Study library · 20 sources in the current map

The studies behind the AI job claims

Each card says what was measured. That is the difference between a warning and a claim you can use.

Broad U.S. monitor: no clear AI jobs footprint yet

Source: Yale Budget Lab, “Tracking the Impact of AI on the Labor Market,” updated 15 September 2026
Population and place: U.S. labor-market measures built around Current Population Survey data; the update incorporates August 2026 microdata.
Period: Monitoring through August 2026, compared with historical and pre-AI patterns.
Outcome: Occupational churn, AI exposure among unemployed workers, AI-use measures and an exposure-based synthetic difference-in-differences comparison.
Method: Ongoing descriptive monitoring plus a synthetic difference-in-differences analysis of occupational AI exposure.
Finding: Yale reports no clear AI-related labor-market disruption in the measures it tracks: indicators are flat, within historical ranges or continue on pre-AI trends.
Inference: A strong broad counter-signal to a claim that AI has already caused an economy-wide U.S. jobs collapse.
Limit: The tracker does not randomize employer adoption or identify a named system’s causal effect. Broad measures can miss a concentrated effect on new entrants, a region, an occupation or job quality.

Swedish AI grants: more vacancy ads, no detected headcount effect

Source: Hellsten, Khanna, Lodefalk and Yakymovych, IZA Discussion Paper 18267, November 2025
Population and place: 190 Swedish small- and mid-sized firms that applied to Vinnova for first-time AI-project support: 53 were awarded and 137 were not.
Period: Grant decisions in 2019–2020; job-vacancy data from 2017–2024 and outcomes up to five years after award.
Outcome: Whether a firm posted a vacancy, number of vacancies, total employment, hiring and separations.
Method: Synthetic difference-in-differences comparing awardees with non-awarded applicants, using near-universal Swedish vacancy data and population registers.
Finding: Five years after award, recipients were 24 percentage points more likely to post a vacancy and 22.5 points more likely to post a white-collar vacancy. The study found no statistically detectable effect on total employment, hiring or separations.
Inference: A close-to-causal grant effect on recruitment effort among Swedish AI-project applicants, not a simple employment verdict.
Limit: The intervention is a grant, not random use of a named system, and it largely predates broad chatbot diffusion. The sample is small, vacancies are not hires, and Swedish labor-market frictions may not travel.

A 4% cognitive-employment decline—inside one 2030 scenario

Source: Korinek, Jones, Sacher, Cotter and McCrory, CEPR Discussion Paper 21939, September 2026
Population and place: Not a worker or firm outcome sample. The paper models an economy and separately surveys U.S. adults about AI expectations.
Period: Illustrated paths from 2026 to 2030.
Outcome: Modelled cognitive employment, GDP, labor share, wages, reallocation and unemployment.
Method: A structural scenario framework that maps assumed AI capability and automation paths into economic outcomes; it illustrates modest, substantial and extreme change.
Finding: In the authors’ substantial scenario, cognitive employment declines 4% by 2030. They say median U.S. survey expectations are consistent with that path.
Inference: A useful stress test for a possible future—not a measured 2026 labor-market result.
Limit: The figure depends on the scenario’s assumptions and has no stated probability on the accessible summary page. It does not count layoffs, identify a worker’s risk or establish that any observed hiring change was caused by AI.

Danish firms reporting AI adoption: slower growth through lower recruitment

Source: Bonin, Darougheh and Kuchler, Danmarks Nationalbank Working Paper 223, September 2026
Population and place: Danish firms reporting AI use, matched to monthly employer–employee records; the reported effect is concentrated in smaller firms.
Period: Firms first reporting AI adoption in 2023, followed through late 2025.
Outcome: Employment growth and recruitment, including recruitment in AI-exposed occupations.
Method: Firm-level event-study comparison of adopters and non-adopters using prior employment trends and industry differences.
Finding: By late 2025, 2023 adopters had employment growth about 11% below non-adopters relative to prior trends. The gap reflects lower recruitment; aggregate Danish employment had not shown a major change.
Inference: A high-value adoption-linked warning that slower hiring can appear before an economy-wide unemployment signal.
Limit: Adoption is not random, so firm differences or other concurrent changes may explain part of the gap. Denmark, firm size and the period through late 2025 limit generalization; this is not a layoff count or a personal forecast.

