AI Infrastructure Risk — the white paper

The AI infrastructure buildout is the largest capital deployment cycle of the decade, yet the compute layer remains structurally unfinanceable at scale. GPU clusters depreciate in ways that no measurement standard can currently quantify — leaving lenders, insurers, and rating agencies without the numerical basis they need to price the asset. This paper explains why the gap exists and how metrologically traceable measurement closes it.

The paper is written for institutional investors, infrastructure lenders, residual value insurers, and rating analysts who encounter AI infrastructure in deal flow and need a rigorous framework for evaluating GPU fleet risk. It assumes familiarity with project finance and structured credit but no background in GPU architecture or machine learning.

Readers will come away with a working understanding of how Machina generates a Remaining Useful Life distribution from first-principles measurement, how that distribution maps onto covenant structures used in other infrastructure asset classes, and what a credit committee needs to underwrite an AI compute tranche as investment-grade debt rather than equity or private credit.

In this paper

1. 1.The Measurement Gap in AI Infrastructure Finance
2. 2.A Metrologically Traceable Degradation Standard
3. 3.The Covenant Feed Specification
4. 4.Residual Value Modelling and RUL Distributions
5. 5.Path to IG-Rateable AI Infrastructure Debt
6. 6.Case Studies: Barranquilla and CoreWeave DDTL