The rapid rise of artificial intelligence (AI) is reshaping many sectors of the global economy — and with it, the energy landscape.
While AI brings transformative potential, its massive energy needs, primarily through the operation of large-scale data centers, are creating new challenges for electricity systems around the world.
Meeting this surging demand requires a combination of innovation, investment, and strategic planning. Several solutions are being explored to ensure that AI’s growth aligns with energy sustainability and reliability.
AI’s Soaring Energy Needs
Training and running advanced AI models require substantial computing power.
According to the International Energy Agency (IEA), a typical AI-focused data center today can consume as much electricity as 100,000 households, with the largest centers under construction expected to use the equivalent of up to 2 million households.
Data centers already accounted for 1.5% of global electricity demand in 2024, and this figure is set to more than double by 2030, reaching approximately 945 TWh — about the current electricity consumption of Japan.
Key points:
- In the United States, data centers could account for nearly half of all electricity demand growth through 2030.
- Global data center energy use could reach 1,200 TWh by 2035 under base-case projections.
Global Data Centers Electricity Consumption Trends
Data source: International Energy Agency
The Energy Gap: Why Current Systems Are Under Strain
Several factors make the challenge particularly difficult:
- High-capacity power lines and substations needed for data centers are scarce, with grid connection times often stretching to four to eight years.
- Transmission and distribution networks were not originally built for highly localized, intensive loads like those demanded by hyperscale AI facilities.
- Supply chains for transformers, gas turbines, and backup generators are already constrained, risking delays in grid reinforcement.
Without action, AI growth could exacerbate local grid bottlenecks, increase energy prices, and even threaten reliability in areas of high data center concentration.
Global Power Demands Driven by AI Surge
Data source: Goldman Sachs, IEA
Nuclear Energy: A Central Pillar in Emerging Solutions
One of the most important solutions being actively pursued is the expansion of nuclear energy, particularly small modular reactors (SMRs).
Nuclear power offers several advantages in the context of AI-driven energy demand:
- Baseload stability: Nuclear plants provide 24/7 power, unlike solar and wind, whose output is variable.
- High energy density: A single nuclear plant can provide large quantities of energy on relatively small land footprints, critical near urban or high-demand areas.
- Grid reliability: Nuclear’s consistent output helps stabilize the grid against the intermittent surges associated with AI computing loads.
Recent developments highlight growing interest:
- Companies such as Microsoft have signaled intent to procure SMRs or invest in nuclear partnerships to power new AI data centers.
- The U.S. Department of Energy is funding advanced nuclear designs, aiming for modular reactors to be deployable by early 2030s, offering lower construction costs and faster permitting compared to traditional nuclear plants.
In addition to SMRs, some technology firms are exploring on-site microreactors as a future-proof solution to ensure self-sufficient clean power without waiting for broader grid upgrades.
Broader Energy Strategy: Diversifying the Supply Mix
Although nuclear is gaining momentum, other energy sources will also play key roles:
- Renewables Expansion: Large investments in wind and solar farms are underway, especially for data centers in sunny or windy regions. However, their intermittency requires robust storage solutions or supplementary baseload power.
- Energy Storage: Battery systems are being deployed alongside renewable energy to stabilize supply, although large-scale storage remains expensive.
- Natural Gas: Flexible gas turbines continue to provide peaking power, though their long-term role will depend on carbon capture economics and emissions targets.
- Geothermal and Hydrogen: Pilot projects for direct geothermal energy and hydrogen-fueled backup systems are under development by several hyperscale providers.
Some companies are increasingly choosing to build near energy-rich areas where renewable and nuclear power are already abundant, rather than waiting for new transmission lines.
Annual Average Data Center Power Supply Capacity by Fuel and Region
Data source: International Energy Agency
Smarter Infrastructure and Direct Investment
Another critical piece of the puzzle is smarter grid planning and direct investment by the technology sector:
- Tech giants like Amazon, Microsoft, and Google are signing long-term clean energy power purchase agreements (PPAs) to secure dedicated capacity.
- Innovative ideas, such as co-locating data centers with power plants or creating microgrids with independent energy sources, are becoming more common.
- Some new facilities are designed to participate in demand response programs, adjusting load to assist with grid stability during peak periods.
These private-sector actions complement public policy efforts aimed at modernizing and expanding grid capacity to handle future AI-driven growth.
AI’s energy needs represent one of the most significant new challenges for global energy systems.
While the demand growth is steep, a mix of clean energy generation, nuclear expansion, smarter grid integration, and direct private investment offers a credible path forward.
Nuclear energy, particularly through the deployment of small modular reactors, is poised to become a cornerstone of this future, offering the consistent, low-carbon power needed to sustain AI’s promise without undermining climate goals.
As the digital and energy revolutions converge, proactive planning and innovation will be critical to ensuring that progress in one domain does not come at the expense of sustainability in another.