AI and India

The global conversation about AI is dominated by perspectives from a small number of contexts: Silicon Valley, a handful of elite universities, and the technology media that covers them. These perspectives shape which applications of AI are considered important, which problems are considered worth solving, and which futures are considered worth building toward.

India's relationship with AI doesn't fit this conversation well. The constraints are different. The most important opportunities are different. And the version of AI that matters most for India — for its 1.4 billion people, for its economic trajectory, and for its specific combination of ambitions and challenges — is not the version that dominates the global discussion.

Where India starts from

India has a large, young, and growing population with a median age of around 28. It has one of the world's largest concentrations of engineering talent — produced by IITs, NITs, and hundreds of other technical institutions, employed in the global technology industry, and increasingly building companies. It has a rapidly expanding digital infrastructure, including UPI (one of the world's most successful digital payment systems), Aadhaar (a biometric identity system at national scale), and a government that has been more actively interventionist in digital infrastructure than most comparable economies.

It also has significant structural challenges: a public education system that is large and uneven in quality, a healthcare system that is under-resourced relative to the population it serves, a large informal economy where economic activity is not well captured by formal systems, and deep regional disparities in economic development.

The AI applications that are most relevant to India are determined by these starting conditions, not by the starting conditions of Silicon Valley.

Where AI actually matters in India

Healthcare at scale. India's doctor-to-population ratio is significantly below the WHO recommended level. The gap between the healthcare needs of 1.4 billion people and the supply of trained healthcare providers is a structural problem that cannot be solved at the pace required by training more doctors alone. AI tools that support diagnosis, that help healthcare workers at lower training levels extend their effective scope, that make specialist knowledge accessible at the primary care level, and that help manage chronic disease in populations that have limited access to regular specialist care are directly relevant to this gap.

Agriculture and rural livelihoods. India has roughly 600 million people whose livelihoods are connected to agriculture. AI tools that provide better crop disease identification, more accurate yield prediction, more useful market price information, and more accessible advisory services for farmers can improve the economic outcomes of a population that is otherwise poorly served by information-based services.

Education access and quality. India's education system produces millions of graduates annually, but the quality is highly variable. AI tutoring tools that provide expert-level explanation and feedback to students in schools that cannot currently provide it are particularly relevant to a country where the gap between the best and worst educational environments is large and consequential.

Language and inclusion. India has 22 officially recognized languages and hundreds of others with significant speaker populations. Most of the best AI tools are built primarily for English. The extension of capable AI tools to Hindi, Telugu, Tamil, Marathi, Bengali, and the other major languages of India is an access question of significant consequence — the version of AI that reaches the majority of India's population will need to work well in languages that currently have far less AI capability than English.

Infrastructure and urban management. India is in the middle of one of the largest urban expansions in human history. AI tools that support better urban planning, better traffic management, better infrastructure maintenance, and better delivery of government services at city scale have direct applications in a country building new cities and managing the rapid growth of existing ones.

India's AI production, not just consumption

India is not only a consumer of AI — it is a producer. Indian engineers are significant contributors to the global AI research and engineering effort, employed at the major AI labs and building AI companies. The Indian AI startup ecosystem is growing, with companies building applications across healthcare, agriculture, fintech, and enterprise software.

The most interesting question for India is not whether it can produce world-class AI researchers and engineers — it demonstrably can. The question is whether India builds the frontier models and the infrastructure layer, or primarily builds applications on top of models built elsewhere. This is a question about capital, about data access, and about the strategic choices that Indian institutions and companies make over the next decade.

The distinctive India AI opportunity

The opportunity that is most distinctive to India is the opportunity to build AI applications at scale for populations that are currently underserved by the global technology industry. The majority of the world's population does not speak English as a first language, does not have reliable internet access, does not have smartphones with the compute to run demanding applications, and does not have the economic profile that most Silicon Valley product decisions are optimised for.

Building AI that works for these populations — that is multilingual, that runs on lower-end hardware, that is designed for intermittent connectivity, that addresses problems relevant to agriculture and healthcare and education in lower-income contexts — is not a charity project. It is a large market opportunity that the global AI conversation consistently underestimates.

India is one of the few places where the talent to build this AI, the populations who would use it, and the institutional will to invest in it are all present at the same time. What gets built with that combination, over the next decade, is genuinely open.