Insider Brief
- AI’s rapid growth is increasing energy consumption, but its innovations in efficiency and clean energy applications could help mitigate its environmental impact.
- Large-scale infrastructure investments, such as Stargate, are expanding data center capacity, while new AI models like DeepSeek demonstrate that AI can be developed with greater energy efficiency.
- AI is already improving grid management, streamlining clean energy permitting, and accelerating the discovery of energy-related materials, making it a potential tool for addressing climate challenges.
The artificial intelligence boom is reshaping energy infrastructure. Companies are pouring billions into new data centers, while breakthroughs like DeepSeek show AI can be scaled more efficiently. But even as AI’s energy consumption grows, its most promising tool for mitigating its environmental impact may be AI itself.
Cully Cavness, co-founder, president, and COO of Crusoe, argues in an opinion piece in Fortune that while AI’s energy needs are rising, the same technology driving the demand could also unlock solutions for clean energy adoption, grid efficiency, and climate-friendly innovation.
“Having spent the better part of the past decade building a business determined to confront the paradox of computing innovation and computing energy demand, I know that one of the most powerful tools for building energy and climate solutions will be AI itself.” writes Cavness.
The AI-Energy Paradox
AI is accelerating investment in infrastructure, Cavness points out.
The U.S. is in a global race to expand its data center capacity, illustrated by the scale of projects like Stargate, a multibillion-dollar initiative. At the same time, new models such as DeepSeek, an open-source AI system, suggest that AI development can be made more energy-efficient. While some see these as contradictory trends, Cavness believes they are interconnected.
“Stargate reflects the urgency to build out America’s infrastructure to compete in a global race for AI dominance—infrastructure that’s going to be necessary, regardless of advancements in model efficiency,” Cavness writes. “And DeepSeek shows that innovation will continue to far outpace our initial expectations, which will further drive AI’s demand for infrastructure and energy as that brings down the cost of scaling new AI applications. This is why Crusoe and others have seen an increase in demand for chips and GPUs since DeepSeek was announced.”
The paradox is that while AI’s growth is increasing energy consumption, advances in AI are also making computing more efficient. The result is a race between energy demand and technological efficiency.
AI’s Role in Energy Innovation
Rather than an obstacle to climate progress, AI could be a crucial tool in addressing energy challenges. Cavness added that there are multiple ways AI is already improving energy efficiency.
AI is being used to streamline the permitting process for clean energy projects, a bottleneck that often slows renewable deployment. It is also improving forecasting models that help balance electricity supply and demand, allowing for more efficient use of renewable power sources.
Cavness writes: “In fact, Crusoe, OpenAI, Nvidia, and Lowercarbon Capital hosted a hackathon last year in partnership with the Department of Energy to tackle this very problem. By more accurately forecasting electricity supply and demand, AI is helping to balance power grids more efficiently. This is critical to our ability to integrate more clean energy directly from the source or from battery storage systems.”
The technology is accelerating materials science research, cutting down development timelines for energy-related materials like battery electrolytes. Cavness cited SES AI, which used AI to shrink the time needed to map new battery electrolyte molecules from 8,000 years to two months. AI is also playing a role in fusion energy, with companies like Avalanche Fusion using it to refine reactor designs.
A Manageable Climate Risk?
Critics worry AI’s energy appetite could worsen climate change. The computing power behind large AI models requires massive amounts of electricity, often drawn from fossil-fuel-powered grids. But Cavness argues that the benefits of AI-driven clean energy innovation could outweigh these risks.
“Yes, AI’s energy-intensive nature poses a climate risk, but it’s one that can be mitigated,” he adds.
Crusoe’s own business focuses on reducing the carbon footprint of data centers by using power from clean or wasted energy sources.
The broader challenge, Cavness suggested, is making sure AI’s growth is paired with sustainable energy solutions. Without them, he warned, widely accepted climate goals could become unattainable.
It’s unlikely AI’s momentum can be reversed, according to Cavness. Instead, he writes that companies and policymakers must focus on directing its capabilities toward climate solutions.
“There’s no turning back the clock on AI innovation—but by harnessing its incredible potential, we might just be able to turn back the clock on climate change,” he concludes.
