人工智能带来的总体收益及其分配：基于使用数据的全球证据（英）

AggregateGainsfromAIandTheirDistribution:

GlobalEvidencefromUsageData

RachelYutingFanandHaMinhNguyenWP/26/147

IMFWorkingPapersdescriberesearchin

progressbytheauthor(s)andarepublishedtoelicitcommentsandtoencouragedebate.

TheviewsexpressedinIMFWorkingPapersare

thoseoftheauthor(s)anddonotnecessarily

representtheviewsoftheIMF,itsExecutiveBoard,orIMFmanagement.

2026

JUL

AT

N

★★

A

R

*WethankKarimBarhoumi,SandraBaquie,ChenChen,EraDabla-Norris,NisreenFarhan,MercedesGarcia-Escribano,AlejandroGuerson,MumtazHussain,PatrickImam,MohammadM.Khabbazan,AndrasKomaromi,RoseKouwenhoven,RaphaelLam,

FrancescoLuna,AnhDinhMinhNguyen,MehdiRaissi,EmmaRockall,TolgaTiryaki,ShuYu,FelipeZanna,YunhuiZhao,andparticipantsattheIMFseminarforhelpfulcomments.TheauthorsusedAIforeditingandcodingassistance.Allerrorsareour

own.AllviewsexpressedinthispaperarethoseoftheauthorsanddonotnecessarilyreflectthepositionoftheWorldBank,theIMF,theirExecutiveBoards,ortheirmanagement.

©2026InternationalMonetaryFundWP/26/147

IMFWorkingPaper

InstituteforCapacityDevelopment

AggregateGainsfromAIandTheirDistribution:

GlobalEvidencefromUsageData

PreparedbyRachelYutingFanandHaMinhNguyen*

AuthorizedfordistributionbyMercedesGarcia-Escribano

July2026

IMFWorkingPapersdescriberesearchinprogressbytheauthor(s)andarepublishedtoelicit

commentsandtoencouragedebate.TheviewsexpressedinIMFWorkingPapersarethoseofthe

author(s)anddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,orIMFmanagement.

ABSTRACT:HowlargeisthelaborcostsavedbyAI,andhowisitdistributedacrossoccupations?Using

fivewavesoftheAnthropicEconomicIndex(January2025toFebruary2026),weconstructtwonovel

measuresfromobservedAIusageacrosscountriesovertheworld.TheAIconcentrationindex(ACI)shows

thatindevelopingeconomies,virtuallyallAIusage-basedvalueisgeneratedinasmallprofessionalenclave

(ACInear1.0);high-incomeeconomiesaverage0.4to0.5,withconcentrationdeclininginmanycountries.Thelaborcostequivalent(LCE)valuesthetimecurrentlysavedbyAIat$2.7trillionannually(3.4%ofGDP),an

indicativemeasureofthelaborcostofthattime.Incomeandregulatoryreadinesspredictconcentration;lackinganofficialEnglishlanguageslowsbroadening,abarrierfordevelopingcountries.

RECOMMENDEDCITATION:RachelYutingFanandHaMinhNguyen(2026)“AggregateGainsfromAIand

TheirDistribution:GlobalEvidencefromUsageData”,IMFWorkingPaper26/147

JELClassificationNumbers:

D63,O33,O14,O12,J31

Keywords:[TypeHere]

AIadoption,AIconcentrationindex,occupationalcomposition,technologydiffusion,developingcountries

Author’sE-MailAddress:

hnguyen7@;yutingf@

1

AggregateGainsfromAIandTheirDistribution:

GlobalEvidencefromUsageData

RachelYutingFan*HaNguyen*

July2026

Abstract

HowlargeisthelaborcostsavedbyAI,andhowisitdistributedacrossoccupations?UsingfivewavesoftheAnthropicEconomicIndex(January2025toFebruary2026),weconstructtwonovelmeasuresfromobservedAIusageacrosscountriesovertheworld.TheAIconcentrationindex(ACI)showsthatindevelopingeconomies,virtuallyallAIusage-basedvalueisgeneratedinasmallprofessionalenclave(ACInear1.0);high-incomeeconomiesaverage0.4to0.5,withconcentrationdeclininginmanycountries.Thelaborcostequivalent(LCE)valuesthetimecurrentlysavedbyAIat$2.7trillionannually(3.4%ofGDP),anindicativemeasureofthelaborcostofthattime.Incomeandregulatoryreadinesspredictconcentration;lackinganofficialEnglishlanguageslowsbroadening,abarrierfordevelopingcountries.

