Key Takeaways
- AI development remains highly concentrated: only a handful of countries host most AI‑specialized data centers, and the Global North controls the bulk of compute, talent, and investment.
- Energy and water demands of data centers create hidden constraints; regions that secure renewable power gain a competitive edge in the AI race.
- The Global South generates vast amounts of data but often lacks the infrastructure to process it locally, leading to “digital extractivism” where value accrues elsewhere.
- Governance of AI is dominated by OECD, G7 and major tech firms, leaving the Global South largely as rule‑takers rather than rule‑setters.
- AI reshapes labor markets, offering productivity gains but also risking job displacement, skills mismatches, and heightened inequality—especially for informal workers who make up over 60 % of the global workforce.
- South‑South cooperation, renewable‑energy‑powered AI hubs, and digital public infrastructure present concrete pathways to narrow the divide.
- Building sovereign AI ecosystems—through public compute pools, localized data governance, and inclusive skill‑development programs—can empower Global South actors to shape AI on their own terms.
- A proactive, inclusive global governance architecture that centers Global South voices is essential to ensure AI promotes shared prosperity rather than entrenched inequality.
Concentration of AI Infrastructure and Investment
The current AI landscape is marked by stark geographic concentration. According to recent data, only 32 countries host AI‑specialized data centers, and the majority of these facilities are located in the Global North. Africa and Latin America together account for roughly 3 % of global AI compute capacity. Investment mirrors this pattern: of the 23 gigawatts of data‑center capacity under construction in September 2025, about 75 % was sited in the United States. The International Monetary Fund’s AI Readiness Index underscores the gap, with advanced economies averaging 0.68, emerging markets 0.46, and low‑income countries just 0.32—meaning the richest nations score more than double the readiness of the poorest. Early, sustained investment in digital infrastructure is a common trait among the highest‑scoring countries, reinforcing a self‑reinforcing cycle where capital and expertise stay clustered in a few hubs.
Energy as a Hidden Constraint
AI’s computational hunger translates directly into massive electricity and water demands. Globally, data centers consume about 1 % of total electricity, but the share varies dramatically—Irish data centers, for example, draw 20 % of national power, prompting moratoria on new builds until 2028. Water use is equally pressing; facilities in India and Brazil compete with agriculture for scarce supplies, and rising temperatures exacerbate cooling challenges. Countries that can secure abundant, low‑cost renewable energy and efficient water‑recycling systems will gain a decisive advantage in the AI race. Conversely, regions constrained by fossil‑fuel dependence or water scarcity may find their AI ambitions throttled before they even begin.
Unequal Innovation Ecosystems
While cutting‑edge AI research remains concentrated in the North, the Global South is forging a distinct innovation path centered on “applied AI” and efficiency‑driven solutions. Rather than chasing ever‑larger models, many Southern actors prioritize compact, purpose‑built systems that deliver high relevance with modest compute needs. A salient example is InkubaLM, a South‑African‑based language model that targets Swahili, isiXhosa, Yoruba, Hausa, and Zulu, employing edge computing for localized tasks. This focus on linguistic specificity and resource efficiency allows Southern innovators to create tools that are both affordable and directly applicable to local challenges, even as they operate outside the dominant large‑model paradigm.
Data Extraction Without Value Creation
The Global South produces enormous volumes of data through expanding mobile networks and subsea cables, yet limited local processing capacity means much of this information is exported for analysis abroad. This dynamic has been termed “digital extractivism”: the raw data is harvested in the South, refined and monetized primarily by Northern firms, and the resulting value rarely returns to the data’s origin. Without investment in local storage, computing, and analytics capabilities, the South remains a supplier of raw material rather than a beneficiary of the insights derived from its own digital footprints.
Governance and Power Asymmetry
Global AI governance is currently shaped by institutions such as the OECD, the G7, and major technology corporations, while the Global South occupies a marginal role—often acting as rule‑takers rather than rule‑setters. Less than 10 % of all AI governance frameworks originate in Southern countries. This asymmetry skews policy toward the interests and risk tolerances of powerful Northern actors, potentially overlooking contextual challenges such as informal labor markets, data sovereignty concerns, and differing ethical norms. Closing this gap requires deliberate mechanisms to amplify Southern voices in standard‑setting bodies, funding for regional policy research, and inclusive multilateral forums that treat equity as a core design principle.
