智能体时代谁为认知劳动定价？计算锚定工资

摘要

关于AI代理经济学的一个自然直觉是，由于代理可以以极低的边际成本复制，当代理劳动与人类劳动高度替代时，其供给可能极具弹性，从而对认知劳动工资产生下行压力。我们认为这一框架在机制上存在错误，但结论部分正确，且纠正这一偏差对理论与政策均具有重要意义。代理并非劳动力，而是将算力资本K_c转化为有效认知劳动单位L_A的生产技术。一旦认识到这一点，锚定均衡工资的弹性供给边际便从劳动力市场转移至算力资本市场。基于经典要素定价框架mankiw2020，我们推导出算力锚定工资（CAW）约束：在人类与代理生产的认知劳动可相互替代的任务中，竞争性人类工资的上界为λ·k·r_c，其中r_c为算力资本的租赁价格，k为生产一个有效代理认知劳动单位所需的算力强度，λ为人类与代理的相对生产率。我们通过常替代弹性（CES）聚合方法推广该结论，区分可替代任务与互补任务，并探讨要素份额的影响。结论简洁明确：认知劳动的定价者已不再是劳动力市场。

English

A natural intuition about the economics of AI agents is that, because agents can be replicated at very low marginal cost, agent labor may be supplied highly elastically, placing downward pressure on cognitive-labor wages when it closely substitutes for human labor. We argue this framing is wrong in mechanism but partially correct in conclusion, and that the correction matters for both theory and policy. Agents are not labor; they are a production technology that converts compute capital K_c into effective units of cognitive labor L_A. Once this is recognized, the elastic-supply margin that anchors the equilibrium wage migrates from the labor market to the compute capital market. Building on the classic factor-pricing framework mankiw2020, we derive a Compute-Anchored Wage (CAW) bound stating that, on tasks where human and agent-produced cognitive labor are substitutes, the competitive human wage is bounded above by λcdot k cdot r_c, where r_c is the rental rate of compute capital, k is the compute intensity of one effective agent-produced cognitive labor unit, and λ is the relative human-to-agent productivity. We generalize the result through constant elasticity of substitution (CES) aggregation, separate substitutable from complementary tasks, and discuss factor-share consequences. The conclusion is concise: the price-setter for cognitive labor is no longer the labor market.

智能体时代谁为认知劳动定价？计算锚定工资

Who Prices Cognitive Labor in the Age of Agents? Compute-Anchored Wages

摘要

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