RaceAgainsttheMachineHow the Digital Revolution is Accelerating Innovation, Driving Productivity, and IrreversiblyTransformingEmploymentandtheEconomy.

ErikBrynjolfssonandAndrewMcAfee

DigitalFrontierPressLexington,Massachusetts

©2011ErikBrynjolfssonandAndrewMcAfee

Allrightsreserved.Nopartofthebookmaybereproducedinanyformbyanyelectronicormechanicalmeans(includingphotocopying,recording,orinformationstorageandretrieval)withoutpermissioninwritingfromthepublisher.

Forinformationaboutquantitydiscounts,emailinfo@raceagainstthemachine.com

www.RaceAgainstTheMachine.com

LibraryofCongressCataloging-in-PublicationData

Brynjolfsson,ErikRace against the machine : how the digital revolution is accelerating innovation, driving productivity, and irreversibly transformingemploymentandtheeconomy.p.cm.

eISBN978-0-9847251-0-61.Technologicalinnovations–EconomicAspects.I.McAfee,Andrew.II.Title.

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Contents1.Technology’sInfluenceonEmploymentandtheEconomy

2.HumanityandTechnologyontheSecondHalfoftheChessboard

3.CreativeDestruction:TheEconomicsofAcceleratingTechnologyandDisappearingJobs

4.WhatIstoBeDone?PrescriptionsandRecommendations

5.Conclusion:TheDigitalFrontier

6.Acknowledgments

Tomyparents,AriandMargueriteBrynjolfsson,whoalwaysbelievedinme.

Tomyfather,DavidMcAfee,whoshowedmethatthere’snothingbetterthanajobwelldone.

Chapter1.Technology’sInfluenceonEmploymentandtheEconomyIf,inlikemanner,theshuttlewouldweaveandtheplectrumtouchthelyrewithoutahandtoguidethem,chiefworkmenwouldnotwantservants.

—Aristotle

Thisisabookabouthowinformationtechnologiesareaffectingjobs,skills,wages,andtheeconomy.Tounderstandwhythisisavitalsubject,weneedonlylookattherecentstatisticsaboutjobgrowthintheUnitedStates.

Bythelatesummerof2011,theU.S.economyhadreachedapointwhereevenbadnewsseemedgood.Thegovernmentreleasedareportshowingthat117,000jobshadbeencreatedinJuly.ThisrepresentedanimprovementoverMayandJune,whenfewerthan100,000totaljobshadbeencreated,sothereportwaswell received.Aheadline in theAugust6 editionof theNewYorkTimes declared, “USReportsSolidJobGrowth.”

Behind those rosy headlines, however, lay a thorny problem. The 117,000 new jobs weren't evenenough to keep upwith population growth, let alone reemploy any of the approximately 12millionAmericanswhohad lost their jobs in theGreatRecessionof2007-2009.EconomistLauraD’AndreaTysoncalculatedthatevenif jobcreationalmostdoubled, to the208,000jobspermonthexperiencedthroughout2005,itwouldtakeuntil2023toclosethegapopenedbytherecession.JobcreationatthelevelobservedduringJulyof2011,ontheotherhand,wouldensureonlyanever-smallerpercentageofemployedAmericansovertime.AndinSeptemberthegovernmentreportedthatabsolutelynonetnewjobshadbeencreatedinAugust.

OfallthegrimstatisticsandstoriesaccompanyingtheGreatRecessionandsubsequentrecovery,thoserelatedtoemploymentweretheworst.Recessionsalwaysincreasejoblessness,ofcourse,butbetweenMay2007 andOctober 2009 unemployment jumped bymore than 5.7 percentage points, the largestincreaseinthepostwarperiod.

AnEconomyThat’sNotPuttingPeopleBacktoWork

Anevenbiggerproblem,however,was that theunemployedcouldn't findwork even after economicgrowth resumed. In July of 2011, 25 months after the recession officially ended, the main U.S.unemploymentrateremainedat9.1%,lessthan1percentagepointbetterthanitwasatitsworstpoint.Themeanlengthoftimeunemployedhadskyrocketedto39.9weeksbythemiddleof2011,adurationalmost twice as long as that observed during any previous postwar recovery. And the workforceparticipationrate,orproportionofworking-ageadultswithjobs,fellbelow64%—alevelnotseensince1983whenwomenhadnotyetenteredthelaborforceinlargenumbers.

Everyoneagreedthatthiswasadireproblem.NobelPrize-winningeconomistPaulKrugmandescribedunemploymentasa“terriblescourge…acontinuing tragedy.…Howcanweexpect toprosper twodecades from nowwhenmillions of young graduates are, in effect, being denied the chance to getstartedontheircareers?”

Writing inTheAtlantic,DonPeckdescribedchronicunemployment as “a pestilence that slowly eatsawayatpeople,families,and,ifitspreadswidelyenough,thefabricofsociety.Indeed,historysuggeststhat it is perhaps society’smost noxious ill.…This eraof high joblessness… is likely towarpourpolitics,ourculture,andthecharacterofoursocietyformanyyears.”HiscolleagueMeganMcArdleaskedherreaderstovisualizepeoplewhohadbeenunemployedforalongtime:“Thinkaboutwhatishappeningtomillionsofpeopleoutthere…whosesavingsandsocialnetworksareexhausted(orwereneververybigtobeginwith),whoareintheirfiftiesandnotyoungenoughtoretire,butveryhardtoplacewithanemployerwhowillpaythemasmuchastheywereworthtotheiroldfirm.Thinkofthepeoplewhocan'tsupporttheirchildren,orthemselves.Thinkoftheirdespair.”

ManyAmericansdidthinkofsuchpeople.Twenty-fourpercentofrespondentstoaJune2011Galluppollidentified“unemployment/jobs”asthemostimportantproblemfacingAmerica(thisinadditiontothe36%identifying“economyingeneral”).

ThegrimunemploymentstatisticspuzzledmanybecauseothermeasuresofbusinesshealthreboundedprettyquicklyaftertheGreatRecessionofficiallyendedinJune2009.GDPgrowthaveraged2.6%inthe sevenquarters after the recession’s end, a rate 75%ashigh as the long-termaverageover 1948-2007.U.S.corporateprofitsreachednewrecords.Andby2010,investmentinequipmentandsoftwarereturnedto95%ofitshistoricalpeak,thefastestrecoveryofequipmentinvestmentinageneration.

