AI as a Credence Good: Quality Competition,Limited Verification, and the IndustrialOrganization of Disclosure

AI as a Credence Good: Quality Competition,Limited Verification, and the IndustrialOrganization of Disclosure | 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 AI as a Credence Good: Quality Competition,Limited Verification, and the IndustrialOrganization of Disclosure Xufeng Zhang, Han Li, Shenghui Bao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9077074/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 In this paper we study competition between AI providers when users cannot fully verify model quality. Many AI services are not well described as standard search goods, and they are not pure experience goods either. Price, interface quality, and latency are usually observable, but reliability, hallucination risk, and the downstream cost of error are often only imperfectly observable even after use. Building on the emerging view that algorithmic advice can exhibit credence-good features, we embed that insight in a static industrial-organization model of vertical quality differentiation, costly certification, and limited user comprehension. Two firms choose whether to offer a low-quality or high-quality model. High-quality AI reduces error risk, but it is slower and costlier. A high-quality firm can purchase credible certification or disclosure, yet only a subset of users can interpret it. We characterize the pure-strategy equilibrium set, show how low-quality pooling can arise even when superior technology exists, and identify a quality-trap region in which the unique market equilibrium is low-quality pooling although an allocation with one high-quality provider is welfare superior. We then analyze policy. Standardized certification works through the demand side by increasing the fraction of users who can reward quality; minimum quality standards work directly but bluntly; liability shifts firms' cost incentives and weakly shrinks the region in which a low-quality industry outcome can be sustained. Contrary to a common rhetorical move in AI policy debates, these instruments are not interchangeable. The model also clarifies that our framework is a certification model rather than a full Grossman-Milgrom unraveling game: the key distortion comes from limited user comprehension of costly, truthful quality communication. The results offer a tractable industrial-organization foundation for current debatesover hallucinations, model evaluation, AI documentation, and governance. JEL Codes: D82, L13, L15, L51, O33 Generative AI Credence goods Disclosure Quality competition Liability Full Text Additional Declarations No competing interests reported. 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-9077074","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603500976,"identity":"6795a572-f77a-4fae-9387-6eb6c8c1331c","order_by":0,"name":"Xufeng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFACxgdAwgbC5iFOC7MBkEgjXcthErQY3EhmfFzw63we/4wExgdv24jQIjkjmdl4Zt/tYokbCcyGc4nRwi+Rf0yat+d2YsONBDZpXmK0sEkks//m7TmXOP9GApBBnC3JbMw8Pw4kbgDawkyUFsmex8zSvA3JiRvPPGyWnHOOCC0Gx5MZP/P8sUucdzz54Ic3ZURoAQNGsHsYG4hVDwJ/SFE8CkbBKBgFIw4AACDoNWLN5k56AAAAAElFTkSuQmCC","orcid":"","institution":"Resp AI Research Lab, CIIOE","correspondingAuthor":true,"prefix":"","firstName":"Xufeng","middleName":"","lastName":"Zhang","suffix":""},{"id":603500977,"identity":"3bbeb8bd-80a5-4c3c-b0ea-cf48010a9a31","order_by":1,"name":"Han Li","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Li","suffix":""},{"id":603500978,"identity":"6d2b3349-fe69-40bd-8fe1-587824545127","order_by":2,"name":"Shenghui Bao","email":"","orcid":"","institution":"National University","correspondingAuthor":false,"prefix":"","firstName":"Shenghui","middleName":"","lastName":"Bao","suffix":""}],"badges":[],"createdAt":"2026-03-09 22:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9077074/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9077074/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106556406,"identity":"ff82e3b4-2683-42c3-a9a4-75a447e6f717","added_by":"auto","created_at":"2026-04-09 20:09:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":340082,"visible":true,"origin":"","legend":"","description":"","filename":"IO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9077074/v1_covered_f96d3128-2552-4446-9290-9b84b6fb1d6c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI as a Credence Good: Quality Competition,Limited Verification, and the IndustrialOrganization of Disclosure","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Generative AI, Credence goods, Disclosure, Quality competition, Liability","lastPublishedDoi":"10.21203/rs.3.rs-9077074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9077074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper we study competition between AI providers when users cannot fully verify model quality. Many AI services are not well described as standard search goods, and they are not pure experience goods either. Price, interface quality, and latency are usually observable, but reliability, hallucination risk, and the downstream cost of error are often only imperfectly observable even after use. Building on the emerging view that algorithmic advice can exhibit credence-good features, we embed that insight in a static industrial-organization model of vertical quality differentiation, costly certification, and limited user comprehension. Two firms choose whether to offer a low-quality or high-quality model. High-quality AI reduces error risk, but it is slower and costlier. A high-quality firm can purchase credible certification or disclosure, yet only a subset of users can interpret it. 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