2026AI品牌资产建设发展白皮书 2026 White Paper on AI Brand Equity Development-国家广告研究院

2026WhitePaperonAIBrandEquityDevelopment

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TableofContents

Preface 6

Chapter1 9

GlobalAIEcosystemsandtheLandscapeofCognitiveEntryPoints 9

1.1GlobalAIEntryArchitecture:MainstreamPlatformsandCapabilityBoundaries 9

1.2China’sAIEntryEcosystem:Super-PlatformIntegrationandClosed-LoopCommerce

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1.3GlobalMigrationofAIBrandEntryPoints:FromSearchtoAIAssistants 12

Chapter2 13

ParadigmShift:From“TrafficDominance”to“CognitiveDominance” 13

2.1ChangesinUserBehavior:From“FindingInformation”to“SeekingConclusions” 13

2.2TheParadigmReconstructionofSearchEngineOptimization(SEO):FromRanking

LogictoGenerationLogic 13

2.3TheMeaningof“CognitiveDominance”:WhetheraBrandCanBeConsistently

UnderstoodandReferencedbySystems 14

2.4“CognitiveDilution”andtheRiskofimplicitexclusionintheGenerativeAIContext.14

Chapter3 16

AIBrandEquityDevelopment:From“BeingDiscovered”to“BeingReferenced” 16

3.1WhyAIBrandEquityDevelopmentIsNecessary:ItIsNotaChangeinTerminology,

butaChangeinCompetitiveLogic 16

3.2TheThreeLevelsofAIBrandEquityDevelopment:BeingDiscovered,BeingSelected,

andBeingReferenced 16

3.3TheObjectofAIBrandEquityDevelopment:From“ContentProduction”to“Brand

InformationGovernance” 17

3.4AIBrandEquity(AIBE):TheNewLocusofBrandAssets 18

3.5TheFive-LayerPathofAIBE:FromIdentifiabilitytoGovernability 19

Chapter4 20

HowAI“Sees”Brands 20

4.1GenerativeAIIsNotSimply“SmarterSearch” 20

4.2SourcesofCognition:Long-TermKnowledge,BehavioralBoundaries,andReal-Time

Evidence 20

4.3Workflow:RetrievalRouting,Recall,Reranking,andGeneration 21

4.4WhyBrandContentGets“FilteredOut” 22

Chapter5 24

GlobalDevelopmentStatusandGovernanceFrameworkofAIBrandEquity 24

5.1GlobalDevelopmentOverview:RegionalDifferencesandCommonCharacteristics 24

5.2CurrentStatusofChina’sAIBrandEquityIndustry:FromConceptualenthusiasmto

RuleConvergence 25

5.3GlobalAIGovernanceandBrandTrustFrameworks:EvolutionofInternationalRules

andStandards 26

5.4CommonIndustryMisconceptions:StrategicMisalignment,ExecutionDistortion,and

Evaluationmisalignment 27

Chapter6 29

IndustryImplementationGuide:TheCompetitionfor“CognitiveDominance”AcrossDifferent

Sectors 29

6.1EnterpriseServices(SaaS/Industrial/B2B):From“TrafficExposure”to“Professional

Substitution” 29

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6.2RetailandE-Commerce:From“SearchRanking”to“PreciseMatchingofConsumer

