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The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Large language model providers are compute constrained, and their universal response to congestion is to degrade service: route queries to smaller models, cut reasoning effort, truncate context. The industry's accounting says this saves money. We show the accounting is wrong, because it prices a query when the customer buys an answer. A degraded answer fails with some probability, and a failed answer either returns as a retry, inflating arrivals when the system is most loaded, or departs as churn, destroying lifetime value on a ledger no cost dashboard displays. We model inference allocation w

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