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. \textbf{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 \citep{mankiw2020}, we derive a \emph{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 $λ\cdot k \cdot r_c$, 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: \emph{the price-setter for cognitive labor is no longer the labor market.}

翻译：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，代理劳动的供给可能具有高度弹性，从而在其与人类劳动高度替代时给认知劳动工资带来下行压力。我们认为，这种框架在机制上是错误的，但结论部分正确，且这一纠正对理论和政策都具有重要意义。\textbf{代理并非劳动；它们是一种将算力资本$K_c$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界便从劳动力市场迁移至算力资本市场。基于经典要素定价框架\citep{mankiw2020}，我们推导出一个\textit{算力锚定工资}（CAW）边界，表明在人力和代理生产的认知劳动为替代关系的任务中，竞争性人力工资的上限为$λ\cdot k \cdot r_c$，其中$r_c$是算力资本的租赁率，$k$是一单位有效代理生产认知劳动的算力强度，$λ$是人力与代理的相对生产率。我们通过常替代弹性（CES）聚合方法推广该结果，区分替代性任务与互补性任务，并讨论要素份额的影响。结论简洁明了：\textit{认知劳动的价格制定者不再是劳动力市场。}