The Cost of Powering AI: Distributional Impacts and an Operational Standard for Self-Powered Deployments

The Cost of Powering AI: Distributional Impacts and an Operational Standard for Self-Powered Deployments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Cost of Powering AI: Distributional Impacts and an Operational Standard for Self-Powered Deployments Alec Pow, Lora Stonden This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7783422/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial intelligence (AI) is scaling amid grid congestion, volatile peak pricing, and uneven water availability. New data-center load can shift public costs—scarcity-hour prices, capacity upgrades, marginal emissions, and water withdrawals—onto disadvantaged communities with elevated energy burdens. This paper treats AI as an electrical load and proposes an operational, auditable Self-Powered AI Standard. Deployments must (i) supply attributable additional clean energy (AACS), (ii) match consumption hour-by-hour within the balancing area (HCC/EHCC with Clean Matching Shortfall), and (iii) maintain firm self-supply availability (FSSA) while demonstrating near-zero Scarcity-Adjusted Import Exposure (SAIE) in top-decile price/CO₂ hours. Using public indicators—wholesale prices, interconnection queues, marginal emissions, congestion, and water context—we construct a screening of “where an extra MW hurts,” outline siting/operation patterns (islanded training parks; grid-tied inference with islanding), portfolio 24/7 matching, and a Levelized Cost of AI Power (LCAP) for self-supplied stacks. A telemetry case study demonstrates end-to-end computation of AACS, HCC/EHCC/CMS, FSSA, and SAIE. The framework enables growth in compute without leaning on the public during scarcity and aligns reporting with standard telemetry and open datasets. Energy Engineering Theoretical Computer Science AI telemetry energy accounting cost modeling self-powered AI distributional impacts Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7783422","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524836897,"identity":"d709aa29-4f78-468d-a1f5-8e3f7747d6a8","order_by":0,"name":"Alec Pow","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACdiD+wHAASDI2EKmFGah2RgKpWph5wFqIBfzNzAc/2/64I2cufbjxw8c9DHLm/Qvwa5E4zJYsnZPwzNiyL7FZcsYzBmOZGw8IWHOYx4w5J+Fw4oYzjG3MPAcYEmdIEHCl/GH+b8wWCYfrwVr+EKPF4DAPGzNDwuEEA5AWBpAW/gb8WgwPsxlL9qQdNtzZw9gs2XNAwlhCgoBX5I43P/zww+awvDkPO5BxwEZOgp+AwxAuhFBAKyQSSNMCBETbMgpGwSgYBSMFAAClg0KmwvTa/AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-1648-0926","institution":"ThePricer Media, LLC","correspondingAuthor":true,"prefix":"","firstName":"Alec","middleName":"","lastName":"Pow","suffix":""},{"id":524836898,"identity":"c649f959-e1b6-455a-b860-443e90d95706","order_by":1,"name":"Lora Stonden","email":"","orcid":"","institution":"Independent Researcher","correspondingAuthor":false,"prefix":"","firstName":"Lora","middleName":"","lastName":"Stonden","suffix":""}],"badges":[],"createdAt":"2025-10-05 07:48:48","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7783422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7783422/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92910800,"identity":"b7a200f8-aff4-4368-9719-75cbd7bbedcb","added_by":"auto","created_at":"2025-10-07 03:24:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":911434,"visible":true,"origin":"","legend":"","description":"","filename":"PowStonden2025SelfPoweredAIv1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7783422/v1_covered_87fa33c8-413e-4d56-9b77-bb8946eb9562.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Cost of Powering AI: Distributional Impacts and an Operational Standard for Self-Powered Deployments\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"ThePricer Media, New York City, USA","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI telemetry, energy accounting, cost modeling, self-powered AI, distributional impacts","lastPublishedDoi":"10.21203/rs.3.rs-7783422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7783422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) is scaling amid grid congestion, volatile peak pricing, and uneven water availability. New data-center load can shift public costs—scarcity-hour prices, capacity upgrades, marginal emissions, and water withdrawals—onto disadvantaged communities with elevated energy burdens. This paper treats AI as an electrical load and proposes an operational, auditable Self-Powered AI Standard. Deployments must (i) supply attributable additional clean energy (AACS), (ii) match consumption hour-by-hour within the balancing area (HCC/EHCC with Clean Matching Shortfall), and (iii) maintain firm self-supply availability (FSSA) while demonstrating near-zero Scarcity-Adjusted Import Exposure (SAIE) in top-decile price/CO₂ hours. Using public indicators—wholesale prices, interconnection queues, marginal emissions, congestion, and water context—we construct a screening of “where an extra MW hurts,” outline siting/operation patterns (islanded training parks; grid-tied inference with islanding), portfolio 24/7 matching, and a Levelized Cost of AI Power (LCAP) for self-supplied stacks. A telemetry case study demonstrates end-to-end computation of AACS, HCC/EHCC/CMS, FSSA, and SAIE. The framework enables growth in compute without leaning on the public during scarcity and aligns reporting with standard telemetry and open datasets.\u003c/p\u003e","manuscriptTitle":"The Cost of Powering AI: Distributional Impacts and an Operational Standard for Self-Powered Deployments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 03:16:36","doi":"10.21203/rs.3.rs-7783422/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebaae95f-1754-458e-abfa-e3a2229e50f8","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55782593,"name":"Energy Engineering"},{"id":55782594,"name":"Theoretical Computer Science"}],"tags":[],"updatedAt":"2025-10-07T03:16:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 03:16:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7783422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7783422","identity":"rs-7783422","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}