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By Sujit Bhar
Under the glow of chandeliers and LED screens, the recent World AI Summit in New Delhi presented itself as an eager participant in the next great technological revolution. Policymakers spoke of compute capacity, start-up ecosystems and digital public infrastructure; global executives nodded approvingly. Yet, beneath the spectacle lies a cluster of hard questions. Can India truly attract major AI firms? Can it become a credible techno-partner? Does it possess the power, water and financial depth to sustain the AI boom? And most importantly, does it need to?
India undeniably has assets. Its vast pool of software engineers, a thriving IT services industry, and a large digital consumer base make it attractive. Companies such as Google, Microsoft and Amazon have already established cloud regions and research centres in India. The country’s Digital Public Infrastructure— Aadhaar, UPI, ONDC—has drawn global admiration as a scalable model.
However, attracting “major AI companies” in the current context means more than setting up offices. It means persuading firms like OpenAI, Nvidia and Meta to locate high-value compute clusters, R&D labs and potentially fabrication units on Indian soil.
Why might they come? First, market access. India’s 1.4 billion people represent one of the largest untapped AI application markets. Second, geopolitical diversification. In a world wary of overdependence on any one geography— particularly China—India offers democratic legitimacy and relative political stability. Third, policy signalling. Production-linked incentives (PLIs), data centre policies and tax sops create a welcoming narrative.
Yet, attraction will hinge on execution. AI companies seek regulatory clarity, contract enforcement, stable taxation and long-term infrastructure commitments. India’s track record in policy flip-flops— retrospective taxation being a historic example—may temper enthusiasm.
TECHNO-PARTNER OR TECHNO-CONSUMER?
The harder question is whether India can be a genuine techno-partner rather than merely a customer or a low-cost back office.
Consider semiconductors. Despite ambitious announcements, India has yet to produce advanced chips domestically. High-end fabrication requires decades of ecosystem-building—materials science, precision manufacturing, supply chain integration. Companies such as TSMC and Intel built their prowess over generations. India’s semiconductor foray remains nascent.
AI, meanwhile, is increasingly compute-bound. The dominance of Nvidia in GPUs underscores that hardware leadership translates into AI leadership. Without a domestic chip ecosystem, India risks being perpetually dependent on imports for core AI infrastructure.
However, partnership need not require full-stack sovereignty. India could position itself as a software innovation hub, leveraging its data diversity and linguistic complexity to build AI models suited for the Global South. Its strength lies in applied AI—agriculture, health diagnostics, financial inclusion—rather than foundational model training at trillion-parameter scale.
The risk is overpromising. If India pitches itself as an equal partner in cutting-edge chip design without the requisite base, it may undermine credibility. A realistic techno-partnership would focus on complementary strengths: data, software talent, market scale and regulatory experimentation.
PARTNERSHIP OR EXPLOITATION?
Will foreign firms seek partnership or exploitative advantage? Multinationals act in shareholder interest. They will come if India offers cost arbitrage, regulatory flexibility and market depth. They may also benefit from comparatively lighter data enforcement or environmental scrutiny, if such gaps exist.
India’s challenge is to negotiate from strength. If policies are overly accommodative—cheap land, subsidised power, lax oversight—foreign AI firms may extract value without embedding durable local capability. This would echo earlier manufacturing experiences where India became an assembly base without deep technology transfer.
Yet, India is not without leverage. Its digital market is too large to ignore. Strategic use of procurement, local data requirements and research collaboration mandates could ensure deeper integration. The question is whether governance institutions are prepared to drive such nuanced bargains.
AI data centres are power-hungry. Training large language models consumes megawatt-hours on an industrial scale. Even inference workloads require steady, high-density electricity.
India’s power grid has improved dramatically, but per capita consumption remains way below global averages. Coal still dominates generation. If hyper-scale AI centres proliferate, they could strain regional grids, especially during peak domestic demand seasons.
