We document and explain the gap between measures of AI exposure and measures of AI adoption in the workplace. This leads us to propose a new AI adoption index based on comparative advantage. Using the representative German DiWaBe employee survey linked to worker and establishment information, we compare worker-reported AI use to prominent exposure measures and find that the relationship is weak. Motivated by this gap, we develop a framework in which adoption depends not only on technical feasibility—AI's absolute advantage measured by exposure—but on profitability—AI's comparative (dis)advantage relative to a specific worker—balancing AI productivity against AI user costs and worker productivity against wages. We operationalize this framework at the task level by (i) estimating worker productivity relative to pay, (ii) mapping exposure indices into AI productivity, and (iii) inferring task-specific AI user costs from revealed-preference adoption. The resulting occupation-level index accounts for 60% of cross-occupation variation in observed AI adoption, compared to 14% for an exposure-only model. The two approaches diverge substantially for approximately 30% of workers, highlighting that comparative advantage—not exposure alone—is crucial for assessing AI's labor-market impact.

Internal labor markets have become more active over the past two decades, and the rise is concentrated higher up in the firm. Using online resume data that separates internal moves from external hires at each seniority level within U.S. firms, I show that the internal share of hires has grown sharply at mid-to-senior levels while remaining flat at entry levels. I build a framework combining a knowledge hierarchy on the firm side with frictional labor markets on the worker side, and use it to study the forces driving this pattern.

This project examines the impacts of artificial intelligence (AI) adoption on both workers and firms by addressing two central questions: (1) How is AI changing worker employment and wages? And (2) What drives differences in AI adoption across firm sizes? In both cases, we use a combination of Census microdata on workers and firms, including the Annual Business Survey (ABS), the Business Trends and Outlook Survey (BTOS), the Longitudinal Employer-Household Dynamics (LEHD) and the Longitudinal Business Database (LBD). On the worker side, we use a task-based framework to measure actual AI adoption and its effects on workers. On the firm side, we investigate the mechanisms behind the observed J-shaped relationship between firm size and AI adoption, specifically that AI adoption is highest among the smallest and largest firms and lower among medium-sized firms. We explore the roles of firm entry, small-firm bias, and workers' skill in explaining how AI adoption shapes firm behavior and its implications for firm dynamism and inequality.