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NVIDIA’s September 21, 2026 roundup names five companies using its accelerated-computing software or platforms for different clean-energy bottlenecks: ThinkLabs AI for grid-interconnection studies, Atomic Canyon for nuclear information work, Redwood Materials for battery storage, TerraPower for advanced-reactor planning, and Commonwealth Fusion Systems for fusion simulation. They are not equivalent projects or proof that commercial clean electricity is already being produced. The strongest reported operating result is a Southern California Edison evaluation workflow that NVIDIA says ThinkLabs reduced from 30–45 days to two minutes. Other examples are platform descriptions, design claims, system-development work or future deployment plans. The useful question is therefore where AI is being applied, how mature each effort is, and which claims remain vendor-reported rather than independently verified.
What are the five companies using NVIDIA AI for clean energy?
The five examples address separate parts of the electricity system rather than one shared product. ThinkLabs AI targets grid connection and planning; Atomic Canyon organizes nuclear knowledge; Redwood Materials combines batteries, power electronics and controls for flexible storage; TerraPower develops a digital twin for its Natrium reactor; and Commonwealth Fusion Systems (CFS) uses simulation tools for its SPARC tokamak and planned ARC plant.
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| Company | Energy bottleneck | NVIDIA technology described | Project stage | Evidence type |
|---|---|---|---|---|
| ThinkLabs AI | Grid-interconnection analysis and variable-resource planning | CUDA, digital twins and software agents | Operational software use reported at Southern California Edison | Reported timing result: 30–45 days to two minutes per application |
| Atomic Canyon | Nuclear procedures, licensing and technical information | Neutron workbench and NIVA knowledge layer | System development and fleet-oriented deployment work | Platform and partnership description |
| Redwood Materials | Responsive supply and storage for AI facilities and the grid | Blackwell-based AI control layer with power electronics | System development | Architecture and capability claims |
| TerraPower | Siting and delivery of future advanced reactors | Omniverse-powered digital-twin platform | Future Natrium plant development | Design and project description |
| Commonwealth Fusion Systems | Fusion-device simulation and experimentation | Omniverse libraries and OpenUSD | SPARC development; ARC is a planned 2030s project | Design comparisons and future target |
NVIDIA presents all five as examples of AI and accelerated computing applied to clean-energy challenges. The company’s article is a corporate account, so performance, safety and schedule statements below should be read as attributed claims, not independent validation.
How is ThinkLabs AI using NVIDIA AI for grid interconnection?
ThinkLabs AI curates digital twins and software agents with NVIDIA CUDA so utilities can model changing grid conditions and evaluate clean-energy connections more quickly. NVIDIA says Southern California Edison used the software to cut the evaluation of each interconnection application from 30–45 days to two minutes.
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What the workflow is intended to do
Interconnection studies test how a proposed solar, wind, storage or other resource could affect a network under many operating conditions. ThinkLabs’ digital-twin approach represents the grid as a changing system rather than a single fixed snapshot. Agents can then run or organize analyses while accounting for uncertainty and potential utility actions.
ThinkLabs founder and CEO Josh Wong described the rationale this way: “The grid is getting less and less certain, so the ability to see things not statically, but as a probability — hence all the utility actions — should be risk informed.” The statement explains the company’s focus on probabilistic planning, not a guarantee that every application can be processed in two minutes.
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How to interpret the two-minute figure
The 30–45-day-to-two-minute comparison is a result NVIDIA attributes to ThinkLabs software at Southern California Edison in 2026. NVIDIA’s cited article does not provide the baseline workload, study boundaries, staffing assumptions, validation method or evidence that the result applies across utilities. It should therefore be treated as a reported workflow example, not a general industry benchmark or a measured reduction in project-construction time.
What does Atomic Canyon’s AI do for nuclear operators?
Atomic Canyon’s Neutron is described as an AI workbench that turns nuclear operating procedures, regulatory guidance, experience, licensing records, design calculations and other structured or unstructured data into a searchable knowledge layer. Its NIVA platform—expanded as Nuclear Industry Virtual Assistant—is described as a unified capability for the national nuclear fleet.
Why information management matters
Nuclear organizations rely on large bodies of controlled technical and regulatory information. Bringing those sources into one knowledge layer can help professionals locate relevant material, connect records and work across documents that were previously separated by format or department. The announced build involved the Institute of Nuclear Power Operations, the Electric Power Research Institute and the Nuclear Energy Institute.
