The World Bank has issued an urgent call for developing economies to capitalize on artificial intelligence as a potential accelerant for growth, arguing that nations acting decisively on infrastructure and workforce readiness could compress enormous development gains into remarkably short timeframes. In a report released this week, the Washington-based institution contends that emerging markets face a genuinely rare historical opening—one that their predecessors fundamentally missed during earlier technological transitions, with consequences that reverberated across generations.
Indermit Gill, the World Bank's chief economist, frames the opportunity in starkly consequential terms. Rather than presenting AI as primarily a threat to livelihoods, Gill emphasizes that developing economies are uniquely positioned to harness artificial intelligence in ways that leapfrog traditional development stages. The core insight underpinning this optimism is straightforward: emerging markets need not build the enormous, expensive infrastructure that wealthy nations are currently constructing. Instead, by adapting smaller, locally tailored AI applications to address pressing challenges, these countries can deliver meaningful improvements across sectors that directly affect hundreds of millions of people.
The practical applications identified by the World Bank span critical areas where emerging economies have long faced persistent deficits. Healthcare delivery, perpetually constrained by insufficient personnel and expertise, could be substantially enhanced when health workers deploy AI diagnostic tools. Educational systems struggling with resource limitations could benefit as teachers employ AI to customize lesson planning and content delivery. Agricultural extension services—vital in nations where farming remains a primary livelihood—could help farmers make data-informed decisions about planting schedules and crop selection. These applications illustrate a broader principle: emerging economies need not wait for cutting-edge large language models, but rather can deploy purpose-built, lower-cost tools calibrated to their specific conditions.
A critical distinction separates the employment outlook for developing nations from that of wealthy countries, according to the World Bank's analysis. While generative AI threatens jobs in high-income economies at three times the rate observed in developing nations, the figures reveal a more nuanced reality than simple panic narratives suggest. In wealthy countries, approximately 14.2 percent of employment faces meaningful exposure to AI-driven disruption, whereas in low- and middle-income countries, only 4.5 percent of jobs fall into this category. This disparity reflects structural differences: emerging economies maintain larger shares of employment in sectors—agriculture, informal services, small-scale manufacturing—where current AI applications have limited penetration. Simultaneously, the productivity benefits that AI can generate appear distributed relatively evenly, with developing economies positioned to see 16.2 percent of jobs benefit from meaningful productivity enhancements compared to 18.7 percent in high-income nations.
Yet realizing this potential hinges on resolving foundational infrastructure and human capital challenges that have long constrained development in the Global South. Reliable electricity remains unavailable or unreliable across vast swaths of emerging economies, creating an immediate bottleneck for any AI-dependent systems. Internet connectivity, particularly broadband access in rural areas, remains patchy across much of Africa, South Asia, and parts of Southeast Asia. Equally important, the digital literacy and technical skills required to implement, manage, and adapt AI tools remain concentrated among relatively small elite populations. Governments must therefore simultaneously expand electrical generation capacity, build robust telecommunications infrastructure, invest substantially in digital education, and ensure that affordable computing devices and smartphones penetrate far beyond current ownership levels. This represents an extraordinarily ambitious agenda that demands sustained political commitment and substantial capital investment.
The International Monetary Fund has independently reinforced this perspective through its own analysis of AI's potential impact on Sub-Saharan Africa, projecting that the continent's economy could expand by approximately four percent over the coming decade if policymakers create enabling conditions for AI adoption. This regional calculation offers concrete illustration of the magnitude of gains that could materialize. For a continent grappling with limited growth rates and persistent poverty, a four-percentage-point boost would translate into substantially improved resources available for schools, hospitals, and basic services. The regional focus also underscores that AI's benefits are not uniformly distributed globally, but rather contingent on deliberate policy choices and infrastructure investments.
The World Bank's analysis also acknowledges the darker possibilities that AI implementation could introduce into developing societies. The technology could amplify existing income inequality rather than reducing it, particularly if access to AI-enhanced productivity remains concentrated among elites and formal-sector workers. The capacity to generate convincing misinformation through AI tools poses genuine threats to developing democracies already struggling with weak institutional safeguards and limited media literacy. Authoritarian governments could exploit AI's surveillance capabilities to enable unprecedented political repression. These risks are not inevitable, but they underscore that AI's trajectory in emerging markets will depend substantially on governance choices and regulatory frameworks that remain, in most cases, underdeveloped and inadequate to the challenge.
The historical parallel that Gill invokes carries particular weight for nations in the Global South. The Industrial Revolution unfolded initially in Western Europe and North America, and the technological and economic gap that opened during that period has never fully closed despite two centuries of subsequent development. Nations that industrialized early accumulated vast advantages in capital, technological capacity, institutional sophistication, and global economic influence. Those that arrived late to industrialization faced entrenched competition from established powers and spent generations struggling to achieve comparable productivity and living standards. The specter of repeating that pattern with AI—with wealthy nations capturing most benefits while emerging economies fall further behind—motivates the World Bank's urgency. The window for emerging economies to position themselves as active participants in the AI revolution, rather than passive consumers of imported AI services, may be narrower than it appears.
Malaysia and other Southeast Asian economies find themselves positioned at a critical juncture. The region possesses some structural advantages relative to other emerging markets: relatively robust electricity infrastructure in major urban areas, expanding but still-developing digital connectivity, and growing technical expertise concentrated in urban centers and technology hubs. Yet the region also exemplifies the broader challenges: digital divides between urban and rural areas remain significant, digital skills are unevenly distributed, and most nations lack comprehensive policy frameworks explicitly designed to guide AI adoption. The World Bank's report implicitly suggests that Southeast Asian governments cannot afford to treat AI as an abstract technology policy question; instead, deliberate investment in foundational infrastructure, workforce development, and enabling regulation has become immediately essential for any nation seeking to participate in AI-driven development rather than merely experiencing its disruptive effects.