Economic history teaches that when companies grow, earn profits, and buy equipment, they alsotypicallyhireworkers.ButAmericancompaniesdidn’tresumehiringaftertheGreatRecessionended.Thevolumeoflayoffsquicklyreturnedtopre-recessionlevels,socompaniesstoppedsheddingworkers.Butthenumberofnewhiresremainedseverelydepressed.Companiesbroughtnewmachinesin,butnotnewpeople.

WhereDidtheJobsGo?

Whyhasthescourgeofunemploymentbeensopersistent?Analystsofferthreealternativeexplanations:cyclicality,stagnation,andthe“endofwork.”

The cyclical explanation holds that there’s nothing new or mysterious going on; unemployment inAmerica remains so high simply because the economy is not growing quickly enough to put peoplebacktowork.PaulKrugmanisoneoftheprimeadvocatesofthisexplanation.Ashewrites,“Allthefacts suggest that high unemployment in America is the result of inadequate demand—full stop.”FormerOfficeofManagementandBudgetdirectorPeterOrszagagrees,writingthat“thefundamentalimpedimenttogettingjoblessAmericansbacktoworkisweakgrowth.”Inthecyclicalexplanation,anespecially deep drop in demand like the Great Recession is bound to be followed by a long andfrustratinglyslowrecovery.WhatAmericahasbeenexperiencingsince2007,inshort,isanothercaseofthebusinesscycleinaction,albeitaparticularlypainfulone.

Asecondexplanationforcurrenthardtimesseesstagnation,notcyclicality,inaction.Stagnationinthiscontextmeansalong-termdeclineinAmerica’sabilitytoinnovateandincreaseproductivity.EconomistTylerCowenarticulatesthisviewinhis2010book,TheGreatStagnation:

Wearefailingtounderstandwhywearefailing.Alloftheseproblemshaveasingle,littlenoticedroot cause:We have been living off low-hanging fruit for at least three hundred years.…Yetduringthelastfortyyears,thatlow-hangingfruitstarteddisappearing,andwestartedpretendingit

wasstillthere.Wehavefailedtorecognizethatweareatatechnologicalplateauandthetreesaremorebarethanwewouldliketothink.That’sit.Thatiswhathasgonewrong.

Tosupporthisview,CowencitesthedecliningmedianincomeofAmericanfamilies.Medianincomeisahalfwaypoint;thereareasmanyfamiliesmakinglessthanthisamountastherearemakingmore.Thegrowthofmedianincomesloweddownsignificantlyatleast30yearsago,andactuallydeclinedduringthefirstdecadeofthiscentury;theaveragefamilyinAmericaearnedlessin2009thanitdidin1999.Cowenattributesthisslowdowntothefactthattheeconomyhasreacheda“technologicalplateau.”

WritingintheHarvardBusinessReview,LeoTilmanandtheNobelPrize-winningeconomistEdmundPhelpsagreedwiththisstagnation:“[America’s]dynamism—itsabilityandproclivitytoinnovate—hasbroughteconomicinclusionbycreatingnumerousjobs.Ithasalsobroughtrealprosperity—engaging,challenging jobs and careers of self-realization and self-discovery … [but] dynamism has been indeclineoverthepastdecade.”

The stagnation argument doesn’t ignore the Great Recession, but also doesn’t believe that it’s theprinciplecauseofthecurrentslowrecoveryandhighjoblessness.Thesewoeshaveamorefundamentalsource:aslowdowninthekindsofpowerfulnewideasthatdriveeconomicprogress.

Thisslowdownpre-datestheGreatRecession.InTheGreatStagnation,infact,Cowenmaintainedthatit’sbeengoingonsince the1970s,whenU.S.productivitygrowthslowedand themedian incomeofAmerican families stopped rising as quickly as it had in the past. Cowen, Phelps, and other“stagnationists”hold thatonlyhigherratesof innovationandtechnicalprogresswill lift theeconomyoutofitscurrentdoldrums.

AvariantonthisexplanationisnotthatAmericahasstagnated,butthatothernationssuchasIndiaandChina have begun to catch up. In a global economy, America businesses and workers can’t earn apremiumiftheydon’thavegreaterproductivitythantheircounterpartsinothernations.Technologyhaseliminated many of the barriers of geography and ignorance that previously kept capitalists andconsumersfromfindingthelowestpriceinputsandproductsanywhereintheworld.Theresulthasbeena great equalization in factor prices like wages, raising salaries in developing nations and forcingAmerican labor tocompeteondifferent terms.NobelprizewinnerMichaelSpencehasanalyzed thisphenomenonanditsimplicationsforconvergenceinlivingstandards.

ThethirdexplanationforAmerica’scurrentjobcreationproblemsflipsthestagnationargumentonitshead,seeingnottoolittlerecenttechnologicalprogress,butinsteadtoomuch.We’llcallthisthe“endofwork” argument, after JeremyRifkin’s 1995bookof the same title. In it,Rifkin laid out a bold anddisturbing hypothesis: that “we are entering a new phase inworld history—one inwhich fewer andfewerworkerswillbeneededtoproducethegoodsandservicesfortheglobalpopulation.”

Computerscausedthisimportantshift.“Intheyearsahead,”Rifkinwrote,“moresophisticatedsoftwaretechnologies are going to bring civilization ever closer to a near-workerlessworld.…Today, all…sectors of the economy … are experiencing technological displacement, forcing millions onto theunemployment roles.” Coping with this displacement, he wrote, was “likely to be the single mostpressingsocialissueofthecomingcentury.”

Theend-of-workargumenthasbeenmadeby,amongmanyothers,economistJohnMaynardKeynes,managementtheoristPeterDrucker,andNobelPrizewinnerWassilyLeontief,whostatedin1983that

“theroleofhumansasthemost importantfactorofproductionisboundtodiminishinthesamewaythat the role of horses in agricultural production was first diminished and then eliminated by theintroductionof tractors.” Inhis2009bookTheLights in theTunnel, software executiveMartinFordagreed, stating that “at some point in the future—it might be many years or decades from now—machineswillbeabletodothejobsofalargepercentageofthe‘average’peopleinourpopulation,andthesepeoplewillnotbeable to findnew jobs.”BrianArthurargues thatavast,but largely invisible“secondeconomy”alreadyexistsintheformofdigitalautomation.

