and competently. It’s not clear that we’ve actually made much progress on training sample efficiency over the last few years - it seems like more so we’ve dramatically widened and improved the data distribution. > > The main way that AIs have been getting better is from adding[ more and better data](https://epoch.ai/gradient-updates/the-least-understood-driver-of-ai-progress?ref=mbi-deepdives.com), and scaling the compute to develop that data in the first place. Obviously RL is the main way that has happened. You can think of RL as a kind of synthetic data generation - you dump a lot of compute against a verifier in order to find the “good” data. Then you train your model to predict these correct rollouts, much in the same way that you might train it to predict the next word in internet text.” Once you see RL as synthetic data generation against a verifier, you can understand the model needs enormous quantities of human expert demonstrations in every domain you want competence in. Dwarkesh does, however, also argue that sample inefficiency may simply not matter for a huge swath of white-collar work. What the model learns amortizes across billions of sessions, so you can be ludicrously inefficient in training and still make enormous economic sense to pursue it as the labs’ revenue curves demonstrate. If you put Reed and Dwarkesh side by side, there is a sense of a circularity: the researchers in the datacenter would need a real, diverse problem set just to evaluate whether a candidate learning algorithm is better, but that problem set is precisely what the datacenter lacks in many, many domains. So if capability runs through deployment, and the manufactured substitute for real-world signal is staggeringly expensive and bespoke, where does the value accrue? Sarah Guo wrote an interesting [essay](https://saranormous.substack.com/p/the-untrainable?r=1o4vkp&utm%5Fcampaign=post&utm%5Fmedium=web&triedRedirect=true) on that question and she makes the case that anything you can measure and you can train against, that is already on its way to a commodity. Her 2x2 maps work along two axes: whether the answer is publicly verifiable or lives in private data, and whether the task is still at the frontier or already saturated. From Guo’s piece: > “we may ask two things of any kind of work. Is its correctness private and expensive to establish, the kind of truth that exists only inside someone’s data? And is it walled off, locked inside a system you can’t get into? Set those against how saturated the task is, and you get a 2x2\. Saturated work with public answers is commodity tokens, and open models own it. Frontier work with public answers, where coding benchmarks live, is where the labs win, because when the eval is free, owning it counts for nothing. The prize is the last corner, the untrainable one: frontier work whose correctness exists only in private. You can see it in the inference clouds hosting the AI-native pioneers, where the vast majority of tokens are generated by custom models, not generic open ones. > > …Capability eats many things, but a better model does not make private ground truth public. It does not hold the license, sign off on the liability, or own the firm’s files, and it cannot be the party that gets sued when the answer is wrong. Intelligence is not the bottleneck here. Permission is, and so is accountability. You can imagine a model far smarter than any person, and it still has to be let in the door, and someone still has to put their name on what it does.” Coding matured first because the compiler and the test suite are free verifiers, yet even there Guo cited researchers at MIT’s work spanning over 100,000 developers: coding agents lifted code written by roughly 180% while code actually shipped rose only about 30%. The gap between those numbers is the illegible part of the job that is still hard to automate away. The big prize for non-AI labs is the frontier work whose correctness exists only inside a firm's private data. This 2x2 framework is useful way to think through the risk labs may pose to any software company. For the more visually inclined, Latent Space [**captured**](https://www.latent.space/p/ainews-open-models-model-labs-vs?ref=mbi-deepdives.com) this 2x2 framework in the below infographic: ![](https://substackcdn.com/image/fetch/$s_!76lN!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F709bf7b6-3173-4a7f-9099-fcabd2ebd438_1954x2078.png) Source: Latent Space --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Automation's Asymptote URL: https://www.mbi-deepdives.com/automations-asymptote/ Last updated: 2026-06-10T15:14:13.000Z Anthropic [launched](https://www.anthropic.com/news/claude-fable-5-mythos-5?ref=mbi-deepdives.com) “Claude Fable 5” yesterday which is the much anticipated “Mythos-class” model. Anthropic was already leading in most benchmark with its Opus 4.8 model, but Fable 5 created a bit more distance from its competitors. Anthropic’s pace of model release even when its setting the bar at the frontier does give some credence to the notion of “recursive self improvement” of building models. Staring at the benchmark chart may make you wonder whether we are indeed at the cusp of automating a good chunk of white collar work. ![Benchmark table showing Claude Fable and Mythos compared to other leading models](https://substackcdn.com/image/fetch/$s_!1ch4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F496d6462-1acc-4306-aa5c-b5fa9b6bb502_2600x2870.webp "Benchmark table showing Claude Fable and Mythos compared to other leading models") Source: Anthropic Even OpenAI recently explicitly laid out an audacious [vision](https://openai.com/index/built-to-benefit-everyone-our-plan/?ref=mbi-deepdives.com): “*Our internal belief is that by *March of 2028* we may have a *significant fraction of our research* being done by AI systems in tandem with our own researchers*.” When trillion+ dollars are expected to be deployed on an annual basis to build and serve these models, you perhaps do need these eyebrows raising goals in not-so-distant future. Nonetheless, as the model capabilities are increasing over time, it is perhaps reasonable to feel increasing discomfort due to fear of automation. During my break, I read this very thoughtful piece: “[After Automation](https://every.to/p/after-automation?ref=mbi-deepdives.com)” by Every’s Dan Shipper who made the case that you can be simultaneously AI-pilled and yet not fear the impending automation doom. He also appeared on Lenny’s [podcast](https://www.lennysnewsletter.com/p/the-ai-paradox-dan-shipper?ref=mbi-deepdives.com) to discuss the piece. I have read and listened to both and frankly speaking, I found it to be one of the best articulations tackling this topic. So, I recommend you take the time and either read the full piece or listen to the podcast. I do want to highlight a few bits from Shipper’s piece that I found to be quite compelling. One of the highlights from his piece is a discussion on in-house benchmark that Shipper came up with and how the score on that benchmark evolved: > “We built an in-house benchmark called the Senior Engineer benchmark. It is, as its name implies, designed to test how good frontier models are at senior engineer–level coding tasks like a major refactor. > > The Senior Engineer benchmark gives a coding agent a vibe coded production codebase that has gone sideways. It’s from a real codebase for [Proof](https://proofeditor.ai/?ref=mbi-deepdives.com) that I vibe coded and subsequently needed a senior engineer to fix. > > The agent gets the codebase as it was before it was fixed and is the kind of instructions you’d give a senior engineer: “This is vibe coded slop; please rewrite it from first principles.” > > This is a good benchmark because it tests the ability for a coding agent to examine many different, unrelated problems and then sees whether it has enough autonomy, conceptual clarity, and courage to perform a working rewrite. (I also have two rewrites from human senior engineers, who used AI, that I use to compare and grade the model output.) > > Coding agents find this task hard. Not only does the agent need to find the root of the problem, it needs to keep the problem in mind over many turns without getting distracted by existing code. It also needs to be comfortable deleting large portions of the codebase—which agents are trained to avoid. > > Most coding agents can identify the shape of the rewrite, but when it comes to execution, they patch over the problem instead of fixing it. > > Until [GPT-5.5](https://every.to/vibe-check/gpt-5-5?ref=mbi-deepdives.com). > > GPT-5.5 scored a 62/100 on its best run—about 30 points above Opus 4.7.[(6)](https://every.to/p/after-automation?ref=mbi-deepdives.com#marginalia-cite-6) > > GPT-5.5’s result felt like the model has crossed a line: not autocomplete, not assistant, not tool, but something uncomfortably close to a human. A human senior engineer scores in the high 80s or low 90s on the benchmark, so another 30 points and it’ll be at human senior engineer level. That is how benchmark numbers work on the imagination: They turn a strange, qualitative change into a clean number that tells a powerful—scary—story. (Next stop: chart psychosis.) > > My guess is that the models will hit the 80s and 90s on this benchmark within the next year. But it is important to understand what the score contains in order to tell us what it means. In this case, the 62 isn’t just a measure of the model itself.” It turns out even Shipper underestimated the pace of improvement as he revealed yesterday that Fable 5 scored 91 on this benchmark! To his credit, Shipper never intended to be married to a particular timeline of when the benchmark will be saturated by the models. He, in fact, laid out why the score itself reveals far less than what most people may think. Again, from his piece: > The prompt for the Senior Engineer benchmark is generic, but it is a frame. And if we varied it, we would see the model perform at a different level. > > For example, the prompt asks for a “structural rewrite from first principles,” it says the problem is likely in the “document collaboration” part of the code, and it asks the coding agent to find and hold to “invariants.” > > If we removed those particulars, the score would go down. If we replaced the prompt entirely with one asking the model to “solve all of the errors that keep popping up,” the model’s score would be close to zero. It would go straight to identifying and resolving the issues one by one, instead of taking a step back to consider a rewrite.[(8)](https://every.to/p/after-automation?ref=mbi-deepdives.com#marginalia-cite-8) > > I can also trivially raise the model’s score. If I ask it to delete a lot of code and give it exact filenames that should be pared down, or if I ask it to check the results of its work to make sure the app is fully functional before it says it’s done, it will be better at the task. > > …Once the current Senior Engineer benchmark saturates, we’ll change the frame to zero it out again. > > The next benchmark will not ask only, “Can you rewrite the app?” It will ask: Can you decide when a rewrite is needed, choose the scope, preserve the right invariants, manage the migration, and judge whether the result is any good? > > As senior engineers use AI to solve these problems, the models will get better at solving them on their own. > > We will all momentarily freak out. It looks like the model can now decide whether to do a rewrite! They can do everything a senior engineer can do! > > And then a new edge will appear that was not obvious before, we will zero our benchmarks, demand will stimulate, and the process will repeat Shipper framed the dynamic as [Zeno’s paradox](https://en.wikipedia.org/wiki/Zeno%27s%5Fparadoxes?ref=mbi-deepdives.com): humanity is the tortoise with a fifty-yard head start, the model is Achilles, and every time the model reaches where we stood, we have moved because saturating a benchmark immediately prompts us to redraw it. Progress inside any fixed “frame” is exponential, but progress against the moving frontier of what humans actually value behaves like an asymptote. Shipper makes more of a philosophical point here, but it does ring true to me: > “The panic that AI generates when we observe it doing something new keeps coming back to this: We set a frame, watch the models climb it, and then confuse the frame—or whatever climbs it—with the thing itself. > > When we look at a benchmark and compare it to human abilities, we confuse the frame for the framer. The score tells us how well the model operates inside a frame we supplied; it does not tell us that the model has become us. > > That is the category error underneath the panic. We point to the latest edge we drew and say: This is us. Then, when the model climbs it, it feels like it has caught us. But it has caught the frame, not the framer. > > The mistake is wanting something concrete to hold on to. We want to say: Intelligence is this benchmark, but the moment something is concrete enough to point at, it is concrete enough to climb. > > Frames are necessary; they let us get traction on the world. But they are frozen, partial, and therefore optimizable. > > Framers are different. The framer is the one still in contact with what the frame has to discard—the whole situation as it appears to them, moment to moment. > > What is this “whole situation”? The moment you start to say what “the whole situation” contains, you have already begun another frame. You can’t say what “it” is, but it exists because you exist.” I do wonder, however, that it can be simultaneously true that humans remain in this loop and that the economics of being in the loop can deteriorate. If reviewing AI-generated pull requests becomes the core of the engineering job, the supply of people who can do that job adequately may expand faster than the demand for it, especially when the models themselves are being trained, release by release, on exactly the review-and-framing behavior we perform. To what extent most people can retain or improve their economic worth in a world where AI models continue to climb the hill is perhaps an open question. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### FICO: The Contestable Monopoly? URL: https://www.mbi-deepdives.com/fico3/ Last updated: 2026-06-09T12:41:14.000Z This is part-3 of my FICO series. In [***Part 1***](https://www.mbi-deepdives.com/fico1/), I traced the company’s history through the 2018-2025 pricing-power era and pushed back on the popular narrative that FICO’s monopoly was simply gifted to it by the 1995 GSE decision. In [***Part 2***](https://www.mbi-deepdives.com/fico2/), I went deep on the actual mechanics of how FICO gets paid in mortgage, walked through the new pricing, and explained how the score business has eyewatering margins. Today, I want to tackle behind the paywall what I think is the most important question for the stock: how contestable is this monopoly, actually? --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Veeva 1Q'26: Strong Present, Patient Future URL: https://www.mbi-deepdives.com/veev1q26/ Last updated: 2026-06-09T12:38:37.000Z ***Programming Note***: Some of you may have missed my announcement in my last [**post**](https://www.mbi-deepdives.com/abnb%5Fsoul/) that I would be taking a couple of weeks break and reached out to see if everything is okay. Thank you for your concern, and I am back to daily posting schedule. Just a small note first: I have recently moved my billing from Stripe Canada to Stripe US. Nothing should change on your end, but if you notice anything different, please let me know. I have been told payment failure spikes after such a migration, but for vast majority of you, you may not see any difference. --- Veeva had another strong quarter. Not only did they comfortably beat the high end of their revenue guide (reported $883 Mn revenue vs $858 Mn guide on the high end), they posted their highest ever GAAP operating margin exceeding 30%. Operating margin expansion continued despite the fact that gross margin declined by 212 bps YoY. While there was not much to nitpick about the last quarter, their commentary during the Q&A was less than inspiring which I will elaborate more behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!abz9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F488f768c-bcc0-4531-bb9f-dffe721a25db_1356x604.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb's Soul Search Beyond Homes URL: https://www.mbi-deepdives.com/abnb_soul/ Last updated: 2026-05-21T14:17:48.000Z **Programming Note**: I am going to take a Summer Break for a couple of weeks. I am actually traveling to Bangladesh to spend some time with family after three and half years. I expect to start posting daily on June 8th onward. --- Yesterday, Booking CEO Glenn Fogel appeared on JP Morgan Annual TMT Conference. On the same day, Brian Chesky from Airbnb released their annual summer release. Both companies are increasingly encroaching into each other’s territories, so it was interesting to hear them both on the same day. Both CEOs wanted to establish the long-term resilience of travel demand. It is a common observation among investors that travel demand is highly discretionary in nature, and hence can be vulnerable to near-term macro uncertainties. It kind of reminds me of the common trap many investors find themselves in for companies which generate revenue from advertising. Haven’t we all heard how Google and Meta’s advertising revenues are “cyclical” even though it can be really, really hard to see “cycle” if you take a look their financials? Never mind that the very nature of direct response advertising is fundamentally different than brand advertising from our grandparents days. Similarly, while travel demand can indeed be deeply affected near term by multitude of macro factors (war, recession, oil price, pandemic…take your pick), one of the higher conviction predictions you can perhaps make about the next couple of decades is that the average number of trips per thousand people globally will keep increasing. Fogel reminded that such secular tailwind is still very much in place: > “…long term, travel has always in the past, and I absolutely believe always in the future, will be a growth industry. No matter how you want to measure it, you can look back over a number of implements, you can go back and just look at any statistics about total spend on travel. It has exceeded global GDP by 1% to 2% for a very, very, very long time. > > And it’s obvious to see why. As people get wealthier, they generally want to travel more. You have the basics of people who are too poor to travel, enter a lower middle class and now can afford to travel. About half the people on this earth. So you’re talking about 4 billion people cannot afford to travel. Those people are slowly becoming wealthier, becoming able to travel. So that’s an absolute tailwind for travel. > > Then you get into our business where we do stuff digitally. And it’s hard to measure, but we’ll pick round numbers. Maybe 1/3 of the people do not buy their travel digitally. So that’s another tailwind. Those people who do it non-digitally, they will die, and then the younger people will come in, and they will be able to -- they’ll do their travel, and that will be digital. So that’s another tailwind for our business. > > I absolutely believe and once you have established your basic needs, what are the things that give you the most enjoyment in life? How many people say, “Well, I’m sick of traveling, I don’t want to go anywhere. I just want to stick at home.” Nobody practically does that. Everybody wants to travel more. So definite tailwind for us. For us, the bigger issue is not the secular industry, which I know, it’s how do we continue to gain share, how do we continue to get more than other ways people can do their travel digitally. And that is where we are focused on. While very few people are skeptical about travel’s long-term appeal, there is usually more debate around which companies within travel can remain relevant for decades to come. OTAs are particularly under the scanner with AI potentially re-writing demand routing system. Fogel emphasized the core tenet of Booking: “in God we trust, everybody else brings data”; he will try everything under the sun and just double down on whatever the data tells him works the best. Chesky, on the other hand, seems to trust someone else other than just “God”: himself! It’s hard to blame someone who built a generational consumer internet company such as Airbnb in his 20s to trust his intuitions. But unlike Fogel, Chesky basically needs to be slapped by data to force him to change his mind which at times makes Airbnb look a bit sluggish for a founder led company. During the [summer release](https://news.airbnb.com/airbnb-2026-summer-release/?ref=mbi-deepdives.com), Chesky revealed quite a few things on which he perhaps begrudgingly needed to change his mind. One of them is “Landmarks”. Back in 2020 right after going public, Chesky had this to say about “landmarks” Take his comments about Landmarks on Airbnb Experiences from 4Q’20 call: > “I think as the world starts opening back up, I think we're very bullish on experiences over the coming years. Because when people travel, they're going to want to do something interesting. And I don't think they're all going to desire to go back to getting on double decker buses and waiting in line in crowded lobbies or landmarks.” Given this intuition, Airbnb focused on original and more quirky experiences rather than landmarks experiences such as Eiffel tower in Paris or Taj mahal in India. Thankfully, over time, he changed his tone and realized someone who’s visiting Paris for the first time will certainly want to “experience” Eiffel tower no matter how banal it may seem to someone who went to Paris a dozen times. So, yesterday Airbnb revealed that based on their survey they came to know that more than 75% of travelers want to visit a landmark when they travel to a new city. My guess is Airbnb needed to do that survey to convince Chesky that Airbnb must scale their supply in landmarks if they ever hope to scale that business. Airbnb now has more than 3,000 landmark experiences on their platform. Airbnb also launched several new categories of “Services” yesterday: groceries, airport pickups, luggage storage, and car rentals. ![](https://substackcdn.com/image/fetch/$s_!RUx4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5aa98e27-4077-434e-b8e3-7b7860089693_1606x510.png) Source: Airbnb Airbnb typically does two product release per year. Chesky mentioned that later in 2026, Airbnb expects to launch services such as Day passes at local gyms, at-home pet services etc. Interestingly, instead of building their own supply (an approach they have taken in experiences), they’re launching these services by partnering with existing players in those categories: “Instacart” for groceries, “Welcome Pickups” for airport pickups, and “Bounce” for luggage storage. So this is a classic asset-light, take-rate aggregation model Chesky is layering a services marketplace on top of the core stays business rather than building vertical supply. If Airbnb ever hopes to scale these businesses, I do think that is the right approach. ![](https://substackcdn.com/image/fetch/$s_!oFsT!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb39081bc-0a32-43f7-b00f-e326e2014e3d_1147x741.png) Source: Airbnb While Airbnb didn’t launch any loyalty program yet, the sketch of such a potential program is gradually starting to take shape. When Airbnb discussed their ramp up in getting more boutique hotels on Airbnb, the screenshot showed guests booking hotels will receive “Airbnb credit”. ![](https://substackcdn.com/image/fetch/$s_!lGdW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbd678de7-4c63-453f-9796-2553d88bfb1a_472x972.png) Source: Airbnb Similarly, when they were showing their newly re-designed home page, it included things like “Book a home and get $100 off Experiences” ![](https://substackcdn.com/image/fetch/$s_!njMb!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F68462e8b-677a-473f-a385-dc2b10c111f2_528x1021.png) Source: Airbnb Chesky really wants “Airbnb” brand to mean something much more than homes. In an interview with [WSJ](https://www.wsj.com/business/hospitality/how-airbnb-ceo-brian-chesky-learned-to-stop-hating-hotels-89f4a281?st=6jKs7M&ref=mbi-deepdives.com) yesterday, Chesky was quite explicit about it. From the interview: > “I want the atomic unit of Airbnb, the center of Airbnb, not to be homes, but to be people. > > I want us to be a community, and around the community, I’d like you to be able to get a home, an experience, a service, a hotel, and then 10 or 20 other things that you can offer. And our app will become an agent.” His focus on “people” i.e. guests/customers and looking at all the services and cross-discounts across categories makes me think Airbnb is likely to launch a subscription tier perhaps next year once they aggregate a good chunk of services categories. Chesky has been waxing praises on Amazon Prime for a while now, especially how there is no such thing as Amazon Prime for services. Airbnb's structural vulnerability is that travel is a low-frequency, high-deliberation purchase where the median Airbnb guest in the future may be willing to search across Google, direct sites, or AI chatbots and Airbnb has no mechanism today to collapse that consideration set in its favor unless the trip itself makes Airbnb more of an obvious choice (e.g. group travel, longer stay, areas where there is no hotel available etc.) . A paid membership is perhaps the most effective tool for solving this problem and the Services layer may be the lynchpin to make this work. Imagine something like “Airbnb One”, priced at $99/year (or whatever), which gives a $50 stay credit applied automatically to the subscriber's first booking of the year. On top of this, the subscriber can get an unlimited 15% discount on Services and Experiences (chefs, cleanings, photographers, training sessions, local experiences etc), capped at **$20 per transaction but uncapped in frequency**, structured to nudge subscribers toward many small redemptions across the year rather than one large one. Airbnb’s goal shouldn’t be to make money on that subscription program, rather create a platform by aggregating demand and integrating numerous services so that Airbnb can graduate from being a low frequency, high deliberation app to higher frequency, lower deliberation app. If they can build something like that, it may be much more valuable over time even if the unit economics of subscription program itself merely break evens (or incurs losses in initial years). To be clear, as of today, Airbnb Experiences and Services “fair value” is likely closer to zero since Airbnb hasn’t quite delivered on being something beyond the core homes business yet. The stock also appropriately values the company on the core accommodation business, but it is an option intriguing enough to keep in mind if Airbnb ever gets closer to product market fir beyond homes. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### FICO: Deconstructing the Monopoly Margins URL: https://www.mbi-deepdives.com/fico2/ Last updated: 2026-05-20T15:01:03.000Z **Programming Note**: This is **part-2** of my FICO series.In[Part 1](https://www.mbi-deepdives.com/fico1/), I covered FICO’s history through the 2018-2025 pricing-power era, where the score business finally started capturing the rents it had been giving away to the credit bureaus for three decades. In today’s post, my focus is on understanding the business itself in a bit more granular level. --- FICO reports its results in two segments: **Scores** and **Software**. The Scores segment is, of course, the legendary monopoly i.e. the FICO Score business that drives the stock, and well…the antitrust lawsuits The Software segment is essentially everything else FICO does such as a decision management platform suite built originally on top of the HNC Software acquisition in [2002](https://investors.fico.com/news-releases/news-release-details/fair-isaac-completes-merger-hnc-software-combination-talent-and?ref=mbi-deepdives.com). Both segments serve overlapping financial institution customers, but the economics are so different that you really should think of FICO as two businesses stapled together. In FY’25, Scores generated $1.2 Billion in revenue at operating margins of \~88%, while Software generated $822 Million at far more modest operating margin of…30%. Given the margin differential, even though \~40% of FICO’s overall revenue comes from the software segment, only \~20% of its operating income is driven by software segment. ![](https://substackcdn.com/image/fetch/$s_!3R8F!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F014985f1-b3e1-45af-95de-78664c07b562_648x159.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let’s start with Scores, because that’s clearly where the action is. The **Scores segment** splits cleanly into **B2B Scores** (FICO sells its score wholesale to lenders, via the credit bureaus) and **B2C Scores** (FICO sells directly to consumers through myFICO.com and a handful of partner channels). B2B is \~80% of segment revenue and effectively all of the profit. Back in 2019, B2B used to be \~70% of Scores revenue. Given B2B is the faster and almost all the profit of Score segment, I’ll focus more on B2B Scores. ![](https://substackcdn.com/image/fetch/$s_!84ys!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc2f58e92-31b9-4b9e-93f0-20786a65ba32_1042x100.png) Within B2B, FICO breaks the business down by what kind of lending decision the score is being used for. The buckets are mortgage originations, auto originations, and credit card, personal loan, and other originations. Mortgage has become by far the most important, both because the per-score price is highest, and because the strategic conversation about FICO’s pricing power is almost entirely a conversation about mortgage. The rest of this post will be behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Floor & Decor's Cyclical Malaise vs Structural Moat URL: https://www.mbi-deepdives.com/fnd_cycle/ Last updated: 2026-05-19T14:00:41.000Z One of the ironies of operating in a cyclical end market is that management can sound like genius to somewhat hopeless depending on the stage of the cycle they are in. And if you are caught in the wrong side of the cycle for long enough, investors often start questioning whether this is really just a cyclical downturn or symptom of a rather deep malaise. Of course, if you operate on the other extreme for slightly longer than what a typical cycle predicts, all sorts of secular bull narrative comes at your doorstep. Floor & Decor (FND) is currently at the wrong side of the cycle and given four consecutive years of same store sales decline (assuming negative SSS in 2026), even the more “settled” bull thesis is under the scanner these days. Even though it was quite consensus not so long ago that FND is a secular share gainer in US flooring industry, if you listen to their Q1 call you will find analysts asking management why they are losing share. Management disagreed that they are losing share. I too received a couple of questions from readers also wondering why FND is in such a persistent negative SSS trajectory while some of the other housing related companies seem to be holding up better. It can be tricky to compare and contrast FND with something like Sherwin Williams (SHW), for example, since even though they can be “housing” related, their demand drivers are substantially different. Ultimately, FND is not in the business of selling paints. What it does sell makes FND currently part of a bad neighborhood. Of course, while looking at market share data, it is more useful if you start from what exactly is going on in their end markets. Flooring Sales Weekly [reports](https://bt.e-ditionsbyfry.com/publication/?i=849944&p=6&view=issueViewer&ref=mbi-deepdives.com) last two decades of US Flooring sales data and based on the data, we can see the overall US flooring revenue itself declined by 10% in 2024 from its peak in 2022\. We don’t have 2025 data yet, but my guess is it declined in 2025 as well. ![](https://substackcdn.com/image/fetch/$s_!hQfr!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed2f9ab8-fbcf-4f8c-9754-cda7ad316f19_780x571.png) Source: [Flooring Sales Weekly](https://bt.e-ditionsbyfry.com/publication/?i=849944&p=6&view=issueViewer&ref=mbi-deepdives.com) Based on this US flooring industry sales data, we can calculate market share of Home Depot (HD), Lowe’s (LOW), FND, and the rest of the industry. As you can see below, FND’s market share increased from 7.5% in 2019 to 13.4% in 2024\. This share gain appears to have come from both the big box retailers and the independents. While HD’s share somewhat stabilized post-2022, LOW and other independent stores continue to donate share to FND. ![](https://substackcdn.com/image/fetch/$s_!D5RF!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F14c3b6fd-11f7-41b0-b118-6bd4ace86a1b_1215x666.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) To be fair, FND critics are more focused on per store metrics than overall market share since they believe FND is gaining share primarily via opening new stores. If the new stores cannibalize too much, FND’s economics can deteriorate even if they gain market share. Some cannibalization certainly occurs as FND has broadened its store base from 160 in 2021 to 270 in 2025\. However, the problem with store level metrics is it often masks quite a few important nuances. For example, a common observation is even though FND’s sales per store used to be 3.6x of HD and LOW’s flooring revenue combined, that ratio has been going down since then, indicating deteriorating economics on a per store level. However, if you go back a little further and notice revenue per store back in 2014-15 when FND had only one-fifth of its current warehouse base, FND’s revenue per store looks much more stable. ![](https://substackcdn.com/image/fetch/$s_!FuzL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa6036495-1d27-4c22-89b6-db83addd7beb_1398x240.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) In fact, I think one of the key reasons I think 2014-15 level revenue per store for FND is more indicative than 2021-22 is the evolution of FND’s warehouse size. Back in 2015-16, average FND’s warehouse size was 78.9k sqft which is actually closer to their current average warehouse size in sqft. There was an upward trend in warehouse during 2021-22 period which inflated their sales per store. But in recent years, FND started opening smaller warehouse formats. For example, FND is expected to open 20 new stores mostly in tier 1 and tier 2 markets in 2026 and the average size of these new stores is expected to be only 55k sqft which is noticeably lower than average warehouse size. Why is FND focusing on smaller warehouse formats all on a sudden? Management explained in 1Q’26 call: > “the reality is the 75,000 to 80,000 square foot box isn’t available in those markets. And if it is available, it’s very, very expensive.” Given these nuances, it can be quite tricky to look at HD and LOW’s per store level metrics and form any conclusion on FND. ![](https://substackcdn.com/image/fetch/$s_!Joks!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5de66d27-bfba-4a0c-b7f4-452f8f711120_958x496.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Ultimately, big box retailers such as HD and LOW are structurally unable to respond effectively to FND, some of which I have covered in my [**Deep Dive**](https://www.mbi-deepdives.com/fnd/) in the past. However, while discussing FND with a shareholder, I came to appreciate that it may be even harder than I assumed for HD and LOW to compete against FND. The investor explained to me that flooring has unfavorable physical economics for shipping and handling: it's heavy and takes up significant volume (cube) relative to its dollar value. This unfavorable ratio makes flooring supply chains genuinely difficult, and working-capital intensive to operate. It's also why customers prefer close product proximity: shipping flooring long distances to a consumer is expensive, and consumers themselves are making multiple trips, hauling samples, etc. The value-to-cube problem creates a specialized supply chain requirement that rewards focus and scale within flooring specifically and punishes generalists. HD/LOW's supply chains are optimized for a generalist big-box assortment (paint, lumber, tools, appliances, garden, plumbing, etc.) where the average product has very different weight/cube/value characteristics. Their distribution network isn't specialized for the heavy, bulky, lower-value-density nature of flooring. They can move flooring through it, but not as efficiently as a flooring-dedicated network. So the value-to-cube argument is really a structural argument about why flooring rewards specialization. A generalist like HD/LOW can't justify the parallel build-out for what is 3-4% of their sales, so they remain at a structural disadvantage that compounds with their supplier-side disadvantage (forced reliance on Mohawk/Shaw etc.) and their retail-format disadvantage (3-7k sq/ft departments). These advantages show up in FND’s continued market share gain in overall flooring industry, but that story is entirely masked by the current [**cyclical malaise**](https://www.mbi-deepdives.com/unprecedented-lock-in/). --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Honeymoon Phase of Capacity URL: https://www.mbi-deepdives.com/honeymoon/ Last updated: 2026-05-18T14:25:01.000Z Although I am yet to write any Deep Dive on any neocloud, I still like to pay close attention to what they are saying during their earnings calls. Both CoreWeave and Nebius had some interesting nuggets in their 1Q’26 call. Let’s start with CoreWeave. Just like the hyperscalers, CoreWeave’s backlog continues to grow at a rapid pace. Their backlog now reached almost $100 Billion in 1Q’26 (vs just $25.9 Billion in 1Q’25). While their backlog likely remains concentrated, they did mention that they now have 10 customers committed to spending at least $1 Billion with CoreWeave. CoreWeave indicated that “materially in excess of 50%” of their compute is currently being used for inference. They slightly raised the low end of the capex guide from $30-35 Billion to $31-35 Billion for 2026\. The increase on the low end is primarily due to memory price increases. What is Nebius saying? Nebius raised their capex guide too, but unlike CoreWeave, they mentioned the impetus for capex increase was primarily higher demand. From the call: > Demand is high. Everything we build with is sold. That is what is driving us to build more and to raise our 2026 CapEx guidance to between $20 billion and $25 billion, which is up from our prior range of $16 billion to $20 billion. Perhaps the more interesting tidbit from the Nebius call is how Nebius management elaborated on their deal with Meta Platforms (emphasis mine): > “First, I want to say that we love working with Meta, and we’re excited that they chose to buy more capacity from us. This expanded new agreement is to make sure that we all understand this, a 5-year contract for a total of $27 billion, and it is structured in 2 parts. First, there’s a $12 billion commitment to dedicated compute capacity with delivery starting in early ‘27\. And then second, as you pointed out, **there’s another $15 billion of additional capacity that we, at our discretion, can either allocate to Meta or sell to our AI cloud customers as it comes online for the duration of the 5-year contract.** > > Let me explain this in a bit more detail. Meta is committed to buy up to $15 billion of any capacity in these clusters at our option during the entire 5-year contract. This commitment will likely allow us to finance the clusters with asset-based -- asset-backed financing **at attractive terms**, while selling them to, as I think you pointed out, to our AI cloud customers at potentially higher market prices. > > The unique combination of being able to sell at a premium, along with the commitment by Meta to purchase any capacity during the contract should provide us with higher margins, less risk and more visibility in our revenue. **If the market remains strong, we should generate more than $27 billion in revenue from this great agreement**.” Meta has effectively written Nebius a put on $15 Billion of capacity. If the market stays hot and Nebius can sell to higher-margin customers, Meta may get none of that $15 Billion capacity. If the market softens and Nebius can't place it at premium prices, Nebius puts it to Meta at the contracted price. Now what is Meta’s incentive for being so charitable here? We cannot be sure for Meta’s motivations but we can make some pretty good guesses. The most likely explanation is Meta is getting the first $12 Billion tranche of capacity at a noticeably below current market rates. But the overall structure of the deal makes me think Meta may be fairly confident of generating decent return on the additional $15 Billion capacity at current contracted rates in case Nebius is unable to sell capacity to customers at more attractive prices. Overall, both companies sound as good as you would expect in an environment compute demand continues to outstrip supply. The real question, of course, is whether these business will prove to be durable or are these just “stop-gap” solutions for the major hyperscalers. Gavin Baker in his recent Sohn interview made the case that some of these neoclouds will be durable business model. Here’s an [excerpt](https://www.youtube.com/watch?v=2Ryr95iiYNk&ref=mbi-deepdives.com) from Gavin Baker’s interview: > …the hyperscalers for a long time were stuck in a cost mentality. The hyperscalers were competing with people running these Formula 1 cars and they were like, you know, doing like overnight shifts and 18-wheelers trying to stay awake, deliver the lowest cost. And that's not what AI is about. Now, I thin they're making this mental and cultural shift and they've made it, but I think some of these Neoclouds have a very durable business model. Baker also pointed out that Amazon’s Trainium is likely to be very underestimated by the market. As I have alluded before, in a compute constrained environment, the market signals can get so weak and hazy that makes it very challenging to assess the longer term competitive dynamics. In current environment, everyone’s capacity is getting sold out and if you have a chip, give some of the hyperscalers a call because they may buy whatever you’re selling. I have laid out more qualitative concerns (see [**here**](https://www.mbi-deepdives.com/aws/), and [**here**](https://www.mbi-deepdives.com/aws2/)) before about the long-term economics of 3P hyperscalers. I will highlight a few data points behind the paywall that continue to keep me unenthusiastic about 3P hyperscaler business model even though investors are currently showing zero inclination of concern right now. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Best Yardstick URL: https://www.mbi-deepdives.com/yardstick/ Last updated: 2026-05-17T15:33:10.000Z After a very good 2023 and 2024, I [**wrote**](https://www.mbi-deepdives.com/2024/) the following in my 2024 annual letter: > “given the somewhat elevated valuation multiples among stocks I have looked at, I think I wouldn't be too upset if I underperform the S&P 500 next year **in case** we see another >25% performance year for the index in 2025\. There are times when it may make sense to underperform the index for you to be able to outperform over a longer period of time.” Since then, MBI portfolio has been basically flat whereas index has generated +30% return in the meantime. So, I certainly got my wish granted even though I don’t seem to feel great about it today! If Buffett read this earlier sentence, I’m not sure he would be pleased about such sentiment. Back in 1997 shareholder [letter](https://www.berkshirehathaway.com/letters/1997.html?ref=mbi-deepdives.com):, he actually took a dig at such a strange behavior among investors: > “A short quiz: If you plan to eat hamburgers throughout your life and are not a cattle producer, should you wish for higher or lower prices for beef? Likewise, if you are going to buy a car from time to time but are not an auto manufacturer, should you prefer higher or lower car prices? These questions, of course, answer themselves. > > But now for the final exam: If you expect to be a net saver during the next five years, should you hope for a higher or lower stock market during that period? Many investors get this one wrong. Even though they are going to be net buyers of stocks for many years to come, they are elated when stock prices rise and depressed when they fall. In effect, they rejoice because prices have risen for the “hamburgers” they will soon be buying. This reaction makes no sense. Only those who will be sellers of equities in the near future should be happy at seeing stocks rise. Prospective purchasers should much prefer sinking prices.” Berkshire did just fine in 1998, but started materially underperforming the index in 1999 just when the internet mania went overdrive. ![chart](https://substackcdn.com/image/fetch/$s_!wAk3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6cd2c88a-1209-430f-8bd6-db6619fab568_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) After such underperformance in 1999, Buffett himself was not as zen as he sounded in his 1997 shareholder letter. I went back to his 1999 shareholder letter and found Buffett was admonishing himself for trailing the benchmark. From the 1999 letter (emphasis mine): > “The numbers on the facing page show just how poor our 1999 record was. We had the worst absolute performance of my tenure and, compared to the S&P, the worst relative performance as well. **Relative results are what concern us: Over time, bad relative numbers will produce unsatisfactory absolute results.** > > Even Inspector Clouseau could find last year’s guilty party: your Chairman. My performance reminds me of the quarterback whose report card showed four Fs and a D but who nonetheless had an understanding coach. “Son,” he drawled, “I think you’re spending too much time on that one subject.” > > My “one subject” is capital allocation, and my grade for 1999 most assuredly is a D. What most hurt us during the year was the inferior performance of Berkshire’s equity portfolio -- and responsibility for that portfolio, leaving aside the small piece of it run by Lou Simpson of GEICO, is entirely mine. **Several of our largest investees badly lagged the market in 1999 because they’ve had disappointing operating results**. We still like these businesses and are content to have major investments in them. But their stumbles damaged our performance last year, and **it’s no sure thing that they will quickly regain their stride**.” Reading such letters is a good reminder why Buffett is a special investor. The more mere mortals would take 1999 as an opportunity to point out the general excesses in the market and explain away their underperformance. Buffett, of course, also thought the investors went a little crazy with the internet stocks, but he started the letter not by pointing fingers at fellow investors, rather at himself. Anyways, what was particularly interesting is that even the GOAT himself couldn’t be as self-reassuring at the face of trailing benchmark return! It turns out the pressure of the benchmark comes even for the very best. So, I guess I shouldn’t be surprised that I don’t feel great about trailing S&P 500 in the last year and half even though I am hoping to be net buyer of stocks for years to come. In the stock market, there may be a lot of random walk along the way, but having a benchmark is indeed a decent barometer of how you are doing. As an investor who is not managing other people’s money, you could argue I have the luxury of being rather nonchalant even if I underperform the index for a few years. However, I suspect too much nonchalance is not healthy for anyone’s long-term track record either. One perhaps needs a healthy balance of nonchalance and self-introspection by looking at the mirror every once in a while to assess whether something needs to change. But when should such serious introspection begin? While going through Buffett’s writing during the late 90s yesterday, I noticed that in his nearly six decades at the helm of Berkshire, Buffett had to endure three consecutive years of underperformance **only once**: 2003-2005 period. I’ll take that as a yardstick and if MBI portfolio happens to lag the index for three consecutive years, I should probably get out of my cocoon of nonchalance. A reader recently messaged me: “how do you maintain such a highly concentrated portfolio when so many names seem to be going parabolic just outside your core holdings?” One of the challenges of fintwit is you can constantly feel you’re not doing as well as others even when you’re outperforming the index. The reality is, in fact, decidedly the opposite. If you happen to be outperforming the index over 5-10 year period, you are almost certainly ahead of **SUPERMAJORITY** of investors. Supermajority, not just majority. A great disservice that the benchmark’s performance does is it makes almost everyone feel that it is the “average” performance among active investors. Of course, you can never know what the twitter anons actually generate in return over any long period of time (since they’ll just go silent whenever they have a terrible year or even worse, they’ll simply lie through their teeth and claim how they sold at the peak…sure!), but we do have high quality audited data how professional investors do over the long term and such audited data should humble any active investor out there. However, many investors look at these data, and there is a strong urge to “explain” away professional investors’ sustained underperformance over any long period: “oh, these guys are index huggers”, “oh, these guys cannot structurally fish at the right areas”, “oh, these guys are dumb” etc etc. ![](https://substackcdn.com/image/fetch/$s_!00zv!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a93cfb8-5b53-47a2-80f5-66f4675b74d2_1466x1082.jpeg) Source: [Aswath Damodaran](https://aswathdamodaran.substack.com/p/finding-your-investment-lodestar) As you can perhaps tell, I do not think any of those are good explanations. In fact, it is “by design” that supermajority of professional investors fail to beat the benchmark. Let me mention a story Howard Marks [shared](https://www.oaktreecapital.com/insights/memo/fewer-losers-or-more-winners?ref=mbi-deepdives.com) to make my point here: > “My memos got their start in October 1990, inspired by an interesting juxtaposition between two events. One was a dinner in Minneapolis with David VanBenschoten, who was the head of the General Mills pension fund. Dave told me that, in his 14 years in the job, the fund’s equity return had never ranked above the 27th percentile of the pension fund universe or below the 47th percentile. And where did those solidly second-quartile annual returns place the fund for the 14 years overall? Fourth percentile! I was wowed. It turns out that most investors aiming for top-decile performance eventually shoot themselves in the foot, but Dave never did.” I actually shared this story before on twitter and I received a DM from a reader telling me that they do not believe such a thing is mathematically possible. In case I receive similar response again, I have preemptively asked Claude to create a visualization to explain this point. Claude even gave it a name “The Steady Eddie Paradox”. Play with it if you’re incredulous too! # The Steady Eddie Paradox 30 pension funds. 14 years. Watch what happens to the one that's never above the 27th percentile or below the 47th in any single year. "In his 14 years in the job, the fund's equity return had never ranked above the 27th percentile of the pension fund universe or below the 47th percentile. And where did those solidly second-quartile annual returns place the fund for the 14 years overall? Fourth percentile." — Howard Marks, recalling David VanBenschoten of General Mills Annual Rank (This Year) — Press Play to begin Cumulative Rank (To Date) — All funds start at $100 Steady Eddie The other 29 funds ▶ Play ↺ Reset 🎲 New Simulation Year 0 of 14 All 30 funds start with $100\. Eddie's strategy: never aim for the top — just don't blow up. Press **Play** to see what 14 years of that discipline produces. Tip: hit **New Simulation** to roll a fresh 14-year run. Eddie almost always lands in the top few — but not by being a hero in any single year. I understand the difficulty of active investing well enough to know that supermajority of investors will not beat the index, including potentially yours truly. If you want to see whether you’re doing well enough, you just need to pay attention to the index. What you definitely don’t want to pay too much attention is the random people on the internet who are somehow posting market beating returns year after year. If you happen to be ahead of the index after 5-10 years of investing, good for you as you’re almost certainly ahead of supermajority of active investors and you may want to stay focused to remain ahead. If you’re lagging the index for years, perhaps it’s time for some introspection and do some soul searching. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Figma 1Q'26: "Not Dead" URL: https://www.mbi-deepdives.com/fig1q26/ Last updated: 2026-05-16T14:01:46.000Z “Quick update: not dead”…that’s what Dylan Field [tweeted](https://x.com/zoink/status/2055018215152234926?ref=mbi-deepdives.com) after releasing Figma’s 1Q’26 earnings! For the second consecutive quarters, Figma’s growth re-accelerated, growing \~**46% in 1Q’26**. ![](https://substackcdn.com/image/fetch/$s_!Skjd!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F06793ae3-a5e7-4a67-a759-029fd6a99ddc_936x515.jpeg) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) These sequential quarters of acceleration was due to multiple factors: the pricing tailwind from the March 2025 hikes layered on top of seat expansion, deepening international penetration, and now (finally) AI revenue. Figma’s CFO noted that the pricing tailwind from last year is starting to wane, but the newer levers are picking up the slack. The other topline metric I always want to see is Net Dollar Retention. Figma’s NDR for customers with >$10k in ARR **jumped 3 percentage points to 139% , the highest reading since 2Q’23**. Customer count growth was similarly super impressive. Paid customers with >$10k ARR **grew by 1,357 in the quarter which was the largest sequential add in Figma’s history** (vs. 951 in 4Q’25 and 1,004 in 3Q’25). The $100k+ ARR cohort added 120 customers which was a step down from 3Q’25 and 4Q’25’s 143, but still comfortably ahead of all the quarters before that. Overall paid customer count was up 54% YoY and reached 690k in 1Q’26 which means only 2.2% of Figma’s paying customers spend >$10k ARR. Clearly, Figma has a pretty deep funnel of paying customers. ![](https://substackcdn.com/image/fetch/$s_!oxht!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa3c7ee3d-601f-4277-a696-cfd14da8d2a9_1305x159.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Given their hypergrowth compared to Adobe’s more steady growth in recent years, Figma’s revenue as a percentage of Adobe’s Digital Media business went from just 4.2% in 1Q’24 to 7.1% in 1Q’26\. More importantly, Figma’s incremental revenue growth as percentage of Adobe’s digital media incremental revenue growth was almost **20%** in 1Q’26! ![](https://substackcdn.com/image/fetch/$s_!mMki!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F70f86013-3401-4aae-bd5b-25ba2114300a_927x534.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As you can see, Figma’s topline is not only “not dead” but very much alive and kicking. But how is it handling the constant drumbeat of AI threats? And how about margins? I will expand on those topics behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### FICO: Pricing the Standard URL: https://www.mbi-deepdives.com/fico1/ Last updated: 2026-05-15T13:53:00.000Z ***Programming note***: As I have mentioned earlier, I am making some changes in how I publish my Deep Dives. Instead of publishing one big Deep Dive, I will do a series of posts on a particular company over multiple weeks. Today I am publishing first of such multiple posts on FICO series. --- Any modern financial system operates on a foundation of trust, but historically, quantifying that trust was a highly subjective and presumably flawed endeavor. Prior to the widespread adoption of statistical credit scoring, lending decisions were governed almost entirely by qualitative judgments. These rudimentary frameworks relied heavily on the personal discretion of local loan officers who evaluated applicants based on isolated knock-out criteria or, more nefariously, subjective character judgments that routinely penalized borrowers based on gender, marital status, race, or geographic location. If a borrower failed to meet a single arbitrary criterion, their application was denied without any holistic consideration of compensating positive factors. The solution to this systemic flaw had been conceptualized back in 1950s. In 1956, an engineer named William Fair and a mathematician named Earl Isaac pooled a modest investment of $400 to found Fair Isaac Company or **FICO** . The two founders had previously met at the Stanford Research Institute, where they applied operations research and statistical analysis to complex military problems. Seeking a civilian application for their highly specialized expertise, they pivoted to consumer finance. By 1958, they had developed their first primitive credit scoring algorithm, introducing the “radical” notion that a borrower's past financial behavior could statistically and reliably predict their future likelihood of repayment. The legal foundation for FICO’s eventual ubiquity was laid in the 1970s. Congress enacted the Equal Credit Opportunity Act ([ECOA](https://en.wikipedia.org/wiki/Equal%5FCredit%5FOpportunity%5FAct?ref=mbi-deepdives.com)) in 1974, initially prohibiting credit discrimination on the basis of sex and marital status. A 1976 [amendment](https://www.federalregister.gov/documents/2026/04/22/2026-07804/equal-credit-opportunity-act-regulation-b?ref=mbi-deepdives.com) expanded the protected classes to race, color, religion, national origin, age, and receipt of public assistance. ECOA effectively gave a green light to statistical credit scoring, but only if any such system was “[empirically derived” and “demonstrably and statistically sound](https://cei.org/studies/the-cfpb-and-the-equal-credit-opportunity-act/?ref=mbi-deepdives.com)”. That single phrase transformed FICO’s quirky math-consulting practice into a legally privileged way of making lending decisions. A properly built statistical model became an affirmative defense against discrimination claims while a loan officer’s gut feel was a liability waiting to happen. With the emergence of credit score, ECOA led to a cascade of improvements in credit access. Despite the regulatory tailwind, FICO’s path to scale was rather slow. Through the 1970s and into the early 1980s, FICO’s bread and butter was actually custom, bespoke scoring models built for one client at a time e.g. a department-store scorecard for one retailer, a credit-card scorecard for one bank etc. The economics was lumpy; in 1981 the company posted \~[$6 Million](https://www.fundinguniverse.com/company-histories/fair-isaac-and-company-history/?ref=mbi-deepdives.com) of revenue but still booked a loss. By the time of its IPO in 1987, revenue had reached $18 Million with \~$2 Million of profit. The truly category-defining move came two years after the IPO. In [1989](https://www.marketplace.org/episode/2022/07/05/the-history-of-credit-score-algorithms-and-how-they-became-the-lender-standard?ref=mbi-deepdives.com), FICO released the first general-purpose FICO score: a single algorithm any lender could buy off the shelf rather than commissioning a custom-built scorecard. This algorithm synthesized the raw, highly complex data housed within credit-bureau files into a single, easily digestible three-digit number ranging from 300 to 850, providing a standardized measure of default risk across the entire industry. A classic FICO score mathematically weighs five core components of a consumer’s credit profile, prioritizing payment history and total credit utilization heavily above length of credit history, mix of credit types, and recent inquiries. By [1991](https://www.fico.com/blogs/fico-celebrates-30th-anniversary-fico-score?ref=mbi-deepdives.com), FICO had made its credit-bureau risk scores available at all three major U.S. consumer reporting agencies or CRAs (Equifax, Experian, and TransUnion) so that a lender could pull the FICO scores from any bureau. ![How FICO Scores are calculated](https://substackcdn.com/image/fetch/$s_!xL_d!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6d36bd96-eddc-4e10-932d-c7ffdba74578_2550x2469.png "How FICO Scores are calculated") Source: [myFICO](https://www.myfico.com/credit-education/whats-in-your-credit-score?ref=mbi-deepdives.com) I went through FICO’s financial statements since 1991 (I couldn’t find their 1989-90 financials on their website). I have [noticed](https://www.youtube.com/watch?v=PL5bLlPAoOk&ref=mbi-deepdives.com) many investors tie FICO’s utter dominance in the 90s to be direct consequence of the GSE decision in 1995\. Bristlemoon Capital made a compelling argument that FICO’s monopoly didn’t quite derive from the blessings of the US government. From Bristlemoon Capital’s [piece](https://www.bristlemoonresearch.com/p/fair-isaac-corporation-fico-pricing?utm%5Fsource=publication-search) on FICO: > “The pivotal moment for the FICO Score came in 1995 when the GSEs Fannie Mae and Freddie Mac directed lenders to use FICO scores for all new residential mortgage applications. Notably, this was when Fannie Mae and Freddie Mac were for-profit enterprises owned by private shareholders; these corporations voluntarily adopted the FICO Score, given that lenders were already using it to evaluate credit risk in mortgage and non-mortgage markets (i.e., FICO’s monopoly in U.S. mortgages was not granted by government decree but rather guided by market forces). This cemented the FICO Score as the industry standard for U.S. residential mortgages, but lender adoption more broadly also saw the FICO Score become dominant in other verticals such as auto loans and credit cards. > > The erroneous notion that FICO is only dominant because of its government mandated use in the conforming mortgage loan market is best rebutted when we see that the majority of FICO Scores are used outside of mortgage originations. In a blog post by the President of FICO’s Scores business, Jim Wehmann revealed that **99% of FICO Scores are used outside of mortgage originations**.” Indeed, if you look at FICO’s operating performance before the 1995 GSE decision, it does seem the company didn’t really need a helping hand from the government to utterly dominate their niche. FICO’s revenue was already compounding at almost 40% annually between 1991 and 1995\. By the end of the decade, FICO was approaching $300 million revenue and $50 million operating profit even though they started the decade with just \~$30 million revenue. ![](https://substackcdn.com/image/fetch/$s_!-GlM!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fccd7b393-e6e3-4cff-a1f3-5c074e9a0909_522x286.png) Source: Company Filings, MBI Deep Dives Multiple structural forces were doing the work. First, U.S. credit-card issuance was booming, and credit-card issuers had neither the time nor the manpower to manually underwrite tens of millions of applications. Automated, score-based underwriting became the only viable workflow. Second, ECOA was now well past its effective date and was being actively enforced, putting any remaining holdouts on legal notice. Third, the 1991 availability of FICO at all three bureaus meant FICO simply was the empirically derived, demonstrably and statistically sound standard the regulators were asking for. By the time the GSEs were debating which credit score to require for mortgages, the industry had already voted with its workflow. By one [estimate](https://www.thecreditpeople.com/bureaus/was-the-fico-fair-isaac-score-first-introduced-in-1989?ref=mbi-deepdives.com), \~30% of lenders were using FICO in 1995; that share rose to over 90% by 1999. The 1995 GSE adoption, combined with FICO’s already-dominant share in credit cards and autos, set up a glorious early-2000s run for the company. Mortgage origination volumes ballooned through the housing boom, and FICO got paid per score on essentially every mortgage origination. Then in [August 2002](https://investors.fico.com/news-releases/news-release-details/fair-isaac-completes-merger-hnc-software-combination-talent-and?ref=mbi-deepdives.com) acquisition of HNC Software brought in the Falcon Fraud Manager, Capstone Decision Manager, RoamEx, and Blaze Advisor product suites under FICO’s umbrella. The HNC deal essentially built FICO’s software business outside of their traditional Scores business. However, the structural weakness of the Score business surfaced in painful fashion when the housing cycle turned. Revenue peaked in 2005 at almost $800 million and operating income peaked the same year when it was approaching $200 million. From there, it was a long, grinding round-trip. FICO didn’t exceed its 2005 revenue level **FOR ALMOST A DECADE** and operating income took **even longer**! Why was a company with \~90%+ market share so utterly hostage to the macro cycle? Because price was effectively frozen. FICO’s wholesale royalties on mortgage scores were originally set in 1989 and as FICO CEO Will Lansing not-so-fondly [reminisced](https://www.fico.com/blogs/fico-s-adoption-and-pricing-mortgage-origination-market?ref=mbi-deepdives.com), “remained at those low amounts for decades due to contractual and technical constraints. As a result, the royalty rates that FICO received from each of the CRAs were essentially flat for nearly 30 years after the FICO Score was introduced in 1989.” ![](https://substackcdn.com/image/fetch/$s_!s5_-!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34e1f758-a8f6-4690-a67f-dd719cc80e01_517x561.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Lansing is, however, determined to correct this historical “wrong”. FICO had no contractual right to push prices higher without renegotiation with the credit bureaus/CRAs. In 2012, FICO began the multi-year process of renegotiating those agreements; the new agreements eventually [gave](https://www.fico.com/blogs/fico-s-adoption-and-pricing-mortgage-origination-market?ref=mbi-deepdives.com) FICO “the right to adjust its base royalty rates for FICO Scores once per year, with advance notice to the CRAs”. With the renegotiated contracts finally in hand, FICO began raising mortgage royalties in 2018 and the impact on the income statement is hard to overstate. Revenue went from $1 Billion in 2018 to almost $2 Billion in 2025, but the incremental operating margin on these revenue was eyewatering **75%!!** Most of that operating leverage is being generated in the asset-light Scores segment where almost every incremental royalty dollar drops straight to the bottom line. ![](https://substackcdn.com/image/fetch/$s_!N2lD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5c8f187b-c935-4b23-abc4-d178a23ab484_514x256.png) Source: Company Filings, MBI Deep Dives Predictably, the stock soared as investors woke up to the reality of such an unbelievable incremental economics of a monopoly that is finally able to exercise pricing power. From the end of 2018, the stock became a 10-bagger by the end of 2024! ![chart](https://substackcdn.com/image/fetch/$s_!yRP8!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2e9eeb49-dd6a-4f39-bb96-51bd1f595467_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) The only problem is while Lansing was determined to ensure FICO gets to keep what he thinks it truly deserves, the percentage increase in prices ended up being, to say it mildly, a little…obscene. > I can’t stop laughing at the FICO management team. I’ve been thinking about it non-stop since I learned about it > > ChatGPT can’t find any examples of anyone doing something even remotely like what they did. I doubt it exists. [pic.twitter.com/7AMMung9nX](https://t.co/7AMMung9nX?ref=mbi-deepdives.com) > > — BuccoCapital Bloke (@buccocapital) [July 9, 2025](https://twitter.com/buccocapital/status/1943051614027170264?ref%5Fsrc=twsrc%5Etfw&ref=mbi-deepdives.com) As you can imagine, such price increases attracted plenty of [attention](https://x.com/pulte/status/1942589271702962447?ref=mbi-deepdives.com) in DC! Bill Pulte, Director of the Federal Housing Finance Agency (FHFA) which oversees the secondary mortgage market and regulates GSEs, appears to be very eager to introduce some competition to FICO which led to plenty of nervousness among FICO’s shareholders. ![chart](https://substackcdn.com/image/fetch/$s_!ScJT!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd4021f67-7ccc-49a6-8b16-21d5a52f862b_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) For much of FICO’s existence, the Score business had monopoly market share but virtually no monopoly pricing power; the economic rents flowed downstream to the credit bureaus and tri-merge resellers, who marked up the score on the way to the lender. As I alluded before, the 2018–2025 chapter is the story of those rents migrating back upstream to the monopoly itself. I’m only scratching the surface here, but the rest of this series will dig into more granular details. I hope to publish the next part of this series later next week. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dive* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Constellation Software 1Q’26: Leaning In Amidst the SaaSpocalypse URL: https://www.mbi-deepdives.com/csu1q26/ Last updated: 2026-05-14T13:07:31.000Z During the current “SaaSpocalypse”, Constellation Software (CSU) delivered a slightly uneven but, in important ways, also encouraging quarter. Organic growth softened and margins took the usual Q1 hit (and a bit more), but capital deployment pace (which is why anyone really owns the stock) remains a key bright spot. Maintenance and other recurring revenue, which is \~77% of total revenue, decelerated from 6% in in 4Q’25 to 4% (FXN) in 1Q’26\. Since 1Q’19, CSU’s average recurring organic growth was 4.8%, so this was a below average quarter for CSU on organic growth front. Excluding Altera, organic growth in recurring revenue was +5% (vs +6% in 4Q’25 but the same as 1Q’25). ![](https://substackcdn.com/image/fetch/$s_!G1XG!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F67c17db6-af6c-456c-84b7-ad32cd927d54_1378x672.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) This is the part where 5-6 years ago, an investor might shrug. But today when much of the bear case on CSU is some flavor of “AI is going to eat your shitty software,” even a slightly below average number invites scrutiny and discomfort. To be clear, CEO Mark Miller also wants CSU to post better organic growth. From the call: > *“*I continue to pressure our businesses on organic growth generally…I really would like to see them doing a better job on organic growth across the board, and I think this is an opportunity to push them harder on that with the advent of some tools to allow you to do things a little bit faster and a little bit better.*”* CFO Jamal Baksh was more measured and thought the internal AI tooling will eventually show up in revenue, but it will take “some time”. Miller also reminded the typical reasons for how CSU loses customers and Why AI should lead to additional opportunities. From the call (emphasis mine): > *“…*the way we lose customers is, they get essentially go out of business, which happens, you can’t do much about that. They’re acquired by other customers, particularly larger customers. That’s another way of losing. You can’t do much about that. Other than you hope you -- the other customer that buys them is your customer. **Pricing is the third and pricing, rarely, we lose customers on pricing because the switching is painful for customers** and it’s working and they’re using it and retraining all their users and adapting the interfaces to make it work and make it harder. Where you lose customers is when the competitor can provide something much different than you can provide that the customer really needs. And that’s where I always worry the most, just generically forgetting about AI. So that’s kind of how I sort of look at it. > > Now as far as these tools, we’re all using them internally. And I’ve been fortunate enough to travel around. I think each week, I’ve met with a different group of Constellation in different location and just see what they’re using and what they’re doing, and they’re adapting to these tools, using them internally to help them run their portfolios, their businesses better. But they’re also using -- **to try to develop more software to actually expand our presence inside of customers more so than defend our presence is kind of the thinking**, but it’s going to depend on our business. So **I look at these tools as an opportunity to do more for customers, not do what we currently do more efficiently, although that will happen in some cases.** *.”* These arguments will sound lot more credible if CSU can accelerate their organic growth. EBITA margin came in at 24.0% for the quarter, \~60 bps below 1Q’25 and the customary \~300 bps below 4Q (the latter is just Q1 seasonality, as payroll taxes reset and you eat the bill in Q1). CFO mentioned CSU had a couple of acquisitions that were drag on margins which was expected and they plan on improving the margins on those acquisitions (typical CSU playbook). So, slight down tick in margins doesn’t seem to be concerning. ![](https://substackcdn.com/image/fetch/$s_!SEp3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fef7fcbfb-5d91-4738-a4fd-d69aa4a7c663_1402x706.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The highlight of this quarter, however, is acquisitions. CSU deployed $809 million in Q1 (cash + estimated deferred). On top of that, the filing disclosed that through the first \~six weeks of Q2 the company has already closed or has open commitments on a further $786 million worth of deals. Combined, that’s \~$1.6 billion in roughly four and a half months, **an amount that’s higher than their cash deployment in the entire 2025**. In fact, LTM acquisitions as a percentage of FCFA2S has now crossed 100% for the first time since 2024\. This is without even considering their recent [**PEMS**](https://www.mbi-deepdives.com/csu4q25/) deals. Clearly, CSU is leaning in to deploy capital in size which may be getting overshadowed by Saaspocalypse concerns. ![](https://substackcdn.com/image/fetch/$s_!dbEp!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc7259c7-790d-43ab-b900-d2c9c69c9591_1210x624.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The natural follow-up question is whether the implied price has gone up too. The answer is: yes, a little but it’s pretty much within what we have seen historically. One would imagine CSU would be able to buy assets cheaper than usual amidst the SaaSpocalypse narrative, but management mentioned valuation hasn’t really gone down that much in areas they typically play. Well, when you’re paying \~1-1.5x revenue for acquisitions, the supply of such sellers willing to part with their lifetime work for even lower than 1x revenue is not exactly elastic. Miller also explicitly mentioned “*There’s a real disconnect between the SaaSpocalypse publicly traded stuff and private markets”* which likely explains why they’re interested in PEMS in the first place. ![](https://substackcdn.com/image/fetch/$s_!uw3W!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9016f68e-67f4-4d14-b554-76a9f6a48e7e_1048x517.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Thanks to their valuation discipline for these acquisitions, CSU’s ROIC remains quite stable. (FYI, read this post to understand how I calculated CSU’s [ROIC](https://www.mbi-deepdives.com/what-exactly-is-csus-roic/). Also note that I have excluded PEMS investments from invested capital base since the associated return is not incorporated in numerator. More on this later.) Three years ago, CSU had \~$8 Billion of invested capital with 17.0% LTM ROIC. If a genie came to you then and told you in the next three years CSU would deploy **incremental \~$8 Billion capital while maintaining their overall ROIC**, you probably would think that was your lucky day**!** Well, not quite. The stock is almost flat for the last three years despite delivering results that should be quite satisfactory to most bulls. In case you needed another reminder “investing is hard”, let this be a good example. While investors are often judged based on past 3-5 year performance (if they’re lucky), the reality is even 3-5 year return for a stock can be largely dictated by the narrative around your “exit multiple” year. Unless you experience it first hand yourself, it’s sort of hard to appreciate the punishing nature of being on the wrong side of the narrative. I had that [moment](https://x.com/borrowed%5Fideas/status/1537901203773804544?ref=mbi-deepdives.com) back in 2022 with Meta. ![](https://substackcdn.com/image/fetch/$s_!tQQm!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1bc388bd-a026-4626-85be-298cfd373b57_1381x670.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) To be clear, bears will rightly argue that ROIC is a lagging metric and cannot tell you how the future will pan out. Of course, that’s true and at current prices, market is clearly telling you that the future trajectory of these metrics will likely be decidedly different. I will expand on CSU’s current valuation behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Roblox 1Q'26: Age Check Hangover, But Aiming to Go Beyond the Blocks URL: https://www.mbi-deepdives.com/rblx1q26/ Last updated: 2026-05-11T14:45:29.000Z *Programming Note*: In case you missed it yesterday, I have [released](https://www.mbi-deepdives.com/mbi%5Fskill/) "MBI" Skill for Claude and Codex. --- Back in 4Q’25 call, Roblox management announced that they will not provide any annual guidance from 2027 onwards: > we've learned that it's difficult to predict exactly where this business will land 12 months out. I mean if you look back at 2025, when Roblox set guidance, Steal a Brainrot and Grow a Garden had not even launched. And that's created a situation where the company has had to provide relatively conservative guidance. I don't think that's helpful to investors, and it's certainly not helpful to day-to-day operation of our business. So we're going to get out of that cycle. We're going to give everyone a long runway. We're providing detailed guidance for 2026\. But as we get into 2027, you'll see us starting to guide one quarter at a time. What happened in 1Q’26 call not only made their decision to move away from annual guide more justified in retrospect, they probably should have stopped providing guidance for 2026 as well. While their initial booking guide for 2026 implied 22-26% growth in 2026, their updated booking guide now assumes only 8-12% growth this year. The fact that Roblox management guided only 8-12% growth despite booking growth of 43% in Q1 hints at their not-so-rosy outlook for the rest of the year. Then again, if a company can be so wrong about their own annual guide just 90 days earlier, why would you not think they may have to change it again (who knows in which direction) 90 days later? Public market investors tend not to be charitable towards such uncertainties. As a result, Roblox not only went from almost $100 Billion Enterprise Value (EV) in September 2025 to below $30 Billion now, it is also comfortably below its valuation at IPO back in 2021. ![chart](https://substackcdn.com/image/fetch/$s_!G7N-!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F38cd7044-8ae0-45af-9f90-6ca2a9d8e523_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) But what did management get so wrong about their initial 2026 guide that they needed to materially downgrade just a quarter later? From the call (emphasis mine): > we launched age check in January. And as we said, we expected to see some headwind both in terms of hours and DAUs. I think **what we did not fully understand until we have now the benefit of 3, 4 months of experience is how that impacts the platform more generally with respect to comms engagement and the knock-on impacts from that**. Now I want to point out a couple of really important things. Number one, when we look at what has happened over the last few months, as I noted, engagement has actually remained quite strong. Monetization has remained strong and retention has remained strong. **What we have seen is challenges at the top of the funnel, i.e., new users coming in. And the reality is when we think about the rest of the year**, we’re not going to see the bookings impact of that right away, hence, the performance in Q1. > > But **we do know that the fact that we had more sign-up headwind over the last few months is going to put pressure on bookings over the remainder of the year**. However, when we start to get back to DAU -- sequential DAU growth in Q3, given the strength of monetization and engagement, we feel confident that we will be able to drive the bookings growth that we’re guiding to, which given the comps in the back half of the year, equates to something like relatively low single digits. So we’re not trying to do something heroic in the back half of the year, and it is largely driven by return to DAU growth. As a reminder, following the age check, Roblox started implementing the below rigid framework in terms of who can chat with others and if someone didn’t go through the age check requirement, they were simply not allowed to chat with others which apparently hurt communication engagement on the platform. As of Q1, only 51% of Roblox Global DAU have age checked which they hope to increase to above 90% “long term”. In the US, the number is already 65% and in Australia where age check was rolled out the earliest, it is at 70%. ![](https://substackcdn.com/image/fetch/$s_!_k67!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7a2d6e1-18aa-4591-b208-213c3c2c4683_1396x769.png) Source: Roblox While the guidance cut got all the attention during the call, there are some interesting developments at Roblox that got overshadowed a bit which I will highlight behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### "MBI" Skill for Claude and Codex URL: https://www.mbi-deepdives.com/mbi_skill/ Last updated: 2026-05-10T13:36:57.000Z Today’s post is about how to get more out of MBI Deep Dives. When MBI Deep Dives was only about the monthly Deep Dives, it wasn’t very difficult to keep track of everything I was writing about. However, since I started publishing daily in July last year, it has become slightly unwieldy, at times even for me to find the exact post I am looking for that I wrote several quarters ago! Of course, AI can help a lot here. So, I created the “MBI” skill on Claude and Codex that lets me ask questions about my own writing and get answers grounded in past posts, with citations back to the specific post. Once I created the skill on Claude, I could ask questions like “how has MBI’s view on Spotify evolved?” The word “MBI” automatically triggers the skill, so Claude went through five separate posts (one from 2024, two from 2025, and two from 2026). You can see Claude’s answer [**here**](https://claude.ai/share/a906da83-3dfc-4f24-aa30-d383e76662cc?ref=mbi-deepdives.com). Now that I had created the skill for myself, I thought: why not make it available for all my subscribers as well? So I am sharing the step-by-step instructions for installing the skill on Claude [**here**](https://docs.google.com/document/d/1-Wy7--jtcJYvtDAWXL7QS7xEk2FRHEonQJP6OUHk2dI/edit?tab=t.0&ref=mbi-deepdives.com#heading=h.3lgxx5mslm05). If you are using Codex instead, you can find the instructions for Codex [**here**](https://docs.google.com/document/d/1Xt8plwrdfWv1CHA3Pey3QyLGspfz4vKwhy0A%5F7WrP9g/edit?tab=t.0&ref=mbi-deepdives.com). The process is quite simple. However, a couple of things I want to highlight first. The skill runs entirely on your machine, against your own Gmail. Your queries don't pass through me or the MBI Deep Dives website. So I don't see what you ask, or what gets surfaced. The skill is just a set of instructions; the actual searching and reading happens between your local Claude app and your own Gmail account. For the skill to work, you need to connect the email you used to subscribe to MBI Deep Dives and your email needs to contain the past emails from MBI Deep Dives. As a result, this skill becomes more useful the longer you have remained a subscriber of MBI Deep Dives. The other thing I want to highlight here is that I often receive emails from paying subscribers saying that even though they are a paying subscriber on my [website](https://www.mbi-deepdives.com/), they cannot access the paywalled content on my [Substack](https://mbideepdives.substack.com/). My website is hosted on the “Ghost” platform whereas Substack is an entirely separate platform, which is why a subscription on one doesn't automatically carry over to the other. Since I publish the same content in both places, you don’t need to subscribe to both. I launched my own website on Ghost first, but since I kept hearing from readers that they would like to read MBI Deep Dives on Substack, I ended up launching a Substack as well. While there is no difference in content published on either the Ghost website or the Substack, I want to make it clearer why Substack might be more useful for some readers. If you are someone who prefers to listen over reading, it may make more sense for you to choose Substack. The audio experience on Substack is substantially better than what you can get via my website on Ghost. However, the Ghost website is much better organized by stock tickers which can make it easier for you navigate and find any past post you may be looking for. In any case, if you install this skill on Claude or Codex, it works on both Substack and Ghost. So this benefit of the Ghost website may be less relevant at this point. What if you already subscribed via the website but would like to move over to Substack? In that case, I suggest you unsubscribe from your current subscription and re-subscribe on Substack. You can drop me an email after doing so and I will refund you for the unused portion of your subscription. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Digital Advertising Industry Snapshot 1Q'26 URL: https://www.mbi-deepdives.com/digital_ad_1q26/ Last updated: 2026-05-09T13:29:13.000Z As I do after every quarter, let me share the snapshot of the overall digital advertising industry. Please note that digital advertising industry is defined as Alphabet’s advertising revenue+ Meta’s advertising revenue+ Snap revenue+ Pinterest revenue+ Microsoft Search advertising revenue+ Amazon advertising revenue+ Trade Desk revenue+ AppLovin revenue. Also note that for visual convenience, I have colored all the Q1 of the last five years. Obviously, the actual digital advertising industry is larger than this, but this snapshot helps me gauge the broader digital advertising industry’s big picture. TikTok and Walmart advertising are notably missing here. Well, TikTok is a private company and Walmart doesn’t disclose their advertising revenue consistently even though Walmart now actually has a larger ad business than everyone in this snapshot except the big tech i.e. Alphabet, Meta, Amazon, and Microsoft. **Some key takeaways from 1Q’26:** Digital advertising industry saw a material growth acceleration in 1Q’26 as it increased by 22.1% YoY! This was the highest growth rate since 4Q’21. However, **nearly half** **of the incremental growth came from just one company: Meta Platforms!** Given that 2022 numbers was affected by ATT impact, it’s more instructive to see Meta’s incremental share from 2023 onwards. Digital advertising industry is one of the more efficient markets out there as the incremental dollar tends to flow to wherever the ROAS is the most compelling. Meta’s utter dominance in incremental share in digital advertising industry clearly shows it is getting harder and harder for advertisers to find better ROAS elsewhere. Google Search seemed to have held onto a steady \~33% share in incremental advertising dollar over the last three years. So, even though Meta is winning the larger pie of the incremental dollar, Google Search is doing okay so far. As I have made the case before, scaled advertising players are clearly some of the largest beneficiaries of AI. But for more sub-scaled players, the bar to remain relevant is getting harder and harder. **Except for Google Search and Meta, pretty much everyone else lost incremental market share in 1Q’26**. The biggest incremental share loser was Microsoft Search advertising as their incremental share went from 2.6% to just 1.1%. Nadella wanted everyone to know they made Google dance; since Google “invented” these dance steps, they were nimble enough to respond but it is Microsoft search that seems to be left out of the dance floor. Life is difficult for other sub-scaled players as well: Snap’s incremental share got halved and yet, that is somehow better than Trade Desk whose incremental share is approaching zero. AppLovin was again the fastest growing digital advertising player even though they too lost incremental market share. Nonetheless, their incremental share was actually **twice** that of current share in digital advertising market. I should, however, note that some of the definitions of advertising here are not quite apple to apple as some companies report advertising dollar on a gross and some report on a net basis which is why I like to focus more on incremental share data. ![](https://substackcdn.com/image/fetch/$s_!g4VC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9a21a8b5-5a13-4cdf-87ec-125acb8ece10_1233x988.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Airbnb 1Q'26: Growth Acceleration Despite War Headwind URL: https://www.mbi-deepdives.com/abnb1q26/ Last updated: 2026-05-08T15:09:40.000Z Airbnb reported +15% (FXN) revenue growth in 1Q’26 which was its highest topline growth over the last eight quarters. It is particularly impressive given the the backdrop of Iran war. Airbnb mentioned growth accelerated from January to February but then experienced a “slight deceleration” in March due to cancellation across EMEA and APAC region once the war started. Airbnb estimates Nights growth would have been 10% (vs reported 9%) in the absence of the war. ![](https://substackcdn.com/image/fetch/$s_!1Ilv!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4a849c69-6761-4c9f-b11a-20b9c2d27ebe_1705x361.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The reported revenue growth by region appears much more impressive in regions outside North America, but FX had a substantial impact on such regions, I would focus on Nights & Experiences (N&E) growth and Average Daily Rate (ADR) on FX neutral basis to assess the underlying growth. ![](https://substackcdn.com/image/fetch/$s_!H7X4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faa94f293-48de-4df9-9071-47d372405b9d_1372x184.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Geographically, North America’s nights growth finally moved upwards from LSD-MSD range to HSD in 1Q’26\. APAC region nights growth also accelerated from mid-teens in the last four quarters to high teens in 1Q’26\. First time booker accelerated to 10% growth (which was highest since early 2022) primarily due to growth in expansion markets such as Brazil, India, and Japan. ![](https://substackcdn.com/image/fetch/$s_!Tv6x!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9a65971a-82f6-4789-a7d5-4860b3393e34_1246x208.png) Source: Company Filings, MBI Deep Dives For the second consecutive quarters, origin nights booked from India grew 50% YoY and in Brazil, it maintained over 20% growth for three consecutive quarters. First-time bookers in India increased 75% YoY. Airbnb’s success in Brazil is particularly encouraging and as I will explain later, I think such success has offered Airbnb bit of a playbook that they now need to replicate and execute in other expansionary markets. Moreover, demand from Airbnb app is growing much faster than the overall business. In 1Q’26, nights booked on the app accelerated to +22% YoY (vs +17% in 1Q’25 and +20% in 4Q’25). Airbnb app accounted for 63% of total nights booked in 1Q’26 (vs 58% in 1Q’25). Airbnb’s quarter looks particularly more impressive when we benchmark it against Booking which I will discuss behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Insurance Brokers 1Q'26 Update URL: https://www.mbi-deepdives.com/insurance-brokers1q26/ Last updated: 2026-05-07T14:32:34.000Z Insurance brokers are currently in a world of pain as the halcyon days of hard market is clearly behind us. At first glance, the organic growth still seems to be holding up reasonably well (ex BRO), but nonetheless investors are somewhat spooked. ![](https://substackcdn.com/image/fetch/$s_!m-z3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faaf5ec4f-0abb-4856-8c74-0621921835ed_1714x153.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Every broker reported sharper property declines. The quantification varies with mix (admitted, large account, E&S cat-exposed), but no broker characterized property as anything other than firmly negative. Q1 FY26 was the quarter that confirmed the property-led soft market is accelerating. Property is clearly past peak: -9% (Marsh global, same as last quarter), -7% RPC (AJG), -15% (AON), -15% to -35% (BRO E&S), -25% to -35% (RYAN cat-exposed). Capacity is returning across primary and reinsurance which is exerting pressure on rates. On the other hand, the unanimous read is that the casualty rate environment is bifurcated. Excess and high-hazard classes are still seeing meaningful rate increases driven by social inflation and reserve actions, while small-to-medium hazard primary casualty is starting to see fresh capacity compete. US excess casualty was +18% per MRSH; high-hazard E&S classes >+10% per RYAN. As you can see above in the organic growth table, BRO was the clearest under-performer in 1Q’26\. BRO CEO Powell Brown explicitly named four idiosyncratic headwinds (Accession integration, Howden start-up litigation, faster-than-expected property declines, pharmacy revenue model change) and conceded BRO is “slightly lower than the peers”. While the hard market in property is behind us, brokers identified data centers and AI-related infrastructure as a meaningful contributor or pipeline opportunity. AON took it furthest, raising its data center life-cycle insurance program capacity by $1 Billion to $3.5 Billion; AJG and Guy Carpenter (MRSH) flagged similar exposure; RYAN noted strong data center activity within its construction pipeline. Of course, AI is also considered a potential disintermediation risk for brokers by some investors. So, each call featured extensive AI commentary during prepared remarks, analyst questions, or both. No surprise that all the brokers uniformly framed AI as a catalyst rather than a threat, leaning on relationship complexity, proprietary data, and scale as moats. Ultimately, brokers’ earnings call would be the last place where you will find any sympathy to such risk. Even though they all have vested interest in framing AI as an opportunity rather than a threat, I also do not see much of an evidence yet that such AI is shaping to be a risk yet. While much of the AI related commentary was qualitative in nature, RYAN gave the most quantified disclosure related of AI’s impact. From RYAN’s call: > “Within Ryan Re, we have built an AI-powered underwriting platform for our facultative reinsurance business. > > We have reduced average processing time per submission from approximately 2 hours to minutes while increasing the number of submissions each underwriter can evaluate by roughly 10x. Within Velocity, our property catastrophe MGU, we deployed an AI-driven platform that scores every submission on appetite fit and propensity to bind. The result being an 11x uplift in submit to bind ratios for our highest appetite category compared to our lowest. > > Simultaneously, the speed to quote has improved by 36% on a median basis. These capabilities are changing how our underwriters work every day, and we are preparing to deploy them more broadly across the firm.” Every single brokers explicitly cited the sector-wide stock pullback as a reason to lean into share repurchases. Capital allocation framing was nearly identical: continue M&A where deals meet criteria, but lean buyback-heavier when the pipeline is muted. The drawdown chart is horrific for brokers. RYAN is down \~60% from its peak and believe it or not, BRO is currently in its worst drawdown in its entire history! The big three are still holding up reasonably well compared to the smaller/mid-sized brokers. ![chart](https://substackcdn.com/image/fetch/$s_!h7Lb!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F25c62ba7-5c29-43f3-9490-307a26a62f0d_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Given the drawdown, it is good to see all the brokers have materially increased their buyback cadence in 1Q’26\. AJG management was quite explicit about their stock being undervalued. From AJG’s call (emphasis mine): > “we repurchased approximately $310 million of our shares. We continue to believe our equity is **woefully undervalued** by the market, so this repurchase was opportunistic.” The outlook for the year wasn’t too bad either except perhaps RYAN. RYAN cut full-year organic from HSD to MSD and Q2 organic growth to **zero**. Every other broker reaffirmed: AON at MSD+; AJG at 5.5% Brokerage; MRSH at “similar to 2025”; BRO at sequential improvement to \~2.5% upper bound. RYAN and BRO are most exposed to the Q2 hit because Q2 is seasonally the heaviest CAT property placement quarter. AJG noted property “is going to take its biggest hole in the second quarter” but said they have less property stress in 2H. AON flagged April 1 reinsurance renewals down 15-20% on rate. MRSH and WTW are the most insulated from the property cycle given their advisory/HWC mix. RYAN is pure-play wholesale and specialty; when the marginal flow into E&S decelerates, RYAN's revenue line decelerates with it because RYAN doesn't have a large-account retail book to offset. E&S has been a big beneficiary of the hard property market during 2019-2024 cycle, but they are now in front of the eye of the storm. The other brokers may start to feel this later if the soft pricing cycle lingers for a while. While RYAN is clearly in a spot of bother in the current environment, I was heartened to see how Pat Ryan decided to use his own capital ($52 million) to fund SBC for RYAN management. From Pat Ryan (emphasis mine): > We have announced a onetime option grant program in the second quarter, **funded entirely by a portion of my own holdings to make sure the broader team is properly aligned over the long term.** > > **It is structured to be neutral to the company’s outstanding share count and will function as a direct reinvestment for me into the team that has built this platform**. I believe in this team, I believe in this platform, and I believe in the direction Ryan Specialty is heading. As we look forward to the work of the next several years, I want every leader at this company to be aligned to our mission, and **I’m offering a meaningful piece of my own capital to support that conviction**. That’s certainly reassuring for minority shareholders to see the largest shareholder of RYAN putting his money to work especially when things are heading towards a difficult period. Admittedly, I have been surprised by how investors valued insurance brokers over the last three years. When they were enjoying the hard market driven pricing tailwind, perplexingly investors were very happy to put an elevated multiple on such earnings power. I [**expressed**](https://www.mbi-deepdives.com/umg/) my concern about valuation when the cycle turns back in October 2024 as I decided to start selling BRO: > While BRO doesn’t take underwriting risk, it is not immune from P&C cycle. When the P&C cycle turns and let’s be clear I have no idea when, the subsequent years would probably be dramatically different from what we experienced last couple of years. Looking at their multiple and trailing 3-year EBITDA growth CAGR, **I wonder whether investors are currently valuing the peak earnings on peak multiple**. As I have noted before, I love the insurance broker industry, but I think it is perhaps a good time to lighten my exposure a bit. Now it has surprised me in the opposite direction. While the cycle has indeed finally started to turn the other way now, investors, again somewhat perplexingly, want to put anemic multiple on their earnings power today. While I do believe these stocks will likely do well from current depressed valuation level, considering the frequency of my bewilderment in how investors valued these companies over the last 3 years (not the most reassuring sign that I know what I’m doing in an industry), I have decided to keep observing them instead of adding more capital for now. ![chart](https://substackcdn.com/image/fetch/$s_!Ndzk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9186b2e7-8bec-4cc3-8ad1-8fb88b795672_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I have made a slight change to my portfolio yesterday. _This post is for paying subscribers only._ ### Shopify 1Q'26: Impressive, But Still Not Enough to Justify Valuation URL: https://www.mbi-deepdives.com/shop1q26/ Last updated: 2026-05-06T14:31:17.000Z 1Q’26 was yet another quarter for Shopify in which it added almost the same GMV as Amazon retail likely added in the same quarter. ![](https://substackcdn.com/image/fetch/$s_!YwxD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6bb199c8-cbf5-413f-bb0d-233ce49bed16_1158x702.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Back in 2017, Shopify’s GMV was only \~10% of Amazon’s estimated GMV. Given the way Shopify is growing, it seems likely that it will be half of Amazon’s GMV by the end of next year. To go from just \~10% of Amazon’s GMV to half of Amazon in just a decade is a remarkable feat! It’s not like Amazon has been struggling to grow during this period; it’s just that Shopify has access to a reservoir of growth that is somewhat inaccessible to Amazon retail which is helping Shopify close this gap quarter after quarter. ![](https://substackcdn.com/image/fetch/$s_!Tlyd!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa59fc66d-f7d2-4552-811b-33a3afcb6573_1489x832.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) What is this reservoir of growth Shopify has access to? Shopify’s platform business model can inherently scale much faster internationally than Amazon’s marketplace business model can. Shopify’s international GMV grew by a whopping 45% in 1Q’26 with cross-border GMV representing 16% of total. Shopify highlighted why they’re growing so much faster internationally: > “We are consistently rolling out new updates and products to grow our international footprint. In Q1, we quietly shipped updates that individually may not make headlines, but together are steadily making Shopify more native to more places. Things like merchant billing, which is now in 7 new European currencies or capital now available in France or smart market and smart language recommendations where merchants get relevant recommendations based on the markets they sell into. Every quarter, we build more. And we removed more barriers for merchants all over the world to choose Shopify.” Given the results they are seeing, Shopify actually spent \~40% of their marketing dollars in Europe. Moreover, off-line GMV was +33%, accelerating from Q4, and B2B GMV grew +80% in Q1\. It is mind boggling that Shopify has been organically growing at >30% in each of the last four quarters. I’m not sure many people would have predicted that five years following the pandemic, Shopify’s growth would have a 3-handle! ![](https://substackcdn.com/image/fetch/$s_!CKKv!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa347e071-e3ae-4384-b1f6-84a61fefec7c_1105x601.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss more about their margins and key takeaways from the earnings call behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Floor & Decor 1Q'26: Persistent Macro Challenges, but Pro Growth Encouraging URL: https://www.mbi-deepdives.com/fnd1q26/ Last updated: 2026-05-05T15:03:52.000Z Floor & Decor (FND) posted an ugly quarter and provided an uninspiring outlook for the rest of 2026\. In fact, given that FY25 was guided down at every revision i.e. three consecutive cuts across the four reports and that’s exactly also how 2026 started, I am a bit surprised that they haven’t given up on providing annual guidance yet. Ultimately, I don’t quite think FND management is in a position to provide an accurate outlook 12 months from now given macro is the primary driving force for their business in the near term. Despite somewhat easy comp, US existing home sales (EHS) went down in each of the first three months in 2026\. There’s really not much FND management can do in the near term if EHS trend continues to deteriorate. The fact that FND stock is largely flat after last week’s earnings is an indication how much pessimism was already embedded in FND’s stock price these days. Most investors suspected FND would have to navigate tough comp following the pandemic era EHS boost, but the length of the cycle perhaps surprised almost everyone. FND’s same store sales (SSS) was negative in 12 of the last 13 quarters! The only quarter that was positive in last three years was actually 2Q’25\. Given such “touch comp”, you can be near certain that 2Q’26 SSS will also be negative. In fact, management mentioned April so far was -4.5%. Overall 2026 comp range outlook was guided down to flat to down 4% vs the initial guide range of -2.0% to +1.0%. We will see if they need to update it again next quarter! ![](https://substackcdn.com/image/fetch/$s_!AJ0R!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6ca8d6b8-550c-42a8-96e4-e4441e5bce8e_1426x826.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Considering such a brutal backdrop, it is a welcome surprise that FND actually posted its highest ever gross margin in 1Q’26\. The biggest driver for margin expansion from 2021-22 trough levels has been the normalization of ocean container rates. Pricing has also been a tailwind. FND has good, better, and best product assortments. Even as transactions declined, the customers who are shopping continue to skew toward better/best price points, which carry higher margin. In four of the last five quarters (including 1Q’26), average price was actually positive. It’s the number of transactions that has been the primary culprit for persistent negative SSS. ![](https://substackcdn.com/image/fetch/$s_!XQSR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd2879bd-189b-4328-868e-efceb4b49cd8_1246x705.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) However, there is perhaps an underappreciated driver for FND’s long-term future which is getting masked during this terrible EHS trends which I will elaborate behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### "Never Sell" Episode: Big Tech earnings, S&P and Moody’s, AI URL: https://www.mbi-deepdives.com/never_sell_15/ Last updated: 2026-05-04T13:19:34.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode yesterday. While I have already covered big tech's earnings over the last few days ([Meta](https://www.mbi-deepdives.com/meta1q26/), [Alphabet](https://www.mbi-deepdives.com/goog1q26/), [Amazon](https://www.mbi-deepdives.com/amzn1q26/), and [Microsoft](https://www.mbi-deepdives.com/msft3q26/)), we expanded on how IT budget reallocation towards AI may not lead to any sustained long-term advantage for the enterprises. I also touched on why I find the recent circular financing deals between Anthropic and hyperscalers particularly questionable. Finally, Scuttleblurb had a more in-depth discussion on AI risk for S&P and Moody's. You can listen to the conversation here: [Spotify](https://open.spotify.com/episode/62vkIPlUJQ8PlU9MNixn1P?si=d040038265b24faf&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/big-tech-earnings-s-p-and-moodys-ai/id1786912203?i=1000765932750&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=31G92cfh77g&ref=mbi-deepdives.com), [RSS feed](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com) As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](https://www.mbi-deepdives.com/msft3q26/#/portal/signup) ### Microsoft FY 3Q'26: Multi-Model Mirage, Copilot Momentum URL: https://www.mbi-deepdives.com/msft3q26/ Last updated: 2026-05-03T15:17:28.000Z **Programming Note**: While I typically don’t write on company specific topics on Sunday, I’m making an exception during the earnings season. --- Microsoft’s own narrative about their future has [**evolved**](https://www.mbi-deepdives.com/nadellas-flip-flop/) quite a bit following the ChatGPT moment in 2022\. While they used to highlight how their exclusive access to OpenAI models would give them a decisive competitive advantage in attracting customers, they have noticeably shifted the tone to emphasize the importance of having a multi-model environment for customers. Notice the following from their FY 3Q’26 earnings call: > Over 10,000 customers have used more than one model on Foundry. 5,000 have used open source models, and the number who have used Anthropic and OpenAI models increased 2x quarter-over-quarter. Microsoft hasn’t quite spelled it out clearly for investors, but once you triangulate these data points with previous disclosures, a more interesting picture emerges. During the FY 2Q’26 call, Microsoft actually disclosed that \~1,500 customers were using both OpenAI and Anthropic models. Since they mentioned that such customers have doubled QoQ, we can estimate that the number has reached 3k customers now. That provides better context for understanding that more customers are using at least one open-source model than are exclusively using OpenAI and Anthropic’s models. Perhaps the more interesting takeaway that’s (deliberately?) not clear from Microsoft’s commentary during the call is that the vast majority of their Foundry customers are still likely using a model from just one company instead of adopting the multi-model environment Microsoft wishes customers would adopt. Microsoft hasn’t disclosed their total number of Foundry customers during the call, but they did mention in the FY 1Q’26 call that Azure AI Foundry had 80k customers, which is \~80% of the Fortune 500**. If only 10k customers used more than one model last quarter, that likely means \~90% of customers are still using just one model.** Maybe multi-model adoption will gain more momentum over time, but I wanted to highlight that the data indicates Microsoft’s own customers are still far away from such a world. While both Microsoft and Amazon propagate the value of a multi-model environment, they should perhaps have more appreciation for the appeal of standardization on a single stack, since they themselves were large beneficiaries of such a reality in the earlier cloud era. Everyone similarly argued that enterprises must be multi-cloud, yet most CIOs rationally pay the lock-in premium and standardize, using a second cloud only for narrow specialty workloads. The lock-in risk is abstract and deferred, but since the operational tax of multi-cloud is concrete and recurring, there is a gravitational pull to standardize your IT stack mostly on a single cloud. I suspect most customers may gravitate to a single frontier model for the same reason they gravitated to a single cloud, even though you can make a compelling argument for why it can be quite risky for an enterprise to do so over the long term. Microsoft wants the harness layer to be decoupled from the model layer and would love their customers to have a multi-model environment, but the AI labs not only have little interest in being commoditized but are also clearly indicating that it will be harder and harder to untangle the harness layer from the model layer. Notice the following exchange between Ben Thompson and Sam Altman in the recent Stratechery [interview](https://stratechery.com/2026/an-interview-with-openai-ceo-sam-altman-and-aws-ceo-matt-garman-about-bedrock-managed-agents/?ref=mbi-deepdives.com): > **Ben Thompson (BT):** How important is the harness, the runtime around the model, the tools, state — to your point, a very important word to you — memory, permissions, evals, to making agents actually work? > > **Sam Altman (SA):** Hard to overstate how critical it is. I no longer think of the harness and the model as these entirely separable things, like my experience of using these, I am very aware of the fact that I don’t always know when I fire something off in Codex and it does an amazing thing for me. I don’t know how much credit — > > **BT:** Was it that the model is amazing or the harness was amazing? > > **SA:** Yeah, exactly. > > **BT:** To what extent is the harness developed in conjunction with the model? Where does that integration happen? Is it in post-training? Is it in the prompt? What makes this integration work? > > **SA:** Both of those. It’s not really part of the pre-training process but I would say you can look at it — there’s a more interesting thing here which is the fact that we’ve seen examples of this many times in the past of where things that we thought were very separable get baked in more and more and more. Like the way we initially thought about tool-calling, which is now a critical part of how we use these models, was not something that we thought about deeply integrating into the training process and over time we’ve done more and more of that. > > I would also suspect that model and harness come together more over time and I would for that matter, I would expect that pre-training and post-training eventually come together more over time as well. It’s such a cliché to say, but I’ll do it anyway, because I think it’s very, very true — we’re so early in the paradigm of all of this, this is still like the Homebrew Computer Club days of how much this is like really matured as an industry. > > **BT:** This is why I think so interesting, I wrote about this a few weeks ago, in any value chain, ultimately a point of integration emerges that that’s where it’s really important, these two pieces have to go together to make it work. And over time, that’s obviously where a lot of value collects — my thesis then is that this harness-model integration is the key point. It’s to your interest, but it sounds like you agree. > > **SA:** It is to my interest, I do agree, but I also would say even more broadly, what you care about is that you go type into Codex what you want to happen and that it happens. > > **BT:** You don’t care about the implementation details. > > **SA:** I don’t think you do. The more pressing matter for Microsoft is their own cash cow software business, which is why they continue to highlight that they expect to balance the incoming supply of compute between Azure and their own first-party workloads. Microsoft doesn’t have a frontier model of their own, but Satya Nadella highlighted that they have access to OpenAI’s royalty-free frontier model with all the IP rights until 2032\. The implicit assumption is that by the time those rights expire, Microsoft will be able to build their own frontier model. I’m not sure how much credence or confidence investors should have in Microsoft’s ability to build a frontier model on their own when they noticeably lag behind today’s frontier models despite having such unfettered access to OpenAI’s IP. Nonetheless, it does appear that Copilot has started to see noticeable momentum. From the call (emphasis mine): > We have seen a surge in usage of our first-party agents with monthly active usage up 6x year-to-date. Copilot queries per user were up nearly 20% quarter-over-quarter. **To put this momentum in perspective, weekly engagement is now at the same level as Outlook**, as more and more users make Copilot a habit. Copilot engagement at the same level as Outlook? Now that’s pretty impressive. Moreover, while investors worry about the seat-based subscription model in a world of agents, Microsoft reassured investors that the transition won’t be too messy for them. From the call (emphasis mine): > “customers want predictability, especially for budgets and procurement. And the seat-based pricing is just entitlements to some consumption, right? And so that’s, I think, the way to think about it, which is, **there is some base usage rights that get bundled in or packaged in to seats, it’s a convenient way for people to buy some essentially consumption packs that happen to be assigned to seats or agents**. > > And then beyond a certain level, there’s overages that go into pure consumption. And even there, if you have commitments, long-term commitments to consumption, you get discounting that is appropriate with it. So I feel like that’s the direction of travel. > > And then -- the other thing you mentioned is how is this going to -- from a customer perspective, they’re going to evaluate it by evals. Where are they seeing the value of tokens, as simple as that. So where they see the outcome, the eval and the token, whether it’s improving revenue, improving efficiency, and that’s what will refine. Like when we talk about IT budgets, IT budgets are going to have to be reshaped by a combination of business outcomes, making their way into IT budgets and maybe reallocation from other line items on the income statement like OpEx.” One concern I do have about enterprise AI revenue momentum in 2026 is that the current [**tokenmaxxing era**](https://www.mbi-deepdives.com/tokenmaxxing/) will inevitably go through a period of optimization once the hype settles. Today, everyone is still figuring out how to embed AI into their workflows or how to create entirely new workflows that are only possible due to AI, but as the early phase of experimentation winds down, enterprises will look much more closely at how to optimize token spending. How such optimization will affect revenue growth ambitions for AI labs remains to be seen. However, even though Nadella thinks enterprise customers will allocate IT budgets based on the revenue or cost improvements they see, I suspect the reality may be that when everyone has access to the same tools, those customers may not see much sustained improvement in a relative sense. That is, of course, not to say enterprise customers won’t see value from using AI, but sustained value is unlikely to accrue to customers who all have access to the same tools. The end result might just be lower margins for everyone as enterprises feel forced to hand their margins to the machine gods just to keep up with their competitors. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Amazon 1Q'26: Rosy Near-term but "Cloudy" Long-term URL: https://www.mbi-deepdives.com/amzn1q26/ Last updated: 2026-05-02T16:19:01.000Z Amazon’s highlight from 1Q’26 was that AWS has accelerated to 28%, which was the highest growth rate in the last 15 quarters. In fact, looking at the guide, it appears even a more pronounced acceleration is likely for 2Q’26\. I will talk more about AWS, but as I typically do every quarter, I will first start with non-AWS segments of Amazon. ![](https://substackcdn.com/image/fetch/$s_!eLOS!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fec9ac732-1864-4db8-a84a-59cc64f04364_1456x243.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Both North America and International segment experienced margin expansion YoY. ![](https://substackcdn.com/image/fetch/$s_!7E0n!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F88a7b295-89ff-4f17-8a87-1f078df4a5f9_1114x676.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q’22\. Since then, unit growth has largely been faster than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. The health of the core retail business remains in a very good shape. Units accelerated to 15% YoY, which was the highest since the tail end of Covid. What I found even more remarkable is that Amazon mentioned “*in Q1, the average prices of products offered on Amazon.com *decreased* compared to the same period last year*.” When I think about what has happened since 1Q’25 especially tariff and oil price shock, it is a pretty incredible stat. ![](https://substackcdn.com/image/fetch/$s_!qdOg!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F96c37caf-5356-45b0-9d87-accbc6871bf4_1327x553.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) A big driver for retail profitability is advertising. Given Amazon ads are perhaps more of a competitor to Google than Meta, I pay close attention to Amazon’s incremental share in advertising compared to Google Advertising. It is interesting to observe a noticeable drop in Amazon ads incremental share compared to Google advertising which itself is [**losing**](https://www.mbi-deepdives.com/meta1q26/) share to Meta’s Family of Apps (FOA) ads. ![](https://substackcdn.com/image/fetch/$s_!Rv4X!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8a68207c-e33d-4472-a59f-99cf30603c0f_973x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) For the last four consecutive quarters, Amazon ads grew at 22% rate (FXN). In contrast, Google advertising accelerated in each of the last four quarters from 10.4% in 2Q’25 to 12.6% in 3Q’25 to 13.6% in 4Q’25 to 15.5% in 1Q’26. ![](https://substackcdn.com/image/fetch/$s_!Cvpf!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8b63a870-a40c-499d-924d-af0a289f0411_1329x156.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While I consistently made the case that AI is a massive tailwind for scaled digital advertising players, I suspect Amazon ads may be structurally positioned worse compared to Google and Meta. I will have much more sympathy to this thesis if I continue to see incremental share for Amazon ads (vs Google advertising) declining in coming quarters since I don’t want to extrapolate too much based on just one quarter. Why do I suspect this might be the case? Amazon Ads is overwhelmingly bottom-funnel i.e. sponsored products served at the moment of purchase intent, where AI adds little ("buy AirPods Pro" doesn't need a reasoning model). Google Search, on the other hand, has always lived slightly further upstream (vs Amazon), where AI Mode and AI Overviews can genuinely improve the considered, comparative shopping query. AI also expands Google's ad inventory in ways Amazon's may not quite grow. Think shopping carousels inside AI responses, conversational query types that didn't previously exist whereas Amazon's inventory is structurally capped by SERP and product-page real estate that's already heavily loaded. Amazon mentioned Rufus which had MAU growth of 115% and engagement growth of 400%, but I wonder how Rufus is being used by customers. Personally speaking, I mostly ask Rufus to summarize reviews of a particular product on Amazon, but for more extensive product comparisons and considerations, I personally (and likely most customers) find general chat bots (Gemini/ChatGPT) more useful. Of course, Amazon's first-party purchase data remains the highest-quality targeting signal in digital advertising, so I certainly don’t think Amazon ads business will be impaired, but Google's funnel position and inventory expansion can produce more incremental lift per dollar of AI investment, at least in this cycle. I’ll keep a close track and update my opinion accordingly. A more nefarious bear case for Amazon ads is whether they become increasingly [**abstracted away**](https://www.mbi-deepdives.com/the-great-abstraction/)by the OS layer. I share some of my readers [**skepticism**](https://www.mbi-deepdives.com/feedback-great-abstraction/) of such bear cases, but given Google owns the OS layer, you can see why they may have more control than Amazon to navigate the evolving digital advertising landscape in the coming years. One interesting tidbit from the call was that Amazon mentioned Prime Video is now a “profitable business in its own right”. For context, Amazon spent 22.4 Billion in video and music content spending in 2025, supermajority of which I think is related to video. Amazon reported $51.3 Billion LTM subscription revenue, majority of which is related to Prime subscription. If we assume $18-20 Billion content was spent on Video, Amazon is perhaps allocating \~40-50% of Prime subscription value to Video which, in addition to recent [doubling](https://www.adweek.com/media/amazon-doubles-prime-video-ad-load/?ref=mbi-deepdives.com) of ad load on Prime Video, may have propelled Prime Video to be profitable. Okay, enough about non-AWS business. Let’s get into AWS related discussion which will be behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Alphabet 1Q'26: Stratospheric Heights in Performance and Expectations URL: https://www.mbi-deepdives.com/goog1q26/ Last updated: 2026-05-01T15:26:57.000Z Alphabet’s revenue growth has accelerated for five consecutive quarters. Except for Google Network business which is increasingly irrelevant, every part of the Alphabet business is humming right now. ![](https://substackcdn.com/image/fetch/$s_!vTxC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F936922c0-7bee-4966-9ac9-2bb448e3ccb0_1810x358.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As mentioned [**yesterday**](https://www.mbi-deepdives.com/meta1q26/), despite growing at an incredible \~19% in 1Q’26, Google Search still continued to lose share in digital advertising industry. While YouTube ads revenue growth appears quite uninspiring in light of Meta and Google Search performance, management highlighted that YouTube subscription is growing faster than YouTube ads which makes me think the overall health of YouTube business remains in good shape. In fact, I actually wonder whether the fact that more and more YouTube users are choosing subscription over ad-supported experience itself is creating some pressure on Meta’s (and TikTok’s) ad auctions (which would increase ad prices) since a lot of valuable impressions are simply going beyond the reach of advertisers on YouTube. The standout segment from 1Q’26 was, of course, Google Cloud. More on Cloud later. ![](https://substackcdn.com/image/fetch/$s_!erhP!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc2d9dbc2-c1ce-4bf8-b7d0-ac3f290413ce_1795x316.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) There also appears to be no end in sight when it comes to margin expansion. Google Services operating margin reached a new peak at 45.3%. Given that incremental margins continue to be 60%+, I wouldn’t be surprised if margin continues to expand here. Google Cloud’s operating income tripled YoY; as a result, a business that was unprofitable as recent as 4Q’22 posted 32.9% operating margin in 1Q’26. In both these segments, I should highlight that there is a **slight** wrinkle in comparing current segment margins to historical segment margins. In [2Q’24](https://www.sec.gov/Archives/edgar/data/1652044/000165204424000076/googexhibit991q22024.htm?ref=mbi-deepdives.com), Alphabet disclosed that “*AI model development teams previously under Google Research in our Google Services segment are included as part of Google DeepMind, reported within Alphabet-level activities, prospectively beginning in the second quarter of 2024”*. As you can see below, there has been a noticeable step up in “Corporate costs” which includes the “Alphabet-level activities”. In any case, the consolidated EBIT margin, which incorporates all the costs, reached the highest ever in Alphabet’s history. Given that its consolidated incremental EBIT margin was still 10 percentage point higher than reported overall operating margin, you can see why margins may continue to expand in the near term. ![](https://substackcdn.com/image/fetch/$s_!Q3Xb!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2ff52026-b3b5-43ca-88ba-56cf5950fb4f_1821x322.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let me quickly go through the key segments of Alphabet. ## **Search** Paid Clicks grew at 13% in 1Q’26, which was the highest since 1Q’22\. Google has dismantled so many bear cases (including the ones I worried about) that I have lost count at this point. While the dominant narrative has been that clicks may be under secular pressure due to the rise of zero click answers especially from chat bots, the data does not seem to support at least for paid clicks. In fact, looking at the pricing trajectory for cost per click and some of the commentary during the call, it seems highly likely that cost per click will grow at a healthy rate in the near term. From the call (emphasis mine): > “…more than 30% of our customer search spend now uses AI-enabled campaigns, AI Max or Performance Max. And **these advertisers are seeing more conversion for the same spend**.” ![](https://substackcdn.com/image/fetch/$s_!KGzQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F880ed319-5b91-49b3-b1bf-ca8f8836e293_1092x664.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Management reiterated that AI is driving more search queries and given the nature of queries has been changing in the post-ChatGPT era, Google has found the changing nature of query to be a tailwind for their business. From the call (emphasis mine): > AI is boosting our ability to deeply understand user intent for a given search query and **to find the most relevant ad**. **Even when we don’t have a direct user query, we’re making significant strides in improving relevance**. > > In Maps, we’re using Gemini to ensure promoted pins are deeply relevant to user surroundings, location of interest, history and intent. **This work is improving ads relevance by nearly 10%,** leading to significant increase in user engagement. We’re pairing this strengthened prediction-driven relevance with bottom-of-funnel precision. While another concern was AI will raise the cost per query, looking at the margins you can tell such concerns haven’t quite panned out. Thanks to Google’s “hardware and engineering breakthroughs”, management mentioned they reduced search latency by more than 35% over the last five years and since upgrading AI Overviews and AI Mode to Gemini 3, they have reduced the cost of core AI responses by more than 30%. Management was asked about whether they will launch ads on Gemini. They didn’t rule it out but also mentioned “we’re not rushing anything here”. I will be a bit surprised if Google launches ads on Gemini in 2026 as I believe they may want to stay focused on keeping the pressure high on ChatGPT by offering a zero-ads experience for free users. ### **YouTube and Subscriptions** Here’s how Alphabet’s total number of paid subscribers has grown since 1Q’25: 1Q’25: 270 Mn 3Q’25: 300 Mn 4Q’25: 325 Mn 1Q’26: 350 Mn The recent growth was primarily driven by YouTube and Google One which itself was benefited by Google’s AI subscription plans. One interesting data point was that management mentioned “*YouTube Music and Premium offering saw its largest quarterly increase in the total number of non-trial subscribers, both globally and in the U.S. since YouTube Premium launched in June 2018*.” That’s bit of a negative [read-through](https://www.mbi-deepdives.com/spot1q26/) for Spotify which only added 3 million Premium subscribers in 1Q’26 (vs 5 million in 1Q’25). That makes me think Spotify has lost share in the recent quarter. I will talk about Google Cloud and some interesting read-throughs from the call behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta 1Q'26: Nothing to Complain About URL: https://www.mbi-deepdives.com/meta1q26/ Last updated: 2026-04-30T19:36:10.000Z I know Meta’s stock is down after yesterday’s earnings, but I have a hard time complaining much as a shareholder. I will discuss why market may not share my enthusiasm later, but let me first share and explain why my own enthusiasm hasn’t abated post-earnings. When I first saw yesterday that revenue from Google Search grew 19% YoY in 1Q’26, I thought that was downright incredible for a business of such scale. Then I looked at Meta’s numbers and updated my model. Even such mindboggling search number paled in comparison with Meta’s Family of Apps (FOA) ad revenue growth. In 4Q’25, Meta’s ad revenue surpassed Google Search by \~$2.3 Billion. In 1Q’26, the gap increased to almost $4 Billion! Just to contextualize how fast Meta has been gaining share in digital advertising market, in 1Q’23, Google search LTM revenue was 42.2% higher than Meta’s FOA ads. Just three years later, Google search LTM revenue is now 11.6% larger; I won’t be surprised if Meta’s FOA actually becomes \~15-20% larger business than Google Search in five years. As Meta becomes better and better in AI-induced recommendations, it seems plausible that they can show you ads on their feed even before you decide to query it yourself on Google Search. As a result, the incremental share can continue to flow much more to Meta than Google search. ![](https://substackcdn.com/image/fetch/$s_!Wu4B!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F33746c9a-4c48-42ef-ac14-1a678991c491_1444x781.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) It is hard to realize looking at such numbers, but the war in Iran did have an impact on Meta. Internet outages in Iran and blocks in Russia led Meta’s Daily Active People (DAP) to decline QoQ. Meta also saw reduction in advertiser spend at the end of February which continued in March. While that understandably had a more pronounced impact in the Middle East, Meta mentioned in the follow up call that they also saw some softer trends in the US and Western Europe (they did mention the trends are improving a bit now, but their outlook embeds a range of scenario). The fact that Meta’s ad revenue grew in the US by 30% and in Western Europe by 40% (!!) **DESPITE** such softer trends in almost one-third of the quarter speaks volume of all the other tailwinds the company is currently enjoying. Worldwide ad impression grew by 19% and average ad prices increased by 12%. There is a lot of concern among some investors that Meta may be intentionally juicing impression to show higher growth. I do not share this concern. Meta has explained again that **larger** driver of the impression growth is coming from users and engagement, not higher load. Ad loads did increase overall, but Meta has made it clear that they’re investing in the infrastructure to figure out users who don’t mind seeing ads and show materially fewer ads if you do. So the fact that ad load has increased on an average is not necessarily an indication that Meta is deliberately doing it to juice revenue growth, rather it is the mere byproduct of the fact that the ads are so good and relevant that most people not only do not mind seeing more ads and some of them may even enjoy seeing them. ![](https://substackcdn.com/image/fetch/$s_!PXfj!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F91279faa-4f15-4fee-8788-b5c96e4fd81b_1410x805.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Some of the tidbits Meta shared during the call which explains much more why impression growth has been increasing so fast (emphasis mine): > On Instagram, **the ranking improvements** that we made in Q1 drove a **10% lift in Reels time spent.** **On Facebook,** **total video time increased more than 8% globally in Q1**, **the largest quarter-over-quarter gain in 4 years.** > > Within the U.S. and Canada, **ranking improvements we made drove a 9% increase in video watch time** on Facebook in Q1\. These gains are benefiting from advances we’re making across the full stack. Starting with data, we doubled the length of user interaction sequences we use for training on Instagram in Q1 and increase the richness of how each user interaction is described, enabling our systems to develop a deeper understanding of user interests. > Within our models, we’ve significantly increased the speed with which our ranking models index new posts, which is enabling us to recommend them sooner after they are published. We’re also applying more advanced content understanding techniques, which is enabling us to quickly identify posts that may be interesting to someone even if they haven’t engaged with a lot of similar content. **These and other improvements have enabled us to increase the diversity and recency of recommended content with same-day posts now representing more than 30% of recommended reels on both Instagram and Facebook more than double the levels 1 year ago.** > > We’re also **using AI to unlock more inventory by auto translating and dubbing videos into a viewer’s local language, enabling us to recommend a more diverse set of content. Over 0.5 billion users on each of Facebook and Instagram are now watching AI translated videos weekly**. > > In Q1, enhancements we made to Lattice’s modeling and learning techniques, along with advances in our GEM model architecture, **drove a more than 6% increase in conversion rate for landing page view ads…**In the second half of last year, we began rolling out our new adaptive ranking model, which is an LLM scale adds recommender model that we use for inference. **This model improves our inference ROI by routing requests to more compute-intensive inference models when it determines there is a higher probability of conversion**. (**MBI Note**: *I have covered this more extensively* [***here***](https://www.mbi-deepdives.com/meta-strikes-back/)*)* > > In Q1, we expanded coverage of our adaptive ranking model to support off-site conversions, **which drove a 1.6% increase in conversion rates across the major surfaces on Facebook and Instagram**. > > Usage of our ad creative tools is also scaling **with more than 8 million advertisers** using at least one of our Gen AI ad creative tools and particularly strong adoption among small- and medium-sized advertisers. These tools are benefiting performance as well with advertisers using our video generation feature seeing more than 3% higher conversion rates in tests. (**MBI Not**e: *this was only 4 million in 2024, so number of advertisers using Meta’s Gen AI ad creative tools doubled in just 15 months. Meta mentioned most of them are SMBs who probably didn’t have any video ad budget before)* > > We also continue to invest in the value optimization suite, which helps advertisers maximize their return on ad spend by prioritizing the highest value conversions rather than optimizing solely for the most conversions at the lowest cost. Adoption by businesses has been strong following performance improvements we’ve made over the past year with the annual revenue run rate of our **value optimization suite now over $20 billion, more than doubling year-over-year**. Drilling further into Meta’s segments, everything outside ads is obviously negligible. But I do want to highlight “other revenue” which was growing \~50%+ YoY in recent quarters. In 1Q’26, other revenue growth accelerated to \~74%! It won’t surprise me if “other revenue” continues to compound at more than 50% for the next five years! Meta’s messaging apps are still not quite as well monetized as their other properties are, so with AI, I think they are still in the early stage of ramp up in monetization there. Notice the following commentary from Meta (emphasis mine): > “…t**here are over 10 million weekly conversations between people and business AIs on our messaging platforms. That's up from 1 million at the start of the year,** and we're going to continue expanding globally in Q2\. And business AIs today are currently free for most businesses on our messaging apps. But as we make more progress, we expect that **we will also work towards establishing a longer-term monetization model**.” It is also good to see that Reality Labs losses has gone down YoY. In fact, Zuckerberg mentioned even though they remain the largest investor in VR space, they would like their VR segment within Reality Labs to be “sustainable”. They will still incur losses due to investments in AI glasses, but I think the losses in Reality Labs may have already peaked in 2026 and it will go down over time from here. ![](https://substackcdn.com/image/fetch/$s_!vwq0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1ea75d15-e57f-4d6d-a67c-7a506bb74b70_1318x480.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Superintelligence** While Muse Spark did pretty well in benchmark, the more reassuring confirmation of Muse Spark came from engagement gains Meta saw due to the model. From the call: > In tests we ran leading up to the launch, **we saw meaningful engagement gains that accelerated week-over-week with each new iteration of the model**. We’re seeing similar games within Meta AI following the broad rollout of our new model with **double-digit percent increases in Meta AI sessions per user**. > > …before this, we have been prototyping a bunch of things using other different models, whether it was our previous older models or kind of using the APIs from other companies. And now **we’re unlocked to be able to go build things and get them to scale on top of our own models.** Agents are the new buzz word in AI, but Zuckerberg mentioned that he is yet to see agents that are suitable for mass consumer use as he mentioned “there aren’t that many that I would want to give to my mother”. I think that’s a good framing for a consumer internet company such as Meta. I am optimistic about their recent acquisition of [Dreamer](https://www.cnbc.com/2026/03/25/hugo-barras-return-to-meta-5-years-after-exit-underscores-ai-urgency.html?ref=mbi-deepdives.com) which was also more focused on solving agents for consumer use cases. One of the key points that I think Zuckerberg wanted to land in yesterday’s call is that thanks to AI, Meta’s scope is materially increasing. See his comments below (emphasis mine): > Right now, our apps primarily help people accomplish 3 important goals: connecting with people, learning about the world and entertainment. But **we’ve always wanted our apps to understand more of people’s goals so we can help improve their lives in all the ways that they want. These new AI models will let us understand this in more detail.** So instead of just looking at statistical patterns of what types of people engage with what content, **for the first time in Meta’s history, we’re going to be able to develop a first principles understanding of what you care about and what each piece of content in our system is about** \-- is that way we can show you more useful things for what you’re trying to accomplish. And we’ll also be able to create personalized content specifically for people to help you achieve your goals as well. Zuckerberg again highlighted how AI is boosting productivity of some employees as one or two people today can build something that previously took dozens of people months. As I have [**mentioned**](https://www.mbi-deepdives.com/tokenmaxxing/) earlier, many tech companies may be forced to layoff the bottom decile or quintile to make room for token budget for the top decile. Many Meta shareholders seem to think Meta doesn’t need to have a SOTA model. Zuckerberg seems to have a different opinion on this. While I do think Zuckerberg cannot acknowledge that they don’t necessarily need SOTA model for their core business to thrive since that would hurt his recruiting pitch to marquee AI researchers, I do suspect he genuinely believes that Meta needs SOTA AI model. Notice his comments below (emphasis mine): > “…**you’re not going to have leading models in the future if your models can’t improve themselves**, right? So you’re getting to a point where today, the models are still able to learn from people -- and then I think at some point, **the models will have to improve themselves**. And that’s how the growth is going to -- an improvement in the models is going to happen. And if you don’t -- i**f we don’t have an ability to do that, then we or anyone else, I think the companies that don’t do that are not going to be leading labs, then they’re not going to produce leading products**. So I think at that like that is a table stakes thing that we are focused on. > > Now does that make us a developer tools company? Not necessarily. I mean, I’m not against having an API or coding tools or anything like that. But it’s not our primary focus. But **I actually think people conflate coding with self-improvement more than they should. Coding is one ingredient for the model self improving. It’s not the only thing. And we are focused on all of the parts that are going to be necessary for self-improvement in service of the personal super intelligence vision that we have for people and businesses**. So which way do I lean more? The investors who think Meta doesn’t need SOTA to thrive are likely right for the next year or two, but I suspect Zuck is indeed more right over the next 5-10 years timeframe. A self-improving model will be too useful to not have in-house in 5 years and any company who doesn’t have such model may face a high likelihood in **NOT** being labeled “big tech” in 10 years. As a long-term shareholder of Meta, I concur with Zuckerberg’s approach here even though success is far from guaranteed in getting **AND** staying at the frontier. Investors also probably didn’t like the fact that Meta raised capex guide for 2026 from $115 Bn-$135 Bn range to $125 Bn-$145 Bn. However, this increase was primarily due to higher memory prices. Meta has also been building up their capacity through 3P cloud and I thought a particular comment from CFO is worth highlighting here (emphasis mine): > we’re going to continue building out our infrastructure **with flexibility in mind. And if we end up not needing as much as we anticipate, we can choose to bring it online more slowly or reduce our spending in future years as we grow into the capacity that we’re building now**. That’s a pretty good position to be in. As long as compute constrained environment continues, Meta will utilize 3P cloud more. But once that’s not the case anymore, they can either lower future capex or utilize less 3P cloud or some combination of both. I suspect they may be contractually bound to use 3P clouds for the next 5-7 years in which case they can lower their own capex if it appears that they have overestimated their compute need. Meta also highlighted their resilient chip strategy which I have [**covered**](https://www.mbi-deepdives.com/mtia2/) before. Nonetheless, investors seem to have elevated concern for Meta’s capex vs any other hyperscalers at the moment. I will explain the investors’ concerns and what I think behind the paywall. --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Spotify 1Q'26: Recalibrating Expectations URL: https://www.mbi-deepdives.com/spot1q26/ Last updated: 2026-04-29T15:01:41.000Z I didn’t realize until yesterday that Spotify is actually currently approaching its second worst drawdown since becoming a public company as the stock has experienced a drawdown of \~44% so far from its peak in June, 2025! At the same time, now that I have been following this company for nearly five years, I don’t remember a time the company was better positioned. The reality is the stock just went a bit ahead of its results last year and investors are likely just re-calibrating their expectations. I’ll go through the quarterly numbers, some key takeaways from the earnings call, and my current commentary on valuation behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!bL04!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6dc35b59-b237-492c-a4b3-6912c9c1d25a_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *Subscribers get the daily journal and five+ years of Deep Dives, i.e. full-length analyses with financial models on* [***65+***](https://www.mbi-deepdives.com/models/) *companies. The daily is just how I think out loud between the Deep Dives!* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Roblox: Aggregating the Attention of Next Generation URL: https://www.mbi-deepdives.com/rblx/ Last updated: 2026-04-28T13:47:19.000Z I was a tad bit surprised to learn that Roblox was founded the same year Facebook was: 2004! Roblox, however, wasn’t quite the rocket ship Facebook was, but at $40 Billion current Enterprise Value (EV), Roblox is currently worth more than twice of Snap and Pinterest **combined!** ![chart](https://substackcdn.com/image/fetch/$s_!UvmO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fde961022-ecbb-40ac-aafc-146a1eb0d6c1_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) By the time David Baszucki decided to start Roblox, he was actually already reasonably successful. Back in 1989, Baszucki cofounded “Knowledge Revolution”, a software development company focused on educational simulation technology. Their flagship product was a program called “Interactive Physics”. It was designed strictly as an academic supplement to help students model two dimensional mechanical problems and visualize textbook equations. Human behavior quickly subverted the software's academic intent. Baszucki observed that instead of diligently solving physics homework, children were utilizing the engine to construct elaborate virtual contraptions. From Roblox’s S-1 filing in 2020: > “The story of Roblox began in 1989 when our founders, David Baszucki and Erik Cassel, programmed a 2D simulated physics lab called Interactive Physics, which would later go on to influence our approach to building the groundwork for Roblox. Students across the world used Interactive Physics to see how two cars would crash, or how they could build destructible houses. In starting Roblox in 2004, we wanted to replicate the inspiration of imagination and creativity we saw in Interactive Physics on a much grander scale.” Knowledge Revolution proved moderately successful and was acquired by MSC Software in December 1998 for \~$20 million dollars. Baszucki spent a few years as a Vice President at MSC before departing to manage an angel investment firm, where he actually provided early seed funding to the pioneering social network: Friendster. In 2003, he reconnected with Cassel, the former lead engineer from Knowledge Revolution. Together, they set out to actualize the vision sparked by those early educational simulations. They wanted to build a platform that married rigid mechanical physics with limitless user generated creativity. Development began under the working title eBlocks, which was soon changed to DynaBlocks to reflect the dynamic nature of the physics engine. They envisioned a digital realm governed by the laws of gravity and momentum, yet entirely moldable by its inhabitants. By early 2004, recognizing that DynaBlocks was somewhat cumbersome for a younger demographic to remember, they merged the concepts of robots and blocks to officially call the platform Roblox. After over two years of intensive coding to build out the foundational architecture, user interface, and community tools, Roblox officially launched to the public on September 1, 2006. At launch, Roblox was pretty basic. It included games such as paintball games, haunted houses, model trains. But the real surprise came from the users as Baszucki [recalled](https://www.engadget.com/2012-05-30-mmo-family-roblox-ceo-david-baszucki-talks-blocks-building-an.html?ref=antoinebuteau.com): > "The biggest surprise is the quality and ingenuity of the users. When we launched, the team literally put up one prototype game that they made. The second they allowed the users to make games, the quality surpassed what they had done almost immediately." In its first few years, the platform functioned primarily as a niche community for dedicated tinkerers and amateur programmers. But a major shift occurred in late 2012 with the launch of Roblox Mobile. Porting an intricate, physics heavy desktop game engine to the iOS ecosystem was a monumental engineering gamble. It required unifying the rendering, physics, and web teams to create a native application that did not compromise the established desktop user experience. By removing the friction of desktop exclusivity, Roblox tapped directly into a younger, mobile first generation. If the mobile launch provided the sheer volume of audience, the introduction of the Developer Exchange (DevEx) program in 2013 provided the platform's economic engine. DevEx allowed creators to convert their earned in game virtual currency Robux into real world fiat money. Before DevEx, developing games on Roblox was a passionate hobby. After DevEx, it became a potentially lucrative career path. The more engaging the games the community built, the more players arrived to spend money, and the more capital the developers earned. This economic architecture catalyzed a gold rush of a better quality content, effectively outsourcing the traditional game development studio model to a much more decentralized crowd. Of course, once Covid happened, Roblox exploded during the pandemic. Daily Active Users (DAU) increased from \~19 million in 2019 to \~37 million in 2020\. After such a massive year, Baszucki decided it was time to go public. First Round’s Chris Fralic recalled in this [podcast](https://podcasts.apple.com/us/podcast/20vc-the-roblox-memo-first-rounds-chris-fralic-on-the/id958230465?i=1000519211017&ref=mbi-deepdives.com) that while the journey to IPO was anything but smooth for Roblox, Baszucki appeared to have a deep conviction on the long-term success of Roblox. From the podcast (emphasis mine): > “…as the company was going public, I was going back and looking through some notes of my takeaways from board meetings and just general updates, and **there were periods of big misses on the financial side. There were key hires that weren't working out as hoped, needed to be replaced, and in particular, like we went out for a fundraise and couldn't get anybody interested. So that was a tough period, but again it never seemed to overly phase Dave**. And he had complete conviction on what Roblox could be in spite of all those facts, and never stopped, and then the numbers caught up. And the reality caught up. And the world started to get a sense of what Roblox really, really was.” The big question, however, was whether the pandemic driven boost in user metrics and engagement was just a temporary fad. Now that it’s five years since Covid, we can answer more definitively that Roblox was no pandemic fad. To contextualize, **Roblox’s DAU in 2025 was almost \~4x the number of its DAU in 2020!** In fact, one of the key reasons I have decided to spend more time in studying Roblox was when I listened Matthew Ball mentioning the following in his recent Stratechery [interview](https://stratechery.com/2026/an-interview-with-matthew-ball-about-gaming-and-the-fight-for-attention/?ref=mbi-deepdives.com): > “…If you can believe **60% of net growth outside of China since 2021 went to Roblox, 67% of global non-China spending growth last year, 67% went to Roblox**. In effect, if you say the gaming industry had a great year last year and you net out Roblox as the sole participant, no, gaming did not have a great consumer spend year.” For the more visually inclined, here are the relevant charts from Ball’s [presentation](https://www.matthewball.co/all/presentation-the-state-of-video-gaming-in-2026?ref=mbi-deepdives.com) on the state of video gaming in 2025: ![](https://substackcdn.com/image/fetch/$s_!Nvbq!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F71eaab16-9e4b-423b-b27b-ed25ddb3d62c_2152x1110.png) Source: Matthew Ball [Presentation](https://www.matthewball.co/all/presentation-the-state-of-video-gaming-in-2026?ref=mbi-deepdives.com) If you don’t have a young kid at home, I suspect you may not quite grasp the popularity of Roblox among kids. In fact, whenever we invite guests at our place and if they have young kids, I have seen almost **100%** of these kids play Roblox while their parents are socializing. This was also another sign that I really should follow Roblox more closely. I also noticed that the stock basically went nowhere since the IPO despite revenue became more than 5x since IPO! ![chart](https://substackcdn.com/image/fetch/$s_!-D0P!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3623831c-f3cd-4a13-aa43-742195b10a2f_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In the rest of this Deep Dive, I will first discuss Roblox the product as well as the business. I will then elaborate on its moats as well as broader competitive dynamics and risks the company faces. I will then talk about management incentives and explore what is currently being priced in Roblox’s valuation. Subscribe to read the rest of this Deep Dive. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Personal Day URL: https://www.mbi-deepdives.com/personal-day/ Last updated: 2026-04-27T13:14:31.000Z I’m taking a personal day today, but wanted to share a couple of quick updates. I was working on a Deep Dive on Roblox this month and it will go out tomorrow. After that, I’ll be working on Deep Dives on FICO and DoorDash over the next couple of months. One change: while I’ve typically published each Deep Dive as a single \~50–60 minute read, going forward I’ll be breaking them into three or four separate posts. Roblox will still come out as one piece, but FICO and DoorDash will each be published as a series over the course of the month. I think it is not only easier for readers to digest relatively shorter posts but also helpful for me to dig deeper in questions where I may want to spend more time. Thank you. [Subscribe](#/portal/signup) ### A (Largely) Functional World: Part 2 URL: https://www.mbi-deepdives.com/functional2/ Last updated: 2026-04-26T15:07:06.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- Early this month, I wrote a [**piece**](https://www.mbi-deepdives.com/functioning-world/) making the case that the world is (largely) functional by pointing out the elevated success rate of rapid social mobility that I have observed among my talented peers despite being born in a second-tier city in Bangladesh. I was careful not to make too sweeping a statement. I narrowed my case to point out that it is perhaps underappreciated by many that as long as you are reasonably intelligent, you have a pretty good shot at social mobility regardless of where you were born. That, of course, wasn’t always the case and the rate of improvement the world has made in this regard is what I believe to be underappreciated. In fact, the first time I heard the story of the great mathematician [Ramanujan](https://en.wikipedia.org/wiki/Srinivas%5FRamanujan?ref=mbi-deepdives.com), I wondered how the world must have missed out on numerous great mathematicians and scientists in much of the history of our civilization just because they were not born in the “right” place! Thankfully, Ramanujan was agentic enough to reach out to G.H. Hardy at Cambridge and the world could benefit from Ramanujan’s unique insights. However, a reader later quipped to me that isn’t intelligence itself a great [Ovarian lottery](https://www.businessinsider.com/warren-buffett-on-the-ovarian-lottery-2013-12?ref=mbi-deepdives.com)? How functional is really the world if such Ovarian lottery dictates our fate? It is an understandable desire to want to give everyone a fair shot regardless of intelligence. Many people perhaps gravitate to a society that is designed based on Rawls’ philosophy of “[veil of ignorance](https://en.wikipedia.org/wiki/Original%5Fposition?ref=mbi-deepdives.com)”. But I have often wondered whether too much dogmatism around fairness may lead us to the world of [Harrison Bergeron](https://www.tnellen.com/westside/harrison.pdf?ref=mbi-deepdives.com). Even though I don’t want to live in a society that is obsessed with fairness by finding the lowest common denominator among its population, I myself also wonder that the world may be lot less functional if you failed the Ovarian lottery in intelligence. It is perhaps in the realm of possibility that while the world made enormous progress in making it more functional for intelligent people regardless of their background, it may have become less hospitable for others. In fact, I came across this book a couple of years ago by Gregory Clark: “[The Son Also Rises](https://www.amazon.com/Son-Also-Rises-Surnames-Princeton/dp/0691168377?ref=mbi-deepdives.com)” which did make me question the typical narrative that the wealthy loses much of their wealth by the third generation. Clark argued that it takes 10 to 15 generations (300 to 400 years) for families at the extreme ends of the socioeconomic spectrum to regress to the mean. He claimed this slow rate of mobility is a universal constant, roughly 0.7 to 0.8 across generations (meaning 70% to 80% of a family’s underlying status is transmitted to the next generation). Conventional mobility studies look at parent-child correlations in income or education and find correlations around 0.3–0.5, suggesting fairly rapid regression to the mean. Clark argued this dramatically overstates mobility because single-generation data is noisy. To avoid the “noise” of short-term parent-child income fluctuations, Clark went for a novel empirical technique: tracking rare surnames over centuries. By examining the concentration of specific surnames in elite institutions (e.g., Oxford and Cambridge graduates, medical associations etc.) against their proportion in the general population, he mapped long-term status persistence. He looked at this in multiple regions. Take India, for example. For India, Clark used Bengali surnames as his anchor. He identified a set of high-caste Kulin Brahmin and Baidya surnames (Mukherjee, Banerjee, Chatterjee, Bhattacharya, Ganguly, Sengupta, and similar) that were elite markers in pre-colonial and colonial Bengal, and contrasted them with surnames associated with lower-caste and Muslim populations. He then tracked their representation across generations in elite registers: e.g. physicians on the Indian Medical Council rolls, judges of the High Courts, professors at top universities, and Bengali engineers and civil servants from the late 19th century through the 2000s. The Kulin Brahmin and Baidya surnames are wildly overrepresented at the start of the series (often 10x+ their population share) and decay only slowly despite Indian independence, and affirmative action. Based on his data, Clark made a pretty bold claim that the underlying rate of mobility is immune to social engineering. He argued that modern welfare states (Sweden), communist revolutions (Qing vs. Maoist China), and democratic capitalism (the US and UK) all exhibit the same sluggish mobility rates. Clark thought the common denominator in all these cases was “social competence” i.e. some bundle of cognitive ability, work ethic, and behavioral traits transmitted across generations. One of the challenges that I have observed kids born in less fortunate circumstances is that their parents can often be awfully unfamiliar with the world of success. While my parents knew to instill the value of formal education in me, my father’s wildest dream around economic success for his kids appears to become a government civil service professional in Bangladesh. He obviously wanted the very best for me, but the “best” in his world could have held me back much more than he might have imagined. I often joke with my friends that if you are born in a less fortunate circumstances, you would be better served by looking outside the imagination available at home. On the other hand, if you are born into a more fortunate circumstances, your parents may impart wisdom in dinner table that can guide you for decades to come. Even in that case, the timeliness of such guidance can depend on the volatility of the world itself. If a world three decades from now is largely an incremental progress from here, our advices for our kids can retain a large fraction of the value. However, if the world becomes much more volatile in the coming decades, especially because of AI, today's social competence may be of little use. and our kids may need to prepare themselves fundamentally differently to navigate such a distinctively different world. To be fair, if Gregory Clark were writing an addendum to his book today, he would likely argue that AI will have zero impact on the long-term rate of social mobility. The striking feature of his data is its insensitivity to enormous structural changes. He looked at the printing press, mass literacy, industrialization, universal schooling, two world wars, and the internet. of course, most of these were supposed to be a great leveler and yet, none visibly moved the underlying coefficient of “social competence” in his data. So, the base case perhaps should be that AI will be no different. However, historically, elite status has been protected by moats of credentialism and exclusive access to high-level cognitive training. A vast amount of underlying “social competence" is perhaps just access to institutional knowledge. A great deal of "social competence" in Clark's sense is tacit i.e. it's knowing which questions to ask, which paths exist, which moves are available, what "people like us" do in this situation. This kind of knowledge has historically been almost impossible to transmit outside of close relationships because the recipient doesn't know what they don't know; they can't search for the answer because they can't formulate the question. AI is unusually good at surfacing this kind of meta-knowledge precisely because it can respond to vague, malformed, embarrassing questions and progressively refine them into useful ones. If AI makes these skills universally accessible at an eventual marginal cost of zero, perhaps it will create such volatility in the “social competence” layer that the world may become finally (largely) functional for people despite not winning the Ovarian lottery of intelligence. We don’t quite know how and where the value will accrue to what skills in such a world decade(s) in advance. I don’t have a lot in common with AI doomers, but even though there may be some merit in worrying about AI’s existential impact on society, I also wonder to what extent it is more of a reflection of people’s concerns about gradual slipping of existing social competence. Just as Uber drivers cannot possibly be a big fan of rising Waymos in the city, can the status quo winners of cognitive ability be truly welcoming of an AI revolution that may make their very skills less rewarding? --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Era of Tokenmaxxing URL: https://www.mbi-deepdives.com/tokenmaxxing/ Last updated: 2026-04-25T14:59:48.000Z A couple of weeks ago, Meta’s internal dashboard for “tokenmaxxing” got a lot of attention after The Information [reported](https://www.theinformation.com/articles/meta-employees-vie-ai-token-legend-status?rc=4lgoj7&ref=mbi-deepdives.com) on it. Although such tokenmaxxing phenomenon only started getting attention recently, Shopify has been doing something [similar](https://newsletter.pragmaticengineer.com/p/how-ai-is-changing-software-engineering?ref=blog.pragmaticengineer.com) since mid-2025\. It is also hardly a Meta or Shopify specific thing; Microsoft and Salesforce are also apparently [following](https://blog.pragmaticengineer.com/the-pulse-tokenmaxxing-as-a-weird-new-trend/?ref=mbi-deepdives.com) something similar. In a recent [podcast](https://www.latent.space/p/shopify?ref=mbi-deepdives.com), Shopify’s CTO Mikhail Parakhin shared some interesting data on AI tool usage at Shopify. Perhaps the most interesting data point is nearly everyone on Shopify is using at least one of the AI tools daily. In the image below, the green line indicates the percentage of employees in Shopify who uses at least one AI tool daily. In early 2026, only half of Shopify employees used one of the AI tools daily. Today, this number is basically approaching 100%. Opus 4.5 was basically a gamechanger for adoption which has been driving the tokenmaxxing era in full speed. Parakhin didn’t share exactly what each of the line below denotes to, but since he mentioned CLI-based tools are becoming more popular within Shopify, it’s probably fair to assume that the brown/tan line refers to Claude Code (Anthropic), and Orange to Codex (OpenAI) which are currently used by \~70% and \~55% of employees respectively on a daily basis in Shopify. Interestingly, he also indicated that tools that require IDEs such as GitHub Copilot, and Cursor are still growing but they’re losing share rapidly (red line in the image). Any Shopify employee can use any AI tool with unlimited token budget. ![](https://substackcdn.com/image/fetch/$s_!ZAo6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F72b2e1c3-51e5-4ae4-8484-196d350600b9_1861x1083.png) Source: Screenshot from Latent Space podcast One interesting tidbit from the conversation is even though everyone is using AI tools on a daily basis, there is fair amount of inequality in usage. The top 10 percentile is actually growing significantly faster than the rest in Shopify and I suspect the trend is similar in other organizations. From the podcast: > the super interesting part here is that you could see that the distribution becoming more and more skewed. > > The top percentiles grow faster. So that means- the people in the top ten percentile, their consumption grows faster than seventy-five and so forth. So, the distribution skews more and more towards the highest users, which is... I don’t know what it tells me. It’s like it feels not ideal, to be honest. Looking at this data, I wondered if many tech companies will be forced to lay off the bottom 10 or 20 percentile token spenders to create budget for tokens for the top 10 or 20 percentile in the organization. Of course, the obvious retort to such tokenmaxxing trend is that it may fall into the trap of Goodhart’s law: "When a measure becomes a target, it ceases to be a good measure". I’m sure these tech companies know that and will look closely into the data to see whether higher token usage correlates highly to highly productive work and not some mindless token spending for the sake of it. And if the data suggests that top percentile users do highly productive work with higher token spending, more budget will naturally follow to them until it stops making sense. ![](https://substackcdn.com/image/fetch/$s_!ymHO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdeb3a821-5cab-402e-99a4-6a294e7ade3e_1780x1033.png) Source: Screenshot from Latent Space podcast There are at least two other reasons why tokenmaxxing may actually more sense than it may appear at first glance. The reality is tokens are likely still quite cheap; just yesterday, The Information reported that Cursor had [negative 23% Gross Margin](https://www.theinformation.com/articles/behind-cursors-deal-spacex-anthropic-compute-costs-loomed-large?rc=4lgoj7&ref=mbi-deepdives.com) in 2025\. While gross margins for Claude and Codex are likely lot better than that, I think token buyers should be able to pick a lot of long hanging fruit with such tools in the early stage of adoption of such tools. You can always optimize the costs later once you let everyone use these tools at their heart’s content for a few months and then look at the data closely to figure out the right optimization process later. There is another interesting explanation why companies such as Meta whose models are not as good at coding as Claude Code or Codex are essentially using such wanton usage of token as a training data for their own model. The Pragmatic Engineer Newsletter [mentioned](https://blog.pragmaticengineer.com/the-pulse-tokenmaxxing-as-a-weird-new-trend/?ref=mbi-deepdives.com) this potential rationale (emphasis mine): > “Putting a leaderboard in place was always going to incentivize much more AI usage. And **more AI usage means producing a lot more real-world traces. These traces can then be used to train Meta’s next-generation coding model better**. > > I believe this was the goal, even if no one said it out loud. > > It’s an expensive way to generate data for training, but if any company has the means to do so, it’s Meta.” Indeed, having a high quality training data perhaps also explains SpaceX’s deal with Cursor. Kevin Kwok [articulated](https://kwokchain.com/2026/04/23/cursor-and-spacex-in-search-of-a-complete-loop/?ref=mbi-deepdives.com) this rationale really well in this post (emphasis mine): > Cursor must train its own models. And they’ve done this, starting with post-training an open source model in Composer 1, then extended pre-training and post-training for Composer 2, and now beginning to pre-train their own models from scratch. But it is one thing to build budget models with better margins and another to compete head to head on the state of the art. The compute expense is in the billions–if you can even get the compute. > > **If Cursor believes it can compete at the highest levels but will see its position degrade without matching the AI labs on compute and model training, then Elon and SpaceX are their perfect complement.** > > Elon merged xAI into SpaceX. Since then, its research leadership has been entirely hollowed out. A morbid joke is that it’s been like Iranian leadership: every day a new head of research is battlefield promoted, and every next day they are gone. > > In recent months **Elon has become convinced of the importance of coding models, moving from a small team working on them to it being the entire lab’s priority**. But building a coding model from scratch without any of the data or harness is hard to bootstrap. Even more so without research or product leadership. > > xAI has tremendous compute capacity, with plans to scale it as much or more as any of the other labs. Everything datacenter-related says xAI should get stronger every year, but it is clearly underperforming that compute capacity. It has the cheapest cost of compute perhaps not just because it is so good at building datacenters but also because no one is using them. It has been a lab with neither product/research directions nor heads. And it is not converging on its competitors–it is falling behind. > > **Cursor is running out of time. xAI is in a race against time. Together they solve each other’s problems.** > > Cursor gives SpaceX the research and product leadership that has shown it knows how to build in this space. It’s not a sure thing, but no team outside of the labs or China has done more. And the product immediately solves xAI’s coldstart problems around data and harness. Meanwhile, SpaceX gives Cursor the compute to compete long-term on both model training and inference scaling. > > Between them they have the complete loop. Neither does alone. I have noticed many investors seem to be too eager to think the coding tool market is likely to be settled in a bit of a duopoly market between Claude Code and Codex. If that indeed turns out to be the case, future tokens may become quite expensive over time as such market structure may allow attractive margins for Claude Code and Codex. However, for a market that has only started couple of years ago, I suspect it is simply too early to declare winner(s) here. Anthropic was coding pilled more than any other labs, and as other labs are waking up to the extent of PMF coding tools have (and implication for such in the product downstream), I expect to see concerted effort from other labs to make this space more competitive. So, instead of coding market being settled to a monopoly or duopoly, we may actually be in the early stage of a more competitive market. As Gavin Baker [pointed](https://x.com/NVIDIADC/status/2043751071600759099?ref=mbi-deepdives.com) out, in the future you may be either token producer or token consumer and if you’re not a token producer, your ability to efficiently and effectively consume tokens as an organization may dictate your future. More competition, of course, would also be welcome if you are consuming tokens. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Pricing "The Compute Theory of Everything", Texas Instruments Hitting Inflection Point URL: https://www.mbi-deepdives.com/txn1q26/ Last updated: 2026-04-24T14:22:57.000Z Believe it or not, the iShares Semiconductor ETF (SOXX) was essentially flat on the year as of March 30…less than four weeks ago. Since then, semiconductor stocks appear to have chugged something with serious kick: the sector is up **47% YTD(!!)**, and given Intel's print last night, it's probably adding to that today. ![chart](https://substackcdn.com/image/fetch/$s_!iRk9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3df8a3f3-b648-4bdc-926c-8b4626a2833a_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I am a tad bit surprised that after three and half years since ChatGPT was released in November 2022, there was still so much money to be made in the “Picks and shovels” of this AI revolution. I’m not sure people are looking at Semi’s valuation that much anymore given that consensus seems to be clustering around the view that we may simply have not enough compute as far as the eyes can see. Given such “reassurances”, SOXX ETF is currently trading at valuation multiples not seen at least since 2001 (I don’t have data prior to 2001; I’m also taking LTM instead of NTM because KoyFin doesn’t have NTM data that far back). Imagine for a moment you are in early 2000s and you just read Hans Moravec’s seminal [paper](https://jetpress.org/volume1/moravec.htm?ref=mbi-deepdives.com) “**When will computer hardware match the human brain?”** Let’s say the paper is compelling enough foryou to start believing in “[**the compute theory of everything**](https://www.mbi-deepdives.com/the-compute-theory-of-everything/)” and you then decide to buy SOXX to express such view through a diversified instrument. Even though your broader view on the unrelenting value of more compute would be correct, you would experience a disorienting volatility over the next two decades. At this point, we are indeed perhaps starting to price “the compute theory of everything” given today’s stratospheric valuation demands fundamentals to remain rosy for years to come for investors to make an acceptable return from here. ![chart](https://substackcdn.com/image/fetch/$s_!bXuJ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa78b4ddc-169e-4f57-a0b3-7581f8c6a278_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Having said that, I will acknowledge that MBI Deep Dives has utterly missed the boat on the picks and shovels in the last 3-4 years. As a generalist, I knew very little about semis when ChatGPT was released and even though I tried to make amends by studying them since [early 2024](https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/), the market moved at too frenetic a pace for a novice semi observer like me to feel comfortable to act. To add insult to the injury, I did buy one semi stock in 2024: Texas Instruments (TXN) and after 18 months of holding the stock, I decided to sell it late last year to re-allocate the proceeds in Meta and Alphabet. ![Ben Affleck Smoking Through the Pain of Existence](https://substackcdn.com/image/fetch/$s_!YryA!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0258d558-50d0-4ab5-8ca1-4a13fdbb00ae_710x1065.jpeg "Ben Affleck Smoking Through the Pain of Existence") “MBI” reminiscing his TXN trade It now appears the TXN stock was “waiting” patiently for me to sell and it has only gone up since then. ![chart](https://substackcdn.com/image/fetch/$s_!_Wip!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F67c3ae4a-dd8b-4506-8d48-2eba722bf18c_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Self-deprecating jokes aside, Texas Instruments’ Q1 does seem to have reached an inflection point as analog revenue has exceeded 20% mark for the first time since 4Q’21. ![](https://substackcdn.com/image/fetch/$s_!pA9D!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8398c7db-e196-4b47-a25b-a7bc40bb0c89_1978x232.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) This growth was primarily led by industrials which was up 30% YoY and \~20% sequentially. Data center revenue was predictably also up \~90% YoY. ![](https://substackcdn.com/image/fetch/$s_!1foX!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3e8aa716-606d-42f8-a90e-1d53dc697035_2236x265.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) TXN management reminded despite this inflection in revenue, they are still noticeably below the previous peak. From the call (emphasis mine): > “we had a very good quarter in industrial…**but still 15% lower than the peak that was back in 2022**. And as I say many times, there is a secular growth continuing in industrial. So **we deserve a higher peak, right, 4 years later**. So I think there is a lot of room to grow. The encouragement I would have on industrial this time is that I see it as a broader application. > > So all of them, not only the data center-related energy infrastructure or power delivery, not only aerospace and defense and we know the geopolitical tensions and the market is establishing new peaks every quarter. **I saw it across all sectors in industrial and also across all customers in terms of regions but also the size of customers. It’s the first quarter where we saw the broad market, basically the tail starting to wake up again after a long hibernation period**, I would call it. So I am encouraged about the fact that we are seeing growth over there. But I think there is, I mean, I want -- I would like to see a secular growth in industrial continuing and then higher peaks establishing in 2026 or later versus the 2022 peak. So in that sense, trend line are suggesting we still have room to go. However, management also mentioned last year also started with a similar buoyant sentiment which turned out to be bit of a head fake. Indeed, the latter half slowdown in 2025 fooled me thinking 2026 would be a rather slow recovery. Again, from the call: > We had a similar, let's say, strong beginning of the year last year. Maybe the year-over-year growth last year was a little lower but it was still in the teens and it looks like it was getting stronger. But it was whatever you want to call it, a head fake, a false start or whatever. We had a good year in analog but it did not accelerate in the second half. It actually slowed down a little bit, right? So I think we need to be cautious…There is geopolitics. There is a macro that we are watching. Nonetheless, the demand now appears to be strong enough that even pricing lever is on the table which makes me think this inflection may not be “head fake”. TXN management sounded pretty upbeat while discussing the pricing environment during the call (emphasis mine): > right now, the demand signals are strong, if demand continues to be strong, and we are monitoring the market price and there is definitely at least an average price increase in the last several months across the analog market. **I think it’s likely that prices may go up in the second half of the year**. Again, this is going to be a case-by-case discussion in our case but that’s the pricing environment as I see it right now. > > And again, it’s always a function of supply and demand and the unknown for me right now is the sustainability of demand. So I want to see it playing out one more quarter and then we’ll figure out for the second half. So high level, not immediate support on growth, both sequentially and year-over-year on pricing. Now what we have seen is just breadth of demand, right, what I said before, multiple sectors or all sectors, all regions, all type of customers, small or large and supported by a data center market where we do pretty well. Given the revenue inflection, predictably margins have inflected as well. Gross margin increased to \~58% in 1Q’26 (+212 bps YoY), but still significantly below last cycle’s peak of \~70%. Similarly, operating margin expanded to \~39% (+669 bps YoY), but well below last cycle’s peak of \~52%. So, TXN should have a lots of headroom for further margin expansion if the cycle continues. ![](https://substackcdn.com/image/fetch/$s_!0NON!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff3e201cb-4d13-4c14-9c40-def72707d5a8_1969x289.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Consensus EBIT margin estimates are in the low \~40s in 2026\. At \~30x NTM EV/EBIT, buy-side may be penciling a higher margin inflection than consensus numbers imply at the moment. ![chart](https://substackcdn.com/image/fetch/$s_!qhBg!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb1e2a39f-96f6-40e0-ba3d-38482a946a9e_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Tesla's Terafab Dream URL: https://www.mbi-deepdives.com/terafab/ Last updated: 2026-04-23T13:44:40.000Z Tesla had their earnings yesterday, but given the company trades at \~226x NTM EV/EBIT multiple, it is bit of a waste of time to spend time dissecting their existing business. Ultimately, Tesla has become a moonshot bet on several projects that currently do not contribute materially to their numbers. Full Self Driving or FSD, Robotaxi, Optimus, and more recently, Terafab are some of their key moonshot projects. It’s not often that I come across companies in public markets which are generously valued purely based on optional value attached to ongoing projects with high degree of uncertainty attached to it, so it speaks volume of the level of confidence investors have shown on Elon Musk’s ability to unlock value even from what it may appear to be pretty improbable scenarios. Even by Tesla’s standard, Terafab project is perhaps the epitome of Musk’s eternal desire to swing for the fences. So, what exactly is this project about? [Terafab](https://www.terafab.ai/?ref=mbi-deepdives.com) is a planned \~$25 billion semiconductor fab that Elon Musk announced last month as a joint venture between Tesla, SpaceX, and xAI. The name comes from its headline target: 1 terawatt of annual compute output, which Musk mentioned to be \~50x what all existing global fabs produce today combined. It will target 2nm process technology and an initial capacity of 100,000 wafer starts per month. Almost every aspect of this project screams of quixotic dream, so I was curious to hear more about Terafab project in yesterday’s call. When asked about Terafab, Elon Musk said the following: > “we’re still working out the details of the Terafab deployment. In the near term, Tesla will be building the research fab on our Giga Texas campus. This is something we expect to be probably a $3 billion-ish initiative and capable of maybe a few thousand wafers per month, but it’s really intended to try out ideas. The research fab, it was in terms of maybe -- we have some ideas for improving the fundamental technology of how chips are made and some new physics we’d like to test out, but we also want to test out the ability to -- to see if something is working in production. So you need kind of like a few thousand wafer starts a month to make sure that a production process is sound. > > And then SpaceX is going to take care of like the initial phase of the scaled up Terafab. And that’s what we figured out thus far. > > I think this will be unique in the world, or at least I’m not aware of any a place where you have the lithography mask creation, and then logic, memory and packaging in under one roof in one building. That’s about the fastest I could possibly imagine doing \[ recourse \] of research and development and being able to try out some pretty radical ideas, some of which have -- it’s kind of long-shot stuff, but if some of these long shots pan out, it would be radical improvements in the way \[indiscernible\] work.” Indeed, that does sound like a pretty long shot! A conventional leading-edge fab actually handles only wafer fabrication and ASML provides the lithography tools. Design happens at fabless customers, memory is made at separate fabs (Micron, Samsung, SK Hynix), and advanced packaging and test are typically done at OSAT facilities or dedicated packaging sites. Terafab's claim is that design, lithography, fabrication, memory production, advanced packaging, and testing all happen at a single site. My guess is that anyone other than Elon Musk would not dare propose something like this. But once you've figured out how to catch a returning rocket booster with a pair of mechanical arms, the word "impossible" probably stops meaning what it means to the rest of us. If anything, such consensus of incredulity may have only propelled him to give this a shot. We don’t need to believe this project has any credible path when Musk himself acknowledges the long shot nature of the bet, but admittedly I’m glad that he’s giving this a go without accepting the “fate” that the semi value chain has reached almost the end state. To be clear, Musk isn’t doing this because he’s annoyed by the ever burgeoning profit pool of the players in the semi value chain, but because he thinks the compute need is going to be so great that it would be pretty much impossible to meet demand with existing ecosystem. From the call yesterday: > Terafab is not some sort of mechanism to generate leverage over our chip suppliers. It's just literally, we don't see a path to having enough efficient quantity of AI chips down the road. As we scale production to high levels, just the rate at which the industry is growing in logic, but even more so in memory, it's just doesn't -- we just anticipate hitting a wall if we don't make chips ourselves. As a reminder, TSMC was also asked about Tesla’s Terafab project during their earnings call. They didn’t sound concerned. From the call (emphasis mine): > “…both Intel and Tesla, they are TSMC’s customers. But again, they are our competitors, and we view Intel as our formidable competitor and do not underestimate them. But having said that, **there are no shortcuts. The fundamental rules of the foundry game never change**. They need the technology leadership, manufacturing excellence and customer trust, and most of all, the service, which has been mentioned by Jensen; thank you for his wording. > > Again, let me say that **it takes 2 to 3 years to build a new fab, no shortcuts. And it takes another 1 to 2 years to ramp it up. Again, that’s the fundamental of foundry industry.** And whether we try to win them back, actually, they are still our customer. And we are very confident in our technology position, and we work very hard to capture every piece of business possible. I will continue to listen to Tesla’s earnings calls to stay updated on their moonshot projects, especially Terafab. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Danaher 1Q'26 Update URL: https://www.mbi-deepdives.com/dhr1q26/ Last updated: 2026-04-22T14:36:45.000Z Danaher had another somewhat uneventful quarter. Looking at Danaher’s numbers every quarter is a stark reminder just how anemic the growth has proved to be for a company that enjoys some supposedly secular tailwinds. Even if you adjust for their spin-off of Environmental & Applied solutions segment in 2023, their topline in 1Q’23 would be $5.95 Billion. Three years later in 1Q’26, their topline remains the same: $5.95 Billion! ![](https://substackcdn.com/image/fetch/$s_!SK22!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb6ad5830-90c4-4e0a-a187-54d5f804eb53_1504x393.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The picture is similar in their biotechnology segment. The inventory destocking that affected them in 2023-24 period is clearly behind them, but growth is still uninspiring at \~6-7%. Sartorius Stedim Biotech, which is more of a pure-play bioprocessing comp, will announce their earnings tomorrow, but their trend has been somewhat similar over the last few years. But there are signs that we may see a better growth trajectory in the second half and beyond. I will particularly highlight management’s commentary on order growth: > Equipment declined modestly in Q1, but we were encouraged to see **orders growth of more than 30%, marking the first quarter of year-over-year equipment order growth in nearly 2 years**. QoQ decline is not surprising since there is some seasonality to it, but if order growth continues at a sustained pace, Danaher’s bioprocessing business should return to comfortable double digit growth. ![](https://substackcdn.com/image/fetch/$s_!dC9B!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F323a436d-c847-43ff-951d-f39d7b266b6b_1501x367.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Margin in biotechnology segment improved YoY to reach 42.7% in 1Q’26\. For context, margin in this segment peaked at 46.4% in 2021. ![](https://substackcdn.com/image/fetch/$s_!zflM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fda6c69a6-a837-4ef0-a2c9-3953ecd2006a_1495x229.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Life Sciences segment’s core revenue grew at only 0.5% despite having an easy comp of -4% in 1Q’25\. At least, that’s better than the Diagnostics segment whose core revenue declined by 4% in 1Q’26 despite, again, pretty easy comp of -1.5% in 1Q’25\. I won’t bore you with details of headwinds described by management, but these segments have been going through some really uninspiring performance for a while now. ![](https://substackcdn.com/image/fetch/$s_!HrYF!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc102bb7c-ee69-41ed-bbe1-685d3e9a49a5_1507x775.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The more inspiring bit during the call yesterday was when an analyst asked about AI’s potential impact on Danaher’s Life Sciences and Diagnostics segment. Rainer Blair, CEO of Danaher, said the following in response (emphasis mine): “…we think AI is going to be a growth accelerator for the pharma and biotech industry, both in the near and in the long term. And the reason for that is we think that AI will accelerate the drug development and commercialization flywheel and result in better development pipeline yields. So as you know, **the average yield in the drug development pipeline today is just above 10%. There’s an enormous opportunity here to improve the yield of the pipeline** and to accelerate the biopharma flywheel along with the flywheels of life science tool providers like ourselves. And so this improved yield drives both growth and profitability and reinvestment in the pharma industry. And that, of course, in turn, drives more investment into discovery, including wet lab validation, development in the clinic as well as commercial drug manufacturing. So **in the short term, what we’re seeing actually is incremental more demand, which we expect to accelerate in the building of biologic models**. **Autonomous science is the current buzzword that refers to the building of biologic models**, and of course, that requires automation, which we’re very well represented in. **It requires more analytical instruments and it requires more reagents as well. So that’s the short-term impact as this practically new market segment of autonomous science starts to play out here, and that plays out first in discovery and then continues to accelerate through the development pipeline**. And of course, we’re very well positioned here with our life science tools. I mentioned automation, analytical instruments that, of course, increasingly are AI-enabled reagents that support all of those models going forward. And that’s a several year driver. **These biologic models are in the single-digit percentage of information coverage required, very different than large language models. These biologic models require significantly more information in order to become general use type of model**. So that’s the short term. And as I indicated then in the long term, **what we’re going to see is the cycle time of pharma development being compressed and the hit rate, i.e., the yield to be increased**. And that flywheel is going to be very good for patients. It’s going to be very good for the pharma industry and those partners like ourselves that support that industry. Now as you think about that going through development…**these more commercialized drugs means more business for our bioprocessing business. We're the best positioned there with the broadest and deepest portfolio…**then lastly, a lot of these drugs are going to be more sophisticated. They are going to require **more sophisticated, more accurate diagnostics**. If they're not personalized diagnostics, they will require near personalized diagnostics to come online.” I haven’t done enough work to assess Danaher management’s point of view, but let me share what gives me bit of a pause here. Last week, Max Jaderberg, chief AI officer, and Sergei Yakneen, chief technology officer at Isomorphic Labs appeared on Nvidia AI [podcast](https://blogs.nvidia.com/blog/isomorphic-labs-ai-drug-discovery/?ref=mbi-deepdives.com) and shared a vision for the future that may have bit of a tension with the future Danaher likes to imagine. Notice what Nvidia mentioned in their blog (emphasis mine): “By modeling cellular processes with AI, Isomorphic’s teams can predict molecular interactions with exceptional accuracy. Their advanced AI models enable scientists to computationally simulate how potential therapeutics interact with their targets in complex biological systems. **Using AI to reduce dependence on wet lab experiments accelerates the drug discovery pipeline** and creates possibilities for addressing previously untreatable conditions.” If AI lets you do order of magnitude more simulation than you ever could through actual experimentation, wouldn’t that be a potentially secular headwind for at least some part of Danaher’s business? As I have mentioned before, I haven’t done enough work here, so I don’t have a strong opinion yet. But I’m also quite not ready to believe the halcyon future of AI being a secular tailwind for Danaher that management likes me to believe. Management kept their core revenue guidance of \~3-6% but raised the high end of 2026 EPS guidance from $8.5 to $8.55. On capital allocation, Danaher maintains its bias to do more M&A. They have already announced they’re going to [acquire](https://investors.danaher.com/2026-02-17-Danaher-To-Acquire-Masimo-Corporation?ref=mbi-deepdives.com) Masimo for $9.9 Billion. Management mentioned that they believe the acquisition will be accretive at all levels (gross and operating margins) and reach HSD ROIC in year 5\. HSD ROIC in year 5? This type of uninspiring ROIC is exactly why I decided to [**sell**](https://www.mbi-deepdives.com/anthropics-focused-bet-portfolio-change/) the stock late last year. Scuttleblurb in a more recent [**post**](https://www.scuttleblurb.com/scuttlebit-dhr/?ref=mbi-deepdives.com) also explained why such ROIC trajectory is unlikely to change anytime soon, if ever (emphasis mine): “The high-single digit ROIC that management expects to achieve on Masimo within 5 years may be disappointing to those who remember the days when the bogey for large platform acquisitions was set at 10%. But targets of significant enough size to move the needle for a buyer as large as Danaher are in scarce supply and **generally aren’t managed by idiots who sell at cyclical bottoms**. **Everyone knows these are valuable properties and I expect them to be taken out at prices that reflect this reality. The days of opportunistically seizing hidden gems at single digit forward EBITA multiples are long over**. I don’t doubt that Danaher will pull out of the current growth slump and eventually return to high-single digit organic growth. China is recovering, A&G demand will eventually come back, and the biologics tailwind remains as strong as ever. Over time, AI may accelerate the pace of drug discovery and streamline processes downstream of that. But it’s reasonable to expect that most of whatever free cash flow drops down from that will be deployed into M&A, **the returns from which will drive much of the returns that shareholders realize**. **Over the last decade, Danaher has spent \~$47bn on life sciences-related acquisitions. EBITDA (ex. spin-offs) has grown by $5.6bn over the same period, implying somewhere in the neighborhood of \~9% after-tax returns over that period (if anything, this is generous since my calculation implicitly gives acquisitions credit for all of the $5.6bn increase in EBITDA over the past 10 years). Danaher will continue to compound value for shareholders, though perhaps at a more modest pace over the next 10 years compared to the last 20**.” Indeed, the next 10 years will likely be more modest than the last 20 for the stock. The starting valuation, despite the stock being flat for the last five years is still on the high-teens. You can, of course, rightly argue that the quality of the business Danaher owns today is likely much better than the collection of industrial assets it owned in 2000-10 period, but the point still remains. ![chart](https://substackcdn.com/image/fetch/$s_!2vsD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff486a2bc-06bb-411f-a5e7-8bac6f6f427f_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Meta's Heterogeneous Fleet URL: https://www.mbi-deepdives.com/mtia2/ Last updated: 2026-04-21T17:35:43.000Z Last month, I mentioned the following on [Meta’s Chip Resilience](https://www.mbi-deepdives.com/mtia/): > “…in an earlier [blog post](https://engineering.fb.com/2025/09/29/data-infrastructure/metas-infrastructure-evolution-and-the-advent-of-ai/?ref=mbi-deepdives.com) last year, Meta’s own engineers mentioned dealing with **5–6 hardware SKUs a year** makes it harder to move workloads around and can create **underutilization and software friction**. From Meta’s own blog post last year (emphasis mine): > “From an operator point of view, **it is difficult for Meta to deal with 5-6 different SKUs of hardware deployed every year. Heterogeneity of the fleet makes it difficult to move workloads around, leading to underutilized hardware**. It is difficult for software engineers to think about building and optimizing workloads for different types of hardware. If new hardware necessitates the rewriting of libraries, kernels, and applications, then there will be strong resistance to adoption of new hardware. In fact, the current state of affairs is making it hard for hardware companies to design products because it is difficult to know what data center, rack, or power specifications to build for.” Given this context, while I was encouraged to see Meta’s diversified chip strategy, I was also a tad bit concerned whether the trade-off has too high a cost. However, I have now **updated** my opinion that Meta is increasingly quite enviably positioned due to their heterogenous hardware fleet which I will elaborate further behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Token Cost Conundrums URL: https://www.mbi-deepdives.com/token-cost/ Last updated: 2026-04-20T14:29:53.000Z A couple of weeks ago, I [highlighted](https://www.mbi-deepdives.com/fat-tail-ai/) a couple of pieces by Anjali Shrivastava who made the point that a token is not a fixed unit of cost. Today’s piece is a follow-up driving the point home that even though every model charges its API customers per token, tokens are far from a standard unit. Each model has its own **tokenizer** that decides how many tokens your prompt becomes. Feed the exact same prompt to GPT-5.4 and Claude Opus 4.7, and Claude might slice it into 2–3x as many pieces. So even if the headline price were exactly the same, you'd pay 2–3x more for identical content. It’s like two movers quoting you a rate per box. Mover A quotes you $10 per box but uses large boxes. On the other hand, Mover B charges you $8 per box, but uses small boxes. Mover B’s rate is 20% cheaper, but your apartment takes 10 boxes at Mover A and 18 at Mover B. As a result, the final bill turns out to be $100 vs. $144 i.e. the “cheaper” quote end up costing you 44% more. The key thing I want to highlight is Mover B wasn’t being dishonest with you; it was your job to understand the nuances of the quote and choose accordingly. Instead of seeing texts as letters of words, a language model sees numbers. The tokenizer is the translator sitting at the door: you hand it English or code, and it hands the model back a sequence of numeric IDs. Every model has its own tokenizer with its own vocabulary, typically 50,000 to 250,000 “tokens.” Each token is a chunk of characters that got a dedicated ID. I asked Claude to explain this with an analogy: > “Think of a court stenographer. She has shortcut keystrokes for super common words — “the,” “court,” “objection” — each one keystroke. Uncommon words she has to spell out letter by letter. Her speed on any given trial depends entirely on how well her shortcut system matches the vocabulary of that case. A routine contract dispute flies by. A medical malpractice case full of “anastomosis” and “laparoscopic cholecystectomy” crawls, because those words aren’t in her shortcut set.” Tokenizers work the same way, just learned from data. During training, the algorithm scans a huge pile of text and asks: which character sequences show up so often that they deserve their own token? Frequent stuff gets compressed e.g. “information” is probably one token, maybe two. However, rare stuff gets shattered i.e. an obscure chemical name might take eight tokens, one per syllable or letter cluster. Tokenization is essentially a compression algorithm, and compression inherently only works on patterns you’ve seen before. These all may seem unnecessary details, but after reading a recent [piece](https://www.tensorzero.com/blog/stop-comparing-price-per-million-tokens-the-hidden-llm-api-costs/?ref=mbi-deepdives.com) on TensorZero, I appreciated how these details can have a material impact on how much the customers are paying to the model developers. Some excerpt from TensorZero: “We sent identical inputs through each provider’s official token counting API and normalized against OpenAI’s: ![](https://substackcdn.com/image/fetch/$s_!CfKM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7040dc19-7624-47fb-a96c-973ad4400096_1009x1041.png) Multiplying list price by tokenizer efficiency gives you what you actually pay to process the same input. ![](https://substackcdn.com/image/fetch/$s_!trcI!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8aa4bf6b-4a84-4e1d-ba33-fe57b1f93cf5_991x1168.png) **The differences are dramatic.** On tool-heavy workloads, `claude-opus-4-7` costs 5.3x more than `gpt-5.4` even though their list prices are only 2x apart. The rankings also flip depending on what you’re sending: Gemini is the cheapest option for text and structured data, but becomes 46% more expensive than OpenAI on tool definitions. **The only way to know what you’re actually paying is to measure it.”** As you can see, tokenization is a non-transparent billing unit that each vendor controls unilaterally. If a provider bills for hidden reasoning or other opaque internal operations, the customer may be paying for a large share of compute they cannot directly observe or verify. Remember what I said before: the mover wasn’t being dishonest (okay fair, you can probably say they were being clever) to quote you a cheaper price but using a smaller box; it was your job to figure out the difference in box dimension and other details before hiring the mover. Similarly, after understanding these nuances, I think any enterprise would be really imprudent to standardize on just one model developer. This is because the customer loses bargaining power, a benchmark, and the ability to distinguish real quality differences from billing artifacts. If the seller controls both the meter and the service, and the buyer has no parallel benchmark, the buyer is highly likely to end up paying more over the long term. Even if the model developer isn’t sneakily charging you higher price, without any benchmark, how will the customer press the model developer to lower their price or even understand that they’re paying too high a price? I have gone through a couple of papers on Arxiv which laid out these concerns as well. Here’s one [paper](https://arxiv.org/html/2505.21627v2?ref=mbi-deepdives.com) pointing out the asymmetry of information between the model developers and customers: > “Our key observation is that, in the interaction between a user and a provider, there is an asymmetry of information. The provider observes the entire generative process used by the model to generate an output, including its intermediate steps and the final output tokens, whereas the user only observes and pays for the (output) tokens shared with them by the provider. This asymmetry sets the stage for a situation known in economics as moral hazard, where one party (the provider) has the opportunity to take actions that are not observable by the other party (the user) to maximize their own utility at the expense of the other party. > > The core of the problem lies in the fact that the tokenization of a string is not unique. For example, consider that the user submits the prompt “What is the oldest city in the world?” to the provider, the provider feeds it into an LLM, and the model generates the output “|Dam|ascus|” consisting of two tokens. Since the user is oblivious to the generative process, a self-serving provider has the capacity to misreport the tokenization of the output to the user without even changing the underlying string. For instance, the provider could simply claim that the LLM generated the tokenization “|D|a|m|a|s|c|u|s|” and overcharge the user for eight tokens instead of two!” Another [paper](https://arxiv.org/html/2506.06446v2?ref=mbi-deepdives.com) also highlighted the point that pricing variation can be quite arbitrary: > “We find empirical evidence that, particularly for non-english outputs, both proprietary and open-weights LLMs often generate the same (output) string with multiple different tokenizations, even under the same input prompt, and this in turn leads to arbitrary price variation.” Even though it may be prudent to no standardize on one model, I do wonder whether the gravitational pull of standardization will be too much to ignore. Enterprises may standardize on a model because they've written prompts against its quirks, built evals around its output style, fine-tuned retrieval to its context window, and trained their engineers on its API. The cost of rewriting a production AI stack for many enterprise customers may be so high that even if they knew Anthropic's tokenizer was less efficient than GPT's, they might rationally stay with Anthropic. I’ll go back to what Dario Amodei [**said**](https://www.mbi-deepdives.com/golden-age-of-digital-ads-llm-p-l/) why he thinks API business will be much more sticky than some might think: > Dario Amodei: > > So often I’ll talk about the platform and the importance of the models. For some reason, sometimes people think of the API business and they say, “Oh, it’s not very sticky.” Or, “It’s going to be commoditized.” > > John Collison: > > I run an API business. I love API businesses. > > Dario Amodei: > > No, no, exactly, exactly. And there are even bigger ones than both of ours. I would point to the clouds again. Those are $100 billion API businesses, and when the cost of capital is high and there are only a few players... And relative to cloud, the thing we make is much more differentiated, right? These models have different personalities, they’re like talking to different people. A joke I often make is, if I’m sitting in a room with ten people, does that mean I’ve been commoditized? > > John Collison: > > Yes, yes, yes. > > Dario Amodei: > > There’s like nine other people in the room who have a similar brain to me, they’re about the same height, so who needs me? But we all know that human labor doesn’t work that way. And so I feel the same way about this > > …we’re like one of the biggest customers of the clouds, and we use more than one of them. And I can tell you, the clouds are much less differentiated than the AI models Nonetheless, the smart move does seem to be multi-model capability (even if 95% of volume goes to one vendor) plus internal benchmarks run on your actual prompts. That gives you the optionality to switch and more importantly, the negotiating leverage to push back at contract renewal. Given this context, I believe it will be exceptionally unlikely that enterprise AI will ever be dominated by one model developer. Anthropic may be dominating enterprise AI today, but OpenAI and Google will also likely have plenty of opportunities to gain further ground. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Counterintuitive Truths URL: https://www.mbi-deepdives.com/counterintuitive/ Last updated: 2026-04-19T16:07:30.000Z In my investing career, there are perhaps couple of truths that felt counterintuitive to the extent that my first instinct was to fight it. Not only didn’t it feel right, what complicated it further is accepting these truths felt deeply uncomfortable. The first was the paradox of skill. As Mauboussin [put ](https://cdn.porchlightbooks.com/legacyassets/system/manifestos/pdfs/000/000/558/original/100.03.SuccessEquation.pdf?ref=mbi-deepdives.com)it: “**Greater skill doesn’t decrease the dependence on luck, it increases it**.” As counterintuitive as it may sound, the explanation is rather pretty straightforward. As much as we may want to pretend otherwise, investment outcomes are clearly a mix of skill and luck. What matters for **relative** performance i.e. beating a benchmark/index is your skill **differential** versus the competition rather than your absolute level of skill. As any field matures, absolute skill rises across the board, but **skill variance** compresses. When the skill distribution narrows, the spread between the best player and the median player shrinks with it. But luck's variance by definition doesn't compress at the same rate. So luck's share of the explained variance in outcomes goes up unabated. I have noticed when people hear about paradox of skill, one tendency is to infer that since your investing outperformance (or underperformance) is increasingly becoming more dependent on luck, your skill doesn’t matter. The way I interpret it, however, is you need to be increasingly more skilled for luck to have a decent shot at helping you. It’s particularly easy to explain this via a sport as I have done in the past with [Cricket](https://x.com/borrowed%5Fideas/status/1277789517219614721?ref=mbi-deepdives.com). It should be intuitive that if you are not honing your skill as an athlete while growing up, it’s impossible to imagine that you would find yourself suddenly in the NBA or NFL when you’re in your 20s. So, you need a lot of skill and hard work to get to the desired stage you want to compete for. But once you are in that league, luck may have a disproportionate role in your outcome while competing against other similarly skilled athletes. This is, of course, not to deny the existence of the likes of Michael Jordan or Sachin Tendulkar in their respective sports, rather highlighting the cold, harsh reality of the vast majority of less than legendary players playing the same sport. Buffett is, of course, the Jordan/Tendulkar of investing. It also perhaps explains why any time someone was labelled as the “next Buffett” has the unfortunate habit of falling terribly short of Buffett’s track record. I often joke with my friends that if you are actively managing your or other people’s money and you have a tendency to feel luck has typically been not on your side in life, you may want to look for a different line of work as I suspect people short on luck in investing will have a harder job than Sisyphus rolling a boulder uphill. A lot of it may mostly come down to your general attitudes towards life rather than your specific circumstances. Charlie Munger had a failed first marriage, lost a young child, and his left eye due to a botched cataract surgery in his 30s and yet, he didn’t wallow in his own woes. While perhaps not as dramatic, I suspect very few people are short of stories in their lives that could make a compelling case that luck hasn’t been as generous as it could have been. It is, of course, the stories we like to focus on that end up dominating our general attitude in life. Most of us also likely have plenty of stories that we could tell ourselves to notice more intently how lady luck has enriched our lives. In fact, sometimes I wonder that if I were an allocator, the question that I really wanted to answer is how deeply a particular investor appreciates the role of luck. Anyone who is too enamored how their skills are driving the last three/five years result may be much more prone to have warped understanding of investing and the world in general. Skills are largely table stakes in professional investing and frankly speaking, I suspect impossible to differentiate unless you are literally talking to a young Buffett (and as mentioned earlier, let’s not forget the error bar in identifying the young Buffett). The problem with paradox of skill is it routinely counters to our own experience in the world which is why it is very counterintuitive. All professional investors know a lot of investors (both professional AND non-professional) who are clearly worse at this job than they are. This leads to my second counterintuitive truth. Again, from another Mauboussin [paper](https://operators.macro-ops.com/wp-content/uploads/2020/06/ExplainingWisdom-1-1.pdf?ref=mbi-deepdives.com) (I also wrote a [thread](https://x.com/borrowed%5Fideas/status/1276645654132031488?ref=mbi-deepdives.com) on this paper in the past): > “…a diverse crowd will always predict **more accurately** than the average individual. So the crowd predicts better than the people in it. **Not sometimes. Always**. > > …**the collective is often better than even the best of the individuals. So a diverse collective always beats the average individual, and frequently beats everyone**. **And the individuals who do beat the collective generally change**, suggesting they are more of a statistical vestige than super-smart people. The collective “**frequently beats everyone**”! That’s one of the most counterintuitive truths I have encountered (read the paper or the thread linked above if you need some convincing). Understanding the **diversity level** of the crowd may be just as important as listening to the individuals within the crowd. One challenge with appreciating the diversity level is we tend to **hear** mostly one side of the story at any point of time: the winning side! The recent stock price tends to be a comforting refuge for your opinions and the people who have a markedly different opinion than the market tend to keep their opinions to themselves when the stock price has very little inclination to oblige to such opinions. The survivorship bias of good stories from the winning side makes us particularly susceptible in imagining a preponderance of success while fundamentally underestimating much greater diversity of outcomes. I do want to note that you cannot just “solve” for diversity merely by looking at prices. Indeed, remember “the collective is **often** better than even the best of the individuals**.”** So, there is no easy way out there. Munger emphasized about the requirement of equanimity if you want to invest for the long term. I seem to appreciate such requirement with every passing year. Ultimately, these counterintuitive truths are very antagonistic to how much of the investing world operates: you must come up with how your skills are differentiated from others and show the past 3-5 years of track record of outperformance as an evidence of such differentiation. What I lean towards much more is that somewhat low turnover and long-term focused investors may need much, much longer time than typically appreciated to realize whether they’re truly a timeless good investor. It is possible that you may be a good investor in a particular era due to your differentiated skillset but as the rest of peers catch up and completely nullify your advantage, you will be forced to rely more on luck to outperform the market. Like Munger, Mauboussin also came to similar conclusion of equanimity. To go back to Mauboussin’s paper on Paradox of Skill: > Once you’ve embraced the paradox of skill, you’ll see that **it’s appropriate to have an attitude of equanimity toward luck**. If you’ve done everything you can to put yourself in a position to succeed, you should accept whatever results appear. Some days you’ll be lucky, and the results will exceed your expectations. Some days the results will be disappointing because of bad luck. The best plan will be to pick yourself up, dust yourself off, and get ready to do it again tomorrow. Given the outsized role of luck and this potentially more than decadal feedback loop, I have often felt that people who **should** actively manage their money are the ones who would like to do so not necessarily to beat the market, rather who want to do so DESPITE the glaring possibility that they will fall short of all their peers who are indolently, and mindlessly throwing their savings to the “collective wisdom” instruments! Of course, in reality, active investing ends up attracting people who tend to be much more money obsessed than the general population, and it is through the pile of their dead bodies (including potentially our very own) rather than vicarious learnings, we may finally appreciate the difficulty level of the “sport” we chose to engage with. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Huang Isn't Planning to Wake Up a Loser URL: https://www.mbi-deepdives.com/not-a-loser/ Last updated: 2026-04-18T15:30:09.000Z You may have already listened to Jensen Huang on the recent [episode](https://www.dwarkesh.com/p/jensen-huang?ref=mbi-deepdives.com) of Dwarkesh podcast. If you haven’t, I highly recommend you listen to the entire episode and not just the bits and pieces clips. This wasn’t the kind of episode that you want an AI to summarize either. What made the episode so enjoyable to listen to is I thought Dwarkesh did an excellent job in eliciting Huang’s opinions on a range of topics that are hotly debated. Frankly speaking, I came to the conversation with a slight bias to Huang’s point of view of selling chips to China, but came away thinking Huang’s arguments weren’t convincing enough. Here on MBI Deep Dives, I am more interested in micro economics concerns such as competitive dynamics, moats etc as I consider the broader macro, geopolitical concerns are largely beyond my paygrade even though they can be quite impactful at times. There were plenty of materials in the episode that were very much in my narrow micro economics interest even though most people are mostly talking about the debate around selling chips to China. Speaking of competitive dynamics, the below excerpt on Huang’s opinions about Trainium and TPUs are particularly worth highlighting: > I would welcome Trainium to demonstrate their 40% that they claim all the time. I would love to hear them demonstrate the cost advantage of TPUs. It makes no sense in my mind. It makes absolutely zero sense. On first principles, it makes no sense. > > So I think the reason why we’re so successful is simply because our TCO is so great. Secondly, **you say 60% of our customers are the top five, but most of that business is external**. For example, most of Nvidia in AWS is for external customers, not internal use. Most of our customers at Azure, obviously all of our customers are external. All of our customers at OCI are external, not internal use. **The reason why they favor us is because our reach is so great**. We can bring them all of the great customers in the world. They’re all built on Nvidia. And the reason why all these companies are built on Nvidia is because our reach and our versatility is so great. > > So **I think the flywheel is really install base, the programmability of our architecture, the richness of our ecosystem**, and the fact that there’s so many AI companies in the world. There’s tens of thousands of them now. If you were one of those AI startups, what architecture would you choose? You would choose an architecture that’s most abundant. We’re the most abundant in the world. You’d choose the one that has the largest installed base. We’re the largest install base. And you’d choose the one that has a rich ecosystem. > > So that’s the flywheel. That’s the reason why, between the combination of: one, our perf per dollar is so great that they have the lowest cost tokens. Second, our perf per watt is the highest in the world. So if one of these companies, if our partners, built a one gigawatt data center, that one gigawatt data center better deliver the maximum amount of revenues and number of tokens, which directly translates to revenues. You want it to generate as many tokens as possible, maximize the revenues for that data center. We are the highest tokens per watt architecture in the world. Lastly, **if your goal is to rent the infrastructure, we have the most customers in the world.** So that’s the reason why the flywheel works. Jensen correctly notes that Amazon (AWS), Microsoft (Azure), and Google (GCP) buy mountains of GPUs primarily because their **external cloud tenants demand them**. Hyperscalers, simplistically speaking, are increasingly acting as capital-intensive distributors for Nvidia. It may take a while, but I still wonder unless we get past this compute constrained environment, the true competitive dynamics between ASICs and Nvidia GPUs may be hard to ascertain. In the current environment, you will probably sign a contract whoever can offer you compute. As a result, Trainium, TPUs, and GPUs all can coexist and thrive in this environment. I am noticing a growing consensus among investors that “we may never have enough compute” which implies worrying about competitive dynamics in a relatively compute abundance scenario largely irrelevant. I, however, lean towards believing in the power of capitalism and there is hardly any shortage that capitalism has failed to eliminate over time. As a result, while it’s excruciatingly challenging to pinpoint a timeline **when** we will reach such relative compute abundance scenario, I suspect we will eventually have to deal with such question. If you value these businesses assuming such question will never arrive but it does arrive five years from now, that has much more valuation implications than the most investors may imagine. I continue to think that in a relative compute abundance scenario, TPU is the most protected precisely because Google has enormous captive internal workloads: Search query serving, YouTube ranking, ad targeting, Gemini across every surface, Waymo, and Workspace AI features. Even if external TPU demand goes to zero, internal workload volume alone justifies continued investment and tape-outs. They’re obviously not immune, just less exposed than Trainium and Nvidia’s GPUs. My guess is DeepMind would be very happy to get higher compute allocation if Google Cloud faces any difficulty for selling compute to external customers at attractive ROIC. Huang also made an interesting point about ASICs vs GPU margins: > “don’t forget, even in ASICs margins are really quite high. Nvidia’s margin is 70%, let’s say. But ASIC margins are 65%. What are you really saving? > > You’ve got to pay somebody. I think the ASIC margins are incredibly good, from what I can tell. They believe it too. They’re quite proud of their incredible ASIC margins.” I found this argument to be a bit misleading. Epoch AI [suggests](https://epoch.ai/data-insights/b200-cost-breakdown?ref=mbi-deepdives.com) that Blackwell chips sell for $30k to $40k whereas the bill of materials (BOM) costs only \~$5.7k to $7.3K which implies an eye-watering 82% margin. They, however, did point out that “*Since most Blackwell revenue comes from servers and rack-scale systems, which may carry* [*lower margins*](https://s201.q4cdn.com/141608511/files/doc%5Ffinancials/2025/Q325/Q3FY25-CFO-Commentary.pdf?utm%5Fsource=chatgpt.com)*, NVIDIA’s realized margins on Blackwell sales may be lower than these chip-level estimates*.” ![](https://substackcdn.com/image/fetch/$s_!wYZ9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faeb5eb08-3b28-4913-8c2f-068bc4410c78_1084x633.png) Source: [Epoch AI](https://epoch.ai/data-insights/b200-cost-breakdown?ref=mbi-deepdives.com) When a hyperscaler pays Nvidia $30k+ for a Blackwell unit, Nvidia's margin is captured on the entire bill of materials plus the software stack. Broadcom's or Marvell's "65% ASIC margin" is on a much narrower slice. The hyperscaler pays TSMC directly for wafers and pays SK Hynix, Samsung, or Micron directly for HBM, which together are the majority of the BOM on a current-generation accelerator. Broadcom's margin sits on top of its contribution, not on top of the whole chip. Saying "65% vs 70%" makes it sound like near-parity, but the 65% is applied to perhaps a quarter of the system cost while the 70% is applied to all of it. A hypothetical chip with \~$6k of real BOM cost sold by Nvidia at $30k captures $24k of margin per unit. The same chip produced through a design-services relationship with Broadcom might carry \~$8k of Broadcom margin plus the same \~$6k BOM, landing the hyperscaler at $14-15k all-in. Even if Broadcom's percentage margin on its slice is as high as Nvidia's, the dollar savings per unit are enormous because Broadcom's slice is so much smaller. Custom silicon programs do, however, carry real costs that doesn’t get surfaced in this simple framing. There is internal engineering headcount, inference and training framework support, and the opportunity cost of engineers who could be working on revenue-generating products instead. These are fixed and amortized, but they are not zero, and for a program that fails to reach sufficient volume, the amortized per-unit cost can be meaningful vs just merely comparing the BOM. This is the actual reason most companies do not build custom silicon because you need Google-scale or AWS-scale internal demand **AND** a top-tier engineering team to make the amortization math work. For the handful of operators where the math does work, the savings are not 65% vs 70%. They are likely closer to single-digit-thousands vs tens-of-thousands per chip, and I’m sure Huang knows this. However, there are different layers to this debate. Gavin Baker [pointed](https://x.com/GavinSBaker/status/2044861680015069571?ref=mbi-deepdives.com) out that while model portability used to make investors think model developers might gain leverage over the chip suppliers over time, that may be changing with Blackwell and even more so with Rubin. From Baker’s post (emphasis mine): > As system level architectures diverge (torus vs. switched scale-up topologies, memory hierarchies, networking primitives), **true portability is eroding**. The Mi300 and **Mi325 had roughly the same scale-up domain size as Hopper while Blackwell’s scale-up domain is 9x larger than the Mi355 scale-up domain**, etc. > > Many frontier models are now being explicitly co-designed for inference on specific hardware like GB300 racks. Codex on Cerebras is another example. **Those models run less efficiently on other systems and the performance differentials will only widen.** A model that runs well on Google’s torus topology will run less efficiently on Nvidia’s switched scale-up topology and vice versa - the data traffic is fundamentally different as a byproduct of the models being parallelized across the different topologies. > > Google’s internal teams - and increasingly the Anthropic teams as they become the most important customer of almost every cloud - have the luxury of operating across the stack (models, chips, networking) - but that is not the case for the rest of the market and other prospective users. Anthropic is the exception, not the rule. **To wit, Anthropic and Google allegedly have a mutual understanding where Anthropic can hire the TPU engineers they need every year to ensure that they can continue to get the most out of the TPU.** > > Given the overwhelming importance of cost per token to the economics of the labs, models will be run where they run best. Most extremely large MoE models will run best on GB300s given the importance of having a switched scale-up network like NVLink for MoE inference. **When training was the dominant cost for labs and power was broadly available, labs were optimizing to minimize capex dollars. Model portability was a way to create leverage over suppliers. I think that drove a lot of the focus on portability.** > > Today, inference costs as measured by tokens per watt per dollar are everything. **Inference is way more important than training costs (inference is effectively now part of training via RL). Labs are therefore now optimizing for inference. This means increasing co-design and higher go-forward switching costs for individual models between systems. I do think this explains why Anthropic and Nvidia came togethe**r: Anthropic needed Blackwells and Rubins to inference at least \*some\* of their models economically. And Mythos might just end up being released coincident with the availability of Rubins for inference. > > TLDR: **as labs shift their focus from training to inference, the costs of portability and the upside of co-design to maximize tokens per watt per dollar both rise. Portability is likely to begin decreasing as a result**. Reading Baker’s post made me think that the hyperscalers will have to pay the “Nvidia tax” whether they like it or not; as long as the hyperscaler customers see **material** benefit for using Nvidia GPUs over hyperscaler ASICs, hyperscalers hands will be tied. And if hyperscalers appear unwilling or hesitant to pay such exorbitant taxes, Nvidia will be happy to allocate more of their chips towards neoclouds, and many customers, especially AI startups will likely follow. Always remember, “[**You’re not talking to somebody who woke up a loser**](https://www.dwarkesh.com/p/jensen-huang?ref=mbi-deepdives.com)**.**” --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### TSMC 1Q'26 Update URL: https://www.mbi-deepdives.com/tsm1q26/ Last updated: 2026-04-17T14:37:35.000Z Yesterday’s TSMC earnings call was yet another confirmation that the AI momentum right now is one-way street: up and to the right! TSMC’s CEO C.C. Wei in his prepared remarks encapsulated the sentiment around AI (emphasis mine): > “AI-related demand continues to be extremely robust. **The shift from generative AI and the query mode to agentic AI and command and action mode is leading to another step-up in the amount of tokens being consumed**. This is driving the need for more and more computation, which supports the robust demand for leading-edge silicon. **Our customers and customers’ customers, who are mainly the cloud service providers, continue to provide us with their very strong signal and positive outlook**. Thus, our conviction in the multiyear AI megatrend remains high, and we believe the demand for semiconductors will continue to be very fundamental.” The transformation of AI can be more tangibly gauged just by looking at how the mix of High Performance Computing (HPC) in which AI Accelerators, Data Center GPUs and ASICs are embedded, and smartphone’s contribution to TSMC revenue evolved over the last five years. Back in 4Q’19, HPC was only 29% of TSMC’s revenue whereas 53% of their revenue came from smartphones. Today, their positions have completely flipped as HPC is now 61% of TSMC’s revenue vs Smartphones contributing only 26%. ![](https://substackcdn.com/image/fetch/$s_!FwFA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F57d1516c-75a6-47b4-918d-2388faa7a38a_1249x621.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) TSMC’s revenue from the most advanced nodes (7 nm or below), where they are a de-facto monopoly, is now almost three-quarter of their revenue. ![](https://substackcdn.com/image/fetch/$s_!ApmP!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1abdaecb-5369-4de9-b5a0-45ea343b3401_1296x730.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) TSMC was asked about potential competitive intensity rising for the advanced nodes, but management reminded everyone that there is no “short cuts” in this business. From the call (emphasis mine): > “…both Intel and Tesla, they are TSMC’s customers. But again, they are our competitors, and we view Intel as our formidable competitor and do not underestimate them. But having said that, **there are no shortcuts. The fundamental rules of the foundry game never change**. They need the technology leadership, manufacturing excellence and customer trust, and most of all, the service, which has been mentioned by Jensen; thank you for his wording. > > Again, let me say that **it takes 2 to 3 years to build a new fab, no shortcuts. And it takes another 1 to 2 years to ramp it up. Again, that’s the fundamental of foundry industry.** And whether we try to win them back, actually, they are still our customer. And we are very confident in our technology position, and we work very hard to capture every piece of business possible. It doesn’t sound like TSMC is losing much sleep over the competition. In fact, given the demand, TSMC confirmed that their capex will likely be closer to high end of capex range of $52-56 Billion provided last quarter. Moreover, management also mentioned they expect revenue growth to outpace capex growth. Gross Margin reached a new high: 66.2% in 1Q’26! Given the recent gross margin trends, analysts probed whether their through-the cycle gross margin guidance for long-term (2024-2029) of 56%+ is actually too low, but management was not ready to upgrade it further...yet! ![](https://substackcdn.com/image/fetch/$s_!eQ_E!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F136ebba0-a34f-4750-91eb-eac79ad4af1c_1399x715.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Like ASML, TSMC management was also asked by analysts whether they are pricing appropriately given the compute constrained environment. This is what TSMC management said in response (emphasis mine): > Let me say that we always view our customers as our partners. Of course, we know our value; of course, we know our position, but we also view our partners as very important business partners, so that **we don't change our pricing dramatically or something like that**. We just try to make sure that our customers can be successful in their market. And at the same time, we grow together, and we also earn our value, so that we can continue to expand our capacity to support them. That fundamentally is, number one, our customer got to be successful. That's our consideration, number one, and we grow together. And again, there's a keyword please pay attention to. Customer is our partner. Indeed, it makes a lot of sense to gradually take price over time if AI demand remains as insatiable as it is today. If TSMC maintains its monopoly in advanced chips in 5-10 years and AI demand continues to skyrocket unabated, the gross margin is very likely to be much higher than the mid-50s through the cycle. It should be no surprising that TSMC revenue estimates continues to go higher. Nonetheless, it’s pretty incredible that revenue estimates for 2026 went up by \~25% since September last year! If the business fundamentals are improving so fast, it is less surprising that the stock too went up by \~60% during this period. ![chart](https://substackcdn.com/image/fetch/$s_!0cT0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc846203c-003c-44e1-a83c-de63693c5e7a_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### ASML 1Q'26 Update URL: https://www.mbi-deepdives.com/asml1q26/ Last updated: 2026-04-16T14:42:04.000Z A new earnings season has arrived. If ASML’s earnings is any guide, be prepared to hear another quarter of “demand is outstripping supply” from all the players in the semi value chain. ASML’s CEO Christophe Fouquet started his prepared remarks with the following which set the tone: > “…for the foreseeable future, demand will continue to outpace supply. This creates constraints across end markets from AI to mobile and PCs, which is driving our customers to aggressively add capacity.” The last question on the call was about whether ASML may become the bottleneck for supply to get closer to demand. Fouquet does not think that is the case: > I know the question of bottleneck comes back very often. I think we don’t feel at all that we are the bottleneck today. We’re very closely working with our customer. And again, we have many, many, many tools in our hands to make sure we keep it this way. During the call, ASML management explained how much they are expanding capacity to meet the growing demand (emphasis mine): > last year, we had 44 tools. if you are just looking at 80 tools, we say at least 80, but if you just look at 80 tools, **those 80 tools give you double the wafer per hour capacity that we would have shipped in 2025**. And on top of that, we’re helping customers upgrade their installed base…we’re really working hand in glove with the customers to look at what is your capacity need, what’s the easiest way and also the most economical way for you to get to the productivity that you need. And we’re executing on all fronts on availability, on productivity, on unit numbers, capacity and then upgrading the installed base. I find it quite remarkable how quickly ASML was able to expand the capacity despite having a maze like supply chain. For context, ASML had 5,150 suppliers in 2024; therefore, ramping up capacity means ASML needs most of these suppliers also step up their game simultaneously to meet ASML’s wish list. ![](https://substackcdn.com/image/fetch/$s_!Yr73!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9241b038-ca2d-4f4f-adde-9756ea985da4_1009x756.png) Source: ASML 2024 Annual Report It is, of course, not magic rather ASML’s own meticulous preparedness across their supply chain that helped their ability to expand the capacity in a relatively short time. From the call (emphasis mine): > “I think we have been mostly explaining in the last few years that we were preparing the supply chain basically to be able to go to a capability of 90 on Low NA and to a capability of 600 for deep UV, total deep UV. And I think what we see in these ramps is that a lot of the preparation is paying off. So I think that, of course, you always have challenge with the supply chain. But I will say, so far, **our supply chain has been able to support our move rate increase quarter-by-quarter. And that includes, by the way, ZEISS** to name them, that include the optic, **where I think we had major challenges a few years ago in the previous ramps. I think we are in a much better shape there**.” It also helps that given Samsung and Intel’s ability to attract customers in light of the compute constrained environment as well as potential geopolitical risks for relying solely on TSMC over the long run, TSMC’s monopsony power over ASML is almost certainly not increasing. From the call: > “It’s pretty clear that the demand in the foundry business is huge and is outweighing the supply, right? So that leaves a bit of room for others than the market leader. And I think that’s room that the other players are trying to enter into. We all know about the plans of Samsung in Taylor. So that’s real. And of course, **that also requires shipment for us, which is happening**. The U.S. player in this business already has quite some capacity, I would say. So we’ve said it before that for this year, **we’re not counting on a huge number of shipments in that regard because they already have quite a bit of capacity**. > > Then you ask about the longer term. Of course, **the market that is characterized by multiple players at least will sort of guarantee innovation**. And that, I think, is what is important. I think the market leader has been tremendously innovative. So you cannot say that even in the market that was dominated by one player, you would still see a lot of innovation. I think that’s what we’ve seen in the past couple of years. But **having 3 players in there will probably guarantee even more innovation**. And I ultimately think that, that is good for the ecosystem.” If there are multiple players and one of them adopts high NA EUV which then yields better results than others who do not, you can see how a oligopoly instead of a monopoly in advanced logic chips can potentially increase the pace of high NA EUV adoption. If it remains just a TSMC monopoly, the pace of adoption of high NA EUV can be dictated by solely on TSMC’s wishes. Of course, logic chips is only half of ASML’s system sales while the other half comes from memory companies. While listening to the call yesterday, I almost thought ASML management were pitching memory stocks! ASML has seen a major adoption of EUV among memory players and management made the case that the move from low NA to high NA EUV will also play out similarly over time as the customers see the performance and productivity benefits over time. From the call (emphasis mine): > **I think DRAM has been a bit the perfect storm for ASML** because, of course, we have this capacity buildup. But as we mentioned a few times, we have seen a major adoption of EUV in DRAM in 2025\. And you may have noticed that our, I will say, U.S. DRAM customer also made this announcement that they were shifting also pretty strongly on EUV. And the reason for that is, of course, performance, but it’s also capacity because **if you are going to use more EUV layers, you are going to need less multi-patterning and multi-patterning takes a lot of space also in the fab**. > > …Now of course, **what’s true for Low NA today, I think we expect to be true for High NA in the future**. So it’s, again, not prime time for High NA today. But I can only say that more Low NA EUV adoption today can only help for more High NA adoption in the future because the logic of High NA is the same. It’s going to single expose, simplifying the process, getting more space, et cetera, et cetera. So I think DRAM has been really a good story when it comes to litho intensity in ‘25\. And I think it’s translating very strongly into EUV demand this year and most probably in the years to come. One question investors often wonder whether ASML is leaving too much money on the table in a compute constrained environment by not sufficiently increasing prices. ASML management declined to take advantage of its customers for what could turn out to be a temporary phenomenon. From the call (emphasis mine): > “Now in our model of pricing, as you know, **our model of pricing is not based on the squeeze that our customers find ourselves in**. That’s not the way we do business. **The way we do business is that we look at the value that we provide to our customers, generation on generation, tool on tool, and we take our fair share in that**. And you might say in the current climate, can’t you squeeze out a little bit more? I understand that. But it’s also true that when the market is good, it goes down a little bit and the customers are going through more difficult times that it also pays these fees. > > So fundamentally, we believe that the model that we have is a fair model. **It’s also a model that is fair to all the players because I would find it difficult to explain why we’re charging more in, let’s say, the memory environment versus the logic environment.** That’s just not the way we do business. So we’re very, very happy with the business model we have, which is based on the value of our tools, and we would gladly continue with that approach.” Indeed, while ASML’s customers (TSMC and Micron, for example) have higher gross margins today than ASML, looking at more long-term gross margin trajectory clearly depicts ASML’s more steady gross margin while memory companies’ margin whipsaw from one extreme to another. Even if TSMC’s gross margins were more volatile than ASML’s over the last decade, it is true that TSMC’s gross margin was almost consistently higher than ASML, perhaps a reflection of their [monopsony power](https://www.mbi-deepdives.com/asml/) over ASML. ![chart](https://substackcdn.com/image/fetch/$s_!lHVk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b6ae197-640d-4176-ab60-1126e1bcefe4_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) ASML increased the revenue guidance from EUR 34-39 Billion to EUR 36-40 Billion for 2026\. Even in late last year, analysts were expecting only EUR 33 Billion revenue for 2026 which has now increased \~20% over the last 6 months. Even though the stock has gone down a bit following the earnings, the stock is still +25% YTD. My broad takeaway from ASML call was that if you are looking to get a hint of capex peaking or demand-supply gap narrowing during any of the earnings call in semi value chain, you are unlikely to get it during this earnings season. ![chart](https://substackcdn.com/image/fetch/$s_!L8bi!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F784832b2-a2f1-4184-a7d0-05b4bacf6b74_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### AI Economics in the East: Part 2 URL: https://www.mbi-deepdives.com/ai-economics-in-the-east-part-2/ Last updated: 2026-04-15T14:47:27.000Z Yesterday, Flo Crivello [tweeted](https://x.com/Altimor/status/2044108104816832576?ref=mbi-deepdives.com) something that caught my attention: > We've tested new OSS models the moment they're released for a while at Lindy. Inference is our #1 cost by a lot (more than payroll) — cutting it by 2-5x would be transformative. > > Last year, OSS models were "not even close." > > 3 months ago, "almost there." Came close to making Kimi K2.5 our default. > I think we are right now crossing the line to "at the frontier, for most use cases." GLM-5.1 in particular is incredible and will likely be our default soon. > Surprised by this development — OSS caught up. For context, here’s the head-to-head on Anthropic’s Claude Opus 4.6 and Zhipu’s GLM-5.1 flagship model API pricing (per million tokens): ![](https://substackcdn.com/image/fetch/$s_!VjbJ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3e4901a6-b5b5-4292-a00b-6016d84eb843_1107x340.png) Rohit Krishnan [captured](https://x.com/krishnanrohit/status/2044123219926372672?ref=mbi-deepdives.com) how strong open source models can change the economics in AI (emphasis mine): > “I think a most fundamental open question today is this - how much the open to closed source gap will continue to exist. If it does, and it's perpetual per model, **we have a concentrated pharma like future for frontier labs.** They have 6/13/18/24 months to squeeze the profits before it gets competed away. > > The default amount of profit is not zero either, since not everyone can create a model. But **it's not going to be 60% gross margin inference if Zhipu can compete for it.** > > Now, it's possible there are some things where the open-to-close gap is longer. Claude's personality maybe as an example. Not everyone will care, but some will! And for them this is the price vector that matters. So a consumer business here can probably continue to command due to brand and utility. > > And **if the frontier models truly hit a scale where OS can't reach, say $100B training runs, then they can have more enduring advantage**. Revenues can grow, expenses can become more about maintenance and sustainability, and usage changes. You'd use the best possible model to do the thing you want to, or to explore, and for anything that's settled, a workflow, you'd get that done for much cheaper with the lowest cost model possible. > > **Which means the distribution of future profits are either highly crunched (few years to squeeze) or long tail (for exploration and super smart work**). It'd be interesting to see how this plays out, and what it means for how to price the OpenAI/ Anthropic IPO.” The more I think about it, the less likely it appears that the most advanced models will be available via API in the long-term (see yesterday’s [**piece**](https://www.mbi-deepdives.com/frontier-ais-economic-engine/) for more on this). Given this context, if a company’s product directly competes against frontier model developers’ first-party products in which using the most advanced models would lead to differentiated product experience, multiple for those companies’ earnings **should be** under pressure. Considering how the Chinese models remain deeply relevant in the question of long-term economics of AI over the world, I am going to follow closely the two publicly listed AI labs in the East. I have already covered [Zhipu](https://www.mbi-deepdives.com/ai-economics-in-the-east/) a couple of days ago, and today I will discuss MiniMax which also IPO-ed early this year. The stock has nearly tripled since its IPO and is currently worth \~$40 Billion Enterprise Value (EV). Like Zhipu, the stock is richly valued as it trades at \~180x NTM revenue. I will discuss more about their economics behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!YWG_!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F65d515fa-58da-40ff-aa11-ccc0224c04ca_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Frontier AI's Economic Engine URL: https://www.mbi-deepdives.com/frontier-ais-economic-engine/ Last updated: 2026-04-14T14:54:55.000Z It should not surprise anyone that coding has found the most product market fit in AI, but it is notable that how much it surpasses everything else in enterprise segment so far which explains so much why Anthropic has been on a tear. a16z recently highlighted AI’s adoption and use cases of AI in the enterprise world. From [a16z](https://a16z.com/where-enterprises-are-actually-adopting-ai/?ref=mbi-deepdives.com): > “On the revenue momentum, enterprise adoption of AI is dominated by a clear set of use cases and industries. **Coding, support, and search** represent the lion’s share of use cases by far (with coding being an order-of-magnitude outlier even among this set), while the **tech, legal, and healthcare sectors** have been the industries most eager to adopt AI.” It may be obvious to most people, but it is still worth spelling out why coding is such a perfect use case for AI. Again, from 16z (emphasis mine): > In many ways, coding represents the ideal use case for AI, both in terms of what the technology can do and how readily the enterprise market will embrace it. **Code is data dense, meaning there is a massive amount of high-quality code available online for the models to train on. It is also text-based, making it easy for models to parse. It is precise and unambiguous, with strict syntax and predictable outcomes. And crucially, it is verifiable: anyone can run it and know if it works, creating tight feedback loops for models to learn from and improve**. ![](https://substackcdn.com/image/fetch/$s_!uysH!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcb594b57-e7d4-4f2b-bed3-d1987b671d69_2000x1759.png) Anthropic has been quite [**focused**](https://www.mbi-deepdives.com/anthropics-focused-bet-portfolio-change/) on nailing the coding use case for the last couple of years as they believe it can not only accelerate their own research but also provides the most compelling economic return. From Sholto Douglas on a [podcast](https://www.youtube.com/watch?v=FQy4YMYFLsI&ref=mbi-deepdives.com) 6 months ago why Anthropic has been so focused on coding (emphasis mine): > Two reasons. > > First, we think it’s the thing that will allow us to assist ourselves in AI research faster. There’s this notion of automating AI research. The speed of takeoff—the speed of progress—is driven by how much AI can assist AI research. Pre-fetching this is important. > > Second, we think **coding is the nearest‑term tractable problem domain in terms of economic impact**. For Anthropic to be a viable research program that can work on the things we think are important, we need economic return. **Coding is a huge market full of keen early adopters who are excited to try and switch tools.** > > There’s massive demand. **There is dramatically more demand for software than there is good software**. We’ve seen this in previous generations of compilers, web abstractions, etc.—demand for software keeps growing. > > **Models are better at coding earlier than almost anything else because coding is uniquely tractable**: the data exists, you can containerize and run things in parallel, you can run unit tests and know when something works. > > Self‑driving is uniquely hard because the car needs to work the first time. Coding is different: the model can fail a hundred times; as long as it succeeds once, it’s fine. There’s tractability and replayability that don’t exist when you directly touch the real world…You wouldn’t want an AI lawyer arguing your case in court right now OpenAI alluded in a recent [memo](https://www.theverge.com/ai-artificial-intelligence/911118/openai-memo-cro-ai-competition-anthropic?ref=mbi-deepdives.com) that such monolithic product market fit may be ultimately limiting for Anthropic, but Ben Thompson today [made](https://stratechery.com/2026/openais-memos-frontier-amazon-and-anthropic/?ref=mbi-deepdives.com) the point that may not be the case (emphasis mine): > I’m not sure I buy this, given that coding underlies so much of AI’s potential. **A lot of AI products will ultimately be about applying coding in a seamless way to business problems without needing to know that coding is happening** The decision to focus on a use case that has the highest immediate return also can be key for keeping the questions on AI economics at bay. Jigar Doshi [pointed](https://www.jigarkdoshi.com/writings?ref=mbi-deepdives.com) out this week that even though AI revenues are exploding in each successive generation of models, the frontier window has been narrowing which kept the recovery of the costs uninspiring. ![](https://substackcdn.com/image/fetch/$s_!OHf0!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6e984b1a-9209-443a-a10c-e3fb09a11420_1098x322.png) Source: Jigar Doshi; he provides more extensive references for these numbers in his [blog](https://www.jigarkdoshi.com/writings?ref=mbi-deepdives.com) However, this may be no solace in the long-term if frontier models become increasingly more protective of their most advanced models to prevent distillation by competitors as well as use the model capabilities itself to build far more compelling first-party products. The frontier model developers may have some asymmetric advantage here. Again, from Jigar Doshi (emphasis mine): > “…labs and their strategic partners get first access to the strongest models, **extract the highest-value discoveries, and then open the model to everyone else once the frontier has moved on**. A startup using GPT-5 via API pays $5/$20 per million tokens. OpenAI's internal team pays **inference at cost: same model, 10x the compute budget**.” A research agent doing genuine deep work might burn 50 million tokens to produce one insight; at external pricing that may be prohibitively expensive, but at internal cost the economics can be far more palatable. So the experiments that are **economically possible** internally for frontier model developers vs the ones done via API differ **by category**. Lab's internal R&D productivity is partially a function of having privileged access to its own frontier which can turn into a recursive advantage no API customer can replicate. In this scenario, app-layer companies aren't just one model generation behind, rather they're paying gross margins to the labs on compute the labs themselves get at cost, while being rate-limited out of the very techniques that produce the highest-value outputs. There is, of course, no free lunch. Every GPU-hour spent on internal research is one not spent serving paying API customers, so there’s a real opportunity cost the labs pay in foregone revenue if their internal research team doesn’t deliver compelling first-party products. But given the talent these labs are hiring, it may be a decent bet that the compute budget for internal teams is unlikely to be wasted. There are all sorts of hypotheses and speculation around why Anthropic may have held Mythos back from public release, but Zuckerberg’s point about frontier model’s availability via API in 4Q’25 call increasingly seems more prescient. From Zuckerberg: > “…my guess is that **Frontier AI for many reasons, some competitive, some safety oriented are not going to always be available through an API to everyone.** So I think like it’s very important, I think, to be able to have the capability to build the experiences that you want if you want to be one of the major companies in the world that helps to shape the future of these products. So that I think is -- it’s going to be, I think, important from a business perspective. And I think it’s just important from like a creative and mission perspective to be able to actually design and build the experiences that we believe that we should be building for people. But yes, I mean I think it’s quite important. Otherwise, we wouldn’t be so focused on this. We’re clearly extremely focused on this.” Given that context, without frontier model capability what you can build can be fundamentally limiting if the players at the frontier becomes a monopoly. If OpenAI or Alphabet (or Meta) eventually gets closer to Mythos’ capability, Anthropic may be forced to release their model via API, but in case a monopoly arises in any particular capability, you can easily imagine the profits will start to flow…to the monopoly! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### AI Economics in the East URL: https://www.mbi-deepdives.com/ai-economics-in-the-east/ Last updated: 2026-04-13T15:07:24.000Z In the most recent All-in [episode](https://www.youtube.com/watch?v=DVBJQQCjgXU&ref=mbi-deepdives.com), Brad Gerstner from Altimeter (which owns stakes in both OpenAI and Anthropic) pushed back at the gross margin concerns for AI labs. From Brad Gerstner (emphasis mine): > “Their gross margins are exploding higher. Like the fastest increase in gross margins I've probably seen out of any technology company. > > …**it's gone from meaningfully negative 18 months ago to, you know, very, very positive. I've seen rumored out there 50 to 60 percent**” Given that Gerstner owns these companies, I think he’s highlighting numbers that he knows are at least directionally accurate. Nonetheless, it is frustrating to follow these AI labs while they remain private companies and everyone’s job would be lot easier if both OpenAI and Anthropic became public companies. Since that is yet to be the case, our best bet to gauge the evolving economics of AI labs may be to study the publicly listed Chinese labs more closely. One such company is Knowledge Atlas Technology JSC which is more commonly known as “Zhipu AI” in China. Since going public early this year, the stock is up a cool \~6x in just three months! Despite such a meteoric rise, the Enterprise Value (EV) is still hovering around \~$50 Billion. Don’t be misled thinking Zhipu has an overly conservative shareholder base though; the stock currently trades at \~127x NTM revenue! But to their credit, revenue has become nearly 6x since 2023! Let’s take a bit deeper look into how they’re growing at such a rapid pace. ![chart](https://substackcdn.com/image/fetch/$s_!PrQg!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5019f5a6-224e-4061-9bb3-67cc84e017ec_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Zhipu discloses revenue in two broad categories: On-premise deployment and Cloud-based deployment. On-premise deployment means the customer takes Zhipu’s model and runs it inside their own infrastructure. A bank, a telecom operator, a state-owned enterprise, or a government agency signs a contract, Zhipu's engineers show up, install the model weights and the serving stack on the customer's servers (or in the customer's private cloud), tune it for the customer's data and workloads, and hand it over. In China, this channel exists largely because data security, and regulatory comfort make many large customers unwilling to send sensitive data out to someone else's cloud. In cloud-based deployment, as you can imagine, the customer does not install anything. They get an API key, they call Zhipu’s model over the internet, and Zhipu serves the tokens from its own GPU infrastructure. Revenue is recognized over time, either ratably for **subscriptions or based on actual usage for pay-as-you-go**. As you can see below, revenue from cloud-based deployment is basically **tripling** every year in each of the last three years. ![](https://substackcdn.com/image/fetch/$s_!4gvW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcf7ecc13-6955-4eb2-9f5a-10a6decd507f_871x442.png) In the most recent earnings, Zhipu also provided a separate segment disclosure of revenue by product line. Zhipu runs what it calls a MaaS platform, short for Model-as-a-Service, which is essentially the Chinese analogue to Anthropic’s or OpenAI’s API business. Customers can either call Zhipu's GLM models over the cloud and pay by usage, or they can have the models deployed on their own infrastructure for security and customization reasons, which Zhipu calls on-premise deployment. As you can see below, the API business is growing the fastest. It’s not just API business, the agents are also in very high demand in China. Notice the “enterprise agents” revenue below which increased by 249% YoY. The underlying offerings in enterprise agents include things like CoCo (their enterprise agent), AutoGLM (the general-purpose mobile agent), and increasingly the productized agent systems built on top of GLM for specific workflows. In practice, this line captures situations where the customer is not buying raw model access but rather a **packaged** agent that plans, uses tools, and completes tasks end-to-end. While the API business and agents were only \~30% of the overall revenue in 2024, they were almost half of the revenue in 2025. ![](https://substackcdn.com/image/fetch/$s_!9wDh!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2b2845bc-4b05-4974-8eb2-1f509a93ee02_459x514.png) Coding in particular is where Zhipu has gone from being one of several credible Chinese labs to being a global frontier participant. If you look at Arena’s [leaderboard](https://arena.ai/leaderboard?ref=mbi-deepdives.com), Zhipu’s model (GLM 5.1) is actually only behind Anthropic’s right now. I asked Claude to translate Zhipu’s press release in English; you can sense the increasing confidence of Zhipu’s differentiation especially in coding as they have been **raising price** to meet the demand in a “compute panic” market. From the translated press release: > Our GLM Coding Plan, launched in 2025, rapidly gained global coverage on the strength of native high-quality engineering reasoning capabilities, with paid developers exceeding 242,000\. With confidence from technical leadership, **we proactively raised prices by 30% in February 2026** **and removed first-purchase discounts.** > > MaaS Platform: Through BigModel.cn, our MaaS platform has become the hub connecting foundation models with industry applications. Within 24 hours of GLM-5’s release, ByteDance, Alibaba, Tencent and other top platforms officially integrated it; 9 of China’s top 10 internet companies have deeply integrated GLM. As of March 2026, registered users exceeded 4 million. **Even with API pricing increased by 83% from late last year, the market still showed a supply-shortage “compute panic.**” ![](https://substackcdn.com/image/fetch/$s_!1ECx!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0f5ae79c-6a1c-45d2-b99d-10ade6d68a15_676x640.png) Source: Arena [Leaderboard](https://arena.ai/leaderboard?ref=mbi-deepdives.com) But revenue is one thing; how about the margins? That was indeed where I have seen some interesting developments which I will elaborate behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Too Cheap to Meter, Too High to Measure URL: https://www.mbi-deepdives.com/too-cheap/ Last updated: 2026-04-12T15:23:15.000Z **Programming Note**: As a reminder, every Sunday, I write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- I moved to the US in 2017 to pursue my MBA. Following a long and arduous process, I have recently become a permanent resident in the US. Since I boarded my flight from Dhaka to New York back in 2017, I have lived in Ithaca (NY), NYC, Madison (WI), Ottawa (ON, Canada), and finally Sacramento (CA). When you move from one city to another every couple of years, the very idea of “Home” starts to feel quite elusive. As a self-employed “vagabond,” the only place that has felt closest to home over the last decade is the internet. The life of a first-generation immigrant is inherently fraught with a lingering sense of what you left behind. However, I suspect it may not be widely appreciated that the true cost of immigration (from immigrants’ perspective) has gone down materially over the last few decades, especially since the advent of the mobile internet. Back in the 1990s (or even the 2000s), when someone from Bangladesh moved to anywhere in the Western hemisphere, the true cost for such immigrants was quite staggering in retrospect. A mere five-minute phone call home would cost almost an arm and a leg. In fact, the phone call used to be a bit of an occasion for the family and extended family to get together. Everyone would gather around the household that happened to have a telephone and listen intently for a precious few minutes to the voice of loved ones thousands of miles away. If you immigrated back in those days from Bangladesh, it certainly made economic sense. But it is excruciatingly challenging to determine the true cost these immigrants had to endure to make that economic leap. There was hardly any witness to their life of toil abroad, and there was hardly anyone who could grasp the context of their lives pre- and post-immigration. More often than not, they were out of sight, out of mind. Technology enriched the lives of these immigrants in a way that is almost impossible to appreciate for people who didn’t experience both of these worlds firsthand. No economic numbers can truly encapsulate how technology has positively influenced the lives of these members of society. Facebook, out of their self-interest, went above and beyond to ensure everyone in this world is connected. Even though my son is 15 months old, his paternal grandparents are yet to see him in real life, since they live ten thousand miles away. But they are watching him grow through Facebook Messenger almost every day, at a cost that is too cheap to meter but at a value that is too high to measure. I sometimes meet Bangladeshis in Sacramento who came here in the 1970s or 1980s. They tell me there were only a handful of Bangladeshis back in those days. This was perhaps the case in almost every city in the US. As someone who has lived in multiple cities in North America over the last decade, I can tell you how my own experience today is markedly different from that of anyone who immigrated in those years. When I was interviewing for full-time roles during my second year at Cornell, I often came across a recurring question from interviewers, especially when I interviewed for roles in cities not named San Francisco or NYC: why do you want to move here? For a first-generation immigrant, that was often slightly confusing because the answer seemed too obvious: “It’s all the same to me. Be it Alaska, Louisiana, or Wisconsin… they’re all the same to a person who just immigrated from ten thousand miles away.” It took me a while to appreciate why this was such a recurring question. But let me provide a bit more context. Two things were simultaneously happening in the US starting in the 2000s. The utter dominance of US big tech was gradually unfolding, and these companies wanted to hire the best talent from anywhere in the world. These are also the same companies that were speed-running to diminish the “true” cost of immigration. As Apple launched the iPhone, Google released Android, and Facebook connected everyone, immigrants stopped becoming vanishing memories for their loved ones after crossing the borders. At the same time, much of the rest of the world hasn’t quite kept up in providing opportunities for their homegrown talent. My wife went to the best engineering school in Bangladesh, and her first post-college job paid her only \~$150/month. She doesn’t work in a tech company, but we both have plenty of friends who do. Many of these peers essentially came for higher studies in the US and got jobs at some of these big tech companies that paid $150 per working hour! When the visible economic returns skyrocketed for such skilled immigrants in the US, the invisible costs were also rapidly declining, thanks to these very tech companies. Given such twin forces, skilled immigration from Bangladesh has only gained momentum over the last couple of decades. It is hard to find these data, but I suspect the majority of the graduates from my wife’s engineering college ended up moving abroad after graduating. This has also created a counterintuitive reality for many skilled immigrants. I can go to pretty much any random city in any part of the Western hemisphere and find a ready Bangladeshi community without much effort. Of course, being Bangladeshi doesn’t mean we will be good friends or that we will have everything in common. But first-generation immigrants (especially if you’re from the same college) have so much shared context of each other’s lives that connecting becomes significantly easier, even if our politics, religion, or opinions diverge. There is just so much that a fellow first-generation immigrant can grasp without ever needing to elaborate, which is why such relationships can move on a fast track minutes after you meet for the first time, often followed by connecting via a WhatsApp group or a DM on Messenger. There is so much overlap in what worries you, what you are looking forward to, and what life was like pre- and post-immigration that you can bond over them in no time. It only occurred to me recently that moving from one city to another for someone born in the US is a vastly different and arguably more psychologically challenging ordeal than it is for an immigrant like me. My son was born in California. It is hard to imagine he would be as indifferent to moving to Wisconsin as I was, if his entire family and friends remained in California. There will be plenty of Bangladeshis in Wisconsin, but it probably won’t mean much to my son. As a kid born in the US, he would likely be utterly oblivious to much of the shared context that made bonding so much easier for first-generation immigrants. His social life in a “foreign” city might be much more void than what I would face moving to a random city in the US. For my son, it would be a very legitimate question to ask, “Why do you want to move to this XYZ city?” Contrary to popular belief, I have often wondered whether skilled immigrants to the US have been one of the largest beneficiaries of technology’s global adoption. This is hardly talked about in the mainstream narrative; anytime we do show up in the mainstream, it’s usually because of some rule or regulation change that may make life difficult for immigrants. Those aren’t false or less impactful narratives in our lives, but they can often mislead even the very immigrants who have benefited from the windfall riches technology has bestowed on us. However, I also wonder whether this windfall is closer to its end for many skilled immigrants. Ultimately, these companies and countries weren’t doing charity. Both sides had something compelling to offer: the country had disproportionate economic returns that made digesting the non-economic costs more than bearable for the immigrants, whereas the skilled immigrants had, well, skills that were in demand. But is technology evolving from a friend to a foe for legal immigrants of the future, if intelligence itself becomes too plentiful to command high demand? As the great [**decoupling**](https://www.mbi-deepdives.com/the-great-decoupling-of-labor-and-capital/) of capital and labor accelerates in the age of AI, it may not be surprising if legal immigration faces increasingly more social, political, and philosophical questions. These countries may still remain open to skilled immigration, but there may be a rapid evolution of which skills will be in high demand, and the number of such skilled immigrants may pale in comparison with what these countries have taken in over the last 50 years. Of course, it’s not just skilled immigrants; almost any white-collar professional wonders about the relevance of their skillset for the coming decades. The anti-AI sentiment is broadly reflective of the tension many people feel about their potential irrelevance in how they contribute to society. These are very broad and likely too unwieldy questions to have any concrete answers. But I cannot help but wonder at times that we may be too prone to believing we live in the most consequential times in history. Life is always evolving, perhaps always faster than we would like to think in hindsight, but slower than we like to imagine looking forward. I will leave you with the excerpt below from Dwarkesh Patel and Ada Palmer’s recent [conversation](https://www.dwarkesh.com/p/ada-palmer?ref=mbi-deepdives.com), which deepened my belief that while I cannot be certain my own current skillset will remain relevant in a decade or two, Homo sapiens overall has always moved ahead in step with the pace of technology itself! --- **Excerpt from the Dwarkesh** [**Podcast**](https://www.dwarkesh.com/p/ada-palmer?ref=mbi-deepdives.com)**:** **Dwarkesh Patel:** Maybe other eras also have this and I just haven’t read the books about them, but from your book, I thought, “Oh, history just seems to be happening really fast, and seems to have sped up, especially religious and political history.” Obviously, the things happening in Italy, but even aside from that, you have Martin Luther and the Reformation, and then just twenty years later England splits off from the Catholic Church, which is unprecedented in two millennia. **Ada Palmer:** Then it has a bunch of tumults that flop, flop, flop so that every decade feels different. Here you are in 1506 being nostalgic for how the world was completely different in 1490\. And you’re like, “That’s pretty fast.” Here we are in 2026 often feeling nostalgic for how things were in the year 2000. **Dwarkesh Patel:** Is it fair to trace that back to the printing press or its offshoots, or is it just embedded? **Ada Palmer:** It’s more that history has always moved fast. But when we teach it in high school, we’re trying to move over large chunks of time quickly, and so we pretend that it moved slowly. We have this lie that there were long periods of stagnation. But you can zoom in anywhere, and you’re going to find every decade feels different, and people in the 1320s are nostalgic for people in the 1300s. It’s always felt like history was moving very quickly, and things rose and things fell. It’s the lies we tell ourselves in history books written in the 19th century that are trying to group all of these things together and make modernity special that confuse us about this. I’m working on a paper right now about the video game Civ. Civ is the number one teacher of history in the world. It has shipped 70 million copies, and 65 percent of people on Earth who have technology play video games. Civ is the number one teacher of history, bar none, since 1991. What does Civ tell you? Civ tells you that in antiquity, a turn is fifty years, and then in the Middle Ages, a turn is twenty-five years. Once you get into the Industrial Revolution, a turn is ten years, and then five years, and in modernity, a turn is just one year because in one year, as much happens now as happened in fifty years in antiquity. That lie is also what our textbooks tell us. But it doesn’t matter where we zoom in. Any time I go to a talk where any historian is zooming in on any decade in any time and place, it always feels like it’s moving as fast as our present is moving. **Dwarkesh Patel:** I guess the difference is that technologically, we know that they weren’t moving as fast. **Ada Palmer:** Technologically, they were moving fast. We just don’t care about those technologies anymore. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Meta Strikes Back! URL: https://www.mbi-deepdives.com/meta-strikes-back/ Last updated: 2026-04-11T14:58:20.000Z Following the Llama 4 fiasco and almost 9 months after rebuilding the Meta Superintelligence team (MSL), Meta has finally released a new model: [Muse Spark](https://ai.meta.com/blog/introducing-muse-spark-msl/?ref=mbi-deepdives.com)! Just a month ago, The New York Times [depicted](https://www.nytimes.com/2026/03/12/technology/meta-avocado-ai-model-delayed.html?ref=mbi-deepdives.com) a pretty unflattering picture of how Meta’s efforts in developing the frontier model are not going in the right direction and claimed Meta would delay the release of the model to May. Meta was a month early of that timeline which was a positive surprise for investors. Perhaps the more welcome surprise is that this actually appears to be a more than a decent model. In Arena’s (formerly LMArena) [leaderboard](https://arena.ai/leaderboard?ref=mbi-deepdives.com), Muse Spark currently lags behind just Claude 4.6 in text and vision although it still lags materially in some areas such as coding. Nonetheless, given Meta AI’s target consumer market, coding may not be the use case they need to be best at. ![](https://substackcdn.com/image/fetch/$s_!McMt!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F45673a88-6ad5-4922-95e6-d047b3663c23_679x1354.png) Source: [Arena.ai](https://arena.ai/leaderboard?ref=mbi-deepdives.com) Of course, there are legitimate concerns related to “Benchmaxxing” which means the most credible sign of Meta being back in model race may actually come from app store ranking. As of this writing, Meta AI is actually ahead of all other AI apps on iOS App Store. Zuckerberg has been making the case for a while that once the underlying quality of Meta’s model improves, it will lead to higher usage and engagement. From 3Q’25 call: > “what we see is that as we improve the quality of the model, primarily for post-training Llama 4 at this point. We are -- we continue to see improvements in usage. So our view is that when we get the new models that we’re building in MSL in there and get like truly frontier models with novel capabilities that you don’t have in other places, then I think that this is just a massive latent opportunity” ![](https://substackcdn.com/image/fetch/$s_!oq0u!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F011d9e99-c60f-48e1-bf83-bf447faa2e8b_784x1144.png) Source: Screenshot from App Store I don’t think Meta has unleashed any novel capability with Muse Spark yet. I actually don’t think they need to get such capability for increasing adoption of Meta AI. Ultimately, what Meta (and Google) needs is to offer a noticeably better model than what ChatGPT or Claude offers for their free users. Hence, I think it is not even of paramount importance for Meta (and Google) to have the most frontier model for competing in consumer AI use cases; they just need to comfortably beat the model and product experiences offered to free users of ChatGPT. The Information [reported](https://www.theinformation.com/articles/openai-forecasts-advertising-hit-102-billion-2030?rc=4lgoj7&ref=mbi-deepdives.com) that OpenAI is projecting $102 Billion ad revenue by 2030 which implies that they will have Weekly Active Users (WAU) of 2.75 Billion by then. Color me skeptical, especially on the WAU front! OpenAI’s easiest period for customer acquisition is actually behind them. Since there is no noticeable network effects, it is highly likely that the next 500 million WAU will be much more expensive to acquire than the last 500 million. They will have to do so while Google and Meta will likely surpass the underlying model quality and rate limits that ChatGPT can offer for free users. That is an uphill task, especially in a compute constrained environment when OpenAI is simultaneously competing for the potentially highly lucrative enterprise dollars against Anthropic! The fact that ChatGPT’s MAU in the US appears to be essentially flat for the last six months is likely a strong indication that it has already been a pretty uphill task for ChatGPT to maintain WAU growth in the face of competition from Gemini 3.0 (and later Claude). If Meta AI can keep their shipping cadence for the rest of this year, that will make a massive dent in OpenAI’s ambitions in growing WAU. ![Image](https://substackcdn.com/image/fetch/$s_!2uic!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe60274db-dfdd-4783-b61f-d132a97129c2_970x549.jpeg "Image") Source: Sensor Tower; Image source from [here](https://x.com/HedgeyeComm/status/2041489829960019972?ref=mbi-deepdives.com) Google may still need to compete against Anthropic and OpenAI much more fiercely in enterprise markets. Meta has no such “baggage”. At least theoretically, Meta actually is incredibly well positioned to stay extremely **focused** on nailing the consumer use cases in AI. While many seem to be wondering whether consumer AI is nearly as appealing as enterprise AI, I do want to [**highlight**](https://www.mbi-deepdives.com/openai-dilemma/) again that the opportunity in consumer AI is also pretty substantial as long as you have the userbase and attention: > “Let’s **imagine** Meta is able to get 1 billion DAU on “Meta AI” in three years (\~25% of their Daily Active People on their properties). Let’s say the average DAU on Meta AI asks 360 queries per year. If 20% of these queries have commercial intent, that’s 72 high-intent queries for which Meta can show ads to users later. If \~10% of these high-intent ads convert and each conversion is worth $10-20, that’s \~$70-140 Billion incremental revenue opportunity for Meta. Remember, an average Meta DAU likely sees [more than 30k](https://www.mbi-deepdives.com/expanding-the-scope-of-digital-advertising/) ads on Meta per year. Even if there is some “cannibalization” here (perhaps Meta would have been able to show some of these ads without the help from “Meta AI” anyway), you can still sense that the incremental opportunity can become quite large for incumbent companies with existing userbase and SOTA advertising infrastructure.” Indeed, I continue to think AI’s impact on large, scaled incumbent advertising infrastructure is still underappreciated by investors which I will expand more behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Jassy's Defense URL: https://www.mbi-deepdives.com/jassys-defense/ Last updated: 2026-04-10T14:58:29.000Z Since Andy Jassy became CEO of Amazon on July 5, 2021, the stock has not only **noticeably** lagged broader benchmarks such as S&P 500 and Nasdaq 100 but also **underperformed every single big tech companies in Mag7** (Nvidia not shown in the chart since it transforms the Y-axis a bit too much…so you get the idea!) Such sustained underperformance understandably provides a plenty of ammunition to pontificate at the leadership, especially when you are not the founder of the company. Jassy certainly received more than his fair share of criticism over the last five years. Yesterday, he penned a thoughtful [shareholder letter](https://www.aboutamazon.com/news/company-news/amazon-ceo-andy-jassy-2025-letter-to-shareholders?ref=mbi-deepdives.com) to explain why he believes Amazon’s current position is misunderstood by investors. ![chart](https://substackcdn.com/image/fetch/$s_!z3ru!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Facd46e5f-0e1a-4b51-8b54-5b1e3cad9a1f_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Being misunderstood is hardly a new phenomenon in Amazon’s history as Jassy poignantly pointed out even as late as 2014 (The “[AWS IPO](https://stratechery.com/2015/the-aws-ipo/?ref=mbi-deepdives.com)” happened in 2015), not all of the internal Senior leadership was entirely convinced why they want to be in the cloud business: > At our 2014 AWS operating plan review, the discussion started with a senior leader at the company musing, “Tell me again why we’re doing this business?” Jassy basically took the time to explain in his letter why Amazon is making somewhat currently confusing bets today which can deliver exceptional returns in the future. Take their investments in rural delivery network, for example. Following Amazon’s investments in rural areas, average number of monthly same-day customers doubled in 2025 and by the time Amazon’s expansion in rural areas is complete, Amazon will deliver over a billion more packages each year. For a long time, rural areas were considered almost uneconomic for e-commerce players and hence, these areas continued to be dominated by physical retailers, especially Dollar General. Given Amazon’s commitment to rural areas, it’s hard not to worry a bit about companies such as Dollar General’s ability to **compound** revenue over the long-term. Behind the paywall, I would like to share some thoughts on three of Amazon’s key current bets: Amazon LEO, Amazon’s investments related to drone delivery, and of course, the massive capex spree on AWS. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- Let’s start with the Amazon LEO bet. Jassy wrote in his letter (emphasis mine): _This post is for paying subscribers only._ ### The Jagged Intelligence of Humans URL: https://www.mbi-deepdives.com/jagged/ Last updated: 2026-04-09T16:16:43.000Z Today’s piece is slightly different from my typical investing musings. I have been thinking about this for the last few days, and I would like to get this off my chest. In a recent [episode](https://www.youtube.com/watch?v=kwSVtQ7dziU&t=8s&ref=mbi-deepdives.com) on “No Priors” podcast, Andrej Karpathy mentioned the following: > “…these models still are not, you know, they've improved a lot, but they're still, like, rough around the edges as maybe the way I would describe it. **I simultaneously feel like I'm talking to an extremely brilliant PhD student who's been like a systems programmer for their entire life and a 10-year-old.** And it's so weird because humans, like there's, I feel like they're a lot more coupled. Like you have, you know, everything is a lot more coupled. You wouldn't encounter that combination. This jaggedness is really strange. And humans have a lot less of that kind of jaggedness, although they definitely have some.” It’s not just Karpathy; I have heard this sentiment echoed by many others in recent months. Admittedly, I am a bit surprised how consensus this conventional wisdom appears to be. If you are on X (formerly twitter), you are routinely bombarded with takes from objectively smart people that can perhaps only be explained by jagged intelligence of humans. I suspect human intelligence is just as jagged as the machines and yet, we tend to notice lot less of it primarily for a couple of reasons: a) most of us have more self-awareness than the machines (the bar is low since I’m comparing against something that has likely no self-awareness) and hence, we avoid topics where me may divulge our naivete, and b) we seem to find it fundamentally challenging to have opposing opinions about someone and crave a desire to have consistency which perhaps explains if someone thinks Elon Musk is the greatest entrepreneur ever lived, they are also particularly prone to ignore or downplay any other negative traits shown by Musk. You may roll your eyes and think the capitalists such as Musk or Gates are far from epitome of human intelligence. Well, I can point to Isaac Newton, perhaps one of the greatest scientists ever lived! While Newton is still revered today for his contributions to some of the core tenets of our scientific understanding of the world, we mostly ignore the fact that he also spent enormous amounts of time in his later years on alchemy and biblical chronology. He actually wrote more on these subjects than on physics and mathematics **combined**. He tried to transmute metals, searched for the philosopher’s stone, and attempted to decode hidden prophecies in the Bible, including calculating dates for the apocalypse. Boy, that sounds pretty jagged intelligence to me! In fact, in a recent Dwarkesh podcast [episode](https://www.dwarkesh.com/p/michael-nielsen?ref=mbi-deepdives.com), Michael Nielsen mentioned an [essay](https://mathshistory.st-andrews.ac.uk/Extras/Keynes%5FNewton/?ref=mbi-deepdives.com) that John Maynard Keynes wrote about Newton in which Keynes perhaps aptly called Newton “the last of the magicians” rather than the first of the age of reason. From the essay (emphasis mine): > "In the eighteenth century and since, Newton came to be thought of as the first and greatest of the modern age of scientists, a rationalist, one who taught us to think on the lines of cold and untinctured reason. > > I do not see him in this light. I do not think that any one who has pored over the contents of that box which he packed up when he finally left Cambridge in 1696 and which, though partly dispersed, have come down to us, can see him like that. **Newton was not the first of the age of reason. He was the last of the magicians, the last of the Babylonians and Sumerians, the last great mind which looked out on the visible and intellectual world with the same eyes as those who began to build our intellectual inheritance rather less than 10,000 years ago**. Isaac Newton, a posthumous child born with no father on Christmas Day, 1642, was the last wonderchild to whom the Magi could do sincere and appropriate homage." Indeed, even if I look at scientists who are the torchbearers of the age of reason, it’s not hard to see noticeable jaggedness in their accomplishments. Think about Einstein. At 26, while working as a patent clerk in Switzerland, he published four groundbreaking papers. Photoelectric effect, Brownian motion, Special relativity, and Mass–energy equivalence. Those four papers were published in March, May, June, and September of the **same year: 1905**! Any single one of these would have been a career-defining contribution; doing all four in one year, outside academia, is why 1905 is "annus mirabilis" (miracle year) in physics. If you are blown away by the cadence of AI model releases in 2026, imagine what you would have thought about annus mirabilis in 1905! Alas, the “annus mirabilis” didn’t repeat in Einstein’s lifetime! Almost ten years after “annus mirabilis”, Einstein published his seminal paper on **general relativity**. However, from roughly the mid-1920s until his death in 1955, Einstein devoted the bulk of his research energy pursuing “[**unified field theory**](https://en.wikipedia.org/wiki/Unified%5Ffield%5Ftheory?ref=mbi-deepdives.com)**”** but couldn’t pull it off. Perhaps nothing proves the Jaggedness of human intelligence than the [Planck’s principle](https://en.wikipedia.org/wiki/Planck%27s%5Fprinciple?ref=mbi-deepdives.com) itself: > “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die and a new generation grows up that is familiar with it” The jaggedness of our intelligence is so embedded that perhaps it is only through our death can we relieve the rest of society from our own ignorance. In fact, one of my counterintuitive beliefs is the world may find itself in a relative tech stasis or stagnation if we are able to materially extend our life span. I am still not quite an “AGI” believer, but the more I hear about jagged intelligence of the machines, the more I think we may want to look us in the mirror a bit more closely! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Man and Machine URL: https://www.mbi-deepdives.com/man-and-machine/ Last updated: 2026-04-08T14:41:08.000Z For a typical company, analysts and investors evaluate a company’s capital allocation by looking at how the company allocates the cash it generates. Ultimately, there are only five things you can do with your cash flow from operations: a) invest in your own business to grow more in the future i.e. capex, b) acquire or invest in a different company, c) pay a cash dividend to shareholders, d) repurchase shares, and/or e) repay any debt. However, assessing capital allocation is relatively lot easier for companies such as Floor & Decor than a big tech company such as Alphabet. Before AI made all the big tech companies (except Apple and Nvidia) materially capex intensive businesses, a good chunk of their actual capital allocation happened through the income statement rather than the balance sheet. Unfortunately, it is relatively more challenging to not only objectively segment growth opex vs maintenance opex but also evaluate the return on such investments on a regular basis. Nonetheless, for businesses such as Alphabet and Meta, I have long felt a better way to gauge about their capital allocation framework is to think from a **gross profit** level, rather than cash flow from operations (CFO) level. For supermajority of the gross profit Meta or Alphabet generates in a typical year, it is unlikely to be driven by the opex associated with the current year. Of course, to compound the gross profit for years (or decades) to come, they must front load the opex investments which gets expensed as incurred but the benefits will likely be reflected in income statement often years after such opex investment. These benefits, however, are far from guaranteed (just look at Meta Reality Labs, for example). So, the variance in return from such opex investments through income statement is likely to be noticeably higher than return variance in capex investments through balance sheet. Nonetheless, that doesn’t invalidate the reality that there is a deep, qualitative difference in how a company such as Alphabet and Meta think capital allocation decisions compared to more plain vanilla businesses such as Floor & Decor. The qualitative difference in capital allocation framework in companies such as Alphabet vs most other traditional businesses was quite apparent when John Collison asked Sundar Pichai about capital allocation questions in a recent Cheeky Pint [episode](https://cheekypint.transistor.fm/31?ref=mbi-deepdives.com). See the question from John Collison and excerpts from Sundar Pichai’s answer below (emphasis mine): > **John Collison:** > > Can I ask, I’m curious, how capital allocation actually works at Google? What I mean by that is the idea good capital allocation is about internalizing the opportunity cost for capital and putting the cash that a business generates towards its highest and best use. In the toy example in a business school book, maybe you’re Boeing, and we have this cash that our business generates, and we can either go bid on the next defense contract, and we’ll invest this much in R&D dollars, and we model this much revenue from the contract, or we go develop a clean-sheet commercial airliner, and we’ll put in this money, and we model this thing. It’s like a 16% IRR versus a 19% IRR. I prefer the 19%. In Google’s case, the projects are extremely heterogeneous where it’s like, we can give the YouTube team more funding so they can go improve the recommender algorithm and therefore time on site increases, and so does monetization. Or, we can give the Waymo team more funding so that they can actually get to market faster or scale up faster, or we can invest in this new AI approach that might pay off in five years time. I’m curious, if you are trying to put capital towards the highest and best use, and you’re ultimately comparing, how do you compare initiatives that are so different in nature and so different in payoff curve shape? > > **Sundar Pichai:** > > It’s a good question. I feel it today more than ever, ironically, **because of TPU allocation**. In some ways, I feel it even Waymo needs TPUs. **Computers made the question, ironically, much more front of mind.** By the way, of all the things I do, I’m really looking forward to how AI, as a companion, at least, gives inputs to this task. > > I think once we can actually get all the data connected and flowing through. I think models are already capable. It’s more of getting all the data unlocked, I think it will be helpful…Historically, I think at Google, **one of the advantages we have had is sometimes we make these decisions very early in the cycle**. It’s almost like going back to that roots as a deep technology orientation. > > On a constant basis, look, I’ve always viewed it as you have to assess the long-term value of these things. It’s almost like in some intuitive way, **you’re thinking about the option value and the TAM of something 5–10 years down the line, and you assume a crazy growth and think through whether those decisions make sense**. > > **The TPU investments have been great that way**. We’ve steadily invested in that. Waymo was a great example where I think we increased our investment two to three years ago when the rest of the world got pessimistic on it. When others, some of the people were backing off. > > …if Waymo had reached this point earlier, I think I would have invested the capital earlier. To some extent, I think you were judging it by... You want to be good stewards of capital. **To the extent you’re bullish on ROIC, you want to invest every last dollar you can there**. > > …This is why we’ve invested in other companies…But we always thought about it with the lens of being good stewards of it. **We felt our investment in Stripe was being a good steward of our capital. SpaceX, and Anthropic and so on**. I think now with the AI shift, there are more opportunities on which we can deploy capital in a good way, and so we are doing that. > > …**we’ve always had a compute budget, even in classic compute**. I would say with ML, and we use both TPUs and GPUs, by the way, extensively. ML compute planning is... **We are super thoughtful about headcount planning, too,** but we have always had to plan that. ML compute, we’ve gone through phases where they’ve been easy, and then there have been phases where we’ve been constrained as a company. > > But now it is really acutely constrained. You spend a lot more time. **I at least spend a dedicated hour a week thinking about that question at a pretty granular level**. I will know by projects and by teams, the compute units they are using, or at least I have that information, and I’m looking at it and assessing it. In some ways, it’s a really important thing to be doing right now, I feel. > > **…The scarce resource is compute in a lot of cases, and so you’re ensuring that Google’s precious compute resources are being spent on the most worthwhile”** I think Pichai’s response to capital allocation question is pretty revealing how Alphabet thinks through investing their capital both through income statement and balance sheet. Alphabet and Meta actually both started disclosing their employee compensation expenses separately which gives us bit of a peek how they allocate their gross profit in both income statements and balance sheets. I think it’s quite interesting that while Zuckerberg received quite the limelight to pen “[The Year of Efficiency](https://about.fb.com/news/2023/03/mark-zuckerberg-meta-year-of-efficiency/?ref=mbi-deepdives.com)” memo in 2023, you could perhaps argue Sundar Pichai paid closer attention to efficiency mantra than Zuckerberg himself did since then. Alphabet’s employee compensation expense was **essentially flat** over the last three years whereas Meta’s employee compensation **increased by 34%** during the same period. You may wonder about the noticeable jump in Alphabet’s opex ex cost of revenue and employee compensation line item below which increased from \~$26 Billion in 2024 to $44 Billion in 2025\. A couple of drivers for this sudden jump is: a) Alphabet has moved its shared AI R&D to “Alphabet-level initiatives” which is embedded here, and b) EU regulatory fines. Similarly, Meta also expenses its non-revenue generating GPU depreciation through R&D line item which also helps explain their noticeable increase as well. I still think it perhaps reveals how much discretionary expenses Meta still harbors in its income statement given the fact that back in 2023 and 2024, both companies had similar opex ex cost of revenue and employee compensation despite Meta generating \~60-70% of Alphabet’s gross profit and Alphabet operating in businesses that are naturally much more opex intensive (think Cloud’s S&M, for example) than Meta’s operations. As long as Meta’s topline continues to grow at a healthy clip, you can perhaps appreciate why shareholders may not need to worry too much about margin sustainability over the **medium term**. Anytime Zuckerberg re-reads his own efficiency memo, he can probably find more opportunities to sustain or improve Meta’s margins. One way to look at Meta and Alphabet’s capital allocation over the last three years is that \~30% of their gross profit went to employees, \~30% to machines, and \~30% to shareholders. The mix, however, is changing very rapidly. In both companies, the gross profit share that went to employees and shareholders is dropping noticeably every year and given the capex outlook in both companies, this mix shift will accelerate further in 2026 (and likely beyond 2026). **Alphabet** ![](https://substackcdn.com/image/fetch/$s_!_8Nx!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9611f780-8208-49d7-b8cc-89f0b28f9857_694x411.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Meta Platforms** ![](https://substackcdn.com/image/fetch/$s_!4AKz!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5062484b-142c-4422-8d60-4d07e023311a_685x421.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) In 2026, \~55-60% of Meta and Alphabet’s gross profits will go to the machines, \~20-25% will be paid to employees, and \~15-20% to shareholders. Of course, shareholders don’t necessarily want the money back from these companies as long as the machines keep producing compelling ROIC. So, this isn’t quite man vs machine, rather man **and** machine. The machine is still working for the man…for now! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Fat-Tailed Economics of AI URL: https://www.mbi-deepdives.com/fat-tail-ai/ Last updated: 2026-04-07T17:55:39.000Z Anthropic yesterday [disclosed](https://www.anthropic.com/news/google-broadcom-partnership-compute?ref=mbi-deepdives.com) that their run-rate revenue has surpassed $30 Billion (I appreciate that unlike most people in tech, they didn’t call it ARR). To contextualize how mind boggling the growth is, Anthropic ended 2025 with annual run rate of just $9 Billion which then shot to $19 Billion by February. Basically, Anthropic seems to be adding its entire 2025 run-rate revenue **every month** now! The meteoric growth starts to make some sense when you see how some of its customers are behaving. The Information [reported](https://www.theinformation.com/articles/meta-employees-vie-ai-token-legend-status?rc=4lgoj7&ref=mbi-deepdives.com) yesterday Meta (likely one of Anthropic’s major customers) is literally running an internal “competition” among its employees to see who can spend the highest number of tokens. Meta employees apparently used 60 trillion tokens in just last 30 days. To understand the scale of such token consumption, The Information helpfully mentioned that all the books that were ever published are only estimated to be worth \~20 trillion tokens! You can probably see now how Anthropic is growing their revenue so fast when its end customers are essentially “bragging” to use as many tokens as possible. ![Image](https://substackcdn.com/image/fetch/$s_!a79s!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a78e8cf-aa44-4a73-8e2d-57bf0134852a_1320x1539.jpeg "Image") Image Source: [The Information](https://www.theinformation.com/articles/meta-employees-vie-ai-token-legend-status?rc=4lgoj7&ref=mbi-deepdives.com) Given how fast Anthropic is growing their revenue and how willing its customers seem to be to spend more tokens, does it mean Anthropic is also making gross profit hand over fist in inference? That may sound like a rhetorical question, but it is actually surprisingly a more difficult question to answer. I have mentioned about AI’s speculative economics before (see [**here**](https://www.mbi-deepdives.com/lipstick-on-frontier-ai-pigs/), and [**here**](https://www.mbi-deepdives.com/frontier-ai-economics/)), but I have a newfound appreciation about the uncertainty in AI economics after reading Anjali Shrivastava’s couple of pieces on this topic. [Anjali](https://x.com/anjali%5Fshriva?ref=mbi-deepdives.com) once left a couple of thoughtful replies to my tweets which made me follow her on X. I then came across her [article](https://www.anjalishriva.com/token%5Fpricing.html?ref=mbi-deepdives.com) “**A token is not a fixed unit of cost”.** Unfortunately, I am one of those guys who always have **at least** 50 tabs open on his PC and Anjali’s piece ended up in that graveyard. Thankfully, one of her recent [tweets](https://x.com/anjali%5Fshriva/status/2040858797464604751?ref=mbi-deepdives.com) showed up on my feed yesterday which reminded me that I never finished reading her piece. After reading her pieces, I can say her pieces should be **required** reading for anyone interested in understanding AI’s economics. In the first [piece](https://www.anjalishriva.com/token%5Fpricing.html?ref=mbi-deepdives.com) titled “**A token is not a fixed unit of cost”** (originally published in August 2025)**,** Anjali highlighted how AI is just fundamentally so different from the traditional software cost structure. Traditional software businesses thrive on the “law of large numbers”. The low costs of light users subsidize the high costs of heavy users, resulting in highly predictable, profitable gross margins. AI truly breaks this economic law because its underlying costs are fundamentally non-linear. When an AI generates a response, it must continuously "re-read" the entire preceding conversation to produce the next word. Therefore, generating the 10,000th word requires exponentially more computing power than the 1st word. This creates infinite-variance, "fat-tailed" financial risk: a tiny fraction of power users running complex tasks can rack up massive compute bills that can materially wipe out a good chunk of the gross profits generated by majority of the “regular” users. I asked Gemini to give me a simple analogy to drive this point home and this is what Gemini came up with: “Imagine running a taxi company with a flat $10 fare. In a normal business, fuel consumption is predictable. In the AI world, the engine burns 1 gallon of gas for the first mile, 2 gallons for the second, and 100 gallons for the tenth. A short trip is highly profitable; a long trip bankrupts the driver. Standard software pricing assumes fixed fuel efficiency, but AI compute costs compound with every mile.” Anjali then wrote a follow up [piece](https://www.anjalishriva.com/fat%5Ftails?ref=mbi-deepdives.com) in January 2026 titled "**Why fat-tailed costs emerge at scale**". It is quite understandable if you thought “well, even if the cost compounds, AI labs can just fix the problem by simply charging users “per token” (by the mile)”. However, true unit costs are still unpredictable because they depend on the real-time congestion of the entire data center. To be profitable, AI providers must process multiple users on the same physical servers simultaneously (batching). But complex AI tasks devour massive amounts of temporary working memory. If a random spike of users submit long tasks at the exact same millisecond, their combined memory needs multiply rapidly. The system hits a physical “memory wall,” servers slow to a crawl, and efficiency plummets. Thus, **the true cost of processing an AI request is a moving target, dictated entirely by the aggregate traffic jam happening on the server at that exact moment**. Again, to make the point even more clear, let me go back to Gemini for an analogy: “Think of an airline trying to maximize profit by filling every seat. Normally, a passenger's weight is fixed. In the AI airline, passengers' luggage magically expands while the plane is in the air depending on how long their trip is. If too many people with expanding luggage happen to be on the same flight, the plane gets too heavy and stalls. **You cannot accurately price a ticket in advance if the plane's maximum capacity fluctuates dynamically during the flight**.” Make no mistake that Anthropic’s exponential growth is downright incredible, but I hope Anjali’s pieces provide an ample food for thought why investors still have lot more work left to do to value an AI lab such as Anthropic beyond looking at the revenue chart. In fact, reading Anjali’s pieces made me re-think Microsoft’s position in the value chain which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- In her follow up piece, Anjali had a very interesting observation (emphasis mine): _This post is for paying subscribers only._ ### "Serious" Era of Software Investing URL: https://www.mbi-deepdives.com/serious-saas/ Last updated: 2026-04-06T14:22:31.000Z A couple of weeks ago, Redpoint published an interesting [presentation](https://www.redpoint.com/reports/2026-market-update/?ref=mbi-deepdives.com) on the state of software industry. It’s a good recap of what has been going on in both the public and private software market. Redpoint pointed out that the SaaS selloff in the last 12 months hasn't been indiscriminate. It's been almost a rational sorting of software by defensibility against AI. Vertical SaaS has held up the best because these companies own irreplaceable moats in proprietary, industry-specific data and compliance workflows; AI can augment them but can't displace decades of accumulated system-of-record data, making the switching cost existential in nature. Infrastructure software also held up just fine because AI is a direct tailwind: more AI deployment means more compute, data, and observability spend flowing to names like Snowflake, Datadog, MongoDB, and Cloudflare. Horizontal SaaS, by contrast, has been crushed because these products were “designed to serve every industry equally, which often meant integrating deeply with none” and their core function of tracking who does what and when is precisely the coordination problem AI handles natively. Market is basically saying today that while in vertical software AI is a feature and in infrastructure it's a demand driver, in horizontal SaaS it's a substitute for the product itself. ![](https://substackcdn.com/image/fetch/$s_!XXUO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F19ef7065-db22-4232-90df-5d12b44e5409_1381x720.png) One of the problems Redpoint identified is excessive stock based compensation (SBC) in software industry. As I have often pointed out, SBC itself is hardly a problem. However, it is the deliberate decision of ignoring SBC **by investors** where lies the key challenge. If investors are okay with valuing a company by ignoring very real costs, management has every incentive to give what investors want. As a result, I have hardly felt any sustained annoyance at tech management for excessive SBC; rather I have been utterly confused why there is a plethora of investors who are overly eager to value companies by ignoring these costs and often seem very willing to propagate inane talking points such as “SBC won’t make or break a company; Accelerate revenue or die”. Of course, a company won’t live or die because of SBC but an investor who wants to compound his or her hard earned savings over years to come cannot possibly ignore these costs while valuing these companies. ![](https://substackcdn.com/image/fetch/$s_!XPVY!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F99e79e3a-33a0-45b1-a625-fb59ff02d0a3_1858x961.png) I’m not singling out Redpoint; even a16z recently [published](https://a16z.com/there-are-only-two-paths-left-for-software/?ref=mbi-deepdives.com) a think piece outlining their advice for software companies. Notice the following (emphasis mine): > Software companies got very good at talking about free cash flow margins over the last decade. But **if we are serious**, we should stop excluding stock comp and pretending dilution is not an expense borne by owners. For companies that are not about to reaccelerate growth, I think the right target is 40% or even 50%+ true operating margins, including SBC, within 12-24 months. “If we are serious”? I guess it makes sense to ignore SBC if you were not being “serious” until the falling stock prices finally forced you to think through some basics. So anytime I hear VCs talk about software’s SBC problem, I cannot help but think about this meme: “we’re all trying to find the guy who did this”. To be clear, public market tech investors also are equally to be blamed. It is still astonishing to me that almost all the data providers ignore SBC for software companies (unless you’re big tech) in their forward estimates which makes much of the forward valuation multiples below Gross Profit completely meaningless. ![Hot Dog Man GIF - Find & Share on GIPHY](https://substackcdn.com/image/fetch/$s_!x7Ip!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8b882c3b-7958-4c60-bcfa-2ad6c96dc168_498x280.gif "Hot Dog Man GIF - Find & Share on GIPHY") Given this context, I also cannot help but wonder whether VC investors are still currently in “non-serious” stage of evaluating AI companies and perhaps they will discover the obvious problems of quoting ARR multiples on companies whose revenues are neither necessarily recurring nor are blessed with zero marginal cost. The reason it made at least some sense to look at revenue multiple for loss making software companies in the past is because of the near zero marginal cost which led to somewhere between 70% and 90% gross margin. That is almost certainly not the case in any of the AI native startups and somehow this obvious flaw is currently being largely ignored by VCs who keep quoting ARR multiples. The reality is this is not a VC specific problem; this inertia to update your opinions or ignoring obvious realities is primarily a feature of “**zero volatility**” nature of these investments. Even in public market, investors are often found to ignore obvious risks until stock price starts going down and then the same investors discover the risks they should have been thinking all along. Yours truly also falls for this classic problem every once in a while and unlike in VC world, public market investors are not afforded to dwell on their cozy, but apocryphal consensus for too long! Redpoint mentioned that once you adjust for growth in private companies, the valuation discrepancy between public and private market software companies not only disappear but you can argue private companies are trading at a substantial discount! ![](https://substackcdn.com/image/fetch/$s_!MSWb!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6728c324-30f0-49bc-951e-90ab827863b3_1990x1041.png) To be fair, they did say these adjustments are “quite imperfect”, but apart from the obvious limitations pointed earlier of using ARR multiples for these private software companies, I thought Akram had a very good [response](https://x.com/akramsrazor/status/2038196993441546581?ref=mbi-deepdives.com) why this comparison is largely non-sensical. Some excerpts from Akram’s counter (emphasis mine): > “…no more growth adjusted comp slides to public mature names plz, which is for some reason happening way more. Series B/C companies trading at 61x ARR but growing 640%, giving a growth-adjusted multiple of 0.05x vs. public software at 0.37x is cool but **this completely ignores that a Series B company growing 640% off a $3M ARR base faces entirely different scaling physics than a public company growing 29% off a $5B base. The growth rate convexity at small revenue bases is almost free. You close three enterprise deals and you’ve tripled**. That 640% growth rate is a description of being small, not evidence of anything beyond that yet really. > > The efficiency slide though is where **the real debate should be**. Cursor at $6.1M ARR/FTE and Lovable at $3.4M are being framed as “unprecedented software efficiency.” But that **ARR/FTE ratio is many ways just a description of how thin the product layer is for these companies. If you have 50 people and $300M ARR because you’re reselling Claude with a great IDE wrapper, are you operationally excellent vs atlassian/servicenow or really just a thinner biz? You could argue these metrics are a reflection of how little proprietary value-add sits between the customer and the foundation model API’s. Today, that looks awesome, but can in fact be EVEN WORSE then the very software 1.0’s who have TERMINAL VALUE questions now**.” Indeed, I still haven’t heard a compelling explanation why all the terminal value question for public software companies isn’t doubly applicable for private AI-native software companies. My best explanation is since these private companies are inherently “zero volatility” assets and the investors know much of the potential buyers in the private market agree to the idea of valuing these companies based on ARR, the VCs can leave the terminal value question for later round and “hopefully” for public market investors. Perhaps one of the more intriguing slides from Redpoint’s presentation is that they pointed out incumbent software companies are likely to get preferential treatment from their customers as these customers start adopting AI in their workflows. Indeed, this is why I believe for many incumbent software companies, they still have a decent shot at controlling their destiny. ![](https://substackcdn.com/image/fetch/$s_!sHV-!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2d866e5-ffed-4eda-b7ab-afad9dfda753_1875x954.png) Unfortunately, the limit to such optimism is more intangible realities in any large incumbents: [culture](https://x.com/loganbartlett/status/2040081771711099163?ref=mbi-deepdives.com). I too am quite sympathetic to this argument and suspect this is where indeed the game will be won and lost between incumbents and startups in the next three to five years! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### A (Largely) Functioning World! URL: https://www.mbi-deepdives.com/functioning-world/ Last updated: 2026-04-05T15:26:30.000Z **Programming Note**: On every Sunday from now on, I will try to write pieces that are predominantly based on personal experiences which may or may not be loosely connected with investing. If you are reading MBI Deep Dives everyday, I think it would be rather useful for my readers to understand my personal lens a bit better since that presumably affects (at least in some capacity) the way I analyze businesses as well. --- If you go to any newspaper website (or still hold onto the relic of actually reading the physical paper) on any random day, it is commonplace to get bombarded by a deluge of negative news flow. Any person who ardently follows news may require a conscious effort to stay optimistic about the world. Indeed, there are always legitimate reasons to be deeply concerned even if you personally live in a safe distance from any of the headline grabbing events that are playing out on a daily basis. Nonetheless, I often get this feeling that most people underestimate how **largely** functioning the world is. That may seem like a banal observation, but frankly speaking, I cannot help but wonder whether that is gradually becoming almost a contrarian belief. Of course, the world is so complex and our experiences in the world are so diverse that we all are running a little blind trying to grasp the “elephant”. So let me contextualize what I mean by “functioning” world. One of my rules of thumb to think about the world is to imagine how much your life can be largely dictated just based on where you are born regardless of your intelligence. Back in 2013, Warren Buffett [called](https://www.businessinsider.com/warren-buffett-on-the-ovarian-lottery-2013-12?ref=mbi-deepdives.com) it the “Ovarian Lottery”: > My political views were formed by this process. Just imagine that it is 24 hours before you are born. A genie comes and says to you in the womb, “You look like an extraordinarily responsible, intelligent, potential human being. \[You're\] going to emerge in 24 hours and it is an enormous responsibility I am going to assign to you — determination of the political, economic and social system into which you are going to emerge. You set the rules, any political system, democracy, parliamentary, anything you wish — you can set the economic structure, communistic, capitalistic, set anything in motion and I guarantee you that when you emerge this world will exist for you, your children and grandchildren. > > What’s the catch? One catch — just before you emerge you have to go through a huge bucket with 7 billion slips, one for each human. Dip your hand in and that is what you get — you could be born intelligent or not intelligent, born healthy or disabled, born black or white, born in the US or in **Bangladesh**, etc. You have no idea which slip you will get. Not knowing which slip you are going to get, how would you design the world? Do you want men to push around females? It’s a 50/50 chance you get female. If you think about the political world, you want a system that gets what people want. You want more and more output because you’ll have more wealth to share around. > > The US is a great system, turns out $50,000 GDP per capita, 6 times the amount when I was born in just one lifetime. But not knowing what slip you get, you want a system that once it produces output, you don’t want anyone to be left behind. **You want to incentivize the top performers, don’t want equality in results, but do want something that those who get the bad tickets still have a decent life. You also don’t want fear in people’s minds** — fear of lack of money in old age, fear of cost of health care. I call this the **“Ovarian Lottery”** Well, the slip I happened to “pick” back in 1991 was Bangladesh. Just before my brother (who was two years older than I am) was born, my parents moved from a small village in Bangladesh to a tier-two city named Bogura. My father sub-leased a room in the city, and all four of us lived in a single room for a few years before my father was able to manage to buy a land and build a tin-shed house. My childhood memory is filled with rain pouring on our tin-shed house and everyone in our house then frenetically trying to put buckets under the tin roof to stem the water flowing to different corners of the house. Fun times! Given that context, I was reasonably confident that my household was in the bottom quintile of the world back in 1991\. But after chatting with Claude today, I realized I was likely born in a household with income somewhere between 45th and 55th percentile of the world. That actually makes me realize why I almost never felt poor in my life despite being objectively poor for a good part of my life. The reason is perhaps quite simple: I was always surrounded by even poorer people. Just as millionaires cannot quite feel rich if they’re surrounded by billionaires, the poor people have oddly similar dynamic. I grew up reading about “[Monga](https://en.wikipedia.org/wiki/Monga%5F%28Bangladesh%29?ref=mbi-deepdives.com)” (an annual phenomenon in the 1990s and early 2000s); every year, some people in the Northern part of Bangladesh used to die of hunger. Thankfully, my family never had to endure starvation. So, in the poverty “Olympics” back then, I can understand why I never felt poor given the visceral awareness that there were people dying out of hunger in nearby villages back then in Bangladesh. In this backdrop, my parents harbored this belief that the genie that can outweigh the Ovarian lottery is education. So they made it their life’s mission to send us to a good school, and it was my mother’s responsibility to ensure we truly excel in academics. I certainly did. Given my own academic success, most of my friends also happen to have similar academic prowess and almost all of them came from similar socioeconomic backgrounds as mine. Thanks to Facebook, I am still connected with almost all of them despite moving abroad. In fact, this allows me to assess and calibrate my views of how “functioning” the world may be. If only \~10-20% of these peers experienced social mobility despite similar intelligence, I would be tempted to infer that the world may be less functioning, and it may just be randomness as well as “ovarian lottery” that are playing too big of a role in dictating our progress. However, when I thought about this yesterday and discussed with my wife (who have similar background and peers as mine), we realized nearly 75% of our **talented** peers (who were born somewhere between 40th and 60th percentile of global income) are now firmly in the **top decile** of global income. That is a remarkably rapid social mobility in the span of three decades! In fact, while this is nearly impossible to prove as no such data likely exists, the rate of social mobility for **talented kids** regardless of their originshas likely quietly **accelerated** in my lifetime without much fanfare. It can be tempting to think the world is getting worse and worse over time, but my personal experience in life has always given me pause in giving into such temptations. Of course, human beings have innate ability to take everything for granted and we are all very good at moving the goal posts almost constantly, but part of me thinks the 15-year old me would probably burst into tears with joy if he got to “see” how my life has turned out to be. In fact, I believe there are a lot of these “15-year olds” in the world who may have lost the Ovarian lottery but would react similarly if they could see how their “35-year olds” are living like today. Perhaps you can see why it is hard for me to be dismayed by any recent news flow and would rather prone to be perennially almost romantic about the steady, uneventful progress! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### "Founder Mode" Complacency URL: https://www.mbi-deepdives.com/founder-mode-complacency/ Last updated: 2026-04-04T15:46:18.000Z Colossus recently [published](https://colossus.com/article/project-mario-demis-hassabis-deepmind-mallaby/?ref=mbi-deepdives.com) a chapter from Sebastian Mallaby’s new book on Demis Hassabis titled “[The Infinity Machine](https://www.penguinrandomhouse.com/books/752231/the-infinity-machine-by-sebastian-mallaby/?ref=mbi-deepdives.com)”. I haven’t read the book, but did read this chapter. It was a riveting read. While I knew DeepMind had second thoughts about staying within Alphabet’s umbrella, I didn’t appreciate before how close it got for DeepMind to untangle themselves from Alphabet to become an independent company. Perhaps the man who deserves a lot of credit that Alphabet still has DeepMind in their wing to navigate the ever shifting AI landscape is Sundar Pichai. While it was almost commonplace to call for Pichai’s resignation during the post-ChatGPT almost embarrassing wobbling “Bard” period, I was more indifferent towards his role. To be clear, my mind was also consumed about Google’s search primacy in the age of AI, but I was worried more about structural concerns that wouldn’t be alleviated by the change of management. Google has so far assuaged such concerns, but this behind-the-scene saga with DeepMind made me update my opinion on Pichai in a much more positive light. When DeepMind was plotting to extricate themselves from Alphabet almost a decade ago, Pichai was prescient enough to foresee AI’s paramount importance in their core business. Let me share a couple of excerpts from the chapter (emphasis mine): > “In the summer of 2016, Hassabis held his fifth round of talks with Larry Page, and the details of a DeepMind spin‑out were laid down in a formal term sheet. A few months later, to ensure that everyone was on the same page, Hassabis met with the new CEO of Google, Sundar Pichai, who had assumed the top job when Page had moved upstairs to head Alphabet. An engineer with an MBA from Wharton and a background as a management consultant, Pichai had a boyish grin, an affable manner, and a dislike of confrontation. His discussions with Hassabis and Suleyman were cordial but bland. Pichai was not going to rock the boat, the DeepMinders concluded. > > …Four days later, the DeepMind duo got on the phone with Pichai. This time the CEO revealed the steelier side of his personality. He said that **turning DeepMind into a semi‑independent Alphabet company might not be in Google’s interests, after all. The “bet” option was for moonshots unrelated to Google’s core business, he said—projects such as autonomous cars or the science of life extension. Artificial intelligence did not belong in that bucket. To the contrary, AI was destined to become strategically important to Google’s flagship products, such as search and cloud computing**.” As these negotiations became more tense over time, all the big guns of Alphabet planned to meet to resolve the issue at hand. Alas, some big guns didn’t seem to appreciate what was at stake. From the book: > When the two sides met again, the conversation underscored the gulf between them. Hassabis and Suleyman argued that DeepMind did not fit under Google’s umbrella: Its mission was AGI, not consumer‑internet products. **Pichai objected that AI was central to his vision for Google, and that he would not allow his scientific bench to be depleted**. **Hassabis had hoped that Larry Page would weigh in on his side and push the Alphabet plan to a conclusion. But Page showed up for the meeting two hours late, and Sergey Brin was even later. Their version of what later came to be known as “founder mode” was that they were nowhere to be found, disproving the Silicon Valley mantra that founders deserve the right to control their companies indefinitely**. With Page and Brin effectively checked out, Pichai was the man DeepMind had to deal with. I have been thinking about the aforementioned excerpt for the last couple of days. If you glanced at my portfolio, it’s not difficult to see that I drank my fair share of kool-aid of “founder mode”. Perhaps fittingly the “[founder mode](https://paulgraham.com/foundermode.html?ref=mbi-deepdives.com)” propaganda originated from a founder himself: Brian Chesky. The more I ruminated over “founder mode”, the more I came to the conclusion that there is a glaring missing aspect in “founder mode” mantra: **Complacency**. It is telling that Chesky proudly recalls every chance he gets about how he figured out during Covid that Airbnb doesn’t need to do search advertising; as an investor I was actually a bit alarmed that he was running Airbnb pre-pandemic without paying close attention whether his advertising dollars was being deployed with appropriate ROAS guardrail. I can guarantee you that despite operating in “Manager Mode”, Glenn Fogel at Booking was looking at advertising ROI with a microscope and he certainly didn’t need a global pandemic to remind him how to deploy his precious advertising dollars at Booking. Is there any manager who would survive in any company if one of your segment operating performance looks like Meta’s Reality Labs? It is a tad bit astonishing that a founder who has built one of most generation defining businesses in the last couple of decades also managed to fund one of the ugliest cost structures of a business segment I have ever seen. I don’t think there is any manager out there who would fund a segment whose revenue in 2025 was essentially flat since 2021 despite losses becoming \~5x during this period. The only other business that I can think of with similarly lopsided, ugly cost structure is CoStar’s Homes.com. Of course, Homes.com is also a brain child of “founder mode”. ![](https://substackcdn.com/image/fetch/$s_!Wo-P!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1c126abd-2b8c-4c22-b5ef-340128634fbe_1171x733.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) In fact, for all the effusive praises Daniel Ek at Spotify or Tobi Lutke at Shopify receive these days, I do wonder at times how on earth these founder led companies became so incredibly bloated in the first place. At least, Zuck had his “monopoly” profits to fund science projects at Reality Labs, but Spotify and Shopify both operate in a much lower gross margin and competitive market. ![chart](https://substackcdn.com/image/fetch/$s_!yFXI!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd55ef3b-af6f-4ec6-92b8-322fbe388fc9_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I guess I shouldn’t act too surprised how this happened in those companies if I look inward a bit more closely. I launched MBI Deep Dives in September 2020 and I too was enamored by the rapid growth that followed in 2021\. In fact, I was so infatuated with the initial growth that I thought I should hire another analyst to help me with my Deep Dives. Fortunately, the guy I really wanted to hire decided to launch his own startup unrelated to investment research and turned down my offer. By the time I wanted to think about other potential candidates, 2022 arrived and Mr. Market schooled this very founder some lessons about investment research newsletter industry. It turns out my business operates in a much more procyclical fashion than I initially appreciated and it would indeed be a mini-disaster if I had hired someone at the peak of 2021\. As you can see, founders don’t have any special or magical power that will insulate themselves from the very human nature of complacency that naturally creeps into your mind following some success. Now imagine building companies such as Alphabet, Meta, Shopify, Airbnb, or Spotify? Given the scale of their success, it would be almost miraculous if these founders didn’t suffer even a more pernicious form of complacency from time to time than I ever did. Let me be clear: Chesky or Zuckerberg are indeed generational founders. But the “[halo effect](https://www.amazon.com/Halo-Effect-Business-Delusions-Managers/dp/1476784035?ref=mbi-deepdives.com)” of the “founder mode” can go a bit too far, especially during bull markets. I still, however, maintain that all else equal, I would rather have founders at the helm especially when a company’s back is against the wall. During more existential periods, managers can indeed be more susceptible in saving their jobs in the near-term potentially at the expense of saving the company in the long-term. But investors deploying their hard earned savings should be careful in not giving wanton license to founders coasting on past glories. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### "Never Sell" Episode: AI Doom, Veeva, Research and Writing URL: https://www.mbi-deepdives.com/never-sell-14/ Last updated: 2026-04-03T13:54:49.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode covering investors’ increasing fascination around AI doom scenarios, why we think Veeva is likely to be more insulated from AI disruption, and the value of approaching investment research more as a judge rather than as a lawyer defending the thesis or a stock. You can listen to it here: [Spotify](https://open.spotify.com/episode/2c39nOdsEBLBS1tjAtb3Aj?si=5eeeb81017af4230&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/ai-doom-veeva-research-and-writing/id1786912203?i=1000758940048&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=PfCIPBrUdeI&ref=mbi-deepdives.com), [RSS feed](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com) During the conversation, we mentioned this piece “[Infinite midwit](https://www.experimental-history.com/p/infinite-midwit?ref=mbi-deepdives.com)” which I really enjoyed reading. While I recommend you read the entire piece, I highlighted the following excerpts while reading it myself: > It’s cool that AI can fold proteins, create websites, fact-check journal articles, etc. but it can’t write anything that I am interested in reading. The problem isn’t that it hallucinates or makes mistakes. It’s that everything it writes vaguely sucks. I drag my eyes across the words and I feel nothing. That’s not quite right, actually—I feel like, “I would like this to be over as soon as possible.” > > …the faster you go, the sooner you hit the wall. I have found myself facing all of those limitations at one time or another, and as soon as I overcame them, I was immediately stymied by some other obstacle. I think all of us suffer from this *bottleneck blindness*: we assume our *current* bottleneck is our *only* bottleneck. When you’re strapped for cash, you think *all* of your problems are cash problems. But once you’ve got some money in you pocket, you realize that what you really need is *time.* Free up some time, and you discover that you’re actually lacking *motivation*. Acquire some motivation, and you realize what you’re missing is *ideas*. Then you need *direction*, then you need *discipline*, then you need *buy-in*, and so on, forever. > > As Montaigne [put it back in 1580](https://www.gutenberg.org/cache/epub/3600/pg3600-images.html?ref=mbi-deepdives.com), “though we could become learned by other men’s learning, a man can never be wise but by his own wisdom”. What does it look like to have all the learning ever created, but no wisdom of your own? I hope the conversation, as well as the aforementioned piece, is a good food for thought for your Friday. As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) ### Unprecedented Lock-in URL: https://www.mbi-deepdives.com/unprecedented-lock-in/ Last updated: 2026-04-02T14:37:06.000Z Apollo recently [published](https://www.apolloacademy.com/wp-content/uploads/2026/03/US-housing-market-033026%5Fv2.pdf?ref=mbi-deepdives.com) their US housing outlook which is an interesting read to get up to speed on how the housing market is faring. Let me highlight a couple of things from their piece that stood out to me. One of the things that jumped out to me is just how unusual the current housing cycle is compared to almost any period of history. Historical cycles typically followed a sharper, shorter trajectory. The downturns of 1974 and 1979 essentially played out and bottomed within 12 months. The 1980 cycle, triggered by the Volcker disinflation, found its floor around month 18\. Even the catastrophic 2005 housing bubble, which saw a deeper plunge of roughly 45% in existing home sales, largely ran its course within 36 months before establishing a new equilibrium. The 2021 cycle, by contrast, has been strikingly different. It was shallower in its initial decline, but rather than bouncing back, the market has flatlined at a depressed level with no meaningful recovery. ![](https://substackcdn.com/image/fetch/$s_!NkSf!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff8b3188f-c65b-423e-ba37-d93ce02dee4d_1831x825.png) Image Source: [Apollo](https://www.apolloacademy.com/wp-content/uploads/2026/03/US-housing-market-033026%5Fv2.pdf?ref=mbi-deepdives.com) That pattern is, of course, consistent with the “lock-in effect” story: homeowners who locked in 3-4% mortgages in 2020–2021 have very little incentive to sell and take on a 6–7% rate, so existing home inventory stays suppressed and transaction volumes remain depressed. Nonetheless, I was still surprised just how unprecedented this dynamic is compared to the last two decades. The percent of outstanding mortgages with rate more than 100 bps below the existing market rate peaked at **88% in 1Q’23** which has been very gradually coming down over time. ![](https://substackcdn.com/image/fetch/$s_!ln8E!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc47f62cd-a3f2-4fa7-a071-cc21717dd5c3_1123x658.png) Image created by Claude using the data sources mentioned The gap between effective rate of interest on mortgage and the current 30-year fixed mortgage rate is unusually wide which is the primary culprit for the lock-in effect. ![](https://substackcdn.com/image/fetch/$s_!3G1v!