🔍 Read the full analysis: AI Layoffs And The Scars Of Disruption: A 40-Year Comparison on ThorstenMeyerAI.com
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TL;DR
A Goldman Sachs client note dated October 29, 2026, says workers likely displaced by AI since 2025 took about a month longer to find new jobs and were roughly 10 percentage points more likely to remain unemployed a year later than other laid-off workers. Fortune reported the findings; the note is not public, and the estimate depends on an occupation- and industry-based measure rather than direct confirmation that AI caused each layoff. Goldman’s public historical research finds that workers displaced by earlier technology also faced prolonged job searches and weaker earnings.
A Goldman Sachs client note dated October 29 says workers likely displaced by AI since 2025 took about one 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. Fortune reported the findings from the note, which is not publicly available; Goldman’s comparison also suggests the employment setbacks resemble patterns seen after earlier waves of technological change.
Goldman economist Pierfrancesco Mei based the new estimate on U.S. government data covering more than 15,000 people who lost jobs between 2016 and 2025. Because government surveys do not ask whether AI caused a job loss, Goldman reportedly built a measure using occupational exposure to AI, industry adoption and whether a position was eliminated. It classified the top 30% of that measure as “likely AI-displaced.”
For job losses since 2025, Fortune reported, the group took about one month longer to find another job and was roughly 10 percentage points more likely to still be unemployed one year after losing work than other displaced workers. Workers aged 35 and older reportedly took about five weeks longer than comparable laid-off workers to return to full-time employment; workers under 35 took about three weeks longer.
The estimate is not a direct count of layoffs explicitly attributed to AI. Goldman reportedly found no significant difference between its likely-AI-displaced group and other laid-off workers for job losses before 2023, which it used as a check on the measure. Fortune is the source for the new note’s figures; other coverage cited in the source material repeats Fortune’s reporting, rather than independently verifying the underlying note.
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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Job Losses Can Leave Long Pay Scars
The reported findings focus on what happens after a layoff, when workers search for new roles, take temporary work or leave an occupation. A longer spell without work can mean lost income, while accepting a new position may involve a pay cut or a move into a different line of work. The Goldman estimate suggests those adjustment costs may already be showing up among workers it classifies as likely displaced by AI.
Goldman’s public historical research gives the comparison a longer horizon. It found that workers displaced by earlier technology took about a month longer to find work and earned more than 3% less in real terms when rehired than workers laid off from stable or growing occupations. Their real earnings growth over the following decade was nearly 10 percentage points lower than for workers who were never laid off. These historical results do not prove that AI will produce the same outcomes, but they show why counting job cuts alone can miss persistent effects on earnings and employment.
The distinction matters for both workers and employers. A role may disappear while a worker finds employment elsewhere, but the new position may pay less, offer less security or require additional training. For policymakers, the figures point to questions about how retraining and job-placement support work in practice, especially for older workers and during an economic downturn.
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Four Decades of Technology Displacement
The comparison draws on Goldman research published publicly in its June 2026 Top of Mind report and a September summary. Mei and colleagues Jessica Rindels and David Mericle examined more than 300 occupations and followed workers through National Longitudinal Surveys that have tracked more than 20,000 people over their careers. They identified occupations most exposed to technology in each decade and examined what happened to workers who lost jobs.
That research found longer searches and weaker subsequent pay among workers displaced by technology. It also reported that those workers’ real earnings grew nearly 10 percentage points less over the next decade than earnings for workers who had not been laid off. Separately, the new AI estimate uses a more recent period and a constructed likelihood measure; it should not be treated as a direct survey of people confirming that AI took their jobs.
Government data cited in the source material show that age is already associated with different reemployment outcomes among long-tenured workers who lost jobs, regardless of whether AI was involved. By January 2026, 72.9% of workers aged 25 to 54 in that group were working again, compared with 57.3% of workers aged 55 to 64. Those figures describe laid-off workers generally, not a separately verified AI-displaced group.
““comparable in magnitude””
— Pierfrancesco Mei, Goldman Sachs economist, as quoted in Fortune
How Much AI Caused the Layoffs
The client note is not publicly available, so its methodology and detailed results are known here through Fortune’s reporting. Goldman’s estimate identifies workers whose occupations and industries make AI displacement more likely; it does not establish that AI directly caused each person’s job loss. The top-30% threshold is a classification choice, not a government finding about individual layoffs.
The new analysis covers a short period of AI adoption and reported job losses since 2025. Mei said it was too early to assess outcomes across education and region. The source material also does not provide enough detail to judge how the reported comparisons vary by industry, the exact number of workers represented in the post-2025 results, or whether the effects will persist as AI use and labor demand change.
Goldman’s historical data identify associations between technological displacement and later employment or earnings outcomes. They cannot establish that every observed difference was caused by technology alone, and past waves do not guarantee that AI’s effects will match them.
Tracking Reemployment and Pay
The next evidence to watch is whether subsequent labor-market data show the reported longer searches and higher unemployment risk continuing as more employers adopt AI. A longer follow-up would also help show whether displaced workers return to full-time jobs, move into different occupations or industries, and recover their prior earnings.
Goldman’s public research links retraining within three years of technology-related displacement with about 2 percentage points more real wage growth over ten years and a roughly 10-point lower chance of becoming unemployed again. Those are historical associations, not a guarantee that any particular training program will produce the same results. The source material says Goldman expects AI-related job losses to speed up if the economy slows and finds displacement costs are larger in downturns; the extent of any such change remains to be seen.
Key Questions
What did Goldman Sachs report about workers likely displaced by AI?
According to Fortune’s account of a Goldman client note dated October 29, 2026, workers classified as likely AI-displaced after 2025 took about one month longer to find new work and were roughly 10 percentage points more likely to remain unemployed a year later than other laid-off workers.
Did the workers confirm that AI caused their layoffs?
No. The source material says government surveys do not ask whether AI caused a person’s job loss. Goldman reportedly estimated likelihood using occupation-level AI exposure, industry adoption and whether a position was eliminated; the measure does not verify the cause of each layoff.
How do the reported findings compare with earlier technology layoffs?
Goldman’s public research on earlier technology displacement found that affected workers took about a month longer to find work, lost more than 3% in real pay upon reemployment, and had nearly 10 percentage points less real earnings growth over the next decade than workers who had never been laid off.
Were older workers more affected in the new estimate?
Fortune reported that workers aged 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. These are figures reported from Goldman’s estimate, not a direct government count of AI-caused layoffs.
What remains unknown about AI-related job losses?
The note is not public, and the available account does not establish how many layoffs AI directly caused or whether the reported outcomes will persist. Goldman said it was too early to assess effects across education and region; longer-term data will be needed to track earnings, job stability and occupational moves.
Source: ThorstenMeyerAI.com
