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OpenAI is reportedly planning a major investment in India. This move is a part of the "Stargate" project, a multi-billion-dollar initiative to build hyperscale data centres that will power the next generation of artificial intelligence. If it moves forward, the Indian facility, projected to have a capacity of at least one gigawatt, would represent a significant escalation of OpenAI's international strategy.
This seems more than a simple business expansion. It signals India's rising importance in the global technology race. The decision to invest in a major AI data centre in India aligns with the country's own ambitions, including the IndiaAI Mission, a government-led initiative, with a budget of over USD 1.2 billion.
Economics and market implications
India's data centre market is experiencing rapid growth, fueled by rising internet penetration, increased digitalisation, and government policies that encourage local data storage. The market, valued at approximately USD 5 billion in 2024, is projected to more than double to USD 12.85 billion by 2033. Data centre capacity is also set for a significant expansion, with a projected increase from 0.9 GW in 2023 to 2 GW by 2026.
OpenAI's planned investment places it among other major players already active in the Indian market. Google and Microsoft, along with local entities such as Reliance and AdaniConneX, are heavily investing in this sector. This influx of investment is driving competition and accelerating the development of India's digital infrastructure.
Technical and operational considerations
The Stargate project's sheer scale highlights a critical challenge for the AI industry, which is energy consumption. A single gigawatt of power can supply hundreds of thousands of homes, and these facilities require a massive amount of electricity to operate. This demand puts pressure on local power grids and raises questions around sustainability.
OpenAI and its partners, including SoftBank and Oracle, must navigate India's energy landscape. The success of this project will depend on securing reliable and, ideally, renewable energy sources to power the tens of thousands of GPUs and custom chips needed for advanced AI model training and deployment.