Indian conglomerate Adani Group said on Monday it would invest $100 billion over the next decade to build data centers specialized for AI across the country, a move that underscores India’s ambition to play a larger role in the global AI race.
The investment, which will run through 2035, is aimed at building renewable-energy-powered data centers designed to support AI workloads, the company said. It expects the plan to catalyze an additional $150 billion in related investments and result in a $250 billion AI infrastructure ecosystem in India over the decade.
Adani is making this commitment against a backdrop of skyrocketing investments in AI infrastructure as companies increasingly look beyond the U.S. for computing power, energy and friendly regulation. India, with its expanding digital economy and growing renewable-energy capacity, has emerged as a major destination for data centers and AI-related infrastructure over the past couple of years.
The announcement coincides with India’s ongoing AI Impact Summit in New Delhi this week, where leaders from some of the world’s top AI companies, including OpenAI, Nvidia, Anthropic, Microsoft and Google, are meeting policymakers and industry executives.
Adani Group chairman Gautam Adani (pictured above) described the plan as a long-term bet on the convergence of energy and computing. “India will not be a mere consumer in the AI age,” he said, adding that the group aims to help build a domestic AI infrastructure base.
The plan is to build atop Adani’s own existing data-center platform and its partnerships with companies like Google and Microsoft. The conglomerate is developing large-scale AI data-center campuses in Visakhapatnam and Noida, and has plans for more facilities in Hyderabad and Pune. An expanded partnership with Walmart-owned Flipkart will focus on another AI data center.
Adani said the broader plan calls for deploying up to 5 gigawatts of data-center capacity. The company said the facilities will be developed as a unified system that would scale power generation and processing capacity in parallel.
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