2025年下游市场中的人工智能与竞争动态研究报告 Artificial intelligence and competitive dynamics in downstream markets-OECD经合组织

ARTIFICIALINTELLIGENCEANDCOMPETITIVEDYNAMICSIN

DOWNSTREAMMARKETS

OECDRoundtablesonCompetitionPolicyPapers,No.331

Artificialintelligenceandcompetitivedynamicsindownstreammarkets

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Abstract

Thispaperexamineshowtheadoptionofartificialintelligence(AI),

particularlygenerativeandagenticsystems,isreshapingcompetitionin

downstreammarkets.ItexploresmechanismsthroughwhichAImaylowerbarrierstoentry,substituteforlabour,reduceminimumefficientscale,andsupportinnovationandproductdifferentiation.Atthesametime,it

highlightsemergingrisksrelatedtodataaccess,modelrestrictiveness,andthedownsidesofAIuse.

ThepaperanalyseshowAIaffectsmarketstructureandmayshapefirmbehaviour,findingthatitscompetitiveimpactishighlycontext-dependent,shapedbysectoralexposuretoAIuse,firmsizeandcapabilities,and

accesstoenablinginputs.Itconcludesbydiscussingenforcement,

advocacy,andregulatorytoolsthatmayhelppreservecontestability,andidentifiesareasforfutureresearch,includingattributionofliabilityandtheimplicationsofagenticAIsystems.Theanalysisisintendedtosupport

competitionauthoritiesinnavigatingAI-relatedmarketdevelopments.

Keywords:AgenticAI,Algorithmiccollusion,ArtificialIntelligence,Barrierstoentry,CompetitionenforcementinAI,Dataaccess,Efficiencyandproductivitygains,GenerativeAI,Marketcontestability,Pricediscrimination,SectoruptakeofAI

JELcodes:L40,L86,D43,D82,O33,K21

Thispaperispartoftheseries“OECDRoundtablesonCompetitionPolicyPapers”,

/10.1787/20758677

.

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Acknowledgements

ThisnotewaspreparedbyAniaThiemannoftheOECDCompetitionDivision.HelpfulcommentsandreviewwereprovidedbyOriSchwartz,AntonioCapobianco,RichardMayandConnorHogg,alsooftheOECDCompetitionDivision.ThispaperispartoftheOECDHorizontaIProjecton6ThrivingwithAI:EmpoweringEconomies,SocietiesandCitizens.,IthasalsobenefitedfromusefulcommentsfromcolleaguesintheOECDDirectorateforScienceTechnologyandInnovation,particularlyJuliaCarroattheDigitalPolicyCo-ordinationTeamaswellasLuisArandaandJeffMollinsfromtheArtificialIntelligenceandEmergingDigitalTechnologiesDivision.Thenotewaspreparedtoserveasbackgroundmaterialfordiscussionson“CompetitionandArtificialIntelligence”takingpIaceattheDecember2025sessionofthe24thOECDGlobalForumonCompetition,andinparticuIarforthesessionon“Artificialintelligenceandcompetitivedynamicsindownstreammarkets”.

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Tableofcontents

Disclaimers2

Abstract3

Acknowledgements4

Executivesummary6

AIasageneral-purposetechnology6

Mechanismssupportingcompetition6

Emergingrisks,limitsandcompetitionconcerns7

Conclusionandfuturedirections8

1Introduction9

1.1.Keyconceptsused9

2TheimpactofAIadoptiononmarketdynamics11

2.1.WhatarethemechanicsbywhichAIcanfostercompetition?11

2.2.LimitstotheusefulnessofAIsystems21

2.3.SectorexposureanduptakeofAIvarysignificantly22

2.4.TheemergingcaseofAgenticAI25

2.5.Conclusion25

3AI-relatedcompetitionconcernsindownstreammarkets27

3.1.Competitionenforcementconcerns27

3.2.Anemergingconcern:theattributionofliability32

3.3.ActionstosupportcompetitioninAI-enableddownstreammarkets33

4Conclusionandfuturedirections37

References39

Notes50

BOXES

Box1.AIandknowledgeworkersubstitution12

Box2.UseofAIinthemanufacturingsector14

Box3.AI-drivencreditscoringcanboostalternativefinance16

Box4.ExperimentalevidenceontheproductivityeffectsofgenerativeAI17

Box5.GenerativeAIforagriculturalsupportinMalawi18

Box6.AIuptakeanddiffusionindownstreamsectorsinIndia24

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Executivesummary

Thispaperexamineshowtheadoptionofartificialintelligence(AI),particularlygenerativeandagenticsystems,isreshapingcompetitionindownstreammarkets.WhilemuchofthepolicydebatehasfocusedoncompetitioninAIinfrastructureandfoundationmodeldevelopment,thispaperconsidershowAIisusedasaninputintoproduction,servicedelivery,logisticsandcustomerengagement.

