Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data Authors Guy Lichtinger — Harvard University Department of Economics Seyed Mahdi Hosseini Maasoum — Harvard University Date August 31, 2025 Summary This study investigates if generative artificial intelligence (AI) acts as a form of seniority-biased technological change, meaning it disproportionately affects junior workers compared to senior workers. Data and Methodology Examined nearly 62 million U.S. workers across 285,000 firms from 2015 to 2025. Used résumé and job posting data to analyze within-firm employment dynamics segmented by seniority. Identified AI adoption by detecting job postings for "AI integrator" roles through text analysis, signaling active use of generative AI. Employed difference-in-differences and triple-difference econometric methods to estimate effects starting 2023 Q1. Key Findings Junior employment in AI-adopting firms declined sharply relative to firms that did not adopt AI. Senior employment continued to increase in those adopting firms. The decline in junior positions was mainly due to slower hiring rates rather than workers leaving their jobs. The most significant declines appeared in the wholesale and retail trade sectors. When examining education levels, a U-shaped pattern emerged: Mid-tier graduates experienced the most substantial declines in junior roles. Elite and low-tier graduates were less impacted. Implications Early evidence suggests generative AI adoption impacts labor markets by creating a seniority-bias, possibly altering career progression and employment opportunities at entry and junior levels. These dynamics may shape future workforce structures and skill demands in AI-adopting firms. Keywords Generative AI, Technological Change, Labor Market, AI Adoption, Job Postings, Résumé Data, Career Ladders, Entry-Level Employment, U.S. Labor Market JEL Classification J24: Human Capital; Skills; Occupational Choice; Labor Productivity J31: Wage Level and Structure; Wage Differentials J63: Labor Turnover; Vacancies; Layoffs O33: Technological Change: Choices and Consequences; Diffusion Processes L23: Organization of Production Paper Details Length: 35 pages Downloads: 9,997 Abstract Views: 26,973 Ranking: 1,305 on SSRN Access Download PDF DOI: 10.2139/ssrn.5425555 --- Contact Information Guy Lichtinger Harvard University Department of Economics Cambridge, MA, United States Seyed Mahdi Hosseini Maasoum Harvard University Cambridge, MA, United States --- This research provides valuable insight into how generative AI technologies reshape employment patterns, particularly affecting junior holdings in firms, with notable variation across sectors and education strata.