PIIE-衡量人工智能经济 Measuring the AI Economy

WORKINGPAPER

26-9MeasuringtheAIEconomy

AntonKorinekandPatrickMcKelvey

May2026

ABSTRACT

AntonKorinekhasbeenanonresidentseniorfellow

WeconstructamacroeconomicestimateoftotalAIproductionfor

atthePetersonInstitute

theUnitedStates,combininginferenceandR&D/trainingactivities

forInternational

andapplyingqualityadjustmentsbasedontheevolutionofAPIprices

Economics(PIIE)sinceFebruary2026andhead

atfixedperformancelevelsandthepaceofalgorithmicprogress.We

ofTransformativeAI

estimatethatnominalAIcomputespendinggrewover140percent

EconomicStudiesatthe

peryeareachin2024and2025,rawcomputecapacitygrewover

AnthropicInstitutesinceMay5,2026.Heison

200percentperyear,andquality-adjustedAIoutputgrewover2,000

leavefromtheUniversity

percentperyear.Thesegrowthratesreflectthreecompounding

ofVirginia(UVA),

forces:expandingdata-centercapacity,continuedimprovementsin

whereheisprofessorofeconomicsandfaculty

chipefficiency,andrapidalgorithmicprogress.Wethenemployour

directoroftheEconomics

estimatestodevelopanascentframeworkfor“AIGDP”thattracksthe

ofTransformativeAI

AIeconomyasacoherentwholeratherthandispersedacrossstandard

(EconTAI)initiative.Thisworkwasconducted

industryclassifications.Quality-adjustedAIGDPgrewbymorethan

inhiscapacityasPIIE

2,500percenteachin2024and2025.Ourmeasurescomplement

nonresidentseniorfellow

traditionalnationalaccountsbyprovidingvisibilityintoafast-moving

andprofessoratUVA.PatrickMcKelveyisa

sectorwhoseactivityisdifficulttoisolateinexistingstatistics,and

seniordatascientistatthe

theymayserveasbuildingblocksforsatelliteaccountsthattrackAI’s

BankofCanada.

growingroleintheeconomy.

TheauthorsthankMartinChorzempa,Cullen

Hendrix,PatrickHonohan,

JELCodes:E01,O33,O47,E22

AdamPosen,andDavid

Keywords:artificialintelligence;nationalaccounts;GDPmismeasure-

Wilcoxforexcellent

ment;AIsatelliteaccounts;quality-adjustedprices;algorithmicprog-

comments;KodyKarmodyandDylanRyfeforreliable

ress;AIGDP

researchassistance;

LeopoldBrownand

YuvalRhymonfortheir

Thefindings,interpretations,views,andconclusionsexpressedhereinare

contributionstoearly-

solelythoseoftheauthorsanddonotnecessarilyrepresentthoseofthe

stageresearchanddata

BankofCanada,theAnthropicInstitute,orthePetersonInstitutefor

collection;FutureImpact

InternationalEconomics.TheauthorshaveusedAIextensivelyatevery

Groupfortheirsupport;

stageoftheresearchandwritingprocessandhavesubjectedallAI-

andAndreyFradkinfor

generatedoutputtocarefulhumanreview.

generouslysharingdataoninferenceprices.

2

Contents

1Introduction

3

2ApproachandContributions

4

3Methodology

7

3.1MeasuringAIProduction

7

4Results

10

4.1Quality-AdjustedAIProduction

11

5FromAIProductiontoAIGDP

11

6Discussion

15

7Conclusion

18

3

1Introduction

Amongartificialintelligence(AI)researchersandleadingtechnologycompanies,thereisbroadagreementthatAIcapabilitiesareadvancingataremarkablepace—withsomearguingthatartificialgeneralintelligence(AGI)maybeachievedsoon.Yetwhenwelookattraditionaleconomicstatistics,weseeonlyupstreaminvestmentindatacenters,whiledownstreamimpactsfromthisrevolutionremainnearlyinvisible.GDPgrowthintheUnitedStatesandotheradvancedeconomieshasremainedmoderate,andproductivitystatisticshavebarelytickedup.Thequestion“whenwillweseeAIintheGDPstatistics?”hasbecomearecurringthemeineconomiccommentary.Onenaturalresponseispatience:AIadoptiontakestime,andtransformativeeconomicefectsmaysimplylieahead.Thisisalmostcertainlypartofthestory.

