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TL;DR
A Goldman Sachs note reported by Fortune says workers likely displaced by AI since 2025 took about a month longer to find new jobs than other laid-off workers and were roughly 10 percentage points more likely to remain unemployed a year later. The findings resemble Goldman’s historical research on technology-driven job losses, which found lasting effects on pay and earnings. The new figures are estimates, and the client note is not public.
Workers likely displaced by AI since 2025 have taken about a month longer to find new jobs than other laid-off workers, and were roughly 10 percentage points more likely to remain unemployed a year later, according to a Goldman Sachs note reported by Fortune. The results suggest the early effects of AI-related job loss may resemble the prolonged employment and pay setbacks associated with earlier technology shifts, though the new figures are estimates rather than a direct count of confirmed AI layoffs.
The Goldman note, dated October 29, 2026, was sent to clients and is not publicly available. Fortune reported its findings the next day, quoting the note. The figures have not been independently verified against the original document. Goldman economist Pierfrancesco Mei and colleagues estimated AI displacement using Labor Department data on people who had lost jobs, rather than a government survey that directly asks whether AI caused a layoff.
For its estimate, Goldman examined more than 15,000 job losses from 2016 to 2025, combining an occupation’s exposure to AI, the pace of AI adoption in its industry and whether a position was eliminated. Goldman classified the top 30% of its resulting scores as “likely AI-displaced.” The firm reportedly found no significant difference in outcomes for this group before 2023, when AI adoption began to rise. That comparison is a check on its method, not proof that AI caused each job loss in the later period.
For workers losing jobs since 2025, Fortune reported that those classified as likely AI-displaced took about one month longer to find work and were about 10 percentage points more likely to be unemployed one year later than other laid-off workers. Workers aged 35 and older took about five weeks longer to return to full-time work, compared with around three weeks for those under 35. The report also said displaced workers were more likely to leave their former occupation or industry, while some who found work moved into jobs with similar or higher AI exposure.
Infographic 01 · What happens after the pink slip
The first AI layoffs look like 40 years of tech layoffs
A longer search, a smaller paycheck and slower growth for years. Goldman’s new estimate for AI-exposed workers lines up with its long-run history.
The first AI wave, since 2025
Likely AI-displaced vs. other laid-off workers, US. Note not public; figures as reported by Fortune.
Forty years of tech shocks
Workers displaced from technology-disrupted occupations, US.
“Comparable in magnitude” to earlier waves of technological change, whose damage was “long-lasting.”Goldman Sachs economist Pierfrancesco Mei, as quoted by Fortune
How Goldman spots “AI-displaced” workers: no survey asks about AI, so it scores each job loss by the occupation’s AI exposure, the industry’s AI adoption and whether the position was eliminated, and treats the top 30% as likely AI-displaced (more than 15,000 workers, 2016–2025). An estimate, not a count.
Sources: Fortune, 30 Oct 2026 (reporting the Goldman note of 29 Oct 2026) · Goldman Sachs, Top of Mind #149, 25 Jun 2026, p. 15 · Goldman Sachs Insights, 11 Sep 2026. pp = percentage points.
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Recovery Can Outlast the Layoff
The findings shift attention from the number of announced cuts to the cost of finding stable work again. If job searches take longer and unemployment persists, the consequences can extend beyond a company’s restructuring: workers may face reduced income, a change in occupation or difficulty returning to full-time employment. The Goldman note’s reported age differences also suggest that recovery may not be equal across workers.
Goldman’s public historical research gives the new estimate a longer-term comparison. Its research on earlier technology-related displacement found that affected workers took about one month longer to find another job, lost more than 3% in real pay when reemployed and experienced nearly 10 percentage points less real earnings growth over the next decade than workers who were never laid off. Those historical results do not establish that AI will produce the same outcomes, but they show why a rehire alone may not capture the full cost of displacement.
The stakes extend to employers and policymakers weighing how to respond to changing work. Goldman’s historical analysis linked retraining within three years of technology displacement with about 2 percentage points more real wage growth over a decade and a roughly 10-point lower chance of becoming unemployed again. That is an association in the historical data, not a guarantee that training will prevent job loss or improve every worker’s outcome.
