Preliminary.Pleasedonotciteorcirculate.

ArtificialIntelligenceandtheModernProductivityParadox:

AClashofExpectationsandStatistics∗

ErikBrynjolfsson,MITSloanSchoolofManagementandNBERDanielRock,MITSloanSchoolofManagement

ChadSyverson,UniversityofChicagoBoothSchoolofBusinessandNBER

September2017Abstract.Weliveinanageofparadox.Systemsusingartificialintelligencematchorsurpasshumanlevelperformanceinmoreandmoredomains,leveragingrapidadvancesinothertechnologiesanddrivingsoaringstockprices.Yetmeasuredproductivitygrowthhasfalleninhalfoverthepastdecade,andrealincomehasstagnatedsincethelate1990sforamajorityofAmericans.Wedescribefourpotentialexplanationsforthisclashofexpectationsandstatistics:falsehopes,mismeasurement,redistribution,andimplementationlags.Whileacasecanbemadeforeachexplanation,wearguethatlagsarelikelytobethebiggestreasonforparadox.ThemostimpressivecapabilitiesofAI,particularlythosebasedonmachinelearning,havenotyetdiffusedwidely.Moreimportantly,likeothergeneralpurposetechnologies,theirfulleffectswon’tberealizeduntilwavesofcomplementaryinnovationsaredevelopedandimplemented.Theadjustmentcosts,organizationalchangesandnewskillsneededtoforsuccessfulAIcanbemodeledasakindofintangiblecapital.Aportionofthevalueofthisintangiblecapitalisalreadyreflectedinthemarketvalueoffirms.However,mostnationalstatisticswillfailtocapturethefullbenefitsofthenewtechnologiesandsomemayevenhavethewrongsign.

∗Contactinformation:Brynjolfsson:erik.brynjolfsson@gmail.com;MITSloanSchool,E62-414,100MainStreet,Cambridge,MA02142;Rock:drock@mit.edu;100MainStreet,Cambridge,MA02142;Syverson:chad.syverson@chicagobooth.edu;UniversityofChicagoBoothSchoolofBusiness,5807S.WoodlawnAve.,Chicago,IL60637.

1

Thediscussionaroundtherecentpatternsinaggregateproductivitygrowth

highlightsaseemingcontradiction.Ontheonehand,thereareastonishingexamplesof

potentiallytransformativenewtechnologiesthatcouldgreatlyincreaseproductivityand

economicwelfare(seee.g.BrynjolfssonandMcAfee,2014).Therearesomeearlyconcrete

signsofthesetechnologies’promise,therecentleapsinartificialintelligence(AI)

performancebeingthemostprominentexample.However,atthesametime,measured

productivitygrowthoverthepastdecadehasslowedsignificantly.Thisdecelerationis

large,cuttingproductivitygrowthbyhalformoreofitslevelinthedecadeprecedingthe

slowdown.Itisalsowidespread,havingoccurredthroughouttheOECDand,morerecently,

amongmanylargeemergingeconomiesaswell(Syverson2017).1

WethusappeartobefacingareduxoftheSolow(1987)Paradox:wesee

transformativenewtechnologieseverywherebutintheproductivitystatistics.

Inthispaper,wereviewtheevidenceandexplanationsforthemodernproductivity

paradoxandproposearesolution.Namely,thatthereisnoinherentinconsistencybetween

forward-lookingtechnologicaloptimismandbackward-lookingdisappointment.Bothcan

simultaneouslyexist.Indeed,therearegoodconceptualreasonstoexpectthemto

simultaneouslyexistwhentheeconomyundergoesthekindofrestructuringassociated

withtransformativetechnologies.Inthispaperweargueandpresentsomeevidencethat

theeconomyisinsuchaperiodnow.

SourcesofTechnologicalOptimism

PaulPolman,Unilever’sCEO,recentlyclaimedthat“Thespeedofinnovationhas

neverbeenfaster.”Similarly,BillGates,Microsoft’sco-founderobservesthat“Innovationis

movingatascarilyfastpace.”VinodKhoslaofKhoslaVenturessees“thebeginningsof...[a]

rapidaccelerationinthenext10,15,20years”.EricSchmidt,ExecutiveChairmanof

AlphabetInc.,believes“we’reentering…theageofabundance[and]duringtheageof

abundance,we’regoingtoseeanewage…theageofintelligence”.RayKurzweilfamously

predictsthatTheSingularity,whenAIsurpasseshumans,willoccursometimearound

1Aparallelyetmorepessimisticallyorienteddebateaboutpotentialtechnologicalprogressistheactivediscussionaboutrobotstakingjobsfrommoreandmoreworkers(e.g.,BrynjolfssonandMcAfee,2011;AcemogluandRestrepo,2017;Bessen,2017;AutorandSalomons,2017).

2

2045.2Assertionsliketheseespeciallyarecommonamongtechnologyleadersandventure

capitalists.

Inpart,thesereflectthecontinuingprogressofITinmanyareas,fromcore

technologyadvanceslikefurtherdoublingsofbasiccomputerpower(butfromeverlarger

bases)tosuccessfulinvestmentintheessentialcomplementaryinnovationslikecloud

infrastructure,andnewservice-basedbusinessmodels.Butthebiggersourceofoptimism

isthewaveofrecentimprovementsinAI,especiallymachinelearning.Machinelearning

representsafundamentalchangefromthefirstwaveofcomputerization.Historically,most

computerprogramssucceededbymeticulouslycodifyinghumanknowledge,step-by-step,

mappinginputstooutputsasprescribed.Incontrast,machinelearningsystemsfigureout

therelevantmappingontheirown,typicallybybeingfedverylargedatasetsofexamples.

Usingthesemethods,machineshavemadeimpressivegainsinperceptionandcognition,

twoessentialskillsformosttypesofhumanwork.Forinstance,errorratesinlabelingthe

contentofphotosonImageNet,adatasetofover10millionimages,havefallenfromover

30%in2010tolessthan5%in2016andmostrecentlyaslowas2.2%withSE-ResNet152

intheILSVRC2017competition(seeFigure1).3Errorratesinvoicerecognitiononthe

Switchboardspeechrecordingcorpus,oftenusedtomeasureprogressinspeech

recognition,haveimprovedfrom8.5%to5.5%overthepastyear(Saonetal.2017).The

fivepercentthresholdisimportant,becausethatisroughlytheperformanceofhumansat

eachofthesetasksonthesametestdata.

Whilenotatprofessionalhumanperformanceyet,Facebook’sAIResearchteam

recentlyimproveduponthebestmachinelanguagetranslationalgorithmsavailableusing

convolutionalneuralnetsequencepredictiontechniques(Gehringetal.2017).Deep

learningtechniqueshavealsobeencombinedwithreinforcementlearning,apowerfulset

2http://www.khoslaventures.com/fireside-chat-with-google-co-founders-larry-page-and-sergey-brin

https://en.wikipedia.org/wiki/Predictions_made_by_Ray_Kurzweil#2045:_The_Singularity

https://www.theguardian.com/small-business-network/2017/jun/22/alphabets-eric-schmidt-google-artificial-intelligence-viva-technology-mckinsey

3http://image-net.org/challenges/LSVRC/2017/results.ImageNetincludeslabelsforeachimage,originallyprovidedbyhumans.Forinstance,thereare339,000labeledasflowers,1,001,000asfood,188,000asfruit,137Kasfungus,etc.

3

oftechniquesusedtogeneratecontrolandactionsystemswherebyautonomousagentsare

trainedtotakeactionsgivenanenvironmentstatetomaximizefuturerewards.Though

nascent,advancesinthisfieldareimpressive.Inadditiontotheirvictoriesinthegameof

Go,GoogleDeepMindhasachievedsuperhumanperformanceinmanyAtarigames

(Fortunatoetal.2017).

Thesearenotabletechnologicalmilestones.Buttheycanalsochangetheeconomic

landscape,creatingnewopportunitiesforbusinessvaluecreationandcostreduction.For

example,asystemusingdeepneuralnetworkswastestedagainst21boardcertified

dermatologistsandmatchedtheirperformanceindiagnosingskincancer(Estevaetal.

2017).TheneuralnetworksatFacebookareusedforover4.5billiontranslationseach

day.4

Figure1.AIvs.HumanImageRecognitionErrorRates

Anincreasingnumberofcompanieshaverespondedtotheseopportunities.Google

nowdescribesitsfocusas“AIfirst”,whileMicrosoft’sCEOSatyaNadellasaysAIisthe 4https://code.facebook.com/posts/289921871474277/transitioning-entirely-to-neural-machine-translation/]

4

“ultimatebreakthrough”intechnology.TheiroptimismaboutAIisnotjustcheaptalk.

TheyaremakingheavyinvestmentsinAI,asareApple,Facebook,andAmazon.Asof

September2017,thesecompaniescomprisethefivemostvaluablecompaniesintheworld.

Meanwhilethetech-heavyNasdaqcompositestockindexmorethandoubledbetween

2012and2017.AccordingtoCBInsights,globalinvestmentinprivatecompaniesfocused

onAIhasgrownevenfaster,increasingfrom$589millionin2012toover$5billionin

2016.5

TheDisappointingRecentReality

Whilethetechnologiesdiscussedaboveholdgreatpotential,thereislittlesignthat

theyhaveyetaffectedaggregateproductivitystatistics.Laborproductivitygrowthratesin

abroadswathofdevelopedeconomiesfellinthemid-2000sandhavestayedlowsince

then.Forexample,aggregatelaborproductivitygrowthintheU.S.averagedonly1.3%per

yearfrom2005to2016,lessthanhalfofthe2.8%annualgrowthratesustainedover1995

to2004.Fully28of29othercountriesforwhichtheOECDhascompiledproductivity

growthdatasawsimilardecelerations.Theunweightedaverageannuallaborproductivity

growthratesacrossthesecountrieswas2.3%from1995to2004butonly1.1%over2005

to2015.6What’smore,realmedianincomehasstagnatedsincethelate1990sandnon-

economicmeasuresofwell-being,likelifeexpectancy,havefallenforsomegroups(Case

andDeaton,2017)

Figure2replicatestheConferenceBoard’sanalysisofitscountry-levelTotal

EconomyDatabase(ConferenceBoard,2016).Itplotshighlysmoothedannualproductivity

growthrateseriesfortheU.S.,othermatureeconomies(whichcombinedmatchmuchof

theOECDsamplereferredtoabove),emerginganddevelopingeconomies,andtheworld

overall.TheaforementionedslowdownsintheU.S.andothermatureeconomiesareclear

inthefigure.Thefigurealsorevealsthattheproductivitygrowthaccelerationinemerging

5Andthenumberofdealsincreasedfrom160to658.Seehttps://www.cbinsights.com/research/artificial-intelligence-startup-funding/6Theseslowdownsarestatisticallysignificant.FortheU.S.,wheretheslowdownismeasuredusingquarterlydata,equalityofthetwoperiods’growthratesisrejectedwithat-statisticof2.9.TheOECDnumberscomefromannualdataacrossthe30countries.Here,thenullhypothesisofequalityisrejectedwithat-statisticof7.2.

5

anddevelopingeconomiesduringthe2000sendedaroundthetimeoftheGreatRecession,

causingarecentdeclineinproductivitygrowthratesinthesecountriestoo.

TheseslowdownsdonotappeartosimplyreflecteffectsoftheGreatRecession.In

theOECDdata,28ofthe30countriesstillexhibitproductivitydecelerationsif2008-09

growthratesareexcludedfromthetotals.Cette,Fernald,andMojon(2016),usingother

data,alsofindsubstantialevidencethattheslowdownsbeganbeforetheGreatRecession.

