Is Fusion Clean Energy’s Wild Card?

​​The most dangerous aspect of climate change isn't uncertainty it's underestimating what's possible.​ Advancements in artificial intelligence (AI), materials science, and private-sector investment are accelerating fusion's development, transforming it from a distant aspiration into a rapidly emerging wild card. In a world urgently seeking scalable, clean energy solutions, it's time to incorporate such wild cards into our climate strategy.

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Distinguishing Fusion from Fission in Principle and Safety

Nuclear fusion and nuclear fission are fundamentally different nuclear processes. Fission, used in today’s nuclear power plants, works by splitting heavy atomic nuclei (like uranium) to release energy, and it relies on a self-sustaining chain reaction – neutrons released by one split atom trigger others to split. In contrast, fusion generates energy by joining light nuclei (such as isotopes of hydrogen) under extreme conditions, mimicking the reactions that power the Sun [1]. These differences lead to profound implications for safety and waste. Because fusion requires extraordinary temperatures (on the order of 100 million °C) and precise control, a fusion reaction cannot run away or “melt down” like a fission reactor accident [1]. If any aspect of the conditions deviates from the narrow optimal range, the fusion plasma simply cools and fizzles out. In other words, fusion is inherently self-limiting as there is no long-lived chain reaction, so a Chernobyl-like scenario is physically impossible [2].

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Fusion’s byproducts also differ markedly from fission’s. A fission reactor produces highly radioactive spent fuel (containing long-lived isotopes) that must be isolated for thousands of years. Fusion produces no high-level, long-lived nuclear waste [2]. The primary radioactive materials in a fusion system are the reactor’s structural components that become mildly activated by neutron bombardment and the short-lived radioisotope tritium (a hydrogen isotope) used as fuel. Crucially, the activated fusion materials are low-level waste that decays to safe levels on human-relevant timescales as their residual radioactivity diminishes significantly within decades, rather than remaining dangerous for millennia [3].

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Fusion in Climate Models: Omitted by Assumption, Not by Merit

Despite fusion’s promise, most climate-energy models and decarbonization roadmaps have historically excluded fusion power as a contributor, chiefly because of assumptions about its commercial readiness. Leading analyses of pathways to limit global warming, for example, the IPCC 1.5°C scenarios, rely almost entirely on scaling up technologies available today (renewables, efficiency, some fission and carbon capture) and do not count on fusion becoming available in time [4]. In the IPCC’s modeling of how to hold warming below 1.5°C, renewables are projected to supply 70–85% of global electricity by 2050 (with most of the rest from nuclear fission or fossil fuels with carbon capture), and fusion is essentially absent from these near-midterm scenarios [5].

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A concrete illustration comes from the One Earth Climate Model, a detailed 1.5 °C energy roadmap funded by the Leonardo DiCaprio Foundation. This model’s creators explicitly aimed to solve the climate puzzle “without resorting to geo-engineering or nuclear” power, focusing on 100% renewable energy and known technologies [6]. Sven Teske, the project’s lead scientist, explained that their goal was to show a pathway to stay below 1.5 °C warming with aggressive deployment of renewables and natural climate solutions [6].

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Climate Change Outpacing Predictions: Earth’s Energy Imbalance and Arctic Warming

It is increasingly clear that climate change is not only happening, but happening faster than early models anticipated. One key metric is Earth’s energy imbalance which is the difference between the solar energy Earth absorbs and the heat it radiates back to space. A positive imbalance means the planet is gaining energy (mostly stored as heat in the oceans), which leads to global warming. Alarmingly, measurements show that Earth’s energy imbalance roughly doubled between 2005 and 2019 [7]. NASA and NOAA researchers confirmed that the planet’s rate of heat uptake has surged in just the past two decades [7]. By 2023, Earth was retaining an estimated 1.8 W/m² more energy than it emits equaling roughly twice what climate models had projected [8].

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Nowhere is the speed of climate change more evident than in the Arctic. Since about 1979, the Arctic has warmed nearly four times faster than the Earth as a whole [9]. This ratio is higher than most models predicted. The consequences are profound: one result is the precipitous loss of Arctic sea ice. This matters because of the ice–albedo feedback: white ice reflects most incoming sunlight back to space, but when ice melts and exposes dark ocean water, the ocean absorbs much more solar energy, heating up and in turn melting more ice [10].

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Another accelerating impact is the thawing of permafrost which is the perennially frozen soils that underlie vast areas of the Arctic tundra. Permafrost holds enormous quantities of organic material that, when thawed, begin to decompose and release carbon dioxide and methane into the atmosphere. This transforms the Arctic from a carbon sink to a net source of greenhouse gases [11]. The northern permafrost region contains an estimated ~1,500 billion tons of carbon – nearly double the amount currently in the atmosphere. Even if only a fraction of that were released, it could undermine global climate goals [12].

