Chase Lochmiller

Crusoe’s founding thesis was that energy would become the bottleneck to scaling AI, so compute should migrate to wherever power is cheap and available. Bitcoin mining initially monetized excess energy while Crusoe built its AI platform; ChatGPT’s November 30, 2022 launch then made infrastructure demand unmistakable. “The infrastructure to support AI would not be centralized.”

The data-center shortage is ultimately a shortage of places where GPUs can be connected, powered, and put to work. Crusoe promised roughly 200 MW in Abilene within one year versus the next-best 2.5-year proposal, then manufactured a medium-voltage distribution system internally in 28 weeks when suppliers quoted 100. Vertical integration is less about component margin than “availability” and the ability to route around shifting bottlenecks.

Chase Lochmiller argues that the loudest community objections to AI data centers—water use and higher power prices—are contradicted by Crusoe’s operating evidence. A roughly 140 MW Abilene building uses about as much annual water as 10 homes because GPU cooling is closed-loop; he says energy prices have typically fallen where large facilities stimulate new generation. The real near-term costs are traffic, dust, and noise, while Crusoe expects to provide more than one-third of local tax revenue and more than double school funding.

Crusoe manages commodity risk by selling three products across the AI stack: “data centers, GPUs, and tokens.” Five-year GPU contracts with reliable customers provide payback and cash flow, shorter contracts carry higher margins and renewal risk, and managed training or inference can monetize the same infrastructure at still higher margins. The analogy is an oil supermajor whose profits migrate between upstream, midstream, and downstream as commodity prices change.

The interview’s sharpest challenge to consensus is that GPUs may remain economically useful far longer than investors assume. Crusoe depreciates chips over six years, yet Hoppers bought in 2023 now command higher usage prices than when new; older, slower chips can still deliver cheaper intelligence when services abstract away the hardware. “People underestimate the ingenuity of applications and developers in turning computing power into value.”

The real inference race is not simply dollars per token, because tokens differ in value and customers also optimize latency, throughput, and utilization. Since GPUs are “the most valuable thing in the entire data center,” idle time is burning money; KV-cache orchestration across HBM, DRAM, NVMe, and object storage becomes a core economic capability. Lochmiller expects customers to spend more on closed models but generate more tokens through open-source models.

Lochmiller has become skeptical that durable AI moats exist amid accelerating technical change. “Most of these moats [are] an illusion”; the lasting advantage is moving quickly and adapting to the changing chessboard. The host introduced Crusoe as having raised $3.9 billion in a Series F at a $30.9 billion valuation; Lochmiller sees public-market capital as ultimately advantageous but is unsure whether Crusoe will be public by the end of 2028.