{"slug": "why-compute-might-get-10x-more-expensive-in-coming-years", "title": "Why compute might get 10x+ more expensive in coming years", "summary": "Compute costs could rise 10x or more in coming years, according to an analysis of AI lab economics. If a human-level software engineer could run on an H100 equivalent, that GPU should rent for over $250,000 a year at current software engineer market rates—15 times today's spot price. The analysis notes that Anthropic's revenue has 10xed year over year and could reach $100–150 billion by year-end, requiring $1 trillion in revenue next year to sustain the trend, which would necessitate higher margins, more expensive compute, or a greater share of compute spent on inference.", "body_md": "# Why compute might get 10x+ more expensive in coming years\n\n### 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.\n\nI 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.\n\nToday I want to talk about the compute situation of the labs over the coming years.\n\nAnthropic 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?\n\nLab compute [3x-es](https://epoch.ai/gradient-updates/frontier-labs-dont-use-most-ai-compute) 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.\n\nMy 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 inference[1](#footnote-1) (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](https://epoch.ai/data-insights/openai-compute-spend), and it’s certainly closer to 50% if not higher now.\n\nLabs 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.\n\nSo 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.\n\nSo 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](https://finance.yahoo.com/sectors/technology/articles/google-paying-spacex-over-900-151816933.html) 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.\n\nI 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.\n\nOf 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 15 more revenue than it currently does. But I actually don’t know if that’s true. If we applied this argument to people, then this would be the classic lump of labor fallacy. Economists generally believe that high-skilled immigration does not decrease the wages of high-skilled laborers because of the gains from specialization and innovation and so on. Maybe this labor supply shock is so big and so fast that the general heuristic no longer applies. But if we believe what standard economics says about labor, then the marginal value of compute (and thus the marginal price of compute) could become astonishingly high.\n\nWhat would happen in such a world?\n\nAs the top models get better and better at monetizing compute, it becomes harder and harder to catch up. If by 2028 we’ve automated software engineering and the price of compute is 15x higher than it is right now, then it’s going to be much more difficult for you, with no revenue, to compete for compute against the frontier labs.\n\nIf you have the best model, then you’ll be able to charge much higher margins than you currently can. This is the\n\n[Alchian–Allen effect](https://en.wikipedia.org/wiki/Alchian%E2%80%93Allen_effect)in economics, but to explain very simply, if instead of $2 per H100 hour, you’re spending $20, then it would be stupid to run a dumber model on that compute, which may take more tokens (thus wasting this expensive compute) to get you the same result. Of course if you’re using an API then you’re paying for that compute indirectly, but the logic is still the same. If you’re gonna have to pay so much for the underlying compute in the first place, you might as well pay to use the very best, most efficient model that runs on that amount of compute.A lot of current popular applications of AI get priced out. The reason AI is relatively cheap right now, at least in comparison to human labor, is partly that it can’t do a lot of things that top humans can do. At some point that will no longer be the case. And so using GPUs to make short-form video slop will just get priced out.\n\nAt the same time, this kind of prediction does pattern match onto the ways people have been wrong about scarcity in the past. I’m thinking of the Simon–Ehrlich wager, where Paul Ehrlich bet that the cost of a basket of commodities would increase rather than decrease in the decade leading up to 1990. This wager is famous in popular economics discussion, because it’s supposed to illustrate how Ehrlich’s Malthusian worldview was falsified — he underestimated the way in which market signals and human ingenuity would find ways to better economize scarce inputs. (Though other\n\n[analysis](https://en.wikipedia.org/wiki/Simon%E2%80%93Ehrlich_wager#Analysis)shows that if this bet had been made in a different decade, Ehrlich would have won).I’m guessing the Simon-Ehrlich basket of commodities is not the correct reference class for compute, because compute supply is much less elastic, and much less capable of absorbing large demand shocks, than the extraction of different metals is. That 3x in compute capacity per year comes from multiplying the following numbers together\n\n~~;~~: 1.4x from Moore’s Law, 1.2x from new fab construction ([bottlenecked through at least 2030 by EUV tool supply](https://www.dwarkesh.com/p/dylan-patel)), 1.8x from AI taking leading edge wafer allocation from other devices (which will start to hit a wall by end of 2027, when AI will go from 60% of N3 to 86%).\n\nI want to clarify that at some point in the future compute becomes cheap again. At some point robots will be able to turn shores of silica sand and mines of copper into computers. At which point the price of compute should fall closer to the cost of raw inputs and tools. I’m just talking about this current regime where AI compute merely 3x-es year over year, which is not fast enough to offset the price effect of how much more useful AI is becoming year over year.", "url": "https://wpnews.pro/news/why-compute-might-get-10x-more-expensive-in-coming-years", "canonical_source": "https://www.dwarkesh.com/p/why-compute-might-get-10x-more-expensive", "published_at": "2026-07-29 15:01:35+00:00", "updated_at": "2026-07-29 16:01:32.160014+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure", "ai-policy"], "entities": ["Anthropic", "OpenAI", "Google", "SpaceX", "Epoch AI", "H100", "GB200", "GB300"], "alternates": {"html": "https://wpnews.pro/news/why-compute-might-get-10x-more-expensive-in-coming-years", "markdown": "https://wpnews.pro/news/why-compute-might-get-10x-more-expensive-in-coming-years.md", "text": "https://wpnews.pro/news/why-compute-might-get-10x-more-expensive-in-coming-years.txt", "jsonld": "https://wpnews.pro/news/why-compute-might-get-10x-more-expensive-in-coming-years.jsonld"}}