机器经济：无限认知市场（上篇）

INDUSTRYRESEARCH

MachineEconomy

Rising:TheMarketof

LimitlessCognition

PartIofII

Contents

Reportsnapshot1

Introduction2Reportsnapshot

Sketchingamarketofunboundedcognition3

Machineeconomysignals8Whatthemachineeconomyis

Whyagenticeconomicactorsare

notpricedinyet12•ThemachineeconomyisamarketinwhichAIagentsdiscover,transact,and

generatevaluewithlittletonohumaninvolvement.

POV:YouareanAIagent16

PartIconcludingtakeaways19•Valuecanbemeasuredfromthreebroadlayers:existingsoftwareandlabor

References20spendingrotatingtoagents,theinfrastructureagentsconsumetooperate,andnet

newvaluefromanagent-onlymarketthatdidnotpreviouslyexist.

•Themarketexpandsasintelligencebecomescheaperandmorecapable,allowing

agentstodopreviouslyuneconomicalworkandtransactwithotheragents.

EarlyestimatesfromForsyputglobalagentGDPat$36billionannualizedasof

July2026.1

Whythemachineeconomyisemerging

•ExistingsoftwareandlaborspendingisalreadyrotatingtowardAI.We

estimateabout$20trillionofAI-exposedwork,thoughonlyabout1%flowing

throughagents.

•Tokenvolumesarecompoundingfarbeyondwhathumanusagecanexplain.

Googleprocessed3.2quadrilliontokensinMay2026,upfrom9.7trilliontwoyears

prior.2OpenAIandAlphabeteachprocessmorethan15billiontokensperminute

viadirectAPIaccess.3,4

•Webtrafficisshiftingtowardmachines,withbotsnowaccountingfor57.5%of

HTMLrequests.ObservedagenticAItrafficisgrowingquickly,andbrowser-based

agentsmakeupabout71%ofobservedagentictraffic.5

•Machine-facinginfrastructureisspreadingfast.Wequeried12,594modelcontext

protocol(MCP)serversthatwereindexedasofJune18,2026,whileStripereports

that70%ofcommand-lineinterface(CLI)APIrequestsnowcomefromagents.6

1ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

•APIsarebecomingmachine-scaledistributionchannels.Salesforceprocessed

almost1trillionAPIcallsinQ12026alone.7Manyplatformsarealsomovingto

meteredpricing,whileroughly25%ofdevelopersnowdesignAPIswithagentsas

theprimaryendconsumer.8

Whatstandsintheway

•Agentsarenotyetpricedaseconomicactorsbecauseautonomyremains

constrainedacrossfourdimensions:scope,controls,supervision,andlongevity.

•Ourscopingexerciseof40deployedagentproductsshowsthattoday’sagents

arestillnottrueautonomouseconomicactors,withaveragescoresof3.2for

autonomyand3.5forscopeonascaleof1to5.

•Inliveworkflows,autonomytypicallybreaksatthreestructuralpoints:when

informationisnotmachine-readable,whenanagentlackscredentialstoact,and

whenapaymentrequireshumanintervention.

Whythisshiftmatters

•Thegapbetweentoday’sagentsandfullyautonomousonesiswherenew

opportunitiesareemerging.Companiesthatsolvefordiscovery,access,usage,

andpaymentatagentscalewillbebestpositionedasthemarketgrows.

•Successwillbecomeincreasinglydependentonservicedirectories,standardized

dataformats,command-lineaccess,andpricingmodelsthatworkforagent-

scaleusage.

•Newrevenuemodelswillemergearoundper-call,per-outcome,andper-execution

pricing,especiallyasagentsstartbuyingservicesfromotheragents.

•Solvingtheagenticpaymentslayeriscrucialbutdifficult.Agentsneedtohold

funds,initiatetransactions,andoperatewithindefinedspendinglimitswithouta

humanhavingtore-entertheflow.

•Therearebigopenquestionsaroundtrust,identity,intent,andreversibility.

Companiesthatsolvethislayerearlywillhaveadurablefirst-moveradvantage.

