{"id":4691,"date":"2026-09-21T10:46:28","date_gmt":"2026-09-21T10:46:28","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2026\/09\/21\/5-companies-using-nvidia-ai-for-clean-energy\/"},"modified":"2026-09-21T10:46:28","modified_gmt":"2026-09-21T10:46:28","slug":"5-companies-using-nvidia-ai-for-clean-energy","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2026\/09\/21\/5-companies-using-nvidia-ai-for-clean-energy\/","title":{"rendered":"5 Companies Using NVIDIA AI for Clean Energy"},"content":{"rendered":"
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Clean energy isn\u2019t hard to come by, but the pace of large-scale adoption has historically been slow due to bottlenecks \u2014 including out-of-date infrastructure, elongated research and development timelines, and upfront cost barriers.\u00a0<\/span><\/p>\n

At New York Climate Week, NVIDIA is highlighting five companies pioneering clean energy projects with AI baked into their foundation, accelerating research-to-inception pipelines and ultimately helping build a low carbon grid.\u00a0<\/span><\/p>\n

ThinkLabs AI Drives Toward An Autonomous Grid With Digital Twins<\/b><\/h2>\n<\/p>\n

ThinkLabs AI is curating digital twins and agents \u2014 using the <\/span>NVIDIA CUDA<\/span><\/a> platform \u2014 to speed up interconnection timelines, seamlessly integrate clean energy sources and optimize the grid to handle ongoing variability.\u00a0<\/span><\/p>\n

\u201cThe grid is getting less and less certain, so the ability to see things not statically, but as a probability \u2014 hence all the utility actions \u2014 should be risk informed,\u201d said Josh Wong, founder and CEO of ThinkLabs, a member of the <\/span>NVIDIA Inception<\/span><\/a> program for cutting-edge startups. \u201cThat hits not just reliability objectives, but also affordability, so we know how to maximize and optimize investments.\u201d\u00a0<\/span><\/p>\n

Southern California Edison used ThinkLabs software to reduce the time required to evaluate each grid interconnection application from 30-45 days to just two minutes.<\/span><\/p>\n

The speedup came from a ThinkLabs agent that runs grid simulations and identifies solutions to interconnection barriers.\u00a0<\/span><\/p>\n

Atomic Canyon\u2019s AI Platform Helps Redefine Operations in the Nuclear Sector\u00a0<\/b><\/h2>\n
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PG&E\u2019s Diablo Canyon, optimized by Atomic Canyon<\/figcaption><\/figure>\n

Atomic Canyon, another NVIDIA Inception member, is bringing AI-powered knowledge management and assistance to reactor operators with its NVIDIA accelerated compute-powered Neutron and NIVA platforms to streamline nuclear power plant operations.\u00a0\u00a0<\/span><\/p>\n

Neutron acts as an AI workbench built for nuclear professionals. It turns all of their\u00a0 procedures, regulatory guidance, operating experience, licensing records, design\u00a0 calculations, and structured and unstructured data into a knowledge layer.\u00a0\u00a0<\/span><\/p>\n

NIVA, short for Nuclear Industry Virtual Assistant, was built with the Institute of Nuclear\u00a0 Power Operations, Electric Power Research Institute and the Nuclear Energy Institute as a unified platform bringing AI-powered capabilities to the national nuclear fleet.\u00a0\u00a0<\/span><\/p>\n

\u201cNuclear is a known technology we\u2019ve been doing for 50 to 60 years, but the way we\u2019ve\u00a0 been doing things simply will not scale to meet the moment that\u2019s in front of us; i<\/span>t needs to be reinvigorated by artificial intelligence,\u201d said Trey Lauderdale, founder and CEO of Atomic Canyon.<\/span><\/p>\n

Redwood Materials\u2019 Repurposed EV Batteries Power AI Without Waiting on the Grid<\/b><\/h2>\n

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Electricity demand from AI is accelerating faster than new grid infrastructure can be built, leading to a bottleneck in the industry\u2019s growth.\u00a0<\/span><\/p>\n

Redwood Materials is closing that gap by harnessing its expertise in battery engineering, power electronics and systems design to build large-scale, off-grid power solutions for AI factories \u2014 enabling new capacity to come online in months rather than years.<\/span><\/p>\n

