摩根士丹利-北美具身AI：世界模型-AI从数字世界走向物理世界的征程-Embodied AINorth America World Models：AI's Journey from Digital to Physical-20260322

MorganstanleyRESEARCH

March22,202609:00PMGMT

EmbodiedAI|NorthAmerica

WorldModels:AlⅠsJourneyfromDigitaltoPhysical

Alismovingbeyondlanguagetowardmodelsthatunderstand)simulateandnavigatethephysicalworld.MajorAllabsand

startupssuchasWorldLabsandAMlLabsaredevelopingⅠworldmodelsⅠthatcouldunlocknewapplicationsacrossrobotics)

gaming)design)andmore.

AI’sNextChallenge:ModelingthePhysicalWorld.LLMstrainon,process,and

generatetextinitsmanyforms.Whiletheyhaveproventobepowerfultoolsforwhite-collartaskssuchascoding,search,andwriting,AI’sbroaderpotentialmaylieinthephysicalworld,whichisgovernedbythelawsofphysics-substances,

thermodynamics,fluiddynamicsandthebehavioroflight-inaconstantlychanging3-dimensionalspace.

'WorldModels'areAIsystemsdesignedtounderstand,simulate,andreason

aboutenvironments,withapplicationsspanningvideogames,design,robotics,

autonomousvehicles,andmore.Potentialusecasesrangefromgeneratingvideogamecontentfromtextprompts,torobotssimulatingoutcomesbeforeacting,

autonomousvehiclestrainingonbillionsofrareedgecases,orarchitectsmodelingentirecitiesbeforeconstructionbegins.Someanecdotesoncurrentprogress:

•

Recentresearch

hassuggestedthatrobotstrainedusingdatageneratedbyworldmodelscanperformcomparablytothosetrainedonreal-world

interactiondata.

•

Waymo(LeveragingDeepMind'sGenie3WorldModel)

hasreportedrunningbillionsofmilesofvirtualdrivingteststotrainandvalidateitssystems

acrossrareedgecasesthatwouldbedifficultordangeroustoencounterintherealworld.WaymoisasubsidiaryofAlphabet-coveredbyBrianNowak.

•Lastyear,

Microsoft(coveredbyKeithWeiss)unveiled

afullyAI-rendered,playableversionof1997shooterQuakeIIbuiltonitsMuseworldmodelinsteadofatraditionalgameengine.

•Earlierthisyear,

Roblox(coveredbyMattCost)announced

anewresearchprojectwiththegoalofusingitsownworldmodeltogenerativeimmersiveenvironmentsanditerateongamesthroughnaturallanguageprompts.

Anumberofmajortechnologycompaniesarealreadydevelopingworldmodels,includingGoogleDeepMind,Meta,Microsoft,Tesla,andNVIDIA.Theyare

increasinglybeingjoinedbystartupsfoundedbyleadingAIresearchers,includingFei-FeiLi’sWorldLabsandYannLeCun’sAMILabs.ThegrowingconcentrationoftalentandcapitalinthisareasuggeststheracetobuildAIsystemsthatunderstand

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MoRGANSTANLEy&Co.LLCAdamJonas,CFA

EquityAnalyst

Adam.Jonas@

+1212761-1726

MatthewCost

EquityAnalyst

Matthew.Cost@

WilliamTackett,CFAResearchAssociate

+1212761-7252

William.Tackett@

CelaVanLieshout

ResearchAssociate

+1212761-6028

Cela.Vanlieshout1@

+1212761-2679

MorganStanleydoesandseekstodobusinesswith

companiescoveredinMorganStanleyResearch.Asaresult,investorsshouldbeawarethatthefirmmayhaveaconflictofinterestthatcouldaffecttheobjectivityofMorganStanley

Research.InvestorsshouldconsiderMorganStanley

Researchasonlyasinglefactorinmakingtheirinvestmentdecision.

