2023大语言模型和未来工作

IncollaborationwithAccenture

JobsofTomorrow:LargeLanguageModelsandJobs

WHITE PAPER

SEPTEMBER 2023

Images:GettyImages

Contents

Foreword 3

Executivesummary 4

Introduction:Howwilllargelanguagemodelsimpact 5

thejobsoftomorrow?

Identifyingexposurepotentialoftasksandjobs 7

Exposedtasks 7

Detailedexamplesofexposedjobs 9

Analysisbyoccupation 10

Analysisbyindustry 14

Analysisbyfunction 16

LLMsandthegrowthanddeclineofjobsandtasks 17

Expectedgrowthanddeclineoftasks 17

Expectedgrowthanddeclineofjobs 17

Conclusion:Ensuringthatlargelanguagemodelsworkforworkers 19

Appendices 20

A1Exposurepotentialbyindustrygroups 20

A2Exposurepotentialbyfunctiongroups 28

A3Methodology 30

Contributors 32

Endnotes 33

Disclaimer

Thisdocumentispublishedbythe

WorldEconomicForumasacontributiontoaproject,insightareaorinteraction.Thefindings,interpretationsandconclusionsexpressedhereinarearesultofacollaborativeprocessfacilitatedandendorsedbytheWorldEconomicForumbutwhoseresultsdonotnecessarily

representtheviewsoftheWorldEconomicForum,northeentiretyofitsMembers,Partnersorotherstakeholders.

©2023WorldEconomicForum.Allrightsreserved.Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,includingphotocopyingandrecording,orbyanyinformationstorageandretrievalsystem.

JobsofTomorrow:LargeLanguageModelsandJobs 2

Executivesummary

Asadvancesingenerativeartificialintelligence(AI)continueatanunprecedentedpace,largelanguagemodels(LLMs)areemergingastransformativetoolswiththepotentialtoredefinethejoblandscape.Therecentadvancementsinthesetools,likeGitHub’sCopilot,MidjourneyandChatGPT,areexpected

tocausesignificantshiftsinglobaleconomiesandlabourmarkets.Theseparticulartechnologicaladvancementscoincidewithaperiodofconsiderablelabourmarketupheavalfromeconomic,geopolitical,greentransitionandtechnologicalforces.The

WorldEconomicForum’s

FutureofJobsReport

2023

predictsthat23%ofglobaljobswillchangeinthenextfiveyearsduetoindustrytransformation,

includingthroughartificialintelligenceandothertext,imageandvoiceprocessingtechnologies.

Thiswhitepaperprovidesastructuredanalysisofthepotentialdirect,near-termimpactsofLLMsonjobs.With62%oftotalworktimeinvolvinglanguage-basedtasks,1thewidespreadadoptionofLLMs,suchasChatGPT,couldsignificantlyimpactabroadspectrumofjobroles.

ToassesstheimpactofLLMsonjobs,thispaperprovidesananalysisofover19,000individualtasksacross867occupations,assessingthepotentialexposureofeachtasktoLLMadoption,classifyingthemastasksthathavehighpotential

forautomation,highpotentialforaugmentation,lowpotentialforeitherorareunaffected(non-languagetasks).ThepaperalsoprovidesanoverviewofnewrolesthatareemergingduetotheadoptionofLLMs.

Thelonger-termimpactsofthesetechnologiesinreshapingindustriesandbusinessmodelsare

beyondthescopeofthispaper,butthestructuredapproachproposedherecanbeappliedtootherareasoftechnologicalchangeandtheirimpactontasksandjobs.

TheanalysisrevealsthattaskswiththehighestpotentialforautomationbyLLMstendtoberoutineandrepetitive,whilethosewiththehighestpotentialforaugmentationrequireabstractreasoningandproblem-solvingskills.Taskswithlowerpotential

forexposurerequireahighdegreeofpersonalinteractionandcollaboration.

ThejobsrankinghighestforpotentialautomationareCreditAuthorizers,CheckersandClerks(81%ofworktimecouldbeautomated),ManagementAnalysts(70%),Telemarketers(68%),Statistical

Assistants(61%),andTellers(60%).