Introduction

Thisisatwo-partreportseriesonthemachineeconomyandagenticpayments.Part

Iexaminestheemergingmachineeconomy.PartIImakesthecaseforwhymachine-

firstpaymentsinfrastructureisneededtounlockit.

AIagentsarealreadymovingtowardgreaterautonomy.Theyareincreasingly

consumingpaidservices,spawningmoresubagents,andcompletingmoretasks

withlesshumaninvolvement.Overtime,theyhavethepotentialtocreatenetnew

economicvalueandsupportamarketthatcanscaleatunprecedentedspeed.

2ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

Thisshifttowardanagent-firstmarketmaylookinevitable,butitwilltakeenormous

investment.Keyinfrastructuregapsstillstandintheway.InPartI,weexaminewhat

themachineeconomymaylooklike,howmuchvalueitcancreate,whyitappears

inevitable,and,importantly,whatispreventingitfromarrivingsooner.Withinthese

gapsarenewemergingopportunitiesandimportantsignalsthatcompaniesshouldbe

buildingaroundnow.

Sketchingamarketofunboundedcognition

Inthissection,wedefinethemachine

economy,outlineaframeworkforUnderneaththeriseofagenticAIisamarketworthtrillionsofdollars,forgedby

measuringitskeycomponents,andautonomousAIagentstransactingandcreatingvalueatmachinespeed.Eachtime

considerwhataconceptuallynewanagentcallsamodel,invokesanorchestrationplatform,parsestheweb,tapsan

agentmarketcouldlooklikeasitAPI,ortransactsonitsown,itisparticipatinginamachine-nativevaluechain.In

takesshape.aggregate,thisnetworkofinterlockingservicesenablesagentstoactautonomously

andgenerateeconomicallymeaningfuloutput.

Thisleadstotheriseofabroadermachineeconomy,whichwedefineasanew

marketinwhichAIagentsdiscover,transact,andgeneratevaluewithminimaltozero

humanintervention.Inthisneweconomiclayer,therelevantmarketisanydigitally

addressableworkflowwherecognitionisworthpayingfor.Agentsperformthework

insidesystems,turninginputsintodecisions,actions,andoutputsthatgenerate

measurableeconomicvalue.

However,definingashiftofthisscaleisdifficultbecauseitblurstheoldboundaries

betweensoftware,services,labor,anddigitalinfrastructure.Insteadofsizingmarkets

forsoftwareandITbudgetsalone,theformulaevolvestomeasurehowmuchofthe

world’scognitiveworkbecomeslegible,executable,andbillablethroughagents.

Therefore,akeychallengeisquantifyingamarketthat,intheory,mapstothedemand

forintelligence.

Definingthismarketisimportantbecauseithelpsspecifywherespendingwillgo,

whichlayersofthetechnologystackwillmattermost,andwherenewvalueislikelyto

accrue.Inourview,themachineeconomy’svaluestemsfromthreebroadlayers:

•Existingspendingshiftingtoagents:Moneythatcompaniesalreadyspendon

software,outsourcing,andsalariedstaff,whichrotatestoagentsinsteadof

traditionaltoolsorpeople.

•Agentstackrevenue:Spendingallocatedtotheinfrastructurethatagentsrunon,

whichcanbethoughtofasanagent’scostofgoodssold.Thisincludescostson

models,APIs,orchestrationplatforms,databases,storage,andsecurity.

•Newagent-generatedvalue:Valueandeconomicactivitycreatedbyagent-only

productsandservicesthatwerepreviouslyimpossibleinhuman-onlyworkflows.

Itismadepossiblewhenintelligenceischeap,reliable,andabletotransactwith

otheragents.

3ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

Themachineeconomy

BudgetsfundingAutonomous

agentstackinfrastructure

spending

ExistingspendingAgentcostofNewagent-only

shiftingtoagentsgoodssoldmarket

Software,services,Models,APIs,Valueunlocked

andlaborbudgetsorchestration,bycheapand

payments,storage,reliableintelligence

andsecurity

Source:PitchBook

Existingspendingshiftingtoagents

Oneoftheearliestmeasurablesurfacesofthemachineeconomyisthereallocation

ofexistingsoftware,services,andlaborspendingintoagentworkflows.Already,

moneythatcompaniesoncespentonsoftwareseats,outsourcingfirms,andsalaried

staffisnowbeingredirectedtowardagentsthatcanoperateacrosstools,complete

tasks,anddeliveroutputswithinbusinesses.Wehavewrittenaboutthisindetailinour

previousnotesSaaSisDead,LongLiveSasandThroughtheLookingGlass:TheRace

toBuildEnterpriseAI.

Outcome-basedpricinghasexpandedenterprisesoftware’saddressablemarket

beyondthe$1.4trillionannualsoftwarespendingtoencompassaportionofthe$50

trillionglobalknowledgelabormarket.WeestimatethecurrentlevelofAI-exposed

workacrossthesecombinedmarketstoberoughly$20trillion,withonlyabout1%of

thatexecutedbyAIagents.Thisimpliesthatwhileapproximately$200billionofwork

todayflowsthroughagent-mediatedworkflows,mostsoftwareandknowledge-worker

spendinghasyettoberewired.

Inevitably,thisshiftwillcontinueasintelligencebecomescheaperandmorecapable

perdollar.Ramp’sdatafrommorethan70,000USbusinessesshowsthattheshare

offirmsspendingonAI,includinglargelanguagemodel(LLM)subscriptions,coding

agents,APItokens,andGPUcloudspending,roseto55%inJune2026from42.7%one

yearearlier.9ItsdataalsohighlightshowrapidlyAIspendingperemployeeisrising.

ThemedianAIspendingperemployeeincreased435%to$10.70fromaJuly2023

baselineof$2.00.Forthehighestspenders,theincreaseisevenmoredrastic.From

July2023toJune2026,thetop1%offirmsincreasedAIspendingperemployeefrom

$453.68to$4,883.33,whilethetop10%rosefrom$53.54to$516.20.10

4ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

PercentagechangeinAIspendingperemployee

1,000%

800%

600%

400%

200%

0%

AugOctDecFebAprJunAugOctDecFebAprJunAugOctDecFebAprJun

2023202420252026

Top1%firmsTop10%firmsMedian

Source:Ramp•Geography:US•AsofJune2026

Agentstack

Thereallocationofspendingfromsoftwareandlaborpartlyfundstheagentstack,

anotherhighlyvisiblelayerofthemachineeconomy.ThisiseffectivelyanAIagent’s

costofgoodssold:theruntimesoftwareandservicesanagentactivelyconsumesto

operate,includingmodelinferenceAPIs,orchestrationandmiddleware,application

hosting,vectorandretrievalservices,third-partytoolAPIs,andsecurityand

observability.Weexcludephysicalinfrastructurebeneaththislayer—includingGPU

hardware,datacenters,anddedicatedcapacityproviders—asmanyofthosecosts

arealreadyembeddedintheper-tokenpricesagentspay.Modelproviders,cloud

platforms,andinfrastructurevendorsarealreadybillingforthislayeratscale.

Forexample,anagentthatresearchesvendorsanddraftsarecommendationwould

searchthroughstoredcontracthistory,pulllivepricingfromtheweb,andcalla

languagemodelseveraltimestoreasonacrosstheresultsandproduceasummary.

Eachstepismetered.Asimpleworkflowlikethismightconsumeseveralthousand

tokensacrossmodelcalls,databaselookups,andAPIcalls,totalingafewcentsper

run.Acrossthousandsofagentsexecutingworkflowsatmachinespeed,thisspend

accumulatesquickly.