TheanalysissuggeststhatAImaysupportcompetitionindownstreammarketsbyloweringbarrierstoentry,reducingminimumefficientscale,andenablingproductdifferentiationandinnovation.Atthesametime,itidentifiesemergingrisksrelatedtodataaccess,modelrestrictiveness,verticalintegrationandalgorithmicconduct.Thepaperdoesnotaimtoprovideanexhaustivesectoralreview,butrathertohighlightmechanismsandconditionsthatmaysupportorhindercompetitioninAI-enabledmarkets.

AIasageneral-purposetechnology

GenerativeAI(GenAI)exhibitscharacteristicsofageneral-purposetechnology,includingpervasiveness,continuousimprovementandinnovation-spawningpotential.Estimatesofproductivitygainsvary,withstudiessuggestingannualincreasesintotalfactorproductivity(TFP)rangingfrom0.07to1.3percentagepoints.However,thediffusionofAIremainsunevenacrosssectorsandfirms,raisingconcernofanemergingAI-divide.

Mechanismssupportingcompetition

AIadoptionmayfostercompetitionthroughseveralchannels:

Laboursubstitutionandaugmentation:GenAIsystemscanautomatecognitivetasks,particularlyroutineorrepetitivetasks,loweringtheskillsthresholdformarketparticipation.Fieldexperimentsalsosuggestsignificantproductivitygains,particularlyforlessexperiencedworkerswhentheyuseAItohelpperformhigher-skilledtasks.Thismayreduceentrybarriersinknowledge-intensivesectorsbyenablingsmallerteamstoperformfunctionsthatpreviouslyrequiredlarger,highlyskilledworkforces,supportingleanerandmoreagilebusinessmodels.

Productimprovementandinnovation:AIcanenablemasscustomisationandpersonalisedservices,supportingdifferentiation.Smallerfirmscanaccessdesignandcommunicationtoolspreviouslyreservedforlargerincumbents.Thisexpandstherangeofviablebusinessmodelsandopensspaceforchallengerstocompeteonquality,userexperienceandnewproductfeatures,ratherthansolelyonscale.

Costreduction,efficiencyandproductivitygains:AIadoptioncanreduceoperatingcoststhroughautomation,predictiveanalyticsandmodulardeployment,whichmaylowerfixedcostsandsupportincrementalscaling.Atthesametime,empiricalstudiesreportproductivitygainsintheformoftimesavingsandqualityimprovementsacrossarangeofprofessionaltasks.Takentogether,theseeffectscanenableleanerbusinessmodelsandfacilitateentryintomarketsthatmightpreviouslyhavebeeninaccessible.

Reducedsearchandswitchingcosts:AIcanalsopromotecompetitionindownstreammarketsbyreducingconsumers’searchandverificationcosts.AI-enabledsearch,recommenderandconversational

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systemshelpfilter,rankandpersonaliseoptions,whichcanbroadeneffectivechoicesets,improvematchingefficiencyandintensifycompetitivepressureonpriceandquality.Theoreticalandempiricalworkshowsthatthesesystemssavetimeandreduceeffort,drawinginpreviouslyinactiveconsumersandenablingmoreefficientcomparisonacrossalternatives.

Thesebenefitsmaynotbeevenlydistributedasthecompetitiveeffectsarehighlycontextdependent.Adoptioncosts,integrationchallengesandaccesstoenablinginputssuchasdataandcomputemaylimituptake,particularlyamongsmallerfirms.Moreover,somesectorsremainlessexposedtoAI,andcertaintasksmaynotbeeasilyautomated.DataaccessandtheconditionsunderwhichfirmscanuseoradaptAImodelsalsoinfluencecompetitionbothupstreamanddownstream.Whileconcentratedcontrolofdataandcloudinfrastructuremaycreateadvantagesforlargeproviders,widespreadaccesstointeroperablemodelsandtheabilitytofine-tunethemcansupportdifferentiationandentry.Thecompetitiveimplicationsthereforedependonaccessterms,portability,andthepracticalabilityoffirms–particularlysmallerones–totailorandintegrateAIintotheiroperations.

