AI Could Transform the Developing World Within Just Ten Years

How AI Could Rewrite the Future of the Developing World

World Bank Says AI Could Give Poorer Countries a Century of Progress in a Decade

Artificial intelligence could allow developing countries to achieve within ten years advances that might otherwise take a century, according to one of the World Bank’s most ambitious assessments of the technology yet. The claim is not a precise economic forecast, but it captures an extraordinary possibility: AI could distribute scarce knowledge faster than poorer countries can train enough doctors, teachers, engineers, administrators and agricultural specialists through conventional systems.

That could turn the developing world from a presumed casualty of the AI revolution into one of its greatest potential beneficiaries. Yet the same technology could also deepen dependency, strengthen authoritarian surveillance and leave countries paying foreign companies for systems they cannot inspect, control or easily replace.

Why AI Could Change Development So Quickly

Previous industrial revolutions spread slowly. The World Bank estimates that the steam engine took around 80 years to reach lower-income countries, electricity took roughly 40 years and the internet took about 20 years. Middle-income countries, by contrast, generated half of ChatGPT’s worldwide traffic within six months of its launch.

That speed matters because AI does not always require a country to build factories, research laboratories or vast fleets of machinery before people can begin using it. A farmer with a basic phone could receive local weather and crop advice. A rural health worker could use an AI-assisted screening tool. A teacher could generate lessons adapted to a child’s language and ability.

The most powerful development effect may therefore come from expanding access to expertise. Rich countries have large pools of trained professionals, while poorer states frequently struggle with severe shortages. AI cannot manufacture a hospital, repair a road or replace a competent government, but it can help a limited number of skilled workers serve far more people.

This is why the World Bank’s argument is more credible than the phrase “a century in a decade” initially sounds. The institution is not claiming that every poor country will resemble a wealthy one by 2036. It is arguing that selected capabilities which previously took generations to distribute could become accessible almost immediately.

What Has Already Been Achieved

Some of the earliest gains are appearing in agriculture. AI-generated weather forecasts can give farmers more precise information about rainfall, temperature and planting conditions in places where trained agricultural advisers are scarce. Better decisions over when to plant, irrigate, fertilise or harvest can protect both incomes and food supplies.

Healthcare offers another opening. AI-assisted screening can help frontline staff identify patterns in scans, symptoms and patient data, directing scarce medical attention towards higher-risk cases. The technology remains fallible and requires human oversight, but even an imperfect assistant may provide value where the alternative is an overwhelmed clinic or no specialist at all.

AI is also being used behind the scenes in public administration. Predictive systems can support tax enforcement, identify unusual transactions, organise public records and help governments decide where limited resources are most urgently required. These applications may appear less dramatic than a medical breakthrough, but improved state capacity can influence everything from school funding to infrastructure maintenance.

The evidence from businesses is promising but uneven. An experimental AI adviser delivered through WhatsApp to small businesses in Kenya produced gains exceeding 15 per cent among stronger-performing firms that could understand and apply its recommendations. The result also exposed a warning: access to AI alone does not ensure equal benefits, because firms with better skills, equipment and management are often best placed to use the advice.

Why “Small AI” Could Matter More Than Supercomputers

The World Bank does not recommend that every developing country attempt to build a rival to the largest American or Chinese AI systems. Frontier models require advanced chips, enormous data centres, plentiful electricity, deep capital markets and highly specialised researchers. For most poorer governments, trying to replicate that infrastructure would consume money needed elsewhere and still be unlikely to succeed.

The more realistic opportunity is “small AI”: specialised systems designed for a particular language, institution, crop, disease or public service. These models can be cheaper to operate, easier to adapt and capable of running through smartphones, voice calls or basic devices where bandwidth is limited.

A local agricultural assistant does not need to understand every subject in human knowledge. It needs reliable information about regional soils, pests, crops, markets and weather. A classroom tool does not need to imitate a global chatbot if it can explain mathematics accurately in the language spoken by local pupils.

This approach could allow developing countries to import the expensive underlying intelligence while controlling the final application. It is the difference between attempting to manufacture an entire aircraft and building a national transport system using aircraft acquired from elsewhere.

What Governments Are Planning

The World Bank’s framework divides the path ahead into three stages: adopt, adapt and advance. Countries can begin by adopting proven tools, move towards adapting those tools to local languages and needs, and eventually advance into developing more of their own AI technology where their resources allow it.

The immediate priorities are less glamorous than futuristic robots. Governments need reliable electricity, affordable internet access, secure digital identities, interoperable payment systems, usable public data and workers who understand how to evaluate AI outputs. Without those foundations, AI pilots may impress at conferences but fail when deployed across thousands of schools or clinics.