Theend-of-workargumentisanintuitivelyappealingone;everytimewegetcashfromanATMinsteadof a teller or use an automated kiosk to check in at an airport for a flight, we see evidence thattechnologydisplaceshuman labor.But lowunemployment levels in theUnitedStates throughout the1980s,’90s,andfirstsevenyearsofthenewmillenniumdidmuchtodiscreditfearsofdisplacement,and ithasnotbeenfeatured in themainstreamdiscussionof today’s joblessrecovery.Forexample,a2010 report published by the Federal Reserve Bank of Richmond, titled “The Rise in Long-TermUnemployment:PotentialCauses and Implications,”doesnot contain thewordscomputer,hardware,software, or technology in its text.Working papers published in 2011 by the InternationalMonetaryFund,titled“NewEvidenceonCyclicalandStructuralSourcesofUnemployment”and“HastheGreatRecessionRaisedU.S.StructuralUnemployment?”aresimilarlysilentabouttechnology.Astechnologyjournalist FarhadManjoo summarized in the onlinemagazine Slate, “Most economists aren't takingtheseworriesveryseriously.Theideathatcomputersmightsignificantlydisrupthumanlabormarkets—and,thus,furtherweakentheglobaleconomy—sofarremainsonthefringes.”

OurGoal:BringingTechnologyintotheDiscussion

We think it’s time to bring this idea into themainstream and to paymore attention to technology’simpact on skills,wages, and employment.We certainly agree that aGreatRecession implies a longrecovery,andthatcurrentsluggishdemandisinlargeparttoblamefortoday’slackofjobs.Butcyclicalweakdemandisnotthewholestory.Thestagnationistsarerightthatlongeranddeepertrendsarealsoatwork.TheGreatRecessionhasmadethemmorevisible,butthey’vebeengoingonforawhile.

Thestagnationistscorrectlypointout thatmedian incomeandother importantmeasuresofAmericaneconomichealthstoppedgrowingrobustlysometimeago,butwedisagreewith themaboutwhythishashappened.Theythinkit’sbecausethepaceoftechnologicalinnovationhassloweddown.Wethinkit’sbecausethepacehasspedupsomuchthatit’sleftalotofpeoplebehind.Manyworkers,inshort,arelosingtheraceagainstthemachine.

Andit’snotjustworkers.Technologicalprogress—inparticular,improvementsincomputerhardware,software, and networks—has been so rapid and so surprising that many present-day organizations,institutions, policies, and mindsets are not keeping up. Viewed through this lens, the increase inglobalization is not an alternative explanation, but rather one of the consequences of the increasedpowerandubiquityoftechnology.

Soweagreewiththeend-of-workcrowdthatcomputerizationisbringingdeepchanges,butwe’renotaspessimisticastheyare.Wedon’tbelieveinthecomingobsolescenceofallhumanworkers.Infact,some human skills aremore valuable than ever, even in an age of incredibly powerful and capabledigitaltechnologies.Butotherskillshavebecomeworthless,andpeoplewhoholdthewrongonesnowfindthattheyhavelittletoofferemployers.They’relosingtheraceagainstthemachine,afactreflectedintoday’semploymentstatistics.

Wewrotethisbookbecausewebelievethatdigitaltechnologiesareoneofthemostimportantdrivingforces in the economy today. They’re transforming the world of work and are key drivers ofproductivity and growth.Yet their impact on employment is notwell understood, and definitely notfully appreciated. When people talk about jobs in America today, they talk about cyclicality,outsourcing and off-shoring, taxes and regulation, and thewisdomand efficacy of different kinds ofstimulus.Wedon’tdoubttheimportanceofallthesefactors.Theeconomyisacomplex,multifacetedentity.

Buttherehasbeenrelativelylittletalkaboutroleofaccelerationoftechnology.Itmayseemparadoxicalthat faster progress can hurtwages and jobs formillions of people, butwe argue that’swhat’s beenhappening.Aswe’llshow,computersarenowdoingmanythingsthatusedtobethedomainofpeopleonly.Thepaceandscaleofthisencroachmentintohumanskills isrelativelyrecentandhasprofoundeconomic implications. Perhaps themost important of these is thatwhile digital progress grows theoveralleconomicpie,itcandosowhileleavingsomepeople,orevenalotofthem,worseoff.

Andcomputers(hardware,software,andnetworks)areonlygoingtogetmorepowerfulandcapableinthefuture,andhaveanever-biggerimpactonjobs,skills,andtheeconomy.Therootofourproblemsisnotthatwe’reinaGreatRecession,oraGreatStagnation,butratherthatweareintheearlythroesofaGreatRestructuring.Our technologies are racing ahead butmany of our skills and organizations arelaggingbehind.Soit’surgentthatweunderstandthesephenomena,discusstheirimplications,andcomeupwithstrategiesthatallowhumanworkerstoraceaheadwithmachinesinsteadofracingagainstthem.

Here’showwe’llproceedthroughtherestofthisbook:

HumanityandTechnologyontheSecondHalfoftheChessboard

Whyarecomputersracingaheadofworkersnow?Andwhat,ifanything,canbedoneaboutit?Chapter2discussesdigitaltechnology,givingexamplesofjusthowastonishingrecentdevelopmentshavebeenandshowinghowtheyhaveupsetwell-establishedideasaboutwhatcomputersareandaren’tgoodat.What’smore,theprogresswe’veexperiencedaugursevenlargeradvancesincomingyears.Weexplainthesourcesofthisprogress,andalsoitslimitations.

CreativeDestruction:TheEconomicsofAcceleratingTechnologyandDisappearingJobs

Chapter3explores theeconomic implicationsof these rapid technologicaladvancesand thegrowingmismatchesthatcreatebotheconomicwinnersandlosers.Itconcentratesonthreetheoriesthatexplainhow such progress can leave some people behind, even as it benefits society as awhole. There aredivergencesbetweenhigher-skilled and lower-skilledworkers, between superstars and everyone else,andbetweencapitalandlabor.Wepresentevidencethatallthreedivergencesaretakingplace.

