IEA Asks: How is AI Reshaping Global Energy Demand?

As AI development accelerates and becomes more integrated into our daily lives, its impact on energy infrastructure is becoming impossible to ignore. 

In light of this, the International Energy Agency (IEA) has released its ‘Energy and AI’ report, offering the first in-depth global analysis of the critical relationship between AI and energy systems. 

The report details how AI is both a powerful catalyst of energy security and innovation and a new source of electricity demand.

Dr Fatih Birol, Executive Director of the IEA, explains: “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.

“This has major consequences for the global energy sector. There is no AI without energy – specifically electricity. At the same time, AI can potentially transform the sector’s future. However, policymakers and the market have often lacked the tools to fully understand these wide-ranging impacts. 

“Recognising this gap, the International Energy Agency (IEA) stepped up to address it by leveraging our expertise in data collection and analysis, as well as our convening power, to inform and strengthen the global dialogue on these issues.”

AI’s growing electricity demand

The IEA identifies AI’s growing electricity demand as one of the most complex and urgent challenges which has arisen from the digital age.

In 2024, global data centres consumed around 415 TWh of electricity, making up 1.5% of global electricity use.

By 2030, data centre electricity demand is expected to more than double to 945 TWh. The usage exceeds Japan's total current electricity use.

The total could rise to 1,720 TWh by 2035, depending on efficiency trends and AI uptake. 

The largest increase will be from the United States, where data centres could be responsible for more electricity than the heavy industry sector. 

Digital workloads, such as video streaming and cloud computing and AI, are the primary causes of this immense increase.

Since 2015, accelerated servers (which feature specialised chips for AI training) have become four times faster than other server types.

To mitigate the impact, the IEA recommends locational flexibility, diversifying supply with renewable energy and operational flexibility. 

These methods will help reduce the immense AI usage of global data centres.

AI for energy: opportunities beyond consumption

The IEA points to several key opportunities for AI in the energy sector, including end uses, supply chains, power systems and innovation.

AI optimises energy and mineral supply in exploration, production and environmental monitoring. This will ensure a more reliable resource evaluation, automated processes and methane leak detection. 

Across the electricity sector, AI can reduce emissions by forecasting variable renewables. AI-based fault detection systems can shorten grid outages by around 30-50%.

According to the IEA, wider adoption of AI in industrial processes could save as much energy in a year as Mexico consumes today. 

The IEA points to several other areas which can utilise AI:

Transport

• AI can be used in route optimisation, the development of autonomous vehicles and vehicle maintenance prediction

Buildings

• AI can enhance the efficiency of heating, ventilation and cooling (HVAC) systems and enable smarter energy use
• AI-led building optimisations could save 300 TWh of electricity a year if scaled globally

Cybersecurity

• AI can point and respond to cyber and physical threats to energy infrastructure faster than traditional systems (this can be up to 500 times quicker in the case of sensors and satellites)

Sustainability

• By 2035, the wide application of AI across industries could cut energy-related emissions by up to 5%

Fatih highlights that: “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.  According to our analysis, there is a role for established technologies such as renewables and natural gas, as well as emerging technologies like small modular nuclear reactors (SMRs) and advanced geothermal. Deciding which options to prioritise may depend on other policy priorities. 

“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. That will mean accelerating investment in grids – and working to ensure that data centres, as well as the wider electricity system, are as efficient and flexible as possible.

“Making this a reality will hinge on the final pillar: bolstering dialogue between policy makers, the tech sector and energy industry. This is an area in which the IEA is proud to have taken a leadership role – and will continue to do so.”

The need for collaboration

The report from the IEA stresses the need for the energy and technology sectors to collaborate to handle the growing challenge of electricity demand from AI. 

The IEA states that the rapid growth of AI models, infrastructure and data centre capacity is altering global energy demand. AI tools offer benefits for forecasting, innovation and grid flexibility.

However, it continues to point to siloed decision-making and policy inertia as risks to avoid. To avoid this, organisations can streamline permitting and connection processes to avoid bottlenecks, maintain joint planning and enhance collaborative investment in renewables and smart grid technologies.

The IEA emphasises the need to craft public-private partnerships to develop skills in the energy workforce and invest in collaborative training initiatives between universities, tech companies and governments to tackle the shortage of digital and AI expertise.

Fatih continues: “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 sector on key issues such as building digital skills in the energy workforce.”

As organisations strive to craft a more connected relationship between AI and energy, the IEA highlights that decision-makers must fund AI innovation for low-carbon technologies, set frameworks to encourage AI adoption and enhance interoperability.

Doing so will cut emissions and make sure AI can help make progress toward energy affordability and security.

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