Public procurement will be decisive. Governments can use their purchasing power to test systems in education, healthcare, agriculture and the legal system, measure whether they work and scale successful programmes. They will also need rules covering privacy, cybersecurity, discrimination, accountability and the right to challenge automated decisions.

Local-language data will become a strategic asset. AI trained mainly on wealthy, English-speaking societies may misunderstand laws, customs, illnesses, dialects and economic conditions elsewhere. Countries that digitise their records and languages responsibly will be better placed to build tools that serve their own citizens rather than merely importing foreign assumptions.

How These Countries Could Look by 2036

In a successful ten-year scenario, a rural clinic could possess diagnostic support once available only in a major city. Teachers could create personalised material for crowded classrooms. Farmers could receive constantly updated advice about weather, disease, prices and logistics, while small companies gain access to accounting, marketing and legal guidance that they could never previously afford.

Governments could become faster and more capable. Tax authorities might identify evasion more effectively, courts could process documents more quickly and citizens could access public services through multilingual voice assistants. Digital records could make welfare payments more accurate while reducing the time and corruption associated with paper systems.

Economic geography could also change. A skilled worker in Nairobi, Dhaka or Accra could use AI to perform work that once required a much larger corporate support system. Small firms could reach international customers, translate products, analyse contracts and produce professional material without employing entire departments.

None of this would automatically turn low-income countries into high-income economies. Roads, ports, electricity, housing, security, capital and effective institutions would still matter. AI can amplify capacity, but where capacity is almost absent it may amplify little.

There is also a darker scenario. Connected cities and internationally competitive firms could accelerate while rural communities remain offline. Well-educated workers might capture most of the gains, leaving poorer citizens facing automated bureaucracy, unreliable decisions and fewer routes into traditional white-collar employment.

The New Geopolitical Battleground

The developing world may become the most contested market in the global AI race. American companies currently dominate many of the leading commercial models and cloud services, while China offers increasingly capable, affordable and adaptable systems backed by its own infrastructure, telecommunications and financing networks.

Governments will not choose between these systems on performance alone. They will consider cost, political alignment, cybersecurity, data storage, sanctions exposure and whether a foreign supplier could withdraw access during a diplomatic dispute. AI procurement could begin to resemble decisions over military equipment, telecommunications networks or energy infrastructure.

Countries that depend completely on one foreign provider may discover that digital capability comes with political leverage. A supplier could influence prices, standards, permitted uses and access to future upgrades. Sensitive health, identity, financial and government data could also become strategically valuable far beyond its original purpose.

China could use cheaper models and infrastructure packages to deepen its influence across Africa, Asia and Latin America. The United States and its allies could respond with trusted cloud partnerships, investment, open models and development finance. India, the Gulf states and other emerging technology powers may offer additional options, giving smaller countries more room to negotiate.

The contest will also affect global voting blocs and diplomatic relationships. A country whose education, healthcare and government systems operate through another power’s technology stack may gradually align with that power’s technical standards, regulatory philosophy and security interests.

The Risk of Digital Colonialism

The danger is that poorer countries become users of AI without becoming owners of any meaningful part of its value. Their citizens could provide data, their governments could pay subscription fees and their businesses could depend on foreign platforms while most profits flow to technology companies based elsewhere.

That would create a new form of extraction. Instead of exporting only minerals, crops or labour, developing countries would also export data and economic dependence. Local companies could struggle to compete with foreign systems trained using far greater resources.

AI could strengthen authoritarian governments as easily as it strengthens public services. The same systems that improve benefit payments can support population tracking. The same language tools that help citizens access government information can generate propaganda, censor opposition or monitor private communications.

Successful countries will therefore need something more practical than absolute “AI sovereignty,” which few can afford. They need selective control: ownership of critical public data, the ability to move between suppliers, independent evaluation of important systems and enough local expertise to understand what they are buying.

A Historic Opportunity With No Guaranteed Outcome

The World Bank’s century-in-a-decade message should be read as a statement of possibility, not destiny. AI can spread knowledge at extraordinary speed, but it cannot compensate indefinitely for failing electricity grids, weak schools, corrupt institutions or unaffordable internet access.

The countries most likely to benefit will not necessarily be those that spend the most on impressive data centres. They will be those that choose narrow problems, build reliable foundations, adapt systems to local conditions and scale only what produces measurable results.

If that happens, AI could help billions of people gain access to expertise that their societies have never been able to provide universally. If it does not, the technology may instead divide the developing world between connected winners and dependent outsiders.

The next decade will therefore decide more than whether poorer countries adopt AI. It will decide whether they use it to compress development—or allow another technological revolution to be controlled elsewhere.