[2604.03501] The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading

Computer Science > Human-Computer Interaction arXiv:2604.03501 (cs) [Submitted on 3 Apr 2026] Title:The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading Authors:Michael Caosun, Sinan Aral View a PDF of the paper titled The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading, by Michael Caosun and Sinan Aral View PDF HTML (experimental) Abstract: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 pote...