Furthermore,thepro-competitiveeffectsofreducedsearchcostsmaydependonsystemdesign:ifrankingorrecommendationprocesseslacktransparencyorembedbiases,theymaychanneldemandtowardsparticularsuppliersandlimitcontestability.Ensuringfairvisibilityandportabilityacrossintermediariesisthereforecentraltorealisingthesebenefits.

Emergingrisks,limitsandcompetitionconcerns

ThepaperhighlightspotentialdownsidesofAIadoption,suchashomogenisationofoutputsandreducedcreativediversity,securityvulnerabilitiesandqualitycontrolchallenges,andsectoraldivides,withprofessionalservicesandICTshowinghigheruptakethanmanualorin-personservicesectors.Theconceptofan“AIdivide”isemerging,withadvancedeconomies,andlargerfirmsbetterpositionedtoabsorbupfrontcostsandintegrateAIintoworkflows,andsomeemergingmarkets,orfirmsthatlackaccesstocomputeandtalent.Largerfirmsmayalsobeabletoacquirepotentialemergingcompetitors.Thismayreinforceincumbencyandlimitcontestability.

Potentialcompetitionconcerns

ThepaperalsooutlinesbothtraditionalandemergingcompetitionconcernsthatmayarisefromthedownstreamuseofAIsystemsbyfirms:

•Horizontalco-ordination:AImayfacilitatealgorithmiccollusion,includinghub-and-spokearrangementsandtacitco-ordination.Enforcementchallengesincludeattributionofintentandevidentiarystandards.

•Unilateralconduct:DominantfirmsmayuseAItoexcluderivalsthroughrankingbias,personalisedpricingorbundling.VerticalintegrationacrosstheAIstackmayenablecross-layerleveragingandinputforeclosure.

•Attributionofliability:Autonomousoptimisationcomplicatesenforcement.Thepaperdiscussestheneedforauditability,transparencyandoversightmechanismstosupportaccountability.

•AgenticAIandmarketstructure:ArelatedemergingconcernisthedevelopmentofAIagentsandAgenticAI.AIagentsaresystemsthatcanperceiveandactontheirenvironment,oftenautonomously,toachievespecificgoals,andcanadapttheirbehaviourinresponsetochanginginputsorcontexts(OECD,2025[1]).AgenticAIreferstosystemscomposedofmultipleco-ordinatedAIagentsthatcanbreakdowntasks,collaborate,useexternaltoolsandpursuegoalsoverextendedperiodswithlimitedhumansupervision.Thesesystemsaredesignedformoreopen-endedandlesspredictableenvironments,enablingautonomousplanningandactionacrossworkflows.Whilestillnascent,AgenticAIcouldreshapecompetitioninmarketssuchassearch,

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workflowautomationandcustomerengagement.Theirintegrationintocloudandhyperscalerecosystemsmayalsoincreaseverticaldependenciesandraiserisksofleveragingorforeclosure,particularlywhereaccesstocompute,dataordistributionbecomesstrategicallycontrolled.

Conclusionandfuturedirections

AIadoptionindownstreammarketspresentsbothopportunitiesandchallengesforcompetition.WhileAImaylowerentrybarriersandsupportinnovation,itsimpactisshapedbyaccesstoenablinginputs,marketstructureandinstitutionalcontext.Thepapersuggeststhatamulti-prongedapproach-combiningenforcement,advocacy,includingmarketmonitoring,regulationandco-operation-maybeneededtoensurethatAI-enabledmarketsremainopen,pro-competitiveandinnovation-friendly.

Furtherempiricalresearchisneededtoassesssector-specificimpacts,particularly(butnotexclusively)inareassuchashealth,finance,professionalservices,platformservices,search,logisticsandcreativeindustries.TheriseofagenticAIalsowarrantsclosemonitoring,givenitspotentialtoreshapeintermediationandconsumerchoice.

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1Introduction

Thispaperexamineshowtheadoptionofartificialintelligence(AI)byfirmsintraditionalandemergingsectorsmayaffectcompetition.WhilemuchattentionhasfocusedoncompetitioninAIinfrastructureandfoundationmodeldevelopment,theemphasishereisonhowAIisusedasaninputintoproduction,servicedelivery,logistics,andcustomerengagement.ThepaperispartoftheOECDHorizontalProjecton‘ThrivingwithAI:EmpoweringEconomies,SocietiesandCitizens’andalsocontributestoaworkstreamofOECD“CompetitionandAI”papersthatdiscussstepsintheAIvalue-chain,aswellasvariousaspectsofcompetitionwithinthefieldofAIitself(seenotably(OECD,2024[2];OECD,2025[3];OECD,2025[4])).