Keywords:AIadoption,distributionaltilt,AIconcentrationindex,occupationalcomposition,technologydiffusion,developingcountries

JELcodes:D63,O33,O14,O12,J31

*RachelYutingFanwasanEconomistattheWorldBankatthetimeofwriting.HaNguyenisaSeniorEconomistattheIMF.WethankKarimBarhoumi,SandraBaquie,ChenChen,EraDabla-Norris,NisreenFarhan,MercedesGarcia-Escribano,AlejandroGuerson,MumtazHussain,PatrickImam,MohammadM.Khabbazan,AndrasKo-maromi,RoseKouwenhoven,RaphaelLam,FrancescoLuna,AnhDinhMinhNguyen,MehdiRaissi,EmmaRockall,TolgaTiryaki,ShuYu,FelipeZanna,YunhuiZhao,andparticipantsattheIMFseminarforhelpfulcomments.TheauthorsusedAIforeditingandcodingassistance.Allerrorsareourown.AllviewsexpressedinthispaperarethoseoftheauthorsanddonotnecessarilyreflectthepositionoftheWorldBank,theIMF,theirExecutiveBoards,ortheirmanagement.

2

1Introduction

WhobenefitsfromusingAI,andhowlargearethegains?Theexistingliteraturehasmadesub-stantialprogressmappingAI’spotentialreach:exposureindicesscoreoccupationaltasksagainstAIcapabilitiesandoffersystematiccross-occupationcomparisons(

Feltenetal.

,

2021

;

Eloundouet

al.

,

2024

).TheseexposureindicesaskwhichoccupationsAIcouldaffect;usagedata,bycontrast,showwhereadoptioniscurrentlyoccurring.Theycannotquantifyaggregategainsfromrealizeduse,andtheywerenotdesignedtotrackhowthedistributionalconsequencesofAIevolveasadop-tionspreadsacrosscountriesandincomelevels.ThesequestionsmatterespeciallyfordevelopingeconomiesweighingAIasadevelopmenttool.

Thispaperoffersadifferentapproach.RatherthanaskingwhichjobsAIcouldaffect,weobservewhichjobsareusingAI,inwhichcountries,andhowthepatternischanging.WeconstructtwonovelmeasuresfromobservedAIusagedataacrossoveronehundredcountries.OurfindingsrevealthatAI’sgainsflowdisproportionatelytohigh-wageoccupations.Whethertheseusage-basedgainsultimatelyaccruetoworkersorfirmsmaydependontheextenttowhichAIcomplementsordisplacesworkersintheseoccupations,anopenquestionbeyondthescopeofthispaper(

Cazzaniga

etal.

,

2024

).Theconcentrationismoreextremeindevelopingcountries:inUgandaandCambodia,virtuallyallAIgainsaccruetoasmallprofessionalenclave,whileinAustraliaandtheUnitedKingdom,gainsarespreadmorebroadly.Notably,thisconcentrationisdecliningasAIspreadsbeyonditsinitialsoftware-engineeringcoreintoeducation,sales,andofficework.

WeusefivewavesoftheAnthropicEconomicIndex(AEI),arecordofonemillionClaudeAIconversationsperwavespanningJanuary2025toFebruary2026,geo-locatedtoover100countries.EachconversationismatchedtoanO*NEToccupationaltaskandmappedtooneof22SOC-2occupationgroups;forcross-countryanalysesrequiringconsistentemploymentandwagedata,wecrosswalkthesetonineISCO-08one-digitgroups,thefinestlevelatwhichILOdataareavailableacrosscountries.ThisgivesusapanelofobservedAIusagebyoccupationacrossfivewavesatthegloballevel,andatthecountrylevelacrossthreewaves(R3toR5),somethingunavailableineithersurveystudiesorexposure-indexapproaches.

