The World Bank has delivered an optimistic assessment of artificial intelligence's potential for emerging markets, suggesting that developing economies stand to gain disproportionate advantages from the technology if they act decisively on critical infrastructure and workforce development. In a report released this week, the multilateral lender argues that nations in the Global South could compress what historically took wealthier countries a century to accomplish into just a single decade, fundamentally altering the trajectory of economic development across Africa, Asia, and Latin America.
This represents a striking reversal of the typical pattern in which technological revolutions have favoured wealthy nations first, leaving developing economies perpetually chasing behind. Indermit Gill, the World Bank's chief economist, framed the opportunity in stark terms, noting that AI has essentially thrown developing economies a lifeline they must seize immediately. His observation carries particular weight given that many emerging nations missed the initial wave of industrialisation in the 19th and early 20th centuries, a missed opportunity whose economic consequences persisted for generations. The prospect of AI-driven leapfrogging offers a historical corrective that developing nations simply cannot afford to ignore.
Understanding why emerging economies possess this advantage requires examining the nature of modern AI deployment. Rather than requiring massive resources or the creation of bespoke large language models—the kind of capital-intensive undertaking that favours technologically dominant nations—Gill emphasised that developing countries can adapt small, low-cost AI tools to their specific local contexts. This democratisation of artificial intelligence technology means that an agricultural extension service in rural Indonesia, a health clinic in rural Kenya, or a judicial system in Southeast Asia need not wait for proprietary solutions designed by Silicon Valley companies. Instead, they can immediately implement tailored applications that address pressing local challenges.
The practical applications underscore why this technological opportunity differs fundamentally from previous waves of innovation. Health workers armed with AI diagnostic tools could accelerate disease detection and treatment in regions where physician scarcity remains chronic. Teachers could leverage AI to personalise lesson plans at scale, addressing quality variations that plague many developing-world education systems. Farmers confronting variable weather patterns and soil conditions could receive precision recommendations about crop selection and planting timing, dramatically improving yields in regions vulnerable to climate variability. These are not hypothetical benefits but concrete service improvements that could reach millions of people currently underserved by existing infrastructure.
The employment impact analysis presented in the World Bank report provides further grounds for optimism when compared to prospects in wealthy nations. Generative AI emerges as three times more likely to displace workers in high-income countries, where 14.2% of jobs face exposure to disruption, compared to just 4.5% in low- and middle-income economies. This disparity reflects structural differences in labour markets and industry composition—wealthy nations concentrate employment in sectors like finance, law, and professional services where AI substitution occurs more readily, while developing economies retain larger proportions of workers in agriculture, construction, and services less amenable to algorithmic replacement. Simultaneously, the share of jobs expected to register meaningful productivity improvements tracks nearly equivalently: 16.2% in developing economies versus 18.7% in high-income countries, suggesting that emerging markets can expect broadly comparable gains in worker effectiveness.
However, the World Bank's analysis identifies formidable prerequisites for capturing these advantages. Governments across the developing world must urgently expand electrical generation capacity and upgrade internet infrastructure, recognising that data centre operations and continuous connectivity form the technological bedrock upon which AI deployment rests. Equally critical is systematic investment in digital skills training at scale, ensuring that populations possess not just device access but genuine competency in leveraging AI tools. The distribution of hardware—smartphones, computers, and related equipment—must broaden far beyond current penetration rates to avoid concentrating benefits among already-privileged populations in major cities.
The International Monetary Fund has projected that Sub-Saharan Africa alone could achieve roughly 4% additional economic growth over the coming decade if AI adoption proceeds under conducive circumstances, a contribution equivalent to what many nations currently derive from their entire manufacturing or agriculture sectors. For context, such acceleration would meaningfully reduce the gap between African growth trajectories and those of faster-expanding Asian economies, potentially altering long-term development patterns across the continent.
Yet the World Bank simultaneously warns that AI deployment carries genuine risks requiring active policy management. Widening income inequality could result if AI benefits accrue disproportionately to already-educated urban populations, leaving rural areas and lower-skill workers increasingly marginalised. Misinformation campaigns could proliferate with greater sophistication as synthetic media becomes cheaper and more convincing, potentially destabilising democratic institutions still consolidating themselves in many emerging nations. Governments facing limited democratic accountability might weaponise surveillance AI to entrench political repression, transforming technology touted as liberating into an instrument of control.
These countervailing risks explain why the World Bank frames AI not as an automatic solution but as an opportunity requiring carefully calibrated policy responses. The cost of passive inaction, however, appears increasingly unacceptable. Developing economies that missed industrialisation's first wave spent two centuries absorbing the economic consequences of that miss. A comparable failure to engage meaningfully with artificial intelligence could condemn emerging nations to permanent relative backwardness in an economy increasingly structured around AI-enhanced productivity. For Malaysia and other Southeast Asian nations integrating into global value chains and competing for investment capital, the imperative to build AI capabilities—whether through indigenous research, strategic adoption of international technologies, or targeted talent recruitment—has become as consequential as earlier decisions regarding industrialisation itself.
