🔍 Read the full analysis: Goldman Sachs On What Follows AI Layoffs And Earlier Tech Shocks on ThorstenMeyerAI.com
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TL;DR
A Goldman Sachs client note dated October 29, as reported by Fortune, estimates that workers likely displaced by AI since 2025 took about a month longer to find work than other laid-off workers. They were also roughly 10 percentage points more likely to remain unemployed a year later. The estimate is not a direct count of AI-caused layoffs, and Goldman says it is too early to assess several effects.
Goldman Sachs estimates that workers likely displaced by artificial intelligence since 2025 have taken about one 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 come from a client note by economist Pierfrancesco Mei dated October 29 and reported by Fortune; the note is not public, and the figures are estimates rather than a government count of AI-caused job losses.
Goldman’s method addresses a gap in official data: government surveys do not ask whether AI caused a person’s job loss. According to Fortune’s account of the note, Goldman used Labor Department data on displaced workers, examining more than 15,000 people who lost jobs between 2016 and 2025. It combined occupational exposure to AI, the pace of AI adoption in an industry, and whether a position was eliminated to create a measure of likely displacement. Workers in the top 30% of that measure were classified as “likely AI-displaced.”
For workers who lost jobs from 2025 onward, Fortune reports that the group identified as likely displaced took about a month longer to find work than other displaced workers. They were also around 10 percentage points more likely to be unemployed a year later. The note reported different recovery times by age: workers 35 and older took about five weeks longer than comparable laid-off workers to return to full-time work, while those under 35 took about three weeks longer.
The reported analysis also found that likely AI-displaced workers were more likely to leave their previous occupation or industry. Yet among those rehired, some—especially older workers—entered jobs with similar or higher AI exposure. Fortune’s report says younger workers were less likely to take a second job alongside their new full-time work and cycled through fewer temporary jobs on the way back.
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 After a Job Loss
The findings shift attention from the number of announced cuts to the length and quality of workers’ recovery. A longer search can mean a longer spell without earnings, while returning to work at lower pay can affect household finances well after a layoff. The Goldman estimates suggest these effects may be relevant to people whose roles are exposed to AI, but the study’s classification does not establish that AI directly caused each individual job loss.
Goldman’s public research on earlier technology-related displacement provides a longer view. It found that workers who lost jobs in occupations exposed to technological change took about one month longer to find work, earned more than 3% less in real terms when rehired, and had nearly 10 percentage points less real earnings growth over the following decade than workers who had not been laid off. Those historical results describe a broad pattern; they do not prove that current AI-related job losses will produce identical outcomes.
The distinction matters for employers and policymakers weighing how to respond to AI adoption. If workers face extended searches or lower pay, the effects of automation may reach beyond the positions eliminated. Goldman’s historical analysis also links retraining with better outcomes, though that association alone cannot show that training caused the improvement.
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Four Decades of Technology Displacement
The recent estimate is set against research Goldman published in June 2026 and summarized again in September. Mei, Jessica Rindels and David Mericle examined more than 300 occupations over several decades, using National Longitudinal Surveys that followed more than 20,000 workers through their careers. The researchers identified occupations most exposed to technology in each decade and tracked what happened to the people who worked in them.
In that historical analysis, technology-displaced workers had slower reemployment and weaker real-pay outcomes than workers laid off from stable or growing occupations. Goldman also reported that workers who retrained within three years had about two percentage points more real wage growth over ten years and a roughly 10-point lower chance of becoming unemployed again. These are findings from the historical data, not a guarantee that retraining will produce the same result for every worker.
Other labor-market data point to age-related differences among laid-off workers generally, not only those identified as likely displaced by AI. The source material cites Bureau of Labor Statistics figures showing that by January 2026, 72.9% of long-tenured workers 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 before or more, down from about 62% two years earlier. These figures describe laid-off workers overall and should not be read as AI-specific statistics.
““comparable in magnitude””
— Pierfrancesco Mei, Goldman Sachs economist, as quoted in Fortune
Limits of the AI Layoff Estimate
The figures for recent AI displacement are reported estimates, not independently verified counts. Goldman’s client note is not publicly available; the figures in the source material come from Fortune’s report, and other coverage is described as repeating its numbers. The method classifies workers by a combination of exposure, industry adoption and job elimination. It cannot establish that AI was the reason a particular employer eliminated a particular position.
Goldman reportedly found no significant difference between the likely-displaced group and other laid-off workers for job losses before 2023, when AI adoption began to rise. That check offers historical context for the method, but it does not remove the uncertainty involved in identifying cause from survey data. It is also too early, Mei said, to assess effects across dimensions such as education and region. The source material does not provide enough detail to evaluate all parts of the model or its margin of error.
Nor is it clear whether the recent outcomes will persist as AI adoption changes, or how they will differ across employers, occupations and economic conditions. Goldman’s historical work finds displacement costs are larger in downturns and its economists expect AI-related job losses to accelerate if the economy slows; that is an assessment about a possible risk, not a report that such an acceleration has already occurred.
Tracking Reemployment and Pay
The next evidence will come from continued observation of displaced workers: how long they remain out of work, whether they return to full-time roles, what those jobs pay, and whether they stay employed. Further data may also clarify outcomes by age, education and region, areas Goldman says are too early to assess fully.
For now, readers should distinguish the preliminary AI-related estimate from Goldman’s longer historical findings and from government statistics covering laid-off workers as a whole. The October note’s approach may help researchers study a cause that official surveys do not directly record, but more evidence is needed to show how reliably it identifies AI displacement and whether the reported patterns continue.
Goldman’s historical results point to retraining as one factor associated with better employment and wage outcomes. Whether similar programs help workers affected by current AI adoption, and which workers benefit most, remains to be established. Any broader judgment about the labor-market effects of AI will depend on how these workers fare over time, not just on the initial number of positions eliminated.
Key Questions
What did Goldman Sachs report about workers likely displaced by AI?
According to Fortune’s account of a Goldman client note, workers classified as likely AI-displaced since 2025 took about one month longer to find work and were roughly 10 percentage points more likely to remain unemployed a year later than other laid-off workers.
Does the analysis prove that AI caused these job losses?
No. Goldman used a measure combining occupational AI exposure, industry adoption and whether a position was eliminated. It identifies workers considered likely displaced; it does not confirm AI as the cause of each individual layoff.
Are the recent figures independently confirmed?
The client note is not public. The figures are reported through Fortune’s coverage, so they should be treated as reported estimates, not independently confirmed government statistics.
Goldman’s public research found that workers displaced by earlier technological changes took longer to find work, lost more than 3% in real pay when rehired and had nearly 10 percentage points less real earnings growth over the next decade than workers who were not laid off.
What remains unknown about AI-related job losses?
The analysis does not yet establish how outcomes vary by education or region, whether the recent patterns will persist, or how accurately its method identifies AI-caused displacement. Goldman’s economist said it is too early to assess several of these effects.
Source: ThorstenMeyerAI.com
