2026机器原生经济白皮书：数字资产如何连接智能、商业与算力 The Machine-Native Economy How digital assets connect intelligence,commerce,and compute

TheMachine-

NativeEconomy

Howdigitalassetsconnectintelligence,commerce,andcompute

BlackRock.

ExecutiveSummary

Theextraordinarygrowthinartificialintelligenceisthedefiningtechnologythemeofthisera.Theriseof

digitalassets,meanwhile,representsaconcurrenttechnologythemewithparticularlyrelevantimplicationsforfinancialinfrastructure.Thesethemeshavehistoricallydevelopedalonglargelyparalleltracks,buttheyarebeginningtoconvergeasAIsystemsbecomemorecapableofinteractingwithfinancialandeconomicnetworks.ThispaperexaminesthisgrowingrelationshipandexplainswhybroadAIadoptionmayrepresentanunderappreciatedsourceofdemand,utility,andapplicationgrowthacrossthedigitalasseteconomy.

Atthecoreofthisconvergence,AIanddigitalassetsbotharisefromacommonfoundation:AIrepresents

machine-nativeintelligence,whiledigitalassetsrepresentmachine-nativemoney.Thisalignment

becomesparticularlyimportantwiththeriseofagenticAI,whichreferstosystemsthatcanplanand

executemultisteptaskstowardadefinedobjectivebyinteractingwithexternaltoolsandinfrastructurewithlimitedhumanintervention,withblockchainsprovidingtheprogrammableinfrastructurethatconnects

intelligencewitheconomicactivity.ThesecapabilitiesextendAIbeyondcontentgenerationtowardreal-worldaction,includingmakingpurchasesandinitiatingfinancialtransactions.

Specifically,weexplorethreekeyareasofoverlap:

•LLMsandblockchainsshareanalogoustokenizationarchitectures.Largelanguagemodelsdivide

humanlanguageintotokensandencodethemnumericallyformodelinterpretationandprocessing.

Blockchainssimilarlyrepresenteconomicvalueandentitlementsasdigitalassettokensdesignedfor

machine-verifiabletransferandsettlement.Thefunctionsaredistinct,butbothworkflowstranslatereal-worldinputsintoformatsthatmachinescanusenatively.

•Agenticcommercerequiresmachine-nativepaymentrails.TheriseofagenticAIandmachine-to-

machinepaymentswilllikelyincreasedemandforblockchainsandotherprogrammablepayment

infrastructure;stablecoins,nativecryptoassets,andotheron-chainassetscanserveasmachine-nativeinstrumentsforpaymentandsettlementacrosstheserails.ExistingrailssuchasACHandcard

networkssupportsubstantialautomation,althoughtheironboardingrequirementsandsettlement

economicscanmakethemlesssuitedtoalways-on,verylow-valuetransactionsrequiring

programmableexecution.Emergingprotocolssuchasx402andACParebeingdeployedonblockchainnetworksandalongsideadaptationstotraditionalpaymentrails,creatingatransactionandsettlementlayerformorecomplexagenticworkflows.

•Computeisemergingasanewandpotentiallylargemarketfordigitalassets.Compute,the

processingcapacityrequiredtotrainandrunAIsystems,isbecominganincreasinglyimportant

economicresource.Analystestimatessuggestthathyperscalercloudrevenuescouldexceed$1trillionannuallyby2030.Asagentsbecomemorecapableandpersistent,standardizedclaimsoncompute

capacitycouldbecomeasignificantdigitalassetusecaseforfinancingandprogrammablesettlement.

Together,thesedevelopmentspositionAIasastructuralcatalystfordigitalassetadoptionanddigitalassetsasapotentialfacilitatoroftheAIeconomy:AIinterpretsinformationanddirectsaction,while

blockchainsprovidemachine-readableassetsandprogrammablesettlement.Thisrelationshipremainsunderappreciatedandcouldexpandtheroleofdigitalassetsascoreinfrastructureforanincreasinglyautonomousdigitaleconomy.

