In a world obsessed with building the biggest AI models, India is making a quieter, more consequential bet—on relevance, restraint, and strategic indispensability.
The global conversation around artificial intelligence has settled into a familiar rhythm. Progress is measured by scale—larger foundational models, denser compute clusters, and ever-rising capital expenditure. In the West, technological leadership is increasingly equated with the ability to outspend rivals in an AI arms race. India, however, appears to be reading from a different script.
The Economic Survey 2025–26 makes a quiet but consequential argument: India’s success in artificial intelligence will not come from competing at the frontier of size and brute-force compute, but from becoming strategically indispensable through application-led innovation. This is not a retreat from ambition. It is a recalibration of priorities—one that values relevance over prestige.
Turning Constraints into Strategy
Building frontier-scale AI models demands resources that remain scarce in India: vast pools of patient capital, uninterrupted access to high-end GPUs, and energy-intensive data infrastructure. Attempting to replicate the Silicon Valley model under these conditions would be fiscally risky and strategically limiting, potentially locking the country into long-term dependence on foreign proprietary technologies.
Instead, the Survey points toward a more pragmatic path—developing smaller, domain-specific AI systems designed for real-world deployment. These models may not dominate global benchmarks, but they are better aligned with India’s realities. They are computationally efficient enough to run on local devices, optimized for sectors such as banking, healthcare, manufacturing, and governance, and compatible with India’s existing digital public infrastructure rather than bespoke hyperscale data centres.
In this approach, constraint becomes a catalyst. Limited resources force sharper choices, and sharper choices can yield more durable advantages.
The Advantage of Arriving Late
Conventional wisdom treats late entry into emerging technologies as a disadvantage. The Survey challenges this assumption. India’s position allows it to observe the growing pains of early adopters—regulatory uncertainty, escalating energy costs, and unclear monetisation pathways for massive AI investments.
Learning from these experiences, policymakers are urged to adopt a sequenced strategy. Institutional coordination must come first, followed by the scaling of proven, application-specific use cases. Regulation, the Survey argues, should evolve alongside markets rather than racing ahead of them. This sequencing reduces the risk of overregulation while preserving space for innovation.
Rethinking India’s IT Model
Perhaps the most profound implications lie in the future of India’s IT sector. For decades, its global competitiveness has rested on cost-efficient, high-volume service delivery. The rapid spread of powerful general-purpose AI systems threatens to erode this foundation by automating routine coding, testing, and maintenance work.
The response, as outlined in the Survey, is a shift from “back office” to “AI front office.” This transition demands a workforce skilled not just in execution, but in reasoning, problem-solving, and domain understanding. The emphasis is on labour augmentation rather than displacement—using AI to raise productivity and move Indian talent up the value chain.
Crucially, building indigenous AI tools ensures that the economic value generated from India’s vast and diverse data ecosystems remains within the country, rather than flowing outward through imported platforms.
Frugal Innovation as the Real Frontier
India’s AI strategy does not dismiss frontier research altogether. Large foundational models can generate valuable spillovers across the economy. But the Survey is clear that scale should not be the primary objective. The real opportunity lies in relevance—AI systems tailored to India’s linguistic diversity, agricultural complexity, healthcare access gaps, and administrative scale.
This philosophy, described as “disciplined swadeshi,” reflects a world increasingly shaped by export controls and technological fragmentation. Strategic indispensability, in this context, means developing AI solutions that solve problems global models often overlook.
India’s AI moment, then, is not about building the biggest machines. It is about building the right ones. For policymakers and industry alike, the message is unmistakable. The future belongs less to those who chase scale, and more to those who solve problems that truly matter.
