高盛-解读智能体经济：人工智能应用与利润率即将迎来拐点-Decoding the Agentic Economy The Coming Inflection in AI Usage and Margins-20260505

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EQUITYRESEARCH|May5,2026|8:33PMEDT

DecodingtheAgenticEconomy

TheComingInfiectioninAIUsageandMargins

AgenticAIhasburstontotheevolvingAIscenewithsignificantpromise,capturingtheimaginationofindustrypractitionersandinvestorsalike.Atitsfullest,AgenticAIcanhandleawiderangeoftaskscurrentlydonebyhumansinafullyautonomousway.Ontheotherhand,AgenticAIcouldbemisdirectedandcounterproductive,consumingvastresourceswithlittlereturn.Inthisreport,weoutlinesomeofthelikelyusecasesweseeforAgenticAIacrosstheenterpriseandconsumersphere-andquantify

potentialupsidetobusinessoutcomes,alongwiththeinvestmentlevelsrequired.

Movingpastconceptstonumbers:WeseeAgenticAIdrivingadramaticincreaseintokenconsumptionof24Xor120quadrillion

tokenspermonthby2030.Wethinkenterpriseagentswillbethelargesttokenmultiplier,liftingtokenconsumption55Xby2040.Webelieveconsumeragentswillbroadenusageawayfromepisodicchatstoutilitybeyondtraditionalsearch,driving12Xtoken

consumptionby2030.

Whynowisthetime-aneconomicinfiectionpointforhyperscalersandmodelmakers:Critically,weoutlinewhyweseean

infiectionintokeneconomicsaheadforhyperscalersandmodelproviders,enabledbycontinuedtokencostdeclines(poweredby

leadingsemicompaniesdriving60%-70%lowerannualizedcostpertoken)andstabilizingtokenprices(whichhavemovedfrom~40%annualdeclinestofiatorincreasingpricing).Webelievetheseimprovingeconomicsarelikelytoimprovemarginsandtheoverall

economicmodelforhyperscalersandmodelproviders-withapositivegrossmargininfiectionlikelyoverthenext3-12months-thusmakinginfrastructurespendingmoresustainablefortheentireecosystem.

Wherewearedi仟erentiated:Wehavetakenabottom-upapproachtoarriveatourforecasts,building"realworld"implementableagentsinpseudo-codetoestimatetokenconsumptionandoverallcosts.Similarly,onthecomputecostside,wehaveusedchip

performance,benchmark,andpricingdatatoarriveatourestimatesofall-intokencostsandestimatemargininfiectionpointsfortheindustry.

Investmentviews:InSemiconductors,wepreferBroadcom(leaderincustomsilicon),Nvidia(high-performancemerchantsolutionsleader),andAMD(enterpriseCPUleader&emergingGPUplayer).InInternet,wepreferAlphabet(cloud&consumercomputing

utility),Amazon(leaderincloudcomputing,eCommerce),andMeta(digitaladvertising&spatialcomputing).InSoftware,wepreferMicrosoft(leaderinbroadenterpriseworkfiows),Cloudfiare(leaderinedgecompute),andAccenture(AIbusinesstransformation).

JamesSchneider,Ph.D.

EricSheridan

GabrielaBorges,CFA

LuyaYou

AnmolMakkar

+1(212)357-2929

+1(917)343-8683

+1(212)902-7839

+1(212)902-5297

+1(212)357-1366

jim.schneider@

eric.sheridan@

gabriela.borges@

luya.you@

anmol.makkar@

GoldmanSachs&Co.LLC

NoahNaparst

+1(917)343-6395

noah.x.naparst@

GoldmanSachs&Co.LLC

GoldmanSachs&Co.LLC

EmmaHuang

+1(212)902-7229

emma.huang@

GoldmanSachs&Co.LLC

GoldmanSachs&Co.LLC

GoldmanSachs&Co.LLC

GoldmanSachs&Co.LLC

GoldmanSachsdoesandseekstodobusinesswithcompaniescoveredinitsresearchreports.Asaresult,investorsshouldbeawarethatthefirmmayhaveaconfiictofinterestthatcoulda仟ecttheobjectivityofthisreport.Investorsshouldconsiderthisreportasonlyasinglefactorinmakingtheirinvestmentdecision.ForRegACcertificationandotherimportantdisclosures,seetheDisclosureAppendix,

orgoto/research/hedge.html.Analystsemployedbynon-USaffiliatesarenot

registered/quali

fi

ed

asresearchanalystswithFINRAintheU.S.