CE0926-M-5936357-EXP0927-2/11

Alanddigitalassettokenizationconvertreal-

worldinputsintomachine-nativerepresentations

Atthearchitecturallevel,tokenizationinartificialintelligenceanddigitalassetsservesananalogous

purpose:translatinginformationandeconomicentitlementsintodiscrete,standardizedrepresentationsthatmachinescanprocessnatively.Altokensencodeinformation,whiledigitalassettokensrepresent

unitsofvalue,ownership,oreconomicclaims.Inlargelanguagemodels(LLMs),atokenizerdivides

human-readabletextintosmallerunits,typicallywords,sub-words,orcharacters,andmapsthemto

numericalidentifiers.Anembeddinglayerthenconvertsthoseidentifiersintovectorrepresentationsthatthemodelcanuseforcomputation.ThemodelgeneratesnumericaltokenIDsthataredecodedbackintohuman-readabletext.Bycontinuouslytranslatingincomingtextintostandardizednumericalunits,LLMscanprocesslargeinputstreamsefficientlyusingthehighlyparallelcalculationsforwhichmodernchipsareoptimized.

Blockchainsapplyafunctionallycomparable,thoughtechnicallydistinct,processtostoresofvalueand

economicclaims.Digitalassettokenizationistheprocessofrepresentingafinancialorreal-worldasset

(RWA)suchascash,afundinterest,asecurity,oranotherownershipclaimasastandardizeddigitaltokenrecordedonadistributedledger.Existingon-chainassetssuchasstablecoinscanbetransferredthroughsmartcontracttransactions.Atthetransactionlevel,theseassetsareexpressedthroughmachine-readabletransactiondataandgoverningrules.Thenetworkverifiesauthorizationandtransactionvalidity,while

smartcontractsortransactionscriptsapplyasset-specificpermissionsandconditions.Together,these

mechanismsupdatetheledgerbytransferringtokenbalances.Onceatransactionhasbeenrecordedandsufficientlyfinalizedunderthenetwork'sconsensusrules,itbecomespartofthenetwork'scanonical

ledgerstate.

Intheblockchainworkflow,smartcontractsexecutetransactionlogicandapplyrulesspecifictofinancialassets.Anti-moneylaundering(AML),know-your-customer(KYC),andknow-your-agent(KYA)checks

generallyoccuroff-chain,whereidentityandcompliancedatacanbeassessed,withverifiedresultspassedon-chaintodeterminetransactioneligibility.FortokenizedRWAs,thesecontrolssitwithinabroaderlegalandregulated-serviceframeworkthatreliesonauthoritativeoff-chainregistries,whilereducing

dependenceoncloseddatabasesandmanualreconciliation.

3

CE0926-M-5936357-EXP0927-3/11

Figure1:IllustrativeTokenizationWorkflowsinLLMsandBlockchainTransactions

AITokenization:

TexttoLLMRepresentation

Text

“AIischangingtheworld”

AItokenization

Tokens

“AI”,“is”,“changing”,“the”,“world”

Encoding

NumericalIDs

[101,23,4587,5,982]

Embedding

Embedding

matrix

[0.48,-0.37,0.20,-0.88,0.12][-0.62,0.88,-0.45,0.32,0.38][0.29,-0.55,0.90,-0.62,0.45][-0.07,0.32,0.79,0.50,-0.92][-0.34,0.15,0.68,0.72,-0.22]

Forillustrativepurposesonly.Source:BlackRockDigitalAssetsResearch.