Whilethereisnosinglequantifierforagentcosts,webelievetensofbillionsofdollars

peryeararebeingcapturedbythisrapidlyscalinglayer.Asa2025baseline,Menlo

VenturessizedAIinfrastructurespendingat$18billion,comprisedof$12.5billionfor

foundationmodelAPIs,$4billionformodeltraininginfrastructure,and$1.5billionfor

AIinfrastructurecoveringstorage,retrieval,andorchestration.11

However,runratesfor2026suggestthestackhasalreadygrownseveral-fold.The

twolargestmodelproviders,AnthropicandOpenAI,areprojectedtogeneratenearly

$80billionincombinedannualizedrevenuealoneonarun-ratebasis.12,13Notallof

thisreflectsagentactivitybecausebothlabsalsoearnsubscriptionandconsumer

income,butweestimatethemajorityisincreasinglyweightedtowardenterpriseAPI

consumption.Beyondthetwoleadinglabs,additionalmodelproviders,orchestration

5ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

platforms,applicationhostingservices,retrievalinfrastructure,andsecuritytooling

contributemeaningfullytotheoverallfigure.Nettingoutconsumerrevenuesand

accountingforthesesurroundingsoftwarelayers,agentstackspendingin2026could

beintherangeof$80billionto$100billion.

Newagent-generatedvalue

Futureagenticrevenuesarrivefromaconceptuallynewlayerformedbythemachine

economy.Thiswillbeabyproductofthecollapseofthinkingpower,bringing

previouslyuneconomicalworkflowswithinreach.Putanotherway,machine

intelligencewilleventuallybecomecheapandreliableenoughtodoworkthathumans

previouslyskipped,suchasthetasksdeemedtoosmall,frequent,orexpensiveto

justifytheopportunitycost.

ThisfollowsapatternAmiasGerety,partnerandheadofUSatQEDInvestors,sees

acrossmosttechnologywaves.AccordingtoGerety,“Technologyalmostalways

movesinthreephases.Thefirstiscostefficiency.Thesecondisconsumption

increase—asthepricegoesdown,youconsumemore.Andthethird,whichishardest

topredictbutultimatelythebiggest,iscompletelynewformsofeconomicactivity.”14

Newagenticvalueunlockedbyfallingmachineintelligencecosts

Costofmachineintelligence

Costpertask

Minimumcostworthahuman’saction

Newvaluecreatedbyagents

Time

Source:PitchBook

Thesecouldbetaskswhereagentscontinuouslytweakinterfacesforindividual

usersbasedonhundredsoftinysignals,disputeminusculebillingerrorsthatonly

becomemeaningfulinaggregate,ormaintaineveryinternalrecordbyclosingstale

tickets,archivingoldfiles,andupdatingoutdatedtagsthemomenttheygostale.Akey

componentofthisnewagenticmarketistheinteractionsbetweenmachineswithout

humaninvolvement.Withproperinfrastructure,agentswillbeabletobuyandselltiny

tasklevelunitsofcapabilityfromeachother.

6ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

Conceptualexamplesofmachine-to-machineinteractionsinanewagent-onlymarket

ScenarioAgentactionCounterpartyFormatofexchange

Real-timetrafficorAgentbuyslivetrafficorincidentConnectedvehiclenetworkorroadsidePayspercaptureorper

eventdatapurchasedatabeforereroutingorrepricingsensoroperatordatapull

logisticsdecisions

On-demandbuildingtelemetryBuildingagentcheckscarbondioxide,Third-partytelemetryservicePaysperreadingorperminute

particulatematter,oroccupancydataofaccess

beforeadjustingHVACorscheduling

On-demandassetinspectionAgenthiresanearbydroneorsensorAutonomousdroneoperatororInternetPaysperinspectioncompleted

toinspectanassetbeforetriggeringaofThingssensornetwork

maintenanceorreorderdecision

Per-taskagentdelegationAgentidentifiesataskoutsideThird-partydomain-specificagentPayspercompleted

itsdomainanddelegatesittoataskoutput

specializedagent

MicrocreditlineleasingAgentborrowsshort-termcredittoLiquidityagentthatfractionalizesaPaysfeesandinterestforthe

coveratemporarycashflowgaplargercreditfacilityintomicrocreditdurationthecreditisheld

slotsallocatabletocounterparties