Butwebelievethereisanadditional,complementaryissueworthtakingseriously.Nationalaccountsweredesignedforaneconomyinwhichallproductionisultimatelyorganizedaroundhumansasthecentralpointofvaluecreation.Thiswasanentirelyappropriatedesignformostofeconomichistory,anditcontinuestoserveitscorepurposewell.However,therapidgrowthoftheAIsectorintroducesmeasurementchallengesthatexistingstatisticalcategorieswerenotbuilttoaddress.Thedi岱cultyisthatAIactivityishardtoseethroughthelensoftraditionalnationalaccountsandinthewayswetypicallymeasureGDP.

Thechallengeoperatesthroughseveralchannels.First,AIactivityisscattered:spendingonAIcompute,modeldevelopment,andAI-poweredservicesisspreadacrossdozensofindustrycategories—dataprocessing,cloudcomputing,softwarepublishing,professionalservices—makingitdi岱culttotracktheAIeconomyasacoherentwhole.Second,AIqualityimprovementsareunusuallyrapid:thepaceofimprovementinAIcapabilitiesisfarfasterthaninmostsectorsforwhichstatisticalagencieshavedevelopedqualityadjustmentmethods,raisingquestionsaboutwhetherstandardhedonictech-niquescapturewhatishappening.Third,AI’sroleintheeconomyisevolving:asAIsystemsbecomemorecapable,theymaytransitionfrombeingoneamongmanyinterme-diateinputstoplayingamorecentralroleinproduction,potentiallystrainingcategoriesthatweredesignedforaworldinwhichmachinesarepassivecapitalratherthanactivecontributorstoeconomicoutput.

AIisthelatestinaseriesofnewrapidly-growingtechnologiestointroducenewmeasurementchallenges,suchassemiconductorsandtheinternet.Butincontrastwithpreviousepisodes,AIwon’tnecessarilybeconstrainedbythesupplyofcomplementaryhumanlabor,whichcouldopenthedoortomuchlargermismeasurement.

Thesemeasurementchallengesmattertoday,andtheywillmattermuchmoreinthenearfuture.IfAIcapabilitiescontinuetoadvancerapidly,policymakersandresearcherswillneedtoolsthatcantracktheAIeconomy’sgrowthalongsidetraditionalstatistics.

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Thecaseforbuildingsuchtoolsnow—whiletheAIsectorisstillrelativelysmall—restsonthesimpleobservationthatstatisticalinfrastructuretakestimetodevelop,andwaitinguntilmeasurementgapsbecomeacutemeansarrivingtoolate.

Thispapermakestworelatedbutseparablecontributions.First,weconstructproduction-sideestimatesofU.S.AIoutputcombininginferenceandtrainingactivities,quality-

adjustedusingAPIpricesatfixedperformancelevelsandestimatesofalgorithmicprogress.TheseestimatesrevealthatrealAIoutputhasgrownatratesvastlyexceedingnominalspending—afindingthatcanbeincorporatedintoconventionalGDPstatisticsasahedo-niccorrection,withoutrequiringanyreorganizationofexistingnationalaccounts.Second,weproposeaframeworkfor“AIGDP”thattreatstheAIsectorasacoherenteconomicentitytradingwiththehumaneconomy,anduseittoproduceanintegratedpictureoftheAIeconomy’ssizeandgrowth.

2ApproachandContributions

Inthispaper,weconstructamacroeconomicestimateoftotalAIproductionfortheUnitedStatesandacorrespondingquality-adjustedAIproductionindex.Ourmeasuresaggregatethroughseverallayers.First,webeginwithestimatesofrawcomputecapacity,anchoredinelectricityusageprojections(

Pateletal.