Figure 2. Smoothed Average Annual Labor Productivity Growth (Percent) by Region

Bothcapitaldeepeningandtotalfactorproductivity(TFP)growthleadtolabor

productivitygrowth,andbothseemtobeplayingaroleintheslowdown(e.g.,Fernald

2014andOECD2015).Disappointingtechnologicalprogresscanbetiedtoeachofthese

components.TFPdirectlyreflectssuchprogress.Capitaldeepeningisindirectlyinfluenced

bytechnologicalchangebecausefirms’investmentdecisionsrespondtoimprovementsin

currentorexpectedcapitalquality.

Thesefactshavebeenreadbysomeasreasonsforpessimismabouttheabilityof

newtechnologieslikeAItogreatlyaffectproductivityandincome.Gordon(2015)argues

thatproductivitygrowthhasbeeninlong-rundecline,withtheIT-drivenaccelerationof

1995to2004beingaone-offaberration.Whilenotclaimingtechnologicalprogresswillbe

nilinthecomingdecades,Gordonessentiallyarguesthatwehavebeenexperiencingthe

6

new,low-growthnormalandshouldexpecttocontinuetodosogoingforward.Cowen

(2011)similarlyoffersmultiplereasonswhyinnovationmaybeslowatleastforthe

foreseeablefuture.Bloometal.(2017)documentthatinmanyfieldsoftechnological

progress,researchproductivityhasbeenfalling,whileNordhaus(2015)findsthatthe

hypothesisofaspeedupoftechnology-drivengrowthfailsavarietyoftests.

Thispessimisticviewoffuturetechnologicalprogresshasenteredintolong-range

policyplanning.TheCongressionalBudgetOffice,forinstance,reducedits10-yearforecast

foraverageU.S.annuallaborproductivitygrowthfrom1.8percentin2016(CBO2016)to

1.5percentin2017(CBO2017).Whileperhapsmodestonitssurface,thatdropimplies

U.S.GDPwillbeconsiderablysmaller10yearsfromnowthanitwouldinthemore

optimisticscenario—adifferenceofequivalentsizetoalmost$600billionin2017.

PotentialExplanationsfortheParadox

Therearefourprincipalcandidateexplanationsfortheconfluenceoftechnological

optimismandpoorproductivityperformancethattheworldfindsitselfin:1)falsehopes,

2)mismeasurement,3)concentrateddistributionandrentdissipation,4)implementation

andrestructuringlags.7

Falsehopes

Thesimplestpossibilityisthattheoptimismaboutpotentialtechnologiesis

misplacedandunfounded.Perhapstechnologieswon’tbeastransformativeasmany

expect,andwhiletheymighthavemodestandnoteworthyeffectsonspecificsectors,their

aggregateimpactwillbesmall.Inthiscase,theparadoxwillberesolvedinthefutureas

realizedproductivitygrowthneverescapesitscurrentdoldrums,ultimatelyforcingthe

optimiststomarktheirbeliefstomarket.

Historyandsomecurrentexamplesofferaquantumofcredencetothispossibility.

Certainlyonecanpointtomanytechnologiesthatdidliveuptoinitiallyoptimistic

expectations.Nuclearpowerneverbecametoocheaptometer,andfusionenergyhasbeen

20yearsawayfor60years.Marsmaystillbeckon,butit’sbeenover40yearssinceEugene

7Tosomeextent,theseexplanationsparalleltheexplanationsfortheSolowParadox(Brynjolfsson,1993).

7

Cernanwasthelastpersontowalkonthemoon.Flyingcarsnevergotofftheground8and

passengerjetsnolongerflyatsupersonicspeeds.EvenAI,perhapsthemostpromising

technologyofourera,iswellbehindMarvinMinsky’s1967predictionthat“Withina

generationtheproblemofcreating‘artificialintelligence’willbesubstantiallysolved”.

Ontheotherhand,thereremainsacompellingcaseforoptimism.Asweoutline

below,itisnotdifficulttoconstructback-of-the-envelopescenarioswhereevenamodest

numberofcurrentlyexistingtechnologiescouldcombinetosubstantiallyraiseproductivity

growthandsocietalwelfare.Indeed,knowledgeableinvestorsandresearchersarebetting

theirmoneyandtimeonexactlysuchoutcomes.Thus,whilewerecognizethepotentialfor

over-optimism—andtheexperiencewithearlypredictionsforAImakesanespecially

relevantreminderforustobesomewhatcircumspectinthisessay—wejudgethatitwould

behighlypreliminarytodismissoptimismatthispoint.

Mismeasurement

Anotherpotentialexplanationfortheparadoxisoutputandproductivity

mismeasurement.Inthiscase,itisthepessimisticreadingoftheempiricalpast,notthe

optimismaboutthefuture,thatismistaken.Indeed,thisexplanationimpliesthatthe

productivitybenefitsofthenewwaveoftechnologiesarealreadybeingenjoyed.Itisjust

thateconomicstatisticsarenotuptothetaskofaccuratelymeasuringthesebenefits,

makingtheslowdownofthepastdecadeillusory.This“mismeasurementhypothesis”has

beenforwardedinseveralworks(e.g.,Mokyr2014;Alloway,2015;Feldstein2015;Hatzius

andDawsey2015;Smith2015).

Thereisaprimafaciecaseforthemismeasurementhypothesis.Manynew

technologies,likesmartphones,onlinesocialnetworks,anddownloadablemedia,involve

time-intensiveconsumptionwithlittlemonetarycost.Theymightdeliversubstantialutility

eveniftheyaccountforasmallshareofGDPduetotheirlowrelativeprice.Guvenen,

Mataloni,Rassier,andRuhl(2017)alsoshowhowgrowingoffshoreprofitshiftingcanbe

anothersourceofmismeasurement.

8Atleastnotyet:https://kittyhawk.aero/about/.

8

However,asetofrecentstudieshaveshownthereisgoodreasontothinkthat

mismeasurementisnottheentire,orevenasubstantial,explanationfortheslowdown.

CardarelliandLusinyan(2015);Byrne,Fernald,andReinsdorf(2016);Nakamuraand

Soloveichik(2015);andSyverson(2017),eachusingdifferentmethodologiesanddata,

presentevidencethatmismeasurementisnottheprimaryexplanationfortheproductivity

slowdown.Afterall,whilethereisconvincingevidencethatmanyofthebenefitsoftoday’s

technologiesarenotreflectedinGDPandthereforeproductivitystatistics,thesamewas

undoubtedlytrueinearliererasaswell.

ConcentratedDistributionandRentDissipation

Athirdpossibilityisthatthegainsofthenewtechnologiesarealreadyattainable,

butthroughacombinationofconcentrateddistributionofthosegainsanddissipative

effortstoattainorpreservethem(assumingthetechnologiesareatleastpartially

rivalrous),theireffectonaverageproductivitygrowthismodestoverall,andisvirtuallynil

forthemedianworker.Forinstance,twoofthemostprofitableusesofAItodatehavebeen

fortargetingandpricingonlineads,andforautomatedtradingoffinancialinstruments,

bothapplicationswithmanyzero-sumaspects.

Oneversionofstoryassertsthatthebenefitsofthenewtechnologiesarebeing

enjoyedbyarelativelysmallfractionoftheeconomy,butthetechnologies’narrowly

scopedandrivalrousnaturecreateswasteful“goldrush”-typeactivities.Boththoseseeking

tobeoneofthefewbeneficiaries,aswellasthosewhohaveattainedsomegainsandseek

toblockaccesstoothers,engageinthesedissipativeefforts,destroyingmanyofthe

benefitsofthenewtechnologies.9

Recentresearchofferssomeindirectsupportforelementsofthisstory.Productivity

differencesbetweenfrontierfirmsandaveragefirmsinthesameindustryhavebeen

increasinginrecentyears(Andrews,Criscuolo,andGal,2016;FurmanandOrszag,2015).

Differencesinprofitmarginsbetweenthetopandbottomperformersinmostindustries

havealsogrown(McAfeeandBrynjolfsson,2009).Asmallernumberofsuperstarfirmsare

gainingmarketshare(Autoretal,2017,Brynjolfssonetal.2008)whileworkers’earnings 9Stiglitz (2014) offers a different mechanism where technological progress with concentrated benefits in the presence of restructuring costs can lead to increased inequality and even, in the short run, economic downturns.

9

areincreasinglytiedtofirm-levelproductivitydifferences(Song,Price,Guvenen,Bloom,

andvonWachter2015).Thereareconcernsthatindustryconcentrationisleadingto

substantialaggregatewelfarelossesduetothedistortionsofmarketpower(e.g.,De

LoeckerandEeckhout,2017;GutiérrezandPhilippon,2017).Furthermore,growing

inequalitycanleadtostagnatingmedianincomesandassociatedsocio-economiccosts,

evenwhentotalincomecontinuestogrow.

Whilethisevidenceisimportant,itisnotdispositive.Theaggregateeffectsof

industryconcentrationarestillunderdebate,andthemerefactthatatechnology’sgains

aren’tevenlydistributedisnoguaranteethatresourceswillbedissipatedintryingto

capturethem—especiallythattherewouldbeenoughwastetoerasenoticeableaggregate

benefits.

ImplementationandRestructuringLags

Eachofthesethreepossibilities,especiallythefirsttwo,reliesonexplainingaway

thediscordancebetweenhighhopesanddisappointingstatisticalrealities.Oneofthetwo

elementsissomehow“wrong”.Inthemisplacedoptimismstory,theexpectationsfor

technologyareoffbase.Inthemismeasurementexplanation,thetoolsweusetogauge

empiricalrealityaren’tuptothetaskofaccuratelydoingso.Andintheconcentrated

distributionstories,theprivategainsforthefewmaybeveryreal,buttheydon’ttranslate

intobroadergainsforthemany.

Butthereisafourthexplanationthatallowsbothhalvesoftheseemingparadoxto

becorrect.Itassertsthattherereallyisgoodreasontobeoptimisticaboutthefuture

productivitygrowthpotentialofnewtechnologies,whileatthesametimerecognizingthat

recentproductivitygrowthhasbeenlow.Thecoreofthisstoryisthatittakesa

considerabletime—oftenmorethaniscommonlyappreciated—tobeabletosufficiently

harnessnewtechnologies.Ironically,thisisespeciallytrueforthosetechnologies

importantenoughtoultimatelyhaveanimportanteffectonaggregatestatisticsand

welfare.Thatis,thosewithsuchbroadpotentialapplicationthattheyqualifyasgeneral

purposetechnologies(GPTs).Indeed,themoreprofoundandfar-reachingthepotential

restructuring,thelongerthetimelagbetweentheinitialinventionofatechnologyandits

fullimpactontheeconomyandsociety.

10

Thisexplanationimpliestherewillbeaperiodwherethetechnologiesare

developedenoughthatonecanimaginetheirpotentiallytransformativeeffectseven

thoughtheyhavehadnodiscernableeffectonrecentproductivitygrowth.Itisn’tuntilthe

necessarybuild-upandimplementationtimehaspassedthatthepromiseofthetechnology

actuallyblossomsintheaggregatedata.

Therearetwomainsourcesofthedelaybetweenrecognitionofanewtechnology’s

potentialanditsmeasureableeffects.Oneisthatittakestimetobuildthestockofthenew

technologytoasizesufficientenoughtohaveanaggregateeffect.Theotheristhat

complementaryinvestmentsarenecessarytoobtainthefullbenefitofthenewtechnology,

andittakestimetodiscoverwhatthesecomplementsareandtoimplementthem.While

thefundamentalimportanceofthecoreinventionanditspotentialforsocietymightbe

clearlyrecognizableattheoutset,themyriadnecessaryco-inventions,obstaclesand

adjustmentsneededalongthewayawaitdiscoveryovertime,andtherequiredpathmay

belengthyandarduous.Nevermistakeaclearviewforashortdistance.

