Tools of the trade: AI and the restructuring of the experience premium

This is the authors' abstract. We don't add key points for this paper.

Purpose We examine how artificial intelligence (AI) reshapes the relative economic positioning of entry-level and experienced workers within industries. Rather than focusing on job elimination, we investigate whether AI changes the returns to experience. Design/methodology/approach We construct an industry-quarter panel from 337 million US Lightcast job postings across 16 NAICS2 industries, with finer-grained inference and decomposition across 86 NAICS3 industries (Q1 2017 to Q1 2026). Difference-in-differences models use a leave-own-industry-out shift-share measure, with industry and quarter fixed effects and small-cluster-robust inference. Lightcast AI-skills rate and Felten task-exposure provide comparisons, with Census firm-reported AI adoption and BLS occupational data providing triangulation. Findings Entry-level posting shares decline modestly but not differentially with AI-exposure. By contrast, the experienced-entry pay gap widens with greater shift-share AI exposure; a one-percentage-point increase in exposure predicts a significant $2,780 widening. Occupational reallocation toward higher-paid jobs accounts for 61% of the widening, and stronger within-occupation wage growth concentrated among experienced workers accounts for 39%. Research limitations/implications The findings suggest AI and labour-market research should distinguish potential task exposure from realized adoption and examine how occupational restructuring alters within-industry career stage inequality. Originality/value We shift attention from displacement to experience repricing, introducing career-stage-biased technical change as a two-channel account in which AI may alter both occupational demand and the rewards to experience-mediated capabilities.