产生人工智能和坚实的价值 Generative AI and Firm Values

NBERWORKINGPAPERSERIES

GENERATIVEAIANDFIRMVALUES

AndreaL.Eisfeldt

GregorSchubert

MiaoBenZhang

WorkingPaper31222

/papers/w31222

NATIONALBUREAUOFECONOMICRESEARCH

1050MassachusettsAvenue

Cambridge,MA02138

May2023

GregorSchubertgratefullyacknowledgesfundingfromtheUCLAFinkCenterforFinanceandtheUCLAEastonTechnologyManagementCenter.TheviewsexpressedhereinarethoseoftheauthorsanddonotnecessarilyreflecttheviewsoftheNationalBureauofEconomicResearch.

NBERworkingpapersarecirculatedfordiscussionandcommentpurposes.Theyhavenotbeenpeer-reviewedorbeensubjecttothereviewbytheNBERBoardofDirectorsthataccompaniesofficialNBERpublications.

©2023byAndreaL.Eisfeldt,GregorSchubert,andMiaoBenZhang.Allrightsreserved.Shortsectionsoftext,nottoexceedtwoparagraphs,maybequotedwithoutexplicitpermissionprovidedthatfullcredit,including©notice,isgiventothesource.

GenerativeAIandFirmValues

AndreaL.Eisfeldt,GregorSchubert,andMiaoBenZhang

NBERWorkingPaperNo.31222

May2023

JELNo.E0,G0

ABSTRACT

WhataretheeffectsofrecentadvancesinGenerativeAIonthevalueoffirms?OurstudyoffersaquantitativeanswertothisquestionforU.S.publiclytradedcompaniesbasedontheexposuresoftheirworkforcetoGenerativeAI.Ournovelfirm-levelmeasureofworkforceexposuretoGenerativeAIisvalidatedbydatafromearningscalls,andhasintuitiverelationshipswithfirmandindustry-levelcharacteristics.UsingArtificialMinusHumanportfoliosthatarelongfirmswithhigherexposuresandshortfirmswithlowerexposures,weshowthathigher-exposurefirmsearnedexcessreturnsthatare0.4%higheronadailybasisthanreturnsoffirmswithlowerexposuresfollowingthereleaseofChatGPT.Althoughthisreleasewasgenerallyreceivedbyinvestorsasgoodnewsformoreexposedfirms,thereiswidevariationacrossandwithinindustries,consistentwiththesubstantivedisruptivepotentialofGenerativeAItechnologies.

AndreaL.EisfeldtMiaoBenZhang

AndersonSchoolofManagementUniversityofSouthernCalifornia

UniversityofCaliforniaatLosAngeles701ExpositionBlvd

110WestwoodPlazaHOH-722

SuiteC4.10LosAngeles,CA90089

LosAngeles,CA90095miao.zhang@

andNBER

andrea.eisfeldt@

GregorSchubert

UCLAAnderson

SchoolofManagement

gregor.schubert@

1

RecentadvancesinGenerativeArtificialIntelligencearewidelyseenasamajortechnologyshockwithimportantimplicationsforfirmvalues.Relativetoearlierartificialintelligencemodels,GenerativeAImodelscandigestmorecomplexinputs,andcanproducehuman-likeoutput,makingGenerativeAImoreversatileandscalablethanpriorinnovationsinAIandmachinelearning.Asaresult,GenerativeAIhasthepotentialforwidespreadcorporateadoption,withimplicationsforfirmvaluesbothacrossandwithinawidearrayofindustries. OneofthebiggestquestionssurroundingadvancesinGenerativeAIiswhateffectthesetechnologieswillhaveoncorporatevaluationsasaresultoftheimpactofGenerativeAIonfirms’laborinputs.Weconstructanoveldatasetcontainingfirm-levelworkforceexposurestoGenerativeAI.WeprovideaquantitativemeasureoftheimpactofGenerativeAIbasedonourfirm-levelexposuredatacombinedwithfinancialmarketdata.UsingthismeasurewecomputethefirstestimatesoftheeffectofGenerativeAIonfirmvaluesbystudyingtheimpactofthereleaseofChatGPTonfirmswithvaryingexposurestothetechnologyshock.