Intent” 30

6.3LocalLifestyleServices(Dining/TravelandHospitality/LifeServices):From“High

Ratings”to“ImmediateDecisionEntryPoints” 30

6.4ContentIPandEducation/CulturalTourism:From“One-WayOutput”to“Interactive

EcosystemDevelopment” 31

6.5HighlyRegulatedIndustries(Healthcare/Finance/Law):From“VagueAnswers”to

“AuthoritativeComplianceSources” 31

Chapter7 33

KnowledgeNetworkofIntegrity&TrustDevelopmentMethodology:FromCognitive

InfrastructuretoGEOOperatingMechanism 33

7.1FromContentSupplytoCognitiveInfrastructure:WhyEnterprisesNeedKnowledge

NetworkofIntegrity&Trusts 33

7.2DefinitionofKnowledgeNetworkofIntegrity&Trusts:UpgradingDispersed

InformationintoaKnowledgeSystemAdoptablebyAI 33

7.3Three-LayerArchitecture:TheOverallStructureofKnowledgeNetworkofIntegrity&

Trusts 33

7.4Six-LayerDevelopmentPath:FromCognitiveDiagnosistoCognitiveReinforcement34

7.5AuthoritativeHigh-QualityCorpus:TheKnowledgeFoundationofKnowledge

NetworkofIntegrity&Trusts 35

7.6FromSemanticPositioningtoAnswerAssets:OperatingMethodsofKnowledge

NetworkofIntegrity&Trusts 36

7.7FromKnowledgeDevelopmenttoCognitiveGovernance 37

Chapter8 39

NewEvaluationSystem:FromClickMetricstoCognitiveGovernanceMetrics 39

8.1WhyMetricsNeedtoBeRebuilt 39

8.2CoreMeasurementObject:TheBrand’sAbilityto“InfluenceAnswers” 39

8.3ShareofAnswer(SOA)Metric:“HowMuchAnswerSpaceIsOccupied”inKey

Questions 40

8.4CitationRateMetric:WhethertheBrandIsUsedas“TrustedEvidence” 40

8.5CognitiveConsistencyMetric:WhethertheAISystem’sJudgmentoftheBrandIs

StableandReproducible 41

8.6SentimentOrientationMetric:NegativeImpactIsMoreConcentratedinGenerative

Environments 41

8.7AIBE:ConvergingMetricsinto“ManageableAIBrandEquity” 42

8.8AIBVIndexSystem:AUnifiedEvaluationFrameworkforGenerativeContent

Environments 42

Chapter9 44

IndustryStandardsandGovernanceFramework:FromPrincipleStatementsto

ImplementationGuidelines 44

9.1WhyIndustryStandardsAreBecomingaNecessaryComponentofAIBrandEquity

Development 44

9.2FundamentalIndustryPrinciples:From“IndustryInitiatives”to“SharedStandards”

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9.3ListofProhibitedPractices:WhatShouldNoLongerBeConsideredan“Effective

Method” 45

9.4RecommendedDevelopmentStandards:WhatConstitutesSustainableDevelopment.47

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9.5EvaluationStandards:HowAIBVBecomesaSharedIndustryLanguage 47