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe26e6667-3160-498b-86f6-beb5772a22d7_1830x940.png) Source: [Apollo](https://www.apolloacademy.com/wp-content/uploads/2026/03/US-housing-market-033026%5Fv2.pdf?ref=mbi-deepdives.com) Indeed, when you look at housing markets such as Toronto which isn’t blessed with 30-year fixed mortgage rates, it becomes abundantly clear that lock-in effect is the dominant force in keeping the housing turnover low in the US. ![](https://substackcdn.com/image/fetch/$s_!MEHL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F735a0a4f-2345-4fd8-8271-ce80fea92b6d_832x718.png) Image Source: From this [paper](https://www.philadelphiafed.org/-/media/FRBP/Assets/Economy/Articles/economic-insights/2025/q3/eiq325-how-mortgage-lock-in-affects-the-price-of-housing.pdf?ref=mbi-deepdives.com) In fact, this [piece](https://www.philadelphiafed.org/-/media/FRBP/Assets/Economy/Articles/economic-insights/2025/q3/eiq325-how-mortgage-lock-in-affects-the-price-of-housing.pdf?ref=mbi-deepdives.com) by a Senior Economist at the Federal Reserve Bank of Philadelphia highlights the pernicious impact of lock-in effect as it also tends to increase housing price. From the piece (emphasis mine): > Indeed, the very language and logic of “lock-in” presupposes that potential sellers are reluctant to re-enter the market as buyers because they are unwilling to reset the terms of their mortgage. Hence, **lock-in of sellers is also “locking out” potential buyers, meaning demand has shifted with supply. If lock-out suppresses demand enough, the buyer/seller ratio could remain steady or even decrease**. > > The effect of lock-in on moving propensity is directly measured from mortgage and transaction data. But since we cannot see the housing market in a counterfactual world with only seller lock-in and not buyer lock-out, researchers have turned to models of the housing market. Using these models, they can estimate the net effects of the rate increase on the buyer/seller ratio and prices. The findings to date show that, on balance, lock-in is making markets slightly tighter, with a modest to moderate effect on prices. Using their estimates of sale probability and a model of housing tenure choice, Batzer, Coste, Doerner, and Seiler find that **lock-in has prevented 1.7 million transactions and increased home prices by 7 percent**. Unfortunately, this lock-in effect so far can only be eroded through the typical [5 D](https://beneworleans.com/the-5-ds-of-real-estate/?ref=mbi-deepdives.com)’s of real estate (Divorce, Downsizing, Diapers, Diamonds, and Death). We are already [seeing](https://calculatedrisk.substack.com/p/fhfas-q3-national-mortgage-database?ref=mbi-deepdives.com) some of that as the percent of loans over 6% bottomed in Q2 2022 at 7.3% and has increased to 21.2% in Q3 2025\. But relying on 5Ds will likely take us much longer than history to normalize the housing market. ![](https://substackcdn.com/image/fetch/$s_!psIE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76ec948c-deed-4f7d-a71f-2ba987a41cc7_1840x966.png) Source: [Apollo](https://www.apolloacademy.com/wp-content/uploads/2026/03/US-housing-market-033026%5Fv2.pdf?ref=mbi-deepdives.com) Given this context, I will share some thoughts on Floor & Decor behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Social Media's Defense URL: https://www.mbi-deepdives.com/social-medias-defense/ Last updated: 2026-04-01T14:27:22.000Z Last week, I shared my concern that algorithmic content may force companies such as Meta to share some liabilities for the content shown on their properties because of the product design choices these companies made. From my [piece](https://www.mbi-deepdives.com/fat%5Ftail/): > I…do sympathize with the concerns related to algorithmic content on all social networks these days…Meta may invoke [section 230](https://en.wikipedia.org/wiki/Section%5F230?ref=mbi-deepdives.com) defense or/and their first amendment right, but I’m not sure even the Supreme Court will be sympathetic towards tech companies’ right to control an algorithm that ends up showing questionable or disturbing content to minors. I have been pondering about this point and would like to present the other side of the arguments. Techdirt published a [piece](https://www.techdirt.com/2026/03/26/everyone-cheering-the-social-media-addiction-verdicts-against-meta-should-understand-what-theyre-actually-cheering-for/?ref=mbi-deepdives.com) last week that made the case that the distinction between design and content is quite flimsy. From Techdirt: > Plaintiffs’ lawyers have been trying to get around Section 230 for years, and [**these two cases represent them finally finding a formula that works**](https://blog.ericgoldman.org/archives/2026/03/comments-on-the-jury-verdict-in-the-los-angeles-social-media-addiction-bellwether-trial.htm?ref=mbi-deepdives.com): don’t sue over the *content* on the platform. Sue over the *design* of the platform itself. Argue that features like infinite scroll, autoplay, algorithmic recommendations, and notification systems are “product design” choices that are addictive and harmful, separate and apart from whatever content flows through them. > > The trial judge in the California case bought this argument, ruling that because the claims were about “product design and other non-speech issues,” Section 230 didn’t apply. The New Mexico court reached a similar conclusion. Both cases then went to trial. > > This distinction — between “design” and “content” — sounds reasonable for about three seconds. Then you realize it falls apart completely. > > Here’s a thought experiment: imagine Instagram, but every single post is a video of paint drying. Same infinite scroll. Same autoplay. Same algorithmic recommendations. Same notification systems. Is anyone addicted? Is anyone harmed? Is anyone suing? > > Of course not. Because infinite scroll is not *inherently* harmful. Autoplay is not *inherently* harmful. Algorithmic recommendations are not *inherently* harmful. These features only matter because of the *content* they deliver. The “addictive design” does nothing without the underlying user-generated content that makes people want to keep scrolling. While the thought experiment is thought provoking, we can actually look at a more intriguing example to make the same argument. TBPN made a compelling [argument](https://tbpn.substack.com/p/the-social-media-addiction-placebo) by pointing out Sora’s failure despite borrowing the same “design” choices Meta or TikTok made. From TBPN (emphasis mine): > “So what to make of these two situations? It feels a bit like a placebo-controlled trial to me. Sora absolutely used all the social media “best practices” (or addictive & harmful neurobiological techniques if you want to use the court’s language). The Sora app was basically the same as TikTok, IG Reels, and YouTube Shorts in terms of UI & UX design. It had infinite scroll, algorithmic recommendations, notifications, a “Like” button! **All the same features that were found to be addictive when applied to human-generated content, very much did not seem addictive when applied to AI generated content** (at least if estimated usage rates are to be believed). > > As people compare social media to the cigarette industry, **it might be worth revisiting what exactly is addictive about cigarettes. Nicotine is the addictive chemical in cigarettes that causes addiction, and nicotine is addictive even when it’s not administered via combustible tobacco. It’s addictive in vapes, it’s addictive in pouches, it’s even addictive in nicotine gum and lozenges**. > > So we now have an experiment where we applied all the same UI/UX features to different content, and maybe you can’t read too much into it, but it certainly seems like **what pulls people into social media is more the humans that create content on the platform. Some creators create very compelling content that can lead to high screen-time. Some people go on social media and make horrible content that depresses people that land on it**.” Indeed, if it’s the content rather than design choices that made you “addicted” to a particular app, that sounds like more of a content issue (which means section 230 defense might apply) than a design issue. In case you wonder OpenAI shut down Sora because it was too expensive to operate in a compute constrained environment, WSJ [reported](https://www.wsj.com/tech/ai/the-sudden-fall-of-openais-most-hyped-product-since-chatgpt-64c730c9?gaa%5Fat=eafs&gaa%5Fn=AWEtsqf-4pViaKbK5wkU%5FCbEeRa-ZcYxsUiJJ-3aJ1MfO1h7ivtdV1cn2mOqT01pjno%3D&gaa%5Fts=69cd1ef2&gaa%5Fsig=EmPEpHxTD4Ib3PVyuWiGlTYYWpMgx7NULRTChH%5FddcQZD86PSmlrMrAmSLbhcmx8CVm7xQteSMeNKCqlKv2Nmw%3D%3D&ref=mbi-deepdives.com) that while number of users peaked at 1 million for Sora, the number dwindled to less than half of that in recent months. So, clearly it wasn’t going in the right direction even if compute constraints were not a factor in their decision. I personally have never used the word “addiction” when it comes to social networking apps. Given the measurement of this “addiction” is far from scientific and often relies on survey questions, this [paper](https://link.springer.com/article/10.3758/s13428-020-01462-9?ref=mbi-deepdives.com) made the case it is almost too easy to find “addiction” because it showed using similar standard medical criteria you could make the case young people are “addicted” to their real-life friends. Indeed, I think my parents could be easily convinced I was “addicted” to watching Test Cricket on TV and they would have perhaps loved it if government banned or limited my watch time for a game that often ends in draw after playing six hours for five consecutive days! Joking aside, I still think these lawsuits will likely end up in the Supreme Court and hence, it makes more sense to pay closer attention to what the Supreme Court has already indicated about their opinions on these topics. I think [Moody v. NetChoice](https://www.supremecourt.gov/opinions/23pdf/22-277%5Fd18f.pdf?ref=mbi-deepdives.com) provides a good ground to gauge Supreme Court’s point of view on some of these topics. The Supreme Court explicitly used "Facebook's News Feed" throughout the opinion in this case as the primary example to analyze how First Amendment protections apply to algorithmic content curation. Justice Kagan’s majority opinion strongly signaled that Meta's core content moderation practices on its main feeds are protected by the First Amendment. From the opinion (emphasis mine): > “**To the extent that social media platforms create expressive products, they receive the First Amendment’s protection**. And although these cases are here in a preliminary posture, the current record suggests that some platforms, in at least some functions, are indeed engaged in expression. **In constructing certain feeds, those platforms make choices about what third-party speech to display and how to display it. They include and exclude, organize and prioritize—and in making millions of those decisions each day, produce their own distinctive compilations of expression. And while much about social media is new, the essence of that project is something this Court has seen before**. Traditional publishers and editors also select and shape other parties’ expression into their own curated speech products. And we have repeatedly held that laws curtailing their editorial choices must meet the First Amendment’s requirements. **The principle does not change because the curated compilation has gone from the physical to the virtual world**." The court in its opinion continued later (emphasis mine; deleted some reference for readability): > “The individual messages may originate with third parties, but the larger offering is the platform’s. It is the product of a wealth of choices about whether—and, if so, how—to convey posts having a certain content or viewpoint. Those choices rest on a set of beliefs about which messages are appropriate and which are not (or which are more appropriate and which less so). And **in the aggregate they give the feed a particular expressive quality**. Consider again an opinion page editor, as in Tornillo, who wants to publish a variety of views, but thinks some things off-limits (or, to change the facts, worth only a couple of column inches). “The choice of material,” the “decisions made \[as to\] content,” the “treatment of public issues”—“whether fair or unfair”—all these “constitute the exercise of editorial control and judgment.”. **For a paper, and for a platform too**…**That those platforms happily convey the lion’s share of posts submitted to them makes no significant First Amendment difference**.” While Justice Alito concurred with the Court’s opinion, it does appear he hasn’t quite made up his mind on whether platforms such as Meta and Alphabet should receive the same first amendment defense as human beings would. From Justice Alito (emphasis mine; deleted some reference for readability): > “…consider how newspapers and social-media platforms edit content. Newspaper editors are real human beings, and when the Court decided Tornillo (the case that the majority finds most instructive), editors assigned articles to particular reporters, and copyeditors went over typescript with a blue pencil. The platforms, by contrast, play no role in selecting the billions of texts and videos that users try to convey to each other. And the vast bulk of the “curation” and “content moderation” carried out by platforms is not done by human beings. Instead, algorithms remove a small fraction of nonconforming posts post hoc and prioritize content based on factors that the platforms have not revealed and may not even know. After all, many of the biggest platforms are beginning to use AI algorithms to help them moderate content. And when AI algorithms make a decision, “even the researchers and programmers creating them don’t really understand why the models they have built make the decisions they make.” **Are such decisions equally expressive as the decisions made by humans? Should we at least think about this?** Other questions abound. **Maybe we should think about the enormous power exercised by platforms like Facebook and YouTube as a result of “network effects.”** And maybe we should think about the unique ways in which social-media platforms influence public thought. To be sure, I do not suggest that we should decide at this time whether the Florida and Texas laws are constitutional as applied to Facebook’s News Feed or YouTube’s homepage. My argument is just the opposite. **Such questions should be resolved in the context of an as-applied challenge. But no as-applied question is before us, and we do not have all the facts that we need to tackle the extraneous matters reached by the majority**.” Moreover, Justice Barrett too may be quite open in evaluating platforms’ first amendment rights differently. From her opinion (emphasis mine; deleted some reference for readability): > “what if a platform’s algorithm just presents automatically to each user whatever the algorithm thinks the user will like—e.g., content similar to posts with which the user previously engaged? **The First Amendment implications of the Florida and Texas laws might be different for that kind of algorithm. And what about AI, which is rapidly evolving? What if a platform’s owners hand the reins to an AI tool and ask it simply to remove “hateful” content? If the AI relies on large language models to determine what is “hateful” and should be removed, has a human being with First Amendment rights made an inherently expressive “choice . . . not to propound. a particular point of view”?** **In other words, technology may attenuate the connection be tween content-moderation actions (e.g., removing posts) and human beings’ constitutionally protected right to “decide for \[themselves\] the ideas and beliefs deserving of expression, consideration, and adherence.” So the way platforms use this sort of technology might have constitutional significance**.” Given these contexts, I don’t quite think it’s an open and shut case that the Supreme Court will certainly side with the platforms, especially for their products/services aimed at non-adults. However, Meta et al do have a strong case at hand and as I alluded before, I suspect Meta won’t be heartbroken either if there are onerous regulations to make kids experience on these platforms boring as long as the rules apply to everyone, including its current and future competitors not just in social media but also on any digital apps. For context, kids spend on an average 2.7 hours on Roblox per day which contain much of the social elements Meta provides on their properties. So, I’m not sure why whatever regulations that apply to Meta won’t also largely apply to platforms such as Roblox. Meta makes [\~1% of their revenue](https://www.japantimes.co.jp/business/2026/02/19/tech/zuckerberg-instagram-age-limits/?ref=mbi-deepdives.com#:~:text=Zuckerberg%20testified%20that%20while%20it's,disposable%20income%2C%E2%80%9D%20Zuckerberg%20said.) from teens; so what they really need is regulators to make it abundantly clear what these companies can or cannot do in their properties. Meta cannot come up with these policies on their own if their competitors decide to eat their lunch by making very different choices in product design. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Pendulum Between Intelligence and Knowledge URL: https://www.mbi-deepdives.com/pendulum/ Last updated: 2026-03-31T18:56:48.000Z Over the last week or so, I have started noticing an interesting development that is worth highlighting. Let’s start with Microsoft’s announcement yesterday. From Microsoft’s [blog](https://techcommunity.microsoft.com/blog/microsoft365copilotblog/introducing-multi-model-intelligence-in-researcher/4506011?ref=mbi-deepdives.com) yesterday (emphasis mine): > “Today, Researcher—Microsoft 365 Copilot’s deep research agent for work—takes a significant step forward. Designed to tackle complex research in the flow of work, Researcher now goes further with two new multi-model capabilities that raise the bar for accuracy, depth, and confidence: Critique and Council. > > **Critique is a new multi model** deep research system designed for complex research tasks. It separates generation from evaluation and utilizes a **combination of models** from Frontier labs including Anthropic and OpenAI. One model leads the generation phase, planning the task, iterating through retrieval, and producing an initial draft, while a second model focuses on review and refinement, acting as an expert reviewer before the final report is produced. **Our evaluations show that this architecture exceeds traditional single model approaches** and delivers best in class deep research quality. This design provides clear optionality across generator and reviewer roles, with the ability to support and expand these roles over time as the system evolves. > > **Council brings multiple model responses side-by-side in the Researcher experience**. Additionally, a cover letter provides valuable insights on **where the models agree, where they diverge, and the unique insights each brings on the topic**.” ![](https://substackcdn.com/image/fetch/$s_!TDD6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0649848b-7a2f-413e-b2ba-784b593e652c_1968x1149.png) Image Source: Microsoft Blog As someone who often copies and pastes the same queries to ChatGPT, Gemini, and Claude and then **manually** reads their responses and evaluates the quality afterwards, I can certainly see the appeal for a product such as “Council”. “Critique” is perhaps more useful if you want to automate something (hence you’re not in the loop) and want to ensure AI’s work goes through multiple phases of refinement before it presents the final work to you. Let’s move onto Intercom now which is a customer service suite software company. Intercom shipped their own customer service AI model called “Fin Apex 1.0”. From Intercom’s [blog](https://www.intercom.com/blog/announcing-fin-apex-the-age-of-vertical-models-is-here/?ref=mbi-deepdives.com) this week (emphasis mine): > “As of last week, \~100% of all (English language, chat and email) customer conversations are now running on Apex. Since day 1, **the Fin engine has comprised a system of models, and last year we started replacing the off-the-shelf models with our own, custom trained on our proprietary data**. But the core answering model was always a frontier labs offering—initially versions of GPT and recently Sonnet 4.0\. But now that core answering model is Apex 1.0. > > This model resolves customer issues at a materially higher rate than any other model available. One of our largest customers in the gaming space saw their resolution rate improve overnight from 68% to 75% (i.e. a reduction in unresolved conversations of 22%). We’ve never seen a jump this large from a single improvement since we started Fin. > > But **importantly it’s also dramatically faster, has fewer hallucinations, and is far cheaper than all other available models—all factors that weigh significantly in the consideration of companies deploying these agents to their service operations**.” ![](https://substackcdn.com/image/fetch/$s_!O2-b!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe2374249-5d18-4cc1-a15e-d30e6cfb1e85_2102x912.png) Source: Intercom Blog Last week, I also highlighted a paper by Meta in my [piece ](https://www.mbi-deepdives.com/metas-agentic-ai-ambitions/)“Meta’s Agentic AI Ambitions”. From my piece: > The most interesting takeaway from the paper is that **a great setup can compensate for a less powerful AI**. The researchers proved that a weaker model (Claude 4.5 Sonnet) using the Confucius scaffolding successfully fixed more bugs (52.7%) than a stronger, more expensive model (Claude 4.5 Opus) using Anthropic’s standard setup (52.0%). When powered by the GPT-5.2 model, Confucius Code Agent successfully resolved **59%** of the real-world bugs on the SWE-Bench-Pro test, beating both prior academic research and the official corporate systems built by OpenAI and Anthropic under identical conditions. ![](https://substackcdn.com/image/fetch/$s_!HhMD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F43e3ca6b-8bf0-4fa8-a208-b78c03a0b25d_969x465.png) Image Source: From [paper](https://arxiv.org/pdf/2512.10398?ref=mbi-deepdives.com) “Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases” Behind the paywall, I will share some thoughts what these different announcements hint at the future of AI and potential implications for frontier model developers. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Energy's Moment URL: https://www.mbi-deepdives.com/energy/ Last updated: 2026-03-30T14:21:59.000Z S&P 500 is down almost 7% Year-to-date. As you can see below, Energy sector ETF has been by far the best performing so far in 2026 with \~40% return YTD. Tech, on the other hand, was close to the other extreme as it is down \~10% YTD. ![chart](https://substackcdn.com/image/fetch/$s_!jAyM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7de92e25-ee26-42eb-a546-6b788e51fbe8_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In fact, John Arnold recently [made](https://x.com/johnarnold/status/2037559346402378235?ref=mbi-deepdives.com) a tongue-in-cheek observation: if you had half of your portfolio in tech and the other half in energy ETF since 2020, your portfolio would have pretty good return every year. Don’t be too excited though as this [reply](https://x.com/AlgoManX/status/2037769047580766660?ref=mbi-deepdives.com) showed that this strategy would not yield such halcyon results from 1999 to 2020! As they say, you can prove almost anything you want in investing if you just get to pick and choose the start and end dates. Alas, the world is far more complicated and unpredictable to have easy fixes! ![Image](https://substackcdn.com/image/fetch/$s_!8KEA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6dc02eb9-da60-4c92-b331-0261e1990b76_355x251.png "Image") Source: John Arnold [post](https://x.com/johnarnold/status/2037559346402378235?ref=mbi-deepdives.com) on X MBI Deep Dives doesn’t really follow energy markets closely and as I have mentioned earlier, I don’t have anything intelligent to say either about oil price or about the ongoing war in Iran even though these will obviously have an effect on the companies I personally own. Even though I don’t have much of an opinion how these events will unfold, during the weekend I did go through Michael Cembalest’s recent piece: “[Pandora’s Bog: the global energy shock of 2026](https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/pandoras-bog.pdf?utm%5Fsource=news.theideafarm.com&utm%5Fmedium=newsletter&utm%5Fcampaign=mom-s-401-k&%5Fbhlid=db8ad837217ce1c4115e1962885bd7104ea7a2d1)”. It’s a good read, so let me share a couple of my highlights from the piece. Cembalest started the piece with the good news: > “…the oil intensity of global GDP has plummeted since its 1970’s peak and is only half the level it was at the time of the Gulf War in 1990\. Even the natural gas intensity of GDP has declined since 1980 despite natural gas consumption tripling since then.” ![](https://substackcdn.com/image/fetch/$s_!VhSv!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6b287963-1046-4170-82be-f22ea81816dc_1681x606.png) Image Source: JP Morgan The bad news is “commodity prices are often set on the margin” and given commodities such as oil being a global market, the pricing trend can look remarkably similar across the world regardless of whether you are a net importer or net exporter of oil. In fact, let me highlight one of the things that I had to update after reading this report. Given that I am a tourist here, I had this naive assumption that since the US has now become a net oil exporter and China remains heavily dependent on imported oil, any oil shock would be net negative for China far more than it would affect the US. ![](https://substackcdn.com/image/fetch/$s_!fZNa!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbb76a25f-63e3-4f87-9f8a-dcedfd973299_619x841.png) Image Source: JP Morgan So, it was surprising for me when I noticed that China was actually ahead of the US in terms of “total insulation factor” when it comes to global oil & gas shocks. The "total insulation factor" indicates the share of a country's useful final energy that is less exposed to global oil and gas shocks. JPM calculated it by adding together a country's reliance on four specific energy sources: domestically produced gas, domestically produced coal, nuclear power, renewables (such as biofuel, hydro, wind, solar, and biomass). China has a total insulation factor of **76%**, while the US has a total insulation factor of **70%**. China scores higher primarily because of its massive reliance on domestic coal (54% of useful final energy), which accounts for a larger share of its energy mix than the US's primary domestic buffer: natural gas (44.5% of useful final energy). Even though China is the world’s largest oil importer nation, oil imports make up 13% of China's primary energy consumption. When you combine all oil consumption and imported gas, it only accounts for 20% of China's primary energy. None of these may be news to anyone following energy markets closely, but since like me, I suspect most of my readers are likely to be more ardent followers of technology than energy sector, I wanted to share this potential blind spots that I personally had until I read this research report. ![](https://substackcdn.com/image/fetch/$s_!-Fir!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feccf4496-7153-4514-8f24-b649e21b6f6e_1735x646.png) Image Source: JP Morgan --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Thoughts on Last Week URL: https://www.mbi-deepdives.com/last-week/ Last updated: 2026-03-28T15:58:42.000Z ***Programming Note***: I will take tomorrow off as I have some family commitment. I will be back on Monday. --- Ouch! Last week was quite terrible for MBI portfolio. I will share some thoughts on how I am thinking through the portfolio behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Digital Engagement's Fat Tail Risk? URL: https://www.mbi-deepdives.com/fat_tail/ Last updated: 2026-03-27T14:55:47.000Z *“There are decades where nothing happens; and there are weeks where decades happen.”* Indeed, Meta, along with other social networks and potentially **any company** that relies on engagement online, may be passing through a week that can have a much deeper implication over the next decade. Meta specifically suffered two separate blows in [Los Angeles](https://www.wsj.com/tech/meta-and-youtube-lose-landmark-social-media-trial-33e4c5cb?gaa%5Fat=eafs&gaa%5Fn=AWEtsqfbbdpyQa92al04G%5Fnb-oJuECmVXWxeJaQvqPUPxwRdZglgUWsMiBVWpWB6LEY%3D&gaa%5Fts=69c555fe&gaa%5Fsig=NlN1P2vJKpvmQo3ClkrQgDDyzlK1b%5Fig4AWVFN7V3l-OQ7JXWrwFifUJuuZ-eK77MgAb5CJQm9wu8Kwm0ejc%5FQ%3D%3D&ref=mbi-deepdives.com) and [New Mexico](https://www.wsj.com/tech/landmark-verdict-says-meta-harmed-children-allowing-adults-to-prey-on-them-cb3ad674?mod=article%5Finline&ref=mbi-deepdives.com) lawsuits this week; the LA [litigation](https://www.motleyrice.com/sites/default/files/documents/social%5Fmedia%5Faddiction-redacted%5Fmaster%5Fcomplaint.pdf?logging%5Fmedia%5Fid=3861180572484512896%5F63084623262&logging%5Fmedia%5Fauthor%5Fid=63084623262&ranking%5Finfo%5Ftoken=GCA1YWVmMGFlN2Y2ZjA0OTE1YWFiMTk1YWJmMTVhYWYwNSWmsvQCFZADFviMq5wNGBMzODYxMTgwMzIzODcxMzQ2MTE4KANzbmIA&utm%5Fsource=ig%5Ftext%5Fpost%5Fpermalink) focused on algorithmic addiction and youth mental health, whereas the New Mexico [lawsuit](https://reason.com/wp-content/uploads/2026/03/2023-12-05-NM-v.-Meta-et-al.-COMPLAINT-REDACTED.pdf?ref=mbi-deepdives.com) centered on child sexual exploitation and predator facilitation. Meta was ordered to pay [$375 million](https://www.pbs.org/newshour/nation/whats-next-in-social-media-legal-battles-after-a-new-mexico-jury-finds-meta-platforms-harm-children?ref=mbi-deepdives.com) in civil penalties for New Mexico lawsuit and the LA jury awarded [$6 million](https://www.wypr.org/2026-03-25/jury-finds-meta-and-google-negligent-in-social-media-harms-trial?ref=mbi-deepdives.com) in total damages ($3 million compensatory, $3 million punitive). For the LA case, the liability was split: Meta was found 70% responsible ($4.2 million) and YouTube was found 30% responsible ($1.8 million). Notably, TikTok and Snap settled out of court **before** the LA trial began. At first glance, for a company such as Meta that generated $201 Billion revenue in 2025, the penalties may seem inconsequential. However, WSJ [reported](https://www.wsj.com/tech/meta-and-youtube-lose-landmark-social-media-trial-33e4c5cb?gaa%5Fat=eafs&gaa%5Fn=AWEtsqfbbdpyQa92al04G%5Fnb-oJuECmVXWxeJaQvqPUPxwRdZglgUWsMiBVWpWB6LEY%3D&gaa%5Fts=69c555fe&gaa%5Fsig=NlN1P2vJKpvmQo3ClkrQgDDyzlK1b%5Fig4AWVFN7V3l-OQ7JXWrwFifUJuuZ-eK77MgAb5CJQm9wu8Kwm0ejc%5FQ%3D%3D&ref=mbi-deepdives.com) that “*more than 3,000 other similar lawsuits against Meta, YouTube, Snapchat and TikTok that are pending in California courts*.” Of course, if these lawsuits have teeth in California, it will add further ammunition across the US (and eventually for the entire world) to go after companies such as Meta for their alleged harm caused to the children. Market’s reaction to this lawsuit yesterday essentially captured this **fat tail risk**. Meta and YouTube both mentioned that they will appeal these decisions and it appears highly likely that these may end up in Supreme Court. Given that context, I think there was another monumental decision by the US Supreme Court during this very week which may prove to be potentially indicative of what happens in the lawsuit against Meta et al. Back in **2018**, Sony and other major recording and publishing companies sued Internet Service Providers (ISPs) arguing that ISPs are legally responsible for providing internet services to known infringers who downloaded and distributed songs without permission. A jury in 2019 even [awarded](https://www.wiley.law/alert-Cox-Communications-Penalized-With-1-Billion-Jury-Verdict-in-Copyright-Infringement-Lawsuit?ref=mbi-deepdives.com) **$1 Billion** in damages. Federal appeals court then [upheld](https://www.wsj.com/business/media/court-tosses-1-billion-verdict-against-cox-communications-for-music-piracy-30a3877a?mod=article%5Finline&ref=mbi-deepdives.com) the verdict, but ordered a new trial to reconsider the damages amount. **After nearly six years of the Jury’s original $1 Billion awards back in 2019**, Supreme Court this week [**overturned**](https://www.wsj.com/us-news/law/supreme-court-limits-liability-for-internet-service-providers-0cabcb46?gaa%5Fat=eafs&gaa%5Fn=AWEtsqea3yp72gwYq63BDWreJ34hb15gp1v3-s6VjcxsEpxOeO8pCFY8vBLdo14WCB4%3D&gaa%5Fts=69c68665&gaa%5Fsig=ZStGIa-8xbc-EgH2x%5FXCrAbGl1dlz0XPho8q6MuHQywrB3FGVvI76PzSTThBpW%5FIgccsaAkUx2ZudfxL57g9FQ%3D%3D&ref=mbi-deepdives.com) this decision. Perhaps more importantly, the Supreme Court’s decision was **unanimous 9-0**! The two major takeaways from the ISP lawsuit that I would like to highlight here are: a) how long it took for the entire process to go from Jury award to Supreme court, and b) how the Supreme Court had a very different attitude than the Jury which increasingly seem to suffer from a pernicious form of [social inflation](https://agentsync.io/blog/insurance-101/what-is-social-inflation?ref=mbi-deepdives.com). As you can understand, this is likely going to be a long, arduous process for these tech companies and they have a decent probability of prevailing, especially in the Supreme Court. One of the reasons I think these lawsuits may eventually need to be severely narrowed in their scope is in its current form, these lawsuits will have much greater ripple effect across many companies than people may be currently appreciating. Let me highlight this particular paragraph from the LA lawsuit: > “It is a matter of common knowledge in the social media industry that the Snap Streak product feature is designed to be addictive. Meta bluntly acknowledged as much in its internal documents, stating: “Streaks are a very important way for teens to stay connected. They are usually with your closest friends and they are addictive.” Nonetheless, Snap continues to provide this feature to its adolescent users” If Streaks are considered too “addictive”, wouldn’t this also affect companies such as Duolingo? Of course, you can extend this argument to almost any other app on your phone that send you push notifications to encourage you to engage with the app. It is possible that the courts will force these companies to adopt a very different product design choices for minors and allow plenty of freedom in product design for adult users. In this case, almost all apps will be devoid of any engagement optimizations for non-adult users. Ultimately, if the citizens want this to be implemented, it indeed needs to be forced from top-down as it is nearly impossible to pursue from a bottom-up perspective. Even if a company such as Meta decides to avoid any engagement tricks for minors but their competitors relentlessly pursue it, it is very difficult for Meta to watch its competitors building competing networks of young users that can hurt its competitiveness in the long term. This is precisely why despite Meta generating only [1% of its revenue](https://www.japantimes.co.jp/business/2026/02/19/tech/zuckerberg-instagram-age-limits/?ref=mbi-deepdives.com#:~:text=Zuckerberg%20testified%20that%20while%20it's,disposable%20income%2C%E2%80%9D%20Zuckerberg%20said.) from teens, it cannot willingly cede this market to its competitors. To the extent the courts force all the companies to make the apps boring for minors, it may end up narrowing the path of future competitors for incumbent social networks such as Meta. Moreover, I think it is likely that the court may also order Apple and Alphabet to take more active role in age-gating certain experiences. In the LA case, Kaley, the lead plaintiff, opened an YouTube account at the age of 6 and an Instagram account at the age of 9 (even though one is not permitted to open these accounts until the age of 13) and went onto spend up to 16 hours per day on these apps as a teen. One can certainly argue these apps should do a better job in verifying the age of the users and force time limits, but the internal discussion on these topics from these very companies clearly indicate that it is easier said than done. Here’s a paragraph from the LA lawsuit (emphasis mine): > “Meta offered to its users a feature that purported to show how much time users had spent on Instagram. And Meta touted this feature “when speaking to consumers, the press, and stakeholders about our efforts to combat social media addiction.” But internally, Meta acknowledged that the data reported by this tool was fundamentally “incorrect”: “**It’s not just that Apple / Google have better data. Ours is wrong.** Far worse. We’re sharing bad metrics externally. We’ve been unable to right it despite several person-months of efforts” Snap also echoed similar sentiment: > “Snap’s executives have admitted that Snapchat’s age verification “is effectively useless in stopping underage users from signing up to the Snapchat app.” I, however, do sympathize with the concerns related to algorithmic content on all social networks these days. I will highlight this particular paragraph from the lawsuit (emphasis mine): > “In December 2022, the Center for Countering Digital Hate (“CCDH”) conducted a similar study, creating TikTok accounts with a registered age of 13 in the United States, United Kingdom, Canada, and Australia. For the first 30 minutes on the app, the accounts paused briefly on videos about body image and mental health and liked them. “Where researchers identified a recommended video matching one of the below categories, they viewed the video for 10 seconds and liked it. **For all other videos, researchers would immediately scroll the For You feed to view the next video recommended by TikTok.” TikTok’s algorithm seized on this information and within minutes began recommending content about eating disorders and self-harm**” This isn’t, of course, TikTok specific concern. The New Mexico lawsuit demonstrated how one can be exposed to disturbing content through the algorithm on Meta’s properties. While these algorithm made all of our feeds much more relevant and engaging, it also unfortunately makes some of the worst individuals’ job lot easier to exploit minors. If the choice of liking or watching one “bad” content for a few seconds leads to a flood of similar content on the feed and sucks the user into that orbit, it is hard to see how or why the tech companies would have zero liability for such product design. As of 4Q’25, almost two-fifth content on Facebook content on the feed come from unconnected network. Meta may invoke [section 230](https://en.wikipedia.org/wiki/Section%5F230?ref=mbi-deepdives.com) defense or/and their first amendment right, but I’m not sure even the Supreme Court will be sympathetic towards tech companies’ right to control an algorithm that ends up showing questionable or disturbing content to minors. ![](https://substackcdn.com/image/fetch/$s_!D_st!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb14905a1-1d12-4327-a89c-4f4731fcd838_1447x793.png) Source: [Meta Transparency Center](https://transparency.meta.com/reports/widely-viewed-content-report/?ref=mbi-deepdives.com#prior-reports) One challenge with these lawsuits is by the time we may get the final verdict from the Supreme Court, these apps may look very different. For example, Meta has already started to allow users to design your algorithm on Instagram (see image below) and perhaps soon Meta will **require** anyone less than 18 years old to have their parents design/choose an algorithm for their kids which cannot be edited by the kids. ![](https://substackcdn.com/image/fetch/$s_!msqh!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F512b5d02-575c-424f-8dd1-4fdb2c29a626_723x1210.png) Source: My personal Instagram Algorithm Of course, in the future even if these apps address all the criticisms currently labeled at them, it won’t relieve them from the potential liabilities that may have been allegedly already incurred in the past. Nonetheless, given how expansive the scope of these lawsuits currently are and how far reaching the implications might be even beyond the social networking companies, it is highly likely that the scope will be narrowed down materially by the time Supreme Court delivers its decision. It’s hard to pinpoint or forecast the long-term liability here, but given confounding factors that contribute to our mental health and at best [mixed evidence](https://www.nature.com/articles/d41586-024-00902-2?ref=mbi-deepdives.com) that social media itself played a primary role in such crisis, these companies will likely be able to mount a strong defense in keeping the liability low. As a result, while these lawsuits do threaten to be a potential fat tail risk for companies such as Meta, I will be surprised if that is indeed the case eventually. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Follow-up on Floor & Decor URL: https://www.mbi-deepdives.com/fnd3/ Last updated: 2026-03-26T17:04:18.000Z Following my piece “[**Why I am buying Floor & Decor**](https://www.mbi-deepdives.com/fnd2/)”, I have received a couple of pushbacks. I will address those and expand on FND’s competitive dynamics a bit more behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta's Agentic AI Ambitions URL: https://www.mbi-deepdives.com/metas-agentic-ai-ambitions/ Last updated: 2026-03-25T15:17:09.000Z When Meta started building their Superintelligence team (MSL) in mid-2025, I certainly expected to see faster progress than what we have seen from them so far. I wonder if the Llama 4 fiasco as well as the backlash for “Vibes” last year made Meta much more tentative than usual for releasing new products/features. This is making Meta increasingly look like a laggard in the AI race despite spending commensurate capital on compute and talent compared to other players leading the race. Despite looking like a laggard, Meta also seems to be on a tuck-in acquisition spree every other week these days. Just in March, Meta acquired [Moltbook](https://www.cnbc.com/2026/03/10/meta-social-networks-ai-agents-moltbook-acquisition.html?ref=mbi-deepdives.com), a social media platform for AI agents, and made a non-exclusive license deal with [Dreamer](https://finance.yahoo.com/sectors/technology/articles/meta-hires-former-google-stripe-180500636.html?ref=mbi-deepdives.com), the startup that helps consumers build AI agents. Since the Dreamer co-founders joined MSL as part of the deal, this is essentially yet another roundabout “acquisition” Silicon Valley invented in the current anti-trust environment. The fact that Dreamer raised $56 million at $500 million valuation back in December 2024 and Dreamer’s CEO was a former CTO of Stripe make me think Meta may have paid a good premium to get the Dreamer team in MSL. Just last week, Dreamer’s now former CEO demoed the product in this [podcast](https://www.youtube.com/watch?v=TvmxWWfiYWI&ref=mbi-deepdives.com), and it indeed looks quite promising. In fact, while reacting to the acquisition news, Alex Heath [mentioned](https://sources.news/p/sheryl-sandberg-ai-race-nscale?ref=mbi-deepdives.com) the following in his newsletter: > I was recently given a demo of the product by co-founder (and former Stripe CTO) David Singleton. I was so intrigued that I demanded to be added to the beta. I clearly wasn’t the only one who was impressed. With [Manus](https://manus.im/blog/manus-joins-meta-for-next-era-of-innovation?ref=mbi-deepdives.com) and Dreamer deals, it appears Meta is getting more serious on agent infrastructure above the model layer. In fact, even if Meta remains a laggard in building a SOTA model for the foreseeable future, their core business continues to offer compelling opportunities to integrate AI on top of the model layer. Even though Meta may seem largely absent in the current agentic AI fever, a blog [post](https://engineering.fb.com/2026/03/17/developer-tools/ranking-engineer-agent-rea-autonomous-ai-system-accelerating-meta-ads-ranking-innovation/?ref=mbi-deepdives.com) (h/t [Eric Seufert](https://mobiledevmemo.com/metas-agentic-approach-to-ad-ranking-experimentation/?ref=mbi-deepdives.com)) from Meta last week made me appreciate how Meta is already using AI agents in their advertising infrastructure. Some key excerpts from Meta’s blog post (emphasis mine): > Optimizing these ML models has traditionally been time-consuming. Engineers craft hypotheses, design experiments, launch training runs, debug failures across complex codebases, analyze results and iterate. Each full cycle can span days to weeks. As Meta’s models have matured over the years, finding meaningful improvements has become increasingly challenging. The manual, sequential nature of traditional ML experimentation has become a bottleneck to innovation. > > To address this, Meta built the **Ranking Engineer Agent (REA)**, an autonomous AI agent **designed to drive the end-to-end ML lifecycle and iteratively evolve Meta’s ads ranking models at scale**. > > REA addresses three core challenges in autonomous ML experimentation: > > **Long-Horizon, Asynchronous Workflow Autonomy:** ML training jobs run for hours or days, far beyond what any session-bound assistant can manage. REA maintains persistent state and memory across multiround workflows **spanning days or weeks, staying coordinated without continuous human supervision**. > > **High-Quality, Diverse Hypothesis Generation:** Experiment quality is only as good as the hypothesis that drives it. REA synthesizes outcomes from historical experiments and frontier ML research to surface configurations unlikely to emerge from any single approach, and it improves with every iteration. > > **Resilient Operation Within Real-World Constraints:** Infrastructure failures, unexpected errors and compute budgets can’t halt an autonomous agent. REA adapts within predefined guardrails, keeping workflows moving without escalating routine failures to humans. > > In the first production validation across a set of six models, **REA-driven iterations doubled average model accuracy over baseline approaches. This translates directly to stronger advertiser outcomes and better experiences on Meta platforms**. > > REA amplifies impact by automating the mechanics of ML experimentation, enabling engineers to focus on creative problem-solving and strategic thinking. **Complex architectural improvements that previously required multiple engineers over several weeks can now be completed by smaller teams in days.** > > **Early adopters using REA increased their model-improvement proposals from one to five in the same time frame. Work that once took two engineers per model now takes three engineers across eight models**. One of the interesting bits from the blog post is that Meta mentioned for long-horizon workflow autonomy, Meta built REA on an internal AI agent framework called “Confucius” which they elaborated further on this [paper](https://arxiv.org/pdf/2512.10398?ref=mbi-deepdives.com) back in February 2026\. Often, when tech companies try to improve AI coders, they focus on making the underlying AI models (like GPT or Claude) smarter. However, the paper argued that the **“scaffolding”** i.e. the software environment, memory systems, and tools built around the AI is just as important. When working on big codebases, AI agents frequently get overwhelmed by reading too much code, forget their original plan during long tasks, or repeat the same mistakes. The most interesting takeaway from the paper is that **a great setup can compensate for a less powerful AI**. The researchers proved that a weaker model (Claude 4.5 Sonnet) using the Confucius scaffolding successfully fixed more bugs (52.7%) than a stronger, more expensive model (Claude 4.5 Opus) using Anthropic’s standard setup (52.0%). When powered by the GPT-5.2 model, Confucius Code Agent successfully resolved **59%** of the real-world bugs on the SWE-Bench-Pro test, beating both prior academic research and the official corporate systems built by OpenAI and Anthropic under identical conditions. If such scaffolding itself can consistently beat the more expensive SOTA models, it can provide a ceiling on SOTA model developers’ ability to exercise pricing power. It remains to be seen whether such scaffolding can outperform more expensive SOTA models in a wide range of scenarios. Nonetheless, the key takeaway is quite encouraging for all the tech companies that will not have a SOTA model and those tech companies may still be able to capture value from better scaffolding. ![](https://substackcdn.com/image/fetch/$s_!HhMD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F43e3ca6b-8bf0-4fa8-a208-b78c03a0b25d_969x465.png) Back in 2Q’25 call, Zuckerberg mentioned the following: > Over the last few months, we’ve begun to see **glimpses of our AI systems improving themselves. And the improvement is slow for now, but undeniable and developing superintelligence** The paper and the blog post essentially validated Zuckerberg’s claims made a couple of quarters ago. The autonomous and self-recursive nature of REA is yet another indication that Meta has a pretty good shot at monetizing their capex even if they remain a laggard in building SOTA model. Perhaps to hint at their confidence, Meta yesterday disclosed a new **stock options program** for their Chief Technology Officer Andrew Bosworth, Chief Product Officer Chris Cox, Chief Operation Officer Javier Olivan, Chief Financial Officer Susan Li, Chief Legal Officer C.J. Mahoney and Vice Chairman Dina Powell McCormick. While Meta historically largely doled out RSUs, this is a welcome change from shareholders perspective as the options would expire worthless if the stock price doesn’t exceed the hurdles. To receive even the first tranche of the options, Meta stock needs to increase by 86% from current price, and to receive the full options package, the stock needs to be 6x from current price **within the next five years**! For your reference, I am showing the stock options program for CTO below (although the numbers vary for each executive, the structure is quite similar for other executives) ![](https://substackcdn.com/image/fetch/$s_!FqQi!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F49c23587-09b3-4ce4-9141-55bebf2a710b_1375x1132.png) As a minority shareholder of Meta, I also very much appreciate that Zuckerberg **excluded** himself from this options program as he probably doesn’t need any more incentive to see Meta thrive. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 67 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Scuttleblurb on Ryan Specialty URL: https://www.mbi-deepdives.com/scuttleblurb-ryan/ Last updated: 2026-03-24T13:56:16.000Z It doesn’t happen often, but right after publishing my [**Deep Dive**](https://www.mbi-deepdives.com/ryan/) on Ryan Specialty Group last week, my friend Scuttleblurb also [**published**](https://scuttleblurb.substack.com/p/ryan) on the same company. Scuttleblurb’s work is excellent as usual, and there are a couple of things that I want to highlight from Scuttleblurb’s piece which I believe are additive to my Deep Dive. I have heard this murmur from insurance broker bears that the rise of MGAs may have led to lower underwriting quality since they don’t take the actual underwriting risk on their balance sheets. However, Scuttleblurb shared an interesting data point that showed the loss ratios are broadly similar for MGAs compared to the broader P&C industry. He also highlighted that the popularity of MGAs itself may be cyclical in nature, something I haven’t quite fully appreciated before. From Scuttleblurb (all emphasis mine): > “many MGAs earn substantial contingent commissions tied to the profitability of the business they write, which creates **a direct economic incentive for disciplined risk selection**. For example, in the 12 months through June 2024, after several years of exceptional underwriting performance, US Assure – an MGA later acquired by Ryan – generated 30% of its revenue, and an even larger share of its EBITDA, from profit commissions. > > Overall, **the loss ratios of MGA-focused carriers appear to be broadly in line with those of the wider P&C industry** > > …as with the E&S market more broadly, **MGA penetration tends to rise and fall with the underwriting cycle, and a large portion of MGA share gains over the past decade came after 2020, as the market began to harden**. MGAs may well represent a secular growth segment, but it is also possible that, after several years of extraordinary expansion, we are closer to a cyclical high-water mark and that future share gains will slow, or even reverse, if the E&S market softens. I’d be wary of extrapolating recent growth rates too far out.” Another tidbit that is worth highlighting from Scuttleblurb’s piece is his benchmarking RYAN’s compensation related costs against Brown & Brown, and Arthur J. Gallagher. From Scuttleblurb (emphasis mine): > “Where Ryan appears to stand out is in how much of that value it is willing to share. In 2025, it paid out 60% of net commissions and fees as compensation (\~$295k+ per employee) while **Brown & Brown’s combined wholesale and delegated underwriting segments paid out a combined 39% ($116k per employee)**. In 2022, management disclosed employees other than Pat Ryan owned nearly 20% of the company. As of 2025, around 16% of Ryan’s employees counted themselves shareholders. The firm retains \~97% of its producers, all top 50 of whom own stock.” ![](https://substackcdn.com/image/fetch/$s_!c0cO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0abecea4-136e-43d4-98ca-afc2e259175f_819x168.webp) Source: Scuttleblurb I didn’t notice until reading Scuttleblurb’s piece the difference in compensation intensity between Brown & Brown and other brokers such as RYAN and Gallagher. Frankly speaking, when I looked at this data, it quickly reminded me of Brown & Brown’s lawsuit against Howden. I highlighted this lawsuit [**last month**](https://www.mbi-deepdives.com/insurance-brokers-4q25-update/) and wrote the following: > I am not a lawyer, but some evidence seems quite damning for these employees who seem to have deliberately siphoned customers away from BRO to Howden. Nonetheless, it doesn’t reflect great on BRO’s culture that a couple hundred employees chose to do such deliberate attempt to hurt the company. Indeed, looking at the difference in compensation intensity, I wondered whether the employees who left Brown & Brown for Howden are just indicative of much broader problem of stingy compensation culture at Brown & Brown relative to the industry. I will share some additional thoughts behind the paywall on how I decided to incorporate these thoughts in my portfolio. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Why I am buying Floor & Decor URL: https://www.mbi-deepdives.com/fnd2/ Last updated: 2026-03-23T22:09:02.000Z Since becoming a public company in 2017, Floor & Decor (FND) is currently going through its largest ever drawdown in its history. The stock is currently down \~65% from its peak in late 2021. ![chart](https://substackcdn.com/image/fetch/$s_!Ni6c!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fffa917a4-e300-4beb-af94-f3e9c9bff176_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) However, looking at NTM EV/EBIT multiple, it may appear the stock is still trading at not-so-cheap multiple and it may be tempting to attribute much of the drawdown just to lofty valuation multiples. ![chart](https://substackcdn.com/image/fetch/$s_!kKhs!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F71fde328-0624-49fe-b628-6f6a038fa98e_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In fact, after three consecutive years of declining same store sales (SSS), the company guided for yet another tame SSS guide for 2026: -2% to +1%. ![](https://substackcdn.com/image/fetch/$s_!M8Fd!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F11632603-6642-467e-ab1e-6edc747795dd_1164x624.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Despite the near-term meh outlook, I actually believe Floor & Decor may be the most attractive stock from risk-reward perspective in my coverage universe today. I will expand my rationales behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Ryan Specialty: The Unconflicted Middleman URL: https://www.mbi-deepdives.com/ryan/ Last updated: 2026-03-20T14:03:43.000Z **Programming Note:** I will take the next two days off. MBI Deep Dives will be back on Monday next week. --- *You can listen to the Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- Pat Ryan, currently 88-year old, is a living legend in the insurance industry. Soon after graduating from college, he entered insurance industry as a life insurance agent, but quickly pivoted into an idea he brought to an insurer: selling insurance coverage through auto dealerships. In 1964, at just the age of 26, he formalized that instinct into his own firm: Pat Ryan & Associates. This was the first glimpse of a recurring thesis: **find a distribution flow others underestimate, then professionalize it with talent and capital**. By 1968, his firm was already writing $15 million premium, and in 1971 he took it public to fund a broader ambition: move from a single product idea into a multi-line platform. A consequential moment arrived in 1982 when his brokerage merged with the Combined Insurance Company of America. This laid the foundation for Aon Corporation, an entity Ryan would ultimately build into the **second largest** insurance brokerage in the world. However, in the 1990s, prevailing industry sentiment held that the rapid rise of the internet and direct market access would inevitably render reinsurance brokers obsolete. Does that sound familiar? All the AI related disintermediation fears in insurance brokerage industry were very much alive back in the 1990s. Ryan bet against that conventional wisdom and recalled the grim sentiment around brokers in the 1990s in this [piece](https://www.businessinsurance.com/conventional-wisdom-often-wrong-ryan/?ref=mbi-deepdives.com) published back in 2010: > “Many observers thought that reinsurance brokers would be replaced by the direct market and the Internet…People were looking at the reinsurance brokerage business saying it’s done. They were wrong” Indeed, the conventional wisdom couldn’t possibly be more wrong as Aon’s $10 million reinsurance brokerage in 1988 ended up becoming the largest reinsurance broker by 1998\. Another decade later in 2008, Ryan decided to retire from the CEO role at the age of 71 in March 2008\. I couldn’t get the data from their IPO in 1971, but I could find Aon’s performance data from 1980 on KoyFin. It appears Pat Ryan led Aon and compounded shareholder return at mid-teen rate for almost four decades in the public market before calling it a day at Aon! ![chart](https://substackcdn.com/image/fetch/$s_!VXzL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7c5e1385-34e2-4d25-bd72-de3d5e7d4b3a_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In your 70s, more mere mortals would probably just want to enjoy the rest of their lives reminiscing the good old days. Ryan, on the other hand, conjured up vigor and vitality to start a new company: Ryan Specialty Group! In a Business Insurance [interview](https://www.businessinsurance.com/aon-founder-seeks-to-build-new-insurance-venture/?ref=mbi-deepdives.com) around the launch of his new venture, Ryan said that even though he had retired from active involvement at Aon in 2008, he never intended to fully retire from the insurance industry. He, however, did not want a sequel that competed with Aon head-on. He described a deliberate boundary: the new company would not operate in retail brokerage, reinsurance brokerage, or human resource consulting, and it would avoid overlapping with Aon’s existing managing general underwriter activity. Instead, he wanted to build a specialty wholesale company aimed at new and emerging needs to skate to “[where the puck is going](https://www.sec.gov/Archives/edgar/data/1849253/000119312521195083/d146849ds1.htm?ref=mbi-deepdives.com)”. As someone who built Aon from the ground up, Ryan was intimately aware that industry’s most durable profit pools may end up sitting in the more awkward spaces: risks too complex for standard processes, and distribution relationships that need technical judgment. He saw several emerging trends: demand rising for specialists as risks became larger and more complex (cyber, climate change etc.), retail brokers narrowing their wholesale relationships, and consolidation among retail brokers accelerating which only feeds further to the earlier point. If Aon was built as a broad global intermediary that could touch most major lanes of risk, then the founding thesis of Ryan Specialty was about building a purpose-built specialist broker where technical underwriting and specialist distribution matter most while keeping the mandate tight enough that it is clearly **additive** to the ecosystem. It was not until June 2018, eight years since its founding, that Ryan Specialty Group sought external institutional backing. The firm secured a [strategic investment](https://ryanspecialty.com/news/rsg-announces-investment-by-onex/?ref=mbi-deepdives.com) from Onex Corporation which injected $175 million into the business, split between $150 million in preferred equity and $25 million in common equity. This capital infusion accelerated M&A and revenue surpassed $1 Billion in 2020\. The very next year, RYAN came to IPO at \~$7 Billion valuation. At its peak in April 2025, RYAN’s market cap reached almost $20 Billion, but then the stock has been cut in half. While other brokers also have been going through material drawdown thanks to property insurance hard market ending, RYAN has been the affected the most. More on this later. ![chart](https://substackcdn.com/image/fetch/$s_!UTSY!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3beb7278-cf96-46a6-a3ef-6a1c0696ed7c_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In the meantime, Pat Ryan [retired](https://ir.ryanspecialty.com/ir-news-events/press-releases/detail/74/ryan-specialty-to-implement-leadership-succession-plan-in?ref=mbi-deepdives.com) from the CEO role at Ryan in October 2024\. I wouldn’t worry about Ryan starting a different company though. As I said earlier, he is 88 years old, but that’s perhaps not as reassuring as it would be about most people. More importantly, he not only remains Executive Chairman of Ryan, but also appears to be quite intimately involved with the company despite retiring from the CEO role as he still shows up in the company’s earnings calls. So, the legend is still definitely very much engaged in RYAN. The person who succeeded Pat Ryan as CEO is Timothy Turner. RYAN was actually [sued](https://www.businessinsurance.com/ryan-specialtys-raid-on-crc/?ref=mbi-deepdives.com) by CRC, one of their closest competitor in wholesale insurance brokerage today, back in 2010 when RYAN poached 10% of CRC employees. Turner was one of those poached employees. In fact, RYAN’s wholesale specialty division was always called “RT Specialty”. The “T” stands for Turner. As you can imagine, while Pat Ryan understandably consumes all the limelight here, Turner is effectively a a co-founder of RYAN. But what exactly is wholesale insurance brokerage business about? Why does it need to exist? Let me now get into these more fundamental questions. I will discuss that as well as the broader industry context, business overview, competitive dynamics, capital allocation, management incentives, and valuation behind the paywall. Subscribe to read the rest of the Deep Dive as well as any of the [**66 Deep Dives**](https://www.mbi-deepdives.com/models/) published earlier. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Nadella's Flip-Flop URL: https://www.mbi-deepdives.com/nadellas-flip-flop/ Last updated: 2026-03-19T14:44:22.000Z There are perhaps few things in the current AI revolution that keeps my opinions grounded than watching Microsoft’s evolving position in the post-ChatGPT world. It can be tempting to imagine investors just frenetically changing their labels to Microsoft as “AI winner” or “AI loser” every few months, but the reality is Microsoft’s CEO himself has quite conspicuously evolved the way he wants to position Microsoft in the AI landscape. Ben Thompson recently [alluded](https://stratechery.com/2026/agents-over-bubbles/?ref=mbi-deepdives.com) to Nadella’s flip-flop in a recent piece. It is quite instructive to actually go through Nadella’s own words from October 2023 earnings call: > “…the approach we have taken is a full-stack approach all the way from whether it's ChatGPT or Bing chat or all our Copilots **all share the same model**. **So in some sense, one of the things that we do have is very, very high leverage of the one model that we used, which we trained, and then the one model that we are doing inferencing at scale. And that advantage sort of trickles down all the way to both utilization internally, utilization of third parties**.” As you can tell, the “one model” was the sun, and Microsoft’s products were the planets orbiting it. It may seem strange today, but Microsoft saw the tight coupling of the underlying foundation model with the user-facing application as a massive competitive advantage. They had **exclusive** commercial access to the best model in the world at the time, so a paradigm where the model was very much entangled with the product suited them perfectly. However, when Sam Altman got temporarily fired by OpenAI’s board, the ensuing drama laid bare all the limitations of this approach of relying on a third-party company’s model. So, Nadella started softening his tone about the benefits of having exclusive access to OpenAI’s models. From celebrating a “full-stack approach” sharing the “same model” in 2023, you can clearly see how much Nadella’s position has changed in his own words from Morgan Stanley TMT Conference early this month: > “I think at this point, it’s fairly clear industry structure-wise when it comes to the narrow way we talk about frontier models, like the American ones are closed, a lot of the Chinese are open. That, **I think, is going to be multi-model**, right? > > …if you’re building any product, whether it’s for coding agents or for knowledge work or wealth management or whatever, **you want to access multiple models**, right? That’s going to be the case. And that means there’s lots of very careful design that you want to do, which is you want to have the harness not get coupled with the model layer. So people are getting pretty sophisticated in making sure that the harness layer is decoupled. > > The other is the context layer also should be decoupled, right? You don’t want effectively the one model to, in fact, vertically integrate into these 2\. And that will be the game that will be played. But I’m pretty clear industry structure-wise, where we will go.” Why is Nadella suddenly preaching the gospel of modularity and decoupling of different layers? Because Microsoft controls the harness (VS Code, GitHub, Azure AI Foundry) and the context (Microsoft 365, enterprise data). What they do not own outright is a frontier model. If the industry structurally integrates the model, the harness, and the context, Microsoft’s right to lion’s share of the profit pool can be legitimately questioned. Nadella **needs** the model layer to become a commoditized, interchangeable plug-in so that Azure’s orchestrators and development tools can capture the profit. To be fair, Nadella’s argument makes sense even from enterprise customers’ point of view. But what makes rational sense to preserve their own interests and how AI products actually work to provide customers the best experience can diverge over time. “Copilot Cowork” (which is powered by only Anthropic’s models) is perhaps an early indication of this potentially persistent tension. Ben Thompson eloquently [explained](https://stratechery.com/2026/agents-over-bubbles/?ref=mbi-deepdives.com) this dichotomy (emphasis mine): > “what made Opus 4.5 compelling was not the model release itself, but changes to the Claude Code harness that made it suddenly dramatically more useful. **What this means is that model performance isn’t the only thing that matters: the integration between model and harness is where true agent differentiation is found.** > > This is a very big deal when it comes to figuring out the future structure of the AI industry and where profits will flow, because **profits flow away from modular parts of the value chain — which are commoditized — and flow towards integrated parts of the value chain, which are differentiated.** Apple is of course the ultimate example of this: its hardware is not commoditized because it is integrated with their software, which is why Apple can charge sustainably higher prices and capture nearly the entirety of the PC and smartphone sector profits. > > It follows, then, that **if agents require integration between model and harness, that the companies building that integration — specifically Anthropic and OpenAI (Gemini is a strong model, but Google hasn’t yet shipped a compelling harness) — are actually poised to be significantly more profitable than it might have seemed as recently as late last year**. And, by the same token, companies who were betting on model commoditization may struggle to deliver competitive products. For what it’s worth, Sam Altman in a recent TBPN interview [indicated](https://x.com/tbpn/status/2033949259440230611?ref=mbi-deepdives.com) OpenAI will also launch their own version of “Claude Cowork”: > OpenAI will obviously" have a version of Codex that "can do other knowledge-work tasks, and control your computer." > > "Of course we should have an ability to kick off new tasks from mobile, and we'll do that." > > "Really what you want is your **single AI that's working for you on a unified backend. Access to all your data and ideas, and your stuff and your memory, and the ability to work across a lot of surfaces**." So perhaps Microsoft will soon have an opportunity to choose from multiple models to offer “Copilot Cowork”. As you can sense, Microsoft and AI labs such as OpenAI/Anthropic seem to have very different ideas on how to integrate the harness layer to the model. They are both talking their own books of course, but the real verdict will come from the customers. If Microsoft wants to make the harness layer decoupled from the model layer but it ends up affecting the quality of the product itself, customers may demand an integrated experience and Microsoft may need to comply to offer the best experience to the users regardless of their own interests. It also doesn’t help that Microsoft’s relationship with OpenAI only seems to be deteriorating over time. Financial Times (FT) [reported](https://www.ft.com/content/e814f4c3-4fb5-4e2e-90a6-470044436b39?syn-25a6b1a6=1&ref=mbi-deepdives.com) Microsoft is weighing legal actions against OpenAI for the partnership with Amazon. Tom’s Hardware [explained](https://www.tomshardware.com/tech-industry/artificial-intelligence/microsoft-considering-suing-openai-over-altmans-recent-deal-with-amazon-report-claims-exclusivity-dispute-revolves-around-frontier-multi-agent-service?ref=mbi-deepdives.com) the key source of tension about this partnership: > “[The PR](https://blogs.microsoft.com/blog/2025/10/28/the-next-chapter-of-the-microsoft-openai-partnership/?ref=mbi-deepdives.com) about that latest agreement states that “API products developed with third parties will be exclusive to Azure. Non-API products may be served on any cloud provider.” Under that logic, OpenAI has the freedom to develop and implement new products, but if they offer them as APIs, they have to go through Azure. > > Redmond believes that OpenAI’s offering access to Frontier via Amazon Web Services (AWS)’s Bedrock platform would be in breach of the agreement. Getting even more technical, the dispute may well come down to the definition of a “stateless” versus “stateful” when applied to AI models. > > Even though it appears to remember your information, a standard chatbot is actually stateless — adding a new question requires the bot to re-process the entire conversation again. A storage and orchestration layer to facilitate something like Frontier is arguably a “stateful” implementation, more specifically a “Stateful Runtime Environment.” > > According to FT’s sources, Microsoft thinks that running Frontier on AWS instead of Azure would breach either the spirit or the letter of the contract. This is illustrated by a report that Amazon is pointedly instructing its staff to never say that SRE “enables access” or “calls on” ChatGPT as a backend, instead preferring vaguer terms like “powered by,” “enabled by,” or “integrates with.” I am not a lawyer, so I don’t know if the court would consider OpenAI’s partnership with AWS a breach of contract with Microsoft. However, we can indeed infer that if you don’t own the model and must rely on legal interpretations of contractual terms to defend your position, your position is a bit…fragile. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Survival of the Fastest URL: https://www.mbi-deepdives.com/nvda_gtc26/ Last updated: 2026-03-18T14:44:47.000Z More than a decade ago, Peter Thiel [wrote](https://www.wsj.com/articles/peter-thiel-competition-is-for-losers-1410535536?gaa%5Fat=eafs&gaa%5Fn=AWEtsqeLK44VfGzGEDP8iu%5F0cbTOwOl%5F1cyOgDKQNdi8mXkoYRO4W9XrMf0g-YekVUU%3D&gaa%5Fts=69ba0552&gaa%5Fsig=S3OWBNcPi53fW4-FIR2Ojya-bd2RnmJSUWfc0QJdB9lL3jnepobtSsTAQ68rlQ597Kpo4jPkTYsia9b9lmXpWg%3D%3D&ref=mbi-deepdives.com): > “Monopolists lie to protect themselves. They know that bragging about their great monopoly invites being audited, scrutinized and attacked. Since they very much want their monopoly profits to continue unmolested, they tend to do whatever they can to conceal their monopoly—usually by exaggerating the power of their (nonexistent) competition. > > Non-monopolists tell the opposite lie: "We're in a league of our own." Nvidia doesn’t quite fit that description. For all intents and purposes, they are indeed a monopoly in general purpose computing, but it is so obvious that the competitors are deeply incentivized to come up with an alternative to GPUs that Jensen Huang doesn’t even bother to “lie” as a monopoly and rather spent much more time in this year’s GTC to establish that Nvidia is in a league of their own. It’s not hard to believe Huang because many of the companies who are trying to offer competing chips are also the same ones ordering tens of billions of chips from Nvidia **every quarter**! Investors, however, still feel nervous about Nvidia’s lofty gross and operating margins. Can such margins be sustainable? Huang went gung ho yesterday during analyst Q&A to defend his margins. From the Q&A session yesterday: > “Every CEO of every cloud service provider, I would challenge them all to go and create that chart for themselves. And I’ll help them. And you pick your favorite other configuration, third-party chips, built your own chips, and you put it into that model faithfully and then you can decide would you like to have higher revenues or lower. Would you like to have higher ASPs or lower, would you like higher margins or lower because that’s all it means. > > Look, TSMC’s wafers are the highest in the world, but they’re the best value in the world. And I gladly pay for it. And so the idea ASML systems are the most expensive in the world, they’re worth it. There’s no question about it. And so the question is simply, do you want to make more money? Or do you want to buy the lowest cost equipment? Do you want to make more money? Or do you want to buy the lowest cost equipment? That’s the difference. Now what I just said is a new concept, and I think we can all acknowledge that. I just treated a computer system. The way I treat TSMC chip factory, the way I treat ASML manufacturing equipment. And that’s not the way people thought about it in the past if I have 2 CPUs, 1 of them is 256 cores, the other 1 is 256 cores. **Tell me which one is the better one. Well, the cheaper one’s the better one because I’m running it by the core anyways. But that’s not the way tokens are created**. > > You don’t rent by the core, **you monetize by the tokens per second.** And so it’s a different economic. Does it make sense? You’re not renting cores, you’re not renting nodes. **You’re producing tokens, which is the reason why everything changed**. It was necessary to make sure that everybody understands the economics of the new world. They’re trying to buy the lowest equipment, lowest cost equipment. My equipment costs 30% cheaper. What does that mean to your factory? What does that mean to your factory? That’s really the question. And so I think people -- **anybody who says my chips are 50% cheaper. Put that in the context of the factory, and that person is actually demonstrating to you they don’t understand AI**.” It’s important to internalize Huang’s point of view here. He is well aware of the margins he is making and how emboldened his competitors/customers must feel to get off the Nvidia tax, but he is highlighting the fact that as long as Nvidia’s chips are superior in producing lowest-cost tokens, the math can remain decidedly in Nvidia’s favor, especially since tokens can skyrocket with the rise of agents. From Huang’s prepared remarks: > “…the price of the computer and the cost of the token are only marginally related. > > Remember, **people are buying these computers to produce tokens**. The effectiveness of the production of those tokens matter greatly. They’re not reselling the computer. If you bought a computer and it’s expensive, if you resold it and that’s it, then it’s expensive. But **you bought a computer and it’s expensive because the technology is incredible, but it produces tokens at such incredible rates, you have -- simultaneously have purchased the most expensive computer and produce the lowest-cost tokens**. Huang’s point is well taken; however, I think the reason Huang is so happy to pay TSMC and ASML’s margins because he is at least so far able to defend his own margins. His own customers i.e. hyperscalers may be grappling with a different reality. I asked Gemini to show me an illustrative example how $100 sales by ASML used to flow through the compute value chain to hyperscalers ten years ago. ![](https://substackcdn.com/image/fetch/$s_!NM_e!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdb8332fe-a421-43c8-96c6-7309e55d39c8_1504x196.png) Source: Gemini I then asked Gemini to do the same illustrative analysis for the compute value chain today. ![](https://substackcdn.com/image/fetch/$s_!0T7f!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8b4ccfcd-b79b-4cc8-801d-5e58614ea850_1519x196.png) Source: Gemini As you can see, the economic multiplier from a $100 piece of ASML equipment to a hyperscalers' end revenue shrank by more than half (from \~618x down to \~285x), illustrating how **value capture has concentrated upstream**. Just a quick look at the evolution of gross margin of the players in the upstream makes it quite apparent of their increased ability to capture value. Hyperscalers also did pretty well over the last decade because given their fragmented customer base and oligopolistic industry structure, they too were able to live in a cozy manner. ![chart](https://substackcdn.com/image/fetch/$s_!Pt0C!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd26649c8-1ddd-4abb-b4d6-f76a280d4a9f_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Unfortunately, Huang seems pretty determined to disrupt such coziness of his customers who are also trying to compete against Nvidia. At one point during the Q&A yesterday, one analyst pointed out that while hyperscalers account for 60% of datacenter revenue, these hyperscalers also currently account for majority of neocloud’s revenues. So, in a sense, the hyperscalers may actually account for \~80% of data center revenue. Huang in his response alluded which customers he would like to see thrive more: > “Even in the cloud, we compete with some part, but **we also bring customers to the other part.** And so **some part of that chart of 60%, we have to compete**. And our job is just to deliver that chart better than anybody else in the world, and we’re doing very, very well, and we’re actually increasing our position day in and day out. And then **the other part, we bring customers to them. They’re just grateful**. > > …I think if you test against, do they design the -- do we -- does NVIDIA compete with them on chips?…then you got to figure out where are we in our position and what’s our opportunity and so on and so forth. **I don’t think OCI will design their own chips. I don’t think it’s sensible for them to do it. Obviously, CoreWeave’s not going to design their own chips. And so there, we -- so where do we compete and where do we bring the cloud service provider customers? And their cloud revenues, a lot of them, a lot -- a big part of it, obviously, I nearly 100% of that is because of NVIDIA**” ![](https://substackcdn.com/image/fetch/$s_!wnx9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe81cdbe5-2fff-4983-9f53-0a3cc33b9f45_1449x855.png) Source: Nvidia In fact, Nvidia mentioned they expect to utilize half of their FCF to buyback shares and use the other half to fund their “ecosystem”. Since Huang indicated before at MS TMT conference that they are unlikely to participate in Anthropic and OpenAI’s funding going forward (since they may IPO this year), majority of the rest of its FCF post buyback may go to funding neoclouds to ensure AI workloads become much more fragmented than traditional cloud workloads ever were. Just for context, Nvidia is expected to generate **almost $700 Billion cumulative FCF in just the next three years**. So even if \~25% of this FCF goes to neocloud, that’s $175 Billion potential funding to neoclouds. Let me also highlight that such circular financing is also what makes valuing Nvidia increasingly challenging for me given this capital allocation policy isn't just one-off, rather structural in nature. How do I capitalize (and at what multiple) Nvidia’s earnings without having an opinion on the future of neoclouds? Nvidia looks cheap in earnings multiples, but half of the earnings goes to funding the ecosystem, you need to have an opinion on the valuation of the ecosystem itself to evaluate Nvidia’s capital allocation. I suspect that’s not an easy job for most investors, certainly not for me. Nonetheless, I have no problem in admitting that Nvidia is running at a breakneck pace which will probably make it very, very difficult for its competitors to catch up with Nvidia in the near future. Huang with his strategic mind is reshaping the compute value chain to outmaneuver and outcompete everyone to protect his castle. A bet on Nvidia may be largely a bet on Jensen Huang which reminds me the following [quote](https://ecorner.stanford.edu/wp-content/uploads/sites/2/2003/01/1125.pdf?ref=mbi-deepdives.com) from him more than two decades ago: > Someone said recently that I’m the most tenacious CEO they’ve ever seen. I’m not exactly sure whether that’s a compliment or not, but my will to survive exceeds almost everybody else’s will to kill me.” Perhaps the most admirable quality of Jensen Huang is his ability to maintain the raw desire for survival even when his company is literally the largest company in the world! 🫡 --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### OpenAI's Dilemma URL: https://www.mbi-deepdives.com/openai-dilemma/ Last updated: 2026-03-17T15:14:05.000Z WSJ yesterday had an interesting reporting on why OpenAI is pondering to focus more on enterprise. Some excerpts from the [piece](https://www.wsj.com/tech/ai/openai-chatgpt-side-projects-16b3a825?mod=hp%5Flead%5Fpos1&ref=mbi-deepdives.com): > “OpenAI’s top executives are finalizing plans for a major strategy shift to refocus the company around coding and business users, recognizing that a “do everything all at once” strategy has put them on the defensive. > > “We cannot miss this moment because we are distracted by side quests,” Simo told staff last week, according to remarks reviewed by The Wall Street Journal. “We really have to nail productivity in general and particularly productivity on the business front.” > > Computing resources often shifted from one team to another at the last minute, and the company’s organizational structure grew complicated, the employees said. For example, OpenAI’s Sora team was housed under the research division, even though it was responsible for launching one of the company’s most high-profile products, they said.” In a compute constrained environment, it can be more expensive than usual to allocate compute on bets with high degree of uncertainty around monetization, especially for an AI labs that requires enormous funding from investors to pursue their ambitions. What made it even more complicated is if a competing AI lab can consistently allocate compute in products/services that drive materially higher revenue and margin than OpenAI can. Right now, AI monetization appears to be **much more clear and immediate** in enterprise and productivity setting. The rise of agents can make this distinction between enterprise and consumer in monetization even more clear. Consumer market can move in a glacial fashion due to [hardcoded habits](https://www.mbi-deepdives.com/hardcoding-habits/) whereas enterprise customers can switch or transform their workflow much faster the moment ROI makes compelling sense. If Anthropic ends up dominating that market, it is conceivable to imagine that OpenAI’s ability to monetize their compute allocation can increasingly pale in comparison with Anthropic which can directly affect OpenAI’s fundraising ambition and maintain a valuation gap with Anthropic if Anthropic surpasses them in revenue. So, it makes sense to me why OpenAI considers it rather urgent to not cede the enterprise opportunity to Anthropic. One “advantage” that Anthropic has is they don’t have the “baggage” of almost one billion free weekly active users. I’m saying “baggage” with my tongue in cheek because anyone would kill to have such baggage. The problem is in a compute constrained environment for a deeply FCF negative company, it can be super expensive to host one billion free users. Of course, OpenAI cannot be too generous for too long here and hence, they’re launching ads. I think a big moment of truth for OpenAI will be in the next 6-12 months is how well they are able to monetize their free users. If the monetization is too poor or users react quite negatively to ads on ChatGPT, I wonder how aggressive OpenAI will remain for gaining the incremental user. It already doesn’t help that once incumbents such as Google integrated their models both in Gemini and traditional search, the impetus for the incremental free user to go to ChatGPT is already showing signs of exhaustion. For example, while the percentage of Google visitors who also visited ChatGPT kept growing for much of the 2023-24 period, this number has been largely flat since August 2025\. As per Similarweb, the percentage of Google visitors who also visited ChatGPT was 14.3% in August 2025\. Their most recent data in February 2025 was 14.1%. ![Image](https://substackcdn.com/image/fetch/$s_!fUlu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feeb46f5c-9c2b-4805-bd83-54674084a176_700x610.png "Image") Source: Similarweb You can argue that OpenAI can serve the free users with a materially cheaper model and keep the most advanced models for paying subscribers. They’re currently using GPT-5.2 for free users whereas GPT 5.4 is only available for paying subscribers. Could they use even older model to serve free users or can they keep using GPT 5.2 for years to serve free users? I have no doubt that deploying GPT 5.2 in 2028 will be significantly cheaper for OpenAI, and they would love to do exactly that if they didn’t have competition. The challenge is Google (and perhaps Meta in the future) may use better models than GPT 5.2 to power their AI experience for free users. If the free users can get better experience in Google or Meta, OpenAI may have much harder time in retaining and particularly attracting incremental free users in this scenario. Perhaps OpenAI is okay with that outcome as long as they are able to deploy their compute in areas with much better visibility to monetization than powering free ChatGPT users. Of course, if ChatGPT stops trying tooth and nail to grow their free userbase, that can be a huge boon for Alphabet and Meta. But you may wonder if OpenAI finds consumer AI is not as worthwhile as enterprise AI, does it matter if Meta and Alphabet gain material share of the incremental users going forward? Unlike OpenAI, Alphabet (or Meta) doesn’t need to see immediate visibility to revenue as long as they believe there is a lot of money to be made in consumer AI. Indeed, I do think consumer AI can prove to be lot easier to scale for incumbent companies with existing distribution to billions of users than companies such as OpenAI and Anthropic who must spend incremental S&M dollars as well as constrained compute to stay in this space in the long term. While OpenAI faced bit of a backlash online for introducing ads on ChatGPT, Meta is quite blatant that they will use your interactions with Meta AI to power ads that you will see elsewhere on their platform. From Meta’s own [blog post](https://about.fb.com/news/2025/10/improving-your-recommendations-apps-ai-meta/?ref=mbi-deepdives.com): > “if you chat with Meta AI about hiking, we may learn that you’re interested in hiking — just as we would if you posted a reel about hiking or liked a hiking-related Page. As a result, you might start seeing recommendations for hiking groups, posts from friends about trails, or ads for hiking boots.” Unlike OpenAI though, Meta won’t show you ads directly on “Meta AI” chat which may put users off wondering if the chat bot’s answer itself is influenced by the advertiser. The user will see such ads, just as they would normally, while scrolling any of the Meta’s apps. The size of the opportunity is quite easy to grasp and it’s not small. Let’s **imagine** Meta is able to get 1 billion DAU on “Meta AI” in three years (\~25% of their Daily Active People on their properties). Let’s say the average DAU on Meta AI asks 360 queries per year. If 20% of these queries have commercial intent, that’s 72 high-intent queries for which Meta can show ads to users later. If \~10% of these high-intent ads convert and each conversion is worth $10-20, that’s \~$70-140 Billion incremental revenue opportunity for Meta. Remember, an average Meta DAU likely sees [more than 30k](https://www.mbi-deepdives.com/expanding-the-scope-of-digital-advertising/) ads on Meta per year. Even if there is some “cannibalization” here (perhaps Meta would have been able to show some of these ads without the help from “Meta AI” anyway), you can still sense that the incremental opportunity can become quite large for incumbent companies with existing userbase and SOTA advertising infrastructure. I did ask you to imagine because Meta doesn’t have a good model yet, but this is the bet they’re likely making. As their models get better over time and for plain vanilla consumer AI queries model quality becomes lot less important than something like coding, Meta thinks they can gain strong adoption with Meta AI. For Alphabet, you won’t have to imagine much because they already have a pretty good model for consumer AI queries and unlike OpenAI, they won’t be in dilemma to serve free users with better model because they do have a clear visibility towards monetization. From Alphabet’s 4Q’25 earnings call: > “We've been deploying Gemini models to improve query understanding at a rate of almost a launch per month for the last 2 years. **These improvements drive better query matching, ranking and quality, making search ads even more effective**. With Gemini across our ads quality stack, **we evaluate relevance with greater accuracy** than with previous generations of models. This has significantly improved our ability to **systematically deliver more helpful high-quality ads, contributing to a meaningful reduction in irrelevant ads served**. Gemini's understanding of intent has increased our ability to **deliver ads on longer, more complex searches that were previously challenging to monetize**. **Gemini models also have a significant impact on query understanding in non-English languages, expanding opportunities for businesses to scale globally”** While reading the WSJ piece on OpenAI’s potential pivot, I was reminded Brian Chesky’s point in an interview that how he noticed so few companies in Y-Combinator to pursue consumer market in AI and most are just focused on enterprise. While there is certainly a lot of truth to the fact that enterprises may have far greater willingness to pay directly for AI than consumers ever will, it can mask an important truth: consumer AI is simply too hard for startups, and incumbents may be destined to dominate consumer AI in the long term. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Neocloud Trojan Horse URL: https://www.mbi-deepdives.com/neocloud/ Last updated: 2026-03-16T14:43:17.000Z One of the most frequent pushbacks that I have received to my recent posts on AWS (see [**here**](https://www.mbi-deepdives.com/aws/), and [**here**](https://www.mbi-deepdives.com/aws2/)) is that the neoclouds are not a legitimate concern for hyperscalers such as AWS and Azure in the long term. Frankly speaking, I myself operated under this assumption for the last couple of years. What made me eventually question such comforting hypothesis is the capex outlook of CoreWeave and Nebius for 2026\. For context, CoreWeave guided $30-35 Billion capex whereas Nebius guided for $16-20 Billion capex for 2026\. I don’t know about you, but I would be at least somewhat concerned about long-term industry structure of cloud if someone mentioned to me that neoclouds capex would be almost one-third of AWS capex size by 2026. It also doesn’t help that almost on a **weekly basis** we are seeing Nvidia providing more funding to these neoclouds or/and these neoclouds are partnering with major tech companies to become more relevant players in the AI workloads. For example, just today Nebius [announced](https://nebius.com/newsroom/nebius-signs-new-ai-infrastructure-agreement-with-meta?ref=mbi-deepdives.com) that Meta agreed to buy $12 Billion of AI capacity by 2027 and an option on another $15 Billion over five years. At this point, it seems almost foolhardy to consider these neoclouds fringe or irrelevant players. In fact, if you listen to these neoclouds earnings calls or recent Morgan Stanley TMT conference sessions, they seem to be not oblivious about the bear cases against their business model. I will share some key takeaways from these calls behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Veeva Model Update URL: https://www.mbi-deepdives.com/veev26/ Last updated: 2026-03-15T17:42:13.000Z As I have [**promised**](https://www.mbi-deepdives.com/veevafy4q26/) earlier, I have updated my Veeva model yesterday. If you are not familiar with the company, I suggest you read my [**Deep Dive**](https://www.mbi-deepdives.com/veev/) (September 2024), and an [**update**](https://www.mbi-deepdives.com/veeva-update/) on the company from December last year. I will briefly go through the model and share some thoughts on valuation behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta at the AI Crossroads URL: https://www.mbi-deepdives.com/meta-at-the-ai-crossroads/ Last updated: 2026-03-14T14:54:06.000Z A couple of days ago, NYT [reported](https://www.nytimes.com/2026/03/12/technology/meta-avocado-ai-model-delayed.html?ref=mbi-deepdives.com) that Meta will delay releasing its new foundational AI model to May this year. While Meta never really publicly disclosed when they would likely release their new model, it was widely believed to be sometime in March. However, NYT’s reporting indicated that while the model outperformed Llama 4 as well as Gemini 2.5 (released in March 2025), it fell short of Gemini 3.0 which was released in November 2025. Not being able to match Gemini 3.0 is certainly a blow to Meta’s ambition in staying at the frontier. While Meta spokesperson [tried](https://x.com/andymstone/status/2032271660896907733?ref=mbi-deepdives.com) to downplay the reporting, I do think the reporting was incremental. If they get there by May, Meta will be six months behind the frontier models which has been sort of consensus timeline. But if they cannot match Gemini 3.0 even by May or delay it again, the gap between Meta and the frontier models will diverge from the consensus timeline. Given the Llama 4 fiasco, Meta’s ambition as a model developer was already under the scanner and this news certainly did not help. In light of this news, some investors started to wonder whether Meta even needs a SOTA model. Interestingly, Alex Heath [reported](https://sources.news/p/starboy-anti-ai-wearable?ref=mbi-deepdives.com) yesterday that “*An interesting question that has been asked *internally* is whether Meta even needs to compete at the frontier of AI*”. I will share my thoughts on this question as well as Meta’s potential layoffs behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Editing Adobe URL: https://www.mbi-deepdives.com/editing-adobe/ Last updated: 2026-03-13T14:34:28.000Z At first glance, everything seems to be chugging along just fine at Adobe. Their earnings yesterday continued to show that revenue is still growing at double digit rate with operating margins remaining quite stable. In fact, Adobe mentioned their ARR from “AI first” offering tripled YoY with monthly active users (MAU) across Acrobat, Creative Cloud, Express and Firefly growing 17% YoY to reach 850 million. Moreover, creative freemium (which includes web and mobile versions of Firefly, Express, Premiere, Photoshop and Lightroom) MAU crossed 80 million, growing 50% YoY. Given that context, Adobe shareholders may be forgiven if they wonder what exactly this company needs to do to get back in investors’ good book again. Let me suggest what they may need to accomplish to make investors re-think about Adobe’s future. Adobe shared the below datasheet in their press release yesterday. I will particularly highlight “Total Adobe ARR” row in this table. Notice that Adobe’s ARR (when adjusted for currency) has **consistently decelerated for every single quarter in the last nine quarters**. In fact, it is the rate of deceleration that slightly accelerated in 1Q’26 which obviously doesn’t inspire a lot of confidence among investors. Adobe did mention that they experienced a “greater-than-anticipated decline” in their stock images book of business which is a $450 million segment for them, but even if you completely adjusted for this segment, its ARR would grow at 11.2% YoY (FX adjusted) which still indicates deceleration. If such deceleration continues and given Adobe projects 10.2% ARR growth for FY’26, it is quite conceivable that the business may end up with HSD ARR growth by FY4Q’26\. As I have laid out in my “[How Elephants May Die](https://www.mbi-deepdives.com/how-elephants-may-die/)” piece, you don’t need to believe in a sudden decline in revenue for you to get concerned about a business’s long-term future, but a **persistent** deceleration can be sufficient to make you concerned. ![](https://substackcdn.com/image/fetch/$s_!ediD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6644d5a2-50e4-448f-9ca3-2cd86c086197_1920x1056.png) Source: Adobe Moreover, while Adobe’s margin doesn’t seem to show much of a pressure today, you will notice that Sales & Marketing (S&M) in the last four quarters has consistently grown at least \~200 bps faster than revenue. They managed to maintain their operating margins by keeping R&D expenses growth noticeably slower than revenue growth. ![](https://substackcdn.com/image/fetch/$s_!HH4W!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F134325db-aaec-4771-9465-6d880e365e3c_1056x259.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I have also updated Adobe’s Digital Media business and compared with Figma’s revenue trajectory. Even though Figma was only 6.6% of Adobe’s Digital Media business in 4Q’25, Figma’s share of incremental growth was much higher and seems to be picking up. Of course, there is a swarm of other (new and old) competitors in creativity software industry which are private companies. If we could have access to the total creativity software industry revenue data every quarter, it would be hardly surprising to learn that Adobe’s share loss in creativity software industry has accelerated quarter after quarter post-ChatGPT. ![](https://substackcdn.com/image/fetch/$s_!HBm1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F808b2692-f79e-42b6-bef5-ee3814935a3a_721x406.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Of course, the most likely reason for the stock to be down post-earnings is that Shantanu Narayen, who has been Adobe’s CEO for the last 18 years, has abruptly announced his retirement yesterday. He’ll remain as CEO until the company finds a replacement. Narayen became CEO of Adobe in December 2007 and successfully transitioned Adobe in the SaaS era. It is actually remarkable that despite the current \~60% drawdown in Adobe stock, Narayen’s tenure almost matched S&P 500 since he became the CEO of Adobe. ![chart](https://substackcdn.com/image/fetch/$s_!fmHA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1f10d0f8-8889-42e2-95f4-b78d6dcba1f6_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Clearly, the fact that Adobe didn’t announce a replacement right away indicates this is not a typical succession plan for Adobe. I do think it may help Adobe to have a fresh perspective on how to set this company for re-accelerating growth. Adobe has entered a materially higher competitive intensity environment than Narayen likely experienced in his 18-year tenure as CEO, and the company probably needs a different leader to navigate the current phase. This isn’t a criticism of Narayen who is perhaps one of the better software CEOs in Silicon Valley, rather an acknowledgement to the reality that creativity software industry has graduated from stable, quasi-monopoly profit pool structure to a much more competitive one which likely requires bit of an “editing” of Adobe itself. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Meta's Chip Resilience URL: https://www.mbi-deepdives.com/mtia/ Last updated: 2026-03-12T13:25:37.000Z AWS, Google, and Microsoft have all deployed a similar chip strategy with varying degree of success. Google has its TPUs, AWS has Trainium, and Microsoft has its Maia chips. The success of their chips so far are also in that order with TPU being ahead of the pack and Maia being the clear laggard. All three, of course, also buy massive amounts of Nvidia hardware. However, the fate of their in-house chips in 3P cloud workloads can be heavily constrained by their cloud customers. Since the vast majority of external AI developers rely on Nvidia’s proprietary software platform **CUDA**, hyperscalers’ customers can continue to have a strong preference for Nvidia chips. Even if AWS or Azure builds a cheaper, highly efficient custom AI chip, their external customers overwhelmingly may demand Nvidia hardware. As a result, the cloud hyperscalers may be essentially forced to buy Nvidia GPUs at whatever price Nvidia dictates, or risk losing those enterprise customers to a rival cloud. Meta is entirely immune to this customer-driven vendor lock-in, making its multi-vendor approach significantly more resilient to supply chain shocks and pricing leverage. Meta can legitimately pit Nvidia, AMD, and its own MTIA foundries against one another to secure the absolute lowest prices and best supply allocations, whereas cloud providers are largely price-takers in the Nvidia ecosystem. I came to this realization after reading my friend Liberty’s [thoughts](https://www.libertyrpf.com/p/618-nvidia-photonics-meta-as-semi?ref=mbi-deepdives.com) on this topic: > **The major hyperscalers (Google, AWS, Microsoft) are in at least a partial conflict of interest**. They build chips, but they also have to sell cloud space to external developers who often demand the ‘industry standard’ (Nvidia), or are locked into a certain stack by legacy decisions. > > They have to offer a full menu, or they may lose customers. > > Meta, however, is **its own customer**. They use compute to do 1st party workloads like recommend Insta Reels, moderate crazy-uncle comments on Facebook, and serve ads. > > Because it doesn’t have to convince external buyers of anything and has full control over their stack, they can pivot to AMD or TPUs or their own custom silicon the second the math (TCO) makes sense. > > Meta can more easily move $10B of capex from Nvidia to AMD than the cloud vendors, because they don’t need the Nvidia brand name to sell cloud services to third parties. > > **This makes them the ultimate “swing voter” in the semiconductor market.** > > Meta is simultaneously committed to a multi-year Nvidia deal covering Blackwell and next-gen Rubin GPUs, has signed a 6GW AMD deal for custom MI450 silicon, is developing its own MTIA chips, and is reportedly exploring TPU access from Google. **That’s the compute-agnosticism thesis playing out in real time, not just in theory.** As Liberty mentioned, Meta’s custom-built silicon is explicitly designed to run the company's most predictable, high-volume internal tasks: the **ranking and recommendation algorithms** that decide which ads, Reels, and posts show up on your Facebook and Instagram. Meta recently announced an aggressive roadmap to roll out four new generations of these chips (MTIA 300, 400, 450, and 500) through 2027\. A [Blog](https://about.fb.com/news/2026/03/expanding-metas-custom-silicon-to-power-our-ai-workloads/?ref=mbi-deepdives.com) post by Meta yesterday elaborated their philosophy on in-house chips. Some key excerpts from Meta’s blog post (all emphasis mine): > “As our current AI workloads continue to grow and evolve, **we’re taking a portfolio approach** to scale our infrastructure capacity by sourcing silicon from a range of industry leaders, while keeping our own MTIA custom silicon at the center of our AI infrastructure strategy. > > We deploy hundreds of thousands of MTIA chips for inference workloads across both organic content and ads on our apps. These chips are specifically designed for our workloads, and are part of a custom full-stack solution, **helping us create a highly optimized system that’s tailored to our needs. This system achieves greater compute efficiency than general use chips for our intended purposes, making MTIA much more cost efficient**. > > While the industry typically launches a new AI chip every one to two years, **we’ve developed the capacity to release ours every six months or less by building on our modular, reusable design**s. This accelerated pace enables us to quickly adapt to evolving AI techniques, adopt the latest hardware technologies, and minimize costs associated with developing and deploying new chip generations. > > Mainstream chips are typically built for the most demanding workload — large-scale GenAI pre-training — and then applied, often less cost-effectively, to other workloads like GenAI inference. **We take the opposite approach: MTIA 450 and 500 are optimized first for GenAI inference, and they can then be used to support other workloads as needed,** including ranking and recommendations training and inference, as well as GenAI training. This keeps **MTIA well-tuned to the anticipated growth in GenAI inference demand.** > > **There is no single chip that can meet all the demands across our varying needs,** which is why we’re working to deploy a variety of chips that are optimized for each of our different workloads. We believe our portfolio approach will enable us to advance and innovate at an unmatched pace, bringing us closer to our goal of creating personal superintelligence for all.” ![](https://substackcdn.com/image/fetch/$s_!ZAse!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd03990a7-4ca2-4a6b-b8a5-ad3dd26b2c0f_1270x1162.png) Source: Meta While Meta’s approach is operationally superior, it’s not all puppies and kittens for Meta. There are indeed some trade-offs here. The other hyperscalers can, of course, amortize the cost of building these in-house chips over larger number of customers and workloads, and given their relative diversity of customer base, they’re not putting all the eggs on the advertising basket. Perhaps more importantly, in an earlier [blog post](https://engineering.fb.com/2025/09/29/data-infrastructure/metas-infrastructure-evolution-and-the-advent-of-ai/?ref=mbi-deepdives.com) last year, Meta’s own engineers mentioned dealing with **5–6 hardware SKUs a year** makes it harder to move workloads around and can create **underutilization and software friction**. From Meta’s own blog post last year (emphasis mine): > “From an operator point of view, **it is difficult for Meta to deal with 5-6 different SKUs of hardware deployed every year. Heterogeneity of the fleet makes it difficult to move workloads around, leading to underutilized hardware**. It is difficult for software engineers to think about building and optimizing workloads for different types of hardware. If new hardware necessitates the rewriting of libraries, kernels, and applications, then there will be strong resistance to adoption of new hardware. In fact, the current state of affairs is making it hard for hardware companies to design products because it is difficult to know what data center, rack, or power specifications to build for.” So while this portfolio approach creates incredible resilience in Meta’s chip strategy, there is predictably no free lunch. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Follow-up on "AWS: Pre and Post-ChatGPT" URL: https://www.mbi-deepdives.com/aws2/ Last updated: 2026-03-11T17:52:29.000Z Last Saturday, I published a [**piece**](https://www.mbi-deepdives.com/aws/) outlining my reasons for nervousness about the long-term future of AWS. This led to a noticeably more constructive feedback from readers which I would like to address in this follow-up piece. I will also expand on a couple of points further behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Constellation Software 4Q'25 Update URL: https://www.mbi-deepdives.com/csu4q25/ Last updated: 2026-03-10T14:39:48.000Z Amidst an unprecedented drawdown relative to its history, Constellation Software (CSU) had a rather reassuring quarter. Things are, at least so far, chugging along just fine. The maintenance or recurring revenue segment, which is \~75% of their revenue, grew by 6% (FX adjusted) YoY in 4Q’25 which is a \~200 bps acceleration from 3Q’25. ![](https://substackcdn.com/image/fetch/$s_!WOhp!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F20f63c7b-ca78-4cff-b0df-f5887503ec13_1374x679.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Is AI accelerating CSU’s organic revenue growth? Management confirmed during the call that AI has **so far** neither contributed nor contracted CSU’s organic revenue. Is AI making price increases harder for CSU? Again, no impact **yet**. Of course, CSU management is smart enough to know that just because AI hasn’t made a dent yet doesn’t mean their businesses will remain immune from it in the long-term. As I have mentioned before, given CSU has hundreds of different VMS businesses, CSU management will not be short of evidence if AI is indeed impacting some niches, and they will also have a much better clarity what type of software businesses have more durability. The company is actively encouraging all of their operating groups to share their learnings and findings from the field. From the call: > “**Best practice sharing across our operating groups is one of our genuine differentiators**. And I’ve seen more cross-portfolio collaboration around AI in the past year than on any topic in recent memory. Over the past 12 to 24 months, we’ve directed our culture of best practice sharing to helping our businesses navigate the AI transition thoughtfully. > > …But I want to be direct about something, **building products and features faster will not be what differentiates us long term**. That capability will become widely available. It’s going to be table stakes. **What will matter is what our businesses have spent many years developing, deep vertical knowledge, a genuine understanding of customer workflows and processes, the data inside their solutions and the trusted relationships they’ve built**. I believe AI will help us do all of this better. When I look at where this leads, the opportunity **I find most interesting is what I described as knowledge networks, connecting our domain expertise, customer process knowledge and data assets in ways AI now makes possible**. That’s a long-term build, and we’re in early days, but the foundation is real. > > Our customers rely on us for **mission-critical** software. We believe that the trusted partner position we’ve earned in our verticals is now extending to guiding them on how to safely and effectively bring AI into their own businesses.” Indeed, writing the code itself was never quite the core differentiator for almost any software business. Listening to the CSU earnings call reminded me of the similar point raised by Benedict Evans in a Stratechery [interview](https://stratechery.com/2026/an-interview-with-benedict-evans-about-ai-and-software/?ref=mbi-deepdives.com) last month. From Benedict Evans: > “I never thought that the hard part of building a piece of software that manages the thing for the other thing in the thing deep inside a big company was writing the code. > > That’s not the hard part. **The hard part is working out that that problem exists and then working out the right way of solving it and then going out and building a go-to-market and working out how to get your customers to buy it. It’s the implementation, the execution, the route to market, the right way of solving the problem**.” Perhaps the most reassuring moment for CSU shareholders was when management explained how they plan to follow essentially a [Schumpeterian](https://en.wikipedia.org/wiki/Creative%5Fdestruction?ref=mbi-deepdives.com) process in their capital allocation during AI’s potential disruption. From the call: > “…we’re very decentralized, and we trust each of our businesses to sort of have a strategy around this and learn from each other, and our coaches pushed them hard on making sure they’re moving things forward. But we don’t look at it as it’s going to be a situation where, as always, some of our businesses do better than others adapting to disruptions in their markets. And we’ll make sure that we, **as a conglomerate, we will put the capital in the hands of the people who we feel will continue to invest at good returns for our shareholders.** > > ….as a good conglomerate, **we want to make sure we’re putting the capital behind the people who are winning. And if you’re losing, we’ll take your capital and put it elsewhere, which doesn’t sound very nice, but that’s what we’ll do**.” I will talk more about CSU’s margins, acquisitions, ROIC, and valuation behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Nvidia's Burgeoning Moats, Pichai's Comp URL: https://www.mbi-deepdives.com/nvda-moat-pichai-comp/ Last updated: 2026-03-09T14:28:14.000Z Jensen Huang mentioned at Morgan Stanley (MS) TMT Conference last week that Nvidia just reported “*the single best print in the history of humanity*”. While such glib comments attracted a lot of attention, I thought Huang’s session was quite instructive in explaining to investors how Huang is busy at work to enhance Nvidia’s moats. I will particularly highlight two comments from the session on this topic. First, Huang’s point about Nvidia’s full-stack approach and why Nvidia is able to accelerate their launch schedule makes me increasingly pessimistic about most of the ASICs. From Jensen Huang: > “…we’re quite comfortable with this full stack, full system approach. And without being able to do that, it is impossible to stay at the bleeding edge. It is literally impossible to keep up with a company that’s building not just one chip each year, but we’re building an entire infrastructure each year because-- we own the CPU. We revolutionized the new way of designing CPUs, and you’ll see more examples of that. We revolutionized the way we do CPUs, revolutionized the way we obviously do GPUs, connect them together using this thing called NVLink, which revolutionized the way you build computers all together, connected together with a new type of AI Ethernet called Spectrum-X, we connected everything together. **Now we own the entire stack**. We know all the chips inside. > > **When you own the entire stack and you own all the chips inside, you could change it every single year. If you don’t own the entire stack and you don’t own all the chips, it’s hard to innovate every year.** And the reason for that is because you’re connecting too many cats and dogs, and there’s too much innovation to pull together once a year if you can’t control it because it’s a full stack problem. So that’s how we got here.” Second, while Nvidia’s moats related to CUDA is well understood by investors now, their procurement advantages is perhaps still underappreciated which I highlighted in my [**AWS piece**](https://www.mbi-deepdives.com/aws/) a couple of days ago. Huang drove the point home even more clearly: > “I love constraints. And the reason for that is because in a world of constraint, you have no choice but to choose the best. You can't squander your choice. If the data centers -- if the land power and shell is constrained, you're not going to randomly put something in there just to try it out. You're going to put something that you know for certain is going to deliver the tokens per watt, that you know for certain is going to allow you -- from the moment you secure the capacity, we're going to be able to stand up an entire factory for you. We're the only company in the world that can come into your company and help you stand up an entire AI factory. > > And this is one of those questions now for all the CEOs that are in the cloud -- they’re cloud service providers or software providers, if they make poor choices, **this is no different than me choosing the wrong foundry**. This is no different than me choosing the wrong memory, the wrong anything because I have so little -- everything is so constrained. If I choose poorly, my revenues are affected, everything is affected. And so they can’t choose poorly. > > The second thing is NVIDIA is, as you mentioned, working at such a large scale. Our supply chain, **one of the things that we do with our money, of course, is to secure our supply chain**. One of the things that we do with our capital is to secure supply chain so that when Satya asked me to help them stand up a few gigawatts, the answer is no problem. And the reason for that is **I got all the memories, I’ve got all the wafers, I got all the CoWoS. I’ve got all the packaging, I’ve got all the systems, I’ve got all the connectors, I got all the cables. Everything from copper to multilayer ceramic capacitors, everything is secured. That’s one of the reasons why NVIDIA’s balance sheet being strong is so strategic.** > > A strong balance sheet today is not only helpful, **it’s strategic**. And so you look at the amount of revenues we’re shipping into, just look backwards and **look at the amount of supply chain capacity we had to go secure for that** they have to believe. If you set up a factory, a plant -- a DRAM plant, and I come in and say, you know what, go ahead and set up the DRAM plant because I’m going to use it. That goes a long ways. You might as well take that to the bank, as many of them have. And so **I think the fact that everything is scarce is fantastic for us**.” Indeed, as long as these scarcities continue, Nvidia likely remains very much in the pole position in the compute value chain and continues to rake profits higher than anyone else. The question, however, remains if we ever get to the other side of scarcity since betting on scarcity to last forever doesn’t seem like the most sensible bet. Perhaps by the time we will largely close the gap between demand and supply in compute, Huang with his “strategic” mind will figure out other ways to defend his castle. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Sundar Pichai’s Comp** Sundar Pichai was [awarded](https://www.sec.gov/Archives/edgar/data/1652044/000165204426000026/goog-20260304.htm?ref=mbi-deepdives.com) a new incentive comp by Alphabet board last week. Some components of Pichai’s comp is largely similar to what he was awarded before. For example, like his last comp, Pichai’s Performance Stock Units (PSU) will vest based on Alphabet stock’s performance against **S&P 100**. While they didn’t disclose the exact details of what Total Shareholder Return (TSR) Alphabet needs to hit relative to S&P 100, they will share those details following 1Q’26\. For context, in his earlier comp, Pichai needed to hit 75 percentile or above to receive 200% of his target PSUs. My guess is it’s going to be quite similar again this year. ![](https://substackcdn.com/image/fetch/$s_!w7S5!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd2d39a4-4ab9-4fab-a4d3-fe7519c723b3_835x424.png) Source: Company Proxy Statement 2025 What is actually new this year is that Alphabet tied a big chunk of Pichai’s economics directly to later-stage **Other Bets**. At target, $175 million of Pichai’s comp package is tied to Waymo ($130 million) and Wing ($45 million); at max, that piece can be worth $350 million. Waymo/Wing awards use internal grant-date fair values for those subsidiaries’ common units which is not publicly disclosed. Given Waymo just [raised](https://waymo.com/blog/2026/02/waymo-raises-usd16-billion-investment-round/?ref=mbi-deepdives.com) $16 Billion last month at $126 Billion valuation, we can probably assume this as “fair value” at grant date for the Waymo piece. Wing, as far as I can tell, hasn’t raised external capital which means we don’t quite have much of an idea about the valuation of Wing. In any case, given that most Alphabet shareholders essentially capitalize “other bets” losses in their models, this is a welcome development that the CEO is directly paid for value creation of “Other Bets”. It does appear more and more “other bets” are gradually graduating from their “science experiments” phases and turning into a real standalone businesses. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Notes from MS Conference: Meta, and Microsoft URL: https://www.mbi-deepdives.com/meta-msft/ Last updated: 2026-03-08T15:01:30.000Z Morgan Stanley (MS) had their annual TMT conference last week. Let me share some notes from Meta, and Microsoft’s sessions today. **Meta Platforms** Susan Li, CFO of Meta, attended the MS conference. As you can imagine, there were plenty of questions around ROIC on their capex. It turns out it’s not just investors or analysts like us, but even the CFO was also surprised at Meta’s continued ability to find ways to improve their ad performance. From the session: > “…we have an internal metric called IREV, which is basically how we measure the performance of ads. And here’s the list and here’s what they add up to. And it is, I think, **one of the maybe modern wonders of the world** that we have continued to generate basically half after half a list of improvements that continue to generate IREV gains every half and those continue to **compound on each other**…that’s true on the organic side, too. And I would say the core business is very healthy.” As I have argued [before](https://www.mbi-deepdives.com/metas-ai-advantage/), we may be underestimating how much AI is reshaping Meta’s ad infrastructure. Meta’s monetization ability was always pretty good, especially relative to most of their sub-scaled counterparts, but AI is likely accelerating the distance between Meta and the sub-scaled players’ (current and future) ability to match Meta’s monetization abilities. It’s not just monetization; a lot of the work on improving the ads relevance is also pretty relevant for improving the ranking of organic content as well. From Susan Li: > “…we have a healthy pipeline of work ahead of us to basically to continue making the content more relevant through a couple of things. **One is just scaling up the amount of data we can use that lets us increase sort of the history of content interactions, makes the overall corpus of data available to the recommendation engine larger**. The second thing is we're really focused now on -- in the same way that we talked in the past couple of quarters, **the way we are really trying to redistribute ad loads so that what we care about is right now, are you in a position where you're interested in engaging with an ad where you want to buy something where you're in a period of commercial intent**. > > …we're also investing in using LLMs to deepen our content understanding. They are -- as the models continue to become smarter and the sort of understanding and reasoning capabilities become better. **Using LLMs to kind of help us understand content helps with recommendations in part because the traditional recommendation engine relies a lot on engagement signals and then you need a lot of engagement to happen to get the engagement signals, but LLMs can reason in real time about whether this is a piece of content that would likely be interesting to you based on what we know**. The bear case for Meta’s capex is that it can be hard to know beforehand where exactly we are currently on the curve of ad performance or organic content relevance improvement. Are we in early, mid, or late innings here? As the CFO suggested, they have always been able to come up with new improvements but past may not always be a great guide for the future, especially in light of noticeable revenue acceleration at a much larger revenue base in recent years. Li seems to have sympathy for these concerns as well: > “…the thing that I think now at some point I was up here talking about this, **it used to worry me, and it still does**, to be clear, I’m just like engineered that way, that **if you added up all these initiatives, sure, you could measure the return on each one because of that individual experiment, but you didn’t know where on the slope of the curve you were**. And so maybe actually, if you add up these 20 things, then you need to discount them by 80% because like the slope becomes much steeper. **That has not turned out to be the case**. > > **These -- the work that we have done has turned out to be more additive than we expected. And there is a virtuous cycle that you get into with advertisers**, right? Like you make the ads perform better. That, in turn, drives costs down for advertisers. That in turn drives their budgets on us up. And then on the platform up. And then hopefully, that’s good for their business, and that’s a like long-term virtuous flywheel because now it’s good for their business. They have more money to spend in the next cycle around doing this with us. That’s really hard to measure, right? **That’s a multi-month, sometimes multiyear process. It’s hard for us to measure that very directly**...But from everything we can observe, that appears to be happening on the platform. And our goal every day is to be the best place advertisers can come and spend their money **relative to anywhere else**.” My takeaway is Meta is likely fairly confident on their ability to generate compelling ROIC on their capex related to core ad business in the near term, but is still tentative about their long-term ability to generate attractive ROIC here. Another big unknown for Meta appears to be their spending on inference. Li has hinted that Meta will try to introduce more interactive content formats within their apps which will be very inference heavy than current formats. But since they haven’t been launched yet, Meta doesn’t know how the demand for such formats would look like and hence also the associated inference spending. Meta is also currently noticeably behind on frontier models, but Li appeared optimistic about their ability to get back to the race and once they do, she believes Meta has a clear path in integrating Meta AI much more deeply within its existing distribution. From Li: > “I think both based on just your deep history of interacting with the platform already and our ability to understand that information and sort of use it to make sure we're building a good experience for you. So I feel -- **I think that when we have a frontier model, I feel quite confident that the combination of that, the combination of the distribution graph, the network effects, the fact that there are a lot of very natural places to have Meta AI interact with you**…I think the ways in which the family of apps as it exists today, I think, are a great scaffold for AI experiences to fit very neatly within them” I will share my notes from Microsoft’s session behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Microsoft** _This post is for paying subscribers only._ ### AWS: Pre and Post-ChatGPT URL: https://www.mbi-deepdives.com/aws/ Last updated: 2026-03-07T18:19:37.000Z Back in 2014, Jeff Bezos wrote to Amazon shareholders: > “A dreamy business offering has at least four characteristics. Customers love it, it can grow to very large size, it has **strong returns on capital**, and it’s durable in time – with the potential to endure for decades. When you find one of these, don’t just swipe right, get married.” Bezos went onto discuss how promiscuous Amazon has been in finding and marrying such great businesses. One of the key focus areas for Bezos in that letter was Amazon Web Services (AWS) which he labeled as “***market size unconstrained***”. The very next year after Bezos writing that letter, Amazon divulged AWS numbers to the broader public. To most investors’ surprise, AWS turned out to be mid-20s operating margin business with wonderful returns on capital that even some of the best businesses would envy. Before ChatGPT came to the scene, AWS essentially proved to be a “dreamy” business pretty much every year since its numbers were revealed. In fact, despite Azure and GCP frenetically trying to catch up to a business with such envious characteristics, the numbers continued to be better and better with each passing year. By 2021, AWS operating margin became \~30% and ROIC improved to mid-20s. The incremental ROIC looked even more attractive as AWS routinely posted mid-30s ROIIC in the pre-ChatGPT world. ![](https://substackcdn.com/image/fetch/$s_!_cr1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F649c106c-7493-4a8d-b25f-86ef5be77ccb_853x496.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Then the world **changed** permanently in November 2022 when OpenAI released ChatGPT. I will explore what happened to AWS post-ChatGPT **so far** and what may happen **in the next five years** behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Why AI Is Unlikely to Kill OTAs URL: https://www.mbi-deepdives.com/ai-vs-ota/ Last updated: 2026-03-07T04:50:06.000Z Online Travel Agency (OTA) stocks such as Booking and Expedia both reacted very positively yesterday after The Information reported OpenAI’s retreat from direct checkouts. From [The Information](https://www.theinformation.com/articles/openai-scales-back-shopping-plans-chatgpt?rc=4lgoj7&ref=mbi-deepdives.com): > **OpenAI is scaling** back its plan to introduce shopping directly inside ChatGPT, marking a change in its high-profile effort to put checkouts inside the chatbot. Instead of allowing users to make purchases directly from product listings that show up in ChatGPT search results, the company is now focused on having checkouts take place inside of specific apps that plug into ChatGPT, an OpenAI spokesperson said. The stock reaction following this reporting makes it apparent that disintermediation has been a top-of-mind concern among investors. In fact, I too received multiple emails over the last month or so asking me about my thoughts on OTA’s disintermediation risk. In response, I have largely reiterated that disintermediation risk is highly likely to be overstated for OTAs, especially for Booking even before OpenAI’s retreat. I will elaborate my thoughts on why Booking’s disintermediation risk is likely overstated behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Veeva FY4Q'26 Update URL: https://www.mbi-deepdives.com/veevafy4q26/ Last updated: 2026-03-05T15:04:47.000Z Veeva had a really fine quarter. While the consensus was expecting $811 Million revenue in FY4Q’26, Veeva posted $836 Million revenue. As you can see below, revenue growth of 16% in Q4 comfortably surpassed opex growth of only \~7% which led Veeva to post their **highest ever** LTM **GAAP** operating margin of 28.7%! Gross margin went down slightly due to professional services related margin which has bit of a seasonality but subscription margins remain stable and above mid-80s. Stock-based compensation (SBC) as a percentage of revenue went down by almost two percentage point YoY. I will note, however, that during FY16 to FY19 period, Veeva’s SBC as a percentage revenue used to be only MSD to HSD level. SBC intensity crept up materially post-pandemic, but now that tech companies are operating in a different talent market environment, it won’t surprise me at all if SBC intensity in the next 5 years keeps going down every year. ![](https://substackcdn.com/image/fetch/$s_!v9gz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7c65d4c-aeef-4733-ac62-12f3cdfae830_2290x598.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Of course, software investors are hardly appeased these days with trailing performance. The question of AI loomed large during the Q&A which I will elaborate more behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Never Sell: MBI Interviews Asianometry URL: https://www.mbi-deepdives.com/asianometry/ Last updated: 2026-03-04T13:55:04.000Z For this month’s "Never Sell" podcast, I had the privilege of speaking with [**Asianometry**](https://www.youtube.com/@Asianometry?ref=mbi-deepdives.com). If you have any interest in semiconductors, you have probably come across his exceptionally useful YouTube videos before. I myself spent countless hours watching several of his [playlists](https://www.youtube.com/@Asianometry/playlists?ref=mbi-deepdives.com) related to semiconductors over the last 3-4 years. As you can imagine, we discussed chips at length, and went through the semiconductor value chain. You can listen to it here: [Spotify](https://open.spotify.com/episode/7am5pHqErmwC0T0XhNcwCD?ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/mbi-interviews-asianometry/id1786912203?i=1000752956818&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=rAhXJ1MOGgU&t=29s&ref=mbi-deepdives.com), [RSS feed](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) ### Frontier AI Economics URL: https://www.mbi-deepdives.com/frontier-ai-economics/ Last updated: 2026-03-03T13:37:44.000Z When I say frontier AI model developers have somewhat “speculative” economics, I do not intend to mean it is destined to be bad even though “speculative” does have negative connotation. What I want to highlight is the **range of outcomes for frontier AI model developers is still fairly wide**, an observation that appears to be largely uncaptured in their private market valuation since their valuations today almost certainly imply model developers to be increasingly in a pole position over time to enjoy attractive **net profit** margins. Yesterday, I came across a piece that also hinted at wider range of outcomes from a very different angle: what if one model developer (e.g. Anthropic) indeed becomes a monopoly? How does the rest of the value chain respond in that scenario? I will bow out today with this short blurb because I would recommend you read this piece by Soren Larson Instead: [**The Coase Conjecture in AI Inference Markets**](https://x.com/hypersoren/status/2028611491365003618?ref=mbi-deepdives.com) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### How Elephants May Die URL: https://www.mbi-deepdives.com/how-elephants-may-die/ Last updated: 2026-03-02T19:18:49.000Z Hendrik Bessembinder's [research](https://www.sciencedirect.com/science/article/abs/pii/S0304405X18301521?ref=mbi-deepdives.com) indicates that **\~4% of companies account for virtually all the excess return** over the one-month **Treasury bill rate** in the US stock market since 1926\. You have probably already come across this study before, but one of my inferences from this study was we may not fully internalize just how deeply challenging compounding is over the long term. As investor time horizon has shortened over time, we want to see signs of disruption right here and now even though in the real world a business may appear to chug along just fine quarter after quarter before starting its final phase towards obsolescence. By now, you have likely consumed plenty of back and forth on SaaS debate and why one side is being obtuse or making the wrong arguments. However, this Benedict Evans’ [thread](https://www.threads.com/@benedictevans/post/DVHK4gcFLAb?ref=mbi-deepdives.com) is worth highlighting: > “One of my memories of the dotcom crash and the telecoms crash was every month or so, people said something that was really obviously stupid, both at the time and now looking back, and the stocks would go down 10-15% > > But also, the stocks were way too expensive. > > So really, what was happening was that these stories catalysed much more well-founded nervousness about valuation” There may be plenty of stupid arguments the other side is making, but high valuation multiples can make stocks particularly amenable to even naive arguments, but can often later be backfilled with real, cogent case why a multiple de-rating makes much more sense. But how about SaaS stocks that are objectively trading at a low multiple on actual GAAP earnings? Perhaps there is no better example than Adobe which is currently trading at \~12x **LTM** EV/EBIT multiple. At this point, it’s hard to argue anymore that the valuation multiple is not incorporating a lot of the AI risk in their valuation. ![chart](https://substackcdn.com/image/fetch/$s_!DVR8!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1ad95295-ed17-41b4-9a90-8efaa0f72370_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In fact, many Adobe shareholders often like to point out their revenue charts and ask rhetorically, “**where is the AI disruption?**” I wouldn’t, however, want to caricature all Adobe shareholders in a similar way. Bristlemoon Capital, despite owning Adobe, recently [wrote](https://www.bristlemoonresearch.com/p/adobe-inc-adbe-slowly-then-all-at?ref=mbi-deepdives.com) perhaps one of the most thoughtful pieces on the Adobe debate in which they steel manned the other side. Once they have done that, they came away **without** high conviction that Adobe will be immune from AI risk. ![](https://substackcdn.com/image/fetch/$s_!s60B!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff2f61410-798c-4888-948c-dc62a339f6a8_1147x780.png) The reality is AI model companies essentially run a psychological assault on current and potential Adobe shareholders almost on a weekly basis now. Just see Google’s recent blog posts related to [Flow](https://blog.google/innovation-and-ai/models-and-research/google-labs/flow-updates-february-2026/?ref=mbi-deepdives.com), [Nano Banana 2](https://blog.google/innovation-and-ai/technology/ai/nano-banana-2/?ref=mbi-deepdives.com), and [Pomelli](https://labs.google.com/pomelli/about/?ref=mbi-deepdives.com). Notice what Pomelli says in its tagline: “*Easily generate on-brand content for your business*”. But isn’t 12x LTM EBIT enough to price these risks at this point? I will explore that question behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Hardcoding Habits URL: https://www.mbi-deepdives.com/hardcoding-habits/ Last updated: 2026-03-01T15:52:33.000Z In early 2025, Mark Zuckerberg [mentioned](https://x.com/tsarnick/status/1877829155842408610?ref=mbi-deepdives.com) that he estimates EU fined the US tech companies more than $30 Billion over the last decade or so. He went onto lament that instead of US government defending their tech companies from this indirect form of “tariff”, US government itself often led such opposition. You can understand why big tech shareholders would be rather incensed with this constant barrage of regulations from both sides of the Atlantic, but I often joke with others that thanks to EU and FTC’s in-depth reports on big tech, we actually have a much firmer grasp on big tech’s moat today than we otherwise would have. Indeed, a lot of my understanding related to Google Search’s moat was [**solidified**](https://www.mbi-deepdives.com/goog/) through UK CMA’s report. The regulators typically have access to a level of granular data that these companies simply do not share with the investors, and even if much of the confidential data points are redacted in the reports available to general public, these reports have provided a strong foundation for me in understanding many of the US big tech companies. It’s not just the reports, but also random regulations here and there that also gives us a sneak peek into the key source of moats for many big tech companies whose competition is often theoretically just a click away. EU’s Digital Markets Act (DMA) was one such regulation. In early 2024, the DMA went into effect across the EU. This legislation designated companies such as Alphabet as gatekeepers and mandated they cease practices that “unfairly” funnel users from their dominant search engine into their secondary services. I recently came across a very interesting paper that looked into how it affected Google following such regulation came into effect which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta’s AI Advantage is "In the Water" URL: https://www.mbi-deepdives.com/metas-ai-advantage/ Last updated: 2026-03-01T03:33:23.000Z ChatGPT or any chat bots have the benefit of novelty factor to capture our imagination in the last three years. However, one challenge with the chat bot paradigm is it requires a level of intentionality from the users that may not be as pervasive and persistent than most power users imagine. Benedict Evans recently [pointed](https://www.ben-evans.com/benedictevans/2026/2/19/how-will-openai-compete-nkg2x?ref=mbi-deepdives.com) out this tension that it appears ChatGPT’s “usage is a mile wide but an inch deep”. ![](https://substackcdn.com/image/fetch/$s_!epST!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faf3e1331-35ed-4f65-a579-fd4958952c87_1920x1080.png) Image Source: [Ben Evans](https://www.ben-evans.com/benedictevans/2026/2/19/how-will-openai-compete-nkg2x?ref=mbi-deepdives.com) One of the reasons I suspect the impact of AI on Meta’s Family of Apps and incumbent digital advertising in general is somewhat still underappreciated is it does not have the novelty factor of chat bots. On the surface, it feels we are still using the same apps, scrolling the same feed, and clicking on ads once in a while. The core user experience feels remarkably similar and there is a sense of continuity without sensing AI being front and center of these apps. Yet, AI is essentially in the “water” for these apps. Because it’s in the “water”, users don’t even need to change their age old habits. They can continue to do what they have been doing, and digital advertising companies, especially Meta can deeply embed and integrate AI in a way that can make their already state-of-the-art advertising infrastructure from a few years ago appear increasingly “primitive”. Behind the paywall, I will discuss a particular paper that I came across recently to elaborate how Meta’s advertising infrastructure is being radically reshaped by AI. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Digital Advertising Industry Snapshot 4Q'25 URL: https://www.mbi-deepdives.com/digital-advertising-industry-snapshot-4q25/ Last updated: 2026-02-27T15:35:42.000Z ***Programming Note***: I will be in San Francisco Bay Area **next Tuesday**. If any of you wants to meet for lunch around Larkspur, please feel free to reach out. Since it will be one-on-one lunch meeting, I may have to decline if there are multiple interests. --- As I do after every quarter, let me share the snapshot of the overall digital advertising industry. Please note that digital advertising industry is defined as Alphabet’s advertising revenue+ Meta’s advertising revenue+ Snap revenue+ Pinterest revenue+ Microsoft Search advertising revenue+ Amazon advertising revenue+ Trade Desk revenue+ AppLovin revenue. Obviously, the actual digital advertising industry is larger than this, but this snapshot helps me gauge the broader digital advertising industry’s big picture. TikTok and Walmart advertising are notably missing here. Well, TikTok is a private company and Walmart doesn’t disclose their advertising revenue consistently even though it reached $6.4 Billion revenue in 2025 which means Walmart now actually has a larger ad business than everyone in this snapshot except the big tech i.e. Alphabet, Meta, Amazon, and Microsoft. ![](https://substackcdn.com/image/fetch/$s_!O5HB!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84a0057b-b71e-4321-8ec2-6754c6798245_1483x1009.png) Some key takeaways from 4Q’25: **Meta continues to gain share**. Meta’s share in digital advertising troughed at 28.2% in 3Q’22 and has since then kept gaining ground to reach market share at 34% in 4Q’25\. Given their incremental share has almost consistently been 40%+ in the last three years, Meta seems destined to keep growing its share in digital advertising industry. Looking at their guide in 1Q’26, we can be pretty confident that their incremental share in digital advertising will remain comfortably above 40% next quarter as well. **Alphabet, on the other hand, keeps losing share**. Notice how Alphabet has been losing share in each of the last four years. Alphabet’s market share in digital advertising: 4Q’22: 54.6% 4Q’23: 52.2% 4Q’24: 50.1% 4Q’25: 48.1% However, there are some important nuances which make it not as bad as it may appear for Alphabet at first glance. While Alphabet lost \~650 bps of market share in the last four years, almost half of the share loss was due to Google Network business which is their lowest margin business in advertising whose revenue is also consistently declining every quarter. As search is evolving from browsing the internet to seek answer to mostly just “answer machine”, network revenue may continue to decline. As you will notice, even YouTube ads lost share in the last four years. But since YouTube’s subscription business continues to gain momentum, YouTube’s ad business is not a reliable indicator of the health of YouTube. Not only more and more formerly ad-supported users graduate to become YouTube subscribers every year, most of these subscribers are also likely to have higher purchasing power which creates a double whammy for the YouTube’s ad revenue. As these valuable impressions become unreachable through ads, I actually wonder if this itself can create some upward pressure on Meta’s CPM as Meta remains one of the handful ways for advertisers to reach scaled advertising userbase. Nonetheless, Google Search also did lose share. There are a couple of things at play here. When Apple implemented ATT, Google search was largely unaffected due to such policy which enhanced their market share in digital ads industry. Once Meta regained some of the lost signal, Search ended up relinquishing such share gains. Besides, AI is highly likely to be a much larger boon for discovery based advertising such as Meta than it is for high-intent search advertising. One interesting competitive dynamic that I am curious to observe in the next couple of years is between Google Search and Amazon advertising. Both have high intent search ad business; given Google’s entire AI ecosystem of products and tightly integrated SOTA model capabilities, I wonder if it can give Google a slight tailwind for regaining some query share from Amazon. Finally, even though the overall advertising industry grew by 18.4% in the last two quarters, much of the fruits of this high growth went to the larger scaled players. With the exception of AppLovin (a company I haven’t closely studied), all the subscale players are growing at a noticeably slower pace compared to the advertising behemoths such as Meta despite having order of magnitude larger base than these sub-scaled players. In the age of AI, the digital advertising infrastructure can become even more complex and the scaled players are moving at such a frantic pace that the sub-scaled players may have harder time staying relevant both for users and advertisers in the next 5-10 years. I made a slight change yesterday in my portfolio which I will discuss behind the paywall --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** _This post is for paying subscribers only._ ### The Nvidia Math URL: https://www.mbi-deepdives.com/the-nvidia-math/ Last updated: 2026-02-26T19:35:21.000Z Nvidia posted yet another amazing quarter, and their guide for next quarter was comfortably ahead of the elevated consensus estimates. Yet, the stock has gone down?? In fact, one of the recurring bemusements among many investors is why Nvidia stock seemed to have lost its momentum despite the massive capex outlook upgrades by its major customers. I will lay out the key source of tensions in Nvidia’s forward estimates behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!wu33!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84a995dc-513c-4798-9c21-78741fec4cce_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### CoStar's Obfuscation, and AI vulnerabilities URL: https://www.mbi-deepdives.com/csgp4q25/ Last updated: 2026-02-25T21:03:59.000Z While I used to enjoy Andy Florance’s humor during CoStar’s earnings calls, I cannot say I relished listening to the 45-minute prepared remarks by Andy Florance and almost 90-minute earnings call yesterday. Perhaps the stock being flat for almost the last seven years is not helping me find humor at Florance’s typical jabs at competitors during the earnings call. What perhaps exacerbated it further is I suspect CoStar is likely setting up their disclosure in a way that may make it incrementally challenging to hold management accountable. I will expand on it behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!-paE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb71474ef-d380-42b5-947d-373d2c8e11df_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Scuttleblurb on Veeva URL: https://www.mbi-deepdives.com/scuttleblurb-veeva/ Last updated: 2026-02-24T14:56:48.000Z My friend Scuttleblurb yesterday wrote a very thoughtful piece on Veeva (link to his [Website](https://www.scuttleblurb.com/veeva-ai/?ref=mbi-deepdives.com), and [Substack](https://www.scuttleblurb.com/veeva-ai/?ref=mbi-deepdives.com)). With his permission, I want to highlight three specific bits from his piece. First, Scuttleblurb explained Veeva’s myriad products through the lens of a typical clinical workflow which I thought was really helpful to grasp how Veeva’s products fit in. He mentioned it might be a bit “tedious” description, but frankly speaking as an analyst who studied Veeva, I found it to be more clarifying than monotonous. From his piece: > “The sheer number of Development and Quality apps and the alphabet soup of acronyms assigned to them is enough to confuse even a diligent analyst. Rather than cataloguing each offering in isolation, it’s more instructive to walk through a typical clinical trial workflow and see where they slot in along the way (product names in **bold**). > > Before a trial even starts, the sponsor uses **QualityDocs** to draft and publish the official playbook that everyone across participating clinics is expected to follow. That playbook combines written procedures and standardized forms covering how clinics should run the study, which systems will house key documents, who must be trained on what, what monitors should verify to ensure protocol compliance, and how serious protocol deviations should be handled. > > Once the trial guidelines are established, the sponsor or CRO must clear the study plan and recruitment materials with the Institutional Review Board, finalize site budgets, and ensure that lead investigators and site staff have been trained on the protocol. Each of these steps generates its own thicket of paperwork – delegation logs specifying who’s responsible for what, contracts governing how sites get paid, training certifications, and regulatory packets containing the study protocol, safety information, and the credentials of everyone involved. > > **Study Startup** is essentially a checklist for all of that pre-enrollment groundwork, used to assign tasks to relevant parties and track whether contracts have been signed, staff have been trained, physician credentials are on file, and so on. **eTMF** (electronic trial master file), meanwhile, serves as the study’s document repository, housing the study plan, signed patient consent forms, training records, safety reporting paperwork, and anything else a sponsor might need to demonstrate a clean paper trail to regulators. In short, Study Startup gets a trial to the starting line while eTMF organizes the receipts that prove everything was done by the book. > > With site preparation complete, the trial moves to patient enrollment and randomization. Age, weight, lab results, medication history, blood markers, and any exclusionary criteria are all captured in an **EDC** (electronic data capture) and reviewed to determine each participant’s eligibility. Those who qualify are then randomly assigned to control or treatment groups by an **RTSM** (Randomization and Trial Supply Management), which also ensures the right kits reach the right patients and that those kits remain adequately stocked throughout. > > The EDC, in addition to storing patient data for purposes of determining trial eligibility, stores just about every vital piece of information about the patient from start to finish. It provides structured forms for staff to enter updated labs and vitals, adverse events, and endpoint measurements. It flags missing values and inconsistencies, and provides an audit trail for who changed what and why. > > Each of these activities takes place across a distributed set of clinics. The **CTMS** (clinical trial management system) is the “command center” that looks across them all so a sponsor can see how enrollment is tracking versus the plan, what major deadlines are at risk, when monitoring teams are scheduled to review what the site, which sites are experiencing high staff turnover or protocol deviations, etc. Think of it like a project management hub purpose-built to oversee clinical trial operations. > > Running alongside CTMS, the sponsor also deploys a **QMS** (Quality Management System) to detect and remediate problems and document proof of resolution. So, let’s say a serious adverse side effect was reported late due to some process failure, like maybe a patient shouldn’t have been eligible for the trial to begin with or a site investigator overlooked early warning signs in lab results. This event would be logged, an investigation opened to determine the root cause, and corrective steps – more training, clearer protocol language – put in place. > > At several junctures along the path from site prep to study completion, the clinical trial workflow branches off into still more workflows, each with their own stringent data requirements. For example, the blood sample that a nurse draws from a trial participant is sent for analysis to a centralized lab, which registers those samples into a **LIMS** (laboratory information management system) that keeps track of who has custody of those samples, who performed what tests, the test results themselves, and any abnormal readings. > > The lab’s quality team also relies on QMS and QualityDocs to transform raw instrument readings into a sponsor-ready report, one that documents how samples were tested, validates the results, and records any issues that arose along with how they were resolved. QMS and Quality Docs – as solutions that codify standard operating procedures, monitor deviations, and log remedial measures – also extend naturally into the CDMOs that make the drugs. And just as labs use LIMS to store patient sample test results, manufacturers use it to track batch records and drug testing outcomes. > > Or, again, consider adverse side effect cited earlier, the one caused by a process error. Well, the mishap, beyond being logged in QMS, spawns its own structured workflow. The sponsor’s safety team opens a case record in **Safety**, which captures in exhaustive detail the patient’s symptoms, when they emerged, the dose they received, follow-up responses from site staff, the assessment of medical reviewers, and any regulatory reports that need to be filed. **SafetyDocs** stores the supporting documents tied to these cases, while **Safety Signal** is used to spot patterns across them. > > As the drug moves toward filing (and often well before the trial’s conclusion), sponsors turn to Veeva’s Regulatory software. They use **Submissions** to manage the documents that comprise the NDA package; **Publishing** to format and organize those documents to the exact technical specifications regulators require; **Registrations** to track of what they are allowed to sell in which markets, and at which doses, etc.; and **Labeling** to draft the official, regulator-approved text that explains how the product should be used safely. > > These regulatory applications address a problem that is somewhat removed from the challenges of running a clinical trial, but they depend on the information gathered along the way. The patient trial data in EDC, the proof of compliance documents stored in eTMF, the cases of adverse side effects logged in Safety all feed into Veeva’s regulatory products. You can even think of the trial workflow itself as a regulated production line that culminates in an agency-ready submission package.” After going through Veeva’s products, Scuttleblurb then discussed the key moat from the entire workflow to drive the point home (emphasis mine): > “…the main point of walking through that somewhat tedious description of the clinical trial process was to give you a sense of the **fractal succession of tasks that cascade from each action, because that’s key to understanding why Veeva’s attempt to unify its development suites makes so much sense**. Each step of the workflow begets its own burst of data, documents, and approvals, while also activating the next step, which triggers its own deluge of records and sign-offs, and so on. A serious adverse event logged in Safety, for instance, could be linked to supporting patient data in EDC and activate a process change within QMS. > > **Clinical systems operate at a different order of complexity than CRM.** Where CRM orchestrates engagement workflows and segments clients, clinical systems must store and reconcile vast volumes of data, documents, images, patient events, and regulatory approvals; maintain longitudinal records of clinical measurements and lab values; and preserve regulator-grade audit trails for every change made along the way. **Keeping all of these artifacts aligned across a patchwork of point solutions is a formidable challenge.** > > Veeva Vault provides the shared building blocks – security, reporting, APIs, user management – that make this technically feasible. But **it is the chain reaction of dependent, regulatorily mandated steps inherent in clinical trials that provides the business logic for why a sponsor or CRO would choose to standardize on Veeva rather than juggle a patchwork of point solutions**.” Of course, no software related piece these days will be complete without an extensive discussion why AI will or will not annihilate the software company at hand. Scuttleblurb had an extensive thoughtful discussion on this point. From his piece (all emphasis mine): > “Pretty much any workflow that can be described in words can be trivially instantiated in software without any programming experience…if not today, then soon. Gone are the days when creating a vertical SaaS requires *both* domain knowledge and coding skills. A small team of trial safety experts could conceivably create a dedicated safety suite in much less than 8 years. > > But **this underestimates the value of iteration. Even seasoned domain experts can’t anticipate the full range of edge cases that surface over time in a complex workflow**. And could those same safety experts describe in granular detail the QMS workflow that Safety feeds into, or the downstream changes in SOPs and training it triggers in QualityDocs? **The interconnected nature of R&D workflows means each of Veeva’s apps is strengthened by its integration with adjacent ones on the same platform. To truly rival Veeva’s R&D offering, a competitor would need to not only match any given app feature-for-feature, but also build out the surrounding upstream and downstream modules that pipe into and depend on it, and then stitch them all into a unified system**. > > **Given the regulatory burden, this can’t just be 80% right; it needs to be all the way there.** You can get away with building a stripped down financial research terminal, charging $100/month, and peeling away like 20% of users who are overserved by Factset and CapitalIQ. That doesn’t work in systems where being 90% right is functionally equivalent to being wrong. > > The notion that AI is going to annihilate deeply embedded systems of record is kind of a strawman at this point. I’m not sure too many folks really believe this. Instead, the woke thing to argue now is that SoR’s are dumb data repositories and most of the future value will be claimed by third party agents driving workflows on top. **My intuition runs in the opposite direction here. Veeva is a repository for data and content, sure, but to get that data in the first place requires chaining together workflows on workflows on workflows, creating audit trails, chains of custody, and organizing it all in a regulatorily compliant way. Everyone will have agents, but who else will rival Veeva’s foundational R&D substrate? And why would that substrate be worth less than the agentic workflow layered on top of it?** > > I can understand how horizontal agents can cut across the enterprise. Microsoft 365, Workday, and Salesforce are not deeply regulated systems of record. Agents can sit above them, stitching workflows together. They might surface overdue accounts in Salesforce, cross-reference contract terms in SharePoint, and generate revenue recovery projections in Excel. Or open a job requisition in Workday and schedule interviews in Outlook. Maybe a constellation of specialized agents collaborate to accomplish these tasks, much the way humans do. > > But **unlike general-purpose enterprise workflows, clinical studies are a bounded, regulated process. The R&D modules used to manage them don’t need to coordinate with external enterprise systems and the more of them Veeva controls, the less it needs to coordinate with third-party apps at all.** Veeva’s platform, fully realized, is as circumscribed an ecosystem as the clinical trials it helps manage. > > Veeva R&D’s core function is to maintain an exhaustive, defensible record of every meaningful trial event. In that context, management sees AI playing an increasingly labor-displacing role, starting with the mundane, manual work that still consumes human time, like interpreting and filing eTMF documents and verifying that forms have been completed according to protocol. More ambitiously, AI could expand into generative territory…designing trial protocols, writing safety case narratives, or drafting responses to questions posed by regulators. > > For such use cases, **agents that are native to the SoR and work off the full context of structured records, documents, permissions, and audit trails would seem epistemically advantaged vs. external agents that have to pull data through connectors and reconstruct the full story from partial signals. Why would a sponsor that has already standardized on Veeva’s R&D platform look outside it for agents rather than use the ones built directly into the system?** > > There seems to be this growing fear that: 1) agents will capture more valuable than the SoRs they leverage and 2) the agent layer will be separate from the SoR. In Veeva’s case, I don’t think “2)” holds and as a consequence I’m not even sure “1)” makes much sense given how intertwined the two are. The more legitimate concern is that in their ability to coordinate workflows across systems, AI agents make it easier for large sponsors to continue running mixed best-of-breed stacks, reducing the urgency to migrate to a unified platform. **Even so, I’d wager that a unified platform with native agents retains a meaningful edge over horizontal agents stitching together incomplete context across disconnected systems**.” As I [mentioned](https://www.mbi-deepdives.com/file-over-app-veeva/) before, if Veeva proves to be vulnerable to AI disruption, then my best wishes to every other enterprise software company out there. I do, however, have a slight disagreement with Scuttleblurb which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Alphabet: From Search to AI to "AGI" URL: https://www.mbi-deepdives.com/goog2026/ Last updated: 2026-02-23T19:26:03.000Z Google is perhaps one of the most recognized brand in the world. I wouldn’t, however, confuse familiarity with understanding of Alphabet’s business. By the end of this piece, it will likely be clear how complex this business has become over time. It is perhaps not a stretch to say Alphabet is currently the most complex business under my coverage. I first wrote a long form piece on Alphabet back in [**early 2023**](https://www.mbi-deepdives.com/goog/) (which a later reader survey showed to be the most popular piece on MBI Deep Dives in 2023). I, however, sold the stock [later](https://x.com/borrowed%5Fideas/status/1732083595874009587?ref=mbi-deepdives.com) in 2023 as my mind became consumed with search related concerns in the post-ChatGPT world. Thankfully, I changed my mind and became a shareholder again in [**early 2025**](https://www.mbi-deepdives.com/googl/). As a result, I am updating and publishing my somewhat complex Alphabet model after nearly three years. My objective in this piece is to lay out Alphabet’s business in a more granular level; it would be quite surprising if you and I agree with all the assumptions made in this model. Frankly speaking, even I will perhaps need to update a lot of these assumptions a few months from now given how the landscape in AI keeps shifting these days. So, I will focus on explaining the model (and how I thought about it) so that it is easier for you to incorporate your own assumptions. Over the last twelve months, I have written numerous pieces on Alphabet. For the sake of avoiding redundancy, I will not repeat much of the qualitative narratives around Alphabet which has been adequately covered on MBI Deep Dives. This piece is largely about the numbers behind the narratives. The rest of this piece is behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Long-term Reality of Hyperscalers: The Good, The Bad, and The Ugly Scenarios URL: https://www.mbi-deepdives.com/hyperscalers/ Last updated: 2026-02-22T15:46:21.000Z In my “[**Lipstick on Frontier AI Pigs**](https://www.mbi-deepdives.com/lipstick-on-frontier-ai-pigs/)**”** piece, I wrote the following: > Apart from ignoring the cost of training, another deeply questionable assumption Amodei makes in his argument is the gross margin for inference. In his “stylized” example, he assumes gross margin to be 67% which is directly responsible for their ability to fund training the next model. How can we assume the gross margin will remain so high when its more well funded competitors know they can exert unduly amount of pressure on Anthropic **if they take the gross margin on inference to…say 30%?** If Anthropic cannot get enough gross profit from today’s model, its hands will be tied for training the next one. Three days later after publishing my piece, The Information [reported](https://www.theinformation.com/articles/openai-boost-revenue-forecasts-predicts-112-billion-cash-burn-2030?utm%5Fcampaign=article%5Femail&utm%5Fcontent=article-16638&utm%5Fmedium=email&utm%5Fsource=sg&rc=4lgoj7) about OpenAI’s financials from which this particular excerpt stood out to me: > “OpenAI has told investors the costs of running its AI models, a process known as inference, quadrupled in 2025\. As a result, the company’s adjusted gross margin—defined as revenue minus the costs of inference—**fell to 33% from 40% the year prior**. That’s lower than the gross margin expectations of 46% it had set for itself for 2025.” The Information mentioned OpenAI suggested the falling margin was largely due to “*the company having to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models*”. While this supports Dario Amodei’s point about “hellish demand prediction” between inference and training, it is hard not to think that finding the right balance can remain just as challenging which may not alleviate the gross margin challenge. Of course, OpenAI then promised with a straight face that they expect inference margin to increase to 52%-67% in the next 5 years. Let me emphasize this point. As of today, **OpenAI (or anyone else) alone does not decide what their gross margins will be this year, or the next…let alone in 2030**. Apart from the challenges related to “hellish demand prediction”, the model race itself remains quite competitive which is the **primary driving force** for gross margin today. Ultimately, improving gross margin is primarily a function of pricing power and better cost structure. Even if you made perfect demand prediction for inference, your more well-funded competitors can keep their pricing low if they have numerous other sources of internally generated cashflows. This may of course force OpenAI and Anthropic to lower their API prices much faster than they would prefer. The problem exacerbates even further when such well-funded competitor e.g. Alphabet also has a better cost structure since they’re much more vertically integrated in their model development stack than OpenAI and Anthropic are. I will also note that I’m discussing the gross margin question by accepting the labs’ lenient definition of gross margin which ignores the cost of training. The Information also mentioned OpenAI “*plans to increase its training costs to $32 billion this year and $65 billion next year, or about *$44 billion more than previously expected*.*” Again, the cost of training is also not quite under OpenAI or Anthropic’s control. If their competitors can invest $100 Billion on training and performance of future models largely follow scaling law, OpenAI and Anthropic must be willing to invest in training in the same vicinity to stay competitive. I think you can appreciate why I find the end state economics of AI labs rather a bit…hazy! One of the strangest counters I have heard from my readers in the last few days is why I am assuming OpenAI and Anthropic cannot make the math work at scale. The reason it is perplexing to me is I am certainly not making the case that it is an impossible scenario. If either or both of these companies are able to find a technological breakthroughs that are time consuming or very hard to replicate by their more well-funded competitors, I can imagine how they may ace the economics questions in the future. However, it is very important to **acknowledge** that the investors of these labs have largely decided that is **exactly** going to be the case which explains their recent funding rounds at massive valuations. All I am pointing out is that there are compelling reasons to be skeptical that the economics question will be solved in the private AI labs favor. Again, if your argument is only a handful of companies will survive this race in 3-5 years and hence the oligopolistic industry will have attractive terminal margins, I have already addressed this point in my “[lipstick in frontier AI Pigs](https://www.mbi-deepdives.com/lipstick-on-frontier-ai-pigs/)” piece. I’m not saying that it’s impossible, but I’m trying to drive the point home that we have far more limited predictability of such “rational” margin structure in the long-term and hence, there are wider possible scenarios than are being appreciated in private AI labs valuation. In most scenarios of AI labs future, one industry whose long-term economics is potentially more vulnerable than currently appreciated by investors is hyperscalers. While we can come up with endless scenarios in our head, I will outline three specific scenarios behind paywall to help you appreciate the range of potential outcomes here. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Walmart’s Trillion-Dollar Mojo URL: https://www.mbi-deepdives.com/wmt4q25/ Last updated: 2026-02-21T17:05:23.000Z Walmart was the most recent member in the [trillion dollar club](https://www.libertyrpf.com/p/trillion-dollar-club-with-mostly?ref=mbi-deepdives.com) companies. After coming to IPO in 1970, it took them almost 54 years to reach half a trillion dollar of Enterprise Value (EV). The next $500 Billion took just two years! As they say, the first $500 Billion is the difficult one. ![chart](https://substackcdn.com/image/fetch/$s_!7o5S!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcfb1af0c-46f2-4838-82b4-f391129e9870_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I initially found it a bit strange that investors enthusiasm about Walmart somehow coincided with two of the most consequential technological revolution. Walmart’s EV peaked in December 1999 and it took them almost two decades to surpass such EV again in 2019! Perhaps it’s less of a surprise considering Walmart’s deep penchant to stay at the cutting edge of retail technology even from the 1980s. A key reason Walmart could run circles around other retailers in the 80s and 90s was its technological lead over others. Some excerpt from this [podcast](https://www.stepchange.show/data-centers?ref=mbi-deepdives.com) would help you appreciate that history: > “…you're at the register at your neighborhood Walmart location: you scan the toothpaste, the barcode beeps. Within seconds, a satellite dish behind the store sends that transaction to the sky and over to Bentonville, Arkansas, where they're hosting their mainframe computer to record the sale. > > A few minutes later, a massive data warehouse would update how many tubes of toothpaste you had just bought. Then, perhaps before the end of the day, Procter & Gamble's factory would receive an update that they needed to make more toothpaste. > > This was the cutting edge of retail in the late 80s, and Walmart made a massive decision to take this a step further, truly driving innovation across the retail industry. They invested $24 million to build their own private satellite network linking all Walmart stores to headquarters. This was fairly unprecedented at the time…**It was the largest private satellite network**. > > So any single event that happened within the Walmart ecosystem rode on this private network, enabling them to mine their data in a way that was unheard of before. > > By mining their sales data – which could now be collected in real time across all Walmart stores over their private satellite network – they discovered that when hurricanes approached, the sale of Pop-Tarts increased 7x over their normal rate.” While Amazon took the lead on defining the future of retail post-2000, Walmart has gradually found its mojo and now increasingly seem very well-positioned to stay at the frontier of retail for the decade(s) ahead. I will discuss more behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Figma 4Q'25 Update URL: https://www.mbi-deepdives.com/fig4q25/ Last updated: 2026-02-20T16:03:07.000Z While Figma’s stock reacted positively to its 4Q’25 earnings, the stock is still down \~33% YTD. Considering the SaaS-ocalypse backdrop, Figma posted pretty strong numbers. Its revenue growth **accelerated** from 38% YoY in 3Q’25 to 40% in 4Q’25. ![](https://substackcdn.com/image/fetch/$s_!-d8u!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fec856668-f11e-41bd-b16a-789298990d3a_850x537.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Figma’s Net Dollar Retention (NDR) for customers who spent at least $10k ARR was its **highest** ever in the last 10 quarters: **136%!** Its Gross Retention Ratio was **97%**, so clearly customers are not leaving Figma to “vibe design” yet. ![](https://substackcdn.com/image/fetch/$s_!ygl1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbfb24d02-c1d7-4429-8fbd-c96974a52cca_912x538.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Figma’s incremental customer count with $10k and $100k ARR were also its highest ever in 4Q’25\. Even the customers with more than $1 million ARR reached 67, growing by 68% YoY. ![](https://substackcdn.com/image/fetch/$s_!EYaj!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0f99ed04-f6e8-423d-9871-4755a295a2f1_1524x100.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) It’s not just 4Q’25, Figma’s revenue outlook also exuded plenty of confidence in their business model. Their 1Q’26 revenue outlook implied 38% growth YoY and FY’26 guidance at the mid-point implied 30% growth. For context, sell-side estimates only implied \~24% growth in 2026 although I do suspect buy-side estimates was higher than that. In fact, Figma’s commentary suggests that their guide is likely conservative and it won’t surprise me at all if their actual revenue growth in 2026 turns out to be close to mid-30s. I will discuss more about that as well as some key takeaways from the call behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Booking vs Airbnb's 4Q'25, Booking's Response to Existential Question URL: https://www.mbi-deepdives.com/bkng4q25/ Last updated: 2026-02-19T15:29:04.000Z Now that both Booking and Airbnb reported its 4Q’25, I will first compare and contrast their respective quarter through different KPIs. Then I will discuss some key takeaways from the call behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Some Portfolio Changes URL: https://www.mbi-deepdives.com/some-portfolio-changes-2/ Last updated: 2026-02-20T21:10:39.000Z Perhaps it’s hardly a surprise if you read yesterday’s piece “[Lipstick on Frontier AI Pigs](https://www.mbi-deepdives.com/lipstick-on-frontier-ai-pigs/)”, but I made some changes to my portfolio yesterday which I will elaborate behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Lipstick on Frontier AI "Pigs" URL: https://www.mbi-deepdives.com/lipstick-on-frontier-ai-pigs/ Last updated: 2026-02-17T19:37:11.000Z Dario Amodei recently did an [interview](https://www.dwarkesh.com/p/dario-amodei-2?ref=mbi-deepdives.com) with Dwarkesh Patel. While plenty of people found it rather reassuring for the “AI trade” to continue, I came away with much more consternation not only about Anthropic but also about anyone whose revenue or earnings is hinged on Anthropic spending ever increasing amount of compute hand over fist going forward. I will elaborate my concerns behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb 4Q'25 Update URL: https://www.mbi-deepdives.com/abnb4q25/ Last updated: 2026-02-13T16:31:14.000Z ***Programming Note***: I will be traveling with my family for the next three days. Hence, I will take some time off, but will be back on **Tuesday** **next week**. --- Earnings for most internet or SaaS stocks these days appear to be just another event for these stocks to go further down. Airbnb somehow managed to buck this trend. While consensus estimates for revenue were $2,711 Million and $2,528 Million respectively in 4Q’25 and 1'Q’26, Airbnb posted $2,778 Million revenue in 4Q’25 and guided $2,610 Million revenue (mid-point) for 1Q’26 which appears to be good enough for the stock to be up \~4% post-earnings (as of this writing). Airbnb’s acceleration in Gross Booking Value (GBV) continued in 4Q’25 and posted its **highest GBV growth in the last two years**. “Reserve Now, Pay Later” is proving to be a strong tailwind for GBV as it not only lengthened the booking lead time but also incentivized people to gravitate towards higher ADR homes. Airbnb also mentioned that guest travel insurance products (available in just 12 countries) grew by \~40% YoY. You will notice that despite such tailwind, Airbnb’s LTM take rate actually went down in 4Q’25\. The most significant factor distorting the take rate in 2025 was the acceleration of booking lead times. Airbnb records GBV at the time of booking, but recognizes revenue only upon check-in. Management noted a “continued lengthening of lead times” and an acceleration in nights booked for future travel (particularly for major 2026 events like the World Cup and Winter Olympics). When users book further in advance, the denominator (GBV) swells immediately, but the numerator (Revenue) is delayed. This mathematically suppresses the implied take rate in the current period, even if the underlying monetization potential of those bookings remains unchanged. In fact, Airbnb mentioned they expect Q1 take rates to be modestly higher YoY. ![](https://substackcdn.com/image/fetch/$s_!ZrSu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd508c54e-9e21-4dba-8b25-c6087f9fe1a8_1594x367.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I’ll dig into the quarter in more detail behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Regional mix shift** _This post is for paying subscribers only._ ### Shopify's Miraculous Momentum URL: https://www.mbi-deepdives.com/shop4q25/ Last updated: 2026-02-12T15:06:28.000Z One of the most impressive things about Shopify is their ability to grow GMV in tandem with Amazon in recent quarters. Shopify was able to maintain such admirable momentum in 4Q’25. ![](https://substackcdn.com/image/fetch/$s_!2Kd2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2dd8f6aa-d41a-4a52-9885-6706aaf4b495_1185x718.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) One of the key reasons for Shopify to be able to match Amazon’s growth is that their business model is inherently much more scalable globally than Amazon’s. Even though \~14% of US e-commerce now goes through Shopify’s platform, international is increasingly a very important growth driver for Shopify which is hard for Amazon to match. Just like in 3Q’25, almost half of their incremental GMV dollars came from outside the North America. Their GMV from European merchants actually increased by 45% (35% in FXN) in 4Q’25\. Their B2B GMV growth of 84% also was a tailwind. The new cohorts are actually performing even better than the earlier ones which is a terrific sign for the health of the platform. From the call: > In Q4, our growth was led **by the 2024 and 2025 cohorts**, which have proven to be larger and more productive than prior cohorts, outperforming older cohorts in GMV and revenue after similar periods of time on the platform. Quite remarkably, 4Q’25 GMV alone was higher than Shopify’s GMV back in entire 2020, a year which was massively benefitted by pandemic! Shopify’s operating performance, no matter whether you’re a bull or bear, seems almost bit of a miracle in the last five years. The fact that the stock is still mostly flat during this period tells all you need to know what an embarrassing bubble 2021 was for SaaS stocks (to be clear, embarrassing for investors, not for the management or the companies). ![](https://substackcdn.com/image/fetch/$s_!Lk7_!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F08d8539d-a55d-42e5-ae25-735aa131d16a_1084x609.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) This somewhat miraculous GMV growth in recent quarters was achieved while materially improving profitability. I will discuss more about their margins and key takeaways from the earnings call behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Spotify's Teachable Moments URL: https://www.mbi-deepdives.com/spot4q25/ Last updated: 2026-02-11T15:50:45.000Z Spotify launched its premium subscription plan back in 2009 and it took them nearly a decade to reach the milestone of 100 million premium subscribers back in 1Q’19\. It took them 16 quarters to add the next 100 million premium subscribers. As they are set to reach 300 million premium subscribers by 2Q’26, they are going to add the next 100 million premium subscribers **even faster**. Despite such growth, Spotify emphasized that only \~3.5% of the world currently subscribes to their service and even though they’re not a monopoly, they still see a path towards \~10-15% of world’s population as their premium subscribers which implies \~3-5x of their current subscriber base. ![](https://substackcdn.com/image/fetch/$s_!sFw3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff14e7730-af97-43b2-8a2d-96d8e12d57a1_1473x163.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Over the last few years, Spotify has successfully raised subscription prices in key markets while simultaneously reducing its effective content costs through the expansion of marketplace tools, which allows labels to accept lower royalty rates in exchange for algorithmic promotion. The reclassification of its Premium tier as a **"bundle"** (by adding audiobooks) likely also helped with their gross margin expansion. Even in 2023, Spotify’s gross margin in premium subscription hovered around 28-29%; in 4Q’25, Spotify reported its highest ever gross margin of 34.8%! Ultimately, aggregating demand is so powerful that even when you have a handful of suppliers (music labels), market power gradually shifts towards you over time. ![](https://substackcdn.com/image/fetch/$s_!QgHK!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff547e685-fbda-4417-a67d-56bd7335fd03_1434x820.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As I have covered before in “Music’s AI Mess” (see [**here**](https://www.mbi-deepdives.com/musics-ai-mess/), and [**here**](https://www.mbi-deepdives.com/musics-ai-mess-part-2/)), AI should be a further tailwind for demand aggregators such as Spotify. A Cambrian explosion of music may not be good news for labels given how Spotify shares the revenue based on labels’ listening share, this is business as usual for aggregators since they were already operating under such a world. As I have [mentioned](https://www.mbi-deepdives.com/bull%5Fcase%5Fai%5Fugc/) before, adding infinite content to another infinite content doesn’t really change the core “job-to-do” for these aggregators. It also helps that their core business model also happens to be the right business model in the age of AI. From Spotify’s 4Q’25 call: > “…new technology is seldom disruptive on its own. Significant disruption happens when new technologies enable new asymmetric business models. For example, this is what Spotify did to music downloads. This is what Uber did to taxi service. So the question everyone should be asking is, **does this evolution create new business models?** Or are we mostly just seeing new technologies? For example, in SaaS, there is currently a lot of fear that the perceived business model will be challenged by more outcome-based models, which is reasonable. > > However, in the consumer space that we are in, **we believe the dominant business model will continue to be ads plus subscription, both places where Spotify excels. This puts Spotify in an outstanding position because we already have the right business model**.” Of course, AI can also help companies such as Spotify a great deal in accelerating new product features. Coding is clearly one of the best product market fits for AI models, and Spotify shared some neat examples in how things are changing in how they’re doing their work: > “As a concrete example, an engineer at Spotify on their morning commute from Slack on their cell phone can tell Claude to fix a bug or add a new feature to the iOS app. And once Claude finishes that work, the engineer then gets a new version of the app pushed to them on Slack on their phone so that he can then merge it to production, **all before they even arrive at the office**. We call this system internally Honk, and we've been told by key AI partners that our work here is industry-leading. Now as Daniel said in his remarks, we are a tech company, and we consider ourselves the **R&D department for the music industry.**” I particularly liked this below example to illustrate the sea change that has happened over the last year: > “A year ago, **only a very senior developer at Spotify could answer the question of what was the first track I've listened to on Spotify. Please take the ones I listened to more than 3 times and match them against what was popular at the time. Now anyone can do that, just using English**.” You will notice an ever rising number of people who write code for a living are getting spooked by model’s increasing capabilities, especially post-Opus 4.6 and Codex 5.3\. How can you not be spooked if you thought something only a brilliant coder could do just 12 months ago can now be trivially done by even a random tech luddite? The very nature of these jobs is likely going through a rapid transformation which is understandably unsettling to the people near the blast area. If you are yet to experience such a feeling of unease, you may not have to wait for too long. Having experienced such unease last year myself, I can tell you that such feeling tends to be somewhat ephemeral. After embracing the pace of change, you can usually find lot more to get done than you could ever before. And despite the pace of change, Spotify convincingly argues that their position in the broader music value chain should be stable and even more relevant now: > “I would say that I think it’s obvious to everyone, but over Christmas, Christmas this year was an event, a singular event in terms of AI productivity. Certainly, I spent my entire vacation coding rather than being on holiday, and I think most people in tech did. A lot of things happened in December, including Opus 4.5 coming out Claude Code. And we crossed the threshold where things just started working. So a lot has actually changed very recently. And when I speak to my most senior engineers, the best developers we had, they actually say that they haven’t written a single line of code since December. They actually only generate code and supervise it. So it is a big change. It is real and it’s happening fast. Now as I said, we’ve discussed for the last at least 1.5 years, not if this should happen, but when it should happen. And we’ve started building systems like Honk that I explained for this type of world. So I feel very well positioned to capture this. But I want to be clear, this is the beginning of the change. There is going to have to be a lot of change in these tech companies if you want to stay competitive. > > …When the Internet came along, everyone thought that we would all have our own web pages. What actually happened was there ended up being very few web pages. In times of lower friction, things actually tend to aggregate, not disaggregate. That’s the opportunity we see in front of us. I think companies such as us are simply going to produce massively more software. Up until our limiting factor is actually the amount of change that consumers are comfortable with.” I personally consider Spotify one of the companies that taught me more than most companies I studied in my investing career. I will elaborate on such teachable moments behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Zuckerberg's Lens URL: https://www.mbi-deepdives.com/zuckerbergs-lens/ Last updated: 2026-02-10T15:21:39.000Z 10 years ago, Alphabet was the largest company among today’s big tech companies. (I’m going to ignore Tesla and Nvidia in this discussion as they are more recent additions to this list). Remarkably, these five companies were clustered quite close to each other back in 2016 as their Enterprise Value (EV) were as follows: Alphabet $413 Billion Apple $374 Billion Microsoft $332 Billion Meta $265 Billion Amazon $225 Billion Today, Meta has the lowest EV among this group, and even the company that is just above them i.e. Amazon is worth \~$600 Billion more than Meta. Both Apple and Alphabet are worth more than twice that of Meta. ![chart](https://substackcdn.com/image/fetch/$s_!6hBF!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F88fe7aba-f8ff-46fe-a3ce-45f56e9c0e27_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Of course, a lot of the increase in EV was driven by multiple expansion for some of these companies. Apple, for example, was trading at **5x LTM** EV/EBIT multiple (no typo) back in 2016\. Today, Apple, Amazon, and Alphabet all trade at high 20s LTM EBIT multiple whereas Microsoft and Meta trade at low 20s multiple. ![chart](https://substackcdn.com/image/fetch/$s_!5CpP!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0a1bb001-376d-4467-aac7-5a94340565f0_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) While this decadal stat almost makes Meta look like a laggard, I think there is a very good chance that Zuckerberg is positioning Meta to re-order this ranking differently within the next 3-5 years which I will elaborate behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Amazon: 2026 Update URL: https://www.mbi-deepdives.com/amazon-2026-update/ Last updated: 2026-02-09T17:36:31.000Z As I do it every year, following Amazon’s 10-K disclosure, I have updated my Amazon model. I’ll expand on the model and elaborate my thoughts behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Never Sell: Episode 13-Big Tech Earnings URL: https://www.mbi-deepdives.com/never-sell-13/ Last updated: 2026-02-08T13:38:03.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode on Big Tech earnings. One of the key topics that I highlighted is why I think the street is likely mismodeling Meta. We also touched on Microsoft and Amazon’s current predicament. You can listen to it here: [Spotify](https://open.spotify.com/episode/4U80uTFWsUBT99wmnV0O2J?si=rDu8kj0fRtuXvpgG8GhzjQ&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/big-tech-earnings/id1786912203?i=1000748706224&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=Al5R2bRgJE8&ref=mbi-deepdives.com), [RSS feed](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com) As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. I will publish my annual model update on Amazon tomorrow. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) ### Some thoughts from last week URL: https://www.mbi-deepdives.com/some-thoughts-from-last-week/ Last updated: 2026-03-04T15:39:24.000Z Phew! What a week. I will share some thoughts behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Amazon 4Q'25 Update URL: https://www.mbi-deepdives.com/amzn4q25/ Last updated: 2026-02-06T15:30:34.000Z After Meta and Alphabet’s eyebrow raising capex plan for 2026, Amazon came up with the biggest number: **$200 Billion**! Market basically said this in response: ![It's gonna be weeks of this, isn't it?](https://substackcdn.com/image/fetch/$s_!nCF4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd7c2d3f-145d-44b7-a61a-cc8962d0af6c_1200x675.jpeg "It's gonna be weeks of this, isn't it?") While market may be currently in the mood of hating large capex numbers, I remain quite optimistic that it is indeed the right strategy long-term and I would do pretty much the same if I were in Amazon’s position. Here are my highlights from yesterday’s call. **Revenue** Apart from Amazon’s 1P retail business, everything else continues to grow at double digit rate. AWS, in fact, grew the fastest in 13 quarters. I’ll discuss more about AWS later, but let’s talk more about Amazon ex-AWS first. ![](https://substackcdn.com/image/fetch/$s_!vbDk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc66352f6-0f0f-4853-b52b-450f5446b168_1446x265.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** As I repeatedly mention in almost every quarter, Amazon retail business continues to be somewhat underestimated. Investors have complained a lot about retail overbuild during the Covid period, but notice what happened to the margin now. Even though North America segment was largely \~3% to \~5% operating margin business pre-Covid, it just reported 9% operating margin. In fact, Amazon had several one-off charges in Q4: a) $1.1 Billion tax dispute which affected the international segment, b) $730 Million severance costs which impacted all three segments, and c) $610 million asset impairments for their store business affecting North America segment. When you adjust for these one-off charges, the adjusted operating margin in North America was likely \~10%. Similarly, even though it may appear international segment’s operating margin went down significantly in 4Q’25, adjusting those one-off items would actually lead to margin expansion YoY. ![](https://substackcdn.com/image/fetch/$s_!tmvd!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F75effcfb-b21b-4b11-a032-84351865a3c6_1117x672.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Fulfillment+ Shipping** If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q’22\. Since then, unit growth has largely been faster than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. ![](https://substackcdn.com/image/fetch/$s_!7EpH!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa6961b9c-0f29-4341-98d4-af8990587f16_1236x549.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Every Amazon earnings call makes me increasingly wary of owning any other physical retailer. It is quite clear that this company will not stop investing as long as they sniff a corner of profit pool in retail. Of course, not everyone will be vanquished in this competition against Amazon, but when it’s all said and done, it may just be a handful of retailers that can survive and compound their business over time. Amazon used to [**mention**](https://www.mbi-deepdives.com/groceries%5F2/) that their grocery business was “over $100 Billion” even in early 2025\. Yesterday they mentioned it’s now “over $150 Billion”. They are planning to launch more than 100 new Whole Foods stores in the next few years to serve the groceries market better. For context, that is almost \~20% expansion of Whole Foods current footprint. Some key quotes from the call yesterday on their retail business: > Last year, U.S. Prime members received over 8 billion items the same or next day, **up more than 30% year-over-year**, with **groceries and everyday essentials making up half of the total items.** For the third year in a row, globally, in 2025, we achieved both our **fastest ever delivery speeds for Prime members while also reducing our cost to serve.** > > In the U.S., **we delivered nearly 70% more items same day than the year before**. We also continue increasing speed for rural customers with nearly 2x more average monthly customers in rural areas receiving same-day delivery year-over-year. Same-day is our fastest-growing delivery offering and nearly 100 million customers used it last year in the U.S. > > our recently launched feature add to Delivery, which enables Prime members in the U.S. to add items to their upcoming Amazon deliveries with just one tap without going through checkout again or paying additional shipping fees. **Just six months after launch, ad to delivery already makes up about 10% of all Prime volume fulfilled through the Amazon network each week**. While this seems simple on the surface, this feature is supported by a lot of invention where we need to figure out in real time and with incredibly low latency, what items among Amazon's hundreds of millions of products are available to add to a customer's upcoming deliveries, surface them, find a way to include in their packages and deliver within the same customer promise. There wasn’t much incremental about Rufus or their strategy around agentic commerce. Amazon has a strong hand here. They already own half the demand in e-commerce in the US. They’re now expecting Rufus will allow them to get a pie of the other half as well as it can buy for you from other websites **beyond Amazon**. They appear quite willing to partner with other chat bots at the “**right value exchange**”. Basically, as long as you give Amazon a lion’s share of the profit pool, your chat bot can crawl to their marketplace. **Advertising** A big driver for retail profitability is advertising. Given Amazon ads are perhaps more of a competitor to Google than Meta, I think it’s interesting to track how Amazon is gaining share here. While Amazon ads is still just \~26% the size of Google Advertising revenue, Amazon ads incremental revenue as a percentage of Google advertising incremental revenue was 41% in 4Q’25 (vs 38% in 4Q’24 and 40% in 3Q’25). While sponsored ads on the marketplace likely remains the majority of advertising revenue, Prime Video ads is gaining momentum. From the call: > We saw continued growth in Prime Video ads, which is now available in 16 countries and **is contributing meaningfully to our revenue growth**. Prime Video has an average ad-supported audience of **315 million viewers globally, up from 200 million in early 2024**. ![](https://substackcdn.com/image/fetch/$s_!E2mZ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2f0a7c25-deab-40a1-a1eb-685a3bbfd202_966x535.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss AWS and the rest of this update behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **AWS** Okay, now let’s talk about AWS. _This post is for paying subscribers only._ ### Alphabet 4Q'25 Update URL: https://www.mbi-deepdives.com/goog4q25/ Last updated: 2026-02-05T17:55:05.000Z Alphabet came to this earnings with elevated expectations from investors. It delivered. The stock is down a bit post-earnings despite that, but it’s hard to imagine Alphabet shareholders not having a good sleep last night. Here are my highlights from the earnings. **Revenue** Who amongst us really thought search revenue would be growing at high-teen rate three years after the asteroid named ChatGPT hit Google? While search is still the lynchpin in Alphabet, amazingly Google now has **14 different products** that exceeds $1 Billion revenue! While YouTube’s ad growth may look quite tepid, migration from ad-supported YouTube to YouTube Premium really makes the comparison increasingly misleading. YouTube is now $60 Billion business across ads and subscriptions. After lagging behind Azure’s growth in recent quarters, Google Cloud is finally leading the growth among the hyperscalers with a material acceleration. ![](https://substackcdn.com/image/fetch/$s_!d5Ha!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7cd6d83-2b33-4fa3-9d71-6907520edf48_1770x364.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Take a look at growth rates by segment since 4Q’22: ![](https://substackcdn.com/image/fetch/$s_!jKUM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb5e49961-e1d7-44d1-aec8-f3bb3c136d9e_1708x336.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Google Services continues to post >40% operating margin. The fact that **for the last eleven quarters their incremental margins have been consistently above 50%** (it was 62.0% in 4Q’25) indicates that we may still have room for further margin expansion. The jaw dropping moment from the earnings release was, however, Google Cloud’s margin. Even in 2Q’20, Google Cloud had -47.4% operating margin. Yes, that’s negative. In 4Q’25, they posted +30.1% operating margin! I don’t think I have ever seen such a margin turnaround in my investing career. From a distant unprofitable laggard in hyperscaler race to reach a touching distance of the operating margin of AWS while simultaneously accelerating revenue growth to \~50% is nothing short of mind boggling. ![](https://substackcdn.com/image/fetch/$s_!i3A2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2c51f064-20eb-4125-9e99-a528e2dde2fe_1797x369.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss the rest of this update behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Insurance Brokers 4Q'25 Update URL: https://www.mbi-deepdives.com/insurance-brokers-4q25-update/ Last updated: 2026-02-04T15:33:03.000Z After years of steep increases, property insurance rates, especially in E&S (Excess & Surplus) and Reinsurance are falling due to a quiet hurricane season and an influx of capital. The insurance brokers, however, noted a split pricing environment. While property rates are softening (a headwind for organic growth), Casualty rates remain "hard" and still increasing. Overall, the brokers more or less grew their organic revenue around Mid Single Digit (MSD) rate with the exception of Brown & Brown (BRO) who had a difficult comparison from flood claims processing revenue in 4Q’25\. I would also highlight that AJG earlier disclosed in their Investor Day that they would change the way they calculate their organic growth. As per their earlier definition, 4Q’25 organic growth would be \~2%. Although I didn’t go through MRSH or AON’s transcripts in detail, they do seem to have done better than the mid-sized players such as AJG and BRO. Even though you will notice current organic growth rate is well below these companies’ last 5 years average, the last 5-year average is influenced heavily by the hard rates market seen in 2021-23 period which makes this average somewhat unrepresentative of the long-term normalized organic growth rate. ![](https://substackcdn.com/image/fetch/$s_!U8Nl!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc1d1f10b-bdce-49bc-a2b4-c5e6e4f4ddfe_1521x133.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) More importantly, now that valuations has come down in insurance brokerage industry, this is creating an excellent reinvestment opportunity for these brokers to deploy capital. For much of the last three years, these brokers used to trade at mid to high teens. Today, multiples have come down to low teens. Arthur J. Gallagher (AJG) management mentioned during their call that they cannot remember the last time a seller asked 16x multiple for their assets. They further mentioned that for tuck-in acquisitions, multiples have come down to \~10x, and \~12-13x for larger deals. Nonetheless, AJG thinks there is a slight slowdown in deals. From AJG call: > “I also believe there's maybe a little systemic slowdown here as sellers come to the realization that maybe valuations are coming down, and it takes a while for people to realize there's a new norm in that.” ![chart](https://substackcdn.com/image/fetch/$s_!E7Ug!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc84086b6-878c-4277-9206-f9b3cee61b28_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) With \~60k insurance brokers around the world, there is ample reinvestment runway for the public insurance brokers. Of course, serial acquirers with MSD organic growth profile with lots of tuck-in acquisitions every year and occasional large deals have been a bit out of favor in this current market. One key difference between companies such as Constellation Software (CSU) and insurance brokers is there is likely less existential concerns around the core business due to AI. Having said that, investors are increasingly trying to be “creative” in finding AI risks everywhere, so don’t be surprised of a renewed disintermediation concern for brokers. In fact, AJG management was asked about AI risks in the call. This is what AJG management said in response: > “I think that we stand to benefit from that because if there is a product that can be sold with AI, we will likely be the ones that can put it out there and then -- and put it out there, have AI, get it to the point of sale and then have a producer do the final piece of it. The second thing is, remember, onboarding a customer is different than servicing a customer too. So it might tell you what the best product to buy is. Let’s just say that works. But then you got to service that policy. > > And then you’ve got to handle the claims on it. And then you’ve got to interface with the carrier. I doubt that there’s an AI tool that will sell a policy to somebody who will have a serious issue in their bar or restaurant and then AI is going to tell AIG to pay the claim. It just doesn’t work that way. There’s going to have to be an adjuster there. There’s going to have to be a counselor called the producer that helps them understand how -- claims a bit. > > Maybe they can put some policies on the books, but the service load that will come along with that. Now on the other hand, we see AI as being a terrific benefit for us to get better, faster at lower cost. We have spent 20 years working on standardizing our processes, centralizing them in our low-cost centers of excellence, driving the quality very high. AI is going to help us automate a lot of that. So the service layer, I think that we’re going to be able to deliver a better, faster and less expensive service offering.” There are couple of things I would highlight here. Since everyone else will also have AI, I think all the benefits that AJG management is highlighting will mostly be competed away and will likely just accrue to their customers. However, the primary barrier for AI to do brokers’ job may be around how intensely regulated the industry is and the undertaking of re-architecting the entire system for AI to service the entire lifecycle of the insurance policy is indeed likely to be order of magnitude more difficult than building an analytics tool for tech-savvy developers. One interesting incident that I would highlight from the insurance brokers’ calls is Howden’s alleged talent raid in some of these public insurance brokers. BRO was particularly affected by this. From BRO’s call: > “As of today, approximately 275 of our former teammates have joined this start-up, taking with them customers currently representing known annual revenues of $23 million. As we've done in the past, we will defend our rights in court and already have obtained an injunction.” There were some colorful details in BRO’s lawsuit against Howden (you can read the filing [here](https://www.insurancejournal.com/app/uploads/2025/12/brown-n-brown-lawsuit-complaint.pdf?ref=mbi-deepdives.com)). I am not a lawyer, but some evidence seems quite damning for these employees who seem to have deliberately siphoned customers away from BRO to Howden. Nonetheless, it doesn’t reflect great on BRO’s culture that a couple hundred employees chose to do such deliberate attempt to hurt the company. While it’s certainly not ideal, one slightly comforting thing is Howden appeared to have used similar tactic to hire people from Marsh, and Willis Towers Watson. Despite these idiosyncrasies, the broader thesis around insurance broker remains more or less the same. These companies will likely keep growing organically at MSD level and deploy earnings into acquiring dozens of tuck-in acquisitions every year while doing a large deal every once in a while. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I made some slight changes in the portfolio yesterday which I will discuss behind the paywall. _This post is for paying subscribers only._ ### The Fog of AV Economics URL: https://www.mbi-deepdives.com/the-fog-of-av-economics/ Last updated: 2026-02-03T15:21:59.000Z Thomas Reiner yesterday [wrote](https://www.platformaeronaut.com/p/av-rideshare-fleet-sizing-why-the?ref=mbi-deepdives.com) a thoughtful piece about a key question about AV ridesharing: what should be the optimal size of AV fleet in a given area? Should it try to cater to peak demand or average demand? Reiner makes the case that “the profit-maximizing fleet size is **just shy of peak demand**”. This particular paragraph stood out to me (emphasis mine): > “The central tradeoff in AV fleet sizing is between utilization and pricing power and **it is fundamentally non-linear**. At small fleet sizes adding vehicles increases utilization and captures unmet demand, but eventually once the fleet approaches full demand coverage incremental vehicles no longer create meaningful new trips, they simply reallocate demand across more idle assets. **At that point utilization falls faster than volume rises, as incremental trips are spread across an expanding base of idle vehicles rather than concentrated on high-value hours.** > > Unlike human drivers, **AV fleets do not naturally exit the market when prices fall, so oversupply persists and price becomes the only clearing mechanism. This is also why cost optimization alone cannot fix an oversized fleet. Once pricing power is lost, no reasonable reduction in depreciation, charging, or maintenance can restore profitability.”** When supply is tight, you can charge for speed and certainty. When supply is abundant, everyone is fast, and the only remaining lever is price. Given this dynamic, there are some interesting implications, especially in light of Tesla’s comments in their 4Q’25 call which I will discuss behind paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Betting Big on Meta URL: https://www.mbi-deepdives.com/meta2026/ Last updated: 2026-02-02T15:56:33.000Z I won’t burry the lede here. After updating my model on Meta following 4Q’25 earnings, I have become materially more bullish on Meta and decided to add to the stock to make it my largest position. I will elaborate more behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Trillion Dollar Club URL: https://www.mbi-deepdives.com/trillion-dollar-club/ Last updated: 2026-01-31T14:01:14.000Z A couple of weeks ago, I recorded a podcast with my friend [Liberty](https://www.libertyrpf.com/?ref=mbi-deepdives.com) to discuss all the companies in “trillion dollar club”. Enjoy! --- From [**Liberty’s Highlights**](http://Trillion Dollar Club): “At nearly two hours, this one is massive, but you know what they say: *Too much of a good thing can be wonderful!* My friend [MBI](https://www.mbi-deepdives.com/) and I sat down to do something that sounds simple but turned out to be a seminar. In 2018, Apple became the first $1T company. Fast forward to now, and we’re living in the era of the **Trillion Dollar Club**, a small group of companies that collectively represent an almost comical amount of market cap (**Nvidia**, **Apple**, **Google**, **Microsoft**, **Amazon**, **TSMC**, **Meta**, **Broadcom**, and **Tesla**). We went through all of them one by one and shared our honest thoughts, bouncing between the big picture and small details that may turn out to be crucial. If the market is a card game, we wanted to figure out which hands we like best. ♥️♦️♣️♠️ We touch on many interesting ideas that help us better understand the forces at play for Big Tech these days. Early on, we dive into the “compute theory of everything.” MBI found this [1998 Hans Moravec paper](https://jetpress.org/volume1/moravec.htm?ref=mbi-deepdives.com) with a sentence so good that he repeats it to himself like a mantra on his daily walks! 🚶‍♂️ (*gotta get those 10k steps!* 👣) Among many other things, we discuss: - Jensen’s “circular financing” strategy, and why it might actually be brilliant 🔄 - East Coast vs. West Coast investing philosophy. - Why the job of the analyst is getting *harder*, not easier. - How academic papers create trillions in value (cat paper, “Attention is All You Need,” Chinchilla). - Apple as “Switzerland,” and their risky bet that we’re at the hardware end-state 📱 - Whether Google has the best hand but keeps getting in its own way, and DeepMind as Google’s “reverse takeover” and saving grace - Why Microsoft seemed to have everything and somehow ended up in the doghouse 💾 - TSMC as the true choke point, the bottom of the inverted pyramid, and why MBI is Zen about geopolitical risk 🧘‍♂️ - Meta’s confusing inability to reach the frontier despite checking every box, and what are the necessary ingredients for a frontier AI lab? 🧑‍🍳🍜 - Tesla as the ultimate “West Coast philosophy” stock and Musk’s loss of interest in EVs. - Broadcom’s IP blocks + custom ASIC enablement + networking… with the risk of hyperscaler disintermediation. - Amazon’s retail business being underestimated and Walmart as a sneakily smart company 🛒 - A tribute to Buffett and Munger, where our emotions come out a bit. - Which CEOs are overrated and underrated? 👍👎 We close with the hardest question: if you could own only one of these companies for the next 20 years, which would it be? We both landed on the same answer. Listen to the end to find out why 🎧 ## **🎧 Listen on Spotify** If you prefer to listen on **Spotify**, here’s the feed: - [Liberty’s Highlights podcast feed on Spotify](https://open.spotify.com/episode/1Mqr4tQf8YaELoVj6lYFso?ref=mbi-deepdives.com) ## **🎧 Listen on Apple Podcasts** Here’s the podcast feed on **Apple Podcasts**: - [Liberty’s Highlights podcast on Apple](https://podcasts.apple.com/ca/podcast/trillion-dollar-club-with-mostly-borrowed-ideas-mbi/id1616884594?i=1000747362769&ref=mbi-deepdives.com) ## **📺 Watch on YouTube** ### Microsoft in the "Dance Floor" URL: https://www.mbi-deepdives.com/msft_dance/ Last updated: 2026-01-30T15:35:51.000Z If you need any reminder how challenging active investing is, it can be humbling to know that if you bought Microsoft the day ChatGPT was launched, you would not only underperform QQQ but also underperform Alphabet (the very company the consensus assumed to be the primary “victim” of ChatGPT) by a whopping 140 percentage points in the next three years. ![chart](https://substackcdn.com/image/fetch/$s_!3tHq!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe3f0b2a9-cbd3-42c9-a79a-bfa74cc3543d_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) After wanting to make Google dance, it is Satya’s Microsoft that is now in the dance floor! ![List : Michael Scott Quote - Well, well, well, how the turntables. (Photos Collection)](https://substackcdn.com/image/fetch/$s_!SFco!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9488b8f2-5cee-4bc5-adee-050bc1086ca9_736x414.jpeg "List : Michael Scott Quote - Well, well, well, how the turntables. (Photos Collection)") I briefly owned Microsoft when I bought the stock during the “liberation day” carnage, but sold the stock as the valuation seemed quite full by [August](https://www.mbi-deepdives.com/msft2025/) 2025\. Interestingly, when I sold the stock, the consensus estimates for 2030 revenue and operating income were $501 Billion and $239 Billion respectively. As of today, consensus estimates for 2030 since then actually moved up to $579 Billion and $279 Billion respectively. As a result, while out-year estimates for operating income increased by \~17% in the last 6 months, the stock went down \~20% which makes this an interesting set up. In fact, after trading comfortably above Alphabet and Meta’s multiples almost consistently since ChatGPT’s launch, the company is now trading now at a material discount to Alphabet and even a slight discount to Meta. So, I went through their earnings call yesterday to understand the concerns a bit more closely which I will discuss behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!7ztJ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9d823228-adcc-40e0-bcca-aec579c207ad_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta 4Q'25 Update URL: https://www.mbi-deepdives.com/meta4q25/ Last updated: 2026-01-29T19:28:00.000Z Admittedly, I was confused looking at the initial reaction of Meta’s earnings when the stock went down. Mr. Market promptly corrected its “mistake” later. One of the reasons I think some may have reacted negatively is the ever burgeoning capex outlook by Meta which inevitably exerts pressure on FCF. I suspect a good fraction of investor base has a very mechanical understanding of FCF i.e. they understand that more FCF is better, but what many seem to find relatively harder to internalize is if a company has rich reinvestment opportunity, the optimal FCF should be as low as possible, especially if you have a strong balance sheet. This shouldn’t be controversial, but I suspect it is among many investors. It is, however, fair to debate whether the reinvestment opportunity is attractive or whether there is a rapidly diminishing return as the scale of capex becomes larger. While it’s reasonable to wonder about these questions, I come away from 4Q’25 call feeling quite optimistic about Meta’s reinvestment runway. Let’s get into 4Q’25 earnings highlights. **Engagement and Advertising** Historically, Meta’s ad prices and ad impressions tend to go in opposite directions. While in 3Q’25 both ad prices and number of ad impressions grew simultaneously, 4Q’25 saw the familiar pattern again: accelerating ad impression but lower ad prices. Investors tend to prefer accelerating ad impression as it implies further headroom for ad prices to catch up over time. Ad revenue grew higher than 20% in **every** region. These are quite incredible growth numbers at this scale. ![](https://substackcdn.com/image/fetch/$s_!0bcL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4a95e413-a35a-4026-adda-b79d7e3d3010_1416x816.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While some wonder whether AI slop will hurt UGC content platforms such as Meta, I recently [**explained**](https://www.mbi-deepdives.com/bull%5Fcase%5Fai%5Fugc/) why I not only **reject** such hypothesis but also investors are likely still underestimating the true bull case of AI assisted or generated content on UGC platforms. This call essentially validated some of the points I mentioned. The fact that IG Reels watch time was +30% in 4Q’25 YoY in the US whereas Netflix watch time was +2% in 2H’25 YoY (I know apples and oranges, but directionally likely accurate) should give anyone at least a pause about the hypothesis that AI will incentivize users to find refuge in Netflix content. As I have mentioned in my piece, the core “job to do” for Meta is to surface engaging content in front of you. If you don’t seem to like AI content, the algorithm simply won’t show it to you. Some key excerpts that highlighted the engagement improvement in Meta’s apps: > we’re continuing to drive incremental engagement from ranking and product improvements. **Instagram Reels had another strong quarter with watch time up more than 30% year-over-year in the U.S**. Engagement is benefiting from several optimizations we made to improve the quality of recommendations including simplifying our ranking architecture to enable more efficient model scaling. This unlocks the ability for our systems to consider longer interaction histories to better identify a person’s interests. > > On Facebook, **video time continued to grow double digits year-over-year in the U.S**., and we’re seeing strong results from our ranking and product efforts on both feed and video surfaces. **The optimizations we made in Q4 drove a 7% lift in views of organic feed and video posts on Facebook, resulting in the largest quarterly revenue impact from Facebook product launches in the past two years**. > > We’re continuing to increase the freshness and originality of content recommendations as well. **On Facebook, our systems are surfacing over 25% more reels published that day than the prior quarter. On Instagram, we grew the prevalence of original content in the U.S. by 10 percentage points in Q4 with 75% of recommendations now coming from original posts.** Threads is also seeing strong momentum again, benefiting from recommendation improvements. **The optimizations we made in Q4 drove a 20% lift in threads time spent.** > > Turning to 2026\. We see a lot of opportunity to drive additional gains. This includes scaling the complexity and amount of training data we use in our models while continuing to make our systems more responsive to people’s real-time interest. **We’re also focused on incorporating LLMs to understand content more deeply across our platform, which will enable more personalized recommendations**. > > One area we’re already seeing promise is with AI dubbing of videos into local languages. We are now supporting 9 different languages with hundreds of millions of people watching AI translated videos every day. **This is already driving incremental time spent on Instagram**, and we plan to launch support for more languages over the course of this year. > > Nearly 10% of the reels people view each day are now created in our Edits app, **almost tripling from last quarter.** **Within Meta AI, the number of daily actives generating media tripled year-over-year in Q4**. Okay, so engagement is great as AI is surfacing the relevant content in front of you. At the same time, users are using AI to generate more content. Win-win for Meta. Now let’s look at the other side of the equation: advertisers which I found, frankly speaking, to be even more bullish for Meta. I will discuss rest of this piece behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Texas Instruments 4Q'25 Update URL: https://www.mbi-deepdives.com/txn4q25/ Last updated: 2026-01-28T15:32:28.000Z I owned Texas Instruments (TXN) for almost 18 months and then managed to sell what appears to be a local bottom in hindsight. Although I did [**re-allocate**](https://www.mbi-deepdives.com/digital-advertising-industry-snapshot-portfolio-change/) my proceeds from TXN sale to Alphabet and Meta equally, Texas Instruments performed better than either of those companies. The chart below doesn’t even include the earnings pop of TXN; it went up \~30% since I sold in November. As you can see, markets are a humbling machine! ![chart](https://substackcdn.com/image/fetch/$s_!5GqW!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F851ca9f0-ec34-468b-b18e-44297377aa73_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Let me dig into 4Q’25 earnings of Texas Instruments (TI). The big reveal in this call was TI’s revenue from data center which includes sectors related to data center compute, data center networking, rack power, and thermal management. TI’s [website](https://www.ti.com/applications/data-center/overview.html?ref=mbi-deepdives.com#aem-application-Browse) has a good interactive section to help you understand their offerings in data center. ![](https://substackcdn.com/image/fetch/$s_!AMS_!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb107b71a-8912-4bc7-85b4-0e6e0beca607_1324x805.png) Source: TI Website Back in September, TI’s CEO [mentioned](https://www.mbi-deepdives.com/texas-instruments-data-center-and-robotics-opportunity/) that data center can eventually be \~20% of their business which implied \~$4-5 Billion revenue opportunity for them. They ended Q4 with $450 million run-rate which was \~10% of their 4Q’25 revenue. Data center has been growing for 7 consecutive quarters and looking at their estimate of eventual size of data center business, it is likely to continue to grow in the coming years. Management also clarified they are not chasing a single “socket”. Instead, they are targeting the “thousands of different parts” in a server rack, including power management, thermal sensing, and signal chain components. Beyond data centers, all the major segments were down sequentially: industrials down MSD sequentially, automotive down LSD sequentially, and personal electronics down mid-teens sequentially. ![](https://substackcdn.com/image/fetch/$s_!4IqQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffd65468c-eb0e-4004-af1f-137e580b35aa_1939x259.png) Source: MBI Deep Dives, Company earnings transcript Management re-iterated that while they believe the recovery has legs in industrials, their visibility is limited beyond a quarter. From the call: > “…even if I go back to Q3, which was, I think, the highest industrial quarter for 2025, it was still about 25% from the previous peaks in year 2022, right? So I do believe that the secular growth continues in industrial. We are looking at end equipment and generation to generation, we see just more content growth per system. So I expect industrial to establish new highs in the future. This is why I talked in the last quarter about maybe a more moderate recovery, especially on the industrial side. > > I do want to remind us all that earlier in 2025, I would say, the first half of ‘25, we saw a pickup of industrial and then it kind of came down. We want to see how sustainable this wake up in orders is.” In terms of margin, gross margin was down both QoQ and YoY; however, operating margin improved by \~86 bps YoY in 4Q’25\. As you can see below, operating margins of mid-30s in 4Q’25 are still closer to trough margins of this cycle last year, and still materially lower than peak margins of \~50% in 2021-22 period. Even if you consider the average of peak and trough as more “normalized” margins, there should be decent margin expansion over the course of the cyclical recovery. ![](https://substackcdn.com/image/fetch/$s_!k-NB!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6c1c5fb7-cbe1-4eed-9b8c-a66e2adc103e_1884x294.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While TI’s guide for Q1 tends to go down sequentially due to seasonal factors, they guided for $4.32 billion to $4.68 billion revenue in 1Q’26\. If they meet the high end of the guide, TI’s revenue will grow at +5.8% QoQ. More importantly, while some analysts speculated whether such unusual QoQ growth is pricing related, TI dispelled it during the call. From the call: > “we have 80,000 products. Prices always go up and down. But for the company, the overall price effect like-for-like in ‘25, we expected it to be low single digits down. Now we finished ‘25, it was exactly there. When you say low single digits, think about 2% or 3% down. > > That’s my assumption for 2026\. That’s what we expect the market conditions to be. If anything changes with pricing, as you know, we’ll see -- of course, TI will respond. But right now, that’s our assumption moving forward. That’s why I was so convinced that the Q1, I think we have a little sequential growth there. **It’s not due to pricing.** > > Actually, **usually, Q1 pricing usually goes down a little bit because of yearly negotiations.** That’s usually what we see in Q1. > > we are just seeing growing orders, and it behaved the same through the quarter. I can’t speculate on what, but I do know the industrial market, there needs to be a correction. And the second point is data center is now a bigger part of our business, so it starts to move the numbers for us, right? This is a market that is now growing every quarter, and it’s not insignificant. So I think that also helps to change the guide compared to previous years.” While TI received some flak for building too much inventory in the last couple of years, this cyclical recovery can now turn into a boon for them, especially if the pace of recovery surprises their competitors. So far, that’s not been the case given the weak recovery. From the call: > Our lead times are very competitive, unchanged, I think, on average, **below 13 weeks, many of our parts at 6 weeks.** Part of our ambition and objective as we prepare to the next cycle, was to be -- to be able to maintain very competitive lead times across the cycle. S**o far this cycle has not been very tough to meet, right, as I said, been a slow recovery, but our lead times continue to stay very, very competitive, probably the lowest in the industry,** and our inventory position allows us to support customers. Overall, the tone in the call was quite positive even though I don’t think a whole lot has changed in the last three months. The stock currently trades at \~31x NTM EBIT. Given the cyclical recovery and margin expansion opportunity, I can see why the stock would trade at optically elevated multiple. Nonetheless, even if you adjust for normalized margins, the stock trades around mid-20s NTM EBIT multiple which is fine, but not quite exciting. ![chart](https://substackcdn.com/image/fetch/$s_!W9vo!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F41c95f4a-b939-4319-ba31-ca3a27ea9e8d_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### A "Sneaky" Bull Case for Tesla's Operating Performance? URL: https://www.mbi-deepdives.com/tsla_sneaky_bull_case/ Last updated: 2026-01-27T15:54:01.000Z There is perhaps hardly any stock out there which bamboozle most value investors more than Tesla. Given Tesla’s eye-watering multiple, I have seen people often mentioning Mag7 multiples, **excluding** Tesla. Even if I look at consensus EPS estimates in 2029, the stock currently trades at \~55x of 2029 EPS. So, clearly Tesla bulls must believe consensus is off the mark significantly from the future they envision for the company. ![chart](https://substackcdn.com/image/fetch/$s_!6vZh!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F92bf68b7-3b16-4d86-abbc-e17624700dd7_2400x1240.png "chart") Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Indeed, I have recently thought about a "sneaky" bull case for Tesla’s operating performance that is likely not embedded in consensus estimates. I will expand on my thoughts behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 66 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Figma: The Design OS at the AI Crossroads URL: https://www.mbi-deepdives.com/fig/ Last updated: 2026-01-26T14:07:13.000Z *You can listen to the Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- Back in 2011**,** Dylan Field, then a 18-year old intern at the social magazine startup Flipboard, found himself wrestling with clunky design software. He was frustrated by [Adobe Fireworks](https://en.wikipedia.org/wiki/Adobe%5FFireworks?ref=mbi-deepdives.com) and wondered aloud why designing on a computer couldn’t be as seamless and collaborative as writing in Google Docs. This simple question perhaps planted a seed on his mind. Field wasn’t even a Design major (he was a computer science student at Brown University), but he sensed an opportunity. If documents and spreadsheets had moved to the cloud, why not design? While at Brown, Field met Evan Wallace who was apparently nick named “CJ” (stands for “Computer Jesus”). Wallace was teaching assistant (TA) in one of the courses Field took and they got to know each other. Wallace interned at Pixar and Microsoft and while he did get return offers, he also realized that he wouldn’t really feel “challenged” in those companies. So, when Field asked Wallace whether the “Computer Jesus” would like to start a company with him, Wallace was quite enthusiastic about that. The only problem is they still didn’t know what they would build together. Field’s grand idea was basically just build “something” with Wallace at this point. That conviction proved stronger than any fully baked business plan. With backing from the Thiel Fellowship and a seed check from Index Ventures whose partner Danny Rimer had been tipped off by LinkedIn’s CEO [saying](https://www.indexventures.com/perspectives/figma-goes-public-thirteen-unforgettable-years-with-dylan-field/?ref=mbi-deepdives.com), “We have this incredible intern I’m trying to hold onto, but the kid wants to start his own company. It sounds interesting. You should talk to him.” Once Field and Wallace narrowed their business idea to an online, collaborative design tool, turning that into reality proved to be an uphill technical battle. For the first few years, the team operated in stealth mode, quietly building and rebuilding their prototype while many wondered what they were up to. It even became a running joke at Index venture, as recalled in this [blog](https://www.indexventures.com/perspectives/figma-goes-public-thirteen-unforgettable-years-with-dylan-field/?ref=mbi-deepdives.com): “When’s the product launching?” And every time, Field would respond enthusiastically, “Next year! We want to get it just right.” Behind the scenes, there were moments of doubt and even downright weird detours. In one comical episode, Field temporarily convinced himself that the killer application of their [WebGL](https://en.wikipedia.org/wiki/WebGL?ref=mbi-deepdives.com) technology was… a **meme generator**. For five strange days, the duo actually explored making a tool to create internet memes, until they [realized](https://wondery.com/shows/how-i-built-this/episode/10386-figma-dylan-field/?ref=mbi-deepdives.com) this was **“**the darkest week of Figma**”** and promptly steered back to more serious, productive endeavor. Through countless whiteboard sessions and late-night coding marathons, they zeroed in on the biggest pain point designers faced: **collaboration**. At the time, design files were passed around like homework assignments i.e. emailed, versioned, and often out of sync. Field and Wallace envisioned a world where **multiple people could jump into the same design file at once**, **edit in real-time, and leave behind the mess of exporting and emailing.** By late 2015, after three long years, they felt ready to show their work to the world. Figma’s public debut came with a blog post in December 2015 titled **“**[**Design: Meet the Internet**](https://www.figma.com/blog/design-meet-the-internet/?ref=mbi-deepdives.com)**”:** > “Ever since Writely (now called Google Docs) launched ten years ago, I’ve believed that all software should be online, real-time and collaborative. Creative tools haven’t made the leap because the browser has not been powerful enough. Now, with WebGL, everything has changed. > > I first glimpsed the power of WebGL in April 2011\. My classmate, Evan Wallace, had just returned to the Brown CS lab after a weekend hackathon. He pulled me aside and showed me how he had re-implemented a server side image processing API in WebGL. “You know,” said Evan, “we could use this thing to build creative tools in the browser.” Designers, despite being stuck using desktop-bound tools from the Adobe suite, weren’t quite banging the door for something radically new. Figma’s beta product (released as a free-to-use tool in early 2016) initially received, to put it mildly, a mixed reaction. Field recalled in “[How I built this](https://podcasts.apple.com/us/podcast/figma-dylan-field/id1150510297?i=1000711868022&ref=mbi-deepdives.com)” podcast: > “…a lot of designers at that point were coming from an agency culture where you know you go explore, you come up with a few solutions for the client, you present three solutions, and then you have a grand reveal of like here is the big amazing thing that you should obviously do. And that's just not the way that product development should work on a team internally. Instead, you know, there's a wide range of things you consider. You collaboratively need to work through them. But people were so used to that agency method of working that some of the initial comments in the launch were stuff like, **"If this is the future of design, I'm changing careers." This is 2015, I mean that this idea that design being a collaborative thing was *not* a thing necessarily. Like one comment was "A camel is a horse designed by committee.”** > > I think that was maybe the the mainstream view of the design culture then. But there were also a lot of people that signed up for the waitlist. And then we started to every week let more people off the waitlist into the product and we saw right away that people were using it. Even without everything we knew that they needed. And then every time we added additional functionality to the product, we saw that conversion of people going from "Okay, you're off the waitlist" to "You're actually using it," it would go up.” After designers understood the essence of Figma, designers around the globe began to adopt it; finally, designers got a graphics editor that felt as fluid as a web app and as social as a Google Doc. More than half of Figma’s users would soon come from outside the US, validating Field’s hunch that **great design is a** **global need**. By the time Figma introduced a paid professional plan, it wasn’t a hard sell. If anything, some of their largest users were begging Figma to mature their enterprise sales motion. Claire Butler, Figma’s first GTM hire, [recalled](https://www.lennysnewsletter.com/p/an-inside-look-at-figmas-unique-bottom?utm%5Fsource=publication-search) in a podcast how Microsoft almost forced Figma to grow some enterprise sales muscle: > “…we’ve never gone through Microsoft procurement, Microsoft security. It just started popping up throughout the organization. And we have these really cool Node graphs that show this too, where you’d have these little pockets of people and then it would jump to another, like they’d have one more collaborator and then jump from another pocket. **There were these really cool maps of how that spread within the organization.** And eventually, we got to the point where that was a very comprehensive Node graph that had this massive thing of all of these people from Microsoft using the product. > > But still, it was only on credit cards. I don’t think we even had an enterprise product at this point, and so there was no salesperson for them to talk to. And Microsoft was like, “Wait a minute. We need to organize this. We need security. We need account management. We need procurement involved.” > > They wanted to pay for it and they wanted us to have this enterprise product, because they had these requirements and they wanted to have a better control over it, because it was just popping up within the organization without their control. > > And so that’s probably a good example of what that looked like as this bottoms in motion just spread to a really large organization.” Always a good sign when your customers want you to get things in order just so that they can use your product! As you almost certainly know by now, Figma’s rise did not go unnoticed by the industry’s incumbent behemoth: Adobe. In September 2022, Adobe made a eyebrow raising \~$20 billion bid to acquire Figma. However, over the next year, the acquisition ran into regulatory hurdle. The “innovation” of just licensing the product and “acquihire” a bunch of top leaders wasn’t quite normalized in Silicon Valley yet. So, by [December 2023](https://www.figma.com/blog/figma-adobe-abandon-proposed-merger/?ref=mbi-deepdives.com), the deal fell through, forcing Adobe to abandon the takeover. Field [encapsulated](https://www.youtube.com/watch?v=tPoH3WuqgPQ&ref=mbi-deepdives.com) the state of his mind during this saga in a podcast with Jack Altman: > “At the start, it was kind of like, ‘Oh, 95% certainty it’ll go through, no worries.’ But every time we checked in, the certainty went down. Eventually, at the end, it was maybe a 5% chance. > > Somewhere in that arc, we realized quickly: this is not a sure thing. Keep the foot on the gas, keep building. That’s the best outcome whether we join Adobe or we’re independent. You can’t be in this constant state of ‘we’re so back’ and ‘it’s so over.’ Rapid cycling through that makes your brain fall apart and turn to mush, in my opinion. > > So, the word of the year for me was **equanimity**. I think I said equanimity more times that year than I ever will for the rest of my life. It’s just: how do you find peace in every option and know that everything’s going to be okay? We’re building a great company for amazing customers and we have a lot of stuff to do. Let’s go do it. > > Whatever happens, we’re going to put our best foot forward. We signed a contract with Adobe, and we’re going to make sure that we do everything we can to close this. If it doesn’t work out, we’ll have a great independent path. > > When you get to the end of it, the relief the team felt at knowing an answer—and having that superpositional state collapse into ‘Okay, we know we’re going to be independent’—that was real relief.” The entire saga turned out to be much more ironic for Adobe than Figma. I [wrote](https://www.mbi-deepdives.com/adbe2q23/) in a piece back in June, 2023: > “…Figma raised $333 Mn over seven funding rounds. If the deal with Adobe doesn't go through, Figma will receive $1 Bn breakup fee. So, **effectively Adobe shareholders will pay \~3x money that Figma raised over its entire life to receive exactly 0% ownership of Figma**. To put it differently, Adobe shareholders will be giving $1 Bn charity to Figma to potentially compete much more directly with Adobe in the medium term.” Indeed, rather than seeing it as a setback, Figma’s leadership rallied. The company refocused on its independence with renewed energy, rolling out an ambitious slate of new features and even entire new products (to be discussed later) in the following year. By mid-2025, Figma had channeled that momentum into an **IPO.** While Adobe offered \~$20 Billion to Figma back in 2022, Figma IPO-ed at [$19 Billion](https://www.wsj.com/finance/stocks/figma-shares-jump-over-200-in-stock-market-debut-605f6212?ref=mbi-deepdives.com) valuation in July, 2025\. Following the IPO, the stock reached a stratospheric valuation of as high as \~$70 Billion at the peak, but then went onto plummet to only \~$13 Billion current Enterprise Value (EV). ![chart](https://substackcdn.com/image/fetch/$s_!v8RT!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F369c072d-defd-4fc5-9a09-14beabd691af_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In the next section, I’ll discuss Figma’s products as well as key metrics related to the business. Then I will look into its competitive dynamics, management incentives, and dig into valuation assumptions. Subscribe to read the rest of the Deep Dive as well as any of the [**65 Deep Dives**](https://www.mbi-deepdives.com/models/) published earlier. --- [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### The Social Media Ban Wagon URL: https://www.mbi-deepdives.com/the-social-media-ban-wagon/ Last updated: 2026-01-25T15:52:31.000Z ***A programming note***: I hope to publish my Deep Dive on Figma tomorrow. Next month, following the big tech’s earnings I will review Meta, Amazon, and Alphabet’s 10-Ks and update my models. As it’s been the case for the last couple of years, I won’t publish a new company Deep Dive in February, but will work on a new Deep Dive in March. --- In “[Zero to One](https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296?ref=mbi-deepdives.com)”, Peter Thiel mentioned a question that he likes to ask: “What important truth do very few people agree with you on?” By now, perhaps everyone reading this has heard this question, and yet, this question likely remains surprisingly difficult to answer for everyone. Not only the question demands something that is already difficult enough: you have to be contrarian **AND** right, it also has to be something that is deemed to be “important truth”. For example, while applying for Thiel Fellowship, Dylan Field, one of the co-founders of Figma, [wrote](https://aletteraday.substack.com/p/f5-college-admissions-essays) that he finds chocolates repulsive. That is indeed something very few people would agree with him (I certainly wouldn’t), but I’m not sure that quite qualifies as “important truth”. What is my answer to this question? **We have far from conclusive evidence whether social media is bad for kids**. This may seem tame at first glance, but think about the statement which I would consider opposite of what I’m saying: “the effect of social media on kids is decisively bad”. If I pick random 100 people from any part of the Western hemisphere and poll how many people would agree with that “opposite” statement, I believe every time supermajority of people would agree with this “opposite” statement. So my position here is clearly quite contrarian even if it may sound a bit evasive at first. Like everyone else, I have read fair bit of essays and think pieces that make the case that we need to save our kids from social media. The conventional wisdom of how the mental health crisis in the West can be directly tied to the rise of social media has led to [ban](https://www.bbc.com/news/articles/cwyp9d3ddqyo?ref=mbi-deepdives.com) social media in Australia for under 16 years old. Given the near consensus that I see among almost everyone (even among many Meta shareholders I personally know), it would hardly surprise me if such ban will eventually spread towards other countries in the West, if not the entire world over time. A consequential policy imposed on a section of society who really have no say on such policy despite very scant evidence to support the ban is why I think it qualifies as “important truth”. One of the things that convinced me to dig more into this is when I [listened](https://open.spotify.com/episode/0SPB4z431KhDM8JxaMZbXD?si=Lj3UWFzVSh6jOYmQRDMTyQ&nd=1&dlsi=be012c085a484057&ref=mbi-deepdives.com) to Tyler Cowen explaining his own skepticism on this topic. Some excerpt from that conversation: > “There’s a lot of meta studies that look at the correlation between individual happiness and social media usage, and those correlations are very, very weak. I think they’re stronger for girls in the age range of something like 12 to 14…But to blame societal pessimism on social media usage, I don’t see that supported by the data. > > What I observe in broader history is you have very large social mood swings, such as before World War I that do not seem to have obvious causes. So if today’s does not have an obvious cause, of course that’s a puzzle, but in a way, it’s the kind of puzzle we should have been expecting. > > I see a lot of people my age or older who watch a great deal of cable TV and they become negative from that. So, no, I’m not convinced it’s social media. I don’t see that in the data” The unfortunate reality is while adults have reached the consensus that social media is bad for kids and are enacting laws based on such belief, the kids have, by and large, [positive perception](https://www.pewresearch.org/internet/2022/11/16/connection-creativity-and-drama-teen-life-on-social-media-in-2022/?ref=mbi-deepdives.com) about social media. ![](https://substackcdn.com/image/fetch/$s_!gSlk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb48daa33-c1a3-4a89-bbd3-6a32bc1945db_693x1168.png) You may argue that of course, a few decades ago teens also likely had “positive perception” about smoking, but that doesn’t mean we should have let them smoke. Well, unlike smoking, research shows that zero use of social media is not quite optimal for kids. Just a couple of weeks ago, JAMA [published](https://jamanetwork.com/journals/jamapediatrics/article-abstract/2843720?ref=mbi-deepdives.com) a research after studying 100,991 Australian adolescents (grades 4-12) for three years and came to the following conclusion: > “A U-shaped association emerged where **moderate social media use was associated with the best well-being outcomes**, **while both no use and highest use were associated with poorer well-being.** For girls, moderate use became most favorable from middle adolescence onward, while for boys, **no use became increasingly problematic from mid adolescence**, **exceeding risks of high use by late adolescence.** > > Social media’s association with adolescent well-being is **complex and nonlinear, suggesting that both abstinence and excessive use can be problematic depending on developmental stage and sex**.” I am not holding my breath that Australian government will promptly change their mind and cancel their ban based on these findings. Remember what Tyler Cowen said about how the data barely shows anything about the impact of social media on kids’ mental health? This [Substack](https://mikemales.substack.com/p/researchers-agree-on-a-solid-consensus) named “Mike’s Substack” looked at some of these studies and carefully jotted down their findings: > “…social media’s association with teenagers’ mental health) is various levels of nothing. > The standard Cohen’s d-statistic (a common measure of “effect size”) shows: > [**Ferguson et al**](https://www.christopherjferguson.com/Like%20This%20Meta.pdf?ref=mbi-deepdives.com)**’s two analyses: d < 0.08\. Zero effect.** > [**Rausch/Haidt**](https://www.afterbabel.com/p/fundamental-flaws-part-2?ref=mbi-deepdives.com)**’s reanalysis: maximum d = 0.17 to 0.20\. Trivial.** > [**Twenge/Haidt**](https://www.sciencedirect.com/science/article/pii/S0001691822000270?ref=mbi-deepdives.com)**’s estimate for girls: maximum d = 0.20\. Trivial.** > [**Stein**](https://shoresofacademia.substack.com/p/some-notes-on-two-community-notes)**’s reanalysis: maximum d = 0.20\. Trivial.** > [**Jané’s**](https://matthewbjane.github.io/blog-posts/blog-post-6.html?ref=mbi-deepdives.com) **initial re-analysis: d < 0.09, nothing.”** While it’s much easier to rally around the big, bad social media companies, it can be quite uncomfortable when we try to see eye-to-eye to the variables that do show strong association with the falling mental health of kids. From the same [Substack](https://mikemales.substack.com/p/researchers-agree-on-a-solid-consensus): > “Compare the nothing-values found for social media effects with the powerful d-values from the 2023 CDC survey for the effects of troubled parenting on teenagers: > **Teen’s depression and parent-inflicted emotional abuse: d = 0.88, very strong** > **Teens’ depression and parents’ depression: d = 0.73, strong** > **Teens’ depression and parents’ domestic violence: d = 0.66, strong.”** While mental health can be challenging to standardize just by surveying people, nothing is more black and white than looking at suicide rates to gauge the sign of mental health of the population at large. The rising teen suicide rate in 2010s, which coincided with the rising penetration of social media, seems to bolster the critics of social media at first glance, but again, this misses important drivers not named “social media companies”. From a different piece by the Mike’s [Substack](https://substack.com/@mikemales/p-184471246?ref=mbi-deepdives.com) > “Teen suicide rates rose in tandem with parents’ suicide rates from 2007 to 2017, then both leveled off and fell from 2017 through 2024\. That teen suicide rates have been falling for 7 years during the height of the social media era wasn’t supposed to happen, so of course it is being ignored. > > In the early 1990s, parent-age suicide rates were just 23% higher than teen rates, but by 2024 had risen to 60% higher. But because in Haidt’s, Twenge’s, and authorities’ fantasy, only teens commit suicide, never grownups, these crucial trends offering insights into real causes and prevention are being swept aside.” ![](https://substackcdn.com/image/fetch/$s_!xhZy!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6307ecaf-80d7-4abd-b1b6-ff5a9658694e_1135x820.png) Image Source: [Mike’s Substack](https://substack.com/@mikemales/p-184471246?ref=mbi-deepdives.com) I read that paragraph and looked at the chart, and I chuckled to myself “don’t give them ideas. Maybe they’ll try to ban social media for adults as well”! Thankfully, unlike kids, we can vote. I’m actually surprised that my seemingly tame position would be considered contrarian given even the American Psychological Association (APA) [says](https://www.apa.org/topics/social-media-internet/health-advisory-adolescent-social-media-use?ref=mbi-deepdives.com) the following: > “**Using social media is not inherently beneficial or harmful to young people**. Adolescents’ lives online both reflect and impact their **offline lives**. In most cases, **the effects of social media are dependent on adolescents’ own personal and psychological characteristics and social circumstances**—intersecting with the specific content, features, or functions that are afforded within many social media platforms. Given the consensus, banning social media for kids would make us likely temporarily feel good about ourselves as society, but don’t be surprised if the problems remain weirdly persistent. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Some charts that caught my attention URL: https://www.mbi-deepdives.com/some-charts-that-caught-my-attention/ Last updated: 2026-01-24T16:04:56.000Z a16z recently published their “[State of Markets](https://docs.google.com/presentation/d/e/2PACX-1vQXsMMv5ZCWm77za7oXJcz1X-Th5Mz15g5nYBxbUjnomStVcjn8lXPjE5LzAlvc%5Fhg4yHKgwASWLo5a/pub?slide=id.g3b6e2578ab2%5F8%5F4858&ref=mbi-deepdives.com)” presentation. I will share some of the charts from the presentation that caught my attention. First of all, I do want to highlight a key limitation of a16z’s data; they mention that the presentation only includes data voluntarily provided to a16z by certain companies, including both portfolio companies and companies in which a16z has not invested. If things are not going well in your private companies, I can imagine simply not responding to a16z’s request for data, especially if they hadn’t invested in your company. So, there may be a systemic upward bias in some of these numbers. Nevertheless, given the size of their AUM ([\~$90 Billion](https://finance.yahoo.com/news/andreessen-horowitz-raises-15-billion-130217803.html?guccounter=1&guce%5Freferrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce%5Freferrer%5Fsig=AQAAAAWBW1y2RZ9iumH1EEmYOCvrMb1Dk-XXWWUy9id2IK2nXx1PIZaekviRQhob-YJTj9sr%5FMjHzyxv58F2yxKOYmeXMiHcvkFKt78V8vrajlh4r5w8BEYe480tljaJzBSiqevoWB5sQPeznuy5YxBHhQsvaJtOjBLXQ%5FFEohhDerD4&ref=mbi-deepdives.com)), they tend to have a good grasp of where things are in the private market. The first chart that stood out to me was how revenue growth in private companies accelerated in 2025\. However, for the top-tier companies (i.e. 75th and 90th percentile companies), margins appeared to have worsened significantly in 2025\. While revenue growth accelerated from 97% to 119% for the 75th percentile and 231% to 250% for the 90th, their “[Rule of 40](https://en.wikipedia.org/wiki/Rule%5Fof%5F40?ref=mbi-deepdives.com)” scores actually declined (dropping from 51% to 47% and 171% to 133%, respectively). Since the Rule of 40 is the sum of growth and margin, a falling score amidst rising growth suggests that margins are plummeting; specifically, the implied margins for the 90th percentile drop from -60% in 2024 to -117% in 2025, indicating these companies are burning more cash to fuel that extra growth. Given this dynamic, the economics of incremental growth remains an open question. ![](https://substackcdn.com/image/fetch/$s_!MIeU!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F36de7c34-aee3-449a-944d-df49baeebbb5_1782x789.png) One counterintuitive finding, especially given the narrative that AI companies should be hyper-efficient "software that writes software", was that the median non-AI company actually had higher ARR per FTE (full-time employee) compared to an AI company. However, as the AI companies become larger, they do become substantially higher ARR/FTE vs non-AI companies. It is also good to see that non-AI and AI companies have similar gross margin although I do wonder if I’m looking at apple-to-apple comparison of gross margins given AI companies are quite [notorious](https://www.theinformation.com/newsletters/ai-agenda/helping-perplexitys-60-gross-profit-margin?rc=4lgoj7&ref=mbi-deepdives.com) in defining “gross margin” in a very generous way. ![](https://substackcdn.com/image/fetch/$s_!d5cI!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0d6cd476-18e6-403e-954f-01162c70fd82_2389x673.png) It also stood out to me that there is indeed a very strong product market fit in some domains. We all know about coding, but Harvey’s (the dominant AI for law firms) numbers also seem quite encouraging. Both queries per MAU and hours spent per MAU continue to go up. ![](https://substackcdn.com/image/fetch/$s_!RTuQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F16bf0f83-bb60-4ed4-aee4-6ca4dd1cd84c_1225x1174.png) a16z also had some discussions on public markets, and I did find it odd that they showed things such as “Price to Book” or “Debt to equity” charts while discussing publicly listed AI companies. a16z may need a couple of folks who have a better grasp on how public market investors look at balance sheets! A couple of charts that I thought were discouraging for AI bulls are retention rates for both Claude 4 and Gemini 2.5 Pro. Let me just say that if this were the retention rate for MBI Deep Dives, I would be currently looking for a job! The most worrying thing is the retention rate is deteriorating for the newer cohorts. You would imagine as the models get more and more capable, the newer cohorts would have higher retention, and yet we are seeing the exact opposite. a16z captioned this chart as “early users stick with models a bit longer, but later users quickly switch to the new hot thing”. If that’s true, it may mean the model’s ability to generate incremental revenue is directly tied to staying at the cutting edge and in that case, it is perhaps an indication that training costs may need to be included in “cost of revenue” and not shoved down to “R&D”. How we treat training cost may become a key tug of war between AI bulls and bears if the model race doesn’t show any sign of slowdown. ![](https://substackcdn.com/image/fetch/$s_!iOZo!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa099bd34-b625-4e23-984d-39e7d34b9334_550x1048.png) I was curious to see how the retention rate looks like for ChatGPT. An older a16z blog [post](https://a16z.com/ai-retention-benchmarks/?ref=mbi-deepdives.com) seems to indicate it settles around \~40% and even have a tendency to exhibit “smile curve”. Again, the “smile curve” may be an indication that cost for training should be considered as directly responsible for inducing churned users to come back. ![](https://substackcdn.com/image/fetch/$s_!RD7_!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2a58af1c-9811-473f-9869-ee3da01a7087_1051x552.png) The other quite discouraging chart was the massive scale of subsidies AI companies still seem to be offering their customers as free tokens continue to dwarf paid tokens. While total tokens consumed continue to go up unabated, the paid tokens processed still don’t seem to have any clear direction. As I have [mentioned](https://www.mbi-deepdives.com/chatgpts-value-capture-problem/) before, while I do not doubt for a moment that these AI models can be incredibly valuable for their users, we may be underestimating the challenges in capturing such value by the model developers given the model race remains fairly competitive. Ultimately, value capture is often a function of industry structure rather than your ability to generate value for your customers. ![](https://substackcdn.com/image/fetch/$s_!VGkE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fae1f5611-cdf0-4c63-b5f8-e59c26a79b72_871x1081.png) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Bull Case for AI Content on UGC Platforms URL: https://www.mbi-deepdives.com/bull_case_ai_ugc/ Last updated: 2026-01-23T21:49:11.000Z Yesterday, Ben Thompson [interviewed](https://stratechery.com/2026/an-interview-with-netflix-ceo-greg-peters-about-engagement-and-warner-bros/?ref=mbi-deepdives.com) Netflix co-CEO Greg Peters. I don’t cover Netflix, but this particular bit on “AI Slop” caught my attention: > **Ben Thompson: Is AI slop going to save you? If it overwhelms the UGC platforms and basically it’s like you’re a refuge, so this is all actual, real.** > > **Greg Peters:** I think it’s a credible — I don’t know if that’s the reality so I can’t say with certainty that’s where we’re going to land, but it’s a credible possibility, I think. Ben Thompson is basically alluding to the idea that abundance of AI content creates noise that can overwhelm the system. I understand the intuitive appeal of the argument because I myself did fall for it once. Back in [late 2023](https://x.com/borrowed%5Fideas/status/1732083595874009587?ref=mbi-deepdives.com) when I decided to sell Alphabet, I was worried that “AI Slop” may overwhelm the open web, making Google Search increasingly unreliable. The problem is I severely underestimated how spamming has been an endemic challenge in search from the early days of web and it is literally Google’s job to differentiate spamming from actual, high quality information. If two decades of sifting information in the internet doesn’t equip them to be prepared for AI generated low quality content, it is hard to see any other company who could do this job any better. Similarly, we already have infinite content, and multiplying infinite human generated content by infinite AI slop gets you…well, infinite total content. The primary “job to do” for the algorithm of UGC (User Generated Content) platforms has been to surface the content you or I **personally** will find intriguing. So, the core job isn’t really changing at all although the volume is likely going to accelerate. While I intuitively rejected this hypothesis that AI generated content may be bad for UGC platforms, even I underestimated some of the potential upsides these platforms may enjoy due to AI content. I will discuss a couple of papers behind the paywall which highlight such upsides. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Case for Giving Employees More Agency in Compensation URL: https://www.mbi-deepdives.com/agency_sbc/ Last updated: 2026-01-22T20:49:47.000Z Whenever software companies take a beating in the market, their elevated stock based compensation (SBC) becomes a talking point between bulls and bears. As the stocks tumble, some atypical investors start to look at SaaS companies and often get dissuaded pretty fast after taking a look at their excessive executive compensation. Software bulls, on the other hand, are pretty insistent that whenever people start talking about SBC too much, that’s your hint to get long of SaaS companies. Bulls do not live in the fantasy land that assumes SBC is not a real expense, but their point is a bit more nuanced: SBC alone is almost never a reason for a stock’s poor return, rather it’s usually some business or industry related questions that drive the stock. So, just as SaaS stocks took a beating after the ZIRP era ended and IT budget optimizations began in 2022, AI related concerns today are the primary reason for these stocks to fare poorly. Since these stocks aren’t really faltering due to excessive SBC, it is bit of a waste of time to overthink on SBC. If you’re looking to catch a random 50% pop on a revenue acceleration (especially more than what is implied in buy-side expectations), the arguments put forward by bulls are fine. But if you intend to be long-term shareholders of any of these SaaS companies, excessive SBC can absolutely be a huge drag for long-term compounding on these stocks. Apologies for stating something so obvious, but somehow I find this to be underappreciated among many investors. When I was going through Avenir’s excellent presentation “[The Future of SaaS-A Fork in the Road](https://docsend.com/view/iknzz8xwkzkjf88z?ref=mbi-deepdives.com)”, the following SBC chart stood out. It is indeed strange that the **SBC intensity** has increased by \~50-60% since 2018 even though if you time travel and asked investors what they expect to see SBC intensity in 2025, they would almost certainly say the opposite. Remember, the SBC number reported in Cash Flow Statement or Income Statement considered the stock price at issuance and since stocks have generally gone up (at least until recently for these companies), the actual compensation has gone up even more. Nothing is more unpleasant for public market investors when we observe these same companies at times get acquired by Private Equity investors and then quickly rationalize their expense base and at times re-IPO when they find enough gullible bunch to perhaps do the whole shenanigan all over again. While many investors seem to think the presence of PE bid is a good thing to protect your downside, I find it discouraging for long-term investors that PE can snatch assets from public market exactly when the stocks appear to be attractive. Given this dynamic, I have largely avoided SaaS companies led by non-founders. At least founders (especially with voting control) can keep the company public and presumably are deeply invested in making the business a success over the long-term. ![](https://substackcdn.com/image/fetch/$s_!VGJZ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82c4e90a-5789-4ed4-adfb-8829ec29c438_2121x921.png) Source: [Avenir](https://docsend.com/view/iknzz8xwkzkjf88z?ref=mbi-deepdives.com) I want to make it absolutely clear that I have nothing against SBC as a compensation tool. I also understand the reality of the market; when you are competing for talent against Mag7 which seemed to have more money than “God” (at least until the GPUs showed up), it is hard not to be generous in compensation although PE companies ability to rationalize expenses do make me question to what extent this itself is driving the compensation up. In any case, as investors, our job is to value the companies taking all of these into consideration. If company A is too generous whereas B is more prudent in doling out SBC, we should value B more than A and hopefully with the passage of time, company A will amend its compensation if they want to receive the valuation premium. Without rationalization of these expenses, these stocks may remain largely trading sardines that go nowhere in the long-term. Perhaps the worst offense some of these companies can commit is the inherent “heads I win, tails you lose” strategy in SBC. If you issue RSU at $10 and the stock becomes $100 in three years, everyone is understandably too happy to ask questions. But god forbid if you issue RSU at $100 but the stock then goes to $10 in three years, sometimes the companies want to make employees “whole” for the “lost” compensation. This is, of course, quite demoralizing for the investors who don’t have such a nice cushion in the downside. Take Shopify, for example. The stock traded at \~50x NTM revenue in the height of 2021 SaaS bubble, but then came back to earth to 4.5x NTM revenue at the bottom in 2022\. It is, of course, not helpful for employees to have so little clue how much money they actually are going to make in the coming year as such volatility seems to happen every few years these days. For the average employee in most of these SaaS companies, they have almost no agency in selecting the stock and cash mix, neither do they have much of an impact on the stock’s direction on a year-to-year basis. ![chart](https://substackcdn.com/image/fetch/$s_!cDJ6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fde658980-8fa1-4110-9252-a321f62a2f65_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) This isn’t some hypothetical concern; Tobi actually specifically mentioned why he decided to change Shopify’s compensation approach after experiencing the drawdown in 2022\. In his recent [podcast](https://www.youtube.com/watch?v=ZSM2uFnJ5bs&ref=mbi-deepdives.com) with David Senra, Tobi discussed this at length: > “one of the effects was—and this is right—people got stock options at the level up there the peak. They rightfully ask, “Well, how about it?” Even if they say, “Okay, I’m super game to get us back there,” I realized the psychological problem with everyone being so underwater. They have to spend years basically getting to the zero point where the stock options are worth even a penny again. > > Of course, they felt that the company gave them those things at the time. They felt, “I received them. **I was passive in this interaction. Therefore the company has some responsibility to make me whole here**.” > > I don’t necessarily see it this way because the risk was there. Other people voting is what causes the price; I didn’t set it. However, **I do concede the point that they had no agency in the process.** > > So, we rebuilt our compensation system to work completely the opposite of everyone else’s. **We give people their total value.** You go into an internal system, look at the number, and you get sliders. > > You can choose: How much do you want in stock? How much do you want in RSUs? How much do you want in cash? > > You can change that every quarter. You decide how you want your money. You can even use a tool to lock in the value of the stock you receive for three years. > > Sometimes “orthodoxy” can come back on the table if you get there from good principles. **You can actually join Shopify and get exactly the same stock option deal that you get at other places by using the tool we give you. But you have full agency and you make this choice.** > > The consequence of this compensation system is beautiful. First, it’s super predictable. Second, if you choose stock and it appreciates, you make more money. If the stock goes down, you get more stock units in your next quarter. It rebalances against the actual value every quarter. It works really well. > > It is very popular. It was hard to do because doing this worldwide, with people in many different countries, was **a legal nightmare due to rules regarding changing salaries**. We figured it all out. So we have a blueprint of how people want to do it. We’re proud of it because it’s a point of differentiation and we believe it works much better. > > Again, I want people at the company to feel like this is a company that never sleepwalks into anything. We are deliberate about things.” Giving some agency to the employees for choosing compensation feels like a basic common sense, but as they say common sense is hardly common. If I were running a SaaS company, I would probably contact Tobi or someone at Shopify to understand how to implement this in my company. At the very least, giving back the agency to employees will dissuade the employees from demanding the company make them whole when the stocks are near the bottom which, from the investors’ perspective, is clearly the worst possible time to issue too many stocks. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Great SaaS Debate URL: https://www.mbi-deepdives.com/the-great-saas-debate/ Last updated: 2026-01-21T15:39:21.000Z Software is having one of its periodic identity crises. Instead of writing yet another think piece on the future of SaaS, let me curate today a wide range of point of views that I came across on this debate which I think will help you think through the high pitched noise in the great SaaS debate. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Slow Singularity URL: https://www.mbi-deepdives.com/the-slow-singularity/ Last updated: 2026-01-20T14:59:33.000Z I often come across almost a steady stream of tweets from people mostly in tech (and sometimes in finance) that imply you have a very short period of time left to “escape the permanent underclass”. These tweets have a tinge of humor and warning associated with such an outlook of the future. So, how do you escape such “permanent underclass”? These tweets allude that you must amass a lot of capital and use that capital to buy equities into companies that will lead the age of machines to automate almost everything in sight. Stanford Professor Charles Jones and Christopher Tonetti’s recent [working paper](https://web.stanford.edu/~chadj/JonesTonetti%5FAutomation.pdf?ref=mbi-deepdives.com) titled, “*Past Automation and Future A.I.: How Weak Links Tame the Growth Explosion*” may pour some cold water on such simplistic framework of the future. Their research suggests that while AI will indeed drive accelerating growth, the transition may be far slower than the hype suggests. To understand why the future might be sluggish, the authors first had to decode the past. In a methodological twist that fits the subject matter perfectly, they employed OpenAI’s Deep Research to dig through economic history and construct a dataset of 150 essential tasks over the last century. This analysis revealed a counterintuitive "Zero Productivity Paradox" as switching a task from labor to capital contributes **zero** to Total Factor Productivity ([TFP](https://en.wikipedia.org/wiki/Total%5Ffactor%5Fproductivity?ref=mbi-deepdives.com)) growth at the exact moment it happens. This is because firms switch exactly when the costs are equal. The growth comes entirely from what happens **after** the switch: the task is now performed by a machine that improves exponentially faster than a human. They estimate that while machine productivity on automated tasks grows at a blistering 5% **annually**, human task efficiency grows at a meager 0.5% and in some sectors, human efficiency appears to be declining. To prove how vital this dynamic is, they calculated a "frozen" counterfactual: if we had stopped automating new tasks in 1950, but allowed computers to keep getting faster at the things they were already doing, US economic growth would have essentially flatlined for the last 70 years. We may be accustomed to growth given our own life experiences, but it is a good reminder that our life experiences are in stark anomaly to much of the history of human civilization which largely operated in stagnation for centuries. Without constantly pushing the technology frontier, growth may again become elusive. Growth basically requires a constant widening of the automated circle, and not just better machines inside the existing circle. The paper mentioned the economic irony we all intuitively know: abundance often leads to a loss of value. The following excerpts from the paper was particularly eye-opening: > …it is useful to consider the following question: We know that the share of factor income paid to capital has risen in recent years. What has happened to the share of factor income paid to computers? On the one hand, computers are everywhere. The number of transistors on a computer chip today is 50 million times more than it was in the 1970s. On the other hand, the price of compute has plummeted, suggesting that the marginal product of computing power has as well. Which effect dominates? > > During the dot-com era of the late 1990s, the factor share of income for computers rose from around 3.7 percent to 4.3 percent. But since 2000, the share has fallen substantially to 3.0 percent. In other words, even though the amount of computing power has exploded, we pay less of our GDP as a return to computers today than in the past. This is exactly what a production function with an elasticity of substitution less than one would predict. And this fact may itself be very informative about the effects of future A.I.-driven automation on the economy. ![](https://substackcdn.com/image/fetch/$s_!oN_s!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F65058fce-43d0-4b04-85f2-f5053ea06492_919x556.png) The same logic explains why the AI “singularity” is likely to be a slow burn rather than an explosion. The economy operates on a “weak link” principle. Production requires a chain of complementary tasks; you need high-speed coding, but you also need management, legal compliance, physical logistics etc. Because these tasks are interlinked, the economy is constrained by its slowest components. Even if AI automates cognitive tasks with infinite speed, total output remains bottlenecked by the essential tasks that still require slow-improving human labor. The math is quite sobering here even if you assume a fantastical automation future in cognitive tasks. From the paper (please note that sigma represents the **elasticity of substitution** between the different tasks required to produce the final output): > Given the advances in LLMs at coding, software is generally thought to the one of the first industries that will be largely automated by A.I. The share of software in GDP is around 2%. This means that automating all the tasks that are currently done by software with infinite productivity would only raise GDP by about 2% when σ = 1/2 > > More speculatively, transformative A.I. is thought to move on to automating all cognitive tasks — anything that could be done by a remote worker with a computer could potentially be done by an A.I. agent. Around two thirds of GDP is paid to labor. We consider what would happen if half of this were fully automated with infinite productivity. With σ = 1/2…gives a gain of…1.5; that is, infinitely automating 1/3 of GDP would only raise GDP by 50%. At some level, this number seems quite small; after all we have infinite productivity on a third of current GDP. However, the logic is again one of weak links. **The economy is constrained by the other two-thirds of tasks that are not automated**. But an alternative way to view the 50% gain is that if it were to occur over a decade, this would correspond to an increase in GDP growth of around 5% per year; over two decades it would correspond to more than 2pp of extra annual growth.” As long as there are essential tasks that only humans can do, our own limitations will act as a governor on the engine of growth. The singularity may be coming, but it may have to arrive at a human pace. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Incentives > Intelligence: The Real Barrier(s) to Agentic AI URL: https://www.mbi-deepdives.com/incentives-intelligence/ Last updated: 2026-01-19T15:14:57.000Z A few days ago, I came across this [reels](https://www.instagram.com/reel/DS0b82wEhLB/?ref=mbi-deepdives.com) through which I got to know about Amazon’s new “Buy For Me” feature. This feature, however, created some controversies. From [Modern Retail:](https://www.modernretail.co/technology/brands-are-upset-that-buy-for-me-is-featuring-their-products-on-amazon-without-permission/?ref=mbi-deepdives.com) > “Amazon’s “Buy For Me” feature allows users to purchase products from third-party websites without leaving Amazon’s app or site. (Amazon also has a “Shop Direct” button that links out to brands’ third-party websites for customers to complete a purchase.) When products appear on Amazon through “Buy for Me,” they are shown alongside standard Amazon search results but are clearly labeled as coming from “other brands,” with a prominent button that says “Buy for Me.” > > “Buy For Me” uses “agentic AI capabilities” to provide third-party websites with shoppers’ encrypted payment and shipping information, according to Amazon. Still, several merchants said that, to shoppers accustomed to scrolling Amazon’s marketplace, the listings can resemble a typical Amazon product page, potentially giving the impression that a brand is selling directly on Amazon, even if the transaction ultimately happens elsewhere.” Amazon’s stance on agentic shopping does seem a little disingenuous albeit quite clever; on one hand, they are sending “[cease and desist](https://www.aboutamazon.com/news/company-news/amazon-perplexity-comet-statement?ref=mbi-deepdives.com)” letter to Perplexity preventing them to browse Amazon’s own marketplace whereas Amazon is automatically opting in all the individual third-party websites in their own search results **unless those sellers email Amazon directly to opt out of the program**. Amazon can perhaps still claim that they’re following the “golden rule” i.e. they’re treating others the same they would like to be treated. Just as they let anyone opt out of the program once they let Amazon know, Amazon also asks other AI agents to respect their wishes of not “trespassing” in their marketplace. Economically, Amazon’s position seems coherent. Amazon wants to remain the primary interface to shopping, which means resisting any external agent that weakens its control of the customer relationship and data. At the same time, Amazon also wants maximum selection which means reaching beyond its own inventory and pulling the rest of the web into Amazon’s interface. The key here is, of course, aggregating demand. Even Amazon’s position can be malleable to third-party agents if they start suspecting they’re losing a rising fraction of sales by not allowing 3P agents. Such “disingenuous yet clever” strategy is actually a good glimpse of the barrier to agentic AI’s adoption. While most of us focus too much on technical capabilities of AI, we may still be underestimating the challenges related to (lack of) incentives of incumbents as well as legal frameworks for agentic AIs to flourish. “Ghosts of Electricity” had a very good [piece](https://aleximas.substack.com/p/why-cant-your-ai-agent-book-a-flight) explicitly laying out couple of real headaches: > “we highlight two main obstacles that stand in the way of AI agents becoming true digital partners. The first has to do with the design of the internet itself–the interface of nearly every website was meticulously optimized for humans. But what works for humans does not necessarily work for AI agents. Until AI can truly emulate every aspect of a human being, we will likely need to design a parallel internet for agentic commerce to work. But there’s reasons to suspect that this will not happen soon: some firms have little to gain, and potentially much to lose, from investing and facilitating a machine-readable web. This leads us to the second obstacle, which is even simpler: many use-cases for AI agents are **illegal, or at least legally ambiguous**. The rights around AI agents need to be clarified and developed in order for agents to participate meaningfully in economic transactions and interactions.” In the piece, they substantiated these headaches with a couple of examples. Some excerpts below: > “Let’s say you tell your favorite AI tool (ChatGPT Atlas, Perplexity Comet, Claude, Gemini Antigravity) to purchase a concert ticket for you or to shop on Amazon. Take seat selection. The agent reaches the seat map and gets stuck because it can’t tell what’s actually available or what counts as a “good” choice. The map isn’t a simple list: seats change color when you hover, prices only appear after clicking, and availability updates every second as other people buy tickets. While the agent pauses to figure out what to do, the seat disappears, the page refreshes, and it loses its place. Every pause, waiting for pages to load, retrying after errors, handing control back to you, adds friction. **What takes a human a few minutes to do turns into a brittle, ten-minute ordeal** > > …Imagine you deploy an AI agent to shop for you. The agent logs into your Booking.com account using your credentials, stored locally on your device. It browses hotels, compares prices, and completes a purchase—all at your explicit direction, acting solely on your behalf. > > Have you done anything wrong? Has your agent? > > **The answer is surprisingly unclear, and the current legal framework is not favorable to agents**. The core question is whether a BYO agent inherits your rights to access a website. You, as a human, can browse Booking. **You agreed to their Terms of Service. Does your agent automatically have the same permission?**” Some of these may seem trivial issues that will be ironed out over time, but remember these are not just technical questions which would indeed probably be solved via more compute, data, and better algorithm. But solving incentives when every incumbent is hyperaware of their share of profit pool can delay your AI timeline to an extent that can surprise most AI researchers. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Misunderstood Utility of Search Ads URL: https://www.mbi-deepdives.com/search-ads/ Last updated: 2026-01-18T16:11:28.000Z I have been increasingly noticing an overwhelming negative perception on search ads. Ben Thompson often mentions how search ads can feel like skimming the advertisers as Google collects rent on economic activity that would likely happen anyway. Notice this piece from [2022](https://stratechery.com/2022/digital-advertising-in-2022/?ref=mbi-deepdives.com): > “…oftentimes search ads feel like [**a rake on organic results**](https://stratechery.com/2019/the-google-squeeze/?ref=mbi-deepdives.com) that would have given you what you were looking for anyways. Facebook-style display advertising, on the other hand, is the foundation upon which an entirely new host of Internet-only businesses are built.” Ben Thompson has always been a strong defender of Meta’s advertising, but it appears there is a growing consensus of “pro-social” nature of Meta’s advertising model. While announcing the launch of ads on ChatGPT, even Sam Altman explicitly [mentioned](https://x.com/sama/status/2012253252771824074?ref=mbi-deepdives.com) Instagram ads as the “yardstick” for useful ads: > An example of ads I like are on Instagram, where I’ve found stuff I like that I otherwise never would have. We will try to make ads ever more useful to users If you read OpenAI’s [press release](https://openai.com/index/our-approach-to-advertising-and-expanding-access/?ref=mbi-deepdives.com) on their launch of ads, you can almost sense they’re a bit cagey about ads. In an “ideal” world, it does seem they would prefer not to have any ads on ChatGPT. From their press release: > People trust ChatGPT for many important and personal tasks, so as we introduce ads, it’s crucial we preserve what makes ChatGPT valuable in the first place. That means you need to trust that ChatGPT’s responses are driven by what’s objectively useful, never by advertising. > > We’ll always offer a way to not see ads in ChatGPT, including a paid tier that’s ad-free. I was recording a podcast with my friend [Liberty](https://www.libertyrpf.com/?ref=mbi-deepdives.com) a couple of days ago and he also echoed similar opinions of search ads being less useful compared to Meta’s ads. So, the prevailing assumption seems to be that if we could magically strip away ads from a search engine, the user experience would be unambiguously better. I actually grew curious to see if there was any research paper which explored such a question in a more objective manner. I did come across one such paper with full of counter-intuitive findings. I will discuss the paper behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### TSMC's Green Signal URL: https://www.mbi-deepdives.com/tsmcs-green-signal/ Last updated: 2026-01-16T13:24:45.000Z ***A Programming Note***: I will take a personal day tomorrow, so there won’t be any new post tomorrow. --- TSMC’s 4Q’25 call was quite reassuring for AI bulls. Many investors look at TSMC as an important barometer for assessing the cycle as TSMC is deeply incentivized to ensure they are not left holding the bag by spending tens of billions of dollars in capex right at the peak and then suffer through the underutilized fabs for the entire cycle. TSMC has spent \~$101 Billion cumulative capex in the last three years, and they were quite confident that the number will be “**significantly higher**” in the next three years. Considering they guided for $52 Billion to $56 Billion capex in 2026, it is not hard to see that indeed this number may be lot higher in the next three years. Given the elevated risk for TSMC, TSMC management takes more of a 360-approach to receive feedback not from just its customers, but customers’ customers to gauge the health of their end markets. Clearly, whatever TSMC has seen was encouraging enough for them to increase capex and revenue outlook. From the call: > Our customers continue to provide us with a positive outlook. In addition, **our customers' customers who are mainly the cloud service providers are also providing strong signals and reaching out directly to request the capacity to support their business**. Thus, our conviction in the multiyear AI megatrend remains strong, and we believe the demand for semiconductor will continue to be very fundamental…To address the structural increase in the long-term market demand profile, TSMC works closely with our customer and our customers' customer to plan our capacity. > > Based on our planning framework, **we raised our forecast for the revenue growth from AI accelerator to approach a mid- to high 50s percent CAGR for the 5 years period from 2024 to 2029**. Underpinned by our technology differentiation and broad customer base, we now expect our overall long-term revenue growth to approach 25% CAGR in U.S. dollar terms for the 5-years period starting from 2024. The first question in the call was about what TSMC is hearing from its customers, and customers’ customers. While investors seem to be starting to express a tad bit skepticism on TSMC’s customers’ customers i.e. hyperscalers, TSMC management seems much more convinced about the ROI on such spending. Some excerpt from the Q&A: > …you essentially try to ask us, say, whether the AI demand is real or not. I'm also very nervous about it. You bet because we have to invest about USD 52 billion to USD 56 billion for the CapEx, right? **If we didn't do it carefully, and that would be big disaster to TSMC for sure. So of course, I spend a lot of time in the last 3, 4 months talking to my customer and end customers' customer**. I want to make sure that my customers demand are real. So I talked to those cloud service providers, all of them. > > The answer is that **I'm quite satisfied with the answer**. Actually, they show me the evidence that the AI really help their business. So they grow their business successfully and healthy in their financial return. > > So no doubt, I also asked specifically that what's application, right? I mean that's -- for one of the hyperscalers, they told me that, that helped their social media software. And so the customer continue to increase. So I believe that. And **with our own experience in the AI application, we also help to our own fab to improve the productivity**. > > …so all in all, I believe in **my point of view, the AI is real, not only real, it's starting to grow into our daily life.** And we believe that is kind of -- we call it AI megatrend, we certainly would believe that. So you -- another question is **can the semiconductor industry to be good for 3, 4, 5 years in a row, I'd tell you the truth, I don't know.** > > But I look at the AI, **it looks like it's going to be like an endless, I mean, that for many years to come.** No matter what, TSMC stick on the fundamental technology leadership, manufacturing excellence, and we work with customers to get their trust. And I think that fundamental thing position TSMC to be very good future growth, let me say that, 25% CAGR as we projected, and **we used to be conservative**. One analyst asked about TSMC’s assumptions about the token growth behind their revenue CAGR outlook. TSMC’s CEO C.C. Wei gave an endearing response: > I also try to understand what is the tokens of growth. But my customers, their product improvement continue to increase…it’s a well-known from Hopper to Blackwell to Rubin, that almost double, triple their performance. So the one they can support the tokens of growth or the one they can continue to support the compute power is enormous. > > And **so I lose the track to be frank with you**. You and me both, Mr. Wei! One point that I would highlight here is the fact that TSMC is upgrading their capex outlook for 2026 likely indicates they believe AI still has legs to continue the momentum and this is more of an affirmation of their outlook on AI in the medium term. From the call: > If you build a new fab, it takes 2 to 3 years to build a new fab. So **even we start to spend the 52 billion to 56 billion, the contribution to this year almost none and to 2027, a little bit. So we actually are looking for 2028, 2029 supply.** > > **Our headache right now, if I can call it a headache, is a demand and supply gap**. We need to work hard to narrow the gap. TSMC also reminded that silicon still remains the key bottleneck, not power: > Today, from my point of view, still the **bottleneck is TSMC's wafer supply**. **Not the power consumption, not yet.** So we also look at carefully. To answer your question, say that TSMC's wafer can support how much of the gigawatt, still not enough. They still have abundant of power supply in the U.S. One particular dimension about power “bottleneck” narrative that I didn’t appreciate before is this “prisoner dilemma” dynamic highlighted by Jeremie Eliahou Ontiveros in a recent Stratechery [interview](https://stratechery.com/2026/an-interview-with-jeremie-eliahou-ontiveros-and-ajey-pandey-about-building-power-for-ai/?ref=mbi-deepdives.com#power-shortage): > “The way we put it is it’s sort of a prisoner’s dilemma. If everyone could coordinate, we could all find power in a more easy way, but because everyone is spamming the utilities all around the country, actually all around the world, it creates this vicious circle where because you have requests everywhere, you need to put requests elsewhere because you don’t know if you’re going to get one in this location, and so it just only gets worse. And so that’s why you’ve seen this crazy behavior where every state has seen tens of gigawatts of data center requests for every month, which is insane because **if you aggregate the number or total number of data center requests close to a terawatt in the US alone, which for context, the peak load in the US is 750 gigawatts, so we’re saying we are requesting more than double that!** > > …they’re fake, and it’s just not possible, the US is not going to be able to handle all of that. But **the reason they’re fake is that prisoner’s dilemma”** Don’t get me wrong; power can still prove to be a bottleneck, but the situation may be not as dire as many might have assumed. TSMC’s commentary seemed also quite encouraging for semi cap equipment companies. The rising complexity in chip manufacturing in advanced nodes is a boon for semi cap equipment companies such as ASML, Applied Materials, Lam Research, and KLA Corp. etc. From the TSMC call: > “the cost of tools are becoming more expensive and process complexity is increasing. As a result, the CapEx dollar required to build 1,000 wafer per month capacity of N2 is substantially higher than 1,000 wafer per month capacity for N3\. The CapEx per k cost for A14 will be even higher.” The other data point that I thought was very encouraging is the confirmation from TSMC that Arizona’s fabs’ yield or defect density is now almost equal to its fabs in Taiwan. All in all, TSMC not only provided a buoyant outlook for their own business in the short to medium term, it also largely assuaged investors that the “AI trade” is still very much on! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Airbnb: Inbound Headwinds, AI Moat Question, and a CTO Shakeup URL: https://www.mbi-deepdives.com/abnb_cto_ai_headwind/ Last updated: 2026-01-16T11:46:32.000Z The travel industry has received some not-so-encouraging news in the last couple of days. First, Skift [reported](https://skift.com/2026/01/12/overseas-travel-to-the-u-s-slumps-for-8th-straight-month/?ref=mbi-deepdives.com) that international travel to the US went down for **eight consecutive** months. Overall overseas visitation to the US fell 2.5% in 2025\. With this year’s Football (Soccer) World Cup in sight, US inbound travel certainly has an easy comp, but the overall posture to international travelers to the US doesn’t quite seem very appealing for most travelers these days. Any time these news flows gain traction, I have noticed some people seem to think the flailing inbound travel to the US must be quite damaging to Airbnb, especially given \~40% of their revenue comes from the US. While such inbound travel data is certainly far from ideal, it is largely immaterial for Airbnb. Airbnb management directly addressed this back in 1Q’25 call: > “U.S. travel is predominantly domestic. And as a result, that corridor of foreign travelers coming to the U.S. is approximately **2% to 3% of our overall business**. So it’s frankly not quite material. > > At the same time, what we’re seeing is within that corridor, **guests who would have in a prior year come to the U.S. are simply choosing a different location**. So I think Canada is the most obvious example where we see Canadians are traveling at a much lower rate to the U.S. but they’re traveling more domestically, they are traveling to Mexico, they are going to Brazil, they’re going to France, they’re going to Japan.” Brian Chesky did multiple media appearances yesterday. I will discuss some notes from these behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### "File over App", Veeva at JPM Healthcare Conference URL: https://www.mbi-deepdives.com/file-over-app-veeva/ Last updated: 2026-01-14T23:00:09.000Z Back in 2023, Steph Ango, CEO of Obsidian, wrote an intriguing piece titled “[File over app](https://stephango.com/file-over-app?ref=mbi-deepdives.com)”. It’s a bit philosophical piece. Some excerpt from the piece: > *“File over app* is a philosophy: if you want to create digital artifacts that last, they must be files you can control, in formats that are easy to retrieve and read. Use tools that give you this freedom. > > *File over app* is an appeal to tool makers: accept that all software is ephemeral, and give people ownership over their data. > > In the fullness of time, the files you create are more important than the tools you use to create them. Apps are ephemeral, but your files have a chance to last.” As you may have noticed, Claude Cowork (which Anthropic describes as “Claude Code for the rest of your work”) is currently having a moment. I stumbled onto “File over app” piece through this [tweet](https://x.com/dannypostma/status/2011065092843139291?ref=mbi-deepdives.com) which essentially expressed the same philosophy as Ango: > “The longer I work with Claude Code, the more I realise I should start to have all my files locally instead of scattered around many cloud SaaS apps. > > Can’t give context without owning it all on your device. Sure you can call the tools, but Claude just browsing your folders is so much more powerful for context. > > True renaissance for local, open-source tools.” What are the implications for software in “file over app” world? I will touch on that and share my notes from Veeva’s discussion at JPM Healthcare conference behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Ecosystem Advantage: Part 2 URL: https://www.mbi-deepdives.com/the-ecosystem-advantage-part-2/ Last updated: 2026-01-13T15:35:21.000Z While explaining Alphabet’s “[ecosystem advantage](https://www.mbi-deepdives.com/the-ecosystem-advantage/)”, I mentioned “the real advantage of Google is its ability to monetize Gemini through ads even though they are not putting ads on the core Gemini chat bots itself.” To say it differently, I consider it exceptionally likely that Google will insert ads in the short to medium term everywhere in its ecosystem of products except on the core Gemini app. Ultimately, it is only a matter of when, not if, that ChatGPT may feel pressured to launch ads in their app which can give Google another opportunity to gain further market share by pointing out their ad-free chat experience on Gemini. Eventually, when or if consumer habit graduates from traditional search and Gemini gains a material percentage of query share (relative to traditional Google search), we will likely see ads on Gemini as well, but I expect it to be the one of the last places where Google will insert ads. In that earlier piece, I also mentioned about “[CC](https://labs.google/cc?ref=mbi-deepdives.com)” feature in Gmail. I have an upcoming short trip to Miami and since Gmail already has my flight details, notice how the “CC” AI feature is suggesting me to book hotels during the trip (see below image). It may be obvious, but let me explicitly mention the key distinction between ChatGPT and Google here. Unless I personally query anything about my Miami trip on ChatGPT, ChatGPT will never know anything about this trip. On the other hand, even if I don’t Google or ask Gemini anything Miami related, Google will still know where I am going (through Gmail, Maps, Android etc.) and hence can show me appropriate ads in other surfaces beyond the chat bot. In this sense, the rest of the world tries to give Google various context about my life to Google whereas it is only me who can provide such context to ChatGPT, so if/when I don’t provide such context, ChatGPT can remain blind. Again, as mentioned in my earlier piece, if you’re top 1-5% of ChatGPT users, ChatGPT will have pretty deep understanding of who you’re, so these questions are particularly relevant for the \~90-95% of users. ![](https://substackcdn.com/image/fetch/$s_!hQ3I!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa8420dff-dd2b-4a2b-8a27-531a25aa07c7_601x94.png) Image: A snapshot from my personal Gmail Moreover, Google had a slew of [press releases](https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/nrf-2026/?ref=mbi-deepdives.com) in the last couple of days which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### China's Rise in Innovation Ladder and Questions for ASML URL: https://www.mbi-deepdives.com/chinas-rise-in-innovation-ladder-and-questions-for-asml/ Last updated: 2026-01-12T14:48:36.000Z JP Morgan in its recent “[2026 Eye on the Market Outlook](https://am.jpmorgan.com/content/dam/jpm-am-aem/global/en/insights/eye-on-the-market/smothering-heights-amv.pdf?utm%5Fsource=weekly.moiglobal.com&utm%5Fmedium=newsletter&utm%5Fcampaign=member-news-and-reading-inspiration&%5Fbhlid=d259cbccb5d123283adc7ebdd4c1d39e1e814b39)” discussed four specific “What Could Go Wrong” for the market over the medium term. One that caught my attention is a section titled “*China scales the moat with its own lithography-semiconductor technology…when, not if*”. The section starts with the following paragraph and accompanied the below striking graphs: > “Let’s start with a basic premise: China has been steadily climbing the innovation food chain across a range of industries. The first chart shows China breaking into the top ten on innovation, while the second shows China and the US converging with respect to the complexity of exported products. China has also been outpacing the rest of the world on clean energy patents, AI patents and nuclear engineering research.” ![](https://substackcdn.com/image/fetch/$s_!JXTf!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F621fae8c-566b-457e-9123-c26281dd6f82_1050x730.png) Image Source: JPM [2026 Eye on the Market Outlook](https://am.jpmorgan.com/content/dam/jpm-am-aem/global/en/insights/eye-on-the-market/smothering-heights-amv.pdf?utm%5Fsource=weekly.moiglobal.com&utm%5Fmedium=newsletter&utm%5Fcampaign=member-news-and-reading-inspiration&%5Fbhlid=d259cbccb5d123283adc7ebdd4c1d39e1e814b39) How did China manage to radically close the gap and in some instances surpass the US by a significant margin? Dan Wang in his recent [letter](https://danwang.co/2025-letter/?ref=mbi-deepdives.com) had a very lucid explanation of how many people in the West fundamentally misunderstand China’s rise in innovation ladder (emphasis mine): > “…western elites keep citing the wrong reasons for China’s success. When members of Congress get around to acknowledging China’s tech advancements, they do not fail to attribute causes to either industrial subsidies (also known as cheating) or IP theft (that is, stealing). These are legitimate claims, but China’s advantages extend far beyond them. That’s the creation of deep infrastructure as well as extensive industrial ecosystems that I describe above. > > Probably the most underrated part of the Chinese system is the ferocity of market competition. It’s excusable not to see that, given that the party espouses so much Marxism. **I would argue that China embodies both greater capitalist competition and greater capitalist excess than America does today.** Part of the reason that China’s stock market trends sideways is that everyone’s profits are competed away. > > …western elites keep holding on to a distinction between “innovation,” which is mostly the remit of the west, and “scaling,” which they accept that China can do. I want to dissolve that distinction. Chinese workers innovate every day on the factory floor. By being the site of production, they have a keen sense of how to make technical improvements all the time. American scientists may be world leaders in dreaming up new ideas. But American manufacturers have been poor at building industries around these ideas. The history books point out that Bell Labs invented the first solar cell in 1957; today, the lab no longer exists while the solar industry moved to Germany and then to China. While Chinese universities have grown more capable at producing new ideas, it’s not clear that the American manufacturing base has grown stronger at commercializing new inventions. > > I sometimes hear that the US will save manufacturers through automation. **The truth is that Chinese factories tend to be ahead on automation: that’s a big part of the reason that Chinese Tesla workers are more productive than California Tesla workers.** China regularly installs as many robots as the rest of the world put together. They are also able to provide greater amounts of training data for AI. **We have to be careful not to let automation, like superintelligence, become an excuse for magical thinking rather than doing the hard work of capacity building**.” Speaking of capacity building, perhaps nothing exemplifies the divergence between China and the rest of the world than what has transpired in power generation since 2019\. JPM mentioned that since 2019, power generation changes was +2,500 TWh in China, only +221 TWh in the US, and an outright embarrassing -110 TWh (yes, negative) in Europe. Now that we know power is a key bottleneck in the current AI race, this trend is even more worrisome for the entire Western hemisphere. Ben Thompson [interviewed](https://stratechery.com/2026/an-interview-with-jeremie-eliahou-ontiveros-and-ajey-pandey-about-building-power-for-ai/?ref=mbi-deepdives.com) a couple of analysts from Semianalysis recently who made the case that the power bottleneck in the US may not be as dire as many people imagine; nonetheless, the trend since 2019 is a sobering reminder that the West needs to pick up the speed if they don’t want to be laggards. Having said that, as Dan Wang noted, there are still two particular sectors in which China remained noticeably behind the West: aviation, and semiconductors. Within semiconductors, one particular bottleneck is advanced lithography. A recurring topic between ASML bulls and bears is if, when, or how China may catch up in lithography to put a dent on ASML’s outright monopoly in EUV. There is an interesting angle to this debate which I will discuss more behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Software in Crosshairs: Part 2 URL: https://www.mbi-deepdives.com/software-in-crosshairs-part-2/ Last updated: 2026-01-11T15:47:28.000Z After publishing “[**Software in Crosshairs**](https://www.mbi-deepdives.com/software-in-crosshairs/)” last week, I received some interesting feedback. One email from someone who works in enterprise SaaS stood out as he took bit of an issue with how Foundation Capital [outlined](https://foundationcapital.com/context-graphs-ais-trillion-dollar-opportunity/?ref=mbi-deepdives.com) incumbent companies’ inability to capture the context graphs. I am publishing his email with his permission: > “I want to correct a few things on Foundation Capital’s piece that they either over simplified or didn’t know: > > 1\. Salesforce as a current state memory system: this is extraordinarily easy to accomplish in Salesforce. It’s true we can’t capture conversations in the hallway but no one is proposing that nor would that make economic sense to store 90% of hallway garbage conversations in a database. If it’s on video, a phone call, a slack message, etc. We can trace these things, I think they have a misunderstanding of our product offerings and capabilities. These are not new. > > 2\. Zendesk/Slack example- most enterprises aren’t using Zendesk, it’s either Service Cloud or ServiceNow. This is do-able (tying context together). > > They are right that software in theory is at risk, core applications less so in my view but there are challenges for each group and their lack of knowledge is a big one (we have a lot of people who have built terrible systems due to this). **Undoing decades of short term decision making is a monstrous task and very few are doing this because a CEO isn’t measured on all of the system cleanup they did** nor does IT understand the context of the data for a LOB to transition these into rich insights or create new ones. **Incentives, awareness and skill sets are the big issue**. > > What people don’t appreciate is that building all of these foundational tools and modules (the modules are the key) natively is not worth the effort nor will you get them all right so you’ll create the same silos eventually. > > I can go on but I’ll save the rant. There is a fundamental misunderstanding of core software applications like CRM, HRIS, ERP, etc. They’re misunderstanding the downsides, the upsides and the actual risks.” Indeed, the difficulty for incumbents may largely come from their culture and org structure while competing with companies designed from ground up keeping AI models’ increasing capabilities in mind. I go back to a pithy [remark](https://cheekypint.transistor.fm/19?ref=mbi-deepdives.com) made by John Collison during his conversation with Satya Nadella last year: > …people point out a lot that current companies are modeled after manufacturing companies and Alfred Sloan type stuff, despite the fact that we're doing knowledge work today and not running a little manufacturing line. I will discuss more on this as well as Veeva’s approach to AI behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### "The Compute Theory of Everything" URL: https://www.mbi-deepdives.com/the-compute-theory-of-everything/ Last updated: 2026-01-10T16:03:46.000Z One of my favorite recent genre of content these days is annual reflection from AI researchers. Dan Wang isn’t an AI researcher, but I think his fantastic [annual letters](https://danwang.co/2025-letter/?ref=mbi-deepdives.com) was inspiration among many. I will admit that through his letters, Wang has singlehandedly elicited from me a sense of affection and admiration for China and its people. In fact, his writings at least partly influenced me on how I would like the AI race between China and the US to be settled. As someone living in the US and heavily invested in the US companies, I certainly have my biases towards the US, but nowadays I basically don’t want the race to be settled overwhelmingly in the US’s favor. I would like a **genuine** uncertainty about the outcome of this race for hopefully forever. If there is no such genuine uncertainty, I suspect policies such as [moratorium](https://x.com/SenSanders/status/2001057004370948131?ref=mbi-deepdives.com) on data centers in the US (and perhaps the entire Western hemisphere) may gain a decisive popularity over time. I also wonder whether each of these super powers may behave a lot better with the rest of the world if either of them does not have a decisive and sustained advantage over the other. Anyways, I do think essays are generally deeply underrated, and I wish more bright people penned how they’re perceiving the world through their lens. Some of the annual letters/reviews/reflections that caught my attention this year are by [Andrej Karpathy](https://karpathy.bearblog.dev/year-in-review-2025/?ref=mbi-deepdives.com), [Zhengdong Wang](https://zhengdongwang.com/2025/12/30/2025-letter.html?ref=mbi-deepdives.com), and [Samuel Albanie](https://samuelalbanie.substack.com/p/reflections-on-2025). It was Albanie who coined “the compute theory of everything” in his letter. Albanie referred two seminal essays by Hans Moravec: “[The Role of Raw Power in Intelligence](https://stacks.stanford.edu/file/druid:ws563sd6050/ws563sd6050.pdf?ref=mbi-deepdives.com)” (1976), and “[When will computer hardware match the human brain?](https://jetpress.org/volume1/moravec.htm?ref=mbi-deepdives.com)” (1998) I glanced through the first essay, but read the second one. I was moved just by reading the abstract of the paper: > “This paper describes how **the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware.** The processing power and memory capacity necessary to match general intellectual performance of the human brain are estimated. Based on extrapolation of past trends and on examination of technologies under development, **it is predicted that the required hardware will be available in cheap machines in the 2020s**.” Moravec is a familiar name to me because I have heard about [Moravec’s paradox](https://en.wikipedia.org/wiki/Moravec%27s%5Fparadox?ref=mbi-deepdives.com) before, but it was in this 1998 essay he provided a metaphor for AI progress that defied the intuition that reasoning is hard and “sensing” is easy. He described human skills as a landscape where AI is a rising flood. From the paper (all emphasis mine): > “Computers are universal machines, their potential extends uniformly over a boundless expanse of tasks. Human potentials, on the other hand, are strong in areas long important for survival, but weak in things far removed. Imagine a "landscape of human competence," having lowlands with labels like "arithmetic" and "rote memorization", foothills like "theorem proving" and "chess playing," and high mountain peaks labeled "locomotion," "hand−eye coordination" and "social interaction." We all live in the solid mountaintops, but it takes great effort to reach the rest of the terrain, and only a few of us work each patch. > > **Advancing computer performance is like water slowly flooding the landscape.** A half century ago it began to drown the lowlands, driving out human calculators and record clerks, but leaving most of us dry. Now the flood has reached the foothills, and our outposts there are contemplating retreat. We feel safe on our peaks, but, at the present rate, those too will be submerged within another half century. I propose that we build Arks as that day nears, and adopt a seafaring life!” Indeed, AI conquered chess (foothills) decades before it could reliably fold laundry or navigate a cluttered room (the peaks), which remain difficult engineering challenges today. Hence, the Moravec’s paradox. As I have mentioned [before](https://www.mbi-deepdives.com/scaling-law-skepticism/), Moore’s law had plenty of skeptics along the way. Moravec was no such skeptic. Despite acknowledging valid reasons to harbor skepticism, Moravec relied on his simple observations on computing: > “Computers doubled in capacity every two years after the war, a pace that became an industry given: companies that wished to grow sought to exceed it, companies that failed to keep up lost business. In the 1980s the doubling time contracted to 18 months, and computer performance in the late 1990s seems to be doubling every 12 months ![](https://substackcdn.com/image/fetch/$s_!oP36!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4947daf3-b5e2-4f79-8c7c-910452582979_1057x1083.png) > **At the present rate, computers suitable for humanlike robots will appear in the 2020s.** Can the pace be sustained for another three decades? The graph shows no sign of abatement. If anything, it hints that further contractions in time scale are in store. But, **one often encounters thoughtful articles by knowledgeable people in the semiconductor industry giving detailed reasons why the decades of phenomenal growth must soon come to an end**.” What perhaps moved me the most from Moravec’s 1998 paper was his lucid explanation why AI suffered a long winter. Moravec offered a fascinating economic explanation for why AI stalled between 1960 and 1990\. From his paper (note: MIPS stands for Millions of Instructions Per Second): > “In the 1950s, the pioneers of AI viewed computers as locomotives of thought, which might outperform humans in higher mental work as prodigiously as they outperformed them in arithmetic, if they were harnessed to the right programs. Success in the endeavor would bring enormous benefits to national defense, commerce and government. The promise warranted significant public and private investment. For instance, there was a large project to develop machines to automatically translate scientific and other literature from Russian to English. There were only a few AI centers, but those had the largest computers of the day, comparable in cost to today's supercomputers. A common one was the IBM 704, which provided **a good fraction of a MIPS.** > > By 1960 the unspectacular performance of the first reasoning and translation programs had taken the bloom off the rose, but the unexpected launching by the Soviet Union of Sputnik, the first satellite in 1957, had substituted a paranoia. Artificial Intelligence may not have delivered on its first promise, but what if it were to suddenly succeed after all? To avoid another nasty technological surprise from the enemy, it behooved the US to support the work, moderately, just in case. **Moderation paid for medium scale machines costing a few million dollars, no longer supercomputers. In the 1960s that price provided a good fraction of a MIPS** in thrifty machines like Digital Equipment Corp's innovative PDP−1 and PDP−6. > > The field looked even less promising by 1970, and support for military−related research declined sharply with the end of the Vietnam war. Artificial Intelligence research was forced to tighten its belt and beg for unaccustomed small grants and contracts from science agencies and industry. The major research centers survived, but became a little shabby as they made do with aging equipment. **For almost the entire decade AI research was done with PDP−10 computers, that provided just under 1 MIPS.** Because it had contributed to the design, the Stanford AI Lab received a 1.5 MIPS KL−10 in the late 1970s from Digital, as a gift. > > Funding improved somewhat in the early 1980s, but the number of research groups had grown, and the amount available for computers was modest. **Many groups purchased Digital's new Vax computers, costing $100,000 and providing 1 MIPS**. By mid−decade, personal computer workstations had appeared. Individual researchers reveled in the luxury of having their own computers, avoiding the delays of time−shared machines. **A typical workstation was a Sun−3, costing about $10,000, and providing about 1 MIPS.** > > **By 1990, entire careers had passed in the frozen winter of 1−MIPS computers, mainly from necessity, but partly from habit and a lingering opinion that the early machines really should have been powerful enough. In 1990, 1 MIPS cost $1,000 in a low−end personal computer. There was no need to go any lower. Finally spring thaw has come. Since 1990, the power available to individual AI and robotics programs has doubled yearly, to 30 MIPS by 1994 and 500 MIPS by 1998\. Seeds long ago alleged barren are suddenly sprouting. Machines read text, recognize speech, even translate languages. Robots drive cross−country, crawl across Mars, and trundle down office corridors. In 1996 a theorem−proving program called EQP running five weeks on a 50 MIPS computer at Argonne National Laboratory found a proof of a boolean algebra conjecture by Herbert Robbins that had eluded mathematicians for sixty years. And it is still only spring. Wait until summer.**” I don’t know for sure, but I suspect most of my readers, including yours truly, are “AGI Atheists” (or maybe “AGI Agnostics”), but going through Moravec’s decade old essays did make me wonder whether my non-belief is actually on shakier grounds than believers’ belief on the compute theory of everything! The religious fervor of summoning “God” into data centers seems ridiculous at first glance, but maybe “summer” is almost here! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Climbing the Scaling Wall URL: https://www.mbi-deepdives.com/climbing-the-scaling-wall/ Last updated: 2026-01-09T15:30:57.000Z Last month, Gavin Baker in ILtB [podcast](https://colossus.com/episode/nvidia-v-google-the-economics-of-ai/?ref=mbi-deepdives.com) mentioned the following: > “I think there's been a big misunderstanding of maybe in the public equity investing community, or the broader more generalist community, based on the scaling laws of pre-training, **there really should have been no progress in '24 and '25.** > > …all the progress we've had, and we've had immense progress since October '24 through today, was based entirely on these two new scaling laws. And Gemini 3 was arguably the first test since Hopper came out of the scaling law for pre-training, and it held, and that's great, because **all these scaling laws are multiplicative**. So, now we're going to apply these two new reinforcement learnings, verifiable rewards and test-time compute, to much better base models.” As one of the generalists, I was initially comforted by the idea of the multiplicative nature of these scaling laws. But it turns out there are indeed plenty of discomforting questions lurking around scaling laws. After publishing “[**Scaling Law Skepticism**](https://www.mbi-deepdives.com/scaling-law-skepticism/)” a couple of days ago, I received several interesting emails. One smart reader stood out when he shared some of his thoughts on the “church” of scaling; his thoughts included a couple of reading materials which I went through yesterday and I came away with more nuanced concerns around Scaling Laws. I will discuss some of these nuances behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### CoStar's Outlook URL: https://www.mbi-deepdives.com/costars-outlook/ Last updated: 2026-01-08T15:31:58.000Z Rich Simonelli, the Head of Investor Relations (IR) of CoStar, wrote only two sentences in his “About” section on [LinkedIn](https://www.linkedin.com/in/richardsimonelli/?ref=mbi-deepdives.com). The first sentence reads the following: “**I maximize shareholder value.**” Since becoming Head of CoStar IR in August 2024, this is how CoStar performed: ![chart](https://substackcdn.com/image/fetch/$s_!oYYl!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8e02c05a-9aa4-47fe-90a7-3c686568dcff_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Hmm, [not quite my tempo](https://www.youtube.com/watch?v=GBvBu5ErSSo&ref=mbi-deepdives.com). In fact, the stock has been **flat from July 2019!** ![Not Quite My Tempo Jk Simmons GIF - Not Quite My Tempo Jk Simmons Fletcher - Discover & Share GIFs](https://substackcdn.com/image/fetch/$s_!KLpS!,w_2400,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4bef7f89-ebdd-4a75-84d8-cd4bf4e91f33_640x358.gif "Not Quite My Tempo Jk Simmons GIF - Not Quite My Tempo Jk Simmons Fletcher - Discover & Share GIFs") Understandably, CoStar management has been under the scanner these days. The company issued a press release yesterday providing both 2026 and a medium term outlook for the business which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Scaling Law Skepticism URL: https://www.mbi-deepdives.com/scaling-law-skepticism/ Last updated: 2026-01-07T15:28:38.000Z Peter Lee, Head of Microsoft Research, once quipped a [joke](https://newsroom.lamresearch.com/Whats-After-Moores-Law-The-Economist-Takes-a-Look-Ahead?blog=true&ref=mbi-deepdives.com) about Moore’s law: “There’s a law about Moore’s law…the number of people predicting the death of Moore’s law doubles every two years.” As a reminder, Moore’s law is not a law of physics or mathematical theorem. It is just an prediction/observation that ended up being incredibly prescient. I like how Mark Liu, the former Chairman of TSMC, [characterized](https://www.wired.com/story/i-saw-the-face-of-god-in-a-tsmc-factory/?ref=mbi-deepdives.com) Moore’s Law as essentially a “shared optimism”. Chris Miller said in “Chip War”: *“The making of Moore’s Law is as much a story of manufacturing experts, supply chain specialists, and marketing managers as it is about physicists or electrical engineers.”* Given the qualitative elements to Moore’s law, in retrospect it doesn’t surprise me that it had so many skeptics along the way. Of course, Moore’s law is so yesteryears; there’s a new law in town: scaling laws! If you throw more compute, more training data and more parameters, you will get a better AI model. Unlike Moore’s law, I have hardly come across any ardent skeptics of scaling laws. I’m sure they are out there, but they’re probably still few and far between for me to notice. So, I was particularly curious to read yesterday [Sara Hooker’s](https://www.linkedin.com/in/sararosehooker?ref=mbi-deepdives.com) recent [essay/paper](https://papers.ssrn.com/sol3/papers.cfm?abstract%5Fid=5877662&ref=mbi-deepdives.com): “On the slow death of scaling”. Hooker is former Head of Cohere Labs and former Research Scientist at Google DeepMind. After reading her essay, I went through a couple of her prior work, and I particularly enjoyed “[The Hardware Lottery](https://arxiv.org/abs/2009.06489?ref=mbi-deepdives.com)” paper from 2021 which argued that AI progress is often determined by what runs best on available hardware rather than what is scientifically the best idea; if you’re visually inclined, you can watch her discuss the paper [here](https://www.youtube.com/watch?v=POozdlZAyQc&ref=mbi-deepdives.com) in four minutes. I will discuss some key excerpts from her most recent paper on scaling laws behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Alphabet's Alpha Bet URL: https://www.mbi-deepdives.com/alphabets-alpha-bet/ Last updated: 2026-01-06T15:40:00.000Z A couple of months ago, Demis Hassabis [indicated](https://sources.news/p/demis-hassibas-on-gemini-3-world?utm%5Fcampaign=email-post&r=1r85f&utm%5Fsource=substack&utm%5Fmedium=email) what he is more focused on these days: > World models are the thing I’m mostly spending my research time on. You probably saw [SIMA 2](https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/?ref=mbi-deepdives.com) come out just a couple of days ago. I sometimes call it SIMA playing in the mind of [Genie](https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/?ref=mbi-deepdives.com). These things fascinate me, and I think they will be critical components in getting to AGI. I suspect over the course of 2026, we will hear a lot more about world model from Alphabet, and other frontier labs. What exactly is a “world model”? A world model is basically an internal simulation of how the physical environment works. Just as humans can mentally imagine what will happen if they drop a glass on a concrete floor (it will shatter) versus a carpet (it might bounce), a world model allows an AI to predict the future state of the world based on its current state and a proposed action. A recent [paper](https://arxiv.org/pdf/2512.10675?ref=mbi-deepdives.com) by Google DeepMind hints that it is likely possible to accelerate building such world models. I will discuss the paper and some implications behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Software in Crosshairs URL: https://www.mbi-deepdives.com/software-in-crosshairs/ Last updated: 2026-01-05T14:56:34.000Z I came across this [tweet](https://x.com/JaredKubin/status/2007209156181405916?ref=mbi-deepdives.com) on last Friday that indicates that the first trading day in 2026 was the worst day for software stocks since 2009 (and third worst in the last 20 years)! ![](https://substackcdn.com/image/fetch/$s_!6Pb0!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4995b3e9-e30c-4e4e-8a2d-ffc997459d43_877x984.png) During the holidays, I came across several pieces on the current and future potential state of software companies which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Pursuit of Better Explanations URL: https://www.mbi-deepdives.com/the-pursuit-of-better-explanations/ Last updated: 2026-01-04T16:03:28.000Z A couple of weeks ago, Instagram algorithm showed this reel to me about James Cameron. It’s a two part series ([part 1](https://www.instagram.com/reel/DSvHiU8kT5b/?igsh=YzAyMDM1MGJkZA%3D%3D&ref=mbi-deepdives.com), [part 2](https://www.instagram.com/reel/DSvH1Tcke0y/?igsh=YzAyMDM1MGJkZA%3D%3D&ref=mbi-deepdives.com)) which in aggregate will take you four and half minutes to watch. I recommend you watch the videos, but in case you don’t, this is how the part 1 starts: > “James Cameron is the most interesting person on earth and it is because he is absolutely insane. When he was younger, he had no idea what he wanted to do with his life and he was working as a truck driver and a janitor. But that all changed when one day he went and saw Star Wars, of all things. You see, he went and saw Star Wars and he said, "I want to get into movies.” Then in near the end of part-2, the reel provides a good gist of Cameron’s non-exhaustive accomplishments: > “Just to recap, he is a self-taught painter. He is a self-taught director. Self-taught illustrator. He is a self-taught screenwriter. He is practically a self-taught marine biologist. First director to ever have a film that made over $1 billion. First director to have a movie that earned over $2 billion as well. He currently holds three of the top 4 highest-grossing movies of all time. Maybe he's gonna get a fourth one." I want to emphasize again: this is a non-exhaustive list. If you’re curious, I recommend you spend the four and half minutes to watch the videos. During my daily 10k-step walk, I cannot tell you how many times my mind kept going back to thinking about this particular reel in the last few days and as you can see, I feel compelled to write about it even though none of this is really quite related with investing. But hey, it’s Sunday which I may use as an excuse to share some of these random musings with my readers. The question that I kept wondering how on earth Cameron had such deep reservoir of curiosity in such wide ranging fields! It is one thing to have child like curiosity but a different thing altogether to be so deeply proficient at it. A couple of years ago, I tried to read David Deutsch’s book “[The Beginning of Infinity](https://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359?ref=mbi-deepdives.com)”. I found it a bit dense but after listening to Deutsch in a couple of podcasts, I found his ideas more accessible through that medium. In fact, I find many of his ideas quite captivating that resonate well with how I perceive the world. As I kept thinking about these reels on James Cameron, I wondered how Deutsch would explain Cameron’s insatiable pursuit to new problems. So, like any normal person would do in 2026, I asked both ChatGPT and Gemini Deutsch’s epistemological explanations for Cameron’s accomplishments. I had some back and forth for quite a while, so let me share some key excerpts below from Gemini’s responses: > “According to Deutsch, the difference between Cameron and the “average” person is not biological hardware (e.g., a “genius” brain vs. a normal brain), but rather the software (ideas) they use to process reality. > > Deutsch argues that **all humans are Universal Explainers**. > > The human brain is a universal computer. If a brain can understand one complex domain (like the grammar of a native language), it possesses the computational capacity to understand *any* domain that can be understood (physics, engineering, directing). There is no such thing as a "math brain" or an "art brain." > > Cameron’s ability to jump from screenwriting to deep-sea engineering to optical physics is not a biological miracle; it is the standard function of a human mind that has not arbitrarily limited itself. He treats "physics" and "storytelling" not as separate identities, but as different applications of the same universal explanatory capacity. > > The majority of people operate under the **"Bucket Theory of the Mind,"** believing that knowledge is a fluid held by authorities (teachers) that must be poured into them. If they want to learn engineering, they wait to be taught. > > Deutsch thinks knowledge is created *only* through **Conjecture and Criticism** (Karl Popper’s epistemology). We learn by guessing a solution to a problem and then criticizing/testing it to correct errors. Cameron is a radical practitioner of this method. He did not go to film school to "receive" knowledge. He went to the USC library to solve specific problems (e.g., *"How does an optical printer work?"*). He would form a conjecture (a design for a camera), build it, fail, and correct the error. Because he was actively creating knowledge to solve a problem he cared about, his learning was exponential compared to a passive student. > > Deutsch argues that **coercion** (forcing someone to learn against their will) destroys the capacity for creative thought. To solve difficult problems, the mind must be free to follow its interest. Deutsch calls this the **"Fun Criterion"**—not frivolous entertainment, but the deep, conflict-free engagement with a problem. > > Cameron dropped out of college because he found it coercive and limiting. He only learned physics because he *needed* it to build submersibles, making the learning functional and deeply integrated. The majority of people learn to pass tests (coercion), which results in shallow knowledge that is quickly forgotten. > > Deutsch warns of **"Anti-Rational Memes"**—cultural ideas that survive by disabling critical thinking (e.g., "You aren't qualified," "Trust the experts," "Stay in your lane"). Most people are infected by the meme of **Credentialism**. They believe they are *not allowed* to do engineering without a degree. This is a "bad philosophy" that acts as a self-imposed prison. > > Cameron operates on Deutsch’s **Principle of Optimism**: *“Anything that is not forbidden by laws of physics is achievable, given the right knowledge.”* When told a camera couldn't film at 11,000 meters depth, the majority would accept the impossibility. Cameron viewed it merely as a lack of knowledge, which he then created. He respects the authority of the *problem*, not the authority of the *experts*. > > Epistemologically, James Cameron is what a human looks like when their natural **Universality** is not suppressed by **static, anti-rational memes**. He is not a specialist; he is a general-purpose problem solver who treats ignorance not as a permanent state, but as a temporary lack of explanation.” Just chatting with ChatGPT/Gemini is a daily reminder that education has permanently and radically changed even if much of the world (especially educational institutions) hasn’t quite woken up to that reality. I am not sure I quite believe there will be millions of Einsteins or James Camerons in the data center, but it does seem highly likely that AI may embolden many more Homo Sapiens than ever before to accelerate their pursuit of knowledge and solve problems. Sure, there will be some LLM-induced psychosis along the way, but I will be surprised if the world doesn’t have more Camerons and Einsteins hopping from one problem to another in the pursuit of better explanations of the universe! [Subscribe](#/portal/signup) ### The Battle for Attention URL: https://www.mbi-deepdives.com/the-battle-for-attention/ Last updated: 2026-01-07T13:19:48.000Z During 1Q’17 Netflix earnings call, Reed Hastings mentioned something that raised some eyebrows or perhaps created a muffled laughter: > Think about it when you watch a show from Netflix and you get addicted to it, you stay up late at night. You're really -- **we're competing with sleep on the margin**, and so it's a very large pool of time. (1Q’17 call) While some may have thought that was a bit tongue on cheek comment back then, Hastings was really onto something. Of course, we all intuitively grasped this over time, but a new [working paper](https://ideas.repec.org/p/feb/framed/00828.html?ref=mbi-deepdives.com) that I came across during the holidays had more concrete evidence and some nuanced inferences. I have actually covered some of these findings [**before**](https://www.mbi-deepdives.com/metas-biggest-competitor-s/) following the court’s verdict in favor of Meta against FTC’s lawsuit. But after going through the actual paper (and just the judge’s verdict), I think there are certain findings that are worth highlighting from the paper itself which can have interesting implications for Meta. I will discuss them behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### 2025 Annual Letter URL: https://www.mbi-deepdives.com/2025/ Last updated: 2026-01-01T01:42:04.000Z Broadly speaking, I tend to reflect on my year in three buckets: a) operating performance of MBI Deep Dives, b) performance of my personal portfolio, and c) my personal health as well as relationships with my friends and family. For the second consecutive year, MBI Deep Dives has been in a really solid trajectory. Gross revenue and ARR were up +55% and +25% respectively in 2025\. For context, Gross revenue and ARR increased by +33% and +70% respectively in 2024\. While for first half of the year ARR growth threatened to be anemic, that quickly changed when I [**shifted**](https://www.mbi-deepdives.com/from-monthly-to-daily/) from posting monthly to daily from July 2025\. In hindsight, that was perhaps the most important change I have ever made since starting MBI Deep Dives in September 2020\. Given that context, let me share three key learnings from this experience. One of my interesting realizations is format doesn’t travel across platforms well. When I announced to shift to posting daily, I basically thought I would mostly continue what I was already doing for my WhatsApp group i.e. sharing excerpts from interesting pieces that I came across on that day. However, once I started posting the content on my website and not confine it within WhatsApp’s walled garden, I felt compelled to evolve the format a bit. The “Daily Dose” nowadays is mostly an eclectic mix of what I have been thinking within my coverage companies and weaving that with what I have been reading. This directly leads to my second key takeaway. One of the striking realizations I had was if I imagined “Daily Doses” exactly what it has eventually become, I would be far more hesitant to launch it because I would be much more concerned thinking I would not be able to pull it off. Thankfully, I underestimated what it would take to do it on a daily basis, and once it was launched, I could iterate it a bit based on your feedback and listening to my own gut more closely. While many seem to wonder about burnout given such high frequency of output, I found it quite energizing to stay deeply intellectually engaged on a daily basis. This is, of course, perhaps not a good fit for everyone, but I suspect for certain type of individuals who tend to consume copious amount of content everyday and want to organize their own thoughts in a careful, focused way, publishing daily can be a terrific fit. While some readers may argue about the pitfalls of “too much content”, the reality is I have no qualms whatsoever that MBI Deep Dives today is more valuable for the readers than it has ever been since its inception which leads to my final key realization after moving to daily cadence. While not the only focus, tech companies do receive a lion’s share of attention in my coverage. Although companies such as Brown & Brown and Sherwin Williams may not need quarterly or even annual updates, most tech companies operate in a much more dynamic environment. As a result, a Deep Dive on any particular point of time suffers the risk of obsolescence far faster than my coverage on some non-tech companies. This can be only addressed in a much higher frequency format. Moreover, **Deep Dives and Daily Doses are incredibly complementary in nature**. It’s lot easier for me to write Daily Doses on companies that I already did a Deep Dive before. For example, it would be lot harder for me to do some of my recent Veeva updates (see [here](https://www.mbi-deepdives.com/veeva-update/), and [here](https://www.mbi-deepdives.com/veeva-clinical/)) if I didn’t already publish the Deep Dive a year ago. Overall, the business is in a strong foundation today. I do feel the need to do some updates on companies that I did a few years back (e.g. Uber, Copart etc.). I am still thinking through whether to lower the number of new Deep Dives in 2026 to spend more time in updating some of the coverage I did few years ago. I haven’t made up my mind yet, but I will keep you posted when/if I do. On personal health front, I did something that I am really proud of this year. Given MBI Deep Dives suffers from “key man” risk, I want to prioritize my health and want to give myself the best chance to operate this business as long as physically and mentally possible. Just a month before I launched Daily Doses, I started walking at least 10,000 steps everyday. I have maintained that consistency every month in the last seven months! Admittedly, I have been surprised how energized I feel just by walking everyday. I actually wonder if I didn’t take this habit almost out of a whim, I would have a lot harder time with the daily publishing cadence. Slacking or procrastinating is simply not an option in my “job” nowadays, and a general consistency and rhythm almost feels essential to execute my work everyday. I have lost \~20 pounds during the last seven months, and physically haven’t felt better in a long, long time. I didn’t start walking with any particular goal in mind. I just wanted to keep moving everyday and it has been personally transformative for me. ![](https://substackcdn.com/image/fetch/$s_!rnDC!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F84577d67-58d5-48c3-8b24-0861a19b301a_1189x1261.png) Yes, I have essentially become this [meme](https://x.com/DzambhalaHODL/status/1878122361888600185?ref=mbi-deepdives.com) 😂 ![Just Keep Walking! : r/Funnymemes](https://substackcdn.com/image/fetch/$s_!C4K2!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb1a28f4d-519f-4ea5-af26-ddfb6f486c5e_1283x908.png "Just Keep Walking! : r/Funnymemes") On family front, I will keep it short since I already [reflected](https://www.mbi-deepdives.com/one-year-of-parenting/) on my experience of one year of parenting. We celebrated Nile’s first birthday over the holidays. If I pick two words to encapsulate my family life in the last twelve months, I will go for "unbridled joy"! ![May be an image of baby, smiling and tree](https://substackcdn.com/image/fetch/$s_!2D6t!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd6c0f982-29c4-4fd1-8673-a3fdd2126387_1638x2048.jpeg "May be an image of baby, smiling and tree") Let me look back at my portfolio’s performance now! [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### One Year of Parenting! URL: https://www.mbi-deepdives.com/one-year-of-parenting/ Last updated: 2025-12-25T03:31:25.000Z When the world was celebrating Christmas last year, we welcomed our son Nile to the world! “Oh, a Christmas baby!!”— we kept hearing from the doctors and nurses in the hospital. I didn’t grow up celebrating Christmas, but who knew my greatest cause for celebration would arrive on the Christmas day! Looking back at the past year, Nile has been a constant source of unbridled joy for us. We are perhaps among the luckiest parents since I feel like we got almost all of the upsides and none of the downsides of having a new born at home. After the first eight weeks, Nile started sleeping mostly throughout the night. My personal sleep quality was basically unaffected after those first eight weeks. Not sure many parents of a new born can say that, so perhaps I owe a lot to Nile! We did have plenty of help though. My in-laws flew from Bangladesh to stay with us for six months. My mother-in-law has essentially become a personal hero of mine. She has always been a homemaker throughout her life, but just observing how relentless she has been day-in-day-out is simply awe inspiring. She wakes up before the sun rises, says her prayer, prepares food for all of us, and then looks after Nile once she’s done. It’s a crazy amount of workload for a 60-year old, but she insists carrying out the responsibility with such rigor and precision that I can tell she enjoys having these responsibilities. In some sense, having a job with a very clear expectation of what you are supposed to accomplish today and then going to bed knowing you have done everything you were supposed to do on that day can be oddly satisfying. It’s not for everyone, of course, especially since many people I assume could be put off by the mundane nature of the work. Frankly speaking, observing my mother-in-law was partly a contributor for me to think of publishing something everyday. I myself noticed that whenever I published my monthly Deep Dive, I felt a sense of accomplishment. It actually made me think it would be nice to chase that feeling everyday! Eventually, we did hire a nanny for Nile. Since I work from home, my presence at home perhaps has no obvious charm (sigh!) to Nile at this point. But my wife gets to enjoy work from home only half the time and goes to office for work for the other half. Nile doesn’t seem to wonder where his mom is throughout the day, but whenever my wife comes home from work, his body curls up and makes almost this automatic reflex with hands stretched outward to signal as if “can you please hold me, woman?” It’s a real treat for me to watch this ordeal. My wife’s face lights up like I have never seen before when she holds Nile after coming from work. I quipped to my wife the other day, would you please love me 20% of how much you love Nile? She asked me back, “how much do you love me if Nile is the scale?” I sheepishly had to mention “20%!” and then pointed out there’s a reason I only want 20% from her; you see, I am a fair and reasonable person! While Nile acts like he wants to remain attached to his mother whenever he sees her, I noticed he is much more willing to copy what I do. The little guy is already learning to mostly borrow from his Dad! Nile would play with me for almost an hour at night before going to bed. He audibly smiles and tries to copy my hand gestures which he then shows to his grandparents the next day! But looking at how he closely observes me made me wish I actually was way cooler than I am (Sorry, Nile!). It also made me realize if my son is looking at me so intently, there is a certain weight I feel in showing my best version to him as much as possible. Almost a decade ago, my manager at my old job in Bangladesh once mentioned to me that our lives are basically like “balance sheets”. Just as a company’s balance sheet contains almost everything that has happened since its inception, whatever happened with our ancestors and whatever our ancestors did, our genes contain all the remnants of the past. However, just like the recent history has obviously much more influence to a company’s balance sheet than what happened 50 years ago, our parents can have similar effect on us. Nonetheless, human beings are lot more complicated than “balance sheets” and I do see myself more as a “[shepherd and not an engineer](https://www.instagram.com/reel/DMQAcgBI2fe/?ref=mbi-deepdives.com)” for Nile. But since we both grow from the “same vine”, I want to be a good shepherd (great song!) I recently came across this [quote](https://www.goodreads.com/quotes/10442-people-say-that-what-we-re-all-seeking-is-a-meaning?ref=mbi-deepdives.com) by Joseph Campbell while listening to a podcast: > “People say that what we’re all seeking is a meaning for life. I don’t think that’s what we’re really seeking. **I think that what we’re seeking is an experience of being alive**, so that our life experiences on the purely physical plane will have resonances with our own innermost being and reality, so that we actually feel the rapture of being alive.” There is hardly anything that made me feel alive more than looking at Nile smile! That may seem rather banal way to feel the rupture of being alive, but I personally sense it is indeed the deepest source of feeling alive. If we could only feel alive by climbing the Mount Everest or being Billionaires, the essence of life would be obviously beyond most of our reach. But mother nature has, fortunately, kept it far simpler than that. It doesn’t require us to keep accomplishing ever crazier adventures to feel alive; perhaps all most of us need is to just stare at our own offspring! Merry Christmas, everyone! I will be taking some time off between December 25th and December 30th to keep staring at Nile for an uncomfortably long period. I hope to publish my annual letter on the 31st and then take another couple of days off in the new year before starting to publish everyday in 2026 from January 3rd! [Subscribe](#/portal/signup) ![](https://substackcdn.com/image/fetch/$s_!PSYs!