Author: mallorysnowden

  • From Papers to Plant Economics: Costing 38 Fusion Concepts in One Pipeline

    From Papers to Plant Economics: Costing 38 Fusion Concepts in One Pipeline

    We have built and run an automated pipeline that takes 38 fusion reactor concepts, from established tokamaks to early-stage and exotic ideas, and turns each one into a researched dossier, a cost model, and an LCOE estimate within a single framework.

    The mission of 1cFE is to understand what must be true for fusion energy to reach a levelized cost of electricity at or below $0.01/kWh. Answering that requires a map of the full design space, not just the approaches with the most published data. The concepts in this first pass were selected for technical distinctiveness and commercial seriousness. If you believe we are omitting a promising approach, please contact us.

    Other resources, such as Fusion Energy Base, have compiled similar concept landscapes. We are not aware of any that integrates agentic research and deterministic costing into a single reproducible pipeline, which is the gap this work addresses.

    The pipeline pursues four goals: a common ontology so every concept is described by the same set of characteristics; a structured analysis of each concept covering what is novel, which hypotheses the cost model should test, and how the biggest risks and assumptions are captured (sensitivities, scenario branches, or explicit flags); a costing model for each concept built on the deterministic framework of 1costingFE where possible; and tools for exploring the results and building intuition.

    Given the volume of concepts and information to process, we took an AI-centric approach. That choice introduces specific challenges to design around:

    • Reliably automating research and data extraction
    • Enabling traceability and verification to mitigate hallucinations
    • Keeping a level playing field so that, to the extent possible with varying amounts of public data per concept, comparisons stay apples-to-apples
    • Incorporating human review, feedback, and management
    • Treating each analysis as living: none of these are “done,” and any analysis should be able to absorb new data as it arrives

    ​🚧 Caveat: We are still auditing these concepts against public information. Every value in the database carries uncertainty, all results are predicated on the underlying physics of each concept working as intended, and the figures shown are the pipeline’s outputs at our stated assumptions, not forecasts of any company’s plant and not any company’s own numbers. We publish at this stage because external scrutiny improves the work. The single best thing a reader can do is find an error and tell us.

    Pipeline and Process

    Building an Ontology

    In this phase we ran a brute-force research cycle to gather enough data on each concept to complete the ontology table below with moderate confidence, producing an initial dossier per concept.

    Automated Analyses

    At the highest level, the pipeline looks like this:

    Stage 0 is the ontology and dossier work above. That data seeded the cold-start Stage 1 run.

    Stage 1 is an iterative cycle. Each iteration is kicked off using the agentic feedback produced by the prior cycle, optionally new data sources (from our research agent or provided by the user), and optionally specific feedback from the user.

    After any number of iterations, a human can review three artifacts:

    • analysis.md : the structured concept report
    • model_setup.py: the cost model
    • model_output.txt: results of running the model, including LCOE and sensitivity data

    Stage 2: The user approves the analysis or provides feedback and continues iterating.

    Stage 3: Once approved, a final command produces a unified synthesis, including comparison across concepts.

    We built the agentic layers on Claude, which gave us a single stack for research, code generation, and orchestration. To operate the workflow efficiently we deployed a /manage-concepts agent.

    The analysis loop (run_analysis.py) takes a few flags:

    • --add-passes N : run for another N loops autonomously
    • --research: insert an agentic research process at the start of the cycle; the agent uses the previous iteration’s feedback.md to target gaps
    • --feedback PATH: point at a feedback file, either custom or generated from Stage 2

    A few design patterns proved useful for this style of iterative agentic process:

    • The filesystem is the state machine. Where we are in the process is dictated fully by what files exist on disk. You can run the run_analysis.py script an any time, and it will pick up where left off.
    • Composable prompts. Since file presence indicates state, we were able to utilize simple {{var}} substitution, {{#if var}} conditionals, and {{@path}} file inclusion in our prompts to keep control code minimal.
    • Iteration directories as transcripts. Every loop pass writes a complete new directory. Provides full record of how analysis evolves over time. Undoing work is as simple as deleting a directory.

    Worked Example

    The clickable image below walks through the full Automated TEA Pipeline using a compact spherical tokamak based on Tokamak Energy’s ST-E1 design. This interactive walkthrough serves to illustrate source selection, dossier synthesis, the research gaps that require human intervention, and the final LCOE and sensitivity results. Note that all costing values have a high degree of uncertainty and are predicated on the underlying physics of each concept working as intended. The complete pipeline database is available in the Concept Explorer, which is covered in the following section.

    https://1cfe.github.io/fusion-tea-walkthrough-visualization/

    Two framing notes apply here and throughout the database. First, these are our model’s outputs at a single published design point under our stated assumptions, including 1 GWe normalization. They are not Tokamak Energy’s numbers, and differences from any company’s internal estimates are expected. Second, as with everything in this analysis, the results assume the concept’s underlying physics performs as intended. In keeping with our living-analysis approach, we welcome corrections from any team whose concept appears in the database, including pointers to better primary sources or design points.

    Concept Explorer

    The Concept Explorer is a tool for inspecting the results of the automated analyses. It supports side-by-side comparison of all concepts in the database, plus agentic research insights, cost breakdowns, and the key sensitivities of each design. It is an alpha release that we expect to improve with user feedback.

    Three confinement families compared at a normalized 1 GWe plant scale: a spherical tokamak, a heavy ion beam inertial concept, and a magnetized target concept

    A hosted, no-install version of the Concept Explorer is in development. The local alpha is for readers who want to dig in now, and feedback from early users will directly shape the web release

    Setup

    To run the Concept Explorer locally:

    • Follow the README.md to clone (at least) fusion-tea and 1costingfe side-by-side
    • Install uv and run uv sync
    • cd ~/1cfe/fusion-tea
    • uv run python exploration/concept_explorer/server.py

    The server will run on http://localhost:8421. See the concept-explorer/README.md for more.

    Results

    LCOE results, sensitivity analyses, and key findings from the agentic research can be viewed by selecting any concept.

    A concept becomes eligible for approval once it has passed strategic review (Review-Status in {proceed, addressed, clean}) and a synthesis.md file has been generated. A human expert then formally approves it by running scripts/run_analysis.py approve <id>, which writes Status: approved and Approved-Date to the front matter of both analysis.md and synthesis.md. Approval has one downstream side effect being aware of: approved concepts enter the cross-concept reuse pool, so their assumptions become reference material the agent pulls into subsequent analyze runs.

    The images below provide a preview of the Concept Explorer UI, but to fully explore and interact with individual concepts, it is best to follow the instructions in the Setup section above.