Thisexplanationresolvestheparadoxbyacknowledgingthatitstwoseemingly

contradictorypartsarenotactuallyinconflict.Rather,theyareinsomesensebothnatural

manifestationsofthesameunderlyingphenomenonofbuildingandimplementinganew

technology.

Whileeachofthefirstthreeexplanationsfortheparadoxmighthaveapartin

describingitssource,theyalsofaceseriousquestionsintheirabilitytodescribekeyparts

ofthedata.Wefindthefourth,theimplementationandrestructuringlagsstory,themost

compellinginlightoftheevidencewediscussbelow.Thusitisthefocusofourexplorations

intheremainderofthispaper.

TheArgumentinFavoroftheImplementationandRestructuringLagsExplanation

Implicitorexplicitinthepessimisticviewofthefutureisthattherecentslowdown

inproductivitygrowthportendsslowerproductivitygrowthinthefuture.Webeginby

establishingoneofthemostbasicelementsofthestory:thatslowproductivitygrowth

todaydoesnotruleoutfasterproductivitygrowthinthefuture.Infact,theevidenceis

clearthatitisbarelypredictiveatall.

11

Totalfactorproductivitygrowthisthecomponentofoveralloutputgrowththat

cannotbeexplainedbyaccountingforchangesinobservablelaborandcapitalinputs.Ithas

beencalleda“measureofourignorance”(Abramovitz,1956).Itisaresidual,soan

econometricianshouldnotbesurprisedifitisnotverypredictablefrompastlevels.Labor

productivityisasimilarmeasure,butinsteadofaccountingforcapitalaccumulationsimply

dividestotaloutputbythelaborhoursusedtoproducethatoutput.

Figure3andFigure4plot,respectively,U.S.productivityindicessince1948and

productivitygrowthbydecade.Thedataincludeaveragelaborproductivity(LP),average

totalfactorproductivity(TFP)andFernald’s(2014)utilization-adjustedTFP(TFPua).

Figure3.U.S.TFPandLaborProductivityIndices,1948-2016(1990=100)

Figure4.U.S.TFPandLaborProductivityGrowth(%)byDecade

0

20

40

60

80

100

120

140

160

180

1948

1951

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

LP

TFP

TFPua

12

Productivityhasconsistentlygrowninthepost-warera,albeitatdifferentratesat

differenttimes.Despitetheconsistentgrowth,however,pastproductivitygrowthrates

havehistoricallybeenpoorpredictorsoffutureproductivitygrowth.Inotherwords,the

productivitygrowthofthepastdecadetellsuslittleaboutproductivitygrowthinforthe

comingdecade.Lookingonlyatproductivitydata,itwouldhavebeenhardtopredictthe

decreaseinproductivitygrowthattheendofthe1960sorforeseethebeneficialimpactof

ITinthe1990s.

Asitturnsout,whilethereissomecorrelationinproductivitygrowthratesover

shortintervals,thecorrelationbetweenadjacentten-yearperiodsisnotstatistically

significant.Wepresentbelowtheresultsfromaregressionofdifferentmeasuresof

averageproductivitygrowthonthepreviousperiod’saverageproductivitygrowthfor10-

yearintervalsaswellasscatterplotsofproductivityforeach10yearagainstthe

productivityinthesubsequentperiod.ThedataaresourcedfromtheFernald(2014)TFP

andutilization-adjustedTFPseriesandspanfrom1948to2016(annually).10TheR2of

theseregressionsislowinallcases.Thecorrelationcoefficientsinthefollowingtable

10Available: http://www.frbsf.org/economic-research/indicators-data/total-factor-productivity-tfp/

0

0.5

1

1.5

2

2.5

3

3.5

4

1950s 1960s 1970s 1980s 1990s 2000s 2010s

Avg.LP

Avg.TFP

Avg.TFPua

13

representthecorrelationinproductivitygrowthseriesforagiventen-yearperiodandthe

subsequentten-yearperiod.

CorrelationCoefficients(1stvs.2nd10-yearPeriod)

LaborProductivity 0.09TFP 0.15TFP(util.adj.) 0.17

RegressionresultsforLaborProductivity,TFP,andutilization-adjustedTFPare

includedbelow.ForLaborProductivity,theR2is0.009.Whiletheinterceptissignificantly

differentfromzero(productivityispositive,onaverage),thecoefficientontheprevious

periodisnotsignificant.ForTFPtheR2is0.023,andagainthecoefficientontheprevious

periodisnotstatisticallysignificant.Utilization-adjustedTFPisslightlyhigher,butstill

smallandstatisticallyinsignificant.

14

00.51

1.52

2.53

3.54

1 1.5 2 2.5 3 3.5 4

Subsequent10Years

First10YearPeriod

10YearAvg.LaborProducWvityGrowth(%)

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

Subsequent10Years

First10YearPeriod

10YearAvg.TFPGrowth(%)

0.00

0.50

1.00

1.50

2.00

2.50

0.00 0.50 1.00 1.50 2.00 2.50

Subsequent10Years

First10YearPeriod

10YearAvg.TFP(uWl.adj.)Growth(%)

15

Theoldadagethat“pastperformanceisnotpredictiveoffutureresults”applieswell

totryingtopredictproductivitygrowthintheyearstocome,especiallyinperiodsofa

decadeorlonger.Historicalstagnationdoesnotjustifyforward-lookingpessimism.

ATechnology-DrivenCaseforProductivityOptimism

Simplyextrapolatingrecentproductivitygrowthratesforwardisnotagoodwayto

estimatethenextdecade’sproductivitygrowth.Doesthatimplywehavenohopeatallof

predictingproductivitygrowth?Wedon’tthinkso.

Insteadrelyingonlyonpastproductivitystatistics,wecanconsiderthe

technologicalandinnovationenvironmentweexpecttoseeinthenearfuture.Inparticular,

weneedtostudyandunderstandthespecifictechnologiesthatactuallyexist,andmakean

assessmentoftheirpotential.

Onedoesnothavetodigtoodeeplyintothepoolofexistingtechnologiesorassume

incrediblylargebenefitsfromanyoneofthemtomakeacasethatexistingbutstillnascent

technologiescanpotentiallycombinetocreatenoticeableaccelerationsinaggregate

productivitygrowth.Webeginbylookingatafewspecificexamples.Wewillthenmakethe

casethatAIisaGPT,withbroaderimplications.

First,let’sconsidertheproductivitypotentialofautonomousvehicles.Accordingto

theUSBureauofLaborStatistics,in2016therewere3.5millionpeopleworkinginprivate

industryas“motorvehicleoperators”ofonesortoranother(thisincludestruckdrivers,

taxidrivers,busdrivers,andothersimilaroccupations).Supposeautonomousvehicles

weretoreduce,oversomeperiod,thenumberofdriversnecessarytodothecurrent

workloadto1.5million.Wedonotthinkthisisafar-fetchedscenariogiventhepotentialof

thetechnology.Totalnonfarmprivateemploymentinmid-2016was122million.

Therefore,autonomousvehicleswouldreducethenumberofworkersnecessarytoachieve

thesameoutputto120million.Thiswouldresultinaggregatelaborproductivity

(calculatedusingthestandardBLSnonfarmprivateseries)increasingby1.7percent(=

122/120).Supposingthistransitionoccurredover10years,thissingletechnologywould

provideadirectboostof0.17percenttoannualproductivitygrowthoverthatdecade.

Thisissignificant,anditdoesn’tincludemanypotentialproductivitygainsfrom

complementarychangesthatcouldaccompanythediffusionofautonomousvehicles.For

16

instance,self-drivingcarsareanaturalcomplementtotransportation-as-a-servicerather

thanindividualcarownership.Thetypicalcariscurrentlyparked95%ofthetime,making

itreadilyavailableforitsownerorprimaryuser(Morris,2016).However,inlocationswith

sufficientdensity,aself-drivingcarcouldbesummonedondemand.Thiswouldmakeit

possibleforcarstoprovideusefultransportationservicesforalargerfractionofthetime,

reducingcapitalcostsperpassenger-mile,evenafteraccountingforincreasedwear-and-

tear.Thus,inadditiontotheobviousimprovementsinlaborproductivityfromreplacing

drivers,capitalproductivitywouldalsobesignificantlyimproved.

Asecondexampleiscallcenters.Asof2015,therewereabout2.2millionpeople

workinginover6,800callcentersintheUnitedStatesandhundredsofthousandsmore

workashome-basedcallcenteragentsorinsmallersites.11Improvedvoice-recognition

systemscoupledwithintelligencequestion-answeringtoolslikeIBM’sWatsonmight

plausiblybeabletohandle60-70%ormoreofthecalls,especiallysince,inaccordance

withtheParetoprinciple,alargefractionofcallvolumeisduetovariantsonasmall

numberofbasicqueries.IfAIreducedthenumberworkersby60%,itwouldincreaseUS

laborproductivityby1%,perhapsagainspreadover10years.Again,thiswouldlikelyspur

complementaryinnovations,fromshoppingrecommendationandtravelservices,tolegal

advice,consulting,andreal-timepersonalcoaching.

Beyondlaborsavings,advancesinAIhavethepotentialtoboosttotalfactor

productivity.Inparticular,energyefficiencyandmaterialsusagecouldbeimprovedin

manylarge-scaleindustrialplants.Forinstance,ateamfromGoogleDeepMindrecently

trainedanensembleofneuralnetworkstooptimizepowerconsumptioninadatacenter.

Bycarefullytrackingthedataalreadycollectedfromthousandsofsensorstracking

temperatures,electricityusage,pumpspeeds,thesystemlearnedhowtomake

adjustmentsintheoperatingparameters.Asaresult,theywereabletoreducetheamount

ofenergyusedforcoolingby40%comparedtothelevelsachievedbyhumanexperts.The

algorithmwasageneral-purposeframeworkdesignedtoaccountcomplexdynamics,soit

iseasytoseehowsuchasystemcouldbeappliedtootherdatacentersatGoogle,orindeed

11https://info.siteselectiongroup.com/blog/how-big-is-the-us-call-center-industry-compared-to-india-and-philippines

17

aroundtheworld.Overall,datacenterelectricitycostsintheUSareabout$6billionper

year,includingabout$2billionjustforcooling.12

What’smore,similarapplicationsofmachinelearningcouldbeimplementedina

varietyofthecommercialandindustrialactivities.Forinstance,manufacturingaccounts

forabout$2.18trillionofvalue-addedeachyear.ManufacturingcompanieslikeGEare

alreadyusingAItoforecastproductdemand,futurecustomermaintenanceneeds,and

analyzeperformancedatacomingfromsensorsontheircapitalequipment.Recentworkon

trainingdeepneuralnetworkmodelstoperceiveobjectsandachievesensorimotorcontrol

atthesametimehaveyieldedrobotsthatcanperformavarietyofhand-eyecoordination

tasks(e.g.unscrewingbottlecapsandhangingcoathangers)(Levineetal.2016).Liuetal.

(2017)trainedrobotstoperformanumberofhouseholdchores,likesweepingand

pouringalmondsintoapan,usingatechniquecalledimitationlearning.13Inthisapproach,

therobotlearnstoperformataskusingarawvideodemonstrationofwhatitneedstodo.