These 100%-recycled electric vehicle batteries are firming up power suppliers by acting as onsite energy storage to create a secure, flexible energy system for AI factories and the national grid at large.\u00a0<\/span><\/p>\n

The batteries are integrated into existing clean energy infrastructure with an AI intelligence layer \u2014 running on the <\/span>NVIDIA Blackwell<\/span><\/a> platform \u2014 that makes the system hypervigilant and adaptable to the real-time energy needs of the data center it\u2019s supplying power to.\u00a0<\/span><\/p>\n

This approach also lowers the cost of power. Redwood\u2019s simplified architecture \u2014 built on in-house power electronics \u2014 requires far fewer transformers and inverters, eliminates the need for an uninterruptible power supply, and can deploy repurposed or new electric vehicle batteries.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cUsing 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,\u201d said Colin Campbell, chief technology officer of Redwood Materials, another NVIDIA Inception member.<\/span><\/p>\n

TerraPower\u2019s Natrium Reactor Sets New Standard for Safe, Sustainable Nuclear Power<\/b><\/h2>\n
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TerraPower Natrium Reactor<\/figcaption><\/figure>\n

Nuclear energy currently powers 20% of the nation\u2019s electricity. TerraPower is working to make sure that more power is safe, sustainable and quickly harnessable through its emission-free Natrium reactor.<\/span><\/p>\n

\u201cTwenty years ago, our founders, including Bill Gates, realized that emissions avoidance should also be a part of this energy solution,\u201d said Chris Levesque, president and CEO of TerraPower.\u00a0<\/span><\/p>\n

TerraPower is connecting an <\/span>NVIDIA Omniverse<\/span><\/a>-powered platform to create digital twin software that will support its efforts to accelerate the siting and delivery of future plants from years to months.\u00a0<\/span><\/p>\n<\/p>\n

Natrium reactors have a unique design that separates them from traditional nuclear power; they\u2019re cooled with liquid metal sodium instead of water \u2014 allowing them to operate at a lower pressure, equivalent to atmospheric pressure. This reactor doesn\u2019t require offsite water or electricity to keep it stable \u2014 in the event of an emergency, it can keep itself cool without any intervention.\u00a0<\/span><\/p>\n

Commonwealth Fusion Systems Creates Fusion Energy Ready for the Grid<\/b><\/h2>\n

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Fusion energy is poised to join the clean energy stack in the 2030\u2019s, thanks to Commonwealth Fusion Systems (CFS). <\/span>\u00a0<\/span><\/p>\n

With the help of NVIDIA Omniverse libraries and OpenUSD, CFS is compressing years of experimentation into weeks for its SPARC tokamak demonstration fusion machine, which can successfully replicate the sun\u2019s power source on Earth.\u00a0 <\/span>\u00a0<\/span><\/p>\n

Fusion energy is a carbon-free, safe source of power.\u00a0 <\/span>\u00a0<\/span><\/p>\n

\u201cWith fusion, there\u2019s no running out of control,\u201d said Brandon Sorbom, cofounder and chief science officer of CFS. \u201cIt is passively safe, since the default mechanism is shutting itself down.\u201d\u00a0 <\/span>\u00a0<\/span><\/p>\n

Using high-temperature superconductors, CFS enables stronger magnetic fields than previous fusion systems.\u00a0 <\/span>\u00a0<\/span><\/p>\n

These magnets allow SPARC\u2019s design to be 40x smaller and, by extension, cheaper to build and operate. <\/span>The company\u2019s ARC power plant \u2014 the first of which will be built in Chesterfield County, Virginia, and will connect to the grid in the 2030s \u2014 is more than 10x smaller.\u00a0 <\/span>\u00a0<\/span><\/p>\n

\u201cWe will be able to build a first-of-its-kind plant that will be cost-competitive with both renewable and nonrenewable sources of energy,\u201d said Sorbom.<\/span>\u00a0<\/span><\/p>\n

Explore more <\/span><\/i>NVIDIA-powered sustainability use cases<\/span><\/i><\/a>.<\/span><\/i><\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

https:\/\/blogs.nvidia.com\/blog\/clean-energy-nvidia-ai\/<\/p>\n","protected":false},"author":0,"featured_media":4692,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4691"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=4691"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/4691\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/4692"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=4691"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=4691"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=4691"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}