Caleb Harding is a Mandarin-speaking BYU CS graduate. He previously interned at the US Embassy in Jakarta and Doublethink Lab in Taiwan. He is currently based in D.C.

When you think of the biggest technologies of today, the most promising fields for the future, what comes to mind? If your first two thoughts were AI and quantum tech, congratulations — the Chinese Communist Party agrees with you. But what they listed third on the list of “Cutting-edge S&T breakthrough efforts” (前沿科技攻关) in their 15th Five-Year Plan might surprise you: nuclear fusion.

The detailed table entry for nuclear fusion indicates that the CCP is paying close attention to nuclear fusion and is invested in its success. Their goals for the next five years are described as follows:

“Achieve breakthroughs in key fusion technologies including tritium fuel preparation and recycling, materials irradiation testing, high-performance lasers, and superconducting magnet manufacturing; conduct plasma operation experiments on deuterium-tritium fusion and feasibility verification across multiple technical approaches; advance the engineering development process for nuclear fusion R&D.”

Who will execute on this? A whole network of researchers, national labs, and SOEs is driving ahead on the necessary research and manufacturing developments. But China’s most promising assets may lie outside of that system: a handful of startups that are iterating aggressively to take fusion commercial.

Yang Zhao 杨钊 is the CEO and cofounder of China-based Energy Singularity (能量奇点), one of the key players in this space. After graduating with a PhD in theoretical physics1 from Stanford in 2017, Yang spent a year drifting before deciding on his mission in life: to accelerate the timeline for commercial fusion.

After getting a grasp of start-up operations at an AI education firm, Yang Zhao and three other friends2 founded Energy Singularity in Shanghai in 2021. Their approach is similar to that taken by Commonwealth Fusion Systems (CFS), one of the most well-known US companies in the US. With a new kind of more powerful magnet, both companies intend to make fusion viable by shrinking the scale of reactors and, by extension, their cost.

Energy Singularity has had some significant breakthroughs since then. Last year, they achieved first plasma on Honghuang 70 (HH-70, 洪荒70), the world’s first functioning high-temperature superconducting (HTS) tokamak. Design and construction of that experimental reactor was completed in just two years, at record speed. This year, they created a magnet capable of producing a magnetic field of 21.7 teslas, passing CFS’s previous record of 20 teslas.

CFS may yet beat them to the punch. Energy Singularity built HH70 as a proof-of-concept device for HTS tokamaks — an impressive feat. But it doesn’t achieve a Q value greater than 1. The Q-value is a ratio of energy output to input; Q = 1 is break-even, and achieving Q >= 10 is considered the key milestone to prove the commercial viability of fusion. With significant funding and a few years’ head start, CFS is skipping the proof-of-concept device and already working on their Q >= 10 device, SPARC.

First plasma (systems operational) for SPARC is expected in 2026, with net energy production aimed for 2027. Construction on HH170, Energy Singularity’s Q >= 10 device, is expected to finish by the end of 2027, with first plasma and energy production to follow.

But Energy Singularity has some advantages. With their stronger magnets, design experience, and domestic supply chain, they believe their reactors will be the most cost-effective in the world. They report that HH70 cost them USD$16 million (120 million RMB) to build, and project HH170 will cost $420 million. Having already built a first-in-class HTS tokamak under budget and on time, I trust their estimate.

When SPARC was announced in 2018, the budget was $400 million, and it was supposed to achieve net power in 2025. Currently at 65% complete, the new estimate is around $500 million, and the timeline has already been pushed back two years. That being said, both Energy Singularity and CFS’ cost estimates are on the order of 50 times cheaper than the International Thermonuclear Experimental Reactor (ITER) currently under construction in France, which also has Q > 10 as a key goal.

The US may be in for another DeepSeek moment, and China may be poised for explosive growth in fusion come 2035.

The interview has many fascinating tidbits. But at 2.5 hours long, the full transcript might be a bit much for most. Below I’ve provided some extended snippets with occasional commentary. Or if you want to put your nuclear fusion Mandarin vocabulary to the test (惯性约束 is definitely not a term you hear everyday), you can listen to the podcast or watch the video.