AIadoptionisheterogeneousacrossfirmsandsectors.Productivityeffectsalsovary,withsignificantgainsinprofessionalservicesandmanufacturing,butlessinmarketsthatlargelyrelyonmanualworkandin-personservices(Calvino,ReijerinkandSamek,2025[5]).ThecentralquestioniswhetherusingAItechnologieshelpslowerbarrierstoentryandpromotecompetition,orconverselymayreinforceincumbencythroughcontrolofdata,compute,andecosystems.AIcanallowfirmstoautomate,scale,personalise,andoptimiseoutputsandservices,supportingentryandinnovation.Atthesametime,itmayfacilitateconcentrationofcapability,verticalleveragingbydominantplatforms,ornewformsofanti-competitivebehaviour,includingalgorithmiccollusion.

Theanalysisdoesnotaimtoprovideanexhaustiveaccountofallsectors.Rather,theaimistoshedlightonthefactorsandmechanicsthatsupportorhindercompetitioninthemarketsthatadoptAIbasedtools.GiventhatissuesspecificallyrelatedtoAIinfrastructurewillbeaddressedinaseparateOECDpaper(OECD,2025[4]),thispaperreferstothemonlyinsofarastheyhavedirectimplicationsfordownstreamcompetition.

Asthetopicisasyetunder-researchedandthesectorisincrediblyfast-moving,withdailybreakthroughs,start-uplaunchesornewusesreportedinthemedia,manyexamplesrelyonpress-reportsorInternetresearch.Examplescitedhereinshouldbetakenasillustrativeasopposedtoauthoritative.Takentogether,theaimisthattheseperspectiveswillcontributetoabodyofworktohelpreadersbetterunderstandinwhichcircumstancestheuseofAIislikelytobepro-competitiveandhence,whereregulatorsshouldseektopromoteorprotectitsentryandusefromvestedinterestsorregulation;orconversely,tounderstandwhentheuseofAImightbeanti-competitive,andhencenationalcompetitionauthorities(NCAs)shouldbevigilanttoprotectcompetitionacrossawiderangeofdownstreammarkets.

1.1.Keyconceptsused

Thissectiondescribes,forthepurposeofthispaper,severalconceptsinartificialintelligence(AI)thatarecentraltotheanalysisofitsimpactoncompetition.TheterminologyisgroundedinOECDframeworks,includinganalyticalandpolicy-orientedpublications.TheOECDdefinesanAIsystemasamachine-basedsystemthat,forexplicitorimplicitobjectives,infers,fromtheinputitreceives,howtogenerateoutputssuchaspredictions,content,recommendations,ordecisionsthatcaninfluencephysicalorvirtualenvironments.DifferentAIsystemsvaryintheirlevelsofautonomyandadaptivenessafterdeployment(OECDCouncil,2024[6]).TheAIstack(infrastructure,modeldevelopmentanddeployment),itstechnicalcomponentsaswellasthekeyenablersofcompute,dataandtalent,alongwiththeircompetitiveimpact,

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havealreadybeenextensivelyanalysedinpreviousOECDcompetitionwork(OECD,2024[2];2025[3]),andarethereforetakenasgivenhere.

FoundationModels(FMs)refertoaclassofvery-largeAIsystems(oftenadeepneuralnetwork)trainedonvastanddiversedatasets(oftenknownascorpora1)usingself-supervisedlearningtechniques.Theyaredesignedtoperformawiderangeofgeneraltasksacrossdomainssuchaslanguage,vision,andspeech,andtobegeneral-purposeinnature.FMscanpoweravarietyofdownstreamapplicationsandunderpinmanyofthemostadvancedAIsystemscurrentlyinuse,includingthoseusedinlanguageprocessing,imagegeneration,andscientificdiscovery(OECD,2024[2];Calvino,HaerleandLiu,2025[7];Zenner,2023[8]).