California claims: no August surge in high-exposure unemployment claims

Source: California Employment Development Department and California Policy Lab AI-Unemployment Tracker, August 2026 update
Population and place: California unemployment-insurance claimants, grouped by the AI exposure of their self-reported last occupation.
Period: Monthly claims through August 2026; the update reports three-month moving averages.
Outcome: New initial unemployment-insurance claims, not employment, hiring or all job losses.
Method: Descriptive tracking by potential AI exposure and a task-use measure derived from Claude conversations, with linked occupation crosswalks.
Finding: High-exposure three-month claims declined about 1.2% under the potential measure and 1.0% under the observed-use measure, within recent historical fluctuations.
Inference: A current California counter-signal to a broad exposure-linked unemployment surge.
Limit: Exposure and task-use scores are not employer adoption. Claims exclude many workers who do not apply or are ineligible, and self-reported occupation codes can be missing or inaccurate.

Recent graduates: no summer unemployment spike in a new U.S. check

Source: Fairlie and Wu, IZA Discussion Paper 18945, September 2026
Population and place: Recent U.S. college graduates in Current Population Survey microdata, compared with older college graduates and young adults without a college degree.
Period: June through August 2026, compared with earlier summer months.
Outcome: Unemployment, plus a broader measure that includes “sidelined unemployed.”
Method: Difference-in-differences and event-study interaction models using two comparison groups; occupation interactions test AI exposure and remote-work availability.
Finding: No statistically significant summer-2026 relative unemployment increase appears in the reported comparisons, including the broader unemployment measure. The paper finds some evidence of a positive relationship with remote-work availability in occupation interactions.
Inference: A material U.S. counter-signal to a broad graduate-unemployment claim.
Limit: The paper does not observe employer AI adoption or measure initial job matches, earnings, vacancies or later career progression. It is a short-window unemployment result, not an all-clear for entry-level work.

Norway: no robust young-worker displacement yet

Source: Facius and Iacono, CESifo Working Paper 12752, June 2026
Population and place: Norwegian private-sector workers with a full-time main job; population-wide employer-employee records, focused on ages 22–25.
Period: January 2015 to March 2025, around ChatGPT’s November 2022 release.
Outcome: Employment, wages and labor-market mobility.
Method: Within-firm composition difference-in-differences, occupation-level synthetic difference-in-differences and a firm shift-share design, using two occupational exposure indices.
Finding: Young-worker employment estimates are negative but not individually statistically significant across the three designs. Backdated placebos can be larger than the post-ChatGPT estimate.
Inference: A serious cross-country limit on a universal early-career AI displacement story.
Limit: Norway is not the U.S. The study maps U.S.-based exposure scores to Norwegian occupations and does not observe firm-level AI adoption; late estimates trend negative and the post-period is still short.

European firms: productivity up, no adverse headcount result

Source: Aldasoro and colleagues, EIB Working Paper 2026/02, January 2026
Population and place: More than 12,000 non-financial firms in the European Union and United States.
Period: Pooled firm cross-sections from 2019 to 2024.
Outcome: Firm labor productivity and employment.
Method: Matched EIBIS-ORBIS firm data; the paper instruments EU-firm AI adoption with adoption rates of matched U.S. peers.
Finding: The paper reports 4% higher labor productivity and no adverse firm-level employment effect in its instrumented specification.
Inference: A broad short-run counterpoint to a firm-level job-collapse claim.
Limit: The measure includes big-data analytics and AI, not generative AI alone. The pooled design does not measure entry-level hiring, occupation mix or a randomized rollout.

Actual AI spending: heavy adopters added headcount

Source: Kharazian, Simon and Stevens, Ramp Economics Lab and Revelio Labs working-paper presentation, June 2026
Population and place: More than 21,000 Ramp-linked U.S. firms, joined to Revelio workforce data.
Period: Firm outcomes over the two years after the study’s adoption date.
Outcome: Total and entry-level headcount.
Method: Firm-level comparison by recorded AI-vendor spending intensity; high intensity is the top third of spend per employee in the first three post-adoption months.
Finding: The high-intensity group grew total headcount 10.2% and entry-level headcount 12%; low-intensity adopters had no statistically significant change.
Inference: A valuable actual-spending counterpoint to exposure-only studies, but still an association.
Limit: The accessible report is company-authored and uses proprietary Ramp-linked data. High-intensity adopters were already larger, more technical, more venture-backed and faster growing; the result is not a causal or economy-wide estimate.