    One concept’s full cost picture: headline metrics and the ranked CAS breakdown
    Every result ships with elasticities. Drag any assumption and the headline updates live; slider bounds and population whiskers come from each parameter’s assumed range

    What We Are Seeing So Far

    Three category-level observations from the first full pass over the concept landscape.

    1. Uptime and the cost of money are the only universal levers. Only two parameters rank near the top of the sensitivity tables for nearly every concept, and neither is physics: availability is the most sensitive input for 29 of 38 concepts, and interest rate makes the top five for 33 of 38, both with elasticities near one. The physics levers rotate family by family: major radius and field strength for magnetic confinement, repetition rate and driver parameters for inertial. These elasticities are local to each design point under our 1 GWe normalization, which disadvantages concepts that scale geometrically rather than by replication.
    2. For roughly half the concept landscape, the economics rest partly on our assumptions. Seven of 38 concepts have no published quantitative design point at all. For another 13, the engineering gain that sets recirculating power is a library default rather than a value derived from the concept’s published physics. We model these concepts anyway, but we suppress ungrounded headline LCOEs and flag default-driven inputs rather than print artifacts of our own library. This measures where fusion’s public record runs out; it is not a finding about any concept’s design.
    3. The recurring failure modes were source problems, not arithmetic problems. Concepts typically took two to three iterations to pass review, and what forced another pass was almost always upstream of the cost model: design points stitched from publications that disagree, library defaults silently carrying the economics, cost anchors imported from older studies of different architectures, and small attribution slips. The deterministic costing layer rarely failed. The hard part is establishing what a concept actually claims; every catch and fix is recorded in the iteration history.

    Validation Steps

    Validation is built into the process rather than bolted on at the end. Our team ran targeted deep-dive spot checks comparing expert technoeconomic analyses against the AI-generated LCOE results. Those checks surfaced a meaningful number of misapplied model inputs from the concept research, exactly the failure mode we expected from LLM-based research agents, and drove iteration cycles that substantially strengthened the research-derived inputs through two changes:

    • Single-design-point anchoring. Each concept’s primary sources are now anchored to one consistent design point, rather than combining elements from multiple design points.
    • A tighter override process. Cost and performance overrides are applied only when supported by high-quality data and a clear, well-justified rationale that the concept differs materially from the baseline costing model’s assumptions.

    Validation efforts are ongoing. The spot checks are aggregated here for transparency.

    Known Limitations

    1. Modularity scaling penalty. All plant designs are scaled to 1 GWe for an apples-to-apples LCOE comparison. Smaller plants scale by replicating modules, which can inflate LCOE because many reactor-core costs scale approximately linearly with capacity. One large tokamak will typically require less HTS conductor than the equivalent fusion capacity distributed across multiple smaller modules, since larger reactors benefit from favorable surface-area-to-volume scaling. As a result, smaller plant designs tend to be disadvantaged by the 1 GWe normalization, though this still provides a fairer comparison than evaluating each concept at its native power level.
    2. Freeform codegen analyses. Exotic concepts that fit no 1costingFE archetype bypass the standardized cost model, so their outputs are not directly comparable with archetype-based runs. These concepts are flagged “Non-Standard” to signal that their LCOEs come from a different methodology and carry greater uncertainty and reduced comparability.
    3. Limited physical consistency checks. 1costingFE, relies on accurate inputs, and the agentic research can occasionally misattribute values. Basic sanity checks exist on power balance and efficiency attributes, but a physics-driven check of reactor dimensions against claimed power output is out of scope. This can distort LCOE values for certain concepts.

    Next Steps

    The research data compiled for each concept will now be systematically analyzed to down-select the five to ten most promising concepts for deeper evaluation. An upcoming 1cFE post will set out the down-select criteria and the resulting subset. Those concepts will receive closer scrutiny: plant physics modeling upgrades, uncertainty bounds, and corridor mapping to ultra-low-cost fusion scenarios.

    In the meantime, the Concept Explorer captures a broad range of fusion technologies with enough first-order detail to enable meaningful comparison. We encourage readers to explore, challenge, and stress-test the results. AI enables rapid analysis across a large design space, but expert review and community feedback remain essential for catching errors, refining assumptions, and improving the underlying research. If you find a problem, that is the tool doing its job: tell us, and the analysis gets better.

  • Space Sector vs. Fusion Sector: What the Analogy Teaches Us

    Space Sector vs. Fusion Sector: What the Analogy Teaches Us

    After 14 years in the aerospace industry, I recently moved to fusion. I was drawn to fusion for the same reason I was drawn to aerospace: an intergenerational goal that demands technical rigor and complex coordination across disciplines and systems, with huge potential upsides for humanity.

    A black and white image of a rocket with 'UNITED' printed on its side, next to a laboratory scene featuring engineers in white coats working on a rocket engine test setup.
    Left: Mercury Redstone rocket (NASA c. 1961) , Right: Tokamak T3 (Kurchatov Institute c. 1968)

    The two industries share more structural features than is often appreciated. Both pursue capabilities once considered the exclusive domain of national governments. Both require the integration of advanced materials, cryogenics, high-power electronics, and precision manufacturing at scales that take decades to mature. Both depend on long-horizon capital that sits uneasily within standard venture timelines. And both have followed a similar institutional progression — from state-led megaprojects driven by geopolitical competition, to international cooperation efforts, to a more recent wave of privately funded companies pursuing diverse technical approaches.

    Decisions being made today about fusion regulation, public-private partnership structure, capital allocation, and inter-company collaboration are being justified in part by reference to what worked or failed in the space industry. That makes it worth examining carefully. This post addresses three questions:

    1. Which structural features of the space industry’s evolution actually translate to fusion, and which break down on closer inspection?
    2. What lessons from the space industry’s transition to private commercialization are most directly applicable to fusion’s current stage?
    3. Where will fusion need to develop its own playbook — because the underlying problem differs from space in ways the analogy obscures?

    The answers point to a coherent set of policy choices, drawn from space’s hard-won lessons, that could position U.S. fusion to lead. Some of the groundwork has been laid, but the current federal commitment is underfunded and fragmented.

    Parallel histories

    The US space launch industry went through roughly three eras. The Apollo Program was a single architecture with state-driven, geopolitical motivation. This era of intense competition, called “the space race”, offered no promise of economic return – just the prestige and validation that came with beating the USSR to the moon. This era was succeeded by the development of the Space Shuttle and ISS, which brought international cooperation, megaproject overruns, and design-by-committee critiques. With the Space Race won, the absence of intense competition led to some slowdown in both federal funding and the pace of innovation. Then in the early 2010’s, the New Space era introduced commercial demand, venture capital, and fresh innovation. New technocratic rivalries emerged, such as SpaceX vs. Blue Origin or, in the smallsat arena, RocketLab vs. Stoke Space.

