Due Diligence Report: AI Infrastructure Margin & Compute Economics
Testing whether committed AI infrastructure capital is tied to a real margin model, or only to a capability narrative.
Executive Summary
This review tested whether capital committed to specialized AI infrastructure, including GPU clusters and custom compute, was tied to a margin model or justified primarily by a capability narrative.
Findings
No formal model existed linking infrastructure spend to the revenue or adoption threshold required to justify it; the investment case rested on capability description alone.
Recommendation: Build an explicit margin model before further capital is committed, tying spend to a specific adoption threshold.
Infrastructure procurement was running ahead of actual product adoption, rather than scaling with it.
Recommendation: Resequence remaining spend to track realized adoption rather than projected adoption.
The infrastructure decision had been approved by technical leadership without a corresponding finance or margin sign-off.
Recommendation: Require joint architecture and margin sign-off for future AI infrastructure commitments of this scale.
Conclusion
The infrastructure itself was technically sound. The risk was entirely in the absence of a margin model governing it, which this review converted from an unpriced bet into a tracked, threshold-based decision.
Frequently Asked
What is the biggest risk in AI infrastructure capital decisions?
Approving spend based on capability alone, without a margin model tying it to a specific adoption or revenue threshold that justifies the capital.