Foranalystcertificationandotherimportantdisclosures,refertotheDisclosureSection,locatedattheendofthisreport.

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andsimulatethephysicalworldisalreadyunderway.SpatiallyawareAImayrepresentthenextmajorfrontierinartificialintelligence.

Notallworldmodelsarealike.Somemodelsgeneratedynamicenvironmentsthatrespondtoagentactionsinrealtime,whileothersfocusonconstructingcoherent3Dworlds,predictingfutureobservations,orlearningabstractrepresentationsofhowenvironmentsevolve.Thesedifferingapproachesreflectabroaderdesign

spectrum-fromhighlygenerativesystemsthatrendervisualworldstomoreefficientarchitecturesthatfocusonpredictinglatentrepresentationsofreality.

Overtime,thesecategoriesmayincreasinglyconvergeassystemsintegratemultiplecapabilities.

Inthisnote,weprofiletwoemergingstartupspursuingdifferentapproachestoworldmodels:WorldLabsandAMILabs.

•WorldLabs,foundedbyStanfordprofessorandcomputevisionpioneerFei-FeiLi,isfocusedonbuildinggenerativeworldmodelswithspatial

intelligence.Itscurrentmodel,calledMarble,aimstogiveAIsystemsa

nativeunderstandingofthree-dimensionalenvironments,enablingcoherent,navigableworldsthatcouldsupportapplicationsacrossrobotics,design,andsimulation.Thecompanyemergedfromstealthin2024andwasrecently

valuedatover$5bn(accordingtoPitchBook)

followinga$1bnfundinground

inFebruary.

•AMILabs,foundedbyMeta’sformerChiefAIScientistYannLeCun,is

pursuingadifferentpathcenteredonlearningefficientinternal

representationsofhowtheworldbehaves.Ratherthangeneratingfullvisualenvironments,itfocusesonmodelsthatpredicthigh-levelstructureand

dynamicsinlatentspace,anapproachdesignedtosupportreasoning,

planning,andphysicalAIsystemssuchasrobotics.Thecompanyexitedstealthearlierthismonth,

raising>$1bna

ta$4.5bnpost-moneyvaluation(accordingtoPitchBook).

WorldModels,SimplyExplained:WorldmodelsareAIsystemsthatlearnhowenvironmentsworkbybuildinganinternalrepresentationoftheworld.Insimpleterms,theyactlikeanAI“imaginationengine,”allowingmachinestosimulate,explore,andreasonaboutenvironments-fromgenerating

virtualworldstotestingactionsandscenariosbeforeactingintherealone.

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Exhibit1:WorldLabsCompanyProfile

Source:WorldLabs,PitchBook,MorganStanleyResearch

Exhibit2:AMILabsCompanyProfile

Source:AMILabs,PitchBook,MorganStanleyResearch

ForFurtherReading:

•

PhysicalAI:TheMorganStanleyRobotAlmanac(7Dec2025)

•

Internet:ShakingtheWorldModelBlues?3TakeawaysfromRBLXand

TTWO(8Feb2026)

•

Internet:WorldModelsAreHere...SoWhatDoTheyMeanforU,RBLX,

TTWOandAPP?(2Feb2026)

•

Internet:WhatAPPandUHadtoSayAboutAIDisruption(17Feb2026)

•

Space:VardaSpace:SpaceManufacturing,PharmaMadeinOrbit(19Feb

2026)

•

EmbodiedAI:AIRoboticsDisruptors:PhysicalIntelligence(19Dec2024)

•

EmbodiedAI:AutonomousVehicleDisruptors:Wayve(14Oct2024)

•

Robotics:VulcanForms:MetalsManufacturing,MadeSimpler(5Feb2026)

•

Space:Starcloud:DataCentersinSpace-AIGoesOrbital(27Jan2026)

•

Robotics:BedrockRobotics:TheBotsBreakGround(12Jan2026)

Thecontentaddressingprivatecompaniesisbeingprovidedforinformational

purposesonlyanddoesnotconstituteasolicitationorimplyfutureresearch

coverageifthecompanygoespublic.Noinvestmentrecommendationisprovidedasthereislimitedpublicinformationavailableforprivatecompanies.Investorsshouldconducttheirownduediligenceandbeawarethatadditionalordifferentinformation

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maybeprovidedbythecompany.

