摩根士丹利-宏观视野 人工智能 十条投资真知 Big Picture - Artificial Intelligence Ten Investment Truths

BIGPICTURE

ArtificialIntelligence:TenInvestmentTruths

BIGPICTURE|2Q2026

Threeyearsago,ArtificialIntelligence(AI)wasatopicofmere

curiosity.Todayitisthesubjectofcapitalallocation.The

challengeforinvestorsisnotonlyunderstandingthatAIis

consequential,butbuildingframeworkspreciseenoughtoacton.

ThisBigPicturecoversteninvestmenttruthsaboutAIthateveryasset

ownermustunderstand:fromtheinfrastructurebeingbuiltatextraordinaryspeed,totheagentsbeginningtoactautonomouslyintheworld,tothetwocompetingarchitecturesthatwillshapethegeopoliticalorderfordecades.

Theopportunityspanseverylayerofthestackandeveryassetclass.Sodoestherisk.Neitherrewardsanarrowframework.

AUTHOR

JITANIAKANDHARI

DeputyCIO,Solutions&Multi-AssetGroupPortfolioManager,

PassportEquity

HeadofMacro

&Thematic

Research,EmergingMarketsEquity

Theseareteninvestmenttruthsforassetownerswhoneedframeworks,notforecasts.

1.4-WAYCONVERGENCE:ALGORITHMS,COMPUTE,TALENT&CAPITAL

Compoundingsimultaneouslywithnohistoricalprecedent.

2.MOORE'SLAWNOMORE:PHYSICSBECOMESTHEBOTTLENECK

Everytechcyclehasabottleneck.WithAI,itkeepsmoving.

3.THETOKENECONOMY:WHENCOMPUTEBECOMESREVENUE

Datacentersarefactories.Tokensaretheproduct.

4.FROMREACTIVETOAUTONOMOUS:THEAGENTICTRANSITION

AIisnotwaitingtobeasked.Itisalreadyatwork.

5.DATA,DOMAINANDDISTRIBUTION:THENEWSOFTWAREMOATS

Themoatisnolongerthecode,itisthethreeDs.

6.YESTERDAY'SSCI-FIISINCREASINGLYINREACH

AIstopsanalyzingtheeconomyandstartsoperatingit.

7.AIISAFULL-STACKCAPITALCYCLE

Thisisacross-asset,cross-sectorcapitalcycle.

8.COMPETINGCOMPUTE:TWOARCHITECTURES,ONERACE

AIisamatterofnationalsecurity.

9.AIISSTRATEGICINFRASTRUCTURE,BUTNOBODYISINCHARGE

Capabilitiesareadvancing.Governanceisnot.

10.FROMTELEGRAMSTOTOKENS:HISTORYASAROADMAP

Tokenswillcomebeforetheapplications

2MORGANSTANLEYINVESTMENTMANAGEMENT|BIGPICTURE

1

Four-WayConvergence:Algorithms,Compute,TalentandCapital

COMPOUNDINGSIMULTANEOUSLYWITHNOHISTORICALPRECEDENT.

AIistransitioningfrombreakthrough

technologytofoundationalinfrastructureatapacefewanticipated.Google’s

2017Transformerbreakthroughwastheinflectionpoint.Whatfollowedwasaself-reinforcingflywheelacrossfourforcesscalingsimultaneously:

algorithmsadvancingthroughsuccessivearchitecturalbreakthroughs,compute

(i.e.,resourcesfortraininganddeployingAI)expandingatextraordinaryspeed,

talentconcentratinginahandfulof

institutionsandcapitaldeployingata

scaleandvelocitywithnoprecedentintechnologyhistory.Approximately$2.3trillionhasbeencommittedinAIcapexsincetheTransformerbreakthrough,andthepaceisaccelerating,notplateauing.

Theresultsaremeasurable.

Consumptionoftokens(basicunitsoftextsthatanAImodelprocesses)grewmorethan10xin2025alone.Modelsarebecomingmulti-modalacrossvoice,imageandvideo.Thevirtuouscycle

remainsintact:modelimprovementsdrivehigherusage,higherusage

driveshigherinvestmentandhigherinvestmentdrivesbettermodels.