Jobswiththehighestpotentialfortaskaugmentationemphasizemathematicalandscientificanalysis,suchasInsuranceUnderwriters(100%ofworktimepotentiallyaugmented),BioengineersandBiomedical

Engineers(84%),Mathematicians(80%),and

Editors(72%).

Jobswithlowerpotentialforautomationoraugmentationarejobsthatareexpectedto

remainlargelyunchanged,suchasEducational,Guidance,andCareerCounsellorsandAdvisers(84%oftimespentonlowexposuretasks),Clergy(84%),ParalegalsandLegalAssistants(83%),andHomeHealthAides(75%).

Inadditiontoreshapingexistingjobs,

theadoptionofLLMsislikelytocreatenewroleswithinthecategoriesofAIDevelopers,InterfaceandInteractionDesigners,AIContentCreators,DataCurators,andAIEthicsandGovernanceSpecialists.

Anindustryanalysisisdonebyaggregatingpotentialexposurelevelsofjobstotheindustrylevel,notingthatjobsmayexistinmorethanoneindustry.Resultsrevealthattheindustrieswiththehighestestimatesoftotalpotentialexposure(automationplusaugmentationmeasures)arebothsegmentsoffinancialservices:financialservicesandcapitalmarketsandinsuranceandpensionmanagement.Thisisfollowedbyinformationtechnologyanddigitalcommunications,andthenmedia,entertainmentandsports.Additionallistsofjobsrankedbyhighestexposurepotentialforeachmajorindustrycategoryarecompiledintheappendix.

Similarly,afunctiongroupanalysisrevealsthatthetwothematicareaswiththegreatest

totalpotentialexposuretoLLMsareinformationtechnology,with73%ofworkinghoursexposed,andfinance,with70%ofworkinghoursexposed.Aswiththeindustrygroups,additionallistsofjobsrankedbyhighestexposurepotentialforeachfunctiongroup

arecompiledintheAppendices.

ThesenewfindingsconnectdirectlytoearlierworkdonebytheCentrefortheNewEconomyandSocietyinthe

FutureofJobsReport2023

.ManyofthejobsfoundtohavehighpotentialforautomationbyLLMswerealsoexpected

bybusinessleaderstoundergoemploymentdeclinewithinthenextfiveyears,suchasbanktellersandrelatedclerks,dataentryclerks,andadministrativeandexecutivesecretaries.Meanwhile,jobswithhighpotentialforaugmentationareexpectedtogrow,suchasAIandMachineLearningSpecialists,DataAnalystsandScientists,andDatabaseandNetworkProfessionals.Together,thesetwo

publicationsidentifyandreaffirmsalientthemesintheconnectionbetweentechnologicalchangeandlabourmarkettransformation.

ThefindingsofthisreportshedlightonhowimplementingLLMscouldalterthelandscapeofjobs,providingvaluableinsightsforpolicy-makers,educatorsandbusinessleaders.Ratherthanleadingtojobdisplacement,LLMsmayusherinaperiodoftask-basedtransformationofoccupations,requiringproactivestrategiestopreparetheworkforceforthesejobsoftomorrow.

JobsofTomorrow:LargeLanguageModelsandJobs 4

Introduction:Howwilllargelanguagemodelsimpactthejobsoftomorrow?

Languagecapabilitiesoverlapsubstantiallywithtasksperformedonthejob,

withestimatessuggestingthatupto62%ofworktime

involveslanguage-basedtasks.

Labourmarketsareundergoingrapidtransformationfromthetrajectoryofgrowth,geoeconomics,sustainabilityandtechnology.TheFutureofJobsReport2023foundthatbusinessleadersexpect23%ofglobaljobstochangeinthenextfiveyears.2Inparticular,generativeartificialintelligence(AI)

hasundergoneaprofoundleapincapabilities,embodiedinproductssuchasGitHub’sCopilotforprogramming,MidjourneyforimagegenerationandChatGPTasauniversallanguageassistant.TheFutureofJobsReport2023alsofoundthatAIandtext,imageandvoiceprocessingtechnologiesmoregenerallyaretopofmindforbusinesses.Thereportfoundthat75%ofsurveyrespondentsreporthavingplanstoadoptAIintheirorganization’soperations,and62%reporthavingplanstoadopttext,imageandvoiceprocessingtechnologies.3Thishasraisedquestionsabouthowthisnewtechnologywillaffectorganizationsandlabourmarketsaroundtheworld.

