India’s Role in AI: The World’s Most Valuable Unpaid Intern?

India, Feb 27 : India is rapidly emerging as one of the world’s largest artificial intelligence user bases. The challenge now is ensuring that this scale translates into technological leadership rather than simply serving as a training ground for global tech giants.

The foundations of AI rest on three pillars: talent, compute infrastructure and data. While India produces a vast pool of engineers, it lacks large scale foundational AI research capacity and sufficient access to advanced chips and high-performance computing systems in public institutions. What it possesses in abundance, however, is data  and that resource is increasingly becoming strategic.

With nearly a billion people online and a mobile-first digital ecosystem, India generates enormous volumes of messages, voice recordings, transactions and human feedback every day. These inputs are critical to improving AI systems. The country is the second-largest user base after the United States for platforms like ChatGPT and Claude, yet contributes only a small share of their global revenues. This imbalance highlights how valuable India currently is for training purposes rather than monetization.

The aggressive push by US technology firms into the Indian market reflects this reality. Free services and promotional access may appear consumer-friendly, but they enable foreign AI systems to learn Indian languages, speech patterns and behavioral nuances first. Without safeguards, India risks repeating an old economic pattern: exporting raw materials cheaply and importing finished products at a premium — this time in digital form.

India’s linguistic diversity adds urgency to the issue. With more than 20 officially recognized languages and dozens of widely spoken regional tongues, AI systems must understand local contexts to function effectively in education, healthcare, legal services and governance. Without adequate training on indigenous data, these tools could misinterpret users and reinforce inequalities. Bridging this gap is central to the government’s vision of making artificial intelligence accessible beyond English speaking urban elites.

Several startups and nonprofit initiatives are attempting to build localized datasets, particularly for regional languages. These efforts could form the backbone of a domestic AI ecosystem if supported properly. Policymakers must ensure that data collection and labeling practices are fair while also recognizing that such datasets are foundational digital infrastructure.

The need extends beyond language. High-quality, sector-specific data in healthcare and finance could enable breakthroughs in diagnostics, risk assessment and personalized services. Much of this information remains locked in fragmented systems. Creating secure frameworks that allow responsible access while protecting privacy would strengthen India’s innovation capacity and align with its “AI for good” ambitions.

Ultimately, the debate centers on control and value capture. Rather than isolating itself, India can negotiate partnerships that include access to high-end chips, shared computing resources, robust research training pipelines and deeper technological collaboration. It can establish transparency standards requiring foreign AI developers to disclose how Indian data shapes their models and how risks are evaluated in local contexts.

By crafting equitable data policies and treating information as a strategic national asset, India has the opportunity to lead emerging economies in shaping the AI era. Without such measures, it risks becoming an open resource base powering systems that automate domestic jobs while concentrating technological gains elsewhere.