Thevaluationinformationprovidedhereisincludedforillustrativepurposesonly.Theinformationisbasedonpubliclyavailableinformation(fundingrounds,company

disclosure,thirdvendors,)andproducedendorsedby

-partyetc.wasnotorMorgan

StanleyResearch.

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WorldModels101

WhatareWorldModels?A

worldmodel

isanAIsystemthatbuildsaninternal,usable

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representationofanenvironmentsoitcansimulate“whatmighthappennext”,often

includinghowthefuturechangeswhenanagenttakesactions.Intoday’susageofthe

term,worldmodelsincreasinglymeangenerative,interactivesimulators:systemsthatcangeneratecoherentvideo/3Dscenesandkeepthemstableenoughforexploration,editing,anddownstreamtaskslikeroboticstrainingorcontentcreation.

•Ausefulmentalimagefornon-technicalreaders:AworldmodelislikeanAI“imaginationengine”thatcan1)simulateandinferwhattheworldislikenow;2)

rollitforwardintimeunder“what-if”conditions;and3)exposethatsimulationtohumansoragentsassomethingyoucanexplore,query,oredit.

MajorTypesofWorldModels:Therearenouniversallyagreed-oncategoriesforWorldModels.However,giventhewiderangeofdifferentapproaches,weattempttodivideintoprimarycategories.Overtime,wewouldexpectthesecategoriestograduallybluras

modelsintegrateseveralofthesecapabilities-andanumberofmodelstodayalreadydonotfitcleanlyinonecategory.

•InteractiveAction-ConditionedWorldModels:Thesemodelsgenerate

environmentsthatdynamicallyrespondtoanagent’sactionsinrealtime,allowingAIsystemstoexplore,plan,andlearninsidesimulatedworlds.Theybehavelikealearnedgameenginewherethemodelpredictshowtheworldchangeswhen

actionsoccur.

°KeyFocus:Predictshowtheenvironmentchangesgivenagentactions.

°Example:GoogleDeepMindGenie

•CoherentWorldGenerators:Thesemodelsgeneratecoherentandpersistent3Denvironmentsfromtext,images,orvideo.Theirfocusisonconstructingstable

spatialworldsthatmaintaingeometricconsistencyandcanbeexploredor

renderedfrommultipleviewpoints.Unlikeaction-conditionedmodels,they

typicallyemphasizescenegenerationandspatialconsistencyratherthandynamicinteraction.

°KeyFocus:Creationofstable,navigable3Dworlds

°Example:WorldLabsMarble

•AbstractRepresentation/Non-GenerativeModels:Thesemodelspredicthigh-levelrepresentationsofmissingorfuturepartsofdataratherthanrawsensoryinputslikepixels.Byfocusingonabstractstructure(objects,motion,and

relationships)theylearnhowtheworldbehaveswithoutneedingtoreproduceeveryvisualdetail.Thisapproachemphasizeslearningtheunderlyingstructureofrealityratherthanreconstructingexactobservations-overallemphasizingmodelefficiencyvs.precisedetail.

°KeyFocus:Predictinglatentworldrepresentationsinsteadofpixels°Example:MetaV-JEPA,AMILabs

•PredictiveGenerativeWorldModels:Thesemodelspredictwhattheworldwilllooklikenext,oftengeneratingthenextvideoframeorobservation.Theysimulateenvironmentalchangesbutusuallydonotcreatefullyinteractiveworlds.TheyarecommonlyusedforphysicalAIplanning,forecasting,andautonomousdriving

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reasoning.