WhatIstoBeDone?PrescriptionsandRecommendations

Oncetechnicaltrendsandeconomicprinciplesareclear,Chapter4considerswhatwecanandshoulddotomeetthechallengesofhighunemploymentandothernegativeconsequencesofourcurrentraceagainstthemachine.Wecan’twinthatrace,especiallyascomputerscontinuetobecomemorepowerfulandcapable.Butwecanlearntobetterracewithmachines,usingthemasalliesratherthanadversaries.Wediscusswaystoputthisprincipleintopractice,concentratingonwaystoaccelerateorganizationalinnovationandenhancehumancapital.

Conclusion:TheDigitalFrontier

We conclude in Chapter 5 on an upbeat note. This might seem odd in a book about jobs and theeconomywrittenduringatimeofhighunemployment,stagnantwages,andanemicGDPgrowth.Butthisisfundamentallyabookaboutdigitaltechnology,andwhenwelookatthefullimpactofcomputersandnetworks,nowandinthefuture,weareveryoptimisticindeed.Thesetoolsaregreatlyimprovingourworld andour lives, andwill continue todo so.Weare strongdigitaloptimists, andwewant toconvinceyoutobeone,too.

Chapter2.HumanityandTechnologyontheSecondHalfoftheChessboard

Anysufficientlyadvancedtechnologyisindistinguishablefrommagic.—ArthurC.Clarke,1962

Weusedtobeprettyconfidentthatweknewtherelativestrengthsandweaknessesofcomputersvis-à-vishumans.Butcomputershavestartedmakinginroadsinsomeunexpectedareas.Thisfacthelpsustobetterunderstandthepastfewturbulentyearsandthetrueimpactofdigitaltechnologiesonjobs.

A good illustration of howmuch recent technology advances have taken us by surprise comes fromcomparing a carefully researchedbookpublished in 2004with an announcementmade in 2010.Thebook is The New Division of Labor by economists Frank Levy and Richard Murnane. As its titleimplies,it’sadescriptionofthecomparativecapabilitiesofcomputersandhumanworkers.

Inthebook’ssecondchapter,“WhyPeopleStillMatter,”theauthorspresentaspectrumofinformation-processing tasks. At one end are straightforward applications of existing rules. These tasks, such asperformingarithmetic,canbeeasilyautomated.Afterall,computersaregoodatfollowingrules.

At the other end of the complexity spectrum are pattern-recognition tasks where the rules can’t beinferred.TheNewDivision of Labor gives driving in traffic as an example of this type of task, andassertsthatitisnotautomatable:

The…truckdriverisprocessingaconstantstreamof[visual,aural,andtactile]informationfromhis environment.… To program this behavior we could begin with a video camera and othersensorstocapturethesensoryinput.Butexecutingaleftturnagainstoncomingtrafficinvolvessomany factors that it is hard to imagine discovering the set of rules that can replicate a driver’sbehavior.…

Articulating [human] knowledge and embedding it in software for all but highly structuredsituations are at present enormously difficult tasks. … Computers cannot easily substitute forhumansin[jobsliketruckdriving].

The results of the first DARPA Grand Challenge, held in 2004, supported Levy and Murnane’sconclusion.Thechallengewastobuildadriverlessvehiclethatcouldnavigatea150-mileroutethroughthe unpopulatedMohave Desert. The “winning” vehicle couldn’t even make it eight miles into thecourseandtookhourstogoeventhatfar.

InDomainAfterDomain,ComputersRaceAhead

Justsixyearslater,however,real-worlddrivingwentfrombeinganexampleofataskthatcouldn’tbeautomatedtoanexampleofonethathad.InOctoberof2010,GoogleannouncedonitsofficialblogthatithadmodifiedafleetofToyotaPriusestothepointthattheywerefullyautonomouscars,onesthathaddrivenmorethan1,000milesonAmericanroadswithoutanyhumaninvolvementatall,andmorethan140,000mileswithonlyminorinputsfromthepersonbehindthewheel.(Tocomplywithdrivinglaws,

Googlefeltthatithadtohaveapersonsittingbehindthesteeringwheelatalltimes).

LevyandMurnanewerecorrect thatautomaticdrivingonpopulated roads is anenormouslydifficulttask,andit’snoteasytobuildacomputerthatcansubstituteforhumanperceptionandpatternmatchinginthisdomain.Noteasy,butnotimpossibleeither—thischallengehaslargelybeenmet.

TheGoogletechnologistssucceedednotbytakinganyshortcutsaroundthechallengeslistedbyLevyandMurnane,butinsteadbymeetingthemhead-on.TheyusedthestaggeringamountsofdatacollectedforGoogleMapsandGoogleStreetViewtoprovideasmuchinformationaspossibleabouttheroadstheir cars were traveling. Their vehicles also collected huge volumes of real-time data using video,radar, and LIDAR (light detection and ranging) gear mounted on the car; these data were fed intosoftwarethattakesintoaccounttherulesoftheroad,thepresence,trajectory,andlikelyidentityofallobjects in the vicinity, driving conditions, and so on. This software controls the car and probablyprovides better awareness, vigilance, and reaction times than any human driver could. The Googlevehicles’onlyaccidentcamewhenthedriverlesscarwasrear-endedbyacardrivenbyahumandriverasitstoppedatatrafficlight.

Noneof this iseasy.But inaworldofplentifulaccuratedata,powerfulsensors,andmassivestoragecapacityandprocessingpower,itispossible.Thisistheworldweliveinnow.It’sonewherecomputersimprovesoquicklythattheircapabilitiespassfromtherealmofsciencefictionintotheeverydayworldnotoverthecourseofahumanlifetime,orevenwithinthespanofaprofessional’scareer,butinsteadinjustafewyears.

LevyandMurnanegivecomplexcommunicationasanotherexampleofahumancapabilitythat’sveryhard for machines to emulate. Complex communication entails conversing with a human being,especiallyinsituationsthatarecomplicated,emotional,orambiguous.Evolution has “programmed” people to do this effortlessly, but it’s been very hard to programcomputerstodothesame.Translatingfromonehumanlanguagetoanother,forexample,haslongbeenagoalofcomputerscienceresearchers,butprogresshasbeenslowbecausegrammarandvocabularyaresocomplicatedandambiguous.

