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AI could raise global productivity and improve public services, but it could also displace work, deepen inequality and strain energy systems. IMF, OECD and G7 analyses converge on a conditional verdict: outcomes will depend on adoption, worker support, competition, safeguards and each country’s capacity to implement them.
The central bargain: faster growth, uneven consequences
Global policy leaders are not treating artificial intelligence as either an automatic economic cure or an inevitable jobs catastrophe. They describe a transition whose benefits depend on who can deploy reliable systems, who owns the productive assets and whether institutions can distribute gains.
IMF Managing Director Kristalina Georgieva said at the World Government Summit on February 3, 2026, that AI could increase global productivity by up to 0.8 percentage points per year. That is an upper-bound possibility, not a guaranteed annual increase. OECD analysis likewise says macroeconomic gains depend on how widely and effectively firms, sectors and countries adopt AI.
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The same institutions warn that adoption can widen gaps between advanced and developing economies, large and small firms, and workers whose tasks complement AI and those whose tasks are easiest to automate. The policy question is therefore not simply whether AI creates growth, but whether economies can convert technical capability into broad-based income, secure work and trustworthy public services.
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What the productivity upside actually means
IMF: a potential macroeconomic lift
The IMF’s 0.8-percentage-point figure describes a possible boost to global productivity from AI-driven efficiency and innovation. Productivity gains can raise output without a proportional increase in hours worked, allowing firms to produce more, lower costs or pay more for scarce skills. The IMF also links AI to growth, economic diversification and potential wage and employment gains for workers in demand.
Those gains are scenario-based. They require businesses to redesign workflows, invest in complementary software and equipment, train employees and trust the systems enough to use them in consequential tasks. Poor data, unreliable outputs, weak cybersecurity or legal uncertainty can delay adoption and reduce the realized benefit.
OECD: adoption breadth matters more than headline capability
OECD work emphasizes that an advanced model in a handful of companies cannot deliver economy-wide gains by itself. Benefits are more likely when AI reaches many firms, including smaller businesses, and when workers can combine it with domain expertise. Knowledge-intensive services such as information and communications technology, finance and professional services have especially large opportunities because much of their work involves information processing.
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Public services and small firms
G7 leaders identify public-sector efficiency and small and medium-sized enterprise competitiveness as major potential benefits. AI could help agencies process cases, detect fraud, translate information and provide more accessible services; for smaller firms, it can lower the cost of analysis, customer support and software development. These uses still require human review, clear liability and protection for sensitive data.
Why “40% of jobs impacted” does not mean 40% unemployment
What the IMF number measures
The IMF estimates that 40 percent of jobs globally will be impacted by AI, rising to 60 percent in advanced economies (IMF, 2026). “Impacted” includes jobs that are upgraded, eliminated or transformed. It is an exposure measure, not a forecast that those shares of workers will lose employment.
Advanced economies have more office-based and cognitive work that can be affected by current systems, which explains their higher exposure. Emerging and developing economies may face less immediate task exposure in some sectors, but they can lose opportunities if they lack the infrastructure and skills needed to adopt AI.
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AI can automate portions of routine clerical, analytical and administrative jobs while raising the value of judgment, communication and technical oversight. That creates a squeeze in the middle: some roles shrink, some become more productive and new roles emerge around implementation, evaluation, security and compliance.
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The IMF reports that about one in 10 job postings in advanced economies now requires at least one new skill. The figure signals changing skill demand, not a complete inventory of future occupations. Workers may need employer-funded training, portable credentials and time to learn while remaining employed.
What determines whether exposure becomes displacement
- Task design: jobs with repeatable digital tasks are easier to automate than jobs requiring physical dexterity, accountability or nuanced relationships.
- Business choices: employers can use AI to augment staff, reduce headcount, increase output or combine all three.
- Labor institutions: bargaining arrangements, notice periods, unemployment insurance and retraining determine how quickly households absorb change.
- Demand growth: lower costs can expand output and create work, but the timing and distribution of that effect are uncertain.
Which countries and firms are positioned to benefit
Infrastructure, skills and capital
Countries with dependable broadband and electricity, access to computing capacity, skilled workers, investment capital and capable regulators can adopt earlier. They are also better placed to build local companies and retain more of the value created by AI. Countries without these foundations may remain users of imported systems, paying for services while capturing less of the productivity and tax base.
Competition and concentration
OECD analysis highlights competition bottlenecks and the risk that a small number of companies control essential models, data, chips, cloud capacity or distribution channels. Concentration can raise prices, limit experimentation and give dominant firms leverage over customers and workers. Policymakers are considering open-source options, access to data and compute, scrutiny of acquisitions and coordination among competition authorities.
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An IMF scenario-planning note describes AI as a “macro-critical transition rather than a standard technology shock” and identifies “state capacity” as a binding constraint on inclusive outcomes. In practical terms, governments need agencies that can set rules, enforce them, update tax and benefit systems, supervise financial risks and deliver training at scale. A formal strategy without implementation capacity will not protect workers or attract responsible investment.