Thiswhitepaperexaminesthepotentialnear-term,directimpactonjobsofaparticulartypeof

generativeAI,largelanguagemodels(LLMs),whichhavebeenhighlyvisibleinpublicdebateoverthepastyearduetotheirhuman-likeabilitytocreateandunderstandlanguage.AsLLMserviceshaveexplodedinpopularity,withfreeservicessuchasChatGPTreachingasmanyas100millionactiveuserswithinthefirsttwomonthsofitsdebut,4

thecapabilitiesofthesemodels,pairedwiththeiraccessibilityandrapidadoptionrate,suggestthatmanyworktasks–andjobsthatemphasizethem–couldbeimpactedbytheuseofLLMsintheyearstocome.Bysomeestimates,upto62%ofworktimeinvolveslanguage-basedtasks.5Yet,artificialintelligenceandtext,imageandvoiceprocessingtechnologiesalsohavethepotentialtoaugmentworkandcreatenewjobs.Inaddition,manyrolesremainwhollyunaffectedbythesedevelopments.

Rapidtechnologicalchangeoftengeneratesanticipationregardingitseffectsondailylife,particularlyjobs.Intheaggregate,previousinnovationshaveledtomoreemploymentopportunities,better-qualityjobsandahigherqualityoflife,buttheyalsocreatedisruptionanddisplacement.6Thispaperaimstosupportthedetailedanalysisrequiredtotakeaclear-eyedviewaroundimpact,opportunityandpreparation.

GenerativeAI,LLMsandlanguagetasks

ThenewestformsofgroundbreakinggenerativeAImodelsarecreatedviadeeplearning,whichistheprocessoftrainingfoundationmodelsonverylargesetsofdata.Thesefoundationmodelsaretypicallycreatedintheformofaneuralnetwork,whosestructureisinspiredbythearrangementofneuronsinthehumanbrain.Largefoundationmodelsaretrainedonvastamountsofdataandhaveseeminglysuper-humanlevelsofpredictivecapacity,whichcanbeharnessedbyproducingtextorimagesinresponsetoawrittenprompt.7

Sofar,generativeAImodelshavebeenconfiguredintoavarietyofdifferenttoolstoservedifferentcontexts,suchasimage,audioorvideo

creation,identifyingfinancialfraudandothersecurityrisks,andahostofgenerallanguagecapabilities,includingtheabilitytogeneratenatural,mathematicalandcomputationallanguage.WhilethereisabroadrangeofimplementationsofgenerativeAI,thisstudywillfocusonLLMsandtheiruniquelanguage-generatingcapabilities,asthesemodelshavethegreatestpotentialtoimpactthelargestnumberofjobsinthenearterm.

LLMscanperformabroadspectrumoflanguagetasks,usuallyinresponsetoasimpleuserprompt,onnearlyanytopic:

LLMscanreformulateandprovidedetailedfeedbackonaprovidedsetoftext,includingsummarizingit,translatingittoanotherlanguage,proofreadingit,discussingitsstyleortoneofvoiceandevenrewritingitinadifferentstyleortoneofvoice.

LLMscanalsogeneratenewtextandprovidesomedegreeofexpertiseontopicspresentintheLLM’strainingdata,suchasintheformofaliteraturerevieworcompletingatasktypicallydonebyaresearchassistant.8

Asprogramminglanguagesaretext,LLMscanserveasprogrammingassistants.

ImplementationslikeGitHub’sCopilothavebeenshowntoincreaseprogrammerproductivityby56%.9

JobsofTomorrow:LargeLanguageModelsandJobs 5

ByintegratingLLMswithothersystems,thesecapabilitiescanbeextendedtoagreaterrangeofabstracttasks,suchasschedulingmeetings,placingorders,respondingtoemails,orprovidingresearchonaparticulartopic.