,

2024

)anddata-centercharacter-istics(

EpochAI

,

2026b

),andcross-checkedagainstchipsalesdata(

EpochAI

,

2026a

).Second,wemaprawcomputetonominalspendingusingacollecteddatasetofGPU-hourrentalprices.Third,weconstructqualityadjustments:forinference,usingtheevolutionofAPIprices

1

atfixedbenchmarkperformance(

Demireretal.

,

2025

);fortraining,usingthepaceofalgorithmicprogress(

Hoetal.

,

2024

)asmeasuredbythecomputerequiredtoreachagivenlevelofmodelperformance.

WeusetheseestimatestodevelopanascentbroaderframeworkforcalculatingAIGDP—theportionofeconomicvaluecreationmorecloselyassociatedwithAIcompu-tationratherthanhumancomputation.ThisframeworkprovidesapotentialconceptualfoundationfortrackingAI’scontributiontotheeconomyonitsowntermsratherthanasanincidentalbyproductofhuman-centeredaccountingcategories,oferinganovelandcomplementaryperspectiveontheforcesshapingoureconomy.

RelationshiptoTraditionalNationalAccounting

Itisusefultospelloutconcretelyhowourapproachrelatestostandardnationalac-countingpracticeandwhereitprovidesadditionalinformation.OurmeasurementsofAI

1API(ApplicationProgrammingInterface)pricesrefertotheper-unitfeeschargedbyAIproviders—suchasOpenAI,Anthropic,andGoogle—foraccesstolargelanguagemodels(LLMs)viasoftwareinterfaces.Pricesaretypicallyquotedpermilliontokensprocessed,whereatokenisroughlyawordfragment.

5

productionrepresentrefinementsthatfollowthespiritoftraditionalnationalaccountingrulesbutadaptthemtoprovideaclearerpictureofAI:

TrackingtheAIsectorasacoherentwhole.Nationalaccountsorganizeeconomicactivitybyinstitutionalunitandindustry.AI-relatedactivityisthereforedistributedacrossmanycategories,includingcloudcomputing,softwareandprofessionalservices.OurapproachinsteadtrackstheAIeconomyasacoherentwhole—allcomputeproduc-tion,modeldevelopment,andinferenceoutput—regardlessofwhichindustryclassifi-cationitfallsunder.Thisisanalogoustohowtradeeconomistssometimesconstructaccountsforthe“tradablesector”orhowenergyeconomiststracktheenergysectoracrossstandardindustryboundaries.Severalofthemeasuresweconstruct,particularlyourestimatesofnominalAIcomputespendingandrawcomputecapacity,couldserveasbuildingblocksforanAIsatelliteaccountwithintheexistingnationalaccountingframe-work,providingastructuredviewofAIactivitywithoutrequiringchangestoheadlineGDPmethodology.ThisisimportantbecauseAImaysoonbecomeoneoftheprimarydriversofvaluecreation,makingitnecessarytohaveanintegratedpictureofitsimpact.

Moregranularqualityadjustment.Standardstatisticalagenciesapplyhedonicpriceadjustmentsconservatively.Quality-adjustedpricedeclinesof20–30%peryearinfast-movingtechnologysectorslikesemiconductorscountasoutliers.Ourinferencedefia-tordeclinesapproximately94%peryear—roughlya16-foldincreaseinquality-adjustedoutputforagivendollarspent.Thisrefiectsthecompoundingoftwoforces:contin-uedchipe岱ciencyimprovementsandrapidalgorithmicprogress.Thischallengeisnotwithoutprecedent:

