Jean-Stanislas Denain, head of Epoch AI's Insights Team, sits down with Nathan Lambert to stress-test the most consequential claims in AI right now: recursive self-improvement timelines, the true size of the US-China capability gap, and whether distillation explains Chinese labs' rapid progress. Neither host nor guest arrives with confident answers. That's the point.
The most concrete data point discussed is from OpenAI's public blog post: a 2x-per-month increase in Codex spending by internal researchers. Denain reads this as weak-to-moderate evidence of productivity gains, not as a signal that a software intelligence explosion arrives in six months. He also flags that Chinese job postings offer a surprisingly legible window into lab strategy, and that the open-versus-closed safety debate is less settled than either side admits.
The full episode runs past the 90-minute mark and covers what a frontier post-training recipe actually looks like, the role robotics plays in any acceleration scenario, and how Epoch AI structures its research function. The distillation segment starting at 27:39 is the section most worth your time if you hold strong priors on the US-China gap.
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