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A new startup called Fusionality is quietly building the software backbone that could help fusion power finally reach the grid. Founded by alumni of Google DeepMind, the company is developing AI-powered control systems and simulation environments designed to help fusion reactor developers skip years of trial-and-error engineering, according to an exclusive report from TechCrunch.
Fusion power has always had a timing problem. Scientists have chased the promise of clean, near-limitless energy from atomic fusion for decades, and the joke inside the industry has long been that commercial fusion is perpetually ‘thirty years away.’ Now a new startup called Fusionality thinks it can shave real time off that clock, not by building a reactor itself, but by building the software that every reactor needs to actually work.
According to TechCrunch’s exclusive reporting, Fusionality was founded by alumni of Google DeepMind, the AI lab famous for teaching machines to beat world champions at Go and fold proteins with AlphaFold. The founders are now pointing that same machine learning muscle at plasma physics, developing control systems and simulation environments meant to help fusion power startups iterate faster on one of the hardest engineering problems in existence: keeping a superheated plasma stable long enough to generate usable energy.
[Image: Plasma containment visualization inside a tokamak reactor]
That plasma control problem is exactly where DeepMind has already shown its hand. Back in 2022, DeepMind researchers published work in Nature demonstrating that reinforcement learning, the same family of algorithms behind AlphaGo, could control the magnetic fields inside a tokamak reactor at Switzerland’s Swiss Plasma Center. That project proved AI could shape plasma in real time without needing a human at the controls making split-second physics calculations. Fusionality’s pitch is essentially: let’s take that proof of concept and turn it into a product every fusion startup can plug into, instead of every lab reinventing the wheel.
It’s a classic picks-and-shovels play, and it comes at a moment when fusion has never had more money or attention behind it. Private fusion companies have raised billions in the past few years, with players like Commonwealth Fusion Systems and Helion Energy racing toward pilot plants by the early 2030s, backed by everyone from Microsoft to OpenAI chief Sam Altman personally. Every one of those companies still has to solve the same nagging problem Fusionality is targeting: how do you control a plasma that behaves unpredictably at 100 million degrees, using sensors and actuators that can’t afford to lag by even a few milliseconds.
“We kept seeing fusion teams solve the same control and simulation problems from scratch,” one person familiar with Fusionality’s pitch told TechCrunch, describing the redundant engineering effort across the industry. That redundancy is expensive in an industry where a single reactor prototype can cost hundreds of millions of dollars and years of build time.
The bet Fusionality is making mirrors what happened in autonomous vehicles a decade ago, when simulation environments from companies like Waymo became just as valuable as the cars themselves, because testing control software in a virtual world is far cheaper and faster than testing it on a real, extremely expensive piece of hardware. Apply that logic to fusion, where a botched experiment can mean months of reactor downtime, and the value of a good simulation environment goes up dramatically.
Fusionality’s timing also rides a broader wave of AI infused into climate and energy tech, a trend The Tech Buzz has tracked as AI labs increasingly spin out talent into deep tech and hard science startups rather than staying purely in software. DeepMind itself has been a proving ground for this kind of talent migration, having already spun out AI-for-science efforts in areas like weather prediction and materials discovery.
What’s still unclear is exactly which fusion companies Fusionality is working with, how it plans to make money, whether through licensing its control software, a SaaS-style platform, or something more bespoke, and how big a check it has raised to build all this out. TechCrunch’s report doesn’t detail funding figures, and Fusionality has kept a relatively low public profile so far. But the involvement of DeepMind alumni alone is likely to get the attention of fusion investors who’ve already shown they’re willing to bet big on speculative, capital-intensive physics.
The next few months should clarify a lot. If Fusionality lands a marquee fusion startup as a customer or announces a funding round of its own, it’ll be a strong signal that the ‘AI as fusion’s missing infrastructure’ thesis has real legs. If not, it joins a long list of ambitious ideas trying to finally make fusion’s thirty-year promise come true on schedule.
Fusionality’s emergence signals a subtle but important shift in how the fusion industry might finally cross the finish line, not through a single breakthrough reactor design, but through shared AI infrastructure that lets every player move faster. If DeepMind’s plasma-control research can be productized the way Fusionality is attempting, it could compress years off fusion’s notoriously slow development timeline. For an industry that’s raised billions on the promise of clean, limitless energy, the software stack underneath the reactors might end up mattering just as much as the reactors themselves.