Artificial intelligence is an innovation that catalyses further innovation
Like the steam engine and electricity, artificial intelligence (AI) is a general-purpose technology that could profoundly transform the global economy and the world's energy system. Though key uncertainties remain, it stands to have major impacts. High on the list is its potential role in accelerating innovation.
Impressive technological advancements - both incremental and radical - have helped drive down the cost of key energy technologies in recent years. But to achieve global energy security and emissions goals, existing clean energy technologies need to keep improving, and novel energy technologies must reach the market. AI will enhance the capacity and creativity of scientists in generating and testing new ideas. But for AI-accelerated innovation to really deliver for the energy sector, policymakers and the scientific community need to build a common understanding of the most promising applications and key enablers - and address critical gaps.
This is a key focus of the IEA's new workstream on energy and AI, which also involves analysing how the adoption of AI will affect electricity consumption by data centres and how AI can be applied to optimise complex parts of energy systems, such as electricity networks. The upcoming Global Conference on Energy & AI, which is bringing together leaders from government, the energy sector, the tech industry and civil society to discuss these topics for the first time, will provide a space to kickstart and advance public-private dialogues on these subjects at a critical moment.
Does AI represent a step-change in the speed of energy innovation?
For energy analysts, a fundamental question is whether the application of AI will cause the rate of technology progress to deviate from current projections. In the field of semiconductors, Moore's Law - an observation from the 1960s that the number of transistors in an integrated circuit doubles about every two years, which proved startlingly accurate for several decades - is well known. Similarly, for many energy technologies, it is common to project cost reductions for each doubling of cumulative deployment, known as the "learning rate."
However, progress in the semiconductor sector has slowed, and Moore's Law has not been a good guide for technological development since around 2010. Experts question whether the learning rate for a technology like electric vehicle batteries, which IEA analysis projects at 15%, can be maintained over future decades. Recent inflation in technology prices, partly caused by mismatches between supply and demand for critical material inputs, are a reminder that factors such as manufacturing capacity and trade can also impede the innovation process.
Some analysts see AI as a means to keep current learning rate projections on track despite these concerns. Others see it as a more disruptive force that could make today's rates look very conservative. To inform this debate, it is necessary to take a closer look at the specific ways in which AI could boost the pace of innovation.
Early examples of AI discoveries on energy-related materials are very promising
Finding a higher-performing material for a task, or one that does not contain certain undesirable inputs, has typically relied on human ingenuity and knowledge of how different compounds behave. But the number of possible options is often vast. AI techniques are already excellent at solving problems by optimising for well-understood relationships across large and well-structured data sets.
In July 2024, researchers from a US government laboratory and Microsoft published results of a study that used AI to assess 32.5 million possible new solid-state electrolytes for lithium-based batteries and found 23 new ones with the right characteristics. Scientists in Sweden recently screened 45 million potential new battery cathode molecules and found nearly 4 600 promising candidates. Other teams have achieved similar results, and one has pursued their findings through to synthesis and testing. Notably, these types of techniques are increasingly attracting financing: Anionics, an AI start-up, recently partnered with the battery manufacturing subsidiary of Porsche, while Mitra Chem has raised USD 80 million with its promise of shortening the lab-to-production timeline by over 90%.
Recent breakthroughs have not only been battery-related. Researchers using AI tools have also found they can engineer enzymes for biofuel synthesis, predict high-yielding biofuel feedstocks, identify industry-beating catalysts for hydrogen-producing electrolysers and generate materials for carbon dioxide (CO2) capture. And as AI becomes an increasingly indispensable part of the research process for energy technologies, innovators will be also benefit from developments in adjacent areas, including improved robotics and automation. A recent study of the impact of using AI tools in an industrial research setting showed a 39% increase in patenting by the company in under two years.