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7a85e784-7626-4ffd-874c-e6abc69faba8_745x1207.png) ### The Ecosystem Advantage URL: https://www.mbi-deepdives.com/the-ecosystem-advantage/ Last updated: 2025-12-23T20:02:16.000Z Like “Spotify Wrapped”, ChatGPT yesterday let its users see their 2025 stats which was fun to go through. I almost exclusively use ChatGPT Pro model (with extended thinking) which makes it hard for me to casually chat with ChatGPT as it typically takes 10-20 minutes for each of my queries. Nonetheless, ChatGPT is telling me I was top 5% users in 2025. ![](https://substackcdn.com/image/fetch/$s_!YrzL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F848315ce-512c-41ee-9d2b-04a822418914_901x1162.png) Image: My personal ChatGPT stats in 2025 I think ChatGPT has such a great product market fit (PMF) for investment, and tech folks that I suspect \~50% of people working in analyst roles or people who needs to code a lot are likely in the top 5% users of ChatGPT this year. In fact, when I shared my stats in a couple of investing group chats I am active in, it turns out **everyone else is top 1% users**! I will be surprised if my usage doesn’t increase next year; my usage (I think) has gradually increased over the course of 2025. However, given such an incredible PMF in our particular use cases can also make us susceptible to extrapolate our own experiences to the wider world perhaps a bit too much. One of my friends was wondering if usage keeps increasing on ChatGPT, will there be any person or any company that can get to know you better than ChatGPT can in a few years? The answer to that question can have profound implications, especially in economic context. But I think the answer depends a lot on who the user is. If you are consistently in the top 1% ChatGPT users for a few years, it is highly likely that ChatGPT will “know and understand” you better than any person or company can, and it is an innate desire for humans to interact with people/things that just “get” them. But it can remain a pretty uphill task for ChatGPT to “understand” \~90% its users who likely just don’t spend as much time on the app as top 1-5% does. Ultimately, it requires a lot of agency for the user to come up with a question they can ask. If you don’t have a question to ask (or asks one very sparingly), ChatGPT is close to a blank slate in terms of outlining a picture of who you are, and what you may be interested in. Contrast this with Google who has an entire ecosystem of products to know you better which will likely come pretty handy as they let AI features gather more context and data across the ecosystem to create a holistic picture of its users. Take Google’s new feature called “[CC](https://labs.google/cc?ref=mbi-deepdives.com)” in Gmail. Google hasn’t released this feature for the wider public yet, but you can join the waitlist. I signed up for this last week, and basically what “CC” does is it goes through all my emails/calendar everyday and then send me one email in the morning that I should pay attention to. On the very first day, it reminded me to update the payroll for the nanny we recently hired for our son. Even if I stop using Google search tomorrow, Google will still have a lot of these handy contexts to make its products more useful for me. I’m not sharing my personal email’s screenshot, but here’s one from Google: ![](https://substackcdn.com/image/fetch/$s_!WY-U!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6429b1c0-2e7b-47ef-b4ed-9f7bb08ea9af_1451x2019.webp) Source: [Google Labs](https://labs.google/cc?ref=mbi-deepdives.com) While clicking the Gemini’s diamond icon on top right on my Chrome browser yesterday to summarize a piece I was reading, Google asked me the following. Notice what it says: “when you use this feature, page content and URLs are sent to Google.” Even if I don’t use Google search/Gemini chat, Google will have a pretty clear understanding what I am reading and browsing across the internet. ![](https://substackcdn.com/image/fetch/$s_!wPKD!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F374a34ae-8bc4-42e9-97ec-2e860bc84c98_742x708.png) Then consider YouTube. Recently, I have been going through a lot of YouTube videos to understand the host experience on Airbnb. As we all know, YouTubers are incentivized to lengthen their video to increase watch time and hence almost all the videos about their experience are 10-20 minutes long. I could basically cut down the time spent on these videos by 90% just by clicking “Ask” button on YouTube to get a summary of the video. I could probably go through 20 such videos in 20 minutes instead of just one. I can probably go on half a dozen other Google products to make the same point. As you can see, Gemini is quite useful for me even though Gemini is not my primary chat bot. ![](https://substackcdn.com/image/fetch/$s_!HwP2!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3a6a8216-1b8d-4464-aac7-fe6697356f17_1687x1185.png) The real advantage of Google, however, is its ability to monetize Gemini through ads even though they are not putting ads on the core Gemini chat bots itself. People speculate when Google may insert ads in Gemini; frankly speaking, I will be surprised if they insert ads anytime soon. It is far more strategically advantageous for them to keep core Gemini experience ad free to essentially force ChatGPT to not introduce ads as well. Google already has the widest canvas you can think of monetizing Gemini through ads, a point eloquently made by Eric Seufert in a recent [piece](https://mobiledevmemo.com/gemini-in-chrome-and-gmail-and-googles-ai-distribution-conduit/?ref=mbi-deepdives.com): > “whether ads are ever inserted into Google’s Gemini *app* is largely irrelevant: **Gemini already monetizes with ads through the vast surface areas of AI Overviews and AI Mode.** And in fact, for that reason, the user base scale of the Gemini app is also mostly inconsequential: **Google has consistently applied Gemini to its portfolio of consumer-facing products, like Search, Chrome, and Gmail, in ways that may not directly incorporate advertising but nonetheless support the advertising business model.**” The question of economics is likely to become more front and center over time. The Information recently [reported](https://www.theinformation.com/articles/openai-getting-efficient-running-ai-internal-financials-show?rc=4lgoj7&ref=mbi-deepdives.com) that OpenAI’s computing margin has substantially improved from \~35% in Jan’24 to \~68% in Oct’25\. That sounds very impressive, but remember, this is only for paid users which is typically \~5% of OpenAI’s users. The cost associated with the rest \~95% who are using ChatGPT are not considered in this “margin” calculation. For ChatGPT, it is perhaps all but necessary to introduce ads at some point soon, but they know it’s almost certainly going to be bit of a headwind for incremental growth, especially when Gemini chat experience will likely remain ad free. And of course, the question that looms large is the training related costs. Even if you make respectable margins on inference, the question of when can you really get off or at least slow down the treadmill of training the next model is the most uncomfortable question in AI. ![](https://substackcdn.com/image/fetch/$s_!ul0u!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F665ed595-ed7a-4aa9-a424-6872e1599f3e_1441x754.png) Image Source: [The Information](https://www.theinformation.com/articles/openai-getting-efficient-running-ai-internal-financials-show?rc=4lgoj7&ref=mbi-deepdives.com) A reader recently highlighted a [piece](https://hellochinatech.com/p/running-out-of-runway?r=9x0z5&utm%5Fmedium=ios&shareImageVariant=overlay&triedRedirect=true&ref=mbi-deepdives.com) on China’s two frontier AI labs: Zhipu, and MiniMax—both of which filed for IPO last week. It’s an interesting piece that I recommend reading and really bears light to the questions I have been alluding here. Now, there are certainly massive differences between these Chinese labs go-to-market and OpenAI's, but the core questions are still valid. Some excerpts from the piece: > “Zhipu’s gross margin sits at 56%. Strong by any standard, especially for a company selling complex enterprise software. MiniMax’s cloud API business generates 69.4% gross margins. These are SaaS-level economics. > > Yet Zhipu lost ¥2.47 billion on ¥312 million in revenue last year. The loss is eight times the revenue. MiniMax lost $465 million on $30.5 million in revenue. Fifteen times. > > Research and development consumed ¥2.2 billion of Zhipu’s budget in 2024\. That’s a 26x increase from the ¥84 million spent in 2022\. **Within that R&D figure, ¥1.55 billion went directly to compute services. Computing infrastructure alone ate 70% of the entire R&D budget**. > > MiniMax shows better cost discipline but faces the same fundamental pressure…The ratio of training costs to revenue dropped from 1,365% in 2023 to 266% in the first three quarters of 2025\. But even at 266%, you’re spending nearly $3 on training for every $1 of revenue. > > This creates the first paradox. At the transaction level, these businesses are profitable. Sell an API call or a subscription, you make money. Scale that up, you should make more money. **But scaling requires maintaining competitive model quality. Competitive model quality requires constant compute investment. The compute investment grows faster than revenue**…**competition determines the required investment level, not customer demand.** > > Zhipu released six core models in less than three months following R1’s debut. That kind of release cadence doesn’t happen with planned budgets. It happens when competition forces your hand. Each model release requires compute for training, evaluation, and deployment. The costs compound. > > MiniMax faced the same pressure despite its efficiency advantages. Being 100x more capital-efficient than OpenAI means nothing when a domestic competitor proves you can do more with even less. The bar keeps rising. The cost of staying relevant keeps climbing. > > **This reveals what makes the situation structural rather than cyclical. Your strategy becomes irrelevant when competitive dynamics dictate behavior**. Zhipu chose scale. MiniMax chose efficiency. DeepSeek’s emergence forced both to spend more regardless of their chosen path. > > The competitive advantage has shifted from technical capability to balance sheet depth. Zhipu and MiniMax both demonstrated they can build competitive models. That’s no longer sufficient. **The question becomes: can you afford to keep building competitive models quarter after quarter as the bar keeps rising**? When DeepSeek forces another iteration cycle, can you write another ¥1 billion check? > > Platform companies gain structural advantage because they can sustain losses that would bankrupt independents. ByteDance, Alibaba, and Tencent control compute infrastructure. They own distribution channels. They generate cash from other businesses. A ¥2 billion annual loss is a rounding error in their consolidated P&L statements. They can treat AI model development as a strategic investment, not a profit center that must justify itself quarterly.**”** Speaking of ecosystem advantage, let’s go back to Eric Seufert who ended his [piece](https://mobiledevmemo.com/gemini-in-chrome-and-gmail-and-googles-ai-distribution-conduit/?ref=mbi-deepdives.com) with the same conclusion as well: > “The entirety of Google’s consumer-facing product suite (including YouTube), as well as the Google Cloud Platform, is a distribution conduit for the company’s AI initiatives. This spans multiple billions of consumers, *all of which* are already monetized through ads. Every dollar Google invests in AI research and development can immediately improve an existing revenue line item.” While the questions around Google search seemed existential not so long ago, the market has come to eventually appreciate this ecosystem advantage as Alphabet is fittingly ending 2025 being the best performing megacap tech stock this year! ![chart](https://substackcdn.com/image/fetch/$s_!M9eO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fad653222-9d10-453d-ac78-456de0cfa4dd_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Veeva's Opportunity in Clinical Software URL: https://www.mbi-deepdives.com/veeva-clinical/ Last updated: 2025-12-22T15:47:31.000Z In my [**Veeva update**](https://www.mbi-deepdives.com/veeva-update/) a couple of days go, I highlighted the below chart to make the point that Veeva clearly expects clinical segment to be their major growth opportunity in their journey to reach $6 Billion revenue run-rate by 2030\. Given that context, I wanted to deepen my understanding on clinical software which I will discuss behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!ZSdY!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcde9ac05-c817-45e3-831f-70d6e01be9d7_1297x781.png) Source: Veeva 2025 Investor Day --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Sam Altman's Explanation URL: https://www.mbi-deepdives.com/sam-altmans-explanation/ Last updated: 2025-12-21T16:05:43.000Z Jensen Huang in his recent Joe Rogan [podcast](https://www.youtube.com/watch?v=3hptKYix4X8&t=3376s&ref=mbi-deepdives.com) had an interesting story from early years of Nvidia: > “**we convinced ourselves** that chip is going to be great. And so I had to call some other gentleman. So I called TSMC…and I explained to them what we were doing. And I explained to him (Morris Chang) I had a lot of customers. **I had one**, you know, Diamond Multimedia…the demand's really great, and we're going to tape out a chip to you, and I like to go directly to production because I know it works. > > And they said, "Nobody has ever done that before. Nobody has ever taped out a chip that worked the first time. And nobody starts out production without looking at it." > > But I knew that if I didn't start the production, I'd be out of business anyways. And if I could start the production, I might have a chance. > > …as we were starting the production, Morris flew to United States. He didn't so many words asked me so, but **he asked me a whole lot of questions that was trying to tease out do I have any money** but he didn't directly ask me…**so the truth is that we didn't have all the money but we had a strong PO from the customer and if it didn't work some wafers would have been lost. I'm not exactly sure what would have happened but we would have come short, it would have been rough**.” History doesn’t repeat, but it does rhyme. Two and half decades later, there is another American CEO who is trying to convince everyone that they have a lot of demand! Perhaps the key distinction is while both Nvidia and TSMC back then were hardly a footnote in the tech industry, OpenAI today is at the front and center of perhaps the most consequential technological revolution in history. If their demand forecast is substantially off, the value destruction in “AI trade” can be whole lot larger than if Nvidia couldn’t pay TSMC in mid 1990s. In a recent appearance on Big Technology [podcast](https://www.bigtechnology.com/p/sam-altman-on-openais-plan-to-win?ref=mbi-deepdives.com), Sam Altman was asked about OpenAI’s $1.4 trillion “commitment” to various players in the AI value chain. This was a good podcast with thoughtful, reflective answers from Sam Altman. It is worth listening to the entire episode, but I will focus primarily on his comments regarding infrastructure commitment. Here’s the excerpt on this point: > “…my learning in the history of this field is once the squiggles start and it lifts off the x-axis a little bit, we know how to make that better and better. But that takes huge amounts of compute to do. So that’s one area—throwing lots of AI at discovering new science, curing disease, lots of other things. > > A kind of recent, cool example: we built the Sora Android app using Codex. **They did it in less than a month.** They used a huge amount—one of the nice things about working at OpenAI is you don’t get any limits on Codex. They used a huge amount of tokens, but they were able to do what would normally have taken a lot of people much longer. And Codex kind of mostly did it for us. And you can imagine that going much further, where entire companies can build their products using lots of compute. > > People have talked a lot about video models pointing towards these generated, real-time generated user interfaces that will take a lot of compute. Enterprises that want to transform their business will use a lot of compute. Doctors that want to offer good, personalized health care that are constantly measuring every sign they can get from each individual patient—you can imagine that using a lot of compute. > > It’s hard to frame how much compute we’re already using to generate AI output in the world, but these are horribly rough numbers, and I think it’s undisciplined to talk this way, but I always find these mental thought experiments a little bit useful. So forgive me for the sloppiness. > > Let’s say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models. More, but it’s not like a quadrillion tokens for anybody, I don’t think. Let’s say there’s 8 billion people in the world, and let’s say on average, the average number of tokens outputted by a person per day is like 20,000—these are, I think, totally wrong. But you can then start—and to be fair, we’d have to compare the output tokens of a model provider today, not all the tokens consumed—but you can start to look at this, and you can say, we’re gonna have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that. > > In some sense, it’s like a really silly comparison, but in some sense, it gives a magnitude for how much of the intellectual crunching on the planet is human brains versus AI brains, and those relative growth rates there are interesting. This answer is a good encapsulation of why analyzing companies in the AI value chain has become more challenging. Altman’s explanation for demand is not non-sensical at all; it is certainly possible the average user in 5-10 years will utilize order of magnitude more tokens per day than we are today. But the more challenging aspect is to project how pricing of such token will evolve over time. While thinking about compute demand, I keep thinking about what I pointed in my [Illumina Deep Dive](https://www.mbi-deepdives.com/ilmn/): > “I do find it quite interesting that Illumina’s revenue will be basically flat from 2021 to 2026\. As alluded earlier, studying Illumina is bit of a cautionary tale how Jevons paradox doesn’t really absolve us from difficult questions. Just to give you perspective, while Illumina’s core revenue slightly declined in both 2023 and 2024, their sequencing volume data kept growing at a pretty healthy rate. Given cost of sequencing fell faster thanks to transition to higher throughput instruments as well as due to potentially competitive factors, Illumina couldn’t grow their revenue. **When cost of sequencing kept falling, Illumina’s customers did increase volume of sequencing materially but that wasn’t enough to outweigh the pricing pressure**. ![](https://substackcdn.com/image/fetch/$s_!Duet!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa410c3df-7556-492b-9ff4-fd85490845e3_1024x640.png) Illumina's sequencing volume growth data Later in the podcast, Altman shared how they plan to reach profitability: > “As revenue grows and as inference becomes a larger and larger part of the fleet, it eventually subsumes the training expense. So that’s the plan. Spend a lot of money training but make more and more. If we weren’t continuing to grow our training costs by so much, we would be profitable way, way earlier. But the bet we’re making is to invest very aggressively in training these big models.” The only problem is…that is everyone’s plan! **The optimal strategy for Google is to keep inference prices so low that it remains very hard for inference revenue to subsume training runs for all the other model developers**. If OpenAI had a monopoly in building frontier models, you can bet their inference revenues would be easily able to supersede training costs and perhaps make respectable operating margins. But if everyone remains in lock-step in the red queen race of training the next model while the pricing for inferences keeps falling precipitously, the **economics** can remain far from compelling. It’s a risky bet when such questions are still pretty much up in the air, especially at [$830 Billion valuation](https://techcrunch.com/2025/12/19/openai-is-reportedly-trying-to-raise-100b-at-an-830b-valuation/?ref=mbi-deepdives.com). But hey, it worked out just fine for Nvidia even though Jensen too didn’t really have demand lined up for his chips. The age old American audacity of "just go for it" without having all the answers may still have its final say! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Veeva Update URL: https://www.mbi-deepdives.com/veeva-update/ Last updated: 2025-12-20T18:28:40.000Z Most software stocks are in bit of a tough spot in 2025\. Even till late October this year, IGV (tech-software ETF) was slightly trailing both SPY and QQQ, but today its YTD is lagging SPY and QQQ by \~10 and \~14 percentage points respectively. Veeva has experienced a more dramatic version of this route as it went from +45% YTD in late October to just +5% YTD now. ![chart](https://substackcdn.com/image/fetch/$s_!BP5h!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fef951da2-01d0-4be7-965b-45f694b80b7a_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I have [**explained**](https://www.mbi-deepdives.com/tyler-technologies-portfolio-change/) before how I try to pay attention to potential dislocation in stock prices, and it did catch my attention that while I was [**modeling**](https://www.mbi-deepdives.com/veev/) \~$3.13 Billion revenue for Veeva in FY’26, they guided for \~$3.17 Billion revenue in FY’26\. More interestingly, I was only modeling $780 Million GAAP EBIT in FY’26\. However, their LTM GAAP EBIT is already $859 Million, thanks to their operating margin running \~400 bps higher than what I was modeling. Their actual EBIT in FY’26 may turn out to be **\~15% higher than my estimates** even though the stock is largely flat since my Deep Dive. That is usually a good set up for me to dig a little deeper to assess whether the stock is now sufficiently attractive. So, I did go through their October Investor Day as well recent earnings and sell-side transcripts to gauge more recent developments and contexts. I will elaborate my findings as well as my current thoughts on the stock behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Why I am not writing a Deep Dive this month URL: https://www.mbi-deepdives.com/why-i-am-not-writing-a-deep-dive-this-month/ Last updated: 2025-12-19T13:56:56.000Z I was working on this month’s Deep Dive on Nvidia. I was excited to finally study the largest company in the world with focus and patience it deserves, but after spending the last three weeks on the business, I have decided to give up on writing the Deep Dive. I still feel too much gaps in knowledge to be comfortable writing a Deep Dive on the company. While this is unfortunate and I apologize to you for the inconvenience, I think it can be instructive to understand how I came to realize this. I listened to all the Acquired series on Nvidia (Part [1](https://www.acquired.fm/episodes/nvidia-the-gpu-company-1993-2006?ref=mbi-deepdives.com), [2](https://www.acquired.fm/episodes/nvidia-the-machine-learning-company-2006-2022?ref=mbi-deepdives.com), [3](https://www.acquired.fm/episodes/nvidia-the-dawn-of-the-ai-era?ref=mbi-deepdives.com), [interview](https://www.acquired.fm/episodes/jensen-huang?ref=mbi-deepdives.com) with Jensen Huang). Huang’s very recent [interview](https://www.youtube.com/watch?v=3hptKYix4X8&t=3376s&ref=mbi-deepdives.com) with Joe Rogan was also helpful to appreciate some of the near death moments Nvidia faced. I was particularly touched by Huang’s upbringing and I came away quite appreciative of his innate ability to evolve and adapt to almost any situation Huang (and Nvidia) may find themselves in. I listened to a bunch of episodes ([here](https://joincolossus.com/episode/376-jensen-huang-founder-of-nvidia/?ref=mbi-deepdives.com), and [here](https://joincolossus.com/episode/403-how-jensen-works/?ref=mbi-deepdives.com)) on Founders podcast to get to know Nvidia and Huang’s story better. After having a reasonable grasp on the history, I started going through their SEC filings. I read Huang’s all the shareholder letters. There are too many good bits to quote, but let me share a couple that stood out. > “Foreseeing the importance of **energy efficiency, we set out half a decade ago to build high-end parallel and mobile processors**. These groundbreaking initiatives sought to address **two of our fundamental convictions: that power will limit the number of computers in large data centers,** and that energy efficiency will define our experience with mobile devices” > > “There is already well over 1 exabyte of images and videos in the cloud, more than 100,000 times the books in the Library of Congress. Hundreds of millions of photos are uploaded daily. And the amount of data created will rise 50-fold this decade, according to IDC. **Our GPUs can make a real contribution to processing this deluge — enabling computers to learn how to help us search a world filled with images. It is an opportunity that could potentially require millions of our GPUs**. Here’s a quiz: when do you think Huang wrote these sentences in his shareholder letters? The first quote was taken from **2011** shareholder letter, and the latter from **2013**. This was my reaction after reading these quotes... ![Lisan Al Ghaib Meme - Lisan al ghaib - Discover & Share GIFs](https://substackcdn.com/image/fetch/$s_!EeMt!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F99a0d6f0-c76f-4707-a734-f1a075a3eb41_220x180.gif "Lisan Al Ghaib Meme - Lisan al ghaib - Discover & Share GIFs") I built a shell excel model for the company and the way their revenue and earnings ramped up, it is fair to say I have never seen anything like it. I wanted to see what some analysts were saying about Nvidia before some of these eyepopping numbers showed up in their financials. I read a bunch of them, and I was pleasantly surprised how Scuttleblurb’s 2021 series on Nvidia (Part [1](https://www.scuttleblurb.com/nvda1/?ref=mbi-deepdives.com) and Part [2](https://www.scuttleblurb.com/nvda2/?ref=mbi-deepdives.com)) touched on so many key areas that became part of the conversation in the next few years (The Bitter Lesson, Jevon’s Paradox, competition from ASICs, detailed explanation of CUDA moat etc.). I let Scuttleblurb know how much I enjoyed reading his pieces, and fortunately, he took paywall off his Nvidia pieces later. So, you can all go and read it yourself. I also want to highlight this [piece](https://www.fabricatedknowledge.com/p/gpt-3-and-the-writing-on-the-wall?ref=mbi-deepdives.com) by Fabricated Knowledge back in 2020 who was admirably early in outlining the “future”. To get a sense of the current competitive landscape of Nvidia, I went through a bunch of work done by Semianalysis (see their work on [AMD](https://newsletter.semianalysis.com/p/amd-advancing-ai-mi350x-and-mi400-ualoe72-mi500-ual256?utm%5Fsource=publication-search), [TPU](https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-swing-at-the?ref=mbi-deepdives.com), [Trainium](https://newsletter.semianalysis.com/p/aws-trainium3-deep-dive-a-potential?ref=mbi-deepdives.com), for example). Gavin Baker’s recent [podcast](https://joincolossus.com/episode/nvidia-v-google-the-economics-of-ai/?ref=mbi-deepdives.com) on ILtB was also helpful in understanding the rivalry between Google and Nvidia. I probably mentioned only \~30% of the content I went through during my research process, but you can sense there is a deluge of content on Nvidia. After consuming a good chunk of it and feeling reasonably good about my understanding, I started writing the Deep Dive myself. After making 20% progress on writing the Deep Dive, I only started appreciating the gaps of my knowledge when I got to more meaty parts of the Deep Dive. Frankly speaking, if I didn’t try to write a Deep Dive, it would have been lot easier to convince myself that I understand the business reasonably well. But writing is a forcing function in making you realize your gaps in understanding. AI didn’t quite help because AI’s answers were only making it more apparent to me that the gaps may be wider than I appreciated. It also didn’t help that there is some news flow about Nvidia that is happening on a daily basis which further eroded my confidence in my ability to write a Deep Dive that can maintain its relevance even a year form now. So, after writing a Deep Dive every month since September 2020, I came to this unfortunate decision to pause writing the Deep Dive for this month. However, since I have an “okay-ish” foundation of knowledge on Nvidia now, it will be marginally easier for me to follow the company going forward. I would like to follow and cover their quarterly earnings, and major events such as GTC more closely for a couple of years before making another attempt of writing a Deep Dive on the company. While everyone is talking about semis, the whole experience was a stark reminder that the technical nature of the industry remains quite challenging for not only to get up to speed but to keep up with the industry. I am not giving up on covering semis in the future, but just appreciating the patience it will require to get there. It’s not ideal, and I wish I had a better update for you. Thank you for your understanding. **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Digital Advertising's "Bitter Lesson" Moment URL: https://www.mbi-deepdives.com/digital-advertisings-bitter-lesson-moment/ Last updated: 2025-12-18T13:48:48.000Z Eric Seufert recently had an interesting [podcast](https://podcasts.apple.com/us/podcast/season-6-episode-21-does-genai-outperform-humans-with/id1423753783?i=1000740506093&ref=mbi-deepdives.com) with a couple of professors who authored the [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract%5Fid=5638311&ref=mbi-deepdives.com) titled “The Impact of Visual Generative AI on Advertising Effectiveness**”.** The authors pursued four specific lines of inquiry to understand how visual Generative AI (genAI) fits into the advertising landscape. Here are the four questions from the paper (emphasis mine): > Does allowing visual genAI to **modify** existing expert-created advertisements enhance advertising effectiveness? In other words, do **“GenAI-modified ads” outperform their original “human expert-created” counterparts?** > > Does allowing visual genAI to **create new advertisements from scratch** enhance advertising effectiveness? In other words, **do “GenAI-created ads” outperform “human expert created” (and “GenAI-modified”) ads?** > > Does allowing visual genAI to also **redesign product packaging** shown in the advertisements further enhance advertising effectiveness? > > Does **disclosing** to consumers that genAI was involved in producing the ads, either by modifying or fully creating them, affect advertising effectiveness? To answer these questions, the authors set up a rigorous “Man vs. Machine” competition involving both lab tests and real-world spending. First, they built a library of advertisements. They took real, historical ads from a beauty retailer (the “Human Expert” baseline). Then, they acted as creative directors for the AI, using tools like Midjourney, Stable Diffusion, and DALL-E to create two types of challengers: a) “**Modified” Ads:** They asked the AI to act as an editor, tweaking the human ads by adding faces, nature scenes, or artistic filters, while forcing it to keep the original layout; and b) **“Created” Ads:** They asked the AI to act as an artist, generating brand-new ads from scratch based on text prompts, sometimes even letting it redesign the product packaging itself. You can see one of the examples from the paper below: ![](https://substackcdn.com/image/fetch/$s_!sWQ1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7f99caa9-8746-486c-bdd2-2aa24bb7262f_1705x874.png) Image Source: Click [Here](https://download.ssrn.com/2025/12/11/5638311.pdf?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEM3%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQDgc3kR1I0lDM2zc1XEUhX6o85u%2B%2FgRkFwhAnaFZzQ0RgIgGWGkyrdLQl1d4RDK5UYP6qJKvNl1aV%2Fm0cGobHKpS1gqxgUIlv%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAEGgwzMDg0NzUzMDEyNTciDFAPCkrcD04rTa9U7SqaBTc2KxDqkLuYMyT%2BZQkEgjQCKIL9x12lp%2F8%2FoYPc9pLgwxbpO2ZtB5BlTdA9FxisYYTD2gvVKbrg0eBSWrF5jvRG80iwOH%2FRiyx%2Fo42zBd2mBIdmvtGaVsjUL2av4Y29NwE4m79%2BlRhOWj9%2FjqmPxxKQNc8M3Wv%2BhgnAXJJEI6HL4o2lbLGMo%2B8401dLX9ZyHbY1gloF%2Ffptl3Ynq1SBTLQAnVLTLpZEalEuE9iOQf46ukKySdmnHImdzlFUVMiSTXhDiRcdRUbG6mkb0hYtPXyy1a961agUPPOSYPXYUajnx%2BrEuRmCCTqmIY%2BwgYL5teQRyjH69ks3seAUmshSwatFyTXPMewAhv0WgQOE0eK0G4z729lfkZdBxZavK0j9IJmOqc8IdHyaCjXn5Byi96fkMze44vCCXcuqEZXCsLYkIc3FmhI2tP8lxa81cuV9BfH85OcDQVzCwC9YAvuiPLiKYQG5VJ0jwAjtbbATkEnqCPckW95s%2BOtI4QNVA7tBrS155QTGp1HMFbaW6IKsOo82X0uYYOyyeBXQyHENrL2gnPqVP75ZZgLfAK0gaYseYbRc9PYvyfjNqarMi3lh58%2BKjOBxdoZx9vTu4gPFIupL8Dn79bFdLsN4pcRpk7aJHYXJ3n6EcWChYcehtRwZ1yl%2ByWdZSTQzxDvdxgDIBnkCHxNw%2BSrLGNuqqFmN7pkU1V0a11iLy8gN79ZWbA3ZRs05dVTqP5ePdfbY4PVNKvibb83C0sIuILIfaKxUQlfivhKcMTJWLVh7xfNhorScAftkn10UrD2XvezwEmmJ6rz5XLmOnWEn%2FwtRAma1A3ASty%2BAJ1g0koPeJmqzkG3jizr6WLjqY5AoKL4IrrA9gx6lqjrvu1Rmh3XCxjDr5I%2FKBjqxAaysC95NeZxri1llj5kGyw74xfKr8czyLwtlJJmNFkA6u2AhDM5BaxnrZ8awiVKMnlKArI5xOUj7KP6boXfvpVdTTROc2m5QD4SuQuzcih7PUlkkDdy9%2FP%2F%2F%2FpX0EMzveZESiHtcZEO4Huiz5sZTGq5GTSbFZ9Y4cNShPp%2FCZ1MBHu89VEMswAOAi8OPhmiYhrB0a52p0%2F%2Fu%2Bh11kse1dDYLnBJf3HA1VoGXAPSCWBXh3w%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20251218T130900Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAUPUUPRWE6HKIAKK2%2F20251218%2Fus-east-1%2Fs3%2Faws4%5Frequest&X-Amz-Signature=9290667547e6660db634ee6773c805a5e611cc1adb2c235a6b9727872f487dc9&abstractId=5638311&ref=mbi-deepdives.com) The researchers then ran two major tests: a) they recruited nearly 700 participants and showed them these ads in a controlled setting. They asked how likely they were to buy the products and measured psychological factors like how “real” or emotionally engaging the ads felt., and b) to prove this worked in the “wild,” they spent money on a real Google Ads campaign. They ran the human, AI-modified, and AI-created ads for cosmetic brands over four months, tracking over 100,000 views to see which versions real people actually **clicked** on. So, they only had click-through rate (CTR) data for this study, and not actual conversions. The study found a massive performance gap between asking AI to “fix” an ad and asking it to “make” one. In the Google Ads campaign, **GenAI-created ads** (made from scratch) achieved a **19% increase** in CTR compared to human-expert ads. Interestingly, **GenAI-modified ads** provided no significant improvement over human designs. They also found **no difference** in performance based on which AI models were used to create the ads. The effectiveness was amplified when the AI was given **even more creative freedom**. When genAI created both the advertisement **and** the product packaging design, it boosted the CTR by \~**15%** relative to genAI ads that used the standard packaging. It didn’t come up during the podcast, but listening to it was yet another reminder to me of Rich Sutton’s “[the bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html?ref=mbi-deepdives.com)” in AI! Sutton’s “bitter lesson” (in spirit) is that systems that scale i.e. learning/search with lots of compute/data tend to **beat** systems where humans hand-engineer clever structure, **even when** the hand-engineering looks smart in the short run. That’s precisely why the lesson feels bitter. We all like to imagine us trying to act clever **should** triumph over more hands-off, simpler approach. When the researchers in this paper tried to inject human constraints i.e. forcing the AI to work **within** the layout and structure of a human-designed ad, the AI performed poorly. It was limited by the “human prior” and struggled to make the edits look natural. However, when genAI gets to create from scratch (and even design the packaging), it has more degrees of freedom to build a coherent visual story. As the researchers removed the constraints and let the model generate the entire artifact from scratch (leveraging its massive training on billions of images), it outperformed the human experts. That feels to me yet another evidence of “bitter lesson” in AI. These research findings are super consistent with the vision Zuckerberg tried to outline in 3Q’25 call: > “…advertisers are increasingly just going to be able to give us a business objective and give us a credit card or bank account and like have the AI system basically figure out everything else that’s necessary including generating video or different types of creative that might resonate with different people that are personalized in different ways, finding who the right customers are. All of these -- all of the capabilities that we’re building, I think, go towards improving all of these different things. So I’m quite optimistic about that.” It is, however, interesting that this study also found that explicitly labeling an ad as “AI-generated” or “AI-edited” caused the CTR to plummet by **31.5%.** Thankfully, that’s hardly a problem. Just as nobody cares whether someone created an ad using Adobe or Canva, consumers don’t really need to know whether an ad is AI generated or not. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Reality of Meta's Reality Labs URL: https://www.mbi-deepdives.com/the-reality-of-metas-reality-labs/ Last updated: 2025-12-17T15:46:19.000Z You have all seen the chart of Meta’s Reality Labs (RL) losses. Just as a reminder, ever since Meta started reporting RL as a separate segment, it has reported a cumulative loss of $73 Billion in the last 20 quarters. Obviously, they were incurring losses even before reporting the segment separately; so it is very conceivable that Meta’s accumulated losses tally may be **$100 Billion** by 2025. Sometimes, I joke with my friends that as a Meta shareholder, I often wonder if I will receive a Christmas gift from RL employees since all the shareholders have been paying for this with eyewatering losses year after year. As the largest shareholder of Meta, Mark Zuckerberg is the Chief Generosity Officer here. Thankfully, there were recent reports that Meta intends to cut “[up to 30% of Metaverse budget](https://www.reuters.com/business/meta-ceo-zuckerberg-plans-deep-cuts-metaverse-efforts-bloomberg-news-reports-2025-12-04/?ref=mbi-deepdives.com)”. It appears much of this cut will affect VR and related investments in Metaverse whereas Meta’s investments in AR glasses is likely to remain unaffected. For context, half of Meta’s investments in RL is already in AR glasses. However, another recent news report that received lot less attention really soured my optimism even for Meta’s efforts in AR Glasses which I will discuss behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!MMm6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0c4fbad1-949b-4729-a5ef-6302b6298dc7_1237x706.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/) [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### CoStar and Zillow's Google Risk URL: https://www.mbi-deepdives.com/costar-and-zillows-google-risk/ Last updated: 2025-12-16T17:40:54.000Z It seems Google’s masks are off now that the regulatory monkeys are increasingly off its back! Yesterday, Inman [reported](https://www.inman.com/2025/12/15/analyst-portals-like-zillow-face-long-term-risk-from-google-real-estate-experiment/?ref=mbi-deepdives.com): > “Real estate data and analytics firm HouseCanary shook up the industry over the weekend with its Google partnership, which prominently displays ComeHome listings at the top of search results for real estate in several key markets, including Chicago, Denver and Austin. ComeHome is HouseCanary’s home search site and is **available in 13 markets**. > > Buyers can scroll through listing information and schedule a home tour with a top-rated real estate agent selected by Google. The platform transfers the homebuyers’ information to the agent, with an expected response time of 15 minutes or less. The feature is **only available on mobile**.” Google understandably likes queries where the user is about to do an expensive thing. Real estate is among the most expensive things most people can do. So, Google would certainly want to keep the user on Google and capture the “contact an agent” step in our real estate buying journey. The screenshot from Mike DelPrete’s [blog](https://www.mikedp.com/articles/2025/12/12/google-enters-the-portal-wars?ref=mbi-deepdives.com) (which probably broke this news) should feel familiar to anyone who ever searched for a flight or book a hotel, but this time Google is going up the ladder in real estate! ![](https://substackcdn.com/image/fetch/$s_!s1qk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd8066a27-57c7-4a42-87f9-26ee69bc5076_1430x782.png) Image Source: [Mike DelPrete](https://www.mikedp.com/articles?author=57b1c7569de4bb3199d69106&ref=mbi-deepdives.com) Wait a minute, is Google even allowed to do that? To answer that question, let’s take a step back and understand how the system currently works. I will elaborate on that and how this may affect CoStar and Zillow behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Adobe and Airbnb URL: https://www.mbi-deepdives.com/adobe-and-airbnb/ Last updated: 2025-12-15T15:20:31.000Z Yesterday, I stumbled onto this time-series chart of Adobe and Airbnb’s EV to LTM Gross Profit (GP) multiple. I thought it was quite interesting that they trade at almost the same LTM EV/GP multiple. ![chart](https://substackcdn.com/image/fetch/$s_!RVmi!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F78f19813-c00d-411e-b8b4-5ba0eba014c1_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) While Adobe consistently grew gross profit at low double digit rate, Airbnb used to grow at a much higher rate post-pandemic, but now largely converged to Adobe’s growth profile in the last few quarters. ![chart](https://substackcdn.com/image/fetch/$s_!hNKi!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F63825b91-2747-400b-b484-9f3d23de42e6_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I posed a question to my X (formerly twitter) followers: In 5-10 years from now, which company will have higher maintenance opex as a percentage of their core revenue? Interestingly, more people thought it was Adobe which will have **higher** maintenance opex as percentage of their core revenue! ![](https://substackcdn.com/image/fetch/$s_!Rmz3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff43bf4ad-7e0e-4dd3-9f47-8484af5114e1_913x564.png) I will elaborate how I myself think about this question behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Booking on the Offense URL: https://www.mbi-deepdives.com/booking-on-the-offense/ Last updated: 2025-12-14T15:39:30.000Z Last week, Booking’s CFO Ewout Steenbergen attended Nasdaq Morgan Stanley London TMT Conference. Booking’s CFO re-iterated their “8-8-15” algorithm for the “next few years”. For the uninitiated, “8-8-15” stands for 8% gross bookings growth, 8% revenue growth, and 15% EPS growth. I also really liked how the CFO framed their buyback framework, and given the stock was in a 20% drawdown in late November, the CFO wasn’t really shy about disclosing what they were doing: > “When there is a dislocation of the share price, according to the grid that we put in place, there is really quite a large increase in the daily buyback volume. **So one can assume that, that is happening now at this moment in the fourth quarter.**” ![chart](https://substackcdn.com/image/fetch/$s_!5dbo!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4494bf84-2625-41c8-a2e6-0d2da586db64_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) There's a reason I called them "[shareholder friendly OTA King](https://www.mbi-deepdives.com/bkng/)"! Of course, the meat of the discussion revolved around Booking’s approach to AI agents which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Synopsys' Pricing Evolution in Design IP URL: https://www.mbi-deepdives.com/snps_ip_pricing/ Last updated: 2025-12-14T01:40:01.000Z While design IP was as high as 39% of Synopsys total revenue back in 2022, it has come down to \~25% of revenue following Ansys acquisition. So, although this segment has somewhat declined in significance, the performance of this segment can still be needle mover for the company. It certainly doesn’t help that design IP revenue declined by more than 20% in FY 4Q’25 despite facing a pretty easy comp. To be clear, this was hardly a surprise for the investors. Management spoke at length about IP’s weakness in FY 3Q’25 call which drove the stock to be down 35% after that call which I [**covered**](https://www.mbi-deepdives.com/why-synopsys-sank/) earlier. ![](https://substackcdn.com/image/fetch/$s_!NFfL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6ffee9eb-5c83-4dbb-8d47-c0a3a58179a7_1147x571.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) One of the primary culprits for IP’s weakness is China. For the four consecutive quarters, Synopsys China revenue was down. While it may seem China revenue was down 9.3% in 4Q’25, after excluding Ansys contribution, it was actually down 22%! As a result, China’s contribution in Synopsys revenue mix declined from 16.1% in FY’24 to 11.5% in FY’25 (including Ansys). Synopsys management explained their predicament in China, especially for IP business: > “On the classic Synopsys side, what we’re facing in China is primarily our inability to sell to the market that needs the most advanced solution…**From a Synopsys standpoint, where it impacts us the most is in IP, given the proportion of our business and our leadership in IP, not only in China broadly, but in China that has a fairly big impact on Synopsys**. > > From EDA in general…we’re not losing share to our standard or the peers that you think about. **When we’re losing share in China are for customers our industry cannot sell to. And therefore, there’s an erosion that is happening on the EDA side on customers that we cannot deliver or support due to entity restriction or technology**. So we believe we have derisked it in our guide for ‘26\. And of course, when you pass ‘26 and assuming the environment is the same, meaning no additional restrictions, then the comps get easier in terms of comparing year-over-year.” ![](https://substackcdn.com/image/fetch/$s_!nfyR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5fde3a2a-4ca3-45eb-a453-20788a073540_1141x567.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Design IP margins predictably took a nosedive given the declining revenue. Design IP’s adjusted operating margin was 47% in 4Q’23, but it came down to only 13.8% in 4Q’25\. It’s actually even worse than that since these are adjusted numbers that add back SBC. Considering SBC as a percentage of overall revenue was 10.5% in 4Q’25, the real margin in design IP segment was likely barely breakeven. ![](https://substackcdn.com/image/fetch/$s_!S7mY!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F07be536f-8c39-4376-b890-51a82db4cd2e_1216x748.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Given this context, Synopsys likely feel increasing pressure to capture more value from their IP business. Back in 2024 Investor Day, Synopsys management explained their pricing structure in IP business: > …most of our contracts are a committed agreement with the customer. So we call them FSAs, flexible spending account, where the customer commits x millions of dollars for a duration of time, and they pull the IP when they need it. So they burn that flexible spending account. > > So we provide them that flexibility. It gives us the ability to forecast our business because those agreements are committed. The reason we don’t move to a subscription base in terms of the time base where you sell them a USB for 3 years, and they can pretty much use it on any chip start, it will impact our monetization ability. > > …The customer pays us even if the chip does not go in production…even if they design in the IP and the chip does not go into mass production, we get paid. However, Synopsys appears to be willing to make some changes to capture further upside. I will discuss this evolution, as well as a slight change in my portfolio, behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Lululemon's Succession URL: https://www.mbi-deepdives.com/lululemons-succession/ Last updated: 2025-12-12T14:59:17.000Z Regular readers are likely aware that I have lost my shirt by investing in Lululemon. To add further salt to my injury, the stock started going up a bit only after I sold it. Joking (or lamenting) aside, Lulu’s board came to the [**same conclusion**](https://www.mbi-deepdives.com/lulu2q25/) I did three months ago: Calvin McDonald should not remain CEO of Lululemon! McDonald became CEO of Lululemon back in [August 20, 2018](https://corporate.lululemon.com/media/our-stories/2018/lululemon-announces-new-chief-executive-officer?ref=mbi-deepdives.com). I’ll first let McDonald summarize his tenure at Lulu: > “Since 2018, lululemon tripled its annual revenue, and we expect to generate $11 billion this fiscal year. > > We have broadened our global reach from 18 to over 30 geographies and grown the company’s China Mainland business into our second largest market. We expanded the horizon for what’s possible for lululemon, quadrupling our international business, growing our men’s business as well as our online channel and extending into new categories and activities. And I am proud we are the #1 women’s active apparel brand in the United States. We have done this while increasing our profitability. > > Based on our guidance for 2025, we will achieve a compound annual growth rate in EPS of approximately 20% from 2018 to 2025\. And the company has strong cash flow and a balance sheet with $1 billion in cash and no debt.” To be fair, that is indeed very impressive! So, shareholders must have done really well under McDonald’s leadership, right? RIGHT? The answer would be different if you asked the question a year ago! For almost the entire period between August 2018 to early 2025, Lululemon’s stock outperformed both the S&P 500 and QQQ. But when McDonald announced to step down as CEO, the stock only managed to do \~5% CAGR during his tenure. ![chart](https://substackcdn.com/image/fetch/$s_!kIep!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F57129c46-b98b-4441-9c78-6a9e235ef786_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) When McDonald became CEO, the stock was trading at \~40x P/E whereas the stock is currently trading at \~15x P/E. So, McDonald had bit of a “Ballmer” fate here, but that would mask a sense of real erosion of the brand in the last couple of years under his leadership. ![chart](https://substackcdn.com/image/fetch/$s_!FlO3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6966d983-fb79-43eb-8b24-dfea34fda05c_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Take a look at this [tweet](https://x.com/MattJMcClintock/status/1998844768315678904?ref=mbi-deepdives.com), for example: Lulu’s lack of consistency even for its logo perhaps encapsulates how lost they seem to be to rekindle growth in North America. Perhaps my biggest frustration was McDonald was not “consistently candid” with investors for the last couple of years. For example, Lululemon, as far as I (or the couple of AI models I asked) can tell, never mentioned about consumers “trading down” affecting their performance until yesterday: > “We held share in premium athletic and lost some slight share in the performance apparel as we see guest behavior and **trading down**.” Then when asked a question about “trade down”, management claimed they have seen such behavior **throughout 2025\.** Management didn’t bother to share such information before. Moreover, Lulu mentioned several points yesterday that bolster the case that Lulu management did not do many basics that were under their control. From the call: > We know that our current merchandising mix, particularly in North America does not fully reflect the go-forward vision we have for our brand. > > We’ve let product life cycles run too long within some of our key franchises. And we have not inspired our high-value guests to purchase as we had in the past. > > Our mainline product development process currently runs 18 to 24 months, and we are working to reduce it to 12 to 14 months. Given this context, it is less of a surprise that for the first time both the US and Canada experienced revenue **decline** YoY. International markets, especially China still maintained its momentum, but unless North America business reaccelerates to MSD level over time, the question of brand saturation will eventually arise for those regions as well which will exert pressure on the valuation multiples. ![](https://substackcdn.com/image/fetch/$s_!5VS6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbb27b351-37a5-4765-8a03-0f9bc0668c88_1110x822.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Lulu’s operating margin in Americas was below 30% even though it used to be consistently mid to high 30s. Admittedly, Lulu was quite unfortunate here as the regulation around de minimis and tariff are the primary culprit here. Their overall gross margin went down by \~290 bps YoY. Moreover, they expect tariffs and de minimis to have 410 bps impact on margins. On top of that, they now expect 100 bps higher markdown than 2024. ![](https://substackcdn.com/image/fetch/$s_!pVUq!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F95cbd538-c07a-4ffc-9db1-a92287c5b5c7_1105x268.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Management reminded that 2026 will face margin headwinds as well: > “In terms of 2026 operating margin it is fair to assume that the negatives will outweigh a positive. The margin push though will be a multiyear effort... It will be our first full year of tariffs.” Following McDonald’s departure, Andre Maestrini (Chief Commercial Officer) and Meghan Frank (CFO) will be Co-CEOs until the board hires a new CEO for the company. Whoever the new CEO may be, I think he or she will have a difficult hand in compounding this business given the competitive dynamics today. At least, he or she will have a much more reasonable P/E to start with! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Adobe and Figma's Divergent Path URL: https://www.mbi-deepdives.com/adobe-and-figmas-divergent-path/ Last updated: 2025-12-11T15:23:34.000Z The last time I covered Adobe’s earnings was back in [**2Q’23**](https://www.mbi-deepdives.com/adbe2q23/). In that update, I explained why I decided to sell the stock. In retrospect, that was perhaps one of my well-timed exits **so far**. ![chart](https://substackcdn.com/image/fetch/$s_!OXzv!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a3fd08a-390d-4ea8-a8ed-38bd7a6ca938_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) But has the market been punishing Adobe too much? I wouldn’t focus too much answering that question here, but I will explore an interesting divergence between Adobe and Figma behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The "Edge AI" Bear Case URL: https://www.mbi-deepdives.com/the-edge-ai-bear-case/ Last updated: 2025-12-10T14:58:47.000Z On “Invest Like The Best” [podcast](https://joincolossus.com/episode/nvidia-v-google-the-economics-of-ai/?ref=mbi-deepdives.com) yesterday, Gavin Baker mentioned the following: > “In three years, on a bigger phone, you’ll be able to run a pruned-down version of Gemini 5, Grok 4, or ChatGPT. And that’s free. This is clearly Apple’s strategy - we’re going to make it privacy-safe and run on the phone. **Other than scaling laws breaking, edge AI is by far the most plausible and scariest bear case**.” One of my hypotheses in investing in general is most bear cases never truly die; they just keep re-appearing time and again and most investors start paying more attention to these perpetual bear cases only when stock prices force them to i.e. when stocks go down a lot. To Baker’s credit, he’s highlighting a risk when stocks are actually doing fine. But indeed, edge AI has been touted as a plausible bear case for the “AI trade” for much of the last couple of years. Nilesh Jasani was one of the investors who actually took this bear case seriously and [explained](https://www.geninnov.ai/blog/edge-computing-a-eulogy-for-a-future-that-wasnt?ref=mbi-deepdives.com) the appeal of edge AI: > “In late 2023 and early 2024, the promise of edge computing was not a niche technical idea; it was a palpable hum of excitement. The logic was clean and compelling. Moving artificial intelligence to the edge would solve the technology’s most pressing problems. > > First, there was privacy. Processing data on your own device meant it never had to travel to a server owned by someone else. Your secrets would remain your own. Second was latency or annoying delays. With the thinking done locally, responses would be instantaneous, a crucial feature for everything from conversations with voice assistants and augmented reality to autonomous vehicles. Finally, there was cost and access. Why rent time on a remote supercomputer when your own device could do the work? This would democratize AI, making it reliable even without a perfect internet connection.” However, Jasani admitted that it was “wrong”. The reality didn’t match edge AI’s promises at all. From his [piece](https://www.geninnov.ai/blog/edge-computing-a-eulogy-for-a-future-that-wasnt?ref=mbi-deepdives.com) again: > “Top-tier smartphones like Apple’s iPhone 16 and Samsung’s latest Galaxy look and work pretty much like the models from a few years prior. Yes, they tout AI-powered camera modes and smarter assistants, but **there’s little truly new that runs independently on the device.** All the major phone launches emphasized AI features, yet those features often amounted to things like slightly better autocorrect, AI image editing, or voice dictation – useful, sure, but hardly the seismic shift envisioned by edge computing evangelists. > > **Notably, many of these “AI features” still lean on the cloud.** Apple’s vaunted Apple Intelligence gave iPhones some on-device smarts, but even Apple admitted that for more “sophisticated commands” the system quietly reaches out for “help from ChatGPT” or other cloud models. **In other words, your iPhone might rewrite a text message offline, but if you ask it to compose a complex email or summarize a document, it’s likely pinging a server somewhere**. Apple’s commitment to privacy means those requests are anonymized and encrypted, but they’re not happening on your phone’s CPU alone.” I recently bought an “AI PC” and the salesperson at “Best Buy” was passionately making the case that conversations on CoPilot stays on device and not on the cloud. That’s [not true](https://support.microsoft.com/en-us/topic/privacy-faq-for-microsoft-copilot-27b3a435-8dc9-4b55-9a4b-58eeb9647a7f?ref=mbi-deepdives.com). So, there is clearly a gulf of difference what edge AI has promised so far and how much they could make it work. But is it just a timing mismatch and much of the promises of edge AI will eventually bear fruit? If edge AI becomes reality in the medium to long term, the biggest losers are likely to be hyperscalers who are hoping to generate billions of inference revenue on their ongoing capex spree. Since they are Nvidia’s biggest customers today, you might imagine Jensen Huang to be highly incentivized to not believe in edge AI’s promises. Interestingly, Huang himself in his recent Joe Rogan [podcast](https://www.youtube.com/watch?v=3hptKYix4X8&ref=mbi-deepdives.com) appearance made the case that edge AI is inevitable: > “the fact of the matter is your phone’s going to run AI just fine all by itself in a few years. Today, it already does it fairly decently. And so the fact that every country, every nation, every society will have the benefit of very good AI. It might not be tomorrow’s AI. It might be yesterday’s AI, but yesterday’s AI is freaking amazing. You know, in 10 years time, 9-year-old AI is going to be amazing. You don’t need 10-year old AI. You don’t need frontier AI…we need frontier AI because we want to be the world leader. But for every single country, everybody, I think the capability to elevate everybody’s knowledge and capability and intelligence, that day is coming.” If edge AI starts to deliver its promises, I wonder if it’s an underappreciated headwind for OpenAI. Following GPT-5 backlash, Sam Altman once [tweeted](https://x.com/sama/status/1954603417252532479?ref%5Fsrc=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1954603417252532479%7Ctwgr%5E7ef1c623bac6ec6e12750fe394342de7fbf55645%7Ctwcon%5Es1%5F&ref%5Furl=https%3A%2F%2Fgizmodo.com%2Fopenai-brings-back-fan-favorite-gpt-4o-after-a-massive-user-revolt-2000641214&ref=mbi-deepdives.com), “the percentage of users using reasoning models each day is significantly increasing; for example, for free users we went from <1% to 7%, and for plus users from 7% to 24%.” It was a bit surprising to me that vast majority of ChatGPT users are not using reasoning models. If your queries can be responded without reasoning, those are exactly the type of queries that can be easily done by on device models in a few years. And if the free model on your device can have private, uninterrupted (personality won’t change due to any model changes) conversations, would ChatGPT remain compelling enough for incremental users to pay for subscription? There is indeed a real possibility that “System 1” (fast/reflexive) queries are just commodities and it is the “System 2” (deep thinking) queries that prove to be high-value ones. It may be instructive to think why Anthropic is almost solely [**focusing**](https://www.mbi-deepdives.com/anthropics-focused-bet-portfolio-change/) on “System 2” queries whereas ChatGPT’s vast majority of queries are still likely to be “System 1” queries. System 1 queries can be quite profitable (very low inference costs now which may even be almost zero in edge AI scenario), but it is also going to be very hard to convince users that it is worth paying subscription for in a few years. Of course, we cannot quite outline an impact of a certain risks playing out imagining ceteris paribus. Sam Altman has already declared “[code red](https://www.wsj.com/tech/ai/openai-sam-altman-google-code-red-c3a312ad?gaa%5Fat=eafs&gaa%5Fn=AWEtsqcXV3kBq3BAzVH-LfwKFXak8tzcOHAyCoTkCcV6VckV5A1XBOd4kZF3VGYByMI%3D&gaa%5Fts=69398a87&gaa%5Fsig=%5FQdes3GunI9BMP7h1eqbtiGyENeETuJ2TI4sfs7Uoo5cZ-RFr6oODrn3MvjlsdOUl%5FgPSnTBHVtaDPu5qagwOw%3D%3D&ref=mbi-deepdives.com)” in the face of Gemini’s recent growth spurt, and OpenAI will certainly not sit idly if edge AI ever starts proving to be a headwind for user growth. Let’s not forget that OpenAI still has the “Midas touch” when it comes to product itself. They may be behind on model quality, they may be less focused than Anthropic, and they clearly don’t have the infrastructure advantage as Google does, but AI still is synonymous with “ChatGPT” for most people. That itself will likely buy them time, but the clock is ticking. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Waymo's Surge URL: https://www.mbi-deepdives.com/waymos-surge/ Last updated: 2025-12-09T15:02:14.000Z In May 2023, Waymo was doing just [10k](https://www.theverge.com/2023/5/4/23709962/waymo-phoenix-san-francisco-service-area-robotaxi?ref=mbi-deepdives.com) commercial public rides per week. Then exactly a year ago, Sundar Pichai disclosed Waymo was doing [175k](https://www.youtube.com/watch?v=OsxwBmp3iFU&t=934s&ref=mbi-deepdives.com) rides per week. Yesterday, this [tweet](https://x.com/dee%5Fbosa/status/1998064172811305070?ref=mbi-deepdives.com) indicated weekly number of rides has now shot to **450k**, growing 157% YoY! Of course, Waymo is still only available in handful of cities. But in cities they are operational for a while, their market share gain seems to be one-way street! ![Image](https://substackcdn.com/image/fetch/$s_!knBw!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F54e3a85d-5dfe-4b2e-9346-c4735b65f21f_1242x1274.jpeg "Image") Image taken from this [tweet](https://x.com/bearlyai/status/1995188405001695690?ref=mbi-deepdives.com) Even for the cities where Waymos are currently available, Waymo is further geofenced to a fraction of the overall city. As a result, their true market share in those markets need to be further adjusted for their actual availability. In April 2025, Bond Capital [cited](https://www.bondcap.com/reports/tai?ref=mbi-deepdives.com) Yipit data to show Waymo went from 0% market share to surpass Lyft’s market share and reach 27% in just 20 months in Waymo’s San Francisco operating zone. Remember, this was back in April when Waymo was doing [250k](https://waymo.com/blog/2025/05/scaling-our-fleet-through-us-manufacturing??ref=mbi-deepdives.com) rides per week. Since current weekly ride is \~80% higher, Waymo may have continued to gain market share. ![](https://substackcdn.com/image/fetch/$s_!WFNv!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9e28167f-2777-4671-b75f-377f05aeecae_1624x1212.png) Given that Waymo captured this market share with just [1,000 cars](http://bloomberg.com/news/articles/2025-11-12/waymo-launches-driverless-robotaxis-on-freeways-in-first-for-us?srnd=undefined&ref=mbi-deepdives.com) in SF operating zone, should Waymo launch more cars here to keep increasing market share? Not so simple! You cannot build the supply with peak demand in mind since that will add to a pesky little problem called **deadhead miles** which refers to the distance the vehicle travels without carrying any paying passenger, essentially driving empty to get to the next pickup, or reposition to areas where pickups are more likely. California Public Utilities Commission (CPUC) further breaks deadhead into [two segments](https://ww2.arb.ca.gov/sites/default/files/2019-12/SB%201014%20-%20Base%20year%20Emissions%20Inventory%5FDecember%5F2019.pdf?ref=mbi-deepdives.com): > Period 1 (P1) is the period of time after a driver logs into a TNC application but is not yet matched with a passenger. During this time period, the driver awaits a ride request through the TNCs; (TNC= Transportation Network Company) > > Period 2 (P2) starts when a match is made and accepted by the driver, but before the passenger has entered the vehicle. During this period of time, the driver is en route to pick up the passenger What does Waymo’s deadhead miles look like? CPUC [data](https://docs.google.com/spreadsheets/d/109epdBMshkEnzhtDR9U%5F5gN18CAdpbzg/edit?gid=498277451&ref=mbi-deepdives.com#gid=498277451) shows **51.5%** of total Vehicle Miles Traveled (VMT) was deadheading back in January 2024\. As the density of demand picked up, deadheading came down to **44.3%** of total VMT in September 2025. How does that compare against Uber or Lyft? This paper [suggests](https://www.sciencedirect.com/science/article/abs/pii/S1361920918309878?ref=mbi-deepdives.com) for ridesharing apps such as Uber/Lyft, deadheading ranged between 36% and 45% in different cities. So, Waymo in California is getting pretty close to average Uber/Lyft deadheading. But the deadheading constraint also indicates that Waymo cannot really inject more vehicles in their operating zones without risking a material increase of deadhead miles. Driverless Digest highlighted this trade-off in their [piece](https://www.thedriverlessdigest.com/p/what-cpuc-data-reveals-about-waymos?ref=mbi-deepdives.com#footnote-anchor-2-179382118): > Ideally, P1 should be as close to zero as possible, since all you’re doing in that period is waiting for the next trip. With a fixed fleet size though, Waymo may be forced to reposition more after drop-offs — or simply keep moving because there aren’t many legal places to wait. At a high level, the data suggests their fleet spends a lot of time driving around between trips while waiting for a request, rather than staying put, which is why P1 miles remain so high. ![](https://substackcdn.com/image/fetch/$s_!ylXa!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc2d7baa5-063e-4f7b-b261-8b24e763789a_1200x742.png) Source: [The Driverless Digest](https://www.thedriverlessdigest.com/p/what-cpuc-data-reveals-about-waymos?ref=mbi-deepdives.com#footnote-anchor-2-179382118) In the long term, AVs should do better than traditional transportation network company or TNCs, as [explained](https://www.linkedin.com/posts/mraifman%5Ftransportation-mobility-waymo-activity-7395220802973106177-pe8O/?utm%5Fsource=share&utm%5Fmedium=member%5Fdesktop&rcm=ACoAAAlbMNEBjlrhOke2Nkx9SxDncEod2ib9wdc) by Matthew Raifman: > AV fleets should be able to best TNCs on deadheading, though. Predictive routing is a challenging one to solve, but human fleet TNCs have to motivate their drivers to go where the modeling suggests they should be for efficiency. That generally takes some monetary investment from the TNC. Sometimes that’s worth it (e.g. end of a baseball game perhaps); sometimes it is not (a brief uptick in demand from a club). Waymo, and other AV fleets, do not need to consider driver motivation. They can just allocate vehicles to where they think demand will be. Nonetheless, the current high mix of deadheading can exert a lot of pressure on Waymo to slow down scaling. Is the rational play “multi‑channel”: use Uber/Lyft **AND** Waymo app simultaneously in all the markets, especially given the high upfront fixed costs and low marginal costs involved? In more mature zones (SF downtown), Waymo is already constrained more by road geometry and peak‑hour spatial mismatch than by lack of demand. You’ll still deadhead between the residential peak origins and entertainment/office peak destinations. If Waymo is available on all ridesharing apps, they will probably lower deadheading by 10-15 percentage point max which does ease pressure in the short term. But making Waymos available in all markets can prove to be suboptimal long-term strategy since Uber/Lyft own the customer, and own the price. Partners are much more useful to launch faster and fill early capacity, especially where brand awareness is low. But in the long term if Waymo’s technology and regulatory approvals remain a differentiating factor, it will be surprising to me if Waymo doesn’t lean more towards owning the customer relationship. Right now, they are partnering with others in different permutations and combinations in different markets, perhaps only to find the data necessary to lean towards the best long-term strategy. ![](https://substackcdn.com/image/fetch/$s_!Dgsr!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F67fc8d34-1ff8-4563-8eb8-a269aca7a50c_909x620.png) Image Source: [Platform Aeronaut](https://www.platformaeronaut.com/p/the-state-of-rideshare-and-autonomous?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I did make a small change yesterday which I will mention behind the paywall. _This post is for paying subscribers only._ ### Music's AI Mess: Part 2 URL: https://www.mbi-deepdives.com/musics-ai-mess-part-2/ Last updated: 2025-12-08T15:26:53.000Z Just as I have highlighted in [**Music’s AI Mess**](https://www.mbi-deepdives.com/musics-ai-mess/), people seem to find it very hard to differentiate between AI-generated music and human-made music. Deezer/Ipsos recently did a [survey](https://newsroom-deezer.com/2025/11/deezer-ipsos-survey-ai-music/?ref=mbi-deepdives.com) in which they asked people to listen to three tracks and determine whether or not they were fully AI-generated. **97%** couldn’t differentiate them and 52% of them felt uncomfortable for their inability to do so, perhaps indicating they expected them to be able to spot the difference. As you can tell, even if AI generated music flooded the music scene, almost all of us would have hard time spotting it without proper disclosure. In fact, Deezer mentioned that AI-generated tracks are already **34%** of music uploaded everyday. Even Spotify recently [mentioned](https://newsroom.spotify.com/2025-09-25/spotify-strengthens-ai-protections/?ref=mbi-deepdives.com) that they removed over 75 million spammy tracks which are largely created by generative AI tools. While there is a Cambrian explosion of AI-generated music, Deezer also mentioned that it only accounts for **0.5%** of total streams on their platform. I started appreciating the scale of the AI-generated music explosion when I came across the recent Billboard [piece](https://www.billboard.com/pro/suno-creates-spotify-catalog-music-two-weeks-pitch-deck/?ref=mbi-deepdives.com) on Suno: > **Every two weeks, users on the AI music platform Suno create as much music as what is currently available on Spotify**, according to Suno investor presentation materials obtained by *Billboard*. Those users are primarily male, aged 25-34, and spend an average of 20 minutes creating the some 7 million songs produced on the platform daily, according to the documents and additional sources. Suno recently raised $250 million at a $2.5 Billion valuation. Suno already has 1 million subscribers, which grew 300% YoY although they have only 25% retention rate after 30 days. So, a lot of people seem to want to experiment with the tool and then quickly churn. However, Suno claimed they were able to “reactivate” a lot of these churned subscribers later. It’s possible that many music enthusiasts do not feel the need to have access to Suno on an ongoing basis, but may just use it for inspiration or on an ad hoc basis. Their “Pro” Plan charges only $8/month which is lower than any music streaming app. In October, The Information [reported](https://www.theinformation.com/articles/music-app-suno-nearly-quadruples-annual-recurring-revenue-150-million?rc=4lgoj7&ref=mbi-deepdives.com) that Suno currently has $150 Million ARR. The company mentioned they expect to generate $1 Billion revenue by 2028 before considering “monetizing consumption”. ![](https://substackcdn.com/image/fetch/$s_!j0iw!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F456a1edb-24f9-49f4-a030-ec2b046e481c_1843x1051.png) Source: Suno website The number of uploaders on Spotify in 2025 was [12 million](https://loudandclear.byspotify.com/?ref=mbi-deepdives.com#takeaway-3). If half of them subscribe to Suno by 2028, you can get to $600 Million ARR assuming $100 ARPU. Of course, the number of uploaders on Spotify is perhaps a very limiting view of the possible size of the market here. While the internet made it possible to any musician to upload their music for anyone to listen to, Suno is making it feasible for **ANYONE** to create music. So, it may not be non-sensical to imagine a multiple of Spotify uploaders as Suno’s TAM. Of course, Suno is far from a monopoly. There’s [Udio](http://The number of uploaders on Spotify in 2025 was 12 million. If half of them subscribe to Suno by 2028, you can get to $600 Million ARR assuming $100 ARPU. Of course, the number of uploaders on Spotify is perhaps a very limiting view of the possible size of the market here. While the internet made it possible to any musician to upload their music for anyone to listen to, Suno is making it feasible for ANYONE to create music. So, it may not be non-sensical to imagine a multiple of Spotify uploaders as Suno’s TAM.). And then there’s SOTA model developers such as [OpenAI](https://www.theinformation.com/articles/openai-plots-generating-ai-music-potential-rivalry-startup-suno?ref=mbi-deepdives.com&rc=4lgoj7) who itself already expressed interest in getting into this business. So, I am lot less concerned about the size of the opportunity here, but do wonder about anyone’s ability to generate sustained profit here given that tech itself may not be a differentiator here. Suno likely senses that too, and hence they mentioned even though they can reach $1 Billion revenue just by selling the music generation tool, they do have a much broader ambition as their goal is to create “high-value, high-intent music discovery” and “artist-fan interaction.” While DSPs such as Spotify initially seemed out of Suno’s crosshair, Suno seems to have a different idea. I don’t quite think Spotify shareholders need to lose their sleep over it. Why doesn’t Spotify itself build these tools and make it available for the creators? I wouldn’t rule this out, especially now that labels seem to be changing their tunes a bit about Suno. While all the major labels sued these AI-generated music creation platforms, they have started settling these lawsuits and Warner Music has recently even [partnered](https://bluntmag.com.au/music/warner-music-group-settles-lawsuit-begins-partnership-with-ai-music-platform-suno/?ref=mbi-deepdives.com) with Suno. So, I won’t be surprised if Spotify builds on their own or partners with a SOTA model developer and then make the models available to all their creators on the platform. Given the rise of AI generated music, perhaps one of the key stakeholders that may be the biggest loser in the current music value chain is the musicians themselves. If someone with no talent in music can generate a rather catchy tune on their own using these tools, wouldn’t we fundamentally be less impressed with someone’s ability to do so **without** these AI tools? We may be deeply averse to AI generated music, but the fact that almost every musician will likely use AI in some shape or form within their music may make us less drawn to the artists themselves. That may seem like almost bit of dystopian, but frankly speaking, I was more uncomfortable when I came across this slide in UMG’s investor presentation last year. Is society fundamentally better off when the musicians are some of the most influential people around? If technology commoditizes their raw talent and they lose their gravitas, aura, and appeal to impressionable youth (and old), I cannot say I feel compelled to shed tear over it. Of course, you can have a different opinion and that’s fine. ![](https://substackcdn.com/image/fetch/$s_!V1kC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcf8d9f22-9450-4960-9047-5123524bded4_1881x1036.png) Image Source: UMG Investor Presentation --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Dollar Store Earnings 3Q'25 URL: https://www.mbi-deepdives.com/dg-dltr3q25/ Last updated: 2025-12-07T16:02:02.000Z We have all heard about the “K-shaped” economy, and looking at how dollar stores’ stocks i.e. Dollar Tree (DLTR), and Dollar General (DG) fared so far this year certainly seems to give credence to such theory. ![chart](https://substackcdn.com/image/fetch/$s_!SkcT!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F68f7425e-af41-497c-b5ae-20139e4bab53_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Nonetheless, it is important to contextualize the current year performance as these stocks were in the gutter while entering 2025\. Despite such massive outperformance this year, DG and DLTR are still down \~46% and \~30% from their respective peaks in 2021! ![chart](https://substackcdn.com/image/fetch/$s_!rLrE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F02f194e4-b50b-41b1-928d-3217fde7d12e_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) DG and DLTR had 2.5% and 4.2% Same Store Sales (SSS) growth respectively in 3Q’25\. They both make Walmart’s (WMT) SSS number look even better as WMT reported 4.5% SSS in 3Q’25 despite being \~17x the size of DG. Interestingly, DG and DLTR had very different drivers for SSS in 3Q’25\. While DLTR experienced negative traffic, DG’s SSS was entirely driven by traffic. ![](https://substackcdn.com/image/fetch/$s_!_cE5!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97177e84-ea7e-404b-8b3e-6796fd992594_1444x103.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Both retailers mentioned about being beneficiary of increasing trade downs. DLTR highlighted that they gained 3 million incremental households in the last 12 months, 60% of which makes over $100k, 30% make $60-100k, and only 10% making below $60k. While they did gain more higher income households, last quarter’s growth was driven more by lower income households. From the DLTR call: > “At the same time, higher income households are trading into Dollar Tree, lower-income households are depending on us more than ever. For example, the average spend for lower-income households grew more than twice as fast in the third quarter as the average spend for higher income households.” Similarly, DG highlighted that their core customer feels “more pressured”, and they’re also enjoying some trade down effects. From DG call: > This traffic and basket composition is consistent with what we have historically observed when our **core customer feels more pressured on their spending as they come in more often but have smaller basket sizes**. > > we’re pleased to see growth once again in our total customer count with **disproportionate growth coming from higher income households**. In terms of margin, DG’s gross margin improved by 107 bps YoY even though it was below 30%. Gross margin improved primarily from higher inventory markups and a 90 bps benefit from reduced shrink, partially offset by LIFO. DG mentioned that shrink continues to improve at a much higher and faster rate compared to the expectations implied in their long-term financial framework, and they expect continued improvement over time. Merchandise inventory was also down 6.5% YoY. Operating margin also improved from 3.2% in 3Q’24 to 4.0% in 3Q’25\. To contextualize, the average operating margin in 3Q for DG between 2017 and 2022 was 7.7%. It was 7.0% even if we exclude the post-pandemic numbers. So, despite the recent improvement, their operating margin is still substantially lower than what they used to report. One key driver for recent margin improvement for DG is the return to growth in non-consumables segment. For the last three consecutive quarters, non-consumables has outpaced consumables growth. Given non-consumables tend to be higher margin segment, it is a tailwind for the overall margin. ![](https://substackcdn.com/image/fetch/$s_!wW5w!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F243d501e-7480-4477-9cba-60f018e86977_1342x103.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Just like DG, DLTR’s non-consumables also outpaced consumables growth in 3Q’25\. Moving to multi-price segment (above $1) has proved to be the real gamechanger here. Management had a good detailed discussion on how multi-price can have a profoundly positive impact on profitability by explaining what they witnessed in Halloween assortment: > “This year, our Halloween assortment generated over $200 million in sales, an all-time record. But to see the full impact of multi-price, let’s go back to Halloween 2022 when multi-price was still in its infancy. That year, multi-price represented about 3% of units sold, 10% of sales and 7% of merchandise gross margin across our full Halloween assortment. Fast forward to 2025 on a 25% larger base of sales where multi-price accounted for roughly 1/4 of our total Halloween sales and merchandise gross margin but only 8% of Halloween units sold. > > Across Halloween this year, **each multi-price item that we sold generated 3.5x more profit than each non-multi-price item we sold. This is a full turn higher than Halloween 2022\. By combining this increase per unit profitability with a higher multi-price mix, we were able to generate approximately 25% more margin dollars from our Halloween assortment this year compared to 2022 while selling approximately 10% fewer units**. And this is just the positive impact on merchandise margin. It doesn’t take into consideration any labor or distribution cost savings that come from handling fewer units. Looking at it this way, multi-price is a powerful growth and profitability driver.” Despite all the multi-price moves, DLTR mentioned 85% of sales dollars are still at $2 or below, insisting the value perception is intact. ![](https://substackcdn.com/image/fetch/$s_!c2g8!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdb009f1f-722a-46e7-9eab-2e4447a048f2_1702x118.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) One interesting discussion in DG call was their assessment on delivery and how it strengthens their convenience moats. From the call: > “with 80% of our stores in those small towns across America, **very, very difficult to replicate, whether it be brick-and-mortar or whether it be on a digital basis**. And again, we didn’t sit back. We moved swiftly once we saw that our core consumer was starting to venture into her digital journey. We’ve always said our core customer, she’s a fast follower. She’ll get there, and she’s starting to move that way. So we move that way. > > And the great thing is we move that way with a lot of intentionality and that intentionality was really centered around rural America and **be able to deliver that customer an hour or less where no one else can touch that at this point**.” DG highlighted that delivery orders via DoorDash/Uber/DG Delivery are **\~70% incremental, carry larger baskets, and 75%+ are delivered in under an hour**. To my surprise, DG also mentioned that these deliveries are “**profit accretive**”. DoorDash or Uber doesn’t do delivery in my area for DG yet, so I cannot quite test it yet. Their website or app is also not quite rich with information about this service. I can, however, see that they charge $1 fee per order if you want delivery under an hour. This [website](https://thekrazycouponlady.com/coupons-for/dollar-general/dollar-general-delivery?ref=mbi-deepdives.com) mentioned $3.49 - $7.49 Service Fee (depends on the size of your order) that customers pay. Given typical DG basket size, $5-7 can actually be a material percentage of the overall basket size. With enough volume, I can see how it can be profit accretive for DG. ![](https://substackcdn.com/image/fetch/$s_!ild6!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0eb781c7-b14d-45e9-b26d-f4a7e12c5adb_1705x460.png) Source: DG Website But if a significant fraction of DG’s customers are willing to pay these potentially exorbitant fees, I still do suspect that many of these customers may eventually shift to Walmart or Amazon given their much greater selection and better value once they inevitably narrow the delivery speed over time. If you have to wait 30 minutes longer but can get it cheaper on Walmart, wouldn’t many of DG’s customers opt for that? I have always thought the core moat for DG is their convenient locations, but as the consumer behavior and expectation is changing in how they shop, this moat may be gradually eroding. It may be too early for DG to declare victory by looking at early cohort data. While I do wonder about these businesses’ long-term relevance given the changing retail landscape, they do seem well positioned in the near term as the K-shaped economy runs its course. DLTR, in fact, guided for 4-6% SSS growth next quarter and re-iterated \~12-15% EPS growth CAGR till 2028\. It certainly helped that they bought back 8% shares outstanding so far this year at $90/share (stock is currently at $122). --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### My experience on Airbnb Experience: Part 2 URL: https://www.mbi-deepdives.com/abnb_experience2/ Last updated: 2025-12-06T16:51:13.000Z My first Airbnb Experience can be found [**here**](https://www.mbi-deepdives.com/my-experience-on-airbnb-experience/). --- One of the benefits of living in Sacramento is it’s just two hours driving distance from both San Francisco and Lake Tahoe. You can basically organize a day trip to see mountains, beach, or forest and you only have to pay probably a third of the rent compared to the Bay Area! Having lived in Ithaca (NY), Madison (WI), and Ottawa (ON) between 2017 and 2023, I sometimes miss seeing several feet deep snow creating winter wonderland all around me. Lake Tahoe can usually come to the rescue when I want to witness some snow during the winter. I have been to Lake Tahoe several times since moving to Sacramento, but I always felt like there are more to explore here than I did so far. So, I opened my Airbnb app and looked for some experiences in Tahoe. There are a couple that stood out to me: this “[Wild Nevada off-Road Adventure Tour](https://www.airbnb.com/experiences/3569099?adults=1&children=0&infants=0&pets=0¤tTab=all%5Ftab&federatedSearchId=&searchId=&source=wishlist)”, and “[Hike with a Tahoe Local](https://www.airbnb.com/experiences/207629?adults=1&children=0&infants=0&pets=0¤tTab=all%5Ftab&federatedSearchId=&searchId=&source=wishlist)”. The former wasn’t available during winter season, but I could book for the hike. Right after the booking, Jen (the host) created a messaging group within the app. There was also a couple from Oklahoma (let’s call them “L&E” who would be attending the hike. So, a local Tahoe resident, L&E from Oklahoma, and yours truly from Bangladesh…I was already intrigued about the hike! In fact, while listening to Hiroki Asai (Chief Experience Officer in Airbnb) in a recent [podcast](https://www.youtube.com/watch?v=1QbDGZGaYxQ&t=9s&ref=mbi-deepdives.com), I came to know that a **third of the people who book an Experience in Airbnb are doing so to meet other people**. Let me go off on a tangent here and mention that it only occurred to me somewhat recently that there are certain aspects about moving to a different city that are likely lot easier to navigate as an immigrant than even American citizens. A few months ago, I threw a dinner party and invited \~40 people to come to my place. When I mentioned this to a couple of American friends, one of them was surprised that I even know so many people given I moved to California only a couple of years ago. It was only then I started appreciating why my experience may be a little anomalous compared to many Americans. Both my wife and I attended colleges in Bangladesh from which vast majority of the graduates (\~70% for my wife’s college, and \~25% for mine) move to somewhere in the Western hemisphere. To contextualize, fresh graduates from even the top engineering and business schools in Bangladesh get paid $300 to $600 per month. So, you can understand why most of these graduates consider it economically ruinous to stay there post-graduation. This, however, has created a strange situation that I can move to any random major city in the entire Western hemisphere and can probably easily find \~30-40 people within a year whom I will know on a first name basis and I myself will be invited to probably dozens of dinner parties. In fact, when I was interviewing for jobs post-MBA, I was often confused why many interviewers were so curious about why I would want to move to a particular city. I didn’t say this, but in reality whenever I heard that question, I used to think “my brother in Christ, do you really think I left my entire friends and family back home only to nitpick between moving to Alaska and Dallas? I simply do not care; give me the job, I will go wherever you want me to”. Perhaps what I didn’t quite appreciate back then was my attitude towards moving implicitly took a ready community for granted given the reality I described above…I knew I would find a Bangladeshi community full of our alma maters no matter where we move. It took me a while to appreciate that ironically, it can be much harder for many Americans to find such community if they need to move to a random city for a job. My personal experience probably only holds true for most first-generation immigrants who can more easily connect to other fellow immigrants on the basis of their shared journey and life experiences. But even second generation immigrants, including my son will probably have lot harder time finding such ready community given he probably wouldn’t have much “connection” with Bangladesh by the time he is in my age. So, **meeting other people,** even just for the sake of meeting people, can be potentially a very unmet want (need?) for anyone moving to a different city. And if you’re traveling, most people probably want to meet other people—both the locals or/and the people who also came from different parts of the country/world to visit the same place. So, perhaps it makes a lot of sense why third of the people booking Airbnb experience are just trying to meet other people! Back to my own Airbnb experience. Right after publishing my “daily dose” yesterday, I started driving for Lake Tahoe as the experience starts from 9 am PT. I was exactly on time and found the group in the meeting spot. Our host Jen has lived in Tahoe for 26 years. However, she mentioned she discovered the trail we were about to embark on only six years ago. L&E are in town to celebrate L’s birthday. After reaching Tahoe yesterday, L&E were trying to figure out what activities they can do; they looked into Airbnb app and found this morning hike. It was a beautiful, sunny Friday morning and I thought about leaving my jacket in my car. Jen suggested otherwise as she warned that I might feel a little chilly in the shades during the hike. I am glad that I paid heed to her. There were some remnants of snow, but the trail was largely unaffected. The “core” hike was completed by 45 minutes; then Jen asked the group if we were willing to keep going to go near the water. We all nodded, so we kept going. Jen showed us some eagle nests near the top of dead trees. Apparently, bears climb the trees to get to the nests; however, if it’s a dead tree, it’s lot harder for the bear to climb. She mentioned there’s a [YouTube video](https://www.youtube.com/watch?v=uU2qMn-eeYE&ref=mbi-deepdives.com) you can watch a bear climbing to a tree while eagles are trying to defend their nests. I recommend watching it to appreciate how mother nature can be quite ferocious! When we reached near the water, Jen offered us hot chocolates which was a nice touch! I had a brief conversation with L&E and Jen; Jen offered tons of recommendations for L&E since they will be staying in Tahoe for the next four days. Jen later texted all her recommendations (activities, food, sightseeing etc.) to the group. Here’s a one-minute video of our hike (to respect the group’s privacy, I didn’t include any group photo). 0:00 /0:56 1× I also had a restaurant recommendation for L&E. Last year, I found this Argentine restaurant called “[Empanash](https://www.empanash.com/?ref=mbi-deepdives.com)” which I highly recommend to anyone visiting Lake Tahoe. I always order the Hammers with golf sauce. After the hike, I drove for another 20 minutes, had a couple of Hammers by the beach, and then bought some more for the family as I started driving back to Sacramento at noon. ![](https://substackcdn.com/image/fetch/$s_!-cn0!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0ad41796-5b5b-4985-9ba9-c47b2c43a3da_3024x4032.jpeg) You came for the business analysis, but you ended up getting food recommendations! Okay, fine. Let me wear the analyst hat now. For the hike, there were total 20 spots available, each of which costs $64/guest. The unit economics of a “filled capacity” Airbnb experience from the host’s (and Airbnb’s) perspective is more compelling than hosting your home. Of course, the problem is it seems really, really difficult to fill capacity in most days. Jen was telling me that she typically gets groups visiting Tahoe, but it’s usually “a few times” in a week, and not everyday. And it’s rare to get 20 spots filled. How did Jen get started? While she’s a PE instructor, she looked for some side hustle during the pandemic. She found a woman in Colorado who was doing hiking experiences on Airbnb and had a Zoom call with her to learn how to do something similar in Tahoe. Jen came to know that woman was making >$100k/year from doing these experiences; Jen was encouraged by it and decided to launch this hiking tour in Airbnb. Unfortunately, demand has been lot more spotty for her, and after a while, she decided to list her experience on Viator as well. She mentioned once she was available on Viator, she started getting more customers from Viator, but Airbnb has been picking up for the last couple of months. This isn’t surprising since Viator has \~$4 Billion booking whereas Airbnb’s undisclosed experiences booking may be a couple hundred millions (just guessing)! What was perhaps a bit more surprising to me was why nobody except Jen was offering similar services on Airbnb given Tahoe is one of the top tourist spots in California. It turns out it may not be exactly legal to offer such services. You must apply for a permit, but the permit process is really antiquated. You can only get a “concessionary” permit i.e. if you want to sell snacks or any food items within national parks, you need to get a permit. I guess that’s why Jen was offering us hot chocolates? The reason we probably don’t see many people offering similar services is it is nearly impossible to get these permits! Of course, Airbnb would be more worried about such a supply bottleneck if Jen had her capacity filled everyday. But as mentioned earlier, that’s not the case at all. Demand is quite spotty for Jen despite her “monopoly” within Airbnb in providing hiking tours at Lake Tahoe. I have noticed some Airbnb hosts for stays offer “experiences” on the platform as well. If the consumer awareness is there over time and demand picks up, I wonder if we will see a noticeable supply increase from some of the existing individual hosts themselves. Such an integrated offerings would certainly make guests even more loyal to Airbnb. Speaking of loyalty, I consider Airbnb launching a loyalty program to be likely imminent (perhaps later next year?). One way to instigate demand in experiences is if anyone staying on Airbnb for 5-10 nights a year gets a free experience. Since most Airbnb stays are group stays anyway, you can sweeten the “experience” by offering one free experience if you’re booking the same experience for 3+ group i.e. you basically pay for n-1 group. Airbnb is in a very envious position to design the loyalty program in a way that only creates further differentiation of the overall travel experience from any other OTA out there! Once the demand for experiences is there, perhaps nothing begets more supply than having more demand. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Reader's feedback on "The Great Abstraction" URL: https://www.mbi-deepdives.com/feedback-great-abstraction/ Last updated: 2025-12-05T14:07:11.000Z A couple of days ago, I wrote a piece titled “[**The Great Abstraction?**](https://www.mbi-deepdives.com/the-great-abstraction/)”, highlighting the impending tension between OS-layer agent and the entire corpus of companies operating in the app layer. [Catapult Capital](https://x.com/catapultcap?ref=mbi-deepdives.com) had some constructive thoughts on that piece which I think is worth highlighting. So, I am sharing their thoughts below: > I have been thinking about this as well with respect to Amazon in particular. There are a lot of unknowns still but I think it is an uphill battle to achieve this, even for Google within Android. > > A few of the challenges for Google I have been pondering: > > 1) The biggest underlying challenge is that there really isn’t much of a problem to fix. Amazon works REALLY well already. They show you a broad range of options with best-in-class review depth and an extremely simple and easy purchase workflow. Google will have to stretch to show an advantage big enough to change user habit. The most obvious path is to pitch that they are able to show offerings across the whole internet and not just Amazon, but can they actually achieve that breadth? That brings me to the second challenge. > > 2) Will Google even be able to include Amazon in this agentic workflow? Amazon can easily ban this in their terms of service and a large corporation like Google won’t subvert that. I suspect the ability to ban agents will be something that is eventually tested in court/congress and I wouldn’t be surprised if Google wins out eventually (i.e. I wouldn’t be surprised if Amazon is forced to allow agents to interact with their website on behalf of users) but this is far from certain and is the first battle Google must win. If Amazon is not in the offering the service will only take share on the margin in my view. Amazon is half of ecommerce and its the important half and the majority of Americans are locked in via Prime. If these users are going to only use one protocol I strongly suspect most will opt for the Amazon app and not the Gemini workflow if it lacks Amazon access. > > 3) Lets assume Google is allowed to take action on Amazon’s website. The next legal question will be how much of Amazon’s data can Google take and reproduce in its workflow. When I search for sneakers or whatever, what I really want to see is all the different options laid out in a grid the way Amazon does things. For most things I don’t want Gemini to grill me with a bunch of questions and then present a short-list. I want to see what’s out there. So to match this, Gemini will have to show something like the Amazon format, but with the hook being that other vendors are included. That sounds appealing, but as a user, I will also want to see Amazon reviews. I will want to be able to click the items and see more pictures. Maybe read some of the reviews. See the product listing page. Google will have to be allowed to read all this data from Amazon (and other websites) and present it all in its workflow to match Amazon. I suspect this will be a bridge too far for courts/congress and I don’t think they will be allowed to do this. Especially if a product is only on Amazon and its just a straight lift of data from one page rather than a mixing of data across many pages into an aggregate. If Google can’t take all this data, it will be at a serious disadvantage versus Amazon for most purchase workflows. > > 4) The final thing worth mentioning is the AI capabilities needed for Google to even pull this off without errors and with the lightning quick load times, they will need to compete with Amazon’s offering. It is popular to talk about the “jagged” frontier of AI capabilities and I would note that computer use is one of the areas where AI models are surprisingly weak relative to other capabilities. Think about ChatGPT’s operator system. Models just aren’t completely robust at reading web pages and taking actions over long contexts. Most likely this will get really good at some point in the future but that is another thing that will take time. And then there is also the “long march of 9s” to consider. This has to be done fast and on a reasonable compute budget, but I suspect even 99% success will be too low when the traditional Amazon workflow has 100% success. You can’t order the wrong thing 1 out of 100 times or even 1 out of 100,000 times or courts will side with Amazon and you won’t be allowed to operate on their site. With AI it seems to take just as much effort/time to go from 90 to 99% success as to go from 99 to 99.9% success and 99.9 to 99.99% success. Look at driverless cars and how long that has taken relative to expectations (and how we still have one remote operator for every 5 cars for Waymo to handle tail events). I suspect this will eventually be technologically feasible but it may take much longer than you’d guess. > > A final point. The main assumed advantage Google is offering consumers here is offering the whole internet vs just Amazon. It’s worth keeping in mind that Amazon seems to be working hard to close this potential advantage by offering more and more links to outside websites in its search results. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Understanding AWS Graviton Playbook URL: https://www.mbi-deepdives.com/aws-graviton/ Last updated: 2025-12-04T15:06:26.000Z I was watching AWS Re-invent [Keynote](https://www.youtube.com/watch?v=q3Sb9PemsSo&ref=mbi-deepdives.com) yesterday, and one of the things that stood out to me was the below quote: > “For the third year in a row, **more than half of the CPU capacity that we’ve added to the AWS cloud comes from Graviton**” While this isn’t a newly disclosed info (they shared the same data in last year’s [keynote](https://x.com/Arm/status/1864804203559854499?ref=mbi-deepdives.com)), the continued success of Graviton deserves a deeper attention. To contextualize how big of a success Graviton has been, let me share a quote from 2024 keynote: > Graviton is growing like crazy. Let’s put this into context. **In 2019, all of AWS was a $35 billion business. Today, there’s as much Graviton running in the AWS fleet as all compute in 2019.** It is indeed kind of crazy to think that Graviton was only launched in [November 2018](https://aws.amazon.com/blogs/aws/new-ec2-instances-a1-powered-by-arm-based-aws-graviton-processors/?ref=mbi-deepdives.com), and in just 5-6 years, it has become such an integral part of AWS offerings! In some [estimates](https://africa.businessinsider.com/news/over-90-of-awss-biggest-data-center-customers-are-using-its-homegrown-graviton-chips/04rvl3q?ref=mbi-deepdives.com), Graviton was \~20% of AWS CPU usage by mid-2022, and given the added capacity in the last three years, it may be close to \~30% of CPU usage today. You can tell how AWS feels quite emboldened by Graviton’s success and they very much want to replicate this success beyond the realm of CPUs. I will explore a brief history of Graviton and elaborate on Amazon’s desire to replicate and constraints it may face in repeating Graviton’s success behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Great Abstraction? URL: https://www.mbi-deepdives.com/the-great-abstraction/ Last updated: 2025-12-03T15:20:33.000Z Last week in my [**Solving the “DoorDash Problem**](https://www.mbi-deepdives.com/solving-the-doordash-problem/) piece, I mentioned the following [excerpt ](https://www.theverge.com/decoder-podcast-with-nilay-patel/672087/uber-dara-khosrowshahi-waymo-ai-bus-transit-tesla-self-driving?ref=mbi-deepdives.com)between Nilay Patel from The Verge and Uber CEO Dara Khosrowshahi: > **Nilay Patel: So, I come to you and I say, “I want to be able to get cars from Uber.” What is the percentage toll? What is the extra margin you would have to charge me, in dollars and cents, to make it worth it for me to take the customer away from you in that way?** **Because when the customer opens your app, you get to cross-sell them into Uber One, or ask “would you like some food when you arrive?” There’s all of these other incremental opportunities you have to forgo if I take that customer.** > > **Dara Khosrowshahi:** So I have a weird philosophy on this. Initially, **I charge you zero**…People spend so much time trying to figure out what the economics might be when the first thing is to try it out. Is it going to be a good experience or not? Is your scheduling actually going to work, or is it going to be off and the driver has to wait for 10 minutes, which is terrible? Let’s just figure it out. Then, once you optimize the experience, we can measure. **Are you an incremental consumer for Uber or are you totally cannibalistic?** > > If it’s cannibalistic, then I’m going to charge a lot of money. You can’t have any money because you’re getting the benefit and my content. You’re not bringing me any business at all. If it is incremental, then I would pay some take rate. Is it a 5, 10, or 20 percent take rate? It depends on the incrementality. > > But I think so much innovation has slowed down because companies try to figure out the economics first. Figure out the experience first and then the economics. Listen, if I do a bad deal for a year, who cares? **I’m going to renegotiate with you.** I’m building stuff for the next 10 years. **Success or failure isn’t going to be determined by my take rate being 5 or 20 percent in year one. It can set precedent, and precedents are dangerous. That’s why I would say to charge zero**. Let’s try it out. Let’s see what the experience is. **Let’s try to measure out what the value add is, and then the economics essentially will take care of themselves** I thought that was a very cogent response to the problem at hand until one of my readers made me re-think when he said the following: > “what happens if it’s not another application layer app trying but actually the OS layer doing that? > > iOS and Android will set the rules for Uber et al to play. There may be no (re)negotiation. OS does it for the user whether Uber/DoorDash likes it or not?” I promised him that I will think about his pushback a bit more. Well, I did and I will explore this more behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Why is AWS partnering with GCP? URL: https://www.mbi-deepdives.com/aws-gcp/ Last updated: 2025-12-02T14:23:27.000Z On last Sunday, Google Cloud published a blog post about their new collaboration with AWS. From the [blog post](https://cloud.google.com/blog/products/networking/extending-cross-cloud-interconnect-to-aws-and-partners/?ref=mbi-deepdives.com): > “Today, we [announced](https://cloud.google.com/blog/products/networking/aws-and-google-cloud-collaborate-on-multicloud-networking?ref=mbi-deepdives.com) a significant collaboration with Amazon Web Services (AWS)to offer a managed, private and secure, on-demand, solution for **cross-cloud connectivity**. This solution is designed to enable customers to easily build enterprise-grade applications that span both Google Cloud and AWS environments. This collaboration is particularly timely, as the adoption of multicloud applications is rapidly accelerating, driven in part by the rise of AI. A Forbes [survey](https://www.forbes.com/sites/rscottraynovich/2024/12/03/its-been-a-big-year-for-multicloud-networking-2024-will-be-bigger/?ref=mbi-deepdives.com) highlighted that 82% of respondents anticipate that the arrival of AI services will increase the demand for multicloud networking due to the scarcity of specialized accelerator resources and the availability of diverse AI agents across different vendors. The surge in multicloud adoption is a strategic imperative for organizations looking to build agentic AI applications, optimize workloads, access best-of-breed services, meet data residency requirements, and ensure the necessary resiliency for modern hybrid and multicloud applications.” Remember, cross-cloud interconnect was first “generally available” since [May, 2023](https://docs.cloud.google.com/network-connectivity/docs/interconnect/release-notes?ref=mbi-deepdives.com#:~:text=May%2031%2C%202023,-Announcement&text=Cross%2DCloud%20Interconnect%20is%20now,and%20another%20cloud%20service%20provider.). This is how Google [explains](https://docs.cloud.google.com/network-connectivity/docs/interconnect/concepts/cci-overview?ref=mbi-deepdives.com) their cross-cloud interconnect product: > Cross-Cloud Interconnect is a product that helps you establish high-bandwidth dedicated connectivity between Google Cloud and another cloud service provider. > > When you buy Cross-Cloud Interconnect, Google provisions a dedicated physical connection between the Google network and that of another cloud service provider. So, how is this partnership with AWS any different from what was already available? Google helpfully explained the key differences in their [documentation](https://docs.cloud.google.com/network-connectivity/docs/interconnect/concepts/partner-cci-for-aws-overview?ref=mbi-deepdives.com): ![](https://substackcdn.com/image/fetch/$s_!fVrM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0fdf90fb-b76b-44c0-96ee-18fce091ff6b_1324x583.png) Source: [Google Cloud Documentation](https://docs.cloud.google.com/network-connectivity/docs/interconnect/concepts/partner-cci-for-aws-overview?ref=mbi-deepdives.com) While the explicit purpose of extending Cross-Cloud Interconnect is to improve networking performance and simplify multi-cloud architectures, there may be some strategic impetus for such an extended collaboration between GCP and AWS which I will explore behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Behind the scenes: Airbnb vs Booking URL: https://www.mbi-deepdives.com/abnb_bkng/ Last updated: 2025-12-01T15:17:59.000Z A subscriber from Thailand recently reached out to me and let me know that he has experience of hosting his home through Booking.com, Airbnb, and Agoda. He was gracious enough to allow me access by making me a co-host. It was intriguing to observe these platforms through the eyes of a host. I will share screenshots of what I observed and my thoughts behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Never Sell: Episode 12-Serial Acquirer ROIC, Fiserv Debacle and Management Credibility, Wise and Stablecoins URL: https://www.mbi-deepdives.com/never-sell-episode-12-serial-acquirer-roic-fiserv-debacle-and-management-credibility-wise-and-stablecoins/ Last updated: 2025-11-30T13:35:37.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode. You can listen to it here: [Spotify](https://open.spotify.com/episode/7MklmTYaWLXuaWFfCE7YtS?si=771012c38a5f49af&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/serial-acquirer-roic-fiserv-debacle-and-management/id1786912203?i=1000738939144&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=enYRlJfgqGU&t=10s&ref=mbi-deepdives.com), [RSS feed](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com) Some of the relevant posts that we touched on this episode: 1. [What exactly is CSU’s ROIC?](https://www.mbi-deepdives.com/what-exactly-is-csus-roic/) 2. [Fiserv’s October Massacre](https://www.scuttleblurb.com/scuttlebit-fi1/?ref=mbi-deepdives.com) 3. [Wise is probably going to be fine](https://www.scuttleblurb.com/scuttlebit-wise1/?ref=mbi-deepdives.com) 4. [The Depreciation Battleground](https://www.mbi-deepdives.com/the-depreciation-battleground/) As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) ### The Depreciation Battleground URL: https://www.mbi-deepdives.com/the-depreciation-battleground/ Last updated: 2025-11-29T22:34:59.000Z I was in my Freshman year in college when I first watched this Khan Academy video “[Is short selling bad?](https://www.youtube.com/watch?v=zAkMhEqWFF0&ref=mbi-deepdives.com)” To this day, it remains a key foundation for my belief that short sellers are **a net positive force** in capital markets. Despite such belief, I myself never shorted any individual stock (but I did buy puts on index at times). John Hempton [wrote](https://x.com/borrowed%5Fideas/status/1617712689157124096?ref=mbi-deepdives.com) pretty persuasively the pitfall of shorting individual stocks: > “In shorting frauds, this is the sort disaster that sometimes befalls you: > > a) You short a stock at $10 run by a promoter who you suspect is a liar. You are (as nearly as possible) certain that this stock is worthless. You hope to cover at $1. > b) The promoter makes up a story that somehow retail seems to think is real and the stock trades at $40. > c) You are forced to buy some back – because there is no conceptual reason why the stock can’t trade at $80\. After all it is no sillier at $40 or $80 than it was at $10 (it was worth 100 percent less at all times). > d) After you cover the stock normally goes to $1 (as you expected all along) though it might go through $100 on the way. > > This is actually a fairly common event for us. **We manage well over 200 shorts with the specific goal of blunting the impact of any such individual disaster**. And the diversity normally works.” As an individual investor managing my own money, it just didn’t seem quite feasible to short stocks well, especially after considering the return on brain damage and the potential for a random stock turning into a meme for obscure reasons. I’m sure some people can do this well even within such constraints, but I have decided long ago that I am not going to be one of them. One person who essentially achieved the celebrity status for shorting the GFC correctly was Michael Burry. Burry recently launched a Substack: “[Cassandra Unchained](https://michaeljburry.substack.com/)”. Burry is an incredibly gifted writer, and I enjoyed reading every single piece published so far. However, it was during college that I learned first hand not to confuse good rhetoric with accuracy of the logic. During my Junior year, I went to Manila in 2012 to participate as an adjudicator in the World Universities Debating Championship. It was quite the teachable moment for me as I got to observe some of the world’s best debaters closely. In case you don’t know how debate competition works, the debaters are given a motion or topic just \~15 minutes before the debate starts. There are four teams (two in favor of the motion, and two opposing the motion), but they don’t get to pick a side. They are assigned **randomly** whether they are supposed to speak in favor or against the motion. After they all speak for seven minutes each, the debaters leave the room and adjudicators then discuss among themselves how to rank the four teams. Following this discussion, the debaters come back to the room and adjudicators then explain the results to the debaters. To this day, this was one of the most stressful moments for me because I was often explaining my decision to a group of people who are almost always superior to my own rhetorical ability. Nonetheless, the whole experience showed me there are gifted people out there who can basically argue for or against a topic in a moment’s notice even if they need to take opposite position of what they actually believe. But what I took the most from that experience is **I should never confuse a good rhetoric with its accuracy**. In fact, I often wonder that being able to write or speak too well comes with a profound, hidden risk: you can always come up with compelling reasons why you are not wrong! Perhaps this is mother nature’s way to eliminate or at least negate a lot of the advantage of such a gift. Is Burry inaccurate in some of the concerns he laid out about big tech’s depreciation schedule? Before I dig into that, I should start by saying that Burry’s Substack is bit of a fresh air. Not only are there too many bullish think pieces on AI these days, the few bearish voices often fall so short on making their cases that they end up losing their credibility altogether. Burry, on the other hand, is a very good student of the market. You can tell he has been at this for a much longer than most of us. I only read about tech bubble (and crash), but Burry lived (and invested) through it. In one of his [pieces](https://michaeljburry.substack.com/p/the-cardinal-sign-of-a-bubble-supply), these couple of sentences stood out to me: > I can tell you firsthand how the market peak appeared on March 10, 2000\. That is, it happened for no apparent reason…As 2000 progressed, parts shortages and capacity constraints were the rule. He then cited a few quotes from Cisco’s earnings calls in 2000\. Some quotes from Cisco calls that Burry Cited: ***“We see no indications in the marketplace that the radical Internet business transformation… is slowing — in fact, we believe it is accelerating globally.”*** ***Cisco CEO: Q4 earnings release, August 2000*** ***“Cisco is fortunate to be at the center of an economic revolution that is reshaping not only the economy, but all facets of the society.”*** ***Cisco CEO: press release September 24, 2000*** ***“We haven’t seen any sign of a slowdown. We have guided the Street accurately, and we can execute to plan.”*** ***Cisco Chief Strategy Officer: Nov 3, 2000*** Reading these made me realize that it is highly likely that hyperscalers will probably continue to say “demand outstrips supply” for a quarter or two in their earnings calls **after** the peak! In fact, when I wondered how big tech communicated during 2022 slowdown, I realized they weren’t quite proactive in giving the signal to the investors. In retrospect, Meta was noticeably different from the pack then. As a founder led company with voting control, Meta likely feels more empowered to communicate in a more authentic manner with the investors. During 4Q’21 call, Meta explicitly quantified the headwinds from ATT: > we believe the impact of iOS overall as a headwind on our business in 2022 is on the order of **$10 billion, so it’s a pretty significant headwind for our business**. Perhaps how Meta navigates this AI capex bonanza, especially at the face of potential investor skepticism can be important signal to where we are in the cycle. Going back to Burry’s arguments on big tech’s depreciation schedule. Regular readers are likely aware that I myself was deeply concerned about this in [**early 2025**](https://www.mbi-deepdives.com/big-tech-earnings-quality/). However, I later [**changed**](https://www.mbi-deepdives.com/why-i-dont-worry-as-much-about-big-techs-depreciation-schedule/) my mind and explained why I don’t worry (as much) about big tech’s depreciation schedule anymore. The distinction between AI workloads is key: training frontier models demands the newest chips, while inference (running the models) is less demanding. I mentioned about the “value cascade” model before which works as follows: New chips (e.g. Blackwell) will handle frontier training, displaced chips (H100) move to high-end inference or fine-tuning, and even older chips (A100) will move to bulk inference or other accelerated computing tasks. I cited some historical precedents that show, if anything, big tech’s depreciation schedule had been overly conservative in the past. Burry’s argument is that the historical precedents are obsolete because the pace of innovation has fundamentally changed. Nvidia has moved from an 18–24 month cycle to a 1-year cycle. More importantly, the generational leaps are not just about power, but about efficiency (Total Cost of Ownership or TCO). If a new chip is vastly more efficient, the economic justification for running older hardware may evaporate. To substantiate his case, Burry cited none other than Satya Nadella who [said](https://www.dwarkesh.com/p/satya-nadella-2?ref=mbi-deepdives.com) the following in Dwarkesh podcast: > “**The other thing is that I didn’t want to get stuck with massive scale of one generation**. We just saw the GB200s, the GB300s are coming. By the time I get to Vera Rubin, Vera Rubin Ultra, guess what, the data center is going to look very different because the power per rack, power per row, is going to be so different. The cooling requirements are going to be so different. That means I don’t want to build out a whole number of gigawatts that are only for a one-generation, one family.” Burry’s interpretation from this quote was while Microsoft is still depreciating chips and servers over 6 years, and data center buildings at 15 years or longer, this quote itself is an indication that the accounting useful lives are fiction. That wasn’t my interpretation. Nadella is talking about **capex pacing and concentration risk**. He is not explicitly commenting about the **true** economic life of a chip. That’s consistent with a world where top‑tier training usage of a GPU generation might be 1–3 years, but **the same silicon can still earn its keep for another few years in lower tiers** (inference, smaller models, other accelerated workloads). Phasing your build so you’re not massively overweight one vintage is just normal capital discipline when the tech curve can be steep. It doesn’t logically require that the **total** economic life is only 2.5–3 years. Even if the older chips are materially less energy‑efficient than the new chips, there are workloads where latency doesn’t matter much, or the alternative is CPU, which can be worse on both cost and performance. Let me give a concrete example. Let’s say you’re YouTube. You have billions of videos, and for each video you want to store a little **“**fingerprint**”** i.e. a list of numbers that capture what the video is about (for search, recommendations, etc.). You don’t make these fingerprints one‑by‑one while a user is waiting. You do it in huge background jobs. Maybe every night you scan all the new videos, and you run a big model that turns each video into its fingerprint. This job might run from 2:00–4:00 am in some data center. No one obviously cares if it finishes at 3:00 or 3:40\. It’s not blocking a user click. What matters is the **total cost** to chew through that giant pile of videos. So you take a bunch of **older GPUs (like A100s)** that you already own, feed them huge batches of videos at once, and keep them busy close to 100% the whole time. Even though these GPUs are “old” and less efficient than new ones, they are much faster and cheaper for this math‑heavy job than CPUs would be. This is one way old GPUs, for example, can still be very useful and cheaper than trying to do the same work on regular CPUs. Having said that, I still have sympathy for one particular argument against big tech’s depreciation schedule. Depreciation is supposed to allocate cost over the period you get economic benefits from the asset. If benefits are front‑loaded, an **accelerated method** may be preferable than straight‑line. The reality for hyperscalers is the new GPUs generate way more revenue per unit in the early “frontier training + premium inference” years. The economic value curve is non‑linear i.e. big in years 1–2–3, then tapers as chips get pushed down the value cascade. Picking straight line depreciation schedule is obviously flattering to near‑term EPS and ROIC. If you switched from straight‑line to an accelerated method, you would take a larger depreciation hit **right when the market is obsessed with AI.** It is perhaps not a surprise that no CFO is volunteering for that. If you squint harder, there may be a plausible justification: the cascade + workload diversity means GPUs do meaningful work for many years, so simple straight‑line isn’t **wildly wrong**, and any more sophisticated method would be **guesswork**. And given how flattering the straight line depreciation potentially is for big tech’s EPS today, who would take the risk of such a guesswork, especially if it turns out to be unnecessary conservatism? In many ways, there are many layers to what the “correct” depreciation schedule is. And it may not be a boring accounting question, but at its heart, this may be a technological question. You can even wonder whether it’s a geopolitical question; if SOTA model training clusters can be built more and more in a more power abundant locations (i.e. middle east), I can see how that may introduce a different dimension to the overall debate. Nonetheless, I do acknowledge that Burry is probably sniffing in the right areas. If you want to build a proper short case against big tech today, this is probably the area you should focus on. But I continue to believe Burry is still short of evidence. That doesn’t mean he’s wrong; he might simply be early. As someone on the other side of his bet, I intend to follow this debate closely wearing my old adjudicator hat. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Anthropic's focused bet, Portfolio Change URL: https://www.mbi-deepdives.com/anthropics-focused-bet-portfolio-change/ Last updated: 2025-11-26T15:26:47.000Z ***A programming note***: As a reminder, I will take the next two days off for Thanksgiving, and hope to be back on Saturday. Happy Thanksgiving, everyone! --- For a brief moment, Gemini was leading the benchmarks until Anthropic [released](https://www.anthropic.com/news/claude-opus-4-5?ref=mbi-deepdives.com) Opus 4.5 this week. For the uninitiated, **Sonnet** and **Opus** are two different models within Anthropic’s family of AI systems, each optimized for different use cases. Sonnetis the practical, efficient workhorse designed for speed and scale at a lower cost, while Opus is the premium reasoning engine for mission-critical tasks demanding the highest level of intelligence and accuracy. Opus 4.5 outshines everyone else, including Gemini 3 Pro in several areas, as shown below. ![Comparison table showing frontier model performance across popular benchmarks](https://substackcdn.com/image/fetch/$s_!sQse!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F772d5f57-9d5d-47c6-8c48-244ad58238bb_2600x2236.webp "Comparison table showing frontier model performance across popular benchmarks") Source: Anthropic [blog post](https://www.anthropic.com/news/claude-opus-4-5?ref=mbi-deepdives.com) Speaking of Gemini 3.0, let me take this opportunity to share my experience of using the model as well. While benchmarks are important to gauge standardized evaluation, our own personal experience can differ from such benchmark. That is indeed the case for me personally in the case of Gemini 3.0 although I’m not willing to make any big claim since I’m not sure how generalizable my own experience is. Let me show you one example to make my point. I asked this prompt to both the models: “what are the top 5 hotel chains in the US? what are their aggregate market share in the US?” Click [ChatGPT](https://chatgpt.com/s/t%5F69270b414f4081918ef90efc137dbbf1?ref=mbi-deepdives.com) and [Gemini](https://gemini.google.com/share/417ec4f0b638?ref=mbi-deepdives.com) to see both of their answers. Not only ChatGPT’s answer appears to be more on the money here, it gives me ample links and references that I can click to check, ponder, and research the quality of its answers. Gemini didn’t bother to include any clickable links. This isn’t really one-off experience for me either. The question that I wondered this week is would I be able to tell that Alphabet launched a new Gemini model recently. The answer for me is no when it comes to text based reasoning. However, the answer is yes if I think about just photos and video generation in Gemini 3.0\. Given that context, I intend to stay subscriber of both ChatGPT and Gemini. While we are talking about benchmarks, Ilya Sutskever also [wondered](https://www.dwarkesh.com/p/ilya-sutskever-2?ref=mbi-deepdives.com) yesterday that there does seem to be a disconnect between what the evals show and the real world impact of these models: > This is one of the very confusing things about the models right now. How to reconcile the fact that they are doing so well on [evals](https://www.lesswrong.com/posts/2PiawPFJeyCQGcwXG/a-starter-guide-for-evals?ref=mbi-deepdives.com)? You look at the evals and you go, “Those are pretty hard evals.” They are doing so well. But the economic impact seems to be dramatically behind. > > One thing you could do, and I think this is something that is done inadvertently, is that people take inspiration from the evals. You say, “Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?” I think that is something that happens, and it could explain a lot of what’s going on. > > If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance Perhaps many of these AI companies are falling for Goodhart’s law: “When a measure becomes a target, it ceases to be a good measure”. That’s why it may be increasingly more relevant to observe what real world users are saying instead of focusing too much on evals. On that point, I have noticed several people, including Ben Thompson [echoing](https://open.spotify.com/episode/38v8a2tplNyOgcDyBD6u5A?si=cf9a26ef81bc4fcb&ref=mbi-deepdives.com) my concerns about Gemini 3.0 although there is indeed a broad consensus around Gemini’s superior image generation capability. Given this context, I thought there was a particularly interesting [blog post](https://cognition.ai/blog/devin-sonnet-4-5-lessons-and-challenges?ref=mbi-deepdives.com) from Cognition **a couple of months ago**. Cognition’s primary product is “Devin” which is an “AI software engineer” capable of independently handling entire software development projects. Some excerpts from the blog post I am talking about: > We rebuilt Devin for Claude Sonnet 4.5. > > Why rebuild instead of just dropping the new Sonnet in place and calling it a day? Because this model works *differently*—in ways that broke our assumptions about how agents should be architected. > > Because Devin is an agent that plans, executes, and iterates rather than just autocompleting code (or acting as a copilot), **we get an unusual window into model capabilities**. Each improvement compounds across our feedback loops, giving us a perspective on what’s genuinely changed. With Sonnet 4.5, we’re seeing the biggest leap since Sonnet 3.6 (the model that was used with Devin’s GA): planning performance is up 18%, end-to-end eval scores up 12%, and multi-hour sessions are dramatically faster and more reliable. > > In order to get these improvements, **we had to rework Devin not just around some of the model’s new capabilities, but also a few new behaviors we never noticed in previous generations of models**. It is likely much higher signal about model quality when your customer is writing such a blog post than any benchmark out there. Remember, this was Sonnet 4.5; so the recently released Opus 4.5 is expected to be even better. Anthropic is currently “[valued](https://www.cnbc.com/2025/11/24/anthropic-unveils-claude-opus-4point5-its-latest-ai-model.html?ref=mbi-deepdives.com)” $350 Billion in private market. Even in early 2024, they were “[valued](https://www.cnbc.com/2023/12/21/openai-rival-anthropic-in-talks-to-raise-750-million-funding-round.html?ref=mbi-deepdives.com)” $18 Billion. So, the company basically became 20-bagger in less than two years! One of the key differences between OpenAI and Anthropic is while OpenAI’s canvass appears to be very, very open ended and they cannot say “no” to almost any opportunity, Anthropic is almost in the opposite extreme. Anthropic is essentially a very focused bet on coding! Sholto Douglas from Anthropic [mentioned](https://www.youtube.com/watch?v=FQy4YMYFLsI&ref=mbi-deepdives.com) why Anthropic made such a choice: > Anthropic has been laser‑focused on coding, computer use, and things we think will have direct economic impact within the next six months. > > **One thing Anthropic has noticeably not focused on compared to DeepMind and OpenAI is mathematical reasoning**. DeepMind and OpenAI have been pursuing mathematical reasoning because of implications for science and because many people there love math and want to see it progress. > > We’ve had to reluctantly sacrifice focus on that to focus on near‑term economic impact with models. Later in the podcast, he elaborated more why coding is the key bet for them. Some excerpts from the podcast (slightly edited for clarity): > Two reasons. > > First, we think it’s the thing that will allow us to assist ourselves in AI research faster. There’s this notion of automating AI research. The speed of takeoff—the speed of progress—is driven by how much AI can assist AI research. Pre-fetching this is important. > > Second, we think **coding is the nearest‑term tractable problem domain in terms of economic impact**. For Anthropic to be a viable research program that can work on the things we think are important, we need economic return. **Coding is a huge market full of keen early adopters who are excited to try and switch tools.** > > There’s massive demand. **There is dramatically more demand for software than there is good software**. We’ve seen this in previous generations of compilers, web abstractions, etc.—demand for software keeps growing. > > **Models are better at coding earlier than almost anything else because coding is uniquely tractable**: the data exists, you can containerize and run things in parallel, you can run unit tests and know when something works. > > Self‑driving is uniquely hard because the car needs to work the first time. Coding is different: the model can fail a hundred times; as long as it succeeds once, it’s fine. There’s tractability and replayability that don’t exist when you directly touch the real world…You wouldn’t want an AI lawyer arguing your case in court right now So far, the bet is clearly working! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I have made a couple of changes yesterday which I will discuss behind the paywall. _This post is for paying subscribers only._ ### Solving the "DoorDash Problem" URL: https://www.mbi-deepdives.com/solving-the-doordash-problem/ Last updated: 2025-11-25T14:44:14.000Z Nilay Patel from “The Verge” has recently been talking about the “DoorDash problem” for some consumer internet companies. What is the “DoorDash problem”? From [The Verge](https://www.theverge.com/podcast/823909/the-doordash-problem-ai-agents-web-amazon-perplexity-lawsuit?ref=mbi-deepdives.com): > “Briefly, it’s what happens when an AI interface gets between a service provider, like DoorDash, and you, who might send an AI to go order a sandwich from the internet instead of using apps and websites yourself. > > That would mean things like user reviews, ads, loyalty programs, upsells, and partnerships would all go away — AI agents don’t care about those things, after all, and DoorDash would just become a commodity provider of sandwiches and lose out on all additional kinds of money you can make when real people open your app or visit your website.” As you can tell, this isn’t really a DoorDash specific problem, rather emblematic of a larger problem for many consumer internet companies, especially marketplaces that rely on ads to make their overall economics work. Predictably, these consumer internet companies don’t think (or at least don’t want you to think) AI agents can really threaten their position. They believe their specific data, vetted networks, or brand trust create a defensive “moat” that AI agents cannot easily cross. Verge quoted C-suites from Lyft, Zocdoc, Taskrabbit etc who all sort of echoed this sentiment. However, the CEO who I felt had the most cogent strategic argument to AI agents is Uber’s Dara Khosrowshahi. Notice this [excerpt](https://www.theverge.com/decoder-podcast-with-nilay-patel/672087/uber-dara-khosrowshahi-waymo-ai-bus-transit-tesla-self-driving?ref=mbi-deepdives.com) between Nilay Patel and Uber’s CEO: > **Nilay Patel: So, I come to you and I say, “I want to be able to get cars from Uber.” What is the percentage toll? What is the extra margin you would have to charge me, in dollars and cents, to make it worth it for me to take the customer away from you in that way?** **Because when the customer opens your app, you get to cross-sell them into Uber One, or ask “would you like some food when you arrive?” There’s all of these other incremental opportunities you have to forgo if I take that customer.** > > **Dara Khosrowshahi:** So I have a weird philosophy on this. Initially, **I charge you zero**…People spend so much time trying to figure out what the economics might be when the first thing is to try it out. Is it going to be a good experience or not? Is your scheduling actually going to work, or is it going to be off and the driver has to wait for 10 minutes, which is terrible? Let’s just figure it out. Then, once you optimize the experience, we can measure. **Are you an incremental consumer for Uber or are you totally cannibalistic?** > > **If it’s cannibalistic, then I’m going to charge a lot of money. You can’t have any money because you’re getting the benefit and my content. You’re not bringing me any business at all. If it is incremental, then I would pay some take rate. Is it a 5, 10, or 20 percent take rate? It depends on the incrementality.** > > But I think so much innovation has slowed down because companies try to figure out the economics first. Figure out the experience first and then the economics. Listen, if I do a bad deal for a year, who cares? **I’m going to renegotiate with you.** I’m building stuff for the next 10 years. **Success or failure isn’t going to be determined by my take rate being 5 or 20 percent in year one. It can set precedent, and precedents are dangerous. That’s why I would say to charge zero**. Let’s try it out. Let’s see what the experience is. **Let’s try to measure out what the value add is, and then the economics essentially will take care of themselves**. That really says it all! Nobody really needs to make a big claim whether any of these will work; the data can simply guide these companies where to strike the right balance over time. The key word here is “incrementality”. Incrementality is why it makes enormous sense for Walmart to partner with ChatGPT as I suspect the people who shop at Walmart and the people who use ChatGPT daily may have a tiny overlap **today**. So, if people try to use AI agents to shop their groceries and Walmart pops up, they are highly likely to be incremental to Walmart. On the other hand, I suspect all of these ChatGPT DAUs are likely Amazon Prime members which makes it lot more cannibalistic than incremental for Amazon. And of course, if you’re using AI agents to order things from online, Amazon is not making its ad revenues which is essentially the key pillar for their retail business. As I [**mentioned**](https://www.mbi-deepdives.com/walmart-and-target-cy-3q25-earnings/) recently, Amazon’s LTM advertising and subscription fees was \~$113 Billion which was **3.5x of its LTM operating income ex-AWS.** While Amazon has strong incentive to resist third-party AI agents roaming around its website, it does have compelling argument why such an experience is unlikely to resonate with consumers, at least at its current state. From Amazon’s 3Q’25 earnings call: > “search engines are a very small part of our referral traffic and third-party agents are a very small subset of that. But I do think that we will find ways to partner. We have to find a way, though, that makes the customer experience good. Right now, I would say the customer experience is not -- **there’s no personalization. There’s no shopping history. The delivery estimates are frequently wrong. The prices are often wrong. So we’ve got to find a way to make the customer experience better and have the right exchange value**. But I do think that the exciting part of this and the promise is that **AI and agentic commerce solutions are going to expand the amount of shopping that happens online**. And I think that’s really good for customers, and I think it’s really good for Amazon because at the end of the day, you’re going to buy from the outfit that allows you to have the broadest selection, great value and continues to deliver for you very quickly and reliably. And I think that bodes well for us.” I have tried AI agents and my initial [**underwhelming**](https://www.mbi-deepdives.com/first-impression-of-chatgpt-agent-and-apps-on-chatgpt/) reaction still persists today. But as I said before, there is a pretty clear [**distinction**](https://www.mbi-deepdives.com/the-appeal-of-conversational-commerce/) between the full autonomous agentic AI and conversational AI experience. And I am a much more ardent believer of conversational commerce than truly agentic commerce. My friend [Liberty](https://www.libertyrpf.com/?ref=mbi-deepdives.com) was telling me yesterday that he’s looking into buying luggage for his next family trip. He shared the below search result on Amazon; you can see why it would be a much better experience if he just provided some key aspects he’s looking in a luggage (price range, size, color etc.) and then ask the AI to give him three options which he then can click to buy or go to the specific website himself to complete the purchase. At least I am certainly not there yet that I would just ask a prompt to any AI and let it make the final purchasing decision, including payments. The reality is there are so many contexts that reside in our head that can be difficult to write down in minute details while prompting to AI, but AI can certainly narrow down the decision process from \~25-30 similar looking luggage to just three and allow me to pick one. ![](https://substackcdn.com/image/fetch/$s_!p3kB!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe316c74b-87f9-4957-82ac-7edc00da2d2b_2506x2868.png) Given this context, I do think OpenAI’s yesterday’s launch of “[Shopping Research](https://openai.com/index/chatgpt-shopping-research/?ref=mbi-deepdives.com)” is very appropriately named. I would love AI agents to do research on my behalf to help in shopping decision process, but not take the final decision itself. Such a feature should pose an immediate threat to Google’s networking business which has already been declining in revenue for the last [**13 consecutive quarters**](https://www.mbi-deepdives.com/goog3q25/). [![](https://substackcdn.com/image/fetch/$s_!uutX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F05a5ec8f-c1fe-499e-be8e-0606fa068144_1576x799.png)](https://openai.com/index/chatgpt-shopping-research/?ref=mbi-deepdives.com) Source: OpenAI The core Google search or Amazon’s advertising can also be under pressure if ChatGPT does become the “superapp”. However, customers seem to [trust](https://www.mbi-deepdives.com/the-appeal-of-conversational-commerce/) retailers’ on-site agents 3x more than third-party agents such as ChatGPT, but that can change over time. Of course, Amazon shouldn’t be actively helping ChatGPT to become a superapp by making all their inventory available to shop on ChatGPT given much of it is going to be cannibalistic for them. The two most important factors for e-commerce online is selection and delivery speed, and it is difficult for anyone to match Amazon on either front. OTAs also have this “DoorDash problem” although there are important nuances to it. Unlike many other marketplaces, Booking makes less than 5% of their revenue from ads. Airbnb doesn’t even have any ad revenue. However, ceding the customer relationship to ChatGPT can make Booking’s “connected trip” dream difficult to execute. Last week when Google [launched](https://blog.google/products/search/agentic-plans-booking-travel-canvas-ai-mode/?ref=mbi-deepdives.com) some AI features for travel, all the OTA stocks took bit of a beating as investors started to entertain the possibility that Google may end up making OTAs irrelevant. [Skift](https://skift.com/2025/11/20/google-agentic-ai-travel-booking-no-intention-become-ota/?utm%5Fcampaign=Newsletter%20%7C%20The%20Daily&utm%5Fmedium=email&%5Fhsenc=p2ANqtz-%5FVLhgNNAgqaq0D21QlzxOzvwAe6KKjKae%5FVtRvo9REunuVeQ5qsevyumoT%5FxWUmb2P8Qh4%5FgksGymfuhKzKhlpqv4QiQ&%5Fhsmi=390984138&utm%5Fcontent=390984607&utm%5Fsource=hs%5Femail) later reported that Google clarified that it was not their intention: > “In a follow-up with Skift, Julie Farago, Google’s vice president of engineering for Travel and Local, sought to clarify the company’s intentions. “Google has no intention of becoming an online travel agency,” she said. > > “For many years, we’ve helped travelers compare their options across different providers and complete the booking with the partner of their choice. Our work to enable agentic booking for flights and hotels builds on this framework.” > > Farago stressed that “**Google won’t be the merchant of record**. Partners will continue to service the booking and manage the customer relationship.**”** In my [**Booking Deep Dive**](https://www.mbi-deepdives.com/bkng/), I also made the case that the risk to OTAs are likely overstated. From my Deep Dive: > While agentic booking remains a mirage, conversational booking or planning the entire trip on Google or ChatGPT seems like a real possibility. But even if that happens, I am not sure that’s necessarily a big negative for Booking. Booking already lives in a world where \~35-40% of its customers don’t start their travel journey on their app or website. Instead of fighting for those customers on core Google search, they will just have to do that within the confined box of ChatGPT or Gemini. Is this fundamentally a different paradigm? Even if it’s different, is it necessarily a negative development for Booking? > > On one hand, you could say if we move away from the search environment where Google has \~90%+ market share to a relatively more bifurcated chatbot experience, I wonder if that may lead to some CAC **deflation** compared to status quo. On the other hand, if consumers become so habituated with general chat bots that the starting point of planning a trip shifts more to indirect channel, Booking’s direct or organic traffic may already have peaked. While you can start planning the trip on Booking app itself, ultimately the question may come down to integration and personalization. Which app or company will have much richer context of your personal preferences? ChatGPT or Booking? If you plan the entire trip in Gemini, perhaps Gemini will be able to embed your trip details automatically in Google Calendar, use Google Maps directly from the Gemini app environment, and utilize Gmail to easily find booking confirmation or flight codes. It is not difficult to imagine how such an integrated experience may seem much better than Booking’s connected trip experience. Nonetheless, Booking can respond to such a headwind by dialing up the benefits of their Genius loyalty programs as long as doing so is accretive for them from margin perspective. As you can imagine, it is simply too early to know how any of these may evolve. > > Airbnb should be more immune from some of these headwinds though. Most of their exclusive listings don’t have their own websites, so they will remain reliant on Airbnb in driving their bookings. Airbnb itself will come up with their own AI-powered search and if unique listing is what you’re looking for, your best bet will continue to be to go directly to Airbnb app or website. Given this context, I did make a small change in my portfolio yesterday which I will mention behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 65 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Booking: The shareholder friendly OTA king URL: https://www.mbi-deepdives.com/bkng/ Last updated: 2025-11-24T14:58:50.000Z You can listen to the Deep Dive [here](https://www.mbi-deepdives.com/audio/) --- Booking’s modern identity as a dominant, European-centric travel aggregator obscures its chaotic origins as a quintessentially American dot-com darling: Priceline.com. The original company, founded by Jay S. Walker, launched in 1998 as Priceline.com. It was built on a strange, patented mechanism called “Name Your Own Price”. This model was a form of reverse auction, meaning consumers bid a price for a service, like a hotel or flight, without knowing the specific brand until after the purchase was complete. The concept cleverly targeted the perishable inventory of airlines and hotels, which have a marginal cost of essentially zero for an empty seat or room. The model allowed suppliers to offload this excess inventory at a discount without cannibalizing their primary, price-transparent distribution channels. While revolutionary, as you can imagine, the model was built on a foundation of psychological friction. Despite this inherent friction, Priceline.com became a high-flyer of the internet bubble. I mean if you look at numbers for Priceline back then, you would legitimately wonder “friction? what friction?” After starting the priceline.com service on April 6, 1998, the company reported $35 million revenue in 1998\. Next year’s revenue? $482 million. The year after that, revenue was $1.2 Billion. If you are shocked to see how AI startups are scaling revenues, it looks like this is pretty much what happens when you are in the early phase of a true technological transformation! ![](https://substackcdn.com/image/fetch/$s_!eKXS!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faf361938-15cf-41f1-a527-375eb775c341_573x109.png) Source: Company Filings, MBI Deep Dives If this is what happens in the early phase of transformation, let’s pray (!) that we can skip the middle phase in AI. Priceline was smart enough to go public in March 1999 and by **May of 1999**, its market cap exceeded **$20 Billion**. Of course, tech bubble popped next year and to make matter considerably worse, 9/11 almost seemed like a death blow to them. Market cap went below **$200 million by December 2000!**! From a tech bubble darling, Booking actually became a penny stock. So, the company had to do a 1 for 6 reverse stock split. What perhaps surprised me more is how long it took Booking to **exceed their revenue in 2000**. It’s one thing for the stock to experience \~99% drawdown after a bubble, but it really puts things in perspective when you see **it took Booking SEVEN years to exceed their revenue in 2000!** Despite internet being obviously a truly revolutionary technological phenomenon and Booking being one of the winners, their fundamentals (not just stock price) weren’t immune from a long suffering! ![](https://substackcdn.com/image/fetch/$s_!O6Mz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6de8932e-4567-46cc-9c52-a1296c2d36e0_568x219.png) Source: Company Filings, MBI Deep Dives This period, however, forged the company’s core competency: resilience. The management team that survived the dot-com abyss was immediately tested again by the 9/11 attacks, followed by the SARS-1 outbreak in 2003\. Booking’s current CEO Glenn Fogel did a recent [interview](https://stratechery.com/2025/an-interview-with-booking-ceo-glenn-fogel-about-travel-and-aggregation/?ref=mbi-deepdives.com) with Stratechery in which he indicated the battle-hardened, pragmatic culture was perhaps the most valuable asset to survive the crash. It created a leadership team that was unsentimental about its original, failing business model and actively searched for one that actually worked. The search for that new model led Fogel, then in charge of the company’s small European operation, to an uncomfortable conclusion. The “Name Your Own Price” model just didn’t make sense in a European market where low-cost carriers like Ryanair and EasyJet already offered transparent, rock-bottom prices. Fogel realized “Name Your Own Price” can only be a niche market and to pursue the larger opportunity, Priceline needed customers to offer a platform that allowed the customers to see the price and exactly what they were getting. This search led Priceline to acquire a small UK-based company called Active Hotels in 2004 for $161 million, and, in 2005, a small Amsterdam-based outfit named **Booking.com** for $133 million. These acquisitions, which barely registered compared to the 1999 IPO valuation, would become the most important strategic decisions in the company’s history. Active Hotels and Booking.com operated on a completely different framework known as the “agency model.” At the time, the dominant US player, Expedia, used the “merchant model,” where it bought rooms from hotels at a discount and then resold them, capturing the margin. This model was good for Expedia’s cash flow, as it collected the customer’s money upfront, but it was not as good for hotels, which had to be paid later. The agency model, in contrast, was bit of a godsend for hotels, particularly the small, independent properties that dominate the fragmented European market. Hotels simply listed their rooms, and the customer paid the hotel directly upon arrival. Booking would then send an invoice to the hotel **after** the stay to collect its commission. This model was operationally superior for rapidly scaling inventory, as it required zero risk or complex integration from the hotel. But it was less than ideal for Booking. Booking.com was an early and aggressive user of search engine marketing, paying Google for traffic. This created bit of a cash-flow paradox: Booking had to pay Google for ad clicks **upfront**, in cash, but would not get paid its commission from the hotel for **months**. As Fogel [explained](https://stratechery.com/2025/an-interview-with-booking-ceo-glenn-fogel-about-travel-and-aggregation/?ref=mbi-deepdives.com), “The faster you grew, the worst your negative cash flow can get worse, worse, worse, worse, worse.” As a result, Booking.com was the perfect acquisition target for a cash-rich, publicly traded company like Priceline, which could inject the capital needed to solve this working-capital crisis and unleash the model’s explosive potential. For the next decade, the small European acquisition proceeded to completely eclipse its American parent. The agency model, fueled by Priceline’s capital and basically a **scientific** approach to Google advertising, became an unstoppable engine of growth. The original “Name Your Own Price” business became a rounding error. By the mid-2010s, Booking.com accounted for the vast majority of the group’s revenue and growth. In, 2018, the company acknowledged this reality by officially changing its corporate name from The Priceline Group to Booking Holdings. After the topsy-turvy first decade in the public market, Booking also matured into an incredibly well run, scalable marketplace in the internet! It’s hard to even “see” GFC in their numbers as the business just kept growing topline at higher than 20% every year between 2008 and 2014 (although some of these growth was inorganic in nature) while increasing operating margins from 15% in 2008 to 36% in 2014. Of course, the pandemic was a major hiccup and, Booking was a [**laggard**](https://www.mbi-deepdives.com/airbnbs-mix-shift/) in recovering from the pandemic because their higher booking mix was geared towards what the pandemic crushed and took longer to recover: hotels, cross‑border Europe, short urban stays, and business travel. Nonetheless, their 2025 revenue will be \~75% higher than 2019 revenue, exhibiting yet again the resilience of their business model. ![](https://substackcdn.com/image/fetch/$s_!X1X-!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F049534c1-98f8-4ea3-97a1-8c774e2d620c_568x505.png) Source: Company Filings, MBI Deep Dives Today, Booking is a \~$155 Billion market cap company and despite IPO-ing during the super hot tech bubble period, Booking still managed to generate a respectable \~9% CAGR over the last two and half decades. And if you’re one of the lucky ones to bottom tick the stock after it crashed, it was **almost** a 1,000-bagger! Booking pivoted from “Know Your Own Price” to “Agency” model to “Merchant” model while navigating a frenemy relationship with the 800 pound gorilla named Google and responding effectively to the Silicon Valley’s favorite: Airbnb. That is some rich history! 🫡 ![chart](https://substackcdn.com/image/fetch/$s_!awf_!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F70bc0d3c-e6e9-4460-8277-3ddeccb79c22_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) But how does Booking travel from the status quo to the future where AI may re-write a lot of assumptions in how we choose our destinations? Before I explore such questions, let’s first deepen our understanding of the status quo of the business. The rest of the Deep Dive will be behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### The limits to our imagined vision of the future URL: https://www.mbi-deepdives.com/the-limits-to-our-imagined-vision-of-the-future/ Last updated: 2025-11-23T16:57:48.000Z ***A programming note***: I hope to publish my Deep Dive on Booking Holdings tomorrow. Since this is my first year of publishing daily at MBI Deep Dives, I am still thinking about some vacation policy. I will let you know once I finalize something, but for now, I would like to take Thursday and Friday off during Thanksgiving next week. I would also take the last week of the year off. My son, who is a Christmas baby, will be turning one this year; so I think I will have my hands full during that time! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- Earlier this year, I read this [piece](https://www.noemamag.com/finding-awe-amid-everday-splendor/?ref=mbi-deepdives.com) “Finding Awe Amid Everyday Splendor” which made the point that seeking “brief moments of awe is as good for your mind and body as anything you might do.” The writer of the piece interviewed Dacher Keltner (the author of the book “[Awe](https://www.amazon.com/Awe-Science-Everyday-Wonder-Transform/dp/1984879685?ref=mbi-deepdives.com)”) while strolling through Point Reyes beach. The first time I went to Point Reyes North Beach was in October 2024 and I remember telling my wife how the vastness of the sea in that foggy October afternoon in a nearly secluded beach added a surreal element to our life events! It almost felt like we were saying good byes to our past selves before entering a new phase in our life: parenthood! Then when I read that piece about “awe”, it rekindled a deep desire to go back to that beach once again. So, I have been thinking about going back for a while, but finally, after publishing my “daily dose” on Friday last week, I started driving for Point Reyes North Beach! ![](https://substackcdn.com/image/fetch/$s_!eIPR!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5f132312-4381-490d-94e7-1f073ae82431_1440x1800.jpeg) It took me three and half hours to get there. This time, it was lot sunnier. With a clear sky and constant sound of crashing waves, I found the beach even more secluded this time. I took a stroll for half an hour around the beach and then started driving back to my home for another three hours! 0:00 /0:49 1× Admittedly, I was less awestruck about Point Reyes North Beach the second time around, but I actually conjured lot more awe while driving to and from the beach itself because during the entire 7-hour trip, I was **finally** listening to Acquired’s three Nvidia episodes (part [1](https://www.acquired.fm/episodes/nvidia-the-gpu-company-1993-2006?ref=mbi-deepdives.com), [2](https://www.acquired.fm/episodes/nvidia-the-machine-learning-company-2006-2022?ref=mbi-deepdives.com), and [3](https://www.acquired.fm/episodes/nvidia-the-dawn-of-the-ai-era?ref=mbi-deepdives.com)). It was a bit **surreal** to listen to these episodes knowing what we know today: Nvidia is the largest market cap company in the world! While listening to the episodes, there are a few things that really stood out to me. Jensen Huang was born in Taiwan and his family later moved to Thailand. At age Nine, his family sent him to a boarding school in the US to chase the American dream. While the school seemed affordable, his family’s knowledge about the school was clearly quite…limited! From the podcast: > “It turns out that the reason that this school, OBI (Oneida Baptist Institute) was so cheap was it’s actually not a prep school. It’s a reform school. This is a school for troubled kids. It’s a reform school. Jensen’s roommate, when he shows up as a 9-year-old, is a 17-year-old kid who had just gotten out of prison and was recovering from 7 stab wounds that he got in a knife fight.” Many people may laugh or even raise eyebrow at such callousness of Huang’s parents, but as an immigrant father, I can confirm feeling a bit sentimental when my son’s American passport arrived. I almost felt like I did something “very important” for my son. The other thing that really stood out from the series was a particular Marc Andreessen quote. I googled it and found the quote in a Forbes [piece](https://www.forbes.com/sites/aarontilley/2016/11/30/nvidia-deep-learning-ai-intel/?ref=mbi-deepdives.com) from Nov 30, 2016\. Here’s the quote from Marc Andreessen: > “We’ve been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia’s platform,” says Marc Andreessen of venture capital firm Andreessen Horowitz. “It’s like when people were all building on Windows in the ‘90s or all building on the iPhone in the late 2000s. > > “For fun,” adds Andreessen, “our firm has an internal game of what public companies we’d invest in if we were a hedge fund. We’d put all our money into Nvidia.” Oh, he would have so much more fun if he (or any of us) actually did that! The stock compounded at a cool **62% CAGR** **AFTER** that Forbes piece was published. A few days ago, I was lamenting to a couple of investor friends that how hard investing may be evolving to be, especially in tech given how seeds of the major value unlock in recent years came from “academic papers” 7-8 years ago (think “Attention is all you need”). Isn’t it going to be increasingly more difficult to understand these inflection points? Admittedly, listening to the Nvidia series was a good reminder that indeed “attention is all you need”! You don’t have to take any prominent VCs (or anyone) words as gospel obviously since just like anyone else, they can often be wrong but in retrospect, there was enough “easter eggs” out there for a good student of the market to **at least start paying attention** to what’s going on in Nvidia. ![chart](https://substackcdn.com/image/fetch/$s_!J-9P!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8803dced-37d3-4b7b-b7ec-85e67ecf1437_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Of course, investing is never going to be so easy that just by paying “attention” you will magically sense the future! When Acquired published its Part 1 of Nvidia series in March 2022, its Enterprise Value (EV) was \~$700 Billion. A month later when they published part 2, Nvidia’s EV dropped to $500 Billion. It was interesting how the bull-bear cases that Acquired discussed in April 2022 still remains relevant to this day even though the company is today almost \~10x larger. Notice the below excerpt from part 2, for example: > “Google is sort of counter positioned against NVIDIA here, where they’re saying, we want to differentiate Google Cloud with this offering that depending on your workload, it might be much cheaper for you to use TPUs with us than for you to use NVIDIA hardware with us or anyone else. They’re probably willing to eat margin on that in order to grow Google Cloud’s share in the cloud market. It’s kind of the Android strategy, but runs in the data center.” In fact, while listening to episode 2 of Nvidia series, I didn’t know when they published it since I was driving. But it became clear that it was definitely before ChatGPT as they were kind of struggling a bit to justify what could explain Nvidia’s $500 Billion valuation. By the time, Acquired released their part 3 of Nvidia series in September 2023, the world became obsessed with ChatGPT and Nvidia’s EV reached $1 Trillion. ![chart](https://substackcdn.com/image/fetch/$s_!M_JO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd926b1c2-294a-4a92-ae21-3d3b87cd3256_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) When I got back home, it was wild to see the earnings revisions of Nvidia’s in recent years. In January 2023, Nvidia’s consensus operating income or EBIT estimates for FY’26 was just $20 Billion. Today, estimate for FY’26 shot to $135 Billion!! You bet I found “awe” looking at this chart! ![chart](https://substackcdn.com/image/fetch/$s_!msnY!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feb6b322b-8f33-43d7-8c3e-761ae82c2837_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) One of the easiest way to be humble in market is to imagine time traveling and wonder if you could possibly foresee now that you know how in reality it actually turned out. More often than not, I quickly come to the realization how investing would still be very, very difficult even if I knew some (but not all) key data points beforehand. Even if I were following Nvidia like a zealot for the last 10 years and became infatuated with ChatGPT right after it came out in November 2022, the harsh reality is I might still have been quite tentative about investing my money in Nvidia. Investing is inherently forward looking, but I find time traveling in the past is quite underappreciated. Because we don’t know the future, all our debates are understandably centered around how any of these will pan out, but if we just go back even a couple of years, it should be crystal clear that how our imagined vision of the future likely stands on a foundation of sands! I hope that should make it apparent that the distribution of outcomes is likely always wider than we like to imagine! **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Meta's biggest competitor(s) URL: https://www.mbi-deepdives.com/metas-biggest-competitor-s/ Last updated: 2025-11-22T15:21:44.000Z FTC’s[ lawsuit](https://www.ftc.gov/legal-library/browse/cases-proceedings/191-0134-facebook-inc-ftc-v-ftc-v-meta-platforms-inc?ref=mbi-deepdives.com) against Meta may be a colossal waste of time for everyone involved, but it did have one silver lining. Thanks to this lawsuit, we got access to a lot of documents and research work (both internal and external) which helped investors understand the company a bit better. As expected, the court has [ruled](https://www.bbc.com/news/articles/crklgrpdke8o?ref=mbi-deepdives.com) last week that Meta does not have a monopoly. While I have covered many of the documents that were unearthed during this lawsuit before, I found some discussions particularly interesting from the [verdict](https://assets.bwbx.io/documents/users/iqjWHBFdfxIU/rww8JGP.20cc/v0?ref=mbi-deepdives.com) which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Walmart and Target CY 3Q'25 Earnings URL: https://www.mbi-deepdives.com/walmart-and-target-cy-3q25-earnings/ Last updated: 2025-11-21T14:57:29.000Z It is a bit strange co-incidence, but CY3Q’25 was the last earnings call for both the current CEOs of Walmart and Target. However, the backdrop in which they are passing down the baton to the next person perhaps couldn’t be any more different. In both cases, the new leadership is coming from internal talent bench. Target’s new CEO Michael Fiddelke joined the company as a finance intern back in 2003 and rose through the ranks to become CFO in 2019, then COO in 2024, and now finally CEO. Walmart’s new CEO John Furner had an even more endearing story. He joined Walmart as a part‑time hourly associate in 1993\. As if that were not convincing enough that Furner lives and breathes the Walmart ethos, he even got his degree from Sam Walton College of Business at the University of Arkansas in 1996. The reason the mood around the leadership change is very different in these two retailers can be encapsulated very quickly if you look at their same store sales (SSS) growth in the last four years. Remarkably, **nine out of the last 10 quarters**, Target’s SSS growth was negative whereas Walmart just kept cruising along at close to mid-single digit SSS growth during this period. ![](https://substackcdn.com/image/fetch/$s_!Shis!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff30b95c4-ba7f-41b6-9d47-5e86b9f2ffb6_1161x673.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Their stock performance appropriately reflected this reality. In the last five years, Walmart’s stock was comfortably ahead of S&P 500’s return whereas Target’s stock declined **\~44%** over the last five years. Given how challenging it is to turn around a retailer, the new CEO at Target will have a much more difficult hand. ![chart](https://substackcdn.com/image/fetch/$s_!awQR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F223d5698-1d64-4c68-9d52-3857cb655357_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) One of the key reasons for such a massive divergence between these two retailers performance is e-commerce. Walmart has been growing their US e-commerce business for 20%+ in the last seven consecutive quarters and growth has, in fact, accelerated in the last two quarters. Target’s digital comp, on the other hand, has limped towards low single digit! Walmart’s e‑com is tied to **high‑frequency, non‑discretionary** missions (grocery is \~60% of their US sales), while Target’s digital gains are trying to swim upstream against a **discretionary slowdown**. That alone explains a big chunk of the growth gap, but perhaps far from the entire story. ![](https://substackcdn.com/image/fetch/$s_!aYvi!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe4f34ac4-1dc4-4995-9fb1-67bb826d89c1_1150x669.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While Target painted a picture of a somewhat pressured consumer, citing sentiment at a “3-year low” and that shoppers are “stretching budgets”, Walmart’s commentary hints at much of the pressure is limited to lower income families. Inflation in Walmart is also at LSD. From the call: > As we look at our customers and members here in the U.S., they’re still spending with upper and middle income households driving our growth. **We continue to benefit from higher income families choosing to shop with us more often. Middle income households have been steady, and while lower income families have been under additional pressure of late**, we’re encouraged by how our teams are meeting them with greater value across necessities and doing what we can to help them stretch their dollars further. > > For the quarter, like-for-like inflation in Walmart U.S. was 1.3% with food and general merchandise up low single digits. Walmart mentioned advertising and membership fees was one-third of their operating income now. While that’s impressive, it still pales in comparison with Amazon. This isn’t quite apple-to-apple comparison since \~10-20% of Amazon’s advertising revenues come outside its marketplace business and its Prime subscription has broader scope than Walmart’s membership, they can still be helpful to gauge the context. Amazon’s LTM advertising and subscription fees was \~$113 Billion which was **3.5x of its LTM operating income ex-AWS**! Nonetheless, Walmart clearly got the memo where the future of retail is heading while Target appears to have been caught sleeping in the last 5 years. Walmart correctly realized that you need 3P sellers and your entire retail infrastructure needs to undergo technological transformation to be able to compete against the 800 pound gorilla named Amazon. In CY 2Q’25 call, Walmart mentioned \~44% of their marketplace flew through Walmart Fulfillment Services (WFS) and in CY 3Q’25 call they mentioned, half of the fulfillment center volume is now automated. Target’s in‑store fulfillment model now looks **less scalable and less efficient** than the robotics‑heavy systems at Walmart/Amazon, and I’m, frankly speaking, not sure they can ever catch up at this point. One reason for such pessimism is what I discussed in my [**Instacart Deep Dive**](https://www.mbi-deepdives.com/cart/) last month: > Walmart has been investing in perfecting delivery for the last few years. While they don’t disclose how much of their orders are coming from Instacart compared to demand coming from their own app, my guess is supermajority of their e-commerce sales is coming from their owned and operated app/website. They invested in their own “[Spark](https://www.sparkdriverapp.com/en%5Fus.html?ref=mbi-deepdives.com)” platform which is Walmart’s own last-mile delivery service for independent contractors to deliver grocery to Walmart’s customers. Even in 2022, they [mentioned](https://corporate.walmart.com/news/2022/08/17/growing-the-spark-driver-platform-now-and-in-the-future?ref=mbi-deepdives.com) \~75% of their deliveries were fulfilled by drivers on Spark platform. **Despite such end to end control and order density, Walmart only achieved overall e-commerce profitability for the first time in FY 1Q’26**. It is safe to assume that it will take a while for the most of the rest of the retailers to reach profitability, let alone matching the profitability level seen in their own stores. Walmart’s speed of delivery is really the killer differentiation. \~35% of their digital orders were delivered in under 3 hours in 3Q’25\. In their China operation where e-commerce has \~50% penetration (vs \~20% in the US), Walmart delivers \~80% of the orders in under an hour. I don’t quite expect that to replicate anytime soon in the US, but speed of delivery is certainly going to be one-way street. Walmart mentioned in the call that their fastest growth channel is the orders that get delivered in less than an hour and as the order density grows, fulfillment and shipping cost will decline further and they can afford to invest more aggressively to accelerate delivery speed even more. From the call: > more than 50% of our volume from fulfillment centers is coming from automation. And that translates into lower shipping costs. **Our shipping costs have been down consistently for many quarters in the 30% range**. This was another quarter where we saw double-digit improvements. And that really helps our e-commerce economics, but also helps the overall SG&A of the company. The best bet for most of the retailers like Target is to partner with the likes of Instacart/DoorDash to stay competitive in delivery speed. It’s still likely going to be inferior given the lack of end-to-end control over the entire value chain, and perhaps more importantly, having another middleman to share your already thin margin with is not going to be pretty outcome for most retailers. Walmart’s [partnership](https://corporate.walmart.com/news/2025/10/14/walmart-partners-with-openai-to-create-ai-first-shopping-experiences?ref=mbi-deepdives.com) with ChatGPT was briefly discussed, but we didn’t learn anything new. During the call, management did mention that more than 40% of the new code is either AI-generated or AI-assisted. Walmart also had a very interesting example about how they are using WhatsApp to drive e-com in Chile. As I have [**mentioned**](https://www.mbi-deepdives.com/messaging-opportunity-and-group-chat-dynamics/) recently, it can be hard to appreciate the power of WhatsApp unless you live outside North America. > One of the things that’s useful about having an international segment is that we have markets where we can trial new capabilities. And we’ve been trialing something called Carrito Listo down in Chile, where we actually create customers’ orders for them, send them a WhatsApp prompt to ask them if they’re interested in buying that basket. They go into the app, and they can see we’ve created a basket for them that actually has all the brands that they normally buy in the kind of intervals that they like to buy it. And they have full agency over whether they want to accept that basket, whether they want to add to the basket, whether they want to take from the basket. And what we’re seeing is that it’s really attractive, and it’s become up to **about 20% of our e-comm business in Chile already**. Bill Gates once [described](https://stratechery.com/2018/the-bill-gates-line/?ref=mbi-deepdives.com) how to identify whether a company is platform or not: *“A platform is when the economic value of everybody that uses it, exceeds the value of the company that creates it. Then it’s a platform.”* WhatsApp is so little monetized relative to the value millions of companies likely generate in most international markets in the world, I wonder if it’s the platform that has the highest differential between the value it captures and the value others generate from it. Perhaps Meta will look to narrow this differential in the next 5-10 years! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The corporations of the future URL: https://www.mbi-deepdives.com/the-corporations-of-the-future/ Last updated: 2025-11-20T13:39:54.000Z Why do companies even exist? Why isn’t the economy just a vast collection of freelancers buying and selling services from each other in the open market? The answer, according to economist Ronald Coase, is “[transaction costs](https://en.wikipedia.org/wiki/Coase%5Ftheorem?ref=mbi-deepdives.com).” These are the frictions and overhead involved in using the market. Every time you want to hire someone externally, you must spend time and resources searching for the right person, negotiating the price, writing a contract, and ensuring the work is done correctly. A corporation exists because it is often significantly cheaper and faster to organize these activities internally. By hiring employees and establishing internal management structures, a company avoids the constant hassle of market negotiation. The firm thrives as long as its **internal coordination is more efficient than dealing with the external marketplac**e. Satya Nadella has been doing bit of podcasting rounds and his recent “Cheeky Pint” [episode](https://cheekypint.transistor.fm/19?ref=mbi-deepdives.com) had plenty of interesting nuggets, but his musings on what the future corporations may look like was quite intriguing to me. Nadella connected Coase theorem directly to the profound challenge posed by advanced and ever more capable AI. Traditionally, a company maintained its efficiency advantage (its lower internal transaction costs) through “tacit knowledge” i.e. the unique know-how, processes, and expertise residing within its employees and organizational culture. This specialized internal knowledge is what made the company faster and smarter than the open market. Nadella’s concern is that powerful, generalized AI models can radically disrupt this balance. If an AI model available to everyone “knows everything,” the cost of acquiring expertise and executing complex tasks in the open market suddenly plummets. The traditional advantage of having specialized knowledge inside the company dissolves if that knowledge is universally accessible via AI. In this scenario, the Coasean justification for the corporation is threatened, as the marketplace becomes just as efficient as the internal organization. Therefore, Nadella argues that for a company to survive, it must redefine how it maintains its advantage. It must take its unique **tacit knowledge** and embed it into its own proprietary AI models or specialized layers. This customized “Corporate AI” becomes the new mechanism for lowering transaction costs. By ensuring its internal AI allows it to deploy and compound knowledge faster than the generalized AI available to everyone else, the company preserves its “sovereignty” and its reason for existing. Here’s the specific back and forth between Satya Nadella and John Collison: > **Satya Nadella**: “…the ultimate sovereignty question is more of what’s the future of a corporation, right? I mean, if you sort of start to go to the core of the Coase theorem, you say, “Wow, what the heck? If the model is the thing that knows everything, why do I even… I’m supposed to have some tacit knowledge that makes the transactional costs inside my organization lower than just being in the marketplace.” So they’re a mind bender. So in fact, one of the ways I think is, the sovereignty that matters is your company’s sovereignty in an age where there are continual learning increasing returns to a model. So I’m increasingly thinking that hey, the company’s ability to have that intelligence layer that’s a scaffold or even weights embedded in the model. So it’s not somebody else’s foundation model. It’s about do you have sovereignty in your foundation model? So **my new concept is the future of a company is that company has its own foundation model that captures essentially the tacit knowledge that makes the transactional costs of how knowledge gets accrued and diffused inside the organization faster**. So that’s sort of a long speech on sovereignty. > > **John Collison: “**Well, there’s two versions… That’s very interesting. The idea that AI maybe just changes the nature of companies, and you are saying that if some companies are already collections of IP, right? Disney or we had Dave Ricks from Eli Lilly here, that is an IP company in a big way. And some companies are already collections of IP, but right now that IP is in all the emails and documents and people’s heads most importantly, whereas maybe the IP could be in a single model over time. Where I thought you were going to go with that is just maybe the—people point out a lot that current companies are modeled after manufacturing companies and Alfred Sloan type stuff, despite the fact that we’re doing knowledge work today and not running a little manufacturing line. And do you get more just weird-looking companies? Do you get the famous really tiny billion-dollar company? Do you get more highly distributed internet companies? Do you get some DAOs? I thought that’s where you’re going to go with that.” > > **Satya Nadella**: “I think that those are also possibilities. So the structure itself could change and it’s going to be more possible for whatever the few, the one-person billion dollar company, what have you, maybe could happen or DAOs could happen. But **the interesting question, at least for me, is where does tacit knowledge reside? Clearly it resides in people’s heads and it’s the classic know-how that accrues and compounds. I think it’ll also reside and compound as weights in some** [**LoRa**](https://www.ibm.com/think/topics/lora?ref=mbi-deepdives.com#:~:text=Low%2Drank%20adaptation%20%28LoRA%29,number%20of%20trainable%20model%20parameters.) **layer that is unique to your company.** I feel like the new intellectual property at Eli Lilly or at Microsoft or at Stripe at some point can be also, besides all the humans, besides all the other artifacts we have, I think we’ll also say, “Oh, they are in some embedding.” > > And so one of the questions for all of us is how do you protect that from essentially leaking over to the base foundation model? Is it just like one capability hop away because it learned how to even do fraud detection? Is it just some other multidimensional, or not? And that I think is the key question to me. I think there are two arguments. One argument is that argument that the models are going to eat the world. You can kind of easily, oh yeah, after all, everything is just a pattern and I’ll learn it all and what have you. But then the thing though is, to your point about Stripe, it can take multiple models, build this unbelievable, sort of, I’ll call it fraud detection layer that is model-forward. And then there is this memory and tools use and action space that’s all unique to Stripe. That to me is the future of a corporation, whether it’s a pharma company, a payments company, or a software company. That I think is the work that we all are doing and will do. And I think that to me that is sovereignty.” Nadella is basically arguing that modern AI reshapes those transaction costs around knowledge. If a company can encode its tacit know‑how into its own model layer, then the cost of moving, sharing, and applying that knowledge inside the firm drops sharply. Internal coordination gets easier and the firm becomes a faster learning machine. In Coase’s language, the “make vs. buy” frontier shifts inward again because the in‑house path becomes cheaper relative to the market. To say it differently, Coase explained firms as islands of lower coordination cost. Nadella’s update is that those islands might soon be defined by weights and memories as much as by org charts and processes. If a corporation’s essential knowledge is distilled into the weights of a proprietary AI model, that model may become the single most valuable asset. Valuation may shift significantly from traditional assets or human capital to the sophistication and uniqueness of the AI itself. As Nadella points out, the critical challenge then becomes preventing this specialized intelligence from “leaking” back into the base foundation models used by competitors, as such leakage could instantly neutralize a company’s advantage. It is perhaps no surprise that Nadella said “they’re a mind bender”! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Some Portfolio Changes URL: https://www.mbi-deepdives.com/some-portfolio-changes/ Last updated: 2025-11-19T17:15:39.000Z I made a few changes in my portfolio yesterday which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Messaging Opportunity and Group Chat Dynamics URL: https://www.mbi-deepdives.com/messaging-opportunity-and-group-chat-dynamics/ Last updated: 2025-11-18T15:04:28.000Z OpenAI has started [piloting](https://techcrunch.com/2025/11/14/chatgpt-launches-pilot-group-chats-across-japan-new-zealand-south-korea-and-taiwan/?ref=mbi-deepdives.com) group chats for ChatGPT, initially in Japan, New Zealand, South Korea, and Taiwan. It’s a simple idea with potentially big implications: bring multiple people and the model into the same conversation so the AI can search, summarize, draft, compare options, and keep a running context for everyone at once. This is OpenAI’s another clear attempt to evolve from a single‑player Q&A box to try to graduate to a social surface. Session‑based usage has a ceiling; chats with friends, family, or teams can basically run all day. Making ChatGPT the “nth participant” in a thread can potentially fill the engagement gap. As Scuttleblurb mentioned in our recent podcast ([**Spotify**](https://open.spotify.com/episode/5NyeYMWe09WzUTxB82vJer?si=bf59aa2e555f4c9e&ref=mbi-deepdives.com)**,** [**Apple**](https://podcasts.apple.com/us/podcast/the-big-tech-capex-debate-amazon-retail-aligns/id1786912203?i=1000734933500&ref=mbi-deepdives.com)**,** [**YouTube**](https://www.youtube.com/watch?v=LQa6HvR5D5s&ref=mbi-deepdives.com)**,** [**RSS feed**](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com)**)**, there are some apps that require effort to use, and there are some that require conscious effort to NOT use! The reality is despite ChatGPT’s incredible capabilities, the median user likely requires too much agency to stay engaged as DAU over the long term. A persistent stream of notifications from multiple group chats on ChatGPT will shift ChatGPT from the latter to much closer to the former over time. Perhaps very few people appreciate more how messaging has become the base layer of social interaction these days than the CEO of the company owning two largest messaging apps in the world: Mark Zuckerberg. In an [interview](https://stratechery.com/2025/an-interview-with-meta-ceo-mark-zuckerberg-about-ai-and-the-evolution-of-social-media/?ref=mbi-deepdives.com) with Stratechery in May 2025, Zuckerberg laid out how our social interaction evolved over time: > It used to be that you interacted with the people that you were connecting with in feed, like someone would post something and you’d comment in line and that would be your interaction. > > Today, we think about Facebook and Instagram and Threads, and I guess now, the Meta AI app too and a bunch of other things that we’re doing, as these discovery engines. Most of the interaction is not happening in feed. What’s happening is the app is like this discovery engine algorithm for showing you interesting stuff and then, **the real social interaction comes from you finding something interesting and putting it in a group chat with friends or a one-on-one chat. So there’s this flywheel between messaging which has become where actually all the real, deep, nuanced social interaction is online** and the feed apps, which I think have increasingly just become these discovery engines. Given this context, I have seen some people expressing their surprise online that Meta hasn’t run after this opportunity before OpenAI did. Well, Meta did. WhatsApp does let you mention Meta AI in individual and group chats, but end‑to‑end encryption means the AI only sees what you explicitly send it or what you opt to summarize; by design, it can’t freely graze the entire conversation. You cannot wish for end-to-end encrypted messaging and the AI being a unprompted participant. And even when you explicitly ask for AI’s help, it may not perform as well as you would like due to their lack of context of the entire conversation. That’s not going to be a problem for ChatGPT. ChatGPT will have complete context, allowing it to function as a fully informed, dynamic “nth participant.” More importantly, users starting a group thread on ChatGPT are doing so clearly to utilize the AI, mitigating any immediate privacy concerns that plague established messaging apps trying to retrofit AI into a privacy-first environment. I suspect it is going to be very, very difficult for Meta to change their tune on end-to-end encryption. It’s not only technical constraint, rather perhaps this is yet another “strategy tax” for Meta for having a dubious privacy perception from its users. As a result, I echo with [Ben Thompson](https://stratechery.com/2025/chatgpt-group-chats-meta-and-the-encryption-trade-off-network-effects-and-ad-models/?ref=mbi-deepdives.com) that it will be lot harder to borrow the page from OpenAI here even if it becomes quite popular. However, such analysis underestimates the layers of moats of the current messaging apps. Take WhatsApp, for example. If you are reading this sitting in the US or perhaps much of the western hemisphere, it may be fundamentally difficult for you to appreciate just how deeply reliant some countries are on apps such as WhatsApp. Dharmesh Ba last month wrote an intriguing [piece](https://newsletter.theindianotes.com/p/whatsapp-owns-india?mc%5Fcid=1404c3e931&ref=mbi-deepdives.com) “**WhatsApp owns India!”** which might help you understand this dynamic: > “In 2023, I met a cosmetic store owner in Nagpur who had accidentally invented demand forecasting. > > He sells beauty products - Korean serums, Indian sunscreens, foreign moisturizers. His challenge wasn’t competition or pricing. It was more existential: his shelf space could hold maybe 200 SKUs, but his customers now wanted access to 2,000. > > Ten years ago, this wasn’t a problem. A woman would walk into his store, and he’d guide her. “Try this Lakmé sunscreen. Let me show you how to apply it.” He hired young women specifically for this - people who could explain products, who could teach. > > But Instagram changed the game. Women no longer came to his store to discover products. They came with screenshots, asking for specific Korean brands they’d seen on reels. If he didn’t have it, they left. And he couldn’t stock everything. > > So he invented a system. > > He joined a vendor WhatsApp group - wholesalers who posted about new products arriving in the market. When something looked promising, he’d post the image to his WhatsApp status. Or he’d share it in a customer group he’d built over years. > > Then he’d wait. If even two or three people asked for the price, he knew there was demand. He’d place an order before the product even touched his shelf. > > He wasn’t using WhatsApp for communication and marketing alone but turned it into a just-in-time inventory system. > > **This was India’s answer to the Shopify + Stripe stack.** **Discovery happened on Instagram. Demand validation happened on WhatsApp Status. Payment happened on UPI. Small businesses had discovered that consumer apps could be hacked into business tools. In this WhatsApp owns India’s communications layer. And unlike every other tech platform, there’s no competitor even close. Instagram competes with Twitter and YouTube. WhatsApp has... nothing.”** Of course, WhatsApp got to this status by aggregating the users first. Moving everyone to a new group chat sounds very simple at first, but let me tell you my personal experience. I am in several DM groups on twitter (or X). If you use twitter DM, I don’t need to explain to you what a sub-par experience the whole thing is compared to any other messaging apps. What used to really bother me is I couldn’t search anything in my conversation (we can now), a basic feature that’s been available on WhatsApp or Messenger for forever. So, I once took the initiative to move couple of my twitter DM groups to WhatsApp. Well, half the people became completely inactive there probably because they barely open the app. So, instead of solving any problem, I became more confused where to share my thoughts and eventually had to come back to twitter. Just last week, Twitter was able to somehow exacerbate the messaging experience even more, but no matter what Twitter does, I think it is more likely than not that my DM groups will remain stuck on Twitter. If we have such hard time to move off twitter, I am fairly comfortable thinking it will be even harder to move away from messaging apps that are much more functional and useful. Nonetheless, I do think there will be some chats that are obviously a better fit for ChatGPT than WhatsApp/Messenger. Imagine going for a trip to Europe with your family or/and friends. Instead of everyone individually making plans by searching things on Google, everyone going to the trip can just simultaneously plan on ChatGPT. I can totally see how that can be materially a better experience than what the current messaging apps can deliver today. However, while ChatGPT’s hands are not tied due to privacy, they do have different set of limitations. While everyone can join your group chats on ChatGPT, ChatGPT’s responses to your queries can depend on your subscription tier. If someone in the group chat has Pro tier and hence has access to the most advanced models compared to a free user who can be bound not only by lower quality models but also rate limits, that can create a bifurcated experience even within the group chat, diluting the overall experience for all participants. Even if OpenAI eventually launches ads and addresses these limitations by offering more uniform group chat experience, people may become less comfortable with the idea that ChatGPT is showing them ads based on the private conversations they are having with their friends. Perhaps the revealed preference of going to ChatGPT to open a group chat itself will be strong enough to subdue such “AI is reading my conversation” concerns, but it can still be tricky to navigate the privacy quagmire. While much of the focus of this launch seemed to be focused on consumer use cases, I believe where this gets strategically much more interesting is at work. In an enterprise, the company can just standardize the plan, eliminating the “fairness” problem, and the group can collaborate on the same thread with predictable capability. That looks a lot less like “threat to WhatsApp” and perhaps eventually more of a problem for Slack and Teams. Of course, Slack and Teams aren’t asleep. Slack AI already summarizes channels and threads, produces recaps, and is layering in richer context features. Microsoft’s Copilot can be added to group chats and channels and will summarize discussions, answer questions, and ground responses in the prompter’s permissions. As with many things in AI these days, the competitive dynamic seems quite fluid. OpenAI itself likely acknowledges this which is why they keep approaching the opportunity set in front of them in a much more open ended fashion than a narrow focus on a specific market. Of course, once they can see what the users are mostly using group chats for, they can always focus on building more features to cater to such use cases later. If ChatGPT “group chats” proves to be mostly people collaborating for work, WhatsApp may be wrong place to wonder how that will evolve. But if it’s more about couples doing joint therapy sessions with ChatGPT, it may pose a threat to an entirely different market altogether! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The appeal of conversational commerce URL: https://www.mbi-deepdives.com/the-appeal-of-conversational-commerce/ Last updated: 2025-11-17T14:17:21.000Z First things first, let’s define “conversational commerce”. When you ask queries to an AI model or chat bot about something you want to buy, and based on the AI’s research you then take decisions and complete the purchase by ordering the product on the retailer’s website, that’s conversational commerce. On the other hand, when you just ask query and the AI itself does the research, goes to a particular website, put the item in a cart, and then just ask for payment confirmation from you, that’s “agentic commerce”. In the latter case, you are largely out of the picture after the initial prompt except appearing in the final stage of the buying process again just to click “confirm payment”. While most AI companies want to graduate to “agentic commerce”, we aren’t quite there yet. You can theoretically do it today, but the experience is usually so hit or miss that most people still do shopping online either through “conversational commerce” or just the traditional Google/Amazon search. Of course, AI companies (and frankly speaking, everyone) have all broadly adopted “agentic commerce” as an umbrella term which includes conversational commerce as well even though they do seem sufficiently distinct to me. In a recent Stratechery interview, Michael Morton [highlighted](https://stratechery.com/2025/an-interview-with-michael-morton-about-ai-e-commerce/?ref=mbi-deepdives.com) this distinction although he readily acknowledged that the ship has sailed. People are probably not going to adopt the term “conversational commerce” and we will have to live with the less clarifying term “agentic commerce”. So, what’s the appeal of conversational commerce? The appeal of conversational commerce is perhaps the greatest when you have to carefully think through the decision. If you are just buying diaper for your kid, conversational or agentic commerce would be an overkill. Just go to Amazon/Google, search for it, and click the buy button. Michael Morton had a good [example](https://stratechery.com/2025/an-interview-with-michael-morton-about-ai-e-commerce/?ref=mbi-deepdives.com) when conversational shopping would essentially feel like “god mode” of online shopping: > a colleague had been planning to do a multi-month hike, and he had been nerding out on Reddit. Like, outdoor gear people, it’s a whole world of reviews, right? And he had spent two months looking for the right tent, he went into ChatGPT, explained where he was doing his hike, what he was going to be doing, and the first thing he got back was the tent he ended up buying. It is very intuitive to me how such conversational shopping would drastically reduce our decision making process which is great news for both the end customers and merchants. There are likely a lot of leakages in the traditional product search process for more complex, conversational queries since it is fundamentally architected differently. Again, from Michael Morton’s interview: > What we started to do is we took a couple different products and we ran them through the traditional funnel and we’ll go back to the first example I used, shoes for flat-footed runners. What I did to start the exercise was I did hours and hours of research reading literally podiatry magazine posts, and every single post about the best running shoes for flat feet, I organized them, I ranked them, so what shoes got first and second, and we came out with some clear winners. “Here are the one, two, and three best running shoes for people with flat feet”, so we know what the best answer is. > > Now let’s put it in Google search, and what you found was the PLAs at the top, the carousel you’ll see a set of icons that are horrible for getting the right answer. > > for the work we did, one of the six was of the top ranked running shoes and when you looked at the models, their slugging percentage was, I would say 60 to 80% of the time, what they showed you out of the five icons were the best running shoe. So if they had five, they’d get one bad one. > > So I sat down, I asked ChatGPT, I asked Gemini, I asked all the different models, “Hey, when I ask you what’s the best running shoe, what do you do?”, and they’ll tell you, “We go read all the expert websites”…Google is looking at this more formulaic. Who’s bidding? What’s the conversion rate? Where’s the information? Given this context, it should be no surprise that shopping on ChatGPT is gaining momentum. While only \~7% of total referral shopping traffic in the US came from ChatGPT in October 2024, that number jumped to [16%](https://www.bain.com/insights/agentic-ai-in-retail-how-autonomous-shopping-redefining-customer-journey/?ref=mbi-deepdives.com) a year later. ![](https://substackcdn.com/image/fetch/$s_!kPf0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F68718bc0-1151-47bb-a76f-3a49854e9ea1_1639x925.png) Image Source: [Bain & Company](https://www.bain.com/insights/agentic-ai-in-retail-how-autonomous-shopping-redefining-customer-journey/?ref=mbi-deepdives.com) I should, however, note that even though AI (well, mostly ChatGPT for now) accounts for nearly quarter of total **referral traffic** for mass merchants, marketplaces, and specialty retailers in the US, **AI-driven traffic as a percentage of total traffic is largely well below 1% today**! So, if conversational continues to take off, it’s likely very, very early days here. ![](https://substackcdn.com/image/fetch/$s_!xDXY!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30530fa6-f368-4346-b41d-2442ff430b27_1594x883.png) Image Source: [Bain & Company](https://www.bain.com/insights/agentic-ai-in-retail-how-autonomous-shopping-redefining-customer-journey/?ref=mbi-deepdives.com) What I found quite interesting from this [piece](https://www.bain.com/insights/agentic-ai-in-retail-how-autonomous-shopping-redefining-customer-journey/?ref=mbi-deepdives.com) by Bain & Company is nearly half of the customers seem hesitant to trust AI for their shopping. Even more interestingly, customers seem to trust retailers’ on-site agents 3x more than third-party agents such as ChatGPT. ![](https://substackcdn.com/image/fetch/$s_!9mWB!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5604b9ee-f578-49ce-b47d-b6bbd8c9a5c7_1116x610.png) Image Source: [Bain & Company](https://www.bain.com/insights/agentic-ai-in-retail-how-autonomous-shopping-redefining-customer-journey/?ref=mbi-deepdives.com) In fact, Amazon did point out in the recent earnings call why “agentic” commerce through third-party platform currently have too many limitations to be very useful for customers today. From Amazon’s 3Q’25 call: > “search engines are a very small part of our referral traffic and third-party agents are a very small subset of that. But I do think that we will find ways to partner. We have to find a way, though, that makes the customer experience good. Right now, I would say the customer experience is not -- **there’s no personalization. There’s no shopping history. The delivery estimates are frequently wrong. The prices are often wrong. So we’ve got to find a way to make the customer experience better and have the right exchange value**. But I do think that the exciting part of this and the promise is that **AI and agentic commerce solutions are going to expand the amount of shopping that happens online**. And I think that’s really good for customers, and I think it’s really good for Amazon because at the end of the day, you’re going to buy from the outfit that allows you to have the broadest selection, great value and continues to deliver for you very quickly and reliably. And I think that bodes well for us.” While it could be hard to know to what extent Amazon’s portrayal of agentic commerce on 3P chatbots largely self-serving in nature, Bain’s survey work here seems to confirm Jassy’s comments in the earnings call. Rufus, which is Amazon’s own AI assistant that can cater to your conversational queries, may [not](https://www.amazon.science/blog/the-technology-behind-amazons-genai-powered-shopping-assistant-rufus?ref=mbi-deepdives.com#:~:text=The%20answer%20for%20Amazon%20is,a%20custom%20LLM%20from%20scratch) be powered by a SOTA model, but customers can still find it more useful in most instances because of Amazon’s end-to-end control over your shopping experience, **especially** logistics and fulfillment. Again, from Amazon’s 3Q’25 call: > Rufus, our AI-powered shopping assistant has had **250 million active customers** this year with **monthly users up 140% year-over-year**, **interactions up 210% year-over-year and customers using Rufus during a shopping trip being 60% more likely to complete a purchase**. **Rufus is on track to deliver over $10 billion in incremental annualized sales.** --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### On the eve of Gemini 3.0 URL: https://www.mbi-deepdives.com/gemini3/ Last updated: 2025-11-16T14:48:48.000Z Prediction markets [indicate](https://polymarket.com/event/gemini-3pt0-released-by?tid=1763302104823&ref=mbi-deepdives.com) Alphabet will release Gemini 3.0 next week. Sundar Pichai’s [tweet](https://x.com/sundarpichai/status/1989481514393121239?ref=mbi-deepdives.com) also makes it all but certain that is indeed likely the case. At least my twitter or X timeline is increasingly filled with tweets from Alphabet employees with giddy excitement about the launch. It’s not just internal folks; many people who got a glimpse of the model’s capabilities have also been posting about pretty effusively. It is perhaps the first time I have noticed such a reaction on the eve of a launch of a model by Google. ![](https://substackcdn.com/image/fetch/$s_!KQDS!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdf11d11f-b44a-4ef5-8e14-8bd7c5abef24_1458x591.png) Source: [Polymarket](https://polymarket.com/event/gemini-3pt0-released-by?tid=1763302104823&ref=mbi-deepdives.com) I haven’t got a chance to play with the model yet, and given the level of anticipation, I suspect there is a decent chance that the model may underwhelm many people. Frankly speaking, I am not confident that I can tell any noticeable difference between the models anymore for the kind of queries I like to ask. I usually run the same queries simultaneously in both Gemini and ChatGPT and while I still have slight preference for ChatGPT, it is far from a strong preference these days. It won’t surprise me if that remains the case even after Gemini 3.0. While the differentiation between the models will likely remain an open question, it does seem extremely likely that model capabilities will almost certainly keep improving over time. Even if it may not be quite apparent to most users, I have recently read an interesting [piece](https://generativehistory.substack.com/p/has-google-quietly-solved-two-of) by Mark Humphries (Professor of History at Wilfrid Laurier University) on Gemini 3.0’s alleged capabilities that helped me appreciate why people closest to building these models seem to be “AGI” pilled. Of course, there is a cynical view that these people are incentivized to propagate such beliefs, but I think it is perhaps also worth pondering that such beliefs may be earnestly warranted. Mark Humphries says an unreleased Google model he accessed via Google AI Studio (appearing as an A/B test with two answers to pick from) is **nearly perfect** at **handwritten text recognition (HTR)** on messy 18th‑century manuscripts and also shows **spontaneous, step‑by‑step symbolic reasoning**. He (and others) speculate this may be a Gemini‑3‑era model. Humphries used his specialized work, which analyzes obscure 18th-century handwritten accounting ledgers, as a benchmark. He notes that this task is exceptionally difficult because it requires more than just visual recognition of messy script; it demands an integration of historical context, linguistic nuance, and logical deduction. From the piece: > “Most people think that deciphering historical handwriting is a task that mainly requires vision. I agree that this is true, but only to a point. When you step back in time, you enter a different country, or so the saying goes. People talk differently, using unfamiliar words or familiar words in unfamiliar ways. People in the past used different systems of measurement and accounting, different turns of phrase, punctuation, capitalization, and spelling. Implied meanings were different as were assumptions about what readers would know. > > While it can be easy to decipher most of the words in a historical text, without contextual knowledge about the topic and time period it’s nearly impossible to understand a document well-enough to accurately transcribe the whole thing—let alone to use it effectively. The irony is that some of the most crucial information in historical letters is also the most period specific and thus hardest to decipher.” The model displayed astonishing accuracy in transcription. Humphries reports that the previous state-of-the-art (Gemini 2.5 Pro) achieved a Character Error Rate (CER) of about 4% on these complex documents i.e. roughly equivalent to a professional human transcriber. The new model reduced the CER to just 0.56% and the Word Error Rate (WER) to 1.22%: > “The new Gemini model’s performance on HTR meets the criteria for expert human performance. **These results are also 50-70% better than those achieved by Gemini-2.5-Pro**. In two years, we have in effect gone from transcriptions that were little more than gibberish to expert human levels of accuracy. And the consistency in the leap between each generation of model is exactly what you would expect to see if scaling laws hold: **as a model gets bigger and more complex, you should be able to predict how well it will perform on tasks like this just by knowing the size of the model alone**.” The most profound implication is the potential transition of AI from sophisticated “stochastic parrots” to systems capable of genuine understanding. Again, from his piece: > “The safer view is to assume that Gemini did not “know” that it was solving a problem of eighteenth-century arithmetic at all, but its internal representations were rich enough to emulate the process of doing so. But that answer seems to ignore the obvious facts: it followed an intentional, analytical process across several layers of symbolic abstraction, all unprompted. This seems new and important. > > If this behaviour proves reliable and replicable, it points to something profound that the labs are also starting to admit: **that true reasoning may not require explicit rules or symbolic scaffolding to arise, but can instead emerge from scale, multimodality, and exposure to enough structured complexity**.” In a narrow sense, near-perfect HTR combined with contextual understanding would allow for the rapid digitization and analysis of centuries of trapped knowledge, potentially **rewriting our understanding of the past**: > “For historians, the implications are immediate and profound. If these results hold up under systematic testing, we will be entering an era in which large language models can not only transcribe historical documents at expert-human levels of accuracy, but can also reason about them in historically meaningful ways. That is, they are no longer simply seeing letters and words—and correct ones at that—they are beginning to interpret context, logic, and material reality. **A model that can infer the meaning of “145” as “14 lb 5 oz” in an 18th-century merchant ledger is not just performing text recognition: it is demonstrating an understanding of the economic and cultural systems in which those records were produced…and then using that knowledge to re-interpret the past in intelligible ways**.” An AI that can reason can begin to automate complex cognitive tasks previously thought to be the exclusive domain of human experts. The implications of such a system can be even more profound than just re-writing our understanding of the past which itself is no small feat! The more I spend time on understanding and covering AI, the more my worldview comes closer to Ilya Sutskever’s [tweet](https://x.com/ilyasut/status/1710462485411561808?lang=en&ref=mbi-deepdives.com) a couple of years ago: ![](https://substackcdn.com/image/fetch/$s_!ChGN!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b773aa8-e618-4f9b-b8c4-3c8eb05d10c4_733x214.png) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Great Concentration of Productivity URL: https://www.mbi-deepdives.com/the-great-concentration-of-productivity/ Last updated: 2025-11-15T14:43:47.000Z A couple of weeks ago, I [**wrote**](https://www.mbi-deepdives.com/the-great-decoupling-of-labor-and-capital/) about the great decoupling of labor and capital in which I pointed out that today’s megacap tech companies achieved staggering revenue growth with progressively fewer incremental employees, a phenomenon well underway even **before** the advent of Gen AI. However, that was bit of a micro view; a recent [piece](https://www.chicagofed.org/publications/chicago-fed-letter/2025/515?ref=mbi-deepdives.com) published on the Chicago Fed Letter provided a more profound, macro context. It turns out this decoupling of revenue from labor is a symptom of a much larger, economy-wide phenomenon: the **Great Concentration** **of productivity**. The Chicago Fed paper analyzed US Total Factor Productivity ([TFP](https://en.wikipedia.org/wiki/Total%5Ffactor%5Fproductivity?ref=mbi-deepdives.com)), and found that it has been “highly concentrated” for the past four decades. The data is astonishing. Since the late 1980s, the **Information Technology (IT) sector** has been responsible for \~**45% of all TFP growth** in the U.S. economy. It has done this while accounting for only **8% of the private business sector’s** total value added. Over the 1988-2023 period, annual TFP growth in the IT sector averaged **2.9%**. In all **non-IT sectors combined**, it averaged a mere **0.31%** From the paper: > “As the figure shows, aggregate TFP has increased by a factor of 1.4, or 40%, in the 36 years since the start of 1988, while the IT sector’s TFP has increased by a factor of about 2.78, or 178%. In contrast, TFP in all the non-IT sectors combined has increased by a factor of only about 1.12, or 12%. In panel B of figure 3, we similarly plot the cumulative contributions of TFP growth in the IT and non-IT sectors to aggregate TFP growth. The IT sector has contributed approximately 17 percentage points to cumulative TFP growth since 1988, and the non-IT sectors about 20 percentage points. Thus, in cumulative terms, the IT sector again accounts for roughly 45% of aggregate productivity growth” ![](https://substackcdn.com/image/fetch/$s_!LV75!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7912d3e7-fed3-4c92-b628-08a8ae092ba1_936x1150.png) Image Source: click [here](https://www.chicagofed.org/publications/chicago-fed-letter/2025/515?ref=mbi-deepdives.com) Of course, you may be wondering if the IT sector is so hyper-productive, why hasn’t it taken over much more of the entire economy? Why is it still **just 8% of value added**? Because prices have collapsed. While the real value added has exploded by over **2,300%** since 1988, the price of that output has **declined by 70%**. Again, from the paper: > “Figure 6 shows why this approximate constancy over the longer run holds. In panel A, we plot the cumulative growth in real value added for the IT sector: In real terms, value added of the IT sector increased by over 2,300% between 1988 and 2023\. In panel B, we plot the cumulative growth rate of the price of a unit of value added of the IT sector: This price declined by almost 70% over the 1988–2023 period. Putting these results together, along with the fact that aggregate nominal value added of the entire private business sector grew about 500% over the same period, leaves the share of the IT sector only slightly higher in 2023 than in the late 1980s.” ![](https://substackcdn.com/image/fetch/$s_!KL5y!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5be7bd28-f8f6-4dad-810b-780965ba5843_964x1015.png) Image Source: Click [here](https://www.chicagofed.org/publications/chicago-fed-letter/2025/515?ref=mbi-deepdives.com) The above graph is perhaps good indication of massive consumer surplus we get to enjoy because of the tech companies! The scalability of software and networks is the same force that drops prices so dramatically, delivering immense value to consumers and keeping the sector’s share of the economy in check. The precipitous drop in price IS the **diffusion fuel** as it turns hospitals, farms, and factories into downstream beneficiaries of IT’s TFP without making them IT companies which likely helps maintain this paradox: IT drives growth everywhere while its measured share of the economy hardly rises. Nonetheless, this extreme concentration of productivity does have potentially uncomfortable implications, especially now that AI is into the scene. The reliance of US economy on a single engine of productivity can make aggregate growth “more fragile”. From the paper: > “if similar trends continue, e.g., because of the introduction and rollout of AI (artificial intelligence) technologies, the **medium-term outlook for U.S. economic growth may well hinge on developments in the IT sector**. And third, given the concentration of growth in a single sector, maintaining a steady pace of aggregate economic growth may be more fragile than at times when the sources of growth were more widespread. Will the rapid TFP growth in the IT sector stall out, or will it accelerate based on new innovations in AI and other cutting-edge computing technologies? Given the key role of IT in driving aggregate TFP growth over the past few decades, the answer may have profound implications for future advances in economic conditions.” After looking at how almost half of the last few decades of productivity growth came from just one sector, it may help contextualize the level of concentration we are seeing in equity markets these days. While everyone in the economy eventually becomes the beneficiary of such diffusion of technology largely via consumer surplus, the shareholder value may accrue to increasingly fewer and fewer companies. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Meta's GEM URL: https://www.mbi-deepdives.com/metas-gem/ Last updated: 2025-11-14T14:44:16.000Z Back in [March 2025](https://www.facebook.com/business/news/ai-innovation-in-metas-ads-ranking-driving-advertiser-performance), Meta published an interesting blog post highlighting their innovation in their ad infrastructure. One such key innovation was their Generative Ads Recommendation Model or **GEM**. GEM, the **largest** foundation model for recommendation systems, is essentially a massive reorganization and upgrade of the AI that runs advertising on Facebook and Instagram. It is described as the “central brain” for ads recommendation. The aforementioned blog post had an intuitive explanation of what GEM is about: > “Imagine having a super brain that can read an entire library of books in seconds, understand the relationships between all the characters, remember every single detail, and connect the details into an understanding of the sequence of events a person goes through across all types of activities. That’s what GEM does for Meta’s ad system: **catalogs, analyzes, and connects trillions of pieces of information, making it incredibly intelligent and effective**. With GEM, Meta’s recommendation system learns from an enormous amount of data, **recognizes subtle patterns, and provides the most relevant ads to the right person at the right time with low latency**.” I will expand more on GEM and the implications behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Nadella's truth URL: https://www.mbi-deepdives.com/nadellas-truth/ Last updated: 2025-11-13T14:22:06.000Z Satya means “Truth” and Nadella’s [**interview**](https://www.dwarkesh.com/p/satya-nadella-2?ref=mbi-deepdives.com)withDwarkesh and Dylan Patel yesterday was indeed his way of disseminating his truth. It is one of the most interesting interviews I have listened to so far this year. What made the interview so compelling was that the interviewers did not tiptoe around asking the real questions to elicit answers from Nadella, and Nadella obliged gracefully and did not dodge questions. Let me share some key excerpts from the interview that stood out to me. Nadella rightly aspires to be in the right neighborhood in the current AI revolution and wouldn’t mind losing market share to startups and other incumbents as long as the pie is big enough. As I have [**mentioned**](https://www.mbi-deepdives.com/the-great-decoupling-of-labor-and-capital/) before, focusing too much on “us vs them” framework in big tech land hasn’t been quite productive as the market expansion itself has been the dominant theme for many key competitive markets in the last decade or so. In Nadella’s words: > “Take Office 365 or Microsoft 365\. Having a low [ARPU](https://en.wikipedia.org/wiki/Average%5Frevenue%5Fper%5Fuser?ref=mbi-deepdives.com) is great, because here’s an interesting thing. During the transition from server to cloud, one of the questions we used to ask ourselves is, “Oh my God, if all we did was just basically move the same users who were using our Office licenses and our Office servers at the time to the cloud, and we had COGS, this is going to not only shrink our margins but we’ll be fundamentally a less profitable company.” > > Except what happened was the move to the cloud expanded the market like crazy. We sold a few servers in India, we didn’t sell much. Whereas in the cloud suddenly everybody in India also could afford fractionally buying servers, the IT cost. In fact, the biggest thing I had not realized, for example, was the amount of money people were spending buying storage underneath [SharePoint](https://en.wikipedia.org/wiki/SharePoint?ref=mbi-deepdives.com). In fact, [EMC’s](https://en.wikipedia.org/wiki/EMC%5FCorporation?ref=mbi-deepdives.com) biggest segment may have been storage servers for SharePoint. All that sort of dropped in the cloud because nobody had to go buy. In fact, it was [working capital](https://en.wikipedia.org/wiki/Working%5Fcapital?ref=mbi-deepdives.com), meaning basically, it was cash flow out. So it expanded the market massively. > > So this AI thing will be that. If you take coding, what we built with [GitHub](https://en.wikipedia.org/wiki/GitHub?ref=mbi-deepdives.com) and [VS Code](https://en.wikipedia.org/wiki/Visual%5FStudio%5FCode?ref=mbi-deepdives.com) over decades, suddenly the coding assistant is that big in one year. That I think is what’s going to happen as well, which is the market expands massively. > > You have new competitors, new existential problems. When you say, who’s it now? Claude’s going to kill you, Cursor is going to kill you, it’s not boreland. Thank God. That means we are in the right direction. > > This is it. The fact that we went from nothing to this scale is the market expansion. This is like the cloud-like stuff. **Fundamentally, this category of coding and AI is probably going to be one of the biggest categories. It is the** [**software factory**](https://economictimes.indiatimes.com/news/new-updates/its-not-enough-satya-nadella-says-bill-gates-idea-that-made-him-worlds-richest-person-is-now-obsolete/articleshow/123321513.cms?from=mdr&ref=mbi-deepdives.com) **category. In fact, it may be bigger than knowledge work. I want to keep myself open-minded about it.** > > You could say we had high share in VS Code, we had high share in the repos with GitHub, and that was a good market. But the point is that even having a decent share in what is a much more expansive market… > > Y**ou could say we had a high share in** [**client-server**](https://en.wikipedia.org/wiki/Client%E2%80%93server%5Fmodel?ref=mbi-deepdives.com) **server computing. We have much lower share than that in hyperscale. But is it a much bigger business? By orders of magnitude**.” Nadella made his case that he doesn’t think there will be one single model that will be materially ahead compared to the rest in all workflows. Nadella’s “truth” aligns very well to how Microsoft is positioned today. Just like Amazon who doesn’t have and likely has no path to building leading edge models, they both do not believe in model developers’ supremacy over the entire value chain. I myself am closer to Nadella’s position, but I remain open minded about model’s **eventual** capabilities. To be fair, Nadella himself is open minded too, as evidenced by his willingness to keep investing in in-house model developing capability and hiring marquee AI talent (aka the Meta playbook). From Nadella: > “Structurally, I think there will always be an open source model that will be fairly capable in the world that you could then use, as long as you have something that you can use that with, which is data and a scaffolding. **I can make the argument that if you’re a model company, you may have a** [**winner’s curse**](https://en.wikipedia.org/wiki/Winner%27s%5Fcurse?ref=mbi-deepdives.com)**. You may have done all the hard work, done unbelievable innovation, except it’s one copy away from that being commoditized. Then the person who has the data for** [**grounding**](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview?ref=mbi-deepdives.com) **and** [**context engineering**](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents?ref=mbi-deepdives.com)**, and the liquidity of data can then go take that checkpoint and train it.** > > if there’s one model that is the only model that’s most broadly deployed in the world and it sees all the data and it does continuous learning, **that’s game set match and you stop shop**. The reality that at least I see is that in the world today, for all the dominance of any one model, that is not the case. Take coding, there are multiple models. In fact, everyday it’s less the case. There is not one model that is getting deployed broadly. There are multiple models that are getting deployed. It’s like databases. It’s always the thing, “Can one database be the one that is just used everywhere?” Except it’s not. There are multiple types of databases that are getting deployed for different use cases. > > I think that there are going to be some network effects of continual learning—I call it data liquidity—that any one model has. Is it going to happen in all domains? I don’t think so. Is it going to happen in all geos? I don’t think so. Is it going to happen in all segments? I don’t think so. It’ll happen in all categories at the same time? I don’t think so. So therefore I feel like the design space is so large that there’s plenty of opportunity. > > But your fundamental point is having a capability which is at the infrastructure layer, model layer, and at the scaffolding layer, and then being able to compose these things not just as a vertical stack, but to be able to compose each thing for what its purpose is. **You can’t build an infrastructure that’s optimized for one model. If you do that, what if you fall behind? In fact, all the infrastructure you built will be a waste. You kind of need to build an infrastructure that’s capable of supporting multiple families and lineages of models. Otherwise the capital you put in, which is optimized for one model architecture, means you’re one tweak away, some** [**MoE**](https://en.wikipedia.org/wiki/Mixture%5Fof%5Fexperts?ref=mbi-deepdives.com)**\-like breakthrough that happens, and your entire network topology goes out of the window. That’s a scary thing**.” Nadella also sees software as a key differentiator in the capital intensive hyperscaler business: > "I kind of look at it and say that our business, which today is an end-user tools business, will become essentially an infrastructure business in support of agents doing work. It’s another way to think about it. In fact, all the stuff we built underneath [M365](https://en.wikipedia.org/wiki/Microsoft%5F365?ref=mbi-deepdives.com) still is going to be very relevant. You need some place to store it, some place to do archival, some place to do discovery, some place to manage all of these activities, even if you’re an AI agent. It’s a new infrastructure. > > **Some people ask me, what is the difference between a classic old-time hoster and a hyperscaler? Software. Yes, it is capital intensive, but as long as you have systems know-how, software capability to optimize by workload, by fleet**... That’s why when we say fungibility, there’s so much software in it. It’s not just about the fleet. > > It’s the ability to evict a workload and then schedule another workload. Can I manage that algorithm of scheduling around? That is the type of stuff that we have to be world-class at. So yes, I think we’ll still remain a software company, but yes, **this is a different business and we’re going to manage**. At the end of the day, the cash flow that Microsoft has allows us to have both these arms firing well." Dylan Patel probed Nadella about Microsoft’s strategy of letting Oracle build for OpenAI instead of keeping the business for themselves. Nadella’s answer makes it clear that if he were an investor, he would likely be very reluctant to buy Oracle’s stock as Oracle’s strategy may prove to be quite risky. From Nadella: > **“We didn’t want to just be a hoster for one company and have just a massive book of business with one customer. That’s not a business, you should be vertically integrated with that company**. > > Given that OpenAI was going to be a successful independent company, which is fantastic. It makes sense. And **even Meta may use third-party capacity, but ultimately they’re all going to be first-party. For anyone who has large scale, they’ll be a hyperscaler on their own**. To me, it was to build out a hyperscale fleet and our own research compute. That’s what the adjustment was. So I feel very, very good. > > By the way, **the other thing is that I didn’t want to get stuck with massive scale of one generation. We just saw the** [**GB200s**](https://www.nvidia.com/en-us/data-center/gb200-nvl72/?ref=mbi-deepdives.com)**, the** [**GB300s**](https://www.nvidia.com/en-us/data-center/gb300-nvl72/?ref=mbi-deepdives.com) **are coming. By the time I get to** [**Vera Rubin, Vera Rubin Ultra**](https://en.wikipedia.org/wiki/Rubin%5F%28microarchitecture%29?ref=mbi-deepdives.com)**, the data center is going to look very different because the power per rack, power per row, is going to be so different. The cooling requirements are going to be so different. That means I don’t want to just go build out a whole number of gigawatts that are only for a one-generation, one family. So I think the pacing matters, the fungibility and the location matters, the workload diversity matters, customer diversity matters and that’s what we’re building towards.** > > The other thing that we’ve learned a lot is that every AI workload does require not only the [AI accelerator](https://en.wikipedia.org/wiki/Neural%5Fprocessing%5Funit?ref=mbi-deepdives.com), but it requires a whole lot of other things. **In fact, a lot of the margin structure for us will be in those other things.** Therefore, we want to build out Azure as being fantastic for the long tail of the workloads, because that’s the hyperscale business, while knowing that we’ve got to be super competitive starting with the [bare-metal](https://en.wikipedia.org/wiki/Bare-metal%5Fserver?ref=mbi-deepdives.com) for the highest end training. > > But that can’t crowd out the rest of the business, because **we’re not in the business of just doing five contracts with five customers being their bare-metal service. That’s not a Microsoft business.** That may be a business for someone else, and that’s a good thing. What we have said is that we’re in the hyperscale business, which is at the end of the day a long tail business for AI workloads. And in order to do that, we will have some leading bare-metal-as-a-service capabilities for a set of models, including our own. And that, I think, is the balance you see. > > I don’t want to take away anything from the success Oracle has had in building their business and I wish them well. **The thing that I think I’ve answered for you is that it didn’t make sense for us to go be a hoster for one model company with limited time horizon** [**RPO**](https://www.fool.com/terms/r/remaining-performance-obligation/?ref=mbi-deepdives.com)**.** Let’s just put it that way. > > **The thing that you have to think through is not what you do in the next five years, but what you do for the next 50.”** Nadella also took a slight jab at some of the revenue forecasts laid out by OpenAI and Anthropic and reminded that they are highly incentivized to come up with lofty revenue projections to help them raise money: > “In the marketplace **there’s all kinds of incentives right now**, and rightfully so. **What do you expect an independent lab that is sort of trying to raise money to do? They have to put some numbers out there such that they can actually go raise money so that they can pay their bills for compute** and what have you. > > And it’s a good thing. Someone’s going to take some risk and put it in there, and they’ve shown traction. It’s not like it’s all risk without seeing the fact that they’ve been performing, whether it’s OpenAI, or whether it’s Anthropic.” Indeed, but in the same token, it’s also worth pointing out that Nadella, as the CEO of the largest incumbent software company in the world, has his own incentives to believe a specific version of the future. Admittedly, I find his version to be highly credible, but the future can often be surprising! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Why I may have underestimated the potential for Airbnb Experiences URL: https://www.mbi-deepdives.com/why-i-may-have-underestimated-the-potential-for-airbnb-experiences/ Last updated: 2025-11-12T20:47:23.000Z Brian Chesky has made quite the uproar in re-launching Airbnb Experiences in the last 6 months. As far as I can tell, almost none of the investors shares remotely the same enthusiasm about experiences as Chesky appears to do. I too was more on the “unenthusiastic” or “indifferent” camp when it comes to Airbnb’s potential in experiences, but after studying this market a bit, especially after 3Q’25 earnings, I am starting to appreciate Chesky’s enthusiasm. The data point that really woke me up here is when Chesky mentioned the following: > “in Q3, almost **half of the people who booked an experience did not have an Airbnb stay**. So we’re giving people another reason to use Airbnb.” If you asked me to guess this number, I would probably say somewhere close to \~10-20%. I think it’s a pretty big deal that Airbnb seems to be able to find customers **only** for “Experiences” which makes experiences not just a mere services that you cross-sell when someone books a Stay on Airbnb, rather a new market or use case that can deepen the relationship. Moreover, even though Chesky used to have a somewhat condescending tone when it comes to the typical tourist experiences and activities and why Airbnb’s “differentiated” supply would stand out, he seems to have a change of heart here. I liked how he segmented the experiences market in three categories and Airbnb’s willingness to participate in all of them. From the call: > “one of the lessons we learned in Paris for experiences, for example, is that there are really 3 types of travel people. There’s people where it’s their first time to a city like Paris. There are people who’ve been to Paris repeatedly and then there’s local. And that **each of them want totally different types of supply**. So for people for whom it’s the **first time to a city, they really want to go to landmark**. They want see Eifel Tower…And this is really what you see when you see other platforms where they’re really focused on traditional tourist experiences. So **we’ve been adding a lot of landmark experiences**. We think we provide some of the very best high-quality experiences. They’re very local in nature, but they’re very much appealing to first-time visitors to a city. And **this has been very, very popular**. Then you’ve got people who’ve been to the city already. This is nearly as big and in many cities, it’s a bigger market. > > A lot of people that go to Paris, they have been there before. **If it’s your second or third time to Paris, you’re not going to see Eifel Tower. You want to see something different. So now you want more local experiences**. You might want to do a cooking class. Now you might do some other type of activity. And **then locals want to do something really unique**...They want to book originals. And so we see these 3 audiences, and that’s been really interesting. And **the year-over-year growth in Paris has been very encouraging.** > > In Paris, **70% of Airbnb originals are booked by locals**. And so we think with Airbnb Originals, we’ve figured out a product that is appealing to people in their own city.” It is also worth noting that Viator, one of the leaders in Experiences market, has seen their growth accelerate to low double digit in the recent quarter. However, I was always a bit underwhelmed when I looked at Viator’s numbers. If one of the leading players only generate \~$4.5 Billion Gross Booking Value (GBV), it doesn’t seem that exciting to enter such market, especially given Airbnb’s \~$90 Billion LTM GBV today. ![](https://substackcdn.com/image/fetch/$s_!KOnz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F78a6aa3c-4dcd-4001-809b-5cc6d058a545_997x180.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Nonetheless, one of the things that really prompted me to update my thinking about experiences potential is when I went through Klook’s IPO [**filing**](https://www.sec.gov/Archives/edgar/data/2071502/000121390025108023/ea0248324-08.htm?ref=mbi-deepdives.com#T9912). Most of you may not have heard about the company, but Klook is one of the leading players in the Asia-Pacific (APAC) region aggregating attractions, tours, transport etc. \~80-90% of their revenue came from APAC region in the last three years. Despite such a narrow regional focus, their Gross Transaction Value (GTV) actually increased from $660 Million in 2022 to \~$3 Billion in 2025\. Their GTV grew \~179% in 2023, but this primarily reflects the post-pandemic pent up travel demand; please note that APAC region generally lagged most of the world in terms of opening up post-pandemic and relaxing travel restrictions. In any case, given they are still growing at \~30-35% in 2024 and 2025, you can see even the normalized growth rates is pretty healthy for Klook. SoftBank-backed Klook raised more than [**\~$1 Billion**](https://skift.com/2025/02/11/klook-raises-100-million-from-vitruvian-to-fuel-travel-experience-sales/?utm%5Fsource=ask%5Fskift) to get to $3 Billion GTV. However, I’m not sure the company used to be quite well run. The crazy thing is they mentioned they had **3% more employees in 2019** than they do today. For context, their GTV back in 2019 was $918 Million which means their **GTV became \~3x despite reducing headcount in the last 6 years**. The [**decoupling**](https://www.mbi-deepdives.com/the-great-decoupling-of-labor-and-capital/) of labor and capital is increasingly more global! Klook’s current take rate is hovering around high teen (for context, Airbnb’s experiences take rate is 20%); I suspect things such as transportation may have lower take rates than attractions. GTV per experience was almost $50 for Klook which is admittedly higher than I would imagine. While only \~25% of Airbnb’s gross profit is spent on S&M, Klook spends \~70-75% of their gross profit in marketing. Interestingly, they mention \~70% of their traffic comes organically and they seem to have more focus on leveraging social media (e.g. [Klook Kreator](https://www.klook.com/en-AU/tetris/promo/kreator-program/?ref=mbi-deepdives.com) program). Thanks to their more prudent approach to headcount, the company is now profitable in adjusted EBITDA level and almost breakeven this year in GAAP EBIT basis. ![](https://substackcdn.com/image/fetch/$s_!2bG3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F02cc9501-bb51-4267-989a-6b296fff996f_751x960.png) Source: Company Filings, MBI Deep Dives While the overall number itself can clearly show the impressive growth in recent years, I was more encouraged to see their cohort level data. Not only transacting users are spending more over time, the newer cohorts are also spending more compared to earlier cohorts. As of 2024, they had 10.7 million annual transacting users from 200 geographic markets. Given the total experiences booked in 2024 was 54 million, it implies the average transacting users are using the app at least **five times** a year or once every two months. If Airbnb can get to such engagement level in the next 5 years, you can sense why it can become a needle mover for them. More importantly, I think if Airbnb can graduate from an app that you open a couple of times a year to a couple of times a month, it can also open up the opportunity to cross-sell other products/services than they are going to launch. ![](https://substackcdn.com/image/fetch/$s_!6ZcH!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F072db154-a37d-49ad-b994-e0ba1edbd93b_859x423.png) Source: Company Filings Of course, in a marketplace, you need both demand and supply. Again, I was surprised to see Klook had only **310k offerings** in their platform (which itself has grown \~30% YoY). For context, Airbnb mentioned they received 110k applications from Experiences hosts just in 3Q’25 (although they haven’t disclosed how many of them were accepted). Given Klook’s number of offerings, it implies an average merchant hosted \~176 experiences per year. This number tells me Klook is likely much more focused on scaling the first category of experiences that Chesky highlighted. Klook also mentioned “merchants who made a sale on our platform in 2023 generated **127%** of their 2023 GTV in 2024”. Klook highlighted that experiences lagged in terms of online penetration compared to accommodations and flights. However, given the pace of increase in penetration from 23% in 2018 to 34% in 2024, it seems rather safe to assume that this penetration is pretty much one way street and has ample headroom for future growth. ![](https://substackcdn.com/image/fetch/$s_!56HP!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F15bb5e85-75d5-46c9-b3c5-077550109719_652x213.png) Source: Company Filings Overall, it is quite telling that even just an APAC focused experiences platform managed to build a $3 Billion GTV business. Given Airbnb’s global marketplace, I would actually be disappointed now if they don’t generate $5 Billion GBV from experiences in 2030\. If they do get there, Airbnb may generate $1 Billion revenue from Experiences in 2030 (assuming \~20% take rate) which is \~10% of their current revenue. They should have a structural CAC advantage compared to almost any other region specific marketplace. As alluded earlier, if Airbnb can make experiences successful, there may be additional benefits that can accrue to the marketplace even beyond Experiences revenue or profit contribution. If they can graduate to become an app that you want to use on a monthly basis, it may lead to much more compelling opportunities that Airbnb can pursue in the coming years! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### What exactly is CSU's ROIC? URL: https://www.mbi-deepdives.com/what-exactly-is-csus-roic/ Last updated: 2025-11-12T13:42:45.000Z Yesterday, I [**mentioned**](https://www.mbi-deepdives.com/csu3q25/) “While CSU’s heydays of \~50-60% ROIC is clearly behind us, CSU’s ROIC in the last 12 quarters has been quite sticky around \~30% which is plenty good!” A couple of readers pushed back on how I calculated ROIC and after thinking through their points, I think they are correct in pointing out the shortcomings of my approach. Let me explain. To calculate Net Operating Profit After Tax (NOPAT) for my ROIC calculation, I started with CSU’s reported consolidated EBITA and then just multiplied it by 79% to get to NOPAT, assuming a uniform 21% tax rate to negate the volatility associated with taxes. One shortcoming to this approach was this is a consolidated number and hence, overstates the NOPAT since I did not subtract non-controlling interests of Topicus and Lumine. I will come back to this shortcoming later. Now for denominator “invested capital”, I started with total assets and then subtracted cash, restricted cash, derivatives, equity securities available for sale, and non-interest bearing current liabilities (to be specific: accounts payable and accrued liabilities, deferred revenue, income taxes payable, and provisions). With this approach, CSU’s invested capital came out to be \~$8 Billion. However, a couple of my readers mentioned that I should also add back **accumulated amortization** to calculate CSU’s invested capital. CSU’s current accumulated amortization is \~$6.5 Billion. So, if you add accumulated amortization, CSU’s invested capital becomes **\~$14.5 Billion and ROIC comes down to \~17%.** While ROIC in this approach is substantially lower than what I indicated yesterday, one of the points that I made yesterday remains valid: despite almost doubling their invested capital since 1Q’23, CSU’s ROIC has largely remained the same (but it’s high teens and not \~30%). Some CSU bulls may think adding back accumulated amortization is too punitive, so let me explain why I think adding back accumulated amortization is closer to economic reality than punitive. ![](https://substackcdn.com/image/fetch/$s_!LI1y!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbef4d1e5-ee49-40e0-b3ba-6b384ed964e9_2125x259.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) When calculating Invested Capital, the objective is to measure the total **cumulative** amount of cash that has been invested in the business to generate its operating profits. For serial acquirers, accounting conventions related to acquisitions significantly distort the book value of their invested capital. The primary reason we add back accumulated amortization is to **restore the Invested Capital base to reflect the actual cash outlay** used for acquisitions and to ensure a meaningful measure of return on that capital. When a company acquires another business, the purchase price typically exceeds the book value of the target’s net assets. The excess purchase price is allocated to identifiable intangible assets, such as customer relationships, patents, trademarks, and proprietary technology. The identifiable intangible assets recognized must then be **amortized** which is systematically expensed over their estimated useful lives. If Invested Capital is calculated using the reported balance sheet figures (which are **net** of accumulated amortization), the investment base will appear to shrink over time. This shrinkage is merely an accounting artifact. The fundamental goal of analyzing a serial acquirer is to determine if management is allocating capital effectively. To do this, management must be held accountable for the **full price** paid for their acquisitions. An example would make this abundantly clear. If a company spends $100 million to acquire intangible assets, that $100 million is the capital deployed. If, over five years, $50 million of this value is amortized, the balance sheet will show a net asset value of $50 million. However, the company still needs to generate a return on the original $100 million it invested. By adding back the accumulated amortization, we restore the Invested Capital base to its original cost, ensuring the calculation reflects the total cash invested. Imagine in the example above: The $100 million acquisition generates $15 million in NOPAT annually. So, in year 1, you generate \~15% ROIC. If NOPAT doesn’t grow at all in the next 5 years but invested capital shrinks to $50 Million due to amortization, it would appear the business is now generating \~30% ROIC in year 5\. The unadjusted calculation misleadingly suggests ROIC has doubled. It is the adjusted calculation that shows the **true economic return** on the cash spent. Now, please notice that both my numerator and denominator in ROIC calculation takes CSU’s consolidated numbers, but as mentioned earlier, CSU does have non-controlling interest in Topicus and Lumine. So, you can fine tune this approach further by deducting the impact of Topicus and Lumine. I did some back of the envelope math for the current quarter, and it’s close enough to the consolidated number that I decided not to go through the brain damage on calculating “core” CSU’s LTM ROIC in each of the last \~60-70 quarters. Ultimately, what I wanted to see is a time series data on how ROIC is evolving and with the consolidated number, while not perfect, you can get much closer to the truth. Okay, so what does this amended ROIC inform me in terms of how I approach CSU’s valuation? To simplify, CSU currently owns hundreds of businesses whose recurring revenue grows at \~MSD rate and deploys almost all of the cash flow coming from these businesses at mid-to-high teen ROIC. Given the company currently trades at \~4.5% LTM FCF yield, I still do find the valuation to be attractive, but this amended approach helps me better in thinking through how I deploy incremental capital for my own portfolio. Frankly speaking, I would be prone to be more aggressive in increasing CSU from a relatively small position to a much larger over time when I thought the company is generating \~30% ROIC on their invested capital. Based on my current understanding, I will be much more gradual in doing so than I would be otherwise, especially considering the AI disruption risk which will take time to understand what impact (positive or negative) it will have on CSU’s fundamentals. I appreciate the feedback and thank you for helping me think this through. While not fun to make mistakes in public, it is a cost I am very much willing to pay to improve my thinking and process. Speaking of my portfolio, I did make some changes yesterday which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Constellation Software 3Q'25 URL: https://www.mbi-deepdives.com/csu3q25/ Last updated: 2025-11-11T14:58:14.000Z Constellation Software reported earnings on Friday last week. Unlike other companies, they do not hold quarterly earnings calls although I do wonder whether they will have a change of heart under the [**new CEO**](https://www.mbi-deepdives.com/constellations-succession/), especially if the stock continues to remain under pressure for an extended period of time. I will briefly recap some KPIs from the quarter and then provide an update on valuation behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### How would we know if market were "AGI" pilled? URL: https://www.mbi-deepdives.com/how-would-we-know-if-market-were-agi-pilled/ Last updated: 2025-11-09T14:43:12.000Z During [Meta 2Q’25](https://www.mbi-deepdives.com/meta2q25/) call, Mark Zuckerberg mentioned something potentially quite profound: > “Over the last few months, we’ve begun to see **glimpses of our AI systems improving themselves. And the improvement is slow for now, but undeniable”** Then in [Meta 3Q’25](https://www.mbi-deepdives.com/meta-3q25-update/) call, he described Meta’s Family of Apps (FOA) business in a way I have never previously heard him depict it in this manner. From the call: > This quarter, we saw meaningful advances from unifying different models into simpler, more general models, which drive both better performance and efficiency. And now **the annual run rate going through our completely end-to-end AI-powered ad tools has passed $60 billion**. And one way that I think about **our company overall is that there are 3 giant transformers that run Facebook, Instagram and ads recommendations. We have a very strong pipeline of lots of ways to improve these models by incorporating new AI advances and capabilities.** > > And at the same time, **we were also working on combining these 3 major AI systems into a single unified AI system that will effectively run our family of apps** and business using increasing intelligence to improve the trillions of recommendations that we’ll make for people every day. If you connect these two excerpts from Meta’s last two earnings calls, is it ridiculous to think a growing percentage of Meta’s future unified model improvements will actually come from the AI systems themselves? Is it too reach to imagine that in the next 5-10 years, almost all of the model improvements will eventually come from the AI systems? Of course, the better the model gets, the better the content and ad recommendations will be and higher Meta’s revenue will be (assuming people still have jobs then; only half-kidding!) If in 5 years, almost all the incremental revenue growth starts to come from improvement in Meta’s models that are also initiated/originated by the AI systems themselves, it is hard to imagine Meta’s incremental headcount need. At the very least, there should be a massive reallocation of headcount resources over time in these big tech companies. Meta’s Super Intelligence team might be just a glimpse of what is about to come in big tech companies for the next decade. If most skills become commodities, the price for bottleneck resources is expected to skyrocket which is what happened with AI researchers in the last couple of years. Of course, the bottleneck resources or skillset can also evolve over time dramatically. Notice OpenAI’s blog [post](https://openai.com/index/ai-progress-and-recommendations/?ref=mbi-deepdives.com) published last week: > “Although AI systems are still spikey and face serious weaknesses, **systems that can solve such hard problems seem more like 80% of the way to an AI researcher than 20% of the way. The gap between how most people are using AI and what AI is presently capable of is immense**.” It is certainly possible that today’s bottlenecks will prove to be rather short-lived as companies increasingly learn to utilize the full extent of AI’s (current and future) capabilities. Sam Altman in a recent [interview](https://conversationswithtyler.com/episodes/sam-altman-2/?ref=mbi-deepdives.com) with Tyler Cowen tried to highlight some things that are relatively under-discussed: > People talk a lot about the recursive self-improvement loop for AI research, where AI can help researchers, maybe today, write code faster, eventually do automated research, and this thing is well understood, very much discussed. Very little discussed or relatively little discussed are the hardware implications of this: robots that can build other robots, data centers that can build other data centers, **chips that can design their own next generation. There’s many hard parts, but maybe a lot of them can get much easier. Maybe the problem of chip design will turn out to be a very good problem for previous generations of chips**. Sam Altman doesn’t have any option other than being a fund raising machine, so we may need to take everything he says with a grain of salt. Nonetheless, Altman does hint at something that I also wonder about. A good chunk of Nvidia’s moat comes from CUDA (Compute Unified Device Architecture) which is a parallel computing platform and programming model that has created immense developer lock-in. CUDA exists to bridge the gap between human programmers and the complex architecture of the GPU. As AI systems become capable over time, AI should not require human-friendly abstraction layers, SDKs, or documentation. It could theoretically look at any piece of hardware i.e. an Nvidia GPU, a Google TPU, or a novel architecture it just designed and write perfectly optimized machine code for it. If humans are increasingly out of the loop, shouldn’t the friction that keeps developers locked into CUDA also materially diminish over time? In fact, the very idea of a picks and shovels company being the largest company in the world should perhaps create cognitive dissonance to AI bulls. Picks and shovels are immensely valuable when you have no idea where exactly the gold can be found, and yet, there is an army of people rushing to dig for gold anyway. However, if there are only a handful of companies left to dig gold and one or two consistently start finding gold in higher quantity, they may eventually try to abstract away picks and shovels and vertically integrate the entire gold digging process. The company who has already made this bet is Alphabet. They have been vertically integrated and have a full-stack approach to AI that its nearest competitors are trying to replicate as soon as possible. ![Image](https://substackcdn.com/image/fetch/$s_!0A7q!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe675349b-a391-4764-adea-d31f1b76d584_1119x750.jpeg "Image") Among the major players in AI value chain, I think **until recently** Jensen Huang used to be far from “AGI” pilled. If you truly believed in “AGI”, would you sell your chip designs to third parties as much as possible instead of trying to build your own lab to develop the model itself? I mentioned “until recently” because Huang did seem to be getting “AGI” pilled as Nvidia is now investing in OpenAI. I have always wondered one of the biggest question in big tech land in the next 3-4 years is what Nvidia will do with its expected \~$817 Billion cumulative Free Cash Flow (FCF) in the next four years (consensus estimates between FY2027 and FY2030)? Nvidia seems to have a [bitter](https://fortune.com/article/nvidia-anthropic-ai-chips-china-smuggle-telling-tall-tales/?ref=mbi-deepdives.com) relationship with Anthropic; it wouldn’t surprise me if he distributes a good chunk (majority?) of this cumulative FCF to OpenAI and xAI. In hindsight, Alphabet actually appears much more “AGI” pilled than most companies; selling TPU to others stop making sense if you think there is a viable path to “AGI”. I know there are strong murmurs of [evolution](https://www.mbi-deepdives.com/googles-evolving-tpu-strategy/) of such a strategy, but I will wait to see who exactly they’re selling these TPUs to before inferring too much. If it’s other model developers such as Anthropic or [SSI](https://ssi.inc/?ref=mbi-deepdives.com) (who has the least pragmatic approach but by far the most “AGI” pilled), it would still indicate Alphabet is still quite “AGI” pilled and they’re just hedging their bets around a bit. With a full-stack approach to AI (and some hedges through Anthropic ownership), Alphabet will likely be the most valuable company in the world if market were getting increasingly “AGI” pilled. I suspect that may still not be enough to infer confidently that market is getting “AGI” pilled especially since Alphabet is already also the most profitable company in the world. However, **if OpenAI and Anthropic IPOs and either of them enjoys a persistent higher market cap than some of the big tech companies (e.g. Meta), you could say market is warming up to the idea that developing the best model itself will ultimately be the best business**, near-term earnings and cash flows be damned! Elon Musk, Sam Altman, Mark Zuckerberg, Demis Hassabis+ Sundar Pichai…I suspect all of them are higher on the scale of being “AGI” pilled than their shareholders today (okay, maybe OpenAI’s investors are more than others). If most investors get over time closer to where these CEOs probably are today, especially **if more evidence of recursive self-improving models emerge in the next couple of years**, I would like to own more Alphabet in such a world which should explain my [**decision**](https://www.mbi-deepdives.com/digital-advertising-industry-snapshot-portfolio-change/) to add more to Alphabet recently. While the stock isn’t “cheap” anymore, we don’t quite need to be “AGI” pilled to see Alphabet’s potential either. As I have [**mentioned**](https://www.mbi-deepdives.com/the-great-decoupling-of-labor-and-capital/) earlier, I believe “AGI” is not a moment in time, rather an asymptote to which we may perennially march towards, but just as we didn’t make any audible gasp when Turing test was passed, our days may seem surprisingly similar as we get ever closer to “AGI”. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Digital Advertising Industry Snapshot, Portfolio Change URL: https://www.mbi-deepdives.com/digital-advertising-industry-snapshot-portfolio-change/ Last updated: 2025-11-08T15:21:29.000Z As is the tradition in every earnings season, I am sharing my digital advertising Snapshot after 3Q’25 earnings. Digital ads market growth has accelerated to 18.4% in 3Q’25 (vs 15.7% in 2Q’25 and 14.1% in 3Q’24). Meta continues to gain market share every quarter. For the first time, Google’s advertising market share was below 50%. From 3Q’21 to 3Q’25, Meta and Amazon gained \~300 bps and \~400 bps market share, largely at the expense of Google who lost \~760 bps market share during this period. Almost half of Google’s market share loss actually came from its low margin network business which is perhaps going to be increasingly irrelevant over time as search is gradually graduating to “zero click answer machine”. YouTube also lost market share during this period, but it is difficult to infer anything definitive from this data point given YouTube’s subscriptions has accelerated during this time. As Alphabet [**mentioned**](https://www.mbi-deepdives.com/goog3q25/) during 3Q’25 call, YouTube Music and Premium subscriber generates a **meaningful higher gross profit** than ad-supported users which certainly implies Alphabet is very happy to “lose” this market share to other advertising players. As a result, it seems likely that a huge chunk of Alphabet’s market share “loss” in the last 4-5 years is a intentional retreat from some advertising markets. In fact, a part of me wonders whether this planned retreat may eventually expand to Search as well if Gemini gains further momentum, especially after they launch Gemini 3.0 later this year. If that happens, it might make inferring the health of Google’s search business more challenging than it may appear at first glance. Let me explain more. ![](https://substackcdn.com/image/fetch/$s_!eM0z!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F86602bf4-eb09-4e7f-9435-465241154f3c_1252x913.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) *\*Digital Ads Market is defined as Alphabet reported ad revenue+ Meta’s ad revenue+ Amazon Ads+ Microsoft Search+ Pinterest+ Snap+ Trade Desk+ Applovin revenue* --- In one of the Google’s recent anti-trust trials, there was an interesting [d**isclosure**](https://assets.bwbx.io/documents/users/iqjWHBFdfxIU/r0EtnNkJfNzk/v0?ref=mbi-deepdives.com) about an internal meeting with Google’s top executives in October 2024, including Liz Reid (Head of Search), Vidhya Srinivasan (VP and General Manager of Ads and Commerce), and Nick Fox (SVP of Knowledge and Information). From the meeting summary, the following stood out to me: > “Nick’s main point: “we have 3 options: (1) Search doesn’t erode, (2) we lose Search traffic to Gemini, (3) we lose Search traffic to ChatGPT. **(1) is preferred but the worst case is (3) so we should support (2)”** > > Vidhya essentially said that analysis keeps telling them we aren’t losing Search/Ads traffic yet, but **she feels like this is inevitable**, and we should prepare for Gemini’s success. She wants to accelerate monetizing Gemini with Ads ASAP ... **“writing is on the wall”** > > Discussion on use cases in Gemini led to lots of questions around Gemini monetization strategy > > Desire from Vidhya, Shashi, and others to more explicitly come up with a connected strategy, e.g. **thinking about when Gemini should kick back to Search, build out Shopping solutions**, etc > > Maria mentioned and pinged after about exploring opportunities to build **in Shopping experiences in the Gemini user experience more explicitly** It’s been a year since this meeting, but I suspect the main takeaways from the meeting still have validity. Gemini already has **650 million MAUs (vs 450 million in 2Q’25)**, and queries on Gemini increased **3x from 2Q’25**. While many queries are certainly incremental today, it does seem likely that traditional core Google search will continue to bleed share to Gemini and ChatGPT. The more aggressive Google becomes to promote Gemini, the more uninspiring Search ad revenues may seem comparing and contrasting to other digital ads players. However, if users turn more into Gemini instead of Google and eventually become paying subscribers, I wonder it may eventually play out the same way as it did in YouTube: the **average** gross profit from paying subscribers may turn out to be higher than ad-supported search users. Admittedly, I am not super confident of this as it is hard to imagine an outcome better than current Google search. This is particularly intriguing because Google does have an opportunity to build the highest consumer surplus subscription product in the planet. They already have 300 million paying subscribers in Google One and YouTube premium. Imagine if Google eventually introduces a “**custom**” subscription plan that lets you add all the Google Services in one bundle and get a \~20-50% discount to the a la carte prices if you pick 2+ products/services from Google, I can imagine how Google may be able to get to 1 Billion+ subscribers in the next 5-7 years. Ads will still play an important role given ad-supported users will likely continue to be the majority of Google’s user base, but in this scenario, Google’s intrinsic value may continue to increase even if they lose ads market share to Meta and Amazon et al over time. ![Who the Heck Is Gonna Pay $250 for Google AI Ultra? - CNET](https://substackcdn.com/image/fetch/$s_!pQuL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F191e667f-2b67-4310-aab5-9ce11fcb8d63_1200x675.jpeg "Who the Heck Is Gonna Pay $250 for Google AI Ultra? - CNET") I also wonder what it means for the rest of the digital ad players if a behemoth such as Google very gradually retreats from ads. Given Meta and Amazon generates majority of their ad revenue primarily from direct response or performance ads, it’s not like direct response advertisers will just switch more to Meta and Amazon. Ultimately, these advertisers care about ROIs and once they cannot meet their ROI threshold, they won’t increase their spending on Meta. However, there is one interesting possibility; if the very nature of search queries change and transition more to Gemini/ChatGPT, we may see a revival of brand advertising. In a recent Stratechery [interview](https://stratechery.com/2025/an-interview-with-michael-morton-about-ai-e-commerce/?ref=mbi-deepdives.com), Michael Morton hinted at this possibility: > It’s funny how the world goes circular. Limited shelf, unlimited shelf, and now maybe back to a more constrained shelf, and then to where everything was brand and now everything was performance-based marketing. Well, now if you’re getting down to a world where there’s a couple of icons, and people are more trusting to the model and the model doing the due diligence for them, some ability for brand recall and spending money on brand advertising could rise in importance again. It is also worth noting that measuring the performance of brand advertising can be more challenging than direct response ads. As a result, brand advertising can be more cyclical than performance advertising, but in a world of zero click search, companies may be increasingly under more pressure to spend on brand advertising than they were in the last 5-10 years. That might leave even less margins for brands overall, but that may lead to greater auction liquidity and higher ad prices on Meta, Amazon etc. I will share the digital advertising industry snapshot spreadsheet and explain some portfolio changes that I made yesterday behind the paywall --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb 3Q'25 Update URL: https://www.mbi-deepdives.com/abnb3q25/ Last updated: 2025-11-07T15:05:15.000Z Airbnb had a notable acceleration in Gross Booking Value (GBV) growth as it increased from 10.8% in 2Q’25 to 14.3% in 3Q’25\. This acceleration was driven by the twin acceleration of both the nights & experience or N&E (from 7% in 2Q’25 to 9% in 3Q’25), and Average Daily Revenue or ADR (which increased from 2.9% in 2Q’25 to 4.7% in 3Q’25). While revenue growth was slower than GBV (\~10% vs \~14%), GBV tends to be a **leading indicator of future revenue growth** since Airbnb records GBV when a guest makes a reservation but recognizes the associated revenue only when the guest actually checks in for their stay. One of the reasons for such acceleration in GBV growth was Airbnb launched “Reserve Now Pay Later” in the US in Q3\. This feature (which was only available for US domestic travel for homes that had flexible cancellation policies) encouraged customers to book their travel further in advance which led to strength in longer lead time bookings. Airbnb expects higher cancellation rates than usual for these bookings, but does anticipate net impact to be positive. Nights & Experiences (N&E) and ADR by geography are shown below: ![](https://substackcdn.com/image/fetch/$s_!3yCa!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F265b672e-b899-443f-bfba-de79ac2f7d5a_498x193.png) Source: Company Filings, MBI Deep Dives, I have also shown the KPI trends below: ![](https://substackcdn.com/image/fetch/$s_!kDTM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7bc3f36-9b77-4f1a-9a5c-266bb585fa48_1768x364.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I’ll dig into the quarter in more detail behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Regional mix shift** _This post is for paying subscribers only._ ### Corpay 3Q'25 Update, Portfolio Change URL: https://www.mbi-deepdives.com/corpay-3q25-update-portfolio-change/ Last updated: 2025-11-06T14:34:10.000Z Many payments companies are going through bit of a rough patch in the latter half of this year. Unfortunately, Corpay hasn’t been an exception. Thankfully, they reported a pretty decent quarter. I will cover my highlights from the quarter as well as some risks I see behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!v_u2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F45cbd07b-960d-4cd2-8db4-2bb11cb543e5_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Shopify 3Q'25 Update URL: https://www.mbi-deepdives.com/shop3q25/ Last updated: 2025-11-05T15:07:48.000Z Despite having the good fortune of owning Shopify after the 2022 drawdown, I had the “misfortune” of selling it at less than one-third of the current stock price in early 2023\. Given how Shopify powers increasingly a sizable portion of e-commerce market, I still try to follow them closely. Every time I update the below chart, it is hard not to admire this Canadian company keeping pace with the 800-pound gorilla called Amazon! ![](https://substackcdn.com/image/fetch/$s_!pVbF!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc4ec1d00-c59d-45f5-ac6a-9a6182598e2b_1150x628.png) \*Amazon GMV estimated as online sales+ physical store sales+(3P sales/25%); Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) One of the key reasons Shopify’s incremental GMV growth nearly matches Amazon’s is their much stronger presence outside North America. While Amazon marketplace operates in [**23 countries**](https://www.mbi-deepdives.com/amazon-marketplace-trends/), it is still very much US centric. For example, while we don’t know Amazon’s exact growth by geography only in retail business, we do know Amazon’s North America revenue increased by $10.7 Billion whereas International revenue increased by only $5 Billion YoY in 3Q’25\. In contrast, Shopify mentioned **half of their GMV dollar growth in Q3 came from outside North America**. It is lot easier to scale Shopify’s platform internationally than to scale Amazon’s marketplace. Another important driver for Shopify GMV growth was their growing B2B business. For the last two years, B2B GMV consistently grew at “over 100%” and it doubled again in Q3\. They don’t disclose how big B2B GMV exactly is, but if you keep doubling it every year, it’s gotta be a very strong tailwind to their GMV growth. From the call: > “…large enterprise, especially the more -- the older ones, the ones that have been around for decades, they’re having conversations in their boardrooms talking about where do we go to future-proof platform, where do we go to make sure that we don’t miss out on agentic commerce? And **all roads lead to Shopify.** > > we nearly doubled B2B GMV again in Q3, up 98% year-over-year. This isn’t just 1 cohort or 1 region; **we’re seeing broad GMV growth across both new and established merchant cohorts.** For example, in Canada, Q3 B2B GMV was up over 155% year-over-year.” I will cover rest of their quarter behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Insurance Brokers 3Q'25 Update URL: https://www.mbi-deepdives.com/insurance-brokers-3q25-update/ Last updated: 2025-11-04T14:03:40.000Z Insurance brokers have been going through a not-so-fun period, especially relative to the broad market over the last one month. Now that all the major five insurance brokers released their 3Q’25 earnings, let me dig into them a bit behind the paywall. ![chart](https://substackcdn.com/image/fetch/$s_!E8t2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9a9b0197-56f5-461e-9000-5cfdc165dfb1_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Never Sell: Episode 11-The Big Tech Capex Debate, Amazon Retail, Align’s Pricing Challenge URL: https://www.mbi-deepdives.com/never-sell-ep11/ Last updated: 2025-11-03T12:50:52.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode. You can listen to it here: [Spotify](https://open.spotify.com/episode/5NyeYMWe09WzUTxB82vJer?si=bf59aa2e555f4c9e&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/the-big-tech-capex-debate-amazon-retail-aligns/id1786912203?i=1000734933500&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=LQa6HvR5D5s&ref=mbi-deepdives.com), [RSS feed](https://rss.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com) Unlike yours truly, Scuttleblurb rarely talks about big tech in public, so I really enjoyed discussing and thinking through the thorny capex debate. David (Scuttleblurb) is one of the sharpest analysts I know, and the primary reason I was pestering him for the last couple of years to launch a podcast with me is I need a good excuse to discuss stocks with him. It's one of those things that I frankly do not quite care much if anyone else listens to these conversations (I know many of you do), but it's certainly something I personally look forward to every month. Maybe one day I will be able to convince David to do it more than once a month! As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) ### The Great Decoupling of Labor and Capital URL: https://www.mbi-deepdives.com/the-great-decoupling-of-labor-and-capital/ Last updated: 2025-11-02T15:43:23.000Z ***A programming note***: I initially wanted to cover Microsoft’s earnings today, but I am changing the schedule a bit as I felt more inspired to write today’s piece. --- Almost two decades ago, Hewlett-Packard (HP) was the[ **first**](https://www.britannica.com/money/Hewlett-Packard-Company?ref=mbi-deepdives.com) tech company to **exceed $100 Billion** annual revenue threshold in **2007**. At that time, HP had 172k employees. The very next year, IBM joined the club, but IBM had almost 400k employees. Today’s megacap tech companies all exhibit a common characteristics: their growth is pretty much **decoupled** from their headcount. Intuitively, this might not be a news to anyone, but when I sat down and carefully jotted the numbers, the extent of the decoupling even before Generative AI truly came to the scene was a bit astonishing to me. Let me show you one by one. # **Apple** Apple reached its first $100 Billion revenue with just 60k employees in 2011\. The next incremental $100 Billion revenue took just half of the incremental number of employees! The most recent incremental $100 Billion revenue for Apple took only \~17k incremental employees. Of course, a $100 in December 2011 is $144 today after [adjusting](https://www.bls.gov/data/inflation%5Fcalculator.htm?ref=mbi-deepdives.com) for inflation. Moreover, while Apple was certainly not the worst offender of pandemic over hiring, their journey from $200 Billion to $300 Billion did break the overall pattern. Let’s see some other companies to see how their journey to become a behemoth evolved over time. ![](https://substackcdn.com/image/fetch/$s_!vGLq!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7f910cbb-ed97-48f7-a0e5-e1c750d44a0c_922x537.png) Source: MBI Deep Dives # Alphabet Alphabet hasn’t quite reached $400 Billion revenue yet, but they are likely to get there next quarter! Alphabet required 76k employees to get to their first $100 Billion. Their most recent incremental $100 Billion? Just **11,000**! (assuming they add another 3k employees in 4Q’25) Now, Alphabet certainly did over hire people post-Covid which they [corrected](https://www.reuters.com/business/google-parent-lay-off-12000-workers-memo-2023-01-20/?ref=mbi-deepdives.com) later. Despite such over hiring, Alphabet fits the broader pattern: each incremental $100 Billion revenue took a noticeably fewer incremental employees! ![](https://substackcdn.com/image/fetch/$s_!AhAz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc2b232b2-8d18-4d14-8809-251038353e1b_924x535.png) Source: MBI Deep Dives # Microsoft Microsoft also hasn’t reached $300 Billion revenue milestone yet, but they will get there next quarter. Historically, Microsoft used to be much more human capital intensive company as they required 124k and 97k incremental employees to get to $100 Billion and $200 Billion revenue milestones respectively. But their most recent $100 Billion? Only **SEVEN** thousand! ![](https://substackcdn.com/image/fetch/$s_!00Mh!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feab5d62c-a799-4363-9fd0-324e1d8a03b0_919x534.png) Source: MBI Deep Dives # Meta Meta is the youngest of these companies. They will likely reach $200 Billion revenue milestone next quarter. Their first $100 Billion took 63k employees while the recent one will likely take one-third of that number! As they say, the first hundred billion is the most difficult one! ![](https://substackcdn.com/image/fetch/$s_!cykM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F17acfda0-6ec2-4666-8e81-a81e2ac5d80d_904x531.png) Source: MBI Deep Dives # **Amazon** I almost didn’t want to include Amazon here given their massive retail employee base. But after taking a peek at their number, I decided to include them as well. Unlike the other tech companies, Amazon really didn’t exhibit much of a pattern for their journey to $500 Billion revenue milestone. In fact, their hiring pattern is perhaps the poster child of post-pandemic over hiring as the company really was in the thick of pandemic induced massive upward demand shock and misread the post-pandemic hangover. While historically they took 200k to 400k employees for their incremental $100 Billion revenues, they added their last **$200 Billion revenue with only 36k incremental employees**! I do want to note that Andy Jassy in his June 2025 [**memo**](https://www.aboutamazon.com/news/company-news/amazon-ceo-andy-jassy-on-generative-ai?ref=mbi-deepdives.com) to employees said the following: > As we roll out more Generative AI and agents, it should change the way our work is done. We will need **fewer people** doing some of the jobs that are being done today, and more people doing other types of jobs. It’s hard to know exactly where this nets out over time, **but in the next few years, we expect that this will reduce our total corporate workforce** as we get efficiency gains from using AI extensively across the company. I wouldn’t be surprised if Amazon reaches $1 Trillion revenue in 3-4 years by adding only \~100-200k incremental headcount. If that happens, it would mean while Amazon required 1.5 million employees to get to $500 Billion revenue, **the next $500 Billion revenue would come with only \~10-15% of incremental headcount**! ![](https://substackcdn.com/image/fetch/$s_!Rb4E!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8392fbed-d83c-4831-8d89-96bdf9b50a69_936x540.png) Source: MBI Deep Dives Of course, I haven’t even mentioned the largest company in the world: Nvidia! When they reached $100 Billion LTM revenue in 2024, they only had 30k employees and they will likely reach their next $100 Billion with only \~6-8k incremental headcount! The trend isn’t necessarily just confined to tech companies either. Walmart’s full-time employees number remained relatively constant for the last 10 years while their revenue grew by $200 Billion during this period. In fact, Walmart recently [mentioned](https://www.cnbc.com/2025/09/29/walmart-ceo-ai-is-literally-going-to-change-every-job.html?ref=mbi-deepdives.com) that the headcount will remain static for the next three years as well. So, it is likely that Walmart will add $300 Billion incremental revenue since 2015 with basically no incremental headcount! If you add Apple, Microsoft, Alphabet, Meta, Nvidia’s last $100 Billion incremental revenue, then add Amazon’s last $200 Billion, and Walmart’s \~$300 Billion revenue, we are essentially looking at **\~$1 Trillion incremental revenue with only \~100k headcount growth**! Remember, this has happened even before generative AI. **The next $1 trillion incremental revenue may require even fewer employees!** This is what I mean by the great decoupling between labor and capital has been an ongoing theme for a while now. It’s also why I believe the narrative around “AGI” may be a little misguided. Many people talk about “AGI” as if you would receive an email on one fine Monday from your company that would read “we have reached AGI. We no longer need your service”. Personally, I think what is much more likely is that **AGI is perhaps not a moment in time, rather we are simply marching towards “AGI” which itself is an** **asymptote**. AI will accelerate the pace at which we would be going towards this “AGI”. Even if in a strictly theoretical sense we never reach “AGI”, the actual difference of reaching and not reaching “AGI” may prove to be minimal in a couple of decades. There are, of course, deeper societal, political, and philosophical implications for such a world. Exploring the implications in full detail is a bit beyond the scope of this piece. But I do want to highlight some musings related to investment implications. While I used to snicker a bit on OpenAI’s revenue ambitions in the next 5 years, going through big tech’s earnings last week made me want to tone it down a bit. If Reels can [**scale**](https://www.mbi-deepdives.com/meta-3q25-update/) from $1 Billion revenue run rate to $50 Billion in just **three years**, perhaps I should be less cynical about OpenAI’s ability to go from $30 Billion revenue in 2026 to $200 Billion revenue in 2030\. I am not sure you can quite “model” these things in any precision, but I am updating my opinion that reaching $200 Billion is not a top 0.1 percentile outcome for OpenAI, rather perhaps a top 1-2 percentile outcome (which is \~10-20x higher probability). Given this possibility, I am updating **the distribution of outcomes** in my mind for OpenAI. ![](https://substackcdn.com/image/fetch/$s_!d5Cm!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F42eea1b9-dee5-4b3e-885c-41aae3e6deb6_1063x622.png) Source: [The Information](https://www.theinformation.com/articles/openai-says-business-will-burn-115-billion-2029?rc=4lgoj7&ref=mbi-deepdives.com) If OpenAI reaches $200 Billion revenue in 2030, what does that imply for the current crop of big tech companies? Should I update the distribution of outcomes for them as well? It’s a difficult question, but let me share some brief thoughts. One of the things that I have consistently noticed in my last seven years of following big tech companies is the way investors (including me) often frame the debates in “us vs them” framework. As fundamental analysts and active investors, we are understandably geared to assess the companies in a relative sense, and yet, all these companies have all performed pretty well over the last 10 years even though a lot of concerns around some of these companies indeed proved to be valid. Is there any doubt that Google search ads consistently [**lost**](https://www.mbi-deepdives.com/amzn3q25/) share to Amazon ads? Is there any doubt that Meta has lost share in time spent to TikTok? Is there any doubt that Google search has and will continue to lose overall query share to ChatGPT? Of course, one of the reasons such “us vs them” framing turned out to be misguided is we persistently underestimated the potential for a new dynamic to expand the market itself. Another thing that I believe is often under appreciated for all these tech companies is the massive **consumer surplus** they tend to leave on the table which gives them ample time to respond to somewhat unforeseen concerns. Imagine how many articles we have all read about the mental health crisis that is allegedly accelerated by social media companies. I don’t doubt that these companies can inflict negative impact on some users, but I have always believed the core consumer surplus for social media companies is deeply underappreciated. Take the recent hysteria around short form video. Perhaps almost everyone reading this piece is convinced that this is just “brain rot” imposed on us. I personally don’t find short-form videos too addicting (but I don’t doubt that it might be too addicting for many), but I do think it is deeply underappreciated that during any point of the day, I can open an app and I am absolutely guaranteed to watch a video that would make me smile for free! Similarly, while countless pieces will be written on ChatGPT related psychosis, it is perhaps not quite deeply internalized how incredible it is that we can type something to a query box and learn about anything homo sapiens has ever figured out so far! One of the reasons I think they are not deeply internalized is it actually feels uncomfortable to write about. You almost feel like a “Meta or OpenAI shill”. It may be much more socially acceptable to worry about the impact of big tech companies than to write think pieces on how our lives have become consistently better thanks to these tech companies! One of my current major takeaways from this exercise was the broader economy may increasingly be more of a [**smiling curve**](https://en.wikipedia.org/wiki/Smiling%5Fcurve?ref=mbi-deepdives.com): on one hand you have these big tech companies dominating the economy without adding much headcount in the process, on the other hand they will keep empowering “little” guys: think more Amazon 3P sellers, more content creators, more niche app developers etc. while everyone in the middle continues to be squeezed. As I have mentioned repeatedly, this has been an ongoing theme. AI is simply a further accelerant. Even if this is true, this won’t preclude us from interim hiccups, and I expect narrative to swing from one extreme to the other even though the destination may not quite change much. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Alphabet 3Q'25 Update URL: https://www.mbi-deepdives.com/goog3q25/ Last updated: 2025-11-01T14:31:25.000Z Alphabet is a $3.4 Trillion company. Yet, the stock is up almost **\~50%** since 2Q’25 earnings. The stock has largely shredded the initial search related jittery post-ChatGPT and is now firmly seen as one of the key winners in AI. 3Q’25 call only bolstered this view. Here are my highlights from the earnings. **Revenue** Alphabet revenue accelerated to **15.9%**, its highest growth **since 1Q’22**! For the **13th** consecutive quarters, Google network’s revenue went down. Everything except the network business grew at a healthy double digit rate. Google Cloud is now at $60.6 Billion revenue run-rate, growing at \~34% YoY in 3Q’25. ![](https://substackcdn.com/image/fetch/$s_!R8jE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1dbda516-386b-4d7b-a442-625bef48c569_1660x361.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Take a look at growth rates by segment since 1Q’23: ![](https://substackcdn.com/image/fetch/$s_!vcNe!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0028ad7f-9a89-4f13-877a-c41fc84b1064_1489x319.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Alphabet had $3.5 billion charge related to European Commission (EC) fine. I have adjusted their earnings by subtracting this charge. Frankly speaking, given how recurring EC’s fines are, perhaps I shouldn’t have adjusted it. Anyways, adjusting for EC fines, Google Services had 42.5% operating margin in 3Q’25, which was their **highest** ever! Covering Alphabet’s earnings is bit of a humbling exercise for me every quarter these days as I wondered Alphabet may have reached peak margin in 2021 (40% in 3Q’21)! Google Services incremental operating margins **stayed above 50% for last 10 quarters now**! As the low margin network business continues to go down, it is a natural tailwind for margins; Traffic Acquisition Cost (TAC) as % of ad revenue went down from 21.9% in 1Q’22 to 20.1% in 3Q’25\. It likely also helps that **half of all codes** are now generated by AI at Google, so higher productivity is also likely a tailwind for margins. ![](https://substackcdn.com/image/fetch/$s_!dBmE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4a1b7abd-2a20-41b4-be80-8b81ddedf5fc_1944x79.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Google Cloud’s margin ramp up continues at an unbelievable pace. **From -47.4% operating margin in 2Q’20 to +23.7% in 3Q’25**…if you told me such margin ramp up potential 5 years ago, I don’t think I would have believed you! ![](https://substackcdn.com/image/fetch/$s_!w25t!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5ca20378-8980-4664-b694-6192c09d03d9_2023x321.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss the rest of this update behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- ## **Search** _This post is for paying subscribers only._ ### Amazon 3Q'25 Update URL: https://www.mbi-deepdives.com/amzn3q25/ Last updated: 2025-10-31T12:45:56.000Z Amazon reported a fantastic quarter yesterday. Not only AWS growth has accelerated, it does appear such acceleration may continue at least in the near term. Moreover, the rest of the business, especially the core e-commerce continues to be a secular winner. Here are my highlights from yesterday’s call. **Revenue** Overall revenue grew by 13.4%. After 10 quarters, AWS growth finally exceeded 20% again. Ads grew 22% (FX adjusted) YoY. I’ll discuss more about AWS later, but let’s talk more about Amazon ex-AWS first. ![](https://substackcdn.com/image/fetch/$s_!gfJP!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe464707c-036b-48f2-a6fa-4befba3f9397_1603x240.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** At first glance, it may seem North America and International segment’s operating margin took bit of a nosedive last quarter, but Amazon actually had two special charges: a) a legal settlement with FTC that impacted the North America segment, and b) their recent layoff related severance costs affected all three segments. Excluding these charges, North America would have 6.9% operating margin which was \~100 bps operating margin expansion YoY. While Amazon didn’t mention the exact number, international segment’s margin would also increase YoY after adjusting for the one-off charges. So, the margin expansion story is very much under way even if the headline numbers may appear otherwise. ![](https://substackcdn.com/image/fetch/$s_!sD65!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F747d1396-ca33-43cc-8403-5ac5b2d572e3_1108x667.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) It’s kind of incredible that while tariff took so much of airtime for much of this year for Amazon retail, **the word “tariff” was not even mentioned once** either during the prepared remarks or during the Q&A of the call. **Fulfillment+ Shipping** If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q’22\. Since then, unit growth has largely been faster than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. These two numbers were pretty close last quarter though, so maybe incremental margin expansion in retail segment will be harder from here. ![](https://substackcdn.com/image/fetch/$s_!mYya!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0aa9a428-394f-421a-a00f-697c5857739b_1153x547.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The earnings call, however, increased my conviction that it is increasingly getting very, very challenging to compete against Amazon in e-commerce. Unless your name is Walmart or Costco, I really think you may suffer from insomnia in the next 10-20 years. Every year, Amazon’s shipping speed will get faster. Moreover, I imagine as the demographic cohort of millennials and Gen Z age over time, e-commerce penetration for **everything** will continue to increase. For physical stores that live and die by the assumption of \~2-4% SSS growth, Amazon may challenge such assumptions deeply in the next 10 years. Some quotes from the call: > “we’ve already **increased** the number of rural communities with access to our same-day and next-day delivery by **60%**, reaching roughly half of the total communities we plan to expand to by the end of the year. > > for the third year in a row, we are on track to deliver our **fastest speeds** ever for Prime members in 2025\. We continue to tune and improve our fulfillment operations and our regionalized network is operating at scale. We see many benefits of our inbound process improvements, including a reduction of U.S. inbound lead time by nearly **4 days** compared to last year. This allows us to be more efficient with our inventory purchasing, which benefits working capital. We’re also placing inventory more strategically throughout the network. And by leveraging our existing infrastructure, we’re now offering U.S. customers the ability to order perishable groceries and receive them the same day and as little as **5 hours**. > > We’re seeing positive early results since launching in January, **when customers start shopping groceries on Amazon, they are visiting the site more often and returning twice as often as nonperishable shoppers**. > > we continue to experiment with various formats. But **the one that we are most excited about is…the ability to provide perishable groceries with same-day deliveries**…we started with a few markets about a year ago, and **we were really taken aback at the adoption**, not just the number of people that started buying perishables from us very quickly but **how often they came back downstream to buy perishables and groceries from us in the future**. And so we’ve now expanded that to 1,000 cities around the U.S. and will be in 2,300 by the end of the year. And it’s really changing the trajectory and the size of our grocery business. As explained in my [Instacart Deep Dive](https://www.mbi-deepdives.com/cart/), I don’t quite think Amazon is existential threat to Instacart as Amazon cannot match Instacart’s (or Walmart’s) speed anytime soon. However, with robotics, over time Amazon can materially increase pick up speed that shoppers or other retailers cannot really match, and once the density of perishable demand forms, I can see how Amazon can take a decent bite in grocery in the long term. Given the early traction, Amazon does seem adamant in cracking the grocery market. There already seems to be pretty sizable demand in non-perishable groceries, so it may not be a long shot for them to encroach into perishable over time. Amazon also shared some concrete data related to Rufus: > Rufus, our AI-powered shopping assistant has had **250 million active customers** this year with **monthly users up 140% year-over-year**, **interactions up 210% year-over-year and customers using Rufus during a shopping trip being 60% more likely to complete a purchase**. **Rufus is on track to deliver over $10 billion in incremental annualized sales.** Amazon was also asked about agentic commerce; while they do think it is quite promising in the long term, Amazon’s response made it clear that they think it’s way too early ([**I agree**](https://www.mbi-deepdives.com/first-impression-of-chatgpt-agent-and-apps-on-chatgpt/)). From the call: > “search engines are a very small part of our referral traffic and third-party agents are a very small subset of that. But I do think that we will find ways to partner. We have to find a way, though, that makes the customer experience good. Right now, I would say the customer experience is not -- there’s no personalization. There’s no shopping history. The delivery estimates are frequently wrong. The prices are often wrong. So we’ve got to find a way to make the customer experience better and have the right exchange value. But I do think that the exciting part of this and the promise is that **AI and agentic commerce solutions are going to expand the amount of shopping that happens online**. And I think that’s really good for customers, and I think it’s really good for Amazon because at the end of the day, you’re going to buy from the outfit that allows you to have the broadest selection, great value and continues to deliver for you very quickly and reliably. And I think that bodes well for us.” **Advertising** A big driver for retail profitability is advertising. Given Amazon ads are perhaps more of a competitor to Google than Meta, I think it’s interesting to track how Amazon is gaining share here. While Amazon ads is still just \~24% the size of Google Advertising revenue, Amazon ads incremental revenue as a percentage of Google advertising incremental revenue was 40% in 3Q’25 (vs 37% in 3Q’24 and 43% in 2Q’25). ![](https://substackcdn.com/image/fetch/$s_!pXg1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5b7a75f0-8bc8-4d7a-b05c-d112df778950_955x543.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss AWS and the rest of this update behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **AWS** Okay, now let’s talk about AWS. _This post is for paying subscribers only._ ### Meta 3Q'25 Update URL: https://www.mbi-deepdives.com/meta-3q25-update/ Last updated: 2025-10-30T14:59:05.000Z Let me start with the **conclusion**: while Meta’s stock is down \~10% after earnings, the company remains extremely well positioned for the long-term. Meta can, however, still be uncomfortable to own in the short-term as the company is going to navigate pretty aggressive capex plans next year. Nonetheless, I have slept pretty well last night after digesting yesterday’s earnings report. Here are my highlights from the quarter. **Users** Meta is still adding users to its Family of Apps (**FOA**). Instagram reached **3 Billion** MAUs. Threads, the most recent addition to FOA, now has 150 million DAUs. ![](https://substackcdn.com/image/fetch/$s_!S3cT!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fff34b4ec-1687-4a71-8eb1-127cf58ce12c_1234x91.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad revenue by Geography** In 3Q’25, Meta’s **slowest** growing region was US & Canada, but it grew at **23% YoY!** Overall ad revenue growth was +26% YoY which was driven by 14% ad impression growth and 10% ad price growth YoY, both of which accelerated vs 2Q’25\. AI continues to be a strong tailwind for **both impression growth and improved conversion**. Some excerpts from the call: > Across Facebook, Instagram and Threads, our AI recommendation systems are delivering higher quality and more relevant content, **which led to 5% more time spent on Facebook in Q3 and 10% on Threads**. > > Video is a particular bright spot with **video time spent on Instagram up more than 30% since last year**. And as video continues to grow across our apps, **Reels now has an annual run rate of over $50 billion.** For context, here’s how Reels scaled over the last four years: [**2Q’22**](https://www.mbi-deepdives.com/meta2q22/): $1 Billion [**3Q’22**](https://www.mbi-deepdives.com/meta3q22/): $3 Billion [**2Q’23**](https://www.mbi-deepdives.com/meta2q23/): $10 Billion 3Q’25: $50 Billion Wow, that is some scaling! It may be instructive to remember this pace of scaling when we think about Meta’s monetization of its investments in AI. How did AI improve Meta’s conversion? From the call: > In Q3, we rolled out Lattice to app ads, **which drove a nearly 3% gain in conversions** for that objective. Since introducing Lattice back in 2023, along with other back-end improvements, -- we have now cut the number of ads ranking and recommendation models by **approximately 100** as we consolidated smaller and more specialized models into larger ones that use the Lattice architecture to generalize learnings across surfaces and objectives. We continue to observe performance improvements as we combine models and **expect to drive additional gains as we consolidate another 200 models over the coming years into a smaller number of highly capable models.** > > we began piloting a new run time ads ranking model in Q3 that leverages more compute and data than our prior models to select more relevant ads. **In testing, we’ve seen this new model drive a more than 2% lift in conversions on Instagram**. We also significantly improved performance of Andromeda in Q3 and by combining models across retrieval and early-stage ranking into a single model, **driving a 14% increase** in ads quality on Facebook Surfaces. ![](https://substackcdn.com/image/fetch/$s_!HeCU!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5f0237c9-9dfa-4c62-b269-4f80f9eac1df_1333x808.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Again, to appreciate Meta’s brisk pace in FOA, you need to compare and contrast with Google Search. In 2Q’25, the delta between Meta’s FOA incremental ad revenue and Google Search incremental revenue YoY was $2.5 Billion. That delta has **increased to $3 Billion** in 3Q’25\. Google Search’s LTM revenue is now just $30.7 Billion higher than Meta’s FOA ads (vs $49 Billion in 4Q’22). In the [**last quarter**](https://www.mbi-deepdives.com/meta2q25/), I wondered if FOA may eclipse Google Search by 2030\. At this rate, that may prove to be conservative! ![](https://substackcdn.com/image/fetch/$s_!WvIk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e557bdf-94be-4cd6-a2d3-c050b3011eed_1387x774.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will cover the quarter in detail and explain my current thoughts on valuation behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*.* [Subscribe](#/portal/signup) --- **Segment Reporting** _This post is for paying subscribers only._ ### CoStar 3Q'25 Update URL: https://www.mbi-deepdives.com/csgp3q25/ Last updated: 2025-10-29T14:20:22.000Z CoStar had a decent quarter, but the stock is down 15% post-earnings. To be quite frank, the market reaction is quite perplexing to me as I couldn’t quite find anything that would warrant such a drop. Here are my highlights from the call. **Revenue** CoStar acquired Domain for $1.9 Billion on August 27 which contributed $25 million revenue, 90% of which was residential and the rest 10% was split between commercial marketplaces and information services. Excluding Domain, CoStar’s revenue was $808 million in 3Q’25 which exceeded their high end of the guidance. More detailed revenue growth trajectory by segment and region are shown below both from YoY and QoQ perspective. ![](https://substackcdn.com/image/fetch/$s_!2-FY!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe0fdea93-3f20-4ca3-964a-b60bd4b876c3_1138x811.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Margins** While CoStar used to report \~30% EBITDA margins during 2016 to 2021 period, their reported margins don’t really depict their underlying profitability anymore given their massive investments in Homes.com. In fact, their core adjusted EBITDA margins continued to go higher as it reached **47% in 3Q’25** (vs 43% in both 2Q’25, and 3Q’24)! When I say core margins, it refers to their commercial information and marketplace businesses which exclude the impact of Homes.com, OnTheMarket, Domain, and Matterport. ![](https://substackcdn.com/image/fetch/$s_!256h!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F00c1a974-4263-44f7-8e17-e4217ef24c46_1147x712.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Management also mentioned that they expect their marketplace businesses (Apartments.com, Homes.com, Domain, OnTheMarket) to reach \~40% adjusted EBITDA margins. For context, in aggregate, these marketplace businesses are posting negative EBITDA even though Apartments.com is obviously very profitable. However, they didn’t provide any timeframe to get there. Andy Florance basically pointed out companies such as Rightmove, REA Group, Idealista, or SeLoger are posting 40% to 70% EBITDA margins at scale, so given their similarity of business model, there is no compelling reason for them to get there over time. One key differences I think these scaled marketplaces have vs some of marketplace CoStar owns ex Apartments.com is they are not market leaders. Given the increasing return on scale phenomenon, there can be significant differences in margins for the second or third tier marketplaces compared to the market leader. So, I wouldn’t be too eager to model \~40% EBITDA margins for CoStar’s marketplaces business in terminal year just yet! ![chart](https://substackcdn.com/image/fetch/$s_!FWIh!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe1b04008-bc5d-4a7b-990c-38809d6ebf31_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Let’s look at segment by segment now which I will discuss behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **CoStar Suite** _This post is for paying subscribers only._ ### Amazon marketplace trends URL: https://www.mbi-deepdives.com/amazon-marketplace-trends/ Last updated: 2025-10-28T13:23:54.000Z ***A programming note***: We are heading towards a pretty packed earnings week. As a reminder, I will cover only one company’s earnings every day. My tentative earnings coverage schedule for the next few days is: CoStar (Wednesday), Meta (Thursday), Amazon (Friday), Alphabet (Saturday) , Microsoft (Sunday), and Insurance Brokers (Monday). All of the earnings coverage will be behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- “Marketplace Pulse” publishes a very data driven report on Amazon marketplace trends every year. They recently released their report for this year which you can download [here](https://www.marketplacepulse.com/reports/amazon-marketplaces?ref=mbi-deepdives.com). They estimate Amazon marketplace currently has $575 Billion third-party (3P) GMV. While Amazon operates its marketplace in 23 countries, \~83% of its GMV comes from just five countries: US, UK, Germany, France, and Italy. ![](https://substackcdn.com/image/fetch/$s_!JwVx!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F752de1f5-488c-4ad6-a941-99820d2a3daa_733x678.png) Source: [Marketplace Pulse](https://www.marketplacepulse.com/reports/amazon-marketplaces?ref=mbi-deepdives.com) One of the interesting points the report made is that the US market is truly a goldmine for its “**micro niche viability that simply doesn’t exist elsewhere**”. While I imagine a significant percentage of men in Saudi Arabia have beards, the term “beard oil” was searched only 50 times per month in Saudi Arabia whereas it received **25k monthly** searches in the US. Similarly, “sourdough starter jar” received 20k searches whereas it received only 200 in Australia. US consumerism is truly off the charts, especially relative to any other country! As you can expect, sellers exhibit a power laws in the marketplace. From the report: > “Amazon’s competitive paradox intensified in 2025: the number of million-dollar sellers has nearly doubled in four years, from 60,000 to over 100,000, while the total number of active sellers has declined by 25% > > Even more dramatically, sellers exceeding $100 million in yearly sales surged from approximately 50 to over 230 across global marketplaces…The marketplace’s power law has intensified – in the U.S., **just 2% of sellers generate over 50% of total revenue**” ![](https://substackcdn.com/image/fetch/$s_!hvYQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F81ed8a8a-2394-4dfb-a1f8-2b9abc851bab_1042x501.png) Source: [Marketplace Pulse](https://www.marketplacepulse.com/reports/amazon-marketplaces?ref=mbi-deepdives.com) The dominance of Chinese sellers only seems to be increasing in Amazon’s US marketplace. Again, from the report: > “The U.S. marketplace offers the clearest path to substantial revenue, with 43% of sellers generating $100,000 or more annually, compared to a 19% global average. Among the 146 sellers achieving $100 million or more in the U.S. marketplace, 117 are based in the U.S., while 22 are Chinese companies. However, **at the million-dollar threshold, Chinese sellers represent 57% of approximately 51,000 Amazon.com sellers.** > > **Chinese sellers crossed the 50% threshold among Amazon’s top U.S. sellers in 2024**, and then the global active seller base in 2025\. With 47-68% of new seller registrations across international markets, **Chinese merchants are systematically capturing market share through manufacturing advantages, government support, and operational sophistication that other sellers struggle to match**. > > **US sellers account for approximately $157 billion of Amazon.com’s $305 billion in third-party GMV, compared to $132 billion for Chinese sellers**. The average US seller generates $884,958 in revenue, more than double that of their Chinese counterparts at $393,557 > > Tariffs **paradoxically** **worsen** competitive positioning. When U.S. sellers source from Chinese manufacturers, they pay tariffs on marked-up wholesale prices. Chinese manufacturers selling directly face tariffs on manufacturing costs – a significantly lower baseline, maintaining pricing flexibility even with duties. However, revenue performance remains strong and can continue to be so with strategic moat building beyond price alone > > **The pattern extends beyond the U.S.** \- European marketplaces show Chinese seller penetration above local representation, while **Canada demonstrates near-complete capture with only 4% local seller representation**” ![](https://substackcdn.com/image/fetch/$s_!mlAH!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7d6f0697-b0a7-4297-be50-2de188c8844e_1129x565.png) Source: [Marketplace Pulse](https://www.marketplacepulse.com/reports/amazon-marketplaces?ref=mbi-deepdives.com) Looking at these data, it is hard not to agree with the report’s conclusion that the Amazon marketplace has “evolved from “Made in China, Sold by America” to “Made, Sold, and Marketed by China.” Amazon marketplace, in a sense, perhaps perfectly encapsulates the two distinctive axis of power of the US and China. The exceptional consumerism exhibited by the US consumers leads the US to enjoy enormous influence and power whereas China’s manufacturing capacity and ability to serve that very demand is increasingly difficult to match. The symbiotic dance of the US and China may need to sustain for the greater good of both the countries until and unless either of them has any good alternative! --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Music's AI mess URL: https://www.mbi-deepdives.com/musics-ai-mess/ Last updated: 2025-10-27T14:25:20.000Z Most of you may not have heard about Xania Monet, but “she” created a bit of a stir in the music industry last month when Hallwood Media, an independent music label company, [**signed**](https://www.billboard.com/pro/ai-music-artist-xania-monet-multimillion-dollar-record-deal/?ref=mbi-deepdives.com#:~:text=9/16/2025) a $3 million deal with her as her new track “[**How Was I Supposed To Know**](https://www.youtube.com/watch?v=opuDZYJuAz0&list=RDopuDZYJuAz0&start%5Fradio=1&ref=mbi-deepdives.com)” reached No. 1 on Billboard’s R&B Digital Song Sales chart. She is no one trick pony either as another song “[**Let Go, Let God**](https://www.youtube.com/watch?v=sE4oFCRLaqY&list=RDsE4oFCRLaqY&start%5Fradio=1&ref=mbi-deepdives.com)” currently number 3 on Hot Gospel Songs chart. ![](https://substackcdn.com/image/fetch/$s_!rElN!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F787b8694-0517-40fa-a651-eb7a99dd0ea1_1020x376.png) Source: Billboard Chart Why is this a big deal? Well, Xania Monet is **NOT** a real person! Monet is an **AI-generated virtual R&B persona**. The vocals and the instrumental music released under the name Xania Monet are created using the AI music generation platform Suno. The lyrics are written by a human creator, Telisha “Nikki” Jones, a poet and designer based in Mississippi. Jones writes poetry drawn from her personal experiences and then inputs those lyrics into the Suno platform. Suno then generates the melody, instrumentation, and the vocal performance to create the finished track. If we define “AI artist” strictly as an artist whose music and/or vocals are primarily created using **generative AI** tools (like Suno or Udio), Xania Monet appears to be the **first** to achieve such significant success on the US Billboard charts. I doubt that she will be the last artist. Interestingly, while a label signed Monet (or should I say Telisha Jones since she’s the person behind Monet), there has been an ongoing [**lawsuit**](https://www.riaa.com/wp-content/uploads/2024/06/Suno-complaint-file-stamped20.pdf?ref=mbi-deepdives.com) between the labels and AI-based music generation platforms. In my [**UMG Deep Dive**](https://www.mbi-deepdives.com/umg/) last year, I highlighted this tension between these two parties: > “The record labels’ primary arguments against Suno revolve around copyright infringement and the improper use of copyrighted music to train its AI models. The labels claim that Suno has engaged in “mass infringement” by using their copyrighted sound recordings without permission to develop and train its music-generating AI. The labels further mention that Suno’s activities violate copyright law because the AI-generated songs are based on pre-existing recordings, and no licenses or permissions were obtained for this usage. > > Suno’s main argument against the lawsuit filed by major record labels revolves around the issue of fair use. Suno acknowledges that its AI models were trained on publicly accessible music, including songs owned by these labels, but argues that this use falls under the fair use doctrine. Suno claims its AI systems do not directly copy the music but rather learn from it in a way similar to how humans learn from existing works. > > Labels, of course, reject Suno’s fair use defense, arguing that fair use applies to limited, transformative scenarios involving human creativity, not machine-generated content that mimics or dilutes the value of original music recordings. > > How this lawsuit will be settled is above my paygrade” But can Monet’s songs be copyrighted? Because the lyrics are written by Telisha Jones, they are protected by copyright. The US Copyright Office allows the use of AI as an “assistive tool,” but [**may not grant** ](https://www.skadden.com/-/media/files/publications/2023/08/district-court-affirms-human-authorship-requirement/districtcourtaffirmshumanauthorshiprequirementforthecopyrightabilityofautonomouslygeneratedaiworks.pdf?rev=0f79f2bf88c343e6b771686caf8ef6e8&ref=mbi-deepdives.com)copyright protection if “AI technology, and not a human, determines the expressive elements of the output.” The legal uncertainty lies in the copyright status of the final sound recording i.e. the combination of human lyrics and AI-generated music and vocals. The **extent** to which the AI-generated elements are protected remains legally untested. UMG in the last earnings call put a brave face and emphasized that their agreements with DSP partners such as Spotify will be sufficient in curbing the proliferation of AI generated music and diluting labels’ share of the profit pool. From UMG management: > “…AI models will not be trained on our artists work without consent. AI recording to train on our content will be removed. AI-generated music will not dilute our artist royalties and AI content that misappropriate our artist identities and infringes upon their right of publicity will also be removed. And various monitoring requirements are also included in these agreements.” UMG also mentioned how consumers themselves don’t really want to listen to “simulation or imitation of artists”: > “We talked to consumers about their interest in AI and music. 51% of U.S. consumers expressed interest in AI integration and music, but half of those really wanted to focus AI integration to improve their music consumption experience, meaning better recommendations, better discovery, better content interaction. > > Of the various AI music categories, I don’t think anyone will be surprised to hear that **simulation or imitation of artists rank the lowest**. And there’s tremendous interest by consumers in a connection to the artists. I mean we kind of define that as a moral code, where 75% of these consumers interested in AI said, they believe that human creativity is essential. A similar percentage, say, connection to artists is key to their interest, and this is consistent across age groups.” While UMG may think there is a consumer distaste to AI slops, Monet’s success should make them rethink a bit. I do think the general attitude to “AI slop” is a bit misguided; I believe people has aversion to “slop” in general regardless of whether AI or a human is behind it. Similarly, a good song may just be a good song and if history is any guide, our “moral code” can evolve with the passage of time. It’s interesting that Verge mentioned Xania Monet’s songs as “[**somewhat passable**](https://www.theverge.com/ai-artificial-intelligence/785792/ai-generated-music-record-deal-copyright?ref=mbi-deepdives.com)”. In fact, it wouldn’t surprise me if some of you clicked the songs (mentioned above) and snickered on “AI slops”. I am no music connoisseur, but I can tell you that as a listener, I did find “Let Go, Let God” to be quite compelling. Perhaps more importantly, what if consumers simply cannot differentiate between AI generated and human crated music anymore? In fact, back in June 2025, there was a [**paper**](https://arxiv.org/pdf/2506.19085?ref=mbi-deepdives.com) showing exactly that: people cannot seem to differentiate between AI generated music and music created by human artists; in fact, they increasingly like AI generated music more! The researchers conducted a comprehensive benchmarking study. This involved generating a dataset of 6,000 songs using 12 state-of-the-art music generation models and then conducting a large-scale survey with 2,500 human participants, collecting over 15,000 pairwise audio comparisons to understand listener preferences and perceived text-audio alignment. For music created by human artists, they used MTG-Jamendo which is a large library of real music created by human artists (containing 55,000 tracks). What makes it useful for researchers is that every song is labeled with descriptive tags detailing its genre, the instruments used, and the mood of the music. The researchers used it as a high-quality standard for human-made music to see if the AI-generated compositions could measure up or even surpass it. To evaluate different models, they used the Elo rating system which is a scoring system used to calculate the relative skill levels of competitors in head-to-head matchups; it is typically used to rank chess players and sports teams. In this study, the “competitors” are the different AI music models. The system works by comparing two models at a time. When a human participant listened to two songs and preferred the song by Model A over the song by Model B, Model A “won” that matchup. When a model wins, its Elo score goes up; when it loses, its score goes down. After thousands of these comparisons, the final Elo ratings provide a clear ranking of which AI models consistently produced the music that people liked the most. The human evaluations established a clear ranking of the models, revealing that commercial systems currently dominate the field. Specifically, **Suno v3.5, Suno v3, and Udio demonstrated superior performance, even outperforming the human-created MTG-Jamendo reference dataset in terms of listener preference.** ![](https://substackcdn.com/image/fetch/$s_!URNM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F807d77b5-d3b0-4e18-9bcb-447bc14b12bf_1120x994.png) Source: [Benchmarking Music Generation Models and Metrics via Human Preference Studies](https://arxiv.org/pdf/2506.19085?ref=mbi-deepdives.com) While this paper used Suno v3.5, Suno recently launched [v5](https://help.suno.com/en/articles/8105153?ref=mbi-deepdives.com). You can imagine v5 might have scored even higher in this paper. Not only this seems to be one-way street from here, the SOTA model folks are also showing strong interest in getting into action. From [The Information](https://www.theinformation.com/articles/openai-plots-generating-ai-music-potential-rivalry-startup-suno?rc=4lgoj7&ref=mbi-deepdives.com): > “OpenAI staff have been taking steps to develop AI that generates music, according to a person with knowledge of the work. For instance, the company has been working with some students from the Juilliard School to annotate music scores, according to a second person. They are providing the kind of training data that would be needed to develop AI that produces music. > > OpenAI has privately discussed generating music with text and audio prompts: for example, enabling people to ask the AI to add guitar accompaniment to an existing vocal track, according to a third person who has been involved in the discussions. The resulting product could also help people add music to videos. > > If OpenAI releases a music-generating tool, that could help the company’s own expansion into advertising someday. An ad agency, for instance, could use OpenAI’s tools for tasks related to generating an ad campaign, such as brainstorming ideas for lyrics, creating a catchy jingle based on music samples or recordings, or uploading a video to mimic in terms of style, according to one of the people. Even if wholly AI generated music is deemed outside the purview of copyrighted content, it is extremely likely that supermajority of human artists will utilize these tools to increase the pace of their own output. While a Cambrian explosion of AI generated or AI assisted music may not be a drastically different reality than the status quo for DSPs such as Spotify, such a deluge of content may accelerate the dilution of major labels’ share over time. ![](https://substackcdn.com/image/fetch/$s_!yYDb!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F07d948fa-8083-447e-ac10-99155409a1b5_1159x550.png) Source: Spotify Filings, MBI Deep Dives --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Amazon's robot army URL: https://www.mbi-deepdives.com/amazons-robot-army/ Last updated: 2025-10-26T13:27:08.000Z Back in June 2025, WSJ [reported](https://www.wsj.com/tech/amazon-warehouse-robots-automation-942b814f?mod=djem10point&ref=mbi-deepdives.com) that while the average number of employees per Amazon facility continues to go down every year, the number of packages that Amazon ships per employee each year has increased from 175 in 2015 to almost 4,000 in 2025\. This massive productivity gain largely came from Amazon’s deep investments in robotics in its facilities. ![](https://substackcdn.com/image/fetch/$s_!0P3p!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3d22675b-ba8f-4557-8b1e-5d3126a13daa_739x457.png) Then last week WSJ published another report highlighting Amazon’s further investments in robotics. Some excerpts from the WSJ [piece](https://www.wsj.com/tech/amazon-testing-new-warehouse-robots-and-ai-tools-for-workers-8e3d885a?gaa%5Fat=eafs&gaa%5Fn=AWEtsqe4sXd6z%5FoAIFz3GZJmGNswdr0yg4IPG4SDAHkZ4mCN0MYyuZNwg6Mt0%5FfPaH0%3D&gaa%5Fts=68fd49f3&gaa%5Fsig=d13whh1v29lmVc-rOuW2rD0NyBR6KIlRaxHnbxKnq8lIULootzg3khSOzVqzcg1QSkHwGULc%5FHvfGgdL91M9kg%3D%3D&ref=mbi-deepdives.com): > “The retail giant unveiled a trio of new technologies Wednesday that it is testing or preparing to deploy in its warehouses and delivery vans. They include **a robot arm called Blue Jay, designed to sort packages**; an artificial-intelligence agent called Eluna, intended to help human managers deploy workers and avoid bottlenecks; and augmented-reality glasses to be worn by delivery drivers in the field. > > The announcements are the latest in a yearslong effort by Amazon to automate more warehouse tasks, an effort that began with the company’s [$775 million acquisition of Kiva Systems](https://www.wsj.com/articles/SB10001424052702304724404577291903244796214?mod=article%5Finline&ref=mbi-deepdives.com) in 2012\. **Around three-quarters of Amazon’s deliveries are in some way assisted by robots**, the company has said.” > > Brady said the Shreveport facility, which is **the model for Amazon’s ambitions, contains 10 times as many robots as a typical warehouse, allowing packages to move through 25% faster. “We plan to pass on that low cost to customers,**” he said. Amazon also published a [blog post](https://www.aboutamazon.com/news/operations/new-robots-amazon-fulfillment-agentic-ai?utm%5Fsource=amazonnewsletter&utm%5Fmedium=email&utm%5Fcampaign=102525&utm%5Fterm=bluejay) about Blue Jay last week: > Blue Jay is already being tested in production at one of our facilities in South Carolina, where it’s already able to pick, stow, and **consolidate approximately 75% of all the various types of items we store at our sites**. Over time, it will serve as a core technology helping power Amazon’s Same-Day sites. For customers, that means [faster deliveries](https://www.aboutamazon.com/news/transportation/rural-small-town-america-us-prime-same-next-day-delivery?ref=mbi-deepdives.com) at low cost. ![Blue Jay in action](https://substackcdn.com/image/fetch/$s_!4vPG!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F25919fb3-e53b-4644-a518-78795ab885b5_1600x900.gif "Blue Jay in action") Source: Amazon Retail is perhaps one of the most deceptively simple industries. We all visit stores, or just click a couple of buttons to order something, and voila, it shows up in a couple of hours or a day or two later. I say “deceptively simple” because the supply chain operations behind the scene is excruciatingly challenging to execute. In my most recent Deep Dive on [Instacart](https://www.mbi-deepdives.com/cart/), I mentioned that one of the things that I think many people may underappreciate is how much **technological investments** that may be required to operate the best-in-class grocery delivery experience. Of course, this is true for not just grocery delivery, but any e-commerce delivery operations as well. Speed of delivery is increasingly the key differentiating factor and robots are the key conduit for ever increasing efficiency in retail operations. I mentioned this McKinsey[ report](https://www.mckinsey.com/~/media/mckinsey/industries/retail/how%20we%20help%20clients/the%20state%20of%20grocery%20retail%202022%20north%20america/mck%5Fstate%20of%20grocery%20na%5Ffullreport%5Fv9.pdf?ref=mbi-deepdives.com) in my Instacart Deep Dive, but I think is worth highlighting how much the pick-up speed itself can be a major contributing factor to increasing efficiency and lowering costs: > “**Picking costs typically represent the greatest aggregate cost for online fulfillment**. The average grocery basket contains about 30 items. Assuming a pick speed of 60 UPH (unit per hour), the 30 minutes spent picking a single order translates to about $8 to $10 an hour. This total includes the labor costs associated with picking as well as order consolidation, staging, customer contact, and handover. > > Grocers can take advantage of a range of picking options with different degrees of centralization, capacity, and automation. Grocers need to build a portfolio of fulfillment options by geography, with the mix varying by the density of current and projected demand and their fundamental online value proposition (for example, cost or speed of delivery).” ![](https://substackcdn.com/image/fetch/$s_!yz9D!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2fa6bbbc-034a-4fb6-b804-7f0d7cb4e838_1140x1021.png) Source: McKinsey Given the massive technology investments required, I think the US retail industry is going to be even more consolidated in the next couple of decades. Retail is a low margin industry, and perhaps only a handful of the players can afford to make such long-term sizable bets to stay at the productivity and efficiency frontier. One consequence of this massive investments in robotics is Amazon will require fewer headcount for incremental growth. Another [piece](https://www.nytimes.com/2025/10/21/technology/inside-amazons-plans-to-replace-workers-with-robots.html?unlocked%5Farticle%5Fcode=1.vE8.FwGC.6M9rnks4nUaO&smid=url-share&utm%5Fsource=substack&utm%5Fmedium=email) by NYT last week corroborated that Amazon intends to hire fewer people as they expect to enjoy efficiency gains through their investments in robotics: > “Amazon’s U.S. work force has more than tripled since 2018 to [almost 1.2 million](https://assets.aboutamazon.com/89/0f/be7269b44b25b166030d7b2bfe27/2024-eeo1-amazon-report.pdf?ref=mbi-deepdives.com). But Amazon’s automation team expects the company can avoid hiring more than 160,000 people in the United States it would otherwise need by 2027\. **That would save about 30 cents on each item that Amazon picks, packs and delivers to customers.** > > Executives told Amazon’s board last year that they hoped robotic automation would allow the company to continue to avoid adding to its U.S. work force in the coming years, even though they expect to sell twice as many products by 2033\. That would translate to more than 600,000 people whom Amazon didn’t need to hire. > > Amazon plans to copy the Shreveport design in about 40 facilities by the end of 2027, starting with a massive warehouse that just opened in Virginia Beach. And it has begun overhauling old facilities, including one in Stone Mountain near Atlanta. > > That facility **currently has roughly 4,000 workers. But once the robotic systems are installed, it is projected to process 10 percent more items but need as many as 1,200 fewer employees**, according to an internal analysis.” Of course, there are some [predictable](https://x.com/SenSanders/status/1980735982048522527?ref=mbi-deepdives.com) concerns from some political corners about potentially lower employment opportunities. I am not unsympathetic to such concerns (haven’t we all wondered about obsolescence of our livelihood post-AI), but the following Milton Friedman [anecdote](https://www.wsj.com/articles/SB124355131075164361?ref=mbi-deepdives.com) can be clarifying why such concerns are unlikely to withstand in a capitalistic society: > “At one of our dinners, Milton recalled traveling to an Asian country in the 1960s and visiting a worksite where a new canal was being built. He was shocked to see that, instead of modern tractors and earth movers, the workers had shovels. He asked why there were so few machines. The government bureaucrat explained: “You don’t understand. This is a jobs program.” To which Milton replied: “Oh, I thought you were trying to build a canal. If it’s jobs you want, then you should give these workers spoons, not shovels.” --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The coming debt deluge? URL: https://www.mbi-deepdives.com/the-coming-debt-deluge/ Last updated: 2025-10-25T14:36:30.000Z One of the core differences of the current AI revolution from the earlier bubble periods was that almost all of the funding so far has come from operating cash flow (OCF) of some of the most profitable companies on earth! Despite massive capex increases in recent years, all the major public companies (except Oracle) participating in this investment cycle has healthy Free Cash Flow (FCF) so far. Meta, for example, generated \~$50 Billion FCF in the last 12 months although one-third of it was just SBC. But cash is cash…if you need hundreds of billions over multiple periods to get to the promised land, there is still a healthy difference between OCF and Capex of some of these big tech. Investments funded by internally generated cash can go on for a long time as long as market remains receptive to such investments. ![chart](https://substackcdn.com/image/fetch/$s_!VbLe!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F318796db-735c-4457-a476-0aa005af70fb_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) However, we are starting to see some changes in funding mix as debt has gradually come to the scene. One thing about debt entering the conversation is debt itself can be a great forcing function to manage the potential overinvestment cycle as interest payment obligations and balance sheet leverage can put some hard constraints to keep you disciplined. Big tech understands this and hence are resorting to some “helping hands” in their investment journey. For example, last week Meta [entered](https://about.fb.com/news/2025/10/meta-blue-owl-capital-develop-hyperion-data-center/?ref=mbi-deepdives.com) in a Joint Venture (JV) with Blue Owl Capital for their $27-Billion Hyperion Data Center campus, of which Meta will own 20% and the rest will be owned by funds managed by Blue Owl Capital. Meta is signing an “operating lease” with an initial term of only **four years**. They have the **option to extend the lease every four years**, but they are not obligated to. To persuade the JV to accept the short four-year leases, Meta provided a **“Residual Value Guarantee**” (RVG) covering the first 16 years of operations. If Meta decides to leave (by not renewing or terminating the lease) within the first 16 years, they **guarantee** the campus will still be worth a certain amount of money (undisclosed). This payment is “capped” i.e. there is a pre-agreed **maximum** limit to how much Meta would have to pay. Again, we don’t know the exact capped limit in this deal. The structure of this deal, featuring short 4-year leases combined with a long-term RVG on a highly specialized asset, closely resembles a financial tool known as a [**Synthetic Lease**](https://en.wikipedia.org/wiki/Synthetic%5Flease?ref=mbi-deepdives.com). In a synthetic lease, the tenant (Meta) gains the flexibility of short commitments and favorable accounting treatment (keeping the debt off their balance sheet). However, to convince investors (Blue Owl Capital) to fund the construction, the tenant must assume the **majority** of the financial risks of ownership. The RVG achieves this risk transfer. To secure financing for such a massive, specialized asset, this cap must be set very high. While we don’t know the exact number, my guess is it’s likely somewhere between 80% to 90%. If we assume it to be 85%, for the $27 Billion Hyperion campus, Meta’s maximum possible exposure is $22.95 Billion. If Meta decides to terminate the lease within the 16-year RVG period, the payout is determined by the following calculation: Guaranteed Value at time of exit - Actual Market Value = Shortfall Meta pays the **shortfall**, but only up to the agreed-upon cap (estimated at $22.95B). The guaranteed value is likely just a a **pre-agreed schedule** that **decreases** over the 16 years, representing the value the investors expect the asset to hold as they recoup their investment through Meta’s lease payments. Of course, actual market value (AMV) is the real variable here. If the specialized technology becomes obsolete or the market softens, the AMV could plummet. Given Meta’s backing, the bonds issued to fund this investment received investment grade credit rating. However, the bonds were [issued](https://www.wsj.com/finance/investing/blackrock-etfs-among-biggest-investors-in-metas-giant-data-center-debt-deal-087fe671?mod=tech%5Flead%5Fpos2&ref=mbi-deepdives.com) at 6.58% yield which is closer to junk bond yield. Why is the yield so high? If the value of the data center catastrophically collapses due to obsolescence or for some other reasons, Meta’s RVG covers most of the loss, but the investors bear the portion **exceeding** the cap. Moreover, the debt belongs to the project entity, it is “structurally subordinated” to Meta’s own corporate debt. Investors demand a higher yield to compensate for this “tail risk”. More importantly, the underlying collateral is a hyper-specialized AI data center. If Meta leaves, it’s likely that the facility cannot be easily repurposed. While the RVG mitigates the financial loss, the specialized nature of the underlying asset still influences the perceived risk and pushes the yield higher. My guess is Meta (and other big tech) will do more of these deals going forward. In fact, just yesterday, Oracle appears to be raising debt even larger than Hyperion deal: [$38 Billion](https://www.bloomberg.com/news/articles/2025-10-23/record-38-billion-debt-sale-nears-for-oracle-tied-data-centers?srnd=phx-technology&ref=mbi-deepdives.com) for building data centers in Texas and Wisconsin. If the deal goes through, it would be the largest debt deal **so far** in AI infrastructure. I am very curious to see what the yields will be for debts issued by Oracle, especially given their cash position and balance sheet leverage is considerably inferior than Meta’s. Moreover, if these companies keep doing these deals, the yield may only go higher as the risk for later debt deals will gradually increase for the bondholders. Perhaps AI infrastructure spree can cool a lot when the debt yields get close to double digit yield. Indeed, while debt funded infrastructure investments will certainly raise the risk profile of these companies, having some debt into the system can make everyone all on a sudden a lot more disciplined in their AI infrastructure investments. There are, however, compelling reasons for companies such as Meta to deploy less cash from their own balance sheet and get as much helping hand as they can get as long as the market remains receptive to such deals. While Meta is confident that the demand for compute will continue to grow massively, they are likely less certain about **what kind of** compute infrastructure will be optimal in five or ten years. A data center is typically a 20-30 year asset. If Meta built and owned Hyperion, they would be committed to the physical footprint, power delivery, and cooling design made in 2025. I do want to note that in a separate blog post, Meta [indicated](https://about.fb.com/news/2025/10/metas-new-ai-optimized-data-center-el-paso/?ref=mbi-deepdives.com) that their infrastructure is built in a way to accommodate **flexibility** for their 1 GW data center project in El Paso, Texas: > AI, and its inference and training needs, is still evolving, so our design needs to balance what we know today with what we might know in the future. Different AI configurations will require different approaches to hardware and network systems designs, so our new data centers are built to accommodate flexibility. For example, we’ve designed the El Paso data center to have systems that can support both the traditional servers of today and future generations of AI-enabled hardware If they can build such flexible design, why is the obsolescence concern still valid? My guess is “flexible design” often means that a future retrofit is **possible**, **not that it is easy or cheap**. At some point, the cost of retrofitting an old “flexible” design exceeds the cost of simply moving into a new facility optimized for the new technology. In any case, such flexibility likely only addresses the known unknowns and may not able to cater to unknown unknowns 5-10 years from now. The flexibility Meta is buying with the Hyperion lease structure is “strategic flexibility”. Whiledesign flexibility lets you **adapt the asset**, strategic flexibility affords you the ability to **exit the asset**, but of course, at a price! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Instacart: "Webvan done right" URL: https://www.mbi-deepdives.com/cart/ Last updated: 2025-10-24T13:30:19.000Z *You can listen to this Deep Dive* [*here*](https://www.mbi-deepdives.com/audio/) --- With a 10-month old son, MBI household understandably doesn’t quite enjoy going to grocery stores. So our habit of ordering grocery online survived longer than perhaps many families did since the pandemic. Since every member of MBI household, including my son, owns Amazon shares, I cajoled my wife to use Amazon for ordering grocery as well. To my persistent annoyance, I keep finding my wife ordering grocery via Instacart. But that’s how I also knew I should study Instacart closely. Instacart was founded by Apoorva Mehta in 2012\. Born in India but raised in Canada, he studied engineering at the University of Waterloo, and then actually went to Amazon to work on supply‑chain systems. In his early 20s, he then had the brilliant idea of starting a company…**any** company! Mehta didn’t have any particular “calling”; from an ad network for social games, a Groupon‑for‑food variant, even a social network for lawyers, he tried many things but thankfully, he failed fast. So, he didn’t toil in obscurity to pursue these ideas for too long. Then the story goes, the spark for Instacart came from a near‑empty fridge when he was living in San Francisco and a feeling that everything except groceries had already moved online. Mehta wanted a low‑friction way to buy basics without dedicating hours to aisles and checkout lines. The thesis was simple: groceries are enormous, habitual, and still offline; **smartphones could turn that habit into an app**. He wrote the first version of the app in three weeks and stress‑tested it the only way a one‑person startup can: by ordering from himself. He placed an order, drove to the store, shopped, delivered, and of course, tipped himself. That small loop proved the mechanics worked: if the app could connect a buyer to a nearby human with a car, there is no compelling reason why groceries cannot be ordered online and then get delivered to people’s homes. Of course, this simple idea didn’t just occur to Mehta first. Webvan tried the idea of online grocery delivery and spectacularly failed by filing for bankruptcy in just five years after the company was founded. Mehta [recalled](https://blog.eladgil.com/p/video-and-transcript-apoorva-metha?ref=mbi-deepdives.com) how one of his meetings with a VC went: > I remember there was an investor meeting that I had in the early days and I walk into this meeting and I start presenting. And this is like 24-year-old Apoorva. I wanted to really stand out. The title under my logo was **Webvan done right**. And I started, got to the second slide, third slide, and this investor literally got up and left the room and I was like, Is the meeting over? It’s very clear I didn’t get the term sheet. But then he came back and he slapped this floppy disk on the desk on the table. And he was like, this has the Webvan business plan, you should go home and study it and you will never do this company. This story reminded me of Corry Wang’s [tweet](https://x.com/corry%5Fwang/status/1763998533261135987?ref=mbi-deepdives.com): > I always joke that predicting the future is easy - most good ideas are “obviously” good ideas. And over a long enough time horizon, almost every good idea will work eventually. The hard part is figuring out **whether this time is different** ![Image](https://substackcdn.com/image/fetch/$s_!uWaa!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F60dca2f9-1f5a-4c52-8cac-503bd42d2d5d_598x577.jpeg "Image") Image Source: [X](https://x.com/corry%5Fwang/status/1763998533261135987?ref=mbi-deepdives.com) Indeed, grocery delivery is one of those “obviously good ideas” and fortunately for Instacart, the timing was right! While smartphones were certainly a godsend for Instacart’s adoption, it was far from a panacea. The idea was still ahead of its time as the overall grocery value chain was lagging behind in its tech adoption. From [Apoorva Mehta](https://blog.eladgil.com/p/video-and-transcript-apoorva-metha?ref=mbi-deepdives.com): > …what we did was we invented this thing called ninja shopping, which was effectively that we would just go to a store, buy the stuff off the shelf, go to the checkout, and deliver the groceries to the customers. And the only problem was we didn’t know what any of the stores actually carried…and you couldn’t find it anywhere online. I was totally okay scraping stuff, but you just couldn’t find it. And some of these SKUs didn’t even have any online information about them at all. > > So what we decided to do was we went to many different stores and picked up one of every single thing from the store, took it to a studio, photographed everything, and then uploaded all that onto Instacart. And it cost us like $50,000 to do that, to buy the entire grocery store. But that allowed us to bootstrap our supply. And so now it immediately became something that customers wanted. And I remember we did this for one of the stores, and overnight **our demand doubled**. Mehta ended up meeting his co-founders: Max Mullen and Brandon Leonardo at YC, and they all focused on how to avoid Webvan’s fate. Instacart’s operating principle was simple and strikingly different from Webvan: **use existing grocers as decentralized “warehouses,” crowdsource shoppers and delivery, and focus on dispatch, routing, batching, and reliability**. That asset‑light stance was in stark contrast to capital‑heavy parallel supply chains. The decision let Instacart launch quickly and iterate where it held leverage: software and marketplace liquidity. Instacart expanded beyond San Francisco, wiring inventory from partners into the app and leaning on flexible shoppers to hit one to two‑hour delivery windows. The company’s bet of layering software atop retailers instead of replacing them saved millions in startup costs and let it spread faster than warehouse‑based rivals. However, Instacart still faced some near death experience; one such experience was when Amazon [acquired](https://media.wholefoodsmarket.com/amazon-to-acquire-whole-foods-market/?ref=mbi-deepdives.com) Whole Foods in 2017! Mehta [explained](https://blog.eladgil.com/p/video-and-transcript-apoorva-metha?ref=mbi-deepdives.com) how that particular saga turned out to be actually **positive** for Instacart: > I got a call from the Whole Foods CEO at 6:00 AM in the morning. All right, at this time, **Whole Foods was the largest partner** of ours. They had about, I think **30% to 40% of our overall sales were coming from Whole Foods**. And so of course, I was going to take the call. It didn’t matter if it was 06:00 AM. And I get on a call with him. He tells me that Amazon had just paid like $18 billion to buy Whole Foods. > > Now, I’m a very paranoid person, and you tend to be that when you’re a founder, you have to understand where risks are in your business. But this was not in my risk bingo card and was a very short call because I didn’t know what to say to him. And I was just refreshing my social feed. And as soon as that announcement happened, the next set of announcements were, oh, Instacart’s dead. And we started getting text messages from investors and parents asking us if we’re okay. And it kind of felt like this was going to be it. Like this was going to be my 21st failure of a company because **now our largest competitor owned our largest partner**. > > And so at the time I called in all hands, told the team that we were in war mode and the only thing that mattered at this point was to fight this battle. In the next couple of weeks, we came together with a plan, and this was a high beta plan, very high-risk plan, which was that we were going to sign every major grocery retailer onto our platform and we were going to rapidly increase the Instacart membership so that we would be able to retain most of these customers. And me and the team, we were on the phone with effectively every retail CEO in America talking about what we could do. > > We had this concept of the alliance of the willing. We were calling it internally, but really it was how do we figure out how do we work with retailers? And we looked at every single thing that we could do to get them over the line, which was how do we rapidly expand nationwide? So that we were everywhere. We were in Rockford, Illinois. We were in Lubbock, Texas. In the smallest cities, Instacart worked. And we figured out how to make the economics work in the smallest cities because that’s what it would take for some of these larger retailers to sign with us. We scaled Instacart enterprise with all kinds of functionality that would make it so that these retailers felt comfortable putting their brand onto Instacart. And there was no meeting that we would not do in person. Regardless of how many red eyes we had to take, we made it happen. > > And **by the time Whole Foods finally left the Instacart platform, they were less than 5% of our sales.