Crusoe AI puts energy first to power sustainable AI infrastructure at scale

For Crusoe AI, sustainable AI infrastructure has always been the founding premise, a welcome alternative amidst a surge of antipathy for data centers in local communities.

The company takes a vertically integrated, energy-first approach to building AI infrastructure, sourcing power and deploying managed AI services on top, according to Omar Lari (pictured), senior director of infrastructure as a service at Crusoe AI. This method has enabled deployments in locations that are far from traditional (wind and natural gas in Abilene, Texas, or geothermal and hydroelectric in Iceland), as well as a partnership with Redwood Materials, powering thousands of Blackwell GPUs with recycled EV batteries.

“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.”

Lari spoke with theCUBE’s Christophe Bertrand and Alison Kosik at Pure Accelerate 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Crusoe’s energy-first strategy differentiates its sustainable AI infrastructure offering and what enterprises need to prioritize as they scale AI workloads. (* Disclosure below.)

Sustainable AI infrastructure success depends on reliability, performance and data governance

Crusoe’s partnership with Everpure reflects three priorities Lari says matter the most to AI infrastructure customers: reliability, empathy for the service provider model and supply chain expertise. These qualities are of enormous import because AI-native customers running massive GPU training clusters have no tolerance for downtime. Roughly 800 Blackwell GPUs represent a quarter petabyte of high-bandwidth memory, and idle time burns capital at scale.

“Performance is a really important piece,” Lari said. “Think about if you have 800 Blackwell GPUs — that represents about a quarter petabyte of high-bandwidth memory. If you’re waiting 20, 30 minutes for that to get hydrated, you’re burning through an enormous amount of capital just waiting for bytes to float around.”

Looking ahead, Lari said the most important opportunity is connecting AI-native model builders with enterprise data owners. AI natives have the models, and enterprises have decades of accumulated data and domain expertise. Securely combining those two worlds, along with proper governance, is where the next wave of industry-specific AI value will be created. AI infrastructure demands being treated as a step-function increase in complexity instead of an incremental upgrade — building that expertise now is critical, Lari noted.

“The intelligence that you deploy is only going to be as good as the expertise and the data that you feed it,” Lari said. “AI adoption across the enterprises is going to accelerate massively.”

(* Disclosure: TheCUBE is a paid media partner for the Pure Accelerate event. Neither Everpure, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE