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Incentivizing Learning in AI-Assisted Work: A Model of Skill Dynamics
This paper extends an accept-or-solve model to sequential tasks where worker skills evolve through independent practice or reliance on AI. It shows that myopic workers can get trapped at low skill levels or lose skills, while strategic workers may choose independent work to preserve future capabilities. The authors characterize incentive payments that encourage skill acquisition and retention, finding that independent work can become self-sustaining and that higher skill targets may reduce long-run costs.
Why it matters: Provides a concrete economic model and actionable insights for organizations designing AI-assisted workflows to sustain human expertise.
AI-assisted workskill dynamicsincentive design