Topics Included:

• What’s in a Name?

• When Cost is Key, Build a Startup

• How to Compare Reactors

• How to Design a Novel Reactor

• Build Your Own Supply Chain

• Science Risk vs. Engineering Risk

• Why Not to Invest in Helion

• China and the US: Independent Fusion Ecosystems

• AI Can Accelerate Fusion

• Fusion => Interstellar?

• Contribute Where You Have Leverage

What’s in a Name?

Zhang Xiaojun: How did you come up with [the name for] your first-generation device, Honghuang 70? Why call it Honghuang?

Yang Zhao: Honghuang is from Chinese mythology — a very primordial, abundant state [Note: before the formation of the universe]. It’s chaotic but full of energy. Fusion is similar: you take a lot of originally disordered energy and convert it into electricity. So we named this series Honghuang. The “70” is a key design parameter — the major radius. It’s 70 centimeters, so we call it “70.”

The Oxford Chinese-English dictionary definition for 洪荒 is “primeval chaos.” If we were picking a fusion winner based on the coolest name, Energy Singularity has got it, hands down.

When Cost is Key, Build a Startup

The idea of ITER (the International Thermonuclear Experimental Reactor) was first conceived in the 80’s, and the groundbreaking for the massive reactor took place in 2007. 18 years later… it still has 10+ years to go, with massive cost and time overruns (more on that later). In Yang Zhao’s mind, the science is there, it is simply a matter of building it cheap enough.

Yang Zhao: So in 2021 I set the goal: reduce fusion’s cost per kWh to coal levels or lower. The value our company offers is to continuously improve cost-performance and lower fusion kWh cost through every possible means. That’s why we insisted on designing the entire device ourselves. From magnet design, manufacturing to final testing and operation, we had to do it ourselves because those are the things that most significantly affect device cost. Subsequently, we developed most core subsystems in-house.

From the perspective of cost-effectiveness, small design changes can lead to huge cost differences. Your core subsystems affect interfaces with every other system; even minor design changes can drastically change the entire device. If I can push my costs to be mostly raw-material costs, meaning the team discovers and owns the knowledge, then we can lower the costs, and the higher upstream you go in production the cheaper the raw materials can be.

So we decided in design to do everything ourselves: core subsystems, in-house manufacturing, design, production, final commissioning and operation. Only when the device is not a black box and everything is transparent can you set new targets and know which systems to adjust to optimize cost at higher parameters. We figured this out in 2021. At the beginning I had only four people; for example, Dong was responsible for the overall work, the physics design, and later the experimental operation. Our most critical initial system was the magnet, which we fully manufactured ourselves. That was beneficial. Of course, this approach requires high demands on team operations and funding. New team members joined; initially about four people were doing this work.

Zhang Xiaojun: Why do it in the form of a startup? Why not use more efficient paths, like existing institutions?

Yang Zhao: That’s exactly the point. What we need to do is achieve, in the shortest time and with the least cost, a rapid, order-of-magnitude improvement in fusion cost-performance. That is essentially what a startup is suited for. From the industrial perspective, what we’re doing is similar to what SpaceX did.

Organizationally, the shortest decision pipelines and most efficient execution to take something from the lab to low-cost, large-scale use is what a commercial company does best. That’s not what universities or research institutes are best at.

So once the problem of fusion shifted from proving scientific and engineering feasibility to proving commercial feasibility, the best vehicle to do that turned out to be a startup. Once we knew our goal and what kind of team and organizational form we needed, we started doing this around 2021.

…

Zhang Xiaojun: You claim your cost will be half of comparable US efforts and the device will be smaller. How do you achieve that? Chinese teams tend to be more economical, with today’s AI being one example.

Yang Zhao: That’s our team goal and reflects our values: extreme efficiency combined with pragmatism. Our target is the “170” device: the world’s lowest-cost, highest cost-performance machine that achieves Q ≥ 10. From the start of design, everything — overall device layout, raw material choices, supplier selection, and manufacturing routes — has been done with that target in mind.

