2025哈佛大学生成式 AI 与工作的本质

WorkingPaper25-GenerativeAIandtheNatureof

生成式AI与工作的本质GenerativeAIandtheNatureofWorkingPaper25-Copyright©2024byManuelHoffmann,SamBoysel,FrankNagle,SidaPeng,andKevinWorkingpapersareindraftform.Thisworkingpaperisdistributedforpurposesofcommentanddiscussiononly.Itmaynotbereproducedwithoutpermissionofthecopyrightholder.Copiesofworkingpapersareavailablefromtheauthor.FundingforthisresearchwasprovidedinpartbyHarvardBusiness

生成式AI与工作本质Copyright©2024byManuelHoffmann,SamBoysel,FrankNagle,SidaPeng,andKevin处获得。FundingforthisresearchwasprovidedinpartbyHarvardBusinessGenerativeAIandTheNatureofAbstract:Recentadvancesinartificialintelligence(AI)technologydemonstrateconsiderablepotentialtocomplementhumancapitalintensiveactivities.Whileanemergingliteraturedocumentswide-rangingpro-ductivityeffectsofAI,relativelylittleattentionhasbeenpaidtohowAImightchangethenatureofworkitself.Howdoindividuals,especiallythoseintheknowledgeeconomy,adjusthowtheyworkwhentheystartusingAI?Usingthesettingofopensourcesoftware,westudyindividualleveleffectsthatAIhasontaskallocation.WeexploitanaturalexperimentarisingfromthedeploymentofGitHubCopilot,agener-ativeAIcodecompletiontoolforsoftwaredevelopers.Leveragingmillionsofworkactivitiesoveratwoyearperiod,weuseaprogrameligibilitythresholdtoinvestigatetheimpactofAItechnologyonthetaskallocationofsoftwaredeveloperswithinaquasi-experimentalregressiondiscontinuitydesign.WefindthathavingaccesstoCopilotinducessuchindividualstoshifttaskallocationtowardstheircoreworkofcodingactivitiesandawayfromnon-coreprojectmanagementactivities.Weidentifytwounderlyingmechanismsdrivingthisshift-anincreaseinautonomousratherthancollaborativework,andanincreaseinexplorationactivitiesratherthanexploitation.Themaineffectsaregreaterforindividualswithrelativelylowerability.Overall,ourestimatespointtowardsalargepotentialforAItotransformworkprocessesandtopotentiallyflattenorganizationalhierarchiesintheknowledgeeconomy.JEL-Classification:H4,O3,Acknowledgement:TheauthorsaregratefulforfinancialandadministrativesupportfromGitHuband,inparticular,forgenerousadvicefromPeterCihon.WethankShaneGreenstein,TimSimcoe,DavidAutor,andSamRansbothamfortheirfeedback.TheauthorsarealsoindebtedforcommentsbyseminarparticipantsattheresearchseminarsfromtheHarvardLaboratoryforInnovationScience,BostonUniversity,theMassachusettsInstituteofTechnology,andtheUniversityofPassau.Wearefurthergratefulforfeedbackfromparticipantsatthe“LaborintheAgeofGenerativeAI”conferenceattheUniversityofChicago,theNBERSI2024DigitalEconomicsandArtificialIntelligenceinCam-bridge,MA,the2024NBERProductivitySeminarinCambridge,MA,the2024AcademyofManagementScienceinChicago,IL,the22ndZEWEconomicsofICTconference,inMannheim,Germany,the20thSymposiumonStatis-ticalChallengesinElectronicCommerceResearchinLisbon,Portugal,theACMCollectiveIntelligenceConferenceinBoston,MA,theMITCodeConferenceinCambridgeMAandtheCESifoAreaConferenceonEconomicsofDigitization2024inMunich,Germany.