    Fusion has followed a similar arc to the space launch industry. Both rocketry and fusion research projects were initially funded for military applications (see: ICBM’s and H-bombs). The early fusion era was led by national labs in the US, UK, and Russia, with machines like the T-3 Tokamak and the ZETA Z-pinch. ITER is the international megaproject of fusion’s middle era– starting in 1988 and projected to begin operations in 2035, it will cost at least $22B between 35 member countries. Like the middle era of the space industry, fusion innovation stagnated somewhat in the absence of competition and geopolitical urgency. The current private fusion era was galvanized by technological advancements in high-temperature superconductors (HTS), power electronics, and AI, plus the growing demand for non-fossil power generation.

    In the case of the space launch industry, significant cost reductions weren’t achieved until its third era, approximately 50 years after the Space Race began. What conditions led to this inflection point, and which of them are reproducible for fusion? Answering this is the underlying work of 1cFE, which uses calibrated technoeconomic models to map feasible corridors to ultra low-cost fusion power across a range of future scenarios. In the sections below, we break down those enabling conditions and identify which are most applicable to accelerating commercial fusion.

    A scatter plot illustrating the cost per kilogram of payload to low Earth orbit (LEO) over time, displaying various rocket launch systems from different eras, including the Apollo Era and the New Space Era. The trend line indicates a decrease in launch costs, with specific rockets like Falcon 9, New Glenn, and Starship highlighted.
    Sources: Our World In Data, scaled to 2025 USD; Added Datapoints: Wikipedia, “New Glenn”; CNBC, “Blue Origin’s first New Glenn rocket reaches orbit” (Jan 16, 2025). Wikipedia, “SpaceX Starship”. Starship reuse projection: NextBigFuture, “SpaceX Starship Roadmap to Lower Launch Costs by 100 Times” (Jan 20, 2025).

    Where the analogies break— Fusion vs. Space

    The players are different. While the Cold War space race was driven by the United States and Russia, today’s race to commercialize fusion power is largely led by the United States and China. The Cold War space race was symmetric: two state programs running essentially the same architecture (multi-stage liquid propellant rockets) toward the same goal. The US-China fusion competition is largely asymmetric. China runs an Apollo-style program centered around a single architecture (D-T tokamaks) with strong government support, a large trained workforce, and a rapidly-maturing supply chain. The U.S. runs a portfolio model: dozens of private companies pursuing different physics, supported by private investors and, to a lesser extent, federal mechanisms like ARPA-E and the DOE Milestone Program. But the interesting question isn’t who produces net power first— it’s which model is better suited to converge on a commercially-viable power plant given the current state of unresolved physics and engineering.

    Fusion has a higher technical bar than space did at commercialization. Aerospace had working demonstrations to build from before private capital arrived. NASA demonstrated orbital flight and lunar landing in the 1960s. While most of NASA’s heritage designs are classified under International Traffic and Arms Regulations (ITAR), private companies like SpaceX and Blue Origin were given reasonably good blueprints to work from. In contrast, fusion has not yet demonstrated net-power generation. NIF achieved scientific breakeven (Q_plasma > 1) in December 2022, but engineering breakeven — Q_engineering > 1, accounting for end-to-end plant efficiencies — has not yet been demonstrated. Private fusion companies are betting on physics breakthroughs and engineering cost reductions simultaneously, which is a structurally riskier proposition than what New Space took on.

    Despite this, capital keeps flowing. As seen in the graph below, fusion equity investment in 2025 surpassed the historic 2021 peak to set a new annual fundraising record. Per the Fusion Industry Association’s 2025 Global Fusion Industry Report, cumulative industry funding now stands at $9.7B across 53 surveyed companies. China, meanwhile, has spent at least $6.5B on public and private fusion projects since 2023, focusing mainly on D-T tokamak architectures.

    Line graph illustrating public and private funding for fusion and space industries from 2007 to 2025, showing NASA Budget, Fusion DoE Budget, Private Sector Fusion Funding, Private Sector Space Funding, and Chinese Fusion Funding.
    Data sources: Public and private funding for fusion and space industries, 2007–2025, in inflation-adjusted 2025 USD. Sources: NASA budget — Wikipedia, Budget of NASA; U.S. fusion R&D — CRS Report R48866, Toward Commercial Fusion Energy; private fusion — FusionX Invest, “Private Fusion Funding Thriving in 2025”; private space — BryceTech, Start-Up Space 2025.

    The customer problem is fundamentally different. Launch demand has consistently exceeded supply for over a decade. Every new rocket company has had a built-in customer base across NASA, DoD, ESA, and the increasing number of private satellite constellations. Conversely, fusion power will enter a competitive power market with many low-cost alternatives that are continuously improving. Forward-thinking tech companies such as Google and Microsoft have signed Power Purchase Agreements (PPA’s) with CFS and Helion, respectively – but these symbolic First-Of-A-Kind agreements will only persist if fusion power plants are able to produce electricity at competitive rates.

    Even so, multiple New Space companies have pivoted from launch provision to higher-margin or higher-demand industries after disappointing ROI’s. Faced with SpaceX dominance and cooling VC interest, multiple New Space companies have had to pivot from their original launch theses in order to survive. SpinLaunch has redirected towards developing a comm-sat constellation, abandoning kinetic launch as its near-term revenue path. Astra leaned into in-space propulsion via its acquired Apollo Fusion business, reducing its near-term reliance on Rocket 4. ABL and Stratolaunch exited commercial launch entirely, repositioning around missile defense (ABL) and hypersonic flight testing (Stratolaunch) for the DoD.

    We are already seeing early signs of similar hedging in the fusion industry, even before any company has faced the aforementioned “customer problem”. There should be more room for profitable players in the fusion industry given its potential market size, but electricity is a pure commodity with little room to differentiate on anything but price. And fusion’s competitors — gas, solar, fission, batteries — are continuously adapting and improving. Despite these structural differences, fusion companies can still draw clear lessons from the New Space survivors, which will be explored in detail in the following sections.