LargeLanguageModels(LLMs)areaprominenttypeofFMs.Theyaredesignedtounderstandandgeneratehumanlanguage,andcanperformawidearrayoftaskssuchassummarisation,translation,questionanswering,andcontentgeneration.TheyareoftenaccessedusingchatbotinterfacessuchasChatGPT(Open.AI)Claude(Anthropic)andLeChat(Mistral).Theseinterfacesdeterminehowend-usersaccessandbenefitfromthecapabilitiesoffoundationmodels(OECD,2023[9];byby.dev,2025[10]).

GenerativeAI(GenAI)referstoAIsystemsthatarecapableofproducingnovelcontent,suchastext,images,audio,video,orcode,basedonpatternslearnedfromtrainingdata.TheGenAIsectorcanbebroadlydividedintotwosegments:upstream,wherefoundationmodelsaredeveloped,anddownstream,wherespecialisedmodelsarebuiltfromFMs.Developersmaydecide(i)toverticallyintegratetheirfoundationmodelswithdownstreamAIservices,(ii)toprovideaccesstothird-partydevelopers,(iii)andthelevelofaccesstheygivetotheirmodels.AccesstoFMsisoftengrantedviacloudservices,whichmayinturnverticallyintegratewithspecialisedmodels,orprovideservicestothird-partydevelopers.TheextenttowhichaFMisverticallyintegratedormadeaccessibletocloudprovidersortothird-partydevelopersistheresultofastrategicdecisionmadebytheFMdeveloper(AdC,2024[11]).

GenAIhasbeendescribedasageneral-purposetechnology.Withcharacteristicssuchaspervasiveness,continuousimprovementandinnovation-spawningpotential,itmayreshapeproductivity,innovation,andmarketstructuresacrossawiderangeofsectors(Calvino,HaerleandLiu,2025[7]).AswithpreviousGPTs,suchaselectricityortheinternet,thediffusionofgenerativeAIisexpectedtobeuneven,withsignificantimplicationsforcompetitionpolicy,includingtheriskofmarketconcentrationandtheadventofnewbottlenecksintheAIvaluechain.

ApplicationsbuiltonFMsaretypicallyfine-tunedtoperformspecifictasksorserveparticularusergroups.ItisadistinctandcriticalphaseintheGenAIlifecycle(OECD,2024[2]).Fine-tuninginvolvestrainingthemodelfurtherondomain-specificorfirm-specificdatatoimproveperformance.Forexample,enterprisecopilotsmaybefine-tunedonproprietarybusinessdata,whileconsumerchatbotsmaybeadjustedfortoneorcontentmoderation.Fine-tuningiskeytohowGenAIsystemsaredeployedindownstreammarkets.Itinfluencesaccessibility,differentiation,andthepotentialforinnovationacrosssectors.

UnderstandinghowAIsystemsfunction,isessentialforanalysinghowusingAImayaffectmarketentry,firmbehaviour,andcompetitivedynamics.Asthesetechnologiesevolve,sotoowilltheirimplicationsforcompetitionenforcementandpolicydesign.Throughoutthepaper,theterms“AI-systems”,“AI”and“GenerativeAI(GenAI)”willbeusedinterchangeablyandwillalsosometimesencompassthefeaturesofFMsandLLMs.IfanexampleorusecasereliesonaparticularformofAI,thiswillbespecified.2

Thepaperisstructuredasfollows:Section

2

examinestheuptakeofAIacrosssectorsandfirms,highlightingboththeopportunitiesforgreaterefficiencyandthebarriersthatcanlimitadoption,includingaccesstodataandmodelopennessshape,withimplicationsfordownstreamcontestability.Section

3

explorestraditionalandemergingcompetitionconcernsassociatedwithAIsystems,includingalgorithmiccollusionandverticalforeclosure,andoutlinesactionstosupportcompetitivedynamicsinAI-enabledmarkets,includingenforcement,advocacy,regulation,andco-operation.Thepaperconcludeswithreflectionsonfutureresearchdirections.

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2TheimpactofAIadoptiononmarketdynamics

Asageneral-purposetechnology,GenAIexhibitsdefiningcharacteristicssuchaspervasiveness,continuousimprovementovertimeandinnovationspawning,withimplicationsforfutureproductivitygrowth(Eloundouetal.,2024[12];Calvino,HaerleandLiu,2025[7];AghionandBunel,2024[13]).Estimatesoffuturegainstototalfactorproductivity(TFP)fromtheuseofAIrangefromaconservativeannualincreaseof0.07percentagepoints(Calvino,HaerleandLiu,2025[7]),toanestimateof0.68ppsofadditionalTFPgrowthperyear(Ag