°KeyFocus:Predictingfuturestatesoftheworld

°Example:WayveGAIA,1XWorldModel,NVIDIACosmos(Predict)

•Physics-GroundedSimulatedDataEngines:Thesesystemsaredesignedto

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simulatereal-worldphysicsandenvironmentssothatrobotsandembodiedAI

systemscansafelytraininvirtualworldsbeforeactinginreality.Theyoften

combineworldmodelswithphysicsenginesandlarge-scalephysicaldatasetstogeneraterealisticsynthetictrainingdata.

°KeyFocus:Physics-consistentsimulationforroboticstraining

°Example:NVIDIACosmos(Transfer)

SimulatedDataEngines

Generationofphysics-groundedsimulateddata

forphysicalAItraining

NVIDIACosmos(Transfer)

PhysicalAITraining

MajorWorldModelTypes

MSDefinitions(Noofficiallyagreedontypes)

Modelscanbluracrossmultiplecategories

Focus

Examples

KeyUseCases

Exhibit3:MajorWorldModelTypes

InteractiveAction-ConditionedModels

CoherentWorldGenerators

AbstractRepresentation

Focus

Generatedynamicenvironmentsthatrespondto

anagent’sactionsinrealtime

Creationofstable,persistent3Dworlds

Efficientlypredictabstract,high-levelstructureof

missingorfuturedata

Examples

GoogleDeepMindGenie

WorldLabsMarble

MetaV-JEPA&I-JEPA

KeyUseCases

AgentTraining&VideoGames

3DEnvironmentDesign(DigitalTwins,Digital

AssetDesign,PhysicalAITraining,etc.)

PhysicalAIReasoning&EfficientWorld

Understanding

PredictiveGenerativeWorldModels

Predictwhattheworldwilllooklikenext,oftenthenextvideoframeorobservation

WayveGAIA,1XWorldModel,NVIDIACosmos

(Predict)

PhysicalAIReasoning&ScenarioForecasting

Source:MorganStanleyResearch

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High-LevelMechanismsofaGenerative/PredictiveWorldModel

•Learnacompactinternalstate(latentrepresentation):Themodelreceives

observations(text,images,video,sensordata)andcompressesthemintoaılatent

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stateı-acompactinternalrepresentationthatcapturestherelevantspatial,

temporal,andcausalstructureoftheenvironment.Ratherthanstoringrawpixels,themodellearnsastructuredrepresentationsufficientforpredictionandcontrol.

•Predictdynamicsovertime:Themodellearnshowthelatentstateevolvesovertime,oftenbypredictingthenextlatentstate(andsometimesthenext

observation).Thisenablesittoırollforwardıintoplausiblefutures.Someworldmodelsexplicitlyreconstructvideoobservations,whileothersoperatepurelyinlatentspaceforplanning.

•Conditiononactionsfor'what-if'simulation:Manypracticalworldmodelsacceptactionsasinputs,suchassteeringcommands,robotmotions,orgamecontrols,andpredicthowtheenvironmentchangesunderdifferentchoices.

•Generateusableoutputs:Dependingonthesystem,outputsmayinclude:Predictedvideoframes,structured3Dscenes,gamestates,robotmotion.

•Usethemodelforongoingplanningandlearning:Oncesufficientlyaccurate,a

worldmodelallowsanagentto:Evaluatecandidateactionsinimagination,

comparedifferentpossiblefuturesbeforeacting(inaprocesscalled

'Model-Based

Reasoning')

,trainpoliciespartlyorentirelyinsidethesimulatedenvironment,

transferlearnedbehaviortoreasonabouttherealworld.

•Formoretechnicaldetails,werecommendthefollowingresearchpapers:

°

"WorldModels"byDavidHa&JürgenSchmidhuber,2018

°

"GAIA-1:AGenerativeWorldforAutonomousDriving"byHu,etal.,2023

Exhibit4:GenerativeWorldModelsallowforanagenttobuildandinteractwithentirelyimaginedworldsbasedonaninputtedstate.

