Meeting AI Demand as Consumption Doubles by 2030

AI is rapidly becoming a cornerstone of our global economy, driving innovation across countless sectors. From accelerating scientific discovery to making manufacturing smarter, AI's potential seems boundless. But this technological revolution hinges on a critical resource: energy. The International Energy Agency's (IEA) recent "Energy and AI" special report delves into this crucial relationship, exploring both AI's burgeoning energy demands and its potential to transform the energy sector itself.  

The Growing Thirst: AI's Energy Footprint

There's no AI without energy, specifically electricity. The data centers that train and run AI models are significant power consumers.  

• Current Consumption: In 2024, data centers accounted for roughly 1.5% of global electricity use, consuming about 415 TWh. The United States leads this consumption, followed by China and Europe.  

• Projected Growth: Driven significantly by AI and other digital services, this demand is expected to more than double by 2030, reaching around 945 TWh – slightly more than Japan's total current electricity consumption.  

• Regional Impact: While a fraction globally, the impact is pronounced locally. In the US, data centers could account for nearly half of electricity demand growth by 2030. The concentration of these facilities also puts significant strain on regional grids.  

Powering the AI Revolution: Challenges and Solutions

Meeting this surging demand requires a robust and diverse energy supply and significant infrastructure upgrades.  

• Energy Mix: The IEA projects a mix of sources will be needed. Renewables, supported by storage, are expected to meet half the growth in data center demand by 2035. However, dispatchable sources like natural gas and nuclear power (including emerging small modular reactors) will also play crucial roles.  

• Grid Strain: Existing electricity grids are already under pressure. Without upgrades, connecting new data centers faces potential delays. Bottlenecks include long queues for grid connections and extended wait times for essential components like transformers.  

• Smarter Integration: Solutions involve not just building more generation and transmission, but also smarter integration. This includes strategic placement of data centers in areas with grid availability and exploring operational flexibility within data centers (using spare server capacity or backup power assets).  

AI for Energy: Optimisation and Innovation

The relationship isn't one-sided. AI offers transformative potential for the energy sector.

• Optimising Operations: AI is already being used to enhance efficiency across the energy value chain.

  • Oil & Gas: Optimising exploration, production, maintenance, and detecting methane leaks.  

  • Electricity: Improving forecasting for renewables, reducing grid outages, and potentially unlocking significant transmission capacity without new lines.  

  • End-Use: Driving energy savings in industry through process optimisation, improving transport efficiency, and making building heating/cooling systems smarter and more flexible.  

   

• Accelerating Innovation: AI can drastically shorten the long innovation cycles typical for energy technologies. By rapidly analysing vast datasets, AI can help discover and test new materials for batteries, solar cells, or carbon capture, accelerating the path to commercialisation.  

Navigating the Path Forward

Realising AI's benefits while managing its energy impact requires addressing key challenges:

• Skills Gap: The energy sector needs more AI-literate workers.  

• Data & Security: Access to data, digital infrastructure, and managing cybersecurity risks are critical barriers. Supply chain security for components like critical minerals (e.g., gallium) is also a concern.  

• Collaboration: Deeper dialogue and collaboration between the tech sector, the energy industry, and policymakers are essential to navigate uncertainties and develop effective strategies.  

The rise of AI presents both immense opportunities and significant energy challenges. As the IEA report highlights, proactively managing AI's energy demands while harnessing its power to optimise our energy systems will be crucial for a secure, affordable, and sustainable energy future.

Read the full report here: https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai