Nadella's truth
Satya means “Truth” and Nadella’s interview with Dwarkesh 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 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 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. In fact, EMC’s 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, 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 and VS Code 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 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…
You could say we had a high share in client-server 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. 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 and context engineering, 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-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 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, the GB300s are coming. By the time I get to Vera Rubin, Vera Rubin Ultra, 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, 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 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. 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!
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