生成式AI与工作本质有越来越多的文献记录了AI的广泛生产力影响，但相对较少关注AI如何改变工作的本质本身。当个人，尤其是知识经济中的个人开始使用AI软件的设置，研究AI对任务分配的个体层面影响。我们利用GitHubCopilot的部署这一自然实验，GitHubCopilot是一个面向软件开发者的生成式AI代码补全工具。利用两年期间数百万个工作活动，我们使用项目资格门槛来调查AI不连续设计。我们发现，能够访问Copilot的个人会促使他们将任务分配转向核心编码活动，并言，我们的估计表明，AIJEL分类：致谢：作者感谢GitHub在财务和管理方面的支持，特别是感谢PeterCihonShaneGreensteinTimSimcoeDavidAutorSamRansbotham“生成式人工智能时代的劳动”会议、NBERSI2024数字经济学和人工智能会议（马萨诸塞州剑桥）、2024年NBER生产力研讨会（马萨诸塞州剑桥）、2024年管理科学学会会议（伊利诺伊州芝加哥）、22ZEW20会、ACM集体智能会议（马萨诸塞州波士顿）、麻省理工学院代码会议（马萨诸塞州剑桥）以及CESifo数字化经济领域会议2024（德国慕尼黑）的参与者提供的反馈。Throughouthumanhistory,therehavebeenahandfuloftechnologicalinnovationsthatfundamen-tallyshifthowtheeconomyworks.Theprintingpress,internalcombustionengine,andcomputersareoft-citedexamplesofsuchgeneralpurposetechnologies.Althoughartificialintelligence(AI)hasexistedforsometime,manyhavearguedthatrecentadvancesmaypushitintothiselitecate-goryoftechnologiesthatalterthecourseofhistory(Crafts,2021;Goldfarb,Taska,andTeodoridis,2023;Eloundouetal.,2024).IfAI—broadlydefinedastheuseofcomputersandmachinestomimichumanintelligence––isdestinedtohavesuchasubstantialimpact,wearelikelystillatthebeginningofthistechnologicalrevolutionthatisslowlyandsteadilyreachingallsectorsoftheeconomy(Acemogluetal.,2022).Importantly,thehighesteconomicimpactofAIispredictedtobeonproductivitygrowththroughthelabormarket,especiallyinknowledgeintensiveindus-tries(BughinandManyika,2018;Sachs,2023).However,duetothenoveltyandbreadthofAI,researchisonlystartingtoelucidateitsimpactonthenatureofworkandtaskallocationinproduc-tionsettings.ThisisparticularlytrueofgenerativeAI(generativeAI)—asubsetofAIbuiltonlarge-languagemachine-learningmodels(LLMs)—whichexplodedontothescenein2022andcurrentlyrepresentsthecutting-edgeofAI.Thesemodels,includingOpenAI’sGPT4,Google’sGemini,1Meta’sLLaMa,andnumerousothers,aretrainedonmassive,Internet-scaledatabasesandusebillionsofparameterstoconstructaprobabilisticmodelthatpredictswhatthenextwordinananswertoapromptfromausershouldbe.Thesemodelscanalsobetrainedondatasetsthataremorefocusedonspecificcontexts—e.g.,health,finance,customerservice,softwaredevelop-ment,etc.Whetherandhowthesenewtechnologieswillshapethenatureofworkremainopenquestions.Further,whetherAIcanbeacomplementtoskilledworkers(Autor,2024)andhelpaddresscriticalaspectsofteamproduction,especiallyinthecontextofdistributedwork,hasgoneAlthoughsomeearlystudiesongenerativeAIhaveshownpositivehigh-levelproductivityimpacts(Brynjolfsson,Li,andRaymond,2023;Dohmke,Iansiti,andRichards,2023;NoyandZhang,2023;Pengetal.,2023),itislessclearwhatthemechanismsbehindtheseimprovements1FormerlyknownasBard.

