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

Experimental evidence suggests that AI tools raise worker productivity, but also that sustained use can erode the expertise on which those gains depend.

To explore the consequences of this tradeoff, 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, a decision-maker who fully anticipates skill erosion still rationally adopts AI when front-loaded gains outweigh long-run skill costs, lowering long-run productivity.

The decomposition sorts deployments into five regimes by their long-run effect, separating beneficial from harmful adoption.

Second, the tradeoff introduces the potential for misaligned incentives.

When the decision-maker does not bear the long-run skill cost, AI use can leave the worker worse off than with no AI, the outcome we call the augmentation trap.

Third, when AI productivity depends little on worker expertise, the model can generate permanent divergence, with high-skill workers realizing their potential and low-skill workers deskilling.