1

WemeasuretheimpactofamajoreventintheadvancementanddisseminationofGen-erativeAItechnology,namely,thepublicreleaseofChatGPT,onequityreturnsatthefirmlevel.Thiseventhadasubstantialimpactonfirmreturns,consistentwithGenerativeAIadvancementrepresentingamajortechnologicalshock,oneforwhichwecanmeasurethearrivalandimpactinrealtime.Whilefirmsmayprogressivelyadoptthetechnology,theunmatchedmediaattentionanduserbasethatChatGPThasgarneredwithinjustmonthsindicatesthatfirmsandinvestorsareactivelyassessingthepotentialfastdiffusionofthistechnology.WeshowthatTwittermentionsandearningscallmentionsofGenerativeAIincreasedsubstantiallyfollowingthereleaseofChatGPT.Moreover,themassiveinformationgatheringandprocessingabilityofChatGPTitselfallowsustoassesseachfirm’sexposuretoChatGPT’sdisruptioninreal-time.

OurkeyfindingisthatthearrivalofChatGPThadasizablepositiveeffectonthevalueoffirmswhoselaborforcesaremoreexposedtoGenerativeAIandrelatedLargeLanguageModels(LLMs).FirmswithhigherexposuretothereleaseofChatGPT,asmeasuredbytheexposureoftheirlaborforcetobeingmademoreproductivebytoolslikeChatGPT,outperformfirmswithlowerexposuresbyover40basispointsindailyexcessreturnsduringthetwoweeksfollowingitsrelease.Notably,thesereturndifferencesarenotonlyduetodifferencesinlaborforceexposuresacrossindustries.Returnsoffirmswithhighlaborforceexposuresalsooutperformfirmswithlowexposuresbyabout40basispointsdailyin

1RecentstudiesofGenerativeAIinclude

Eloundou,Manning,Mishkin,andRock

(

2023

)whostudytheimpactofGenerativeAIonindustries’laborforces,

NoyandZhang

(

2023

)whostudythedisplacementeffectsofGenerativeAIonprofessionalwritingtasks,and

Brynjolfsson,Li,andRaymond

(

2023

)whostudytheeffectsofGenerativeAIoncustomersupportagents,and

Felten,Raj,andSeamans

(

2023

)whoconsiderheterogeneityinoccupationalexposure.

2

industry-neutralportfolios.

OurmethodologybuildsontheideathatChatGPTandrelatedtechnologieswillin-creasefirm-levelfreecashflowsthroughalaboreffectthatcanworkthroughtwopotentialchannels.First,firmswhoselaborforcecanbesubstitutedforwithcheaperGenerativeAI-basedcapitalwillexperiencehigherfreecashflowsbyloweringinputcosts.

2

Second,firmswhoselaborinputsaremorecomplementarytoGenerativeAIwillexperiencehighercashflowsduetothetechnologicalimprovementinaninputthatiscomplementarytotheirworkforce.

3

Whilewedonottakeastandonwhether(andforwhichworkers)GenerativeAIisasubstitutefor,oracomplementaryto,labor,weareabletoshowthatfirmsthathaveahighershareofoccupationsexposedtoGenerativeAIexperiencegainsinvalueacrossawidearrayofindustries.Atthesametime,theeffectofthereleaseofChatGPTonfirmvaluesvarieswidelyacrossindustries,aswellaswithinindustriesacrossfirms.Indeed,wefindasignificantlynegativeimpactfromthereleaseofChatGPTforsomeindustries.Value

lossesforincumbentsareconsistentwiththeideathatforsomeindustriesGenerativeAIwillleadtonewentrantsanddisplacementofexistingfirms.WhileadvancesinGenerativeAIcanhaveeffectsthroughtheproductmarketaswellasthroughthelabormarket(forexample,increasingdemandforcloudcomputingservices),ourresultssupporttheideathatAIadvanceswillhaveabroadimpactontheeconomythroughitseffectsonlaborinputs.

ThefactthattheoverallimpactofthearrivalofChatGPTonfirmswithmoreexposuretoGenerativeAIissignificantlypositiveisconsistentwithrecentstudiesshowingthatitisincreasinglymoredifficultfornewentrantstodisplaceincumbentfirms.

4

Wemeasurefirm-levelexposuretoGenerativeAIintwosteps.First,webuildon

Eloun-

douetal.

