Now here is the dimension that ties everything together and makes nuclear energy not just important but urgent for India in 2026. Let us talk about AI, data centres, and the jaw-dropping amount of electricity they consume.
Think of it this way. When you ask ChatGPT or a similar AI model a question, that query does not float in the air. It runs on thousands of specialised processors (GPUs) packed into massive server racks, housed inside data centres the size of football fields. These data centres need electricity — not sometimes, not during the day, but every second of every day, without a single interruption. A single AI server rack consumes five to six times more power than a conventional server rack. Training a large AI model can consume as much electricity as a small town uses in a year.
Now multiply that by the scale of what is happening. Globally, data centres consumed roughly 300–400 terawatt-hours (TWh) of electricity in 2022 — about 1–2% of all electricity used on the planet. The International Energy Agency (IEA) projects this could reach 945 TWh by 2030 and 1,200 TWh by 2035. To put that in perspective, 1,000 TWh is more than the total electricity consumption of Japan.
And India is right at the centre of this explosion. Despite hosting less than 5% of global data centres, India accounts for about 20% of global data consumption. The gap is closing fast. The Indian AI market is projected to grow to over $17 billion by 2027. Data centre capacity in India is expected to rise from under 2 GW in 2025 to 8–15 GW by 2030. Reliance Industries has announced a ₹1.6 lakh crore investment in a 1.5 GW AI cluster in Visakhapatnam. Google has committed $15 billion for a 1 GW hyperscale hub. Microsoft's CEO Satya Nadella announced $17.5 billion in India investments over four years starting 2026. India is being called the "next hyperscale battleground."
Here is the problem. Solar and wind energy are cheap, but they are intermittent — the sun does not shine at night, the wind does not blow on demand. AI data centres cannot tolerate downtime. They need what energy engineers call "base-load power" — electricity that is available 24 hours a day, 365 days a year, rain or shine. Coal can provide this, but it defeats India's climate goals. Gas is expensive and import-dependent.
This is where nuclear energy enters — not as a nice-to-have, but as perhaps the only realistic clean energy option that can provide guaranteed, round-the-clock, carbon-free power at the scale data centres need. Union Minister Ashwini Vaishnaw said explicitly after the SHANTI Act was passed that small and modular nuclear reactors offer "feasible and practical solutions" to meet the huge energy demand of data centres. He noted that new reactor designs can be set up on as little as 14 acres of land — compact enough to sit alongside a data centre campus.
Globally, this marriage between nuclear and AI is already happening. Microsoft signed a deal to restart the Three Mile Island nuclear plant in the US specifically to power its data centres. Amazon, Google, and Meta are actively investing in nuclear energy projects. France, where 70% of electricity comes from nuclear, is marketing its nuclear-powered grid as an AI advantage.
For India, the connection is direct: the SHANTI Act opens the door for private companies (many of whom are also building data centres) to invest in nuclear reactors. SMRs, with their smaller footprint and dedicated output, are particularly suited for powering data centre clusters in cities like Hyderabad, Mumbai, and Visakhapatnam. The Nuclear Energy Mission's target of five SMRs by 2033 is not an abstract clean-energy goal — it is a practical necessity if India wants to remain competitive in the global AI race.
There is a deeper strategic point here. Whoever controls the energy supply for AI infrastructure controls the pace of AI development. If India has to depend on imported fossil fuels to power its data centres, it is building its AI future on someone else's energy. Nuclear energy — especially indigenous nuclear energy — makes AI infrastructure truly self-reliant. This is the indigenisation argument applied to the digital economy: just as India built its own rockets when others refused to share technology, India now needs to build its own energy backbone for AI when the stakes are equally high.
But there are real concerns too. Data centres are enormous water consumers — AI data centres in India could drive water usage to 1,068 billion litres annually by 2028, according to some estimates. Nuclear reactors also need cooling water. Placing both near the same water sources creates competition. Regulatory approvals for nuclear plants near data centre hubs (often in or near cities) will face scrutiny. And the public perception challenge is real — communities may resist having a nuclear reactor next to their neighbourhood, even a small modular one.