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TL;DR
A Goldman Sachs client note dated October 29, 2026, found that workers it classified as likely displaced by AI since 2025 took about a month longer to find new jobs than other laid-off workers. Fortune reported the findings; the note is not public, and the AI-displacement group is an estimate rather than a government category. Goldmanās published research on earlier technology-driven job losses also links displacement to lower pay and weaker earnings growth over time.
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 still be unemployed a year later, according to a client note reported by Fortune. The findings suggest the effects of AI-related job losses may persist after the initial layoff, but the estimate is based on an indirect measure and the note itself is not public.
Goldman economist Pierfrancesco Mei compared workers who lost jobs between 2016 and 2025, using Labor Department survey data. Because the government does not ask displaced workers whether AI caused their job loss, Goldman built a classification based on occupational AI exposure, industry adoption and whether a position was eliminated. It designated the top 30% of its resulting scores as ālikely AI-displaced.ā
Fortune reported that, for job losses since 2025, the group took about one month longer to find work and was approximately 10 percentage points more likely to remain unemployed after a year than other displaced workers. Workers aged 35 and older took about five weeks longer than comparable laid-off workers to return to full-time employment; workers under 35 took about three weeks. Goldman also found that AI-classified workers were more likely to leave their former occupation and industry, while some rehired workers moved into jobs with similar or higher AI exposure.
The October note is not publicly available, and these details come from Fortuneās account, which quoted it. Goldmanās public research offers a longer-term comparison: workers displaced by earlier technological changes took longer to find work, lost more than 3% in real pay when rehired, and recorded nearly 10 percentage points less real earnings growth over the following decade than workers who were never laid off.
Infographic 01 Ā· 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. Goldmanās 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.
āComparable in magnitudeā to earlier waves of technological change, whose damage was ālong-lasting.āGoldman Sachs economist Pierfrancesco Mei, as quoted by Fortune
How Goldman spots āAI-displacedā workers: no survey asks about AI, so it scores each job loss by the occupationās AI exposure, the industryās AI adoption and whether the position was eliminated, and treats the top 30% as likely AI-displaced (more than 15,000 workers, 2016ā2025). An estimate, not a count.
Sources: Fortune, 30 Oct 2026 (reporting the Goldman note of 29 Oct 2026) Ā· Goldman Sachs, Top of Mind #149, 25 Jun 2026, p. 15 Ā· 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 jobs cut to the length and quality of workersā recovery. A longer search can mean an extended spell without wages; a lower-paying replacement job can affect earnings well beyond the first year. Goldmanās historical figures suggest that these consequences may accumulate, although they describe earlier technology-related displacement and do not establish the long-term outcome for workers affected by AI today.
The reported age gap also points to an uneven adjustment. Older workers in Goldmanās estimate took longer to return to full-time work than younger workers. Separate government data cited in the source material show that among long-tenured workers aged 25 to 54 who had lost jobs, 72.9% were working again by January 2026, compared with 57.3% of those aged 55 to 64. Those figures cover laid-off workers generally, not workers identified as displaced by AI, so they should not be read as an AI-specific comparison.
The findings matter for employers and policymakers weighing AIās labor effects. A layoff count alone cannot show how easily displaced employees find comparable work, or whether they return at similar pay. Goldmanās historical analysis associates retraining within three years with about two percentage points more real wage growth over ten years and a roughly 10-point lower chance of becoming unemployed again. That association does not by itself prove retraining caused the better outcomes.
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Four Decades of Technology Displacement
Goldmanās comparison draws on its public June 2026 Top of Mind report and a September summary by Mei and colleagues Jessica Rindels and David Mericle. The researchers tracked people across more than 300 occupations, using National Longitudinal Surveys that have followed over 20,000 workers through their careers. They identified occupations exposed to technology in each decade and examined what happened to workers who lost jobs.
That historical work found a slower return to employment and a real-pay loss exceeding 3% among workers displaced by technology, compared with negligible losses for the comparison group described in Goldmanās research. It also found nearly 10 percentage points less real earnings growth over the next decade than for workers who were never laid off. These historical results provide a reference point, not a forecast that every AI-related layoff will have the same effects.
For its newer estimate, Goldman examined more than 15,000 people who lost jobs from 2016 to 2025 in the Labor Departmentās Displaced Worker Supplement to the Current Population Survey. The researchers reported no significant difference between their likely AI-displaced group and other laid-off workers for losses before 2023, when AI adoption began to rise. That check is part of Goldmanās method; it does not independently confirm that AI caused any particular job loss.
“ācomparable in magnitudeā”
ā Pierfrancesco Mei, Goldman Sachs economist, as quoted in the client note reported by Fortune
Limits of the AI Job Estimate
No government survey directly identifies AI-caused layoffs, so Goldmanās group is a modeled estimate, not a count of confirmed cases. A worker may have lost a job for several reasons, and the method combines exposure, industry adoption and position elimination rather than establishing that AI caused an individual layoff.
The client note is not public. The recent findings in this report are drawn from Fortuneās coverage, which quotes the note; the figures have not been independently checked against the underlying analysis here. The sample also covers a relatively short period of AI adoption, leaving long-term pay, employment and career effects uncertain. Mei said it was too early to assess outcomes across education and region. The source material also says Goldman expects AI-related job losses to accelerate if the economy slows, while finding that displacement costs are larger during downturns; that is an outlook, not a confirmed future outcome.
Separate Bureau of Labor Statistics figures in the source material show that about 49% of long-tenured workers who returned to full-time work earned as much as before or more, down from about 62% two years earlier. Those statistics concern long-tenured workers overall, not a group identified as displaced by AI.
Evidence to Watch in Coming Years
The next useful evidence will come from repeated worker surveys and longitudinal follow-up showing whether people classified as likely AI-displaced return to work, what they earn, and whether they remain in their previous occupations. Results broken down by age, education, region and industry could clarify who faces the longest recovery and where retraining or other support is associated with better outcomes.
Goldmanās public historical research provides a framework for tracking those measures, but the latest client noteās underlying data and full methodology are not publicly available in the source material. For now, the reported findings are an early estimate of job-search outcomes, not a final assessment of AIās effect on employment. Whether the gap persistsāand how it changes in a weaker economyāremains to be seen.
Key Questions
What did Goldman Sachs find about workers likely displaced by AI?
According to the client note as reported by Fortune, workers classified as likely displaced by AI since 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.
How did Goldman identify likely AI-displaced workers?
Goldman combined measures of occupational exposure to AI, industry adoption and position elimination. It classified the top 30% of its scores as likely AI-displaced. This is an estimate, not a government determination that AI caused a specific layoff.
Is the Goldman Sachs note publicly available?
No. The October 29, 2026, note was sent to clients. The recent figures in this report are attributed to Fortuneās account of the note.
What does Goldmanās older research say about technology layoffs?
Goldmanās public research says workers displaced by earlier technological changes took about a month 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 following decade than workers who were never laid off.
Does the research show retraining prevents the effects of displacement?
No. Goldmanās historical data associate retraining within three years with better later wage growth and a lower chance of becoming unemployed again. The reported association does not prove retraining alone caused those outcomes, and the long-term effects for workers displaced by AI remain unclear.
Source: ThorstenMeyerAI.com
