Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data

Abstract

We study whether generative artificial intelligence (AI) constitutes a form of seniority-biased technological change, disproportionately affecting junior relative to senior workers. Using U.S. résumé and job posting data covering nearly 62 million workers in 285,000 firms (2015-2025), we track within-firm employment dynamics by seniority. We identify AI adoption through a text-analysis approach that flags postings for dedicated « AI integrator » roles, signaling active implementation of generative AI. Difference-in-differences and triple-difference estimates show that, beginning in 2023Q1, junior employment in adopting firms declined sharply relative to non-adopters, while senior employment continued to rise. The junior decline is driven primarily by slower hiring rather than increased separations, with the largest effects in wholesale and retail trade. Heterogeneity by education reveals a U-shaped pattern: mid-tier graduates see the largest declines, while elite and low-tier graduates are less affected. Overall, the results provide early evidence of a seniority-biased impact of AI adoption and its mechanisms.