    Where the analogies hold – Fusion vs. Space

    Public-to-portfolio transition. Both space and fusion have moved from government-led megaprojects toward more privately funded, diverse approaches — but the transition looks very different in the US and China. China’s significant government funding remains focused primarily on a single architecture: the D-T tokamak. This is a defensible bet given China’s record-breaking progress in that domain. The US, by contrast, is funding dozens of reactor concepts — tokamaks, stellarators, magnetic mirrors, magneto-inertial, laser inertial, aneutronic fuels — through a mix of private capital and, to a lesser extent, federal programs. Whether this portfolio diversity proves to be inefficient diffusion or prescient hedging won’t be clear for some time. But the space analogy offers some reassurance for the U.S. fusion industry: had the New Space era focused only on the architecture requiring the least technical development, the breakthroughs in additive manufacturing, advanced propulsion, and reusability might never have materialized.

    A step-change unlock. In the space sector, launcher reusability was perhaps the greatest insight of the New Space wave. A reusable booster (and, in some cases, a reusable second stage) allows for increased launch cadence and lower cost per kg to orbit. Fusion has no consensus equivalent — different companies are betting on different unlocks. HTS magnets enable smaller, higher-field tokamaks. Aneutronic fuels like D-³He and p-B11 and would reduce costs associated with neutron damage and radioactivity. Like reusable rockets, these potentially transformative technologies will require significant upfront investment before any long-term benefits are seen.

    A series of three images showing a rocket on a launch pad igniting its engines, with flames and smoke as it prepares for launch against a sunset backdrop.
    SpaceX’s “Mechazilla” seen here catching the Starship’s booster during its fifth flight test. Mechazilla improves performance by eliminating heavy landing gear, combining recovery and launch infrastructure into one system, and enabling faster reuse cycles.

    Cost has historically been treated as secondary to performance in both fields. Apollo, the Shuttle, ITER, and JWST all optimized for capability rather than unit economics. New Space changed that for rockets. SpaceX drove launch costs down through reusability and vertical integration, and companies like LEAP 71 are now employing AI optimization to drastically accelerate design timelines. Fusion is starting to follow the same playbook in different ways. Commonwealth Fusion Systems is manufacturing its own HTS tape at scale, while DeepMind has demonstrated reinforcement-learning control of plasma instabilities in tokamaks. Thea Energy is exploring modular, planar coils on its Helios reactor, which should improve the manufacturability issues with stellarators. These companies have prioritized manufacturability while exploiting recent technological developments across industries.

    A close-up view of a propulsion device expelling a bright, focused jet of fluid, with visible mist and light effects in the background.
    Leap 71 showcases an AI-driven methalox aerospike engine that went from “specification to first flame” in under three weeks
    3D visualization of a plasma containment system showing a coil array with gaps, indicating that a quarter can be removed without impacting the magnetic field.
    Thea Energy’s planar coil stellarator concept

    Transferable lessons from space to fusion

    Risk-based regulation matters. This seems like an obvious statement, but improper regulation could end the fusion industry before it begins. To illustrate this, let’s consider the commercial drone industry. Pre-2016, U.S. drone operators had to individually petition the FAA for case-by-case exemptions designed for much larger manned aircraft — a process that typically took six months or more. Only after the FAA introduced a risk-based regulatory framework (Part 107) did the commercial drone industry scale rapidly, now valued globally at ~$15B and rising.

    The fusion equivalent is the NRC’s October 2023 decision to regulate fusion under 10 CFR Part 30 (byproduct material) rather than Part 50 (the utilization facility framework used for fission). Applying Part 50 to fusion would have been like applying Boeing 737 rules to a quadcopter — a framework built for a different risk profile, and compliance costs that would likely have killed most private fusion economics.

    The next regulatory question is whether the framework will further differentiate between neutronic and primarily aneutronic approaches. The radiological risk profiles are meaningfully different, and a one-size-fits-all approach within Part 30 leaves performance on the table. Primarily aneutronic fuel companies such as Helion and TAE are thus lobbying the NRC for risk-based regulations that recognize their significantly lower amounts of radioactivity as compared with a D-T fusion plant. Innovation stalls when regulation is poorly fitted to the technology it governs.

    Megaproject opportunity cost is the real cost. ITER, JWST, and SLS share a critique that goes beyond schedule and budget overruns: they crowd out the rest of the portfolio. JWST cost ~$10 billion and forced a $1.4 billion reallocation from other NASA astrophysics missions. Astronomer Adam Frank has famously asked whether the field could have produced more science with ten $1 billion missions instead of one $10 billion observatory. ITER absorbed a generation of public fusion funding that could have supported a more diverse portfolio of smaller experiments. These megaprojects cost not only their budget (often inflated due to bureaucratic impediments) but also the optionality of everything that didn’t get funded.

    The New Space response to this was incremental, fail-fast development — SpaceX’s “fail early and often” approach to Falcon 9 development, where rapid iteration on hardware that occasionally explodes turns out to be cheaper and faster than the zero-failure ULA approach. Fusion is starting to adopt the same posture. CFS will use the SPARC (Smallest Possible ARC) demonstrator to validate magnet and plasma performance well before the full ARC reactor gets built. OpenStar Fusion is developing its second of four reactor iterations on the way to a commercial powerplant. Helion has built and tested seven incremental prototypes to date. Helion recently unveiled its “Tiny Merge”, a new testbed less than one-eighth the size of its latest machine Polaris, designed to run experiments on FRC formation and merging. With a 2028 Microsoft delivery deadline rapidly approaching and fundamental physics questions still open on Polaris, Helion essentially concluded that the seventh-generation machine alone wasn’t iterating fast enough. The lesson is that “fail-fast” doesn’t mean abandoning incremental scaling, but running the iteration loop on the cheapest hardware that can answer the next question.

    Timeline of significant advancements in fusion energy research highlighting key projects and milestones from 1999 to 2028, including 'LSX', 'IPA-C', 'Grande', 'Polaris', and 'Trenta' with specific years and details about each project's focus.
    A visualization of Helion’s incremental approach. Source: December 2022 update from Helion CEO David Kirtley and GeekWire, “Helion makes big bet on ‘Tiny Merge’ fusion testbed” (May 2026).

    Not every fusion company is taking the incremental approach. Pacific Fusion emerged from stealth in 2024 with a $900M Series A and plans to go from a clean sheet design to a demonstration system of approximately 156 identical 2TW pulser modules, targeting net facility gain by 2030. There is planned iteration and validation at the pulser component level, but no intermediate integrated fusion machine between the component tests and the full demonstrator system. The bet is that capital plus speed beats iteration — a defensible position given fusion’s compressed timeline against Chinese competition, but the opposite of the SpaceX-style incremental development that defined New Space’s most successful players.