TheGoldmanSachsGroup,Inc.

GoldmanSachsA

tetrpcsa:s/n.

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ExecutiveSummary

WeSeeAIUnitEconomicsandMarginsInfiectingasAgenticAIUsage-TakesO仟

nTokenexplosionmeetsmargininfiection.WebelieveAgenticAIcoulddrivea

step-functionchangeintokenconsumption,justastokeneconomicsarebeginningtoimprove.Althoughtheindustryiscompute-capacityconstrainedin2026,our

inferencepricevs.costanalysiscurvesuggeststhatuniteconomicsoftokensaresettoimprove.LeadingLLMtokenpriceshavenowstartedtostabilize-butunderlyingcomputecostpertokenacrossNvidia,AMD,GoogleTPU,andTrainiumcontinuestofallsignificantlyfaster.Inotherwords,theAgenticAIexplosionintoken

consumptionwemodelby2030maynotonlyrefiectademandstory,butalsoamarginexpansionandprofitabilitystoryacrosstheAIvaluechain.

Exhibit1:Tokeneconomicsarebeginningtoinfiect,ascomputecostdeclinescontinueandtokenpricesstabilize

Cost/Priceper1MTokens

$1.20

MarginsInflectingin

$1.00

1H26

LeadingLLMTokenPrice

$0.80

$0.60

$0.40

$0.20

Trainium

AMD

$0.00

GoogleTPU

Nvidia

-$0.20

Source:DatacompiledbyGoldmanSachsGlobalInvestmentResearch

SizingtheConsumerAgentOpportunity

nWeestimateconsumerAIagentscanliftglobaltokenconsumption12Xby2030.ConsumerAIqueriesarealreadyalargeandgrowingmarket,butthemixisshiftingquicklyasAIoverviewsandLLMagenticqueriestakesharefromtraditionalsearch.At~23billionAIqueriesperdayby2030,upfrom~5billionin2025,weestimateupto30%willbedirectedtoagentsacrosssearch,shopping,travel,email,andother

personalproductivityfunctions.Thiswouldadd60quadrilliontokenspermonthby2030,or12Xcurrentglobaltokenconsumptionasof2026.Oursimulationsof

basicconsumeragentssuggestthelargesttokenstep-upwillcomewhenagentsmovefromuser-initiatedtaskstopersistent,“always-on”backgroundactivitythatcontinuouslymonitorscontextandactswhenneeded.

5May20262

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5May20263

Exhibit2:By2030,weestimateconsumerandenterpriseagentscouldpushtokenconsumption24Xabovetoday’sestimatedglobalcapacity.

120,000,000,000,000,000

100,000,000,000,000,000

TokensProcessedperMonth

80,000,000,000,000,000

60,000,000,000,000,000

40,000,000,000,000,000

20,000,000,000,000,000

-

TokenConsumptionUp>24Xby2030

EnterpriseAgents

TokenEconomicsTurn

Positivein1H26

ConsumerAgents

GlobalTokenCapacityasof2026

Non-AgentWorkloads

Apr-24

Jun-24

Aug-24

Oct-24

Dec-24

Feb-25

Apr-25

Jun-25

Aug-25

Oct-25

Dec-25

Feb-26

Apr-26

Jun-26

Aug-26

Oct-26

Dec-26

Feb-27

Apr-27

Jun-27

Aug-27

Oct-27

Dec-27

Feb-28

Apr-28

Jun-28

Aug-28

Oct-28

Dec-28

Feb-29

Apr-29

Jun-29

Aug-29

Oct-29

Dec-29

Feb-30

Apr-30

Source:DatacompiledbyGoldmanSachsGlobalInvestmentResearch

SizingtheEnterpriseAgentOpportunity

nWeestimateenterpriseAIagentscanliftglobaltokenconsumption24Xby2030and55Xby2040.EnterpriseadoptionofagenticAIisstillearly:whilesurveys

