Modelling lock-in of AI adoption through skill erosion
This paper presents a mathematical model showing that generative AI can improve immediate performance but erode human skills, leading to bistability and hysteresis between low-adoption/high-skill and high-adoption/low-skill states. The model derives exact skill distributions for users and non-users, and shows that AI adoption can become locked in due to history-dependent switching costs. It also finds asymmetric transitions: a single AI user can spread among experts if AI pay exceeds expert pay, while a single non-user can only reverse adoption if they regain sufficient skill before returning to AI.
Why it matters: Provides a rigorous theoretical framework for understanding AI adoption lock-in and skill erosion, with practical implications for policy and workforce planning.