    Public-private partnerships work when the government acts as a customer, not a designer. NASA’s COTS (2006) and Commercial Crew (2014) programs are the archetype. NASA defined what it needed (cargo or crew transport to the ISS), set fixed-price milestones, awarded multiple companies to preserve competition, and let the contractors design the vehicles. It was the COTS program that helped to save SpaceX from bankruptcy after multiple failures of their Falcon 1 rocket. The result: SpaceX delivered Crew Dragon at roughly half the cost of Boeing Starliner and on a faster timeline. The closest fusion analog is the DOE’s Milestone-Based Fusion Development Program, launched in 2023 with $46M to eight companies. The structure is effective, but the funding level is not yet at the scale needed to materially accelerate first-of-a-kind power plants.

    Consider Alternate Revenue Streams. The only consistently profitable private launch provider is SpaceX, and that is almost entirely because of Starlink. Falcon 9’s launch business alone barely breaks even at 75% gross margins; SpaceX is a profitable company because it anticipated a downstream, recurring-revenue product that its launch capability uniquely enabled. The lesson for fusion is that betting solely on electricity sales (a low-margin commodity) may not produce a profitable fusion company even if the technology works. Successful fusion firms will need to anticipate and capture downstream value streams. Marathon Fusion’s idea to use fast neutrons from the fusion reaction to transmute mercury to gold is one such revenue stream. Other candidates include radioisotope production, process heat-as-a-service, and direct sales of HTS tape or pulsed-power electronics to the medical or defense industries.

    Co-design with the customer in mind. Cowboy Space (formerly Aetherflux) has recently raised $275M to build its own rockets after concluding that no commercial launch provider could scale its orbital data center business fast enough. Cowboy Space isn’t building a generic rocket and selling it to data center customers — they’re building a rocket where the data center is the second stage. Co-designing payload and vehicle as one product eliminates entire integration layers and the duplicated systems they require. The fusion equivalent is co-designing the plant and the customer’s facility, perhaps a large data center, from the start. A co-located fusion plant can share cooling infrastructure, security, and grid interconnection costs with an AI data center. Some fusion fuels and reactor architectures can also output DC power directly and modulate output to match real-time demand, eliminating AC-DC conversion losses and reducing the on-site storage that data centers typically require. For fusion plants using steam turbines, the large quantities of waste heat — typically 50–60% of total thermal output — could be routed through absorption chillers to provide data center cooling, thus reducing one of the data center’s largest operating expenses. Sharing infrastructure with an external partner brings real complications around ownership, liability, and design control. But the CAPEX and operational synergies are large enough that the fusion companies willing to navigate this complexity may end up with a meaningful cost advantage over those who design in isolation.

    Cross-industry collaboration is more important for fusion than it ever was for space. Though many aerospace companies share launch infrastructure, test stands, and strategic vendors, direct cost or IP sharing between competing space companies is essentially nonexistent. Fusion companies may need to chart a new course here, given their structural incentive to collaborate: a large number of relatively small fusion companies are trying to solve many shared technical problems, from plasma control to fast neutron bombardment to tritium breeding to cycle-limited power electronics. It’s unlikely that any single company will solve all of these issues better than the rest. And unlike the space sector, no individual fusion company can credibly claim to reach the revenue stage within a typical VC’s timeline.

    This is why the fusion industry needs more collaboration and support than space did if the U.S. wants to continue as a leader in fusion power. The Special Competitive Studies Project (SCSP) has suggested a $10B federal fusion investment to ensure that the US beats China to market. Roughly 50% of this funding would go to public R&D investments to address many common knowledge gaps across the industry. 40% would go towards building demonstrator plants for a select few fusion companies. 10% would go into public-private mechanisms such as the Milestone-based development program and research grants. Whether this is the right structure for the money is a question worth examining carefully, which I will return to at the end of this post.

    In the private sector, the CFS-Realta partnership announced earlier this year is a notable example of symbiotic collaboration. CFS is supplying integrated HTS magnet systems for Realta’s prototype and eventual power plants — a deal CFS describes as having multi-billion-dollar potential. Two companies pursuing different fusion architectures (tokamak and magnetic mirror) are sharing manufacturing capability and supply chain access. Similarly, Avalanche Energy’s shared lab space called FusionWERX has potential to collectively benefit small fusion companies while generating intermediate revenue streams for Avalanche.

    What this means for fusion’s next few years

    New Space peaked around 2021–22, and the shakeout has been ongoing. The companies that survived built cost discipline, customer-driven design, manufacturability focus, rapid iteration, and incremental testing into their cultures from the start. They also have successful public-private partnerships and innovative alternate revenue streams (see: Starlink).

    The transferable lessons from space to fusion are important: risk-based regulation enables industries while misapplied regulation kills them, a rapid, incremental approach beats out the megaproject approach, public-private partnerships work when governments buy outcomes rather than specific designs, customer-driven design can unlock economic synergies, and alternate revenue streams are essential in high-risk industries.

    But the analogy breaks where it matters most. Fusion has not yet demonstrated engineering breakeven, and its customers — utilities buying a pure commodity in a market full of cheap, improving alternatives — bear no resemblance to the aerospace customers with limited launch options. Fusion companies are betting on physics breakthroughs and cost reductions simultaneously, which is structurally riskier than what companies like SpaceX faced.

    China and the U.S. are facing these commercialization risks in very different ways, as discussed previously. China is relying on its large government funding, skilled workforce, and built-out supply chain to commercialize the D-T Tokamak. To counter this, the SCSP has proposed a $10B U.S. investment across three categories: public R&D infrastructure to close scientific gaps, public-private partnerships with milestone and grant mechanisms, and a demonstration tier to help fund first-of-a-kind powerplants. In an October 2025 report, the SCSP stated “The race [between the US and China fusion industries] is no longer theoretical; it is unfolding now, and the consequences of losing would reverberate across energy security, economic leadership, and national power.

    Venn diagram comparing U.S. and China in fusion energy metrics, highlighting areas of leadership and contention.
    Source: Fusion Forward: Powering America’s Future, October 2025, SCSP

    While I agree with many of the recommendations from the SCSP, this framing seems a bit oversimplistic, as if trying to catalyze fusion innovation in the same way that Kennedy and Khrushchev catalyzed the space race. Being first to market is not the same as winning the market. Take SpaceShipOne, which won the Ansari X Prize in 2004 and proved private human spaceflight was possible. Its successor, Virgin Galactic, has been unable to successfully monetize this technology because its suborbital architecture fundamentally limits its market reach. This is a prime example of designing for a milestone rather than a market demand. Fusion’s frontrunner architectures could face a similar fate if their complexity, scale, or operating costs preclude them from competing in future power markets.