** We had continued to drive a lot of growth and we had virtually every major grocery retailer on our platform. And so at the time, of course, this felt like we’re not going to make it, but actually ended up being one of the best things that could have happened to the company. Instacart survived **and then** **thrived** even after departure of Whole Foods from Instacart app. Then of course, the pandemic happened which was basically manna from heaven for Instacart. The company then raised a whopping [$265 million](https://www.instacart.com/company/updates/instacart-announces-265-million-in-new-funding-led-by-existing-investor/?ref=mbi-deepdives.com) at an eyepopping valuation of **$39 Billion** in April 2021\. That has proved to be the absolute peak for Instacart’s valuation…at least for quite some time. Just a couple of months after the capital raise, Mehta [left](https://www.wsj.com/articles/instacart-co-founder-raises-30-million-for-new-healthcare-company-11669754224?gaa%5Fat=eafs&gaa%5Fn=ASWzDAj46vXwoyDoXI12ZQNp0EqnMu%5F0k-CGxi4R-PCT2j%5Fd3gzBR9qNNRiMZueG5wA%3D&gaa%5Fts=68e9bd95&gaa%5Fsig=ApNckPfN8f-L-Ie9vSicyfCVJeYbwLCLUEl1ACw5U67YORk-nCpImLs-yfaO9pjzA6wjb-cXa1Mo4eFSCXFLbA%3D%3D&ref=mbi-deepdives.com) the CEO role back in August 2021 and handed the leadership baton to **Fidji Simo** who used to lead the Facebook app. Given ads are of paramount importance for profitability, Simo’s pedigree perhaps outweighed any concerns that would naturally arise from a founder’s exit. Instacart finally came to IPO at just \~$9 Billion valuation in 2023, almost \~70% below its peak valuation in 2021\. Aswath Damodaran [shared](https://aswathdamodaran.substack.com/p/putting-the-instacart-before-the) the below table back then which made it clear even an IPO exit may mean a very shabby return for a lot of VCs! ![](https://substackcdn.com/image/fetch/$s_!Jh8C!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6e84258b-3fde-4687-96cf-5e8b28d872a9_1350x470.jpeg) Image Source: [Aswath Damodaran](https://aswathdamodaran.substack.com/p/putting-the-instacart-before-the) Despite coming to IPO at \~70% below its peak valuation, Instacart stock lagged both S&P 500 and QQQ considerably. **Instacart’s valuation has been almost flat since 2018! To put this in context, Instacart’s revenue increased by \~16x between 2019 and 2024 and yet its shareholders didn’t make much money during this period**. It may be helpful to remember no matter how “clear” the story seems in the short-term, the weight of long-term fundamentals may come for all. ![chart](https://substackcdn.com/image/fetch/$s_!YPLs!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F89af450f-0a2f-41b7-aedc-f7c614258cc6_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) So where does Instacart go from here? Before we ponder about that question, let’s have a deeper understanding of their business today. Then I will assess the competitive dynamics they are facing as well as Instacart’s capital allocation philosophy and management incentives. Finally, I will show what assumptions are embedded in current stock price. The rest of the Deep Dive is behind the paywall. --- *MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 64 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. Subscribers also receive one email everyday on topics/companies I am interested in.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb's bet on anti-trend URL: https://www.mbi-deepdives.com/airbnbs-bet-on-anti-trend/ Last updated: 2025-10-23T16:01:39.000Z ***A programming note*:** I will publish my Deep Dive on **Instacart** tomorrow. As a reminder, I only send one email per day; therefore, on the day I publish my Deep Dive on a company, I won’t publish anything else. --- Airbnb typically releases product updates twice a year (May and October each year). They just [released](https://news.airbnb.com/introducing-social-features-for-airbnb-experiences/?ref=mbi-deepdives.com) their winter product updates a couple of days ago. One of the key features that they launched this year is “Connections”. Everyone who you have traveled with or booked an experience or service on Airbnb, they will show up on your “Connections” tab on the app. ![The image shows two smartphone screens displaying an app interface. The left screen is titled "Upcoming availability," displaying a specific time slot, "Thursday, October 23, 1:00 PM - 4:00 PM," with "3 spots available." Below this, there are circular icons with smiling faces, and a note says "Join guests from United States, France, and South Korea." The right screen shows a section titled "Connections" with information about people with whom the user has booked trips. Two profiles are shown: one of a person named Cory, with "3 trips together," and the other of a person named Sophia, with "2 trips together." Each profile has circular images of the persons and icons resembling passport stamps with city names.](https://substackcdn.com/image/fetch/$s_!IOSb!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76522454-64f1-4e87-871d-ef932056b31a_2500x1666.jpeg "The image shows two smartphone screens displaying an app interface. The left screen is titled "Upcoming availability," displaying a specific time slot, "Thursday, October 23, 1:00 PM - 4:00 PM," with "3 spots available." Below this, there are circular icons with smiling faces, and a note says "Join guests from United States, France, and South Korea." The right screen shows a section titled "Connections" with information about people with whom the user has booked trips. Two profiles are shown: one of a person named Cory, with "3 trips together," and the other of a person named Sophia, with "2 trips together." Each profile has circular images of the persons and icons resembling passport stamps with city names.") Source: Airbnb While this is clearly a “social” feature, Airbnb isn’t trying to reinvent another messaging app, rather they are trying to lead the “touch grass” movement. Travel, experiences, or services all are intertwined with real life experiences beyond your digital screen. Airbnb is just trying to be the connective tissue as you transition from digital to real world every once in a while. While time spent on digital screen has been a secular one-way trend, Chesky believes we may yearn for more real experiences in reaction to that as he explained in [TBPN](https://www.youtube.com/watch?v=evcjN-w95EY&ref=mbi-deepdives.com): > “I like to say you want to **ride a trend or ride the opposite trend**. And so if we’re basically creating this fantasy digital realm that is highly artificial, I think in reaction to that, people want what’s real. > > This is not an anti-phone rant. This is not an anti-AI thing. It’s just about the fact that **we need to have a balance**. Do you ever notice that devices and screens aren’t usually in your dreams? There’s something about the digital realm that doesn’t quite stick in your memory the way physical experiences do. And I think increasingly if AI frees up more of more of our time, hopefully that time can be spent in the real world having meaningful experiences with people we care about. And to me, that’s what life is really going to be about. And I want to be a part of that.” It’s a a bit counterintuitive thought whether betting on both the secular trend and anti-trend indeed can work at the same time. The New York Times is perhaps a good example here to make Chesky’s point. While traditional news organizations have largely been hollowed out in the age of Google and Meta, the NYT stock actually did pretty well as their subscale competitors fell off the charts and they themselves did a reasonable job in transitioning to subscriptions. It is remarkable that despite being in the eye of the storm, NYT stock almost matched Meta and Google’s last 10-year performance post-liberation day drawdown in the market. Of course, as the big tech recovered since then, the performance gap widened but NYT still did a very respectable \~17% CAGR in the last 10 years. ![chart](https://substackcdn.com/image/fetch/$s_!4kQz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5a4536bd-1a4f-4a0a-b593-64d6b000f0da_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) It’s not the only anti-trend Airbnb is betting on. While Airbnb’s competitors such as Booking.com or Expedia are integrating with ChatGPT, Airbnb has been strangely missing from these integrations. It’s particularly odd given that Brian Chesky and Sam Altman are good friends, so Airbnb clearly consciously decided not to integrate with ChatGPT. Despite being friends with Altman, Chesky doesn’t seem to be “AGI-pilled” and he also doesn’t seem to have “short timelines” to AGI. Chesky actually makes the case there’s no way one company can entirely run the economy and the model companies need to have a much more performant software development kit (SDK) before Airbnb will think about integrating with them. From Chesky: > “Imagine as a thought experiment. this is not a perfect analogy it’s a little bit flawed in some ways. but imagine you replace AI with electricity and it was like a 100 years ago and three companies had electricity and no other company had electricity. > > Suddenly these electric companies would have a huge advantage but we have this mental model as if these companies are the only ones with electricity. Every company’s going to have the access to all the same models unless companies start limiting their models only to their applications. > > But then other competing models would then get more widely adopted because they will have an API. So you have to make a choice. Do you want to limit your model or do you want to be like AWS? > > …you’re going to see a huge change where on the one hand we all have to decide how to participate with platforms like ChatGPT and I think if they build a really great SDK and **we can still own the customer relationship,** there’s probably not a huge problem. It has to just be integrated correctly. > > At the same time, you have to remember we’re also going to have nearly as good a AI via the fact that even if we don’t produce our own models, there will be an entire economy that will allow those models to be accessible via APIs. There may be some advantages to the companies that build apps within. Do you want to go to one destination that then is like a macro agent that connects to all other agents or do you use different apps and those become different agents? So now we’re starting to debate these mental models and there’s a trade-off. The trade-off is the advantage of going just o ChatGPT is now one agent can kind of cross-pollinate and organize everything. > > But then **if the SDK is limited, it will be not as powerful as going direct to the app that is an AI app that can go really really deep and do your job really well.** And so this is the balance. And where do I think this lands? Where I think it goes is I think **ChatGPT has to build an SDK that’s really robust and it will be just a channel**. That’s my guess, but we’ll see. Given Airbnb’s organic demand and unique supply on its marketplace, it is definitely a non-starter for them to not own the customer relationship. Chesky also made the case that it is going to be near impossible for ChatGPT to do everything on its own if it doesn’t empower others to build on top of it. The diffusion of the use cases of these models will be slower if it’s largely self-contained within the models. Again, from Chesky in the TBPN interview: > I’m on the board of Y Combinator. Almost all the startups we are seeing are enterprise. There is not a lot of companies doing consumer. There’s a couple reasons why. Number one, I think some people are nervous about ChatGPT killing their startup. I think they’re too worried. I think companies are too worried. I keep telling people, and I told this to Sam Altman, one of my best friends, that no one company can run the entire economy. First of all, governments won’t allow that. But second of all, it’s just too much bureaucracy in a company to do that. And there’s a reason that when Apple created that the iPhone, they didn’t make every app in the app store because **can you imagine how big of a bureaucracy that would have had to be for Apple to build Airbnb and Uber and Instacart and Instagram**. So there’s going to be a whole series of companies, but they’re going to take time. My prediction is that in the next three to five years, not in the next year, you’re going to see a huge boom in the consumer space of AI. One other takeaway from the interview was Chesky seems well aware of the disintermediation risk Airbnb faces as it embarks on services. If someone books a recurring service from the same serviceperson, what is the compelling reason for you to pay the middleman (Airbnb) a fee? Chesky seems to realize for recurring services, they need to charge a lower take rate. I personally think he’s not being aggressive enough. The take rate for services should be near zero (maybe charge enough just to cover the payment processing fees). Services is going to be excruciatingly challenging to take off on Airbnb, and they are perhaps not being radical enough to propel people (both demand and supply) give services a shot on Airbnb. Chesky briefly hinted about loyalty membership program; while I’m not sure how serious he is about such a program, that indeed sounds like a much better idea to incentivize people avail experiences and services on Airbnb. If I get 50 points by booking a $50 service on Airbnb which I can use to pay for booking a home on Airbnb when I am traveling, I will have a much higher incentive for paying the service person via Airbnb than venmo-ing the person directly, especially if the service take rate is also near zero (which would also not incentivize the serviceperson to ask me to pay him or her directly). If Airbnb does that, what’s the business case for services if take rate for recurring services is almost zero? Airbnb deserves a cut anytime they are connecting the customer and the serviceperson **for the first time**. For the recurring services, I should just receive points and Airbnb takes almost no cash for itself but those points will almost certainly incrementally increase “loyalty” for the customer to Airbnb. From an overall Airbnb “ecosystem” perspective (home, experiences, and services), I think the unit economics can make a lot of sense even if just services itself may have lackluster unit economics. Unfortunately, in the last 5 months since Airbnb launched experiences and services, I haven’t quite noticed anything radical about it. It just feels similar version of approaches others already took to serve these markets. Airbnb has an opportunity to be more aggressive here, but they haven’t taken that opportunity yet. One of the complaints from investors about Airbnb that I have noticed is that Airbnb doesn’t move fast enough. For a founder led company, such complaints should perhaps bother Chesky a bit. He has been talking for a while about how suppliers are currently under-monetized or how sponsored listings make a lot of sense on Airbnb, but he hasn’t translated these musings into actual monetizable product yet. Perhaps he can borrow a page from his friend Altman and learn to move a little faster and be a more radical while pursuing more ambitious projects! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I have made a slight change in my portfolio which I will discuss behind the paywall. _This post is for paying subscribers only._ ### Texas Instruments 3Q'25 Update URL: https://www.mbi-deepdives.com/txn3q25/ Last updated: 2025-10-22T13:20:23.000Z “The overall semiconductor market recovery is continuing, though, **at a slower pace than prior upturns**, likely related to the broader macroeconomic dynamics and overall uncertainty.” That quote from yesterday’s call captures the tone of Texas Instruments (TI) management. Things are chugging along, but just not as well as perhaps many expected in the beginning of 2025. I will discuss more about the quarter behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Revenue** _This post is for paying subscribers only._ ### Danaher 3Q'25 Update URL: https://www.mbi-deepdives.com/dhr3q25/ Last updated: 2025-10-21T14:46:32.000Z Following Sartorius 3Q’25 call, I [mentioned](https://www.mbi-deepdives.com/sartorius-3q25-update/) in my update that I am actually a bit surprised that Danaher stock didn’t react more positively. Well, Danaher stock did pop \~8% after confirming bioprocessing’s continued growth and an initial 2026 guide that outlined HSD earnings growth even in a conservative topline growth scenario. Let me recap the business performance of 3Q’25. **Overall Danaher** Danaher’s revenue slightly accelerated QoQ last quarter from 3.4% in 2Q’25 to 4.4% in 3Q’25. Danaher reports its business in three broad segments: a) Biotechnology, b) Life Sciences, and c) Diagnostics. Majority of revenue in all three segments come from recurring revenue through consumables that are spec’d into regulated process or the specific equipment Danaher sells to the customers. This recurring portion of revenue gradually increased from 75.4% in 3Q’23 to 82.9% in 3Q’25. In terms of profitability, overall adj. EBITA margin improved both from QoQ and YoY perspective. ![](https://substackcdn.com/image/fetch/$s_!Osis!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Facb5382a-2356-4f2c-9eb6-2270fc026dc6_1317x394.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss the quarter segment by segment behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Biotechnology** _This post is for paying subscribers only._ ### ChatGPT's value capture problem URL: https://www.mbi-deepdives.com/chatgpts-value-capture-problem/ Last updated: 2025-10-20T13:43:09.000Z Consider a thought experiment. Imagine traveling back to February 2023, the moment OpenAI [introduced](https://openai.com/index/chatgpt-plus/?ref=mbi-deepdives.com) the ChatGPT Plus subscription for $20 per month. At the time, ChatGPT felt revolutionary despite its profuse hallucinations. There was no deep research (which would be launched [two years](https://openai.com/index/introducing-deep-research/?ref=mbi-deepdives.com) later); it couldn’t access real-time information beyond its training data cutoff; and it often struggled with the sustained reasoning required for truly complex tasks (reasoning models were launched in[ September 2024](https://openai.com/o1/?ref=mbi-deepdives.com)). Now, imagine if OpenAI had simultaneously revealed a snapshot of their 2025 product, a model equipped with sophisticated search integration, vastly improved computational bandwidth for nuanced problems, and deep research capabilities. If asked to predict the price of **that** **future** product, given the $20 baseline for the nascent 2023 version, I bet many people would likely have estimated a noticeably higher price than $20/month for the “2025 version” of ChatGPT. The leap in utility is clearly astronomical compared to what we could get back in February 2023\. And yet, here we are in 2025, and the subscription cost remains exactly $20. This price stagnation presents a paradox in the economics of innovation. There is no question that ChatGPT, and AI tools generally, have created immense value for consumers. But the key debateis about **value capture**. It is tempting to attribute this stable pricing solely to technological efficiency i.e. the idea that token prices and operational costs have decreased significantly. While efficiency gains are part of the equation, the true governor on pricing power is the **competitive landscape**. In a market where innovation is quickly commoditized and substitutes are a click away, the ability to raise prices is severely constrained, regardless of the improvements made. Value capture is ultimately, more often than not, a **function of industry structure and competitive dynamics**. Notice how Google’s AI plan gets cross‑sold with storage and Workspace. In that environment, raising price is self‑sabotage unless you some sort of lock‑in. Of course, most of the existing ChatGPT users do perhaps already feel some sort of habitual lock-in, but the real battle between Gemini and ChatGPT is not for ChatGPT’s existing DAUs, rather it’s the next 500 million to 1 billion DAUs. ![](https://substackcdn.com/image/fetch/$s_!AYye!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F80ea4a6d-f170-4c00-836e-d72c4a40f0f5_837x1237.png) Source: [Google](https://gemini.google/subscriptions/?ref=mbi-deepdives.com) A couple of weeks ago, Greg Brockman [mentioned](https://x.com/MatthewBerman/status/1975996514125443431?ref=mbi-deepdives.com) if OpenAI had 10x more compute, they could likely 5x their revenue. I am skeptical that compute is the only constraint for OpenAI to capture value. Brockman then mentioned how “[Pulse](https://openai.com/index/introducing-chatgpt-pulse/?ref=mbi-deepdives.com)” is such a great product but they had to limit it only for Pro subscribers ($200/month) due to compute constraints. I’m sure many Plus subscribers would love to have “Pulse” feature, but I don’t think even 10% of Plus subscribers would pay additional $1/month to have “Pulse” feature. Feature velocity can be become a very good retention tool, but unlikely to be a pricing lever. Of course, almost for any other case, poking at a stable subscription prices when subscriber numbers keep increasing at a healthy rate would be non-sensical especially in a world where aggregating demand is the key to the castle. ChatGPT is a bit different because of OpenAI’s exceptional burn rate. Nonetheless, OpenAI perhaps will have no problem in raising money from VCs or from public market in current environment. Things can, however, become dicey depending on how Gemini 3.0 fares [later this year](https://sherwood.news/tech/googles-gemini-3-0-reportedly-due-to-be-released-in-december/?ref=mbi-deepdives.com), especially given that OpenAI [won’t](https://ositcom.com/blog/sam-altman-confirms-no-gpt6-launch-in-2025?ref=mbi-deepdives.com) launch GPT-6 this year. ChatGPT’s [traffic share](https://x.com/Similarweb/status/1979863740670480674?ref=mbi-deepdives.com) in GenAI went from 87.1% a year ago to 74.1% now whereas Gemini went from 6.4% to 12.9% during the same time. Of course, when you invent a category, you tend to lose market share over time as your success inevitably attracts others. ![Image](https://substackcdn.com/image/fetch/$s_!4GoN!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F044743a2-96e5-4dee-a833-77bcd5d53dfe_956x766.jpeg "Image") Source: [Similarweb](https://x.com/Similarweb/status/1979863740670480674?ref=mbi-deepdives.com) Nonetheless, if Gemini 3.0 receives a very warm reception in December 2025 and Gemini’s market share accelerates, I wonder if OpenAI will start feeling a bit nervous. Chrome entered the browser market pretty late, and it took them just four years to win the browser war. ![](https://substackcdn.com/image/fetch/$s_!jUur!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F14c3e82f-da4f-4949-b34a-9a887aec11f3_1069x796.png) Image Source: [Wikimedia](https://commons.wikimedia.org/wiki/File%3AWeb%5Fbrowser%5Fusage%5Fshare%5FStatCounter.svg??ref=mbi-deepdives.com) Ironically, I wonder if at least some part of the “AI trade” can potentially experience a jolt if Gemini 3.0 hits it out of the park. Ultimately, ChatGPT will almost certainly continue to create value primarily through enormous consumer surplus; but the ecosystem its competitors have may continue to succeed at capping how much of such value can be captured by OpenAI. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### TSMC's Margins URL: https://www.mbi-deepdives.com/tsmcs-margins/ Last updated: 2025-10-19T14:19:04.000Z It is kind of incredible to see TSMC’s margin trajectory over the last five years. Their **current operating margin is now basically same as their gross margins pre-pandemic**. ![](https://substackcdn.com/image/fetch/$s_!Ar6O!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F981fd35d-a1e5-492a-984c-f26dedcff0f6_1327x742.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) What is even more remarkable is how they were able to expand margins so much despite a persistent headwind from ramping up wafer revenue from 3 nanometer (nm). Back in 3Q’23 when TSMC first disclosed revenue coming from 3 nm, it was only 6%, Within a year or so, it shot up to quarter of their wafer revenue. To put this in context, in 3Q’23, their overall LTM gross margin was 54.5% and it has expanded by \~500 bps in the next two years despite the headwind from 3 nm. ![](https://substackcdn.com/image/fetch/$s_!9hza!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb0027457-97ab-41e8-9e16-eed070084460_1069x529.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Why was 3 nm wafer revenues a headwind for TSMC? TSMC management explained in 1Q’24 call: > it is true that **N3 is taking a longer time to reach the corporate margin** than the other nodes like N5 or N7\. N5 or N7 before**, it was like 8 to 10 quarters to reach the corporate. But for N3, we think it will take about 10 to 12 quarters**. And this is probably because N3 process complexity has increased, and also our corporate average gross margin also increased during the period. But another reason is that **we set the pricing of N3 very early, several years ahead of production. However, we experienced a lot of cost inflation pressures in the following years**. So as a result, N3 will take a longer time than N5 and N7 to reach the corporate average gross margin. In the recent earnings call, TSMC management mentioned N3 is finally going to match the overall corporate average margins in 2026\. The fact that margins could have such expansion despite this headwind tells you how the overall numbers can mask the business’ much higher underlying profitability. Now that N2 is coming out, there will be a new source of margin headwind for TSMC just when N3 will reach overall corporate margins. However, TSMC has made sure N2’s structural profitability is higher than N3\. From the 3Q’25 earnings call: > …as all the new node, when they just come out, the **N2 will have dilution in our gross margin in 2026**. But at the same time, the N3 dilution is gradually coming down, and we expect the N3 to catch up to the corporate average sometimes in 2026. > > **N2’s structural profitability is better than the N3…**it’s less meaningful nowadays to talk about how long it will take for a new node to reach to a corporate average in terms of profitability. And that’s because the corporate profitability, the corporate gross margin moves and generally, it has been moving upwards. So less meaningful to talk about that Beyond N2/N3 dynamics, TSMC will also continue to face some margin headwinds due to their overseas fab expansion which management quantified explicitly in the call: > While the cost of overseas fabs remain higher, thanks to the company’s overall larger scale, w**e now expect the gross margin dilution from the ramp-up of our overseas fabs to be closer to 2% in the second half of 2025\. For the full year 2025, we now expect it to be between 1% to 2% as compared to 2% to 3% previously**. > > Looking ahead, **we continue to forecast the gross margin dilution from the ramp-up of our overseas fabs in the next several years to be 2% to 3% in the early stages and widen to 3% to 4% in the latter stages.** We will leverage our increasing size in Arizona and work on our operations to improve the cost structure. We will also continue to work closely with our **customers and suppliers** to manage the impact. Typically, one might expect that as manufacturing operations mature and scale up, efficiency would improve, costs would decrease, and therefore the margin dilution would **narrow**. So it may seem a bit counterintuitive that the margin dilution will widen to 3-4% in latter stages. In the initial phase, only the first few overseas fabs are ramping up (e.g. Arizona Fab 1). While these fabs are much more expensive to operate on a per-wafer basis, they represent a small percentage of TSMC’s total global output today. Their impact on the overall corporate average margin is therefore limited. in the “latter stages” when multiple overseas fabs (e.g. Arizona Fabs 2 and potentially 3, Japan Fabs 1 and 2, Germany Fab 1) are expected to operate at high volume, a much larger proportion of TSMC’s total manufacturing will occur in these higher-cost regions. Even if the efficiency of the individual overseas fabs improves slightly over time, the sheer volume shifting away from the low-cost base in Taiwan **mathematically** drags the blended corporate gross margin down further. Moreover, during the initial ramp-up, not all equipment may be installed, and the depreciation expenses have not yet reached their maximum level. Once the fabs are fully equipped and running at mass production volumes, the depreciation expense hits the P&L statement fully. As subsequent fabs come online, the cumulative weight of depreciation from multiple massive investments being recognized simultaneously is much higher, putting greater downward pressure on gross margins. Besides, costs for specialized engineering labor, electricity, and water are fundamentally higher in the US and Europe than in Taiwan. In the early stages, staffing is focused on setup; in the latter stages, the fabs require full 24/7 staffing, magnifying the impact of higher labor costs. Investors may not need to worry about minute details of such margin headwinds as long as TSMC’s absolute monopoly in leading edge chip manufacturing remains largely uncontested. Even if US cajoles (forces?) TSMC to build [half](https://www.cnbc.com/2025/09/30/taiwan-should-only-produce-half-of-americas-chips-says-us-commerce-chief.html?ref=mbi-deepdives.com) of US demand locally, I guess as long as TSMC stays a monopoly in the leading edge, they can always just price the chips appropriately to protect their margins, and hence the burden of onshoring can be largely passed to its customers: mostly US big tech companies. Taiwan already [rejected](https://www.cnbc.com/2025/10/02/taiwan-us-chip-production-tsmc-trade-tariffs.html?ref=mbi-deepdives.com) the idea of splitting chip production half to the US; the geopolitical tussle is a much larger consideration here than worrying about pesky margins. It is hard for me to imagine that US will ever be at ease with such a glaring vulnerability at place, so not only they may push for such “50-50” idea again but also in the meantime likely would like to try to find (and fund) for an alternative to TSMC. As of today, there is essentially none. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Why I bought some puts URL: https://www.mbi-deepdives.com/why-i-bought-some-puts/ Last updated: 2025-10-18T13:21:31.000Z Yesterday, I decided to buy some puts. I will share my rationale and the details around the puts behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Sartorius 3Q'25 Update URL: https://www.mbi-deepdives.com/sartorius-3q25-update/ Last updated: 2025-10-17T14:32:09.000Z It’s been difficult few years post-pandemic for Danaher and Sartorius. Most quarterly earnings call in the last couple of years for these companies were spent on explaining and updating on inventory destocking issues which is certainly not quite a fun exercise. Fortunately, those days increasingly seem to behind us and Sartorius had a good quarter. If you are unfamiliar with Sartorius business, I suggest you read my [Deep Dive on Sartorius](https://www.mbi-deepdives.com/dim/) first. I will share some thoughts on the quarter behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### ASML's China question URL: https://www.mbi-deepdives.com/asmls-china-question/ Last updated: 2025-10-16T14:59:45.000Z In my [ASML Deep Dive](https://www.mbi-deepdives.com/asml/) last month, I wrote about China’s gigantic and increasing contribution on ASML’s business: > ASML revenue increased from €18 Billion in 2021 to €28 Billion in 2024\. During this period, their revenue from China increased by €7.5 Billion; so **basically \~75% of ASML’s incremental growth in the last three years came from China**. As a result, China’s contribution to ASML’s revenue went from \~15% in 2021 to \~36% in 2024. Coming into 2025, ASML management indicated China’s contribution to its revenue will come down to \~20%. As the year progressed, China remained stubbornly high in ASML’s revenue mix. ASML discloses China’s contribution in ASML’s net system sales on a quarterly basis (system sales is \~75% of ASML’s overall revenue). In both Q1 and Q2 of 2025, China was 27% of ASML’s system sales. In Q3, it shot up to **42% of ASML’s system sales!** Now ASML management expects China revenue to “**decline significantly**” compared to 2024 and 2025 period. Clearly, ASML management was surprised by China’s performance in 2025, so who knows perhaps China can surprise them again to the upside. Here’s what management said about ASML’s China business yesterday in Q3 call: > on China, we have been very consistent that we thought that the level of business in the last 2, 3 years was very high and **in no way normal**. So I think we have been experiencing a very high cycle in China, especially through the last couple of years. And again, our expectation and the visibility we have right now is that next year, we go back to more reasonable business. > > …**we were actually quite surprised that the China sales this year are as strong as they are**. But that -- but still the underlying assumption and our underlying perspective on the Chinese market is still the way it was a year ago. And that has to do with the fact that the Chinese market is a very specific one, right? > > **It’s focused on mainstream logic**, as we call it. And simply given the dynamics, of that market. It is our assessment that the sales level that we currently see this year is **very high in comparison to what we would think is a normalized level** for that -- for the mainstream market. So that’s the reason why we’ve indicated this assumption of a **significant decline**. So that’s based on our understanding of the market. It’s based on the dialogues that we have with our customers**. Could that change? Absolutely. I think we’ve seen that this year.** That could change in comparison to our perspective. But if you ask us for an honest assessment at this stage, how we think it’s going to be in ‘26, it is as we communicated. Indeed, it is quite surprising that Chinese companies are buying all these DUV machines from ASML for “mainstream logic” which are typically manufactured on older, more mature process nodes, generally considered to be 28 nanometers (nm) and larger. Texas Instruments mentioned in their 2Q’25 call that overall WSTS without memory is still **12-13% below the trendline**. So it’s quite unlikely that the demand was through the roof for Chinese companies in the last couple of years to go for a buying spree of DUV machines. It is highly likely that geopolitics is a much greater consideration for Chinese buyers which is perhaps not the most reassuring news for ASML investors, especially given how the trade tension between China and the US has resurfaced. Even the Dutch government recently took [control](https://www.reuters.com/world/china/dutch-government-intervenes-chinese-owned-computer-chip-firm-nexperia-2025-10-12/?ref=mbi-deepdives.com) of Nexperia from the Chinese company Wingtech who bought Nexperia for $3.63 Billion in 2018. ![](https://substackcdn.com/image/fetch/$s_!Y2Cx!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6e4ff64a-7ad9-4dc4-9cc1-c37b5586a459_987x594.png) Image Source: Texas Instruments Of course, China cannot quite retaliate directly by banning ASML; it’s good to be monopoly in this circumstance. Nonetheless, it is always quite challenging to map out geopolitical implications ex-ante; perhaps China is stockpiling enough DUV machines that even if DUV gets banned tomorrow, they will be fine for 5 years (if not longer). Beyond five years, China may be betting that they can [rely](https://www.digitimes.com/news/a20251008PD216/china-duv-lithography-patent-smee-28nm.html?ref=mbi-deepdives.com) on SMEE. Again, for context, ASML generated a **cumulative** **€5 Billion revenue from China during 2016 to 2019 period**. Now just in 2024 and 2025, their revenue from China in these two years in aggregate will be almost **€20 Billion**! Given the flaring trade tensions in recent weeks, I wouldn’t be surprised one bit if ASML has another \~€10 Billion revenue year from China next year**.** China’s ordering pattern in recent years doesn’t make it seem like they are very confident that they will have access to ASML’s DUV machines for too long! For now, China has been a good business for ASML. It’s not only revenue contribution, their margins in China are also higher. From ASML in 3Q’25 call: > as you know, what we ship to China today to a very large extent is immersion. **Immersion comes with a very good gross margin. So less China business would be dilutive on that front** Even though ASML management is assuming “significant decline” in China business in 2026, they nonetheless expect to see revenue growth in 2026: > …there has been a positive news flow across the industry in recent months that has helped to reduce the level of uncertainty that we were reporting last quarter. First, there were a number of announcements around the continued investment in AI infrastructure that supports demand in both leading edge logic and advanced DRAM. Second, the **positive momentum around AI seems to extend to more customers in both logic and DRAM**. Third, we see continued momentum around customers adopting more EUV layers in both logic and DRAM, migrating multi-patterning deep UV to single exposure EUV and continuing to support litho intensity. > > We believe that the impact of these dynamics will only **partially affect 2026**; however, overall, **we do not expect 2026 total net sales to be below 2025.** In this environment, we also expect the 2026 EUV business to be up driven by the dynamic in advanced DRAM and leading-edge logic and the deep UV business to be down compared to 2025, driven by the dynamics with our Chinese customer. While ASML’s tone about 2026 has certainly positively shifted from “*we cannot confirm growth in 2026”* during 2Q’25 call to *“we do not expect 2026 net sales to be below 2025”* in 3Q’25, I think it’s worth highlighting what revenue estimates for 2026 **used to be** in the last couple of years. Revenue estimates for 2026 peaked at **€40.6 Billion** in mid-2023, however, current estimates for 2026 are now almost \~20% below that! Moreover, I doubt many people were forecasting \~**€10 Billion** revenue contribution from China in 2024 and 2025\. Surprisingly, even though today 2026 estimates are 20% below the peak in mid’23, the stock is up \~35% since then! That perhaps is more of an indication that ASML shareholders are happy to look through temporary issues as long as the monopoly remains firmly in tact! ![chart](https://substackcdn.com/image/fetch/$s_!cWo9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0d78fbf9-65b9-4f64-9e9a-26a840337205_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Why I don't worry (as much) about big tech's depreciation schedule URL: https://www.mbi-deepdives.com/why-i-dont-worry-as-much-about-big-techs-depreciation-schedule/ Last updated: 2025-10-15T14:07:56.000Z In [**early 2025**](https://www.mbi-deepdives.com/big-tech-earnings-quality/), I wrote about big tech’s deteriorating earnings quality primarily because of their gradual extension of useful lives for their servers in the last five years. Over the course of this year, these concerns have reverberated in many corners of the market. Just last month, The Economist [warned](https://www.economist.com/business/2025/09/18/the-4trn-accounting-puzzle-at-the-heart-of-the-ai-cloud?ref=mbi-deepdives.com) that if server useful lives were reduced to one to two years, it could shave off $2 trillion to $4 trillion market cap from big tech’s valuation. Similarly, Rihard Jarc at “Uncovered Alpha” [echoed](https://www.uncoveralpha.com/p/too-much-ai-too-soon?r=znly&utm%5Fcampaign=post&utm%5Fmedium=web&ref=mbi-deepdives.com) similar concerns about depreciation schedule: > “The life of the current generation of GPUs is shorter than most think, and what many companies are projecting in their amortization plans...But some might say, well, you still see people renting Nvidia H100, which are chips that Nvidia started selling 3 years ago. Yes, but there are two factors to that. The first one is that you have two clients pushing demands sky high, as they are subsidizing the end users, as the computing to do the services that they offer is much more expensive than the price that they are charging the end users. This works out only to the point where investors are willing to give you the money to continue doing that. And the second, even more important point is that the H100 is still useful despite being 3 years old, because NVDA switched to a 1-year product cycle between H100 and Blackwell, so this is in late 2024\. Before that, the cycle was 18-24 months. So, in terms of cycle times, the chip isn’t that old from a generation perspective compared to looking at it in years. However, with Nvidia now on a one-year product cycle, this change affects things significantly. In my view, the real amortization of these chips should be in 1-2 years.” If Jarc or The Economist are right about chips useful lives of just 1-2 years, it would indeed be quite concerning. So, why don’t I worry about it as much as I used to almost a year ago? Thanks to the “[Cunningham’s Law](https://bigthink.com/thinking/cunninghams-law/?ref=mbi-deepdives.com#:~:text=This%20is%20known%20as%20%E2%80%9CCunningham's,corrections%20to%20come%20flying%20in.)”, the best way to get the right answer online is not to ask a question, but to post “wrong” answer. Following my piece on big tech’s earnings quality, I have received some pushback and over time, I came to the conclusion that the critics to that piece had more cogent arguments than I did. The argument for a rapid, 1-2 year depreciation cycle for GPUs overlooks the critical distinction between different types of AI workloads. The newest and most powerful chips such as Nvidia’s Blackwell series are essential for the computationally immense task of **training** next-generation foundation models. However, once a model is trained, the task of **inference** creates a long and valuable life for older chips, which can be efficiently repurposed for these high-volume inference workloads, as well as a broad spectrum of other “accelerated computing” tasks. This logic is **especially true for big tech**, whose infrastructure supports an incredibly diverse array of services. Jarc cites Groq founder Jonathan Ross who himself [believes](https://www.youtube.com/watch?v=VfIK5LFGnlk&t=3s&ref=mbi-deepdives.com) the chips should be depreciated over just one year. A specialized AI company like Groq might see its hardware’s value tied almost exclusively to a narrow set of inference workloads, making it more susceptible to rapid obsolescence. In contrast, a hyperscaler like Google, Amazon, or Microsoft runs everything from cloud databases and video transcoding to scientific simulations and internal analytics. For them, a three-year-old H100 may not be obsolete, and rather can be redeployed to accelerate countless other tasks, delivering a significant performance uplift over traditional CPUs and generating economic value for years. Big tech operates on a “value cascade” model for their hardware. A new Blackwell GPU takes the top-tier training jobs. The displaced H100s then cascade down to power high-end inference, model fine-tuning, or less-demanding training runs. The A100s they replace might cascade further to handle standard inference or other non-AI accelerated computing tasks. This **systematic repurposing ensures that the chip continues to generate economic value** long after it has been dethroned as the performance king. One of the pieces that really made me re-think and re-evaluate my concerns around depreciation schedule is this particular [piece](https://appliedconjectures.substack.com/p/tsmc-and-then-there-was-one?r=9x0z5&utm%5Fmedium=ios&triedRedirect=true) from Applied Conjecture. I think they made a compelling argument that the evidence so far doesn’t suggest big tech being aggressive about the depreciation schedule. Some excerpts from the piece below: > “If the latest GPUs become obsolete and uneconomic within a year or two of introduction, ROI on AI CapEx would be hugely negative. > > In my view, the consensus is vastly underestimating the useful life of GPUs and thus their lifetime economics > > The existence of this large category of throughput-oriented workloads creates a structural demand for older, “good enough” hardware. An older, fully depreciated A100, while slower than a new B200 for a single, latency-sensitive query, can be highly cost-effective for throughput-sensitive workloads. **When running large, batched workloads, the A100 can be driven to high utilization delivering a lower TCO for that workload than a brand new, expensive B200 that might be under-utilize**d. > > This creates a situation where hyperscalers and enterprises will deploy their newest, most powerful GPUs for latency-critical tasks, **while repurposing prior-generation GPUs to serve the massive, cost-sensitive market for batch inference**. This dynamic fundamentally alters the traditional IT depreciation curve, giving older hardware an economically valuable and extended useful life. > > Essentially, an A100 purchased in 2021 for foundational model training can be strategically repurposed in 2024 for a premium, low-latency inference tier. By 2026, as even faster GPUs (i.e. B100/B200) take over that role, the same A100 can be shifted again to a bulk, low-cost, throughput-oriented inference tier. This deployment model extends the useful economic life of the asset from the oft-cited 2 years to a more favorable 6-7 years. > > Real-world evidence supports this model of extended lifecycles. Azure’s public hardware retirement policies provide a clear precedent. **For example, Azure** [**announced**](https://learn.microsoft.com/en-us/previous-versions/azure/virtual-machines/sizes/retirement/ncv2-series-retirement?ref=mbi-deepdives.com) **the retirement of its original NC, NCv2, and ND-series VMs (powered by Nvidia K80, P100, and P40 GPUs) for August/September 2023\. Given these GPUs were launched between 2014 and 2016, this implies a useful service life of 7-9 years. More recently, the retirement of the NCv3-series (powered by Nvidia V100 GPUs) was** [**announced**](https://learn.microsoft.com/en-us/azure/virtual-machines/ncv3-retirement?ref=mbi-deepdives.com) **for September 2025, approximately 7.5 years after the V100’s launch**. This demonstrates the viability of extracting value from GPUs over a much longer period than the consensus implies.” The Chip Letter also [shared](https://thechipletter.substack.com/p/obsolescence) some historical analogies yesterday to make a similar point: > There was a famous saying in the 1990s: ‘what Andy giveth, Bill taketh away’ otherwise known as ‘Andy and Bill’s law’. Andy was Intel CEO Andy Grove, and Bill, of course was Microsoft’s Bill Gates. As Intel delivered performance increases, Microsoft’s software updates ate up that performance, making older hardware unusable. This was good news for Andy (and Intel) as users were forced to upgrade to the latest Intel chips. > > The AI equivalent of this is driven by the ‘bitter lesson’. The latest and best models need a lot more compute. Older hardware just won’t do, at least if you need to see the results of your training run this year. > > The lifetime of those ‘AI chips’ is complicated by the fact that they are doing two distinct tasks, training and inference. Both involve lots of matrix multiplications, but being optimal for one doesn’t necessarily mean that it’s the best for the other. Hyperscalers see Nvidia’s chips as the best for training. **Part of Jensen’s sales pitch is that these designs can be gently retired to the less onerous challenges of inference, roughly in the same way that you might bequeath your gaming laptop to another family member to do their emails on**. Given the implications of shorter useful lives and the impact on earnings, it is understandable why many investors are concerned about this. However, many may not appreciate how much talent and effort have always been deployed to keep the infrastructure useful and reliable over the last two decades. The Chip Letter mentioned an interesting [paper](https://arxiv.org/pdf/2503.21165?ref=mbi-deepdives.com) titled “Extending Silicon Lifetime: A Review of Design Techniques for Reliable Integrated Circuits” which made me appreciate that the estimates of useful life for chips aren’t a random number out of a hat and people are constantly trying to figure out new techniques to enhance reliability and useful lives of chips. From the paper: > ICs have been widely adopted across various sectors and play a vital role in modern electronics. However, they face significant reliability challenges in nearly all of the application domains. These challenges are exacerbated by the increasing replacement costs of ICs at advanced technology nodes and the demand for uninterrupted operation, such as during the training of AI models. Aging remains a major threat to the lifetime of IC chips, arising from a combination of device degradation mechanisms, including Bias Temperature Instability (BTI), Hot Carrier Injection (HCI), and Time-Dependent Dielectric Breakdown (TDDB), which affect transistors, as well as electromigration (EM) in on-chip metal interconnects. The confluence of these effects leads to degraded performance and shortened lifespan. The need to understand and address these aging effects has grown significantly. Figure 2 shows **the number of publications addressing each aging mechanism over the past 25 years…The data illustrate that interest in these issues has been steadily increasing, particularly in advanced technology nodes below the 10 nm regime.** ![](https://substackcdn.com/image/fetch/$s_!7xAp!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff67a86bb-042d-4cde-8c70-b68cc4746425_790x651.png) Given the hundreds of billions of dollars of capex, managing the infrastructure efficiently is going to be a key core part of all big tech’s operations. It was always the case, but infrastructure is increasingly at the top of everyone’s list. I expect big tech to deploy a lot of talent in improving useful lives of chips, and the diversity of their workloads will make a lot of old chips useful longer than most bears think. At the end of the day, useful life of chips is an estimate, so the estimate can be off by a little on either side, but I have dialed down my concerns considerably that the big tech may be assuming useful lives of chips \~50-100% longer than they should. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Gartner may not be "AI loser" URL: https://www.mbi-deepdives.com/gartner-may-not-be-ai-loser/ Last updated: 2025-10-14T13:11:56.000Z While the broad index makes all-time high almost every other day these days, Gartner shareholders are actually currently experiencing the worst drawdown since the GFC. ![chart](https://substackcdn.com/image/fetch/$s_!5NuJ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4c7a4a57-94bc-4502-be01-a7d863928201_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Gartner went from trading at almost \~40x LTM EBIT in late 2024 to just \~16x today. As you can imagine, there are all sorts of narrative flying around now to make the case that Gartner’s best days may not be ahead of them. A couple of weeks ago, my friend David Kim from Scuttleblurb wrote an interesting [**piece**](https://www.scuttleblurb.com/is-gartner-doomed/?ref=mbi-deepdives.com) on Gartner which I think helped contextualize some of these concerns. ![chart](https://substackcdn.com/image/fetch/$s_!ox2g!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F941839e1-d090-4dea-ba53-f4877ad4d43f_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) At first glance, it is quite understandable why investors are concerned. While Gartner’s median revenue growth was low double digit for the last 20 years, it has come down to just mid-single digit now. ![chart](https://substackcdn.com/image/fetch/$s_!oLFB!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1af1ab89-b75f-4418-ac3c-8000b7263c94_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) For the uninitiated, Gartner’s core is subscription research sold to IT buyers (GTS or Global Technology Solutions) and to non‑IT leaders (Global Business Solutions); both GTS and GBS are part of their core “Insight” segment. Tiers range from a \~$20–25k “read‑only” seat to \~$80–100k for access that offers unlimited analyst calls and senior “executive partner” time; conferences and consulting are adjuncts with a much smaller profit contribution. Is Garner’s research mission critical for its customers? I don’t have any compelling argument to doubt its usefulness for its customers, but Scuttleblurb points out the reality of any non-mission critical but merely useful subscription research services (including yours truly). Churn in this business is inevitable, especially when “ROI” of spending on any research can be often a bit squishy: > “that Gartner has managed to grow contract value by double-digits nearly every year over two decades while starting each CV anniversary \~18% in the hole is testament not to the inherent retentive properties of subscription research but to the effectiveness of its sales organization. > > …that Gartner assists in mission critical spend doesn’t render Gartner itself mission critical. It is often just one of several sources, from competing research shops to consulting firms, that enterprise buyers rely on to compare vendors and craft strategy. Clients find it tough to quantify the ROI of Gartner’s services and, make no mistake, they do complain about Gartner’s pricing, often questioning whether they’re getting they’re money’s worth. **Some of this may be a matter of perception**. Subscribers don’t live in Gartner the way they do in a CRM or HR system and commonly access the company’s services an ad hoc basis, when procurement decisions or a big internal project comes to the fore. The lack of consistent, habitual use may lead some clients to think they get less value from Gartner’s services than they actually do. Also, **gains to knowledge are notoriously hard to measure and often take the form of intangible benefits, like looking smart in front of one’s boss or a future interviewer, that go easily overlooked.**” ![](https://substackcdn.com/image/fetch/$s_!qV7J!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F672d86ef-75fa-4b0b-824c-c8a000adab18_1024x472.webp) Source: Gartner Indeed, a friend (who is also a subscriber) recently was telling me that he was interviewing for a job, and he tried to steer the interview conversation to a direction so that he can deliver some of the insights he gathered from MBI Deep Dives. Even if he gets the job, it’s very difficult to assign any specific or objective value to MBI Deep Dives for his success other than some vague idea that it likely helped him during the interview process. While Scuttleblurb sounded a bit underwhelmed about the quality of Gartner’s business due to the leaky bucket and hence the inherent treadmill nature for future growth, Gartner’s numbers is actually much closer to “mission critical” than it may appear. Let’s compare and contrast with CoStar, for example. CoStar is certainly mission critical for its customers; it’s difficult to scale a commercial real estate business without subscribing to CoStar. Nonetheless, its retention ratio hovers around 90% whereas Garner’s client retention ratio is usually around mid-80s. That’s pretty impressive? ![](https://substackcdn.com/image/fetch/$s_!Q745!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F98902d01-7cba-4270-9691-ec6755ea0786_1267x699.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Of course, there are lot of nuances in comparing such different businesses. First of all, the benefits of being a mission critical is deeply understood during economic downturn. CoStar can likely go through a recession without much of a noticeable change in renewal rates whereas Gartner will almost certainly feel the vicissitude of economy much more. Moreover, the differences in definition of retention rate itself can mask a lot of differences. For example, imagine Company A, B, and C all report 95% retention rates for their subscription services, but customers only renew the contracts in every 1, 3, and 5 year respectively. Even though they will all report 95% retention rates, their underlying retention rate is actually 95%, 85%, and 75% respectively. Since company C has a 5-year contract, its reported retention can mask the inability of its customers to cancel their subscription. ![Image](https://substackcdn.com/image/fetch/$s_!uvKD!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7fd7f406-3ba4-4e6d-bb67-1500bac98234_624x103.png "Image") Source: MBI Deep Dives Indeed, Gartner actually mentions \~75% of its contracts are multi-year in nature whereas CoStar’s subscriptions typically renew at one-year increment. Given the average duration of the contract is 1.7 years for Gartner, the underlying retention rate for its subscription services is likely a bit worse than reported retention compared to CoStar, but unlikely to be materially worse than reported retention. Of course, these are just quirks of the nature of business model and services Gartner is selling, but the stock is selling off for not these reasons. The primary culprit is their contract value growth decelerated from \~17% to just 5% in three years. Is Gartner getting “Chegg-ed” by ChatGPT? Scuttleblurb makes a convincing case that is quite unlikely: > Even if publicly available data were trustworthy and unbiased by vendor hype, information discovery alone has never been the chief reason for subscribing to Gartner. Were that the case, Google search would have decimated Gartner’s research business long ago. I think LLMs, in their current state of maturity, are akin to an advanced form of search, more useful for packaging publicly available data than for generating sound opinions. > > it doesn’t fly for the domains that Gartner plays in, or for the kinds of questions its clients ask, questions that go well beyond simple prompts, like “give me the pros and cons of the top five endpoint security tools”. You will not find a reliable publicly available baseline for how the CFO of a $1bn revenue manufacturer should structure and budget an FP&A program or on how the CIO of a bank with $100bn of assets across Europe and North America should evaluate zero-trust network security vendors in an environment running on legacy technology. > > Try telling the Board you used Google searches and ChatGPT to narrow down the list of ERP vendor finalists. See how that goes. It’s not just that Gartner is a more credible authority for evaluating IT decisions than ChatGPT; **it’s perceived that way too, which is just as important for a CIO whose foremost concern is career preservation**. “ZissouCapital**”** also [corroborated](https://x.com/scuttleblurb/status/1974109734069547169?ref=mbi-deepdives.com) similar arguments about Gartner: > “It’s an expensive, discretionary service, but their sales force is an insane machine. I don’t think they’re really the “industry currency” they were once regarded as, but from calls I’ve done, MSFT, Amazon, etc. aren’t cutting spend as vendor clients. Talked to probably a dozen customers over the last couple of months trying to find someone who would tell me yeah we are substituting Gartner with ChatGPT et al and couldn’t find a single person. Closest I got was a guy at a federal health agency with a massive budget who had to cut $5bn in costs and slashed Gartner subs for all the people he fired. He was reflecting the AI-fear narrative in the stock price on the call, but when I pushed him he said if DOGE hadn’t cut his budget, he wouldn’t have fired those people and he wouldn’t have cut their Gartner subs for AI tools instead. He also said he personally upgraded his Gartner seat to the highest tier with all the CTO access etc and wouldn’t be cutting it” The data Scuttleblurb shared also makes a pretty convincing case that the primary reason for Gartner’s recent soft operating performance is likely DOGE and tariffs, not AI: > The US Federal Government, influenced by by DOGE scrutiny, retained just 50% of its Gartner contracts by value, and was responsible for nearly 80% of the year-to-date contraction in GTS NCVI3\. With nearly all US Federal contracts, \~4% of CV, coming up for renewal this year, spending weakness should not carry over into 2026. > > Subscribers in industries heavily dependent on imports and exports, who made up 35% to 40% of Gartner’s total contract value (with GBS slightly more exposed than GTS), performed much worse than those in sectors not impacted by tariffs. Moreover, although deals are being delayed and escalated up the chain, the deal pipeline – a forward looking measure of demand – is up double-digits across both GTS and GBS, which one would not expect to see if AI were culprit. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### AI diffusion URL: https://www.mbi-deepdives.com/ai-diffusion/ Last updated: 2025-10-13T13:02:51.000Z Microsoft recently published an interesting [paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/09/AI-Diffusion%5FTechnical-Report.pdf?ref=mbi-deepdives.com) on “AI diffusion” which made me appreciate the true diffusion of AI may be higher than most people think and bottlenecks for further adoption may currently be underestimated. This paper titled, “Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage,” introduces the “**AI User Share**,” a novel metric designed to estimate **the percentage of a country’s working-age population (15-64) actively using AI tools.** The methodology leverages anonymized Microsoft telemetry data. They define “AI User Share” as: percentage of Microsoft users who used AI tools (e.g. ChatGPT, Gemini, Microsoft Copilot etc.) multiplied by percentage of population with a desktop device multiplied by country‑specific mobile scaling, and then normalize to the working‑age population (15–64). It’s a rare **population‑normalized** usage signal across **148 economies**, updated in near–real time. The authors acknowledge that the metric’s primary limitation is its reliance solely on Microsoft telemetry. This introduces inherent biases toward desktop platforms and the Microsoft user demographic. Although adjustments are made to account for mobile usage and overall device penetration (using third-party data like StatCounter), the metric assumes that Microsoft user behavior is representative of the general population, which may not always be accurate. Perhaps the most crucial insight is that the **primary barrier to AI adoption in developing economies is internet access, rather than a lack of interest**. While overall AI User Share is low in these regions, the adoption rate among the population that already has internet access is robust. The analysis examined the 15 economies with the lowest internet penetration and found: **their average overall AI User Share is only 9%. However, among their connected populations, the AI User Share jumps to 23%.** This 23% adoption rate among connected users is actually **higher** than the 20% average for connected populations in the rest of the countries analyzed. For example, Zambia has an overall AI User Share of 12%, but this rises to 34% among its connected population. Similarly, Pakistan jumps from 10% overall to 33% connected. Many of these countries are lagging developed economies considerably because of their lack of access to internet itself. ![](https://substackcdn.com/image/fetch/$s_!fhCC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5159cff9-7816-4b96-b48a-717c63f6ba08_1027x535.png) Source: Microsoft’s [paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/09/AI-Diffusion%5FTechnical-Report.pdf?ref=mbi-deepdives.com) titled “Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage” The paper also contextualizes AI adoption within a “technology funnel” correlated with GDP per capita: from Electricity to Internet Connectivity to Digital Skills to AI Usage. From the paper: > “These four can be considered a funnel, since it is difficult to use AI without having basic digital skills. Likewise, basic digital skills are unlikely to be present without internet connectivity, and even moreso, electricity.” The analysis shows significant drop-offs at each stage of the funnel. While electricity and connectivity approach saturation in high-GDP countries, the levels of digital skills and, particularly, AI usage lag significantly behind. ![](https://substackcdn.com/image/fetch/$s_!f5l2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F907a24cf-27a4-4992-8d82-8b9153a3c75f_1014x664.png) Source: Microsoft’s [paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/09/AI-Diffusion%5FTechnical-Report.pdf?ref=mbi-deepdives.com) titled “Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage” While wealthier nations generally lead adoption, the data suggests **a potential ceiling** under current conditions. Apart from outliers like the UAE and Singapore, most advanced economies cluster within a **25%–40% adoption range.** ![](https://substackcdn.com/image/fetch/$s_!dWir!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F03c86b69-6566-41f6-9ea2-7c22d29fb9c5_538x792.png) Source: Microsoft’s [paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/09/AI-Diffusion%5FTechnical-Report.pdf?ref=mbi-deepdives.com) titled “Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage” Some high-income countries, including the United States (26.3%) and Denmark (26.6%), fall below the trend line, indicating they are underperforming relative to peers with similar GDP levels. ![](https://substackcdn.com/image/fetch/$s_!1022!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7561c69-c613-4e7c-94d0-56184c19899b_1017x739.png) Source: Microsoft’s [paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/09/AI-Diffusion%5FTechnical-Report.pdf?ref=mbi-deepdives.com) titled “Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage” The AI User Share metric is sensitive enough to capture almost real-time market dynamics following major product launches. The introduction of DeepSeek in January 2025 illustrates this clearly. Following DeepSeek’s launch, AI adoption in China accelerated dramatically. **China’s AI User Share more than doubled from 8% to 20% in the subsequent months**. In contrast, the US AI User Share remained steady at around 25%. This surge propelled **China to become the world’s largest AI market by volume, with an estimated user base exceeding 195 million**. ![](https://substackcdn.com/image/fetch/$s_!yqZL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd7b9dbba-ce38-4f9d-9cb7-86bb9568a297_970x535.png) Source: Microsoft’s [paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/09/AI-Diffusion%5FTechnical-Report.pdf?ref=mbi-deepdives.com) titled “Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage” Overall, the big takeaway from the paper is while global AI adoption is currently concentrated in wealthy nations and strongly correlated with GDP, usage rates among connected populations in developing countries are pretty similar across the world which implies that **the primary barrier to worldwide AI diffusion is access to internet itself.** --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Rare earth shenanigans URL: https://www.mbi-deepdives.com/rare-earth-shenanigans/ Last updated: 2025-10-12T13:12:38.000Z Rare earth materials are again in the news! From [WSJ](https://www.wsj.com/economy/trade/china-imposes-new-controls-over-rare-earth-exports-35a4b106?gaa%5Fat=eafs&gaa%5Fn=ASWzDAiJf2kvVukZh45ZFQ5UCY1%5FPBW0V0bQujGdLA%5Ffw2H4u1mhOLhvrvJmjpfHQYs%3D&gaa%5Fts=68eb9dba&gaa%5Fsig=LW27z3AbbH6ejdui0NwOq3NM7PC7d9r-3cDWpa3XgWAVdVrWqenbzHlzYd8pUG7VwTTKj671mTYhEWSow5H5Zg%3D%3D&ref=mbi-deepdives.com) last week: > China’s Commerce Ministry said Thursday that foreign suppliers must obtain approval from Beijing to export some products with certain [rare-earth materials](https://www.wsj.com/world/china/china-flexes-chokehold-on-rare-earth-magnets-as-exports-plunge-in-may-c1adac50?mod=article%5Finline&ref=mbi-deepdives.com) originating from China **if they account for 0.1% or more of the good’s total value**. Goods produced with certain technologies from China are also subject to the export controls. **Both restrictions apply to products manufactured outside of China**. A significant development in the new policy is its “extraterritorial” reach. It does seem China is essentially borrowing a page from the US semiconductor policy even though they may only have a knife in a gunfight! You may already be aware that the term “rare earths” is a misnomer. This [group ](https://en.wikipedia.org/wiki/Rare-earth%5Felement?ref=mbi-deepdives.com)of 17 chemically similar elements is relatively abundant in the Earth’s crust. The “rarity” stems from several factors: a) Rare-earth elements (REE) are typically dispersed and rarely found in concentrated, economically viable deposits; b) the extraction process is complex and often environmentally intensive; and c) separating and refining REEs from their ores is a sophisticated and capital-intensive process that requires specialized technology and expertise. It is in this processing stage that China has established a near-monopoly, controlling [\~85-90%](https://cleantech.com/whats-reeally-happening-with-rare-earths/?ref=mbi-deepdives.com#:~:text=Estimates%20suggest%20China%20controls%2060,crucial%20for%20high%2Dtemperature%20magnets.) of the global capacity for refining rare earths and manufacturing downstream products like permanent magnets. While that may seem damning for the US at first glance, US actually imported only [**$170 million** ](https://pubs.usgs.gov/periodicals/mcs2025/mcs2025-rare-earths.pdf?ref=mbi-deepdives.com)REEs in 2024 which itself was down 11% from 2023 imports! Of course, even if REEs are insignificant input materials, they can be **critical element** of the value of the end products. I listened to a couple of podcasts yesterday to gauge how concerned we should be about China’s ability to create a long-term damage in the US supply chain through this policy. Javier Blas on [Odd Lots](https://www.youtube.com/watch?v=z4LVJFeFVwQ&ref=mbi-deepdives.com) podcast back in April 2025 seems to have largely brushed away concerns around REEs as mostly a nothingburger. He posited that China’s monopoly isn’t necessarily hinged on some special skillset or control over the value chain, rather its mostly due to lack of interest of anyone else to be in this business. So, if China enacts policies that bar the US to access China’s REEs, the prices of REEs will rise and people will be interested in figuring out how to do it domestically or in other US friendly countries. From the podcast: > About 80-85% of the world’s rare earth metals come from China. It’s a question of digging them out of the ground and then processing. The big difficult part is processing because it’s very polluting and it’s a reason why all the processing has moved from everywhere else in the planet into China because no one wanted to deal with how nasty the process is. And here is also the other question. If you want to do rare earth metals processing in particular outside China, **what you need is much higher prices. If anything, the problem today with rare earth metals and if we want to develop an industry of rare earth metals outside China is that prices are too low. We need much higher prices and then everyone will do rare earth metals.** As mentioned earlier, US only imported $170 million REEs in 2024, so even if prices go up by 10x, that’s going to be quite manageable in the medium to long-term. ChinaTalk also had an[ “emergency” episode](https://www.chinatalk.media/p/emergency-pod-rare-earth-export-controls?ref=mbi-deepdives.com) on China’s rare-earth restrictions. Chris Miller, the author of the seminal book “[Chip War](https://www.amazon.com/Chip-War-Worlds-Critical-Technology/dp/1982172002?ref=mbi-deepdives.com)”, thinks these restrictions may not have much teeth on semiconductors but it will likely have larger impact on other parts of the economy. From Chris Miller: > The interesting dynamic to me is that if you look at the use of rare earths in the chipmaking process, they’re predominantly — at least magnets are — used in the machines that make chips, where magnets are indeed required. Although many of these companies have done a fair amount of stockpiling, it’s not the case that if you stop selling magnets, the chip industry grinds to a halt. Maybe it gets more complicated to build new tools for expansion. > > The other direct chip industry impact that the regulations called out was non-magnets — other rare earths that are used in some of the materials and consumables. They specifically mentioned sputtering targets, for example. It’s really unclear how strong of a position China has here. We’ve just never run the experiment in real life. It’s possible that China can really limit production of these items, but we’re also talking about really small volumes. **It’s also possible that if China does implement the controls, there are ways to source from other companies or source secretly in ways that China can’t detect**. > > All that’s to say, if China actually carries the controls out, it might not be as immediately impactful in the chip industry as China hopes, with a pretty wide uncertainty interval. But we should probably turn to the question of what it means for the rest of the economy if China carries them out, because that’s where you would have probably pretty disruptive impacts. > > **We could shut down much of China’s chip production domestically because they require a larger share of materials and consumables than we require from them.** > > But what we saw in April was that China bet it could respond in a different sphere. We impose tariffs, they impose magnet controls. That had a big impact on the automotive sector, for example. My worry is less about the semiconductor-specific dynamics and more about what happens if China follows through with this. What’s the impact on the rest of the manufacturing base in the United States, which, as we know, does need magnets and other materials that are mostly sourced from China? > > In April and May, we found that the White House was very sensitive to any disruptions in the auto supply chain — not surprisingly. **That, to me, is where the uncertainty lies. What happens if these controls ricochet through other segments of the economy where it’s less clear that the US has this position of escalation dominance?** Then you end up with a standoff: the US threatening to escalate in one sphere, China threatening to escalate across the manufacturing base. Who feels most compelled to back down? Who feels most able to bear economic cost? > > I don’t know the answer to that, but I worry about it. > > The other key dynamic here is that the Chinese now clearly believe — and the rest of the world has increasingly bought into the thesis — that they have a durable long-term position in their dominance over rare earth mining, but especially refining. One way to look at this is: **what’s easier to replicate, a rare earth processing facility and mining for heavy rare earths, or an EUV tool? We’re betting on the latter.** **Big steps that would show China’s making the wrong bet if it’s betting on processing facilities — and help the rest of the world realize that this is not a real credible threat over the long run — would shape how the rest of the world responds to this.** The last point is the key. It is the same risk US was/is running in their semiconductor policy on China. If China indeed becomes independent of western chip supply chain due to chips restrictions imposed by the US, that may turn out to be a huge boon for them in the long-term. Of course, that is exponentially harder and may take years (a decade or two?) for China to get there. However, since China is making the same regulatory posture in REEs and it is highly likely to be considerably easier for western countries to actually be able to mine and process REEs domestically and become independent of China here, China may be making the same mistake…a lot faster and they may quickly lose this leverage in a couple of years **permanently**. From Chris Miller again: > One other point on the Chinese side — if they threaten it but don’t implement it because we’ve got some retaliatory threat that we then negotiate and both pause — but this is still hanging in the background, **it might actually be a pretty dangerous strategy for China**. If they’ve got this sword of Damocles hanging over everyone, people look at it and begin building their own rare earth processing facilities. We find out that after a couple of years, this actually degrades pretty rapidly. > > It seems like a risky thing for China to threaten and not actually use. If we’re right that this degrades pretty quickly in terms of its durability as a choke point, then this might be something that, if you threaten it and don’t use it, it actually ends up going away. US, of course, already [responded](https://www.wsj.com/politics/policy/trump-china-tariffs-rare-earths-xi-meeting-8053c81a?gaa%5Fat=eafs&gaa%5Fn=ASWzDAiR%5F8x6ln27xQX57vUizNPw0rH1DfYSKEpXSWXw9IXZ0Y2y8Qq9MeI2su-rxFU%3D&gaa%5Fts=68eb9dba&gaa%5Fsig=v1VKNBZm-rSi%5FanJXtSF%5FtsPhQZLbPvGVkwFI2E-DtMOGih9bQarT-mL3Vtc5lNj7E3-eq2VhZi4LMroh0fn%5Fg%3D%3D&ref=mbi-deepdives.com) to China’s policy by imposing additional 100% tariff on imports from China and a new export controls on “critical software products”. Perhaps by next week, the two countries will have an entirely different posture and these may prove to be all just negotiating tactics for a “grand bargaining”. Even if that happens, it would be prudent for the US government to make sure China doesn’t get to use REE as any sort of leverage going forward; hence, they should and must look into domestic mining, processing, and refining of REEs. It would actually make a lot of sense to impose an absurd level of tariffs on any REE import from China regardless of the outcomes of the trade talks! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Less is more? URL: https://www.mbi-deepdives.com/less-is-more/ Last updated: 2025-10-11T14:32:56.000Z Just yesterday, I was wondering about the profound impact “the cat paper” had on so many companies and [wrote](https://www.mbi-deepdives.com/the-cat-paper/) “*Perhaps someone is going to publish a paper today or in a couple of years that may fundamentally alter the tech landscape 5-10 years from now. This makes analyzing some of these tech companies and investing in them potentially harder than appreciated*.” Then I came across a tweet by Gavin Baker later yesterday: “*get ready to hear a lot about TRMs*”. When I asked Grok about the tweet, Grok thought Baker is talking about “Trust, Risk, and Security Management”. Well, Grok is wrong. He is almost certainly talking about **“Tiny Recursive Models” (TRM)**, an idea which just came out of Samsung SAIL (Samsung Advanced Institute of Technology AI Lab). ![](https://substackcdn.com/image/fetch/$s_!37pD!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F45ec46d5-cc53-42a1-80d6-b34c02cfc77d_766x193.png) SAIL’s [Alexia Jolicoeur-Martineau](https://x.com/jm%5Falexia?ref%5Fsrc=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor&ref=mbi-deepdives.com) published a [paper](https://arxiv.org/abs/2510.04871?ref=refetch.io) just last week titled: “*Less is More: Recursive Reasoning with Tiny Networks*” which can indeed cause some uproar in the coming days. The paper challenges the prevailing assumption that larger models are inherently better. It demonstrates that tiny, specialized models can outperform the largest language models on complex reasoning tasks by employing an iterative self-correction mechanism. The central thesis of this paper is that for difficult logic problems, **how** an AI model thinks (its reasoning process) is more important than its sheer size. Large Language Models (LLMs) like Gemini or GPT-4 typically generate answers auto-regressively i.e. one word or token at a time. When solving a complex problem, they try to produce the solution in a single forward pass. If they make an error early on, they struggle to recover, often rendering the entire answer invalid. To compensate, LLMs use techniques like Chain-of-Thought (CoT), where they write out their reasoning steps, and Test-Time Compute (TTC), where they generate multiple answers and select the best one. However, these methods are computationally expensive and still fail on tasks requiring deep, multi-step logic, such as complex Sudoku puzzles, maze solving, and abstract reasoning benchmarks (i.e. [ARC-AGI](https://arcprize.org/arc-agi?ref=mbi-deepdives.com)) The paper introduces the **Tiny Recursive Model (TRM)**, which builds upon a previous approach called the Hierarchical Reasoning Model (HRM). HRM showed that small networks using “recursive reasoning” i.e. repeatedly checking and refining their answers could beat LLMs on these hard tasks. However, HRM was overly complex and inefficient. TRM significantly simplifies and improves this concept. It operates much like a human solving a difficult puzzle: iteratively making guesses, evaluating the consequences, and revising the guesses until a solution is found. Instead of a huge model that tries to think once and be done, use a **tiny model that thinks in loops**. It keeps two things in memory: **y:** its current best guess for the answer, and z: an internal “scratchpad” of reasoning. At each step it **updates z a few times**, then **updates y once**, and repeats. That’s it. The loop lets a 2‑layer, \~7M‑parameter network iteratively fix its own mistakes (“less is more”). Surprisingly, the authors found that smaller networks (only 2 layers deep) performed better. This is because when training data is scarce (e.g., only \~1,000 examples), large models tend to “overfit” (memorize the data), whereas tiny models are forced to learn the underlying logic. TRM, **with only 7 million parameters**, outperforms models like Gemini 2.5 Pro on the hard reasoning ARC-AGI benchmarks, **despite having less than 0.01% of the parameters.** It also significantly advanced the state-of-the-art on Sudoku-Extreme (from 55% to 87% accuracy). What are the implications of this paper? I will share some thoughts behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The Cat Paper URL: https://www.mbi-deepdives.com/the-cat-paper/ Last updated: 2025-10-10T13:35:20.000Z I finished listening to Acquired’s [final episode](https://www.youtube.com/watch?v=lCEB7xHer5U&ref=mbi-deepdives.com) on Alphabet yesterday. One of the interesting stories from this episode was the significance of the “[cat paper](https://static.googleusercontent.com/media/research.google.com/en//archive/unsupervised%5Ficml2012.pdf?ref=mbi-deepdives.com)”. Here’s Acquired highlighting this particular moment in history: > You talk to anyone at Google, you talk to anyone in AI, they’re like, “Oh yeah, the cat paper.”…just to like underscore how seminal this is, we actually talked with Sundar in prep for the episode. And he cited seeing the cat paper come across his desk as **one of the key moments that sticks in his brain in Google’s story**. A little later on, they would do a TGIF where they would present the results of the CAT paper and you talk to people at Google, they’re like, “That TGIF, oh my god, **that’s when it all changed**.” I knew the cat paper was an important milestone, but perhaps I didn’t appreciate the extent of it, especially the role it played in other tech companies as well. In 2012, the Google Brain team undertook an experiment at an unprecedented scale. They connected 16,000 computer processors to create a massive neural network (a computer system loosely modeled on the human brain) with over a billion connections. They then fed this network 10 million random, unlabeled still images taken from YouTube videos and essentially told the system: “Find the patterns.” When the researchers looked inside the artificial brain they had built, they discovered something astonishing: **the network had taught itself to recognize cats.** One specific digital “neuron” activated strongly whenever the system was shown an image containing a cat. The importance of this result wasn’t that Google could now find cats. It was *how* the system learned to find them. Before this paper, the dominant approach was **Supervised Learning**. This is like teaching with labeled flashcards. To train an AI, humans had to painstakingly label vast amounts of data (e.g., tagging thousands of photos as “Cat” or “Not a Cat”). As you can understand, this process was slow, expensive, and impossible to do at the scale of the internet. The Cat Paper demonstrated the power of **Unsupervised Learning** at scale. The AI was never told what a cat was. It invented the **concept of a cat** simply by observing recurring patterns in the raw data. The internet is composed almost entirely of messy, unstructured, and unlabeled data i.e. billions of hours of video, trillions of images, and endless audio clips. The Cat Paper proved that neural networks, if large enough and given enough data, could begin to make sense of this vast ocean of information without explicit human instruction. The Acquired guys mentioned not only this was instrumental for Google but it also was perhaps directly responsible for **hundreds of billions of revenue** in companies such as Meta and ByteDance. How does recognizing a cat translate into the staggering revenues of Google, Meta, and ByteDance? The connection is **Recommendation Engines**. User engagement is the key foundation on which companies such as Google (especially YouTube), Meta or ByteDance are built. To maximize engagement, they must continuously show you content you are interested in. This requires the platform to understand two things at an incredible scale: a) **Understand the Content:** What is this video, image, or post actually about? b) **Understand the User:** What does this specific user like? The techniques validated by the Cat Paper unlocked **the ability to understand content at scale**. Without this paper, the internet today might have looked very different. The paper also led to the events that made Google realize GPUs would be much better fit for certain workloads. In fact, in **2014**, Google decided to put a big order for GPUs from Nvidia. From Acquired: > …they settle on a plan to order **40,000 GPUs** from Nvidia…For a cost of $130 million. > > That’s a big enough price tag that the request gets elevated to Larry Page who personally approves it even though finance wanted to kill it. > > As an aside, let’s look at Nvidia at the time. This is a giant giant order. **Their total revenue was $4 billion. This is one order for 130 million**. I mean Nvidia is primarily consumer graphics card company at this point and their market cap is $10 billion. **It’s almost like Google gave Nvidia a secret that hey, not only does this work in research…but neural networks are valuable enough to us as a business to make a hundred plus million dollar investment in right now**. Back in 2017, Bezos made an interesting [point](https://www.cnbc.com/2017/05/08/amazon-ceo-jeff-bezos-long-term-thinking.html?ref=mbi-deepdives.com#:~:text=Amazon%20CEO%20Jeff%20Bezos%20has%20a%20pretty%20good%20idea%20of,Not%20next%20quarter.) how business results are often lagging indicator of all the decisions the company made few years ago: > “When somebody … congratulates Amazon on a good quarter … I say thank you. But what I’m thinking to myself is … those quarterly results were actually pretty much fully baked about 3 years ago. Today I’m working on a quarter that is going to happen in 2020\. Not next quarter. Next quarter for all practical purposes is done already and it has probably been done for a couple of years.” Indeed, the cat paper likely created so many ripple effects within and outside Google that it perhaps played a key role in shaping our internet today. It also made me think it may be more important for investors today to spend more time going through research papers to stay at least somewhat informed where the future may be heading. “[Attention is all you need](https://arxiv.org/abs/1706.03762?ref=mbi-deepdives.com)” was published in 2017 which directly led to ChatGPT moment in 2022\. Perhaps someone is going to publish a paper today or in a couple of years that may fundamentally alter the tech landscape 5-10 years from now. This makes analyzing some of these tech companies and investing in them potentially harder than appreciated. A DCF of a tech company can be very fragile if technical breakthrough can reshape the competitive dynamics profoundly. There’s no DCF anyone can ever build that would lead you to think Nvidia may go from **$10 Billion market cap to $4.7 Trillion in just 10 years**! As someone who builds DCF models, I am not saying we should invest based on YOLO vibes, rather I’m highlighting the inherent limitations in analyzing technology companies. When Buffett [indicated](https://acquirersmultiple.com/2024/11/warren-buffett-only-5-10-of-companies-fall-within-my-circle-of-competence/?ref=mbi-deepdives.com) many technology companies don’t fall under his “circle of competence”, he is certainly not saying he doesn’t understand their business models **today;** I suspect he recognizes his limitations in assessing the wide range of outcomes for tech companies more than most investors do. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### First impression of ChatGPT Agent, and apps on ChatGPT URL: https://www.mbi-deepdives.com/first-impression-of-chatgpt-agent-and-apps-on-chatgpt/ Last updated: 2025-10-09T13:35:30.000Z OpenAI has been at the forefront of AI narrative these days to the extent it is difficult to keep track of their press releases and their implications. So, I first want to go back and discuss something that they [launched](https://openai.com/index/introducing-chatgpt-agent/?utm%5Fsource=chatgpt.com) almost three months ago: ChatGPT Agent. In their launch post, the first use case OpenAI showed for “ChatGPT Agent” is booking flights. So, I asked my ChatGPT the following query: “*Find me cheapest flight from SFO to Dhaka. Travel date December 6 to February 6*” The agent then worked for **ten minutes,** and provided me the below wall of texts. ![](https://substackcdn.com/image/fetch/$s_!Kkk7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F95d06bb8-0e90-4a4b-81c0-a30d6d5685d4_1438x954.png) If you asked me to guess what the agent might do to respond to my query, I would have said it would find the cheapest flight (and perhaps a couple other options), and give me a clickable link in case I decide to book the flight. To my surprise, they not only gave me the wall of texts, but when I clicked the link shown in ChatGPT’s response, it just led me to…**screenshots from flight comparison websites, including Google**! There was actually no way for me to click to get to the actual website and book the flight. You can see my query and ChatGPT’s response [here](https://chatgpt.com/share/68e5de3d-fa14-800b-897a-46438549f404?ref=mbi-deepdives.com). ![SFO to DAC, 12/6 – 2/6](https://substackcdn.com/image/fetch/$s_!jhGz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbdf14bd9-e614-4bbe-a0d4-c2a202b3f92f_1024x768.png "SFO to DAC, 12/6 – 2/6") I would have been more forgiving if it were a recently launched feature that needs more finetuning, but the fact that it was launched three months ago and yet remains a less than a half baked idea today does not bode well. In contrast, it took Google less than 30 seconds to give me a **clickable** result. ![](https://substackcdn.com/image/fetch/$s_!ytwQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F20f8acfa-3a21-40a8-ab32-7ba3f0836a65_1618x1120.png) Now, let’s try something that OpenAI indeed launched just a couple of days ago: integrating other apps on ChatGPT within the prompt. This is OpenAI’s third attempt to essentially graduate from just a chatbot to a platform that aggregates other apps in it. [ChatGPT plugins](https://openai.com/index/chatgpt-plugins/?ref=mbi-deepdives.com) was their first attempt in March 2023 which was later deprecated. Then they launched [GPT Store](https://openai.com/index/introducing-the-gpt-store/?ref=mbi-deepdives.com) in January 2024\. Sam Altman in an interview with Ben Thompson [suggested](https://stratechery.com/2025/an-interview-with-openai-ceo-sam-altman-about-devday-and-the-ai-buildout/?ref=mbi-deepdives.com) yesterday that “*GPTs actually did work, GPTs get a surprising amount of usage, but inside a company or someone for their own workflows or whatever*.” Now, with “[Apps in ChatGPT](https://openai.com/index/introducing-apps-in-chatgpt/?ref=mbi-deepdives.com)” launch, some **selected apps** are directly integrated with ChatGPT that you can summon just by typing their name on the query box. One such selected app is Booking.com So, I tried to book a hotel through ChatGPT. I typed “Booking” in the query box and nothing showed up. Then I tried “Booking.com” and it did appear then. I finished my query: “*Booking.com find me an accommodation near Pacifica, CA on October 30-31 for two people*” This time, the agent was reasonably fast and showed me **five results** that I can actually click and go to Booking’s website to book the hotel. You can see my query and ChatGPT’s response [here](https://chatgpt.com/share/68e7b187-1ee4-800b-a1e9-215061b766d7?ref=mbi-deepdives.com). ![](https://substackcdn.com/image/fetch/$s_!aN7Q!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F26ea0205-46c7-473d-8be2-40133117ef08_988x891.png) Google too shows five results at the top, but you can scroll down to see many more results, or just click the map on the right to select a hotel, both of which you cannot do on ChatGPT. ![](https://substackcdn.com/image/fetch/$s_!P02B!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa6b83d1b-0615-453d-a665-19968ff70eaf_2137x1206.png) Despite the integration with ChatGPT, I still need to enter all my details on Booking.com if I try to book the hotel. That is also the case if you try to book a hotel through Google and go to Booking.com website from there. Ultimately, Booking is deeply interested in not just another transaction, but a clear way to potentially own a direct relationship with the customer. If you just do this via Booking’s app, you won’t need to enter all the details again after the first time. Booking a hotel was a much better experience than trying to book a flight through ChatGPT (of course, that’s an incredibly low bar and it was still slightly worse than booking through Google or directly on Booking’s app). It is worth highlighting why booking the hotel was a relatively better experience. Sam Altman seems to think he’s doing almost a charity to these apps by not abstracting them away from the user experience completely. See the interaction between Ben Thompson and Sam Altman from the aforementioned [interview](https://stratechery.com/2025/an-interview-with-openai-ceo-sam-altman-about-devday-and-the-ai-buildout/?ref=mbi-deepdives.com) below: > **Ben Thompson (BT): Well, with these apps in there, is there a sense on your side it’s like, just to go back to the, “We have all this usage, it’s going to be better if I can just use Zillow in the app”? This idea of going somewhere else and Zillow says, “Oh, I’d rather them be in our app, we spent so much time on it”, do you feel you have the power to dictate, “For the user, it’s a better experience and because the users are here, if you’re not there, someone else will be” — and you’re sort of able to, dictate sounds bad, but if it’s a better user experience, that’s better for everyone else?** > > **Sam Altman (SA):** No. Here’s another place that I think my early career training was useful. There was a version of this we could have done where it was a better user experience, but terrible for the partners. > > **BT: What would that look like?** > > **SA:** Well, I mean, on that Zillow example, what if you just said like, **“Hey, ChatGPT, find me all of the houses that meet these things”, and we said we’re going to control the UI.** > > **BT: Oh, right. So there’s no even a presentation layer of the Zillow app, you’re just getting the results.** > > **SA:** Yeah, yeah. But I felt really strongly that when we do this, it’s something that the whole ecosystem benefits from, and specifically that new startups can rocket into existence because of it. So we did this in a way where you very much have the relationship with the other site. You’re calling them by name, we’re suggesting them by name, they’re taking over the UI, they’re linking their account. So I think there was something we could have done that was maybe slightly more user-friendly, but not good for the other companies, and I really didn’t want us to do that. I hope my examples made it clear to you that if OpenAI did try to completely abstract away the apps, booking a hotel would probably feel similar to booking a flight: it would probably work for 10 minutes and then give me screenshot of booking websites!! Of course, if they did try to abstract away the apps completely, the apps could simply block OpenAI (or any AI crawlers). OpenAI is still somewhat early in aggregating demand, so the apps are still not as powerless as they are in Google’s properties today. I am skeptical that even these integrations will lead to any material shift in consumer behavior in booking a hotel (definitely not booking a flight as it currently stands), so how these integrations will evolve remains to be seen. Altman did indicate in that interview that “*hopefully this works better and if it doesn’t, *we’ll keep trying**.” --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Meta's monetization game URL: https://www.mbi-deepdives.com/metas-monetization-game/ Last updated: 2025-10-08T13:22:52.000Z Back in 2022, Meta was facing onslaught from multiple directions: a) Apple’s App Tracking Transparency (ATT), b) regulatory threats from Europe (and sometimes even the US), c) competition from TikTok, d) rising capex with uncertain pay-off, and e) persistently rising operating losses in Reality Labs with even more uncertain pay-off. If you look closely, while the stock increased by 7-8 fold from the bottom, almost all of these concerns except ATT are still sort of simmering around. Europe still has plenty of zeal for coming up with novel (and often nonsensical) regulations. TikTok is still around and while Reels is a very effective response so far, with OpenAI’s Sora, Meta’s clock may be ticktocking for another race. Meta’s recent capex guide (\~$100 Billion for 2026) dwarf its 2023 capex outlook in 3Q’22 call ($34-39 Billion) which raised so many eyebrows back then. There is still plenty of debate around pay-off for such elevated capex. For reality labs, I suspect there is not even much of a debate. There is almost a consensus among investors that all these spending in reality labs won’t yield much return for the company in the future. I am yet to come across a Meta bull pounding the table how reality labs investments will contribute materially to Meta’s business even 5 years from now. Even though it can be hard to pinpoint which of the aforementioned reasons primarily led to Meta’s \~75% drawdown in 2022, I would argue the vast majority of the decline could likely be explained by ATT. The rest of the concerns weren’t invalid, but I suspect the rest of the concerns would have harder time having a sustained relevance in investor psyche if Meta continued to post strong revenue growth in 2022\. In reality, Meta actually posted **negative** revenue growth in 2022 for the first time in its history largely due to ATT, and all the concerns essentially fed each other to depict a draconian future for the company. ![chart](https://substackcdn.com/image/fetch/$s_!si0o!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4cf66abb-015c-45b8-aaf3-689f2f84c511_2400x1240.png "chart") Figure: Meta's drawdown in 2022; Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) The reason I am repeating the history of investor concerns around Meta is to point out that much of the concerns in the near-term are almost always likely to be outweighed as long as the company’s monetization game remains strong. Of course, the stock can go down for variety of reasons but the company today is decidedly in a very advantageous position when it comes to monetization. I have written about “golden age of digital advertising” before (see [here](https://www.mbi-deepdives.com/golden-age-of-digital-ads-llm-p-l/), and [here](https://www.mbi-deepdives.com/expanding-the-scope-of-digital-advertising/)), and Meta’s recent initiative about “Business AI” is another step in the direction of deepening the effectiveness of its ad infrastructure. From Meta’s blog [post](https://www.facebook.com/business/news/introducing-business-ai): > “[Meta is launching new Business AIs](https://www.facebook.com/business/news/advertising-week-2025) – a turnkey sales concierge and AI agent designed to help you engage, convert and build lasting relationships with your customers in the AI era. With just a few clicks, you can configure Business AI for your WhatsApp and Messenger chats, Facebook and Instagram ads, and now, also on your website. > > Business AI extends to more than just Meta’s apps. Starting today, qualifying businesses in the U.S. can also add Business AI **directly to your website** to answer frequently asked questions, make product recommendations and help drive sales.” ![](https://substackcdn.com/image/fetch/$s_!ft8o!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F867fad7f-9184-40ff-84d9-5bf2edb05bb9_748x1486.gif) Source: Meta In another [blog post](https://www.facebook.com/business/news/advertising-week-2025), Meta expanded on Business AI’s use cases: > Business AI from Meta is unique because it adds further value to your existing Meta business account and campaigns. You don’t need to know how to code or spend months configuring the agent: **Business AI learns from your existing social posts, ad campaigns, and website to provide more immersive responses for customers**. Business AI on ads will be free to use for eligible advertisers, and at a fraction of the cost of alternatives on website and messaging apps. > > To give shoppers more ways to visualize a product and inspire confidence, we’ll begin testing **the ability to see how clothing featured in an ad looks on them** after they upload a photo of themselves. ![](https://substackcdn.com/image/fetch/$s_!xJBv!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb03ba518-758b-4c80-af59-a9b4ed292c8d_640x1388.gif) Source: Meta As I [mentioned](https://www.mbi-deepdives.com/expanding-the-scope-of-digital-advertising/) before, the fact that UK CMA[ reported](https://assets.publishing.service.gov.uk/media/68598b13eaa6f6419fade67b/Proposed%5Fdecision.pdf?ref=mbi-deepdives.com) that Google’s search revenue in the last decade was largely driven by increasing click-through rate (CTR) and improving conversion rate (CVR) must be a very positive signal for Meta’s monetization runway. Since search users already have much higher intent than Meta’s users, improving CTR and CVR are likely harder for Google than Meta. With features such as Business AI, I can see how Meta can accelerate user journey from awareness to consideration to purchase. Of course, capitalizing on this monetization runway is predicated on the assumption that Meta’s properties will continue to dominate the attention economy. However, this is likely a perennial concern. No matter how many times Meta squashes or largely mutes the threat to its dominance in attention economy, something new almost always emerges. At least so far, many of these threats eventually turned out to be a very good outsourced “R&D” for Meta to figure out how to make their scaled properties more compelling for its own users! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Airbnb's mix shift URL: https://www.mbi-deepdives.com/airbnbs-mix-shift/ Last updated: 2025-10-07T14:18:45.000Z One of the more interesting, gradual shifts that have happened over the last five years in Airbnb is their geographic mix shift. The impact of geographic mix itself is often an **underappreciated** factor among investors for Airbnb’s recent operating performance. I will share some thoughts on this topic behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### GDP's absurdity URL: https://www.mbi-deepdives.com/gdps-absurdity/ Last updated: 2025-10-06T13:39:44.000Z When I read the following excerpt from the book “[Poor numbers](https://www.amazon.com/Poor-Numbers-Development-Statistics-Political/dp/080147860X?ref=mbi-deepdives.com)” last year, it was a stark reminder of lack of state capacity in many parts of the world even for collecting some routine data: > In 2010, I returned to Zambia and found that **the national accounts now were prepared by one man alone**… Until very recently he had had one colleague, but that man was removed from the National Accounts Division to work on the 2010 population census. To make matters worse, lack of personnel in the section for industrial statistics and public finances meant that **the only statistician** left in the National Accounts Division was responsible for these data as well. Coming from Bangladesh, I was well aware that it is hard to rely on statistics produced by the government. While that lack of trust often came from lack of integrity in the process, I realized I didn’t put as much weight as I should have on many countries’ lack of resources to even collect, organize, and report the data. However, after reading “[How GDP Hides Industrial Decline](https://letter.palladiummag.com/p/new-article-how-gdp-hides-industrial?ref=mbi-deepdives.com)” by Patrick Fitzsimmons (former founding engineer at HubSpot), I am starting to entertain the idea that at least in case of GDP, it may be an impossible task even if you have plenty of resources and no apparent desire to “game” the numbers. Like the author, I too have read that US manufacturing output has continued to go up over the last few decades even though labor’s share of manufacturing jobs kept going down primarily due to automation. Unlike the author, I never quite questioned this conventional wisdom. However, when he took a peek under the hood, it didn’t appear that such “wisdom” is based on strong foundation. To understand the flimsy nature of such belief, we first need to understand the nuances around calculating GDP: > Discourse over GDP is frequently confused because there are actually three different calculation approaches: the *income* approach, the *expenditures* approach, and the *value-added* approach. > > Each approach has its uses, but you have to be careful with which you use. What percent of GDP is healthcare? You get two different numbers depending on the approach. With the expenditures approach, healthcare is 17% of GDP, but for the value-added approach only 8%. Why? Because the value-added approach only counts expenditures on hospital and clinic workers toward the healthcare category. Money spent on manufacturing medical devices counts as manufacturing; money spent building hospitals counts as construction. For measuring healthcare’s share of the economy, it is probably better to use the expenditures approach because it is reasonable to include pharmaceutical production and hospital electricity bills as part of healthcare. > > **GDP is a very complicated statistical construct** that is made by government bureaucrats behind closed doors without any ability of the public to replicate, audit, or verify assumptions. Sometimes, these kinds of constructs can be useful for accurately representing real-world phenomena, like manufacturing capacity. But a dive into how the sausage is made makes clear that GDP is not one of them. So, how is the sausage made here? It is particularly striking to take a look at manufacturing: > If you want to see what percent of the economy is manufacturing, and how that has changed over time, **you can only use the value-added approach.** Only the value-added approach separates out each step in the economic chain: from mining the iron ore to transporting it to the factory to manufacturing the product to selling it at the store. The value-added approach categorizes each step, so you can sum together just the increase in price from the manufacturing step across all categories of spending. Why is that a problem? The below example should make it abundantly clear that while we may look at very tangible looking number such as manufacturing output, we actually have very little clue about what the number even means: > let’s say that, in 1997, car sales were $100 billion, and were still $100 billion twenty years later in 2017, with no changes due to inflation or input costs. Input costs in both years were $75 billion, meaning $25 billion in value-added in both years. The only thing that changed, let’s say, was that the “quality” of cars got 10% higher thanks to software innovations like Apple CarPlay and design improvements like crumple zones for safety—neither of which add to recurring production input costs. So, let’s say, our economists would adjust the 2017 figure to be $110 billion in “real” terms and show a small 10% increase, right? > > Instead, the way it works is that a recent “base year” is taken, in this case 2017, and the base year is never adjusted. So **rather than adjusting from $100 billion to $110 billion, the “real” output of 1997 is retroactively adjusted to be *lower*, in this case $91 billion, to get the same 10% increase. But then, our value-added in 1997 has fallen to $16 billion, and the *increase* in “real value-added manufacturing” has jumped from 10% to around 50%! We have created a 50% increase in car manufacturing not by actually producing 50% more cars or “objectively” making cars 50% better, but just by playing around with statistics and definitions.** > > The effect becomes even more extreme as the quality adjustment gets higher and makes the original value-added shrink to zero or negative. **If the quality adjustment is 32%, the value-added increase becomes 652%! And after that it goes infinite and then becomes undefined.** Of course, there are further complications. If the inputs are quality-adjusted in the same way as the outputs, the effect might be less, but this probably won’t happen because the methodology is quite different. **This is all to demonstrate that value-added is *not* a measure of how much stuff the United States makes**. It is a number that produces wild results and thus should not be mixed into aggregate statistics. > > I do not know if these scenarios described above are the actual reasons for why value-added is so greatly outpacing gross output. **No one else knows either**. I almost felt bad for Zambia for having just one statistician to prepare some critical data related to National Accounts, but I don’t know how to feel about it after reading how the sausage is made even in the most advanced economies of the world. As much as we pay attention to GDP numbers, I’m not sure we actually understand what we are looking at most of the time. While a lot of these concerns may be a feature rather than a bug, data quality concerns have also been affecting some other critical macro data as well. Take inflation, for example. From [Apollo](https://www.apolloacademy.com/the-quality-of-the-cpi-data-continues-to-deteriorate/?ref=mbi-deepdives.com): > When data is not available, BLS staff typically develop estimates for approximately 10% of the cells in the CPI calculation. However, the share of data in the CPI that is estimated has increased significantly in recent months and is now above 30%, see chart below. ![Significant increase in the share of alternate estimation in the CPI](https://substackcdn.com/image/fetch/$s_!8mVu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9c348d41-8b2c-47cd-82a9-6655f10c2c95_1366x768.jpeg "Significant increase in the share of alternate estimation in the CPI") Sources: BLS, Apollo Chief Economist Looking at these data, it was just another strong reminder that it may not be worth spending a lot of time looking at macro data which are much more black box than we may think. It may be much more worthwhile to follow specific companies and try to form opinions about them; perhaps it may be relatively easier to create a mosaic of the economy through this bottom-up approach than the other way around. Of course, that’s not easy either, but at least, it may be less made up than the top-down approach. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Thoughts on Alpha School URL: https://www.mbi-deepdives.com/thoughts-on-alpha-school/ Last updated: 2025-10-05T14:34:01.000Z It’s Sunday, so I thought about sharing some thoughts on a non-investing topic: schools for our kids. Before we had our first kid in December last year, I was pretty firm on sending my kid to public school. However, since both my wife and I grew up in Bangladesh (we actually met in high school), we do not have much of a clue about schooling in the US. I could pretty much summarize our knowledge about schooling in the US in two sentences: private schools are exceptionally expensive, so we can just eliminate that option. From the public schools, you can find pretty decent option if you live in a good neighborhood. We leaned pretty heavily to public school and honestly I didn’t think much of this decision until I read this [blog post](https://www.astralcodexten.com/p/your-review-alpha-school?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com) three months ago about Alpha School. For the uninitiated, Alpha School is a private school in the US that make three eye-popping promises about their schools: a) kids **must love** school, and b) kids can just do **two hours** of academic work everyday at their school and still learn at **twice the speed**, thanks to their AI-powered learning platform, and c) for the rest of the time, kids spend more time on learning life skills (think public speaking, or even running a business; some kids apparently even managed an Airbnb). Joe Liemandt, the Principal of Alpha School and the youngest member of the Forbes 400 in the 1990s, did a [podcast](https://joincolossus.com/episode/building-alpha-school-and-the-future-of-education/?ref=mbi-deepdives.com) with Invest Like The Best where he outlined Alpha’s philosophy and vision in greater detail. It’s easy to make these promises, but I was quite intrigued by the aforementioned blog post as they detailed their experience (both good and bad) of sending their kids to Alpha. Here are some excerpts from the very long [blog post](https://www.astralcodexten.com/p/your-review-alpha-school?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com): > “After twelve months I’m persuaded that Alpha is doing something remarkable—but that almost everyone, including Alpha’s own copywriting team, is describing it wrong: > **It isn’t** **genuine two‑hour learning**: most kids start school at 8:30am, start working on the “two-hour platform” sometime between 9am-930am and are occupied with academics until noon-1230pm. They also blend in “surges” from time to time to squeeze in more hours on the platform. > > **It isn’t** **AI** in the way we have been thinking about it since the “[Attention is all you need](https://arxiv.org/abs/1706.03762?ref=mbi-deepdives.com)” paper. There is no “generative AI” powered by OpenAI, Gemini or Claude in the platform the kids use – it is closer to “turbocharged spreadsheet checklist with a spaced‑repetition algorithm” > > **It definitely isn’t** **teacher‑free**: Teachers have been rebranded “guides”, and while their workload is different than a traditional school, they are very important – and both the quantity and quality are much higher than traditional schools. > > **The bundle matters**: it’s not just the learning platform on its own. A big part of the product’s success is how the school has set up student incentives and the culture they have built to make everything work together” So, what are these incentives Alpha came up with to propel kids learn faster? Again, from the blog post: > Alpha schools have their own in-house currency. Alpha has “Alpha bucks”; GT (Gifted and Talented which is one of the versions of Alpha) School has “GT bucks”. My understanding is that they work a little differently on each campus, but the overall philosophy is the same. This review will focus on the details of the GT system since it is what I know best. > > If the students complete their 2-hour learning “minimums” each day they earn about 10 GT Bucks. They get additional bonuses for every lesson they complete beyond their minimums. They also get a bonus if they finish their minimums within the scheduled time (vs going home and doing them later), additional bonuses if the entire class completes their minimums during the allotted time, and weekly bonuses for hitting longer term targets. > > They only get credit if they both complete their lessons AND get 80% or higher on the problem sets within the lesson. If they get 79% they still move on (with the questions they missed coming back later for review), but they don’t get the GT bucks associated with the lesson (this stops gaming where the kids rush through the lessons just to get “bucks”) > > A GT buck is worth 10-cents. So if they are really pushing a kid could be earning roughly $2 per day. > > Once a kid has earned a collection of GT bucks they can spend those bucks at the GT-store. The Alpha store has a wide selection of offerings. The GT store, because it is a much smaller school, is more like a catalog. The kids can select what they want and the school will order it so it is ready when they earn enough “bucks”. Every kid has their own personalized incentive – do the school work and they will get their personalized prize. As someone in the investing profession, I am aware of the power of a well designed incentives system. I was particularly drawn to it because of how simple it is. If this is a core reason for Alpha’s success (as the parent put it), that’s great news since it would be not that challenging to come up with your own version for your kid and implement it. Of course, we all know we are supposed to eat vegetables, and yet not many people do. So, I am not ignoring the implementation challenges, but it was encouraging to read that with some simple incentive tricks, you may be able to get some of the results Alpha is seeing in their schools. After googling a bit, I came to know Alpha was actually coming to do an information session in a nearby city which is just 20 minute drive from our place. My wife and I both, along with \~50 other parents, attended the session a couple of months ago. One of the things that really stood out to me from the session is how many of their ideas are incredibly simple and intuitive. In traditional schools, a kid who is in 6th grade learns 6th grade math, science, and language lessons. But isn’t it much more likely that if the kid is much more interested in math, they might be able to accelerate their learning to 8th grade level math while they may actually be in 5th grade language skill (or vice versa)? This was perhaps always the case, but in post-AI world, a lack of personalized learning pace seems awfully anachronistic to me. Alpha’s personalized learning platform just seemed “common sense” in today’s world. Unfortunately, I doubt public schools in the US have the capacity to change their learning environment rapidly to meet such demand. To my disappointment, Alpha decided not to launch a school in the nearby city we visited. They didn’t provide any rationale, but my guess is it is due to lack of interest from parents. I do think many parents who attended the session were interested but may have been dissuaded by the price tag which was \~$50k/year. While such costs for private schools are quite common in NYC or Bay Area, private schools are hardly a thing in Sacramento. In fact, when I discussed this with a couple of my acquaintances, they were utterly shocked that I did not laugh at the price tag. As you know, it can be excruciatingly difficult to compete against “free” even if the “free” comes with potentially a large long-term hidden but uncertain cost. Both my wife and I did pretty well in traditional school. Of course, schooling was dirt cheap in Bangladesh, and I pretty much excelled academically wherever I went both in Bangladesh and in the US. So, I’m not speaking from some deep seated negative experiences I personally felt, rather I believe given how the world is changing, it seems less than ideal for my kid to go through the same schooling experience we did. I will end with what perhaps draws me to Alpha school the most. If my kid can learn at his own pace and finish his high school potentially faster, it may offer him some options to tinker with certain things before heading to college. Maybe he can take a year or two to pursue something he’s deeply interested in intellectually (teens are often underestimated by society how much they can intellectually accomplish), maybe he can start a business, maybe he can just travel with me to a bunch of countries and see the world, or maybe he can live in Bangladesh for a while and do nothing. Having options in life is valuable. Instead of staying in a well trodden path, sometimes you can get a much better idea about yourself and what you would like to do if you can afford to wonder and wander a little. We haven’t made any decisions yet, but at the very least, I will perhaps implement some sort of incentive system to guide our kid’s learning. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### OpenAI's admirable pace of iterations URL: https://www.mbi-deepdives.com/openais-admirable-pace-of-iterations/ Last updated: 2025-10-04T15:20:36.000Z I often see people admonishing Google for literally inventing the transformer architecture and yet not being able to productize it before OpenAI launched ChatGPT. We may not have to wonder about the hypothetical scenario where Google would be today if they were the ones who launched “ChatGPT” first. Exactly a week before ChatGPT was launched as a “research preview”, Meta did unveil its LLM: Galactica. Despite launching it before OpenAI did, Meta did not enjoy any “first mover advantage”; in fact, it was widely ridiculed. You could get a glimpse of the reaction in this MIT Technology Review [piece](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science/?ref=mbi-deepdives.com): > Like all language models, Galactica is a mindless bot that cannot tell fact from fiction. Within hours, scientists were sharing its biased and incorrect results on social media. > > It’s a tantalizing idea. But suggesting that the human-like text such models generate will always contain trustworthy information, as Meta appeared to do in its promotion of Galactica, is reckless and irresponsible. It was an unforced error. Galactica survived three days before Meta decided to shut it down. Four days later, ChatGPT was [launched](https://openai.com/index/chatgpt/?ref=mbi-deepdives.com). ChatGPT too had plenty of hallucinations even when Bing [integrated](https://dkb.blog/p/bing-ai-cant-be-trusted?ref=mbi-deepdives.com) ChatGPT three months later. In retrospect, it clearly was a mistake by Meta to shut Galactica down. They should have ridden through the vitriol by resetting people’s expectations about the model’s capability. It is rarely the case that big incumbents are completely blindsided by a new technology’s capability, rather it is their lack of imagination to build a product out of new technology that is often the real reason for their downfall. In a recent “Cheeky Pint” [episode](https://cheekypint.transistor.fm/12/transcript?ref=mbi-deepdives.com), Marc Andreessen explained this recurring theme: > …the other twist on the big company failing thing, which I think is really underrated, is the big companies that fail, the way the story gets written is they never figured it out. And the easy example of this is always Kodak for example, they never figured out digital photography. Well, you often find in the backstory is no, they actually figured it out and they did it too soon. Kodak had actually a very active digital camera program. > > Yahoo had mobile early. Yahoo was all over mobile between 2002 and 2006\. And then they got burned so hard on it that by the time the iPhone appeared it was too late. > > quite frankly, I think a lot of the tech you mentioned the big tech companies, a lot of the big tech companies, they had internet fully deployed internally. They had TCP/ IP products. They actually knew it quite well. They were running it. It just was something that they were very used to that they didn’t really think about in any way. ChatGPT’s success was a surprise to everyone, including OpenAI and Microsoft. However, it wasn’t the ChatGPT launch itself that I found most impressive about OpenAI. Once OpenAI became the “accidental consumer tech company”, what I have found the most admirable about the company is their elevated pace of iteration. After the initial product market fit with ChatGPT’s launch, they have consistently set the pace for the entire industry, and the rest of the companies have mostly played catch up for the last three years. OpenAI was also the first to launch **reasoning** models which was certainly an accelerant for me personally to be increasingly less concerned about hallucinations. Their studio Ghibli moment was another viral reminder for most people about ChatGPT’s use cases beyond just seeking information. And now with Sora 2 launch, OpenAI maintained its hot streaks as despite being an invite only app, it has reached the top spot in the App Store. ![](https://substackcdn.com/image/fetch/$s_!ncjJ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff23286a8-ba39-43fa-9962-46350c89cde7_1284x1784.jpeg) The original ChatGPT launch may have been an “accidental” success, but you cannot evoke luck too many times to explain a recurring phenomenon. Even though they have lost plenty of marquee talent over the last three years, OpenAI still has their magic. For most public market investors, Sam Altman and consequently OpenAI have been polarizing topics, but I don’t think it should be controversial to admire how the company navigated the last three years to still stay ahead of some of the most well funded incumbents in the history of capitalism. Like the Galactica launch, Meta was again ahead of OpenAI in launching its own AI generated video content feed named “Vibes” on Meta AI app. Again, they were lambasted by almost everyone (**excluding** Ben Thompson) for launching “AI Slop”, only to find people looking for invite codes for Sora 2 a couple of days later. I don’t quite participate in people’s hysteria around short-form video or “AI Slops”, but it didn’t take me too long to understand “Vibes” is likely to be doomed not because of people’s aversion to “AI Slops” but because it’s just a bad product. As if it were already not quite apparent, Sora 2 made it painfully clear to Meta what a good product sense can deliver for many people to rave about “AI slops”. At least, Meta hasn’t shut down “Vibes” yet and perhaps they will fast follow OpenAI soon. But it can be hard to find the same charm when you’re just copying others, and unlike Instagram copying from TikTok or Snap, “Meta AI” app doesn’t have the distribution to compensate for being laggard. What is perhaps more likely is “Sora 2” like feature will be embedded in IG DM, WhatsApp, or Messenger to allow users have fun with this in their existing chat groups. That’s been my experience so far; we mostly exchanged sora videos in our existing DM groups instead of opening a new DM group on Sora app. However, it may be challenging to embed a Sora-like **feed** on Meta’s existing scaled distribution. While people seem to have strong opinions about “AI slops” vs “human slops” today, my weakly held opinion is in the long run, people won’t care much about such differences and what will ultimately matter is the quality of the content regardless of how it was created. If I find something funny, I wouldn’t stop laughing just because AI made it. Reels feed already have plenty of AI generated content and while some seem to have some aversion to such content, algorithm will and can easily respond to such preferences. However, if it’s just another instance of the dichotomy between revealed vs stated preferences, AI generated videos increasingly will have greater presence in people’s existing feed. Remember, we already are in a content glut; so the more durable “product” may just be the algorithm itself. While Sora’s success is unlikely to be a harbinger of doom for Meta, it is nonetheless a concern just how badly the company has been fumbling in AI in recent months. They are coattailing on their existing distribution moat a bit, but their execution muscle has been lacking in some of the recent events/launches. Google does seem to have found some of its mojo back, thanks to its own viral moment related to Nanobanana. Even though Meta stock has experienced a 10% drawdown, market still seems to acknowledge that AI remains largely a boon for Meta. ![chart](https://substackcdn.com/image/fetch/$s_!zI-O!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc43f404-9637-4a51-b4bb-9cd90cbb9404_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Interestingly, OpenAI also seems to be moving faster this time to monetize their viral moment. Unlike human generated content, the marginal cost of these AI generated content is not zero. Sam Altman yesterday [hinted](https://blog.samaltman.com/sora-update-number-1?ref=mbi-deepdives.com) at moving to monetization very soon: > we are going to have to somehow make money for video generation. People are generating much more than we expected per user, and a lot of videos are being generated for very small audiences. Given the “feed” on any app can be exceptionally conducive for ads, we may see OpenAI launch ads in Sora soon. It’s also interesting that Sora is only available on iPhones so far. While that is another recognition of the power of iOS ecosystem, it also made me wonder that when OpenAI launches its own hardware, they may make some features exclusive only for their hardware for the first few weeks/months. And if they can maintain their hot streaks of coming up with new product iterations, that may directly influence their hardware sales. A friend recently told me that every big tech company will eventually have their OpenAI scare moment. Google has already experienced it and has been dealing with it, and Meta is probably in the early stage of that similar process. Perhaps Apple is next when OpenAI launches their hardware. No matter what your opinion is about Sam Altman or OpenAI, there is something admirable about a company with \~5-10% of employees of all of these big tech companies legitimately creating a sense of fear for their respective empire! --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### First impression of Meta Ray-Ban Display, Taking a big swing at Airbnb URL: https://www.mbi-deepdives.com/first-impression-of-meta-ray-ban-display-taking-a-big-swing-at-airbnb/ Last updated: 2025-10-03T14:01:52.000Z Yesterday, I drove for 40 minutes to go to a Best Buy store where I scheduled my demo for [Meta Ray-Ban Display](https://www.meta.com/ai-glasses/meta-ray-ban-display/?ref=mbi-deepdives.com) glasses. When I reached there, I could see that Best Buy had a prominently displayed corner for Meta Ray-Ban. I saw a guy who missed their demo on the 30th and was asking the salesperson whether he could get the demo now. The salesperson said they’re fully booked for the demo for the whole day, and he would have to reschedule the demo to get a slot. He asked if he could buy the glass without the demo; he could and he did. There was also another woman who seemed to really want to buy the glass but ultimately decided otherwise because she wanted to wait for the sand colored frames (they only had black ones). Unfortunately, my demo did not go well. The salesperson claimed the glass and wrist band were low on battery which is why they weren’t working properly. I later realized that the salesperson made me wear the neural wristband incorrectly which made it near impossible to operate the glass through gestures. After hearing that I could return the glass in the next two weeks if there are any issues, I decided to just buy it despite the negative demo experience. So I came home, charged it for about an hour or so, and then decided to try it out. It turns out you really don’t need the demo. Meta’ instructions and set up process were simple and clear. However, it definitely takes a while to acquaint yourself with how to operate the glass through gestures via your neural wrist band. I kept making silly mistakes, but after an hour or so, it started feeling quite natural as it almost became a muscle memory. They have a game called “hyper trail” which I suggest you should play to learn to use your gestures to operate the glass. You can also operate the glass by touching the right frame, but once you get used to the gestures, it’s more convenient to operate through the neural band. Here’s what Ray-Ban Meta (the earlier version) and Meta Ray-Ban Display glasses look like side by side. I prefer the earlier case, but the Display’s frame feels much nicer. ![](https://substackcdn.com/image/fetch/$s_!XrmV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F39cc4429-94c3-4637-8198-a9c0e9a64d7e_4021x2111.jpeg) Once you get a hang on the wrist band, it really is an incredibly cool experience. Just like the earlier version, I could take photos and record videos, but I used to have to open my phone to actually see those photos and videos I took. Now I could see them directly on my glasses, browse my photos, and zoom into them if I want. When I ask questions to Meta AI, I can now actually read the response as well which I thought was a pretty neat experience. I connected my WhatsApp, Instagram, Messenger, and Facebook to the glass. I could read my messages through the glass; however, I could only read like last \~15-20 messages, not the entire chat conversation. Similarly, I could only read last 10 chats, so I didn’t have access to all my chats. I could dictate to respond to my chats and send message through the glass, but I couldn’t write it through gestures yet (I believe this feature will be available later). Interestingly, I could watch Reels if someone sent me one in IG DM, but there was no way to browse my IG or Facebook feed. Watching Reels on the glass is a pretty cool experience, and I would definitely browse through some Reels if I could do that. The biggest disappointment in the glass is **there is no browser**. So if I clicked a link, it wouldn’t open it on the glass and just send me a notification of the link to the “Meta AI” app on the phone. Similarly, there is no YouTube. It would be really amazing to watch YouTube videos while walking, cooking, or doing some other work. Audio experience on Meta Ray-Ban Display wasn’t a huge upgrade either. Just like earlier, I could listen to music and podcast through the glass but there was very limited functionality of the Spotify app through the glass. I couldn’t browse the Spotify app or watch video. It would be really nice if I could listen to music/podcast and pull up the lyrics/subtitle along with it through the glass. I tried “captions” which let you see live captions of your conversation with someone. It’s not 100% accurate, but it’s decent enough. Not sure I will trust its translation yet if I’m speaking with someone in a different language. The “Map” app is also nice, but given its limited functionality (you can search by dictating, but it often gets it wrong), I can’t imagine myself using it much over Google Maps on my phone. Meta AI remains spotty. Sometimes it just doesn’t respond anything. It is nowhere close to be as natural as it seems to talk to ChatGPT. There was also no way to connect your emails, so you definitely need your phone if you want to check or read your emails. **The killer feature in Meta Ray-Ban Display glasses is video calling**. While Zuck and Boz couldn’t make it work during this year’s Connect, it really is an amazing experience. While the person on the other side only sees your POV, you can see the person on the other side pretty clearly. I have a 9-month old son and given my parents live in Bangladesh, we do a lot of video calling. Personally, a POV video calling is quite a handy feature in this context. Overall, without doubt, it’s a pretty cool experience. If you already didn’t have Meta Ray-Ban glasses, you would probably be more blown away by this than I was. For me, it was cool but more of an incremental progress than a massive jump. It does cost more than double that of Meta Ray-Bans though, so unless you do a lot of video calling, Ray-Ban Meta (the earlier version) may be good enough for most people. Even if you love video call, presumably in most cases, the person on the other side wants to see you and not just your POV; so, the phone still does have an advantage there. It is quite clear that Meta needs an app store for the glass. With a thriving app ecosystem, it is not hard to imagine how the glass could be much more interesting and useful over time. Smartphones are still very much safe, but Meta Ray-Ban Display glasses are a pretty clear glimpse of the future. Apple and Google may still have a decided advantage here if they could somehow export their current app ecosystem to glasses as well. Apple’s recent [push](https://9to5mac.com/2025/10/02/apple-glasses-now-rumored-to-follow-rare-strategy-for-launch/?ref=mbi-deepdives.com) to unveil glasses in 2026 (and ship in 2027) is perhaps an acknowledgement that they too realize that the glass may indeed become the next dominant computing device and that reality may be sooner than we (or Apple) thought. While Meta Ray-Ban Display felt incremental to me personally, the path seems increasingly clear that this is indeed the right race for Meta to stay focused on. Google and Apple may have theoretical (and real) advantages here, but so far, it is only Meta who is shipping. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- I decided to take a big swing at Airbnb yesterday. I will share some thoughts on this behind the paywall. _This post is for paying subscribers only._ ### Constellation's Succession URL: https://www.mbi-deepdives.com/constellations-succession/ Last updated: 2025-10-02T13:20:21.000Z ***A programming note***: I am currently working on a Deep Dive on **Instacart** which I expect to publish by 25th of this month. I have recently spent some time in understanding Amazon’s ambition in groceries (see [part 1](https://www.mbi-deepdives.com/groceries%5F1/) and [part 2](https://www.mbi-deepdives.com/groceries%5F2/)); given Instacart’s success in grocery delivery, I feel it necessary to spend some time on studying their business. --- After Mark Leonard’s sudden resignation due to health concerns, Constellation Software had a conference call yesterday to discuss their succession plans and give the new CEO Mark Miller the opportunity to calm the investors. In the late 80s, Miller founded a startup which developed software for transit agencies. He ended up joining Constellation when his company was the first company Constellation acquired back in 1995\. In just six years after joining Constellation, he became Chief Operating Officer of the whole company. So, he is certainly no stranger to Constellation’s culture, philosophy, and playbook. As a result, during the call yesterday, Miller largely reminded investors that even though Mark Leonard is not at the helm anymore, investors should not expect to see much changes to Constellation’s strategy going forward. His capital allocation philosophy seems to largely mirror Leonard’s. Miller explicitly mentioned that he is not a fan of lowering hurdle rates. Predictably, there were quite a few questions on AI. Miller mentioned they intend to be “fast followers”. From the call: > We tend to be fast followers. So our business leaders, particularly our better business leaders are usually quick to respond to changes inside of their markets due to moves their competitors make or new entrants make into the market. Last week, Miller [bought](https://x.com/CJ0pp3l/status/1971852688733491244?ref=mbi-deepdives.com) 275 CSU shares in the open market. An analyst asked if they might think about buying back shares. Miller said the following: > we’re not considering buying back shares, and I don’t think there’s anything you can extrapolate into that other than both John and the Chairman and myself acquired some shares. We really just felt it was sending a signal to people that we were very comfortable with the company and its long-term future. That’s all that’s to be read into that. I don’t mind CSU not buying back shares. If anything, you could argue if they started buying back shares at current prices, it might give a negative or at least a confusing signal about their capital deployment opportunities. However, the very last question in the call reminded me that Miller seems to have the exact same blind spots as Mark Leonard had about buybacks. Notice the interaction between an analyst and Mark Miller: > Analyst: just a clarification, the answer you provided before on the buybacks, is that just a philosophical thing where we’re not interested in buybacks? Or is it a reflection of when you look at the valuation today relative to the opportunities > > Mark Miller: From my perspective. philosophical thing. We’ve never been a fan of doing buybacks at Constellation. The question was such a softball, and yet Miller’s response reminded me Mark Leonard’s 2013 shareholder letter in which he discussed his “philosophy” on buybacks: > “Buybacks are tempting to management and boards: they tend to improve the lot of managers and insiders, while being applauded by the business press. I think they are frequently a tolerated but inappropriate instance of buying based upon insider information. Instead of shareholders being partners, they become prey.” I wrote the following in my CSU Deep Dive back in [March 2022](https://www.mbi-deepdives.com/csu/): > It all seems puppies and kittens when your stock just continues to CAGR at 20-30% in the last 10-15 years, but I bet if/when the stock ever experiences a 50% drawdown and many of his millionaire employees become non-millionaires, Leonard will think about “preying” on some shareholders In some sense, CSU management has the opposite disease of many Silicon Valley tech companies’ CFOs who like to “offset SBC by buying back shares”. Perhaps you get CSU’s philosophical nonsense when your stock never experiences 30%+ drawdown. It is extremely likely that their “philosophy” will be tested at some point in the next couple of decades, and it may prove to be very expensive philosophy if the then management holds onto the idea of not “preying” on shareholders. This doesn’t mean CSU will just let its cash pile up in the absence of compelling capital deployment opportunities in the long term; they will probably return capital via either common or special dividend when they reach such a phase. So, I don’t worry a great deal about their “philosophy” on buybacks. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Airbnb Model Update URL: https://www.mbi-deepdives.com/airbnb-model-update/ Last updated: 2025-10-02T01:59:36.000Z I first published my [Deep Dive on Airbnb](https://www.mbi-deepdives.com/abnb/) three years ago. The stock has been largely flat since then whereas its closest peer Booking’s stock price tripled during the same time. Now that I have started buying Airbnb stock this year, I thought about updating the model and then explain why I consider the risk-reward to be quite attractive here. I will share the model and the rest of my thoughts behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!QYHk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F87c6356d-530a-4768-965b-1f471bf114c6_1408x871.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Expanding the scope of digital advertising URL: https://www.mbi-deepdives.com/expanding-the-scope-of-digital-advertising/ Last updated: 2025-09-30T15:49:39.000Z Every once in a while, you may have heard (or wondered it yourself) that Meta must be listening to us given how well targeted its ads are compared to almost any other platform. Indeed, compared to most other platforms (other websites, apps, or TV, radio, podcast ads), Meta’s ads are much better. But still, it feels like the “dumbest smart algorithm” out there. I have always thought that one of the enduring bull cases for Meta is that there must be a long way until we reach an asymptote of good personalized ads relevant only for an individual user. Let’s think for a moment how many ads an average user on Meta’s Family of Apps (FOA) properties see everyday. Say, they spend 60 minutes per day. Within this 60-minute, they spend 20 minutes scrolling the feed, 25 minutes watching reels and stories, and 15 minutes on messaging (likely higher for a global user than US user for messaging). Meta doesn’t disclose these details specifically, and there are different estimates out there for all these variables; for the point I’m trying to make here, I am not striving to be accurate, rather just some ball-park estimates. Feeds have the highest ad density (1 ad every 3-4 posts). If you assume each post takes 5 seconds to consume, you will see 12 piece of content per minute, \~3 of which will be ads (25% ad load). **Assuming the user spends 20 minutes on the feed per day, they will see 60 ads per day.** Ads do not appear within private conversations. However, display ads are present within the Messenger inbox. Exposure depends on how often a user returns to the inbox (e.g., to switch conversations) rather than a continuous scroll. Let’s assume minimal exposure during this time, estimating **2 ad exposures** from navigating the inbox. Ad load is increasing in video formats but likely remains lower than the feed. Video requires slightly longer viewing time than static posts, balanced by quick skips. Let’s assume the user views 6 pieces of short-form video content per minute which means they will watch 150 videos or stories per day. **Assuming 20% ad load, that’s 30 ads per day.** If you add it all, an average Daily Active User (DAU) is seeing **92 ads per day or \~34k ads per year**. If you assume Click-through-rate (CTR) is \~1-2%, a DAU is clicking 510 ads every year at mid-point of that CTR assumption. Following the CTR, let’s assume \~5-10% ads actually convert (CVR) to sale which implies only 38 ads (at mid-point of CVR estimates) converted to sale from the initial \~34k ads that the user has seen over a whole year. Despite the sheer volume of ads that a DAU sees on Meta’s properties, it basically is only **able to convert just three ads per user per month**! UK CMA’s [report](https://assets.publishing.service.gov.uk/media/68598b13eaa6f6419fade67b/Proposed%5Fdecision.pdf?ref=mbi-deepdives.com) on Google also bolstered my belief that there should be a long runway for Meta to improve CTR and CVR. Compared to Meta, Google’s search queries have much higher intent and hence, targeting and attribution are lot more straightforward. Despite that, the report suggests **improving CTR and CVR were the primary growth drivers for Google search in the last decade or so**. The CMA report mentioned while commercial query growth was **below 3%** during 2015 to 2024, **real search revenue growth (**not nominal**) i**n the UK was still growing **low double digit CAGR** primarily through higher conversion and click-through rates. If higher CTR/CVR contributed so disproportionately for Google, Meta should have a decent runway for revenue growth once they run out of users to grow their userbase. ATT was a major headwind to this story, but AI has resurfaced the possibility of durable ad revenue growth. For Meta, AI should be a durable tailwind in improving their ad infrastructure. Eric Seufert has [written](https://mobiledevmemo.com/will-ai-efficiency-impact-ad-load/?ref=mbi-deepdives.com) about how AI efficiency could shape ad loads: > “Generative tools, coupled with AI-empowered automation, **will result in ads being better targeted and more resonant with consumer preferences**. > > As ads become better aligned with consumer tastes, **the probability that any given ad results in a conversion increases**.” In fact, as targeting improves, ad load may decrease on Meta over time as disposable income can be the real constraint. In that case, it may make more sense to “entertain” the user without showing too many ads: > “…ads do not follow a Bernoulli process: each trial (ad exposure resulting in a conversion or not) is *not* independent. **The success of an ad exposure is influenced by a consumer’s available disposable income**, which serves as a hard constraint on conversion. The success of any ad exposure influences subsequent ad exposures, since an ad-driven purchase reduces a consumer’s disposable income. > > **if a consumer’s disposable income is depleted, the conversion probability for all future ad exposures falls sharply**. > > As a result, **ad platforms may reduce ad load to better align the volume and timing of ad impressions with a consumer’s capacity to make purchases**, moderated by higher conversion rates in the ads they see.” It is also possible that instead of decreasing ad load, more and more ads can move to brand advertising once disposable income is depleted for a particular period: > “Another potential adaptation that ad platforms could make to navigate the disposable income constraint that becomes more acute with improved conversion rates is to shift more impressions to awareness ads that aren’t intended to incite an immediate response. This could result in ad load mostly remaining constant, with the mix between direct response and brand changing.” In a more recent [piece](https://mobiledevmemo.com/ai-enabled-advertising-and-the-sparse-data-consumer/?ref=mbi-deepdives.com), Seufert made another interesting point about scope of digital advertising potentially expanding as the ad infrastructure is improving over time, especially for SMBs: > I’d contend that the businesses — almost entirely SMBs — onboarded to the advertising economy will bring customers who were likewise not being targeted by advertising with them. In its [**Q2 report**](https://www.census.gov/retail/mrts/www/data/pdf/ec%5Fcurrent.pdf?ref=mbi-deepdives.com), the Census Bureau of the Department of Commerce estimates that eCommerce comprised 16.3% of total sales. Capital One [**estimates**](https://capitaloneshopping.com/research/online-shopping-statistics/?ref=mbi-deepdives.com) that **while 84% of US consumers shop online, only 30% of US consumers shop *primarily* online.** > > In other words, **the local component of the real economy still captures the overwhelming majority of consumer spend**. But it’s eCommerce and the app economy that provide the data inputs for advertising targeting: online conversions are captured and propagated in the digital sphere and aggregated for advertising targeting based on digital identity. While offline retail sales can be attributed to digital campaigns (for instance, [**Meta’s CAPI can receive offline events**](https://developers.facebook.com/docs/marketing-api/conversions-api/offline-events/?ref=mbi-deepdives.com)), this almost certainly represents a small fraction of the overall data used for targeting. > > **What this means is that some proportion of consumers are currently undermonetized through digital advertising, given that their retail engagement takes place outside of the scope of traditional digital advertising targeting. In other words, these consumers are undervalued by eCommerce advertisers** — they either have no data footprint or their footprint is too sparse to be useful for targeting. If the local retailers they *do* engage with are onboarded to digital advertising platforms, those users become targetable *for those retailers* given their geographic and interest-based relevance. This is expansionary. > > Note that my argument isn’t that these consumers are not social media users currently, or that they don’t generally use the internet. Rather, my point is that the number of consumers who receive targeted, direct response ads will increase as more local, SMB retailers are onboarded to advertising platforms. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Never Sell: Episode 10-Lululemon, Align, LLMs and Newsletters, Portfolio Holdings, Right for the Right Reasons URL: https://www.mbi-deepdives.com/never-sell-episode-10-lululemon-align-llms-and-newsletters-portfolio-holdings-right-for-the-right-reasons/ Last updated: 2025-09-29T12:53:16.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode. You can listen to it here: [Spotify](https://open.spotify.com/episode/698Qic75tIlmcqYoV7RqzX?si=f3msNmUuSveyXLwVWsmJVw&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/lululemon-align-llms-and-newsletters-portfolio/id1786912203?i=1000728947337&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=XC6EP4QRX3o&ref=mbi-deepdives.com), [RSS feed](https://feeds.buzzsprout.com/2435713.rss?utm%5Fsource=substack&utm%5Fmedium=email) As the title suggests, we tried to cover a bunch of topics, but I wanted to highlight our discussion on Lululemon. Lululemon has been a big loser for me in 2025 and through my introspection, this episode may be some source of vicarious learnings for you. As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. [Subscribe](#/portal/signup) ### Reader Response to "AI Overinvestment" URL: https://www.mbi-deepdives.com/reader-response-to-ai-overinvestment/ Last updated: 2025-12-05T14:10:05.000Z A couple of days ago, I wrote a piece titled “[**What if there’s an overinvestment in AI?**](https://www.mbi-deepdives.com/what-if-theres-an-overinvestment-in-ai/)**”** I received some thoughtful emails in response to that piece. One reader named Craig Melillotook the time to share a more detailed thoughts which he granted me the permission to share with all of you. I am copying his email below which I think adds to my aforementioned piece (slightly edited; emphasis mine): --- “This is/was a great exercise. I am sure you are not alone in attempting this (I know that I am and have). I think the four incumbent hyperscale spenders (AMZN, META, GOOG, MSFT) are in somewhat of a “tails I win, heads you lose” situation given their AI spend to date is discretionary and it is not damaging the balance sheet. Some capacity will get utilized immediately, and some capacity will get utilized eventually. The attached revenues might not be what investors hope for, but it doesn’t kill them and IRR is likely >0%. In your scenario, NVDA would be in for a really tough time, which likely means in the short term, at least for stock prices, that everyone would be in for a tough time (especially ORCL, neo clouds, power supply chain). Open AI is its own case, and the $500B post-money from this past month plus a $20-25B exit rate annualized revenue will keep private shareholders trapped. Maybe the bubble popping solves inflation, enables rate cuts, and strengthens the dollar. I am not exactly going out on a limb with this next statement, but I see three potential “events” that could act as rubber meeting the road event that pours some water on the fire: 1. Scaling laws asymptote and there’s a realization that digital God isn’t around the corner which removes the biggest totem that public markets and CEO’s both have in terms of giving permission to chase the dream. How does this manifest? More respected skeptics speak on it, and the frontier models level off. 1. I think Dwarkesh Patel made a really good [point](https://www.dwarkesh.com/p/timelines-june-2025?ref=mbi-deepdives.com) a few months ago when he said that **the more time that goes by without achieving AGI, the less likely it becomes that we get there at all**. If we assume that we do not get AGI within 3 years, then how much incremental value does a general purpose model that “just” provides longer context windows, memory, and faster response times actually provide if tomorrow’s small models are good enough (and what is the willingness to pay?). Training frontier zero-revenue models on chips that become obsolete for training purposes every 18 months is not sustainable. Ben Thompson made the point that early on in his career, he initially underappreciated society’s collective demand for better technology for the sake of better technology (maybe I am making that same mistake), but in this instance the cost of the incremental improvement is quite literally measured in the tens of billions of dollars (and you don’t know the return ahead of time, so it’s another leap of faith after the previous leap of faith did not deliver). Also, the predominant use case of today’s models are “free to me” inference, so the enterprise NEEDS to adopt this stuff, and if you listen to Accenture’s call yesterday, basically the large enterprise is nowhere near ready for it. Obviously GPT5 is much more accurate than GPT4 due to better reasoning, but if enterprise use cases are going to be grounded in domain specific data, then hallucinations should be lower in those cases lessening the demand for improvements in reasoning that are intended to reduce hallucinations. So how many bites at the apple will OAI get to train the next frontier model if they cannot live up to their own promises? 2. Any one of the cloud companies stops saying “demand exceeds supply” or implies that they have sufficient capacity to meet demand (implies AI demand decel, not growth decline) 1. If it takes 18-24 months to stand up an AI datacenter, then we really only have like 12-18 months of AI data center investment online since the hyper scalers actively committed to going pedal to the medal on investing in 2024\. So, it makes sense that collective demand exceeds supply. But if collective supply goes up by 2-3x over the next 18-36 months, will there be a smooth 1:1 balance with demand? i.e. can AI tokens continue to growth exponentially for the current base? Maybe, but what is the willingness to spend in the face of excess supply? We don’t know, but if MSFT doesn’t want to build training infrastructure, then we know someone will say “we’re good”. 3. We see one/some of the negative margin AI model wrappers run out of funding and there’s reluctance for VC’s to step in (this is the least likely one given sovereign money will be there as long as the first two events don’t happen). You point out that if you were in Zuck’s shoes then you would continue to invest too, and I agree that it is the right move. **Everyone in the value chain is acting rationally and in their own best interest given everything they currently know: scaling laws have more or less held up keeping the dream for “digital God” alive, demand for tokens/AI inference capacity exceeds CURRENT supply, and all competitors are choosing NOT to cooperate**. I have a short take on each, and figured I would share. - **META** – META is competing for consumer screen time directly with LLM’s as well as traditional competitors YouTube and TikTok. They need to make the product “AI native” to retain/grow mindshare. I don’t know if they will get a model that will act as “the draw” to the app for consumers, but they need a model that will drastically improve the recommendation engine, enable pro-grade multi-media content creation for users and advertisers, and then be good enough to act as an LLM destination for people that don’t want to leave the app. This is putting the augmented reality dream to the side for now, because I’m sure there is a lot synergistic spend for RL in here. - I think Meta will prove to be the most difficult company in terms of disaggregating “AI revenue” versus “non-AI revenue”, as they may already be generating AI revenue via ad creation tools, improved targeting, - **GOOG** – they are doing as well as you could hope with balancing monetization and disrupting oneself. YouTube will likely only increase in value and perhaps search has a much higher floor than the worst case fears. OpenAI may need to launch an ad supported model to keep the dollars flowing, and this could potentially steal from Google’s ad revenue (and ignite some ‘search is dead’ fears for a little). But Google Cloud, YT, and Gemini are crushing it. - **MSFT/ORCL/OpenAI** – the only one you could argue who is not acting rational is MSFT, unless they firmly believe we will not see the digital God dream (in which case, then they are acting rational and are optimizing for future inference demand that needs to be available when enterprise clients ultimately need it). MSFT is pretty much telling us that the incremental value of training frontier models will have diminishing returns. **We want to applaud Oracle as “the winner” but they’re winning because MSFT is allowing it**, and because OpenAI doesn’t have an existing business that prints $100B+ of operating cash flow per year to fund the spend. Maybe MSFT is wrong, and this will be the most obvious fumble ever, but they have skin in the game, but they were early to OpenAI so there’s a track record of seeing around the corner a bit with Satya. I don’t think they have an innovator’s dilemma even as/if OAI says they want to takeover the workplace application market. the G Suite is essentially free, and it MSFT has done quite well with its coexistence. Oracle has no other path to fast track itself into the hyperscaler conversation. Ellison is 81 and in it for glory, so he figures why not attach myself to a potential anchor customer in OAI (and now TikTok) that can maybe do $100B+ per year with me. I suspect there will be a lot of collateralized, non-recourse debt in their Stargate arrangement. - **OAI/NVDA** \- OAI wants to be everything yesterday, but they only have one product (though it’s a general/world model), and they don’t have the organic cash flow to get there. So how do they solve it? They sell the dream to anyone and everyone who will listen to raise the money. The backers (NVDA/ORCL) need to adopt the same approach, and their leaders have no problem doing so. 18 months ago, Altman tried to say he would manufacture chips and TSMC laughed at him because they can’t afford to waste their time. Today, Altman has aligned himself with two owner operators in Huang and Ellison who have long histories of selling the dream. Since there is a hype market for it, it feels dumb for people to push back on it in the moment. NVDA also needs as many customers as possible to apply pressure to MSFT/AMZN/GOOG who are developing their own chips internally. To the extent NVDA can keep the market tight for GPU’s it means competitors are running fast and providing less leash for the ASIC customers to experiment with pushing internal chip efforts, and it forces the HPC’s to keep buying. This is also why Huang is so keen on keeping the chips flowing to China (and Singapore).” --- I thought this was very well grounded discussion. The only people I disagree strongly with are people at the both extremes: “AGI is just around the corner” and “this is the most obvious bubble in the history of mankind”. Of course, there are gazillions of scenarios between these two extremes and navigating these scenarios remains the key challenge for the next few years. Thank you, Craig for sharing your thoughts with me and allowing me to publish it on MBI Deep Dives. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Buying Constellation Software amidst its consternation! URL: https://www.mbi-deepdives.com/buying-constellation-software-amidst-its-consternation/ Last updated: 2025-09-27T14:57:27.000Z After owning Constellation Software (CSU) for almost three years, I wrote the following in [April 2025](https://www.mbi-deepdives.com/snps/) (see final section): “I do not see anything particularly concerning about the business today. It’s a very well run business and run by pretty competent and high integrity management. However, I think the risk-reward from valuation perspective is far from attractive at current valuation. Speaking of better ideas from risk-reward perspective, I re-allocated my CSU sale equally to four big tech companies (Meta at $500, Amazon at $165, Microsoft at $378, and Google at $154).” In a year I had some material misses, this turned out to be one of the better decisions! ![](https://substackcdn.com/image/fetch/$s_!Ueu-!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fceb0736e-59c3-4e45-be99-9276d9d03ae9_1410x868.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) One of the beauties of public market investing is even the best companies go through a period of discomforting volatility. CSU almost defied this axiom pretty much its entire life as the company barely experienced even a 30% drawdown. We will learn shortly whether that will remain true or not in coming months as the stock is currently on the cusp of being in a 30% drawdown. ![chart](https://substackcdn.com/image/fetch/$s_!A141!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faa304673-daa6-4035-b468-b4afb131b527_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Admittedly, I was a bit confused looking at the stock going up after a rather shabby Q1 quarter this year; my confusion only compounded further when the stock started going down after a rather reassuring Q2\. So, it’s not quite the fundamentals that are the primary culprit for their very recent drawdown. One key source of Constellation’s current consternation is AI. As an owner of a thousands of “shitty” software companies, CSU is now being considered by many a potential victim in the road to “AGI”. While CSU management doesn’t even have usual quarterly calls, they decided to address AI related concerns in a conference call last week. Mark Leonard had a more nuanced perspective on AI throughout the call. He didn’t strike me as someone who is “AGI” pilled; based on the ground reality of software businesses he has seen over the last few decades, he thinks there will be plenty of scope for creating value: > I believe that vertical market software is the distillation of a conversation between the vendor and the customer that has gone on frequently for a couple of decades. > > And you distill those work practices down into algorithms and software and data and reports and it captures so much about the business. And being able to examine that in a new way because of AI, creates new opportunity to modify and change and suggest new approaches. So yes, I’m hopeful that, that unique and proprietary information will be of value. Of course, even if he’s wrong on the margin, CSU buys companies with a lot of margin of safety thanks to their high hurdle rates (we don’t know exactly what, but it’s definitely not 10%): > So the advantage of using a high discount rate already is that **it minimizes the value of the terminal value** and your overall assessment of the attractiveness of the investment. So we’ve got that going for us inherently. We’re already discounting the future a lot. CSU knows it owns not-so-fancy software products that happen to be mission critical for their customers, and given their R&D efforts on these products is quite minimal, the potential obsolescence of these products were always much more in CSU’s calculation even before AI than perhaps any other software companies in the world. As a result, while CSU obviously wasn’t considering “AI” as the key risk for their software companies 5-10 years ago, the price they paid for these companies always factored in some sort of “asteroid” coming towards them. If the AI is indeed such an asteroid to software, I have mentioned before in my [Deep Dive](https://www.mbi-deepdives.com/csu/) that I don’t expect CSU to be married to Vertical Market Software (VMS) and will look to allocate capital beyond software over time. Mark Leonard pretty much addressed this in his 2018 shareholder letter: > *“*If Constellation had started in 1895 instead of 1995, we might have had the objective of being a great perpetual owner of daily newspapers. The newspaper industry underwent a long period of high growth which attracted many new entrants, followed by local consolidation, conglomeration, and eventual decline. **I anticipate that the VMS industry will evolve similarly.** > > One day Constellation may find that VMS businesses are too expensive to rationally acquire. If that happens, I hope we’ll have had the foresight and luck to find some other high ROE **non-VMS businesses in which to invest at attractive prices**. **I am already casting about for such opportunities.** If we don’t find attractive sectors in which to invest, then we’ll return our FCF to our investors. Even if re-investment opportunities become scarcer, Constellation doesn’t end… it will continue to be a good (hopefully great) perpetual owner of its existing VMS portfolio, and will still deploy some capital opportunistically.” It is more likely that AI may be a double edged sword for CSU. It is hard to exactly know where the debate will settle, but there is a scenario in which CSU is able to create more value to their existing customers and capture greater wallet share over time even if they lose some fraction of the customers to “vibe coding”: > You know, we capture the small companies as they graduate from horizontals. We take them and some of them grow enormously and become very successful, large companies. And then they graduate to no longer using our systems, but to using a much more proprietary system that they have a much stronger hand in driving. Now, **AI has the potential to allow us to do way more work on making the client happy and customizing our solutions. But it also allows the client to potentially do that. And so there’s a natural tension there.** > > We obviously would love to capture that. Our clients, **if they don’t have a list of five years worth of IT projects to get to**, would obviously love to capture that as well. And so I think to some extent. Whenever we go see a large clients, IT director. We’re in a negotiation. Regarding what we’ll do and what they’ll do. And, you know, it’s not going to be an easy answer. It’s it’s going to be somewhere in between. And **AI makes it potentially way more exciting for us to provide customization . But it also makes it much more likely that the client will do it themselves*.*** > > it’s important to consider that even if we rebuild with AI, **we’re still going to have to maintain it**. So I think in some cases **it will enable us to modernize and rebuild our solutions more effectively than we were able to do so before**. But at the same time, how we maintain, troubleshoot and bug fix our solutions, we’re still going to have to do that. Whether the code has been written by AI or by humans ten years ago. While nobody knows how these debates will be settled, I would rather be a company providing mission critical software to customers who are perennial laggard in adopting the shiny new tech. So, even if the AI fears have a lot of merit, CSU will have plenty of time to allocate their capital appropriately. Of course, these pivots can still be painful to go through, but as I said before, this is a company run by very competent and more importantly, high integrity people. Alas, CSU also just abruptly announced Mark Leonard’s retirement due to health reasons. There was no such indication even during the conference call couple of days ago, but through the grapevine I have heard it was indeed some sudden health related issues that led to such an unfortunate decision. Leonard is one of those rare personalities that I can only think of Buffett as his true peer. One of my earnest beliefs about Warren Buffett is there will **never** be another one like him in my lifetime. It’s not that there may not be any other investor who will have higher return over 50-60 year period, rather there will never be one who will allow you to buy their investing vehicle without essentially any compensation at all. Buffett’s true **charity** was that anyone could buy Berkshire and for that privilege, they would have to pay essentially nothing to Buffett. Leonard too didn’t take any salary since 2014 (he did take compensation in the first 20 years of CSU operations), and anyone could enjoy the fruits of Leonard’s talent by buying CSU without paying him anything at all. I’m, of course, not saying anyone should be obligated to do such charity, but I do want to highlight and applaud something extraordinary when I see it. Both Leonard and Buffett’s writing are deeply imbued with integrity and humility. In an investing world increasingly filled with scammers, fraudsters, and meme stocks, I feel deeply sad that both of them retired in the same year. Buffett is obviously sort of expected to due to his age, but Leonard’s retirement is a real shocker. I wish him a speedy recovery. Leonard will remain in the board, and I earnestly hope CSU will continue to be blessed with his acumen and guidance for years to come. Leonard is going to be succeeded by Mark Miller who used to be the Chief Operating Officer of CSU. While it may be tempting to presume Leonard’s departure a nothingburger due to CSU’s deeply decentralized culture, I don’t want to understate the importance of towering figures in any company. CSU should prove to be quite resilient, but such sudden departure can affect the morale and may create some additional upheaval, especially when you are also dealing with big technological questions such as AI. Having said that, I do think the valuation is starting to appear somewhat attractive from risk-reward perspective which is why I started a 2% position yesterday. Speaking of valuation, if you ask 10 CSU shareholders what **LTM** multiple the stock is currently trading at, you may get 10 different answers. I will explain why risk-reward is starting to appear attractive and show some valuation work (including excel file) behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### What if there's an overinvestment in AI? URL: https://www.mbi-deepdives.com/what-if-theres-an-overinvestment-in-ai/ Last updated: 2025-09-26T14:27:59.000Z ***A programming note***: In case you missed it, I published my Deep Dive on ASML yesterday which you can read [**here**](https://www.mbi-deepdives.com/asml/). --- Without the benefit of hindsight while it can be hard to figure out where we are in the AI capex cycle, it is extremely likely that we will at one point end up overinvesting in AI. Both Alphabet (see [2Q’24 call](https://www.mbi-deepdives.com/alphabet-2q24-update/)) and Meta (see Zuck’s recent[ comments](https://x.com/ShanuMathew93/status/1970295152062115874?ref=mbi-deepdives.com)) pretty much explicitly expressed their desire to err on the side of overinvestment than under-investment. The inevitability of overinvestment was eloquently [explained](https://capitalgains.thediff.co/p/economic-cycles?ref=mbi-deepdives.com) by Byrne Hobart a couple of months ago: “The only way for the GPU industry never to have a down cycle, even if the face of growth, is some combination of: 1. Every single person who can make big capital spending decisions chronically underestimates long-term AI demand. (If even one of them doesn’t, that person is a larger share of the next round of capital allocation decisions.) 2. It’s a straight shot from here to the Singularity. In any other case, there will still be an air pocket or two; even if spending rises 30% annualized, there will be times when it’s closer to 25% and the capex for that period supports something more like 35% growth. And a good working explanation for why cyclicality won’t go away is that Mag7 executive who had read the above description of cyclical dynamics in AI capex a few years ago, taken it to heart, and decided to spend less than planned would have ended up regretting that choice. Many cyclical industries were born as growth industries. Airlines had great returns in the 1960s, automakers did similarly well in the 1920s (in the aggregate, with many failures made up for by a few huge success stories), and in the chip industry the *slowest* growth from 1950-60 was just over 40%, in 1960\. (The next year, the industry shrank year-over-year for the first time, and [settled into a cycle thereafter](https://www.economist.com/business/2020/01/09/a-revival-is-under-way-in-the-chip-business?utm%5Fsource=capitalgains.thediff.co&utm%5Fmedium=referral&utm%5Fcampaign=cyclical-into-secular-and-vice-versa))” Since overinvestment is all but inevitable, does that mean stocks are bound to crash on the other side of the cycle? I have seen the below chart from Goldman Sachs being shared on twitter. While some pointed out how today’s mag7 valuation pales in comparison with stocks at bubble peak back in 2000, one stock particularly caught my attention: AT&T. The stock was trading at valuation level below all of mag7 currently trades at. So, what happened to AT&T during the internet crash? ![Image](https://substackcdn.com/image/fetch/$s_!rcD-!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb81c78ea-7719-42bc-a631-e2d1712aa383_1108x528.jpeg "Image") Of course, it wasn’t just internet bubble, early 2000s was also very much about telecom capex bubble. Despite appearing to trade at somewhat reasonable multiple, AT&T wasn’t spared from the carnage. It experienced a \~65% drawdown from the peak and had a negative 35% return during 2000 to 20004 period. ![](https://substackcdn.com/image/fetch/$s_!zPQE!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0f98dbeb-d43f-4860-9782-dcf0540d8308_1405x895.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) The “reasonable” P/E didn’t save AT&T shareholders from a painful experience as it went from \~24x P/E to bottom at \~9x P/E. It’s a good reminder that just because some of today’s mag7 aren’t trading at nose bleeding multiples doesn’t mean the shareholders will be immune from a traumatic aftermath if it turns out the eyewatering investments in AI yields sub-par return. ![chart](https://substackcdn.com/image/fetch/$s_!M9_s!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5a2de11c-2f47-4d5f-a970-0493ab48a6a4_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) The trillion dollar question now is whether we are already in the overinvestment phase and if we are, how much drawdown might we face once the realization of overinvestment sets in? I don’t know the answer to the former, but I will make an attempt to answer the second question in this piece. Of course, different companies will face varying degree of impact within big tech land. Companies such as Nvidia is likely to be hit particularly hard, but what about the overall market? I’m not presumptuous enough to have any strong opinion on the overall market in this scenario, but I would like to focus on Meta Platforms to gauge the potential impact of overinvestment in AI. Before talking about potential overinvestment in AI, let me give you some context on Meta so that I can provide some guesstimate of Meta’s investments in AI. When I think of Meta’s capital intensity, I can sense three distinct phases. In the first phase (2010-2017 period), Meta’s Family of Apps (FOA) business is mostly about texts and photos. Every dollar of capex added $2 to $2.5 of incremental revenue to Meta’s topline. ![](https://substackcdn.com/image/fetch/$s_!Vy7e!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe11b70bf-d2b4-41ef-a69d-41c66354028e_1201x124.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Then video consumption took off on Meta. While video used to be just \~15-20% of overall time spent on Meta’s platform in 2015-17 period, it has steadily crept upward to be half of all time spent on Facebook (likely even more on IG). ![](https://substackcdn.com/image/fetch/$s_!-n9K!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9cf92f6d-13c9-4df4-820c-1c77fd86b39a_733x411.png) Source: Meta-FTC Trial Video consumption is obviously much more capex intensive and every dollar of capex added just $1 of incremental revenue during 2018-20 period. During ZIRP era, it temporarily shot up to 1.7x, but then ATT happened. To regain the lost signal, Meta had to amp it up in capex and while the attribution infrastructure is never going to be as perfect as it was pre-ATT, Meta has largely regained the signal (especially relative to other competitors ex Google) and the company returned to its enviable growth trajectory. Moreover, given Meta has shifted more and more to recommended content to fill your feed, the core FOA product is also much more compute intensive than the earlier versions of newsfeed. During 2022-24 period, Meta spent a cumulative \~$95 Billion of capex and added \~$47 Billion of incremental revenue. Given the ever increasing compute intensity, let’s assume Meta’s “normalized” incremental revenue to capex ratio would be 0.6x during this 2022-24 period which implies the incremental $47 Billion of incremental revenue would require $78 Billion of capex. That means Meta spent a cumulative \~$15-20 Billion on “AI” during 2022-24 period. Please note Reality Labs related investments largely flow through income statement through opex, so Reality Labs is likely to be quite capex light. That’s why I think these $15-20 Billion was spent on AI that didn’t generate any revenue yet. ![](https://substackcdn.com/image/fetch/$s_!gVsu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc6633e52-755e-43ec-a9cb-475cf7065c14_1075x112.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Phase 3 is next 5 years when Meta’s core business is expected to even more capital intensive. If you look at consensus estimates, you can see analysts are modeling consistently $30 Billion incremental revenue in the next 5 years for Meta even though capital intensity just creeps up over time. As a result, incremental revenue to capex ratio is expected to go from \~2-2.5x during phase 1 (2010-17 period) to \~1x during phase 2 (2018-24 period) to only 0.3x during phase 3 (next 5 years). Of course, some of these deterioration is due to “Nvidia tax”, but I find it remarkable that the assumption of persistent capex increases going forward despite no acceleration in incremental revenue. ![](https://substackcdn.com/image/fetch/$s_!5g3D!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F992cccb5-575d-4943-b4c1-07cc7ee196e7_904x117.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink), Tikr If we assume the core incremental revenue to capex ratio will hold at 0.6x (as it was likely the case during 2022-24 period), it shows my guesstimate of investments in non-revenue generating AI going forward. Just to help to you understand how I calculated this number, let me explain my estimates for 2026\. I took the consensus capex and incremental revenue estimates for 2026\. Since Meta is expected to grow revenue by $32 Billion next year and if we assume for every dollar of capex would lead to $0.6 incremental revenue, it implies Meta would need to spend $53 Billion in capex in 2026\. However, since they actually are expected to spend \~$97 Billion, the difference between these two numbers is what I am guesstimating as Meta’s investments in non-revenue generating capex (mostly AI). ![](https://substackcdn.com/image/fetch/$s_!6NRQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F909cabfe-4ed9-4776-8aaa-f4ddedf56b6e_883x552.png) Source: Company Filings, MBI Deep Dives, Tikr Even with these consensus estimates, incremental revenue to capex of just 0.3x is uninspiring enough, now imagine if the revenue estimates turn out to be too optimistic for some reason. Ultimately, Meta’s revenue comes from advertising and if the broader macro experiences a slowdown for some reason, Meta won’t be immune. It is highly unlikely that in any such scenario, Meta would post anywhere close to $30 Billion of incremental revenue growth. So let me imagine a scenario and show my math to estimate how much Meta’s intrinsic value might be down if a lot of these non-revenue generating investments turn out to be largely a waste. I will discuss this behind the paywall. --- *In addition to “Daily Dose” (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### ASML: The Machine that Builds the "Machine God(s)" URL: https://www.mbi-deepdives.com/asml/ Last updated: 2025-09-25T12:49:58.000Z *You can listen to this Deep Dive* [*here*](https://www.mbi-deepdives.com/audio/) --- > “It is perfectly normal to feel dizzy in this place. Not because of the sterile air, but because the mere thought of the extremely complex tools needed to make the chips destined for the phones of the future can make you lightheaded. A machine invented in Veldhoven, manufactured with parts from all over Europe and shipped to chip factories in Asia and the US, where it produces silicon wafers full of processors and memory chips that, in just a few years, will end up in the palm of your hand.” > > From the book [**Focus: The ASML Way**](https://www.amazon.com/Focus-Inside-struggle-complex-machine-ebook/dp/B0CW1FLCD4?ref=mbi-deepdives.com) (it is perhaps a required reading if you want to study ASML) Indeed, I felt a little dizzy too even reading about the most complex machines mankind has ever built. I know almost everyone, including me, feels a little nervous about the fragility of semiconductor value chain these days, but while working on this Deep Dive, I at times felt that there is an inherent beauty in the fact that people from so many different regions, ethnicities, and beliefs play such an instrumental role in creating tiny little chips which in aggregate will perhaps one day create our own “Machine God(s)”. Europe’s biggest contribution to these “Machine God(s)” comes from a company started in 1984 in Veldhoven, a somewhat nondescript town in Netherlands. This company was initially named “ALS”, but once they realized it could be misconstrued as the [motor neuron disease](https://en.wikipedia.org/wiki/ALS?ref=mbi-deepdives.com), they rebranded to “ASM Lithography” and finally shortened it further to just “**ASML**” in 1996. What exactly is lithography? ASML says in its annual report: “*A lithography (more formally known as ‘photolithography’) system is essentially a projection system, with light projected through a blueprint of the pattern that will be printed (known as a ‘mask’ or ‘reticle’). With the pattern encoded in the light, the system’s optics shrink and focus the pattern onto a photosensitive silicon wafer. After the pattern is printed, the system moves the wafer slightly and prints another copy.*” I’m sure many of you are already probably feeling like Michael Scott! ![](https://substackcdn.com/image/fetch/$s_!kVud!,w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F499d1875-f8c0-4945-8fb3-4d7c60563b6f_443x250.gif) Okay, let’s try again then. Imagine you have a stencil, like a piece of paper with a star cut out of it, and a flashlight. If you shine the flashlight through the stencil onto the wall, you see a big star made of light. Photolithography is very similar, but it’s a magic projector that works backward. Instead of making the picture bigger, it makes the picture incredibly tiny. Scientists create a blueprint of the tiny wires that will go inside a computer chip. This blueprint is called a "mask." A big machine shines a very special light through the mask. As the light passes through, the machine uses powerful lenses to shrink the pattern from the mask until it is much, much smaller than a human hair. The tiny light pattern is projected onto a shiny disc called a "wafer." This disc is covered in a special coating that is sensitive to light. The light "draws" the pattern onto the wafer. The machine stamps the pattern, moves the wafer a tiny bit, and stamps it again. It does this over and over until the whole wafer is covered in tiny copies of the chip. I **strongly** encourage you to watch this YouTube [video](https://www.youtube.com/watch?v=9RZreu5z%5FGc&ref=mbi-deepdives.com) to visualize this process. Don’t be fooled by the simple analogy; these machines are anything but simple. As late as the 1960s, engineers used rudimentary methods like applying wax to etch simple circuits. However, as the number of transistors on a chip scaled from a handful to trillions, the components became infinitesimally small, making physical methods unfeasible. This led to a technological shift toward "etching with light," or photolithography. To pattern these microscopic features, the light used must be incredibly precise. This demand for precision has forced the industry to utilize ever-narrower wavelengths, pushing the technology far beyond the visible spectrum. Today, the most advanced ASML machines are the **size of a steam locomotive and requires seven Boeing 747s to transport** to a chip factory!! If that didn’t raise your eyebrows, wait till you hear the sticker price for the most advanced lithography machines: [**400 Million**](https://www.reuters.com/world/asia-pacific/tsmc-still-evaluating-asmls-high-na-intel-eyes-future-use-2025-05-27/?ref=mbi-deepdives.com)**!** Despite such price tag, these machines aren’t always perfect: > You may think a lithography machine with a price tag of hundreds of millions of euros would be able to make perfect copies of chip structures. Think again. > The mask, which carries the original image of the chip, does not leave an exact replica on the photosensitive layer. The chemical processes that occur on the wafer create rough and messy lines. However, with mathematical models, you can calculate how to shape the mask so that, despite these deviations, a pattern emerges that will transmit the electric signals on the chip without fault. You can compare it to the beauty filters on TikTok or Instagram, which correct the 'imperfections' on your face to make you look like a model. However, making a mask takes much more time than snapping a selfie, and it takes weeks for the pattern of the chip to be converted into a flawless image. (From the book “Focus: The ASML Way” With 100,000 components doing a “tightly coordinated dance”, a lithography machine required a deep scientific understanding of optics, mechatronics, physics, chemistry, and who knows what else for ASML to get here. Like Boeing and Airbus, ASML outsources most components to focus on just designing the system and essentially become the orchestrator of one of the most complex, and yet tight-knit supply chains in the world. ![](https://substackcdn.com/image/fetch/$s_!v5M2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F50656043-5856-4217-898e-3bf533478a44_1498x526.png) Source: [ASML](https://www.asml.com/en/products/euv-lithography-systems?ref=mbi-deepdives.com) Of course, ASML didn’t get here overnight! Philips, the 19th century Dutch electronics giant, played a crucial and foundational role in the creation of ASML. In the early 1980s, Philips had been developing its own lithography systems, called "[wafer steppers](https://en.wikipedia.org/wiki/Stepper?ref=mbi-deepdives.com)". They had developed a series of prototypes, but the project lacked the resources to become a commercially viable product. After facing the high costs of further development, Philips decided to spin off this activity. This led to a joint venture with ASM International, a chip-machine manufacturer, creating ASML in 1984\. Philips provided the initial technology, intellectual property, and a team of engineers, which formed the core of the new company. The early years were very challenging, but focusing too much on those years is a bit beyond the scope of this Deep Dive. ASML muddled through those initial years and reached profitability in 1993 and then went for an IPO in two years after being profitable. As you can imagine, there were few times in history more favorable than going public in mid-90s for a tech company. I went back to read their 1999 annual report which is perhaps the height of the frenzy for anything remotely related to tech. You could sense the unbridled optimism for the future and technology’s role in it in that [1999 annual report](https://edge.sitecorecloud.io/asmlnetherlaaea-asmlcom-prd-5369/media/project/asmlcom/asmlcom/asml/files/investors/financial-results/a-results/1999/asmluk99.pdf?ref=mbi-deepdives.com). Some excerpts: > …Today **the artists for the most diverse applications are the lithography systems**, patterning the structures in silicon to create integrated circuits. > > Society today is seeing rapid innovation in the electronics industry with many new products and services coming on to the market every year. The rapid improvements in the performance of these devices are made possible by revolutions in the IC industry. > > The structure of an IC resembles that of a building with multiple floors. Rooms compare to functional components like transistors, the corridors and the stairs to the interconnections between the components. A further analogy exists in building the ICs as they are built layer after layer (floor after floor). Lithography plays the **key role in defining the precise structures** to form the layers and hence the components. Of course, it is hard not to be optimistic about the future as their revenue grew from €1 Billion in 1998 to €2.7 Billion in 2000 and operating margin doubled from 10% to 20% in just two years! (Note: Since the Euro was introduced in January 1 1999, I have decided to show results only from 1998) ![](https://substackcdn.com/image/fetch/$s_!v8It!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc24a9eff-09d3-466a-93f8-46c6fd1fcda0_943x145.png) Source: Company Filings, MBI Deep Dives Accelerating topline and margins is obviously not just a recent love affair for investors; it is a timeless one! So, ASML stock was a cool \~23x in just five years since its IPO in 1995! ![](https://substackcdn.com/image/fetch/$s_!KY0o!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbed412bb-9a58-4d52-bc74-dd7ef4eb50ee_1393x867.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) You can probably guess what happened afterwards. The lines do not go up and to the right unabated forever! ASML was expecting to sell 700 machines in 2001\. They could only sell 197! Customers started cancelling orders as the internet revolution increasingly seemed to be a temporary chimera. ASML had to take a massive inventory write-off, leading to an infinitesimal gross margin of **TWO** percent! ASML laid off 23% of their company and held on for their dear lives as they reported operating loss in **three consecutive years!** ![](https://substackcdn.com/image/fetch/$s_!SlV0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd94410f5-c244-48ae-9674-9d66ca9ff574_948x151.png) Source: Company Filings, MBI Deep Dives By the end of 2002, the summer of 2000 must have felt like a distant mirage for almost all tech companies! ASML shareholders experienced a brutal \~80% drawdown. ![](https://substackcdn.com/image/fetch/$s_!yqR7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6f75bc4e-eedf-4e13-a5d9-24e8995b159b_1402x862.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) ASML did start to see light at the end of the tunnel as business came back to profitability in 2004\. By 2007, ASML’s revenue was 40% higher than its revenue in 2000 and operating margin exceeded tech bubble’s peak. ![](https://substackcdn.com/image/fetch/$s_!_o-P!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F09139064-15b4-478f-937c-01526da49d79_940x177.png) Source: Company Filings, MBI Deep Dives Predictably, stock prices have recovered from the dog days of 2002! ![](https://substackcdn.com/image/fetch/$s_!ddxF!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1e0bd866-1c41-4993-8f26-4237d4296415_1393x864.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Then, of course the GFC happened. The entire world economy almost came to a halt; ASML revenue dropped by **46%**! Just to put that into context, **ASML’s 2009 revenue was 40% lower than their revenue in 2000**. Ouch! Thankfully, this was an intense but short-lived pain. ![](https://substackcdn.com/image/fetch/$s_!7tmO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbec66321-a6f7-4c24-a303-0d4982d83b5b_934x139.png) Source: Company Filings, MBI Deep Dives iPhone was launched and the promises of the internet was about to be fully unleashed through the smartphone and cloud revolution in the next decade or so. Business roared back in 2010 with operating margin reaching a new high. ![](https://substackcdn.com/image/fetch/$s_!CFsy!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd597bbfb-d2d6-4427-b428-a2984bc76e89_1411x879.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) After the topsy-turvy 15 years in public market both in operating performance and stock price volatility, ASML more or less experienced one-way street in operating performance since 2010\. There were some hiccups here and there (e.g. 2012) but none of them had any sustained bite. ASML’s revenue increased \~18xed in the last 15 years! **Their operating profit last year was the same as their revenue in…2017**! As it turns out, being the monopoly of machines building the machine God(s) is a very, very good business! ![](https://substackcdn.com/image/fetch/$s_!_WRQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe24d8746-4617-4c2d-9d5f-42d3a11fa67b_946x492.png) Source: Company Filings, MBI Deep Dives So, it’s no surprise that the stock did spectacularly well in the last decade and half; ASML stock was almost a 50-bagger during this period! ![](https://substackcdn.com/image/fetch/$s_!FMCf!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2e344f3f-a092-4926-adb1-37d48a3aaf83_1396x871.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I like to do these historical operating performance and return trajectory to help you (and me) internalize how the march to greatness is anything but linear. Of course, investing is always about the future and to understand where we are heading, we need to have a deeper understanding of the present which I will dig into the Business Overview section. While the past is the foundation of where the business stands today, intricate details about the past is not going to be the primary focus in my Deep Dive. I do strongly recommend the book “Focus: The ASML Way” if you want to gain more in-depth understanding of the past. I have greatly enjoyed reading the book and excerpts the book will re-appear multiple times in this Deep Dive, but for the sake of brevity, I will largely skip the “past”. After the business overview, I will dig into competitive dynamics and risk ASML faces today which will be followed by a discussion on their capital allocation and management incentives. As usual, I will share my model that tries to capture what expectations are embedded in current stock price. Subscribe to read the rest of the Deep Dive. --- *MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 63 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. Subscribers also receive one email everyday on topics/companies I am interested in.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Nvidia and OpenAI's Gambit URL: https://www.mbi-deepdives.com/nvidia-and-openais-gambit/ Last updated: 2025-09-24T13:55:01.000Z ***A programming note***: I will publish my Deep Dive on **ASML** tomorrow. As a reminder, I only send one email per day; therefore, on the day I publish my Deep Dive on a company, I won’t publish anything else. --- Early this year, I remember looking at Nvidia’s consensus FCF estimates for the next five years and wondered what exactly Nvidia will do with all these money. To put this in perspective, from FY’26 to FY’30, Nvidia is expected to generate a cumulative **$867 Billion of FCF** in the next five years! Of course, these are just consensus estimates, so who knows what the actual numbers will be. But looking at the number made me wonder that what Nvidia will choose to do with all these FCF may have a profound impact on the big tech landscape. I was a bit skeptical that all they would do is buyback shares. Indeed, we may have a slightly better idea now what Nvidia is going to do with the hyperscalers’ “recurring” gift to them. It appears Nvidia may be going to fund the most capital intensive private company the world has ever seen: OpenAI. OpenAI and Nvidia [entered ](https://nvidianews.nvidia.com/news/openai-and-nvidia-announce-strategic-partnership-to-deploy-10gw-of-nvidia-systems?ref=mbi-deepdives.com)into a “strategic partnership” to to build and deploy at least 10 gigawatts of AI data centers which will require millions of GPUs. Nvidia mentioned that it “intends to invest up to $100 billion in OpenAI **progressively** as each gigawatt is deployed”. ![](https://substackcdn.com/image/fetch/$s_!KeC4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe907c4e3-8929-4c8e-a6a1-b2b573eea947_1888x556.png) While Nvidia stock went up initially (which must mean market took the deal positively), I have seen plenty of negative reaction to the deal which is taken as a sign of bubble or froth in AI. The web of relationships and scratching each other’s back among several companies in the AI ecosystem is likely to raise eyebrows of even the most ardent AI believers! There may be a lot of merit to such skepticism, but admittedly, if I were in Jensen’s shoes, I am not sure I would do anything differently. To say it differently, from Nvidia’s perspective, I think it makes much more sense to allocate capital in a way to try to **design** the AI ecosystem to maintain or bolster their competitive position. I do want to note that I feel a little uncomfortable writing anything about OpenAI. The company seems to throw some really eye-popping numbers and partnerships to shock everyone even though they just have a low double digit billions of revenue today. Just yesterday, WSJ [reported](https://www.wsj.com/tech/openai-unveils-plans-for-seemingly-limitless-expansion-of-computing-power-d0b39b9b?gaa%5Fat=eafs&gaa%5Fn=ASWzDAiljcSFtluuJ8mVHCXhv5P0UjdiVjpy9ic-iQnzrEx075zeX1BMesdh6cRiFrY%3D&gaa%5Fts=68d3ee94&gaa%5Fsig=mNYx2%5FGBuV3XyHdimRt1lsa8yYVn0bBx-CHC3vopEGyzdrARtqorwA3zPUtuli7xEP2a4Kb2abjvs3aKBSKxwA%3D%3D&ref=mbi-deepdives.com) that OpenAI is planning to build $1 trillion of computing infrastructure. I may never see another company in my lifetime throwing trillion dollar numbers with so little revenue! Having said that, I do want to update my opinion a bit post-Nvidia deal. OpenAI can sign $300 Billion of RPO with Oracle but since neither of them has the cash or debt capacity to fund it, you could be genuinely skeptical about some of OpenAI’s audacious proclamations. Given the size of funding requirement, I was also not quite a believer that VCs can continue to fund it, especially considering they also need to find checks to fund Anthropic, xAI etc. But now that Nvidia appears to be willing to fund OpenAI’s ambitions, OpenAI may have found their most credible partner ever for their funding needs. Given that hyperscalers seem to compete against each other who can give money to Nvidia faster, OpenAI may not have to worry for a while about funding **as long as they stay at the frontier in AI**. However, this partnership may cast a long shadow over the rest of Big Tech. An unconstrained OpenAI is a far more formidable competitor. The impact on hyperscalers (AWS, Azure, GCP, and Meta) is particularly acute from a supply chain perspective. In a market already characterized by severe GPU scarcity, a 10-gigawatt commitment will inevitably cannibalize available supply. With Nvidia heavily prioritizing this massive allocation to OpenAI, the GPU supply for other major cloud providers will remain squeezed, potentially stifling their own AI infrastructure buildouts and making it harder to serve their customers. This development makes one thing abundantly clear: Application-Specific Integrated Circuits (ASICs) are no longer a “fun little project” or a mere negotiating tactic; they may be an absolute necessity. Hyperscalers cannot afford to have their largest capex dictated by a single supplier, especially one that is now heavily backing a key competitor in various niches. The motivation to develop viable in-house silicon has never been stronger. I, however, don’t want to romanticize ASICs too much. Custom silicon shines once the model architecture and software stack settle; until then, general‑purpose GPUs and Nvidia’s end‑to‑end tooling can keep winning. When might the model architecture and software stack stabilize enough for ASICs to shine? Unfortunately, that question is above my paygrade. However, over the long-term, ASICs feel increasingly essential about gaining bargaining power, ensuring supply chain diversity, and maintaining control over each of hyperscalers’ technological destiny. Relying solely on Nvidia is too great a strategic risk, regardless of GPU price. Google, of course, occupies a somewhat unique position. They were the earliest to invest in custom silicon and possess arguably the best talent pool to execute on it. Internally, Google is largely [insulated](https://www.mbi-deepdives.com/googles-evolving-tpu-strategy/) thanks to their robust TPU infrastructure. Yet, their cloud business (GCP) remains exposed because enterprise customers overwhelmingly prefer the familiarity and established ecosystem of Nvidia’s GPUs. I wonder if Nvidia-OpenAI partnership further incentivizes Google to make their TPUs a more compelling external offering in other neoclouds. If there is too much demand-supply mismatch in GPUs, more and more customers may be willing to give TPUs a shot. Given Microsoft’s own massive investment in OpenAI, I am a tad bit surprised that the market hasn’t reacted more negatively to Microsoft after this partnership. OpenAI seems to be cultivating a very “promiscuous” relationship strategy, taking significant capital from multiple strategic partners whose long-term goals may not perfectly align. The deal with Nvidia potentially dilutes Microsoft’s influence over OpenAI’s strategic direction. We will have to wait for more details about Microsoft-OpenAI’s latest negotiations as their [joint statement](https://openai.com/index/joint-statement-from-openai-and-microsoft/?ref=mbi-deepdives.com) said they are “actively working to finalize contractual terms in a definitive agreement”. I was confused about such a joint statement (why not just wait to finalize the agreement and then issue a press release), and my confusion only compounded after reading Nvidia’s press release about OpenAI partnership which mentioned “*NVIDIA and OpenAI look forward to finalizing the details of this new phase of strategic partnership in the coming weeks*.” If the deal hasn’t been finalized yet, why is there any hurry for issuing a press release? --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Airbnb's Evolution URL: https://www.mbi-deepdives.com/airbnbs-evolution/ Last updated: 2025-09-23T15:04:52.000Z How does Airbnb make money? Let’s go through an illustrative example first assuming an individual host lists their home at $100 per night. If a host lists their home at $100/night, Airbnb charges the host \~$3 to cover the payment processing costs; hence, the host receives $97 for a night's booking. Essentially, Airbnb doesn't make money from the individual hosts; it's the guests who, on that illustrative $100 booking, will pay \~$12 to Airbnb, $4 in lodging taxes, and therefore, $116 to book for a night. To recap, guests pay $116, hosts receive $97, local authorities/govt. receive $4, and $15 goes to Airbnb (implied take rate \~13% of the gross booking paid by the Guest). ![](https://substackcdn.com/image/fetch/$s_!Hj6K!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd9c613b6-e1df-4a27-891b-7cec6debf558_1155x484.png) Source: Airbnb S-1 As we all know, marketplaces are bit of a chicken and egg problem. In the initial years, both supply and demand are critically important. But at scale, it is aggregating demand that increasingly becomes the dominant force over time. The incremental supply to the marketplace may be less valuable (still valuable, but incrementally less so) in year 15 than it was in year 3, and once the overall marketplace infrastructure is in place, it is usually the supplier that pay for the privilege to be able to sell their products/services on the marketplace. That is exactly the case if you look at OTAs such as Booking or Expedia. So why does Airbnb have such a strange commission structure after all these years? I used to think that it is indicative of Airbnb’s assessment that unique supply is still of paramount importance for them and that’s why individual hosts don’t pay much to Airbnb. I’m not sure why but I was also under the impression that if you are connected to Airbnb via Property Management System (PMS), the hosts pay \~15% of the booking to Airbnb. That has indeed been the case in Europe and other non-US markets since 2020-21, but apparently that hasn’t been implemented in the US until **this year**. ![](https://substackcdn.com/image/fetch/$s_!vjAA!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F11a64d88-a380-4eae-820a-93149c82e6ee_1092x892.png) Source: Airbnb website ![](https://substackcdn.com/image/fetch/$s_!d6Zm!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F46f2f605-ee2f-496d-a556-5b0c391be30d_994x744.png) Source: Airbnb website Booking.com and Expedia have long used a **host‑only commission** model. Airbnb’s shift makes cross‑listing price parity simpler for property managers and removes the “Airbnb looks \~12-15% higher” problem that came from layering a guest fee on top. Brian Chesky in a recent [interview](https://www.youtube.com/watch?v=CiHSV6XReLI&ref=mbi-deepdives.com) with Skift provided more context how Airbnb came up with the original pricing model and why they are shifting now: > “Let’s just back up. How do we—why do we—have this kind of weird commission structure? Because when I was like 26 years old and I didn’t know anything—I went to art school—Joe, Nate, and I realized, like, we need to have a business model. We handled money; no vacation app handled money. And we said, ‘Let’s have a 5% commission,’ and it was a guest fee. Then eventually we realized, ‘Oh, credit‑card processors are taking 3% from us,’ and that’s how we came up with 3% on the host fee. And eventually the 5% on the guest fee we, over time, increased to about 12%, and we ended up with this legacy of 12 + 3, or 12 1/2 + 3. > > It was purely organic. **I would never have designed it that way**. **But once you have this inertia, it’s like a major switch.** Once we were designing service experiences, we’re like, ‘Let’s just do it right from the first time.’ A lot of guests complain about: there’s taxes, there’s cleaning fees, there’s service fees—there’s a lot of drip pricing—and, ‘Let’s just have one price.’ Like, you go to a coffee shop, there’s a price of coffee and there’s tax. So that’s what we did, and we did it for service experience. > > And we said, if we’re going to do it for that, why don’t we make the switch to homes? That was for two reasons. Number one, for ease of use. People were confused; they were frustrated—why is the price changing? But the other thing—and this is related to a competitive issue—**a lot of API host cross list, property managers were charging more on Airbnb because they charge $200 a night on Airbnb and then we mark the price up, and they charge $200 on a competitive site, but they didn’t mark the price up. So now our prices are 12% higher.** > > And so this was—you know, we were trying to educate them—but we started realizing people were just being really confused. And so that’s partly why we went to just one one host fee, and this is how the hotel industry does it. So we think it’s going to be much simpler.” Given that this simple pricing only applies to PMS-connected hosts, will this give some sort of advantage to individual hosts? Any “advantage” for individual hosts is mostly optical and small. Guests now see **total price by default**, so what matters is the all-in number, not how Airbnb slices its fee between guest and host. PMS-connected hosts can (and will) reprice to keep their owner payout whole, which erases most of the gap. It will, however, stop the confusion from consumers end who used to think prices may be higher on Airbnb for the same property compared to other OTA websites. While this pricing shift itself won’t be a material tailwind for Airbnb in the near term, I do think they are setting themselves up for potentially consequential changes in monetization which may prove to be significant tailwind for their topline in the next \~3-5 years. I will discuss this point as well as some portfolio changes behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### The resilience of consumer spending in the US URL: https://www.mbi-deepdives.com/the-resilience-of-consumer-spending-in-the-us/ Last updated: 2025-09-22T13:59:37.000Z Federal Reserve Bank of Boston had an interesting [piece](https://www.bostonfed.org/publications/current-policy-perspectives/2025/why-has-consumer-spending-remained-resilient.aspx?ref=mbi-deepdives.com) last month about the resilience of consumer spending which is \~70% of GDP in the US. They looked at credit card spending data for low, middle, and high-income group (representing first, third, and fifth quintiles in their dataset respectively) in the last 10 years. Please note the below graph has different axes for different income groups. ![](https://substackcdn.com/image/fetch/$s_!MOjo!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F874b2f66-5fda-4457-9a8e-ac84d91f845b_1285x820.png) The most interesting graph (shown below) from the piece was related to aggregate credit card debt by income group in the last 10 years. From the piece: > The left panel shows that real credit card debt for low-income consumers trended up from 2015 to 2019\. Debt levels fell about 15 percent from approximately $80 billion in 2019 to $65 billion in 2021, but these declines have reversed, and **the current (as of April 2025) level is close to the level implied by the pre-pandemic trend—the level that would have prevailed if credit card debt had continued to grow at the pre-pandemic pace**. > > The middle panel shows that middle-income consumers’ real credit card debt declined from about $100 billion in 2019 to roughly $75 billion in 2021\. While this 20 percent drop is larger than the 15 percent drop for low-income consumers, **real credit card debt for this group has grown rapidly from the pandemic-era lows and is now above the 2019 level**. > > The right panel shows that high-income consumers’ real credit card debt levels fell from about $190 billion in 2019 to $140 billion in 2021\. This decline of more than 25 percent is the largest of the three income groups. Notably, **credit card debt for this income group remains below the 2019 high and is well below the level implied by the pre-pandemic trend**. ![](https://substackcdn.com/image/fetch/$s_!V7AR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F09bb0e94-a7f0-4e5e-8410-eb0a4e6c92ab_1449x861.png) This graph basically helped me understand how the broader economy is chugging along just fine even though “vibecession” has increasingly become part of the conversation. The vibes are not great because a lot of people are indeed feeling the pinch whereas the high income group remains remarkably resilient. It is because of this high income group macro data may continue to be strong for a while: > the fact that **credit card debt levels for the highest-income consumers are currently well below the pre-pandemic trend implies that these consumers have room to spend out of unused credit even if their cash on hand has been depleted**. US economy has increasingly been driven by the high income group for a while as [half ](https://www.wsj.com/economy/consumers/us-economy-strength-rich-spending-2c34a571?mod=livecoverage%5Fweb&ref=mbi-deepdives.com)of the consumer spending (vs \~36% three decades ago) basically comes from just top decile of earners. WSJ [highlighted](https://www.wsj.com/economy/consumers/us-economy-strength-rich-spending-2c34a571?mod=livecoverage%5Fweb&ref=mbi-deepdives.com) early this year this growing disparity of how the economy has fared for high and low-income people: > Between September 2023 and September 2024, the high earners increased their spending by 12%. Spending by working-class and middle-class households, meanwhile, *dropped* over the same period. > > Taken together, well-off people have increased their spending far beyond inflation, while everyone else hasn’t. The bottom 80% of earners spent 25% more than they did four years earlier, barely outpacing price increases of 21% over that period. The top 10% spent 58% more. ![](https://substackcdn.com/image/fetch/$s_!XOyp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c9195d-9419-4b5d-b73f-6fc5c92ffa53_529x823.png) Image source: [WSJ](https://www.wsj.com/economy/consumers/us-economy-strength-rich-spending-2c34a571?mod=livecoverage%5Fweb&ref=mbi-deepdives.com) All these data points basically tell me the economy may prove to be quite resilient on an aggregate basis in the near term even if the consumer sentiment continues to exacerbate for valid reasons. However, it also does indicate the economy can be much more vulnerable to any shock to high-income group. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### My Experience on Airbnb Experience URL: https://www.mbi-deepdives.com/my-experience-on-airbnb-experience/ Last updated: 2025-09-21T13:53:39.000Z A few months ago, Airbnb has made a big splash about **re-launching** “[Airbnb Experience](https://www.airbnb.com/resources/hosting-homes/a/introducing-completely-reimagined-airbnb-experiences-742?ref=mbi-deepdives.com)”. While I stayed at 17 different Airbnb homes over the last 7 years, I have never booked an Airbnb Experience before. So a few days ago, I went to the Airbnb app and looked for experiences near my city. It immediately became apparent that **supply is a huge bottleneck** in Airbnb experiences. There were seven listings related to experiences near my city and six of them were about “paint and sip”. Well, I am no painter. But I was curious about the only experience unrelated to painting: a guided hike in a nearby city named “Cool”. I googled to find out that it’s a 50-minute drive from my place. So I went and experienced my first Airbnb experience yesterday. ![](https://substackcdn.com/image/fetch/$s_!WDvo!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fffd283e3-dbc3-4879-9740-3e2f5c42483d_1284x2622.jpeg) Despite having a “monopoly” on guided hiking tours in nearby cities on Airbnb Experiences, the demand for such experiences appears to be spotty as well. Even though there were 10 spots available for the tour, I was the only one who signed up for the weekend hike. When I asked Richard (the host of the experience), he also confirmed that the demand is very volatile. Sometimes he would go months without any new guest. He did try to broaden his reach by trying to provide guided hike near Lake Tahoe which is a major tourist hotspot. But apparently Airbnb is yet to give him permission to operate such experience as they claimed he needs some “license” to operate such guided hikes in that area. He remains a bit confused about the exact requirements. He was also visibly annoyed by Airbnb’s “insistence” that he let AI to describe his listing. If the AI suggested some description but let him edit as he sees fit, I think that’s fine? I’m starting to notice that while many people are enchanted by the magic of AI, there are increasingly some who are a bit fatigued and almost have bit of a resistance to it. In fact, I wonder if there is a recession in the next 1-3 years, it may be a bit difficult to discern whether people are losing jobs because of AI or the recession which may lead to a more conspicuous backlash against AI. I really liked how Seth Godin [thinks](https://www.youtube.com/watch?v=LFOvsXvO63k&ref=mbi-deepdives.com) about AI (I recommend the episode which I incidentally listened to it while driving to my Airbnb experience): > I think it's more interesting to talk about AI like it's the weather. You can be pro-snow or anti-snow, but if it's snowing out, it's still snowing. > > It's the talking dog thing, which has two parts. Part one is if you meet a talking dog and its grammar isn't very good, don't forget that it's still a talking dog. It's still a miracle. But number two is just because a talking dog said it doesn't mean it's important. Richard certainly seemed quite knowledgeable about the area and the history around it. It was a bit nerve wracking to hear that a mountain lion killed someone in this trail [last year](https://www.sfgate.com/bayarea/article/mountain-lion-kills-man-northern-california-19365657.php?ref=mbi-deepdives.com) and there are also plenty of rattle snakes around. So, I felt a bit reassured looking at his preparedness for any potential mishaps during our hike. It was a two-hour hike. While I did see few people in the trail, they were few and far between. 0:00 /0:08 1× During our hike, I had a good chat with Richard about many topics. I have moved around quite a bit in the last 8 years before living in California (Ithaca, NY for a few years, Madison, WI for a year, Ottawa for a year; also spent a few months in NYC and Texas). One of the things I have noticed that may be a bit different about people in California is many of them tend to assume your politics whereas people in most other areas seemed more reserved before assuming what you believe. I wonder if it’s due to relative homogenous political beliefs around here or the ready assumption of politics of an immigrant; people just feel more comfortable of espousing their political beliefs even when they’re at “work”. Given how divisive politics has turned out to be in the US, I do lament how all encompassing politics seems in most conversations these days, even with apparent strangers. Anyways, I do always enjoy meeting and chatting with someone who is from a very different background and has very different worldviews than I do. Of course, nobody has monopoly on truth and it is much more likely that different people contain some ounces of truth. I would have loved to go for more of these guided hikes in areas I travel to or in nearby cities (especially at a $30 price point, it’s quite reasonably priced). But like I mentioned before, I do not see any such listing on Airbnb experience. If Airbnb is actually serious about making experiences a success, they really should focus on increasing supplies. I should also receive messages about interesting experiences from Airbnb when I travel somewhere. Now that they re-launched Airbnb experiences, I hope such low hanging fruits will be picked by management sooner rather than later. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Data Center Story: Part 2, Portfolio Change URL: https://www.mbi-deepdives.com/the-data-center-story-part-2-portfolio-change/ Last updated: 2025-09-20T13:28:56.000Z So I finished the Data Center [episode](https://www.stepchange.show/p/data-centers-the-hidden-backbone?ref=mbi-deepdives.com) by stepchange yesterday. But let me offer a correction from yesterday’s [piece](https://www.mbi-deepdives.com/the-data-center-story-part-1/) first. I quoted “stepchange” saying, “*Many enterprises had PUE numbers around 2, meaning twice as much power went to lights, cooling, and everything else compared to the amount of power that actually ran the IT hardware. Google pushed it down to 1.1, meaning only 10% of the power was not directly used to power the computers*.” The quote was a bit sloppy. As a reminder, PUE is total facility power divided by IT power. A PUE of 2.0 actually means the total power used by the facility is double the power used by the IT hardware. If the computers use 1 MW, the facility uses 2 MW total. The overhead (cooling, lights, etc.) accounts for the remaining 1 MW. Therefore, a PUE of 2.0 means the power used for overhead **equals** the power used for IT hardware (a 1:1 ratio). Similarly, A PUE of 1.1 means roughly 9% overhead and 91% to IT. Thanks to the couple of readers who pointed it out. Speaking of PUE, as indicated yesterday, it’s not just Alphabet or Meta anymore, the entire industry has become much more efficient now. Over the last decade, compute power actually increased by 40x and yet power consumption grew by only 2.5x, thanks to all these efficiency gains. One of the surprising stats is hyperscalers energy consumption doubled in just four years during 2017 to 2021 period even before the AI boom took place. From the podcast: > One study found that the cloud is 93% more efficient than running your compute power on-premises because you have less wasted assets combined with everything we talked about with PUE and driving down that efficiency. > > Well, now all of a sudden you had COVID plus ZIRP, where you're not waiting to get more efficient; you're just trying to build more and more. > > We'd also kind of run out of that low-hanging fruit, right? **PUE is at 1.1.** > > if you look at the big four that we talked about before — Amazon, Microsoft, Google, and Meta — between 2017 and 2021, in those four years, **they doubled their energy use to 72 terawatt-hours in 2021**. > > So much greater demand, combined with a loss of the PUE benefits and the cloud efficiency, means we're in a new paradigm of power usage, and the industry is having to reckon with that. I knew mining crypto was energy intensive, but never appreciated the extent of it. I would not have guessed that by 2022 crypto was half of the energy consumption of the data centers (at the high end, that’s **double** the energy consumption of hyperscalers): > Let's talk a little bit about crypto because it's not just a hobby, but actually ends up being a competitive buyer in the data center infrastructure build-out. > > **By 2022, 100 to 150 terawatt hours. So that's almost 50% of what the rest of the data centers are consuming now going to crypto minin**g. Go back to 2009, Bitcoin launches, and it's pretty much just a dark corners of the internet thing, right? Someone mining on a PC in their bedroom. > > Then, finally, folks figure if you buy GPUs off the shelf, you can go faster, and people start even designing custom ASICs that are designed to do nothing but mine Bitcoin and other coins. By the mid-2010s, mines had become significant power loads, and folks had started to build these out wherever they could find cheap electricity. Of course, the AI data centers (which are only \~10% of total datacenters today) have some distinct characteristics from the traditional data centers which make the power question the key focus: > One of the things driving this power conversation we've been having is, again, not only the size of these data centers, but now **the density of power required within these data centers**. This is what makes an AI-driven data center so much different from a traditional data center. > > Just 20 of these training racks in a pod is one megawatt of power. That's on the order of a small, 800-person town powering electricity. So, if you get 200 of these racks, you get to an 8,000-person neighborhood very quickly. You get to a gigawatt of Seattle-scale power. > > As you pack these GPUs that have higher power density, they run hotter, and you can't let them run hotter. So now, how you cool your GPUs and your racks needs to evolve. > > We've crossed the threshold of physics where air can cool this amount of heat being emitted at this level of density from the racks. And so now it is a liquid-cooling world. > > You don't really want to think about it as one GPU or a rack of GPUs or a building of GPUs. You want, as much as possible, **the whole data center to act together**. Because the way that a transformer actually trains is that it makes guesses about what should come next, and then it compares that to reality, and then it tunes the weights across it. And you're doing that collectively across the whole model. And to do that, you need all of the computers working on the problem to be able to communicate quickly. Otherwise, the whole thing is training slowly. The faster those exchanges can happen, the better. And so you want the connectivity between chips to be as fast as possible. You want the connectivity between boxes of chips to be as fast as possible. And so it does not work if all of a sudden half of your computers are on the East Coast and half are on the West Coast, because the speed of light across the country is going to slow you down by multiple orders of magnitude than within one campus, within one center. And that's why Meta wants to build a 5-gigawatt campus because that's going to get them the biggest training model possible. While data centers are expected to contribute much more to overall electricity use in the next 5-10 years, it is only 4% today. But looking at this number on a national level may be misleading as it masks the very real implications in the local context, especially when you take into account aforementioned “*You want, as much as possible, *the whole data center to act together**.” From the podcast: > it's worth pausing there for a second to talk about that 4% electricity use. When I hear that number, it actually seems shockingly small. For all of the discussion of data centers and electricity, it's like, okay, it's 4%. Let's say it goes to 8%. Compared to industry, compared to cooling and heating buildings. It all feels small. So why might this be such an issue of conflict? > > It might have to do with the fact that it's misleading to think about it in terms of total national electricity use, because a data center has a localized and concentrated impact, right? You're not spreading this load across multiple utilities. And not only is it localized and concentrated, it's localized and concentrated in similar areas. Because, as we've been talking about, the network effect of the value of a data center being positioned near the undersea cables and near other network points is where you get a lot of performance gains. And so you end up concentrating yourself in Virginia, in California, in Texas. There's just basically 10 states where you're seeing new data centers come online. And so there's a tremendous impact at a local level—at the electricity prices for that community, that county, and in that state—but not necessarily at a national level. There are 11,800 data centers in the world, and nearly half of them are in the US, followed by Germany, UK, China, and Canada. But who owns all these data centers? > …you can kind of think of it in four broad categories. That's representative of the US but also it fits globally. > > And so the first category are…the colocation centers, the ones we've talked about: Equinix, Digital Realty, and others. There are about a dozen of these that are building 10 to 100 megawatt blocks in the US and around the world. The second category are the hyperscalers: the Facebooks, Amazons, Microsofts; you can add in Apple, Oracle in there. **They account for almost half of global data center capacity, and they're the lion's share of new growth**. But importantly, you still have thousands of private server rooms in banks and retail facilities and public institutions that are out there. So **while large in count, they're relatively smaller in capacity, but today still make up about 35%**. And finally, you've got legacy telcos that are still owning and operating data centers. > > Now the interesting thing about this is the trend, right? You're seeing **enterprise and public sector data centers trending down, and new build increasingly going to hyperscalers and these purpose-built colocation facilities**. I do disagree with one thing with Stepchange guys though. At one point, Anay Shah (one of the hosts) lamented about a “new digital divide, exacerbating global inequality” due to the disparity of data center locations and loss of “national security” for many countries. I think he got it completely backward. It is certainly very much in the interest of the **governments** of many weak or non-democratic countries to want to have data centers within their national territory so that they can seize control if it’s necessary, but is that actually in the interest of people living in those countries? I don’t live in Bangladesh anymore, but I can tell you I would rather not want to give **any** Bangladeshi government such power even if it means my internet connection would be a little slower than Americans. “Inequality” is one of those persistent problems that **almost** all the solutions basically ignore the unintended consequences and more often than not, these “solutions” are much worse than the problem itself. However, I do wholeheartedly echoed with them that these datacenters are indeed “modern marvel”. Without them, the podcast or MBI Deep Dives itself would not exist. Of course, we take all the technological miracles for granted and our ability to adapt to new technological paradigms may be close to infinite (although AI may test this hypothesis), but it is quite striking just how “invisible” the datacenters are in our everyday lives even though it has quickly become the core foundation of our lives. From the podcast: > …I think about data centers that started off occupying closets, growing to whole floors, then buildings, then warehouses. And I start to think of the cloud as a building, as a factory, where a bit comes in, gets massaged, gets put together in the right way, and then it gets sent out to its destination, and it really brings the internet home. > > For me, this connects to one of my reflections, which is threaded through the invisibility of this infrastructure and what makes data centers and the connecting internet almost the perfect abstracted infrastructure. And what I mean by that is you can access them, you can use them, you can get all of the power of them without ever seeing them or touching them. > > Now, with us living in a mostly wirelessly blanketed internet ourselves, you have access to all of these warehouses and all these buildings via all these undersea cables. And if you're an engineer developing software, you can deploy to all these regions around the world and never actually look at a CPU or a hard drive in your life. > > It is, I think, **the best-abstracted physical infrastructure that humanity has built**. It is physical, it is silicon, it is electricity, it is fiber. There is no real magic. It is all physics. > > But the only infrastructure I can think of in humanity that is as well abstracted is maybe money, but money is actually not physical anymore. In the same way, this actually is still doing physical work. And that struck me. > > Invisible, and so frequently in our life. Even though it is invisible, our entire day is mediated by this infrastructure…if your entire day is spent on the train, you're very aware of where the tracks are, and you can see them, and you're on the train. > > Even fire, we probably used it selectively throughout the day. The railroads, we got on and off. The car, we got in and out of. Electricity, we turned the lights on and off. This is **a modern marvel** that we're only not using when we sleep. And even then, it's doing stuff for us…It's tracking my sleep. I'm wearing a ring that's tracking my sleep, sending bits to Oura's servers. > > You're living in this infrastructure, but we never see it. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I did make one change to my portfolio yesterday which I will mention behind the paywall. _This post is for paying subscribers only._ ### The Data Center Story: Part 1 URL: https://www.mbi-deepdives.com/the-data-center-story-part-1/ Last updated: 2025-09-19T14:30:22.000Z I came across this recently launched podcast named “[stepchange](https://www.stepchange.show/?ref=mbi-deepdives.com)”. The podcast is basically Acquired-style story telling of “human progress through the lens of transformative technologies, systems, and infrastructure”. They have covered two topics so far: Coal, and Data Center. Naturally, I started with their [Data Center](https://www.stepchange.show/p/data-centers-the-hidden-backbone?ref=mbi-deepdives.com) episode. Since I grew up in Bangladesh, loadshedding was a daily occurrence in my childhood. In fact, one of my lasting childhood memories of my brother and me studying in the evening using kerosene powered hurricane lamp. For the uninitiated, I am not some old dude reminiscing about yesteryears; I’m just a guy in his mid-30s who was born in a country where it used to take awfully long time for future to arrive. But the diffusion of progress and technology has undeniably got [faster](https://x.com/borrowed%5Fideas/status/1880630162188030108?ref=mbi-deepdives.com) and faster at an unprecedented speed in my lifetime. ![](https://substackcdn.com/image/fetch/$s_!VwHG!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F26a87ac4-4110-46ef-b30c-ea06248053d0_800x680.png) Figure: A kerosene powered hurricane lamp; Image Source [here](https://www.sumitool.com/en/column/column%5F01.html?ref=mbi-deepdives.com) While the playing field will never quite be leveled across the world, today’s revolutionary technologies (e.g. chat bots) is much more accessible to kids in Silicon Valley and “most” kids born in Bangladesh. I quite marvel at the **diffusion** of technology, and it is the rate of diffusion for which I have always had a soft corner for Alphabet and Meta who both went above and beyond to ensure their products reach every nook and cranny of the world. While it is quite fashionable to drone about their shortcomings in the pursuit of such diffusion, you probably needed to study under kerosene powered hurricane lamps to appreciate the accelerating pace of diffusion for more people to have a chance at social mobility over time. One technology that certainly was an accelerant of such diffusion was data centers. While today’s data centers look gigantic, the early days of “data centers” looked nothing like it. The stepchange guys argue IBM’s [punch cards](https://en.wikipedia.org/wiki/Punched%5Fcard?ref=mbi-deepdives.com) were essentially the first data centers. It is hard to appreciate how different the world looked when processing and storing information were entirely manual; for example, it took seven years just to tabulate the data collected for 1880 census data. Even in 1960s when air travel started to take off, you had to call your travel agent who would then call the airline, the airline would then have to run clerks around, pulling cards out to book a seat…the whole process apparently used to take 90 minutes for each booking! The episode also enhanced my admiration for Walmart even further. Walmart may not have a conspicuous revenue and profit driver such as AWS to make them look like a “tech company” today, but Walmart’s penchant for technology ran pretty deep. Perhaps it isn’t a surprise why despite starting to sell groceries in 1988, Walmart became the largest seller in groceries in just [13 years](https://www.mbi-deepdives.com/groceries%5F1/)! In the late 80s, Walmart was essentially at the frontier in integrating technology in every facets of their business. An excerpt from the podcast: > …you're at the register at your neighborhood Walmart location: you scan the toothpaste, the barcode beeps. Within seconds, a satellite dish behind the store sends that transaction to the sky and over to Bentonville, Arkansas, where they're hosting their mainframe computer to record the sale. > > A few minutes later, a massive data warehouse would update how many tubes of toothpaste you had just bought. Then, perhaps before the end of the day, Procter & Gamble's factory would receive an update that they needed to make more toothpaste. > > This was the cutting edge of retail in the late 80s, and Walmart made a massive decision to take this a step further, truly driving innovation across the retail industry. They invested $24 million to build their own private satellite network linking all Walmart stores to headquarters. This was fairly unprecedented at the time…**It was the largest private satellite network**. > > So any single event that happened within the Walmart ecosystem rode on this private network, enabling them to mine their data in a way that was unheard of before. > > By mining their sales data – which could now be collected in real time across all Walmart stores over their private satellite network – they discovered that when hurricanes approached, the sale of Pop-Tarts increased 7x over their normal rate. I am 75% into the four-hour episode, and one thing that stood out from the episode is their discussion on **PUE** or “Power Usage Effectiveness”. What is PUE? From [Wikipedia](https://en.wikipedia.org/wiki/Punched%5Fcard?ref=mbi-deepdives.com): > PUE is the ratio of the total amount of energy used by a computer data center facility to the energy delivered to computing equipment. PUE is the inverse of data center infrastructure efficiency…PUE was published in 2016 as a global standard The interesting bit was that PUE as a metric was only presented in a paper in 2006 and established as a global standard in 2016\. Of course, data centers were around long before people were optimizing for PUE, but it was still instructive for me to understand just how inefficiently many data centers were operated not so long ago. From the podcast: > It hit the industry like a storm because the PUE race was now on, and **Google wanted to win**. Many enterprises had PUE numbers around 2, meaning twice as much power went to lights, cooling, and everything else compared to the amount of power that actually ran the IT hardware. **Google pushed it down to 1.1, meaning only 10% of the power was not directly used to power the computers**. > > Their internal teams took hold of this and ran with it. They used this rethinking of the data center from the ground up as one computer system, with software being the reliability layer, not the hardware. This, along with innovative geographic placement, drove performance and PUE. It's come to a point where it essentially can't be optimized past one. We have all heard about Google’s infrastructure advantage, but these advantages were often consequences of their ability to assemble such technically competent teams. Of course, Google (or any company) never had monopoly on talent. Facebook understood Google’s deep advantages here and realized they needed a different approach to play catch up to Google. From the podcast: > “so they're (Facebook) realizing, 'Why is everyone making so much money on us?' There's got to be a better way to do this. And there are two broad ways that they go about this. One is similar to what we've seen with Amazon, Google, and Microsoft, which is the thought that, 'Hey, we can do this better, cheaper if we build our own custom data center.' And so their first purpose-built facility was announced on January 10th and began operations in 2011 in Prineville, Oregon. And they soon followed with North Carolina, Sweden, and Iowa, and expanded rapidly. But Prineville was, just like all the others, quite intentional and quite strategic. > > …if you look at the numbers of server growth, if you go back just to 2008, they had 10,000 servers; in 2009, 30,000 servers. Supposedly by 2010, when they are really kicking off and building the Prineville project, they have around 60,000 servers. They're trying to figure out how to scale up. > > so they decide to engineer the building and the servers together. This very purpose-built facility. And just like the others, they realized that they can beat industry standards. And so they very famously and publicly came out with this first purpose-built facility **with a very aggressive target of a 1.15 PUE. Industry average is 1.5\. Historically, they were north of 2**, and they were able to report 38% less energy, 25% lower cost against prior facilities. And do you think they ended up beating their PUE? > > Smoked it: **1.07**. > > So that means that only 7% of the energy going to run the data center went to anything other than powering the IT equipment. That is phenomenal. > > Did they keep this to themselves, how they did this? I think this is what sets Facebook's approach apart. > > This first part they needed to do, and they executed extremely well. But the second thing they did was far more revolutionary. In a very secretive world of data center development where each hyperscaler kept their builds to themselves, in April of 2011, Facebook open sourced their blueprints. They announced the Open Compute Project. > > It's hard to overstate how radically different an approach this was. This is an industry that kept the design of the servers in the data centers extremely secretive. They viewed that as core IP, differentiation, and notions of security. Google would publish papers, especially on things like PUE, and be visible about metrics that they wanted to highlight. But they famously didn't let anyone into their data centers until this really changed the game. > > Their motivation is pretty interesting. They look around at all the other big data center companies and they're seeing all the margins that are being made. They're like, 'Well, wait a minute, if we publish our blueprints and we get everyone else to buy in and publish theirs, that means we can drive down the costs by standardizing what we're building.' > > Ultimately, what they want is for their suppliers to be in increased competition. The best way to do that is not to make one deal with one supplier, but to say, 'Hey, suppliers, this is what we need, this is what we like. You make this, we'll buy it as long as it's the cheapest one out there.' > > …it flips the power dynamic. Now, instead of the vendors and suppliers dictating what the specs are and having multiple different specs for multiple different customers, they're able to standardize it and say, 'You're going to respond to the Open Compute Project standards.” I knew about “Open Compute Project” and Meta’s strategy to “commoditize your complement” which they have tried to replicate in SOTA model as well (so far, not so successfully), but it was still quite interesting to learn just how much better efficiency in infrastructure may have been a tailwind for both Meta and Alphabet in much of 2010s. Of course, many of these low hanging fruits are now gone, but it is hard to appreciate in the moment what else these companies may be cooking to improve their infrastructure. Given their ever increasing depreciation expenses, the question may have been never more relevant than it is today. I hope to write more about the episode tomorrow as well. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Thoughts on Meta Connect 2025 URL: https://www.mbi-deepdives.com/thoughts-on-meta-connect-2025/ Last updated: 2025-09-18T13:32:16.000Z I bought my first Meta Ray-Ban Glasses back in 2023\. After using it for a few days, I reviewed my initial thoughts [here](https://x.com/borrowed%5Fideas/status/1725282443526082886?ref=mbi-deepdives.com). For almost the last two years, I have been a Daily Active User (DAU) of these glasses. In fact, after losing a pair early this year, I could survive only 48 hours without them and then went to a nearby Ray-Ban store to buy a new pair. What do I mostly use my glasses for? Since I intend to hit 10k steps everyday, I try to take all my calls with my glasses during my walk. I use my glasses to listen to all my podcasts and earnings calls. Of course, I could use Airpods for these as well, but it’s more convenient to be able to pause or change volume. The fact that I live in California also helps since I probably should wear a sunglass while walking under California sun during most of the year anyway. These glasses are particularly handy for being present while taking photos or videos of my 9-month old son. You know what I’m not quite using these glasses for yet? AI. While Meta seems hell bent on calling these glasses “AI glasses”, my interaction with “Meta AI” using these glasses have often been hit or miss. It is this particular reason I may be less charitable about Meta’s demo failures for its “[Meta Ray-Ban Display](https://www.meta.com/ai-glasses/meta-ray-ban-display/?ref=mbi-deepdives.com)” launch (see [here](https://x.com/nearcyan/status/1968468841786126476?ref=mbi-deepdives.com), and [here](https://x.com/ns123abc/status/1968473847901880678?ref=mbi-deepdives.com)) in yesterday’s Connect keynote. Tech companies understand the risk of these live demos and hence mostly show pre-recorded videos to precisely avoid these mishaps. While I can appreciate Meta willing to take such risks, my bigger concern is to what extent these failures were indeed emblematic of actual consumer experience. Having tried Meta AI through the glasses, I can tell you these “mishaps” are not super rare. While the demo failures did get plenty of attention, the product itself is super cool. These [ads](https://x.com/SawyerMerritt/status/1968496473009700957?ref=mbi-deepdives.com) give you a sense of the experience. One demo that did work was “[live subtitle](https://x.com/BasedBeffJezos/status/1968561531127312670?ref=mbi-deepdives.com)” and you can even get the subtitles in your own language while the other person can speak in a different language. Traveling or living in a different country can be a fundamentally different experience in just a couple of years! But I would like to reserve my judgment before I can actually try the glass myself. Interestingly, while I could buy the earlier Meta Ray-Ban glasses directly on the website, “Meta Ray-Ban Display” requires you to schedule a demo first. I will get a demo in a nearby store on October 2\. I will report back what I think of the glasses after the demo. ![](https://substackcdn.com/image/fetch/$s_!5gfk!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d9ed266-8c9a-4125-a0a8-1a9e7c9eea21_651x810.png) While glasses were the highlight of this year’s Connect, Meta also talked about making it easier to create virtual worlds (thanks to AI) to propel VR adoption. I have a Quest 3 headset and while I used to be DAU in playing Cricket in VR, I barely touched my headset since my son was born. Perhaps more importantly, during my nearly two years of playing Cricket in VR, I didn’t find anything else that I would like to do on a recurring basis with my VR headset. Meta may want to be patient with VR, especially given AI can indeed make it trivial to create 3D content but it’s hard to deny VR’s adoption has been a huge disappointment so far for Meta. I do think the clock **should** be ticking for VR. I suspect Meta may be losing \~$8-10 Billion per year on their investments in VR and given the lack of momentum here since 2021, I think the investments may need to be scaled down materially to \~$1-2 Billion losses in VR if even AI cannot change the direction noticeably in a couple of years. The glasses indeed seem much more promising and I am fine with Meta investing $10-15 Billion/year here for another 3-4 years as they perfect the form factor and technology over time. Every Connect Keynote feels like a referendum to me on the question of Meta’s Reality Labs losses. While it may be non-sensical to capitalize these losses in valuing Meta, this year’s Connect wasn’t super inspiring to think these losses are going to change direction soon. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/09/image-2.png) --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Amazon's "unfair" advantage in advertising URL: https://www.mbi-deepdives.com/amazons-unfair-advantage-in-advertising/ Last updated: 2025-09-17T14:18:20.000Z Amazon started disclosing their advertising revenue from 4Q’21\. Before 4Q’21, their ad revenue was “hidden” in “other” segment. Everyone, of course, knew ads was \~80-90% of “other” revenue. Amazon’s other segment grew from quarterly revenue of $1.1 Billion in 3Q’17 to a whopping $8.1 Billion in 3Q’21 after which they disclosed ad revenues separately. While modeling Amazon’s ad revenue, I consistently underestimated its potential in the past. For example, in [early 2024](https://www.mbi-deepdives.com/amzn2024/), I modeled Amazon’s ad revenue to be \~$62 Billion in 2025\. Given their LTM ad revenue is already \~$61 Billion, it is highly likely that they will end up generating ad revenue \~10% higher than what I was modeling for 2025 early last year. It took me a while to appreciate Amazon’s “unfair” advantage in advertising (something that may cause some headaches for Trade Desk) which I will discuss behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!C4Up!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F671ef325-6d45-4745-9179-a1378873b626_912x592.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### How people use ChatGPT and its implications, Portfolio Change URL: https://www.mbi-deepdives.com/how-people-use-chatgpt-and-its-implications-portfolio-change/ Last updated: 2025-09-16T13:49:11.000Z There was a lot of chatter about an NBER working paper “[How People Use ChatGPT](https://www.nber.org/system/files/working%5Fpapers/w34255/w34255.pdf?ref=mbi-deepdives.com)” yesterday. Unlike most survey work or academic paper looking into this question, what differentiates this paper is it is written by people at OpenAI. As you can imagine, there is a treasure trove of interesting and more importantly, high quality data here since there is lot less guess work. So the paper provides analysis of the chatbot's consumer usage from its November 2022 launch through **July 202**5 based on actual usage data and pattern. By July 2025, ChatGPT reached 700 million weekly active users (\~10% of the global adult population) who collectively sent **18 billion messages** per week. This rapid diffusion continues, with total message volume growing **5x** between July 2024 and July 2025, driven by both new users and increased engagement from existing cohorts. A **key revelation** is the dominance of non-work-related usage. While work usage grew, non-work messages expanded much faster, increasing from **53% of total volume in June 2024 to 73% in June 2025**. From the paper: > “Table 1 shows the growth in total message volume for work and non-work usage. Both types of messages have grown continuously, but non-work messages have grown faster and now represent more than 70% of all consumer ChatGPT messages. While most economic analysis of AI has focused on its impact on productivity in paid work, the impact on activity outside of work (home production) is on a similar scale and possibly larger. The decrease in the share of work-related messages is primarily due to changing usage within each cohort of users rather than a change in the composition of new ChatGPT users.” ![](https://substackcdn.com/image/fetch/$s_!6LXq!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5c8262b4-d7ee-4768-a2c9-7fcbc4783181_946x307.png) Source: [How People Use ChatGPT](https://www.nber.org/system/files/working%5Fpapers/w34255/w34255.pdf?ref=mbi-deepdives.com) I have heard before that for most people, chat bot usage is mostly one-way street i.e. you find more and more new ways to use chat bots in your work and personal lives. Indeed, that’s exactly what the data shows. Every cohort’s usage has only **increased** over time. > “The figure shows that earlier sign-ups have consistently had higher usage, but that usage has also consistently grown within every cohort, which we interpret as due to both (1) improvements in the capabilities of the models, and (2) users slowly discovering new uses for existing capabilities” ![](https://substackcdn.com/image/fetch/$s_!-uTG!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F54764763-70c7-4e27-94fe-78934ecf4991_1021x651.png) Source: [How People Use ChatGPT](https://www.nber.org/system/files/working%5Fpapers/w34255/w34255.pdf?ref=mbi-deepdives.com) Okay, so people are using it more over time and non-work messages are increasingly being more and more dominant. Let’s now look into the types of queries people are asking ChatGPT. Usage is highly concentrated in three main categories accounting for nearly 80% of all conversations: "Practical Guidance" (e.g., tutoring, advice, ideation), "Seeking Information" (e.g., factual search, current events), and "Writing" (e.g., drafting, editing, translation). When used for work, Writing is the dominant application (40% of work-related messages), though two-thirds of these tasks involve modifying user-provided text rather than generating new content. The study classifies user intent as "Asking" (decision support) or "Doing" (task execution). While "Doing" makes up 56% of work usage, "Asking" is growing faster overall, constitutes 51.6% of total usage by June 2025. ![](https://substackcdn.com/image/fetch/$s_!57oS!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F36901f11-9525-4bb5-8cf3-cd613ca3e21e_897x1135.png) Source: [How People Use ChatGPT](https://www.nber.org/system/files/working%5Fpapers/w34255/w34255.pdf?ref=mbi-deepdives.com) While considering both work and non-work related queries, there has been bit of an evolution of the query mix over time. From the paper: > Practical Guidance has remained constant at roughly 29% of overall usage. Writing has declined from 36% of all usage in July 2024 to 24% a year later. Seeking Information has grown from 14% to 24% of all usage over the same period. As you may know, these data points can be Rorschach test for Google shareholders. On one hand, you can argue this paper makes it abundantly clear that chat bot usage expands the query TAM and “seeking information” is just a small sub-set of the TAM. That’s true, but it is also less assuring that query mix has gone from 14% to 24% in just one year for “Seeking information”. Perhaps the bear case for Google is not that ChatGPT is a Google replacement, rather ChatGPT makes Google increasingly irrelevant since the core “seeking information” function is just a small subset of what you can accomplish with ChatGPT. As we all know, Google is responding. The reality is Google is hardly just a “query box”; it is also dozen other things that all feed to protect and grow the golden goose of search. So, even though they do make supermajority of their profits from the search query box, our interaction with search for a while has already been only a subset of all of Google’s properties (Chrome, YouTube, email, calendar, Google Drive, Photos etc.). ![](https://substackcdn.com/image/fetch/$s_!k38x!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9c283429-d6af-44fa-99fa-379770f12c57_1284x1506.jpeg) The other data point that will somewhat reassure Google shareholders is not only “seeking information” is just one-fifth of the overall queries, queries that can lead to “purchasable products” is only \~**2%**! Of course, Google's advertising revenue relies heavily on searches with commercial intent and it appears queries with such intent is rarer than most investors might have imagined. For context, [\~15%](https://sparktoro.com/blog/new-research-we-analyzed-332-million-queries-over-21-months-to-uncover-never-before-published-data-on-how-people-use-google/?ref=mbi-deepdives.com) of Google queries have commercial intent. One interesting data point from the paper was computer programming accounts for only 4.2% of messages, and companionship or social-emotional topics account for just 1.9%. Imagine how much airtime people give to use cases such as “AI boyfriend or girlfriend” and it turns out most of us are not actually weirdos trying to date our ChatGPT. Of course, there is a shock value in pointing out such weird behaviors, but it’s good to remember these data as we may increasingly see some chatbot related hysteria going forward. ![](https://substackcdn.com/image/fetch/$s_!Fv9R!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F42cb4b28-dc7e-4c41-a9ef-cd49b8a1474f_1273x796.png) Source: [How People Use ChatGPT](https://www.nber.org/system/files/working%5Fpapers/w34255/w34255.pdf?ref=mbi-deepdives.com) Demographically, the user base has evolved significantly. Early adoption was overwhelmingly male (80%), but this gap closed entirely by June 2025\. The user base remains young, with nearly half of all messages sent by adults under 26\. Geographically, growth rates over the past year have been disproportionately high in low and middle-income countries, signaling rapid diffusion in emerging markets. From the paper: > “Comparing May 2024 to May 2025, we see that the adoption of ChatGPT grew dramatically, but also that there was disproportionate growth in low to middle-income countries ($10,000–40,000 GDP-per-capita). Overall, we find that many low-to-middle income countries have experienced high growth in ChatGPT adoption.” It’s incredible to see just how much this graph has shifted in just one year! It’s good to see such diffusion of technology in different parts of the world in such a short time. ![](https://substackcdn.com/image/fetch/$s_!Umok!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6f4a0c42-08dd-412d-b750-00bf168fa014_1465x886.png) Source: [How People Use ChatGPT](https://www.nber.org/system/files/working%5Fpapers/w34255/w34255.pdf?ref=mbi-deepdives.com) Overall, the paper helped me internalize something I already knew even more: ChatGPT’s canvas goes way beyond traditional search. Given the general purpose nature of SOTA models, the lines are inherently blurring when it comes to what exactly these chat bots are competing against. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I have made some changes to my portfolio which I will discuss behind the paywall. _This post is for paying subscribers only._ ### Why we need Intel foundry spin-off, funded by a mag7 consortium URL: https://www.mbi-deepdives.com/why-we-need-intel-foundry-spin-off-funded-by-a-mag7-consortium/ Last updated: 2025-09-15T13:45:35.000Z In July 2024, I appeared on Liberty’s [podcast](https://www.libertyrpf.com/p/semiconductors-industry-going-deep?ref=mbi-deepdives.com) to discuss semiconductor industry. Near the end of the podcast ([43 minute](http://v/?ref=mbi-deepdives.com)), I proposed an idea that I considered to be potentially a wild but reasonable idea. The global tech ecosystem is dangerously reliant on a single point of failure: TSMC. This concentration of advanced chip manufacturing in a geopolitical hotspot poses a profound systemic risk, visualized as an inverted pyramid where TSMC supports tens of trillions of dollars in global market capitalization, alongside critical national security infrastructure. For the visually inclined, Liberty later showed the below diagram. ![](https://substackcdn.com/image/fetch/$s_!OHIJ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc671ec10-fc88-4611-9796-da71193b0206_1012x807.png) Source: [Liberty’s Highlights](https://www.libertyrpf.com/p/semiconductors-industry-going-deep?ref=mbi-deepdives.com) A disruption in Taiwan would not merely cause a temporary shortage; it would likely trigger a severe global recession and potentially halt the trajectory of technological progress. The "Magnificent Seven" (mag7) tech giants, whose valuations and operations are almost entirely dependent on uninterrupted access to leading-edge silicon, are particularly exposed to this catastrophic tail risk. To mitigate this vulnerability, my “wild” idea was to spin off Intel’s foundry division into an independent, dedicated manufacturing company, primarily **funded** by a consortium of the Mag7\. While government subsidies distribute costs across the entire tax base, the primary beneficiaries of a resilient supply chain should assume a more direct financial stake. If you asked “what keeps you up at night” to every single CEO of mag7, Taiwan risk would probably be up there for all of them. The current reliance on TSMC represents an unacceptable strategic vulnerability. By forming a consortium, they can at least give their best shot to a viable, Western-based alternative although success is, of course, far from guaranteed. Intel has historically lacked the customer-service orientation required for a pure-play foundry. A spin-off, however, would provide the singular focus needed to excel, free from the conflicting priorities of Intel’s integrated model. Operating with a startup mentality and offering fresh equity incentives would make the entity far more attractive to top engineers as well. If each of the mag7 contributed, for example, $10 billion (which isn’t a massive number for any of the mag7 and can be disbursed over multiple years which will make it even easier), the resulting capital would radically accelerate the foundry's development. The opportunity cost of this investment pales in comparison to the trillions in market value that would evaporate if access to TSMC were compromised. Despite potential coordination challenges among these competitors, this collaborative investment is perhaps a necessary insurance premium to secure the future of the economy itself. Why am I writing about this today? During the weekend, I was listening to Dan Kim’s [interview](https://stratechery.com/2025/an-interview-with-dan-kim-about-intel-nvidia-and-the-u-s-government/?ref=mbi-deepdives.com) with Ben Thompson. Mr. Kim is the former chief economist and director of strategic planning and economic security at the U.S. Department of Commerce for its CHIPS for America program. During the interview, Mr. Kim suggested quite a similar idea as I did. It was a bit gratifying to understand that perhaps it wasn’t such a wild idea after all. Read the excerpt of the relevant section below (BT= Ben Thompson; DK=Dan Kim): > **BT:I think the scale of devastation that would come from a Taiwan war is drastically understated and underappreciated, but there is a bit where we don’t have the luxury of thinking about the trailing edge if Intel goes out of business, because guess what? The trailing edge for the next 100 years is screwed, so we’ve got to get this one right first.** > > **DK:** No, you have to seed it, so we’re focusing on one particular problem, but the demand driver, so one very small solution that I thought of when I was at CHIPS was, okay— > > **BT: Before you move on, I want to put a pin in what you just said because I think it’s brilliant, I think it’s the answer. I’m annoyed I didn’t write this in my Article, which is — this is exactly it — Intel is saying we need a customer, the answer is not to give them a customer, it’s to give them all the customers.** > > **DK:** Every customer. > > **BT: That is it’s introducing economic inefficiency, but it’s socializing the harm of doing this. In this case the US has the leverage to do it. Micron was going to complain or I don’t know if it’s going to be founding, memory’s a whole different thing, but just as an example, because that’s a US competitive space where they’re competing with SK Hynix and a Samsung. In that case, you don’t want to unfairly impinge on one, you need to do it all and I just want to double down before you move on to your point to say, you nailed it. That’s exactly it. That is exactly the next step. That makes sense.** > > **DK:** I’m not actually suggesting that any of this is a good idea, I think we all recognize that these are just- > > **BT: They’re all horrible ideas, it’s choosing the least bad one and in that context, I’m saying that is a good idea, it’s a good one.** > > **DK:** If I were in a company situation, I would hate this. Meaning if I were in a customer situation, I would hate all of this. But in the spirit of having the least bad idea, lets at least choose an idea that’s equally terrible for everybody. > > **BT: Exactly.** > > **DK:** Where they share it. > > **BT: It’s a tax. Taxes suck for everyone, but they’re equally applied and this is the Intel Tax. What you’re proposing is the Intel Tax.** > > **DK:** And it could be that maybe the outcome in 2035 looks like in which maybe there is no Intel that we recognize it as today, but it’s like a joint venture among all the leading edge customers as an alternative to TSMC, and you call it whatever you want. Maybe it’s Intel, maybe it’s something else, but they all have a stake in the game. > > **BT: Here’s the thing though, don’t you have to split Intel to do this though? One of the problems is no one trusts Intel, no one has trusted Intel for decades, that’s a big problem here. And yes, they’re talking about putting up a firewall and separating all these sorts of things, but if you’re going to go all in and you’re going to break every single norm, at what point should you just say, “You know what Intel Products, you go somewhere else”? We know we need your products for volume, that’s the argument to keep Intel together is, Intel is the core customer that gives volume for these new nodes, but at the end of the day, if we’re going to mandate volume, don’t we need to cut the Intel Product organization out?** > > **DK:** Well, this is where I would think if we were to steelman your argument even further, which is the government should take a stake, but maybe all the customers should take a stake too, and then they dictate what trust looks like that’s good enough for them to be able to use them. Maybe it’s complete separation, maybe it’s some other system, but the point is that the customers then dictate instead of saying, “We don’t trust you”. > > **BT: If you’re going to force them to do this, they should get the financial upside too. They should have the input upside as well.** > > **DK:** Not even the financial upside. I’m not sure if any customers will look at this and look at it as a financial investment. > > **BT: We’re not recording video, but I was smiling cheekily because yes, I completely agree with you.** > > **DK:** Again, just also underscoring the point, I’m not suggesting any of these are good ideas or even endorsing it, but these are some creative ideas that you could be thinking about as to like, “Okay, break the glass scenario, here we are, let’s solve it”. One parallel problem that I thought about when I was at CHIPS was, “If there is a dominant foundry and there’s such a demand for the chips that TSMC would make in Arizona, then presumably they could demand the price premium for that, if there is such a demand”. In that case the government- > > **BT: Well, that’s what’s happening, they are getting a price premium.** > > **DK:** Well, the way that I thought about it was, is it then the government’s position to say to these customers, “You pay for that fab because it’s either you pay for it or the taxpayer pays for it”. In some ways, if I give money to this company that could demand the price premium then are not in effect giving that money to an Nvidia or an Apple from a government perspective, and that made me very uncomfortable until it became very clear that you actually did need the incentives to push them to commit further, so I made peace with that. > > But then it also got me in a really uncomfortable position of then what is the government’s role then? Only fund losers that the industry doesn’t want to fund? But is that really where the best thing is, or do you actually also want to fund some winners too? These are all interesting philosophical questions about industrial policy, but the principle of having the customers having a skin in the game and having that turn into a demand driver that is enduring, I think is a principle that the government should think about for sure. These all make sense to me. I really hope someone at the current US administration is indeed exploring such an idea. This isn’t, of course, an ideal scenario; US government shouldn’t exert pressure on companies to invest in a particular company, but given the current administration’s penchant for pursuing unusual ideas, I won't be surprised if something like this is also on the table (I hope it is)! --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Texas Instruments' data center and robotics opportunity URL: https://www.mbi-deepdives.com/texas-instruments-data-center-and-robotics-opportunity/ Last updated: 2025-09-14T14:19:12.000Z Texas Instruments (TXN) hardly ever breaks out “data center” or “robotics” opportunity in quarterly calls with hard numbers. “Data center” shows up intermittently under **enterprise systems** segment, and “robotics” is usually rolled into **factory automation** during quarterly calls. So, it did catch my attention when TXN CEO Haviv Ilan provided some concrete numbers for the data center opportunity in the recent GS Communicopia conference. I will discuss both these opportunities for TXN behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb's untapped monetization lever(s) URL: https://www.mbi-deepdives.com/airbnbs-untapped-monetization-lever-s/ Last updated: 2025-09-13T14:11:38.000Z Brian Chesky had an interesting session at GS Communicopia last week which made me think Airbnb has quite a few untapped levers of monetization. While it’s unlikely that they will tap **all** these levers anytime soon, I think it’s worth thinking through these potential monetization pathways which can come to fruition over the next two to four years. I have said this before, but it’s worth repeating that Airbnb is one of those rare companies that unlocked somewhat unique demand and supply largely organically on both side of the marketplaces. I have touched on their unique demand driver [before](https://www.mbi-deepdives.com/airbnbs-unique-demand-driver/), so let me spend more time on their supply today. Hotels are concentrated in cities, and classic tourist zones, while Airbnb supply is more geographically dispersed into small towns and rural tracts. In fact, in 2022, Airbnb [mentioned](https://news.airbnb.com/us-travel-dispersal-report/?ref=mbi-deepdives.com) that in the US “Airbnb welcomed more than 44 million guest arrivals to areas where there are **no hotels,** generating more than $10.5 billion in Host earnings”. Given \~60% of Airbnb’s 2022 Gross Booking Value (GBV) of $63 Billion came from the US, it implies almost one-third of Airbnb’s GBV came from such supplies where hotels are not available. Moreover, most of these supplies are only on Airbnb and nowhere else, creating this unique supply that travelers can access **only** on Airbnb. ![](https://substackcdn.com/image/fetch/$s_!5tVa!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffec8b9f1-e1ce-4880-a1c7-9b755ed2507a_1669x1161.png) Source: [Airbnb](https://news.airbnb.com/us-travel-dispersal-report/?ref=mbi-deepdives.com) Airbnb obviously deeply cares about fostering such unique element of their marketplace. They launched co-host network late [last year](https://news.airbnb.com/airbnb-2024-winter-release-highlights/?ref=mbi-deepdives.com) which will certainly help convincing a lot more people to bring more unique supplies on Airbnb marketplace. From Chesky at GS: > We have about 10 million listings in Airbnb and for most of our history, we had the great fortune of most of supply coming organically to Airbnb, and **the vast majority of supply still comes organically**. Now this is a great characteristic of our business. My friends at like Uber and other companies did not have that quite that great fortune so they had to really build out these supply machines and have a lot of like driver referral programs. > > At the same time, **we want to grow supply much faster** than organic nature. So we've done a number of things. I mean we've worked also on host referrals. We built this thing called the Co-Host Network. We observed something. We noticed there's a whole bunch of people that have homes. They would love for them to be rented out and make extra money, but they don't have time to host. There's a bunch of hosts that are making a lot of money, and they love to host more frequently, but they don't have the capital to get another home. And we thought we're basically existing in a very narrow Venn diagram of people that have time to host and have a home. So what if we actually created a marketplace to match those 2 together? We can unlock millions more listings. So we did that, and it's just early on. We've gotten 10 million nights booked through the Co-Host Network. Admittedly, I was surprised that Co-Host network led to only 10 million bookings so far given that’s only \~2% of Airbnb’s overall nights booked on the platform. My surprise came from my own personal experience on Airbnb. When your booking is confirmed by your host, Airbnb automatically creates a messaging group to facilitate conversations between the host and the guest(s). Of the five Airbnbs I stayed so far this year, four of them had co-hosts and I just assumed this has been scaled throughout the platform pretty extensively already. But the disclosed number made me realize my personal experience may have been influenced by the fact that I only stayed in different parts of California and Airbnb is still in the very early stage of ramping up their co-host network across all the states in the US (and beyond). It does feel Airbnb is onto something here, and while networks can take time to gain momentum, a thriving co-host network should lead to a faster growth in supplies. I also think next year’s world cup could be a great timing to see this idea of co-host network flourish to a much larger extent: > The World Cup is coming here next year. It will, I think, be the largest event in human history. It's coming to U.S., Canada, Mexico. Events are how we started Airbnb, and it's the best way to get supply. And by the way, **it's a great way to get supply, also a great way to normalize relationships with policymakers** because this is the one time they're reaching out to you More than acquiring new supplies, retaining such supplies is equally, if not more, important and Chesky mentioned this is the key area of focus for them: > take a guess what the #1 reason someone stops hosting us? **It's too much work**. So if you could help people host and reduce the burden of hosting that not only we create more hosts, you'll monetize the listing better and there'll be less like either churn. And the churn is something that we (think) is probably even more important than the supply acquisition. And so absolutely, as you add more services, more people come on the platform, they stick longer, they don't churn and they pay you more. So, what could be some untapped monetization levers? Chesky gave some hints: > We have so many things they want us to do. I mean, like they want help with pricing. They want help with hosting. They want help with parts of hosting like help me register with the city, help me clean my apartment, help me get my place photographed. They want their house restocked with items that we could recommend to them. They want financial help with like doing their taxes and kind of doing essentially financial planning because these are essentially small businesses. And a lot of these people, they're not actually set up as businesses. They don't have accountants or CFOs. They're usually sole proprietors. So they need help with all those different things. They want to help to expand their business, add more properties. They want to help promote their listings. I understand that helping hosts do all these tasks could be quite challenging for Airbnb in pre-AI world, but today I wonder whether Airbnb can launch some “Airbnb host agent” in a year or two that can indeed keep track of all these mind numbing tasks for hosts. Airbnb will likely have the most in-depth contexts of their hosts’ tasks/to-do lists than a general purpose LLM models, and by launching such an agent, Airbnb can truly address some key pain points of their hosts. I imagine they may be able to charge some sort of subscription or pro tier for hosts using such agents. Of course, the more pain points Airbnb solve for their hosts, it will also automatically create newer hosts and additional supplies on the marketplace itself. Chesky also seems to have increasingly warmed up to the idea of promoted listings: > I think promoted listings is really, really interesting. We've looked a lot at this. > > I don't think there's has to be a trade-off between ads and a great user experience. I think with AI, the whole paradigm of an ad has to change. I think the way Google did ads, and I think that's going to be different in a world of AI. **Booking.com has done some really interesting things around the Genius program in loyalty, which essentially is a monetization program.** > > So I think there's like **this company is not only under monetized, it almost like isn't really monetized essentially**. We have travel insurance that we offer but we keep every year offering services for hosts for free, and **they're telling us they want to pay for premium services. So I think a host ecosystem is a massive opportunity for monetization in the future**. Indeed, Airbnb may not be fully capturing the value it has created in both sides of the marketplace, but there may be too much surpluses left especially in the host side of the equation. Even for their [services launch](https://www.airbnb.com/resources/hosting-homes/a/introducing-airbnb-services-741?ref=mbi-deepdives.com), I wonder if they missed a mark by focusing so much on the guests. Every single host on the platform needs to call for cleaning services every time a guest books, and it might have made more sense to address such an endemic demand for cleaning (and other related) services from hosts than focusing on guests. Beyond the supply side, Chesky also had some interesting thoughts on building a “social network” in the real world: > I would argue that **social network is the most successful product in human history that was invented and then uninvented**. Because in 2012, social networking became social media and your friends became your followers. Instead of connecting you started performing. So suddenly, there's really no social networks anymore. There's no way to connect with people. > > And we're not trying to build a social network. But **I'd love to build something like a social network in the real world**, where we can match you to people and communities all over the world to do all these products and services. So to do that, we'd have to build one of the most definitive profiles on the Internet, a really robust profile. We have 200 million verified identities. I think that's more than the U.S. passports in circulation at this moment I’m not sure what exactly “a social network in the real world” would entail, but there are some hints on Airbnb today. On the “Connection” tab on your profile, you can see your friends you stayed with on Airbnb, and then it mentions: “Coming soon: connect with people you’ve met through experiences”. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/09/data-src-image-ebd1bb90-9a26-4b55-8827-6982a3d742c7.png) Figure: Screenshot from Airbnb website I do wonder given the increasing [consumer habit of “browsing” the app](https://www.mbi-deepdives.com/airbnbs-unique-demand-driver/) and Airbnb stays gradually transitioning from intent to discovery, wouldn’t it make sense to have a feed tab on the app? I would love to know where my “connections” stayed on Airbnbs (if they choose to make it public), and what experiences/services they availed from Airbnb (and their respective reviews). Any such feed would also be quite conducive to show promoted listings. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Notes and thoughts on Meta and Microsoft discussion at GS Communicopia URL: https://www.mbi-deepdives.com/notes-and-thoughts-on-meta-and-microsoft-discussion-at-gs-communicopia/ Last updated: 2025-09-12T14:26:58.000Z **Correction**: I would like to issue a correction from yesterday’s [piece](https://www.mbi-deepdives.com/why-synopsys-sank/) on Synopsys. I mentioned the company is trading at \~56x LTM EBITA. The problem is Synopsys’ LTM EBITA doesn’t incorporate Ansys’ EBITA which they just acquired. As a result, while the EV includes the impact of acquisition, my LTM EBITA did not. When I added Ansys’ last four quarters **reported** EBITA (again, deducted SBC to keep it apple-to-apple), Synopsys is actually trading at \~36x EBITA (assuming $90 Billion EV). While I still am not interested in Synopsys at this valuation, it is not as egregious as I implied yesterday. Thanks to everyone who pointed out the error. --- # Meta CFO at GS Communicopia There’s been a lot of discussion about Zuck claiming during the White House meeting that Meta is going to “invest” $600 Billion in the US by 2028, but CFO Susan Li explained that this number includes all the capex and opex in the US. I estimate Meta is probably going to spend $450-500 Billion in opex (ex-depreciation) between 2025 and 2028\. If you assume \~75% of this opex will be spent in the US, that leads us to \~$350 Billion. When you add another \~$250 Billion of capex i.e. \~$60 Billion/year in the US between 2025-28 period, you can get to $600 Billion “investments”. That actually seems not as outlandish as it may have sounded when Zuck first said it. Of course, these are staggering numbers, but Li reminded that they remain committed to **operating profit growth**: > I believe Mark has publicly committed to delivering operating profit growth. And I realize that, that is not stand-alone a benchmark that is extremely exciting and that in practice, **we, in fact, have to make sure that over the long run, we are an attractive investment relative to any of the many other public equity investments that are available to all of you**. > > So there will be obviously like lumps in the years. It would be a truly amazing thing if you could sort of just deliver nice linear compounding returns in a predictable way forever. But we're committed to making sure that we deliver attractive financial returns over time and across and we sort of think about managing the portfolio of investments in that way. She also made some interesting comments on the evolution of ad loads. Ben Thompson, for example, has been a bit skeptic and wondered whether Meta intentionally dialed up the ad load in recent times to receive “permission” from investors to go after more speculative investments. That’s a fair concern. From [Ben Thompson](https://stratechery.com/2025/meta-earnings-meta-turns-the-dial-social-network-r-i-p/?ref=mbi-deepdives.com): > I don’t think it’s a coincidence that, in the same quarter where Meta decided to very publicly up its investment in the speculative “Superintelligence”, users got pushed more Reels and Facebook users in particular got shown more ads. The positive spin on this is that Meta has dials to turn; by the same token, investors who have flipped from intrinsically doubting Meta to intrinsically trusting them should realize that it was the pre-2022 Meta, the one that [**regularly voiced the importance of not pushing too many ads**](https://seekingalpha.com/article/4018524-facebook-fb-q3-2016-results-earnings-call-transcript?part=single&ref=mbi-deepdives.com) in order to preserve the user experience, that actually deserved the benefit of the doubt for growth that was purely organic. This last quarter is, to my mind, a bit more pre-determined. However, Susan Li alluded that their ad load has gotten much more discerning in terms of **when** to show ads: > "with ad load, it is also a story of personalization and of increasingly trying to infer when you are using our product, when you are in a session, are you interested in buying something? Are you in a commercial state of mind? > > there will be times when I'm like clearly scrolling through friends and family content and probably not thinking about shopping, and that's a good time to show me fewer ads. And that enables us without any meaningful sort of engagement impact to really optimize the impressions that we show you and increase the value of those impressions. > > And so there's a lot of work that's being done, I think, to really make ad load, it's gone away from kind of like 12.5%, the 1 in every 8 stories as an ad to something that feels really, really sort of tailored to when you are most likely to want to have a commercial experience. > > “On the demand side, reported CPM is really an output of the work that we are actually doing to drive prices down… even as we bring that cost down, you should see reported CPMs go up because we are making each impression convert more frequently and be more valuable.” One inference I had from this is reels may be even more conducive to show you more ads compared to newsfeed. Meta may not be dialing up ad load mindlessly, rather carefully capitalizing on every opportunity of increasing ad loads whenever it doesn’t hurt user engagement. It’s not just ad load personalization, even the ad content itself is likely going to be personalized in not-so-distant future. When I think of Meta’s ads, the path to personalization feels more of asymptote to me…there’s always going to be more and more work to do to personalize our experience even more over time. From Li: > "...the idea that ads can be super tailored to each person without the advertiser knowing that you and I could get the like a hotel in Hawaii could target you and I with the same ad... But we know that you should get an ad that's oriented around like Big Wave surfing and I should get an ad that's oriented around like hiking. And it just creates those for us because it knows that those are our interest and the hotel doesn't have to, we do all that on behalf of the advertiser." Li also pointed out the paradox of productivity gain from AI; while the concern seems to be around whether companies will hire fewer people because of higher productivity, Li mentioned the opposite should be the case although there is certainly a point at which the marginal return would taper off: > "You can imagine that there are teams like if each of your engineers can produce twice as much product impact because the Al tools have made them twice as productive as they previously were, then we should probably hire a lot more of those engineers. There are sort of other areas around the company where I think this will be more of an efficiency gain..." > > "**The thing that's a little bit unknown** is but like where are you on the sort of marginal curve of returns right now? So sure, maybe we know sort of each 25 engineer unit of work is going to generate some amount of return, but what happens is you add like 1,000 engineers, like how quickly does the curve drop off?" There was, of course, a lot of discussion on Meta’s investments in Superintelligence team, but there was nothing incremental there. She did mention that Meta AI is being widely used despite the fact that the model powering Meta AI is not a SOTA model. One of my hypotheses is the kind of interaction people likely do with Meta AI doesn’t really need the most advanced SOTA model and Meta may be discouraged to invest ever increasing amount on SOTA models once they realize the incremental engagement gain is not enough to justify to stay in the race. As a result, it may make a lot more sense to trail the SOTA model by 6-18 months and play catch up with a fraction of the cost for SOTA model developers. Of course, even that may not be easy to do as we are finding out Meta’s recent struggles with Llama models. If the Superintelligence team can get Meta back on track, I think that’s what they may focus on and spend much more time in productizing and infusing AI in every facets of the user experience through Meta’s apps. --- ### Microsoft at GS Communicopia [Jared Spataro](https://www.linkedin.com/in/jaredspa/?ref=mbi-deepdives.com), Chief Marketing Officer of “AI at Work” at Microsoft, attended GS Communicopia as well. I will share my notes and thoughts on this session as well as OpenAI’s recent partnership with Microsoft behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Why Synopsys Sank URL: https://www.mbi-deepdives.com/why-synopsys-sank/ Last updated: 2025-09-12T14:37:54.000Z First, let me follow-up on yesterday’s [piece](https://www.mbi-deepdives.com/more-alphabet/) related to Oracle’s eye-popping quarter and what it may have implied for the broader ecosystem. Later yesterday, WSJ [reported](https://www.wsj.com/business/openai-oracle-sign-300-billion-computing-deal-among-biggest-in-history-ff27c8fe?gaa%5Fat=eafs&gaa%5Fn=ASWzDAgOmD5QS82BFm-eLCL2PfEN18XlqvFQJ-3kBnAH9Np2UfziFtZRD3aNDIjyCfk%3D&gaa%5Fts=68c2c279&gaa%5Fsig=Qlp6Y6j37HUgaO%5FmDs26JfEXZnEN312kXE-GFIZhSnXiC9-eU3MGYOdXpizGSNMBSZOEMAGol2hXgUeoKQNLFg%3D%3D&ref=mbi-deepdives.com) that it was OpenAI who signed a contract with Oracle to buy **$300 Billion** of compute in the next five years! It is surprising to me that Oracle didn’t bother to mention such a material information during the earnings call. My skepticism certainly notched up a bit after realizing almost the entire increase in RPO was primarily attributed to OpenAI whose ability to pay Oracle is pretty much hinged on its ability to raise money from VCs/public market. As a result, there is a counterparty risk to this contract. Of course, OpenAI is not obligated to buy if they don’t need the compute for some reason, but a bet on Oracle at this point has become somewhat a bet on OpenAI’s continued ability to scale and ability to raise money. As I have mentioned earlier, sentiment in capital market is not really forecastable. AI can be the most consequential technology in the history of mankind and yet, capital market can shut off for a couple of years for some unexpected reasons in the meantime (See Amazon [post-tech bubble](https://www.mbi-deepdives.com/the-appeal-of-cash/)). Admittedly, I am surprised that Oracle stock maintained much of its gain after it became clear that OpenAI was almost solely responsible for the RPO growth. To be clear, we already knew OpenAI was the primary customer here, but the amount still surprised me. One reader pointed out to me that if you assume non-cloud software margins of 90% and 70% gross margin for SaaS within cloud revenue for Oracle, the implied incremental gross margin for IaaS in FY’1Q26 was only 25%. That perhaps explains that even though Oracle didn’t shy away from mentioning eye-popping revenue numbers in out years, they were much more reticent to talk about margins or earnings power. Oracle has an analyst day coming up next month; I hope they share more details about their earnings power from these partnership(s). --- In April 2025, I published a [Deep Dive](https://www.mbi-deepdives.com/snps/) on Synopsys. Later, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I did a podcast together on Cadence and Synopsys ([Spotify](https://open.spotify.com/episode/2FF3stedGzyN3vJe3avYbo?ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell-episode-5-cadence-and-synopsys/id1786912203?i=1000705553510&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=LBnNCv%5F2BEw&ref=mbi-deepdives.com), [RSS Feed](https://feeds.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com)). We both inferred that the stock appears to be richly valued when it was around $450\. Naturally, the stock went up by \~45% in the next three months. However, on the same day Oracle was experiencing 40% pop on eye-popping RPO numbers, Synopsys went down by 35% after their 3Q’25 call. I will explain why the stock experienced such a precipitous fall behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### More Alphabet! URL: https://www.mbi-deepdives.com/more-alphabet/ Last updated: 2025-11-25T00:03:04.000Z > “Eventually, AI will change everything…though not everyone fully grasp the extent of the tsunami that is approaching…Training AI models is a gigantic multitrillion-dollar market. It's hard to conceive of a technology market as large as that one. But if you look close, you can find one that's even larger. And it's the market for AI inferencing. Millions of customers using those AI models to run businesses and governments. In fact, the AI inferencing market will be much, much larger than the AI training market.” > > \-Larry Ellison (Oracle FY 1Q’26 Earnings Call) Last night’s Oracle earnings call set almost a giddy tone for AI infrastructure investments! Oracle CEO mentioned in the call “Oracle Cloud Infrastructure will grow 77% to $18 billion this fiscal year and then increase to $32 billion, $73 billion, $114 billion and $144 billion over the following 4 years.” For context, when I did a [Deep Dive on Oracle](https://www.mbi-deepdives.com/orcl/) late last year, I was modeling only \~$62 Billion revenue in FY30\. Oracle is now saying more than double the number I thought was somewhat aggressive! Moreover, Oracle reported Remaining Performance Obligations (RPO) reached at an almost obscene number: **$455 Billion**! However, after listening to Oracle’s earnings call as well as Thomas Kurian’s discussion at GS Communicopia conference yesterday, I inferred that I should buy even more Alphabet. I explain more behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### "Innoveracy", Finding Awe URL: https://www.mbi-deepdives.com/innoveracy-finding-awe/ Last updated: 2025-09-13T12:07:07.000Z # Innoveracy A couple of weeks ago, a friend asked for my opinion about Meta’s historical struggle of building “new products” (he sent me this [tweet](https://x.com/adropboxspace/status/1958718825152819266?ref=mbi-deepdives.com)). I mostly explained to him that going from 0 to 1 repeatedly is a challenging task for any incumbent, and I also reminded him that when Zuck acquired Instagram, Instagram had thirteen employees and literally **zero** revenue. Meta basically [took](https://x.com/borrowed%5Fideas/status/1913241843078443096?ref=mbi-deepdives.com) the business from zero revenue to the highest revenue generating consumer internet business since Facebook itself (yes, even more than YouTube and Netflix). Last week, I came across this [piece](https://asymco.com/2014/04/16/innoveracy-misunderstanding-innovation/?ref=mbi-deepdives.com) by Horace Dediu (h/t [SharpTech](https://open.spotify.com/episode/2u05CGIGFupPLZiSWOxXuf?si=d267ca703a724058&ref=mbi-deepdives.com)) who did a better job more than a decade ago in differentiating “innovation”. Dediu coined a new term “**Innoveracy”** which he defined as *“the inability to understand creativity and the role it plays in society*”. If I read it earlier, I would have probably just sent this piece to my friend. From the piece: > The definition of innovation is [easy to find](http://en.wikipedia.org/wiki/Innovation?ref=mbi-deepdives.com) but it’s one thing to read the definition and another to understand its meaning. Rather than defining it again, I propose using a simple taxonomy of related activities that put it in context. - Novelty: Something new - Creation: Something new and valuable - Invention: Something new, having potential value through utility - Innovation: Something new and uniquely useful ![Screen Shot 2014-04-18 at 7.54.26 AM](https://substackcdn.com/image/fetch/$s_!rEk6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F893eb7ad-3be9-4d3a-a4a3-e5d545bf4cec_620x458.png "Screen Shot 2014-04-18 at 7.54.26 AM") Image [source](https://asymco.com/2014/04/16/innoveracy-misunderstanding-innovation/?ref=mbi-deepdives.com) The piece further explained: > Note that the taxonomy has a hierarchy. Creations are novel, inventions are creations and innovations are usually based on some invention. However inventions are not innovations and neither are creations or novelties. Innovations are therefore the most demanding works because they require all the conditions in the hierarchy. Innovations implicitly require defensibility through a unique “operating model”. Put another way, they remain unique because few others can copy them. > > > To be innovative is very difficult, but because of the difficulty, being innovative is usually well rewarded. Indeed, **it might be easier to identify innovations simply by their rewards.** It’s almost a certainty that any great business is predicated on an innovation and that the lack of a reward in business means that some aspect of the conditions of innovation were not met. It’s thought provoking, and of course, the challenge of such taxonomy is you may find many examples in real world that may violate the specific details, but that doesn’t necessarily negate the core essence of the piece. --- # Finding Awe _This post is for paying subscribers only._ ### The Appeal of Cash URL: https://www.mbi-deepdives.com/the-appeal-of-cash/ Last updated: 2025-09-08T14:52:19.000Z Last month, Michael Mauboussin and Dan Callahan published a [report](https://www.morganstanley.com/content/dam/im/assets/publication/thought-leadership/consilient-observer/article%5Fconsilient-observer-cash-holdings%5Fltr.pdf?1754415308165=&utm%5Fsource=theideafarm.com&utm%5Fmedium=newsletter&utm%5Fcampaign=global-opportunities&%5Fbhlid=c1ac15eb05e7d335dc5e1d29f03aa2c400f6289f) on history and implications of cash holdings among publicly listed companies in the US. Excluding financial firms, US companies had $2.5 Trillion cash at the end of 2024 (4.7% of total market cap). From 1970 to 2000, the average ratio of cash to total asset was 5.8%, but it has increased to 9.7% during 2001 to 2024 period. ![](https://substackcdn.com/image/fetch/$s_!C2e7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F73b47c0e-c0cd-4de1-840b-4e6125219704_1048x615.png) Of course, you need some cash to run the business on a day-to-day basis. If we assume 2% of sales is what you require to operate the business and define anything above 2% of sales as excess cash, we can see the chart still shows more or less the same picture. US companies didn’t have much excess cash for much of 1970s and 1980s, but post-2000s, they seem to want to operate with much more excess cash. ![](https://substackcdn.com/image/fetch/$s_!U8Ha!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe26c8ade-dc04-44fd-9fd0-647425c55fe2_1027x529.png) Big Tech almost consistently run their business with net cash. Perhaps the shadow of the dot com bubble still looms so large in their minds. It is worth remembering that Amazon was tantalizingly close to go out of business; thankfully, Amazon’s then CFO who was advised by Ruth Porat (Alphabet’s penultimate CFO) to raise some cash as the internet mania was almost at its peak. Brad Stone’s book “[The Everything Store](https://www.amazon.com/Everything-Store-Jeff-Bezos-Amazon/dp/0552167835?ref=mbi-deepdives.com)” mentioned this chilling story (emphasis mine): > “While other dot-coms merged or perished, Amazon survived through a combination of conviction, improvisation, and **luck**. Early in 2000, Warren Jenson, the fiscally conservative new chief financial officer from Delta and, before that, the NBC division of General Electric, decided that the company needed a stronger cash position as a hedge against the possibility that nervous suppliers might ask to be paid more quickly for the products Amazon sold. Ruth Porat, co-head of Morgan Stanley’s global-technology group, advised him to tap into the European market, and so in February, Amazon sold $672 million in convertible bonds to overseas investors. This time, with the stock market fluctuating and the global economy tipping into recession, the process wasn’t as easy as the previous fund-raising had been. Amazon was forced to offer a far more generous 6.9 percent interest rate and flexible conversion terms— another sign that times were changing. **The deal was completed just a month before the crash of the stock market, after which it became exceedingly difficult for any company to raise money. Without that cushion, Amazon would almost certainly have faced the prospect of insolvency over the next year**” I imagine Amazon (and others) in hindsight inferred that while they survived, it was always unacceptable to be in such a position in the first place. They should have comfortable cushion long before the music stopped. I don’t necessarily interpret this as some sort of “cultural” preference for robust cash position among tech companies though. There are actual constraints which sort of forced them to run their business with net cash position. Mauboussin and Callahan wrote: > The most **important driver** of the increase in cash holdings appears to be the **growth in intangible relative to tangible investments** > > The basic idea is that **intangible assets have limited value as collateral, which caps the debt capacity of those companies reliant on them.** Further, many of these companies have good prospects for growth and do not want to be subject to the vagaries of capital markets or economic shocks. Indeed, now that big tech have lots of tangible capital and will almost certainly increase tangible capital much much over the course of this decade, they are increasingly much more active in debt markets. If scaling law persists, it is probably not even controversial to expect that all of the big tech (except Nvidia and Apple) will likely have material net debt on their balance sheet by 2030\. Although these happened for different reasons, Meta (buyback+ capex), Microsoft (Activision acquisition), and Amazon (both retail and AWS capex spree) already saw material shift in their net cash position. While Google’s net cash position has also declined noticeably in the last three years, Google remains the only company in the AI race with a $50 Billion+ net cash position. ![chart](https://substackcdn.com/image/fetch/$s_!pL3s!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc1e5a233-5e8e-4dbb-97f8-d875fe995ffe_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) From Google’s perspective, that’s a pretty neat position to be in given the capex requirement in the AI race, especially when your primary threat OpenAI just allegedly let their investors know their cash burn would be $80 Billion higher than expected for the rest of this decade. From The [Information](https://www.theinformation.com/articles/openai-says-business-will-burn-115-billion-2029?rc=4lgoj7&ref=mbi-deepdives.com): > OpenAI projected its cash burn this year through 2029 will rise even higher than previously thought, to a total of $115 billion. That’s about $80 billion higher than the company previously expected. ![](https://substackcdn.com/image/fetch/$s_!9qb1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F91379681-d069-4866-a2f8-3ac76eee2e33_1006x679.png) The appeal of cash actually appears to be much stronger outside the US. Mauboussin and Callahan showed the below country comparison and US public companies are actually nowhere near the top. Of course, cash becomes the most prized asset during period of extreme volatility. I remember back in Covid, Keyence [boasted](https://www.ft.com/content/247469ca-25b7-4bd8-8ac3-becf5530f15b?utm%5Fsource=chatgpt.com) they could survive **17 years** without ANY revenue! While that might have been comforting during Covid, that seems bit of an overkill. Of course, hoarding cash is no free lunch; it has very real opportunity costs. There is bit of a conflict of interest between shareholders and management here. Mauboussin and Callahan explained: > “Broadly speaking, companies generally want to hold more cash than investors want them to. Investors prefer less cash because they commonly hold the stock of a company within a diversified portfolio and prefer that management focus on building long-term value per share. Executives prefer more cash because it generally reduces their idiosyncratic risk and increases their ability to make decisions that benefit them and not necessarily their shareholders. > > In this context, the main risk shareholders face is that of executives using corporate resources for their benefit at the expense of the shareholders.” ![](https://substackcdn.com/image/fetch/$s_!fWIv!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F38c8a055-6a17-4b54-9d38-52dde247761a_1053x684.png) The last thing I want to highlight is the report mentioned “*there is research that suggests cash-rich companies are *more likely to do acquisitions* and that they often fail to create value*”. Lina Khan’s FTC was certainly an impediment for big tech to go for large deals; while I wouldn’t say current FTC is friendly to big tech, the current administration appears to be in a “cozier” relationship with big tech. As they say, “be careful what you wish for”; now that big tech has found a “loophole” to acquire companies (see Windsurf, ScaleAI, Inflection acquisitions etc.), I actually wonder if such a permissive environment would prove to be more **value destructive** for shareholders in the long run. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Google's evolving TPU strategy URL: https://www.mbi-deepdives.com/googles-evolving-tpu-strategy/ Last updated: 2025-09-07T15:07:10.000Z Google built the first Tensor Processing Unit (**TPUs**) because general‑purpose chips were too slow and too power‑hungry for the surge of machine‑learning inference inside Google Search, Ads, Photos, speech etc. As [Moore's Law](https://en.wikipedia.org/wiki/Moore%27s%5Flaw?ref=mbi-deepdives.com), the long-standing observation of exponential growth in transistor density, began to falter, the need for a more specialized and efficient solution became paramount. Google Cloud in a blog post from 2[017](https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu??ref=mbi-deepdives.com) explained the history behind TPUs: > Although Google considered building an [Application-Specific Integrated Circuit](https://en.wikipedia.org/wiki/Application-specific%5Fintegrated%5Fcircuit?ref=mbi-deepdives.com) (ASIC) for neural networks **as early as 2006**, the situation became urgent in 2013\. That’s when we realized that the fast-growing computational demands of neural networks could require us to double the number of data centers we operate. > > Usually, ASIC development takes several years. In the case of the TPU, however, we designed, verified, built and deployed the processor to our data centers in just **15 months**. 15 months!! While listening to the recent Acquired episode on [Alphabet](https://www.acquired.fm/episodes/alphabet-inc?ref=mbi-deepdives.com), I was reminded that it was Google which was able to gather the true “Superintelligence” team at a likely fraction of the cost of Meta did this year. In fact, we may not see such talent density again for several decades. From Acquired (slightly edited for clarity): > What if I told you that, between 2015 and 2016—the 12 months after the Alphabet transition—all of the following people were Google employees: Alex Krizhevsky of AlexNet (often cited as the dawn of modern machine-learning AI); his PhD advisor, Geoffrey Hinton (the “godfather of AI” and his collaborator on the AlexNet paper); Ilya Sutskever (founding scientist of OpenAI); Dario Amodei (co-founder, with his sister, of Anthropic); Andrej Karpathy (until recently chief AI scientist at Tesla); Chris Olah; Noam Shazeer; Ian Goodfellow; and, of course, the co-founders of DeepMind—acquired by Google in 2014—Demis Hassabis, Shane Legg, and Mustafa Suleyman (who runs AI at Microsoft today); Andrew Ng from Stanford; and, in addition to all of those people, the authors of the Transformer paper, since Google invented the Transformer and published the paper in June 2017. Of course, most of these people left Google since then. It is easy to blame Google’s culture and what not for such an exodus, but few people seem to be willing to ponder how on earth Google was able to assemble such a talent dense team in the first place. The reality is among all the big tech, Google was the quintessence playground for most talented technologists in much of the 2000s and 2010s and while critics aren’t necessarily wrong about the general decadence and increasingly bureaucratic culture in the company over time, it was always going to be almost impossible to retain such a highly talent dense team. Moreover, a two-decade monopoly would probably be corrosive to most companies culture anyway; perhaps the threat that their money printing machine i.e. Search faces in the AI world was a great time for a cultural reset. Looking at Google’s shipping cadence since some early fiascos post-ChatGPT likely hints at such a reset. Anyways, back to TPUs. The initial goal was not to sell chips; it was to keep Google’s products fast and keep power bills sane. Training‑capable TPUs (v2/v3) came later, followed by large, pod‑scale systems (v4/v5) and, most recently, an inference‑first generation ([Ironwood](https://blog.google/products/google-cloud/ironwood-tpu-age-of-inference/??ref=mbi-deepdives.com)). Unlike Nvidia, which became the largest company in the world by selling its GPUs, Google chose to keep its TPUs proprietary. I will discuss the rationale for such a strategy and why they may be changing it now behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Texas Instruments and Analog Devices at Citi TMT Conference URL: https://www.mbi-deepdives.com/texas-instruments-and-analog-devices-at-citi-tmt-conference/ Last updated: 2025-09-06T14:04:15.000Z Both Texas Instruments (TXN) and Analog Devices (ADI) attended Citi TMT Conference last week. Let me share some of my notes from these calls. TXN stock reacted negatively a bit after management highlighted they are not seeing a “snapback” in the current recovery: > …it's **not quite happening a snapback** as maybe some people anticipated or as other recoveries have happened. So that part is a little different. And some of that, we think, could be due to uncertainties at the macro level. Some of that, as I mentioned, is because automotive is not quite recovering like others, but we are in recovery. It's just not quite like it's been in other places now. Similarly, ADI also talked about the uncertainties they are sensing in their end markets: > it's uncertainty both ways because it's the **tariff and trade uncertainty**. And for us, and we've talked about this before, the tariff and trade uncertainty is less about the direct impact to us, right? The impact of tariffs as they exist today is pretty nominal for us. But what it does matter is what is it doing to GDP and what is it doing to demand creation/demand destruction because our products are in a lot of end markets where there are tariffs already and does that drive down demand? And what does that do to GDP? So that's certainly an uncertainty, and that clouds the picture because two of the largest economies in the world, **they're still in negotiations** with our administration around the trade policies, right? So **we still don't yet know where China and India will land on the spectrum.** **We've still got some potential even additional tariffs in our space specifically to come**, right? So I think that's a level of uncertainty. Second piece, I mentioned a little bit, if you look at which for us, industrial this is a really good barometer for us. the PMIs have been choppy, right? They're hovering around 50, but we've had a couple of months of contractionary PMI. So you got to pay attention to that because it does -- has historically been a leading indicator for where we're headed. The other piece is uncertainty around SAAR, right? The number of vehicles are going to be produced. You look at the pressure, the rebates being removed in the U.S., the tariffs and the impact they're having on auto demand. And **it's unclear what auto production plans might look like in '26**. Despite these uncertainties, ADI management pointed out how they’re still considerably below their peaks which means the recovery is likely to continue: > if you think about it from a cycle perspective, we were out in front of reducing inventories pretty quickly. So we've been reducing inventory, both in our channel and at our customers for the better part of 2 years now. And I think that positions us really well because **we are at some of the lowest channel levels we've ever been**. **We're well below our sort of historical 7- to 8-week channel model**. So we feel good about where we're positioned there. > > …And I still think there's room to run because if you think about the sort of the consumption line, assuming that, that industrial business would typically grow 5% or 6% CAGR over any period of time, you run that line out from pick your starting point to where we are today, even with the strength of the industrial recovery, we'll still be below that trend line. **We're also still 30% below our peak in industrial**. And I think that's an important thing. So I think that we continue to see room for us to grow given we're below that -- still below the trend line and below our peaks. So I think that's an important piece on the industrial side. And other than auto, all of our businesses are still below their peak values. TXN talked about low lead times and given their substantial inventory, they can maintain very low lead time throughout the recovery: > …our lead times remain really low. And that is by design. So we have capacity in place. We have inventory in place. We have new processes internally where we are building more buffers than we used to before. So our business is largely a catalog-based business, not entirely, of course. But largely, more than half of our revenue is on catalog-based business, so we can build to stock. And so even though we're already in an upward progress with the cycle, we can keep those lead times short. And we expect to continue to do that through the upturn. If we are successful in our strategy, we will keep those lead time short through the upturn. Market’s enthusiasm for these analog players have dissipated a bit given the liberation day likely obfuscated the true nature of this current recovery. While TXN didn’t provide any guide for 2026, they did mention in the conference that it is “more likely than not” that the revenue will be closer to the lower end of their revenue guide in 2026 (\~$20 Billion) which would prompt them to do capex closer to $2 Billion next year (they guided a $2-5 Billion range earlier dependent on various revenue scenarios) Both companies kind of told the same macro story: industrial is back, auto is messy, China is where the growth actually showed up, and tariffs/politics are the fog machine. Despite "China for China" localization efforts, China has been the strongest geography for both companies in 2025\. Both noted China was the first region into the downturn and the first out. Nonetheless, visibility remains low given the macro uncertainties. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Lululemon 2Q'25 Update URL: https://www.mbi-deepdives.com/lulu2q25/ Last updated: 2025-09-05T13:18:35.000Z Over the last few quarters, there have been a murmur around Lululemon potentially needing a new management to right their ship. Given that they tripled their revenue in the last six years under Calvin McDonald’s leadership, I was reluctant to join this chorus. However, after yesterday’s call, admittedly I too feel McDonald’s days at Lululemon may (or should) be numbered. I will explain more and share my thoughts on the quarter behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Technology vs Platform Shift, Portfolio Change URL: https://www.mbi-deepdives.com/technology-vs-platform-shift-portfolio-change/ Last updated: 2025-09-04T13:04:33.000Z Is the current AI phenomenon a technology or a platform shift? Or is it both? I read couple of pieces yesterday that makes a pretty convincing case that it is only a technology shift so far. Casey Winters made this [point](https://caseyaccidental.substack.com/p/on-platform-shifts-and-ai) almost a couple of years ago which I think still holds up pretty well: > What I realized having gone through the internet and mobile platform shifts is that the technological and distribution shifts *did not happen at the same time*. Platform shifts that create both technological and distribution opportunities happen in a sequence, not all at once…AI has come out and definitely created a technological shift that enables new ways to solve problems that couldn’t be done before. But **AI lacks a new distribution channel**. ChatGPT is “not it”, as the kids would say. At least not yet. Sameer Singh [elaborated](https://breadcrumb.vc/ai-technology-shift-not-platform-shift-accd008e4333?ref=mbi-deepdives.com) this further yesterday with a thought experiment: > Let’s start with a thought experiment: ***In each of these eras, where would you find the early products?*** 1. **Where did you find early internet products?** *On* the internet, i.e. you could access websites via web browsers. 2. **Where did you find early mobile apps?** *On* smartphones, i.e. via app stores on the iPhone and Android phones. 3. **Where do you find AI products?** … on AI models? Of course not. You find them on the internet or via app stores. This is the first hint that the shift we’re seeing with AI is *not* the same as the internet or mobile. > …a platform shift can only exist if the adoption of a new underlying technology gives users ***access*** to new products from that era. This creates a network effect between adoption of the technology and builders tinkering on top of the technology. The mass adoption of a new underlying technology with these characteristics can be described as a platform shift. > > Adopting ChatGPT does not make it easier to access AI apps like Cursor or Lovable. And adoption of AI apps like Cursor or Lovable has no bearing on ChatGPT usage (or of any other LLM for that matter). Casey Winters, however, does make the point that it is pretty early days. As he said, these shifts may not happen at once, but rather in a sequence which is what happened during earlier platform shifts as well. From Casey Winters: > we shouldn’t really expect new distribution shifts to have happened yet. The App Store launched in 2008, and even though there was fervor around discovering apps on the App Store for a while with the “there’s an app for that” campaigns, that fervor died as did most of the apps featured. **It was when Facebook launched mobile ads four years later in 2012 that apps exploded into multi-billion dollar companies**. This is similar to the internet. People started getting online around 1994\. Google didn’t come out until 1998\. Sure, there were search engines before that (Lycos, Yahoo!), but **they lacked the predictable distribution** of Google. Word of mouth can’t scale technological shifts alone. They need scalable distribution methods, and usually new ones that take time to become obvious. > > So, **as an operator, this feels like 1997 or 2008**. Sameer Singh also points out that “*All platform shifts are, by necessity, also technology shifts. But not all technology shifts are platform shifts*” and the diagram below makes that abundantly clear: ![](https://substackcdn.com/image/fetch/$s_!tXVS!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fabcabd8a-5b8f-4eee-985d-55406b778470_631x454.png) Image Source: [Sameer Singh](https://breadcrumb.vc/ai-technology-shift-not-platform-shift-accd008e4333?ref=mbi-deepdives.com) Sameer also points out that in a technology shift, users may not even be aware about the tech (it just works) whereas in a platform shift, the change is front and center for the user: > In a technology shift, form factor does not and should not matter. For example, scaling Snapchat’s picture messaging functionality would not have been possible without the shift to cloud computing. While Snapchat’s [cloud hosting costs](https://www.vox.com/2017/2/7/14526832/snap-ipo-snapchat-s1-wall-street-business-google-cloud?ref=mbi-deepdives.com) were significant, it would not have been possible to scale it as quickly if it relied on large, operationally complex investments into server infrastructure. The most important part — Snapchat’s end users did not know or care about this in any way. The user interface did not change to call out Snapchat’s “Cloud powered” technology. The biggest changes happened in the backend, not the frontend. While scrolling twitter yesterday, I stumbled onto this [tweet](https://x.com/antonosika/status/1963270847554363493?ref=mbi-deepdives.com) by Loveable’s founder. He was sharing how someone built a $1 million revenue business on top of Loveable in just 5 months, but one particular sentence caught my attention from that screenshot: “they have no idea it was crafted with AI” which does give credence to AI being a technology shift rather a platform shift. ![](https://substackcdn.com/image/fetch/$s_!cbnL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F53b54053-7ed0-49c9-b1ee-5d941c0c2d8b_1182x601.png) A platform shift which creates new distribution channel is obviously more uncomfortable to deal with for existing incumbents. If you think from this perspective, it is easier to appreciate why OpenAI paid such [top dollars](https://www.reuters.com/business/openai-acquire-jony-ives-hardware-startup-io-products-2025-05-21/?ref=mbi-deepdives.com) to acquire “LoveFrom”. I interpret this mostly as OpenAI’s ambition to propel AI graduate from technology shift to platform shift. At least that’s what I think Sam Altman is likely assigning Jony Ive to accomplish. I may sound a little skeptical about OpenAI’s soaring valuation these days, but I do not doubt that OpenAI is the most dangerous company which can create a jolt in the current big tech landscape and almost three years after ChatGPT’s launch, it may still be early to sketch their level of ambition. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Why I bought more Alphabet! URL: https://www.mbi-deepdives.com/why-i-bought-more-alphabet/ Last updated: 2025-09-03T14:16:21.000Z Believe it or not, it’s not necessarily because of Judge Mehta’s ruling yesterday! After coming [back to Google](https://www.mbi-deepdives.com/googl/) as a shareholder back in April this year, I will elaborate on my decision to **add** to my existing position in Alphabet behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### The Changing Healthcare Landscape URL: https://www.mbi-deepdives.com/the-changing-healthcare-landscape/ Last updated: 2025-09-02T15:21:35.000Z Last month, JP Morgan published a very interesting healthcare [piece](https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/sick-as-a-dog.pdf?utm%5Fsource=theideafarm.com&utm%5Fmedium=newsletter&utm%5Fcampaign=american-unexceptionalism&%5Fbhlid=2ab9c84cacf055c431022caf2e0a04b8c31df0ec) titled “Sick as a Dog” which I read yesterday. They also published a podcast on this [piece](https://open.spotify.com/episode/4ow2QrKViGBL8wC2AN9hCG?si=390df70cc0ce4382&ref=mbi-deepdives.com), but I think you would enjoy reading the piece instead. I would like to highlight some interesting points from the piece. One of the startling data points for me was healthcare pretty much matched technology sector’s return from 1989 to 2019 period with dramatically lower volatility (15% vs 24%). Imagine how much the world has changed thanks to technology during this period and yet, if you put your money in healthcare, you would not only have a much better sleep but also enjoyed just as good a return as tech investors. Well, even if you did that, your sound sleep has been abruptly disrupted since 2019\. While tech’s run has continued unabated, healthcare sector has been limping around since then. ![](https://substackcdn.com/image/fetch/$s_!vcWr!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F026850f7-0ef6-49b3-baeb-1e906ebd1326_1165x394.png) Another topic that surprised me is the data related to patent thickets, an idea which I originally came across from this Lina Khan’s [tweet](https://x.com/linamkhan/status/1870185009958510795?ref=mbi-deepdives.com). Patent thicket happens when so many overlapping patents exist in one area that it becomes hard to make or sell something new without running into legal trouble. While I did know the idea, I didn’t know “*almost 75% of all patents granted and all patent applications were filed *after* FDA approval*”. From the report: > Multiple overlapping patents for a given drug are known as “patent thickets”, and typically must all expire or be settled with manufacturers before generic and biosimilar drugs can be sold. Regardless of a patent’s strength or validity, patent thickets can deter competition by raising the perceived litigation cost of entry. A 2024 JAMA article analyzed patent thickets for the 10 brand name drugs with the highest US sales. The authors found that patents filed after FDA approval, most of which were unrelated to each drug’s active ingredient, can substantially lengthen the effective period of patent protection and delay the impact of generic and biosimilar drugs. For the ten small molecule and biologic drugs in the JAMA analysis, almost 75% of all patents granted and all patent applications were filed after FDA approval > > Patent reform has the potential to substantially impact US drug prices. While the US has the highest generic drug utilization rate in the world at 90% by volume, generic drug consumption represents just 17.5% of total drug spending. The remaining 10% of all drug prescriptions account for the other 82.5% of drug spending. These figures are remarkable; in other words, the issue is not that patients aren’t using generic drugs; it’s that the branded drug market remains heavily impacted by increasingly “creative” patent thickets that may exceed the original goal of protecting intellectual property investments in pharmaceuticals. ![](https://substackcdn.com/image/fetch/$s_!mUKC!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85e921db-325d-401d-8a14-286de1c5eecc_873x310.png) I knew biotech investors have suffered a lot in the last few years, but it still shocked me to know that “*since 2018, half of all biotech IPOs have lost 80%+ of their value, and only 20% had positive holding period returns*.” ![](https://substackcdn.com/image/fetch/$s_!5Ixk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2e4e34e1-ec1d-46fa-a33f-2bcaa09182dd_943x640.png) There are some interesting issues raised near the end of the report. Aging population driving healthcare spending has been a secular thesis among most healthcare investors, but we may have hit some sort of ceilings on healthcare spending. From the report: > “…personal consumption spending on healthcare as a share of GDP rose from 5% in 1976 to 11% by 2008 but has flatlined since 2008; the same trend is true for national health expenditures. > > The cost of Medicare and Medicaid entitlements relative to non-defense discretionary spending shown in the last chart is continuing to rise. I believe we’re getting closer to an informal national referendum to narrow this gap, rather than taking steps to keep increasing it. ![](https://substackcdn.com/image/fetch/$s_!sTEO!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97508fda-8d53-4f08-8c23-5cc47c24f7c0_721x985.png) Healthcare is obviously a very heated political topic in the US. I will admit that some of the healthcare related debates have been bit of a cultural shock to me when I moved to the US in 2017\. While growing up in Bangladesh, I have basically never known anyone with health insurance. All of our healthcare related spending were out-of-pocket expenses. If you couldn’t afford private healthcare, you could go to government hospitals which are always overflowing with patients. Therefore, your access to proper healthcare was pretty much directly tied to your wealth. To my surprise, most of the western countries, including the US has largely eliminated your need to be wealthy to access proper healthcare, and yet, it remained such a thorny political topic. People seem to deeply underappreciate how close almost all the western countries are to the “utopia” of equality when it comes to access to quality healthcare. The reality is it is nearly impossible for private health insurance companies to be popular because most people do not consume healthcare and vastly “overpay” to subsidize the chronically ill, old, and poor people of the society in the West. If you look at these [numbers](https://www.healthsystemtracker.org/chart-collection/health-expenditures-vary-across-population/?ref=mbi-deepdives.com#Share%20of%20total%20population%20and%20total%20health%20spending,%20by%20age%20group,%202021) below, it should be clear why health insurance companies will always structurally have hard time gaining any popularity anytime soon: > In 2021, 5% of the population accounted for nearly half of all health spending…At the other end of the spectrum, the 50% of the population with lowest total health spending accounted for only 3% of all health spending…Roughly 14% of the population had $0 in health expenditures in 2021. I suspect the reason nationalized healthcare seems to be more popular is it abstracts away the way it is funded and many people feel like they are accessing healthcare for “free”. Of course, there is no free lunch. If someone asked me “what’s the conspiracy theory you think is true”, I am not sure I have a good answer today. But when I look at how skewed the data is in healthcare spending, sometimes I wonder if the western governments will be highly incentivized in the future to make it socially popular (or even desirable) for the chronically ill and/or very old people to choose assisted suicides. Take a look at the age distribution and spending mix of healthcare in the US below. Considering the fertility rate, the population mix will be increasingly dominated by older and older population for the next few decades. Given the broader social consensus around uniform access to healthcare in most western countries, these trends can lead to pretty uncomfortable political conundrums. To be clear, I personally do not want a broader social consensus to form to make it more desirable to choose assisted suicides, but frankly speaking, societies do have history of taking nearly unfathomable or cruel policies to solve an anticipated problem (e.g. one child policy in China). Perhaps we do need AGI to usher us in an accelerating growth to make these pesky problems go away! ![](https://substackcdn.com/image/fetch/$s_!_b2x!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9cfcf52c-d0ce-494f-a374-5d3760235a30_1621x727.png) --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: ![](https://substackcdn.com/image/fetch/$s_!_wAv!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F27dd7f4b-24ba-4c84-925a-a911031c4be5_1102x595.png) *\*Based on closing prices as of August 30, 2025 (time-weighted YTD: +6.7%); Since inception (August 24, 2018) time-weighted annualized return +17.3%* **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### MBI turns five! URL: https://www.mbi-deepdives.com/mbi-turns-five/ Last updated: 2025-09-13T12:07:29.000Z On this day five years ago, I decided to start an independent research service. I had a simple idea: just one Deep Dive on a publicly listed company every month. It may seem there are a plethora of Substacks or independent research services today, but even five years ago, there was only a handful of decent analysts publishing their work at a regular frequency online. Even the ones who did, very few actually did long form write-ups on publicly listed companies. Over time, I realized why there weren’t as many decent analysts publishing their work online. The reality is if you are any good, you would inevitably get picked up by the industry. Market is usually efficient enough that good analysts are spotted fairly quickly. So, the only way any decent analyst can remain “online” for a long time is through their sheer commitment to stay independent. I have no doubt that the likes of [Ben Thompson](https://stratechery.com/?ref=mbi-deepdives.com) or [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) needed to make conscious, firm decision to stay the course. Admittedly, I was, at times, in two minds. Every year since starting MBI Deep Dives, I did receive interest from my own subscribers every once in a while whether I would be interested in going back to buy-side. In **some** cases, I did engage in and entertained these conversations. But over time, I realized I have increasingly become, for better or worse, “unemployable”. I have simply fallen in love with the work I do at MBI Deep Dives, and it is perhaps going to be pretty much impossible to replicate this feeling anywhere else. Of course, the joys of working for yourself also exposes one to the vagaries of capitalism itself much more directly. My initial bet with MBI Deep Dives was very few people actually have the focus, interest, and discipline to do a proper Deep Dives every month and only a handful will do so online. So, if I can accomplish that and do a good job at it, it is highly likely to have a market for that. Indeed, that bet was correct! However, once “Deep Research” came to the scene, my business model increasingly seemed anachronistic to me. Although the raw numbers didn’t quite show any noticeable sign of tension, I couldn’t quite be at ease while thinking about the future of MBI Deep Dives in three to five years! I could see multiple tensions brewing over time. In a pre-AI world, you could read a Deep Dive and easily appreciate the effort that went behind producing such a piece. Deep Research completely abstracts away or/and cheapens these efforts in the eyes of readers. If I came across MBI Deep Dives and saw this guy meticulously published Deep Dive after Deep Dive every month in the last 60 months, frankly speaking I would be impressed at the persistence and discipline. AI, however, likely already has or is going to rewire these perceptions in a profound way for all written content. Remember how I thought only a handful of people could be focused, disciplined, and interested enough to do these Deep Dives regularly? Well, after Deep Research, that number certainly, theoretically, shot up to tens of thousands (if not more). As someone whose job is to analyze businesses, it didn’t take me too long to infer “Houston, we have a problem!” While AI was posing these uncomfortable questions, the actual process of analyzing businesses didn’t quite get a lot simpler overnight. To understand, absorb, digest a new company and write my own thoughts on a business, I still needed to do almost all the work that I was doing earlier. Nonetheless, I decided to take a deeper look at my work and what I could do to tweak my business model a bit. One of the things I always try to focus on is to take a closer look at what I am **already** doing in my day-to-day life and whether any of it would be useful to other people. One thing that jumped out to me was I am an infovore who prodigiously devours content and I suspect I have developed a pretty decent taste on content published online. Moreover, thanks to studying different businesses over the years, I myself have a lot of my own thoughts and inferences while consuming content everyday. So, I thought what if I write **everyday**? Admittedly, it was a jarring thought. Publishing something everyday requires a level of self-discipline and productivity that I wasn’t sure I quite had it. After some self-introspection, I decided to lean into my fear and gave it a shot. Voila, MBI Deep Dives went from monthly to daily! How do I work these days? I wake up somewhere between 4 to 5 am. I basically get three uninterrupted hours to myself before my son wakes up. So, I intend to publish my daily post within three hours before my son wakes up. I usually have a pretty good idea what I intend to write the night before, and typically have a brief outline before going to bed. I do the actual writing after waking up. After publishing my daily post, I spent about an hour or so with my son. Then I focus on the monthly Deep Dives for the rest of the morning and afternoon. I tend to have pretty early dinner these days and post-dinner, I spend some time creating an outline for the post next day. Actually, \~80% of the idea of daily posts comes during my daily walk. As I try to hit 10k steps every day, these are usually great fodder for gathering my thoughts circling in my head. It’s been two months since I made these adjustments. I surprised myself how easily I adapted to this schedule. I don’t think I have ever been as cognitively active as I had been in the last couple of months. The rigid deadline of publishing something everyday turned out to be a great way to stay deeply intellectually engaged. And it doesn’t hurt that the “market” responded; MBI revenues last month reached new all-time highs with lowest ever monthly churn in the last five years! I don’t quite think Deep Dives are any less important today than it was five years ago. I want to do this job because investing is my lens to understand the world. To understand the world, I still need to dive deep, ponder, and wrestle with my thoughts before writing down my understanding and assessment of a business. But I do feel I needed to wake up to the market reality. I believe this will happen to a broad swath of knowledge economy jobs; as you can see, I don’t quite think AI is quite the wholesale replacement for the work most of us do, but we may need to take a deeper look to tweak some things to keep our work economically relevant in the post-AI world. As far as success or failure goes, I remind myself the following by Viktor Frankl: “*Don't aim at success. The more you aim at it and make it a target, the more you are going to miss it. For success, like happiness, cannot be pursued; *it must ensue**” Indeed, it must ensue! One of my core beliefs is mere survival online over a very long period of time will be rewarded in many positive, unexpected ways. Here’s to the next five (and hopefully many, many more)! Thank you so much for your support! [Subscribe](#/portal/signup) ![](https://substackcdn.com/image/fetch/$s_!S1fj!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F331e718b-12e5-4451-9a8e-f45d512f1fa9_3072x4080.jpeg) ### The ROI Question URL: https://www.mbi-deepdives.com/the-roi-question/ Last updated: 2025-08-31T13:37:24.000Z A friend recently DM-ed me to highlight one of the quotes from Nvidia’s CFO in their recent earnings: "New NVFP4 4-bit precision and NVLink 72 on the GB300 platform delivers a 50x increase in energy efficiency per token compared to Hopper, enabling companies to monetize their compute at unprecedented scale. For instance, **a $3 million investment in GB200 infrastructure can generate $30 million in token revenue, a 10x return**." 10x return? That’s a bit eye-popping number. My friend was understandably a bit skeptical of this claim, so he asked ChatGPT to show the math and some reasonable assumptions behind this claim. He then shared couple of screenshots from his ChatGPT which are shown below: ![](https://substackcdn.com/image/fetch/$s_!kTGz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcb8ed155-da52-4d41-97bf-aadf6eae3609_1576x1222.png) ![](https://substackcdn.com/image/fetch/$s_!SCNV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb64213a0-7472-42af-af58-1bc50fda0eb5_1852x1183.png) Why was my friend a bit skeptical? Well, clearly hyperscalers aren’t realizing such revenue from their investments in Nvidia chips yet. He also pointed out since a material percentage of chips are going to train the models, this math isn’t quite relevant yet but we both agreed that it does show latent monetization potential of their massive capex investments once supermajority of the capex shifts from training to inference. Looking at the math, I myself wanted to gain a bit deeper understanding of the drivers of the assumptions here. So I chatted with both ChatGPT and Gemini for almost a couple of hours yesterday; let me summarize my understanding based on these chats to discuss the three key variables in the ROI question: Token per second, utilization, and price per token. **Token/sec or throughput** Batch size, which is the number of concurrent user requests processed simultaneously, is the single most important operational factor for maximizing throughput. The highest throughput numbers are always achieved with the largest possible batch sizes. However, large batch sizes increase latency, specifically the Time-to-First-Token (TTFT). The system must wait to assemble a large batch before processing begins. For interactive applications (chatbots, real-time coding assistants), a high TTFT is understandably unacceptable. To maintain low latency, providers must deliberately use smaller batch sizes. This inherently sacrifices aggregate throughput to ensure a good user experience. The 1M tokens/sec benchmark mentioned in the ChatGPT screenshot above is likely achieved at latencies that would be unacceptable for real-time use. Efficient batching works best when requests are similar. If one request asks for 10 tokens and the next asks for 2,000 tokens, the batching mechanism becomes inefficient, as resources may be tied up waiting for the longest request to complete. Moreover, some models support increasingly large context windows (100k+ tokens). Processing these long contexts significantly degrades throughput. The longer the context, the larger the KV cache (a memory optimization that speeds up AI text generation by storing past calculations, allowing the model to focus only on generating the next word instead of reprocessing the entire conversation history each time) required to store that context in GPU memory. **Utilization** While 20% utilization may seem conservative, achieving this average utilization consistently (24/7/365) with **monetized** workloads may not be super easy in inference. AI inference demand is often "peaky." Infrastructure built for peak load sits idle during off-hours. We also need to consider time lost to maintenance, updates, and optimization. Of course, in the long run, when AI is diffused across different industries throughout the world, we should expect to see a materially high utilization. The argument of “peaky” infrastructure requirement reminds me of skepticism around cloud, but we know with enough diversity around end market demands, this can be solvable and utilization can be maintained at a pretty high level. **Token price** Competition among major providers and the rise of capable open-source models have driven down token prices significantly in the last couple of years. However, as I [noted](https://www.mbi-deepdives.com/constraints-and-challenges-of-value-capture-in-the-ai-race/) recently, token price seems to have stabilized a bit in recent months. ![](https://substackcdn.com/image/fetch/$s_!2hud!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff81be772-3f05-407e-902f-fdee11e7ab30_667x429.png) However, the GB200 itself can accelerate token price decline. By making inference vastly more efficient and increasing the supply of compute, it may force providers to lower prices to remain competitive. Of course, hyperscalers do not only run the most expensive models. A likely material portion of their workload involves smaller, cheaper models (often <$1 per 1M tokens), reducing the actual blended revenue shown in my screenshot above. If the average realized price drops to $2/M tokens, the idealized revenue drops from $31.5M to $12.6M in my example (ceteris paribus). Nvidia is clearly not stopping anytime soon either. If a new architecture doubles performance in two years, the older GB200 hardware will no longer be able to command premium pricing. So, pricing can be quite dynamic. Overall, the whole exercise gave me a slightly better sense around sensitivity of different variables to realize ROI on these massive capex investments by big tech. The question on ROI is obviously far from solved especially if a material percentage of incremental capex continues to go for training the next model, but you can perhaps appreciate a bit more why it is such a good debate. It is a much closer debate than perhaps both the bulls and bears like to think. