机器经济：无限认知市场（上篇）

ApitchBOOK

aMorningstarcompany

INDUSTRYRESEARCH

MachineEconomy

Rising:TheMarketofLimitlessCognition

PartIofII

Contents

Reportsnapshot1

Introduction2Sketchingamarketofunboundedcognition3

Reportsnapshot

Machineeconomysignals8

Whatthemachineeconomyis

Whyagenticeconomicactorsare

notpricedinyet12

•ThemachineeconomyisamarketinwhichAIagentsdiscover,transact,and

POV:YouareanAIagent16

generatevaluewithlittletonohumaninvolvement.

PartIconcludingtakeaways19

•Valuecanbemeasuredfromthreebroadlayers:existingsoftwareandlabor

References20

spendingrotatingtoagents,theinfrastructureagentsconsumetooperate,andnetnewvaluefromanagent-onlymarketthatdidnotpreviouslyexist.

•Themarketexpandsasintelligencebecomescheaperandmorecapable,allowing

agentstodopreviouslyuneconomicalworkandtransactwithotheragents.

EarlyestimatesfromForsyputglobalagentGDPat$36billionannualizedasofJuly2026.1

Whythemachineeconomyisemerging

•ExistingsoftwareandlaborspendingisalreadyrotatingtowardAI.We

estimateabout$20trillionofAI-exposedwork,thoughonlyabout1%flowingthroughagents.

•Tokenvolumesarecompoundingfarbeyondwhathumanusagecanexplain.

Googleprocessed3.2quadrilliontokensinMay2026,upfrom9.7trilliontwoyearsprior.2OpenAIandAlphabeteachprocessmorethan15billiontokensperminuteviadirectAPIaccess.3,4

•Webtrafficisshiftingtowardmachines,withbotsnowaccountingfor57.5%of

HTMLrequests.ObservedagenticAItrafficisgrowingquickly,andbrowser-basedagentsmakeupabout71%ofobservedagentictraffic.5

•Machine-facinginfrastructureisspreadingfast.Wequeried12,594modelcontextprotocol(MCP)serversthatwereindexedasofJune18,2026,whileStripereportsthat70%ofcommand-lineinterface(CLI)APIrequestsnowcomefromagents.6

1ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

2ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

•APIsarebecomingmachine-scaledistributionchannels.Salesforceprocessedalmost1trillionAPIcallsinQ12026alone.7Manyplatformsarealsomovingtometeredpricing,whileroughly25%ofdevelopersnowdesignAPIswithagentsastheprimaryendconsumer.8

Whatstandsintheway

•Agentsarenotyetpricedaseconomicactorsbecauseautonomyremains

constrainedacrossfourdimensions:scope,controls,supervision,andlongevity.

•Ourscopingexerciseof40deployedagentproductsshowsthattoday’sagentsarestillnottrueautonomouseconomicactors,withaveragescoresof3.2forautonomyand3.5forscopeonascaleof1to5.

•Inliveworkflows,autonomytypicallybreaksatthreestructuralpoints:when

informationisnotmachine-readable,whenanagentlackscredentialstoact,andwhenapaymentrequireshumanintervention.

Whythisshiftmatters

•Thegapbetweentoday’sagentsandfullyautonomousonesiswherenew

opportunitiesareemerging.Companiesthatsolvefordiscovery,access,usage,andpaymentatagentscalewillbebestpositionedasthemarketgrows.

•Successwillbecomeincreasinglydependentonservicedirectories,standardizeddataformats,command-lineaccess,andpricingmodelsthatworkforagent-

scaleusage.

•Newrevenuemodelswillemergearoundper-call,per-outcome,andper-executionpricing,especiallyasagentsstartbuyingservicesfromotheragents.

•Solvingtheagenticpaymentslayeriscrucialbutdifficult.Agentsneedtoholdfunds,initiatetransactions,andoperatewithindefinedspendinglimitswithoutahumanhavingtore-entertheflow.

•Therearebigopenquestionsaroundtrust,identity,intent,andreversibility.