(

2023

)anduseChatGPTitselftoassesswhethereachofthe19,265taskscur-rentlyperformedbyvariousoccupationscanbedonebythecurrentChatGPTorbyfutureChatGPTafterinvestmentinadditionalcapabilities.Following

Eloundouetal.

(

2023

),weaggregatethetask-levelexposuremeasurestotheoccupationsintheO*NETdatabase.Sec-ond,andnoveltoouranalysis,wemapoccupationstopublicly-tradedfirmsusingdatafromRevelioLabs.ThisdatasetisconstructedfrommillionsofpublicemployeeprofilessuchasLinkedIn.Ourfirm-levelexposuremeasurethuscapturestheabilityofthetaskscurrentlyperformedbylaboratthosefirmstobeperformed(ormademoreefficient)byGenerativeAI.Tothebestofourknowledge,ourstudyisthefirsttocreateafirm-levelmeasureofexposuretoGenerativeAI.

Wenextvalidateourlabor-basedmeasureoffirms’exposuretoGenerativeAIbyexamin-

2See

Autor,Levy,andMurnane

(

2003

)and

Zhang

(

2019

)formeasuresoffirmexposuretoautomationand

Webb

(

2019

)and

LaneandSaint-Martin

(

2021

)fortheimpactofAIonfirms.

3See

Krusell,Ohanian,R´ıos-Rull,andViolante

(

2000

)and

Eisfeldt,Falato,andXiaolan

(

2022

).

4See,forexample,

Guti´errezandPhilippon

(

2019

)and

AkcigitandAtes

(

2020

).

3

ingfirms’earningscalltranscriptsin2023.WedocumentastrongrelationshipbetweenourmeasureofexposuretoGenerativeAIandfirms’discussionsofGenerativeAIandrelatedtechnologiesinfirms’earningscallsfollowingthereleaseofChatGPT.Incontrast,firmswithhigherexposuretoGenerativeAIdonotincreasediscussionscommontechnologicaltopicssuchasEngineeringfollowingthereleaseofChatGPT.Moreover,thesefindingsremainevenafterweexcludeallfirmsfromthemostIT-relatedsectors,

5

suggestingthatfirms’recentdiscussionsaboutGenerativeAIgobeyonditsimpactonrelatedproducts,andextendtotheimpactonoperationsincludinglaborinputs.

WestartbyshowingthetypesofoccupationsthatwillbeaffectedbyadvancesinGen-erativeAI.Wefindthatthemostaffectedoccupationsarethosethatinvolvenon-routinecognitivetasks.Thisisinstarkcontrastwithpriorfindingsthatautomationmainlydis-placesoccupationsinvolvingroutinetasks(

Autoretal.

(

2003

)).Indeed,themostaffectedoccupationsarethosewithahighshareofnon-routinecognitiveanalyticaltasksorroutinecognitivetasks,whilemanualphysicaltasksarerelativelyunaffected.InterpersonaltaskslieinbetweencognitiveandmanualtasksintermsoftheirexposuretoGenerativeAI.Occupa-tionswithhigherwagesalsohavehigherexposuretoGenerativeAI.Ourresultisconsistentwithrecentfindingsby

Kogan,Papanikolaou,Schmidt,andSeegmiller

(

2019

),whofindthattechnologicaladvancesimpactworkersatthehigherendofthewagedistribution.

ExposuretoGenerativeAIthroughfirms’laborinputshasanintuitiverelationshiptoaveragefirmcharacteristicsacrossandwithinindustries.Attheindustrylevel,moreexposedsectorshavehigherwages,consistentwiththosesectorsemployingmoreworkersinhigher-paidoccupationsthatalsotendtobemoreexposedtoGenerativeAI.Regardinglaborinputs,firmsinmoreexposedindustriestendtohavehigherlaborintensityintermsofthenumberofemployeesperunitofcapital,andlowerassettangibility.Moreexposedfirmsalsohavehigherratiosoforganizationaltototalcapital.

6

Forthecharacteristicsrelatedtofirmvaluation,moreexposedsectorshaveloweraveragefirmsizeasmeasuredbytotalassetsandhigherTobin’sQ.Importantly,wealsoobservesimilarrelationshipsbetweenfirms’exposuretoGenerativeAIandfirmcharacteristicswithinindustrysectors.Therobustpatternsofvariationinindustryandfirm-levelexposureswithfirmcharacteristicssupportourstudyofstockreturnsbothacrossandwithinindustries.

