Nathan Lambert on how open models can avoid being absorbed by closed labs
Overview
Nathan Lambert says open model builders should cut inference costs sharply on specialized, repetitive tasks rather than compete on cost with frontier labs in general agents.
He argues the open model economy is fragmented, which makes it hard to turn application revenue into funding for the next model cycle.
The argument comes from a practice recording for Fireworks Forge 2026 in San Francisco, dated 3 November 2026, which covers whether people use open models, how close they are to the frontier, and how open models win.
Written by AI from the articles below · updated Nov 2, 4:51 PM ET
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Nathan Lambert@natolambertXNathan Lambert outlines how open models could avoid being swallowed by closed labsAINathan Lambert says open model builders should cut inference costs sharply on specialized, repetitive tasks rather than compete on cost with frontier labs in general agents. He argues the open model economy is fragmented, making it hard to turn application revenue into funding for the next model cycle. His talk, a practice recording for Fireworks Forge 2026 in San Francisco on 3 November 2026, covers whether people use open models, how close they are to the frontier, and how open models win.

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