Too Fast to Adjust: Adoption Speed and the Permanent Cost of AI Transitions

We study how the speed of Artificial Intelligence (AI) adoption affects labor market outcomes during technological transitions. In a dynamic model where displaced routine workers enter a retraining pipeline with finite capacity, faster adoption compresses the displacement window without reducing total displacement, overwhelming the pipeline and generating permanent labor force exit through worker discouragement. The central result is that, even when two economies share the same long-run automation level, adoption speed alone determines transition welfare: faster adoption produces a larger discourage stock, a steeper and more persistent decline in labor force participation, and a sustained compression of the labor share throughout the transition window. Non-routine employment and wages exhibit a crossing pattern initially higher under fast adoption, then lower so that faster adoption can simultaneously raise long-run wages for survivors while permanently reducing participation. Social welfare is strictly concave in adoption speed and maximized at an interior optimum below the market rate, because firms do not internalize the congestion externality they impose on the retraining queue, the irreversibility of permanent exit, or the wage depression borne by non-routine incumbents. The socially optimal speed and retraining capacity are complements: stronger institutions raise the optimal adoption speed.