One solution lies in renewable integration. Data centres could be co-located with solar or wind farms, supported by battery storage. India’s ambitious renewable targets provide an opportunity to green AI growth. But renewables are intermittent; AI computing requires reliability. Hybrid models—renewables backed by gas or hydro—may be necessary.
The trade-off is stark. Should scarce power serve elite AI clusters or households, MSMEs and agriculture? Political economy will inevitably prioritise domestic consumers. Therefore, any AI expansion must internalise its energy footprint and invest in additional generation rather than diverting existing supply.
THE WATER CONSTRAINT
Cooling AI servers demands water—often millions of litres annually for large facilities. India is already classified as water-stressed, with groundwater depletion rampant in several states.
Locating data centres in arid zones without sustainable water sourcing would exacerbate local scarcity. Advanced cooling technologies—air cooling, liquid immersion systems, recycled wastewater use—can mitigate impact. Singapore, for instance, has imposed water efficiency norms on data centres.
India will need similarly stringent environmental clearances. The optics of tech giants consuming scarce water in drought-prone districts could trigger social backlash. Strategic sites near coastal regions, with desalination options, may offer one path — though energy costs would rise.
FINANCIAL MARKETS AND COMPLEX PRODUCTS
AI booms elsewhere have been fuelled not only by technology but by financial engineering. The interwoven investments among OpenAI, Microsoft and Nvidia reflect a sophisticated capital ecosystem comfortable with derivatives, structured products and high-risk valuations.
India’s stock market has matured significantly, yet it remains susceptible to volatility and retail-driven surges. Complex options and futures products tied to AI valuations could introduce systemic risk if regulatory oversight lags.
The Securities and Exchange Board of India (SEBI) has historically been cautious. That caution may prove beneficial. Rapid introduction of opaque financial instruments, without deep institutional participation, could destabilise markets.
A calibrated approach—encouraging AI listings, venture capital and private equity while limiting speculative excess—would better serve long-term stability.
DOES INDIA NEED THIS?
This is the central question. AI promises productivity gains across sectors. For a country aspiring to a $5 trillion economy and beyond, technology-led growth appears indispensable.
Yet scale matters. Should India invest billions in hyper-scale foundational models when global leaders already dominate that space? Or should it channel resources into domain-specific AI that directly addresses domestic challenges— crop forecasting, language translation across hundreds of dialects, judicial backlog reduction?
Massive capital inflows can distort priorities. The current global AI surge has seen circular capital flows—investments feeding back into partner companies, inflating valuations and reinforcing market dominance. India must guard against becoming a peripheral node in such financial loops.
If AI investment crowds out spending on health, education and infrastructure, the net social return may diminish. Conversely, if AI enhances those sectors’ efficiency, the multiplier effect could be transformative.
Optimistically, AI could catalyse a new services export wave, akin to the IT boom of the 1990s. Indian firms could build affordable AI solutions for emerging markets, leveraging cost advantages and contextual knowledge. This would reinforce India’s role as a technology bridge between the West and the Global South.
THE FLIP SIDE
Pessimistically, India could become primarily a consumption market and data provider, with limited capture of intellectual property or high-value manufacturing. Energy and water pressures could intensify, while financial exuberance fuels asset bubbles detached from real productivity gains.
The outcome depends less on summit optics and more on institutional depth. Regulatory competence, environmental prudence, infrastructure planning and educational reform will determine whether AI becomes a ladder or a mirage.
India does not need AI for prestige. It needs AI that solves Indian problems. If massive investment serves that end— building resilient grids, water-efficient data centres, skilled talent pipelines and robust capital markets—the gamble may pay off. If it merely amplifies existing inequalities and dependencies, the cost will be borne not by Summit delegates but by the broader citizenry.
In the final reckoning, the question is not whether India can host AI giants. It is whether India can shape AI’s trajectory in a way that aligns with its developmental priorities. The answer will define not only its technological standing but the character of its economic future.
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