Consider a water-skier. Surfing in the wake of a speedboat, they may at times go faster than the vessel pulling them along—but they never overtake it. Lately some experts have turned to this analogy to describe China’s race with America for supremacy in artificial intelligence. They suggest that the impressive progress of Chinese labs largely relies on the pace of their American peers pushing out the frontier. If America speeds up, so might China. If America were to throttle down, Chinese labs would slow too.
A small model trained by DeepSeek, a Chinese lab, on 800,000 examples produced by their most powerful model went from a score of 14.6% to 45.8% on a popular mathematics test, according to a study published in the Findings of the Association for Computational Linguistics in July. Anthropic has said these new measures may not be lastingly effective, but have so far reduced the amount of data harvested by distillers by 90-99%. As a result it might give up about 5-7% of its revenue.
The reason is “distillation”: the training of an AI model on the output of another, rather than from scratch. And America wants to stay far ahead of China: President Donald Trump has said that “whoever wins AI, wins”. As AI becomes more capable, the extent to which Chinese firms are distilling the best American models has become an urgent question. America’s companies need users to pay for their expensive models rather than use cheap knock-offs. Yet the widespread distillation of frontier AI models may not be quite as bad for America, nor as good for China, as it seems. Chinese firms can thus distil the capabilities that American models learn through expensive RL training. Like a student with a tutor, their AIs learn faster than American ones working things out alone. Far more data are needed to match the capabilities of frontier models—but Chinese labs appear to be distilling at scale. A report published in September by Anthropic found that five Chinese firms had collected more than 180m messages from its models in five months. Chinese models bear a measurable resemblance to American ones (see chart), and sometimes even identify themselves as Claude or ChatGPT. Anthropic and OpenAI are keen to cut the rope. They have moved to end an exploit, found in August, that gives Chinese firms access to the working-out that their models produce while solving problems. They have also stepped up their detection of distillation campaigns. Anthropic has begun charging for Claude requests even if the model senses what is going on and refuses to respond—making failed attempts at distillation costly. The company argues that Chinese model-makers were releasing near-frontier models without guardrails against misuse, and that such advanced models posed a national-security risk.