FirmswithhigherexposuretoGenerativeAIexperiencehighervolatilityaroundthereleaseofChatGPT.However,itappearsthatittakessometimefortheinformationinChatGPT’sreleasetobeimpoundedintostockprices.Thecumulativeexcessreturnsfor

5Tobeprecise,weexcludetheNAICS51“Information”andNAICS54“Professional,Scientific,andTechnicalServices”sectors.

6See

EisfeldtandPapanikolaou

(

2014

)and

EisfeldtandPapanikolaou

(

2013

).

4

Figure1:GenerativeAIexposurequintileportfolioreturnsovertime:marketfactor-adjusted.Thegraphshowsthecumulativeexcessrealizedreturnsonportfoliosbasedonvalue-weightedsorts.Allportfolioreturnsshownarenetoftherisk-freerate.ThedatasetconsistsofdailystockreturnsfromYahooFinanceforNov.15,2022-March31,2023.Thefigureshowsreturnsadjustedformarketfactorexposure.

thehighest-exposurequintileoffirmsvs.thelowest-exposurequintiledivergeforseveralweeksfollowingthereleaseofChatGPT.Figure

1

plotsthereturnsofthehighest-exposurequintile,thelowest-exposurequintile,andalong-shortportfolio,whichwedenoteAMHfor“ArtificialMinusHuman”.CumulativereturnstoholdingtheAMHportfoliothatislongthehighest-exposurequintile,andshortthelowest-exposurequintilefromthereleaseddatethroughMarch31,2023,areover9%.

WestudytheeffectofGenerativeAIonfirmvaluesbycomparingthereturnsoffirmswithhigherandloweroccupationalexposuretoGenerativeAIduringandoutsidethetwo-weekwindowfollowingthereleaseofChatGPTonNovember30,2022.Theeffectsaresubstantial,andmonotonic,withinindustriesacrossGenerativeAI-exposurequintiles.Adjustingforthemarketfactor,theexcessreturnstoquintileportfoliosformedbasedonfirm-leveloccupa-tionalexposuretoGenerativeAIaremonotonicallyincreasing,withthehighest-exposurequintileoffirmswithinindustriesearningpositiveexcessdailyreturnsofover40basispointswhilethelowestexposurequintileexperiencesnegativeexcessreturnsofaround25basispoints.ThefactthatthesestrongeffectsexistwithinindustriesformanyindustriesprovidesevidencethatGenerativeAIcanhaveabroadimpactonfirmvaluesthroughtheeffectsontheirlaborinputs.

5

Acrossindustries,theeffectsofGenerativeAIonfirmvaluealsovarywidely.Publishing,informationandcomputing-relatedindustrieshavepositivereturnsfollowingthereleaseofChatGPT,whilefinanceandtransportation-relatedindustriesexperiencenegativereturnsoverall.Dispersioninindustryreturnsismuchhigherduringthetwo-weekperiodfollowingthereleaseofChatGPTthanoverthefullsamplefromNovember30,2022toMarch31,2023overall.

Ourwithin-industryresultsalsodisplaystrikingdifferencesacrossindustrysectors.Withinfinance,thereturnofmoreexposedfirmsrelativetolessexposedfirmsissubstantiallyandsignificantlypositive.Combinedwiththeoverallnegativeindustryeffect,thisisconsistentwithsomefirmsbenefittinggreatlyfromGenerativeAIadvanceswhileoveralltheimpactofthereleaseofChatGPTwasnegativeforvalueinthefinanceindustry.FirmswithhigherexposurestoGenerativeAIwithinmanufacturingaswellastheadministrativesupport,wastemanagement,andremediationservicesindustryalsosignificantlyoutperformfirmswithlowerexposures.Ontheotherhand,firmswithhigherexposuresintherealestateandrentalandleasingindustrysignificantlyunderperformfirmswithlowerexposures.ThiscouldmeanthatexistingfirmswithlargeexposurestoGenerativeAImaybedisplacedbynewentrantsinthoseindustries.Finally,severalindustriesdonotdisplaysignificantre-turnspreadsfollowingthereleaseofChatGPT,includingconstructionofbuildings,mining,andheavyandcivilengineeringconstruction.Thenegligibleimpactintheseindustriesisconsistentwithmanualtasks’lowerexposuretoGenerativeAI.