Thesefourforcesareconverging

towardArtificialGeneralIntelligence,orAGI,meaningAIsystemscapable

ofperforminganyintellectualtaska

humancan,acrossanydomain,withouttask-specificretraining.Today’sAIis

extraordinarybutnarrow.Itautomatesspecificworkflowswithinitstraining

domain.AGIremovesthatconstraintentirely,autonomouslyidentifying,designingandexecutingsolutionstoproblemsithasneverencountered.BeyondAGIliesASI(Artificial

Superintelligence)systemsthat

surpassthebesthumanperformanceacrossalldomains.Anthropic’sCEOhasframedwhatthislookslikein

practice:“acountryofgeniusesinadatacenterbyendof2027.”

AIcapabilitiesarecurrentlydoublingeveryfourmonths,implyingsystems250timesmorepowerfulby2028

thantoday.Historyoffersnoreliabletemplateforcompoundingatthisrate.

2

Moore’sLawNoMore:PhysicsBecomesTheBottleneck

EVERYTECHNOLOGYCYCLEHASABOTTLENECK.INTHISONE,IT

KEEPSMOVING.

Moore’sLaw,the60-yearprinciplethateverytwoyearscomputegotcheaperandmorepowerful,nolongerholds.

Thisisnotatemporaryslowdown.Thephysicshaverunout.Thatmakestheshiftstructural,meaningtheindustryhastofindentirelynewwaystodeliverperformance.

Theindustry’sresponseismulti-

dimensional.Atthechiplevel,chipletarchitectures(multiplesemiconductorsplacedsidebysideorstacked)are

deliveringperformanceimprovementsbeyondthelimitsoftraditionaldesigns.Atthesystemlevel,co-designed,

multi-chiparchitecturesareproducingstep-functiongainsinAIcompute

density.Atthedatacenterlevel,siliconphotonicsisemergingasthesolution

tobandwidthconstraintsthatcopperinterconnectscannotaddressatscale.

Componentspreviouslytreatedas

commodityinputsarebeingrepricedasstrategicsupplychainassets.Memoryiscurrentlyundersuppliedthroughendof2026.AIanddatacenterdemandisexpectedtocreate75–100exabytesofincrementalmemorydemandin2027,doublingagainin2028.Hyperscaler

buyingbehaviorhaschanged

structurally:Supplysecuritynowtakespriorityoverpriceoptimizationand

long-termsupplyagreements,whichhavehistoricallybeenrareinmemorymarkets,arebeingsignedtoday.

Thepatternisconsistent.The

bottleneckmigratesupthesupply

chain:firstchips,thenpower,then

memory,thennetworking,thencooling.Eachmigrationturnsyesterday’s

commodityintotomorrow’sscarce

asset.Thesemiconductorstoryisnolongeraboutwhomakesthebestchip.Itisaboutwhichlayerofthesupply

chainbecomesindispensablenext.Forinvestors,identifyingthatlayerbeforeconsensusistheopportunity.

3

TheTokenEconomy:WhenComputeBecomesRevenue

DATACENTERSAREFACTORIES.TOKENSARETHEPRODUCT.

Threeyearsago,theworldwas

askingwhatAIcoulddo.Today,the

moreimportantquestioniswhatAI

generates.Theansweristokens.Everyqueryanswered,everydocument

drafted,everyagentactionexecutedgeneratestokens.Theyaremeasurable,priceableandscalable.Tokensaretheoutputofanewkindoffactory.Data

BIGPICTURE|MORGANSTANLEYINVESTMENTMANAGEMENT3

“Thesinglemostimportant

technicalmetricinthisnew

economyistokensperwatt,themeasureofintelligenceproducedperunitofenergyconsumed.”

centershavecrossedathreshold:Theyarenolongercostcenterssupportingabusiness.Theyareproductionfacilitiesmanufacturingintelligenceandtheir

outputispricedatdollarspermilliontokens,muchlikeelectricityispricedperkilowatthour.

Theeconomicsarealreadyestablished.Computecapacityandrevenuehave

becomedirectlyproportionalinawaythathasnoprecedentinthehistory

ofenterprisetechnology.Companieswiththreetimesmorecomputeare

generatingthreetimesmorerevenue.Thesinglemostimportanttechnical

metricinthisneweconomyistokensperwatt,themeasureofintelligenceproducedperunitofenergyconsumed.

