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Bringing AI to the next generation of fusion energy

Science Published 16 October 2025 Authors The Fusion team We’re partnering with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality.Fusion, the process that powers the sun, promises clean, abundant energy without long-lived radioactive waste. Making it work here on Earth means keeping an ionized gas, known as plasma, stable at temperatures over 100 million degrees Celsius — all within a fusion energy machine’s limits. This is a highly complex physics problem that we’re working to solve with artificial intelligence (AI).Today, we’re announcing our research partnership with Commonwealth Fusion Systems (CFS), a global leader in fusion energy. CFS is pioneering a faster path to clean, safe and effectively limitless fusion energy with its compact, powerful tokamak machine called SPARC.SPARC leverages powerful high-temperature superconducting magnets and aims to be the first magnetic fusion machine in history to generate net fusion energy — more power from fusion than it takes to sustain it. That landmark achievement is known as crossing “breakeven,” and a critical milestone on the path to viable fusion energy.This partnership builds on our groundbreaking work using AI to successfully control a plasma. With academic partners at the Swiss Plasma Center at EPFL (École Polytechnique Fédérale de Lausanne), we showed that deep reinforcement learning can control the magnets of a tokamak to stabilize complex plasma shapes. To cover a wider range of physics, we developed TORAX, a fast and differentiable plasma simulator written in JAX.Now, we’re bringing that work to CFS to accelerate the timeline to deliver fusion energy to the grid. We’ve been collaborating on three key areas so far:Producing a fast, accurate, differentiable simulation of a fusion plasma.Finding the most efficient and robust path to maximizing fusion energy.Using reinforcement learning to discover novel real-time control strategies.The combination of our AI expertise with CFS’s cutting-edge hardware makes this the ideal partnership to advance foundational discoveries in fusion energy for the benefit of the worldwide research community, and ultimately, the whole world.Simulating fusion plasmaTo optimize the performance of a tokamak, we need to simulate how heat, electric current and matter flow through the core of a plasma and interact with the systems around it. Last year, we released TORAX, an open-source plasma simulator built for optimization and control, expanding the scope of physics questions we could address beyond magnetic simulation. TORAX is built in JAX, so it can run easily on both CPUs and GPUs and can smoothly integrate AI-powered models, including our own, to achieve even better performance.TORAX will help CFS teams test and refine their operating plans by running millions of virtual experiments before SPARC is even turned on. It also gives them flexibility to quickly adapt their plans once the first data arrives.This software has become a linchpin in CFS’s daily workflows, helping them understand how the plasma will behave under different conditions, saving precious time and resources. “ TORAX is a professional, open-source plasma simulator that saved us countless hours in setting up and running our simulation environments for SPARC. Devon Battaglia, Senior Manager of Physics Operations at CFS Finding the fastest path to maximum energyOperating a tokamak involves countless choices in how to tune the various “knobs” available, like magnetic coil currents, fuel injection and heating power. Manually finding a tokamak’s optimal settings to produce the most energy, while staying within operating limits, could be very inefficient.Using TORAX in combination with reinforcement learning or evolutionary search approaches like AlphaEvolve, our AI agents can explore vast numbers of potential operating scenarios in simulation, rapidly identifying the most efficient and robust paths to generating net energy. This can help CFS focus on the most promising strategies, increasing the probability of success from day one, even before SPARC is fully commissioned and operating at full power.We’ve been building the infrastructure to