Artificial Intelligence: Capitalising the Future

INDIA HAS ENTERED the AI era where the technology is moving from experimentation to everyday operations. A McKinsey survey finds that 88 per cent of organisations used AI in at least one business function in 2025, yet only 31 per cent were scaling it and just 7 per cent had fully deployed it, suggesting an economy where AI is easy to try but hard to integrate. This gap between experimentation and integra­tion is where the economic story begins for India.

Globally, AI is diffusing faster than ownership. While many economies are learning to deploy AI at scale, only a few nations, notably the US and China, are accumulat­ing the compute, capital, and research depth needed to capture most of the economic rents. These two remain the dominant AI superpowers, supported by general purpose models, advanced chips, and deep pools of private capital.

India sits differently in this landscape as it has yet to build sov­ereign frontier models. The country is already a major AI economy by breadth and India’s share of AI publications in computer sci­ence reached 9.22 per cent in 2023, comparable to the US (9.20 per cent). However, AI publications per capita in 2025 are far lower for India (4.45) than the US (22.90) and China (15.16), showing that India’s research intensity is diluted by population scale even when aggregate output looks large. The International Monetary Fund AI Preparedness Index places India at 0.492 (rank 72/174), below China (0.63) and far below the US (0.77), signalling gaps in digital infrastructure, innovation depth, and governance capacity that make scaling harder than piloting.

India’s AI development lags not because adoption is absent, but because capability formation is constrained. India is not producing globally competitive frontier LLMs at scale and most general purpose LLM usage is through foreign platforms. Domes­tic efforts do exist—Krutrim, Sarvam AI and initiatives such as BharatGPT—but they are not yet the default model layer across large-scale Indian enterprise and public deployments. This is ex­actly why India’s scale advantage matters: if it can turn adoption into replicable platforms, it can capture productivity gains now while building domestic capability in the layers that compound.

India’s edge is not frontier training runs but it is implementation capacity through skills, talent integration, and the ability to diffuse workflow change across a very large economy. India is among the world’s largest producers of science and engineering graduates, giving it an unusually large pipeline for AI integration across firms and public systems. AI adoption requires redesigning workflows, training workers, building quality checks, integrating systems, and continuously improving outcomes and not just buying software. India’s large STEM pipeline will be a core advantage here.

That implementation capacity is already showing up on the ground in the form of frugal, problem-driven deployments across sectors. In health, non-invasive AI-enabled thermal imaging for early breast cancer screening in low-resource settings, and low-cost AI-assisted oral cancer screening devices deployed through primary health centres and outreach camps. In public goods and climate risk, it points to AI-based urban water management in Bengaluru and sensor networks plus machine learning for real-time landslide alerts in Himalayan regions. AI-enabled agricul­tural networks improving market access, price discovery and logistics for 1.8 million farmers across 12 states.

These examples show what an Indian comparative advan­tage can look like: small, task-specific AI deployed in constrained environments where costs matter and multilingual access is essential, while domestic capability is progressively built in the sector-relevant layers. They also reveal a second economic truth: pilots are easier than platforms. If India wants diffusion to com­pound into national productivity gains, the enabling environ­ment must reduce the cost of replication. That means treating AI less like software and more like infrastructure, while also being honest about market structure and capability gaps.

On the supply side, AI workloads are constrained by physical inputs like power, data centres, advanced chips, and GPUs. As per the World Bank, 73 per cent of data centres are in high-income countries as of June 2025, China accounts for 11 per cent, other up­per-middle-income countries for 11 per cent, and India for about 3 per cent. At a global level, utilisation remains concentrated: high-income countries accounted for 58.4 per cent of AI usage in April 2025, while upper-middle-income and lower-middle-income countries accounted for 22.5 per cent and 18.7 per cent, respectively. The competitiveness opportunity for India lies in using AI widely but doing so in a way that is resilient to global concentration in hardware, capital, and proprietary systems.

India needs steady capacity building alongside compute-efficient design choices, which is why the India Semiconductor Mission and Semicon India programme aim to improve compute predictability through a ` 76,000-crore incentive framework with up to 50 per cent fiscal support across the semiconductor stack.

After establishing compute as the visible foundation, data now becomes the invisible multiplier. Fragmentation in data availabil­ity and quality, lack of standardisation, and weak interoperability across systems and datasets prevents local ideas and innovation from adding up to real, countrywide strength. In economic terms, this behaves like a recurring transaction cost. Each institution rebuilds similar pipelines. Each successful pilot remains local. Productivity gains do not compound because systems cannot plug and play with each other. AI can be framed as a public good where the sovereign is a monetary stakeholder.

What separates isolated pilots from economy-wide productiv­ity gains is infrastructure. Common data standards, interoper­able systems, and reusable evaluation methods make replication cheap and predictable. India’s comparative advantage in frugal deployment becomes dramatically more valuable when the plumbing cost of replicating solutions drops. A centralised code repository under the IndiaAI Mission, as proposed by the Economic Survey is indeed the most practical idea.

India’s AI capability build-out is constrained by a weak private innovation engine and thin industrial depth: the private sector funds only about 36 per cent of national R&D spending while gov­ernment contributes roughly 64 per cent, whereas in economies where R&D exceeds 2 per cent of GDP, the private share is typically above 50 per cent, a gap that matters because frontier AI progress de­pends on sustained, firm-led productisation and scale-up. Over the next five years, more than 95 per cent of India’s data centre capacity additions are expected to be driven by leased facilities, across both retail and wholesale segments, with the remaining share coming from hyperscale building dedicated AI infrastructure.

Therefore, the bottom-up approach becomes a genuine ad­vantage. Instead of making the national ecosystem hinge on a small number of compute-intensive frontier efforts, India can scale value through application-specific, small models that are computationally efficient, easier to fine-tune, and capable of run­ning on locally available hardware such as smartphones and per­sonal computers. This widens the set of actors who can innovate, including startups, research institutions, public agencies, and domain-specific firms, while reducing exposure to global GPU tightness or financial constraints.

At the same time, India can keep the long-term agenda of im­proving compute predictability and building interoperable data rails. In the short run, the economic play should be adoption-led: accelerate applied AI in priority sectors including education, help firms move from pilots to process redesign through shared playbooks and reference architectures, reduce trust and procure­ment friction through common evaluation norms, and expand affordable compute access for startups and smaller firms. Used this way, global models deliver productivity today, while domestic capability in fine-tuning, evaluation, security, and integration ensures India captures more of the value tomorrow.