By default, Chinese AI falls very behind

This work is hosted on MCNAIR, but does not reflect the views of my employer or the Center.

In the last couple of weeks, domestic coordination has looked more likely. Therefore, I am increasingly concerned about the Chinese government’s (and various Chinese labs’) incentives over the next 6-18 months with respect to international coordination on mitigating potential existential risks from powerful AI. In this series, I attempt to model these incentives and what they imply for Chinese AI policy.

• In Part I, I argue that the Chinese AI outlook is very bad, and that by default China will enter takeoff far behind the US, possibly leading the way to disempowerment.
• In Part II, I will walk through several domestic policy interventions the CCP may use to improve the situation, and analyze whether these are likely to be sufficient.
• In Part III, I will discuss foreign policy interventions such as sabotage and escalating tensions, excluding bilateral treaties or other international coordination.
• In Part IV, I will describe some considerations on China’s plate walking into hypothetical negotiations for an AI treaty with the United States.

The situation is quite bad

Perhaps the most important (and underrated) fact about the US-China AI situation is that China’s outlook, by default, is quite poor. Here, I use “by default” to mean “assuming China’s AI-relevant policy stays ~the same over the next 6-18 months,” and I use “quite bad” to mean “the US-China AI capability gap increases significantly.”

Previously, we found that the capability gap between the US external frontier (i.e., the best publicly available American models) and the Chinese external frontier (i.e., the best publicly available Chinese models) is between three and nine months. However, the gap that matters most is between the US and Chinese internal frontier (i.e., the best models used internally at frontier labs), because the best internal models, not the best external models, are likeliest to be used for AI R&D. So what is the gap between the best internally and externally deployed models, in both the US and China?

• In China, the internal-external gap is likely extremely small (e.g., hours to days). Zhipu AI’s Director of Product has mentioned in a podcast that “We open source it [our models] within a few hours”; the large number of competitive Chinese players, a focus on open-source releases, and several second-hand conversations with Chinese AI researchers confirm this.
• In May, Redwood Research predicted that the information-value of being inside a frontier American lab is similar to looking ~2.5 months into the future. Similarly, METR's Frontier Risk Report assessed that, as measured by Time Horizon 1.1 50%, the internal frontier was on average ~2 months ahead of the public frontier. Claude Mythos was deployed internally on February 24th, and externally on April 7th to Glasswing partners. In recent months, the internal-external gap has likely increased, with Astra being released to the public about four months after a similarly capable model (which caused the OpenAI/Hugging Face incident) was evaluated internally. Therefore, the internal-external gap is likely between two and six months.

Based on these predictions, the capability gap between the US and Chinese internal frontier is very likely between six and twelve months, and increasing over time as the US internal-external gap increases. So, on current trends, the situation is quite bad. Under current Chinese AI policy, several factors that could influence these trends are unlikely to have much effect:

• Compute: Currently, the United States controls the vast majority (>70%) of AI-relevant compute. Despite new Huawei chips coming online, the gap between Huawei and Nvidia is increasing, and Huawei is unlikely to catch up to Nvidia by 2030, meaning the compute capacity gap is unlikely to decrease. Accelerated chip smuggling could mitigate this somewhat, but is unlikely to reverse the overall trend.
• Talent: There is currently no mass exodus from American AI labs to Chinese AI labs, nor are there any public Chinese or American policies that would make such an exodus significantly more likely. It is unclear what percentage slowdown US frontier labs would incur if their top Chinese researchers left.
• Data: The data ecosystem in China is growing quickly. However, there is no public indication that China plans to mobilize large parts of state machinery in the near future to subsidize or assist data production, and in general it seems like compute capacity dominates capabilities progress.
• Power: Although China has the ability to quickly mobilize much more electricity than the United States, China’s data center growth is largely constrained by access to chips, not power. Similarly, power is unlikely to be the largest bottleneck to US datacenter growth in the next couple of years.

Therefore, the gap between US and Chinese AI capabilities is large and likely to increase. As we approach fully automated AI R&D, Chinese AI labs are likely to be left behind, paving the road for potential disempowerment of the Chinese state in the future. Their outlook is quite poor, and we now focus our attention on domestic interventions the CCP may employ to alleviate the situation.

1. I expect to make many major and minor mistakes throughout this sequence, both due to my non-expertise but also because of the unusually low rigor with which I’m approaching this incredibly complex topic.

This work is hosted on MCNAIR, but does not reflect the views of my employer or the Center.