Hausman

(

1999

)showedthattheBLStelecommunicationsCPIwasbiasedupwardbyroughly2.3percentagepointsperyearsimplybyfailingtoaccountfortheintroductionofcellulartelephoneservice,anewgoodthato岱cialstatisticswereslowtoincorporate.Theextenttowhichthisaggressiveadjustmentisappropriatede-pendsonthedegreetowhichbenchmark-basedperformancegainstranslateintoeconomicvalue—aquestionweacknowledgeasanimportantcaveat.Butwebelievetheexerciseisinformativepreciselybecauseithighlightshowmuchthechoiceofdefiatormethodol-ogymattersforourunderstandingofAI’seconomictrajectory.IfAIcrossesathresholdsuchasAGI,behindwhichitbecomesbroadlyusefulacrosstheeconomy,ourestimatesmayshedimportantlightontheresultingmacroeconomicimplications.Inthatcase,thedefiatorforAIGDPmaynolongerdeclineatitscurrentpace,andtherapidincreasesinAIproductionmaytranslatemoredirectlyintotraditionalGDPgrowth.

Treatingmodeldevelopmentascapitalformation.OurframeworktreatsAItrainingasinvestmentin“modelcapital”—intangibleassetsthatimprovethequalityoffutureinferenceoutput.Thisextendsthelogicalreadyembeddedinnationalaccounts

6

sincethe2008SNArevision,whichcapitalizedR&Dasintellectualpropertyproducts.AsAImodeldevelopmentgrowsinscale,statisticalagenciesmayfinditusefultoidentifyitasadistinctassetcategorywithintheexistingIPPframework.

Ourproposalfordevelopinganascentframeworkfor“AIGDP”representsamorefundamentalreorientationofnationalaccountingstatisticsbutmaybecomeincreasinglyusefulastheroleofAIinourworldgrows:

ANascentFrameworkforAIGDP.Instandardnationalaccounting,mostofAIproductioncountsasanintermediategoodthatnetsoutinthecalculationoffinalGDP.ThisobscurestherapidchangesoccurringintheAIsector.WeproposeanewframeworkthatseparatesouthumanGDPandAIGDP,wherethelatterconsistsofalleconomicactivitythatisdrivenbyAI-basedcomputation.Inputssuchaselectricity,compute,andmaintenancethatAIsystemsrequiretooperateareclassifiedasimportstotheAIeconomy,whereasinferenceoutputsthataresoldtothehumaneconomyarecountedasexpensesandthusimports.Modeltrainingcountsasinvestmentinmodelcapital.Thisdepartureismoreconsequentialthantheothers:itrefiectsaview—developedfurtherinourAIGDPframeworkinSection

5

—thatasAIsystemsbecomemorecapable,theboundarybetween“capitalbeingusedup”and“anagentproducingoutput”maybe-comeincreasinglyblurred.WepresentthisasanalternativelenstocurrentpracticesofcompilingGDPthatwillbecomemoreandmoreusefulasAIcapabilitiesgrow.

OurframeworkofAIGDPisdesignedtoremaininformativeacrossarangeofsce-nariosforAI’sfutureeconomicrole.StandardaccountshandleAIadequatelyfortoday’spurposesbecauseAIisarelativelysmallintermediateinput.ButifAI’sroleexpandssubstantially—whethergraduallyorthroughamorerapidtransition—thentrackingtheAIeconomyonitsowntermsprovidesanearlysignalthatcomplementswhattraditionalstatisticscanofer.

Weemphasizethatourapproachisintendedtocomplement,notreplace,traditionalGDPmeasurement.Standardaccountswilllargelycontinuetoservetheircorepurposeoftrackingeconomicwelfarefromahumanperspective,althoughtheymayhavetobeadjustedonthemargins.Ourmeasuresprovideadditionalvisibilityintothisfast-movingsector.

DataLimitationsandUncertainty.OurestimatesrepresentaninitialattemptattakingamacroeconomicviewoftheAIeconomy,anddatalimitationsmeanthatstrongassumptionswererequired.Theallocationofcomputebetweentrainingandinferenceislargelyunobserved,forcingustoleanonanecdotalevidencethatthereisaroughlyequalsplit.OurvisibilityintoAIcompanies’grossmarginsislimited.Andtherelationshipbetweenbenchmarkperformance—whichanchorsourqualityadjustments—andactualeconomicvalueremainsuncertain.Thesegapspointtowardvaluablecollaborationsbe-

7

tweenstatisticalagencies,AIcompanies,andresearchers.AstheAIeconomygrows,sodoesthecaseforsystematicmeasurementinfrastructure.