Thispapermakesthreecontributions.First,wemeasureaggregateAIgains.Weconstructalaborcostequivalence(LCE)thatvaluesAI’stimesavingsateachcountry’sownwages.UnderassumptionsaboutClaude’smarketpenetrationdescribedinSection

2.3

,weestimateaggregateLCEatapproximately$2.7trillionannually,

1

about3.4%ofthe86samplecountries’combinedGDP.

2

TheLCEisbestreadasanindicativemeasureoftheproductivitygainsimpliedbycurrentAIusage,thelaborcostofthetimeAIsaves,ratherthanadirectestimateofGDPimpact,whichwouldrequireadditionalassumptionsabouthowtimesavingstranslateintooutputandhowmarketsadjust.Theseaggregategainsreflecttwooffsettingforces.AsAIspreadsfrom

1ThisfigureisasnapshotofcurrentAIusage,notasteady-stateorpotentialestimate;itrangesfrom$1.6to$6.1trillionunderalternativescalingassumptions(AppendixTable

A2

)andexcludesenterpriseAPIusageandnon-Claudeplatforms.

2Thecombined2025nominalGDPofthese86countriesis$79.2trillion.BoththeLCEandtheGDPdenominatorareinnominalUSdollars.

3

softwaredevelopmentintoeducation,officework,andsales,thetypicalAIconversationincreasinglyserveslower-paidoccupations:theusage-weightedaveragewagefell5.5%over13months.ButaggregateLCEisrising,becausethoselower-wageoccupationsemployvastlymoreworkersthanthesoftwarecorewhereAIbegan.ThesheervolumeofAI-assistedworkmorethanoffsetsthelowerwageperconversation.Theaggregatefigureatthegloballevelmasksadistributionalasymmetryacrosscountries:low-incomecountriescontributeadisproportionatelysmallshareofglobalLCEnotonlybecausewagesarelowerbutalsobecausetheirAIconversationsareconcentratedinanarrowoccupationalslice,limitingtheemployment-scaleeffectthatdrivesaggregateAIgainselsewhere.Thispaperdoesnotaddresslabor-marketoutcomes,includingjobdisplacementandwagecompression;see

Cazzanigaetal.

(

2024

)and

Jaumotteetal.

(

2026

),whofindthatAIexposureandcomplementarityconcentratedisplacementriskandreduceemploymentinsomeoccupationsevenaswagesrise.

Second,weconstructanAIconcentrationindex(ACI),adaptedfromtheconcentrationindexinhealtheconomics(

Wagstaffetal.

,

1991

;

Kakwanietal.

,

1997

),thatmeasureswhetherAIusageisconcentratedinhigher-paidorlower-paidoccupationsrelativetotheiremploymentshare.TheACIispositivewhenAIgainstilttowardthetop,negativetowardthebottom,andzerowhengainsareneutral.Innearlyeverycountryineverywave,theACIispositive:AI’sgainstilttowardhigher-paidoccupations.Incomeshapeshowconcentratedthosegainsare.Inthehigh-incomeUnitedStates(ACI0.49),theyarespreadrelativelybroadlyacrossoccupations;inlow-incomeTanzania(ACI0.98),almostallofthemflowtothetopofthewagedistribution.ProfessionaloccupationsinTanzaniaemployfewerthan5%ofworkers,soanACIthishighmeansAI’sproductivitygainsarecurrentlygeneratedprimarilyinprofessionaloccupations,withlimiteddirectengagementfromtheagricultural,elementary,andserviceworkerswhomakeupmostofthelaborforce.Butthetiltisdeclininginagrowingnumberofcountries.BetweenAugustandNovember2025,32of108countries,or30%,sawtheirACIfall;betweenNovember2025andFebruary2026,thenumberroseto53of110,nearlyhalf.