Ourstudycontributestotheliteraturestudyingtheimpactofdisruptivetechnologiesonfirmvaluations.

7

Papanikolaou

(

2011

)and

KoganandPapanikolaou

(

2014

)studytheeffectsofinvestment-specifictechnologicalchangesonassetprices.

EisfeldtandPapanikolaou

(

2013

)and

EisfeldtandPapanikolaou

(

2014

)studyfirms’exposuretotheorganizationcapitaltechnologyfrontier.

Zhang

(

2019

)studiesfirms’exposuretoroutine-biasedautomation.Inaseriesofpapers,

Babina,Fedyk,He,andHodson

(

2020

),

Babina,Fedyk,He,and

Hodson

(

2021

),and

Babina,Fedyk,He,andHodson

(

2022

)studytheeffectsofAIonfirmgrowth,compensation,andworkforcecomposition.Seealso

Webb

(

2019

)fortheimpactofAIonfirms.

Kelly,Papanikolaou,Seru,andTaddy

(

2021

)studyfirms’exposuretodisruptivetechnologicalshocksusingpatenttextualdata,and

Koganetal.

(

2019

)assessesworkerdisplacementfromtechnologicalchangeoveraverylongsample.Thesestudiesofferimportantinsightsintoinvestors’andfirms’responsestotechnologicalshocksinhistoricalsamples.

Ourstudydepartsfromtheseworksbyfocusingonmeasuringfirms’exposuretoGen-

7See

Greenwood,Hercowitz,andKrusell

(

1997

)foranearlycontributiononthelong-runimpactsofinvestment-specifictechnologicalchange.

6

erativeAIandassessinginvestors’reactiontothetechnologyshockuponitsarrival.WearguethatthereleaseofChatGPTinNovemberof2022isanobservable,largetechnologyshock.Wealsohighlightourcontributionofmeasuringinvestors’reactionstothisshockinreal-time.Indeed,theinformationinmarketpricescanpotentiallyinformemployees’andfirms’ultimateresponsestotechnologicaldisruption.Timelyassessmentofthemarket’sexpectationsofGenerativeAI’simpactonfirmscanalsohelppolicymakerstoeffectivelyevaluateregulatorypoliciesinresponsetothearrivalofthenewtechnology.

Whileothercontemporaneousorrecentstudiessuchas

Eloundouetal.

(

2023

)alsoaddresstheexposureofoccupationstoGenerativeAIadvances,ourpaperisnovelinitscontributionstotheeffectonfirms.OuruseoftheRevelioLabsdatatolinkoccupationstofirmsyieldsauniqueopportunitytostudycorporateoutcomes.

8

.

Thepaperproceedsasfollows:Section

I

describesourdataandmeasureoffirms’expo-suretoGenerativeAI.Section

II

providesdescriptivefactsaboutGenerativeAIexposuresacrossoccupations,industries,andfirms.Section

III

documentscorporatecommunicationstoinvestorsregardingGenerativeAI,andtherelationshipbetweenthosecommunicationsandourmeasureofGenerativeAIexposures.Section

IV

presentsourresultsdocumentingthesubstantialchangesinfirmvaluationsfollowingtheintroductionofChatGPT.Finally,Section

V

concludes.

I.DataandMeasurement

Wemeasureafirm’slaborexposuretoGenerativeAIintwosteps.First,wemeasureeachoccupation’sexposuretoGenerativeAIbasedontheoccupation’staskstatementsfromtheO*NETdatabase.Second,weaggregatetheoccupation-levelGenerativeAIexposuremeasuretothefirmlevelusingthefirm-occupationalemploymentdatafromtheRevelioLabsWorkforceDynamicsdatabase.

A.MeasuringoccupationalexposuretoGenerativeAI

OccupationaltaskdataToassesswhetheranoccupationwilllikelyexperienceachangeinabsoluteorrelativeproductivityasaresultofGenerativeAImodelsbecomingwidelyavailableandused,weuseatask-basedapproach.Thatis,similarto

Eloundouetal.