Source:GoogleDeepmind("Genie:GenerativeInteractiveEnvironments")

NotableChallenges:

•ErrorAccumulationandTemporalDrift:Manyworldmodels

struggletostay

coherentoverlonginteractions:

objectsdrift,geometrymorphs,oreventherulesofphysicscanchangeasthesimulationruns.Forexample,

GoogleDeepMindıs

Genie3

(generallyviewedasoneofthemostadvancedgenerativemodelstoday)currentlysupportsonlyafewminutesofcontinuousinteraction.Extending

memoryhorizonsaddsnoiseandcanincreasecomputationaloverheadandcostsignificantly.

•Controllability:Aworldmodelmaygeneratebeautifulworldsbutstilloffer

limitedmeaningfulactions(whatyoucanreliablydoinsidetheworld)otherthan

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basicnavigation/movementevenforsomeofthemostadvancedinteractive

modelstoday,whichconstrainsitsusefulnessforagentsandinteractiveproducts.

•Multi-AgentandSocial/InteractiveDynamics:Simulatinginteractionsamong

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multipleindependentagents(people,vehicles,robots)issubstantiallyharderthansimulatingasinglecameramovingthroughascene.ForGenie3,DeepMind

specifically

callsout

multi-agentinteractionasanotableongoingchallenge.

•DataScaleandDiversity:Buildingrobustworldmodelsoftenrequiresenormous,diversedatasetsandcarefulcuration,especiallyforphysicalAl/robotics,where

collectinglabeledreal-worldsensordatacanbecostlyandslow.

•LackofBenchmarkingFrameworks:Thereiscurrentlynowidelyaccepted

benchmarkformeasuringworld-modelqualityacrosslonginteractivehorizons.Asaresult,evaluatingprogressinworldmodelsoftenreliesonqualitative

demonstrationsortask-specifictestsratherthanstandardizedmetrics.

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UseCasesSpanningBothDigitalandPhysical:

•VideoGames:Worldmodelscangeneratefullyinteractivegameenvironments

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fromminimalpromptsordemonstrations,enablingrapidcontentcreationanddynamicworld-building.Thisisaverytopicaldebateforvideogamestocksgiventhedisruptionpotentialifotherscanessentiallypromptamodeltocreatetheirownvideogame.ForviewsfromourMSvideogameanalystMatthewCost,

see

here.

•VFX/Animation:Worldmodelscangeneratecoherentscenesthatmaintainobjectpermanence,lightingconsistency,andphysicalplausibilityacrosstime,reducing

manualanimationandsimulationwork.Artistscoulditerativelyexplorealternativestorylines,cameramovements,orsceneeditsbyinteractingdirectlywitha

simulatedworldratherthanrenderingframe-by-frame.

•AutonomousVehicles:Worldmodelscansimulatecomplexdrivingscenarios,includingrareedgecases,allowingsystemstoevaluatedecisionssafelybeforedeployment.Theyalsosupportmodel-basedplanning,enablingvehiclesto

anticipatehowtrafficparticipantsmayrespondtodifferentmaneuvers.AVcompaniesleveragingworldmodelstodayinclude:

Tesla,Waymo,

and

Wayve,

amongothers.

•Robotics:Robotscantraininsidesimulatedenvironmentsgeneratedbyworld

models,practicingmanipulation,navigation,orcoordinationbeforeoperatingin

therealworld.Overtime,improvedsimulationfidelitycannarrowthesim-to-realgap,makingtransferoflearnedbehaviorsmorereliableandefficient.Notable

examplesofroboticscompaniesusingworldmodelsinclude:

1X,Tesla,SkildAl,

andmanyothers.

Exhibit5:

WHAMM

isaMicrosoftResearchworld-model

(basedon

Muse)

thatgeneratesandupdatesaplayablegameenvironmentinrealtimebypredictingeachframefromplayerinputsinsteadofusingatraditionalgameengine.

