An analysis of US payroll data across 730+ occupations: employment among workers ages 22 to 25 in highly AI-exposed jobs is now shrinking by 3.8% per year
Context & Ripple Effects
This finding extends a consistent coverage arc: earlier BLS-based reporting showed employment in a set of AI-exposed occupations slipping while the overall labor market grew, and Stanford-related coverage identified a larger decline among entry-level workers in the most exposed fields since 2022.
The new payroll analysis narrows the signal to workers ages 22 to 25 across more than 730 occupations, making the apparent pressure on early-career hiring more specific than a broad technology-sector slowdown.
First-order effects
- Young workers seeking roles with high AI exposure face a shrinking employment base, potentially reducing the number of conventional entry points into those occupations.
- Employers in those roles can meet more routine junior-level work with fewer hires or redesign junior jobs around supervising, validating, and applying AI output.
Second-order effects
- Schools, training providers, and early-career programs face pressure to emphasize skills that complement AI tools rather than workflows the tools can perform directly.
- Employers may shift recruiting toward candidates with demonstrated experience, while workers compete for a smaller set of roles that still provide the experience needed to advance.
Third-order effects
- If the pattern persists, the labor-market ladder in AI-exposed knowledge work could become less accessible at its first rung, changing how firms develop future talent.
- The divergence between AI-exposed occupations and the broader labor market will make it increasingly important to distinguish AI-linked displacement from cyclical weakness when assessing employment data.
The trend: This is one data point in the emerging trend of generative AI reshaping entry-level hiring before its effects are equally visible across the wider workforce.