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Google's "first mistake" URL: https://www.mbi-deepdives.com/googles-first-mistake/ Last updated: 2025-08-30T13:54:55.000Z I have been listening to Acquired’s latest episode on [Google](https://www.youtube.com/watch?v=QhAftC%5FzFr8&ref=mbi-deepdives.com). I haven’t finished the episode yet; I’m half-way into the four-hour episode. As you can imagine, there are a lot of cool anecdotes from the spree of some of the best acquisitions Google has made over the years. I was particularly intrigued by some data from YouTube’s early days. Following Google’s acquisition, YouTube was generating $30 Million revenue but they were losing $1 Billion per year. We may be accustomed to such numbers today, but Google’s CFO was spooked by the staggering losses. From the podcast: > **The amount of money they lost was almost exactly equal to a penny per view**. So just imagine every time you loaded YouTube in those years, Google would just flush a penny down the drain. They got to figure out something to do about this. So for the first couple years, the CFO at the time was terrified of it scaling. Like, please don't scale in its current state. But of course, there's nothing they can do. The cat's out of the bag. It's scaling. And the **CFO was exploring, hey, can we sell this to one of the other companies who was bidding on it?** That's right. Because Yahoo and the media companies also wanted to buy YouTube. Given this context, apparently YouTube used to be considered Google’s “first mistake”. We may laugh today, but it is an understandable sentiment if you could time travel and imagine yourself looking at this lopsided operating cost structure in late 2000s. It is easy to feel sympathy for the CFO’s fear about YouTube scaling when I heard Acquired mentioning that YouTube in 2007 consumed as much bandwidth as the entire internet did in the year 2000\. Even in 2014, YouTube was 20% of the bits on the internet. Thankfully, YouTube eventually figured out ways to effectively monetize the aggregated attention. In [Q3 2024](https://variety.com/2024/digital/news/youtube-q3-2024-advertising-revenue-growth-1236193926/?ref=mbi-deepdives.com), YouTube surpassed $50 Billion LTM revenue. More importantly, I think YouTube may prove to be the most durable consumer internet asset. When I was looking at Meta’s recent transparency [report](https://transparency.meta.com/data/widely-viewed-content-report/?ref=mbi-deepdives.com#prior-reports), it did catch my attention that **YouTube is, by far, the most widely viewed domain on Facebook in Q2 2025**. ![](https://substackcdn.com/image/fetch/$s_!cAMA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F54b687ac-a6dc-41df-8b7d-3d2d24683d6d_1356x604.png) Source: Meta Transparency [Report](https://transparency.meta.com/data/widely-viewed-content-report/?ref=mbi-deepdives.com#prior-reports) What’s quite intriguing is the value of YouTube to Google may go far beyond YouTube’s own revenue and profit numbers. A few months ago, Jack Morris made the [point](https://blog.jxmo.io/p/there-are-no-new-ideas-in-ai-only?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com) that “**There Are No New Ideas in AI… Only New Datasets”** and it is YouTube which will prove to be the treasure trove of “new datasets”. From his [post](https://blog.jxmo.io/p/there-are-no-new-ideas-in-ai-only?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com): > Our breakthrough is probably not going to come from a completely new idea, rather it’ll be the resurfacing of something we’ve known for a while. > > But there’s a missing piece here: each of these four breakthroughs **enabled us to learn from a new data source:** > > 1\. AlexNet and its follow-ups unlocked [ImageNet](http://%28https//www.image-net.org/?ref=mbi-deepdives.com), a large database of class-labeled images that drove fifteen years of progress in computer vision > > 2\. Transformers unlocked training on “The Internet” and a race to download, categorize, and parse all the text on [The Web](https://arxiv.org/abs/2101.00027?ref=mbi-deepdives.com) (which [it seems](https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications?ref=mbi-deepdives.com) [we’ve mostly done](https://arxiv.org/abs/2305.16264?ref=mbi-deepdives.com) [by now](https://arxiv.org/abs/2305.13230?ref=mbi-deepdives.com)) > > 3\. RLHF allowed us to learn from human labels indicating what “good text” is (mostly a vibes thing) > > 4\. Reasoning seems to let us learn from [“verifiers”](http://incompleteideas.net/IncIdeas/KeytoAI.html?ref=mbi-deepdives.com), things like calculators and compilers that can evaluate the outputs of language models > > …The obvious takeaway is that our next paradigm shift isn’t going to come from an improvement to RL or a fancy new type of neural net. It’s going to come when we unlock a source of data that we haven’t accessed before, or haven’t properly harnessed yet. > > One obvious source of information that a lot of people are working towards harnessing is video. According to [a random site on the Web](https://www.dexerto.com/entertainment/how-many-videos-are-there-on-youtube-2197264/?ref=mbi-deepdives.com), about 500 hours of video footage are uploaded to YouTube \*per minute\*. This is a ridiculous amount of data, much more than is available as text on the entire internet. It’s potentially a much richer source of information too as videos contain not just words but the inflection behind them as well as rich information about physics and culture that just can’t be gleaned from text. > > It’s safe to say that as soon as our models get efficient enough, or our computers grow beefy enough, Google is going to start training models on YouTube. They own the thing, after all; it would be silly not to use the data to their advantage. So, it is certainly in the realm of possibility that Google’s “first mistake” can eventually prove to be its true savior. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Dollar General 2Q'25, Microsoft's Cybersecurity business URL: https://www.mbi-deepdives.com/dollar-general-2q25-microsofts-cybersecurity-business/ Last updated: 2025-08-29T15:41:23.000Z Yesterday, Dollar General published its 2Q’25 earnings and Microsoft attended Deutsche Bank's 2025 Technology Conference to discuss their cybersecurity business. I will share some thoughts on both of them behind the paywall. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta's (lack of) Agency URL: https://www.mbi-deepdives.com/metas-lack-of-agency/ Last updated: 2025-08-28T14:34:21.000Z Back in 2018, Mark Zuckerberg [wrote](https://www.facebook.com/zuck/posts/10104413015393571?ref=embed%5Fpost) on Facebook: > “One of our big focus areas for 2018 is making sure the time we all spend on Facebook is time well spent. > > …recently we've gotten feedback from our community that public content -- posts from businesses, brands and media -- is crowding out the personal moments that lead us to connect more with each other. > > It's easy to understand how we got here. Video and other public content have exploded on Facebook in the past couple of years. Since there's more public content than posts from your friends and family, the balance of what's in News Feed has shifted away from the most important thing Facebook can do -- help us connect with each other. > > …I want to be clear: by making these changes, I expect **the time people spend on Facebook and some measures of engagement will go down**.” During 2018-20 period, one KPI Meta (then Facebook) often used to mention in earnings calls is MSI or **M**eaningful **S**ocial **I**nteraction which prioritizes social interactions over passive consumption of content. It is, of course, quite rare for a business to knowingly pursue a strategy that will certainly harm both their top and bottom line in the near term in the **hope** that it may yield long-term benefits. This was also perhaps indicative of Meta’s level of comfort of their own position in consumers lives to the extent that they wanted to exert how their users **should** spend their time. Meta thought connection is the long-term moat and if they can build more meaningful interactions through Meta’s properties, that should lead to long-term relevance of its business even if it means some short-term sacrifices in top and bottom line. However, the promised benefits in the long-term were far more uncertain than even Meta perhaps appreciated. The attention landscape is just too volatile to “model” the financials with very high confidence. You could go back to 2010 and worry about Meta’s transition to mobile. Even in 2015, you could be understandably a bit worried if someone told you that the two most interesting formats for the “next five years” would be ephemeral stories and short-form videos and both of them would be invented by Meta’s competitors which would force Meta to play catch-up. Even in 2021 when TikTok threat was way more apparent, majority of Facebook feed consisted of content from your friends and people you chose to follow. While looking at a chart like this, I used to be deeply concerned about a major risk Meta faces. Meta doesn’t publish this data, but I suspect if we could see content posted on feed per MAU per month over time, it is likely to be in secular decline. So, if Meta doesn’t have enough content to fill our feeds to keep it engaging, will we really spend much time on Meta’s apps in the long run? ![](https://substackcdn.com/image/fetch/$s_!Snbc!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbe965ea6-0486-47fc-acbc-a1ffea43a353_1024x624.png) Source: [Meta Transparency Center](https://transparency.meta.com/data/widely-viewed-content-report/?ref=mbi-deepdives.com#prior-reports) A lot of people look at big tech companies and overestimate how much agency these companies have over our lives. I think what is perhaps likely much closer to reality is these companies, especially Meta are in the business of very closely observing our own behavior and then trying their bloody best to serve our own preferences. To the extent they fail to do this job well, the users still have plenty of agency to go elsewhere to satisfy their preference. Given this reality, it is perhaps less surprising that Meta’s 2Q’25 transparency [report](https://transparency.meta.com/data/widely-viewed-content-report/?ref=mbi-deepdives.com#prior-reports) shows a dramatically different picture of their feed compared to just four years ago! ![](https://substackcdn.com/image/fetch/$s_!0K3J!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd3a021de-dab5-47ab-9167-b2099d80cecc_1401x658.png) Source: [Meta Transparency Center](https://transparency.meta.com/data/widely-viewed-content-report/?ref=mbi-deepdives.com#prior-reports) I do want to highlight that connection remains a core dynamic in Meta’s properties, but the way we connect these days has fundamentally shifted over the last few years. Adam Mosseri hinted at this in this [thread](https://www.threads.com/%40mosseri/post/C1RKqYOuSOR?hl=en&ref=mbi-deepdives.com): > People are sharing to feeds less, but to stories more and (even photos and videos) in messages even more still…So **it's no so much that people are sharing less, but rather than they're sharing differentl**y. Ultimately, feed itself has lost its relevance a bit over time and conversation has gradually shifted to private DMs. Mosseri mentioned in Rick Rubin’s [podcast](https://podscripts.co/podcasts/tetragrammaton-with-rick-rubin/adam-mosseri?ref=mbi-deepdives.com) that “**young people literally spend more time in DMs than they do in stories or feed*.*” Meta perhaps entertained the audacity that their users would rather adapt to Meta’s own goals of increasing meaningful interactions online than spending more of their time online elsewhere outside of Meta’s properties. It took them a few years to realize that they likely underestimated their users’ agency. You may not like the fact that so many people CHOOSE to “waste” their time watching short-form videos online, but I think it is helpful to remember how we ended up here and not get the cause and effect backward. And if we can appreciate our own agency in building the current attention landscape, it would be easier to solve the problem (if you do consider it a problem). Personally speaking, I have always struggled with binge watching shows on Netflix. The cliffhangers at the end of each episode feel too tempting for me to stop watching the next one; my wife doesn’t seem to have this problem, so this is clearly a “me” problem. If you ever binge watched a show, you know after a few episodes it definitely starts to feel like your 4th ice cream in four hours. After experiencing this a few times, I remember that there is an easy solution for this. I can actually unsubscribe from Netflix. So a couple years ago, I did exactly that. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Amazon's Ambition in Groceries: Part 2 URL: https://www.mbi-deepdives.com/groceries_2/ Last updated: 2025-08-27T14:22:39.000Z Part 1 of this series can be read [**here**](https://www.mbi-deepdives.com/groceries%5F1/) --- Doug Herrington, the current CEO of Amazon Retail, became part of the S-team at Amazon in 2011\. Herrington has been a key figure to push Amazon to groceries. Brad Stone in his book “[Amazon Unbound](https://www.amazon.com/Amazon-Unbound-Invention-Global-Empire/dp/1982132612?ref=mbi-deepdives.com)” mentioned a particular memo by Herrington published internally back in 2012 (emphasis mine): > Herrington had joined the vaunted leadership council a year before, and his blunt memo would resonate inside the S-team for years. Even its title was provocative: “**Amazon’s Future is CRaP.**” > > In company parlance, CRaP stood for “can’t realize a profit”…in his memo, Herrington was talking largely about the inexpensive, bulky items stocked by supermarkets, such as bottled water, Diet Coke, or even a bag of apples. In the wake of the Webvan fiasco, most online retailers at the time considered these types of products to be economic quicksand. To the extent it sold them at all, Amazon had developed an “add-on” program to minimize their harmful financial impact. Customers could only include CRaP in their orders when they were making a broader assortment of purchases, such as books or electronics at the same time. > > Herrington’s memo pointed out that Walmart, Carrefour, Tesco, Metro AG, and Kroger were the world’s five largest retailers at the time. **“All of them anchor their customer relationship in groceries,”** he wrote. If Amazon’s retail business was going to grow to $400 billion in gross merchandise sales, it needed to transform a model based on infrequent shopping for relatively high-priced goods to more regular shopping for low-priced essentials. In other words, **if the company was going to join the ranks of the biggest retailers, the S-team had to figure out a way to profitably sell supermarket items.** If they didn’t, Amazon was going to be vulnerable to rivals who already enjoyed the shopping **frequency and cost advantages** of the grocery model. Indeed, grocery delivery is one of those “CRaP” businesses that require massive scale to even enjoy positive unit economics. Former Instacart CEO Fidji Simo mentioned in their very first earnings call as a public company the punishing reality of grocery delivery business: > “…**it took us 100 million orders before we were able to get to positive unit economics**. > > So scale matters enormously in order to deliver this business not only profitably but also at scale and efficiently. And so the reason you're seeing all of our large partners partnering with us year after year, choosing to continue our relationship with us is because **we are the most efficient, and we are offering them a service that they know is both efficient for their own P&L, but also allows them to keep the service as affordable as possible for their customers**, which does drive growth.” Today, Amazon’s grocery business is segmented into broadly three categories: a) nonperishables (consumables, canned goods, pet food, health and beauty products), b) Whole Foods Market (organic grocery), and c) Amazon Fresh (mass physical presence). Amazon actually got into nonperishables long before they entered the rest of the grocery market. So, they entered the nonperishables grocery business back in [2006](https://www.sec.gov/Archives/edgar/data/1018724/000119312506152620/dex991.htm?ref=mbi-deepdives.com), launched “Amazon Fresh” in [2007](https://www.seattlepi.com/business/article/amazon-starts-grocery-delivery-service-1245445.php?ref=mbi-deepdives.com), and acquired Whole Foods in [2017](https://media.wholefoodsmarket.com/amazon-to-acquire-whole-foods-market/?ref=mbi-deepdives.com). Amazon seems to have made the highest progress in their nonperishables segment since even if you exclude Whole Foods and Amazon Fresh, their gross sales exceeded $100 Billion. From Q1 2025 call: > Amazon's grocery business, which includes everyday essentials, grew **more than twice as fast as the rest of their business**. Grocery accounted for **one out of every three units sold** on Amazon in the U.S. Even without Whole Foods Market and Amazon Fresh, Amazon is one of the largest grocers in the U.S., with over $100 billion in gross sales last year. While acquiring Whole Foods, Amazon mentioned in Q2 2017 call that they are "experimenting with a number of formats for groceries, including physical pickup points, Amazon Go, online ordering, and delivery through Prime Now and Amazon Fresh", and that "there will not be one single solution for grocery delivery." Unlike Walmart which got into grocery in 1988 and became the market leader in just [thirteen years](https://www.mbi-deepdives.com/groceries%5F1/), Amazon’s experiments in groceries weren’t as smooth. Amazon even took $720 million in impairment charge in Q4 2022 for Amazon Fresh and Amazon Go physical stores. Despite some of these failures, given what they experienced post-pandemic there was hardly any doubt that Amazon would invest heavily on groceries. In Q2 2020 call, Amazon mentioned their online grocery sales **tripled** YoY. However, this success didn’t quite reverberate in every facets of the grocery business. In Q1 2023 call, Amazon management sort of lamented that they haven’t quite found the right approach in groceries especially in the stores format. From Q1 2023 call: > We wish we were further along at this point. We’ve tried lots of ideas. We haven’t yet found conviction around the format that we want to go expand much more broadly. We have a set of experiments and ideas and concepts that we’re working on across our dozens of stores there. And we’re pretty optimistic that we have something that may very well work. And we’re hopeful over this next year we find that Did they find the right format a year later? They indeed sounded quite optimistic about Amazon Fresh’s new store formats in 4Q’23 and 1Q’24 calls: > We've been testing V2 of our Fresh format in a few locations near Chicago, in a few locations in Southern California. It's very early, it's just a few months in, but the results thus far are very promising and on almost every dimension (Q4 2023) > > Amazon Fresh V2 format shows "meaningfully better" results in almost every dimension. (Q1 2024) Then just a couple of weeks ago, Amazon announced “one of its most significant grocery expansions”. From the company’s [press release](https://www.aboutamazon.com/news/retail/grocery-delivery-amazon-same-day-store?utm%5Fsource=amazonnewsletter&utm%5Fmedium=email&utm%5Fcampaign=081625&utm%5Fterm=groceries): > “Amazon is undergoing one of its most significant grocery expansions by introducing thousands of perishable grocery items at a great value to its [Same-Day Delivery service](https://www.aboutamazon.com/news/amazon-prime/amazon-same-day-delivery?ref=mbi-deepdives.com). Customers in more than 1,000 cities and towns across the U.S. can now order fresh groceries with their Same-Day Delivery orders with plans to expand to over 2,300 cities by the end of 2025. > > For [Prime members](https://www.aboutamazon.com/news/retail/lesser-known-amazon-prime-benefits?ref=mbi-deepdives.com), Same-Day Delivery is free for orders over $25 in most cities. If your order doesn’t meet the minimum, you can still choose Same-Day Delivery for a $2.99 fee. For [customers without a Prime membership](https://www.aboutamazon.com/news/retail/how-to-sign-up-for-a-prime-membership?ref=mbi-deepdives.com), the service is available with a $12.99 fee, regardless of order size.” This announcement shouldn’t come as a surprise if you read Andy Jassy’s 2023 shareholder letter in which he pretty much hinted at their ambition in perishables: > “We have a very large and growing grocery business in organic grocery (with Whole Foods Market) and non-perishable goods (e.g. consumables, canned goods, health and beauty products, etc.). We’ve been working hard on building a mass, physical store offering (Amazon Fresh) that offers a great perishable experience; however, **what if we used our same-day facilities to enable customers to easily add milk, eggs, or other perishable items to any Amazon order and get same day? It might change how people think of splitting up their weekly grocery shopping, and make perishable shopping as convenient as non-perishable shopping already is**.” So, making perishable grocery part of the same-day delivery promise is something Amazon has been chasing for a while, but we may finally be getting closer to the reality in couple thousands cities across the US by 2025\. In the latest earnings call, Amazon highlighted some positive data points about consumer adoption on buying perishables on Amazon: > **75% of customers** who used the perishables service that year were first-time shoppers for perishables on Amazon. **20% of customers** who used the service returned multiple times within their first month. Frankly speaking, Amazon had a lot of confusing subscription plans for their grocery offerings, and I much prefer the simplicity of same-day delivery that comes with the Prime membership. But is same-day delivery enough? Walmart seems to be way ahead of Amazon in this regard. From Walmart’s most recent [call](https://www.mbi-deepdives.com/walmart-and-target-earnings/): > **“Speed of delivery is important to customers, and we're continuing to get faster.** Approximately 1/3 of deliveries from store in recent weeks were fast delivery in 3 hours or less, reinforcing the value of our store network and driving speed and **20% of those deliveries arrived to our customers in 30 minutes or less.**” While I do think that grocery market is large enough and still have plenty of laggard grocery operators for Amazon to catch up to Walmart’s speed over time, the reality of retail is that it is inherently a deeply relative game. Customers can flock to the **best** value proposition and even if you improve but consistently falls short of the best operator, it may not be enough. To drive this point home, let me share an excerpt from the book “[The Halo Effect](https://www.amazon.com/Halo-Effect-Business-Delusions-Managers/dp/1476784035?ref=mbi-deepdives.com)”: > To show how company performance is intrinsically relative, I’ll present some data about a major U.S.-based retailer, a well-known company with hundreds of stores nationwide. I’ve made an effort to include only things that seem objectively verifiable and not shaped by the Halo Effect. To disguise the identity of this company, I’ll give it a fictitious name: “Qual-Mart.” According to the report of an independent industry analyst, Alex. Brown & Sons, during the early 1990s, “Qual-Mart” did these things: - Installed point-of-sale terminals in its stores, which provided better information on sales by item and improved the inventory planning process. - Expanded central buying to 75 percent of its merchandise, helping to reduce the costs of procurement. - Modernized its inventory management and thereby significantly improved its “in-stock position.” One result: better management of seasonal inventory, boosting Christmas and Halloween sales by 60 percent. - Conducted physical inventory counts more frequently, not just once at year-end, resulting in greater accuracy and efficiency. - Reduced its expense levels as a percentage of sales. - Improved its merchandise assortment to match current demand trends, helping to raise sales. - Installed a toll-free customer service number, which led to a sharp improvement in customer satisfaction. - Implemented a sophisticated client/server technology that led to better merchandise management and savings of $240 million. > Thanks to these many steps, “Qual-Mart” saw an improvement in inventory turns — that is, how many times in a year it sold its inventory, a key measure of retailing efficiently — from 3.45 in 1994 all the way to 4.56 in 2002\. That’s a jump of 32 percent, not bad at all. > > Would you say “Qual-Mart” improved its performance? Of course you would — it got significantly better at a number of important things, each one measured objectively. So you might be surprised to learn that the company we’re talking about is Kmart. That’s right: Kmart, the Evergreen Project’s Loser with a capital L, the poster child of mismanagement, the guys who supposedly got everything wrong. How can a company seem to do so many things better and still wind up in the bone yard? Because its rivals improved at an even faster rate. Over the same eight years, Wal-Mart’s inventory turns went from 5.14 all the way to 8.08, up 63 percent. Wal-Mart had faster turns at the start of the eight-year period than Kmart had at the end. Kmart got better in absolute terms and yet fell further behind at the same time — and the gap between the two retailers was growing ever wider. > > As for other measures of performance, Alex. Brown & Sons noted that Kmart improved in “the key areas of expense ratio reduction, in-stock position, and visual presentation,” but its major rivals also got better — in fact, much better. It went on: “Both Wal-Mart and Target, by our estimate, continue to enjoy significant advantages on the expense ratio front — allowing them to be quite assertive on price and to post still higher financial returns than Kmart.” And that wasn’t all. By the early 1990s, while Kmart raised the amount of centrally purchased inventory to 75 percent, Wal-Mart reached 80 percent. Kmart installed point-of-sale scanning in its stores by 1990, but Wal-Mart had done the same two years earlier. No wonder Kmart was scrambling. Its rivals were driving down costs and improving logistics at an even faster rate. By 2002- just as inventory turns were reaching an all-time high!­- Kmart hoisted the white flag and shuffled off to bankruptcy court. It’s irresistible to infer that a bankrupt company must have been poor at execution, but the evidence doesn’t support that view at all, at least not if we’re talking about execution in an absolute sense.*”* Given this reality, I will take a closer look at competitors, especially Instacart and Walmart in the next part of my “Amazon’s ambition in groceries” series. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The Promise and Pitfall of Agentic Commerce URL: https://www.mbi-deepdives.com/the-promise-and-pitfall-of-agentic-commerce/ Last updated: 2025-08-26T14:44:11.000Z We are entering the age of agentic commerce, where autonomous AI agents, acting as our digital deputies, will navigate the endless aisles of online stores, evaluating products and making purchases on our behalf. Agentic commerce promises to crush search frictions and expand the practical consideration set from a handful of clicks to the whole shelf. While the promises seem quite lofty, I came across this [paper](https://arxiv.org/pdf/2508.02630?ref=mbi-deepdives.com) recently which was quite instructive to appreciate the challenges as well. The paper explored four specific questions: > Do agents satisfy basic instruction following and simple economic dominance tests? > > What are product market shares when purchases are fully mediated by AI agents, and how do such market shares vary across AI agents? > > How do AI agents respond to observable attributes (price, rating, reviews, text) and platform levers (position, promotions, sponsorship)? > > How might outcomes change when sellers and/or marketplace platforms deploy their own optimizing AI agents? The paper discussed some experiments they did to understand how well the AI agents perform in different contexts. In these experiments, even SOTA models struggled with basic economic rationality, sometimes failing to select the lowest-priced or highest-rated product when all other attributes are identical. From the paper: > In one of the price-based rationality tests, we construct a scenario with all listings being identical except for one listing having a lower price. Here, even state of the art models (GPT4.1) can register failure rates exceeding 9%. The failure rate tends to decrease with the increase in price difference. In rating dominance tests, all listings are identical except that one listing has an average rating which is higher by 0.1\. We see significant heterogeneity in performance, with some models registering no failures (Gemini 2.0 Flash) and others registering up to 71.7% failures (GPT-4o, on which OpenAI Operator is built). However with GPT-4.1, this fail rate comes down to 16.0%, supporting the insight that failures reduce with more advanced models. **These findings imply consumers delegating purchases may sometimes pay more or obtain lower-rated products, and sellers cannot rely on modest price cuts or rating advantages to guarantee being selected by agents**. It is perhaps not surprising that a product's placement on a webpage dramatically influences an AI agent's purchasing decision, but unlike humans who likely focus on just a first couple of options, the choices made by AI agents can be quite confounding. Again, from the paper: > Holding all attributes constant, each model assigns a clear premium to the top row relative to the bottom row. However, the horizontal (column) patterns vary sharply. GPT-4.1 strongly favors the first column; Claude Sonnet 4, in contrast, largely ignores the first column and prefers the two middle columns; and Gemini 2.5 Flash tilts toward the third column, while columns one and two are comparatively disfavored. > > The position can lead to drastic changes in selection rates. For example, **for Claude Sonnet 4, moving a product from the bottom right corner (where it is selected 4.5%) to the top row in the second or third column leads to a 5-fold increase** in selection rate! Interestingly, the top left corner would only yield ![](https://substackcdn.com/image/fetch/$s_!24y7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4f8316d6-183f-4e68-bd25-d871f1f8f855_1762x553.png) Image take from this [paper](https://arxiv.org/pdf/2508.02630?ref=mbi-deepdives.com) Like humans, AI agents do prefer cheaper products with better ratings and more reviews. However, the degree to which they value these attributes differs significantly across various AI models. This means a small boost in a product's rating can result in a much larger increase in purchase probability for one AI compared to another, creating an unpredictable market dynamic. The paper showed in an experiment that a product with a baseline selection probability of 10%, an +0.1 increase in rating lifts the probability to 15.4%, 20.3% and 16.0% with Claude Sonnet 4, GPT-4.1 and Gemini 2.5 Flash, respectively. This, of course, leads us to the ultimate "meta game" of agentic commerce: the interaction between buyer and seller agents. As sellers deploy their own AI to optimize product descriptions and pricing in response to the behavior of buyer agents, we may enter a new era of algorithmic cat-and-mouse. The paper showed that even minor, AI-driven tweaks to a product's description can lead to substantial gains in market share in some product segments. ![](https://substackcdn.com/image/fetch/$s_!__4U!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa2b0ee72-f538-40aa-b456-644fecd7a051_1069x616.png) Image taken from this [paper](https://arxiv.org/pdf/2508.02630?ref=mbi-deepdives.com) Of course, in real world, it won’t just be one seller agent tweaking the descriptions, rather EVERY seller out there will do exactly that! This sets the stage for a kind of "SEO game of chicken," where sellers are constantly trying to outsmart each other's algorithms. The promise here is one of a hyper-efficient marketplace, where supply and demand are perfectly matched in real-time. The pitfall, however, is a potential race to the bottom, where the richness and diversity of the marketplace are sacrificed in the name of algorithmic optimization. If you hated the SEO slops in the pre-AI world to satisfy Google’s algorithm, we may be entering a world with exponential slops trying to cater to multiple AI agents with varying level of biases. Can a technology with a non-deterministic approach solve all these challenges? It's unlikely that we will ever achieve a perfectly predictable and rational system, but that may not be the point. We humans aren’t quite the embodiment of rationality either. But going through the paper makes me deeply unwilling, as of today, to use agents to buy anything through any of the chat bots. Ultimately, I think for agentic commerce to thrive, it needs to largely imitate my preferences. So, instead of agents deploying their own internal logic, it needs to decipher my own “internal” logic of how I decide to buy products online. Perhaps it’s possible if such an agent can observe me taking decisions in variety of contexts for weeks (months?), it can truly become my personal agent and I will feel lot more comfortable in allowing such an agent to buy things online for me. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Never Sell: Episode 9-Global Payments, AI and Software, OpenAI and Anthropic Bubble? URL: https://www.mbi-deepdives.com/never-sell-episode-9-global-payments-ai-and-software-openai-and-anthropic-bubble/ Last updated: 2025-08-25T13:09:18.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I published a new episode today. You can listen to it here: [Spotify](https://open.spotify.com/episode/1AJCzOLHeQrmETVfeaPJvP?si=377a485024be404b&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell-episode-9-global-payments-ai-and-software/id1786912203?i=1000723428464&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=iDR-CN-hBis&ref=mbi-deepdives.com), [RSS feed](https://feeds.buzzsprout.com/2435713.rss?utm%5Fsource=substack&utm%5Fmedium=email) As a reminder, if you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our future episodes. ### Challenges for Self driving Semi, ADI 3Q'25 Earnings URL: https://www.mbi-deepdives.com/challenges-for-self-driving-semi-adi-3q25-earnings/ Last updated: 2025-08-24T14:45:54.000Z # Challenges for Self driving Semi I touched on the potential for self driving semi in my recent [Deep Dive](https://www.mbi-deepdives.com/unp/) on Union Pacific, and mentioned the lack of entrenched unions for trucking industry can be a boon for them to integrate automation compared to railroad industry which is heavily unionized. The limit to this argument is trucker is the most common job in 29 states. As J Paul Getty [said](https://www.goodreads.com/quotes/214064-if-you-owe-the-bank-100-that-s-your-problem-if?ref=mbi-deepdives.com), “*If you owe the bank $100, that's your problem. If you owe the bank $100 million, that's the bank's problem.*” So, when your job is literally the most common job in 29 states in the US, you may not need to be unionized to push back against automation; the politicians may be amply incentivized to make life difficult for trucking automation by implementing ever stringent regulations. We will probably see this conversation about social implications much more once self driving semis start to hit escape velocity, but we aren’t there yet. Chris Paxton wrote a good piece yesterday explaining the technical challenges of self driving semis and why companies such as Waymo have abandoned their self driving semis operations for now. A key excerpt from the piece: > Fully-loaded trucks are *massive*, with a legally-mandated maximum of 80,000 lbs. This makes everything a truck does notably less responsive. > > If we want to discuss how serious a problem this is, we should look at stopping distance; i.e. how long it takes a semi truck to come to a complete stop because, say, there was an accident on the road ahead of it. > > Stopping distance for a fully-loaded semi truck traveling at 65 mph is approximately [525 feet](https://www.smithlawcenter.com/blog/semi-truck-stopping-distance?ref=mbi-deepdives.com#:~:text=Commercial%20trucks%20must%20have%20longer,for%20the%20braking%20to%20begin.) to [about 600 feet](https://www.wkw.com/truck-accidents/blog/stopping-distance-semi-trucks-vs-cars/?ref=mbi-deepdives.com). Even though most US highways have higher speed limits, trucking companies *usually* limit speed to 65 mph for safety and fuel efficiency reasons; it seems reasonable to expect that autonomous truckers would do the same. But note that this is under ideal conditions; stopping distances can as much as [double on icy roads](https://www.edmontontrailer.com/blog/what-is-the-stopping-distance-of-a-semi-truck-acheson-ab/?ref=mbi-deepdives.com#:~:text=Speed%20limits%20are%20not%20arbitrary,icy%20roads%20can%20double%20it.). > > Now, a good long-ranged lidar could have 1000 feet of range. Aurora has a [particularly good in-house lidar](https://techcrunch.com/2025/07/30/auroras-autonomous-trucks-are-now-driving-at-night-its-next-big-challenge-is-rain/?ref=mbi-deepdives.com), with about 450 meters (\~1500 feet) of range - much farther than many other options. But maximum range isn’t [effective range, which is far more important](https://www.baraja.com/en/blog/effective-range-matters-more-than-maximum-range?ref=mbi-deepdives.com). This is hard to estimate — it varies depending on conditions, on objects, and of course on the quality of the particular classifiers being used to interpret objects. This quantity is notably shorter than the maximum range on practically any sensor, [by as much as about half](https://www.robosense.ai/en/tech-show-63?ref=mbi-deepdives.com#:~:text=RoboSense%20RS%2DRuby%2C%20a%20new,wide%20field%20of%20view%2C%20which); and we’ll also need to classify if this was a spurious detection (a plastic bag blowing onto the road, a cardboard box) or a serious issue. > > And that’s setting aside other concerns: what if there’s a patch of black ice ahead on the road? The lidar can’t detect this at all, and it’s a huge issue for highway driving. There was a [famously horrific 133-car pileup in Fort Worth, Texas in 2021,](https://www.wfaa.com/article/news/local/ntsb-releases-factual-report-2021-133-car-pileup-crash-i-35w/287-a8d2543f-9848-493d-b461-d6a278613c55?ref=mbi-deepdives.com) caused by black ice, which led to 65 injuries and six fatalities. If you watch video, you’ll see skilled semi truck drivers carefully bringing their vehicles to a halt through the event, minimizing damage to other drivers as much as possible. > > All this is to say, we’re talking about a really important and very high-stakes perception problem. You cannot make any mistakes in this, or trucks *will* crash, and people *will* die. --- # ADI 3Q’25 Earnings I will share some thoughts on ADI’s earnings behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Airbnb's unique demand driver URL: https://www.mbi-deepdives.com/airbnbs-unique-demand-driver/ Last updated: 2025-08-23T15:03:16.000Z Traditional travel accommodation is still quite search driven. You go to Google or an OTA website/app and insert some details from the dropdown menu (destination, travel dates, number of guests, budget etc.) to pick your accommodation. But ever since going public, Brian Chesky has been highlighting that they would rather be in “inspiration” business i.e. it’s okay if you don’t know where you want to go, Airbnb can **inspire** you to find your perfect spot. This “browse and discovery” mode is quite unique to Airbnb, and this behavior may be reaching an inflection point among Airbnb users. From the recent earnings call: > We're seeing a giant uptick in the number of people that are booking a home from the homepage on Airbnb. So this has been a major behavioral change from basically the last 17 years of Airbnb's history. **So if you go to most apps, especially OTAs, you open the app and every single person goes essentially to the search box. they type in something in the search box and they enter dates and then they get a bunch of search results. And this is how everyone search for travel over the last 20, 25 years. The holy grail is to get more and more people to be in browse and discovery mode**, almost like on Netflix or say DoorDash. **DoorDash was very search-driven. They're now more of a browse and discovery application**. And it's been a really hard not to crack within travel, but we think we've done it because what we've seen is that increasingly more and more guests are engaging not just the service experience from a homepage, but with homes. Now this is very strategic. Why is this strategic for us? **The reason why is if people can engage with our homepage rather than typing in a destination, then we can divert travel more broadly to where we have available supply, thereby increasing conversion rate of our traffic**, if this makes sense. Indeed, that’s exactly how I found the most recent Airbnb we stayed last week. On Wednesday, my wife and I felt we would like to go somewhere for a couple of days. So, I started browsing the Airbnb app, and this particular [listing](https://www.airbnb.com/rooms/1240948581470424905?source%5Fimpression%5Fid=p3%5F1755958711%5FP35VY5ufnuWI3p9Y&ref=mbi-deepdives.com) caught our attention. And the next day, we were there! See (at 3x speed) video below: 0:00 /0:48 1× Airbnb has been sitting on this unique demand driver for a while, but I do believe the best days of generating and extracting value from this demand driver is ahead of them. One of the key value propositions for Airbnb is for majority of their listings, you cannot find on Google or any other OTA websites. An AI powered search (instead of a dropdown menu) can really turbocharge this behavior. Imagine if you could ask an “Airbnb AI” which has access to all the listings, availability, reviews etc. and provide a much more expansive query such as *“I am planning to do some focused work for the next couple of weeks while on an Airbnb. I would like to be surrounded by enchanted trees, the sound of water streaming downhill, and a morning sun that can peek through my window. I would like some decent restaurants within 3-5 miles from the place. I also would like to have the option to go for a couple of unique local experiences e.g. sightseeing or some local activity to get a feel for the place. Please show me listings within 3-4 hours of driving distance from my address.”* This will likely become a reality sometime next year. From Airbnb’s 2Q’25 call: > Next year, **we're going to bring AI into travel search**. So all this brings us back to the question you asked about travel planning. **Over the next couple of years, I think what you're going to see is Airbnb becoming an AI-first application**. And this leads to the bigger question around AI. Over the last almost 3 years since ChatGPT spin out, if you look at the top 50 apps in the App Store, almost none of them are AI apps. The #1 app in the App Store, I think, as we speak, is ChatGPT. And if you go through 2 through 50, maybe only 1 or 2 others are AI native applications. So you've got basically AI apps and kind of non-AI native apps. And Airbnb would be a non-AI native application. **Over the next couple of years, I believe that every one of those top 50 slots will be AI apps. either start-ups or incumbents that transform into being AI native apps.** And I think at Airbnb, we are going through that process right now of transitioning from a pre-generative AI app to an AI native app. We're starting to customer service. We're bringing into travel planning. So it's really setting the stage. > > I think that the key thing is going to be for us to lead and **become the first place for people to book travel on Airbnb**. As far as whether or not we integrate with AI agents, I think that's something that we're certainly open to. **Remember that to book an Airbnb, you need to have an account, you need to have a verified identity. Almost everyone who books uses our messaging platform. So I don't think that we're going to be the kind of thing where you just have an agent or operator book your Airbnb for you because we're not a commodity.** But I do think it could potentially be a very interesting lead generation for Airbnb. Airbnb likely spends more on R&D than all other OTAs combined. Of course, higher R&D dollars doesn’t necessarily mean better output, but Airbnb does have a compelling opportunity to re-imagine the core travel search experience on the website/app in the AI world much more than any other competitor out there. A superior execution here can truly unlock new demand which may lead to higher occupancy rates for Airbnb hosts. Higher occupancy rate can then move the needle for ADR, and if existing hosts make more money, that will inevitably unlock even more supply over time. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Walmart and Target Earnings URL: https://www.mbi-deepdives.com/walmart-and-target-earnings/ Last updated: 2025-08-22T14:24:21.000Z Walmart and Target announced their earnings this week and while I don’t follow these businesses rigorously, going through their earnings simultaneously was an interesting exercise. If you looked at Walmart and Target’s same store sales growth trajectory in the last eight quarters, you might be tempted to think they must be operating in two different industries! In eight out of the last nine quarters, Target posted negative same store sales comp whereas Walmart’s worst comp during this period was +3.8% (FY 1Q’25). ![](https://substackcdn.com/image/fetch/$s_!gUUl!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe965767b-7a5e-40c2-90e0-d21638211924_1165x673.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Perhaps it’s not a surprise that Target has chosen a [new CEO](https://x.com/pitdesi/status/1958229524770128215?ref=mbi-deepdives.com). I have been saying for the last few quarters that the secular rise of same day (or increasingly two-hour) shipping will have profound impact on most physical retailers. It’s difficult to compete against the likes of Amazon if the key competitive vector shifts to changing consumer behavior and an execution muscle requiring technological expertise. The new CEO tried to reassure that they have identified the challenges but frankly speaking, there are few things easier in business world than a retail turnaround. From the call: > "We've identified the biggest challenges that slow us down, **legacy technology that doesn't meet today's needs**, manual work that can be automated, unclear accountabilities, slow decision-making, siloed goals and a lack of access to quality data." It’s not just same store sales growth that’s under pressure, even Target’s digital comps appear increasingly lethargic, especially when you peek at Walmart. My admiration for Walmart only grows when you see how much they have transitioned to the new reality of retail. E-commerce used to be just 12.7% of their US sales just two years ago, but it reached 17% in FY’25 and the growth has continued so far in FY’26. I do want to highlight that the below chart is not quite apple to apple comparison (*Walmart defines eCommerce sales as “omnichannel sales where a customer initiates an order digitally and the order is fulfilled through a store or club, as well as net sales from other business offerings that are part of the Company's ecosystem such as certain advertising arrangements, fulfillment services, and data insights” whereas Target Digital comparable sales include “all merchandise sales initiated through Target’s digital channels (apps/websites, fulfilled by stores or other means”*), but still probably much closer to the truth than otherwise. ![](https://substackcdn.com/image/fetch/$s_!rYb4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc73d2e74-cccb-437b-83b2-f0a3a4f644d6_1159x684.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Walmart's incredible logistical advantage, using its store footprint to compete effectively on speed in the convenience-driven digital marketplace was on full display during the call. It really does sound like they got the memo of where retail is heading. From the call: > "Walmart U.S. e-commerce sales grew 26%, stepping up from the low 20% growth range we delivered over the prior 4 quarters. All fulfillment channels increased, **led by delivery from store**, which was up almost 50%. > > **Speed of delivery is important to customers, and we're continuing to get faster.** Approximately 1/3 of deliveries from store in recent weeks were fast delivery in 3 hours or less, reinforcing the value of our store network and driving speed and **20% of those deliveries arrived to our customers in 30 minutes or less.**” Retail is too large a market to make it either/or winner between Amazon and Walmart, and given the most retailers will find it excruciatingly hard to adapt to new retail environment, both Amazon and Walmart will likely keep snatching market share from more feeble operators. Frankly speaking, Walmart increasingly sounds more like Amazon…I almost had to double check if I’m reading the right transcript: > With strong growth in e-commerce, our advertising business globally increased nearly 50%, including VIZIO. Walmart Connect in the U.S., ex VIZIO grew more than 30% > > …**50% of our incremental profit, excluding claims, was related to advertising, membership and marketplace**. > > …it's pretty rare to find a company of our size with a roughly $700 billion revenue base that is growing organically 5% to 6% each period. We're really pleased with what we're seeing there. And if you look at the contribution to that growth, it's primarily e-commerce…We are more than just a standard brick-and-mortar retail business. **We have a much more diversified set of profit streams now that are both higher growing as well as higher margin.**” While Walmart does have a reputation of serving more lower income households, they did mention their comp was driven by “*all income cohorts with upper income households contributing the largest gains."* How about tariff? Walmart has hinted that we may not be out of the woods from tariff related impact yet, and in fact, the more difficult days may be ahead of us unless tariffs are changed materially downward in the coming months. From the call: > With regards to our U.S. pricing decisions, given tariff-related cost pressures, we're doing what we said we would do. **We're keeping our prices as low as we can for as long as we can**. Our merchants have been creative and acted with urgency to avoid what would have been additional pressure for our customers and members. They've done a terrific job managing pricing and mix across merchandise categories. They managed to generate rollbacks. They've made good quantity and flow decisions, and they've set us up well as we start the back half of the year. > > As it relates to what we're experiencing with customers and members here in the U.S., their behavior has been generally consistent. **We aren't seeing dramatic shifts.** The way things have played out so far, **the impact of tariffs has been gradual enough that any behavioral adjustments by the customer have been somewhat muted**. But as we replenish inventory at post tariff price levels, **we've continued to see our costs increase each week, which we expect will continue into the third and fourth quarters.** > > Not surprisingly, we see more adjustments in middle and lower-income households than we do with higher-income households. **In discretionary categories where item prices have gone up, we see a corresponding moderation in units at the item level as customers switch to other items or in some cases, categories.** As always, our customers are aware, smart and value conscious. **We have approximately 7,400 price rollbacks across our assortment, which is about 2,000 more than last quarter**. While I don’t follow the stock closely, I have been quite impressed with Walmart and I intend to do a Deep Dive on the company sometime next year to hone my understanding of their business (and the stock) a bit more. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Constraints, and challenges of value capture in the AI race URL: https://www.mbi-deepdives.com/constraints-and-challenges-of-value-capture-in-the-ai-race/ Last updated: 2025-08-21T14:21:57.000Z There are a couple of podcasts I would like to highlight today. First, Acquired interviewed Sierra’s co-founders: Bret Taylor (who is currently Chair of OpenAI board and former Salesforce co-CEO) and Clay Bavor (who was at Google for 18+ years). They have an interesting vantage point; they both made their career in generation defining incumbent tech companies and were clearly doing very, very well and yet chose to leave those careers behind to venture into AI. I thought the below paragraph is particularly interesting to contextualize Meta’s pivot to building their small and yet high talent density Superintelligence team: > “…the primary constraint I don't think is capital. It starts with the people and there's a **small set of people** who know how to architect these models, do the pre-training runs, do post-training RL runs and and so that would be the the starting place and then to to your point the capital outlay for building a a data center that can train these multi- trillion parameter count models is just enormous and you have to amortize the cost of the people, amortize the cost of the capital to build out the data centers and then do that, to your point, in a pretty short period of time in order to make the math work. And so I do think there there will be a very small number of these frontier models and research labs producing them. You know, **they will optimize all the way down to the memory, the chips, power delivery, and build this highly vertically integrated stack to get as much value out of the model as quickly as possible and at at lowest cost possibl**e. And by the way though, just with all the press around the talent, it's still a **rounding error** compared to the infrastructure. So I think it's worth keeping that in mind.” Their point about vertical integration is also a reminder why Google may be way ahead of where all their competitors in the current AI race **want to be in a few years**. Dylan Patel from Semianalysis made the point in a16z [podcast](https://www.youtube.com/watch?v=xWRPXY8vLY4&t=1344s&ref=mbi-deepdives.com) that if frontier model is concentrated in just a handful of model developers that open source models cannot quite match, custom silicon will do better. In this scenario, Google’s deep vertical integration of their AI stack through TPUs will become much more visible. Another bit that I thought was interesting in the Acquired interview was their point about how they think about creating leverage through AI: > …we always like to say the way we think about an AI first company is we're building a machine to produce happy customers…And I think that's important because it's like if something comes off the assembly line of machine that's malformed, you don't just fix that thing. You say what part of the machine broke to produce the malformed item. > > And so just as it relates to, for example software engineering, we have this philosophy like when cursor, which is the most popular co-pilot for software engineers to like write code and now having some sort of more agentic flavors of it, if it produces incorrect code, **our philosophy is don't fix the code, fix the context that cursor had that produced the bad code.** And I think that's a big difference when you're trying to make like a company driven by AI. **So essentially, if you just fix the code, you're not adding leverage.** If you go back and say, what context did this coding AI not have that had it had it, it would have produced the correct code. So I don't want to pretend we're perfect here, but that's the way we think about it. I really like thinking of our business as a machine. In theory, any company should be able to “add leverage” through AI, but given their experience in large companies, they also know AI will require a cultural change which many incumbents will have hard time navigating through. I know they are highly incentivized to point out the incumbents’ weaknesses, but the point does have merit. I do think it would help to have founders or management team at the helm that cast a long shadow in their companies so that they can diffuse plenty of agility to embed AI deep in the organizational workflows. From the podcast: > “…we're a new company. So it's just so easy to do these things at a small scale. I observe just like having everyone in our company, you know, you didn't use chat GPT deep research before your sales meeting? Are you kidding me? Like that's a best practice that everyone should do. Imagine doing that with, you know, 10,000 salespeople, you know, to roll that out. So I think about it a lot and then just having the vantage point of having come from larger I just have a ton of empathy for for lack of a better word like the cultural change management of absorbing these technologies into larger organizations. So we're trying to be the poster child of it and then because we are a partner to so many larger firms. **I have a lot of empathy for the challenges of adopting technology into cultures. I think it's really really hard** and I have a ton of respect for leaders who are able to do t at a larger scale.” One big challenge in the AI race, however, is the difficulty of capturing the proportionate value by the model developers. Dylan Patel made this point in the a16z podcast: > I think the main thing is that **AI is already generating more value than the spend. It's that the value capture is broken**, right? Like I legitimately believe OpenAI is not even capturing 10% of the value they've created in the world already. And I think the same applies to, you know, Anthropic and Cursor and and whoever else you're looking at. I think the value capture is really broken. > > Even like internally, I think like what we've been able to do with like four devs with in terms of like automation, like our our spend on Gemini API is absurdly low and yet we go through every single permit and regulatory filing around every single data center with AI and we we take satellite photos of every data center and we're able to label our data set and then recognize what generators people are using, what cooling towers and the construction progress and substation. All this stuff is like automated and it's only possible because of GenAI but and we do it with very few developers and then the value capture that I'm able to generate by selling this data by consulting with it is so high but the companies making it (the model)…they get nothing out of it I think many SOTA model developers are gradually waking up to this reality. The Information [pointed ](https://www.theinformation.com/articles/cost-buying-ai-creeping-boosting-microsoft-sellers?rc=4lgoj7&ref=mbi-deepdives.com)out yesterday how the token price seems to be stable in recent months compared to the last couple of years. The subscription model just doesn’t seem appropriate in many of the use cases. For example, this Reddit post [points](https://www.reddit.com/r/Anthropic/comments/1mqs5rf/this%5Fguy%5Fconsumed%5F50000%5Fin%5F30%5Fdays%5Fon%5Fa%5F200/?ref=mbi-deepdives.com) out how one dev basically consumed $50k worth of tokens while paying $200 for the monthly subscription. This is, of course, a business model problem. There are business models that perfectly captures the value without much leakage at all. A hedge fund is perhaps epitome of the business model to perfectly capture value (and a lot of times, beyond the value it generated). On the other hand, a newsletter subscription business is usually pretty bad at capturing the value it creates (if it does indeed create any value). Of course, the key difference here is building a newsletter doesn’t cost much whereas we are spending tens of billions to develop SOTA models. ![](https://substackcdn.com/image/fetch/$s_!2hud!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff81be772-3f05-407e-902f-fdee11e7ab30_667x429.png) It may be tempting to think it won’t be that difficult to capture value over time. While I have no doubt that SOTA model developers will get better at it, there is a [long list](https://chatgpt.com/share/68a72841-8fb4-800b-865b-347d4316d257?ref=mbi-deepdives.com) of revolutionary technology which had hard time capturing the value. Let me share a personal example. Recently, I opted for “ChatGPT Pro” subscription ($200/month) just to see if there is a noticeable difference between Plus and Pro subscription. One of my family members asked me to run a query that had important career implications for her. After I sent ChatGPT Pro’s response, she was really glad and was telling me that it would probably cost her $1,000 to get such information if not for ChatGPT. At first, I thought even $200/month could be considered incredible value if it can solve at least one such problem in every couple of months. The only problem is when I ran the same query on Gemini 2.5 Pro for which I pay $20/month, it also came up with a very, very good response. ChatGPT Pro was slightly better in some marginal details, but now I was starting to feel $200/month wasn’t worth for those marginal improvement. You see the challenge here? The consumer surplus through these models is quite undeniable but SOTA models remain competitive enough that the value capture can prove to be much, much harder challenge than is currently being appreciated by AI bulls. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 62 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Union Pacific: Chasing the Transcontinental Dream URL: https://www.mbi-deepdives.com/unp/ Last updated: 2025-08-20T12:15:53.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- **Note**: I first published my Deep Dive on CSX in [July 2023](https://www.mbi-deepdives.com/csx/). The introduction and industry overview section basically remain pretty relevant today for Union Pacific as well, so I have copied (and edited a couple of things here and there) those sections to this Deep Dive. The rest of the Deep Dive following industry overview is new. Even if you read the CSX Deep Dive, it’s still probably a good idea to review the first couple of sections to refresh your memory. --- Warren Buffett isn’t quite fond of making bold predictions, so I certainly took [note](https://twitter.com/borrowed%5Fideas/status/1497586138730708994?ref=mbi-deepdives.com) when he mentioned the following in 2021 shareholder letter: > “I’ll venture a rare prediction: BNSF will be a key asset for Berkshire *and* our country **a century from now**.” Buffett started buying Burlington Northern Santa Fe i.e. BNSF Railway in 2006 and accumulated 22.5% of BNSF’s shares by early 2009\. But in November 2009, Berkshire announced to buy the whole company. Delving deep into BNSF transaction is bit of beyond the scope for this piece, but let’s just say it has been a home run for Buffett. And after a decade of owning BNSF, Buffett still felt pretty good about BNSF’s chances for the next hundred years! It’s not just Buffett; Soroban Capital’s Eric Mandelblatt also shared similar sentiment on a [podcast](https://www.joincolossus.com/episodes/43906331/mandelblatt-investing-in-the-industrial-economy?tab=transcript&ref=mbi-deepdives.com) in March 2022: > …given how developed the country is at this point, Union Pacific can't raise their hands and say, "Hey, we'd love to run a railroad track through downtown Houston." It doesn't work like that. So the tracks are the tracks. We're not laying new tracks here. And at my old firm we used to talk about **what are the businesses we'd be comfortable buying a 100-year bond from?** Because it's almost the definition of incumbency, barriers to entry longevity. **To me, the railroads are my number one.** When I think about what's a business I know a hundred years from now, that business is going to be around, it's going to be cash flowing. Very hard to say that, very hard to look 100 years in the future. **To me, railroads are the definition of the 100-year asset.** And by the way, they're one of the very few 100-year bond issuers in the United States market. The steam locomotive was invented in 1797 and [three decades](https://www.aar.org/chronology-of-americas-freight-railroads/?ref=mbi-deepdives.com#!) later, the first railroad in North America was introduced. Railroads had a monumental impact on capitalism. When transcontinental railroad was built in the 1860s, one could travel from the east coast to the west coast of the US in three days that used to take three weeks before the railroads came to the scene! In fact, in the beginning of the 20th century, railroads [contributed](https://www.credit-suisse.com/media/assets/corporate/docs/about-us/research/publications/credit-suisse-global-investment-returns-yearbook-2021-summary-edition.pdf?ref=mbi-deepdives.com) \~63% of the US and \~50% of the UK total stock market capitalization. Today, it is less than 1%. While one may be tempted to think railroads are on the brink of irrelevance in our current world, the reality is a bit different. Even though the industry went through a dizzying number of M&A over the last couple of centuries, the last players standing today are in an enviable position. Surface Transportation Board (STB) [defines](https://www.stb.gov/reports-data/economic-data/?ref=mbi-deepdives.com) **Class I railroad companies** as having operating revenues of, or exceeding, $1 Billion per year, and there are only 6 class I railroad companies today: BNSF Railway (owned by Berkshire), Union Pacific (Ticker: UNP), CSX Corporation (Ticker: CSX), Norfolk Southern (Ticker: NSC), Canadian National (Ticker: CNI), and Canadian Pacific Kansas City (Ticker: CP). The class I railroads [contain](https://www.aar.org/wp-content/uploads/2023/04/AAR-Facts-Figures-Fact-Sheet.pdf??ref=mbi-deepdives.com) \~67% of the industry’s mileage, \~87% of its employees, and \~94% of its freight revenue. The utter dominance of class I railroads, as well as their significance in broader supply chain in North America, has consistently been somewhat underappreciated by the market as every single one of them outperformed the S&P 500 Index over the last two decades! While S&P 500 only increased \~6.5x over the last two decades, shareholders of North American railroad companies enjoyed somewhere between \~7-20x return during the same time. ![chart](https://substackcdn.com/image/fetch/$s_!BrD4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8d32d2b5-2bce-45ca-8d0f-3a6e036e1c2d_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for this month’s Deep Dive: **Section 1 Understanding Railroad Industry**: This section provides a brief history of North American railroad industry, how a railroad business operates, geographical segmentation of North American railroad industry, and some basic concepts in the industry such as intermodal shipping, Precision Scheduled Railroading (PSR) etc. **Section 2 UNP’s Business**: Following the discussion on overall industry, I outlined how UNP makes money as well as its operating cost structure. **Section 3 Competitive Dynamics**: The focus of this section UNP’s recent acquisition announcement of NSC and how that may shape the competitive dynamics in railroad industry, especially relative to trucking industry. **Section 4 Management and Capital Allocation**: UNP and NSC’s capital allocation history over the last two decades and UNPs current incentive structure is highlighted here. **Section 5 Valuation/Model Assumptions**: Model/implied expectations are analyzed here. **Section 6 Final Words**: Concluding remarks on Union Pacific, and disclosure of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Global Payments, Tax Shenanigans URL: https://www.mbi-deepdives.com/global-payments-tax-shenanigans/ Last updated: 2025-08-19T13:36:42.000Z ***A programming note***: I will publish my Deep Dive on Union Pacific (ticker: UNP) tomorrow. Following Union Pacific, the next Deep Dive will be on ASML. I also wanted to remind you that after switching to a more topical discussion format for the “Never Sell” podcast (see [here](https://www.mbi-deepdives.com/never-sell-episode-8-ai-meta-process-pods-writing/)), I will mostly try to respond to your more open ended questions through the podcast. Since moving to daily publishing schedule, the number of emails I receive per day has increased significantly. It is difficult for me to respond individually, but I encourage you to send questions for the podcast. --- # Global Payments (GPN) One of the reasons I am not a big fan of **pitching** stocks is it can inherently put people in a certain mode of discussing a company. In fact, I have noticed at times that if someone is long a stock, they often only mention risks that they have a decent counter against. And if they’re short, many people seem almost too worried to concede anything positive about the company. Of course, good investors and analysts can spot this and/or are self-aware enough to not fall for this “natural” biases themselves. One investor and analyst who certainly doesn’t fall for this is [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com). Scuttleblurb wrote about Global Payments (GPN) yesterday, a company I have never studied. One of my highlights from the piece was his meticulous track down of all the hodgepodge of acquisitions GPN and other legacy payment acquirers did over the years which was a strong reminder of Adyen’s key strength in competing against these companies. Some excerpts from Scuttleblurb [piece](https://www.scuttleblurb.com/gpn/?ref=mbi-deepdives.com): > “…Scarcely in the history of payments have so many bankers been paid such enormous fees for so little value creation. > > …From an enterprise value of just \~$3bn in 2012, Global spent $8bn on growthy forward-looking acquisitions, until by 2018 nearly half its revenue came from integrated payments, e-/omnichannel commerce, and vertical software, business lines that hadn’t existed prior to this transformative journey. In the heady days of the late-2010s, with the market gushing over software, management encouraged analysts to value the company on a sum-of-the-parts basis. > > …I mean, just an absolutely bewildering frenzy of acquisitions that go so many layers deep and encompass so many different processing engines across so many geographies that it comes as no surprise whatsoever that all attempts to consolidate them have utterly failed and long been abandoned. First Data tried for close to a decade before giving up. Vantiv eventually dropped the phrase “single platform” from its 10K. Adyen scanned at the mosh pit and rightly determined that the only way forward was to “start over again”. > > …The only time investors in legacy acquirers ever seem to hear the ugly God’s honest truth about how things are going is when it comes out of the mouth of a new CEO, when the incentive to come clean and heap blame on the prior regime are at their peak. > > …Legacy acquirers are masters at financial engineering but amateurs at organic development and execution. Their hungry eyes are sensitized to the nickels of cost synergies laying right before them and blind to the dollars that could be captured further down the road by tuning in to marketplace realities and adapting accordingly. > > …Legacy players chant “scale” like a hallowed incantation, as if size were the end all, regardless of how many regional platforms it was distributed across. And I’ll concede that decades ago legacy acquirers could get away with trumpeting cost synergies, neglecting product development, and forsaking the hard work of integrating acquired platforms. All sucked at innovation and platform integration together; all enjoyed close relationships with the banks and ISOs that controlled distribution. But that doesn’t cut it anymore. Stripe abstracted its payments apparatus behind a simple API that was rapidly adopted by developers; Adyen sold directly to e-commerce enterprises; Square terminals could be picked off the shelves of major retailers or ordered online. All prioritized easy onboarding and beautiful design, and eschewed the legacy model of scaling through transformative M&A, understanding that the easy wins (immediate scale, cost synergies) from such an approach could morph into an addiction that eventually concretizes into a millstone around the necks of those who succumb to it. In short, you can’t buy your way to relevance anymore. After reading all that, it may be surprising to some that he actually is long the stock: > As you’ve no doubt gathered, I dislike the Worldpay acquisition. Global shareholders would be much better served by the focused agenda that management had in place prior to this year. But here we are! And God help me, for all the knocks against the company, the stock looks pretty damn cheap. On a standalone basis, Global trades at less 7x this year’s net earnings. The Worldpay transaction is expected to be accretive on day 1, so the stock is even cheaper than that on a pro-forma basis, even without taking into account the $600mn of cost synergies (18% of WP’s cost base). At the current price, an investor need not believe that Global will crash payments Olympus and claim a seat alongside Adyen and Stripe. Just that it won’t be snuffed out of existence. Humbly, I don’t think this is too difficult a bet to underwrite. I have always learned a lot about payments by studying Scuttleblurb’s work. I always appreciate the nuances embedded in his pieces instead of a narrow lens of a particular long/short view. While many readers perhaps would rather read stock pitches over research pieces rambling around to sometimes nowhere, building foundational knowledge over time is critical for survival of any active investor. --- # Tax Shenanigans Brad Setser wrote quite a revealing [piece](https://www.cfr.org/blog/time-end-roundtripping-big-pharma?ref=mbi-deepdives.com) on how pharmaceutical companies have used tax shenanigans to avoid paying taxes in the US: > “…America’s top pharmaceutical firms have basically stopped paying corporate income tax in the U.S. > > …The top seven pharmaceutical companies are paying $10 billion or so in tax on their $70 billion in offshore profit. They are just paying all that tax abroad. ![](https://substackcdn.com/image/fetch/$s_!mfyy!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F881e0edb-c0da-4295-8580-3bca4fe59f76_1026x805.png) Source: [Brad Setser](https://www.cfr.org/blog/time-end-roundtripping-big-pharma?ref=mbi-deepdives.com) This looks really, really bad when you juxtapose US revenue vs reported profit in the US. ![Image](https://substackcdn.com/image/fetch/$s_!PvuM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7eea981-3a4f-4b28-8e1e-5fde6536187e_1165x775.jpeg "Image") How do these companies do it? From Brad Setser: > Conduct a few transactions to move the right to profit from the firms’ intellectual property to an offshore subsidiary in a low-tax jurisdiction, produce those drugs abroad, and import them to the U.S. (the pre-Trump pharmaceutical tariff was zero) and pay the 10.5 percent global intangible low-taxed income (GILTI) rate rather than the 21 percent headline tax rate on profits derived largely from U.S. sales. Of course, pharmaceutical companies are not the only companies doing these shenanigans. Again, from Brad Setser: > [Apple reported](https://investor.apple.com/sec-filings/default.aspx?ref=mbi-deepdives.com) $80 billion ($78.3 billion) in offshore profit in 2024, up from around $70 billion in 2023\. [Microsoft reported](https://microsoft.gcs-web.com/static-files/1c864583-06f7-40cc-a94d-d11400c83cc8?ref=mbi-deepdives.com) $45 billion in offshore profit in 2024, and $36 billion in 2023. > > That is just under $200 billion in offshore profits from fewer than ten companies, and a large share of the $350 billion that the U.S. balance of payments data shows that American firms earned in low tax jurisdictions in 2023. I own some of these big tech companies, and I have always disliked these tax shenanigans as I believe given the size of these big tech companies and how the rest of the world will increasingly look to milk these companies through random regulations, they need US government to be on their side to vociferously make their case. US government would be lot less willing (or worse, they could try to milk them as well) if these companies don’t actually pay much taxes here. Paying lower taxes can lift short-term EPS for these big tech companies but can really hurt their long-term earnings power if governments around the world start thinking they’re easy target. I have always felt a little uneasy whenever I see effective taxes for all the big tech are consistently noticeably below the statutory rate. In fact, when I model these companies, I always assume the statutory tax rates going forward as I do believe that’s indeed the long-term equilibrium for these companies. ![chart](https://substackcdn.com/image/fetch/$s_!fLvF!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F03000fe2-8273-4d69-b78a-67b08bd9a875_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) It can be instructive to notice when big tech are trying to pay less taxes in the US, Warren Buffett is trying to ensure everyone knows how much taxes Berkshire is paying. From Buffett’s 2024 shareholder letter: > “…Berkshire Hathaway – paid far more in corporate income tax than the U.S. government had ever received from any company – even the American tech titans that commanded market values in the trillions. > > To be precise, Berkshire last year made four payments to the IRS that totaled $26.8 billion. That’s about 5% of what all of corporate America paid. As these companies become larger and larger over time, their roles will be increasingly scrutinized in every society. The value proposition they bring not just through economic and consumer surplus but their direct contribution to government coffers can be an important tool to have the most powerful government in the world to be on their side. I hope and expect the big tech companies, especially the founder led ones optimize their future for the very long term. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Market Bubble Talk, Frozen Housing Market Implications URL: https://www.mbi-deepdives.com/market-bubble-talk-frozen-housing-market-implications/ Last updated: 2025-08-18T14:22:38.000Z Ever since I started investing in the US in 2018, I have consistently heard about valuation being in “bubble” territory. Early 2023 was perhaps the only time I can recall the bubble talk was a bit subdued, but then again, a recession in 2023 was thought to be on the cards. Sometimes when I look back at the prices and multiples many of the “compounders” used to trade at during early 2010s, it feels investors were playing such an easy game. Companies such as Visa, Sherwin Williams, Google, Union Pacific…some cherry picked compounders from four very different industries, but they all traded around mid-teens earnings multiple! ![chart](https://substackcdn.com/image/fetch/$s_!mEN7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc61b0f8-b08c-46c9-81e5-95607533ebfc_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) The bets on compounders seem lot less straightforward these days! While Google and Union Pacific’s multiples stay somewhat grounded, Visa and Sherwin Williams unlikely to enjoy any material tailwind from multiple expansion going forward. ![chart](https://substackcdn.com/image/fetch/$s_!oLMR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb171c4fa-41b2-4f29-8125-da48fb49d42e_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Of course, there is always something to worry about. While today we know Google had enormous runway left for global domination, there were legitimate concerns around transition to search on mobile. With GFC still fresh on everyone’s memory, the fear of double dip global recession (Eurozone crisis also didn't help) was likely quite vivid among most investors’ mind. So, it is highly likely that even if I was investing back then, I may not have quite felt like a kid in a candy store. In any case, given the frequency of proclamations around market being in a “bubble” territory, I am admittedly deeply suspicious of any kind of bubble talk. I do think there’s always some bubble in some corner of the market, but a broad market level **valuation** **bubble** is historically incredibly rare. Of course, if China invades Taiwan tomorrow, the broad index may go down \~30% perhaps in a week. That’s not a sign of valuation bubble though; it’s just “Taiwan risk” is not being captured in price. To say it differently, even if S&P 500 were 20% below today’s level and China invaded Taiwan, I imagine the index would still go down somewhere between 20-30% in a week. It is incredibly simplistic to look at historical market level multiples and think current index is trading at such and such percentile compared to past 30, 50, or 100 years without not looking at nuances embedded in the index. We may be looking at S&P 500 index, but the index “portfolio” is incredibly dynamic over time, and hence looks dramatically different over 30-50 year period. We did, of course, see a true valuation bubble over the index level back in early 2000s. S&P 500 experienced a lost decade in the first decade of this century. But you know what, I always found it to be quite instructive that broader market needed to experience TWO largely unrelated massive jolts for investors in the US market to experience a lost decade! Perhaps index could still eke out LSD-MSD type return DESPITE market being in such an obnoxious bubble in early 2000s if there were no GFC. LSD-MSD CAGR is clearly a bad outcome in most market environments, but probably not nearly as bad when market is recovering from an absolute mania. While we have started hearing about current market environment being similar to the tech bubble, I think there are still more differences than similarities between these two periods. I do feel a bit hesitant to point out the early 2000s tech bubble period to claim “hey, we aren’t that crazy yet”…that’s hardly reassuring! I did come across some numbers from Patient Capital Management that [contextualizes](https://www.patientcapitalmanagement.com/articles/secular%5Fbull%5Fmarket%5Fpeaks%5F%5Fare%5Fwe%5Fthere%5Fyet?ref=mbi-deepdives.com) the tech bubble vs current period: > “The S&P 500 at 22x forward twelve-month earnings is \~15% cheaper than the peak of the tech bubble at 25.5x despite having 60% higher profit margins and 10% better ROE. > > **Market concentration today looks much more aligned with fundamentals.** During the tech bubble, the concentration of the top 10 largest stocks at 27% was nearly 2x above its earnings contribution. The expected earnings growth that was priced in failed to materialize. Today, the weight of the top 10 stocks relative to their earnings contribution is much more aligned at 35% and 32%, respectively. While the top 10 stocks in the S&P at 38.3x is above the tech bubble at 34.4x, Tesla at 145x is meaningfully skewing the data. Excluding Tesla, the top 10 today trade at 26.5x, or \~25% below the tech bubble peak despite returns on capital that are >2x higher.” The real bear case for the broad index is increasingly related to AI. If all these AI capex leads to quite sub-par return, the mag7 earnings will prove to be overstated as depreciation will keep rising over time without much commensurate gain in revenue and profits! I am obviously playing close attention to this debate, but I don’t quite think we need to be worried…yet! --- # Frozen Housing Market Implications Last week, WSJ published an interesting [piec](https://www.wsj.com/economy/american-job-housing-economic-dynamism-d56ef8fc?st=rr51ev&reflink=article%5FcopyURL%5Fshare&ref=mbi-deepdives.com)e about the implications of frozen housing market and the ripple effects it is causing in various ways. Some excerpts from the piece: > The frozen housing market means growing families can’t upgrade, [empty-nesters can’t downsize](https://www.wsj.com/economy/housing/baby-boomers-big-homes-real-estate-inventory-3a047cb6?mod=article%5Finline&ref=mbi-deepdives.com) and first-time buyers are [all but locked out](https://www.wsj.com/economy/housing/first-time-home-buyers-are-mia-landlords-are-the-winners-4c60cdb2?mod=article%5Finline&ref=mbi-deepdives.com). When people can’t move for a job offer, or to a city with better job opportunities, they often earn less. When companies can’t hire people who currently live in, say, a different state, corporate productivity and profits can suffer. > > In the 1950s and ’60s, some 20% of Americans would typically move each year. > > The share of people moving has steadily slowed since then, in part because the U.S. population has aged, and older people tend to move less. More Americans also live in households with two earners, which makes uprooting more challenging. > > By 2019, the year before the Covid pandemic, 9.8% of Americans moved. > > During Covid, there was a well-publicized increase in [people decamping](https://www.wsj.com/us-news/americans-up-and-moved-during-the-pandemic-heres-where-they-went-11620734566?mod=article%5Finline&ref=mbi-deepdives.com) farther away from work and deeper into the suburbs. That surge was brief. In 2023, only 7.8% of Americans moved, the lowest rate logged since U.S. Census records began in 1948\. That figure held relatively steady in 2024, the most recent data available. ![](https://substackcdn.com/image/fetch/$s_!_IT6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0d917bfc-a1f1-4859-86bc-8602dabd9ab9_763x514.png) Image Source: WSJ If people feel locked in their current house and can’t upgrade because they cannot afford the mortgage payments, I wondered if it can affect fertility rate. This NBER [paper](https://www.nber.org/digest/feb12/impact-real-estate-market-fertility?ref=mbi-deepdives.com) did seem to confirm my intuition: “*a 10 percent increase in home prices leads to a 1 percent decrease in births among non-homeowners in an average metropolitan area. However, the negative effect among non-owners is offset by a 4.5 percent increase in births among current homeowners, who are now wealthier.*” I also wondered about political polarization deepening over time if the composition of the county/city/state remain more or less the same for decades. Regions can become stronger echo chambers, making national consensus and political compromise increasingly difficult to achieve. Reading the WSJ piece made me appreciate that the housing affordability crisis can become a top-tier political issue in coming years (even more so than it is today). The "correct" solution may become a major battleground for political parties, and I suspect we will see plenty of bad ideas (e.g. rent control, increasing pressure on Fed to lower rates regardless of the data) as potential solutions. I don’t have any company specific point (although I do suspect this may be good for home improvement retailers?), but I wanted to highlight this as it may prove to be important in sociological and political context in the next few years. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Amazon's Ambition in Groceries: Part 1 URL: https://www.mbi-deepdives.com/groceries_1/ Last updated: 2025-08-17T14:54:23.000Z Back in 2023, Terry Smith from Fundsmith [wrote](https://www.fundsmith.co.uk/media/cygbfqd0/fef-2023-semi-annual-letter-web.pdf?ref=mbi-deepdives.com) the following as their rationale to exit their position in Amazon (emphasis mine): > Relatively new CEO Andy Jassy enunciated some principles of investment which investment projects had to have, namely: > > 1\. Be big and capable of delivering good returns on capital. > > 2\. Serve an area of the market in which consumers are not already well served. > > 3\. Amazon had to have a differentiated approach to competitors’ and > > 4\. Amazon had to have or be able to acquire the competence to execute. > > Our view was that there was a lot to like about that statement, and it gave us some comfort in purchasing a stock we had shied away from before. However, it is always easier to talk the talk than it is to walk the walk and the CEO’s pronouncement that he wanted Amazon to seek routes to **get bigger in grocery retail ran counter to all these principles. In our view grocery retail has none of these characteristics** and Amazon has already stubbed its toe in this sector with the Whole Foods acquisition. > > Moreover, our recent experience of engagement with companies which we believe are making capital allocation and other mistakes has produced a much longer list of those who have ignored us than of those who have listened and so we are likely to be more active in exiting such situations where we disagree with the manner in which our investors’ capital is being allocated. **Where companies choose to invest outside a powerful core franchise in which they already have expertise we believe they are likely to destroy value, and especially so where they are entering a sector which already has poor returns**. While it is hard to know, my guess is many Amazon shareholders (including me) perhaps largely agreed with Mr. Smith that Amazon’s big bet in groceries is bit of a headscratcher but likely chalked up Smith’s decision to exit Amazon as missing the forest for the trees. However, I recently came across a data point that startled me a bit and persuaded me to spend some time understanding the grocery market. My objective is to better understand Amazon’s bet in groceries. While understanding Amazon’s bet on groceries is ultimately my objective, today’s focus is not about Amazon. It is about Walmart and their decision to get into grocery business. So, what did exactly startle me a bit? Walmart was founded in 1962 and became a public company by 1970\. However, it wasn’t until **1988(!)** they started selling groceries. Then in just **thirteen (!!) years** Walmart became the largest grocery player in the US. Imagine getting into a highly competitive, “mature” sector after two and half decades of your operations and then become market leader in that segment in just over a decade! I went back to Walmart’s [1988 annual report](https://stock.walmart.com/%5Fassets/%5Fcc753cf12bdf0e606eeb187b6d8e924c/walmart/db/950/9615/annual%5Freport/1988-annual-report-for-walmart-stores-inc%5F130199394950449861.pdf?ref=mbi-deepdives.com#:~:text=%5BPDF%5D%201988%20Annual%20Report%20,tives%20is%20still%20unknown) to see what exactly they were talking about their bet on groceries. While we are familiar today with Walmart “Supercenters”, these used to be called “Hypermart” back then. This is what Sam Walton wrote in his annual shareholder letter about this new “experiment”: > Hypermart USA opened it's doors in Garland, Texas, a suburb of Dallas, in December, 1987 and Topeka, Kansas in January, 1988\. Hypermart USA may be described as a blend of the best of a Wal-Mart store, a combination supermarket/general merchandise store and a Sam's Wholesale Club. **A true experiment**, Hypermart USA has received strong initial customer acceptance, but **our ability to achieve satisfactory profit objectives is still unknown**. This “experiment” with “unknown” profitability led to **$276 Billion sales (\~60% of Walmart’s sales in the US)** in FY’2025. ![](https://substackcdn.com/image/fetch/$s_!h9Jp!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fda6e2a08-2198-4f71-b8f8-4a656eb3427c_1798x175.png) Source: Walmart SEC Filings Although Sam Walton was still Chairman of the board, interestingly, Walmart’s experiment with groceries coincided with a new CEO: David Glass. It was Glass, who originally joined Walmart in 1976 and took the helm in 1988, aggressively expanded the Supercenter concept. Thankfully, Glass didn’t mind investing “outside a powerful core franchise”**.** By the time Glass left the CEO role, Walmart was on the verge of becoming the market leader in groceries! So, while Sam Walton still today likely casts his long shadow on Walmart, David Glass deserves plenty of credit and adulation. Walmart’s success in groceries was the result of a perfect storm of competitive advantages: an entrenched low-price strategy i.e. Every Day Low Prices (EDLP), massive economies of scale, supply chain mastery, a disruptive store format, savvy merchandising (including private brands), and a corporate ethos fixated on cost reduction. In 1988, none of the traditional grocers had more than \~5–10% of the national grocery market. The industry was still regionally oriented and competitive. Walmart’s competitive advantages deepened over time, and in the [2001 shareholder letter](https://www.annualreports.com/HostedData/AnnualReportArchive/w/NYSE%5FWMT%5F2001%5F423b0948dc0248d6ac90d8fa58b27ba2.pdf?ref=mbi-deepdives.com), the new CEO wrote the following: > “This year, Wal-Mart became the largest retailer in the U.S. grocery industry…That is truly a remarkable achievement, and I think **Sam Walton would be proud.**” Sam Walton passed away in 1992, but I imagine he would be quite proud indeed! Meanwhile, the legacy chains consolidated aggressively to defend their positions. Fifteen of the top 20 national grocers in the 1980s [either merged or were acquired](https://www.foodandpower.net/retailers?ref=mbi-deepdives.com#:~:text=But%20a%20spate%20of%20mergers,doubled%20in%20under%20a%20decade) by the 2000s*,* reflecting a massive shake-up of the industry. By 2001, **Walmart, Kroger, Albertsons, and Safeway** stood as the top four grocery seller, with Walmart firmly in the lead. As you can imagine, Walmart stock did exceedingly well during this period as the stock became \~18x during this 14-year period! ![chart](https://substackcdn.com/image/fetch/$s_!OkK2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F70fba318-151d-409e-a3d1-7ed7ef164346_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) If you look at the chart closely, you will see Walmart stock was dead money from early 1992 to early 1997! I am highlighting this particular period because these periods get often “lost” in a long-term stock charts with gangbuster returns. But take a moment to appreciate the doubts, and criticisms that must have been directed at Walmart’s shareholders back then. Having experienced one such period with Meta Platforms, I can tell these are never fun to go through, but perhaps almost unavoidable for most long-term compounders! ![chart](https://substackcdn.com/image/fetch/$s_!kggs!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb1939448-160f-4bfb-a1da-7cc58fa83ba4_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) But why was the stock flat for five years despite essentially steamrolling competitor after competitor in grocery market in mid-90s? Walmart’s net profit actually doubled from $1.6 Billion in FY’92 to $3.1 Billion in FY’97\. But growth was decelerating as the base got huge (net-sales growth stepped down from 35% in FY’92 to 12–13% by FY’96–’97). That’s exactly the recipe for de-rating. P/E was basically cut in half. ![chart](https://substackcdn.com/image/fetch/$s_!mNOH!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa30cd4b2-fc25-438f-8d85-7b1d85bb20a7_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) It also likely didn’t help that mid-90s Walmart spent \~$3–3.5B a year to open/convert stores and build DCs (supercenter roll-out). Operating cash flow couldn’t cover capex (negative FCF) for much of this period, only flipping positive in FY’97\. It’s not hard to imagine people complaining about negative FCF year-in-year-out. While those investors must have felt pretty good about their “judgment” to avoid such a capex heavy retailer investing so much in lower margin grocery business, that proved to be a short sighted judgment in hindsight! None of this means history will repeat for Amazon, but it’s useful historical pre-text before I spend more time understanding Amazon’s bet on groceries. I am not sure how many parts I will write in this series, or when the next parts will come out, but expect me to do more work here in the next few months. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Tyler Technologies, Portfolio Change URL: https://www.mbi-deepdives.com/tyler-technologies-portfolio-change/ Last updated: 2025-08-16T14:12:07.000Z I have published 61 [Deep Dives](https://www.mbi-deepdives.com/models/) over the last five years. Obviously, that’s a lot to keep track for one person. However, given that I already spent a month studying each of these businesses, it usually doesn’t take too long for me to get up to speed on a company that I already did a Deep Dive on sometime in the last five years. While I still read the entire earnings transcripts for almost all companies I own in my portfolio, for companies I am tangentially interested in I mostly resort to AI these days to quickly go through the key takeaways from their earnings calls since I last looked at the company. Nonetheless, even with AI and all, I still need to pick and choose whether to spend more time on a particular company or if a stock is now sufficiently attractive. I want to discuss one of my processes to quickly decide whether I should spend more time on a particular company that I already studied before. The company I will use as an example to explain this process is Tyler Technologies on which I published a Deep Dive back in [May, 2023](https://www.mbi-deepdives.com/tyl/). Let me explain it as well as my most recent portfolio change behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Cursor's Conundrum URL: https://www.mbi-deepdives.com/cursors-conundrum/ Last updated: 2025-08-15T14:11:01.000Z I don’t quite follow private companies closely. OpenAI and Anthropic are perhaps the only exceptions; I had to make the exception because they are simply too big to ignore and likely will have profound impact on how AI shapes the broader tech industry in the next 5-10 years. Cursor isn’t quite *that* big, but still is currently “valued” \~[$10 Billion](https://techcrunch.com/2025/06/05/cursors-anysphere-nabs-9-9b-valuation-soars-past-500m-arr/?ref=mbi-deepdives.com). What really caught my attention is despite being just a three year old company, other startups were starting to pitch “[Cursor for X](https://medium.com/intuitionmachine/cursor-for-x-is-rewriting-the-rules-of-ai-startups-3c2bd181f480?ref=mbi-deepdives.com)”, and yet when I spent some time on Cursor, I was slightly confused why this company is considered a blueprint for success for other startups! I came across this Edward Zitron [piece](https://www.wheresyoured.at/ai-is-a-money-trap/?ref=mbi-deepdives.com) “*AI is a Money Trap*” that made me wonder about Cursor’s long-term future. To be frank, I read half of the article and gave up because the piece was getting progressively weaker. However, the way I read any content on the internet is to not look for all the weaker points an author makes, rather only look for the interesting and good arguments that make me think. So, while I think Zitron makes a lot of weak arguments in his piece, he did outline Cursor’s conundrum well: “*Cursor makes — before, at least, their massive changes to their service — $500 million in annualized revenue, so around $42 million a month. This makes it the single-highest earning generative AI company that isn’t called OpenAI or Anthropic, and the highest-earning company built on top of (primarily) Anthropic’s technology. Its success is symbolic to the greater movement, and just as it hit its peak, Anthropic (and OpenAI, to a lesser extent) decided to add priority processing and priority service tiers, demanding more money up front and causing Cursor to have to massively degrade its service. I* [*explain in detail in my premium piece from a few weeks ago*](https://www.wheresyoured.at/anthropic-and-openai-have-begun-the-subprime-ai-crisis/?ref=mbi-deepdives.com)*.* 1. *To explain in short, Cursor’s AI-powered coding editor used to have fairly unrestrained access to the various models provided by these companies. In mid-June — a few weeks after Anthropic introduced “*[*priority tiers*](https://docs.anthropic.com/en/api/service-tiers?ref=wheresyoured.at#get-started-with-priority-tier)*” that required companies to pay up-front and guarantee a certain throughput of tokens and increased costs on using prompt caching, a big part of AI coding —* [*Cursor massively changed the amount its users could use the product, and introduced a $200-a-month subscription.*](https://techcrunch.com/2025/07/07/cursor-apologizes-for-unclear-pricing-changes-that-upset-users/?ref=wheresyoured.at) 1. *As an aside to this, Anthropic also competes with Cursor’s AI coding product with their own service, Claude Code.* 1. *Cursor, as Anthropic’s largest client (*[*the second largest being Github Copilot*](https://www.theinformation.com/articles/anthropic-revenue-pace-nears-5-billion-run-mega-round?rc=kz8jh3&ref=wheresyoured.at)*), represents a material part of its revenue, and its surging popularity meant that they were sending more and more revenue Anthropic’s way.* 2. *Anthropic used this opportunity to raise prices on accessing its models to continue providing service at an acceptable level to Cursor’s customers by introducing “*[*Priority Tier*](https://docs.anthropic.com/en/api/service-tiers?ref=wheresyoured.at#get-started-with-priority-tier)*” access on May 30 2025.* 3. *This has allowed Anthropic to juice its revenues, and due to the upfront nature of these contracts, Cursor is locked-in regardless of how well it does. The net result of these cost increases means that Cursor’s product is less attractive to its customers, and will thus make it less money.”* Chris Paik yesterday also wrote a [piece](https://x.com/cpaik/status/1956071009779638316?ref=mbi-deepdives.com) on Cursor that highlighted the double whammy Cursor faces: > Cursor’s users expect the best coding performance, which is currently delivered by the frontier labs. That pins Cursor’s COGS to OpenAI/Anthropic price cards. Cursor doesn’t control two critical dials: 1. Model performance frontier (what users demand). 2. Model input/output pricing (what Cursor pays). > If Cursor steps down to cheaper, weaker models, the users who care about performance will notice and churn; those who can tolerate weaker models can get them cheaper elsewhere. If it stays at the frontier while keeping prices flat, the variable, real cost to service their heaviest users will explode. In an effort to combat this, Cursor has been forced to raise prices and institute usage caps leading to user outrage and churn. While Cursor is indeed structurally exposed to supplier power on both axes that matter (frontier capability and token economics), Cursor understandably has a slightly different perspective to this conundrum. In an[**interview**](https://stratechery.com/2025/an-interview-with-cursor-co-founder-and-ceo-michael-truell-about-coding-with-ai/?ref=mbi-deepdives.com)with Ben Thompson (BT), Cursor co-founder Michael Truell (MT) hinted at what he thinks is Cursor’s differentiator (emphasis mine): > **BT**: Is that a real sustainable advantage for you going forward, where you can really dominate the space because you have the usage data, it’s not just calling out to an LLM, that got you started, but now you’re training your own models based on people using Cursor. You started out by having the whole context of the code, which is the first thing you need to do to even accomplish this, but now you have your own data to train on. > > **MT:** Yeah, I think it’s a big advantage, and I think these dynamics of high ceiling, you can kind of pick between products and then this kind of third dynamic of distribution then gets your data, which then helps you make the product better. I think all three of those things were shared by search at the end of the 90s and early 2000s, and so in many ways I think that actually, **the competitive dynamics of our market mirror search more than normal enterprise software markets**. > > **BT:** Yeah, I do want to come back to that point, I think that’s really interesting. > > What role do the large LLMs still play in Cursor today? Obviously that was what mattered to start, but where do they make a difference in the experience given that you use your own models to do some of this work that you’re talking about? > > **MT:** They definitely still play a big role. So **sometimes we’re using custom models entirely without any of the big API models**, as an example, [**Tab**](https://www.cursor.com/cn/blog/tab-update?ref=mbi-deepdives.com), in the prediction side of things, that’s because that’s a task that’s very specialty, those models need to be incredibly fast to deliver suggestions within 200 milliseconds, 300 milliseconds, but **then the API models are an important feature of Cursor.** Often they’re used not on their own, but with our own models on the input side of things and on the output side of things. > > **BT:** So you’re writing custom prompts and deciding where to go to, it’s like an AI orchestration layer. > > **MT:** Yeah. On the input side of things, we have a whole suite of models that is picking the best parts of a code base to show these models and optimizing things on the input, and then on the output side of things, the API models are very slow, and so to make an actual change across the code base, the API model gives us kind a sketch of the change. Then it gets turned into a multi-thousand line diff by a specialty model and some inference tricks that takes that plan and then does the actual text editing. > > So even in the cases where we use the API models, which are an important feature of Cursor and we definitely benefit from those getting better, and are excited by all the recent developments and for progress to continue there, **there’s custom models around them**. Why might Cursor’s “custom” models beat Claude/OpenAI/Google? Cursor doesn’t need to dominate at broad reasoning; Cursor needs to outperform on *narrow, code‑specific tasks* under hard latency constraints. Nonetheless, I do think the concerns are pretty real. In some sense, it reminds me of a tweet I saw (I tried to look for it but cannot find it) that lamented why Shopify chooses at times to compete with third party developers on its ecosystem by providing a competing solution. Someone from Shopify replied (like I said, I can’t find any of these but I don’t think I’m hallucinating this) which left an impression on my mind how any platform approaches these decisions. So, someone from Shopify essentially implied Shopify can’t (and shouldn’t) build for every edge case. That’s what the ecosystem is for. But for something like checkout/Shop Pay? These must be fast, secure, and universal, so they’re native. **If it’s table-stakes for nearly all merchants** (he may have mentioned \~80% of merchants)**, Shopify builds and hardens it**; partners extend it for edge cases. If it’s a broad need but fragmented in implementation, Shopify may ship a baseline while encouraging 3P depth. If partners already deliver superior depth and the category isn’t core to Shopify’s moat, Shopify defers to 3P partners. I think the challenge for Cursor is coding is such an incredibly good product market fit for these SOTA model developers and particularly a massive revenue driver for Anthropic, it’s hard to imagine Anthropic not doubling down in perfecting Claude Code! In a recent interview, Anthropic Co-founder Dario Amodei [said ](https://cheekypint.transistor.fm/5/transcript?ref=mbi-deepdives.com)the following: > …I think there was quite a lot of uncertainty two or three years ago. But I think we might be relatively close to the final set of players, if not necessarily the final market structure or the roles of the players. **I would say there's probably somewhere between three and six players**, depending on how you count, and those are the players that are capable of building frontier models and have enough capital to plausibly bootstrap themselves. If I were Cursor, I would be praying the number of players turns out to be closer to six! --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Beyond the "Search" Box URL: https://www.mbi-deepdives.com/beyond-the-search-box/ Last updated: 2025-08-14T13:22:51.000Z Anecdotally, we have all been hearing some version of this in the last few months, at least on twitter: “*After I started using ChatGPT, my Google search usage went down a LOT!*” While Google bears increasingly depict a visceral visualization of Google increasingly being replaced by ChatGPT, Google search bull thesis remains reliant on the arguments related to default distribution, consumer habits, and AI being potentially query expansionary in nature. The crux of the debate basically comes down to whether AI is going to expand query market or ChatGPT is mostly going to replace traditional search over time. So, I was particularly intrigued by this [study](https://www.semrush.com/blog/google-usage-after-chatgpt-adoption/?ref=mbi-deepdives.com) conducted by Semrush which tried to answer this question. They started with two hypotheses: > **Substitution Hypothesis:** ChatGPT adoption pulls users away from Google Search. People “substitute” ChatGPT for how they previously used Google. > > **Expansion Hypothesis:** ChatGPT adoption does not reduce Google usage. In other words, it simply expands overall information-seeking behavior. Semrush tracked 260 billion rows of clickstream data on U.S. desktop users who began using ChatGPT in Q1 2025, comparing their Google Search sessions in the 90 days before and after adoption to a control group that never used ChatGPT. This setup allowed them to isolate whether ChatGPT adoption caused changes in traditional search behavior compared to natural trends over time. The overall result of the study shows after adopting ChatGPT, users **increased** their Google Search sessions from 10.5 to 12.6 per week while also adding about 5 ChatGPT sessions weekly, suggesting ChatGPT use complemented rather than replaced Google searches. ![AD_4nXf52toHwzzCP5tZKSFCUpauDr1sqRrwWTINchC6HnOp8yX0UqI74XFOZOmhelKI5nx-mMY-j1rTjuDMZpRHjOVFbV3f7_JvSnX6u6bx_hHQlqUW2ya--LXacsz91UZDx5oAVhIGow?key=Fzv_zuKop8N473NG043a0A](https://substackcdn.com/image/fetch/$s_!oZN3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F083d6d8b-00f4-459c-9dc8-e0f41d49230e_1600x1041.png "AD_4nXf52toHwzzCP5tZKSFCUpauDr1sqRrwWTINchC6HnOp8yX0UqI74XFOZOmhelKI5nx-mMY-j1rTjuDMZpRHjOVFbV3f7_JvSnX6u6bx_hHQlqUW2ya--LXacsz91UZDx5oAVhIGow?key=Fzv_zuKop8N473NG043a0A") Source: Semrush Semrush shared some cohort level data by month which all show that despite sustained ChatGPT usage after adoption, Google Search usage remained resilient. ![AD_4nXdIaoG1GuhUHUiC7h2G511Tfu0ntiJ8oFE_Iv-AV9NvJjgJGutXtFhV_c_VcZHPj41Gsk6ksrHsQ90p4k-xMOxLmOCQmMIM0z_CaiEM6_2iFY3bVaEf7FF_ILBMIdm2f-8138O1Ew?key=Fzv_zuKop8N473NG043a0A](https://substackcdn.com/image/fetch/$s_!yNCB!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffb24120b-4060-4bd1-a96a-c0eb26fc9376_1600x1486.png "AD_4nXdIaoG1GuhUHUiC7h2G511Tfu0ntiJ8oFE_Iv-AV9NvJjgJGutXtFhV_c_VcZHPj41Gsk6ksrHsQ90p4k-xMOxLmOCQmMIM0z_CaiEM6_2iFY3bVaEf7FF_ILBMIdm2f-8138O1Ew?key=Fzv_zuKop8N473NG043a0A") Source: Semrush ![AD_4nXcdmPFqjz9teVBPYKmXTLY7HiUGkisoE2bA02ou3oMskRTmpLC8YiaomY5EfY1CrQ39i89Bx-Xdo4U1-o2CR74WkCu2IszeHwqGmyoSCCMeqMSFtCoRyvrMs6JJGM5BpPTT1QfE?key=Fzv_zuKop8N473NG043a0A](https://substackcdn.com/image/fetch/$s_!HWK2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbace2d8e-8a78-43ba-b7ba-8be496c03c22_1600x1486.png "AD_4nXcdmPFqjz9teVBPYKmXTLY7HiUGkisoE2bA02ou3oMskRTmpLC8YiaomY5EfY1CrQ39i89Bx-Xdo4U1-o2CR74WkCu2IszeHwqGmyoSCCMeqMSFtCoRyvrMs6JJGM5BpPTT1QfE?key=Fzv_zuKop8N473NG043a0A") Source: Semrush ![AD_4nXfc53JZCu0Zk9KFwUj3xoJjEv11XNCDJEwzhtabW0jYw_diSGTcSkrBLB24yRSjMmddhIQBsZ4Wr2WkSfDZHBwSePLbtL13rHHh-Pv_Lt5vboehFTw7iGPhkj89eZo94nviFBsDLg?key=Fzv_zuKop8N473NG043a0A](https://substackcdn.com/image/fetch/$s_!g9PJ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc8c5dc1d-dcc5-4f83-b451-ea2d576b52ac_1600x1486.png "AD_4nXfc53JZCu0Zk9KFwUj3xoJjEv11XNCDJEwzhtabW0jYw_diSGTcSkrBLB24yRSjMmddhIQBsZ4Wr2WkSfDZHBwSePLbtL13rHHh-Pv_Lt5vboehFTw7iGPhkj89eZo94nviFBsDLg?key=Fzv_zuKop8N473NG043a0A") Source: Semrush One may wonder if you keep using ChatGPT for longer than a year, perhaps it eventually changes your Google usage. That also doesn’t quite seem to be the case yet since a 500-day study by Semrush of users who began using ChatGPT in January 2024 found that Google search activity remained steady while ChatGPT usage stayed consistent after adoption. ![AD_4nXeVf-kINXUCjg8lCw1dCRAHYA8i_Eur0mXu50_u84PVvPz0Va8M2xjl1oo5oVpt7VcOrCNR3Y2o6WWcO0hntYuFPINRSswIIDDFpIVtKKNrmnHmcQdX2xIVNjw5B7Iim7PtHYj6DQ?key=Fzv_zuKop8N473NG043a0A](https://substackcdn.com/image/fetch/$s_!6d3H!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc54e26dd-1dbe-4a99-96fb-31f3349691a0_1600x1486.png "AD_4nXeVf-kINXUCjg8lCw1dCRAHYA8i_Eur0mXu50_u84PVvPz0Va8M2xjl1oo5oVpt7VcOrCNR3Y2o6WWcO0hntYuFPINRSswIIDDFpIVtKKNrmnHmcQdX2xIVNjw5B7Iim7PtHYj6DQ?key=Fzv_zuKop8N473NG043a0A") Source: Semrush Of course, it’s not just ChatGPT. Google Search potentially faces another vector of competition as Gen Z increasingly[ search ](https://www.emarketer.com/content/gen-z-tiktok-instagram-product-discovery?ref=mbi-deepdives.com)on TikTok and Instagram. A recent AlphaSense expert interview of a former Product Lead at Meta (free trial link for AlphaSense [here](https://www.alpha-sense.com/mbi/?ref=mbi-deepdives.com)) I came across discussed this topic at length. The expert highlighted how the search activity on IG vs Google are fundamentally different: > Instagram was never built to serve utility. Now, with Meta AI layered into this, it is starting to serve some of it. Now, the point is that people do search on Instagram but not the way that they search on Google. It's not a hardcore utility. Instagram has always been a discovery mechanism. > > Here, the searches, if you look at the kind of searches people do, as I said, it's not "I want to buy a Nike shoe," and "Give me Nike shoes." It is like, "Give me inspiration for summer outfits," or "Give me healthy meal preparation plans," or "What are the best venues for hiking in summer in London in U.K.?" for instance. These are not in traditional ways, not that utility of search, but it is utility adjacent. The goal is real, but the form of answer is not very specific. > > Meta knows that users won't replace Google for let's say, "What is the capital of United Kingdom," or things like that. People never search for that on Instagram, and Meta knows that. That is why that's not the goal. The goal is for every interest-led, creator-first or visual topics there is where the objective is to make results more relevant and give fast summaries and reduce friction. You can think of it as not replacing Google but actually consuming the unsolved cases, like beauty, fashion, lifestyle. When asked to stack-rank the verticals that matter most for Instagram search volume, the expert mentioned five categories (in that order): a) beauty and skincare, b) fashion and apparel, c) Pop culture, entertainment, d) Local discovery, e) Creator-led commerce. So, while Instagram and TikTok do seem to be helping users discover new products/services not just through their ads but also through search queries (and perhaps through AI chats in the future), Google does seem to have a steady position of answering high intent and more utility type queries. Speaking of utilities, one thing that I’m increasingly confident about is that the AI chat bots are gradually creating deep consumer surplus as they keep launching new features and consumers get acquainted with more and more use cases over time. These really aren’t mere amorphous query box; these feel much, much deeper than that. I’ll share a particular feature in Gemini that recently blew me away behind the paywall. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Maravai 2Q'25 Update URL: https://www.mbi-deepdives.com/mrvi2q25/ Last updated: 2025-08-13T14:06:30.000Z I cannot recall a company’s stock going up 30% after management decided to withdraw its previously shared guidance. Of course, despite the 30% pop after Q2 earnings, most shareholders likely feel like the below meme! ![](https://substackcdn.com/image/fetch/$s_!0xyV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F15dc8229-ad04-470e-b5f3-cf91b104de99_500x707.png) There are, however, good reasons why the stock reacted like that. I will share the rest of the update behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Golden age of Digital Ads, LLM P&L URL: https://www.mbi-deepdives.com/golden-age-of-digital-ads-llm-p-l/ Last updated: 2025-08-12T13:08:03.000Z # Golden age of Digital Ads Last month, some researchers at Meta published a very interesting [paper](https://arxiv.org/pdf/2507.21983?ref=mbi-deepdives.com) that highlighted Meta’s ability to monetize LLMs through its core advertising platform. Meta management sort of alluded to some of these during earnings calls, but this paper really drove the point home for me. Meta has a feature named “Text Generation” in their Ads Manager tool. The Text Generation tool helps advertisers put in their own ad text i.e. their original idea, and then the LLM behind it suggests several different variations of that texts (see the figure below). You can think of it as a brainstorming partner for the advertiser but the advertiser retained full control. They could use it for inspiration, edit heavily, or just stick with their own original text. ![](https://substackcdn.com/image/fetch/$s_!bAZf!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F962a63ae-571b-4d64-a0d8-436aeef92691_1405x681.png) [Image Source](https://arxiv.org/pdf/2507.21983?ref=mbi-deepdives.com) This paper talks about Reinforcement Learning with Performance Feedback, or **RLPF**, and a model called AdLlama. Instead of imitating how people write, the system optimizes for **what people do**. Unlike writing a poem or something subjective, ad text effectiveness can be tied to something very concrete and measurable: click-through rate **or CTR** which is basically clicks divided by impressions. It's a direct and quantifiable measure of engagement, so no opinion needed. The paper talks about a two-step process. First, they train something called a **performance reward model**. This model's job is to predict the CTR of any given piece of ad text based purely on historical data. They used loads of past ad data, where the only difference between ads shown to similar audiences was the text itself. This let them create pairs. This text got a higher CTR than *that* text, so they could directly compare text performance. See the below diagram to get a sense of the tests. Feeding millions of these comparisons into the reward model teaches it to score ad text based on its likely click performance. Think of it like millions of Facebook users voting with their clicks over time. Then step two, they use this reward model to fine-tune the base LLM, which was Llama 2 Chat 7B in this case. The goal is to adjust the LLM so that it becomes more likely to generate text that the reward model gives a high score to**.** So, it's actively trending to write text that will get more clicks based on what the reward model learned from real user behavior. ![](https://substackcdn.com/image/fetch/$s_!KVbz!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85fdbcfa-1507-4213-a3a6-c006e3924666_1266x751.png) [Image Source](https://arxiv.org/pdf/2507.21983?ref=mbi-deepdives.com) They ran a big A/B test on Facebook. The test ran for 10 weeks early 2024, and involved almost 35k advertisers and generated \~640k ad variations. Advertisers were randomly split. Half got the old system, the Imitation LLM V2\. That was the control group. The other half got the new system, AdLlama, trained with RLPF, the test group. The main result was a statistically significant **6.7% increase in CTR for the advertisers using AdLlama** compared to the ones using the old imitation model. Getting 6.7% gain in CTR on a platform of Meta’s size, especially that's already so highly optimized is pretty impressive. More importantly, they found AdLlama increased the total clicks per advertiser, but didn't just do it by showing ads more often. The total impressions weren't affected. The ads themselves were just genuinely more effective at getting clicks. Moreover, they found advertisers using AdLlama actually generated **18.5% more ad variations** which likely indicates the advertisers found the outputs more useful and more aligned with their goals. So they were more willing to adopt them and create more versions for their campaigns. How much improvement is still possible? Well, the theoretical limit for CTR is 100%! Joking aside, it’s certainly possible for further optimization as model quality improves over time. I will again highlight some key excerpts by Zuck from Meta’s recent earnings call: > “we're seeing with teams internally being able to **adapt Llama 4 to build autonomous AI agents that can help improve the Facebook algorithm to increase quality and engagement are like -- I mean, that's like a fairly profound thing if you think about it**. I mean it's happening in low volume right now. So I'm not sure that, that result by itself was a major contributor to this quarter's earnings or anything like that. But I think the trajectory on this stuff is very optimistic. > > I think that **for developing superintelligence, at some level, you're not just going to be learning from people because you're trying to build something that is fundamentally smarter than people**. So it's going to need to learn how to -- or you're going to need to develop a way for it to be able to improve itself. > > So that, I think, is a very fundamental thing. That is going to have **a very broad implications for how we build products, how we run the company, new things that we can invent, new discoveries that can be made, society more broadly**. I think that that's just a very fundamental part of this.” Of course, this is not a Meta specific innovation. I expect almost all digital advertisers to benefit from this, and the ad dollars will likely accelerate from other channels to digital ad platforms. Beyond the ad platforms, the paper hinted at some broader implications which I found to be quite interesting. The core principle of RLPF, using real-world performance metrics to train the AI, could apply anywhere you have language linked to some kind of measurable outcome**.** From the paper: > “The ability to generate more engaging ad content not only improves existing advertisers’ return on investment, but could also **lower the barrier to entry for new and inexperienced advertisers** (e.g., small businesses) by reducing the need for extensive marketing expertise and resources. > > …the principles of RLPF can be **adapted to other domains where aggregate performance metrics are available**. By using performance data as a feedback mechanism, organizations can fine-tune LLMs to optimize for their desired outcomes. For example, the core methodology can easily be extended to closely related settings like personalized email campaigns or e-commerce product descriptions. RLPF can also be extended to settings with multiple rounds of interactive feedback, such as AI customer support agents, using metrics like resolution rates, satisfaction scores, or user response times. There are also less obvious settings where RLPF could be applied. For example, in online learning platforms, student performance data (test scores and engagement metrics) could guide the generation of adaptive learning content, while for certain public awareness campaigns (e.g., vaccination, energy consumption), performance data could enable LLMs to rewrite communication materials to better resonate with their intended audience.” --- # LLM P&L Stripe Co-founder John Collison recently interviewed Anthropic Co-founder Dario Amodei. It’s an interesting [interview](https://cheekypint.transistor.fm/5/transcript?ref=mbi-deepdives.com), and I recommend listening/watching. There were quite a few quotable sections but I found the below excerpt particularly interesting: > John Collison: > I would love to understand how the model business works, where you invest a bunch of money up front in training, and then you have this fast-ish depreciating assets, though maybe with a long tail of usefulness, and hopefully you pay that back. Thus far, I think the image people have from the outside world is ever larger amounts of CapEx, and how does all that— > > Dario Amodei: > Get kind of burned. There's two different ways you could describe what's happening in the model business right now. So, let's say in 2023, you train a model that costs $100 million, and then you deploy it in 2024, and it makes $200 million of revenue. Meanwhile, because of the scaling laws, in 2024, you also train a model that costs $1 billion. And then in 2025, you get $2 billion of revenue from that $1 billion, and you've spent $10 billion to train the model. > > So, if you look in a conventional way at the profit and loss of the company, you've lost $100 million the first year, you've lost $800 million the second year, and you've lost $8 billion in the third year, so it looks like it's getting worse and worse. If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million, and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume, in this cartoonish cartoon example, that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model, in this example, is actually profitable. > > What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's much more expensive and requires much more upfront R&D investment. And so the way that it's going to shake out is this will keep going up until the numbers go very large and the models can't get larger, and then it'll be a large, very profitable business, or, at some point, the models will stop getting better, right? The march to AGI will be halted for some reason, and then perhaps it'll be some overhang. So, there'll be a one-time, "Oh man, we spent a lot of money and we didn't get anything for it." And then the business returns to whatever scale it was at. > > Maybe another way to describe it is the usual pattern of venture-backed investment, which is that things cost a lot and then you start making it, is kind of happening over and over again in this field within the same companies. And so we're on the exponential now. At some point, we'll reach equilibrium. The only relevant questions are, at **how large a scale do we reach equilibrium, and is there ever an overshoot?** It’s always tricky to take such excerpts literally especially in a free flowing conversational format such as podcast, but part of me wonders whether Amodei was particularly generous in his assumptions about profitability by model cohorts (who knows!). More importantly, model developers enjoy far from a monopolistic market today which can push price per token down faster than cost per token. Frankly speaking, when I observe each part of the chips or even cloud value chain, model developers seem to operate under far more competitive intensity than anyone else. The entire chip value chain is dominated by a bunch of monopolies or duopolies. Hyperscalers such as Google are much more vertically integrated than any other SOTA model developers. So, even when we do reach the equilibrium, I’m not sure we can be so confident that SOTA model developers will just be able to pull the trigger and start milking the “final model run” for eternity. It could even be the opposite. Once competitors realize the “final model” has been run and it’s time to focus on efficiency and deep optimizations, I have hard time believing an amicable outcome for every surviving SOTA model developers. Amodei did point out that he believes models are much more differentiated than hyperscalers, so presumably if hyperscalers can generate \~20-40% operating margin, SOTA model developers should be just fine. From the podcast: > Dario Amodei: > So often I'll talk about the platform and the importance of the models. For some reason, sometimes people think of the API business and they say, "Oh, it's not very sticky." Or, "It's going to be commoditized." > > John Collison: > I run an API business. I love API businesses. > > Dario Amodei: > No, no, exactly, exactly. And there are even bigger ones than both of ours. I would point to the clouds again. Those are $100 billion API businesses, and when the cost of capital is high and there are only a few players... And relative to cloud, the thing we make is much more differentiated, right? These models have different personalities, they're like talking to different people. A joke I often make is, if I'm sitting in a room with ten people, does that mean I've been commoditized? > > John Collison: > Yes, yes, yes. > > Dario Amodei: > There's like nine other people in the room who have a similar brain to me, they're about the same height, so who needs me? But we all know that human labor doesn't work that way. And so I feel the same way about this > > …we're like one of the biggest customers of the clouds, and we use more than one of them. And I can tell you, the clouds are much less differentiated than the AI models These are interesting point of views, but I’m not sure I fully buy it. Cloud lock‑in comes from data gravity, IAM/policy, networking, compliance, and dozens of interdependent managed services. “Model personality” may be more of a durable differentiator in consumer use cases, but I’m less convinced when it comes to enterprise use cases where the decision makers may not necessarily be actual end users, especially when we may be talking about billions of dollars in API bills. --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### ChatGPT's Edge, Tech Talent Retention URL: https://www.mbi-deepdives.com/chatgpts-edge-tech-talent-retention/ Last updated: 2025-08-11T13:34:37.000Z GPT-5 had bit of a mixed reaction. I was initially excited about the much cleaner UI (no more selecting models; to be fair, I was using o3 for 95% of my queries anyway). However, it didn’t take me too long to realize GPT-5 is “thinking” considerably less than o3 in most of my queries as it started making some rookie mistakes. Given my personal preference for o3 and reasoning’s apparent superiority to non-reasoning models, I was a bit surprised to read the following [tweet](https://x.com/sama/status/1954603417252532479?ref=mbi-deepdives.com) from Sam Altman yesterday: > "the percentage of users using reasoning models each day is significantly increasing; for example, for free users we went from <1% to 7%, and for plus users from 7% to 24%. > > i expect use of reasoning to greatly increase over time, so rate limit increases are important." I am seeing two things at once in that tweet. First, there is clear latent demand for deeper “reasoning” once people bump into tasks that need it. Second, the product has gained a massive adoption **without** it. Most day-to-day jobs to be done are simple asks, lookups, rewrites, and summaries. If a free user can get value from that shallow band of use, they form a habit around the entry point, not the model class. The mental shortcut becomes “open ChatGPT” rather than “pick the best model.” Good enough wins the default, and if we know anything from Google’s history, the “default” compounds over time. ChatGPT already has a giant top of funnel, a known UX, and months of chat history, saved prompts, and personal setup that is quietly raising switching costs purely from customer habit perspective. Even if a challenger ships a model that benchmarks better, the gap must be **large and obvious** in everyday tasks to overcome habit, trust, and distribution. Most consumers will not A/B test models. They will notice whether the answer appears quickly, sounds right, and does not break. As rate limits rise and reasoning improves, users push heavier work into the same front door instead of seeking a new one. That pulls more of the market into ChatGPT’s gravitational field and raises the threshold a rival must clear. If ChatGPT ever falls behind on raw quality, the brand buys time, and time can prove to be enough to close the gap because users default to the place that already works for their lighter tasks. Big tech today pay a premium to stay on the frontier because it buys independence and a shot at the next default surface. That premium has a limit. If the probability-weighted value of winning a new interface or agent layer drops, or if OS-level distribution and enterprise bundling make displacement **unlikely**, the option is overpriced. At that point it may be more rational to ride fast-follower strategies. When rivals can replicate 95% of capability with smaller models and better orchestration, super-linear capex may just buy bragging rights rather than cash flow. The capex ceiling will arrive at different levels for each firm. Hyperscalers can amortize capex across customers, so their limit may be higher. Consumer platforms with mostly internal monetization may hit diminishing returns sooner. The constraint may not be “can we dislodge ChatGPT in consumer chat,” rather it may be “does the next tranche unlock a step-change use case or structural cost edge.” When the answer becomes no, rational CFOs should taper frontier capex and shift spend to utilization, inference efficiency, and productization. It certainly feels like the clock is ticking for Big Tech. It will be very hard to divert existing ChatGPT users to a new model, but it may be still be fair game at least in consumer use cases for the **incremental users**. I have mentioned TikTok-Meta saga before; TikTok didn’t really lose the existing user base to Reels. It’s just their growth trajectory slowed down as by the time Reels became a pretty decent product for IG’s existing userbase, they didn’t need to install another app to get more or less a similar thing. It’s also why OpenAI may not quite be out of the woods yet, especially if their model quality lags behind big tech. GPT-5’s reaction so far has likely been underwhelming and if the future incremental users notice the consensus opinion is shifting to ChatGPT is behind in model quality compared to Google/anyone else’s AI offerings, ChatGPT can still face potent competition in consumer space even if the current existing users remain loyal. Ultimately, there are still far more AI DAUs up for grabs than the DAUs ChatGPT has today. Enterprise users can prove to be much more “rational” and less beholden to “habits”. Hence, the competitive dynamics can evolve there for much longer than we may see in consumer. To say it differently, if you want to put a dent in ChatGPT’s adoption for consumer use cases, you probably have max 2-3 years left before the train may permanently leave the station, but the fight for market share in enterprise use cases can remain open. --- # Tech Talent Retention SignalFire [published](https://www.signalfire.com/blog/whos-winning-the-engineering-talent-war?ref=mbi-deepdives.com) some interesting data around tech talent retention. One of the metrics they mentioned is “hiring-to-attrition ratio” which tracks how many engineers a company hires for every one who leaves. From the report: > The chart below highlights who’s pulling ahead. These companies are successfully scaling engineering headcount by **hiring faster than they’re losing talent**. > > Anthropic, OpenAI, and Meta are growing engineering teams 2-3x faster than they’re losing them, while Tesla, Bloomberg, and Walmart are losing talent faster than they can hire. > > Companies above the 100 mark are **retaining and scaling sustainably,** while those below are backfilling losses or burning out their teams. Of course, Anthropic and OpenAI are relatively new companies, so I’m not sure how comparable they are relative to the incumbents on this list. I used to hear how Meta’s employer “brand” is damaged in Silicon Valley 3-4 years ago, but I guess a rising stock price can heal most things. ![](https://substackcdn.com/image/fetch/$s_!VbBA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb659a0b1-0930-4dd4-8069-bec437caca15_1017x700.png) Long-term (4-year) retention in tech companies has gradually fallen over the last decade; still, more than half of the engineers stay at the same company for four years. The report also highlighted the recent falling retention rate may just be more of a function of what we experienced in the last five years instead of a secular theme: > Considering the last five years of chaos with a global pandemic, remote-work shifts, return-to-office whiplash, layoffs, reorgs, and now the AI gold rush, that’s surprisingly stable. It points to a deeper truth: **strong engineering cultures still stick. The best teams are holding onto top talent, even as the market churns.** ![](https://substackcdn.com/image/fetch/$s_!cttg!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd710423f-9c34-4175-9339-122123b9e57e_1021x577.png) --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### CoStar Multifamily vs Zillow Rentals URL: https://www.mbi-deepdives.com/costar-multifamily-vs-zillow-rentals/ Last updated: 2025-08-10T14:39:55.000Z Each quarter, I closely track Zillow rentals revenue to gauge how the competitive dynamics between Zillow Rentals and CoStar multifamily segment (mainly Apartments.com) is evolving. At first glance, it may appear Zillow Rentals is really eating Apartments.com’s lunch for the last few years as Zillow Rentals revenue increased from \~39% of CoStar multifamily revenue in 2Q’22 to \~54% in 2Q’25. ![](https://substackcdn.com/image/fetch/$s_!AT3D!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1c6dabfe-7d33-4e90-9f15-eff264ba7b8c_990x525.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) However, there is a bit more nuance to this. On February 6, 2025, Zillow entered into a partnership with Redfin to become exclusive provider of multifamily rental listings on Redfin and its sites, including Rent.com and ApartmentGuide.com. While we don’t know exactly how much incremental revenue came from this new partnership, I would venture a guess of \~$10 million looking at incremental revenue trajectory history of Zillow rentals. ![](https://substackcdn.com/image/fetch/$s_!XXPC!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F86647e5d-2503-4946-85ea-41bca9f7de11_969x595.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Zillow’s Rentals revenue was $159M in 2Q’25, up 36% year over year. Multifamily led the charge: revenue up 56% and advertised properties up 45% to \~64k. That gap implies revenue per multifamily property rising roughly 7–8% year over year. Management says growth is coming from both **more properties** and **upgrades to higher-priced packages.** During the call, they did mention “*We're also gaining wallet share with large property managers*” Andy Florance at CoStar likely knew that his shareholders may fret over these numbers, so he addressed this during the recent [earnings cal](https://www.mbi-deepdives.com/csgp2q25/)l: > “I think we're conflating 2 different things here. Obviously, the product is extremely strong with very high NPS, renewal rates, growing bookings, robust sales bookings, growing ASP. And that's been conflated a little bit with looking at a lot of **purchasing clients by our competitor paying top dollar to buy share from Redfin and from Realtor**. That's relatively low quality advertisers coming in.” Indeed, historically Zillow never had much problems with generating revenue. Their only persistent issue was making actual profit (or even defining what actual profits is). If you look closely to this Redfin deal, it’s fair to wonder about the quality of this revenue stream. Zillow paid Redfin **$100 Million** up front for the partnership which is amortized over nine years. This is, of course, a very real expense but conveniently added back to Zillow’s adjusted EBITDA calculation. Moreover**,** Zillow needs to make ongoing payments to Redfin for leads for an initial five-year term (with **two** **optional two-year** extensions). Practically, that means unit economics include a fixed amortization drip and a variable (not disclosed) cost-per-lead layer against each dollar of multifamily revenue. Some CoStar bears/Zillow bulls argue that Florance knows Zillow is becoming a much fiercer competitor in multifamily rentals, so he felt forced to go after Zillow’s core business. As a result, the eye-watering investments in Homes.com is not necessarily an opportunistic or offensive investments, rather a defensive strategy to protect its attractive profit pool in Apartments.com. It’s hard to know exactly what prompted Florance to go after residential segment, but put yourself in Florance’s shoes. Imagine opening Zillow’s financial statements in one fine morning and discover that this must be world’s worst run “monopolies” as they generated losses after losses every single year in the last decade. ![](https://substackcdn.com/image/fetch/$s_!44sC!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffd0f25a6-b7d1-4c59-9588-90234125c2f2_928x567.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) In case you think GAAP EBIT is not depicting a fair picture, let me point out that in the last 10 years, Zillow’s **cumulative** **operating cash flow** was $2.2 Billion. However, during this period, their cumulative SBC itself was $2.5 Billion. So, basically they generated no real cash at all. Zuckerberg had a terrific quote more than a decade ago to encapsulate the state of Twitter: “*Twitter is such a mess, it’s as if they drove a clown car into a gold mine and fell in.*" You could perhaps say the same about Zillow. So, I find it more convincing to think a founder such as Florance would think he may have an opening to really go after Zillow, especially during a regulatory upheaval to see if he can get a piece of the goldmine and then run the business much more profitably than Zillow management ever could. He does seem quite willing to make life difficult for Zillow (see the recent [lawsuit](https://www.mbi-deepdives.com/meta2q25/)). I do find the rivalry at times quite funny; just see the email I received from Homes.com yesterday: ![](https://substackcdn.com/image/fetch/$s_!ZOtJ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F45526fff-f6f7-4aa7-bdd3-f387b66040e5_562x1080.png) Source: Homes.com marketing email --- *In addition to "Daily Dose" (yes, *DAILY*) like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Shopify's Surprise, XPEL 2Q'25 URL: https://www.mbi-deepdives.com/shopifys-surprise-xpel-2q25/ Last updated: 2025-08-09T15:00:31.000Z # **Shopify’s Surprise** I started buying Shopify in February 2022 in the $80s. I eventually averaged down which led to my average buy price to be \~$39/share, but the stock kept going down and found bottom in the mid-20s. I eventually sold my position in mid-$40s (sold at various prices between 30 and 50, but average was in the mid 40s) in February 2023\. After just two and half years, the stock is now at \~$150\. I’m sharing this history because I wanted to discuss a particular point. It’s quite common among investors to stare at a stock price chart and lament about missing the large gains. But more often than not, these frustrations are often quite misplaced. Of course, in hindsight I look a bit stupid to sell the stock at mid 40s, but take a look at what happened to GMV growth after I sold i.e. 1Q’23\. Obviously, I never imagined the Shopify would grow at such an astonishing pace. Why couldn’t I imagine that? Whatever the reason, if someone modeled this growth trajectory I would probably think “You drank the Tobi Kool-Aid a bit too much. You need to calm down a bit.” ![](https://substackcdn.com/image/fetch/$s_!WU9A!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F00754128-0cfd-43f6-82a5-27b4599e3c1e_1095x613.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) In fact, I was wondering that Shopify might be graduating from high-flying GMV growth stock to HSD to LDD GMV growth in the next few years which will put them under the scanner to improve their margins to drive earnings growth to sustain their lofty multiples. Again, look at the below chart that I very closely track: Shopify GMV added YoY as a percentage of Amazon GMV added YoY. Shopify truly punched above its weight during the pandemic and came down to more historical level of “market share” relative to Amazon. To my utter astonishment, they have now gone back to their heyday of “market share” gains relative to Amazon. Perhaps a lot of it was always just the ATT impact. Once Meta recovered the signal, Shopify coasted through it (and then some) and the slowdown in 2022-23 proved to be a false signal. Looking at these two charts makes me feel there is something fundamental about Shopify that I have never quite appreciated enough and it is perhaps only “**fair**” that I didn’t get to enjoy those large gains without such insight and appreciation. ![](https://substackcdn.com/image/fetch/$s_!PJjV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5a82079a-c59e-4945-a979-31751b3094ec_1081x663.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) It wasn’t just GMV growth acceleration, Shopify achieved this by largely maintaining their take rates and improving their operating margins substantially since I sold the stock. Their gross margin remains stuck around \~50% and you may not need to drink too much Tobi Kool-Aid to think \~20% operating margin is possible at scale. ![](https://substackcdn.com/image/fetch/$s_!7BJ7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffc2a39bc-31f3-4b87-b557-80a817a926b0_1915x184.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Of course, let’s not forget the stock currently trades at \~120x LTM EBIT. But I don’t want to give an impression that’s the real story behind my missed profits. The real reason is I am utterly astonished by the company’s operating performance in the last couple of years. My hats off to Shopify management! 🫡 --- *In addition to "Daily Dose" like this (yes, *DAILY*), MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- # XPEL 2Q’25 I will share my earnings recap of XPEL behind the paywall. _This post is for paying subscribers only._ ### Digital Advertising Snapshot, Corpay 2Q'25, Portfolio Change URL: https://www.mbi-deepdives.com/digital-advertising-snapshot-corpay-2q25-portfolio-change/ Last updated: 2025-08-08T13:23:30.000Z # **Digital Advertising Snapshot** As is the tradition in every earnings season, I am sharing my digital advertising Snapshot after 2Q’25 earnings. Three key takeaways: 1. For the **nine consecutive quarters**, Meta has outpaced Google in terms of incremental share. If Meta’s guide is any indication, that will almost certainly continue in the next quarter as well. 2. It is incredible that Meta and Amazon grew **faster** last quarter compared to much smaller companies such as Pinterest, Snap, and Trade Desk. What a remarkable exhibit of increasing returns to scale. 3. AppLovin, a company I haven’t studied, seems to be operating in entirely different stratosphere than anyone else. If anytime you think Meta’s margins are too lofty, take a look at AppLovin (not shown in the dashboard below). If Meta’s margins appear too high, AppLovin’s margins are basically obscene. A couple of subscribers asked for the spreadsheet for the dashboard; I will share the spreadsheet and recap CorPay 2Q’25 earnings behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!zVrh!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9bda1398-8815-4468-bbe1-05024aee82b2_1209x915.png) \*Digital Ads Market is defined as Alphabet reported ad revenue+ Meta's ad revenue+ Amazon Ads+ Microsoft Search+ Pinterest+ Snap+ Trade Desk+ AppLovin revenue; Source: Company Filings, MBI Deep Dives, Daloopa --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- Spreadsheet: _This post is for paying subscribers only._ ### Airbnb 2Q'25 Update URL: https://www.mbi-deepdives.com/abnb2q25/ Last updated: 2025-08-07T15:34:51.000Z Airbnb had a decent quarter. While nights booked decelerated to 7%, both Gross Booking Value (GBV) and revenue increased by double digit rate. Q2 started with a lot of economic uncertainty from tariffs but demand on Airbnb accelerated from April to July. However, the company cited impending hard comps for Q3 and Q4 which soured the sentiment about outlook a bit. The stock went down \~8%, but I think it’s quite interesting time at Airbnb. I’ll explain more, but let’s first look at the Key Performance Indicator (**KPI**s) first. ![](https://substackcdn.com/image/fetch/$s_!HPyB!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2785d817-5779-4358-ab9f-cc41aec6c4b5_1666x364.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Region** Although Airbnb doesn’t report specific segment details, Airbnb discusses its business in two broad segments: core, and expansion markets. They have only 5 **Core** markets: US, Canada, Australia, France, UK. And they identify the following countries as “**Expansion”** markets: Spain, Italy, Germany, Mexico, Brazil, China, India, Korea, Japan. They didn’t mention this in this call explicitly, but did mention in 1Q’25 call that \~70% of their business is still within the core markets. For the last six consecutive quarters, nights booked on expansion markets has grown at **twice the rate of core markets**. That bodes well for the Airbnb bull case that Airbnb will become much more mainstream over time across many more countries beyond just five. Again, we don’t know exactly what the “core” market grew at, but if we just look at North America region, unfortunately it hasn’t grown at double digit rate for the last seven consecutive quarters. So, such growth rates in expansion markets may be slightly less impressive than it appears at first glance. ![](https://substackcdn.com/image/fetch/$s_!bC93!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc658fb8f-f711-4552-949c-1543329e0c28_1657x367.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) So, the big question is whether Airbnb has been “saturated” in the US? Airbnb has upgraded its total price display, and pricing tips yet still needs more work to ensure strong value and broader demographic appeal in the United States. It is gaining traction with previously underpenetrated groups, especially Hispanics and consumers in Heartland states. Additional efforts to improve usability and expand payment choices are expected to lift US performance further. But when you don’t grow at double digits for seven consecutive quarters, the question of saturation will only get louder every quarter. Europe is another important region for travel where Booking is much fiercer competitor than they are in the US. While homes are the soul of Airbnb, management seems to be leaning to boutique hotels as well, especially in Europe. From the call: > We've spent a lot of time looking at hotels as a business. We think it's really compelling, and we think that there's going to be a lot more to do with hotels on Airbnb. Our take rate is very, very competitive. We've spoken with hotels around the world, especially independent boutiques in bed and breakfast, a huge percent of hotels in Europe are independents. And one of the things they said is **they really want incremental travelers**. They know that they have another booking channel. They would love to have high-income American young travelers. We're probably the biggest travel brand in the United States. So I think we're really, really compelling. And when we have -- I think homes and accommodations homes will be the heart and soul of Airbnb. But that being said, in our top markets, top markets, especially during high season, **people often don't find a home. We think hotels would be a great supplement**. Brian Chesky does seem to understand that the current trajectory isn’t good enough: > “We are looking to reaccelerate the growth of Airbnb. **We are not satisfied with the company growing approximately 10% year-over-year**. We want the company to reaccelerate.” For someone who basically originated the “[Founder Mode](https://www.paulgraham.com/foundermode.html?ref=mbi-deepdives.com)” saga, it is certainly less than ideal if your business is limping around \~10% growth rate and is actually being beaten by a metrics driven machine called Booking. Indeed, for the last three consecutive quarters, Booking has grown at a faster rate than Airbnb despite its revenue being more than double the size of Airbnb’s. ![](https://substackcdn.com/image/fetch/$s_!KVh1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff33ed2cb-0cc6-47af-890f-d0f46a85f16e_1659x187.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Margins** Airbnb is a very profitable business with low to mid-20s LTM EBITDA margin. Please note I do not add back SBC for this EBIDA calculation and it is a better metric than FCF given the structural working capital benefit. Moreover, considering their humongous cash balance, I wouldn’t want to capitalize interest income either. Their LTM EBITDA margins peaked at 25.4% in Q1’24 and since then, margins have been under pressure. In recent quarters, they have been investing in their recent experiences and services launch which was a further headwind to their margins. One particular cost line item that is worth highlighting is “Operations and Support” which used to be \~12-15% of revenue but came to only 10.7% of revenue in 2Q’25\. Airbnb mentioned they’re using AI customer service agent which reduced the percentage of hosting guests who need to contact a human agent by 15%. I expect they will get further leverage here as they roll it out over other markets in coming years. ![](https://substackcdn.com/image/fetch/$s_!RNy0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F38f41786-ed3b-460b-b49e-4ccc67c8b2a4_1657x676.png) \*Yellow colored cell indicates I have subtracted $935 mn non-recurring tax withholding expenses and lodging tax reserves; Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AI** Speaking of AI and customer service, Chesky has some interesting bits on how AI will impact travel. From the call: > We've chosen a very specific way to approach AI. A lot of companies have chosen what I would say is the lower stakes part of travel, which is travel planning and inspiration. For AI, we actually start with the hardest problem, which is customer service. **Customer service is the hardest problem because the stakes are high, you need to answer this quickly and the risk of hallucination is very, very high**, and you cannot have a high hallucination rate. And when people are locked out, they want to cancel reservation, they need help, you need to be accurate. And so what we've done is we built a custom model or we've built a custom agent built on 13 different models that have been tuned off of tens of thousands of conversations. We rolled this out throughout the United States in English. And this has reduced, as I mentioned in the opening remarks, 15% of people needing to contact a human agent when they interact instead with this AI agent. We're going to now, over the course of this year, bring this to more languages. And throughout next year, it's going to become more personalized and more agentic. So what this means is that when you reach out to an agent, the AI agent, it will not only tell you how to cancel your reservation, it will know which reservation you want to cancel, it cancel it for you, and it can be agentic as in it can start to search and help you plan and book your next trip. > > Next year, **we're going to bring AI into travel search**. So all this brings us back to the question you asked about travel planning. **Over the next couple of years, I think what you're going to see is Airbnb becoming an AI-first application**. And this leads to the bigger question around AI. Over the last almost 3 years since ChatGPT spin out, if you look at the top 50 apps in the App Store, almost none of them are AI apps. The #1 app in the App Store, I think, as we speak, is ChatGPT. And if you go through 2 through 50, maybe only 1 or 2 others are AI native applications. So you've got basically AI apps and kind of non-AI native apps. And Airbnb would be a non-AI native application. **Over the next couple of years, I believe that every one of those top 50 slots will be AI apps. either start-ups or incumbents that transform into being AI native apps.** And I think at Airbnb, we are going through that process right now of transitioning from a pre-generative AI app to an AI native app. We're starting to customer service. We're bringing into travel planning. So it's really setting the stage. > > I think that the key thing is going to be for us to lead and **become the first place for people to book travel on Airbnb**. As far as whether or not we integrate with AI agents, I think that's something that we're certainly open to. **Remember that to book an Airbnb, you need to have an account, you need to have a verified identity. Almost everyone who books uses our messaging platform. So I don't think that we're going to be the kind of thing where you just have an agent or operator book your Airbnb for you because we're not a commodity.** But I do think it could potentially be a very interesting lead generation for Airbnb. Indeed, I don’t think Airbnb is commodity. I myself actually enjoy browsing Airbnb very much. It turns out I’m not the only one, and the implications can be profound over the long term. From the call: > We're seeing a giant uptick in the number of people that are booking a home from the homepage on Airbnb. So this has been a major behavioral change from basically the last 17 years of Airbnb's history. **So if you go to most apps, especially OTAs, you open the app and every single person goes essentially to the search box. they type in something in the search box and they enter dates and then they get a bunch of search results. And this is how everyone search for travel over the last 20, 25 years. The holy grail is to get more and more people to be in browse and discovery mode**, almost like on Netflix or say DoorDash. DoorDash was very search-driven. They're now more of a browse and discovery application. And it's been a really hard not to crack within travel, but we think we've done it because what we've seen is that increasingly more and more guests are engaging not just the service experience from a homepage, but with homes. Now this is very strategic. Why is this strategic for us? **The reason why is if people can engage with our homepage rather than typing in a destination, then we can divert travel more broadly to where we have available supply, thereby increasing conversion rate of our traffic**, if this makes sense. **Experiences and Services** Airbnb made a huge splash on their launch of experiences and services. They mentioned in the call that the average guest rating for service and experience since launch is 4.93 stars (vs 4.8 for homes during the same period). They received 60k applications to host a service or experiences. They shared a couple of interesting data points: a) \~40% of bookings for Airbnb Originals are from locals, and b) \~10% of bookings for services are from locals. While these are somewhat encouraging data points, neither of them moves the needle and frankly speaking, I remain skeptical, especially about services. Airbnb again talked about the potential of hiring chefs or masseuse through the app, but I’m not sure why I would want to keep hiring them through Airbnb once I found someone I like. In any case, these are not huge headaches for me. Management does have a right approach here. They’re focusing on a handful of key cities (Paris, LA etc.) and trying to improve attach rates of experiences and services in these key cities. Whatever strategies prove to be successful, they will roll it out over other cities. But in case they cannot make it work, they will be forced to move on. They are spending only $200 million per year on these new launches, so it’s not material enough even if it doesn’t work. But if it does work and my skepticism proves to be unfounded, that’s all the better because as I will show later, I don’t think the stock price assigns much value to these bets anyway. **Ad dollars** Unlike Booking, Airbnb doesn’t spend much on customer acquisitions via Google since \~90% of their traffic is organic in nature. The way management explained how they’re thinking about spending ad dollars in the future can itself be thought as an ad for Meta (only half-kidding). From the call: > We think that probably going forward, the best way to market services and experiences is to actually market the entire offering of Airbnb. So immediately upon the launch, we did launch some Airbnb experiences, specific ads. But this fall, we're going to be launching ads that market home services and experiences, the bundled offering. And we think this is a really, really key principle that only Airbnb offers all of this in one app. And so we don't think that the marketing intensity per se has to increase because we think we get a lot more for our dollar by marketing all of our offerings. > > If you book a home, you're very likely to want a service or experience, so we can market all 3\. The second thing is channel. So that's just the strategy. **We think that the channels for services experiences and homes is increasing in the shift to social.** Now why is this going to be the case? Well, one of the things we're noticing, obviously, in the whole world seeing is that **a lot of travel is switching from desktop to mobile and from Google search to social media**. > > And so increasingly, people are spending time on social media and **social media is gradually taking over as the #1 place for travel search from Google and travel is becoming more of an inspiration base than a high-intent search-based destination platform**. So Airbnb, we think, is really primed for social media. We are probably the most relevant brand for young American travelers that is the kind of heart and soul of kind of the social media audience. And I think that you're going to see a lot more of social media native advertising. **So we're shifting a lot of our advertising from TV to social**. And we are -- and **the great thing about social is we can target. We know a lot more about the customers. We know if they're Airbnb customers. We can actually when they watch an ad, we can link it to inventory and get them to go directly to the app. So it's actually, we think, very, very performative**. So this is what we're going to be doing with marketing. **Capital Allocation** Since Airbnb started buying back stocks in 3Q’22, diluted shares outstanding declined by \~8%. Over the last four quarters, their LTM buyback is essentially more or less the same as their FCF. I do want to highlight that I’m taking FCF just as the company reports it (so I haven’t made any SBC or any other adjustments). However, for buybacks, apart from share repurchases, I have also included taxes paid related to net share settlement of equity awards. I used to not include this, but after a feedback from a subscriber, I decided to add it to buyback as well. The subscriber explained “*it is effectively a share buyback since employees receive shares net of tax and the company pays the cash tax due to the tax authorities. It is equivalent to the company issuing the entire vesting amount of shares to employees, who then sell the number of shares required to settle their taxes back to the company and use those funds to pay their taxes.”* I agree; as a result, going forward, you will see me including this number for other companies as well. ![](https://substackcdn.com/image/fetch/$s_!z8Sl!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8b706981-2f7c-47aa-adbe-91e4d571ab5d_1450x358.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While Airbnb hasn’t done many acquisitions, they seem more open to it now. From the call: > We've historically primarily focused on building organically, but we absolutely are open to acquisitions, and we are going to be looking at it. And I think that **we are now in a better place to consider acquisitions now that we've rebuilt our tech platform from the ground up**, and we have this new expanded strategy where we're focused not just on all aspects of traveling, but also living and so I think there's absolutely acquisitions on the table that we could be looking at. **We always want to make sure that if we do an acquisition, it is one of the most perishable opportunities that the integration costs don't outweigh the benefit of the revenue that we get**. But we are absolutely opportunistic when it comes to acquisitions. **Outlook** Airbnb guided revenue of $4.02-$4.1 Billion revenue in Q3, which implies \~8-10% revenue growth. As mentioned earlier, management also highlighted that their nights booked grew by 8% in 3Q’24 which then re-accelerated to \~12% in 4Q’24\. As a result, they expect tougher comp for Q4\. So, Airbnb will likely to remain confined within HSD to LDD growth for the rest of the year. I will share some thoughts on valuation behind the paywall. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Novelty bias and future of software engineering, Social's growing share in ad dollars URL: https://www.mbi-deepdives.com/novelty-bias-and-future-of-software-engineering-socials-growing-share-in-ad-dollars/ Last updated: 2025-08-06T13:23:59.000Z Alex Telford, who mostly writes on healthcare adjacent topics, recently published this interesting [piece](https://atelfo.github.io/2025/08/03/llms-and-software-development.html?ref=mbi-deepdives.com): “*Thoughts on the future of software development as a non-developer*”. Allow me to digress from the core point of the piece, but the below paragraph stood out to me: > One of the larger workflow changes post-LLMs for me is that **I hardly use Figma** and write detailed design docs anymore because LLMs are such powerful tools for rapidly exploring the product space. If the generated code runs and it feels good to play around with it I’ll expend effort to understand it in more depth and refine it into something potentially mergable. I don’t really care if the LLM writes broken code at this point because I can quickly test and reject it, and doing so doesn’t consume much of my time. Some of our best features have come from this rapid prototyping process. Figma’s S-1 [mentions](https://www.sec.gov/Archives/edgar/data/1579878/000162828025033742/figma-sx1.htm?ref=mbi-deepdives.com) that two-thirds of Figma’s 13 million monthly active users are non-designers. So I took notice when a non-designer writes about “hardly using Figma”. It is a bit startling to think when Adobe is being left for dead by the market, Figma is being bid up to the moon. One trades at \~6x LTM revenue whereas the other is at…45x!! Of course, Figma grows much faster and in any case, this is more of a function of a low-float IPO than perhaps a wider reflection of investor rabid enthusiasm, but even then, I notice this incumbent vs challenger dichotomy way too often in the market. You had to be there in 2022 to appreciate the news flow, but there was a time when TikTok really seemed to be disrupting everybody (according to constant press coverage of their new product/feature launch). Gen Z was searching on TikTok instead of Google, Meta’s properties appeared “uncool” and TikTok was creating trends that uncool Meta users were copying weeks later, and TikTok was even launching music streaming…if you squinted hard enough, you might be able to see a “superapp”. There was very little attention to all the barriers that may show up into their path to glory or how may the incumbents respond over time. Of course, this is 2025…TikTok is still incredibly popular but hardly seen in the light of indomitable spirit of 2022\. I do sense bit of a deja vu today for OpenAI vs Google dynamic. The story that “the old moat is crumbling” is vivid and measurable, while the story that “the new thing will actually scale and keep its economics” is exciting and conveniently fuzzy. Incumbent risks are on the surface. We see their margin mix, product overlaps, and cannibalization path, so we mark them down. Challenger risks are hidden behind NDAs, short track records, and selective disclosure, so we mark them up. New platforms are modeled like call options with uncapped upside and limited apparent downside. That convexity pulls multiples higher even when the base-rate odds of durable dominance are low. Of course, it is safer reputationally to predict disruption than to defend the incumbent. If Google “wins”, we can always point later that Google had unfair advantage in so many ways and you won’t receive any intellectual brownie points for that. But if OpenAI decimates Google moat? You better have some blog posts or at least tweets to show people how you saw this outcome long ago market did. In an era of almost religious conviction from analysts and investors to only buy stocks with “accelerating topline”, investors prefer clean growth stories to messy cash-cow transitions. We remember the few challengers that won and project those paths forward. We forget the many that stalled on distribution, unit economics, or regulation. You can quantify incumbent erosion in detail, but cannot easily quantify the challenger’s future dependence on capex, partners, or regulators, so those risks get hand-waved. So perhaps it’s less of a surprise that investors can simultaneously haircut Adobe for “Figma + AI compress the design stack” while granting Figma a multiple that assumes frictionless scaling. Well, for now at least. Anyways, none of this was the point of Telford’s piece. The below excerpt captures the main essence of his piece: > As useful as they are, considering how much I have to babysit LLMs to get them to produce anything workable the concept of them replacing software engineers in 6-12 months feels totally fake to me. Our codebase is only about a year old and is written in typescript/python (common languages), and already the LLMs struggle with it. I can imagine that people working on decade-old code bases in rare languages get hardly any utility from LLMs at the moment. > > I find the replacement discourse similar to when people suggest that AlphaFold will “solve” drug discovery. Yeah, it’s super useful but there are also a million other things that need to be done. Even for the engineers most of the work these days is [not actually writing code](https://ordep.dev/posts/writing-code-was-never-the-bottleneck?ref=mbi-deepdives.com), but higher order tasks like working with customers to prioritize features or choosing an architecture. Even supposedly simple tasks like configuring 3rd party providers take up a lot of time; LLMs can’t use the Google Cloud Console for me, so far my least enjoyable software engineering task. > > However, LLMs do unambiguously accelerate parts of software development work, and what they will do is exacerbate certain bottlenecks in software development. Both through increasing the scope of products that get built and the amount of code that an organization can generate. > > The first big impact will be that **non-developers can now have the capacity to push *a lot* of code if so they wish**. The debate around software engineering may continue, but software engineers will continue to add a lot of value for the foreseeable future, and software engineering will likely go through plenty of evolution in the next few years as everyone starts using these AI tools to write code. --- **Social’s growing share in ad dollars** Rentokil, valued $18 Billion in Enterprise Value, is a global services company specializing in pest control, hygiene, and facility management solutions. Their recent earnings call had some interesting tidbits how they’re spending their ad dollars. From the call: > “…we need to improve our inbound lead generation through a broader range of marketing and brand initiatives. **Historically, we've been overly reliant on paid for digital channels, which whilst effective in many respects, has limited our overall lead generation potential and has incurred higher associated cost per lead**. Our renewed focus is on a broader range of full funnel lead growth activities. In the second quarter, **spend has been refocused on awareness channels like Meta and YouTube.** > > …we did talk about the fact that we became **over-reliant on paid search**, and that's because we weren't yet able to get the organic channels working as we needed to. So what you've seen **over the last 2 quarters, and in particular in Q2, is a progressive move by us to take money out of paid search and put it into organic search and into other broader channels, like I say, with Meta and top of funnel marketing advertising as well. So it's a migration of spend.** We've kept the spend broadly the same across the period, but spending less in the paid area, but also happily the cost per lead coming down in the paid. As explained [yesterday](https://www.mbi-deepdives.com/the-humorless-and-timeless-machine-of-booking-holdings-portfolio-change/), paid search is still the most reliable way to harvest explicit intent and capture bottom-funnel demand. When the query is “rent termite control now,” search beats social on speed and often on ROAS. Google is also pushing its own automation (e.g., PMax) and retail-style ad products to defend its turf, and search/retail media themselves are still set to grow. Of course, given the size of the ad verticals, Booking is lot more reliable signal than Rentokil. Therefore, while for Rentokil it’s “migration of spend”, I think the overall pie is getting bigger; **social is likely just taking more of the next dollar rather than taking the last dollar.** Nonetheless, it’s an interesting data point. While it’s not quite relevant to Rentokil’s products/services, it does seem consumer behavior is drifting toward “entertainment-led shopping” and creator-driven discovery; that naturally routes budgets into Meta, YouTube, and TikTok as the places where demand is sparked rather than harvested. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### The "humorless" and timeless machine of Booking Holdings, Portfolio Change URL: https://www.mbi-deepdives.com/the-humorless-and-timeless-machine-of-booking-holdings-portfolio-change/ Last updated: 2025-08-05T14:17:23.000Z Almost three years ago, my friend Scuttleblurb [described](https://www.scuttleblurb.com/abnb%5Fbking/?ref=mbi-deepdives.com) Booking in a way that I believe truly encapsulated the essence of the company: > Booking is metrics – it iterates on performance marketing game mechanics and humorlessly monitors every bit of performance down the hour. Indeed, I doubt there is any better company out there which perhaps turned every art of performance marketing into its most “scientific” form. Therefore, how they invest their precious ad dollars often piques my interest. Their most recent earnings call left some clues how they think about it. Performance **search** remains the travel industry’s most durable acquisition engine because it captures the moment of highest commercial intent. Google now lets a traveler move from “Paris boutique hotels under $300” to price-filtered results, reviews, and a booking call-to-action without ever leaving the search environment. Take reviews for example. Some studies show [88%](https://www.siteminder.com/r/hotel-reviews-manage-online-property/?ref=mbi-deepdives.com) of travelers filter out hotels with an average star rating below three, and Google [captures](https://wisernotify.com/blog/google-review-stats/?ref=mbi-deepdives.com) majority of the reviews written on the internet. ![](https://substackcdn.com/image/fetch/$s_!f4XX!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3027d4ea-0fc6-46b2-805f-8e16a19bfa1e_2134x1243.png) Source: Google A vast majority of hotel decisions still start with a search, underscoring how embedded this habit is in the purchase journey. Google is also stimulating instead of cannibalizing that habit through its AI features in search, effectively enlarging the funnel that performance marketers such as Booking can bid on. Against that backdrop it is less surprising that Booking’s Q2 call noted its Google clicks “continue to hold up quite well” and are still growing YoY for accommodations: > …if you look at the performance marketing channels, it's actually, to some extent, interesting that the Google clicks continue to hold up quite well. Actually, **they're still growing for accommodations**, slightly still period-over-period. So we don't see yet a decline in that. Booking’s deep connectivity gives it near-real-time rates and room types, so its quality score on Google stays high and CPC inflation is kept in check. Moreover, attribution still favors last-click channels. Even when users browse on social or in an AI assistant, many may finish the transaction on Google, where Booking’s brand-bidding strategy secures the converting click. As long as these mechanics persist, Google will keep delivering predictable, high-margin traffic. If search wins on intent, social wins on inspiration and that part of the funnel continues to gain momentum. From the call: > But we would like to, of course, really **diversify** our performance marketing channels to other channels like we are doing with social media. Just to give you another data point there, actually, **the spend in social media channels was up this quarter, 25% compared to the second quarter of last year**. OpenAI’s new ChatGPT Agent mode already prototypes a world where a user [says](https://www.youtube.com/shorts/vMqUJSaeC%5F8?ref=mbi-deepdives.com) “find a 5-star hotel in Mexico City” and the assistant fetches live rates, compares hotels, and completes the reservation, using Booking.com inventory. In the medium term, that shift could turn agents into the new meta-layer of distribution. Traffic that arrives via an agent will likely be priced on a cost-per-action or revenue-share basis rather than a CPC auction. If agents scale, Booking’s marketing mix may tilt toward variable commissions and away from unpredictable bid markets, potentially smoothing ROAS. One can wonder if current search performance marketing requires a lot of skill in which Booking is better than its competitors, an agent dominated world may make it a simpler world for everyone, including its competitors which may put pressure on take rates. But I think there will still be plenty of complexities left even in agent dominated world. The assistant abstracts away the UI, so loyalty has to live in service quality, and breadth of supply. Booking’s deep inventory, reviews corpus, and payments stack may give it defensibility, but only if those assets remain the agent’s easiest way to fulfill a request. Agents will expect structured, real-time availability feeds and semantically rich content. The OTAs that invest earliest in API performance, room-level attributes, and adjacent travel products (ground, experiences, insurance etc.) will train the agent to default to them, reinforcing share. Winning may come down to who can guarantee the agent’s promise e.g. instant confirmation, no rate parity conflicts, and reliable customer support at the lowest latency. Booking reminded investors that they intend to be quite ready for however the future of travel accommodation unfolds: > So also there, we're continuing to learn and experiment finding new modern channels that travelers are using to get inspired for travel and finding ways to ultimately book and then also, as you know, **we are very actively working together with all the hyperscalers and what they are doing with respect to their agent development**. So for example, we're very proud that we were mentioned as one of the key partners for ChatGPT agent mode and Booking.com was clearly highlighted in the demos that they showed a couple of days ago. So a little bit overall, still too early to say. But what you should take away, I think, main point is **we are really trying to expand to learn and the more channels we can use, the better it is for the company in the future**. For now, Booking’s strategy is pragmatic: keep milking high-intent search while redirecting incremental budget to whichever new channels (social video today, AI agents tomorrow) show additive economics. The longer agents take to mainstream, the more cash flow Booking can recycle into inventory breadth and tech infrastructure, positioning it as a default plug-in when or if conversational booking finally hits scale. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. I made some changes in my portfolio yesterday which I will discuss behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Some notes from Big Tech 10-Qs URL: https://www.mbi-deepdives.com/2q2510q/ Last updated: 2025-08-04T22:59:11.000Z I went through Meta, Amazon, and Alphabet’s 10-Qs during this weekend, so just wanted to highlight some interesting points from their 10-Qs. ## Meta 2Q’25 10-Q Both Alphabet and Meta now disclose total employee compensation expenses. One of the surprising data points for me was that almost half of the operating expenses of Reality Labs was non-employee compensation related in 2Q’25\. If I had to guess, I would probably have said \~20-30%. Of course, Reality Labs sell hardware products (Glasses and VR headsets) which in aggregate likely have **negative** **gross margin,** but even then Meta likely spent \~$2 Billion in expenses ex hardware, and employee comp. That’s not a small amount if you annualize it, but we don’t quite know what exactly Meta spends this \~$8 Billion on. Meta doesn’t disclose Depreciation &Amortization (D&A) expenses by segment, but now that we have employee compensation, we can see the overall company spent \~$12 Billion in employee comp last quarter, \~$4.5 Billion in D&A, and \~$11 Billion in “other costs and expenses ex D&A and employee comp” which would be \~$45 Billion if you annualize the number. Of course, D&A is certainly going to be a growing number for years given the skyrocketing capex, and employee comp is somewhat variable (Meta can hire or layoff depending on business context, but only to a certain extent). But it’s quite interesting to see Meta spends $40-45 Billion in opex that is outside the employee comp and D&A expenses and a significant part of this can be more discretionary than employee comp and D&A expenses. Looking at this breakdown of opex, I actually feel more comfortable that Meta can maintain its operating margin just fine even though capital intensity is rising. I expect the more discretionary expense buckets will grow much slower than overall revenue growth which will help them maintain their operating margins. Of course, there will be fluctuations in random quarter or year, but the point should stand over the course of 3-5 years. ![](https://substackcdn.com/image/fetch/$s_!K4di!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F21789e18-3306-45b0-be4e-37e56694404a_549x903.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will share some thoughts from Amazon and Alphabet 10-Q and what I find the most interesting point across these three companies behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Never Sell: Episode 8 - AI, Meta, Process, Pods, Writing URL: https://www.mbi-deepdives.com/never-sell-episode-8-ai-meta-process-pods-writing/ Last updated: 2025-08-03T13:21:29.000Z For the “Never Sell” podcast, [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I recorded an “AMA” episode. You can listen to it here: [Spotify](https://open.spotify.com/show/3Fdub8zkhm4xwN1ZcDbv1j?ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell/id1786912203?ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/@scuttleblurb?ref=mbi-deepdives.com), [RSS feed](https://feeds.buzzsprout.com/2435713.rss?utm%5Fsource=substack&utm%5Fmedium=email) Thank you for asking thoughtful questions. If you have any questions for either MBI Deep Dives or Scuttleblurb, please feel free to email us the questions which we may try to respond/discuss in our podcast. ### Microsoft FY2025 Update URL: https://www.mbi-deepdives.com/msft2025/ Last updated: 2025-08-05T15:49:20.000Z Since Microsoft’s financial year ends in June 30th, I have decided to update my Microsoft model instead of doing the usual quarterly recap. I first published my [Microsoft Deep Dive](https://www.mbi-deepdives.com/msft/) back in April 2023\. However, Microsoft announced some changes to the composition of their reporting segments in August 2024\. As a result, I had to make some noticeable changes in my model as well. I will share my new abridged version of Microsoft model, discuss the assumptions, and my inferences to take a decision on my Microsoft position behind the paywall. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Amazon 2Q'25, Portfolio Change URL: https://www.mbi-deepdives.com/amzn2q25/ Last updated: 2025-08-01T14:50:22.000Z ***A programming note***: For my next Deep Dive, I am currently working on **Union Pacific** which I will publish sometime later this month. After Union Pacific, I will do a Deep Dive on **ASML** in September. You can find all the past 61 Deep Dives [here](https://www.mbi-deepdives.com/models/). [Subscribe](#/portal/signup) --- # Amazon 2Q’25 Update Amazon stock is down \~7% post-earnings. What happened? In 1Q’25 call, Amazon guided revenue and EBIT for Q2 to be $159-164 Bn and 13-17.5 Bn respectively. They actually reported $167.7 Bn revenue and $19.2 Bn EBIT. Perhaps consensus estimates was higher than guide? Going into the call, consensus estimates for revenue and EBIT were $162.1 Bn and $16.8 Bn respectively. Maybe Q3 guide was poor? 3Q’25 revenue and EBIT consensus estimates before the call were $173.2 Bn and $19.5 Billion respectively. Amazon guided revenue and EBIT for Q3 to be $174-179.5 Bn and $15.5-20.5 Bn respectively. Amazon **usually** beats the high end of the guide. So, why is the stock down so much today? I can invent reasons if you like, but the closer to the truth explanation is: [nobody quite ever knows](https://www.youtube.com/watch?v=BVaKqfaUBTE&ref=mbi-deepdives.com)! Here are my highlights from yesterday’s call. **Revenue** Overall revenue grew by \~13% (\~12% FXN). I’m quite encouraged to see that both 1P and 3P revenue accelerated to double digit growth in 2Q’25 compared to MSD growth in 1Q’25\. In fact, every single revenue segment grew at double digit rate; AWS at high-teen and Ads grew at 22%. I’ll discuss more about AWS later, but let’s talk more about Amazon ex-AWS first. ![](https://substackcdn.com/image/fetch/$s_!h6s7!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa4d28e12-0721-4708-9668-033a87f37eaa_1671x228.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** North America and International segment’s margin expansion theme remains very much in tact. In 2Q’25, operating margin in North America was +7.5% (vs +5.6% in 2Q’24) and in international was +4.1% (vs +0.9%). It’s quite remarkable to see how consistently they have been able to expand the margins and all indications are there that there is more room to go (to be discussed shortly)! ![](https://substackcdn.com/image/fetch/$s_!kFSS!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6d9030e0-f13a-4219-a35c-9c7d6a55179f_1119x684.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Tariff remains a black box and a source of ongoing uncertainty for the retail business. From Andy Jassy in the call: > “It's hard to know where the tariffs are going to settle, particularly in China. It's hard to know what will happen **when we deplete some of the prebuys that we did on our own first-party retail** and then some of the forward deploying that we saw of our third-party selling partners. And if costs go up over time, **we're unsure at this point who's going to end up absorbing those higher costs**. What we can tell you is what we've seen so far in the first half of the year, in the first half, we just haven't seen diminished demand. And **we haven't seen any kind of broad scale ASP increases. And so that could change in the second half.**” **Fulfillment+ Shipping** If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q'22\. Since then, unit growth has largely been faster than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. ![](https://substackcdn.com/image/fetch/$s_!oTwS!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85cd743a-a2b7-4aac-be85-6a8caf28080e_1159x547.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Every time I read about what Amazon has been doing in their fulfillment and logistics, it makes me more comfortable about their moats in retail. Some key quotes from the call: > “In Q2, we increased the share of orders moving through direct lanes where packages go straight from fulfillment to delivery **without extra stops by over 40% year-over-year.** We've also reduced the average distance packages traveled by 12% and lowered handling touches per unit by nearly 15%. We've made progress on order consolidation with more products positioned locally, we're able to pack more items into each box and send fewer packages per order. That has helped drive higher units per box and improved overall cost to serve. Taken together, these improvements are making the network faster and structurally more efficient. > > In the U.S., **we delivered 30% more items same day or next day than during the same period of last year.** > > we're working to further improve delivery speeds **no matter where customers live**, we've recently announced plans to expand our same-day and next-day delivery to tens of millions of U.S. customers and more than 4,000 smaller cities, towns and rural communities by the end of the year. Today, it's already available in more than 1,000 of these communities across the U.S. The early response from customers in these areas have been very positive. **They're shopping more frequently and purchasing household essentials and meaningfully higher rates**. Automation and robotics are also important contributors to improving cost efficiencies and driving better customer experiences over time. We deployed our 1 millionth robot across our global fulfillment network and unveiled innovations in our last-mile innovation center, such as automated package sorting and a transformative technology that brings packages directly to employees in an ergonomic height. We rolled out DeepFleet, our AI improves robot travel efficiency by **10%.**” I continue to think Amazon retail is underestimated; while Amazon is never going to be a monopoly in retail, it’s hard to imagine why they won’t be a headwind to most physical retailers over the next few decades as the convenience of ordering something which magically appear on your doorstep in a couple of hours is a timeless value proposition. **Advertising** A big driver for retail profitability is advertising. For the second consecutive quarters, Amazon ads grew the fastest among the top three digital advertising players. Given Amazon ads are perhaps more of a competitor to Google than Meta, I think it’s interesting to track how Amazon is gaining share here. While Amazon ads is still just \~20% the size of Google Advertising revenue, Amazon ads incremental revenue as a percentage of Google advertising incremental revenue increased from 33% in 1Q’24 to 43% in 2Q’25. ![](https://substackcdn.com/image/fetch/$s_!2pYL!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdb2b581c-bb96-4ca8-b933-89d1c0fdcffc_955x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) I will discuss AWS and the rest of this update behind the paywall. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta 2Q'25, CoStar suing Zillow, Portfolio Change URL: https://www.mbi-deepdives.com/meta2q25/ Last updated: 2025-07-31T12:54:09.000Z **Final Reminder**: *Prices for new subscribers will increase to $30/month or $250/year from *tomorrow*. Anyone who joins by July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** *I have been publishing *everyday* since the beginning of this month. I hope it is clear by now that it takes a lot of commitment and energy to produce this everyday. I don’t think I have ever been as productive in my life as I was in the last 30 days…publishing something everyday certainly keeps you on your toes! I have been particularly encouraged by how many of you have voted with your wallet so far which only emboldened my confidence in this direction. Thank you very much!* [Subscribe](#/portal/signup) --- # **Meta 2Q’25 Update** Wow! That’s what I said to myself a couple of times while going through Meta’s earnings yesterday. Let me go through the earnings to explain my reaction! **Users** Meta still hasn’t run out of people to add incremental users to one of their apps. I do want to note that they didn’t disclose monthly active users for Threads after disclosing it for the last seven quarters. My guess is MAU growth has noticeably slowed there in the last quarter. ![](https://substackcdn.com/image/fetch/$s_!3ZoM!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7934e4ed-02e2-4f3a-9b5e-55ddd5732352_2082x100.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad revenue by Geography** After a dip in impression growth in 1Q’25, it accelerated to \~11% YoY in 2Q’25 while also increasing average ad price by \~9%. The combination of these two drove overall ad revenue to grow by \~21% YoY. Growth was pretty indiscriminate across regions. I am quite encouraged to see the uptick in impression growth; I’m much more confident about Meta’s ability to monetize the impression over time, so growing impression boosts my confidence that there is ample headroom here to scale monetization further as targeting and conversion continues to improve thanks to AI. Zuck in his prepared remarks did mention that the new AI-powered recommendation model for ads improved its performance by using more signals and longer context which led to 5% more ad conversions on Instagram and 3% on Facebook. These numbers may seem low for casual observers at first glance, but please keep in mind the scale of these platforms; every 1% improvement may unlock \~$1 Billion revenue for Meta! It’s not just conversion, it’s also helping grow impressions. From Zuck: > “AI is significantly improving our ability to show people content that they're going to find interesting and useful. Advancements in our recommendation systems have improved quality so much that it has led to a **5% increase in time spent on Facebook and 6% on Instagram, just this quarter**. There is a lot of potential for content itself to get better too, we're seeing early progress with the launch of our AI video editing tools across Meta AI and our new Edits app.” CFO Susan Li later also shared additional data points: > We continue to see momentum with video engagement, in particular. In Q2, Instagram video time **was up more than 20%** year-over-year globally. We're seeing strong traction on Facebook as well, **particularly in the U.S., where video time spent similarly expanded more than 20% year-over-year.** These gains have been enabled by ongoing optimizations to our ranking systems to better identify the most relevant content to show. > > We are also making good progress on our longer-term ranking innovations that we expect will provide **the next leg of improvements over the coming years**. Our research efforts to develop cross-surface foundation recommendation models continue to progress. We are also seeing promising results from using LLM in Threads recommendation systems. The incorporation of LLMs are now **driving a meaningful share of the ranking related time spent gains on Threads**. > > We're now exploring **how to extend the use of LLMs and recommendation systems to our other apps**. So, AI is basically working as a twin tailwind in both side of the business: it increases time spent on the platform through more engaging and relevant content AND it increases conversion for the advertisers. **Win-win!** ![](https://substackcdn.com/image/fetch/$s_!uvHs!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fadc9e9bd-da06-4aa9-b76a-1c1f71594f2c_1264x853.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) You can perhaps appreciate Meta’s revenue trajectory more when you contrast it with Google Search. The delta between LTM Google search revenue and LTM FOA revenue was **$48.4 Billion** in 1Q’23\. That delta has narrowed to **just $33.7 Billion in just two years.** If this continues, it may be in the realm of possibility that Family Of Apps (FOA) revenue may eclipse Google search revenue **in the next 5 years**! ![](https://substackcdn.com/image/fetch/$s_!xfT8!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9e1af5cd-c8b3-4663-9137-17e53216be65_1378x769.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While thinking of the possibility of FOA revenue eclipsing Google Search in 2030 or so reminded me of Zuck’s joke to someone “*it’ll be a while before we can buy Google*”. 🫡 ![](https://substackcdn.com/image/fetch/$s_!HMdJ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F80e89619-06d3-4253-97b2-b5b43ac90c6d_1705x403.png) **Segment Reporting** Meta initially guided $42.5 Bn to $45.5 Bn revenue for 2Q’25, but generated **$47.5 Bn** revenue. Overall 2Q’25 revenue was **+22% YoY (vs consensus estimates of \~15% YoY and \~19% FXN in 1Q’25).** This is a sizable beat! “Other revenue” accelerated to \~50% YoY, driven by Meta verified subscriptions and WhatsApp messaging. This revenue line item has been growing at a rapid pace for the last couple of years, but it is still a rounding error in FOA revenue. Business messaging is gaining traction even in the US: > we're seeing good momentum in business messaging, **particularly in the U.S., where click to message revenue grew more than 40% year-over-year in Q2**. The strong U.S. growth is benefiting from a ramp in adoption of our **website to message ads**, which drive people to a business's website for more information before choosing to launch a chat with the business in one of our messaging apps. FOA continued to post >50% operating margins while **maintaining \~60% (or more) incremental margins for the last 13 consecutive quarters**. Between 2Q’22 to 2Q’25, Meta added \~$19 Billion incremental quarterly revenue at an eye-watering \~74% incremental operating margin!! For Reality Labs, another quarter of $4 Billion+ losses. Thankfully, Meta Ray-Ban glasses is a glimmer of hope that all these investments are going to put Meta in a pole position if AI glasses do gain wide adoption which I am pretty optimistic about. From the call: > The growth of Ray-Ban Meta sales accelerated in Q2, with **demand still outstripping supply for the most popular SKUs** despite increases to our production earlier this year. We're working to ramp supply to better meet consumer demand later this year. ![](https://substackcdn.com/image/fetch/$s_!2i4U!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6dfa566a-1601-47d8-ba0e-b4d01aeb24cc_1750x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Superintelligence** Meta has been on the constant news about their recruiting efforts in building the Superintelligence team. I will let Meta management speak for themselves on this topic. It’s a long excerpt from various parts of the call but it is important enough that I’m quoting so much: > Over the last few months, we've begun to see **glimpses of our AI systems improving themselves. And the improvement is slow for now, but undeniable and developing superintelligence**, which we define as AI that surpasses human intelligence in every way, we think, is now in sight. Meta's vision is to bring personal superintelligence to everyone, so that people can direct it towards what they value in their own lives. And we believe that this has the potential to begin an exciting new era of individual empowerment. > > A lot has been written about all the economic and scientific advances that superintelligence can bring, and I'm extremely optimistic about this. But I think that if history is a guide, then an even more important role will be how superintelligence empowers people to be more creative, develop culture and communities, connect with each other and lead more fulfilling lives. > > To build this future, we've established Meta Superintelligence Labs, which includes our foundations, product and FAIR teams as well as a new lab that is focused on developing the next generation of our models. We're making good progress towards Llama 4.1 and 4.2, and in parallel, we are also working on our next generation of models that will push the frontier in the next year or so. > > We are building an elite, talent-dense team Alexandr Wang is leading the overall team, Nat Friedman is leading our AI Products and Applied Research, and Shengjia Zhao is Chief Scientist for the new effort. They are all incredibly talented leaders, and I'm excited to work closely with them and the world-class group of AI researchers and infrastructure and data engineers that we're assembling. > > At a high level, I think that there are all these questions that people have about what are going to be the time lines to get to really strong AI or superintelligence or whatever you want to call it. And I guess that each step along the way so far, **we've observed the more kind of aggressive assumptions, or the fastest assumptions have been the ones that have most accurately predicted what would happen. And I think that, that just continued to happen over the course of this year, too.** > > And so I've given a number of those anecdotes on these earnings calls in the past. And I think, certainly, some of the work that we're seeing with teams internally being able to **adapt Llama 4 to build autonomous AI agents that can help improve the Facebook algorithm to increase quality and engagement are like -- I mean, that's like a fairly profound thing if you think about it**. I mean it's happening in low volume right now. So I'm not sure that, that result by itself was a major contributor to this quarter's earnings or anything like that. But I think the trajectory on this stuff is very optimistic. > > And I think it's one of the interesting challenges in running a business like this now is there's just a very high chance it seems like **the world is going to look pretty different in a few years from now.** And on the one hand, there are all these things that we can do, there are improvements to our core products that exist. > > And then I think we have this principle that we believe in across the company, which **we tell people take superintelligence seriously**. And the basic principle is this idea that we think that this is going to really shape all of our systems sooner rather than later, not necessarily on the trajectory of a quarter or 2, but on the trajectory of a few years. And I think that that's just going to change a lot of the assumptions around how different things work across the company. > > I think that **for developing superintelligence, at some level, you're not just going to be learning from people because you're trying to build something that is fundamentally smarter than people**. So it's going to need to learn how to -- or you're going to need to develop a way for it to be able to improve itself. > > So that, I think, is a very fundamental thing. That is going to have **a very broad implications for how we build products, how we run the company, new things that we can invent, new discoveries that can be made, society more broadly**. I think that that's just a very fundamental part of this. > > In terms of the shape of the effort overall, I guess **I've just gotten a little bit more convinced around the ability for small talent-dense teams to be the optimal configuration for driving frontier research**. And it's a bit of a different setup than we have on our other world-class machine learning system. > > So if you look at like what we do in Instagram or Facebook or our ad system, we can very productively have many hundreds or thousands of people basically working on improving those systems, and we have very well-developed systems for kind of individuals to run tests and be able to test a bunch of different things. You don't need every researcher there to have the whole system in their head. But I think for this -- for the leading research on superintelligence, you really want the smallest group that can hold the whole thing in their head, which drives, I think, some of the physics around the team size and how -- and the dynamics around how that works. Zuck’s voice also wavered a bit when asked about open source. To be fair, Zuck was always clear that he’s not making any promises that Meta will always open source everything, but my guess is if the Superintelligence team delivers, Meta’s open source approach may look somewhat similar to what Google and OpenAI have been doing. You can even argue by potentially not choosing to open source their most advanced model, Meta may allude that they are quite confident of their internal talent pool to stay at the frontier. We’ll see how their tone evolves over time. **Capital Allocation** For the second consecutive quarters, Meta returned more capital to shareholders than FCF they generated. That and thanks to their investment in ScaleAI (wouldn’t surprise me if they take an impairment charge a couple of years down the line here), their net cash balance was only $18 Billion. At this rate, Meta may have net debt position by sometime next year. While they’re doling out eye-popping offers to marquee AI researchers, headcount actually declined QoQ. Meta highlighted “performance related reductions” to explain the decline. I suspect this may remain a theme for foreseeable future in every big tech company as headcount becomes increasingly more scrutinized in their budgeting than perhaps anytime in the 2010s. ![](https://substackcdn.com/image/fetch/$s_!9rZR!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc992d250-9eda-409d-b5bd-19f4cc10ecd0_1143x325.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex and Opex** Speaking of headcount being increasingly more scrutinized, just take a look at the below chart. I looked at total opex of Meta in each quarter since 1Q’13 and subtracted Depreciation & Amortization (D&A) expenses to get a pretty good proxy for headcount related expenses i.e. “labor”. I calculated this number as % of revenue and then compared with capex as % of revenue in each of those quarters. Notice how close these two lines are getting. Given what Meta has been hinting for capex in 2026 (discussed more below), it may not be too long before these lines meet. It is stunning to look at this chart; I’m not quite a believer of AGI mumbo-jumbo, but I gotta say if we were indeed heading towards AGI, this is what this chart would look like! ![](https://substackcdn.com/image/fetch/$s_!3JRV!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F07622da0-bb38-4c86-88de-d76ed2f430c9_1405x894.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Regulation** Europe can still derail the Meta party sooner than you think although I don’t lose sleep over it as I think Meta has more leverage in this tussle, especially under the current US administration. It can also help that Europe basically is making up rules arbitrarily to hurt US tech companies on which their own citizens remain deeply dependent. From the call: > “we continue to monitor an active regulatory landscape, including the increasing legal and regulatory headwinds in the EU that could significantly impact our business and our financial results. For example, we continue to engage with the European Commission on our Less Personalized Ads offering or LPA, which we introduced in November 2024 and based on feedback from the European Commission in connection with the DMA. > > As the commission provides further feedback on LPA, we cannot rule out that it may seek to impose further modifications to it that would result in a materially worse user and advertiser experience. **This could have a significant negative impact on our European revenue as early as later this quarter.** We have appealed the European Commission's DMA decision, but any modifications to our model may be imposed during the appeal process.” **Outlook** Meta guided 3Q’25 revenue $47.5 Bn to $50.5 Bn (1% FX tailwind). Consensus for 3Q before the call was $46.3 Bn. So, if they meet the high end of the guide next quarter, consensus is currently behind \~10%. You can see why the stock was up \~10-12% AH yesterday. They also narrowed the opex range down a bit from $113-118 Bn to $114-118 Bn. Similarly, capex guide range was narrowed from $64-72 Bn to $66-72 Bn. In both cases, most investors were perhaps a bit surprised that they didn’t expand the high end of the range provided earlier. However, they did hint at 2026 opex guide which has interesting implications. For opex, Meta mentioned 2026 YoY opex growth will be **higher than 2025 expense opex growth rate driven by higher depreciation and employee compensation (**those high talent density Superintelligence team are not cheap)**!** I’m assuming it to be \~25% given 2025 opex growth is going to be \~20-24%. For capex in 2026, Meta expects “another year of similarly significant capex dollar growth”. Since 2025 capex is \~$30 Bn higher (at mid-point) than 2024 capex, we are probably going to see $100 Bn capex next year! Remember how mad everyone was back in 3Q’22 call when Meta guided 2023 capex to be $34-39 Bn? Such a quaint time! **Closing Words** Great quarter! I will share some thoughts on valuation behind the paywall later. I will cover **Amazon’s** earnings **tomorrow** and Microsoft’s earnings on **Saturday**. [Subscribe](#/portal/signup) --- **CoStar suing Zillow** On my CoStar [Deep Dive](https://www.mbi-deepdives.com/csgp/), I mentioned: *“it seems perhaps fair to assume that if you are building any real competitor to any of CoStar’s products, you should expect to be sued unless you yourself go to a great length to ensure CoStar’s IP is being respected by anyone who’s using your products/services. And I’m not sure even that would be enough from being sued by CoStar!”* It certainly wasn’t enough for Zillow as CoStar has decided to [sue](https://investors.costargroup.com/news-releases/news-release-details/costar-sues-zillow-rampant-copyright-infringement?ref=mbi-deepdives.com) them. Naturally, I uploaded the [lawsuit](https://nowbam.com/wp-content/uploads/2025/07/CoStar-Complaint-Zillow-July30-2025.pdf?ref=mbi-deepdives.com) to NotebookLM and asked it to create a lucid video on the 38-page PDF. 0:00 /7:12 1× --- _This post is for paying subscribers only._ ### Video on NotebookLM, Amazon's response to WSJ, Brown & Brown 2Q'25 URL: https://www.mbi-deepdives.com/07-30-2025/ Last updated: 2025-07-30T15:00:06.000Z I have been using [NotebookLM](https://notebooklm.google.com/?ref=mbi-deepdives.com) pretty frequently for the last 6 months or so. It’s pretty neat to feed a report to NotebookLM and make a podcast out of it which I can then listen during my daily walk. Yesterday, they launched video feature. So, now I can feed a report and make a “video podcast” out of it. I decided to try the feature by feeding my most recent [Cognex Deep Dive](https://www.mbi-deepdives.com/cgnx/), and I was quite impressed with the output. See it in action below: 0:00 /7:59 1× I was so impressed with the feature that I have decided to launch a [YouTube channel](https://www.youtube.com/watch?v=DLrVEMU-uBo&feature=youtu.be&ref=mbi-deepdives.com) for MBI Deep Dives. I will basically feed my daily updates to NotebookLM, generate a video, and then upload it on my YouTube channel. This is a great showcase of Google’s strength. A neat feature that its billions of users can utilize and then be shared across Google owned surfaces. Content will be created once and then repurposed across all the possible mediums and then users can just choose the medium they prefer to consume the content. No expensive software tools are necessary at least for casual consumer use cases in the entire creation process…imagine the consumer surplus! --- **Amazon’s response to WSJ piece on raising price post-tariff** A week ago, I [wrote](https://www.mbi-deepdives.com/07-22-2025/) about a WSJ piece that looked into \~2,500 items and compared the price changes from January 20 to July 1\. I did mention “Amazon has millions of SKUs. An analysis looking at even 2,500 items may not be representative of overall data, so I am not sure I want to extrapolate too much here.” It turns out Amazon was not pleased with WSJ’s analysis as they responded with a scathing tone: > “The *WSJ*’s article about Amazon's pricing practices isn't just flawed—it seems fundamentally misleading by design. Amazon offers over 6 million everyday essential items, yet the *WSJ* focused their story on just under 2,500 low-priced everyday essentials products—less than 0.04% of our everyday essentials selection. This isn’t responsible sampling; it's surgical cherry-picking > > If the *WSJ* had averaged prices for their subset of about 2,500 products across the months of January and June, they would have found that over 92% had either no price change or a price decrease.” The reality is this was always going to be very difficult analysis to be confident about for any outside observer given the sheer number of SKUs Amazon has. I liked Byrne Hobart’s [take](https://www.thediff.co/archive/the-rising-returns-to-lobbying/?ref=mbi-deepdives.com) on this issue: > there are still retail business ambiguities at work here that make the overall picture hard to read. Retail economics are driven by cross-elasticities—if you bought X, does that make you more or less price-sensitive when buying Y? So in some categories, Amazon might be keeping prices constant, but highlighting the deal to fewer customers, because the product in question is either a newly-expensive way to drive sales for other products or because the complementary sales it leads to are now lower-margin. So the mix of loss-leaders presented to customers might skew towards lower-tariff goods, while Amazon can keep the price of tariff-affected goods fairly low and sell them mostly to customers who are specifically looking for them. There's probably an Amazon-specific CPI that the company can calculate internally, though it wouldn't be that important a number for them to know compared to the more granular price changes that actually drive revenue. And if someone's outside of the company and doesn't know what calculations go into determining what offers they put in front of their customers, it's impossible to know what overall price levels on the site are really doing. --- **Brown & Brown 2Q’25** Brown & Brown (BRO) had bit of a soft Q2 as they reported organic growth of only +3.6% in 2Q’25 which was lower than both AON and MMC. I do want to note that BRO’s comp was pretty challenging as 2Q’24 organic growth was +10.0%. However, comp for the next couple of quarters is also fairly challenging as 3Q’24 and 4Q’24 organic growth was +9.5% and 13.8% respectively. As discussed in detail in my BRO [Deep Dive](https://www.mbi-deepdives.com/bro/), insurance brokers are not immune from P&C pricing cycle. Even though BRO’s average organic growth was +7.6% during 1Q’19 to 2Q’25 period, they posted +3.7% organic growth during 2019-20 period but +9.9% during 2021-24 period. ![](https://substackcdn.com/image/fetch/$s_!Vd7W!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F034f8f13-f3e2-4e05-98fb-1755eefc7f6b_1960x487.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) After listening to AON, MMC, and BRO’s earnings calls, it does seem we may be exiting “firm market” cycle in P&C insurance industry. Here’s what BRO management said in the call: > So based on the consensus of what we were going to grow in Q2 in Retail versus what we delivered, over half that discrepancy was because of rates, so downward pressure on rates. The other half is we basically just had lower new business in the quarter. And so sometimes that can happen. I feel that we have good new business going into the third quarter, but it -- every quarter is a little different, and our visibility into it seems to indicate that we are in good shape for Q3\. But I just want to make sure that everybody understood that over -- more than half the discrepancy was because of rate pressure. ![](https://substackcdn.com/image/fetch/$s_!5t3R!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3c44d10f-1fad-4735-9d75-43010c878420_1404x720.png) Source: Arthur J Gallagher Investor Presentation Later in the call, J. Powell Brown expanded on the cycle: > This is a classic cycle -- and I've only been doing this for 35 years, and I've seen this rodeo a couple of times. And as you know, pricing, particularly in the case of property, typically goes up very rapidly. This is E&S. I'm just using the E&S market as the example. And then it can come down rapidly. > > …And so what you have is you have more pressure today than we have seen. And so there's been a long period of upward pressure on property rates. And depending on your perspective, but if you look at it from our customer standpoint, it is very good for the customers, but it does put pressure on organic growth on any business in the industry. But if you had a lot of property, and we had a lot of property in the Q2, there -- you see it more clearly. > > …I don't want to give you the impression there's some, oh, this is a weird thing. We've never seen this or absolutely not. This is exactly what we expected. I expected it to happen a year ago personally. But again, what you heard me say and my mistake, we -- it surprised us in the speed of decline in Q2 and particularly in the latter part of the quarter. That's the difference that we're talking about, **not that we were surprised by the decline. It was the speed of it**. You can imagine the stock didn’t respond too well following the earnings. I was lucky to have sold my shares in [March](https://www.mbi-deepdives.com/ilmn/) this year at $120/share. Since then, BRO has noticeably underperformed the index. I will share behind the paywall what I’m thinking about BRO now. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-16.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Meta Ray-Ban, Ads in AI Chats URL: https://www.mbi-deepdives.com/07-29-2025/ Last updated: 2025-07-29T13:52:39.000Z I have [reviewed](https://x.com/borrowed%5Fideas/status/1725282443526082886?ref=mbi-deepdives.com) Meta Ray-Ban glasses a couple of years ago, and I have remained a DAU since then. I mostly listen to audio (music/podcast) and receive calls through my glasses. It also allowed to capture lots of spontaneous images/videos during my daily walk. Here’s one of my recent strolls around UC Berkeley (video at 2x speed): 0:00 /0:19 1× Meta is trying to brand these glasses as “AI Glasses”. Ironically, I barely use Meta AI; I don’t prefer to talk to any of the AIs as I still like to write my queries to ChatGPT and read its response. But we don’t have to rely on my personal anecdotes; EssilorLuxottica (see my Deep Dive [here](https://www.mbi-deepdives.com/esloy/)) yesterday during their 2Q’25 earnings call shared some helpful data points about how these glasses are selling. Ray-Ban Meta glasses sales increased by **200% YoY** and they specifically called out the rollout of the Live AI, including new features like the real-time translation and the wider adoption of transition lenses fueling such growth. In fact, 60% of Ray-Ban Meta that were dispensed in Sunglass Hut had transition lenses attached to it. I too use the transition lenses. While there is still a lot we don’t know the economic details about the partnership between Meta and EssilorLuxottica, Essilor confirmed that these glasses are margin dilutive for them. They also emphasized that is the least of their concern. From the call: > “…would like really to comment on this dilution and how its dilution seems to be a bad word. But honestly, what I care is about the money that I can make and the earnings per share that I can really give to my shareholders. And Meta, also with less margin is making a lot of money. And since the volumes are growing very, very fast, that will help the company really for the future investment and also for our journey in the med tech. > > we really, with some positive view, we believe that at a certain point, we will also have the necessity to **increase this capacity above the 10 million pieces that we expected now**. Essilor recounted the difficulties of selling the first version of these glasses. But today? These glasses are “the easiest product to sell”. From the call: > “…because we are the only one into the market. So there is no one that it can help us in this adventure. And this is what is -- makes the rollout quite tough at the beginning but we see the same trend that we saw on the Meta when we launched the story, Ray-Ban story at the beginning. > > It was something really very difficult to communicate and also was difficult from the customer's point to understand how to use it. Now it seems very easy. Everybody are just getting in the store, asked for the AI glasses Ray-Ban Meta, Oakley Meta now is already. We have a customer that are coming in the store and they're asking. And **this seems to be the easiest product to sell**. These are very encouraging words for Meta shareholders given Meta is probably spending $10-15 Billion/year on investments related to AR glasses. I am really looking forward to the next iteration of the glasses to see what they’re cooking inside Reality Labs. --- **Inevitability of Ads in AI Chats** Rohit Krishnan wrote about the inevitability of ads being placed in our chats with AI. From his piece: > “…imagine if Elon Musk is using Claude to have a conversation, the answer to which might well be worth trillions of dollars of his new company. If he only paid you $20 for the monthly subscription, or even $200, that would be grossly underpaying you for the privilege of providing him with the conversation. It’s presumably worth 100 or 1000x that price. > > Or if you're using it to just randomly create stories for your kids, or to learn languages, or if you're using it to write an investment memo, those are widely varying activities in terms of economic value, and surely shouldn't be priced the same. But how do you get one person to pay $20k per month and other to pay $0.2? The only way we know how to do this is via ads. > > And if you do it it helps in another way - it even helps you open up even your best models, even if rate limited, to a much wider group of people. Subscription businesses are a flat edge that only captures part of the pyramid.” As someone running a subscription business, I am well aware of the limitations of subscriptions. Whether you are a billionaire or a sophomore in college, you both pay the same to read my work. If I wanted to capture the value proportionately, I would have to switch to an entirely different business e.g. managing other people’s money. While many people seem to have preconceived notion about pros and cons about one model over the other, the reality is there are trade-offs no matter which path you take. People who think subscriptions are some sort of panacea to all the ills derived from ad dependent business models often choose to ignore ground reality. Again, from Krishnan’s piece: > I also think this is a good thing. I know this pits me against much of the prevailing wisdom, which thinks of ads as a sloptimised hyper evil that will lead us all into temptation and beyond. But honestly **whether it’s ads or not every company wants you to use their product as much as possible. That’s what they’re selling!** I don’t particularly think of Slack optimising the sound of its pings or games A/B testing the right upskill level for a newbie as immune to the pull of optimisation because they don’t have ads. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. My current portfolio is disclosed below: _This post is for paying subscribers only._ ### Google's response, Lululemon Community URL: https://www.mbi-deepdives.com/07-28-2025/ Last updated: 2025-07-28T15:07:31.000Z Google shared couple of blog posts last week that seem worth highlighting. Google is experimenting with a new feature called “[Web Guide](https://blog.google/products/search/web-guide-labs/?ref=mbi-deepdives.com)” which basically looks like Google itself will write a “blog post” in response to your query but instead of links being hyperlinked (as they are in a typical blog posts), they are more prominently displayed within the “write-up”. The result looks a cleaner experience to me than current Google search experience. Web guide uses a custom Gemini model to reorganize the results page into topic-based groups, making it easier to discover relevant web pages you might not have found with a single list of links. See it in action below: 0:00 /0:07 1× Source: Google The likely goals are to increase result quality and discovery for open‑ended or multi‑sentence queries, and to surface a broader set of websites rather than concentrating clicks on a few top links. Strategically, it also gives Google a way to add AI to search without hiding sources behind long AI summaries. This isn’t being rolled out to everyone yet; it’s still a “Google Labs” experiment but if they get positive feedback from Labs users, I can imagine this moving to default search experience. The genie of ChatGPT is out of the bottle, and perhaps the best bet for Google at this point is to neutralize this threat to the extent Meta did for TikTok. TikTok is still incredibly popular, but once Reels got going, the incremental user and engagement growth on TikTok was an uphill battle for them. That’s what Google needs to do well: make the next 500 million DAU a very hard fought battle between Google and ChatGPT and give the existing users fewer reasons to break their search habits to try the new shiny thing. Of course, Google engages in the query battle in two fronts: commercial and non-commercial queries but they’re closely intertwined. You cannot really cede non-commercial queries for forever without commercial queries gradually leaking to the alternatives. Amazon has been doing it for years as Google did lose a lot of share to Amazon for shopping related queries. Google is doing some interesting things here as well to stop or even [reverse](https://www.emarketer.com/content/gen-z-shoppers-turn-google-their-starting-point?ref=mbi-deepdives.com) the bleeding. Google launched a virtual [try‑on](https://blog.google/products/shopping/back-to-school-ai-updates-try-on-price-alerts/?ref=mbi-deepdives.com) tool in the U.S. that lets you upload a full‑length photo of yourself and see how clothing would look on you across Google Search, Google Shopping, and product results in Google Images. See the feature in action below: 0:00 /0:20 1× Source: Google --- **Lululemon’s Community** Lululemon stock trades like death and as someone who has been bagholding it (with the unpleasant experience of watching it from +80% unrealized gain to -50% unrealized losses), I wanted to see first-hand what Lulu’s community driven experiences look like. I went to one of their “[in-store sweat series](https://www.eventbrite.ca/e/lululemon-summer-in-store-sweat-series-sound-mind-tickets-1479023040729?ref=mbi-deepdives.com)” in the nearest Lulu store on Sunday morning. Two dozen people showed up at the store at 9 am (\~90% women, \~10% men). There wasn’t much sweat in that session since it was focused on relaxation through sound healing. So there was lots of soothing sounds of crystal bowls and chimes. Not bad for a Sunday morning session! I would like to go more of these sessions to get a better feel about these events. I also pay very, very close attention to Lulu’s [subreddit](https://www.reddit.com/r/lululemon/?ref=mbi-deepdives.com) which is incredibly active community. Frankly speaking, this community was early in picking up some of the issues Lulu had faced in the last couple of years (faltering quality, lack of newness in color, style etc.), but in the last month or so, I have sensed a slight reversal in sentiment. We’ll see in the next quarter’s earnings whether that is harbinger of an uptick in growth in North America. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Insurance Brokers, Portfolio Change URL: https://www.mbi-deepdives.com/07-27-2025/ Last updated: 2025-07-27T14:45:05.000Z I have written about insurance brokers before (see my [Brown & Brown](https://www.mbi-deepdives.com/bro/) Deep Dive, and write-up on [Aon](https://www.mbi-deepdives.com/may2024/)). I love insurance broker business and while they are certainly not the most exciting businesses around, the entire industry has been a reliable compounding machine for investors for decades. Hence, even though I only own Aon today, I expect myself to own multiple brokers over time. So, yesterday I listened to the recent earnings calls of the big two in this industry i.e. Marsh McLennan (MMC) and Aon (AON). First of all, let’s look at the organic growth. Aon surprised a bit with 6% organic growth while MMC did 4%. Over the long run, I expect both of them to grow organically at MSD rate. If you average their organic growth from 1Q’19 to 2Q’25, the average organic growth during this period was 6.5% for MMC and 6% for Aon. ![](https://substackcdn.com/image/fetch/$s_!5CXA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F106cf734-273c-4e2b-8643-c3de37169b25_1960x229.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Some notes from MMC Call** Management mentioned lower fiduciary interest income, declining Property & Casualty (P&C) pricing and market uncertainty affecting their clients in the U.S. Here’s what they mentioned about P&C pricing: > According to the Marsh Global Insurance Market Index, commercial insurance rates decreased 4% in the second quarter, driven by property despite a surge in cat losses in the first 6 months of the year. This follows a 3% decline in the first quarter of 2025\. As a reminder, our index skews to large account business. **Overall, rates in the U.S. were flat. Latin America, Europe, U.K. and Asia were all down mid-single digits and Pacific was down double digits**. For the full year, MMC guided for MSD organic growth and margin expansion. It’s remarkable to see how much the margins have expanded here over time. ![chart](https://substackcdn.com/image/fetch/$s_!mtQH!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb992dfef-692f-41fa-b9f3-cd6ea7e8efa8_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) One particular comment from the call stood out about how they’re using AI to negotiate better terms for their customers: > We have $1.12 trillion of premium that we've analyzed and over $100 billion of claims are in our databases that gives us a unique insight into that. And most recently, one of the AI tools that we've done is we've laid over an agentic tool over our claims database, and so clients can interrogate that, we can track comparators between how insurance companies pay their claims over which space, which puts us in a good position to negotiate great terms for them. **Some notes from AON Call** What did Aon say about their organic growth drivers? From the call: > In the quarter, new business powered organic revenue growth and contributed 11 points, with an equal contribution from both new clients and expansion with existing clients. > > Net new business contributed 5 points to organic revenue growth in the quarter. Net market impact, which captures the impact of rate and exposure, contributed approximately 1 point to organic revenue growth, consistent with our 0 to 2-point estimated range. Reinsurance was down from rate declines and higher retentions, and rate pressure in Commercial Risk was offset with limit and coverage increases across our book. Health and Wealth both benefited from positive net market impact with rising health care costs and favorable asset performance supporting growth. > > Revenue-generating headcount is up 6% through the first half, and these colleagues are equipped with advanced data analytics and capabilities from ABS, enabling them to win more business. We continue to expect these investments to support sustainable organic revenue growth, with the 2024 cohort projected to contribute 30 to 35 basis points to full year organic revenue growth. NFP acquisition seems to be going well. All the previous guide for synergy remains on track. NFP closed 8 acquisitions with aggregated $20 million EBITDA. AON too reaffirmed their full year guidance of MSD (or greater) organic revenue growth, and 80-90 bps of margin expansion. Like MMC, Aon’s margin expansion history is also pretty impressive. ![chart](https://substackcdn.com/image/fetch/$s_!M7GG!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe18e6df6-cda4-4b4b-9977-17097213e864_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Since I bought Aon [last year](https://www.mbi-deepdives.com/may2024/), they have performed noticeably better than MMC. I will share some thoughts behind the paywall what I’m thinking today about exposure in insurance brokers. ![chart](https://substackcdn.com/image/fetch/$s_!VYN2!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F52a2f88e-ca3c-4257-a464-eed593dfffdd_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### CoStar 2Q'25 Update URL: https://www.mbi-deepdives.com/csgp2q25/ Last updated: 2025-07-26T14:10:59.000Z CoStar may not be a widely followed company (you can read my Deep Dive [here](https://www.mbi-deepdives.com/csgp/) if you’re unfamiliar), but it’s one of the earnings calls that I enjoy the most listening every quarter, primarily because CEO Andy Florance always comes up with new reasons to take a dig at competitors. Many actually seem to detest these antics, but I personally find humorous elements in these constant jabs at CoStar’s competitors. 2Q’25 call was no different. Here are my highlights from the call. **Revenue** It almost seems too good to be true, but 2Q’25 was CoStar’s **57th consecutive quarter of double digit growth**! CoStar also had its highest quarterly net new bookings in CoStar Group's history. CoStar suite and LoopNet’s revenue growth accelerated. Just keep in mind how terrible the overall environment was for Commercial Real Estate (CRE) industry last few years and yet, CoStar suite showed incredible resilience during this period. More detailed revenue growth trajectory by segment and region are shown below both from YoY and QoQ perspective. ![](https://substackcdn.com/image/fetch/$s_!6Eok!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb3cf54bd-c68a-4bd0-a134-73d937db993a_1078x900.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Margins** Anytime I post about CoStar, I usually receive at least one DM/email/reply asking me about why I am even looking at a company trading at stratospheric valuation multiples. So, let me clarify the point here. CoStar’s reported margins are saddled with and weighed down materially by their massive investments in residential segment. Back in 2019, they reported \~32% EBITDA margin, and yet reported only MSD EBITDA margin in 2024. At first glance, it may seem CoStar’s EBITDA margin was only 3.7% in 2Q’25\. But in reality, their core EBITDA margin was 43% in both 1Q’25 and 2Q’25\. This not only shows how much margins have expanded since 2019, but also reminds just how massive CoStar’s investments are in Homes i.e. residential segment. So, while CoStar’s reported EBITDA appears meager and hence multiples appear stratospheric in any data platform providers such as Bloomber/CapIQ/KoyFin, the reality is their current annualized core (ex-residential and Matterport) EBITDA is \~**$1.2 Billion**. ![](https://substackcdn.com/image/fetch/$s_!PqU9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fec5e873d-40c9-44aa-8c33-79aabb178a75_1273x823.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let’s look at segment by segment now. **CoStar Suite** CoStar subscribers increased to 275k, +19% YoY primarily driven by the ongoing migration of STR users into CoStar and the addition of new STR subscribers to the CoStar platform. Renewal rate remained at 93%. The worst for CRE is likely behind them which could be a slight tailwind for CoStar suite going forward. From the call: > “The CRE market continues to face difficulties, particularly in the office segment with persistently high vacancy rates, though moderating and slightly worsening negative net absorption rates. **We're seeing a sharp decline in new deliveries, which should help stabilize** the office market in the near future. Transaction volumes have maintained a positive seasonal trend, with Q2 **up 43%** year-over-year.” **LoopNet** LoopNet generated more net new business in 1H’25 than the entirety of 2024\. As a result, CoStar now expects revenue growth to exceed 10% in 2H’25. **Multifamily** Multifamily (mostly apartments.com) saw $45 million in net new bookings, which was +20% YoY and the fourth highest quarter ever for net new bookings. They are also increasing their sales team to 500 in 2025. As of 2Q’25, 83k multifamily communities are advertising on CoStar’s platform. This increased from 62k in 4Q’22, 71k in 4Q’23, and 75k in 4Q’24\. Just in 1H’25, CoStar added 7.6k new apartment communities, more than what they added throughout all of 2024 (and they highlighted they did so without steep discounting). So, clearly there is a strong momentum here. There have been some concerns that Zillow has been closing the gap with CoStar in multifamily segment. Naturally, Florance took some shots at Zillow. From the call: > “Apartments.com continues to deliver more leads and **nearly twice as many leases as our 2 closest competitors combined** according to Entrata data. > > I think we're conflating 2 different things here. Obviously, the product is extremely strong with very high NPS, renewal rates, growing bookings, robust sales bookings, growing ASP. And that's been conflated a little bit with looking at a lot of purchasing clients by our competitor paying top dollar to buy share from Redfin and from Realtor. That's relatively low quality advertisers coming in. > > **The ASP on those properties is dramatically below the ASP on Apartments.com**. So I would say we feel that we're in a very strong competitive position and nothing is changing.” CoStar’s CFO tried to lower the temperature by reminding investors that both Zillow and CoStar can coexist. From the call: > “the greenfield TAM in this industry is still massive. And so this concept of wallet share taking wallet share really isn't applicable here given how large the TAM are. We're both competing and there's massive TAM.” **Matterport** Matterport is the recent $1.6 Billion acquisition by CoStar. It generated $44 million revenue in 2Q’25, but they’re closing some non-core operations in Matterport which generated $14 million annual revenue but posted $10 million losses. So, their revenue will decline a bit in the next quarter or two but profitability will have a tailwind after shutting down the non-core elements. Management reminded Matterport’s value proposition to CoStar’s businesses: > 40% of apartment seekers look for communities in different cities and 41% are willing to rent site unseen if you provide high-quality imagery. Significantly, 53%, they'll say they will stop considering a rental unit without detailed imagery. Consumers love the Matterport experience on Apartments.com. In Q2, they viewed Matterports 67 million times, up 193% over the same period last year, spending 71% more time on listing detailed pages with the Matterport 3D tour. Listings with the Matterport 3D tour received 23x more leads than those without. Here too CoStar and Zillow have two contrasting approaches. Here’s what Zillow CEO [said](https://stratechery.com/2024/an-interview-with-zillow-ceo-jeremy-wacksman-about-evolving-strategy/?ref=mbi-deepdives.com) in an interview with Ben Thompson last year: > So a traditional listing, which a photographer goes out and shoots 25 to 50 photos of each room, and then you write a description and you put a beds, bath, and price and you upload it to MLS and that’s okay circa 2006\. But as you just said, we all want to really virtually tour our house as much as possible before we can, you have to capture a little bit more data to do that. > > There was a bunch of technology coming that was super high-end, dedicated hardware, set it up in every room and it would kind of do a whole bunch of really high-res panos, and you could do this kind of virtual tour thing. **The problem with that is that in real estate, no one’s going to spend the money on the capture, you have to make the capture free fast and easy, you have to get to the level of like a hundred dollars 360 camera or even the new iPhone camera**. That’s what we’ve spent a bunch of time working on, is you can now create the showcase listings with just an iPhone and with just grabbing a few panos in each room and then we do a bunch of photo location. > > We do a bunch of AI human-in-the-loop assisted annotation for the floor plan and you end up with what you’re describing is an interactive floor plan where all the photos are on the floor plan, and you can see on each photo which way it’s pointing in the room, and you can walk through the house and generative AI stitches all that together to make it feel like it’s a 3D walkthrough. CoStar, on the other hand, is betting real estate industry will choose to spend money. From the call: > We intend to shift Matterport towards a business-to-business or B2B approach. The Matterport Pro 3 camera delivers a superior capture experience and a very superior display experience compared to mobile devices. **Customers using the Pro 3 camera have an 85% renewal rate for our SaaS services, while those using an Android phone only have a 40% renewal rate**. We plan to invest time and capital in developing even more advanced cameras, appropriately named the Matterport Pro 4 and the Pro 4 Ultra specs to be revealed one day. While Zillow’s approach is more capital efficient, I do wonder if CoStar’s approach yields better results that CoStar can quantitatively show to its customers, there’s no reason why customers wouldn’t pay for such capture. But I think the real question is whether the results can stay dramatically ahead of what you can capture through your phone. **Homes** Finally, Homes.com or what I like to call CoStar’s “Metaverse” bet i.e. just as Meta shareholders have to pinch themselves every year that Zuck is indeed plowing $20 Billion losses every year in Reality Labs for years without much to show for so far, CoStar is sort of making similar bet with Homes. Even worse, the “investments” in Homes are so large that it has been almost devouring all of the core EBITDA. The update here is CoStar is doubling down on Homes. Homes.com sales force is increasing to 750 by the end of 2025 (vs 230 in 2024). To be fair, there is some glimmer of hope as the expanding sales force drove 5% MoM growth in May and 15% in June. They 6,300 net new members, +56% increase in membership during the quarter. Some more encouraging quotes from the call related to Homes: > Member agents listings on Homes.com achieved 22x greater reach compared to nonmembers, significantly enhancing consumer engagement. > > Unaided awareness has grown from 4% at the launch in 2024 to over 36% in Q2. > > Listings from members received 7x more detailed views, 4x more favorites and 6x more shares, resulting in faster sales and higher selling prices. Leveraging these marketing advantages, Homes.com members secured 62% more listings than nonmembers with an outstanding return on investment. Especially given the average new listing commission value of $15,000 against a monthly membership fee under $500. > > Our NPS grew from a modest 3 in Q4 of 2024 to 9 in Q1 of '25\. And then it jumped substantially to 38 in Q2, marking a 340% quarter-over-quarter increase. > > The newly launched Boost product has been successful. Boost provides sellers and their agents with a flexible marketing option, allowing single property listings to be boosted on Homes.com to benefit from membership level marketing. Since Q2 launch, we sold 1,270 Boosts. Boosted listings reach over 14,000 homebuyers with an average of 32 views per buyer making boosted listings 25% more likely to go under contract within 10 days. These are all great, but any optimism definitely gets shot a bit when you look at revenue which was just $28.4 million last quarter. CoStar now expects residential revenue to grow 20% to $120 million in 2025\. Given that they’re spending $1 Billion per year for the last couple of years make it…not good enough. With 750 sales people, we better see more than 20% growth in residential in 2026 or the investments here need to be rationalized appropriately. One particular point by Florance that was interesting is he mentioned “*One of the beauties of our business model is that unlike our competitors that can only really sell to 5% of the market, our business model can sell to 60%, 70%, 80% of the market, which is why we love it and why investors should too.*” Since CoStar doesn’t “divert” consumer inquiries away from the listing agent; it connects buyers directly with the seller’s agent. If you target listing agents and brokerages this way, you can, in principle, sell to a very large share of active agents who take listings. CoStar mentioned they size this opportunity to be 500-750K active agents. Given the current average membership fee is $500, that makes $3 to 4.5 Billion revenue opportunity. I’m not sure such an opportunity deserves $1 Billion investments per year, especially given the probability of success here is certainly not high. CoStar management has been battling these strident remarks on their Homes investments from investors for the last few quarters, so perhaps that propelled them to share a new slide in last quarter. They’re basically saying to investors “hey buddy, we know what we are doing. Just chill a bit”. Fair enough, Mr. Florance. But you may not want to coast on past glories for too long! I will share some thoughts on current valuation behind the paywall. ![](https://substackcdn.com/image/fetch/$s_!JuDS!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fab543325-5762-4280-ac80-ebe409fd3733_1458x442.png) Source: To calculate IRR and investment return multiples, CoStar estimates the market value of each brand, as of June 30, 2025, using 10-year historic average commercial revenue and Adjusted EBITDA multiples. The IRR is calculated using the combined investment, estimated market value and the time elapsed since the combined investment was completed, and investment valuation multiples are calculated by dividing market value by the combined investment --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Texas Instruments 2Q'25 Update URL: https://www.mbi-deepdives.com/txn2q25/ Last updated: 2025-07-25T14:23:05.000Z You hardly ever see sell-side analysts being livid to management in an earnings call, but TXN’s 2Q’25 call was a bit spicy. After the prepared remarks, Q&A started with a combative tone from an analyst probing management about “change of tone” compared to last quarter. Then couple other analysts also pressed on “tone”. Just read this interaction between BofA Analyst Vivek Arya and TXN CEO Haviv Ilan: > **Vivek Arya**: Haviv, sorry to go back to this tone change because it's not just from the last earnings call. It's at the end of a conference at the end of May, I think you had suggested that every remaining quarter of '25 will accelerate from the first half up 13%, but your Q3 sales guide is up only 11%. So my question is that versus that reference point, which end market has softened? Is it that the industrial normalization is done? Is it that auto, right, was a little weaker? Or is that just extra conservatism on TI's part? Because the tone change is, as I mentioned, not just from earnings, but from the end of May. > > **Haviv Ilan:** Yes. And again, I don't control probably tone level, but that's you guys are hearing... > > **Vivek Arya**: But you quantified it, Haviv. You quantified; it wasn't just tone. TXN management actually attended BofA conference in early June, so I went back to see what exactly management said. I gotta say the analyst comes across a little too eager to interpret things management didn’t exactly mention: ![](https://substackcdn.com/image/fetch/$s_!2W3r!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0a888d41-1b85-42c3-8f62-0da38cf4c0e0_1786x786.png) Nonetheless, I do admit I too got the the impression of a much better tone in Bernstein conference TXN management attended in May. See the below interaction from May: ![](https://substackcdn.com/image/fetch/$s_!FoV3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1f675818-69ae-4a54-8451-f865cb25ef74_1807x796.png) Given this context, it’s perhaps not a surprise that the stock is down \~15% since reporting earnings a couple of days ago. Here’s my highlights from the earnings. **Revenue** I have shown below revenue growth by segment from both YoY and QoQ perspective. As you can see, the cycle has clearly turned. Analog revenue, which is \~80% of overall revenue, accelerated from \~13% YoY in 1Q’25 to \~18% in 2Q’25\. Similarly, sequential revenue accelerated from \~1% in 1Q’25 to \~8% in 2Q’25. ![](https://substackcdn.com/image/fetch/$s_!iNns!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe27143ef-9be9-4d73-b7be-fd9b147986df_2005x358.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Margins** This is a cyclical business, so margins go up and down along the cycle too. Both gross and operating margin improved both from YoY and QoQ perspective. ![](https://substackcdn.com/image/fetch/$s_!UT6C!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F226ff9a4-41f6-40b7-806d-9000e0121c94_2001x289.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Inventory Days on hand also fell a bit last quarter. Given that there’s not much of an obsolescence risk for TXN’s inventory and will gradually come down through the cycle over time, it’s not a huge headache for me, but it’s good to see inventory days starting to trend in the right direction. ![](https://substackcdn.com/image/fetch/$s_!UYaa!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe638cad5-dc1b-4695-9650-adb7ad298ec7_1444x733.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) In terms of end markets, I have gathered management’s commentary in both 2Q’25 and 1Q’25\. Apart from automotive which declined by LSD sequentially (but was +MSD YoY), every other end market grew both sequentially and YoY. Industrial, which is the largest segment, accelerated from HSD sequential growth in 1H’25 to mid-teens sequentially. ![](https://substackcdn.com/image/fetch/$s_!iEH4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3929c081-2a71-437d-ac12-a63ecda117d3_1212x285.png) Source: MBI Deep Dives Here’s what management said about automotive lagging the rest of the end markets: > “…automotive, let's just remember that it's kind of a year delayed versus industrial, right? Industrial peaked for us at least in the third quarter of 2022\. Automotive picked 1 year later in the third quarter of 2023\. So one could expect automotive to be joining last. The automotive recovery has been shallow, meaning we are running single digits versus the peak, we are running year-over-year. We are actually having some growth in the second quarter from a year-over-year perspective, but at a very low level. So I will say that automotive has not recovered yet. But because of content growth, I think the cycle here is going to be less pronounced and more shallow.” It’s hard to blame management too much for their optimistic tone last quarter as Q2 was indeed pretty good; however, this is not an easy business to forecast with high precision couple of quarters out, especially given the massive volatility around tariff and trade policies which are above their paygrade. Again, from the call: > …what we saw in Q2 is probably a combination of customers wanting to have a little bit more inventory because of tariff and also the cyclical recovery. When customers make orders, they don't tell us why they want more parts. And I would assume that some of it was for building a little bit of inventory on their shelves to protect themselves from tariffs, if you will. So that is my assumption. Again, I don't know how the third quarter will play out, but that's part of the way we are forecasting Q3. **Outlook** Given the uncertainty, TXN provided a wide range in their revenue and earnings outlook. For 3Q’25, their revenue guide is $4.45 billion to $4.80 billion and EPS range is $1.36 to $1.60. Management also reminded that the upcycle may have lot more room to run than earlier ones once (if) we get past the tariff related uncertainties: > This recovery is very, very different from any previous one. You can see it also at the slope of the recovery when you look at the overall WSTS without memory trend, you can see a not very sharp return to trend line. **We are still running 12% or 13%, I believe, below trend line**. And there is a lot of -- usually, when a cycle establishes itself, you first have to get a trend line, and then you have to establish the next peak. **We are still running double digits percentage-wise on units below trend line.** They also highlighted recent changes in US tax legislation which would increase tax rates in 3Q’25 but will decrease cash taxes for the “next several years”. I will share some thoughts below the paywall what I intend to do with my TXN position. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Alphabet 2Q'25, Corpay M&A, Portfolio Changes URL: https://www.mbi-deepdives.com/goog2q25/ Last updated: 2025-07-24T14:53:40.000Z Alphabet had another fantastic quarter. Admittedly, I am a bit surprised that the stock didn’t react even more positively. Here’s my highlights from the earnings. **Revenue** On an FX adjusted basis, Alphabet increased its revenue by 13% in 2Q’25 (\~100 bps tailwind from FX). For the **12th** consecutive quarters, Google network’s revenue went down. I’m kidding of course, but at this rate, open web may die faster than regulators decide Google’s fate in this business! Everything except the network business grew at a healthy double digit rate. Google Cloud is now at $54.5 Billion revenue run-rate, growing at an incredible \~32% YoY in 2Q’25, a 362 bps **acceleration** in growth from 1Q’25. ![](https://substackcdn.com/image/fetch/$s_!_Hda!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F39496e41-d12a-4c26-9dbc-b73dc49b6c34_1998x357.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Take a look at growth rates by segment over the last 14 quarters. ![](https://substackcdn.com/image/fetch/$s_!XEB0!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2a10634e-8253-4e8e-8064-985962fba957_1819x313.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** After 8 consecutive quarters of incremental operating margin of \~50%+, Google service’s incremental and reported operating margin was somewhat similar at \~40% in 2Q’25\. However they did have $1.4 Billion legal charge, so margin would be slightly higher if you adjust for that. These legal expenses may be recurring enough for Google that it may even make sense to not adjust it (only half-joking). Google Cloud’s margin ramp up over the last 5 years is indeed a thing of beauty. From -47.4% operating margin in 2Q’20 to +20.7% in 2Q’25, I am not sure I have ever seen such margin expansion in just five years. ![](https://substackcdn.com/image/fetch/$s_!qgny!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff6b986ba-8dc7-44e7-9327-a1ce041525cd_1920x439.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ## **Search** Most investors (including yours truly) are understandably concerned about search, and thankfully, there were lot of positive data points on search from the call. AI overviews now reach 2 billion MAU (vs 1.5 billion mentioned in 1Q’25 call) across more than 200 countries and territories and 40 languages. AI overviews now drive **10% more** queries globally for the types of queries that show them. AI mode has been rolling out in the US and India, and currently has 100 million MAU. Google Lens search grew 70% YoY which they again reminded investors that majority of lens search are incremental. More importantly, they’re seeing healthy growth for shopping queries using lens which are obviously more commercial queries in nature. Both overall queries and commercial queries on Search **grew** year-over-year, but they didn’t specify the exact number. **Paid click grew 4% YoY. (vs 2% in 1Q’25)**. Given the commotion around paid click number last quarter, Google management tried to downplay the importance of this number. From the call: > “we manage the business to drive great outcomes for our users and an attractive ROI for our advertisers. We actually don't manage to pay clicks and CPC targets. Some of the product and policy changes we make actually drive better monetization at the expense of paid clicks.” I’m not sure if the market would be too generous if paid clicks growth turns flat or negative. In fact, if this number is not so helpful in gauging the health of the business, it may make sense for Google to stop disclosing it. Investors may react negatively at first but then would learn to live without the data. Two interesting data points that Google shared for advertisers are: a) advertisers that activate AI Max and Search campaigns typically see 14% more conversions, and b) campaigns using Smart Bidding Exploration see a 19% increase in conversions on average. Moreover, more than 2 million advertisers now use Google's AI powered asset generation tools to run ads, a 50% increase YoY. Those AI investments are coming handy! Speaking of AI investments, Google laid out its case why they are ahead of the pack in the AI race. From the call: > “We are seeing significant demand for our comprehensive AI product portfolio. Of course, this is all possible because of the long-term investments we have made in our differentiated full stack approach to AI. This spans AI infrastructure, world-class research, models and tooling and our products and platforms that brings AI to people all over the world. > > We have some of the best models available today at **every price point**. Our 2.5 models have been a catalyst for growth and 9 million developers have now built with Gemini. I also want to mention Veo 3, our state-of-the-art video generation model. It's been a viral hit with people sharing clips created in the Gemini app and with our new AI filmmaking tool Flow. Since May, over 70 million videos have been generated using Veo 3. > > At IO in May, we announced that **we processed 480 trillion monthly tokens across our surfaces. Since then, we have doubled that number, now processing over 980 trillion monthly tokens, a remarkable increase**. The Gemini app now has more than **450 million monthly active users**, and we continue to see strong growth in engagement with daily requests growing over **50% from Q1**. In June alone, over 50 million people used AI-powered meeting notes in Google Meet. And powered by Veo 3, our new short video product in Workspace called Google Vids reached nearly 1 million monthly active users. > > There's definitely exciting progress, **including in the models we haven't fully released yet**…I expect 2026 to be the year in which people kind of use agentic experiences more broadly I would not have guessed Gemini **app** has \~450 million MAUs. This is more impressive than Meta’s “Meta AI” MAU data because I’m assuming people had to download the app and use it to be counted in the MAU for Gemini app. Google is one of those rare companies that even its shareholders like to snicker on the management every once in a while. But when I take a step back, I find it hard to share that feeling. I mean Google bought the entire DeepMind at a [price](https://techcrunch.com/2014/01/26/google-deepmind/?ref=mbi-deepdives.com) Zuck is probably paying a single accomplished AI researcher. While Nadella gets all the adulation for turning Microsoft around (he should because it’s pretty damn impressive), it’s hard to deny that he completely missed the boat on AI and got **lucky** with their investments in OpenAI (TBD how lucky given the deal with OpenAI seems rockier by the day; it’s hard to ever feel comfortable if your fate is tied to Sam Altman). Unlike Nadella, Zuck definitely marched ahead on investing in AI long before it was cool, but it was Google which seemed to have prevailed much better in building the AI stack. Tim Cook probably was way behind the ball and still is in AI. Given that context, I really don’t see the reason to participate in a recurring call for Pichai’s resignation every once in a while whenever people are looking for a scapegoat. Pichai did seem to be a bit more forceful than usual in getting the message across in recent calls, and I do want to note that Pichai [tweeted](https://x.com/sundarpichai/status/1948152308333736031?ref=mbi-deepdives.com) the below image yesterday: ![Image](https://substackcdn.com/image/fetch/$s_!0A7q!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe675349b-a391-4764-adea-d31f1b76d584_1119x750.jpeg "Image") ### **YouTube** We knew it already that shorts now earn as much revenue per watch hour as traditional in-stream on YouTube, but Google mentioned that in some countries, it now even **exceeds in-stream's rate**. ### **Google Cloud** There was a slight error in the below chart I shared a few days ago. As mentioned earlier, Google Cloud’s revenue seems to track reasonably well to AWS 16 quarters apart. ![](https://substackcdn.com/image/fetch/$s_!t-Me!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76100825-4657-436e-96e0-1970e5c7c9e5_1234x739.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Some interesting quotes on Google Cloud from the call: > “the number of deals over $250 million, **doubling** year-over-year…in the first half of 2025, we signed the same number of deals over $1 billion **that we did in all of 2024.** > > the number of new GCP customers increased by nearly **28%** quarter-over-quarter > > more than 85,000 enterprises, including LVMH, Salesforce and Singapore's DBS Bank now build with Gemini, driving a **35x growth** in Gemini usage year-over-year. > > Google Cloud backlog increased **18% sequentially in Q2 and 38% year-over-year**, reaching **$106 billion** at the end of the quarter. This growth was driven by strong demand for our products and services from both new and existing customers” ### Waymo Some Waymo update in the call: > “Last month, Waymo launched in Atlanta, more than doubled its Austin service territory and expanded its Los Angeles and San Francisco Bay Area territories by approximately 50%. Waymo also launched teen accounts, starting with riders aged 14 to 17 in Phoenix. Overall, great momentum here. The Waymo driver has now autonomously driven over 100 million miles on public roads. And the team is testing across more than 10 cities this year, including New York and Philadelphia.” ### **Capital Allocation** In 2Q’25, Google returned 3x their FCF to shareholders through buyback and dividend which declined net cash position from $84 Billion to $72 Billion. Given the capex intensity, we may be heading towards zero net cash position (or even net debt?) by the end of this decade. ![](https://substackcdn.com/image/fetch/$s_!BNiq!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F273e63cc-6530-4d46-85b2-8cd69b228767_676x655.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ### **Capex and Opex** Google raised capex from $75 Billion to $85 Billion in 2025\. Market initially seemed to take it negatively, but eventually brushed off the concerns because in the call they again highlighted that they expect to remain in a tight demand supply environment going into 2026 which means 2026 capex is also going to be higher than anticipated. Google also mentioned the vast majority of the capex was invested in technical infrastructure with approximately 2/3 of investments in servers and 1/3 in data centers and networking equipment. I am not that worried about capex increases especially when the increase is driven by tight demand-supply environment. Even if Google overinvests for a year or two, they can easily absorb it for the next few years given the breadth of their own products and services that require similar infrastructure. Of course, depreciation is ramping up. It increased by 35% in 2Q’25 and management highlighted they expect depreciation to accelerate further in Q3. ![](https://substackcdn.com/image/fetch/$s_!Tqyk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F06d28150-b84d-40fd-80b3-c5413e023b4f_1828x232.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ### **Valuation** Since [3Q'22](https://mbideepdives.substack.com/p/goog3q22?utm%5Fsource=publication-search), I share the following valuation framework every quarter. The Services business seems to be currently priced at \~18x LTM EBIT, and Google Cloud at \~8x run-rate revenue. ![](https://substackcdn.com/image/fetch/$s_!tUWQ!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6f4a6ec2-24a1-43d8-b259-3d25ad7dc911_1837x373.png) I will discuss Corpay’s M&A deal and my portfolio changes behind the paywall. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Danaher 2Q'25 Update URL: https://www.mbi-deepdives.com/dhr2q25/ Last updated: 2025-07-23T13:28:53.000Z While S&P 500 is at its all-time high, Danaher is still \~34% below its peak from late 2021\. The company was beleaguered with pandemic hangover and destocking trends for much of 2022-24 period. While the worst seem to behind them, the business isn’t quite humming enough to get investors excited yet. Yesterday’s earnings report also wouldn’t probably excite anyone, but neither would it cause a shareholder to lose much sleep. Let me recap the business performance of 2Q’25. ![chart](https://substackcdn.com/image/fetch/$s_!2VJq!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F68458442-b034-49e4-98f1-02443e59da13_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) **Overall Danaher** Danaher’s revenue grew at Low Single Digit (LSD) rate last quarter. Geographically, reported revenue in North America and “high-growth” markets was basically flat. China declined by Mid Single Digit (MSD). Western Europe was the shining light in the last quarter. In fact, Danaher’s overall reported revenue increased by $193 million YoY in 2Q’25, but revenue from Western Europe **alone** grew by $186 million. Danaher reports its business in three broad segments: a) Biotechnology, b) Life Sciences, and c) Diagnostics. Majority of revenue in all three segments come from recurring revenue through consumables that are spec’d into regulated process or the specific equipment Danaher sells to the customers. This recurring portion of revenue gradually increased by almost 10 percentage point from 74.6% in 2Q’23 to 83.7% in 2Q’25. Danaher took a non-cash impairment charge of $432 million pretax ($328 million after-tax) on a genomics consumables business which led to a noticeable drop of GAAP EBIT margin. If you adjust that and given past acquisitions and resultant amortizations, it’s better to look at adjusted EBITA to gauge the underlying economics here. Overall adj. EBITA margin was flat yoy at 27.3% in 2Q’25. ![](https://substackcdn.com/image/fetch/$s_!yQIu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffe6f05be-9f44-4003-bf95-53d950d3f2b4_1147x379.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let’s look at Danaher now in segment by segment. **Biotechnology** Danaher’s biotechnology segment has two businesses: a) bioprocessing which is \~85% of the revenue in this segment, and b) the rest 15% is Discovery & Medical (D&M) which is more like the life sciences tool business. Bioprocessing revenue was up High Single Digit (HSD), but D&M down LSD. Danaher enjoyed Low Double Digit (LDD) growth in consumables, mostly driven by commercial demand and large pharma CDMO customers. Smaller customers are still below historical trends but they’re stabilizing a bit now. Danaher mentioned [monoclonal antibodies](https://chatgpt.com/share/6880d876-a7dc-800b-b3da-9e53fb7dc24e?ref=mbi-deepdives.com), which is \~75% of bioprocessing revenues, remain the largest investment area for their customers who have a healthy pipeline of new molecules in development. They continue to expect bioprocessing revenue to grow HSD in 2025. Equipment sales declined as customers continue to absorb capacity added over the past several years, and global trade uncertainty apparently delayed in some larger capital investment decisions. Adj. EBITA margins improved a bit YoY and was 40%+ for second consecutive quarters. \~92% of the revenue in this segment was recurring in nature in last quarter which tends to be higher margin business. If you notice, Danaher’s revenue actually declined by $35 Million in 2Q’25 vs 2Q’23, but their adj. EBITA still grew by $19 Million, primarily because the recurring mix of the revenue increased from \~81% to \~92% over that period. ![](https://substackcdn.com/image/fetch/$s_!w7Mk!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2e8d93e4-6a94-4125-9f4c-559c11887329_1150x406.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Life Sciences** Revenue was flat last quarter. Although 1H revenue is down LSD in this segment, Danaher still expects full-year to be flat. What will drive incremental improvement to finish the year flat? From the call: > “I would say that genomics, again, remember the first half, we had those two large customers really fall off. I think that's probably 1/3 of it. > > I would say that we are assuming China -- especially China, the tools with some comps -- easier comps and a better funding environment. There's another 1/3 of it. And then lastly, new products and kind of other things is the final 1/3\. So 1/3, 1/3, 1/3 between China, genomics, new products, other; that's sort of what we're assuming and are baked into the model from step-up from 1H to 2H of roughly $150 million” Danaher mentioned clinical and applied markets held up well globally, while demand from academic and government customers remained weak. Danaher took the impairment charge mentioned earlier in this segment which led to this segment reporting operating loss in 2Q’25\. However, adj. EBITA was 19.3% in 2Q’25, slightly down from 21.1% in 2Q’24. ![](https://substackcdn.com/image/fetch/$s_!VGa5!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F25d22f50-2623-41ee-899a-8637ffaa9edb_1150x381.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Diagnostics** Diagnostics revenue was up LSD, but it was actually up MSD outside of China. Beckman Coulter Diagnostics revenue increased by HSD outside of China which was their fourth consecutive quarters of MSD+ growth outside China. Cepheid global installed base was again mentioned to be 60,000 instruments which seems to be flat since that’s the same number mentioned in 2Q’24 call. During Covid, Cepheid’s installed base doubled and reached 50k by 1Q’23\. Since then, installed base growth has slowed materially (basically they mentioned \~60k in each of the last five earnings calls). Given the anemic revenue growth, margin contracted a bit. ![](https://substackcdn.com/image/fetch/$s_!vZsA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F813f5475-31a7-47c1-90d2-6e1168323b9a_1153x385.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Danaher management was asked whether AI is a headwind for biotech funding. Management thinks AI is likely a tailwind for them: > “…as it relates to AI, we really see that ultimately as a tailwind because we see then less money being spent on getting to compound ideas, if you will, and more money being spent taking great ideas, which have been validated in silico as they say, through the development pipeline, ultimately driving more manufactured and commercialized therapies. > > And of course, that's where our business is, where we have the most volume, of course, the most share. And so we view this really positively. But we have to say that we're at a low activity level currently in the discovery phase of the biotech market. I have highlighted an interesting [piece](https://www.mbi-deepdives.com/07-08-2025/) recently which also corroborated to the idea that AI can really help in drug development process. A world of therapeutic abundance should be very positive for Danaher. **Guidance** Revenue growth guidance for 2025 remains unchanged at 3%. But they did raise full year adjusted diluted net EPS guidance to a range of $7.70 to $7.80 vs previous range of $7.60 to $7.75. **Valuation** Compared to the past few years, Danaher’s multiples have noticeably come down. However, it is hard for a stock to go up if revenue growth hovers around LSD level. After a very extended hangover of Covid and inventory destocking by its customers, I thought 2025 would be the year of modest recovery with at least MSD growth. Of course, the trade tensions and difficult funding environment in broader healthcare delayed that to hopefully next year. It’s interesting to note that they bought back no share at all despite the stock being down over the last quarter. I suspect Danaher is likely preparing to do an acquisition sometime this year. Frankly speaking, I prefer that Danaher does a deal than buying back stocks. The whole sector has been reeling with challenges and there got to be some compelling opportunities which can have more compelling IRR than Danaher’s stock itself. Much of the life science stocks used to trade at nose bleeding multiples; now that they have all come down to a more tolerable level, it probably makes sense for Danaher to take that opportunity. I don’t have any plan to further add to my position, but given that this remains a very high quality recurring revenue with lofty profitability business, I remain comfortable owning a piece of this business. ![chart](https://substackcdn.com/image/fetch/$s_!rPe3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F05df9a7d-ab05-45ad-b47a-184ea0416f16_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Tariff conundrums, AI capex boom, Edge computing fallacy, AI ARR chicanery URL: https://www.mbi-deepdives.com/07-22-2025/ Last updated: 2025-07-22T15:26:54.000Z ***A Programming Note***: A couple of you have given me the feedback that instead of generic title of the Daily Doses, I should mention the topics covered in the title/email subject. As you can see, I have decided to implement it from today. --- **Tariff conundrums** While there was a broad consensus that higher tariffs will lead to price increases, the evidence so far has been largely mixed. Last month, Wirecutter [mentioned](https://www.nytimes.com/wirecutter/reviews/advice-wirecutter-tariff-pick-price-tracking/?ref=mbi-deepdives.com) that they tracked 40 Wirecutter picks over two months and prices of two-third of those products didn’t change at all (three, in fact, went down and the prices of the rest ten did go up). Yesterday, WSJ [published](https://www.wsj.com/business/retail/amazon-price-hikes-essentials-60a7c7f3?mod=djem10point&ref=mbi-deepdives.com) a report that looked into \~2,500 items and compared the price changes from January 20 to July 1\. Interestingly, their analysis found that on an aggregate basis of the sample, Amazon did raise price by mid-single digit percent for cheaper items whereas it actually decreased prices for more expensive items. There are perhaps many ways to look at this data, but I wonder if a good interpretation is that tariffs are much easier to pass through to consumers for basic necessities. The fact that prices for more expensive items declined may be more of a factor of demand softening for consumer discretionary items. Of course, Amazon has millions of SKUs. An analysis looking at even 2,500 items may not be representative of overall data, so I am not sure I want to extrapolate too much here. Earnings calls of these retailers in the next couple of weeks may be more instructive of overall impact from tariffs. ![](https://substackcdn.com/image/fetch/$s_!_l95!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd130c50-4672-4884-b82d-913ef6be0874_753x579.png) Image Source: [WSJ](https://www.wsj.com/business/retail/amazon-price-hikes-essentials-60a7c7f3?mod=djem10point&ref=mbi-deepdives.com) --- **AI Capex Eating Everything?** Paul Kedrosky put the scale of AI capex in context in his [piece](https://paulkedrosky.com/honey-ai-capex-ate-the-economy/?ref=mbi-deepdives.com) “*Honey, AI Capex is Eating the Economy* **”:** > Compare this to **prior capex frenzies**, like railroads or telecom. **Peak railroad** spending came in 19th century, and **peak telecom** spending was around the 5G/fiber frenzy. It's not clear whether we're at peak yet or not, but ... we're up there. Capital expenditures on AI data centers is likely around **20% of the peak spending on railroads**, as a percentage of GDP, and it is still rising quickly. And we've already passed the decades ago **peak in telecom spending** during the dot-com bubble ![](https://substackcdn.com/image/fetch/$s_!7leH!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5b462f9d-3bd6-438f-b625-9d1b842d5bcf_2000x1290.png) \*there’s a typo in this chart. It should be “Telecom (2000s)” Kedrosky has an ominous tone about this capex boom and even lamented the possibility that investing in such “rapidly depreciating technology” may be diverting fund from other productive areas in the economy. I find the argument that this investment rush is "starving" other sectors to be overstated; the primary funders are a handful of cash-rich technology giants reallocating enormous internal profits, a different dynamic than a broad-based diversion of capital across the entire economy. He also made the point that while railroads were century long investments, these AI capex is basically a [red queen’s race](https://en.wikipedia.org/wiki/Red%5FQueen%27s%5Frace?ref=mbi-deepdives.com). That analogy also seems to miss some critical differences between the two capex booms. Railroad expansion was a physically-constrained, sequential process where economic value was unlocked incrementally with each mile of track laid over decades. In contrast, today’s megacap tech companies are layering AI capabilities onto pre-existing digital distribution networks with billions of users. The output of this new AI datacenter infrastructure, e.g. a better algorithm, a new feature, a more efficient ad model can be deployed globally and almost instantaneously via software. Therefore, while he is correct that these are "short-lived, asset-intensive facilities" unlike "century-long infrastructure", the timeline to generate decent revenue from the investment is radically compressed from decades to potentially a couple of years. Nonetheless, the size of the capex relative to historical parallels is an interesting data point worth highlighting. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 61 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- **Edge computing fallacy** It’s much more commonplace to hide the opinions that turn out to be (at least temporarily) incorrect, so I appreciate this [piece](https://www.geninnov.ai/blog/edge-computing-a-eulogy-for-a-future-that-wasnt?ref=mbi-deepdives.com) by Nilesh Jasani self-reflecting why his thesis around edge computing didn’t play out as anticipated. But let’s remind us why edge computing had so much appeal in the first place. From Jasani’s piece: > In late 2023 and early 2024, the promise of edge computing was not a niche technical idea; it was a palpable hum of excitement. The logic was clean and compelling. Moving artificial intelligence to the edge would solve the technology’s most pressing problems. > > First, there was privacy. Processing data on your own device meant it never had to travel to a server owned by someone else. Your secrets would remain your own. Second was latency or annoying delays. With the thinking done locally, responses would be instantaneous, a crucial feature for everything from conversations with voice assistants and augmented reality to autonomous vehicles. Finally, there was cost and access. Why rent time on a remote supercomputer when your own device could do the work? This would democratize AI, making it reliable even without a perfect internet connection. So, what went wrong? The edge‑computing dream fizzled when the headline devices e.g. Humane’s AI Pin, Rabbit R1, Qualcomm‑powered “AI PCs”, and even flagship phones failed to run meaningful models locally and instead routed most “on‑device” features back to the cloud, exposing a gulf between marketing and reality. At the same time, scaling‑law breakthroughs made ever‑larger models dramatically more capable, spurring megacap tech companies to build colossal training clusters that intensified the data‑center’s gravitational pull and undercut the economics of decentralization. These cloud‑resident “agentic” AIs now orchestrate tasks on our gadgets from afar, leaving phones and PCs as sleek terminals and proving that narratives about privacy, latency, and cost cannot beat the brute‑force advantages of centralized compute. --- **Follow-up on AI ARR chicanery** I would like to have a quick follow-up on my Daily Dose from [July 20](https://www.mbi-deepdives.com/07-20-2025/). I mentioned this interesting quote by Replit’s CEO: > **It's very easy in AI to increase ARR while users are not happy because they're spending a lot more and like not getting the results** and in some cases maybe shouldn't grow that fast because like you'd want users to get a better experience for less less money and so it's one thing that we try not to obsess. As it turns out, there may be some historical parallels to this phenomenon. Marc Andreessen wrote this [piece](https://www.founderstribune.org/p/becoming-steve-jobs-foreword-by-marc-andreessen-b165?ref=mbi-deepdives.com) as a tribute to Steve Jobs back in 2024, but the following bit stood out to me: > If you had enough sales and marketing hype, or used enough FUD—Microsoft like IBM before them, was famous for pre-announcing products that weren’t even on the drawing board, to freeze the market—you could bluff your way through to market success while your product wheezed along. > > One of the things that went wrong in Silicon Valley in the 1990s—and one of the things that caused the crash in 2000—was too many Valley companies bought into that approach. So you had too many Valley companies that launched into market too fast and shipped subpar products. A lot of the products people used in the dot-com era, especially business products, were used out of fear—the fear of being left behind. Then 2000 and 2001 came around and everybody collectively said, “Holy Lord, these products are all crap.” And they all got dropped overnight, in many cases killing their companies. We really perhaps need to maintain two opposing ideas in our head at the same time. AI is revolutionary and is likely the most transformative technology of our time. Yet, it will also probably destroy (and create) a humungous amount of wealth. And we will be in various part of the hype cycle along the way. One thing is certain: we are definitely **NOT** in the “trough of disillusionment” part of the cycle. ![The Trough Of Disillusionment And Four Outliers On The ...](https://substackcdn.com/image/fetch/$s_!-ck8!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7fb32616-b142-4cf1-a129-2c6d96b4be48_710x533.jpeg "The Trough Of Disillusionment And Four Outliers On The ...") --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### Cognex: Agents of Automation URL: https://www.mbi-deepdives.com/cgnx/ Last updated: 2025-07-21T14:13:53.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- In 1981, Robert Shillman, a young lecturer in human-visual perception at MIT, resigned his faculty post, and teamed up with two of his top graduate students, Bill Silver and Marilyn Matz, to found a little startup in Massachusetts called **Cognex.** The name “Cognex” itself was a nod to their ambition, short for “**Cognition Expert”.** The very next year after the company’s founding, Cognex unveiled **DataMan**, the world’s first industrial-strength Optical Character Recognition (OCR) system. It was a computer that could **read** letters, numbers, and symbols printed or etched directly on products even where **no ink** was used. Imagine a silicon wafer or a car tire with identification codes laser-etched into its surface which is invisible to the naked eye yet crucial for tracking. DataMan could instantly decipher those codes. Cognex got some traction, and one of the first to notice was IBM. IBM became Cognex’s one of the first major customers, buying a DataMan system to read serial numbers on semiconductor wafers in its factories. That first installation read a humble 15 characters per second, but it proved the concept. In an era when factory automation was still in its infancy, Dataman could read what humans couldn’t, an early indication of machine’s superiority over human eyes. It was the start of a new way to ensure quality and traceability in manufacturing, free from human error or fatigue. ![](https://substackcdn.com/image/fetch/$s_!Yo1v!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffa6d3f88-4e48-4f49-8fd5-25b35e85a340_916x697.png) Image Source: Cognex Shillman or as commonly known inside Cognex as “Doctor Bob”, and his team understood that as products like computer chips and circuit boards got **smaller, faster,** and more complex, human inspectors would struggle to keep up. By the mid-1980s, Cognex had begun targeting high-tech sectors, especially semiconductors and electronics where automation was no longer a luxury but increasingly a requirement. Cognex went public in 1989 at a split-adjusted 34 cents a share. Despite the current \~60%+ drawdown, the stock has still been a 125-bagger in the public market over the last three and half decades. ![chart](https://substackcdn.com/image/fetch/$s_!ihN8!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd5224b20-8188-486c-b518-0e847bc9b9de_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) By the mid-1990s Cognex had morphed from a custom-projects boutique into a catalog company. The vast capabilities of machine vision can be distilled into four primary functions, which Cognex and the industry often remember with the mnemonic "**GIGI**": **G**uidance, **I**nspection, **G**auging, and **I**dentification. Machine-vision systems in manufacturing revolve around these four fundamental tasks: **guidance** to locate parts, **identification** to recognize or read them, **inspection** to detect defects, and **gauging** to measure dimensions. Mastering these capabilities lets automation equipment reliably position components, track and route items, verify quality, and confirm precise sizing on production lines. ![](https://substackcdn.com/image/fetch/$s_!Mcy3!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1db91ac3-a8f3-49e5-bf7e-8dccf12dabe8_1081x472.png) Source: Cognex 10-K A notable development was the introduction of [**In-Sight**](https://www.cognex.com/products/machine-vision/2d-machine-vision-systems?ref=mbi-deepdives.com) in 2000, Cognex’s first **compact, “smart” vision system** that integrated camera, processor, and software into a single rugged unit. In-Sight was designed to be so user-friendly that a line technician could set it up without writing code, a radical departure from earlier systems that practically required a PhD to program. This move opened up a much wider market of potential customers who needed vision but lacked specialized expertise. By bringing vision to the masses (or at least to the average factory engineer), Cognex primed itself for the next phase of growth. Cognex tapped the wider factory-automation market while the dot-com frenzy pushed semiconductor capacity orders to records. From 1992 to 2000, revenue leapt by almost \~9x to reach $251 million, operating income to $90 million, and operating margins to 36%. ![](https://substackcdn.com/image/fetch/$s_!YGPS!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9d057a27-2f25-4c3e-a2b1-740036e97d3d_925x199.png) Source: Company Filings, MBI Deep Dives As a result, the stock was **\~27x in the first 10 years in public market!** ![chart](https://substackcdn.com/image/fetch/$s_!SL9C!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe1ee78a8-bec2-494f-b676-4de246e883ae_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Then gravity returned. When the tech bubble burst, chipmakers froze capital budgets and cancelled tooling projects. Cognex’s top line fell whopping **44% in 2001 and slid another \~20% in 2002**. The collapse exposed a strategic vulnerability: almost \~80% of revenue still traced back to semiconductor and electronics industries whose fortunes rose and fell with memory prices and handset cycles. A revived electronics cycle, the popularity of In-Sight, and the 2004 relaunch of handheld DataMan scanners lifted revenue past its old record in 2010, when Cognex booked $291 million revenue. Profitability took longer. The dam finally broke in 2014, helped by a revenue surge from Apple, propelling operating income to $128 million, well clear of the 2000 watermark. ![](https://substackcdn.com/image/fetch/$s_!sds6!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F478ec0ac-56f2-468e-bda3-a7fd511537f2_1425x199.png) Source: Company Filings, MBI Deep Dives Why did it take fourteen years to eclipse those millennium-era peaks? First, Cognex had to replace its semiconductor dependency with a broader set of growth engines, from barcode readers that could survive the punishing world of logistics to 3-D sensors that measured [tire treads](https://www.cognex.com/industries/automotive/tire-and-wheel-systems/tire-assembly-inspection?ref=mbi-deepdives.com) and [glue beads](https://www.cognex.com/industries/automotive/chassis-systems/bead-inspection?ref=mbi-deepdives.com). Second, the shift from high-ticket OEM boards to lower-priced, high-volume smart cameras meant climbing the same mountain with smaller steps; unit volumes rose even as average selling prices fell. Finally, every expansion required new domain expertise, channel partners, and, critically, the patience to deliver the cost-per-pixel economics that made vision attractive to mass-market factories. Nonetheless, there is no denying the fact that this remains a cyclical business. Cognex’s fortune still rises and falls with the capex cycle of its customers. Historically, investors haven’ quite looked through such cyclicality and the stock went through repeatedly steep drawdowns. ![chart](https://substackcdn.com/image/fetch/$s_!uC4Y!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F63fc08c7-df1d-4855-9d78-fe4938c74b6d_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) As you can see, Cognex’s origin story was filled with vision (literally), rapid growth, and a hard-earned lesson in cyclicality. But what does the business look like today? That’s what I will detail in section 1. In section 2, I will discuss the competitive dynamics in this industry, especially against Keyence and Chinese competitors. I’ll also touch on the relevance of Cognex’s products in the age of AI. In Section 3, I will elaborate on Cognex’s capital allocation history, current philosophy, and management incentive structure. In section 4, I will show what is likely currently embedded into the stock price. Finally, in section 5, I will offer some concluding thoughts and disclose my overall portfolio. Subscribe to keep reading! [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### MBI Daily Dose (July 20, 2025) URL: https://www.mbi-deepdives.com/07-20-2025/ Last updated: 2025-07-20T22:16:03.000Z *Companies or topics mentioned in today's Daily Dose: YouTube, Replit* --- ***A Programming Note***: There will be no Daily Dose email tomorrow as I will be publishing my Deep Dive on Cognex tomorrow. A good rule of thumb is you will only receive one email from me everyday. So on the days I will be publishing Deep Dives, I will skip the Daily Doses. Since we are heading towards earnings season this week, let me set some expectations here. While I used to publish my earnings recap in the evening of the earnings, I am moving the publishing schedule to the next morning. For example, Alphabet is reporting its earnings this Wednesday; I expect to publish a recap on Thursday morning. If there are multiple companies in my coverage reporting earnings on the same day, I will recap one of them the next day and then the other one(s) in the following day(s). --- YouTube has seized the television throne in the US, eclipsing every broadcast, cable and streaming rival. Its ascent reflects mobile‑native audiences aging into the living room and a deliberate push by creators to produce longer, family‑friendly shows while Google makes the TV app more like its phone counterpart with smarter recommendations and remote‑friendly navigation. From [WSJ](https://www.wsj.com/business/media/how-youtube-won-the-battle-for-tv-viewers-346d05b8?mod=djem10point&ref=mbi-deepdives.com) yesterday (emphasis mine): > YouTube became the most-watched video provider on televisions in the U.S. earlier this year, and its lead has only grown, according to Nielsen data. People now watch YouTube on TV sets more than on their phones or any other device—an average of more than one billion hours each day. **That is more viewing than** [**Disney**](https://www.wsj.com/market-data/quotes/DIS?ref=mbi-deepdives.com) **gets from its broadcast network, dozen-plus cable channels and three streaming services combined**. ![](https://substackcdn.com/image/fetch/$s_!p723!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9faae513-1145-4a8c-b21b-17d4c82ff704_640x516.png) Image Source: WSJ More from the same piece: > Research from Tubular Labs shows viewers on YouTube are spending substantially more time with long content—meaning more than 15 minutes—than two years ago, and they are doing it the most on televisions. YouTube producers are noticing and making longer videos, which keep audiences glued to their channel for longer stretches. ![](https://substackcdn.com/image/fetch/$s_!Q5p4!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1e51936a-4077-46ed-856f-b38217ffc283_427x553.png) Image Source: WSJ What strikes me from the above chart is just how quickly TV has gained share in YouTube’s overall viewing. If this continues for the next 3-5 years, I do think YouTube’s recommendation engine could become the **principal gatekeeper** of televised attention, **accelerating** an advertising‑first, creator‑centric and algorithm‑mediated media ecosystem. Of course, these pieces always probe the question what exactly would YouTube be worth if it were a standalone company, especially when most Google shareholders are not quite oblivious to the potential erosion of search moats in the next 5-10 years. Most people tend to look at Netflix to draw a comparison. Netflix is currently worth \~$520 Billion. YouTube still doesn’t report its overall revenue (ads+ subscription), but MoffettNathanson estimates YouTube’s overall revenue last year was $54.2 Billion which was \~40% higher than Netflix’s revenue in 2024\. However, Netflix has \~30% operating margin and given YouTube’s structural lower gross margin, my best guess for YouTube’s operating margin is somewhere between 10% and 20%. If you take the mid-point at 15%, YouTube’s operating income would be \~20-25% lower than Netflix’s reported EBIT last year. One can argue that YouTube has greater moats and durability than almost any internet assets out there today and frankly speaking, I tend to agree. If that’s the case, you can make the case that YouTube’s “fair” multiple would be higher than Meta or Netflix. Of course, this pre-supposes that Netflix itself is trading at fair multiple (and that may not be the case), but it indeed does seem reasonable to think YouTube would be valued at \~$500 Billion if it were a standalone company today. --- I listened to [this interview](https://www.youtube.com/watch?v=kOyIjt6FUrw&t=38s&ref=mbi-deepdives.com) by Replit’s CEO yesterday. The CEO and hosts both made the point that it’s fairly easy for AI companies today to boost ARR given almost everyone is trying to experiment with AI, but the churn is through the roof for most of these companies. It may be rude awakening for many such companies when their customers decide to tighten the belt in AI experimentations. From the interview: > …since Replit agent launch, we're growing 45% compound monthly average. You know it put a lot of strain on the company, and our systems were still relatively small. I I feel like it can get to to your head and you can start optimizing for the wrong thing. **It's very easy in AI to increase ARR while users are not happy because they're spending a lot more and like not getting the results** and in some cases maybe shouldn't grow that fast because like you'd want users to get a better experience for less less money and so it's one thing that we try not to obsess. **We actually don't have ARR goals at at Replit**. We have like more product goals, retention goals just like other methods. > > …it's all kind of a blur for them (investors) because investors, I mean I'm just going to generalize here, but when they start looking at the space they'll use everything for three minutes and everything for three minutes looks the same. It’s a bit ironic and funny that right after I listened to the interview and was scrolling twitter, the following [tweet](https://x.com/GergelyOrosz/status/1946783581570736362?ref=mbi-deepdives.com) showed up at the top of my feed: ![](https://substackcdn.com/image/fetch/$s_!AMpR!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Facbf35a6-711b-4691-93c6-8f4c9dab5571_724x1177.png) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year*.** [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 19, 2025) URL: https://www.mbi-deepdives.com/07-19-2025/ Last updated: 2025-07-20T22:16:27.000Z *Companies or topics mentioned in today's Daily Dose: OpenAI Agent, Elephant Graph* --- OpenAI launched “[OpenAI Agent](https://openai.com/index/introducing-chatgpt-agent/?ref=mbi-deepdives.com)” last week. It’s basically a marriage between “Deep Research” and “Operator”, but what’s new is the orchestration layer on top i.e. an agentic planner that decides when to invoke which skill, manages short‑term working memory, and runs multi‑step workflows inside the familiar chat interface, rather than two separate tools. I still haven’t got access to it, so I haven’t played with it yet. But I was quite impressed with the tool when I saw it in action [here](https://chatgpt.com/share/687a765f-21e4-8000-bd99-c28976837050?utm%5Fsource=substack&utm%5Fmedium=email) which asked the agent to perform a redesign of Wikipedia in the styles of Airbnb and OpenAI websites (I came across this example via “[Enterprise AI Trends](https://nextword.substack.com/p/chatgpt-agent-mode-and-vibe-automations?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcf144821-649f-423d-b5db-143e77cfac04%5F1816x1292.png&open=false)” Substack). The agent worked on it for 50+ minutes; I suggest you click the [link](https://chatgpt.com/share/687a765f-21e4-8000-bd99-c28976837050?utm%5Fsource=substack&utm%5Fmedium=email) to watch it work on this problem (just watch for a couple of minutes). So, here’s the prompt: *I want two variations of a redesign of wikipedia.com in the style of 1) OpenAI's home page, as well as 2) Airbnb's home page. When doing the redesign, use the same aesthetics and design system (typography, spacing, etc) that you can infer from the respective home pages. Just the home page itself will be fine.* Let me show you the final output of the redesign in OpenAI style. It’s pretty damn impressive! ![](https://substackcdn.com/image/fetch/$s_!fsB1!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2529a3a5-5ef0-4281-bb3a-24124f0ad95c_2215x1204.png) --- I recently read this [paper](https://documents1.worldbank.org/curated/en/959251468176687085/pdf/wps6259.pdf?ref=mbi-deepdives.com) which probes into the winners and losers of globalization during 1988 to 2008 period. Most of it lines up with my intuition, but still quite interesting to see the numbers. Some excerpts from the paper (emphasis mine): > What parts of the global income distribution registered the largest gains between 1988 and 2008? As the figure shows, it is indeed among the very top of the global income distribution and among the “emerging global middle class”, which includes more than a third of world population, that we find most significant increases in per capita income. The top 1% has seen its real income rise by more than 60% over those two decades. **The largest increases however were registered around the median: 80% real increase at the median itself and some 70% around it. It is there, between the 50th and 60th percentile of the global income distribution that we find some 200 million Chinese, 90 million Indians, and about 30 million people each from Indonesia, Brazil and Egypt. These two groups—the global top 1% and the middle classes of the emerging market economies— are indeed the main winners of globalization.** > > The surprise is that **those at the bottom third of the global income distribution have also made significant gains, with real incomes rising between more than 40% and almost 70%. The only exception is the poorest 5% of the population whose real incomes have remained the same.** It is this income increase at the bottom of the global pyramid that has allowed the proportion of what the World Bank calls the absolute poor (people whose per capita income is less than 1.25 PPP dollars per day) to go down from 44% to 23% over approximately the same 20 years. ![A graph with blue lines and a white background AI-generated content may be incorrect.](https://substackcdn.com/image/fetch/$s_!aO0l!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F62a95c20-eb8b-43dd-a72a-4f37eb1d10b5_814x635.png "A graph with blue lines and a white background AI-generated content may be incorrect.") This is more commonly known as “Elephant Graph” because it kinda looks like an elephant > **But the biggest losers (other than the very poorest 5%), or at least the “non-winners,” of globalization were those between the 75th and 90th percentiles of the global income distribution whose real income gains were essentially nil. These people, who may be called a global upper middle class, include many from former Communist countries and Latin America, as well as those citizens of rich countries whose incomes stagnated.** > > Global income distribution has thus changed in a remarkable way. It was probably the profoundest global reshuffle of people’s economic positions since the Industrial revolution. Broadly speaking, the bottom third, with the exception of the very poorest, became significantly better-off, and many of the people there escaped absolute poverty. The middle third or more became much richer, seeing their real incomes rise by approximately 3% per capita annually. > > **The most interesting developments, though, happened among the top quartile: the top 1%, and somewhat less so the top 5%, gained significantly, while the next 20% either gained very little or faced stagnant real incomes. This created polarization among the richest quartile of world population**, allowing the top 1% to pull ahead of the other rich and to reaffirm in fact -- and even more so in public perception -- its preponderant role as winners of globalization. > > Who are the people in the global top 1%? Despite its name, it is a less “exclusive” club than the US top 1 percent: the global top 1% consists of more than 60 million people, the US top 1% of only 3 million. Thus, among the global top percent, we find the richest 12 percent of Americans (more than 30 million people) and between 3 and 6 percent of the richest Britons, Japanese, Germans, and French. It is a “club” still overwhelmingly composed of the “old rich” world of western Europe, northern America and Japan. The richest 1% of the embattled Euro countries of Italy, Spain, Portugal and Greece are all part of the global top 1 percentile. However, the richest 1% of Brazilians, Russians and South Africans belong there, too. > > To which countries and income groups do the winners and losers belong? Consider the people in the median of their national income distributions in 1988 and 2008\. In 1988, a person with a median income in China was richer than only 10% of world population. Twenty years later, a person at that same position within Chinese income distribution, was richer than more than one-half of world population. Thus, he or she leapfrogged over more than 40% of people in the world. > > So who lost between 1988 and 2008? **Mostly people in Africa, some in Latin America and post-Communist countries**.” --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 18, 2025) URL: https://www.mbi-deepdives.com/07-18-2025/ Last updated: 2025-07-20T22:16:58.000Z *Companies or topics mentioned in today's Daily Dose: AMA, Voidware, OpenAI's Monetization* --- **AMA**: David Kim from [Scuttleblurb](https://scuttleblurb.substack.com/) and I will record an AMA episode for the next Never Sell podcast. Please reply to this email to send your questions! --- Matan Zinger [acquainted](https://94040.substack.com/p/why-voidware-is-a-big-deal?utm%5Fsource=post-email-title&publication%5Fid=3207683&post%5Fid=168567726&utm%5Fcampaign=email-post-title&isFreemail=true&r=9x0z5&triedRedirect=true&utm%5Fmedium=email) me with a term yesterday that I did not know before: Voidware. What is Voidware? Picture an app that materializes at your tap, evaporates the moment you let go, and lives entirely in an AI’s imagination; no servers, no stored code, just pure on-demand existence. This “model-is-the-app” trick is what Ohad Eder-Pressman [dubs](https://www.ohad.com/2025/07/10/voidware/?ref=mbi-deepdives.com) **voidware**: software that never truly exists before, during, or after you use it, like a soap bubble spun from machine logic. He [showed](https://www.ohad.com/2025/07/10/voidware/?ref=mbi-deepdives.com) a crude demo in his post what it looks like: 0:00 /0:34 1× If an LLM can spin up a bespoke interface, backend, and data model at millisecond cost, every user effectively gets a one-off product shaped by their current intent. Matan points out the implications can be quite profound. From his [piece](https://94040.substack.com/p/why-voidware-is-a-big-deal?utm%5Fsource=post-email-title&publication%5Fid=3207683&post%5Fid=168567726&utm%5Fcampaign=email-post-title&isFreemail=true&r=9x0z5&triedRedirect=true&utm%5Fmedium=email): > Through a Voidware-style AI-generating-code-on-the-fly model, not only do users get their own personal editor, but each user also gets their own PM, designer, and engineering team! > > A user turning to Twitter (*fine, X*) for a quick dopamine hit of memes can’t possibly be expected to share the same UX as the user looking to catch up on the latest insights around AI-assisted coding. While the former is probably well served by the existing app design, the latter would benefit from a NotebookLM-style research canvas with AI summaries. As I sometimes play the role of both of these user personas, it would be amazing if the app could – under different circumstances – understand the job I’m currently hiring it for, and morph itself accordingly. > > Imagine your personal LLM-PM tracking your Instagram usage, saying to itself something like *“Oh we’re diving into recipe ideas now, are we? Perhaps I should add a little button on top, that switches into a table view of your favorite recipes so far.”* Then the LLM-designer sketches something, and the LLM-Software-Engineer goes ahead and adds the button. Instantly. Just for yourself. Voidware hints at a future where apps are more like conversations, with LLMs acting as real-time product studios: think infinitely personalized UIs, ephemeral micro-services, and “feature streaming” instead of downloads. It is far from a crazy idea; Google already [showed](https://developers.googleblog.com/en/simulating-a-neural-operating-system-with-gemini-2-5-flash-lite/?ref=mbi-deepdives.com) that “*an interface could be generated in real time, adapting to a user's context with each interaction*”. --- OpenAI probably cannot afford to not monetize its free users for too long. This may be just step 1 of much broader plan of monetizing the free users; for now they’re going to take a cut from any sales made through ChatGPT by integrating a payment checkout system. There is a striking similarity to how Google also got started in their monetization journey more than two decades ago. See the below excerpt from the recent [Acquired](https://www.acquired.fm/episodes/google?ref=mbi-deepdives.com) episode on Google: ![](https://substackcdn.com/image/fetch/$s_!iHns!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe40ce675-b07a-48e2-989a-424afb17ec07_733x1173.png) Of course, there is a lot of debate whether and how much you can monetize traffic coming from the LLMs. The Diff [mentioned](https://www.thediff.co/archive/the-triple-lock-as-a-bad-derivatives-trade/?ref=mbi-deepdives.com) yesterday this Adobe [study](https://blog.adobe.com/en/publish/2025/03/17/adobe-analytics-traffic-to-us-retail-websites-from-generative-ai-sources-jumps-1200-percent?ref=thediff.co) which I thought was relevant: “*Once users land on a travel site, Adobe Analytics data shows a 45 percent lower bounce rate among consumers coming from a generative AI source — showing a consumer who is more informed and engaged*.” As of early this year, the conversion of this traffic still lagged compared to non-AI conversion, but the way the gap narrowed in less than a year, I suspect eventually AI conversions can dwarf non-AI ones. From the study: > The conversion gap reinforces that AI is being utilized during the research and consideration stage, in advance of when shoppers are ready to hit the buy button. But the narrowing gap shows that consumers are also increasingly comfortable completing a transaction directly after an AI-powered chat experience. ![Monthly AI vs. Non-AI Conversion (Retail)](https://substackcdn.com/image/fetch/$s_!CP_Y!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F60f7e8ef-4caf-4f58-bbb8-0f6576772571_750x466.png "Monthly AI vs. Non-AI Conversion (Retail)") Source: [Adobe](https://blog.adobe.com/en/publish/2025/03/17/adobe-analytics-traffic-to-us-retail-websites-from-generative-ai-sources-jumps-1200-percent?ref=thediff.co) There is perhaps an interesting analogy you can draw from how early internet users behaved and how the current crop of AI users are behaving. Benedict Evans in a recent interview [mentioned](https://www.youtube.com/watch?v=ne2MF-mTpLg&t=3945s&ref=mbi-deepdives.com) the following: > “So in 2004, you go on the internet and you already know what you want and you look for the cheap- what is the cheap X and then you put in a scoop or you put in a product or something. Whereas over time that goes down and best goes up and best goes up and crosses it. It's a perfect X on the chart, and the thesis is you're going further up the funnel. **You're looking more and more for I want someone on the internet to tell me the best X or Y where previously you'd have got that from the magazine or newspaper or something**.” For what it’s worth, I tried to recreate this data, and I could only recreate it for UK google trends (but not US or the worldwide trends), but I think his broader point stands. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-15.png) Just as early internet users first leaned on search engines to locate the “cheapest X” and gradually shifted toward asking for “the best X” as they moved higher in the decision-making funnel, we’re probably now watching a similar crossover between traditional keyword search and conversational AI. Keyword search still excels at quick, specific look-ups, but when people need synthesis i.e. recommendations tuned to their tastes, nuanced comparisons, or help framing the very question, they will increasingly start with ChatGPT-like bots. The bot sits even farther “up-funnel” than the old “best X” query; instead of presenting a ranked list of links, it internalizes vast context and iteratively co-creates an answer, collapsing the research journey the way “best” once collapsed multiple price-checking steps. If the cheap-vs-best curves formed an X, today’s graph plots classic search against chat-based reasoning, with the lines now racing toward their own intersection as users hand off more exploratory, advisory tasks to conversational agents. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 17, 2025) URL: https://www.mbi-deepdives.com/07-17-2025/ Last updated: 2025-07-20T22:17:22.000Z *Companies or topics mentioned in today's Daily Dose: Google, US innovation, Aging gracefully* --- “Search Engine Land” [published](https://searchengineland.com/google-ads-data-shows-query-length-shift-post-ai-mode-458162?ref=mbi-deepdives.com) some interesting data about the evolution of Google Search post-AI Mode launch. Some key excerpts from the piece: > The biggest shift has been from 1-2 word keywords to 3-4 word search terms. > > Short queries dropped from 42% in January to just 31% in June – suggesting **users are increasingly communicating in more natural language**. > > Shorter keywords have a **CTR drop of 50%.** Keywords with more than eight words are down by 26%. > > Shorter keywords accounted for 76% of conversions in January, but dropped to 50% by June. > > **Three- and four-word queries now make up 40% of conversions, up from 20% in January**. Clearly, search behavior is changing, but instead of clinging onto to the old search model, Google seems to be evolving just as fast. In fact, Eric Seufert recently [quoted](https://mobiledevmemo.com/googles-gambit-part-3-consumers-embrace-ai-mode/?ref=mbi-deepdives.com) a survey by Oppenheimer to point out an intriguing data point: > …of users familiar with AI Mode and who pay for ChatGPT, 82% find AI Mode more helpful than Google Search and **75% find AI Mode more helpful than ChatGPT**. Admittedly, this surprised me because of two things: a) these ChatGPT users in this survey are paying subscribers (my initial reaction was they must be comparing free version of ChatGPT vs Google’s AI mode), and b) I personally would respond differently in this survey. Having said that, sample size in this survey was only 263, so not sure how representative this is for more broader population. In any case, I do think the real fight between ChatGPT vs Google is on the free users. If Google AI mode is deemed decisively better than ChatGPT’s free version, users will have no compelling reason to shift their decade long search habits, and Google search can survive and thrive just fine. Speaking of Google thriving, Ben Thompson wrote a [piece](https://stratechery.com/2025/cloudflares-content-independence-day-googles-advantage-monetizing-ai/?access%5Ftoken=eyJhbGciOiJSUzI1NiIsImtpZCI6InN0cmF0ZWNoZXJ5LnBhc3Nwb3J0Lm9ubGluZSIsInR5cCI6IkpXVCJ9.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.PCw47gs%5Fj%5Fqi4i0dfQTb8oAZT54eZbaScrJWVNSWIDRU-K-cqpTRKN5n80ja58KyOFP9LGHeSn9bmgnafHgIzaRQ-viLiVHwQR62PdczlsPnXeR%5F7fJLLVNS14QYbxg0U0nGEkdmM8PorWldP6Q3e3XwSiYGkslTRT9IP%5FruuUnKXWvcxcaEPtupUYRndLNPVWdgdgvbvTKcn6Gm9Gv3mFrhSXIppisbp9T3H5hWnlY5XdxNNqUwm6nkmiym0HF6HHco3Qd2vFhp3DRKO8kZ9t03rJR7H0aSHiNsv6kFfL9MEcZnsD-VX3UR4Q53rBiU66zvJlOHwBeL845LeNpfIg&ref=mbi-deepdives.com) yesterday that has profound positive implications for Google. Thompson highlighted how Cloudflare is trying to reshape the open web by forcing AI crawlers pay for content. This is potentially a bad news for model developers’ margins unless you are Google. From Ben Thompson’s piece yesterday: > Google, which has two crawlers. `Googlebot` crawls the web for Google search, while `Google-Extended` crawls the web to capture data for Gemini. What is critical to understand, however, is that data for Google Search AI products — including AI Overviews and AI Mode (i.e. [**the search funnel**](https://stratechery.com/2025/google-i-o-the-search-funnel-product-possibilities/?ref=mbi-deepdives.com)) — is gathered by `Googlebot`; that means that if you want your website to show up in Google Search you have no choice but to have that data also be used by any AI products that are under the Search umbrella. > > Just to be clear, what Cloudflare is doing is not simply amending `robots.txt`; rather, they are straight-up denying access to AI crawlers — again, except for `Googlebot`, which a Google executive confirmed in court captured data for use in Search AI products > > In short, Cloudflare is actively helping Google’s competitive position, insomuch as Google’s most important AI product is in fact Search I am not super confident that Google can maintain these terms as I imagine Google’s SOTA model competitors will highlight these terms to the regulators and force a level playing field. If such attempts to level playing the field are not successful, this would be a decisive competitive advantage for Google. Of course, if the chatbots need to pay for content, they may try to be much more careful in crawling the number of pages before answering a query or running deep research which can hurt answer quality. Frankly speaking, these terms do seem unfair. I can digest the argument “AI overviews” as part of the core traditional search experience, but including “AI mode” in “Googlebot” terms feels a bridge too far. AI Mode has a chat like interface too which makes it functionally no different than something like ChatGPT. If ChatGPT needs to pay for content, so should “AI Mode” in Google. --- Nilesh Jasani made an interesting [case](https://www.geninnov.ai/blog/the-audacious-american-edge-when-shares-become-currency?ref=mbi-deepdives.com) that one of the true innovation edges for US is its companies’ willingness to wield highly valued shares and even cloud‑compute credits as a de facto private currency, letting them fund outsized acquisitions, poach talent and embark on decade‑long AI‑era capex at an exceptionally low cost of equity. I was a bit surprised to know how cash is the decisively dominant currency for deals everywhere else outside the US. ![](https://substackcdn.com/image/fetch/$s_!NhvV!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa088cc56-ede0-460e-b3c7-bc54bda849e0_1222x900.png) Regions where valuations and dilution tolerance are lower must rely on cash, limiting strategic flexibility and putting them at risk of falling irreversibly behind in the global innovation race. While many people bemoan innovations in financing, this piece points out the other side of it: > Giants like Microsoft, Amazon, and Google strategically offer cloud computing credits to startups and AI innovators, effectively investing through services rather than direct capital. > > Such credits are often seen by purists, including us at times, merely as receivables or deferred revenue, but the reality is far richer. These arrangements provide critical support for cash-strapped innovators, who receive significant resources without immediate financial burden, enabling rapid scaling of ideas. Moreover, this approach locks promising startups firmly within the hyperscalers' ecosystems, ensuring a future revenue pipeline and industry alignment. ![](https://substackcdn.com/image/fetch/$s_!Mk5R!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3c7fe77b-5a3e-4f3a-8d4b-11d842c69180_1216x568.png) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year.* [Subscribe](#/portal/signup) --- I came across a very interesting data point about aging which is worth highlighting. From a [piece](https://www.gspublishing.com/content/research/en/reports/2025/05/20/2d3fe290-10b1-44be-8d0e-77b8d303928f.pdf?utm%5Fsource=theideafarm.com&utm%5Fmedium=referral&utm%5Fcampaign=50-facts-from-1h-2025) by Goldman Sachs: > In addition to living longer, people are also living healthier lives, in the sense that the functional capacity of older individuals is improving over time. A recent IMF study, using micro-data of individuals aged 50+ (including physical and cognitive tests) from a sample of 41 developed and emerging economies, found that “**on average, a person who was 70 in 2022 had the same cognitive ability as a 53-year-old in 2000”, while the physical frailty of a 70-year-old corresponded to that of a 56-year-old in 2000**. Measured in years, these improvements are larger than the reported increases in life expectancy, emphasizing the need to focus on biological rather than chronological age. This feels like a big deal, especially given the increasing aging population across the world. If our cognitive and physical ability can be sustained for an extended period of time compared to before, perhaps we can age more gracefully than earlier generations did. Another interesting data from the same piece: > while median expected life expectancy in developed economies has increased by 5% since 2000 (from 78 to 82 years), the median effective working life has risen by 12% (from 34 to 38 years) and the share of the total population in employment has increased from 46.0% to 48.3%. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 16, 2025) URL: https://www.mbi-deepdives.com/07-16-2025/ Last updated: 2025-07-20T22:17:44.000Z *Companies or topics mentioned in today's Daily Dose: Extreme leverage of AI Research team with high talent density, GLP-1 impact on Life Insurance* --- I have been pondering about Zuck’s recent hiring spree of AI researchers. I walk \~10,000 steps everyday and during my afternoon walk yesterday, I was thinking about three different pieces I came across yesterday that all touched on this topic. First, Zuck gave an [interview](https://www.youtube.com/watch?v=qDDOy90V4Jo&ref=mbi-deepdives.com) with The Information in which he said some things that I think are likely preview of what he may be going to say during the earnings call in a couple of weeks: > I think that the physics of this is **you don’t need a massive team to do this**. **You actually kind of want the smallest group of people who can fit the whole thing in their head. So there’s just an absolute premium for the best and most talented people.** > > I think we’ll see how the technology trends, and we’ll see what the results are. In running the company, **I’m sort of always looking for ways that I can convert capital into a higher-quality service for people**. > > And one of the benefits of reinforcement learning is it gives you a venue to, you know, potentially convert very large amounts of capital into a better and better service, and potentially a better service than other less well-funded or less bold competitors will be able to do so. I view that as a competitive advantage. > > I think **we’re going to have the largest compute fleet of any company, and focusing on that on being powered by a small and talent-dense team, I think we’re gonna have by far the most compute per researcher to do leading edge work**. > > Like I said before, a lot of the numbers specifically have been inaccurate, but I think it discounts the other key reasons why people are super excited to come work on Meta Superintelligence Labs. > > And one of the biggest is that **you can just have more leverage as a researcher**. You have more compute right? I mean, basically historically, **when I was recruiting people to different parts of the company, you know, people are like, OK, what’s my scope going to be? And, you know, here, people say, I want the fewest number of people reporting to me and the most GPUs**. And so having basically the most compute per researcher is definitely a strategic advantage, not just for doing the work, but for attracting the best people. When I was reading the [reflections](https://calv.info/openai-reflections?ref=mbi-deepdives.com) of Calvin French-Owen, a former OpenAI employee who helped launch Codex, about his time at OpenAI, I started appreciating Zuck’s strategic direction here. The following excerpt is particularly relevant to the topic at hand: > Andrey (the Codex lead) used to tell me that you should think of researchers as their **own "mini-executive"**. There is a strong bias to work on your own thing and see how it pans out. There's a corollary here–**most research gets done by nerd-sniping a researcher into a particular problem.** > > …back in November 2024, OpenAI had set a 2025 goal to launch a coding agent. By February 2025 we had a few internal tools floating around which were using the models to great effect. And we were feeling the pressure to launch a coding-specific agent…From start (the first lines of code written) to finish, the whole product was built in just **7 weeks**. 7 weeks!! This is a good glimpse of what a small team with high talent density can accomplish. Finally, I read this [piece](https://kwokchain.com/2025/07/15/the-halo-effect/?ref=mbi-deepdives.com) by Kevin Kwok which further drove this point home for me: > Tech, and especially AI, is increasingly deflationary. **Every year the advances in AI are obsoleting the last year’s models. The knowledge gleaned from training the last generation of models or from building products that best utilized them might be essential for working with the latest models–but the actual old models or products will be outdated fast**. Conversely, as it gets easier to build software every year, the value of owning the legacy codebase falls or can even go negative. > > An interesting note on the hiring done by Zuckerberg is **how much of his hiring is of a profile that maps closer to being founders than AI researchers**. Across the industry we are increasingly seeing companies figuring out how to create setups that work well hiring “founders.” And there’s a lot to unpack in this blurring by the market of the founder role. As I was digesting all these pieces, it did strike me that Zuck indeed has an incredibly compelling pitch for all these AI Researchers. Meta’s new hiring binge is less a numbers game than a deliberate experiment in leverage. Zuck’s recent comments make the formula explicit: take a “talent-dense” handful of researchers, surround them with a GPU arsenal no one else can match, and let them run with minimal management overhead. Models, codebases, even entire product lines can decay faster each year. In a world where yesterday’s model is tomorrow’s technical debt, Meta’s competitive edge can be that every one of its AI researchers operates with founder-level autonomy atop their largest GPU fleet, turning capital into breakthroughs faster than the rest of the field can depreciate. If you want to win in AI, the inputs are pretty simple to list and brutally hard to assemble: absurd compute, top-decile researchers, deep capital reserves, high quality data, scaled distribution, leadership urgency/vision, and execution muscle that doesn’t flinch. Meta has lined up the first six; we’re about to learn whether it can deliver the seventh. I’ve been closely following Meta since 2017, and I’d be surprised if execution is what trips them up. The optimistic read from Owen’s piece is that we may not be stuck in a three-year wait for proof. Give it three quarters and we should know whether Zuck’s leverage experiment is paying off. Sure, pack that much firepower into one org and egos will spark. Some sparks are fine. With the level of autonomy and resources on offer, I doubt ego drama will define Meta’s Superintelligence team when we look back three years out. --- Let’s change gear a bit and talk about GLP-1. One of the underappreciated aspects of revolutionary technologies is it is nearly impossible to map out the ripple effects of such invention over time. I was reading this very interesting [piece](https://www.glp1digest.com/p/how-glp-1s-are-breaking-life-insurance?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com) by Ashwin Sharmaabout how GLP-1 drugs started having noticeable impact on life insurance business! Life insurers set premiums using decades-deep mortality tables and a few core biomarkers such as HbA1c, cholesterol, blood pressure, BMI that forecast death with ruthless, 98 % accuracy. GLP-1 drugs like semaglutide swiftly improve those exact metrics, so applicants who recently used them can appear pretty healthy even while an underlying metabolic syndrome still lurks. Because prescription records from direct-to-consumer providers often stay hidden, underwriters may grant decades-long “preferred” rates to people who were obese a year ago and are highly likely to regain the weight once they stop medication, something almost two-thirds do within 12 months. When those gains reverse, the insurer is stuck with a badly mispriced policy, a phenomenon the industry calls “mortality slippage.” Since 2019, slippage has nearly tripled to 15.3 %, meaning almost one in six life policies now carries a hidden, multimillion-dollar risk. ![](https://substackcdn.com/image/fetch/$s_!eUBD!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fae10e607-35f1-454d-bb0d-b5bf98e28907_1774x1250.png) Source: SwissRe --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*.* *Prices for new subscribers will increase to $30/month or $250/year from August 01, 2025\. Anyone who joins on or before July 31, 2025 will keep today’s pricing of $20/month or $200/year.* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 15, 2025) URL: https://www.mbi-deepdives.com/07-15-2025/ Last updated: 2025-07-20T22:18:16.000Z *Companies or topics mentioned in today's Daily Dose: Future of Enterprise software, Some cold water on AI productivity, AWS vs GCP* --- **A Programming Note**: A number of you have asked whether the Daily Dose might be a short-term experiment. Given the positive feedback I have received so far, rest assured it’s here to stay, and so are the monthly Deep Dives. Starting **August 2025**, Daily Doses will be **behind a paywall every other day**. Monthly Deep Dives will, of course, all be paywalled. To sustain the growing breadth and depth of my research, subscription rates for ***new*** ***subscribers*** will rise on **August 1, 2025** to **$30/month** or **$250/year**. Anyone who joins on or before **July 31, 2025** will keep today’s pricing of **$20/month** or **$200/year**. Thank you for reading and for your continued support! [Subscribe](#/portal/signup) --- Enterprise software is going through yet another transition. The center of gravity inside enterprises is likely evolving from human-driven SaaS front-ends to AI-native “control planes” where agents act directly on unified data. For incumbents that do not refactor their stacks, Karpathy’s “[Software 3.0](https://www.youtube.com/watch?v=LCEmiRjPEtQ&ref=mbi-deepdives.com)” warning looms large: LLMs are now the users and prompts are the API. Because LLMs will soon be the main “users,” click-heavy GUIs and hand-coded logic flip from assets to liabilities; winning teams will expose clean APIs, markdown docs and declarative flows that an agent can read and execute. Karpathy speculates even a mass rewrite in which legacy code is deleted or wrapped by governance controls that expose an “autonomy slider,” letting managers dial AI from a single text-completion to a full repo overhaul. Scuttleblurb wrote a thoughtful piece yesterday where he strikes a balance between existential questions and more prosaic concerns about AI’s impact on enterprise software. From Scuttleblurb’s [post](https://scuttleblurb.substack.com/p/day-wday?utm%5Fsource=post-email-title&publication%5Fid=1278575&post%5Fid=168191915&utm%5Fcampaign=email-post-title&isFreemail=false&r=9x0z5&triedRedirect=true&utm%5Fmedium=email): > The obligatory question of the 2010s – “What is your data strategy?” – gradually bled into “What is your AI strategy?”. The explosive rise of LLMs and AI agents collapsed the distinction. Given the relentless copy-catting that has long characterized the space, it should come as no surprise that literally every one has the same pitch for why *they* are uniquely advantaged to win in AI: we have the cleanest data, the most curated data, the most contextualized data for agents to consume. > > AI is an intelligence layer that augments a vendor’s incumbent strengths. Its benefits, quantifiable in terms of time savings and improved match rates, can be priced for just like any other product. But it is not, in itself, a decisive wedge. Enterprise buyers won’t pick a vendor because of its AI or defect because their current vendor is charging a bit more for it than another. AI lifts ARPU but it doesn’t improve win rates. “AI lifts ARPU but it doesn’t improve win rates”….indeed, you know what else it may not improve if everyone has basically the same pitch? Margins. If these transitions aren’t headache enough, SOTA model developers in their pursuit of AGI can decide to form end-to-end software stack that truly cater to their contexts. These risks are increasingly becoming a bit more tangible over time. This isn’t quite confirmed yet, but a recent Guggenheim [piece](https://x.com/techfund1/status/1944432927103652003?ref=mbi-deepdives.com) alluded to the possibility that OpenAI may decide to move away from Datadog: > "OpenAI has already completed building and testing the internal observability solution onto which Datadog workloads will move. In fact, a recent video called “Scaling Clickhouse to Petabytes of Logs at OpenAI,” shows members of technical staff at OpenAI discussing an internal observability solution built on Clickhouse, which we believe corroborates our view. Meanwhile, the company may be evaluating other more cost- effective alternatives to other Datadog functionalities. We believe this would be consistent with OpenAI’s approach to using in house and primarily open source infrastructure software solutions, which enable lower costs together with higher flexibility and security." If OpenAI can pull it off, how about Anthropic and other SOTA model developers? Of course, Datadog is just an example and it’s not really just a question about just model developers vs incumbent software players. Given the size of each of the SOTA model developers, they would all like to productize their R&D efforts as much as possible and eventually externalize many of the tools/software they’re internally using. Of course, the models themselves can be further ammunition to arming their own users to create more point solutions. I don’t want to paint a broad brush on the overall sector; the real world always tends to move slower than narratives and spreadsheets. Software is far from dead and [Jevons paradox](https://en.wikipedia.org/wiki/Jevons%5Fparadox?ref=mbi-deepdives.com) is indeed a more likely outcome. There will certainly be some incumbents who will remain unscathed during this transition, but the transition may not be trivial for many incumbent enterprise software companies. --- There are few use cases that had as much product market fit as AI-assisted coding in this new AI world. We have been hearing about how “English” is now the most popular coding language, implying how trivial the skillset is becoming even for non-technical users. Of course, companies are also falling over themselves to mention in their earnings call what percentage of their codes are now being written by AI and how much that is leading to higher productivity. Given that context, this super interesting [post](https://secondthoughts.ai/p/ai-coding-slowdown?utm%5Fsource=cross-post&publication%5Fid=1214734&post%5Fid=167765295&utm%5Fcampaign=1501429&isFreemail=true&r=9x0z5&triedRedirect=true&utm%5Fmedium=email) definitely poured some cold water in that narrative. The whole piece is worth reading, but I will share some key excerpts: > METR performed a rigorous study ([blog post](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/?ref=mbi-deepdives.com), [full paper](https://metr.org/Early%5F2025%5FAI%5FExperienced%5FOS%5FDevs%5FStudy.pdf?ref=mbi-deepdives.com)) to measure the productivity gain provided by AI tools for experienced developers working on mature projects. The results are surprising everyone: a 19 percent **decrease** in productivity. Even the study participants themselves were surprised: they estimated that AI had **increased** their productivity by 20 percent. If you take away just one thing from this study, it should probably be this: when people report that AI has accelerated their work, they might be wrong! > > The paper states that “developers were instructed to use AI to whatever degree they thought would make them most productive”. However, some subjects seem to have gotten carried away, and this may have contributed to the observed slowdown. > > Based on exit interviews and analysis of screen recordings, the study authors identified several key sources of reduced productivity. The biggest issue is that the code generated by AI tools was generally not up to the high standards of these open-source projects. Developers spent substantial amounts of time reviewing the AI’s output, which often led to multiple rounds of prompting the AI, waiting for it to generate code, reviewing the code, discarding it as fatally flawed, and prompting the AI again. (The paper notes that only 39% of code generations from Cursor were accepted; bear in mind that developers might have to rework even code that they “accept”.) In many cases, the developers would eventually throw up their hands and write the code themselves. > > Several aspects of the study play to the weaknesses of current tools. First, it was conducted on mature projects with extensive codebases. The average project in the study is over 10 years old and contains over 1 million lines of code – the opposite of “greenfield”. Carrying out a task may require understanding large portions of the codebase, something that current AI tools struggle with. ![Forecasted vs observed slowdown chart](https://substackcdn.com/image/fetch/$s_!53MT!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9797fd06-ad35-4503-88f7-952e362b19a7_2562x1540.png "Forecasted vs observed slowdown chart") --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- It’s not just Azure. The Information yesterday [published](https://www.theinformation.com/articles/google-finds-crack-amazons-cloud-dominance?utm%5Fcampaign=article%5Femail&utm%5Fcontent=article-15386&utm%5Fmedium=email&utm%5Fsource=sg&rc=4lgoj7) a piece indicating AWS may be ceding some share to GCP thanks to GCP’s superior offerings in AI: > **“**Earlier this year**,** when The Browser Company was looking for a cloud provider to power the artificial intelligence features in a web browser it was developing, the startup’s leaders asked Amazon Web Services if it could handle the work. > > But as the two sides neared an agreement, The Browser Company found that Dia’s AI features—such as analyzing text and images on webpages in less than a second—ran faster and more cheaply on Google Cloud, where they were powered by Gemini, an AI model [Google](https://www.theinformation.com/org-charts/google?rc=c48ukx&selected%5Femployee=sundar-pichai&ref=mbi-deepdives.com) developed in-house. > > Startups are important to cloud providers because they can quickly become meaningful customers. Snowflake and Pinterest launched their companies on AWS, and each now spends at least $500 million a year on its cloud servers and other products, according to [The Information’s Cloud Database](https://www.theinformation.com/projects/cloud-database?rc=c48ukx&ref=mbi-deepdives.com).” It’s not all doom and gloom for AWS; the same piece mentioned some key startups that use AWS almost exclusively and some have multi-cloud approach. ![](https://substackcdn.com/image/fetch/$s_!R5kd!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7b083200-361d-47e3-8cd9-d68d40b8acf3_654x522.png) Here’s the thing though. I have been hearing about GCP potentially enjoying a superior hand in AI ever since ChatGPT came to dominate public consciousness. The narrative makes sense, but it’s not quite there in the numbers…yet! I have mentioned before how Google Cloud (Google doesn’t disclose GCP only numbers) tends to mimic AWS revenue 16 quarters apart. This somewhat stupid rule of thumb had remarkable consistency as Google Cloud consistently was \~90-100% of AWS revenue 16 quarters apart. It is bit of a surprise that the lowest number in this series was 1Q’25 when Google Cloud was supposed to capitalize on AWS weakness! Perhaps we are on the cusp of Google Cloud inflecting materially; we’ll see. ![](https://substackcdn.com/image/fetch/$s_!2gV4!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc0543dd-31bb-4842-b2ed-662aa05f8d0b_1378x817.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 14, 2025) URL: https://www.mbi-deepdives.com/07-14-2025/ Last updated: 2025-07-20T22:18:36.000Z *Companies or topics mentioned in today's Daily Dose: AWS growth, Interest rate in AI boom scenario, LLM Adoption at work* --- As Anthropic scaled its annualized revenue from $1 Billion in December 2024 to [$4 Billion](https://www.theinformation.com/articles/anthropic-revenue-hits-4-billion-annual-pace-competition-cursor-intensifies?rc=4lgoj7&ref=mbi-deepdives.com) by mid-2025, people are starting to wonder what it means for AWS revenue growth. Morgan Stanley (MS) recently [published](https://www.businessinsider.com/amazon-deploy-cursor-employee-interest-spikes-ai-coding-2025-6?ref=mbi-deepdives.com) a note estimating Anthropic’s potential impact on AWS revenue. MS assumed Anthropic’s revenue will increase from $4 Billion in 2025 to $10 Billion in 2026 and $19 Billion in 2027\. If you assume Anthropic’s gross margin is \~60% and three-quarter of Anthropic’s cost of revenue is spent on AWS, Anthropic alone could contribute $5.6 Billion revenue in 2027\. That would still make it only LSD revenue mix of overall AWS, but given how many investors are super focused on betting on stocks that are about to experience “accelerating” topline, even such incremental contribution can matter for the stock in the near term. ![](https://substackcdn.com/image/fetch/$s_!cxUr!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc37afa8-f126-4c26-bfd7-4c7bcfc62b0c_934x462.png) How about the long-term impact given that’s much more important for long-term shareholders? It’s no denying that Anthropic’s recent momentum has been pretty impressive, but long-term questions still feel quite murky to me. Coding has been a a very popular use case in Anthropic, but if there are multiple SOTA models around in 5 years and companies such as Cursor can still offer multiple model alternatives to choose from, will model developers cede more gravity to the layer above them over time? Cursor is the big name in this space, and their CEO Michael Truell’s recent [interview](https://stratechery.com/2025/an-interview-with-cursor-co-founder-and-ceo-michael-truell-about-coding-with-ai/?ref=mbi-deepdives.com) at Stratechery had some interesting angle in terms of how Cursor thinks about this space in the long term. Notice this below excerpt: > **Ben Thompson:** Is that a real sustainable advantage for you going forward, where you can really dominate the space because **you have the usage data**, it’s not just calling out to an LLM, that got you started, but now you’re training your own models based on people using Cursor. You started out by having the whole context of the code, which is the first thing you need to do to even accomplish this, but now you have your own data to train on**.** > > **Michael Truell:** Yeah, I think it’s a big advantage, and I think these dynamics of high ceiling, you can kind of pick between products and then this kind of third dynamic of distribution then gets your data, which then helps you make the product better. I think all three of those things were shared by search at the end of the 90s and early 2000s, and so in many ways I think that actually, **the competitive dynamics of our market mirror search more than normal enterprise software markets.** One other interesting bit is at one point Truell mentioned in that interview, “*We’ve actually kind of already seen that where some of the most enthusiastic and loud users of these tools are people who are slightly technical or not really technical*” Shopify’s Head of Engineering pretty much corroborated that in a recent [interview](https://x.com/SouthernValue95/status/1944530984679956758?ref=mbi-deepdives.com): > “We’ve been using AI tools a long time in engineering. We were the first to use GitHub Copilot outside of GitHub in 2021, we weren’t charged for 2 years and in exchange we gave them lots of feedback. > > We then deployed Cursor 1 year ago. We’re trying all these things to see what’s working and what’s not and whatever works we let more people use it. > > The most interesting thing about Cursor is that the growth in Cursor at Shopify is happening a lot outside of engineering and R&D. Finance, Sales, Support, those are the teams using Cursor.” Of course, Anthropic has Claude Code, and Google just “licensed” Windsurf which was supposed to be originally acquired by OpenAI. Google also already has [Gemini CLI](https://blog.google/technology/developers/introducing-gemini-cli-open-source-ai-agent/?ref=mbi-deepdives.com) and with Windsurf in the wing, I am expecting a vigorous attempt by Google to dominate this space. I’m sure OpenAI will still launch something on their own. The opportunity for these tools may be quite large given the technical bar to develop a software will perhaps exponentially go down; nonetheless, it remains a bit hazy to think how the competitive dynamics will be settled here in a couple of years. So, it remains to be seen whether it makes sense to be enthusiastic about Anthropic’s momentum that may result into AWS growth acceleration in the next few quarters. --- Seth Benzell at “Empiricrafting” [examined](https://empiricrafting.substack.com/p/could-ai-save-us-from-making-hard) whether a potential AI-driven economic boom could make large government debts more sustainable. His simulation suggests that for the US, the resulting GDP growth could help manage debt, but this is complicated by **rising** interest rates and negative impacts on developing nations. Ultimately, he concludes that the high potential of AI makes it even more crucial to reduce government debt to avoid "crowding out" the private investment needed to realize that potential. It’s a bit speculative piece, but I think is worth highlighting that interest rate for govt financing could continue to increase if AI boom actually materializes (I have noticed plenty of people assume the opposite). Some excerpts from the piece: > In 2017 the government’s borrowing costs were at record lows (1.2% of GDP), but have exploded upwards since. In 2024, the US spent over 3% of GDP paying interest on the national debt. > > This line item will not get smaller with the AI productivity boom. Instead, the opposite. This is because an AI boom will make it costlier for the government to borrow money. > > The countries that benefit the most from AI will be the ones with (currently) high wages and TFP, and low costs of capital — the US is the lead example here, with high wages, a well developed financial system, and low corporate taxes. > > …when the marginal cost of capital is high, that means there are productive uses for it that are being unexploited! Therefore, it’s even more important to save when interest rates are high. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- A recent [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract%5Fid=5136877&ref=mbi-deepdives.com) looked at LLM adoption at work, and the pace has clearly been picking up: > In our own nationally representative surveys of U.S. workers, we find that LLM adoption at work among survey respondents above 18 has increased rapidly from 30.1% as of December 2024, to 43.2% as of March/April 2025, and to 45.9% as of June/July 2025, a substantial increase in 2025 which we find to be largely due to an increase in ChatGPT and Generative AI use. > > …Among those who report to use Generative AI at work, about 33% claim to use it every workday, while selectively using it for a limited number of hours per week. Generative AI seems to decrease the time spent on a task by an average of one hour, resulting in tripling the productivity in tasks where this technology is used. While more and more people are using LLMs at work, only mid-teen percentage of people use it daily. As you know, these data tends to be Rorschach test. The AI optimists focus on accelerating adoption whereas skeptics probably would highlight DAU being only mid-teen percentage of users despite broad awareness. It is an open question what percentage of workers will religiously use AI if it requires as much agency as it currently does on users’ part (formulating proper prompts, uploading files, verifying claims/data etc.) I do think DAUs will be lot higher than mid-teens in 3-5 years, but a lot of unlock may need to happen through lowering agency required to become core part of people’s workflows. That’s precisely why OpenAI (and others) wants to launch browser and hardware products. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 13, 2025) URL: https://www.mbi-deepdives.com/07-13-2025/ Last updated: 2025-07-20T22:19:17.000Z *Companies or topics mentioned in today's Daily Dose: Future of logistics, Rise of Dupes, Meta vs EU, follow-up on Wealth Ladder* --- Austin Vernon [argues](https://austinvernon.substack.com/p/the-outsize-impact-of-ai-logistics) that AI-driven autonomy and battery electrification may profoundly transform the labor-plus-fuel cost stack that makes trucking and last-mile delivery so expensive, putting almost a third of US GDP in play. Removing the driver lets fleets switch to much smaller, ultra-low-maintenance “pallet hauler” EVs, making even single-pallet point-to-point moves competitive and slashing less-than-truckload prices by 80-90%. Cheap aerial and ground drones push the last-mile price floor toward $1, giving most parcels air-freight transit times at a fraction of today’s ground-shipping costs. He does acknowledge that these things of course won’t happen overnight but it’s a thought provoking piece how much logistics may be transformed in the next couple of decades; here’s an excerpt on the economics of aerial drone delivery: ![](https://substackcdn.com/image/fetch/$s_!6_zU!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F167761c4-840f-4568-b329-996b1ccefa70_1155x1084.png) I was wondering about the impact on Amazon if such future eventually unfolds over time. Logistics is one of the key tenets of Amazon’s current moats in retail. If point-to-point shipping really lets a mid-Atlantic phone-case factory deliver nationwide for 75 cents and in 12–13 hours, the 35–40% take-rate sellers currently swallow for FBA, ads and commissions may prove challenging to sustain. Amazon may double-down on its scale advantages (proprietary drone corridors, exclusive micro-hubs, even licensing its logistics stack), but such massive potential transformation in logistics always runs the risk of offering some opening to new entrants to disrupt its logistics moat. --- I have mentioned before that Lululemon sued Costco for alleged dupes. The [Streisand-effect](https://en.wikipedia.org/wiki/Streisand%5Feffect?ref=mbi-deepdives.com) risk is real as yesterday WSJ published a piece which can almost be deemed as an [ad](https://www.wsj.com/business/retail/costco-kirkland-signature-pants-lululemon-792becb2?mod=djem10point&ref=mbi-deepdives.com) for Costco’s Kirkland brand: > The store brand now accounts for roughly a third of Costco’s revenue—and it’s growing faster than the company as a whole. Costco’s total sales have almost doubled since 2017\. Kirkland’s have almost tripled. > > At this point, it’s bigger than many of the world’s biggest companies. . Kirkland alone brought in $86 billion last year—more than all of [Procter & Gamble](https://www.wsj.com/market-data/quotes/PG?ref=mbi-deepdives.com). In fact, this brand known for no-frills affordability generated roughly the same annual revenue as luxury giant [LVMH](https://www.wsj.com/market-data/quotes/FR/XPAR/MC?ref=mbi-deepdives.com). > > It all started when Sinegal noticed something curious about his business: Even when the cost of raw materials went down, the price of brand-name products kept going up. That inefficiency became his opportunity. > > Costco launched a private label in 1995 and called it Kirkland Signature, a nod to the company’s headquarters in Kirkland, Wash. > > But the Lululemon lawsuit might just turn out to be free advertising for Costco, which is the company’s preferred form of advertising. > > After all, press coverage of the suit is how many shoppers who would never spend $128 on pants found out their favorite company was selling a look-alike product at a fraction of the price. I don’t think it was stupid for Lulu to sue Costco, but I’m not sure they expected mostly positive coverage Costco ended up enjoying. Lulu dupes were always there, but they were never quite considered as good as the real thing. But I have heard the dupes have gotten better and the quality gap between dupes and the real products is shrinking. These dupes are not just a concern for Lulu; WSJ also ran another [piece](http://com/?ref=mbi-deepdives.com) covering the rise of superfakes in luxury handbag industry. This particular bit stood out from the piece: > Luxury resale website Fashionphile has a counterfeit Louis Vuitton handbag on display alongside a real one at its New York flagship store—an “authenticity challenge” to see if shoppers can spot the real from the fake. The company’s founder Sarah Davis says people who work as sales assistants for top luxury brands haven’t been able to tell the bags apart.” --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- Meta might be heading towards potentially a high-stakes standoff against EU regulators. From [Reuters](https://www.reuters.com/sustainability/boards-policy-regulation/meta-wont-tweak-pay-or-consent-model-further-despite-risk-eu-fines-sources-say-2025-07-11/?ref=mbi-deepdives.com): > Meta platforms is very unlikely to offer more changes to its pay-or-consent model, meaning it is almost certain to be hit by fresh EU antitrust charges and hefty daily fines, people with direct knowledge of the matter said on Friday. > > The Facebook owner was hit with a 200-million-euro ($234 million) [fine](https://www.reuters.com/sustainability/boards-policy-regulation/apple-fined-570-million-meta-228-million-breaching-eu-law-2025-04-23/?ref=mbi-deepdives.com) in April after the EU antitrust enforcer said its pay-or-consent model breached the DMA from when it was introduced in November 2023 to November 2024. > > Meta had tweaked the model in November 2024 to use less personal data for targeted advertising, which prompted additional EU scrutiny and the subsequent Commission comments in June. > > That in turn will likely result in fresh EU antitrust charges in the coming weeks and daily fines following shortly, of as much as 5% of Meta's average daily worldwide turnover starting from June 27, one of the sources said, although a final decision has yet to be made. This has been an ongoing saga for a while. The Substack “**How EU Law Influences Tech**” has a pretty good background [reading](https://eutechreg.com/p/discussing-the-european-commissions?ref=mbi-deepdives.com) on this whole saga. Meta already implemented some revisions and currently offer three choices to EU users. From the [substack](https://eutechreg.com/p/discussing-the-european-commissions?ref=mbi-deepdives.com): “Since November 2024, Meta has implemented a significantly revised model: - Reduced subscription prices by 40% (€5.99/month on web, €7.99 on mobile). - Introduced a third option: "less personalized ads" with non-skippable ad breaks. - Created a two-step choice flow where users first choose between paid and free, then can opt for less personalization.” It is par for the course that EU regulators take absurd approach in regulating US big tech. But this probably takes the cake: “Their core thesis is striking: **if Meta chooses to offer its primary service for free, any equivalent alternative must also be free** of monetary charge to have equivalent access conditions.” I think it makes a lot of strategic sense for Meta to play hardball here. If they continue to give in, I don’t think EU will ever run out of increasingly outlandish demands. This doesn’t only affect Meta; it affects all the other gatekeepers too (Google, TikTok et al). Moreover, giving into EU’s demands consistently also can invite equally outlandish demands from governments of other countries (see [Canada](https://www.cbc.ca/news/politics/online-news-act-meta-facebook-1.6885634?ref=mbi-deepdives.com) for example). If EU really dares to fine Meta 5-10% of their **global revenue** for non-compliance, that will almost certainly attract attention from the current US administration. I doubt EU will do it; even if they do, Meta can legitimately threaten to leave the region as complying with such anti-business regulations may be counterproductive and create terrible precedents for the long-term health of the business. It is important to note that EU only drives \~[10%](https://s21.q4cdn.com/399680738/files/doc%5Ffinancials/2023/q1/META-Q1-2023-Earnings-Call-Transcript.pdf?ref=mbi-deepdives.com) of Meta’s revenue today, so EU doesn’t quite have as much negotiating leverage than one may perceive. --- I mentioned about the book “[The Wealth Ladder](https://www.amazon.com/Wealth-Ladder-Proven-Strategies-Financial/dp/0593854039?ref=mbi-deepdives.com)” yesterday. The book had this table segmenting wealth in six levels. ![](https://substackcdn.com/image/fetch/$s_!vdw4!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F846b010d-8bd6-490e-9bff-31eda96c07f2_2570x1958.jpeg) Later in the book, I came across this interesting data. It turns out you are more likely than not to stay at your wealth level even after 10 or 20 years. Social mobility is more sluggish than I thought at all levels. If you have moved two levels up the ladder over 10 or 20 years, it is quite an anomaly and not as common experience than you may think. ![](https://substackcdn.com/image/fetch/$s_!J94q!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd869e9dd-ab8d-45f7-85df-68e66589ef29_2540x3703.jpeg) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 12, 2025) URL: https://www.mbi-deepdives.com/07-12-2025/ Last updated: 2025-07-20T22:19:47.000Z *Companies or topics mentioned in today's Daily Dose: Stripe, Google, Meta, The Wealth Ladder* --- Emily Glassberg Sands, Stripe’s Head of Information, had an interesting [podcast](https://www.youtube.com/watch?v=nvwglW-PRYI&ref=mbi-deepdives.com) recently. She explained why Stripe has been building a foundation model. Stripe's massive volume of payment data is a unique asset, different from the data used to train language or image models. At this immense scale, however, the financial data begins to show language-like patterns, allowing them to apply similar AI techniques to learn the complex relationships and semantics between transactions. Later in the podcast, she gave a specific example where foundation models can be more useful than traditional ML models. Imagine a fraudster tests a list of stolen credit card numbers with very small transactions to see which ones are still active. The goal is to either use the working cards for larger thefts later or sell the validated list to other fraudsters. Traditionally, fraud detection systems struggled with this. A fraudster could hide a few hundred tiny, seemingly insignificant purchases (like for less than $1) within a sea of hundreds of thousands of legitimate transactions on a large retail website. For the older models, this fraudulent activity was too scattered and small to be noticed; it was lost in the noise. However, the new "foundation model" operates differently. Instead of looking at each transaction in isolation, it analyzes the **sequence and pattern** of events over time. It can recognize that hundreds of nearly identical, low-value requests perhaps coming from slightly different IP addresses but at regular intervals are related. This whole group of suspicious activities is flagged as a potential card testing attack. The key advantage is that you only need a small amount of evidence to confirm that the entire cluster of activity is fraudulent. Once the pattern is identified as an attack, the whole group of transactions can be blocked. By combining this new pattern-seeking approach with their existing models, Stripe was able to improve its detection rate for this type of fraud on major merchants from 59% to an impressive 97%. We are perhaps still in the early stages of exploring all the potential applications of LLMs in all sorts of industries. I use Stripe to process payments at MBI Deep Dives, and I can sense it would actually be quite useful to be able to “chat” with a bot instead of clicking around to find certain things within their platform. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- I have been listening to Acquired’s recent episode on [Google](https://www.acquired.fm/episodes/google?ref=mbi-deepdives.com) for the last couple of days. It’s a three and half hour marathon and they will probably need to do another six hours of podcast to get to 2025 since they ended the first episode of the series in mid-2000s. If you listened to any of the Acquired’s episodes, they have a lot of interesting nuggets. It was striking how incredibly aggressive the early days of Google were. The moment they understood scale and distribution is of paramount importance in search, they were almost willing to move mountains. This is not quite a novel insight, but still useful to internalize how Ben explained search is an amazing “increasing-returns-to scale” type business: ![](https://substackcdn.com/image/fetch/$s_!hm82!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8d748b59-457c-4c15-976f-d4148a1e2bd0_735x1156.png) One of the things that stood out from the episode was how luck played such a role in Google’s formation. My sense is Page and Brin would probably end up being billionaires anyway even if they didn’t start Google (they would probably just found something else), but they were indeed likely lucky to start Google which ended up being the most profitable company in the US in less than three decades after its founding. David mentioned if the Google boys tried to start a company a few years earlier than Google, they would probably end up building a portal (i.e. Yahoo) since the internet was just too small to require a search engine. But if they tried this a couple years later, they might not have done it because it would take too much capital to test their idea being a grad student at Stanford. Another funny bit is Google actually had very little idea about their business model and their first attempt was to sell the search technology to enterprise customers. I guess consumer is just so hard that even entrepreneurs don’t consider it a viable path; you need bit of a lightning on a bottle moment to think that’s a feasible path. Of course, the future of the most profitable business in earth is now under the scanner these days. But like I said, they have only covered up to mid-2000s so far. They will get to AI et al in the later episode(s). --- Semianalysis shared some thoughts on Meta’s newly formed Superintelligence team. The piece had good context around Meta’s challenges in Llama 4 and Meta’s aggressive plan for building datacenters. While I think Zuck’s strategy here is right, it is still disappointing to see Meta trying to be on catch up mode, especially when Zuck was [so early](https://x.com/modestproposal1/status/1943781132387987646?ref=mbi-deepdives.com) in understanding AI is a big deal! From [Semianalysis](https://semianalysis.com/2025/07/11/meta-superintelligence-leadership-compute-talent-and-data/?access%5Ftoken=eyJhbGciOiJFUzI1NiIsImtpZCI6InNlbWlhbmFseXNpcy5wYXNzcG9ydC5vbmxpbmUiLCJ0eXAiOiJKV1QifQ.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.dIL74uAZiYqDlYwdjgE6vvbJ7pqht-KXRWDgzZ-dO7LVX8%5FsMiOnXj6e7Y%5FSl4i-enm13pmSIm8BfcQD6Y1RlQ&ref=mbi-deepdives.com): > Mark Zuckerberg understands the talent gap relative to leading AI labs and has taken over recruiting. He’s on a mission to build a small but extremely talent-dense team, casually offering signing bonuses in the tens of millions of dollars. The goal is to create a “flywheel effect”: top tier researchers join the adventure, bringing credibility and momentum to the project. > > The recruiting pitch is powerful: unrivaled compute per researcher, a shot at building the best open-source model family, and access to over 2 billion Daily Active Users. The offers that generally range from $200M to $300M per researcher for 4 years also strengthens this pitch. As such Meta has acquired awesome talent from OpenAI, Anthropic, and many other firms. > > …While some have noted that Zuckerberg “settled” for Scale AI, we do not think this is the case…core to many of the Llama 4 issues were data problems and the Scale acquisition is a direct move to address that. It’s interesting to see while EPS estimates for 2025 was revised upward after 1Q’25, 2026 and 2027 estimates are still somewhat below pre-liberation week. Given the level of opex spending increase in AI that Meta essentially unveiled in the last couple of months and may have more impact to 2026 numbers, I wonder if there is further downward adjustments needed for 2026. ![chart](https://substackcdn.com/image/fetch/$s_!lDyu!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6a4c17bf-d1a1-43a7-932f-23cc07c1c4ec_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) --- I received a copy of the book “[The Wealth Ladder](https://www.amazon.com/Wealth-Ladder-Proven-Strategies-Financial/dp/0593854039?ref=mbi-deepdives.com)”. The book had an interesting table dissecting wealth in six levels. One can, of course, not feel quite rich despite being on level 4 even though they are, objectively speaking, the “top 1%” in the world if they are “stuck” there for quite some time. ![](https://substackcdn.com/image/fetch/$s_!vdw4!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F846b010d-8bd6-490e-9bff-31eda96c07f2_2570x1958.jpeg) But being “stuck” in level 4 is sort of expected. The book had a couple of good paragraphs about it: > Imagine you just hit Level 4 and you have $1 million in investments. If you don’t save another dollar and your investments return 5 percent per year (after inflation), it would take forty-seven years for you to get to $10 million. Of course, you probably aren’t going to stop saving once you hit $1 million. So let’s assume you save $100,000 after tax per year, a significant sum of money. How long would it take you to get to $10 million with a 5 percent annual return? Twenty-eight years. What about if you saved $200,000 per year? Then it would take about twenty-one years. What about if you saved $300,000 annually? It still takes seventeen years! > > As you can see, even with a very high-paying job (e.g., $500,000 per year or more), it will take multiple decades to get to Level 5\. And note that this would be *after* you’ve already made it to Level 4\. If you manage to get to Level 4 in your thirties or forties, this is doable, but will still require a lot of effort. Unfortunately, as I will cover later, only 5 percent of people in their thirties and 15 percent of people in their forties are in Level 4\. Since the median age for those in Level 4 is sixty-two, most people don’t enter Level 4 until later in life. This means that the typical person in Level 4 would need to work an extremely demanding job into their seventies and eighties to get to Level 5\. And who wants to do that? As they say, five million is a nightmare 😂 While it is easy to mock people in level 4 complaining about anything, I do think the experience of climbing the ladders over time may be more satisfying experience to most people, and people who are stuck likely crave the experience of graduating one level to another. Ultimately, wealth does feel mostly psychological in my opinion after a certain threshold. If you’re born in level 1 but currently in level 3, you may feel much happier than someone who was in level 4 but ended up in level 3 even though objectively they may both have similar wealth. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 11, 2025) URL: https://www.mbi-deepdives.com/07-11-2025/ Last updated: 2025-07-20T22:20:17.000Z *Companies or topics mentioned in today's Daily Dose: Correction to yesterday's daily dose, Adobe's headwinds, Floor & Decor* --- **\*Correction**: Yesterday I [mentioned](https://www.mbi-deepdives.com/07-10-2025/) "CMA reported Google’s **real** revenue (so not nominal) increased by 130-140% during 2015 to 2024 period, which at mid-point implies only 3.4% CAGR during the period.” I apologize for the stupid mistake as the correct revenue growth CAGR was \~10%, not \~3.4% (I entered the wrong numbers in excel). This means one of my takeaways was incorrect. Yesterday I said UK nominal search revenue growth CAGR (\~5-6%) materially lagged overall search revenue CAGR (\~12%) it may be challenging to extrapolate too much from UK numbers. However, now that we know real revenue growth was 10% and nominal growth likely was similar to overall company’s search revenue growth, it means CMA’s report may be much more applicable to the overall business than I implied yesterday. It also means that while commercial query growth was below 3% during 2015 to 2024, revenue growth was still growing low double digit CAGR primarily through higher conversion and click-through rates. AI may continue to help in this instance, but as I said yesterday, commercial queries today seem plenty optimized already, so it’s just hard to be very confident about Google’s ability to continue to grow search revenue at HSD-LDD rate if commercial query grows at LSD rate. Of course, commercial query grew at LSD rate when there wasn’t ChatGPT around, so even replicating such LSD query growth going forward might prove to be challenging. --- I watched this Kalshi [commercial](https://x.com/PJaccetturo/status/1943347391815651736?ref=mbi-deepdives.com) which was entirely created by AI tools (I recommend reading the entire thread), and it only reaffirmed my concern that these tools are likely a permanent headwind for Adobe’s creative cloud business. When brands can swap a full Creative Cloud production pipeline for a chat window and a text-to-video model, it’s hard to imagine how Photoshop, Illustrator, and Premier Pro can maintain their relevance for Creative Cloud customers in 10 years. If you are trying to learn editing or design skills today, are you spending time with Adobe’s software or Figma, Runway, Veo3, and numerous other AI tools? These AI tools will likely only make it easier to create digital content, become cheaper, and more scalable as compute gets more abundant over time. Even if Adobe tries to develop their own models, it’s hard to imagine they can do a better job than SOTA model developers. Perhaps one way Adobe can still win is if the SOTA models are deemed to infringe copyright content, but the courts have so far sided (see [here](https://www.wsj.com/tech/ai/anthropic-lands-partial-victory-in-ai-case-set-to-shape-future-rulings-e3560114?ref=mbi-deepdives.com), and [here](https://news.bloomberglaw.com/legal-ops-and-tech/meta-beats-copyright-suit-from-authors-over-ai-training-on-books?ref=mbi-deepdives.com)) with the model developers. I have received questions on Adobe every once in a while, especially since I owned the stock in the past. However, it has decidedly moved to my too-hard-pile the more I have observed and studied the AI tools. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- Floor & Decor (FND) is probably in every compounder bro’s watchlist (including yours truly) if they don’t own it already. However, their operating performance in the last few years has been less than inspiring to pull the trigger despite the stock being down \~38% from its peak in October 2021. While there are macro factors that are weighing FND’s performance, it is Home Depot’s (HD) operating performance that makes FND look a bit mediocre. If you compare 2024 sales on a per store basis vs 2019’s (so pre-Covid), average flooring sales in HD, LOW, and FND increased by 13%, 8%, and 2% respectively. Another way to look at it is while HD’s flooring sales per store only declined by 8% from its peak in 2021, FND’s sales per store declined by 19% from 2021. FND is a good business, but it may be a good time for them to reassess whether it is more important to increase productivity in existing stores instead of continuing to add more stores. ![](https://substackcdn.com/image/fetch/$s_!WVi5!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faa1bc4c7-a030-48a6-8716-eba9c6283580_1003x505.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Alex Morris from TSOH had some [more thoughts](https://thescienceofhitting.com/p/the-dominant-force-in-flooring?ref=mbi-deepdives.com) on some of the macro factors that are weighing on FND: > Starting with existing home sales, which CFO Bryan Langley has called “[our highest correlated metric](https://thescienceofhitting.com/p/in-a-transitional-period?ref=mbi-deepdives.com)”, the picture remains dire: the latest data from the National Association of Realtors (NAR) showed a subdued level of seasonally adjusted unit sales at [\~4.0 million in May 2025](https://www.nar.realtor/newsroom/nar-existing-home-sales-report-shows-0-8-increase-in-may?ref=mbi-deepdives.com); as you can see below, that is [\~25% lower](https://www.nar.realtor/sites/default/files/2025-07/2025-07-residential-real-estate-market-snapshot-report-07-07-2025.pdf?ref=mbi-deepdives.com) than the level typically reported pre-pandemic. Langley framed the historical context of these figures on [the Q1 FY25 call](https://ir.flooranddecor.com/news-events/ir-calendar/detail/8913/q1-2025-earnings-conference-call?ref=mbi-deepdives.com): “In March 2025, existing home sales fell to a seasonally adjusted annual rate of 4.02 million, **the lowest reading for the month of March** [**since 2009**](https://www.huduser.gov/portal/sites/default/files/pdf/Housing-Market-Indicators-Report-May-2025.pdf?ref=mbi-deepdives.com) ([down 2.4% YoY](https://www.nar.realtor/blogs/economists-outlook/blogs/economists-outlook/latest-existing-home-sales-data-graphs?ref=mbi-deepdives.com)).” > > …I struggle to understand what would drive the sizable improvement required to get back in-line with long-term trends. ([J.P. Morgan U.S. Real Estate research](https://www.jpmorgan.com/insights/podcast-hub/research-recap/us-housing-market-outlook?ref=mbi-deepdives.com): “What we've always looked at is the long-run, historical turnover of housing… How often do people move?We've looked at that over decades, and if we take that rate and apply it to the existing stock of owner-occupied homes in the United States right now, **it would suggest that we should be running at about 5.8 million existing home sales**.”) > > While new homeowners / existing home sales is a key driver for FND, the company is also dependent upon current homeowners – today, the people who have built up meaningful home equity due to rising prices, with [\~55%](https://www.redfin.com/news/wp-content/uploads/2025/06/2025-02-06%5FRedfin%5FReports%5F17%5Fof%5FHomeowners%5FWith%5FMortgages%5F1262.pdf?ref=mbi-deepdives.com) of homes tied to a mortgage at a sub-4% rate (as noted at [Home Depot’s 2022 Investor Day](https://research.alpha-sense.com/?docid=GDS-648a11db5811710d98d00261&utm%5Fsource=alphasense%20platform&utm%5Fmedium=document%20share&utm%5Fcontent=GDS-648a11db5811710d98d00261&utm%5Fcampaign=1752000951322&utm%5Fdoc%5Fid=GDS-648a11db5811710d98d00261&utm%5Fdoc%5Ftype=IRPres), \~40% of owner-occupied homes don’t have a mortgage) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 10, 2025) URL: https://www.mbi-deepdives.com/07-10-2025/ Last updated: 2025-07-20T22:20:40.000Z *Companies or topics mentioned in today's Daily Dose: Commercial query growth in Google, Social commerce, Generative UI* --- I have mentioned about UK CMA’s report on Google [before](https://www.mbi-deepdives.com/07-01-2025/), but one astute reader highlighted a couple of data points from the [appendix](https://assets.publishing.service.gov.uk/media/6859812cdb8e139f95652e0a/Annex%5FB%5FMarket%5Foutcomes.pdf?ref=mbi-deepdives.com) of the report that I feel compelled to mention. CMA report mentioned total monthly search query in the UK increased by \~50-60% during 2017 to 2024 period which implies \~6-7% query growth CAGR. ![](https://substackcdn.com/image/fetch/$s_!3beZ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcd433d1f-ecb2-4f1f-a808-8778d18e630d_1042x649.png) My initial impression to this MSD+ query CAGR was positive since persuading people to query more tends to be challenging. This [quote](https://www.mbi-deepdives.com/goog/) by Sridhar Ramaswamy, who is a former SVP of Google's Ads business and current CEO of Snowflake, is quite apt in this context: > ...It turns out that persuading people to query more is next to impossible. All of us have a certain propensity to use search, and it varies from person to person. There are people, and you can observe this among your friends, as soon as there is a disagreement about something or a question about something, they'll be like, "Let me take my phone out," and they will do this in front of you. > > ...In general, it's really hard to change this number. However, it was the another data point that was more interesting. CMA later also reported that Google’s commercial queries increased from 30-40 Billion in 2015 to 40-50 Billion in 2024, which at the mid-point implies a meager 2.8% CAGR. Since the report also shared that total monthly queries hovered between 10 Billion and 25 Billion, let’s take the mid-point of that and multiply by 12 to get to total queries in the UK to be 210 Billion in 2024\. This means every one in five queries is currently commercial in nature. Of course, this is just query, but how about the revenue? CMA reported Google’s **real** revenue (so not nominal) increased by 130-140% during 2015 to 2024 period, which at mid-point implies only 3.4% CAGR during the period. (Note: some [correction](https://www.mbi-deepdives.com/07-11-2025/) here) Google didn’t report search only revenues for the company in 2015, but their overall advertising revenue in 2015 was $68 Billion. Since they reported $198 Billion search revenue in 2024, the company actually enjoyed \~12-13% CAGR in search during this period. I have three takeaways from these data points: a) Even if we add \~2-4% inflation to Google search’s revenue growth in the UK, search revenue growth in the UK almost certainly materially lagged the overall company’s growth in search revenue. Therefore, we need to be **cautious** not to extrapolate too much from UK numbers to the overall company. (Note: some [correction](https://www.mbi-deepdives.com/07-11-2025/) here) b) I was admittedly surprised at the anemic commercial query growth which grew at less than half the overall query growth. If I had to guess, I would have predicted the opposite i.e. commercial query growth outpaced overall query growth. Data points like this often turn out to be bit of a Rorschach test, but I think it is likely an incrementally negative data point for Google investors. With AI, we will almost certainly ask more questions which will be a natural tailwind for commercial query growth but the extent of such tailwind is likely more limited than anticipated. Most of the queries with commercial intent are already being asked on Google, and it will be harder to move the needle in commercial queries even with AI chatbots. Moreover, commercial queries in traditional search are already super high intent queries, so I’m not sure how much a conversational query will increase click-through rates or CPMs. c) Of course, chatbots such as ChatGPT are considered preeminent threat to Google’s traditional search. If the nature of commercial query is inherently not as open ended as overall queries, it may be easier for chatbots to gain share in such queries eventually and if the pie of commercial queries grows at anemic rate, encroaching market share in commercial queries may lead to a more pronounced impact to Google search. Overall, Google remains a pretty difficult stock to bet in size in my opinion. Looking at their multiple, I can tell I am not the only one whose mind gets muddled every once in a while. --- Social networking companies have been trying to make social commerce happen in the West for quite some time, but it hasn’t quite worked so far. Admittedly, I was more optimistic about its chances after looking what my own sister-in-law could do with her Facebook page. She opened a Facebook page right around Covid and started live streaming to sell women’s dresses online. My elder brother thought the business had potential and took a risk by leaving his job to help his wife scale the business. Today, their business makes \~30-40x of what they were earning in their old jobs pre-Covid (they live in Bangladesh, so the jobs don’t pay as well as it does in the US). It’s a bit funny that both my brother and I went into Covid with deep uncertainty about our respective careers and we both ended up building our own businesses. Anyways, it turns out Eric Seufert was also quite positive about social commerce’s potential, so he [revisited](https://mobiledevmemo.com/whats-wrong-with-social-commerce/?ref=mbi-deepdives.com) why it didn’t quite work in the US: > why are TikTok and Meta reversing course on social commerce? I believe there are three possible general explanations and one that is specific to Meta. > > First, **the commerce tools offered by TikTok and Meta are inferior to those provided by other retail platforms**, specifically Shopify. > > Second, **Western audiences never acclimated to social commerce**, preferring to transact on dedicated retail platforms (such as Amazon) or directly on brands’ websites. This could be a function of a lack of trust, a lack of product discoverability, or simply habit. > > And third, **retailers simply preferred to operate and drive traffic to their own storefronts,** given the benefits of owning the relationship with the consumer. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- Google recently published a very interesting blog [post](https://developers.googleblog.com/en/simulating-a-neural-operating-system-with-gemini-2-5-flash-lite/?ref=mbi-deepdives.com) about simulating an OS itself with Gemini 2.5 Flash-Lite (h/t [Liberty’s Highlights](https://www.libertyrpf.com/p/575-apple-perplexity-anthropics-success?utm%5Fsource=post-email-title&publication%5Fid=70226&post%5Fid=166901445&utm%5Fcampaign=email-post-title&isFreemail=false&r=83442&triedRedirect=true&utm%5Fmedium=email)). From the post: > In traditional computing, user interfaces are pre-defined. Every button, menu, and window is meticulously coded by developers. But what if an interface could be generated in real time, adapting to a user's context with each interaction? > > Our prototype simulates an operating system where each screen is generated on the fly by a large language model. It uses [Gemini 2.5 Flash-Lite](https://deepmind.google/models/gemini/flash-lite/?ref=mbi-deepdives.com), a model whose low latency is critical for creating a responsive interaction that feels instantaneous. Instead of navigating a static file system, the user interacts with an environment that the model builds and rebuilds with every click. > > By default, our model generates a new screen from scratch with each user input. This means visiting the same folder twice could produce entirely different contents. Such non-deterministic, stateless experience may not always be preferred given that the GUI we are used to is static. Reading this post reminded me of Hugo Barra’s interview with Ben Thompson in [October, 2024](https://stratechery.com/2024/an-interview-with-hugo-barra-about-orion-and-metas-ar-strategy/?ref=mbi-deepdives.com#agents). It’s a very intriguing interview and I highly recommend it if you have missed it earlier. Barra had some provocative ideas and he did mention about generative UI and apps as real possibilities for AR glasses. We may be gradually heading towards such possibilities. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 09, 2025) URL: https://www.mbi-deepdives.com/07-09-2025/ Last updated: 2025-07-20T22:21:09.000Z *Companies or topics mentioned in today's Daily Dose: Constellation Software, AGI timeline, Waymo vs Tesla* --- I listened to an interesting AlphaSense interview of former Head of M&A at Constellation Software (free trial link [here](https://www.alpha-sense.com/mbi/?ref=mbi-deepdives.com)). Let me share some bits and pieces that stood out to me. Constellation doesn’t seem to be bothered by copycats: > From Constellation standpoint, Constellation acknowledges that there are companies trying to emulate its success. Constellation itself came up with the term copycats. > > Constellation seems to think that there's a bit of the pie for everyone, but it just focuses on doing what it does best. > > If Constellation is competing with a copycat, especially if it's a start-up, all the copycat has to offer at that moment is capital. > > Constellation will acquire just about anything for the right price. It'll look at smaller businesses, it'll look at restructurings, it'll look at businesses that haven't been growing that much or that aren't that profitable. Interestingly, it is the internal competition that may be more of a headache than facing competition from copycats: > If you work at one of Constellation's operating divisions, such as Volaris, for example, the largest one, your main competitor is actually another operating division of Constellation, such as Vela or Topicus or Harris or whatever it is, because you're competing for the same deal. It's very cutthroat within the organization itself. That's one of the pet peeves of Constellation's employees. > > Suppose I work at Volaris and I'm pitching Constellation to you, and I'm interested in acquiring your business and tell you all the great things Constellation does, say we hang up the phone, and then all of a sudden, you might get another email from another operating division of Constellation with a completely different name and then another call from yet another one, and you might be thinking, "What is going on? Why are these three different people from different organizations claiming to be affiliated with Constellation?" That does happen, stepping on the toes of your peers. It creates sometimes a very convoluted process, and it requires a lot of handholding and mitigating internally. The discussion on impact of AI on CSU was quite nuanced. The expert talked about how CSU basically stripped out R&D spending following acquisition and quickly get to pretty high margin. So, most of CSU’s software is understandably subpar quality, but many of their customers still live in the stone age while the rest of the world is heading towards AGI. As a result, AI may not have much of an impact for such niche software anytime soon: > AI is probably the most talked about topic now. It only started appearing as a priority on Constellation's agenda as of last year. It's not to say that Constellation was dismissive of it…There are several ways of looking at it. There's Constellation's perspective as an operator, there's Constellation's perspective as an acquirer, and then obviously you have the client's perspective, which impacts Constellation. > > Constellation's default stance when it acquires a business like that is to almost instantly scrap or get rid of all the R&D expenditure to save on money and help that business generate cash flow, especially in the early years. > > A lot of Constellation's platforms, if you were to rank the platforms according to their functionality and how modern they are, Constellation's average platform, I would say, is Tier three, Tier four, Tier five, whereas some of the private equity owned ones which they pay up for are Tier one. Constellation's are typically legacy software for people or clients who don't want to pay up or can't afford to pay for a Tier one software. What that means is that they're probably less prone to want to pay up for an AI model that's unproven at this stage. However, one intriguing bit in the interview was the expert mentioned CSU does have some “blue-chip” customers who are in financial services, legal, or insurance industries which tend to be more tech forward than the typical VMS software customers. When asked about the mix of revenue between smaller and blue-chip customers, the expert said 50-50\. My sense is CSU is likely to be largely immune from AI threat when it comes to smaller niche VMS software customers (it may not be worth attacking such niche segments), but it will probably face competition higher than anticipated in “blue-chip” segment. --- Dwarkesh wrote a good [post](https://www.dwarkesh.com/p/timelines-june-2025?ref=mbi-deepdives.com) explaining why he has extended his AGI timeline. One of the reasons he mentioned is AI’s lack of continual learning. Dwarkesh writes: > How do you teach a kid to play a saxophone? You have her try to blow into one, listen to how it sounds, and adjust. Now imagine teaching saxophone this way instead: A student takes one attempt. The moment they make a mistake, you send them away and write detailed instructions about what went wrong. The next student reads your notes and tries to play Charlie Parker cold. When they fail, you refine the instructions for the next student. > > This just wouldn’t work. No matter how well honed your prompt is, no kid is just going to learn how to play saxophone from just reading your instructions. But this is the only modality we as users have to ‘teach’ LLMs anything. It is important to note that Dwarkesh only extended his AGI timeline to 2032 which maybe “conservative” among his peers in SF, but it is quite an aggressive timeline anywhere else. He did make an interesting point that beyond 2030, the probability of reaching AGI diminishes every year we don’t get there given various constraints of scaling compute: > AGI timelines are very lognormal. It's either this decade or bust. (Not really bust, more like lower marginal probability per year - but that’s less catchy). AI progress over the last decade has been driven by scaling training compute of frontier systems ([over 4x a year](https://epoch.ai/blog/training-compute-of-frontier-ai-models-grows-by-4-5x-per-year?ref=mbi-deepdives.com)). This [cannot continue](https://benjamintodd.substack.com/i/160703377/bottlenecks-around) beyond this decade, whether you look at chips, power, even fraction of raw GDP used on training. After 2030, AI progress has to mostly come from algorithmic progress. But even there the low hanging fruit will be plucked (at least under the deep learning paradigm). So the yearly probability of AGI craters. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- It is not easy to find a somewhat neutral and sobering analysis on Tesla. Timothy Lee had a lot of nice things to say about Tesla’s robotaxi launch, but had some interesting angles while comparing and contrasting against Waymo. Tesla vs Waymo has been a hot topic, and this [piece](https://www.understandingai.org/p/what-i-learned-watching-79-videos?utm%5Fsource=post-email-title&publication%5Fid=1501429&post%5Fid=167841270&utm%5Fcampaign=email-post-title&isFreemail=true&r=9x0z5&triedRedirect=true&utm%5Fmedium=email&hide%5Fintro%5Fpopup=true) did a really good job in explaining why the debate is far from settled: > Waymo’s vehicles are only available in a handful of metropolitan areas. They don’t operate on freeways. And Waymo has remote operators who can intervene if the vehicles get stuck. For years, Tesla fans argued that these precautions showed that Waymo’s technology was brittle and unable to generalize to new areas. They claimed that Tesla, in contrast, was building a general-purpose technology that could work in all metropolitan areas and road conditions. > > But in a [piece last year](https://www.understandingai.org/p/on-self-driving-waymo-is-playing?ref=mbi-deepdives.com), I argued that they were misunderstanding the situation. > > “Tesla hasn’t started driverless testing because its software isn’t ready,” I wrote. “For now, geographic restrictions and remote assistance aren’t needed because there’s always a human being behind the wheel. But I predict that when Tesla begins its driverless transition, it will realize that safety requires a Waymo-style incremental rollout.” > > Last month, Waymo [published a study](https://arxiv.org/abs/2506.08228?ref=mbi-deepdives.com) demonstrating that self-driving software benefits from the same kind of “scaling laws” that have driven progress in large language models. > > “Model performance improves as a power-law function of the total compute budget,” the Waymo researchers wrote. “As the training compute budget grows, optimal scaling requires increasing the model size 1.5x as fast as the dataset size.” > > When Waymo published this study, Tesla fans immediately seized on it as a vindication of Tesla’s strategy. Waymo trained its experimental models using 500,000 miles of driving data harvested from Waymo safety drivers driving Waymo vehicles. That’s a lot of data by most standards, but it’s far less than the data Tesla could potentially harvest from its fleet of customer-owned vehicles. > > So if bigger models perform better, and bigger models require more data to train, then the company with the most data should be able to train the best self-driving model right? > > I posed this question to Dragomir Anguelov, the head of Waymo’s AI foundations team and a co-author of Waymo’s new scaling paper. He argued that the paper’s implications are more complicated than Tesla fans think. > > “We are not driving a data center on wheels and you don’t have all the time in the world to think,” Anguelov told me in a Monday interview. “Under these fairly important constraints, how much you can scale and what are the optimal ways of scaling is limited.” --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 08, 2025) URL: https://www.mbi-deepdives.com/07-08-2025/ Last updated: 2025-07-20T22:21:59.000Z *Companies or topics mentioned in today's Daily Dose: Therapeutic abundance, Agent Archetypes, OpenAI's SBC, Rent growth* --- I was admittedly a bit skeptical about AI revolutionizing therapeutics. While I understood the AI’s potentially profound impact on drug discovery process, I wasn’t sure if it can have a sustained impact given the regulatory process can act as a bottleneck. So, even if some part of the overall drug value chain speeds up materially thanks to AI, I thought it would still be difficult to accelerate the overall process. However, Jacob Kimmel’s [piece](https://blog.jck.bio/p/creating-therapeutic-abundance?ref=mbi-deepdives.com) “**Creating therapeutic abundance”** has prompted me to change my mind a bit. I overestimated the regulatory bottleneck and underestimated scientific bottlenecks in medicine in the status quo and if AI can help us with the latter, perhaps therapeutic abundance is still on the cards. It is bit of a long excerpt, but that only shows my appreciation of the overall piece: > Most of the knowledge of drug program lifecycles remains locked within drug companies. Nonetheless, we can bucket the failures into a two broad categories of safety and efficacy and make informed estimates. 1. **Safety failures – \~20-30% of all candidates** A molecule was developed, but proved unsafe in patients. These are typically detected as failures in Phase 1 trials. 2. **Efficacy failures – 70-80% of all candidates** The remainder of all drug candidates that fail – 63% of *all* drugs placed into trials period – fail due to a lack of efficacy. Even though the drugs are safe, they don't provide benefit to the patients by treating their disease. > From these coarse numbers, it's clear that **the highest leverage point in our drug development process is increasing the efficacy rate of new candidate medicines** > > …**our main challenges are scientific. We simply don't know how to make effective drugs that preserve health or reverse disease**! *If we want more medicines, we need to understand why they don't work and fix it.* > > Concretely, a scientist sits and thinks hard about the problem, makes a guess at the responsible molecular players based on their intuition, prior art, and their new data, then tests to see if the molecule is causal. The *vast* majority of these hypotheses are wrong! The few that prove to be correct often become the basis of our modern target-based drug discovery process and several companies quickly launch programs to prosecute them. > > *Is it possible to build a more deterministic, less constrained discovery process? Can we discover target biologies with a complexity matching the origins of disease?* > > Two technological revolutions argue in the affirmative. Functional genomics methods now enable us to test far more hypotheses than ever before. From the resulting data corpuses, artificial intelligence models can search otherwise intractably large hypothesis spaces, like the space of possible genetic circuits or combinatorial therapies. By performing most experiments in the world of bits rather than atoms, it’s possible to address questions that were inaccessible to a previous generation of scientists. > > **In practice, this allows researchers to *treat* *the cell as the unit of experimentation*, increasing the throughput of many target discovery questions by 100-1000X**. These methods aren't applicable to *every* target discovery problem (e.g. some pathologies only manifest across tissue systems), but they nonetheless unlock a class of putative interventions that were previously too numerous to search effectively. > > Artificial intelligence models can learn general models from the data generated in functional genomics experiments of many flavors, predicting outcomes for the experiments we haven't yet run. If we manage to construct a performant model for a given class of target biologies, we may be able to increase the efficiency of target discovery by many orders-of-magnitude. **The cost of discovering a target could conceivably go from >$1B to <$1M**. --- Agents are the topic du jour these days. The Information yesterday [published](https://www.theinformation.com/articles/seven-kinds-ai-agents?utm%5Fcampaign=article%5Femail&utm%5Fcontent=article-15352&utm%5Fmedium=email&utm%5Fsource=sg&rc=4lgoj7) a helpful piece on agents, segmenting them in different categories, relevant products, and common use cases: ![](https://substackcdn.com/image/fetch/$s_!Jms1!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7e133cbb-a1b9-4294-8b1b-35cb96e3dfd3_955x829.png) --- On a different [piece](https://www.theinformation.com/articles/openais-stock-compensation-reflect-steep-costs-talent-wars?utm%5Fcampaign=article%5Femail&utm%5Fcontent=article-15354&utm%5Fmedium=email&utm%5Fsource=sg&rc=4lgoj7), The Information published about OpenAI’s Stock Based Compensation (SBC) which apparently was \~120% of their revenue in 2024\. Of course, OpenAI expects SBC to have tremendous leverage over time as they expect the revenue (the denominator here) to increase much faster than SBC. Meta’s hiring spree will almost certainly force OpenAI to be even more generous with their SBC program. Is this some 4D chess by Zuck to make life difficult for a company that is legitimately threatening to join the big tech club? I am not a big believer in that. I think the surge in compensation for AI “super talent” is the natural market outcome, especially after Meta hit some road blocks in their existing effort. If building compelling AI products requires abundant compute and AI talent, Meta has a lot of compute but it is lot less useful without the latter. If OpenAI continues to march forward without much of a hiccup, it will give us a clear indication that the talent density may run pretty deep in OpenAI. At the same time, if Meta’s strategy proves successful in building SOTA models and AI products, that may stamp the primacy of AI talent even more, increasing compensation for AI super talents even higher. People talk about how AI may enable a billion dollar company run by one individual, but we may see a billion dollar “employee(s)” first! ![](https://substackcdn.com/image/fetch/$s_!bxjr!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F860bc1c8-5cd5-4d88-969a-a11a63cbb2d1_682x544.png) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- US apartment[ rent growth](https://investors.costargroup.com/news-releases/news-release-details/apartmentscom-releases-multifamily-rent-growth-report-second-0?ref=mbi-deepdives.com) YoY was below 1% in 2Q’25\. One anecdotal experience that I can share is I moved to Sacramento in December 2023, and recently moved out to rent in a different neighborhood. I was talking to my previous landlord a couple of weeks ago and it turns out he had to rent the place at the same rent he was renting me back in December 2023\. Considering it’s an annual lease for the next tenant, my landlord effectively “froze” rent for 30 months! ![](https://substackcdn.com/image/fetch/$s_!e1xV!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3a0d4967-7080-4f88-819e-5be3592eb369_937x520.png) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 07, 2025) URL: https://www.mbi-deepdives.com/07-07-2025/ Last updated: 2025-07-20T22:22:23.000Z Companies or topics mentioned in today's Daily Dose: Fiscal tightrope, Productivity boom, ChatGPT traffic, History of Intelligence --- UBS [published](https://mebfaber.com/wp-content/uploads/2025/06/Signal-over-noise-10-The-fiscal-tightrope.pdf?utm%5Fsource=theideafarm.com&utm%5Fmedium=newsletter&utm%5Fcampaign=mid-year-outlooks&%5Fbhlid=f612fd5a3a6d35893b95a5966d63826ebe78e085) this interesting table comparing and contrasting US’s current high debt level to similar situations in the past. Considering the recent US administrations’ poor fiscal discipline, an AI-driven surge in productivity may now be the one of the very few (only?) “get-out-of-jail-free” card left. ![](https://substackcdn.com/image/fetch/$s_!acW3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F91dc5fed-937a-4817-8635-135a9b4f8b71_640x825.png) --- Speaking of AI led productivity boom, we can be quite encouraged to see the recent productivity numbers in the US which is noticeably above pre-2020 trend. Any comparison about pre and post-2020 is, of course, quite challenging given how many key variables decidedly shifted at the same time. This blog [post](https://conversableeconomist.com/2025/07/03/the-weird-and-lovely-surge-of-us-productivity-growth/?ref=mbi-deepdives.com) mentions three such variables that likely contributed to productivity boom seen in numbers: rise of work from home, higher job switching to match your skill, and rise of entrepreneurship post-2020\. Despite these factors, it is AI which is likely leading the productivity boom: > …let’s remember if this surge were caused by work from home, labor reallocation, or more startups, we might expect to see broad-based, one-time-boost-type gains across many industries or concentrated in sectors with more work from home, et cetera. But that’s just not really what drove it. If you look at the industries experiencing the most significant productivity surge, seven or eight out of the top ten look tech or AI intensive … We’re talking about things like internet publishing, e-commerce, computer system design, renting intangible assets, motion picture and sound recording, and miscellaneous professional, scientific, and technical services. ![](https://substackcdn.com/image/fetch/$s_!_5Qi!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb522ef2f-d525-4613-a090-df6ca0293894_712x501.png) --- ChatGPT’s [traffic](https://techcrunch.com/2025/07/02/chatgpt-referrals-to-news-sites-are-growing-but-not-enough-to-offset-search-declines/?ref=mbi-deepdives.com) to news sites is exploding while traffic from traditional search is declining. It’s also interesting to note how stocks and finance related topics dominate the traffic. You may no longer need to make up an explanation on your own to your PM’s query “why is this stock up/down 3%?”. From TechCrunch: > From January through May 2024, ChatGPT referrals to news sites were just under 1 million, Similarweb says, but have grown to more than 25 million in 2025 — a 25x increase. > > Topics like stocks, finance, and sports are currently accounting for the majority of these ChatGPT news-related prompts, but Similarweb’s report notes other topics are seeing growth, too, like politics, the economy, weather, and others. > > This, the firm theorizes, may signal a move away from more “reactive information” and toward deeper “issue-driven engagement” via AI. ![](https://substackcdn.com/image/fetch/$s_!gdXp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F11786f5d-834a-4815-ba2e-0ffdd44fbe28_1024x982.png) ![](https://substackcdn.com/image/fetch/$s_!kQNr!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa81a6c5a-156d-4e59-93ce-0328ba07eb82_1014x883.png) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- While driving around Big Sur yesterday, I was [listening](https://www.youtube.com/watch?v=sscaPRtf9XQ&ref=mbi-deepdives.com) to this podcast which interviewed the author of the book: “[The History of Intelligence](https://www.amazon.com/Brief-History-Intelligence-Humans-Breakthroughs/dp/0063286343?ref=mbi-deepdives.com)”. I didn’t need a coffee break thanks to this podcast as my jaw dropped several times while listening to it. I recommend listening to the podcast, but I asked Gemini (I’m usually a ChatGPT user but noticed Gemini is better if I ask it any query on YouTube videos) to summarize some of the studies mentioned in the podcast. I’m copy-pasting Gemini’s response to a couple of the studies below: ##### **The Devaluation Study on Habit Formation** This classic experiment is used to distinguish between goal-directed actions and ingrained habits. A mouse is first trained to press a lever to receive a specific food treat. Later, in a separate environment, the mouse is given that same treat but laced with a substance that causes sickness, devaluing the reward. When the mouse is returned to the original box, its behavior reveals its motivation. If the training was brief, the mouse will avoid the lever, showing it connects the action to the now-undesirable outcome. However, if the mouse had performed the action hundreds of times, it will often continue to press the lever compulsively, demonstrating the action has become a rigid habit that is independent of the goal it once served. ##### **Emil Menzel's Chimpanzee Deception Study** Researcher Emil Menzel was initially studying spatial memory in chimpanzees. He would hide food in a large, one-acre enclosure and show its location to only one chimp, a subordinate female named Bella. At first, Bella would lead the group to the food, but a dominant male named Rock began to aggressively hoard the treat. In response, Bella's strategy evolved. She first began waiting for Rock to be distracted before retrieving the food. Eventually, she started to actively practice deception, deliberately leading the group in the wrong direction to mislead Rock and secure the treat for herself, demonstrating a sophisticated capacity for strategic thought and planning. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 06, 2025) URL: https://www.mbi-deepdives.com/07-06-2025/ Last updated: 2025-07-20T22:22:51.000Z *Companies or topics mentioned in today's Daily Dose: Inflection Point for AI adoption in enterprise, Airbnb and host experience* --- I listened to this AlphaSense interview of Head of Generative AI Strategy at Cerebras (free trial link [here](https://www.alpha-sense.com/mbi/?ref=mbi-deepdives.com)). The expert forecasts that within the next 6–12 months enterprises will unleash a wave of previously-shelved use cases, which may be bit of an inflection point for AI adoption in enterprises. Having said that, the amount of capex is also an uncomfortable reminder that ROI needs to deliver in a major way in not-so-distant future for even some of the most profitable companies in the world to be able to keep investing here. It may not be likely that everyone will enjoy compelling ROI across the board. So, the big question to me is not that whether AI capex will have compelling ROI, but rather how the value creation will be distributed among the current crop of Big Tech (and beyond). ![](https://substackcdn.com/image/fetch/$s_!ZARs!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F79dd2d55-50c9-48b1-987c-348a54f7e94e_1077x739.png) --- Following up on my Airbnb experience [mentioned](https://www.mbi-deepdives.com/07-05-2025/) yesterday. I had an opportunity to chat with my Airbnb host yesterday for 30 minutes whom I stumbled onto while walking around the pond at the property. I asked about her experience of being a host at Airbnb. She has been an Airbnb host for 5+ years. She only listed the property on Airbnb and seemed quite happy with the steady stream of income she was able to generate from the platform. She did have one complaint though. Airbnb once put her property on hold for 10 days once a guest brought their dog despite her “no pet policy”. It’s not clear what the guest said to Airbnb, but Airbnb took the side of the guest and “punished” the host by making the property unavailable to book for 10 days. The whole process took place over a chat interface. This happened early last year, so by that time she already hosted on Airbnb for four years and had almost 100 glowing reviews from guests. She was so livid with that experience that she considered leaving Airbnb altogether but decided against it later. While I have no comment on her specific incident, I do think it is a growing pain in a world dominated by large scaled tech platforms. While with AI into the scene it can potentially feel even more dehumanizing experience going forward, I think there is also a potential scenario that some of these companies may feel empowered to provide a better customer experience leveraging actual persons precisely because AI will handle almost all of the routine issues. Zuckerberg actually [talked](https://www.dwarkesh.com/p/mark-zuckerberg-2?ref=mbi-deepdives.com) about this potential a couple of months ago: > “…it wouldn't make sense to staff out calling for everyone. But let's say AI can handle 90% of that. Then if it can't, it kicks it off to a person. If you get the cost of providing that service down to one-tenth of what it would've otherwise been, then maybe now it actually makes sense to do it. That would be cool. So the net result is that I actually think we're probably going to hire more customer support people. > > The common belief is that AI will automate jobs away. But that hasn't really been how the history of technology has worked. Usually, you create things that take away 90% of the work, and that leads you to want more people, not less.” Of course, it would be politically incorrect for Zuck to paint out a world where the technology he’s investing billions in would replace a vast swath of labor. Nonetheless, while thinking about AI’s potential, I am often reminded Benedict Evans’ [joke](https://medium.com/ualterai/when-you-automate-things-you-create-more-jobs-not-fewer-jobs-benedict-evans-f37a31a1f651?ref=mbi-deepdives.com) on this point: > “The joke I make about this is that before Excel, investment bankers used to work really long hours**.** But now, thanks to Excel, Goldman Sachs associates can finish their work at lunchtime on Friday and go home. … What actually happened is that when you gave everybody Excel you do more analysis, not less—because if you can answer in five minutes instead of a week you just ask more questions.” The question in any major tech transition is, of course, whether this time it is indeed different. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 05, 2025) URL: https://www.mbi-deepdives.com/07-05-2025/ Last updated: 2025-07-20T22:23:23.000Z Companies or topics mentioned in today's Daily Dose: Student loan, Progress in AI, AGI implications for rise and fall of nations, Traveling --- Apollo [published](https://www.apolloacademy.com/wp-content/uploads/2025/07/StudentLoanOutlook%5Fv2.pdf?ref=mbi-deepdives.com) a presentation titled “*The pause on reporting delinquent federal student loans to credit rating bureaus has ended: Implications for consumer spending*”. This is, unfortunately, one of the tangible headwinds for consumer discretionary spending going forward. Some excerpts: > 45 million people have a federal student loan. 24% are delinquent among those obligated to pay. > > Eleven million consumers could see their credit scores decline and therefore lose access to borrowing if they don’t start repaying their student loans. > > Millions of people had their student loan payments paused during the pandemic, but now they have to start paying them back again, which makes it harder for households to afford other things, and that can slow down consumer spending. ![](https://substackcdn.com/image/fetch/$s_!ymWf!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb29edceb-33c2-4a49-a71c-601ab6bce8f2_1792x970.png) --- Tyler Cowen made the point that while progress in AI has been “staggeringly high” than expected so far, you probably shouldn’t expect such rate of progress to continue. Some excerpt from his [post](https://marginalrevolution.com/marginalrevolution/2025/07/a-consumption-basket-approach-to-measuring-ai-progress.html?ref=mbi-deepdives.com): > It would be interesting to chart the rate of LLM progress, weighted by how people actually use them. The simplest form of weighting would be “time spent with the LLM,” though probably a better form of weighting would be “willingness to pay for each LLM use.” > > I strongly suspect we would find the following: > > 1\. Progress over the last few years has been staggeringly high, much higher than is measured by many of the other evaluations For everyday practical uses, current models are much better and more reliable and more versatile than what we had in late 2022, regardless of their defects in Math Olympiad problems. > > 2\. Future progress will be much lower than expected. A lot of the answers are so good already that they just can’t get that much better, or they will do so at a slow pace. (If you do not think this is true now, it will be true very soon. But in fact it is true now for the best models.) For instance, once a correct answer has been generated, legal advice cannot improve very much, no matter how potent the LLM. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives* [*here*](https://mbideepdives.substack.com/p/deep-dives)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- What if Cowen’s call for subdued expectation proves to be premature and we do end up in “AGI” land? I can imagine some eyerolls here (perhaps including yours truly), but I have to admit if someone explained “Deep Research” to me three years ago and asked me to estimate my probability of such a technology being available at nearly zero marginal cost, I think I would have estimated the probability of that being reality to be \~5%. What that tells me is it’s better to be a bit humble about my own ability to predict the pace of AI’s progress and be more psychologically and intellectually open to more wide ranging possibilities. To that point, Rand published a provocative [report](https://www.rand.org/pubs/research%5Freports/RRA3034-2.html??ref=mbi-deepdives.com) last week titled “**How Artificial General Intelligence Could Affect the Rise and Fall of Nations”.** The report outlines eight potential AGI futures, as shown below. ![](https://substackcdn.com/image/fetch/$s_!PXqD!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F42548670-2e6f-4234-a9c2-e4cdcd191d47_832x394.png) In the conclusion section, the report summarized the implications of these potential AGI scenarios: ![](https://substackcdn.com/image/fetch/$s_!0u2l!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5d2b407b-7d26-44ee-af62-74164c9662ef_823x1018.png) I am currently traveling with my family for the 4th of July weekend, so I didn’t read the entire report. But I did feed the report to NotebookLM and listened to the [podcast version](https://notebooklm.google.com/notebook/88172703-8264-4ce6-95eb-a3580a5dd507/audio?ref=mbi-deepdives.com) of it which I think did a pretty good job of compressing the report. --- Speaking of traveling, we came to this Airbnb (a ranch in the middle of nowhere) in California. I had to jog for three minutes to record the whole place (video is at 2x speed), and there are still parts of the ranch (for example, a swimming pool and a farm with several other animals) that weren’t captured here. It’s hard to find gems like this at a reasonable ADR except for Airbnb ;) 0:00 /1:31 1× --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### MBI Daily Dose (July 04, 2025) URL: https://www.mbi-deepdives.com/07-04-2025/ Last updated: 2025-07-20T22:24:05.000Z *Companies or topics mentioned in today's Daily Dose: Follow-up on golden age of digital advertising, LLM adoption in enterprise, Anthropic, Money* --- Happy Fourth of July! It’s a great day to remind myself never to bet against America. While on my walk yesterday, I was thinking about Eric Seufert's podcast that I [mentioned](https://www.mbi-deepdives.com/07-03-2025/) in the last Daily Dose and I was making a connection with an earlier post by Ben Thompson. Ben [posted ](https://stratechery.com/2025/encryption-and-the-uneasy-compromise-netflix-earnings-the-aggregators-compounding-advantage/?ref=mbi-deepdives.com)about this chart below (originally by MoffettNathanson) in early 2025 and quoted MoffettNathanson to make a striking point: *"The average Paramount+ subscriber is paying double in subscription fees for each hour of viewing pleasure compared to the average Netflix subscriber in the U.S."* ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-13.png) As I was noodling what Eric Seufert was saying in the podcast ("As ads become more effective at generating conversions, it makes sense that **platforms would show fewer of them per session given an upper bound on consumers’ discretionary income and appetite for various goods"**) and the point MoffettNathanson analysts raised, I wonder if this dynamic will be even more prominent for scaled social networking apps. Netflix spreads its fixed content costs over a vast audience, letting it charge less per effective viewing hour while still out-investing rivals; the same principle now favors the largest advertising platforms. As generative-AI targeting and creative tools lift conversion rates, each impression becomes more valuable. For example, Meta, armed with the deepest behavioral dataset and the most liquid auction, can reach any revenue or ROAS goal with fewer ad exposures than smaller networks, then fill the reclaimed feed space with friends’ posts, Reels, or new AI features. **A cleaner, more engaging experience drives higher usage, which feeds more data into Meta, attracts higher bids, and reinforces its advantage**. Less-scaled competitors, lacking comparable data and auction depth, may feel compelled to keep ad load high to maintain revenue, degrading user experience and falling further behind. Some of this dynamic was already sort of going on, but AI may be bit of a further accelerant. --- Kong [surveyed](https://assets.prd.mktg.konghq.com/files/2025/06/685c3aec-kong-2025-report---genai-in-the-enterprise.pdf?ref=mbi-deepdives.com) 550 users, developers, engineers, and IT decision-makers to understand LLM usage/adoption. With the caveat of the sample size and other limitations for these surveys, here's a quick look at key highlights from the report: ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-6.png) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-7.png) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-8.png) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-9.png) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-10.png) Source: all images are from Kong [Report](https://assets.prd.mktg.konghq.com/files/2025/06/685c3aec-kong-2025-report---genai-in-the-enterprise.pdf?ref=mbi-deepdives.com) --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives, including excel models,* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- Semianalysis [mentioned](https://semianalysis.com/2025/07/03/deepseek-debrief-128-days-later/?ref=mbi-deepdives.com) yesterday that Anthropic has some similarity with DeepSeek in that they are both compute constrained and despite popular belief, their partnership with Amazon is still "work-in-progress": "In the world of AI, the only thing that matters is compute. Like DeepSeek, Anthropic is compute constrained. Having noticed the success of token consumers like Cursor, the company launched Claude Code, a coding tool built into the terminal. Claude Code usage has skyrocketed, leaving OpenAI’s codex in the dust. Google, in response, also released their own tool: Gemini CLI. While it is a similar coding tool to Claude Code, Google uses their compute advantage with TPUs to offer unbelievably large request limits at no cost to users. Anthropic is getting more than half a million Trainium chips, which they will then be used for both inference and training. This relationship is still a work-in-progress as, despite popular opinion, Claude 4 was not pretrained on AWS Trainium. It was trained on GPUs. Anthropic also turned to their other major investor Google of compute. Anthropic rents significant amounts of compute from GCP, specifically TPUs. Following this success, Google Cloud is expanding their offerings to other AI companies, striking a deal with OpenAI. Unlike previous reporting, Google is only renting GPUs to OpenAI – not TPUs. " ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-12.png) Source: Google --- I received an early copy of "[The Inner Compass](https://compass.moretothat.com/?ref=mbi-deepdives.com)" yesterday which is Lawrence Yeo's first book. I will share my thoughts later once I finish reading. The book is going to be available to buy on July 08th, but you can join the waitlist now. If you are not familiar with Yeo's work, I can tell you that he once wrote a piece on *Money* that is, frankly speaking, the best piece I have ever read on anything related to money. Yeo writes on money in a way that took me to places I may have never gone just thinking by myself. If you haven't read his piece: "[Money Is the Megaphone of Identity](https://moretothat.com/money/?ref=mbi-deepdives.com)", give it a read. --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### MBI Daily Dose (July 03, 2025) URL: https://www.mbi-deepdives.com/07-03-2025/ Last updated: 2025-07-20T22:24:40.000Z *Companies or topics mentioned in today's Daily Dose: Digital advertising, semiconductor cycle, Kevin Kelly* **A programming note*: All the content of MBI Deep Dives are published on both "MBI Deep Dives" website and* [*Substack*](https://mbideepdives.substack.com/)*. I launched the Substack a couple of years ago because a number of subscribers requested for it as some people prefer to read on Substack app/website. I do want to highlight that the website and the Substack are on two different platforms and hence, subscription on one doesn't automatically export to another. I suggest you pick just whatever that is more convenient for you since the content is exactly the same.* *If you currently are a paying subscriber on the website but would like to switch to Substack, just subscribe on Substack and then send me an email so that I can refund you for your subscription on the website. Thank you!* --- Eric Seufert from Mobile Dev Memo published quite an insightful [podcast](https://mobiledevmemo.com/commerce-at-the-limit/?ref=mbi-deepdives.com) making a compelling case that the golden age of digital advertising is likely ahead of us. A key excerpt from the podcast: "...Generative AI will similarly create competitive friction for the discovery of all forms of content. If every advertiser is operating at maximum creative efficiency through the use of shared platform-centric creative production tools, and creative iteration takes place at the blinding speed unlocked by generative AI, then the only recourse an advertiser will have to improve performance in auctions is through increased bids. But this dynamic might actually justify lower ad load more broadly. As ads become more effective at generating conversions, it makes sense that **platforms would show fewer of them per session given an upper bound on consumers’ discretionary income and appetite for various goods.** People can only buy so many things. Advertising outcomes in a product session or across product sessions within a given time period do not follow a Bernoulli process. The ad exposures are not independent and the probability of conversion is non-stationary. If a user converts early in the session, subsequent ads are likely to be less effective. So if the effectiveness of early ads increases due to AI-enabled targeting or creative platforms, they may optimize by reducing total ad volume." **MBI Note**: If conversion keeps improving, and ad load, in fact, goes down, this can lead to a win-win-win scenario for all stakeholders involved: improved conversion may increase ROAS for the advertiser (although as Seufert mentions “satisfier’s regret” may always be there), and lower ad loads can improve user experience- both of which can eventually lead to longer growth runway for the advertising platform itself since it can create not only more advertisers over time but also unlock ad budgets further. --- *In addition to "Daily Dose" like this, MBI Deep Dives publishes one Deep Dive on a publicly listed company every month. You can find all the 60 Deep Dives, including excel models,* [*here*](https://www.mbi-deepdives.com/models/)*. I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- Byrne Hobart [makes](https://capitalgains.thediff.co/p/economic-cycles?ref=mbi-deepdives.com) a convincing case why the current GPU mania is destined to experience some sort of cyclicality at some point: "The only way for the GPU industry never to have a down cycle, even if the face of growth, is some combination of: 1. Every single person who can make big capital spending decisions chronically underestimates long-term AI demand. (If even one of them doesn't, that person is a larger share of the next round of capital allocation decisions.) 2. It's a straight shot from here to the Singularity. In any other case, there will still be an air pocket or two; even if spending rises 30% annualized, there will be times when it's closer to 25% and the capex for that period supports something more like 35% growth. And a good working explanation for why cyclicality won't go away is that Mag7 executive who had read the above description of cyclical dynamics in AI capex a few years ago, taken it to heart, and decided to spend less than planned would have ended up regretting that choice. Many cyclical industries were born as growth industries. Airlines had great returns in the 1960s, automakers did similarly well in the 1920s (in the aggregate, with many failures made up for by a few huge success stories), and in the chip industry the *slowest* growth from 1950-60 was just over 40%, in 1960\. (The next year, the industry shrank year-over-year for the first time, and [settled into a cycle thereafter](https://www.economist.com/business/2020/01/09/a-revival-is-under-way-in-the-chip-business?utm%5Fsource=capitalgains.thediff.co&utm%5Fmedium=referral&utm%5Fcampaign=cyclical-into-secular-and-vice-versa))" --- I enjoyed Brie Wolfson's [piece](https://joincolossus.com/article/flounder-mode/?ref=mbi-deepdives.com) on Kevin Kelly. Loved the title of the piece- "Flounder Mode": "I asked him the difference between “following your interests” and being scatterbrained or having shiny object syndrome, like I sometimes worry I do. “The people who become legendary in their interests never feel they have arrived,” he said. When he talked about the power of passion and obsession in that process, I asked him if passion is enough. “Enough for what?” he asked, somewhat rhetorically. He had an impression of what I meant. “I think one of the least interesting reasons to be interested in something is money,” --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### MBI Daily Dose (July 02, 2025) URL: https://www.mbi-deepdives.com/07-02-2025/ Last updated: 2025-07-20T22:25:18.000Z *Companies mentioned in today's Daily Dose: Amazon, Cloudflare, Microsoft, Figma vs Adobe* --- WSJ had an interesting [piece](https://www.wsj.com/tech/amazon-warehouse-robots-automation-942b814f?mod=djem10point&ref=mbi-deepdives.com) yesterday on how Amazon has scaled its robotic efforts over the years: "The e-commerce giant, which [has spent years automating tasks](https://www.wsj.com/articles/amazons-new-robotic-warehouse-will-rely-heavily-on-human-workers-f95e06b6?mod=article%5Finline&ref=mbi-deepdives.com) previously done by humans in its facilities, has deployed more than one million robots in those workplaces, Amazon said. That is the most it has ever had and near the count of human workers at the facilities. Now some 75% of Amazon’s global deliveries are assisted in some way by robotics, the company said. The growing automation has helped Amazon improve productivity, while easing pressure on the company to solve problems such as [heavy staff turnover ](https://www.wsj.com/business/retail/how-amazon-workers-in-new-york-became-first-to-unionize-11648998000?mod=article%5Finline&ref=mbi-deepdives.com)at its fulfillment centers. The average number of employees Amazon had per facility last year, roughly 670, was the lowest recorded in the past 16 years, according to a Wall Street Journal analysis, which compared the company’s reported workforce with estimates of its facility count. The number of packages that Amazon ships itself per employee each year has also steadily increased since at least 2015 to about 3,870 from about 175, the analysis found, an indication of the company’s productivity gains." ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-1.png) --- *I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- Perhaps micropayments do have a future. From [TechCrunch](https://techcrunch.com/2025/07/01/cloudflare-launches-a-marketplace-that-lets-websites-charge-ai-bots-for-scraping/??ref=mbi-deepdives.com): **"**Cloudflare, a cloud infrastructure provider that serves 20% of the web, announced Tuesday the launch of a new marketplace that reimagines the relationship between website owners and AI companies — ideally giving publishers greater control over their content. It’s called Pay per Crawl, and Cloudflare is launching the “experiment” in private beta on Tuesday. Website owners in the experiment can choose to let AI crawlers, on an individual basis, scrape their site at a set rate — a micropayment for every single “crawl. This June, Cloudflare says it found that Google’s crawler scraped its websites 14 times for every referral it gave them. Meanwhile, OpenAI’s crawler scraped websites 17,000 times for every one referral, while Anthropic scraped websites 73,000 times for every referral." --- Another day...another mindboggling progress in AI. From [Microsoft AI](https://microsoft.ai/new/the-path-to-medical-superintelligence/?ref=mbi-deepdives.com): "The Microsoft AI team shares research that demonstrates how AI can sequentially investigate and solve medicine’s most complex diagnostic challenges—cases that expert physicians struggle to answer. Benchmarked against real-world case records published each week in the New England Journal of Medicine, we show that the Microsoft AI Diagnostic Orchestrator (MAI-DxO) correctly diagnoses up to 85% of NEJM (The New England Journal of Medicine) case proceedings, a rate **more than four times higher than a group of experienced physicians.** MAI-DxO also gets to the correct diagnosis more cost-effectively than physicians. Across Microsoft’s AI consumer products like Bing and Copilot, we see over 50 million health-related sessions every day. MAI-DxO boosted the diagnostic performance of every model we tested. The best performing setup was MAI-DxO paired with OpenAI’s o3, which correctly solved 85.5% of the NEJM benchmark cases. For comparison, we also evaluated 21 practicing physicians from the US and UK, each with 5-20 years of clinical experience. On the same tasks, these experts achieved a mean accuracy of 20% across completed cases. Importantly, we found that MAI-DxO delivered *both* higher diagnostic accuracy and lower overall testing costs than physicians or any individual foundation model tested." ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-2.png) Comparison of AI powered diagnostic agents by accuracy and average diagnostic test cost per case. Top performing agents appear toward the top left quadrant, reflecting higher accuracy and lower cost. The lower dotted line represents the performance range of the best individual foundation models. The purple line traces the performance of MAI-DxO across different configurations. The red cross indicates the average performance of 21 practicing physicians.; Image Source: [Microsoft AI](https://microsoft.ai/new/the-path-to-medical-superintelligence/?ref=mbi-deepdives.com) --- We now have Figma [S-1](https://www.sec.gov/Archives/edgar/data/1579878/000162828025033742/figma-sx1.htm?ref=mbi-deepdives.com#i8dccdb6f4d824d4aba81e7c665eaa62d%5F40), so we can compare and contrast with Adobe's Creative Cloud business. I hope Canva comes to IPO soon so that I can track the competitive dynamics much more closely. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/07/image-3.png) Source: Company Filings, MBI Deep Dives --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### MBI Daily Dose (July 01, 2025) URL: https://www.mbi-deepdives.com/07-01-2025/ Last updated: 2025-07-20T22:26:03.000Z Last week, UK's Competition and Markets Authority (CMA) published an interesting report titled "[Strategic market status investigation into Google's general search services](https://assets.publishing.service.gov.uk/media/68598b13eaa6f6419fade67b/Proposed%5Fdecision.pdf?ref=mbi-deepdives.com)". CMA has been historically quite useful in understanding search market dynamics. While I do want to note that the regulator has bit of an incentive to make Google look more entrenched, here are some interesting excerpts from the report: "The research also found that all consumers reported still using traditional search engines alongside AI assistants. In other words, traditional search engines and AI assistants are perceived as complementary rather than fully substitutable. For example, although consumers do sometimes use AI assistants for ‘search-like’ tasks, they may resort to traditional general search engines to confirm that the output they receive is reliable, and/or carry out a ‘follow-up’ search task such as navigating to a website ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/06/image.png) Google still accounts for around a 90% share of queries to traditional general search providers and AI assistants combined in the UK. In December 2024 the volume of AI assistant queries was about 0-5% of the volume of Google’s general search queries, albeit that use of AI assistants has been growing quickly with query volume on AI assistants growing by 20-30% in the last three months of 2024\. Amongst AI assistants, we estimate that ChatGPT receives by far the greatest volume of queries in the UK, accounting for 80-90% of UK queries to AI assistants in December 2024\. In contrast, Google’s Gemini AI assistant only accounted for 0-10% of queries to AI assistants. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/06/image-1.png) Another way users engage with generative AI is when Google (as AI Overviews) and Microsoft (as Bing Generative Search) display AI summaries in response to certain queries on their SERPs. Figure 5.3 shows that Google’s AI Overviews are shown in response to more queries than ChatGPT receives. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/06/image-2.png) Bing is the only English-language provider to have developed search infrastructure which is comparable to Google’s. As shown in the table below, all third parties, both traditional general search providers and AI assistants, which have developed their own search infrastructures have a web-index which is a fraction the size of Google’s and Bing’s and which costs a fraction of the sum spent by Google and Microsoft to maintain." ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/06/image-3.png) --- *I would greatly appreciate if you share MBI content with anyone who might find it useful!* [Subscribe](#/portal/signup) --- Excerpt from Will Cathcart's [interview](https://economictimes.indiatimes.com/tech/technology/et-exclusive-whatsapp-ads-will-not-bother-most-users-privacy-stays-intact-assures-head-will-cathcart/articleshow/121893601.cms?ref=mbi-deepdives.com) discussing WhatsApp's India business: **"**[**WhatsApp Payments**](https://economictimes.indiatimes.com/topic/whatsapp-payments?ref=mbi-deepdives.com) **hasn't picked up the way it was envisioned. What went wrong?** We're still really committed to making payments simple, reliable, private, making it work well on WhatsApp. It is growing really quickly in India. It has more than doubled (for businesses) in India in the past year. **How will WhatsApp do personalised ads if private data is encrypted?** One of the challenges is going to be, we have more limited data in WhatsApp. And so, that is one of the trade-offs with the fact that it is a more private service. These ads will be based on more limited data, including about your updates tab usage. We’re going to try and do our best to make the ads relevant." --- From [WIRED](https://www.wired.com/story/mark-zuckerberg-welcomes-superintelligence-team/?ref=mbi-deepdives.com), Everyone Meta hired in their "Superintelligence" team so far: ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/06/image-4.png) --- Not quite investing related, but I really enjoyed this piece. [Face it: you're a crazy person](https://www.experimental-history.com/p/face-it-youre-a-crazy-person?ref=mbi-deepdives.com) "We tend to overestimate the prevalence of our preferences, a phenomenon that psychologists call the “false consensus effect”. This is probably because it’s really really hard to take other people’s perspectives, so unless we run directly into disconfirming evidence, we assume that all of our mental settings are, in fact, the defaults. Our idiosyncrasies may never even occur to us. ...once we invented agriculture, [almost everyone](https://ourworldindata.org/employment-in-agriculture?ref=mbi-deepdives.com) was a farmer the next 10,000 years. “What should I do with my life?” is really a post-1850 problem, which means, in the big scheme of things, we haven’t had any time to work on it. The beginning of that work is, I believe, unpacking. As you slice open the boxes and dump out the components of your possible futures, I hope you find the job that’s crazy in the same way that *you* are crazy." --- **Current Portfolio:** Please note that these are **NOT** my recommendation to buy/sell these securities, but just disclosure from my end so that you can assess potential biases that I may have because of my own personal portfolio holdings. Always consider my write-up my personal investing journal and never forget my objectives, risk tolerance, and constraints may have no resemblance to yours. _This post is for paying subscribers only._ ### From Monthly to Daily URL: https://www.mbi-deepdives.com/from-monthly-to-daily/ Last updated: 2025-06-30T14:45:51.000Z I have decided to make some changes to **MBI Deep Dives**. Before I get to the changes, here is a quick update on next month’s Deep Dive: I am currently studying **Cognex (CGNX)** and expect to publish the Deep Dive by **July 25**. For this month’s *Never Sell* podcast episode, David Kim from [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I discussed **APi Group** ([Spotify](https://open.spotify.com/episode/32t5RFZd9Jys3Zf05etxOP?si=a6923f09fd9548cd&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell-episode-7-api-group/id1786912203?i=1000715131730&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=YC%5F2bsLphH4&ref=mbi-deepdives.com), [RSS Feed](https://feeds.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com)). So, about the changes. Starting tomorrow, I will send **one email every day** (yes, including weekends). I will call it **“MBI Daily Dose.”** Each Daily Dose will cover interesting content I come across that day. If you are currently in our WhatsApp Community, the format will be largely familiar. However, I am going to archive the WhatsApp Community because, unfortunately, it is not a great solution for what I am trying to do. With a member limit of 1,024 on WhatsApp, it cannot scale over time. While I used to disclose and discuss any changes to my portfolio in the last section of the monthly Deep Dives, I will now share my portfolio (and any changes) in the Daily Dose emails. This will allow me to communicate more quickly whenever something changes on my end. I will, of course, still publish my Deep Dives. These changes are additive to your experience in MBI Deep Dives. The point of these changes is to move closer to what I keep repeating about MBI Deep Dives: **it is my investing journal.** My objective is to share this journal in its fullest sense. The Daily Dose brings me closer to that ideal end state by allowing me to chronicle, every day, what I am reading and listening to and, perhaps, share a few thoughts while reflecting on it. Of course, my content diet will be largely biased toward companies I already own or would like to own eventually. The modus operandi at MBI Deep Dives has always been to ask myself whether I would find it useful if someone else did the same. While I cannot speak for anyone else, I am confident that I would find a "Daily Dose" email very useful. Even before AI arrived, the search cost of finding interesting and useful content had already become unwieldy, so I believe MBI Deep Dives can add value here since such search cost may materially increase over time. As an "infovore**"**, I do not consider it extra work (or "work"); I would devour content every day anyway! You do not have to do anything on your end and there is no change in price; just expect an email to show up everyday sometime between 9 am-12 pm PT everyday from tomorrow. Thank you for your support! [Subscribe](#/portal/signup) ### APi: Safety-as-a-"Subscription" in a Fragmented Market URL: https://www.mbi-deepdives.com/apg/ Last updated: 2025-06-24T15:17:38.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- For the second consecutive months, I am writing a Deep Dive on a company that came to public market via a SPAC. [Martin Franklin](https://en.wikipedia.org/wiki/Martin%5FE.%5FFranklin?ref=mbi-deepdives.com), who is the main character behind **J2** SPAC that ended up acquiring APi Group in October 2019, doesn’t seem to like the term SPAC. Here’s Franklin on a [podcast](https://www.blog.boyarvaluegroup.com/boyar-value-group-blog/blank/the-world-according-to-boyar-episode-15-sir-martin-e-franklin?ref=mbi-deepdives.com) in 2020: > I wish what I did wasn’t called a SPAC, but I started pretty early in this. I used it as a way to diversify my own investments. I partnered up with a guy called Nicholas Berggruen and we invested in three vehicles early on and what we decided to do was different from anybody at the time. We put a lot of our own money to work in these things rather than just being promoters. > > …US SPAC structure tends to do more speculative transactions because the non-speculative targets won’t deal with US SPACs…I do a very different thing, I buy very fundamental companies that make a lot of money today, made a lot of money for many, many years before and hopefully, will make even more money in the future. It’s a hunting license to do publicly-listed acquisitions without having a foundation company prior to that. > > My goal, personally, is to have a portfolio of five large nine-figure investments in great companies **whose capital allocation decisions I have a say in**, not a dictatorship. It’s a collaborative process but I can influence those outcomes. APi Group does fit the description of a company that “made a lot of money for many, many years before and hopefully, will make even more money in the future.” But before we dig into APi, let me spend more time on Franklin who owns \~11% of APi Group, and will likely have a “say” in capital allocation decision. Why was the SPAC called J2? Franklin dubbed the blank-cheque vehicle “J2” as a deliberate hat-tip to his earlier successful empire: **Jarden**. The “J” in J2 stands for “Jarden”. To understand the fable of Jarden, we have to return to the early-2000s when Franklin snapped up a modest \~$300 million spin-off from Ball Corporation called Alltrista out of obscurity, renamed it **Jarden**, and spent the next 15 years turning odds-and-ends household brands into a $15 billion consumer-products empire. The playbook was simple: buy specialist labels that led their niche, wire them into Jarden’s sprawling sales network, and fund the next purchase without over-stretching the balance sheet. FoodSaver in 2002, Coleman and Sunbeam in 2005, K2 Sports in 2007…each deal widened the company’s reach while giving distributors a thicker catalogue to push at little incremental cost. As Jarden became larger over time, the company expanded its ambition beyond bolt-on deals. Three of their largest deals (Yankee Candle for $1.75 Bn in September 2013, Waddington for $1.4 Bn in July 2015, and Jostens for $1.5 Bn in October 2015) came pretty late in the Jarden journey just before it was sold to Newell in December 2015. By the time Newell Rubbermaid arrived with a \~[$15 Bn](https://www.wsj.com/articles/newell-rubbermaid-and-jarden-strike-merger-deal-1450094318?gaa%5Fat=eafs&gaa%5Fn=ASWzDAjOrJv-aAj3XJZAWQtWZ%5F8-BbC34iqphHoVzr2zPdF3b211EglaIcAaVelv27A%3D&gaa%5Fts=6851a10d&gaa%5Fsig=t8GSW2PFDcj8TY0sk4Cz2votSelC62LTrwX8Lnnl1Mwa5qLeakHGX3l43LJzZgfEu159jLZpDRHZiwr1Z5Bj3g%3D%3D&ref=mbi-deepdives.com) cash-and-stock offer, Jarden had generated a \~50x return for early shareholders and an estimated half a billion dollar payday for Franklin himself. Interestingly, even though Jarden just did aforementioned large deals at HSD EBITDA multiples, it was able to sell Jarden to Newell at 13.7x LTM EBITDA multiple. Franklin clearly got the “buy low, sell high” memo! The marriage, however, was rockier for the buyer. Integrating more than a hundred brands across two corporate cultures proved harder than PowerPoint promised. Synergy targets were met on paper, but debt swelled, growth stalled, and by 2018 Newell was locked in a public board-room brawl with activist fund Starboard. Jarden’s investors had exited near the top and Franklin wasn’t involved to see Jarden limping around within Newell. So, the fable of Jarden is a happy memory that Franklin wanted to recreate even though Jarden deal is perhaps a nightmare in hindsight for its current owner. ![chart](https://substackcdn.com/image/fetch/$s_!npI9!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2fba5f4d-504f-4b83-a2f6-3bdd5901688b_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I am not quite sure how to interpret the chart above. Should we give more credit to Franklin and co that they managed to compound at such high rate for so long and the difficulty of the task could only be appreciated even more after the change of ownership? Or did the seller sense that the hodgepodge of numerous brands start to become too difficult to manage and was able to make it someone else’s problem at the right time? There might be a bit of both, but the latter is an admission that Franklin’s success with Jarden comes with an asterisk. While being intimately involved with Jarden, Franklin realized that acquisitions can scale and still compound if you keep bureaucracy [flat](https://ritholtz.com/2020/07/transcript-martin-franklin/?ref=mbi-deepdives.com). He also developed a conviction that public money that is [raised](https://www.blog.boyarvaluegroup.com/boyar-value-group-blog/blank/the-world-according-to-boyar-episode-15-sir-martin-e-franklin?ref=mbi-deepdives.com) quickly and parked in a SPAC trust lets you be just as competitive (if not more) as private equity to pounce on a deal. Since 2006 he has floated a procession of blank-cheque vehicles, each aimed at a different sector and each repeating, with mixed success, the basic Jarden formula of “buy, bolt on, and professionalize.” Let me very briefly mention these deals. **Freedom Acquisition Holdings** (2006) raised [$528 Mn](https://www.kblhealthcare.com/spac/Year%5Fof%5FSPAC.html?ref=mbi-deepdives.com) and merged with GLG Partners, then one of Europe’s hedge-fund behemoths. If you bought “Freedom” when they acquired GLG in 2007, you may have lost your freedom since it lost \~60% by the time GLG was acquired by [Man Group](https://www.reuters.com/article/business/man-group-to-buy-glg-in-bid-to-kick-start-growth-idUSTRE64G23U/?ref=mbi-deepdives.com) in 2010. **Liberty Acquisition Holdings** (2007) spent [$900 million](https://www.reuters.com/article/business/prisa-has-deal-with-liberty-worth-up-to-900-mln-idUSTRE624463/?ref=mbi-deepdives.com) for a stake in Spain’s debt-laden media group Prisa in 2010\. The Spanish recession and streaming’s assault on legacy broadcasters left the equity limping, and an investor in the SPAC likely lost almost all of their money. Ouch! **Justice Holdings** (2011) was first big hit for Franklin in the SPAC world. They took a [29%](https://www.sec.gov/Archives/edgar/data/1547282/000119312512274675/d348065d424b3.htm??ref=mbi-deepdives.com) slice of Burger King for $1.4 billion, relisted the chain on the NYSE, and watched it roll into Restaurant Brands International (QSR) alongside Tim Hortons three years later. If you bought this SPAC, you almost 5x-ed your money since then. Franklin, however, was mostly involved in QSR until 2019\. Given that QSR stock didn’t do much in the last 5 years, that proved to be a good exit for him. Now, let’s talk about **four active investing vehicles** that Franklin currently has in public markets. **Platform Acquisition Holdings** (2013) bought specialty-chemicals group MacDermid for $1.8 billion and morphed into Platform Specialty Products, today’s Element Solutions. **Nomad Holdings** (2014) paid €2.6 Bn for Iglo, Europe’s largest frozen-food company, and kept consolidating frozen peas and fish fingers across the continent. Then came **J2 Acquisition Ltd.** (2017) which is the vehicle that married Franklin to APi Group. He and long-time Jarden lieutenants James Lillie and Ian Ashken injected $1.25 Bn of SPAC cash and their operational blueprint into APi’s fire-and-safety roll-up in late 2019. Finally, **Admiral Acquisition Ltd.** (2023) raised about $550 million to buy Acuren, a North-American non-destructive-testing and industrial-inspection group, for \~[$1.85](https://www.reuters.com/markets/deals/blank-check-firm-admiral-buy-north-americas-acuren-185-bln-deal-2024-05-22/??ref=mbi-deepdives.com) Bn. As you can see below, performance of all these companies in the last five years have been largely a mixed bag except for APi group. Like Jarden, APi has been a home run for Franklin so far. ![chart](https://substackcdn.com/image/fetch/$s_!ByXA!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F72e410ae-c1fb-4346-8154-5b400588dbff_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) What threads these deals together is less the sector than the style: Franklin looks for markets where brand, route-to-market, or service density can be scaled through disciplined M&A, and then grafts a lean holding-company culture on top. If there’s any takeaway from all this is you cannot just blindly coattail Franklin; what matters much more is the company specific fundamentals. However, it’s a good prologue to internalize the track record, history, and philosophy of the major shareholder who will likely to continue to play a key role in strategic direction in APi. So what exactly is APi Group? From fire suppression and detection, emergency lighting, CCTV, access control, alarm panels, elevators, HVAC loops and mandated safety inspections to covering the mechanical, electrical and life-safety needs of buildings, APi Group designs, installs, monitors and maintains a full spectrum of services. Its crews also tackle specialty utility and infrastructure work such as water and wastewater lines, electric-grid upgrades, fiber-optic cabling and structural fabrication, giving customers a single provider for nearly every critical service in and around their properties. While none of these are cutting edge technology or services, they are quite critical to normal functioning of buildings and facilities APi Group serve. ![](https://substackcdn.com/image/fetch/$s_!IdWy!,w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0e5d6fcb-dca6-403a-9156-fcb8a3b9d5d3_1852x943.png) Source: Company Presentation I will expand on APi’s business with a more detailed discussion on its two segments, margin structure, and operating philosophy. Then I will talk about competitive dynamics, capital allocation history, and management incentives. I will show what is likely currently embedded into the stock price. Finally, I will offer some concluding thoughts and disclose my overall portfolio. Subscribe to keep reading! [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Lululemon 1Q'25 Update URL: https://www.mbi-deepdives.com/lulu1q25/ Last updated: 2025-06-06T00:13:35.000Z *Disclosure: I own January 2026 $165 Call Options* While the quarter looks mostly okay, Lulu’s guide is the primary culprit for the stock to be down 20%+ after-hours today. Before we get into that, let me recap the quarter first and then I will share some highlights from the call. [Subscribe](#/portal/signup) **Sales by Region** Lulu is again back to LSD growth in the US; what makes it more disappointing is they actually had a somewhat easier comparison since 1Q’24 growth was just 2%. Canada, despite a low double digit growth in 1Q’24, again had higher growth than the US this quarter. China and Rest of the World (RoW) maintained healthy growth rates but growth has materially decelerated. However, due to the timing of Chinese New Year, Lulu’s China growth was impacted by four points. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6fc7dd66-2e56-40f0-ba7d-5a58aea327c6_1236x811.png) Given US is still the majority of the business (albeit declining in overall mix; US as % of overall mix has been trending downward for a while: 68% in 1Q’22, 66% in 1Q’23, 61% in 1Q’24, and 57% in 1Q’25), the main focus is still around the anemic growth in the US. What explains the struggle in the US? Management mostly blamed “macro uncertainty” and “consumer confidence” as the key reasons which led to traffic decline in stores. They mentioned conversion remains mostly consistent and average order value actually increased YoY. While most management teams typically like to shift the blame to macro, I wonder if the real challenge here is the broader athleisure segment which definitely took plenty of share in overall apparel industry in the last few years post-Covid and now may be ceding some of the shares. The reason I suspect this may be at least partly the case is **Athleta’s persistent struggle as well which posted 1Q’25 revenue that was \~15% lower than what they did in 1Q’22\. In contrast, Lulu’s revenue in the US was actually +24% during that same period.** In fact, Lulu management highlighted that they believe they continue to take share in the US: > When I look at our performance vs the market, **we gained market share** in the premium activewear. We had strong performance gains vs our peers in this segment of the market where we compete…We're definitely not happy where the growth is in the U. S. But relative to the market and our performance vs others, we are pleased that we're putting on share Of course, we cannot track some of the key competitors such as Alo or Vuori since they aren’t public companies. Moreover, if the broader Athleisure segment is under pressure, we may see increased promotional activity or increased markdown in the US. Lulu management did confirm that they suspect the back half of the year is likely to experience that: > from a competitive perspective, there's nothing we're seeing globally on a price promotional play other than in the U.S., where I would say we continue to monitor that closely because we do see ongoing promotional activity across the market, across the competitors as we've seen the certain consumer the more cautious. > > We know that to lever other pulls and we continue to monitor it and quite frankly, **anticipating a bit of a spike in the back half if the macro headwinds continue**. But we are a full price business, and we'll lead with innovation and our core assortment, we'll continue to play that. But we are seeing and do anticipate probably a dynamic competitive market. in the U.S. Lulu’s markdown so far is **down 10 bps** compared to last year, but they expect it to increase over the course of the year by 10-20 bps YoY. Management thinks traffic is the leading indicator when it comes to markdown and given the declining trend in traffic in the US (traffic hasn’t improved in QTD as well), they assume overall markdown will rise in 2025 vs 2024. Lack of newness was mentioned as the key reason for last year’s lackluster growth in the US, but they seem to have corrected the newness mix and plan to rollout some of the recently launched styles over the course of the year (which was launched in just limited number of stores so far): > In terms of the composition of our merchandise mix, we are back at our newness percentages…I think the way the guest is reacting and responding within that newness, she is reacting very positively to the new core or intended core silhouette styles that she has not seen before… Align no line, the Daydrift, to Be Calm to name just a few, and there's a number of those. > > Those as a percentage of our newness mix, we are increasing in the back half so that we're reacting to what the guest is responding to, and as a result, we are shifting some of the seasonal colors, patterns and graphics in the remaining core to maintain that sort of ratio that we're seeing. But as I look to the back half, the percentage of newness remains strong above historical as we lean into a little bit of these areas where the guest has really responded well, and we weren't at full store distribution. One good thing, however, is that management mentioned unaided brand awareness in the US increased from mid-30s in 4Q’24 to 40% in 1Q’25\. Of course, we are not seeing such positivity in the financials yet, but this unaided brand awareness is critical for Lulu to eventually get back to healthy growth rates in their US business. **Operating Margin** In both the Americas and in China, Lulu’s operating margin improved YoY. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fde5b28d8-ca6f-4bff-ad55-5f453e238363_940x288.png) I will discuss tariff more in the outlook section, but Lulu thinks they can absorb some of these increased costs and likely pass some to consumers given the “elasticity” of their consumer base: > We have an industry leading operating margin. This allows us to continue investing across our strategic road map to enable long term growth while managing any increased cost associated with tariffs. > > our premium positioning in the performance athletic apparel category yields different elasticity for our products relative to fashion oriented brands. **Sales by Gender** All three segments grew at largely similar rates in 1Q’25 ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fac43abe3-3a34-410f-a5c4-7a2516e274b4_1228x262.png) **Inventory** In 4Q’24 call, Lulu guided inventory to grow at high-teens as they chase the newness mix but inventory actually grew by 23%. However, management indicated this was mostly due to FX and tariff; when considering inventory in terms of units, it increased by 16% which was more in line with the guide. > When looking at inventory, we expect units to increase in the low double digits in Q2, with dollar inventories up in the low 20s and due in large part to the impact of higher tariff rates and foreign exchange. We expect a similar dynamic in inventory growth for the remainder of the year. **Capital Allocation** Lulu maintained their buyback intensity throughout last quarter. They bought back $430 mn shares at $316/share (vs $332 Mn in 4Q’24 and $297 Mn in 1Q’24). As a result, Lulu’s shares outstanding declined by 4.3% YoY in 1Q’25\. Management’s buyback activity at the face of headwinds faced by business does seem to project confidence about the long-term health of the business. Given they still have $1.3 Billion cash on balance sheet and the stock price reaction to this earnings, I expect them to remain quite active in buying back shares. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1f634259-74b6-48a5-b320-ff72119c36e5_964x387.png) **Outlook** For 2025, topline guide remains unchanged $11.15-11.3 Bn, implying \~5-7% YoY growth. If you exclude the 53rd week impact, this implies \~7-8% growth. However, they lowered the EPS guide for 2025 from $14.95-15.15 to $14.58 to $14.78 (vs $14.64 in 2024). Why was EPS guided lower despite keeping the topline guide? > For the full year, we now expect gross margin to decrease approximately 110 basis points versus 2024\. Relative to our prior guidance for a 60 basis point decrease, we expect the additional 50 basis points of deleverage to be driven predominantly by increased tariffs, offset somewhat by our enterprise-wide efforts to mitigate these costs and slightly higher markdowns. When looking specifically at tariffs, the assumptions we've made regarding rates include 30% incremental tariffs on China and an incremental 10% on the remaining countries where we source. > > From a mitigation standpoint, as Calvin said, we've looked across the enterprise and have identified several levers which will help offset much of the impact of these higher rates. Based on our implementation strategies, we expect our mitigation efforts to be most impactful in the second half of the year. > > Turning to SG&A for the full year. We expect deleverage of approximately 50 basis points versus 2024, relatively in line with our prior guidance, driven by FX headwinds and ongoing investments into our Power 3x 2 road map, including investments in marketing and brand building aimed at increasing our awareness and acquiring new guests, investments to support our international growth and market expansion and continued investment in technology. > > Looking at operating margin for the full year 2025, we now expect a decrease of approximately 160 basis points versus 2024 So, basically additional \~50 bps pressure in the gross margin due to tariffs than what was communicated in 4Q’24 call; tariff was already assumed to be \~20 bps headwind during 4Q’24 call, but of course that was before the broad based tariff that Trump later imposed. They do expect to raise price a little in some assortments to minimize this impact but nonetheless will take some margin hit. > When we think about the tariff impact to mitigation actions, I'd highlight, one would be pricing. We are planning to take strategic price increases looking item by item across our assortment as we typically do, and it will be price increases on a small portion of our assortment, and they will be modest in nature. > > And then on the sourcing side, we are also pursuing some efficiency actions there, some of which will impact the second half of this year, and then we are also focused on that into '26 as well. **Final Words** I bought Lulu in April 2024; so I clearly underestimated the challenges Lulu has been facing in the US. While it is possible that Lulu has been gaining share, there is lot of noise in verifying such claim given I don’t have data related to Alo/Vuori etc. In any case, gaining share is no panacea if the segment itself is facing headwind. The reality is without persistent MSD-HSD growth in the US, Lulu will find it very difficult to get back mid-20s P/E multiple (or higher). It current trades at \~17x P/E (based on high end of their current 2025 EPS guide). I have mentioned last quarter that I don’t intend to add to my position unless Lulu trades at \~10x NTM EBIT which is basically another 20% down from current price in after-hours. Given that I think Lulu will likely still manage to grow at MSD+ rate over the next 3-5 years even in the scenario that US remains stuck in LSD growth, it is sufficiently attractive for me to start adding if the stock experiences 20% drawdown from here. Thank you for reading. ### June 2025 Update URL: https://www.mbi-deepdives.com/june-2025-update/ Last updated: 2025-06-03T13:22:19.000Z Just a few quick updates for this month: I am going to publish my Deep Dive on **APi Group (APG)** by 26th of this month. For this month's "Never Sell" podcast episode, David Kim from [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I discussed Booking and Airbnb. I even had some product ideas for Brian Chesky; it was a fun conversation! ([Spotify](https://open.spotify.com/episode/3lm2F5UaiddZ38eJDbAc6D?si=4fefd357c37449e9&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell-episode-6-airbnb-and-booking/id1786912203?i=1000710964051&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=--M8CxJh%5F70&ref=mbi-deepdives.com), [RSS Feed](https://feeds.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com)). Finally, for new subscribers, I would like to let you know that you can access the full library of Deep Dives, including excel models, [here](https://www.mbi-deepdives.com/models/). I also encourage you to join the [WhatsApp community](https://chat.whatsapp.com/DYCGwtHfIteDKua1JHkAso?ref=mbi-deepdives.com) where I share interesting tidbits on the companies I follow almost everyday. Thank you for your support! [Subscribe](#/portal/signup) ### Perimeter: Private-Equity GP Economics in Public Markets URL: https://www.mbi-deepdives.com/prm/ Last updated: 2025-05-21T14:28:59.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- It wouldn’t shock me if the word “[SPAC](https://www.investopedia.com/terms/s/spac.asp?ref=mbi-deepdives.com)” itself has a negative connotation in your mind, but of course, it doesn’t inherently imply anything nefarious. A SPAC or “Special Purpose Acquisition Company” is just a publicly traded shell corporation that raises capital through an IPO to merge with or acquire a private business, enabling that target to become publicly listed without undergoing its own traditional IPO. EverArc Holdings, an acquisition company listed in London in December 2019, raised \~$340 million and staffed its board with some heavy hitters: TransDigm co-founder [Nick Howley](https://www.howleyfoundation.org/founders-staff/?ref=mbi-deepdives.com), “Outsiders” author-investor [Will Thorndike](https://www.evergreensg.com/team/will-thorndike/?ref=mbi-deepdives.com), and Berkshire alumna [Tracy Britt Cool](https://en.wikipedia.org/wiki/Tracy%5FBritt%5FCool?ref=mbi-deepdives.com), among others . EverArc had the mandate of building a platform that can let public shareholders enjoy “private-equity-like returns with the liquidity of the public markets”. I kind of find it amusing how that’s supposed to be appealing. What percentage of PE or VC firms actually generated higher returns than S&P 500 in the last 10-15 years? If you read between the lines, EverArc people are alluding private equity has better return profile than public equities and they would like to “democratize” this elusive return profile to random Joe out there. The reality is, of course, that a small minority of investors in all asset classes will be able to beat the public equity benchmark index such as S&P 500 over the long-term. While Warren Buffett rightly receives a lot of adulation, the average Joe out there really should be much more grateful to Jack Bogle for truly **democratizing** investing vehicle such as S&P 500; it may not be a stretch to even claim that Jack Bogle may have sowed the seed to create many more millionaires than any active investor out there, including the GOAT Buffett! So, the talk of “private-equity-like returns with the liquidity of the public markets” is mostly a sales pitch. However, if we look beyond this sales pitch, EverArc did have a solid approach in finding a potential target. EverArc sifted through more than 200 prospects, scoring each against five economic filters it considered non-negotiable: revenues that recur with high visibility; an industry enjoying secular tailwind; products that cost a rounding error to customers yet mission critical; free-cash-flow margins and returns on tangible capital that leave room for error; and a landscape ripe for bolt-on acquisitions. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82b0e35f-f765-4f43-998e-d01d0f1b7799_985x586.png) Source: Perimeter Solutions While I don’t quite think Perimeter ticked every box, its business does have compelling characteristics. Perimeter’s primary business revolved around making long-term fire-retardant chemicals that are mixed with water and dropped from planes, helicopters, or applied from the ground to coat vegetation and structures ahead of a blaze. The phosphate-based solution, which is tinted bright red so pilots can see their coverage, changes how cellulose burns, effectively rendering grasses, brush, and timber non-flammable. A common misperception is that those vivid aerial drops extinguish fires on contact, when they really just delay the flame’s advance until ground firefighters can secure control lines. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0aefc2ad-b915-487a-ab34-d2eb24db28fd_800x512.jpeg) Source: Perimeter Solutions How did this business meet EverArc’s stringent criteria? Wildfire seasons, amplified by climate volatility, provide the secular tailwind. Although it is impossible to predict any particular year’s revenue in this business given somewhat fickle nature of severity of wildfire season from one year to another, the long-term decadal trend is one way street. As a result, you may have very limited visibility from year-over-year perspective, but can potentially be more confident about decadal trajectory. It is one of those rare businesses that may be easier to predict 10 years out than to guess next year’s revenue. Retardant and additive purchases are indeed a rounding error in government firefighting budgets, yet determine whether an advancing fire front is checked or rages on. Based on six decades of operational history, we know this business demonstrated extremely sticky customer retention. The business changed many hands over the decades, and while there is plenty of volatility in revenue **and** margins from one year to another, the business model boasted strong EBITDA margins with low capex requirements over the longer term. It also certainly helped that Perimeter had an absolute monopoly in fire retardant industry. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F26802383-ffa7-439a-8d72-3ff5e7bc7a24_1762x621.png) Source: Perimeter Solutions Like most other SPACs, Perimeter had quite a tumultuous couple of years since becoming a public company. While Perimeter came to the public market with the narrative of an absolute monopoly and resultant high margin, investors became quite wary when they came to know about a new entrant which threatened Perimeter’s “private equity-like return”. Ultimately, while “private equity-like return” is far from guaranteed, the only reality for any public company is **volatility**. The real “moat” for private equity is not perhaps in the actual returns, but in their magical ability to mask volatility. Indeed, Perimeter’s stock precipitously fell by \~75% by November 2023, but as the competitive dynamics largely turned out to be bit of a false flag, the stock has rebounded and nearly come back to its IPO price. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6ba49ae5-f71e-44c5-b1bf-9feb2a019e4c_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) While fire retardant remains the most important segment for Perimeter today, it is \~60% of their revenue today. So, let’s take a much closer look at their current businesses and a more granular understanding of the industry tailwinds as well as economics of the business. That’s section 1. In Section 2, I will discuss how Perimeter was able to maintain their monopoly in fire retardants for decades and some potential risks that can potentially hinder their dominance. In Section 3, I will elaborate on Perimeter’s capital allocation philosophy. Moreover, Perimeter’s incentive structure deserves to be unpacked in greater detail than usual which is the key focus on this section. In section 4, I will show what is likely currently embedded into the stock price. Finally, I will offer some concluding thoughts and disclose my overall portfolio. Subscribe to keep reading! [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### May 2025 Update URL: https://www.mbi-deepdives.com/may-2025-update/ Last updated: 2025-05-02T15:32:41.000Z Some quick updates for this month: I am going to publish my Deep Dive on **Perimeter Solutions (PRM)** by 25th of this month. Given that this is a busy earnings season, I wanted to study a small cap company instead of undertaking a large cap company this month. For this month's "Never Sell" podcast episode, David Kim from [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I discussed Cadence and Synopsys. ([Spotify](https://open.spotify.com/episode/2FF3stedGzyN3vJe3avYbo?ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell-episode-5-cadence-and-synopsys/id1786912203?i=1000705553510&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=LBnNCv%5F2BEw&ref=mbi-deepdives.com), [RSS Feed](https://feeds.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com)). Speaking of earnings season, I have covered [Alphabet](https://www.mbi-deepdives.com/goog1q25/), [Meta](https://www.mbi-deepdives.com/meta1q25/), and [Amazon](https://www.mbi-deepdives.com/amzn1q25/) this quarter. You can read some notes from Meta's follow-up call [here](https://x.com/borrowed%5Fideas/status/1917983681282752652?ref=mbi-deepdives.com). As a reminder, I encourage you to be part of the [WhatsApp community](https://chat.whatsapp.com/DYCGwtHfIteDKua1JHkAso?ref=mbi-deepdives.com) where I share more updates frequently. While I typically haven't covered Microsoft's quarters in the past (I did do a Deep Dive in [April 2023](https://www.mbi-deepdives.com/msft/)), I intend to start covering their quarterly earnings as well from next quarter. One of the big tech companies I have never covered at all in MBI Deep Dives is Nvidia. I hope to do a Deep Dive on Nvidia later this year. These big tech companies are clearly the most important companies, and without following them closely, it will increasingly create wider gaps in understanding the broader economy and the world. Therefore, I expect myself to be diligent in filling these gaps over time. Finally, for new subscribers, I would like to let you know that you can access the full library of Deep Dives, including excel models, [here](https://www.mbi-deepdives.com/models/). Thank you for your support! [Subscribe](#/portal/signup) ### Amazon 1Q'25 Update URL: https://www.mbi-deepdives.com/amzn1q25/ Last updated: 2025-05-02T00:12:02.000Z *Disclosure: I own shares of Amazon* Amazon has another pretty decent quarter. Here are my highlights from today’s call. [Subscribe](#/portal/signup) **Revenue** Overall revenue grew by 10% (FX neutral). While Amazon’s 3P business usually grows faster than 1P, both 1P and 3P retail business grew at similar rate in 1Q’25\. Ads revenue continued its momentum at 19% growth YoY which was higher than both Google and Meta. After growing at \~19% YoY for the last three consecutive quarters, AWS growth decelerated this quarter to 17%. I’ll discuss more about AWS later, but let’s talk more about Amazon ex-AWS first. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F384b31e0-0ad8-4235-be09-9ee4279583b9_1579x226.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** Amazon recorded “one time charges related to some historical customer returns has not yet been resolved and some costs to receive inventory that was pulled forward into Q1 ahead of anticipated tariffs” Excluding this impact, North America and international segment’s margin would be 7.2% and 3.7% respectively. Given that 1Q’24 operating margins for North America and international segment were 5.8% and 2.8% respectively, the margin expansion continues to be an ongoing theme. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F355564aa-ea1d-465a-a148-ff44ac07207f_1120x673.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Of course, tariffs were a big talking point in the call. I thought it was interesting that Amazon was somewhat forceful in reminding investors that the value proposition that Amazon retail provides to customers and how they may be much more resilient than they’re given credit for (perhaps compared to other retailers such as Walmart or Costco): > …We haven't seen any attenuation of demand yet, To some extent, **we've seen some heightened buying in certain categories** that may indicate stocking up in advance of any potential tariff impact. We also have not seen the average selling price of retail items appreciably go up yet. Some of this reflects some forward buying we did in our first party selling, and some of that reflects some advanced inbounding our third party sellers have done But a fair amount of this is that most sellers just haven't changed pricing yet. Again, this could change depending on where tariffs settle. **Amazon is not uniquely susceptible to tariffs**. > > As it relates to China, **retailers who aren't buying directly from China are typically buying from companies who themselves are buying from China**. Marking these items up, rebranding, and selling to US consumers. These retailers are buying the product at a higher price than Chinese sellers selling directly to US consumers in our marketplace. So t**he total tariff will be higher for these retailers than for China direct sellers**. It's also sometimes easy to forget what Amazon sells. We're not mostly selling high average selling price items, though we certainly sell a bunch. In the first quarter, **our everyday essentials grew more than twice as fast as the rest of our business, and represented one out of every three units sold** in the US on Amazon. Even if you exclude Whole Foods Market and Amazon Fresh, Amazon is one of the largest grocers in the US with over 100 billion dollars in gross sales last year. People are buying a lot of their everyday essentials at Amazon. We also have extremely large selection. > > …Finally, when there are uncertain environments, customers tend to choose the provider they trust most. Given our really broad selection, low pricing, and speedy delivery, we have emerged from these uncertain areas with **more relative market segment share than we started** and better set up for the future. I'm optimistic this could happen again. **Fulfillment+ Shipping** If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q'22\. Since then, unit growth has largely been faster than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F26a1f6f0-2eb2-40fe-ad7a-09e0cc59f2c9_1168x526.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Advertising** As mentioned before, Amazon ads grew the fastest among the top three digital advertising players. Given Amazon ads are perhaps more of a competitor to Google than Meta, I think it’s interesting to track how Amazon is gaining share here. Amazon ads incremental revenue as a percentage of Google advertising incremental revenue increased from 33% in 1Q’24 to 40% in 1Q’25. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe3a7b31b-be76-460c-8547-0dd534c104c3_877x511.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AWS** Okay, now let’s talk about AWS. AWS added $481 Mn incremental revenue QoQ which was slightly disappointing to me. Current backlog stands at $189 Bn, which is +20% YoY. Weighted average remaining life of this backlog is 4.1 years. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6b40f7ea-4be2-448e-a51b-bc1e453ee867_1042x538.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Let’s take a quick look at hyperscalers growth. Azure, Google Cloud, and AWS revenue grew by 35%, 28%, and 17% respectively. OpenAI clearly is adding bit of a torque to Azure’s growth here. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa61b1af8-dd55-41e4-a258-f5ed70ca378b_1402x775.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) One thing I would like to track is Google Cloud’s operating performance trajectory against AWS. Back in 2020, Google Cloud was only about a quarter of the size of AWS, but now it’s two-fifth of AWS revenue. We don’t know exactly how much of this is GCP, but we can be pretty confident that GCP is leading this catch-up with AWS. Google Cloud hasn’t made much progress this quarter relative to AWS. While revenue as a percentage of AWS increased by 36 bps QoQ, opex as a percentage of AWS actually increased by 258 bps which isn’t quite indicative of efficiency from Google’s perspective. However, Google tends to look worse in this comparison in Q1 and gradually improves over the course of the year. I will be curious to track if that continues to be the case in 2025. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff165ecd2-2af5-4d94-b64f-2e11a83dad2e_1050x637.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2c877a13-ce07-4be6-8d05-59cab13e9fbf_1102x658.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While AWS topline growth was slightly disappointing, they did post their highest ever operating margin of \~40% this quarter. I am mostly used to seeing AWS operating margins hovering around \~25-30%, but for the last five consecutive quarters, AWS is posting 35%+ operating margin. What’s interesting about such margins this quarter is AWS actually decreased useful life of servers last quarter which was a headwind this quarter. Moreover, when you consider their “multi-billion” AI revenue run-rate growing at triple digit which is presumably lower margin segment today, it is mighty impressive that they are posting \~40% operating margins! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe4d83389-0712-4966-906f-0310e0422ca6_1600x120.png) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8100a861-4284-434d-9d8e-6bcfdef8330b_1194x646.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Couple of quotes on AWS from the call: > Our AI business has a **multibillion dollar annual revenue run rate**, continues to grow **triple digit** YoY, and is still in its very early days. While there is good reason for the high optimism about AI, I conclude my AWS comments with a reminder that there is still so much on premises infrastructure yet to be moved to the cloud. Infrastructure modernization is much less sexy to talk about than AI but fundamental to any company's technology and invention capabilities, developer productivity, speed, and cost structure. And **for companies to realize the full potential of AI, they're going to need their infrastructure and data in the cloud**. > > …**as fast as we actually put the capacity in, it's being consumed. So, you know, I think we could be helping more customers and driving more revenue for the business if we had more capacity…**I expect that, you know, there are other parts of the supply chain that that are a little bit jammed up as well, you know, motherboards and some other componentry, some of that is just because there is so much demand right now. But I do believe that the supply chain issues and the capacity issues will continue get better as the year proceeds. **Opex+Capex** Just like other big tech, Amazon’s capital intensity continues to increase as well. Capex as a percentage of revenue was \~16% in 1Q’25 (vs \~10% in 1Q’24). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8627fad8-6a02-45f2-8474-6503e8d82b10_1453x316.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** Amazon’s guidance for 2Q’25 is below. Please note consensus 2Q’25 revenue and EBIT before the call were $161 Bn and $17.7 Billion respectively. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fda40c2e5-b0ae-4c6a-b418-e6f0ed28b49d_1044x237.png) Source: Company Filings **Closing Words** I agree with Jassy’s characterization that even in a deteriorating tariff scenario, my best guess would be Amazon retail would fare okay and may even gain share. Of course, they can still be hurt if consumer spending goes down in a recession scenario, but I feel comfortable about Amazon retail’s competitive position which is more important to me as a long-term shareholder than guessing how tariff will affect this year’s EPS. On AWS side, while the margins are quite eye popping, I expected to see them grow faster, especially in light of the increasing capex spending. These things can be lumpy and as Jassy mentioned in the call, it isn’t a question about demand. I intend to stay invested, and if I decide to deploy some capital over the next month, Amazon would be on top of my list. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Meta 1Q'25 Update URL: https://www.mbi-deepdives.com/meta1q25/ Last updated: 2025-05-01T03:00:58.000Z *Disclosure: I own shares of Meta Platforms* > Our goal is to make it that any business can basically tell us what objective they're trying to achieve, like selling something or getting a new customer and how much they're willing to pay for each result and then we just do the rest…if we deliver on this vision, then over the coming years, I think that the increased productivity from AI will make advertising **a meaningfully larger share of global GDP than it is today**. > > \-Mark Zuckerberg (1Q’25 Earnings Call) Digital advertising has surpassed the days of “[Mad Men](https://en.wikipedia.org/wiki/Mad%5FMen?ref=mbi-deepdives.com)” a while ago and thanks to AI, it seems even better positioned to unlock new markets and more opportunities. Meta is, of course, one of the companies leading this march. Here are my highlights from today’s call. [Subscribe](#/portal/signup) **Users** Daily Active People (DAP) across its Family of Apps (FOA) accelerated to 80 mn QoQ in 1Q’25\. I wonder when Zuckerberg starts to get concerned about the fertility crisis as well since Meta may run out of people to sign up for their products in a few years! (only half-kidding) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97d6e77c-ad77-4ab3-8a41-494a963c04ac_1825x93.png) **Ad revenue by Geography** You can take a look at the table below and tell these numbers are quite impressive, but let me contextualize how impressive they are, especially in light of Google’s numbers. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffe7e9c5a-8994-4617-a566-5a9b38b46dfd_1933x337.png) Google advertising’s incremental revenue was $5.2 Bn in 1Q’25 YoY (of which Google Search added $4.5 Bn). Meta’s Family of Apps (FOA) ad business generated $5.8 Bn incremental revenue! FOA’s growth has now surpassed Google’s even from pre-ATT days. Imagine facing an existential crisis and then come out stronger than ever before…a true sign of antifragility! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc25689b-baef-40ae-82d3-e4ae0a5cd2a2_706x145.png) **Ad Impression and Avg. Price Per Ad** I know a lot has been said about Google’s paid click growth of just 2% in 1Q’25\. Meta’s ad impression growth was 5% YoY. On one hand, I think it makes Google’s number look more okay than many might think. On the other hand, Meta just seems to have more unmonetized impressions they can unleash if they ever feel too saturated. For example, Threads is just starting to monetize with 350 Mn MAUs. In the call, they also mentioned “tens of billions of views of status posts on WhatsApp each day”. I know they don’t monetize these via ads, but never say never. I wouldn’t be surprised if they eventually decide to monetize these; I’m obviously not suggesting anything in the near term but think long term i.e. 5-10 years and I sense it gives us more margin of safety in growth runway at Meta (vs Google). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5ca88659-3f2c-4b98-b26f-266f3741b50e_1060x496.png) This call also again **drove the point home** how AI is such a big deal in digital advertising and why even if impression growth stalls, we may have decent runway just by improving monetization of current impressions: > In just the last quarter, we are testing a new ads recommendation model for reels, which has already **increased conversion rates by 5%**. And we're seeing 30% more advertisers are using AI creative tools in the last quarter as well. > > we continue to see **conversions grow at a faster rate than ad impressions** in Q1, so reflecting **increased conversion rates** and ads ranking and modeling improvements are a big driver of overall performance gains. > > we continue to evolve our ads platform to drive results that are optimized for each business' objectives and the way they measure value. One example of this is our incremental attribution feature, which enables advertisers to optimize for driving incremental conversions or conversions we believe would not have occurred without an ad being shown. We're seeing strong results in testing so far, with advertisers using incremental attribution in tests, **seeing an average 46% lift in incremental conversions** compared to their business as usual approach. We expect to make this available to all advertisers in the coming weeks. **Segment Reporting** Overall 1Q’25 revenue was +16.3% YoY (\~**19%** on constant currency). FOA’s “other revenue” was +34% YoY which was driven by business messaging and Meta verified subscriptions (I don’t think Meta called out “verified subscriptions” as growth driver until this quarter. Of course, it’s probably meaningless given the scale of ads business) FOA continued to post >50% operating margins. For Reality Labs, another quarter of $4 Billion losses! More embarrassingly, revenue **declined** YoY! I know Meta is re-allocating a lot of expenses to AR glasses. I wonder if Meta is very close to admitting “defeat” in VR and scaling down their investments substantially by the end of 2026 if AI advancements don’t lead to sustained acceleration in VR in the next few quarters. AR likely deserves continued investments, so I don’t expect the Reality Losses to reverse course anytime soon even if they scale down investments in VR. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8984d12f-17a7-401d-b19c-d4cfe0b3c438_2098x549.png) **AI** Some quotes from the call all of which are good evidence for ROI on Meta’s AI investments: > In the last 6 months, improvements to our recommendation systems have led to a **7% increase in time spent on Facebook, a 6% increase on Instagram and 35% on Threads.** > > Over the long term, as AI unlocks more productivity in the economy, I also expect that **people will spend more of their time on entertainment and culture**, which will create an even larger opportunity to create more engaging experiences across all of these apps. > > We began testing using Llama in Threads recommendation systems at the end of last year given the app's text-based content and have already seen a **4% lift in time spent from the first launch**. It remains early here, but a big focus this year will be on exploring how we can deploy this for other content types, including photos and videos > > we shared on the **Q3 2024 call that improvements to our AI-driven feed and video recommendations drove a roughly 8% lift in time spent on Facebook and and a 6% lift on Instagram over the first 9 months of last year. Since then, we've been able to deliver similar gains in just 6 months' time, with improvements to our AI recommendations, delivering 7% and 6% time spent gains on Facebook and Instagram, respectively.** **Meta AI** Meta AI monthly actives reached 1Billion (vs 700 million in 4Q’24 and 500 million in 3Q’24). While by this metric Meta AI looks better than even ChatGPT, I wouldn’t be surprised if number of conversations on Meta AI is not even 20% of ChatGPT gets today. What are the top use cases? > The top use case right now for Meta AI from a query perspective is really around information gathering as people are using it to search for and understand and analyze information followed by social interactions from, ranging from casual chatting to more in-depth discussion or debate. We also see people use it for writing assistance, interacting with visual content, seeking help. And we see Meta -- people engage with Meta AI from several different entry points. WhatsApp continues to see the strongest Meta AI usage across our Family of Apps. Most of that, WhatsApp engagement is in one-on-one threads, followed by Facebook, which is the second largest driver of Meta AI engagement, where we're seeing strong engagement from our feed deep dives integration that lets people ask Meta AI questions about the content that's recommended to them. How is Meta AI different from all the other chat bots out there? > I'm not sure that people are going to use multiple agents for the same exact things, but I'd imagine that something that is more focused on kind of enterprise productivity might be different from something that is somewhat more optimized for personal productivity and that might be somewhat different from something that is optimized for entertainment and social connectivity. So then there were different experiences. One of the trends that I think we're starting to see now is personalization across these. Right now if the experience is unpersonalized then you can kind of just go to different apps and get reasonably similar answers to different questions. > > But once an AI starts getting to know you and what you care about in context and can build up memory from the conversations that you've had with it over time, I think that will start to become somewhat more of a differentiator. While ChatGPT is certainly a productivity amplifier, it also very much satisfies a lot consumer use cases as well. And like Zuck said, it can definitely know me well and personalize the responses over time. I’m not super convinced yet that I will use different chat bots based on different query types. Given OpenAI is still figuring out how to monetize free users and doesn’t have as prodigious cash flows as Google/Meta does, I wonder if the fight for the next 500 million users will be more closely fought than the first 500 million users was. In this call, Meta did indicate that they will lean to their usual playbook of ad based model to monetize Meta AI: > Our focus for this year is deepening the experience in making AI the leading personal AI with an emphasis on personalization, voice conversations and entertainment. I think that we're all going to have an AI that we talk to throughout the day, while we're browsing content on our phones, and eventually, as we're going through our days with glasses. And I think that this is going to be one of the most important and valuable services that has ever been created. In addition to building Meta AI into our apps, we just released our first Meta AI stand-alone app. It is personalized. So you can talk to it about interests that you've shown, while browsing reels or different content across our apps. And we built a social feed into it. So you can discover entertaining ways that others are using Meta AI. And initial feedback on the app has been good so far…**I think that there will be a large opportunity to show product recommendations or ads as well as a premium service for people who want to unlock more compute for additional functionality or intelligence**. But I expect that we're going to be largely focused on scaling and deepening engagement for at least the next year before we'll really be ready to start building out the business here. **Facebook, and Instagram** > In the first quarter, we saw strong growth in video consumption across both Facebook and Instagram, particularly in the U.S., where **video time spent grew double digits year-over-year.** **Messaging** Some good color on messaging opportunity: > …there are now **as many messages sent each day on Instagram as they are on Messenger** > > …business messaging should be the next pillar of our business. In countries like Thailand and Vietnam, where there is a low cost of labor, we see many businesses conduct commerce through our messaging apps. **There's actually so much business through messaging that those countries are both in our top 10 or 11 by revenue, even though they're ranked in the 30s in global GDP**. This phenomenon hasn't yet spread to developed countries because the cost of labor is too high to make this a profitable model before AI, but AI should solve this. **Threads** Threads Monthly Active Users (MAU) over time: 3Q’23: 100 Million 4Q’23: 130 Million 1Q’24: 150 Million 2Q’24: 200 Million 3Q’24: 275 Million 4Q’24: 320 Million 1Q’25: 350 Million MAU growth has decelerated a bit here. **AR** > Ray-Ban Meta AI glasses have **tripled** in sales in the last year. > > We're seeing very strong traction with Ray-Ban Meta AI glasses, **with over 4x as many monthly actives as a year ago, and the number of people using voice commands is growing even faster as people use it to answer questions and control their glasses.** **Capital Allocation** Interesting to see Meta was much more aggressive in 1Q’25 in buying back shares (vs last quarter). They haven’t filed 10-Q yet, but I will be curious to see the prices at which they bought back these shares given both the upside and downside volatility the stock experienced in the quarter. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd98708eb-beb7-4389-b027-d36aad43c559_2088x316.png) **Capex and Opex** Looking at this table below, I wondered in which year Meta’s (and other big tech) depreciation expense may surpass their employee compensation expense! At the pace big tech is spending on capex, maybe it’s not as nonsensical as it may seem at first glance. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0d7c7b01-a223-417c-96df-0e2ca78f6762_2091x105.png) **Regulation** > The European Commission recently announced its decision that our subscription for no ads model is not compliant with the DMA. Based on feedback from the European Commission in connection with the DMA, we expect we will need to make some modifications to our model, which could result in a materially worse user experience for European users, and a significant impact to our European business and revenue as early as the third quarter of 2025\. We will appeal the commission's DMA decision, but any modifications to our model may be imposed before or during the appeal process. Meta later clarified that it would affect 16% of their overall revenue. There is not much merit to EU’s logic and it is clearly hostile to US tech companies as many EU companies do exactly what Meta has done. I would expect current US admin to not look the other way if US big tech is treated such a way by EU or anyone else in the world. **Outlook** Meta guided 2Q’25 revenue $42.5 Bn to $45.5 Bn (1% FX tailwind). Consensus is $43.8 Bn. They also guided opex range down a bit from $114-119 Bn to $113-118 Bn. However, capex guide was increased: > We anticipate our full year 2025 capital expenditures, including principal payments on finance leases will be in the range of $64 billion to $72 billion, increased from our prior outlook of $60 billion to $65 billion. This updated outlook reflects additional data center investments to support our AI efforts as well as an increase in the expected cost of infrastructure hardware. The majority of our CapEx in 2025 will continue to be directed to our core business. **Closing Words** Overall, this was a super impressive quarter and the guidance is reassuring even in the volatile tariff environment. The takeaway is pretty clear: Meta is very well positioned in navigating and riding along the secular theme of AI. It’s not just Meta of course; when I looked at Microsoft’s numbers tonight, perhaps the real surprise to me is the volatility that the big tech stocks routinely experience every now and then despite having such rock solid underlying business, growth, profitability, and balance sheets. Meta trades at below 25x NTM P/E even after \~5% AH rally, so the valuation is quite reasonable as well. I intend to stay invested. Having said that, I am somewhat disappointed at Meta’s recent missteps in Llama and Zuck’s somewhat [disingenuous](https://x.com/modestproposal1/status/1917593373705150892?ref=mbi-deepdives.com) explanation later. I don’t think Meta necessarily needs to have the best model for the stock to do well for long-term shareholders, but it does make me think whether the company may be losing their usual execution muscle a bit. I will cover **Amazon’s** earnings **tomorrow**. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Synopsys: Harnessing Complexity URL: https://www.mbi-deepdives.com/snps/ Last updated: 2025-04-25T12:44:37.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- Chip design has long been a paradoxical craft. On one hand, engineers must trust sophisticated software to help them craft circuits of mind-boggling complexity; on the other, they maintain a healthy skepticism, knowing a single flaw can doom a chip. The old mantra "trust, but verify" could well have been coined for this process. Hardly any company embodies this delicate balance between automation and human oversight better than Synopsys, a company that was founded just three months prior to TSMC in the mid-1980s as a scrappy spin-off from General Electric (GE). Yes, this was Jack Welch’s GE which apparently spread its tentacles even into semiconductors. Synopsys’s origin story unfolds amid a turning point in semiconductor history. In 1985, a severe industry downturn prompted GE to pull back from its semiconductor venture, leaving a small team of engineers led by Dr. Aart de Geus at a crossroads. They had developed an innovative logic synthesis tool, a software capable of taking a high-level description of a digital circuit and automatically generating the intricate logic gates to implement it. At the time, this was revolutionary. Chip designers were used to managing that task manually, drawing out circuits gate by gate. Indeed, to create a schematic in the mid-1980s, an engineer would often hand-draw everything i.e. every logic gate, resistor, and connection on vellum paper since computer schematic tools were not yet available. This laborious process could take weeks or months for a complex circuit, with frequent re-drafts and “cut-and-paste” edits done by literal scissors and tape​. The Synopsys founding team envisioned a better way. In 1986, they spun out of GE (with the company’s blessing and even a modest technology license), forming a startup originally named Optimal Solutions​. By the following year, they had relocated to Silicon Valley, aptly renaming the venture **Synopsys** (short for *synthesis and optimization systems*) and setting out to commercialize their new method of chip design​. For decades, chip designers viewed their work as equal parts engineering and art. The thought of ceding the “artistic” control to a software program met with natural resistance. In the early years, letting a tool automatically modify a circuit was almost *taboo*. On the Acquired [podcast](https://www.acquired.fm/episodes/the-software-behind-silicon-with-synopsys-founder-aart-de-geus-and-ceo-sassine-ghazi?ref=mbi-deepdives.com), Aart de Geus (founder of Synopsys), and Sassine Ghazi (the current CEO of Synopsys) recollected some of those early days. They half-jokingly referred to their synthesis engine as having a “license to kill,” because giving a tool free rein to alter a circuit design felt as dangerous as it sounds. Many engineers were simply uncomfortable with software intervening in what they saw as their creative process. “Please only aid me, do not automate for me,” was a common sentiment among chip architects of the time. Underneath this reluctance was a deep issue of trust: designers weren’t convinced that an algorithm could make better decisions than an experienced human, and they worried that automated changes might introduce hidden bugs​. This cultural skepticism meant that Synopsys not only had to invent the technology, but also persuade an entire industry to believe in it. What problem was Synopsys solving at inception? In a word: **complexity**. The mid-1980s marked the dawn of an era when microchips were growing so intricate that traditional design methods were reaching a breaking point. Each new chip generation packed in more transistors than the last, making the schematics exponentially harder to manage. Synopsys’s breakthrough was to transition chip design “from schematic to language-based” representation​. Instead of physically drawing every gate, a designer could write code (in a hardware description language) describing the chip’s behavior, and Synopsys’s software, their flagship **Design Compiler** introduced in 1987, would automatically synthesize that code into an optimized gate-level implementation. This *logic synthesis* automation was like a compiler for hardware, translating abstract designs into silicon-ready blueprints. It drastically accelerated design work and reduced human error, at a time when such a leap was desperately needed. Just take a look at the diagram below and you may appreciate why humans may not be best suited to draw something like this by hand. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb3d8edfe-e012-4c0f-ad30-9ee91aa7ef7f_1000x519.png) Image Source: [Here](https://infoscience.epfl.ch/server/api/core/bitstreams/2f5b97bb-e550-4a15-94e1-8eda2ba95494/content?ref=mbi-deepdives.com) Even though there was skepticism initially, those who tried the tool found that it could shave months off design cycles by handling the grunt work of logic gate optimization. One by one, major semiconductor companies gave this new approach a chance. The results spoke for themselves: chips reached timing goals and functioned correctly, even when much of the design had been generated by Synopsys’s algorithms. By the early 1990s, Synopsys had established relationships with virtually all of the world’s leading chipmakers. The young startup expanded its portfolio beyond synthesis, adding simulators for testing chip behavior, timing analyzers to ensure speed targets, and other tools to round out a complete design suite. Synopsys grew rapidly on this acceptance. In fact, the company’s momentum carried it to an IPO in 1992, barely six years after its founding​, a testament to the value it was delivering to chip designers desperate for solutions. In the year it IPO-ed, Synopsys posted \~$92 million revenue and \~$8 million operating profit. By the end of the 1990s, Synopsys reached $806 Million revenue and $251 Million operating profit. Of course, tech bubble crashed afterwards and the stock experienced \~60% drawdown in 2000\. The stock fully recovered in just four years but then experienced another \~60% drawdown. Like many tech companies, Synopsys eventually took more than a decade to decisively exceed its peak during tech bubble! Despite these rise and fall, Synopsys is more than a \~50-bagger since its IPO in 1992. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4708edbb-2607-4e17-a6e6-8c2f6925ddd5_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Today, Synopsys stands as a linchpin of the semiconductor ecosystem. A company that was born from a risky idea that software could design hardware is now entrenched as a trusted ally in virtually every advanced chip project. The introduction of automated design tools fundamentally changed the economics and timeline of chip development. To understand and appreciate their current role in the broader semiconductor value chain, let me provide you a more granular understanding of their current business. While it is not essential, I would like to note that if you’re largely unfamiliar with semiconductor industry, it may be more helpful to start with my semiconductor [primer](https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/) first and then read this Deep Dive. In section 1, I will go deep to Synopsys’ current business, economics, and growth drivers for both of its two segments: design automation, and design IP. In section 2, I expanded on Synopsys’ potential acquisition of Ansys. Then in section 3, I looked into the moats of Synopsys as well as the competitive dynamics between Cadence and Synopsys. In section 4, I will show what is likely currently embedded into the stock price. Finally, I will offer some concluding thoughts and disclose my overall portfolio. Subscribe to keep reading! [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Alphabet 1Q'25 Update URL: https://www.mbi-deepdives.com/goog1q25/ Last updated: 2025-04-25T01:45:12.000Z *Disclosure: I own shares of Alphabet* While Google continues to fend off concerns related to long-term future of search, Alphabet’s business keeps chugging along. Here’s my highlights from today's earnings. [Subscribe](#/portal/signup) **Revenue** On an FX adjusted basis, Alphabet increased its revenue by 14% in 1Q’25 (\~200 bps headwind from FX). For the **11th** consecutive quarters, Google network’s revenue went down. Just when regulators are lambasting Google for their network business in court, it keeps dwindling to oblivion. If Google just spins it off to get rid of the legal hassle, that’s probably an even worse news for the open web. Eric Seufert today made a [compelling](https://stratechery.com/2025/an-interview-with-eric-seufert-about-digital-advertising-during-political-uncertainty/?ref=mbi-deepdives.com) case to publishers: *“be careful what you wish for”*. Both Search and YouTube ads grew by 10% YoY. To appreciate YouTube’s momentum, we may increasingly have to rely on “Subscription, platform, and devices” revenue (formerly known as “Google other” segment). More on this later. Google Cloud is now at almost $50 Billion revenue run-rate, growing at an incredible \~28% YoY in 1Q’25. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5824a569-f125-441b-8af5-d22241661707_1768x364.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Take a look at growth rates by segment over the last 13 quarters. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffa079e84-e9f4-494e-9274-9b28eb2d8419_1603x313.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Google continues to post pretty unbelievable margins. Both Google Services and Google Cloud posted their highest ever operating margins. Google Services had another mind-boggling \~70% incremental margin quarter. It’s easy to forget but almost everyone expected the opposite to unfold since ChatGPT came to the scene. With monetization headwind from AI and rising cost per query, it certainly surprised me how much Google was able to expand its margins. For context, Google posted \~30% operating margin in 4Q’22 when ChatGPT was released. They just posted \~42% operating margin for Google Services. Couple of things helped expand margins: a) the persistent decline of Google network business which has the highest TAC rate and likely one of the lowest margin business for Google Services; and b) the increase of depreciation schedule over the last few years. Google, of course, also enhanced its focus on “durably reengineering the cost base”. The fact that sales & marketing expense was down 4% in 1Q’25 is a good evidence to that approach. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff4d055d0-b130-4c96-803d-dbd0ea5ddb3f_1687x468.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ## **Search** Some interesting quotes on Search from the call; I will include my comments/notes in the parentheses: > …AI Overviews is going very well with over **1.5 Billion users** per month and we are excited by the early positive reaction to AI mode. > > …We released Gemini 2.5 Pro last month, receiving extremely positive feedback from both developers and consumers. 2.5 Pro is state of the art on a wide range of benchmarks and debuted **at number one on the chatbot arena by a significant margin**. (**Note**: it definitely feels Google got its mojo back in model development. It’s TBD whether it is noticeable enough for consumers to care as OpenAI seems to be still better in productizing the model. My guess is it may matter if Google consistently continues to lead and actually ends up increasing the lead over time. Other than that, models may remain mostly commodity and they will have to just capitalize on their existing distribution to compete against OpenAI and others) > > …On average AI mode queries are **twice as long as traditional search queries**. We're getting really positive feedback from early users about its design, fast response time and ability to understand complex nuanced questions. We also continue to see significant growth in multimodal queries. Circle to Search is now available on more than 250 million devices (**Note**: mentioned to be 200 million last quarter) with usage increasing nearly **40% this quarter**. And monthly visual searches with Lens have increased by 5 Billion since October. > > …with the launch of AI Overviews, the volume of commercial queries has increased. Q1 marked our largest expansion to date for AI overviews, both in terms of launching to new users and providing responses for more questions. > > …For AI overviews overall, we continue to see **monetization at approximately the same rate**. (**Note**: this was also mentioned in last quarter, so not a new info) > > …In Q1, the number of people shopping on Lens grew by over 10% and the majority of Lens queries are **incremental**. > > …Thanks to dozens of AI part improvements launched in 2024, businesses using DemandGen now see an average 26% YoY increase in conversions per dollar spent for goals like purchases and leads. And when using DemandGen with product feed, on average, they see more than double the conversion per dollar spent year over year. > > …We are continuing to make a lot of progress there in terms of people using coding suggestions. I think the last time I had said the number was like 25% of code that's checked in. It involves people accepting AI solutions. **That number is well over 30% now.** > > …Search and Gemini obviously will be **two distinct efforts**, right? I think there are obviously some areas of overlap, but they're also you know, like expose very, very different use cases. And so, for example, in Gemini, we see people iteratively coding and going much deeper on a coding workflow, as an example. So I think **both will be around**. Within Search, would think of AI overviews scaling up and working for our entire user base, but an AI mode is the tip of the tree for us pushing forward on an AI forward experience. There will be things which we discover there which will make sense in the context of AI overviews, so I think will flow through to our user base. But you almost want to think of what are the most advanced 1 million people using Search for, the most advanced 10 million people, and then how do 1 billion people use Search for. And we want to innovate and so I think this allows us to do that. But the **true north star through all of this is user feedback, user satisfaction, user experience**. From personal perspective, LLM feels like a mix of productivity and search tool. So, the surface area of long-term potential does feel pretty wide. I think it makes sense for now to approach these from multiple angle to gauge what resonates with the users the most. ### **YouTube** > “YouTube now has over 1 Billion monthly active podcast users. YouTube Music and Premium reached over 125,000,000 subscribers, including trials globally.” I wish Google stopped including the trials in their subscriber numbers. Just report the paying subscriber number! ### Subscriptions, Platforms, and Devices Google announced they not have 270 million subscribers! Back in 1Q’22, both YouTube ads and the then “Google other” (now “subscription, platforms, and devices) segment had \~$6.8 Billion revenue. Three years later, this segment just reported \~$1.5 Billion more revenue than YouTube ads in 1Q’25\. Google mentioned this growth is primarily driven by YouTube and Google One subscriptions. If Google can maintain its recent momentum in releasing SOTA models, I think Google One can be a pretty large business for them. Their [offering](https://x.com/borrowed%5Fideas/status/1911436715111419991/history?ref=mbi-deepdives.com) is quite compelling! Search’s long-term future can be hard to decisively answer, and while Google may be too dependent on search advertising revenue, I think they have plenty of defense to remain relevant for a long time: > All 15 of our products with a half a billion users now use Gemini models. Android and Pixel are two examples of how we are putting the best AI in people's hands, making it super easy to use AI for a wide range of tasks just by using their camera, voice or taking a screenshot. ### **Google Cloud** Google Cloud more than doubled its revenue in just three years as it grew from $5.8 Billion in 1Q’22 to $12.3 Billion in 1Q’25\. Just as Google cloud grew its revenue by 28%, one interesting thing that I noticed is when AWS had \~$12 Billion quarterly revenue in 4Q’20, they also grew revenue by 28%. Two years ago, I [mentioned](https://www.mbi-deepdives.com/goog/) that Google Cloud’s revenue tends to mirror AWS revenue four years apart, but I was skeptical that it would continue. So far, Google cloud is largely still keeping pace with AWS four years apart. I will discuss more on Cloud when Amazon posts later this week. Management reiterated that demand-supply is still not in an equilibrium: > …we're in a tight demand-supply environment and given that revenues are correlated with the timing of deployment of new capacity, we could see variability in cloud revenue growth rates depending on capacity deployment each quarter. > > …We expect relatively higher capacity deployment towards the end of twenty twenty five. ### **Capital Allocation** In 1Q’25, Google returned 92% of their FCF to shareholders through buyback and dividend. Share count declined by 46 bps QoQ. They also increased dividend by 5% going forward. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fec0e3393-dd95-4500-a9b5-822f90a918a2_679x628.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ### **Capex and Opex** Management re-iterated that they expect capex to be $75 Billion in 2025\. My sense is no matter what happens in the economy, we will see $75 Billion capex this year, and the real impact of economic situation will sway their capex plan in 2026. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0f104c90-a3ff-47dd-aa45-9a67dac301cd_1605x238.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ### **Outlook** Google doesn’t provide guidance, but did remind the headwind from “APAC based retailers” and the tsunami of depreciation expense that’s coming: > With regard to Q2, we're only a few weeks in, so it's really too early to comment. I mean, we're obviously not immune to the macro environment, but we wouldn't want to speculate about potential impacts beyond noting that the changes to the de minimis exemption will obviously cause a slight headwind to our ads business in 2025 primarily from APAC based retailers. > > We had about a 31% year over year growth in depreciation this quarter and it will be higher as we go throughout the year. So think about that kind of as a headwind that we have to manage against. ### **Valuation** Since [3Q'22](https://mbideepdives.substack.com/p/goog3q22?utm%5Fsource=publication-search), I share the following valuation framework every quarter. The Services business seems to be currently priced at \~15x LTM EBIT, (a segment that has grown EBIT by 14.1% CAGR over the last three years) and Google Cloud at \~5x run-rate revenue. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1f635f0b-8de5-443f-a5ef-065f1da87491_1840x379.png) I will publish my Deep Dive on **Synopsys** tomorrow, and will cover the other big tech earnings next week. Thank you for reading. **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Portfolio Discussion URL: https://www.mbi-deepdives.com/portfolio-discussion/ Last updated: 2025-04-08T00:32:57.000Z The last couple of trading days has been unusually active days for me. I have deployed a total \~12% of cash in the last two days; I have another \~6% left which I expect to deploy soon if the market continues its downward trend. I will share some brief thoughts on what I did. While I usually reserve this discussion for the final section of the monthly Deep Dives, let me share essentially my unpolished journal notes while navigating the current volatile market. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Back to Google! URL: https://www.mbi-deepdives.com/googl/ Last updated: 2025-04-05T02:07:08.000Z There's a popular meme on Twitter that goes like this: *"I'm starting to be a real believer in AI. Will be a megatrend like no one is really imagining right now. I'm sure lots of ramps and drops along the way though"* Like any good meme, there is indeed an ounce of truth to it. Maybe not an ounce; I actually think there is likely to be a lot of truth to it. However, as former Bernstein Internet Analyst [Corry Wang](https://x.com/corry%5Fwang?ref=mbi-deepdives.com) (who is incidentally currently at Google AI Strategy team) [put it](https://x.com/corry%5Fwang/status/1300635628460220418?ref=mbi-deepdives.com) : *"There is a misguided obsession in many tech circles around predicting the future of technology. In contrast, I would posit that predicting the future is actually pretty easy - the hard part is making any money on it."* Indeed, a couple of years ago, it was somewhat easy to foresee that the narrative around the future of search would likely continue to sour. It did, but the details have been somewhat more difficult to be right about. For example, since the rise of GenAI and chat bots, one common concern has been a potential double whammy for incumbents such as Google i.e. GenAI queries are harder to monetize and at the same time, the cost to serve such queries would be materially higher than traditional search queries. I doubt anyone was expecting Google's operating margin would go **higher** from 30.6% in 2021 to 32.1% in 2024\. I myself thought Google Services operating margin in 2021 might be "peak margin" for Google as they would have to navigate much higher cost to serve queries and lower monetization phenomenon (admittedly, part of this margin expansion was due to extending depreciation schedule, but even if you adjust that, 2021 and 2024 Google Services operating margin would be somewhat similar). What actually happened was Google reduced cost to serve these GenAI queries by 90% in 18 months through " hardware, engineering and technical breakthroughs" (3Q'24 call). Moreover, Google has stumbled onto new query markets e.g. in 4Q'24 call, they mentioned how lens is used for 20 billion visual search queries, majority of which are **incremental**. How about monetization of these GenAI queries? It's less reassuring so far, but in 1Q'24 call Google management mentioned *"based on our testing so far, I'm comfortable and confident that we'll be able to manage the monetization transition here well as well."* This isn't a Deep (or even shallow) Dive on Google, so I'm not going to focus much on numbers or try to address/discuss all the Google related concerns (read this [piece](https://www.mbi-deepdives.com/goog/) if you're more interested in numbers; I may also do an update later in the year). Look, when I [sold](https://x.com/borrowed%5Fideas/status/1732083595874009587?ref=mbi-deepdives.com) Google in December 2023, I did mention I expect search to go through a significant transformation in the next 10-15 years. It's always dangerous to change your mind on something in two years that you expect to play out over much longer period. I still have plenty of sympathies for search related concerns. And even from personal perspective, while it is true that Google did lose my query share mostly to OpenAI, Google hardly lost any of my monetizable query share. However, if you want to disrupt Google, you always were going to disrupt the non-monetizable queries first and then work your way to monetizable queries later. So, I am certainly not claiming Google to be out of the woods yet. My appreciation of the risk is certainly why I made Google only a 3% position so far with an average cost of $150\. I will pay close attention to price and broader developments in AI before sizing it up more. However, I do want to mention a couple of things to give you a sense what prompted to change my mind. Let me be very frank. When I sold Google in 2023, I hardly knew much about semiconductors and I knew even less about TPUs. As I gained a bit more [understanding](https://www.mbi-deepdives.com/models/) over the last 15 months, I started appreciating there are certainly scenarios in which Google's deep infrastructure advantage and breadth of existing relationship with users can mask almost all of their weaknesses. I have been following big tech since 2018-19 fairly closely and it never became easier to predict how the narratives will shift just in 2-3 years down the line. Just imagine trying to predict the narrative around Meta today back in 2022 (or "Apple 2021 narrative" back in 2018). I certainly do not rule out the possibility that search can chug along just fine, and thanks to Google's almost end to end control over their infrastructure (unfortunately, they still do have dependency on TSMC for fabricating TPUs), Google's narrative can change materially especially in the post-regulatory clarity era. I am not super confident in predicting how the big debates will be settled in AI, but it does seem there is a distinct scenario in which Google can potentially be materially ahead of everyone else in 5 years. Outlining the exact path is hard to do, but I have come to the view that while the search related debates consume all the airtime, most investors are potentially missing or underappreciating a scenario in which Google may just topple everyone else by leveraging their infrastructure advantage. But isn't Google pretty bad at productizing their AI advancements? They have already deployed quite impressive models and capabilities, but if they're bad at productizing it, will this infrastructure advantage matter at all? While I was pondering about the point about infrastructure advantage for almost a year now, the recent Sharp Tech podcast [episode](https://open.spotify.com/episode/7dkl5Eshi4HV2USY4UleEp?si=dac0253f554540d0&ref=mbi-deepdives.com) really drove the point home and also reminded me why Google may be able to succeed **despite** its weakness in productizing model capabilities. A listener of the pod sent the following musing for Ben and Andrew: *"When exactly was Google good at building new products? The answer is never. Google has always sucked at building new products. Consider the epic failures of Google Buzz, Wave and Plus or G chat, and Google Hangouts.* *Google was only ever successful at innovating and building one of the following three things:* *•Google search* *•A critical infrastructure required and custom tailored for Google search.* *•Products where search-like characteristics, huge scale, and a data flywheel turned out to be critical for success.* *Google pioneered amazing concepts such as map reduce, batch processing, zero trust security, containers, software defined networking to name a few. However, it sucked when it came to wrapping them as general purpose infrastructure products. This goes back to the famous 2011* [*Stevey's platform rant*](https://gist.github.com/chitchcock/1281611?ref=mbi-deepdives.com)*.* *Stevey's platform rant predicted so well why Google Cloud Provider was destined to lag behind AWS. This is also why despite employing a phenomenal collection of talent with security researchers and engineers, Google could never have built a product like Wiz for external use. All of this is to say, I don't think there's anything new going on. Maybe this is just Google being Google, the awkward nerd that gains his advantage by staying up in the data center all night figuring out how to stack 20 times more servers compared to Inktomi who occupied the cage next door. Of course, OpenAI has the better polished product. That was never Google's advantage. And if that's what what it takes to win an AI, then well, Google is just not going to win. Not now and not ever in its history.* *The only hope is if it turns out AI models aren't a commodity. Search engines were considered a commodity back in the Inktomi, Alta Vista days very much like LLMs today. Google's only hope is if just like in the early 2000s, *it turns out the consensus is wrong and there is a sustainable long-term advantage to be gained by better infrastructure engineering.**" But is this a good time to buy Google when majority of the revenue is largely dependent on advertising revenue given the recently introduced tariffs may even cause a global recession? Tariffs are certainly a risk, but let me offer some brief thoughts on them. Let me provide a historical analogy that I think can have some resemblance to today. Imagine we are in late 2001 to early 2002\. Tech bubble just crashed and people just experienced 9/11\. I bet 9/11 consumed almost all of our attention (and rightly so), and perhaps more and more people started laughing at people who thought internet would revolutionize everything. In my mind, these tariffs (if they remain unchanged) are like 9/11 i.e. incredibly impactful for the world and will certainly affect us in ways we may not be realizing today, just as 9/11 did. But from purely long-term market perspective, 9/11 wasn't the main story that mattered; what mattered was the internet. Today's "internet" is "Generative AI". In a decade (likely lot sooner), it is much more likely than not that Gen AI will be dominant driver of the market, and while tariffs may be impactful in the meantime, they are mostly going to be adapted and absorbed by different stakeholders in the value chain. Google, Meta, Amazon have all been down 30% from their highs. I have started deploying my capital to all of them today (3% Google at $150, and adding more to my existing Amazon and Meta positions: 1% Amazon at $168, 1% Meta at $500). I am keeping an eye on Microsoft as well, but haven't pulled the trigger yet. I expect myself to buy more if stocks keep going down. There is a very good probability that people may "forget" about AI amidst the tariff tantrum, but I am of the opinion that might lead to a compelling opportunity to steer my portfolio to the future of the world even more. [Subscribe](#/portal/signup) ### April, 2025 Update URL: https://www.mbi-deepdives.com/april-2025-update-2/ Last updated: 2025-04-01T14:28:52.000Z Just a couple of quick updates for this month: I am going to publish my Deep Dive on **Synopsys** by 28th of this month. I know I mentioned before that I would cover ASML this month, but I am shifting ASML to next month as I wanted to study an EDA tools company first. For this month's "Never Sell" podcast episode, David Kim from [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I discussed Adobe. ([Spotify](https://open.spotify.com/episode/2TnWaXDtZZwnYWHUVPdCe0?ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell-episode-4-adobe/id1786912203?i=1000701577625&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=qXzTX5ive1A&ref=mbi-deepdives.com), [RSS Feed](https://feeds.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com)). I have also uploaded an updated Adobe model for the paying subscribers at the end of this post. Finally, for new subscribers, I would like to let you know that you can access the full library of Deep Dives, including excel models, [here](https://www.mbi-deepdives.com/models/). Thank you for your support! [Subscribe](#/portal/signup) --- **ADBE Updated Model:** [ADBE Mar 29, 2025ADBE Mar 29, 2025.xlsx2 MBdownload-circle](https://www.mbi-deepdives.com/content/files/2025/04/ADBE-Mar-29--2025.xlsx "Download") ### Lululemon 4Q'24 Update URL: https://www.mbi-deepdives.com/lulu4q24/ Last updated: 2025-03-28T00:30:36.000Z *Disclosure: I own January 2026 $165 Call Options* Since its IPO back in 2007, Lululemon always posted double-digit revenue growth every single year. While there was plenty of skepticism throughout 2024, they managed to eke out double digit growth last year. But 2025 topline guidance of 5-7% implies the era of persistent double digit growth regardless of the economy is likely behind us! Here are my highlights from the quarter. [Subscribe](#/portal/signup) **Sales Growth by Region** After three quarters of anemic growth in the US, Lulu managed to post MSD growth in the US in 4Q’24\. Canada was double digit. China remains on a different growth stratosphere and even Rest of the World (RoW) segment’s growth was \~30% last quarter. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc971f881-11c8-4f0a-9710-32a1e0282c08_1158x564.png) Lulu’s struggle in the US was mostly due to lack of its newness last year which they have largely corrected now: > “Looking at quarter one, **we have increased our level of newness on par with the past.** We believe this increase along with a robust pipeline of innovation will enable us to meet the expectations of our guests and I'm excited about what the product teams are bringing to market this spring throughout the year. We started the year strong with the launch of several new innovations. > > **Initial response has been very strong and we've been selling out across several sizes and colors**. The teams are chasing into it now and we have several additions planned for later this year. Based on this response and performance, **we believe Daydrift become a new core franchise**.” While Lulu talked about increased newness, they guided for LSD-MSD revenue growth in the Americas; to be more specific, they expect US to be closer to the lower end of the range and Canada to be on the higher end. Why such lackluster growth despite the increased newness? > we started this year with several compelling new product launches, but we also believe the dynamic macro environment has contributed to a more cautious consumer. In fact, based on a survey we conducted earlier this month in conjunction with Ipsos, **consumers are spending less due to increased concerns about inflation and the economy**. **This is manifesting itself into slower traffic across the industry in The U. S.** **in Q1 which we are experiencing in our business as well**. However, we see guests who visit us responding to the newness and innovations we brought into our assortment. We believe this is a positive indication as we continue to flow new product engage with our guests through unique and compelling activations and launch brand campaigns. We are controlling what we can control and we expect to see modest growth in U. S. Revenue for the full year of 2025. Later, they clarified it’s mostly a US thing and they haven’t seen similar traffic trend in other regions: > “So in terms of traffic, I would say the notable trend we saw was that shift in The U. S. Nothing materially different in terms of either Canada or the international markets. I would call out just the difference in Lunar New Year timing, a shift in the timing this year. Have a little bit of a headwind on Q1 in terms of our China trend and overall international. And then in terms of U. S. Regional, we aren't seeing any meaningful differences regionally” Lulu emphasized that their new guest acquisition is still strong and when guests arrive at the store, their conversion and average order size has increased; so it’s the decline in traffic itself that warranted the caution for the guide. In their guide, they assume Q1 traffic trend to continue which means they don’t expect improvement or further deterioration in traffic trend from here. Their guide for other regions remains healthy: China \~25-30% and RoW at \~20%. Also, if you look at the comps for the US, Q2 and Q3 were pretty weak last year, so if traffic improves later in the year, that can boost their US revenue growth. But at their current size, it is quite clear they cannot be insulated from broader macro trend. Lulu emphasized the long-term opportunity is still there given low unaided awareness across the world: > our unaided brand awareness in France, Germany and Japan is in single digits In China Mainland, it's in mid to high teens In The UK and Australia, it's in the 20s And in The U. S, unaided brand awareness is in the 30s. **Margin** In 4Q’24, Lulu’s operating margin was more or less flat YoY in Americas but China and RoW margins were comfortably up. I would highlight RoW’s margin progression throughout the year. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F19808bf0-7574-4151-bf1a-434cd94d3f69_865x267.png) Lulu’s gross margin in 2024 was 59.2% and and operating margin was 24.3%, both at or near all-time high for the company. Given the lower revenue growth in Americas, it is impressive that they were able to improve margins for the overall company, mostly thanks to operating margin expansion of 250 bps in China and 460 bps in RoW. I would highlight though that Lulu’s spending on advertising as % of sales increased from 4.1% in 2022 to 4.5% in 2023 to 5.1% in 2024\. Such increased spending may be indicative of higher competitive intensity. As a side note, in my recent visit to LA, I would guesstimate the number of people I noticed wearing Alo and Lulu was almost 50-50\. Despite the competitive intensity, Lulu’s product gross margin did improve by 40 bps in 2024 in Americas, but the higher SG&A led to 50 bps decline in operating margin last year. After hearing Lulu’s plan to more community activation planned throughout this year, I think Lulu will keep their marketing spending intensity and mostly look for other areas to maintain/improve operating margins. **Sales by Gender** Women’s segment returned to double digit growth in 4Q’24\. Both men and other segment also grew at low to high teen despite tough comps. I am somewhat disappointed that Lulu couldn’t make much inroads in shoes, especially in light of Nike’s woes. They are still trying to test out products here, but given that they haven’t been mentioning anything about shoes during the call tells me these experiments haven’t quite gone well. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffcc3ed76-d2d7-4545-b695-78ce53e43bb3_1159x255.png) **Inventory** Even though Lulu guided inventory to increase by low double digits, it grew by 9%. They, however, guided for high-teen inventory growth for Q1 as they chase the newness. Tariff is assumed to be 20 bps headwind this year. **Capital Allocation** Lulu maintained their buyback intensity throughout last quarter. As you can see below, their buyback intensity varies materially over time which implies their activity is somewhat indicative of management’s opinion on the stock. Their actions suggest management continues to think the stock is quite attractive as they repurchased $332 million last quarter. They still have $2 Bn cash on the balance sheet, and given the stock price today and how cash generative this business is (they generated $3.2 Bn cumulative FCF in last two years), I expect buyback activity to continue unabated for the coming months. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F67817faa-e6cb-497e-a26d-dd6a8f3fb89d_496x592.png) Thanks to these buybacks, Lulu’s shares outstanding declined by 3.7% YoY in 4Q’24. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F14cf470a-a6e4-48eb-ae07-a42bfb9c6c3a_949x391.png) **Outlook** I have already somewhat touched on the outlook, but here’s a more granular breakdown: For 2025, topline guide is $11.15-11.3 Bn, implying \~5-7% YoY growth. If you exclude the 53rd week impact, this implies \~7-8% growth. FX is assumed to 100 bps headwind. Gross margin is expected to be down 60 bps, driven by deleverage on fixed cost, FX, and tariff impact. Operating margin is expected to be down \~100 bps, but half of the decline is driven by FX headwind). EPS guide for 2025 is $14.95-15.15 vs $14.64 in 2024, implying only 2-3.5% growth. Please note FX is assumed to be $0.3-0.35 drag this year. **Final Words** After last year’s uninspiring growth in the US, I came to 2025 hoping Lulu’s US business will pick up the pace this year as they introduce more newness to their products. Unfortunately, with potential macro softness it appears we may be set for longer wait for the US business to get back to MSD-HSD growth. In my [interview](https://open.spotify.com/episode/0MOQhtHLwYOUQ8QaCmnoHl?si=13e51b6e18044c79&ref=mbi-deepdives.com) with Speedwell early this year, I mentioned how I worry about recession for a company such as Lululemon which sells consumer discretionary products. Given Lululemon’s size, they cannot be immune from macro headwinds anymore. A recession also makes the job of differentiating broader macro headwinds and the impact from higher competitive intensity very difficult, especially given many of Lulu’s competitors are not public (Alo, Vuori, Gymshark etc.). As a result, admittedly my enthusiasm for the stock has abated a bit in the current macro environment. So, I won’t be adding to my position here unless the stock trades at 10x NTM EBIT. Thank you for reading. ### Illumina: A "Monopoly" in a Knife Fight URL: https://www.mbi-deepdives.com/ilmn/ Last updated: 2025-03-25T03:18:28.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- The Human Genome Project was an epic scientific quest launched in 1990 to decode the complete blueprint of human life i.e. the 3.2 billion letters that form our DNA. Scientists around the globe collaborated tirelessly, meticulously assembling this genetic puzzle one fragment at a time. Initially slow, relying on painstaking sequencing techniques, the project unfolded over thirteen years. By 2003, most of the genome was revealed. While even a “draft” human genome sequence took 15 months and cost [$300 million](https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost?ref=mbi-deepdives.com) in early 2000s, today a human genome can be sequenced in hours, and it can cost as low as \~[$100](https://genomics.umn.edu/news/ultima-ug-100-arrives?ref=mbi-deepdives.com) (and we are perhaps not done yet). The progress is so breathtaking that even Moore’s law appears to be shabby in comparison! The company that helped lead this progress is Illumina through their Next-Generation Sequencing (NGS) technology. ![Graph: Sequencing Cost Per Genome](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe6d79911-052a-496c-947e-87f79e4b76ae_4000x2251.jpeg "Graph: Sequencing Cost Per Genome") Source: [NIH](https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data?ref=mbi-deepdives.com) Illumina, however, was bit of an accidental torchbearer in leading this progress. “The Century of Biology” had a good [piece](https://centuryofbio.com/p/illumina?ref=mbi-deepdives.com) covering the history of Illumina’s rise: > “A small startup in San Diego founded by a blind venture capitalist, a veterinarian with an MBA, a Harvard professor, a chemist, and a molecular biologist produced a genomics instrument that would become synonymous with DNA sequencing. Ironically, this company played no role in [The Genome War](https://www.goodreads.com/en/book/show/4595?ref=mbi-deepdives.com)—the first ideas for its technology had nothing to do with genomics.” (**Note**: while this piece is overall informative, you may also want to read a more critical [review](https://aseq.substack.com/p/everything-wrong-with-illumina-the) of the piece by “ASeq”) Indeed, as the human genome project was ongoing in the ‘90s, Illumina was incorporated in 1998 and believe it or not, they came to IPO just two years after being incorporated. On their [S-1](https://d18rn0p25nwr6d.cloudfront.net/CIK-0001110803/8c75fb64-bcc2-4647-a558-107165c652ef.pdf?ref=mbi-deepdives.com), Illumina indicated the far-reaching potential of sequencing a human genome and how they want to sell the picks and shovels to power this revolution: > “Understanding genetic variation and function is critical to the development of personalized medicine, a key goal of genomics. Our tools will provide information that could be used to improve drugs and therapies, customize diagnoses and treatment, and cure disease. > > Completion of the sequencing of the human genome will **drive demand for tools that can assist researchers in processing the billions of tests necessary to convert raw genetic data into medically valuable information**. This requires functional analysis of highly complex biological systems, involving a scale of experimentation not practical using currently available tools and technologies. Using our technologies, we are developing a comprehensive line of products that can address the scale of experimentation and the breadth of functional analysis required to achieve the goals of molecular medicine.” There was just one pesky little problem. The same S-1 mentioned the below first thing in their “risk factors”: > “We Have Generated **No Revenue** from Product Sales to Date…To date, we have derived all of our revenues from grants and partnerships.” No revenue? Actually, that’s even better because you could then be a “[potential pure play](https://www.youtube.com/watch?v=BzAdXyPYKQo&ref=mbi-deepdives.com)” 😉 In the early 2000s, it wasn’t just the tech and telecom bubble that plagued the investors; biotech bubble was also going on a full swing back then. So a company with zero product revenue (but a very open-ended possibilities) quickly ballooned to reach $1.4 Billion market cap. The bubble, of course, eventually popped and Illumina stock came back to earth to reach just $58 Million market cap, a whopping 96% decline from the then peak! As you can imagine, Illumina had quite the tumultuous beginning as a public company! ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F29aadad1-cd5a-468a-9308-a1e77d1e7976_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Thankfully, Illumina did figure out some products to generate revenue. In the early 2000s, Illumina's bead array technology placed tiny beads coated with DNA probes into microscopic wells on fiber-optic slides. When sample DNA matched these probes, the beads lit up, allowing scientists to quickly study many genetic markers at once. If the last two sentences are not quite legible to you, that’s okay. Considering Illumina’s primary business today has evolved considerably since those early years, we can skip a granular discussion on a segment that has barely any impact on the company today. After losing money for the first seven years, Illumina posted \~20% operating margin in 2006\. However, its the acquisition in early 2007 that truly changed the fate of the company…for the better! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F12fb0c16-841d-413e-8a3d-956d7e7c8251_463x276.png) Source: Tikr, MBI Deep Dives On January 26, 2007, Illumina acquired Solexa for [$600 Million](https://www.wsj.com/articles/SB116343413347821623?ref=mbi-deepdives.com) stock. Given Illumina’s market cap was still below $2 Billion back then, it was a pretty significant acquisition. The acquisition of Solexa indeed proved to be quite consequential for Illumina. By acquiring Solexa, Illumina gained a powerful sequencing technology called [sequencing-by-synthesis](https://en.wikipedia.org/wiki/Illumina%5Fdye%5Fsequencing?ref=mbi-deepdives.com), which allowed DNA to be read quickly and affordably. This technology transformed Illumina from a company focused mainly on DNA microarrays into the leading provider of DNA sequencing instruments. Before moving any further, let me briefly explain what exactly DNA sequencing is. DNA sequencing involves determining the exact order of the bases in DNA — the As, Cs, Gs and Ts that make up segments of DNA. A, T, C, and G are letters representing the four chemical building blocks or bases of DNA. A, T, C, and G stands for Adenine, Thymine, Cytosine, and Guanine respectively. If you imagine DNA as a story written in an alphabet of just four letters i.e. A, T, C, and G, each combination forming words guides how living things grow, move, breathe, and become. For example, look at the image below. To read these letters, scientists first create many fragments of DNA that end at different lengths, each ending with a special "tagged" letter (colored letters in the image). These tagged letters glow or fluoresce, making it possible to see exactly where each fragment ends. By lining up these fragments from shortest to longest, scientists can see the glowing letters clearly in order. This tells them the exact sequence of the DNA. At the bottom of the image, you see the original DNA sequence, pieced together from these glowing, tagged letters: G-A-C-T-T-C-G. In essence, DNA sequencing helps scientists read the exact order of the letters inside DNA, revealing the instructions hidden within each cell. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9f00e5dc-9c1f-4d6d-b926-83dfbb97e09d_1129x714.png) Source: Click [here](https://puppeteer.cognitoedu.org/coursesubtopic/b3-alevel-ocr%5FsAtaYBgv?ref=mbi-deepdives.com) With the acquisition of Solexa and its technology, Illumina almost immediately became THE player in DNA sequencing. In fact, in 2008 10-K Illumina mentioned that “*Instrument revenue increased by $64.8 million over prior year, of which $63.0 million was due to increased sales of our sequencing systems”.* There was no looking back since then. Even in 2008 to 2010 period when the entire world was grappling with GFC, their revenue grew by 56%, 16%, and 35% respectively. Since 2006 (just the year before Solexa acquisition), Illumina’s revenue became \~25x in the next 15 years. For all intents and purposes, Illumina had monopoly in DNA sequencing tools during this period. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6bd14651-75e9-4ad5-abb6-0f4bca194b96_301x421.png) Source: Tikr, MBI Deep Dives The stock price predictably followed the business performance. Since the acquisition of Solexa was announced, the stock became \~24x at the peak of 2021\. Then everything changed! ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F87b72cfb-d88e-42a9-a2a3-004aa8cd9c1a_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Since its all-time high in August 2021, Illumina experienced a whopping 84% drawdown. Like Solexa, Illumina hoped for another consequential acquisition: Grail for which they paid [$8 Billion](https://investor.illumina.com/news/press-release-details/2020/Illumina-to-Acquire-GRAIL-to-Launch-New-Era-of-Cancer-Detection/default.aspx?ref=mbi-deepdives.com). This opened pandora’s box which is beyond the scope of this Deep Dive since much of these details are behind the company now following its [divesture](https://www.genengnews.com/topics/omics/illumina-to-divest-of-grail-after-appeals-court-defeat/?ref=mbi-deepdives.com) last year (if you want to explore more on this saga, [Nongaap](https://www.nongaap.com/p/illumina-malignant-governance?ref=mbi-deepdives.com) is an excellent source). The whole ordeal led to [resignation](https://www.nongaap.com/p/illumina-ceo-abruptly-resigns?utm%5Fsource=publication-search) of the then CEO Francis deSouza; it also attracted attention from [Carl Icahn](https://www.cnbc.com/2023/09/07/carl-icahn-supports-new-illumina-ceo-thaysen-after-proxy-fight-.html?ref=mbi-deepdives.com) for a proxy fight. Just as people started thinking the worst may be behind Illumina, China just announced Illumina to be in their “[unreliable entities](https://www.wsj.com/world/china/illumina-to-cut-100-million-in-costs-lowers-guidance-because-of-china-sales-ban-ff8b495a?ref=mbi-deepdives.com)” list as part of the retaliation to tariff imposed by the US which likely means Illumina cannot sell new sequencers in China anymore. The recent uncertainty around NIH funding is also adding further fuel to the uncertainty. If that weren’t enough already, the sustainability of Illumina’s monopoly status is currently being seriously questioned by investors. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc64a61ad-1204-49d8-b15d-6ce9452d9089_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Before we dig into these issues, let’s start with a deeper understanding of Illumina’s business, size of the opportunity, unit economics, and overall cost structure. That’s section 1. In section 2, I will dissect the very nature of the knife fight Illumina is dealing with today. In section 3, I will discuss capital allocation and management incentives. Then in section 4, I will show what is likely currently embedded into the stock price. Finally, I will offer some concluding thoughts and disclose my overall portfolio. Subscribe to keep reading! [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### March, 2025 Update URL: https://www.mbi-deepdives.com/march-2025-update-2/ Last updated: 2025-03-03T18:13:11.000Z A few quick updates for this month: I am going to publish my Deep Dive on **Illumina** by 25th of this month. I'm frequently asked about the tools I use in my research process. In my recent podcast, David Kim from [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) and I tackle that question. Moreover, we dive into what's driving our excitement (and a hint of trepidation) about Deep Research. ([Spotify](https://open.spotify.com/episode/3lT4pp31ESH7aFMCMfhDFY?si=bfb64b3897994749&ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/never-sell-episode-3-deep-research/id1786912203?i=1000695541831&ref=mbi-deepdives.com), [YouTube](https://www.youtube.com/watch?v=cAi9HJU3BkY&ref=mbi-deepdives.com), [RSS Feed](https://feeds.buzzsprout.com/2435713.rss?ref=mbi-deepdives.com&utm%5Fsource=substack&utm%5Fmedium=email)) I have received quite a few emails letting me know that many of you were not able to join my WhatsApp community. As it turns out, WhatsApp allows only 1,024 members in a community. Since we have reached that limit, I have decided to open a new community. Please do **NOT** join this community if you are already a member of my previous WhatsApp community since I will post the same content in both communities. Click this [link](https://chat.whatsapp.com/DYCGwtHfIteDKua1JHkAso?ref=mbi-deepdives.com) to join. Just to give you an idea in terms of what to expect from the WhatsApp community: I mostly post interesting articles that I come across. I also post my earnings recap for companies that I personally own (usually for companies that tend to have lower weight in the portfolio; for larger positions, I usually do full recap on the website). Moreover, I have recently started posting interesting excerpts from expert network transcripts from AlphaSense (you can get a free trial [here](https://www.alpha-sense.com/mbi/?ref=mbi-deepdives.com)). Overall, this community allows me to share my process of learning and studying companies that I follow closely. If you may be interested in that, I encourage you to join the community. Finally, for new subscribers, I would like to let you know that you can access the full library of Deep Dives [here](https://www.mbi-deepdives.com/models/). Thank you for your support! [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* [ ](https://www.mbi-deepdives.com/meta4q24/) ### Amazon: 2025 Update URL: https://www.mbi-deepdives.com/amzn2025/ Last updated: 2025-02-21T21:24:27.000Z *Disclosure: I own shares of Amazon* Amazon, once a scrappy online bookseller, has long since shed its modest origins, revealing itself as something far more elemental: a relentless, ever-multiplying force of commerce and technology. Like an ancient hydra, each time it masters one domain, another head emerges: from e-commerce to logistics, from cloud computing to entertainment, from groceries to AI. Matt Yglesias had a famous backhanded compliment for Amazon that perhaps also [captured](https://slate.com/business/2013/01/amazon-q4-profits-fall-45-percent.html?ref=mbi-deepdives.com) the bewilderment of many investors back in 2013: > “…Amazon, as best I can tell, is a charitable organization being run by elements of the investment community for the benefit of consumers. The shareholders put up the equity, and instead of owning a claim on a steady stream of fat profits, they get a claim on a mighty engine of consumer surplus. Amazon sells things to people at prices that seem impossible because it actually is impossible to make money that way. And the competitive pressure of needing to square off against Amazon cuts profit margins at other companies, thus benefiting people who don’t even buy anything from Amazon. > > It’s a truly remarkable American success story.” What was once a company squeezing out razor-thin profits in its early years is now a machine of unfathomable scale, posting $638 Billion revenue and $60 billion in **profit** in 2024 which nearly **doubled** year over year! After updating my Amazon model, I remain largely reassured that Amazon’s earnings power remains somewhat underestimated. Let me show the source of such steady confidence in this year’s update. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Meta Platforms: 2025 Update URL: https://www.mbi-deepdives.com/meta2025/ Last updated: 2025-02-14T13:24:34.000Z *Disclosure: I own shares of Meta Platforms* --- Meta’s stock is on a tear. The stock has ended higher in the last **19 consecutive** trading sessions. It is by far the best stock among the “magnificent seven” so far this year. Of my almost seven years of being a Meta shareholder, I do not recall investors being so nearly unanimously positive about the company’s prospects. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faa765bdf-d23d-4354-ac5f-3f3a38da2b66_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Given such optimism, it is perhaps even more relevant to probe into what exactly is **embedded** in today’s stock price of Meta. As is the annual tradition at MBI Deep Dives (see [2023](https://www.mbi-deepdives.com/meta2023/) and [2024](https://www.mbi-deepdives.com/meta2024/) updates), that exactly is the topic at hand. Let’s start with Reality Labs which, for all intents and purposes, is currently the “problem child” at Meta. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Amazon 4Q'24 Update URL: https://www.mbi-deepdives.com/amzn4q24/ Last updated: 2025-02-07T17:11:22.000Z *Disclosure: I own shares of Amazon* I know Amazon stock went down by \~4% after-hours, but I actually liked the quarter. Here are my highlights from today’s call. [Subscribe](#/portal/signup) **Revenue** Amazon faced \~900 Mn FX headwind in 4Q’24 which was \~700 mn higher than assumed. For the third consecutive quarters, AWS grew 19% YoY. Ads grew by +18% YoY, and revenues in other segments increased by mostly High Single Digit (HSD) rate. I’ll discuss more about AWS later, but let’s talk more about Amazon ex-AWS first. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9cbe255c-c5d8-435f-bc69-98e2813d171b_1344x229.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** Despite North America segment being more than 2.5x the size of International segment, it grew by 10% whereas international revenue increased by 9% (both FXN). For the 8th consecutive quarters, both North America and international segment experienced YoY operating margin expansion. Amazon reported its highest operating margin in North America segment at least since 2013\. Moreover, please note that they currently expense majority of their costs associated with development of the satellite network which will be capitalized when services reach commercial viability. Therefore, the “actual” retail margin is almost certainly even higher. While most investors are usually more excited by AWS’ prospects and infatuated by their lofty margins, I may be in the minority in being more optimistic about Amazon retail’s long-term profitability. AWS has a couple of pretty capable competitors and thanks to Nvidia being the key bottleneck, the industry value chain may not evolve in a favorable way for AWS. But when I think about Amazon retail’s long-term future, the gap between Amazon and the competitors may keep growing. More on this below. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff460fa1a-3bcf-4e22-8c08-2eef6b3461c7_1120x682.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Fulfillment+ Shipping** If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q'22\. Since then, unit growth has largely been faster than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. The gap between the two has, in fact, widened in 4Q’24. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F334dc981-1849-45b9-988e-dcc7f396b12a_1167x550.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Amazon continues to indicate that they are far from done in optimizing their logistics footprint as well as improving speed of delivery. Some key quotes on this topic below: > 2024 also marks **the second year in a row where we've lowered our global cost to serve on a per unit basis**. > > We expanded the number of **same-day delivery sites by** **more than 60% in 2024**, which now serve more than 140 metro areas. And overall, we delivered over 9 billion units the same or next day around the world. Our relentless pursuit of better selection, price and delivery speed is driving **accelerated growth in Prime membership.** > > …I'll also tell you that this group of, call it, **a half a dozen or so new initiatives** is not close to the end of what we think is possible with respect to being able to **use robotics to improve the productivity cost to serve and safety in our fulfillment network**. And we have kind of the next wave that we're starting to work on now. But I think this will be a **many-year effort** as we continue to tune different parts of our fulfillment network where we can use robotics. And we actually don't think there are that many things that we can't improve the experience with robotics. > > …**we have not yet seen diminishing returns** and being able to continue to improve the speed of delivery…if you look at what we're doing with **Prime Air, the promise there is for a number of items that we'll be able to deliver items to customers inside an hour**. And I think when you're ordering everyday essentials where you need something more quickly, it's a big deal. And you see it, it's had a big impact on our everyday essentials. It's had a big impact on our pharmacy business **Advertising** Now that we have the earnings of major digital advertising players, I have updated my industry dashboard. Since I started tracking this in 4Q’20, Meta has its highest ever market share while Google posted its lowest ever share in 4Q’24\. Amazon’s market share also kept growing both YoY and QoQ. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2025/02/image.png) **AWS** Okay, now let’s talk about AWS. AWS added \~$1.3 Billion incremental revenue QoQ which was the second highest incremental growth QoQ ever. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F698d4c90-76a8-4844-baea-4b1bb32bdf81_1035x535.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Let’s take a quick look at hyperscalers growth. Azure, Google Cloud, and AWS revenue grew by 31%, 30%, and 19% respectively. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9406f771-8266-4c30-b4d7-155b0618d818_1398x784.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); \*Google Cloud includes Google Workspace, so not quite apple-to-apple and in reality, GCP tends to grow faster than Google Cloud One thing I would like to track is Google Cloud’s operating performance trajectory against AWS. Back in 2020, Google Cloud was only about a quarter of the size of AWS, but now it’s two-fifth of AWS revenue. We don’t know exactly how much of this is GCP, but we can be pretty confident that GCP is leading this catch-up with AWS. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7b8aa1f6-cc6d-45c4-aa83-fd64a2f89fe9_1051x639.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc56d95bc-efbc-47ec-9188-0e36f1dc2ce4_1102x669.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) AWS had 36.9% operating in 4Q’24, with incremental operating margin being 76%! Please note that their decision to increase server useful life expanded AWS operating margin by 200 bps. As you may know, I have been wondering about big tech’s [deteriorating earnings quality](https://www.mbi-deepdives.com/big-tech-earnings-quality/), Amazon had some interesting comments related to useful life of some PP&E for 2025: > …in Q4, we completed a useful life study for our servers and network equipment and observed an increased pace of technology development, particularly in the area of artificial intelligence and machine learning. As a result, **we're decreasing the useful life for a subset of our servers and networking equipment from 6 years to 5 years**, beginning in January 2025\. We anticipate **this will decrease full year 2025 operating income by approximately $700 million.** > > In addition, **we also early retired a subset of our servers and network equipment**. We recorded a **Q4 2024 expense of approximately $920 million from accelerated depreciation and related charges and expect this will also decrease full year 2025 operating income by approximately $600 million**. Both of these server and network equipment useful life changes primarily impact our AWS segment. > > Lastly, **we also completed a useful life study for certain types of heavy equipment used in our fulfillment centers and are increasing the useful life from 10 years to 13 years beginning in January 2025\. We anticipate this will increase full year 2025 operating income by approximately $900 million**. So, the net impact appears to be a decline of operating income of $400 million in 2025 which doesn’t seem to be a big deal. but given the ever increasing size of capex (more on capex later), this can gradually become more important 3-4 years down the line. It would be interesting to see how Microsoft, Google, and Meta respond the following year. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff5457996-b228-4daa-b0bf-e135f0260e09_1537x123.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F12ab16c8-5225-4d5b-b75e-aabe320ce316_1197x655.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Lots of interesting comments on AWS during today’s call: > AWS is a reasonably large business by most standards, and though **we expect growth will be lumpy over the next few years** as enterprise adoption cycles, capacity considerations and technology advancements impact timing, it's hard to overstate how optimistic we are about what lies ahead for AWS' customers and business. > > Trainium2 just launched at our AWS re:Invent Conference in December. And EC2 instances with these chips are **typically 30% to 40% more price performant than other current GPU-powered instances available**. That's very compelling at scale. > > We're already hard at work on Trainium3, which we expect to **preview late in '25 and defining Trainium4 thereafter**. Building outstanding performant chips that deliver leading price performance has become a core strength of AWS', starting with our Nitro and Graviton chips in our core business and now extending to Trainium and AI and something unique to AWS relative to other competing cloud providers. > > …we also just launched Amazon's own family of frontier models in Bedrock called Nova. These models compare favorably in intelligence against the leading models in the world but offer lower latency, lower price, about **75% lower than other models in Bedrock, and are integrated with key Bedrock features like fine-tuning, model distillation, knowledge bases of RAG and agentic capabilities**. Amazon mentioned they could have grown faster, if not for certain constraints: > It is hard to complain when you have a multibillion-dollar annualized revenue run rate business in AI, like we do, and it's growing triple-digit percentage year-over-year. It's hard to complain. However, **it is true that we could be growing faster, if not for some of the constraints on capacity.** > > And they (constraints) come in the form of, I would say, **chips from our third-party partners, come a little bit slower than before** with a lot of midstream changes that take a little bit of time to get the hardware actually yielding the percentage healthy and high-quality servers we expect. It comes with our own big new launch of our own hardware and our own chips and Trainium2, which we just went to general availability at re:Invent, but **the majority of the volume is coming in really over the next couple of quarters**, the next few months. It comes in the form of **power constraints** where I think the world is still constrained on power from where I think we all believe we could serve customers if we were unconstrained. **There are some components in the supply chain, like motherboards too, that are a little bit short in supply for various types of servers**. So I think the team has done a really good job scrapping and providing capacity for our customers they can grow. We're still growing at a pretty reasonable clip, as I mentioned earlier, but **I do think we could be growing faster if we were unconstrained. I predict those constraints really start to relax in the second half of '25.** Like Microsoft, Amazon seems to be betting that frontier models may be commodity and hence, they want to offer broadest selection of models to their customers: > I think if you run a business like AWS and you have a core belief like we do, that **virtually all the big generative AI apps are going to use multiple model types, and different customers are going to use different models for different types of workloads.** > > You're going to provide **as many leading frontier models as possible** for customers to choose from. That's what we've done with services like **Amazon Bedrock**. And it's why we moved so quickly to make sure that DeepSeek was available both in Bedrock and in SageMaker faster than you saw from others. And we already have customers starting to experiment with that. Amazon also made the case for “[Jevon’s paradox](https://en.wikipedia.org/wiki/Jevons%5Fparadox?ref=mbi-deepdives.com)”: > I think what's -- one of the interesting things over the last couple of weeks is sometimes people make the assumptions that **if you're able to decrease the cost of any type of technology component, in this case, we're really talking about inference, that somehow it's going to lead to less total spend in technology. And we just -- we have never seen that to be the case.** We did the same thing in the cloud where we launched AWS in 2006, where we offered S3 object storage for $0.15 a gigabyte and compute for $0.10 an hour, which, of course, is much lower now many years later. People thought that people would spend a lot less money on infrastructure technology. And what happens is companies will spend a lot less per unit of infrastructure, and that is very, very useful for their businesses. But then they get excited about what else they could build that they always thought was cost prohibitive before, and they usually end up spending a lot more in total on technology once you make the per unit cost less. > > And I think that is very much what's going to happen here in AI, which is **the cost of inference will substantially come down**. What you heard in the last couple of weeks that DeepSeek is a piece of it. But everybody is working on this. I believe the cost of inference will meaningfully come down. I think it will make it much easier for companies to be able to infuse other applications with inference and with generative AI. > > And I think it's going to -- if you run a business like we do, where we want to make it as easy as possible for customers to be successful building customer experiences on top of our various infrastructure services, **the cost of inference coming down is going to be very positive for customers and for our business**. > > …at the stage we're in right now, AI is still early stage. I**t does come originally with lower margins and a heavy investment load as we've talked about**. And in the short term, over time, that should have -- **be a headwind on margins**. But over the long term, we feel the **margins will be comparable in non-AI business** as well. While Amazon’s explanation may be compelling, here’s a counter perspective from [@akramsrazor](https://x.com/akramsrazor/status/1887649655842086976?ref=mbi-deepdives.com) (slightly edited for clarity): > “Reminder that in 1999 server revenue was $58.5 billion on just under 4 million units. The dollar number was not passed till 2017 despite units 3x.Not that these numbers even matter as much when you consider the $ number on anything is a moving target, but it shows how deflationary certain breakthroughs were in computing. Also when everyone talks about capex related infra stocks, the assumption seems to be that arms race of training foundation models goes on for forever for every giant. That's obviously a bad assumption. And who knows where inference ASICs/GPU ASPs ends up with multiple players battling it out for this compute.” **Opex+Capex** Amazon spent $26.3 Billion in capex in 4Q’24 and indicated 2025 capex will likely be annualized figure of that number i.e. $105 Billion. Where they going to spend all these capex? > Similar to 2024, **the majority of the spend will be to support the growing need for technology infrastructure**. This primarily relates to AWS, including to support demand for our AI services as well as tech infrastructure to support our North America and international segments. AWS hinted that the higher capex is basically harbinger of revenue growth in coming years: > **The vast majority of that CapEx spend is on AI for AWS. It's the way that AWS business works and the way the cash cycle works is that the faster we grow, the more CapEx we end up spending because we have to procure data center and hardware and chips and networking gear ahead of when we're able to monetize it.** > > **We don't procure it unless we see significant signals of demand**. And so when AWS is expanding its CapEx, particularly in what we think is one of these once-in-a-lifetime type of business opportunities like AI represents, I think it's actually quite a good sign, medium to long term, for the AWS business. And I actually think that spending this capital to pursue this opportunity, which from our perspective, we think virtually every application that we know of today is going to be reinvented with AI inside of it and with inference being a core building block, just like compute and storage and database. > > If you believe that plus altogether new experiences that we've only dreamed about are going to actually be available to us with AI, AI represents, for sure, the biggest opportunity since cloud and **probably the biggest technology shift and opportunity in business since the Internet**. And so I think that both our business, our customers and shareholders will be happy medium to long term that we're pursuing the capital opportunity and the business opportunity in AI. Amazon also indicated they’re going to increasingly focus on logistics and fulfillment in rural areas which is not quite music to your ears if you’re Dollar Store shareholders: > We also have CapEx that we're spending this year in our Stores business, really with an aim towards trying to continue to improve the delivery speed and our cost to serve. And so you'll see us expanding the number of same-day facilities from where we are right now. **You'll also see us expand the number of delivery stations that we have in rural areas. We can get items to people who live in rural areas much more quickly, and then a pretty significant investment as well on robotics and automation so we can take our cost to serve down and continue to improve our productivity.** ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe6dc0459-1dea-4276-a8b1-bcf4ac2fafa8_2002x289.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** Amazon’s guidance for 1Q’25 is below. Please note consensus 1Q’25 revenue and EBIT before the call were $158.3 Billion and $18.2 Billion respectively. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F17c9253b-906d-4795-80d7-f21369b43429_1072x279.png) Source: Company Filings **Closing Words** This was another strong quarter by Amazon. There’s hardly anything to complain about as an Amazon shareholder. Since we will have more disclosure in the 10-K to work with, I will publish my annual update on Amazon a couple of weeks from now. I will share more thoughts on the valuation then. Please feel free to share with your friends and network. Thank you for reading. **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Alphabet 4Q'24 Update URL: https://www.mbi-deepdives.com/goog4q24/ Last updated: 2025-02-05T13:44:01.000Z Alphabet had bit of a mixed earnings call. While there were some encouraging data points related to the future of search, their tone in the near-term outlook clamped down on the enthusiasm a bit. Here’s my highlights from the earnings. [Subscribe](#/portal/signup) **Revenue** Alphabet maintained their low double digit revenue growth. For the **10th** consecutive quarters, Google network’s revenue went down. Cloud revenue growth decelerated from 35.0% YoY in 3Q’24 to 30.1% in 4Q’24\. I will note, however, that Google usually discloses every quarter that GCP grew at higher rate than overall Cloud, but in this call, they mentioned “GCP grew at a rate that was **much higher** than cloud overall”. Therefore, the deceleration may have been mostly driven by the rest of cloud e.g. Google Workspace. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8eb36736-dd40-43fa-8eb5-2766bc89057a_1669x358.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Google mentioned all of their advertising revenue was impacted by tough comp in 4Q’23, especially because of “APAC-based retailers”. While a year ago most investors were concerned about “APAC based retailers” driven tough comps for Meta, they weren’t even mentioned once during Meta’s 4Q’24 call and it was Google which highlighted tough comp here. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea95fea8-2ca2-4f15-9b72-77332f7a7a77_1503x238.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) If you look at how Meta’s Family of Apps (FOA), Google Search, and YouTube ads fared over the last three years, it is abundantly clear that Meta is well past the dark days of ATT and continues to gain share. I will update my digital advertising market share dashboard once Amazon posts earnings this week. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7754251b-8cfd-4360-aa7c-bf0a65fac903_606x147.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Google maintained strong profitability across the board. Google Services posted 39% operating margin and high 70s incremental operating margin. Google Cloud also posted its highest ever margin. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcfd65782-de82-4025-a517-b02270b69272_1503x465.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ## **Search** Some interesting quotes on Search from the call: > AI overviews are now available in more than 100 countries. They continue to drive **higher satisfaction and Search usage**. Meanwhile, Circle to Search is now available on over 200 million Android devices. > > We have 7 products and platforms with over 2 billion users and all are using Gemini. That includes Search where Gemini is powering our AI overviews. **People use Search more with AI overviews and usage growth increases over time as people learn that they can ask new types of questions. This behavior is even more pronounced with younger users** who really appreciate the speed and efficiency of this new format. > > We are also pleased to see how **Circle to Search is driving additional search use** and opening up even more types of questions. This feature results are popular among younger users. **Those who have tried Circle to Search before now use it to start more than 10% of their searches.** As AI continues to **expand the universe of queries** that people can ask, 2025 is going to be one of the biggest years for Search innovation yet. > > We've already started testing Gemini 2.0 in AI overviews and plan to roll it out more broadly later in the year. All these are quite encouraging, but one that stood out to me was the below quote: > **Google is already present in over half of Journeys where a new brand product or retailer are discovered**. By offering new ways for people to search, we're expanding commercial opportunities for our advertisers. Shoppers can now take a photo of a product and using Lens quickly find information about the product, reviews similar products and where they can get it for a great price. **Lens is used for over 20 billion visual search queries every month and the majority of these searches are incremental.** In 3Q’24, Google shared the same data that lens is being used 20 billion times per month and highlighted that 1 in 4 query had commercial intent. So in this call, Google is basically confirming not only a significant percentage of these queries are monetizable, they are also **incremental**. Sundar later reiterated that the opportunity space is “far from zero-sum” > I think the opportunity space expands. I think there's plenty of it, feels very **far from a zero-sum game**. There's plenty of room, I think, for many new types of use cases to flourish. And I think for us, **we have a clear sense of additional use cases**. We can start to tackle for our users in Google Search. And all the early work with AIO view shows that users will react positively to that. > > …**We are continuing to see growth in Search on a year-on-year basis in terms of overall usage**. Of course, within that, AI overviews has seen stronger growth. Moreover, AI Overviews are monetizing “at approximately the same rate”": > we recently launched the ads within AI overviews on mobile in the U.S., which builds on our previous rollout of ads above and below. And as I talked about before, for the AI Overviews, overall, **we actually see monetization at approximately the same rate**, which I think really gives us a strong base on which we can innovate even more. ### **YouTube** While Netflix and Spotify keep hitting new highs, it’s interesting that YouTube likely remains their closest long-term competitor: > …data shows YouTube continues to be #1 in streaming watch time in the U.S. **with our share of streaming now at a record high**. > > …We are now **the most frequently used service for consuming podcast in the U.S.** according to a recent Edison report. This success reflects our long-term approach of investing in emerging trends from mobile to the living room. While election wasn’t even mentioned in Meta’s call, Google highlighted the impact of election for YouTube’s results in 4Q’24 which makes me think YouTube will face tough comp in 4Q’25: > The 14% growth in YouTube advertising revenues was driven by **strong spend on U.S. election advertising with combined spend from both parties almost doubling from what we saw in the 2020 elections**. YouTube is making rapid progress in shorts monetization: > In 2024, the monetization rate of short relative to in-stream viewing **increased by more than 30 percentage points in the U.S., and we expect to make additional progress in 2025**. ### **Google Cloud** Some interesting data points on Google Cloud: > Google data centers deliver nearly **4x more computing power per unit of electricity compared to just 5 years ago…Cloud customers consume more than 8x the compute capacity for training and inferencing compared to 18 months ago.** > > Last year, we closed several strategic deals over $1 billion, and **the number of deals over $250 million doubled from the prior year** > > In Q4, we saw strong uptake of Trillium, our sixth-generation TPU, which **delivers 4x better training performance and 3x greater inference throughput compared to the previous generation.** While some worry about “AI bubble” especially given the capex spree by big tech, Google, like Microsoft, is currently capacity constrained in Q4: > we do see and have been seeing very strong demand for our AI products in the fourth quarter in 2024\. And **we exited the year with more demand than we had available capacity.** Google highlighted their end-to-end stack in infrastructure will be a competitive advantage in the AI race: > part of the reason we have taken the end-to-end stack approach is so that we can definitely drive a **strong differentiation in end-to-end optimizing and not only on a cost but on a latency basis, on a performance basis…I think our full stack approach and our TPU efforts all play give a meaningful advantage. And we plan -- you already see that. I know you asked about the cost, but it's effectively captured when we price outside, we pass on the differentiation.** I will discuss more on Cloud when Amazon posts later this week. DeepSeek predictably came up, and Google management highlighted their models far well vs DeepSeek. Moreover, Google likes how things are trending more towards inferences: > both our 2.0 Flash models, our 2.0 Flash thinking models, they are **some of the most efficient models out there, including comparing to DeepSeek**'s V3 and R1\. And I think a lot of it is our strength of the full stack development end to end optimization, **our obsession with cost per query**. All of that, I think, sets as well for the workloads had both to serve billions of users across our products and on the cloud side. > > A couple of things I would say are if you look at the trajectory over the past 3 years, **the proportion of the spend towards inference compared to training has been increasing, which is good because, obviously, inferences to support businesses with good ROIC**. And so I think that trend is good. > > I think the reasoning models, if anything, accelerates that trend because it's obviously scaling upon inference dimension as well. And so I think -- look, I think part of the reason we are so excited about the AI opportunity is**, we know we can drive extraordinary use cases because the cost of actually using it is going to keep coming down, which will make more use cases feasible.** ### **AI** Some more quotes on impact of AI across different businesses in Google: > Last quarter, we introduced a reinvented Google shopping experience, rebuilt from the ground up with AI. This December saw roughly **13% more daily active users in Google shopping in the U.S., compared to the same period in 2023**. > > …we believe that AI will revolutionize every part of the marketing value chain…Based on the Nielsen meta analysis of marketing mix models, on average, Google AI-powered video campaigns on YouTube delivered **17% higher return on advertising spend than manual campaigns**. ### **Google Other** Google Other or what is currently categorized as “subscriptions, platforms, and devices” have been trending well: > Google One's performance has been outstanding and is **one of our fastest-growing subscription products** in terms of subscribers and revenue growth. > > We continue to have significant growth in our subscription products, primarily due to increase in the number of paid subscribers across YouTube TV, YouTube Music Premium and Google One. With regards to platform, we saw a slight increase in the growth rate in play, primarily due to a strong increase in the number of buyers. ### **Other Bets** > Waymo, which made tremendous progress last year, safely serving more than **4 million passenger trips. It's now averaging over 150,000 trips each week** and growing. Looking ahead, Waymo will be expanding its network and operations partnerships to open up new markets, including **Austin and Atlanta this year, and Miami next year.** And in the coming weeks, Waymo vehicles will arrive in **Tokyo for their first international road trip.** We are also developing the sixth-generation Waymo driver, which will **significantly lower hardware costs**. ### **Capital Allocation** In 4Q’24, Google returned 72% of their FCF to shareholders through buyback and dividend. Share count declined by 57 bps QoQ. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feb4a4cb5-0846-4e03-a33a-fa949fbe531b_669x601.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ### **Capex and Opex** Google’s capex increased by 63% YoY in 2024\. Management guided it to increase to $75 Billion in 2025 i.e. +43% YoY. They understandably are not interested in being capacity constrained. Let’s see in a year whether $75 Billion capex solves it. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7318e60-2c58-461e-89a4-3b53e5373d5f_1500x247.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ### **Outlook** Google doesn’t provide guidance, but did mention the below during the call which all sounded somewhat defensive to me: > “in terms of revenue, I'll highlight two items that will have meaningful impact on Q1 revenue across the company. The first is the impact of foreign exchange rates. At the current spot rates, we expect a larger headwind to our revenues from the strengthening of the U.S. dollar relative to key currencies in Q1 versus Q4 2024\. Second is the impact of leap year… These are understandable heads up, but after mentioning tough comp due to “APAC based retailers” to explain 4Q’24 growth numbers, Google somewhat cautioned about tough comp on advertising for entire 2025: > As for our segments, Google Services, **advertising revenue in 2025 will be impacted by lapping the strength we experienced in the financial service vertical throughout 2024**. And in Cloud, given that revenues are correlated with the timing of deployment of new capacity, we could see variability in cloud revenue growth rates depending on when new capacity comes online during 2025. As explained in my recent [piece](https://www.mbi-deepdives.com/big-tech-earnings-quality/) on Big Tech’s deteriorating earnings quality, Google also cautioned about increased “pressure on the P&L” due to “higher depreciation”: > the increase in our investment in CapEx over the past few years will increase **pressure on the P&L, primarily in the form of higher depreciation**. In 2024, we saw **28% year-over-year growth in depreciation** as we put more technical infrastructure assets into service. Given the increase in CapEx investments over the past few years, **we expect the growth rate in depreciation to accelerate in 2025**. ### **Valuation** Since [3Q'22](https://mbideepdives.substack.com/p/goog3q22?utm%5Fsource=publication-search), I share the following valuation framework every quarter. Given I created this table in 2022 which was a very different market than what we have today from sentiment perspective, you can argue this is overly conservative. I’m going to keep it consistent. But I acknowledge the reality that Google not only trades at the lowest NTM P/E multiple among Mag-7 stocks. It appears market is largely valuing the Service operating income at 18x and the cloud business at \~10x revenue. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F719d3c02-7ef7-458f-87f6-4ac625176eee_1839x382.png) Source: MBI Deep Dives Interestingly, there has been a noticeable multiple differential between Meta and Google these days. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1adf3e03-c5b2-40d5-baf5-d12ee1931268_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) I will cover earnings of **Amazon** this week. Thank you for reading. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### February, 2025 Update URL: https://www.mbi-deepdives.com/feb2025/ Last updated: 2025-02-03T15:59:04.000Z Few quick updates for this month: 1. In February, I will do my annual, more detailed updates on Meta Platforms, and Amazon. I expect to publish my update on Meta by the end of next week, and hope to publish Amazon update the week after that. In the meantime, I will also cover Alphabet and Amazon earnings this week. 2. Let me give you a schedule for the next couple of Deep Dives: Illumina in March, and ASML in April this year. 3. Over the last couple of weeks, I did two podcasts: one with [Scuttleblurb](https://www.scuttleblurb.com/?ref=mbi-deepdives.com) on XPEL ([Spotify](https://substack.com/redirect/3732c519-ad73-4639-80b9-47ef926b9b1a?j=eyJ1IjoiOXgwejUifQ.QLatekrDQ1JxAFwqYZIS7Eu9DEeYbRkK73W9whI8srw&ref=mbi-deepdives.com), [Apple](https://substack.com/redirect/22afe24c-6828-448c-a603-9969db25c134?j=eyJ1IjoiOXgwejUifQ.QLatekrDQ1JxAFwqYZIS7Eu9DEeYbRkK73W9whI8srw&ref=mbi-deepdives.com), [RSS feed](https://substack.com/redirect/1d442b7a-19f1-421e-ada3-d376ff23c153?j=eyJ1IjoiOXgwejUifQ.QLatekrDQ1JxAFwqYZIS7Eu9DEeYbRkK73W9whI8srw&ref=mbi-deepdives.com)), and the other one with [Speedwell](https://www.speedwellmemos.com/?ref=mbi-deepdives.com) on Spotify and Lululemon ([Spotify](https://t.co/sjrimy2rfH?ref=mbi-deepdives.com), [Apple](https://podcasts.apple.com/us/podcast/interview-spotify-case-study-and-learning-from/id1699073398?i=1000685007125&ref=mbi-deepdives.com)). 4. Since I have updated my [portfolio](https://www.mbi-deepdives.com/portfolio/) last week (disclosed on the last day of every month), I do want to disclose that I have started a new position in Maravai LifeSciences (MRVI) **today** which was my [Deep Dive](https://www.mbi-deepdives.com/mrvi/) in December last year. In my Deep Dive, I mentioned I would be looking to be a shareholder if the stock comes down to $4-4.2 price range, but I started this position at slightly higher prices than what I considered to be more compelling entry price because a) while the upside can be debated, the downside is likely to be limited from current prices (elaborated in the Deep Dive), and b) I am unwilling to let my cash balance exceed 20% and would rather swing at opportunities at slightly lower IRR than letting my cash balance grow over time. While cash provides optionality in period of volatility, too much of it can be a drag for the overall portfolio return; therefore, I try to maintain a ceiling of 20% cash for my portfolio. 5. Finally, in case you're not already a member of WhatsApp community, I encourage you to [join](https://chat.whatsapp.com/HKJLqkvhIkQBgtBdEIf5jV?ref=mbi-deepdives.com) since I do share more earnings coverage, as well as interesting pieces that I come across, on WhatsApp. For new subscribers, let me also highlight that you can access the full library of 55 Deep Dives [here](https://www.mbi-deepdives.com/models/). Thank you for your support! [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Meta 4Q'24 Update URL: https://www.mbi-deepdives.com/meta4q24/ Last updated: 2025-01-30T16:42:26.000Z *Disclosure: I own shares of Meta Platforms* > “This is going to be a really big year. I know it always feels like every year is a big year, but more than usual, it feels like the trajectory for most of our long-term initiatives is going to be a lot clearer by the end of this year.” > \-Mark Zuckerberg in Meta’s 4Q’24 Call In recent weeks, Meta stock has mostly been one-way street: up! That continued to be the case after-hours post 4Q’24 earnings. [Subscribe](#/portal/signup) Here are my highlights from today’s call. **Users** Daily Active People (DAP) across its Family of Apps (FOA) accelerated to 60 mn QoQ in 4Q’24\. It’s kind of mind boggling that Meta added 1 Billion DAP since 1Q’20. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb9bd84aa-2fa5-42c8-81d7-a4f95d53e0b6_1842x105.png) **Ad revenue by Geography** I remember many investors were worried about “tough comps” this time last year which is understandable when you look at the comps. Despite growing ad revenue by 24% in 4Q’23, Meta still managed to increase revenue by 21% in 4Q’24. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3c36927a-7652-40f1-a04a-8a81637aba65_1942x328.png) **Ad Impression and Avg. Price Per Ad** Overall impression grew by 6% YoY and avg. price per ad grew by 14% YoY. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9638fa54-eec0-41f5-84b6-7b6e48529caf_910x498.png) While many prefer revenue growth driven through impressions, Meta mentioned they think they will have opportunity to drive revenue growth across both pricing and impression growth: > “we generally expect that we are going to be able to deliver ongoing ad performance improvements through a lot of the ongoing work that we're doing across our monetization road map and that will have the sort of effect of benefiting pricing overall…Overall, we are seeing healthy cost per action trends for advertisers for whatever is the action that they are optimizing for. And we believe we'll continue to get better at driving conversions for advertisers. And when we do, that will have the effect of continuing to lift CPMs over time because we're delivering more conversions per impression served, resulting in higher value impressions.” Later in the call, Meta expanded further on how they are working on improving monetization: > In the second half of 2024, we introduced an innovative new machine learning system in partnership with NVIDIA called Andromeda. This more efficient system enabled a 10,000x increase in the complexity of models we use for ads retrieval, which is the part of the ranking process **where we narrow down a pool of tens of millions of ads to the few thousand we consider showing someone**. The increase in model complexity is enabling us to run far more sophisticated prediction models to **better personalize which ads we show someone. This has driven an 8% increase in the quality of ads that people see on objectives we've tested**. Andromeda's ability to efficiently process larger volumes of ads also positions us well for the future as advertisers use our generative AI tools to create and test more ads. > > Adoption of Advantage+ shopping campaigns continues to scale with revenues surpassing a **$20 billion annual run rate and growing 70% year-over-year in Q4**. Given the strong performance and interest we're seeing in Advantage+ shopping and our other end-to-end solutions, we're testing a new streamlined campaign creation flow. > > …**More than 4 million advertisers are now using at least one of our generative AI ad creative tools, up from 1 million six months ago**. There has been significant early adoption of our first video generation tool that we rolled out in October, image animation with hundreds of thousands of advertisers already using it monthly. **Segment Reporting** Overall 4Q’24 revenue was +21.2% YoY; on a 2-yr and 3-yr CAGR basis, Meta’s topline increased by 22.3% and 12.8% respectively. FOA’s 4Q’24 operating margin of **\~60% (!!)** was \~550 bps higher than previous peak margin. Incremental margin at FOA was 88% last quarter! As you know, inside Meta, “there are two wolves”: one with eyepopping profitability and the other with mind numbing mounting losses quarter after quarter. Reality Labs managed to report $5 Billion losses in 4Q’24\. It’s almost hard to fathom that these two segments are within the same company! There was also no indication in the call that losses at Reality Labs have either peaked or close to be peaking. So, I guess we’ll have to keep watching this bleeding in almost suspended disbelief. To put it in context, since breaking out Reality Labs as a separate segment, Meta has reported a cumulative **$60 Billion losses** in the last 17 quarters in Reality Labs. Given there has been some recent optimism around Meta’s AR glasses, we are probably looking at **tens of billions** of continued investments **every year**. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9ef6ad41-d9a7-45fa-b73d-e94a29908e09_1938x535.png) Let’s look at some interesting comments from the earnings call: **AI, AI, AI** > “I expect that this is going to be the year when a highly intelligent and personalized AI assistant reaches more than 1 billion people, and I expect Meta AI to be that leading AI assistant. Meta AI is already used by more people than any other assistant. And **once a service reaches that kind of scale, it usually develops a durable long-term advantage**. > > …I also expect that 2025 will be the year when it becomes possible to build an AI engineering agent that has coding and problem-solving abilities of around a good mid-level engineer. And **this is going to be a profound milestone and potentially one of the most important innovations in history, like as well as over time, potentially a very large market, whichever company builds this first, I think it's going to have a meaningful advantage in deploying it to advance their AI research and shape the field**. So that's another reason why I think that this year is going to set the course for the future. Meta AI monthly actives reached 700 million (vs 500 million in 3Q’24). The following bit is also an indication how Meta AI can drive incremental growth over time but don’t expect much monetization of Meta AI anytime soon as they’re still mostly focused on delivering the consumer experience first: > We're now introducing updates that will enable Meta AI to deliver more personalized and relevant responses **by remembering certain details from people's prior queries and considering what they engage with on Facebook and Instagram to develop better intuition for their interest and preferences**. **Llama** Some very bold predictions and ambitions on Llama: > I think **this will very well be the year when Llama and open-source become the most advanced and widely used AI models as well**. Llama 4 is making great progress in training, Llama 4 Mini is doing with pretraining and our reasoning models and larger model are looking good too. **Our goal with Llama 3 was to make open source competitive with closed models. And our goal for Llama 4 is to lead.** Llama 4 will be natively multimodal. It's an omni model, and it will have agentic capabilities. Recent developments around DeepSeek only emboldened Meta’s strategy to double down on open source: > I also just think in light of some of the recent news, the new competitor DeepSeek from China, I think it also just puts -- it's one of the things that we're talking about is there's going to be an open source standard globally. And I think for our kind of national advantage, it's important that it's an American standard. So we take that seriously, and we want to build the AI system that people around the world are using and I think that if anything, some of the recent news has only strengthened our conviction that this is the right thing for us to be focused on. **Facebook, and Instagram** > In Q4, global video time grew at **double-digit percentages** year-over-year on Instagram, and we're seeing particular strength in the U.S. on Facebook, where video time spent was also up double-digit rates year-over-year. We see continued opportunities to drive video growth in 2025 through ongoing optimizations to our ranking systems. > > In the U.S., we recently launched a new destination in reels that consists of content your friends have left a note on or liked. **We're seeing very positive early results**, and we'll look to expand this globally in the coming months. **WhatsApp** One thing I would like to highlight is FOA’s “other revenue” which is mostly revenue from WhatsApp grew at 55%, 68%, and 50% CAGR over 1-yr, 2-yr, and 3-yr respectively. While it’s only \~$2 Billion run-rate business, it’s an interesting area to watch given the growing importance of business messaging in the next 5-10 years. So, it’s pretty goo that WhatsApp has continued to gain momentum in the US: > I expect WhatsApp to continue gaining share and making progress towards becoming the **leading messaging platform in the U.S.** like it is in a lot of the rest of the world. **WhatsApp now has more than 100 million monthly actives in the U.S**. **Threads** Threads Monthly Active Users (MAU) over time: 3Q’23: 100 Million 4Q’23: 130 Million 1Q’24: 150 Million 2Q’24: 200 Million 3Q’24: 275 Million 4Q’24: 320 Million MAU growth has decelerated a bit here, but momentum is still intact. **AR/VR** > “This will be a defining year that determines if we're on a path towards many hundreds of millions and eventually billions of AI glasses and glasses being the next computing platform like we've been talking about for some time or if this is just going to be a longer grind. > The number of people using Quest and Horizon has been steadily growing. And this is a year when a number of the long-term investments that we've been working on that will make the Metaverse more visually stunning and inspiring will really start to land. So I think we're going to know a lot more about Horizon's trajectory by the end of this year. I have noticed some people interpreted this as “make-or-break” year for Meta’s AR/VR investments. I don’t think Meta meant anything drastic changes here, but in case glasses adoption continues to accelerate, it is likely that they’ll double down here with more aggressive investments. If not, they will still very much persist in their investments but perhaps at a more measured pace. However, VR investments do seem to be increasingly bit of a suspect. I doubt “stunning visual” at Metaverse will be any real breakthrough for engagements. However, like Zuck, I too am quite optimistic about the glasses. It seems much easier bet that the people will upgrade to smart glasses in the next 10-15 years: > It's kind of hard for me to imagine that a decade or more from now, all the glasses aren't going to basically be AI glasses as well as a lot of people who don't wear glasses today, finding that to be a useful thing. So I'm incredibly optimistic about this. **Capital Allocation** Meta didn’t repurchase any shares which I applaud. The stock isn’t nearly as attractive as it was over the last couple of years and given the size of investments Meta is planning, it makes sense to not hurriedly return cash to shareholders. There is perhaps a scenario in which Meta’s FCF can be severely pressured (imagine a recession in a year or two, for example) but still would like to be committed to their investments to not fall behind compared to its competitors. To exacerbate my concerns around earnings quality (see my recent [piece](https://www.mbi-deepdives.com/big-tech-earnings-quality/) on this topic) even further, Meta changed its depreciation schedule for certain servers and network assets from 5 to 5.5 years. Interestingly, Meta did mention in 3Q’24 follow-up call that they had “no current plans to extend the useful lives in our servers”. I will do more work on this when I publish more detailed annual update on Meta in couple of weeks. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F332eb4ea-2204-4d1e-b25d-d7548eb5ab3c_2008x316.png) **Capex and Opex** Given Meta is going to spend $60-65 Billion on capex in 2025, this topic deserves detailed notes. First, some interesting commentary on Meta’s custom chips MTIA: > …we're pursuing cost efficiencies by deploying our custom MTIA silicon in areas where **we can achieve a lower cost of compute by optimizing the chip to our unique workloads**. **In 2024, we started deploying MTIA to our ranking and recommendation influence workloads for ads and organic content. We expect to further ramp adoption of MTIA for these use cases throughout 2025, before extending our custom silicon efforts to training workloads for ranking and recommendations next year**. > > we expect that **we are continuing to purchase third-party silicon from leading providers in the industry. And we are certainly committed to those long-standing partnerships, but we're also very invested in developing our own custom silicon for unique workloads, where off-the-shelf silicon isn't necessarily optimal and specifically because we're able to optimize the full stack to achieve greater compute efficiency and performance per cost and power** because our workloads might require a different mix of memory versus network, bandwidth versus compute and so we can optimize that really to the specific needs of our different types of workloads. Meta was quite willing to credit DeepSeek to inject some novel advancements which may or may not affect long-term capex intensity: > …on the DeepSeek question. I think **there's a number of novel things that they did that I think we're still digesting**. And there are a number of things that they have advances that we will hope to implement in our systems. > > …I don't know -- it's probably too early to really have a strong opinion on what this means for the trajectory around infrastructure and CapEx and things like that. There are a bunch of trends that are happening here all at once. > > There's already sort of a debate around how much of the compute infrastructure that we're using is going to go towards pre-training versus as you get more of these reasoning time models or reasoning models where you get more of the intelligence by putting more of the compute into inference, whether just will mix shift how we use our compute infrastructure towards that. **That was already something that I think a lot of the -- the other labs and ourselves were starting to think more about and already seemed pretty likely even before this, that -- like of all the compute that we're using, that the largest pieces aren't necessarily going to go towards pre-training. But that doesn't mean that you need less compute because one of the new properties that's emerged is the ability to apply more compute at inference time in order to generate a higher level of intelligence and a higher quality of service, which means that as a company that has a strong business model to support this, I think that's generally an advantage that we're now going to be able to provide a higher quality of service than others who don't necessarily have the business model to support it on a sustainable basis.** > > …I continue to think that investing very heavily in CapEx and infra is going to be a strategic advantage over time. **It's possible that we'll learn otherwise at some point, but I just think it's way too early to call that. And at this point, I would bet that the ability to build out that kind of infrastructure is going to be a major advantage for both the quality of the service and being able to serve the scale that we want to**. Meta’s 2024 opex turned out to be $95 Billion (vs guide of $96-98 Billion in 3Q’24). 2025 opex guide is $114-119 Billion. So, while opex increased by \~$7 Billion in 2024, it is expected to grow by \~$20 Billion in 2025. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b9789cf-ac18-4204-be42-2bee61ee294c_2010x106.png) **Outlook** Meta guided 1Q’25 revenue growth to be +11% to +18% on a constant currency basis. Consensus was closer to the high end of the guide. **Closing Words** Near the end of the call, Zuck tried to inject some caution: > I guess my note of caution or just my kind of periodic reminder…the actual business opportunity for Meta AI and AI studio and business agents and people interacting with these AIs remains outside of '25 for the most part. And **I think that's an important thing for for us to communicate and for people to internalize as you're thinking about our prospects here**. But nonetheless, we've run a process like this many times. We built a product. We make it good. We scale it to be large. We build out the business around it. That's what we do. I'm very optimistic, but it's going to take some time. While the animal spirits are running high in this market and Meta is one of the prime beneficiaries of that, it is indeed good to remember the exceedingly high bar Meta is going to face this year. To put this in context, Meta will have to grow its incremental revenue by $30 Billion to grow its operating profit by \~15% in 2025\. Bulls might say they did grow revenue by $30 Billion this year and they can do it again, and bears may see the impending wall that Meta may hit at some point as their business is gradually becoming more and more fixed cost heavy (more PP&E) which will make it harder for them to be agile in case they hit an idiosyncratic or macro-wide rough patch. Meta is well positioned for the long-term, but better to not expect the path towards long-term a linear one. As alluded earlier, I will expand more on my thoughts on Meta in a couple of weeks on my annual update on Meta. Notes from follow-up call [here](https://x.com/borrowed%5Fideas/status/1885005302065697275?ref=mbi-deepdives.com). I will cover **Google** and **Amazon’s** earnings **next week**. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Big Tech's Deteriorating Earnings Quality URL: https://www.mbi-deepdives.com/big-tech-earnings-quality/ Last updated: 2025-01-26T20:08:33.000Z *Disclosure: I own shares of Meta Platforms, and Amazon* Almost one and half years ago, I wrote about “[The Curious Case of Big Tech](https://www.mbi-deepdives.com/the-curious-case-of-big-tech/)”, in which I highlighted that much of the big tech was actually “deep value” investments back in 2013\. In fact, I also mentioned *“Meta, and Amazon are *currently trading at* *lower* OCF multiple than they were trading back in 2013*”. Both Meta and Amazon have comfortably outperformed both S&P 500 and Nasdaq 100 since then (not claiming any causal relationship, of course). ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8c7f2412-c92d-495b-b1db-a9bca0489c43_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) [Subscribe](#/portal/signup) However, as the title of this piece suggests, I have a slightly different tune today. I am increasingly concerned about Big Tech’s deteriorating earnings quality. While some people started murmuring about this topic a few quarters ago, I would like to show through this write-up that big tech shareholders (including me) should perhaps indeed be **at least** slightly concerned. Moreover, as almost all the big tech are going to continue to invest in their capex hand over fist in the short to medium term, this concern may accentuate even further. To make my case, I am going to focus on three companies within Big Tech: Microsoft, Meta Platforms, and Alphabet. Apple and Nvidia aren’t quite capex heavy, so this may not be a concern relevant for them. While it is very much relevant for Amazon, Amazon’s capex numbers are slightly convoluted given their retail AND cloud operations as well as the nature of my exercise (to be explained later). Even though Amazon won’t be under my scanner in this exercise, it was Amazon which essentially sowed the seed of deteriorating earnings quality in big tech back in 2020\. In 4Q’19 earnings call, Amazon announced to increase the useful life for their servers from **three to four years**: > …*there's enough trend now to show that the useful life is exceeding four years. We have been – for our servers and we had been depreciating them over three years. So, we are going to start depreciating them on a *four year* basis."* (Amazon 4Q'19 Call) This led to similar adjustments at Microsoft, Alphabet, and Meta a year later as they all decided to extend the useful life for their servers to four years. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0937daca-6ec8-46a0-b9d7-a382d15e1dc8_579x431.jpeg) Two years later, Amazon did it again. The useful lives for their servers were extended from **four years to five years**, and for their networking equipment from **five years to six years**. This time they tried to provide bit more justification why this isn’t just “accounting change” out of thin air, but rather a careful assessment of the reality: > As a practice, we monitor and review the useful lives of our depreciable assets on a regular basis to make sure that our financial statements reflect our best estimate of how long the assets are going to be used in operations…**Although we're calling out an accounting change here, this really reflects a tremendous team effort by AWS to make our server and network equipment last longer**. We've been operating at scale for over 15 years, and we continue to refine our software to run more efficiently on the hardware. This then lowers stress on the hardware and extends the useful life, both for the assets that we use to support AWS' external customers as well as those used to support our own internal Amazon businesses. (4Q’21 Call) That sounds reasonable…except miraculously Alphabet, Microsoft, and Meta again came to the same conclusion just **a year later**. Basically, it’s the same meme I mentioned above. Microsoft extended the depreciable useful life for server and network equipment assets in cloud infrastructure **from 4 to 6 years.** So did Alphabet. Meta, however, extended the useful life to five years. Given how quickly they all followed Amazon’s changes, it makes me wonder why they weren’t proactively carefully assessing the useful lives of their assets in the first place. Or if you’re cynical, you may think they may not have robust rationales anyway; they’re just doing it because a big tech peer gave them the “signal” that it can be done. Of course, higher useful life leads to lower depreciation expense which leads to higher operating profit. While shareholders all love ever increasing higher profits, it may be prudent to ask difficult questions to big tech management or ask them to provide a more detailed reporting on how they came to these re-assessments so quickly after years of “inefficiently” managing their servers and networking equipment. Of course, not all big tech shareholders readily assume that something nefarious is going on here. Some understandably wonder whether the mix of PP&E itself may have contributed to lower depreciation rate in recent years. For example, land is not depreciated at all, and buildings are usually depreciated at 25-30 year period. So overall depreciation rate would go down if such mix shift occurs. However, we don’t quite see that in their financials. Let me show you Microsoft, Meta, and Alphabet’s more granular PP&E to substantiate this point. Let’s start with Microsoft. **Microsoft** Microsoft’s gross PP&E increased from $22 Billion in FY’2013 to $212 Billion in FY’2024\. While the gross PP&E basically 10xed in the last 11 years, computer equipment and software was consistently \~40-45% of their gross PP&E. However, thanks to the changed depreciation schedule mentioned above, **overall depreciation rate as a percentage of net PP&E declined from \~30-34% during FY’2014-2020 to just \~15% in FY’2024.** While one can perhaps legitimately claim that some of these computer equipment’s useful life may have been extended thanks to more innovative engineering and efficient management, that is perhaps equally (if not more) counterbalanced by increasing mix of GPUs in their PP&E. This is not controversial to say that the useful life of these GPUs are lot lower compared to when this cycle of extending useful life started anyway. Here’s Rohit Krishnan in a recent [piece](https://www.strangeloopcanon.com/p/what-would-a-world-with-agi-look?hide%5Fintro%5Fpopup=true&ref=mbi-deepdives.com): “*The actual service life of H100 GPUs in datacenters is relatively short, ranging from 1-3 years when running at high utilization rates of 60-70%.”* Ben Thompson in his recent [interview](https://stratechery.com/2025/an-interview-with-daniel-gross-and-nat-friedman-about-models-margins-and-moats/?ref=mbi-deepdives.com) with Nat Friedman and Daniel Gross made the same point: *“you have a data center, which is I think a 30-year depreciation, and then the GPUs are I think accounted for in a five-year depreciation, but actually are unusable after about 36 months. So, you already have a problem there in terms of your accounting for the GPUs”* Given this context, these companies perhaps should face difficult time in maintaining their historical depreciation rate, yet we are seeing the **opposite**. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2be5eebc-ed0d-4437-8eea-3c1f120fd843_1453x583.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let’s look at Meta now. **Meta** Meta made some changes in how they classify certain PP&E items in 2021\. To make it more apple to apple, I have created a line item myself titled, “sub-total Servers and Network Assets”. In Meta’s case, we do see a somewhat noticeable mix shift within PP&E for servers and network assets as it declined from \~45-60% in 2013-2018 period to \~38-42% in the last five years. Like Microsoft, Meta’s depreciation rate as a percentage of beginning net PP&E declined from \~25-30% during 2013-2018 period to \~14-15% in the last two years. Unlike Microsoft, however, Meta’s incremental depreciation rate was somewhat consistent over the last decade. In Meta’s case, there was indeed some mix shift in PP&E, but given Meta’s ever increasing GPUs (Zuckerberg mentioned they expect to have [1.3 million GPUs](https://www.facebook.com/zuck/posts/pfbid0219ude255AKkmk4JAueXZeZ9zpjNYio2tBkd7bNmCaRbJ6iJaVVjypUgDg78CNdq5l) which clearly indicates these GPUs will be a significant part of their PP&E), the question remains just as valid whether we perhaps should expect to see depreciation rate to increase, instead of being down or steady going forward. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8a3efc0c-5e51-4359-bb45-f9149f7242b9_1407x886.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Now let’s look at perhaps the most interesting one: Alphabet. **Alphabet** Of these three companies, Alphabet is likely the worst “offender” in this exercise. “Information technology assets” has largely been steady in their PP&E mix over the last 10 years. When Alphabet’s net PP&E doubled from $42 Billion in 2017 to $85 Billion in 2020, their depreciation expense also doubled. So far, so good. However, **while their net PP&E increased by a whopping $50 Billion in 2023 compared to 2020, their annual depreciation expense has actually declined by $1 Billion during the same period**. Those changes in useful lives certainly came in pretty handy for Alphabet. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd4847c90-2725-4f25-a027-83477e9d830e_1330x580.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); the cells colored yellow indicates that Alphabet later re-stated its depreciation expenses in 2021-22 period It almost doesn’t pass the smell test when I noticed Meta and Google reported almost the same depreciation expense in 2023 even though Meta’s net PP&E as % of Google’s was only \~70% in 2023\. Given their network assets in the PP&E mix are kind of similar, I’m not sure why such discrepancy exists. Perhaps Google is much better than Meta in managing their assets! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F63f09817-3e56-4840-9d65-7ae1cfa889ff_1108x592.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While many may consider it “analysis paralysis” on accounting shenanigans, there may be important implications for these companies. Even though changes in depreciation schedule doesn’t have any impact on cash flow, since all these big tech companies are investing heavily in their capex, investors understandably pay more attention to their earnings these days than free cash flow in valuing these companies. If somewhat questionable depreciation schedule leads to higher reported profit and higher reported ROIC, investors can go a bit astray in assessing their fundamentals. This can become especially more important as the size of their PP&E grows which is almost certainly going to be the case in the short-to-medium term. Thank you for reading. I will cover Google, Meta, and Amazon's earnings in the next couple of weeks. [Subscribe](#/portal/signup) ### IDEXX: Leading the Humanization of Pet Care Through Diagnostics URL: https://www.mbi-deepdives.com/idxx/ Last updated: 2025-01-23T13:23:49.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- A couple of weeks ago, Instagram showed me this funny [reel](https://www.instagram.com/reel/Cvzg0HEARJF/?igsh=YzAyMDM1MGJkZA==&ref=mbi-deepdives.com): 0:00 /1:21 1× Like most good comedy bit, it is funny probably because there is an element of truth to it. As a society, we are increasingly humanizing our pets. When we look at our pets, we increasingly don’t just consider them our mere properties, rather we sense a much deeper bond to a sentient beings. Former Petco CEO Ron Coughlin [provided](https://www.washingtonpost.com/business/2022/12/30/american-pet-spending/?ref=mbi-deepdives.com) some numbers to contextualize society’s growing sense of devotion to our pets: *“Pet parents don’t want to be called pet owners. Seventy-seven percent say they want to be called pet parents, and 60 percent say they love spoiling their pets.”* While pets have been a core part of many American family experience for decades, I do think pets may play a even more critical role in many people’s lives in the coming decades. Pets may fill a potentially growing void in millions of childless households not just in the US, but in the world. Pew Research [published](https://www.pewresearch.org/social-trends/2024/07/25/the-experiences-of-u-s-adults-who-dont-have-children/?ref=mbi-deepdives.com) in 2018 that among adults aged 18 to 49 years old in the US, 37% said they are unlikely to have children in the future. Since then, that number has increased to 44% in 2021 and 47% in 2023! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F671b1f15-4fe4-4d48-8d48-fb16ff22b4c2_520x490.png) Source: [Pew Research](https://www.pewresearch.org/social-trends/2024/07/25/the-experiences-of-u-s-adults-who-dont-have-children/?ref=mbi-deepdives.com) While one may think perhaps many of these 18-49 year old may change their minds later (and I hope they do), the reality is even the status quo already indicates that too many of them will not. When we look at the historical trend of the number of biological children women aged 40-44 year old have, only 10% such women had no children in 1980, and another 10% had one child. Fast forward to 2022, both those numbers have basically doubled. If those survey data is even directionally correct, it seems highly likely that these numbers may increase even further. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdba972b2-386f-4c3f-aa03-9933ee24fe17_948x904.png) Source: [NCFMR](https://www.bgsu.edu/ncfmr/resources/data/family-profiles/guzzo-loo-number-children-women-aged-40-44-1980-2022-fp-23-29.html?ref=mbi-deepdives.com) Childless or single child households, relatively speaking, have a lot more time, money, and energy to spend on their pets. While pet ownership in general has mostly steadily risen across household types, the most noticeable expansion has [occurred](https://www.washingtonpost.com/business/2022/12/30/american-pet-spending/?ref=mbi-deepdives.com) among childless households. For such households, it is quite conceivable that pets may indeed be key source of bonding, love, and meaning that human beings generally crave. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F37bd1a41-3a38-4d63-84da-4717253ca6e9_534x703.png) Image Source: [Washington Post](https://www.washingtonpost.com/business/2022/12/30/american-pet-spending/?ref=mbi-deepdives.com) I do not own a pet. So when I asked around my friends and acquaintances who own pets, they all let me know that their pets are very much part of their family. Then when I asked them whether they own pet insurance, the answer was almost consistently no. Even though pet insurance has been [growing](https://naphia.org/news/na-pet-health-insurance-market-more-than-doubles-in-four-years/?utm%5Fsource=chatgpt.com) at double digit rate for years, pet insurance penetration is still hovering around just 2.5%. Therefore, almost all of the pet healthcare expenses come out of pocket of pet parents. If we are going to truly humanize our pets, we will probably need to see much higher pet insurance penetration in the US. If my friend’s dog gets affected by cancer (or some other terminal disease) and he chooses to put the dog down instead of spending tens of thousands of dollar, most people are not going to judge him for such a decision. Perhaps in 2050, that may change and society may increasingly find such trade-off distasteful, and to not have pet insurance will perhaps be considered irresponsible behavior. With this context in mind, IDEXX sits on the sweet spot to ride on this secular theme of humanization of pets. It was founded in 1983 by David Shaw in Westbrook, Maine, with a vision to revolutionize the veterinary diagnostics industry. Shaw recognized a gap in the market for advanced diagnostic tools tailored to veterinarians, a profession that had traditionally relied on outdated and less efficient methods borrowed from human medicine. IDEXX began with a focus on developing innovative diagnostic products that offered rapid and reliable results, empowering veterinarians to make timely decisions about their patients' care. Over the years, IDEXX expanded its offerings to include a wide range of diagnostic solutions, including in-clinic analyzers, reference laboratory services, and software tools to streamline veterinary workflows. The company IPO-ed in 1991 and has compounded at almost 20% rate over more than three decades to have current Enterprise Value of \~$36 Billion! ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0047d87a-a486-4f9a-9a2f-d384a12282e5_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Before we get into the weeds of IDEXX business, let me first discuss the size of the opportunity here. That’s section 1. In section 2, I will give you an overview of the business as well as the economics of different segments. In section 3, I will elaborate on the competitive dynamics in this industry. Section 4 highlights capital allocation and management incentives. Then in section 5, I will show what’s likely embedded in the current stock price. Finally, in section 6, I will share some concluding thoughts and disclose current portfolio holdings. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### 2024 Annual Letter URL: https://www.mbi-deepdives.com/2024/ Last updated: 2025-08-04T17:00:57.000Z _This post is for paying subscribers only._ ### Some updated thoughts on AppFolio URL: https://www.mbi-deepdives.com/appf3/ Last updated: 2024-12-23T14:34:00.000Z Over the weekend, I recorded a podcast on AppFolio which will be released within a few days. Before recording the podcast, I started reviewing my own as well some others’ work on the company. I have come to find some glaring gaps in my analysis published before on AppFolio which is why I feel compelled to post an update right away even before the podcast is released. I will briefly explain how I was incorrect in my earlier analysis and how I updated my thoughts on the company. While explaining my current thinking about the company, I will show you the earlier model I shared and how I changed the model now to fit my current thinking. I will also make the assumption that you have a decent understanding of the company as I won’t repeat the basics of the company. If you’re unfamiliar with the company, you can start with my [Deep Dive](https://www.mbi-deepdives.com/appf/) first and then come back to this post to see how I have updated my opinion. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Maravai: A Cornerstone in mRNA and Biologics Innovation URL: https://www.mbi-deepdives.com/mrvi/ Last updated: 2024-12-20T23:39:00.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- While I usually focus on covering large-cap companies, I decided to spend some time studying Maravai LifeSciences (Ticker: MRVI) which is only $1.5 Billion market cap company. Admittedly, it caught my attention when one of my friends started tweeting about it. Since I’m also quite keen to widen my circle of competence in Life science industry, I have decided to study Maravai this month. Maravai LifeSciences was formed in March 2014 as a partnership between Carl Hull, Eric Tardif, and [GTCR](https://www.gtcr.com/?ref=mbi-deepdives.com), a Private Equity (PE) firm. Hull, a thirty-year veteran of the diagnostics and life sciences industry, had served as CEO of Gen-Probe where he led a strategic transformation that culminated in its $3.75 billion [sale](https://www.reuters.com/article/business/healthcare-pharmaceuticals/hologic-to-buy-gen-probe-for-375-billion-idUSBRE83T10G/?ref=mbi-deepdives.com) to Hologic. Tardif, who joined Hull at Gen-Probe as Senior Vice President of Corporate Development after spending over a decade at Morgan Stanley, was responsible for corporate strategy and mergers & acquisitions. After selling Gen-Probe to Hologic, they both decided to partner with GTCR with the mission to build a company in the life sciences tools and in vitro (i.e. studies, experiments, or procedures that are performed outside of a living organism) diagnostics markets. GTCR committed to investing up to $300 million of equity capital. Maravai’s strategy focused on acquiring businesses in the life science research market and accelerating their growth through investment, strong leadership, and best practices. This approach led to a series of strategic acquisitions that expanded Maravai's portfolio and capabilities over the years. A common theme across these acquisitions is that they were all **founder-owned, scientifically driven, category-leading** companies in their own niche. Couple of years after forming the partnership, Maravai's acquisition spree began with Vector Laboratories in April 2016 (later divested in September 2021), followed by Cygnus Technologies, and TriLink BioTechnologies in the same year. Cygnus specialized in developing and manufacturing test kits that helped pharmaceutical and biotech companies detect and identify impurities in their biologic drugs. Trilink, founded in 1996, was focused on creating high-quality nucleic acid and mRNA products that were essential for research, diagnostics, and therapeutics. If this starts to feel already a bit intimidating, don’t worry; I will discuss the basics before getting into the weeds of Maravai. For the time being, I am just going to give you a very brief history of how this company was formed. The following year in 2017, Maravai acquired Glen Research. Glen Research is even older company than Trilink as it was founded in 1987\. They made high-quality chemical reagents used to create synthetic DNA and RNA molecules. Following this deal, Maravai took a hiatus in acquisitions. As the biotech “bubble” started forming following the pandemic, Maravai [raised](https://www.spglobal.com/marketintelligence/en/news-insights/latest-news-headlines/maravai-lifesciences-raises-1-86b-in-ipo-61444501?ref=mbi-deepdives.com) $1.86 Billion from its IPO and became a public company in November 2020\. Maravai was valued \~$7 Billion at the time of its IPO, and quickly doubled by August 2021 to reach its peak market cap of \~$15 Billion. It has gone down almost 90% from its peak in 2021\. It’s not just the stock price; the fundamentals of the company has mostly followed similar trajectory. Thanks to the Covid boom, its revenue in 2022 was \~6x of what it was in 2019, but then revenue went down a whopping \~67% in 2023 and it is expected to go down another 10% in 2024\. Of all the Deep Dives I have written so far, Maravai is the first company to experience such a precipitous decline in revenue. So, I am certainly not used to seeing revenue of a business tumbling so wildly like that. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F95c567c8-a8d0-451a-b055-4447a3e263cd_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Although a lot of Covid induced revenue has gone away, Maravai has utilized its pandemic driven cash flows to acquire new companies. Between 2020 and now, Maravai made four more acquisitions. **MockV** (acquired in March 2020**)** developed non-infectious particles that mimic real viruses used in testing biotherapeutic manufacturing processes. Their products helped pharmaceutical companies test their viral clearance procedures without the risks and costs of working with actual viruses. **MyChem** specialized in making ultra-pure nucleotides - essential building blocks used in mRNA synthesis, diagnostics, and research. They had been a supplier to Maravai since 2018, and eventually was acquired by Maravai in January 2022. **Alphazyme** (acquired in January 2023**)** was a provider of custom, scalable molecular biology enzymes. The company specialized in manufacturing enzymes used in genetic analysis and nucleic acid synthesis. Maravai acquired Alphazyme for \~$150 million (with potential additional performance-based payments of up to $75 million). Then just a month ago, Maravai [announced](https://investors.maravai.com/news-events/press-releases/detail/102/maravai-lifesciences-plans-to-acquire-dna-and-rna-business-of-officinae-bio-advancing-support-for-innovative-nucleic-acid-research?ref=mbi-deepdives.com) the acquisition of DNA and RNA business of Officinae Bio. The acquisition is expected to enhance Maravai's mRNA manufacturing capabilities through its TriLink division. Even though its revenue is in “low single-digit” millions, Maravai mentioned it to be “self-funding”. Overall, Maravai today is segmented in two broad segments: a) Nucleic Acid Production (NAP), and b) Biologics Safety Testing (BST). As you may already be thinking, this is not the kind of Deep Dive that can be intelligible without understanding some basics in biology. So, I will discuss the basics while elaborating on the economics of these two business segments. I will then expand on the ownership structure and management incentives at Maravai. Finally, I will try to figure out what is priced in today’s stock price. _This post is for paying subscribers only._ ### Lululemon 3Q'24 Update URL: https://www.mbi-deepdives.com/lulu3q24/ Last updated: 2024-12-11T17:56:40.000Z *Disclosure: I own January 2026 $165 LULU Call Options* While the overall market continues to hit All-time High almost every other day, Lulu has been battling the skeptics for much of this year. There is still a long way to go, but with the stock now up almost 50% over the last three months, Mr. Market has likely started acknowledging that the bear narratives perhaps went a bit too far. Glancing through the numbers for 3Q’24 may seem eerily similar to 2Q’24, but management’s tone was much more upbeat tonight. Lulu’s CEO Calvin McDonald started the call with an apparent dig at Alo (which ran 30% discount on all products during this Black Friday) while assuaging investors that the current quarter is trending well so far: > We are pleased with our business over the extended Thanksgiving weekend and the traffic trends we saw across both our store and e-commerce channels. In fact, on Black Friday, **we had the most visits ever to our Shop app and e-commerce site.** **Unlike others in this space, we do not run sale events across our entire store.** We leveraged the increased traffic over this period to clear through product we are not taking forward and to feature full-price style. While some competitors such as Alo notched up the promotional intensity, Lulu remained disciplined. McDonald later explained: > ..We are happy with **how the guests responded to both with full price sales driven by some of our key franchises** > > ..From a year-over-year perspective, I don't think the overall market is any more intense. There's pockets where certain brands and retailers are more promotional and where others are less. I think it obviously depends on the momentum in their business coming in of how they've chosen to play that. > > But when I look at the premium athletic space, **we've continued to play a non-promotional markdown only reg price business unlike others within this space** and pleased to see the results to kick off the holiday and the way the guest responds to our product. [Subscribe](#/portal/signup) **Sales Growth by Region** For the two consecutive quarters, US revenue was flat. US traffic was positive in e-com but slightly lower in stores than last year’s. Given Lulu’s bear thesis mostly revolves around alleged saturation in the US, Lulu won’t be able to kill this bear thesis until US revenue/comp starts growing. Remember, despite the roaring rally in the last three months, the stock is still down \~33% from its peak. Canada remains a bright spot in North America. Lulu closed their Mexico franchise and instead added 15 company operated stores in Mexico. While every other brand seems to be struggling in China, Lulu seems to be operating on a different gear there. I have shared more about their China strategy on my [WhatsApp community](https://chat.whatsapp.com/HKJLqkvhIkQBgtBdEIf5jV?ref=mbi-deepdives.com) a couple of months ago, so I won’t elaborate much here. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F49f12872-2268-4357-871b-533d293977d1_847x859.png) Source: Company Filings While Lulu’s US trajectory remains an issue, management mentioned that guest retention remains high and their membership program reached now 24 million in North America. If you take LTM revenue of \~$9.5 Billion in North America and assume \~80% of this revenue was derived from Lulu’s “members”, it implies each member spent \~$300 on Lulu in the last 12 months. This is **average** number, but it is likely that top 10% of Lulu’s customers perhaps spend multiple thousands of dollars per year. This should give you a good idea about the nature of the cult Lulu is for its core customers. Given the high retention of these members, I’m only half joking when I say Lulu is perhaps almost like a “subscription” for many of these core customers. Of course, these customers are not going to buy the same color every year which is why newness is an important driver for Lulu. Unfortunately, they have faltered on this for the last few quarters and management reiterated that by Q1, they expect to reach their historical level of newness: > We continue to see good response to newness from our guests, and **we're on plan to hit that historical number by quarter 1**. And when I look at our guests, I'm happy with the absolute growth number. Our retention with our guests remains very strong, and the opportunity remains, as I've spoken to in the past revenue per guest related to newness. Beyond North America and China, Rest of The World (RoW) also continues to grow at a rapid pace. Lulu plans to open company-operated stores in Italy in 2025; they have also decided to enter Denmark, Belgium, Turkey and the Czech Republic under a franchise model. I wish there were more questions why Lulu decided to opt for franchise model in these markets, but there wasn’t much discussion on this during Q&A. But the fact that Lulu is just entering market such as Italy (fourth largest GDP in Europe and 10th in the world) makes me optimistic that there is ample growth runway left in RoW. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51951927-03a0-455f-991f-85711d68e0ff_1159x264.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Lulu’s sqft growth gained a bit more momentum in the last quarter; adding 15 stores in Mexico certainly helped. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc01b815f-6466-4215-b367-44529ed46feb_880x453.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Margin** Lulu’s operating margins in North America and China segment may look pretty elevated, but management hinted that there’s more margin to be gained in China in due time: > it's not a region where we're looking right now to optimize operating margin, really focused on driving our long-term trend, but certainly see opportunity there over the longer term. Lulu’s Gross Margin (GM) was ahead of their expectation which was driven by +50 bps increase in product margin, lower inventory provision offset by higher freight costs. Markdown was flat YoY; there was also 20 bps deleverage of fixed costs and 10 bps positive FX impact. SG&A, on the other hand, had 20 bps negative impact from FX. 3Q’24 operating margin was still highest in the last 10 years. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F591c0a80-6a13-4354-9901-c714d41720c4_1411x799.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F91d304aa-a9c2-4456-a282-b649dbd7985c_1198x673.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Sales by Gender** After MSD growth in women’s category in 2Q’24, it has picked up in 3Q. Lulu also seems to be planning to focus more on “play category” which I think is a good idea: > In quarter 3, in women's, **we saw strength in short skirts and leggings and seasonal colors**…We built upon our success this past spring in Golf by focusing on another play category, tennis, when we dropped our Lululemon tennis club collection during the U.S. opened in New York this quarter. > > Based on the strong guest response we continue to see an opportunity to grow our play activities and intend to evolve our strategy from a seasonal approach to one where we introduced newness into these collections consistently throughout the year. Lulu’s accessories business (other category) continues to defy the tough comp and maintained its HSD growth despite facing mid-20s comp from last year. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F45c41a50-b327-43c1-9960-187db255b34e_1081x258.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Inventory** Even though Lulu guided inventory to increase by mid-teens, it only grew by 8%. They, however, guided for mid-teen inventory growth for Q4 as they chase the seasonal newness. There was a question about potential impact of tariff. It sounds to be quite manageable: > we have very limited exposure in China, we sourced approximately **3% of goods from China**. So exposure there is relatively small. **Our sourcing from Mexico is less than half of percentage, and we don't source anything from Canada.** So also a very small exposure there as well. So I would expect those are probably under some of the competitive landscape. **Capital Allocation** I am so, so impressed by Lulu management’s capital allocation in the recent few quarters. As the market warmed up to Alo/Vuori bear thesis, management decided to be quite aggressive in buying back the stock. When the stock was trading below $300 in July-August, they really ramped up their buyback activity. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb4727f97-da96-4064-9c28-2228fe4deadf_487x520.png) Source: Company Filings, MBI Deep Dives I was curious to see whether management chose to be similarly aggressive in the past. Indeed, I had to go back to 2018-19 when Lulu was similarly aggressive in buying back stocks as the stock was trading at $120-160/share. The stock did 2.5-3x since then, so these buybacks turned out to be quite accretive for remaining shareholders. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffa798572-548c-4662-b41f-c359d896abab_489x406.png) Source: Company Filings, MBI Deep Dives The current aggressive buybacks already started proving to be fruitful as Lulu’s shares outstanding declined by 3.1% YoY in 3Q’24. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb655c220-f5b0-4e2f-9018-d6fa8b21279c_954x393.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** Even though Lulu comfortably beat topline in 3Q, they guided rather conservatively for 4Q. They increased the mid-point of annual guidance after taking it down by 3% last quarter: > Starting with full year 2024, we now expect revenue to be in the range of $10.452 billion to $10.487 million. This range represents growth of 9% relative to 2023…For the full year, we now expect gross margin to increase approximately 10 to 20 basis points versus our adjusted gross margin in 2023\. We continue to expect markdowns to be relatively flat with last year…When looking at operating margin for the full year 2024, we continue to expect a decrease of 10 to 20 basis points versus adjusted operating margin in 2023, which expanded 110 basis points versus 2022. **Final Words** Lulu remains a very important holding in my portfolio. I am encouraged to see growth momentum continuing in international markets, but for Lulu to get out of the woods, we will have to wait for the US growth to return. I am optimistic that we will see such return to growth in the US in 2025 which may help evaporate the clouds on Lulu’s business. More reading on Lululemon: [here](https://www.mbi-deepdives.com/lulu/), [here](https://www.mbi-deepdives.com/apr2024/), and [here](https://www.mbi-deepdives.com/tsm/) (see section 5) Thank you for reading. ### December, 2024 Update URL: https://www.mbi-deepdives.com/dec24/ Last updated: 2024-12-02T15:30:10.000Z Some quick updates for this month. I am currently working on **Maravai Lifesciences (Ticker: MRVI),** and I expect to publish my Deep Dive later this month. I plan on covering earnings of Dollar General and Lululemon this week. Since DG has become somewhat unimportant holdings for me at this point, I will share my notes and brief thoughts on DG's earnings in our WhatsApp community; join the [community](https://chat.whatsapp.com/HKJLqkvhIkQBgtBdEIf5jV?ref=mbi-deepdives.com) if you haven't already. I will, however, cover Lululemon earnings in detail on the website. As I do after every month, I have disclosed my updated portfolio [here](https://www.mbi-deepdives.com/portfolio/). After receiving some feedback, I have now included a more granular breakdown of the portfolio on the "Portfolio" tab of the website itself. While this disclosure is not new since I have disclosed this at the end of my monthly Deep Dives, it is more convenient for readers to quickly see the current overall portfolio. For new subscribers, let me highlight that you can access all the past 53 Deep Dives, including excel models, [**here**](https://www.mbi-deepdives.com/models/). Thank you for your support. [Subscribe](#/portal/signup) ### Oracle: Ellison's Voyage to Software and Beyond URL: https://www.mbi-deepdives.com/orcl/ Last updated: 2025-01-23T06:19:21.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) (Note: *while I usually use my own AI voice to narrate the Deep Dive, I have decided to use a "professional AI voice" and utilized a different AI tool this time. It's better.)* --- > *“Wh*y do we do these things? George Mallory said the reason he wanted to climb Everest was because ‘it's there’. I don't think so. I think Mallory was wrong. It's not because it's there. It's because *we're* there, and we wonder if we can do it. > > …It's hard for me to quit when I'm losing—and it's hard for me to quit when I'm winning. It's just hard for me to quit. I'm addicted to competing.” > > \-From the [book](https://www.amazon.com/Billionaire-Mechanic-Sailing%C2%92s-Greatest-Americas/dp/0802121357?ref=mbi-deepdives.com) *“The Billionaire and the Mechanic: How Larry Ellison and a Car Mechanic Teamed up to Win Sailing s Greatest Race, the Americas Cup, Twice”* This addiction to competing even for the sake of it has served Larry Ellison, the Co-founder of Oracle, exceedingly well in life. Ellison, along with Bob Miner and Ed Oates, started Oracle with just $2,000 in 1977; Larry contributed $1,200\. They never raised money from venture capital and Ellison was largely allergic to raise equity. As a result, Ellison still owns \~42% stake in Oracle which is currently worth \~$220 Billion. While working at Ampex, Ellison came across a [research paper](https://www.seas.upenn.edu/~zives/03f/cis550/codd.pdf?ref=mbi-deepdives.com) authored by an IBM employee named E.F. Codd in 1970\. The paper introduced the relational model for databases, which revolutionized how we organize and access data. Before this, databases were structured like rigid hierarchies or networks, making them inflexible and requiring users to understand complex data organization. Codd proposed representing data as simple tables (relations) with rows and columns, where each column represents a domain (type of data) and each row contains related values. The paper explained how this simpler model eliminates three major dependencies that plagued earlier systems: ordering dependence (how data is physically stored), indexing dependence (how data is looked up), and access path dependence (how data relationships are navigated). Ellison thought it was a profound insight, and decided to start a company named “Software Development Laboratories” or SDL with two of his cofounders-Bob Miner and Ed Oates. Ellison was, however, still working at Ampex, but managed to secure a $50,000 contract from the CIA to build a database program. “Oracle” was the codename for this CIA project; it was meant to be able to answer any question about anything, similar to the classical meaning of an oracle as a source of wisdom and predictions. Later, they changed the company name to Relational Software Inc. or RSI in 1979\. In the same year, Oracle released their first version of Database. Ellison believed that customers would be skeptical about purchasing a Version 1 product since first versions are often perceived as untested or incomplete. By starting with “Version 2”, Oracle positioned their database as a more mature and reliable solution, suggesting that the product had already undergone revisions and improvements, even though there was never actually a publicly released Version 1. By 1983, the company finally changed its name to “Oracle”. You may be wondering why IBM itself wasn’t pursuing this revolutionary idea at full force at that time given the paper that inspired Ellison was written by an IBM employee. IBM was initially reluctant to implement Codd's relational database model primarily to protect their revenue from their existing hierarchical database product called IMS Database. IBM viewed Codd’s concept as merely an "intellectual curiosity" that could potentially undermine their existing products. In response to IBM's resistance, Codd started demonstrating the value of his model directly to IBM's customers, who then subsequently pressured IBM to implement it. IBM's cautious approach and focus on their mainframe business meant they were late to fully embrace the relational database market, releasing their first commercial product SQL/DS in 1981 and DB2 in 1983, but by then Oracle had already established themselves in the market. IBM’s sluggishness was well captured by Mike Wilson in his [book](https://www.amazon.com/Difference-Between-God-Larry-Ellison/dp/0060008768?ref=mbi-deepdives.com) *“The Difference Between God and Larry Ellison: \*God Doesn't Think He's Larry Ellison”:* > “Think of the marketing of relational technology as a race, with Ellison and IBM as two of the main entrants. IBM taught Ellison to walk, bought him a pair of track shoes, trained him as a sprinter, and then gave him a big head start. How could he lose?” Of course, there are always numerous ways to lose in business even when you have a head start. Bruce Scott, Co-architect and Co-author of the first three versions of Oracle Database, [gives](https://www.oracle.com/us/corporate/profit/p27anniv-timeline-151918.pdf?ref=mbi-deepdives.com) a lot of credit to Larry Ellison to be able to stay ahead of competition almost though sheer will: > “I’ve thought a lot about why Oracle was successful. I really think that it was Larry Ellison. There were a lot of other databases out there that we beat. It was really Larry’s charisma, vision, and his determination to make this thing work no matter what. It’s just the way Larry thinks. I can give you an example of his thought processes: We had space allocated to us, and we needed to get our terminals strung to the computer room next door. We didn’t have anywhere to really string the wiring. Larry picks up a hammer, crashes a hole in the middle of the wall, and says, ‘There you go.’ It’s just the way he thinks—make a hole, make it happen somehow.” This “make it happen somehow” attitude from the founder itself rippled through the company, and almost everyone else in the company picked up on that energy. While Bruce Scott laid out bit of a positive spin of Ellison’s “by hook or by crook” tactics, “Tech History Channel” had a more sobering take on how the [database wars](https://www.youtube.com/watch?v=nG5hYn93GQ8&t=59s&ref=mbi-deepdives.com) were won by Oracle: > “Ellison built a highly aggressive sales force that was focused on closing deals quickly at any cost. Oracle salespeople were known for their relentless pursuit of customers and the ability to sell the product's vision even when the software was still lacking in functionality. One of Oracle's most famous sales tactics was its promise of future functionality. Oracle would often promise features that didn't exist yet or that were still in development, and many businesses bought into this vision. By the time the promised features were delivered, customers were already locked into Oracle's ecosystem. > > Oracle also used aggressive pricing strategies to win over customers. In some cases, Oracle would offer deep discounts or special terms to undercut competitors like Ingress. This allowed Oracle to win contracts even when its product was technically inferior. Larry Ellison was also a master at forming strategic partnerships and alliances, which helped Oracle gain credibility and expand its customer base. One of Oracle's most important early alliances was the US Government, specifically the CIA. > > Oracle's first major contract was to build a relational database for the CIA, a project that not only provided critical funding for Oracle, but also gave the company a level of legitimacy that few startups could match.” > > \-From Tech History Channel on “[The Database Wars](https://www.youtube.com/watch?v=nG5hYn93GQ8&t=59s&ref=mbi-deepdives.com)” As Oracle found a strong traction in the database market and generated $55 million sales with 450 employees in 1986, they decided to go public on March 12, 1986\. The *very next day*, Microsoft went public, and a few months later, so did Adobe! Although Microsoft and Oracle didn’t compete against each other at that time, Ellison intentionally picked a fight against Bill Gates. Matthew Symonds in perhaps the most famous [book](https://www.amazon.com/Softwar-Intimate-Portrait-Ellison-Oracle/dp/0743225058?ref=mbi-deepdives.com) on Oracle “*Softwar: An Intimate Portrait of Larry Ellison and Oracle* **”** outlined Ellison’s almost bizarre rationale to go after Microsoft and Gates: > “The terrifying thing about Bill is that he's smart enough to understand what ideas are good, what's worth replicating, and he has the discipline and resources to get on with it and make it just a little bit better. That's very Japanese. That's very scary. Add that to Bill's ruthless perseverance and the fact that Microsoft has more money than God, and you get a most formidable foe, the ultimate foe, the perfect enemy. We pick our enemies very carefully. We decided to pick a fight with the biggest, most dangerous bully in the schoolyard. There's no way to avoid this fight, so let's start it." Partly because of this somewhat entertaining psyche of Ellison and partly due to my own curiosity to see how Oracle and Microsoft fared once they went public, I am going to show Oracle and Microsoft’s both operating performance and stock performance simultaneously. I couldn’t get a hold onto operating performance of Oracle during 1986-1989 period, so let’s start with their stock performance after they both went public in March, 1986\. Both had a flying start in the public market as Oracle and Microsoft stock became \~6x and \~5x respectively in less than four years. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9ae23f03-2fb8-4f3a-b17a-1e929c7c8f9f_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) After breezing through the first few years in the public market, Oracle started the 1990s with more than a mere hiccup. Ellison’s “take-no-prisoners” attitude towards growing the business eventually caught up with Oracle. Oracle salespeople promised products that never were delivered and the company recorded sales that had not yet happened. Oracle had to restate its financials in early 1990s and the stock went down \~80% in 1990\. Ellison blamed the lack of seasoned management at the company for the mishap. The lack of seasoned management is indeed quite apparent when you see how Oracle used to price its products globally. The book “Softwar” had a great example to substantiate the chaos Oracle was dealing with in much of the 1990s during its heyday of growth. From the book: > "The pricing committee was 'responsible' for deciding how much we would charge for our products—our new application server for example. So we'd do some competitive analysis—what's IBM charging, what's BEA charging, what's Microsoft charging? How does our product compare? What are the market dynamics? After that we'd make a decision, say, $10,000 per processor. > > "We'd then produce an updated price list and send it across the hall to our global sales headquarters. Unfortunately, our global sales team had their own pricing people who felt they weren't doing their jobs unless they did their own analysis and—for the good of the company, of course—corrected any pricing mistakes we might have made. They'd say, 'Larry's an engineer, what does he know about setting prices? We're salespeople, we know about pricing.' So they'd reset the price to $20,000 per processor and send out a pricing memo to our European headquarters in Geneva. Of course we had another pricing team in Geneva that redid the analysis and reset the price once more. They'd say, 'What do Americans know about selling software in Europe? We're Europeans, we live in Europe.' So a team in Geneva decides that the right price for Europe is $15,000 per processor. They then send that price out to Paris, Munich, London. The same thing happens all over again. The guys in Germany ask, 'What do French people in Geneva know about selling software in Germany? The right price in Germany is $25,000 per processor.' Every country had a different price. It was crazy. > > "We had about two hundred people around the world involved in analyzing and reanalyzing, setting and resetting prices. It cost us a fortune in duplication of effort. It delayed the process and confused most of our customers—but not all of them. One day I get a call from one of our largest customers, located in the northeastern United States. He tells me, 'Larry, we've decided to buy all of our Oracle software from Oracle Brazil.' I said, 'But you're headquartered in Connecticut, Connecticut's not part of Brazil. Why are you doing that?' He said, 'They gave us the best price.' Shit. Everything is so duplicative and decentralized, the right hand doesn't know what the left hand is doing. We're competing against ourselves. This is embarrassing.". This whole story reminded me a quote by [Brent Beshore](https://x.com/BrentBeshore?ref=mbi-deepdives.com): “*All businesses are loosely functioning disasters, and some are profitable despite it.”* Despite this growing pain of hypergrowth, Oracle grew their revenue and operating profit at \~29% and \~34% respectively in the 1990s. However, it still paled in comparison against Microsoft which was just on a different stratosphere of growth. Even though Oracle started the decade with just \~20% lower revenue than Microsoft, Microsoft’s **operating profit** was \~12% higher than Oracle’s **revenue** by the end of the decade! (**Note**: Oracle and Microsoft fiscal year ends on May 31st and June 30th respectively) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd6b7935d-ea2a-45d8-a65e-449a108a983a_1014x352.png) Source: Tikr, MBI Deep Dives In light of such operating performance, both Oracle and Microsoft stocks were understandably rocket ships as these stocks became almost \~50x and \~100x respectively during the decade of 1990s. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97ac5b24-274e-47b7-94b1-61d5e21191af_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Many may wonder whether such stock price performance was just mere representation of the tech bubble we experienced during the 1990s. When I looked closely, I was admittedly somewhat surprised to see how reasonably Oracle and Microsoft were valued for much of the 1990s despite their mindboggling growth year after year. In fact, while Microsoft and Oracle earnings grew at \~34% and \~43% CAGR respectively during 1990s, they consistently traded below 40x **LTM P/E** during 1990s. It is only in the second half of 1999 when euphoria seemed to have kicked in. By the end of 1999, Oracle and Microsoft were trading at \~120x and \~80x LTM P/E, far higher than what they typically traded during much of 1990s. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcb1f73af-b4c5-4cca-8e01-3f2d36c55da0_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) As we all know, the tech bubble burst right after that. Oracle’s LTM P/E crashed from \~170x in March 2000 to just \~12x in March 2001\. Oracle’s stock kept going down and bottomed in 2002 after experiencing \~80% drawdown from the tech bubble peak. The stock later started recovering until Global Financial Crisis (GFC) led to a \~40% tumble in 2008-09 period. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F89c45769-06be-4154-9adc-2b05f98b3a28_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) As a result, both Microsoft and Oracle experienced a lost decade (and then some) in the 2000s as they ended the decade being down \~36% and \~12% respectively. In fact, Microsoft and Oracle shareholders had to wait till 2014-15 period to get back to their peak during tech bubble. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed7ecaf5-0c46-42d6-bbdc-94c7728b207f_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Even though the stocks were getting decimated following the tech bubble, the businesses did just fine. Oracle’s operating profit almost tripled in 2009 vs 1999 whereas Microsoft’s profits doubled. As they say, multiple expansion is a helluva drug, but multiple compression is such a bitch! Once the bubble crashed, analysts wondered after two decades of relentless growth whether the database market had reached its maturing phase. Ellison couldn’t disagree more as he thought over time, database market could be 10x or even 100x of what it was in early 2000s as the internet would exponentially increase both the number of database transactions and the number of people who would interact with Oracle's databases. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe5727bde-1cce-4bb9-97dd-52ebe728ac97_907x318.png) Source: Tikr, MBI Deep Dives While the stock was going through a tough period, Oracle did some consequential acquisitions during 2000s. Between 2005 and 2015, Oracle spent more than $50 Billion on acquiring other companies. In [December 2004](https://www.oracle.com/corporate/pressrelease/oracle-buys-peoplesoft-121304.html?ref=mbi-deepdives.com), Oracle acquired PeopleSoft for $10.3 billion after an 18-month hostile takeover battle. Large enterprises requiring sophisticated HR and financial software systems typically faced a choice between three major vendors: SAP, Oracle, and PeopleSoft. While SAP was a major player, some customers found their choices effectively limited to just Oracle and PeopleSoft due to SAP's product limitations in certain areas. By acquiring PeopleSoft, Oracle reduced the competitive intensity in this area of the market. Then in [September 2005](https://www.oracle.com/us/corporate/acquisitions/siebel/investor-072298.pdf?ref=mbi-deepdives.com), Oracle purchased Siebel Systems for $5.9 billion, acquiring a major player in Customer Relationship Management (CRM) software. After a couple of years of pause from major deals, in [January 2008](https://www.zdnet.com/article/surprise-oracle-buys-bea-systems/?ref=mbi-deepdives.com) Oracle acquired BEA Systems for $7.2 billion ($8.5 billion including BEA's cash on hand). BEA was a significant player in middleware and Service Oriented Architecture (SOA) technologies. The very next year, Oracle [bought](https://www.oracle.com/corporate/pressrelease/oracle-buys-sun-042009.html?ref=mbi-deepdives.com) Sun Microsystems in 2009 for $7.4 billion, outbidding IBM's offer of $6.85 billion. Tech History Channel [explained](https://www.youtube.com/watch?v=nG5hYn93GQ8&t=59s&ref=mbi-deepdives.com) why this acquisition helped Oracle maintain their leadership in “database war”: > “The success of MySQL underscored a fundamental shift in the database market. The rise of the Internet and cloud computing, along with the increasing importance of web applications, meant that traditional enterprise databases like Oracle were no longer the only game in town. While Oracle continued to dominate in industries like finance, healthcare and government, where large scale database management and advanced features were essential, MySQL was rapidly becoming the de facto choice for web developers and startups. Oracle saw this as a massive threat, because in time, these small startups would emerge as big competitors that used MySQL instead of Oracle. While Oracle maintained its dominant position in the present, it saw that MySQL had the potential to be a threat to its future…In 2008, the company behind MySQL was acquired by Sun Microsystems for approximately $1 billion. However, the real twisted maestro story came two years later when Oracle, out of nowhere, shocked the tech industry and acquired Sun Microsystems in a 7.4 billion dollar deal in 2009” Even during 2010s, Oracle kept its acquisition spree with major deals such as MICROS System for $5.3 Billion ([2014)](https://www.oracle.com/corporate/pressrelease/oracle-buys-micros-systems-062314.html?ref=mbi-deepdives.com), and NetSuite for $9.3 Billion ([2016](https://www.oracle.com/corporate/pressrelease/oracle-buys-netsuite-072816.html?ref=mbi-deepdives.com)). While Oracle started the 2010s decade well, the company’s operating profit stopped growing despite these large acquisitions. Between 2012 and 2019, Oracle’s operating profit largely stayed around $14 Billion. Microsoft’s operating profit also stayed largely flat between 2011 and 2016, but after Satya Nadella went full speed ahead towards cloud, Microsoft ended up more than doubling its profit in 2019 vs what it posted in 2009\. If you are curious to explore more about Microsoft, I suggest you read my [Deep Dive](https://www.mbi-deepdives.com/msft/) on the company. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4c80b27b-a0d4-456d-b8ed-b4f7b089d9f0_901x322.png) Source: Tikr, MBI Deep Dives Oracle was still in the middle of its transition from licenses and maintenance revenue of the on-prem world to subscriptions in cloud software. I will elaborate more on this in the next section of this Deep Dive. The stock did okay in 2010s; however, it paled in comparison with Microsoft. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbc5e0206-635a-4a99-b144-00f8c63b5b3c_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) So far in 2020s, however, Oracle has not only accelerated its transition from on-prem to cloud for its software, it has also found their way into joining the hyperscaler party. Even though Microsoft is still very much around the center of the current AI revolution thanks to their investment and partnership with OpenAI, Oracle too is now very much part of the conversation and in fact, the stock has so far performed better than Microsoft in this decade. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34b25600-d7f9-4bc8-98e8-0bea48222e78_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In the next section, I would like to dig deep and provide a more comprehensive overview of what Oracle does today. Then I will discuss the competitive dynamics Oracle faces today, especially in the context of OCI. I will later discuss what’s embedded in the stock price today as well as capital allocation history and incentive structure of Oracle management. Finally, I will share some concluding thoughts, and disclose my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### November, 2024 Update URL: https://www.mbi-deepdives.com/november-2024-update-2/ Last updated: 2024-11-04T15:28:12.000Z Some quick updates for this month. I am currently working on **Oracle Corporation (Ticker: ORCL),** and I expect to publish my Deep Dive on Oracle by November 26th. While I mentioned earlier that I would work on a couple more semiconductor companies this year, I am shifting it to next year. So far, I have published a [Primer](https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/) on semis, and Deep Dives on [Texas Instruments](https://www.mbi-deepdives.com/txn/) and [TSMC ](https://www.mbi-deepdives.com/tsm/)this year. Next year, I hope to cover at least two more companies from semiconductor industry. For new subscribers, let me highlight that you can access all the past 52 Deep Dives, including excel models, [**here**](https://www.mbi-deepdives.com/models/). I also disclose my [portfolio](https://www.mbi-deepdives.com/portfolio/) on a monthly basis. Thank you for your support. [Subscribe](#/portal/signup) ### Amazon 3Q'24 Update URL: https://www.mbi-deepdives.com/amzn3q24/ Last updated: 2024-11-01T01:10:23.000Z *Disclosure: I own Jan 2025 $55 call options of Amazon* Like clockwork, Amazon has posted another impressive quarter. Every part of the business seems to be trending well, and moving in the right direction. Here are my highlights from today’s call. [Subscribe](#/portal/signup) **Revenue** Except 1P, every segment of the business is growing at double digit rate. Both AWS and advertising grew at 19% YoY. I’ll discuss more about AWS later, but let’s talk more about Amazon ex-AWS first. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6f2d9767-984d-49b4-bd2d-bbe29ba798e6_1501x232.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** North America and international sales grew +9% and +12% respectively. Both North America and international segment posted their seventh consecutive YoY operating margin improvement. Amazon’s operating margin improvement is well telegraphed and understood by investors, but still nice to see them execute so well here. Lower cost to serve, greater contribution from advertising, improved selection, faster delivery speeds driving consumer demand are some of the things mentioned by management for retail business’ continued improvement. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F54d097a3-d8be-435f-9419-a8cab4158186_852x94.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Some key quotes on retail business below: > Prime remains a core contributor to this growth. Year-over-year paid membership growth **accelerated in Q3 from both the U.S. and globally**. > > When you look at the split between revenue growth and unit growth, you do see some impact of the lower ASP products that we're selling as well as **some of the trade down that consumers are doing**. But we take that as a real positive, seeing the growth in everyday essential categories, which are really predicated on speed. > > So you have to have fast delivery to be able to sell the those products to customers. And when you do, it results in a **stickier consumer relationship, higher orders, building larger baskets, which help our ship economics, and repeat orders are stronger**. So those are all positive signs, and **we'll take any short-term degradation in ASP because what we're focused on primarily is free cash flow here.** > > …**it's easy to lower prices but it's much harder to be able to afford to lower prices**. And the same thing is probably true about lower ASP items. It's pretty easy to choose to supply them but **it's much harder to be able to afford to economically supply them.** And so one of the reasons that we have been so maniacal about cost to serve over the last few years is that as we're able to take our cost to serve down, it just opens up the aperture for more items, particularly lower ASP items that we're able to supply in an economic way. Given how focused Amazon seems to be in serving lower ASP items, you gotta wonder about the long-term questions that may pose for Dollar stores, especially in urban/semi-urban areas. **Fulfillment+ Shipping** If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q'22\. Since then, unit growth is faster (was at par in 3Q’23) than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. That theme continued in 3Q’24 which makes me confident that Amazon retail remains very much on track. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2aef1bc6-b66a-4aff-90c3-ed0a5a619da4_1108x469.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The good news is there is likely more upside left here. Their regionalization efforts in logistics is continuing to reap benefits for the company that will likely keep improve customer experience and margins even more over time: > First, we continue to believe there are **more gains on top of what we've captured thus far in outbound regionalizatio**n and getting more items closer to end consumers. As such, we're in the process of significantly changing the way we inbound items into our fulfillment network and subsequently spread them to our regional fulfillment nodes. > > In the last few months, we've made hundreds of changes to our U.S. inbound network and opened more than 15 inbound buildings. While still relatively early in this re-architecture, **we've already improved our ability to spread inventory across our fulfillment centers by 25% YoY**, allowing us to have more of the requisite items in fulfillment centers closest to the customer so we can compile shipments and ship to customers even more quickly. As we scale and optimize this new design, we expect these changes will **further improve inventory placement, offer faster delivery time, save transportation costs, and enable us to increase units shipped per box.** > > Second, we continue to roll out same-day delivery facilities, which is **not only the fastest way to get products to customers but also one of our lowest cost ways to deliver**. Over 40 million customers this past quarter have had their orders delivered for free with same-day delivery, **an increase of more than 25% year-over-year**. And third, we continue to innovate in robotics to speed delivery, lower cost to serve, and further improve safety in our fulfillment network. > > We recently launched our 12th-generation fulfillment center design with the first building launching in Shreveport, Louisiana. This is the first facility that incorporates our newest robotics inventions to simplify stowing, picking, packing, and shipping processes. **Thus far, this new design reduces fulfillment processing time by up to 25%, increases the number of items we can offer for same-day or next-day delivery and is expected to drive a 25% improvement in our cost to serve during peak within this next generation facility.** Though we believe we have more expansive automation and robotics than other retail peers, **it's still early days in how much automation we expect in our fulfillment network.** > > we really do believe that **AI is going to be a big piece of what we do in our robotics network**. We had a number of efforts going on there. We just hired a number of people from an incredibly strong robotics AI organization. And I think that will be a very central part of what we do moving forward, too **Advertising** > …Sponsored Products, we're seeing **meaningful growth on a very large base**, and we see further opportunity in driving **even better performance for advertisers by further improving the relevancy of the ads** we show and by providing additional optimization controls. At the same time, some of our newer offerings are in their very early days. We're just entering our first broadcast season for Prime Video advertising, following a very strong showing at upfronts. And we're continuing to support brands of all sizes with our generative AI-powered creative tools across display, video and audio, including our video generator that uses a single product image to curate custom AI-generated videos. While we're generating a lot of advertising revenue today, **there remains considerable upside**. **AWS** Okay, now let’s talk about AWS. AWS added \~$1.2 Bn incremental revenue QoQ. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe5a7a6fd-223c-4f7a-a057-bd5b0888a2fe_1018x532.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Let’s take a quick look at hyperscalers growth. While both Azure and Google Cloud accelerated in 3Q, AWS growth remained steady at 19%, same as 2Q’24\. Do keep in mind AWS’ large base though now that it has annualized run-rate of $110 Billion revenue. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbd3b5750-e291-4e67-99f3-03872afd0880_1399x796.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); \*Google Cloud includes Google Workspace, so not quite apple-to-apple and in reality, GCP tends to grow faster than Google Cloud One thing I would like to track is Google Cloud’s operating performance trajectory against AWS. Back in 2020, Google Cloud was only about a quarter of the size of AWS, but now it’s almost two-fifth of AWS revenue. We don’t know exactly how much of this is GCP, but we can be pretty confident that GCP is leading this catch-up with AWS. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0c783133-871e-41bb-8524-9e7e7dc9b8fa_1000x616.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0cc3fd1a-0b8a-43ab-b600-b0ed35f09ace_1077x664.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Like Google and Meta, AWS also kept posting almost “obscene” incremental operating margins of \~80% in 3Q’24\. AWS, in fact, posted its highest ever operating margin of 38.1% in 3Q’24\. What’s driving these incredible operating margins? > …continued focus on cost control, including a measured pace of hiring, a focus on driving efficiencies in our infrastructure, and reducing costs across the business. Additionally, we increased the estimated useful life of our servers starting in 2024, **which contributed approximately 200 basis points** to the AWS margin increase year-over-year in Q3\. As we said in the past, we expect the AWS operating margins to fluctuate, driven in part by the level of investments we're making at any point in time. I know investors don’t love these margin expansion from extending useful lives of servers, but it’s extremely unlikely that they’re making these numbers up without supporting evidence for doing so. So, I don’t quite frown on this practice. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F189d29bb-1114-4b30-8e4b-428a19bec78f_1465x115.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe28a1dfe-0b0d-4a5d-ae4b-69ce4d0077e0_1192x649.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The following quote from Andy Jassy on AWS was quite insightful: > I think **one of the least understood parts about AWS, over time, is that it is a massive logistics challenge**. If you think about, we have 35 or so regions around the world, which is an area of the world where we have multiple data centers, and then probably about 130 availability zone through data centers, and then we have thousands of SKUs we have to land in all those facilities. > > And **if you land too little of them, you end up with shortages, which end up in outages for customers. So most don't end up with too little, they end up with too much.** **And if you end up with too much, the economics are woefully inefficient. And I think you can see from our economics that we've done a pretty good job over time at managing those types of logistics and capacity.** And it's meant that we've had to develop very sophisticated models in anticipating how much capacity we need, where, in which SKUs and units. > > And so I think that the AI space is, for sure, earlier stage, more fluid and dynamic than our non-AI part of AWS. But it's also true that **people aren't showing up for 30,000 chips in a day. They're planning in advance. So we have very significant demand signals giving us an idea about how much we need. And I think that one of the differences if you were able to get inside of the economics of the different types of providers here is how well they manage that utilization and that capacity. It has a very direct impact on what kind of margins you have over time and what kind of capital efficiency you also have over time.** > > And so I think you're right…that **there are some similarities in the early days here of AI, where the offerings are new and people are very excited about it. It's moving very quickly and the margins are lower than what I think they will be over time. The same was true with AWS. If you looked at our margins around the time you were citing, in 2010, they were pretty different than they are now. I think as the market matures over time, there are going to be very healthy margins here in the generative AI space.** **Opex+Capex** Just like other big tech, Amazon's capital intensity has also gone up materially with their increased scale. In 1H’24, they spent $30.5 Bn in capex, \~$22 Billion in Q3, and guided another \~$23 Billion for Q4\. Back in 2018-19, capex as % of sales used to be \~5-6%. Capital intensity has almost tripled as it reached 14.2% of sales in 3Q’24. What are they spending these capex on? > The majority of the spend is to support the growing need for technology infrastructure. This primarily relates to AWS as we invest to support demand for our AI services while also including technology infrastructure to support our North America and international segments. > > Additionally, we're continuing to invest in our fulfillment and transportation network to support the growth of the business, improve delivery speeds and lower our cost to serve. This includes investments in same-day delivery facilities, in our inbound network and as well in robotics and automation. Management emphasized that this may be “once-in-a-lifetime” opportunity, so they would rather be aggressive in their investments: > so the thing to remember about the AWS business is the cash life cycle is such that **the faster we grow demand, the faster we have to invest capital in data centers and networking gear and hardware**. And of course, in the hardware of AI, the accelerators or the chips are more expensive than the CPU hardware. And s**o we invest in all of that upfront in advance of when we can monetize it with customers using the resources.** > > But of course, a lot of these assets are many-year useful life assets. Data centers, for instance, are useful assets for 20 to 30 years. And so I think we've proven over time that we can drive enough operating income and free cash flow to make this very successful return on invested capital business. And we expect the same thing will happen here with generative AI. **It is a really unusually large, maybe once-in-a-lifetime type of opportunity. And I think our customers, the business, and our shareholders will feel good about this long term that we're aggressively pursuing it.** For opex, Amazon has started to find its efficiency religion. They mentioned office staff is down slightly YoY (and flat from 2023 end). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F61396f60-e86c-4cf1-8ce4-f2045e695f4f_2109x256.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Other Bets** Nothing on Kuiper in this call, but Amazon continues to show optimism around Alexa although I’m not sure such optimism around Alexa is shared by many: > We've about 0.5 billion devices out there with a couple of hundred million active endpoints. And when we first were pursuing Alexa, we had this vision of it being the world's best personal assistant and people thought that was kind of a crazy idea. > > And I think if you look at what's happened in generative AI over the last couple of years**, I think you're kind of missing the boat if you don't believe that's going to happen.** It absolutely is going to happen. So **we have a really broad footprint where we believe if we rearchitect the brains of Alexa with next-generation foundational models, which we're in the process of doing, we have an opportunity to be the leader in that space.** **Outlook** Amazon’s guidance for 4Q’24 is below. Please note consensus 4Q’24 revenue and EBIT before the call were $186.3 Billion and $17.3 Billion respectively. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff99bc97c-7960-4e90-ab44-0c5266194a6d_1054x220.png) Source: Company Filings **Closing Words** Amazon seems to be in a pretty strong place in both of its core business: retail and cloud. Both of them are staggeringly large markets with sizable profit pool across the value chain and Amazon remains in a prime position to capture attractive economics. I intend to remain invested at current price. For more in-depth valuation discussion of Amazon, see my analysis [**here**](https://www.mbi-deepdives.com/amzn2024/) (February, 2024). Please feel free to share with your friends and network. Thank you for reading. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Meta 3Q'24 Update URL: https://www.mbi-deepdives.com/meta3q24/ Last updated: 2024-10-31T16:13:42.000Z *Disclosure: I own shares of Meta Platforms* Meta’s earnings was fine. But there is almost a certain sense of exhaustion in terms of reaction to Big Tech earnings this quarter so far. Here are my highlights from today’s call. [Subscribe](#/portal/signup) **Users** Daily Active People (DAP) across its Family of Apps (FOA) decelerated to 20 mn QoQ in 3Q’24. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffe8fca7f-15bd-4b68-bb79-f18631413602_1879x81.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad revenue by Geography** Even though easy comp ended in 1Q’24 and 3Q’24 faced a fairly difficult comp, Meta posted quite strong YoY growth rates across regions in 3Q’24. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F09318ee2-7298-4a86-85e7-ea0a7d4c1699_1978x361.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Meta’s Family of Apps (FOA) ads revenue growth is comfortably ahead of both Google Search and YouTube ads in each of the last 1-yr, 2-yr, and 3-yr timeframe. Given the growth levers Meta have today, I reckon they will grow noticeably faster compared to both Google Search and YouTube ads in the next couple of years. More on the growth levers later. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F781e9966-d829-4397-be94-958345517f10_514x157.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad Impression and Avg. Price Per Ad** Overall impression grew by 7% and avg. price per ad grew by 11% YoY. Interestingly, Asia Pacific had the slowest growth in 3Q’24, mostly because it was lapping a period of stronger demand from China-based advertisers. I remember how much airtime these “China-based advertisers” were getting last year from the analysts, and how they may have “inflated” Meta’s ad revenue. Today, this felt almost a trivial point. Meta’s ad infrastructure is going through some consequential changes which I suspect will keep improving monetization at all the FOA properties over multiple quarters going forward. Some interesting quotes from the call: > Pricing growth was driven by increased advertiser demand, in part due to improved ad performance. > > …Similar to organic content ranking, we are finding opportunities to achieve meaningful ads performance gains by adopting new approaches to modeling. For example, we recently deployed new learning and modeling techniques that enable our ad systems to consider the sequence of actions a person takes before and after seeing an ad. Previously, our ad system could only aggregate those actions together without mapping the sequence. This new approach allows our systems to better anticipate how audiences will respond to specific ads. Since we adopted the new models in the first half of this year, **we've already seen a 2% to 4% increase in conversions** based on testing within selected segments. > > In Q3, we introduced changes to our ad ranking and optimization models to take more of the cross-publisher journey into account, which we expect to **increase the Meta attributed conversions** that advertisers see in their third-party analytics tools. > > we care a lot about **conversion growth, which…continues to grow faster than impression growth**. And are we seeing healthy cost per action or cost per conversion trends. And as long as we continue to get better at driving conversions for advertisers that should have the effect of lifting CPMs over time, **because we're** **delivering more conversions per impression served and that will result in higher value impressions.** ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3f561e84-ee6e-4564-9218-b52d08b30923_877x504.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Segment Reporting** Overall 3Q’24 revenue was +18.9% YoY; on a 2-yr and 3-yr CAGR basis, Meta’s topline increased by 21.0% and 11.8% respectively. FOA’s 3Q’24 operating margin of 54.0% was slightly below its peak operating margin of 54.4% in 4Q’20\. Incremental margin at FOA remains super impressive at 67.2% in 3Q’24. Here’s a funny stat. Meta added $12.9 Billion incremental quarterly revenue in 3Q’24 vs 3Q’22\. Their incremental operating income at FOA increased by $12.4 Billion during this time. So, basically they were able to grow their revenue at effectively 100% margin. The magic of zero marginal cost runs deep into Meta’s business. The less magical segment: Reality Labs (RL) continues to bleed. But an optimistic would say the silver lining here is QoQ losses here declined. Does this mean we are very close to peak RL losses? Meta was asked this question, but didn’t answer definitively. My guess is they may provide more clarity on this next quarter. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6aee5bd8-2ad5-48eb-bec7-1d5b980b1af3_1836x540.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let’s look at some interesting comments from the earnings call: **AI, AI, AI** Lots of good data points how AI is helping Meta’s core products: > Meta AI now has more than **500 million monthly actives**, improvements to our AI-driven feed and video recommendations have led to an **8% increase in time spent on Facebook and a 6% increase on Instagram this year alone**. More than 1 million advertisers used our Gen AI tools to create more than 15 million ads in the last month. And we estimate that **businesses using image generation are seeing a 7% increase in conversions and we believe that there's a lot more upside here**. Meta’s new approach to content ranking will likely act as tangible lever of growth in terms of time spent and surfacing more relevant content for the users, but it’s going to be probably a multi-year journey: > Previously, we operated separate ranking and recommendation systems for each of our products because we found that performance did not scale if we expanded the model size and compute power beyond a certain point. However, inspired by the scaling laws we were observing with our large language models, last year, we developed **new ranking model architectures capable of learning more effectively from significantly larger data sets.** > > To start, we have been deploying these new architectures to our Facebook ranking video ranking models, which has enabled us to deliver **more relevant** recommendations and unlock meaningful gains in launch time. Now we're exploring whether these new models can unlock **similar improvements to recommendations on other services**. After that, we will look to introduce **cross-surface data to these models, so our systems can learn from what is interesting to someone on one surface of our apps and use it to improve their recommendations on another**. This will take time to execute and there are other explorations that we will pursue in parallel. > > However, over time, we are optimistic that this will unlock more relevant recommendations while also leading to higher engineering efficiency as we operate a smaller number of recommendations. **Llama** Meta again explained how their “open” approach is going to benefit them in the long run: > There's sort of the quality flavor and the efficiency flavor. There are a lot of researchers and independent developers who do work and because Llama is available, they do the work on Llama and they make improvements and then they publish it and it becomes -- **it's very easy for us to then incorporate that both back into Llama and into our Meta products** like Meta AI or AI Studio or Business AIs > > Perhaps more importantly, is just **the efficiency and cost**. I mean this stuff is obviously very expensive. When someone figures out a way to run this better if that -- **if they can run it 20% more effectively, then that will save us a huge amount of money**. And that was sort of the experience that we had with open compute and part of why we are leaning so much into open source here in the first place, is that **we found counterintuitively with open compute that by publishing and sharing the architectures and designs that we had for our compute, the industry standardized around it a bit more. We got some suggestions also that helped us save costs and that just ended up being really valuable for us.