India’s AI strategy offers a blueprint for Global South

“The strong do what they can and the weak suffer what they must.”

— Thucydides

The idea of the “Global South” is not merely geographic; it is a story shaped by colonial extraction, uneven industrialisation, and the late arrival of technological revolutions. Today, as artificial intelligence (AI) emerges as the defining technology of the 21st century, the Global South stands at a pivotal crossroads. It represents both the largest opportunity for inclusive growth and the greatest risk of deepening inequality. This is because AI is not just a tool but a new layer of power, which intersects with history, economics, and sovereignty.

Historical Context

To understand AI in the Global South, one must begin with history. For centuries, economies across Africa, Asia, and Latin America were structured around resource extraction. Raw materials and resources flowed outward, while value creation remained concentrated in Western nations. In the modern digital era, data has become the new resource. Yet the colonial-type structural asymmetry remains strikingly familiar. The Global South often produces data, labour, and raw materials for AI but captures limited value from AI systems built on these inputs.

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This continuity is not accidental. Early AI development was heavily concentrated in the Global North, where capital, research institutions, and computing power were already abundant. The result is a new form of dependency called digital colonialism, where intelligence systems are imported rather than created locally.

The Compute Divide

If oil defined the 20th century, compute defines the 21st. Artificial intelligence is not just built on algorithms; it is built on infrastructure — data centres, energy systems, advanced chips, and high-speed connectivity. Over 70 per cent of the world’s data centre capacity is concentrated in North America, Europe and China. The United States alone accounts for roughly 40 per cent of global data centres, while Africa hosts less than 1 per cent of total global capacity. Large parts of Latin America and South Asia remain similarly underrepresented.

This means that while the Global South generates vast and growing volumes of data, the storage, processing, and monetisation of that data largely occur outside its borders. Without deliberate intervention, compute risks becoming not just a technical resource, but a gatekeeper of economic power and digital sovereignty.

Recent adoption data reflects this imbalance. By 2025, AI adoption in the Global North reached approximately 24.7 per cent of the working-age population, compared to just 14.1 per cent in the Global South. It reflects deeper asymmetries in access to infrastructure, capital, and technical capability. AI is projected to add over $15 trillion to the global economy by 2030. The distribution of this value is deeply unequal. Estimates suggest that, excluding China, only a small fraction of this economic gain may accrue to the Global South.

Data Sovereignty

Perhaps the most critical dimension of AI in the Global South is sovereignty. In the past, sovereignty was about land and resources. Today, it is about data, algorithms, and digital infrastructure.

Many Global South nations face a dilemma. Relying on foreign cloud providers and AI systems offers immediate access but risks long-term dependency. Building domestic infrastructure requires significant investment but enables control over data and innovation pathways.

Sovereignty also extends to governance. Global AI norms and ethical frameworks are often shaped by high-income countries, leaving limited representation from the Global South. This creates a mismatch between technology design and local realities.

Encouragingly, new approaches are emerging. Countries like India are investing in public compute infrastructure, language-specific AI models, and policies that reflect local cultural and social contexts. These efforts signal a shift from passive adoption to active participation.

AI as a Development Multiplier

Despite the challenges, AI offers transformative potential for the Global South. Unlike previous industrial revolutions, AI can leapfrog traditional infrastructure gaps. In agriculture, AI can optimise crop yields and predict climate patterns. In healthcare, it can enable diagnostics in regions lacking doctors. In education, it can personalise learning at scale.

Moreover, the rise of open-source AI models is lowering barriers to entry. Platforms that offer free or low-cost access to advanced capabilities are enabling developers in underserved regions to build locally relevant solutions.

This democratisation of AI could redefine innovation. Instead of importing solutions, the Global South can create systems tailored to its unique challenges — languages, cultures and economic realities.

The Risk of Repeating History

To avoid repeating the history of dependency and extraction, the Global South must adopt a proactive strategy. Four key pillars are essential:

1. Infrastructure First

Investment in digital infrastructure such as 5G connectivity, local cloud systems, and compute clusters is foundational. Without it, AI remains inaccessible to most local populations. Public-private partnerships and South-South collaborations can accelerate progress. A fund can be conceived to support AI start-ups emerging from countries in the Global South.

2. Talent Development at Scale

No AI strategy can succeed without people. Education systems across the Global South must move beyond traditional curricula and integrate AI, data literacy, and digital skills from an early stage. This is not only about producing elite researchers, but about building broad-based capability among engineers, technicians, policymakers, and informed citizens who can engage with AI systems critically and productively.

3. Localised Innovation Ecosystems

The Global South cannot rely solely on imported AI solutions. Technologies developed elsewhere often fail to account for local languages, cultural nuances, and economic realities. Governments and institutions must actively support research labs and open-source communities that focus on context-specific challenges. This includes developing AI models in local languages, designing tools for informal economies, and addressing region-specific issues such as agricultural variability or public health access.

4. Global Equity

AI is a global technology, but its governance remains uneven. Standards, ethical frameworks, and regulatory norms are often shaped by a small group of advanced economies, with limited representation from the Global South. This imbalance risks creating systems that do not reflect diverse social, cultural, and economic contexts.

For the Global South, participation in global AI governance is not optional but essential. Multilateral institutions, international forums, and cross-border partnerships must evolve to ensure inclusive representation. This includes not only governments, but also academia, civil society, and industry voices from emerging economies.

Conclusion: A New Paradigm

India offers one of the most compelling examples of how the Global South can approach AI strategically. Through its Digital Public Infrastructure — from Aadhaar to UPI — it has already demonstrated how technology can scale inclusively. Now, this approach is extending into AI.

Initiatives around sovereign compute, public datasets, and support for indigenous language models reflect a deliberate push towards digital self-reliance. The focus on building accessible AI platforms, combined with a vibrant start-up ecosystem, positions India not merely as a consumer of AI, but as a potential architect of scalable, population-level solutions.

If sustained, India’s model could serve as a blueprint for other emerging economies seeking both growth and sovereignty.

(Sudhir Tiku is a Singapore-based AI thought leader focused on the intersection of technology, policy, and economic development in the Global South. He is the author of the seminal work, ‘AI: Global South – Power, Progress and Policy’, published by World Scientific, Singapore. Views expressed in the above piece are personal and solely those of the author. They do not necessarily reflect Firstpost’s views.)