GiventhelargeoverlapbetweenLLMcapabilitiesandcurrentjobtasks,howwillintroducingLLMsintotheworkplacechangejobs?Whichpartsofajobwillbeimpactedthemost,andwhichjobswillbeimpactedmost?Finally,withtheintroductionofthesenewtechnologies,whichnewjobscanbeexpectedtoarise?

Atask-basedapproachtojobexposure

Toanswerthesequestions,themethodsdeployedinthiswhitepaperassessthepotentialexposureoflanguage-basedjobtaskstotheabilityofLLMstoperformthesetasks.TheapproachistofirstthinkofajobasconsistingofmanydifferenttasksandthenassesshoweachtaskmaybeaffectedbyLLMs.Themagnitudeofimpactonajobultimatelydependsonthedegreeoflanguage-basedskillsrequiredforspecifictasksinthatjobandthetimespentonthosetasks.Language-dependent,standardized,routineandprocess-orientedtasksareprimecandidatesforautomationandreplacementbyLLMs.Atthesametime,thoserequiringagreaterdegreeofhumaninteraction

aremorelikelytobeaugmentedandperformedincollaborationwithLLMs.

lessonsandclasses(27%).10SoftwareDeveloperscouldturntoLLMstogeneratestandardizedblocksofcodewithclearfunctionalparameters,speedingupthedevelopmentprocessandallowingformoretimetobespentonhigh-levelarchitecturaltasks.

SoftwareDevelopersalsoperformmanytaskswithhighpotentialforautomation,suggestingthatmanyjobswillbetransformedratherthanautomated

oraugmented.

TheresearchmethodsemployedinthispaperaimtoidentifywhichtaskswillbeexposedtoLLMsandhowtheywillbeimpacted:whethertheyhavethepotentialtobeautomatedandreplacedbyLLMsoraugmentedandenhancedbyLLMs.DataforanalysiscomesfromO*NETandtheUnitedStates

BureauofLaborStatistics(BLS),whichcharacterizes867jobswithrespecttoover19,000individualtasks.Usingbothmachinelearningandmanualmethods,jobtasksareindividuallyratedwithrespecttotheirpotentialexposuretotheadoptionofLLMs,therebyclassifyingthemintooneoffourcategories:

Highpotentialforautomation:Goingforward,

thetaskwillbeperformedbyLLMs,nothumans.

Highpotentialforaugmentation:Humanswillcontinuetoperformthetask,andLLMswillincreasehumanproductivity.

Lowpotentialforautomationoraugmentation:HumanswillcontinuetoperformthetaskwithnosignificantimpactfromLLMs.

Unaffected(i.e.non-languagetasks).

ThreeintenteachershavealreadyusedChatGPTforlessonplanning(30%),generatingcreativeideasforclasses(30%)andbuildingbackgroundknowledgeforlessonsandclasses(27%).

Forexample,somejobtasksareroutineandpredictableandareperformedbypeopleworkingindividually,suchasclerksandadministrators,whichinvolvesreadingandenteringdata,cross-referencingrecordsbetweendifferentdatabasesandreviewingtransactions.Thesetasksaremorelikelytobeexposedtoandultimatelyautomatedbythe

introductionofLLMs,implyingthattheywillnolongerbeperformedbyhumans.Theoutcomeisthatjobsemphasizingthesetaskswilleithertransformtotakeonnon-automatabletasksorgointodecline.Otherjobtasksrequireagreatdealofabstractreasoning,creativityandproblem-solving.Whilelanguage

tasksmaynotbetheirprimaryproduct,theymayrelyheavilyonlanguageandcommunication.Forexample,MathematiciansandEditorsrelyheavilyuponlanguage,yetneedtoincorporatecreativeinsightsfromtheirfieldsofexpertise.Similarly,SoftwareDevelopersworkalotwithcomputerlanguagesbutalsoneedtograspcomplexsystemsatvariouslevelsofabstractiontocreateafinishedsoftwareproduct.WorkersinthesejobswouldnothavetheirtasksreplacedbyLLMs;rather,LLMswouldsuperchargetheirabilitytocompletethesetasks.Teachers,forexample,couldrelyonLLMsforassistanceinlessonplanningandcorrectingstudentwork.AccordingtoonestudyintheUS,threein

tenteachershavealreadyusedChatGPTforlessonplanning(30%),generatingcreativeideasforclasses(30%)andbuildingbackgroundknowledgefor

