> With LLMs, the real expertise isn’t in doing the work. It’s knowing how to guide and evaluate the work. It’s knowing what is worth prompting, which is another way of 4saying *knowing what’s worth doing.* > -- [[<Raw> How I Stopped Worrying About AI and Learned to Value My Humanity]] Meaning > Execution [[From Proof-of-work to Proof-of-meaning]] Knowing what you want Knowing how to ask for it ([[Managing AI agents is like managing people]]) [[Value of human = objective function & alignment layer]] [[Intent engineering & meaning architecture]] [[Use AI for meaning not efficiency]] > AI 可以生成一万个方案，但选择哪个，需要判断。 > AI 可以模仿任何风格，但什么是美，需要品味。 > AI 可以完美执行，但为什么要做这件事，需要人性。 > > 当技术越暴力，越高效，越无所不能时，那些最「软」的东西反而变得最硬核。 > > -- 李继纲 ## 人类管理的核心：赋义而非执行 执行变得不在稀缺 -- AI agents 任你派遣 稀缺的在于价值、意义和方向 这才是人类管理的核心——不是执行，而是赋义。 未来的管理者不只是会编排代理，而是要成为「意义架构师」。他们用技术设定秩序，用叙事构建愿景，用制度引导方向。他们既写代码，也讲故事。既懂逻辑，也擅长唤起人的激情。 [[Quality of idea over productivity]] ![[From expertise to taste, value judgement, courage, and agency.png]] > Things will appear simple or easily replicable on the surface. But the more and better access everyone has to AI tools, the clearer it becomes that the final bottleneck to great work is not knowledge or information. It’s not even intelligence. It’s that elusive, intangible, quality—call it taste, creativity, courage, imagination, agency.