🔍 Read the full analysis: What Happens After An AI Layoff? Goldman Sachs Looks To Tech’s Past on ThorstenMeyerAI.com
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TL;DR
Goldman Sachs estimates that workers 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 a year later. The findings come from a client note reported by Fortune; Goldman’s method identifies likely AI displacement rather than confirming the cause of individual layoffs.
Workers Goldman Sachs classifies as likely displaced by AI have taken about one month longer to find new jobs than other laid-off workers and are roughly 10 percentage points more likely to remain unemployed a year later, according to a bank note reported by Fortune. The findings suggest the costs of AI-related job losses may extend beyond the layoff itself, though the estimate relies on a statistical method rather than records confirming why each person lost work.
Goldman economist Pierfrancesco Mei presented the findings in a client note dated October 29, 2026. Fortune reported the figures the next day, quoting the note. The note is not public, so the new estimates cannot be checked directly against the underlying document; the available details should be treated as reported findings, not independently verified results.
Because government surveys do not ask whether AI caused a worker’s job loss, Goldman constructed a proxy using Labor Department data on displaced workers. The analysis covered more than 15,000 people who lost jobs between 2016 and 2025, scoring their occupations’ exposure to AI, the pace of AI adoption in their industries and whether their positions were eliminated. The top 30% of the score were categorized as “likely AI-displaced.” Goldman reportedly found no significant difference between this group and other laid-off workers for job losses before 2023, when AI adoption began to rise.
For losses since 2025, the note’s reported estimates point to differences in both job-search duration and outcomes: the likely AI-displaced group took about a month longer to find work and was about 10 percentage points more likely to still be unemployed after a year. Workers aged 35 and older took about five weeks longer than comparable laid-off workers to return to full-time employment; those under 35 took about three weeks longer. The note also says AI-displaced workers were more likely to leave their previous occupation and industry, but some who found new jobs moved into roles 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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The Costs Continue After Layoff
The estimates focus attention on what happens after a worker loses a job: time out of work, the quality of reemployment and longer-term pay. If job searches take longer and workers struggle to find stable roles, the economic effects of AI adoption could reach household finances and local labor markets well after a company announces cuts. Goldman’s estimates do not establish that AI caused each loss, but they offer an early indication of how workers in more AI-exposed roles may fare.
Goldman’s public historical research provides a longer-term comparison. Its analysis of technology-related displacement over roughly four decades found that affected workers took about a month longer to find work, lost more than 3% in real pay when reemployed, and recorded nearly 10 percentage points less real earnings growth over the following decade than workers who had not been laid off. Those historical findings concern earlier technological changes, not a measured forecast of what will happen to every worker affected by AI.
The outcomes also appear uneven. The new note’s reported age differences point to a longer return to full-time work for people 35 and older. Separately, government data for long-tenured workers laid off for any reason show that 72.9% of those aged 25 to 54 were working again by January 2026, compared with 57.3% of those aged 55 to 64. That broader data is not specific to AI, but it shows that displaced older workers already face a more difficult return-to-work path.
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A Historical Benchmark for AI Displacement
Goldman’s new estimates draw on research the bank has made public. In a June 2026 “Top of Mind” report and a September summary, Mei, Jessica Rindels and David Mericle examined workers across more than 300 occupations, using National Longitudinal Surveys that have tracked over 20,000 people through their careers. The researchers compared workers in occupations most exposed to technology with other workers and followed their outcomes over time.
That earlier research found a slower return to work after technology-related job loss, lower real pay after reemployment and weaker earnings growth over the next decade. It also found better outcomes among workers who retrained within three years: their real wage growth over ten years was about 2 percentage points higher, and their chance of becoming unemployed again was roughly 10 percentage points lower. These are historical associations and do not prove that retraining alone caused the difference.
Other available figures also show pressure on reemployed workers. Bureau of Labor Statistics data cited in the source material indicate that, among long-tenured workers who returned to full-time employment, about 49% earned as much as before or more, down from about 62% two years earlier. That measure covers laid-off workers generally, not only those affected by AI.
“Comparable in magnitude”
— Pierfrancesco Mei, Goldman Sachs economist, as quoted in the client note reported by Fortune
Limits of the AI Layoff Estimate
No government survey directly identifies AI-caused layoffs, and Goldman’s classification is an estimate based on occupation, industry adoption and job elimination. A worker counted as likely AI-displaced may have lost a job for another reason. The analysis also does not establish that AI was the sole or direct cause of an individual layoff.
The underlying client note is not publicly available. Fortune reported its contents, and the new figures have not been independently checked against the original analysis here. It is also too early to know whether the reported differences will persist as more workers lose jobs, find new employment or move between occupations. Goldman’s reported note says that outcomes by education and region remain difficult to assess.
The source material says Goldman expects AI-related job losses to accelerate during an economic slowdown and finds displacement costs are larger in downturns. That is a stated expectation based on the bank’s analysis, not confirmation that a recession or a specific wave of AI layoffs is imminent.
More Data Will Test the Pattern
The next test is whether the reported gap persists as additional labor-market data become available. Researchers will need to track how long workers remain unemployed, whether they return to full-time work and how their real wages and job stability compare with their previous positions. More detail on age, education and region could clarify which workers face the greatest barriers.
For now, Goldman’s note offers an early comparison rather than a settled account of AI’s employment effects. Its historical research suggests that retraining is associated with better outcomes, while a weaker economy could make displacement harder to absorb. Whether those patterns apply to workers displaced specifically by AI will become clearer only as more cases are observed and the measurement methods are tested.
Key Questions
What did Goldman Sachs find about workers likely displaced by AI?
According to the client note reported by Fortune, workers classified as likely AI-displaced since 2025 took about a month longer to find work than other laid-off workers and were roughly 10 percentage points more likely to remain unemployed a year later.
How did Goldman identify likely AI-related job losses?
Goldman reportedly combined measures of occupational AI exposure, industry adoption and whether a position was eliminated. It classified the top 30% of its score as likely AI-displaced. This is a proxy, not confirmation of the reason for any individual layoff.
Are the new findings independently confirmed?
The figures come from a Goldman client note that is not public and were reported by Fortune. They should be treated as reported estimates; the underlying note and data have not been independently verified here.
What does Goldman’s historical research say about retraining?
In Goldman’s historical analysis, technology-displaced workers who retrained within three years had about 2 percentage points more real wage growth over ten years and a roughly 10-point lower chance of becoming unemployed again. The findings show an association and do not prove retraining alone caused the better outcomes.
Source: ThorstenMeyerAI.com