Thesetechniqueswillsurelybeimportantforautomatingmanufacturingprocessesinthe

future.Theresultssuggestthatartificialintelligencemaysoonimproveproductivityin

householdproductiontasksaswell,whichin2010wereworthasmuchas$2.5trillionin

nonmarketvalue-added(Bridgmanetal.2012).

Whiletheseexamplesareeachsuggestiveofnon-trivialproductivitygains,theyare

onlyafractionofthesetofapplicationsforAIandmachinelearningthathavebeen

identifiedsofar.JamesManyikaandhiscolleaguesanalyzed2000tasksandestimatedthat

about45%oftheactivitiesthatpeoplearepaidtoperformintheUSeconomycouldbe

automatedusingexistinglevelsofAIandothertechnologies.Theystressthatthepaceof

automationwilldependonfactorsotherthantechnicalfeasibility,includingthecostsof

automation,regulatorybarriersandsocialacceptance.

ArtificialIntelligenceisaGeneralPurposeTechnology

ImportantasspecificapplicationsofAImaybe,wearguethatthemoreimportant

economicseffectsofAI,machinelearning,andassociatednewtechnologiesstemfromthe

12According to personal communication, August 24, 2017 with Jon Koomey, Arman Shehabi and Sarah Smith of Lawrence Berkeley Lab.13Videos of these efforts available here: https://sites.google.com/site/imitationfromobservation/

18

factthattheyembodythecharacteristicsofgeneralpurposetechnologies(GPTs).

BresnahanandTrajtenberg(1996)arguethataGPTshouldbepervasive,abletobe

improveduponovertime,andbeabletospawncomplementaryinnovations.

Thesteamengine,electricity,theinternalcombustionengine,andcomputersare

eachexamplesofimportantgeneralpurposetechnologies.Eachofthemnotonlyincreased

productivitydirectly,butalsobyspurringimportantcomplementaryinnovations.For

instance,thesteamenginenotonlyhelpedpumpwaterfromcoalmines,itsmostimportant

initialapplication,butalsospurredtheinventionmoreeffectivefactorymachineryand

newformsoftransportationlikesteamshipsandrailroads.Inturn,theseco-inventions

helpedgiverisetoinnovationsinsupplychainsandmassmarketing,toneworganizations

withhundredsofthousandsofemployees,andeventoseeminglyunrelatedinnovations

likestandardtime,whichwasneededtomanagerailroadschedules.

AI,andinparticularmachinelearning,certainlyhasthepotentialtobepervasive,to

beimproveduponovertime,andtospawncomplementaryinnovations,makingita

candidateforanimportantGPT.

AsnotedbyAgrawal,Gans,andGoldfarb(2017),thecurrentgenerationofmachine

learningsystemsisparticularlysuitedforaugmentingorautomatingtasksthatinvolveat

leastsomepredictionaspect,broadlydefined.Thesecoverabroadrangeoftasks,

occupationsandindustries,fromdrivingacar(predictingtherightwaytoturnthesteering

wheel)anddiagnosingadisease(predictingitscause)torecommendingaproduct

(predictingwhatthecustomerwilllike)andwritingasong(predictingwhichnote

sequencewillbemostpopular).Thecorecapabilitiesofperceptionandcognition

addressedbycurrentsystemsarepervasive,ifnotindispensable,formanytasksdoneby

humans.

Machinelearningsystemsarealsodesignedtoimproveovertime.Indeed,whatsets

themapartfromearliertechnologiesisthattheyaredesignedtoimprovethemselvesover

time.Insteadofrequiringaninventorordevelopertoconsciouslycodify,orcode,eachstep

ofaprocesstobeautomated,amachinelearningalgorithmcandiscoveronitsowna

functionthatconnectsasetofinputsXtoasetofoutputsYaslongasitsgivenasufficiently

largesetoflabeledexamplesmappingsomeoftheinputstooutputs(Brynjolfssonand

Mitchell,2017).Theimprovementsreflectnotonlythediscoveryofnewalgorithmsand

19

techniques,particularlyfordeepneuralnetworks,butalsotheirsynergieswithvastlymore

powerfulcomputerhardwareandtheavailabilityofmuchlargerdigitaldatasetsthatcan

beusedtotrainthesystems(BrynjolfssonandMcAfee,2017).Moreandmoredigitaldata

iscollectedasbyproductofdigitizingoperations,customerinteractions,communications

andotheraspectsofourlives,providingfodderformoreandbettermachinelearning

applications.14

Mostimportantly,machinelearningsystemsspuravarietyofcomplementary

innovations.Forinstance,machinelearninghastransformedtheabilitiesofmachinesto

performanumberofbasictypesofperceptionandthesemakepossibleabroadersetof

applications.Considermachinevision—theabilitytoseeandrecognizeobjects,tolabel

theminphotos,andtointerpretvideostreams.Aserrorratesinidentifyingpedestrians

improvefromoneper30framestoaboutoneper30millionframes,self-drivingcars

becomeincreasinglyfeasible(BrynjolfssonandMcAfee,2017).

Improvedvisionalsomakesavarietyoffactoryautomationtaskspractical,aswell

asimprovedmedicaldiagnoses.GillPratthasmadeananalogytothedevelopmentof

visioninanimals500millionyearsago,whichhelpedignitetheCambrianexplosionanda

burstofnewspeciesonearth.(Pratt,2015).Healsopointedoutthatmachineshaveanew

capabilitythatnobiologicalspecieshas:theabilitytoshareknowledgeandskillsalmost

instantaneouslywithothers.Specifically,theriseofcloudcomputinghasmadeit

significantlyeasiertoscaleupnewideasatmuchlowercostthanbefore.Thisisan

especiallyimportantdevelopmentforadvancingtheeconomicimpactofmachinelearning

becauseitenablescloudrobotics—thesharingofknowledgeamongrobots.Onceanew

skillislearnedbyamachineinonelocation,itcanbereplicatedtoothermachinesvia

digitalnetworks.Dataaswellasskillscanbeshared,increasingtheamountofdatathat

anygivenmachinelearnercanuse.

Thisinturnincreasestherateofimprovement.Forinstance,self-drivingcarsthat

encounteranunusualsituationcanuploadthatinformationwithasharedplatformwhere

enoughexamplescanbeaggregatedtoinferapattern.Onlyoneself-drivingvehicleneeds

toexperienceananomalyformanyvehiclestolearnfromit.Waymo,asubsidiaryof 14Forexample,throughenterpriseresourceplanningsystemsinfactories,internetcommerce,mobilephones,andthe“InternetofThings.”

20

Google,hascarsdriving25,000“real”autonomousandabout19millionsimulatedmiles

eachweek.15AlloftheWaymocarslearnfromthejointexperienceoftheothers.Similarly,

arobotstrugglingwithataskcanbenefitfromsharingdataandlearningswithotherrobots

thatuseacompatibleknowledge-representationframework.16

WhenonethinksofAIasaGPT,theimplicationsforoutputandwelfaregainsare

muchlargerthaninourearlieranalysis.Forexample,self-drivingcarscouldsubstantially

transformmanynon-transportindustries.Retailcouldshiftmuchfurthertowardhome

deliveryondemand,creatingconsumerwelfaregainsandfurtherfreeingupvaluablehigh-

densitylandnowusedforparking.Trafficandsafetycouldbeoptimized,andinsurance

riskscouldfall.Withover30,000deathsduetoautomobilecrashesintheUSeachyear,and

nearlyamillionworldwide,thereisanopportunitytosavemanylives.17

WhyFutureTechnologicalProgressIsConsistentwithLowCurrentProductivity

Growth

Havingmadeacasefortechnologicaloptimism,wenowturntoexplainingwhyitis

notinconsistentwith—andinfactmayevenbenaturallyrelatedto—lowcurrent

productivitygrowth.

LikeotherGPTs,AIhasthepotentialtobeanimportantdriverofproductivity.

However,asJovanovicandRousseau(2005)pointout(withadditionalreferencetoDavid’s

(1991)historicalexample),“aGPTdoesnotdeliverproductivitygainsimmediatelyupon

arrival.”(p.1184).Thetechnologycanbepresentanddevelopedenoughtoallowsome

notionofitstransformativeeffectseventhoughitisnotaffectingcurrentproductivity

levelsinanynoticeableway.Thisispreciselythestatethatwearguetheeconomymaybe

innow.

WediscussedabovethataGPTcanatonemomentbothbepresentandyetnot

affectcurrentproductivitygrowthifthereisaneedtobuildasufficientlylargestockofthe 15 http://ben-evans.com/benedictevans/2017/8/20/winner-takes-all 16 Rethink Robotics is developing exactly such a platform. 17 These latter two consequences of autonomous vehicles, while certainly reflecting welfare improvements, would need to be capitalized in prices of goods or services to be measured in standard GDP and productivity measures. We will discuss AI-related measurement issues in greater depth below. Of course it is worth remembering autonomous vehicles also hold the potential to create new economic costs if, say, the congestion from lower marginal costs of operating a vehicle is not counteracted by sufficiently large improvements in traffic management technology or certain infrastructure investments.

21

newcapitalorifcomplementarytypesofcapital,bothtangibleandintangible,needtobe

identified,produced,andputinplacetofullyharnesstheGPT’sproductivitybenefits.

Thetimenecessarytobuildasufficientcapitalstockcanbeextensive.Forexample,

itwasn’tuntilthelate1980s,morethan25yearsaftertheinventionoftheintegrated

circuit,thatthecomputercapitalstockreacheditslong-runplateauatabout5percent(at

historicalcost)oftotalnonresidentialequipmentcapital.Itwasonlyhalfthatlevel10years

prior.Thus,whenSolowpointedouthisnoweponymousparadox,thecomputerswere

finallyjustthengettingtothepointwheretheyreallycouldbeseeneverywhere.

David(1991)pointsoutasimilarphenomenoninthediffusionofelectrification.At

leasthalfofU.S.manufacturingestablishmentsremainedunelectrifieduntil1919,about30

yearsaftertheshifttopolyphasealternatingcurrentbegan.Initiallyadoptionwasdriven

bysimplecostsavings.Thebiggestbenefitscamelater,whenmanagersbeganto

fundamentallyre-organizeworkbyreplacingthecentralizedpowersourceandgiving

everyindividualmachineitsownelectricmotor.Thiscreatedmuchmoreflexibilityinthe

locationofequipmentandmadepossibleeffectiveassemblylinesmaterialsflow.

Thisapproachtoorganizingfactoriesisobviousinretrospect,yetittookasmuchas

30yearsforittobecomewidelyadopted.Why?AsnotedbyHenderson(1993;2006),itis

exactlybecauseincumbentsaredesignedaroundthecurrentwaysofdoingthingsandso

proficientatthemthattheyareblindtoorunabletoabsorbthenewapproachesandget

trappedinthestatusquo—theysufferthe“curseofknowledge.”18

Similarly,BrynjolfssonandSmith(1999)documentthedifficultiesincumbent

retailershadadaptingtheirbusinessprocessestotakefulladvantageoftheinternetand

electroniccommercerelativetoborn-digitalcompanieslikeAmazon.Thepotentialof

ecommercetorevolutionizeretailingwaswidelyrecognized,andevenhypedinthelate

1990s,butactualshareofretailcommercewastrivial,0.2%ofallretailsalesin1999.Only

in2017,aftertwodecadesofwidelypredictedyettime-consumingchangeintheindustry,

18AtkesonandKehoe(2007)notemanufacturers’reluctancetoabandontheirlargeknowledgestockatthebeginningofthetransitiontoelectricpowertoadoptwhatwas,initially,onlyamarginallysuperiortechnology.DavidandWright(2006)aremorespecific,focusingonthe“theneedfororganizationalandaboveallforconceptualchangesinthewaystasksandproductsaredefinedandstructured”(p.147,emphasisinoriginal).