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Risks that extend beyond employment
Rights, privacy and fairness
Systems trained on incomplete or biased data can produce discriminatory outcomes in hiring, credit, insurance, education or public services. Collection and reuse of personal data create privacy risks, while opaque decisions can make it difficult for people to challenge an error. Risk-based governance should require impact assessments, data protections, documentation, human oversight and remedies proportionate to the stakes.
Safety, security and malicious use
AI can generate convincing misinformation, assist cyberattacks, expose confidential information or behave unpredictably in high-risk settings. Organizations need access controls, testing, monitoring, incident reporting and the ability to disable or roll back systems. Security cannot be treated as a one-time certification because models, data and attack methods change.
Trust, autonomy and financial stability
Overreliance on automated recommendations can weaken human judgment and autonomy. In finance, correlated AI decisions, rapid trading or dependence on a few technology providers could amplify shocks. IMF scenario work therefore includes fiscal, monetary, financial-stability and governance uncertainty alongside labor-market effects.
Energy and environmental pressure
G7 leaders warn that rising AI adoption will place growing pressure on energy grids, create negative externalities and affect energy security, resilience and affordability. New data centers can increase electricity demand and compete for water or grid capacity. Governments must measure these effects, improve efficiency and direct AI toward uses that strengthen energy-system resilience rather than assuming efficiency gains will offset new demand.
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How major institutions frame the policy trade-off
| Institution | Economic opportunity | Labor and inclusion concern | Safeguard and governance emphasis | Distinct constraint |
|---|---|---|---|---|
| IMF (2026) | Up to 0.8 percentage points of global productivity growth per year; possible growth, diversification and wage gains. | 40% of jobs globally and 60% in advanced economies impacted; middle-skilled transition risks. | Tax, social-protection, financial-supervision and data-governance readiness. | State capacity and macroeconomic uncertainty. |
| OECD (2025) | Productivity and income-per-capita gains, especially in knowledge-intensive services. | Uneven adoption across countries, sectors and firms; digital divides. | Trustworthy, interoperable, risk-based rules and effective competition. | Concentration, weak competition and fragmented regulation. |
| G7 Leaders’ Statement on AI for Prosperity (June 17, 2025) | Public-sector efficiency, SME competitiveness, scientific progress and social benefits. | Workforce preparation and partnerships with emerging and developing economies. | Human-centric AI that protects rights, privacy, safety, security, fairness and accountability. | Energy-grid pressure, negative externalities and affordability. |
What governments are proposing
Prepare workers before disruption peaks
- Fund reskilling, retraining and transition assistance tied to real vacancies.
- Support talent exchanges and ensure workers can carry credentials between employers.
- Update curricula so digital, analytical and interpersonal skills complement AI.
Make adoption broader, not just faster
- Help SMEs obtain affordable computing, secure data infrastructure and technical advice.
- Expand public-sector pilots with procurement standards, audit trails and human review.
- Close connectivity and skills gaps in emerging and developing economies through partnerships.
Set guardrails that preserve useful experimentation
The emerging consensus favors interoperable, risk-based governance rather than identical rules for every application. Low-risk experimentation should remain possible, while high-impact uses face stronger requirements for testing, transparency, accountability, privacy and security.
Protect competition and open options
Policy proposals include preserving open-source alternatives, improving access to data and computing resources, reviewing acquisitions and vertical strategies, and coordinating competition enforcement across borders. The goal is to prevent control of essential inputs from becoming a barrier to entry.
Update the macroeconomic toolkit
Governments may need tax rules that capture new forms of value without discouraging investment, social protection that follows workers through job changes, financial supervision that accounts for technology concentration and data-governance systems that permit responsible sharing.
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Plan for energy demand
Grid planning, efficiency standards, transparent reporting of electricity use and investment in generation and transmission can reduce bottlenecks. AI applications that improve forecasting, maintenance or demand management should be evaluated alongside their own resource requirements.
How to judge whether the promise is being delivered
Headline model performance is an incomplete scorecard. Policymakers and the public should track:
- Productivity and wage growth by sector, firm size, region and worker group.
- The share of workers receiving paid training and the time required to move into new roles.
- AI adoption among SMEs and public agencies, not only large technology firms.
- Incidents involving privacy, discrimination, unsafe behavior and cybersecurity, plus resolution times.
- Market concentration, access prices for compute and data, and the number of viable suppliers.
- Electricity demand, grid congestion and environmental impacts associated with AI infrastructure.
- Whether lower-income countries gain local skills, investment and public-service capacity rather than only importing tools.
AI’s economic balance will be decided by these distributional and institutional outcomes. The technology can raise productivity, but inclusive prosperity requires deliberate investment in people, competition, public capacity and safeguards at the same time.
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