So within the limits of our understanding and design constraints, we aimed for the lowest-cost when designing the 170. Based on the entire construction process of the 70, we have a very clear and detailed BOM model for the cost of each subsystem, which we use to optimize the whole device. The final design resulted in a device costing roughly 3 billion RMB (USD$420 million). We’re not really sure why in the US this would require 1 billion USD — they haven’t publicly shared their cost breakdown. But having optimized to this extent, we feel further cost optimization would be quite difficult.

Achieving such low cost requires that the overall design is cost-minimal. We use suppliers available on the market with high competition and, frankly, overcapacity. Otherwise, if it were relatively monopolistic, or only one or two suppliers could do it, they would have strong bargaining power. If it’s a piece of equipment that we are going to need to use long-term, we develop it ourselves. Then we only need to buy the materials.

So through this approach — from design to manufacturing, to processes, to experimental operation — we optimize with the lowest-cost mindset. The final design may well be the lowest-cost device in the world capable of achieving this level of performance.

How to Compare Reactors

As of 2024, there were 45 different fusion startups pursuing 23 different reactor designs. How can you compare them, and tell who is up to snuff? One of the key things to look at is the “triple product” values that they have published. Yang Zhao explains what that is all about.

Yang Zhao: This comes from the past sixty or seventy years of fusion research, summarized from hundreds of devices and thousands of experiments. To achieve a sufficiently high energy gain — the so-called energy gain is your output power divided by input power, that is, the energy you produce divided by the energy you consume — that’s called energy gain.

Zhang Xiaojun: That’s the key break-even value, right?

Yang Zhao: Right. If it equals one, that’s break-even. For a power plant, it has to be much greater than one. For example, if it equals ten, your output energy is ten times your input. After all, in real operation there are losses, right?

So energy gain is actually determined by a physical parameter called the triple product. Simply put, it’s the plasma density multiplied by the temperature multiplied by the confinement time — these three numbers multiplied together, hence “triple product.” When this product reaches roughly 10^21 in a certain, relatively complex set of units, physics from first principles tells you that no matter what method you use, if you take deuterium and tritium as fuel, that triple product corresponds to Q≈1. If it’s slightly higher, in the range of 10^21 to 10^22, the energy gain Q can grow from one to very large values, almost like an avalanche. Once you pass this break-even line, even a small increase in parameters can yield a very large energy gain.

So if a startup’s intended reactor design has only published triple product values of 10^10 or even 10^17… it might be best to stay away for the time being. Read more on that in the “Why Not to Invest in Helion” section.

So what does this logic tell us? To increase energy gain, you need to increase the triple product, because it determines the energy gain. Over the past sixty or seventy years of research, engineers have found that the most effective ways to increase the triple product are either to make the device large enough or to make the magnetic field strong enough. These are the two main approaches.

This is exactly the difference between ITER and CFS/Energy Singularity. Production for HTS magnets didn’t really reach the required scale until 2018 - long after plans had been made and construction begun on ITER, which consequently had to take the “go big” approach — at great expense. With HTS magnets, the second route is now an option, and promises to be much more cost-effective.

How to Design a Novel Reactor

I have never had to approach this complicated a problem before. However, after hearing him describe the process in detail, it isn’t quite as formidable as I imagined it. Extremely hard - yes. But even an elephant can be eaten, one bite at a time.

Yang Zhao: A device’s design goes through several stages.

First is the physics design: what is the core goal you want the device to achieve? Based on that goal, you determine the plasma state — the core physical parameters the plasma must reach.

From the physics design you move to conceptual design: what must each subsystem achieve in terms of parameters to meet your overall physics goals? For example, how strong and what shape must the magnetic field be? What does the vacuum vessel look like? What are the operating temperatures of each subsystem? When do you add fuel, when do you run diagnostics to observe its current state, and when do you apply control? Based on the physics targets, you define each subsystem’s core objectives, its operating conditions, and its interfaces with other subsystems. If you don’t do that, subsystems will conflict and you won’t be able to assemble the machine.

…

After finishing the conceptual design and converting it into physical targets, every system has a design concept that shows feasibility — basically whether the thing can be built.