However,thedemandtrajectoryisnotlinear.AIhasevolvedthroughthree

distinctphases,eachrepresenting

adramaticstep-changeintoken

consumption.GenerativeAI(using

modelstogeneratetext,images,

videos,audioandotherformsofdata)establishedthebaseline.ReasoningAI,whichintroducedself-reflectionand

errorcorrection,requiredapproximately1,000timesmorecompute.Agentic

AI,systemsthatactratherthanmerelyanswer,usingtools,executingtasks

andrunningcontinuously,requires

approximatelyonemilliontimes

morecomputethantheoriginal

conversationalmodel.Eachphasehasexpandedthemarket,notreplaced

it.Moreusers,moreagentsperuser,morecomplexworkflows,more

always-onsystemsmeanthedriversofdemandaremultiplyingtogether.

The$2trillionsoftwareindustrywas

builtonlicensingseats.Thenextchapterwillbebuiltonconsumingtokens.

4

FromReactivetoAutonomous:TheAgenticTransition

AIISNOTWAITINGTOBEASKED.

ITISALREADYATWORK.

ThefirstgenerationofAIwasreactive.Youtypedinaquestion,AIanswered.Theinteractionwastransactional,

theinterfacefamiliarwiththehumanfirmlyincontrol.Thatmodelisgivingwaytosomethingfundamentally

different.AIistransitioningfroma

toolthatonlyrespondstoasystemthatacts,autonomouslyexecuting

tasks,managingworkflows,operatingcontinuouslyandtransacting

independently.Theshiftfrom

retrievaltoexecutionisnotasimpleproductupdate.Itisarevolutionaryrestructuringofhowworkgetsdone.

Theearlyevidencecanbeseenin

thenumbers.Developeradoption

hasmovedfastest.Someengineers

arealreadymanagingfourormore

agentsconcurrently,delegatingratherthancoding,overseeingratherthanexecuting.Agenttrafficonmajor

internetinfrastructurenetworkswentnear-verticalinearly2026.

Theorganizationalimplicationsare

assignificantasthetechnicalones.

Microsoftdescribesthreedistinct

modesofAIinteractionatwork:simplechat,delegatedtasksandfulldigital

workerswiththeirownidentities,toolsandworkspaces.Althoughadoption

iscurrentlyhappeningunevenly

acrossfirmsandfunctions,overthe

nextthreeyearsmostindividual

contributorswillinvolvemanaging

groupsofagentsratherthanexecutingtasksdirectly.Everyemployee

willmanageagents.Thequestion

organizationswillfaceisnotwhethertoadoptagenticAI,buthowquicklytheycanredesignworkflowsarounditratherthanmerelylayeritontopofexistingprocesses.

Themostunderappreciateddimensionoftheagentictransitioniswhathappenswhenagentsbegintotransact,not

justact.Agenticcommerce,where

AIagentsactuallyexecutepurchases,

settlepaymentsandmanagefinancial

interactionsautonomously,isalready

emergingasinfrastructure.Infact,

agent-to-agenttransactionswill

naturallyfavorpaymentrails(paymentplatformsornetworks)thatrequire

nohumanauthorization,haveno

bankinghoursandsettleinseconds.

Thearchitecturerequiredisnowbeingbuiltacrosspaymentplatforms,internetinfrastructureanddigitalassetnetworks.

4MORGANSTANLEYINVESTMENTMANAGEMENT|BIGPICTURE

DISPLAY1

Data,DomainandDistribution:TheNewSoftwareMoats

D

D

D

DISTRIBUTION

Anetworkbuiltovertwo

decadescannotbereplicated.Themoatwasalwayswhatthesoftwareaccumulated,notthe

softwareitself.

DOMAIN

Compliancedepth,

implementationcomplexityanddomain-specificlogicbuiltoverdecades.AIdoesnoterodethesemoats.Itdeepensthem.

DATA

Theorganizationsthatcontrol

theirownunifieddatalayer,

enablingagentstoreasonacrossallsystemsatthesametime,willcompoundtheiradvantage.

