How Much AI Compute Do Frontier Labs Use? | Epoch AI

Newsletter<br>May 20, 2026

Frontier labs don't use most AI compute (yet)<br>But Anthropic and OpenAI may rapidly grow their compute share in the next few years. After that, continued scaling would require an economic transformation.

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By Josh You

Gradient Updates shares more opinionated or informal takes on big questions in AI progress. These posts solely represent the views of the authors, and do not necessarily reflect the views of Epoch AI as a whole.

Disclaimer: the estimates of frontier developer compute discussed below are more tentative than our standard data work.

OpenAI kicked off the AI boom when it launched ChatGPT in 2022. Frontier LLMs soon accrued hundreds of millions of users and billions in revenue, sparking a massive investment boom in AI compute infrastructure, with Nvidia’s AI-related sales spiking more than fourfold in 2023. Global AI computing power has now grown to the equivalent of around 20 million Nvidia H100s, funded by hundreds of billions of dollars in annual capital expenditures.

Yet while OpenAI launched the compute boom, they don’t dominate AI compute usage. I estimate that the compute OpenAI uses for research, training, and inference as of the end of 2025 made up around 10% to 15% of the world’s operational AI compute supply, and this share was even smaller a year ago. Even after adding the other most well-resourced frontier developers — Anthropic, xAI, and the AI labs within Google and Meta — the combined total is probably still under half of the world total.

In other words, there is a lot of AI compute that top frontier labs are not using. Anthropic and OpenAI have seen rapid growth in revenue and funding, enabling them to grow their AI compute faster than the world overall, and this will continue in 2026.

But the top labs may capture a much larger share of global compute within a few years. At that point, compute growth at top labs would be more directly tied to the pace of total compute production, which could slow down the rapid growth we’ve seen in both model capabilities and AI deployment/revenue. For scaling to continue, the overall compute buildout would need to accelerate. Given that AI capital expenditure (capex) is already approach $1 trillion per year, such an acceleration in compute production would require dramatic economic changes.

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Most AI compute probably doesn’t go to frontier AI<br>More details for each company can be found in the Appendix, and the accompanying research document.<br>I don’t have a great estimate of the compute used by each of the five most resource-rich frontier developers, but we know enough to estimate their share of world AI compute.1<br>OpenAI helpfully disclosed the total electric power capacity of its data centers, which can be converted to ~1.7 million in H100-equivalent (H100e) compute. We also know a lot about xAI’s Colossus data centers. I’m less certain about Anthropic, which had significantly less compute than OpenAI at the end of 2025, though probably still over 1 million H100e. The situation at Google DeepMind and Meta Superintelligence Labs is also unclear, since the compute owned by their parent companies (roughly one-third of the world total) is split across frontier AI, cloud, and other internal uses.2 It’s not clear that the frontier labs at Google and Meta use even half of the total. For more details on each lab, see the Appendix.<br>But it’s still clear that a lot of AI compute isn’t used by the top labs. My best guess, in terms of the equivalent number of Nvidia H100 GPUs, are that OpenAI, Anthropic, and xAI together probably had fewer than 4 million H100e at the end of 2025.<br>My best guess is that DeepMind uses slightly under half of Google’s total. Meta also rents external cloud compute (not shown on the graph), starting in late 2025. Estimated world total of 16 million H100e assumes a one-quarter lag between chip sales (est. 20 million) and operations. This is a reference scenario; a longer lag would imply a higher frontier compute share.<br>Meanwhile, cumulative sold AI compute was roughly 20 million H100e as of the end of 2025. But not all of this was necessarily operational — I don’t know exactly how much, but a rough estimate would look at chip sales at a time lag based on typical installation periods for AI clouds like CoreWeave.3 If there’s a one-quarter lag between delivery and deployment, deployed compute at the end of 2025 would be comparable to sold compute as of Q3 2025, which was ~16 million H100e. If the delay is two quarters, deployed compute goes down to ~12 million H100e.<br>Under these varying deployment assumptions, Anthropic, OpenAI, and xAI’s total H100e would make up around 20% to 30% of the world total at the end of 2025. If you also count the inference compute that the hyperscalers use to run their own APIs on OpenAI and Anthropic models, this may contribute up to another ~5%.<br>Meanwhile, we estimate that Google...