suggest70–90%ofenterprisesareexperimenting,lessthanone-quarterarescalingagents.Ultimately,wethinkthecurvewillbeS-Shaped.Tovalidatetokenintensity,webuiltsimulatedagentsacrossAI-exposedoccupationstoestimatetheminimumtokensrequiredforanAIagenttoreplicatecoreworkfiows.Wefoundthatsome

agentscanconsumeveryhightokenvolumesbutremainrelativelylowcostifthe

workfiowismostlytext-based(suchascoding),butotherscanconsumefewer

tokensandcarrymuchhigherAPIcostbecausetheyrequiremulti-modalprocessing(suchasreal-timecallsandvideo).ThistensionmeanswedonotexpectagenticROItobeadoptedevenlyacrossallenterpriseworkfiows.

InvestmentImplications

nForhyperscalersandmodelproviders,wepreferthefollowingstocks(coveredbyEricSheridan):

oAMZN(Buy,$32512-mPT):WecontinuetoseevisibilityintoreturnsasAWSrevenuescompound,supportedbyareported$364bnrevenuebacklog,

drivenbybothAIworkloadsandrisingmomentumarounditscustomsilicon(Trainium,Graviton,etc.).

oGOOGL(Buy,12-m$450PT):AlphabetisseeingmomentumacrossitsCloudbusinessandSearchmulti-modality,leveragingafull-stackapproachas

managementcontinuestoseeAIrepositioningthecompanyforsustainedgrowth.

oMETA(Buy,12-m$830PT):Metaremainsaleaderinitscoreadvertising

business(significantlyoutpacingtotaldigitaladindustrygrowth)asthe

applicationofAI-relatedcomputeisdrivingmomentumaroundengagementandadsmonetization.

5May20264

nForsemiconductorcompanies,wepreferthefollowingstocks(coveredbyJimSchneider):

oAVGO(Buy,12-m$480PT):Asthemarketleaderincustomcomputing,weseemorehyperscalers(Google)andLLMmodelprovidersturningto

Broadcomtodelivercost-optimizedchipsolutionstailoredtotheirspecificworkloads.

oNVDA(Buy,12-m$250PT):WebelieveNvidiacanretainitsdominantmarketleadershipinthemediumtermasitremainstheleaderinAIperformance

acrossabroadrangeoftrainingandinferenceworkloads.

oAMD(Buy,$45012-mPT):WeseeAMD’smarketpositionstrengtheningasthecompanyscalesitshigh-performancedatacenterGPUo仟eringsoverthenexttwoyears.Importantly,webelieveAMDisalsopoisedforanincreasingshareofagenticAIworkloadsintheenterpriseasitgainsshareinX86serverCPUsandtheCPUattachrateincreases.

nForsoftwareandITservicescompanies,wepreferthefollowingstocks:

oMSFT(Buy,$61012-mPT,coveredbyGabrielaBorges):CopilotfeedbackisgettingbetterandtheE7upgradecyclemaydrivefurtheraccelerationin

Microsoft365.ThemostlikelyscenariomaybeanecosystemwhereCopilotcoexistsalongsidedomain-specificagentsanddomain-specificappsoftware,andtheusageofonepullsthroughusageoftheothersreciprocally.

oNET(Buy,$25012-mPT,coveredbyGabrielaBorges):WeexpectCloudfiaretotakeoutsizedshareofAIinferenceworkloadsbecauseofitsperformanceandcostadvantages,inturndrivenbyitsarchitecturalnetworkadvantages

andthesophisticationofitsisolatessoftware.

oACN(Buy,$30012-mPT,coveredbyJimSchneider):WeexpectAccenturetoseegrowingtailwindsfromagenticadoptionasenterprisesincreasingly

movefromAIpilotstoscaledagentdeployments,drivingdemandforintegration,workfiowredesign,governance,andchangemanagement.