Third,weexaminewhatpredictscross-countrydifferencesinaggregateAIgainsandtheirconcentration.AIregulatoryreadinessisthedominantpredictorofhowmuchAIvalueacountrycapturesrelativetoitsGDP;economicstructurealsomatters,withmoreservice-orientedeconomiescapturinglargergains.FortheACIlevel,richercountriesandcountrieswithhigherregulatoryreadinesshavemorebroadlydistributedAIgains.ButwhatpredictswhetherconcentrationisfallingisEnglishasanofficiallanguage,sothatsufficientinstitutionalknowledgeenterstrainingdataforEnglishdominatedLargeLanguageModelstoprovideaccurateandreliableassistanceacrossabroaderrangeofoccupations.

Thepaperconnectstofourstrandsofliterature.First,andmostdirectly,itcontributestomeasuringAIadoptionanditseconomicconsequences.Survey-basedstudiesaskfirmswhethertheyuseAI:

Bonneyetal.

(

2024

)trackUSfirm-leveladoptionratesthroughtheCensusBureau’sBusinessTrendsandOutlookSurvey,while

Yotzovetal.

(

2026

)poolparallelfirmsurveysacrossfourcountries(US,UK,Germany,Australia)andfindthatmostfirmsreportnomeasurableimpact

4

onemploymentorproductivitysofar.ThesesurveyscaptureadoptionatthefirmlevelbutcannotobservewhichtasksAIperformsorhowtimesavingsdistributeacrossthewageladder.Second,exposure-indexstudiesmapAIcapabilitiestooccupationaltaskdescriptionstomeasurewhichjobsAIcouldaffect:

Feltenetal.

(

2021

)constructanAIOccupationalExposureindexlinkingAIapplicationareastoO*NETabilities,while

Eloundouetal.

(

2024

)estimatetheshareoftaskswhereLLMscouldreducecompletiontimebyatleast50%.

Cazzanigaetal.

(

2024

)extendthisapproachacross142countries,findingthatadvancedeconomiesfacegreaterexposurebutarebetterpositionedtobenefitthankstostrongerdigitalinfrastructureandhumancapital.TheseindicesaskwhichoccupationsAIcouldaffect;weuseobservedconversationdatatoshowwhereadoptioniscurrentlyoccurring,forwhom,andhowthepatternshiftsovertime.Third,micro-levelexperimentsfindthatAIraisesproductivityby15to40%,withgainsconcentratedamonglower-skilledworkerswithinagivensetting:

Brynjolfssonetal.

(

2025

)showthatanAIassistantinacustomer-servicecallcenterraisedproductivityby15%onaverage,withthelargestgainsamonglessexperiencedandlower-skilledagents;

Noy&Zhang

(

2023

)findthataccesstoChatGPTreducedtimeonwritingtasksby40%andcompressedthequalitydistribution,benefitinglower-abilityworkersmost.

Thispapercomplements

Fan

(

2026

),whichusesthesamedatatodocumentcross-countryvariationinAIadoptionintensityandbreadth;weusetheoccupationalcompositionofthatusagetomeasureaggregategainsandtheirdistribution.Fourth,wecontributetothetechnology-diffusionliterature(

CominandHobijn

,

2010

)byprovidingthefirstmulti-wavecross-countryevidenceonhowAIusagecompositionevolves,notjustadoptionlevels.Previousworkmeasuredhowtheinternet(

Hjort&Poulsen

,

2019

)andmobilephones(

Aker&Mbiti

,

2010

)reshapedlabormarketsacrosscountries;thecontrastwithAIisinstructive.Thosetechnologiesdiffusedacrossawiderangeofoccupations,whereasAIusagesofarisconcentratedinanarrowersetofhigher-paidoccupations,especiallyindevelopingeconomies.

3

2DataandMethodology

2.1Data

AnthropicEconomicIndex.WeusefivereleasesoftheAnthropicEconomicIndex(AEI),

4

spanningJanuary2025toFebruary2026.EachreleasesamplesonemillionClaudeAIconversations.An-thropic’sownclassifiermapseachconversationtoanO*NEToccupationaltask,providingarecordofobservedAIusageratherthanpotentialexposure.Thefirsttwowaves(R1andR2)reportAIusagebyoccupationfortheworldasawhole,withnocountry-levelbreakdown.Fromthethirdwaveonward(R3inAugust2025,R4inNovember2025,andR5inFebruary2026),conversations

3Thesepatternsmarkastageinanongoingdiffusion,notapermanentstructuralfeature,showingwhereAIstandsasitspreadsfromitssoftwarecoreintolower-wageoccupationsandfromhigh-incometolower-incomecountries.

