What You Have to Believe for OpenAI to Break Even in 2030

Programming Note: MBI Deep Dives will be off tomorrow, but I will be back on Friday.

Yesterday I mentioned 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 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 every year since 2023, it has told investors it plans to reach 30 GW by 2030, its audited 2024 and 2025 financials leaked in June, its cloud contracts carry implied prices, and the WSJ has reported both its equity-comp trajectory and its own compute budget through 2030. 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.

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