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

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.