(

2023

),

8Indeed,aswedraftthisstudy,IBM,thecompanyranked#1inourexposuretoGen-erativeAImeasureamongthelargestU.S.firms(seeTable

II

)announcedtohalthir-ingof7,800jobsthatcouldbereplacedbyAI.See

https://www.businessinsider.

com/ibm-halts-hiring-for-7800-jobs-that-could-be-replaced-by-ai-report-2023-5?

utm_source=&utm_medium=newsletter&utm_campaign=

ibm-starts-replacing-jobs-with-ai&r=US&IR=T

7

weconsideranoccupationtobeasetoftasks-to-be-doneandevaluateforeachtaskwhetheritcanbedonemoreproductivelyusingChatGPTandsimilarlargelanguagemodels(LLMs)orfutureapplicationsthatwillbebuiltbasedontheircapabilities.

WeobtaininformationonthetasksinvolvedineachoccupationfromtheO*NETdatabase,whichprovidesalistoftaskstatementscreatedbypractitionersorexperts.

9

Ataskstate-mentisusuallyonesentence,andanoccupationhasonaverage22tasks.The19,265pairsoftaskstatementsandtheoccupationsthattheybelongtothenneedtobecodedasbeingexposedtoGenerativeAItechnologiesornot.

TaskscoringWebuildontheapproachforscoringtasksthatwassuggestedandvalidatedby

Eloundouetal.

(

2023

).Inparticular,weuseGPTitselftoscoreexposureoftasksbasedonwhetherthetaskcanalreadybedonedirectlyusingtheChatGPTinterface,orcanbedonewithadditionaltoolsbuiltontopofit.TwoadvantagesofusinganLLMtoassesstaskstatementsarethatitallowsforbetterreplicabilityoftheresearchintermsofcostandspeedofexecution,andrapidscalingofthemethodtothefullsetof19,265taskstatements.

10

Specifically,weuseOpenAI’sGPT3.5Turbomodeltoclassifythefullsetoftaskstatementsandvalidateitsreliabilityonasmallersubsampleoftasks.

11

ThemodelisgivenanoverallrubricforscoringLLMexposure,aswellastwoexampleinteractionsbetweenauserandanassistantthatshowcasethekindofoutputitisexpectedtoproduce.Then,ataskstatementissubmittedtogetherwithitsO*NETtitle,andthemodelreturnsascore.Thescorescapturewhetherthetimetakentocompletetaskisreducedbyatleasthalf,atconstantquality,iftheworkercanaccessChatGPT-liketools.Thescoresfallintothefollowingcategories:E0indicatesnoexposureasthetooliseitherinsufficientlyusefulforthistask,orcannotbebroughttobearasaresultoftheintrinsicnatureofthetask,e.g.ifitinvolvesphysicalactivities;E1isappliedifa50%reductionincompletiontimeisalreadyfeasiblewiththeexistinglargelanguagemodelinterfaces;E2requiresthatsuchaproductivitygainisfeasible,butonlyoncethecurrentcapabilitiesofthemodelcanbedeployedthroughapplicationswithfurtherinputs(e.g.accesstointernetorproprietarydatabases),orifitistrainedondomain-specificissuesordata;E3isappliedwhentheproductivityincreasewouldrequireimageprocessingcapabilitiesinadditiontocurrenttextprocessing.Importantly,the

9ThisdatacanbeaccessedviatheO*Netdatabaseat/

10Whilesimilarlarge-volumeclassificationtasksinthepastoftenreliedoncrowd-workersononlineplat-formssuchasAmazonMechanicalTurk(MTurk),ChatGPThasrecentlybeenshowntooutperformhumancrowd-workersinaccuracyintextclassificationtasks,whilealsoexhibitinglowervariabilityinscoresacrossmultiplerunsoftheprogram(

Gilardi,Alizadeh,andKubli

,

2023

).Economistshavealsorecentlyusedotherlargelanguagemodelstoclassifyunstructuredtextfromjobpostingsandfoundthattheyoutperformed

othermachinelearningmethods

Hansen,Lambert,Bloom,Davis,Sadun,andTaska

(

2023

).

11ThestructureofthepromptsubmittedtotheOpenAIGPTAPIisshowninAppendix

A

.

8

modelisaskednotonlytorespondwiththescorebu