How are AI capabilities distributed between rich and developing countries?

AI capabilities are concentrated in high-income countries, which account for about 87 percent of notable AI models, 86 percent of AI start-ups, and 91 percent of venture capital funding, despite holding about 17 percent of the global population.

How much of the labor market is exposed to AI in emerging markets?

About 40 percent of employment in emerging-market economies is exposed to AI, according to IMF staff analysis, compared with near 60 percent in advanced economies and about 26 percent in low-income countries.

The Economic Impact of AI on Development

Artificial intelligence is reshaping economic growth, work, and public services, and the gains are not arriving evenly. This page sets out what the World Bank and IMF data show, with an interactive dashboard and an Arab-region lens. The capacity to build AI is concentrated in high-income countries, while use is spreading to developing markets and exposure to AI now reaches a large share of the global workforce.

Key Takeaways

AI capacity is concentrated. High-income countries account for about 87 percent of notable AI models, 86 percent of AI start-ups, and 91 percent of venture capital funding, while holding about 17 percent of the world's population.
Exposure to AI is broad and uneven. The IMF estimates that about 40 percent of global employment is exposed to AI, close to 60 percent in advanced economies and about 26 percent in low-income countries.
Use is shifting toward developing markets. By mid-2025, more than 40 percent of global ChatGPT traffic came from middle-income countries, led by Brazil, India, Indonesia, and Vietnam.
Readiness has four parts. The IMF AI Preparedness Index measures digital infrastructure, human capital and labor-market policies, innovation and economic integration, and regulation and ethics across around 174 economies.
Smaller, local AI can fit better. The World Bank argues that affordable, purpose-built AI in local languages, running on basic devices, is a more realistic path for many developing economies.

How does the World Bank use AI?

The World Bank treats AI as a general-purpose technology with a developing-country reading. Its World Development Report 2026 addresses AI for development. The International Finance Corporation released an AI-powered ESG analysis tool as a free public good. The development-impact unit built a generative-AI research assistant grounded on validated studies, and a news-driven early-warning approach for food crises. A randomized trial in Edo State, Nigeria, used a GPT-4 assistant as a supervised after-school tutor and recorded large learning gains at low cost. The Bank frames readiness around four foundations, connectivity, compute, context, and competency, and argues for "Small AI."

How does the IMF use AI?

The IMF measures readiness and analyzes AI's effect on work. Its AI Preparedness Index scores around 174 economies across four dimensions. IMF staff analysis on generative AI and the future of work produced the headline exposure figures: about 40 percent of global employment exposed, close to 60 percent in advanced economies, around 40 percent in emerging markets, and about 26 percent in low-income countries. The analysis notes AI can both displace and complement jobs, and warns it may widen inequality without supportive policy.

Common Questions

What is the impact of AI on economic development?

AI affects economic development unevenly. The capacity to build AI is concentrated in high-income countries, while use is spreading to developing markets and about 40 percent of global employment is exposed to AI. The economic result depends on whether developing economies build the infrastructure, skills, and governance to adopt AI on their own terms.

What is AI readiness and how is it measured?

AI readiness is a measure of how prepared an economy is to adopt and benefit from AI. The IMF measures it through the AI Preparedness Index across digital infrastructure, human capital and labor-market policies, innovation and economic integration, and regulation and ethics.

The interactive dashboard explores these measures with World Bank context indicators and an Arab-region lens. Related work: Last-Mile AI, AI governance in the United Nations, and the AI projects portfolio.