In Januaryof2011,however, the translation services companyLionbridge announcedpilot corporatecustomers for GeoFluent, a technology developed in partnership with IBM. GeoFluent takes wordswritteninonelanguage,suchasanonlinechatmessagefromacustomerseekinghelpwithaproblem,and translates them accurately and immediately into another language, such as the one spoken by acustomerservicerepresentativeinadifferentcountry.

GeoFluent isbasedonstatisticalmachine translationsoftwaredevelopedat IBM’sThomasJ.WatsonResearchCenter.This software is improvedbyLionbridge’sdigital librariesofprevious translations.This“translationmemory”makesGeoFluentmoreaccurate,particularlyforthekindsofconversationslargehigh-techcompaniesarelikelytohavewithcustomersandotherparties.Onesuchcompanytestedthe quality of GeoFluent’s automatic translations of online chat messages. These messages, whichconcerned the company’s products and services, were sent by Chinese and Spanish customers toEnglish-speaking employees. GeoFluent instantly translated them, presenting them in the nativelanguage of the receiver. After the chat session ended, both customers and employees were askedwhether theautomatically translatedmessageswereuseful—whether theywereclearenoughtoallowthe people to take meaningful action. Approximately 90% reported that they were. In this case,automatictranslationwasgoodenoughforbusinesspurposes.

The Google driverless car shows how far and how fast digital pattern recognition abilities haveadvanced recently. Lionbridge’sGeoFluent shows howmuch progress has beenmade in computers’abilitytoengageincomplexcommunication.AnothertechnologydevelopedatIBM’sWatsonlabs,thisoneactuallynamedWatson,showshowpowerfulitcanbetocombinethesetwoabilitiesandhowfarthecomputershaveadvancedrecentlyintoterritorythoughttobeuniquelyhuman.

WatsonisasupercomputerdesignedtoplaythepopulargameshowJeopardy!inwhichcontestantsareasked questions on a wide variety of topics that are not known in advance.1 In many cases, thesequestions involvepunsandother typesofwordplay. Itcanbedifficult tofigureoutpreciselywhat isbeing asked, or how an answer should be constructed. Playing Jeopardy!well, in short, requires theabilitytoengageincomplexcommunication.

ThewayWatsonplaysthegamealsorequiresmassiveamountsofpatternmatching.Thesupercomputerhasbeenloadedwithhundredsofmillionsofunconnecteddigitaldocuments,includingencyclopediasandotherreferenceworks,newspaperstories,andtheBible.Whenitreceivesaquestion,itimmediatelygoes to work to figure out what is being asked (using algorithms that specialize in complexcommunication), thenstartsqueryingall thesedocuments to findandmatchpatterns in searchof theanswer.Watsonworkswithastonishingthoroughnessandspeed,asIBMresearchmanagerEricBrownexplainedinaninterview:

Westartwithasingleclue,weanalyze theclue,and thenwego throughacandidategenerationphase,whichactuallyrunsseveraldifferentprimarysearches,whicheachproduceontheorderof50searchresults.Then,eachsearchresultcanproduceseveralcandidateanswers,andsobythetimewe’vegeneratedallofourcandidateanswers,wemighthavethreetofivehundredcandidateanswersfortheclue.

Now,allofthesecandidateanswerscanbeprocessedindependentlyandinparallel,sonowtheyfanouttoanswer-scoringanalytics[that]scoretheanswers.Then,werunadditionalsearchesfortheanswers togathermoreevidence, and then rundeepanalyticsoneachpieceofevidence, soeachcandidateanswermightgoandgenerate20piecesofevidencetosupportthatanswer.

Now, all of this evidence canbe analyzed independently and in parallel, so that fans out again.Nowyouhaveevidencebeingdeeplyanalyzed…andthenallof theseanalyticsproducescoresthatultimatelygetmergedtogether,usingamachine-learningframeworktoweightthescoresandproduceafinalrankedorderforthecandidateanswers,aswellasafinalconfidenceinthem.Then,that’swhatcomesoutintheend.

Whatcomesout intheendissofastandaccuratethateventhebesthumanJeopardy!playerssimplycan’t keep up. In February of 2011,Watson played in a televised tournament against the twomostaccomplishedhumancontestantsintheshow’shistory.Aftertworoundsofthegameshownoverthreedays, thecomputerfinishedwithmorethanthree timesasmuchmoneyas itsclosestflesh-and-bloodcompetitor.Oneofthesecompetitors,KenJennings,acknowledgedthatdigitaltechnologieshadtakenoverthegameofJeopardy!Underneathhiswrittenresponsetothetournament’slastquestion,headded,“Iforonewelcomeournewcomputeroverlords.”

Moore’sLawandtheSecondHalfoftheChessboard

Wheredid theseoverlords come from?Howhas science fictionbecomebusiness reality soquickly?

Twoconceptsareessentialforunderstandingthisremarkableprogress.Thefirst,andbetterknown,isMoore’s Law, which is an expansion of an observation made by Gordon Moore, co-founder ofmicroprocessormakerIntel.Ina1965articleinElectronicsMagazine,Moorenotedthatthenumberoftransistorsinaminimum-costintegratedcircuithadbeendoublingevery12months,andpredictedthatthissamerateofimprovementwouldcontinueintothefuture.Whenthisprovedtobethecase,Moore’sLawwasborn.

Later modifications changed the time required for the doubling to occur; the most widely acceptedperiodatpresentis18months.VariationsofMoore’sLawhavebeenappliedtoimprovementovertimein disk drive capacity, display resolution, and network bandwidth. In these andmany other cases ofdigitalimprovement,doublinghappensbothquicklyandreliably.

It also seems that software progresses at least as fast as hardware does, at least in some domains.Computer scientistMartinGrötschelanalyzed the speedwithwhicha standardoptimizationproblemcould be solved by computers over the period 1988-2003. He documented a 43 millionfoldimprovement,whichhebrokedownintotwofactors:fasterprocessorsandbetteralgorithmsembeddedin software. Processor speeds improved by a factor of 1,000, but these gains were dwarfed by thealgorithms,whichgot43,000timesbetteroverthesameperiod.