3Methodology

ThecentralfocusofourmeasurementefortsisAIproduction,whichencompassesthecreationofinferenceoutputs(tokengenerationfortherestoftheeconomy)andtheformationofmodelcapital.Weprovideanoverviewofourmeasurementmethodologyanddatasourceshere.Thein-depthmethodologyisdescribedinthe

technicalappendix

.Below,wewillalsouseAIproductiontodevelopanascentconceptualframeworkformeasuringAIGDP.

3.1MeasuringAIProduction

Figure

1

showsaconceptualoutlineofAIproduction.ThecentraldriverofAIproductionisthegenerationofAIcompute,whichrequiresdatacentercapital(i.e.,GPUs)andtheelectricitytorunthem.Computeisthenallocatedbetweeninferenceandtraining.InferencecomputeisthecomputeusedtoproduceAIoutputsforuseinothertasks.InferencecomputeiscombinedwithAImodels(intangiblecapital)toproduceinferenceoutputs.TrainingandR&DcomputeincludesallcomputeusedintheAIR&Dprocessincludingpretrainingandpost-trainingofAImodels,experiments,andsyntheticdatageneration.TrainingcomputeiscombinedwithR&DalgorithmstoproduceAIR&D,whichleadstotheaccumulationofintangiblemodelcapital.Inthisway,trainingoutputimproveswiththeimprovementofR&Dalgorithms,andinferenceoutputsimprovewiththedevelopmentofnewmodelcapital.

8

Figure1:ConceptualoutlineofAIproduction.Computeisthecentralinput,allocatedbetweeninferenceandtrainingactivities,eachofwhichbenefitsfromqualityimprove-mentsovertime.

Power-basedestimatesofU.S.AIcompute.Ourprimarymethodologyforcal-culatingAIcomputeproductionfortheUnitedStatesstemsfromtheinsightthat,foragivencompositionofthestockofAIchips,computeoutputscaleswithpowerusage.Startingfromelectricityusageprojectionsfrom

Pateletal.

(

2024

),weapplychip-levelcharacteristicsfromtheEpochAIMLHardwaredataset(

EpochAI

,

2024

)—notablyThermalDesignPower(TDP)andbit-levelprocessingperformance—togetherwithGPUcapital-stocksharesfromtheEpochAIGPUClustersdataset(

EpochAI

,

2026b

),toconverttotalfacilityenergyintotheassociatedworkingtimeinGPU-hoursforeachchiptype.WethenapplyhourlyrentalratesforGPUs,collectedindependently,toobtainestimatesoftotalnominalspendingonAIcompute,onanimputedrentalpricebasis

2

.Thesamecharacteristicsletuscomputetotalphysicalcomputeproduction(inFLOPsorH100-equivalents)

3

.Derivationsandassumptionsbehindthesecalculationsareprovidedinthe

onlinetechnicalappendix

.

Asacross-check,foranalternativeestimateofcomputeproduction,weleveragere-centlyreleaseddatafromEpochAIonglobalchipsalesfromleadingproducers(

EpochAI

,

2026a

).Byassumingaconstantusageintensityandaccumulatingquarterlydeploymentsintoacumulativestock,weobtainabottom-upestimateofAIcomputespendingwith

2Assimplifyingassumptions,weholdrentalpricesconstantovertimeforagivenGPUtype,andweapplythelowest-availablecontractrateforeachgivenGPUtype,asmuchlarge-scalecomputeispurchasedthroughlong-termprivatecontracts.

3ItshouldalsobenotedthatFLOP-basedcomputemeasureslikelyunderstateimprovementsinchipe优ectivenessastheyfailtocaptureimprovedmemorycapacityandconnectionbandwidthinnewerchipgenerations.