DATokenization:

RWAstoOn-ChainOwnershipRecord

Real-worldasset(RWA)

$100beneficialinterestinamoneymarketfund

Tokenized

ownership

Digitalassettokenization

Tokenizedfundsharesassignedtoinvestor’sdigitalwallet

Encoded

transactionfields

Validation,locking,encoding

[AssetID,SenderID,RecipientID,Units,EligibilityFlag]

Blockchain

transactionrecord

Executionandfinalization

tx_hash:0x8F3A...91C2

asset_id:0x2D7B...44E1

from_id:0x91C3...0A77

to_id:0x71A9...3F06

token_units:100.000000

block:21845902

status:finalized

Theseparallelworkflows,oneencodinghumancontextandtheotherencodingeconomicentitlement,arebecomingmorerelevantassystemsofintelligenceandtransactionexecutionconverge.Bothsystemsusestructured,machine-readablerepresentations,whichcangiveLLM-basedAIagentsamoredirect

interfacewithblockchaindatathanwithmanyfragmentedlegacysystems.Inturn,greaterstandardizationacrossassetclassescanreducerelianceonbespokeintegrationsandmakeiteasierforagentsto

orchestratemorecomplexmulti-assetworkflows.Throughprogrammableinterfaces,agentscanevaluatebalancesandrulesbeforeexecutingauthorizedtransactionsandverifyingsettlement,withlimitedrelianceonmanualprocesses.

RecentBitcoinPolicyInstituteresearchofferspreliminarysupportforthisframework,reportingthatmodeloutputsacrosscontrolledsimulationsgenerallyfavoredstablecoinsforeverydaypaymentsandbitcoinforlong-termvaluepreservation.1Thesefindingsreflectsimulatedmodelresponsesratherthanobserved

agentbehavior,butpointtoapotentialAI-nativemonetaryarchitectureinwhichstablecoinsserveas

transactionmoneyandbitcoinasastoreofvalue.Asweexploreinthenextsection,thissharedmachine-nativefoundationcouldsupportmeaningfulagenticpaymentusecasesthatsometraditionalfinancialrailsmayservelessefficientlyoreconomically.

1.BitcoinPolicyInstitute,“WhichmoneydoAIagentsprefer?”,

/articles/study

-

ai

-

models

-

overwhelmingly

-

prefer

-

bitcoin

-

and

-

digital-

native

-

money

-

over

-

traditional-

fiat

.

4

CE0926-M-5936357-EXP0927-4/11

Machine-nativetransactionsrequirepurpose-buildagenticpaymentprotocols

AsAIagentsbecomemorecapableandastheirreal-worldapplicationsexpand,theyincreasinglydemandpaymentandassetinfrastructuredesignednativelyformachine-speedcommerce.Crypto-native

blockchainrailsareparticularlywellsuitedtohigh-frequency,sub-cent,machine-to-machine(M2M)

transactionsthattakeplacearound-the-clock,includingAPIcalls,on-demanddata,andconsumption-

basedcompute.Inparallel,modifiedtraditionalpaymentsystemswillremainimportantforconnecting

agentswithhuman-operatedbusinessesandconsumersinbusiness-to-machine(B2M)andconsumer-to-machine(C2M)settings.

Stablecoins,nativecryptoassets,tokenizedreal-worldassets,andotherprogrammableinstrumentscan

supporttransactionsanddigitalownership,includingcollateraluse,withtherequiredgranularityonan

always-onbasis.Morebroadly,tokenizationcanprovidestandardized,machine-readablerepresentationsacrossassetclasses,reducingbespokeintegrationsacrossfinancialinfrastructureandenablingagentstoorchestrateincreasinglycomplexworkflowsmoreefficiently.

Manyexistingpaymentrailsarelesswellsuitedtohigh-volume,low-denominationagentictransactions.Theyinvolve:

•Accountsetup,credentialing,andauthorizationprocessesthatmayrequirehumaninvolvement;

•Merchantacceptancefeesthatcanmakeverylow-valuetransactionsuneconomic;

•Settlementandfinalityconstraints,asmostACHvolumesettleswithinonebusinessdayorless,whilecardauthorizationisnear-instantbutmerchantsettlementanddisputefinalitycantake

longer;and

•Potentialscalabilitylimitationsasmachine-generatedtransactionvolumesgrow.

AgenticpaymentprotocolsbuildonfoundationalstandardssuchasMCPandA2A,whichconnectagentswithexternalsystemsandoneanother.MCP2(ModelContextProtocol),introducedbyAnthropicin

November2024,standardizeshowAIapplicationsaccessexternaldataandworkflows.A2A3

(Agent2Agent),launchedbyGoogleinApril2025,enablesagentstocommunicateandcoordinateacrossplatforms.