9.6CertificationMechanisms:FromCorpusReviewtoServiceGrading 49

9.7StandardizationRoadmap:FromPrinciplestoIndustryImplementationGuidelines.50

Chapter10 52

TenPrinciplesforGlobalAIBrandEquityDevelopment 52

10.1PrincipleofIdentifiability 52

10.2PrincipleofAuthoritativeSources 52

10.3PrincipleofMultilingualConsistency 53

10.4PrincipleofStructuredKnowledge 53

10.5PrincipleofVerifiability 53

10.6PrincipleofRiskBoundaries 54

10.7PrincipleofAIcitation-readiness 54

10.8PrincipleofGlobalPlatformConsistency 55

10.9PrincipleofContinuousGovernance 55

10.10PrincipleofComplianceandEthics 55

Conclusion 57

Appendix 58

A.AIBrandEquityDevelopmentPerformanceIndexSystem(AIBV1.0) 58

B.Glossary(AbbreviatedVersion) 58

C.MetricDefinitions 58

Appendix:AIBrandEquityDevelopmentPerformanceIndexSystem(AIBV1.0) 60

Preface 60

Chapter1GeneralProvisions 61

1.1PurposeandPositioning 61

1.2ScopeofApplication 61

1.3ContentOutsidetheScopeofEvaluation 61

1.4FundamentalPrinciples 62

Chapter2TermsandDefinitions 62

2.1AIBrandEquity 62

2.2AIBV 62

2.3ThreePrimaryIndices 63

2.4TwoCalibrationFactors 63

Chapter3OverallFramework 63

3.1“3+2”CompositeStructure 63

3.2ScoreRange 63

3.3OutputRequirements 64

Chapter4AIP:AIFoundationalPerformanceIndex 64

4.1DimensionalComposition 64

4.2CompositeFormula 64

4.3DimensionA:Accuracy 64

4.4DimensionB:Visibility&Recall 65

4.5DimensionC:ScenarioFitness 66

4.6DimensionD:Consistency&Tone 66

Chapter5AIC:AIConstructionIndex 67

5.1IndexDefinition 67

5.2IndicatorComposition 67

5.3CalculationMethod 67

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Chapter6AIR:AIRisk&ReliabilityIndex 68

6.1IndexDefinition 68

6.2IndicatorComposition 68

6.3CalculationMethod 68

6.4DisclosureRequirementsforSevereErrorClassification 68

Chapter7UAFandMCI 69

7.1UAF:User–AIAlignmentFactor 69

7.2MCI:MethodConfidenceIndex 69

Chapter8CompositeIndex 70

Chapter9DataCollectionandMeasurementStandards 70

9.1Multi-Model,Multi-EntryPrinciple 70

9.2QuestionSetStratificationPrinciple 70

9.3SourcesofQuestions 70

9.4RecordRetentionandAuditability 71

9.5AnnotationandQualityControl 71

Chapter10Anti-ManipulationandQualityAssurance 71

10.1HiddenQuestionandRandomQuestionMechanism 71

10.2AnomalyDetection 71

10.3DataQualityManagement 72

10.4ResultPublicationRequirements 72

Chapter11ResultPresentationandClassification 72

11.1ResultPresentation 72

11.2ExampleClassificationLevels 73

Chapter12UsageSpecificationsandComplianceRequirements 74

12.1BoundariesofIndexUsage 74

12.2ExternalCitationStandards 74

12.3DisputeResolutionandAppealsMechanism 74

12.4InformationDisclosureforHigh-RiskBrands 74

12.5StandardsforReferencingCertificationResults 74

Chapter13VersionManagementandExtendedApplications 75

13.1VersionManagement 75

13.2DomesticandInternationalApplications 75

13.3ImplementationGuidelines 75

ClosingRemarks 76

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Preface

Overthepasttwodecades,digitalmarketinghasbeenfundamentallybuiltuponsearchvisibility,contentdistribution,andclick-throughbehavior.Brandcompetitionfocusedprimarilyongainingvisibility,attractingclicks,andinfluencingconsumerdecisionsafterusersenteredawebpagethroughcontent,productinformation,pricingexplanations,userreviews,customerinteractions,andconversion-orienteddesign.

However,withtherapidadoptionofgenerativeartificialintelligence,themechanismsthroughwhichinformationisacquiredanddecisionsareformedareundergoingaprofoundtransformation.Anincreasingnumberofusersnolongerbrowselargevolumesoflinksbeforeindependentlydrawingconclusions.Instead,theyobtainconclusionsdirectlythroughsemanticinteractionscenariossuchasAI-poweredquestionanswering,AIsearch,AIrecommendations,AIassistants,andintelligentagents.Thesesystemshelpusersconductpreliminaryevaluationsandadvancetowardsubsequentactionsmoreefficiently.Forusers,thismeanslowerinformationacquisitioncosts,shorterdecision-makingjourneys,andgreaterefficiency.Forbrands,itmeansthatthecompetitivefrontierisshiftingfromvisibilitytomachinecomprehension,authoritativecitation,andinclusionwithinAI-generatedoutputs.

The2025WhitePaperonBrandValueManagementintheNewAIErahighlightedthatgenerativeAIisacceleratingthetransitionfromamodelof“linkindexing”tooneof“directanswergeneration.”Today,morethan80percentofChineseinternetusershavedevelopedthehabitofusingAI-poweredsearchenginesorAIassistantsaspartoftheirdailyinformation-gatheringroutines.ThistrendsignalsaclearmigrationofbrandcommunicationentrypointsandcompetitivedynamicstowardAIrecommendationsystems.