5May20265

WeseeuniteconomicsinfiectingforAgenticAI:Implicationsformargins,ROI,andCapEx

Tokenvolumeexplosionmayfinallygivewaytoapositivemargininfiectionforthehyperscalers

TheBottomLine:AgenticAIislikelytodriveastep-functionchangeintoken

consumption,justastokeneconomicsarebeginningtoimproveforhyperscalers

andLLMproviders.By2030,ourbottom-upframework(builtonoursimulated

agentsthatvalidatereal-worldtokenintensity)suggeststhatglobaltokendemandcouldriseby2,400%versus2026levels,withconsumeragentspotentiallyreaching~60quadrilliontokenspermonthandenterpriseagentsreaching~56quadrillion

tokenspermonth(or278quadrillionbypeakadoptionin2040),assumingadoptionbroadens.

Moreimportant,ourinferencetokenpricevs.costcurvesuggeststhatthe

underlyinguniteconomicsoftokensaresettoimprovesignificantlyfor

hyperscalersandLLMproviders.LeadingLLMtokenpriceshavedeclinedrapidly,buthavenowstartedtostabilizeorevenincreaseinsomecases.Atthesametime,ourcalculatedall-intokencomputecostsforhyperscalersandLLMproviders-

poweredbyNvidia,Broadcom,AMD,andMarvellappeartobefallingfaster.Simplyput-thedramaticincreaseintokenconsumptionwehavemodeledby2030may

notonlyrefiectademandandrevenuenarrative,butalsoamarginexpansionandprofitabilitystoryacrosstheAIvaluechain.

InthefirstphaseoftheAIcycle,investorslargelyviewedcomputeandtokensasacostdriver:moreusagemeantmoreinferenceload,moreaccelerators,morepower,and

moreCapEx.Buttheshapeoftheinferencepricevs.costcurvesuggeststhatthistrendischanging.AlthoughleadingLLMtokenpriceshavedeclinedmeaningfullyovertime

theyhavenowstartedtostabilizeorevenincreaseinsomecases.Atthesametime,ourcalculatedall-incostofcomputepertokenforNvidia,GoogleTPU(Broadcom),AMD,

andTrainium(Marvell)continuestofallrapidlyandmoreconsistently.Iftokenpricesstabilizeatlevelshigherthantokencosts,thisimpliesthatanincreaseinagenticAI

adoptioncouldproducepositivemarginexpansion,notjustrevenuegrowth.Inotherwords,wenowbelievetheindustrycouldbemovingfromaphasewhereinferenceeconomicswereuncertainandpotentiallydilutivetomarginstoaphasewhere

tokengrowthincreasinglydropsthroughatattractiveincrementalmargins.

5May20266

Exhibit3:AgenticAImaydrivethenextwaveoftokendemand

By2030,weestimateconsumerandenterpriseagentscouldpushtokenconsumption24Xabovetoday’sestimatedglobalcapacity.

120,000,000,000,000,000

100,000,000,000,000,000

TokensProcessedperMonth

80,000,000,000,000,000

60,000,000,000,000,000

40,000,000,000,000,000

20,000,000,000,000,000

-

TokenConsumptionUp>24Xby2030

EnterpriseAgents

TokenEconomicsTurn

Positivein1H26

ConsumerAgents

GlobalTokenCapacityasof2026

Apr-24

Jun-24

Aug-24

Oct-24

Dec-24

Feb-25

Apr-25

Jun-25

Aug-25

Oct-25

Dec-25

Feb-26

Apr-26

Jun-26

Aug-26

Oct-26

Dec-26

Feb-27

Apr-27

Jun-27

Aug-27

Oct-27

Dec-27

Feb-28

Apr-28

Jun-28

Aug-28

Oct-28

Dec-28

Feb-29

Apr-29

Jun-29

Aug-29

Oct-29

Dec-29

Feb-30

Apr-30

Non-AgentWorkloads

Source:DatacompiledbyGoldmanSachsGlobalInvestmentResearch

Exhibit4:Tokeneconomicsarebeginningtoturnmorefavorable

LLMtokenpricinghasfallendramatically,buthasnowstartedtostabilize-whileunderlyingcomputecostscontinuetofallfaster-creatingroomforpositivemargininfiection

