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：实验证据表明，AI工具能够提升劳动者生产率，但持续使用也可能侵蚀其赖以形成收益的专业技能。我们构建了一个动态模型，在该模型中决策者需随时间推移决定劳动者对AI工具的使用强度，在即时生产力与劳动者技能侵蚀之间进行权衡。我们将工具的生产率效应分解为两个渠道：一个独立于劳动者专业能力，另一个则随专业能力而变化。该模型产生三个主要结果：第一，即使决策者完全预期到技能侵蚀，也会在前期生产力收益超过长期技能成本时理性采用AI，从而导致稳态损失——劳动者最终的生产率反而低于采用前的水平。第二，当管理者存在短视行为或劳动者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"，使劳动者境况比从未采用AI时更差。第三，当AI生产率对劳动者专业能力的依赖程度较低时，劳动者技能可能出现永久性分化：经验丰富的劳动者充分发挥其潜能，而经验不足的劳动者则技能退化至零。管理者激励机制的微小差异可能决定劳动者走向不同路径。这一生产力分解机制将AI部署划分为五种模式，可区分有益采用与有害采用，并识别出易陷入陷阱的部署场景。