Once you reach that stage, the next step is the engineering design. For example, if I need a low-temperature system with a certain flow rate, temperature, and flow speed, engineering design answers how to actually implement it: what distribution valves and boxes are needed, what liquid helium tanks, what refrigerants, etc. All those engineering devices are fully designed. At that point, after having the concept for each system, you make an engineering design package that can be used for manufacturing, machining, or equipment procurement — you produce drawings and technical specifications. That’s the third step: engineering design.

After completing engineering design, you enter the manufacturing stage. For some components, we give drawings to external machining or manufacturing suppliers, such as vendors who do welding and fabricate tanks or vacuum pressure vessels, and have them manufactured and returned to us. For some items, like magnets, we manufacture them ourselves in another workshop.

…

After subsystems are manufactured, they go through acceptance: does each subsystem, at the subsystem level, meet your design specifications? If yes, you accept it; if not, you fix what needs to be fixed or send it back to the manufacturer. Once subsystem acceptance is complete, you begin overall assembly: you install different subsystems and turn them into a complete tokamak, like the device you see downstairs.

During assembly there are of course tests. After installation you do system integration and commissioning to see whether the whole system can operate according to design and within the design parameters. Then you reach the final experimental operation stage where you test whether you can accomplish the original design goals, like achieving first plasma. Or, for our goal this year, can you maintain a thousand second steady operation?

From initial design, step-by-step detailed design, manufacturing, assembly, to final operation, it’s basically an acceptance process: does the completed machine meet your originally defined design goals? That completes the whole cycle. Each stage requires different capabilities.

Build Your Own Supply Chain

The approach they have taken to cutting costs (discussed in the “When Cost is Key” section) and basically building things from scratch is indeed reminiscent of researchers at DeepSeek, who in the face of compute constraints dramatically increased the efficiency of their training.

Zhang Xiaojun: What does the industry supply chain look like?

Yang Zhao: The supply chain is still at a very early stage. Different groups build devices differently. Many universities and research institutions build small experimental devices, and these are often outsourced or assembled by other research units or groups that can piece a device together. Partial subsystems are sometimes handed to other research units to finish and return, so the supplier might itself be another research institute.

Our approach was different: we didn’t want black boxes in device design and construction. We do full in-house design and make the core systems ourselves. That means we directly contact raw material suppliers and, once we have drawings, we send them to competitive machining, welding, and manufacturing vendors to produce parts.

Upstream for us is mostly raw materials, plus highly competitive machining, welding, manufacturing suppliers, and common electronic components and mass-produced parts. The industry chain hasn’t really formed yet, so under our working model a lot of things have to be self-developed.

Science Risk vs. Engineering Risk

You’d think that a company designing a nuclear fusion reactor would be chock full of nuclear physicists. Not so. The core of Energy Singularity’s approach is to avoid anything that is a “scientific risk” - they want “engineering risks.”

Zhang Xiaojun: What backgrounds did they [the early design team] have? Physics?

Yang Zhao: Not many pure physicists. Early on there were a few theorists and experimentalists, but most were engineers: structural engineers, cryogenics engineers, vacuum engineers. We had to develop our own magnets, so we had magnet process engineers as well — lots of engineering staff. Even now, people doing pure physics research are not that many — maybe around twenty. The engineering team is much larger.

…

Yang Zhao: The basic logic is this. From design to delivery of a device, you have a physics design, conceptual design, and engineering design. We’re following the HTS tokamak route, and in the physics-design stage we chose a relatively conservative approach, the same design path that ITER used 30 years ago. We don’t want to take on physics or scientific risk; we base our design on physics that already has a lot of experimental evidence.

In other words, if you use those well-established formulas and parameters for the physics design, then as long as your engineering parameters meet the design targets, the probability of achieving the intended plasma performance is very high. Because our physics assumptions are very conservative and traditional, the only thing you need is that the engineering input parameters meet the design requirements. So we transformed the risk that the final device might not reach, say, Q > 10 — a system-level physics risk — into engineering risk.

Engineering risk itself splits into two parts. First: since my device requires very high engineering parameters, can I actually build subsystems with those high parameters? ... The other point is integration. Even if you can build all these subsystems, can you assemble them and still get the expected performance?

