Nathan Lambert 演讲：开放模型如何在与 OpenAI、Anthropic 竞争中胜出
Nathan Lambert 新演讲，讲开放模型社区怎么跟 OpenAI、Anthropic 打：别拼通用 Agent，去卷专用任务推理成本，思路很具体。
Nathan Lambert 为 Fireworks Forge 2026（2026 年 11 月 3 日，旧金山）录制了题为《How Open Models Win》的演讲练习版。他主张开放模型社区不应在通用 Agent 场景与 OpenAI、Anthropic 等前沿实验室拼成本，而应靠在专用、重复性任务上大幅压低推理成本立足。演讲还覆盖开放模型的实际使用情况、与前沿模型的差距，以及开放模型经济碎片化导致应用收入难以回流到下一轮模型训练的问题。
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.
