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TL;DR
A Goldman Sachs client note, reported by Fortune, says workers classified as likely displaced by AI since 2025 took about a month longer to find work than other laid-off workers and were more likely to remain unemployed a year later. The estimate is not a direct count of AI-caused layoffs, and the note is not public; Goldman’s published historical research links technology-related displacement to lasting pay and employment costs.
Workers Goldman Sachs classifies 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 a year later, according to a client note reported by Fortune. The findings suggest that the effects of AI-related job losses may extend beyond the initial cuts, though Goldman’s estimate is indirect and the note is not publicly available.
Fortune reported the findings from a note dated October 29 by Goldman economist Pierfrancesco Mei. The figures have not been independently checked against the note, which was distributed to clients. Goldman used Labor Department data on displaced workers and constructed an estimate because the government survey does not ask whether AI caused a person’s job loss.
According to Fortune’s account, Goldman examined more than 15,000 people who lost jobs between 2016 and 2025. It combined occupational exposure to AI, the pace of AI adoption in each industry, and whether the position was eliminated to identify the top 30% as “likely AI-displaced.” Goldman reportedly found no significant difference in outcomes for that group before 2023, when AI adoption began to rise.
For job losses since 2025, the reported note says the likely AI-displaced group took about one 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 around five weeks longer than comparable laid-off workers to return to full-time work, compared with about three weeks for those under 35. These are reported estimates, not a government count of layoffs directly caused by AI.
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 Layoffs
The findings shift attention from the number of job cuts to what happens after workers leave. A longer search can mean more time without wages, while a lower-paying return to work can affect household finances well after a layoff. The reported age gap also suggests that recovery may differ among workers, even when they face the same broad change in technology.
Goldman’s public historical research offers a longer-term comparison. Its June 2026 report and a September summary examined workers across more than 300 occupations over several decades, drawing on National Longitudinal Surveys that have tracked more than 20,000 people. Goldman found that workers displaced by technology took about a month longer to find work, 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 had not been laid off. Those historical results do not establish what will happen to today’s AI-exposed workers, but they show why reemployment alone may not capture the full cost.
The report also says outcomes varied with retraining. In Goldman’s historical data, 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. That association does not prove retraining alone caused the difference, but it identifies a potential area for employers and policymakers to examine.
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A Four-Decade Technology Comparison
Goldman’s new estimate is presented alongside its longer historical analysis, rather than as a complete record of AI layoffs. In the published work, Mei and colleagues Jessica Rindels and David Mericle identify occupations exposed to technological change in different decades, then follow workers through their careers. The work compares people who lost jobs in technology-affected occupations with workers laid off from stable or growing ones.
Government figures cited in the source material show that reemployment is already uneven among long-tenured workers who lost jobs, though the figures cover all causes of displacement, not AI specifically. By January 2026, 72.9% of 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, according to the Bureau of Labor Statistics figures provided in the source.
Goldman’s reported comparison also points to the possibility that workers move between occupations or industries after displacement. Fortune said likely AI-displaced workers were more likely to leave their former occupation and industry, yet some who were rehired entered jobs with similar or higher AI exposure. The available account does not explain how common those moves were or whether they lasted.
““comparable in magnitude””
— Pierfrancesco Mei, Goldman Sachs economist, as quoted in Fortune
Limits of the AI Layoff Estimate
The central estimate is a constructed category, not a verified count of workers whose jobs were eliminated because of AI. The government survey does not record AI as the reason for job loss, and Goldman’s method combines exposure, adoption and position elimination to identify a group it calls “likely AI-displaced.” A worker’s occupation being exposed to AI does not by itself show that AI caused the worker’s layoff.
The underlying client note is not public. Fortune reported its figures and quoted Mei, while other coverage described in the source material repeated Fortune’s numbers. The estimates therefore cannot be independently checked against the full analysis here. Goldman also said it was too early to assess outcomes across factors including education and region. The available reporting does not provide enough detail to establish how results vary by industry, the type of AI adoption, or the reasons employers gave for each job cut.
It is also unclear whether the reported gap will persist as hiring conditions change. Goldman’s historical work finds that displacement costs are larger during downturns, and its economists expect AI-related job losses to accelerate if the economy slows. That is an assessment of risk, not confirmation that a recession or faster wave of cuts is underway.
Tracking Reemployment and Pay
The next useful evidence will be follow-up data showing whether workers identified as likely AI-displaced return to work, how long searches last, and how their new pay compares with their former earnings. Further analysis by age, education, region, occupation and industry could help test whether the differences reported by Goldman apply broadly or are concentrated among particular groups.
For now, readers should distinguish the client-note estimate from official displacement statistics and treat comparisons with earlier technology waves as historical context, not a forecast for every worker. The source material provides no timetable for a public release of Goldman’s underlying note or a new government survey that directly asks whether AI caused a job loss.
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 than other laid-off workers and were roughly 10 percentage points more likely to remain unemployed a year later.
Does the estimate count confirmed AI-caused layoffs?
No. The government survey Goldman used does not ask whether AI caused a job loss. Goldman created a proxy using occupational AI exposure, industry adoption and whether a position was eliminated, then classified the highest-scoring 30% as likely AI-displaced.
What does Goldman’s historical research say about technology layoffs?
Goldman’s public research says workers displaced by technology took about a month longer to find work, lost more than 3% in real pay when reemployed, and had nearly 10 percentage points less real earnings growth over the following decade than workers who were not laid off.
Were older workers reported to have a longer recovery?
Yes. Fortune reported that workers aged 35 and older took about five weeks longer than comparable laid-off workers to return to full-time work, compared with about three weeks for workers under 35. The source also cites government data showing lower reemployment among displaced workers aged 55 to 64 than those aged 25 to 54; that government comparison covers layoffs from all causes.
What remains unknown about the findings?
The full Goldman note is not public, and the AI-displacement group is an estimate rather than a confirmed list of AI-caused job losses. It is also too early to establish how outcomes differ by education or region, or whether the reported job-search and unemployment gaps will persist.
Source: ThorstenMeyerAI.com
