Gen Z women losing ground to men in entry-level jobs—but AI isn't the main cause

Stanford Digital Economy Lab and ADP Research have released new data from the Canaries Dashboard, a joint project led by Erik Brynjolfsson and Nela Richardson, showing that young women ages 22–25 are falling behind young men in entry-level employment. The dashboard, one of the country's most closely watched labor-market trackers, has documented how generative AI is reshaping hiring across 4.6 million workers and more than 730 occupations.

Since ChatGPT's debut in late 2022, the dashboard established a clear pattern: workers ages 22–25 in the most AI-exposed occupations, such as software development and customer service, have seen employment decline sharply, even as overall U.S. employment growth remained healthy. That divergence has deepened steadily, growing by roughly half a percentage point per month since the researchers' original paper last year. The latest update breaks the numbers down by gender for the first time, revealing that young women in the sample show weaker employment growth than young men in the same 22–to–25 age bracket.

However, the researchers identified two distinct forces at work. First, women are more likely to work in AI-exposed occupations: 43.8% of women in the sample work in the most-exposed category and 21.2% in the second-most-exposed, compared with 32.4% and 18.1% of men respectively. Second, women see somewhat slower employment growth than men at every level of AI exposure. Yet when the researchers isolated how much of the growth gap traced specifically to AI exposure, the connection proved weak. In the least-exposed quintile, employment among 22–to–25-year-old women grew just 1.3% annually after late 2022, compared with 2.7% for men—a gap nearly as wide as in the most-exposed category, where women's employment shrank 4.5% annually against 2.5% for men.

The researchers concluded: "Gender-based differences in the relationship between AI exposure and employment trends appear to be driven primarily by occupational composition, rather than disparate trends within given sets of occupations." This finding challenges prior research, including work from the International Labour Organization, which found women's jobs nearly twice as likely as men's to be exposed to generative AI. The Canaries data add something exposure-based studies could not measure: actual, realized employment outcomes tracked month by month. The core argument from Brynjolfsson and Richardson has been that AI is disrupting tasks before it disrupts jobs, automating mechanical work—summarizing, formatting, scheduling—that typically goes to the newest employees. Occupations where AI augments human work show durable employment growth, while those where it automates tasks outright are contracting, with early-career roles sitting disproportionately in the second group. Women's overrepresentation in AI-exposed roles means they face that dynamic more intensely, but as a byproduct of occupational sorting that predates generative AI by decades, not because AI treats women differently once they are in a given job. The researchers expect further research will investigate what actually drives women's flatter growth curve across occupations—education mix, industry concentration, hours worked, return-to-office effects—over time.

The Canaries Dashboard has emerged as one of the country's most closely watched labor-market trackers since its debut, establishing a clear pattern: workers ages 22–25 in AI-exposed occupations like software development and customer service have seen employment decline sharply since ChatGPT's late-2022 debut, even as overall U.S. employment growth remained healthy. That divergence has not reversed but deepened, growing by roughly half a percentage point per month.

The latest update, released for the first time with gender disaggregation, reveals a paradox that challenges conventional wisdom about AI and gender equity. Prior research from the International Labour Organization and other sources has suggested women's jobs are nearly twice as likely to be exposed to generative AI, and separate estimates have placed women at three times the automation risk of men. Yet the Canaries data—which tracks actual, realized employment outcomes month by month—shows that if AI exposure were the primary driver of the gender gap, the gap should widen sharply as exposure rises. Instead, it remains roughly the same whether a job is barely touched by AI or squarely in its path.

The researchers' conclusion reframes the problem: the real issue is not that AI treats women differently within occupations, but that women are overrepresented in AI-exposed roles as a byproduct of occupational sorting that predates generative AI by decades. Brynjolfsson and Richardson's core argument has been that AI is disrupting tasks before it disrupts jobs—automating the mechanical work (summarizing, formatting, scheduling) typically handed to the newest employees—and early-career roles sit disproportionately in that contracting group. Women's concentration in such roles is a labor-market structure issue, not an AI-specific one, pointing to questions around education mix, industry concentration, hours worked, and return-to-office effects that further research is expected to address.