Stanford Digital Economy Lab
Stanford University research lab directed by Erik Brynjolfsson; calculated AI is suppressing 34 hires for every declared layoff.
Last refreshed: 16 April 2026 · Appears in 1 active topic
If 34 jobs are suppressed for every AI layoff declared, what is the real displacement count?
Timeline for Stanford Digital Economy Lab
Applied JOLTS hiring-rate gap to nonfarm workforce to produce 34-to-1 displacement ratio
AI: Jobs, Power & Money: Stanford: AI costs 34 hires per layoffMentioned in: AI industry raises $125M v. regulators
AI: Jobs, Power & Money- How does Stanford calculate 34 AI jobs lost per layoff?
- Stanford Digital Economy Lab compared the JOLTS 3.1% February 2026 hiring rate with the 2023 baseline, finding 950,000 to 1 million fewer annual hires, against Challenger's 27,645 declared AI layoffs — a 34-to-1 ratio.Source: Stanford Digital Economy Lab / ProCap Insights
- Why are young workers hit hardest by AI job displacement?
- Workers aged 22 to 25 in AI-exposed occupations are 16% below late-2022 employment levels, while colleagues over 30 in the same roles are up 6 to 12%. Entry-level positions are more easily automated; senior roles require contextual judgement current models lack.Source: Stanford Digital Economy Lab
- What is the JOLTS hiring rate and why does it matter for AI?
- JOLTS (Job Openings and Labor Turnover Survey) is the BLS measure of monthly hires. Its February 2026 reading of 3.1% — the lowest since April 2020 — is the starting point for Stanford's estimate that AI is suppressing nearly 1 million annual hires.Source: Bureau of Labor Statistics
- Who is Erik Brynjolfsson and what has he said about AI jobs?
- Erik Brynjolfsson directs Stanford's Digital Economy Lab and co-wrote The Second Machine Age. His April 2026 JOLTS analysis found AI is preventing roughly 950,000 to 1 million annual hires in the US, 34 times the publicly declared AI layoff count.Source: Stanford Digital Economy Lab
Background
The Stanford Digital Economy Lab made its most consequential measurement to date in April 2026, applying the JOLTS (Job Openings and Labor Turnover Survey) 3.1% hiring rate — the lowest reading since April 2020 — to the 158.6 million nonfarm workforce. The result: AI is preventing roughly 950,000 to 1 million annual hires against the 2023 pace, a ratio of approximately 34 hires suppressed for every one declared AI layoff.
Founded at Stanford University and directed by economist Erik Brynjolfsson, the Lab studies how digital technologies reshape labour markets, productivity, and economic growth. Brynjolfsson co-authored The Second Machine Age and has tracked AI labour displacement since the mid-2010s, making the Lab the most cited academic source on AI workforce impact in the US policy debate. Its JOLTS analysis extends earlier work from Update #4 corroborating the age-concentration pattern: workers aged 22 to 25 in AI-exposed occupations are 16% below late-2022 employment levels, while colleagues over 30 in the same roles are up 6 to 12%.
The 34-to-1 ratio carries significant policy weight because it reframes how AI displacement is measured. Official counts — through Challenger, Gray & Christmas and WARN Act filings — capture declared layoffs only; the Lab argues the dominant channel is hires that never happen, invisible to standard labour statistics until cohorts show up missing years later. The figure arrived the same week the Bureau of Labor Statistics skipped its scheduled GenAI workplace publication, shifting the evidence burden further onto academic and regional bank sources.