An economy is held back by its weakest links. That idea helps explain why AI’s promise for developing countries is large, why its gains will take time to arrive, and why some of its risks may not wait. Watch @ChrisTonetti and our Faculty Director, @Susan_Athey, discuss AI as a general-purpose technology and what determines the pace of its impact, in this session from the World Development Report 2026: The Promise of Artificial Intelligence conference at Stanford. This is the first of four posts on takeaways from the conference. The promise is large. Adapted to local needs, AI could help do in a decade what might otherwise take a century. If AI can strengthen an economy’s weakest link, the returns are high. In many developing countries, one weak link is expertise. With AI’s help, nurses, teachers and extension workers without specialist training can take on tasks that once required a specialist. The gaps are wide: Sub-Saharan Africa had 2.9 physicians per 10,000 people in 2019, against a global average of 16.7, and primary schools in low-income countries average about 40 students per teacher, compared with about 15 in high-income countries. But expertise is not the only weak link, and the others will determine how quickly that promise is realized. AI is diffusing faster than earlier general-purpose technologies. Poorer countries adopted steam about 80 years after richer ones, electricity about 40 years, and the internet about 20 years. Within six months of ChatGPT’s launch, middle-income countries accounted for half its traffic. But fast diffusion is not deep use. By April 2025, nearly 25% of internet users in high-income countries had adopted ChatGPT, compared with 5.8% in upper-middle-income, 4.7% in lower-middle-income and 0.7% in low-income countries. AI’s benefits could remain concentrated in richer countries, at least at first. This adoption gap reflects other weak links. AI depends on complements: electricity, connectivity and literacy. In rural Sub-Saharan Africa, 32% of schools lack regular electricity and 68% lack reliable internet access, and across the region, 89% of 10-year-olds cannot read a simple text. Fixing one weak link often exposes the next. In Thai clinics using an AI tool to screen for diabetic eye disease, the limiting factor shifted once the tool was in place. Poor lighting led it to reject over 20% of images, and slow connections stretched uploads that took seconds in testing to over a minute. History points the same way. Electricity paid off only after factories were redesigned, and computers only after firms changed their workflows. In 19th-century France, gains from mechanized cotton spinning came only after a long period of trial and error in reorganizing production. AI faces similar limits: a drug company can use AI to find new medicines faster, but it may still be limited by its capacity to run human trials. Pockets of benefit can come quickly when a specific application meets a specific need. Fitting AI into complex institutions like schools and health systems takes years of tool selection, piloting, evaluation, and rollout. Tonetti’s research with @ChadJonesEcon applies the same logic to the whole economy. In a working paper that uses a model built on US data since 1950, the authors find that most past productivity growth came from automation. Machines get better at their tasks much faster than people do, so each task moved to a machine keeps getting more productive. Looking ahead, AI accelerates growth, but slowly. Output depends on every task, so it remains constrained by the tasks people still perform. In their baseline scenario, US GDP per person in 2050 is only about 4% higher than it would have been if it had continued growing at its pre-AI pace. If AI’s aggregate gains build this slowly even in the US, the pace in developing countries will likely depend all the more on their weak links. The paper ends with a warning. The same weak links that slow gains make the economy fragile: strengthening one link adds little, but breaking one can bring the chain down. AI that automates software engineering may do little for GDP, yet an AI that uses a computer as well as the best humans could, in the wrong hands, hack the power grid or the banking system. The dangers can arrive quickly even as the benefits arrive slowly. The policy lesson: countries need a theory of where change from AI will be slow, where it may be fast, and why. For each use of AI, that means asking which link will bind: a device, a connection, literacy, or an institution willing to change how it works. Where the links are already in place, as with agricultural advice by text message on basic phones, gains can come quickly. Where they are not, public investment should go first to electricity, connectivity, and foundational skills. Next up in this series: what new surveys of firms in six developing countries show about AI adoption and jobs. Full session: youtube.com/watch?v=oq5Rwdnn…