AgentsusingMCPandA2Aleveragemultipleagenticpaymentprotocolstocompletecomplexworkflowsinvolvingpayments.x4024,anopenpaymentprotocoldevelopedbyCoinbase,usesthe

HTTP402

“PaymentRequired”statuscodetofacilitatemachine-initiatedpayments.Theprotocolisblockchain-

agnostic,withstablecoinssuchasUSDCrepresentinganearlyprimaryusecase,andisemergingasonepotentialstandardforhigh-velocityM2Mtransactions.Byproviding24/7,near-real-time,verifiable

settlement,x402canreducetheresourceprovider’scounterpartyexposureandenabletheimmediate

releaseofrequesteddataorservicesuponpaymentconfirmation.Becausex402usesdigitalcurrencies,

includingstablecoinsheldinon-chainwallets,itcansupporthigh-frequency,low-denomination

transactionswithouthumanintervention.Wheresettlementoccursonpermissionlessnetworks,greater

usagecouldincreasedemandforblockspaceandvalidatorservices,creatingapotentialtransmission

channeltonativecryptoassets.Theextentofvaluecapturewilldependoneachnetwork’sfee,staking,andgas-sponsorshipdesign.

2.ModelContextProtocol,

https

://modelcontextprotocol

.

io/docs/getting

-

started/intro

.Analysisbasedonthecurrencybeingheldascash.3.GoogleCodelabs,

https

://codelabs

.

developers

.

google

.

com/intro

-

a

2

a

-

purchasing

-

concierge#

0

.4.Coinbase,x402,

https

://x

402

.

org/wp

-

content/uploads/sites/

10/

2026/

06

/x

402

-whitepaper

.

pdf

.

5

CE0926-M-5936357-EXP0927-5/11

Otheremergingstandardsconnectagentictransactionswithexistingpaymentrailsandestablish

guardrailsfortrustedfinancialexecution.TheMachinePaymentsProtocol(MPP5),developedbyStripe

andTempo,enablespaymentsforAPIsandother

HTTPresourceswithflexiblesettlementviastablecoins

ortraditionalpaymentmethods.TheAgenticCommerceProtocol

(ACP6),developedbyStripeandOpenAI,enablesprogrammaticcheckoutbetweenagentsandbusinesseswhileallowingsellerstoretaintheir

existingcommerceandpaymentinfrastructure.Google’sAgentsPaymentProtocol(AP27)uses

cryptographicmandatesandaudittrailstoprovideevidenceofuserauthorization,whileVisa’sTrustedAgentsProtocol(TAP8)helpsmerchantsverifytrustedagentsandsecurelyreceivepaymentcredentials.

Figure2illustratesasimplifiedagenticpaymentworkflow.Ahumanuser(1)asksanagenttobookflightsandhotelswithinaspecifiedbudget.TheprimaryAIagent(2)accessestheuser’scalendar,preferences,

andapprovedpaymentdetailsthroughMCPconnectors,then(3)delegatesdata-gatheringtoaspecializedAItravelsub-agentthroughA2A.Thesub-agent(4)callspaidairfareandhotel-rateAPIs,withapplicable

paymentshandledthroughx402andsettledon-chain.Usingthereturnedinformation,theprimaryagent(5)completesreservationsthroughairlineandhotelcheckoutsystemsand(6)returnstheitineraryand

receiptstotheuser.