    How smart fusion policy could build the next SpaceX

    Concentrating federal support on a small number of bets early in a technology’s development can foreclose the diversity that often produces the winners. The U.S. fusion industry will require sustained federal support to remain a global leader, and the SCSP is right that significant funding is warranted for public R&D infrastructure. The SCSP’s proposed $4B “demonstration tier”, however, deserves closer scrutiny. Currently, the SCSP’s demo tier is budgeted around two DOE-chosen FOAK plants at roughly $2B each. This structure is modeled after fission’s ARDP, which has shown somewhat dubious results to date. If this demo tier were restructured to mirror NASA’s COTS program, it may be possible to support a wider range of technologies without overextension. Fixed-price milestone payments would cap the DOE’s exposure, and multiple parallel awards with clear reallocation when companies miss milestones would incentivize timely results. Furthermore, DOE Loan Programs Office credit support — direct loans or loan guarantees — can stretch federal dollars further by financing construction at low interest rates, with (initially) subsidized revenue providing the bankable cash flows to service the debt. In this way, the same federal commitment that SCSP would spend on two grants could possibly support construction financing for 4-6 companies, without picking technology winners or absorbing cost overruns.

    Revenue-side support has to extend past first-of-a-kind. A COTS-style demo tier gets the first fusion plants built. It does not, on its own, drive the kind of cost reductions that turn a technology into a competitive commodity. That second phase has historically required sustained, technology-specific revenue support at scale. The two clearest precedents are Germany’s Renewable Energy Sources Act (EEG), which paid out roughly €100 billion in cumulative feed-in tariffs for solar through 2020, and the UK’s Contracts for Difference (CfD) program, which has paid out ~£8.9 billion since 2014 and contracted 39 GW of renewable capacity. Both programs absorbed real cost from consumers and taxpayers in exchange for a learning curve that bent steeply downward.

    Fusion will learn slower than solar did. Wright’s-law learning rates run roughly 20–30% per doubling of cumulative capacity for solar PV, ~35% for lithium-ion batteries, and ~10% for wind. Fission, by contrast, exhibits negative learning across most national programs, due to its large, bespoke nature and high regulation. A realistic planning assumption is that fusion’s learning rate will lie somewhere between fission and solar.

    That argues for a federal CfD or strike-price program sized to support roughly 4-8 GW of cumulative fusion capacity over ten years, at a cost of $15–30 billion in support payments. This is meaningfully larger than the SCSP’s $10B proposal, but an order of magnitude smaller than what Germany spent seeding the global solar industry. It is sized to fund 3–4 doublings of installed fusion capacity, which is the minimum needed to test whether fusion’s actual learning rate is closer to fission’s or to renewables’. This need not come at the expense of other clean-firm technologies. SMRs, geothermal, and long-duration storage each have a similar case for revenue-side support, and a combined clean-firm program would still be a small fraction of the ~$800 billion the CBO projects for federal clean-energy tax credits over the next decade. A subsidized revenue stream of this scale would kickstart the fusion industry on a technology-agnostic basis, rewarding companies that deliver concrete results — and giving the U.S. the empirical basis to decide which architectures deserve scaling further.

    In closing, the Space Race catalyzed innovation for both the U.S. and Russia, but it wasn’t until the New Space era that a competitive launch market emerged. If fusion were to follow that blueprint, it could be decades from first net power to a commercially viable plant. With these lessons in mind, fusion policymakers and founders should prioritize fusion technologies not only for their near-term technical feasibility, but for their commercialization potential in future energy markets.

  • The Unsolved Engineering Behind Fusion Power

    The Unsolved Engineering Behind Fusion Power

    Plasma confinement gets the headlines. Three engineering problems between the plasma and the grid will determine whether D-T fusion can compete on cost.

    Fusion industry headlines tend to focus on similar themes: plasma temperatures hotter than the sun, record-breaking confinement times, and increasing gains towards breakeven. These are extraordinary achievements, and they have rightly dominated the public discourse. But a working fusion power plant requires more than excellent plasma confinement — and several massively underfunded challenges remain in the path to commercialization.

    Between the plasma — typically a deuterium-tritium (D-T) fuel mixture — and the power grid lie three largely overlooked problems whose solutions will ultimately determine whether D-T fusion is viable at scale: tritium breeding, tritium extraction, and remote maintenance. This post examines each in turn, surveys the technological advancements to date, and summarizes the gains that must still be accomplished to achieve commercialized fusion.

    Why Deuterium-Tritium Fusion?

    As of 2025, the Fusion Industry Association counted 53 private fusion companies, the vast majority of which are pursuing reactions between deuterium (D) and tritium (T). Add to that the major public programs to date — ITER, the USA’s NIF, China’s EAST, the UK’s JET— and Deuterium-Tritium is clearly the consensus fuel choice for first-generation commercial plants.

    The reason is primarily physics-driven. As seen in the figure below, the D-T reaction is significantly more reactive than other fuel types, which allows for a lower operating temperature at around 150 million °C. The lower plasma temperature and superior reactivity of D-T enable less plasma heating and a smaller reactor vessel for an equivalent power output.

    Thermal fusion reactivities vs. Ion Temperature, Wurzel & Hsu, 2022

    Thermal fusion reactivities vs. Ion Temperature, Wurzel & Hsu, 2022

    The trade-off is neutrons. Approximately 80% of the energy in a D-T reaction is carried away by high-energy neutrons, which must be caught and converted to heat. In most reactor concepts, this is accomplished with some form of Lithium-based blanket that lines the reactor walls. This blanket must simultaneously absorb and multiply neutrons, breed tritium to sustain the fuel cycle, extract heat for power generation, and shield the rest of the reactor from radiation damage. The tritium fuel then needs to be reprocessed at unprecedented rates for the fuel cycle to be economically viable. Lastly, the radioactivity caused by high-energy neutron bombardment necessitates radiation-hardened, high-strength, and high-precision robots to conduct regular maintenance in the fusion core. For any other endeavor, these challenges would be a focal point of development effort. For fusion power plants, given the extreme challenge of achieving net energy gain, they are relegated to secondary priority.

    Tritium Breeding — Making More Fuel Than You Burn

    Tritium, an isotope of Hydrogen with three neutrons, does not exist in meaningful quantities on Earth. While Tritium is a byproduct of a certain class of fission plants called CANDU reactors, the global supply from CANDU fission is insufficient to supply commercial fusion power at scale. Every D-T fusion plant must therefore breed its own tritium to be economically viable. The Tritium Breeding Ratio (TBR), or the ratio of tritium produced in the blanket to tritium consumed in the plasma, must exceed 1.0 to achieve self-sufficiency. A TBR of at least 1.1 is generally required to offset neutronic and tritium losses, as well as to provide extra tritium stores for future reactor startup fueling .

