Interconnects
Nathan Lambert on post-training and open models; rigorous technical judgment
12:32AI writing has stalled: long-form nonfiction is the real test of model ability
Models are improving only slowly at long-form nonfiction, which exposes a defect in how they organize knowledge — a more worrying signal than their progress in code and math, because it bears on whether AI can solve open scientific problems.
49:05Open models aren't closing the gap by distilling — nobody knows how to distill usefully
Nathan Lambert takes apart, point by point, the narrative that Chinese open models are just distilled: using Fable/GPT 5.6 as a judge inside the RL stage does not pencil out on cost, and even if you were handed the strongest model's reasoning traces for free, the research community still does not know how to convert them into capability.
20:03Open weights now trail closed models by 3-5 months, and that lag is the whole safety margin
Kimi K3 compresses the open-versus-closed gap from the 6-9 months people had been arguing over down to 3-5 months, and that lag is the only risk buffer anyone has — heavy-handed regulation of open weights would merely postpone the inevitable while voiding the buffer itself.