India’s AI value paradox

Source: The post “India’s AI value paradox” has been created, based on “India’s AI value paradox” published in “BusinessLine” on 21st April 2026.

UPSC Syllabus: GS Paper-3- Science and technology

Context: India is witnessing rapid expansion in artificial intelligence infrastructure through large-scale investments in data centres and cloud ecosystems. However, India still faces limitations in developing indigenous AI technologies and capturing the full economic value generated by artificial intelligence. This situation reflects an emerging AI value paradox, where infrastructure capacity is growing faster than innovation capability.

India’s Strength in AI Infrastructure Expansion

1. India’s data centre sector is witnessing significant multi-billion-dollar investments driven by policy incentives and rising digital demand.
2. India possesses competitive operational costs and a large technology workforce, which make it an attractive destination for global AI and cloud infrastructure deployment.
3. India has developed strong digital public infrastructure that supports large-scale digital adoption across sectors.
4. These advantages are positioning India as an important node in the global digital economy.

Infrastructure Expansion Without Ownership of Core Technologies

1. India contributes only about 2–3 percent of global AI patent filings, which reflects limited ownership of core technologies.
2. Public expenditure on research and development in India remains low at about 0.6–0.7 percent of GDP, which constrains frontier innovation capacity.
3. India ranks behind the United States and China in research strength, investment levels and compute capacity according to the Stanford AI Index.
4. Although data and infrastructure inputs are increasingly available domestically, high-value outputs such as advanced AI models and platforms are still largely developed outside India.

Uneven Enterprise Adoption of Artificial Intelligence

1. Artificial intelligence has become an important priority at the boardroom level in large enterprises across India.
2. Large firms are experimenting with AI across business functions and are gradually building internal capabilities.
3. However, many mid-sized firms remain cautious because of concerns related to costs, organisational readiness and uncertainty regarding returns on investment.
4. In many organisations, cloud adoption has focused mainly on migrating legacy systems rather than redesigning workflows around automation and real-time data.
5. As a result, enterprise productivity gains from artificial intelligence remain uneven across sectors.

The Talent Paradox in India’s AI Ecosystem

1. India produces one of the largest pools of technology professionals in the world.
2. However, artificial intelligence development requires deeper research capability, advanced mathematical skills and sustained experimentation capacity.
3. Much of India’s technology workforce remains oriented towards implementation rather than foundational innovation.
4. There is therefore a growing need to develop T-shaped talent, which combines deep domain expertise with the ability to apply AI tools across multiple functions.

Dual Nature of India’s AI Startup Ecosystem

1. A segment of India’s startup ecosystem risks engaging in “AI-washing” by emphasising artificial intelligence without developing strong underlying technologies.
2. Some startups function mainly as intermediaries by building interfaces on top of global AI models instead of creating original platforms.
3. At the same time, several startups are developing domain-specific AI solutions suited to India’s needs.
4. For example, Sarvam AI and Krutrim are developing language models adapted to India’s linguistic diversity.
5. Similarly, Qure.ai is applying computer vision in healthcare diagnostics and CropIn is improving agricultural productivity through artificial intelligence applications.

Structural Challenges Limiting India’s AI Value Capture

1. India continues to face low levels of investment in frontier research and development.
2. India has limited compute-intensive innovation capacity compared to leading global economies.
3. Many enterprises are still in early stages of integrating artificial intelligence into core operational workflows.
4. Mid-sized firms face constraints related to costs, institutional readiness and uncertainty regarding returns on AI investments.
5. There remains a shortage of researchers working at the cutting edge of artificial intelligence technologies.

Way Forward

1. The government should increase public investment in research and development to strengthen India’s frontier innovation ecosystem.
2. Policymakers should support indigenous development of artificial intelligence models and platforms suited to India’s needs.
3. Universities and industries should collaborate to develop T-shaped professionals capable of applying artificial intelligence across sectors.
4. Enterprises should move beyond cloud migration and redesign workflows around automation and real-time decision-making systems.
5. Investors should support deep-technology startups with longer investment horizons to strengthen India’s innovation capacity.

Conclusion: India has already built strong advantages in terms of digital infrastructure, data availability and technology talent. However, the long-term benefits of artificial intelligence will depend on India’s ability to convert these strengths into indigenous innovation capability. Therefore, India must focus not only on hosting AI infrastructure but also on creating the technologies that generate value in the artificial intelligence economy.

Question: Even as India rapidly expands its AI infrastructure, it faces challenges in capturing value from AI innovation and adoption.” Examine the paradox and suggest measures to address it.

Source: Business Line