Source:MSIM,asof5/31/2026.Providedforinformationalpurposesbasedontheresearch,analysis,andopinionsoftheEMETeam;notarecommendationtopurchaseorsellspecificsecurities,ortoadoptanyparticularinvestmentstrategy.

5

Data,DomainandDistribution:TheNewSoftwareMoats

THEMOATISNOLONGERTHECODE,ITISTHETHREED’S

WhileAItrainingiscapitalintensiveandcompetitive,thenextwave

ofvaluewillcomefromAIusage:

inference,orchestration,applications

andworkflowsthatoffermoredurablerecurringrevenueopportunities.New

AI-nativecompanies,leveragingmassivecomputewithfarfeweremployees,arealreadydemonstratingthestructuralcostandspeedadvantagesover

incumbentsbuiltforadifferentera.

Durablemoatsaremovingaway

fromcode,towardworkflowdepth,

proprietarydata,domainexpertise,

networkeffects,complianceand

distribution.Buildingsoftwaretodaycostsafractionofwhatitdid.Featuresthatoncetookmonthsandhundredsofmillionstobuildcanbereplicated

indays.Butreplicatinganetworkbuiltovertwodecadescannot.Themoat

wasneveronlythesoftware.Itwas

alwayswhatthesoftwareaccumulatedovertime.AIhasnotchangedthat

logic.Ithassharpenedit.

InverticalSaaS(SoftwareasaService)especially,thewinnersarelikelytobefirmswiththethreeDs:Data,DomainandDistribution(Display1).Datais

themostcontested.MostenterprisedatasitsfragmentedacrossdozensofsystemsincludingCRM(customerrelationshipmanagement),ERP

(enterpriseresourceplanning),emailanddocuments,eachsiloedbehind

itsowndatabaseandchargingexportfees,eachchargingexportfeesthatmakeunifiedaccessexpensiveand

slow.AnAIagentthatcannotseethefullpictureproducesweaker,more

fragmentedrecommendations.Theorganizationsthatcontroltheirownunifieddatalayer,enablingagentstoreasonacrossallsystemsatthesame

time,willcompoundtheiradvantage.Thevendorswhosilodatabehind

exportfeesarenotprotectingamoat.Theyarelikelyacceleratingtheirowndisplacement.

Domainanddistributionareequally

powerfulbutmoredurable.Systems

embeddeddeeplyinmission-critical

workflowssuchascorebanking,ERP,verticalSaaSrunningentireindustries,carrymoatsbuiltfromyearsof

implementationcomplexity,compliancerequirementsanddomain-specificlogicthatAIdeepensratherthanerodes.

InvestorsneedtodistinguishbetweencompaniesenhancedbyAIandthosevulnerabletoAI-nativecompetition.Thecompanieswiththestrongest

advantagewillhaveallthreei.e.,data,domainanddistributiondominance.

BIGPICTURE|MORGANSTANLEYINVESTMENTMANAGEMENT5

DISPLAY2concentratesvalueatthecoreinputs.

ewillnotneedtopredictwhich

botwins.Wewillneedtoallocatepitaltothebuildingblocksthat

eryautonomoussystemwillrequire.

7

IsaFull-StackCapitalCycle

ISISNOTASECTORSTORY.IT

ACROSS-ASSET,CROSS-SECTOR

PITALCYCLE

AIIsaFull-StackCapitalCycle

AIStack

AgenticAI,software

PhysicalAI,autonomy

Applications

Models

Infrastructure

AI

TH

IS

CA

W

ro

ca

ev

Source:MSIM,asof5/31/2026.Providedforinformationalpurposesbasedontheresearch,analysis,andopinionsoftheEMETeam;notarecommendationtopurchaseorsellspecificsecurities,

ortoadoptanyparticularinvestmentstrategy.

6

Yesterday’sSci-FiIsIncreasinglyinReach

AISTOPSANALYZINGTHEECONOMYANDSTARTSOPERATINGIT.

Intelligenceismovingfromsoftwareintomachinesthatmove,build,

transportandoperate.Ifdigital

AIautomatedknowledgeworks,

embodiedAIwillautomatethephysicaleconomy.Autonomousmobility,

robotics,industrialautomation,dronesandintelligentinfrastructureareno

longerexperimental.Theyarebeing

deployedcommerciallyatscale.A

newtransportationlayerisemerging

betweengroundandtraditional

airspace.Software-definedvehicle

revenuehasdoubledfrom$500millionin2021to$1billionin2024,targeting$2billionby2027.1Government

programsanddefensecontractsare

earlyanchorsofdemand,significantlyexpandingtheaddressableopportunity.

