The International Energy Agency (IEA) has explored the opportunities and challenges brought about by AI with regards to global energy.
Training and deploying sophisticated AI models occur within vast, power-hungry data centres. A “typical AI-focused data centre consumes as much electricity as 100 000 households,” the IEA notes, with the largest facilities under construction projected to demand 20x times that amount.
Surging data centre investments
Global investment in data centres has nearly doubled since 2022, reaching half a trillion dollars in 2024, sparking concerns about escalating electricity needs.
While data centres accounted for approximately 1.5% of global electricity consumption in 2024 (around 415 terawatt-hours, TWh,) their local impact is far more significant. Consumption has grown annually by about 12% since 2017, vastly outpacing overall electricity demand growth.
The US leads this consumption (45%), followed by China (25%) and Europe (15%). Almost half of US data centre capacity is concentrated in just five regional clusters.
Looking ahead, the IEA projects global data centre electricity consumption to more than double by 2030 to reach approximately 945 TWh. To put that in context, that’s slightly more than Japan’s current total electricity consumption.
AI is pinpointed as the “most important driver of this growth”. The US is projected to see the largest increase, where data centres could account for nearly half of all electricity demand growth by 2030. By the decade’s end, US data centres are forecast to consume more electricity than the combined usage of its aluminium, steel, cement, chemical, and other energy-intensive manufacturing industries.
The IEA’s “Base Case” extends this trajectory, anticipating around 1,200 TWh of global data centre electricity consumption by 2035. However, significant uncertainties exist, with projections for 2035 ranging from 700 TWh (“Headwinds Case”) to 1,700 TWh (“Lift-Off Case”) depending on AI uptake, efficiency gains, and energy sector bottlenecks.
Fatih Birol, Executive Director of the IEA, said: “AI is one of the biggest stories in the energy world today – but until now, policymakers and markets lacked the tools to fully understand the wide-ranging impacts.
“In the United States, data centres are on course to account for almost half of the growth in electricity demand; in Japan, more than half; and in Malaysia, as much as one-fifth.”
Meeting the global AI energy demand
Powering this AI boom requires a diverse energy portfolio. The IEA suggests renewables and natural gas will take the lead, but emerging technologies like small modular nuclear reactors (SMRs) and advanced geothermal also have a role.
Renewables, supported by storage and grid infrastructure, are projected to meet half the growth in data centre demand globally up to 2035. Natural gas is also crucial, particularly in the US, expanding by 175 TWh to meet data centre needs by 2035 in the Base Case. Nuclear power contributes similarly, especially in China, Japan, and the US, with the first SMRs expected around 2030.
However, simply increasing generation isn’t sufficient. The IEA stresses the critical need for infrastructure upgrades, particularly grid investment. Existing grids are already strained, potentially delaying around 20% of planned data centre projects globally due to complex connection queues and long lead times for essential components like transformers.
The potential of AI to optimise energy systems
Beyond its energy demands, AI offers significant potential to revolutionise the energy sector itself.
The IEA details numerous applications:
Energy supply: The oil and gas industry – an early adopter – uses AI to optimise exploration, production, maintenance, and safety, including reducing methane emissions. AI can also aid critical mineral exploration.
Electricity sector: AI can improve forecasting for variable renewables, reducing curtailment. It enhances grid balancing, fault detection (reducing outage durations by 30-50%), and can unlock significant transmission capacity through smarter management—potentially 175 GW without building new lines.
End uses: In industry, widespread AI adoption for process optimisation could yield energy savings equivalent to Mexico’s total energy consumption today. Transport applications like traffic management and route optimisation could save energy equivalent to 120 million cars, though rebound effects from autonomous vehicles need monitoring. Building optimisation potential is significant but hampered by slower digitalisation.
Innovation: AI can dramatically accelerate the discovery and testing of new energy technologies, such as advanced battery chemistries, catalysts for synthetic fuels, and carbon capture materials. However, the energy sector currently underutilises AI for innovation compared to fields like biomedicine.
Collaboration is key to navigating challenges
Despite the potential, significant barriers hinder AI’s full integration into the energy sector. These include data access and quality issues, inadequate digital infrastructure and skills (AI talent concentration is lower in energy sectors,) regulatory hurdles, and security concerns.
Cybersecurity is a double-edged sword: while AI enhances defence capabilities, it also equips attackers with sophisticated tools. Cyberattacks on utilities have tripled in the last four years.
Supply chain security is another critical concern, particularly regarding critical minerals like gallium (used in advanced chips,) where supply is highly concentrated.
The IEA concludes that deeper dialogue and collaboration between the technology sector, the energy industry, and policymakers are paramount. Addressing grid integration challenges requires smarter data centre siting, exploring operational flexibility, and streamlining permitting.
While AI presents opportunities for substantial emissions reductions through optimisation, exceeding the emissions generated by data centres, these gains are not guaranteed and could be offset by rebound effects.
“AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments, and companies – how we use it,” said Dr Birol.
“The IEA will continue to provide the data, analysis, and forums for dialogue to help policymakers and other stakeholders navigate the path ahead as the energy sector shapes the future of AI, and AI shapes the future of energy.”
(Photo by Javier Miranda)
See also: UK forms AI Energy Council to align growth and sustainability goals
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