Articles tagged with "AI-in-energy"
Tesla profit tanked 46% in 2025
In 2025, Tesla’s profit declined sharply by 46% to $3.8 billion, marking its lowest profit in years. This downturn was driven primarily by a drop in car sales, which fell 11% year-over-year, as Tesla shipped 1.63 million vehicles globally—continuing a two-year sales decline after years of promised rapid growth. Contributing factors included CEO Elon Musk’s involvement in the Trump administration and the elimination of federal electric vehicle subsidies by Congress, both of which negatively impacted demand. Despite setbacks in automotive revenue, Tesla made strides in diversifying its business. Revenue from its solar and energy storage segments grew 25%, while services revenue—including Full Self-Driving software, insurance, parts, and Supercharging—increased by 18%. The company also improved its gross margin compared to previous quarters. Tesla is shifting focus toward becoming a “physical AI company,” highlighted by a $2 billion investment in Musk’s AI startup xAI. Additionally, Tesla plans to launch
energyelectric-vehiclesTeslasolar-energyenergy-storageAI-in-energyautomotive-technologyUS system to cut nuclear fusion simulation time from months to real-time
The Princeton Plasma Physics Laboratory (PPPL) has introduced STELLAR-AI, a new computing platform designed to drastically reduce the time required for nuclear fusion simulations from months to real-time. By integrating artificial intelligence (AI) with high-performance computing, STELLAR-AI connects computing resources directly to experimental devices, enabling real-time data analysis during fusion experiments. The platform’s hardware architecture combines CPUs for standard tasks, GPUs for AI model training, and quantum processing units (QPUs) to handle complex calculations beyond the capabilities of traditional computers. A key experimental partner is the National Spherical Torus Experiment-Upgrade (NSTX-U), which will benefit from a digital twin model to simulate experiments virtually before physical testing. STELLAR-AI supports the U.S. Department of Energy’s Fusion Science and Technology Roadmap, aiming to accelerate the commercialization of fusion power plants through AI-driven design and optimization. Projects under this initiative include StellFoundry, which uses AI to speed up the design of stellarators
energynuclear-fusionAI-in-energyhigh-performance-computingfusion-simulationfusion-energy-researchplasma-physicsNew Coal-Killing Geothermal Energy Anomalies Found In US
The article highlights a significant shift in the US energy landscape, where geothermal energy is emerging as a formidable competitor to coal, natural gas, wind, and solar power. Historically limited to a few Western states, geothermal energy is now expanding due to advances in drilling technology and innovative approaches like those developed by the startup Zanskar. Zanskar employs custom-built artificial intelligence (AI) to identify previously unknown geothermal fields more efficiently and economically, reducing drilling costs and failure rates. This AI-driven method enables the company to tap into geothermal resources closer to the surface, making geothermal power more scalable and cost-effective. Zanskar recently secured $115 million in Series C funding led by Spring Lane Capital, with participation from previous investors such as Obvious Ventures and Lowercarbon Capital. This funding will support the company’s expansion across multiple Western US locations, aiming to deliver clean geothermal energy on a gigawatt scale before 2030—well ahead of typical timelines for coal, gas, or nuclear projects. The US
energygeothermal-energyrenewable-energyAI-in-energydrilling-technologyUS-Department-of-Energyclean-technologyZanskar thinks 1 TW of geothermal power is being overlooked
The article highlights the untapped potential of conventional geothermal energy in the U.S., which experts at the Department of Energy estimate could provide nearly 10% of the nation’s electricity by 2050. Unlike enhanced geothermal systems that rely on fracking to access deep hot rock, conventional geothermal has been limited by outdated assumptions and reliance on surface indicators like hot springs or volcanoes, which only represent about 5% of geothermal systems. Zanskar, a startup leveraging AI, challenges these assumptions by using machine learning and Bayesian evidential learning (BEL) to identify and validate previously overlooked geothermal sites, significantly expanding the potential capacity from tens of gigawatts to possibly a terawatt scale. Zanskar’s AI-driven approach has already led to the revival of a power plant in New Mexico and the discovery of two new sites with over 100 megawatts of combined potential. These successes helped the company secure $115 million in Series C funding from a diverse group of investors. The startup currently focuses
