Nathan Lambert

Lisha Li × Nathan Lambert

Lambert estimates distillation may give Chinese labs a few months, but its value in RL-heavy post-training remains unclear.Closed labs integrate inference and lead coding, where revenue has gone from zero to $100 billion, while open-model economics fragment builders, inference firms and post-training companies.Open models may still win buyers seeking owned weights and the cheapest adequate model.

Nathan Lambert × Sebastian Raschka

Anthropic’s distillation warning turns frontier API access into a geopolitical and capability-control issue.Nathan Lambert argues GPU-constrained Chinese labs “obviously should do this”: buying Claude outputs is easier than generating comparable synthetic data internally, while Anthropic labels distributed collection an “attack.”Terms of service mainly let providers terminate access, but renewed enforcement raises the possibility…

Lex Fridman × Nathan Lambert × Sebastian Raschka

The 2026 AI race is becoming plural, with ideas spreading across DeepSeek, Qwen, Kimi, MiniMax, Z.ai, Google, OpenAI and Anthropic while compute, hardware access, culture and distribution determine advantage.Scaling laws now span pre-training, post-training and inference, making coding the clearest monetization wedge and open weights strategic infrastructure; gigawatt-scale Blackwell clusters could support longer RL runs and premium inference, but data rights, benchmark contamination and serving economics remain risks.