investigate various SPARC scenarios. We can look at maximizing fusion power produced under different constraints, or optimizing for robustness as we learn more about the machine.Here we illustrate examples of a standard SPARC pulse simulated in TORAX. Our AI system can assess many possible pulses to find the settings we expect to perform the best. Visualizations of a cross section through SPARC. On the left, we see the plasma in fuchsia. On the right, we see an example plasma pulse simulated in TORAX, showing changes in the plasma pressure. On the right side, we show that adjusting control commands changes the plasma performance, resulting in different plasma pulses. Through our growing network of collaborations within the fusion research community, we’ll be able to validate and calibrate TORAX against past tokamak data and high-fidelity simulations. This information will provide confidence in simulation accuracy and help us nimbly adapt as soon as SPARC begins operations.Developing an AI pilot for real-time controlIn our previous work, we showed reinforcement learning can control the magnetic configuration of a tokamak. We’re now increasing complexity by adding simultaneous optimization of more aspects of tokamak performance, such as maximizing fusion power or managing SPARC’s heat load, so it can run at high performance with a greater margin to machine limits.When running at full power, SPARC will release immense heat concentrated onto a small area that must be carefully managed to protect the solid materials closest to the plasma. One strategy SPARC could use is to magnetically sweep this exhaust energy along the wall, as illustrated below. Left: The location of the plasma-facing materials depicted on the right side of SPARC’s interior. Right: Three-dimensional animation of the rate at which energy is deposited on the plasma-facing materials, as the plasma configuration changes (not representative of an actual pulse on SPARC). Image rendered with HEAT (https://github.com/plasmapotential/HEAT), courtesy of Tom Looby at CFS. In the initial phase of our collaboration, we’re investigating how reinforcement learning agents can learn to dynamically control plasma to distribute this heat effectively. In the future, AI could learn adaptive strategies more complex than anything an engineer would craft, especially when balancing multiple constraints and objectives. We could also use reinforcement learning to quickly tune traditional control algorithms for a specific pulse. The combination of pulse optimization and optimal control could push SPARC further and faster to achieve its historic goals.Uniting AI and fusion to build a cleaner futureAlongside our research, Google has invested in CFS, supporting their work on promising scientific and engineering breakthroughs, and moving their technology toward commercialization.Looking ahead, our vision extends beyond optimizing SPARC operations. We’re building the foundations for AI to become an intelligent, adaptive system at the very heart of future fusion power plants. This is just the beginning of our journey together, and we hope to share more details about our collaboration as we reach new milestones.By uniting the revolutionary potential of AI and fusion, we’re building a cleaner and more sustainable energy future. Learn more about our work AcknowledgementsThis work is a collaboration between Google DeepMind and Commonwealth Fusion Systems.Google Deepmind contributors: David Pfau, Sarah Bechtle, Sebastian Bodenstein, Jonathan Citrin, Ian Davies, Bart De Vylder, Craig Donner, Tom Eccles, Federico Felici, Anushan Fernando, Ian Goodfellow, Philippe Hamel, Andrea Huber, Tyler Jackson, Amy Nommeots-Nomm, Tamara Norman, Uchechi Okereke, Francesca Pietra, Akhil Raju and Brendan Tracey.Commonwealth Fusion Systems contributors: Devon Battaglia, Tom Body, Dan Boyer, Alex Creely, Jaydeep Deshpande, Christoph Hasse, Peter Kaloyannis, Wil Koch, Tom Looby, Matthew Reinke, Josh Sulkin, Anna Teplukhina, Misha Veldhoen, Josiah Wai and Chris Woodall.We’d also like to thank Pushmeet Kohli and Bob Mumgaard for their support.Credits: The image of the SPARC Facility, the SPARC renderings and CAD rendering of the divertor tiles are copyright from 2025 Commonwealth Fusion Systems.