5

ACP

Finalize

Reservations

Airline/HotelCheckout

Figure2:IllustrativeAgenticWorkflowInvolvingFoundationalandFinancialProtocols

MCP:helpsagentsaccessexternaltoolsanddata

A2A:helpsagentscommunicatewitheachother

x402:powersfastmachine-to-machinetransactions

ACP:connectsagentstovendors'existingpaymentrails

3

4

x402

A2A

"Getschedules

andfares"

Agentpaysfordata

tobuildoptimal

itinerary

AITravel

Sub-Agent

PrimaryAgent

HumanUser

Airfare/RoomRatesAPI

Userprompt

1

"Bookmytripforunder$2,500"

Itinerary,Receipts

Datadriving

2

purchasedecisions

MCP

MCP

Tools/Data

6

Tools/Data

Calendar,Email,PaymentCredentialsRouting,SeatMaps,Loyalty

Forillustrativepurposesonly.Source:BlackRockDigitalAssetsResearch.

Severaltypesofdigitalassetsmaysupportagenticcommerce,butstablecoinsarelikelytolead

transactionaluse.Stablecoinsaredigitaltokensdesignedtomaintainastablevaluerelativetoareferencecurrency,mostcommonlytheU.S.dollar.Theirpricestabilityprovidesareliableunitofaccountandgreaterpredictabilityinpricingandsettlement.Stablecoinsrepresentthelargestcategoryoftokenizedreal-worldassets,withmorethan$300billionincirculatingmarketcapitalizationasofSeptember20269Adjusted

stablecointransactionvolumeexceeded$11trillionin2025,placingitinthesamebroadrangeasVisaandMastercard’sannualpaymentvolumes.10Adjustedstablecoinvolumeremainedwellbelowthe$93trilliontransferredoverACHin2025;from2020to2025,however,itgrewatan80%CAGR,comparedwith

approximately8.5%forACH.11Growingregulatoryclarity,includingtheGENIUSActintheU.S.,MiCAintheEU,HongKong’sstablecoinlicensingregime,andSingapore’sstablecoinregulatoryframework,should

supportcontinuedstablecoinadoptionandgrowth.

5.Tempo&Stripe,MachinePaymentsProtocol,

https

://mpp

.

dev/overview

.6.Stripe,AgenticCommerceProtocol,

https

://www

.

agenticcommerce

.

dev/docs

.7.Google,AgenticPaymentsProtocol,

https

://ap

2

-

protocol

.

org/

.8.Visa,TrustedAgentProtocol,

https

://developer

.visa

.

com/capabilities/trusted

-

agent

-

protocol

.9.RWA.xyz,

https

://app

.

rwa

.

xyz/

stablecoins

.10.Visa/Allium,

https

://visaonchainanalytics

.

com/transactions

.Adjustedstablecointransactionvolumeusesaddresslabelsandheuristicfilterstoexcludeinternaltransfers,intra-exchangeflows,bots,andotherhigh-frequencyorhigh-volumeactivity.11.Nacha,

https

://www

.

nacha

.

org/content/ach

-

network

-volume

-

and

-value

-

statistics

6

CE0926-M-5936357-EXP0927-6/11

Figure3:StablecoinandMajorCardNetworksTransactionVolumes

StablecoinsVisaMastercard

20

US$Trillions

15

10

5

0

16.7

10.6

11.2

8.5

201820192020202120222023202420251H2026

2026stablecoindatathroughJune2026.Note:measuresarenotdirectlycomparable.Adjustedstablecoinvolumeincludesselectedexchange,DeFi,lending,mint/burn,andrampactivityaftermethodologicalfilters.Visareportstotalvolume,includingpaymentandcashvolume,whileMastercardreportsgrossdollarvolume.VisadataareforthefiscalyearendedSept.30,2025;Mastercardandstablecoindataarepresentedonacalendar-yearbasis.Source:VisaOnchainAnalytics,Allium,VisaandMastercardannualreports,andSECfilings.

Fordigitalassets,theimplicationsofthisgrowthextendbeyondstablecoinstotheblockchainnetworksonwhichtheyareissuedandsettled.Manymajorstablecoinsareissuedacrossmultipleblockchains,allowingmarketparticipantstoselectamongsupportedsettlementvenuesbasedoneconomicsandtechnicalfit.