Cost/Priceper1MTokens

$1.20

MarginsInflectingin

$1.00

1H26

LeadingLLMTokenPrice

$0.80

$0.60

$0.40

$0.20

Trainium

AMD

$0.00

GoogleTPU

Nvidia

-$0.20

Source:DatacompiledbyGoldmanSachsGlobalInvestmentResearch

AgenticAImayalsocreateaself-reinforcingeconomicfiywheelascomputecosts

decline:(1)lowercomputecostpertokenenablesricher,morecomplexagents;(2)

richeragentsconsumeconsiderablymoretokensthroughlongercontext,moreloops,

morevalidation,andmorepersistentmonitoring;(3)higherutilizationimprovesthe

economicsofAIinfrastructureandbettereconomicsallowproviderstokeepinvestinginmodelqualityanddistribution.Thisfiywheelisverydi仟erentfromtheprevailingmarketnarrativethatAIusagewillsimplydriveanincreasingandunsustainablecostburden.

However,therecontinuestobeasignificantriskthatthispositivemargininfiectionisnotguaranteedacrossallAIworkloads.Competitioncouldstillforcetokenpricesdownfasterthancomputecosts,especiallyformorecommoditizedtext-onlychatbots.

5May20267

Butevenassumingsomelevelofpricingpressure,theoverallpricevs.costtrend

suggeststhattheindustryhassignificantroomforeconomicimprovementas

acceleratorefficiency,modeloptimization,routing,caching,andutilizationcontinuetoscale.Thekeyinvestmentconclusionisnotthateverytokenwillbeprofitable,butthatthemarginaleconomicsofagenticAImayimproveatthesametimethattokenvolumesaccelerate.Thatcombinationiswhatmakestheagenteconomypotentiallymuchlarger,moreprofitable,andmoredurablethanasimpleextrapolationof

today’schatbotusagewouldimply.

5May20268

AgentROI:Fallingtokencostspullmoreenterpriseusecasesintothe-money

TheBottomLine:Themarginstoryisnotjustaboutcheapertokens;itisabout

cheapertokensexpandingthesetofenterpriseAIusecasesthatcandeliver

positiveROItotheultimateAIendconsumer:enterprises.Totestthat,wetranslateoursimulatedagentworkloadsintoestimatedcostpertaskandcomparethose

costsagainstthehumanlabortheycouldaugmentorreplaceacrosscoding,call

center,anddata-entryworkfiows.Thekeyconclusionisthatevenworkfiowsthatconsumemillionsoftokensperdaycanstillcomparefavorablyagainsthumanlaborcosts,particularlyastokenpricescontinuetodefiate.

Exhibit5:Notallagentsarecurrentlycosteffectiveagainsthumanlabor

CostperDay($)

TokensperDay

AI

$92.90

AI

$13.39

APIPrice

ComparableCostofHumanLaborTokenIntensity

20,000,000

15,000,000

10,000,000

5,000,000

-

CodingAgentCallCenterAgentDataEntryAgent

$250

$200

$150

$100

$50

$0

30,000,000

25,000,000

Human

$300.00

Human$90.00

Human$80.00

AI

$59.68

$350

$300

Source:DatacompiledbyGoldmanSachsGlobalInvestmentResearch

However,wedonotexpectthisROItounlockevenlyacrossallenterpriseworkfiowsyet.Weestimateacodingagentcouldconsumeroughly7mntokensperdayatonly~$13/day,whichhelpsexplainwhysoftwaredevelopmenthasalreadyseenfasteragentadoption:theworkfiowishigh-value,largelytext-based,andthetoolingecosystemis

comparativelymature.Bycontrast,weestimateacallcenteragentcouldconsumeroughly2mntokensperdaybutcost~$92/dayifitreliesheavilyonreal-timevoice,makingfullvoice-basedautomationmateriallymoreexpensiveandoperationally

complexthanoutsourcedhumanlaborpresently.

