Giovanni Landi, EMEA General Manager, Alpha Ring
Energy crises and scientific advances have delivered us closer to cracking the ‘holy grail’ of fusion energy. But how do we accelerate this much needed advancement into delivery?
Private and public funding for fusion technology is certainly important, however, opening funding to researchers and labs is simply not enough. We cannot hide from the fact that there are technologies at our disposal that can speed up the process to achieving limitless, clean energy. The technology in question is, of course, Artificial Intelligence (AI).
According to the International Energy Association, the global energy market is set to increase by $3.3 trillion in 2025. It’s hardly a secret, as we are constantly seeing tech “superpowers” such as Google, Microsoft, Amazon, Meta, and OpenAI, begin to make massive plays into nuclear fusion and fission. A common theme in these investments is that they see fission and fusion as opportunities to solve the growing global energy demand of AI data centres – but what if we flip this conversation?
Along with energy for AI, we need to be talking about AI for energy.
Globally, there are collections of brilliant minds working to achieve clean energy. The time required is not a question of lacking expertise or scientific progress, it is a question of human resource and engineering capabilities. Worldwide, there are very few individuals who are qualified to experiment with fusion or have access to the data and resources to do so. The capabilities of AI are vast, especially when it comes to mathematical analysis. So, what AI can do for the fusion industry? Simple — it can help us do much more, with much less.
Many industries have already taken the step to use AI to their advantage; engineers are turning to AI-powered design tools to accelerate their design process and financial services firms are using it to analyse large data sets, saving time and resources. These industries are not removing the human element of their work, they are simply using AI to achieve the desired result, faster.By applying the same school of thought in the fusion community, we could accelerate the path to clean, limitless energy.
Many fusion research initiatives incorporate AI, however this is typically through conventional Machine Learning techniques. While these approaches are effective in specific applications, they depend heavily on vast datasets and static training pipelines in a field where experimental data is scarce, costly and time consuming to generate. The issue of data scarcity comes from the reliance on large and expensive Tokamaks or Stellarators, a problem that is partially resolved by the use of compact accelerators like Alpha Ring’s Alpha-E, which is capable of showcasing the fusion process on the desktop of a laboratory. However, even these advances can only provide a fraction of the data needed for a full simulation.
This is where AI goes beyond Machine Learning. The next breakthrough in fusion will not come from more data, but from smarter models. Emerging AI architectures such as foundation models, reasoning agents and self-supervised learning systems offer a paradigm shift. The problem we are facing remains consistent; we are running out of time. If we do not capitalise on the opportunities of AI for fusion, we risk losing both the human expertise we currently have at our disposal and the patience of long-term investors. As in other industries, we must adapt as the world changes around us. If we make predictions based on the way we have always worked, we will fall behind.
Achieving commercial fusion is the most challenging and most rewarding technological challenge that mankind has ever faced. We have a moral imperative to use every tool at our disposal to make it happen.
