A new US-led computing initiative aims to remove one of the biggest obstacles to progress in fusion energy research: the sheer time and computing power required to model, design, and optimise fusion systems. By tightly integrating artificial intelligence with high-performance computing and live experimental data, the project promises to dramatically accelerate the pace of discovery across the fusion sector. The platform, called STELLAR-AI, is being developed under the leadership of the Department of Energy’s Princeton Plasma Physics Laboratory (PPPL). Rather than serving as a standalone facility, STELLAR-AI is designed as a shared computational backbone for the wider fusion community, connecting national laboratories, universities, technology firms and private fusion companies. Ending the simulation bottleneck Modern fusion energy research relies heavily on advanced computer simulations to predict plasma behaviour, test reactor designs and refine operating conditions. These simulations are extraordinarily complex, often taking weeks or even months to complete on conventional computing infrastructure. Training AI systems capable of assisting with fusion design can be just as time-consuming. STELLAR-AI aims to compress these timelines by orders of magnitude. …