AI, Automation, and Expertise
By Bouke Klein Teeselink and Daniel Carey
Abstract:
Occupations with identical AI exposure can experience opposite labor market trajectories depending on which tasks are automated. Building on Autor and Thompson (2025), we formalize this insight in a model that separates two margins of automation: displacement on the labor-demand side, and a supply-side expertise-threshold channel that expands or contracts the pool of qualified workers. The model predicts that these margins interact, with rising expertise requirements dampening employment declines from displacement. We test these predictions using hundreds of millions of job postings across 39 countries and exploiting ChatGPT’s November 2022 release as a natural experiment. We find that expertise-raising automation leads to a small but significant increase in advertised wages, while AI exposure reduces job postings by 6.2 percent, with suggestive evidence that effects are larger in countries with stricter employment protection and lower digital readiness. The employment interaction is positive and significant, confirming that the employment effect of expertise changes depends on the degree of automation displacement.