    Key Tritium Breeding Challenges

    No experiment has yet demonstrated a TBR greater than one. In fact, the best direct measurement of TBR to date was published in 2025 by Delaporte-Mathurin et al., which achieved a TBR of 3.57×10-4 in a small volume of FLiBe (Li2BeF4) molten salt. MIT’s Plasma Science & Fusion Center is currently working on scaling this experiment to a larger testbed called LIBRA, with the aim of eventually achieving a TBR > 1. The UK Atomic Energy Authority is developing a dedicated test facility under its Lithium Breeding Tritium Innovation (LIBRTI) program. The facility will enable engineering-scale lithium blanket modules to be assembled, instrumented, and exposed to fusion-relevant neutron conditions in order to study tritium breeding and heat extraction. In parallel, a 1.4 MW supercomputer called “Sunrise” will use AI-accelerated tritium breeding simulations to anchor LIBTRI’s experimental results. But there is a large gap in blanket performance still left to cover– particularly given that high TBR is just one of many (often conflicting) design requirements.

    Tritium breeding blankets can take a solid or liquid form. Both forms use lithium, which reacts with high-energy neutrons from the fusion reaction to create tritium and helium.  Solid breeders generally use ceramic (Li₄SiO₄ or Li₂TiO₃) pebbles in a packed bed formation. Helium gas sweeps through the pebbles to remove heat and tritium. Solid breeder blankets require periodic batch replacements due to neutron damage and lithium depletion. Liquid blankets, on the other hand, are not susceptible to neutron-induced mechanical damage and can be continuously processed and replenished without affecting plant availability. In liquid-blanket reactors, a lithium-containing liquid metal (such as PbLi) or molten salt (such as FLiBe) circulates through closed channels or, in some cases, through open jets lining the vessel walls. Liquid blankets could enable simpler reactor designs in the long-term, but they come with significant R&D challenges. Their multifunctionality as a breeder, shield, and coolant drives stringent material requirements:

    • Strong neutron multiplication and absorption characteristics to achieve the required TBR
    • High radiation damage tolerance to enable longer service life
    • Melting and boiling points that are compatible with the plant’s required operating temperatures and the surrounding structure
    • High specific heat and thermal conductivity for efficient heat transfer
    • Low tritium solubility to enable clean fuel extraction from the blanket
    • Low electrical conductivity to reduce interference from nearby high-strength magnetic fields
    • Low corrosivity
    • Abundant and cheap feedstock(s)
    • Low toxicity and flammability

    No blanket material has yet been found to satisfy these requirements simultaneously– more R&D work is needed to develop a viable liquid blanket at scale. This material R&D draws from a database consisting of fission data, small-scale breeder experiments, and neutronics simulations– none of which fully replicate the integrated blanket environment of a fusion power plant. Commonwealth Fusion Systems has been one of the more proactive and public actors in the tritium breeding domain, with its ARC blanket concept shown below. Their smaller demonstrator, SPARC, is slated to begin operations in 2027 and does not include a breeder blanket.

    Diagram of a fusion reactor highlighting the Blanket tank, Vacuum Vessel, and LIB (molten FLiBe) components.

    Commonwealth Fusion’s ARC design uses a liquid immersion blanket (LIB), Meschini et al., 2023

    Tritium Extraction and Recycling — Closing the Fuel Loop

    Breeding tritium in the blanket is only the beginning. The tritium must then be rapidly extracted, purified, separated from deuterium, and returned to the plasma — all within a closed loop that loses as little tritium as possible. This makes the tritium plant — comprising fuel clean-up units, isotope separation systems, and storage — as consequential as the blanket itself.

    As mentioned previously, tritium extraction has already been demonstrated within CANDU fission reactors. However, a CANDU reactor produces tritium incidentally at low concentrations, whereas a D-T fusion plant must extract it continuously and return it to the plasma at a rate and purity that no existing tritium plant has ever demonstrated.

    Key Tritium Plant Challenges

    In steady state, a self-sufficient D-T fusion plant’s rate of tritium production must be greater than or equal to its rate of consumption. If on-site tritium production lags, the plant must outsource large amounts of tritium to maintain smooth operation, which is expensive and can drive higher regulatory burdens.*

    A key metric that drives tritium production requirements is the tritium burn efficiency (TBE), or the fraction of injected tritium actually fused. Doubling the TBE will halve the amount of tritium that must travel through the fuel cycle. ITER is expected to burn less than 1% of injected tritium (Abdou et al., 2021). The highest burn efficiencies from inertial confinement experiments are slightly larger, but still in the single digits. Thus, it is imperative that the large amounts of unburnt tritium be quickly recycled to eliminate bottlenecks. While the blanket-bred tritium goes through the full fuel cycle shown below, the purest portion of the unburnt plasma goes through a bypass called Direct Internal Recycling (DIR). This innovation dramatically reduces the required TBR and tritium startup inventory. However, DIR does require real-time isotopic composition monitoring at the pump outlet, since the exact ratio of deuterium to tritium is unknown in the plasma exhaust.

    A flowchart illustrating the inner fuel cycle of a plasma system, featuring key components like the Isotope Separation System, Storage and Management, Fueling System, Vacuum Pump, Fuel Cleanup, and Detritiation System, with arrows indicating the connections and interactions between these components.

    A simplified schematic of tritium flows through each stage of the fuel cycle

    The remainder of the fuel must travel through the inner fuel cycle within hours, including the fuel clean-up unit (FCU) and isotope separation system (ISS). The FCU typically employs membranes that are selectively permeable to Hydrogen gases, which are then separated in the ISS through a multi-stage cryogenic distillation process. Chemical separation of bred tritium from the blanket fluid adds a further time constraint upstream of the inner fuel cycle. A 2023 analysis by Meschini et al. establishes five interlocking performance thresholds that must all be met simultaneously for tritium self-sufficiency to be achievable:

    1. Tritium burn efficiency (TBE) >0.5%
    2. A tritium processing time of <4 hours through the inner fuel cycle
    3. Plant availability > 70%
    4. A DIR fraction > 30%
    5. A required TBR < 1.2

    These targets are not independent: a low burn efficiency means more unburnt tritium must be recovered and reprocessed per pulse, which could increase the required DIR fraction; a slow tritium plant increases the inventory tied up in the system at any given time, which could force a higher TBR to compensate; and low plant availability compounds these effects by reducing the time available for breeding. Miss any single threshold by a meaningful margin and the required TBR climbs above what blankets can physically provide. ITER has the most mature tritium plant design to date, but does not claim self-sufficiency. The project will rely on CANDU-sourced tritium to close the fuel cycle, which is a non-starter for future commercial power plants.