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The Arctic and permafrost are critical components of Earth’s climate system. Loss of sea ice reduces reflectivity and increases global warming. Permafrost thaw releases greenhouse gases, reinforcing the warming. These feedbacks can amplify climate change and are not fully accounted for in many climate models. They highlight the urgency of expanding the toolkit of solutions available to slow warming.

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How AI Is Accelerating Fusion

AI is proving to be a game-changer across the fusion sector. In 2022, researchers at DeepMind and the Swiss Plasma Center used deep reinforcement learning to control a plasma in a tokamak reactor, an environment that requires millisecond-level precision [13]. This marks the first time AI has been used in real-time plasma control, reducing instabilities and improving efficiency.

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AI is also speeding up materials discovery. Finding structural components that can withstand fusion's extreme heat and neutron bombardment has historically been a bottleneck. But machine learning models can now predict material performance, identify new alloys, and accelerate experimental testing [14][15].

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In reactor design, AI allows for "digital twins" which are virtual simulations that can test and optimize thousands of parameters in parallel, vastly reducing the time between concept and construction [15]. These tools are reducing the timeline for building practical, commercial fusion reactors and enabling unprecedented optimization in performance and safety.

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Perhaps most radically, AI is helping predict entire plasma discharge outcomes before they happen. At the Princeton Plasma Physics Lab, neural networks trained on terabytes of historical plasma shot data have been able to forecast disruptions up to 300 milliseconds in advance, enough time to prevent damage to reactor walls or reroute power to auxiliary systems [14]. These forecasting systems now operate in real time, functioning as an early warning system for reactors approaching dangerous thresholds.

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AI is also accelerating fusion-adjacent fields. For instance, Google’s "Optometrist Algorithm" dramatically sped up the alignment process for laser lenses at the National Ignition Facility, reducing adjustment time from hours to minutes, an improvement that directly contributed to the historic 2022 ignition milestone [17]. Meanwhile, supercomputing facilities like Argonne’s Aurora and Oak Ridge’s Frontier are being paired with AI workloads to simulate magnetohydrodynamic behavior at unprecedented resolution, enabling fusion-scale turbulence modeling that was once computationally prohibitive.

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Taken together, these examples illustrate how AI is not merely an optimization tool, it is an enabling force that allows scientists to move beyond trial-and-error and instead operate with predictive precision. In a domain where a single experiment can cost millions and take weeks to prepare, this acceleration is not just helpful, it is transformative.

 

From Wild Card to Warranted Bet

Fusion has long been labeled a "wild card" because of uncertainty about its readiness. But progress is accelerating. The private sector has invested over $6 billion into fusion startups [16], and experiments like those at the National Ignition Facility and the JET tokamak have set records for energy output and ignition [17]. These advances suggest fusion could become viable far sooner than previous models assumed.

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The risks of climate change are growing, and in some cases outpacing predictions. While we must rapidly deploy existing technologies like wind and solar, we also need to invest in transformative options. Fusion may not solve the 2030 climate challenge, but it could play a critical role in 2040 and beyond. Including it in our strategy is not a distraction, it is an act of responsibility.

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Works Cited

1. González de Vicente, S. (2021). The Safety of Nuclear Fusion. IAEA Bulletin.

2. ITER Organization. (2023). Fusion: The safest form of energy?

3. World Nuclear Association. (2023). Radioactive Waste Management.

4. IPCC. (2018). Special Report: Global Warming of 1.5 °C.

5. IEA. (2021). Net Zero by 2050: A Roadmap for the Global Energy Sector.

6. Teske, S. et al. (2019). One Earth Climate Model. University of Technology Sydney.

7. Loeb, N. G. et al. (2021). Satellite and Ocean Data Reveal Marked Increase in Earth’s Energy Imbalance. Geophysical Research Letters.

8. Mauritsen, T. et al. (2023). Amplification of the Earth’s energy imbalance due to aerosol decline. Nature Climate Change.

9. Rantanen, M. et al. (2022). The Arctic has warmed nearly four times faster than the globe since 1979. Communications Earth & Environment.

10. Pistone, K., Eisenman, I., & Ramanathan, V. (2014). Observational determination of albedo decrease caused by vanishing Arctic sea ice. PNAS.

11. Schuur, E. A. G. et al. (2015). Climate change and the permafrost carbon feedback. Nature.

12. Turetsky, M. R. et al. (2019). Permafrost collapse is accelerating carbon release. Nature.

13. Degrave, J. et al. (2022). Magnetic control of tokamak plasmas through deep reinforcement learning. Nature.

14. Kolemen, E. et al. (2022). Prediction and avoidance of fusion plasma disruptions using machine learning. Nuclear Fusion.

15. Zinkle, S. J. & Was, G. S. (2013). Materials challenges in nuclear energy. Acta Materialia.

16. Fusion Industry Association. (2023). Fusion Companies Survey.

17. National Ignition Facility. (2022). First Demonstration of Fusion Ignition.

18. Commonwealth Fusion Systems. (2021). SPARC and High-Temperature Superconducting Magnets.