Goldman Sachs economist Pierfrancesco Mei found that workers displaced from AI-exposed jobs since 2025 took about a month longer to find new work than other displaced workers, according to a research note released Thursday. Those workers were also roughly 10 percentage points more likely to still be unemployed a year after losing their job.
Goldman said the effects are comparable in magnitude to those from earlier waves of technological change, which its prior research found to be long-lasting. That earlier work, built on individual-level data going back to 1979, found that workers who lost jobs to technology saw slower real earnings growth than other displaced workers and remained more likely to face further unemployment.
Age is the sharpest dividing line in the new data. AI-displaced workers aged 35 and older took about five weeks longer than other displaced workers to get back to full-time work, while those under 35 took about three weeks longer. Mei wrote that younger workers may be able to adapt more flexibly to AI disruption, and younger AI-displaced workers cycled through fewer temporary jobs and were less likely to hold a second job alongside a new full-time role.
Key facts
- 01Workers displaced from AI-exposed jobs since 2025 took about one month longer to find new work than other displaced workers, per Goldman Sachs.
- 02They were roughly 10 percentage points more likely to still be unemployed a year after losing their job.
- 03AI-displaced workers 35 and older took about five weeks longer to return to full-time work; those under 35 took about three weeks longer.
- 04Goldman analyzed more than 15,000 displaced workers who lost jobs between 2016 and 2025 using the Displaced Worker Supplement.
- 05Challenger counted 120,136 announced job cuts citing AI through September, about 21% of all cuts.
No government survey asks whether AI caused a job loss, so Goldman built its own measure. Using the Current Population Survey's Displaced Worker Supplement, a biennial survey of people who involuntarily lost jobs, the bank analyzed more than 15,000 displaced workers who lost jobs between 2016 and 2025, including newly released data on 2023 to 2025. Each job loss was scored on the occupation's AI exposure, the industry's AI adoption rate and whether the position was eliminated, with the top 30% treated as likely AI-displaced.
Goldman found no significant gap between the two groups for job losses before 2023, when AI adoption began rising. The bank also flagged limits: the data covers only people who had a job to lose, not new entrants to the labor market, and Goldman said it is still too early to assess the impact along dimensions such as education and geography. The note does not break the one-month delay down by occupation.
Goldman Sachs' historical data points to who tends to recover best. Younger, college-educated and urban workers had cumulative real earnings losses about half as large as other technology-displaced workers over the following decade. Workers with less tenure at the job they lost also fared better, which Mei attributed to skills that were less occupation-specific and therefore more transferable.
AI-displaced workers are more likely than other displaced workers to leave their old occupation and industry altogether. When they are rehired, more of them, older workers in particular, are moving into roles with similar or higher AI exposure.
Federal data shows the wider displacement picture. The Bureau of Labor Statistics counted 7.4 million workers displaced from 2023 through 2025, up from 6.3 million in the 2021 to 2023 survey. Among workers with at least three years of tenure, 72.9% of those aged 25 to 54 were working again by January 2026, compared with 57.3% of those 55 to 64 and 38.6% of those 65 and older. About 49% of long-tenured workers who found full-time work again were earning as much as or more than before, down from about 62% in the prior survey.
Employers are also naming AI more often when they cut jobs. Challenger, Gray & Christmas counted 120,136 announced job cuts citing AI through September, about 21% of all cuts and the most common stated reason.
Other research reads the labor market differently. An August study from the Stanford Digital Economy Lab by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen, using ADP payroll data, found employment for workers aged 22 to 25 in the most AI-exposed occupations was about 19% below where it would have been had it tracked less-exposed peers, with no comparable gap for experienced workers. The researchers attributed that mainly to reduced hiring of young workers rather than more layoffs and said they did not see widespread, economy-wide displacement tied to AI.
The Yale Budget Lab's analysis of Current Population Survey data found that measures of AI exposure, automation and augmentation showed no sign of being related to changes in employment or unemployment.
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