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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallNo—Goldman Sachs did not predict that 300 million people will lose their jobs. Its 2023 estimate was that generative AI could expose work equivalent to 300 million full-time jobs to potential automation as workflows change. Exposure means tasks may be affected; it is not a count of layoffs or a forecast that those jobs will disappear.
What Goldman Sachs meant by “300 million jobs exposed”
In an April 5, 2023 explainer, Goldman Sachs Research economists Joseph Briggs and Devesh Kodnani estimated that generative AI could expose the equivalent of 300 million full-time jobs to automation if the technology is adopted into work processes. The number describes an amount of work potentially affected, not 300 million named positions or people expected to become unemployed. Goldman Sachs Research’s 2023 analysis also estimated that roughly two-thirds of U.S. occupations were exposed to some degree, and that around a quarter to as much as half of the workload in exposed occupations could potentially be replaced.
Those are modeled estimates of potential task automation. They do not tell us how quickly employers will adopt AI, whether they will use it to reduce staffing, or how many workers will be displaced. Goldman Sachs said most jobs and industries were only partially exposed and therefore more likely to be complemented than substituted by AI.
What the ILO’s newer exposure estimate says
The International Labour Organization and Poland’s NASK research institute published a global generative AI exposure index on May 20, 2025. It estimates that one in four workers worldwide is in an occupation with some degree of exposure to generative AI. The share rises to 34% of employment in high-income countries. The index evaluates occupational tasks—nearly 30,000 of them—rather than counting jobs already eliminated. The ILO’s conclusion is that job transformation is more likely than full replacement because many tasks still require human involvement. See the ILO’s global analysis.
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The ILO–NASK index identifies clerical work as the most exposed occupational group. It also notes increasing exposure in highly digitized fields such as media, software, and finance. In high-income countries, 9.6% of female employment and 3.5% of male employment fall into occupations at the index’s highest automation-risk level. These are group-level exposure estimates, not an individual worker’s probability of losing a job.
The ILO’s one-in-four figure is not a revised version of Goldman Sachs’ 300 million. The studies use different methods, dates, and units: Goldman estimated equivalent full-time work potentially exposed, while the ILO classifies workers by the exposure of their occupations. The ILO reports a mean automation score of 0.29 in its 2025 index, compared with 0.30 in its 2023 analysis, after refining its method and reducing variation in scores. That small numerical difference should not be read as evidence that exposure fell by a particular amount.
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Exposure is not the same as implementation or job loss
An occupation can include tasks AI may be able to assist with without being automatable as a whole. Whether exposure changes someone’s employment depends on what an employer actually adopts, which tasks are changed, and how the business responds. AI can help workers complete parts of a job, change the mix of skills employers seek, reduce hiring for some roles, or contribute to job cuts. An exposure estimate alone cannot distinguish among those outcomes.
It is also important to separate several kinds of labor-market evidence:
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- Task exposure estimates whether AI could affect tasks in an occupation.
- Vacancies and hiring show whether employers are posting or filling fewer positions, but do not by themselves establish why.
- Headcount changes and layoffs record employment outcomes, but attributing them specifically to AI requires evidence about causes.
That distinction is why neither a modeled exposure figure nor a decline in hiring in an exposed industry can be treated automatically as a tally of jobs lost to AI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What employment evidence adds—and what it cannot prove
The OECD’s 2023 baseline
The OECD’s 2023 Employment Outlook reviewed empirical studies available at the time and found little to no aggregate employment effect attributable to AI in that evidence base. It noted that limited adoption, slow implementation, employer reliance on attrition, and the creation of new tasks could keep effects small or delay them. The review also discussed evidence of reduced vacancy posting at some AI-exposed firms and displacement in particular sectors. Because it predates much of the subsequent adoption of generative AI, it is a dated baseline—not proof that AI cannot reduce employment. Read the OECD Employment Outlook 2023.
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Goldman Sachs Research’s 2026 labor-market analysis
A September 3, 2026 Goldman Sachs Research update reports slower job-opening growth since late 2022 in more AI-exposed industries, with stronger signs in some countries and sectors. It reports an association in France, Canada, and the United States: a 10% occupational exposure is associated with a 0.1-percentage-point drag on annual headcount growth. Goldman describes that as an association, not proof that AI caused the change, and says economy-wide hiring headwinds remain limited. Its analysis also suggests possible stronger hiring headwinds for junior workers. Read Goldman Sachs Research’s 2026 update.
The same update says call-center employment was 39% below trend in the United States, 33% below trend in Canada, and 27% below trend in Germany. These are Goldman Sachs Research’s estimates of employment relative to trend, which it attributes to AI-related headwinds; they are not counts of positions definitively eliminated by AI. The analysis offers evidence of pressure in selected areas, but not an audited global total of AI-caused job losses.
How to read the headline claim
| Claim or evidence | What it measures | What it does not establish |
|---|---|---|
| Goldman Sachs, 2023: equivalent of 300 million full-time jobs | Modeled work potentially exposed to automation through workflow changes. | That 300 million people will be laid off or that the jobs will vanish. |
| ILO–NASK, 2025: one in four workers worldwide | Workers in occupations with some degree of generative AI exposure. | The number of workers already displaced or an individual worker’s risk. |
| OECD, 2023: little to no aggregate effect in the reviewed evidence | Empirical studies available before much later generative AI adoption. | A current guarantee that AI will not affect employment. |
| Goldman Sachs Research, 2026: hiring and headcount associations | Labor-market patterns in selected countries and sectors, as analyzed by Goldman Sachs Research. | A causal global count of jobs lost specifically because of AI. |
The studies answer different questions, so their figures should not be added together or treated as successive estimates of the same thing. Taken together, they support a narrower conclusion: many jobs include tasks AI may affect; full replacement is not the default outcome in the ILO’s assessment; and recent hiring evidence points to headwinds in some exposed sectors without establishing how many job losses AI has caused worldwide.
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