Experimental evidence confirms that AI tools raise worker productivity, but also that sustained use can erode the expertise on which those gains depend. We develop a dynamic model in which a decision-maker chooses AI usage intensity for a worker over time, trading immediate productivity against the erosion of worker skill. We decompose the tool's productivity effect into two channels, one independent of worker expertise and one that scales with it. The model produces three main results. First, even a decision-maker who fully anticipates skill erosion rationally adopts AI when front-loaded productivity gains outweigh long-run skill costs, producing steady-state loss: the worker ends up less productive than before adoption. Second, when managers are short-termist or worker skill has external value, the decision-maker's optimal policy turns steady-state loss into the augmentation trap, leaving the worker worse off than if AI had never been adopted. Third, when AI productivity depends less on worker expertise, workers can permanently diverge in skill: experienced workers realize their full potential while less experienced workers deskill to zero. Small differences in managerial incentives can determine which path a worker takes. The productivity decomposition classifies deployments into five regimes that separate beneficial adoption from harmful adoption and identifies which deployments are vulnerable to the trap.

翻译：实验证据证实，人工智能工具能提升工人生产力，但持续使用也可能侵蚀其赖以产生收益的专业知识。我们构建了一个动态模型，在该模型中决策者随时间推移为工人选择人工智能使用强度，需在即时生产力收益与工人技能退化之间进行权衡。我们将工具的生产力效应分解为两个渠道：一个独立于工人专业知识，另一个则随专业知识变化。该模型得出三个主要结论。第一，即使完全预见到技能退化的决策者，在前期生产力收益超过长期技能成本时，仍会理性采用人工智能，从而产生稳态损失：工人最终的生产力低于采用前的水平。第二，当管理者目光短浅或工人技能具有外部价值时，决策者的最优策略会将稳态损失转化为增强陷阱，使工人的境况比从未采用人工智能时更差。第三，当人工智能生产力对工人专业知识的依赖程度降低时，工人的技能可能产生永久性分化：经验丰富的工人实现其全部潜能，而经验不足的工人技能退化至零。管理激励的微小差异可能决定工人走向哪条路径。这种生产力分解将部署分为五个类别，区分为有益采用与有害采用，并识别出哪些部署易陷入陷阱。