生成式人工智能的崛起：對美國勞動力市場的暴露、替代和不平等效應進行建模

BISWorkingPapersNo1207

TheriseofgenerativeAI:modellingexposure,

substitution,andinequalityeffectsontheUSlabour

market

byRaphaelAuer,DavidK?pfer,Josef?véda

MonetaryandEconomicDepartment

September2024

JELclassification:E24,E51,G21,G28,J23,J24,M48,O30,O33

Keywords:Labourmarket,Artificialintelligence,Employment,Inequality,Automation,ChatGPT,GPT,LLM,Wage,Technology

BISWorkingPapersarewrittenbymembersoftheMonetaryandEconomicDepartmentoftheBankforInternationalSettlements,andfromtimetotimebyothereconomists,andarepublishedbytheBank.Thepapersareonsubjectsoftopicalinterestandaretechnicalincharacter.TheviewsexpressedinthemarethoseoftheirauthorsandnotnecessarilytheviewsoftheBIS.

ThispublicationisavailableontheBISwebsite

()

.

?BankforInternationalSettlements2024.Allrightsreserved.Briefexcerptsmaybereproducedortranslatedprovidedthesourceisstated.

ISSN1020-0959(print)ISSN1682-7678(online)

1

TheriseofgenerativeAI:modellingexposure,substitution,and

inequalityeffectsontheUSlabourmarket*

RaphaelAuertDavidK¨opfer?Josefv′eda§

August21,2024

Abstract

Howexposedisthelabourmarkettoever-advancingAIcapabilities,towhatextentdoesthissubstitutehumanlabour,andhowwillitaffectinequality?Weaddressthesequestionsinasimulationof711USoccupationsclassifiedbytheimportanceandlevelofcognitiveskills.WebaseoursimulationsonthenotionthatAIcanonlyperformskillsthatarewithinitscapabilitiesandinvolvecomputerinteraction.AtlowAIcapabilities,7%ofskillsareexposedtoAIuniformlyacrossthewagespectrum.AtmoderateandhighAIcapabilities,17%and36%ofskillsareexposedonaverage,andupto45%inthehighestwagequartile.Examiningcomplementaryversussubstitution,wemodeltheimpactonsideversuscoreoccupationalskills.Forexample,AIcapableofbookkeepinghelpsdoctorswithadministrativework,freeinguptimeformedicalexaminations,butrisksthejobsofbookkeepers.WefindthatlowAIcapabilitiescomplementallworkers,assideskillsaresimplerthancoreskills.However,asAIcapabilitiesadvance,coreskillsinlower-wagejobsbecomeexposed,threateningsubstitutionandincreasedinequality.IncontrasttotheintuitivenotionthattheriseofAImayharmwhite-collarworkers,wefindthatthoseremainsafelongerastheircoreskillsarehardtoautomate.

JELcodes:E24,E51,G21,G28,J23,J24,M48,O30,O33

Keywords:Labourmarket,Artificialintelligence,Employment,Inequality,Automation,ChatGPT,GPT,LLM,Wage,Technology

*WethankRyanBanerjee,SebastianDoerr,FiorellaDeFiore,FernandoPerez-Cruz,AndrasValko,andseminarparticipantsattheBISforcommentsandsuggestions.WeacknowledgetheuseofGPT4foreditingand

asitanel.lti,erlcieoftheBIS.

?BankforInternationalSettlements,david.koepfer@§BankforInternationalSettlements,josef.sveda@

2

1Introduction

HowwilltheadvancementofgenerativeAIcomplementandsubstitutedifferentkindsofhumanlabour?RecentbreakthroughshaveenabledgenerativeAItomimichumancognitiveabilitiesinmanyfields,includingin“whitecollar”professionssuchaslaw,medicine,orscience.Ongoingadvancesandintegrationofthetechnologyintoday-to-dayapplicationsandworkflowsraiseurgentpolicyquestions.

