India is set to host the fourth global AI summit, following the inaugural meeting at Bletchley Park in 2023, where the conversation revolved around AI safety and existential risk. Since then, the discourse has shifted sharply toward geopolitical competition, particularly between the United States and China, with the European Union positioning itself as a regulatory arbiter.
With New Delhi’s summit themed sarvajana hitaya, sarvajana sukhaya — welfare for all, happiness for all — India is attempting to redirect the global AI narrative from power politics to purpose-driven development.
From AI Safety to AI Geopolitics
The global AI conversation has evolved rapidly:
- Initial fears centred on catastrophic risks and model safety.
- Today’s focus is on technological supremacy and compute dominance.
- Markets are anxious about overvaluation in AI-driven sectors.
- Workers fear displacement as automation expands.
In this environment, AI is increasingly framed as a strategic asset rather than a public good. India’s emphasis on welfare-oriented AI offers a counterpoint to this race for dominance.
AI-for-Purpose: Food, Health and Human Capital
India’s comparative advantage lies not in building the most powerful frontier model but in applying AI to large-scale development challenges.
Agriculture:
Smallholder farmers produce the bulk of global food yet remain low-productivity. India’s Kisan e-Mitra chatbot handles thousands of farmer queries daily across multiple languages. Telangana’s AI-enabled Saagu Baagu initiative has demonstrated that data-driven crop management can raise yields and incomes while reducing chemical inputs.
Healthcare:
India’s public healthcare system faces a severe doctor shortage. The eSanjeevani telemedicine platform has delivered hundreds of millions of consultations, expanding access in remote areas. AI diagnostic tools such as those developed by Qure.ai have improved early detection of diseases like tuberculosis.
Skills and Education:
With only a small percentage of India’s workforce formally skilled, digital platforms like DIKSHA and FutureSkills PRIME have scaled learning access, including to rural and Tier-II/III cities. AI-powered adaptive learning systems can personalise instruction at population scale.
These examples suggest that India can lead in AI-for-inclusion rather than AI-for-hegemony.
The Structural Bottlenecks: Ten “Potholes”
However, the ambition of sarvajana hitaya requires confronting systemic constraints:
- Connectivity gaps: Rural internet penetration remains significantly lower than urban access; digital gender divides persist.
- Energy constraints: AI workloads require reliable power and robust grids, which remain uneven.
- Skill shortages: AI job demand far exceeds the supply of qualified engineers.
- Semiconductor dependence: Over 90% of advanced chips are imported, exposing India to supply chain vulnerabilities.
- Data quality deficits: Lack of high-quality, annotated datasets, especially in regional languages.
- Regulatory complexity: Fragmented governance and compliance hurdles slow innovation.
- Infrastructure weaknesses: Logistics bottlenecks increase operational costs.
- Capital gaps: Late-stage AI start-ups face funding shortages beyond Series B.
- Cybersecurity risks: Expansion of AI over digital public infrastructure (DPI) increases vulnerability.
- Climate-energy tensions: AI’s energy demands must align with sustainability goals.
Building frontier models without addressing these foundational constraints risks widening inequalities rather than reducing them.
Digital Public Infrastructure as an AI Backbone
India’s Digital Public Infrastructure (DPI) — identity, payments, and data-exchange layers — provides a scalable base for AI-powered public services. Extending DPI with AI applications in agriculture advisories, health diagnostics and education can deliver measurable welfare gains.
However, security, privacy safeguards and interoperable governance frameworks will be crucial to ensure trust.
Beyond the US–China Binary
The global AI debate is dominated by a US–China technological rivalry, with Europe focusing on regulatory guardrails. India presents a fourth model: AI anchored in developmental metrics.
Instead of measuring success by parameter counts or GPU clusters, India could redefine benchmarks around:
- Increase in smallholder farm yields.
- Reduction in preventable diseases.
- Improvement in literacy and skill acquisition.
- Enhanced access to public services.
Such outcome-based metrics align technology with human development goals.
What to Note for Prelims?
- Concept of Artificial Intelligence and machine learning applications.
- India’s Digital Public Infrastructure (DPI) components.
- Government platforms like eSanjeevani and DIKSHA.
- Challenges in semiconductor supply chains.
- Digital divide and gender digital parity indicators.
What to Note for Mains?
- AI as a tool for inclusive development.
- Balancing innovation with regulation and cybersecurity.
- Energy–climate implications of AI expansion.
- India’s role in shaping global AI governance.
- Reframing technological competition around welfare outcomes.