Source:Microsoft

Exhibit6:

Waymo'sWorldModel

BuiltonGenie3-EnvironmentisFullySimulated(noticesnowcoveredpalmtrees...)

Source:GoogleDeepMind

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NotableExamples

•Genie(GoogleDeepMind):Genieisagenerative,interactiveworldmodelthatcan

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transformstaticimagesorinternetvideosintoplayable,controllable

environments.Itlearnsenvironmentdynamicsfromlarge-scalevideodataand

allowsagentstotakeactionsinsideimaginedworlds.

Genie3,

thelatestversion,wasunveiledinAugust2025,andallowsuserstogeneratemultipleminutes(vs.10-20secondsforGenie2)ofinteractive3Denvironmentsata720presolution

and24FPS.Themodelisalsobeingusedasthebasisofthe

WaymoWorldModel

forAVsimulation.AlphabetiscoveredbyBrianNowak.

•V-JEPA(Meta):V-JEPA(Video-basedJointEmbeddingPredictiveArchitecture)isMeta’sworldmodelapproachthatlearnspredictivedynamicsfromrawvideotosupportplanninganddecision-making.Thearchitectureisdesignedtosupport

downstreamreasoningandplanningtaskswithoutrelyingonfullvideogeneration

-particularlyforroboticsystemswhichisheavilyhighlightedinthecompany's

researchpapers.

Thelatestiteration,V-JEPA2wasunveiledin

June2025.

MetaiscoveredbyBrianNowak.

•GAIA(Wayve):GAIAisagenerativeworldmodeldevelopedbyUK-basedstartupWayveforautonomousdrivingthatpredictsfuturedrivingscenesconditionedonvehicleactionsandcontext.Itintegratesvideo,text,andcontrolsignalstosimulaterealisticdrivingscenariosfortrainingandevaluation.Thelatestversion,

GAIA-3,

wasunveiledinDecember2025andrepresentsasignificantupgradeoveritspredecessors,with15billionparameters(abouttwicethesizeofGAIA-2)andtrainingonroughlytentimesmoredata.

•Cosmos(NVIDIA):

Cosmosi

sNVIDIA’sphysicalAIworldfoundationmodel

platformaimedatbuildinglarge-scale,physics-awaresimulatorsforroboticsandautonomoussystems.Itcombinesgenerativemodelingwithsimulation

infrastructure(e.g.,Omniverse)tocreateinteractive,controllablevirtual

environments.Usersinclude(perNVIDIA):1X,AgileRobots,AgilityRobotics,FieldAI,Figure,Hexagon,MenteeRobotics,SkildAI,andanumberofothers.NVIDIAiscoveredbyJoeMoore.

•Muse(Microsoft)

:Muse

isMicrosoft’sgenerativeworldmodeldesignedtolearnthedynamicsofinteractiveenvironments,particularlyvideogames,fromgameplaydata.Themodelpredictshowgamestatesevolveinresponsetoplayeractions,

allowingittogeneratenewgameplaytrajectoriesandsimulateenvironmentsthatrespondtocontrols.MicrosofthaspositionedMuseasatoolforgame

developmentandAIresearch,whereitcanhelpgenerateplayablegame

sequences,assistwithprototyping,andsupportAIagentslearninginsidesimulatedenvironments.MicrosoftiscoveredbyKeithWeiss.

•Marble(WorldLabs):

Marble

isagenerativeworldmodeldevelopedbyWorld

Labsfocusedoncreatingcoherent,navigable3Denvironmentsfromimagesor

textualdescriptions.Thesystememphasizesspatialconsistencyandpersistent

geometry,enablinguserstoexploregeneratedscenesfrommultipleviewpoints

whilemaintainingstableobjectrelationshipsandlayout.Ratherthanfocusing

primarilyonaction-conditioneddynamics,Marbleaimstoconstructhigh-quality

spatialworldsthatcanserveasafoundationforinteractiveenvironments,creativetools,andvirtualproductionworkflows.