    *Regulation is another weighty consideration when assessing the economic potential of D-T fuel. However, regulatory factors lie outside the scope of this posting.

    Remote Maintenance — Working in a Radioactive Environment

    The same high-energy neutrons that necessitate frequent replacements of irradiated components also prevent humans from replacing them. The D-T fusion reaction releases high-energy neutrons that penetrate reactor structures and induce nuclear reactions that convert stable atoms into radioactive isotopes, causing high-exposure reactor materials to become radioactive over time. Although fusion produces low level radioactive waste as compared with an equivalent fission plant, humans still cannot enter a D-T reactor vessel for years after operation. Every task, from tightening a bolt to replacing a 10-tonne blanket module, must be performed by radiation-hardened robots working in confined spaces with limited lines of sight. And these tasks must be completed as quickly as possible, given the economic consequences of operational downtime. A technoeconomic analysis on tokamaks by Lindley et al. cites capacity factor (determined in large part by maintenance downtime) as the single most influential variable on a plant’s levelized cost of electricity. Reactors must therefore be designed with remote handling (RH) considerations in mind.

    Key Remote Maintenance Challenges

    A D-T plant generally requires several distinct RH systems operating in concert. These systems might include: articulating robotic arms for replacing blanket modules and divertor cassettes; autonomous transporters that shield and convey radioactive components between the reactor and the hot cell; and finally, remote cranes and welding equipment inside the hot cell itself.

    There is an inherent tension between optimizing for low RH costs and high plant availability. Key tradeoffs must be considered to balance CAPEX, performance, accessibility, and plant availability. For example, a larger number of ports can improve plant availability by accelerating and simplifying maintenance — but each port is a penetration through the vacuum vessel that reduces tritium-breeding coverage and weakens structural integrity. Conversely, an architecture with few ports may not be able to accommodate as many parallel operations, thus hurting plant availability while also simplifying the reactor vessel design. Other plant concepts like the UK’s Spherical Tokamak STEP have a vertical lid that requires low-resistance joints in the magnet coils but allows for large components to be lifted by an overhead crane directly to the hot cell.

    JET, the UK’s Joint European Torus, operated from 1983 – 2023 and was a pathfinder for fusion remote handling. JET’s RH system, pictured below, has demonstrated in-vessel divertor tile replacement and was used as a foundation for ITER and DEMO remote handling designs. ITER is developing the most ambitious RH system ever built, though these systems will not truly be tested until 2039 when deuterium-tritium operations are scheduled to begin. Meanwhile, the robotics powerhouse of China has reached a significant milestone with the completion of their remote-handling test platform. The system includes a blanket-maintenance robot that has demonstrated the ability to move 60-tonne loads with ±3.1 mm positioning accuracy.

    Renderings of JET’s two-arm manipulator and boom transporter, Todd 2018

    Renderings of JET’s two-arm manipulator and boom transporter, Todd 2018

    While the recent advancements in RH technology are encouraging, there is significant development work ahead. RH systems must demonstrate an unprecedented level of reliability and repeatability within high-radiation environments. Early RH systems will likely be bespoke to their co-designed reactor geometry, limiting economies of scale; in the longer term, standardization of RH components and reactor interfaces could yield significant cost reductions. Each fusion plant design must balance the upfront cost of RH systems with the lost revenue from maintenance downtime. Not prioritizing maintenance considerations when designing an inherently high-maintenance D-T power plant is a financial liability.

    Conclusions

    At the current pace of fusion progress, net power operation may be achieved before a viable blanket design exists to capture that power, or a viable tritium plant exists to close the fuel cycle, or a viable remote maintenance scheme exists to minimize downtime. Each of these fields must advance substantially for D-T fusion to become economically competitive in future power markets with low-cost alternatives like natural gas and solar + storage.

    Many well-funded fusion companies have not yet published or disclosed detailed plans for the breeding, extraction, and remote handling of tritium. They appear to be deferring these questions to publicly-funded programs like LIBRTI and ITER. And yet delaying tackling the engineering challenges posed by tritium can have sizable impacts to a plant’s levelized cost of electricity (LCOE). For example: a tritium breeding or extraction shortfall forces lower plant availability or relies on externally-sourced tritium, thus increasing LCOE. Similarly, a suboptimal remote maintenance configuration could result in high CAPEX, low reactor performance, or prolonged downtimes, all of which hurt the LCOE.

    Plasma gain (Q) alone cannot determine a fusion plant’s cost of electricity. A plant with outstanding plasma performance but poor tritium/RH infrastructure may not be able to produce electricity at an economically viable cost .

    At 1cFE, quantifying these gaps is part of the work, and the early signal from our models is that tritium and maintenance assumptions can shift LCOE estimates by as much as the plasma physics does.

    The fusion industry faces no shortage of ambition, innovation, or capital. As engineering breakeven draws closer, the focus must shift towards the less glamorous but equally existential engineering challenges that lie between the burning plasma and the electricity grid. Ignition is just the beginning.

    References

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    Chinese Academy of Sciences, Institute of Plasma Physics (2025) Remote Handling Test Platform Sets New Benchmark in Fusion Technology. Hefei Institutes of Physical Science. Available at: https://english.hf.cas.cn/nr/ps/202509/t20250923_1055245.html (Accessed: 15 March 2026).

    Crofts, O. and Harman, J. (2014) ‘Maintenance duration estimate for a DEMO fusion power plant, based on the EFDA WP12 pre-conceptual studies’, Fusion Engineering and Design, 89(9–10), pp. 2383–2387. arXiv:1412.4008.

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    Ferry, S.E., Woller, K.B., Peterson, E.E., Sorensen, C. and Whyte, D.G. (2023) ‘The LIBRA Experiment: Investigating Robust Tritium Accountancy in Molten FLiBe Exposed to a D-T Fusion Neutron Spectrum’, Fusion Science and Technology, 79(1), pp. 13–35. https://doi.org/10.1080/15361055.2022.2078136

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    Schwartz, J.A., Ricks, W., Kolemen, E. and Jenkins, J.D. (2024) ‘Valuing maintenance strategies for fusion plants as part of a future electricity grid’, arXiv:2405.01514.

    Segantin, S., Testoni, R. and Zucchetti, M. (2020) ‘Neutronic comparison of liquid breeders for ARC-like reactor blankets’, Fusion Engineering and Design, 160, p. 112013. https://doi.org/10.1016/j.fusengdes.2020.112013

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