AI, Energy and the Data Centre Surge: IEA's Global Warning
As AI becomes embedded across digital systems, it is bringing considerable changes to energy use around the world.
The International Energy Agency (IEA) now warns that these shifts, fuelled in large part by the explosive rise of data centres, require urgent attention from policymakers and the energy sector.
In its newly published report 'Energy and AI', the IEA explores how AI is reshaping electricity demand while also offering new tools for innovation, efficiency and sustainability in energy systems.
Dr Fatih Birol, Executive Director of the IEA, sets the tone: “In recent years, AI has soared to the top of the political and business agenda.
"Once a mostly academic pursuit, it has evolved into an industry with trillions of dollars at stake. Despite significant uncertainties, it is now very clear: AI is coming. In many sectors, it is already here.”
The electricity load behind AI: data centres in focus
At the heart of AI’s rising energy demand lies the data centre.
These vast facilities are responsible for powering everything from video streaming to e-commerce and Gen AI models.
In 2024, the IEA estimates global data centres consume around 415 TWh (terawatt-hours) of electricity—approximately 1.5% of global use.
By 2030, that figure could more than double to 945 TWh.
Under some conditions, consumption could reach 1,720 TWh by 2035. That’s more electricity than Japan uses today.
The US is expected to see the largest increase in data centre energy demand. The report suggests that data centres could surpass heavy industry in terms of electricity consumption in the US alone.
This leap in demand is driven in part by accelerated servers—specialised machines fitted with chips designed for training and deploying AI models.
Since 2015, these have become four times faster than traditional servers, vastly expanding computational capacity but also ramping up energy use.
To counterbalance these pressures, the IEA highlights several measures, including renewable energy sourcing, improving locational flexibility and operational flexibility to reduce load during peak times.
These strategies, if adopted widely, could help data centres become more sustainable despite their expanding footprint.
AI’s promise for smarter, greener energy systems
While the electricity needs of AI are considerable, the IEA also identifies key areas where AI offers benefits to the energy sector.
AI already supports mineral and energy supply chains, including exploration, automation and environmental monitoring.
This improves resource evaluation and helps with methane leak detection—vital in reducing emissions.
In electricity systems, AI plays a growing role in forecasting renewable generation, shortening outage response times with fault detection and improving grid reliability.
The report claims AI tools can cut downtime on power grids by 30% to 50%.
AI’s ability to optimise systems is also being seen across buildings and transport.
Smart HVAC (heating, ventilation and cooling) systems powered by AI can reduce electricity demand significantly. If adopted globally, AI-led building management could save up to 300 TWh annually.
In transport, AI is already in use for route optimisation and predictive maintenance.
The IEA states that if used widely across industrial processes, AI could help save as much energy each year as Mexico currently consumes.
Additionally, broader AI use in cybersecurity offers energy networks rapid defence mechanisms, potentially reacting to threats up to 500 times faster than current methods using satellite and sensor data.
Birol continues: “We suggest three pillars countries should bear in mind as they plan for the future. The first is the importance of finding the right mix of energy sources to deliver the uninterrupted power supply that data centres need to support AI [...]
"Yet a sole focus on increasing electricity generation won’t be enough. To deliver the energy for AI, countries must also think about their infrastructure… Making this a reality will hinge on the final pillar: bolstering dialogue between policy makers, the tech sector and energy industry.”
Collaboration and capacity: where AI and energy meet
The IEA report calls for stronger coordination between governments, the private sector and energy providers.
As AI continues to expand – especially in high-demand areas such as generative modelling and automation – electricity infrastructure must keep pace. Yet siloed policy and slow permitting processes present major risks.
To address this, the IEA urges joint planning efforts, investment in renewables and smart grid technology and an overhaul of permitting and connection systems to prevent delays in meeting AI-led demand.
The report recommends crafting public-private partnerships to bridge skills gaps and deliver AI training programmes across tech firms, universities and governments.
Without these collaborations, the growing AI load on data centres may outpace the capacity of global electricity systems.
Birol concludes: “AI could also be an incredibly powerful tool for the energy sector. It is already helping energy companies optimise their approaches to exploration, production, maintenance and safety – and if AI tools are applied broadly, huge amounts of electricity transmission capacity could be unleashed without building a single new line.
"Yet our analysis shows the sector must do more to seize the moment. This, too, will require strong collaboration between the public and private sectors on key issues such as building digital skills in the energy workforce.”
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