Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence

Source: Brynjolfsson, Chandar, Chen, Stanford Digital Economy Lab · 2026-08-12

Using a sample of high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026, we document six facts about the labor market following the widespread adoption of generative AI.

We find no evidence of widespread, economy-wide job displacement.

However, employment of young workers (ages 22–25) in AI-exposed occupations now stands 19% below where it would be had it kept pace with that of their less-exposed peers; experienced workers show no comparable gap.

This divergence has widened steadily since we first documented it in August 2025.

It operates primarily through reduced hiring of young workers rather than increased separations.

Adjustment is occurring through employment rather than base compensation.

The divergence is not explained by several prominent alternative factors: it persists when excluding technology firms and computer occupations, when controlling for exposure to interest-rate increases and for remote work, and across alternative measures of AI exposure. These patterns attenuate when controlling for education, show some divergent trends predating generative AI, and are more pronounced in the ADP analysis sample than in national survey benchmarks, with some evidence of consistent patterns in government administrative data. We interpret these facts as early, descriptive indicators—canaries in the coal mine—rather than causal estimates, and we provide a public set of AI Economic Indicators to facilitate ongoing tracking of changes in the economy.