The India AI Impact Summit is bringing together figures such as Sundar Pichai, Sam Altman and Emmanuel Macron alongside researchers, entrepreneurs, investors and policymakers. The optics matter. Artificial Intelligence (AI) is not simply another technology wave but a structural reordering of economic and institutional power. When leaders of global technology firms and heads of State converge in India to discuss AI, the implication is clear: India is central to how AI will scale across large, complex societies.
India approaches this moment with real strengths. By most composite measures of talent, research output, startup dynamism and policy orientation, it stands behind only the US and China in overall AI capacity. That position reflects a vast engineering workforce, globally integrated technology companies and a State that has shown unusual competence in building digital public infrastructure. Yet being third in a capital-intensive race offers no guarantee of durable advantage. The frontier of AI is shaped by actors who can mobilise extraordinary financial resources, control advanced semiconductor supply chains and integrate hardware with software at scale. Talent does not neutralise structural asymmetries in capital.
India’s distinctive contribution to the digital age has rarely been about producing the most advanced laboratory breakthrough. It has been the taming of complexity, given its reality of incredible diversity. Aadhaar, UPI and industrialised software delivery for a global market are examples of this. These achievements shared a common architecture: Interoperability, standards driven ecosystems and relentless pressure on transaction costs. AI will reward the same instincts but with higher stakes. Treating AI as a decorative feature bolted onto existing offerings risks strategic shallowness. The question is whether AI becomes infrastructural within India’s economy, embedded in workflows across finance, agriculture, manufacturing, education and public administration. In a country where skilled human capital is scarce relative to need, AI systems can generate measurable productivity gains. The cumulative effect of many small efficiency improvements can be transformative in a large heterogeneous economy.
India’s diversity also constitutes a strategic asset. Building AI systems that function reliably across dozens of languages, accents and administrative contexts imposes technical discipline. Solutions that succeed in such an environment tend to generalise well to other parts of the developing world. India can serve as both a proving ground and export platform for affordable, scalable AI applications. The same demographic scale that complicates governance can, if harnessed, create powerful data and feedback loops.
The countervailing force is capital intensity. Contemporary AI development depends on specialised chips, high performance data centres and significant energy capacity. Global spending on AI infrastructure has reached levels that dwarf traditional research budgets. Nations and corporations are committing sums that would have seemed implausible a decade ago. The structural risk for India is that it becomes an enthusiastic consumer of AI systems whose core intellectual property, compute capacity and pricing power reside elsewhere. Such dependence is not purely economic. When AI systems are embedded in banking, health care, supply chains and governance, reliance on foreign platforms acquires strategic overtones.
India’s historical response to expensive technology has been to reorganise it around affordability. A comparable AI strategy would prioritise model efficiency, optimisation techniques that deliver more performance per unit of compute and architectures suited to Indian use cases rather than imported defaults. It would also require sustained investment in high quality datasets across Indian languages and socioeconomic contexts.
A parallel with India’s space programme is instructive. The country achieved credible launch and exploration capabilities through disciplined mission focus, frugal engineering and iterative learning under constraint. AI differs in that it evolves rapidly and is driven largely by private enterprise. Nonetheless, the underlying lesson endures: Capability emerges from cumulative competence and institutional coherence, not from sporadic bursts of spending. Strategic clarity about which layers of the stack to own and which to access through partnerships is essential.
This leads to a more uncomfortable reflection. Many Indian firms have approached AI as an enhancement layer atop existing services, integrating third party models and monetising implementation expertise. While commercially rational, this strategy risks perpetuating a pattern in which India supplies skilled labour but captures limited intellectual property and platform control. Underinvestment manifests in modest domestic compute ownership, constrained funding for long horizon foundational research and limited ambition in integrated model development. As global leaders vertically integrate chips, infrastructure and algorithms, partial participation may yield diminishing strategic leverage.
Yet India’s strength in rapid adoption should not be underestimated. When digital tools demonstrate clear utility, uptake can be swift and broad. Mass usage generates feedback loops that improve products, inform safety research and stimulate localised innovation. If channelled deliberately, adoption can evolve into capability rather than dependency.
The risks extend beyond capital and competitiveness. AI systems can amplify misinformation, and entrench bias if governance frameworks lag deployment. Automation may disrupt segments of the services workforce that underpin India’s export economy even as new roles emerge unevenly. Data centres may strain energy systems already under pressure. Reliance on externally trained models can embed foreign cultural and epistemic assumptions into domestic institutions.
India’s challenge is, therefore, multidimensional. It must build infrastructure that fosters competition and affordability, invest credibly in domestic capability where strategic autonomy matters and enforce standards of trustworthy deployment that sustain public confidence. The summit in New Delhi signals that the world expects India to shape the trajectory of AI rather than merely absorb it. The decisive question is whether India will position itself primarily as an adept customer of intelligence designed elsewhere or as the architect of affordable widely usable AI that a cost-constrained world will increasingly demand.
Siddharth Pai is co-founder and managing partner, Siana Capital. The views expressed are personal
