The Capability Transfer: Cohort Reversal, Codified Expertise, and the Adaptability Tax of AI Transformation

Most analyses of AI and employment ask which jobs are exposed. This paper asks what happens to the pipeline that produces experts. It extends the model of Caosun and Aral (2026) from a single worker to a firm with two cohorts, juniors and seniors, and studies how expertise formation changes when codified work is automated. Because junior work is the most codified, demand for juniors falls first; the stock of seniors falls later but further, as the pipeline stops replacing the experts who retire. In the baseline calibration, junior demand bottoms out about a fifth below its 2022 level before recovering most of that ground, while the senior stock falls by a quarter and later halves. Because expertise forms through unassisted practice on work that AI cannot do, the time needed to develop an expert from within rises from about five years to nine. The costs fall on a different party from the one that decides on adoption: a manager with a three-year horizon sets AI use to maximise near-term output, cutting the wage bill by 21 percent while the capacity of the firm on work AI cannot do falls by two-fifths and its output on that work goes unserved once the pipeline runs thin. Two key parameters are not yet measured, so the results are scenarios rather than forecasts. The paper proposes three indicators organisations can track: the unaided practice juniors get on work AI cannot do, senior time spent on such work, and the years it takes to develop an expert.