UnderstandinghowthepotentialevolutionofAIwillcomplementorsubstitutehumanskillsisessentialforshapingpoliciestoensureequitablegrowthandemploymentstability.Theliter-

aturehasfocusedontheoccupation-levelimpactofcurrentAImodels,1

experimentalevidence

ofproductivityimpacts(Noy&Zhang,

2023;

Brynjolfssonetal.,

2023;

Pengetal.,

2023),and

thepotentialforcomplementarityandsubstitutioneffectsofAItechnologyataparticularstate

ofAIdevelopment(Pizzinellietal.,

2023;

Acemoglu&Restrepo,

2019,

2018c,a)

.Exceptforcertaintypesoffreelancers(seee.g.

Webb

2020),thebroaderimpactofAIcapabilitiesonthe

labourmarketyetremainstobedemonstrated.

Inthispaper,wetakeaforward-lookingapproach:weaskthequestionof“whatif”and

examinehowanAIofahypotheticallevelofcapabilitieswastoexpose2

differentoccupations.Toshedthefirstlightonthefutureimpact,webuildaparsimoniousbottom-upquantificationwithaspecialfocusonincomedistribution.

Ouranalysisproceedsintwosteps.Inthefirststep,webuildon

Eloundouetal.

(2023);

Feltenetal.

(2021);

Gmyreketal.

(2023);

Pizzinellietal.

(2023);

Acemoglu

(2024)andmodel

theexposuretothetechnologyasthecapabilitiesofAIincrease

.3

Inthesecondstep,weexaminehowthesedevelopmentscouldcomplementorsubstitutehumanlabourthroughthelensoftheirimpactoncoreandsideskills.

Inthefirststep,wearguethatthenear-termimpactofAIislimiteda)tocomputer-relatedinteractionsandb)bythedifficultyoftheskillsthatAIcansubstitutefor.Inthis,weonlyquantifytheimpactonskillsinvolvinginteractionwithacomputer.We,hence,donottakeintoaccounttheimpactofAIonroboticsthatmaysubstituteforphysicalworkorevensocialinteractions

.4

OurfirstdeparturefromtheliteratureistoemployanunderusedpartoftheO*NETdatabasethatclassifiesskillsbytheirdifficulty.Intuitively,anAIofacertaincapabilitylevelcanonlyperformtasksuptoacorrespondingskilllevel.AsthecapabilitiesofAIadvance,an

1Seei.e

.Webb

(2020);

Feltenetal.

(2021);

Tolanetal.

(2021);

Gmyreketal.

(2023);

Yang

(2022)

2Throughoutthepaper,weusetheterms“expose”and“exposure”inaneutralmanner,toimplythatsomepartsofaskill,task,oroccupationcouldbeenhanced,performed,orotherwisebeaffectedbyanAI.

3Similartotheseapproaches,wetakeapartialequilibriumperspectiveanddonottakeintoaccountthe

interplaybetweenskills,relativewages,humancapitalformationanddirectedtechnologicalchange(Acemoglu

&Restrepo,

2018c)

.

4Thisisinlinewith

Acemoglu

(2024),whoarguesthat“AIisnowhereclosetobeingabletoperformmost

manualorsocialtasks”,andwethusassumethatitcanonlyperformcomputerinteractions.

3

increasingshareofcognitiveskillswillhencebeexposedtothetechnology.5

WenestthisnotionofAIcapabilityandskilldifficultyinaquantitativesimulationof711USoccupationsfromtheO*NETdatabaseclassifiedbytheimportanceandtherequiredlevelofcognitiveskillsthatinvolvecomputerinteractions.ThemodelpredictsthatanAIcapableofsubstitutingforsimplecognitivetasks–suchastheminimalcommunicationskillsrequiredforatruckdriver–willexposearound7%ofallskills.AtlowlevelsofAIcapability,thiseffectholdsuniformlyacrosstheentirewagespectrum,butforheterogeneousreasons.Forlow-incomeworkers,asubstantialshareofcognitivecomputerskillsisexposed,buttheoverallshareoftimespentoncomputerinteractionislow.Forhigh-incomeworkers,onlyasmallshareofcognitivecomputerskillsisexposedbecauseofthelargerskillrequirement.However,theshareoftimespentusingsuchskillsishigher

.6

AsAIcapabilitiesincrease,weobserveaprofounddifferenceinoccupationalexposure:upto45%intheupperquartileofthewagedistributionareexposed,whereastheexposureofthelowestquartileisaround26%.

