Nathan Lambert on How the Open Model Economy Can Win
In his talk "How Open Models Win" at Fireworks Forge 2026, Nathan Lambert argued that open model builders should sharply drive down inference costs on specialized, repetitive tasks rather than competing with frontier labs on cost for general-purpose agents. He noted that the open model economy is highly fragmented, and that how to channel application revenue back into the next round of model iteration is a problem that must be solved in the near term. The talk also discussed usage of open models, the gap with frontier models, and the ecosystem landscape.
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New talk on the open model economy. I wanted to break down the case of where open model builders should steer the economics of the industry, if we want to avoid being swallowed by OpenAI and Anthropic in due time. This rests fully on being able to drive the cost of inference down dramatically on specialized, repetitive tasks, rather than trying to compete on cost with the frontier labs in any general agent.
A core piece of it is how the open model economy is very fragmented, leaving an interesting puzzle we need to solve to get revenue from applications to feed back into the next model cycle. This is a very near-term problem as closed labs scale rapidly.
This talk investigates: Are people using open models? How close are open models to the frontier? Mapping the open model economy How open models win
This is a practice recording of my talk "How Open Models Win" for Fireworks Forge 2026 in San Francisco (3 November 2026), which will have improvements over this one.