Everyautonomoussystem,regardlessofenduse,reliesonthesamestack:

chips,compute,sensors,energystorage,electricpropulsionandactuation.

Valueaccruesdisproportionatelyto

whomevercontrolsthesechokepoints.

Humanoidrobotstodayareno

longertrainedintherealworldbutinsimulationenvironmentsthat

compressyearsofphysicallearningintodays,thespecificbreakthroughthatmakesphysicalAIscalable

ratherthanexperimental.Physical

AIcouldtransformtransportation

andlogistics,manufacturing,

construction,agriculture,defenseandhealthcare.Coreplatforms,enablinginfrastructureandthesoftwarelayer,willunderpinthisecosystem.

Autonomyisnotanichevertical.Itisahorizontallayerspanninghardware,software,sensorsandpowersystems.ItmarksthemomentwhenAIstopsanalyzingtheeconomyandstarts

operatingit:ontheground,intheairandinspace.Itlowersthecostoflabor,reshapessupplychainsand

AIrepresentsafull-stackcapitalcycle,andeverylayerisbeingpricedinrealtime.Itwillcreatewinnersacross

semiconductors,foundry,memory,

networking,powerandcooling.Atthemodellayer:training,inference,securityandgovernance.Attheapplication

layer:software,agenticplatforms,

robotics,sensors,actuatorsandedgecomputing.Thescarceassetshiftsasbottlenecksmovethroughthestack.

Thestackhasthreedistinctlayers

(Display2).Atthefoundationsits

theinfrastructurelayerthatiscapitalintensive,supplyconstrained,and

thelayeronwhicheveryAIsystemultimatelydepends.Inthemiddle,themodellayerencompassestraining,

inferenceandorchestration,wherecomputeintensityishighestand

competitivedynamicsaremostfluid.Atthetop,theapplicationlayeris

splittingintotwodistinctcategories:

agenticAI,whichactsautonomouslywithindigitalworkflows,and

autonomousAI,whichoperatesin

thephysicalworldthroughrobotics,sensors,edgecomputingand

autonomoussystems.Forinvestors,

thekeyquestionsarenolonger

simplyaboutwhohasthebestmodel.Theyareaboutwhocontrolsthe

1KPMG-“DrivingtheFutureofMobilityandMonetization”-February2026.Forecastsand/orestimatesaresubjecttochangeandmaynotactuallycometopass.

DISPLAY3

6MORGANSTANLEYINVESTMENTMANAGEMENT|BIGPICTURE

TheU.S.andChina:FrontierLanguageModelIntelligenceOverTime

ReLanguageModelslativePerformanceofLarge

Performancelmprovement

Source:Source:StanfordInstituteforHuman-CenteredArtificialIntelligence:ArtificialAnalysisIntelligenceIndexReport.TheArtificialAnalysisIntelligencelndexisacompositebenchmarkaggregatingtenchallengingevaluationstoprovideaholisticmeasureofAlcapabilitiesacrossmathematics)science)coding)andreasoning.

infrastructure)whoownsthedata)

whocapturestheemergingagent

economy)whoisembeddedinmission-criticalworkflows)whocommands

thephysicalAlsupplychainandwhocantranslateAlcapabilityintodurableearningspower.FocusonwhereAlcanunlocknewdemand)notjustreducecosts.Thebiggestbreakthroughsin

Alarecreatingentirelynewproblems)thensolvingthembeforethemarketrealizestheyevenexist.

Forassetowners)thisarguesfor

abroaderframeworkthansimply

buyinghardwareorsoftware.Alisacross-asset)cross-sectorinvestmentthemewithimplicationsforportfolioconstruction)managerselectionandlong-termcapitalallocation.

