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On October 22, 2026, Nathan Lambert presented his assessment of the state of open-source models in a guest lecture for Harvard CS 2881R "AI Safety." He stated that open-source models lag closed-source frontiers by about 3 to 5 months, and the gap is narrowing; Chinese open-source models lead comprehensively in performance, download volume, and paper citations, while US open-source models lag China by another 3 to 4 months. He believes treating distillation as a core issue is an overestimate, and that the truly enduring US advantage is compute: the total compute of the top five labs averages 16 times that of China's top five; by the end of 2026, Anthropic and OpenAI will each have more than 5 GW of compute, while China's leading labs will have at most 100 to 200 MW. He also pointed out that banning open source is not feasible in reality and would only suppress economic activity, and suggested assessing additional risk on a per-model basis with unified national standards. There are currently no subsequent development facts.
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Oct 22
Shao Meng
Nathan Lambert Harvard lecture: Chinese open-source models are fully ahead; US compute advantage is about 16x
In a guest lecture for Harvard CS 2881R "AI Security," Nathan Lambert judged that open-source models lag the closed-source frontier by about 3 to 5 months and the gap is narrowing; Chinese open-source models are fully ahead in performance, downloads, and paper citations, while US open-source models lag China by another 3 to 4 months. He believes treating distillation as a core issue overestimates it; the US's truly durable advantage is compute, with the combined compute of the top five labs averaging 16 times that of China's top five, and by the end of 2026 Anthropic and OpenAI each having more than 5 GW of compute, while China's leading labs have at most 100 to 200 MW. He also pointed out that banning open source is not feasible in reality and would only suppress economic activity, and suggested assessing additional risk on a per-model basis with unified national standards.