Science

Published
16 October 2025
Authors

The Fusion team

Photograph taken at the Commonwealth Fusion Systems headquarters in Devens, Massachusetts. The image shows construction in progress for SPARC, a compact, powerful tokamak machine called SPARC. A rendering of SPARC when completed is shown on the rear wall. Copyright 2025 Commonwealth Fusion Systems (CFS).

We’re partnering with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality.

Fusion, the process that powers the sun, promises clean, abundant energy without long-lived radioactive waste. Making it work here on Earth means keeping an ionized gas, known as plasma, stable at temperatures over 100 million degrees Celsius — all within a fusion energy machine’s limits. This is a highly complex physics problem that we’re working to solve with artificial intelligence (AI).

Today, we’re announcing our research partnership with Commonwealth Fusion Systems (CFS), a global leader in fusion energy. CFS is pioneering a faster path to clean, safe and effectively limitless fusion energy with its compact, powerful tokamak machine called SPARC.

SPARC leverages powerful high-temperature superconducting magnets and aims to be the first magnetic fusion machine in history to generate net fusion energy — more power from fusion than it takes to sustain it. That landmark achievement is known as crossing “breakeven,” and a critical milestone on the path to viable fusion energy.

This partnership builds on our groundbreaking work using AI to successfully control a plasma. With academic partners at the Swiss Plasma Center at EPFL (École Polytechnique Fédérale de Lausanne), we showed that deep reinforcement learning can control the magnets of a tokamak to stabilize complex plasma shapes. To cover a wider range of physics, we developed TORAX, a fast and differentiable plasma simulator written in JAX.

Now, we’re bringing that work to CFS to accelerate the timeline to deliver fusion energy to the grid. We’ve been collaborating on three key areas so far:

  • Producing a fast, accurate, differentiable simulation of a fusion plasma.
  • Finding the most efficient and robust path to maximizing fusion energy.
  • Using reinforcement learning to discover novel real-time control strategies.

The combination of our AI expertise with CFS’s cutting-edge hardware makes this the ideal partnership to advance foundational discoveries in fusion energy for the benefit of the worldwide research community, and ultimately, the whole world.

Simulating fusion plasma

To optimize the performance of a tokamak, we need to simulate how heat, electric current and matter flow through the core of a plasma and interact with the systems around it. Last year, we released TORAX, an open-source plasma simulator built for optimization and control, expanding the scope of physics questions we could address beyond magnetic simulation. TORAX is built in JAX, so it can run easily on both CPUs and GPUs and can smoothly integrate AI-powered models, including our own, to achieve even better performance.

TORAX will help CFS teams test and refine their operating plans by running millions of virtual experiments before SPARC is even turned on. It also gives them flexibility to quickly adapt their plans once the first data arrives.

This software has become a linchpin in CFS’s daily workflows, helping them understand how the plasma will behave under different conditions, saving precious time and resources.

“

TORAX is a professional, open-source plasma simulator that saved us countless hours in setting up and running our simulation environments for SPARC.

Devon Battaglia, Senior Manager of Physics Operations at CFS

Finding the fastest path to maximum energy

Operating a tokamak involves countless choices in how to tune the various “knobs” available, like magnetic coil currents, fuel injection and heating power. Manually finding a tokamak’s optimal settings to produce the most energy, while staying within operating limits, could be very inefficient.

Using TORAX in combination with reinforcement learning or evolutionary search approaches like AlphaEvolve, our AI agents can explore vast numbers of potential operating scenarios in simulation, rapidly identifying the most efficient and robust paths to generating net energy. This can help CFS focus on the most promising strategies, increasing the probability of success from day one, even before SPARC is fully commissioned and operating at full power.

We’ve been building the infrastructure to investigate various SPARC scenarios. We can look at maximizing fusion power produced under different constraints, or optimizing for robustness as we learn more about the machine.

Here we illustrate examples of a standard SPARC pulse simulated in TORAX. Our AI system can assess many possible pulses to find the settings we expect to perform the best.

Visualizations of a cross section through SPARC. On the left, we see the plasma in fuchsia. On the right, we see an example plasma pulse simulated in TORAX, showing changes in the plasma pressure. On the right side, we show that adjusting control commands changes the plasma performance, resulting in different plasma pulses.

Through our growing network of collaborations within the fusion research community, we’ll be able to validate and calibrate TORAX against past tokamak data and high-fidelity simulations. This information will provide confidence in simulation accuracy and help us nimbly adapt as soon as SPARC begins operations.

Developing an AI pilot for real-time control

In our previous work, we showed reinforcement learning can control the magnetic configuration of a tokamak. We’re now increasing complexity by adding simultaneous optimization of more aspects of tokamak performance, such as maximizing fusion power or managing SPARC’s heat load, so it can run at high performance with a greater margin to machine limits.

When running at full power, SPARC will release immense heat concentrated onto a small area that must be carefully managed to protect the solid materials closest to the plasma. One strategy SPARC could use is to magnetically sweep this exhaust energy along the wall, as illustrated below.

