- AI-driven data centres are rapidly increasing global electricity demand, with consumption projected to reach 945 TWh by 2030.
- Rising AI demand is accelerating investment in power generation, grid infrastructure, renewables, storage and energy efficiency.
Artificial intelligence has been touted as the next frontier of the digital revolution, but its rapid expansion depends on something physical: electricity.
Behind every AI model, automated system and digital assistant are data centres filled with servers, cooling systems and other energy-intensive infrastructure. As companies develop more powerful AI applications, these facilities are creating a new source of electricity demand. That is turning AI into an energy-sector story.
The International Energy Agency (IEA) estimates that data centres consumed about 415 terawatt-hours (TWh) of electricity in 2024, representing around 1.5% of global electricity consumption. By 2030, consumption could reach about 945 TWh.
The question is no longer simply how much electricity AI will use. It is whether energy systems can supply that power while maintaining reliability, affordability and progress toward cleaner energy.
AI’s Growing Energy Appetite
AI requires large amounts of computing power, and that computing power requires electricity. The IEA estimates that a typical AI-focused data centre can consume as much electricity as 100,000 households, while the largest facilities under construction could consume around 20 times more.
Although data centres remain a relatively small share of global electricity demand, their rapid growth can create significant local pressure because they concentrate large electricity loads in specific locations. This makes AI as much an infrastructure issue as a technology issue.
Where Will the Power Come From?
Meeting this demand will require new electricity generation. The IEA projects that electricity generation serving data centres could rise from around 460 TWh in 2024 to more than 1,000 TWh by 2030. Renewables are expected to provide nearly half of the additional supply, with solar, wind and hydropower playing important roles. However, natural gas and nuclear power are also becoming part of the conversation because data centres require reliable electricity around the clock.
Recent developments in the United States illustrate this shift. In Texas, new gas and nuclear projects are being discussed alongside growing demand from AI data centres and semiconductor facilities. In Alberta, Meta’s planned data centre has also prompted discussions about additional power generation and natural gas infrastructure.
The emerging message is clear: building AI infrastructure increasingly means building the energy infrastructure to support it.
The Grid Challenge
Generating more electricity is only part of the problem. The power must also reach the data centre. The IEA estimates that around 20% of planned data-centre projects could face delays because of grid constraints. Transmission lines, substations and other infrastructure can take years to develop, while data-centre capacity can expand much faster.
This creates a growing mismatch between the speed of technological expansion and the speed of energy infrastructure development.
Batteries, on-site generation, renewable power purchase agreements and other flexible resources could help bridge the gap. However, significant investment in transmission and distribution infrastructure will remain necessary.
AI and the Energy Transition
The growth of AI also creates a new tension for the energy transition. The world is trying to increase renewable generation, expand electricity access and reduce emissions. At the same time, AI is creating another major source of electricity demand.
Some of that demand will be met by fossil fuels, particularly natural gas, while renewables are expected to supply a substantial share of new electricity generation. Nuclear power could also become increasingly important in some markets.
The outcome will depend on how quickly countries can add new clean generation capacity, strengthen grids, and deploy storage to meet rising demand.
AI Could Also Transform Energy
AI is not only increasing electricity consumption. It could also help the energy sector operate more efficiently. Energy companies can use AI for renewable energy forecasting, predictive maintenance, grid management, fault detection, and demand optimisation.
The IEA estimates that AI-based fault detection could reduce outage durations by 30% to 50% in some applications. It also suggests that AI could potentially unlock up to 175 GW of transmission capacity without building new transmission lines.
This creates a more complicated relationship between AI and energy. AI is simultaneously “creating new electricity demand and offering tools that could help energy systems manage that demand more efficiently”.
What It Means for Emerging Markets
The impact could be particularly significant in emerging economies. The IEA estimates that emerging and developing economies outside China account for around half of global internet users but less than 10% of global data-centre capacity.
Reliable and affordable electricity could therefore become an increasingly important factor in attracting data-centre investment. For countries with weak electricity infrastructure, however, growing AI demand could expose existing limitations in generation, transmission and distribution.
Nigeria sits within this challenge.
The country needs to expand reliable electricity access and strengthen its power infrastructure, while its growing digital economy could create additional demand for dependable electricity from data centres and other technology infrastructure.
That demand could encourage investment in generation, renewable energy, gas, storage and grid infrastructure. But attracting data centres alone will not solve the country’s power challenges. The wider electricity system must expand alongside them.
Powering What Comes Next
AI’s future will depend on more than computing power. Every new model and data centre requires electricity, while every additional electricity load requires generation, transmission, distribution and investment.
For the energy sector, this creates both a challenge and an opportunity. It must provide enough reliable electricity to support the growth of AI without undermining affordability, energy security or the transition toward cleaner energy.
At the same time, it can use AI to make grids more efficient, integrate renewable energy more effectively and improve the performance of existing infrastructure.
The AI revolution may have begun in software, but its next chapter will increasingly be written in power plants, grids, batteries and the infrastructure that keeps them running.