Meanwhile,themarketforgenerativecontentoptimizationisenteringaperiodofrapidexpansion.AsgenerativeAIandAI-poweredsearchbecomeincreasinglymainstream,China’smarketforgenerativecontentoptimizationandAIBrandEquitydevelopmentreachedapproximatelyRMB5.7billionin2025andisprojectedtogrowtoRMB13.7billionin2026,representingannualgrowthexceeding140percent.Globally,thegenerativecontentoptimizationmarketisexpectedtoreachapproximatelyRMB512.2billionby2030,withacompoundannualgrowthrate(CAGR)of65.3percentbetween2025and2030.Thisgrowthtrajectorydemonstratesthatgenerativecontentoptimizationisrapidlybecomingastrategicdomainforenterprisesseekingtoredesigndigitalmarketingsystemsandreallocatebrandresources.

TheCouplingoftheComputationalBlackHoleandCognitiveEntryPoints:BehindtherapidevolutionofgenerativeAIlieswhatmaybedescribedasa“computationalblackhole.”Single-inferencecosts,trainingcosts,andpeakinferenceconcurrencycontinuetoriseexponentially.Asaresult,AIplatformsareincreasinglyforcedtoreduceretrievalbudgets,limittheamountofevidenceconsideredduringreasoning,andraisethequalitythresholdrequiredforeachindividualpieceofinformation.

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Forbrands,thisimpliesthatbeingcitedwillbecomefarscarcerandmorevaluablethanmerelybeingmentioned.Low-density,low-credibility,andpoorlystructuredcontentwillincreasinglybefilteredoutbytheeconomicrealitiesofAIcomputation.Ineffect,computationalconstraintsareelevatingthescarcityandstrategicvalueofhigh-qualityinformation.Consequently,theprimarychallengefacingbrandsisnolongerlimitedtosearchrankingsormediaexposure.Instead,thecriticalquestionbecomeswhetherabrandcanbeaccuratelyunderstood,appropriatelycategorized,consistentlyrepresented,andreliablyreferencedbymainstreamAIsystemsacrosscomplexqueriesandmulti-turninteractions.Brandequityisthereforeenteringanewdimension.ItmustexistnotonlywithinconsumerperceptionbutalsowithinthesemanticspaceofAIsystems,formingaclear,trustworthy,andverifiableknowledgeprofile.

Againstthisbackdrop,thiswhitepaperintroducestheconceptofAIBrandEquity(AIBE).AIBEservesasanoverarchingframeworkdescribingabrand’scumulativevaluerepresentationwithinmainstreamlargelanguagemodelsandAI-poweredapplicationenvironments.Achievingthisobjectiverequiresenterprisestomovebeyondfragmentedcontentoptimizationandisolatedcommunicationactivities.Instead,organizationsmustundertakeasystematicAI-orienteddevelopmentinitiativefocusedon:Brandfacts;Semanticpositioning;Questionsets;Answerassets;Citationassets;andContinuousgovernancemechanisms.Together,theseelementsformaknowledgesupplysystemcapableofbeingunderstood,calledupon,cited,andreusedbyAIsystems.

Withinthisframework,theKnowledgeNetworkofIntegrity&Trust(KNIT)isdefinedasthefoundationalmethodologyandinfrastructureunderpinningAIBrandEquitydevelopment.KNITdoesnotdirectlydefinebrandequityitself.Rather,ittransformsfragmentedandunstructuredenterpriseinformationintoastandardized,trustworthyknowledgesystemthrough:Real-worlddata;Authoritativeresearch;Structuredknowledgegraphs;Third-partyvalidation;Traceableinformationsources.Asaresult,competitioninAIBrandEquityisnolongercenteredoncontentvolumealone.Instead,itincreasinglydependsonthequality,structure,credibility,andverifiabilityofknowledgesupply.

