{
  "abstract": "Large language model providers are compute constrained, and a common response to congestion is to degrade service. A degraded answer fails with some probability, and a failed answer either returns as a retry or departs as churn, destroying LTV on an unaccounted ledger. We model inference allocation as a newsvendor whose stockout cost is churned lifetime value, a geometric retry multiplier in which the recycled product is dissatisfaction, and a two-regime transient fluid queue whose arrival rate is made endogenous by retries. Statically, there is a regime in which a cheaper model saves energy per initiated task while consuming strictly more capacity per initiated task, so the discount inverts when capacity binds. Dynamically, a reactive throttle fired during a surge can cross an ignition threshold beyond which it manufactures more traffic than it sheds, and a release rule set below the degraded equilibrium converts a transient surge into a permanent degraded regime. With heterogeneous customers, throttling is a transportation problem in retry-inflated load whose optimal policy rations intelligence by critical ratio, and whose dual, the shadow price of intelligence, prices a marginal query by class and by hour. Under congestion, throttling is not a cost lever but a demand lever.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.23986",
  "author_total": 1,
  "authors": [
    {
      "name": "Elioth Sanabria"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3gqcsc0vwsm4b.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.23986",
  "fulltext_url": null,
  "id": "3gqcsc0vwsm4b",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.23986",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.PF"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "1404|Supply Chain and Inventory Management"
    ]
  ],
  "openalex": "W7204228316",
  "pdf_url": "https://arxiv.org/pdf/2608.23986",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3gqcsc0vwsm4b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:42Z",
      "native_id": "2608.23986",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.23986"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem",
  "title_text": "The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:42Z",
  "url": "https://research.draconitic.com/paper/3gqcsc0vwsm4b",
  "venue": "arXiv",
  "versions": []
}