Goldman Sachs: AI Layoffs Are Scarring Workers Like 40 Years of Tech Cuts

Workers displaced from AI-exposed jobs since 2025 took about a month longer to find new work than other displaced workers, Goldman Sachs economist Pierfrancesco Mei wrote in a note published Thursday. They were also roughly 10 percentage points more likely to still be unemployed a year after losing their job. Those effects are "comparable in magnitude" to earlier waves of technological change, Mei wrote, damage he called "long-lasting." Goldman has tracked four decades of Americans who lost jobs to technology, and the first AI-displaced workers are starting to look a lot like them.

The numbers on job cuts are already stark. Employers cited AI in 120,136 announced job cuts through September, about 21% of all cuts and the top reason given, according to Challenger, Gray & Christmas' September report.

Where does the age split show up?

Older workers are having the harder time so far, the same pattern as past tech waves. AI-displaced workers 35 and older took about five weeks longer than other displaced workers to return to full-time work. Workers under 35 took about three weeks longer.

The Labor Department data shows the same gap across all laid-off workers. The Bureau of Labor Statistics reported in August that its survey counted 7.4 million workers displaced from 2023 through 2025, up from 6.3 million in the prior 2021-2023 survey. Among those who had held their jobs at least three years, 72.9% of workers ages 25 to 54 were working again by January 2026. Only 57.3% of those 55 to 64 and 38.6% of those 65 and older were.

The pay hit is broadening too. Of long-tenured workers who lost full-time jobs from 2023 through 2025 and found full-time work again, about 49% were earning as much as or more than before, down from about 62% in the prior survey, according to BLS.

What did four decades of tech layoffs do?

Goldman's earlier research drew on individual-level data back to 1979. Workers who lost jobs to technology saw real earnings grow more slowly than other displaced workers for years afterward. Their odds of another unemployment spell stayed persistently higher.

The damage wasn't even. Younger, college-educated and urban workers saw cumulative real earnings losses about half as large as other technology-displaced workers over the decade after losing their jobs. Workers with shorter tenures also did better, "likely because their skills were less occupation-specific and therefore more transferable," Mei wrote.

The new data repeats the pattern by age. Younger AI-displaced workers cycled through fewer temporary jobs and were less likely to hold a second job alongside their new full-time one. Younger workers "may be able to adapt more flexibly to AI disruption," Mei wrote — "an encouraging sign given concerns that AI-driven job losses could disproportionately affect recent graduates." Goldman also found college students increasingly choosing majors that lead to jobs with lower AI exposure and stronger growth. Goldman cautioned it's "still too early to assess the impact along all of these dimensions," such as education and geography.

Are young workers hit elsewhere?

Stanford's Digital Economy Lab, using ADP payroll data, found in August that employment for workers ages 22 to 25 in the most AI-exposed occupations runs about 19% below where it would be if it had kept pace with similarly aged workers in less-exposed jobs. "Experienced workers show no comparable gap," wrote Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen. The adjustment "appears to operate primarily through reduced hiring of young workers rather than increased separations," they wrote. Goldman's data only covers people who had a job to lose.

How did Goldman count?

The analysis uses the Current Population Survey's Displaced Worker Supplement, run every two years, covering more than 15,000 displaced workers who lost jobs between 2016 and 2025. No survey question asks whether AI cost someone their job, so Goldman built a score from the occupation's AI exposure, the industry's AI adoption rate and whether the position was eliminated, treating the top 30% as likely AI-displaced. Goldman found no significant gap between groups for job losses before 2023, the year AI adoption began to rise.

Not everyone sees AI moving the overall job market. The Stanford researchers wrote they "do not see widespread, economy-wide job displacement associated with AI." The Yale Budget Lab has found that measures of AI exposure, automation and augmentation "show no sign of being related to changes in employment or unemployment."

The historical pattern carries a warning. In past waves, technology-displaced workers were rehired into more routine, less complex work — the kind the next technology could take. Goldman sees early signs of that again: when AI-displaced workers get rehired, more of them, "especially older ones," are moving into roles with similar or even higher AI exposure.