9

globalscope,complementingtheU.S.-onlypower-basedmethod.Thetwoapproachesdrawonlargelyindependentdata,andtheiragreement—onceadjustedforthediferenceingeographiccoverage—providesaninformalvalidationofthepower-basedestimates.

Trainingvs.inferenceallocation.Dataonhowtoattributecomputebetweentrain-ingandinferenceisextremelylimited.Wethereforeimposetheassumption,basedonnarrativeaccounts,thatphysicalcomputeissplitroughlyequallybetweeninferenceserv-ingandR&Dactivities.MoredataonthissplitwouldbeveryvaluableforresearchersaimingtounderstandAIactivityatamacrolevel.

Inferenceoutputanditspricedefiator.RawinferencespendingdoesnotcapturetherapidqualitygainsinAIoutput.Tobuildaquality-adjustedinferencedefiator,weusedatagenerouslysharedby

Demireretal.

(

2025

),whichrecordstheminimumpromptprice(permilliontokens)forthecheapestavailablemodelwithineachintelligenceperformancetieronOpenRouter,observedweekly.WecomputeachainedFisherpriceindexacrossconsecutivemonthlypairs,includingonlytiersalreadypresentinthepriormonth—soanewlyintroducedfrontiertierenterstheindexthemonthafteritsintroduction,ratherthanartificiallyinfiatingit.Inthisway,wecapturepricemovementswithinperformancetiers,averagedacrossallavailableintelligencelevelsinagivenperiod.Thisyieldsatrenddeclineinper-tokenpricesofapproximately97%peryear(a35×e岱ciencygain),asillustratedinFigure

2

.Weadjustthisindexbytheapproximately2.2×annualtrendincreaseinbenchmarkresponselengths(

Embersonetal.

,

2025

),leadingtoanetinferencedefiatorthatdeclines94%peryear(roughlya16×fallintheefectivepriceofAIinferenceoutput).Asdocumentedby

Demireretal.

(

2025

),thisdeclineisbroad-basedacrossintelligencetiersandmodeltypes,consistentwithbroad-basedalgorithmicimprovementsovertimeallowingsmallermodelstomatchtheperformanceofolderlargermodels.

10

Figure2:ChainedFisherPriceIndexforAIinferencetokensatconstantcapability.Theindextrackstheminimumpricepermilliontokenswithineachintelligencetierandchainsthemintoasinglequality-adjustedseries.Therapiddeclinerefiectsfallinginferencecostsatfixedmodelcapability,notashifttowardlower-capabilitymodels.

Trainingoutputanditspricedefator.Toprice-adjusttrainingproduction,weapplythepaceofalgorithmicimprovementestimatedby

Hoetal.

(

2024

),whofindthatthecomputerequiredtotrainamodelatfixedperformancefallsbyroughlytwothirdseachyear—anannualizede岱ciencygainofapproximately65%,ora3×increaseinefectivecomputeperunitofphysicalcompute.

Onecaveatisthatweuseamixedmethodologytoaccommodatelimitationsintheavailabledata:weestimatenominalspendingontheproductionside,andcombineitwithapricedefiatorbasedonthepriceandcharacteristicsofinferenceoutputs.Thisassumesthatinferenceproviders’grossmarginsdidnotmateriallychangeovertime.

Sevillaetal.

(

2026

)findthatAIcompanieshavepositivebutmodestgrossmarginsoninference;sincethisisconsistentwithrelativelystablemargins,webelieveourestimatescapturetheappropriatefirst-orderdynamicsdeterminingtotalgrowthrates.

4Results

ThissectionpresentsourmainempiricalfindingsfortheUnitedStatesAIeconomyfrom2023to2025.

Table

1

reportsannualnominalcomputespendingandphysicalcomputeoutputfortheUSAIeconomy,basedonthepower-basedmethodologydescribedinSection

3.1

.