ThesecondconceptrelevantforunderstandingrecentcomputingadvancesiscloselyrelatedtoMoore’sLaw.ItcomesfromanancientstoryaboutmathmaderelevanttothepresentagebytheinnovatorandfuturistRayKurzweil.Inoneversionofthestory,theinventorofthegameofchessshowshiscreationtohiscountry’sruler.Theemperorissodelightedbythegamethatheallowstheinventortonamehisownreward.Theclevermanasksforaquantityofricetobedeterminedasfollows:onegrainofriceisplacedonthefirstsquareofthechessboard,twograinsonthesecond,fouronthethird,andsoon,witheachsquarereceivingtwiceasmanygrainsastheprevious.

The emperor agrees, thinking that this reward was too small. He eventually sees, however, that theconstantdoubling results in tremendously largenumbers.The inventorwindsupwith264-1 grains ofrice,orapilebiggerthanMountEverest.Insomeversionsofthestorytheemperorissodispleasedatbeingoutsmartedthathebeheadstheinventor.

In his 2000 book The Age of Spiritual Machines: When Computers Exceed Human Intelligence,Kurzweilnotesthatthepileofriceisnotthatexceptionalonthefirsthalfofthechessboard:

Afterthirty-twosquares,theemperorhadgiventheinventorabout4billiongrainsofrice.That’sareasonablequantity—aboutonelargefield’sworth—andtheemperordidstarttotakenotice.

Buttheemperorcouldstillremainanemperor.Andtheinventorcouldstillretainhishead.Itwasastheyheadedintothesecondhalfofthechessboardthatatleastoneofthemgotintotrouble.

Kurzweil’s point is that constant doubling, reflecting exponential growth, is deceptive because it isinitiallyunremarkable.Exponential increases initially looka lot like standard linearones,but they’renot. As time goes by—as we move into the second half of the chessboard—exponential growthconfoundsour intuition andexpectations. It accelerates far past lineargrowth, yieldingEverest-sizedpilesofriceandcomputersthatcanaccomplishpreviouslyimpossibletasks.

So where are we in the history of business use of computers? Are we in the second half of the

chessboardyet?Thisisanimpossiblequestiontoanswerprecisely,ofcourse,butareasonableestimateyields an intriguing conclusion. The U.S. Bureau of Economic Analysis added “InformationTechnology”asacategoryofbusiness investment in1958, so let’suse thatasour startingyear.Andlet’stakethestandard18monthsastheMoore’sLawdoublingperiod.Thirty-twodoublingsthentakeusto2006andtothesecondhalfofthechessboard.AdvancesliketheGoogleautonomouscar,WatsontheJeopardy!championsupercomputer,andhigh-quality instantaneousmachine translation, then,canbeseenasthefirstexamplesofthekindsofdigitalinnovationswe’llseeaswemovefurtherintothesecondhalf—intothephasewhereexponentialgrowthyieldsjaw-droppingresults.

ComputingtheEconomy:TheEconomicPowerofGeneralPurposeTechnologies

Theseresultswillbefeltacrossvirtuallyeverytask,job,andindustry.Suchversatilityisakeyfeatureofgeneral purpose technologies (GPTs), a term economists assign to a small group of technologicalinnovations so powerful that they interrupt and accelerate the normal march of economic progress.Steampower,electricity,andtheinternalcombustionengineareexamplesofpreviousGPTs.

It is difficult to overstate their importance. As the economists Timothy Bresnahan and ManuelTrajtenbergnote:

Wholeerasof technicalprogressandeconomicgrowthappear tobedrivenby…GPTs, [whichare] characterized by pervasiveness (they are used as inputs by many downstream sectors),inherentpotentialfortechnicalimprovements,and“innovationalcomplementarities,”meaningthattheproductivityofR&D indownstreamsectors increasesasa consequenceof innovation in theGPT. Thus, as GPTs improve they spread throughout the economy, bringing about generalizedproductivitygains.

GPTs,then,notonlygetbetterthemselvesovertime(andasMoore’sLawshows,thisiscertainlytrueofcomputers),theyalsoleadtocomplementaryinnovationsintheprocesses,companies,andindustriesthatmakeuseofthem.Theylead,inshort,toacascadeofbenefitsthatisbothbroadanddeep.

ComputersaretheGPTofourera,especiallywhencombinedwithnetworksandlabeled“informationandcommunicationstechnology”(ICT).EconomistsSusantoBasuandJohnFernaldhighlighthowthisGPTallowsdeparturesfrombusinessasusual.

TheavailabilityofcheapICTcapitalallowsfirmstodeploytheirotherinputsinradicallydifferentand productivity-enhancing ways. In so doing, cheap computers and telecommunicationsequipmentcanfosteranever-expandingsequenceofcomplementaryinventionsinindustriesusingICT.

NotethatGPTsdon'tjustbenefittheir“home”industries.Computers,forexample,increaseproductivitynotonlyinthehigh-techsectorbutalsoinallindustriesthatpurchaseandusedigitalgear.Andthesedays,thatmeansessentiallyallindustries;eventheleastIT-intensiveAmericansectorslikeagricultureandminingarenowspendingbillionsofdollarseachyeartodigitizethemselves.

NotealsothechoiceofwordsbyBasuandFernald:computersandnetworksbringanever-expanding-setofopportunities to companies.Digitization, inotherwords, isnot a singleprojectprovidingone-time benefits. Instead, it's an ongoing process of creative destruction; innovators use both new andestablished technologies tomakedeepchangesat the levelof the task, the job, theprocess,even the

organization itself. And these changes build and feed on each other so that the possibilities offeredreallyareconstantlyexpanding.

Thishasbeenthecaseforaslongasbusinesseshavebeenusingcomputers,evenwhenwewerestillinthe fronthalfof thechessboard.Thepersonalcomputer, forexample,democratizedcomputing in theearly1980s,puttingprocessingpowerinthehandsofmoreandmoreknowledgeworkers.Inthemid-1990s two major innovations appeared: the World Wide Web and large-scale commercial businesssoftware like enterprise resource planning (ERP) and customer relationship management (CRM)systems.Theformergavecompaniestheabilitytotapnewmarketsandsaleschannels,andalsomadeavailablemoreoftheworld’sknowledgethanhadeverbeforebeenpossible;thelatterletfirmsredesigntheirprocesses,monitorandcontrolfar-flungoperations,andgatherandanalyzevastamountsofdata.