这类通用技术中经常被引用的例子。尽管人工智能（AI）已经存在了一段时间，但许多人认为最近的进步可能将其推向改变历史进程的精英技术类别（Crafts2021Goldfarb,器来模拟人类智能——注定要产生如此重大的影响，那么我们可能还处于这场正在缓慢AI（生成AI）（LLMs）AI2022AIOpenAIGPT4Gemini1Meta的LLaMa以及许多其他模型，它们在庞大的、互联网规模的数据库上进行训练，什么。这些模型还可以在更专注于特定上下文的数据库上进行训练——例如，健康、金Pengetal.2023），但这些改进背后的机制尚不清楚。之前被称为Bardare.DoestheuseofgenerativeAIshiftuserstofocusonparticulartypesoftasksthatleadtothoseproductivityimprovements?Ifso,whichtasks?HowexactlydoestheworkprocesschangewhenusinggenerativeAI?Toanswerthesequestions,wedevelopatheoreticalmodelthatleadstotestablehypothesesthatofferinsightsintowhereandwhythemostsalientimpactsarelikelytooccur.Understandingtheseimpactsinformslaborstrategyinamannerrelevanttobothfirms(Tamayoetal.,2023)andpolicymakers(U.S.DepartmentofLabor,2024),includinghiringpoli-cies,worktrainingprograms,andupskillingorreskillingeffortsforcurrentemployees.ThekeychallengeintestingourhypothesesandassessinghowAIchangesthenatureofworkforworkershasbeenintroducedinaquasi-exogenousmanner.Oursetting—theintroductionofGitHubCopilot,asoftwaredevelopmentgenerativeAItool,forkeydevelopers(knownasmain-tainers)inopensourcesoftware(OSS)projects—addressesbothofthesecriteria.OSSsourcecodeispubliclyavailableandpermissivelylicensedforuse,modification,andredistribution.Fre-quentlydevelopedbydistributedteamsofdevelopers,OSSisaclassicexampleofaproductthatisproducedthroughthedistributedworkofteamsandisgenerallyfree(MoonandSproull,2002).AlthoughOSScreatessocietalvalueontheorderoftrillionsofdollars(Hoffmann,Nagle,andZhou,2024)andisthereforeimportantinitsownright,weargueandprovidesuggestiveevidencethatthefindingsinthissettinggeneralizetothebroadersetofworkactivitiesthatoccurintheknowledgeeconomy.Further,aswithmanyteamproductionsettings,OSSalsosuffersfromthe“linchpin”problem(Ballester,Calv´-Armengol,andZenou,2006;Godin,2010)asasmallsetofdevelopersarethedrivingforcebehindthewidelyusedandincrediblyvaluabledigitalinfras-tructurethathascometounderliesoftwaredevelopmentandthemoderneconomyasawhole(Eghbal,2020;Geiger,Howard,andIrani,2021;Hoffmann,Nagle,andZhou,2024).Inpractice,aninfluxofnon-expertsenabledbydecreasingcommunicationcosts(Altman,Nagle,andTush-man,2015)createsanadditionalburdenondevelopers,whomusttriagesupportrequests,reviewcontributions,andotherwisemanagetheirproject’sgrowingcommunity.Indeed,surveyevidence

AI是哪些任务？使用生成式AI时，工作流程究竟是如何变化的？为了回答这些问题，我们的地方和原因提供了见解。了解这些影响将有助于企业（Tamayo，2023）定者（美国劳工部，2024）的劳动力战略，包括招聘政策、工作培训计划和为现有员工进AI（1）工作模式是可观察的，并且（2）一种专门为工人定制的AI工具以准外生的方式引入。我们的环境——GitHubCopilot软件开发生成式AI工具的引入，针对开源软件（OSS）项目中的主要开发者（称为维护者）OSS并且具有许可使用、修改和重新分发的许可。通常由分布式开发团队开发，OSS是产品通过团队分布式工作生产的经典例子，通常是免费的（Moon和Sproull，2002）。尽管OSS创造了数万亿美元的社会价值（Hoffmann，Nagle和Zhou，4），因此本身很重要，但我们认为并提供了有说服力的证据，证明在这个环境中得出的结论可以推广到知识经济中发生的更广泛的工作活动。此外，与许多团队生产环境一样，OSS也面临着“关键人物”问题（Ballester，Calvo‑Armengol和Zenou，2006；，0），因为一小部分开发者是广泛使用且价值巨大的数字基础设施背后的驱动力，该基础设施已成为软件开发和整个现代经济的基础（，0；Geiger，Howard和Irani，1；Hoffmann，Nagle和Zhou，4）。实际上，由于通信成本的降低，非专家的大量涌入（Altman，Nagle和Tushman，5）给开发者带来了额外的负担，他们必须对支corework(coding)andtoomuchonmanagerial(projectmanagement)tasks(Nagleetal.,2020a).Withthesefactorsinmind,interventionswiththepotentialtorelaxconstraintsonkeyindividualsareofgreatinteresttothedistributedproductionsettingofOSSandarelikelytogeneralizetonumerousothersettingsasdistributedworkhasbecomeincreasinglycommon.WeexploitaspectsofthegeneralaccesslaunchofGithubCopilottothebroaderpublicinJune2022toestablishcausaleffectsofgenerativeAIwheresomedevelopersbelowacertainthresholdofaninternalrankingreceivedfreeaccesstothecodingAIandothersdidnot.Westartwithapanelof187,489distinctdevelopersobservedweeklyfromJuly2022throughJuly2024,whichresultsinmillionsofdeveloper-weekobservationsforCopilotusageandactivitylevelsinpublicGitHubrepositories.2Withinthedatasetoftopdevelopers,wefindthatthosewhoreceivefreeaccesstoCopilotduringthegeneralaccessperiodincreasetheirrelativeshareofcodingtaskswhilereducingtheirrelativeshareofprojectmanagementactivities.Thedynamicsofthetreatmenteffectsarestableforourtwoyearperiod.Wedigfurtherintothemechanismsunderlyingtheseeffectsandfindthattheyaredrivenbyanincreaseinautonomousbehavior(andarelateddecreaseincollaborativebehavior)andanincreaseinexplorationbehavior(ratherthanexploitation).Further,wefindlowerabilitydeveloperswhoreceiveaccesstoAIincreasecodingandreduceprojectmanagementtoagreaterextentcomparedwiththeirhigherabilitypeers.Theresultsarerobusttothestandardregressiondiscontinuitydesigntestsandtodifferentestimationproceduressuchasdifference-in-differenceandmatching.Further,theresultsareconsistentwhenconsideringwhetherdevelopersareworkingonbehalfoftheiremployersorasvolunteers,addingsupporttothelikelihoodthatthesefindingsgeneralizebeyondtheOSSsettingtoabroadersetofOurresultscontributetoagrowingliteratureontheproductivityimpactsofAIinimportantways.Earlyworkinthisareapositsgeneralproductivitygains(Agrawal,Gans,andGoldfarb,2019;Corrado,Haskel,andJona-Lasinio,2021;RajandSeamans,2018),butthatthegainsmaynotbeevenlydistributed(Brynjolfsson,Rock,andSyverson,2018;FurmanandSeamans,2AGitHubrepositoryisalocationwhereallaspectsofaprojectarestoredincludingitssourcecode,andrevision