Whatdoesthismeanfortheincomedistribution?Wenotethatinlinewiththeliterature,“exposure”hasaneutralmeaninginthatsomepartsofaskill,task,orjobcouldbeperformedbyanAI.

Thismayleadtosubstitutionbutcouldalsocomplementviaincreasedproductivity.7

Toshedlightontheseissues,intheseconddeparturefromtheliteratureandstepofoursimulations,weexaminetheextenttowhichAImightcomplementorsubstitutehumanlabour.Wefocusonthedifferentialimpactoncoreversussideoccupationalskills,arguingthatAIwouldtendtocomplementoccupationswherevertheauxiliary(side)skillsnecessaryfortheprofessionarewithinitscapabilities.Forexample,ifAIcanorganisemeetings,billing,orbookkeepingforlawyers,medicaldoctors,orscientists,thisfreesuptimethatcanbespentoncoreactivitiesandthusincreasesproductivity.However,aprofessionmaybeatriskifthecoreactivityitselfcanbeperformedbytheAI.

ThisexercisesuggeststhatAImayinitiallycomplementallprofessions,assideskillsare

5Wetakenopositiononhowfasttheevolutionofthetechnologywillmaterialise.SomehavearguedthatAImaysoonhavedramaticimpactsonthelabourmarket(ie

Korinek&Juelfs

(2022))

.OthersarguethatfutureadvancementofAImaymaterialisemuchslowerthanexpected.Forexample,

Acemoglu

(2024)arguesthat

earlyevidenceisfromeasy-to-learntaskswithclearoutcomes(thatAIcanoptimisefor),whereasmoreprofoundproductivityimpactsinmoresubtlecontextsmaymaterialisemuchslower.

Perez-Cruz&Shin

(2024)arguethat

currentLLMsarelimitedintheirunderstandingofhumaninteractionandhigher-orderbeliefs.

6Fortheseexamples,“simplecognitivetasks”correspondtothoserequiringaskilllevelof2.0intheO*NETdatabase,forexample,theminimumsocialperceptivenessskillsrequiredforpiledriversortheminimumspeakingskillsrequiredforindustrialtruckoperators.“Mediumcognitivetasks”correspondtothoserequiringaskilllevelof3.0,forexample,problem-solvingskillsofmedicalappliancetechniciansortheoperationsmonitoringskillsofregisterednurses.“Highcognitivetasks”correspondtothoserequiringaskilllevelof4.0,forexample,thepersuasionskillsofpsychiatristsortheactivelisteningskillsofairtrafficcontrollers.

7Svanbergetal.

(2024)furthernotethat“exposure”doesnotmeanautomation:theysurveyworkerswith

“end-use”taskstogetasenseoftherequirementsforautomation,andsecond,theymodelthecostofamodelcapableofmeetingtherequirements.Focusingontheautomatabilityofvision,findthatonly23%ofoccupationsthatare“exposed”inthesenseof

Eloundouetal.

(2023);

Feltenetal.

(2021)couldtodaybeautomatedeco

-nomically.Wenotethatourmeasureofexposureismorenuancedthantheonein

Eloundouetal.

(2023);

Felten

etal.

(2021)aswerestricttheimpacttoskillsinvolvingcomputerinteractionandnotonlymodelwhetheraskill

inprinciplecouldbeautomatedbutalsowhetherthecapabilityleveloftheAIissufficientforsuchautomation.