22

arecompanieslikeAmazonarehavingafirst-ordereffectonmoretraditionalretailers’

salesandstockmarketvaluations.

Anothersourceofthetimegapbetweenatechnology’semergenceanditsmeasured

productivityeffectsistheneedforcomplementarycapitaltobeinstalled(andoften,first

invented).Thisincludesbothtangibleandintangibleinvestments.Thetimelinenecessary

toacquireandinstallthesecomplementsistypicallymoreextensiveasthetime-to-build

considerationsjustdiscussed.

ConsiderchangingaspecificproductionprocesstobenefitlargeinvestmentsinIT.

BrynjolfssonandHitt(2003)examinedfirmleveldataandfoundthatwhilesmall

productivitybenefitswereassociatedwithITinvestmentswhenone-yeardifferenceswere

considered,thebenefitsgrewsubstantiallyaslongerdifferenceswereexamined,peaking

afteraboutsevenyears.Theyattributedthispatterntotheneedforcomplementary

changesinbusinessprocesses.Forinstance,whenimplementinglargeenterpriseplanning

systems,firmsalmostalwaysspendseveraltimesmoreonbusinessprocessredesignand

trainingthanonthedirectcostsofhardwareandsoftware.Thesecanbethoughtofas

investmentsinorganizationalandhumancapital,andtheyoftentakeyearstoimplement.

Atthefirmlevel,additionalcomplementaryinvestmentsarerequiredbeyondthose

attheprocessleveltofullyharnessnewtechnologies.Theorganizationalstructureofthe

companyoftenneedstoberebuilt.HiringandotherHRpracticesoftenneedconsiderable

adjustmenttomatchthefirm’shumancapitaltothenewstructureofproduction.Infact,

Bresnahan,Brynjolfsson,andHitt(2002)findevidenceofthree-waycomplementarities

betweenIT,humancapital,andorganizationalchangesintheinvestmentdecisionsand

productivitylevels.Furthermore,Brynjolfsson,Hitt,andYang(2002)showeachdollarof

ITcapitalstockiscorrelatedwithabout$10ofmarketvalue.Theyinterpretthisas

evidenceofsubstantialIT-relatedintangibleassetsandshowthatfirmsthatcombineIT

investmentswithaspecificsetoforganizationalpracticesarenotjustmoreproductive,

theyalsohavedisproportionatelyhighermarketvaluesthanfirmsthatinvestinonlyone

ortheother.Thispatterninthedataisconsistentwithalongstreamofresearchonthe

importanceoforganizationalandevenculturalchangewhenmakingITinvestmentsand

technologyinvestmentsmoregenerally(e.g.Araletal2012;BrynjolfssonandHitt,2000;

Orlikowski,1996;Henderson,2006).

23

Butsuchchangestakerealtimeandresources,contributingtoorganizational

inertia.Firmsaremorecomplexsystemsthanindividualproductionlines,andthisgreater

complexityrequiresamoreextensivewebofcomplementaryassetstoallowtheGPTto

fullytransformthesystem.Transformingfirmsoftenmustreevaluateandreconfigurenot

onlytheirinternalprocesses,butoftentheirsupplyanddistributionchainsaswell.

Thereisnoassurancethattheadjustmentswillbesuccessful.Indeed,thereis

evidencethatthemodaltransformationofGPT-levelmagnitudefails.Alon,Berger,Dent,

andPugsley(2017)findthatcohortsoffirmsoverfiveyearsoldcontributelittleto

aggregateproductivitygrowthonnet—thatis,amongestablishedfirms,thereisonefirm

becominglessproductiveforeachfirmthatincreasesitsproductivity.Itishardtoteachthe

proverbialolddognewtricks.Moreover,theolddogs(companies)oftenhaveinternal

incentivestonotlearnthem(Arrow,1962;Holmes,Levine,andSchmitz2012).Insome

ways,technologyadvancesinindustryonecompanydeathatatime.

Transformingindustriesandsectorsrequiresstillmoreadjustmentand

reconfiguration.Asnotedabove,retailoffersavividexample.Despitebeingoneofthe

biggestinnovationstocomeoutofthe1990sdot-comboom,thelargestchangeinretailin

thetwodecadesthatfollowedwasnote-commercebutinsteadtheexpansionof

warehousestoresandsupercenters(HortaçsuandSyverson,2015).Itisonlyveryrecently

thate-commercehasbecomeaforceforgeneralretailerstoreckonwith.Whydidittakeso

long?Manycomplementaryinvestmentswererequired.Anentiredistribution

infrastructurehadtobebuilt.Customershadtobe“retrained.”Noneofthiscouldhappen

quickly.EcommercemayhavebeenreadilyforeseeableoncetheInternetbegantoreach

mosthomes,buthastakenover20yearsforecommercesalestorisetoitscurrentshareof

9percentoftotalretailsales.

ViewingToday’sParadoxthroughPreviousGeneralPurposeTechnologies

Wehaveindicatedinthediscussionabovethatweseeparallelsbetweenthecurrent

paradoxandthosethathavehappenedinthepast.ItiscloselyrelatedtotheSolowparadox

eracirca1990,certainly,butitisalsotiedcloselytotheexperienceduringthediffusionof

24

portablepower(wepreferthisto“electrification”soastoalsoreflecttheparallelgrowth

andtransformativeeffectsoftheinternalcombustionengine).

Comparingtheproductivitygrowthpatternsofthetwoerasisinstructive.

Figure5isanupdatedversionofananalysisfromSyverson(2013).ItoverlaysU.S.

laborproductivitysince1970withthatfrom1890to1940,theperiodafterportablepower

technologieshadbeeninventedandwerestartingtobeplacedintoproduction.(The

historicalseriesvaluesarefromKendrick1961.)Themodernseriestimelineisindexedto

avalueof100in1995andislabeledontheupperhorizontalaxis.Theportablepowerera

indexhasavalueof100in1915,anditsyearsareshownonthelowerhorizontalaxis.

Laborproductivityduringtheportablepowererasharedremarkablycommon

patternswithcurrentseries.Inbotheras,therewasaninitialperiodofroughlyaquarter

centuryofrelativelyslowproductivitygrowth.Thenbotherassawdecade-long

accelerationsinproductivitygrowth,spanning1915to1924intheportablepowereraand

1995-2004morerecently.

Thelate-1990saccelerationwasthe(atleastpartial)resolutionoftheSolow

Paradox.Weimaginethelate1910saccelerationcouldhavesimilarlyansweredsome

economist’squeryin1910astowhyoneseeselectricmotorsandinternalcombustion

engineseverywherebutintheproductivitystatistics.19

Figure5.LaborProductivityGrowthinthePortablePowerandITEras

19Wearen’tawareofanyonewhoactuallysaidthis,andofcoursetoday’ssystemofnationaleconomicstatisticsdidnotexistatthattime,butwefindthescenarioamusing,instructive,andinsomewaysplausible.

25

Veryinterestingly,andquiterelevanttothecurrentsituation,theproductivity

growthslowdownwehaveexperiencedafter2004alsohasaparallelinthehistoricaldata,

aslowdownfrom1924to1932.Ascanbeseeninthefigure,andinstructivetothepointof

whetheranewwaveofAIandassociatedtechnologies(orifoneprefers,asecondwaveof

IT-basedtechnology)couldre-accelerateproductivitygrowth,laborproductivitygrowthat

theendoftheportablepowereraroseagain,averaging2.7percentperyearbetween1933

and1940.

Ofcoursethispastbreakoutgrowthisnoguaranteethatproductivitymustspeedup

againtoday.However,itdoesraisetworelevantpoints.First,itisanotherexampleofa

periodofsluggishproductivitygrowthfollowedbyanacceleration.Second,itdemonstrates

thatproductivitygrowthdrivenbyacoreGPTcanarriveinmultiplewaves.

ExpectedProductivityEffectsofanAI-DrivenAcceleration

TounderstandthelikelyproductivityeffectsofAI,itisusefultothinkofAIisatype

ofcapital,specificallyatypeofintangiblecapital.Itcanbeaccumulatedthrough

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

40

60

80

100

120

140

160

180

1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940

PortablePower IT

26

investment;itisadurablefactorofproduction;anditcandepreciate.TreatingAIasatype

ofcapitalclarifieshowitsdevelopmentandinstallationasaproductivefactorwillaffect

productivity.

Aswithanycapitaldeepening,increasingAIwillraiselaborproductivity.This

wouldbetrueregardlessofhowwellAIcapitalismeasured(whichwemightexpectit

won’tbeforseveralreasonsdiscussedbelow)thoughtheremaybelags.

AI’seffectsontotalfactorproductivity(TFP)aremorecomplexandtheimpactwill

dependonitsmeasurement.IfAI(anditsoutputelasticity)weretobemeasuredperfectly

andincludedintheboththeinputbundleinthedenominatorofTFPandtheoutputbundle

inthenumerator,thenmeasuredTFPwillaccuratelyreflecttrueTFP.Inthiscase,AIis

treatedjustlikeanyothermeasurablecapitalinput.Itseffectonoutputwillbeproperly

accountedforand“removed”bytheTFPinputmeasure,leadingtonochangeinTFP.This

isn’ttosaythattherewouldn’tbeproductivebenefitsfromdiffusionofAI;itisjustthatit

wouldbevaluedlikeanyothertypeofcapitalinput.

Therearereasonswhyeconomistsandnationalstatisticalagenciesmightface

measurementproblemswhendealingwithAI.Someareinstancesofmoregeneralcapital

measurementissues,butothersarelikelytobeidiosyncratictoAI.Wediscussthisnext.

MeasuringAICapital

RegardlessoftheeffectsofAIandAI-relatedtechnologiesonactualoutputand

productivity,itisclearfromtheproductivityoutlookabovethatthewaysAI’seffectswill

bemeasuredaredependentonhowwellcountries’statisticsprogramsmeasureAIcapital.

TheprimarydifficultyinAIcapitalmeasurementis,asmentionedabove,thatitwill

largelybeintangible.ThiswillpresentitselfasaproblemforbothAIcapitalitselfaswellas

itsoutputs.ThispotentialissueisexacerbatedbythelikelihoodthatAIwillprimarilybe

usedasaninputinmakingothercapital,includingnewtypesofsoftware,humanand

organizationalcapital,ratherthanfinalconsumptiongoods.Humancapitalperworkeris

risingthroughouttheworld,compoundingthemeasurementissue(JonesandRomer,

2010).Moreover,thisothercapitalwill,likeAIitself,bemostlyintangible.

EffectiveuseofAIrequiresdevelopingdatasets,buildingfirm-specifichuman

capital,andimplementingnewbusinessprocesses.Theseallrequiresubstantialcapital

27

outlaysandmaintenance.Thetangiblecounterpartstotheseintangibleexpenditures,

includingpurchasesofcomputingresources,servers,andrealestate,areeasilymeasured

inthestandardneo-classicalgrowthaccountingmodel(Solow,1957).Ontheotherhand,

thevalueofcapitalgoodsproductionforcomplementaryintangibleinvestmentsisdifficult

toquantify.Bothtangibleandintangiblecapitalstocksgenerateacapitalserviceflowyield

thataccruesovertime.Realizingtheseyieldsrequiresmorethansimplyrentingcapital

stockaswell.Afterpurchasingcapitalassets,firmsincuradditionaladjustmentcosts(e.g.

businessprocessredesignsandinstallationcosts).Theseadjustmentcostsmakecapital

lessflexiblethanfrictionlessrentalmarketswouldimply.MuchofthemarketvalueofAI

capitalinspecificandITcapitalmoregenerallymaybederivedfromthecapitalizedshort-

termquasi-rentsearnedbyfirmsthathavealreadyreorganizedtoextractserviceflows

fromnewinvestment.