Young workers: a 19% relative payroll gap

Source: Brynjolfsson, Chandar and Chen, Stanford Digital Economy Lab, revised August 2026
Population and place: Millions of U.S. workers in ADP payroll data; focus on workers aged 22–25.
Period: Employment trends through June 2026, compared with the period after ChatGPT's release.
Outcome: Employment and base pay by age and occupational AI exposure.
Method: Descriptive payroll comparisons between more- and less-exposed occupations, with robustness checks and alternative controls.
Finding: Young workers in highly exposed occupations were about 19% below the employment level they would have reached by keeping pace with less-exposed peers. Experienced workers showed no comparable gap.
Inference: A serious descriptive early-career warning, not a causal estimate.
Limit: Exposure is not adoption. Pre-trends, education, firm-hiring controls and the difference between the ADP sample and national benchmarks leave attribution and generalization unresolved.

Remote work: a rival explanation for junior-hiring decline

Source: Lambert and Schindler, University of Warwick working paper, September 2026
Population and place: New hires and online postings in the U.S., U.K., Canada and Australia.
Period: 2017–2025.
Outcome: Junior share of new hires and share of ads requiring limited experience.
Method: Difference-in-differences at occupation, region and firm level, testing GenAI and working-from-home exposure separately and jointly.
Finding: Each exposure separately predicts a lower junior share. Jointly, the remote-work effect remains while the GenAI coefficient sharply attenuates and is often not statistically distinct from zero.
Inference: A credible attribution challenge to an exposure-only AI explanation.
Limit: A working-paper result with different countries, outcomes and exposure measures from the U.S. payroll and Census studies. It does not show that remote work fully explains those results.

New graduates: lower employment and pay in the most exposed majors

Source: Orr, Tucker and Warren, U.S. Census Bureau working paper, September 2026
Population and place: Roughly 6.7 million bachelor’s graduates from more than 350 institutions in 24 U.S. states; a subset of U.S. higher education.
Period: Graduation records from 2016–2024 and post-graduation outcomes around ChatGPT's late-2022 release.
Outcome: Initial employment, full-quarter earnings, industry and job switching.
Method: Event-study and fixed-effects comparisons of majors with different pre-ChatGPT occupational task-exposure scores.
Finding: The most-exposed major decile had a five-point lower initial-employment rate and 13% lower full-quarter initial earnings than the bottom six deciles after ChatGPT's release.
Inference: Association consistent with a concentrated disruption at labor-market entry.
Limit: Exposure is not adoption. The design cannot rule out every time-varying shock correlated with major exposure, and the institutions are not all U.S. colleges.

Early-career hiring: a U.S. warning with an attribution gap

Source: Lee C. Tucker, U.S. Census Bureau working paper, April 2026
Population and place: Private-sector workers aged 22–24 in industry-state cells across 45 U.S. states.
Period: Quarterly data through 2025 Q2; compared around ChatGPT's November 2022 release.
Outcome: Hires, employment, earnings and job flows.
Method: Event studies and regression comparisons of industry-state cells with different occupational AI-exposure scores.
Finding: Early-career hires fell immediately by 9% relative to less-exposed cells; regression-adjusted employment in the most-exposed quintile was 12% lower after 10 quarters.
Inference: Association consistent with reduced relative demand; not a clean causal estimate of AI adoption.
Limit: Exposure is not observed use. Pre-existing pandemic-era shifts, remote work and education trends remain plausible contributors.

Texas online postings: a regional AI-related pullback

Source: Dodini and Smith, Federal Reserve Bank of Dallas analysis, September 2026
Population and place: Texas employers’ Lightcast online job postings; a balanced panel separately follows incumbent firms.
Period: Quarterly postings after ChatGPT’s November 2022 release, with incumbent-firm results through early 2026.
Outcome: Online job postings: a measure of advertised labor demand, not realized hiring or employment.
Method: Within-industry comparison of occupations with different shares of tasks classified as automatable by a Claude-use-based index; firm exposure is set from pre-ChatGPT posted occupations.
Finding: More-exposed positions had about 8% fewer postings relative to less-exposed positions by 2025 Q1. The authors calculate a 2.6% aggregate effect on Texas Lightcast postings in 2025; incumbent firms show an 8–9% pullback by early 2026.
Inference: A serious regional, exposure-linked warning for advertised labor demand.
Limit: Claude task use is not verified employer adoption. Lightcast underrepresents some work and postings are not hires, jobs or layoffs. A broad U.S. posting analysis finds little distinct AI decline, so this does not establish a national causal effect.

Broad U.S. postings: no distinct AI drop yet

Source: Audoly, Guerin and Topa, Federal Reserve research analysis, June 2026
Population and place: U.S. postings collected by Lightcast from career pages, job boards and listing aggregators.
Period: Compared after ChatGPT's late-2022 release.
Outcome: Labor demand as represented by job postings.
Method: Tests whether postings for more AI-exposed occupations declined disproportionately.
Finding: Overall hiring slowed, but the analysis finds little indication of a distinct AI-driven decline in postings.
Inference: A broad counter-signal to claims of an economy-wide AI hiring collapse.
Limit: Postings are not realized hires and do not isolate new graduates, college majors or unadvertised recruitment.

U.S. task adoption: broad reach, shallow use

Source: Bick, Blandin, Deming and Schumacher, St. Louis Fed analysis, September 2026
Population and place: Nearly 14,000 U.S. workers in four Real-Time Population Survey waves.
Period: August 2025 to May 2026.
Outcome: Reported workplace AI use by detailed occupation and task.
Method: National survey linked to occupations and the ten O*NET tasks rated most important for each occupation.
Finding: At least one in five workers used AI in more than 80% of occupations and on more than 40% of tasks, but most adoption rates stayed below 50%.
Inference: Actual use differs materially within exposed occupations.
Limit: Self-reported use, not a measure of employment, wages or causal labor-market effects.

Firm AI use: broad enough to watch, too early to call a headcount collapse

Source: Bonney and colleagues, U.S. Census Bureau working paper, April 2026
Population and place: U.S. employer firms in the Business Trends and Outlook Survey.
Period: November 2025 to January 2026 reference period.
Outcome: Reported AI use, business functions, worker tasks and AI-related employment changes.
Method: Nationally representative firm survey with firm- and employment-weighted estimates.
Finding: 18% of firms, or 32% weighted by employment, reported AI use in a business function. Among AI users, 66% reported augmentation only and 2% reported an AI-related employment decrease.
Inference: Descriptive survey evidence, not a time-series or causal employment estimate.
Limit: Firms report their own attribution during one period; indirect or later effects may not appear.

Danish pay and hours: still water, rapid task change

Source: Humlum and Vestergaard, NBER Working Paper 33777, revised March 2026
Population and place: Danish workers and workplaces in 11 exposed occupations, linked to adoption surveys.
Period: The first two years after ChatGPT's launch.
Outcome: Earnings, recorded hours, reported productivity, tasks and occupational movement.
Method: Difference-in-differences using linked administrative records and adoption surveys.
Finding: No detectable differential earnings or recorded-hours effect; the estimates rule out effects larger than 2%. New AI-related tasks and occupational movement appeared among adopters.
Inference: Strong short-run result for the study's outcomes and setting.
Limit: It does not estimate U.S. entry hiring or establish that later effects will remain small.

Global exposure: a risk map, not a layoff total

Source: ILO Working Paper 140, May 2025
Population and place: Global occupations mapped to ISCO-08 tasks.
Outcome: Potential generative-AI exposure of occupational tasks.
Method: Task-level scoring using worker input, expert review and AI-assisted prediction.
Finding: One in four workers are in occupations with some exposure; 3.3% of global employment is in the highest exposure category.
Inference: Potential for task change across occupations.
Limit: No observed employment, unemployment, hiring, wage or hour outcome is measured.