The AI Paradox Reshaping Clean Energy Research
- AI is straining global power grids and energy security even as it becomes indispensable for managing them.
- Researchers in China used AI to identify a new molecule that can revive dead EV batteries for thousands of extra recharge cycles.
- A University of Toronto team used AI to find six new metal alloys for jet engines and nuclear reactors in just a few weeks, a process that once took years.
Artificial intelligence is a double-edged sword for the energy sector. The rapid buildout of data centers to support widespread integration of large language models in virtually every economic sector imaginable, from our energy grids to your electric toothbrush – yes, really – is pushing energy demand growth projections to unprecedented levels, threatening to far outpace energy capacity additions and imperil energy security on a global scale. On the other hand, artificial intelligence holds enormous promise for improving energy efficiency in a wide range of systems and may hold the key to unlocking next-gen clean energy methods and technologies that could be integral to enabling feasible decarbonization pathways.
In the clean energy sector, artificial intelligence is being used to improve forecasting models for more sophisticated and accurate predictions of energy supply and demand, leading to greater grid stability at a time when our electricity grids have never been more stressed. Researchers are also increasingly using large language models to conduct "needle in a haystack" type inquiries to find the best methods and materials for certain use cases, accomplishing in months what would take a whole team of scientists years to achieve through trial and error methodologies.
“Finding new materials, catalysts or processes that can produce stuff more efficiently is the sort of ‘’needle in a haystack’ problem that AI is ideally suited to,” the Financial Times reported last year in an article musing about “How AI might save more energy than it soaks up.”
In China, for example, scientists used large language models to identify a new molecule, lithium trifluoromethanesulfinate (LiSO2CF3), capable of replenishing lithium ions in dead electric vehicle batteries for thousands of recharge cycles, significantly extending their lifespan. “We had no idea what kinds of molecules could do that job or what their chemical structures would be, so we used machine learning to help us,” Chihao Zhao, part of the research team at Fudan University, was quoted by Scientific American last year.
In nuclear fusion research, artificial intelligence is coming in handy for rapidly modelling how different materials will stand up to the extreme temperatures involved in creating and maintaining plasma, an integral component of the fusion process. The scientists at the Ames National Laboratory in Ames, Iowa who have created the modelling tool, called DuctGPT, say that the use of AI has slashed a monthslong process down to just hours of computation. “Now when you ask it, ‘I want to design a material for fusion that has all x, y, z properties that are critical for use in fusion reactors. Tell me the combination of elements which satisfy the criteria,’ it will give you those combinations of elements with properties,” Ames Lab Scientist Prashant Singh told Interesting Engineering.
Just this month, researchers at University of Toronto Engineering have announced that they used large language models to successfully develop six new metal alloys that could transform the functionality and durability of extreme environments including jet engines and nuclear power plants. The AI-powered system identified all six alloys in just a few weeks, an incredible feat compared to traditional scientific methods.
“There’s enormous demand for materials that can stand up to huge swings of temperature and pressure, such as what you would find inside a jet engine or in the steam generators inside nuclear power plants, anywhere conventional steel just can’t survive,” Yu Zou, who led the project, told Interesting Engineering.
These breakthroughs, and the speed that they are being achieved in, could be transformative for the deployment of next-gen clean energy. By slashing research timelines and finding materials that improve the performance and durability of clean energy infrastructure, experimental technologies that would have otherwise been prohibitively expensive to design, test, and develop can now be made scalable and commercially viable. And the timing could not be better, as we draw closer to major global decarbonization deadlines
By Haley Zaremba for Oilprice.com
Energy IPOs Are Booming Thanks to AI’s Insatiable Thirst for Power
- Energy companies have raised $12.6 billion in IPOs so far in 2026, already nearly triple 2025's full-year total of $4.3 billion.
- Investors are chasing next-gen bets like nuclear fusion and space-based solar as AI's power needs balloon, with Meta signing a 1-gigawatt space solar deal with Overview Energy.
- Solar PV led every new source of U.S. power capacity for 28 straight months through the end of 2025, making up 72.6 percent of all electricity additions, per FERC.
The artificial intelligence boom has created an era of unprecedented uncertainty in the global economy. Large language models are evolving as quickly as they are being integrated, creating a major headache for anyone trying to project their future impact or even their current energy footprint. But while we don’t know exactly how much energy will be needed to power the tech sector in coming years, we do know that it will be a whole lot, and the market is already reacting accordingly.
“Energy companies are raising money at IPO at their fastest pace this century, taking advantage of investors’ hunt for new ways to bet on the boom in power-intensive AI data centres,” states a recent report from the Financial Times. In the first half of this year, the money raised in initial public offerings for energy startups was the highest since 1999, when the first dot-com boom spurred a similar gold rush. And the rate of growth is staggering: in 2025, energy companies raised a total of $4.3 billion for the entire year. The total for 2026 is already at $12.6 billion, and we still have another half year to go.
“Investors started by buying AI-linked names like Nvidia. Then they said, ‘hold on, every chip needs energy to power it’,” RBC clean energy analyst Chris Dendrinos was quoted by the Financial Times. “That’s put a huge tailwind behind these companies.”
This spending spree includes a wide range of energy companies, including unproven and next-gen energy technologies that are enjoying a windfall of funding that they may not otherwise have achieved. The tech sector is investing heavily in pie-in-the-sky energy research like nuclear fusion, enhanced geothermal energy, and space-based solar power. Earlier this year Meta, the company behind Facebook and Instagram, signed a deal with startup Overview Energy to develop as much as 1 gigawatt of space-based solar power.
The market is particularly bullish about nuclear fusion, which has finally broken through on Wall Street after years of struggling to go private. Historically, nuclear fusion research is so expensive and seen as so experimental that it’s been funded by governments and huge public projects. But now fusion startups are popping up in astonishing numbers and seeing successful IPOs.
While it may seem risky to put too much attention into unproven technologies, an all-of-the-above approach will be absolutely essential to keep up with the energy monster that AI is creating. “There’s no way to get there without a breakthrough,” Sam Altman, co-founder and CEO of ChatGPT firm OpenAI, said at the 2024 World Economic Forum in Davos, Switzerland. “It motivates us to go invest more in fusion,” he went on to specify.
The new energy gold rush is also keeping clean energy companies highly competitive at a time when the policy environment has never been more antagonistic. Solar photovoltaics was the single largest source of new power generation capacity in the United States for 28 consecutive months through the end of 2025, ultimately accounting for 72.6 percent of all electricity additions according to data from the Federal Energy Regulatory Commission (FERC).
Miguel Stilwell d’Andrade, chief executive officer of Portuguese electric utilities company EDP, recently told Semafor that we are currently “living in what arguably is one of the best periods to invest in renewables in the US over the last 20 years.” As a result, solar power is booming under the Trump administration, despite its best efforts to dampen domestic renewable growth.
The invisible hand of the market is simply too strong, and energy security is the imperative of the day. “For years, clean energy has been sold as a moral imperative. Now it is simply an economic and geopolitical necessity,” Forbes reported earlier this year. “It’s not about emissions. It’s about resilience and price stability.”
By Haley Zaremba for Oilprice.com
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