Why the world's biggest battery maker isn't worried about AI's energy demand

Pursuing a creative approach isn’t a typical suggestion when talking about AI’s energy usage and the need to build data centres to handle this, but creativity – in terms of innovation, thinking and policy-making – is emerging as a response to this defining question.

During a session titled “No Power, No AI” at the Annual Meeting of the New Champions, participants sought to answer three aspects of this question: how do we develop the energy systems that AI will require, how do we build these in a way that strengthens existing energy systems rather than strains them, and can we ultimately create a system in which AI enables savings so great that these exceed the cost of the energy and resources it itself requires?

These are already pressing questions. The International Energy Agency (IEA) forecasts that global electricity consumption from data centres will reach approximately 945 terawatt-hours by 2030, an amount roughly equivalent to that used by the world’s fifth-largest consumer of energy, Japan.

This is creating a sense of unease, particularly for a world in which 730m people lack access to electricity and where questions surrounding energy security have been writ large during 2026. The mood at the session mirrored this: when asked how many of the audience felt energy and grid capacity would be major constraints on AI’s growth in the next five years, approximately 70% of those in the room agreed.

Creativity is essential to innovation. Vanessa Chan, Inaugural Vice-Dean Innovation and Entrepreneurship, University of Pennsylvania, raised the issue of “new architectures” – innovation in the way in which systems are approached, communities engaged, regulation and taxation addressed, and technology applied.

Among these new architectures is a re-evaluation of not just energy supply but also demand. How can large users, like data centres, consume less? Chan and her colleagues are researching ways to make data centres more efficient, for example, by replacing general AI models responding to tasks with specialized ones that are more energy efficient.

Chip configurations are also being studied, including researching monolithic 3D configurations, which are four times faster than the current 2D ones, while battery technology – particularly storage and distribution – is being doggedly pursued.

Regulation in China dictates that all new data centres must employ 80% renewable energy, a situation that is accelerating research into grid stability and battery technology, with energy storage a key issue.

According to Robin Zeng, Founder, Chairman and Chief Executive Officer, Contemporary Amperex Technology (CATL), there are three phases to considering the current maturity of energy storage solutions: technology capability – can the supplier provide a reliable and constant supply of energy to support a big data centre (1 gigawatt of power year-round); is it cost-effective (cost must be equal or lower than traditional energy to be competitive); and long-term reliability and performance. Currently, nearly one in five large-scale energy storage power stations worldwide are underperforming, underscoring why continuing innovation in this area is vital.

Concurrently, supply chain reliability is a key issue. CATL is developing sodium-ion batteries to help reduce China’s dependence on lithium (defined as a critical mineral). Zeng revealed that in three to five years, a sodium-ion battery is expected be able to reach 100 gigawatt-hours every year, and in doing so, will be able to fully support a modern data centre.

A big question for policy-makers is whether AI infrastructure can be compatible with (national) development, particularly at a time, when at a grassroots level, there is growing opposition to local AI data centre development.

For Chan, this is an issue that needs turning on its head, with the question becoming “why would someone want a data centre in their back yard”? For her, the answer comes down to economics: if the trade-off is a permanent reduction in bills and a more reliable energy supply, this will create an opportunity that communities can get excited about.

A natural follow-on question is when there are benefits to hosting a data centre locally, how can we ensure the costs are allocated fairly in proportion to these benefits?

In the US, policy-makers are increasingly demanding that “hyperscalers” “BYOC – bring your own clean energy”. This reflects calls by some states to have tech giants such as Google develop local energy infrastructure and grid modernization, as well as “start funding batteries, heat pumps and electric vehicle charging”. In Virginia, state authorities have been creating “complicated and sophisticated” large load tariffs, while in Georgia, large customers help fund new clean energy resources, receiving energy value credits in return.

There’s also been a shift towards “grayscaling,” where data centres take on stranded assets like retired coal plants and transform them into digital hubs designed to serve the local community.

Out of these questions emerges a central idea - how can we use AI to develop energy systems?

China is already using AI in its data centres, with these systems purchasing electricity when prices are low, while also optimizing energy usage and maintaining operational stability at facilities. But this needs to be taken further.

The panel suggested that the thinking about data centres needs to change. Instead of regarding them as a drain on the grid, they need to become a dynamic system “which can flex”. Chan argued: “We think too much about the wires and all that, but flexibility itself becomes an actual asset. An AI centre could be a flexible place to bring energy back into the grid.”

In China, vehicle-to-grid technology already allows electric vehicles to supply electricity back to the grid. At battery swapping stations, there are large battery capacities that can store energy, particularly overnight, helping to balance renewable energy and supply. Zeng revealed that in the future, he expects EVs to be a lot more than just a transport option, and instead a valuable battery and computing resource that could be used in energy and digital systems when not in use by the owner.

This shift to engaging the whole system requires much broader – and creative – thinking. Reflecting this, co-designing is becoming more common. Instead of designing everything in isolation, power, storage and computing need to be co-designed from the outset, a situation that participants agreed would boost energy affordability, ideally in a way that’s beneficial to the wider community.