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Four Decades of Job Displacement
Goldman’s comparison draws on public research published in its June 2026 “Top of Mind” report and a September summary. Mei, Jessica Rindels and David Mericle examined more than 300 occupations over several decades, identifying jobs most exposed to technology in each period and tracking workers through National Longitudinal Surveys that have followed more than 20,000 people over their careers.
That research found a pattern of slower recovery after technology-related job loss: longer searches, lower real pay on reemployment and weaker earnings growth over time. The government data cited in the source material also show differences among long-tenured workers who had lost jobs. By January 2026, 72.9% of those aged 25 to 54 were working again, compared with 57.3% of those aged 55 to 64. Among long-tenured workers who returned to full-time work, about 49% earned as much as or more than before, down from about 62% two years earlier, according to the Bureau of Labor Statistics. These figures cover laid-off workers generally, not workers confirmed to have lost jobs to AI.
The distinction matters because AI exposure is not the same as AI-caused displacement. An occupation may use AI without eliminating a job, and a layoff can have several causes. Goldman’s scoring approach attempts to estimate likely cases, but cannot replace direct evidence from employers or workers about why a specific position ended.
““comparable in magnitude””
— Pierfrancesco Mei, Goldman Sachs economist, as quoted in the note reported by Fortune
AI’s Role Remains an Estimate
The central limitation is that no government survey cited here directly identifies AI as the cause of a person’s job loss. Goldman’s “likely AI-displaced” category is based on a score combining exposure, industry adoption and position elimination. The figures should be treated as an estimate of a group, not a verified tally of AI layoffs.
The underlying client note is not public, and the new findings are available through Fortune’s reporting rather than direct review of Goldman’s analysis. It is also too early to assess outcomes across all worker groups, including by education and region, or to know whether the reported patterns will persist as AI use changes. The source material provides no detailed breakdown of which sectors or employers account for the estimated losses.
It remains unclear how much of the observed difference reflects AI itself, broader labor-market conditions or other factors affecting the workers and industries in the estimate. The reported comparison with earlier technology shocks provides historical context, but does not show that every AI-exposed worker will experience the same search time, wage loss or long-term earnings path.
More Data Will Test the Pattern
The next step is further tracking of workers who lost jobs in recent years, so researchers can see whether they return to full-time work, what they earn and whether they remain in their new roles. More public detail on Goldman’s method and results would also help readers evaluate how the AI-displacement estimate was constructed and how it compares with other measures.
Future government surveys or research that ask workers and employers directly about AI-related job changes could help distinguish job losses caused by automation from layoffs driven by other factors. For now, the reported figures offer an early comparison with past technology shifts, not a final account of AI’s effect on employment. Goldman’s economists also expect AI-related job losses to speed up if the economy slows; that is a forecast attributed to the firm, not an established outcome.
Key Questions
What did Goldman Sachs report about AI-related job losses?
According to Fortune’s report on a Goldman client note, workers classified as likely displaced by AI since 2025 took about a month longer to find new work than other laid-off workers and were roughly 10 percentage points more likely to remain unemployed after a year.
Are these confirmed counts of people laid off because of AI?
No. The figures are estimates based on a score using occupational AI exposure, industry adoption and whether a position was eliminated. The source material says government data do not directly ask whether AI caused a job loss.
How does this compare with earlier technology layoffs?
Goldman’s public historical research found that workers displaced by technology took about a month longer to find work, lost more than 3% in real pay on reemployment and had nearly 10 percentage points less real earnings growth over the following decade than workers who were never laid off. These historical findings do not prove that AI layoffs will have identical effects.
Which workers may face a longer recovery?
The reported Goldman findings showed a larger delay for workers aged 35 and older: about five weeks longer to return to full-time work, compared with roughly three weeks for workers under 35. The note’s reported results do not settle why the age difference occurs.
What is still unknown about AI’s effect on jobs?
The actual number of job losses caused by AI, the longer-term effects on wages and employment, and differences by education, region and industry remain uncertain. The client note is not public, and the reported estimates need to be assessed as more data become available.
Source: ThorstenMeyerAI.com