Companiesthatsolvethislayerearlywillhaveadurablefirst-moveradvantage.

Introduction

Thisisatwo-partreportseriesonthemachineeconomyandagenticpayments.PartIexaminestheemergingmachineeconomy.PartIImakesthecaseforwhymachine-firstpaymentsinfrastructureisneededtounlockit.

AIagentsarealreadymovingtowardgreaterautonomy.Theyareincreasingly

consumingpaidservices,spawningmoresubagents,andcompletingmoretaskswithlesshumaninvolvement.Overtime,theyhavethepotentialtocreatenetneweconomicvalueandsupportamarketthatcanscaleatunprecedentedspeed.

3ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

Thisshifttowardanagent-firstmarketmaylookinevitable,butitwilltakeenormousinvestment.Keyinfrastructuregapsstillstandintheway.InPartI,weexaminewhatthemachineeconomymaylooklike,howmuchvalueitcancreate,whyitappears

inevitable,and,importantly,whatispreventingitfromarrivingsooner.Withinthese

gapsarenewemergingopportunitiesandimportantsignalsthatcompaniesshouldbebuildingaroundnow.

Inthissection,wedefinethemachineeconomy,outlineaframeworkfor

measuringitskeycomponents,andconsiderwhataconceptuallynewagentmarketcouldlooklikeasittakesshape.

Sketchingamarketofunboundedcognition

UnderneaththeriseofagenticAIisamarketworthtrillionsofdollars,forgedby

autonomousAIagentstransactingandcreatingvalueatmachinespeed.Eachtimeanagentcallsamodel,invokesanorchestrationplatform,parsestheweb,tapsanAPI,ortransactsonitsown,itisparticipatinginamachine-nativevaluechain.In

aggregate,thisnetworkofinterlockingservicesenablesagentstoactautonomouslyandgenerateeconomicallymeaningfuloutput.

Thisleadstotheriseofabroadermachineeconomy,whichwedefineasanew

marketinwhichAIagentsdiscover,transact,andgeneratevaluewithminimaltozerohumanintervention.Inthisneweconomiclayer,therelevantmarketisanydigitally

addressableworkflowwherecognitionisworthpayingfor.Agentsperformtheworkinsidesystems,turninginputsintodecisions,actions,andoutputsthatgenerate

measurableeconomicvalue.

However,definingashiftofthisscaleisdifficultbecauseitblurstheoldboundariesbetweensoftware,services,labor,anddigitalinfrastructure.InsteadofsizingmarketsforsoftwareandITbudgetsalone,theformulaevolvestomeasurehowmuchoftheworld’scognitiveworkbecomeslegible,executable,andbillablethroughagents.

Therefore,akeychallengeisquantifyingamarketthat,intheory,mapstothedemandforintelligence.

Definingthismarketisimportantbecauseithelpsspecifywherespendingwillgo,

whichlayersofthetechnologystackwillmattermost,andwherenewvalueislikelytoaccrue.Inourview,themachineeconomy’svaluestemsfromthreebroadlayers:

•Existingspendingshiftingtoagents:Moneythatcompaniesalreadyspendonsoftware,outsourcing,andsalariedstaff,whichrotatestoagentsinsteadoftraditionaltoolsorpeople.

•Agentstackrevenue:Spendingallocatedtotheinfrastructurethatagentsrunon,whichcanbethoughtofasanagent’scostofgoodssold.Thisincludescostsonmodels,APIs,orchestrationplatforms,databases,storage,andsecurity.

•Newagent-generatedvalue:Valueandeconomicactivitycreatedbyagent-onlyproductsandservicesthatwerepreviouslyimpossibleinhuman-onlyworkflows.Itismadepossiblewhenintelligenceischeap,reliable,andabletotransactwithotheragents.

4ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

Themachineeconomy

Autonomousinfrastructure

spending

Budgetsfundingagentstack

Existingspendingshiftingtoagents

Newagent-onlymarket

Agentcostofgoodssold

Software,services,andlaborbudgets

Valueunlocked

Models,APIs,

bycheapand

orchestration,

reliableintelligence

andsecurity

payments,storage,

Source:PitchBook

Existingspendingshiftingtoagents

Oneoftheearliestmeasurablesurfacesofthemachineeconomyisthereallocation

ofexistingsoftware,services,andlaborspendingintoagentworkflows.Already,

moneythatcompaniesoncespentonsoftwareseats,outsourcingfirms,andsalariedstaffisnowbeingredirectedtowardagentsthatcanoperateacrosstools,complete

tasks,anddeliveroutputswithinbusinesses.WehavewrittenaboutthisindetailinourpreviousnotesSaaSisDead,LongLiveSasandThroughtheLookingGlass:TheRacetoBuildEnterpriseAI.

Outcome-basedpricinghasexpandedenterprisesoftware’saddressablemarket

beyondthe$1.4trillionannualsoftwarespendingtoencompassaportionofthe$50trillionglobalknowledgelabormarket.WeestimatethecurrentlevelofAI-exposed

workacrossthesecombinedmarketstoberoughly$20trillion,withonlyabout1%ofthatexecutedbyAIagents.Thisimpliesthatwhileapproximately$200billionofworktodayflowsthroughagent-mediatedworkflows,mostsoftwareandknowledge-workerspendinghasyettoberewired.

Inevitably,thisshiftwillcontinueasintelligencebecomescheaperandmorecapableperdollar.Ramp’sdatafrommorethan70,000USbusinessesshowsthattheshare

offirmsspendingonAI,includinglargelanguagemodel(LLM)subscriptions,codingagents,APItokens,andGPUcloudspending,roseto55%inJune2026from42.7%oneyearearlier.9ItsdataalsohighlightshowrapidlyAIspendingperemployeeisrising.

ThemedianAIspendingperemployeeincreased435%to$10.70fromaJuly2023

baselineof$2.00.Forthehighestspenders,theincreaseisevenmoredrastic.FromJuly2023toJune2026,thetop1%offirmsincreasedAIspendingperemployeefrom$453.68to$4,883.33,whilethetop10%rosefrom$53.54to$516.20.10

5ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

PercentagechangeinAIspendingperemployee

1,000%

800%

600%

400%

200%

0%

AugOctDecFebAprJunAugOctDecFebAprJunAugOctDecFebAprJun2023202420252026

Top1%firmsTop10%firmsMedian

Source:Ramp•Geography:US•AsofJune2026

Agentstack

Thereallocationofspendingfromsoftwareandlaborpartlyfundstheagentstack,anotherhighlyvisiblelayerofthemachineeconomy.ThisiseffectivelyanAIagent’scostofgoodssold:theruntimesoftwareandservicesanagentactivelyconsumestooperate,includingmodelinferenceAPIs,orchestrationandmiddleware,applicationhosting,vectorandretrievalservices,third-partytoolAPIs,andsecurityand

observability.Weexcludephysicalinfrastructurebeneaththislayer—includingGPUhardware,datacenters,anddedicatedcapacityproviders—asmanyofthosecostsarealreadyembeddedintheper-tokenpricesagentspay.Modelproviders,cloudplatforms,andinfrastructurevendorsarealreadybillingforthislayeratscale.

Forexample,anagentthatresearchesvendorsanddraftsarecommendationwouldsearchthroughstoredcontracthistory,pulllivepricingfromtheweb,andcalla

languagemodelseveraltimestoreasonacrosstheresultsandproduceasummary.Eachstepismetered.Asimpleworkflowlikethismightconsumeseveralthousandtokensacrossmodelcalls,databaselookups,andAPIcalls,totalingafewcentsperrun.Acrossthousandsofagentsexecutingworkflowsatmachinespeed,thisspendaccumulatesquickly.