核心工作（编码）以及过多的管理任务（Nagle等人，2020a）。考虑到这些因素，旨我们利用2022年6月GitHubCopilot向更广泛公众开放的一般访问功能，来建立生成式AI的因果效应，其中一些低于内部排名一定阈值的开发者获得了免费访问编码AI2022720247187,489不同的开发者面板开始，这导致了数百万个开发者周观察结果，用于Copilot在公共GitHub存储库中的使用和活动水平。在顶级开发者的数据集中，我们发现那些在一般访问期间获得Copilot免费访问的人增加了他们的编码任务相对份额，同时减少了他们的探索行为的增加（而不是利用）驱动的。此外，我们发现能力较低的开发者在使用AI我们的结果以重要方式为关于AI生产力影响的日益增长的文献做出了贡献。该领域早期的工作提出了总体生产力收益（Agrawal，Gans和Goldfarb，2019；Corrado，Haskel和Jona‑Lasinio，2021；Raj和Seamans，2018），但收益可能并不均匀（Brynjolfsson，Rock和Syverson，2018；Furman和Seamans，2019）。GitHub存储库是一个项目所有方面都存储的地方，包括其源代码、文档和修订历Subsequentempiricalworkhaslargelyconfirmedthesepredictionsandfoundwide-rangingpro-ductivitybenefitstousingAI,atboththefirmlevel(Czarnitzki,Fern´ndez,andRammer,2023)andtheindividuallevel(F¨generetal.,2022).Particularlyrelatedtothisstudy,researchfo-cusedonCopilotspecificallyhaseitherbeenconductedusingamuchsmallersampleofworkerswithinfirms(Cuietal.,2024)orrelyingonobservationaldatawithoutthebenefitofknowingpreciselywhichcontributorstoOSSweregivenfreeaccesstoCopilot(Yeverechyahu,Mayya,andOestreicher-Singer,2024).Ourworkisconsistentwiththispriorresearchbutaddsadditionalnuancetothelaboraugmentingtechnicalchangeliterature(Acemoglu,2003).Bygoingbeyondproductivitytoexplorehowtechnologychangesthenatureofwork,weprovideoneofthelargestnaturalexperimentsofgenerativeAIandit’simpactonhighlydisaggregatedmeasuresofworkprocesses“inthewild”overatwoyeartimehorizon.OurmainfindingsidentifychangesinthenatureofworkofAIadoptersintheirknowledgeworkprocesses.WeshowthatwhensoftwaredevelopersleverageAImore,theyreallocatetheireffortstowardstechnicalcodingactivitiesandawayfromauxiliaryprojectmanagementactivitiesthatinvolvesocialinteractionswithotherdevelopers.Thisisasignthattheworkerslikelywillintensifytheircorecontributionstopublicgoods,suchasopensourcesoftware,whenleverag-ingskillaugmentingtechnologylikegenerativeAI.Itisalsoconsistentwithreducedcollaborativefrictionsduringtheproblemsolvingprocessofworkandachangeinthewayworkersinteractwitheachotherontheplatform.WecomplementthecurrentliteraturethatleveragesITandconsultancychatsupportAIsandfocusesonhigh-levelproductivityimpactsthroughexperimentation(Bryn-jolfsson,Li,andRaymond,2023;Dell’Acquaetal.,2023)byinvestigatingthenatureofworkthroughchangesinworkactivitiesandhumaninteractionprocessesoverthetwoyearsfollowingtheintroductionoftheprogrammingLLM.BeyondtheidentificationofcausaleffectsthatgenerativeAIhasondecentralizedwork,ourresultssuggestimportantimplicationsforthefutureofOSS.OSShasreceivedgrowingattention(LernerandTirole,2002)asithasbecomeanincreasinglycriticalpartofthemoderneconomy,tothepointwhere96%ofcorporatecodebasescontainsomeopensourcecode(Synopsys,

