What “economic development” means here
Economic development usually refers to rising living standards over time: higher incomes, better health and education, more productive firms and sturdier institutions. It is related to growth in total output but is not the same thing, since a country can grow while many people see little benefit. When asking how AI might affect development, there are really two questions. First, could AI raise overall productivity and growth? Second, who would gain, within and between countries?
Both are open questions. Economists have studied earlier technologies in detail, but AI is recent and evolving, so claims about its economic effects should be treated as informed guesses rather than established results. Numbers quoted in headlines about trillions of dollars of added output depend on assumptions that vary widely.
How AI could support growth
Economic growth over the long run is driven largely by productivity: producing more with the same inputs. AI could contribute through several channels.
Task productivity. Tools that speed up writing, analysis, coding, design or customer support may allow workers to produce more or to spend time on higher-value tasks. The size of the effect is debated and likely varies by occupation. Productivity gains from general-purpose technologies such as electricity and computers historically took years or decades to show up in national statistics, because firms had to reorganize around them.
Better decisions from data. Forecasting demand, spotting fraud, optimizing logistics and detecting disease earlier are examples of where predictive analytics can reduce waste. Applications in smart factories and supply chain optimization illustrate this at the firm level.
Faster innovation. AI might speed up parts of research and engineering, such as screening materials or drug candidates, which could lead to new products and industries. How much remains uncertain.
Wider access to services. Translation, tutoring, basic legal or health information and financial tools delivered through a phone could reach people who lack local experts. Examples like financial inclusion show how digital services can open up access, though quality, language coverage and reliability still vary.
New industries and jobs. Demand for data, computing and related services creates work, though it may be concentrated in a few places.
The challenge of unequal starting points
Many of the possible benefits depend on foundations that are not evenly distributed.
- Connectivity and electricity. Using cloud-based AI requires reliable internet and power. Large gaps persist, and the digital divide may mean that the people who could gain most are least able to use the tools.
- Computing and data. Training advanced systems requires large amounts of computing power and data, concentrated in a handful of countries and companies. Smaller economies may become mainly users of tools built elsewhere, which can mean paying for access and having little say in how systems are designed.
- Language and context. Systems often work best in widely used languages and may perform worse in languages with less written material, or may reflect assumptions from other cultures.
- Skills and institutions. Using AI effectively takes educated workers, capable firms and trustworthy institutions. Countries with strong education systems and clear rules are likely better positioned.
- Capital. Firms with funds to invest, in any country, can adopt new methods more readily.
Because of these differences, there is a real possibility that AI widens the gap between richer and poorer economies. There is also a more hopeful argument that cheap, easy-to-use tools could help latecomers catch up faster than in earlier industrial eras, by sharing knowledge widely. Evidence for either view is still thin, and the outcome probably depends on policy choices.
Development paths under pressure
Historically, many lower-income countries grew by moving workers from farms into manufacturing and then into services, with low labor costs as an advantage. If automation reduces the importance of low-cost labor in manufacturing, or if service tasks such as call-center work and routine back-office processing become easier to automate, that path may become harder. Whether this happens, and how quickly, is uncertain, and countries may find new niches. Still, it is a concern that economists and development organizations discuss.
Inequality within countries
Even within a single country, gains may be uneven. Several patterns are possible:
- Skill-related gaps. Workers who can pair their skills with AI tools may see their earnings rise, while those whose tasks are replaced may see theirs fall.
- Capital versus labor. If AI lets firms produce more with fewer workers, a larger share of income could flow to owners of capital and technology, rather than wages.
- Winner-take-most markets. Digital technologies often favor large firms with data and scale, which could increase concentration.
- Regional differences. Cities with technology industries may prosper, while regions dependent on easily automated work may struggle.
It is not guaranteed that any of these will occur, and effects might also reduce some inequalities, for instance if tools raise the performance of less experienced workers more than that of experts, as some early studies of specific tasks have suggested. Such findings are limited to particular settings and should not be generalized too broadly.
Policy levers
Governments, businesses and international bodies have a number of options, each with trade-offs:
- Investing in infrastructure, education and training
- Supporting local research and small firms so that benefits are not captured entirely abroad
- Setting rules for data, competition, safety and responsible AI development
- Building safety nets and retraining for workers displaced by change
- Encouraging open tools and shared resources that smaller countries and organizations can use
- Cooperating internationally, so that standards and benefits are shared
There is disagreement about the right balance between encouraging rapid adoption and managing risks, and reasonable people weigh these differently.
A cautious conclusion about what to watch
Useful indicators over the coming years include productivity statistics, patterns of investment, who owns the key infrastructure, changes in wage shares, and the pace of adoption outside wealthy countries. Until more evidence arrives, the sensible stance is that AI offers real potential for development, that the benefits are not automatic, and that who gains will depend largely on access, skills and the choices societies make.