YetwhilethestockoftangibleAIassetsisbookedoncorporatebalancesheets,

expendituresontheintangiblecomplementsandadjustmentcoststoAIinvestmentlargely

arenot.WithoutincludingtheproductionofintangibleAIcapital,theusualgrowth

accountingdecompositionsofchangesinvalueaddedcanmisattributeAIintangiblecapital

deepeningtogrowthinTFP.AsdiscussedinHall(2000)andYangandBrynjolfsson(2001)

thisconstitutesanomissionofapotentiallyimportantcomponentofcapitalgoods

productioninthecalculationoffinaloutput.EstimatesofTFPwillthereforebeinaccurate,

thoughpossiblyineitherdirection.Nevertheless,inthecasethatclaimsontheassetsofthe

firmarepubliclytraded,thefinancialmarketwillproperlyvaluethefirmasthepresent

valueofitsrisk-adjusteddiscountedcashflows.

Wecancombineq-theoryofinvestmentwiththeneoclassicalgrowthaccounting

frameworktoimproveestimatesofTFP.Inparticular,weshowintheappendixthatinthe

casethattheshadowpriceofAIinvestmentisclosetothepurchasepriceofinvestment,

thereisnomissinggrowthinoutput.ButiftheintangibleAIcapitalstockisgrowingfaster

thantheaccumulationofordinarycapital,thenTFPgrowthwillbeunderestimated.The

intuitionforthisresultisthatinanygivenperiodt,theoutputof(unmeasured)AIcapital

stockinperiodt+1isafunctiontheinput(unmeasured)existingAIcapitalstockinperiod

t.WhenAIstockisgrowingrapidly,theunmeasuredoutputswillbegreaterthanthe

unmeasuredinputs.(Ofcourse,insteadystatethereisnolongeramismeasurement

28

problemasfurtherinvestmentservespreciselytoreplenishdepreciatedcapital.Inthis

case,theunmeasuredinputsandoutputscancelout.)Furthermore,supposetherelevant

costsneededtocreateintangibleassets,intermsoflaborandotherresources,are

measured,buttheresultingincreasesinintangibleassetsarenotmeasuredas

contributionstooutput.Inthiscase,notonlywilltotalGDPbeundercountedbutsowill

productivity,whichusesGDPasitsnumerator.Thusperiodsofrapidintangiblecapital

accumulationmaybeassociatedwithlowermeasuredproductivitygrowth,eveniftrue

productivityisincreasing.

TheseproblemsmaybeparticularlystarkforAIcapital,asitsaccumulationwill

almostsurelyoutstripthepaceofordinarycapitalaccumulationintheshort-run.AIcapital

isanewcategoryofcapital—newineconomicstatistics,certainly,butwewouldargue

practicallysoaswell.WhiletheconceptofAIisdecadesold,verylittleactualAIcapitalwas

accumulatedinpriordecades.Thusthecurrentstockisclosetozero.

Thisalsomeansthatcapitalquantityindexesthatarecomputedfromwithin-type

capitalgrowthmighthaveproblemsbenchmarkingsizeandeffectofAIearlyon.National

statisticsagenciesdonotreallyfocusonmeasuringcapitaltypesthataren’talready

ubiquitous.Newcapitalcategorieswilltendtoeitherberolledintoexistingtypes,possibly

withlowerinferredmarginalproducts(leadingtoanunderstatementoftheproductive

effectofthenewcapital),ormissedaltogether.Thisproblemisakintothenewgoods

probleminpriceindexes.

Arelatedissueis—onceAIismeasuredseparately—howcloselyitsunitsof

measurementwillcaptureAI’smarginalproductrelativetoothercapitalstock.Thatis,ifa

dollarofAIstockhasamarginalproductthatis10percenthigherthanthemodalunitof

non-AIcapitalintheeconomy,willthequantityindexesofAIreflectthis?Thisrequires

measuredrelativepricesofAIandnon-AIcapitaltocapturedifferencesinmarginal

product.Measuringlevelsrightislessimportantthanhavingproportionaldifferences

(whetherintertemporallyorinthecrosssection)correct.Whatisneededintheendisthat

aunitofAIcapitaltwiceasproductiveasanothershouldbetwiceaslargeinthecapital

stock.

Itisworthnotingthattheseareallclassicproblemsincapitalmeasurementandnot

newtoAI.PerhapstheseproblemswillbesystematicallyworseforAI,butthisisnot

29

obviousexante.Whatitdoesmeanisthateconomistsandnationalstatisticalagenciesat

leasthaveexperiencein,ifnotquiteafullsolutionfor,dealingwiththesesortsof

limitations.

SomemeasurementissuesarelikelytobespecificallyprevalentforAI.Oneisthe

likelihoodthatasubstantialpartofthevalueofAIoutputwillbefirm-specific.Imaginea

programthatfiguresoutindividualconsumers’priceelasticitiesandmatchespricingto

theseelasticities.Thishasdifferentvaluetodifferentcompaniesdependingontheir

customerbases,andknowledgemaynotbetransferrableacrossfirms.Thevaluealso

dependsoncompanies’abilitiestoimplementpricediscrimination.Suchlimitscouldcome

fromcharacteristicsofcompany’smarket,likeresaleopportunities,whicharenotalways

underfirms’control,orfromtheexistenceinthefirmofcomplementaryimplementation

assetsand/orabilities.Likewise,eachfirmwilllikelyhaveadifferentskillmixthatitseeks

initsemployees,uniqueneedsinitsproductionprocessandaparticularsetofsupply

constraints.AsnotedbyBrynjolfssonandMcAfee(2017),firm-specificdatasetsand

applicationsofthosedatacandifferentiatethemachinelearningcapabilitiesofonefirm

fromanother.

Conclusion

In2017,thereareplentyofbothoptimistsandpessimistsabouttechnologyand

growth.Theoptimiststendtobetechnologistsandventurecapitalists,andmanyare

clusteredintechnologyhubs.Thepessimiststendtobeeconomists,sociologists,

statisticiansandgovernmentofficials.Manyofthemareclusteredinmajorstateand

nationalcapitals.Thereismuchlessinteractionbetweenthetwogroupsthanwithinthem,

anditoftenseemsasthoughtheyaretalkingpasteachother.Inthispaper,wearguethatin

animportantasense,theyare.

Whenwetalkwiththeoptimists,weareconvincedthattherecentbreakthroughsin

AIandmachinelearningarerealandsignificant.Wealsowouldarguethattheyformthe

coreofanew,economically-importantGPT.Whenwespeakwiththepessimists,weare

convincedthatproductivitygrowthhassloweddownrecentlyandwhatgainstherehave

beenareunevenlydistributed,leavingmanypeoplewithstagnatingincomes,declining

metricsofhealthandwell-being,andgoodcauseforconcern.Peopleareuncertainabout

30

thefuture,andmanyoftheindustrialtitansthatoncedominatedtheemploymentand

marketvalueleaderboardhavefallenonhardertimes.

Thesetwostoriesarenotcontradictory.Infact,anymanyways,theyareconsistent

andsymptomaticofaneconomyintransition.Ouranalysissuggeststhatwhiletherecent

pasthasbeendifficult,itisnotdestiny.Althoughitisalwaysdangeroustomake

predictions,andwearehumbleaboutourabilitytoforetellthefuture,ourreadingofthe

evidencedoesprovidesomecauseforoptimism.ThebreakthroughsofAItechnologies

alreadydemonstratedarenotyetaffectingmuchoftheeconomy,buttheyportendbigger

effectsastheydiffuse.Moreimportantly,theywillenablecomplementaryinnovationsthat

willmultiplytheirimpact.Entrepreneurs,managersandend-userswillfindpowerfulnew

applicationsformachinesthatcannowlearnhowtorecognizeobjects,understandhuman

language,speak,makeaccuratepredictions,solveproblems,andinteractwiththeworld

withincreasingdexterityandmobility.

Furtheradvancesinthecoretechnologiesofmachinelearningarelikelytoyield

largebenefits.However,ourperspectivesuggeststhatanunderratedareaofresearchis

understandingbetterthecomplementstothenewMLtechnologies,notonlyinareasof

humancapitalandskills,butalsonewprocessesandbusinessmodels.Theintangible

assetsassociatedwiththelastwaveofcomputerizationwereabouttentimesaslargeas

thedirectinvestmentsincomputerhardwareitself.WethinkitisplausiblethatML-

associatedintangiblescanbeofacomparableorgreatermagnitude.Giventhebigchanges

incoordinationandproductionpossibilitiesmadepossiblebyML,thewaysthatwe

organizedworkandeducationinthepastareunlikelytoremainoptimalinthefuture.

Relatedly,weneedtoupdateourmeasurementtoolkits.AsAIanditscomplements

morerapidlyaddtoour(intangible)capitalstock,thetraditionalmetricslikeGDPand

productivitycanbeincreasinglymisleading.Successfulcompaniesdon’tneedlarge

investmentsinfactoriesorevencomputerhardware,buttheydohaveintangibleassets

thatarecostlytoreplicate.Thelargemarketvaluesassociatedwithcompaniesdeveloping

and/orimplementingAIsuggestthatinvestorsbelievethereisrealvalueinthose

companies.What’smore,theeffectsonlivingstandardsmaybeevenlargerthanthe

benefitsthatinvestorshopetocapture,thoughit’salsopossible,evenlikely,thatmany

peoplewillnotshareinthosebenefits.Economistsarewellpositionedtocontributetoa

31

researchagendaofdocumentingandunderstandingtheoften-intangiblechanges

associatedwithAIanditsbroadereconomicimplications.

RealizingthebenefitsofAIisfarfromautomatic.Itwillrequireeffortand

entrepreneurshiptodeveloptheneededcomplements,andadaptabilityattheindividual,

organizational,andsocietallevelstoundertaketheassociatedrestructuring.Theory

predictsthatthewinnerswillbethosewiththelowestadjustmentcostsandtheasmanyof

therightcomplementsinplaceaspossible.Thisispartlyamatterofgoodfortune,butwith

therightroadmap,itisalsosomethingforwhichthey,andallofus,canprepare.

32

References

Abramovitz,Moses.“ResourceandOutputTrendsintheU.S.since1870.”American

EconomicReview,May1956(PapersandProceedings),46(2),pp.5-23.

Agrawal,Ajay,JoshuaGans,andAviGoldfarb.2017.“WhattoExpectfromArtificial

Intelligence.”SloanManagementReviewFeb7,2017.

Alloway,Tracy.2015.“Goldman:How‘GrandTheftAuto’ExplainsOneoftheBiggest

MysteriesoftheU.S.Economy.”BloombergBusiness,May26,2015.

http://www.bloomberg.com/news/articles/2015-05-26/goldman-how-grand-

theft-auto-explains-one-of-the-biggest-mysteries-of-the-u-s-economy

Alon,Titan,DavidBerger,RobertDent,andBenjaminPugsley.2017.“OlderandSlower:

TheStartupDeficit’sLastingEffectsonAggregateProductivityGrowth.”Mimeo.

Andrews,Dan,ChiaraCriscuolo,andPeterGal.2016.”TheBestversustheRest:TheGlobal

ProductivitySlowdown,DivergenceacrossFirmsandtheRoleofPublic

Policy.”OECDProductivityWorkingPapers,No.5,OECDPublishing,Paris.

