Plasma Under Control: How AI is Driving Fusion’s Next Leap
Fusion energy has long promised a clean, virtually limitless power source, but achieving it requires more than just generating plasma. The real challenge lies in controlling and stabilizing the plasma once it's created. Thanks to breakthroughs in artificial intelligence (AI), we are now on the cusp of transforming fusion from a theoretical concept into a scalable, reliable energy source.
AI is not only speeding up fusion research — it's redefining how we approach plasma control, scaling, and commercialization.
The Breakthrough: AI Stepping Into the Reactor
In 2022, DeepMind and EPFL achieved a groundbreaking fusion milestone. For the first time, deep reinforcement learning (DRL) agents were trained entirely in simulation and successfully deployed to control plasma inside a real tokamak — the TCV in Switzerland. This was a "sim-to-real leap," where the AI not only learned how to control plasma but also shaped and stabilized it in real time.
Plasma inside the TCV fusion reactor in Switzerland. Curdin Wüthrich/SPC/EPFL
Key highlights from the breakthrough:
Plasma Shaping: AI controlled complex plasma configurations such as D-shaped, snowflake divertors, and elongated plasmas.
Real-Time Stability: AI managed plasma stability on the fly, marking a huge leap in control systems.
Sim-to-Real Transition: AI was trained in virtual simulations and deployed directly onto reactor coils, showing its practical potential.
This achievement isn't just a milestone in AI for fusion; it's the moment where AI control became central to making fusion energy viable.
Why Plasma Control Matters
Generating plasma is one thing; keeping it stable is the real challenge. Plasmas are inherently unstable, and small fluctuations can lead to catastrophic disruptions that cause shutdowns and costly damage. Maintaining control is essential for any reactor to run safely and reliably.
Here’s why plasma control is crucial:
Instabilities such as tearing modes and edge bursts can damage reactors and cause shutdowns.
AI has proven capable of predicting and suppressing these instabilities, allowing for longer plasma durations and safer operations.
Stronger plasma control equates to fewer costly disruptions, making fusion more economically viable.
By improving stability and extending plasma duration, AI is setting the stage for commercial fusion energy.
Expanding Beyond One Device: Scalability of AI
One of the most exciting developments is AI's scalability. While the initial success at TCV was significant, AI techniques have already been successfully tested on larger reactors like DIII-D in the U.S. These experiments show that AI can work across different fusion devices, proving its transferability and adaptability.
Why scalability matters:
Cross-device validation: AI methods that worked at TCV were successfully applied to DIII-D, ensuring the technology can work in larger-scale reactors.
Multi-device consistency: AI has been shown to improve plasma control and prevent disruptions across different reactor types, making it ready for ITER and DEMO reactors.
Scalable technology: These results indicate that AI can handle the complex challenges of large-scale fusion reactors — not just small, lab-based tokamaks.
This cross-device success is critical for the long-term goal of commercial fusion plants.
New Technical Advances: 2024–2025 and Beyond
The rapid pace of AI innovation in fusion continues to break new ground. One of the most exciting new developments is “reconstruction-free” AI controllers, which could revolutionize how plasma is controlled in future reactors.
Key advancements include:
Reconstruction-Free Control: AI bypasses traditional plasma reconstruction steps, instead using raw magnetic probe data to make decisions in real-time.
Faster Deployment: This new approach reduces manual engineering effort, accelerates deployment, and lowers costs for future reactors.
Plug-and-Play AI: This technology brings us closer to AI control systems that are easy to deploy in commercial fusion reactors without heavy customization.
This technical leap brings us closer to a world where AI can autonomously manage fusion reactors with minimal human intervention.
Navigating the Realities
While AI's progress in fusion is impressive, there are still significant challenges to address.
Challenges to consider:
Sim-to-real gaps: Models trained in simulation may miss rare edge cases found in real plasma environments. Real-world testing will remain essential.
Device-specific adaptation: While AI is scalable, it still needs to be tuned for each reactor's unique geometry and coil configuration.
Regulatory hurdles: Fusion regulators will demand verifiable, interpretable AI before it’s allowed to control ITER-scale machines. Ensuring transparency in AI's decision-making process is vital.
Fusion's primary milestone: This AI breakthrough is focused on control and stability, not yet on net energy gain (Q>1) — an important distinction for long-term fusion goals.
Despite these challenges, AI is shifting fusion's landscape toward safer, more stable, and more scalable reactors.
Strategic & Economic Implications: A New Era of Investment
AI’s role in fusion goes beyond technical breakthroughs — it has profound economic and strategic implications. By improving control systems and reducing reliance on costly hardware, AI is reshaping the cost structure and investment landscape for fusion energy.
Strategic opportunities with AI in fusion:
Faster development cycles: AI shortens the time between plasma experiments, accelerating the roadmap to commercial fusion energy.
Cost reduction: AI shifts complexity from hardware to software, reducing the reliance on expensive reactor infrastructure.
Investment potential: Fusion startups leveraging AI offer asymmetric opportunities by focusing on software-based control systems, offering lower entry costs and potentially high returns.
Expanding talent pools: AI is bringing data science talent into fusion, enabling new avenues of innovation and attracting a wider range of experts.
AI’s impact extends far beyond fusion physics — it’s creating a new market for investors, innovators, and talent.
Southeast Asia’s Role: Plugging In to the Fusion Future
For Southeast Asia, participating in the global fusion race doesn’t require massive reactors or billion-dollar facilities. Instead, AI and data science offer a lower-cost entry point.
Regional opportunities include:
Collaborative research: Singapore and Malaysia are already making strides in plasma diagnostics and fusion simulations, creating valuable opportunities for regional players to plug into global fusion efforts.
AI partnerships: Countries in Southeast Asia can collaborate on AI-driven fusion models, contributing to the growing field without the heavy infrastructure costs.
Developing talent: Institutions in the region can help bridge the gap between plasma physics and data science, creating a multidisciplinary workforce that will be crucial for future fusion advancements.
With AI-driven fusion on the horizon, Southeast Asia has a unique opportunity to carve out a competitive niche in the clean energy space.
Fusion’s Future Is Now
Fusion energy is no longer a distant vision. With AI driving control breakthroughs and materials science overcoming past barriers, the path to commercial fusion is clearer than ever.
The convergence of AI control and advanced materials is setting the stage for practical fusion power — and the future of clean, limitless energy is accelerating toward us.