4WenameAnthropicandClaudefortransparencyandreproducibility,notasanendorsementoftheprovider.Becausethedatacaptureusagefromasingleprovider,theresultsmaynotfullygeneralizetothebroaderAImarket,althoughwescaleconversationvolumebyClaude’smarketsharewhenestimatingthetotal-marketLCE(AppendixSection

B

).

5

arealsogeocodedtocountries.InR5,forexample,176countriesreceiveageographiccode,ofwhich117meetAnthropic’sminimum-conversationthresholdfortaskenrichmentandentertheanalysis.

5

Figure

1

showsthefullbreakdownoftheR5sample,fromonemillionconversationsdowntothefinalanalysisset.Thedatacoverconsumer-streamClaude.aiwebconversationsonly

6

andexcludeenterpriseAPIusage.

7

Occupationmapping.Anthropic’sclassificationsystemmapseachconversationtoaspecificO*NEToccupationaltaskthatcanbemappedtooccupationgroups.

8

WefurthercrosswalktheAEI’s22SOC-2occupationgroupstonineISCO-08one-digitgroups,thefinestlevelatwhichtheILOprovidesconsistentemploymentandwagedataacrosscountries.

9

Timeestimates.InR4andR5,theAEIreportstask-leveltimeestimates:timetocompleteataskwithoutAIandtimewithAIassistance.Thedifferencegiveshourssavedperconversation(ht).Theseestimatesaredifferentbutstableacrosswaves,withhourssavedhighlycorrelatedbetweenR4andR5.

10

R3doesnotreporttheseestimates;weapplytheR4values,thenearestwavewithtimedata,toextendtheanalysistoR3.Section

3.4

boundstheinfluenceofthesemodel-basedestimatesontheaggregateLCE,whichremainswithin$2.5to$2.9trillion.Table

1

illustratestherange.Softwaretaskssaveroughly3.2hoursperconversation;customer-servicetaskssaveroughly0.3hours.

Table1:Illustrativehourssavedperconversation(ht),R5(February2026)

O*NETtask

Occupation

Humanonly

WithAI

Saved

Modifyexistingsoftware

Computer&Math

3.54hrs

17.8min

3.24hrs

Assiststudentswithcoursework

Education

2.82hrs

17.6min

2.52hrs

Writepressreleases

PR/Media

1.54hrs

14.5min

1.30hrs

Adviseondruginteractions

Healthcare

1.19hrs

10.4min

0.97hrs

Answercustomerquestions

Customerservice

0.35hrs

3.4min

0.29hrs

5The59countriesthatreceiveageographiccodebutnotaskbreakdownareexcludedfromtheanalysis.

6TheLCEisscaledbyClaude’smarketshare,buttheACIisnotadjustedfortheoccupationalcompositionofClaude’suserbase,whichhashistoricallyskewedtowardsoftwareengineersrelativetocompetitors;thismayaffectboththeACIlevelandthedeclining-ACItrend,whichcouldpartlyreflectAnthropic’sgrowinganddiversifyingmarketshareratherthaneconomy-wideAIdiffusion.

7IncludingenterpriseAPIusagewould,ifanything,strengthentheconcentrationfindings:enterprisetrafficisheavilyskewedtowardhigh-wageoccupations(Computer&Mathaccountsforapproximately51%ofAPIconversa-tionsinR5),sotheACIcomputedfromconsumerdataaloneislikelyalowerboundonthetrueconcentrationofAIgains.

8O*NETtasksareoccupation-specificbyconstruction,soeachconversationismappedtoasingleoccupationcategory,andsummingoveroccupationsandtaskswithinoccupationscountseachconversationonce.