4

generallylessdifficultthancoreskills.Forexample,anAIonlycapableofperformingsimplecognitivetaskshasnegligibleexposuretocoreskills,whereasit,onaverage,exposesaround12%ofsideskills.However,alreadyformoderateAIcapabilities,thereisdivergenceacrossthewagespectrum,withthecorecognitiveskillsofthelow-wageworkersbecomingroughlyasexposedtoAIastheirsideskills.Incontrast,theupperquartileofthewagedistributionstillseesnegligibleexposureofcoreskills(5%),whereassideskillsareexposedsubstantially(27%).

IfAIcapabilitiesarehigh,around25%ofbothsideandcoreskillsofthelowestquartileofthewagedistributionareexposed.Incontrast,only20%ofthecorebutastaggering62%ofthesideskillsofthehighestquartileoftheincomedistributionbecomeexposed.

Onbalance,ourmodellingoftheimpactonsideandcoreskillshencereversesthenotionthat

generativeAImightdecreaseinequalityinthelabourmarket(Noy&Zhang,

2023;

Brynjolfsson

etal.,

2023)

.Despitebeingatechnologythatisexposingwhite-collarjobsmoreintensively,thiseffectisfocusedonthesideskillsoftheirprofessions,whilethecoreskillsarenotinreach

.8

Incontrast,acapableAIwillalsoexposethecoreskillsoflower-incomeworkers,thusthreateningsubstitutionandwideninginequality.

Thebalanceofthispaperisasfollows:werelateourapproachtotheliteratureinSection

Section2.

Next,

Section3

presentsthemethodologydescribingtheevolutionaryimpactofever-improvingAIonoccupations.ItalsoservesasanAIexposuredependentonAI’scapabilities.Thereafter,wesplittheAIexposurebasedoncoreandsideskills

Section4

thatarethenusedtoidentifycomplementarityandsubstitutionaleffectsforindividualoccupations.

Section5

presentsadditionalrobustnessanalysis,while

Section6

concludes.

2Literaturereview

Historically,technologicaladvancementshavebeenmetwithbothoptimismandconcernre-

gardingtheirimplicationsforthelabourmarket(Bessen,

2016)

.TheadventofAIandmachinelearningtechnologies,ingeneral,hasintensifiedthesedebates,withresearchersseekingtoun-derstandhowthesenewtoolscanreshapethelabourmarketandhowtheimpactcandiffer

fromprevioustechnologicaladvancementsinrobotisationorcomputerisation(Autor,

2015)

.

SeveralrecentstudieshavedirectlyaddressedthepotentialofthelatestadvancementsinAItosignificantlyimpactthecurrentstructureofthelabourmarket.

Brynjolfssonetal.

(2018)

arguethatmostoccupationsintheUSincludeatleastsometasksthataresuitableformachinelearningapplications,and

Eloundouetal.

(2023)suggeststhat80%oftheworkforcecouldbe

affectedbyGenerativePredictiveTransformers(GPTs).Whiletheseestimatesarestaggering,

Arntzetal.

(2016)arguethattheactualvulnerabilityofjobstoautomationislowerwhen

consideringthenuancedskillswithinoccupations.Nonetheless,theproliferationofthelatestLLMsseemstobenon-negligent;

Eloundouetal.

(2023)furtherfind19%ofUSworkersinthe

8Ofcourse,oncethecapabilityoftheAIbecomesextremelyhighsuchthatallskillsarewithinreach,thiseffectabates,andallcognitiveworkersareindangerofreplacement.

5

USmayseeatleasthalfoftheirskillsimpactedand

Hatziusetal.

(2023)finds25%ofcurrent

workskillsinUSautomatable.

CurrentAIcapabilities,insomeinstances,fallshortofprofoundreasoningskills(Perez-Cruz

&Shin,

2024)

.However,animportantissueregardshowthefutureevolutionofAIcapabilitiescanenhancelabourproductivityorcrowdoutworkers.RecentexperimentswiththelatestgenerationofAIshowthatitcanhaveapositiveeffectinspecificoccupationswhilereducingdifferencesamongworkerswithvaryingexperiencelevels.

Noy&Zhang

(2023)demonstrated

thattheuseofChatGPTsignificantlyincreasesaverageproductivitymeasuredbytimespentontasksandreducesdifferencesbetweenhigh-andlow-skilledworkers.