带来了广泛的生产力效益。（Czarnitzki，Fernández和Rammer，2023年）特别是与本研究相关，针对Copilot的具体研究要么是在企业内部使用较小的工人样本进行的年费访问Copilot的贡献者（Yeverechyahu，Mayya和Oestreicher‑Singer，2024年）。（Acemoglu，2003年）。通过超越生产力，探讨技术如何改变工作的本质，我们提供了一项关于生成式人工智能及其对高度细分的“野外”工作流程影响的两项最大自然实我们的主要发现确定了AI采用者在他们的知识工作流程中工作本质的变化。我们表明，当软件开发人员更多地利用AI时，他们将他们的努力重新分配到技术编码活动，而不是涉及与其他开发人员社交互动的辅助项目管理活动。这表明，当利用像生成式AI这编程LLM引入后的两年内工作活动和工作互动过程的变化，补充了当前文献，该文献利ITAI，并通过实验关注高级生产力影响（Brynjolfsson，Li和Raymond，2023年；Dell’Acqua等人，2023年）除了识别生成式AI对去中心化工作产生的因果效应之外，我们的研究结果对开源软的关注（Lerner和Tirole，2002），以至于96%的企业代码库中包含一些开源代码Further,recentstudiesestimatethevalueofOSStobeontheorderofbillionsofdollarsforthesupplyside(Blindetal.,2021;Robbinsetal.,2021)andtrillionsofdollarswhenaccount-ingforusage(Hoffmann,Nagle,andZhou,2024).Additionally,firmusageof,andcontributionto,OSShasimportantimplicationsforfirmproductivity(Nagle,2018,2019),firmcompetition(Boysel,Hoffmann,andNagle,2024)andentrepreneurialactivity(Wright,Nagle,andGreenstein,2023).However,despitetheimportanceofOSS,manycriticalprojectsareunder-resourced(Egh-bal,2020;Nagleetal.,2020b)asnumerousfirmsfree-rideontheeffortsofotherswithoutgivingback(Lifshitz-AssafandNagle,2021)leavingvolunteerdevelopersburntoutandoverwhelmed(Ramanetal.,2020).Asourresultsshow,generativeAImayofferasolutiontohelpaddresstheseconcernsandallowtopdeveloperstomoreeasilycontributetothecommongoodbysolvingmoreissues.PriorresearchhasshownthatOSSdevelopersgenerallycontributetoOSSbecauseitgivesthemacreativeoutletandtheydonotwanttospendtheirtimeonmanagerialtaskslikesecurityanddocumentation(Nagleetal.,2020a).AI-poweredtoolsmaymakeiteasiertoquicklyaddresssuchmanagerialtasks,sodeveloperscanspendtimeinamannertheyprefer,whilestillensuringthesecurity,stability,andusabilityofOSS.Theremainderofthispaperproceedsasfollows.Section1developsamodeloftheimpactofgenerativeAIonindividualworkersleadingtotestablehypotheses.InSection2,wediscusstheenvironmentwithinwhichthestudyoccurs.InSection3wecharacterizeourdatasetanddiscusstheconstructionofoursample.WehoneintothesetofdevelopersthatobtainCopiloteligibilityforfreeviaaninternalrankingfromGitHubandpresentourestimationstrategyinSection4.Wethenpresentourresultsusingaregressiondiscontinuitydesign(Section5)whilealsoexploringthemechanismsatplay,andofferingempiricalsupportforourhypotheses.Wediscussthelimitations,implications,andaback-of-the-envelopecalculationtounderstandhowtheresultsarelikelytogeneralizebeyondourempiricalsettinginSection6.Section7concludes.

