Navigating the Resource Costs of India’s AI Infrastructure
Introduction
India’s data centre sector is rapidly emerging as strategic national infrastructure at the centre of the country’s AI ambitions, fuelled by a combination of technological advancements and the global political economy. Estimates suggest that national data centre capacity is expected to rise from 1.2 GW in 2025 to almost 8 GW by 2030. With a funding of ₹10,372 crore, the IndiaAI Mission aims to establish domestic compute power and expand GPU infrastructure throughout the nation. Simultaneously, the Digital Personal Data Protection (DPDP) Act, 2023 has introduced a form of “soft localisation,” empowering the government to mandate domestic storage for sensitive categories of data.
Together, this push for infrastructure aims to transform India from a passive data market into an active shaper of global data flows. Yet India’s current policy model differs significantly from the approaches being adopted in other major digital economies. A comparison with Singapore and the European Union reveals that while India is focused on aggressive data centre expansion, other jurisdictions are increasingly prioritising sustainability, efficiency, and digital sovereignty.
This raises a critical policy question: can India scale its AI infrastructure ambitions while accounting for the governance and resource challenges that other markets are now attempting to correct?
India’s Incentive-Led AI Infrastructure Push
India’s current approach to data centre expansion is fundamentally facilitative. The state is acting as an enabler of rapid private investment through fiscal incentives and infrastructure prioritisation.
The Union Budget 2022 had classified data centres as “infrastructure,” which enables developers to access cheaper institutional financing and long-term capital. The Union Budget 2026 further introduced tax holidays for foreign cloud providers using Indian facilities for global operations. At the state level, governments such as Maharashtra and Uttar Pradesh are aggressively competing to attract hyperscale investments through electricity duty exemptions, expedited approvals, and “essential service” status designed to guarantee uninterrupted operations.
This approach reflects India’s broader strategic positioning. As global demand for AI compute accelerates, India seeks to establish itself not only as a major digital market, but as a sovereign compute hub for the Global South.
The IndiaAI Mission demonstrates this ambition clearly. By seeking to scale domestic GPU capacity to 100,000 units, the government is recognising that compute infrastructure is increasingly becoming geopolitically strategic. AI leadership will now depend on the ability to control and secure the physical infrastructure powering advanced AI systems.
However, while India’s policy framework strongly incentivises capacity creation, it remains relatively underdeveloped in areas such as sustainability benchmarks, resource management, and operational accountability.
Singapore and the European Union: Governance After Scale
Singapore and the European Union offer models of digital infrastructure governance as rapid infrastructure growth starts to raise resource and sovereignty issues.
With the limited energy resources and land at its disposal, Singapore has shifted from unrestricted data centre growth to a tightly managed sustainability-first model. Through the Data Centre Call for Application (DC-CFA) framework, only projects meeting strict efficiency and economic value criteria are approved. For instance, new facilities are expected to maintain Power Usage Effectiveness (PUE) levels of 1.3 or lower and submit detailed water efficiency plans to comply with advanced environmental standards. The country has also developed tropical cooling standards that allow facilities to run at higher ambient temperatures, reducing cooling energy consumption significantly. Rather than uninhibited growth, Singapore is now geared towards growth efficiency.
The European Union, on the other hand, is pursuing a sovereignty-oriented governance model in response to geopolitical pressures. However, it is still introducing energy reporting requirements and waste heat recovery rules into digital infrastructure rules through the revised Energy Efficiency Directive and proposed EU Cloud and AI Development Act. Simultaneously, the Digital Markets Act (DMA) is being used to investigate hyperscale cloud providers for potential “gatekeeper” behaviour, reflecting concerns about excessive concentration of digital infrastructure power in the hands of a few non-European firms. This approach shows that sovereignty and energy efficiency can go hand-in-hand.
These models illustrate an important trend: digital infrastructure governance is shifting from the promotion of investment to sustainability, competition regulation and strategic autonomy.
India’s Emerging Governance Challenge
India’s current trajectory and global geopolitical tensions suggest that pressures regarding sustainability and sovereignty are set to intensify over the next decade.
AI infrastructure is resource-intensive by design. For example, a single modern AI server rack can consume up to 250 kilowatts (kW) of power, compared to a traditional enterprise server rack which typically requires only 15 kW. Despite the use of water use effectiveness (WUE) technologies, the sheer volume of heat transfer means that AI data centres can still put immense pressure on local water resources, especially in warmer climates. These figures juxtaposed against hyperscale clusters mean the volumes of electricity, cooling systems, land, water, and high-density compute rise by significant orders of magnitude. Yet most Indian policies remain overwhelmingly focused on fiscal incentives rather than long-term resource governance.
This creates the risk of a reactive policy cycle in which sustainability standards are introduced only after resource pressures become acute. Urban concentration, grid stress, water scarcity, and energy reliability may eventually force abrupt regulatory interventions which can lead to higher compliance costs and uncertainty in operations.
At the same time, India’s push for sovereign AI infrastructure also raises broader questions around digital sovereignty and institutional capacity. Procuring GPUs alone does not create an AI ecosystem. Secure hosting environments, skilled infrastructure personnel, cybersecurity preparedness, and interoperable governance mechanisms are equally essential.
This makes workforce development a strategic human resource development issue rather than simply an industrial challenge. Without sufficient thermal engineers, cybersecurity professionals, and digital infrastructure specialists, India’s infrastructure ambitions may struggle to translate into long-term resilience.
Building Governance into the Expansion Phase
India’s current “pre-regulatory” moment also presents a significant opportunity. Because the sector is still evolving, both policymakers and infrastructure actors have the ability to shape governance standards before constraints become restrictive.
It is vital to establishing national sustainability benchmarks through public-private technical partnerships, possibly under the aegis of of NITI Aayog, the Bureau of Energy Efficiency (BEE) and MeitY, before the next resource pressures dictate reactive regulation. Pilot “sustainability sandboxes” focused on liquid immersion cooling, renewable integration, battery energy storage systems, and water-efficient operations could help create evidence-based policy frameworks tailored to Indian conditions. Similarly, Likewise, collaborations with skilling institutions like NSDC and NIELIT can contribute to the development of dedicated digital infrastructure academies for thermal engineering, cybersecurity, and AI infrastructure management.
This would support India to progress towards a sovereign AI infrastructure stack, bringing together compute capacity, sustainability, capacity building and governance resilience into a seamless ecosystem.
Conclusion
With AI systems become increasingly utilised in finance, healthcare, governance, and public services, the infrastructure ecosystem supporting them will become equally politically and strategically significant. The choices India makes today to operationalise sustainability, skilling, competition, and sovereign compute capacity will shape the foundations of its future AI economy.
The central challenge is no longer whether India can become a major AI infrastructure hub. It is whether the country can transition from an incentive-led expansion model toward a governance framework that balances scale with sustainability, sovereignty, democratic accountability, and long-term resilience.
That transition may ultimately define the success of India’s AI century.
References
https://indiaai.gov.in/news/cabinet-approves-india-ai-mission-at-an-outlay-of-rs-10-372-crore
https://www.midcindia.org/wp-content/uploads/2021/09/IT-ITES_Policy_2015.pdf
https://uplc.up.gov.in/en/page/uttar-pradesh-data-center-policy


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