Brynjolfssonetal.

(2023)studiedtheintroductionofgenAIassistanttothecustomersupportagentsandfounda

significantlyhighernumberofcompletedtasksthatweremorepronouncedfornoviceandlow-skilledworkers.

Pengetal.

(2023)suggestscoderswithaccesstogenAIarecapableofcompleting

coding-orientedtasksupto55%faster.AItoolscanalsoserveasthetooltodiscoverpotential

improvementsinbusinesssystems(Cockburnetal.,

2018;

Chengetal.,

2022)

.

However,anincreaseinlabourproductivitymeansthatlesshumancapitalisneededto

maintainthesameoutput,whichcouldleadtolayoffsorwagereductions(Acemoglu&Restrepo,

2020)

.Inthiscontext,

Frey&Osborne

(2017)predictedthatupto47%ofUSemploymentis

athighriskofcomputerisation.

Arntzetal.

(2016)howeverusesadifferentmethodologyand

estimatesanimpactofonly9%.Gmyreketal.

(2023)findthatgenAIcouldautomate5.1%of

totalemploymentinhigh-incomecountries,whereaslow-incomecountriesarenotsosusceptible.Thepotentialforaugmentationissimilarlydistributedacrosscountriesrelativetotheirincomelevels,althoughthepotentialtoaugmentismuchlarger(aroundfourtofivetimes).

Noy

&Zhang

(2023)claimthatChatGPTmostlysubstitutesforworkereffortratherthanpurely

complementingworkerskills.

Yang

(2022)alsoshowsthatAIcanpositivelyaffectproductivity

andemploymentbutadverselyaffectstheemploymentoflessknowledgeableworkers.Some

studiesadditionallydebatetheeffectsrelativetogender(Eloundouetal.,

2023;

Webb,

2020;

Gmyreketal.,

2023;

Aldasoroetal.,

2024)

.

Historicalexperiencewithinnovationshowsthatinthelong-term,thedisplacementcanbe

offsetbyanincreaseintherangeofgoodsandservicesoffered,see(Autor,

2015;

Acemoglu

&Restrepo,

2019)

.Forexample,

Bessen

(2016)showsUSlabourdemandhasincreasedfaster

incomputerisedoccupationssince1980,althoughthecomputerisationledtosubstitutionforotheroccupations,shiftingemploymentandrequiringnewskills.

Acemogluetal.

(2022)find

increasingdemandinAI-exposedoccupationsintheUSsince2015.AutomatisationinJapan

andtheUSgeneratedcostsavings,allowinglargeroutputineconomy(Adachietal.,

2024;

Dekle,

2020;

Acemoglu&Restrepo,

2020)thatoutweighedthedisplacementeffectsofhuman

labour.

Yang

(2022)findsthatAItechnologyispositivelyassociatedwithproductivityand

employmentinTaiwan’selectronicsindustryforthe2002–2018period.

Acemoglu&Restrepo

(2019),

Acemoglu&Restrepo

(2018a)and

Acemoglu&Restrepo

(2018c)thenfocusdirectlyon

thedynamicsofdisplacementandreinstatementoflabourduetoautomation.Basedondata

6

fromtheUSsinceWorldWarII,

Acemoglu&Restrepo

(2019)claimthatdisplacementeffects

occurintuitively,buttheyarecounterbalancedbythecreationofnewtasksinwhichlabourhasacomparativeadvantage.Thesethenchangethetaskcontentofproductioninfavouroflabourbecauseofareinstatementeffectfollowedbyariseinthelabourshareandlabourdemand.

Acemoglu&Restrepo

(2019)pointoutthatthesuccessofreinstatementisnotautomatic

.Itratherdependsonadditionalvariablessuchasthesupplyofnewskills,demographics,orlabourmarketinstitutions

.9

Althoughpreviousinnovationsinautomatisationandcomputerisation,onaverage,broughteconomicgrowth,theystillreshapedthelabourmarketandintroducednewchallengesinre-gionallabourmarketstructuresthataffectedlabourdistributionacrosstheskilldistributionofmarkets.