此外，最近的研究估计开源软件（OSS）对供应方的价值约为数十亿美元（Blind2021年；Robbins等人，2021年），而当考虑到使用时，其价值则达到数万亿美元（Hoffmann、Nagle和Zhou，2024年）。此外，对OSS的使用和贡献对企业的生产力（Nagle，2018年，2019年）、企业竞争（Boysel、Hoffmann和Nagle，2024年）和创业活动（Wright、Nagle和Greenstein，2023年）具有重要意义。然而，尽管OSS的重要性不言而喻，许多关键项目却资源不足（Eghbal，2020年；Nagle等人，2020年b），因为许多企业免费搭乘他人的努力，而不给予回报（Lifshitz‑AssafNagle，2021），（Raman等人，2020年）。正如我们的研究结果所示，生成式AI可能为解决这些问题献。先前的研究表明，开源软件（OSS）的开发者通常因为开源软件为他们提供了一个创意出口，他们不想把时间花在管理任务上，如安全和文档（Nagle2020a）。AI驱动的工具可能使快速解决此类管理任务变得更加容易，这样开发者就可以以他们喜本文的其余部分如下进行。第1节1构建了一个生成式AI对个体工人影响的模型，2233中，我44GitHubCopilot不连续设计（55）来展示我们的结果，同时探讨其中的机制，并为我们的假设提供66中，我们讨论了局限性、影响以及一个估算，以了解结果如何可能推广到我们的实证环境之外。第7节7得出结论。TheoreticalIntheknowledgeeconomy-whichisanincreasinglylargesectoroftheoveralleconomy-,highlyproductiveindividualscanoftenbecomevictimsoftheirownsuccess.Acommonpatternrelevanttoourstudyoccurswhenadeveloperdoesexceptionalcorework,theyareoftenassignedmoremanagerialworkasaresult.Forexample,inthecontextofacademia,whereresearchandteachingarecorework,theresultofdoingagoodjobontheseistogetpromotedandthentobegivenmoremanagerialtasksincludingdepartmentandschoolcommitteeassignments.Thiscanbesummedupbytweakingthewell-knownphrase“Therewardforgoodworkismorework.”tobe“Therewardforgoodcoreworkismoremanagerialwork.”Thisisparticularlytrueinthecontextofpublicgoodswhich,aspublicgoodprojectsbecomemoresuccessfulandmorewidelyused,newusersrequestmorefromthosethatarecreatingthegood.3Thus,theintroductionofanAItoolthatcanhelpreducesomeofthisburdenmayplayanimportantroleinthecreationofpublicInthefollowingsection,wedeveloptheexpositionofourempiricalsettingbyusingasimpleeconomicframeworkwhereindividualworkerschoosebetweentwoactivitiestomaximizeutility:coreworkcandprojectmanagementm.Lettheworker’spreferencesuθ(·)beindexedtheparametervectorθ.Ineachperiod,eachworkerchoosescandmtosolvethefollowingstaticutilitymaximizationproblem:

Theoretical稍作修改来概括，即出色核心工作的回报是更多的管理工作。这在公共物品的背景下尤其如的人提出更多要求。因此，引入一个可以帮助减轻一些这种负担的AI工具可能在公共物好（·）由参数向量（θ）索引。在每个时期，每个工人选择cm来解决以下静态效c,

uθ(c,

c,

uθ(c,

subject pcc+pmm≤wherec,m≥0andpc,pm>0.Thechoiceisconstrainedbyrelativecostsofeach

subject pcc+pmm≤cm≥0pcpm>0。选择受限于每种活动的相对成本，p=（pcpmp=(pc,

),andunitsofanendowmentresource,ω.4Inlinewithsimpleeconomicmodels,

assumethatpreferencesaretime-invariantandthatthereareno3InourempiricalcontextofOSS,this“burden”ofbeinganopensourcedeveloper(Geerling,2022)hasbeencitedassignificantdriverofburnoutandabandonmentofopensourcedevelopment(Nagleetal.,2020a;Ramanetal.,2020).Thus,alleviatingthisburdenisofcriticalimportance.4Inoursetting,theresourceendowmentωcanbeinterpretedastheagent’s“taskbandwidth”theyareabletoacrossvariouswork

