Morgan Stanley has flagged a structural shift in India's digital infrastructure build-out, projecting the country's data centre capacity to surge nearly six-fold to 10.5GW by FY2031, from around 1.8GW currently, as demand accelerates on the back of AI adoption and data localisation policies.
The report notes that geopolitical realignments, coupled with India's regulatory push, are catalysing a multi-year investment cycle in data centres.
“We expect India's installed DC capacity to scale from 1.8GW now to 10.5GW by F31e,” the report said, adding that artificial intelligence workloads alone could account for 6.8GW of this capacity.
According to the brokerage, rising demand for low-latency data processing, tighter localisation norms and increasing compute intensity are key structural drivers.
“Growth will be structurally underpinned by… stronger digital push, tighter localization norms and the need for high-compute intensity with rising AI workloads,” it said.
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The expansion is expected to unlock a significant capital expenditure opportunity. Morgan Stanley estimates an industrial capex pipeline of about $60 billion linked to incremental data centre capacity, spanning land, power systems, cooling infrastructure and networking equipment.
Additionally, the power ecosystem is set to see investments of over $20 billion to support energy-intensive data centres, with operators increasingly pivoting towards renewable energy and storage solutions.
The report emphasises that India's policy framework, including data localisation mandates, infrastructure status for data centres and fiscal incentives, is accelerating capital formation and attracting global hyperscalers. This is expected to deepen foreign investment flows and strengthen India's position as a regional hub for cloud and digital services.
“Strategically, greater domestic data storage enhances digital sovereignty, reduces reliance on overseas infrastructure, and positions India as a regional hub for global technology firms,” the report said.
However, Morgan Stanley cautioned that key constraints remain, particularly around access to reliable and cost-effective power, as well as dependence on imported high-end computing hardware, factors that could influence execution timelines and capital intensity.
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