Whilethereisnosinglequantifierforagentcosts,webelievetensofbillionsofdollarsperyeararebeingcapturedbythisrapidlyscalinglayer.Asa2025baseline,Menlo

VenturessizedAIinfrastructurespendingat$18billion,comprisedof$12.5billionforfoundationmodelAPIs,$4billionformodeltraininginfrastructure,and$1.5billionforAIinfrastructurecoveringstorage,retrieval,andorchestration.11

However,runratesfor2026suggestthestackhasalreadygrownseveral-fold.The

twolargestmodelproviders,AnthropicandOpenAI,areprojectedtogeneratenearly$80billionincombinedannualizedrevenuealoneonarun-ratebasis.12,13Notallof

thisreflectsagentactivitybecausebothlabsalsoearnsubscriptionandconsumerincome,butweestimatethemajorityisincreasinglyweightedtowardenterpriseAPIconsumption.Beyondthetwoleadinglabs,additionalmodelproviders,orchestration

6ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

platforms,applicationhostingservices,retrievalinfrastructure,andsecuritytooling

contributemeaningfullytotheoverallfigure.Nettingoutconsumerrevenuesand

accountingforthesesurroundingsoftwarelayers,agentstackspendingin2026couldbeintherangeof$80billionto$100billion.

Newagent-generatedvalue

Futureagenticrevenuesarrivefromaconceptuallynewlayerformedbythemachineeconomy.Thiswillbeabyproductofthecollapseofthinkingpower,bringing

previouslyuneconomicalworkflowswithinreach.Putanotherway,machine

intelligencewilleventuallybecomecheapandreliableenoughtodoworkthathumanspreviouslyskipped,suchasthetasksdeemedtoosmall,frequent,orexpensiveto

justifytheopportunitycost.

ThisfollowsapatternAmiasGerety,partnerandheadofUSatQEDInvestors,seesacrossmosttechnologywaves.AccordingtoGerety,“Technologyalmostalways

movesinthreephases.Thefirstiscostefficiency.Thesecondisconsumption

increase—asthepricegoesdown,youconsumemore.Andthethird,whichishardesttopredictbutultimatelythebiggest,iscompletelynewformsofeconomicactivity.”14

Newagenticvalueunlockedbyfallingmachineintelligencecosts

Costpertask

Costofmachineintelligence

Minimumcostworthahuman’saction

Newvaluecreatedbyagents

Time

Source:PitchBook

Thesecouldbetaskswhereagentscontinuouslytweakinterfacesforindividual

usersbasedonhundredsoftinysignals,disputeminusculebillingerrorsthatonly

becomemeaningfulinaggregate,ormaintaineveryinternalrecordbyclosingstale

tickets,archivingoldfiles,andupdatingoutdatedtagsthemomenttheygostale.Akeycomponentofthisnewagenticmarketistheinteractionsbetweenmachineswithouthumaninvolvement.Withproperinfrastructure,agentswillbeabletobuyandselltinytasklevelunitsofcapabilityfromeachother.

7ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

Conceptualexamplesofmachine-to-machineinteractionsinanewagent-onlymarket

Scenario

Agentaction

Counterparty

Formatofexchange

Real-timetrafficoreventdatapurchase

Agentbuyslivetrafficorincidentdatabeforereroutingorrepricinglogisticsdecisions

Connectedvehiclenetworkorroadsidesensoroperator

Payspercaptureorperdatapull

On-demandbuildingtelemetry

Buildingagentcheckscarbondioxide,particulatematter,oroccupancydatabeforeadjustingHVACorscheduling

Third-partytelemetryservice

Paysperreadingorperminuteofaccess

On-demandassetinspection

Agenthiresanearbydroneorsensortoinspectanassetbeforetriggeringamaintenanceorreorderdecision

AutonomousdroneoperatororInternetofThingssensornetwork

Paysperinspectioncompleted

Per-taskagentdelegation

Agentidentifiesataskoutsideitsdomainanddelegatesittoaspecializedagent

Third-partydomain-specificagent

Payspercompletedtaskoutput

Microcreditlineleasing

Agentborrowsshort-termcredittocoveratemporarycashflowgap

Liquidityagentthatfractionalizesalargercreditfacilityintomicrocreditslotsallocatabletocounterparties

