Crusoe AI builds its AI infrastructure around energy, not the other way around. The company’s vertically integrated approach starts with sourcing power before deploying managed AI services, a strategy that sets it apart in a sector facing growing backlash over data center energy use.
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According to Omar Lari, senior director of infrastructure as a service at Crusoe Energy Systems LLC, this method allows deployments in unconventional locations. In Abilene, Texas, the company taps wind and natural gas, leveraging the region’s established energy grid and renewable resources to power high-performance computing without straining local utilities. In Iceland, it harnesses geothermal and hydroelectric power, taking advantage of the country’s nearly carbon-neutral electricity mix to minimize environmental impact. A recent partnership with Redwood Materials extends this approach further, powering thousands of Blackwell GPUs with recycled EV batteries, turning retired automotive energy storage into a stable, sustainable source for AI workloads.
“Crusoe’s mission is to accelerate the abundance of energy and intelligence,” Lari said. “Energy is going to drive the next breakthroughs in AI. AI will eventually help us make the next breakthroughs in energy.” This bidirectional relationship between energy and AI underpins the company’s long-term vision, where advancements in one field directly fuel progress in the other.
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Reliability, performance, and data governance are the top priorities for customers, Lari noted. AI-native users running large GPU clusters cannot tolerate interruptions, as even minor delays translate into substantial financial losses.
Lari sees the next big opportunity in connecting model builders with enterprise data owners. AI-native firms specialize in developing cutting-edge models, but their potential is limited without access to high-quality, domain-specific datasets. Enterprises, meanwhile, possess decades of proprietary data—from manufacturing logs to financial transactions—that could refine AI outputs for niche applications. Securely bridging these two ecosystems, with strict governance frameworks, could unlock tailored AI solutions that generic models cannot achieve.
“The intelligence that you deploy is only going to be as good as the expertise and the data that you feed it,” he said. This principle drives Crusoe’s focus on building robust, scalable systems that can evolve alongside AI’s growing sophistication.
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AI adoption across enterprises is expected to accelerate rapidly, making early investments in sustainable, high-performance infrastructure a competitive advantage. As organizations scale their AI workloads, the ability to maintain stability, efficiency, and governance will determine which players lead the next wave of innovation. The focus remains on expanding capacity without sacrificing these core pillars, ensuring that energy and intelligence advance in tandem.