Aral,Sinan,Brynjolfsson,ErikandWu,Lynn(2012)“Three-WayComplementarities:

PerformancePay,HRAnalyticsandInformationTechnology.”ManagementScience,

58(5):913-931.

Arrow,Kenneth.1962.“EconomicWelfareandtheAllocationofResourcesforInvention.”

inTheRateandDirectionofInventiveActivity:EconomicandSocialFactors,Richard

R.Nelson(ed.),Princeton:PrincetonUniversityPress:609–26.

Atkeson,AndrewandPatrickJ.Kehoe.2007.“ModelingtheTransitiontoaNewEconomy:

LessonsfromTwoTechnologicalRevolutions.”AmericanEconomicReview,97(1):

64-88.

Autor,David,DavidDorn,LawrenceF.Katz,ChristinaPattersonandJohnVanReenen.

2017.“ConcentratingontheFalloftheLaborShare,“AmericanEconomicReview

PapersandProceedings,AmericanEconomicAssociation,vol.107(5),pages180-

185,May.

Autor,DavidandAnnaSalomons,2017.“RobocalypseNow–DoesProductivityGrowth

ThreatenEmployment?”MITworkingpaper.

33

Barter,Paul“’Carsareparked95%ofthetime.’Let’scheck!”ReinventingParkingblog.

http://www.reinventingparking.org/2013/02/cars-are-parked-95-of-time-lets-

check.html

Bloom,Nicholas,CharlesI.Jones,JohnVanReenen,andMichaelWebb.2017.“AreIdeas

GettingHardertoFind?”Mimeo.

Bresnahan,T,E.Brynjolfsson,andL.M.Hitt(2002).“InformationTechnology,Workplace

Organization,andtheDemandforSkilledLabor:Firm-LevelEvidence,”Quarterly

JournalofEconomics,Vol.117,Issue1,Feb2002pp339-376.Bresnahan,TimothyF.,ManuelTrajtenberg.1996.“GeneralPurposeTechnologies:‘Engines

ofGrowth’?”JournalofEconometrics,AnnalsofEconometrics,65(1):83–108.

Bridgman,B.,Dugan,A.,Lal,M.,Osborne,M.,&Villones,S.(2012).Accountingfor

householdproductioninthenationalaccounts,1965–2010.SurveyofCurrent

Business,92(5),23-36.

Brynjolfsson,Erik.1993.“TheProductivityParadoxofInformationTechnology”,

CommunicationsoftheACM.

Brynjolfsson,E.,&Hitt,L.M.(2000).BeyondComputation:InformationTechnology,

OrganizationalTransformationandBusinessPerformance.JournalofEconomic

Perspectives,14(4),23-48.

Brynjolfsson,ErikandLorinHitt.2003.“ComputingProductivity:Firm-levelEvidence.”

ReviewofEconomicsandStatistics,85(4): 793-808.Brynjolfsson,Erik,LorinHitt,andShinkyuYang.2002.“IntangibleAssets:Computersand

OrganizationalCapital.”BrookingsPapersonEconomicActivity,2002(1),Brookings

Institution.Brynjolfsson,E.,andMcAfee,A.2011.Raceagainstthemachine.DigitalFrontier,Lexington,

MA.

Brynjolfsson,E.,andMcAfee,A.2014.Thesecondmachineage:Work,progress,and

prosperityinatimeofbrillianttechnologies.WWNorton&Company.

Brynjolfsson,ErikandAndrewMcAfee2017“What’sDrivingtheMachineLearning

Explosion?”HarvardBusinessReview,July18,2017.

34

Brynjolfsson,E.,A.McAfee,M.Sorell,andF.Zhu.2008.“ScaleWithoutMass:Business

ProcessReplicationandIndustryDynamics”HarvardBusinessSchoolTechnology&

OperationsMgt.”UnitResearchPaper07-016.

Byrne,DavidM.,JohnG.Fernald,andMarshallB.Reinsdorf.2016.“DoestheUnitedStates

HaveaProductivitySlowdownoraMeasurementProblem?”BrookingsPaperson

EconomicActivity.Spring,109-182.

Cardarelli,RobertoandLusineLusinyan.2015.“U.S.TotalFactorProductivitySlowdown:

EvidencefromtheU.S.States.”IMFWorkingPaperWP/15/116.

Cette,Gilbert,JohnG.Fernald,andBenoitMojon.2016.“ThePre-GreatRecession

SlowdowninProductivity.”EuropeanEconomicReview,88,3-20.

CongressionalBudgetOffice.2016.TheBudgetandEconomicOutlook:2016to2026.

CongressionalBudgetOffice.2017.The2017Long-TermBudgetOutlook.

Cowen,Tyler.2011.TheGreatStagnation:HowAmericaAteAlltheLow-HangingFruitof

ModernHistory,GotSick,andWill(Eventually)FeelBetter.(NewYork:Dutton)

David,Paul.1991.“ComputerandDynamo:TheModernProductivityParadoxinaNot-Too-

DistantMirror.”In:TechnologyandProductivity:TheChallengeforEconomicPolicy,

OECD,Paris:315–47.

David,PaulA.andGavinWright.2006.“GeneralPurposeTechnologiesandSurgesin

Productivity:HistoricalReflectionsontheFutureoftheICTRevolution.”inThe

EconomicFutureinHistoricalPerspective,Volume13,PaulA.DavidandMark

Thomas(eds.).Oxford:OxfordUniversityPress/BritishAcademy.

Deng,Jia,etal.2009.“ImageNet:ALarge-ScaleHierarchicalImageDatabase.”CVPR.IEEE

ConferenceComputerVisionandPatternRecognitionIEEE,2009.

DeLoecker,JanandJanEeckhout.“TheRiseofMarketPowerandtheMacroeconomic

Implications.”NBERWorkingPaper23687.

Esteva,A.,Kuprel,B.,Novoa,R.A.,Ko,J.,Swetter,S.M.,Blau,H.M.,&Thrun,S.

2017.“Dermatologist-LevelClassificationofSkinCancerwithDeepNeural

Networks.”Nature,542(7639):115-18.

Feldstein,Martin.“TheU.S.UnderestimatesGrowth.”WallStreetJournal,May18,2015.

Fernald,JohnG.2014.“AQuarterly,Utilization-AdjustedSeriesonTotalFactor

Productivity.”FRBSFWorkingPaper2012-19(updatedMarch2014).

35

Fortunato,Meire,MohammadGheshlaghiAzar,BilalPiot,JacobMenick,IanOsband,Alex

Graves,VladMnihetal.2017."NoisyNetworksforExploration."arXivpreprint

arXiv:1706.10295.

Furman,Jason,andPeterOrszag.2015.“Afirm-levelperspectiveontheroleofrentsinthe

riseininequality.”Presentationat“AJustSociety”CentennialEventinHonorof

JosephStiglitzColumbiaUniversity.

Gehring,J.,Auli,M.,Grangier,D.,Yarats,D.,&Dauphin,Y.N.(2017).ConvolutionalSequence

toSequenceLearning.arXivpreprintarXiv:1705.03122.

Gordon,R.J.2014.ThedemiseofUSeconomicgrowth:restatement,rebuttal,and

reflections(No.w19895).NationalBureauofEconomicResearch.

Gordon,RobertJ.2015.TheRiseandFallofAmericanGrowth:TheU.S.StandardofLiving

sincetheCivilWar.Princeton,NJ:PrincetonUniversityPress.

Gutiérrez,GermánandThomasPhilippon.2017.“DecliningCompetitionandInvestmentin

theU.S.”NBERWorkingPaperNo.23583.

Guvenen,Fatih,RaymondJ.Mataloni,Jr.,DylanG.Rassier,andKimJ.Ruhl.2017.“Offshore

ProfitShiftingandDomesticProductivityMeasurement.”NBERWorkingPaperNo.

23324.

Hatzius,JanandKrisDawsey.2015.“DoingtheSumsonProductivityParadoxv2.0.”

GoldmanSachsU.S.EconomicsAnalyst,No.15/30.

Hayashi,Fumio.1982.“Tobin’sMarginalqandAverageq:ANeoclassicalInterpretation.”

Econometrica,50(1):213-24.

Hayashi,FumioandTohruInoue.1991.“TheRelationbetweenFirmGrowthandQwith

MultipleCapitalGoods:TheoryandEvidencefromPanelDataonJapaneseFirms.”

Econometrica,59(3):731-54.

Henderson,Rebecca.1993.“UnderinvestmentandIncompetenceasResponsestoRadical

Innovation:EvidencefromthePhotolithographicIndustry.”RandJournalof

Economics,24(2):248-70.

Henderson,Rebecca.2006.“TheInnovator’sDilemmaasaProblemofOrganizational

Competence.”JournalofProductInnovationManagement,23:5-11.

36

Holmes,ThomasJ.,DavidK.Levine,andJamesA.Schmitz.2012.“Monopolyandthe

IncentivetoInnovateWhenAdoptionInvolvesSwitchoverDisruptions.”American

EconomicJournal:Microeconomics,4(3):1-33.

HortaçsuAliandChadSyverson.2015.“TheOngoingEvolutionofUSRetail:AFormatTug-

of-War.”JournalofEconomicPerspectives,29(4):89-112.

Jones,C.I.,&Romer,P.M.2010.ThenewKaldorfacts:ideas,institutions,population,and

humancapital.AmericanEconomicJournal:Macroeconomics,2(1),224-245.

Jovanovic,BoyanandPeterL.Rousseau.2005.“GeneralPurposeTechnologies.”in

HandbookofEconomicGrowth,Volume1B.PhilippeAghionandStevenN.Durlauf,

eds.ElsevierB.V.:1181-1224.

Kendrick,JohnW.1961.ProductivityTrendsintheUnitedStates.NationalBureauof

EconomicResearch.PrincetonUniversityPress.

Levine,S.,Finn,C.,Darrell,T.,&Abbeel,P.2016.End-to-endtrainingofdeepvisuomotor

policies.JournalofMachineLearningResearch,17(39),1-40.

Liu,Y.,Gupta,A.,Abbeel,P.,&Levine,S.2017.ImitationfromObservation:Learningto

ImitateBehaviorsfromRawVideoviaContextTranslation.arXivpreprint

arXiv:1707.03374.

Lucas,Jr.,RobertE.1967.“AdjustmentCostsandtheTheoryofSupply.”JournalofPolitical

Economy,75(4,Part1):321-334.

Manyika,James,MichaelChui,MehdiMiremadi,JacquesBughin,KatyGeorge,Paul

Willmott,andMartinDewhurst.“Harnessingautomationforafuturethat

works.”McKinseyGlobalInstitute(2017).

McAfee,Andrew,andErikBrynjolfsson.2008.“InvestingintheITthatmakesacompetitive

difference.”HarvardBusinessReview:98.

Brynjolfsson,ErikandTomMitchell,2017.“AutomatingAutomation:HowandWhere

MachineLearningAffectstheWorkforce”Draft.

Morris,DavidZ.2016.“Today’sCarsAreParked95%oftheTime.”Fortune,March13,2016Nakamura,LeonardandRachelSoloveichik.2015.“CapturingtheProductivityImpactof

the‘Free’AppsandOtherOnlineMedia.”FederalReserveBankofPhiladelphia

WorkingPaper15-25.

37

Nordhaus,W.D.(2015).AreWeApproachinganEconomicSingularity?Information

TechnologyandtheFutureofEconomicGrowth(No.w21547).NationalBureauof

EconomicResearch.

