Stanford tracks employment declines among young workers in AI-exposed roles

The AI Economic Indicators show the strongest exposure-related employment declines among workers aged 22 to 25, as Isabelle Hau calls for new apprenticeship, mentorship and experiential learning models

Stanford Digital Economy Lab’s AI Economic Indicators dashboards show employment declines among workers aged 22 to 25 in highly AI-exposed occupations, including software development and customer service.

Isabelle Hau, Executive Director of the Stanford Accelerator for Learning at Stanford University, highlighted the findings on LinkedIn, citing a 4.4% annual rate of employment decline in recent months for young workers in these occupations.

Hau called for new models of apprenticeships, mentorship and experiential learning to help young people develop professional expertise.

“Entry-level jobs have traditionally been essential environments for learning by doing, developing judgment, building relationships, and growing into professional roles,” she wrote.

The employment findings come from the Canaries Dashboard, developed with ADP Research using payroll data from a sample of US firms. The dashboard measures correlations between occupational exposure to AI and employment trends. It does not establish that AI caused the declines, and the lab cautions that its sample is not necessarily representative of the wider economy.

Employment patterns differ by age and occupation

The Canaries Dashboard tracks employment across occupational exposure groups following ChatGPT’s introduction in November 2022.

Among workers aged 22 to 25, the two most-exposed groups show employment declines, while the three less-exposed groups show growth. That pattern becomes less pronounced and ultimately disappears in older age groups.

Software developers and customer service representatives show declines among younger workers alongside expansion among older groups. Home health aides, whose relational and physical work has relatively low AI exposure, show stronger employment gains among the youngest workers.

The dashboard also examines how AI is used within occupations, drawing on the Anthropic Economic Index. Automation involves users fully delegating tasks to AI, while augmentation involves collaboration between people and AI systems.

Occupations with a higher share of automation show employment declines or more limited growth. For early-career workers, the augmentation ratio does not show a similarly clear relationship with employment trends.

Human capabilities as explicit learning outcomes

Hau’s response focused on what education should intentionally develop as AI changes professional work.

“Relational intelligence, creativity, critical thinking, human agency, adaptability can no longer be treated as by-products of education. They must become central to what we intentionally teach, nurture, and assess,” she wrote.

She also urged AI developers to consider how their systems support human potential, assessing tools by the capabilities they enable people to develop alongside the tasks they can perform.

Her proposed changes to apprenticeships, mentorship and experiential learning center on preserving opportunities to learn through work, including developing judgment, building relationships and acquiring professional experience.

Tracking adoption and broader economic change

The AI Economic Indicators bring together government statistics, academic research and private company data across five areas: employment and wages, aggregate economic outcomes, consumer surplus, hiring and skills, and AI usage.

Alongside the Canaries Dashboard, the collection includes an Adoption Monitor tracking individual and business use of AI, a Transformation Tracker examining broader economic measures, and a GDP-B Surplus Observer estimating the value consumers derive from generative AI tools.

The Transformation Tracker assesses 12 indicators, including output growth, labor productivity and capital shares. Its October 21, 2026 update reports no decisive evidence of the shift toward explosive economic growth that it monitors.

The dashboards are publicly accessible. The lab schedules monthly updates on the third Wednesday following the first Monday, with the Canaries Dashboard also last updated on October 21, 2026.

ETIH Innovation Awards 2027 open for entries

The ETIH Innovation Awards 2027 are now open for entries, with 26 categories recognising companies, institutions, projects and individuals making a measurable impact across education technology. The global awards span AI in education, digital learning, higher education, skills and workforce, inclusion, education operations, evidence and impact, with entries managed through ETIH’s new online awards platform.

Entries are open worldwide, with the early-bird deadline on Friday 18 December 2026 and the final entry deadline on Friday 26 February 2027 at 23:59 UK time. 

Explore the 2027 categories and judging criteria, read the How to Enter guide or start an entry through the ETIH Innovation Awards 2027 platform.