The world-saving potential of nuclear fusion just got a huge boost
The goal of limitless clean energy is now clearly on the horizon, thanks to AI accelerating research.
The quest for fusion power — replicating on Earth the same nuclear process that powers the sun and stars — has seen a remarkable surge of interest in the past five years, as reflected by more than $6 billion in private capital and strategic governmental initiatives. At the same time, significant advances in fusion research at national laboratories and facilities worldwide confirm the potential of fusion as a clean, safe and virtually limitless energy source.
Achieving the production of commercial fusion has become a global race. Private companies are touting new techniques and even promising to deliver commercial fusion within a few years. This is exciting, but claims that commercial fusion is only a few years away underplay the research and development still needed. Translating the accomplishments of recent years into viable commercial fusion will still require substantial scientific advances, engineering innovations and cost-reduction strategies.
If the United States is to make progress on the White House’s decadal goal for fusion energy development, we can’t use Thomas Edison’s trial-and-error method, guessing our way to a commercial fusion reactor. That route powered the Industrial Revolution but has proved too costly and slow for fusion. Instead, a revolutionary approach is taking shape: using the combined power of artificial intelligence and high-performance computing to steer fusion innovation, shaving decades off development timelines.
The potential of artificial intelligence to change the world has received extraordinary attention. Many claims might fall short of their promise. But I believe fusion might turn out to be AI’s “killer app” — proving the value of AI to deliver truly world-saving innovations.
At the heart of any fusion system is a burning plasma, an ultrahot — at least 100 million degrees — ionized gas of atoms fusing together. Over the past three decades, computational models of this hot plasma have evolved markedly. They were initially empirical, relying heavily on observational data from expensive, large-scale experiments. Today, these models are predictive, grounded in the fundamental laws of physics and extensively validated through experiments.
Current simulations for fusion devices come close to the real thing, and researchers are hard at work refining the models. This transition to predictive modeling — which I’ve watched firsthand as a student, then faculty member and now as laboratory director at the U.S. Energy Department’s Princeton Plasma Physics Laboratory — has been a monumental scientific achievement.
There’s no better proof of this than the spectacular results last year from the National Ignition Facility at the Lawrence Livermore National Laboratory in California. Those results, which involved “shooting nearly 200 lasers at a cylinder holding a fuel capsule the size of a peppercorn,” as CNN put it, showcased a decade of intense scientific work enabled by predictive modeling, which culminated at least three times in fusion ignition. I would argue that all recent advances in fusion have been powered by this increased predictive capability.
When we combine AI with predictive modeling, we start to see the revolutionary potential. Researchers at Princeton are already using machine learning to predict and eliminate plasma instabilities before they occur. This is critical because important decisions must be made every millisecond to control a plasma and keep a fusion reaction going. With AI, these decisions are more rapid, steering the plasma away from instability much more quickly. We’re also using AI to evaluate the best ways to heat the plasma inside a fusion vessel and to accelerate the latest generation of predictive simulation tools.
Although this is promising, the full potential of AI for fusion is just emerging. There are hundreds of billions of possible fusion reactor designs. How can we know which one is best for commercial fusion power? Finding the answer involves assumptions about the future energy market and construction costs alongside technical analysis. Building a commercial fusion reactor also requires precision engineering and aligned manufacturing. Analyzing a reactor design with traditional models takes months, making an exhaustive search impractical and slow. However, in the past two years, fast AI models are learning from and replacing these traditional models — reducing analysis time from months to hours. With AI, the hunt for the best or most viable commercial fusion reactor is on.
Ultimately, of course, a digital solution is not enough; we must build a fusion pilot plant and generate electricity. This will require investments in the tens of billions of dollars, which is appropriate for a technology that will dominate the multitrillion-dollar-a-year energy industry. We also need to attract top talent, develop a highly skilled workforce and empower strong international collaboration — all of which will enable us to share and design new experimental approaches and techniques more readily.
I am now firmly convinced it isn’t if, but when fusion will be deployed at scale. Undoubtedly by the end of this century, clean, safe, sustainable fusion reactors will power cities, towns, data centers and factories. They will unobtrusively dot the landscape, free of pipelines, coal trains and environmental blight on poor communities. Fusion will put the energy where needed and when needed, without emitting carbon dioxide into the atmosphere. I won’t live to see it happen, but I take comfort in knowing that babies born today might well witness the world transformed.
Steven Cowley is a professor of astrophysical sciences and laboratory director of the U.S. Energy Department’s Princeton Plasma Physics Laboratory.