In the last couple of weeks, domestic coordination on mitigating AI risks has looked more likely. Therefore, I am increasingly concerned about the Chinese government’s (and various Chinese labs’) incentives over the next 6-18 months with respect to international coordination on mitigating potential existential risks from powerful AI. In this sequence, I attempt to model these incentives and what they imply for Chinese AI policy.

• In Part I, I argue that Chinese AI capabilities will fall increasingly behind the US without major policy shifts.
• In Part II, I will walk through several domestic policy interventions the CCP may use to improve the situation, and analyze whether these are likely to be sufficient.
• In Part III, I will discuss foreign policy interventions such as sabotage and weight theft, excluding bilateral treaties or other international coordination.
• In Part IV, I will describe some considerations on China’s plate walking into hypothetical negotiations for an AI treaty with the United States.

The US-China capabilities gap will continue growing

Perhaps the most important (and underrated) fact about the US-China AI situation is that China’s outlook, by default, is quite poor if you believe that powerful AI will become a major source of national power. Here, I use “by default” to mean “assuming China’s AI-relevant policy stays ~the same over the next 6-18 months,” and I use “quite poor” to mean “the US-China AI capability gap increases significantly.”

Previously, I predicted that the capability gap between the US external frontier (i.e., the best publicly available American models) and the Chinese external frontier (i.e., the best publicly available Chinese models) is between three and nine months. However, the gap that matters most is between the US and Chinese internal frontier (i.e., the best models used internally at frontier labs), because the best internal models, not the best external models, are likely to be used for AI R&D. I consider the gap between the best internally and externally deployed models, in both the US and China:

• In China, the internal-external gap is likely extremely small (e.g., days).
  • Zhipu AI’s Director of Product has stated that “We open source it [our models] within a few hours.”
  • This is further encouraged by the large number of competitive Chinese players and a focus on open-source releases.
  • Alibaba uploaded checkpoints daily for Qwen 3.8, as indicated by changing performance on live benchmarks.
• In the US, the internal-external gap is likely between two and five months and growing.
  • METR’s Frontier Risk Report assessed that, as measured by Time Horizon 1.1 50%, the internal frontier during February and March was on average ~2 months ahead of the public frontier.
  • In May, Redwood Research estimated that the information-value of being inside a frontier American lab is similar to looking ~2.5 months into the future.
  • Claude Mythos was deployed internally on February 24th, and externally on April 7th to Glasswing partners.
  • Model 2 from Anthropic’s Frontier Risk Report was used “heavily” in Anthropic as of July 15, 2026, and Opus 5.5, the first model which clearly outperforms it on some AI R&D tasks, was released to the public 2 months later.
  • Astra was released to the public about four months after a potentially similarly capable model (which caused the OpenAI/Hugging Face incident) was evaluated internally.

Based on these predictions, the capability gap between the US and Chinese internal frontier is likely 5-14 months, and likely increasing over time as the US internal-external gap increases. So, following current trends, the situation is quite bad. Under current Chinese AI policy, several factors that could influence these trends are unlikely to have much effect:

• Compute: Currently, the United States controls the vast majority (>75%) of AI-relevant compute. Despite new Huawei chips coming online, the gap between Huawei and Nvidia is increasing, and Huawei is unlikely to catch up to Nvidia by 2030, meaning the compute capacity gap is unlikely to decrease. Accelerated chip smuggling could mitigate this somewhat, but is unlikely to reverse the overall trend.
• Talent: There is currently no mass exodus from American AI labs to Chinese AI labs, nor are there any public Chinese or American policies that would make such an exodus significantly more likely. It is unclear what percentage slowdown US frontier labs would incur if their top Chinese researchers left.
• Data: The data ecosystem in China is growing quickly. However, there is no public indication that China plans to mobilize large parts of state machinery in the near future to subsidize or assist data production, and in general it seems like compute capacity dominates capabilities progress.
• Power: Although China has the ability to quickly mobilize much more electricity than the United States, China’s data center growth is largely constrained by access to chips, not power. Similarly, power is unlikely to be the largest bottleneck to US datacenter growth in the next couple of years.

Therefore, the gap between US and Chinese AI capabilities is likely to continue increasing. As we approach fully automated AI R&D, Chinese AI labs are likely to be left behind.

Next, we'll turn our attention to domestic interventions the CCP may employ to alleviate the situation.

1. I am approaching this from a frame of “what options does the CCP have if it increasingly becomes convinced of transformative AI?”, and not making general predictions about Chinese policy. I expect to make many major and minor mistakes throughout this sequence, both due to my non-expertise but also because of the unusually low rigor with which I’m approaching this.
2. Importantly, I assume that we don't see major weight theft or a Chinese national project to centralize compute and development. I will consider these interventions in Part III and II, respectively.