The global conversation on artificial intelligence has largely centred on the race to generate ever more powerful models. Trillions of dollars are flowing into chips, data centres and computing infrastructure. A small number of firms in a handful of countries dominate the headlines and stock valuations. Yet the deeper economic question is not who builds the largest model, but who puts AI to work at scale in ways that raise productivity, improve services and distribute benefits widely.
India enters this phase with a distinctive advantage. Its experience with digital public infrastructure demonstrates how technology, institutions and market incentives can combine to lower costs, expand access and create platforms for innovation. The same approach that turned identity, payments and data sharing into population-scale public goods offers a practical template for artificial intelligence.
From Infrastructure to Intelligence
India’s digital public infrastructure rests on open, interoperable systems. Aadhaar provided a universal digital identity layer. The Unified Payments Interface created a low-cost, real-time payments network that now handles hundreds of billions of transactions annually. DigiLocker, Account Aggregator frameworks and sector-specific platforms for health, agriculture and commerce extended the model further. These systems reduced friction, cut intermediary costs and enabled new private services to flourish on shared public rails.
The economic effects have been measurable. Direct benefit transfers became more accurate and less leaky. Small merchants gained digital payment capabilities without expensive hardware. Credit assessment improved through verified transaction histories. The architecture avoided lock-in to any single private provider and kept barriers to entry low. This combination of public goods and competitive markets is precisely what AI adoption now requires.
The World Bank Group’s World Development Report 2026 underscores that the largest development gains from AI will accrue not to those who merely own frontier models, but to those who apply the technology to practical problems at scale. Developing economies that already possess digital public infrastructure are well positioned to capture these gains.
Building Capability Without Matching Hyperscalers
India need not attempt to replicate the capital-intensive model of global hyperscalers. Strategic capabilities can be built through targeted public investment. The IndiaAI Mission has already onboarded tens of thousands of graphics processing units into a shared national compute pool, offering subsidised access to researchers, startups and government users. Further expansion is under way, with the goal of making high-quality computing available at affordable rates.
Complementary efforts focus on Indian-language models, open datasets and domain-specific applications. Research, Development and Innovation funding and the Anusandhan National Research Foundation provide additional institutional support. The objective is not to own every layer of the technology stack, but to ensure that Indian institutions, firms and citizens can use advanced AI tools under Indian law, with data remaining subject to domestic governance.
Scaling What Works Across the Economy
The real prize lies in moving from isolated pilot projects to economy-wide productivity gains. In agriculture, multilingual AI systems can deliver crop advisory, weather alerts and pest diagnosis to millions of farmers. In healthcare, tools for tuberculosis screening and diagnostic support are already demonstrating results in public systems. In education, adaptive learning platforms that assess reading proficiency in local languages and generate personalised remediation plans have shown promising pilot outcomes.
The challenge is adoption at population scale. Digital public infrastructure lowers the cost of distribution. Once a reliable AI service is developed, existing platforms can carry it to clinics, classrooms, Common Service Centres and mobile devices. Interoperability remains essential. Just as UPI’s open design prevented dependence on any single payments provider, AI systems should be built to avoid technological lock-in and preserve competition.
Public institutions play a critical role. Results, including failures, must be shared across states and ministries so that successful approaches can be replicated and unsuccessful ones discontinued. Continuous evaluation, rather than one-time announcements, will determine whether AI becomes a genuine productivity multiplier.
Ensuring Gains Are Widely Shared
Technology alone does not guarantee inclusive outcomes. Without deliberate policy, AI risks concentrating benefits among those already equipped with skills, capital and connectivity. India must therefore pair infrastructure investment with broad-based capability building.
Frontline workers—teachers, health staff, agricultural extension officers and civil servants—need practical training to use and question AI tools. Students require exposure to AI literacy alongside traditional subjects. Workers in the informal sector, who form a large share of the labour force, need accessible pathways to new digital skills. Open datasets in Indian languages and low-cost access to compute help level the playing field for smaller firms and researchers outside major metropolitan centres.
Governance frameworks must keep pace. Clear rules on data protection, accountability for automated decisions and transparency in public-sector AI deployments will build trust. The forthcoming consultation paper on AI regulation offers an opportunity to set proportionate standards that encourage innovation while safeguarding citizens.
A Distinctive Path Forward
India’s digital public infrastructure succeeded because it treated technology as a public good that enables private innovation rather than a product to be monopolised. The same philosophy can guide AI strategy. Shared compute, open datasets, interoperable platforms and continuous learning across government create the conditions for widespread adoption.
The global race for model dominance will continue. For India, the more consequential race is the one that turns AI into a tool that raises farm yields, improves clinic efficiency, personalises classroom learning and expands credit to small enterprises. Success will be measured not by the size of domestic models, but by the breadth of productivity gains and the evenness with which they are distributed.
By building on the foundations already laid, India can demonstrate that artificial intelligence need not deepen existing divides. When technology, institutions and market incentives are aligned, the economic impact can be both large and inclusive. That combination remains the country’s most reliable path to turning AI’s promise into shared prosperity.
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