Theseadvancesdon’texpireorfadeawayovertime.Instead,theygetcombinedwithandincorporatedinto both earlier and later ones, and benefits keep mounting. The World Wide Web, for example,becamemuchmoreusefultopeopleonceGooglemadeiteasiertosearch,whileanewwaveofsocial,local,andmobileapplicationsarejustemerging.CRMsystemshavebeenextendedtosmartphonessothat salespeople can stay connected from the road, and tablet computers now provide much of thefunctionalityofPCs.

Theinnovationswe’restartingtoseeinthesecondhalfofthechessboardwillalsobefoldedintothisongoingworkofbusinessinvention.Infact,theyalreadyare.TheGeoFluentofferingfromLionbridgehasbrought instantaneousmachine translation to customer service interactions. IBM isworkingwithColumbia University Medical Center and the University of Maryland School of Medicine to adaptWatsontotheworkofmedicaldiagnosis,announcingapartnershipinthatareawithvoicerecognitionsoftwaremakerNuance.AndtheNevadastatelegislaturedirecteditsDepartmentofMotorVehiclestocomeupwithregulationscoveringautonomousvehiclesonthestate’sroads.Ofcourse,theseareonlyasmall sampleof themyriad IT-enabled innovations that are transformingmanufacturing, distribution,retailing, media, finance, law, medicine, research, management, marketing, and almost every othereconomicsectorandbusinessfunction.

WherePeopleStillWin(atLeastforNow)

Although computers are encroaching into territory that used to be occupied by people alone, likeadvancedpatternrecognitionandcomplexcommunication,fornowhumansstillholdthehighgroundin eachof these areas.Experienceddoctors, for example,makediagnosesby comparing thebodyofmedicalknowledgethey’veaccumulatedagainstpatients’labresultsanddescriptionsofsymptoms,andalsobyemployingtheadvancedsubconsciouspatternrecognitionabilitieswelabel“intuition.” (Doesthispatientseemlikethey’reholdingsomethingback?Dotheylookhealthy,orissomethingoffabouttheir skin tone or energy level?’) Similarly, the best therapists, managers, and salespeople excel atinteracting and communicating with others, and their strategies for gathering information andinfluencingbehaviorcanbeamazinglycomplex.

Butit’salsotrue,astheexamplesinthischaptershow,thataswemovedeeperintothesecondhalfofthe chessboard, computers are rapidly getting better at both of these skills. We’re starting to seeevidencethatthisdigitalprogressisaffectingthebusinessworld.AMarch2011storybyJohnMarkoffin theNew York Times highlighted how heavily computers’ pattern recognition abilities are alreadybeingexploitedbythelegalindustrywhere,accordingtooneestimate,movingfromhumantodigitallaborduringthediscoveryprocesscouldletonelawyerdotheworkof500.

In January, for example, BlackstoneDiscovery of Palo Alto, Calif., helped analyze 1.5milliondocumentsforlessthan$100,000.…

“Fromalegalstaffingviewpoint,itmeansthatalotofpeoplewhousedtobeallocatedtoconductdocumentreviewarenolongerabletobebilledout,”saidBillHerr,whoasalawyeratamajorchemical companyused tomuster auditoriumsof lawyers to readdocuments forweekson end.“Peoplegetbored,peoplegetheadaches.Computersdon’t.”

The computers seem to be good at their new jobs. … Herr … used e-discovery software toreanalyzeworkhiscompany’slawyersdidinthe1980sand’90s.Hishumancolleagueshadbeenonly60percentaccurate,hefound.“Thinkabouthowmuchmoneyhadbeenspenttobeslightlybetterthanacointoss,”hesaid.

AndanarticlethesamemonthintheLosAngelesTimesbyAlenaSemuelshighlightedthatdespitethefactthatclosingasaleoftenrequirescomplexcommunication,theretailindustryhasbeenautomatingrapidly.

Inanindustrythatemploysnearly1in10Americansandhaslongbeenareliablejobgenerator,companies increasingly are looking to peddle more products with fewer employees.…Virtualassistants are taking the place of customer service representatives. Kiosks and self-servicemachinesarereducingtheneedforcheckoutclerks.

VendingmachinesnowselliPods,bathingsuits,goldcoins,sunglassesandrazors;somewillevendispenseprescriptiondrugsandmedicalmarijuanatoconsumerswillingtosubmittoafingerprintscan. And shoppers are finding information on touch screen kiosks, rather than talking toattendants.…

The [machines] cost a fraction of brick-and-mortar stores.They also reflect changing consumerbuying habits. Online shopping has made Americans comfortable with the idea of buying allmannerofproductswithoutthehelpofasalesmanorclerk.

During the Great Recession, nearly 1 in 12 people working in sales in America lost their job,acceleratinga trendthathadbegunlongbefore. In1995,forexample,2.08peoplewereemployedin“salesandrelated”occupationsforevery$1millionofrealGDPgeneratedthatyear.By2002(thelastyear forwhich consistent data are available), that number had fallen to 1.79, a decline of nearly 14percent.

If, as these examples indicate, both pattern recognition and complex communication are now soamenable toautomation,areanyhumanskills immune?Dopeoplehaveanysustainablecomparativeadvantageasweheadeverdeeper into the secondhalfof thechessboard? In thephysicaldomain, itseems thatwedo for the timebeing.Humanoid robotsare stillquiteprimitive,withpoor finemotorskillsandahabitoffallingdownstairs.Soitdoesn’tappearthatgardenersandrestaurantbusboysareindangerofbeingreplacedbymachinesanytimesoon.

Andmanyphysicaljobsalsorequireadvancedmentalabilities;plumbersandnursesengageinagreatdealofpatternrecognitionandproblemsolvingthroughouttheday,andnursesalsodoalotofcomplexcommunicationwithcolleaguesandpatients.Thedifficultyofautomating theirworkremindsusofaquoteattributedtoa1965NASAreportadvocatingmannedspaceflight:“Manisthelowest-cost,150-

pound,nonlinear,all-purposecomputersystemwhichcanbemass-producedbyunskilledlabor.”