Still,thedirectionoftravelisincreasinglyfavorable.Astokencostsfall,the

breakeventhresholdforenterpriseagentsmoveslower,pullingmoreworkfiowsinto

positiveROIovertime.Thisisthecoreeconomicbridgebetweenourbottom-uptokenestimatesandenterpriseadoption:agenticworkfiowsmaybetoken-intensive,butifthecostpertokendeclinesfasterthanthecomplexityoftheworkfiowrises,thenmany

agentscangenerateattractivereturnswellbeforetheyreachfullautonomy.For

investors,thismeanstheenterpriseagentopportunityshouldnotbeevaluated

onlythroughtoday’sproductmaturityornear-termdeploymentfriction.Itshouldbeevaluatedthroughadecliningcostcurve,whereeachstepdownincompute

costsexpandsthesetofworkfiowsthatsoftware,services,andinfrastructureproviderscaneconomicallyautomateandgeneratepositiveROI.

5May20269

InvestmentImplications

nForhyperscalersandmodelproviders,theriseofconsumeragentsandagentic

computingrepresentsagrowingdriverofcomputedemandwhilealsocreatingan

opportunityforvalueunlock,markingashiftwherevalueliesindistributionand

monetizationofintent/utilizationacrossbothconsumerandenterpriselandscapes.Overall,operatorsremainsupplyconstrainedintheirabilitytomeetcurrent/forwardcomputedemand(bothinternallyandexternally)andcontinuetoinvestinthe

necessaryinfrastructuretosupportanevolvingcomputinglandscapeand

broad-basedAIadoption.Ascapexintensityremainselevated(withGOOGL&METAraisingFY2026capexestimatesandAMZNmgmtreiteratingtheirstrategyof

maintainingelevatedcapexcomingoutofQ1’26earnings),weexpectinvestorstoincreasinglylookforevidenceofscope/visibilityforreturns,againstthebroader

marketdebateiflarge-scaleAIusagecanbecomeeconomicallyattractivetojustifythecapexcycle.Wepreferthefollowingstocks:

oAMZN(Buy,$325PT):WecontinuetoseevisibilityintoreturnsasAWS

revenuescompound(growthreacceleratedto+28%YoYinQ1;newGSefor~low30s%YoYgrowthinFY26&FY27)supportedbyareported$364bn

revenuebacklog(notincludinganadditional$100bnforadealannouncedinQ2),drivenbybothAIworkloadsandbuildingmomentumarounditscustomsilicon(Trainium,Graviton,etc.).

oGOOGL(Buy,$450PT):AlphabetisseeingmomentumacrossitsCloud

business(+63%YoYinQ1withbacklognearlydoublingQoQto~$460bn)andSearchmulti-modality,leveragingafull-stackapproachasmgmtcontinuestoseeAIasrepositioningthecompanyforsustainedgrowth.

oMETA(Buy,$830PT):Metaremainsaleaderinitscoreadvertisingbusiness(significantlyoutpacingtotaldigitaladindustrygrowth)astheapplicationofAI-relatedcomputeisdrivingmomentumaroundengagement&ads

monetization(adscreation,targeting,measurement/attribution,etc.).Goingforward,thecompanyremainsfocusedonapplyingitsgrowingcompute

capacityandscaling1PAImodels(MuseSparkbeingthefirstiteration)to

buildingoutadditionalmonetizationopportunitiesaroundagenticcommerce,AIbusinesstoolsforSMBs/creatorsandnewconsumerAIapplications.

nForSemiconductorcompanies,weseeaclearlypositiveimpactfromongoing

CapExspendingfromhyperscalersandLLMproviders.WebelievethatfallingtokencostsenabledbymerchantGPUandASICleaderswillmaketoken-intensiveuse

caseseconomicallyviableandhenceincreasetheaddressablecomputemarketasvolumeelasticitymorethano仟setslowercomputecosts.Justasimportant,the

positivemargininfiectionweseeaheadforhyperscalersandLLMprovidersmeansthattheirmarginscanimprovesignificantly-thuscreatingsignificantlymore

headroomforincreasedCapExandmakingtoday’selevatedinfrastructureinvestmentssustainable.Wepreferthefollowingstocks:

oBroadcom(Buy,$480PT):Asthemarketleaderincustomcomputing,weseemorehyperscalers(Google)andLLMmodelprovidersturningtoBroadcomto

