Goldman: AI job losses show 'long-lasting' scar from Pierfrancesco Mei

Goldman's note rests on a methodological workaround: because no government survey asks whether AI caused a job loss, Mei built a score combining an occupation's AI exposure, its industry's AI adoption rate, and whether the position was eliminated, then treated the top 30% as likely AI-displaced. He found no significant gap between the two groups for job losses before 2023, the year AI adoption began to rise, which is the basis for calling the 2023–2025 cohort the first wave of AI-displaced workers.

The broader data Goldman cites shows the pay hit is already visible. 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. In past tech waves, Goldman found workers were more likely to be rehired into more routine, less complex work, and it sees early signs of that again: AI-displaced workers are more likely to leave their old occupation and industry, but when rehired, more of them, especially older ones, are moving into roles with similar or even higher AI exposure.

Mei flagged that younger workers 'may be able to adapt more flexibly to AI disruption,' which he called 'an encouraging sign.' But Stanford's Digital Economy Lab, using ADP payroll data, found a different kind of damage for young workers: employment for ages 22 to 25 in the most AI-exposed occupations is about 19% below where it would be if it had kept pace with similarly aged workers in less-exposed jobs, an adjustment that appears to operate through reduced hiring rather than increased separations. Goldman itself cautioned it is 'still too early to assess the impact along all of these dimensions.'