From AI Spending Guidance to Grid Commitment: Disclosure Attrition and Tariff-Duration Mismatch

Abstract

An 80% or 90% minimum bill is not necessarily 80% or 90% protection over the life of a grid asset. This paper documents two linked losses in the conversion of AI investment narratives into infrastructure commitment: disclosure attrition upstream and protection-duration attrition downstream. We combine two independently assembled, hand-audited U.S. datasets covering 40 hyperscaler earnings events and 27 large-load tariff chains. Cash capital expenditure is observable in all 40 events, but numeric forward guidance appears in 14, comparable same-target-year revisions in nine, and a blind model supports positive AI attribution in only nine of the 14 author-coded guidance observations; no event reports a standardized AI-specific payoff. Among five evidence-complete tariff architectures, stated minimumpayment floors are 80% or 90%, yet their narrow discounted 30-year coverage ranges from 0.388 to 0.900 at a 7% discount rate. Excluding the single 30-year term option, coverage ranges from 0.388 to 0.773 because support expires before the assumed recovery horizon. The datasets are not transactionmatched and support no causal claim. Their joint contribution is a duration-aware measurement result: better disclosure can improve planning without creating security, while strong headline protection can leave substantial tail exposure.

Author abstract from the research manuscript. Typography and line breaks have been normalized; the linked source may show a different manuscript version.