The data covers just ~10–20% of global aggregate AI supercomputer performance as of March 2025.
And then there's this: https://epoch.ai/data/gpu-clusters-documentation/coverage
> The coverage of Chinese companies is particularly poor. Our average coverage of 8 major companies is 15%.
The bigger issue is that if you're interested in frontier training, the national aggregate doesn't tell you much. The question is whether your top labs can get to the chip scale necessary to train a frontier model and China's labs have already demonstrated they can. See https://newsletter.semianalysis.com/p/deepseek-debates
But it's not even clear that training frontier models is where the real innovation occurs. When it comes to fine-tuning, deploying, and building AI-based products, inference is the name of the game and the chip quality is less important. Huawei's Ascend is already pretty competitive here and then you're also discounting China's open-weight access to good models, a huge base of engineering talent and a massive domestic market to iterate against.
And unless Anthropic, OpenAI et. al. are willing to invest billions of dollars training models that will only be used by a small number of clients (like the government), there's no way to fight distillation. Protecting any model from distillation means forgoing its full revenue potential, improvement via feedback and all the benefits AI is supposed to bring to the broad US economy. And if you restrict your most advanced models, you just push people to Chinese open-weight models, which is what we're already seeing.