5May202610

delivercost-optimizedchipsolutionstailoredtotheirspecificworkloads.

oNvidia(Buy,$250PT):WebelieveNvidiacanretainitsdominantmarketleadershipinthemediumtermasitremainstheleaderinAIperformanceacrossabroadrangeoftrainingandinferenceworkloads.

oAMD(Buy,$450PT):WeseeAMD’smarketpositionstrengtheningasthe

companyscalesitshigh-performancedatacenterGPUo仟erings(MI450and

MI5XX)overthenexttwoyears.Importantly,webelieveAMDisalsopoisedtoanincreasingshareofagenticAIworkloadsintheenterpriseasitgainsshareinX86serverCPUsandtheCPUattachrateincreases.

nForsoftwareandITservicescompanies,webelievethemarginstoryismore

nuancedbutwecontinuetoseelonger-termtailwinds.Lowertokencostsmakeiteasierforsoftwarevendorstoembedagentsintoexistingproductswithout

significantlyimpactinggrossmargins,whilealsoallowingthemtopricearound

outcomes,productivity,orunitsofworkratherthanseatsalone.ThissupportsourargumentthatagenticAIcanexpandsoftwareTAM:ifthecostofdeliveringan

automatedworkfiowfallswhilethevalueofthecompletedworkremainstiedto

laborsubstitutionorproductivitygains,softwarecompaniescancaptureaspreadbetweenfallingAIdeliverycostsandthemuchlargervalueofthetaskbeing

automated.Meanwhile,weexpectITServicescompaniestobenefitasagentsshiftAIconsumptionfromstandalonetoolstoenterprise-wide,integration-heavy

workfiowtransformation-increasingdemandforintegration,governance,andmanagedorchestrationtolevelsnotyetseenwithstandalonetools.

nWepreferthefollowingstocks:

oMSFT(Buy,$61012-mPT,coveredbyGabrielaBorges):CopilotfeedbackisgettingbetterandtheE7upgradecyclemaydrivefurtheraccelerationin

Microsoft365.ThemostlikelyscenariomaybeanecosystemwhereCopilotcoexistsalongsidedomain-specificagentsanddomain-specificappsoftware,andtheusageofonepullsthroughusageoftheothersreciprocally.

oNET(Buy,$25012-mPT,coveredbyGabrielaBorges):WeexpectCloudfiaretotakeoutsizedshareofAIinferenceworkloadsbecauseofitsperformanceandcostadvantages,inturndrivenbyitsarchitecturalnetworkadvantages

andthesophisticationofitsisolatessoftware.

oAccenture(Buy,$300PT):WeexpectAccenturetoseegrowingtailwinds

fromagenticadoptionasenterprisesincreasinglymovefromAIpilotsto

scaledagentdeployments,drivingdemandforintegration,workfiowredesign,governance,andchangemanagement.

5May202611

TheConsumerAgentLandscape

Whatdoestheconsumerlandscapelookliketoday?

SinceourOctober2025notewhereweframedfiveinvestordebatesontheAI

landscape,weseetrendsofincreasedconsumerutilizationandamorerobustproductinnovationlandscapethatcontinuestorefiectprogressofevolvingconsumerhabits

againstthebackdropofanevolvingcomputinglandscape.Inourview,thisshiftis

characterizedasatransitionfromconsumersusingAIforconversationandtool-basedfunctions(promptingchatbotsforinformationretrieval/action)andtowardsapotentialendstatewhereLLMagentsandAIsystemsmayplayamoreinvolvedroleinconsumers’livesday-to-day,giventheautonomytoexecutemulti-stepworkfiowsdirectlywithinauser’scomputingenvironmentwithoutbeingpromptedbytheuser.

Tolevel-set,consumerAIadoptioncontinuestobebroad-basedandaccelerating,

asusersincreasinglyturntogenerativeAIproductswithqueries(awayfromtr