Artificial intelligence is exploding in popularity, and many people have concerns about the sustainability of its energy consumption. AI models need vast amounts of electricity: according to the International Energy Agency, a typical AI data centre can consume as much energy as 100,000 households each year. Energy use by US data-centre servers more than tripled between 2014 and 2023 — reaching 4.4% of the nation’s total electricity consumption. This figure is expected to at least double by 2028¹.

However, AI might also hold the key to improving energy efficiency: offsetting some of the demand it creates. An article published by the World Economic Forum suggests that the use of AI tools across industries — in automating tasks or monitoring real-time energy use, for example — can reduce energy consumption by up to 60%. Ginelle Greene-Dewasmes, a sustainable-development specialist and an author of the article, has been working to lower AI’s energy demands while finding ways to use the tools to reduce wider emissions.

Originally from Trinidad, Greene-Dewasmes became interested in solving energy-security issues for island states with few resources. In 2021, she completed a PhD on the green-energy transition across small island developing states at the University of York, UK, before joining the World Economic Forum in 2022. Now based in Geneva, Switzerland, Greene-Dewasmes has been the Forum’s AI and Energy Lead since 2024. Her work involves bringing together AI and energy leaders from around the world to identify shared challenges, align priorities and develop practical actions that can improve AI’s energy impact.

As well as looking at improving AI’s energy efficiency, Greene-Dewasmes and partners are exploring how models can find innovations that improve existing energy infrastructure, such as that in buildings that are already in use. Nature spoke to her about navigating the increasing energy needs of the technology and whether its benefits could outweigh this demand.

Why are AI models so heavy on energy consumption?

AI needs cloud networks, chips, graphics-processing units and cooling systems, all of which consume large amounts of energy. Developing and training models requires high levels of processing power to crunch the data, but AI use is much more energy intensive overall because of the scale. ChatGPT, the chatbot from technology firm OpenAI in San Francisco, California, has more than 800 million users each month and receives more than 2.5 billion queries daily. As AI becomes mainstream, we need to build more energy infrastructure to keep up with the surge in demand.

But, when it comes to scaling up AI, I think the conversation is too skewed towards the energy needed to power centralized infrastructure, when there are lots of other things that will contribute to AI’s energy requirements. Large data centres will exist in only the few countries that have the money, space and resources to host them. For most of the world, AI infrastructure will be disaggregated and decentralized. For example, if one million people are using their phones more intensively because of AI, that affects the energy infrastructure.

Although more complex LLM queries are currently directed to remote data centres, some smaller tasks can be completed directly on new devices that have AI chips. As this becomes more common, what will that mean for infrastructure planning for energy? It’s much harder to measure this than the outputs from one data centre.

How can AI improve energy efficiency in other areas?

AI’s electricity impact is currently about 0.49% of total global electricity use — so it’s still fairly small, although it’s growing. The World Economic Forum, in collaboration with other specialists, has created a Net-Positive AI Energy Framework. It looks at how we can design AI models and infrastructure for efficiency — by optimizing chips, data-centre locations and liquid cooling, in which fluids are used for cooling owing to their high heat capacity — while using AI to bring down energy usage in high-impact sectors, such as transportation and urbanization, both by improving the efficiency of systems and coming up with solutions to global challenges.

For example, in Azerbaijan, operations that keep buildings running, including lighting, heating and cooling, account for a large proportion of the country’s carbon emissions. The Centre for the Fourth Industrial Revolution Azerbaijan (a public legal entity) is using AI-powered solutions to improve efficiencies as part of its nationally determined contribution commitments — the country’s pledge to reduce emissions. This includes AI solutions that adjust heating, ventilation and air conditioning (HVAC) on the basis of building occupancy and use trends, and that predict the need for maintenance before faults arise. In some cases, this use of AI to hone efficiency has brought down HVAC energy use by 25% and emissions by up to 40%.

We’re also encouraging people to use AI wisely. We want AI to reduce energy consumption overall, rather than adding strain to systems.

What’s involved in your community of AI and energy specialists?

The aim is to gather insights from leading voices in the AI ecosystem to generate thought leadership and encourage cross-discipline learning so people aren’t working in silos. It started off as one-on-one conversations with professionals working with AI, centred on the technology’s biggest challenges and opportunities. We convened a community of about 40 people, which has increased to around 70 in nine industries around the world and is still growing. We try to hear from lots of voices: university academics, public-sector entities and technology, AI and energy companies (including renewables and nuclear) are all involved. Together with our partners, we turn these insights into guidance and frameworks that can support the wider industry.

We’ve published two reports based on the learnings gleaned from these thought leaders. Artificial Intelligence Energy Paradox explores how to balance the challenges and opportunities of AI and energy. The Net-Positive AI Energy Framework is our North Star for clean, efficient and equitable AI. We’re now working on an AI Energy Readiness Compass: a methodology to support responsible AI energy developments and identify the causes of upcoming bottlenecks in the power grid that could be problematic for AI energy use.

One particular success from these collaborations is the willingness of hyperscalers — large organizations that operate cloud computing services and data centres — to embrace grid flexibility. At first, they were reluctant to adjust when they draw power because they wanted continuous access to reliable electricity, but they have come to see the benefits of balancing demand on the grid. As a result, they have embraced power flexibility, and 18 partners supported the launch of the AI Energy Management Alliance: a coalition of companies that promotes dynamic load-shifting (changing when energy is used to align with grid loads, the availability of green energy and the best pricing).