Authored by Haley Zaremba via Oilprice.com,
Biglaw firm Duane Morris argues the energy sector's greatest AI-related risk is not surging power demand but failing to adopt AI tools fast enough to remain competitive.
MIT research challenges industry claims that AI efficiency gains will offset its enormous energy consumption, while new data centers continue to be approved at record pace.
AI shows genuine promise in clean energy applications - from nuclear fusion modeling to EV battery recovery - but the AI investment boom is simultaneously diverting capital away from next-gen energy research.
The artificial intelligence boom has created unprecedented pressure and anxiety in the energy industry. The public and private sector alike are expending enormous amounts of effort trying to quantify the amount of electricity that will be needed to power data centers in the near future, and get ahead of the skyrocketing energy demands headed for our already outdated and beleaguered electric grids. But the answer to the energy monster that AI is unleashing could very well lie in the application of AI tools.
A new article published by Biglaw firm Duane Morris argues that the most prescient AI-related risk for the energy industry is not the one posed by the demands of the sector itself, but the risk of falling behind in AI integration and application. The firm argues that the energy sector has an obligation to consider the ways in which large language models can be an asset, concluding that "AI should not be viewed only through the lens of risk avoidance."
"The risks of AI remain real and must be governed thoughtfully," the Energy Intelligence article goes on to say. "But in a sector responsible for critical infrastructure, the greater long-term risk may not be using AI too aggressively - it may be failing to use it enough."
Indeed, proponents of AI adoption argue that although training and operating large language models eats up an enormous amount of energy, not to mention other finite resources such as water, AI will be instrumental in making a wide array of industries significantly more energy-efficient. In fact, through these widespread efficiencies, some experts say that AI has the potential to save more energy than it consumes overall.
However, critics say that these claims are overblown and the result of wishful thinking rather than rigorous modelling. A 2025 report from MIT challenges such claims, pointing out that touted efficiency gains have not yet come to fruition, and may not be forthcoming. And while numbers on AI's efficiency gains - and even the amount of energy that AI is currently using - are still lacking, new data centers are being greenlit at lightning speed.
"AI's integration into almost everything from customer service calls to algorithmic 'bosses' to warfare is fueling enormous demand," the Washington Post wrote in an article published last summer. "Despite dramatic efficiency improvements, pouring those gains back into bigger, hungrier models powered by fossil fuels will create the energy monster we imagine."
Moreover, it is just this fear of "being left behind" that's fuelling the AI boom, arguably even more than actual demand. There is question as to whether rapid AI integration into everything from our energy grids to our electric toothbrushes - no, really - is going to create a more sophisticated and energy-efficient world, or whether it's just a resource-intensive bid to stay relevant in a rapidly changing global economy.
Wherever you stand on the issue of AI integration, it's increasingly clear that AI has some extremely promising applications in next-gen clean energy technologies. Researchers are using large language models to conduct "needle in a haystack" type inquiries to find the best methods and materials to advance nuclear fusion modelling, for example. In the renewable energy sector, AI is being used to improve forecasting of energy supply and demand for greater grid stability. And AI could even soon be used to give new life to dead EV batteries.
The massive energy needs of AI are also pushing increasing and intensified research efforts into cutting edge clean energy technologies such as nuclear fusion, advanced geothermal, and space-based solar power. But Big Tech is running on natural gas while it powers research into these clean energy ambitions. And, overall, research into next-gen energy is suffering from the AI gold rush as investors redirect their attention.
AI's role in the energy sector is anything but simple. And it's true that avoiding AI integration entirely won't solve the problem. But if the energy sector is going to eschew risk aversion and lean into the AI boom as Duane Morris suggests, it needs to have a strong policy foundation and a much smarter AI strategy going forward.
By Haley Zaremba for Oilprice.com