Why Not to Invest in Helion

Basically, Helion has gone the opposite route of Energy Singularity and CFS in assuming a lot of scientific risk.

Zhang Xiaojun: Is your technical route different from Helion Energy, which Sam Altman invested in?

Yang Zhao:

It’s not quite the same. Helion also uses magnetic confinement, but the configuration of its magnetic field is linear, unlike ours, which is shaped like a torus — a doughnut. Their setup is called a “field-reversed configuration,” or FRC for short. Based on publicly available academic data, the highest-performing FRC device so far has achieved a triple product of around 10¹⁷ [see the “How to Compare Reactors” section to understand this value], maybe not quite reaching 10¹⁸. So there’s still a gap of about four orders of magnitude from 10²¹. That’s why we feel this is a technological path with very high scientific risk.

Let me give an example. Suppose I want to build an airplane, and right now I only have experimental flight data for altitudes between 0 and 10 meters. Then I take that data and try to extrapolate it to design a plane that can fly at 10,000 meters. In the process of extrapolating, I might not even realize that the air gets thinner and the temperature gets lower at higher altitudes. So if I use aerodynamic data from 0 to 10 meters and extrapolate it to 10,000 meters — about a difference of three orders of magnitude — then the aircraft I design might simply not be able to fly at that altitude.

Similarly, if you only have experimental data up to about 10¹⁷ and you extrapolate to 10²¹, you face the same problem. You don’t know whether new, emergent physical processes will appear in the range from 10¹⁷ to 10²¹ that would change the equations — processes that weren’t there before. If such processes exist, your extrapolated design could fail.

If you’re very lucky and no new physics appears, or the new physics even helps you, that’s great. But in my view these are scientific risks — it’s even uncertain whether the answer exists. So, in principle, these kinds of high-scientific-risk problems are more suitable for research institutes or universities to pursue.

Helion’s plane may fly. Maybe. Thankfully for him, even if Sam Altman loses his investment, his finances are secure.

Zhang Xiaojun: Helion claims to build the world’s first fusion power plant in 2028. You’re targeting 2035.

Yang Zhao: Right, building a fusion power plant by 2028 is indeed extremely ambitious. Even within our team, we don’t fully understand from a theoretical standpoint why their approach would work. Of course, that company has released very little information, and there’s hardly any academic material available. So it’s actually quite difficult for us to judge; it’s possible that there are some physical principles we haven’t taken into account and that they have some very unique understanding of the physics. But based on all the publicly available information and on what is generally known in the field of physics, we don’t fully understand how their technical approach will ultimately achieve energy breakeven.

China and the US: Independent Fusion Ecosystems

Zhang Xiaojun: How do you see the China-US fusion landscape and progress — are there differences?

Yang Zhao: The basic situation is that both China and the US are developing very quickly. Most of the investment and progress is concentrated in these two places. The markets are also naturally separate: it’s unlikely China’s fusion tech will rely on the US to realize it, so China needs domestic teams to do it. Likewise, the US probably won’t import fusion technology from China; they will have domestic teams. From demand, funding capacity, talent pool, supply chain and technical reserves, these two regions are the most likely earliest achievers of fusion. Each will have its own teams.

At present, most commercial investment is in the US and Western countries. Total funding in the fusion field is approaching about $6 billion. There are roughly 40 startups in the US/West. In China there are probably fewer than ten startups, just a handful. In China the total funding scale is on the order of ten billion RMB, which corresponds to around one to two billion US dollars. I haven’t audited exact details, but that’s the rough scale.

Our judgment is that China and the US are the most likely earliest places for commercial fusion, and both regions will have relatively independent technical efforts — you don’t really know what others are doing and vice versa; everyone works independently.

Zhang Xiaojun: The technical routes might also differ.

Yang Zhao: The routes are actually similar in many cases. For example, many US startups follow a tokamak + high-temperature-superconductor route similar to CFS. Some domestic startups follow approaches similar to Helion. It’s likely that some leading companies in the US will have comparable counterparts in China.

With cross-border tech sharing, capital investments, and reactor construction totally off the table, it seems likely that the US and China will develop a sort of mirror ecosystem, with their own champions pursuing each of the same families of tech.