Autor

(2019)documentstheseeffectsusingUSdatashowingthatautomation(to

-getherwithinternationaltrade)ledtotheeliminationofthebulkofnon-collegeoccupations,furtherleadingtodisproportionatepolarisationofurbanlabourmarkets.

Acemoglu&Restrepo

(2022)documentthatbetween50%and70%ofchangesintheUSwagestructureoverthelast

fourdecadesareaccountedforbyworkersspecialisedinroutinetasksinindustriesexperiencingrapidautomation.

Acemoglu&Restrepo

(2020)showindustrialrobotadoptionintheUnited

Stateswasnegativelycorrelatedwithemploymentandwages.Theseexamplespinpointtheimportanceofunderstandingthepotentialeffectsoftechnologicaladvancementstonavigateasmoothtransitiontowardsanewstructureofthelabourmarket.

ThequestionremainshowmuchthenewwaveofautomationwithAIiscomparabletoprevi-oustechnologicaladvancements.Previously,automationexposedpredominantlymanuallabourthroughtheinventionofmachinesandrobots.Thetransitionprocesstorobot-drivenproduc-

tion,therefore,affectedatitsfirststageratherlower-skilledlabour(Acemoglu&Restrepo,

2018b)

.EvolvingAIchallenges,however,cognitivetasksandskillsandcreatesapotentialtoaffectdifferentoccupationsbyeithercomplementingorsubstitutingthem.Earlierworkby

Autor&Dorn

(2013)suggeststhatlow-wageoccupationsfacedhighersubstitutiondueto

computerisation.Incontrast,high-wageoccupationswerecomplementedbytechnology.

Webb

(2020)thenfocusesonthenewerinnovationinAIandstatesitisdirectedathigh-skilledtasks,

effectivelyaffectingthehigher-wagequantiles.Asimilarconclusionisreachedby

Eloundouetal.

(2023)and

Pizzinellietal.

(2023)

.

Webb

(2020)arguesthattheimpactofAIisdifferentfrom

theeffectsofsoftwareinnovation,whichexposedmid-wageoccupations(inlinewith

Michaels

etal.

(2014))

.

Pizzinellietal.

(2023)emphasisehighcomplementarityintheuppertailofthe

earningsdistributionbyAI,leadingtoaproductivityboostinsteadofjobdisplacements.TheeffectsofAIalsodiffergeographically.

Pizzinellietal.

(2023);

Gmyreketal.

(2023);

Albanesi

etal.

(2023)showthatmoredevelopedcountriesaremoreexposedtoAIastheirlabourmarkets

aremoreorientedtocognitivetasks.However,asAIsignificantlyprogresses,researchalsoneeds

toaccountfortheevolutionoftechnologytofullyunderstanditspotentialeffects.Examining

9Inasimilarvein,

Aldasoroetal.

(2024)showinageneralequilibriummodelthattheoutputeffectsofAI

mayprimarilyariseviatheindirectimpactondemandandassociatedchangesinrelativepricesratherthanviathedirectinitialproductivityboostfromAIadoption.

7

theimpactofdevelopingAIthroughthelensofwagedistributionseemstobeadvantageousto

formulatetargetedpolicyresponses(Furman&Seamans,

2019)

.AstheadvancementsinAItechnologyprogress,theirinteractionmightchangerapidly.

3MeasuringAIexposure:dataandmethodology

PredictingtheimpactofAIonthelabourmarketischallenging,astheintegrationofthetechnologyintoreal-lifeapplicationsisstillinitsinfancy,andonlysomesyntheticbenchmarksonthepotentialqualityandefficiencyimprovementsoncertainaspectsofworkareavailable(seei.e.

Tolanetal.

(2021);

Pengetal.