Orlikowski,W.J.(1996).Improvisingorganizationaltransformationovertime:Asituated

changeperspective.Informationsystemsresearch,7(1),63-92.

OrganizationforEconomicCooperationandDevelopment.2015.TheFutureofProductivity.

Pratt,GillA.“IsaCambrianExplosionComingforRobotics?”JournalofEconomic

Perspectives29,no.3(2015):51-60.

Saon,G.,Kurata,G.,Sercu,T.,Audhkhasi,K.,Thomas,S.,Dimitriadis,D.,...&Roomi,B.2017.

Englishconversationaltelephonespeechrecognitionbyhumansand

machines.arXivpreprintarXiv:1703.02136.

Smith,Noah.2015.“TheInternet’sHiddenWealth.”BloombergView,June6,2015.

http://www.bloombergview.com/articles/2015-06-10/wealth-created-by-the-

internet-may-not-appear-in-gdp.

Solow,RobertM.1957.“TechnicalChangeandtheAggregateProductionFunction.”Review

ofEconomicsandStatistics,39(3):312-20.

Solow,Robert.1987.“We'dBetterWatchOut.”NewYorkTimesBookReview,July12:36.

Song,Jae,DavidJ.Price,FatihGuvenen,NicholasBloom,andTillvonWachter.2015.

“FirmingUpInequality.”NBERWorkingPaperNo.21199.

Stiglitz,JosephE.2014.“UnemploymentandInnovation.”NBERWorkingPaperNo.20670.

Syverson,Chad.2013.“WillHistoryRepeatItself?Commentson‘IstheInformation

TechnologyRevolutionOver?’”InternationalProductivityMonitor,25:37-40.

Syverson,Chad.2017.“ChallengestoMismeasurementExplanationsfortheUS

ProductivitySlowdown.”JournalofEconomicPerspectives,31(2):165-86.

Wildasin,DavidE.1984.“OnPublicGoodProvisionwithDistortionaryTaxation.”Economic

Inquiry,22(2):227–43.

Yang,S.,&Brynjolfsson,E.2001.Intangibleassetsandgrowthaccounting:Evidencefrom

computerinvestments.Unpublishedpaper.MIT,85(61),28.

38

Appendix:DerivationoftheProductivityBiasfromUnmeasuredAICapital

OursetupadoptstheapproachofYangandBrynjolfsson(2001)asfollows.

Takeaconstantreturnstoscaleproductionfunction

𝑌 = 𝑝𝐹 𝐾,𝑁, 𝑡 (1)

whereYisthefinalgoodsoutputofthefirm,pisthepriceoffinalgoodsoutput,Kisthe

vectorofcapitalgoods,Nisthevectorofvariableinputs(e.g.labor),andtrepresentsthe

leveloftotalfactorproductivityattimet.Withflexiblecapitalandinputprices(r,w),we

havethefollowing,withgrepresentingagrowthrate:

𝑔! =𝑌𝑌 =

𝑝 𝐹!𝐾 + 𝐹!𝑁 + 𝐹!𝑌 =

𝑟𝐾𝑌 𝑔! +

𝑤𝑁𝑌 𝑔! + 𝑔! (2)

Thevalueswithanupperdotrepresentthetotalderivativewithrespecttotime.

Inwords,thegrowthinoutputovertimecanbedecomposedintothegrowthin

capitalstockmultipliedbycapital’sshareofoutputplusthegrowthinflexibleinput

quantitymultipliedbytheexpenditureshareofflexibleinputsandafinaltotalfactor

productivitygrowthterm.ThisisthefamiliarSolowResidual.Asmentionedabove,it

representsakindof“measureofourignorance”inhowafirmconvertsinputstooutputs,

butgrowthinTFPindicatesimprovementinproductiveefficiency.

Toincorporateadjustmentcosts,wemodify(1)followingLucas(1967):

𝑌 = 𝑝𝐹 𝐾,𝑁, 𝐼, 𝑡 (3)

NowtheproductionfunctionincorporatesaninvestmenttermIwithmarketpricezsuch

thatthetotalcostofinvestmentinoneunitofcapitalgoodsis(z–pFI).Fisassumednon-

increasingandconvexinItorepresenttheideathatadjustmentcostsgrowincreasingly

costlyforlargerI.Thishelpsmodelwhyfirmscannot,forexample,instantaneously

replicatethecapitalstocksoftheircompetitorswithoutincurringlargercosts.

Wecanrelatefirminvestmentbehaviortomarketvalueusingthisproduction

function.20Fortheprice-takingfirm,marketvalueisequaltothesumofthecapitalized

adjustmentcosts.Thefirmmustsolve:

20SeeforexampleHayashi(1982),Wildasin(1984),andHayashiandInoue(1991).

39

max!,!

𝜋 𝑡 𝑢 𝑡 𝑑𝑡!

!= 𝑉(0)

where 𝜋 𝑡 = 𝑝𝐹 𝐾, 𝐼,𝑁, 𝑡 − 𝑤!𝑁 − 𝑧′𝐼

and 𝑑𝐾𝑖𝑑𝑡

= 𝐼! − 𝛿!𝐾! ∀𝑖 = 1, 2,… , 𝐽. (4)

Thatis,Kiisthecapitalstockoftypei(indexescapitalvariety),Nisavectorofflexible

goods,u(t)denotesthediscountrateattimet,andδiisthedepreciationrateofcapitalof

typei.Fisassumednon-decreasingandconcaveinKandN,andwithhomogeneityof

degreeoneforFwegetthesolutiontothemaximizationoftheHamiltonianin(5)attime

0:

𝐻 𝐾,𝑁, 𝐼, 𝑡 = 𝑝𝐹 𝐾,𝑁, 𝐼, 𝑡 − 𝑤!𝑁 − 𝑧!𝐼 𝑢 𝑡 + 𝜆!(𝐼! − 𝛿!𝐾𝑖)!!!! (5)

withfirstorderconditions:𝜕𝐻𝜕𝜆𝑗

= 𝐾! = 𝐼! − 𝛿!𝐾! ∀𝑗 ∈ 1,2,… , 𝐽 ,∀𝑡 ∈ [0,∞]

𝜕𝐻𝜕𝐾𝑗

= −𝜆! = 𝑝𝐹!!𝑢 − 𝜆!𝛿! ∀𝑗,∀𝑡

𝜕𝐻𝜕𝐼𝑗

= 0 = 𝑝𝐹!! − 𝑧! 𝑢 + 𝜆! ∀𝑗,∀𝑡

𝜕𝐻𝜕𝑁𝑖

= 0 = 𝑝𝐹!! − 𝑤! 𝑢 ∀𝑖 ∈ 1,2,… , 𝐿 ,∀𝑡

𝜆 ∞ 𝐾 ∞ = 0

leadingtoanequationforthevalueofthefirm:

𝑉 0 = 𝜆! 0 𝐾!(0)!

!!!

(6)

Thevalueofthefirmatt=0isthesumoverallvarietiesofthecapitalstock

quantitiesmultipliedbythe“shadowprice”ofinvestmentoftherespectivevarieties.This

shadowprice,representativeofadjustmentcostsintheoriginalformulation,corresponds

directlytointangibleAIcapitalinourcontext.Assumethatmarketpricescorrectly

representthevalueofclaimsonpubliclytradedfirms.Equation(6)suggeststhata

regressionoffirmvalueondollarquantitiesofassetvarietieswillyieldacoefficientvector

thatrepresentsthevalueofoneunitofeachtypeofcapital.Inafrictionlessefficient

40

market,thatvectorwouldbeequaltounityforallassets.Inthepresenceofadjustment

costs,thecoefficientisequaltounityplusthemarginaladjustmentcostforallasset

varieties.Thisofcourseassumesthatallassetstocksaremeasuredperfectly.

WecanextendthislogictointangibleAIinvestmentsthatarecorrelatedor

complementarytotangibleassetsandimperfectlymeasured.SupposeanAI-intensivefirm

mustinvestintwoassets:datacentersandfirm-specificAIspecialisttraining.Ifafirm

ownsameasurablequantityoftangiblecapitalindatacentersandhasinvestedinfirm-

specifictrainingofAIspecialists,theestimatedshadowpricecoefficientforthedatacenter

investmentwillexceedthe“true”datacentercoefficientbytheamountnecessaryto

representthetrainingaswell.Thespecialisttrainingisnotcapitalizedonthefirm’s

balancesheet,yetthefinancialmarketadequatelyvaluesthetrainingserviceflowifno

arbitrageconditionsaretohold.Themarketvaluepremiumoverbookvalueimpliesa

valuegreaterthanunityforTobin’sQ;thevalueofthefirmishigherthanthesimple

replacementcostofitsobservedassets.Technologyfirmshaveconsiderablyhighervalues

ofQ,suggestingthattheyhavehigherlevelsofadjustmentcosts,intangiblecorrelate

investmentstothebookedassets,orboth.

MARKET

Inthegrowthaccountingframework,thevalueoffinalgoodsinanygivenyearcan

bedividedintothevalueofconsumptiongoodsandthevalueofcapitalgoodsasfollows:

𝑝!𝐶 + 𝑧𝐼 = 𝑌 = 𝑝!𝐹 𝐾,𝑁, 𝐼 = 𝑝!𝐹!𝑁 + 𝑝!𝐹!𝐾 + 𝑝!𝐹!𝐼 = 𝑤𝑁 + 𝑟𝐾 + 𝑧 − 𝜆 𝐼 (7)

Thisisthegrowthaccountingidentity.Thevalueofconsumptiongoodsplusthevalueof

capitalinvestmentisequaltototaloutputY.This,inturn,isequaltothetotalincomeof

flexibleinputs,capitalrentalcosts,andinvestment(bothmeasuredandunmeasured).

If(λ–z)Ivalueofcapitalgoodsproductiongoesunmeasured,thenpartofthe

expenditureoncapitalgoodsismissingwhenthegrowthdecompositionisperformed.In

thecontextofAI,thismeansthatmuchofthetraining,theinvestmentinimplementing

data-drivendecisionprocesses,thereorganizationcosts,andtheincentivedesigns

necessarytogeneratecapitalserviceflowfromAIcapitalareleftout.

41

Furthermore,iftheeconomyisaccumulatingAIcapitalfasterthanitaccumulates

measurablecapital,thenTFPwillbeunderestimated.Toseewhy,wecanupdatethe

growthdecompositionequationasfollows:

𝑔! =𝑌𝑌 =

𝑝 𝐹!𝐾 + 𝐹!𝑁 + 𝐹!𝐼 + 𝐹!𝑌 (8)

followingthefirstorderconditionsfortheHamiltonianabove,wehave

𝜆! 0 = (𝑧! − 𝑝𝐹!!)and

𝑔! =𝑝𝐹!𝐾𝑌

𝐾𝐾 +

𝑝𝐹!𝑁𝑌

𝑁𝑁 + 1−

𝜆𝑧

𝑧𝐼𝑌

𝐼𝐼 +

𝐹!𝐹 (9)

Thegrowthdecompositionnowclearlyshowsthemissingcomponentofinvestment

inthesecondtolastterm.Becausethegrowthofproductivityinthelasttermisaresidual,

itwillalsosubsumethemissinginvestment.

Thus,wehaveshownthatinthecasethattheshadowpriceofAIinvestmentisclose

tozero,thereisnomissinggrowthinoutput.ButwhenthereareextensiveunmeasuredAI

investmentsthatcorrelatethetangiblecapitalgoodsproduction,asislikelytobethecase,

thenestimatesofTFPgrowthwillbebiaseddownward.Theextentofthisbiaswilldepend

onthemagnitudeoftheunmeasuredcapital.