8

CompetingCompute:TwoArchitectures,OneRace

AIISAMATTEROFNATIONALSECURITY

TwodistinctAlecosystemsare

emerging.TheU.S.modelisahigh-

costinnovationengine)fueledby

massivecapitalexpenditureandaccesstocutting-edgechips.ltsprimary

constraintisnotsemiconductorsbutpower)theavailabilityofelectricitytoruntheinfrastructureitisbuilding.TheChinesemodel)constrainedbyexportcontrolsonadvancedsemiconductors)hasevolvedintoalow-cost)efficiency-focusedsystem)leveragingsurplus

powerandopen-sourceecosystems)supportedbydeeppartnerships

acrossemergingmarketfoundriesandhardwaresectorsincludingservers)

memoryandnetworking)designed

tocreateaparallelAlstack.That

supplychainisreinforcedbythe

manufacturingscaleandtechnologicalexpertiseofNorthAsiancompanies

whosecapabilitiesspanthefullhardwarespectrum.

Theperformancegapisclosingfasterthanexpected(Display3).Despite

spendingonly18%ofwhatAmericanhyperscalershaveinvested)Chinese

modelsarenowbenchmarkingbroadlyinlinewithU.S.peers)withthelag

onreportedperformancenarrowingtoapproximatelyonemonth.The

lowercostbasehasallowedChinesemodelstoreducetokencosts

fasterwhileofferingcomparableperformance)drivingmarketsharegainsasmeasuredbycumulativedownloads.China)shyper-digitized

BIGPICTURE|MORGANSTANLEYINVESTMENTMANAGEMENT7

economyisenablingfasteradoption

andapplicationdeployment.The

competitivesignalof2026istheshiftbyonemajorU.S.hyperscaleraway

fromanopen-sourcemodelstrategy

andtowardaproprietarymodel)an

implicitacknowledgmentthatopen

sourcecannotwintheenterprise.lfthistriggersanindustry-widemovetowardclosedmodels)valueconcentratesatfrontierlabs.lfopensourceproves

resilient)inferencemargincompressionacceleratesacrosstheindustry.

HarnessingAlisbecomingamatter

ofnationalsecurity.Militaryand

commercialapplicationsfromadvancedandautonomousweaponrytoreal-

timebattlefieldawarenessisgainingimportance.GovernmentsanddefenseestablishmentswerepassiveobserversofthefirstphaseofAldevelopment)andarenowbecomingactive

participants)andactivecustomers)inthesecond.Theraceisnolongerpurelycommercial.ltisstrategic.

Andstrategicraces)historically)runlongerandattractmorecapitalthancommercialones.

9

AIIsStrategicInfrastructure,butNobodyIsinCharge

CAPABILITIESAREADVANCING.

GOVERNANCEISNOT.

ThepolicyvacuumaroundfrontierAliswidening.Capabilitiesare

advancingfasterthanpublic

policycanrespond)leavingprivate

companies)notgovernments)tomakeimportantdecisionswithgeopoliticalconsequences.Privatecompanies

areeffectivelysettingaccesspolicy)

decidingwhichcountriesandfirmscan

deploymodelscapableofidentifyingdecades-oldvulnerabilitiesincriticalfinancialandphysicalinfrastructure.Thisisprivateregulationofpublic

infrastructure)withoutagovernmentmandate.Noelectedbodyauthorizedthisandnointernationalframeworkgovernsit.

The

U.S.is

especiallyexposed.lts

economyishighlydigitally-connectedwhilemuchofitscriticalinfrastructureisprivatelyowned.Manysectors

stilllackminimumcybersecurity

requirements.Recentvulnerabilitiesacrosstelecom)waterandenergy

havealreadyshownhowcostlythisweaknesscanbe.TheadoptionofAlisexpandingtheattacksurfacefasterthandefensivecapabilitiesarebeingdeployed.Halftheenterprisemarketremainsonlegacycybersecurity

protectiondespiteAl-driventhreatsoperatingatmachinespeedwithnosecondchances.Security)safetyandaccesscontrolshavenotcaughtuptoagentcapabilities.

Theconcernsextendwellbeyond

cybersecurity.Lableadersthemselvesarewarningthatthepaceof

developmentmayneedtoslow.Yetpolicymakersarenotrespondingwithmatchingurgency.Advancedsystemsmaybecomehardertocontrolastheygainaccesstotools)externalAPls

(applicationprogramminginterfaces)andworkflows.