Accordingly,manypracticeshistoricallygroupedunderGenerativeEngineOptimization(GEO)requirereinterpretation.ThiswhitepaperdoesnotrejectthepracticalvalueofGEO.Rather,itseekstoredefineitsrolewithinabroaderstrategicframework.Anypracticethatimprovestheauthenticity,structure,interpretability,andcitation-readinessofbrandknowledgeshouldbeconsideredalegitimatecomponentofAIBrandEquitydevelopment.Conversely,practicesbasedonfalsecontentinjection,fabricatedsources,orforgedevidencechains,datapollution,orresultmanipulationshouldbeexplicitlyexcluded.Fromthisperspective,GEOshouldnolongerberegardedasastandaloneframework.Instead,itshouldbeunderstoodasacollectionofimplementationmethodologiesandoperationalpathwayswithinthebroaderdisciplineofAIBrandEquitydevelopment.

Atthesametime,discussionssurroundingAIBrandEquitycannotremainconfinedtomethodologyalone.Asblack-hatGEO,AIpoisoning,fabricatedevidencechains,fakeauthoritysignals,andmisleadingcontentdisseminationcontinuetoemerge,thefieldis

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transitioningfromconceptualenthusiasmtowardgovernanceprinciples,industrystandards,andregulatoryconvergence.Whattheindustryshouldencourageisnotoutcomemanipulation,butthesystematicimprovementofsupply-sideinformationquality.Thisincludesenhancing:Informationstructure;Authenticity;Accuracy;Transparency;Verifiability.Bystrengtheningthesedimensions,AIsystemswillbeabletogeneratemorereliableanswersbasedonmoretrustworthyknowledgenetworks.

Accordingly,thiswhitepaperproposesacomprehensiveanalyticalframework:

AIBEdefinesboththeobjectiveandtheasset:whatkindofvaluepresenceabrandshouldestablishwithinAIsystems.

AIBrandEquityDevelopmentdefinestheenterprisecapability-buildingprocess.

KNITdefinesthecoremethodologyandunderlyinginfrastructure.

GEOreferstoimplementationmethodsforgenerativeengines.

AIBVservesastheunifiedevaluationframeworkformeasuringAIBrandEquityperformance,capabilitymaturity,andriskexposure.

WeemphasizethatAIBrandEquitydevelopmentshouldnotbeinterpretedasanarrowmarketingupgrade.Rather,itshouldbeviewedasacomprehensiveinformationgovernanceinitiativedesignedfortheAIera.ItspurposeisnottomakeAIsystemsfavorabrand.ItspurposeistoensurethatAIsystemsunderstandabrandmoreaccuratelyandreferenceitmoreresponsibly.Throughthecontinuousdevelopmentofreusable,citable,verifiable,andhigh-qualityknowledgeassets,organizationscanpreserveexpertise,productadvantages,scopeofapplicability,andriskdisclosuresinmachine-readableandtrustworthyforms.Suchknowledgecanthenremainconsistentandinterpretableacrossdifferentmodels,scenarios,andtimehorizons.ThisfoundationisessentialnotonlyforAIBrandEquitydevelopmentitselfbutalsoforfutureindustrystandards,governanceframeworks,andevaluationsystems.

Ultimately,thiswhitepaperseekstoencourageacriticalshiftinindustrythinking:fromcompetingforoutcomestobuildingassets.Fromcompetingfortraffictomanagingcredibility.Fromopportunitynarrativestostandards-drivengovernance.IntheAIera,thebrandsmostcapableofenduringmodelevolution,platformshifts,andregulatoryconvergencewillnotbethoserelyingonshort-termmanipulation.Theywillbethosebuiltuponauthenticity,compliance,transparency,andtraceability.

Theultimatecompetitionamongbrandsmaynolongerbeaboutcommunicationcapabilityalone.Itmayinsteadbeabouttheabilitytobecrediblyunderstoodbytheworld.Thosewhosuccessfullytransitionfromcontentsupplytotrustedknowledgesupplywillbebestpositionedtoestablishlong-termandsustainableinfluencewithintheAI-driveninformationecosystemofthefuture.