Paysfeesandinterestforthedurationthecreditisheld

Source:PitchBook

Eachworkflowgeneratesitsowndemand,withagentsbecomingcustomersofotheragentsandcreatingdownstreamconsumptionofdata,compute,andservices.Thiscompoundsintoanewlayerofmachine-nativespending,achievedwhenadvanced

reasoningisaccessibletoallenterprises,notjusthyperscalers.Intelligenceis

movinginthisdirection.DatafromEpochshowsthatthepricetoreachfixedLLM

benchmarkscoreshasfallenby9xto900xperyear,withamediandeclineofabout

50x.Meanwhile,acrossAIclasschipsreleasedbetween2012and2025,thecomputeperformanceavailableperdollarhasimprovedbyapproximately40%peryear,roughlytranslatingtoa2xgaineverytwotothreeyears.15

However,thismarketisincrediblydifficulttosizetoday.Manyoftheseagent

interactionsdonotexistyet,andthenumberofpossibleagentsandworkflowshasnoobviouslimit.OneearlyattempttomeasuretheeconomicvaluealreadybeinggeneratedbyagentscomesfromForsy,astartupthatisbuildingthelearningloopforagentsoperatinginreal-worldandcyber-physicalsystems.AsofJuly2026,thecompanyestimates$36billioninannualglobalagentGDPonarun-ratebasis—ametricitdefinesastheneteconomicvalueaddedbydeployedagents,adjustedforoverlapwithhumanwork,software,andagentcostofgoodssold.16

Forsy’sagentGDPisnotameasureoftotalAIspendingortotalrevenuefromAI

companies.Instead,itfocusesonoutputswhereanagentwastheprimarydriveroftheresult.Thiscomesfromfourkeycomponents,adjustedforoverlap:

1.Agent-assistedworkvalue:Outputwhereanagentimprovesahuman’sspeedorquality,butthehumanownstheworkflow.

2.Agent-generatedrevenue:Revenuewhereanagentcompletesthecommerciallydecisivestep.

8ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

3.Agentservicerevenue:Incomefromdeliverablessuchasresearchreports,supportresolutions,oroperationsoutputs.

4.Agentassetrevenue:Reusabledigitalassetssuchascode,templates,orknowledgebasesthatretainvaluebeyondasingletask.

Theircalculationbeginswithacountry’seconomicbase,reducedtoworkthatis

digitallyaddressable,thentotasksthatagentscanperform,andfinallytoworkthatisalreadybeingdeployedbyagents.Deploymentisassessedthroughsignalssuchasenterpriseadoption,APIusage,publictoolingadoption,andotherdeployment

indicators.Afinalstepdetermineshowmuchoftheoutputshouldbecreditedtotheagentversusthehumanorsoftwarearoundit,withhigherattributionwhentheagentcompletesmoreoftheworkflowend-to-end.

Top10countriesbyagentGDP

Country

AgentGDP

(permonth)

Agentshareofdigitalwork

Medianagentstackcost

(permonth)

Medianrevenuefromagent-enabledwork(permonth)

Economicvaluegeneratedperdollarofagentstackcost

US

$1.4B

1.0%

$160

$1,190

4.3x

China

$540.0M

0.7%

$50

$640

4.3x

UK

$198.0M

0.8%

$132

$805

3.7x

India

$182.0M

0.7%

$24

$325

4.3x

Germany

$172.0M

0.7%

$128

$745

3.6x

France

$131.0M

0.6%

$112

$675

3.4x

Japan

$120.0M

0.5%

$108

$620

3.2x

SouthKorea

$76.0M

0.6%

$98

$585

3.3x

Canada

$51.0M

0.5%

$98

$605

2.9x

Australia

$37.0M

0.5%

$92

$560

2.8x

Source:Forsy.ai•Geography:Global•AsofJuly2026

AnimportanttakeawayfromForsy’sdataisthattheearlymachineeconomyisalreadyproducingmeasurableoutput.Themedianagent-enabledoperatorisgeneratingabout$90permonthinrevenuedirectlyattributabletoagentwork,andagentsareproducing1.5timestheoutputofahumandoingthesameworkwithoutone.Evenso,global

agentGDPof$3billionpermonthacross200countriesisstillsmall.17Astheenablinginfrastructurematuresandintelligencebecomescheaperandmorecapable,the

marketshouldscalematerially.