China)srapidprogresstonarrowthe

Algapshouldbetreatedasabase-

caseassumption)notadistantrisk.

Washingtoncannotholdoffregulationasthoughithasacommandinglead.

Themostimportantregulatory

challengeiscreatingcredibleguardrails

fastenoughtomatchthetechnology)whilestillcompetingaggressively.TheU.S.needsinternational)nationalandsocialpoliciescapableofmatchingthespeedoftechnology.

10

FromTelegramstoTokens:HistoryasaRoadmap

THECABLESCAMEBEFORETHEINTERNET.THETOKENSWILLCOMEBEFORETHE

APPLICATIONS

ThecurrentAlinfrastructurebuildoutfeelsunprecedented.Thecapital

commitmentsareextraordinary)

thepaceofchangedisorientingandthegapbetweeninvestmentand

monetizationuncomfortablefor

thosewhorememberthelasttimetheworldcollectivelybuiltthisfast.Butthepatternisnotnew.lthas

beenrepeatedacrosseverymajorcommunicationsinfrastructurecyclefor170yearsandeachtimethe

infrastructurethatseemedaheadofitsmomentturnedouttobetheprerequisitefortheadoptionwavethatfollowed.

Theearliestexampledatestothebirthofsubseatelegraphyinthemid-19thcentury.Sendinga30-wordtelegramfromAustraliatotheUnitedKingdomoncecosttheequivalentofthree

weeksofaveragewages.Technologicalimprovement)competitionand

consolidationdrovepricesfrom$1.09to$0.30permessagebetween1866and1900.Volumegrew11-fold)from5.8millionto63.2millionmessages.Lowercostsweremorethanoffset

byhigherusage.Valuetripledevenaspricesfell.By1930)volumehadgrownafurtherthree-foldtonearly212

8MORGANSTANLEYINVESTMENTMANAGEMENT|BIGPICTURE

millionmessages.Theshiftwasalreadyvisible:infrastructurebuildoutleadstopricecompression,pricecompression

drivesadoption,andadoption

generatesdemandthattheoriginalbuildersneverimagined.

Theinterneterarepeatedit

withgreatervelocity.The1996

TelecommunicationsActopenedthedoortoafiberbuildoutthatsaw

telecomoperatorsmultiplyfrom

30to711infouryears.Annualfiber

deploymentquadrupledanddrove

long-distancecallcostsfrom$0.75to$0.55perminute,withsomeroutes

fallingaslowas$0.10.Whatlooked

likeoverbuildingturnedouttobethefoundationoftheconsumerinternet

era.Theapplicationsthatultimately

justifiedtheinfrastructure,including

search,socialmedia,streaming,ride-

hailing,e-commerce,didnotexistwhenthecableswerebeinglaid.

Thispatternrepeatswithconsistency:infrastructurebuildout,price

compression,demandacceleration

andanewapplicationlayerthatwasunimaginableattheinfrastructure

investmentstage.AccordingtoSam

AltmanAItokencostsfell10xin

2025alone.Theapplicationsthat

willconsumetheinfrastructure

beingbuilttodayarestillaheadof

us.Thecompaniesthatgeneratethegreatestreturnsmaynotyethave

beenfounded.Whatwecansaywithconfidenceisthatthispatternhas

repeatedreliablyandAIisfollowingitwithonecriticaldifference:velocity.Thetelegraphtookdecadesto

commoditize.Thefiberbuildouttookadecade.AI’spricecompressiontookoneyear.Theadoptioninflectionmaynottaketenyears.Itmaytaketwo.

“Theapplicationsthatwill consumetheinfrastructurebeingbuilttodayarestillahead ofus.Thecompaniesthat generatethegreatestreturnsmaynotyethavebeenfounded.”

WhatCouldGoWrong

Thefirstriskisthattheconvergencecouldstall.Algorithmsmaybehittingdiminishingreturns.Thereisacrediblebodyofresearchsuggestingthat

scalinglawsareflattening,meaningmorecomputenolongerproducesproportionallybettermodels.The

nextbreakthroughmaynotarriveonthecurrentarchitecturalpath.For

example,ifAGItakeslongerthan

expected,today’scapitaldeploymentcould