9AppendixTable

A1

summarizeswhichoccupationgroupingandwagesourceeachanalysisuses.NotethattheUSSOCsystemplacestechnician-typeroles(ITsupport,engineeringdrafters,labtechnicians)insidebroaderprofessionalSOCgroups,allofwhichmaptoISCO-2(Professionals).Asaresult,ISCO-3(Technicians,~17%ofemployment)contributesnoAIconversationshareinourdata,biasingtheACIslightlyupward,aconservativebiasthatmakesAIgainslookmoreunequalthantheytrulyare.

10ThePearsoncorrelationoftask-levelhourssaved(ht)acrosstheapproximately3,200taskscommontobothR4(November2025)andR5(February2026)isρ=0.80.

6

Figure1:Samplestructuretree(R5,February2026).PanelAshowstheglobalclassificationpipeline:972,636of1,000,000conversationsareclassifiedbytheONETclassifier,ofwhich929,714maptoanoccupationaltask.PanelBshowsthecountry-levelbreakdown:176countriesreceiveageographiccode(805,387conversations),ofwhich117receiveONETtaskenrichment(801,151conversations).

Countryandwagecoverage.The117samplecountriesspanthefullincomespectrum,fromtheUnitedStatesandGermanytoIndia,Vietnam,andUganda,covering79%ofworldGDP.OnenotableexclusionisChina,whereAnthropicdoesnotoperate.TheACIranksoccupationsbyeachcountry’sownwagesratherthanbyUSwages,soeachcountry’sconcentrationofAIgainsismeasuredagainstitsownwageladder.Country-levelemploymentsharesandoccupationwagesarefromILOSTAT(2024orlatestavailableyear).Ofthe117countries,86havedirectILOemployment

7

andwagedataattheISCO-1level,covering67%ofworldGDP;theseformourbaselineregressionsample.

11

WeprovidetwoversionsoftheaggregateLCE.Thebaselineusesonlythese86samplecountries.Theexpandedversioncovers115ofthe117task-enrichedcountries,

12

bysubstitutingemploymentorwagedatafromamatchedpeercountrywhereveracountry’sdirectILOdataareincomplete.

13

Section

3.5

re-estimatestheregressionsonthisexpandedsampleasanextensionandreachesthesameconclusions.

Countrycharacteristics.GDPfiguresarefromtheIMFWorldEconomicOutlook(April2026).Thedenominatorofthelabor-cost-equivalent-to-GDPratioisnominalGDPincurrentUSdollarsfor2025,andtheincomecontrolintheregressionsisGDPpercapitaatpurchasingpowerparity(PPP)for2025;botharein2025values.Theremainingcovariatescomefromseveralsources.FromtheWorldBankWDI:theunemploymentrate,servicesvalueadded(%GDP),andtheshareofindividualsusingtheinternet(%).Incomeinequalityisthedisposable-incomeGinicoefficient(mostrecentyearavailablepercountry)fromtheStandardizedWorldIncomeInequalityDatabase(

Solt

,

2020

).FromtheIMF:AIregulatoryreadiness,theregulatorysub-indexoftheAIPreparednessIndex(

Cazzanigaetal.

,

2024

).TheAIPreparednessIndexspansotherdimensionsofreadinessbeyondregulationthatmayalsoactasbarrierstoAIdiffusionindevelopingcountries;onlytheregulatorysub-indexenterstheregressions,astheotherdimensionsaretoocollinearwithincometoenterseparately.WealsoconstructabinaryindicatorforwhetherEnglishisanofficialordefactoofficiallanguage.Weuseofficial-languagestatusratherthanEnglishproficiencybecausetherelevantchannelisnotconversationalfluency(modernLLMsconverseindozensoflanguages)butwhetheracountry’sinstitutionalknowledgebaseiswellrepresentedinthetrainingdataofEnglish-dominantlargelanguagemodels.

2.2SamplestructureandAEIwaves

EachAEIwavesamplesapproximately1millionClaudeAIconversations,butcountrycountsandclassificationratesdifferacrosswaves.Table

2

liststhefivereleases,andTable

3

reportsthefullsamplestructurebywave.

