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AI is already influencing investment and changing some work tasks, but current evidence does not establish a broad, lasting acceleration in global productivity. Its economic impact will depend on whether firms turn investment and task-level gains into sustained output—and whether workers and countries have the infrastructure, skills, and institutions to share in those gains.
How is AI affecting the global economy now?
Four connected channels help explain the shift: investment, productivity, work and employment, and the distribution of income and economic power. They are moving at different speeds. Investment is visible in current economic activity; productivity and employment effects are harder to measure and remain uneven.
Investment is supporting activity, but returns are uncertain
The OECD’s September 2026 interim Economic Outlook says continued strength in AI-related investment helped sustain production, trade, and growth in the first half of 2026. That is evidence of an investment cycle—not proof that AI has already raised global output permanently or that the spending will earn the returns investors expect. The OECD warns that disappointing returns could weaken growth prospects and prompt asset repricing.
Task-level gains have not yet established a productivity boom
AI can help with particular tasks, but isolated time savings do not automatically become more output, higher wages, or lower costs. An International Labour Organization (ILO) review published on 1 June 2026 finds real but uneven gains, many of them not yet verified in measured outcomes. In the evidence it reviews, workers report saving a few per cent of working hours, but those reported savings have not yet translated into higher measured output, earnings, or employment.
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The review draws on experiments, firm data, platform studies, and surveys in Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom, and the United States. Its findings are informative about those settings and types of evidence; they are not a direct measurement of AI’s effect on the entire world economy.
Could AI raise economic growth?
Yes, but the scale and timing are uncertain. The World Bank’s June 2026 Global Economic Prospects describes a plausible sequence: near-term effects would come mainly through higher investment, with productivity playing a larger role over the medium term. Its optimistic illustration has global growth in the 2030s exceeding the average of the 2000s—but only if AI produces transformative productivity improvements. That is a conditional scenario, not a central forecast or an observed result.
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Estimates vary because the eventual effect depends on several linked questions: how much economically important work is exposed to AI; how much productivity improves on those tasks; how widely and quickly firms adopt the technology; and whether gains are one-off or continue over time. Models also rely heavily on evidence from advanced economies, which makes the effects in emerging markets and developing economies less certain.
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| Figure | What it represents | What it does not establish |
|---|---|---|
| 0.3–0.4 percentage points for Europe | Early estimates of annual labor-productivity growth summarized by the OECD’s 2025 cross-country report, attributed there to other researchers. | A realized increase or an OECD forecast. |
| Almost zero to 1 percentage point for the United States | A range of cited estimates for AI’s possible contribution to annual labor-productivity growth over the next decade, as summarized by the OECD in 2025. | A measured outcome, guaranteed result, or single official estimate. |
| Global growth in the 2030s above the 2000s average | An optimistic, conditional scenario in the World Bank’s June 2026 Global Economic Prospects. | The World Bank’s central forecast for global growth. |
| 2.9% in 2026; 3.0% in 2027 | The OECD’s overall global GDP growth projections in its September 2026 interim outlook. | AI’s estimated contribution to GDP growth. |
The range of productivity estimates should not be collapsed into a single expected gain: the figures come from different studies and assumptions. The OECD summarizes them; it does not present them as realized productivity growth.
What does AI mean for jobs and workers?
Exposure to AI does not mean a job will disappear. A task may be automated, assisted, reorganized, or left largely unchanged. The net effect on a job depends on which tasks matter, how employers adopt AI, and whether new or expanded work offsets tasks that shrink.
Exposure is greater in some advanced-economy occupations
A joint ILO–World Bank analysis, reported by the ILO in 2026, examines 135 countries covering around two-thirds of global employment. It finds higher generative AI exposure in advanced economies, especially in clerical and professional occupations. This is an analysis of potential exposure, not a count of jobs already lost or a measurement of effects in every country.
Lower exposure can coexist with greater barriers to benefit
In developing economies, different task mixes and less reliable internet may limit both exposure to automation and the ability to use AI to raise productivity. Some vulnerable clerical and administrative roles in lower-income countries may still face automation pressure. These jobs can also be pathways to better-paid, more secure work, including for women and young people. A country’s lower aggregate exposure therefore does not mean its workers are insulated from disruption—or positioned to capture productivity gains.
Adjustment may affect job quality and opportunity as well as headcount
The ILO’s June 2026 review finds large-scale displacement limited in the studies it synthesizes, while identifying emerging risks to younger workers’ employment opportunities and changes in work organization, coordination, autonomy, and job quality. These are important pressures to monitor, not a definitive forecast of global job losses.
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Who is likely to gain—and what separates countries?
There is no reliable single ranking of “AI-winning” countries. The ability to benefit depends on more than exposure to AI: it also depends on whether workers and firms can access the technology and use it productively, and on who captures the resulting value.
| Factor | Why it matters |
|---|---|
| Exposure versus adoption capacity | A task may be technically exposed, but firms need the ability and incentive to adopt AI in ways that improve work. |
| Digital foundations | Reliable connectivity, computing, and access to relevant systems affect whether people and businesses can use AI. |
| Skills and task composition | Workers’ skills and the tasks they actually perform—not just an occupation’s name—shape how AI changes work. |
| Investment versus realized productivity | Capital spending can support current demand; lasting economic gains require sustained improvements in output or efficiency. |
| Distribution and adjustment | Training, labor-market institutions, and social protection influence who benefits and who bears disruption. |
The IMF also flags the possibility that income gains accrue unevenly and that market concentration, unequal access to frontier models and computing, and differences in worker preparation could widen gaps. These are distributional risks, not outcomes that are inevitable in every economy.
What policies can help broaden the gains?
The World Bank, ILO, and IMF point to enabling conditions rather than one intervention that guarantees success. Their policy priorities include:
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- Expand reliable digital infrastructure. Connectivity and access to computing and relevant systems are prerequisites for many workers and firms to adopt AI.
- Invest in skills and lifelong learning. Training can help workers adapt as tasks change; the available evidence does not establish a single training program with a guaranteed return.
- Strengthen labor-market institutions and social protection. These can help workers manage transitions and share in gains as jobs and work practices change.
- Use public investment and regulation to widen access and manage risks. Policy choices can shape adoption, competition, and how benefits and costs are distributed.
- Pay attention to system-wide risks. The IMF identifies financial supervision, cybersecurity, energy supply, and concentration in the AI ecosystem as issues requiring attention, alongside international coordination.
These are policy priorities identified by the institutions, not interventions with quantified, proven returns across all countries.
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The clearest test of AI’s economic importance will be whether investment is followed by durable, measured productivity growth—and whether the gains reach workers and countries beyond those with the strongest digital foundations. In the World Bank’s words: “Ultimately, the extent to which AI reshapes global growth will depend on the scale and persistence of the productivity improvements it generates and on whether countries can build the conditions needed to capture them.” — Global Economic Prospects, June 2026, Box 1.1.
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