energygeothermal-powerAI-in-energyrenewable-energymachine-learningenhanced-geothermalenergy-startupsWhy the electrical grid needs more software
The electrical grid, traditionally unnoticed when functioning smoothly, has recently come under intense scrutiny due to crises like California wildfires and Texas freezes, as well as rising electricity demand driven by the AI boom and data center expansion. In 2025, concerns about grid capacity, pricing, and resource strain intensified, prompting utilities to urgently upgrade infrastructure and build new power plants. This environment has created opportunities for software startups offering innovative solutions to optimize grid management, site selection for new infrastructure, and integration of distributed energy resources. Startups such as Gridcare and Yottar use data analytics to identify optimal locations for grid expansion and facilitate connections for medium-sized users amid the data center surge. Others, like Base Power and Terralayr, aggregate distributed battery storage into virtual power plants to provide backup and grid support, while companies like Texture, Uplight, and Camus focus on coordinating renewable energy sources to improve efficiency. Major tech players like Nvidia and Google are also applying AI to enhance grid resilience and streamline connection processes
energyelectrical-gridsoftware-startupsvirtual-power-plantsdistributed-energy-resourcesAI-in-energygrid-modernizationUK achieves 1,000 times faster 5D plasma modeling for nuclear fusion
Scientists from the UK Atomic Energy Authority (UKAEA), Johannes Kepler University Linz (JKU), and Emmi AI have developed GyroSwin, an AI-powered tool that models fusion plasma turbulence up to 1,000 times faster than traditional 5D gyrokinetic simulations. These simulations, which track plasma behavior across three spatial dimensions plus two velocity dimensions, are crucial for designing fusion reactors but typically require days on supercomputers. GyroSwin uses machine learning to learn the underlying plasma dynamics, enabling accurate predictions in seconds while preserving key physical features such as turbulent fluctuations and sheared plasma flows, which are essential for meaningful scientific interpretation. This breakthrough addresses a major bottleneck in fusion research by drastically reducing computational time and cost, facilitating millions of simulations needed to optimize future fusion power plants like the UK’s Spherical Tokamak for Energy Production (STEP). By accelerating the modeling of plasma turbulence, GyroSwin supports the development of practical fusion energy—a clean, virtually limitless power
energyfusion-energyplasma-modelingAI-in-energynuclear-fusionsupercomputingmachine-learningA Startup Says It Has Found a Hidden Source of Geothermal Energy
Zanskar, a geothermal startup, announced a significant breakthrough in Nevada by using artificial intelligence to identify a new commercially viable geothermal resource deep underground. This discovery marks the first major find of its kind in decades and represents a turning point for the geothermal industry, which had long been considered stagnant due to the difficulty of locating hidden or "blind" geothermal systems—hot reservoirs without surface indicators. Zanskar’s cofounders emphasize that their AI-driven approach systematically reduces the risk of exploration by analyzing vast geological data, enabling more precise identification of these elusive energy sources. Geothermal energy, which harnesses steam from underground hot water reservoirs to generate electricity, is a promising renewable resource, especially in tectonically active regions like the western United States. However, most productive geothermal systems lie deep underground without visible surface signs, making them challenging to find. Historically, many geothermal plants were built only after accidental discoveries during unrelated drilling activities. Government efforts in the 1970s attempted systematic exploration, but funding waned
energygeothermal-energyrenewable-energyAI-in-energygeothermal-power-plantsustainable-energyenergy-discoveryProfitable Renewable Energy: Abundant & Scalable - CleanTechnica
The article from CleanTechnica highlights the rapid growth and increasing profitability of renewable energy worldwide. Technological advancements such as artificial intelligence (AI) and the Internet of Things (IoT) have enhanced the viability and scalability of renewables, reducing reliance on government incentives. The global renewable energy market is projected to expand from $1.26 trillion in 2025 to $4.60 trillion by 2035, with a compound annual growth rate of 12.48%. According to IRENA, renewables maintain a cost advantage over fossil fuels due to innovations, competitive supply chains, and economies of scale, alongside benefits like reduced dependence on volatile fuel markets and improved energy security. The business case for renewables is stronger than ever, driven by declining costs and their role in combating climate change. Despite ongoing challenges such as trade tariffs, raw material shortages, permitting delays, and grid capacity limitations, renewable energy technologies continue to mature and expand rapidly. The International Energy Agency (IEA) reports that
