China’s open-model advantage is not free weights; it is cheaper failure. Lambert cites Ai2 and Epoch AI estimates that roughly 80% of frontier compute goes into R&D, not the final training run. If leading Chinese labs keep publishing technical reports, weights, and infra details, peers avoid rerunning the same MoE, RL, and data experiments.
I don’t buy the lazy “open is cheaper” framing. The piece says off-the-shelf open models are usually more expensive for product teams than closed hosted APIs, because inference scale still favors integrated providers. The weak link is the stack: at-scale RL for MoE models still lacks a truly open recipe, and semi-open tools like Thinking Machines’ Tinker or Prime Intellect Lab may not release enough surface area. The compounding only works if labs stop forking the valuable training stack back behind company walls.