Abstract

We document the early empirical patterns in US knowledge-work labor demand over the thirty-three months following the November 2022 public release of large language models, using a panel of approximately 41 million online job postings from January 2023 through September 2025. We classify postings by occupation (SOC 2018 six-digit) and merge with occupation-level AI exposure scores following . Three findings are central. First, between 2023Q2 and 2025Q3, postings in the top exposure quintile declined by 19.4 percent while postings in the bottom quintile declined by 4.1 percent, a 15.3 percentage-point differential that survives controls for industry mix, region, and macroeconomic conditions. Second, the within-occupation composition of skill requirements shifted substantially in highly-exposed occupations: the share of postings mentioning routine cognitive tasks (data entry, standard report generation, first-line response handling) fell by 6.8 percentage points; the share mentioning AI-collaboration skills (prompt engineering, AI verification, AI workflow integration) rose by 7.1 percentage points; the share mentioning judgment-intensive skills (architectural design, ambiguous-case judgment, strategic communication) rose by 4.6 percentage points. Third, the within-occupation posted wage distribution polarized: the 75th-percentile posted wage in highly-exposed occupations rose by 12.4 percent while the 25th-percentile fell by 3.2 percent, a 15.6-percentage-point differential robust to controls. The three margins jointly support the restructuring hypothesis articulated in the contemporary methodology literature : generative AI is substituting for routine cognitive tasks at the lower end of the within-occupation distribution while complementing judgment-intensive tasks at the higher end. We are explicit that the design is descriptive of a cross-sectional differential under a single common shock; the residual confounds from contemporaneous monetary tightening and post-pandemic sectoral reallocation are documented and partial-out diagnostics reported. We close by drawing implications for occupation classification, workforce education investment, and the projection of long-run skill premia.