Talent strong, infrastructure weak: India’s AI reality check

By: Team India Tracker

India’s standout strength is talent, ranking second globally behind the US. Yet the same index reveals a stark weakness: infrastructure, where it stands a low 68th

In the global race to shape the future of artificial intelligence, India finds itself in a curious position: ahead of most, but still some distance from the front-runners. The 2024 Tortoise Global AI Index places India 10th overall in AI capacity. That is no small achievement. But the details tell a more complicated story. 

India’s greatest strength is talent. It ranks second in the world on that metric, just behind the US. It also performs strongly on operating environment, where it stands third. These are not marginal gains. They reflect the depth of India’s engineering base, its thriving start-up culture and a regulatory climate that, so far, has not choked innovation with overreach. 

Yet the same index exposes a glaring weakness: infrastructure. India ranks a low 68th in this critical pillar. In a field where computing power is the fuel, that shortfall matters. Artificial intelligence today is not just about clever algorithms; it is about access to massive computing resources, data centres and advanced chips. Without them, talent can only go so far. 

The top of the table remains predictable. The US sits comfortably at number one overall, ranking first in talent, infrastructure, research and development, and operating environment. China is second overall, with strong showings in development and research, though it lags the US in infrastructure. Singapore, the UK and France round out the top five, each combining policy clarity with robust digital infrastructure. 

India’s profile, in contrast, is lopsided. It is a talent powerhouse but an infrastructure laggard. That imbalance defines its AI challenge. 

Nowhere is this clearer than in high-performance computing (HPC), the backbone of advanced AI systems. HPC refers to supercomputers capable of processing vast datasets and training complex models at speed — an essential requirement for building large language models and frontier AI systems. India accounts for just 1 per cent of the world’s top 500 HPC systems and 1 per cent of total capacity. The US controls 35 per cent of such systems and nearly half of global capacity. China, despite ranking second overall in AI, has 9 per cent of systems but only 2 per cent of capacity, highlighting its own infrastructure constraints. 

The message is blunt: compute power is concentrated in a handful of advanced economies. For India, the gap is structural, not cosmetic. 

Investment patterns reinforce this reality. Between 2014 and 2024, India attracted just 3 per cent of global venture capital investment in AI training data. By comparison, the US drew 56 per cent, China 17 per cent and the European Union 15 per cent. Training data is not a peripheral input; it is the raw material from which AI systems learn. The ability to acquire, curate and process large, high-quality datasets depends heavily on capital availability. Limited funding restricts the scale and sophistication of domestic AI models. 

So what does India’s 10th-place ranking signify? 

First, it signals potential. Ranking second in talent suggests that India has built a formidable human capital base. Its universities, technology institutes and diaspora networks feed a global AI workforce. The strong operating environment ranking indicates policy stability and a market large enough to test and deploy AI solutions at scale. 

Second, it signals vulnerability. Infrastructure deficits mean that much of India’s AI talent may end up building products on foreign platforms, using foreign compute and relying on foreign capital. That creates dependency. In strategic technologies, dependency carries risks — economic, technological and geopolitical. 

Third, it signals urgency. As India hosts global conversations on AI governance and regulation, it must confront a basic fact: regulation without capability is posturing. If India wishes to shape global AI norms meaningfully, it must first invest in its own computing backbone, semiconductor ecosystems and data infrastructure. 

The broader lesson is that AI leadership is not determined by a single variable. It is an ecosystem game. Talent, research, capital, infrastructure and policy must reinforce one another. India has some of the pieces. But the pieces do not yet fit together. 

The AI summit in New Delhi offered a symbolic moment. It showcased ambition and intent. The index ranking provides a reality check. India is in the global top 10—a credible position. But the distance between tenth and first in AI is not incremental. It is exponential. 

Bridging that gap will require sustained public investment, private capital mobilisation and institutional coordination. Talent has given India a seat at the table. Infrastructure will determine whether it remains a participant—or becomes a leader.