倦怠和放弃开源开发的显著驱动因素（Nagle等人，2020a；Raman等人，2020）。因此，减轻这种负担至关重要。在我们的设置中，资源禀赋4σ+β1/σ uθ(c,m)β1/σσ+β1/σσ+β1/σ uθ(c,m)β1/σσ+β1/σ

uθ(c,uθ(c,m)β1/σ

σ−1

σ−1 whereforθ=(σ,βc,βm),σistheelasticityofsubstitutionbetweencandm,andβc,βmareCESshareparameters.Withoutlossofgenerality,afternormalizingpm=1,pcbecomestherelativecostofdoingcorework.Undertheoptimalchoiceofthesetwoactivities,theMarshallianmandsforcoreworkandprojectmanagementcanbeexpressedasfunctionsoftheseproductivity,preference,andendowmentparameters:

其中对于θ(σ,βc,βmσ是c和m之间的替代弹性，而βc,βm是CES分享参数。不失一般性，在标准化pm=1后，pc成为核心工作的相对成本。在这些两种活动的最优选 cp1−σ+

c⋆

p1−σ+βm

m⋆

m⋆

βcp1−σ+ βcp1−σ+βm βmConsistentwithpriorliterature(Acemoglu,Kong,andRestrepo,2024),wechoosetotheinterventionofgenerativeAIasareductioninthecostofcorework,pc.Assuch,thecompar-ativestaticswithrespecttopcareofinterest.DetailsonthecomparativestaticsforachangeinpccanbefoundinAppendixD.AconsequenceoftheCESdemandsystemisthatareductioninpcincreasestheoptimallevelofcoreworkunderanyvalueoftheelasticityofsubstitutionσ>0.Furtherempiricalsupportforthisrelationshipcomesfrompriorliteratureinthefield.BeyondAI,automationandinformationsystemstechnologieshavebeenshowntocomplementskilledlaborandleadtoareshapingoforganizationalpracticesthatallowsworkerstoengageinmorecom-plexandstrategicactivities(Autor,Levy,andMurnane,2003;Orlikowski,2007;Zammutoetal.,2007).Further,whentechnologyreducesthecostoreffortassociatedwithcertaintasks,economicandmanagementtheorysuggeststhatworkerswillincreasetheamountofthattasktheyperform(AcemogluandRestrepo,2018;Bloometal.,2014).Assuch,wearriveatthefollowingprimary

建模为降低核心工作的成本，pcpc的比较静态分析是有趣的。关于pc变化的比较静态分析细节可以在附录D中找到。CES需求系统的结果是，降低pc会增加在任何替代弹性σ>0值下的核心工作的最优水平。这一关系的进一步经验支持来自该领域的先前文献。除了AI之外，自动化和信息系统技术已被证明可以补充熟练劳动力，并导致组织实践的重新塑造，使工人能够参与更复杂和战略性的活动（AutorLevyandMurnane,Hypothesis1a(H1a)AftertheadoptionofanAItoolthatassistswithcoreworktasks,aworker’scoreworktasksincreaseasapercentageofalltasks.Incontrasttotheimpactoncoreworktasks,theimpactofgenerativeAIonmanagerialtasksislessclearanddependentontheelasticityofsubstitutionσ.Adoptionofthetoolmayleadtonochangeintheshareofprojectmanagementwhentheelasticityofsubstitutionσ=1.Alternatively,projectmanagementmaydropwhenthepriceofcoreworkdropsgivenaσ>1(projectmanage-mentisasubstitute),ormayincreasewhen0<σ<1(projectmanagementisacomplement).Thisisconsistentwithpriorliteraturethathasshownthatwhileautomationandtechnologytendtoreducetheburdenofroutinetasks,theydonotnecessarilyeliminatemanagerialresponsibilities,whichmayrequirehumanjudgment,creativity,andinterpersonalcoordination(Autor,Levy,andMurnane,2003;Mintzberg,1994).Consequently,evenasAIcanreducethetimespentonroutinetasks,workersmaystillengageinhigh-leveldecision-makingandteamleadership,leavingtheneteffectonmanagerialtasksuncertainandbestdeterminedempirically.Hypothesis1b(H1b)AftertheadoptionofanAItoolthatassistswithcoreworktasks,thechangetoaworker’smanagerialtasksasapercentageofalltasksisambiguous.Wenextseektobetterunderstandthemechanismsthataredrivingtheseeffects.WhatistheeffectofAItechnologyontaskallocationacrossspecifickindsofcoreworkandprojectmanage-ment?Tothisend,weextendthebaseline2-goodCESmodelintoanestedCESmodel,underwhichcoreworkandprojectmanagementareinsteadmodeledascompositesofmoredisaggre-gatedgoods.