Left: The location of the plasma-facing materials depicted on the right side of SPARC’s interior. Right: Three-dimensional animation of the rate at which energy is deposited on the plasma-facing materials, as the plasma configuration changes (not representative of an actual pulse on SPARC). Image rendered with HEAT (https://github.com/plasmapotential/HEAT), courtesy of Tom Looby at CFS.

In the initial phase of our collaboration, we’re investigating how reinforcement learning agents can learn to dynamically control plasma to distribute this heat effectively. In the future, AI could learn adaptive strategies more complex than anything an engineer would craft, especially when balancing multiple constraints and objectives. We could also use reinforcement learning to quickly tune traditional control algorithms for a specific pulse. The combination of pulse optimization and optimal control could push SPARC further and faster to achieve its historic goals.

Uniting AI and fusion to build a cleaner future

Alongside our research, Google has invested in CFS, supporting their work on promising scientific and engineering breakthroughs, and moving their technology toward commercialization.

Looking ahead, our vision extends beyond optimizing SPARC operations. We’re building the foundations for AI to become an intelligent, adaptive system at the very heart of future fusion power plants. This is just the beginning of our journey together, and we hope to share more details about our collaboration as we reach new milestones.

By uniting the revolutionary potential of AI and fusion, we’re building a cleaner and more sustainable energy future.

Learn more about our work

Acknowledgements

This work is a collaboration between Google DeepMind and Commonwealth Fusion Systems.

Google Deepmind contributors: David Pfau, Sarah Bechtle, Sebastian Bodenstein, Jonathan Citrin, Ian Davies, Bart De Vylder, Craig Donner, Tom Eccles, Federico Felici, Anushan Fernando, Ian Goodfellow, Philippe Hamel, Andrea Huber, Tyler Jackson, Amy Nommeots-Nomm, Tamara Norman, Uchechi Okereke, Francesca Pietra, Akhil Raju and Brendan Tracey.

Commonwealth Fusion Systems contributors: Devon Battaglia, Tom Body, Dan Boyer, Alex Creely, Jaydeep Deshpande, Christoph Hasse, Peter Kaloyannis, Wil Koch, Tom Looby, Matthew Reinke, Josh Sulkin, Anna Teplukhina, Misha Veldhoen, Josiah Wai and Chris Woodall.

We’d also like to thank Pushmeet Kohli and Bob Mumgaard for their support.

Credits: The image of the SPARC Facility, the SPARC renderings and CAD rendering of the divertor tiles are copyright from 2025 Commonwealth Fusion Systems.

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AI’s Fragmented Regulatory Landscape Becomes a Data Center Design Challenge

Designing for Verifiable Operations For an operator serving tenants across jurisdictions, a claim about where a workload runs must be one that the responsible service provider can substantiate. That means identifying who can administer the workload, how data moves between regions, where backups are maintained, and which records document those arrangements. A tenant promising to run a workload within a particular jurisdiction needs assurance that its data, backups, and access arrangements satisfy the applicable requirements. Workload placement alone cannot establish where a backup resides or from where an administrator can access a system. Those questions must be addressed across compute, storage, network paths, security controls, and third-party access, with responsibilities established during procurement and verified before service begins. Not all of those controls necessarily reside with the data center operator. A colocation provider may be responsible for physical access, facility security, and designated infrastructure services, while a tenant or cloud provider controls the applications, storage configuration, backup locations, and logical access to workloads. The operating agreement must distinguish between what the facility provider can verify directly and what remains within the tenant’s systems. The EU AI Act makes that division of responsibility particularly important. Its requirements for covered high-risk AI systems include logging, documentation, human oversight, robustness, and cybersecurity, subject to the law’s applicable implementation dates. These obligations attach to the relevant providers and deployers of AI systems, rather than automatically to every company hosting them. A data center operator may nevertheless be required by contract to provide physical-access records, security evidence, or information about infrastructure location so that a tenant can substantiate its own compliance. A further distinction concerns data residency. The AI Act is not itself a general requirement to keep all AI workloads or associated data within the European Union. Geographic restrictions may instead arise from data-protection

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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