** > > Here, one of the big costs is chips -- a lot of the infrastructure there. **What we're seeing is that as Llama gets adopted more, you're seeing folks like NVIDIA and AMD optimize their chips more to run Llama specifically well, which clearly benefits us**. So it benefits everyone who's using Llama, but it makes our products better, right, rather than if we were just on an island building a model that no one was kind of standardizing around the industry. So that's some of what we're seeing around Llama and why I think it's good business for us to do this in an open way. One thing that really stood out to me was Meta mentioned they are helping the public sector to adopt Llama across the US govt which assuages my concerns related to potential unfriendly regulations to open source approach that are being lobbied strongly by some of the companies pursuing closed approach models: > This quarter, we released Llama 3.2, including the leading small models that run on device and open source multimodal models. We are working with enterprises to make it easier to use. And now we're also working with the public sector to adopt **Llama across the U.S. government.** Llama 4 (the smaller one) is coming early next year: > The Llama 3 models have been something of an **inflection point** in the industry. But I'm even more excited about Llama 4, which is now well into its development. We're training the Llama 4 models on a cluster that is bigger than 100,000 H100s or bigger than anything that I've seen reported for what others are doing. I expect that the smaller Llama 4 models will be ready first, and they'll be ready -- we expect **sometime early next year**. > > It seems pretty clear to me that open source will be the most cost-effective, customizable, trustworthy performance and easiest to use option that is available to developers. **Facebook, and Instagram** > On Facebook, we continue to see **positive trends with the young adults, especially in the U.S.** > > In the third quarter, we continue to see **daily usage grow year-over-year across Facebook and Instagram, both globally and in the U.S**. On Facebook, we're seeing strong results from the global rollout of our unified video player in June. > > Since introducing the new experience and prediction systems that power it, we've seen a **10% increase in time spent within the Facebook video player**. This month, we've entered the next phase of Facebook's video product evolution. Starting in the U.S. and Canada, we are updating the stand-alone video tab to a full screen viewing experience, which will allow people to seamlessly watch videos in a more immersive experience. We expect to complete this global rollout in early 2025. > > Within Facebook, video engagement **continues to shift to short form following the unification of our video player**, and we expect this to continue with the transition of the video tab to a full screen format. This is resulting in an organic video impressions growing more quickly than overall video time on Facebook, which **provides more opportunities to serve ads**. > > Across both Facebook and Instagram, we're also continuing our broader work to optimize **when and where we should show ads within a person's session. This is enabling us to drive revenue and conversion growth without increasing the number of ads.** As you can see, there are a number of growth levers Meta is working on. Each of these levers will probably only add a couple of points of growth, but in aggregate they can really add up over time. **WhatsApp** > For WhatsApp, the U.S. remains one of our fastest-growing countries, and we just passed a milestone of **2 billion calls made globally every day**. > > The other element of revenue on WhatsApp, I would say, is **paid messaging that continues to grow at a strong pace again this quarter**. It remains -- in fact, the **primary driver of growth** in our Family of Apps other revenue line, which was up 48% in Q3, and we're seeing generally a strong increase in the volume of paid conversations driven both by growth in the number of businesses adopting paid messaging as well as in the conversational volume per business. **Threads** Threads Monthly Active Users over time: 3Q’23: 100 Million 4Q’23: 130 Million 1Q’24: 150 Million 2Q’24: 200 Million 3Q’24: 275 Million They are seeing 1 million sign-ups per day. Engagement is growing as well. In Q3, they saw strong user growth in the U.S., Taiwan and Japan. Threads monetization is unlikely to happen in 2025\. But Threads is another “margin of safety” in Meta’s growth trajectory. Given how MAU keeps accelerating every quarter, it is likely they may reach the coveted 1 Billion MAUs sometime in 2026 after which I think Meta will start monetizing Threads. Once this ad inventory becomes available, that’s gotta be bit of a tailwind for Meta sometime in 2026-27. **AR/VR** Meta mentioned their newly launched limited edition Meta Ray-Ban glasses sold out almost immediately and currently “trading” online for over $1,000\. Good signs! VR got very limited attention in the call. Quest 3 will probably do really well during the holiday season, but if this doesn’t lead to better engagement and retention post-Christmas, I suspect Meta may get its “efficiency” religion in VR and re-allocate some resources to move faster so that they can launch “Orion” before Apple gets there. **Capital Allocation** Meta generated $15.5 Billion FCF and returned $10 Billion cash to shareholders via dividend and buyback. They also completed a debt offering of $10.5 billion in Q3. LTM SBC per employee is now at $226k. Mr. Zuckerberg is clearly quite generous with his employees! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6409fc2d-0882-48b5-b402-a2087e9719de_1879x307.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex and Opex** Opex guide for 2024 was narrowed to $96-98 Billion (vs 96-99 Billion) Capex guide range was **again** tightened to $38-40 Billion (from $37-40 Bn in 2Q’24, $35-40 Bn in 1Q’24, and $30-37 in 4Q’23). However, in the first 9 months, they only spent \~$24 Billion. So Meta is going to \~$15 Billion in capex in Q4: > We continue to expect **significant capital expenditure growth in 2025**. Given this, along with the back-end weighted nature of our 2024 CapEx, we expect a significant acceleration in infrastructure expense growth next year as we recognize higher growth in depreciation and operating expenses of our expanded infrastructure fleet. Consensus capex estimate is still $47 Billion. I suspect it’s going to be closer to $50 Billion capex in 2025. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdcaa2a69-7743-49c4-b911-6e74cf03883c_1885x103.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** 4Q’24 topline guide is $45-48 Bn. Mid-point YoY growth is \~15.9% (vs consensus estimates of $46.2 Billion) **Closing Words** At the end of his prepared remarks, Zuckerberg said the following: > This may be the most dynamic moment that I've seen in our industry, and I am focused on making sure that we build some awesome things and make the most of the opportunities ahead. For a change, Meta seems quite well positioned during a potentially seismic shift in the tech landscape. Meta still needs to execute well, but nothing really happens in a straight line. Notes from the follow-up call [here](https://x.com/borrowed%5Fideas/status/1852000815239827865?ref=mbi-deepdives.com). For more in-depth analysis on Meta Platforms, you can read my analysis [**here**](https://www.mbi-deepdives.com/meta2024/) (February, 2024). I will cover **Amazon’s** earnings **tomorrow**. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Alphabet 3Q'24 Update URL: https://www.mbi-deepdives.com/goog3q24/ Last updated: 2024-10-30T03:09:11.000Z Alphabet had a pretty impressive quarter! Here’s my highlights from the earnings. [Subscribe](#/portal/signup) **Revenue** Let’s start with not-so-good news. For the 9th consecutive quarters, Google Network revenue kept declining. Everything else is good news. Despite all the disruption narrative, Search keeps humming along. YouTube is doing okay. But the highlight from last quarter was Google Cloud. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F799e48be-f8d1-4a3f-a944-97c6b9adf644_2005x361.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Google Cloud grew **35% YoY,** **highest** in the last 8 quarters. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F64bb80b7-b5eb-4c8e-9c4d-391cf7bd79b6_1833x235.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Not only did Google Cloud grow at faster pace at a larger scale, they did so at much higher profitability than ever before. After turning profitable in 1Q’23, Google Cloud posted 17.1% operating margin in 3Q’24\. The narrative that Google cannot reliably make money anywhere other than search should be put to rest. After posting its highest ever margin in 2Q’24, Google Services business reached a new margin peak again in 3Q’24\. Thanks to five consecutive quarters of **\>70% incremental operating margin (!!)**, operating margin for Google Services was 40.3% in 3Q’24! Given the qualitative narrative around search in the age of AI, I’m not sure too many people (certainly not me) would predict that Google Service business would keep reaching new heights in terms of operating margin every passing quarter! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd4a5c736-b3f5-446d-b6b4-ed6770f40631_1936x469.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Search** Speaking of search, let me share some interesting quotes on Search from the call: > since we first began testing AI overviews, we have lowered machine cost per query significantly. **In 18 months, we reduced cost by more than 90% for these queries** through hardware, engineering and technical breakthroughs while doubling the size of our custom Gemini model. > > In search, recent advancements, including AI overviews, Circle to Search and new features in lens are transforming the user experience, expanding what people can search for and how they search for it. This leads to users coming to search **more often for more of their information needs driving additional search queries**. Just this week, AI overview started rolling out to more than 100 new countries and territories. **It will now reach more than 1 billion users on a monthly basis.** We are seeing strong engagement, which is **increasing overall search usage and user satisfaction**. People are asking longer and more complex questions and exploring a wide range of websites. What's particularly exciting is that **this growth actually increases over time as people learn that Google can answer more of their questions**. The integration of ads within AI overviews is also performing well, helping people connect with businesses as they search. > > Circle to Search is now available on over 150 million Android devices (**MBI note**: 2Q’24 data was 100 million, so 50 million incremental here over a quarter) with people using it to shop, translate text and learn more about the world around them. **1/3 of the people who have tried circle to search now use it weekly a testament to its helpfulness in potential.** Meanwhile, **lens is now used for over 20 billion visual searches per month.** Lens is **one of the fastest-growing query types** we see on search because of its ability to answer complex multimodal questions and help in product discovery and shopping. For all these AI features, it's just the beginning, and you will see a rapid pace of innovation and progress here. > > AI really supercharges search…with Circle to Search, where we see **higher engagement from users aged 18 to 24**. AI is expanding our ability to understand intend and connect it to our advertisers. This allows us to connect highly relevant users with the most helpful ad and deliver business impact to our customers. > > every month lens is used for almost 20 billion visual searches with **1 in 4 of these searches having commercial intent**. > > …As you remember, we've already been running ads above and below AI overviews. We're now seeing that **people find ads directly within AI overview is helpful** because they can quickly connect with relevant businesses, products and services to take the next step at the exact moment they need. As I've said before, we believe AI will revolutionize every part of the marketing value chain. > > …people are using a lot of buzz words like answer engines and all that stuff. I mean **Google started answering questions about 10 years ago in our search product with featured snippets.** So look, I think, ultimately, you are serving users. User expectations are constantly evolving. And and we work hard to stay a step ahead, anticipate and stay a step ahead. And this is why we've kind of really brought multimodality on the input side and the output side in search pretty natively. Just a lot of very positive data points for Google’s search business. I know management is supposed to highlight the positives, but it’s definitely not getting easier to depict the Search bear case given what Google shared today. You can argue we are still in the early days, but Google Search’s operating performance has so far been much better than most bears would have predicted by mid-2023. **YouTube** In the last 12 months, YouTube's combined ad and subscription revenue has surpassed $50 billion for the first time. Of all the channels uploading to YouTube each month, 70% are uploading shorts. 70 billion YouTube shorts are watched every day. Monetization gap on shorts has continued to narrow. Moreover, Google DeepMind is going to launch its most capable model for video generation which will help creators produce shorts later this year. **Google Cloud** Sundar Pichai had a very good quote on how customers are using Google Cloud products which is worth reading in full: > Customers are using our products in 5 different ways. First, our AI infrastructure. which we differentiate with leading performance driven by storage, compute and software advances as well as leading reliability and a leading number of accelerators. Using a combination of our TPUs and GPUs, LG AI research reduced inference processing time for its multimodal model by more than 50% and operating costs by 72%. > > Second, our enterprise AI platform, Vertex is used to build and customize the best foundation models from Google and the industry. **Gemini API calls have grown nearly 14x in a 6-month period**. When Snap was looking to power more innovative experiences within their “My AI” chatbot, they chose Gemini's strong multimodal capabilities. Since then, Snap all over **2.5x as much engagement** with “My AI” in the United States. > > Third, customers use our AI platform together with our data platform, big query, because we analyze multimodal data no matter where it is stored with ultra low latency access to Gemini. This enables accurate real-time decision-making for customers like Hiscox, one of the flagship syndicates in Lloyd's of London, which reduced the time it took to quote complex risks **from days to minutes**. These types of customer outcomes, which combine AI with data science have led to **80% growth in big query ML operations over a 6-month period**. > > Fourth, our AI-powered cybersecurity solutions Google threat intelligence and security operations are helping customers like BBVA and Deloitte, prevent deduct and respond to cybersecurity threats much faster. We have seen customer adoption of our Mandan power threat deduction **increased 4x over the last 6 quarters**. > > Fifth, in Q3, we broadened our applications portfolio with the introduction of our new customer engagement suite. It's designed to improve the customer experience online and in mobile apps as well as in call centers, retail stores and more. A great example is Volkswagen of America, who is using this technology to power its new IBW virtual assistant. In addition, the employee agents we delivered through Gemini for Google Workspace are getting superb reviews. **75% of daily users say it improves the quality of their work.** I will discuss more on Cloud when Amazon posts next week. **AI** > all **7 of our products** and platforms with **more than 2 billion monthly users** use Gemini models, that includes the latest product to surpass the 2 billion user milestone Google Maps. Beyond Google's own platforms, following strong demand, we are making Gemini even more broadly available to developers. > > We're also using AI internally to improve our coding processes, which is boosting productivity and efficiency. Today, **more than 1/4 of all new code at Google is generated by AI, then reviewed and accepted by engineers.** **Other Bets** > Waymo is now a clear technical leader within the autonomous vehicle industry and creating a growing commercial opportunity. Over the years, Waymo has been infusing cutting edge AI into its work. Now each week, **Waymo is driving more than 1 million fully autonomous miles and serves over 150,000 paid rights.** > > Wing, our drone delivery company recently passed the 1-year university of scale in its partnership with Walmart in the Dallas-Fort Worth area, now **operating in 11 stores and serving 26 different cities and towns.** **Capital Allocation** Google returned capital to shareholders through buyback and dividend almost equivalent to FCF they generated last quarter. Share count declined by 74 bps QoQ. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc9f56f63-8a3c-4311-832a-a77112ff3c54_675x574.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex and Opex** Some good context around Google’s capex: > as you saw in the quarter, we invested $13 billion in CapEx across the company. And as you think about it, it really is divided into 2 categories. One is our technical infrastructure, and that's the majority of that $13 billion. And the other one goes into areas such as facilities, the bets and other areas across the company. Within TI, we have investments in servers, which includes both TPUs and GPUs. And then the second categories are data centers and networking equipment. This quarter, approximately **60% of that investments in technical infrastructure went towards servers and about 40% towards data center and networking equipment**. And as you think about them, we offer both GPUs and TPUs, both internally and to our customers. So we have choices and options based on what our customer needs and what our internal needs are. And as you think about the next quarter and going into next year, as I mentioned in my prepared remarks, we will be investing in Q4 at approximately **the same level of what we've invested in Q3**, approximately $13 billion. And as we think into 2025, we do see an increase coming in 2025, and we will provide more color on that on the Q4 call, **likely not the same percent step-up that we saw between '23 and '24, but additional increase.** For context, Google’s capex is expected to increase by \~55% in 2024 vs 2023, and the consensus estimates imply capex to be $54 Billion in 2025 vs $50-52 Billion in 2024\. So, of course nobody is remotely expecting “same percent step up” but my gut says the fact that they even mentioned it perhaps implies the “additional increase” is probably not just a couple of billions of capex increase in 2025, but rather $8-10 Billion increase. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F065d9ebe-d495-46f9-a07d-060080e87f88_1831x234.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** Google doesn’t provide guidance, but did mention the below during the call: > As we think about the remainder of 2024, there are a couple of dynamics to consider. In terms of revenue, Year-on-year growth in advertising revenue will continue to be impacted by the increase in strength in advertising revenue in the second half of 2023, in part from APAC-based retailers, and there will be a headwind to year-over-year growth in subscription platforms and devices revenue in the fourth quarter due to the pull forward of our Made by Google launches into the third quarter this year. **Valuation** Since [3Q'22](https://www.mbi-deepdives.com/goog3q22/), I share the following valuation framework every quarter. Given I created this table in 2022 which was a very different market than what we have today from sentiment perspective, you can argue this is overly conservative. I’m going to keep it consistent. But I acknowledge the reality that Google not only trades at the lowest NTM P/E multiple among Mag-7 stocks, its multiple is also below S&P 500. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa8e76552-63ba-4ab7-b4fe-9b8618f42299_1831x382.png) Source: MBI Deep Dives Finally, I do want to mention that Google shareholders should be proud to own a company that was the epicenter of AI research for the last decade or so which led to couple of its employees win the Nobel Prize! When I read the news first, I wished I owned Google! There is hardly any doubt that AI is going to have profound impact on our lives, and on society, and no matter which way the stock goes in the next 10-15 years, History will likely remember Google quite positively. I will cover earnings of **Amazon and Meta** this week. Thank you for reading. **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Why I am buying AppFolio URL: https://www.mbi-deepdives.com/appf2/ Last updated: 2024-12-23T14:51:24.000Z Read my updated thoughts [here](https://www.mbi-deepdives.com/appf3/) --- Today, I have started a \~3% position in AppFolio (APPF) at $214.2/share. Back in February this year, I [published](https://www.mbi-deepdives.com/appf/) my Deep Dive on AppFolio. Since then, the stock has underperformed both S&P 500 and QQQ by more than 20%. For the uninitiated, I would encourage you to read the Deep Dive to understand the nitty-gritty details around the business. I will briefly share here what prompted me to buy the stock now. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/10/image-9.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In my original Deep Dive, I wondered if I was being too generous to AppFolio for underwriting \~26% terminal operating margin when it was merely breakeven in 2023\. As the year progressed, I started realizing I was anything but generous. Let me give you more context. In 2022, AppFolio generated $472 Million revenue. Management just guided 2024 revenue to be in the range of $786 to $790 Million. As a result, APPF's revenue has increased by \~67% over the last two years. What really got my attention is their operating expense (R&D+S&M+G&A) is only up \~10%. Everyone knows about near zero marginal cost of software and how beautifully a software company can be scaled, but I think recent silliness around software industry during ZIRP era almost made us oblivious to such simple truths! ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/10/image-1.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While we hear about the tension between growing topline and showing margins for software companies, AppFolio managed to not be constrained with such dichotomy. Their revenue grew by more than 30% in both 2022 and 2023 and this year will likely end at \~27% revenue growth. Moreover, almost all of this growth was organic in nature. APPF just [announced](https://www.globenewswire.com/news-release/2024/10/23/2967815/0/en/AppFolio-Unveils-FolioSpace-to-Transform-the-Resident-Experience-and-Help-Customers-Build-Thriving-Communities-Acquires-LiveEasy-to-Accelerate-its-Vision.html?ref=mbi-deepdives.com) an acquisition of LiveEasy for $80 Million, but before this acquisition, they did their last major acquisition in 1Q'19\. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/10/image-6.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While APPF managed to grow almost \~30% CAGR over the last two years, such growth is extremely unlikely to continue for the next couple of years. As you can see above, 3Q'24 revenue growth YoY came down to 24% and if you take the high end of the management guide, 4Q'24 topline growth will decelerate further to just \~17%. Moreover, if you look at the revenue growth in the first half of 2024, it is easy to see APPF has some really tough comps ahead of them in 1H'25. While I went to a more granular drivers in my Deep Dive, let me simplify here. There are essentially two primary drivers for APPF's revenue growth: a) increasing units under management, and b) increasing Average Revenue Per Unit (ARPU) which is calculated as just revenue divided by average units under management. For much of APPF's history, increasing units under management was their primary driver for growth. However, in the last five quarters, it was ARPU that's driving their topline. One of the key drivers for ARPU growth in recent quarters was eChecks, or electronic checks, which allows tenants to pay rent online. AppFolio started charging **$2.49 per transaction** for all eCheck payments on July 31, 2023\. Right after implementing this, APPF's ARPU growth shot up in 3Q'23 and stayed higher than 20% for the next three consecutive of quarters. As APPF overlapped that quarter now, ARPU growth came down to just \~13% in 3Q'24. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/10/image-11.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Given this context, it is likely that ARPU growth may come down to HSD to LDD in 2025\. The harder question is whether units under management will keep growing at \~10%. Current Consensus estimates imply high-teen revenue growth next year. So, if ARPU grows at \~10% next year, you need units under management to grow at \~8-10% to beat the consensus estimates. Compared to its 2023 units under management, in the first 9 months of 2024 APPF's units under management grew by only \~300k (vs \~500k in 2023, and \~700k in 2022). In fact, one of the concerns mentioned in my Deep Dive was that for APPF to keep its growth momentum, they will have to keep displacing the incumbents in the up market since the end market itself is only going to grow at a very steady pace. I don't have a compelling answer yet in terms of whether 2024 was bit of an anomaly for unit growth or it's the evidence of structural challenges for going up market. There are couple of things that assuage my concerns a bit. After publishing my Deep Dive, I exchanged a couple of emails with one of my subscribers. He mentioned a couple of things that made me much more optimistic about sustainability of revenue growth. While I mentioned I came away a bit unimpressed with AppFolio's unit growth trajectory compared to RealPage (I did give a caveat that lot of the growth was inorganic and I couldn't decipher RealPage's organic growth), he mentioned based on RealPage's disclosure in the past, one could infer they were growing units organically only \~3-4% before they were acquired by Thoma Bravo for $10.2 Billion in 2020\. For context, RealPage was acquired at \~14x LTM gross profit whereas AppFolio today is trading at \~15x LTM gross profit despite growing their revenue **organically** at a much faster rate. He also mentioned overall organic growth for RealPage was still \~10%, thanks to ARPU growth. I'm willing to look past the potentially dicey 2025 consensus estimates and would like to start owning this company for a couple of reasons: a) the structural growth in place remains robust beyond 2025\. ARPU growth can sustain at HSD to LDD rate beyond 2025 once they launch more and more other value added services over time. So I think the probability of mid-to-high teen topline growth for the rest of this decade is likely high. b) There is an element of incentive that I missed in my original Deep Dive which was pointed out by one of my subscribers. [Olivia Nottebohm](https://www.linkedin.com/in/olivia-nottebohm-7095b0/?ref=mbi-deepdives.com) joined on AppFolio's board in 2023\. Nottenbohm is currently the COO of Box. Her former roles include Chief Revenue Officer at Notion Labs, COO at Dropbox etc. She has a very interesting compensation agreement with Reece Duca (the largest and controlling shareholder of AppFolio). Let me directly copy from AppFolio's [proxy statement](https://www.sec.gov/Archives/edgar/data/1433195/000143319524000058/appf-20240429.htm?ref=mbi-deepdives.com): *"Mr. Duca has agreed to pay Ms. Nottebohm 35 percent of the net gain on the equivalent of 35,714 shares of the Company’s Class A common stock each year for the next *seven years*, which amount may be paid in cash or shares of the Company’s Class A common stock. The net gain will be based on a $100 per share starting value and the 10-day average of the final closing price of the shares prior to the date of payment, as reported by The Nasdaq Global Market. *No part of such payment will come from the Company.*"* It is clear that the largest controlling shareholder of AppFolio is very keen to incentivize the board appropriately to keep the company (and the stock) in the right direction. AppFolio is not optically cheap, neither is it trading at nose bleeding territory. But I think the near-term multiple can mask the attractiveness of the stock. The persistence of mid teen revenue growth with high incremental margin can lead to low teen IRR. IRR can improve further if the company ends up doing some consequential acquisitions along the way. I will be interested in buying more if the stock goes down from here. I have uploaded a new excel model on APPF that subscribers can download below. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/10/image-12.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Thank you for reading. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### UMG: The Sound of Music URL: https://www.mbi-deepdives.com/umg/ Last updated: 2025-01-23T06:20:29.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- On a [podcast](https://open.spotify.com/episode/3a1BjRLZ7ZjgqBC8lcfPiL?si=3405352d076b42cc&ref=mbi-deepdives.com) in 2022, [Ted Gioia](https://www.tedgioia.com/?ref=mbi-deepdives.com), who wrote 12 books on Music, aptly captured music's essence in our lives: > “I've always tried to dig into why music is important, Why is it a key part of our society? Why do people like certain songs? And a few years back, I wrote a history of the love song, but I was looking into these hormones that are released in the body when we sing, and a very interesting thing is they make you more trustworthy of the people that are around you. That's why countries have national anthems. That's why labor unions have their songs. That's why sports teams have their songs. And it's also why when you go out on a date, you go on a date to a music club or dancing…the music brings you together with the people you're around. > > …if you don't understand these things, you don't realize how essential and closely connected the musical culture is to other things. I often tell people, many of us are here today because of a song. If our parents hadn't heard a song, we might not be here today.” While music clearly leaves an indelible effect on most of our lives, it has not been easy for capitalism to capture the value created by music. In 1999, global recorded music industry generated $22.2 Billion revenue; it took another two **decades** for the industry to surpass that number! Even though revenue in nominal dollars finally exceeded the former peak revenue in 1999 by the end of 2021, on an inflation-adjusted per capita basis consumers today spend almost **half** of what they used to spend on recorded music in 1999. One can, of course, argue that perhaps we were spending too much on music back then. There was likely little consumer surplus left in late 90s in the music industry as consumers had no option but to spend their hard earned money to buy the entire album of their favorite musicians even if they were only interested in listening to one particular song! This was likely a good deal for the superfans, but certainly quite a suboptimal deal for supermajority of casual fans. Then when Napster came along, pervasive music piracy threatened the very core of recorded music industry since anyone could download almost any music they want for free. It is not a coincidence that recorded music industry revenue temporarily peaked at the very year Napster was founded in 1999\. Record labels such as Universal Music, Sony, and Warner, who owned vast majority of the rights to recorded music at that time, were potentially looking at terminally declining business. Thankfully, Steve Jobs came to rescue the recorded music industry through the launch of iTunes in 2001. As the era of illegal file-sharing threatened to tear down traditional structures in music industry, this new marketplace provided a lifeline, offering listeners a legitimate path to build their own libraries—one song at a time. Suddenly, music could follow fans wherever they went, slipping into pockets and playlists, shaping itself to fit their moods and moments. However, that wasn’t enough; as you can imagine, it is very difficult to compete against free. Recorded music revenue continued its decline for years after the launch of iTunes. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F482c877e-089a-40f8-84d9-341a0b841394_1504x1081.png) Source: [IFPI](https://www.ifpi.org/wp-content/uploads/2020/03/Global%5FMusic%5FReport%5F2023%5FState%5Fof%5Fthe%5FIndustry.pdf?ref=mbi-deepdives.com) While music labels began to unbundle their music offerings by selling songs individually, the simple price tag of a download still failed to capture a tune’s enduring charm. Everything cost the same 99 cents, be it Bob Dylan’s “[Blowin’ in the Wind](https://www.youtube.com/watch?v=MMFj8uDubsE&ref=mbi-deepdives.com)” that you may listen again and again for decades, or some forgettable music that you may only listen once or twice. That was clearly suboptimal for both the listener, and the artist. This paved the way for a new chapter: the era where artists are compensated with each play, where playlists evolve with listeners, and where the world’s music finds a home in the cloud. Services like Spotify reshaped the value of music, making the price of access more appealing than the pursuit of piracy, and transforming digital streams from a niche revenue stream into the industry’s main lifeline. While even ten years ago, streaming was just \~10% of global recorded industry’s revenue, it is now two-third of the overall revenue. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8daa9efd-77af-49e8-97e5-30315ed9d014_1485x1075.png) Source: [IFPI](https://www.ifpi.org/wp-content/uploads/2020/03/Global%5FMusic%5FReport%5F2023%5FState%5Fof%5Fthe%5FIndustry.pdf?ref=mbi-deepdives.com) Music streaming value chain, however, reminds me of credit card transaction value chain. It can be deceptively simple for the consumer; you just pay a fixed subscription to access almost the entire catalog of available music in the world. However, the music streaming value chain can be dizzyingly complex. Before we get to more granular discussion on Universal Music Group, let me dissect the music value chain first. After the discussion on music value chain, the rest of the Deep Dive is outlined below: **UMG Business Overview**: I have covered an in-depth overview of UMG, and the economics of the business. **Bull/Bear Debates**: I have focused on four key debates among bulls and bears: a) The Economics Between Artists and Labels, b) The rising multiple and valuation for catalog music acquisitions, c) The Economics Between DSPs and Labels, and d) Is AI tailwind or headwind for Labels? **Capital Allocation and Management Incentives**: UMG’s capital allocation policy as well as how the management is incentivized is scrutinized in this section. **Model Assumptions and Valuation**: Model/implied expectations in the current stock price are analyzed here. **Final Words**: Concluding remarks on UMG, and disclosure of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### October, 2024 Update URL: https://www.mbi-deepdives.com/october-update-3/ Last updated: 2024-10-02T14:32:08.000Z Just a very quick update for this month. I am currently working on **Universal Music Group (UMG),** and I expect to publish my Deep Dive on UMG by October 25th. Following UMG, I hope to work on **Oracle** Deep Dive next month. For new subscribers, I would like to highlight that you can access all the past Deep Dives, including excel models, [here](https://www.mbi-deepdives.com/models/). You can also follow MBI Deep Dives on [WhatsApp](https://chat.whatsapp.com/HKJLqkvhIkQBgtBdEIf5jV?ref=mbi-deepdives.com). Thank you for your support. [Subscribe](#/portal/signup) ### Veeva: Durable Vertical Cloud Platform URL: https://www.mbi-deepdives.com/veev/ Last updated: 2024-09-25T18:42:24.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- Peter Gassner, founder and CEO of Veeva Systems, apparently had a deep aversion to following the herd from his early childhood. True to his instinct, while in first grade he let his parents know that he doesn’t want to learn how to read. Instead of panicking, his mother was unperturbed: *“That's fine. You know, I never met a 18-year old who didn't know how to read. I'm sure you'll get there.*” More than three decades later, Gassner came up with another seemingly crazy, non-herd following idea: *“I'm going to make something very specific to an industry, very specific to an industry in the cloud.”* Remember, this was a time when people were skeptical of long-term future of cloud itself, let alone a vertical or industry-specific cloud platform. Gassner’s friends and acquaintances were less receptive with his contrarian hunch than his mother which only emboldened his belief that there may be a big market for catering to a specific industry, especially one with a byzantine of regulation and compliance requirements such as Life Science. With Global Financial Crisis (GFC) in the backdrop, there certainly wasn’t easy money sloshing around waiting to fund anyone’s contrarian thoughts. After helping Salesforce IPO in 2004, Gassner, who was SVP of Technology at Salesforce, left the company in 2005 and started seriously pondering about building a vertical cloud platform for life science industry. Gassner raised just $3 million in the seed round to found Veeva in 2007\. He raised another $4 million in 2007 before going for IPO in 2013\. Even though they raised $7 million before IPO, they apparently burned only $3 million, and the company self-funded itself through internal cash generation to become \~$36 Billion market cap company today. In fact, Veeva posted **+23% GAAP operating margin** in the year before they went for IPO! ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F73134275-4b9d-4219-b35e-c7fc9d752be6_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) In an era of tech companies burning cash year-after-year to get to the ever elusive promised land sometimes decade(s) later, Gassner truly was a contrarian through-and-through. In Gassner’s [words](https://www.saastr.com/veeva-biggest-vertical-saas-success-story-time-video-transcript/?ref=mbi-deepdives.com): > The thing is, when we started out, I had a lot of pressure from people, including early investors and stuff to **spend more, spend more, spend more**. I had a lot of pressure on that. I was the guy who just didn't get it. I just didn't get it. > > They were saying, “Spend more,” and they were convinced, I was the guy who just didn't get it. I was too conservative, but that's OK. You get all kinds of outside pressure, and you have no chance for being great if all you're doing is following the herd. > > I thought it wasn't right. I thought, “No, I think it's good to have top line and bottom line, and I don't want to take any more money, because then I'll dilute the company. Why would I do that?” > > …Gosh, if we're building a lasting company, a lasting company should have a reasonable profit margin. > > I always thought, **“God, it's sort of like a drug. If I get on that non profitable drug, like when am I going to get off of it and stuff.”** It just seemed like, “No, you don't do that. That's not the way to do it. Given that all it took just burning $3 Million to generate positive cash flow, it may be tempting to think the business was sailing from the very beginning. But as it goes in any business, the early days were typically challenging. When Gassner spoke with potential customers about their CRM product, they all were uninterested. However, he did pick up from those conversations that the customers weren’t quite “emotionally attached” to their existing solutions either. The very first customer Veeva got actually chose Veeva for completely uneconomic reason. The CEO of the customer company just wanted to buy some software to remind his IT team who was actually in charge. Veeva’s first actual consequential customer was Pfizer. While meeting with Pfizer to discuss Veeva’s offerings, Gassner was met with skepticism as someone from Pfizer wondered how a startup would deliver a complex software product when Veeva had fewer employees than the number of Pfizer employees in that very meeting. Veeva was, nonetheless, able to convince Pfizer that they mean business. When Pfizer signed the deal with Veeva, they paid $3 million cash in upfront payment and thanks to this deferred revenue that leads to positive cash flow cycle, Veeva quickly figured out that they can grow without much dilution if they can just keep executing and delivering their promises to their customers. So, what does exactly Veeva’s products do? Veeva specializes in industry-specific cloud-based solutions—including data, software, and services—to the global Life Sciences industry. Veeva started its journey with their customer relationship management or CRM solution for life science sales representatives enabling a broad range of industry-specific functions such as drug sample tracking with electronic signature capture, healthcare affiliations management, and the ability to conduct interactive, rich media demonstrations with physicians on a mobile device, with or without an internet connection. While Veeva initially built its CRM platform on top of Salesforce (more on this later), Veeva decided to build its own platform when they branched out of CRM. Veeva launched “Veeva Vault” in 2011 which is their regulated content management and collaboration solution that enables the management of complex, content-centric processes during clinical trials and managing the workflows and approvals for promotional materials, in compliance with stringent government regulations. Like its CRM product, Vault also became a huge success for Veeva. Veeva’s revenue increased by more than 10x from just $210 million in 2013 to $2.4 Billion in 2023\. What is remarkable is there was a very detailed short [pitch](https://seekingalpha.com/article/1873601-veeva-systems-saass-biggest-bubble-and-techs-best-short?ref=mbi-deepdives.com) by Suhail Capital on Veeva back in 2013 casting a lot of doubt on Veeva’s TAM (To Suhail Capital’s credit, they [understood](https://seekingalpha.com/article/3823526-veeva-why-went-long-former-favorite-short?ref=mbi-deepdives.com) their mistake and ended up going long Veeva in 2016): > total addressable market for all three Veeva products is currently somewhere between $1 billion and $2 billion. **The market cap of Veeva is now 3x-6x the entire annual revenue opportunity of its industry!** Despite Veeva being down \~33% from its peak in 2021, the stock still 5x-ed since its IPO in October 2013\. Veeva remains very much focused on life science industry, but they have also started growing their ambition beyond life science industry. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff471ea6f-33ee-4995-a186-23c437c18552_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for the rest of this Deep Dive: **Business Overview and Economics:** I discussed a brief company as well as product overview in this section. The economics of the business is also explained here. **Competitive Dynamics:** I elaborated on Veeva’s total addressable market and competitive advantages against point solutions. Recent competitive dynamic, especially against Salesforce is also a key focus in this section. **Capital Allocation and Management Incentives:** Veeva has a challenge that many companies probably would like to have. They generate too much cash to deploy which makes capital allocation perhaps one of the key questions for the shareholders today. I discussed the implications here, and highlighted management incentives. **Model Assumptions and Valuation:** Model/implied expectations in the current stock price are analyzed here. **Final Words:** Concluding remarks on Veeva, and disclosure of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Some more thoughts on Dollar General URL: https://www.mbi-deepdives.com/dg_sept2024/ Last updated: 2024-09-10T14:30:14.000Z After Dollar General’s (DG) disastrous earnings couple of weeks ago, I was quite concerned about their prospects in the near-term. However, a week later after digesting through Dollar Tree (DLTR) as well as DG management’s explanation in the Goldman Sachs Retailing Conference, I have updated some of my thoughts about DG…in the positive direction. Nonetheless, I have decided to abide by my decision **not** to inject more capital to DG, but to increase my notional exposure to DG via long-dated call options. As a result, I now no longer own any share of DG but do own January 2026 $45 Calls for which I have paid $36.8 per share. To be more specific, I still have similar $ exposure to DG as I did before, but doubled the notional exposure now thanks to these call options. Before I discuss what prompted me to be willing to increase my notional exposure to DG, let me start with the acknowledgement that there are indeed plenty of question marks on DG. When a stock is down \~67% from its peak, it should not be surprising that there are some concerning developments for the business. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa9f2ecad-63fc-4c12-90a0-f97c2fd7542e_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://app.koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) There are two distinct vector of concerns: **a) Walmart (WMT)**: while WMT and DG have co-existed and thrived for decades, after WMT’s **14 consecutive quarters** of faster Same Store Sales (SSS) growth- it is certainly a fair question to wonder whether something structurally has changed. . ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F61e048e4-07a2-44ed-9434-8305f2276730_1804x142.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As Alex Morris from “[The Science Of Hitting](https://thescienceofhitting.com/p/the-dollar-store-downturn?ref=mbi-deepdives.com)” (TSOH) discussed today, I agree that it is clear WMT has been outexecuting DG in the last three years or so, but the impact of WMT may largely have been confined to the marginal trip and not necessarily a structural question on the DG business model. From TSOH: > I think we need to answer a fundamental question: **what value does DG provide to its customers**? The primary answer, especially in the \~16,000 stores located in small towns (population of less than 20,000), is convenience. **As a reminder, the majority of their products cost under $5, with an average transaction value around $16\. (To put that into context, free delivery through Walmart+ is only available on $35+ orders.)** The mix is also heavily weighted to consumables, which account for >80% of DG’s revenues. > > Put differently, I think most Dollar General customers walk in the store to pick up a handful of products, with the need for (consumption of) those products likely to be in the **immediate future**. Given this context, I’m not quite losing my sleep over DG’s **consumables** business…yet! The value proposition of convenience of DG stores is likely to remain relevant for many years to come, something that can be easily underappreciated by people living in the urban areas with plenty of retail alternatives to choose from. Moreover, DG management reiterated that they primarily gain share from drug stores and grocery stores and there is likely healthy amount of share left by those stores, so I don’t think DG needs to necessarily win a food fight against WMT in the near to medium term to get back to \~3%+ SSS trend in consumables next year. From DG management: > What we have noticed over the years is that our share gains have been coming no surprise, and we've been very vocal about it from drug first and the grocery sector second. Normally, what you find from those 2 cohorts of retailers is a middle to upper middle and even lower and upper income demographic. And that, on a quarterly basis has been for, gosh, probably the last 10 years, that customer, at least in my mind, has been up for grabs, right?…So when I look at Q2, while our core customer was very stable, $30,000 and under, what we saw different from Q1 to Q2 was that while we gained share in that middle income cohort, we gained it at half the rate we did in Q1\. And it was obvious to us through the data where the other half went and it went to mass. And I think we called out the guys in Bentonville, took a little bit larger piece of that. b) **Amazon/Temu**: As earlier discussed in my [earnings update](https://www.mbi-deepdives.com/dg2q24/), I do have plenty of sympathy for structural concerns when it comes to non-consumables business. After 10 consecutive quarters of decline in LTM non-consumable sales, it is difficult not to see some impact of Amazon and Temu here. There are some [data](https://www.earnestanalytics.com/insights/all-posts/temu-takes-share-of-wallet-from-dollar-general-dollar-tree-customers?ref=mbi-deepdives.com) out there that also seem to substantiate such impact. This is indeed a real concern and unless the trend reverses, it may act as an insurmountable barrier to get back to DG’s long-term operating margin of \~8-9% (vs current \~5%). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fccb76dc5-3b6f-46a5-879d-386347dc4527_1942x162.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) One counter argument to this concern is although declining non-consumables certainly pressured gross margins in recent quarters compared to what we have seen in 2020-21 period, gross margin still looks pretty similar to pre-pandemic era. Given DG reported 8.3% operating margin both in 2018 and 2019 despite reporting somewhat similar gross margin to recent quarters, the mix shift from non-consumables to consumables may be more manageable than many suspect. The key difference between 2018-19 and now, of course, is SSS. While DG is guiding SSS to be 1.3% (mid-point) in 2024, they reported >3% SSS in 2018-19 period. Therefore, SSS remains the most important KPI for the stock. So, the question is can this KPI go in the right direction? ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F246ee6c1-4d0a-4cc3-9cb6-5afa7205bb15_2127x103.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) DG’s SSS increased by only 0.2% in 2023 and is expected to grow by +1.3% in 2024\. Therefore, my first observation here is DG will be up against some pretty soft comp next year. Moreover, given Family Dollar (FDO)’s continued struggle, it is highly likely that we will see an acceleration of store closure to shut down the underperforming stores. As a [reminder](https://www.mbi-deepdives.com/dg/), almost half of FDO stores is within 5 miles of DG store; therefore, DG should be a beneficiary of such a strategy by FDO and should help DG lift SSS a bit next year or two. Perhaps more importantly, it is the macro commentary by DG (and DLTR) management that makes me think DG is a very, very compelling bet especially in my personal portfolio context (more on this later). Here’s what DG management said at GS conference last week: > as we look at the quarter, what we saw was a pretty drastic slowdown in change. And it happened suddenly, I would say, mid-quarter-ish if you will. And what led us to start looking even deeper at, is this macro? Is this something internal? Is it a combination? We started to take a look at a couple of different factors. > > One being, is it broad-based? And with 20,000 stores, it's an advantage, right, because you're coast to coast. And **if it's something macro, normally, you start to see that across the country and it was definitely that. It happened across every region, every division that we had almost the same amount**. > > Second thing was we looked at was well, **is it happening in your new stores?** Well, again, we're advantage there with opening as many new stores as we've done. And **sure enough, our new store base all sort of ships went down in the harbor at the same time as well**. So those are great leading indicators. > > And then lastly, what we noticed was an even tighter core consumer at the very last week of each of the months in Q2\. While that's always a tighter week of the month for our core consumer, it was by far, though, the weakest in each of the -- when you look at each of the 4 weeks of each of the period. So, that led us to believe it's more macro in nature. While we still have a lot to do in our back-to-basics work, I would tell you that we believe that the macro effect of what we're seeing in our core customer is starting to take effect on her. > > …our core customer normally works her 30 to 40 hour a week job but also has a secondary job that she normally works 15 to 25 hours in. What she told us in Q2 was that is going away or has gone away. > > …it looks like the economy is slowing at a pretty decent cliff, at least what we're seeing here from the customer base. And **that natural progression that we see as she moves, that middle income, all roads lead through Bentonville and usually go to Walmart first**, right? And then -- and here's the key. The key is, the trade down in there is this, the customer says, I'm trading in because I'm fleeing, I'm looking for value, right? > > And I think you heard other retailers talk about that. **The next shoe to drop normally is not I'm seeking value, but I must have value. I've got to make ends meet, where our core customer is today. And then when that happens, that usually then that customer trades into Dollar General**. Given how the stock has traded in the recent months, investors seem to be deeply unwilling to give much credence to management’s explanation, and while I have not been a fan of Todd Vasos (CEO of DG), I happen to find the explanation more reasonable than market likes to think today. Not only DLTR has echoed similar concerns, dollar stores’ history also encourage me to be a bit more optimistic here. I have been discussing some of these aspects with Alex Morris over the last week, and he already aptly explained the historical context in his [write-up](https://thescienceofhitting.com/p/the-dollar-store-downturn?ref=mbi-deepdives.com) today. Let me quote from his piece: > In February 2008, Dollar Tree reported [its Q4 2007 results](https://www.sec.gov/Archives/edgar/data/935703/000093570308000004/ex99%5F1.htm?ref=mbi-deepdives.com). Reported comps declined \~1%, a notable change in trend from prior periods. As CEO Bob Sasser noted at the time, the results reflected “continuing pressure on the consumer from a generally challenging economic environment”. Mr. Market wasn’t too pleased with that explanation: the stock, which had traded up to \~$15 per share in mid-2007, was down >50% by early 2008 (split adjusted). > > [But then the results started to improve](https://www.nytimes.com/2009/05/02/business/02dollar.html?ref=mbi-deepdives.com). As you can see below, despite facing intensified macro pressures during the heart of the financial crisis, DLTR started reporting mid-single digit comps. > > …The stock, which bottomed well below $10 per share in early 2008, was trading at \~$25 in early 2011\. (As an aside, it’s interesting to note that DG’s comp trajectory during this period was quite similar: some weakness [in late 2007 and early 2008](https://investor.dollargeneral.com/websites/dollargeneral/English/310010/us-sec-filing.html?secFilingId=97952e49-89d3-4269-97de-6e701ce37252&shortDesc=Securities%20Registration%20Statement&format=convpdf&ref=mbi-deepdives.com) followed by stellar results for full year 2008 / 2009.) > > …At the time, I’d be willing to bet that analysts and investors questioned whether that answer fully explained what was going on, particularly given that a notable competitor like Walmart [was still reporting solid results](https://www.sec.gov/Archives/edgar/data/104169/000119312508033081/dex991.htm?ref=mbi-deepdives.com): “We had a very strong underlying operating performance, exceeding our expectations for the quarter… The price leadership strategy we put in place at the beginning of the year was exactly the right strategy… **Price leadership and improved customer service made the difference**.” To the extent DG management is right about macro potentially deteriorating from here, almost all of my portfolio holdings will likely take at least a temporary hit. Most DG investors prefer exposure to dollar stores precisely because of their countercyclicality. Given the recent missteps, this exposure has unfortunately come at a heavy price. I do suspect, however, that it is much more likely than ever that we may be on the cusp of some much needed countercyclical exposure such as DG. Consensus estimates for 2025 and 2026 operating margins are 5.0% and 5.3% respectively. If DG comes back to 3%+ SSS for the next couple of years, it is very much conceivable to me that actual operating margin may turn out to be \~150-200 bps higher (which would still be \~150-200 bps lower than DG’s long-term operating margins). If we do see return to \~6.5-7% operating margin and multiple re-rates to \~15-16x P/E, the stock can almost double in a couple of years. I don’t think any of my portfolio holdings will double in a couple of years if we face a recession during this time. As a result, despite my concerns about DG, I have decided to ensure that I have appropriate notional exposure to DG. At the same time, I do feel a strong aversion not to inject more capital to water my weeds which is why it still just remains a \~3% position. Thank you for reading. **Further reading**: My [Deep Dive](https://www.mbi-deepdives.com/dg/) on DG (August, 2023) [Subscribe](#/portal/signup) ### September, 2024 Update URL: https://www.mbi-deepdives.com/september-2024-update-2/ Last updated: 2024-09-04T01:06:48.000Z First things first, I am currently working on **Veeva Systems** (Ticker: **VEEV**) which I expect to publish by 25th of this month. Exactly four years ago, I launched MBI Deep Dives to, frankly speaking, help pay off my student loan. Not only did it help completely pay off my loans, it has ended up becoming my full-time livelihood. I have published [**50 Deep Dives**](https://www.mbi-deepdives.com/models/) over the last four years and I hope to continue to write one Deep Dive every month for many years to come. I also would like to let you know that I have started a [**WhatsApp Community**](https://chat.whatsapp.com/HKJLqkvhIkQBgtBdEIf5jV?ref=mbi-deepdives.com)**.** I expect to share more regular updates and thoughts on the companies I cover via this channel. For example, Dollar General, Analog Devices, and Texas Instruments will appear on different sell-side conferences this week. Publishing a post on the website about these sell-side conferences seems to be an overkill, and I'm hoping this channel will be a bridge to share bits and pieces from different earnings calls, conferences, or interesting articles that I read. Thank you for your continued support. I appreciate it very much. [Subscribe](#/portal/signup) ### Lululemon 2Q'24 Update URL: https://www.mbi-deepdives.com/lulu2q24/ Last updated: 2024-09-01T14:44:31.000Z *Disclosure: I own January 2026 $165 LULU Call Options* It is far from common for a stock to be up 4% after missing the revenue guide for the quarter **and** slashing the full-year revenue guide. That’s exactly what happened with Lululemon today which should tell you the kind of sentiment going into the earnings! Here are my highlights from tonight’s call. [Subscribe](#/portal/signup) **Sales Growth by Region** The crux of the bear thesis on Lulu usually circles around their US business. After growing sales +2% YoY in 1Q’24, it was **flat** in 2Q’24\. So, I don’t quite expect bears to bow out unless and until Lulu’s US business dispels their concerns. We are clearly not there yet. More on the US business later, but looking at Canada which is Lulu’s most mature market, I continue to be optimistic that the brand hasn’t peaked in the US. While China revenue grew by +37% YoY this quarter, skeptics might think the deceleration from last quarter (+52% YoY) is also a bit concerning. A good counterargument is the Chinese New Year was in Q1 this year which lifted the growth last quarter. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3b3da6ea-cb6d-4e55-b3a3-a5ae241034fe_1165x472.png) Source: Company Filings Management mentioned in the US *“traffic was up across both channels and Google search queries remain positive”,* andreiterated \~5% sqft growth per annum in Americas through 2026\. They also reminded that comps for the US will be easier next year; my guess is if Lulu can deliver HSD to LDD growth next year in the US, much of the bear concerns will likely evaporate. While the comps will be easy, the big question remains whether the macro will be supportive of such growth trajectory next year. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F03a4ba78-6415-414c-aa08-f6e35910cbf5_987x282.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) After four quarters of YoY decelerating sqft growth trend, 2Q’24 quarter saw a slight uptick. Although this is nice to see, please note Digital was \~38% of the revenue. So, unlike most physical retailers, Lulu story is not necessarily hinged on sqft growth. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F538def64-a8ba-4e92-869a-e20ebea0e162_1243x631.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Margin** Another popular bear case circles around Lulu’s margins which many suspect Lulu over-earned during the pandemic and now that consumer is weak and Lulu is facing intensifying competition from the likes of Alo/Vuori, Lulu will be compelled to defend its turf by sacrificing margins. So far, we don’t see this thesis playing out in the numbers. Americas margins was flat YoY, China was +229 bps YoY, and Rest of the World (RoW) posted +334 bps YoY operating margin **improvement**. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3419f6af-385b-44c4-ac82-185225ecfffd_603x261.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Lulu’s gross margins used to be in the low 50s during 2014-17 period. Lulu posted **its highest ever gross margin in 2Q’24**. Similarly, Lulu also posted **its highest ever 2Q operating margin this year**. For a retailer that’s experiencing some hiccups in the topline and typically have material fixed cost, it is quite impressive that Lulu has been able to pull this off. While I understand the appeal of that bear thesis on margin contraction, there is **so far** scant evidence on this point. We’ll see if that materially changes going forward. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F55d35b1a-f92f-444f-8e0d-113fc0d09feb_1407x790.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb3e31fc6-5444-4f8a-80cf-d5c841c7d6a7_1197x673.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Sales by Gender** One area that can embolden bears is the mediocre results in women’s segment which has come down to a meagre MSD growth in 2Q’24\. Men’s and accessories/others continue to chug along just fine, but women’s segment (\~62% of overall sales) is clearly bit of a headache at this point. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6e5996fc-0b05-47bb-9e26-9b86a44634f1_1074x259.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) So what’s going on with women’s segment? Here are some quotes from the call discussing this segment: > While we continue to see growth in our men's business, we have experienced a slowdown in women's. We have improved our in-stocks in smaller sizes through Q2 and are entering Q3 better positioned. As we've analyzed our women's business in more detail, **we have determined the most significant factor was a product plan that introduced less newness across core and seasonal styles**. By newness, I'm referring to the seasonal updates we bring into the assortment, typically expressed as color, print, patterns and silhouettes. > > I'm not referring to our pipeline of innovation, which remains full…As we have learned more, it's become clear to us that this **reduced newness, which is below our historical levels and stems from earlier product decisions has impacted conversion rates given the fewer new options available to our female guests. While this reduction was seen across our women's assortment, it had a more pronounced impact in bottoms and in our online channel**. The newness that we had performed well, we simply did not have enough to inspire her to purchase. > > …Guest was coming in traffic was positive across all channels and the opportunity was in conversion. > > So I see that as an opportunity that they were there with intent to spend, and there was a noticeable reduction in those historical levels of newness. So those were the product decisions that we made earlier and the new teams in action. And as I alluded to the chase, but definitely, I think majority is within our control. > > …For 2025, we are fast-tracking several new styles within performance shorts, tops and track suits. **We are optimistic that we will begin to see the benefits of these strategies over the upcoming quarters and return to our historical levels of newness no later than spring 2025**. It is *really, really* disappointing to see management score such an own goal. For the two consecutive quarters, we are hearing about this lower conversion rates. I think much of this problem emanates from not what they did this quarter, but due to their poor preparation over the course of last few quarters. I should note about the recent debacle related to Breezethrough which Lulu pulled away after noticing design complaints from their customers. Management mentioned it had immaterial impact on revenue or inventory and since customers really liked the fabric (but not the design), they’ll reintroduce the fabric with a different design in 2025 (or later). The problems management has outlined here made me wonder whether management fell asleep at the wheel here. I harbor mostly positive opinions about Calvin McDonald (just see pre and post Calvin era growth trajectory until this year), but I don’t think McDonald can afford to have any more mishap without attracting activist shareholders. The good thing is McDonald will have the power of Lulu brand to give him some time to course correct; despite management’s recent spotty ability to serve their core customers, Lulu continues to acquire new customers: > In terms of the guest profile, nothing meaningful in that we continue to grow our new guest base and continue to do it across the demographics that we have been growing. **Inventory** Inventory declined by 14% YoY and LULU expects it to increase by mid-teens in Q3 and slightly higher in Q4. **Capital Allocation** While management will get probably a “D” grade from me in terms of operational execution in the last couple of quarters, I am at least pleased to see that as the stock kept going down, they increased their buybacks. While Lulu mentioned they repurchased $1.2 Bn YTD, their 10-Q shows they bought back $889 Mn until Q2 which implies they bought back another $311 Mn so far this quarter. Assuming $260/share, it means they repurchased 1.2 mn shares in August i.e. \~1% shares. (**MBI Note**: updated this section after going through the 10-Q) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4a1330f0-0bb4-4566-9b76-2fa2d7361e8c_1195x220.png) Source: Company Filings ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffd07a2b8-d638-4827-aad2-35c2ca1e7643_1185x214.jpeg) Source: Company Filings Thanks to these buybacks, LULU’s shares outstanding has gone down by \~2% in 2Q’24. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8e29c6e7-2049-44eb-9cd6-b93deb61f0f5_963x439.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** As mentioned earlier, Lulu guided down for the full-year. 2024 revenue outlook is now $10.375-10.475 Bn (down 3% from prior guide). The reason for guiding down was macro uncertainty. Management mentioned in Q2, May was more in line with Q1 trend, then it deteriorated in June, but July was slightly above June. Some more colors around the guide below: > …we are assuming that revenue trends in the second half of the year remained fairly consistent with Q2, when excluding the 53rd week and the impact of a shorter holiday shopping season in Q4 > > …For the full year, we now expect gross margin to be approximately 20 basis points below our adjusted gross margin in 2023, due prominently to deleverage on fixed costs associated with lower forecasted sales and an increase in freight costs relative to our prior estimates. We continue to expect markdowns to be relatively flat with last year. Turning now to SG&A for the full year. We now expect it to be approximately flat versus 2023\. When looking at operating margin for the full year 2024, we now expect a decrease of 10 to 20 basis points versus adjusted operating margin in 2023, which expanded 110 basis points versus 2022. **Final Words** As mentioned earlier, unless and until Lulu starts posting healthy topline growth in the US, it will likely prove difficult for the stock to take a decisive upward trajectory. Over the long run, I do think international growth will be a material driver for the stock, but in the next 12-18 months, Lulu’s fate will likely hinge on the trajectory of their US business. I have no plan to add/trim following this quarter. More reading on Lululemon: [here](https://www.mbi-deepdives.com/lulu/), [here](https://www.mbi-deepdives.com/apr2024/), and [here](https://www.mbi-deepdives.com/tsm/) (see section 5) Thank you for reading. [Subscribe](#/portal/signup) ### Dollar General 2Q'24 Update URL: https://www.mbi-deepdives.com/dg2q24/ Last updated: 2024-08-29T17:33:17.000Z *Disclosure: I own shares of Dollar General* I first wrote my Deep Dive on Dollar General back in [August 2023](https://www.mbi-deepdives.com/dg/). While I wasn't initially excited about owning a piece of the company despite the stock being down 40% from peak then, I changed my mind when the stock went down another \~25% following 2Q'23 earnings. After today's dismal earnings, the stock went down almost another 30% since 2Q'23 earnings. If you're counting, the stock is now down \~67% from its peak in October, 2022\. From listening to today's call, I think it is likely that I underestimated the depth of challenges Dollar General was heading towards. Here are some highlights from today’s call. [Subscribe](#/portal/signup) Let's start with DG's assessment on the **low-end consumers** which is DG's core customer base: > From a monthly cadence perspective, same store sales growth was strongest in June before turning negative in July**. Notably the three softest comp sales weeks of the quarter were the last week of each of the calendar months.** This pattern suggests that our customers are less able to stretch their budgets through the end of the month. With that in mind, as well as our continued softness in discretionary sales and our own customer data and survey work, we believe the softer than anticipated sales performance in Q2 is at least partially attributable to a core customer that is less confident of their financial position. > I want to provide some additional context around what we're seeing and hearing from our customers. The majority of them state that they feel worse off financially than they were six months ago. As higher prices, softer employment levels and increased borrowing costs have negatively impacted low income consumer sentiment. As a result, our core customers who contributes approximately 60% of our overall sales comes predominantly from households earning less than $35,000 annually. Inflation has continued to negatively impact these households with **more than 60% claiming they have had to sacrifice on purchasing basic necessities due to the higher cost of those items**. > In addition to paying more for expenses such as rent, utilities and health care, more of our customers report that they are not resorting to using credit cards for basic household needs and **approximately 30% have at least one credit card that has reached its limit**. And in our latest survey, **25% of our customers surveyed noted they anticipated missing a bill payment in the next six months**. While middle and higher income households are seeking value as well, they don't claim to feel the same level of pressure as low income households. > As customers have felt more pressure on their spending, we have also seen corresponding elevation in the promotional environment beyond what we have anticipated coming into the year. > ...we are increasing our investment in markdown activity in an effort to support our customers, further drive customer traffic and improve sales. While some of these data are helpful in gauging the challenges low end consumers face today, it would be **more** helpful if we could get a time-series data (e.g. what % of consumers claimed they had to sacrifice purchasing basic necessity last year vs today?). My guess is the broader point management is trying to drive would still stand but we could glean more insight in terms of the trend. But wait a minute; isn't DG supposed to benefit in tough economic environment as middle-class customers tend to **trade-down** during such period. DG management had a good explanation why we are not seeing that...yet: > First of all...it takes a few quarters to come out of that, meaning our core customer. What we also see is **it takes a quarter or more for the trade-in to come in at a higher rate**. Now, in saying that, **what we've noticed is a trade-in has been slower to come in to the channel than what we had anticipated and/or have seen in the past**. I believe that's – there's a couple reasons why. I think the main reason and I believe this is true because it appears in every piece of data that we have is that the job market is still pretty decent, right. It's not as robust as it was. But also, unemployment hasn't spiked greatly, if you will, in the last quarter or so. **Normally it takes that jolt to get the trade-in to come in at a heavier clip**, if you will. > Now, the middle and the upper middle income are still looking for value. So I don't want you to believe that they're not. But usually to get them to trade in at a higher rate usually takes something a little bit more substantial than we've even seen to occur. I'm not suggesting I want to see that happen to that customer, but we stand ready and willing to certain her when that happens. **The other thing that we've noticed is that more and more online activity comes from that cohort. Our core customer continues her online journey pretty much the way she was. It's pretty static, if you will. But we've noticed that middle to upper middle continues to rely on online a little bit more. And so as that occurs, I believe the trade-in slows a little bit on that side**. So it's incumbent upon us to take a look at how we offset that piece as well. **Same Store Sales (SSS)** After a more encouraging SSS of +2.4% 1Q'24, SSS decelerated to just +0.5% in 2Q'24\. Traffic remains positive at +1% YoY (vs +4% YoY in 1Q'24) which was offset by transaction amount -0.5% YoY. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/08/image-3.png) SSS increase was driven entirely by consumable category and was partially offset by declines in non-consumables (home, seasonal and apparel categories). After non-consumables grew faster than consumables during 2020, it was almost 12 consecutive quarters of sales decline YoY in non-consumables (excluding 4Q’22)!! ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/08/image-2.png) There are potentially important implications for such persistent decline in non-consumables. LTM non-consumable sales peaked in 1Q'21 at \~$8.2 Bn. In 2Q'24, LTM non-consumables **declined by \~$1 Bn** since then; in the meantime LTM SG&A **increased by $2.4 Bn** since then. Before the pandemic, non-consumable sales and DG's overall SG&A almost mirrored each other in 2019\. During the pandemic, thanks to the stimulus money, non-consumables soared and while the initial slowdown in non-consumables was interpreted as just Covid hangover, the relentless decline quarter after quarter should make everyone wonder whether something has structurally shifted in the post-pandemic period. It is not just Temu, but even Amazon's one-day shipping promises may have structurally damaged DG's non-consumable business. While they can still hold onto their core customers through consumables business, e-commerce may be taking bites on DG's attractive profit pool of non-consumables. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/08/image-10.png) **Shrink** Shrink was 21 bps headwind in Q2 which was in line with DG's expectations and they expect it will turn to a tailwind as they move into Q4 and then much more substantial of a tailwind into 2025\. **Gross Margin** Speaking of profit pool, DG's gross margin declined by 112 bps YoY, attributable to increased markdowns, increased inventory damages, a greater proportion of sales coming from the consumables category and increased shrink. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/08/image-4.png) **Operating margin** Again, since non-consumables is higher gross margin segment, the decline of this segment is a persistent headwind to margins. And since thanks to inflation, labor costs are increasing. As a result, DG is finding itself in a pretty tough spot. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/08/image-5.png) **Inventory** Inventories stood $7 Bn in 2Q’24, -7% YoY and a decline of 11% on a per store basis. Non-consumables inventory was -13% YoY and -17% on per store basis. **Outlook** In light of the tough environment DG has seen in the recent months, DG has **lowered its annual EPS guide by almost 20%**: > we now expect net sales growth in the range of approximately 4.7% to 5.3% and same store sales growth in the range of approximately 1% to 1.6% (**MBI Note**: from earlier guide of 6-6.7% sales growth and SSS of 2-2.7%) > Turning to gross margin, we expect additional pressure as a result of the increased promotional markdown activity that Todd noted as well as increased sales mix pressure due to the customers' need to prioritize their spending on the consumables category > Within SG&A, we are seeing an elevated rate of maintenance expense particularly with HVAC units and coolers in the summer months. We're taking steps in the back half of the year to be more proactive in addressing these opportunities in order to provide a more consistent customer experience across our store footprint while also supporting ongoing sales growth. As a result, we expect incremental pressure from the increased repairs and maintenance expense to continue within SG&A in the back half of the year. > Finally, we are also seeing pressure from wage rate inflation closer to **approximately 4% this year**, which is higher than was contemplated in our initial guidance for the year. With all of this in mind, we are updating our EPS guidance and now expect to deliver EPS in the range of approximately $5.50 to $6.20\. (**MBI Note**: from earlier guide of $6.8-7.55, so EPS is revised down by almost 20%) > ...this guidance really assumes more of a macro neutral to slight softening of that consumer. So the low end of the guidance takes that into consideration. > So on that lower end range, we're looking at a comp similar to the comp that we had in the second quarter whereas the higher end of the range would assume that there's some acceleration. **Final Words** This really has been a brutal quarter for DG and as a result, it is not a surprise that the stock is taking a beating. Given the context of relentless decline in non-consumable sales and the implications for margin, it is unfortunately difficult to add to the stock despite the stock being down \~30% today. And if the trend in non-consumables is more secular in nature than a short-term challenge, DG's operating margins from yesteryears may prove to be incredibly difficult to get back to. Therefore, I do not plan to add further capital to DG; I will, however, stay invested as I think the stock can still prove to be a potential hedge if the softness in low-end consumer ends up spreading to middle class in the coming quarters. **Further reading**: My [Deep Dive](https://www.mbi-deepdives.com/dg/) on DG (August, 2023) Thank you for reading. I will cover Lululemon's earnings tonight. ### EssilorLuxottica: From Eyecare To Eyewear To Smart Glasses URL: https://www.mbi-deepdives.com/esloy/ Last updated: 2024-08-24T13:52:40.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- A recent Freakonomics podcast [episode](https://freakonomics.com/podcast/why-do-your-eyeglasses-cost-1000/?ref=mbi-deepdives.com) started with this riddle: *“Name an item that is both a medical device and a fashion accessory. An item that may cost $50 to make, but often sells for over $1,000\. An item that was* [*invented eight centuries*](https://www.college-optometrists.org/the-british-optical-association-museum/the-history-of-spectacles?ref=mbi-deepdives.com) *ago, has improved billions of lives — and yet many people who need it don’t have it,* [*especially children*](https://www.who.int/publications/i/item/world-report-on-vision?ref=mbi-deepdives.com)*.”* Anyone reading this Deep Dive probably already figured out the answer to this riddle is eyeglasses. The company that absolutely dominates the eyeglasses industry today is EssilorLuxottica. Until 2018, Essilor and Luxottica used to be separate companies. While Esilor focused on **lens** manufacturing, and distributed corrective lenses and optical equipment, Luxottica focused on designing, manufacturing, and distributing **frames** for eyeglasses. Following its all-stock merger of equals in October 2018, the new entity became known as EssilorLuxottica. Essilor itself was the result of merger between Essel and Silor. Essel started as a small network of eyeglass assembly workshops in Paris in 1849, specializing in manufacturing eyeglass frames. It later expanded into lens production. Silor, on the other hand, was established in 1931 by Georges Lissac, and focused on developing new lens materials and treatments. The merger of these two companies combined Essel's expertise in lens design with Silor's innovations in materials, laying the foundation for Essilor’s dominance in the ophthalmic lens industry. Luxottica had a more colorful history. It was founded by Leonardo Del Vecchio in 1961\. He grew up in an orphanage and started apprenticing as a metal engraver at age 14\. After working at a place that produced parts for eyeglass frames, Del Vecchio in his mid-twenties decided to open his own workshop in a smalltown in Italy. From the early days, he quickly understood the value and significance of vertical integration in order to control and ensure attractive economics in the overall eyeglasses value chain. Del Vecchio basically tirelessly worked in his entire adult life to ensure his company’s control in each part of eyeglass value chain. From production of frame parts to assembly and the retail outlets through which these very eyeglasses are sold, Del Vecchio built Luxottica over time in a way that ensured he had tight control over the entire value chain. At the same time, Del Vecchio developed key licensing agreements with global luxury brands that cemented eyeglasses as fashion symbols. Back in 1988, Luxottica signed a licensing agreement with Armani which was a first of its kind and was then followed by numerous similar deals in the following decades that certainly elevated the status of eyeglasses from all sorts of negative connotations. While it may seem surprising today, people did have hesitance in wearing glasses in the past even if they had poor eyesight. From the same Freakonomics podcast: > What we often see in early art are representations of the devil wearing spectacles. They start to take on this magical connotation, whereby glasses allow you to see the things that you shouldn’t see. Another reason why people were reluctant to wear spectacles was because they were so closely associated with aging. You have paintings in which a pair of spectacles are shown alongside a skull. The symbolism of that is quite clear. Such licensing agreements not only helped gradually brush away all the negative perception around wearing glasses, it also de-commoditized eyewear industry from being everyday necessity for people suffering from poor eyesight to a fashion statement for potentially anyone. Del Vecchio may be the most important figure in the history of commercialization of eyeglasses! Del Vecchio actually first retired in 2004, only to come back a decade later to become more actively involved in Luxottica again. In the following couple of years, he ousted three Chief Executives and just when the company appeared to be in bit of a turmoil, Del Vecchio conjured a merger that he labeled as *“the achievement of a lifetime dream.”* One key part of the eyeglass value chain over which Del Vecchio did not have control was lens design and manufacturing. Almost \~30% of lenses used in eyeglasses sold by Luxottica was coming from Essilor. Since \~70% of global optical lenses market was controlled by just three companies with Essilor leading the industry, it was a potential vector of weakness for Luxottica. There were murmurs even in 2014-15 that Essilor wanted to acquire Luxottica, but Del Vecchio was reluctant to cede control to the French company. By the time the two companies did consummate the merger, Del Vecchio became the Executive Chairman and owned 32% stake in the merged company. While Del Vecchio passed away in June 2022, his overarching vision of vertical integration and deep control over the value chain still ripples through EssilorLuxottica. As you can see below, these two companies’ history of acquisitions and licensing agreements led to the behemoth they are today. But before we get into the weeds of the company’s businesses and the economics around it, let’s start with a deeper understanding of the overall eyeglass industry. ![quartr.com](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F18396c24-c2ed-4c15-ad81-17de2223ac92_2062x1517.png "quartr.com") Image Source: [Quartr](https://quartr.com/insights/company-research/essilorluxottica-global-dominance-through-strategic-acquisitions?ref=mbi-deepdives.com) Here’s the outline for the rest of this Deep Dive: **Industry Context**: In this section, I discussed about the size of the eyeglasses market, increasing rate of myopia and why it may be happening, and the potential for solutions to hearing and vision problem to coalesce in the coming years. **Company Overview, and Economics**: After some industry context, this section focuses on the business of EssilorLuxottica-both as a merged entity as well as when Essilor and Luxottica used to be independent companies. **Competitive Dynamics**: I elaborated on EssilorLuxottica’s moats especially on brand licensing deals as well as the competition with Warby Parker and how it evolved over the last few years. I also discussed how some luxury brands are choosing to go in-house instead of brand licensing and explores whether the big tech “partnership” with the incumbents is a boon or a potential bane for the industry. **Capital Allocation and Management Incentives**: I showed EssilorLuxottica’s capital allocation since their merger, as well as management short and long-term incentive structure in this section. **Model Assumptions/Valuation**: Model/implied expectations in the current stock price are analyzed here. **Final Words**: Concluding remarks on EssilorLuxottica, and disclosure of my overall portfolio (including why I changed my mind on Sartorius preference shares and bought the ordinary shares instead). [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### August, 2024 Update URL: https://www.mbi-deepdives.com/aug24/ Last updated: 2024-08-02T14:47:57.000Z Just a few quick updates for this month: 1. While I said last month that I would like to cover another healthcare company in August, I have changed my mind and would rather do a Deep Dive on **EssilorLuxottica**. Considering they may be an interesting piece of the value chain in AR glasses, I would like to have a deeper understanding of the overall industry. I expect to publish the Deep Dive by 25th of this month. 2. If you missed my earnings updates so far this quarter, you can read them here: [Alphabet](https://www.mbi-deepdives.com/alphabet-2q24-update/), [Amazon](https://www.mbi-deepdives.com/amzn2q24/), and [Meta](https://www.mbi-deepdives.com/meta2q24/). I would also encourage you to go through this [thread](https://x.com/borrowed%5Fideas/status/1819028328260817376?ref=mbi-deepdives.com) on Meta's follow-up call. 3. While I didn't publish these on the website, I have tweeted about earnings on some of the other companies that I own: [Bioprocessing](https://x.com/borrowed%5Fideas/status/1816607877454577743?ref=mbi-deepdives.com) (Danaher and Sartorius), [CoStar](https://x.com/borrowed%5Fideas/status/1816637699614474467?ref=mbi-deepdives.com), [Texas Instruments](https://x.com/borrowed%5Fideas/status/1816846592709919223?ref=mbi-deepdives.com), [Insurance Brokers](https://x.com/borrowed%5Fideas/status/1816870881597243628?ref=mbi-deepdives.com) (AON and BRO), and [Sherwin Williams](https://x.com/borrowed%5Fideas/status/1817248634330927150?ref=mbi-deepdives.com). While Lululemon didn't have earnings yet, the stock continued to its downward trajectory on which I have shared some thoughts [here](https://x.com/borrowed%5Fideas/status/1816655490849603815?ref=mbi-deepdives.com). 4. Last month, I also appeared on the podcast: Liberty's Highlights to discuss semiconductors. You can find the podcast [here](https://www.libertyrpf.com/p/semiconductors-industry-going-deep?ref=mbi-deepdives.com). 5. For the new readers, you can access all the past 49 Deep Dives, including excel models, [here](https://www.mbi-deepdives.com/models/). Thank you for supporting MBI Deep Dives. I appreciate it very much. [Subscribe](#/portal/signup) ### Amazon 2Q'24 Update URL: https://www.mbi-deepdives.com/amzn2q24/ Last updated: 2024-08-02T03:02:53.000Z *Disclosure: I own Jan 2025 $55 call options of Amazon* Now that all the big tech reported their quarters, we now have better context to how their quarters went. So, while I will mostly discuss Amazon's earnings in this update, I will briefly touch on some broader themes as well. [Subscribe](#/portal/signup) **Revenue** While Amazon’s 1P business (online and physical stores) continues to limp forward, the rest of the business kept their growth momentum. Advertising decelerated from \~24% YoY in 1Q’24 to \~20% in 2Q’24\. AWS, on the other hand, slightly accelerated from \~17% YoY in 1Q’24 to \~19% YoY in 2Q’24 (FXN). I’ll discuss more about AWS later, but let’s talk more about Amazon ex-AWS first. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa3ad560e-2d1c-49a9-84f8-3eef4d05a68d_1555x226.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** For the second consecutive quarters, North America (NA) operating margins went down QoQ. However, there are more nuances to this headline number as NA store margins actually improved QoQ (management didn’t quantify exact improvement): > If we look at profitability of the core North America stores business, **we actually improved our margin again quarter-over-quarter in Q2**. The overall North America segment operating margin **decreased slightly due to increased Q2 spend in some of our investment areas, including Kuiper, where we're starting to manufacture satellites will launch in the space in Q4.** Some other key quotes on retail business below: > “we're seeing lower average selling prices or ASPs right now because customers continue to **trade down on price** when they can on more **discretionary higher ticket items** > > our seller fees are a **little lower than expected** given the behavior changes we've seen from our latest fee changes. While some of these issues compress short-term revenue, we generally like these trends. While consumers are being careful on price, **our North American unit growth is meaningfully outpacing our sales growth**, as our continued work on selection, low prices and delivery is resonating. > > **On seller fees, lowering apparel fees has spurred substantial year-over-year unit growth in apparel** and the incentive we've given sellers to send their items to multiple Amazon inbound facilities so they can save money where they save us effort and money **is getting more traction than we'd even hoped**. > > These cost improvements won't happen in 1 quarter or 1 fell swoop. They take technology and process innovation with a lot of outstanding execution, **but we see a path to continuing to lower our cost to serve”** As you can see below, international margins also deteriorated QoQ. Since Amazon typically have a step up in SBC during Q2, all the segment margin was affected by it this quarter. There wasn’t any other explanation other than the usual which goes like this: “some countries are tracking just as well as the US whereas the emerging countries where Amazon is ramping up is lowering the overall margins” ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F10391da0-e695-4420-8fb9-02770ea7fada_1116x669.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Fulfillment+ Shipping** If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q'22\. Since then, unit growth is faster (was at par in 3Q’23) than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. That theme continued in 2Q’24 which makes me confident that Amazon retail remains largely on track even if headline margin numbers may give a different impression. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff9c31226-0cc8-4307-ae44-7e4f00c2ae6d_1105x469.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Amazon management also reiterated that there’s more upside left here: > …the first one that you've seen play out over the last year or so has been the regionalization of the U.S. network. And I think one thing to remember about that is that while it had even bigger impact than maybe we theorized when we first architected it, **we're still not done fully honing it**. There's a lot of ways that we continue to optimize that U.S. regionalization that we think will continue to bear lower cost to serve. > > But at the same time, **we found a number of other areas where we believe we can take our cost down while also improving the customer experience**. One of the great things about regionalization was **it not only took our costs served down, but it meaningfully changed the speed with which we're able to get items to customers**. And so we have a number of those other opportunities. > > …as we're able to take cost to serve down, it means that **we're able to afford to have more selection** that we're able to offer to customers. And there are a lot of lower ASP items there, average selling price items, that we don't stock because they're not economic to stock with our current cost to serve. But as we work hard to make progress like we are on lowering our cost to serve, **that allows us to add more selection**. And we see this time in and time out that we -- when we add more selection, customers actually consider us for more of their purchases and spend more with us down the line. **Advertising** For the second consecutive quarters, Meta grew at the fastest pace among all the digital advertising companies. After looking at other companies’ results, Meta’s 2Q’24 numbers (and even the 3Q guide) appear to be even **more** impressive. While Meta has \~30% market share in digital ads (Note: see definition of "digital ads), it has taken >40% incremental ad dollars in the last 6 consecutive quarters. Meta’s incremental share could potentially be higher as Meta’s CFO Susan Li made this interesting comment on the follow-up call: > Our compute needs outstrip our available data center capacity right now. Given the focus that we have on accelerating our GenAI efforts, the new capacity that we've been bringing online is really going more towards GenAI than towards other workloads. We've also had to do a little bit of shifting capacity around to free up capacity for GenAI training. And altogether, **we expect that that will result in some foregone revenue growth from ads and organic content ranking improvements that we would have otherwise made**, but that has been factored into our Q3 outlook. And **we generally expect this to be to be a near term dynamic until we start bringing additional data center capacity online next year, which will meet our capacity needs.** *(*Note*: quite a few subscribers suggested me to include TTD in this table; while I wanted to do that, TTD hasn't reported their Q2 yet, so I will update it once they do)* ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6d2e61d3-2950-43cb-8c79-4b5638345507_979x820.png) Amazon ads is now $50 Bn LTM revenue business, and there are still plenty of avenues for growth left: > Sponsored ads drive the majority of our advertising revenue today, and we see further opportunity there. Even with this growth, it's important to realize we're **at the very beginning of what's possible in our video advertising**. **AWS** Okay, now let’s talk about AWS. AWS added $1.2 Bn incremental revenue QoQ. The current backlog is $156.6 Bn, growing 19% YoY. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd7989aa8-8200-4509-882d-41723f888f69_1024x550.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Let’s take a quick look at hyperscalers growth. While Azure’s growth has decelerated a bit, AWS and Google Cloud slightly accelerated. Many people seem to care about these 1-2 points of growth acceleration/deceleration; I don’t quite think it matters nearly as much as the attention it gets. It’s hard to complain about these growth rates anyway even if it were 1-2 points lower/higher. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0f74e3da-3b42-479a-a5d5-1b08bf1f7ec3_1393x790.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) One thing I would like to track is Google Cloud’s operating performance trajectory against AWS. Google Cloud’s both revenue and opex as % of AWS went in the right direction in 2Q’24 although opex improvement perhaps could have been better. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3b5c68cc-ca42-4b30-a327-045c5bca3151_892x579.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7abcba9c-3a3d-41f9-9978-a07aed8cc08b_912x574.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Google Cloud's opex trajectory is likely less of a concern because AWS posted another quarter of eye-watering margins**,** so it’s hard to keep pace with them. AWS incremental operating margin was **96%** in 2Q’24!!Enjoy while it lasts though as we may not see these margins for too long given the massive capex ramp up we are seeing now (and likely will continue to see next year): > AWS operating margin **includes an approximately 200 basis point favorable impact from the change in the estimated useful life of our servers that we instituted in Q1**. > > we expect AWS operating margins to **fluctuate over time, driven in part by the level of investments we're making at any point in time**. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5b6704d-fc62-4882-96e0-2776ce4d8371_1401x117.png) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4a1768b2-a9e6-4b5a-8889-1531829eefd1_1201x658.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) During Q&A, I found Jassy’s answer to Eric Sheridan’s question really interesting. Just read the whole exchange: ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1170d974-3b3f-40f8-909f-d4dd756b9531_1857x1074.png) Some other interesting tidbits on AWS from the call: > We're continuing to see 3 macro trends drive AWS growth. First, companies have **completed the significant majority of their cost optimization** efforts and are focused again on new efforts. Second, companies are spending their energy again on **modernizing their infrastructure and moving from on-premise infrastructure to the cloud**…And third, Builders and companies of all sizes are excited about leveraging AI. **Our AI business continues to grow dramatically with a multibillion-dollar revenue run rate despite it being such early days**. > > At the heart of this strategy is a firmly held belief which we've had since the beginning of AWS that there is not 1 tool to rule the world. People don't want just 1 database option or 1 analytics choice or 1 container type. **Developers and companies not only reject it, but are suspicious of it.** They want multiple options for flexibility and to use the best tool for each job to be done. > > The same is true in AI. You saw this several years ago when some companies tried to argue that TensorFlow will be the only machine learning framework that mattered and then PyTorch and others overtook it. The same 1 model or 1 chip approach dominated the earliest moments of the generative AI boom, but we have a lot of data that suggests this is not what customers want here either, and our AWS team is determined to deliver choice and options for customers. **Opex+Capex** I would caution readers from getting too excited about Amazon's higher gross margins in 1Q'24 since the cost of sales for AWS is actually reported within R&D (or as they say "Technology & Content"). As a result, we don't really know for sure what Amazon's gross margin is. However, looking at its cost of sales as % of revenue and AWS reported operating margin, it is perhaps safe to assume that its gross margin is indeed improving (but just wanted to remind that the lack of hard data as evidence). Just like other big tech, Amazon's capital intensity has also gone up materially with their increased scale. In 1H’24, they spent $30.5 Bn in capex and expects 2H’24 capex to be higher. What are they spending these capex on? > The majority of the spend will be to support the growing need for AWS infrastructure as we continue to see strong demand in both generative AI and our non-generative AI workloads. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fee77135a-24fb-4647-942a-a6c468961cb1_2023x259.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Other Bets** There are some interesting tidbits on Kuiper in this call. While they haven’t disclosed any number yet (and I suspect Amazon’s total investments on Kuiper may surprise its shareholders if it were disclosed), at least we are starting to get some details: > …for Project Kuiper, our low earth orbit satellite constellation, **we're accelerating satellite manufacturing** in our facility in Kirkland, Washington. > > We've announced a distribution agreement with Vrio who distributes DIRECTV Latin America and Sky Brazil **to offer Project Kuiper satellite broadband network to residential customers across 7 countries in South America** and we continue to field significant demand for the service from enterprise and government entities. **We expect to start shipping production satellites late this year and continue to believe this could be a very large business for us.** > > our Kuiper team is working on how **to figure out how to help the 400 million to 500 million households around the world who don't have broadband connectivity get that connectivity and allow them to do a lot of the things we take for granted today with broadband connectivity.** **Outlook** Amazon’s guidance for 3Q’24 is below. Please note consensus 3Q’24 revenue and EBIT before the call were $158.3 Bn and $15.2 Bn respectively. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6c5a5b75-b47f-4ecf-bbe5-d29580096551_1057x220.png) Source: Company Filings **Closing Words** While the stock went down by \~8% after-hours, this was a fine quarter. Sure, the guidance may be a little soft, but it may likely be because of broader consumer weakness in general. I will stay invested, and may think about adding more to my position at $160 (or below) if it gets there. For more in-depth valuation discussion of Amazon, see my analysis [**here**](https://www.mbi-deepdives.com/amzn2024/) (February, 2024). Please feel free to share with your friends and network. Thank you for reading. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Meta 2Q'24 Update URL: https://www.mbi-deepdives.com/meta2q24/ Last updated: 2024-08-01T02:26:16.000Z *Disclosure: I own shares of Meta Platforms* While the meme of *“nobody uses Facebook”* sort of evolved to *“only old people use Facebook”*, Meta reminded us that it’s just another urban myth: > “…The growth we're seeing here in the U.S. has especially been a bright spot. **WhatsApp now serves more than 100 million monthly actives in the U.S.**, and we're seeing **good year-over-year growth across Facebook, Instagram, and Threads** as well, both in the U.S. and globally. I'm particularly pleased with the progress that we're making **with young adults on Facebook**. The numbers we're seeing, especially in the U.S., really go against the public narrative around who's using the app. A couple of years ago, we started focusing our apps more on 18 to 29 year olds, and it's good to see that those efforts are driving good results. Here are my highlights from today’s call. [Subscribe](#/portal/signup) **Users** After adding 50 mn in two consecutive quarters, Daily Active People (DAP) across its Family of Apps (FOA) decelerated to 30 mn QoQ in 2Q’24\. Still pretty impressive given their scale. I have already mentioned how Young Adults are driving growth on Facebook. What are they doing on Facebook? Posting on Marketplace and Group. > We've seen healthy growth in young adult app usage in the U.S. and Canada for the past several quarters. And we've seen that products like Groups and Marketplace have seen particular traction with young adults. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7e547d0-b7c8-4ab8-b367-4271e32aa5cd_1798x91.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad revenue by Geography** Even though easy comp ended in 1Q’24, Meta continued to post quite strong YoY growth rates in 2Q’24. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fabb951eb-ca29-4cf1-8bbd-32a0d4e560f4_1897x370.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Looking at Family of Apps (FOA) ads revenue growth, YouTube’s number looks a bit perplexing. YouTube’s ads have materially underperformed both Search ads and FOA ads over the last one, two, and three-year period despite being \~18-20% of the size of Search ads and Meta FOA. Of course, as I have [mentioned](https://www.mbi-deepdives.com/alphabet-2q24-update/) earlier, it’s hard to infer any conclusion about YouTube ads with high conviction given we don’t know how much of the revenue simply shifted from ads to subscription segment. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F890a267f-2a80-4676-91e6-f99e98fd7cbf_517x145.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad Impression and Avg. Price Per Ad** We got ad impression and ad price related disclosure by geography since 1Q’24. Overall impression grew by \~10% and avg. price per ad grew by \~10% YoY. Interestingly, the strongest ad price growth was in RoW segment (which includes Africa, Middle East, and LATAM) which incidentally also had the highest revenue growth. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F55da25fc-ef76-44ae-b146-15847bb61f1c_796x496.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Segment Reporting** Overall 2Q’24 revenue was +22.1% YoY; on a 2-yr and 3-yr CAGR basis, Meta’s topline increased by 16.4% and 10.3% respectively. FOA had another near \~50% operating margin quarter and Reality Labs (RL) continues to bleed. Like [Google Services](https://www.mbi-deepdives.com/alphabet-2q24-update/) , Meta continues to post truly incredible incremental operating margins. FOA had 89% incremental operating margin whereas overall Meta posted 77% incremental margins in 2Q’24!! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd0b552b-43f8-4c32-bb5a-b2a2f42a2ca8_1753x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Let’s look at some interesting comments from the earnings call: **AI, AI, AI** Zuck has recently been talking about moving to a unified recommendation/AI system. What stood out to me is such a shift is leading to higher engagement and it seems there is still upside left as Meta completes this transition over time: > In this quarter, we rolled out our full screen video player and unified video recommendation service across Facebook, bringing reels, longer videos, and live into a single experience. And **this has allowed us to extend our unified AI systems, which had already increased engagement on Facebook Reels more than our initial move from CPUs to GPUs did**. Over time, I'd like to see us move towards **a single unified recommendation system that powers all of the content, including things like People You May Know across all of our services. We're not there yet. There's still upside** and we're making good progress here. Of course, advertisers remain the key early beneficiaries of Meta’s AI investments: > …today, advertisers still need to develop creative themselves. And in the coming years, AI will be able to generate creative for advertisers as well, and we'll also be able to personalize it as people see it. > > Over the long term, **advertisers will basically just be able to tell us a business objective and a budget, and we're going to go do the rest for them.** We're going to get there incrementally over time, but **I think this is going to be a very big deal.** > > …We've seen promising early results since introducing our first generative AI ad features, image expansion, background generation, and text generation, **with more than 1 million advertisers using at least one of these solutions in the past month**. Zuck expects Meta AI to be the most used AI assistant by the end of the year. Admittedly, the only time I typically use Meta AI is when I am wearing my [Meta Ray-Ban smart glasses](https://x.com/borrowed%5Fideas/status/1725282443526082886?ref=mbi-deepdives.com) (mostly while I’m driving/walking). It turns out India is driving the strong adoption of Meta AI which is not surprising given the Indian userbase on WhatsApp and their understandable lack of willingness to pay $20/month to access closed models: > People have used **Meta AI for billions of queries** since we first introduced it. We're seeing particularly **promising signs on WhatsApp in terms of retention and engagement, which has coincided with India becoming our largest market for Meta AI usage.** Usage or adoption is one thing, but how is Meta going to make money here? Zuck essentially says “trust me; we have seen this before and we know what we are doing here.” > before we're really talking about monetization of any of those things by themselves, I mean, I don't think that anyone should be surprised that I would expect that, **that will be years, right?** I think that, like what we've seen with Reels. It's what we sell with all these things. **But I think for those who have followed our business for a long time, you can also get a pretty good sense of when things are going to work years in advance.** **And I think that the people who bet on those early indicators tend to do pretty well,** which is why I wanted to share in my comments the early indicator that we had on Meta AI, which is, I mean, look, it's early. > > Last quarter, I think it just started rolling it out a week or 2 before our earnings call. This time, we're a few months later. And what we can say is I think we are on track to achieve our goal of being the most used AI assistant by the end of this year. And **I think that's a pretty big deal**. Is that the only thing we want to do? No. I mean, we obviously want to kind of grow that and grow the engagement on that to be a lot deeper, and then we'll focus on monetizing it over time. > > But the early signals on this are good, and I think that, that's kind of all that we could reasonably have insight into at this point. But **I do think that part of what's so fundamental about AI is it's going to end up affecting almost every product that we have in some way. It will improve the existing ones and will make a whole lot of new ones possible.** Zuck was pushed near the end of the call about these new potential opportunities coming out of Meta AI. > when I was talking before about we have the initial usage trends around Meta AI but there's a lot more that we want to add, things like commerce and **you can just go vertical by vertical and build out specific functionality to make it useful in all these different areas** are eventually, I think, what we're going to need to do to make this just as -- to fulfill the potential around just being the ideal AI assistant for people. Then there is business piece of AI, especially on messaging: > “We're still in alpha testing with more and more businesses. The feedback we're getting is positive so far. **Over time, I think that just like every business has a website, a social media presence and an e-mail address, in the future, I think that every business is also going to have an AI agent that their customers can interact with**…our goal is to make it easy for every small business, eventually every business, to pull all of their content and catalog into an AI agent that drives sales and saves them money. When this is working at scale, I think that this is going to **dramatically accelerate our business messaging revenue**.” For what it’s worth, FOA’s other revenue increased by 73% YoY in 2Q’24 (was +85% YoY in 1Q’24), driven by business messaging revenue growth from WhatsApp business platform. So we clearly have early signs of potential for business messaging even without AI coming into play. **Llama** If you haven’t read it already, I encourage you to read Zuck’s [letter](https://about.fb.com/news/2024/07/open-source-ai-is-the-path-forward/?ref=mbi-deepdives.com) when Meta launched Llama 3 model. A lot of the points from that letter were repeated which I’m not going to mention and I would rather encourage you to read the letter instead. During the call, Zuck also hinted at the ever increasing and kind of mindboggling cost increases for building the next models: > The amount of compute needed to train **Llama 4 will likely be almost 10x more than what we used to train Llama 3**. And future models will continue to grow beyond that. It's hard to predict how this trend -- how this will trend multiple generations out into the future. But at this point, **I'd rather risk building capacity before it is needed rather than too late, given the long lead times for spinning up new infra projects**. And as we scale these investments, we're, of course, going to remain committed to operational efficiency across the company. To be clear, Meta does seem to have ample capacity to build Llama 4\. Llama 3 was trained on \~16k H100s, and Meta expects to have \~600k equivalent H100 capacity by the end of 2024\. If Llama 5 is 10x more than Llama 4, of course that changes the equation dramatically (speaking in hypotheticals, so not a prediction). Just like Google, Meta seems also committed to not face the scenario of under capacity. Perhaps I’m suffering from bit of PTSD in 2022, but it does remind me of the 2021 era hiring spree which almost every big tech did except Apple. Ironically, Apple remains largely absent in the capex spree here as well, and yet remains the consensus AI winner given their end-to-end control and primacy of their devices in our lives. We will see whether ROI on these capex will be much better than the terrible return we have seen on the opex in 2021. Meta, however, reminded the investors about the fungible nature of the capex which somewhat allays my concerns a bit: > …we're continuing to build our AI infrastructure with **fungibility in mind** so that we can flex capacity where we think it will be put to best use. The infrastructure that we build for gen AI training can also be used for gen AI inference. We can also use it for ranking and recommendations by making certain modifications like adding general compute and storage. And we're also employing a strategy of staging our data center sites at various phases of development, which allows us to flex up to meet more demand and less lead time if needed while limiting how much spend we're committing to in the outer years. While Meta takes ROI-based approach for their core AI work, their investments in GenAI products are understandably more speculative/experimental in nature: > On our core AI work, we continue to take a very ROI-based approach to our investment here. **We're still seeing strong returns as improvements to both engagement and ad performance have translated into revenue gains**, and it makes sense for us to continue investing here. > > Gen AI is where we're much earlier, as Mark just mentioned in his comments. We don't expect our gen AI products to be a meaningful driver of revenue in '24\. But we do expect that they're going to open up new revenue opportunities over time that will enable us to generate a solid return off of our investment while we're also open sourcing subsequent generations of Llama. **Reels** > On Instagram, Reels engagement continues to grow as we make ongoing enhancements to our recommendation systems. Part of this work has been focused on increasing the share of original posts within recommendations so people can discover the best of Instagram, including content from emerging creators. Now, **more than half of recommendations in the U.S. come from original posts**. **Threads** Threads Monthly Active Users over time: 3Q’23: 100 Mn 4Q’23: 130 Mn 1Q’24: 150 Mn 2Q’24: 200 Mn Just as you expect an app that benefits from network effects, growth here is accelerating. Zuck seems committed to grow Threads to reach 1 Bn userbase and he appears to be under no hurry to monetize it. When X/Twitter is perhaps wondering whether they can make the next quarterly interest payments, Threads is focused on just growing the userbase with zero ads. While fintwit is barely active on Threads, let’s not underestimate how lopsided this game may prove to be over the next 2-3 years as Meta may continue to leverage its FOA apps to drive adoption and engagement on Threads. > …a lot of other companies that ship something and start selling it and making revenue from it immediately. So I think that's something that our investors and folks thinking about analyzing the business, if needed, to always grapple with is all these new products, we ship them and then there's a multiyear time horizon between scaling them and then scaling them into not just consumer experiences but very large businesses. > > But the thing that I think is just super exciting about Threads is that we've been building this company for 20 years, and **there are just not that many opportunities that come around to grow 1 billion-person app.** I mean, there are, I don't know, maybe a dozen of them in the world or something, right? I mean, there are certainly more of them outside the company than inside the company, but we do pretty well and **being able to add another 1 to the portfolio if we execute really well on this is just really exciting to have that potential.** **AR/VR** Meta mentioned both Meta Quest and Ray-Ban Meta Smart glasses are selling better than they expected. On AR glasses: > Demand is still outpacing our ability to build them, but I'm hopeful that we'll be able to meet that demand soon. > > Ray-Ban Meta smart glasses are showing very promising traction with the early signals that we are seeing across **demand, usage and retention**, increasing our confidence in the long-run potential of AR glasses. Please note 2Q’24 RL sales are largely driven by Quest headsets and it’s not clear how the revenue is recognized by Meta in AR glass sales for which they have a partnership with EssilorLuxottica. **Capital Allocation** Meta returned \~70% of their FCF via dividend and buyback. Net cash balance remains \~$40 Bn, and diluted shares outstanding decline by 57 bps QoQ. LTM SBC per employee for the first time exceeded $200k per employee Despite the buybacks, shares outstanding decreased by only 0.2% QoQ, thanks to Meta’s quite generous SBC program which is currently nearing \~$200k/employee. Meta makes even Google look pretty conservative on SBC as Google’s LTM SBC per employee is “only” $125k. To be clear, I would rather let Meta keep an exceptionally high bar for talent and pay people more than the hiring spree they went on during 2021-22\. After all, these comp packages are likely important source of competitive advantage of big tech as most of these talents get priced out from much of the Silicon Valley startups/smaller companies. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F497d9be2-28d4-4fd1-a356-eaf53d6f243c_1797x310.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Opex Guide** I will stop sharing the below graph after this quarter. While Meta used to decrease their opex guide gradually over the course of the year, clearly this is likely just Dave Wehner phenomenon, and Susan Li doesn’t seem to have any such approach. She appears to mostly stick to her initial opex guide. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc24b1ab-88c8-482a-9b5a-2d4fe3d02be1_1209x582.png) Source: Company Filings, MBI Deep Dives **Capex** Capex guide range was **again** increased to $37-40 Bn (from $35-40 Bn in 1Q’24, and $30-37 in 4Q’23). More importantly, Meta mentioned *“we currently expect *significant* capital expenditures growth in 2025”* What does “significant” mean? Meta didn’t clarify further, but my guess is a number closer to $50 Bn. **Outlook** 3Q’24 topline guide is $38.5-41 Bn (\~2% FX headwind). Mid-point YoY growth is \~16.4% (vs consensus estimates of 14.7%) **Closing Words** After 1Q'24 call, Meta’s stock went down by almost 20% as the market was quite jittery about increased capex. This time the stock went up by +7% (and almost \~30% from 1Q’24 post-earnings low). Why the diametrically opposite reaction despite the fact that Meta is signaling another year of massive capex increases? My best guess is investors are increasingly appreciating Meta’s avenue of opportunities on capitalizing on GenAI, especially the fungibility of capex and hence the risk to the upside is perhaps more likely than to the downside from these investments. For more in-depth analysis on Meta Platforms, you can read my analysis [**here**](https://www.mbi-deepdives.com/meta2024/) (February, 2024). I will cover **Amazon’s** earnings **tomorrow**. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Alphabet 2Q'24 Update URL: https://www.mbi-deepdives.com/alphabet-2q24-update/ Last updated: 2024-07-24T19:12:43.000Z Yesterday’s Google’s earnings may have been bit of “meh” at first glance, but there were nuggets in the call that may have important implications for the broader market. Here’s my highlights from the earnings. [Subscribe](#/portal/signup) **Revenue** Network segment continues to struggle, but the rest of the businesses continue to grow at healthy double digit rate. Search revenue growth surpassed YouTube ads revenue growth last quarter. For the first time, Cloud posted >$10 Bn quarter while maintaining high 20s growth YoY. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F78639dec-6e32-4b7d-90bc-b2eb0005ff5e_1903x358.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Google Services business posted its highest ever margin. Thanks to four consecutive quarters of **\>70% incremental operating margin (!!)**, operating margin for Google Services was 40.1% in 2Q’24! Google Cloud posted its first ever double digit operating margin quarter. TAC continues to tick lower as % of ad revenue. Overall company operating margin expanded from 29.3% in 2Q’23 to 32.4% in 2Q’24. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff48248e4-c6c5-4179-8cb8-799d9cc32ac6_1723x459.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Search** Some interesting comments on Search from the call: > We are pleased to see the **positive trends** from our testing continue as we roll out AI Overviews, including **increases in Search usage and increased user satisfaction** with the results. > > …we see even **higher engagement from younger users aged 18 to 24** when they use Search with AI Overviews. > > …we are seeing that ads appearing either above or below AI Overviews continue to provide valuable options for people to take action and connect with businesses. > > …**AI expands the types of queries we are able to address** and opens a powerful new ways to Search. Visual search via Lens is one. Soon, you'll be able to ask questions by taking a video with Lens. And already, we have seen that AI Overviews in Lens leads to an increase in overall visual search usage. Another example is **Circle to Search, which is available today on more than 100 million Android devices**. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F484c4eb3-4858-4fc6-909f-0f502a305e88_922x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **YouTube** YouTube’s ad revenue missed the consensus numbers by \~3%. After \~21% growth in Q1, the pace of deceleration was a bit surprising. Management explained the reason for such deceleration: > YouTube was lapping negative year-on-year growth in Q1 last year. And then also Q1 benefited from the extra from leap year. And so what you're also seeing here is with YouTube, we were anniversary-ing the ramp in APAC-based retailers that began in the second quarter last year and foreign exchange headwinds as well that we noted. And so there are some timing issues going on. YouTube’s numbers might have spooked investors about broader digital ads industry (Meta, Snap, Pinterest etc.), but Eric Seufert [hypothesized](https://x.com/eric%5Fseufert/status/1816129617868284252?ref=mbi-deepdives.com) that *“broader CTV CPM compression instigated by the influx of inventory from Amazon Prime Video”* also likely contributed to YouTube’s deceleration. Of course, it’s always hard to conclude anything about YouTube ads **definitively** given the subscription side of YouTube. It’s really high time Google started disclosing overall YouTube revenue instead of just ad revenue. If more and more users switched from ads to subscription, that’s far from bad news. However, subscription growth also experienced sequential decline YoY: > we continue to have significant growth in our subscriptions business, which drives the majority of revenue growth in this line. However, there was a sequential decline in the year-on-year growth rate as we anniversaried the impact of a price increase for YouTube TV in the second quarter last year Some more interesting comments on YouTube: > …Views on CTV have increased more than 130% in the last 3 years. According to Nielsen, YouTube is the #1 most watched streaming platform on TV screens in the U.S. for the 17th consecutive month. Zooming out, **when you look not just at streaming but at all media companies and their combined TV viewership, YouTube is the second most watched after Disney**. And this growth is happening in multiple verticals, including sports, which has seen CTV watch time on YouTube grow 30% year-over-year. > > …we continue to see an improvement in Shorts monetization, particularly in the U.S. We're also seeing a very encouraging contribution from brand advertising on Shorts, which we launched on the product in Q4 last year. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9f180c46-d2e8-40bd-bd64-d2e642b490c8_916x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Google Cloud** While Google Cloud revenue growth accelerated to 28.8% in 2Q’24, management mentioned GCP continued to outpace overall Cloud growth. Not sure we will see such persistence of growth at such revenue level/size in too many secular growth industries! > We are the **only cloud provider to offer grounding with Google Search**, and we are expanding grounding capabilities with Moody's, MSCI, ZoomInfo and more. I had to ask Perplexity about grounding; if you are in the same boat, see [here](https://www.perplexity.ai/search/google-said-this-in-the-earnin-nwoyip9lSXeWl90aOEOV4w?ref=mbi-deepdives.com) for explanation. I will discuss more on Cloud when Amazon posts next week. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbe922325-39bc-49e9-a39d-c8fd59419918_924x547.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AI** > **At 2 million tokens, we offer the longest context window of any large-scale foundation model to date**, which powers developer use cases that no other model can handle. Gemini is making Google's own products better. All 6 of our products with more than 2 billion monthly users now use Gemini. Google seems to indicate that while GenAI is contributing positively to their own products, model capabilities are converging for the enterprise customers and the full range of potential of multimodal models are yet to be fully explored: > …I think **there is a time curve in terms of taking the underlying technology and translating it into meaningful solutions** across the board, both on the consumer and the enterprise side. Definitely, on the consumer side, I'm pleased…in terms of how for a product like Search, which is used at that scale over many decades, how we've been able to introduce it in a way that **it's additive** and enhances overall experience and this **positively contributing** there. > > …I think across our consumer products, we've been able -- I think we are seeing progress on the organic side. Obviously, monetization is something that we would have to earn on top of it. **The enterprise side, I think we are at a stage where definitely there are a lot of models. I think roughly, the models are all kind of converging towards a set of base capabilities**. But I think where the next wave is working to build solutions on top of it. And I think there are pockets, be it coding, be it in customer service, et cetera, where we are seeing some of those use cases are seeing traction, but I still think there is hard work there to completely unlock those. > > …we are all building multimodal models. At least Gemini has been natively multimodal from the ground up. But **most of the use cases today that have been unlocked have been around the tech side. So in terms of getting real generative audio, video experience is working well. I think there is still -- it's going to take some time.** **Waymo** Google seems quite encouraged at the pace of adoption of Waymo and has decided to make a multi-year investment of $5 Bn. > Waymo served **more than 2 million trips to date and driven more than 20 million fully autonomous miles on public roads. Waymo is now delivering well over 50,000 weekly paid public rides**, primarily in San Francisco and Phoenix. **Capital Allocation** Google paid its first dividend last quarter. They returned capital to shareholders through buyback and dividend more than FCF they generated last quarter. As a result, net cash balance decreased from $95 Bn in 1Q’24 to $87 Bn in 2Q’24\. If they keep this pace, they may get closer to a balance sheet with zero net cash in the next 3-5 years. There’s been quite a few **rumors** flying around Google’s potential acquisitions (Hubspot and Wiz are recent examples neither of which apparently likely to consummate as per media reporting); so they may get there soon if they use cash to do large deals. Despite buying back $15.7 Bn shares, share count only declined by 13 bps QoQ which is of course disappointing. It is not 100% clear why that’s the case; Google issued 32 mn RSUs in 2Q’24 vs 33 mn in 1Q’24 and bought back 111 mn shares in 2Q’24 vs 92 mn in 1Q’24\. My best guess is they also issued some contingent shares from some earlier acquisitions (Mandiant maybe?) in 2Q’24. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F924ff830-c1df-4a9d-ab26-0c4cc1344d1d_673x576.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex and Opex** Sundar Pichai had a very interesting answer to Ross Sandler’s question on whether hyperscalers are overbuilding capacity. It’s clear that Google is indeed trying to overbuild capacity since the risk of underinvesting is far greater. This answer may have more implications not only for the hyperscalers but also many semiconductor companies as well. See the Q&A below: ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0c82c65b-b478-427d-8fe8-ae965fc96113_2017x553.png) Source: Tikr Google seems quite aware that the impending depreciation expenses from this potential overinvesting need to be managed to continue to grow earnings: > Our leadership team remains focused on our efforts to moderate the pace of expense growth in order to **create capacity for the increases in depreciation and expenses associated with the higher levels of investment in our technical infrastructure**. Given that reality, don’t expect big tech hiring to ramp up anytime soon. Although Google’s headcount declined QoQ for the second consecutive quarter, Google did say headcount will have a “slight” increase in 3Q as they will hire new graduates. Overall capex is expected to be \~$12 Bn or above for each of the quarters in 2024. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3f79d9ae-555f-48c4-8daf-9262cb7cf007_1723x246.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Valuation** Since [3Q'22](https://mbideepdives.substack.com/p/goog3q22?utm%5Fsource=publication-search), I share the following valuation framework every quarter. As a matter of fact, the stock was also at $176 post 1Q earnings when I [mentioned](https://mbideepdives.substack.com/p/alphabet-1q24-update?utm%5Fsource=publication-search) that “For the first time since 3Q’22, each of the following scenario indicates upside to be limited”. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/07/image-2.png) Source: MBI Deep Dives Google’s valuation is far from unreasonable, neither is it very expensive. But is it compelling? The answer appears to be no in my opinion. I will cover earnings of **Amazon and Meta** next week. Thank you for reading. **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Sartorius Stedim: A Pure-play Pick-and-Shovel in Biotech URL: https://www.mbi-deepdives.com/dim/ Last updated: 2024-08-23T15:08:20.000Z *Disclosure: I own Preference Shares of Sartorius AG* *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- In 1870, Florenz Sartorius, at the age of 24, founded Sartorius which was primarily a manufacturer of analytical balances in Germany. By late 1800s, in addition to balances, Sartorius expanded its operations to incubators for poultry farming and heating devices for bacteriological purposes. Before Florenz passed away in 1925, he decided on the succession plan to let his two sons (Wilhelm, and Erich) lead the company. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F56a1b865-2188-43cc-a999-42ad873aef0d_3047x2991.png) Figure: Balances by Sartorius; Source: Sartorius Website When Wilhelm died in 1937, Erich became the sole manager of Sartorius and he started to prepare his adopted son Horst Sartorius for the succession. You may be curious how Sartorius operated during the era of Nazi Germany, and your suspicion would be confirmed. From Sartorius’ own [website](https://www.sartorius.com/en/company/about-sartorius-ag/history/1927-1949?ref=mbi-deepdives.com): > During the Third Reich, Sartorius profited from the armaments industry and used forced laborers during World War II. The company's management upheld the regime by conforming to the system. This resulted in restrictions being imposed immediately following the end of the war, which, however, had no long-term consequences. In 1947, Horst Sartorius, representing the third generation, took over the management of the company and its economic restructuring. Stedim is one of the many acquisitions undertaken by Sartorius. Stedim, founded in 1978 by Bernard Lemaître and Bernard Vallot in France, initially focused on developing intravenous nutrient supply systems for patients with damaged digestive systems. The company pioneered special ethylene vinyl acetate (EVA) bags for parenteral nutrition and began commercial operations in the early 1980s, collaborating with pharmaceutical giants like Pharmacia and Baxter. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd57b3dc7-8ded-474b-98a6-abe4788ee248_3106x4000.jpeg) Source: Sartorius Stedim Website In the 1990s, Stedim developed innovative single-use bag systems for the biopharmaceutical industry to capitalize on the potential of biotechnology. These systems replaced traditional steel and glass containers, offering cost-effectiveness, user-friendliness, and improved safety. Stedim continuously expanded its product range, increasing bag sizes from 50 mL to 1,000 L by 1996 to meet growing industry demands. Then in 2007, Sartorius acquired a majority stake in Stedim, merging it with its own Biotechnology Division to form Sartorius Stedim Biotech. This merger combined Stedim's expertise in single-use technologies with Sartorius' strengths in filtration, separation, and cell culture, creating a leading international partner for biopharmaceutical research, and industry. In 2014, Sartorius further sharpened its focus by selling its industrial weighing business and organizing into two divisions: "Bioprocess Solutions"(BPS) and "Lab Products & Services" (LPS). Sartorius has two separate listed entities: Sartorius AG (listed in Germany), and Sartorius Stedim Biotech (listed in France). To simplify, I will mention the former as SAG, and the latter as SSB in this Deep Dive. While SAG owns 100% of LPS division, it owns 74% of SSB shares (the BPS division) but 85% of voting rights. As a result, SSB has **\~26% free float** and the minority shareholders in aggregate have \~15% voting rights. ![seekingalpha.com](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2af416fe-294d-406f-b751-447255430d0a_1110x650.png "seekingalpha.com") Source: Sartorius Website As this Deep Dive is on SSB, I will ignore the LPS division. However, since Sartorius AG is the majority shareholder, it is important to note the somewhat unique shareholder structure of its own. After Horst Sartorius (third generation of Sartorius family) died in 1998, he left a \~55% stake to his three daughters; the joint heirship is subject to administration of the executor until mid-2028\. This arrangement was stipulated in Horst Sartorius's will, which required his daughters to wait 30 years before receiving their inheritance. In [March 2022](https://hengeler-news.com/en/articles/hengeler-mueller-advises-armira-on-the-acquisition-of-a-participation-in-the-community-of-heirs-of-horst-walter-sartorius?ref=mbi-deepdives.com) a significant transaction took place involving the heirs of Horst Sartorius and LifeScience Holding (LSH) which completed the acquisition of a participation of \~40% of shares of Sartorius heirs. To complicate it even further, in a separate but concurrent deal, Karin Sartorius-Herbst, the eldest daughter of Horst Sartorius, increased her existing stake within the community of heirs (details of the size of stake increases is not disclosed). Apart from Sartorius heirs, \~38% of Sartorius AG shares is owned by Bio-Rad Laboratories (a publicly listed life science company developing, manufacturing, and marketing a broad range of products for the life science research and clinical diagnostics markets), and the rest 7% is free float. SAG also has non-voting preference shares that entitled its owners typically to a higher dividend than ordinary shares, usually receiving an additional €0.01 per share. Bio-Rad (Ticker: BIO) also owns \~28% of SAG’s preference shares. It is likely that BIO may acquire SAG’s ordinary shares outright after 2028 when Sartorius heirs may sell their stakes. While all these may seem quite quaint complexity about SAG shareholding structure, there is likely important implications for SSB shareholders as well. I will touch on the implications later in this Deep Dive, but given the technical nature of the industry Sartorius operates in, let’s start with the basics first. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F72bbb43b-d9bd-4f5b-8383-96be5f9f38f8_1039x345.png) Source: Sartorius The rest of the Deep Dive is organized as follows: **Industry Context:** Given the jargons in this industry, I first discussed the basics related to biologics, monoclonal antibodies, biosimilars, cell and gene therapy, stainless vs single-use bioreactors etc. before getting into the weeds of Sartorius. **Business Overview and Economics**: In this section, I expanded on the upstream and downstream bioprocessing of biologics, followed by a discussion on Sartorius product portfolio, customer concentration, revenue mix by region, as well as margin structure. I also highlighted why I am not as worried about China risk here and the shortcomings of focusing on EBITDA for a company such as Sartorius. **Competitive Dynamics**: First, I mentioned about the broader biopharma value chain, followed by competitive moats of Sartorius. Then I discussed the competitive dynamics within bioprocessing solution providers. **Capital Allocation and Management Incentives:** I showed capital allocation of last decade and half at Sartorius. Then I elaborated on why I am not a fan of management incentives at Sartorius. **Model Assumptions and Valuation**: Model/implied expectations in the current stock price are analyzed here. **Final Words**: Concluding remarks on Sartorius, and disclosure of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Why I am buying CoStar Group URL: https://www.mbi-deepdives.com/csgp2/ Last updated: 2024-07-10T17:30:48.000Z I have started a \~3% position in CoStar (CSGP) at $71/share and would like to increase the position to \~5% if the stock goes down to \~$60. I wrote a [**Deep Dive**](https://www.mbi-deepdives.com/csgp/) on CSGP back in January 2024, so I would suggest reading my Deep Dive for a more comprehensive discussion on the company. I will mostly highlight a couple of points in this piece. While many like to depict CoStar as “Bloomberg for CRE” (Commercial Real Estate), I liked the way Brown Advisory [described](https://x.com/StockCompil/status/1810681563543118033?ref=mbi-deepdives.com) it, *“it’s like Bloomberg, if FactSet did not exist”*. While that is perhaps an apt description of CSGP’s offerings and its competitive advantages within CRE market, CoStar suite and information services are only \~45% of their revenue today. \~45% of their revenue comes from online marketplaces (LoopNet, and Multifamily), and the rest \~10% comes from the nascent residential segment and other marketplaces and services. One of the things that I really like about CSGP is while CoStar’s information & analytics segment is more procyclical, online marketplace segment is a bit countercyclical which subdues the overall cyclicality of the company. From my Deep Dive: *“Looking at the dire situation in commercial office space during post-Covid era, an understandable concern that one might have is whether CoStar is also going to feel indirect pain of their CRE customers. Given CoStar’s revenue declined by 1% during GFC and their almost entire business was information & analytics back then, we can sense their vulnerability in this segment in a potential, secularly challenged environment for office space. Nonetheless, it is perhaps also somewhat impressive that even during GFC, their revenue declined by only \~1%. It likely indicates that unless their customer is going out of business (bankruptcy may just mean restructuring the capital structure and not necessarily shutting down the business), CoStar’s CRE information business is likely to remain (and was) resilient in the last 2-3 years.* *What’s different from GFC now is \~55% of CoStar’s revenue comes from online marketplaces such as LoopNet, Apartments.com etc. During boom economic period, PMCs may have very little vacancies in a low unemployment environment. For example, during 2021-22 period rental vacancies came down to almost 30-year low. In such environment, you simply don’t have much need to sign up for an annual advertising subscription product of CoStar. On the other hand, when vacancies rise during recession, you not only feel the need to advertise your rentals but also feel tempted to upgrade to a different tier to fill up your rentals. Given the low vacancy rate in 2021-22, CoStar’s multifamily segment revenue only grew by \~13% and 10% in 2021 and 2022 respectively whereas it was consistently growing at >20% before that. It started growing at >20% in 2023 as rental vacancies started to rise again in recent quarters*. *Such countercyclical dynamic makes it much more likely that even in a “GFC” scenario, CoStar’s revenue may continue to increase.”* I also recently read Speedwell Research’s Deep Dive on [CSGP](https://speedwellresearch.com/companies/?ref=mbi-deepdives.com) and came away with more confidence that even though CoStar suite and information service business may be procyclical, even this segment is very unlikely to be too volatile. From Speedwell: *“In 3Q08, Andy noted that firms that had been with them for more than 3 years had a 95% renewal rate versus those who were with them for less than 3 years having 70%. This difference was explained by a larger number of new firms and brokers that entered the market during the frothiest years of the real estate boom. For those that stayed in business, as CFO Brian Radecki points out below, *no matter how bad the industry was, their product was still in need**.” Andy Florance, however, is not the kind of CEO who would be content with \~70% retention rate. So, CSGP incentivized salespeople based on usage of their product, not just on new users/seats even though CoStar products are not priced based on usage. They correctly figured out that one way to increase retention is to increase usage of the product. So, salespeople were highly incentivized to make sure their customers are using the product regularly to build habit using CoStar products. By 3Q10, retention of customers who had been with CSGP for less than 5 years increased from 74% to 87%. Today, their overall retention rate is \~90% (\~95% for customers with CSGP for >5 years). So, even their procyclical part of the business may do just fine even during recessions. CSGP is one of those rare companies that grew its revenues double digit for each of the last 51 quarters. CSGP guided for $5 Bn revenue in 2027 even though consensus estimates today is $4.5 Bn in 2027\. Led by its founder Andy Florance, CSGP has been a public company since 1998\. As a result, we have track record of their past long-term guidance and how they fared afterwards. Back in 2012, CSGP had \~$350 Mn revenue. In early 2013, CSGP guided $800 mn annualized revenue in 2016\. Their actual revenue (not annualized) in 2016 was $840 Mn. In early 2014, CSGP guided $1 Bn annualized revenue in 2018\. Their actual revenue (not annualized) was $1.2 Bn. So, at least looking at historical precedence, it does not seem CSGP comes up with their long-term revenue guide out of thin air. CSGP also guided for $2 Bn EBITDA (\~40% EBITDA margin) in 2027\. Let’s go back to GFC again. From Speedwell: *“…despite the worst economic crisis since the great depression, CoStar not only pulled through unscathed, but actually hit their ambitious U.S. EBITDA margin target of 30% by the end of 2008—a quarter early. In 3Q08, their U.S. business hit a 33% EBITDA margin, showing the leverage in the model.”* Having said that, I do acknowledge that it is more likely that CSGP will hit 2027 revenue target than their EBITDA target; Florance is not the type of CEO who would necessarily optimize for near-term profit targets. They are currently in the middle of a massive bet in US residential segment. Just as they spent \~$1 Bn on advertising after acquiring Apartments.com, they are going for the same playbook in residential segment with Homes.com. Today, apartments.com/multifamily segment is their largest revenue driver of the overall company (an incredible feat given it was only launched just \~10 years ago). Replicating success of multifamily will not be easy given the entrenched incumbents here (i.e. Zillow to be specific), but if anyone can disrupt these perennially barely profitable/unprofitable incumbents, it is Andy Florance’s CoStar. The good thing is I don’t think we are quite paying much for CSGP potentially hitting it big with Homes.com. At current $25 Bn EV (with \~$4 Bn net cash on balance sheet), the company is currently trading at \~20x NTM EV/EBITDA multiple (ignoring residential losses which really mask the profitability of the core business). This is capex light business, so EBITDA can be considered a reasonable proxy for FCF here. Paying \~20x multiple for a founder led, highly competitively advantaged business growing at LDD rate for the next 3-5 years seems quite reasonable to me, especially when a major optionality is largely uncaptured in current valuation. Thank you for reading. **Recommended Content** 1. MBI's [Deep Dive](https://www.mbi-deepdives.com/csgp/) on CSGP 2. Speedwell's [Deep Dive](https://speedwellresearch.com/companies/?ref=mbi-deepdives.com) on CSGP ### Buying Puts on QQQ URL: https://www.mbi-deepdives.com/qqq/ Last updated: 2024-07-06T14:23:00.000Z I posted on X and Threads yesterday why I started buying some puts on QQQ. Since not all of my readers may be on X and Threads, let me share my thoughts with you as well. I have also edited a bit and expanded on a couple of points after receiving some feedback on X. **$440 QQQ Puts for January 2026** The current bull market has finally persuaded me to start buying some insurance for the eventual rainy days. While it’s just **\~1%** of my portfolio, it is an acknowledgement of the little upside that I see today. Let me put some of my thoughts into words. Why $440 Puts? Since I paid \~$20 for these puts, I will breakeven at $420\. For each $20 decline from $420, these options would be worth one double in $20 intervals. +100% at $400, +200% at $380, +300% at $360…you get the idea. Anything above $440 would make the options worth **zero**. The reason I am not buying puts for ATM (At-the-Money) options is it’s psychologically not challenging to go through \~10-20% drawdowns. Having experienced 2022 drawdown, I know that it starts to become unpleasant experience when the drawdown extends beyond 20%. Food starts to taste bland at >30% drawdown. At >40% drawdown, you almost start suffering from apathy which is NOT good because ideally you want to remain excited to deploy capital at depressed prices. So, I have decided to give myself some **psychological reprieve** if we end up experiencing >20% drawdown. So, in case QQQ goes down \~30% and my portfolio does the same (TBD), this \~1% put options would increase to become \~5% of my portfolio which I can potentially deploy at more attractive prices. \~5% may not seem much, but near the bottom, every inch of cash deployment counts. Now, why might QQQ go down \~20-30% (or more)? Time for some blunt truth. I (neither does **almost** anyone) have no clue. It might be because inflation scare will come back, maybe deficit and/or stagflation concerns, AI investments can become earnings headwinds for mag7, or something much more catastrophic such as China invading Taiwan or some **real** regulatory challenge for Big Tech in the US/EU. Very few people (if any) could have accurately predicted Covid, stimulus driven demand, HSD inflation, supposedly taming of the said inflation etc. **consecutively**. But one thing is certain today: the multiples at the index level is now trading at nosebleed level. QQQ currently trades at \~32x LTM EV/EBIT multiple (LTM=Last Twelve Months; NTM=Next Twelve Months). For context, it bottomed at \~18x LTM EV/EBIT both in 2020 and 2022 drawdowns and at \~15x during 2018 drawdown. These are all the drawdowns I experienced first-hand since I started investing in the US in August, 2018\. If we go beyond that, QQQ consistently traded below \~20x between 2016 and 2018\. QQQ currently basically trades at 2021 peak LTM EV/EBIT multiple, and we got to go back to during 2000 tech bubble to find multiples higher than this. Given this context, I think not much needs to go wrong for these options to be a worthwhile bet. I, however, do not want to have puts regardless of the market environment. My general framework is to have puts whenever QQQ trades at above >30x LTM EV/EBIT multiple (historically, it happened very rarely). So, if we go to 2026 and QQQ still trades at >30x LTM EV/EBIT and we don't have a compelling reason to think earnings is depressed for one-off reasons (think something like Covid), I will maintain \~1% puts in my portfolio. To put it differently, I am okay with losing \~1% of my portfolio per year to protect my downside a bit when index trades at a nosebleed valuation. If it trades at \~20x multiple, I will not have puts in that case. ![chart](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/07/data-src-image-deea199c-2866-4235-8587-7f26fc2eca5a.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)); \*KoyFin has QQQ valuation multiple data since 2016. Wait, shouldn’t we look at NTM? The reason I’m not looking at NTM numbers here is LTM numbers are facts whereas NTM is opinion. Having gone through 2020-2024 cycle, I have very little confidence on anyone’s ability to forecast NTM numbers with high accuracy. Moreover, data quality is a huge concern for me as analysts account for SBC for some companies (mostly big tech) in NTM numbers whereas for most other companies, they don’t. I wanted to avoid the black box of NTM numbers for index and decided to stick to LTM numbers. It’s a time series comparison anyway, so this isn’t a strong limitation. Why not buying puts for SOXX? I think there are credible (although unlikely) scenarios where mag7 ex-NVDA would engage in an uneconomic GPU war with each other which might (temporarily?) wreck the economics of mag7 ex-NVDA more than SOXX constituents. Although I think it is more likely than not that both SOXX and QQQ would go up/down simultaneously, I wanted to keep it simple by buying puts for QQQ instead of getting too cute with SOXX. Isn't a static put options expiry a bit more risky than having a more dynamic expiry dates i.e. instead of buying Jan 2026 options, shouldn't I have multiple puts in multiple expiry dates? Perhaps, and I may still do it. To be very precise, these puts are 0.8% of my portfolio now and they're roughly \~18 months in duration. Since I am okay with setting aside \~1% on puts **per year,** I can potentially add \~0.3-0.4% more of these puts in my portfolio. I will perhaps do that if QQQ has another \~10-20% rally in the next 6-12 months, so I have some additional capacity left here. Some people wonder whether there is upside risk to QQQ as bears may all just give up simultaneously. In some sense, I think it has already happened in last 2.5 months? Of course, it can always continue moving higher which is why I have some tiny capacity left. ![chart](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/07/data-src-image-a5fc4284-f5bc-4075-af0a-425fa5f33b3f.png) Figure: Performance of selected stocks and indexes since April 19, 2024; Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Why Jan 2026? Timing is the most challenging aspect of options and there aren’t, unfortunately, compelling answers. Index is unlikely to go down \~30-40% in 3 months (unless China invades Taiwan of course), so I wanted to give it “enough” time for the index to go down and minimize my timing risk a bit. Also 12 months is probably enough time if market decides to get nervous about the new administration’s regulations, plans for spending etc. Shouldn't we just raise some cash if QQQ is overvalued, especially in a world with \~5% yield on cash? I do have \~6% cash right now and I expect myself to save at least \~10% of my current portfolio in the next 12 months. So, while it may make a lot of sense for someone else depending on their personal context, it makes more sense to me to add an instrument that provides some torque in the upside if index does go down. Okay, that’s the rough sketch. If these options expire worthless, I am probably not terribly unhappy since it likely means 99% of my portfolio may do just fine. If it becomes worth multiple of what I paid for these put options, I will feel psychologically lot better to have owned at least some insurance for the rainy days. Ultimately, investing is, more often than not, a psychological sport. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* [ ](https://www.mbi-deepdives.com/lulu1q24/) ### July, 2024 Update URL: https://www.mbi-deepdives.com/jul2024/ Last updated: 2024-07-02T13:55:54.000Z Just a very short update for this month. I will publish a Deep Dive on **Sartorius Stedim Biotech** by 25th of this month. After studying [IQVIA](https://www.mbi-deepdives.com/iqv/) last month, I am enjoying working on some healthcare companies and may choose to do a couple more this year to keep extending my learning curve here. For the new readers, I would like to highlight that you can access all the past 48 Deep Dives [**here**](https://www.mbi-deepdives.com/models/). You can also see my portfolio [**here**](https://www.mbi-deepdives.com/portfolio/) (updated on the last day of every month). Thank you so much for your support. [Subscribe](#/portal/signup) ### IQVIA Deep Dive URL: https://www.mbi-deepdives.com/iqv/ Last updated: 2024-06-21T17:06:18.000Z *Note: I recorded the audio version of this Deep Dive, but unfortunately the file got corrupted and for some reason, it has randomly deleted 20 minutes of the final version. So, you won’t be able to listen to this month’s Deep Dive. Apologies for the inconvenience.* --- Back in 2016, IMS Health and Quintiles went through a merger of equals to form “QuintilesIMS” which was later renamed to be “IQVIA” in 2017\. "I" and “Q” stand for IMS Health and Quintiles respectively and "VIA" means "by way of”. Since IMS and Quintiles both used to be separate public companies, let me discuss these two businesses separately at first before getting into the details of their post-merger status. **IMS Health** IMS, which stands for Intercontinental Medical Statistics, was founded in 1954 by Bill Frohlich and David Dubow. Apparently, Arthur Sackler (for the uninitiated, read about [Sackler family](https://en.wikipedia.org/wiki/Sackler%5Ffamily?ref=mbi-deepdives.com) owned Purdue Pharma’s [role](https://www.congress.gov/event/116th-congress/house-event/LC65831/text?ref=mbi-deepdives.com) in the opioid crisis in the US) also had a “[hidden](https://www.healthcommentary.org/2019/09/25/the-real-arthur-sackler-part-4-visionary-data-crook/?ref=mbi-deepdives.com)” ownership stake at IMS. IMS collects comprehensive set of healthcare information around the world, including sales, prescription and promotional data, medical claims, electronic medical records etc. to deliver information and insights on \~90% of the world’s pharmaceuticals. IMS standardizes, organizes, and integrates 61 petabytes of unique proprietary data sourced from \~150k data suppliers covering over one million data feeds globally. A sales executive may want to know “which providers generate highest return on rep visit”; a marketing executive may wonder “is my brand gaining market share quickly enough to hit revenue forecasts”; a R&D executive may explore “how long will trial enrollment take to hit target patient volumes”. IMS database can come quite handy in answering each of these questions. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F43352a06-bc19-49c7-9ab4-4a3679afde7a_2224x370.png) Source: IMS 2015 10-K As you can imagine, allowing such granular data to be disseminated among pharmaceutical companies has its fair share of critics. IMS, for example, purchases prescription records from pharmacies and sells the information back to pharmaceutical companies who want to monitor the return on their promotional efforts. With the rise of privacy hawks who don’t even want social media companies to deliver targeted ads, you can imagine how buying/selling sensitive health related data would seem like a walking lawsuit. While IMS de-identifies these data while maintaining utility at an aggregate level for their customers, IMS already dealt with these lawsuits in 2000s and by the mid-2010s, regulatory environment on the existential question of the business model itself is largely settled. For the more curious readers who want to dig deeper, I recommend this [paper](https://scholarlycommons.law.case.edu/cgi/viewcontent.cgi?article=1065&context=healthmatrix&ref=mbi-deepdives.com) from 2013 which provided good historical background on this issue. IMS first became a public company in 1972\. But it was taken private by late [2009](https://www.sec.gov/Archives/edgar/data/1058083/000110465909062890/a09-33005%5F1ex99d1.htm?ref=mbi-deepdives.com) by TPG Capital, CPP Investment Board and Leonard Green & Partners for $5.2 Bn, including $2 Bn debt. These buyers were able to almost \~[2.6x](https://www.pehub.com/tpg-cppib-and-leonard-green-poised-to-make-2-6x-from-ims-health/?ref=mbi-deepdives.com) their money in \~4.5 years when IMS again became a public company in April, 2014. As you can perhaps imagine, it’s quite a sticky business. IMS mentioned in their 2015 10-K that the average length of relationships with their top 25 clients is over 25 years and the retention rate for their top 1,000 clients was 99%. Moreover, \~70% of revenue was recurring in nature. IMS used to segment their revenues in two categories: a) information, and b) technology services. Both these segments largely contributed half of IMS revenue each in 2015. Their **information** products provide country or/and regional level performance metrics related to sales of pharmaceutical products, prescribing trends, individual prescriber level (depending on regulation in a country/state), medical treatment and promotional activity across multiple channels including retail, hospital and mail order. Their clients use these data to measure relative performance, assess market opportunity, determine brand and company strategy, and understand market dynamics. \~90% of their information revenue came from subscription or license-based contracts that are sold in a range of frequencies from weekly to annual intervals, and are delivered in a variety of formats, including online hosted, PCs and mobile platforms. Revenue from **technology services** consists of a mix of revenue from SaaS licenses such as CRM, performance management, incentive compensation, territory alignment, roster management, call planning, compliance reporting and Master Data Management. Beyond such SaaS licenses, “real-world evidence solutions” enables IMS’ clients to use anonymous patient-level data to understand treatments, outcomes, and costs to inform and advance healthcare decision making. Revenue from workflow analytics and consulting services are also included in this segment. Information segment is typically higher gross margin business than tech services segment. While a very sticky business, information services is an extremely mature business and revenue was basically flat from 2012-2015 period. Tech services grew at mid-teen CAGR which increased the overall revenue at MSD+ CAGR rate. While a material percentage of business is recurring in nature, it is a labor intensive business as IMS employees need to consistently collect data on regular intervals. It is, however, a capex light business and the business typically generated \~25-30% EBITDA margin during 2012-2015 period when it was a standalone company. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0c04951e-0a11-4f3b-b956-4ea34c5cbe76_691x822.png) Source: Company Filings, MBI Deep Dive **Quintiles** Quintiles, founded by Dennis Gillings, a biostatistics professor at UNC Chapel Hill in 1982, is a leading provider of biopharmaceutical development services and commercial outsourcing services. Quintiles first came to IPO in 1994 but was taken private in 2003 in a $1.7 Bn management buyout. Then in 2013, they came back to public market again at \~[$5 Bn](https://www.wsj.com/articles/BL-MBB-1150?ref=mbi-deepdives.com) valuation. \~74% of their 2015 revenue came from their “**Product Development**” segment which is the largest Contract Research Organization (CRO) focused primarily on Phase II-IV clinical trials and related laboratory and analytical activities. CROs are contracted or outsourced by the sponsors of clinical trials (such as pharmaceuticals, medical device, and diagnostics companies) to manage and conduct the trials on their behalf. They can manage all aspects of a clinical trial, from study design and planning to execution and closeout, including developing protocols, selecting trial sites, recruiting patients, monitoring the trial, and managing data. CROs help ensure that trials comply with regulations and ethical guidelines and assist with preparing and submitting regulatory documents to agencies like the FDA. They are responsible for collecting, cleaning, storing and analyzing the data generated in clinical trials, and provide statistical analysis and reporting. By outsourcing these services to CROs, trial sponsors can access specialized expertise, reduce costs, and improve the speed and efficiency of the drug development process. CROs have the infrastructure, resources and experience to run high-quality, compliant clinical trials, allowing pharmaceutical and biotech companies to focus on their core competencies of research and product development. As per the data shared in 2015, all the top 20 largest biopharmaceutical companies (and 98 out of top 100) are customers of Quintiles. Back in 2015, they had $12 Bn backlog, 28% of which came from top 10 biopharmaceutical companies, 23% from companies ranked as 11-20, 24% from companies ranked as 21-50, and 25% with biopharmaceutical companies outside the top 50. \~26% of Quintiles’ 2015 revenue came from Integrated Healthcare Services (IHS) segment. IHS provides healthcare business services for the broader healthcare sector, such as real world and late phase research, market access and consulting, health information analytics, technology consulting, and other healthcare solutions. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fde1e34d0-c9f8-474b-9238-ed5eff5b2f8b_2209x477.png) Source: Quintiles 2015 10-K Both product development and IHS segment historically grew at MSD CAGR, but product development has substantially higher margin than IHS. Product Development reported \~40-42% gross margin and \~20-22% operating margin during 2012-15 period whereas IHS segment reported just \~20% gross margin and \~6-7% operating margin. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe1903517-0f82-4602-9607-d1779a709fa6_706x912.png) Adj. EBITDA is calculated as EBIT+D&A+ other/restructuring expenses; Source: Company Filings, MBI Deep Dive Now that we have bit of an overview of IMS and Quintiles as standalone companies, let’s get to their merger. **IQVIA: The Merger** In May 2016, IMS Health and Quintiles did an all-stock merger of equals which resulted in pro forma ownership of 51.4% by IMS Health shareholders and 48.6% by Quintiles shareholders. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F20a3cff6-c0d6-4ae8-bd48-31971242b7f9_1350x780.png) Source: IMS and Quintiles Merger Presentation 2016 The rationale for the merger was to realize more efficiency in R&D and demonstrate value and measure outcomes for the customers which management thought would accelerate the topline growth of the combined company by \~100-200 bps by year 3. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F857d4839-0879-46e3-8609-67a4cdc38e89_1372x823.png) Source: IMS and Quintiles Merger Presentation 2016 As mentioned earlier, while the merged entity was initially named “QuintilesIMS”, it was later rebranded as “IQVIA” in 2017\. The overall business has now three operating segments: **a)** **Technology & Analytics Solutions** (**TAS**): TAS is mostly the legacy IMS business. Approximately one-third of TAS is the **information/data** business of IMS that grows at Low Single Digit (LSD) rate. As shown earlier, this is relatively higher margin, non-discretionary source of revenue for TAS. Beyond the information business, \~20-25% of TAS revenue comes from **analytics & consulting** which are more discretionary in nature and hence, potentially cancellable/deferrable by the customers depending on the industry/economic cycle. \~30-35% of TAS revenue comes from **Real World Solutions** segment which focuses on generating evidence and insights from real-world data (RWD) to support life sciences companies. IQVIA’s scaled information networks include \~1.2 Bn unique non-identified patient records globally, as well as access to profiles of over 3,400 real world data assets in \~100 countries facilitating data discoverability for healthcare research via the IQVIA Health Data Catalog. The rest \~15-20% of TAS revenue is generated from **technology platforms** which provide range of cloud-based applications and SaaS solutions that support commercial and clinical processes, including CRM, performance management, real-world evidence generation, compliance and safety reporting, incentive compensation, multi-channel marketing, and master data management etc. Overall, TAS is generally mid to high 20s operating margin business. TAS generally contributes \~40% of IQVIA’s revenue and \~45-50% of the overall operating income. **b) Research and Development Solution (RDS):** RDS is primarily the legacy “Product Development” or CRO business from Quintiles. IQVIA’s solutions and services enables the efficient conduct and coordination of multi-site clinical trials (generally Phase II-IV). Within RDS segment, IQVIA’s offerings include project management and clinical monitoring, clinical trial support services, laboratory services, strategic planning and design, patient and site centric solutions etc. I will expand on the appeal of CRO business in a later section of this Deep Dive. At any given point in time, IQVIA is likely working on \~2,500 clinical trials which are staggered through the years, and revenue flows over several years from each one of those trials. RDS currently has \~$30 Bn backlog that has been steadily growing at $2-2.5 Bn per year (except for $3.6 Bn increase in backlog during the pandemic which includes some one-off business). Approximately \~30% of prior year’s backlog tends to convert to revenue in the current year. \~60% of RDS revenue comes from large pharmaceuticals and \~20% of the RDS revenue is generated from Emerging BioPharma (EBP) customers. While EBP segment typically grows faster, \~10% of RDS backlog is from pre-revenue EBP customers which are more speculative in nature i.e. those backlog will not convert to revenue if these clients do not receive funding. RDS is relatively lower gross margin business (\~30% gross margin) but still generates \~20% operating margin. \~15% of RDS revenue is **FSP** revenue (Functional Service Provider) which is a particularly low margin business and much of the rest is from higher margin full-service revenue. FSP solutions can range from supplementing a client's team with a few individuals to providing dedicated teams across functions or large-scale programs. Basically, some clients want to outsource functional aspects of projects while maintaining control over the overall process and data. While increasing popularity of FSP can put pressure on the margin, it is often necessary to receive a broader mandate from the customers in the future. When Quintiles was a standalone company, they weren’t too keen on FSP revenue, but IQVIA decided otherwise in the post-merger era: > If you go back to 2015, right before we did the merger. Quintiles had largely turned away from FSP work because it wanted to focus on the higher-margin full-service work. And when we came in, we -- as IMS and Quintiles merged and already took over. He looked at it and said, "No, we want to participate in the whole market." And it's more than just the revenue potential for FSP. It's also that, for certain clients, you've got to play FSP to be in the full service. They might have 60% or 75% FSP, 25% to 40% full service. But **to get the full service work, you have to be willing to do the FSP work, too**. So we think that was obviously the right move over time to chase that work. And we can talk about the margins, but from a revenue standpoint and booking standpoint, we need to be there. Overall, RDS is \~55% of revenue and \~50-55% of operating profit of IQVIA. c) **Contract Sales and Medical Solutions**: This segment is primarily legacy IHS business from Quintiles. It includes health care provider engagement services, patient engagement services, and medical affairs services. While it’s \~5% of the overall IQVIA’s revenue, revenue from this segment is consistently declining over the years. Since this segment has just \~6-7% operating margin, it contributes only \~1% operating profit of IQVIA. Overall, apart from the one-off pandemic boost, IQVIA has been largely growing at MSD rate with \~20% adjusted EBITDA margin. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30d19d2f-324a-4acf-8f3e-99971661dc3e_775x708.png) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd7951c72-3fb8-4617-b4ae-5bab50bc6f57_783x751.png) \*EBITDA is calculated as EBIT+D&A+ restructuring costs; Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Now that we have an overview of IQVIA’s business and its history, let’s discuss the opportunities, risks, and competitive dynamics IQVIA faces. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Lululemon 1Q'24 Update URL: https://www.mbi-deepdives.com/lulu1q24/ Last updated: 2024-06-06T02:53:09.000Z *Disclosure: I own January 2026 $165 LULU Call Options* Lululemon was facing some really thorny questions from Mr. Market ever since 4Q’23 earnings. Today’s earnings should help calm some nerves. Here are my highlights from tonight’s call. **Sales Growth by Region** At first glance, one may find confirmation to plenty of concerns for LULU. US sales, which was \~61% of overall sales this quarter, grew by only 2%. Growth momentum in international markets, especially in China helped mask the weakness in the US. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F31a696da-6a94-4ee2-9eab-fd1e6ac0a3e4_1107x1002.png) Source: Company Filings Looking at Canada’s continued double-digit growth momentum assuage my concerns related to maturity of the US business as Canada is LULU’s most mature market. There are quite a few factors at play here. US business had much tougher comp than Canada this quarter (1Q’23 US business grew by \~20% vs Canada’s \~3%). The “[Everywhere Belt](https://shop.lululemon.com/p/bags/Everywhere-Belt-Bag/%5F/prod8900747?sz=ONESIZE&ref=mbi-deepdives.com)” bag was much more popular in the US last year than it was in Canada. I will discuss a few other reasons for US weakness later. Let me focus a bit more on international opportunity, which was \~21% of LULU’s revenue in 2023\. Interestingly, while Nike is \~3x the size of LULU’s North America business, Nike’s China business is \~8x the size of LULU’s, hinting at the large runway available for LULU to further penetrate the market. LULU’s China business grew by \~67% in 2023, and yet China revenue grew by 52% FXN in 1Q’24. LULU management expects international to eventually contribute 50% of overall revenue. That may seem overly ambitious, but looking at China growth momentum and low penetration it doesn’t seem inconceivable to me. Moreover, LULU doesn’t even operate in potentially large markets such as India yet (although they may enter [soon](https://economictimes.indiatimes.com/industry/cons-products/fashion-/-cosmetics-/-jewellery/yoga-pants-inventor-lululemon-athletica-plans-to-enter-india/articleshow/109791990.cms?from=mdr&ref=mbi-deepdives.com)). Admittedly, India for LULU may be more of a 2030s story than 2020s story, but in the fulness of time, \~50-50 revenue mix between North America and International seems plausible to me. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1d3d5e37-3fb9-4e44-95ef-61aa9bc1a1c8_901x289.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Margin** What’s perhaps even more interesting is LULU’s margins in China are quite incredible. In 1Q’24, they reported \~39% operating margin in China (vs \~35% in Americas). Operating margins in Rest of the World (RoW) have also improved by \~400 bps YoY. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9c164a95-7660-4c40-81c0-442b2e2644a8_432x259.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) LULU’s overall gross margin was 57.7%, \~20 bps improvement YoY which was driven by 120 bps increase overall product margin (lower product costs, lower air freight costs and lower inventory provisions) but was offset somewhat by a 50 bps increase in markdowns. LULU expects inventory markdown to increase YoY in Q2 as well (but less than in Q1) but annual markdown is still expected to be flat YoY, implying lower markdown in the back half of the year **Sales by Gender** While LULU’s men’s business maintained its robust mid-teen growth, women’s segment reported <10% revenue growth for the first time since 2Q’20 (which was marred by Covid anyway). As discussed earlier, “others” category which includes bags had a particularly tough comp (1Q’23 grew by 54%). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6d302f02-f721-45b1-8364-4b35004fc4bc_987x258.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Why did women’s segment perform poorly? From today’s call: > When looking at women's, we did not maximize the business in the U.S., which was the result of several missed opportunities, **including a color palette and our core assortment, particularly in leggings that was too narrow**. Where we had color guests responded well, we just needed more as they are looking for additional choices. And we are also out of stock in some of our smaller sizes. > > …The 1 color and newness we did have, she responded incredibly well to, but she was looking for more, and our palette that we chose was just more limited than what she was looking for as well as because of the success in Q4, we came into the year with some missed opportunity across our size profile, particularly our smaller sizes. **All of this is within our control**. All of this, the teams have been chasing and **we expect much of that to be addressed in the second half of this year** as well as a lot of the newness and innovation, we did have planned for this year and our women's business was scheduled more for mid to back half of the year. The pain in women’s segment seems mostly self-inflicted to me and something that can be corrected over the course of the year. Lack of product assortment was also an issue in “other” category: > In the accessories business, we know that we're cycling over the success of the Everywhere Belt Bag which is incredible. It really validates and shows what's possible for our brand in accessories, in particular, in bags. And although that bag continues to perform well, not quite to the levels of last year, but the team has introduced a number of new styles of bags that the guests responded incredibly well to. > > We just didn't have the depth of inventory to satisfy the demand that could have offset some of the headwind of the Everywhere Belt Bag success last year. That is something we can control. We know the newness is resonating and the guest is moving beyond just an Everywhere Belt Bag, and we have opportunity and the teams have been chasing into that and expect to be in a better in-stock position in the back half, **the 2-tone bag is a good example of that sold out almost immediately. We were able to chase bring some in, offer it as an Essentials member early access, it again sold and did incredibly well, and we continue to chase into that**. To put LULU’s success in perspective, they did $549 mn revenue in “other” category in 2021 which then more than doubled by 2023 to reach $1.2 Bn. I know competition is the hot topic for LULU; but frankly speaking, I’m not sure whether they were even able to match LULU’s momentum in their “other” category, let alone men’s and women’s category over the last 2-3 years! Perhaps thanks to such outsized success, things had to be a bit rocky in 2024\. I do, however, expect LULU’s management to be better prepared with product assortments than they were in the first half of the year. Management did mention a number of new products to be launched in second half of the year, so we will have better clarity on this issue in a couple of quarters. **Membership** LULU now has 20 mn members in North America (vs 17 mn in 4Q’23). LULU continues to acquire new customers, but it would be bit more helpful if they provided more color on guest retention numbers. In 2019, for example, they mentioned they had [92%](https://x.com/borrowed%5Fideas/status/1791826965756035510?ref=mbi-deepdives.com) guest retention for high value guests. **Inventory** Inventory declined by 15% YoY and LULU expects it to decrease mid-teens in Q2 as well before starting to increase in line with revenue in the second half of the year. **Competition** Has the intensity of competition gone up recently? From the call: > There remains competitors in this space that use promo as a means to drive demand for their product. We've seen that increase over the last few years. > > But **I wouldn't say in this quarter, it's either gone deeper or pulled back**. **It's sort of the same, which I would say is a heightened level from a few years ago, but nothing dramatic in the quarter.** **Capital Allocation** It’s not everyday you see a company increasing the buyback intensity as stock price goes down, so it’s good to see LULU execute that. As the stock went down, they increased buyback intensity from \~117k shares in February to \~186k in March to \~448k in April. We know the stock did even worse in May, and thankfully management mentioned they bought back another $230 Mn (so another \~650-700k shares in my estimates) in May. Moreover, they raised buyback authorization by $1 Bn and have $1.9 Bn cash on balance sheet. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4a1330f0-0bb4-4566-9b76-2fa2d7361e8c_1195x220.png) Source: Company Filings Thanks to these buybacks, LULU’s shares outstanding has been going down by \~1% YoY each quarter. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1bf4e5a9-8ff2-4586-b514-9f3d8a8b58f3_706x421.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** LULU maintained their topline and operating margin guidance. EPS guide was slightly increased 14-14.2 to 14.27-14.47 (without assuming future buybacks). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5642f373-aa54-43d3-9edc-b7020e085b48_1128x261.png) Source: Company Filings **Final Words** Overall, this quarter highlighted that there are indeed some issues for LULU’s product assortments, especially for women’s segment in the US, but it does appear to be largely self-inflicted. The rest of the business seems to be largely continuing its momentum. If LULU manages to address the concerns for women’s segment in the US, I’m still optimistic that they may be able to do better than their high-end of revenue guidance for 2024, and that’s likely not priced in despite the \~10% rise in the stock price After-hours! More reading on Lululemon: [here](https://www.mbi-deepdives.com/lulu/), [here](https://www.mbi-deepdives.com/apr2024/), and [here](https://www.mbi-deepdives.com/tsm/) (see section 5) Thank you for reading. [Subscribe](#/portal/signup) ### June, 2024 Update URL: https://www.mbi-deepdives.com/jun24/ Last updated: 2024-06-03T19:08:23.000Z We are getting close to the half-year mark for this year. Let me take this opportunity to briefly share some thoughts with you: 1. First things first, after spending the last three months on studying semiconductors, I will spend the next couple of months on healthcare industry. I will publish this month's Deep Dive on **IQVIA Holdings** (Ticker: IQV) by 25th of this month. In the following month, I plan on doing a Deep Dive on **Sartorius Stedim Biotech** (Ticker: DIM). 2. One of the things that I have been thinking about is how challenging it has become to figure out the long-term implications of the current Capex bonanza of Mega Cap Tech companies. While it may be tempting to bucket all of these capex in a monolithic fashion, I suspect that is likely to prove to be too simplistic. Google, Microsoft, Meta, Amazon have sufficiently different strategies around AI that they will not necessarily succeed or fail **simultaneously**. The funny thing is when I started investing in the US back in 2018, I remember feeling a bit overwhelmed while studying some of the Big Tech companies and I imagined things will probably feel easier after studying/following these companies for a few years. Six years later, while it doesn't feel as overwhelming, the reality remains it still is quite difficult to figure out how the moats are going to evolve in the future in the big tech land. I don't have all the answers yet and the broader landscape remains quite fluid. While perhaps most of us have been actively thinking about these questions, unfortunately "Magnificent Seven" has been incorporated into our investing lexicon over the last couple of years. I encourage investors to not think about all the capex in uniform terms, rather appreciate the potential for dispersion in future returns due to AI. 3. In my 2021 [Annual Letter](https://www.mbi-deepdives.com/2021/), I wrote the following: > ...because of the business model of MBI Deep Dives, it can be challenging to choose certain companies for deep dives. For example, I am nervous to take a shot at semiconductor companies (crypto is also another area of apprehension) since I suspect one month may not be enough to understand the complexity of the whole industry. Now that I have done [Ethereum](https://www.mbi-deepdives.com/eth/) Deep Dive, a [Primer](https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/) on Semiconductor, and two Deep Dives on semiconductor companies ([Texas Instruments](https://www.mbi-deepdives.com/txn/), and [TSMC](https://www.mbi-deepdives.com/tsm/)), you can see I have tried to address this "weakness" of MBI Deep Dives as I grew more confident about tackling more technically challenging companies over time. Part of the reason for covering semiconductors is simply due to my somewhat recent realization that it is getting quite difficult to follow some of the mega cap tech companies I own without understanding semiconductors itself. I do want to reiterate that I am still on the very early stage of my semiconductors journey and you can expect me to cover two to three semiconductor companies every year at least for the next five years (if not more) regardless of what's going to happen to the stock prices of semiconductor companies during these years. One of the things I have become quite comfortable over time is to trust the market to provide volatility no matter how great a company/industry is. Even if AI is the mega theme for the next 10-20 years like the internet was back in late 90s to early 2000s, I am almost unreasonably confident that Mr. Market will provide us **ample** volatility along the way which is why my only focus is to study the companies closely, and not necessarily catch the "current" wave. I am starting to do the same with healthcare industry as well. Like semiconductors, I am likely to cover 2-3 companies from healthcare sector per year going forward. As a generalist, my goal is to keep studying companies across wide range of industries. 1. One of the questions I received a couple of times is whether I have incorporated Gen-AI in my research workflows. It's early days, so I'm not sure whether my current behavior will stick for years to come. Let me mention a couple of tools I have been using frequently over the last few months. I have started using [Cubby](https://cubby.nyc/?ref=mbi-deepdives.com) to manage all the highlights from annual reports, earnings transcripts, blogs, podcast etc. in one place while working on a Deep Dive. The biggest benefit from having all of my highlights in one place is that it is searchable, so it is lot more convenient to find the source of a particular quote or excerpts quickly. If you want to try it out, you can use the code "**MBI**" (disclosure: I have no financial interest in this recommendation). I have also been using Perplexity for mostly two types of things: a) to understand complex topics which was quite handy while studying semiconductors. Asking follow-up questions and requesting it to simplify the jargons certainly helped while going through jargon filled world of semiconductors, and b) uploading earnings transcripts and asking questions about the recent earnings call. While I still read earnings transcripts in full for my largest holdings, for smaller positions I have resorted to this method to stay updated. As someone running one-person investment research service, I appreciate this efficiency/productivity gain. 2. Finally, last month, I went to Omaha to attend Berkshire's AGM. One of the best things about attending this meeting is the opportunity to meet some of my subscribers. As I work from home writing Deep Dives, my work can feel a little amorphous at times; so I enjoy the fact that it feels a lot more tangible when I meet some of you. Thank you for supporting my work. I appreciate it very much! [Subscribe](#/portal/signup) ### Dollar General 1Q'24 Update URL: https://www.mbi-deepdives.com/dg1q24/ Last updated: 2024-05-30T19:09:10.000Z *Disclosure: I own shares of Dollar General* *“Dollar General (DG)’s stock had an interesting reaction to today’s earnings. First it went up by \~6% in pre-market, but then ended the day 5% down.”* I actually wrote that in last quarter’s update and surprisingly, this event more or less repeated this quarter as well. Stock initially went up \~8%, but currently trading \~7% down. Here are some highlights from today’s call. **Same Store Sales (SSS)** After three consecutive quarters of tepid SSS growth, DG returned to a more healthy SSS growth of 2.4% in 1Q’24\. Like 4Q’23, SSS was again driven by +4% customer traffic growth. This was offset by a decline in avg. transaction amount which was driven by fewer items per basket. DG’s traffic experienced negative growth in 2020, 2021, 2022, and in 1H’2023\. Since then, traffic returned to positive trend which is an encouraging sign. Moreover, DG continues to experience trade downs: > like we saw in Q4, what we're seeing is that the next cohort and the one above that, so let's call it middle- to upper-middle income and then in some of the upper-income strata, we're seeing the trade down still come in. So we feel good that we're getting new customers in. We can see it in our data, and that we're retaining at a high level those core customers of ours in that lower income strata ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7b16ddcd-e24a-4829-a5dd-de674039d04f_1036x691.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) SSS increase was driven entirely by consumable category and was partially offset by declines in non-consumables (home, seasonal and apparel categories). After non-consumables grew faster than consumables during 2020, it was almost 10 consecutive quarters of sales decline YoY in non-consumables (excluding 4Q’22)!! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fce1f1c8e-6ee9-4d9a-9077-b832456f47d1_1894x117.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) DG says non-consumables category is under pressure as consumers exhibit more cautious behavior in their discretionary spending. I wonder if the rise of Temu may have contributed to their struggle in selling discretionary items. **Gross Margin** Since non-consumable segment is relatively higher margin segment, the mix shift has hurt DG’s overall gross margin. Gross margin declined by 145 bps YoY. Apart from mix shift, higher than expected shrink and markdowns contributed to the gross margin pressure: > “Shrink continues to be our most significant headwind and was 59 basis points worse in the first quarter compared to prior year. > > With regards to markdowns, we're seeing promotional levels more similar to 2019 levels, as we anticipated coming into the year.” ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdb57ae28-ad3b-47a3-a8c5-1bccf0e92849_1140x679.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Shrink** To combat shrink, DG converted 12,000 stores (\~60% of total stores) away from self-checkout so far this year. Going forward, DG plans to have self-checkout options available in a limited number of stores, most of which are higher-volume and low shrink locations. DG management seems a bit cagey when it comes to talking about shrink. While at one point CEO said *“what we're seeing on the shrink front right now is what we thought we would”,* CFO clearly indicated otherwise during the call: > …shrink is currently trending worse than we initially expected coming into the year, and we now expect this headwind to be greater in 2024 than what was originally contemplated in the financial guidance we provided on our earnings call in March. We're taking aggressive and decisive action to mitigate this challenge, and we're expecting to see improvement later in the back half of 2024 than we had previously anticipated and more significantly, into 2025. Once self-checkout options become very limited, it’s quite likely that we will see shrink to bottom sometime by this year. **Operating margin** While DG’s operating margin bottomed in 3Q’23 and it came back to close to \~6% in 4Q’23, it has gone slightly in the wrong direction this quarter. SG&A as % of sales increased 97 bps YoY driven by retail labor, depreciation and amortization, incentive compensation, and repairs and maintenance. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F001f6385-5a30-4680-95f5-861fb7343815_1155x684.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Inventory** Inventories stood $6.9 Bn in 1Q’24, -5.5% YoY and a decline of 9.5% on a per store basis. Non-consumables inventory was -19.1% YoY and -22.5% on per store basis. **Store expansion** DG has slightly changed its store expansion cadence. While they initially guided 800 new store openings this year, they lowered the number to 730\. They now expect to remodel 1,620 stores this year compared to previous expectation of 1,500 remodels. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74814b4b-a585-4d47-b5d9-743262bb83a0_649x390.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** DG’s outlook for 2024 remains the same: > we're reiterating our financial guidance for 2024 and continue to expect net sales growth in a range of approximately 6% to 6.7%, same-store sales growth in a range of 2% to 2.7%, and EPS in a range of $6.80 to $7.55\. This guidance continues to assume an estimated negative impact to EPS of approximately $0.50 due to higher incentive compensation expense and an effective tax rate in a range of approximately 22.5% to 23.5%. Although they don’t typically guide by quarter, DG provided some more color on 2Q’24\. 2Q’24 SSS guide is low 2% range and EPS guide is $1.7-1.85. **Final Words** While traffic and SSS trend remain quite encouraging, it’s disappointing to see operating margin trend in the wrong direction. Looking at consensus estimates, market clearly doesn’t expect DG to go back to its earlier \~8-9% operating margin days. As a shareholder, I disagree with market’s pessimism here, but I’m wary that the longer it takes DG to go back to its \~8% operating margin days, the more unlikely it will be for them to return and sustain such margin. As a result, I am drawing a hard line. If DG fails to post operating margin of >6.5% by 2025, I am unlikely to remain a shareholder. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff2fadeb1-286d-426c-8949-b1d79767f614_813x478.png) Source: Tikr, MBI Deep Dives **Further reading**: My [Deep Dive](https://www.mbi-deepdives.com/dg/) on DG (August, 2023) Thank you for reading. I will cover Lululemon's earnings next week. ### TSMC: The Most Mission-Critical Company on Earth URL: https://www.mbi-deepdives.com/tsm/ Last updated: 2024-10-17T14:18:34.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- On my Semiconductor [Primer](https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/), I mentioned the following quote from the book “[Chip War](https://www.amazon.com/Chip-War-Worlds-Critical-Technology/dp/1982172002?ref=mbi-deepdives.com)” to substantiate the pervasiveness of chips in modern world: *“Last year, the chip industry produced more transistors than the combined quantity of all goods produced by all other companies, in all other industries, in all human history.”* As you can imagine, chips are pervasive in our modern civilization. From washing machines to smartphones and fighter jets, chips are indeed indispensable. While in the early years of semiconductor industry almost every company used to own **Fabs** i.e. semiconductor manufacturing facilities, chip manufacturing has gradually been outsourced to **Foundries** i.e. pure-play semiconductor manufacturing facilities that produce chips for other companies (fabless firms). Today, foundries manufacture supermajority of the chips produced in the world, and Taiwan Semiconductor Manufacturing Company (**TSMC**) alone has \~60% market share in the global foundry market. ![Global Semiconductor Foundry Market Share: Quarterly - Counterpoint](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F570fb100-3e6d-4202-ab5d-1428cb84a027_2000x1460.png "Global Semiconductor Foundry Market Share: Quarterly - Counterpoint") Source: [Counterpoint](https://www.counterpointresearch.com/insights/global-semiconductor-foundry-market-share/?ref=mbi-deepdives.com) Perhaps more astonishingly, TSMC has a de-facto monopoly with **\~90%** market share in the leading edge nodes (manufacturing processes with the smallest transistor sizes and highest densities). Leading edge nodes are crucial for applications requiring the highest computing performance like supercomputers, advanced servers, high-end PCs/laptops, smartphones, AI/machine learning, and military/defense systems. As a result, the very basic tenet of modern life is essentially standing on the shoulders of one company based in Taiwan. But how did a company in Taiwan end up being so mission-critical for nearly all leading technology companies today? If Apple is Steve Jobs with ten thousand lives, Morris Chang almost feels like a similar figure for TSMC. Chang is perhaps the epitome of late bloomers as he founded TSMC at the ripe age of [55](https://www.wsj.com/tech/tsmc-morris-chang-taiwan-semiconductor-chips-entrepreneurship-506fcbc4?ref=mbi-deepdives.com) and led the company for almost two decades before retiring at age 74\. His retirement proved to be short lived as he came back again to lead TSMC in 2009\. At the time of his return, TSMC was \~$40 Bn market cap company. By the time he finally retired from the board at age 87 in June 2018, TSMC became a \~$200 Bn company. Today, TSMC is valued **\~$650 Bn**. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F21732153-0c84-453e-a48a-635d095be483_2400x1240.png "chart") Figure: Morris Chang’s “Second” term at TSMC; Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Imagine starting a company at age 55 in a geopolitically fragile country with an unproven business model and then building it to be the most mission-critical company on earth! I am not erudite enough to capture my astonishment and admiration in words for Morris Chang, but it’s important to understand his story and TSMC’s early days to better appreciate what TSMC is today. Chang was born in China. At age six, Chang, along with his mother, left for Hong Kong as the [Second Sino-Japanese War](https://en.wikipedia.org/wiki/Second%5FSino-Japanese%5FWar?ref=mbi-deepdives.com) (often regarded as the beginning of World War II in Asia) started. Then on December 7, 1941, the same day the attack on Pearl Harbor happened, Japan launched a coordinated offensive across the Pacific targeting several territories of the United States and British empires. One of the key targets was the British colony of Hong Kong. Chang quickly returned to Shanghai. That also proved to be a short stay as Chinese Communist Revolution started a few years later, and Chang once again came back to Hong Kong. By the time he was 18, he basically had to endure three major wars; given how his life transpired made me wonder whether Chang went through “Post-traumatic Growth” which is, as Taleb [points out](https://www.youtube.com/watch?v=g2LYTUEOn20&ref=mbi-deepdives.com), much more common than PTSD. Thankfully, Chang got accepted to Harvard and moved to the US at age 18\. However, in his sophomore year, he transferred to MIT to study Mechanical Engineering. After finishing his undergrad and Masters in the next three years, he decided to pursue PhD at MIT but ended up failing the qualifying exam twice. As Chang started looking for a job in 1955, he received two offers: one from Ford offering $479/month and the other one from Sylvania Electric Products offering allegedly just $1 more than Ford’s. Chang took the latter, and started working in Sylvania’s new Semiconductor division. Since he studied Mechanical Engineering, he decided to study some text books to get up to speed on electrical engineering. While studying Shockley’s [book](https://www.amazon.com/Electrons-Holes-Semiconductors-Applications-Electronics/dp/0442075936?ref=mbi-deepdives.com) “Electrons and Holes in Semiconductors**”,** he started showing up with the text book at the bar and buying drinks for senior colleagues so that they would entertain his questions about the book. Chang eventually moved on from Sylvania as he recalled one of the Senior managers musing the company’s predicament: *"We (at Sylvania) cannot make what we can sell and we cannot sell what we can make."* Sylvania was eventually merged with General Telephone in 1959\. A year ago, Chang already moved to Texas Instruments (TI) in 1958\. It was also the same year Integrated Circuit (IC) was invented at TI by Jack Kilby. At TI, Chang’s initial project was a particularly challenging one. While IBM usually manufactures all of their products, they decided to outsource some of the chips manufacturing to TI as the demand was too high for them to do on their own. Moreover, IBM’s own plant had just \~10% yield i.e. for every 100 chips being manufactured, 90 of them had to be discarded. TI, on the other hand, had almost zero yield when Chang was assigned to the project. In just four months, Chang was able to increase the yield to 20%! Sensing Chang’s potential, TI offered to sponsor his PhD at Stanford; Chang finished his PhD this time within just two and half years. After finishing his PhD and returning to TI, Chang became General Manager of one of the divisions within Semiconductor segment at TI. Then in just five years, he became Vice President at TI and was rumored to be the leading candidate to be the next CEO of TI. Although Chang became US citizen in 1962, some allege that Chang might have been passed over for the TI’s CEO role because of his Chinese ethnicity. While that’s a popular theory, it’s far from the only explanation why Chang might have been looked over for the CEO role. After running TI’s semiconductor division for six years, Chang was given responsibility to turn around TI’s struggling consumer products division. Chang wasn’t successful in that role. Then after nearly three decades of career at TI, Chang moved to General Instrument as their COO. He resigned from this role in just 18 months. Even his first marriage was falling apart. As Chang was grappling with both personal and professional challenges, [K.T. Li](https://en.wikipedia.org/wiki/Li%5FKwoh-ting?ref=mbi-deepdives.com), who became subsequently regarded as “Father of Taiwan's Economic Miracle”, invited Chang to come to Taiwan to lead Industrial Technology Research Institute (ITRI) which was supposed to be Bell Labs type research organization. Chang considered it to be essentially his retirement job. That all changed when K.T. Li gave him a new assignment. Li asked Chang to start a new semiconductor company in Taiwan and make it a global leader. Taiwan, despite being underdeveloped country back then, wasn’t quite stranger to semiconductor industry. But the role their companies played was quite low value-add as indicated by their \~**4-5% gross margin**. As Chang mused to come up with a business plan, he realized the only viable path for his new company may be pure-play foundry! Here’s Chang’s thought process in his own [words](https://www.semi.org/en/Oral-History-Interview-Morris-Chang?ref=mbi-deepdives.com): > …one thread of thought as I paused and thought about the task that Mr. K.T. Li gave to me…he wanted me to present a business plan, he wanted me to start a semiconductor company. > > And another thread was what I had already observed, closely observed, for three decades. I had been in the semiconductor business for three decades before I came to Taiwan. And I learned at close quarters how competitive the industry was, and how good some of the players were—companies like Intel, Texas Instruments. Even then the Japanese companies were very fierce also. I knew how competitive it was, and how difficult it would be to carve out a niche for a new Taiwan company. So that was the second thread of thought. The third thread of thought was I paused to try to examine what we have got in Taiwan. And my conclusion was that \[we had\] very little. What strengths have we got? The conclusion was very little. We had no strength in research and development, or very little anyway. We had no strength in circuit design, IC product design. We had little strength in sales and marketing, and we had almost no strength in intellectual property. The only possible strength that Taiwan had, and even that was a potential one, not an obvious one, was semiconductor manufacturing, wafer manufacturing. And so what kind of company would you create to fit that strength and avoid all the other weaknesses? The answer was pure-play foundry. While Chang came to this conclusion, it was far from easy to execute on this conclusion. The conventional “wisdom” back then was “Real Men Have Fabs” i.e. any serious semiconductor company should manufacture their own chips instead of outsourcing it. But thanks to Moore’s law, demand for chips kept growing and the Integrated Device Manufacturers (**IDM**s), i.e. companies who both design and manufacture, were willing to outsource some of their manufacturing demand to pure-play foundries. Given it was mostly “leftover” demand, this business was far from stable. Despite all the uncertainty around the business model, the Government of Taiwan decided to fund half of the $220 mn funding Chang decided to raise for his new company. \~28% of the fund came from a Dutch company named Philips, the same company that started ASML (a joint venture between ASM and Philips). The rest \~22% was raised from various business leaders in Taiwan who were essentially told by the government to invest in this new company. Taiwan Semiconductor Manufacturing Company (TSMC) was founded in 1987\. While Morris Chang was going to lead TSMC, he initially didn’t have **any** equity. In the initial years, TSMC was mostly just manufacturing the “leftover” orders from the IDMs, but it was the rise of fabless companies that truly turbocharged TSMC’s growth. Today’s behemoths such as Nvidia, Qualcomm, and Broadcom were all started right after TSMC was founded and given none of them had fabs to manufacture their own designed chips, they were looking to outsource their manufacturing to someone else. As the Fabless companies grew from just a handful to hundreds or even thousands of them over time, so did TSMC’s revenue. TSMC became publicly listed in Taiwan in 1994 at $4 Bn valuation. It later became listed in the US in 1997. With the rise of Fabless companies, TSMC started to enjoy bit of a flywheel effect! As TSMC gets better and better in manufacturing chips with more and more advanced process nodes, their customers can address and capture higher market share which creates even more demand for TSMC’s chip manufacturing prowess. Even though TSMC started with significant technological disadvantage, it didn’t take them too long to get up to speed of their peers and then gradually surpass everyone else over the years! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F178f181b-1b4e-4e3e-9738-ae48ee0d76fd_3482x2250.jpeg) With \~$8 Bn revenue and $2.8 Bn operating Profit, TSMC appeared to be at pretty solid ground in 2005\. Chang was 74 years old then and decided to retire by handing over the leadership position to Rick Tsai. However, this retirement proved to be only a temporary one as he decided to return to the helm of TSMC at age 78 during the summer of 2009. Even though the economy was still reeling through the ramifications of GFC, the late 2000s also introduced iPhone and smartphones to the world. When competitors were hesitant to make big investments in such macro backdrop, Chang decided to aggressively invest and create a distance from their competitors. While TSMC invested only \~$12 Bn cumulative capex during 2005-2009 period, they invested a whopping \~$40 Bn cumulative capex during 2010-14 period following Chang’s return to the company. It was also the time when they started to forge a close rapport with Apple, the largest customer of TSMC today which alone generated \~$18 Bn revenue for the company in 2023 (more on this later). Chang relinquished TSMC’s CEO role in 2013 but remained Chairman of the board. After three decades with the company since its very beginning, Morris Chang finally left the TSMC board in 2018\. It is remarkable to think that Chang had two 30-year careers with two different companies. I am a tad bit surprised that there hasn’t been a movie on his life yet! I have tried to condense as much as I could, but I strongly encourage readers to listen to Acquired [podcast](https://www.acquired.fm/episodes/tsmc?ref=mbi-deepdives.com) on TSMC which was my primary source to understand the founding story of TSMC. During 2013 to 2018, TSMC was led by two co-CEOs: Mark Liu and C.C. Wei. Following Chang’s departure from the board, Liu became the Chairman and Wei became the sole CEO. Liu is set to retire as well in June 2024 and Wei will then take over both the CEO and chairman role at TSMC. Here’s the outline for the rest of the Deep Dive: **Section 1 The Economics of TSMC**: This section starts with a very brief discussion on the basics, followed by a more granular discussion of TSMC’s revenue, pricing power, partnership with Apple, and operating cost structure of the business. **Section 2 Competitive Dynamics**: This section explores first why Taiwan was so successful in chip manufacturing, and the geopolitical implications on the chip manufacturing industry. This then leads to Intel and Samsung’s recent attempts to be competitive against TSMC at advanced chip manufacturing and what this may mean for pricing power of TSMC. **Section 3 Capital Allocation**: I showed capital allocation of last couple of decades at TSMC in this section. **Section 4 Model Assumptions and Valuation**: Model/implied expectations in the current stock price are analyzed here. **Section 5 Final Words**: Concluding remarks on TSMC, disclosure/discussion of my overall portfolio. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### May, 2024 Update URL: https://www.mbi-deepdives.com/may2024/ Last updated: 2024-05-12T14:15:00.000Z This month's Deep Dive will be on **TSMC** which I'm hoping to publish sometime in the last week of this month. Following TSMC, I will spend some time out of semis for a few months and then come back to semis later in the year. I also wanted to share some brief notes on why I have started buying Aon today. **Why I am buying Aon** I bought a \~3% position in Aon (Ticker: AON) at $282/share and am open to increase exposure if stock continues to go down. I first covered the insurance brokers in June 2023 when I did a [Deep Dive](https://www.mbi-deepdives.com/bro/) on Brown & Brown (Ticker: BRO). After owning BRO and following other insurance brokers for almost a year, I have only appreciated the simplicity of the business even more. These businesses are just as exciting as watching paint dry, but quite a few publicly listed insurance brokers have been able to keep compounding for their shareholders for decades and it’s much more likely than not that it may continue to be the case. Aon is primarily an insurance broker that sells insurance on behalf of the carriers and gets paid largely a fixed percentage of commission on the insurance premium. I love the broker business model because a) they are capital light with \~30% operating margin and \~30% ROIC, b) there is no insurance underwriting risk and c) business is largely immune from inflation over the long-term. However, the business is not quite immune from typical P&C cycle and revenue growth will be affected depending on where we are in the P&C cycle but for long-term shareholders, we need not fret too much over the cycle as long as we get to buy the stock at reasonable price and let the economics of the business prevail over the course of the entire cycle. Unlike BRO, Aon primarily serves the Fortune 500 i.e. large corporate customers across 120 countries. Its revenue mix in 2023 was as follows: Commercial Risk Solutions (53%), Reinsurance Solutions (19%), Health Solutions (18%), and Retirement Solutions (11%). Revenue in terms of geographical mix in 2023 was as follows: US 44%, Americas ex US 9%, UK 14%, EMEA 21%, and APAC 12%. Aon is quite a competitively advantaged business for serving its core customers: large corporates. Its more or less a duopoly with Marsh & McLennan (Ticker: MMC) in that segment. There’s a somewhat of a struggling third player here: Willis Towers Watson (Ticker: WTW) which Aon wanted to acquire back in 2020 but DOJ deemed the deal to to be anti-competitive. Going through DOJ’s [complaint](https://www.justice.gov/atr/case-document/file/1425181/dl?ref=mbi-deepdives.com) of the deal gives you a pretty good picture of the competitive dynamics in this industry. Some key excerpts from DOJ's complaint below: > *“Aon and WTW are the second- and third-largest insurance brokers in the world. Together, Aon, WTW, and Marsh McLennan (“Marsh”) tower above other firms—so much so that they are often referred to as the “Big Three.” The Big Three dominate competition for insurance broking for the largest companies in the United States, *almost all of which are customers of at least one of them.* The Big Three compete with each other directly on price, service, and the development of innovative solutions to the challenges these customers face. *Other broking firms do not offer large customers the same quality and combination of services* that the Big Three currently deliver: extensive global networks of offices, sophisticated data and analytics, a breadth of knowledge across multiple types of employee benefits and risk management strategies, strong reputations, and depth of personnel with specialized expertise. With respect to these qualities, the Big Three distinguish themselves from other firms.* > *High levels of concentration exist because customers view Aon and WTW—along with Marsh— as offering key advantages over other firms. First, through a mix of broad data, deep experience, knowledge, and institutional resources that outstrip smaller insurance brokers, Aon and WTW can customize their products to fit a particular client’s unique needs. Second, Aon and WTW offer, and have deep talent across, the full range of commercial risk and employee benefits products and services, *allowing them to provide advice and insights that would not be possible for a smaller firm with a narrower scope*. Third, Aon and WTW have extensive global networks of offices that facilitate the provision of seamless worldwide service for multinational customers. Finally, as crucial sources of business for insurance carriers, Aon and WTW are able to secure carriers’ attention on behalf of their customers more easily and promptly than could any individual customer (or smaller insurance broker).* > *Among large customers in the United States, *Aon and WTW have a combined market share of at least 40%* for broking property damage risk, third-party liability (or “casualty”) risk, and financial risk, which together account for the majority of most large customers’ commercial risk insurance expenditures.* > **Past attempts have shown that successful entry is difficult*. For example, several years ago a number of employees from one of the Big Three attempted to start their own commercial risk broking firm with a focus on serving large customers. Despite having deep experience in the industry and existing relationships with many potential customers, this new venture failed to take much business from Aon, WTW, and Marsh. Similarly, *at least one major direct-to-consumer provider spent several years attempting to expand into the private multicarrier retiree exchange market, but has since abandoned that effort due to a lack of success*. This direct-to-consumer provider’s foray into private multicarrier retiree exchanges was hampered by, among other things, its lack of reputation and experience with large employers that Aon and WTW have handled for years*.” Although WTW deal didn’t go through, it didn’t quite change the reality of the competitive dynamics in this industry. WTW is still there as an independent company but AON and MMC may gradually prove to be the “the Big Two”. There’s not much of an impetus for intense price competition among the “Big Two”, so it is probably fair to assume stability in the commission structure of their core business. How about disintermediation risk? MMC has been around for 150 years. Insurance broking is a really old business model as customers, especially large sophisticated corporate customers have always intuitively understood the inherent conflict of interest of buying non-standardized insurance products directly from the carriers. As a result, the middlemen i.e. brokers have played a key role in the insurance industry for more than a century in the US. That seems unlikely to change going forward. One interesting thing that AON is currently trying to do is acquire its way to middle market insurance. They recently completed an acquisition of NFP for $13.4 Bn (\~15x EBITDA). Success in middle market is far from guaranteed as it’s effectively a different market from large corporates. However, in case AON attains compelling economics from this deal, AON may end up being lot more acquisitive in middle market as well. If not, they may just contain themselves within large corporates. This is bit of an unknown to me, but even if Aon's foray into middle market insurance turns out to be wrong, it is unlikely to be a major setback. AON has organically grown its topline at \~MSD rate in the last 5-10 years and should continue to grow at nominal GDP+ 0-2% organic growth rate for years to come (again, growth can be affected by P&C cycle but this should hold true over the course of the cycle). Given the stock currently trades at \~18x P/E and they typically enjoy some margin expansion each year, we can get to LDD IRR with reasonable assumptions: \~5% organic revenue growth+ \~0-1% margin expansion+ \~5-6% earnings yield. If they manage to deploy capital at attractive ROIC by acquiring smaller middle market insurance brokers, IRR can be notched up a bit. In the most recent quarter, AON’s organic growth lagged its peers which led to decline in AON’s multiples compared to MMC’s. Over the last 10 years, AON hardly ever traded below MMC and S&P 500 NTM EV/EBITDA multiples, but today they trade at 1-turn and 2-turn below S&P 500 and MMC NTM EV/EBITDA multiple respectively. I consider the stock to be good value. ![chart](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/05/data-src-image-e64a379f-42ab-4ce1-aaad-a213e83d61c7.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Finally, I am writing this from Omaha. Feel free to say hello if we stumble into each other in the streets of Omaha for the next few days! Thank you for your support. [Subscribe](#/portal/signup) ### Amazon 1Q'24 Update URL: https://www.mbi-deepdives.com/amzn1q24/ Last updated: 2024-05-01T02:17:48.000Z *Disclosure: I own Jan 2025 $55 call options of Amazon* Now that Meta, Alphabet, Microsoft, and Amazon all reported their quarters, we now have better context to how their quarters went. So, while I will mostly discuss Amazon's earnings in this update, I will briefly touch on some broader themes as well. [Subscribe](#/portal/signup) **Revenue** Overall revenue was slightly below the high end of Amazon's guidance. However, once you adjust for the FX (\~$700 mn adjustment), revenue was actually higher than high end of Amazon's guide last quarter. It's interesting to note that while all of their businesses basically overcame the pandemic hangover, Amazon's 1P business (online +physical stores) is barely growing on 3-yr CAGR basis. Apart from 1P which is also likely their least profitable segment, every single segment of their business is growing at a healthy rate. Both AWS and 3P are now >$100 Bn run-rate business and yet growing at high teen rates YoY. While most people are focused on Gen AI's implications for AWS, it's interesting to see how this technology can be a boon for Amazon's 3P business as well: > We've recently launched a new generative AI tool that enables sellers to simply provide a URL to their own website, and we automatically create high-quality product detail pages on Amazon. Already, over 100,000 of our selling partners have used one or more of our gen AI tools ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-20.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** While North America's margin slightly went down QoQ, it was still +5.8% in 1Q'24 vs +1.2% in 1Q'23\. International segment's operating margin improvement was even more impressive as it went from -4.3% in 1Q'23 to +2.8% in 1Q'24\. Ads was an "**important contributor**": > Advertising remains an important contributor to profitability in North America and international segments. We see many opportunities to grow our offerings, both in the areas that are driving growth today like sponsored products and in areas that are newer, like streaming TV ads. While North America's operating margin got pretty close to pre-pandemic level, Amazon reminded that it should not be seen as a ceiling since advertising used to be much less of a contributor back then. With ads ramping up, these segments should be more profitable: > We look back to before the pandemic, and we say, first, **we can achieve those operating margins even without the impact of advertising**. And we're not quite there yet. But we're not limiting ourselves to that. We're looking for ways to, again, turn over every rock, look at every process and everything that we do on the logistics side and see **how can we get our cost structure down** and how can we get speed up and selection up. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-21.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Fulfillment+ Shipping** Speaking of logistics, here are some highlights from on this topic from today's call: > In this past Q1, we delivered to Prime members at our fastest speeds ever. In March, **across our top 60 largest U.S. metro areas, nearly 60% of Prime members orders arrived the same or next day. And globally, in cities like Toronto, London, and Tokyo, about 3 out of 4 items were delivered the same or next day**.. > As we further optimize our network, **we've seen an increase in the number of units delivered per box, an important driver for reducing our cost**. When we're able to consolidate more units into a box, it results in fewer boxes and deliveries, a better customer experience, reduces our cost to serve, and lowers our carbon impact.. If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter used to consistently outpace the former pretty much all the time since 2015 until 3Q'22\. Since then, unit growth is faster (was at par in 3Q’23) than shipping+ fulfillment costs, indicating operating leverage in their logistics footprint. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-30.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Advertising** For the first time in a long time, Amazon didn't grow its advertising revenue the fastest among the big three (Google, Meta, and Amazon). To be fair, Amazon didn't have as much of an easy comp as Meta did, so would discourage anyone from inferring much from this data. While Meta has \~30% market share in digital ads (Note: see definition of "digital ads), it has taken >40% incremental ad dollars in the last 5 quarters. It will be interesting to observe how the next 5 quarters play out given their easy comp is now behind them. If Meta AI leads to some share in the bottom of the ad funnel thanks to high intent data from user chats, Meta's high incremental market share may persist even beyond their easy comp duration. *(*Note*: quite a few subscribers suggested me to include TTD in this table; while I wanted to do that, TTD hasn't reported their Q1 yet, so I will update it once they do)* ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/05/image.png) **AWS** Okay, now let’s talk about AWS. This was Amazon's best Q1 ever. If you notice last few years, historically incremental QoQ revenue in Q1 is generally the lowest. If that remains the case in 2024, this may prove to be a pretty robust growth year for AWS after digesting through last several quarters of "optimization" from their customers. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-22.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Amazon believes they continue to enjoy the highest absolute growth over their competitors (hard to validate since others don’t disclose exact numbers): > It's useful to remember that year-over-year percentages are only relevant **relative to the total base from which you start. And given our much larger infrastructure cloud computing base, at this growth rate, we see more absolute dollar growth again quarter-over-quarter in AWS than we can see elsewhere.** ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-23.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); \*Google Cloud includes Google Workspace, so not quite apple-to-apple and in reality, GCP likely grew faster than Google Cloud One thing I would like to track is Google Cloud’s operating performance trajectory against AWS. While Google Cloud’s revenue somehow managed to maintain its gradual momentum against AWS, opex trajectory went to the opposite direction. That may be less of a fault by Google Cloud and more of a credit to AWS. But do remember that given Google Workspace is likely higher margin segment within Google Cloud, GCP's economics is likely considerably worse than AWS and may have plenty of catch up to do in the future. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-26.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-27.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Nonetheless, the reason I was saying Google Cloud's opex trajectory is likely less of a fault on their part is **AWS opex actually declined YoY while their revenue increased by \~$3.7 Bn, which led to an eye-watering operating margin of \~38%.** Don't get used to such margins though as AWS expects to ramp up their capex materially going forward which will lead to higher depreciation and lower margin initially but will get to normalized margin once the utilization improves: > We expect the combination of AWS' reaccelerating growth and high demand for gen AI to meaningfully increase YoY capital expenditures in 2024, which given the way the AWS business model works is a positive sign of the future growth. **The more demand AWS has, the more we have to procure new data centers, power and hardware. And as a reminder, we spend most of the capital upfront. But as you've seen over the last several years, we make that up in operating margin and free cash flow down the road as demand steadies out. And we don't spend the capital without very clear signals that we can monetize it this way**. > ...in Q1, we had $14 billion of CapEx. **We expect that to be the low quarter for the year**...And we continue to see strong CapEx performance in our stores business. Most of that will be related to modest capital or capacity increases in addition to our same-day fulfillment network and some Amazon Logistics upgrades to the fleet. **But for the most part, what you'll see is really going to be on the AWS side**. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-25.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-24.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Some other interesting tidbits on AWS from the call: > ...companies have largely completed the lion's share of their cost optimization and turned their attention to newer initiatives. Before the pandemic, companies were marching to modernize their infrastructure, moving from on-premises infrastructure to the cloud to save money, innovated at a more rapid rate, and to drive more developer productivity. The pandemic and uncertain economy that followed distracted from that momentum, **but it's picking up again**. Companies are pursuing this relatively low-hanging fruit in modernizing their infrastructure. > ...We see considerable momentum on the AI front where we've accumulated a **multibillion-dollar revenue run rate** already. > today, we announced the general availability of Amazon Q, the most capable generative AI-powered assistant for software development and leveraging company's internal data. > ...I think the thing that people sometimes don't realize is that while we're in the stage that so many companies are spending money training models, once you get those models into production, which not that many companies have, but **when you think about how many generative AI applications will be out there over time, most will end up being in production when you see the significant run rates. You spend much more in inference than you do in training because you train only periodically, but you're spinning out predictions and inferences all the time**. > ...**we see both training and inference being really big drivers on top of AWS.** And then you layer on top of that the fact that so many companies, their models and these generative AI applications are going to have their most sensitive assets and data. And it's going to matter a lot to them what kind of security they get around those applications. And yes, if you just pay attention to what's been happening over the last year or 2, **not all the providers have the same track record**. And we have a meaningful edge on the AWS side so that as companies are now getting into the phase of seriously experimenting and then actually deploying these applications to production, people want to run their generative AI on top of AWS. **Opex+Capex** I would caution readers from getting too excited about Amazon's higher gross margins in 1Q'24 since the cost of sales for AWS is actually reported within R&D (or as they say "Technology & Content"). As a result, we don't really know for sure what Amazon's gross margin is. However, looking at its cost of sales as % of revenue and AWS reported operating margin, it is perhaps safe to assume that its gross margin is indeed improving (but just wanted to remind that the lack of hard data as evidence). Just like other big tech, Amazon's capital intensity has also gone up materially with their increased scale. However, given much of the current capex cycle is driven by AWS which has a pretty high visibility to attractive ROIC, I very much applaud this capex. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-28.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Other Bets** What I like less and less is complete lack of disclosure or even paltry discussion on Amazon's "other bets" (i.e. Alexa, Kuiper et al) in their earnings call. **Outlook** Amazon’s guidance for 2Q’24 is below: ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-32.png) Source: Amazon Press Release One thing Amazon highlighted in the call while discussing the guide is they are seeing Europe to fare worse than the US in the current quarter: > As part of our guidance considerations, we also continue to keep an eye on consumer spending and macro level trends, specifically in Europe, where it appears to be a bit weaker relative to the U.S. For more in-depth valuation discussion of Amazon, see my analysis [here](https://www.mbi-deepdives.com/amzn2024/) (February, 2024). Please feel free to share with your friends and network. Thank you for reading. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* [ ](https://www.mbi-deepdives.com/meta1q24/) ### Alphabet 1Q'24 Update URL: https://www.mbi-deepdives.com/goog1q24/ Last updated: 2024-04-26T03:05:17.000Z "...*it took Google more than 15 years to reach $100 billion in annual revenue. In just the last 6 years, we have gone from $100 billion to more than $300 billion in annual revenue*" Despite following Big Tech closely for the last 5 years, this sentence from today's call still somewhat surprised me! The scale and the height of success of Google **truly boggles** my mind. Over the last year or so, Google has had its fair share of skeptics, including yours truly. While the stock price used to embed some tension and debate about Search's future, following after-hours (AH) rally, as I will show, it appears that the stock is finally priced as "AI winner". Here’s my highlights from today’s earnings. [Subscribe](#/portal/signup) **Revenue** For the second consecutive quarters, every important segments i.e. Search, YouTube, Cloud accelerated their topline growth. Overall revenue growth was +15% YoY (+16% FX Neutral) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-5.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Amidst the bearish narrative around Search, Google Search's topline growth of 14.4% was quite impressive. But to appreciate Search's resiliency, I would like to highlight something else. If it were a trivia question which of these three businesses (Meta's Family of Apps' or FOA Ads, Google Search, and YouTube Ads) generated highest topline growth over the last three years, my guess is most people would get it wrong. While many may think growth in Meta's FOA has been extraordinary, **Search actually outpaced FOA and YouTube ads** over the last 3-year period. Meta's FOA just appeared more impressive this quarter primarily due to easier comps. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-15.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ***EBIT*** Google Services business posted 39.6% operating margin which was very close to its all-time high reported margin in this segment back in 3Q'21\. When Search related bearish narrative gained momentum last year or so, I too started entertaining the possibility that we may have seen peak margin for Google Service segment back in 3Q'21\. **With >70% incremental operating margin in the Services segment for three consecutive quarters**, Google seems poised to post record margins sooner rather than later. For the second consecutive quarters, Google Cloud posted +9.4% operating margin. Moreover, TAC as % of Ad revenue was the lowest as far as my eyes could see in the last 5 years (may be the lowest ever, but not 100% sure about it). Sluggish revenue from Network business (which is higher TAC) perhaps made this metric look lot better (maybe higher Android smartphone share too? Not sure though). ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-16.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Search** Some interesting comments on Search from the call: > based on our testing, we are encouraged that **we are seeing an increase in search usage among people who use the new AI overviews as well as increased user satisfaction** with the results. > since introducing SGE about a year ago, **machine costs associated with SGE responses have decreased 80%** from when first introduced in Labs driven by hardware, engineering, and technical breakthroughs > Overall, I think with generative AI in Search, with our AI overviews, I think we will **expand the type of queries we can serve our users**. We can answer more complex questions as well as, in general, that all seems to carry over across query categories. Obviously, it's still early, and we are going to be measured and put user experience at the front, but we are positive about what this transition means Almost near the end of the call, Pichai reminded all the skepticism that was hurled at Google and yet how they have prevailed and expect to thrive going forward: > ...if you were to step back at this moment, there were a lot of questions last year, and we always felt confident and comfortable that we would be able to improve the user experience. People question whether these things would be costly to serve, and we are very, very confident we can manage the cost of how to serve these queries. People worried about latency. When I look at the progress we have made in latency and efficiency, we feel comfortable. There are questions about monetization. And based on our testing so far, I'm comfortable and confident that we'll be able to manage the monetization transition here well as well. It will play out over time, but I feel we are well positioned. And more importantly, when I look at the innovation that's ahead and the way the teams are working hard on it, I am very excited about the future ahead. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-10.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **YouTube** > viewers are watching over **1 billion hours** of YouTube content on TVs daily. > And on subscriptions, which are increasingly important for YouTube, we announced that in Q1, **YouTube surpassed 100 million Music and Premium subscribers globally, including trials**. And **YouTube TV now has more than 8 million paid subscribers**. > In 2023, more people created content on YouTube than ever before, and **the number of channels uploading Shorts year-on-year grew 50%.** > In the U.S., the monetization rate of Shorts relative to in-stream viewing has more than **doubled** in the past 12 months, including a **10- point sequential improvement** in the first quarter alone. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-9.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Advertising Infrastructure** > Advertisers using PMax asset generation are **63% more likely** to publish a campaign with good or excellent ad strength. And those who improve their PMX ad strength to excellent see **6% more conversions** on average. > We're also driving improved results for businesses opting into Automatically Created Assets (ACA), which are supercharged with gen AI. Those adopting ACA see, on average, **5% more conversions** at a similar cost per conversion in Search and Performance Max campaigns. **Google Cloud** Cloud revenue growth again accelerated. Interestingly, Google expects YouTube overall (ads+ subscriptions) and Google Cloud will have combined $100 Bn revenue run-rate by the end of 2024 which implies pretty sustained growth momentum throughout the year. I will discuss more on Cloud when Amazon posts next week. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-8.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Google Other** Google One (subscription service that provides additional storage for Google Drive, Gmail, and Google Photos) now has \~100 million paid subscribers. **AI** Contrary to popular perception, Pichai outlined how AI can be gamechanger for Google: > I think **for the first time**, we can work on AI in a **horizontal way and it impacts the entire breadth of the company, be it Search, be it YouTube, be it Cloud, be it Waymo and so on.** And we see a rapid pace of innovation in that underlying. **So it's a very leveraged way to do it**, and I see that as a real opportunity ahead. **In terms of the challenges, I think it's been a mindset shift, which we've been driving across the company** to make sure that we are embracing this opportunity but being very efficient in how we are approaching it **Capital Allocation** Google mostly utilized all of its FCF to buyback shares, declining shares outstanding by 0.6% QoQ. For the first time, they have declared cash dividend of $0.20/share. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-11.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex and Opex** Google spent $12 Bn capex in Q1 and indicated capex will be around $12 Bn or above for each of the three remaining quarters. So, call it \~$50 Bn capex in 2024 (vs \~$32 Bn in 2023) compared to Meta's $35-40 Bn in 2024\. Google reminded how committed they always have been with their infrastructure (and frankly in my opinion it is perhaps an underrated moat): > We have the best infrastructure for the AI era. Building world-leading infrastructure is in our DNA, starting in our earliest days when we had to design purpose-built hardware to power Search. Our data centers are some of the most high-performing, secure, reliable, and efficient in the world. They've been purpose-built for training cutting-edge AI models and designed to achieve unprecedented improvements in efficiency. We have developed new AI models and algorithms that are more than 100x more efficient than they were 18 months ago. Our custom TPUs, now in their fifth generation, are powering the next generation of ambitious AI projects. Gemini was trained on and is served using TPUs. We are committed to making the investments required to keep us at the leading edge in technical infrastructure. You can see that from the increases in our capital expenditures. This will fuel growth in Cloud, help us push the frontiers of AI models and enable innovation across our services, especially in Search. Google’s headcount declined QoQ, but capex as % of revenue increased to 14.9% in 1Q’24 which was highest since 3Q’19\. Google seems to be balancing between headcount and emboldening their infrastructure investments. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-12.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** Google doesn’t provide guidance, but management did seem to hint at tougher comp ahead: > Looking ahead, two points to call out: first, results in our advertising business in Q1 continued to reflect strength in spend from APAC-based retailers, a trend that began in the second quarter of 2023 and continued through Q1, which means we will begin lapping that impact in the second quarter; second, the YouTube acceleration in revenue growth in Q1 reflects, in part, lapping the negative year-on-year growth we experienced in the first quarter of 2023. > > ...Q1 results reflect the benefit of leap year > > Looking ahead, **we remain focused on our efforts to moderate the pace of expense growth in order to create capacity for the increases in depreciation and expenses associated with the higher levels of investment in our technical infrastructure**. We believe these efforts will enable us to deliver full year 2024 Alphabet operating margin expansion relative to 2023. **Valuation** Since [3Q'22](https://www.mbi-deepdives.com/goog3q22/), I share the following valuation framework every quarter. For the first time since then, each of the following scenario indicates upside to be limited. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-17.png) Source: MBI Deep Dives Market is likely valuing Google Service segment at \~20x LTM EBIT and the Cloud business at \~8-10x revenue multiple. I think it's fair to say despite plenty of bearish narrative around Google, it's hard to find figment of that in the financials. Management seems rather confident that early numbers indicate GenAI is not really a threat to Search economics. As a result, investors seem to be finally willing to embrace Google as one of the winners of "tomorrow". ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-18.png) Source: MBI Deep Dives While almost all big tech seem to be cruising along today, what matters for long-term investors is where each of these businesses will stand 5-10 years from now. I have mentioned this before, but would like to reiterate that understanding, assessing, and predicting competitive dynamics, moats, and durability of such moats for big tech remains quite a challenging task. I will cover earnings of **Amazon** next week. Thank you for reading. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Meta 1Q'24 Update URL: https://www.mbi-deepdives.com/meta1q24/ Last updated: 2024-04-25T18:03:32.000Z *Disclosure: I own shares of Meta Platforms* If you have been following Meta for some time, you probably are accustomed with after-hours (AH) volatility by now. While Meta was -20% at one point AH today, it does seem a bit overdone. Of all the post-earnings drop that I have experienced following Meta since 2018, this one probably made me nervous the **least**. Here are my highlights from tonight’s call. **Users** This was another typical quarter for Meta adding \~50 mn Daily Active People (DAP) across its Family of Apps (FOA). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fded181b9-14ab-4ea0-b1bd-79fcd8a4b0e7_2005x127.png) **Ad revenue by Geography** While ad revenue growth YoY continued at a brisk pace, this was the last quarter with easy comps and hence, 2-yr CAGR is likely better reflective of underlying trend. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe0665fdd-9632-4e0f-9a1a-345ffdc8981c_1809x339.png) **Ad Impression and Avg. Price Per Ad** We got ad impression and ad price related disclosure by geography for the first time this quarter. Overall impression grew by \~20% and avg. price per ad grew by \~6% YoY. Interestingly, the strongest ad price growth was in RoW segment (which includes Africa, Middle East, and LATAM) which incidentally also had the highest revenue growth. Meta mentioned in the call that they grew conversions at a faster rate than impressions over the course of this quarter, making the ads more performant. Moreover, campaigns using Advantage+ audience targeting saw on average a 28% decrease in cost per click or per objective compared to using regular targeting. Ad revenue from Advantage+ shopping and Advantage+ campaigns has more than **doubled** since last year. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F15594ffd-f25d-4fc7-9483-2e9da8d35a25_708x493.png) . Meta didn’t quantify China revenue, but did mention that they “are lapping periods of increasingly strong demand over the course of 2024 given the recovery of China-based advertisers in 2023 from their prior pandemic-driven headwinds.” Ad revenue from North America was 43.4% of overall revenue which was the lowest in Meta’s history, indicating the increasingly global nature of Meta’s business as APAC and RoW segment ramps up and contributes more to revenue. In 1Q’21, these two regions in aggregate contributed 28.2% of Meta’s ad revenue but in 1Q’24, they were 33.3% of ad revenue. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74e9ab07-7150-411e-88f1-fb57ca6b0c8d_2017x160.png) **Segment Reporting** Overall 1Q’24 revenue was +27.3% YoY; on a 2-yr and 3-yr CAGR basis, Meta’s topline increased by 14.3% and 11.7% respectively. FOA’s other revenue increased by 85% YoY, driven by business messaging revenue growth from WhatsApp business platform. Zuck called out business messaging to be “the biggest clear opportunity…it's not like next quarter or the quarter after that scaling thing, but it's not like a 5-year opportunity either.” I expect this momentum in business messaging to continue for years to come. FOA had another near \~50% operating margin quarter and Reality Labs (RL) had another quarter of continued bleeding. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7404b8ef-0be2-4ce9-a9c4-fc0ece2484db_1618x511.png) Last [quarter](https://www.mbi-deepdives.com/meta4q23/), I mentioned: *“I know some Meta bulls are tempted to look at FOA and try to imagine some SOTP by assigning Reality Labs valuation of zero...I would mostly just pay attention to Meta’s consolidated numbers.”* Zuck more or less made the same case as the two segments increasingly become intertwined: > one strategy dynamic that I've been reflecting on is that an increasing amount of our reality labs work is going towards serving our AI efforts. We currently report on our financials as a family of apps and Reality Labs were 2 completely separate businesses. **But strategically, I think of them as fundamentally the same business with the vision of Reality Labs to build the next generation of computing platforms in large part to where we can build the best apps and experiences on top of them**. **Over time, we'll need to find better ways to articulate the value that's generated here across both segments. So it doesn't just seem like our hardware costs increase as our glasses ecosystem scales, but all the value flows to a different segment.** Let’s look at some interesting comments from the earnings call: **Meta AI** Meta AI was the key focus on this call. Meta says the initial feedback is “very positive”: > The initial rollout of Meta AI is going well. Tens of millions of people have already tried it. **The feedback is very positive**. And when I first checked in with our teams, the majority of feedback we were getting was people asking us to release Meta AI for them wherever they are. This has emboldened Meta to continue to invest more aggressively: > Overall, I view the results our teams have achieved here as another key milestone in showing that we have the talent, data and ability to scale infrastructure to build the world's leading AI models and services. And this leads me to believe that we should **invest significantly more over the coming years** to build even more advanced models and the largest scale AI services in the world. Zuck reminded investors that while it may be somewhat painful to go through, they have pretty compelling track record in building features and using their unparalleled distribution to monetize these features. While some investors may wonder whether this is more akin to RL investments or Reels/Stories investment phases, I think it’s exceptionally likely to be the latter. Meta AI is not a speculative/unproven ideas on which Meta is aggressively investing; business messaging, and high intent data from chat bots and hence getting more ad dollars from bottom of the ad funnel is likely to be an **once a decade** opportunity for Meta. It makes perfect sense to me that they would go after this rather quite aggressively: > As we're scaling CapEx and energy expenses for AI, we'll continue focusing on operating the rest of our company efficiently. But realistically, even with shifting many of our existing resources to focus on AI, we'll still **grow our investment envelope meaningfully before we make much revenue from some of these new products**. I think it's worth calling that out that **we've historically seen a lot of volatility in our stock during this phase of our product playbook, where we're investing in scaling a new products but aren't yet monetizing it. We saw this with Reels, Stories, as newsfeed transition to mobile and more. And I also expect to see a multiyear investment cycle before we fully scaled Meta AI, business AIs and more into the profitable services I expect as well.** > > Historically, investing to build these new scaled experiences in our apps has been a very good long-term investment for us and for investors who have stuck with us. And the initial signs are quite positive here, too. > > On the upside, once our new AI services reach scale, **we have a strong track record of monetizing them effectively. There are several ways to build a massive business here**, including scaling business messaging, introducing ads or paid content into AI interactions and enabling people to pay to use bigger AI models and access more compute. **And on top of those, AI is already helping us improve app engagement**, which naturally leads to seeing more ads and improving ads directly to deliver more value. > > So if the technology and products evolve in the way that we hope, each of those will unlock massive amounts of value for people and business for us over time. The point about increased engagement is really crucial here. If there is \~10% higher engagement on Meta’s apps thanks to better recommendation system, simplistically assuming that may result in \~$10-15 Bn revenue opportunity. If you assume FOA’s \~50% operating margin for these incremental ad dollars thanks to higher engagement/time spent, ROIC on these capex is going to be pretty compelling! And Meta already has good evidence that this is the right path to take: > We're seeing good progress on some of these efforts already. Right now, **about 30% of the posts on Facebook feed are delivered by our AI recommendation system. That's up 2x over the last couple of years. And for the first time ever, more than 50% of the content that people see on Instagram is now AI recommended**. > > AI has also been a huge part of how we create value for advertisers by showing people more relevant ads. And if you look at our 2 end-to-end AI-powered tools, Advantage Plus shopping and Advantage Plus campaigns, **revenue flowing through those has more than doubled since last year**. > > …historically, each of our recommendation products, including Reels, in-feed recommendations etc. has had their own AI model. And recently, we've been developing a new model architecture with the aim for it to power multiple recommendations products. We started partially validating this model last year by using it to power Facebook Reels. **And we saw meaningful performance gains, 8% to 10% increases in watch time as a result of deploying this**. This year, **we're actually planning to extend the singular model architecture to recommend content across not just Facebook Reels, but also Facebook's video tab as well. So while it's still too early to share specific results, we're optimistic that the new model architecture will unlock increasingly relevant video recommendations over time**. And if it's successful, we'll explore using it to power other recommendations > > …with Meta AI, I think that **we are on our path to having Meta AI be the most used and best AI assistant in the world, which I think is going to be enormously valuable**. So all of that basically encourages me to make sure that we're investing to stay at the leading edge of this. **Reels** > Video also continues to grow across our platform, and it now represents more than **60% of time on both Facebook and Instagram**. Reels remains the primary driver of that growth. **WhatsApp** > …we're seeing **healthy growth in the US**…where the number of daily actives and message sends in the U.S. keeps gaining momentum **Threads** Threads now has 150 Mn MAU (vs 130 Mn in 4Q’23 and 100 mn in 3Q’23) **AR/VR** > The Ray-Ban Meta glasses that we built with Essilor Luxottica continue to do well and are **sold out** in many styles and colors. > > …As the ecosystem grows, I think there will be sufficient diversity in how people use mixed reality that there will be demand for more designs than we'll be able to build for example, a work-focused headset…Now to be clear, I think that our first-party Quest devices will continue to be the most popular headsets as we see today, and we'll continue focusing on advancing the state-of-the-art tech and making it accessible to everyone. But I also think that opening our ecosystem and opening our operating system will help the overall mixed reality ecosystem grow even faster. I have noticed Zuck put more emphasis on Meta Ray-Ban Glasses than VR headsets during this call. I have both Quest and the glasses and I think the adoption of glasses may inflect sooner than many may think. I myself have noticed after wearing the glasses, I have taken more photos/videos given how friction free it is which then get posted on social media. In the long-term, the biggest risk for Meta is lack of engaging content on its platforms, so the more it can unlock the velocity/quality of content, the better it is for Meta’s FOA platform. **Capital Allocation** Meta bought back \~$15 Bn last quarter (\~120% of FCF). Moreover, it also paid $1.2 Bn dividend. Despite the buybacks, shares outstanding decreased by only 0.2% QoQ, thanks to Meta’s **quite** **generous SBC program** which is currently nearing \~$200k/employee. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa435d261-06d8-421c-b98d-ff874231b9e9_1720x304.png) **Opex Guide** Anyone following Meta perhaps knows by now that Meta tends to decrease their opex guide as the year progresses. But last [quarter](https://www.mbi-deepdives.com/meta4q23/), I did wonder whether this was mostly Dave Wehner-thing and now that we have a new CFO (Susan Li), she may have a different approach. She indeed has bit of a different approach as Meta guided for $96-99 Bn opex (vs $94-99 Bn) for 2024. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F631bb344-633f-4622-8032-d5f58de69e21_1225x577.png) **Capex** Capex guide range was increased from $30-37 Bn to f $35-40 Bn in 2024\. As you can imagine, all the AI infrastructure related investments do not come cheap. Expect capex ramp up to continue: > “While we are not providing guidance for years beyond 2024, we expect CapEx will continue to increase next year as we invest aggressively to support our ambitious AI research and product development efforts.” As mentioned earlier, this is a fundamentally different opex/capex plan for Meta compared to what they have done/are doing with RL. I consider these investments to be lot less speculative. If there is a terrible adoption rate of “Meta AI” and declining engagement by users, Meta can scale back the level of investments and just keep iterating based on what works. It is way less risky capital deployment than AR/VR investments in my opinion. **Regulation** While it does seem increasingly unlikely that the regulators can cause real pain to Meta, it remains a wild card: > we continue to monitor an active regulatory landscape, including the increasing legal and regulatory headwinds in the EU and the U.S. that could significantly impact our business and our financial results. > > We also have a jury trial scheduled for June in a suit brought by the state of Texas regarding our use of facial recognition technology, which could ultimately result in a material loss. **Outlook** 2Q’24 topline guide is $36.5-39 Bn (\~1% FX headwind). Mid-point YoY growth is \~19% (vs consensus estimates of \~20%) **Closing Words** When I [shared](https://www.mbi-deepdives.com/meta2024/) my updated model on Meta, I did mention I sold \~10% of Meta shares I had after last quarter. As I deem market’s reaction to be rather excessively negative today, I would be willing to buy those shares back at \~$400 which would value the company at \~14-15x 2025 (consensus) EBIT. Given Meta’s significant weight in my [portfolio](https://www.mbi-deepdives.com/portfolio/), I am not going to chase the stock if it doesn’t come to $400 or below. For more in-depth analysis on Meta Platforms, you can read my analysis [**here**](https://www.mbi-deepdives.com/meta2024/) (February, 2024). Notes from follow-up call are [here](https://twitter.com/borrowed%5Fideas/status/1783489441090478403?ref=mbi-deepdives.com) I will cover **Alphabet’s** earnings **tomorrow**. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends. [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Texas Instruments: Leading the Analog Chips Industry URL: https://www.mbi-deepdives.com/txn/ Last updated: 2024-04-22T23:14:16.000Z *Disclosure: I own shares of Texas Instruments* *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- Texas Instruments (TI) is a storied company in the semiconductor industry since it’s been there from almost the very beginning of the industry. But believe it or not, it changed its name not once or twice, but **four** times since its founding in 1930\. Initially, its name was “Geophysical Service, Inc.” (GSI) which used the technique of reflection seismology to find and map areas most likely to yield oil. However, the oil exploration business proved to be a bit erratic, and as the World War II was underway, GSI wanted to transfer some of its oil exploration skillsets to help the US military manufacture electronic equipment. GSI became a subsidiary of the parent company named “Coronado Corporation”. After the war, [Patrick Haggerty](https://en.wikipedia.org/wiki/Patrick%5FE.%5FHaggerty?ref=mbi-deepdives.com) joined GSI’s Laboratory and Manufacturing (L&M) division and played instrumental role in TI’s eventual rise in semiconductors. Haggerty believed that the consumer market is much bigger opportunity than the military market. By early 1950s, L&M division was growing much faster than GSI’s Geophysical division and the company was then reorganized and renamed to be “General Instruments”. However, it turned out that the name was already used by another company and hence, they changed it to “**Texas Instruments**”. Its geophysical business was eventually sold to Haliburton in 1988. By 1953, TI became a publicly listed company and went on an acquisition spree by buying seven companies in that very year. As TI was starting to be a major electronics manufacturer, their revenue increased from \~$20 Mn in 1952 to $92 Mn in 1958. It was in 1958 when Jack Kilby joined TI and invented Integrated Circuits (IC) for which he ended up winning the Nobel Prize in Physics in 2000\. What’s an IC? It is a tiny electronic device that combines lots of small electronic parts on a single small piece of material, usually silicon. Imagine it like a mini city where instead of houses and streets, you have tiny components like transistors, resistors, and capacitors all living together on a small piece of land. These tiny parts work together to perform specific tasks, such as processing information, storing data, or controlling other devices. The invention of IC was a pretty big deal as Asianometry [explains ](https://www.youtube.com/watch?v=1Fcf5VuPTGc&ref=mbi-deepdives.com)“how the integrated circuit took us to the moon”. TI built the first computer to use silicon ICs for the Air Force, but it was the consumer market that started to show a lot of promise after IBM started integrating ICs in all its computers in the late ‘60s. Then in early ‘70s, TI entered the consumer-electronic calculator market and disrupted the then ubiquitous [slide rule](https://web.mit.edu/2.972/www/reports/slide%5Frule/slide%5Frule.html?ref=mbi-deepdives.com). Calculators were an immediate hit! After launching the first handheld calculator in the retail market in 1972, sales volume from calculators increased from 17 mn units in 1973 to 28 mn in 1974 to 45 mn units in 1975. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F764f7e29-63ea-403a-aaba-9410c846c739_560x423.png) First scientific calculator (1974); Source: TI Website While new technical gadgets such as calculators, digital watch, and LCD watches started gaining wider adoption among consumers, price war ensued among the competitors which made profits lot more volatile and uncertain. Thanks to strong semiconductor demand and the very profitable defense electronics segment, TI still did okay during the ‘70s as revenue increased from $1 Bn in 1973 to $3 Bn in 1979\. While the stock only generated \~4.6% CAGR, S&P 500 compounded at only \~2% during the ‘70s. ![chart](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F38b7c1c7-e4fb-4a7a-b056-206b28e97427_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Then the 80s was a challenging decade for TI. While the stock generated 3.75% CAGR return in this decade, it severely underperformed S&P 500’s \~13% CAGR return this decade. In the late ‘70s, TI entered the home computer market which also eventually ended up in a price war that led to TI’s first ever loss of $145 Mn in 1983\. TI’s presence in both consumer and industrial markets often led to shortages of components and reduced shipments. They incurred another loss year in 1985\. In the late ‘70s, TI set an ambitious goal of reaching $15 Bn revenue by 1990\. The actual revenue ended up being only \~$6 Bn when TI ended the decade of ‘80s. Their market share in semiconductor also fell precipitously from \~30% to just 5% over the course of this decade. No wonder TI’s stock lagged S&P 500 considerably! ![chart](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5f0977ea-f86e-404b-83c9-fcd74c276100_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Thankfully, TI’s fate radically changed in the ‘90s as the stock almost 23x-ed (\~37% CAGR) vs S&P 500’s performance of \~16% CAGR during this decade. Sure, quite a bit of this massive rally was due to the lead up to the tech bubble in the late ‘90s, but the company also took some decisions to deepen their focus on their core business. They [sold](https://www.semiconductoronline.com/doc/ti-sells-memory-business-to-micron-technology-0001?ref=mbi-deepdives.com) their memory business to Micron for \~$800 Mn and started focusing more on developing new products in analog semiconductor market. For example, in 1999, TI launched 191 analog products (\~7x more than they did in 1996) and became the leading player in analog semiconductor industry (more on analog semiconductor later). In 1999, \~84% of their revenue came from semiconductor market and they were clear beneficiaries of the rise of electronic systems in the ‘90s. TI’s semiconductor products were being used in digital cell phones, computers, printers, hard disk drives, modems, networking equipment, digital cameras and video recorders, motor controls, autos, and home appliances. ![chart](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7133487-c9cf-4b30-92d6-6f0c66f84ba0_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Then of course the first decade of the 21st century started with the crash of the tech bubble. TI’s revenue fell by a whopping \~31% in 2001\. It took TI nearly five years to exceed its revenue in 2000\. Just as it started to recover, Global Financial Crisis (GFC) hit the economy and TI’s revenue declined for three consecutive years. As you can imagine, it was a sorry decade for TI and its shareholders. However, a lot was going on in the background during this decade. In early 2000, TI acquired Burr-Brown for $7.6 Bn. While both companies were analog-focused, their product lines were very complementary with little overlap. Burr-Brown was strong in high-performance, high-precision converters, amplifiers and interface products whereas TI had a broader analog portfolio. The combination enabled TI to offer more complete analog system solutions. TI was also overly reliant on consumer electronics and given the potential short product cycle and OEMs bargaining power in consumer electronics, it was not the easiest business to navigate for TI. So, TI started plotting to lower its dependence on this industry and shift more to industrials and automotive industry, both industries which tend to have larger product/design cycle and diverse, fragmented customer base. TI ended up divesting Sensata for $3 Bn to Bain in 2006\. Sensata later IPO-ed and given its market cap today is only \~$5 Bn, that was clearly a good deal for TI shareholders in hindsight. ![chart](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4edff9cb-827f-469d-b553-d3ad673f7211_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) After the severe beating in 2000s, 2010s was a different story as the stock compounded at \~20% (vs S&P 500 at \~11%). But much of this staggering performance in the 2010s was essentially somewhat architected in 2000s when the stock was going through a tough period in the market. ![chart](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8799728a-3172-4b83-b1f5-a8a9c72ec94e_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) As alluded earlier, over the course of 2010s TI went through a bit of a transformation. Back in 2013, nearly half of TI’s revenue came from personal electronics and communications equipment. Today, that number has come down to only 20%. TI also did their last major acquisition: National Semiconductor (NSC) in 2011\. At the time of the acquisition, TI was generating 22% revenue from industrials whereas 46% of NSC’s revenue came from industrials. NSC had higher gross margin (\~69%) vs TI (\~54%) at that time and contributed 12k analog SKUs to TI’s \~30k products at that time. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9b0d1788-35e8-4d04-a1d3-8301406f1183_933x559.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Now, industrials and automotive contributes nearly three-fourth of overall revenue of TI. With \~$20 Bn revenue, TI is the leading analog chips company in the world. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F05c665f4-e811-409e-9761-d41184d28f08_901x550.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) In 2020s so far, TI has underperformed S&P 500\. Revenue has been declining YoY for the last six consecutive quarters as the industry is currently going through a cyclical downturn. I reckon this is likely to be an interesting opportunity for long-term investors. ![chart](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5b2f3514-1489-46b4-813c-6bec690415ce_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for the rest of this Deep Dive: **Section 1 Basics, Demand Drivers, and Economics**: I start with some basics such as what exactly is analog chip. Then I primarily focused on the two largest sectors that drive TI’s revenue: industrials and automotive to understand the demand drivers of the business. Finally, I discussed the overall economics of TI’s business. **Section 2 Competitive Dynamics**: First, I discussed why both ADI and TI may be attractively positioned in this industry. Then I compared and contrasted between their approaches which was followed by a discussion on the China risk/question posed to both these companies. **Section 3 Capital Allocation and Management Incentives**: I looked at last 20 years of capital allocation history of both TI and ADI. Then I discussed TI’s management incentives. **Section 4 Model Assumptions and Valuation**: Model/implied expectations in the current stock price are analyzed here. **Section 5 Final Words**: Concluding remarks on Texas Instruments, disclosure/discussion of my overall portfolio, as well as couple of updates on my previous Deep Dives. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### April 2024 Update URL: https://www.mbi-deepdives.com/apr2024/ Last updated: 2024-04-02T17:14:16.000Z First things first, this month's Deep Dive on **Texas Instruments** will be published by April 25th. Thank you for all the great feedback on my Semiconductor [Primer](https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/) last month; I'm glad that many of you found it useful to get up to speed on the industry. Although I rarely explain my investment activities in these monthly updates, I will share some thoughts on why I have started buying Lululemon today. Before I explain my rationale to start buying Lululemon (Ticker: LULU), let's go down the memory lane first. I first published my [Deep Dive](https://www.mbi-deepdives.com/lulu/) on the company in November 2020\. I would suggest you read the Deep Dive for more comprehensive and detailed analysis, especially to grasp LULU's competitive advantages. I thought the valuation was a bit rich back then, but later I changed my mind in [June 2021](https://www.mbi-deepdives.com/lulu2/) and started buying the stock at $335/share. However, I later sold the stock at \~$385 in [May 2022](https://www.mbi-deepdives.com/adyey/) (see Section 6) and mentioned this for my rationale to sell LULU to increase my exposure in some of my other portfolio holdings: *"While LULU’s long-term future is likely to be bright, I thought IRR in some of my other portfolio holdings are potentially much higher."* Indeed, while my portfolio nearly doubled since May 2022, LULU has been basically flat since then. Moreover, the stock is down \~25% YTD and is actually flat since August 2020\. Today, I think I am in the opposite situation of May 2022; my guess is LULU is likely to outperform much of my current portfolio holdings in the next 3-5 years and hence took a \~4% position at $380/share (and open to increase my exposure depending on how stock price and/or fundamentals move going forward). This isn't a Deep Dive, so I will be rather brief and highlight just a couple of points on why I like LULU today. Perhaps nothing encapsulates how LULU has somewhat quietly graduated from a fashion cult brand to a scaled, mainstream brand than looking at some of its numbers in comparison with Nike (Ticker: NKE). During 2013-2018 period, LULU's revenue and gross profit was consistently Mid to high single digit as % of Nike's revenue and gross profit. Since LULU has higher operating margin than NKE, operating profit was low double digit to mid-teen as % of NKE's operating profit. Then something changed in 2020! LULU has been on a tear for the last four years since the pandemic. Last year, LULU was almost \~20% of NKE's sales, \~25% of NKE's Gross Profit, and a whopping \~37% of NKE's EBIT. Given their financial years are different from each other, this isn't quite apple-to-apple, but even when we look at the last three years aggregate numbers, it still sort of depicts similar picture. During 2021-2023, NKE generated \~$64 Bn aggregate gross profit. LULU posted $13.7 Bn or 21.5% of NKE's. During this period, NKE generated \~$20 Bn aggregate operating profit whereas LULU posted $5.3 Bn or \~27% of NKE's. You may find this surprising but LULU generated these numbers by spending **\~10% of what NKE spends on advertising**! I'm not going to delve deep into why/how LULU can pull off such a thing and I encourage you to read my initial [Deep Dive](https://www.mbi-deepdives.com/lulu/) where I did explore exactly that. LULU's performance in the last 3-4 years is nothing short of staggering and looking at a massive, global brand such as NKE for comparison really highlights their height of success. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-1.png) Source: Tikr, MBI Deep Dives What surprises me is that despite such a staggering success especially compared to NKE, LULU actually trades at **lower** NTM EV/EBIT multiple today. In fact, it is hardly an anomaly. Historically, NKE often traded at higher multiples than LULU, implying that investors have nurtured a persistent skepticism about LULU's durability and brand power, especially compared to brands such as NKE. Let me explain why I don't share such skepticism and I, in fact, believe LULU should trade at higher multiple than NKE. ![chart](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/data-src-image-ea79ac18-a8ae-4167-9f95-a3a8c1009036.png) Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Back in 2020, [Gavin Baker](https://twitter.com/GavinSBaker?ref=mbi-deepdives.com) wrote a really insightful [piece](https://gavin-baker.medium.com/why-category-leading-brick-and-mortar-retailers-are-likely-the-biggest-long-term-covid-d70b8dfadc20?ref=mbi-deepdives.com) titled *"Why category leading brick and mortar retailers are likely the biggest long term Covid beneficiaries."* Let me share some relevant excerpts here: *"Many of the perceived Covid winners such as e-commerce, videogame and streaming media companies have simply been pulled a few years forward into a future that was inevitable. Their destiny did not change. The future for those businesses simply accelerated whereas the future for category leading “brick and mortar” retailers has changed dramatically as a result of Covid, more so than for any other business of which I can think.* *Said another way, long term steady state FCF will likely be the at the same level for many e-commerce, videogame and streaming media companies as it would have been before Covid. This is not to say that Covid did not increase their value; it did but primarily by pulling their financials forward a few years which obviously matters in a DCF. *Whereas long term steady state FCF will likely be significantly higher for category leading brick and mortar retailers who had reasonably strong e-commerce businesses coming into Covid. Especially so for those category leading retailers who operate in inflationary categories where inventory turns are less important than they are in deflationary categories where e-commerce only companies have structural cost advantages due to faster inventory turns.** *The future was always going to be omnichannel. Pundits have been prematurely predicting this for many years, but it is finally happening.* *Brick and mortar stores have tremendous online value in addition to enabling true omnichannel commerce. Nothing matters more for an e-commerce company than marketing efficiency expressed either as gross margin $ payback period or the ratio of CAC to LTV. Brick and mortar stores significantly lower online CAC by improving marketing efficiency (higher click through rates, higher quality scores for ads). Consumers are more likely to trust a brand they have seen in the real world. Ironic in a world where “CAC is the new rent” that one of the best ways to lower your online rent, i.e. CAC, is to pay rent offline for physical stores. Brick and mortar stores also enable BOPIS (buy online pickup in store) and the in-store return of items purchased online, which consumers value. Economically, BOPIS will always be cheaper than same day delivery and large numbers of consumers are highly cost sensitive*." While Baker didn't mention LULU in his piece, there are hardly better companies that fit and proved this thesis. Like many e-commerce companies, LULU, who already had a pretty decent omnichannel offering to their customers even before the pandemic, had a meteoric growth (\~42%) in 2021\. I'm not sure there are too many e-commerce companies which managed to then grow at \~30% in 2022 and \~20% in 2023\. Remember, this used to be mid-teen to low 20s topline grower before the pandemic. LULU's vertical integration (and hence control over the brand) and direct relationship with its customers by executing a seamless omnichannel experience likely perhaps created a durable change in competitive dynamics. Every person I discuss LULU with (the stock) never forgets to mention the threat from up and coming brands such as Alo, Vuori etc. but I think they fundamentally underestimate and underappreciate that LULU's brand has likely found an escape velocity in the last few years. While those up and coming brands can enjoy some success in their niche, it may require a shocking mismanagement from LULU's management to fumble the structural CAC advantage they now currently enjoy. To be clear, LULU used to be terribly managed business in much of the 2010s; even that wasn't enough to deteriorate the brand too much, implying the strength of the brand in the first place. The current management, led by Calvin McDonald, really deserves some high praise for what they accomplished in the last few years (despite their hiccup with Mirror acquisition). ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2024/04/image-2.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Now that the stock trades at \~18-19x EBIT, we don't have to underwrite heroic assumptions to get to pretty decent returns. LULU's current operating margin is \~23% which seems already quite optimized, so margin upside is rather limited or non-existent. But I think it's quite likely that their topline continues to grow at HSD to low double digits in the next 3-5 years, mirroring the potential IRR from current stock price (assuming no multiple compression/expansion which is likely a fair assumption). The stock recently has gone down \~25% YTD as the management guided low double digit topline growth in 2024; investors were perhaps getting too accustomed to \~20% growth in the last few years. After such a meteoric growth in 2021-23, it is not surprising to me at all that LULU may need bit of a digestion period to grow at a more sustainable pace. HSD to low double digit topline growth seems very much achievable for the next 3-5 years and therefore, I started a new position in the stock. Speaking of new position, I also bought XPEL yesterday. I haven't published a Deep Dive on XPEL yet and I may not publish anything on XPEL in 2024 (highly likely I will do so in 2025). If you're interested in understanding more about XPEL, I suggest you read yesterday's piece by [Scuttleblurb](https://scuttleblurb.substack.com/p/xpel-xpel-inc). Thanks for reading. I will come back with the TXN Deep Dive later this month. [Subscribe](#/portal/signup) ### Semiconductors: "To see a World in a Grain of Sand" URL: https://www.mbi-deepdives.com/semiconductors-to-see-a-world-in-a-grain-of-sand/ Last updated: 2024-03-25T13:30:31.000Z *You can listen to this Primer* [***here***](https://www.mbi-deepdives.com/audio/) --- Semiconductors are almost invisible but indispensable material in our modern life. Semiconductors are hardly tangibly felt by most of us even though anyone reading this piece may be “using” semiconductors almost on an hourly basis. Like [William Blake](https://en.wikipedia.org/wiki/William%5FBlake?ref=mbi-deepdives.com)’s one of the poems starts with “To see a World in a Grain of Sand”, the very tenets of modern world is indeed intertwined with silicon, a component of sand, which is a semiconductor material extensively used in the manufacturing of chips. That mood of indispensability was well captured by NZS Capital in their [White Paper](https://www.nzscapital.com/news/semiconductors?ref=mbi-deepdives.com) on Semiconductors: > *“A good exercise in assessing a potential investment is to ask “could the world get by without this company?”. The answer is usually, “in time, probably just fine.” But should we wake up tomorrow to find that any among Taiwan Semiconductor Manufacturing Co. (TSMC), ASML Holding, Lam Research, Cadence, Synopsys and KLA-Tencor have suddenly ceased to exist, the answer at best is “well, yes, but global progress will suffer a setback of several decades at least.” The loss of a handful of critical chipmakers that depend on these companies – such as NVIDIA, Samsung, Intel, AMD, Texas Instruments, Xilinx, Broadcom and Microchip Technology – would dramatically impede the digital transition of the economy still further.* > > *Why? Because these companies are at the core of a half a trillion-dollar industry that enables us to do pretty much everything, from flying across oceans to booking a ride and ordering a pizza when we get there; from diagnosing and treating a disease to boosting agricultural yields; from supporting the military to powering our cities.”* > > \-[NZS Capital](https://www.nzscapital.com/news/semiconductors?ref=mbi-deepdives.com) The ubiquity of semiconductors or chips is likely a bit shocking to anyone who doesn’t follow semiconductors. Chris Miller put things in perspective about the pervasiveness of semiconductors in his seminal book “[Chip War](https://www.amazon.com/Chip-War-Worlds-Critical-Technology/dp/1982172002?ref=mbi-deepdives.com)” published in late 2022: *“Last year, the chip industry produced more transistors than the combined quantity of all goods produced by all other companies, in all other industries, in all human history.”* Where do we use all these semiconductors? Communication, and computer industry drive more than half of the overall global demand for semiconductors today. Even though automotive, consumer, or industrial sector each constituted \~14% of overall semiconductor demand in 2022, they were all significantly higher growth areas for semiconductor industry. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1712c596-a845-4900-b98f-8dc1d83633df_610x204.png) Figure: Semiconductor Demand Drivers; Source: Semiconductor Industry Association ([2023](https://www.semiconductors.org/wp-content/uploads/2023/08/SIA%5FState-of-Industry-Report%5F2023%5FFinal%5F080323.pdf?ref=mbi-deepdives.com)) Nearly half of global semiconductor sales comes from the US semiconductor companies, but sales outside the US comprise \~80% of sales of the U.S. semiconductor industry. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F049b2d47-5014-45c4-9856-d8f2d5133c04_487x360.png) Source: Semiconductor Industry Association ([2023](https://www.semiconductors.org/wp-content/uploads/2023/08/SIA%5FState-of-Industry-Report%5F2023%5FFinal%5F080323.pdf?ref=mbi-deepdives.com)) While semiconductors can be cyclical industry, the industry’s sales grew from $139 Bn in 2001 to $574 Bn in 2022, implying a \~7% CAGR over this period. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F866bba20-53bd-4df7-b23f-2bf6c839e63d_777x369.png) Source: Semiconductor Industry Association ([2023](https://www.semiconductors.org/wp-content/uploads/2023/08/SIA%5FState-of-Industry-Report%5F2023%5FFinal%5F080323.pdf?ref=mbi-deepdives.com)) These numbers look all big and impressive and it should already be clear by now that this is an enormously important industry, but I would like to take a step back and start from the very basics. This is quite a technical industry and full breadth of basics and technical concepts will require perhaps an entire book. If you are very curious, I do have a separate book suggestion to gain a better understanding on the technical concepts: “[*Understanding Semiconductors*](https://www.amazon.com/Understanding-Semiconductors-Technical-Non-Technical-People-ebook/dp/B0BRCDL3WT?ref=mbi-deepdives.com)*: A Technical Guide for Non-Technical People”*. As a generalist myself, I did read this book and thought it was reasonably helpful. So, I will start with some of the basics in Section 1 so that most readers don’t feel truly lost as we dig further into this Primer. In Section 2, I will discuss some historical contexts in semiconductor industry. In Section 3, I will elaborate on the semiconductor value chain. In Section 4, I will assess the geopolitical contexts in the semiconductor industry. In Section 5, I will conclude the primer with my three broad takeaways after studying the industry for the last month. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Dollar General 4Q'23 Update URL: https://www.mbi-deepdives.com/dg4q23/ Last updated: 2024-03-14T20:38:28.000Z *Disclosure: I own shares of Dollar General* Dollar General (DG)’s stock had an interesting reaction to today’s earnings. First it went up by \~6% in pre-market, but then ended the day 5% down. Despite the somewhat bizarre stock price reaction throughout the day, I think the worst days are likely behind DG. Here are some highlights from today’s call. [Subscribe](#/portal/signup) **Same Store Sales (SSS)** After two quarters of negative SSS growth, DG returned to positive SSS (+0.7%) in 4Q’23 which helped eke out +0.2% SSS growth for FY’23\. More importantly, SSS was driven by +4% customer traffic growth which sequentially improved each month of the quarter (and traffic improvement is persisting in current quarter as well). This was offset by a decline in avg. transaction amount, primarily driven by fewer items per basket. Improving customer traffic trend is quite encouraging as I was always more worried about traffic vs transaction amount growth. Interestingly, DG mentioned they’re seeing some trade down which made me wonder whether the overall consumer may be weaker than it generally appears or it is more of a reflection of DG’s operational improvement that lured some of those customers back: “*what we're starting to see is -- and gives us confidence is that for the first time in many quarters, we're starting to see the trade down come back in. And we hadn't seen that for a few quarters.*” SSS increase was driven entirely by consumable category and was partially offset by declines in the home, seasonal and apparel categories. So, customers seem to be more cautious in their discretionary expenditures. More importantly, after four consecutive quarters DG has finally posted a better SSS comp than Family Dollar (FDO) which is their closest competitor. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd8211bce-a512-4bce-b2bb-161d35c7614e_1230x808.png) **Gross Margin** While gross margin declined by 138 bps YoY, it continued to fare much better compared to FDO. Why did DG’s gross margin fall? > This decrease was primarily attributable to increases in shrink and markdowns, lower inventory markups and a greater proportion of sales coming from the consumables category. These were partially offset by decreases in LIFO and transportation costs. Notably, year-over-year shrink headwinds continued to build during the year, increasing more than 100 basis points for both the fourth quarter and full year. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2d358527-9cff-404f-ae32-20fc39491aaa_1123x733.png) **Operating margin** It appears operating margin has bottomed for DG in 3Q’23 and it came back to close to \~6% in 4Q’23\. There is still a long way to go back to \~8-10% operating margin that DG used to post during much of the 2017-2022 period. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8b25d95d-b464-4de4-a788-5863e803b35b_1126x747.png) **Inventory** Inventories stood $7 Bn in 2023, +3.5% YoY but a decline of 1.1% on a per store basis. Non-consumables inventory was -17% YoY and -21% on per store basis. There’s still plenty of room for improvement here as inventory turnover came down to 3.9x in 2023 (vs 4.2x in 2022 and 4.4x in 2019). **Store expansion** DG guided 800 new stores expansion (Including 30 popshelf and 15 stores in Mexico) in 2024 which will be the lowest since 2015. In 2024, DG will also remodel 1500 stores (vs 2,007 stores in 2023) and relocate 85 stores (vs 129 stores in 2023). DG also mentioned they now have fresh produce in 5,400 stores and will target additional 1,500 stores for fresh produce in 2024. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2deea2bd-ced9-481c-9470-15250657c57a_631x379.png) **Outlook** Here’s DG’s outlook for 2024: > we expect the following for 2024: net sales growth in the range of approximately 6% to 6.7%, same-store sales growth in the range of 2% to 2.7% and an EPS in the range of $6.80 to $7.55\. We currently anticipate an estimated negative impact to EPS of approximately $0.50 due to higher incentive compensation expense. Our EPS guidance assumes an effective tax rate in the range of 22.5% to 23.5%. I’m encouraged to see a return of somewhat healthy SSS growth outlook for 2024\. As you can see below, except for pandemic induced demand in 2020, SSS was consistently hovering around \~2.5%+ during the pre-pandemic period. Following last year’s operational haphazardness which led to management changes, there was certainly a question mark whether DG’s model is indeed broken. If DG returns to it’s \~3% SSS growth, the next beast they need to slay to calm investors is operating margin. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5222a5cb-2836-4a85-8ba4-a09480939e74_997x541.png) 2024 outlook still implies an operating margin closer to \~6% which is long way from DG’s operating margins during the pre-pandemic period. Of course, I don’t expect DG to return to such operating margin level quickly, but a somewhat consistent improvement throughout 2024 should lead us to a clearer pathway to get closer to \~8% operating margin sometime in 2025-26. When DG was trading near $100 in September 2023, the primary question investors needed to get comfortable with to invest in DG is whether DG’s retail model is durable. As DG is about to embark on their 85th year of operation with store locations within 5 miles of approximately 75% of the U.S. population, I was always confident that the answer to that question is highly likely to be yes as their core value proposition of convenience has lasted the test of time. Now that the stock price is at $150, we need to answer an additional question: when (if ever) can DG go back to its historical operating margin? I’m optimistic that over time, DG can get closer to the historical average operating margin. Admittedly, it is a slightly harder question than the first one, but I’m happy to be patient here. **Further reading**: My [Deep Dive](https://www.mbi-deepdives.com/dg/) on DG (August, 2023) Thank you for reading. ### March, 2024 Update URL: https://www.mbi-deepdives.com/mar24/ Last updated: 2024-03-01T15:21:01.000Z After publishing my Deep Dive on [AppFolio](https://www.mbi-deepdives.com/appf/), I have started studying the semiconductor industry. It didn't take me too long to understand that this is one of the more challenging areas for a generalist like me to get up to speed quickly. As a result, I have decided to take this month just to study the overall industry-the basics, the historical background of the industry, and the overall semiconductor value chain. Given that context, instead of publishing a Deep Dive on a company, I would like to publish a **Primer on Semiconductor** later this month (tentative date: March 26, 2024) . Even with ten days of study, I can already sense that generalists like me cannot hide away from semiconductor for too long. While in the past one could perhaps own many of the big tech companies without understanding semiconductor, it does seem that those days are perhaps already behind us. Following the Primer, I would like to do a couple of company Deep Dives on Semiconductor industry. They are most likely going to be **Texas Instruments** (April, 2024), and **TSM** (May, 2024). I plan on doing a few non-tech Deep Dives after that and then eventually come back to semiconductor again in the latter half of the year when I would like to cover **Nvidia** and **AMD**. Even beyond 2024, I expect myself to cover 2-4 companies per year from semiconductor industry in the next 3-5 years. If you are a new subscriber, I just want to highlight that you can access all the past 44 Deep Dives, including their financial models, [**here**](https://www.mbi-deepdives.com/models/)**.** Thanks to your support, I can afford to take the time to study anything I want. I appreciate it so very much! [Subscribe](#/portal/signup) ### AppFolio: Moving Up Market in Property Management Software URL: https://www.mbi-deepdives.com/appf/ Last updated: 2024-12-23T15:03:03.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) Some updated though**ts**[ **here**](https://www.mbi-deepdives.com/appf3/) --- > “…we invested in the IPO, we purchased after the IPO from other investors. And pre IPO, there were other strategic investors and their mandates can change, they make an investment in year two, and in year seven, they have somebody else running their strategic investments and they decide they're willing to sell it and we--so all along this spectrum, from Series A to public companies, and then you look at it, and you say we know the company backwards and forwards, we know the management backwards and forwards, we know the opportunity backwards and forwards. Why would we harvest it and then recycle the money when we have an opportunity to continue to be big owners in an exceptional company?” This quote was taken from the [interview](https://aletteraday.substack.com/p/letter-137-reece-duca-and-bob-casey) of Maurice Duca, who is arguably a good candidate to be on the Mount Rushmore of investing. The company he’s talking about is AppFolio. Mr. Duca alone owns almost one-fifth of the shares outstanding and \~40% of voting power at AppFolio. Mark Leonard, CEO of Constellation Software (CSU), once asked investors for thoughts on AppFolio. CSU executives even said the following during 2023 AGM: > …What we do each quarter is we profile a venture-backed vertical market software company that has done well. And through that process, we can get a sense of how they penetrate markets, what the economics of what they're doing are, what underlying technologies they're using and the advantages they have versus their incumbents. > > And I think we've done 8, 9 of those so far that we do want -- We're doing AppFolio, for instance, this quarter, **which is stunningly attractive property management software company that penetrated a relatively mature market and did so in a very economic fashion**. So we're not blind to technology or to new approaches and we try to learn from them. While I rarely cover companies with <$10 Bn market cap, you can probably understand why I felt compelled to do a Deep Dive on AppFolio. AppFolio was founded by Klaus Schauser and Jon Walker in 2006\. They knew each other for years and started talking about launching web-based software for specific industries that they thought were underserved. They both had startup and tech background. Schauser used to be CTO of Expertcity (now “CitrixOnline”) during 1999-2006 and was visionary behind products such as GoToMyPC, GoToAssist and GoToMeeting. Walker was also CTO of couple of companies (Versora, and Miramar Systems) before founding AppFolio. After brainstorming and speaking with customers from different industries, Schauser and Walker narrowed their focus to property management industry due to its relative laggard status in digital transformation and inefficiencies embedded in the traditional way of doing business. Since they both used to be CTOs, they decided to beef up their management by hiring a CEO in 2007: Brian Donahoo, who worked with Schauser at Expertcity and grew revenue to $240 mn. Donahoo took the company public in 2015 and eventually was replaced by Jason Randall who joined AppFolio back in 2008\. Randall then grew the business from $144 mn in 2017 to $472 mn in 2022, a\~27% CAGR over 5-year period. Then in early 2023, Shane Trigg became CEO who joined the company right after the pandemic started. Although the name “AppFolio” carries the original ambition of the founders to build “a portfolio of business applications for various different vertical markets”, AppFolio basically only doubled down further into the first vertical market they found their product market fit: property management software. The founders are no longer around. Schauser used to be in the board, but left in 2017 although he still owns \~10% shares and \~23% voting power (as per 2023 proxy filings). Walker remained CTO until 2023 and then left to become CTO of Buildertrend, a company that builds software solutions for construction industry. He still owns \~2% of AppFolio shares. As you can see, there has been quite a few management turnover since AppFolio’s founding. While that may seem less than ideal, the stock has been almost a 15-bagger since its IPO in 2015! ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff32be1da-2ebf-4190-8030-244d860fe81c_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 20% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for this Deep Dive: **Section 1 AppFolio Overview**: I discussed a brief overview of the platform, revenue drivers, and overall economics of the business. **Section 2 Competitive Dynamics**: I looked into some of AppFolio’s competitors, compared and contrasted with RealPage’s economics, as well as switching cost of moving away from incumbents’ (Yardi, RealPage etc.) software. As AppFolio navigates to up market, the key question is how difficult or easy it is to unseat the incumbents. **Section 3 Management, Capital Allocation, and Incentives**: I briefly outlined capital allocation philosophy of AppFolio management and incentives of the management. I have also touched on the role AppFolio’s board play. **Section 4 Model Assumptions and Valuation**: Model/implied expectations in the current stock price are analyzed here. **Section 5 Final Words**: Concluding remarks on AppFolio, and disclosure/discussion of my overall portfolio. --- [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Spotify 4Q'23 Update URL: https://www.mbi-deepdives.com/spot4q23/ Last updated: 2024-02-07T03:55:59.000Z While not exactly Meta-like twists and turns, Spotify’s rise from the ashes of late 2022 perhaps slipped through many investors’ mind. The stock was at \~$70 in December, 2022\. Today, it closed at $232. Before I get to my highlights from today’s call, let me first mention that I am no longer a shareholder of Spotify as I sold my shares today at $243\. I will briefly discuss my rationale after discussing the highlights from the call. [Subscribe](#/portal/signup) ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd3e2a86a-dbfc-43b6-9d4b-fb3031a71414_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 15% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) **Users** This was another strong quarter in both premium subscribers and Monthly Active Users (MAU). Spotify’s MAU momentum over the last 5-6 quarters truly defied my expectations. However, premium subscriber as % of MAU declined from \~44% in 4Q’21 to \~39% in 4Q’23\. Spotify again reiterated that they continue to believe that a strong top of the funnel will eventually lead to upgrade to premium subscription, but considering premium mix as % of MAU has declined for six consecutive quarters may hint that Rest of the World (RoW) from where much of the MAU growth is coming from is a structurally different market; premium penetration in RoW may be structurally lower compared to other regions. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F72bf1d40-158a-493e-a99c-3714f75c8708_1812x157.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Netflix vs Spotify** This is something I track every quarter. Netflix has almost entirely eliminated the gap with Spotify in terms of subscriber growth that persisted for the last 12 quarters. I should mention that definition of subscriber of NFLX and SPOT is not apple-to-apple, so I would caution not to infer more than what this data can tell us. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F602dc015-e108-4bfe-8e76-b6daf9889183_838x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Revenue** 4Q’23 enjoyed the full benefit of price increases which helped drive 16.7% growth in premium subscription revenue. While overall reported revenue growth was 16.0%, it was +20% FXN, which was \~300 bps QoQ acceleration. Please note that FXN revenue growth in 3Q’23 also had 300 bps QoQ acceleration. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F96cccdaf-ebfe-4502-956d-c6b05ce8e4df_1519x240.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Podcast** Spotify has been shifting its podcast strategy for the last few quarters; they have clarified their changing strategy during this call: > …we're in a very different position than we were just a few years ago in podcasting because today, Spotify is, in many cases, the #1 podcasting player already. > > …so exclusivity makes sense when you're the smaller playing trying to gain scale. When you're the bigger player, the additional value of the exclusivity is far smaller than it is about being aligned. > > …while exclusivities were net positive on the side, it's not driving as much as the opportunity that we see on the ad side. And so by broadening distribution, we think we can accomplish a number of different goals. Most notable among them, we are going to be more aligned with the creator. The creator obviously wants to be on many different platforms and wants to have as big of an audience as possible. Although Spotify guided last quarter that podcast may reach breakeven for 2024, they were already close to breakeven in 4Q’23 and now guided for full-year profitability on podcasting in 2024. **Audiobook** Some interesting details on recently launched audiobook segment: > Data shows that our entry into this market has dramatically accelerated its overall growth. **In Q4, we became the #2 provider of audio books behind Audible**, which is notable given how entrenched the legacy players are. > > …the biggest surprise has been the type of titles that resonate with consumers. These are not the normal titles that traditionally does well, that do well on Spotify, and that's pleasing to see because **that means we're bringing a whole new audience to audio books**, the format, which is great to see. **Apple** While many had high hopes that EU’s clamp down on iOS ecosystem may lead to some reprieve for a company such as Spotify, we now know Apple seems to be one step ahead of the regulators to kill those hopes. The deadline to be compliant with Digital Markets Act (DMA) in March 07, so EU regulators may still outmaneuver Apple’s tactics. If they do, Spotify did outline the benefits that may accrue to them: > …a la carte purchases, things like superfan things like purchasing of audio books, top-up things **that could be quite meaningful for Spotify's revenues is a significant hindrance today because Apple insists on taking a 30% cut, which in many cases, exceeds even our own cuts that we're able to take inside of the app**. So some of these more innovative things that we would like to do, we are currently restricted in doing on the iOS ecosystem, which limits some of that more innovative things that we would like to do. **Gross margin (GM)** Overall reported GM was +26.7%. Music GM was +29.1% (same as 3Q’23). GM in ads was 11.6% (vs 8.3% in 3Q’23). Expect GM improvement to continue in 2024: > As you look into 2024, we expect to see a continued improvement in our gross margin trends and a continued improvement in our operating income trends as well. > > when you think about the improvements and gross margin moving to 2024, marketplace will be a key contributor, again, along with the podcasting flip and some of the other costs of revenue. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F797bddb2-d3e7-4e11-a00b-6fde8267dd33_874x522.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Opex** Spotify had EUR 143 Mn charges for “efficiency” actions aka mostly layoffs. Excluding this charge, Spotify posted EUR 68 Mn operating profit which was more than double of 3Q’23 operating profit. SBC went down from EUR 100 mn in 4Q’22 to EUR 34 Mn 4Q’23!! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9eb565a3-708a-40a5-ae27-806804833f1f_1813x190.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook for 1Q'24** Perhaps even more importantly, Spotify surprised with \~5% operating margin guide in 1Q’24\. Moreover, they indicated that both gross and operating margin will likely keep improving throughout the year. That is going to be quite the turnaround from 2022 when they posted -5.5% operating margin. Basically, Spotify is now expected to improve operating margins by more than 1,000 bps (no typo) in just two short years. Perhaps rising interest rates and/or a threat of recession isn’t necessarily a bad thing if that’s what it takes for some of these tech companies to wake up and run the business properly. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0a1c6ff9-cc7d-409a-8c40-45011c35c9d7_1276x447.png) Source: Company Filings **Why I sold Spotify** I first [bought](https://www.mbi-deepdives.com/spot/) Spotify in December, 2021 at \~$240\. By the end of 2022, I identified buying Spotify as a [mistake](https://www.mbi-deepdives.com/2022/) mostly due to my naivete around their ads business. However, while I was admitting my mistake on the ads business, especially podcast business to myself, bears were essentially doubting whether Spotify is even a real business when the stock was trading at below $100\. Since I strongly disagreed with such sentiment, I averaged down and my average cost was $115\. Unfortunately, I found it quite hard to hold onto my shares in Spotify and started trimming from $140 and sold whatever I had left today. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7a90f0f0-08fd-4cd3-89ae-2c7010d1fa03_1087x667.png) Source: MBI Deep Dives Why was it much harder to hold Spotify? As I was introspecting about it, I could find at least four reasons: a) It is simply hard to hold onto unprofitable companies. When profits are just figment of your imagination i.e. forecasts in the future instead of reality of today, I suspect it is psychologically more challenging to hold onto such stocks, especially for a company that hardly ever made money despite being founded in 2006\. b) While many bears assume Spotify’s core music business as just a commoditized and easily substitutable product, actual users vote differently by having much higher engagement and lower churn compared to other music streaming alternatives. However, as it’s often the case for most bull/bear cases, there is a point/valuation at which bears (or bulls) argument can start to make a bit more sense. For example, while I consider it unlikely that Amazon or Google will snatch any noticeable percentage of subscribers away from Spotify, I also don’t think it is likely that Amazon or Google will ever leave this business. I don’t agree with bears when they say Google/Amazon/Apple want to subsidize losses here and make money through other parts of their respective ecosystem, there is, however, some ounce of truth that their presence is a hindrance for Spotify’s long-term ability to exercise pricing power. By 2030, we will almost certainly reach saturation across the world in music streaming and hence the ability to raise price may become the primary driver of revenue growth in music streaming. The broader point I am trying to drive here is Spotify’s moats, although not as fragile as bears may think, are not quite as strong as most of my portfolio holdings. c) In retrospect, while $140 may have been too early for me to trim, I didn’t quite envision how quickly they were able to dial up their efficiency button. However, at $240, the stock has somewhat steep expectation embedded in. After today’s earnings, I updated some of my numbers, and I could get to EUR 3-3.5 Bn EBIT (assuming \~8-10% EBIT margin) in 2030 (vs EUR 3.4 Bn **Gross Profit** in 2023). If you take EUR 3.2 Bn EBIT in 2030, I would still need to assume \~25x EBIT multiple in 2030 to get to \~9-10% IRR. \~25x EBIT may be okay for a business that has pretty robust moats with continued pathway for growth. I wasn’t confident Spotify will fit that description in 2030. d) Finally, I think I was a bit jumpy in selling Spotify because of what I own in my portfolio. With a significant exposure in tech and a fresh memory of what that led to in 2022 made me perhaps too eager to lighten my exposure to tech as these stocks just kept rallying. And most of the time when I was looking for trimming my exposure to tech, Spotify seemed a better candidate than other tech companies I own in my [portfolio](https://www.mbi-deepdives.com/portfolio/). Since I sold the stock, I am closing my coverage of Spotify for now, but if there is significant upside/downside volatility from here, I may come back to infer the right lessons in the future. While I have you here, I would like to let you know I am currently working on AppFolio (ticker: **APPF**) for this month’s Deep Dive. I hope to publish by 22nd of this month. Thank you for reading. [Subscribe](#/portal/signup) ### Amazon: Model Update (2024) URL: https://www.mbi-deepdives.com/amzn2024/ Last updated: 2024-02-06T18:11:57.000Z *Disclosure: I own Amazon January 2025 $55 Call Options* Last year, I posted a more [detailed](https://www.mbi-deepdives.com/amzn/) work on Amazon in which I provided more in-depth discussion on the structure of my Amazon model that I am going to discuss today. If you find it hard to follow the discussion below, I suggest you read last year’s piece and/or [download](https://mbideepdives.substack.com/p/models) the model to see how I have worked through these numbers. As mentioned yesterday, I have updated my Amazon model and the intention is to leave some brisk thoughts after updating the model. Let’s start with Amazon’s revenue model. [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### Meta Platforms: Model Update URL: https://www.mbi-deepdives.com/meta2024/ Last updated: 2024-02-05T16:53:06.000Z *Disclosure: I own shares of Meta Platforms* So I took this weekend to update my models on Meta and Amazon. I just wanted to leave some brisk thoughts on these updated models. I will start with Meta today and will share my thoughts on Amazon tomorrow. You can find the updated model on Meta [here](https://www.mbi-deepdives.com/models/) which I encourage you to download to play around with your own narrative as you see fit. Let’s start with the frustrating bit: Reality Labs (RL). # **Reality Labs (RL)** **Revenue Model** Since I just mentioned this is going to be brisk, I am going to assume you have read/followed my [earlier piece](https://www.mbi-deepdives.com/meta2023/) on Meta in which I went to a much greater length in explaining the thought process behind the structure of the model. For Reality Labs model, I have segmented revenue build in two categories: a) Virtual Reality (VR), and b) Augmented Reality (AR). Of course, it is nearly impossible to model Reality Labs revenues long-term given how speculative this segment still is and how spotty the adoption has been so far, so take these numbers with a grain of salt. With Quest launch in late 2023, buzz from Apple Vision Pro, and Meta’s expected launch of [Quest 3 Lite](https://www.uploadvr.com/chinese-analyst-quest-3-lite/?ref=mbi-deepdives.com) in 1H’24, I am modeling an uptick in hardware sales units for VR in 2024 and 2025\. Beyond that is just a pure guessing game. AR glasses is also just as hard to forecast. As Meta mentioned in 4Q’23 call, the new Meta Ray-Ban Smart glasses have been selling well, have seen higher retention than the last version, and with multi-modal AI assistant expected to be active on these glasses soon, I have modeled some momentum in the next couple of years in unit sales. AR glasses are the primary form factor through which Meta wants to put a dent on the dominance of smartphones, and there is an expectation that we may get to see a more tangible glimpses of such consumer ready AR glasses in 2027\. My model assumes lower price per unit than what you see in retail price because I am implying Luxxottica keeps \~50% of the hardware revenue. While there is perhaps a 90% probability of this being “garbage-in-garbage-out” revenue model, it’s the thinking process that counts which we will need to update as we get closer to understanding the adoption rate of such hardware among consumers. Don’t pay too much attention to the revenue/gross profit numbers because I am not modeling Reality Labs to reach anywhere close to profitability even by 2030. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcd50b566-bbdd-48bb-baf2-a5ce90f39bd8_909x942.png) Source: Company Filings, MBI Deep Dives **RL Opex** Looking at Reality Labs numbers is really not for the faint hearted. While things may change in the future, so far this meme probably depicts the reality of Reality Labs aptly! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc3e5a8d-6ea0-46fe-aeba-9b6cdda7b8a9_830x500.jpeg) It was disappointing to see Meta mentioning once again that losses will increase **“meaningfully”** in 2024 despite losing \~$16 Bn in 2023\. What does “meaningfully” mean for 2024? I suspect the answer is closer to $20 Bn. Given how Meta has navigated RL in the last few years, I no longer feel comfortable in assuming RL losses will peak anytime soon. I have modeled continued increase in losses until 2028 when the losses are assumed to have peaked. Nonetheless, even 2030 losses is modeled to be \~$20 Bn. Am I being too pessimistic? Meta bulls would probably think so, but wouldn’t investors be somewhat incredulous as well if someone told them in 2019 that Meta is going to spend $60 Bn aggregate opex against aggregate revenue of mere $8 Bn in 2019-2023 period. I bet you would think that’s overly pessimistic, and yet that’s what Meta spent over the last 5 years with continued guidance for more losses in RL. Looking at these numbers, I have come to the sobering conclusion that there is perhaps less than 1% probability for Meta to generate \~10% IRR on their investments in RL. The only way it may make sense to invest as aggressively as they did/do is to assume the bet on RL is far from “other bets”, and it is very much a core bet that is required to protect and even grow Family of Apps (FOA) business for decades to come once they truly can control their destiny on the next platform shift. Anytime you read how Meta has “pivoted” from Metaverse to AI, remember these losses! As shareholders, it is perhaps not unfair to expect some guidance from management when we can expect RL losses to peak. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F71f750e2-d6ea-4c7e-84eb-56fc1be3cdb6_906x381.png) Source: Company Filings, MBI Deep Dives # Family of Apps (FOA) Users: This is all self-explanatory. Just take a peek at the numbers and assumptions going forward. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3f6733b9-e5eb-4711-aa07-ab1022f162f3_1345x690.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **FOA revenue model** As FOA has regained much of the signal losses from ATT, Meta’s monetization has improved a lot. Since the economy is expected to fare fine and thanks to the continued rollout of Advantage+ and Shop Ads (which quickly became $2 Bn run-rate business after launching just in the US in 2Q’23), I am modeling ARPU momentum to continue in 2024\. Beyond 2024, I am modeling closer to MSD type ARPU growth in 2025-2028 in the US and then reach closer to nominal GDP growth by 2030\. International ARPU growth is modeled to outpace North America. Other revenue, which is largely revenue from WhatsApp business platform, is modeled to grow ARPU at a rapid pace throughout this decade. Because we are still in the early stage of WhatsApp Business monetization, despite such seemingly aggressive assumption, I end up with only \~$1.4 ARPU (worldwide) in 2030\. There is perhaps a non-negligible probability that this number may be substantially higher than I am modeling, especially if SMBs utilize WhatsApp and AI assistants to chat with customers for customer service, repeat purchases etc. It’s hard to underwrite these numbers with higher conviction until the numbers continue to show up quarter after quarter since WhatsApp monetization has been dreamed by investors for a number of years without seeing a lot of tangible evidence to it in financials. Recent numbers hint that it may be changing, but I will wait before underwriting even more optimistic scenarios. I should note that my numbers are slightly conservative than street estimates and likely more conservative than buy-side estimates. I am perhaps a bit more concerned about the tough comp in 2H’24, but it is possible that I may be underestimating the multiple tailwinds (Shop ads, Advantage+, ever improving ad infra post-ATT etc.) especially if the economy continues to grow at a healthy pace. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9c68c9c2-4581-4497-bbdd-f70f1371278a_1297x535.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **FOA Opex** My Opex model below is also self-explanatory. What I would like to highlight is I do suspect most investors are too eager to model the low end of the opex guide for 2024 ($94-99 Bn) whereas I wonder whether the [prior trend](https://www.mbi-deepdives.com/meta4q23/) of guiding higher opex and then lowering opex almost every quarter throughout the year still holds under Susan Li as reliably as did under David Wehner. If RL losses are indeed going to be \~$20 Bn in 2024, I think there’s a decent probability of total opex to reach closer to $99 Bn rather than $94 Bn. While consensus opex estimates seem to be \~$98 Bn, I have been hearing estimates closer to $94 Bn from some of my buy-side friends which I find to be a tad bit aggressive. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7c281648-e7d6-4c94-932f-d43c983415fd_1296x846.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **MBI vs Consensus** Thanks to slightly higher opex and lower revenue than consensus estimates, my operating profit lags the street estimates for this year and continues to trail behind street estimates in out years as well. What if I am closer to the reality than sell/buy-side estimates for Meta in 2024 (and beyond)? While missing estimates may be a terrible news for many investors, I am not sure it should necessarily concern “actual” shareholders of the business since estimates is just one side of the equation, we also need to take a look at valuation multiples. Ultimately, in the near-term (\~1 year), it’s the entry and exit multiple that **often** play a larger role in your return (note: what we have seen in Meta’s stock in the last 12-18 months is highly unusual; it’s **not** the norm), and forecasting multiple expansion/contraction within the next year or so is usually above anyone’s paygrade. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F02409118-1b6f-4ede-a71c-ac351fb676a3_924x498.png) Source: Tikr, MBI Deep Dives **Capex** Before I touch on implied valuation multiples, let me quickly note that I am also modeling capex closer to the high end of Meta’s capex guidance for 2024 (guide: $30-37 Bn). Beyond 2024, I am modeling capex to be persistently above $30 Bn as it may be likely that we are closer to either beginning or mid cycle of AI-related infrastructure investments than to the end of it. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5744ec6-e4ea-42bd-87da-eab653644682_1297x70.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Valuation** To generate \~10% IRR (remember, I’m doing reverse DCF to figure out what I need to believe to generate \~10% IRR), I need to assume \~20x terminal FCF multiple. Please note even the terminal FCF number **includes \~$20 Bn losses from RL segment**. While that may seem to be a draconian assumption and I tend to agree, Meta management has made my job increasingly difficult by continuing to increase losses “meaningfully” year after year without giving even a hint at how far we may be from RL losses peaking. Since there is every chance of AR/VR being a protracted race between Apple and Meta (and who knows who else if this is indeed the future of computing), it is hard to have high conviction on how the losses will evolve in RL. Nonetheless, capitalizing $20 Bn losses with \~20x multiple is effectively implying RL to be worth negative $400 Bn which obviously just sounds wrong, so perhaps bulls can make a rather convincing argument that the “real” terminal multiple is 2-4 turn lower than what you see here. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F36b5d71a-f508-4428-acd2-93cdd3e65eb4_885x300.png) Source: MBI Deep Dives # Closing Words Times have changed a bit; Mark Zuckerberg is now being widely lauded by investors for subscribing to the efficiency religion in operating the company and somehow growing the company faster despite laying off a quarter of the company. If you are not a new reader here, you may be aware that I rate Zuckerberg very highly. Yet, looking back in the last 7-8 years in big tech land, it may be sobering to see Zuckerberg’s company was worth so close to other big tech companies in 2016-17 period, and today those companies are worth $500 Bn to $2 Tn more than Meta is today. Even Nvidia joined this trillion dollar club almost out of nowhere and is even worth more than what Meta is today despite the +400% rally since October, 2022\. If Zuckerberg wants to position Meta near the top in this big tech club by 2030-35, he will have to allocate capital **much more prudently** than he did in the last 5-7 years. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff262c9ea-6541-4318-8af5-0862bfca0265_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 15% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Facebook turned 20 years yesterday; what a Harvard undergrad started as dorm room project turned into a more than a trillion dollar company. It is not lost on me what a downright remarkable achievement this is, but as a shareholder, I hope he continues to have a very high bar for himself for the next couple of decades. I have exercised my January 2025 $50 call options on Friday last week and converted the options to stocks. I did sell 10% of what I received after exercising the options and plan to keep the rest 90% for the time being since despite the dizzying rally in recent months, the stock still seems reasonably valued. I will upload an updated Amazon model and share my thoughts by **tomorrow**. ### Amazon 4Q'23 Update URL: https://www.mbi-deepdives.com/amzn4q23/ Last updated: 2024-02-02T15:45:11.000Z *Disclosure: I own Jan 2025 $55 Call Options of Amazon* While many investors remain obsessed with quarterly topline growth of AWS, the real story continues to be North America’s sustained margin recovery. Here’s my highlights from yesterday’s earnings. [Subscribe](#/portal/signup) **Revenue** 1P revenue grew HSD and Amazon’s revenue ex 1P increased by high-teens last quarter. Since I continue to rate Amazon retail to be relatively overlooked driver for Amazon, let me start with margin discussions on retail. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa735e452-dd7b-4d66-9b28-3718719c3821_1164x259.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** North America’s operating margin bottomed at -2.3% 1Q’22 and has since then increased by more than 800 bps to reach 6.1% in 4Q’23, just shy of the pre-pandemic high of 6.4% in 1Q’19. International margin bottomed at -8.9% in 3Q’22 and also experienced massive margin improvement to reach -1.0% in 4Q’23 although here margin deteriorated a bit QoQ. Amazon tried to reassure that things are on track in international segment: > The International segment represents more than 20 countries of varying degrees of growth. In our largest established countries like the U.K., Germany and Japan, relatively strong revenue growth contributed to the year-over-year improvement in profitability. Additionally, we saw good progress in our emerging countries as they continue to expand their customer offerings while seeking to invest wisely. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd0225eca-5756-49aa-a902-4641a4f4b601_1117x678.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Fulfillment+ Shipping** Shipping+ fulfillment costs, and paid units both increased by \~12% in 4Q’23. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7fb8169-0dd0-4975-95b9-d4ab05c91380_1372x594.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Shipping+ fulfillment cost as % of GMV continued to go in right direction. As % of estimated GMV, shipping+ fulfillment costs were 21.8% in 4Q’23 (vs 26.1% in 1Q’22). We are now lot closer to pre-pandemic level, but Amazon may still have a plenty of runway left for further improvement if you eye at 2016 when this number used to be below 17%. Again, let me contextualize why this is a huge deal for Amazon and its shareholders. I estimate Amazon’s GMV was \~$800 Bn in 2023 (defined as online sales+ (3P sales/25%). So every 100 bps improvement would add \~$8 Bn to Amazon’s bottom line. For the full-year, shipping+ fulfillment cost as % of GMV was 22.7%. If it were \~400 bps lower i.e. 18.7%, it would add \~$32 Bn to Amazon’s total operating profit which would almost double its actual reported profit of \~$37 Bn!! Of course, we cannot expect it to magically happen by next couple of quarters or even years, but this is indeed the most important thesis for Amazon in my opinion that will likely play over multiple years. To understand the valuation implications more clearly, I encourage you to play with my earlier shared Amazon model [**here**](https://www.mbi-deepdives.com/models/). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4bbb4c9f-482d-4971-a785-8a686e3d48d6_1473x748.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) During the call, management reiterated margin upside potential for retail in 2024: > we're seeing a reduction in some of the inflationary factors that hit us especially hard in 2021 and 2022, things like transportation services, fuel and others. So not totally out of the woods there but coming down, and we still see some more upside Jassy also shared quite a few encouraging data points which also made it tangible how hard it will be to compete against Amazon’s logistics: > In 2023, Amazon delivered to Prime members at the fastest speeds ever, with more than **7 billion items arriving same or next day, including more than 4 billion in the U.S. and more than 2 billion in Europe**. In the U.S., this result is the combination of two things. One is the benefit of regionalization where we rearchitected the network to store items closer to customers. The other is the expansion of same-day facilities where in the U.S. in the fourth quarter, **we increased the number of items delivered the same day or overnight by more than 65% year-over-year**. > > …In 2023, **for the first time since 2018, we reduced our cost to serve on a per unit basis globally**. **In the U.S. alone, cost to serve was down by more than $0.45 per unit compared to the prior year.** Lowering cost to serve allows us not only to invest in speed improvements but also afford adding more selection at lower average selling prices, or ASPs, and profitably. We have a saying that **it's not hard to lower prices, it's hard to be able to afford lowering prices. The same is true with adding selection. It's not hard to add lower ASP selection, it's hard to be able to afford offering lower ASP selection and still like the economics.** Like improving speed, adding selection puts us in the consideration set for more purchases. **Shopping Assistant** Amazon launched “Rufus” which is their shopping assistant. It increasingly seems clear to me that query share will continued to be fragmented in GenAI dominated apps and almost every consumer tech company understands and smells this opportunity. It remains to be seen how well this opportunity will be capitalized, but the near certainty of fragmentation of query shares, and the uncertainty around how all these eventually settle is what unsettled me enough to sell Google shares. We’ll see how that ages over time. Here’s more color from management what the assistant can do: > we launched Rufus, an expert shopping assistant trained on our product and customer data that represents a significant customer experience improvement for discovery. Rufus lets customers ask shopping journey questions, like what is the best golf ball to use for better spin control or which are the best cold weather rain jackets, and get thoughtful explanations for what matters and recommendations on products. You can carry on a conversation with Rufus on other related or unrelated questions and retains context coherently. You can sift through our rich product pages by asking Rufus questions on any product features and it will return answers quickly. We're at the start of what Rufus will do with further personalization and expansion coming, but we're excited about how it will make discovery even easier on Amazon. **AWS** AWS is now almost \~$100 Bn annualized revenue business. After five quarters of hiatus, AWS again added more than a billion QoQ revenue which Amazon claimed to be higher than other hyperscalers: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F31215264-b5b4-4663-94c9-f82b0b5a00fb_1027x535.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Now that we have all the hyperscalers reports, here’s their revenue growth trajectory: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd5e2f805-311c-4aac-8730-c8875551aa73_1185x759.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); \*Google Cloud includes Google Workpace, so not quite apple-to-apple and in reality, GCP likely grew faster than Google Cloud We knew GCP had a good quarter, but it does appear to be even better when you benchmark against AWS numbers. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F95875dc5-c174-4c0c-8f72-3fc9c2f73c4f_895x576.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4dd90376-515f-4912-9b9e-54597cb485bb_880x579.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Back to AWS. AWS incremental operating margin continued to be >60% and overall operating margin hovers around \~30%. The incremental margins across the big tech I covered clearly exhibited a ton of efficiency last couple of quarters! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa65c93b1-88e1-4e0d-94b3-5036a27ccc02_1321x118.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa565909b-8c17-4daa-b206-db3941e0c8ee_1264x658.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Some encouraging comments on AWS growth from the call: > Similar to what we shared last quarter, we **continue to see the diminishing impact of cost optimizations**. And as these optimizations slow down, we're seeing more companies turning their attention to newer initiatives and reaccelerating existing migrations. > > …We expect **accelerating** trends to continue into 2024. **Opex+Capex** Looking at cost structure, it’s tempting to see a lot more headroom for further efficiency, especially in sales & marketing. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2ab22a35-f107-4399-ae8b-c5ea42d2a4d7_2094x295.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) If 2023 was the “year of efficiency” in big tech land, 2024 may prove to be “year of capex”. Like Google and Meta, Amazon too indicated capex to increase due to their investments in AWS related infrastructure although they haven’t quantified the increase: > As we look forward to 2024, we anticipate CapEx to increase year-over-year primarily driven by increased infrastructure CapEx to support growth of our AWS business, including additional investments in generative AI and large language models. **Other Bets** We got an update on Kuiper: > In October, we had a major milestone in our journey to commercialize Project Kuiper, which is our low Earth orbit satellite initiative that aims to provide broadband connectivity to the 400 million to 500 million households who don't have it today. > > …We're on track to launch our first production satellite in the first half of 2024 and started beta testing in the second half of the year. **Outlook** Amazon’s guidance for 1Q’24 is below (please note they increased useful life from 5 to 6 years): ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6d386e33-e7cd-413e-952d-4e8b8d7c5c4d_1063x250.png) Source: Company Filings **Closing words** While there is a lot of positive you can take away from this report, I do want to reiterate that a lot of this is already priced into the stock. Amazon trades at \~35x NTM EV/EBIT multiple, so market clearly is paying dearly for the impending growth and margin expansion. We are, unfortunately, currently in a market in which we should perhaps lower our expected return. Please feel free to share with your friends and network. I will cover Spotify's earnings next week. Thank you for reading. [Subscribe](#/portal/signup) ### Meta 4Q'23 Update URL: https://www.mbi-deepdives.com/meta4q23/ Last updated: 2024-02-02T06:21:11.000Z *Disclosure: I own shares and 2025 January $50 Call Options of Meta* By 3Q’23 earnings, almost everyone understood Meta’s turnaround; the layoffs were behind us, topline growth was accelerating, and thanks to Meta’s “efficiency”, margins were expanding. Yet, the stock has gone up another **\~60%** since 3Q’23 earnings (including AH earnings reaction today). Despite being one of the big tech companies, the stock’s volatility in both directions has been nothing short of dizzying for investors. From \~75% drawdown in 2022 to \~+400% from the bottom in just 15 months, there was never quite a dull moment for Meta’s shareholders! Here are my highlights from tonight’s call. [Subscribe](#/portal/signup) **Users** Take a look at the Facebook’s DAU or MAU number for the last time because Meta will stop reporting them from next quarter. Investors usually assume the worst when any important KPI gets deprecated by the company. Thankfully, along with Family “Daily Active People” (DAP), Meta will report YoY changes in ad impressions and average price per ad by **region**. I suspect a granular region level data may prove to be more helpful for investors to gauge Family of App’s (FOA) health than what the deprecated data was providing us. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faf753da2-e2df-4b4d-8f4f-6cdd1bca8701_1804x510.png) **Engagement** DAU/MAU engagement improved QoQ in every single region. Overall DAU/MAU ratio has been inching up for the last **eight** consecutive quarters. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbbace8f7-4c27-4926-bdcb-d9bd6737ec11_1798x544.png) **ARPU** While ARPU exhibited considerable strength, please note the material weakness in YoY comparison and hence, 2-yr CAGR is likely better reflective of long-term trend. As you can see below, we have one more quarter of easy comp ahead of us after which growth may start to mirror closer to long-term trends. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9c1f76ee-8e99-44d0-95ae-1820861bd493_1654x181.png) Annual ARPU (calculated as ad revenue divided by FB MAU) in North America reached \~$220 in 2023 which is a staggering number. While that can also make investors nervous wondering how much larger it can possibly be, it does seem Meta’s ad infra continues to get better which is essential for further runway for ARPU: > Our approach to optimizing ad levels in our apps has become increasingly sophisticated over the years as we've **developed a better understanding of the optimal place, time and person to show an ad, which has enabled us to adopt a more dynamic approach to serving ads**. We expect to continue that work going forward, while services with relatively lower levels of monetization like video and messaging will serve as additional growth opportunities **Ad revenue** Number of ad impression grew by 21% YoY whereas average price per ad increased by 2% YoY, driven by “advertiser demand and currency tailwinds, which were partially offset by strong impression growth, particularly from lower-monetizing services and regions” . ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F611c9bfe-dff6-4c8f-80a9-3178c4c5686e_1113x553.png) China-based advertisers was 10% overall revenue for Meta in 2023 and contributed 5 percentage point of growth. After last quarter, investors started worrying about sustainability of this growth. While the stock price trajectory since then makes me think such concern has largely dissipated, I suspect it can come back at a moment’s notice anytime we start to see softness from these China-based advertisers. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a0ccdb9-b4cf-4c29-bde8-4ca963865f52_1638x576.png) **Segment Reporting** Overall 4Q’23 revenue was +25% YoY(+22% FXN) whereas total expenses was -8% YoY. On a 2-yr CAGR basis, Meta’s topline increased by 9.1% in 4Q’23\. For context, Google Search revenue grew by 5.3% and Google advertising increased by 3.4% during the same time! FOA posted **\~80%** incremental operating margin in 4Q’23 vs 4Q’21 (just as it did for 3Q’23 vs 3Q’21). For the second consecutive quarters, FOA had >50% operating margin. This is damn impressive of course, but let me change gear a little. Looking at such margins, it reminded me about Zuck’s appearance at Senate hearing yesterday. While the hearing is bit of a circus, I cannot help but think a business with >50% operating margin **can, should, and must** do more than what Meta does today to protect their users from harm’s way. While this may depress near-term margins, it may be of paramount importance for long-term health of the business; in fact, it may further entrench these big tech’s moats even if it leads to lower margins in the near term. To be clear, unlike most people, I am [far from convinced](https://www.oii.ox.ac.uk/news-events/no-evidence-linking-facebook-adoption-and-negative-well-being-oxford-study/?ref=mbi-deepdives.com) that usage of social media is harming today’s teens, but I do think Meta may need to do more as society’s (and regulators) expectation from big tech [evolves](https://www.wsj.com/business/retail/amazon-could-soon-be-on-hook-for-safety-of-third-party-products-it-sells-and-ships-be58b697?ref=mbi-deepdives.com) as they get bigger and bigger. The increasing presence of [scammy ads](https://twitter.com/friedoystercult/status/1752518443071340856?ref=mbi-deepdives.com) has also been disappointing and concerning to see. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F021177c4-7542-42bf-a55d-81426243b616_1510x486.png) Okay, back to financials now. Other revenue, which includes “business messaging” from WhatsApp, stood out to me for its pace of growth. Although it’s a small base for now, it increased by 82% YoY (2-yr CAGR 47%) and will be interesting to see if the momentum persists in 2024. Reality Labs remains largely a cause of concern for investors. While it exceeded $1 Bn revenue for the first time, thanks to Quest 3 and Quest 2 sales during Christmas season, losses have no sign of peaking. In fact, Meta mentioned again for 2024, *“for Reality Labs, we expect operating losses to *increase meaningfully* year-over-year due to our ongoing product development efforts in AR/VR and our investments to further scale our ecosystem.”* I know some Meta bulls are tempted to look at FOA and try to imagine some SOTP by assigning Reality Labs valuation of zero. I strongly discourage you to do that as I think it should be clear by now that despite what you may have read about Meta “pivoting from Metaverse to AI”, that is pure fiction. Meta remains fully committed to AR/VR and with Apple’s entry to this space, it is fair to say Meta is going to compete and keep pace against a company that has >3x revenue with deep and entrenched benefits from Apple’s control of incumbent mobile computing. Only way I can see Meta to scale back materially is if Apple itself decides to exit the market if Vision Pro is a massive flop. That wouldn’t be my base case and I expect this to be protracted race between these two companies. In short, I would mostly just pay attention to Meta’s consolidated numbers. Let’s look at some interesting comments from the earnings call: **Reels** > We're seeing sustained growth in Reels and video overall as daily watch time across all video types **grew over 25% year-over-year in Q4** driven by ongoing ranking improvements. **Shop Ads** > Shops ads, we talked about the **$2 billion annual run rate in Q4** after we just **opened availability to all U.S. advertisers in Q2.** > > …eligible Shopify businesses can now onboard to shops on Facebook and Instagram very seamlessly. And we're making it easier for advertisers to turn their existing ads into shops ads. And we'll continue to **focus on deepening integrations with partners and leveraging AI to make shops ads even more performant**. **WhatsApp** WhatsApp Channels now has 500 Mn MAU. > WhatsApp is also doing very well. And the most exciting new trend here is that it is **succeeding more broadly in the United States**, where there's a real appetite for a private, secure and cross-platform messaging app that everyone can use. And given the strategic importance of the U.S. and its **outsized importance for revenue**, this is just a huge opportunity. **Threads** Threads now has 130 Mn MAU (vs 100 mn in 3Q’23). **Open Source** For the second consecutive quarters, Zuck tried to explain to investors why Meta is taking the open source route: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4b1fb682-a8af-44f2-99db-304ce223c429_1080x765.jpeg) One other thing that stood out to me from Zuck’s prepared remarks is Meta’s access to data regardless of how the GenAI related lawsuits will settle: > Now the next key part of our playbook is learning from unique data and feedback loops in our products. When people think about data, they typically think about the corpus that you might use to train a model upfront. And on Facebook and Instagram, there are hundreds of billions of publicly shared images and tens of billions of public videos, which we estimate is **greater than the common crawl data set**. And people share large numbers of public text posts and comments across our services as well. **AR/VR** > “our focus for this year is going to be on growing the mobile version of Horizon as well as the VR one.” I [love](https://twitter.com/borrowed%5Fideas/status/1725282443526082886?ref=mbi-deepdives.com) my Ray-Ban Meta smart glasses, and it seems even Meta was surprised by the demand: > Ray-Ban Meta smart glasses are also off to a **very strong start both in sales and engagement.** Our partner, EssilorLuxottica is already planning on making more than we both expected **due to high demand**. Engagement and retention are also **significantly higher** than the first version of the glasses. **Capital Allocation** Meta bought back $6.3 Bn last quarter ($20 Bn for the full year). What was perhaps bit of a surprise was Meta has initiated a dividend of $0.5/share. While headcount was down 22% YoY, Meta started hiring again as headcount increased by \~1k QoQ. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff4e6fee8-4549-4a26-b00d-0a406e6d34c4_1810x354.png) **Opex Guide** Meta kept opex guide unchanged: $94-99 Bn. Given historical trends, investors typically expect Meta to either lower the opex guide over time or be closer to the low end of the guide. I wonder if this assumption remains relevant under Susan Li. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa3a0ed14-c84d-4ac3-a018-02598dcb624d_1224x583.png) **Capex** Capex guide range was increased from $30-35 Bn to $30-37 Bn in 2024 > We expect growth will be driven by investments in servers, including both AI and non-AI hardware, and data centers as we ramp up construction on sites with our previously announced new data center architecture. Given how well their capex ramp turned out when it was quite controversial among investors, Zuck seemed emboldened by that experience which is quite understandable: > I recently shared that by the end of this year, we'll have about 350,000 H100s and including other GPUs, that will be around 600,000, H100 equivalents of compute. We're well positioned now **because of the lessons that we learned from Reels**. We initially underbuilt our GPU clusters for Reels. And when we were going through that, **I decided that we should build enough capacity to support both Reels and another Reels-sized AI service that we expected to emerge so we wouldn't be in that situation again**. > > And at the time, the decision was somewhat controversial, and we faced a lot of questions about CapEx spending, but I'm really glad that we did this. Now going forward, we think that training and operating future models will be even more compute-intensive. We don't have a clear expectation for exactly how much this will be yet, but **the trend has been that state-of-the-art large language models have been trained on roughly 10x the amount of compute each year**. > > And our training clusters are only part of our overall infrastructure, and the rest obviously isn't growing as quickly. But overall, we're playing to win here, and I expect us to continue investing aggressively in this area. In order to build the most advanced clusters, we're also designing novel data centers and designing our own custom silicon specialized for our workloads. In case you think Zuck has become atheist to the “efficiency” religion, he did have some reassuring words: > …we're in a place now where the business is performing well. And I think the obvious question would be, okay, well, given that, should we just invest a lot more in things? > > And the biggest thing that's holding me back from doing that is that at this point, I feel like **I've really come around to thinking that we operate better as a leaner company.