The Missing Piece in Commercializing Fusion Energy

Can We Power the World Without Warming It?

The climate crisis, driven largely by carbon emissions from energy production, poses an urgent threat to global ecosystems, economies, and human well-being. As the world searches for sustainable alternatives to fossil fuels, one solution stands out not just for its potential, but for its fundamental role in nature. Fusion is the process that powers the sun, where light atomic nuclei combine to release vast amounts of energy. As illustrated in Figure 1, fusion typically involves fusing two hydrogen isotopes at extremely high temperatures and pressures to form a heavier nucleus and a high-energy neutron as shown in the reaction below. 

²H + ³H → ⁴He + n + 17.6 MeV

Fig 1: Fusion reaction (image credit: Department of Energy)

This reaction releases a significant amount of energy, much of which is carried by the neutron and can be captured as heat to generate electricity. 

Fusion is inherently clean and carbon-free, relying on fuel sources like water and lithium that are abundant and widely available, making it a highly sustainable and long-term solution for meeting future global energy demands. Unlike today’s fossil fuels or fission reactors, fusion is virtually limitless, for example, a single gram of fusion fuel (deuterium + tritium) can produce energy equivalent to 11 tons of coal, and without the long-lived radioactive waste.

Yet, despite its promise and ground breaking experiments and billions in investment, commercial fusion has remained "30 years away" for over 70 years. 

Is Fusion Development Stuck in Steam Engine Era?

In the 1700s, the first steam engines were developed through trial and error — engineers tested materials, refined parts, and learned from breakdowns without any predictive tools. 

Fast forward to today, and while fusion engineers have access to advanced simulations, the process still echoes this slow, iterative approach. Most reactor design efforts still rely on manually orchestrated, loosely coupled multi-physics simulations, where outputs from one domain (e.g., neutronics) are passed sequentially to others (e.g., structural or thermal analysis), resulting in long feedback loops and fragmented iteration cycles across disciplines.

Fig 2: Design of steam engine (Image credit: Julianna Schiemann)

For instance, the first published system-level fusion pilot plant concept was by J.E. Menard et al. in 2011 - "Prospects for Pilot Plants Based on the Tokamak, Spherical Tokamak and Stellarator". It took five more years to arrive at the next iteration in 2016 - "Fusion Nuclear Science Facilities and Pilot Plants Based on the Spherical Tokamak", followed by another six-year gap before the 2022 paper - "Fusion Pilot Plant Performance and the Role of a Sustained High Power Density Tokamak".

Even with simulations, each design iteration still takes 5–6 years — an unsustainable pace for a technology racing against the climate crisis. This slow, expensive, and risk-prone approach is a major barrier to commercializing fusion within a relevant timeframe. 

What is the Missing Piece?

Commercializing fusion is not just about achieving ignition. It requires:

• Designing optimized reactors (geometry, materials, targets, magnets and shields etc.);

• Ensuring structural integrity, thermal and physics performance within manufacturing constraints, and;

• Balancing cost, safety, and maintenance needs, all at the same time. It is one of the most challenging problem we have ever faced.

Artificial Intelligence (AI) is reshaping the way we design and optimize complex system. It acts as a multiplier — accelerating discovery, reducing cost, and assisting experts in managing complexity that would otherwise be overwhelming.

Instead of:

Building a prototype → Testing it → Repeating (10+ years),

We move to:

Simulating 100,000 designs → Optimizing virtually → Building one best version.

This is already happening in aerospace and automotive design — and fusion must follow.

By leveraging data from thousands of high-fidelity simulations, experiments, and historical design cases, AI models can learn complex multi-parameter relationships and rapidly predict key performance metrics enabling fast, accurate design evaluations without the need to run full-scale simulations for every iteration.

Bridging the Gap Between Fusion and Commercialization

nTtau Digital Automated AI-driven Co-Design Platform offers a revolutionary approach to fusion power plant design and optimization. The platform employs an automated design workflow that integrates AI based tooling which is informed by a comprehensive costing framework to optimize the design of reactor components by systematically evaluating engineering, physics, and economic constraints within a unified framework. This approach ensures that design iterations are guided by real-time evaluations of trade-offs between performance, manufacturability, and cost-effectiveness, allowing informed decision-making throughout the development process. The system refines the geometries of the components and the material selections, from the first wall up to the site boundary, to achieve optimal configurations that meet stringent operational and regulatory requirements, supporting the development of a reactor that aligns with both technical feasibility and financial viability.

For further details of the tool, review the article by Omer et al 2025: 

“Multi-objective Optimization of a Tandem Mirror and Cross-Comparison with a Tokamak

and Stellarator for given P_NET”

Conclusion

Fusion energy holds the potential to a virtually limitless, carbon-free alternative to fossil fuels. Yet despite decades of research and investment, fusion’s path to commercialization has been hindered not by the laws of physics, but by the limitations of our design process. 

The fusion industry still suffers from slow, fragmented, and manually driven development cycles. Traditional simulation-based workflows, though powerful, are not sufficient to meet the urgency of the climate crisis or the scale of engineering complexity fusion demands.

The missing piece is not more experiments—it's intelligent integration.

If we are to make fusion a reality within this generation, AI must become the backbone of our design philosophy. With AI, we can stop asking "when will fusion be ready?" and start asking "how soon can we deploy it?"

References:

Office of Science, U.S Department of Energy, “Department of DOE Explains...Fusion Reactions”. https://www.energy.gov/science/doe-explainsfusion-reactions 

Julianna Schiemann, “The invention of Steam Engine”. https://steamenginenhd.weebly.com/utility.html