Key Takeaways

  • Near-term AI capacity will rely on renewables, batteries, gas generation and grid upgrades working together
  • Nuclear power could provide firm low-carbon electricity, but deployment timelines remain a constraint
  • China’s fusion magnet milestone matters strategically, though commercial fusion is unlikely to power the current data center cycle

China has completed and tested the world’s largest superconducting fusion magnet system, including a 582-ton toroidal-field coil and a central solenoid coil, at its domestic artificial sun research facility in Hefei. The achievement reinforces China’s position in the long race toward commercial fusion energy. It also arrives as a much more immediate race is unfolding: finding enough electricity to operate the next generation of AI data centers.

The scale of that problem is becoming difficult to ignore. The International Energy Agency estimates that global data centers consumed about 415 TWh of electricity in 2024 and could use more than 900 TWh by 2030, with AI serving as the primary growth driver. That is not simply an energy procurement challenge. It is a generation, transmission, interconnection and local reliability challenge arriving all at once.

The mismatch is stark. Large data centers can be built in two to three years, while connecting them to mature power grids typically takes four to 10 years. Developers can secure land, order servers and construct buildings before utilities can add the substations, transmission lines and generating capacity needed to energize them.

In the near term, the strongest commercial model is likely to be a portfolio combining solar and wind generation, grid-scale batteries, existing grid power and on-site firm generation. Batteries can shift renewable electricity across hours and provide fast-response grid services, but conventional lithium-ion systems generally do not solve prolonged periods of low renewable output. Natural gas generation can fill that gap, though emissions and fuel-price exposure complicate corporate climate commitments.

This is why NextEra Energy and Brookfield Renewable are developing renewable-plus-storage projects and hybrid configurations aimed at data center clusters. Microsoft and Google are also pursuing co-located clean generation and storage while examining nuclear options for longer-duration, round-the-clock supply.

The urgency is visible in the forecasts. Deloitte projects that US AI data center power demand will rise more than 30 times, from 4 GW in 2024 to 123 GW by 2035. Its research found that 72% of utility and data center executives view grid capacity as the leading challenge. Goldman Sachs separately forecasts that AI and data center demand could reach 92 GW as early as 2027, alongside nearly $1 trillion in hyperscale investment.

Adding generation is only part of the answer. Data centers can also become more flexible consumers. AI training workloads may be shifted toward periods or regions with greater electricity availability, provided latency, data sovereignty and operational requirements permit it. Better cooling, higher server utilization and power-aware workload orchestration can reduce pressure at the margin. The ISO/IEC 30134 family, including the power usage effectiveness metric, gives operators a common basis for measuring facility efficiency.

Grid behavior matters too. AI campuses with loads measured in hundreds of megawatts cannot be treated like ordinary commercial buildings. They need sophisticated controls for voltage, frequency, backup generation and demand response. IEEE interconnection and power-quality standards provide an important technical foundation, while ongoing engineering research published through arXiv is examining how data center loads, storage and generation can be coordinated more effectively.

Nuclear power has a credible role, particularly for campuses seeking firm low-carbon electricity. Existing reactors can offer dependable output, while small modular reactors could eventually support dedicated industrial and data center sites. Still, licensing, construction, financing and fuel supply make nuclear a medium- to long-term option in many markets, not an instant fix for projects awaiting power today.

Fusion sits even further out. China’s 582-ton magnet system is strategically significant because powerful superconducting magnets are central to confining plasma inside many fusion reactor designs. Yet a successful magnet test is not the same as a commercially operating power plant connected to an AI campus. Fusion still faces substantial scientific, engineering and economic hurdles.

That said, China’s investment signals where national competition may be heading. Countries that develop abundant firm electricity, stronger grids and faster interconnection processes could attract more AI infrastructure and the industries forming around it. NPR has also documented the growing public debate around data center electricity demand, including questions about who pays for new infrastructure and how communities share the costs.

For the current build-out, renewables, batteries, gas and grid modernization have the advantage because they can be deployed now. Nuclear could become increasingly influential in the 2030s. Fusion remains the high-impact long shot. The likely winner is therefore not a single reactor or storage chemistry, but an integrated power system that can deliver capacity quickly, operate reliably and limit emissions as AI demand continues to climb.