India’s vast and diverse data pool is not just a byproduct of digitisation—it is a strategic national asset. If the country truly wants to emerge as a global AI superpower, it must stop treating its data as a free export for Silicon Valley and instead use it to fuel domestic artificial intelligence development. By investing in talent, computing infrastructure, and data sovereignty, India can democratise AI, generate local employment, and prevent long-term dependence on foreign technology platforms.
India is already one of the world’s fastest-growing AI user bases. The real challenge now is not adoption, but scale—how to move from being a massive consumer of AI tools to becoming a global leader in building them. At the core of this ambition lie three foundational pillars: skilled talent, advanced compute power such as high-end chips and infrastructure, and data. While India has no shortage of engineers, it still lacks large-scale basic AI research training and sufficient access to cutting-edge processors in public universities and laboratories. What it does have in abundance is data—and that advantage is currently underutilised.
This imbalance helps explain why major US tech firms are aggressively expanding in India. With nearly a billion people online and a mobile-first population, the country generates enormous volumes of text, voice notes, digital payments, and human feedback—exactly the inputs needed to train and refine AI systems. India is already the second-largest user base globally for platforms like OpenAI’s ChatGPT and Anthropic’s Claude, after the United States. Yet, despite this massive usage, India contributes only a small share of the revenue—highlighting that user data and training value matter more than direct monetisation.
The free services and generous promotions offered to Indian users are not charity. They are part of a strategic effort to absorb Indian languages, voices, and behavioural patterns, making foreign AI systems more intelligent and globally competitive. Over time, this risks creating a future where the most advanced AI understands India deeply—but is owned and controlled elsewhere.
India’s linguistic diversity makes this issue even more critical. With more than 20 official languages and dozens of widely spoken regional dialects, AI systems that are not trained on local language data and cultural context will fail in real-world settings—from classrooms and hospitals to courts and customer support centres. Bridging this gap is central to Prime Minister Narendra Modi’s vision of democratising AI so that its benefits reach farmers, small business owners, and everyday citizens, not just the English-speaking elite.
This is also why the future AI vision promoted by companies like Meta Platforms and OpenAI—featuring personal AI agents and voice-driven devices—cannot fully succeed in India unless these systems genuinely understand local speech, accents, and social nuance. Some startups, such as Poseidon AI backed by Andreessen Horowitz, along with non-profit initiatives supported by Big Tech, are already working to build local language datasets through crowdsourcing.
However, India cannot achieve “AI for all” by outsourcing the creation of its linguistic and data foundations. If developed strategically, high-quality Indian datasets can become core infrastructure for a homegrown AI economy. The choice is clear: either India continues to power foreign AI systems quietly in the background, or it asserts control over its data and uses it to build intelligence, value, and global influence on its own terms.