renewable-energyclean-energyenergy-marketenergy-technologyIoT-in-energyAI-in-energyenergy-transitionMore Geothermal Energy, Faster, From US Startups
The article discusses the emerging potential of advanced geothermal energy systems developed by U.S. startups, particularly highlighting the work of Utah-based company Zanskar. Traditional geothermal energy in the U.S. has been limited to a few western states with naturally optimal conditions, constraining its contribution to the national energy mix. However, new techniques adapted from the oil and gas industry, combined with artificial intelligence and modern geoscience modeling, are enabling the identification and development of geothermal resources in previously untapped areas. Zanskar’s AI-driven, vertically integrated approach aims to accelerate the discovery and deployment of geothermal power, offering a scalable, reliable, and carbon-free baseload energy source. Zanskar has demonstrated success with two key projects: upgrading the Lightning Dock site in New Mexico, now considered one of the most productive pumped geothermal wells in the U.S., and a recent major discovery at the Pumpernickel geothermal field in northern Nevada. The Pumpernickel site, initially explored unsuccessfully in the
energygeothermal-energyclean-energyAI-in-energyrenewable-energyenergy-startupssustainable-powerNuclearn gets $10.5M to help the nuclear industry embrace AI
Nuclearn, a startup founded by Bradley Fox and Jerrold Vincent, has raised $10.5 million in a Series A funding round led by Blue Bear Capital to advance AI applications in the nuclear power industry. The company focuses on using AI to improve operational efficiency and business processes in nuclear reactors, rather than automating reactor control. Its AI tools are already deployed in over 65 reactors worldwide, helping generate routine documentation and streamline repetitive tasks while ensuring human oversight remains central to liability and safety. Originating from experiments at the Palo Verde Nuclear Generating Station, Nuclearn’s technology incorporates nuclear industry-specific terminology and offers customizable AI models for utilities. The software can operate in the cloud or on-site to comply with strict security protocols. Reactor operators can adjust automation levels based on their confidence in the AI’s performance, with uncertain cases flagged for human review. Fox likens the AI to a “junior employee,” emphasizing that the Nuclear Regulatory Commission views AI as a supportive tool rather than an autonomous
energynuclear-powerartificial-intelligenceAI-in-energypower-industryenergy-technologynuclear-reactorsEV batteries could offer longer lifespan, higher safety with new tech
Researchers at Uppsala University have developed an AI-driven model that significantly enhances the accuracy and robustness of electric vehicle (EV) battery health and lifetime predictions, improving these metrics by up to 65% and 69%, respectively. The model leverages a machine learning framework built on a digital twin approach, which integrates key design parameters with real-world battery behaviors under various fast charging and discharge conditions typical of Nordic climates. This framework enables rapid health assessments within seconds by inferring six critical design parameters from short charging segments, offering a detailed understanding of the chemical processes inside lithium-ion batteries (LiBs) and their aging mechanisms. The study, conducted in collaboration with Aalborg University and published in the journal Energy and Environmental Science, addresses the persistent challenge of EV battery degradation that limits battery lifespan and slows the electrification of transport. By moving beyond treating batteries as “black boxes” and instead modeling their internal chemical reactions, the new approach allows for better battery management and control systems that can extend battery life and improve
energyelectric-vehiclesbattery-technologyAI-in-energybattery-lifespanmachine-learningbattery-management-systemsNew AI hits 94% accuracy in predicting nuclear fusion plasma failures
