Edward Conard

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Upside of Inequality Oxford Unintended Consequences

AI Summary. Employment for workers aged 22–25 in the occupations most exposed to artificial intelligence has declined roughly 18pp relative to less-exposed peers, with no comparable decline in other age groups. The authors find this operates mainly through reduced hiring, concentrated where AI substitutes for human tasks rather than complements them.

Erik Brynjolfsson, Bharat Chandar and Ruyu Chen Stanford University
Date Posted:

If employment of the most AI-exposed workers aged 22–25 had kept pace with that of their less-exposed peers since January 2018, their employment would be 15% higher than at the July 2025 data vintage observed in our previous study, and 19% higher as of June 2026.

Is AI reducing hiring for young workers in exposed occupations?

Core argument: Event study estimates for workers aged 22–25 show employment in the most AI-exposed occupation quintile declining roughly 18pp relative to the least-exposed quintile by mid-2026, with no controls and under four control sets.

Figure 4 plots occupation event study estimates for the most exposed quintile (relative to quintile 1) for workers aged 22–25, tracing the long-difference coefficient as the endpoint of the difference is rolled forward month by month, with no controls and under four control sets: occupational interest rate exposure, education (college share), a work-from-home measure, and all three jointly. The relative decline grows to roughly 18pp by mid-2026. Other age groups show no comparable decline. This divergence, documented in prior versions of this study, has continued to widen; the shortfall was 15% below where it would have been had it kept pace with that of their less-exposed peers at the July 2025 data vintage, and 19% as of June 2026. It operates primarily through reduced hiring rather than increased separations, and declines are concentrated in occupations where AI usage substitutes for human tasks; complementary usage is associated with flat or rising employment.

Takeaways by Macro Roundup® AI

  1. Event study estimates for workers aged 22–25 show employment in the most AI-exposed occupation quintile declining roughly 18pp relative to the least-exposed quintile by mid-2026, with no controls and under four control sets.
  2. The shortfall for workers aged 22–25 relative to their less-exposed peers widened from 15% at the July 2025 data vintage to 19% as of June 2026, while other age groups show no comparable decline.
  3. The decline operates primarily through reduced hiring rather than increased separations and is concentrated in occupations where AI usage substitutes for human tasks, while complementary AI usage is associated with flat or rising employment.

Related Articles:

  • Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence — Using payroll records covering millions of US workers, the authors find employment for workers aged 22–25 in occupations most exposed to artificial intelligence, such as software developers and customer service representatives, declined 6%, while employment for older workers in those occupations rose 6–9%. Employment in less-exposed occupations grew comparably across age groups.
  • The Impact of AI on the U.S. Labor Market — Using observed AI usage data across 321 U.S. occupations, the authors estimate that highly exposed occupations saw a 6.7% decline in real wage growth with no detectable employment effects. The estimated declines are concentrated among the lowest earners, with service workers down 24.3% and the bottom wage quartile down 10.7%, while top earners show no significant effect.
  • You’re (Not) Hired: Artificial Intelligence and Early Career Hiring In The Quarterly Workforce Indicators — Employment among workers aged 22–24 in the most artificial-intelligence-exposed quintile of industry-state employers fell 15.2%, while other exposure quintiles were flat or posted modest gains. The decline is not generally observed among older workers; the 25-34 group's drop was less than one-third as large in percentage terms.
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