Inthissection,weexploreevidenceofhowmachinesarerapidlyingestingknowledgeworkandbecoming

dominantactorsacrosstheweb.

Machineeconomysignals

Strongevidencealreadyexiststhatmachinesarecreatinganincreasingshareofvalueacrosstheinternetandsoftwarestack.Tokenvolumesarecompoundingatrates

humanusagealonecannotexplain.Botsnowaccountformost

HTTPrequestsonthe

web.APIsarebeingrepricedformachine-scaleconsumption

,andtheprotocolsthat

9ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

letagentsplugintoexternalsoftwareareproliferatingfasterthananypriordeveloperstandard.Weexamineeachofthesesignalsbelow.

ModelAPIthroughput

APIthroughputofLLMproviderscanhelpcapturehowmuchworkagentsaredoing.ItisessentiallytheamountofmodelworkperformedviaanAPI,typicallymeasuredasthevolumeoftokensprocessedviaprogrammaticrequestsoveragivenperiod.

Observingthismetrichelpsexplaintheexplosivegrowthinthenumberoftokens

processed,asagentsinteractwithAPIsinfundamentallydifferentwaysthanhumansdo.EveryLLM-poweredaction,whetheradecision,summary,codecommitment,ortransaction,beginswithaninferencecall.WhenahumanusesAI,trafficislinear—asinglepromptoftenyieldsasingleresponse.ButforanAIagent,itcantriggeran

exponentialfan-outeffect,whereasingleinstructionbranchesintomanybackgroundinferencesastheagentplans,invokestools,andchecksitsownoutputs.

Exampleofagentfan-outeffect

Human

Simplifiedexampleofasingularlinearprompt

Agent

Simplifiedexampleofasequentialloopwithparallelsubagentfan-out

Instruction

Orchestrationlayer

Instruction

Initialinference

Reason+plan

Subagent

Subagent

Toolcallandactions

Inference

Websearch,APIs,

documentretrieval,etc.

Inference

Toolcallandactions

Inference

Toolcallandactions

Inference

Augmentedcontext

Finaloutput

Synthesizinginference

Singleorfewinferencecalls,tokenvolumelowandpredictable

Finaloutput

Manyinferencecalls,

tokenvolumehighandcompounding

Source:PitchBook

10ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

Theabilityofagentstoexponentiallymultiplycallsatthisscaleandspeedispushingbroadercognitiveworkloadsontomachines.Itisoneofthecontributingfactorstotheimmensegrowthintokensprocessed.DatafromOpenRouteranda16zindicatethattheaveragepromptlengthnearlyquadrupledbetweenearly2024andlate2025,reflectingmorecomplex,multistepinstructionsbeinggiventomodels.182026trendsshowthis

trajectorycontinuing.Forexample,Google’smonthlytokenprocessingratewas9.7trillioninMay2024,480trillioninMay2025,and3.2quadrilliontokensinMay2026.19

Narrowingtothemodellevel,providerdisclosuresshowhowmuchofthisgrowthis

drivenbymachine-initiatedAPIconsumption.OpenAI’sAPIsprocessed15billiontokensperminuteattheendofMarch2026,withenterprisesnowaccountingformorethan

40%ofrevenueandprojectedtomatchconsumersalesbytheendof2026.20AlphabetreportedinQ12026thatitsGeminimodelswereprocessingmorethan16billiontokensperminuteviadirectAPIusage,up60%QoQ.Italsoseesusageconcentratingamongheavyenterpriseworkloads,with330GoogleCloudcustomerseachprocessingmorethan1trilliontokensand35reachingthe10trilliontokenmilestoneoverthepastyear.21

