Indian manufacturers are ramping up production of cables, power systems, and cooling equipment as AI fuels a rapid expansion of data centre capacity.

This surge represents a "picks and shovels" investment strategy, where the focus shifts from the AI software itself to the physical infrastructure required to run it [1]. As global demand for AI processing grows, India is positioning its industrial base to supply the essential hardware needed for massive compute clusters.

The scale of expansion is significant. Total data centre capacity in India is projected to reach 12 GW by 2030 [2]. A substantial portion of this growth is tied specifically to artificial intelligence, with AI-dedicated capacity expected to rise to 6,546 MW by 2030, up from 275 MW in 2026 [2].

This infrastructure boom is creating a secondary effect on the energy sector. Data centre electricity demand is forecast to grow to 191 TWh by 2040, a massive increase from the 10 TWh recorded in 2026 [2]. To meet these needs, manufacturers are focusing on high-efficiency power distribution and advanced cooling systems to manage the heat generated by AI chips.

Despite the projected growth, the sector faces practical obstacles. Industry reports said that significant hurdles remain regarding land acquisition and the availability of consistent power to support such a rapid scale-up [2]. These bottlenecks could potentially slow the deployment of new facilities even as manufacturing capacity increases.

Companies providing the underlying hardware are capitalizing on this trend. By supplying the cables and cooling units that form the backbone of these facilities, Indian manufacturers are integrating themselves into the global AI supply chain [1].

“Total data centre capacity in India is projected to reach 12 GW by 2030”

India's shift toward manufacturing data centre infrastructure signals a strategic move to capture the physical layer of the AI economy. While software development has long been a strength, the transition to hardware production allows the country to hedge against the volatility of AI application trends by providing the essential utilities—power and cooling—that any AI model, regardless of the provider, requires to operate.