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Chapter1

GlobalAIEcosystemsandtheLandscapeofCognitiveEntryPoints

1.1GlobalAIEntryArchitecture:MainstreamPlatformsandCapabilityBoundaries

Acrosstheworld,generativeartificialintelligencehasevolvedfromastandaloneconversationaltoolintoadiversifiednetworkofcognitiveentrypoints,fundamentallyreshapingtheunderlyinglogicofinformationdistribution.Today’sglobalAIecosystemisincreasinglycharacterizedbyanecosystemofoligopolisticfoundationmodelsplusverticalapplications.General-purposelargelanguagemodelsrepresentedbyOpenAI’sChatGPT,Anthropic’sClaude,GoogleGemini,MicrosoftCopilot,andxAI’sGrokhavebecometheprimaryinfrastructurethroughwhichusersaccessgeneralknowledge,performcomplexreasoning,andgeneratecreativecontent.Poweredbybroadreasoningcapabilitiesandmassiveuserbases,theseplatformshaveestablishedthemselvesasfoundationalgatewaysforhuman–AIinteraction.Beyondsupportinghundredsofmillionsofdailyinteractions,thesemodelsincreasinglyfunctionas“superinterfaces”thatconnectbrands,users,services,anddigitalecosystemsthroughAPIs,plugins,agents,andapplication-layerintegrations.

Atadeeperlevel,competitionamongfoundationmodelsisnolongercenteredsolelyonparametercounts.Instead,ithasevolvedintoanintegratedcompetitionacrosscomputingpower,data,andproducts.OpenAIhasbuiltapowerfulcommercialecosystemthroughitsdeepintegrationwithMicrosoftAzure.AnthropichaspositioneditselfaroundsafetyalignmentandcontrollableAIdevelopmentthroughstrategicbackingfrombothAmazonWebServicesandGoogle.GoogleGeminibenefitsfromthecompany'sverticallyintegratedtechnologystack,combiningproprietaryTensorProcessingUnits(TPUs),foundationmodels,andconsumerapplicationsintoatightlyconnectedecosystem.xAIhasadoptedastrategycenteredonlarge-scalecomputationalexpansion,investingheavilyinitsColossussupercomputingclusterpoweredbymorethan100,000NVIDIAH100GPUs.Meanwhile,Meta’sopen-sourceLlamaecosystemisredefininghowAIcapabilitiesaredistributedandadoptedglobally.Thesedifferingtechnologicalpathwaysinfluencehowmodelsevaluateinformationsources,determinecredibility,andestablishtrust.Forbrands,thisintroducesanewchallenge:ensuringcross-platformconsistency.Thesamebrandfactmustbeaccuratelyunderstoodacrossdifferentmodelarchitectureswhileremainingcompatiblewithvaryingevidence-selectionandrankingmechanisms.

Atthesametime,AI-nativesearchplatformssuchasPerplexity

andY

arerapidlygainingmarketsharefromtraditionalsearchengines,emergingasimportantentrypointsforreal-timeinformationretrieval,in-depthresearch,andfactverification.Thedefiningadvantageoftheseplatformsliesintheir“answer-first”deliverymodelandtheiremphasisonsourcetransparency.Asaresult,brandcompetitionincreasinglyshiftsfromcompetingforclickstocompetingforcitations.Inparallel,AIassistantsembeddedwithinprofessionalworkflows—suchasSalesforceEinstein,AdobeFirefly,andGitHubCopilot—areintegratinggenerative

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capabilitiesdirectlyintoenterpriseoperations.ThesespecializedassistantsarebecomingcriticaltouchpointsthroughwhichB2Bbrandsinfluencedecision-makers.