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Exhibit7:SimulatedEnvironmentsGeneratedbyNVIDIACosmos

Exhibit8:MetaV-JEPA2forPhysicalAIReasoning

Source:NVIDIA

Source:NVIDIA

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WorldModels&VideoGames

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ThisisanexcerptfromthefollowingreportledbyMorganStanley'sVideoGameAnalyst,MattCost:

Internet:WorldModelsAreHere...SoWhatDoTheyMeanforU,RBLX,TTWO

andAPP?(2Feb2026)

LookingAhead,WeSeeTwoScenariosForToday'sGameIndustryLeaders:AdaptorBeReplaced...Weseetwomainscenariosfortoday'sgameindustryleadersoverthelongerterm:Scenario1,whereincumbentsadapttheirtoolsandframeworkstousenewAI

technology;orScenario2,whereincumbentsarereplacedorseverelydisruptedbynewAItechnology.Scenario1assumesthattoolslikeU'sEngineandRobloxStudiodeployAItoolstoautomatemanualprocesseslikecodingand3Dassetcreation.Thestructureof

traditionalgameengineswouldremain,butautomationwouldincreasinglytakecenterstage.

Exhibit9:WeseetwomainscenariosforhowAItoolscouldbeusedtoautomate

videogameproductioninthecomingyears:Scenario1whereincumbentsadapttheirtoolsandframeworkstousenewAItechnology;andScenario2,whereincumbentsarereplacedorseverelydisruptedbynewAItechnology.

Source:CompanyData,MorganStanleyResearch

...ButReplacingExistingGameTechCouldBeHarderThanitAppearsatFirstGlance:

Scenario2isthesimpleroption,sinceworldmodelscanalreadygenerateplayable,videogame-likeworldsfromnaturallanguagepromptsalone.Butweseeanumberchallengesthatmaysloworpreventworldmodelsfromreplacing(asopposedtoaugmenting)existinggametechnology.Someofthesechallenges,likecomputespeedandcostto

operate,haveclearpathstosolutionsandwilllikelybesolvedinthenearterm.Others,includingmetasystemsandlatency,createlargerroadblocksthatwillrequirefurther

researchandinvestmenttoclear,butmayeventuallybesolved.However,certainmajorissueslikedeterminism,memory,andupdatescouldultimatelybedifficulttosolvewithinthecontextofaworldmodel.

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Exhibit10:Severalroadblockspreventworldmodelsfromgeneratingcompleteandplayablegamestoday(i.e.Scenario2),butsomeofthemposesignificantlong-term

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hurdles.

Source:CompanyData,MorganStanleyResearch

Short-TermConstraintsonWorldModelsCreateTimeforIncumbentstoRespond,ButtheLongTermThreatisReal:WhiledemosofGenie3alreadygeneratehigh-fidelity

interactiveworldsinsecondsbasedonuserprompts(seehere),thisisstillanearlytest.Asmentionedabove,webelievetheseworldmodelsfaceshort-termconstraintsintheirspeed,stability,andcost.lnourview,thoseproblemsaresolvable,butimportantlytheyalsoopenawindowforincumbentstorespondandadaptexistingplatforms/offeringstocompete.

Exhibit11:WeprovideaframeworktoassesshowdifferentgameecosystemplayerscouldbeimpactedbyAI-drivenchangestohowvideogamesareproduced.Notably,pureplaymobileadnetworkslikeAPPdonotappearinthisframeworkasweseenoclearreadacrossfromchangestothegamedevelopmentprocess.

Source:CompanyData,MorganStanleyResearch

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WorldModels&PhysicalAI

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Fromourconversationswithroboticsexperts,worldmodelscouldhelpaddress2keychallengesinrobotics:1)theneedforlargevolumesoftrainingdata;and2)enablingrobotstoreasonaboutphysicalenvironmentsbeforeacting.Bylearningpredictive

representationsofho