Nathan Lambert 谈开源模型经济如何取胜
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
Nathan Lambert 在 Fireworks Forge 2026 的演讲《How Open Models Win》中提出，开源模型构建者应把推理成本在专门化、重复性任务上大幅压低，而非在通用智能体上与前沿实验室拼成本。他指出开源模型经济高度碎片化，如何让应用收入回流到下一轮模型迭代是近期必须解决的问题。演讲还讨论了开源模型的使用情况、与前沿模型的差距及生态图谱。
正文
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.