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What's happening? AI is transforming industries at a breakneck pace, but its energy demands are posing a significant challenge. Global energy startups attracted $9.4bn in funding in 2024, a 12% increase from 2023, reflecting a growing emphasis on sustainable solutions to meet AI’s power needs. Among alternative energy options, geothermal energy saw a remarkable rise in investment, tripling to $558m, while nuclear funding doubled to $1.9bn. As AI becomes increasingly embedded across industries, the need for scalable, sustainable energy solutions is clear. (Bloomberg)
Why does this matter? For institutional investors, the intersection of AI and sustainability offers both challenges and opportunities. Data centres and data transmission networks each account for 1-1.5% of global electricity usage. These centres are crucial for AI workloads but exacerbate energy demands, with models like ChatGPT consuming ten times the electricity of a standard Google search.
This surge in energy use can strain grids, a recent Bloomberg report showed that AI data centres are perturbing the supply of domestic electricity to homes. The report found that over 75% of highly distorted power readings in the US are within 50 miles of large data centres. Termed “bad harmonics”, the normally steady waves of electricity can turn into abrupt peaks and troughs, resulting in sudden surges, sags, or in extreme cases sparking. As a result, in Silcon valley there has been an elevated risk of blackouts and rising electricity bills associated with AI data centres. These symptoms are an early indicator of worsening stress on the power grid. For investors, these challenges also represent an opportunity to invest in innovative and renewable energy solutions to these incumbent issues.
An analysis by Schneider Electric suggests that software powering AI should be optimised by “pruning”, reducing the number of parameters in a model and “quantisation”, to make models run faster. Implementing these changes could dramatically cut energy consumption while maintaining regular performance of the software.
Policy changes that could improve AI sustainabiliy include implementing standards for sustainable AI and incentivising re-use and recycling of materials. Collaboration between energy providers, policymakers and AI companies is essential to align these strategies.
Technological innovations are a vital component of decoupling AI growth from environmental degradation. Examples of hardware innovations are emerging, technologies like Supermicro’s liquid cooling systems are addressing the thermal challenges of high-density AI data centres, improving efficiency while reducing carbon footprints. Similarly, emerging designs such as 3D-stacked memory and in-memory computing are minimising energy waste by shortening data transfer distances.
In the UK, Octopus Energy invested £200m ($244m) into tech startup Deep Green, pioneering a scheme to recycle the heat from data processing centres into public swimming pools. This has the dual benefit of providing a community service at a reduced cost and taking advantage of the wasted heat from data processing. Such innovations demonstrate AI’s high energy demands can potentially be met sustainably.
Interestingly, AI itself is contributing to solutions for its energy challenges. Machine learning algorithms are being employed to optimise energy usage in data centres, adapting workloads to align with renewable energy peaks and managing cooling systems more efficiently. Beyond AI, these technologies are improving grid resilience by forecasting energy generation and consumption patterns, accelerating the integration of renewables.
The rapid growth of AI presents significant sustainability challenges, particularly due to its rising energy demands, but it also creates substantial investment opportunities. As stakeholders navigate this evolving landscape, institutional investors have a unique opportunity to support innovative solutions that balance AI’s growth with sustainability, shaping a future where technology and environmental stewardship coexist. Key areas of focus include energy innovations (heat re-use), energy-efficient hardware and infrastructure (liquid cooling, 3D-stacked memory), and AI-optimised software and policy measures. To maximise impact, investors should target regions with high AI adoption, such as North America, and infrastructure growth.