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AI 不仅仅提高生产力：它创造了*过度效率*。个人和小团队现在能够比现有组织更快地生成决策、选项和计划，而这些组织的设计目的正是为了验证、协调或吸收这些内容。瓶颈已经从执行转移到治理。当过剩的生产能力在没有吸收层的情况下积累时，组织不会逐渐适应。从历史上看，它们会冻结：更严格的规则、集中化、禁令、脱钩。我们在 COVID 期间看到了类似的反应：当系统无法在本地吸收冲击时，它们在全球范围内关闭。似乎被讨论不足的是*吸收*：不是“我们能生产多快”，而是*一个组织在不采取防御性关闭措施的情况下，能够消化多少决策、选项和变化*。两种机制似乎相关但理论研究不足：（1）小的、局部的流程更改，重新分配协调和决策负荷；（2）持续的技能和角色转变，人们围绕仍然需要被决策、维护和验证的内容重新定位。我一直在尝试将此问题视为一种“传导”问题，即人类决策和合法性如何与几代人、AI 和人类一起流动。如果你看到过组织很好地处理了这个问题（或严重失败），我会很感兴趣：是什么真正让系统能够吸收 AI 驱动的过度效率，而不会退回到控制、排名、裁员或关闭？

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AI doesn’t just increase productivity: it creates *over-efficiency*.Individuals and small teams can now generate decisions, options, and initiatives faster than existing organizations were designed to legitimize, coordinate, or absorb. The bottleneck has shifted from execution to governance.When surplus productive capacity accumulates without an absorption layer, organizations don’t gradually adapt. Historically, they freeze: tighter rules, centralization, bans, decoupling.We saw a similar reflex during COVID: when systems couldn’t absorb shock locally, they shut down globally.What seems under-discussed is absorption: not "how fast can we produce" but how many decisions, options, and changes an organization can metabolize without defensive closure.Two mechanisms seem relevant but under-theorized: (1) small, local process changes that redistribute coordination and decision load; (2) continuous skill and role shifts, as people reposition around what still needs to be decided, maintained, and legitimized.I’ve been trying to think about this as a kind of "conduction" problem, how human decision-making and legitimacy flow alongside generations, AI and people.If you’ve seen organizations handle this well (or fail badly), I’d be curious: what actually lets systems absorb AI-driven over-efficiency without reverting to control, ranking, layoffs or shutdown?

人工智能创造了过度效率。组织必须适应它。