11AppendixTable

A7

liststhese86samplecountries.

12TwoareexcludedforlackofGDPdata.

13AppendixTable

A8

listseachpeer-countryassignment,showingwhichcountry’sILOemploymentandwagedatasubstituteforthoselackingdirectdata.

8

Table2:AEIreleasesusedinthispaper.

Release

Observationwindow

Geographiccoverage

Globaltotal

Nountry

R1

~Jan2025

Globalonly

~1milliona

n/a

R2

~Mar2025

Globalonly

~1milliona

n/a

R3

Aug4to11,2025

Global+114countries

964,494

602,175

R4

Nov13to20,2025

Global+117countries

999,875

609,954

R5

Feb5to12,2026

Global+117countriesb

1,000,000

565,455

Notes:“Globaltotal”isthesumofallonettaskcountatthegloballevel.Nountryistheclassifiedcountry-levelconversations,includingthoseclassifiedas“none,”summedacrossallcountrieswithONETtaskbreakdown.aR1andR2reporttaskpercentagesonly;Anthropicdescribeseachas“approximatelyonemillionconversations.”b176countriesreceiveageographiccodeinR5;117

receiveONETtaskbreakdown.Theremaining59countrieslacktask-levelbreakdown.SeeFigure

1

andTable

3

forthefullR5samplebreakdown.

Table3:Samplestructurebywave:globalandcountry-levelconversationcounts,classifiedtotals,andanalysissamplesizes.

R1R2

R3

R4

R5

PanelA:Global

Globaltotal~1M~1M

964,494

999,875

1,000,000

N*~1Ma~1Ma

941,239

972,146

972,636

ofwhichonettask=“none”0.5%a1.8%a

55,132

37,119

42,922

onettask=“notclassified”0a0a

23,255

27,729

27,364

PanelB:Countrylevel

ISOcountrieswithONETdatan/an/a

114

117

117

Nountry(classified)n/an/a

602,175

609,954

565,455

onettask=“notclassified”(country)n/an/a

206,927

229,240

235,696

PanelC:ACIanalysissample(86samplecountries)

CountriesinACIanalysisn/an/a

83

85

86

∆ACI:R3→R4

82

∆ACI:R4→R5

84

Countriesinall3waves

81

Notes:N*andNountryareclassifiedconversationcounts:conversationsmappedtoanONETtask

plusthoseclassifiedasonettask=“none”(notoccupational).aR1andR2reporttaskpercentages

only.PanelCisrestrictedtothe86samplecountriesusedinthebaselineregressions(Table

5

);

countriesrelyingonpeer-countryimputationareexcluded.Therobustnesssampleincludingpeer-

matchedcountriescoversupto114countries(Table

6

).

2.3Methodology

Laborcostequivalent.LethtdenotethehourssavedbyusingAIperconversationontasktandCtotal,wtheestimatedtotalAIconversationsperweekacrossallplatformsinwavew.Foreachtask,weestimatehowmanyhoursAIsavesperconversation,scaleuptothetotalnumberofAI

9

conversationseconomy-wide,andvalueeachsavedhourattheoccupation’shourlywage.Thelaborcostequivalentofeachcountry’stimesavingsatitsownILOwagesis:

14

wherenc,tisthenumberofclassifiedconversationsontasktincountryc,WsonthlyistheILOmeanmonthlywageinnominalUSDforoccupationsincountryc,andNuntry=ΣcΣtnc,tisthetotalnumberofclassifiedcountry-levelconversations,equalto565,455inR5.Box

2

worksthroughthiscalculationforasingletask(“modifyexistingsoftware”),showinghowconversationcounts,hourssaved,andwagescombinetoproducetheaggregateestimate.TheLCEisbestreadasanindicativemeasureoftheproductivitygainsimpliedbycurrentAIusageratherthanastrictbound:valuingeveryclassifiedconversationatitsfullestimatedtimesavingtendstooverstaterealizedgains,whileexcludingenterpriseAPIusageentirely(Section

4

)tendstounderstatethetotal,sothenetdirectionofbias