Thethroughputnumbersseentodaysuggestweareinevitablybuildingtowarda

separateagent-ledeconomy.Tensofbillionsoftokensareprocessedthroughmodel

APIseveryminute,withagrowingsharedrivenbyenterpriseworkloadsandagents.Butadoptionisstillearlybecausemostagentstodayoperatewithinsupervised,narrowlyscopedsystems.Whatwearemeasuringtodayislikelyjustthenewbaseline,andthebulkofmachine-initiatedinferencehasyettocome.

Internettraffic

Signalsinweb-crawlingdataandthecompositionofinternettrafficarebeginning

toshowthatmachinesmaybebecomingtheinternet’sdominantactorsintermsof

volume.WhileCloudflareCEOMatthewPrincepredictedinMarch2026thatbottrafficwouldexceedhumantrafficonthewebbytheendof2027,thatcrossoverhasalreadyarrived.22,23Botsnowsurpasshumansin

HTTPrequeststoHTMLcontent.AsofJune

24

,botsaccountedfor57.5%oftheserequests,comparedwith42.5%forhumans.24

Shareof

HTTPrequestsbyusertype

100%90%80%70%60%50%40%30%20%10%0%

May3May17May24May31June7June14June212026

HumanBot

Bots:57.5%

Source:CloudflareRadar•Geography:Global•AsofJune24,2026

11ANALYSTNOTE:MACHINEECONOMYRISING:THEMARKETOFLIMITLESSCOGNITION

However,“bots”isabroadcategory.Itincludeslegacyautomatedtraffic,suchassearchenginecrawlers,uptimemonitors,andsecurityscanners,aswellasmaliciousbotsusedforcredentialstuffing,scraping,andfraud.ItalsoincludesAItrainingcrawlersthatbulk-fetchwebpagesformodeldevelopment,whichconsumethewebpassivelyratherthanactingonbehalfofusers.

Despitethevolumecomingfromotherbotcategories,AIremainsthefastest-growing

driverofautomatedtraffic.Trainingcrawlersmakeup67.5%ofAI-driventrafficobservedbyHUMANSecurity,butthatsharedeclinedthrough2025asagenticcategories

accelerated.Scrapertrafficgrew597%yearoveryear,whileagenticAItrafficgrew

7,851%,albeitfromalowbase.25Browser-basedagentsthatspinupfullbrowser

environments,loadpages,clicklinks,andfillformsnowaccountforaround71%of

HUMAN’sobservedagentictraffic,providingearlyevidenceofagent-ledactivityacrossthewebwellbeforefullymachine-nativeinterfaceshavebeendeveloped.26

Third-partyAPIs

Afterinferenceandreasoning,agentsreachintothefullstackofthird-partyservicestoexecutetasks.ExternalAPIvolumescapturethisdownstreamactivity,suchasretrievingdatasources,initiatingpayments,ortriggeringsoftwareworkflows.Agentsdothis

throughAPIs,sending

HTTPrequeststoendpointsthatexposediscretefunctions

.

Becauseagentscannotnavigatehuman-centricinterfaces,thisrequest-and-responsecyclehasbecometheirdefaultmechanismforinteractingwithexternalsoftware,

leavingmeasurabletracesofautonomousactivityacrosstheweb.

Theeffectsofthisshifthavebeenapparent,as65%oforganizationsgeneraterevenuefromtheirAPIs.27Salesforceprocessedalmost1trillionAPIcallsinonlythefirstquarterof2026.28Alpaca,abrokerageinfrastructureAPIprovider,sawmonthlyAPIusage

growthacceleratefromsingledigitsinQ42025toroughly30%inQ12026,whichit

attributedtoAIagent-drivenmarketparticipation.29Theshiftisalsoshowingupinhowdevelopersbuild.About25%nowdesignAPIswithage