Nathan Lambert：AI 落地长尾知识工作所需的专家投入被严重低估
Nathan Lambert 这次没有谈模型，而是谈专家数据怎么帮 AI 真正进入律师、医生这些长尾行业，还顺带讨论了 SWE 需求为何没崩。他给 Mercor 当顾问的披露也很坦诚。做数据标注、行业落地的团队值得看看这个视角。
AI 研究者 Nathan Lambert 在 X 上提出，将 AI 收益扩散到长尾知识工作所需的人类专家投入，可能被严重低估。
他建议更多数据公司围绕这一方向做品牌定位，让专家帮助 AI 公司改进工具，使从业者更高效而非被替换。以软件工程师为例，他指出当前市场对 SWE 的需求依然旺盛，说明许多知识工作高度依赖人际互动、上下文与实际场景，AI 采纳更多会带来生产力增益。
他认为这种定位与实验室"ASI 将取代所有知识工作"的叙事相冲突，也意味着扩散过程会比评测基准显示的更漫长。他同时披露自己担任数据公司 Mercor 的顾问，认为其可能处于这场扩散进程的中心。
It could be vastly underestimated how much human expertise is needed to diffuse the benefits of AI to the long-tail of knowledge work. I think more data companies should figure out this branding, where experts help AI companies understand how to improve the tools that make them more effective, rather than replaced.
Some jobs will be replaced for sure, but I think a lot of knowledge work is deeply interpersonal, messy, and context driven in a way where AI adoption will be a productivity boon to existing employees. We are seeing this with the demand for SWEs today.
This rebrand will serve the AI industry writ large very well, but goes against the RSI narratives of the labs as "ASI will replace all knowledge work." In some ways, the difference is more philosophical about "what is the nature of work." I believe knowledge work is much more human than the evals give credit for, and this will make diffusion take a long time. Seems like Mercor can be at the center of this diffusion puzzle.
Disclosure: Yes, I advise Mercor on trying to realize this puzzle, as I think it's super interesting.
This report is categorized under AITOP's "Industry" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Mercor, Nathan Lambert, AI安全, 数据标注.
Source attribution: curated from Nathan Lambert (@natolambert).