1a（H1a）AI工具后，工人的核心工作任务占所有任与对核心工作任务的影响相比，生成式AI对管理任务的影响不太明确，并且取决于替代弹性的大小σ。当替代弹性σ=1时，采用该工具可能导致项目管理份额不变。另一方面，当核心工作价格下降时，由于σ>1（项目管理是替代品），项目管理可能会下0<σ<1（项目管理是互补品）时可能会增加。这与先前的研究结果一致，责任，这可能需要人类的判断、创造力和人际协调Autor，Levy和Murnane，2003；1b（H1b）AI工具后，工人管理任务占所有任务的百接下来，我们试图更好地理解驱动这些效应的机制。AI技术对特定类型的核心工作2CESCESu(c,u(c,c,m,m)β1/σu(c,c

σ−1

σ−1 σ+β1/σu(m,m u(c,c,m,m)βσ+β1/σu(m,m u(c,c,m,m)β1/σu(c,cσ+β1/σu(m,m u(m1,m2)arealsoCESfunctionssimilartoequation2butwiththeirrespectivewithin-nest

u（c1c2）u（m1m2）也是类似于方程2的CES函数，但它们的各自嵌套内的替代弹性σc和σm与细分商品之ticitiesofsubstitution

and

thatcorrespondtorelativesubstitutionbetween

goodsc1,c2andm1,m2respectively.HencethenestedCESextensiontothebaselinemodelper-mitsbothmorerefineddefinitionsofworkpatternsandrichersubstitutionpatternsbetweenthesedisaggregatedgoods.DetailsonthefullnestedCESmodel,aswellasthecomparativestaticsachangeinpccanbefoundinAppendixWeusethismodeltoconsidertwomechanismsthroughwhichtheprimaryrelationshipoper-ates.Inthefirstmechanism,weconsiderwhetherworkersengageinworkthatismoreautonomous(lessinteractionwithothersworkingontheproject)ormorecollaborative(moreinteractionwithothersworkingontheproject).Individualscanengageineitherautonomouscorework,c1or

CESCES模型以及pc变化的比较静态分析，可以在附录D中找到。员互动较多）的工作。个人可以从事自主核心工作，c1或协作核心工作，c2或管理等效工作，m1和m2。我们发现，通过AI降低核心工作成本，pc可以提高核心工作的需求laborativecorework,c2orthemanagerialequivalents,m1andm2,Wefindthatareduction

1

<1thecostofcoreworkthroughAI,pccanincreasethedemandofcorework(asinHypothesis

butitdoesnotnecessarilyneedtohappenthroughbothautonomouscoreworkand

<1coreworksimultaneously.Indeed,assumingthattheelasticityofsubstitutionσc>1andthat

1（以及

priceofautonomousworkislowerthanthepriceofcollaborativework,

<1impliesthat

workerwillshifttheireffortstowardsautonomouscoreworkandawayfromcollaborativecoreworksinceautonomouscoreworkislesscostlythancollaborativecorework.Thesame

trueformanagerialworksuchthat

1and

<1.Whiletherearereasonstofindalternativeparameterspaces,

1(and

1)arecredible,wefindthisrestrictedspacewiththepre-existingwedgeofpricesgenerallyplausibleinthecontextofworkersthatarealreadyworkinginahighlycollaborativesettingliketheincreasinglycommonparadigmofdis-tributedwork.Wehypothesizethattheirmainissues—collaborativefrictionssuchasthecostofcoordination,requestsfromotherstosolveproblems,orpersonalconflicts—maybemorecostlythansolvingproblemsbythemselveswhentheyhaveAIasasubstituteavailableatanytime.Thepredictionsofthenestedmodelextensioncansimilarlybederivedfromtheliterature.ThismechanismbuildsontheideathatgenerativeAItoolsreduce(oreveneliminate)muchofthecognitiveandcommunicativefrictioninherentindistributedwork,enablingworkerstotacklecomplextasksautonomously.Priorresearchhasshownthattechnologiesthatstreamlinecommu-nicationanddecision-makingprocessesreducetheoverheadofcollaboration,freeingworkers

嵌套模型扩展的预测可以从文献中类似地推导出来。这种