Jobtasksarethenmappedtotheoccupationsinwhichtheyaredeployedalongwithashareoftimespentoneachtask,andwithbothofthesemetrics,ameasureofpotentialexposuretoLLMsiscreatedattheoccupationlevel.

Chapter1ofthispaperpresentstheseresultsfortasksandjobsindetail,usingthedetailedoccupationslistfromtheStandardOccupationSystem(SOC)fromtheUSBureauofLaborStatistics,thehighestresolutionlistavailable,featuring867occupationtitles.11Thischapteralsoprovidesanalysisofthedifferencesbetweenindustriesandfunctionsintermsoftheexpected

impactofLLMsonjobs.Inchapter2,thesedetailedoccupationsareaggregatedandmappedtotheoccupationclassificationsystemusedintheFutureofJobsSurvey2022todirectlyconnectresultsonexposureofjobstoLLMstosurveyresultsofglobalbusinessleadersonthepotentialforgrowthordeclineofspecificjobs,andtheforcesunderlyingthesetrends,ascoveredingreaterdetailintheFutureofJobsReport2023.

JobsofTomorrow:LargeLanguageModelsandJobs 6

Identifyingexposurepotentialoftasksandjobs

LLMsholdtransformativepotentialandaresettosignificantlyreshapethefutureemploymentlandscape.

ThischapteroutlinestaskexposuretoLLMs,providingdeeperanalysisontwojobshighlylikelytobeimpacted,andranksjobsbytheirautomationandaugmentationpotential.ItalsoidentifiesemergingjobsduetoLLMadoptionandsummarizesexposurerisksbyindustryandjobfunction.

Exposedtasks

Thetaskswiththehighestpotentialforaugmentationrequiremore

abstractreasoningskills,especiallythosethatcombineinteraction

withpeople.

Apreliminaryanalysisrevealswhichtaskshavethehighestpotentialforautomationoraugmentationandwhichhavelowerornopotential(seeTable1).ThetaskswiththehighestpotentialforautomationbyanLLMtendtobemoreroutine,suchasperformingadministrativeorclericalactivities,andsometasksthatrelatetoelementaryanalysis,suchasdesigningdatabasesoranalysingdata.Thetaskswiththehighestpotentialforaugmentationrequiremoreabstractreasoningskills,especiallythosethatcombineinteractionwithpeople.Atthetopofthelistisevaluatingpersonnelcapabilities

orperformance,suchasinthecontextoftheresponsibilitiesofahumanresourcesprofessional,followedbycollectingdataaboutconsumerneedsoropinions.Forthelatter,whilerunningasurvey,forexample,couldbeahighlyautomatedprocess

viaemailandtheinternet,thecraftingandwordingofsurveyquestionsstillrequireahighdegreeofattentionandapprovalbythepersoncollectingthedata.

Taskswithlowerpotentialforexposurerequireahighdegreeofpersonalinteractionandcollaboration,suchasnegotiationofcontracts,developmentofeducationalprogrammes,andotherscientificandtechnicalwork,thelatterof

whichalreadyemployastrongdegreeoftechnicalaugmentation.Finally,non-languagetasksare,

asexpected,thosethatemphasizephysicalmovement,suchasloadingproducts,materialsorequipmentfortransport,assemblyactivities,

agriculturalactivities,andgroomingandhairstyling.

