norgitov/ trends
Технологии · люди · идеи
К обзору/LessWrong10 минут назад

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 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[1]. * 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,[2]” 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 th

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Опубликовано30 сентября 2026 г.Parv Mahajan
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