Why compute might get 10x more expensive in coming years
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Blog<br>Why compute might get 10x+ more expensive in coming years<br>If a human-level software engineer that could run on an H100 equivalent, at current market rates for software engineers, that H100 should rent for over $250k a year. That’s 15x today’s spot price.
Dwarkesh Patel<br>Jul 29, 2026
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I want to experiment with very quick blog post where I time-box writing for 2 hours. I won’t be able to nail down a lot of important sub-questions, but the alternative is just not making any progress on a lot of different topics I’m curious about.<br>Today I want to talk about the compute situation of the labs over the coming years.<br>Anthropic revenue has 10xed year over year. Anthropic likely ends the year with ~$100–150B of revenue. For this trend to continue, Anthropic would have to make $1T in revenue by the end of next year. There’s no deep reason why the trend needs to continue, and it very well might not - it’s ultimately a question about AI capabilities. But suppose it does. What would have to be true about that world?<br>Lab compute 3x-es year over year. For a lab to 10x revenue while continuing to only 3x compute, some combination of the following 3 things has to happen: 1. Lab margins have to increase, 2. The price of compute has to increase, 3. Labs have to spend a greater fraction of their compute on inference.<br>My understanding is that basically all 3 of these things have been happening: 1. Anthropic went from 40% margins in 2025 to probably >80% this year for Fable inference1 (though perhaps not on the marginal compute - see more below). 2. Spot prices for compute are up 40%+ from the February trough, and that likely understates how much more the labs have to pay (again, more below). 3. Roughly a quarter of OpenAI’s 2024 compute spend went to inference according to Epoch, and it’s certainly closer to 50% if not higher now.<br>Labs would prefer not to do 3 (spend greater and greater shares of compute on inference). As has been said jokingly, the point of inference revenue is to convince investors to give you more money to buy more compute to train bigger models. If you’re spending most of your compute on inference, you’re kind of declaring that AI progress has stalled, because it’s not worth investing more in training, and your business is basically that of a cloud provider. The labs do not think this is true - they think they are serving models that will look extremely shitty within a year in order to build up the business case to continue training smarter models.<br>So that leaves 1. lab margins will increase, or 2. compute gets more expensive. It will be more of the former if the leading 1 to 2 labs are significantly ahead of the competition. In a market, your margins are set by how much better you are than the next best alternative. For effect 1 to dominate, margins would have to be in the mid-90s percent by the end of next year. That sounds quite crazy to me. But it does sound plausible to me that AI lab revenues will keep growing astonishingly fast.<br>So that leaves one more effect to explain how this world of crazy $1T revenue by end of next year might come to be: compute gets a lot more expensive. As I mentioned, this is already starting to happen. The price increase is even stronger when we look at the tranche of compute that the labs need - they obviously can’t rely on spot instances - they need security for their weights and customer info, and enough scale to get good utilization and flexibility. To look at how crazy the compute market is in that tranche, consider the price at which Google and Anthropic are renting compute from SpaceX. Google is reportedly paying $900 million a month for 110K GPUs that are a blend of GB200s and GB300s. That’s roughly 2x the spot price per hour for those GPUs. And the current spot price is itself 40% higher than it was in February.<br>I want to emphasize the key conclusion here: as AI models become smarter, they’ll better monetize the same amount of compute. If a true human-level software engineer that could run on an H100 equivalent, at current market rates for software engineers, that H100 should rent for over $250k a year. That’s 15x today’s spot prices.<br>Of course you might expect that if we have 10 million extra software engineers, the marginal value of a software engineer would decrease and so that H100 wouldn’t necessarily be able to produce 15x more revenue than it currently does. But I actually don’t know if that’s true. If we applied this argument to people instead of AI, then this would be the classic lump of labor fallacy. Economists generally believe that high-skilled immigration does not decrease wages in the long run because of how specialization and innovation increase the value of labor. Maybe this labor supply shock is so big and so fast that this general heuristic no longer applies. But if we believe what standard economics says about labor, then the marginal...