(2023);

Noy&Zhang

(2023))

.Particularly,therapidlyevolvingcapabilitiesofAIareamajorsourceofuncertainty.Inthefaceoftheseuncertainties,weconstructaparsimoniousbottom-upmodelcentredonan“AIcapability”parameter,whichallowsustosimulatetheeffectsofevolvingAI.Themodelisbuiltontheskillandoccupationlevelandlateraggregatedtotheindustryorwage-quantilelevel.

Inthissection,weshowhowweconstructtheAIShareAutomatability(AISA)IndexthatdependsonthesophisticationoftheAI(definedas“AIcapability”above).Thisindexrestsontwomainassumptions:

1.Intheshorttomediumterm,automationwillaffectoccupationalactivitieswithcomputerinteractionasopposedtosocialinteractionsorphysicallabour.

2.Theskillsrequiredforperformingtheoccupationsareheterogeneousintheirdifficultylevel.Foraskilltobeimpactedinacertainoccupation,itsdifficultylevelneedstobewithinthecapabilitiesoftheAI.

WeutilisedatafromO*NETversion27.2andthe2022OccupationalEmploymentandWageStatistics(OEWS)SurveyfromtheUSBureauofLaborStatistics.Thesedatasetsdetailaround800differentoccupations(ofwhichwecanuse711afterjoiningacrosstheskillstablesandemploymentstatistics)across22industries,providingaverageincome,employmentnumbers,andratingsforupto35cognitiveskillsforeachoccupationintermsofrequiredskilllevel(1-6)andimportance(1-5).

Furthermore,thedataincludesdetailedtaskdescriptions10

foreachoccupation(onaverage,wehave24taskdescriptionsforeachofthe711occupations).

Inthedescriptionofourmodel,wewillusesubscriptstodenotethedifferentlevelsofaggregation:thelowestlevelsfortheskill,ofortheoccupationandthehighestaggregationlevelsifortheindustryorwforthewagequantile.TheskilllevelLo,sisdistinctforagivenoccupationoandskills.Forinstance,theoccupationofBiophysicistsrequiresalevelof4.75intheskillmathematics,whiletheimportanceofthisskillIo,sis3.88.

10/dictionary/21.0/text/task_statements.html

(releasenumber21.0)

8

3.1OnlycomputerinteractionisautomatablewithAI

Inthispaper,weonlyexaminetheimpactofAIonautomatingtasksthatrequireskillsinvolvingcomputerinteraction.Jobsperformedoncomputersare,intheshortandmediumrun,muchmorelikelytoincorporateAIapplicationscomparedtothoseinvolvingphysicallabour.Weacknowledgethatalsophysicallabourmay,inthefuture,bepronetoautomationthroughimprovedmachinesandrobotics.However,modellingtheimpactofsuchdevelopmentsisoutofthescopeoftheanalysisathand.Similarly,weexpectsocialinteractiontorequirehigherdegreesofsocialacceptancebeforewidespreadautomationmaterialises.Certainly,cost-effectivenessandimprovedsocialskillsoftheAIwillspeeduptheprocess,yet,asforphysicallabour,weexpectlongertimescales.

Weconstructameasureoftheshareofthetimespentoncomputerinteractionsbasedonabout19,000detailedtaskdescriptionsavailableintheO*NETdatabase.Basedonthede-scriptionsofeachoccupation,weinstructedGPT-4toestimatethetimespentwithi)computerinteraction,ii)socialinteraction,andiii)physicallabour.Theexactpromptisshowninthe

BoxA1,andoneexampleoftaskdescriptionisprovidedtotheChatGPT-4in

TableA1.

Notethatcomputerinteractionrepresentsworkingonacomputerthatcommonlydoesnotincludecommunicationviae-meetingsorothersimilarsocialinteraction.

Ingeneral,ChatGPT-4provesveryhighcomparabilitywithconventionalhuman-basedpro-ceduresforcategorisationpurposes.

Eloundouetal.

(2023)usesbothapproaches(human

-andGPT4-based)todirectlyidentifyoccupationalAIexposure,findingaveryhighcorrelationbe-tweenhumanassessmentsandGPT4-basedself-assessments

.11

Gmyreketal.

(2023)follows

theirapproachemp