Thesevenuesincludegeneral-purposepermissionlessnetworkssuchasEthereumaswellaspurpose-builtstablecoinnetworkssuchasCircle’sArc,whereUSDCisdesignedtoserveasthenativegasasset.On

permissionlessnetworks,nativecryptoassets(e.g.ETH)supportconsensus,validatorcompensation,

transactionfees,andsettlement.Asstablecoinactivityscales,greaterdemandforblockspaceandnetworkservicescouldsupportusage-relateddemandandpotentialvaluecapturefortheseassets,subjecttoeachnetwork’sfee,staking,andgas-sponsorshipdesign.Arcalsooffersacomplementarymodelinwhich

greaterpaymentactivitycoulddeepenUSDC’sutilityasbothasettlementassetandthemeansofpayingtransactionfees.

Computeisemergingasanewandpotentiallylargemarketfordigitalassetsasautonomousagentsproliferate

AIsystemsandagentsrequiresubstantialcomputingpowerandenergytooperate.Investorshavelargelyfocusedonthesheerscaleofcapitalexpenditures(CapEx)neededtobuildAIinfrastructure,withsome

estimatesplacingcumulativeAIcapitalspendingabove$5trillionbetween2025and2030.12Butthe

accompanyingriseinongoingoperatingexpenses(OpEx)tosupportAIdeploymentsdeservesequal

attention.AmeaningfulshareofthisoperatingspendflowsthroughtheAIcloudcomputemarket,which

monetizesaccesstoinstalledITequipmentandtheelectricityrequiredtooperateit.Asthismarket

expands,computeisbecomingadistinct,large,andincreasinglyinvestableeconomicresourcethatcouldsupportanewclassofdigitalassets.Usinghyperscalers’majorcloudsegmentsasabroadproxyfor

marketscale,consensusestimatesforAmazonWebServices(AWS),Microsoft’sIntelligentCloudsegment,andGoogleCloudimplycombinedrevenueofapproximately$1.1trillionby2030,representinga29%

CAGRfrom2025levels.13

12.GoldmanSachs,“Privatemarketsareexpectedtohaveagrowingroleindatacenterfinancing,”

/insights/articles/private

-

markets

-

expected

-

to

-

have

-

growing

-

role

-

in

-

data

-

center

-

financing

.13.Bloomberg-compiledsell-sideanalystestimatesasofAug.31,2026.

7

CE0926-M-5936357-EXP0927-7/11

Ascomputebecomesalargereconomicinput,theneedtopriceandallocatecapacitywhilesupporting

financingandhedgingshouldalsoincrease.Historically,largeresourcemarketshavedevelopedtrading

infrastructurethatimprovesliquidityandriskmanagement.Inourview,computemayfollowasimilarpathasAIadoptionscales.Recentmarketdevelopmentsalreadypointinthisdirection,includingGPU-backedfinancingsandfinancingplatformsdesignedaroundlong-duration,usage-linkedcomputerevenue.Thesestructuresreflectthecapitalintensityofsecuringleading-edgeGPUsandbuildingcapacityforfrontier-

modeldevelopment.Demandforbothtrainingandinferenceshouldcontinuetogrowasmodelsimprove.Asreal-worldAIusecasesexpand,inferenceisexpectedtobecomethelargestAIworkloadby2030and

accountforagrowingshareofdata-centerpowerdemand.14Thepotentialinferenceuserbase,spanningenterprisesandindividualconsumers,isconsiderablylargerandmorefragmentedthantheconcentratedsetoftraining-marketparticipants.

Figure4:GlobalDataCenterPowerDemandbyWorkload

AITrainingAIInferenceNon-AI

250

200

Gigawatts(GW)

150

100

50

0

43%

25%28%

28%

20252026E2027E2028E2029E2030E

Percentagesshoweachcategory’sshareoftotaldatacenterpowerdemand.Source:McKinseyDataCenterDemandModel,estimatesandprojectionsasofDec.2025.

