Abstract This study explores how Artificial Intelligence (AI), particularly the rise of Large Language Models (LLMs), is unevenly transforming the U.S. labor market. Focusing on wage growth and employment patterns across various occupations and regions, we introduce two new indices—the Replacement Exposure Index (REI) and the Assistive Exposure Index (AEI)—to measure the susceptibility of jobs to automation or enhancement by AI. Using panel data from the U.S. Bureau of Labor Statistics and the O*NET Resource Center, we conduct a Difference-in-Differences (DID) analysis with the emergence of LLMs in 2022 as a key turning point. Our findings indicate that occupations exposed to AI experience both wage increases and shifts in employment structures, with more pronounced effects in high-tech states like California and Massachusetts. In contrast, low-tech states demonstrate more modest labor responses. These results reveal a pattern of wage polarization and regional inequality driven by exposure to AI. The study contributes to the growing body of research on technological change and labor markets by providing occupation- and region-specific evidence. Our results also underscore the pressing need for policy measures—particularly in education, workforce training, and regional innovation—to mitigate inequality and foster inclusive adaptation to AI-driven changes.
Wang et al. (Mon,) studied this question.