For decades, fusion energy has been the ultimate promise of clean energy. It’s the process that powers the sun, capable of producing enormous amounts of energy without the carbon emissions associated with fossil fuels. Scientists have spent generations trying to recreate it on Earth, convinced that if they can make it work at scale, it could fundamentally reshape the world’s energy future. The problem is that fusion is incredibly difficult, not just from a scientific perspective, but from an economic one. Building and testing experimental reactors costs vast amounts of money, and progress often comes through a frustrating cycle of trial and error. Researchers develop a theory, build hardware to test it, gather data, tweak the design, and repeat the process. Sometimes that cycle takes years. Now, a Chinese startup called VeloAlpha believes artificial intelligence could help break that pattern.
Founded earlier this year by fusion scientist Xie Huasheng, the Beijing-based company is developing FusionAlpha, a simulation platform that lets researchers test fusion reactor designs digitally before committing to expensive physical experiments. It may not sound as exciting as a giant reactor generating limitless clean power. But if VeloAlpha’s technology delivers on its promise, it could end up solving one of fusion’s most expensive and persistent challenges.
The fusion industry’s impossible triangle
According to Xie, fusion researchers have long been stuck with an uncomfortable trade-off. The most advanced simulation software available today can model plasma behavior with remarkable accuracy. Plasma — the superheated, electrically charged gas that fuels fusion reactions — is notoriously difficult to control, and understanding its behavior is critical to designing a viable reactor. The catch is that these simulations require substantial computing resources and can take a long time to complete.
At the opposite end of the spectrum are newer AI-driven systems that can process calculations much faster. While attractive from a speed perspective, researchers often remain cautious because these tools can struggle with reliability and extrapolation beyond the data they were trained on. Then there are simplified physics models, which are computationally efficient but often too crude to accurately guide the design of next-generation reactors. Xie describes this as fusion software’s “impossible triangle”: speed, accuracy, and predictive capability. Historically, researchers have had to sacrifice one to gain another. VeloAlpha’s entire business is built around the idea that this trade-off no longer has to exist.
The company claims that advances in artificial intelligence, combined with new mathematical techniques, can dramatically accelerate simulations without sacrificing the underlying physics. According to Xie, some parts of FusionAlpha can run anywhere from 100 to 10,000 times faster than today’s state-of-the-art fusion codes while maintaining benchmark errors below 5%. Those claims still need independent validation, but if they hold up, they would represent a significant leap forward for the industry.
Building a star is expensive
To understand why software matters so much, it helps to understand what fusion researchers are trying to accomplish. Fusion occurs when the nuclei of light atoms collide and merge, releasing huge amounts of energy. That’s exactly what happens inside stars. Replicating those conditions on Earth requires heating fuel to temperatures hotter than the sun’s core, creating plasma that must then be confined and stabilized long enough for fusion reactions to occur. Most researchers attempt this using machines called tokamaks — massive doughnut-shaped devices that use powerful magnetic fields to contain plasma. Others are experimenting with alternative approaches, including stellarators, linear devices, and laser-driven fusion systems.
Every design comes with its own engineering challenges. Researchers must figure out how to sustain reactions, withstand extreme heat, manage radiation, secure fuel supplies, and ultimately generate electricity cheaply enough to compete with existing energy sources. None of those problems are inexpensive to solve. A single experimental facility can cost hundreds of millions or even billions of dollars. Even smaller design changes often require extensive testing and validation. That’s why simulation software has become increasingly important. The more accurately researchers can predict outcomes before building hardware, the less money they waste on dead-end pursuits.
Fusion’s EDA moment
Xie compares FusionAlpha to electronic design automation (EDA) software, a technology that transformed the semiconductor industry. Modern chip companies don’t build a physical processor every time they want to test a new idea. Instead, they use sophisticated software tools to model, simulate, and optimize designs before sending them to fabrication plants. Without EDA software, the pace of semiconductor innovation would be dramatically slower.
VeloAlpha believes fusion is approaching a similar turning point. Rather than relying primarily on physical experimentation, future fusion companies could use advanced simulation platforms to virtually test thousands of design variations, identify promising approaches, and dramatically reduce development costs. So, the next generation of fusion reactors may be built twice: first in software, then in steel.
Why the timing matters
The startup’s emergence comes at a particularly interesting moment for China’s fusion industry. For years, fusion research was largely driven by governments and national laboratories. That’s beginning to change. China has identified nuclear fusion as a strategic future industry, placing it alongside fields such as quantum computing, embodied AI, biomanufacturing, brain-computer interfaces, and 6G communications. Investors have taken notice, pouring money into a growing ecosystem of fusion startups, component suppliers, and supporting technologies.
Companies focused on reactor development are attracting increasingly large funding rounds, while businesses supplying magnets, materials, power systems, and software are also emerging. VeloAlpha sits at the intersection of two of the biggest technology trends of the decade: artificial intelligence and clean energy. The company recently secured seed funding from investors who appear convinced that fusion’s future won’t be determined solely by advances in hardware.
That doesn’t mean commercial fusion is right around the corner. The industry still faces enormous technical and economic hurdles, and many experts believe that practical fusion power remains years, or even decades, away. But as the sector becomes more competitive, the companies that can iterate fastest may gain a significant advantage. And that’s where software could become as important as the reactors themselves. For years, the fusion industry’s biggest challenge has been figuring out what to build. If AI can help answer that question faster and more accurately, the path toward commercial fusion may suddenly look a little shorter.
