Not yet proven. Current evidence does not establish that AI adoption has increased total employment worldwide. AI can support hiring and new work in some settings while reducing labor demand in others, but exposure estimates, employer forecasts and productivity associations are not proof of a global net jobs gain.
What do the global employment figures actually measure?
Headlines often put percentages and job totals side by side as if they measured the same thing. They do not. A job can be exposed to AI because some of its tasks could be affected; that does not mean the job will disappear, be created or change in headcount. Adoption measures whether organizations use AI, while employment outcomes count jobs, hiring or losses over a defined period.
The scope of the technology matters, too. The International Monetary Fund’s broad estimate of exposure to AI and the International Labour Organization’s estimate for generative AI (GenAI) use different definitions and methods. Their percentages are not competing estimates of one identical measure.
| Measure | Estimate | What it means—and does not mean |
|---|---|---|
| GenAI occupational exposure | One in four workers globally, according to the ILO’s 2025 index | Workers are in occupations with some exposure to GenAI tasks. The index suggests transformation is more likely than full replacement; it is not a count of jobs expected to be lost or gained. |
| Highest GenAI exposure gradient | 3.3% of global employment, according to the ILO’s 2025 index; 4.7% of female employment and 2.4% of male employment | A narrower, highest-exposure category within the ILO framework—not a forecast of the share of jobs that will disappear. |
| GenAI exposure by country-income group | 11% of employment in low-income countries and 34% in high-income countries, according to the ILO’s 2025 index | The shares of employment in occupations with some GenAI exposure. Exposure varies with the kinds of work and their tasks. |
| Exposure to AI broadly | Almost 40% of global employment, according to the IMF in 2024; about 60% of jobs in advanced economies may be impacted | A broader AI measure than the ILO’s GenAI index. “Exposed” or “impacted” does not specify whether AI will complement a worker, reduce demand for their work or change tasks without changing headcount. |
The ILO’s 2025 index estimates occupational exposure using a task-level framework informed by worker input, expert review and AI-assisted scoring. It assesses which tasks may be affected; it does not count jobs created by AI adoption.
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Has AI already raised employment in the evidence available?
The most recent empirical synthesis in the available evidence is an ILO brief published on 1 June 2026. Reviewing experiments, firm-level evidence, platform studies and worker and employer surveys from Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom and the United States, it finds that large-scale job displacement remains limited in the studies reviewed. It also finds that worker-reported time savings—of a few percent of working hours—have not yet translated into higher measured output, earnings or employment.
That is a cautious finding, not proof that AI has no employment effect. The reviewed evidence spans different settings and methods and is not a complete global census. It does not provide a single pooled estimate of how adoption has changed worldwide employment. Productivity gains may be real in some cases, but reported time saved is not the same as a verified increase in output or a net increase in jobs.
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How could AI create jobs—and how could it reduce them?
Routes to more employment
- Lower costs and higher demand: If AI helps a business produce more or serve customers at lower cost, it may expand activity and hire more people. Whether it does depends on how much demand grows and which tasks still require workers.
- Complementary work: AI tools can assist workers rather than replace them. Businesses may need people to review outputs, integrate tools into workflows, handle exceptions or provide work that depends on human judgment and interaction.
- New tasks and roles: Changes in how organizations operate can create work that did not previously exist. The timing, scale and location of that work are uncertain.
Routes to fewer jobs
- Task substitution: When AI performs tasks that previously required paid labor, an employer may need fewer workers for that work or may slow hiring.
- Uneven demand: An organization can increase output without adding staff if existing workers and tools meet demand. Productivity improvement alone does not guarantee job growth.
- Uneven adjustment: New roles may not appear in the same places or require the same skills as displaced work. A national or global net gain, if one occurs, would not mean every worker or region benefits.
These channels can operate at once. The net effect depends on adoption, business decisions, demand, new tasks and how quickly workers and labor markets adjust—not on exposure figures alone.
What do forecasts and historical automation studies tell us?
Forecasts can show a plausible scenario, but they are not observations of employment already created by AI. The World Economic Forum’s 2025 Future of Jobs report projects 170 million jobs created and 92 million displaced by 2030, for a net gain of 78 million. That projection combines employer expectations and multiple macrotrends; it does not isolate AI’s contribution. Its estimates of how tasks may be divided among people, technology and human-machine collaboration also do not measure the absolute amount of work performed.
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Historical analysis offers context but not a direct answer about today’s GenAI. The OECD’s 2024 regional analysis found that, over the prior decade, regions with a 10% higher share of jobs at high risk of automation were associated with 5.6% higher labor productivity over five years. This is an association, not evidence that AI caused global employment growth. The OECD also reports that some regions experienced employment losses and that new jobs did not necessarily benefit the workers displaced by automation.
Which workers and places may feel the effects most?
The ILO’s 2025 index identifies clerical occupations as the most exposed to GenAI. It also finds exposure has grown for some digitized media, software and finance work. Its estimates show greater exposure in higher-income economies than lower-income ones, reflecting differences in the kinds of occupations and tasks found in those economies.
Exposure is also unequal by gender: women have a larger share of employment in the ILO’s highest GenAI exposure gradient than men. Separately, the OECD reports that generative AI exposure differs from earlier automation patterns, with greater exposure among high-skilled workers and women and greater potential impact in metropolitan areas. These are exposure patterns, not direct estimates of job losses or gains.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you assess a claim that AI is creating jobs?
- Check the outcome: Does the claim measure employment, hiring or layoffs—or only exposure, task capability, adoption, productivity or worker opinion?
- Check the technology: Is it about AI broadly, GenAI specifically or a different kind of automation?
- Check the time and place: Which workers, countries, sectors and period does the evidence cover?
- Check what else is included: Does a forecast isolate AI, or combine it with demographic change, economic growth, the green transition and other trends?
- Check who benefits: A net change in jobs does not reveal whether displaced workers can access new roles, or what happens to wages and job quality.
These distinctions explain why exposure statistics and forecasts can be useful without answering the causal question: whether AI adoption has already raised total employment worldwide.
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