A Goldman Sachs research note by economist Pierfrancesco Mei, reported by Fortune on 30 October 2026, finds that workers displaced from AI-exposed jobs since 2025 have taken about one month longer to find new work than other displaced workers. Fortune describes the note as published on the Thursday of that week (29 October).
What the note found
- Workers displaced from AI-exposed jobs since 2025 took about one month longer than other displaced workers to find new jobs.
- They were roughly 10 percentage points more likely to still be unemployed a year after losing their jobs.
- Goldman calls these effects “comparable in magnitude” to earlier waves of technological change, and describes such effects as long-lasting.
- Age matters: AI-displaced workers aged 35 and older took about five extra weeks to return to full-time work, against about three extra weeks for those under 35.
How the estimate was built
According to Fortune, no survey asks whether AI caused a job loss, so Goldman estimated it. It analysed more than 15,000 displaced workers who lost jobs between 2016 and 2025, scored each job loss, and treated the top 30% of scores as likely AI-displaced. Fortune also reports that Goldman considers it “still too early” to assess the full impact. Because the AI attribution is an estimate rather than a measured cause, the results are best read as an indicator, not a precise count.
Wider context reported
- The Bureau of Labor Statistics counted 7.4 million displaced workers from 2023 to 2025, up from 6.3 million in the prior survey period.
- Among long-tenured displaced workers, 72.9% of those aged 25 to 54 were working again by January 2026, against 57.3% for ages 55 to 64 and 38.6% for ages 65 and older.
- About 49% of long-tenured workers who found full-time work were earning as much or more than before, down from about 62% in the prior survey.
- Challenger, Gray & Christmas reported 120,136 announced job cuts citing AI through September, about 21% of all cuts.
Fortune also notes contrary evidence: Stanford researchers found employment for workers aged 22 to 25 in the most AI-exposed occupations about 19% below expected levels while seeing no widespread economy-wide displacement, and the Yale Budget Lab found no clear link between AI exposure and overall employment changes. The picture is contested.
What it means for researchers, universities and research offices
Editorial interpretation, not a finding of the Goldman note: the note concerns the general US labour market, not academia, so any read-across is inference.
- Early-career researchers and graduate students. The age pattern suggests that mid-career staff who lose roles may face a longer search. Doctoral and postdoctoral training offices may want to review how they support transitions into non-academic work.
- Universities. Career services and curriculum planners may want to track labour-market evidence on AI exposure by field, while recognising the evidence is still disputed, as the Stanford and Yale findings above show.
- Research offices. Workforce planning in grant budgets, staffing models and technology investment is increasingly intertwined with AI adoption; see our coverage of AI token costs and institutional budget planning, the EU AI Act and research administration and campus staffing cuts.
Sources
- Fortune, “Goldman Sachs AI layoffs: displaced workers”, 30 October 2026 (read 12 November 2026)