Reshaping Labor Markets
AI promises to be a powerful growth multiplier, with estimates suggesting it could add $1.2 trillion to Africa’s economy by 2030—about 6 % of regional GDP. Productivity gains may arise from automating routine tasks, improving service delivery, and spawning new sectors such as AI‑driven startups and data economies. Yet these benefits are unevenly distributed. A Microsoft report from late 2025 shows that 24.7 % of the working‑age population in the Global North uses AI tools, compared with only 14.1 % in the Global South. Across 15 African nations, merely 9 % of youth aged 15–24 possess basic computer skills, while the continent will need an additional 23 million STEM graduates by 2030 to meet anticipated demand. Informal workers—who constitute roughly 61 % of global employment and over 80 % of the labor force in many Southern countries—are particularly vulnerable; they often earn lower wages, lack protections, and receive little attention in AI‑impact studies. Without targeted upskilling, social safeguards, and inclusive design, AI could deepen existing labor inequalities rather than alleviate them.
South‑South Cooperation and Renewable‑Energy Advantages
Shared constraints have spurred growing South‑South cooperation, enabling countries to pool resources, exchange best practices, and co‑develop solutions tailored to common challenges. Joint infrastructure projects, shared datasets, and technical working groups can amplify impact beyond what any single nation could achieve alone. Several Southern states possess natural advantages for sustainable AI: Brazil sources 88 % of its electricity from renewables (hydro, solar, wind, biomass), positioning it as a potential green AI hub; Kenya and Nigeria score highly on the UNDP’s energy‑and‑sustainability dimension of AI readiness. Aligning AI strategies with renewable‑energy policies—and leveraging platforms like the G20 to coordinate global metrics on AI‑related carbon emissions—could allow the South to leapfrog fossil‑fuel‑dependent infrastructure and establish environmentally responsible AI ecosystems.
Digital Public Infrastructure and Inclusive AI Opportunities
Publicly owned compute resources are emerging as a powerful equalizer. South Africa’s Centre for High Performance Computing offers a regional model that pools demand and shares access across the Southern African Development Community. India’s IndiaAI Mission funds a public pool of roughly 18,000 GPUs, subsidizing access for domestic startups, researchers, and academia. Similar sovereign initiatives are underway in Saudi Arabia and the UAE, though many Southern nations lack the fiscal capacity to replicate such scale. Alternative pathways include community‑led data sovereignty frameworks—such as the Māori initiative in New Zealand—that assert local control over indigenous knowledge while collaborating with government and private partners. AI can also empower informal workers through demand‑matching recommendation engines, translation services that open new markets, and voice assistants that optimize logistics. Investing in STEM education, vocational training, and AI‑specific capacity building—particularly for informal‑sector jobs like electricians, digital plumbers, and technical specialists—will be essential to translate these opportunities into broad‑based prosperity.
Redefining Global Governance and Leapfrogging Opportunities
Looking ahead, novel concepts such as orbital data centers promise to sidestep terrestrial environmental limits, yet they raise fresh governance questions about data sovereignty and jurisdictional reach. A more constructive path lies in co‑creating global AI architecture that centers Global South perspectives. Proposals include a constitutional framework establishing common AI principles, a global operating system of trust ensuring interoperability, and a standing council for cooperative intelligence that aligns national AI strategies, social policies, and private innovation concerns. By embedding inclusive governance mechanisms from the outset, the international community can help ensure that AI serves as a conduit for shared prosperity rather than a vehicle for entrenched inequality.
Conclusion
The AI divide is not an immutable fate; it is the product of current patterns of investment, infrastructure, and power. Targeted interventions—ranging from renewable‑energy‑powered data hubs and South‑South knowledge sharing to sovereign compute pools and inclusive governance—can transform the trajectory. If governments, private actors, and civil society work together to prioritize equity, sustainability, and local relevance, the Global South can not only catch up but also help shape a more just and prosperous AI‑enabled future for all.