Researchers at the Hefei Institutes of Physical Science, Chinese Academy of Sciences, led by Professor Sun Youwen, have developed two advanced AI systems aimed at enhancing the safety and efficiency of nuclear fusion experiments. The first AI tool uses interpretable decision tree models to predict plasma disruptions—sudden events that can damage fusion reactors—achieving a 94% accuracy rate and providing warnings approximately 137 milliseconds before disruptions occur. This early detection focuses on identifying ‘locked modes,’ a common plasma instability, and offers transparent insights into the physical signals behind disruptions. The second AI system employs a multi-task learning model to monitor plasma states, accurately classifying different operating modes and detecting edge-localized modes (ELMs) with a 96.7% success rate, thereby supporting smoother and safer reactor operations. These AI innovations address critical challenges in nuclear fusion, a promising clean energy source that could provide nearly limitless power without carbon emissions or long-lived radioactive waste. As global energy demand rises and the environmental impact of fossil
energynuclear-fusionAI-in-energyplasma-monitoringfusion-reactor-safetyclean-energyfusion-energy-technologyEnergy Storage Breakthroughs Enable a Strong & Secure Energy Landscape at Argonne - CleanTechnica
Researchers at the University of Michigan, leveraging the supercomputing resources at the U.S. Department of Energy’s Argonne National Laboratory, are pioneering the use of artificial intelligence (AI) foundation models to accelerate the discovery of advanced battery materials. Traditionally, battery material development relied heavily on intuition and incremental improvements to a limited set of materials discovered mainly between 1975 and 1985. The new AI-driven approach uses large, specialized models trained on massive datasets of molecular information to predict key properties such as conductivity, melting point, and flammability, enabling more targeted exploration of potential battery electrolytes and electrodes. The scale of possible molecular compounds—estimated at around 10^60—makes traditional trial-and-error methods impractical. The AI foundation models, trained on billions of known molecules, can efficiently navigate this vast chemical space by identifying promising candidates with desirable properties for next-generation batteries. In 2024, the team utilized Argonne’s Polaris supercomputer to train one of the largest chemical foundation models
energybattery-materialsAI-in-energysupercomputingmolecular-designbattery-electrolytesbattery-electrodesNew AI method accelerates plasma heat defense in reactors
Researchers from Commonwealth Fusion Systems, the DOE’s Princeton Plasma Physics Laboratory, and Oak Ridge National Laboratory have developed a new AI method called HEAT-ML to accelerate the protection of fusion reactors from extreme plasma heat. HEAT-ML enhances the existing Heat flux Engineering Analysis Toolkit (HEAT) by using a deep neural network trained on about 1,000 SPARC tokamak simulations to rapidly generate 3D “shadow masks.” These masks identify regions of the reactor’s inner walls shielded from direct plasma contact, which is critical to preventing damage from plasma temperatures exceeding those at the Sun’s core. Traditional HEAT simulations can take up to 30 minutes per run, whereas HEAT-ML produces results in milliseconds, dramatically speeding up the design and operational decision-making processes for fusion systems. The AI was initially tested on 15 tiles near the bottom of SPARC’s exhaust system, the area expected to experience the highest heat loads. By quickly and accurately locating magnetic shadows, HEAT-ML supports
energyfusion-energyAI-in-energyplasma-heat-managementfusion-reactorstokamakenergy-technologyAmazon cloud powers US bid for autonomous next-gen nuclear reactors
Idaho National Laboratory (INL) and Amazon Web Services (AWS) have partnered to leverage AWS’s cloud computing, AI foundation models via Amazon Bedrock, and specialized hardware to advance next-generation autonomous nuclear reactors. The collaboration aims to reduce the cost and time involved in designing, licensing, building, and operating nuclear facilities, with the long-term goal of enabling safe, reliable autonomous operation of advanced reactors to accelerate their deployment. INL will utilize AWS’s AI models and computing power to develop nuclear energy applications, including creating a digital twin—a virtual simulation model—of a small modular reactor (SMR) as a key initial project. This initiative is part of a broader strategy to foster collaboration among government labs, AI firms, and nuclear developers, enhancing reactor safety, efficiency, and responsiveness. The digital twin technology will allow near real-time simulations critical for autonomous control systems. The effort aligns with a growing trend of integrating AI into nuclear energy, exemplified by similar work at Oak Ridge National Laboratory, which
energynuclear-energyautonomous-reactorsAI-in-energycloud-computingdigital-twinsmall-modular-reactors