4

4Globalestimatesbasedonthechip-salesmethodologyshowmorerapidgrowth,withnominalspend-inggrowing291%in2024and166%in2025.Forfurtherdetails,seethe

onlineappendix

.

11

Nominalcomputespendinggrewatroughly144%peryearin2024and2025.Physicalcomputeoutput—measuredinH100-equivalentunits—grewevenfasteratroughly213%peryear,refiectingboththedeploymentofmorechipsandthetransitiontohigher-performancehardware.ContinuedimprovementsinAIchipe岱ciencyfromMoore’sLawmeantthatagivendollarcouldbuymoreFLOPs.Assuch,physicalcomputeproductionoutpacednominalspending.

4.1Quality-AdjustedAIProduction

RapidprogressonAIalgorithmsmeansthateachunitofcomputecouldbeusedtoproducedrasticallymoreAIoutput.Table

2

reportsquality-adjustedgrowthratesforinference,training,andaggregateAIoutput.ApplyingtheinferenceandtrainingdefiatorsdescribedinSection

3.1,

therealvolumeofAIinferenceoutputgrewroughly39×peryear.Quality-adjustedAIproductiongrowthcombinesinferenceandtraininggrowthweightedbytheirnominalshares,withgrowthinourfinalquality-adjustedAIProductionIndexsurpassing2000%peryear.

5FromAIProductiontoAIGDP

Inthissection,wegobeyondthetraditionalnationalaccountingconceptsanduseourmeasureofAIproductiontoconstructanovelframeworkforAIGDP,whichwedefine

12

asalleconomicvaluecreationassociatedwithAIcomputationratherthanhumanbrain-power.Toillustrate,imaginethatallAIactivities(datacentersandR&Dlabs)werelocatedonaseparateisland,withallhumanlaborandotherinputslikeelectricitybeingimportedfromoftheisland.IfthisAIislandwasitsowncountry,thenitisstraight-forwardtocalculatetheisland’sGDPbyaddinguptotalproductionoffinalproductontheisland(i.e.,excludingintermediateinputs)andsubtractinganyimportedinputsfromoftheisland,i.e.,fromthehumaneconomy.Thisquantity,whichwecall“AIGDP,”conceptuallycorrespondstothecontributionofAItotheGDPoftheoveralleconomy.

Figure3:EconomicfiowsintheAIeconomy.Importedinputs(left,blue)combinetoproducefinalproducts(right,red).AIGDPequalsfinalproductsminusimports.

Figure

3

outlinesthekeyeconomicfiowswithintheAIeconomy.AIProduction—theprocessofcombiningimportedelectricitywithAIchipstoproduceAIcomputewhichisthenappliedtoeitherinferenceorAIR&D—formsthecoreoftheeconomy.Alongthetopofthefigure,AIproductionisaugmentedbyincludingconsiderationofthemarginaccruingtocompaniessellinginference-basedAIservices.TheAIservicesmarginrepresentsthereturntointangiblecapitalembodiedinAImodelintellectualproperty(IP)ownership.Wealsonote“imported”laborinputswhichnetoutfromAIGDP.

Inourframework,mostinference-basedAIservicesare卩exported卩fromtheAIecon-

13

omy,withtheexceptionoftheuseofAItoolsintheAIR&Dprocessitself,whichremainsontheisland.AIR&Dandtrainingrepresentinvestmentinintellectualproperty(IPP)andareconsideredtheformationof卩modelcapital.卩ThisisconceptuallysimilartothetreatmentofR&DincurrentNationalAccounts

5

.ThesecontributionsdirectlyincreaseAIGDP,thoughcurrentmethodsarenotdirectlydesignedtocaptureAIefects,andtheintermediatenatureofAImakesitlessvisibleinexpenditureaccounts.

PhysicalcapitalformationconsistsoftheinstallationofAIchipsanddatacenters.AsseeninthebottomsectionofFigure

3

,thesearecurrentlycreatedinthehumaneconomyandare卩impo