Atomic Canyon founder and CEO Trey Lauderdale said, “Nuclear is a known technology we’ve been doing for 50 to 60 years, but the way we’ve been doing things simply will not scale to meet the moment that’s in front of us; it needs to be reinvigorated by artificial intelligence.” That is a statement about the need to modernize workflows. It is not evidence that an AI system has replaced licensed personnel, approved a safety decision or changed a plant’s operating limits.
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What the announcement does not establish
NVIDIA’s description does not publish accuracy rates, user-acceptance results, error-handling procedures or regulatory approvals for Neutron or NIVA. In a safety-critical setting, an information-retrieval system would still need controls for source authority, revision status, traceability and human review. Those requirements are logical safeguards, not reported performance results from this announcement.
How is Redwood Materials using repurposed EV batteries?
Redwood Materials combines repurposed electric-vehicle batteries, in-house power electronics and software controls to provide responsive onsite storage for AI factories and the wider grid. NVIDIA says an AI layer runs on its Blackwell platform and coordinates the system’s response to rapidly changing loads.
Why AI-factory loads are a special use case
AI training can create large, fast fluctuations in electricity demand. Redwood CTO Colin Campbell said, “Using these batteries with our power electronics and systems control software, you can make a highly responsive power source that can deal with the novel fluctuations of AI training.” The architecture is intended to react quickly while reducing the number of separate transformers and inverters needed in a facility.
Repurposed or new batteries
Redwood says its design can use repurposed EV batteries or new batteries. Reuse could give batteries a second application after vehicle service, while new cells could be selected when a project needs a different specification. NVIDIA’s article does not quantify cost, lifecycle emissions, capacity, duration, degradation, safety performance or the number of transformers and inverters avoided. Those outcomes should not be inferred from the architecture description.
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Redwood’s project is a storage-and-controls system, not a claim that batteries alone supply continuous clean electricity. Its value depends on how the batteries, power electronics, software, site load and grid operator work together, as well as on the condition and chemistry of any reused cells.
How is TerraPower using a digital twin for its Natrium reactor?
TerraPower is connecting an NVIDIA Omniverse-powered digital-twin platform to work intended to accelerate siting and delivery of future Natrium plants. NVIDIA describes Natrium as a sodium-cooled reactor that operates at lower pressure than water-cooled reactors.
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What the digital twin is for
A digital twin provides a shared computational representation of a plant or project so teams can visualize designs, test interactions and coordinate information before construction. In TerraPower’s case, the stated goal is to support the siting and delivery process for future Natrium facilities, rather than to operate an already completed commercial plant.
Design features reported by NVIDIA
NVIDIA says the Natrium design does not require offsite water or electricity to remain stable during an emergency. These are project and design descriptions from NVIDIA’s article, not independent regulatory findings or a substitute for licensing decisions. Actual safety, environmental and performance conclusions depend on the design documentation and reviews required by the relevant authorities.
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What is Commonwealth Fusion Systems doing with NVIDIA Omniverse?
Commonwealth Fusion Systems uses NVIDIA Omniverse libraries and OpenUSD to support simulation and experimentation for SPARC, its tokamak demonstration machine. The same work is connected to the company’s longer-term plan for ARC, a fusion power plant that CFS says it intends to connect to the grid in the 2030s.
Why simulation matters for SPARC
Fusion devices combine complex magnetic fields, plasma behavior, structures and heating systems. Shared simulation tools can let engineers examine those interactions and iterate on designs before hardware is built or modified. CFS also uses high-temperature superconductors to create stronger magnetic fields, according to NVIDIA’s account.
How to read the size comparisons
NVIDIA reports that CFS’s SPARC magnet-driven design is 40 times smaller in a stated comparison and that the planned ARC plant is more than 10 times smaller in another comparison. The article does not specify the comparison basis, such as volume, mass, power output or another metric. These figures are therefore company or NVIDIA-reported design comparisons, not independent engineering benchmarks.
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NVIDIA presents fusion as a possible addition to the clean-energy mix in the 2030s. CFS cofounder and chief science officer Brandon Sorbom said, “With fusion, there’s no running out of control,” and, “It is passively safe, since the default mechanism is shutting itself down.” Those are company statements. They should not be treated as an independent safety analysis or as evidence that ARC is already generating grid electricity.
How do the five projects differ in maturity and proof?