** > > Even beyond 2024, my operating assumption is that we will also try to keep it relatively minimal because I think that -- until we reach a point where we're just really underwater on our ability to execute, **I kind of want to keep things lean because I think that's the right thing for us to do culturally**. > > …a big part of why I wanted to improve our profitability is to give ourselves the ability to go through what is a **somewhat unpredictable and volatile period over the next 5 or 10 years. There are different risk factors that are geopolitical or regulatory or different things, but also the technology landscape is somewhat unknown**. And we want the ability to be able to surge investment on things, like building out larger training clusters or just making different investments where that's necessary. > > …being a leaner company is helping us execute better and faster, and we will continue to carry these values forward as a ***permanent*** part of how we operate. **Regulation** Hard to know what to infer from the dizzying number of regulatory worries: > FTC is seeking to substantially modify our existing consent order and impose additional restrictions on our ability to operate. We are contesting this matter, but if we are unsuccessful, it would have an adverse impact on our business. **Outlook** 1Q’24 topline guide is $34.5-37 Bn (+25% YoY at mid-point). **Closing Words** So, what now? The stock has become \~5x in the last 16 months. Is there really much money left on the table here? The reality is I have been listening/reading “the easy money has been made on Meta” since it went from $90 to $150\. Having resisted such proclamations, I too have **finally** started to echo that easy money is likely indeed over. Frankly speaking, I do not see “easy money” anywhere in my portfolio or companies in my watchlist, and it is far from clear to me that Meta is any more “difficult money” than my other [portfolio holdings](https://www.mbi-deepdives.com/portfolio/). I will share more thoughts on how I am thinking about it in my monthly deep dives. My investment in Meta has been by far the most tumultuous one in my career even though it worked out more than fine **so far**. David Poppe’s (Giverny Capital Asset Management) recent letter had this interesting bit that really resonated with me which is perhaps also quite apt on my investment in Meta since 2018: > "The stock market is peculiar in its ability to deliver a satisfactory result over time in a manner that feels unsatisfying. It’s perhaps like a restaurant with amazing food and awful service. Or a slot machine in reverse: you mostly win and over time your wealth increases. But every so often you suffer a debilitating loss that causes real financial pain. On top of this, the losses generate headlines and the gains are often received skeptically" For more in-depth analysis on Meta Platforms, you can read my analysis [**here**](https://www.mbi-deepdives.com/meta2023/) (March, 2023). I will cover **Amazon** earnings **tomorrow**. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends. [Subscribe](#/portal/signup) ### Alphabet 4Q'23 Update URL: https://www.mbi-deepdives.com/goog4q23/ Last updated: 2024-01-31T02:58:10.000Z While I have [sold](https://twitter.com/borrowed%5Fideas/status/1732083595874009587?ref=mbi-deepdives.com) my shares in Alphabet last month, I remain a very curious observer. Here’s my highlights from today’s earnings. [Subscribe](#/portal/signup) **Revenue** While almost every single revenue segment grew by double digits, Google Network was down \~2% YoY and \~10% from 4Q’21\. The Network business may not be terribly important given it’s a relatively lower margin segment compared to Search, but it may be indicative of the general direction of where the open web is heading. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff8160733-6d54-4e32-9c84-afd03a5b1ebe_1693x358.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As I have mentioned [before](https://www.mbi-deepdives.com/goog/), Google’s reliance on non-Google properties has declined consistently since its IPO 20 years ago; this trend has likely accelerated and likely will continue to do so in the age of GenAI. Is that bad news for Google? On one hand, you can argue that open web’s role has been diminished for years without making much of a dent on Google’s health. On the other hand, Dan Taylor, VP of Global Ads for Google, was quoted saying this on a WSJ [piece](https://www.wsj.com/articles/why-google-plays-down-its-ad-tech-business-but-is-determined-to-keep-it-11667292084?ref=mbi-deepdives.com) published in 2022: “*Without websites to be searchable and discoverable content, people would have less need for search engines like ours. In that way our interests are really aligned with supporting publishers through ads*.” It remains an open question in my mind to what extent Google can be insulated if open web becomes increasingly crippled. It would be clearly a terrible news if Google didn't own YouTube; perhaps Google’s YouTube acquisition will prove to be an even better masterstroke than it already is regarded in 5 years time! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b645189-3f6c-4468-a373-f17479e33aaf_1330x916.png) Image Source: [WSJ](https://www.wsj.com/articles/why-google-plays-down-its-ad-tech-business-but-is-determined-to-keep-it-11667292084?ref=mbi-deepdives.com) **EBIT** Google Services business maintained its mid-30s operating margin profile. The real surprise was Google Cloud which accelerated margin from just 3.2% in 3Q’23 to 9.4% in 4Q’23\. In just 16 quarters, Google Cloud’s margin improved from **negative 62% to +9.4%**!! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F24dbc79c-e35d-43f2-acca-a7ffaa6c1618_949x544.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Google’s incremental margin also remained quite healthy at 54% in 4Q’23\. While corporate costs appear inflated, please note it includes restructuring costs of $1.2 Bn in 4Q’23 and $3.9 Bn in 2023. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc039ed64-fd48-4232-999c-3df6ed9ad3f4_1518x418.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Search** Search revenue growth YoY continued to accelerate. While GenAI bear case remains top of mind concern for Search among Google observers, the business seems to be cruising along so far. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1dc1b6d2-c44f-4ee1-8c1c-c58ade546551_822x508.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Some interesting comments on Search from the call: > We are already experimenting with Gemini in Search, where it's making our Search Generative Experience, or SGE, faster for users. We have seen a **40% reduction in latency** in English in the U.S. > We had particular **strength in retail in APAC**, a trend that began in the second quarter of 2023 and **continued through the end of the year**. **YouTube** Like Search, YouTube ads growth also accelerated this quarter. YouTube shorts is now viewed 70 Bn times daily (note, it’s the same data they shared [last quarter](https://mbideepdives.substack.com/p/alphabet-3q23-update)). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F094c61e6-2f13-4362-855a-773fe6da1af0_826x513.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Google Cloud** After disappointing investors last quarter, Google Cloud beat expectations (which was \~22%) this quarter. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8f6db618-8a0d-4b20-a441-3546024326f2_823x514.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As I [noted](https://www.mbi-deepdives.com/goog/) before, Google Cloud’s revenue historically lagged AWS revenue by four years. Looking at how AWS performed in 2020-22 period, I think it is fair to assume Google Cloud will almost certainly **not** be able to maintain this historical trend in 2024-26 period. 2023 was also the first time Google Cloud’s incremental revenue $ growth was lower than it was the year before. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fde91f02c-80ea-4716-ad24-5c30319e75da_645x214.png) Source: Company Filings, MBI Deep Dives Some interesting comments on Google Cloud from the call: > Vertex AI has seen strong adoption with the API request increasing **nearly 6x from H1 to H2 last year.** > > …the cost optimizations in many parts are something we have **mostly worked through** **Google Other** Google Other is now categorized as “Google subscriptions, platforms, and devices”. Subscription revenue (YouTube Premium and Music, YouTube TV and Google One) is now $15 Bn revenue business, which has been \~5x since 2019. While this is quite impressive, please note the primary driver for subscription business is YouTube Premium and Music, both of which are likely lower gross margin business. Some interesting comments from the call: > Google One is growing very well, and we are just about to cross **100 million subscribers**. > > Play had solid growth again in the fourth quarter driven primarily by an **increase in the number of buyers**. In devices, we continue to make **sizable investments with increased emphasis on our Pixel family, particularly with AI-powered innovation** while driving further efficiencies across the portfolio. **Capital Allocation** Google posted $7.9 Bn FCF last quarter and bought back $16 Bn shares. Please note Google made a $10.5 Bn tax payment in October last year which affected their FCF for the quarter. Overall, they generated $69 Bn FCF in 2023 and repurchased $62 Bn shares. They still have $98 Bn net cash on balance sheet. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc8be5e26-2ac1-4af0-8936-d047f1daa738_649x499.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex and Opex** Google’s headcount was largely flat QoQ, but capex as % of revenue increased to 12.8% in 4Q’23 which was higher since 1Q’22\. This increased capex was “driven overwhelmingly by investment in technical infrastructure with the largest component for servers followed by data centers”. Google also indicated that 2024 capex will be “**notably larger**” than it was in 2023 which was \~$32 Bn. Management didn’t quite hint or clarify what is meant by “notably larger”, but I would guess around $40-44 Bn: > The step-up in CapEx in Q4 reflects our outlook for the extraordinary applications of AI to deliver for users, advertisers, developers, cloud enterprise customers and governments globally and the long-term growth opportunities that offers. In 2024, we expect investment in CapEx will be **notably larger** than in 2023. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F169b7998-33c5-481d-8f02-71cdb655d081_1510x240.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** Google doesn’t provide guidance, but management did seem to hint at tougher comp ahead: > As we enter 2024 with advertising revenues of **more than $100 billion higher than 2019**, we remain focused on sustaining healthy growth on this larger base. While topline may face a tougher second half ahead of Google (and other big tech), Google can perhaps navigate that and grow earnings through a better operating discipline: > As we have repeatedly stressed, we remain committed to our framework to durably reengineer our cost base as we invest to support our growth priorities. Key contributors to **moderating our expense growth** include: first, product and process prioritization to ensure we have the right resources behind our most important opportunities and to reallocate resources where we can; second, organizational efficiency and structure. We're focused on **removing layers to simplify execution and drive velocity**. **Valuation** I share the following valuation framework every quarter. Market seems to be valuing Google Service business at \~15x EBIT and the Cloud business at \~8-10x revenue multiple. While Cloud or other segments can provide some downside protection for shareholders, Alphabet remains essentially a bet on Search and its ability to protect, sustain, and grow Search. That reality is unlikely to change anytime soon. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9db3f162-7596-4c0f-9f65-58728463d2cf_1824x376.png) Source: MBI Deep Dives I will cover earnings of **Meta Platforms** and **Amazon** on Thursday this week. Thank you for reading. [Subscribe](#/portal/signup) ### CoStar: The Art of Creating, Capturing, and Protecting Value URL: https://www.mbi-deepdives.com/csgp/ Last updated: 2024-01-23T15:31:22.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- Jeff Bezos may be the most famous graduate from Princeton’s class of 1986, but the 1986 class had another graduate, Andy Florance, who also embodied a remarkable similarity in **relentless** ambition. They were, in fact, Physics lab partners at Princeton. After graduation, their path diverged; while Bezos worked for a couple of firms and then went to work for Wall Street before listening to his true calling- the Internet, Florance was already steeped into the entrepreneurship bug even before graduation. While he was at Princeton, Florance started his first data business: Cornerstone, which was a monthly leasing guide for commercial real estate. He later sold the business and after graduating at age 23, he started a new company named “Realty Information Group” in 1987 which later was renamed to be CoStar Group. It may be hard to believe but after Florance took CoStar public in 1998, the stock’s performance was almost neck-on-neck with freaking Amazon up until January 2015\. Then the “[AWS IPO](https://stratechery.com/2015/the-aws-ipo/?ref=mbi-deepdives.com)” happened, and Amazon stock jumped to a different universe! CoStar (Ticker: CSGP) did, however, almost \~20% CAGR over 25-year period in public market. His Physics lab partner may have won this round, but Florance is clearly not too shabby either! ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F426b7dea-77e4-4b5b-88e9-60bc93cc0acd_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 15% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Although Florance’s father Coke Florance was a [notable](https://www.costar.com/article/2008392110/architect-coke-florance-remembered-for-influencing-nations-capital-%E2%80%94-and-colleagues?ref=mbi-deepdives.com) architect, Andy seemed to have quite a tumultuous childhood. His parents got separated and at age seven, he became homeless. Andy is, therefore, certainly no stranger to adversity and yet, by age eleven, he managed to make enough money to support himself. At twelve, he got into a music school in NYC; during his 2019 VCU Commencement speech, Andy [shared](https://www.youtube.com/watch?v=9CSYRJa6uJc&ref=mbi-deepdives.com) a lesson he absorbed in music school that I thought was profound: > **Getting closer to perfection is a joyous experience**. And I hope it's something that you all strive for your entire lives. It was in music school he came across computers (one of the few schools to have them at that time) and learned how to code which paved his way to Princeton. Florance was an Economics major at Princeton and he was puzzled that while you could easily follow the prices of stocks and bonds, real estate prices, a market larger than stocks and bonds combined, were quite opaque in nature. Florance’s big idea was to make the prices more transparent by digitizing information such as vacancy rates and rent of commercial real estate etc. Florance clearly found a product market fit as just after a year of starting the company, he got an offer to sell his company for half a million dollar. As he was tempted to consummate the deal, he met a lawyer to help him. That lawyer was Michael Klein. Klein specialized in public securities during the Savings & Loan crisis and appreciated how a lack of high quality data contributed to the banks’ difficulty in providing risky commercial real estate loans. After listening to what Florance’s business is about, Klein ended up convincing Florance not only to **not** sell the business, but also investing several million dollars by taking a second mortgage without telling his wife. After IPO, Klein owned 1.8 million shares of CoStar in 1999 which was \~14% of the company. After all these years, he still owns \~2 mn shares that are worth \~$170 Mn today (due to the stock splits and dilution, he owns just \~0.5% of CoStar now). Ultimately, Florance not only built the “Bloomberg” for Commercial Real Estate (CRE) industry, CoStar also undertook some key acquisitions in the last 10-15 years that created a near impenetrable moat in their data/information service business in CRE. Thanks to his relentless ambition, Florance isn’t quite content by building a de-facto monopoly in CRE data business; his eyes are now set in residential real estate. Co-Star’s **growth persistence** is nothing short of remarkable! The company recently posted its **50th consecutive double digit quarterly revenue growth**. I’m not sure how many companies in the US were able to achieve a similar record, but I bet it’s a short list. CoStar does seem to be a special company! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb2983106-e31c-4758-825a-4cbc573a400a_1057x594.png) Source: Tikr, MBI Deep Dives It’s not just topline growth; after it became EBITDA profitable (given the acquisitive nature and low capex intensity, EBITDA is a decent metric here) in 2002, it has increased its margins from \~8% in 2002 to almost \~30% in recent years. The “actual” EBITDA margins on the core business is likely closer to \~40% which is currently masked by CoStar’s aggressive push towards building a marketplace in residential real estate. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3a24c54b-7676-40e8-982a-bee50bcdf9b7_2152x148.png) Source: Company Filings, Tikr, MBI Deep DIves As you can imagine, there’s a lot to unpack here. In this Deep Dive, I first discussed the whole business segment by segment. Then I outlined the CoStar’s economics, capital allocation, and management incentives. Finally, I showed what’s likely embedded into the stock price today and my concluding thoughts on CoStar. --- [Subscribe](#/portal/signup) _This post is for paying subscribers only._ ### January, 2024 Update URL: https://www.mbi-deepdives.com/january-2024-update/ Last updated: 2024-01-04T16:51:07.000Z [Subscribe](#/portal/signup) Happy New year. Just a few quick updates to start 2024: 1. My next Deep Dive is on **CoStar Group** (Ticker: **CSGP**) which I hope to publish by January 23rd. Following CSGP, I plan on studying **AppFolio** (Ticker: **APPF**) in February. 2. Someone asked me about the mix of subscribers at MBI Deep Dives. I meant to include this chart on my [**Annual Letter**](https://www.mbi-deepdives.com/2023/), but forgot about it later. Approximately half of MBI Deep Dives subscribers is professional investors, and the other half is individual investors. ![Image](https://pbs.twimg.com/media/GCh7th_aQAAA9Ik?format=jpg&name=large) 1. Over the last few months, I have heard a specific feedback from a number of subscribers about navigation on my website. Some suggested whether I could make it easier for new subscribers to follow my portfolio changes in the past. For example, I published a Deep Dive on Adyen in May 2022, but only started buying the stock in August 2023\. For new readers, it was hard to figure out earlier why I changed my mind and which post to go to in order to understand the rationale for changing my mind on Adyen. The "[**Portfolio**](https://www.mbi-deepdives.com/portfolio/)" tab on the website will help you navigate that more easily now. This will allow you to also see my portfolio's evolution over time. 2. One other feedback that I have heard from multiple subscribers is many readers would like to receive more updated opinions on the companies that I have covered in the past. One of the features about my investing process at MBI Deep Dives is I am leaning more towards breadth than depth since as a generalist, a better breadth than specialists is how we mostly can add value. While I do keep my eyes on companies that I have covered in the past, it may impede the speed at which I increase my breadth if I go back to updating past coverage too frequently. But I do want to acknowledge that I would like to toggle between new and past coverage a bit more frequently than I have done so far. While there is no change in my process for 2024 and you can expect just 12 new Deep Dives in 2024, I plan on making some changes from 2025\. From 2025, I will do 11 new Deep Dives every year and choose to **update** coverage of **three** companies that I did since 2020\. I will see how it goes and perhaps in 2028-30, I may lean to update 6 companies and do 10 new Deep Dives per year. While these are not yet certain, I just want to convey to my readers how I am thinking about it. 3. Finally, if you are a new subscriber, I just want to highlight that you can access all the past 42 Deep Dives, including their financial models, [**here**](https://www.mbi-deepdives.com/models/). Thank you so much for your support. ### 2023 Annual Letter URL: https://www.mbi-deepdives.com/2023/ Last updated: 2025-08-04T17:02:11.000Z _This post is for paying subscribers only._ ### Floor & Decor: A Special Specialty Retailer URL: https://www.mbi-deepdives.com/fnd/ Last updated: 2023-12-29T13:42:39.000Z *You can listen to this Deep Dive* [***here***](https://www.mbi-deepdives.com/audio/) --- The late Charlie Munger was [asked](https://www.acquired.fm/episodes/charlie-munger?ref=mbi-deepdives.com) whether there is any current imitator of Costco. Munger mentioned Floor & Decor. Floor & Decor (FND) is a specialty retailer primarily selling hard surface flooring products. Management, however, likes to think they are in a “fashion” business since the floor of your home can be exhibition of your taste and aesthetics that can emit a sense of uniqueness. It might be tempting to roll your eyes at the idea that flooring products are a fashion category, but if you look at the history of American homes over the last two centuries, the floor of American homes was indeed bit of a canvas. Brownstoner magazine did an interesting [piece](https://www.brownstoner.com/interiors-renovation/history-flooring-19th-century-carpet-wood-parquet-linoleum-pine/?ref=mbi-deepdives.com) looking at the history of flooring in American homes in the 19th century which made me appreciate that there may be some truth to the claim of flooring being a “fashion” category: > Up through the 1870s, the trends in flooring stayed much the same. Painted floors were recommended, especially for service areas, hallways and bedrooms. Stenciling was still popular, and a viable substitute for carpet in these areas. Tile floors were becoming more popular, especially encaustic tile in vestibules, hallways and sometimes verandahs and porches. The tile was expensive, but long lasting, and worth the expense, as it was easy to clean, and the patterns were very attractive. Very wealthy homes began to tile their receiving rooms and foyers in the European manner, often with encaustic tile, but also with marble, sometimes in patterns of different colored stone. > > Floorcloths were also still popular, as was grass matting, especially in the summer months. Drugget was now used mostly as a rug underlining, and as an insulation for plank floors in winter, when contracting wood allowed drafts to seep up through the cracks and separations in the flooring. But carpeting was king. Indeed, carpeting was king for more than a century! But it went through an incredible boom and bust period in the meantime. After carpet sales saw a meteoric rise to 83 million square yards in 1923, it took nearly four decades for carpet sales volume to consistently exceed that number again. While carpet sales remained sluggish post great depression, it gained quite the momentum when tufted carpets came to the scene in 1950s and outsold woven carpets in less than a decade (see the differences between tufted and woven carpets [here](https://www.youtube.com/watch?app=desktop&v=YT%5FHCfKydNE&ref=mbi-deepdives.com)). Sales volume for tufted carpets increased from \~6 mn in early 1950s to almost \~400 Mn in 1968\. Interestingly, as volume kept rising, price per square yard for tufted carpet kept going down. Between 1955 and 1965, price per square yard for tufted carpets **fell** by \~30% (vs \~[18%](https://tools.carboncollective.co/inflation/us/1955/45078/1965/?ref=mbi-deepdives.com) cumulative general inflation during this period). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F38468a93-12aa-42d3-a8e8-3893d1216b52_589x403.png) Source: Patton, Randall. “A History of the U.S. Carpet Industry” ([Link](https://eh.net/encyclopedia/a-history-of-the-u-s-carpet-industry/?ref=mbi-deepdives.com)) Then came the popularity of hardwood flooring. Hardwood flooring too has been around for centuries, even as early as 1600s when affluent French nobilities opted for hardwood flooring. Hardwood started to show up in the US in the 19th century. Hardwood lost popularity to carpeting after the World War (WW) II as carpeting became more common and inexpensive. Demand for hardwood declined for nearly three decades post WWII and even many hardwood manufacturers were forced to sell carpet to remain in business. But things started to shift in the 1980s and by the time housing boom started gaining momentum in the 90s, hardwood flooring seemed to be more in vogue. In more recent decades, [engineered](https://www.flooranddecor.com/engineered-hardwood-wood?start=0&sz=48&ref=mbi-deepdives.com) hardwood flooring also gave homeowners a relatively cheaper option with much more expansive varieties. By the middle of 2010s, hardwood flooring overtook carpeting in American homes and it continues to gain market share. As of 2021, hard surface had 57% (vs 44% in 2012) market share whereas carpeting had just 37% (vs 49% in 2012) market share. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5d46bda9-9d3e-4fee-904b-1097fe285b38_1582x774.png) Source: Floor & Decor Investor Day, 2022 FND doesn’t sell carpets, and as a specialty retailer selling hard surface flooring products founded in 2000, there was perhaps no other retailer that was perfectly positioned to ride on this secular trend over the last couple of decades. George Vincent started Floor & Decor in 2000 when he allegedly couldn’t find the specific flooring his wife wanted and realized the opportunity for a retailer that can contain a much wider selection than it is typically there in home improvement centers (Home Depot or Lowe’s). But the only way to do such huge number of SKUs to meet customer desire for variety, FND had to specialize in hard surface flooring in a warehouse store format (partly why Munger identified them as “Costco” imitator). Vincent was clearly onto something as just two years after he opened the first store, FND was acquired by a group of investors, including Najeti Ventures, Saugatuck Capital, and TWJ Capital. At that time, FND had only two stores. Over the next decade, the store footprint grew to 25 when the ownership of the company changed hands again to another group of investors (Ares, Freeman Spogli, and FND management itself) in 2010. Following this ownership transition, Vincent left the CEO role and Thomas Taylor, who used to oversee the store operations of Home Depot, joined as CEO of the company in 2012 as FND was eyeing to become publicly listed. There was news report as early as [2014](https://www.reuters.com/article/us-floordecor-ipo-idUSBREA3616V20140407/?ref=mbi-deepdives.com) about FND’s possibility to come to IPO, but FND’s PE investors kept waiting for the “perfect” market conditions, as alluded by Taylor in the 2022 Investor Day: > I joined the company in 2012 with the intention to take the company public. And the private equity teams that I were working with was (saying) we're going to go public pretty quick. And then we stopped and started, and stopped and started so many times because market is not right. This is a problem. There's this story. There's that. So then finally, we're like, well, there always is going to be something. So we went public in 2017. Like other home improvement retailers, FND was a huge beneficiary to post-pandemic boom in housing market. FND’s stock performed better than most retailers even though they too are struggling a little bit at the moment as we are currently going through a slump in existing home sales, thanks to rising interest rates. Despite being a relatively new public company, FND seems to have already attracted a relatively patient shareholder base. Since its IPO in 2017, FND has outperformed not only the home improvement center behemoths, but it also beat both the S&P 500 and Nasdaq 100 index. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F42f1b4fa-b53d-4d1c-bd6c-f12c9dc99940_2400x1240.png "chart") \*Data as of 21 December, 2023; Source: KoyFin (MBI Deep Dives readers get 15% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for the rest of the Deep Dive: **Section 1 Brief Overview and the Economics of Floor & Decor**: I first answer these three questions in this section: **Who** typically shop at a FND store? **What** do customers buy in FND stores? And finally, **why** do they go to a FND store? Then I discuss the unit economics of a FND store and FND’s historical operating performance. **Section 2 Competitive Dynamics**: This section also starts with wondering a couple of questions: can carpeting make a comeback? Will it ever stop ceding market share to hard surface flooring? Is hard surface flooring segment “Amazon proof”? This section also entails a brief case study on Home Depot to instill how difficult it is to outline the long-term path, which is followed by a discussion on current competitive dynamics and the competitive advantages FND enjoys. **Section 3 Management Incentives**: I briefly outlined Floor & Decor management’s annual and long-term incentives in this section. **Section 4 Valuation and Model Assumptions**: Model/implied expectations are analyzed here. **Section 5 Final Words**: Concluding remarks on Floor & Decor, and disclosure/discussion of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Dollar General 3Q'23 Update URL: https://www.mbi-deepdives.com/dg3q23/ Last updated: 2023-12-08T19:17:36.000Z *Disclosure: I own shares of Dollar General* While Dollar General’s (DG) stock rallied +30% over the last two months (still down \~15% pre-2Q’23 earnings though), the business is not quite out of the woods yet. Here are some highlights from today’s call. [Subscribe](#/portal/signup) **Same Store Sales (SSS)** For the two consecutive quarters, SSS is negative for DG whereas Family Dollar (FDO), DG’s primary competitor, maintained its LSD-MSD SSS growth during the same time. Thankfully, management mentioned traffic finally turned positive this quarter; so the SSS decline was driven by average ticket: > …customer traffic was positive in Q3\. After starting the quarter slightly negative, traffic turned positive in the middle period and improved sequentially each period of the quarter. > > ...Customer traffic and same-store sales continue to improve in November. (MBI Note: DG’s third quarter ended on November 3rd and hence sequential improvement in November is not reflected) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F478c32b4-9cea-46bb-bbfe-3a9a49167af9_1369x871.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Gross Margin** Only consumables segment grew YoY and since consumables is relatively lower margin segment compared to other segments, gross margin declined \~147 bps YoY. Management also mentioned increase in shrink, lower inventory markups and increased markdowns for gross margin decline (partially offset by decreases in LIFO and transportation costs): > Shrink is actually 100 basis point headwind for us. And then as we moved into Q3, it's actually running just a little bit higher than that. And so certainly a pressure near term for us, something that we're looking to hopefully- we're mitigating along the way, and it'll show up in the financial results later in 2024. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6030befa-1a90-43c9-a54a-aa706ab692f0_1354x868.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Operating margin** While FDO seem to be gaining traffic share and somewhat narrowed the gap in gross margins, it continued to struggle to generate profits. Both discount retailers struggled last quarter; DG posted their lowest ever quarterly operating margins in the last decade (possibly longer, but I looked at data since 2013 and their lowest operating margin quarter before this quarter was +6.9%). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F25688b41-302d-4f6f-94b0-9b4f5560c52a_1378x876.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) “New” management made some changes (TBD whether these lead to margin improvement over time): > …we have made the decision to redeploy labor hours away from **smart teams** and instead more directly to our store teams and a greater emphasis on customer service and store-level inventory management activities. (Note: Smart teams used to be prior management’s idea which is basically groups of employees that move between multiple stores to organize excess inventory.) **Inventory** Inventory was +3% YoY and -1.8% on per store basis. Non-consumables inventory was -15% YoY and -19% on per store basis. Management expects they have opportunity to take out a “meaningful number of SKUs” to further rationalize inventories (currently they have \~11-12k total SKUs per store) **Store expansion** DG guided 800 new stores expansion in 2024 which will be the lowest since 2015. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2d75448d-4792-4877-b9ac-9827f9eb791f_885x460.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While management thinks slightly slower expansion is more prudent given macro and current context of DG, they remain optimistic about store expansion plans: > we monitor the following 5 metrics of our new store portfolio, including performance against pro forma sales expectations; new store productivity compared to the mature store base; cannibalization, which overall has remained consistent and predictable; cash payback, **which we continue to expect in 2 years or less**; and new store returns, **which we expect to be approximately 18% on average in 2024.** > > I want to note that our expectations for new store returns, while still very strong, are **down modestly from our historical target of 20%-plus**. > > We are placing a **heavier emphasis on rural stores in 2024 with more than 80% of our new stores planned in rural communities** where we believe we can have the most significant and positive impact for our customers The costs of opening new stores is also likely exerting pressure on DG to open stores slower than in recent years: > the initial opening of our 8,500 square foot store has increased more than 30% since we began rolling out the larger format in 2022\. Additionally, nonresidential construction costs have increased significantly since pre-COVID. **Outlook** 2023 Outlook is unchanged from what DG shared couple of months ago. 2023 EPS guidance is $7.35 (mid-point) which implies the stock is currently trading at \~18x P/E. For investors to make decent money in DG, the real debate is whether they can increase their operating margin from \~6.5% (9M’23) to \~8.5% (historical average) over the next couple of years. That debate remains far from settled. **Further reading**: My [Deep Dive](https://www.mbi-deepdives.com/dg/) on DG (August, 2023) Thank you for reading. [Subscribe](#/portal/signup) ### December, 2023 Update URL: https://www.mbi-deepdives.com/december-update-2/ Last updated: 2023-12-04T14:49:27.000Z While it's been a couple of days since the sad news of Charlie Munger passing away, there is certainly a sense of void percolating around. What has consistently amazed me about both Buffett and Munger is not their almost unbelievable track record in investing, but their ability to do much of it in front of the public eye for nearly six decades! Investors with better track record may eventually come along, but I am not sure there will ever be investors who would be able to do so for similar duration **and** let **anyone** leverage their investing prowess by owning a publicly traded stock. Of course, investing track record is hardly the only reason to admire Charlie Munger. I shared my biggest takeaway from Munger's life a couple of days ago: "Munger met Buffett at the age of 35\. How many people actually meet their best friends after 30s? Munger met Li Lu when he was nearly 80 years old. How many people actually form deep intellectual partnerships at such an age? If there's anything I want to learn from his life, it is to live life with intense curiosity and be open to the idea that the best years may be ahead of me. Such belief, even if it proves to be wrong, is deeply optimistic and may lead to more fun and interesting life." Rest In Peace, Charlie Munger. --- Some quick updates for this month: I am currently working on Floor & Decor (Ticker: FND). I hope to publish my Deep Dive before Christmas, but as my family just moved from New York to California, we are still going through all the challenges that come with [moving](https://twitter.com/borrowed%5Fideas/status/1730609526699888729?ref=mbi-deepdives.com) from one coast to another. I would still expect to publish the Deep Dive before Christmas. I will cover Dollar General's earnings this week. I will also do my annual poll among the subscribers, so you will likely receive a short survey link in a couple of weeks. If you are a new reader/subscriber, I would like to highlight that you can access all the past 41 Deep Dives [**here**](https://www.mbi-deepdives.com/models/). Thank you for your support! [Subscribe](#/portal/signup) ### FleetCor: Far from Fleeting URL: https://www.mbi-deepdives.com/flt/ Last updated: 2023-11-20T14:20:09.000Z **Disclosure*: I own shares of FleetCor Technologies* [Subscribe](#/portal/signup) --- Visa and Mastercard built **arguably** two of the most impenetrable payment businesses in the world. Both of them run on an open loop payment system connecting the issuing banks, payment processors, card network etc. While the card networks’ i.e. Visa, Mastercard’s sky-high margins (typically \~60-70% operating margin) caused plenty of envy among payments value chain participants, their three-sided networks (customers, merchants, and banks) have been pretty damn difficult to dislodge. Ben Thompson once [mentioned](https://stratechery.com/2020/visa-plaid-networks-and-jobs/?ref=mbi-deepdives.com): > Once a job is done — and credit cards do their jobs very well — it takes a 10x improvement to get users to switch, and, in a three-sided network, that 10x is 10^3 While Visa and Mastercard were always meant to be pervasive, FleetCor was founded in 1985 with a much narrow focus. It provided a proprietary closed loop card service (known as “FuelMan Fleetcard”) to commercial fleet operators at pre-designated merchant sites that are part of the “FuelMan” network. Why would fleet operators choose such a card instead of Visa/Mastercard? The fleet operators receive a greater discount on their fuel purchases than traditional credit cards. Moreover, FleetCor can restrict unauthorized purchases (cigarettes, food etc.) on the fuel stations. Finally, fleet operators would receive a detailed report on fuel and expense management from FleetCor. In the early years, it was free for fleet operators to use FleetCor’s cards, so there was no downside for fleet operators anyway. Why would merchants be interested in accepting FleetCor’s cards? First of all, fleet operators that are part of the network would be obviously incentivized to go only to outlets accepting FleetCor’s cards, increasing traffic to the stores. Since drivers are likely to go inside the store to buy more high-margin products (food, tobacco, drinks etc.) on their own, accepting fleet cards can lead to more profitable sales. Unlike Visa/Mastercard, FleetCor deliberately avoided ubiquity in the initial years and selected only a few merchants from an area to create a sense of “semi-exclusive” network so that those merchants could enjoy increased traffic and sales. For the privilege of such increased traffic, merchants would pay a transaction fee of 500 bps as percentage of gross revenue which is nearly \~2x the traditional credit cards. Because of the closed loop nature of the network in which FleetCor controls everything, they don’t have to share the economics with everyone in the open loop payment value chain, and can keep the entire economics. It’s a nice business if you can get it! In the early years, FleetCor went for licensing model that effectively created regional franchises who would do the hard work of convincing local merchants to accept FleetCor’s cards. In return, these franchises would pay \~10% of their revenue to FleetCor. Unfortunately, that didn’t turn out so well as many franchises were struggling to remain profitable. Fortunately, FleetCor had the option to buyback these franchises at a pre-defined formula that allowed FleetCor to buy them back at 3.9x EBITDA in 2001. Around the same time, FleetCor hired Ron Clarke, a former President of the ADP Electronic Services Division, who built the business line at ADP from zero to $300 mn in four years. Ron Clarke remains CEO of FleetCor today and given FleetCor was really struggling when he joined in 2000, one can argue almost the entire market cap of FleetCor today has been created under Clarke’s leadership. A decade after Clarke started running FleetCor, it came to IPO in 2010 at \~$2 Bn market cap. Today, its market cap is \~$17 Bn, implying a CAGR of \~18% over \~13 years even though the stock is almost flat over the last 5 years. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd539e54b-678d-469b-be18-1212a16e9120_2400x1240.png "chart") \*Data as Nov 10, 2023\. Source: KoyFin (MBI Deep Dives readers get 15% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) A lot has changed over the last decade or two. While initially it was free to join fuelman network for fleet operators, now you have to pay for analytics, fraud coverage, and to be part of their rewards program. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe079b85c-313d-4f80-a0de-5be4aa32a24f_2013x924.png) Source: FuelMan website Moreover, it’s not just Fuelman network anymore; FleetCor acquired a number of networks over the years to fortify their closed loop network. Today, FleetCor’s closed loop is a combination of four separate networks in North America: Fuelman (accepted in 55k+ gas stations), Comdata (\~8.6k+ truck stops), Commercial Fueling Network or CFN (\~2.6k+ fueling sites), and Pacific Pride Fueling Network (\~1.1k+ fueling sites). Beyond North America, FleetCor has two separate closed loop payment networks in the UK (Allstar and Keyfuels), one in Czech Republic (CCS), Mexico (Efectivale), Brazil (CTF), Netherlands (Travelcard), Australia (Fleet Card), and New Zealand (CardSmart). It’s not just closed loop network either, FleetCor now also partners with Mastercard in North America (accepted in 176k+ fuel sites and 398k+ maintenance sites) and Visa in the UK (\~8.4k+ sites) to provide “white-label” service to customers who want to have their own branded cards. So in some sense, FleetCor has bit of a “Switzerland” approach that allows them to serve their customers any way they want. Of course, FleetCor has fewer controls over these “white-label” cards (easier to implement various specific restrictions over closed loop network than open loop network) and the economics for white label vs their proprietary network is much less lucrative since they do need to share the economics with Mastercard and their brand partners. Fleet cards are sticky business. Both customer retention and revenue weighted volume retention are 91-92% (FleetCor discloses this number for the whole company, so segment specific retention can be slightly different). Half of the churn is due to involuntary reasons (e.g. bankruptcy). \~50-60% of Fuel segment’s revenue is fees revenue, much of which is discount fees earned from the merchants. \~30% of fuel segment revenue in 2022 was directly influenced by absolute price of fuel and \~15% of the segment revenue was tied to fuel price spreads. While higher oil price is largely beneficial to fuel segment revenue, there are some nuances to it. Higher oil price also leads to lower demand for oil and increases bad debt expense. \~10-12% of fuel segment revenue is late fees or financing charges and 30-35% is interchange revenue. While FleetCor’s fuel segment has eclectic sources of revenue, management identifies number of transactions as a Key Performance Indicator (KPI) for this segment. While this KPI has recovered since Covid, number of transaction in 2022 was 471 mn which was still \~5% lower than 2019\. Revenue from this segment, however, surpassed pre-pandemic level, thanks to increase of revenue (adjusted) per transaction from $2.33 in 2019 to $2.68 in 2022\. (Please note the adjustments account for fuel price, FX, acquisitions, divestiture related impacts). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffa104cdf-f059-4694-bd43-5c0b570bb435_895x225.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) When Ron Clarke joined FleetCor in 2000, it had $23 mn revenue and just 10% EBITDA margin. After buying back the struggling franchises and centralizing corporate functions such as accounting, he transformed FleetCor into a money printing machine within just five years as the company reached \~45% EBITDA margin in 2005\. Today, FleetCor Fleet segment’s FCF margin of \~50-60% resembles the margin profile of Visa/Mastercard. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fef147db2-4960-48ef-8a14-e363f30489cd_957x595.png) \*FCF is defined as EBITDA-Capex; Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While FleetCor was founded to serve the commercial fleet operators, its tentacles have moved way beyond fleet business. Since 2002, FleetCor acquired 95 companies, most of which are non-fuel related acquisitions. They correctly and carefully identified the various ways they could serve their core fleet customers. FleetCor understood fuel is just a fraction of all the expenses for their customers; much of their Accounts Receivable/Payables function was done in antiquated fashion which led them to focus on Corporate Payments. FleetCor also realized that they could play a key role in helping the fleet drivers book and pay for hotels when they are on the road. FleetCor largely developed these capacities thanks to acquisition spree Ron Clarke undertook over the last two decades. In doing so, they also expanded their customer footprint way beyond just fleet operators as the non-fuel segments’ customer base expanded to other industries. Today, Fuel is just 40% of FleetCor’s consolidated revenue. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F40c5f3f7-bbc1-4d31-85e1-9fe102c5330d_1395x903.png) Source: FleetCor Even though majority of FleetCor’s revenue is non-fuel related, long-term sustainability question around fuel business casts a shadow among many investors mind. As the world tries to lower their reliance on fossil fuel for transportation, it is a top of the mind concern for FleetCor. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff16c80fc-823b-4d45-b836-69be03648dd5_1026x705.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Before we delve into those concerns, let me give you a more solid overview of the non-fuel segments as well as FleetCor’s EV related initiatives. Then I will discuss the competitive dynamics FleetCor faces which will be followed by capital allocation and management incentive related discussions. Finally, I will elaborate on what’s embedded in today’s stock price and why I have chosen to be a shareholder. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### November Update URL: https://www.mbi-deepdives.com/november-update-2/ Last updated: 2023-11-02T13:51:18.000Z Some quick updates for this month: 1. I will publish a Deep Dive on FleetCor (Ticker: FLT) by November 22nd. In December, my final Deep Dive in 2023 will be on Floor & Decor (Ticker: FND). 2. Now that all the key digital advertisers reported their earnings this quarter, here are some key takeaways from 3Q'23 digital ads market: a) Alphabet's share in digital ads was lowest since 4Q'20 b) Meta's share troughed in 3Q'22; it kept gaining share since then and exceeded 30% market share in digital ads again. c) While Alphabet and Meta's duopoly continues, Amazon keeps taking share. ![Image](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76159142-55f0-4f67-9975-c68cc2c82210_1586x563.png "Image") 1. I have also just updated the below chart following Shopify’s earnings today. I usually track this data and mention it on Amazon’s earnings update. But since Shopify reported later than Amazon this time, I am just posting it now: ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/11/image-4.png) 1. If you are a new reader/subscriber, I would like to highlight that you can access all the past 40 Deep Dives [**here**](https://www.mbi-deepdives.com/models/). 2. Finally, a personal update. I am moving from Ithaca, New York to Sacramento, California by the end of this month as my wife is planning to start a new job. My wife and I plan on driving for about a week to move our lives one coast to another. While I tend to be weather agnostic person, my wife does crave the sunshine. I wouldn’t be surprised if she gets sick of sunshine by this time next year! I am happy to meet any reader if you live around the area. Thank you so much for your support. [Subscribe](#/portal/signup) ### Amazon 3Q'23 Update URL: https://www.mbi-deepdives.com/amzn3q23/ Last updated: 2023-10-27T01:41:32.000Z *Disclosure: I own Jan 2025 $55 call options of Amazon* Imagine opening Amazon’s 3Q’23 earnings report 5 years from now and what do you think you might hope you paid more attention to? It’s very unlikely to be AWS topline growth rate this (**or any**) quarter. If I have to guess, it’s the shipping+ fulfillment costs related developments that you would find **more consequential** 5 years from now. I’ll explain why but let’s first take a look at some numbers quickly before going back to that discussion. [Subscribe](#/portal/signup) **Revenue** 3P revenue grew by almost +20%, ads +25%, subscription mid-teen, and 1P MSD+. AWS, which was the key focus for many, grew by \~12%. More on AWS later; let’s start more segment level discussion with Amazon, ex AWS. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8c10941d-a643-48f6-9e76-eecc0817b5c2_1502x234.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon ex-AWS** This chart is a good indication that the real story is not AWS topline growth, rather the pace of improvement or margin expansion in Amazon “Retail” (defined as everything ex AWS). The crux of this story lies in shipping and fulfillment related costs. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5df1a165-1610-4454-b1e0-af07fc5f2ad4_894x620.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Fulfillment+ Shipping** Amazon re-architected their fulfillment network which is paying a lot of dividends: > Our move earlier this year from a single national fulfillment network in the U.S. to eight distinct regions represented one of the most significant changes to our fulfillment network in our history. This change has gone **more smoothly and made more impact than we optimistically expected**. As delivery speed improved (as an Amazon retail customer living in upstate NY, I experienced the pace of improvement personally), it has unlocked new demand: > we know how important speed of delivery is to customer satisfaction and buying behavior. A good example is **the significant growth we're seeing in consumables and everyday essentials**. When customers are getting items as quickly and conveniently as they are now from Amazon, they're going to consider us more frequently for more of their shopping needs. As we've shared the last few quarters, we've reevaluated every part of our fulfillment network over the last year. What’s more encouraging is Amazon sounds optimistic that they can keep improving here. The difference between Amazon’s integrated logistics experience vs other alternatives will likely keep widening: > …don't think we fully realize all the benefits yet and we continue to make steady improvements in fine-tuning the placement algorithms to enable even more in-region fulfillment and to further increase consolidation into fewer shipments…**We have a long way before being out of ideas to improve cost and speed**. How does this manifest in numbers? If you look at worldwide paid unit growth vs shipping+ fulfillment cost growth, you would notice that the latter have consistently outpaced the former pretty much all the time since 2015 until a year ago. Since then, unit growth is faster (was at par in 3Q’23) than shipping+ fulfillment costs, indicating some leverage in their logistics footprint. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd2a34478-185d-4b6c-b8e0-d7c14fae18e4_1258x606.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Why is this a much bigger deal? In 1Q’22, shipping+ fulfillment costs as % of GMV peaked at 26.1%. Since then, this number has been coming down almost every quarter to reach 22.7% in 3Q’23. If we give some credence to Amazon management’s claims that there is ample room for cost improvement here, naturally a question comes to mind about the extent of this potential improvement. Back in 2016, this number used to be \~17-18%, so almost \~500-600 bps lower from current level. I estimate Amazon will have \~$800 Bn GMV this year (defined as online sales+ (3P sales/25%)). So every 100 bps improvement leads to \~$8 Bn profit to Amazon at 2023 estimated GMV level. Now, I don’t quite expect this to happen anytime soon. Amazon didn’t have 1-day shipping program in 2016, and their international presence was minimal back then compared to today which has important implications here since those countries are at different cost trajectory than more established markets (recently launched countries would have a much higher shipping+ fulfillment expenses as % of GMV compared to Amazon US). On the other hand, Amazon’s further investments in robotics may be an added tailwind to cost curves that didn’t exist back then: > We have a very substantial investment of additional robotics initiatives. I would say many of which are **coming to fruition in 2024 and 2025 that we think will make a further additional impact on the cost and productivity and safety and our fulfillment service** If you look forward to 5-10 years when much of Amazon’s retail will be near maturity, this number can very likely go back to high teen level. There’s an enormous operational leverage embedded in building these logistics and fulfillment network and as GMV increases over time, **every 100 bps improvement will have a quite consequential lift to bottom line**. Frankly speaking, I pay much closer attention to this than AWS topline growth on a quarterly basis. To understand the valuation implications more clearly, I encourage you to play with my earlier shared Amazon model [**here**](https://mbideepdives.substack.com/p/models). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7bd61ede-417e-462e-8c9e-f1b14dcb2445_1481x755.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AWS** Okay, now let’s talk about AWS. While 12% YoY growth doesn’t sound exciting, QoQ incremental revenue in 3Q’23 was highest in the last five quarters. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35db7359-749f-4403-8d1d-9cc754b7e9bb_1003x604.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Of course, 12% sort of pales in comparison with Azure or Google Cloud’s growth number, Amazon believes they continue to enjoy the highest absolute growth over their competitors (hard to validate since others don’t disclose exact numbers). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9ddf8070-73c4-4b5d-9b16-271a277b9d52_1167x754.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); \*Google Cloud includes Google Workpace, so not quite apple-to-apple and in reality, GCP grew faster than Google Cloud One thing I would like to track is Google Cloud’s operating performance trajectory against AWS. While Google Cloud’s revenue somehow managed to maintain its gradual momentum against AWS, opex trajectory really went haywire for Google Cloud. While that’s disappointing for Google, important to remember it’s just a quarter. So we may need a couple of quarters to assess whether this trend is rather sticky. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2bf4e0a7-3334-4897-88f2-146f7ab3554f_894x578.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F927f981c-33aa-4991-9c1c-d0bce73d8e38_861x587.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Back to AWS. AWS incremental operating margin finally turned around. Operating margin returned to \~30%. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3d25bca1-e6a6-41cd-a17f-ca51942cda30_1228x124.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffe5f4028-c9b8-4f80-968c-d02697035f58_1269x664.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While management reiterated margin can fluctuate a bit, the reason cited for margin expansion this quarter was “increased leverage on headcount costs”. Management mentioned optimization is starting to “attenuate”. They used the word “attenuate” **6 times** during the call. They also highlighted that some optimization is actually good for both customers and Amazon: > AWS' year-over-year growth rate continued to stabilize in Q3\. And while **we still saw elevated cost optimization relative to a year ago, it's continued to attenuate** as more companies transition to deploying net new workloads. > …while optimization still remain a headwind, we've seen the rate of new cost optimization slowdown in AWS, and we are encouraged by the strength of our customer pipeline…When we look at the fundamentals of the business, we believe we are in good position to drive future growth as the rates of cost optimization slow down. > You also see a lot of customers **who are moving from the hourly on demand rates for significant portions of their workloads to 1- to 3-year commitments, which we call savings plans**. So those are just good examples of some of the cost optimization that customers are making in less certain economies **where it's really good for customers short and long term, and I think it's also good for us**. Amazon seemed to be really eager to assuage the concerns on AWS being a potential laggard on Gen AI: > we're seeing the pace and volume of closed deals pick up, and we're encouraged by the strong last couple of months of new deals signed. For perspective, **we signed several new deals in September with an effective date in October that won't show up in any GAAP reported number for Q3, but the collection of which is higher than our total reported deal volume for all of Q3.** > …In these early days of generative AI, companies are still learning which models they want to use, which models they use for what purposes and which model sizes they should use to get the latency and cost characteristics they desire. In our opinion, **the only certainty is that there will continue to be a high rate of change**. > …Our generative AI business is growing very, very quickly, as I mentioned earlier. And almost by any measure, it's a pretty significant business for us already. **Opex+Capex** Amazon’s cost structure is starting to show some sign of efficiency. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc1593ba1-386c-4fac-9ade-c0aa8522d02f_2021x291.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Other Bets** Amazon hasn’t given up on Alexa yet: > We continue to be convicted that the vision of being the world's best personal assistant is a compelling and viable one and that Alexa has a good chance to be one of the long-term winners in this arena. **Outlook** Amazon’s guidance for 4Q’23 is below: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8bd353ac-e662-4bd9-86c3-7cda93f20fba_1055x236.png) Please feel free to share with your friends and network. Thank you for reading. [Subscribe](#/portal/signup) ### Meta 3Q'23 Update URL: https://www.mbi-deepdives.com/meta3q23/ Last updated: 2023-10-26T13:10:03.000Z *Disclosure: I own shares and 2025 January $50 Call Options of Meta* Meta had a terrific third quarter which makes the after-hours reaction (down \~3%) tad bit surprising, but perhaps understandable given the wider range of scenarios for advertising going forward. Here are my highlights from tonight’s call. [Subscribe](#/portal/signup) **Users** Since 4Q’19, Meta added 880 Mn Daily Active Users/People (DAU/DAP) to its Family of Apps (FOA) properties. Given Snap currently has 406 mn DAU, this means Meta added **two** “Snap” (and then some) in less than four years!! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F031c9b9f-3f6e-4ea2-b296-e771ef7a9f3c_1712x522.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Engagement** DAU/MAU engagement looks steady across all regions. Overall DAU/MAU ratio has been inching up for the last **seven** consecutive quarters. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7bce0e38-3ac9-4523-837c-6b97f1973dac_1712x552.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **ARPU** While ARPU exhibited considerable strength, please note the material weakness in YoY comparison and hence, 2-yr CAGR is likely better reflective of long-term trend. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faa5f4450-63af-4a80-a5db-2f0219b242b0_1545x185.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad revenue** Number of ad impression grew by 31% YoY whereas average price per ad declined by 6% YoY, driven by higher impression growth in APAC and RoW as well as lower monetizing surfaces (i.e. Reels). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F20215bbb-48c7-454a-8dea-1117f213d8d0_1016x552.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Largest contributor to YoY growth: online commerce, CPG, and gaming respectively. Again, Meta highlighted strong demand Chinese advertisers: > spend from Chinese advertisers **further accelerated** for us in Q3\. We have benefited from strong investments from a few of our larger clients. We've also seen generally broader-based strength from other China advertisers, and we believe factors such as lower shipping costs and easing regulations on the gaming industry have served as tailwinds here. But **I think there has been a broader story of improved growth across all advertiser regions in Q3, and even excluding China, advertisers revenue growth has accelerated nicely**. But is the strong demand from Chinese advertisers sustainable? Meta mentioned even though they enjoyed long-term robust demand from these advertisers, they can be a bit volatile: > you kind of alluded to whether there's – the sustainability of the China advertising revenue. And even though we've seen particularly strong growth this year, I would say that **there has been a longer-term trend of overall growth with this segment dating back to past years** and also periods of volatility in the past, like in the last 2 years, we've seen periods with higher shipping costs with lockdowns, with regulation weighing on demand. So **we recognize there's the potential for volatility in the future as well and especially given that there are so many macro factors at play that are quite hard to predict** I also wanted to highlight the growing contribution from APAC and RoW region. During 2019-20, these two regions used to contribute \~26-28% of Meta’s ad revenue; in each of the last three quarters, these regions were >32% of total ad revenue. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbff1783a-ddba-4825-9438-32fe5e45ea5a_1430x554.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Segment Reporting** Overall revenue was +23% YoY(+21% FXN) Total expenses -7% YoY, FOA expense -9% YoY While easy comp can be thought of as one of the rationales for such growth, I think Meta’s resilience can be understood in comparison with Google. On a 2-yr CAGR basis, Google Search revenue grew by 7.7% and Google advertising increased by 6% whereas Meta’s advertising business grew by 9.1% CAGR. Considering ATT and all the things that happened over the last two years, this has been an incredible turnaround for Zuck and Co. Even more impressive is FOA’s **\~81%** (no typo) incremental operating margin in 3Q’23 vs 3Q’21\. After **eight** quarters, FOA returned to >50% operating margin. Admittedly, I myself started thinking in 2022 that we may never see >50% operating margin in FOA again. Glad to be proved wrong. Reality Labs (RL) continue to hemorrhage losses, but losses are almost flat QoQ. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F99bd2480-20ed-4957-98e6-c7be4d96409e_1423x493.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Reels** Reels has been a massive success: +40% increase in time spent on IG since launch; Monetization milestone reached earlier than expected as it is now net neutral to revenue. Reels is expected to be modest tailwind to revenue in 2024. **Business Messaging** 600 mn conversations happen everyday between people and businesses on Meta’s platforms. \>60% of people on WhatsApp in India message a business app account. Click-to-messaging ads doubled YoY in India. Business messaging revenue is included in FOA’s “other revenue” which was +53% YoY largely driven by Business Messaging growth on WhatsApp. > We're seeing sustained momentum with click-to-message ads. Click-to-WhatsApp ad revenue continues to grow very quickly in particular and is already at a multibillion-dollar annual run rate. We're progressing on our work to enable further down-the-funnel conversions. And longer term, we're excited about the potential of AI to help businesses message with customers more efficiently at scale. **Threads** Threads now has 100 Mn MAU. **Chat Bots** Meta thinks chat bots engagement should be incremental: > this is a new use case that **doesn't take away from people interacting with people**. If anything, it should – we're designing these to make it so that they can help facilitate and encourage interactions between people and make things more fun by making it so you can drop in some of these AIs into group chats and things like that just to make the experiences more engaging. So this should be **incremental and create additional engagement.** **AI** Recommendation system increased time spent on FB and IG by 7% and 6% respectively in 2023\. AI tools for advertisers are also driving results with Advantage+ shopping campaigns with a $10 billion run rate. If you assume similar monetization for this incremental engagement, you can start to see that all those capex may be worthwhile. Perhaps that’s why Meta wants to lean onto hiring for more AI-related projects: > **AI will be our biggest investment area in 2024, both in engineering and compute resources**. But I want to avoid allocating a lot of new headcount. So we're going to **continue deprioritizing a number of non-AI projects** across the company to shift people towards working on AI instead. > we have a **sizable hiring backlog right now** since part of our layoffs earlier this year included teams swapping out certain skill sets for being able to hire others. And we're still going to be hiring those roles into 2024\. So that means that even though we're planning to grow headcount at a much slower rate going forward, the actual rate next year may **temporarily be faster** as we work through this hiring backlog. Meta believes what they’re doing on AI is quite unique: > here's some analogy (note: chat bots) is like what OpenAI is doing with ChatGPT, but that's pretty different from what we're trying to do. Maybe the Meta AI part of what we're doing overlaps with the type of work that they're doing, but the AI characters piece, **there's a consumer part of that, there's a business part, there's a creators part. I'm just not sure that anyone else is doing this**. > I think for the Feed apps, I think that over time, **more of the content that people consume is going to be either generated or edited by AI.** Some of it will be creators will now have all these tools to make content more easily and more fun. And I think over time, **maybe we'll even get to the point where we can just generate content directly for people based on what they might be interested in**. I think that, that could be really compelling. **Llama and Open Source** Llama 2 was downloaded more than 30 mn times last month. What’s the point of making all these work open source? > our CapEx expenses are a big driver of our costs, **so any aid in innovating on efficiency is sort of a big thing there**. > While at the same time, a lot of the secret sauce that goes into our product has specific product logic on top of the model, and we're also able to further train the models with data that we have internally. So I think it's a good balance of **improving the quality of what we do and improving the economics around it and improving recruiting while still enabling us to build a leading product**. **Metaverse** Can FOA ever be benefitted through RL’s work? > Reality Labs is working to build the future of online interactions. And we do expect **you'll see some interesting ways that translate into work with the Family of Apps in the near term**. > …Longer term, obviously, we think there's a lot of value from operating our Family of Apps experiences on top of a new computing platform that we helped develop, for example, **having glasses on that enable you to have our Meta AI assistant with you at all times. And as glasses scale, they'll make it increasingly easy to capture compelling content from a first person point of view** while you're staying in the moment or the activity that you're doing and sharing that content should enrich our content ecosystems even further. > the smart glasses that we just rolled out, we sort of thought were a precursor to eventually getting to displays and holograms for augmented reality, and I think we will eventually get there still. **It's not that far off**. But I think that now the ability to deliver AI through smart glasses may end up being a killer use case for that even before you get to the kind of augmented reality type of use cases. Meta sounded pleased with Quest 3 and Smart Glasses initial reception from people, but didn’t disclose any numbers. **Efficiency** > As part of our 2024 budget, we plan to selectively allocate incremental headcount toward 4 key company priorities: **AI, infrastructure, Reality Labs and monetization as well as toward our regulatory and compliance needs**. Of those areas, we expect AI to be the largest area of increased investment as we further invest in generative AI across our core products, internal tooling and research efforts. We aim to offset some of this growth by continuing our efficiency focus and **reducing planned hiring in other areas across the company in 2024**. > The net effect of our efforts to close out our 2023 hiring underruns and our efficiency-focused 2024 budgeting process is that we expect to end next year with reported in-seat headcount **meaningfully higher than our current headcount but to grow at a slower rate beyond that**. **Capital Allocation** Like Google, Meta too was quite reticent in buying back stocks last quarter. For the second consecutive quarters, shares outstanding actually increased. Why is Meta not buying back shares? Google alluded to pending tax payments. Meta hinted the same but while Google’s amount was $10 Bn, Meta didn’t specify any amount but my guess is tax is playing a role here. In 1Q’20, Meta’s LTM SBC per employee was \~$120k (vs Google at $100k). Now Google is at \~$120k but Meta is at \~$177k! I wonder whether last few years poor stock price performance forced Meta to be a bit generous (among other things, likely not the only reason). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc19f91d5-00fd-4776-b96d-e5d041939206_1710x385.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Opex Guide** Meta narrowed their opex guide range from $88-91 Bn to $87-89 Bn for 2023\. They also provided 2024 expense guide: $94-99 Bn (street was more or less expecting $96-102 Bn, so this guide was better than expected). FOA “will be a larger source of payroll expense growth than Reality Labs in 2024”. Even though Meta is guiding 2024 expense, it seems some of it will depend on what they see in topline: > How the expense and revenue outlook come together? Obviously, that's -- **as we get more information on the revenue outlook for next year, that will influence that**. Going forward, Meta will provide next year’s opex and capex guide in 4Q call instead of 3Q call. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdeff6b7e-3a94-45b8-b5a5-da827d05d545_1228x616.png) Source: Company Filings, MBI Deep Dives **Capex** Speaking of capex, 2023 capex range was narrowed: $27-30 Bn to $27-28 Bn (initial capex guide was $30-33 Bn) 2024 capex guide is $30-35 Bn which was also largely lower than what most people were expecting. **Regulation** Regulation, while ironically a moat for Meta, also remains a key worry for me as a shareholder. Meta touched on these concerns and it sounds like we may get an update on EU soon: > …we continue to monitor the active regulatory landscape, including the increasing legal and **regulatory headwinds in the EU and the U.S. that could significantly impact our business and our financial results. Of note, the FTC is seeking to substantially modify our existing consent order and impose additional restrictions on our ability to operate. We are contesting this matter, but if we are unsuccessful, it would have an adverse impact on our business**. > We're continuing to engage with the DPC and other regulatory authorities on our proposed consent model, but **we're committed to making this move as soon as possible, and we will provide an update when we have it**. **Outlook** 4Q’23 topline guide is $36.5-40 Bn (+2% FX tailwind). The range is wider than usual for following reasons: > coming into Q4, **we've been seeing continued strong advertiser demand in key segments**, including online commerce and gaming. But having said that, we are also **seeing more volatility at the start of the quarter**. That's in part why we widened our guidance range to capture that uncertainty. And so for instance, while we don't have material direct revenue exposure to Israel and the Middle East, **we have observed softer ad spend in the beginning of the fourth quarter, correlating with the start of the conflict, which is captured in our Q4 revenue outlook**. **Closing Words** I will perhaps always vividly remember Meta’s 3Q’22 earnings, a period that truly instilled in my heart, mind, and brain that anything is possible in the market (see below tweet by [Alex](https://twitter.com/TSOH%5FInvesting?ref=mbi-deepdives.com)). As it looks like we are amidst another period of volatility in the market, it is good to remember that. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1f6a191b-5e03-452b-a4bc-4c7dfc9e65a1_904x379.png) More commentary on follow-up call and 10-Q is [**here**](https://www.threads.net/@mostly.borrowed.ideas/post/Cy3NIk9Aymq?ref=mbi-deepdives.com) For more in-depth analysis on Meta Platforms, you can read my analysis [**here**](https://www.mbi-deepdives.com/meta2023/) (March, 2023). I will cover **Amazon** earnings tomorrow. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends. [Subscribe](#/portal/signup) ### Alphabet 3Q'23 Update URL: https://www.mbi-deepdives.com/goog3q23/ Last updated: 2023-10-25T00:59:10.000Z *Disclosure: I am long shares and Jan 2025 $50 Call Options of Alphabet* While Google Service segment did just fine, Google Cloud’s pace of deceleration in topline was a bit disappointing. Here are my highlights from Alphabet’s call tonight. [Subscribe](#/portal/signup) **Revenue** After four consecutive quarters of single digit growth, Google returned to double digit growth this quarter. Both Search and YouTube grew by double digit, but Google cloud’s topline growth came down from \~28% last quarter to 22.5% this quarter. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F71975730-94ba-4e56-84ed-ddb9cf849113_2025x381.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Google Services maintained mid-30s EBIT margin, but after posting QoQ margin expansion for the last 6 quarters, Google Cloud’s margin declined from 4.9% in 2Q’23 to 3.2% in 3Q’23. If you compare Google’s current quarter’s result to the respective pre-Covid results, Google’s 3Q’23 overall revenue increased by 89% compared to 3Q’19 revenue, but EBIT increased by 133% during the same time. Despite the hiring spree post-pandemic, Google’s incremental margins have been quite strong. One thing I would like to highlight here is TAC as % of ad revenue came down to 21.2% which was the **lowest** since Google started disclosing Google Advertising revenue separately in 4Q’18. Given Pixel’s relative success recently, it would not surprise me if this number continues to go down: > Pixel is the fastest-growing smartphone brand in our top markets and the **only one that grew in units sold year-over-year** ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fef4d9e60-9b35-4de8-8050-b5e1d723b109_1835x416.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Search** Google will launch “next generation series of models” in 2024: > we are developing Gemini in a way that it is going to be available at various sizes and capabilities, and we'll be using it immediately across all our products internally as well as bringing it out to both developers and cloud customers through Vertex. So I view it as a journey and each generation is going to be better than the other, and we are definitely investing and the early results are very promising. When asked about whether the future of query is decentralized via many different applications/bots, Google reminded that it was always a possibility that they were and remain focused to defy: > if you zoom back and take an information view of the world, there's always been many different ways to get it. And part of our work we do in making Search be world-class and give users what they're looking for so that we can get it as much of that intent as possible. So I don't see that changing. > With mobile, there were more ways people could get information, but we worked out to make Search work better in the mobile world. And similarly, a view with AI, there'll be many ways people get information, but it also offers us an opportunity in Search and in Assistant to take it to the next level and answer use cases, which we couldn't have done before and expand the diverse set of needs where we are sourced. So that's how I see the opportunity ahead **YouTube** Google disclosed YouTube shorts is now viewed 70 Bn times daily (prior disclosures: 4Q’22: 50 Bn, 1Q’22: 30 Bn) For comparison, Reels was viewed 200 Bn in 2Q’23 (prior disclosure in 3Q’22: 140 Bn) Reels is clearly ahead of Shorts and that gap doesn’t seem to be closing over time. Google Other revenue was +21% YoY, led by YouTube subscription revenues. YouTube’s non-ad revenue is almost certainly growing faster than ad revenues. **Google Cloud** Google Workspace now has 10 mn paying customers (prior disclosures: 1Q’23: 9 mn, 1Q’20: 6 Mn, 1Q’19: 5 Mn) From Q2 to Q3, the number of active generative AI projects on Vertex AI grew by 7x Google Cloud’s topline growth was especially disappointing given the context of Azure, with a higher base, posting +28% YoY growth **Other** Some interesting comments on other/other bets: > We also shared that Chromebooks will now get regular automatic updates for 10 years, more than any other operating system. > …In Other Bets, Waymo is onboarding more riders to its commercial ride-hailing service as it gradually adds over 100,000 people from its San Francisco waitlist. Austin will follow as its next ride hail city. Wing and Walmart announced a new partnership to provide drone delivery service in the Dallas-Fort Worth area. **Capital Allocation** Google utilized \~70% of their FCF in buyback which led to 53 bps decline QoQ in diluted shares outstanding. They still have $106 Bn net cash on balance sheet. Google management sort of hinted why they didn’t utilize all of their FCF to buyback shares in the last couple of quarters: > our cash balance and free cash flow in the second and third quarters benefited from the deferral of certain tax payments to the fourth quarter of 2023. > …On October 16, 2023, we made an estimated tax payment to the IRS of $10.5 billion that will be reflected in our fourth quarter operating cash flow. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3775f032-4f12-44f6-8f4c-6897ec5cf7dc_672x474.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex and Opex** Google started hiring again although at a much subdued pace. They hinted that they expect topline to grow faster than total opex growth. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fadcbc50c-183c-4f0c-86ee-be1bb9951fa8_1840x251.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** > First, cost of sales in the fourth quarter will reflect both higher hardware costs given Pixel family launches as well as increased YouTube as previously noted. > Second, as usual, we expect sales and marketing expenses to be more heavily weighted to the end of the year, in part to support product launches and the holiday season. > …Finally, our reported CapEx in Q3 was $8 billion, driven overwhelmingly by investment in our technical infrastructure with the largest component for servers, data centers, reflecting a meaningful increase in our investments in AI compute. The growth in reported cash CapEx in Q3 is somewhat muted due to the timing of supplier payments which can cause variability from quarter-to-quarter. We continue to invest meaningfully in the technical infrastructure needed to support the opportunities we see in AI across Alphabet and expect elevated levels of investment, increasing in the fourth quarter of 2023 and continuing to grow in 2024. **Valuation** I share this back-of-the-envelope valuation table on Google every quarter to have a quick gut check. While Google seems more or less fairly or reasonably valued today, its long-term future will be very reliant on the durability and sustainability of Search profits. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9726d38d-c8f4-40cc-8b20-412a4e906520_1834x386.png) Source: MBI Deep Dives You can read my more in-depth analysis on Google [**here**](https://www.mbi-deepdives.com/goog/) (March, 2023). I will cover Meta’s earnings tomorrow. Thank you for reading. [Subscribe](#/portal/signup) ### Spotify 3Q'23 Update URL: https://www.mbi-deepdives.com/spot3q23/ Last updated: 2023-10-24T17:15:49.000Z *Disclosure: I own shares of Spotify* --- > We walked into 2023 thinking we would do just over 20 million in net subscriber adds for the full year, but we're actually on track to deliver 30 million, which is a significant beat from where we thought we would be. I’ve been following Spotify for almost two years now and this was perhaps their best quarter. Not surprised that the stock is +10%. Here are my notes from today’s earnings. [Subscribe](#/portal/signup) **Users** Despite price increases for the individual subscription plan first time in the US, Spotify did not lose subscribers on net in North America: > when you think about a price increase, there's really the 2 components you're always going to be focused on. One is anything that elevates churn. And then two, anything that impacts the gross intake in any way. And so what was great was the **churn was right in line with expectations**. And we talked about in the past **when we've raised prices that churn had never been that material, and it was similar to this go around**. And then I guess even just as importantly, **we outperformed on the gross intake side**, which is one of the reasons why we outperformed on overall subs ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fba73115e-2d1d-41dc-97c1-0e4802223f0b_1674x161.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Premium Mix** Premium as % of MAU went down over the last 4-5 years as Rest of the World (RoW) region in the MAU mix went from 13% in 1Q’19 to 31% in 3Q’23\. RoW MAU basically 4xed in less than four years. While MAU is generally considered a pretty good funnel to transition to premium subscription, the relationship appears to be a bit weak for RoW so far. **Netflix vs Spotify** This is something I track every quarter. After significantly lagging Spotify for a while, Netflix is not narrowing the gap at a pretty fast clip. I should mention that definition of subscriber of NFLX and SPOT is not apple-to-apple, so I would caution not to infer more than what this data can tell us. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F74100ad4-45d5-4790-868b-a91fa004c3ad_793x527.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Revenue** While overall reported revenue was +10.6% YoY, it was actually +17% YoY. FXN which was \~300 bps QoQ acceleration. Ads was even better with +24% YoY FXN growth. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F211d720d-8465-43da-803b-46dd15c6e92d_1447x238.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Ads as % of revenue kept going up gradually. It was 13.3% of revenue in 3Q’23 (vs 12.7% in 2Q’23). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F26718e9e-5896-44f8-aea8-22749471804d_742x554.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Gross margin (GM)** Overall reported GM was +26.4% (\~40 bps higher than guided) > As you look into 2024, we expect to see a continued improvement in our gross margin trends and a continued improvement in our operating income trends as well. Music GM was +29.1%. Marketplace continues to be a tailwind to margins: > we're offering more and more products to people on the marketplace side, which is seeing better and better results relative to all the other marketing spend that labels and artists teams are encountering, which, of course, is a great testament for – meaning more and more artists will keep on investing with us there. Moreover, ads segment GM was +8.3%, higher than past 6 quarters. Expect more margin improvement here: > we've seen the improvements in the podcasting business, and we talked about how that's been a drag on our gross margins, and **we expect it to soon reach breakeven** and then **become something that's actually additive to gross profit**. So we're on track on the podcasting side there, and that should continue to be helpful into 2024\. **Same with the music side in terms of incremental gross margins there as well**. Will the launch of audiobooks pour cold water to GM expectations in 2024? While Spotify didn’t share much details about margin structure on audiobooks, they specifically outlined 2024 GM to be higher than 2023: > When you think about the audiobooks side of it, there's obviously some investment anytime you launch a new business. But again, as I said earlier, we feel really good about continuing to have a nice progression in gross margins into 2024. > …we are expecting gross margins to be improved in 2024. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fadd24425-f473-491b-9d89-4701e053016d_867x516.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Opex** Despite lots of efficiency promises in the past, it does seem the efficiency train is finally here. > the new part of the Spotify modus operandi is our focus on efficiencies…you should expect us to continue to look for more improvements going forward because that's just our modus operandi. > …our expectations are now that we will consistently be in the black moving forward… we've hit an inflection point with respect to profitability of the business. The numbers look very encouraging. In 3Q’22, Spotify spent €432 Mn in S&M and added 23 Mn MAU and 7 mn subscribers in that quarter. In contrast, they spent 18% YoY lower in S&M to drive basically similar outcome. G&A was also down 19% YoY. R&D was -4% YoY. You can finally smell some “efficiency” here. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F895cc708-2942-41cf-a76a-48a7c2ab4bb2_1747x194.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AI** GenAI should be pretty neat in lowering cost of ad creatives and creating new demands: > what generative AI has the promise to do is, of course, to allow for that creative **cost to come down. But not only that, but it allows you to scale that creative in unimaginable ways**. So you can translate whatever creative you had lots of different languages. You can use the same voice actor, but instead of producing 1 or 2 ads, you can have 1,000 or 10,000 or even 100,000 ads that are individually created to each user that gets to hear this. **Outlook for 4Q'23** While Spotify faces competition from perhaps the most potent set of competitors in the world (Google, Amazon, and Apple), it has surprised (including me) how it reaccelerated its user growth at scale. Spotify reminded 2023 will be their highest year for net MAU add: > 2023 should finish with the highest net additions for MAUs and the second largest for subscribers in company history, but actually the largest if you exclude the impact of Russia 4Q’23 topline is expected to +20% YoY FXN. Impressive quarter, but probably need a couple of those to truly convince investors that things are different now. The stock doubled this year so far, so perhaps many investors already updated their views on Spotify. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea9bc6ed-911e-49e3-a4a0-76b1d64e4160_1271x435.png) Source: Company Filings You can read my Spotify Deep Dive (December, 2021) [**here**](https://www.mbi-deepdives.com/spot/) I will cover Alphabet’s earnings tonight! [Subscribe](#/portal/signup) ### Enphase Energy: A Solar Roller Coaster URL: https://www.mbi-deepdives.com/enph/ Last updated: 2023-10-23T11:29:10.000Z You can listen to the Deep Dive [**here**](https://www.mbi-deepdives.com/audio/) --- If you invested $1,000 in Enphase Energy’s IPO in 2012, you would have only \~$140 left in 2016\. That $140, if left untouched, would then become \~$44,000 by the end of 2022, a mind boggling >300-bagger stock in just six short years! And then stock fell down \~70%! If anyone held the stock from IPO to today, any actual rollercoaster ride would probably make them drowsy at this point. During the due diligence of Enphase, I spoke with an Engineer who did some solar installation work a few years ago. While he was installing solar panels, he started working with Enphase’s microinverters and thought the microinverters were quite easy to install and solved some of the key challenges string inverters typically have (more on this later). After doing some more research, he decided to buy Enphase stock in 2016 which was trading at \~$1.2 at that time. Fast forward to three years later, the stock became \~6-7x and he sold his shares to realize his gains. Given what happened afterwards, he was telling me that he had to stop looking at the stock price at some point and just before our call, he took a look at the stock price and realized he may have missed out on a life changing money. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc60d804d-f731-44d4-935b-ebfc935f15c5_2400x1240.png "chart") Data as of October 20, 2023; Source: KoyFin (MBI Deep Dives readers get 15% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) The whole saga reminded me of Alan Watt’s “[The Story of the Chinese Farmer](https://jadepanugan.medium.com/the-story-of-the-chinese-farmer-by-alan-watts-b9ca01a16b47?ref=mbi-deepdives.com)”. It is hard to keep your sanity in the stock market unless you maintain a steady stream of equanimity to your fortunes **and** misfortunes with a constant drumbeat of “Maybe” on the question of whether something is good news or bad news. I did, however, mention to him that the skillset required to buy a stock such as Enphase when it was nearly a penny stock (thanks to his ground level experience with the product, he had a clear edge in gauging the potential of Enphase than perhaps most investors) and the skillset required to “dream the dream” to be able to hold the stock are likely to be very, very different. It is extremely rare that they would both be the same investor. Okay, enough about the stock price. Let’s shift our focus to the business now. Before I delve into Enphase’s business, I want to have an elaborate discussion on solar industry so that readers have proper context for Enphase’s business and the rest of this Deep Dive. # Section 1 A Brief Overview of Solar An average US household consumes \~900 kilowatt Hours (KWh) of electricity per month (KWh is a unit of measurement that represents the amount of energy used by an appliance in one hour) and paid \~$140 bill for electricity per month which comes out to be 15.12 cents/KWh in 2022. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F16f78c4d-70f1-4022-8662-d57d9dfb0746_1258x684.png) So I went to Enphase website and left some details such as monthly electric bill (I put $150 which is roughly the average electric bill in the US), home address, credit score (>720) etc. to receive a tentative estimate for my cost to install a solar system. Enphase suggested I install 32 solar panels. Since each panel is expected to produce \~390 Watt (W), this suggested solar system will produce \~12.5 KW per hour when the sun is shining. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F116d4ee2-1c87-416d-b41f-58ae2cc9a879_2176x1090.png) Source: Enphase Website Of course, the sun doesn’t shine all the time. I am currently in Ithaca, NY where the sun [shines](https://www.solardirect.com/archives/pv/systems/gts/gts-sizing-sun-hours.html?ref=mbi-deepdives.com) 4.57 hours per day during summer and only 2.29 hours per day during winter (overall average is 3.79 hours for the year). Solar’s capacity factor, the ratio of energy generated over a period divided by the installed capacity, tends to be the lowest among all other energy generation sources. Depending on the area you live in (sunny or cloudy location), Solar’s capacity factor hovers between \~10-25% whereas coal fired power plants and nuclear power plants have capacity factor of \~70% and \~90% respectively. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F505dc95a-aa18-4270-9bf7-0b81d63b33b3_1640x564.png) Source: Click [here](https://www.whatnextnow.com/home/solar/what-is-capacity-factor-and-how-does-solar-energy-compare?ref=mbi-deepdives.com) In addition to low capacity factor, there is another challenge with Solar. If you don’t have storage or battery in your solar system, self-consumption percentage (how much of the electricity produced by the solar panels that has been consumed by the household) can be quite low. Self-consumption percentage varies a lot depending on whether you are mostly at home or household members are mostly outside (e.g. at schools or at work). If you are mostly at home, this is likely less of a concern for you. But if you are mostly outside during the day, self-consumption can be as low as 10% and it is hard to justify setting up Solar for your place. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4962ead5-1659-47b3-932d-5dc86b70940a_1538x928.png) Source: Click [here](https://www.nea.org.uk/who-we-are/innovation-technical-evaluation/solarpv/self-consumption/?ref=mbi-deepdives.com) One way to increase your self consumption is to buy battery or storage. Buying storage allows you to have more control over when you want to use the energy generated by your solar panels not only during night/peak hours but also when the grid is out (note: if you don’t have battery and the grid is out, you won’t have electricity either even if you have solar system). An average solar battery is [10 KWh](https://www.energysage.com/energy-storage/how-many-solar-batteries-needed/?ref=mbi-deepdives.com) in size (also what Enphase suggested me, see above) and depending on your personal context (do you just want to use when the grid is down for an hour or two? Or do you want to be completely off-the-grid?), you may need to buy multiple batteries. As I have shared last month on my [Tesla](https://mbideepdives.substack.com/p/tesla-a-bet-on-dominance-in-potentially) Deep Dive, the cost of lithium-ion batteries have plummeted over the last decade. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F32ac5d4d-3e29-4a9a-83a6-ae1965e849e9_1492x1300.png) It’s not just batteries, Solar’s LCOE or Levelized Cost of Energy, which is a measure of the average **net present cost** of electricity generation for a generator over its lifetime, has declined almost \~90% over the last decade or so. ![Image](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc332404b-8df4-44a6-9986-4835b65ecb41_1610x1145.jpeg "Image") While the aforementioned graph is one of the favorite charts of Solar industry, it does have limitations. Perhaps one of the biggest shortcomings for LCOE calculation is it does not account for the storage costs that are often required to fully benefit from Solar. [Matt Loszak](https://twitter.com/MattLoszak?ref=mbi-deepdives.com) wrote a pretty intriguing [thread](https://twitter.com/MattLoszak/status/1575864493372243968?ref=mbi-deepdives.com) explaining this limitation: > Millions of people mistakenly believe that Solar is now our cheapest option for powering the grid. What's misleading them? A sneaky metric known as LCOE. > Levelized Cost of Energy, or LCOE, is the total cost of a project, divided by the total energy that it produces in its lifetime. If you build a field of low-cost solar panels that produce a lot of energy over their lifetime, it's a low LCOE and a great investment, right? > What LCOE ignores, is the cost of unreliability. While it might selfishly be a good investment for a solar developer, it could be a terrible investment for the grid, and society at large. > Imagine we were talking about two internet providers: Sun Internet Co, and Atom Internet Co. Sun Co charges $1/GB, while Atom Co charges $5/GB. But Sun Co only works from 9am–5pm, and its speed drops by 80-90% on cloudy days. > Would you opt to save the $4/GB difference, and deal with the inconveniences of Sun Co? Maybe you'd buy an extra hard drive, and download a ton of Netflix shows during the day, to watch later at night. But hard drives also cost money. > The bigger challenge is the 90% speed drop on cloudy days. The internet would randomly become unusable, sometimes for days on end. If you lived in a very sunny area, this might be a small issue. But for most populated places on Earth, there's often unpredictable cloud cover. > To deal with it, you'd pre-download even more content, and buy more hard drives. The hardest part would be the randomness of the outages. Should you download content to last 1 cloudy day? 5? 10? That's a lot of extra data ($)and storage() that might end up wasted… > There are no guarantees - the safer you want to be, the more expensive this game would become. Very quickly that $1 / GB price tag on Sun Internet Co would look a lot more like $6–10 / GB. > In energy planning, the worst case isn't that you forget to download your fav show and feel bored at night. Rather, it's blackouts, causing shortages of water, food, and heating or cooling. > Oil and gas companies love renewables. Today, renewables are always backed 1:1 by oil-and-gas based generators, to fill gaps. In the future, solar purists propose mega storage, and overbuilding (extra panels) as the solution. These extra costs aren't factored into LCOE. > When the cost of unreliability is considered, solar no longer looks like a silver bullet. > This isn't to say that solar is bad - it will still play a role in our future grids. In addition to Matt’s concerns, one thing I would like to add here is that the renewables (solar, hydro, and wind) contribution to overall electricity generation mix itself can have profound impact on the overall stability of the grid. For example, renewables contributed 18% of overall electricity generation in 2021 (vs 11% in 2011); Solar was just 2.7% of overall source of electricity in the US in 2021\. Due to material inclement weather or whatever, let’s say if solar’s contribution to grid goes down by \~10%, it may be quite manageable today. But imagine if solar becomes 50-60% of the overall electricity generation in the US in some distant future, a 10% downward swing in electricity generation would have a materially negative impact on the stability of the overall grid. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F02538299-6974-421d-a91d-324ebdb3269e_628x238.png) Source: Lawrence Livermore National Laboratory, MBI Deep Dives Despite such a material price decline in solar prices over the last decade, Enphase showed me that it would cost me \~$41k to install solar panels in my place. If I add battery as well (e.g. 10 KWh), the cost would go up to \~$55k. Assuming I would pay for these in cash, my payback period would be \~16 years for Solar only and almost \~20 years for Solar & Battery. The payback period takes into account of the 30% Federal tax incentives (more on this later). Enphase also shows me installing solar and battery would reduce my recurring electricity bill by \~70% over 25-year period. If you add the initial installation costs and the total electricity bill after installing solar and battery, Enphase shows you are going to save \~$17k over 25-year period. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F548bdd7a-22d4-4cd9-9438-c3cacad6586b_2220x1410.png) Source: Enphase Website If you notice carefully, one of the assumptions made in that calculation by Enphase is they expect utility bills to increase by 4.4% rate per year. That seems to be bit of an aggressive assumption to me given average residential price for electricity increased by 2.2% rate per year over the last 10 years. Even during 2003-2012 period, electricity price increased by 3.5% per year. So, assuming 4.4% growth rate would imply a material acceleration of price increases compared to what we have seen over the last two decades. That doesn’t mean it’s impossible of course; just it seems a bit aggressive which Enphase is, of course, incentivized to assume to show the appeal of Solar to potential customers. Rising utility bills is an important driver to create demand for solar. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F341b3d55-08c2-4c76-bcce-86c7beed04ef_1030x690.png) Source: EIA What if you don’t have cash to pay for solar? You can take loans. Enphase showed an option for 20-year loan with 9.49% borrowing rate (for >720 credit score) which would lead to \~$270 for just solar panels and \~$360 for both solar and battery. If you annualize the monthly payment and add the reduced electricity bill after installing solar and battery, Enphase showed me it would actually lead to \~$30k higher total expenditure on electricity for me over 25-year period (again, assuming $150 monthly electricity bill today growing at 4.4% CAGR). While Enphase showed me a pretty high interest rate loan option, EnergySage reported that the median interest rate for solar loans increased from 2.99% in 2H’22 to 4.99% in 1H’23. As you can probably tell by now, lower interest rate is another very important driver for solar, especially given [majority](https://www.cnbc.com/2023/05/21/homeowner-basics-of-financing-solar-power-for-residential-real-estate.html?ref=mbi-deepdives.com) of solar installation is financed by loans. As interest rates keep rising, it inherently dampens demand for solar as for many people the choice may not make economic sense. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9b25df11-dd84-41b8-962e-82a1b84f6270_2192x1392.png) Source: Enphase Website Even for cash financing for solar, while Enphase showed me some savings if I opted for solar, the fact is it didn’t account for the opportunity cost of capital. When I looked carefully the assumptions Enphase made to calculate those savings, I noticed cash flow discount rate is assumed to be zero. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd475daa0-1993-4d14-88f9-b69eea38f605_1566x610.png) Source: Enphase Website However, given how much costs have come down for solar, it is tempting to infer that as costs continue to come down over time, opting for Solar may make much more economic sense. That optimism will likely have some hurdles to overcome. A few months ago, John Arnold [explained](https://twitter.com/JohnArnoldFndtn/status/1690818373712482304?ref=mbi-deepdives.com) that even though solar costs have precipitously fallen historically, the road ahead may be much more challenging: > …the story is more complicated, with 3 distinct phases. > **Phase 1: Introduction, 2000-early 2010s**. Sharp cost declines that happened as the industry went from near zero to commercial scale. Significant tech and manufacturing advancements, economies of scale, cost of capital declines, new vendor and developer competition > **Phase 2: Growth, early 2010s-2019**. Smaller cost declines as industry matures. Technology improvements slow, gains from economies of scale and vendor competition plateau, credit spreads tighten, but labor costs increase. > **Phase 3: Maturity, 2020-now**. Costs increase as the deflationary aspects either plateau or have become a small % of total costs. Technology advancements start hitting against physical constraints, materials and labor costs increase, top tier acreage near demand centers is already developed, interconnection delays, interest rate increase severely raises cost of capital, disruption from tariffs on Chinese solar panels, intense vendor competition eases, more accurate pricing of congestion risk, wind turbine manufacturers reprice after losing billions and, also, COVID supply chain interruptions. The NYT writes that, "in 2023, costs rose because of supply-chain problems, inflation and other issues," as if it's a one-time event this year that will quickly reverse. I worry this cost increase is more structural. To flesh out Arnold’s concerns more clearly, let’s take a closer look at solar installation costs today. Solar panels (including warranties) are just \~20-25% of the overall installation costs. 10% of the costs is the inverters which is what Enphase primarily sells (more discussion on Enphase business later). Another 10% is racking, mounting etc. whereas a whopping \~55-60% of the overall cost is the installation itself which comprises of labor and wages, workers’ insurance, permitting fees, interconnection fees etc. (more discussion on overall costs [here](https://blog.gogreensolar.com/solar-panel-installation-cost-breakdown?ref=mbi-deepdives.com)) As Arnold alluded, the deflationary mix of the overall costs is already quite low and even if they keep declining over time, the inflationary mix (labor and wages) of the overall costs may make it challenging for the costs to experience material decline from current level. Moreover, Solar’s supply chain appears to be quite fragile in the context of current geopolitics. IEA [reported](https://www.iea.org/news/the-world-needs-more-diverse-solar-panel-supply-chains-to-ensure-a-secure-transition-to-net-zero-emissions?ref=mbi-deepdives.com) in 2022 that “China’s share in all the key manufacturing stages of solar panels **exceeds 80%…** and for key elements including polysilicon and wafers, this is set to rise to **more than 95%** in the coming years, based on current manufacturing capacity under construction.” From US and many other developed market’s perspective, it is perhaps not the most prudent decision to be over reliant on an energy source whose overall supply chain is utterly dominated by China. While [manufacturing](https://www.youtube.com/watch?v=xDqb6lGNnVk&ref=mbi-deepdives.com) solar panels can likely be replicated by western countries, it will most certainly take some time to scale their manufacturing. If solar becomes larger part of the overall energy mix over time, that may become increasingly even more relevant concern. Nevertheless, the fact remains solar investment has been increasing at a rapid pace almost across the world. Run-rate solar investment in 2Q’23 was near $500 Bn. In [2013](https://www.iea.org/data-and-statistics/charts/oil-production-investment-and-solar-investment-2013-vs-2023?ref=mbi-deepdives.com), total investment in oil production was $636 Bn whereas solar was just $127 Bn. But in [2023](https://www.iea.org/data-and-statistics/charts/oil-production-investment-and-solar-investment-2013-vs-2023?ref=mbi-deepdives.com), total investment in Solar is expected to be $382 Bn, surpassing $371 Bn of investment in oil production. Apart from low interest rates in the past decade and relatively higher utility rates in recent years, the other two primary drivers for Solar are increased awareness and concerns around climate change, and government regulations. ![Photo by Patrick Collison on September 04, 2023. May be an image of text that says 'Solar Investment Reached New Highs in Both 1Q and 2Q 2023 Investment in new large- and small-scale solar assets by region China US Spain Germany Other Europe, Middle East and Africa Other Americas Other Asia Pacific $150billion $150 billion 100 Q1 2018 Q3 50 Q1 2019 Q3 Q3 Q1 2020 Q1 2021 Q3 Q1 2022 Q3 Source: BloombergNEF Note: Includes asset finance for large-scale solar, as well as spending on small-scale solar. Q1 2023 BloombergNEF'.](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F801c3e44-d9ce-483b-aa3b-18c595ec4f07_765x524.jpeg "Photo by Patrick Collison on September 04, 2023. May be an image of text that says 'Solar Investment Reached New Highs in Both 1Q and 2Q 2023 Investment in new large- and small-scale solar assets by region China US Spain Germany Other Europe, Middle East and Africa Other Americas Other Asia Pacific $150billion $150 billion 100 Q1 2018 Q3 50 Q1 2019 Q3 Q3 Q1 2020 Q1 2021 Q3 Q1 2022 Q3 Source: BloombergNEF Note: Includes asset finance for large-scale solar, as well as spending on small-scale solar. Q1 2023 BloombergNEF'.") NEI [estimates](https://www.nei.org/resources/reports-briefs/analysis-of-us-energy-incentives-1950-2016?ref=mbi-deepdives.com) that during 2011-2016 period, “renewable energy received more than three times as much help in federal incentives as oil, natural gas, coal, and nuclear **combined**, and 27 times as much as nuclear energy.” In more recent time, The Inflation Reduction Act of 2022 (IRA) offered [$127 billion](https://guidehouse.com/-/media/www/site/insights/energy/2022/guidehouse-insightsthe-ira-a-boon-for-american-green-energy112022.ashx?ref=mbi-deepdives.com) in clean electricity tax credits to mostly Solar and Wind Energy industry. The most relevant of tax incentives for solar industry is Solar Investment Tax Credit or ITC. Residential homeowners who install solar energy systems between January 1, 2022 through the end of 2032 will receive a tax credit of 30% of the cost from their federal income taxes. Following 2032, the residential ITC will gradually phase out to 26% in 2033, 22% in 2034, and will end in 2035. Even beyond ITC, there are a few states in the US that lets homeowners sell one Solarrenewable energy certificate (SERC) for every megawatt hour (MWh) or 1,000 KWh of electricity generated by their solar system. The value of each SERC[ varies](https://www.energysage.com/solar/srecs/?ref=mbi-deepdives.com) by state (can be \~$5 to a couple hundred dollars). SERCs exist to help utilities meet renewable portfolio standards (RPS) that require utilities to produce a specific percentage of their electricity from renewable energy sources. While much of these regulations are quite conducive to Solar industry, there are some potentially negative developments in California, a state that contributed \~20% of Enphase’s total revenue in 2022\. California used to have Net Energy Metering Policy (NEM) which allowed a billing system between utility providers and customers generating their own power through solar. Under NEM 2.0, self-generators could earn credits for the excess electricity their system provides to the grid. This export rate for solar energy was based on a tiered rate structure that offered a single rate for excess energy exported to the grid, regardless of the time of day or day of the week. This meant that customers received the same credit for excess solar energy exported during peak, shoulder, or off-peak periods. With NEM 3.0, the export rate is now aligned with time-of-use (TOU) periods, offering higher credits for excess energy exported during peak periods and lower credits during off-peak periods. Enphase itself mentioned in their 10-K that following NEM 3.0 implementation, the average export rate in California is expected to be \~5-8 cents per KWh vs \~25-35 cents per KWh earlier. Given California is the largest solar market in the US, this can dampen the demand for solar, especially in the current high interest rate environment. Enphase, however, sounds optimistic that this regulatory change may turn out to be a boon for them as solar households in California would be incentivized to add storage to their solar system to increase self-consumption during peak hours when rates are highest and reduce low-value exports to the grid by using battery storage. SunPower (one of the leading solar installers in the US) mentioned the company saw battery attachment rates of 26%, 32%, and 47% in the first three weeks in California right after NEM 3.0 came into effect in April 2023\. SunPower also mentioned they think battery attachment rate could ultimately exceed 20%. For context, the attachment rate of battery in solar system is \~80% in Germany, Europe’s largest solar market. Hence, 20% could prove to be conservative attachment rate in the long term. Now that we have a good context for Solar industry, let’s delve into the details of Enphase’s business. Here’s the outline for the rest of this Deep Dive: **Section 2 Enphase Business Overview**: I segmented Enphase’s business in three categories: microinverters, storage, and miscellaneous/other. The overall economics and Serviceable Addressable Market (SAM) for Enphase is also discussed in this section. **Section 3 Competitive Dynamics**: I dissected the overall Solar value chain into three broad segments: manufacturers, distributors/installers, and companies such as Enphase who sit at the middle between the two. I discussed why margins are pretty think in other parts of the Solar value chain, why Enphase enjoys relatively attractive margins, and some potential threats to long-term margins. **Section 4 Management and Incentives**: I briefly discussed Enphase management’s capital allocation philosophy and management incentive structure in this section. **Section 5 Model Assumptions/Valuation**: Model/implied expectations are analyzed here. **Section 6 Final Words**: Concluding remarks on Enphase, and disclosure/discussion of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Forward Cap and MBI Discuss Tesla URL: https://www.mbi-deepdives.com/forward-cap-and-mbi-discuss-tesla/ Last updated: 2023-10-05T15:36:37.000Z --- One of the joys of writing about publicly listed companies at a very accessible price point is the interaction with other intelligent and curious readers. I routinely learn a lot from these interactions and MBI Deep Dives is a better product because of these interactions. After I published my [**Deep Dive on Tesla**](https://www.mbi-deepdives.com/tsla/), [Forward Cap](https://twitter.com/forwardcap?ref=mbi-deepdives.com) diligently read my work and sent me his thoughts. I enjoy Forward Cap's work and I encourage you to read his work on his [Substack](https://autoinsights.substack.com/). I suggested him that we make our interactions public which he agreed. **Full disclosure**: as of this writing, Forward Cap owns shares of Tesla and I have no exposure to Tesla. [Subscribe](#/portal/signup) --- **Forward Cap**: Hey – finally got around to reading your deep dive. Great work!! I always love reading a fresh perspective and you have a lot of interesting datapoints, insights, and opinions. Wanted to share some of my feedback: Your S/X deliveries are off (99k act vs 22k in your model) so the ASP in the article is showing $110k vs $76k actual (lower when deducting reg credits which are also 0 in your model for 2018-19). **MBI**: You are right. I linked the cells incorrectly on S/X deliveries for 2018\. I have corrected it now on my model and on my write-up. (For regulatory credits, I do not see related disclosure on my database and hence it was zero.) **Forward Cap:** I like the comparison to the auto industry from 100 years ago, as there are a lot of parallels, specifically with respect to difficult competitive dynamics which are very real here. However, one thing that is much different this time around that’s critical to my thesis is the legacy business/innovator dilemmas that are materially dragging competitor efforts. Between conflicting interests for management, employees, shareholders, unions, dealers, and more – it’s a very difficult uphill battle for incumbent OEMs. **MBI:** I don’t disagree. I am not eager to bet on the ICE incumbents to transition to EV. It’s a tall ask for them. However, I am not super confident that **none** of them will be able to do it. My lack of confidence comes not from deep knowledge about their initiatives, rather the opposite. I certainly didn’t have time to carefully assess each of the incumbents EV initiatives. I do broadly agree with your thesis that the supermajority of them will likely find it too challenging to transition to EV successfully. However, perhaps a couple of them will be able to do it. We are still way too early in EV to form rigid opinions. We may be in the “1920s” and “Toyota” and “Volkswagen” haven’t even been founded yet. The whole discussion on what happened a century ago was to set the context for lack of predictability and a sense of humility required for a long duration bet. **Forward Cap**: You do a good job of highlighting the structural advantages that Tesla has over incumbent OEMs with respect to the DTC distribution vs third party dealers, but I think that often gets mistaken as the only reason why bulls feel that Tesla is differentiated. Both from a margin and pace of innovation perspective, there are several other reasons why Tesla can innovate more quickly and save on costs relative to legacy competitors: - Innovative manufacturing methods that reduce complexity and costs ([see quotes from competitors here](https://x.com/forwardcap/status/1480948169592786946?s=20&ref=mbi-deepdives.com)). There is a lot to unpack here, but fundamentally Tesla is building factories and assembly lines from the ground up and willing to take on additional risk to innovate, whereas there are too many legacy facilities/assets and conflicted parties that would allow this at a legacy OEM. Obviously, Tesla’s ability to dedicate 100% of their effort to developing more efficient manufacturing methods also helps, whereas legacy OEMs are still mostly spending resources on ICE. I highly recommend the Munroe Live YouTube channel to learn more on this, as he/his firm are industry veterans with mountains of knowledge. - Internally developed software, not just FSD, which is an increasingly important aspect of the auto industry. Jim Farley does an excellent job explaining [here](https://x.com/WholeMarsBlog/status/1665626706957438977?s=20&ref=mbi-deepdives.com). In addition to cost and time savings, this allows Tesla to fully capture all of their data, which uniquely positions them for FSD, insurance, understanding vehicle diagnostics/useful parts, and other software-driven applications. - Ability to attract top talent across the org ([more detail here](https://x.com/forwardcap/status/1525835837392490496?s=20&ref=mbi-deepdives.com)). Tesla is really the only automaker competing with big tech for talent and there’s a litany of reasons for this: ability to live in SF or Austin, equity upside (not common at competitors), less bureaucracy/ability to work on smaller teams, ability to work on more internal software and technology, and more. Tesla can also get more out of its workforce because they are not unionized. - Localized production. While geopolitical tensions in China present a risk to Tesla, they are the only foreign automaker to ever operate there independently (i.e. without a JV structure). They are diversifying through their factories in California, Austin, Berlin, Mexico, and eventually other areas which all helps further localize production. This helps them save on costs (i.e. tariffs) and time from production to delivery, which helps drive their industry-leading Days of Inventory. - Economies of scale and first mover advantage. Tesla's lead in EV volumes, specifically domestically and less relevant in China because of BYD, allows them to be more competitive on pricing as a result of lower fixed overhead relative to sales, greater negotiating leverage when sourcing batteries and other key components, etc. Additionally, as a first mover, Tesla’s brand is synonymous with EVs and consumers are proven to check out Tesla specs when considering an EV purchase. This is a big reason why they have been able to scale to a $100B+ revenue business without advertising yet, though I hope they do soon. - Ability to share key manufacturing methods, proprietary technology, and top talent with SpaceX. This happens quite often and is underrated, as there’s more relevant overlap than meets the eye. **Forward Cap:** Great discussion on BYD and I like that you elaborate on them because they are definitely under-discussed. It’s incredible what they’ve accomplished, especially given their presence in mostly just one country, and representative of the much more competitive nature of the Chinese market. BYD has definitely had a negative impact on Tesla’s pricing strategy in China. With that said, I think the article focuses too much on BYD’s volumes and not enough on the differences in market segments (there are pictures but no commentary). A lot of BYD’s volume comes from significantly lower priced vehicles so the volume isn’t exactly apples-to-apples. For example, their top selling cars are the Song starting at 169,800 Yuan and Yuan Plus starting at 135,800 Yan vs Model 3 starting at 259,00 Yuan and Model Y at 263,900\. As a result, BYD has and will likely maintain a much lower share of industry profits. **MBI:** Yes, given I alluded BYD as the “GM” in today’s EV race, I probably should have expanded even more. I guess I was a bit too self-conscious about the length of the Deep Dive as it was \~15k words already. Anyways, I do think volume is quite/likely the most important element here. If you think EVs not just as “hardware” sales but a potential revenue stream of “FSD, insurance, parts sales” etc., protecting gross margin on hardware may prove to be shortsighted. I think Tesla understands that and they seem quite eager to pursue volume instead of protecting automotive’s gross margins; their recent pricing strategy hints at such an approach. It is to BYD’s credit that they could produce low cost EVs that they could sell and the overall profit pool on LTV basis may be quite compelling despite the initial lower gross margin. Tesla clearly has a willingness to produce even cheaper EVs in the long term (which is critical for mass adoption). BYD is likely to be a major exporter to all other regions (except US?) in the next 3-5 years and therefore, BYD and Tesla seem quite destined to be fierce competitors. **Forward Cap:** I agree that China geopolitical risk is a major risk for Tesla. Elon has made it pretty clear in recent interviews that he thinks China will invade Taiwan and tensions will escalate. Pretty wild. **MBI:** Yes, even if China doesn’t end up invading Taiwan, I would argue China could still make Tesla’s life difficult if BYD doesn’t get greenlight to operate in the US. The last thing I expect from China is to allow an American company to dominate one of the largest industry’s profit pools when their own homegrown companies are anathema in the US. I know Apple is still there, but admittedly I’m just as equally concerned about their long-term viability there. **Forward Cap:** I love and agree with this quote “The range of outcome for FSD is really perhaps the widest of anything I have come across in studying businesses over the last decade!” With the trend in ASPs and gross margins this year, the bull case is becoming increasingly dependent on FSD. Not what I envisioned prior to this year. **MBI**: It took me a while to understand FSD’s accounting implications in the financial statements and once I did, I realized it really is perhaps a make-or-break thesis for Tesla shareholders. Elon was indeed right. If FSD “works” eventually, things are likely to turn out to be fine for the shareholders. On the model: **Forward Cap:** Overall, you have very reasonable assumptions. Not far off my volume estimates in 2030 for example. \- I think they’ll gain a bit more operating leverage than you give credit for, but I also think (hope) they will start advertising, so SG&A leverage won’t be as strong as we’ve seen historically. \- You properly show how much value FSD can add without a robotaxi/licensing scenario, though still likely conservative on the GAAP recognition % over time as that is based on available feature set which is expanding. \- The 2% p.a. increase in FDSO is aggressive in my opinion since that has really slowed down recently. For example, FDSO is only 0.1% higher as of Q2 2023 than Q4 2022. **MBI**: To be clear, I was just trying to figure out embedded assumptions in current stock price. I agree that looking at history, one can argue I could have been a bit more generous in giving Tesla credit for further operating leverage. Perhaps I have been burned too many times assuming such operating leverage which very rarely plays out. More seriously, I do think more advertising would be required if competition with BYD heats up across the world. I am not sure Tesla cars would be able to maintain the same inherent appeal to potential customers without doing so. Selling cars to “early adopters” vs “early majority” could be a different ball game. Similarly, I do expect Tesla will have to be more generous in doling out SBC and other benefits. The upside for employees joining at <$100 Bn market cap vs \~$1 Tn market cap is different. It is, however, possible that Elon Musk would be far more conservative in hiring than any other big tech. We will see and I agree that my dilution assumptions could prove to be aggressive. **Forward Cap**: I think it’s likely that non-core revenue streams today will play a much larger role in the latter part of the decade. They have a very large opportunity in Energy for example and are just now beginning to leverage excess battery capacity towards energy as growth in vehicle business slows, which gives them nice optionality. A few other examples: insurance, long-haul transportation (Semi), battery manufacturing, FSD licensing, Optimus, Dojo. All massive opportunities that are adjacent to current areas of focus. With a company as innovative as Tesla, some things are obviously very hard to predict that far into the future, which is where the market is assigning a lot of its value, but nearly impossible to model today so obviously tough to include in any analysis. I’ve always been the biggest Tesla bull I know and looking back at my models from 5+ years ago it’s insane how much even I under-estimated it. Of course, that’s not predictive of future results, but I think they’ve maintained the same culture that got them to where they are today. **MBI**: While I did incorporate some of these opportunities (Energy directly and insurance indirectly in the services segment), I agree that opportunities such as FSD licensing, Optimus etc. are not adequately captured in my model. Valuing optionality for big tech is just incredibly hard and interestingly, almost no other shareholders of other big tech give credit to the companies for such optionality. Google and Meta both are valued based on consolidated numbers despite having large other bets losses (especially for Meta). Similarly, even though Apple is getting into AR/VR and had been working on Car for last 10 years, investors were rarely willing to give these companies any credit for such “investments” until and unless these investments turn into something more tangible. It is possible that Tesla’s shareholder base would act differently here; however, looking at last year's drawdown also gives me pause on that theory. To read my Deep Dive on Tesla, click [**here**](https://www.mbi-deepdives.com/tsla/). Thank you for reading. [Subscribe](#/portal/signup) ### Tesla: A Bet on Dominance in Potentially Compelling Megatrend(s) URL: https://www.mbi-deepdives.com/tsla/ Last updated: 2024-04-08T23:32:18.000Z You can listen to the Deep Dive [**here**](https://www.mbi-deepdives.com/audio/) --- > “…if someone in the year 1900 had to bet on the outcome of the battle between external steam combustion, internal gasoline combustion, and electricity as the future standard for powering cars, they’d have probably put their money on electricity. And at the time, electricity was not only winning the battle over gasoline with far more cars on the road, but the world’s most prominent inventors, including Edison and Tesla, were pouring their efforts into an electric car future. Early in the century, the New York Times referred to the electric car as “ideal,” citing it as quieter, cleaner, and more economical than the gas car. > > But *ideal* wasn’t the driving force of the early auto industry—*scalable* was.” > > \-Tim Urban (“[Wait But Why](https://waitbutwhy.com/2015/06/how-tesla-will-change-your-life.html?ref=mbi-deepdives.com)”) Indeed, in early 1900s, the future of automobile was an open question. \~40% of American cars was powered by steam whereas \~38% was electric. The rest 22% was driven by gas. Despite gas being the underdog in this race, one person through his sheer ingenuity, will power, and raw ambition transformed automobile from mere toys of rich people to a basic necessity for everyday Americans! Henry Ford [invented](https://www.joincolossus.com/episodes/93525062/senra-henry-ford-i-invented-the-modern-age?tab=transcript&ref=mbi-deepdives.com) the “modern age”. Sheer ingenuity, will power, and raw ambition of a different person in a different era are also exactly why we are discussing Tesla today. What Thomas Edison and Nikola Tesla imagined the future of automobile ought to be, Elon Musk is almost on a mission to prove them right. However, when Tesla was founded in 2003, electric automobile was the clear underdog this time as gas powered automobiles had already appeared to have conquered the world. In fact, the last successful American car company (Chrysler) was founded in 1925. Tesla, of course, isn’t an up and coming startup today. It’s an \~$800 Bn juggernaut. The market is betting that not only Electric Vehicles (EV) are the future of automobile but also has likely crowned Tesla to remain utterly dominant in this industry. Warren Buffett, however, [uttered](https://novelinvestor.com/buffett-picking-winners-is-hard/?ref=mbi-deepdives.com) a cautionary tale on the history of automobile: > *Take automobiles first: I have here one page, out of 70 in total, of car and truck manufacturers that have operated in this country. At one time, there was a Berkshire car and an Omaha car. Naturally I noticed those. But there was also a telephone book of others.* > > *All told, there appear to have been at least 2,000 car makes, in an industry that had an incredible impact on people’s lives. If you had foreseen in the early days of cars how this industry would develop, you would have said, “Here is the road to riches.” So what did we progress to by the 1990s? *After corporate carnage that never let up, we came down to three U.S. car companies — themselves no lollapaloozas for investors. So here is an industry that had an enormous impact on America — and also an enormous impact, though not the anticipated one, on investors.** > > *Sometimes, incidentally, it’s much easier in these transforming events to figure out the losers. You could have grasped the importance of the auto when it came along but still found it hard to pick companies that would make you money. But there was one obvious decision you could have made back then — it’s better sometimes to turn these things upside down — and *that was to short horses*.* Shorting horses indeed would be a terrific “trade” in hindsight! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9d70a9a5-be06-4974-ab4f-e16f925d21e0_1358x1346.jpeg) But when I looked closely at this chart, the question that popped out to me is: are we in “1920” in the race between EV and Internal Combustion Engine (ICE)? Is the History of automobiles and the resultant industry structure the right framework to outline the future path here or should we consider the smartphone industry to be more analogous since while EVs may look like cars, they are essentially computers on wheels? Or perhaps trying to overfit pattern recognition to a new industry can lead us astray from all the important nuances? These are indeed the kind of questions I wrestled with over the last month, and it was quite a fun month. Here’s the outline for this month’s Deep Dive: **Section 1 Why EV?** First, I discussed some key skepticisms around transition to EV and dug into the merits of these concerns. **Section 2 How We Got Here:** Tesla has a colorful history in the lead up to its $800 Bn market cap. I touched on some of these key moments and how Tesla defied the odds to get to where they are today. **Section 3 Tesla’s Business Overview:** Following the historical contexts, I got into explaining the business as it stands today. Pay close attention to FSD accounting in this section which can have important implications in the future. **Section 4 Industry Dynamics:** This section is perhaps the meat of the Deep Dive as I delved into historical perspectives around early automobile industry’s evolution and how competitive dynamics evolved over time. GM and Ford’s early rivalry, with data points, is mentioned here. Then I elaborated on Tesla’s closest competitor: BYD and why BYD may be a bit underdiscussed among Tesla bulls. Finally, I discussed Tesla’s variant approach to FSD and why I think FSD may be the widest range of outcome situations that I have ever studied. **Section 5 Management:** I know everyone has already formed their opinions on Elon Musk, so I kept this section relatively short. **Section 6 Valuation:** Model/implied expectations are analyzed here. **Section 7 Final Words:** Concluding remarks on Tesla, and disclosure/discussion of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### September Update URL: https://www.mbi-deepdives.com/sep-email/ Last updated: 2023-09-01T14:24:06.000Z Exactly **three years** ago, I launched MBI Deep Dives in September, 2020\. Thanks to your support, I have been able to keep at it for the past thirty six months writing [**Deep Dives**](https://www.mbi-deepdives.com/models/) every single month. I have never had as much fun as I have had "working" for MBI Deep Dives. I want to keep doing what I am doing for decades, so we are still hopefully in the very early days. Thank you for allowing me to pursue what has been genuinely fun and intellectually stimulating experience for me. [Subscribe](#/portal/signup) Some quick updates for this month: 1. Speaking of fun, I am indeed enjoying studying **Tesla** very much. Tesla is not only just a controversial stock but also perhaps one of the more complicated ones I have studied so far. I hope to publish the Deep Dive sometime during the final week of September. 2. I recently appeared on my friend Liberty's [**podcast**](https://www.libertyrpf.com/p/meta-platforms-and-mark-zuckerberg?ref=mbi-deepdives.com#details) to discuss Meta Platforms. We explored what are likely misunderstood aspects about the company as well as some risks and opportunities ahead of the company. Give it a listen if you are curious. 3. Following Tesla, I plan on covering **Enphase Energy**. I haven't decided on the schedule for the rest of the year yet, but I will let you know once I do. 4. I have received a number of feedback on last month's Deep Dive: [**Dollar General (DG)**](https://www.mbi-deepdives.com/dg/). A few readers let me know that I may have underappreciated the inherent complexities associated with increasing new stores from \~1k/year to 1.5k-2k/year. The constraint is not necessarily capital, rather labor, real estate etc. During my due diligence of DG, I spoke with a couple of shareholders and in fact, they did mention this when I shared my criticism of DG's capital allocation. I, unfortunately, forgot to mention this counterpoint on my Deep Dive. While I do think this is a very fair counterpoint to my criticism, I still think DG management should have been a bit more proactive in penetrating their TAM a bit more quickly since 2015; perhaps not by increasing new stores by 2k/year, but more like 1.2-1.5k/year if they indeed believe the US market opportunity is closer to 30k stores. Another reader mentioned a pretty neat point on why they think DG's historical Same Store Sales (SSS) growth may not be quite indicative of the end-state same store sales growth: *"a dirty trick for retailers same store sales growth is that store roll out is also a tailwind to SSS growth. If it takes 5 years for a store to fully mature, the SSS benefit is counted after year 1 as a tailwind to the group. The implication is that the underwhelming same store sales figures are actually worse if we were to look at the mature subset of long-standing stores (a better proxy for terminal SSS)"* I have also received a couple of emails/messages requesting me to comment on yesterday's large drop on DG's prices. While I'll try to not make it a habit to comment on short term stock price movements on every stock that I have covered, let me address a couple of points here. One of my primary concerns about DG was declining Same Store traffic trend that started in 2020\. Family Dollar (FDO), the closest comp, had the same issue but they turned to positive traffic in the last two quarters whereas DG's traffic remains negative. The traffic comp should be "easy" by now since it declined for three consecutive years. Therefore, it is indeed a bit concerning that traffic trend hasn't turned around yet, especially when the closest competitor's did. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/09/image.png) There has been another [narrative](https://twitter.com/yipitdata/status/1697338854645105136?ref=mbi-deepdives.com) that is increasingly gaining momentum across US retail is the rise of Temu and how much it is potentially affecting other retailers in the US. It is perhaps too early to comment on Temu, but I think it is unlikely that it is the primary reason for DG's recent weakness. If Temu were such a force, I would assume it would show up in FDO's number as well. In fact, one could argue that given FDO over-indexes on more urban regions than DG, delivering low priced items to FDO-adjacent regions would be more convenient than DG-adjacent regions. \~80% of DG's stores are in towns with fewer than 20,000 people. Transaction amount per customer trip to a DG store is \~$15\. I don't see how delivering such low value products to towns with <20k people can make compelling economic sense. Therefore, even if Temu threat were real, I would be lot less worried about DG than most other retailers in the US. Finally, I do think following DG's drop in stock price yesterday, DG's risk-reward is slowly becoming more attractive than I considered them before. I am not a shareholder yet, but I am watching DG with interest. As I have mentioned before, questions are always evolving depending on changes in stock prices and the business fundamentals. Every now and then, the questions just *seem* easier to answer. 5\. I have provided more details on my back-of-the-envelope math on Adyen [here](https://www.threads.net/@mostly.borrowed.ideas/post/CwRGssvsFqq?ref=mbi-deepdives.com). While that thread primarily speaks about the numbers, I encourage you to read my [**Deep Dive**](https://www.mbi-deepdives.com/adyey/) to understand the broader narrative. Moreover, if you want to get up to speed on some of the recent developments on payments industry, especially Adyen, Stripe, and PayPal, I encourage you to listen to this [Stratechery](https://stratechery.com/2023/an-interview-with-lisa-ellis-about-payments/?ref=mbi-deepdives.com) podcast. Thank you again for your support! [Subscribe](#/portal/signup) ### The Curious Case of Big Tech URL: https://www.mbi-deepdives.com/the-curious-case-of-big-tech/ Last updated: 2025-01-26T16:50:08.000Z **Disclosure*: Nearly 60% of my personal portfolio is invested in Meta, Amazon, and Alphabet* --- *“Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years.”* While most market participants want to focus on what happens next quarter or the next year, it is often revealing to look back and see what has been achieved over ten years! What I am about to discuss is something I have discussed with some fellow investor friends over the past month or so, and I have noticed how frequently some of these data startled them. So, I thought about writing a brief note to share this with my readers. In the beginning of 2013, Big Tech’s (defined as Apple, Microsoft, Alphabet, Amazon, and Meta) market cap were: Apple: $500 Bn Microsoft: $225 Bn Alphabet: $232 Bn Amazon: $114 Bn Meta: $58 Bn In aggregate, Big Tech was worth $1.1 Tn at the end of 2012\. **Over the last ten years (2013-2022), Big Tech collectively generated $2.3 Tn Operating Cash Flow (OCF), slightly more than double their aggregate market cap ten years ago**! To say it differently, Big Tech was a form of deep value investing that was deeply underappreciated even though they are all widely followed companies at that time! Believe it or not, back then 10-year treasury was yielding below 2%! Perhaps this is what people call “generational opportunity”! [Subscribe](#/portal/signup) What is perhaps even more strange is that this underappreciation was not quite specific to one or two companies, but to the **entire group**. No company was, however, as underestimated as Meta Platforms (formerly known as Facebook) generating $271 Bn OCF in 2013-2022, which was **470%** of their market cap in 2012! ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/08/image-9.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Skeptics may point out these companies tendency to dole out SBC and the higher capital intensity over the years which necessitate more and more to focus on FCF. As I will show you later, the skeptics may have a point, but the numbers still point out how attractive Big Tech was in retrospect! Amazon is an anomaly here as they relentlessly deploy all of their operating cash flow. Nonetheless, Big Tech generated $1.3 Tn “true” Free Cash Flow (FCF), which is defined as Operating Cash Flow-Capex-Stock-based Compensation (SBC). Again, **this “true” FCF was \~$160 Bn higher than their aggregated market cap in 2012**. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/08/image-7.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Clearly, investors deeply underestimated Big Tech ten years ago, but did things change five years ago? From OCF perspective, it appears they were still quite underappreciated in the beginning of 2018 even though the depth of underappreciation dwindled a bit. All five companies generated \~40-50% of their 2017 year end Market cap in 2018-2022 aggregate operating cash flows. It is certainly quite conceivable that by 2027, perhaps all of them would generate OCF more than their 2017 market caps! ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/08/image-8.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) The narrative, however, gets complicated when it comes to “true” FCF. While Amazon remained true to its color by redeploying their entire OCF, Meta and Alphabet seemed to have followed a similar, but unexpected trajectory. What benefitted Apple and Microsoft (to a large extent) is their primary source of cash machines remained somewhat capital light. As a relatively late entrant, Alphabet ramped up their investments in Cloud. Alphabet was also transitioning to be “AI-first” company which likely required significant human and financial capital investments. Similarly, gone are the days of capital light social media businesses! With everyone’s feeds now being intensely personalized with ongoing shift from text to images to videos, Meta required to grow up to that reality (ATT only worsened the situation). ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/08/image-4.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) These somewhat divergent paths are reflected in their returns since 2018\. Investors were surprised to understand the capital intensity of Alphabet and Meta’s core businesses whereas they likely grew a bit tired of Amazon’s relentlessness and started entertaining healthy amount of skepticism about their ROI on recent investments. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0ac3665c-abd4-4c2d-a230-894b953342cc_2400x1240.png "chart") Source: KoyFin (MBI Deep Dives readers get 15% discount; just click [****here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) The real question, however, is whether this was just an investment cycle required for them to grow to the new reality further extending their moats. **This question will take time to answer** as given the recent Generative AI enthusiasm, the investment cycle is not quite over yet. While nobody quite expects that the capital intensity will return to the level of 2012-2017 period, it is certainly conceivable that investors extrapolating recent capex bonanza till eternity may turn out to be a key source of alpha for long-term investors. Why do I say that? Meta, and Amazon are **currently trading at** **lower** OCF multiple than they were trading back in 2013\. The set up remains quite attractive if the persistence of capital intensity (not just in absolute dollars but as % of their revenues or OCF) turns out to be overstated. Apple and Microsoft, on the other hand, went from “impending terminal businesses” to durable cash gushers in investors mind. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/08/image-3.png) Source: MBI Deep Dives Despite lower multiples, we perhaps cannot expect similar returns from some of these stocks due to important changes in initial conditions. At the end of 2012, Instagram may not have generated its first dollar of revenue yet. AWS “IPO” happened in 2015, so you can imagine Google Cloud or Azure did not even enter investors consciousness back then. LinkedIn was still an independent company and Big Tech were still allowed to acquire companies and could use their massive distribution to scale those businesses. Apple was considered a “hardware business” destined to lose their profit pool to cheaper phones over time. Many of those optionalities could not possibly be modeled back then and the reality is the world has never seen such **a dominant group of global companies** **in the history of capitalism** which led to this deep underappreciation for such a long time which arguably somewhat still persists. But it also introduces questions: what are the current set of optionalities for Big Tech today that are hard to model and hence value appropriately? For Alphabet, the first word that comes to my mind is Waymo. For Apple and Meta, it is AR/VR. Amazon has a whole host of “other bets” that are barely disclosed (Kuiper, Alexa to name a couple). It can also be hard to pinpoint how much Microsoft is the key beneficiary of Generative AI. It may understandably seem to investors that these “optionalities” are far less lucrative than their former versions. Hindsight is 20-20 and perhaps impossible to imagine today how preposterous it would seem to claim in 2012 AWS revenue would be $80 Bn in 2022! Nonetheless, I too broadly agree that these set of optionalities are likely to be much more underwhelming than the last ten years, but companies trading at similar or lower valuation multiples somewhat relieve us from making those bold predictions anyway with the caveat that we need to assume managers/operators of these businesses will act rationally if these optionalities prove to be value destructive. It is a nuanced point; in all likelihood, AR/VR is worth "negative" for Meta *today;* to create value for today's shareholders, AR/VR doesn't have to start a new "smartphone" revolution, but just have to be a real, not an imaginary, business. And if the real business proves to be reasonably attractive, that is quite certainly far from being priced in the stock. I can do these for Alphabet and Amazon as well, but you hopefully get the idea. While all of these may seem quite long-term questions, I am of the opinion that these are the kind of questions, along with durability of their core businesses, are really the **questions that matter**. To substantiate this point, I will leave you with an excerpt from Edward Chancellor’s book “[Capital Returns](https://www.amazon.com/Capital-Returns-Investing-Through-Managers/dp/1137571640?ref=mbi-deepdives.com)”: > While the case for long-term investment has tended to centre around simple mathematical advantages such as reduced (frictional) costs and fewer decisions leading (hopefully) to fewer mistakes, **the real advantage to this approach, in our opinion, comes from asking more valuable questions**. > > The short-term investor asks questions in the hope of gleaning clues to near-term outcomes: relating typically to operating margins, earnings per share and revenue trends over the next quarter, for example. Such information is relevant for the briefest period and only has value if it is correct, incremental, and overwhelms other pieces of information. Even when accurate, the value of the information is likely to be modest, say, a few percentage points in performance. In order to build a viable, economically important track record, the short-term investor may need to perform this trick many thousands of times in a career and/or employ large amounts of financial leverage to exploit marginal opportunities. > > And let’s face it, the competition for such investment snippets is ferocious…Can there really be much of value to say about industry developments over such limited time frames? Of course not. Even so, we would hate to discourage such research as, from time to time, what the short-term guys are selling can turn out to be wonderful long-term investments. > > ...**The longer one owns the shares, however, the more important the firm’s underlying economics will be to performance results. Long-term investors therefore seek answers with shelf life.** What is relevant today may need to be relevant in ten years’ time if the investor is to continue owning the shares. Information with a long shelf life is far more valuable than advance knowledge of next quarter’s earnings. Thank you for reading! For more detailed analysis of Big Tech, click here: [Meta](https://www.mbi-deepdives.com/meta2023/), [Amazon](https://www.mbi-deepdives.com/tag/amzn/), [Alphabet](https://www.mbi-deepdives.com/goog/), [Microsoft](https://www.mbi-deepdives.com/msft/). [Subscribe](#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Dollar General: Rural America's Retailer URL: https://www.mbi-deepdives.com/dg/ Last updated: 2023-08-24T14:26:35.000Z You can listen to the Deep Dive **[here](https://www.mbi-deepdives.com/audio/)** --- I am not sure how many of my readers ever set their foot in a Dollar Store in America, but there are more Dollar Stores in the US than Starbucks, Walmart, and McDonald's **combined**. Dollar General is the leading Dollar Store brand in the US with almost 20,000 locations across 47 states in the US. The origin of Dollar General was reminisced by Cal Turner Jr. ,who was CEO of Dollar General from 1965 to 2003, in his [book](https://www.amazon.com/My-Fathers-Business-Small-Town-Billion-Dollar/dp/1478992980?ref=mbi-deepdives.com) “*My Father's Business*: *The Small-Town Values That Built Dollar General into a Billion-Dollar Company*”**:** > J.L. Turner and Son, as it was called then (James Luther Turner was my grandfather), had 36 retail stores, generally partnerships with local merchants, in small Kentucky and Tennessee towns. The business, headquartered in Scottsville, Kentucky, was grossing about $2 million annually, and my dad was always looking for ways to grow. He was a keen observer of both his customers and the competition, and **he became intrigued by the “Dollar Days” sales put on by the big department stores** in Nashville and Louisville. **Once a month, they would take out huge full-color newspaper ads and sell merchandise with $1 as the single price point. My dad knew what those ads cost, and he understood that if they were spending that kind of money, they were selling a lot of goods. Customers obviously loved that $1 price point. Somehow, it made real value seem even more obvious.** > > “Why couldn’t we simplify all of our operations,” he thought, “by opening a store with only one price—a dollar?” **Every day would be Dollar Day.** In that flash of insight, he saw any number of benefits. Customers could keep track of what they were spending more easily, and checkout would be simplified. While Dollar Stores have become quite prevalent in the US, the original idea of a Dollar Store has become somewhat obsolete. Dollar General stopped selling items only for a dollar decades ago; its closest competitor Dollar Tree held onto the original Dollar Store philosophy for far longer before giving up just a couple of years ago. Therefore, today the Dollar Stores mostly evoke a sense of “value” even though they have moved away from the original idea of Dollar Stores. After Turner family successfully ran the Dollar General business from 1955 to 2003, David Perdue was the first outside CEO and led the company for four years before KKR acquired Dollar General in 2007\. When Dollar General first came to IPO in 1968, one share of Dollar General was worth $16.5 which would be $6,555 in 2007, implying \~16.6% CAGR over 39 years. KKR’s buyout of Dollar General was incredibly well-timed. During the Global Financial Crisis while S&P 500 Index went down by 38% in 2008, Dollar Tree was the best stock in the S&P 500 Index with +64% gain in the year. As the recession forced many people to trade down, Dollar Stores proved to be quite countercyclical. KKR bought Dollar General for $7.2 Bn, but put down only $2.8 Bn equity and the rest was financed via debt. When Dollar General became public again in 2009, its Enterprise Value was \~$12 Bn and the equity was worth \~$8 Bn. Dollar General was a home run for KKR. Dollar General handily beat the S&P 500 index even after it recently experienced its largest ever drawdown since becoming public again. The question is, of course, whether the current drawdown is a great opportunity for investors. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F39bf0c35-8684-4f8f-9335-d1fd440e3e98_2400x1240.png "chart") Data as of August 04, 2023; Source: KoyFin (MBI Deep Dives readers get 10% discount; just click [**here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3c7b4ac2-13f4-418c-ba3d-6938ea1b76bd_2400x1240.png "chart") Data as of August 07, 2023; Source: KoyFin (MBI Deep Dives readers get 10% discount; just click [**here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for this month’s Deep Dive: **Section 1 Dollar General’s Business**: I start the business overview with three key questions: a) who shops at Dollar General, b) why do they shop there?, and c) what do they buy in these stores? Then I discuss the unit economics of a Dollar General store. **Section 2 Competitive Dynamics and Near-term Challenges**: In this section, I explored the rivalry between Dollar General, Family Dollar, and Walmart and some other near-term challenges that have been plaguing Dollar General. **Section 3 Long-term Debates**: Beyond the near-term challenges, I mentioned some longer term concerns and what I think about them. **Section 4 Capital Allocation and Management Incentives**: I explained on this section why I am a fan of neither Dollar General’s capital allocation nor their management incentives. **Section 5 Model Assumptions and Valuation**: Model/implied expectations are analyzed here. **Section 6 Final Words**: Concluding remarks on Dollar General, and disclosure/discussion of my overall portfolio. [Subscribe](#/portal/signup) --- _This post is for paying subscribers only._ ### Amazon 2Q'23 Earnings Update URL: https://www.mbi-deepdives.com/amzn2q23/ Last updated: 2023-10-26T20:17:52.000Z **Disclosure: I own shares, and Jan 2025 $55 call options of Amazon** This was perhaps the best Amazon earnings call from my recent memory, especially on Amazon Retail. Not surprising that the stock was +8% in after hours. Here are my highlights from tonight’s call. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Revenue** 3P, subscription, and AWS grew by mid to high teen while ad segment increased by 20%+ The mix-shift from product to services in Amazon continues as services mix increased from \~40% in 2018 to \~60% in 2023. Let’s start more segment level discussion with AWS. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa41a3018-4ffa-4da1-9d3d-d14cc8b9d545_2154x304.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AWS** After experiencing its first QoQ revenue decline in 1Q’23, AWS added $786 Mn revenue QoQ in 2Q’23\. The 12% YoY growth reflects the tough comp of 2Q’22; since cost optimization mostly started in the latter half of last year, 3Q’23 and 4Q’23 may prove to be easier comp for AWS. More on this later. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa56e069b-6e9d-4ca4-aa84-397bbfcc6978_1418x718.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Azure vs Google Cloud vs AWS** Now that we have all the hyperscalers earnings report this quarter, here’s how their growth stacks against each other. As Jassy reminded during the call, don’t ignore the base effect while thinking about % growth: “…*AWS has almost doubled the revenue of any other provider.*” ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9da45cdc-a38e-4782-b904-cf8ffa7c4b39_1654x994.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink); \*Google Cloud includes Google Workpace, so not quite apple-to-apple and in reality, GCP grew faster than Google Cloud One thing I would like to track is Google Cloud’s operating performance trajectory against AWS. While Google Cloud’s revenue continues to build its gradual momentum against AWS, opex trajectory went slightly in the wrong direction. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2314464c-09eb-49f8-81a5-cd93cc4c6476_1210x766.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0334596e-f5eb-46df-ba7b-a8233233f976_1222x762.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As I have mentioned in Google [update](https://www.mbi-deepdives.com/goog/) early this year, Google Cloud’s revenue closely tracks AWS revenue trajectory just four years apart! ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa8c64ce5-6ee0-449c-a66c-dd4aa2757073_936x330.png) Source: Company Filings, MBI Deep Dives Okay, back to AWS. AWS incremental operating margin continued to struggle this quarter as well. At least, operating margin was slightly up, so hopefully AWS operating margin already bottomed in 1Q’23. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffdd080a9-1269-498d-9568-dfd54e4f9065_1556x164.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F085cd893-607b-47a1-bfa9-0dfa06dfd907_1738x898.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Amazon thinks AWS is pretty well positioned for Generative AI (I’m skipping more details as much of it was mentioned in earlier quarters as well): > Remember, the core of AI is data. **People want to bring generative AI models to the data, not the other way around. AWS not only has the broadest array of storage, database, analytics and data management services for customers, it also has more customers and data store than anybody else**. It seems highly likely that the cost optimization headwinds may be behind us: > What we're seeing in the quarter is that **those cost optimizations, while still going on, are moderating and many maybe behind us in some of our large customers. And now we're seeing more progression into new workloads, new business.** So those balanced out in Q2\. We're not going to give segment guidance for Q3\. **But what I would add is that we saw Q2 trends continue into July. So generally feel the business has stabilized, and we're looking forward to the back end of the year** **Amazon ex-AWS** Personally, the highlight from this call was Amazon Retail. Just see the material margin momentum in North America segment from 1.2% operating margin last quarter to 3.9% this quarter. International segment is also on the right direction: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1e611a80-6c6e-4097-91b2-d5c2f5de8e1b_1262x810.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Management also re-iterated that margins can not only go back to pre-Covid level but do even better: > I continue to believe what I said last quarter…which is I do believe that **we'll get back to margins like what we had pre COVID. And I don't think that's the end of what's possible for us there.** **Shopify vs Amazon** One metric I like to track is Shopify vs Amazon GMV trajectory. Shopify has so far been able to keep up with Amazon. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F93fe95a3-cffa-429f-a5a4-8e6b8804d7a8_1472x888.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Amazon Business is currently at $35 Bn** gross sales run-rate. Jassy thinks it can be $100 Bn+ business over time. **Fulfillment+ Shipping** The primary reason I thought this earnings call was excellent is I am much more optimistic about shipping and fulfillment cost as % of GMV may come down over time. For the fourth consecutive quarters, shipping and fulfillment costs are growing slower than unit growth. 3P units were 60% of overall mix (highest ever) and Amazon saw “good growth in the number of sellers and the unit sold per seller.” In 2017, Amazon’s shipping and fulfillment expenses as % GMV was estimated to be \~19% which consistently crept upwards every year to exceed \~25% in 2022\. This has started to come down this year; I estimate this number was 24.7% in 1H’22 but declined by 190 bps YoY to 22.8% in 1H’23. If long-term shipping and fulfillment expenses is actually closer to \~20% (or lower), the valuation implications are quite intriguing. I encourage you to play with these assumptions in my [Amazon model](https://www.mbi-deepdives.com/models/). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F16c6db77-6fdc-4f1c-af45-9d19095927f1_1674x770.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) But is such cost structure possible for Amazon Retail? Isn’t same day shipping going to make life difficult for Amazon to attain such cost structure? This calls makes me optimistic about these questions: > Central to our efforts has been the decision **to transition our stores' fulfillment and transportation network** **from 1 national network in the United States to a series of 8 separate regions serving smaller geographic areas. We keep a broad selection of inventory in each region, making it faster and less expensive to get those products to customers.** > > Regionalization is working and has delivered **a 20% reduction in number of touches for our delivered package, a 19% reduction in miles traveled to deliver packages to customers and more than a 1,000 basis point increase in deliveries fulfilled within region, which is now at 76%.** This is a lot of progress. Sometimes I hear people make the argument that Amazon is chasing faster speed while driving its costs higher and where it doesn't matter much to customers. **This argument is incorrect. There are 2 things to note. First, customers care a lot about faster delivery. We have a lot of data that shows when we make faster delivery promises on a detail page, customers purchase more often, not just a little higher, meaningfully higher. It's also true that when customers know they can get their items really quickly, it changes their consideration of using us for future purchases, too.** > > Second, **when shipments come from fulfillment centers that are closer to customers, they travel shorter distances, which cost less in transportation, get there faster and is better for the environment. There's a lot of goodness in that equation.** This ability to have shipments closer to customers is the result of a lot of work and invention on the regionalization side, placement logic and local in-stock algorithms. It's also driven by our development and expansion of same-day fulfillment facilities, which is our fastest fulfillment mechanism and one of our least expensive, too. > > Our same-day facilities are located in the largest metro areas around the U.S. so our top moving 100,000 SKUs but also cover millions of other SKUs from nearby fulfillment centers that inject selection into these same-day facilities and **have a design that streamlines getting items from order to being ready for delivery in as little as 11 minutes. The experience has been so positive for customers in our business that we're planning to double the number of these facilities. We believe that we are far from the law of diminishing returns and improving speed for customers.** > > In this last quarter, **across the top 60 largest U.S. metro areas, more than half of Prime members' orders arrived at the same day or next day. So far this year, we've delivered more than 1.8 billion units to U.S. Prime members the same or next day, nearly 4x what we delivered at those speeds by this point in 2019.** **Opex+Capex** The way Amazon has increased SBC intensity is not really a good look. SBC as % of revenue used to be <3% in 2018-2021, but crept up to \~4% in 2022 and was >5% in 2Q’23\. Diluted shares outstanding was +2.7% YoY, not an acceptable dilution for \~$1.5 Tn company. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F209d127c-6c51-43e9-8592-1d0cd89e0590_2516x382.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) Amazon’s LTM Capex+ finance leases was $54 Bn, down from $61 Bn; guidance for 2023 is slightly more than $50 Bn (vs $59 Bn in 2022). Much of the capex is going to AWS: > On the how much generative AI may impact the capital expense spend, included in that number **is a pretty significant amount of capital expense in the AWS business** for large language models and for generative AI. And we have quite a bit of demand right now. And so it's -- like in AWS in general, **one of the interesting things in AWS, and this has been true from the very earliest days, which is the more demand that you have, the more capital you need to spend because you invest in data centers and hardware upfront and then you monetize that over a long period of time.** > > So I would like to have the challenge of having to spend a lot more in capital in generative AI because **it will mean that customers are having success and they're having success on top of our services** and -- but I think that, that's our best estimate right now on that capital expense and we'll update it if we find it's different. **Other Bets** Apart from SBC, another not so great spot is Amazon’s continued reluctance to share “other bets” details with shareholders. I suspect I may care about this detail more than most Amazon shareholders. Let me explain why. Before Meta disclosed Reality Labs (RL) expenses, many investors/analysts were underwriting \~$5 Bn loss estimates/year for RL. But when they started disclosing, we got to know investors materially underestimated the expenses. My primary concern is something similar may be happening here at Amazon as well. While minority shareholders don’t quite have a strong say in how Amazon will allocate their capital (probably nobody has enough % of ownership in Amazon to have material influence even if there is no dual class share structure), at the very least we can make a much more informed decision in valuing Amazon. **Outlook** Amazon’s guidance for 3Q’23 is below: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa3b6760e-fdcb-4741-8b97-46db2327316d_1310x270.png) Thank you for reading. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### Meta 2Q'23 Earnings Update URL: https://www.mbi-deepdives.com/meta2q23/ Last updated: 2023-10-25T21:23:35.000Z *Disclosure: I own shares, and Jan 2025 $50 call options of Meta* While the stock is up +6-7% after hours, I found some mixed signals on this call to keep both bulls and bears interested. In any case, I have never found Meta’s earnings calls monotonous! Here are my highlights from tonight’s call. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Users** Facebook exceeded 3 Bn MAU for the first time, and MAP is now approaching 4 Bn. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F38baec2b-18ba-4164-aed8-eda71bd7f385_2210x646.png) **Engagement** DAU/MAU engagement looks steady across all regions. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F405ce54c-a230-4338-927f-edb12fa9ca44_2212x726.png) **ARPU** While ARPU exhibited considerable strength, please note the material weakness in YoY comparison. One interesting thing is Europe, APAC, and RoW had higher ARPU in 2Q’23 than they had in 4Q’22 which is a holiday season! ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b130009-796b-476d-acdb-c215bab671c1_1832x258.png) **Ad revenue** For the second consecutive quarters, number of ad impression growth and change in average price per ad is moving in the same direction; it’s the derivative that matters! Lower price is driven by higher impression growth in APAC and RoW as well as lower monetizing surfaces (i.e. Reels). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F527287ac-04f1-429e-9848-156f49c23650_1282x734.png) ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0d2d9ece-6192-4040-aa24-b3039630ed34_1832x700.png) **Segment Reporting** Topline grew by 11% (1% FX headwind). Reality Labs (RL) revenue declined by 39% YoY and lost $3.7 Bn this quarter. 2023 RL losses are still expected to exceed 2023 losses which will be higher than 2022’s. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8d54c69b-6dbd-421b-aa03-c597e88dafb0_1658x636.png) **Facebook** AI-recommended content led 7% increase in overall time spent on Facebook. **Reels** In 3Q’22 call, Meta mentioned there are 140 Bn Reels played across Facebook and Instagram each day. That number exceeded 200 Bn last quarter. 75% of Meta’s advertisers are now using Reels. Annual revenue run rate from Reels exceeded $10 Bn in 2Q’23 which was $3 Bn last fall. TikTok [reportedly](https://www.wsj.com/articles/tiktok-struggling-with-slowing-digital-advertising-industry-lowers-ad-revenue-outlook-11668139787?ref=mbi-deepdives.com) generated $10 Bn revenue in 2022; it is perhaps not aggressive to think that Reels may exceed TikTok revenue by next year. Meta is routinely under the wrath of investors (often by its own shareholders), but I have to take a moment and applaud what they achieved here. Admittedly, I was a bit nervous about rise of TikTok and whether Meta will be able to rearchitect FOA properties to build a compelling alternative. They clearly have exceeded my expectations. However, increased time spent will not grow revenue linearly: > we continue to expect time on Reels will monetize at a lower rate than Stories and feed for the foreseeable future since people scroll more slowly through video content. **WhatsApp** WhatsApp Business now has 200 mn users who will “be able to create click to WhatsApp ads for Facebook and Instagram without needing a Facebook account. This is a pretty big unlock, particularly in countries where WhatsApp is often the first step to bring a business online.” > The number of businesses using our paid messaging products has doubled year-over-year. > > click-to-WhatsApp ads revenue continues to grow very quickly at over 80% year-over-year. **Threads** Zuck mentioned the initial reaction to Threads exceeded their expectation and their main focus is on retention now. Don’t model Threads monetization too soon; Meta indicated they want to reach “hundreds of millions” users before thinking about monetization. Zuck hopes Threads might be the 5th big app of Meta’s FOA properties: > I do think it has been sort of this weird anomalous thing in the tech industry that there hasn't been an app for public discussions like this that has reached 1 billion people. As expected, there was a lot of discussion on AI. **AI for Advertisers** > **Almost all our advertisers are using at least one of our AI-driven products**. We've also deployed Meta Lattice, a new model architecture that learns to predict ads performance across a variety of data sets and optimization goals. And we introduced AI Sandbox, a testing playground for generative AI-powered tools like automatic text variation, background generation and image outcropping. One thing that stood out to me was why Meta thinks “AI agent” may finally launch messaging monetization spigot: > You can imagine a world on this where over time, every business has as an AI agent that basically people can message and interact with. > > it should alleviate one of the biggest issues that we're currently having around messaging monetization is that in order for a person to interact with a business, it's quite human labor-intensive for a person to be on the other side of that interaction, which is one of the reasons why we've seen this take off in some countries where the cost of labor is relatively low. > > But you can imagine in a world where every business has an AI agent, that we can see the kind of success that we're seeing in Thailand or Vietnam with business messaging could kind of spread everywhere. My brother runs an SMB which often deals with this issue; they receive far more messages (at times in a very short window if they have a discount offer going on) that any human(s) can deal with. Meta’s rationale makes intuitive sense and it can indeed be gamechanger if the “AI agent” is well executed. **AI for Consumers** On a consumer level, Meta’s plans are also quite expansive, but there will be more clarity and product launches later this year: > We are also building a number of new products ourselves using Llama that will work across our services. I'm going to share more details on that later this year. But you can imagine lots of ways that AI can help people connect and express themselves in our apps. **Creative tools that make it easier and more fun to share content, agents that act as assistance, coaches that can help you interact with businesses and creators and more.** And these new products will improve everything that we do across both mobile apps and the metaverse, helping people create worlds and the avatars and objects that inhabit them as well. If Snap’s “My AI’ [learnings](https://newsroom.snap.com/en-GB/early-insights-on-my-ai?ref=mbi-deepdives.com) is any indication, this can get quite interesting; if this takes off, I expect Meta to take some digital ads market share from Google. **Llama and Open Source** Why open source? > Llama is an open source project, which is a little bit different from building out a developer platform, although there will be an ecosystem around this. What we've seen around open source work that we've done, which we've done a lot of in our core infrastructure work, design of servers and data centers and basic infrastructure as well as in AI. And I pointed out some of these in my remarks upfront, like PyTorch and just a bunch of other models that we've released recently. > > One of the things that we've seen is that when you release these projects publicly and open source, there tend to be a few categories of innovations that the community makes. So on the 1 hand, I think it's just good to get the community standardized on the work that we're doing. That helps with recruiting because a lot of the best people want to come and work at the place that is building the things that everyone else uses. It makes sense that people are used to these tools from wherever else they're working. They can come here and build here. In case Llama indeed becomes the industry standard, Meta made sure their main competitors won’t use the model for free. Anyone with more than 700 mn users will have to pay fees to Meta to use and build on top of Llama: > one of the things that you might have noticed is in addition to making this open through the open source license, we did include a term that for the largest companies, **specifically ones that are going to have public cloud offerings, that they don't just get a free license to use this. They'll need to come and make a business arrangement with us.** > > And our intent there is we want everyone to be using this. We want this to be open. But **if you're someone like Microsoft or Amazon or Google and you're going to basically be reselling these services, that's something that we think we should get some portion of the revenue for. So those are the deals that we intend to be making, and we've started doing that a little bit. I don't think that, that's going to be a large amount of revenue in the near term. But over the long term, hopefully, that can be something.** Zuck is also aware that the conversation around AI safety is evolving and he kept the door open to not make everything open source in case there is real danger but also reminded that the companies who are in the business of **selling** their model may have vested interest in touting AI’s danger to make things “closed” source: > by open sourcing Llama now… we intentionally did not go out there and say we're going to open source every single thing in the future because we do want to have the space to be able to look at how the safety landscape evolves. And if we think that we do cross some kind of critical threshold in the future, it may not be the right thing to open source it in the future. But for our business model at least, since we're not selling access to this stuff, it's a lot easier for us to share this with the community because it just makes our products better and other people, and that, I think, is a really healthy dynamic for the industry. **Metaverse** Quest 3 is going to be launched during this year’s Connect. > Quest 3 is going to be the first mainstream accessible device that we expect many millions of people will get to experience this technology with. The metaverse content and software vision continues coming together as well. We recently announced that Roblox is coming to Quest with an open beta on App Lab. Quite a few push back from the analysts on Meta’s Metaverse spending. Meta tried to defend: > a lot of the investment that's driving the growth here is around conducting the fundamental R&D to solve hard technology problems…A lot of it is around clearing technical hurdles that will make subsequent devices smaller, cost less, weigh less, et cetera. I guess the question that looms large on my mind is whether this is a capital question or a limitation of Physics. Is throwing more money at the problem going to lead to solve the technical challenges in AR glass? For a moment, Zuck seemed to acknowledge how things are not going according to their plan here: > …I'd say the signals that we're getting from the market are it's certainly not getting adopted a lot faster than we expected so **that's sort of the somewhat sobering signal**. Zuck is aware that investors are not quite fond of this bet: > At a deep level, **I understand the discomfort that a lot of investors have with it** because it's just outside of the model of, I think, **even most long-term investors how you would think about this. And look, I mean, I can't guarantee you that I'm going to be right about this bet.** I do think that this is the direction that the world is going in. There are 1 billion or 2 billion people who have glasses today. I think in the future, they're all going to be smart glasses. Zuck reminded how the owners of the current mobile computing platforms made life difficult for Meta. Even though Meta is quite successful despite playing by the platforms’ rules, Zuck probably feels deeply uncomfortable in playing by their ever changing, arbitrary rules: > we've been quite successful at building large-scale social experiences within the constraints of platforms that often our competitors are defining. I think we're going to be able to do even better work. And there's a lot of things that I would like to see us build that we just can't because of the ways that we're constrained by the competitors who build these platforms. My guess is if Quest 3 is not successful (like selling less than 5 mn units in 6 quarters after launch), Meta may scale back on VR but will keep trying to solve AR glass. But there seems to be bit of **emotions** involved here, so it is not a very high confidence “guess”. > I know from an investor standpoint, most people aren't investing on quite as long of a time horizon as we are here, so I kind of get that, a lot of investors might want to see us spending less here in the near term. My view is that we are leading in these areas. I believe that they're going to be big over time. I think we've shown that we can deliver good business results in the near term while investing ambitiously in the long term. So I'm planning on continuing to do that, and I do continue to believe that over time, we will be happy that we did that. **Efficiency** Zuck praised how a small team basically launched Threads and how that chimes well with Year of Efficiency. Zuck re-emphasized on the efficiency theme even though the worst seems to be behind them: > Over the next few months, we're going to start planning for 2024\. And I'm going to be focused on continuing to run the company as lean as possible for these cultural reasons, even though our financial results have improved. I expect that we're still going to hire in key areas, but newly budgeted headcount growth is going to be relatively low. However, the efficiency messaging has bit of a mixed signal. While headcount growth may be limited, expect payroll expenses to go up (AI folks are highly sought after) and Metaverse losses will keep piling up (they did mention that in 1Q’23 as well, so not a new message in this call though): > we anticipate growth in payroll expenses as **we evolve our workforce composition toward higher-cost technical roles**. Finally, for Reality Labs, **we expect operating losses to increase meaningfully year-over-year due to our ongoing product development efforts in AR, VR** and our investments to further scale our ecosystem. **Capital Allocation** Meta mentioned they would like to be near net cash neutral over time which I am very glad to hear. Surprisingly, they hardly did much share buyback this quarter, and I’m not sure I understand why (analysts didn’t ask anything on this). Like many investors, I often complained about Meta’s buyback decisions in the past. While there are certainly valid criticisms to make Meta’s buyback decisions in 2021-22 period, in retrospect I think I have misunderstood Meta’s broader buyback policy. When it comes to buyback, there are two lenses to look at it: Return **Of** Capital, and Return **On** Capital. Supermajority of investors, including me in the past, seem obsessed with return on capital when it comes to buyback. But the data seems pretty evident: big tech mostly doesn’t think deeply about return on capital in their buyback decision; they just want to mostly return the capital back to shareholders i.e. return of capital. Let me share some data to put this in context. From 1Q’20 to 2Q’23, both Google and Microsoft generated \~$208 Bn FCF (yes, same). Microsoft returned 76% of that in the form of dividend and buyback while Google returned 82% of FCF during this time in just buybacks. Meta, on the other hand, generated $98 Bn FCF from 1Q’20-2Q’23 and they bought back $99 Bn shares. Seeing in this light, we can see a clear theme in how big tech allocates capital to buyback. And if you really want big tech to focus on return **on** capital, be careful what you wish for. Let’s be honest: would we really want big tech to accumulate cash on their balance sheet in 2020-21 in the chance that we were on the bubble and they could buyback shares when stock craters? Again, this is not a defense of Meta’s ill-timed buybacks in 2021, rather a general explanation of how I think big tech thinks about buyback. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fac593eed-29d8-4ae6-a672-c8c713380e60_2208x496.png) **Opex Guide** Meta increased their opex guide range from $86-90 Bn to $88-91 Bn. Historically, they usually trim their opex guide over the course of the year, but they had a legal-related expense which probably contributed to the increased range. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feec0d3de-f081-4bb4-ab57-f59e7398bc20_1662x824.png) **Capex** Perhaps surprisingly, Meta lowered capex guide for this year, but it’s mostly just shifted to 2024: > …The other major budget point that we're working through is what the right level of AI CapEx is to support our road map. **Since we don't know how quickly our new AI products will grow, we may not have a clear handle on this until later in the year**. > > **We expect capital expenditures to be in the range of $27 billion to $30 billion, lowered from our prior estimate of $30 billion to $33 billion. The reduced forecast is due to both cost savings, particularly on non-AI servers as well as shifts in CapEx into 2024 from delays in projects and equipment deliveries rather than a reduction in overall investment plans.** Looking ahead, while we continue to refine our plans as we progress throughout the year, we currently expect total capital expenditures to grow in 2024, driven by our investments across both data centers and servers, particularly in support of our AI work. > > ..**we are mindful of our intention to reduce the capital intensity of these investments over time.** **Regulation** The following bit (especially the bold line) stood out to me and to my utter surprise, there was not a single question asked on this point during the call; this remains a pivotal risk for Meta’s business in the long term as the philosophy of “surveillance capitalism” becomes more mainstream: > With respect to EU-U.S. data transfers, we saw a positive development with the European Commission's adoption of a final adequacy decision, which allows us to continue to provide our services in Europe. This is good news, though broadly speaking, **we continue to see increasing legal and regulatory headwinds in the EU and the U.S. that could significantly impact our business and our financial results**. **Outlook** Meta guided $32-34.5 Bn for 3Q’22 (expectation was $31.2 Bn); +20% YoY growth on the high-end. +3% FX tailwind expected in 3Q’23 and 3Q’22 was -4.5% topline quarter, so YoY comparison is a lot easier. Nonetheless, the guide clearly indicates Meta’s relative strength today. **Closing Words** Overall, Meta is at an intriguing place today: on one hand, there are exciting opportunities on the near to mid term horizon (Reels, Messaging, and even AI can have material impact in relatively quick time), but on the other hand, Meta’s long-term bet on Metaverse continues to stand on quite fragile foundation. Bears should marvel a little bit at Meta’s strength and continued ability to adapt at its core business, and bulls should wait a little bit before going euphoric due to near-term wins. More thoughts from the follow-up call, 10-Q, and current valuation **[here](https://twitter.com/borrowed%5Fideas/status/1684542652241395713?ref=mbi-deepdives.com)** I will cover Amazon next week. Thank you for reading. If you are not a subscriber yet, please consider subscribing and sharing it with your friends: [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### Alphabet 2Q'23 Earnings Update URL: https://www.mbi-deepdives.com/goog2q23/ Last updated: 2023-10-24T22:15:42.000Z *Disclosure: I own shares, and Jan 2025 $50 call options of Alphabet* > “With 15 products that each serve half billion people and 6 that serve over 2 billion each, we have so many opportunities to deliver on our mission.” > > \-Sundar Pichai (2Q’23 Earnings Call) Here are my highlights from Alphabet’s call tonight. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Revenue** Topline grew +9% FXN. While Google Services revenue growth was flat in 1Q’23, it was 5.5% YoY in 2Q’23, with growth re-accelerating in every component of Google Services. Despite cost optimization efforts by customers, Google Cloud maintained its momentum with \~28% YoY growth (same as it was in 1Q’23). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcef53831-0ebb-4f0a-a321-389c2cc1f4fd_2176x484.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **EBIT** Google Services maintained mid-30s EBIT margin. But note the following: > Costs associated with the Brain Team, which were previously included in Google Services, are now reported as part of Alphabet's unallocated corporate costs. So, on an apple-to-apple basis, Google Services margin would be slightly lower this quarter than reported (say, \~$500 Mn opex for Google Brain). Google Cloud improved its margin to MSD. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc993015c-af6c-49cd-a9d4-830a8e74b2a9_2084x546.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Search** Google launched “Search Generative Experience” or SGE" in May. Sundar sounds excited on initial feedback: > The feedback has been very positive. We've just **improved our efficiency pretty dramatically** since the product launch. **The latency has improved significantly**. We are keeping a very high bar, but I would say **we are ahead on all the metrics in terms of how we look at it internally**. Google is currently building Gemini, a multimodal Large Language Model, which Google believes will make the user experience even better. How about monetization? If one thing we can be relatively confident about, it is Gen AI will have material impact on how advertisers work with Google’s ad products. Some excerpts on this point below: > Ads will continue to play an important role in this new search experience. Many of these new queries are inherently commercial in nature. We have more than 20 years of experience serving ads relevant to users' commercial queries, and SGE enhances our ability to do this even better. We are testing and evolving placements and formats and giving advertisers tools to take advantage of generative AI. > > It's worth reiterating that while generative AI is now supercharging new and existing ads products with tons of potential ahead, AI has been at the core of our ads business for years. In fact, today, **nearly 80% of advertisers already use at least one AI-powered search ads product.** > > …later this year, **Automatically Created Assets, which are already generating headlines and descriptions for search ads, will start using generative AI to create assets that are even more relevant to customer queries**. > > To…**drive consideration in the mid funnel, we're launching 2 new AI part ad solutions, demand gen and video view campaigns, and both will include (YouTube) shorts inventory.** **YouTube** YouTube’s +4% YoY ad revenue in 2Q’23 (vs -2.6% in 1Q’23) driven by growth in brand, followed by direct response, reflecting further stabilization in advertiser spend. Shorts is now watched by 2 Bn people every month (vs 1.5 Bn in 2Q’22) “Google Other” revenue was bit of a surprise with +24% growth in 2Q’23 (vs +8.8% in 1Q’23). This was largely driven by strong growth in YouTube subscriptions revenues (also, Google Play growth turned positive this quarter). While 70% gen AI unicorns are Google Cloud customers, and more than 750k Workspace users have access to new features such as Duet AI in preview. Google thinks upsell opportunities exist on their 9 mn installed base of Google Workspace customers. Google indicated optimization still is continuing: > we saw a continued moderation in the rate of consumption growth as consumers optimize their spend **Headcount** The recent layoff hit the headcount number last quarter. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4acb954a-11c5-4b0f-9a6d-819fa7b7bb4a_1162x630.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capital Allocation** Google utilized \~70% of their FCF in buyback which led to 46 bps decline QoQ in diluted shares outstanding. They still have $105 Bn net cash on balance sheet. If they don’t want to deplete the net cash balance, at least utilize 100% FCF to buyback shares. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F599629e5-614e-4892-933c-03eb2f0b5830_942x594.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex** Google mentioned earlier that 2023 capex to be modestly higher than 2022\. But so far after 1H, capex in 2023 stands at $13 Bn (vs $16.6 Bn in 1H’22). But Google reiterated their capex guidance which means we probably should expect \~$20 Bn capex in 2H’23\. And the capex train will continue in 2024 as well. Here’s how Ruth explained this capex cadence: > as it relates to CapEx, in Q2, **the largest component was for servers, which included a meaningful increase in our investments in AI compute**. The sequential step up in the second quarter was lower than anticipated for 2 reasons. First, with respect to office facilities, we continue to moderate the pace of fit-outs and ground-up construction to reflect the slower expected pace of headcount growth. Second, there were delays in certain data center construction projects. **We expect elevated levels of investment in our technical infrastructure increasing through the back half of 2023 and continuing to grow in 2024.** **The primary driver is to support the opportunities we see in AI across Alphabet, including investments in GPUs and proprietary TPUs as well as data center capacity.** With all that said, we remain committed to durably reengineering our cost base in order to help create capacity for these investments in support of long-term, sustainable financial value. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb77ad275-cd01-46d2-9329-1ffe908552ed_1144x618.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Valuation** I share this back-of-the-envelope valuation table on Google every quarter to have a quick gut check. When Google was trading at $100 or below, investors mostly needed to be confident about relevance of Google search in 5-10 years. Today, we also need to underwrite both sustainability and growth in Google Services business (led by Google Search, but YouTube will also have to play its role). ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9fbb84ca-5928-4026-a0e4-e2186bf6a93b_2492x500.png) Source: MBI Deep Dives I will cover Meta’s earnings tomorrow! Thank you for reading. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### Spotify 2Q'23 Earnings Update URL: https://www.mbi-deepdives.com/spot2q23/ Last updated: 2023-07-25T16:56:14.000Z *Disclosure: I own shares of Spotify* Spotify stock has been one of the “winners” in 2023\. A week ago, its YTD was +130%. With Generative AI, price increases, and potential margin improvement speculations, the stock crumbled after today’s earnings as it mostly appears to be very much “work-in-progress” story, and there’s not quite inflection point in sight, yet! Here are my highlights from today’s earnings. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Users** Spotify does seem to be hitting on the next gear when it comes to adding users. While Premium net add is quite robust, it is the total MAU (including ad-based segment) that added a record 36 mn users in the last quarter. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7d9e1775-20b2-4925-a998-35d02c713294_2058x210.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Premium Mix** North America and Europe continue to lead Premium subscriber net add growth with 3 mn and 4 mn respectively. Rest of the World (ROW) still seems quite sluggish in converting to premium product; hence, premium as % of total MAU continues to go down. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffaf180ed-144f-4079-ae57-9b6821dd160b_1826x1056.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Netflix vs Spotify** I mention this chart every quarter; it is evident that Spotify’s subscriber growth is reaccelerating. The difference between Spotify and Netflix’s net subscriber adds has narrowed a little since Netflix also accelerated in the last quarter. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F12fa8d7b-68c5-43ae-a12f-abc9b64b0b19_1074x691.png) Source: Company Filings, MBI Deep Dives; Note: definition of subscriber is not apple-to-apple between Spotify and Netflix, so limitations apply. **Revenue** On **constant currency** basis, topline was +14%, Premium segment revenue was +14% and ad revenue +15%. ARPU continues to decline for three consecutive quarters. Podcast ad revenue was +30% while music related ad revenue grew by mid single digit %. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff90c6e47-5f83-4d44-b1c4-f26332b13f14_1796x348.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **ARPU** The recent price increases will probably put an end to QoQ ARPU decline for the next two-three quarters: > Yesterday, we announced broad price increases across more than 50 markets, including most of Europe and North America…And while this won't impact revenue per user much up until the end of Q3, we expect it to have a meaningful impact on Q4 and beyond. The price increases are the most significant in Spotify’s history. Here’s how much price is being increased for different products in the US: Student +20%, Individual +10%, Duo +15%, Family +6% **Gross margin (GM)** Reported GM was 24.1% and adjusted GM was 25.5%. What are these adjustments? > In the quarter, **we took steps to shrink our real estate footprint and rationalize certain areas of our podcasting business. We also exited our Soundtrap marketplace business**. We expect all of these moves to have a positive impact on our rate of profitability on a go-forward basis. However, they did result in roughly **EUR 135 million of net charges in the quarter with EUR 44 million flowing through gross margin and EUR 91 million flowing through our operating expense.** About EUR 25 million of these charges were cash related. Over the last 14 quarters, Premium segment’s GM remains somewhat static at 28%. Analysts asked whether Spotify will enjoy better margin for incremental price increases from the labels. Spotify was pretty tight lipped on that. Ad segment’s margin was positive again, but still miles to go to be considered respectable. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bbad671-8cbc-4946-b3a5-746fef4be133_1124x674.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AI’s Impact** There were some comments on AI’s impact, but the following bit stood out to me: > if you think about advertisers today, the cost of generating new advertisements on Spotify is quite a big thing, especially on audio ads. By using Generative AI and our tools here, **I think you're going to be able to see that we can significantly reduce the cost that it takes for advertisers to develop new ad formats**. And that obviously means that you as an advertiser instead of having 1 ad, you can imagine having thousands and tested across the Spotify networks. **Things that you could easily do today using text, but you haven't been able to do over video or in audio**. **Opex** With 91 mn one-off charges this quarter, total opex trend may appear worse than it is. Nonetheless, Spotify probably needs to try harder to find efficiency in opex structure. Even if we adjust the one-off charges, total opex would be 120% of gross profit. Clearly, more “efficiency” is required. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5949d3c-ec1a-4030-994d-ba6f153b29f5_1800x276.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook for 3Q'23** In 3Q’22, Spotify added 7 mn premium subscribers, but is now guiding 4 mn for 3Q’23\. Why? > Our data would suggest that historical price increases have had minimal impact on growth, but given the breadth of this change and the significant outperformance in the first half of the year, **there is some conservatism baked into our outlook for Q3**. We do expect our net adds through Q3 of this year to be higher than the same point last year, roughly 30% better. Moreover, 600 bps FX headwind is assumed in guide. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F525f144e-37a0-45ed-9974-fb24c201fb7f_1452x502.png) Source: Company Filings You can read my Spotify Deep Dive (December, 2021) [**here**](https://www.mbi-deepdives.com/spot/) I will cover Alphabet’s earnings tonight! [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### CSX: The Old School "Monopoly" URL: https://www.mbi-deepdives.com/csx/ Last updated: 2023-07-24T11:28:37.000Z You can listen to the Deep Dive [**here**](https://www.mbi-deepdives.com/audio/) [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) --- Warren Buffett isn’t quite fond of making bold predictions, so I certainly took [note](https://twitter.com/borrowed%5Fideas/status/1497586138730708994?ref=mbi-deepdives.com) when he mentioned the following in 2021 shareholder letter: > “I’ll venture a rare prediction: BNSF will be a key asset for Berkshire *and* our country **a century from now**.” Buffett started buying Burlington Northern Santa Fe i.e. BNSF Railway in 2006 and accumulated 22.5% of BNSF’s shares by early 2009\. But in November 2009, Berkshire announced to buy the whole company. Delving deep into BNSF transaction is bit of beyond the scope for this piece, but let’s just say it has been a home run for Buffett. And after a decade of owning BNSF, Buffett still felt pretty good about BNSF’s chances for the next hundred years! It’s not just Buffett; Soroban Capital’s Eric Mandelblatt also shared similar sentiment on a [podcast](https://www.joincolossus.com/episodes/43906331/mandelblatt-investing-in-the-industrial-economy?tab=transcript&ref=mbi-deepdives.com) in March 2022: > …given how developed the country is at this point, Union Pacific can't raise their hands and say, "Hey, we'd love to run a railroad track through downtown Houston." It doesn't work like that. So the tracks are the tracks. We're not laying new tracks here. And at my old firm we used to talk about **what are the businesses we'd be comfortable buying a 100-year bond from?** Because it's almost the definition of incumbency, barriers to entry longevity. **To me, the railroads are my number one.** When I think about what's a business I know a hundred years from now, that business is going to be around, it's going to be cash flowing. Very hard to say that, very hard to look 100 years in the future. **To me, railroads are the definition of the 100-year asset.** And by the way, they're one of the very few 100-year bond issuers in the United States market. The steam locomotive was invented in 1797 and [three decades](https://www.aar.org/chronology-of-americas-freight-railroads/?ref=mbi-deepdives.com#!) later, the first railroad in North America was introduced. Railroads had a monumental impact in capitalism. When transcontinental railroad was built in the 1860s, one could travel from the east coast to the west coast of the US in three days that used to take three weeks before the railroads came to the scene! In fact, in the beginning of the 20th century, railroads [contributed](https://www.credit-suisse.com/media/assets/corporate/docs/about-us/research/publications/credit-suisse-global-investment-returns-yearbook-2021-summary-edition.pdf?ref=mbi-deepdives.com) \~63% of the US and \~50% of the UK total stock market capitalization. Today, it is less than 1%. While one may be tempted to think railroads are on the brink of irrelevance in our current world, the reality is a bit different. Even though the industry went through a dizzying number of M&A over the last couple of centuries, the last players standing today are in an enviable position. As of 2022, Surface Transportation Board (STB) [defines](https://www.stb.gov/reports-data/economic-data/?ref=mbi-deepdives.com) **Class I railroad companies** as having operating revenues of, or exceeding, $1 Bn per year, and there are only 6 class I railroad companies today: BNSF Railway (owned by Berkshire), Union Pacific (Ticker: UNP), CSX Corporation (Ticker: CSX), Norfolk Southern (Ticker: NSC), Canadian National (Ticker: CNI), and Canadian Pacific Kansas City (Ticker: CP). The class I railroads [contain](https://www.aar.org/facts-figures?ref=mbi-deepdives.com#4-capacity-amp-service) \~67% of the industry’s mileage, \~87% of its employees, and \~94% of its freight revenue. The utter dominance of class I railroads, as well as their significance in broader supply chain in North America, has consistently been somewhat underappreciated by the market as every single one of them materially outperformed the S&P 500 Index. While S&P 500 only increased \~6x over the last two decades, shareholders of North American railroad companies enjoyed somewhere between \~17-27x return during the same time. ![chart](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F07f6910d-aebc-4439-a238-a371b6cf88df_2400x1240.png "chart") \*Data as of July 09, 2023; Source: KoyFin (MBI Deep Dives readers get 10% discount; just click [**here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for this month’s Deep Dive: **Section 1 Understanding Railroad Industry**: This section provides a brief history of North American railroad industry, how a railroad business operates, geographical segmentation of North American railroad industry, and some basic concepts in the industry such as intermodal shipping, Precision Scheduled Railroading (PSR) etc. **Section 2 CSX’s Business**: Following the discussion on overall industry, I outlined how CSX makes money as well as its operating cost structure. **Section 3 Bull-bear Debate**: I presented both sides’ arguments and mentioned which way I tend to lean on some of these key debates between bulls and bears. **Section 4 Management and Capital Allocation**: CSX, as well as other publicly listed North American Class I railroads, capital allocation history over the last two decades and CSX’s current incentive structure is highlighted here. **Section 5 Valuation/Model Assumptions**: Model/implied expectations are analyzed here. **Section 6 Final Words**: Concluding remarks on CSX, and disclosure of my overall portfolio. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) _This post is for paying subscribers only._ ### Meta's Achilles Heel(s) URL: https://www.mbi-deepdives.com/metabear/ Last updated: 2023-07-13T19:21:45.000Z *Disclosure: I own shares and January 2025 $50 Call Options of Meta* [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) First some caveats: while we have all probably read Charlie Munger’s quote *“I never allow myself to hold an opinion on anything that I don't* ***know the other side's*** *argument better than they do”*, the reality is this is exceptionally uncommon and challenging. In fact, when investors mention risks about a company they like, they often deliberately choose strawman arguments from the other side and advertently or inadvertently ignore steelman arguments. I will try to avoid strawman bear cases (and boy there are many), and only outline the bear cases that indeed concern me as a shareholder. Since I am writing about the bear cases, they are known unknown, but obviously there can be unknown unknowns that I may not even be aware but can certainly affect Meta in the long run. Before we get into those long-term concerns, let me start where I have left off in last week’s post: [Meta’s Moat](https://www.mbi-deepdives.com/metamoat/). I have received quite a few insightful constructive feedback. One of the counterarguments was since the business of social media had dramatically changed over the last decade, it is less useful to track where newer social companies are today at similar scale vs where Meta was a decade ago. While I did mention that “the business of social media likely changed forever”, I could have done a better job outlining where things stand today between Meta vs other social companies. For the purpose of this piece, I will primarily focus on Snap to continue the conversation from the last week’s piece. Let’s start with ARPU. Back in [October 2014](https://www.axios.com/2017/12/15/a-timeline-of-snaps-advertising-from-launch-to-ipo-1513300279?ref=mbi-deepdives.com), Snap launched its ads, so it was just \~4% of Meta’s ARPU (see how it is calculated below) in 2015\. But then it quickly ramped up to 12% of Meta’s ARPU in 2016 and reached 22% in 2020\. Then the progress stopped in the last couple of years. Zuckerberg and Spiegel initially had somewhat opposite tones when it comes to [ATT](https://www.singular.net/glossary/app-tracking-transparency/?ref=mbi-deepdives.com#:~:text=App%20tracking%20transparency%20%28ATT%29%20is,by%20companies%20other%20than%20Apple.); Spiegel almost [welcomed](https://9to5mac.com/2021/05/21/snap-ceo-happy-to-pay-app-store-commission/?ref=mbi-deepdives.com) Apple’s ATT but it is ATT that may have played a significant role in stalling their ARPU momentum against Meta. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc4d3da8a-29ab-43b4-9502-73e731ae6aa7_1476x146.png) Meta’s ARPU is calculated based on the company’s reported advertising revenue divided by last four quarterly average of Facebook’s DAU. Please note Facebook’s DAU includes Messenger but doesn’t consider DAU that uses **only** IG **or** WhatsApp **and** not Facebook. Source: MBI Deep Dives, Company Filings Some may wonder whether user mix differentials between these two companies had contributed to the stalling of progress in ARPU. Not really; in fact, while Snap’s ARPU quickly ramped to \~8-9% of Meta’s by mid-2016, it hasn’t been able to gain much ARPU momentum in North America (NA) since then and in recent quarters, it started going in the wrong direction. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff6a5e2f1-7eed-40bc-bb2b-ea95baf8e0ac_1384x812.png) Facebook NA ARPU is calculated based on company disclosed Facebook DAU (not MAU) to make it apple-to-apple with Snap; Source: MBI Deep Dives, Company Filings What somewhat surprised me is that Snap’s ARPU as % of Meta’s ARPU is actually higher in geographies ex-North America. There are interesting implications here which is relevant with my first bear concern about Meta (to be discussed shortly). Let’s look at Opex now. To make it more apple-to-apple, we need to make some adjustments. Since we want to compare Snap to Meta’s FOA business, I have excluded Reality Labs (RL) related opex. Since RL expenses were disclosed from 2019, I have made some assumptions for RL opex in 2015-2018\. I also excluded restructuring expenses for both Meta’s FOA segment and Snap in 2022 as well as Meta’s large legal bills in 2019\. To be fair, Snap also invests in AR but since they don’t quite disclose it separately (and it seems very much part of the core business), I took Snap’s total opex base (ex restructuring) for calculating their Opex per DAU (Note: Snap IPO-ed in 2017 which distorted that year’s number, so I would ignore it). What we see is on a per DAU basis, Snap spent almost half of Meta in the last three years and yet only generated approx. one-fifth of Meta’s ARPU. Without ARPU momentum, Snap’s ability to invest on its own business through the Income Statement will be limited. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F668bc245-705e-49ee-b9be-2ffcf8aa4d5a_1536x182.png) 2015-2018 RL Opex is estimated but 2019-2022 RL opex is disclosed by company. Source: MBI Deep Dives, Company Filings ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb3d997d3-456c-467a-97d0-5efde370ff79_1538x174.png) Snap IPO-ed in 2017 which led to the increase in Opex in 2017; Source: MBI Deep Dives For Meta’s moat, I highlighted two primary moats that are relevant today: a) ability to attract and price talent, and b) regulatory capture are the primary moats of Meta. As ATT and heightened data privacy concerns wrecked havoc in digital advertising (ex Google Search), Snap’s ARPU momentum lost its venom which is presenting Meta a great opportunity to widen the gap from their competitors. What perhaps shocked me is Snap’s SBC per average employee vs Meta’s: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5d418ea5-c6db-4665-877b-135e0bb2a9d2_1328x782.png) Source: MBI Deep Dives, Company Filings, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) It's a double whammy for a company such as Snap. They need to invest in the next big thing (AR, AI, paying creators for content, ever increasing moderation requirement etc.) while dealing with ATT, GDPR etc. which directly affect ARPU momentum. The way they could have potentially escaped it if their users grew fast so that they enjoy some cost leverage, but despite Facebook’s DAU being 2x Snap’s DAU, Facebook surpassed Snap’s incremental DAU growth YoY for the last couple of quarters. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff5f198b0-7b6b-4d2e-b018-0bb699871ec4_1092x628.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While some readers took an issue with whether execution moat is actually sustainable over the long run (management can change or even the existing management can lose direction for variety of reasons etc.), it is perhaps **far less controversial** to say that Snap (or anyone else) would have to execute out of their skins and hope that Meta loses its way a bit to close the competitive gap with Meta. Therefore, you don’t necessarily need to believe whether Meta has execution moat or not, rather I would invert it: you need to assume Meta would be somewhat incompetent exactly when Snap (or anyone else) would execute extremely well. Of course, it may not have to be binary, but given [power laws](https://twitter.com/borrowed%5Fideas/status/1528494526339420161?ref=mbi-deepdives.com) in consumer internet, it may be harder to generate consistent and durable profit for sub-scaled players. But this piece is not about Snap vs Meta and despite what the stock prices can make shareholders feel these days, not everything is puppies and kittens for Meta. My steelman bear cases are two fold: a) Meta’s persistent dependency on North America, and b) the evolution from “social media” to “media social” and social interactions online. Before I elaborate on my bear cases, let me quickly mention the driver of Meta’s revenue today: a) number of DAU or MAU, and b) the time spent on Meta’s platforms. While much of the bear concerns revolve around users leaving the platform that can be potentially accelerated due to reverse network effects, I think it is the “time spent” function that’s lot more credible bear case. Within the time spent function, Meta’s revenue is driven by: a) the volume or number of ad impressions, and b) the effectiveness of ads which affects price per ad. My first “Achilles heel” for Meta revolves around effectiveness of ads while the second one is about volume of ads or number of ad impressions. ### **Meta’s persistent dependency on North America** At first glance, it may sound like a strange bear concern, so allow me to explain. Back in 2009, 36% of Facebook DAU was based in North America (NA) which contributed two-third of the company’s overall revenue. 4 years later in 2013, NA users became 20% of the DAU and 47% of the revenue. As Meta grew its tentacles all over the world, NA DAU became only 10% of overall DAU in 2022\. Surprisingly, NA’s revenue contribution remain almost half of the company’s revenue. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd98c3a96-0eeb-4fe4-b938-76b79da3bdf3_1158x660.png) \*2009-2012 data is only for US, not NA. Source: Company Filings, MBI Deep Dives So, why is this a concern? Two reasons. First, I suspect while there is a massive difference among regions in terms of ARPU, the cost to serve the users may not have much difference. Meta’s ARPU in NA is \~3x Europe, \~11x Asia Pacific, and \~15x Rest of the World. How about the costs? Back in 2012 10-K (first 10-k after IPO), Meta had an interesting paragraph which they didn’t repeat anytime in their annual filings since 2012: > User geography also has some impact on our costs, though in general new users in Asia and Rest of World **do not require material incremental infrastructure investments** because we are able to utilize existing infrastructure such as our data centers in the United States to make our products available to these users. In addition, user growth by geography does not necessarily affect our overall headcount requirements or headcount-related expenses since we are generally able to support users in all geographies from our existing facilities. I imagine they stopped publishing this paragraph because it may be not true anymore. A [2018 blogpost](https://about.fb.com/news/2018/07/hard-questions-content-reviewers/?ref=mbi-deepdives.com) by the company indicated that Facebook’s “safety and security” team employs 30k people (most of them are contract labor and hence not part of company’s headcount). Such moderation requirement certainly didn’t exist in 2012, and since moderating content requires you to understand local context and nuances, I imagine much of this workforce is region specific. Moreover, if political will to control citizens data within their borders gain critical momentum, data infrastructure costs can also become more region specific. With US leading ARPU momentum that the other regions are finding hard to keep up with, there likely is a massive margin differential across regions. Any weakness in the North America business may make life challenging for Meta, and there seems to be one company which has vested interest in making Meta’s life difficult in North America: **Apple** (Well, their feud is global in nature, but the center of the feud and the potential implications for Meta is most acutely felt in North America since this region is much more mature than others and hence growth primarily depends on effective monetization of MAU.) Mark Zuckerberg is fighting against Apple to keep his company’s economics: > "A guy who rises to the top of a big corporation and owns none of it is much more interested in control than he is in economics. It is just the nature of humanity. A guy who owns his business is already used to control. He never has to fight for control. What he has to fight for is economics” > > \-John Malone iPhone is dominant in North America and if iPhone’s market share among teens is any indication, Apple’s dominance in the US is expected to only increase over time. Apple’s continued and persistent dominance in the US can be bad news for Meta. From 2017-2022, Meta’s MAU in the NA increased by \~2% CAGR whereas ARPU grew by \~20% CAGR during the same time. It is safe to assume Meta’s MAU growth in NA will not exceed \~2% in the next 5-10 years; therefore, the crux of the question around NA’s growth sustainability hinges upon ARPU question. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F66349a47-6037-46a1-8f6e-150304595f80_960x702.png) Source: [WSJ](https://www.wsj.com/articles/why-apples-imessage-is-winning-teens-dread-the-green-text-bubble-11641618009?ref=mbi-deepdives.com) The three largest beneficiaries of the current mobile computing are Apple, Google, and Meta (in that order). Apple reportedly receives [\~$20 Bn](https://macdailynews.com/2023/02/21/google-pays-apple-20-billion-annually-to-be-safaris-default-search-engine/?ref=mbi-deepdives.com) (and it keeps growing) per year from Google which more or less goes directly to Apple’s bottom line. That makes their relationship a bit more symbiotic; despite all the speculation about Apple entering search market, it would not surprise me if the relationship remains in the current equilibrium for the next 3-5 years (but hard to know beyond that). But it is Meta’s rise that must be so damn annoying to Tim Cook (and most likely would be to Steve Jobs too if he were alive) since Apple receives peanuts from Meta. Given Apple doesn’t take a cut of advertising from developers, there’s no direct payment flowing from Meta to Apple, but some may argue rise of social media is what made smartphones much more intriguing to own and hence, Apple and Meta too have symbiotic relationship. I am quite confident that Apple feels they catalyzed smartphone revolution, hosts the wealthiest billion on their platform, and hence **deserves** some share of Meta’s economics. Evan Spiegel [validates](https://9to5mac.com/2021/05/21/snap-ceo-happy-to-pay-app-store-commission/?ref=mbi-deepdives.com) such perception: > We really feel like Snapchat wouldn’t exist without the iPhone and without the amazing platform that Apple has created. A recent [post](https://post.news/@/noam/2RTRvTNNxSCQb3yNjqa0DPfr1Yk?ref=mbi-deepdives.com) on Meta’s near acquisition of Waze (Google ultimately acquired Waze) made me realize if Apple were run by Mark Zuckerberg, he would probably be angry too that Apple doesn’t receive much in return from Meta even though Meta’s majority of revenue (and likely supermajority of profits) is generated on their phones: > Facebook was the natural fit from the product perspective — they feared dependency on the mobile platforms and wanted to own their location stack, both for their upcoming phone and their apps. We spent a lot of time together mapping out potential integration, but Facebook kept running up against the problem of “What if we help you, you become a huge platform, and then Google comes along and acquires you? We would not be able to compete with them financially.” **This was on the heels of the Spotify US launch where Facebook believed they had “built Spotify's business” but did not extract any value from it.** > > \-Noam Bardin (former CEO of Waze) Guess what, Spotify wasn’t anywhere close to making any profit when Zuckerberg felt they had “built Spotify’s business” and lamented that they didn’t get much in return from Spotify. Imagine how Zuckerberg would have felt if a company that runs its products on his platform and generated almost half of Meta’s operating profit but he couldn’t extract much value from that company. Zuckerberg would probably do exactly what Cook had done if Zuckerberg’s role were reversed. Tim Cook and Mark Zuckerberg are, hence, at odds on the question of economics. Zuckerberg wants to maintain status quo, and Cook is constantly looking for tweaking the status quo to get his hands on Meta’s economics. Apple [suggested](https://www.wsj.com/articles/inside-the-apple-vs-facebook-privacy-fight-11660317376?ref=mbi-deepdives.com) Meta pay 30% to Apple for “Facebook boosts”, but Meta declined. Finally, Apple resorted to privacy narrative and wrecked havoc in entire digital advertising ecosystem by introducing ATT. While this may sound like past news to readers, the underlying feud is very much alive and may not be totally solved anytime soon. If ATT turns out to be a boon for Meta and entrenches its moat against sub-scaled players, I cannot imagine Apple thinking the job here is done for them. Given how incentives are stacked, **Tim Cook would actually prefer Google to keep and grow their share in digital ad market at the expense of Meta** since Apple keeps a healthy economics from Google. Our base case likely should be that this is a protracted cat and mouse game between Meta and Apple. Eric Seufert’s [work](https://twitter.com/eric%5Fseufert/status/1671529775880454151?ref=mbi-deepdives.com) indicates (also see this [tweet](https://twitter.com/ErocsJohns/status/1671534568887689216?ref=mbi-deepdives.com)) Apple may continue to make life difficult for digital advertisers until they realize it may be easier to come to a deal with Apple than constantly living life on the edge. Zuckerberg probably feels by giving into Apple’s demands, he makes his business fragile over the long-run (think decades, not years) and would rather endure through the pain but maintain his company’s economics. But make no mistake; if Meta is ever forced to make a deal with Apple because Apple nukes the signal to keep destroying digital ad infrastructure, Meta will get a pretty bad deal from Apple. It’s not just ad infra that can be influenced by Apple’s whims, Apple has every incentive to [help](https://twitter.com/borrowed%5Fideas/status/1532480292103020559?ref=mbi-deepdives.com) Meta’s competitors (harder to negotiate against a monopoly than with a company in a fragmented industry). This is why 2022 was so scary for Meta and its shareholders; when Apple was coming after their ability to monetize users time spent effectively, TikTok was encroaching and threatening Meta’s ability to keep users engaged on their platforms. While the TikTok threat seems well addressed at this point by Meta ([data points](https://twitter.com/borrowed%5Fideas/status/1651407199485165568?ref=mbi-deepdives.com) about Reels popularity as well as political onslaught on TikTok for its ties with CCP makes this threat a bit tame albeit not fully neutralized), Apple’s shadow still looms large over Meta’s future. The fact that they are also the main competitors in AR/VR makes it even more likely that both companies have incentives to see the other stumble on protecting their cash gushing machines they both currently have. Meta’s ability to hurt Apple, however, is quite limited today; Apple’s is probably not. What can Meta do to reduce its dependency on North America? US GDP as % of global GDP (ex China) is \~30%. Therefore, Meta’s best bet is probably North America’s revenue contribution to decline to \~30-35% of overall revenue over time. There are two ways Meta can reduce dependency: a) the bad way: Apple can do it for them by continuously making it challenging to build effective ad infra. and b) the good way: Meta finds other opportunities to monetize its userbase by directly integrating the whole sales funnel within their properties. One of the reasons I think international growth hasn’t quite kept pace is Meta’s lack of significant monetization of WhatsApp. Back in 2014, Meta acquired WhatsApp for $4 Bn cash, 184 mn shares of Facebook (now Meta), and 46 mn RSUs which would imply \~$70 Bn acquisition cost for WhatsApp in today’s price i.e. \~10% of Meta’s Enterprise Value (EV) today. Meta disclosed in 3Q’22 [call](https://twitter.com/borrowed%5Fideas/status/1585444319955066880?ref=mbi-deepdives.com) that click-to-messaging revenue run rate was $1.5 Bn on WhatsApp (growing \~80% YoY) which makes WhatsApp’s contribution to be a measly \~1% of Meta’s overall revenue today. While bulls believe all sorts of ways Meta can monetize WhatsApp going forward and imagine Meta can turn WhatsApp into a “super app” in many important countries especially India and Brazil, Meta has rather been uncharacteristically slow in ratcheting up monetization. That, however, seems to be changing in recent years; WhatsApp business MAU [increased](https://techcrunch.com/2023/06/27/whatsapp-business-crosses-200m-maus-introduces-personlized-messages-feature/?ref=mbi-deepdives.com) from 50 mn in 2020 to 200 mn in 2023\. Meta may be on the verge of a massive monetization spigot in WhatsApp which will largely propel its revenue growth outside NA going forward. Analyzing WhatsApp itself would require a separate piece; thankfully, Invariant did a [good job](https://invariant.substack.com/p/whatsapp-metas-next-growth-engine) following management’s narrative around WhatsApp which paints an optimistic picture for WhatsApp’s future. My opinion on WhatsApp is largely going to be hinged upon their growth momentum; the problem is Meta may not consistently disclose it which will make it difficult to evaluate monetization progress. ### **From “Social Media” to “Media Social”** I don’t remember who coined this term on twitter, but it definitely left an impression on my mind that Meta’s business had gradually evolved from “Social Media” to “Media Social”. In the “Social Media” business, you primarily consume content/media from your **social** connections whereas in the current “Media Social” stage, it is the “Media” that’s the center and the Meta’s attitude towards the source of the “Media” is increasingly becoming agnostic i.e. all Meta cares about is to entertain you which may or may not come from your social connections. When WSJ published "[The Facebook Files](https://www.wsj.com/articles/the-facebook-files-11631713039?ref=mbi-deepdives.com)" a couple of years ago, this particular slide caught my attention. In many ways, Meta has been "lucky" in facing competition from Snapchat and TikTok. Let me explain. ![Image](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7963a764-3187-4501-b987-696d86c78e11_2470x1360.jpeg "Image") Source: [WSJ](https://www.wsj.com/articles/the-facebook-files-11631713039?ref=mbi-deepdives.com) Meta launched "News Feed" in 2006\. Despite vociferous criticism from the users at the time, Feed (as it's later renamed) has been the most scaled profitable "real estate" in social networking industry. Meta doesn't disclose this, but it is highly likely that content posted for Feed per user per month has been on a secular decline. As people become more self-aware of their digital presence, social broadcast is unlikely to experience a renaissance anytime soon, if ever. With diminishing content production over time, users would not have compelling rationale to come back to Meta's properties. Imagine if there were no "Stories", Young Adults (18-29 yr olds) would probably produce far less content. If there were no TikTok, there might not have been any "Reels" (maybe eventually though). Meta would have to clutter your feed in some other ways which could be far less compelling than Reels. Meta, ironically, needed moderate competition to refresh their social networking apps to help them transition from social broadcast to more social entertainment apps in which in 5 years people may mostly consume algorithmic content on Feed and discuss/share them on DM. Meta's competitors currently face the double whammy of monetizing their users and rising CAC in a post-ATT non-ZIRP world. While Meta is relatively better positioned in those dimensions, there are credible risks about the transition from social broadcast to entertainment. Feed currently has \~25% ad load and given how many ads you can watch while scrolling during a typical \~60-minute session per day, Meta may have to increase time spent materially to keep ad impressions growing. When “Stories” came to the scene and took time away from Feed, it wasn’t as concerning since Stories took very little time to navigate and allows Meta to throw you plenty of ads as you browse through your friends’ Stories. For Reels, this time is different. Meta indicated the structural challenges related to Reels in the 1Q’23 call: > There are **structural supply constraints** with the Reels format as people view a Reel for a longer time than a piece of Feed or Stories content, which results in **fewer opportunities to serve ads in between posts**. That will make it likely more challenging to close the monetization efficiency gap than it was with Stories. > > …we're working down the headwind to revenue from the growth of Reels cannibalizing some time that is spent on our more mature ad surfaces, Feed and Stories. And basically, we have been balancing the 2 factors here, which is the degree **to which Reels is driving incremental engagement on the platform versus the lower monetization efficiency of Reels relative to the Feed and Stories engagement that it cannibalizes.** And ultimately, the overall economics of Reels is really going to be determined by the combination of those 2 things. > > so while **we're on track to Reels becoming neutral to revenue by end of year or early next year**, I do think it's important to call out that Reels is structurally different from Feed and Stories. And so **we don't have line of sight of getting Reels to monetization parity per time with Feed or Stories anytime soon because of those structural differences.** It is very much possible such structural changes may mean the best days of profitability of "Feed" is behind us. To substantiate this point, let’s imagine a DAU spent \~50 minutes on Meta’s Family of Apps (FOA) properties in 2019\. The following table outlines why even with the assumption of increased time spent over time, the structural challenges may be a strong headwind for ad impression growth for Meta. Without growing ad loads, impressions, and limited room for DAU/MAU growth, revenue growth will be largely dependent on price per ad which relies on targeting and the quality of ad attribution. If you remember the bear case discussed above, Apple may make things difficult for Meta in their journey to enhancing ad targeting and attribution infrastructure. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdc1f7887-396c-4b3c-9a41-ebe335d8d0c3_1436x608.png) Source: MBI Deep Dives Meta doesn't seem to be oblivious to such glaring risks; monetizing messaging and perhaps shopping will need to pull the lever for Meta this decade both of which have been in the investor conversation for the last 4-5 years but never quite materialized to the extent investors hoped. Rihard Jarc [shared](https://twitter.com/RihardJarc/status/1671905328588308481?ref=mbi-deepdives.com) an insightful expert network interview which outlined why Meta’s shopping efforts failed so far and why that might still change. While I encourage to read the full [thread](https://twitter.com/RihardJarc/status/1671905328588308481?ref=mbi-deepdives.com), the following bit stood out to me: ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F57d14d9e-0678-4bfc-9853-574dc0f93b4e_2070x1144.png) Source: Rihard Jarc (Twitter) As a shareholder, when I look forward, I do think Meta will have to make shopping and messaging to work in a meaningful way for it to be **somewhat** immune from “Apple” risk (i.e. they may not be fully immune from Apple risk; just that they will be hurt less). Once you assess the level of pain Apple can inflict upon Meta, it probably starts making a bit more sense why Meta is so incredibly eager to control the next computing platform. Of the big tech companies, Meta is one of the easier companies to hypothesize bear cases today. 10 years ago, it was actually Apple which was easier to hypothesize bear cases e.g. it’s a hardware company and hardware companies don’t make the kind of margins Apple does; Apple will lose market share to Samsung, Google, Microsoft etc. I try to be a diligent student of big tech companies and the more I studied history, the more it reminded me to be mindful of how hard it is to predict the evolution of moats. As you can probably tell, I have decent amount of sympathies for bear cases about Meta. But Meta’s management’s overall historical track record as well as some potential wild cards still keeps me a shareholder today. Reality Labs which is still likely assigned a steep negative value today by investors is one such wild card, but the other wild card that I am starting to ponder a bit more on is AI chat bots. Snap recently shared their [early insights](https://newsroom.snap.com/en-GB/early-insights-on-my-ai?ref=mbi-deepdives.com) on their chat bot called “My AI”. The whole report is very, very interesting and can have potentially uncomfortable implications for Google Search (disclosure: long). Meta has three separate messaging properties (Messenger, WhatsApp, and IG DM) consisting \~4 Bn MAUs; all three will likely be [swarmed](https://twitter.com/alex193a/status/1665825192398995469?ref=mbi-deepdives.com) with AI chat bots by the end of this year. While we are way too early here (Meta hasn’t even launched anything), the landscape can shift quickly in digital advertising. If chat bots can truly take off on messaging properties, Meta, which generates less than half of Google Service’s revenues, may end up gaining material market share from Google. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fce75c4db-48fa-4b84-87c5-68feb2c74c03_1092x1186.png) Source: [Snap](http://m/en-GB/early-insights-on-my-ai?ref=mbi-deepdives.com) Even if the bear cases for Meta play out, it is unlikely to be a terminally ill business and hence, the question of valuation is quite relevant. Meta currently trades at \~17x NTM EV/EBIT, but if you assign RL a valuation of zero (debatable; can be negative), it trades at \~12-13x NTM EV/EBIT, a significant discount from both S&P 500 Index (\~18x) and Nasdaq 100 (\~23x) most of whose constituents add back SBC (so actual discount is likely even higher). As I have mentioned before, I always consider valuation in terms of questions. The response to those questions is not binary but a probability weighted answers. Most people consider probability to be an inherently quantitative concept with objective and precise answer, but for fundamental active investors (especially with concentrated portfolio) dealing with probability is primarily qualitative in nature. Because it is probability, I do and will routinely assess and update it as fundamentals and valuations change. Thank you for reading. For more detailed valuation work on Meta, read this [post](https://www.mbi-deepdives.com/meta2023/). [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ***Disclaimer:*** *All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Brown & Brown: A Decentralized, Boring, Money Making Machine URL: https://www.mbi-deepdives.com/bro/ Last updated: 2023-06-23T12:48:04.000Z **Disclosure: I am long shares of Brown & Brown** You can listen to the Deep Dive [**here**](https://www.mbi-deepdives.com/audio/) [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) --- When I first read Leonard Read’s seminal [essay](https://fee.org/resources/i-pencil/?ref=mbi-deepdives.com): “I, Pencil”, it became abundantly clear to me that people who solve the co-ordination problem in capitalism are of paramount importance (if you haven’t or don’t have the time to read the essay, watch this [Reels](https://www.instagram.com/p/Cqn7RLiAM7S/?ref=mbi-deepdives.com)). At first glance, intermediaries that generate attractive economics for themselves by connecting seemingly unrelated vast network of people may be deemed leeches of capitalism, yet without the intermediaries, any innovation runs the risk of being siloed to just a few. It is perhaps, therefore, no wonder that intermediaries that prove their worth to the broader value chain often end up more insulated from the direct risks and enjoy a more stable and durable economics among all the players in such value chains. Brown & Brown is one such intermediary. Brown & Brown (Ticker: BRO) is an insurance broker that sells insurance on behalf of the carriers to mostly small/medium sized businesses and gets paid largely a fixed **percentage** of commissions on the insurance premium without taking the insurance underwriting risk. Insurance brokers provide a valuable service to both parties: SMBs get access to brokers’ full breadth of relationships with multiple carriers as well as better price discovery, convenience, and expertise provided by the brokers. Hyatt Brown, current chair of the board and penultimate CEO of BRO, once [mentioned](https://www.youtube.com/watch?v=luinAI0NSq8&ref=mbi-deepdives.com), *“..insurance policies are really the most complex contract that you'll ever enter into without the help of a lawyer*”. Carriers, on the other hand, can leverage brokers’ presence **in the ground and direct relationships** with businesses to scale their insurance products much more efficiently. Insurance broking has a long history in the US. Marsh McLennan, the market leader in this industry, was founded in 1905\. Brown & Brown’s founding root was initiated in 1939\. Jay Adrian Brown, who is the father of Hyatt Brown, and his first cousin CC Owen founded an equal partnership in Daytona Beach, Florida. Brown & Owen eventually became today’s Brown & Brown when Jay Brown’s elder son joined the company. After graduating from University of Florida, Hyatt Brown (the younger son), also joined the company in 1959\. But just two years later, Hyatt ended up being the CEO of the company. Why? Hyatt’s brother was appointed the chairman of the Florida Industrial Commission and hence had to resign from BRO. At the same time, Jay Brown decided to retire at the age of 65. In 1961, BRO generated \~$61k in revenue and $1.4k in profit. Jay wanted to sell the company to Hyatt for $75k. The only problem was Hyatt, having only graduated college two years ago, didn’t have the money to buy. So, Jay made a deal with his younger son: Hyatt would have to pay 10% interest of $75k for both Jay and his wife’s (Hyatt’s mother) lifetime. By 1972, Brown’s revenue increased to $345k, so Hyatt was clearly doing more than a decent job as CEO. But someone apparently dared him to run for the legislature and he accepted the challenge in 30 seconds. He won by a whisker and sat in the Florida House of Representatives, as a Democrat, from 1972 to 1980\. Hyatt then returned to Brown and outlined a plan to double the revenue organically by 1982 from $2 mn to $4 mn. Brown was indeed able to meet the revenue target, but operating margin declined from 16% to 8% leading to pretty much similar profit despite doubling the revenue. For the first time, Hyatt decided to call a consultant to take a look at his operating plan. The consultant carefully looked at Brown’s business and spoke with them three days later. He suggested while they were all great salespeople, they are not good at making money. Hyatt wasn’t offended because the consultant was right. Then the consultant indicated if they followed his recommendations, Brown’s organic growth would likely slow but their operating margin would reach 25%. Why did Hyatt take the consultant seriously? Because the consultant himself ran an insurance agency in upstate Michigan and showed Hyatt his numbers to substantiate how the numbers worked in his firm. Hyatt took the consultant’s suggestions at heart and added a couple of twists on his own to outline Brown’s operating principles. From that initial experience, Hyatt codified the significance of making money as one of the four operating principles of Brown & Brown: “**we are in the money-making business**.” In 1983, Hyatt created a new 7-year operating plan for BRO. Under the base case scenario, he expected BRO to reach $19 Mn revenue in 1990 (\~21.5% CAGR from 1982) and $29.5 Mn revenue (\~28% CAGR) in optimistic scenario; under either scenario, Hyatt wanted to ensure attractive margin for the company. In 1990, BRO posted $25.5 Mn revenue (\~26% CAGR) with 24.5% operating margin. Even though BRO wasn’t a public company back then, they operated almost like a public company. In the early 90s, BRO had \~25 shareholders, almost all of whom were employees at BRO. They even had an annual shareholder meeting. If anyone wanted to sell (a rare event), the company would just buyback the shares from the employee using a pre-specified formula(I don’t know what exactly the formula was). However, one of the shareholders suffered a heart attack and while he survived, he wanted to sell his stocks. The employee was given $700k worth of shares in 1980 which increased in value to $9.3 Mn in 1993\. Hyatt decided to buyback the shares himself; but he also realized it is clearly not a sustainable solution. When the company buys back shares from the employees, it constrains the company’s ability to deploy capital to grow their business. As Hyatt was contemplating how to deal with this challenge, a solution arrived from an unexpected source. One of Hyatt’s friends at Poe & Associates, a publicly listed company, approached him and suggested a merger between Poe and Brown. Poe had $52 Mn revenue in 1993 (vs Brown’s $33 Mn). Following the merger, Poe & Associates became “Poe & Brown”. However, just a year later, Bill Poe (the founder of “Poe & Associates”) left the company; while Hyatt and Poe had respect for each other, they differed in leadership style, as evidenced by Poe’s remarks: *“He's a decentralist and I'm a centralist. He's more of a bottom-line-oriented manager, I'm more of a people-oriented manager. He's the hardest worker you've ever seen. He works about 70 hours a week.”* Poe was happy to let Hyatt run the company and by 1999, the shareholders voted “Poe & Brown” to become “Brown & Brown”. At the time of the merger in 1993, “Poe & Brown” had \~$55 Mn market cap. Today, “Brown & Brown” has \~$18 Bn market cap. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3b3f935e-e617-46c9-9e3e-7e632f44f41d_2400x1240.png) Source: KoyFin (MBI Deep Dives readers get 10% discount; just click [**here**](https://koyfin.com/?via=abdullah&ref=mbi-deepdives.com)) Here’s the outline for the rest of this Deep Dive: **Section 1 Operating Segments**: I explained the basic business model of Brown & Brown and then delved into the economics of four operating segments: Retail, National Programs, Wholesale, and Services. **Section 2 TAM, Competition, and Acquisition Strategy**: I discussed Brown & Brown’s Total Addressable Market, competitive dynamics, their acquisition history as well as their acquisition strategy in this section. **Section 3 Capital Allocation and Management Incentives**: Management’s capital allocation history over the last two decades and their current incentive structure is highlighted here. **Section 4 Valuation/Model Assumptions**: Model/implied expectations are analyzed here. **Section 5 Final Words**: Concluding remarks on Brown & Brown, and disclosure of my overall portfolio. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) _This post is for paying subscribers only._ ### Meta's Moat URL: https://www.mbi-deepdives.com/metamoat/ Last updated: 2023-06-19T12:37:12.000Z **It's not "network effects".** *Disclosure: I own Meta’s shares and January 2025 $50 Call Options* [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) During the weekend, I [asked](https://twitter.com/borrowed%5Fideas/status/1670192933092433920?ref=mbi-deepdives.com) my twitter followers: What do you think Facebook's DAU to MAU ratio was in 2009? The number that got the highest votes was \~80% even though I mentioned the most recent number was \~68%. I asked this question a couple of friends in real life too and they too picked the \~80% number. I asked why they picked this number. Their explanation was, “well, Facebook used to be really cool back then. Everyone was using it and we were certainly oversharing stuffs that would make us cringe today. If DAU to MAU is \~68% now, it must have been higher back then.” I suspect most of my twitter followers had similar rationale in mind. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd32897b3-fb21-4c97-8351-1aff031d820b_822x598.png) The actual number was 48%. While the DAU/MAU has hardly changed since 2016, the ratio rapidly increased from 48% in 2009 to 66% in 2016\. As a result, while Facebook’s MAU increased by 10.7x in 2022 vs 2009, its DAU became 15x during the same time. What did most of the respondents to the poll miss? Smartphone penetration. Global smartphone shipments [increased](https://www.statista.com/statistics/271491/worldwide-shipments-of-smartphones-since-2009/?ref=mbi-deepdives.com) from 173.5 Mn in 2009 to 1.2 Bn in 2022\. Ironically, while transition to mobile was widely believed to potentially “kill” Facebook, Facebook turned out to be perhaps one of the largest beneficiaries (along with Apple and Google of course). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed2291c8-d2a6-450f-978c-f57470573d81_942x556.png) Source: Company Filings, MBI Deep Dives As I was discussing some of these aspects with a couple of friends, they mentioned why the expansion of DAU/MAU ratio isn’t surprising since thanks to network effects, the product is supposed to become stickier. That makes intuitive sense, but did it also happen in some of the other social apps such as Snap, Twitter, Reddit or Pinterest? Unfortunately, we don’t know the answer since Snap and Twitter don’t disclose MAU, and Pinterest doesn’t report DAU. One of the things I think many people often misunderstand about network effects is people tend to think it only needs to be solved **once** as if once you get the flywheel going, you could just sit back and let the network grow on its own. While many investors can be susceptible to thinking in such simplistic terms, that’s not how it works in reality. Here’s a [quote](https://marker.medium.com/the-untold-history-of-facebooks-most-controversial-growth-tool-2ea3bfeaaa66?ref=mbi-deepdives.com) from Zuckerberg reminiscing Facebook’s challenges in growing their network in “early” years: > “Growth had plateaued around 90 million people, I remember people saying it’s not clear if it was ever going to get past a 100 million at that time. We basically hit a wall and we needed to focus on that.” (**MBI note**: Myspace at its peak had 75.9 Mn users) The reality is that network effects need to cross several chasms to keep growing and those chasms don’t get solved on their own; people running the business need to take ingenious approaches to tackle the growth challenges. What complicates this even further is there are competing networks (i.e. other social apps) which makes **speed** a paramount importance, but at the same time, your tech infrastructure needs to be ready to facilitate such speed and retain the userbase after acquiring them. In other words, the moat derived from network effects can be short lived and what needs to follow after the initial spark is just relentless and near flawless execution by the people running the business. But what does “execution” exactly mean? And of course, growing users is not much of a promised land either; you need to know how to monetize your users and you need to learn how to do it profitably. Having interacted with many investors following social networking industry over the last few years, I suspect most investors deeply underestimate how much everyone else in this industry fell behind on the “execution” department. Instead of making qualitative statements, I intend to show this point quantitatively to contextualize what I am talking about. One of the things that made me dig deep into this is I myself started wondering why Meta posted 34% operating margin in 2009 but today’s crop of social networking companies are either unprofitable or barely profitable even at similar scale. Daniel Ek, Spotify’s CEO, [indicated](https://twitter.com/borrowed%5Fideas/status/1659655525762473995?ref=mbi-deepdives.com) in a recent podcast the sea change in unit economics that likely occurred since Facebook’s IPO: ![Image](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb040c972-9722-4aed-bf53-ea1a29be39d4_1290x434.png "Image") Source: Acquired [Podcast](https://open.spotify.com/episode/671O5v5twrIfApPuyBdJTv?si=3e1bba54ce144729&nd=1&ref=mbi-deepdives.com) Here’s what I am going to do: I will show the unit economics of Meta (then Facebook; they also had just “Facebook” website/app back then) in 2009-11 and then show the unit economics of Snap, Pinterest, and Twitter in 2019-2021 period. Snap, Pinterest, and Twitter **somewhat** resemble Meta in terms of userbase 10 years apart and this exercise is indeed quite revealing to gauge what exactly is Meta’s moats against today’s **and** future set of competitors in their social networking business. I will briefly comment on TikTok later. **Meta vs Snap** Seven years after Facebook, Snap was founded in 2011\. In 2019, Snap had 155% of what Facebook’s DAU was in 2009\. But by 2021, Snap came down to 69% of Facebook’s DAU in 2011. Interestingly, while Snap’s Cost of revenue was \~3x higher than Meta’s 10 years ago, their Gross Profit (GP) is largely similar, thanks to much better monetization of the users by Snap and Facebook’s relatively primitive monetization/ad infrastructure back then by today’s standard. It’s the below the gross profit line where things start to diverge dramatically. **On a per DAU basis**, Snap’s R&D was \~6-8x, S&M \~2.5-3x, and G&A \~3-5x of what Meta had 10 years ago at similar scale! Snap had almost double the employees Meta had and while neither company doesn’t exactly disclose salary related expenses, Opex (R&D+S&M+G&A) per average employee for Snap was $644k in 2021 vs Meta’s $493k in 2011. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd9124db6-fa1e-4484-bb1e-b1bff03d7e2f_1734x600.png) \*Avg. MAU or DAU is calculated based on average of the four quarterly reported user date; Source: Company Filings, MBI Deep Dives One of the things that always somewhat surprised me about Snap is how so many people seem to rave about Evan Spiegel’s [leaked](https://wikileaks.org/sony/emails/emailid/139920?ref=mbi-deepdives.com) email **from 2014**. When I first read it a couple of years ago, I was happy to ignore his macro prognostics but was alarmed to see his deeply flawed understanding of the elephant i.e. Facebook in the industry he is building his own business (see the below excerpt). Some of my friends tell me I’m being harsh on Spiegel since he was just 23 at that time. Unfortunately, capitalism doesn’t have any grace period related to age; in any case, when Google came after with all its might after Facebook by launching Google+ in 2011, Zuckerberg was just 27 years old. I know ZIRP world made it easy to forget, but capitalism tends to be sink or swim if you don’t know what you are doing. Ironically, even though Spiegel had a lot to say about Fed and interest rates, his company may be one of the largest beneficiaries of such environment as Snap IPO-ed in March 2017 at $24 Bn valuation despite the fact that they reported **negative 11.7%** gross margin and **negative 128.7% operating margin** (no typo here) in 2016. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5dadee6f-8f9a-402b-824d-031e59a84777_2194x688.png) Source: Excerpt from Evan Spiegel’s [leaked](https://wikileaks.org/sony/emails/emailid/139920?ref=mbi-deepdives.com) email **Meta vs Pinterest** Pinterest was founded in 2008\. Similar to Snap, Pinterest had slightly better GP than Meta at similar scale although their path to slightly better GP per MAU was different to Snap’s. Both their ARPU and Cost of revenue was more or less similar to what Meta had 10 years ago. People likely spend less time on Pinterest than they used to on Facebook back then, so perhaps that’s why they have similar ARPU even with Facebook’s basic ad infrastructure during 2009-2011 period. Since Pinterest IPO-ed in 2019, I am going to ignore 2019 vs 2009 comparison. **On a per MAU basis**, Pinterest’s R&D was \~3-5x, S&M \~2.5-3x, and G&A \~2-3.5x of what Meta had 10 years ago at similar scale! What’s interesting is Pinterest actually had similar number of employees to what Meta had back then. So why exactly Pinterest’s cost structure was still so wildly different? There may be multiple factors at play. But average salary per employee almost certainly increased over time which may be the primary reason for such divergence. Again, as I have [discussed](https://www.mbi-deepdives.com/sbc/) before, there are only a handful of companies in Silicon Valley which **built** de-facto monopoly, and the primary “raw materials” to sustain their monopoly (using the word very loosely; FTC lawyers are encouraged to ignore my phrasings) is their pool of human capital. As you can expect, with their “monopoly” profits, they basically call shot in pricing the labor market and you have to play along even if you don’t have monopoly yourself. To say it differently, everyone in Tech at Silicon Valley or Seattle are rich thanks to a handful of monopolies regardless of whether they work at any of the monopolies themselves. If there were no monopolies in Silicon Valley/Seattle, labor market would likely go through a sea change to reflect the reality of the economics of many of the younger tech companies that were started close to GFC era. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F087c75a3-b654-4432-b7cf-7ac1e3ea8ac0_1734x586.png) Source: Company Filings, MBI Deep Dives **Meta vs Twitter** While Facebook was started in 2004, it became open to general public in 2006, the same year Twitter was founded. Therefore, these two companies are bit of a contemporary but their fates have diverged materially. Mark Zuckerberg had an apt quote about Twitter that gets repeated every now and then: > “Twitter is such as mess — it’s as if they drove a clown car into a gold mine and fell in.” Despite being a contemporary, Twitter’s mDAU or “monetizable DAU” in 2019 (Twitter stopped disclosing DAU or MAU and used to report mDAU) was similar to what Facebook had in 2009\. By 2021, Twitter’s mDAU was less than half of Facebook’s in 2011. Twitter’s ARPU in 2019-21 was actually \~3-4x of Facebook’s 10 years ago which led to GP per DAU to be \~2.5-4x of Facebook’s. Anything below GP line, again, is just a sorry state of affairs. **On a per DAU basis**, Twitter’s R&D was \~7-9x, S&M \~6-7x, and G&A \~4-7x of what Meta had 10 years ago at somewhat similar scale (perhaps these numbers are slightly overstated since I’m using mDAU as DAU for Twitter)! It is perhaps no surprise that Elon Musk got tempted to buy and right size the cost structure a bit, but it seems he had done that at the expense of ARPU so far, so the jury is still out there whether Twitter is in a better position pre or post acquisition by Musk. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1eb724ba-9a87-4989-a3c5-5c63c795a3e0_1726x588.png) \*Ignored Twitter’s litigation expense for 2021 Opex and EBIT calculation; Source: Company Filings, MBI Deep Dives The two other companies that are mostly talked about in social networking industry but yet to be public are Reddit and of course, TikTok (which isn’t necessarily “social”, but they are certainly Meta’s perhaps most potent competition). Reddit, another contemporary of Meta as it was founded in **2005**, likely generated [$350 Mn](https://www.theinformation.com/articles/reddits-ad-revenue-expected-to-double-to-at-least-350-million-this-year?ref=mbi-deepdives.com) revenue in 2021 which is basically **a day’s revenue of Meta today**. TikTok, on the other hand, [generated](https://fortune.com/2023/04/06/tiktok-ban-china-bytedance-increased-ad-spending/?ref=mbi-deepdives.com) $10 Bn revenue in 2022 which would put them closest to Meta in terms of revenue. If WSJ’s [reporting](https://www.wsj.com/articles/tiktok-parent-bytedance-sees-losses-swell-in-push-for-growth-11665071238?ref=mbi-deepdives.com) is true that ByteDance, the owner of TikTok, posted $7.15 Bn losses in 2021, TikTok almost certainly had a pretty deep loss in 2021\. Anecdotally speaking, TikTok pays a hefty premium to lure talent away from Big Tech; but even such premium wasn’t enough to convince a couple of my acquaintances to join TikTok since they were not confident that TikTok would be around in the US in 2-3 years. Therefore, while I don’t expect TikTok’s cost structure to resemble some of the public companies discussed here, it is still likely nowhere close to Meta’s unit economics. What is my broad takeaway from digging into these numbers? While it is tempting to blame management of one company or another (and they likely do deserve some criticisms), we probably should take a step back and wonder why is every single Meta’s competitors being managed so poorly? Why were they all spending money like drunken sailors without any clear sight to compelling economics in the near future? I’m sure low interest rates and investors willingness to look past losses for the elusive economics at scale played their parts too, but I think the primary reason is the business of social media likely changed forever. On the surface level, social networking seems like an amazing business: once you manage to create network effects, the nodes of the network grow and the network just feeds on itself. Since your users generate all the content at zero marginal cost, and your job is basically to just aggregate demand to sell the network’s attention to advertisers vs paying for content in the legacy media business, this sounds like a great business model . Operating margin for such business was understandably expected to be at least \~30% and potentially much more (Meta’s highest ever operating margin was 52.3% in 2010 which likely contributed to such inflated expectations). Why isn’t that happening at all? I already wrote about “raw material” inflation which may not reverse anytime soon. AI may help lowering the demand for software engineers in the medium to long-term, but even then as long as Big Tech have the profits, it shouldn’t be hard for them to maintain a persistent and increasing “inflation” for the talented pool of human capital. Attracting talent at reasonable cost itself would be a difficult challenge, but as societies around the world demand more data privacy, security, and moderations in these companies, it is creating the **unintended benefit** to the incumbent such as Meta (think ATT, GDPR etc.). **So, today these two things: a) ability to attract and price talent, and b) regulatory capture are the primary moats of Meta.** But even beyond that, perhaps yet another challenge for these companies is their competitor is Meta. Tech may seem inherently a [red queen’s race](https://en.wikipedia.org/wiki/Red%5FQueen%27s%5Frace?ref=mbi-deepdives.com), but it may become more difficult to keep pace for everyone else if the company that is way ahead is also run by an operator who is so paranoid about durability of his business that he ends up running faster than everyone else. I would even argue most moats in tech are essentially “execution” moats (maybe also why Buffett mostly avoids them), and except maybe one or two, none of the tech companies can just sell products/services without perennially participating in the red queen’s race. When Michael Nathanson asked the below question to Mark Zuckerberg in 2Q’22 earnings call, admittedly I found Zuckerberg’s response a bit lacking and thought the real answer that he’s not saying is Meta’s moats are evaporating. Not sure if it’s just the stock price that’s influencing me here, but I do think he indeed pointed out the moat Meta likely capitalized the most in its history (the network effects moat expired some time ago) and it’s their **execution**: > **Michael Nathanson** > > Mark, I think going to one of the earlier questions about your advantages at Facebook. The previous moat, we would argue, was just the social graph of billions of people, families and friends. Do you think what you're building now with AI and from digital, how all those content is even a better moat, is a better business than the one you had before, which was a pretty high barrier to entry, just given the social effects of the network you built? (**MBI**: *if it were so high barrier to entry, how could Snap, Pinterest, TikTok enter and gain massive userbase over time? It was always a very weak moat, yet there is almost no profit left for others to eat)* > > **Mark Zuckerberg** > > In terms of building sustainable competitive advantages, in terms of the social graph, right, which you cited from before, people have been able to get that from phones for more than a decade now, right? S**o I don't really think that's been the thing for us. I think it's we're a serious technology company. We invest a lot in building infrastructure. And culturally, we focus on moving and learning faster than everyone else. And I think that those are sustainable advantages.** > > And so certainly, I think that the AI technology infrastructure that we're building, **I think it can compound and be better than others in the industry and that will be an advantage and make the product better over time**. But I think at the end of the day, what that really comes down to is just **I try to push the company to be one that learns faster and just keeps iterating and moving faster than we did in the past and than others in the industry do**. And I think if we can do that well, then we'll continue to succeed. > > **But I think the moment that we stop doing that, then we'll basically fall behind**. It's a very competitive field and we need to keep on pushing ahead. But I think the reason why we have succeeded and seen so good results with Facebook, Instagram and the other social apps is **because we basically focus pretty relentlessly on just pushing to constantly improve them.** Just as it didn’t satisfy me back then, Zuckerberg’s response may not feel convincing to many because it feels a bit fluffy and not the usual kind of moat we talk about. But if you look at Meta’s financials since 2009 and study carefully not only against other social companies but also other big tech, it is hard not to infer their execution has been almost unparalleled. Here’s what I [wrote](https://www.mbi-deepdives.com/meta2023/) back in March this year: “While investors have perennially wondered about the ghost of Myspace, FOA (Family of Apps) continued to reach new and unforeseen heights in the social media industry. A **19-year old** Mark Zuckerberg co-founded Facebook (currently Meta) in 2004 and it is hard not to be awestruck by what he did in less than couple of decades. Let me contextualize Zuckerberg's height of success with Facebook (now Meta). 17 years after being founded, Meta reached $95 Bn Gross Profit in 2021\. To reach similar Gross Profit, Alphabet, Amazon, Apple, and Microsoft took 22, 24, 42, and 45 years respectively. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F201d8296-2c54-4819-a2e0-c2872e7e90df_1056x207.png) Source: Company Filings, MBI Deep Dives How about **GAAP** Operating Profit? Meta posted $45 Bn operating profit (**including** \~$10 Bn losses in Reality Labs) in 2021\. To reach similar operating profit, Alphabet, Apple, and Microsoft took 22, 36, and 44 years respectively. To post such operating profit, only Microsoft required lower invested capital than Meta, so Meta was able to reach such profitability with incredible margins **and** ROIC. Perhaps the AGI will beat Zuckerberg's record by reaching $45 Bn operating profit faster. While Meta may have gotten a bit derailed in 2022, it would be unfair to not acknowledge, appreciate, and applaud what Zuckerberg and his team did in 2004-2021.” ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F56cbe1c5-c86b-4759-88f5-8d6372a717e1_1799x209.png) \*Invested Capital is calculated as total assets-cash and short-term investments-current liabilities; Source: Company Filings, MBI Deep Dives If “execution” is the moat, what is the driver of such moats? It’s the people who have to execute the plan, and it’s the CEO who need to design the plan and recruit the right people to get the job done. Of course, investing is inherently about the future, and what Meta did in 2004-2021 can prove to be just relics of history. But it is historical track record that is the bedrock to form our qualitative insights for the long-term. I do admit Meta’s capital allocation in 2021-22 period was far from anything to be proud of (and Zuckerberg would probably not disagree either); I do want to point out that for most tech companies, capital allocation doesn’t start based on what FCF you generate, so I’m not just alluding to Meta’s ill-timed buybacks in 2021\. It essentially starts at Gross Profit level; how much a tech company spends in R&D, S&M, and G&A are deeply integral to capital allocation framework. From this perspective, Zuckerberg made some very suboptimal decisions and committed mistakes by over hiring which he later needed to correct through a massive and painful series of layoffs. Overhiring is a mistake committed by many CEOs in 2021-22 period, but that’s not a defense for Meta or Zuckerberg because if “execution” is the primary moat, you better be switched on when everyone else is off. At the end of the day, the real question about Meta’s “execution” moat is which “Zuckerberg” reflects closer to reality today going forward: is it 2004-21 Zuckerberg? or is 2021-22 one? Investors generally dislike personality based moats as they can be excruciatingly hard to assess/evaluate/track, and these personalities often tend to test your patience, but if you look at tech history and perhaps the most outlier successes, such personality driven (and not necessarily business driven moats), are not a rarity. Even before the 2022-23 drama, I have always thought the Facebook or Meta’s long-term bull case revolves around Zuckerberg (see my [2020 thread](https://twitter.com/borrowed%5Fideas/status/1253165456850145281?ref=mbi-deepdives.com)). Imagine if someone told you in 2015 that by 2023, the most popular social formats would be “Stories” and “Short form videos”, both of which would be invented (and hence enjoy a material leg up) by Meta’s two closest competitors at the time, you would probably be worried whether Meta would remain relevant in just 8 years. When Meta was transitioning hard to mobile, operating margin fell from 47.3% in 2011 to 10.6% in 2012\. Transitioning to Stories and Reels also required Meta to be willing to give up valuable surfaces or time spent on their apps to materially lower monetized features which create near-term earnings pressure. This is the kind of decisions which are not quite hard to take by a founder but can be quite tricky to pursue for hired management. The social media of 2030-35 would probably be very, very different than what it is today. Any careful analysis of today’s business fundamentals may become obsolete and hence, your ability to be long-term owner of this business may be largely dependent on your opinion about the quality of management as the people running the business would likely need to go through a couple of format/feature shifts every decade or so (maybe OS too every 2-3 decades). Considering almost constant negative press coverage and the difficulty of forecasting/underwriting social media businesses for 5-10 years, the stock may persistently trade at below market multiples. Despite +134% YTD, the stock still trades at \~17x NTM EV/EBIT which is \~8-10 turn lower compared to Nasdaq 100 (when you include SBC in EBIT calculation) and \~3-4 turn lower than S&P 500 (again, similar SBC adjustments to QQQ). To the extent my opinion about Meta’s management is correct is what will likely drive shareholders’ long-term return. **P.S.** I know any piece on Meta’s moat may seem incomplete without any discussion on Metaverse or AR/VR segment. It is simply too early to form any rigid opinion there, but for curious readers, I encourage you to read this Benedict Evans piece which I thought was perhaps the most thoughtful [piece](https://www.ben-evans.com/benedictevans/2023/6/15/vision-pro?ref=mbi-deepdives.com) on Vision Pro/Quest post WWDC. You can also read a bit more detailed analysis on Meta [**here**](https://www.mbi-deepdives.com/meta2023/)(March, 2023) [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ***Disclaimer:*** *All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Adobe 2Q'23 Update URL: https://www.mbi-deepdives.com/adbe2q23/ Last updated: 2023-11-26T13:44:25.000Z *Disclosure: I am long shares of Adobe* First things first, while as of this writing I do own Adobe shares, I have gradually trimmed half of my Adobe holdings for the last couple of weeks. By the time I will publish my Deep Dive next week, it is likely that I may not own any Adobe shares. Before I explain my thought process on selling Adobe, let me quickly recap Adobe's latest earnings. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Revenue** Digital Media net new ARR $470 Mn ($50 mn higher than guide) Digital Media segment revenue $3.5 Bn (guide $3.45-3.47 Bn) Digital Experience $1.22 Bn (guide $1.21-1.23 Bn) Total revenue $4.8 Bn (guide $4.75-4.78 Bn) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image.png) Source: MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image-5.png) Source: MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Gross Margin** Digital Media gross margin remains steady at \~96%, but Digital Experience posted its highest ever gross margin of 67.3% in 2Q'23\. Overall gross margin expanded by 41 bps YoY to 88.1% last quarter. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image-1.png) Source: MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Cost Structure** 2Q'23 GAAP operating margin was 33.7%, down from 34.9% in 2Q'22\. SBC as % of revenue increased from 8.0% in 2Q'22 to 9.0% in 2Q'23\. Working capital benefits and SBC make FCF look better than it is. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image-2.png) Source: MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capital Allocation** Buyback pace has slowed down as Adobe only utilized 50% of their FCF to buyback shares which is noticeably lower compared to the last three quarters. Share count went down by only 0.2% QoQ. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image-3.png) Source: MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Guidance** Digital Media net new ARR increased from $1.7 Bn to $1.75 Bn in 2023. GAAP EPS range was increased from $10.85-11.15 to $11.15-11.25 for 2023. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image-4.png) Source: Adobe Press Release **Why I am selling Adobe** When I published my Deep Dive on Adobe in [August 2022](https://www.mbi-deepdives.com/adbe/), I was cautious about their ongoing and likely intensifying competitive onslaught from Figma and Canva. Moreover, I mentioned I was reluctant to pay premium multiples for Adobe vs Microsoft which is likely the greatest software business in the world today. While Adobe may offer potentially higher growth, I considered the moat and durability unlikely to be as strong as Microsoft to deserve the premium multiples. Adobe was trading at $450/share or in the low 20s NTM EV/EBIT multiple (not really, but more on this later). For context, 10-year treasury was trading at \~2.8% yield back then. Just one month after publishing my Deep Dive, Adobe announced to acquire Figma for an eye-popping valuation which the market absolutely hated and the stock tanked by 40%. While I [agreed](https://www.mbi-deepdives.com/net/) the price paid for Figma is likely to be excessive, I inferred such acquisition would lead to more pricing power for Adobe over time and given the massive drawdown, the valuation concerns were more than taken care of. To me it seemed market almost forgot Adobe still remains an excellent business and despite the valuation concerns, the fact remains they were about to take a likely potent long-term competitor under their own umbrella. Moreover, the more Adobe stock was falling, the lower Figma's price tag would be since the deal was priced as $10 Bn cash+26.5 mn shares+6 mn RSUs. With Adobe stock at $275, Figma would cost \~$19 Bn to Adobe. The odds seemed good to me . I [changed](https://www.mbi-deepdives.com/net/) my mind and took a decent sized position in the stock at an average cost of $315/share. Okay, so where do we stand today? For starters, 10-year is now yielding 3.7%. But that's hardly my only concern. Let's take a look at Microsoft and Adobe valuation multiples again. At first glance it may seem they are both trading at similar multiples, but there is important nuance that's missed in this graph. Not sure how many investors miss this nuance, but I certainly did. While reporting consensus EBIT estimates, analysts consider SBC in their EBIT calculation for Microsoft whereas for Adobe, consensus EBIT adds back SBC. Basically, Microsoft's number is GAAP whereas Adobe's number is non-GAAP which make this whole graph an apple-to-oranges comparison (unfortunately, **EVERY** data provider does this; I'm not taking a dig at KoyFin. You may wonder why such discrepancy exists. For some reason, big tech's NTM EBIT estimates includes SBC impact but except for those handful of tech companies, everyone else just ignores SBC from NTM EBIT estimates. I guess SBC only matters for Big Tech shareholders). Analysts estimate Adobe's NTM EBIT (non-GAAP) to be $8.7 Bn. Adobe's 1H'23 SBC was $849 Mn (vs $674 Mn in 1H'22). Let's double the 1H'23 number to assume NTM SBC to be $1.7 Bn which would make NTM GAAP EBIT to be $7 Bn. After making the numbers more apple-to-apple, Adobe actually currently trades at \~31x NTM EV/EBIT multiple (ignoring AH rally), almost \~5 turn higher than Microsoft. Even if these estimates are revised upwards a little, the multiple isn't going to change much. How about the rest of the concerns? ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image-6.png) With Adobe stock at $510/share, Figma deal is currently priced at \~$26 Bn. If you thought $20 Bn was expensive, well the price may end up being a lot higher thanks to +50% rally over the last month. Perhaps more importantly, will the deal even close? If Microsoft and Activision saga are any indication, the regulatory bodies across the Atlantic will likely fight tooth and nail to stop this deal and I would argue the probability that the deal will go through is likely declining. Adobe bulls think failure to consummate this deal may even be a boon for Adobe as they will be stopped from paying ZIRP like valuation multiples amidst a mid single digit Fed rate world. I am not sure I see it this way. So far, Figma raised $333 Mn over seven funding rounds. If the deal with Adobe doesn't go through, Figma will receive $1 Bn breakup fee. So, effectively Adobe shareholders will pay \~3x money that Figma raised over its entire life to receive exactly 0% ownership of Figma. To put it differently, Adobe shareholders will be giving $1 Bn charity to Figma to potentially compete much more directly with Adobe in the medium term. So, in a sense, today, when I am looking at Adobe, all my concerns deteriorated over time. 10-year yield increased by \~100 bps, stock trades at more expensive multiple, and the competitive concerns from Canva and Figma are very much alive. But what about AI? Isn't this potentially a big boon for Adobe? I suspect if I am wrong about selling Adobe today (I will consider my decision to be wrong if Adobe generates >10% IRR over the next 5-7 year period), it may be because I am underestimating AI's potential for strengthening Adobe's moat. Adobe seems to be taking a bit [conservative approach](https://twitter.com/borrowed%5Fideas/status/1646509218722205696?ref=mbi-deepdives.com) in mitigating some of the legal concerns that may end up affecting much of the leading AI companies today, and they also have a much clearer path to [monetize](https://twitter.com/jiggycapital/status/1661814238589976580?ref=mbi-deepdives.com) their AI capabilities. I mostly agree with bulls that Adobe will likely beat earnings estimates in the next few quarters and I am certainly not expecting any quick doom for Adobe. But I am not as confident as Mr. Market that AI is an unalloyed positive for Adobe in the long-term. Take the below graph for example which Adobe shared on their last Analyst Day. Majority of their net new ARR in Creative Cloud comes from single apps, a likely material percentage of which I assume come from consumer+ SMB segment. If AI is truly as revolutionary tech as the consensus thinks it to be, I wonder if Google and Meta will make Adobe's life difficult in the consumer+ SMB segment. Given the pace at which Meta, Google, or Microsoft (thanks to OpenAI) are moving in AI, it would not shock me if they completely end up disrupting consumer creativity software industry. Kevin Kwok wrote in his piece tiled "[How to Eat an Elephant, One Atomic Concept at a Time](https://kwokchain.com/2021/02/05/atomic-concepts/?ref=mbi-deepdives.com)": > Even more striking, many of the dominant video platforms—like Youtube—are purely distribution focused. **They don’t even have any editing capabilities**. Instead, companies like Adobe end up being large beneficiaries of this need. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/06/image-7.png) Source: Adobe Analyst Day, October 2022 Color me surprised if YouTube and Meta's family of apps business don't end up incorporating significantly better editing tools leveraging their state-of-the-art AI research centers in five years. One counterargument could be thanks to AI tools, if the number of people interested in editing tools or art increase substantially, the market may be large enough to accommodate both big tech and Adobe. That may not be a bad argument, but the fact that Adobe today is $220 Bn EV perhaps doesn't provide much margin of safety if Adobe becomes a consistent market share donors to accommodate other players over time. Moreover, Adobe's AI related capabilities are very unlikely to insulate itself from competitive pressure of Canva and potentially Figma. On top of that, I am not sure I have the skillset to assess where Midjourney or [RunwayML](https://runwayml.com/?ref=mbi-deepdives.com) end up in 5 years both of which are more born and operated in "native" AI environment. Adobe's distribution will continue to be a massive moat, but it may not be unassailable moat. As I often mention on my valuation related discussions, I think of valuation in terms of questions the stock price asks me and my job is to assess my confidence or comfort level in answering the questions. At current valuation, I consider the questions posed by Adobe's stock price are increasingly discomforting. While market seems to have labeled Adobe an "AI" stock, the long-term returns will ultimately be decided not by today's labels, but the company's ability to maintain its competitive moats and profits/FCF in the long run. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Disclaimer:* All posts on “MBI Deep Dives” are for informational purposes only. This is NOT a recommendation to buy or sell securities discussed. Please do your own work before investing your money.* ### Tyler Technologies: Selling Software to the Government URL: https://www.mbi-deepdives.com/tyl/ Last updated: 2023-05-25T11:43:56.000Z You can listen to the Deep Dive [**here**](https://www.mbi-deepdives.com/audio/) [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) --- My friend [Liberty](https://www.libertyrpf.com/?ref=mbi-deepdives.com) once mentioned to me that if someone reads William Thorndike’s “[The Outsiders](https://www.amazon.com/Outsiders-Unconventional-Radically-Rational-Blueprint/dp/1422162672?ref=mbi-deepdives.com)”, they should also read Phil Rosenzweig’s “[The Halo Effect](https://www.amazon.com/Halo-Effect-Business-Delusions-Managers/dp/1476784035/ref=sr%5F1%5F1?crid=2ZFSQFRJR4O47&keywords=the+halo+effect&qid=1684070775&s=books&sprefix=the+halo+effect%2Cstripbooks%2C97&sr=1-1&ref=mbi-deepdives.com)”. Reading just either, instead of both, of them may run the risk of creating lopsided view of the world of business. Reading both lets you elicit a much more balanced understanding; that’s why Liberty thought that the sum of these two books is greater than the parts! When I was reading Tyler’s founder Joseph McKinney’s story, it occurred to me that McKinney is one of those rare characters who could perhaps potentially be featured in both “The Outsiders” and “The Halo Effect”. In the first 20 years of Tyler, McKinney seemed like one of those “Outsider” CEOs diligently creating shareholder value by not only operating the business deftly but also using financial engineering with rare dexterity. But then in the next 10 years, things almost completely unraveled at Tyler. Hence, while his glory days can justifiably be celebrated, McKinney is just as equally a cautionary tale. After graduating from Harvard, McKinney started a Venture Capital firm named Electro-Science Investors in 1960 and became a millionaire after first year of operations. After five years of Electro-Science, McKinney started another company called Saturn Industries which was in the military supplier business. He sold the military supplier business to lessen customer concentration and with the proceeds, he bought a heavy-freight hauler company named C&H Transportation in 1966. More or less everything McKinney touched turned out to be gold in those early years. C&H turned out to be a success and McKinney seemed to prefer the steady, consistent profit streams of established industrial businesses. Therefore, he went on to buy more industrial businesses; in 1968, he bought Tyler Pipe, a manufacturer of sewage pipes with $43 mn sales. Tyler Pipe became such an outsized contributor to revenue and profits that McKinney decided to name his conglomerate after the namesake. After Tyler, McKinney bought dozens of other companies, largely funded by bank loans and bonds. By 1975, Tyler became a Fortune 500 company and the company grew its earnings per share by 23% CAGR during the decade of the ‘70s. In the early ‘80s, McKinney continued his large acquisition spree with Hall-Mark Electronics, a distributor of electronic components, and Reliance Universal, a specialty chemical coatings maker; these two companies contributed 43% of overall sales and 41% of overall profit just two years after the acquisition. While high interest rates and recessions of early ‘80s caused headache for Tyler, it turned out to be just a hiccup for the company. By the mid ‘80s, Tyler became a diversified conglomerate with three of its six primary business units being the leaders in their respective industries. By 1987, Tyler became one of the largest companies in the US with $1.1 Bn sales and \~10,000 employees. As the rest of corporate America became more familiar with the “panacea” of acquisition led growth financed by high yield debt, McKinney developed a growing sense of discomfort and decided to go the other way. He decided to divest and liquidate much of Tyler’s assets; by 1990, McKinney sold \~80% of Tyler’s assets and distributed \~$415 Mn cash and stocks back to shareholders. Tyler, however, still had Tyler Pipe as they entered 1990\. But by mid-90s, even Tyler Pipe was sold. After liquidating supermajority of Tyler’s assets and distributing much of the proceeds back to shareholders, McKinney decided to rebuild Tyler by buying two retail businesses: Forest City Auto Parts (a chain of 61 stores that sold automotive parts and supplies), and Institutional Financing Services (a national education fund-raising services company). Both these companies turned out to be duds, as evidenced by the deteriorating operating performance: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fab9edba8-e522-4f12-9171-960ffaba7123_1082x662.png) Source: [Funding Universe](http://www.fundinguniverse.com/company-histories/tyler-corporation-history/?ref=mbi-deepdives.com), MBI Deep Dives As McKinney’s strategy to reinvent Tyler was clearly failing, he left Tyler in 1996, ending a dramatic rise and fall of the first thirty years of Tyler. A seasoned executive named Bruce Wilkinson then came to lead Tyler. But he didn’t last long as the dispute between Wilkinson and Louis Waters (who owned 10% of Tyler) reached an impasse. Wilkinson wanted Tyler to go back to its roots: industrial businesses whereas Waters wished Tyler to be an information services company. Waters’ wishes prevailed. With the acquisitions of Business Resources Corporation, The Software Group Inc., and Interactive Computer Designs, Waters helped Tyler pivot to selling software to the local governments. Waters’ strategy in focusing on providing information services to public sector proved to be a massive success: > Our plan is to consolidate the information industry for local governments. We are looking at smaller counties, municipalities, cities and appraisal districts or police and court systems. They need to computerize their record keeping, dispatch, tax collections, land records, deeds, probation; the possibilities are vast." While this new strategy had signs of hope, Tyler wasn’t quite out of danger zone for several years. Following the tech bubble, Tyler’s stock also got hammered. In fact, in March 2001, Tyler was trading at only $1.18/share; Lynn Moore, the current CEO and the then in-house legal counsel of Tyler, recalled on a [podcast](https://open.spotify.com/episode/05g19EA1EYGb1usJRDjE3T?si=862b950aa7ea409c&ref=mbi-deepdives.com) that they were concerned that Tyler might be delisted from NYSE. Back then, Tyler’s Enterprise Value (EV) was just \~$50 Mn; today the stock is trading at \~$400/share with \~$17 Bn EV. While it was Waters who could be credited for the strategic pivot to selling information services, John Marr almost certainly deserves bulk of the credit for Tyler’s eventual success in selling software to the government. Marr joined Tyler through the 1999 acquisition of Munis where he led the transformation of Munis from a local provider of municipal information systems to a nationwide leader in public sector software. He became COO of Tyler in July 2003 and a year later, he was promoted to CEO. When Marr became CEO, Tyler’s EV was just \~$300 Mn; by the time he transitioned the CEO role to Lynn Moore and became the Executive Chairman in May 2018, Tyler’s EV exceeded $8 Bn. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8695716d-8c3e-4264-93c5-113e8f41cb00_2400x1240.png) With this rich historical context in mind (much of which is summarized from [here](http://www.fundinguniverse.com/company-histories/tyler-corporation-history/?ref=mbi-deepdives.com)), here’s the outline for this month’s Deep Dive: **Section 1 Tyler’s Operating Segments**: I discussed Tyler’s operating segments, business lines within these segments, and NIC, their largest acquisition to date, in this section. The economics of the operating segments are also outlined. **Section 2 The Industry and Competitive Dynamics**: Tyler’s addressable market, competitive moats, and current competitors are highlighted here. **Section 3 Management, Capital Allocation, and Incentives**: Tyler’s capital allocation history over the last two decades, how it has evolved, and management’s near-term as well as long-term incentive structure are discussed in this section. **Section 4 Valuation and Model Assumptions**: Model/implied expectations are analyzed here. **Section 5 Final Words**: Concluding remarks on Tyler, and disclosure/discussion of my overall portfolio. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) --- _This post is for paying subscribers only._ ### Amazon 1Q'23 Earnings Update URL: https://www.mbi-deepdives.com/amzn1q23/ Last updated: 2023-04-28T02:19:08.000Z *Disclosure: I own shares, and Jan 2025 call options of Amazon* An Analyst asked a legitimate question to Amazon management: *“Does the company ever think about breaking out all the big investments so that we have more clarity on the retail margin structure?”* Andy Jassy provided a word salad in response. As a shareholder, I am not opposed to investing in Alexa, Kuiper etc. But the lack of disclosure to its **owners** is increasingly distasteful. Here are my notes from today’s earnings call. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Revenue** Overall revenue was +9.4% (+11% FXN) While the low margin 1P segment is flat for last two years, everything else is doing just fine. Let me spend more time on dissecting AWS which remains a key focus for investors. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3e1be03e-ea75-4325-881f-35e0b2cffcc1_1886x312.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AWS** For the first time in its history, AWS QoQ sales declined. Please note that 1Q QoQ has consistently been the weakest since 2016. Since AWS mentioned last quarter that they exited January at mid-teens growth, this wasn’t quite a surprise. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc4864f9f-5138-43c1-8fbf-287bc7a6dacd_1360x678.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) What, however, spooked investors a bit was the exit growth rate in April: > customers continue to evaluate ways to optimize their cloud spending in response to these tough economic conditions in the first quarter. And we are seeing these optimizations continue into the second quarter with **April revenue growth rates about** **500 basis points lower than what we saw in Q1**. One thing to note here is 2Q’22 was a very strong quarter, so even if AWS posts 11-12% YoY growth in 2Q’23, we’ll see \~$550-750 Mn QoQ incremental revenue. AWS’ numbers now makes Azure’s numbers look even more impressive than it already was! With AWS having higher exposure to startups (vs Azure’s large enterprises) as well as higher % of revenue coming from IaaS rather than PaaS for AWS (vs Azure) means AWS will be more cyclical than Azure. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd551e3f9-67d9-421f-aaf2-321b85569907_1560x884.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) It’s not just Azure; Google Cloud’s numbers also look pretty good now. One caveat is Google Cloud includes Google Workspace, and Alphabet doesn’t disclose GCP numbers. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F58d34599-b6b7-4c5a-8a18-b2a6ab72f468_1206x754.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4e5fd965-363e-4d0f-b8cf-79ab2ea39d10_1222x750.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) AWS cost structure may also require some further right sizing. 1Q’22 EBIT margin was 35.3% (highest ever), but in 1Q’23, it came down to just 24.0%. Incremental operating margin is now negative for two consecutive quarters. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9f2131f8-aed9-4d38-8b7f-4fc872960cd3_1608x180.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F68f30695-0234-4465-bbb6-6f5173f1ee0e_1736x894.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) As expected, there are plenty of generative AI related talking points which I’m not including here. Amazon remains quite upbeat about cloud’s momentum: > few folks appreciate how much new cloud business will happen over the next several years from the pending deluge of machine learning that's coming. **Amazon ex-AWS** While headline figures still don’t seem encouraging, I am personally quite glad about the progress made at retail. Both North America and International segment’s margins are going in the right direction. North America was profitable in 1Q’23 but still far cry from \~5-6% operating margins seen in 2018 and in 2020. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7da024f-3159-4001-97ea-5154b4630537_1196x730.png) While Amazon retail is dealing with whole host of issues, the operating margin history in North America is perhaps a strong indication that plenty of undisclosed “other bets” are jammed in this segment. If I were a large shareholder of Amazon, I would push Amazon to disclose and quantify such bets. I believe most Amazon shareholders are quite conducive to bold, aggressive bets, but not disclosing the bets is not doing anyone any favor. On International, Amazon reminded us the nuances embedded in margin trajectory there: > I will remind you that, again, that international is an **aggregation of established countries which are already profitable and who look a bit like North America**, perhaps at an earlier stage of development and working their way to parity on profitability. We have forward-loaded Prime benefits in a lot of these countries that are ahead of the curve that we saw in North America. > > We have a large emerging business. **In the last 5 years, we've added more than 10 new countries.** What we're seeing is if you looked back to North America long ago, it took 9 years for us to reach breakeven profitability in the United States. We see a similar curve in a lot of countries overseas. There's, in fact, additional challenges that we usually have to deal with, things like lack of payment methods, lack of the established infrastructure for -- especially for transportation and infrastructure for the Internet and everything else One indication that retail business is progressing well is that for the third consecutive quarters world-wide paid units growth YoY surpassed shipping+ fulfillment cost growth YoY. In fact, the gap is widening, indicating Amazon’s efficiency in dealing with shipping and fulfillment related cost. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffbfcf01d-e6a1-4129-8e21-b9cdf518571f_1530x776.png) **Advertising** Amazon continues to buck the broader advertising trend and even mentioned “*We have a lot of upside still in advertising*”: > On the advertising side, we're continuing to buck wider advertising trends and deliver robust growth. I think there are a few reasons for it. First, even in difficult economies, most people still shop. And with the largest e-commerce shopping venue, we have a lot of customers that companies seek to reach. That, coupled with our very substantial investment in machine learning to make sure customers see relevant ads when they're looking for various items, have meant that these advertisements have performed unusually well for brands, which makes them want to advertise in Amazon. > > It's also worth noting that **we're still very early in our efforts** to find a way to thoughtfully place ads in our broader video, live sports, audio and grocery properties. **We have a lot of upside still in advertising**. **Alexa** There was a question about Alexa’s long-term viability; Amazon still seems confident that they can make it work, especially in light of generative AI. That sounds plausible, but I guess Alexa has a couple of years left to figure out a viable business model before facing severe cut: > I think when people often ask us about Alexa, what we often share is that **if we were just building a smart speaker, it would be a much smaller investment**. But we have a vision, which we have conviction about that **we want to build the world's best personal assistant**. And to do that, it's difficult. It's across a lot of domains and it's a very broad surface area. However, **if you think about the advent of large language models and generative AI, it makes the underlying models that much more effective such that I think it really accelerates the possibility of building that world's best personal assistant**. > > And I think we start from a pretty good spot **with Alexa because we have a couple of hundred million endpoints being used across entertainment and shopping and smart home and information and a lot of involvement from third-party ecosystem partners**. And we've had a large language model underneath it, but we're building 1 that's much larger and much more generalized and capable. And I think that's going to really rapidly accelerate our vision of becoming the world's best personal assistant. **I think there's a significant business model underneath it**. **Opex+Capex** There was $470 Mn employee severance charge in 1Q’23\. But the overall cost structure still seems quite bloated. Capex for 2023 will be lower than it was in 2022\. While analysts asked for more specifics, Amazon didn’t quite outline any specific number for 2023 capex. > For the full year 2023, we expect capital investments to be lower than our $59 billion investment level in 2022, primarily driven by an expected year-over-year decrease in fulfillment network investments. We're continuing to invest in infrastructure to support AWS customer needs, including investments to support large language models and generative AI. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2e060642-dd12-4d23-8760-2b2e5d9d78ae_2414x350.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Outlook** 2Q’23 topline is expected to be $127-133 Bn, \~5-10% YoY growth. Operating income is expected to be between $2.0-$5.5 Bn (vs $3.3 Bn in 2Q’22) Thank you for reading! [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### Meta 1Q'23 Earnings Update URL: https://www.mbi-deepdives.com/meta1q23/ Last updated: 2023-04-27T12:55:24.000Z *Disclosure: I own shares, and Jan 2025 call options of Meta* > “There are two major technological waves driving our road map: a huge AI wave today, and a building metaverse wave for the future.” > > Mark Zuckerberg (1Q’23 Earnings Call) Here are my notes from tonight’s earnings. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Users** When Facebook’s DAU declined in 4Q’21 and MAU declined in 2Q’22 for the first time in its history, some feared (hoped?) for Facebook’s gradual decline to irrelevance. Facebook added 37 mn DAU (now >2 Bn) and 26 mn MAU last quarter. \~3 Bn people now use one of Meta’s apps daily. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F98522b3a-a359-4458-8674-0a6fada5aa2d_2092x692.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Engagement** US & Canada Facebook DAU reached 200 mn. DAU/MAU trends continue to be strong across regions. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8d892fc7-c4dd-4400-80a6-ff117ed75767_2094x726.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **ARPU** ARPU was positive across regions too. Rest of the World (RoW) continues to lead ARPU growth, followed by US & Canada. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9327dfaa-1f82-4f59-a2d1-5a3d541d68d3_1720x236.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Ad revenue** In 1Q’23, number of ad impressions was +26% YoY while average price per ad was -17% due to strong impression in lower monetizing services such as Reels and in lower ARPU regions as well as FX. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faa21c63b-5ea6-46c0-97a8-c1c3ecd8f0eb_2090x724.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Segment Reporting** In 1Q’23, overall revenue was +6% FXN while FOA was +7% FXN After two quarters of \~mid-30s FOA operating margin, now it is back to \~40%. Ex-restructuring, FOA operating margins would be 42.9% in 1Q’23. Overall restructuring cost was $1.1 Bn in 1Q’23\. RL losses $4 Bn (+1 Bn YoY). ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe9dada69-4baa-4c1a-8b14-89c35947b10d_1552x620.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Reels** In 3Q’22, Meta mentioned 1 Bn Reels was shared via DMs on IG **everyday.** That number is **2 Bn** now. While the battle is not over with TikTok, my hats off to Meta for building such a compelling product and scaling it so quickly. They just aren’t as fragile as many so intently seem to believe. Reels seems to be driving incremental engagement: > “Since we launched Reels, AI recommendations have driven a more than 24% increase in time spent on Instagram.” Reels monetization trend is also positive while Advantage+ is doing its magic: > Reels monetization efficiency is up over 30% on Instagram and over 40% on Facebook quarter-over-quarter. Daily revenue from Advantage+ shopping campaigns is up 7x in the last 6 months. While Reels is driving incremental time spent, important to remember the nuances in this format and not get carried away what incremental minutes may mean for revenue in the long run: > There are **structural supply constraints** with the Reels format as people view a Reel for a longer time than a piece of Feed or Stories content, which results in **fewer opportunities to serve ads in between posts**. That will make it likely more challenging to close the monetization efficiency gap than it was with Stories. > > …we're working down the headwind to revenue from the growth of Reels cannibalizing some time that is spent on our more mature ad surfaces, Feed and Stories. And basically, we have been balancing the 2 factors here, which is the degree **to which Reels is driving incremental engagement on the platform versus the lower monetization efficiency of Reels relative to the Feed and Stories engagement that it cannibalizes.** And ultimately, the overall economics of Reels is really going to be determined by the combination of those 2 things. > > so while **we're on track to Reels becoming neutral to revenue by end of year or early next year**, I do think it's important to call out that Reels is structurally different from Feed and Stories. And so **we don't have line of sight of getting Reels to monetization parity per time with Feed or Stories anytime soon because of those structural differences.** **Messaging** Number of businesses using paid messaging on WhatsApp grew +40% QoQ. > click-to-message ads continue to grow and bring incremental demand onto our platform. **This format is mostly used by smaller advertisers today in Southeast Asia and Latin America, and one of the exciting opportunities ahead is to expand adoption to larger advertisers in more markets** by investing in increased automation and reporting to help businesses more easily manage messages and measure results at scale. Expect AI agents to be available on Meta’s messaging apps over time. While Messaging has been perennially undermonetized, I wonder whether generative AI will be big unlock for monetization. Meta is very well positioned here with two separate >1 Bn MAU messaging apps: > I think that there is an **opportunity to introduce AI agents to billions of people in ways that will be useful and meaningful**. We're exploring chat experiences in WhatsApp and Messenger, visual creation tools for posts and Facebook and Instagram and ads, overtime video and multimodal experiences as well. I expect that these tools will be valuable for everyone from regular people to creators to businesses. For example, **I expect that a lot of interest in AI agents for business messaging and customer support** will come once we nail that experience. Now over time, this will extend to our work on the Metaverse too, where people will much more easily be able to create avatars, objects, worlds and code to tie all them together. > > I also think that there's going to be a very interesting convergence between some of the AI agents in messaging and business messaging, where right now, we see a lot of the places where business messaging is most successful are places where a lot of businesses can afford to basically have people answering a lot of questions for people and engaging with them in chat. And obviously, **once you light up the ability for tens of millions of small businesses to have AI agents acting on their behalf, you'll have way more businesses that can afford to have someone engaging in chat with customers.** So I think that, that could be a pretty big opportunity, too. > > We've introduced new features like **in-thread payments and other commerce tools. So we think that there's a big opportunity here. We're trying to make every part of the experience for advertisers, easier, better and more performance**. **AI** Meta is making a bet on open ecosystem for LLM-based products: > Right now, most of the companies that are training large language models have business models that lead them to a closed approach to development. And I think that there's an important opportunity in the industry to help create an open ecosystem. And if we can help be a part of this, then much of the industry, I think, will standardize on using these open tools and help improve them further. So **this will make it easier for other companies to integrate with our products and platforms as we enable more integrations**, and that will help our product to stay at the leading edge as well. > > …I think to some degree, we're just playing a different game on the infrastructure than companies like Google or Microsoft or Amazon, and that creates different incentives for us. So overall, I think that, that's going to lead us to do more work in terms of open sourcing some of the lower-level models and tools. But of course, a lot of the product work itself is going to be specific and integrated with the things that we do. So it's not that everything we do is going to be open. Obviously, a bunch of this needs to be developed in a way that creates unique value for our products. But I think in terms of the basic models, I would expect us to be pushing and helping to build out an open ecosystem here, which I think is something that's going to be important. **Metaverse** Meta called out the nonsense in several Media that are propagating Meta is shying away from “Metaverse”: > A narrative has developed that we're somehow moving away from focusing on the Metaverse vision. So **I just want to say upfront that,** **that's not accurate**. We've been focusing on both AI and the Metaverse for years now, and we will continue to focus on both. The 2 areas are also related. Breakthrough in computer vision was what enabled us to ship the first stand-alone VR device. Mixed reality is built on a stack of AI technologies for understanding the physical world and blending it with digital objects. Being able to procedurally generate worlds will be important for delivering compelling experiences at scale. And our vision for AR glasses involves an AI-centric operating system that we think will be the basis for the next generation of computing. Since last year, number of apps with \~$25 Mn revenue has doubled on Quest store. More than half of Quest daily actives now spend more than an hour using their device. But Meta didn’t mention how many daily actives Quest has. **Efficiency** There’s a growing narrative that Meta may have cut to the bone to appease the Street. Meta doesn’t think so: > A lot of this efficiency work that we've been undertaking and especially this year, **is driven not sort of by solely financial imperative, but really with the focus of increasing operational efficiency**. And that really includes more carefully scrutinizing road maps, winding down projects that are no longer at the top of our priority list, reprioritizing investments. That's really a muscle that, I think, we have spent a lot of time building over the last half year, and I expect that we will be carrying that discipline into the way that we assess our product road maps going forward. > > …The goals of our efficiency work are to make us a stronger technology company that builds better products faster and to improve our financial performance to give us the space in a difficult environment to execute our ambitious long-term vision. **When we started this work last year, our business wasn't performing as well as I wanted. But now we're increasingly doing this work from a position of strength. Even as our financial position improves, I continue to believe that slowing hiring, flattening our management structure, increasing the percent of our company that is technical and more rigorously prioritizing projects will improve the speed and quality of our work**. I also believe that a stronger financial position will enable us to weather a volatile environment while remaining focused on our longer-term priorities. There was a question on recent media report on 1-2% headcount growth going forward. Meta indicated headcount growth will likely exceed 1-2% from the base of post-layoff headcount at least in 2024. **Regulation** Some uncertainty remains around EU-US privacy framework, but one interesting data point was only \~10% revenue comes from EU countries. I honestly expected more questions around data privacy regulation as I do think it is a long-term risk to the business. **Capital Allocation** Meta continues to buyback in excess of FCF which is great to see given the depressed stock price. Share count declined by 1.7% QoQ and 5.3% YoY. Meta still has \~$28 Bn net cash on balance sheet. CFO Susan Li even indicated a better optimized capital structure over time: > As we look forward, I also expect that we will modestly evolve our capital structure over time to improve our overall cost of capital. We expect to do so through periodically accessing the debt markets to diversify our funding sources while **still maintaining a positive or neutral net cash balance over time**. 1Q’23 headcount reflects November layoff but doesn’t reflect the March layoff yet as they are being done in April-May. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0275d050-e438-4217-8aeb-317f8ed3eff0_2094x456.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Opex Guide** After guiding \~$100 Bn Opex for 2023 initially, Meta’s opex guide range has come down by $6-15 Bn. Given last few years trend, it is likely to be closer to $86 Bn (including $3-5 Bn restructuring costs). While thankfully Meta omitted the word “**significantly**”, Reality Labs losses is still expected to increase in 2023. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F970ae19f-3871-4c34-a7d5-3c47095b5096_1644x822.png) Source: Company Filings, MBI Deep Dives **Capex guide** Capex guide is unchanged at $30-33 Bn. I became a little excited after reading the following quote by Zuck and thought capex ramp up is mostly behind us after 2023: > A couple of years ago, I asked our infra teams to put together ambitious plans to build out **enough capacity to support not only our existing products but also enough buffer capacity for major new products as well. And this has been the main driver of our increased CapEx spending over the past couple of years. Now at this point, we are no longer behind in building out our AI infrastructure. And to the contrary, we now have the capacity to do leading work in this space at scale**. But as Susan later explained, capex is likely hinged on how transformative generative AI related investments going to be: > …you can really think about our CapEx investment as having 3 broad buckets. The first, we've talked about before, non-AI compute needs. We do have ongoing general compute and storage needs to support the existing business, but this is an area where we've become much more efficient in terms of capital intensity and are very much focused on continuing to do so over time. > > The second area is in our core AI investments, which is really most of our AI investment today, and that's supporting the building of the discovery engine, ranking unconnected organic content, ranking ads, and we're focused on measuring the return of those investments and making sure that we feel good about the ROI of our spend there, and that really will drive our future plans in terms of that core AI spend. > > And then the third bucket is really around CapEx investments now to support gen AI. And this is an emerging opportunity for us. We're still in the beginning stages of understanding the various applications and possible use cases. And I do think this may represent a significant investment opportunity for us that is earlier on the return curve relative to some of the other AI work that we've done. And it's a little too early to say how this is going to impact our overall capital intensity in the near term. **Outlook** 2Q’23 topline guide is $29.5-32 Bn, \~7% YoY at mid-point (vs consensus of $29.5 Bn) assuming 1% FX headwind Since from 2Q’22-4Q’22 Meta reported negative topline growth, the comps get easier from here. While the mudslinging between Meta bulls and bears continues, it is perhaps a good moment to reflect for everyone involved in this stock about the last 12 months of craziness in one of the most widely followed stocks in the world. Some more thoughts from the follow-up call can be found **[here](https://twitter.com/borrowed%5Fideas/status/1651570464337063944?ref=mbi-deepdives.com)** For a more detailed analysis on Meta, you can read **[here](https://www.mbi-deepdives.com/meta2023/)** (no paywall). I will cover **Amazon** earnings tomorrow! [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### Alphabet 1Q'23 Earnings Update URL: https://www.mbi-deepdives.com/goog1q23/ Last updated: 2023-04-26T10:34:10.000Z *Disclosure: I own shares, and Jan 2025 call options of Alphabet* Google Search is still growing, but YouTube ads was down YoY for three consecutive quarters now. Thankfully, Google Cloud maintained the momentum with +28% topline growth. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd9a0b459-c7b0-4655-ac55-a7fb9b3be556_2056x490.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) The big news in this quarter is Google Cloud became profitable for the first time. But there are some caveats. This profitability came after some adjustment in internal cost allocation methodologies. Google recasted Google Cloud’s last four quarter’s EBIT (from 1Q’22). They also changed estimates for useful life of servers and network equipment which was almost $1 Bn benefit some of which likely flows through Cloud segment. Corporate costs included $2.6 Bn restructuring charges (severance+ office related) and costs related to DeepMind (used to be other bets before). Google Services margin is likely to have a tailwind from 2Q’23 as Google Research related costs will move from Google Services to Google DeepMind within Alphabet's unallocated corporate costs. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5f88a51b-427a-4f9e-a7b5-c25711132520_1968x548.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) It’s really bit of a shame that after acquiring DeepMind in [2014](https://techcrunch.com/2014/01/26/google-deepmind/?ref=mbi-deepdives.com), it took them almost a decade to integrate and pool the intellectual horsepower together. In fact, DeepMind [tried](https://www.wsj.com/articles/google-unit-deepmind-triedand-failedto-win-ai-autonomy-from-parent-11621592951?ref=mbi-deepdives.com) to seek an “independent legal structure” even after being acquired. If not for competition from OpenAI+ Microsoft, I wonder if this integration would ever happen. **Search** Since there has been media reports about Samsung potentially choosing Bing over Google for default search position on their phones, there was a question related to that: > we've always been in a competitive environment for these deals. And while I can't comment on the specifics of any of our partnership agreements, what has served us well is always, first of all, building the best product possible, focused on giving value to users. And when we work with our partners, we work hard to create a win-win experience, and **ultimately, partners end up choosing us because that's what their users want.** And that's always been what's helped Search be widely distributed. So I think it all starts with continuing to innovate and improve Search and making sure we are leading there. So I think we've always approached it very robustly over the many, many years, and I'm comfortable that we'll continue to be able to do so. While Satya Nadella indicated gross margin for Search may have peaked for forever, Sundar Pichai sounds a little more optimistic that costs can be managed: > costs of compute has always been a consideration for us. And if anything, I think it's something we have developed extensive experience over many, many years. And so for us, it's a nature of habit to constantly drive efficiencies in hardware, software and models across our fleet. **And so this is not new. If anything, the sharper the technology curve is, we get excited by it because I think we have built world-class capabilities in taking that and then driving down cost sequentially and then deploying it at scale across the world.** So I think we'll take all that into account in terms of how we drive innovation here. As expected, AI was frequently mentioned, but very little useful info was given in the call in terms of timeline and specifics of product rollout. AI-driven tools, however, have been driving ad performance: > Advertisers who use PMax are, on average, achieving over 18% more conversions at a similar CPA. This is up 5 points in just 14 months, thanks to advances in the AI underlying bidding, creatives, search query matching and new formats like YouTube Shorts. **Google Cloud** > Over the past 3 years, GCP's annual deal volume has grown nearly 500%, with large deals over $250 million growing more than 300%. Nearly 60% of the world's 1,000 largest companies are Google Cloud customers Google Workspace now has 9 mn paying customers. The last time they disclosed paying customers number (6 mn) was back in 2020\. While some seem to think Google Workspace as a threat to Microsoft 365, it doesn’t seem Google Workspace has much of a bite. It just kept growing \~1 mn paying customers per year since 2015\. For context, Microsoft has \~400 mn Office 365 paid seats in commercial segment. While Alphabet never disclosed how many paid seats per paying customer Google Workspace has, it is hard to imagine Microsoft losing sleep over Google’s 1 mn/year incremental growth. It would definitely cause headache if Google Workspace started adding paying customers at an accelerating rate. Some other interesting quotes on GCP: > We are the only cloud provider to announce availability of NVIDIA's new L4 Tensor Core GPU with the launch of our G2 VMs, which are purpose-built for large inference AI workloads, such as generative AI. > > Growth in GCP remained strong across geographies, industries and products. > > in Q1, we continued to see slower growth of consumption as customers optimized GCP costs reflecting the macro backdrop, which remains uncertain. **YouTube** The number of channels that uploaded to Shorts daily grew over 80% in 2022\. No updated data on daily shorts watched per day, so I wonder whether Shorts may have peaked for the time being. Some interesting quotes/data on YouTube: > our creator ecosystem and multi-format strategy will be key drivers of YouTube's long-term growth. And to support this growth, we're focused on, number one, Shorts; number two, engagement on CTV; number three, investing in our subscription offerings; and number four, a longer-term effort to make YouTube more shoppable. > > …In one of our largest marketing mix modeling studies to date, YouTube ROI is 40% higher than linear TV and 34% higher than all other online video, according to a customer analysis from January 2020 to March 2022 of Nielsen Compass ROI benchmarks across 16 countries and 19 billion of total media spend measured. This proves YouTube's ability to drive effectiveness at scale. **Capital Allocation** Alphabet continues to utilize almost all of their FCF in buying back shares which come out to be \~1% share per quarter. They also have \~$100 Bn net cash on balance sheet. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F96783392-e3d0-4baf-9f0b-a35ad840c7a6_948x554.png) Source: Company Filings, MBI Deep Dives **Headcount** While headcount grew by 477 in 1Q’23, the recent layoff will be reflected from 2Q’23\. Google talked about “durably reengineer our cost base” which likely means they would like to match topline and expense growth. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F41e19a8d-828c-4d9b-9b60-2d6590b8cee1_1172x656.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Capex** While street (including me) was expecting capex to be mostly flat YoY, CFO guided 2023 capex to “modestly higher than in 2022.” > CapEx this year will include a meaningful increase in technical infrastructure versus a decline in office facilities. We expect the pace of investment in both data center construction and servers to step up in the second quarter and continue to increase throughout the year. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc0a214f-4a0b-4133-bdd9-0e951a4214d0_1018x612.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Valuation** While Alphabet’s valuation remains undemanding, the long-term questions related to Google Search and evolution of Search economics will decide whether it is mispriced stock or a potential value trap. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb4d5cb1c-4375-4f6b-a181-e93b4e169bbc_2498x498.png) Source: MBI Deep Dives You can read my more detailed analysis (including valuation) on Alphabet [here](https://www.mbi-deepdives.com/goog/) I will cover Meta’s earnings tomorrow. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### Spotify 1Q'23 Earnings Update URL: https://www.mbi-deepdives.com/spot1q23/ Last updated: 2023-10-24T10:50:36.000Z *Disclosure: I own shares of Spotify* Over the last 6 months, Spotify stock has doubled. The business, however, remains largely work-in-progress with users accelerating but operating efficiency yet to reflect in the financials. Here are my notes from today’s earnings. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) **Users** Spotify experienced its largest two MAU growth (mn) quarters in its history for two consecutive quarters. Management mentioned they’re seeing an acceleration in MAU retention, higher DAU/MAU, and lower churn. MAU was 15 mn ahead of guidance! > “retention is higher, the DAU over MAU is higher than before, and the actual engagement is higher. And that's across music, but it's certainly true on podcasting as well. And we've seen a healthy trend sort of up to the right on podcasting for now many, many, many quarters. And we're seeing how both podcast and music is acting in great symbiosis together to drive an overall healthier user funnel on Spotify. > > …The strength was broad-based, and we had record Q1 net additions across nearly all age demographics in both developed and developing regions.” ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbc4a0da7-aafa-4266-978b-eb17fd3b5c13_2278x232.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Premium Mix** Premium as % of MAU went down over the last 4-5 years as Rest of the World (RoW) region in the MAU mix went from 13% in 1Q’19 to 28% in 1Q’23\. RoW MAU basically doubled in two years and it usually takes time to convert MAU to premium. Spotify thinks recent MAU momentum usually bodes well for future premium subscriber growth. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F64c094f5-6acc-4378-ae93-469428b38d11_1430x1086.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Netflix vs Spotify** This is something I track every quarter. The divergence between Netflix and Spotify premium subscriber trend continues to widen every quarter. I should mention that definition of subscriber of NFLX and SPOT is not apple-to-apple, so I would caution not to infer more than what this data can tell us. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb31db038-b1c0-493d-91ef-c12e909770dc_960x632.png) Source: Company Filings, MBI Deep Dives **Revenue** While user growth kept surprising for two quarters, ARPU has been negative for two consecutive quarters. Spotify admitted the recent acceleration of subscribers may be marginally helped by being a lower cost provider (vs competitors most of whom raised price recently) The primary driver for negative QoQ ARPU growth was higher mix of family/duo plans. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0975cb16-ff21-4eeb-8acf-52f6c8aa2ad1_1582x318.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) While ads growth is higher than premium segment, things aren’t quite moving as fast as investors would like. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffcfd52f7-683c-4876-8c66-85f772ea5964_914x720.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Gross margin (GM)** Spotify guided 24.9% GM this quarter, so the actual GM came out \~30 bps higher. Ad’s GM was -3% in 1Q’23 (vs -1.4% in 1Q’22). Building a negative gross margin segment when the primary investor concern about Spotify’s music business is GM is what you call “fate loves irony”. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7950e6ed-860c-4074-81ed-3a64f30c9048_1080x598.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **Content Cost** The primary culprit for negative gross margin in ads is, of course, the podcast related investments which Spotify has vowed to take a much closer look: > we're going to be very diligent in how we invest in future content deals. And the ones that aren't performing, obviously, we won't renew. And the ones that are performing, we will obviously look at those on a case-by-case basis on the relative value. And I would say 2 things here. One, we have very sophisticated tools for measuring impact on the platform where we talked about this at the Investor Day, where we do understand the relative impact on lifetime value in our subscribers and so on and so forth. I think that helps us paying a fair price or understanding what a fair price would be. But then the second part also, because we are now the largest podcasting platform, that means we have a great opportunity to amortize across a larger base. So relative to someone that's smaller, we should be in a better position should we want to renew a deal because we obviously can amortize that against a larger base of users. **Opex** Spotify had €41 mn severance related charge in opex (€44 mn overall, but even if we subtract one-off charges, opex as % of gross profit would be 115%. That’s obviously not sustainable. R&D as % of gross profit increased from mid-30s in 2021 to above 50% in the last three consecutive quarters. Spotify needs to gain leverage on its cost base. With AI in the horizon, I wonder if any material leverage may be hard to come by anytime soon. ![](https://substackcdn.com/image/fetch/w_2400,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F467ffc08-0d2d-46c9-963d-3bbe98d9b904_1962x192.png) Source: Company Filings, MBI Deep Dives, [Daloopa](https://daloopa.com/?utm%5Fsource=MBI&utm%5Fmedium=Organic&utm%5Fcampaign=SourceLink) **AI** Speaking of AI, it does seem Spotify is likely to be beneficiary as music content creation process can get materially easier thanks to AI: > …to caution everyone, this is very early days, and it's an incredibly fast developing space. As I mentioned before, I don't think I've ever seen anything like it in technology, how fast innovation and progress is happening in all the really both cool and scary things that people are doing with AI at the moment. But I think it's important. > > I guess on the risk side would be not just for Spotify, but I think for our -- the entire creative ecosystem is obviously the question around copyrights and who owns what copyrights and what the fairway would be to attribute value when you're doing things in name and likeness situations or inspired \[ by a certain \] artist, et cetera. I think the whole industry is trying to figure that out and trying to figure out training -- and I would definitely put that on the risk account because there's a lot of uncertainty, I think, for the entire ecosystem. > > But on the positive side, to flip on that for a moment because I don't think that's been as highlighted as part of the story. One, I think this could be potentially huge for creativity on the positive side. I go in and talk a little bit more in detail about this on our -- for the record podcasts. > > If you really think about it, with now these conversational interfaces, it will allow people that perhaps don't know anything about how to play a music or even know these complex music production software tools to now create just using their voice, instruct the AI to make something to sound a little bit more upbeat, make something sound a little bit more like add some into the mix when you're creating a drum pattern or something like it. And that has the chance, I think, to meaningfully augment that creative journey that many artists to do. And you could even imagine someone just humming something and then the AI helping you out by creating a backdrop that you then can add it and alter, which is the music sort of software environment that many producers and music creators are doing. **And that should lead to more music. And that more music, obviously, we think it's great culturally, but it also benefits Spotify because the more creators we have on our service the better it is and the more opportunity we have to growing the engagement and growing the revenue.** So that would be on the upside, which a lot of people aren't talking about. And then, of course, there's entirely new potential products that perhaps can happen where you can have users creating their own music and perhaps Spotify could be a conduit of that, but I think it's way too early to speculate on those types of things at present moment. . **Outlook for 2Q'23** In Q2, Spotify is assuming 300 bps FX headwind; FXN topline growth is expected to be 14%. They do expect a steady ramp in gross margins throughout 2023 as well as sequential improvements in our operating loss. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F815a9818-8afc-4a28-9e67-4775bc594ec1_1456x494.png) Source: Company Filings You can read my Spotify Deep Dive (December, 2021) **[here](https://www.mbi-deepdives.com/spot/)** I will cover Alphabet’s earnings tonight! [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) ### Microsoft: The Quintessential Technology Company URL: https://www.mbi-deepdives.com/msft/ Last updated: 2025-04-07T17:28:58.000Z [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) You can listen to the Deep Dive [**here**](https://www.mbi-deepdives.com/audio/) --- > “The personal computer revolution had begun with a game played on a small blue box with blinking lights named after the brightest star in the constellation. 30 years earlier, people in Albuquerque had witnessed the sun come up in the South when the world's first atomic bomb exploded in the pre-dawn darkness a hundred miles away, heralding in the nuclear age. Now another age had dawned in Albuquerque. It began at a ragtag company located next to a massage parlor. > > Its prophets were two young men, not yet old enough to drink, whose computer software would soon bring executives in three-piece suits from all around the country to a highway desert town to make million-dollar deals with kids in blue jeans and T-shirts. > > …You've got to remember that in those days, the idea that you could own a computer, your own computer, was about as wild as the idea today of owning your own nuclear submarine. It was beyond comprehension.” \-*From the* [*book*](https://www.amazon.com/Hard-Drive-Making-Microsoft-Empire/dp/0887306292?ref=mbi-deepdives.com) *“Hard Drive: Bill Gates and the Making of the Microsoft Empire” (*Note*: I haven’t read the book(s); I, however, listened to David Senra’s four* [*podcast episodes*](https://pod.link/founders?ref=mbi-deepdives.com) *that covered three books on Bill Gates, Paul Allen, and/or Microsoft)* Of course, these “prophets” are Bill Gates and Paul Allen who met each other at Lakeside High School, one of the few schools to [have computer](https://www.linkedin.com/pulse/had-been-lakeside-would-have-microsoft-sanket-pai/?ref=mbi-deepdives.com) during late ‘60s. Although Allen was almost three years older than Gates, their mutual passion for computers led to a close friendship; nonetheless, they were very different personalities, as alluded by Allen’s recollection of how his mother used to describe Gates: *“My mother had a term for adrenaline junkies, people who would court risk for the thrill of it. Bill Gates was an *edge walker*, where I was wary of danger, Bill seemed to enjoy it.”* By the time Allen decided to leave his “dead-end job” at Honeywell to start a company with “the edge walker” named Gates, Gates and Allen were just 19-year and 22-year old respectively. While the popular perception of the teen Gates mostly focuses on being a computer nerd prodigy, it was his sales skills, in addition to technical prowess, that became important backbone of Microsoft’s early days. From the book “Hard Drive”: > Gates sustained Microsoft through tireless salesmanship. For several years, he alone made the cold calls and haggled, cajoled, browbeat, and harangued the hardware makers of the emerging personal computer industry, convincing them to buy Microsoft's services and products. He was the best kind of salesman there is. He knew the product, and he believed in it. He approached every client with the zealotry of a true believer, from the day he first articulated the Microsoft mantra: "A computer on every desktop, and Microsoft software in every computer." It wasn’t just sales or technical prowess, Gates’ strategic acumen was also almost unrivaled and Microsoft’s consequential deal with IBM is the perfect testament to that. Microsoft initially paid $25k in December, 1980 to Seattle Computer Products (SCP) for a non-exclusive license of an operating system, but they later bought the license **outright** from SCP for [$50k](https://thisdayintechhistory.com/07/27/microsoft-buys-full-rights-to-86-dos/?ref=mbi-deepdives.com) in July, 1981 just two weeks before IBM started shipping its first PC. Microsoft then renamed the operating system to MS-DOS and licensed it to IBM. But Microsoft made sure it was **non-exclusive** deal with IBM i.e. they could sell the same software to any other PC hardware manufacturers out there (regulators’ intense focus on IBM for monopolistic and anticompetitive behavior might have also prompted IBM not to push on this too hard). IBM did ask for a fixed lump-sum deal with Microsoft initially which Bill Gates rejected; Gates correctly concluded it would be a much better deal to get paid on a per PC basis as the world would likely go through a computer revolution. That one decision turned Microsoft to be effectively a money-printing machine. For every PC sold, Microsoft received royalty for their software. Of course, even if you won the lottery, you still need financial discipline to remain rich for decades to come. Gates’ parents and grandparents were all very financially conservative and such values were passed onto Gates as well. Microsoft never really **needed** to raise money from anyone before the IPO; the only VC money they took was just \~$1-2 Mn primarily for advice. In fact, Microsoft only came to IPO because of their employee stock option policy which meant they were approaching 500 shareholders that [required](https://www.goldmansachs.com/our-firm/history/moments/1986-microsoft-ipo.html?ref=mbi-deepdives.com#:~:text=IPO%20of%20the%20Year%20Puts%20Goldman%20Sachs%20on%20the%20Map%20With%20Tech%20Companies,-Theme%3A%20Clients&text=Goldman%20Sachs%20acts%20as%20book,of%20the%20year%E2%80%9D%20in%201986.) them to register with SEC. Microsoft raised $61 Mn in IPO at $777 mn valuation in 1986\. Today, Microsoft is almost $2 Tn market cap company, a >2,000x over almost four decades in public market! Just looking at those early years’ financials in the lead up to IPO in 1986, you could sense presence of Microsoft’s money printing machine and the financial discipline. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F39aa0b01-b417-47a6-a1a8-98023c32507f_710x170.png) Amount in USD Mn; Source: Company Filings, MBI Deep Dives Even after IPO, Microsoft continued its rapid profitable growth, thanks to their release of Windows in late 1985 which would prove to be a mind boggling cash cow for decades to come. Moreover, 80s was also the decade when Microsoft launched some of the most durable software in the History of software industry: Microsoft Word (1983), Excel (1985), and PowerPoint (1987). Here’s a snapshot of Microsoft’s income statement five years after IPO: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7136f529-f616-44ec-8872-ca952b1690ee_714x206.png) Amount in USD Mn; Source: Company Filings, MBI Deep Dives Just as Microsoft ascended to the glory days with no apparent sight of weakness, some did have prescient warnings for Microsoft. For any investor in technology, the bear case for Microsoft should sound familiar. The obsolescence risk and the rapid shift of technology could theoretically leave Microsoft to be just another tech company from the bygone eras. Bill Joy, one of the co-founders of Sun Microsystem, indeed thought exactly that in early 1990s: *"Microsoft is going to dominate for 5 to 7 years, then everything would change. There would be an industry breakthrough unimagined at the time, and it would be made by a company that didn't exist yet."* Moreover, Joy also suggested that this potential Microsoft disruptor would pursue simplicity instead of complexity to attack Microsoft’s moat. Indeed, by the mid-90s, Netscape came to the scene with their Mosaic browser that only had \~9k lines of code (vs Windows 95 that had \~10-15 mn lines of codes). As internet came to the scene, Bill Gates was infatuated with something entirely different during early to mid 90s: information superhighway. Gates imagined there would be some sort of marriage between the computer and television which would be a critical platform for this so called “information superhighway”. Gates initially massively underestimated internet; in fact, Gates, along with two other Microsoft executives, published a book named “[The Road Ahead](https://www.amazon.com/Road-Ahead-Bill-Gates/dp/B002B1C1YY?ref=mbi-deepdives.com)” in mid-90s to elaborate his vision around “information superhighway” in which he only briefly discussed internet. Funnily, just after the release of the book, Gates realized his mistake and decided to add another 20k words to that book to emphasize the significance of internet and published a revised edition in 1996\. This sudden change of mind was on full display in his seminal internal memo to Microsoft employees: “[The Internet Tidal Wave](https://www.wired.com/2010/05/0526bill-gates-internet-memo/?ref=mbi-deepdives.com)” (some key excerpts from the memo below): *“I think that virtually every PC will be used to connect to the Internet and that the Internet will help keep PC purchasing very healthy for many years to come.* *…Some competitors have a much deeper involvement in the Internet than Microsoft.* *…A new competitor "born" on the Internet is Netscape. Their browser is dominant, with 70% usage share, allowing them to determine which network extensions will catch on.* *…We have to match and beat their offerings* *…I want every product plan to try and go overboard on Internet features.* *…The Internet is a tidal wave. It changes the rules. It is an incredible opportunity as well as incredible challenge.”* In addition to this tidal wave that could potentially capsize the Microsoft vessel, there was another big worry in the 90s. Microsoft was under intense scrutiny of regulators for its anti-competitive as well as alleged monopolistic behavior. As the company was under this regulatory scanner, it complicated their response to competitive threats. While all this was going on, Microsoft stock was on a different universe as it continued to ride PC momentum. In fact, just 12 years after its IPO, Microsoft became the largest market cap company in the world in [1998](https://archive.seattletimes.com/archive/?date=19980905&slug=2770248&ref=mbi-deepdives.com). While Microsoft was battling the technological transformation, competitive threats, and regulatory censures, Wall Street was still drooling over the seemingly never ending eyepopping topline growth and sky-high margins. Microsoft reached \~$20 Bn topline with \~50% operating margin in 1999\. The next decade turned out to be diametrically opposite. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F96917cb6-2d74-4557-acfe-5bcd752a16a2_706x348.png) Amount in USD Mn; Source: Company Filings, MBI Deep Dives After Gates relinquished the CEO role in 2000, Steve Ballmer became the next CEO. I will keep the discussion on Ballmer era short which is perhaps most remembered for how he [laughed](https://www.youtube.com/watch?v=qycUOENFIBs&ref=mbi-deepdives.com) at the launch of iPhone, missed the smartphone era only to exacerbate it later by acquiring the ailing Nokia, and nurtured a [narrow vision](https://stratechery.com/2013/services-not-devices/?ref=mbi-deepdives.com) of Microsoft’s future around Windows. When Ballmer became CEO in 2000, Microsoft’s Enterprise Value (EV) was \~$600 Bn; when he decided to retire in 2014, Microsoft’s EV was just \~$250 Bn, a stunning loss of \~$350 Bn EV over 14-year period. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffb0c30c6-e0b7-4c6b-b63a-ed18849039dc_2400x1240.png) Of course, it wasn’t Ballmer’s fault that investors were willing to pay \~30x revenue for Microsoft at the height of tech bubble just when he assumed the CEO role. Even if he got most of the things right, it would still be likely that Microsoft stock would have a lackluster first decade of the 21st century. Ignoring the stock price, if we just looked at operating performance, Microsoft’s performance during Ballmer era would seem far from a disaster. Nonetheless, the company stopped growing its earnings for four consecutive years (2011-2014) and operating margins went from \~50% in 1999 to \~32% in 2014. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F870f3e56-185e-4be8-8aa8-2ff686c8acc0_714x550.png) Amount in USD Mn; Source: Company Filings, MBI Deep Dives Then came Satya Nadella. In some ways, Nadella had the opposite “luck” of Ballmer as the stock was trading at \~9x EBIT (vs \~60x in 2000) when Nadella became CEO. However, we would gravely underestimate Nadella’s contribution to Microsoft’s turnaround from \~$250 Bn EV to \~$2 Tn today in less than a decade if we focus too much on starting valuation multiples. ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F41e995b2-bf13-48d0-b20e-ea6e40f00ac1_2400x1240.png) After this brief historical background of Microsoft, it is the Nadella era that is my primary focus of this Deep Dive. Here’s the outline for the rest of the Deep Dive: **Section 1 Microsoft’s Operating Segments**: I start the discussion by outlining the different operating segments of Microsoft. **Section 2 A brief primer on Cloud**: While Microsoft’s future is not solely tied to cloud, Cloud is indeed of paramount importance for shareholders today. Hence, I wanted to give a brief primer on Cloud. **Section 3 Competitive Dynamics**: Even though Microsoft’s different operating segments may face different vectors of competition, the primary focus here are the hyperscalers in Cloud, competition in PC OS market, and Alphabet specifically for productivity software. **Section 4 Capital Allocation, Culture, and Management**: I discussed Nadella’s influence in shaping Microsoft’s culture as well as the capital allocation and current incentive structure at Microsoft. **Section 5 Valuation/Model Assumptions**: Model/implied expectations are discussed here. **Section 6 Final Words**: Concluding remarks on Microsoft, and disclosure of my overall portfolio. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) --- _This post is for paying subscribers only._ ### Readers' Feedback, and A Simpler Amazon Model URL: https://www.mbi-deepdives.com/feedback_amzn/ Last updated: 2023-04-02T19:23:38.000Z *Disclosure: I own shares and January 2025 call options of Amazon* There has been a persistent misperception among investors who mostly observe Big Tech from a distance that Big Tech is "consensus" investment. But if you look at realized volatility in some of the Big Tech stocks over the last five years, you can sense a **vigorous debate** among the market participants. ![](https://storage.ghost.io/c/57/41/5741a57f-8779-47c3-a5a5-48acb195f4db/content/images/2023/03/image-50.png) Never have I sensed this debate more tangibly after posting my thoughts on Amazon over the last week. While my update on [Meta](https://www.mbi-deepdives.com/meta2023/) (no paywall) and [Alphabet](https://www.mbi-deepdives.com/goog/) attracted some feedback/debate from readers, my thoughts on Amazon led to a deluge of emails and messages; thankfully, it came from both sides of the table. Thank you for your thoughtful, incisive, constructive, and respectful engagement on Amazon. I have certainly learnt from these interactions; a decentralized feedback from smart, curious readers all over the world is exactly why I love my job so much! Before I share some of these feedback from readers and my own thoughts on these comments, a couple of housekeeping notes: a) I will be in Washington, D.C. on Thursday and Friday (April 6-7) next week; if you're around and would love to meet, reply to this email to set up something, and b) I am currently working on Microsoft Deep Dive which I plan on publishing in late April (think sometime between 20th-25th of April). After Microsoft Deep Dive, I plan on covering a couple of mid-cap companies (<$20 Bn market cap) in May-June this year. Okay then, let me now get into some of the feedback I received and how/why these interactions led me to change some of my assumptions on Amazon. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) _This post is for paying subscribers only._ ### Amazon Update (amended) URL: https://www.mbi-deepdives.com/amzn2023/ Last updated: 2023-03-30T15:55:00.000Z *Disclosure: I own shares and January 2025 call options of Amazon* (Note: In addition to this piece, I encourage you to read this update [**here**](https://www.mbi-deepdives.com/feedback%5Famzn/)) First of all, thank you for the feedback and inputs on yesterday’s[ update on Amazon](https://www.mbi-deepdives.com/amzn/). One such interaction led me today to re-write some key sections of yesterday’s update. I was chatting with my friend [Scuttleblurb](https://twitter.com/scuttleblurb?ref=mbi-deepdives.com) on Twitter, and as we were discussing my piece on Amazon, it became clear to me that one of my assumptions was clearly incorrect. The mistake is material enough that I am writing a separate post on it to explain the mistake and the implications after correcting the mistake. Before I move onto that, I want to thank Scuttleblurb i.e. David Kim for his input. David is one of the best in this business, and I encourage you to subscribe to his [research](https://scuttleblurb.substack.com/). I want to be respectful to the subscribers who already read yesterday’s post, so I will first quickly mention my mistake and then move onto the discussion on all the changes in my estimates. [Subscribe](https://www.mbi-deepdives.com/#/portal/signup) _This post is for paying subscribers only._ _Includes the latest 500 public posts. Use `/sitemap.xml` for the complete archive of public content._