Evenasthismarkettakesshape,meaningfulcontract-designandmarket-structurechallengesremain

beforestandardizedcomputeproductscanscale.Theseincludeaccountingforsubstantialdifferencesinchip-generationproductivityandregionaleconomics,particularlywhereenergycostsdiverge,aswellasestablishingworkablestandardsforbothcashsettlementanddeliveryofcontractedcapacity.Weview

theseasimportantbutultimatelyresolvabledesignconsiderations.Thedevelopmentofbasismarkets,

contractsfordifference,andothermechanismsusedinestablishedcommoditymarketsoffersauseful

precedentformanagingheterogeneousassetsandlocalizedpricing,whileon-chaintokenizedmarkets

mayenablemoregranularregionalandhardware-specificcontractswithinsharedsettlement

infrastructure.Astheseframeworksmature,weexpectstandardizedproducts,includingexchange-tradedcomputefutures,tosupportmoretransparentpricediscoveryandmoreeffectivehedgingforboth

providersandconsumersofcomputecapacity.Standardizedcomputecontractscouldalsocreateclaimsoncomputecapacityandrelatedusagerightsthatcanberepresented,transferred,pledgedascollateral,andsettledthroughprogrammableinfrastructure.Thiscouldinturnbroadeninstitutionalinvestor

participationandestablishcomputeasanewopportunityforthebroaderdigitalassetecosystem.

14.McKinsey,“ThenextbigshiftsinAIworkloadsandhyperscalerstrategies,”

https

://www

.

mckinsey

.

com/industries/technology

-

media

-

and

-

telecommunications/our

-

insights/the

-

next

-

big

-

shifts

-

in

-

ai

-workloads

-

and

-

hyperscaler

-

strategies

.

8

CE0926-M-5936357-EXP0927-8/11

ThisfunctionalitymaybecomeparticularlyvaluableasagenticAIadoptionscalesandagentsincreasinglydiscover,provision,optimize,andpayforcomputethroughprogrammablepaymentrailssuchasx402.

Agentscouldqueryreal-timemarketplaceAPIstocompareavailablecapacitybyprice,performance,

latency,location,andhardwarespecialization,thenprovisiontheresourcesbestsuitedtoagivenworkload.MCPandA2Acouldfacilitatedataaccessandagent-to-agentcoordination,whilex402couldsupporton-demandsettlementonaper-use,per-model-token,orper-jobbasis.Thisframeworkcouldenableelastic,

just-in-timeaccesstocomputewithlimitedhumanintervention.Althoughagenticpaymentactivityremainsnascenttoday,thestructuralfitbetweenautonomousagentsandmachine-nativepaymentsmakesthisanareaworthmonitoringastheecosystemdevelops.

Figure5:IllustrativeAgenticWorkflowforOptimizingandSecuringOn-DemandComputeResources

ComputeProviders

Userprompt

GPU/CPUproviders,specializedcompute,edge/regionalnodes

"Runextended

analysis"

Agent

HumanUser

Returnsoutput

Computecapacitydeliveredtosupportagentictask

Discovery

Tools/Data

Real-timepricing,availability,andreliabilitymetrics

Continouslyexecutestaskandestimatescomputeneeds

MCP

Forillustrativepurposesonly.Source:BlackRockDigitalAssetsResearch.

Stripe’sAugust2026agreementtoacquireOpenRouterprovidesanearlystrategicsignalthatmodel

routingandcompute-usageoptimizationarebecomingpartofthefinancialinfrastructuresurrounding

AI.15OpenRouterdistributesworkloadsacrossmorethan400modelsfromover80providersbasedon

workloadneedsandcost-performancetradeoffs,highlightingtheeconomicvalueofallocatingscarce

computeefficiently.Thebuyeralsomatterstothethesis:givenStripe’sbroaderactivityacrosspayments,stablecoins,billing,andagenticcommerce,thistransactionpointstoapotentialconvergencebetween

computeprocurement,usage-basedbilling,andprogrammablesettlement.Inourview,thiscouldsupportafutureinwhichagentsautonomouslysourceandpayforcomputeoverblockchainsandother

programmablepaymentrails.

15.Stripe,“StripeagreestoacquireOpenRoutertohelpbusin