The examples span an operating software workflow, enterprise information-system development, storage-system development, advanced-fission planning and fusion research. Comparing them as if they were five commercial power plants would blur important differences.
| Dimension | ThinkLabs | Atomic Canyon | Redwood | TerraPower | CFS |
|---|---|---|---|---|---|
| Primary output | Faster grid studies | Nuclear knowledge access | Flexible onsite storage | Reactor project coordination | Fusion simulation and experimentation |
| Reported maturity | Utility use reported | Fleet-oriented platform work | System architecture | Future-plant development | Demonstration-machine development and future plant plan |
| Numerical claim | 30–45 days to two minutes | Not stated in NVIDIA’s article | Not stated in NVIDIA’s article | Not stated for performance | 40x and more-than-10x size comparisons |
| Independent validation in the cited article | Not provided | Not provided | Not provided | Not provided | Not provided |
NVIDIA also states that nuclear power supplies 20% of the nation’s electricity. The article does not identify an underlying statistical source on that page, so the figure should be read as NVIDIA’s stated context rather than a separately documented statistic here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How does this list fit NVIDIA’s wider energy work?
The five companies sit inside a broader set of AI applications that NVIDIA associates with grid interconnection, distribution management, renewable-site planning and equipment monitoring. A separate NVIDIA article discusses AI for solar-site layout and Siemens Gamesa’s work on wind-farm simulation; those examples are not additional companies in the five-company list.
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In a vendor-reported demonstration from that separate case study, a 96-GPU Blackwell Ultra cluster met requested reductions in more than 200 simulated grid events, reduced demand by up to 40% in under a minute, supported requests lasting up to 10 hours and shed about 30% of load within 30 seconds. Those are demonstration results for the separate collaboration, not results attributed to ThinkLabs, Atomic Canyon, Redwood Materials, TerraPower or CFS.
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What can these examples prove about clean-energy impact?
They show where accelerated computing and AI are being positioned: reducing analytical delay in grid planning, organizing nuclear knowledge, controlling flexible storage, coordinating reactor design and simulating fusion systems. They do not, by themselves, establish lifecycle emissions reductions, lower project costs, improved reliability, regulatory approval or commercial electricity production.
Claims that remain open
- ThinkLabs’ timing comparison lacks published methodology and does not prove that all interconnection studies will take two minutes.
- Atomic Canyon’s announcement does not provide retrieval accuracy, error rates or evidence of regulatory acceptance.
- Redwood’s architecture claims do not quantify savings, emissions, battery life, capacity or safety outcomes.
- TerraPower’s emergency-stability and lower-pressure descriptions are design claims pending the applicable reviews.
- CFS’s size figures, passive-safety statements and 2030s ARC target are not independent performance or schedule validation.
How to evaluate future updates
Look for the study boundary, baseline, measurement method and operating conditions behind any new number. For nuclear and fusion projects, distinguish a company design objective from a licensed facility and distinguish a planned date from electricity actually delivered to a grid. For storage and grid software, ask whether results are simulated, demonstrated at one site or replicated across operating systems.
What should a utility or energy planner take from the five cases?
The practical lesson is to match the technology to the bottleneck. Digital twins and agents are most relevant when planning teams face many interconnection scenarios; knowledge systems matter when engineers must navigate controlled technical records; battery controls matter when loads fluctuate quickly; reactor twins matter during design and siting; and fusion simulation matters before a demonstration machine or power plant exists.
None of the five cases is a universal solution. Each depends on domain data, engineering validation, cybersecurity, human oversight and the physical infrastructure surrounding the software. NVIDIA’s roundup is best read as a map of announced applications, with the maturity and evidence level checked separately for every company.
Frequently Asked Questions
Is NVIDIA already generating clean electricity through these projects?
No. The examples concern software, simulation, storage controls, reactor development and fusion research. The only future generation target described is CFS’s planned ARC connection in the 2030s, which is a company plan rather than current grid supply.
Why are digital twins useful in energy projects?
They provide a computational representation that lets teams examine changing conditions and coordinate design information before making physical changes. Their usefulness depends on the quality, currency and completeness of the data represented in the model.
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NVIDIA describes Blackwell as running the system’s AI layer, while Redwood supplies batteries, power electronics and control software. The cited announcement does not disclose a benchmark showing how much Blackwell improves cost, response time or battery life.
Are the 2030s fusion and reactor dates firm construction schedules?
No. The 2030s reference for ARC is presented as CFS’s intended grid-connection timeframe, and TerraPower’s description concerns future Natrium plants. Neither statement is an independently verified commercial-operation date.
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