How AI Can Accelerate Fusion

Here’s Yang Zhao’s thoughts on how AI can continue to drive down the costs of fusion:

Yang Zhao: AI is also a very effective way to cut costs and improve efficiency for fusion. Broadly speaking, AI has several major roles for fusion. First, during device operation it can rapidly and precisely provide real-time AI-driven control.

The real-time demands for control are very high. Traditional physics models are computationally heavy and too complex for real-time control. But with AI acceleration and AI-based surrogate models for very complex physical processes, you can get algorithms that are both precise and fast enough to use in real-time control. That’s a huge help for device control.

A year or two ago, DeepMind used AI to control a tokamak in Europe; with very few iterations and in a short time they achieved experimental configurations that previously required a lot of trial and error to reach. So the first contribution is strong help for real-time control.

Second, AI can help substitute for diagnostic hardware. Many high-end diagnostics are costly and difficult to develop. This is similar to applying AI in imaging or medicine to enhance diagnostic capability: you don’t necessarily need an expensive new hardware device — AI algorithms can give you higher precision or better resolution in diagnosis. Using AI in diagnostics is a major direction people are researching now. It’s another way to reduce cost and improve efficiency.

Third, for plasma simulation: if our simulations were accurate enough in principle we wouldn’t need experiments. But reality and simulation diverge. For example, you may design an ideal device, but manufacturing and assembly have offsets — tenths of a millimeter, a millimeter, a few millimeters — and those gaps can create effects that the first-principles ideal model did not capture.

If we build AI models trained on real experimental data for a specific, already-built machine, and our predictive ability for that machine becomes strong, we can greatly reduce the number of experiments needed to find desired parameters. Where you might originally need 100 experiments, you might only need two, because your simulation environment already gives good predictions. That means many intermediate experiments aren’t necessary and you can move on to the next stage faster.

So by providing faster and more accurate plasma predictions, AI shortens experimental iteration cycles. Overall, AI’s effect on fusion is to cut costs and increase efficiency — saving time and capital. The main application areas are control, diagnostics, and experiment operations; these can all receive substantial help from AI.

Fusion → Interstellar?

Zhang Xiaojun: If D–D fusion[3] becomes possible and energy becomes effectively unlimited, what would the world become like?

Yang Zhao: If energy becomes extremely cheap, civilization would change dramatically. Many issues would be different. For example, whether food needs to be grown naturally or could be industrially synthesized — energy cost is the key factor. If energy is very cheap, many products that currently rely on natural processes could be produced synthetically.

Thinking about leaving Earth: spaceflight consumes enormous energy. If energy is cheap, you wouldn’t worry about that as much; you could provide the energy needed for interstellar colonization. That’s the basic idea.

Contribute Where You Have Leverage

Xiaojun probed Zhao on his choice to go all-in on fusion, and I was impressed with his response.

Zhang Xiaojun: When did you decide to work on controlled nuclear fusion?

Yang Zhao: I first thought about it back in undergrad. As physics students we get exposure to various subfields, and I asked myself: which research areas will have the biggest impact on humanity’s future? I concluded early on that fusion could be one of the most consequential developments. I’m talking about a relatively near-term future — say on the scale of decades rather than a century. For me, fusion felt like a historical inevitability that would have a massive impact on civilization. That kind of project attracts me: things that history will eventually accomplish, where participating means contributing to an inevitable development.

…

Other major trends include quantum computing — that’s clearly a big direction — and artificial intelligence, which is certainly going to happen as well. But some of those areas, like AI, might not be where I’m best able to contribute. There are historically inevitable developments where your participation can accelerate timelines, turning a ten-year progress into five years, for example. But there are also things where your involvement doesn’t change much, so you might choose not to get involved. For AI, it’s an inevitable direction, but it isn’t necessarily the field where my background gives me the greatest leverage.

…

Before graduating I was thinking I might either start a company or become a scientist. I wanted to do things that are hard to do unless you really focus on them, things that take a long time and aren’t easily replicated by just swapping people. For me, whether it’s producing a new theoretical result in research or creating something in the real world through a company that didn’t exist before, both bring strong personal satisfaction.