1

JobsofTomorrow:LargeLanguageModelsandJobs 7

TABLE1

Keytasksimpacted

Level Task

Higherpotentialforautomation

Performadministrativeorclericalactivities

Designdatabases

Analysedatatoimproveoperations

Monitorexternalaffairs,trendsorevents

Obtaininformationaboutgoodsorservices

Documenttechnicaldesigns,proceduresoractivities

Higherpotentialforaugmentation

Evaluatepersonnelcapabilitiesorperformance

Collectdataaboutconsumerneedsoropinions

Readdocumentsormaterialstoinformworkprocesses

Evaluatepatientorclientconditionortreatmentoptions

Prepareinformationalorinstructionalmaterials

Testperformanceofcomputerorinformationsystems

Lowerpotentialforexposure(automationoraugmentation)

Negotiatecontractsoragreements

Advocateforindividualorcommunityneeds

Collaborateinthedevelopmentofeducationalprogrammes

Directscientificortechnicalactivities

Coordinatewithotherstoresolveproblems

Evaluatedesigns,specificationsorothertechnicaldata

Non-languagetasks

Loadproducts,materialsorequipmentfortransportorfurtherprocessing

Assembleequipmentorcomponents

Preparemixturesorsolutions

Performagriculturalactivities

Groomorstylehair

Installenergyorheatingequipment

JobsofTomorrow:LargeLanguageModelsandJobs 8

Detailedexamplesofexposedjobs

ToprovideadetailedexampleofhowthetasksinvolvedinajobdeterminehowLLMswillimpactthejob,Figure1presentsananalysisofanexposedandnon-exposedjobandthetaskexposure

ofeach.TheleftpanelofthefigureprovidesanoverviewofSoftwareDevelopers,ahighlyexposedjobintheanalysis,showinghighpotentialforbothaugmentationandautomationoftasks.Atotal

of28.7%oftimespentintheoccupationhashighpotentialforautomationbyLLMs,including

“analysedatatoimproveoperations”and“analysetheperformanceofsystemsofequipment”.Incontrast,upto43.2%oftimespentontasksintheoccupationhashighpotentialforaugmentation,including“preparinginformationalorinstructionalmaterialsandevaluatingthecharacteristics,

usefulnessorperformanceofproductsortechnologies”.

TherightpanelofFigure1providesanoverviewofHumanResourceManagers,whichisalessexposedjob.Only16.1%oftimehaspotentialforautomation,including“determineresourceneedsofprojectsoroperationsandmanagebudgets

orfinances”,and22.2%oftimehaspotentialforaugmentation,including“explainregulations,policiesorproceduresandtrainothersonoperationalorworkprocedures”.Themajorityof

tasksinvolved,totalling61.7%oftimespent,havelowerpotentialforexposure,asthesetasksinvolveworkingdirectlywithindividualsandcoordinatingandcommunicatingwithlargegroups.

FIGURE1 Exampleofanexposedandnon-exposedjob

SoftwareDevelopers(moreexposed) HumanResourceManagers(lessexposed)

28.7%

28%

43.2%

16.1%

61.7%

22.2%

Higherpotentialforautomation:

Higherpotentialforautomation:

Analysedatatoimproveoperations

Analyseperformanceofsystemsorequipment

Determineresourceneedsofprojectsoroperations

Managebudgetsorfinances

Higherpotentialforaugmentation:

Higherpotentialforaugmentation:

Prepareinformationalorinstructionalmaterials

Lowerpotentialforautomationoraugmentation:

Evaluatethecharacteristics,usefulnessorperformanceofproductsortechnologies

Explainregulations,policiesorprocedures

Trainothersonoperationalorworkprocedures

Coordinatewithotherstoresolveproblems

Communicatewithothersaboutbusinessstrategies

Interviewpeopletoobtaininformation

Coordinategroup,communityorpublicactivities

Lowerpotentialforautomationoraugmentation:

Automation Augmentation Lowerpotential Non-languagetasks

JobsofTomorrow:LargeLanguageModelsandJobs 9

Analysisbyoccupation

Jobswithpotentialforautomation

Resultsfromthetask-basedanalysisrevealthatjobswiththehighestpotentialforautomationoftasksbyLLMsemphasizeroutineandrepetitiveproceduresanddonotrequireahighdegree

ofinterpersonalcommunication.RoleswiththehighestamountofpotentiallyautomatableworktimeareCreditAuthorizers,CheckersandClerks

(81%oftime),ManagementAnalysts(70%),

Telemarketers(68%),StatisticalAssistants(61%)andTellers(60%).Jobswithhighpotentialforautomationoftenincludevariouskindsofofficeclerks,particularlythosefocusedonrecord-keepingandmanaginginformation–taskswhereLLMshavedemonstratedastrongdegreeofcompetency.Forexample,LegalSecretariesandAdministrativeAssistantsspendapproximately54%oftheirtimeontaskswithhighautomationpotential.

