India’s AI strategy offers a blueprint for Global South
India’s AI strategy is highlighted as a potential blueprint for the Global South, addressing historical digital colonialism and infrastructure gaps that leave regions dependent on foreign AI systems. The article argues that without deliberate intervention, the Global South risks missing out on AI’s economic benefits, with only a small fraction of projected $15 trillion gains expected to accrue outside China.
India’s approach to artificial intelligence is being examined as a model for the Global South, a region historically shaped by colonial extraction and uneven technological access. The Global South—comprising Africa, Asia, and Latin America—faces a critical challenge as AI becomes the defining technology of the 21st century. While it holds vast potential for inclusive growth, it also risks deepening inequality, as AI systems built on its data and labor often generate limited local value. Historically, the Global South has supplied raw materials and resources to wealthier nations, a pattern now repeating with data and compute power. Early AI development was concentrated in the Global North, where capital, research, and infrastructure were abundant, reinforcing digital dependency. This dynamic, dubbed digital colonialism, leaves many nations reliant on imported AI systems rather than developing their own. The imbalance in compute infrastructure underscores the issue: over 70% of global data center capacity is in North America, Europe, and China, with the U.S. alone hosting 40%. Africa accounts for less than 1% of global capacity, while much of Latin America and South Asia remains underrepresented. By 2025, AI adoption in the Global North reached 24.7% of the working-age population, compared to just 14.1% in the Global South. Projections suggest AI could add $15 trillion to the global economy by 2030, but most gains will bypass the Global South outside China. Data sovereignty is another key concern. Many nations must choose between relying on foreign AI systems for immediate access or investing in domestic infrastructure for long-term control. Global AI governance frameworks, often shaped by high-income countries, may not reflect local needs. However, some progress is being made: India is investing in public compute infrastructure, language-specific AI models, and policies tailored to its cultural and social context, signaling a shift toward active participation rather than passive adoption.
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