Artificial intelligence is rapidly becoming part of everyday infrastructure–in some places. It helps write emails and software code, filters job applications, powers recommendation systems, and is increasingly being integrated into education, healthcare, finance, and public administration. Industry leaders talk about “AI for everyone,” while governments rush to publish national AI strategies and build sovereign compute.
Yet over the past decade, working on digital inclusion and digital literacy projects in regions from Europe to sub-Saharan Africa and Southeast Asia, I’ve seen the same pattern repeat: each new wave of “transformative” technology lands on a landscape already stratified by connectivity, skills, and institutional capacity. The current AI wave is no exception. If anything, it amplifies those underlying fractures.
Still, some countries areexploring ways of participating in AI development without directly replicating the frontier model race dominated by the United States and China. Recent developments in South Africa and Indonesia illustrate both the possibilities and challenges. The stakes extend far beyond access to AI. Countries that remain primarily consumers rather than creators of AI risk losing opportunities to build local innovation ecosystems, strengthen public-sector capacity, and ensure that their own languages, cultures, and societal priorities are reflected in AI systems. In this sense, the AI divide is also becoming a divide in economic opportunity and technological influence.
AI compute is clustering in a few places
Recent analyses from Stanford University’s 2026 AI Index report that the United States alone hosts more than 5,000 data centers, over ten times as many as any other single country. Because AI workloads are increasingly performed on cloud platforms rather than local infrastructure, this concentration of compute also becomes a concentration of dependency. According to World Bank data, in 2023 the United States accounted for roughly 87 percent of global exports of cloud computing and data storage services.
For most countries, this means that AI development is not just technologically, but commercially and geopolitically outsourced and out of their control. The result is an AI ecosystem where a small number of states and firms host the computational engines that power globally deployed systems.
Systems trained, standardized, and governed within a narrow set of institutional and linguistic environments may struggle to serve a genuinely global public.
Skills and AI literacy are deeply stratified
Even where connectivity and cloud access exist, not everyone is equally positioned to make use of them. Across OECD countries, only around 40 percent of adults possess more than basic digital problem-solving skills, while advanced computational and AI-related competences remain concentrated among highly educated workers and technology-intensive sectors.
At the same time, governments are racing to integrate AI into education, often starting at higher levels of schooling. UNESCO has reported growing efforts worldwide to integrate AI into education, while support for AI literacy in primary and lower secondary education, as well as ethical training for educators, remains uneven.
Those with robust schooling, advanced digital skills, and stable connectivity are best positioned to treat AI as a tool to extend their capabilities. Recent OECD survey data show that participation in AI-related training remains strongly stratified by educational attainment: 36 percent of respondents with tertiary education reported undertaking AI-related training in the previous year, compared with just 18 percent of those with upper secondary education. Those on the wrongt side of the divide are more likely to experience AI as an opaque system acting upon them, from algorithmic welfare systems such as the Dutch childcare benefits scandal to AI-assisted hiring tools such as Amazon’s discontinued AI recruiting system, rather than as a technology they can actively interrogate or shape.
Investment and governance: who gets a seat at the table?
The core agenda-setting power often remains with a narrow set of industry actors and a small group of technologically advanced states. Most other countries remain in a perpetual catch-up posture, adapting imported models, standards, and templates for “trustworthy AI” to their own contexts, and may have limited local capacity to assess trade-offs or propose alternatives.
In countries such as Indonesia and South Africa, communities generate data at massive scale yet still have little voice in how AI systems are designed, governed, or deployed. Their languages are underrepresented in training data; their institutions are under-resourced in regulatory forums; their experiences rarely feature in benchmark datasets. For many countries in the Global South, participation in AI still occurs largely through adapting imported systems rather than shaping how those systems are designed, governed, or deployed.
In South Africa, the Department of Communications and Digital Technologies released a draft national AI policy in April 2026, proposing new oversight institutions. The department withdrew the draft days later after a journalist discovered that at least six of its academic citations did not exist, apparently AI-generated hallucinations. The minister called it “an unacceptable lapse.“ The episode sharply illustrates the gap between AI governance ambition and the institutional capacity needed to implement it, though the new AI panel the country has since constituted has a chance to use South Africa’s unique leverage.
Indonesia presents a case of deliberate, if constrained, public-sector agency. The National Research and Innovation Agency (BRIN) which now leads AI implementation under the national strategy, has built practical AI tools aimed at underserved communities rather than frontier capabilities such as an app that uses satellite data and machine learning to help artisanal fishermen locate schools of fish, multilingual language models trained on Indonesian and local languages such as Javanese and Sundanese, and AI chatbots deployed in government services. In August 2025, the Ministry of Communication and Digital Affairs released a national AI roadmap with a target of training 100,000 AI-skilled workers annually.
The choice is not simply between “AI superpower” and “passive recipient.”
Regional cooperation may also become increasingly important. In 2024 African ministers adopted a Continental AI Strategy and African Digital Compact and participants in the April 2025 Global AI Summit on Africa in Kigali explored how regional coordination, local-language AI models, public universities, and open-source ecosystems might reduce long-term dependence on externally developed AI systems.
A different way to think about the AI divide
None of this means that people should slow or abandon AI, nor that cloud concentration or venture capital are inherently bad. Instead, when we talk about an “AI revolution,” we should also ask who can shape it and who can merely adapt to it.
Digital divide debates once focused on devices and connectivity, later expanding toward skills and outcomes. But the current AI wave adds another layer: disparities in who can meaningfully participate in deciding what AI is for, which problems it is meant to solve, and which social priorities it ultimately serves.
For engineers and policymakers, this raises difficult but necessary questions. Are they designing AI systems and infrastructures that broaden, rather than narrow, participation in shaping technological change? When governments roll out national AI strategies or integrate AI into public services, whose constraints, languages, and institutional realities are they including?
Many observers frame the current AI moment as a competition. But technological competition is never only about speed. It is also about who can influence the direction of change.
AI is already spreading globally. The deeper question is whether the technologists and policymakers responsible for it will ensure that meaningful participation in shaping that future will spread as well.
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