Emerging and developing economies stand at a pivotal crossroads with artificial intelligence, according to a World Bank assessment released this week in London. The international institution's analysis suggests that countries in the Global South have a genuine opportunity to harness AI technologies as a vehicle for rapid advancement across multiple sectors. This represents a markedly different narrative from the anxiety dominating wealthy nations, where AI adoption is increasingly viewed through the lens of labour market disruption and the concentration of technological power.
Indermit Gill, the World Bank's chief economist, framed the opportunity in stark terms, describing AI as a "lifeline" that developing economies must move quickly to seize. The underlying logic hinges on a compressed timeline: if developing countries address critical infrastructure deficiencies and build appropriate human capital now, they could theoretically accomplish in a decade what took industrialised nations over a century. This acceleration would be enabled by adopting and adapting existing AI tools rather than reinventing them from scratch, a crucial distinction that makes the prospect materially achievable for resource-constrained governments.
For Malaysia and other Southeast Asian nations, this assessment carries particular relevance. The region's middle-income status positions it between the extremes of least-developed countries and wealthy economies, meaning the potential gains are substantial but so are the competitive pressures. If neighbouring economies or regional rivals successfully deploy AI to enhance productivity in agriculture, healthcare, education and judicial systems, the consequences for those left behind could be profound. The World Bank's message essentially warns that the window for action is not indefinitely open.
The report identifies three foundational requirements for capturing AI's benefits: adequate electrical power generation and distribution, reliable internet connectivity, and a workforce with basic digital literacy. These prerequisites are deceptively straightforward in articulation but demanding in execution. Many developing economies struggle with chronic electricity shortages and unreliable grid infrastructure. Building the power generation capacity necessary to support AI-enabled services and data processing, whilst simultaneously transitioning towards sustainable energy sources, presents a complex policy challenge that extends far beyond the technology sector itself.
Crucially, Gill emphasised that emerging economies need not invest in constructing massive data centres or developing proprietary large language models to realise meaningful AI gains. Instead, smaller, customised applications tailored to local contexts offer a more pragmatic pathway. Health workers could use AI diagnostic tools to identify diseases more rapidly in under-resourced clinics. Teachers might leverage AI-powered systems to personalise lesson content for diverse student populations. Farmers could access decision-support tools that factor in local climate conditions and crop varieties to optimise planting schedules. These applications require far less computational infrastructure than the industrial-scale AI systems dominating technology company valuations in the West.
The labour market dimension of the World Bank's analysis offers a notably more optimistic picture for developing nations than for wealthy economies. The report found that generative AI poses a direct threat to roughly 14.2 per cent of jobs in high-income countries, whilst the comparable figure for low- and middle-income nations stands at only 4.5 per cent. This disparity reflects the structural differences between advanced and developing economies: wealthier nations have higher concentrations of white-collar, cognitive work vulnerable to AI automation, whereas developing economies still maintain larger agricultural and manual labour sectors less immediately affected by current AI capabilities. Additionally, the potential for productivity enhancements—where workers deploy AI tools to become more effective—is comparable across income groups, suggesting that the net employment impact need not be uniformly negative.
The International Monetary Fund has separately projected that Sub-Saharan Africa's economic output could expand by approximately four per cent annually over the coming decade if AI adoption occurs under favourable policy conditions. Whilst this may appear modest against historical growth rates, the cumulative effect over ten years represents substantial income gains for some of the world's poorest populations. For a region with a population exceeding one billion people, even percentage-point improvements in growth rates translate into millions of individuals lifting themselves above poverty thresholds.
However, the World Bank's analysis does not present an unqualified endorsement of unrestricted AI deployment. The report explicitly acknowledges serious risks accompanying rapid AI adoption, including the potential for widened income inequality between those who benefit from AI-enhanced productivity and those left behind, the emergence of more sophisticated disinformation campaigns tailored to local contexts and languages, and the weaponisation of surveillance and AI capabilities by authoritarian governments seeking to suppress dissent. These dystopian scenarios are not hypothetical: they represent documented harms already materialising in various countries.
Addressing these risks requires deliberate policy choices and institutional capacity that many developing economies currently lack. Regulating AI systems for bias, transparency and accountability demands technical expertise, legal frameworks and enforcement mechanisms that typically exist only in mature institutional settings. Yet the absence of such guardrails before widespread deployment risks embedding existing inequalities and power imbalances into AI systems that could perpetuate harm for decades. Malaysia's policymakers, for instance, must grapple with how to foster AI innovation whilst simultaneously building regulatory architecture capable of managing its risks.
The historical analogy invoked by Gill carries profound weight. Previous industrial revolutions—from mechanisation through electrification to computerisation—created wealth and opportunity, but those nations that delayed adoption or failed to participate meaningfully paid steep prices. Economic historians document how countries that missed early industrialisation required centuries to narrow the development gap with pioneers. The AI revolution, if Gill's assessment proves accurate, may operate on an even more compressed timeline, meaning decisions deferred by even a few years could have consequences extending across generations.
For developing economies in Asia, Africa and Latin America, the World Bank's report functions less as a guarantee of prosperity and more as a conditional invitation. The gains are available, but only to countries willing to simultaneously invest in infrastructure, education and workforce development whilst building the institutional capacity to govern AI responsibly. The opportunity is real, but so is the requirement for comprehensive, forward-looking policy action undertaken immediately. Nations that treat this as a genuine inflection point and allocate resources accordingly could plausibly reshape their development trajectories. Those that treat it as a peripheral concern risk watching their peers accelerate ahead.