FIGURE2 Jobswiththehighestpotentialforautomation

Occupations

Exposure

CreditAuthorizers,CheckersandClerks

88%

81%

7%

12%

ManagementAnalysts

76%

70%

7%

24%

Telemarketers

87%

68%

18%

13%

StatisticalAssistants

74%

61%

13%

26%

Tellers

93%

60%

34%

7%

ForensicScienceTechnicians

60%

58%

1%

4%

37%

ReceptionistsandInformationClerks

69%

58%

11%

31%

BrokerageClerks

Production,PlanningandExpeditingClerks

74%

72%

58%

57%

16%

15%

17%

18%

10%

10%

FileClerks

63%

56%

7% 11%

26%

WordProcessorsandTypists

63%

55%

5%

40%

Bookkeeping,AccountingandAuditingClerks

78%

55%

23%

22%

LegalSecretariesandAdministrativeAssistantsLoanInterviewersandClerks

BillandAccountCollectors

77%

80%

63%

54%

54%

53%

23%

27%

9%

21%

12%

13%

11%

7%

17%

0 10 20 30 40 50 60 70 80 90 100

Automation Augmentation Lowerpotential Non-languagetasks

JobsofTomorrow:LargeLanguageModelsandJobs 10

Jobswithpotentialforaugmentation

ThesameanalysismethodsdemonstratethatthejobswiththehighestpotentialforaugmentationbyLLMsemphasizecriticalthinkingandcomplexproblem-solvingskills,especiallythoseinscience,technology,engineeringandmathematics(STEM)fields(seeFigure3).ToppingthelistisInsuranceUnderwriters,withanalysissuggestingthattheyspend100%oftheirtimeontasksthathave

thepotentialtobeaugmentedbygenerativeAI

systems.ThisisfollowedbyBioengineersandBiomedicalEngineers(84%oftimeaugmentable),Mathematicians(80%)andEditors(72%).Theremainingtop15jobsarelikewisetechnicalorhighlyspecialized,oftenrequiringadvanceddegreesortraining,such

asDatabaseArchitectsandStatisticians.Notethatmanyjobswiththehighestpotentialforaugmentationalsohavesomepotentialforautomation,resultinginveryhightotalexposureforthesejobs,suchasMedicalTranscriptionists,

InsuranceAppraisersandAssessorsofRealEstate.

FIGURE3 Jobswiththehighestpotentialforaugmentation

Occupations Exposure

InsuranceUnderwriters

100%

100%

BioengineersandBiomedicalEngineers

100%

16%

84%

MathematiciansEditors

DatabaseArchitectsStatisticians

93%

72%

86%

84%

12%

15%

15%

72%

80%

72%

68%

28%

7%

14%

16%

TrainingandDevelopmentSpecialistsClerksDatabaseAdministrators

InsuranceAppraisers,AutoDamageGraphicDesigners

PropertyAppraisersandAssessorsAppraisersandAssessorsofRealEstate

74%

83%

100%

79%

88%

88%

6%

16%

14%

26%

26%

34%

68%

66%

65%

62%

62%

66%

26%

18%

17%

3%

12%

12%

OperationsResearchAnalystsMedicalTranscriptionistsInterpretersandTranslators

78%

100%

76%

18%

16%

40%

60%

60%

60%

22%

24%

0 10 20 30 40 50 60 70 80 90 100

Automation Augmentation Lowerpotential Non-languagetasks

JobsofTomorrow:LargeLanguageModelsandJobs 11

Jobswithlower