AndthecapabilityboundariesofAIplatformscontinuetoexpandrapidly.Whatbeganastextgenerationisevolvingtoward:Multimodalunderstanding(images,video,andaudio);Retrieval-AugmentedGeneration(RAG);AutonomousAIagentscapableofexecutingtasks.MultimodalmodelsenableAIsystemstodirectlyinterpretproductimagery,visualbrandingassets,demonstrationvideos,andusagescenarios,integratingtheseelementsintogeneratedresponses.Real-timeretrievalsystemsrequireinformationtoremainbothhighlycredibleandcontinuouslyupdatedifitistoremainvisiblewithindynamicallyevolvingknowledgeenvironments.Forbrands,thistransformationfundamentallyreconstructsthelogicofinformationdistribution.Platformsarenolongerpassivechannelsthroughwhichinformationflows.Instead,theyincreasinglyfunctionascognitivelayersresponsibleforinformationfiltering,semanticrestructuring,andrecommendationgeneration.Consequently,brandsmustadapttoplatform-specificdifferencesinretrievalrouting,evidenceevaluation,answergeneration,andalignmentpolicies.OnlybydevelopingdifferentiatedAIBrandEquitystrategiescanorganizationssecureasustainablepositionwithintheevolvingglobalAIecosystem.

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1.2China’sAIEntryEcosystem:Super-PlatformIntegrationandClosed-LoopCommerce

Comparedwithoverseasmarkets,China’sAIecosystemischaracterizedbyfasteradoptionofgenerativeinteraction,deeperplatformintegration,andmorematurecommercialclosed-loopmechanisms.Thishasgivenrisetoadistinctivemodelofsuper-platformintegration.LeadingdomesticAIapplications—includingYuanbao,Kimi,Doubao,Coze,DeepSeek,ERNIEBot,andTongyi—possessnaturaladvantagesinChinese-languageunderstandingandculturaladaptation.Moreimportantly,theyaredeeplyembeddedwithinexistingecosystemsencompassing:Contentplatforms;Socialnetworks;Localservices;E-commercemarketplaces

Atthefoundation-modellevel,Chinaisdevelopingathree-forcecompetitivelandscape.Thefirsttierconsistsofproprietarymodelsdevelopedbymajortechnologycompanies,includingTencentHunyuan,Doubao,AlibabaTongyi,andBaiduERNIE.ThesecondtierincludesindependentAIcompaniessuchasDeepSeek,MoonshotAI(Kimi),ZhipuAI,MiniMax,andStepFun.Thethirdtiercomprisesindustry-specificandedge-deploymentmodels,representedbyHuaweiPanguandSenseTime.Amongtheseplayers,DeepSeekhasattractedglobalattentionthroughitsfocusonalgorithmicefficiencyasameansofreducingcomputationaldependence.Atthesametime,domesticAIinfrastructureproviderssuchasAscendandCambriconarefosteringacloselycoupledevolutionbetweenChinesecomputingplatformsanddomesticfoundationmodels.Forbrands,thistrendimpliesagrowingfragmentationoftheChinesesemanticlandscape.Asmoremodelsemergeanddiverge,maintainingcross-modelconsistencywillbecomeincreasinglycomplexandresource-intensive.

Ontheuserside,Chineseconsumersshowaparticularlystrongpreferenceforinformationformatsthatprovidedirectconclusions,especiallyinhigh-frequencydecision-makingscenariossuchas:Dining;Travel;Shopping;Localservices.Asaresult,thedistancebetweenAI-generatedanswersanduseractionisremarkablyshort.Simultaneously,China’ssuper-platformecosystemsenableseamlessintegrationamongcontent,search,socialinteraction,andtransactions.Forexample,whenauserasksanAIassistantfor“family-friendlyweekendtravelrecommendations,”thesystemmaynotonlygenerateanitinerarybutalsoimmediatelyconnecttheuserwith:Ticketbookingservices;Hotelcomparisontools;Restaurantrecommendations;Localactivityreservations

Insuchcases,AI-generatedanswersarenolongerinformationalreferences.Theybecomedirectdriversofcommercialtransactions.Thisinfrastructure-levelevolutioncreatesbothnewopportun