Many low- and middle-income countries are preparing for AI to make information abundant. However, the frontier has shifted. Rich economies are preparing for AI to make some forms of intelligence abundant. Will advanced artificial intelligence allow LMICs to leapfrog human knowledge work—or leave them behind?
Information versus intelligence
Basic AI systems, like large language models circa 2024, are good at retrieving existing knowledge. In LMICs, they are being used for health, agriculture, business (Otis et al. 2025), and education (Björkegren et al. 2025). Many countries are pursuing frugal strategies that deliver these services with small models over mobile phones. For communities that the web barely reached, even basic AI offers an improvement in access to information.
However, frontier AI models are increasingly able to not just retrieve information, but apply forms of intelligence: planning, using tools, and completing complex tasks that require multiple steps. Coding assistants like Claude Code or OpenAI’s Codex are already shaking up how software is developed, how data is analyzed, and how computer scientists are educated. AI capabilities are improving and are expected to affect other knowledge sectors: the past few months have seen the release of tools that can perform general computer tasks and create digital designs. Since advanced models require much more computational capacity, firms are building vast data centers and the energy generation to power them.
Why returns to intelligence differ
The economic implications of this transformation can be characterized by the marginal returns to intelligence (Amodei 2024): how much can we improve economic outcomes as we better generate ideas, process data, and apply knowledge? Intelligence allows us to solve scientific problems, design better products, better anticipate demand, and ensure the right quantities are stocked in the right places. Low-income countries will benefit from innovations developed in rich ones. But within many LMICs, the complements to advanced AI are scarcer, including data centers, reliable electricity, and digital records, as well as experienced knowledge workers. Data centers can be located in countries that already have good infrastructure (‘the cloud’) and accessed remotely. But LMICs are less digitally legible: AI will be less able to understand and act in markets, firms, homes, clinics, and schools that do not record data in structured forms. Overall, we would expect LMICs to be at a disadvantage in integrating advanced AI (Korinek and Stiglitz 2021).
A crucial distinction is that LMICs have much smaller knowledge sectors. LMICs employ fewer than 10% of workers in skilled knowledge work, like managers, technicians, and professionals, relative to 41% in high income countries (Silva 2026). Current AI tools require substantial human guidance. So, firms in rich economies are pursuing a grafting strategy: existing knowledge workers are being asked to integrate AI into their roles, starting from producing slides and emails, and scaling to more sophisticated tasks. In countries with smaller knowledge sectors, there are fewer workers and processes to graft AI onto. Thus a key question is whether advanced AI will mainly empower existing workers, or automate knowledge work completely. In wealthy countries, advocates concerned about jobs suggest that AI systems be designed to augment rather than automate (Acemoglu, Autor, Johnson 2026). But in low-income countries, the more urgent question may be how to provide knowledge services when few knowledge workers are available. Fully automating knowledge work could in fact augment less educated workers, who could ask AI to complete macro tasks like developing marketing strategies, rather than micro tasks like reformatting spreadsheets. However, even automated systems will likely require oversight from entrepreneurs and scientists with deep expertise, which may be sufficiently available only in wealthier countries like Brazil and India.
If AI allows LMICs to grow automated knowledge sectors, would the returns be high or low? One indicator is in wages paid to human workers. The wage returns to college education are slightly higher in lower income countries (Psacharopoulos and Patrinos 2018 and 2025), but educated people often earn higher wages abroad, and some domestic knowledge workers are working on rich countries’ knowledge problems in call centers and business process outsourcing. Lower income economies may not currently be structured to fully tap the decision making entailed in knowledge work (Engbom et al. 2025). If we tasked millions of data scientists with helping smallholder farms, the returns are unlikely to be large: agriculture is constrained elsewhere.
However, if the price of some forms of intelligence declines by orders of magnitude, it may become worth applying intelligence to problems that were never worth assigning a human to. Small manufacturers might generate nuanced designs that would have required a team of industrial engineers, and implement advertising campaigns that would have required large creative teams. Many regions have struggled to agglomerate sufficient human talent; since automated intelligence can be accessed anywhere, it could make businesses more mobile. These opportunities could more fundamentally change economic structure.
Small knowledge sectors may also pose less resistance to the adoption of AI. Wealthy country workers are beginning to politically resist AI. But residents of low- and middle-income countries are less concerned that AI will displace jobs, and are more optimistic about AI, according to interviews conducted by Anthropic (Huang et al. 2026). LMICs also have less developed laws and ecosystems of institutions designed to manage human knowledge work. As a result, some uses of AI may become blocked in wealthier countries but allowed in low-income countries: for example, a proposed chatbot law in New York state would restrict AI systems from providing advice typically provided by licensed humans. Ultimately, the constraints in LMICs represent not simply deficits, but a different optimization landscape.
Building for abundant intelligence
The economies that LMICs trade with, compete with, and depend on are being reorganized around abundant intelligence. The question is not whether LMICs will be affected—they will be—but how they will engage. The analysis above suggests that AI that augments knowledge workers is likely to disproportionately benefit rich countries. It is less clear what alternative paths exist, but there are actions that could offer opportunities, particularly for middle-income countries and more advanced regions within low-income countries.
The most capable AI systems currently require large-scale frontier models and large amounts of compute. Governments, firms, and NGOs will need to work with the frontier labs to ensure that the most advanced models speak local languages and understand local contexts. Ensuring that there are multiple suppliers for both models and data centers can reduce prices and risks of lock-in and geopolitical disruption (Athey and Scott Morton 2025).
Governments will also need to push to make economic activity digitally legible, from markets to clinics to schools.
It is also important to ensure that AI can be productively used. That may require training humans to be more productive users of AI, both in applying the tool and having the deeper world knowledge needed to direct it. Firms can also invest in developing AI tools that are complementary to the industrial structure of LMICs, including tools for small scale entrepreneurs who have less education, and for agriculture, like weather forecasting.
The diversity of institutional conditions in low- and middle-income countries may be a comparative advantage. Wealthy countries have evolved similar institutions around human knowledge work; tweaks may lead to local optima. In contrast, systems in low-income countries can differ greatly. Tailoring to different constraints can generate opportunities: for example, Kenyan entrepreneurs coping with unreliable network connections developed techniques to create on-device AI models that are seeing demand around the world (Fastagger). Or, also in Kenya, 90% of people resolve disputes outside the formal justice system (Kenya 2020), and just two doctors serve every 10,000 people, compared to 37 in the United States (WHO, 2022). Firms and NGOs may find creative new solutions, such as offering more efficient ways to settle disputes outside of court, or dynamic medical advice. Governments can take advantage of opportunities to design new regulation for AI, rather than retrofit regulation designed for humans. A lack of established institutions around human knowledge work could also allow harm: what happens when medical AI makes mistakes and there are limited mechanisms to address malpractice? It will take care to develop appropriate new institutions.
We must begin to consider what economies with scarce resources might look like amidst abundant intelligence. The questions are challenging. If LMICs can’t graft AI onto knowledge work, what are they building instead? Will they wait for intelligence to reshape the global economy? Or will they develop new—and possibly different—economic structures?
This piece has benefited from feedback from Jeff Berens, Han Sheng Chia, Johannes Haushofer, and Alex Imas.