Eveninthedomainofpureknowledgework—jobsthatdon’thaveaphysicalcomponent—there’salotofimportantterritorythatcomputershaven’tyetstartedtocover.Inhis2005bookTheSingularity IsNear:WhenHumansTranscendBiology,RayKurzweilpredictsthatfuturecomputerswill“encompass…thepattern-recognitionpowers,problem-solvingskills,andemotionalandmoralintelligenceofthehumanbrainitself,”butsofaronlythefirstoftheseabilitieshasbeendemonstrated.Computerssofarhaveprovedtobegreatpatternrecognizersbutlousygeneralproblemsolvers;IBM’ssupercomputers,for example, couldn’t take what they’d learned about chess and apply it to Jeopardy! or any otherchallengeuntiltheywereredesigned,reprogrammed,andfeddifferentdatabytheirhumancreators.

Andforalltheirpowerandspeed,today’sdigitalmachineshaveshownlittlecreativeability.Theycan’tcompose very good songs, write great novels, or generate good ideas for new businesses. Apparentexceptions here only prove the rule.A prankster used an online generator of abstracts for computerscience papers to create a submission that was accepted for a technical conference (in fact, theorganizers invited the “author” to chair a panel), but the abstractwas simply a series of somewhat-relatedtechnicaltermsstrungtogetherwithafewstandardverbalconnectors.

Similarly, software that automatically generates summaries of baseball gamesworkswell, but this isbecausemuch sportswriting is highly formulaic and thus amenable to patternmatching and simplercommunication.Here’sasamplefromaprogramcalledStatsMonkey:

UNIVERSITYPARK—AnoutstandingeffortbyWillieArgocarriedtheIllinitoan11-5victoryovertheNittanyLionsonSaturdayatMedlarField.

Argoblasted twohome runs for Illinois.Hewent3-4 in thegamewith fiveRBIsand two runsscored.

IllinistarterWillStrackstruggled,allowingfiverunsinsixinnings,butthebullpenallowedonlynorunsandtheoffensebangedout17hitstopickuptheslackandsecurethevictoryfortheIllini.

The difference between the automatic generation of formulaic prose and genuine insight is stillsignificant,as thehistoryofa60-year-old testmakesclear.Themathematicianandcomputersciencepioneer Alan Turing considered the question of whether machines could think “too meaningless todeserve discussion,” but in 1950 he proposed a test to determine how humanlike a machine couldbecome. The “Turing test” involves a test group of people having online chats with two entities, ahuman and a computer. If the members of the test group can’t in general tell which entity is themachine,thenthemachinepassesthetest.

Turinghimselfpredicted thatby2000computerswouldbe indistinguishable frompeople70%of thetimeinhistest.However,attheLoebnerPrize,anannualTuringtestcompetitionheldsince1990,the$25,000 prize for a chat program that can persuade half the judges of its humanity has yet to beawarded.Whateverelsecomputersmaybeatpresent,theyarenotyetconvincinglyhuman.

Butastheexamplesinthischaptermakeclear,computersarenowdemonstratingskillsandabilitiesthatusedtobelongexclusivelytohumanworkers.Thistrendwillonlyaccelerateaswemovedeeperintothesecondhalfofthechessboard.Whataretheeconomicimplicationsofthisphenomenon?We’llturnourattentiontothistopicinthenextchapter.

1Tobeprecise,Jeopardy!contestantsareshownanswersandmustaskquestionsthatwouldyieldtheseanswers.

Chapter3.CreativeDestruction:TheEconomicsofAcceleratingTechnologyandDisappearingJobs

Wearebeingafflictedwithanewdiseaseofwhichsomereadersmaynotyethaveheardthename,butofwhichtheywillhearagreatdealintheyearstocome—namely, technologicalunemployment.Thismeansunemploymentduetoourdiscoveryofmeansofeconomisingtheuseof labouroutrunningthepaceatwhichwecanfindnewusesforlabour.

—JohnMaynardKeynes,1930

The individual technologies and the broader technological acceleration discussed in Chapter 2 arecreatingenormousvalue.There isnoquestion that they increaseproductivity,and thusourcollectivewealth. But at the same time, the computer, like all general purpose technologies, requires parallelinnovation in business models, organizational processes structures, institutions, and skills. Theseintangibleassets,comprisingbothorganizationalandhumancapital,areoften ignoredoncompanies’balance sheets and in the official GDP statistics, but they are no less essential than hardware andsoftware.

Andthat’saproblem.Digital technologieschangerapidly,butorganizationsandskillsaren’tkeepingpace.Asaresult,millionsofpeoplearebeingleftbehind.Theirincomesandjobsarebeingdestroyed,leaving them worse off in absolute purchasing power than before the digital revolution. While thefoundationofoureconomicsystempresumesastronglinkbetweenvaluecreationandjobcreation,theGreatRecession reveals theweakeningorbreakageof that link.This isnotmerelyanartifactof thebusiness cycle but rather a symptom of a deeper structural change in the nature of production. Astechnology accelerates on the second half of the chessboard, so will the economic mismatches,underminingoursocialcontractandultimatelyhurtingboth richandpoor,not just the firstwavesofunemployed.

The economics of technology, productivity, and employment are increasingly fodder for debate andseeminglyfilledwithparadoxes.Howcansomuchvaluecreationandsomucheconomicmisfortunecoexist?How can technologies acceleratewhile incomes stagnate?These apparent paradoxes can beresolvedbycombiningsomewell-understoodeconomicprincipleswiththeobservationthat thereisagrowingmismatchbetweenrapidlyadvancingdigitaltechnologiesandslow-changinghumans.

GrowingProductivity

Oftheplethoraofeconomicstatistics—unemployment,inflation,trade,budgetdeficits,moneysupply,and so on—one is paramount: productivity growth. Productivity is the amount of output per unit ofinput.Inparticular,laborproductivitycanbemeasuredasoutputperworkeroroutputperhourworked.In the long run, productivity growth is almost the only thing that matters for ensuring rising livingstandards.RobertSolowearnedhisNobelPrizeforshowingthateconomicgrowthdoesnotcomefrompeopleworkingharderbutratherfromworkingsmarter.Thatmeansusingnewtechnologiesandnewtechniquesofproductiontocreatemorevaluewithoutincreasingthelabor,capital,andotherresourcesused.

Even a few percentage points of faster productivity growth per year can lead to large differences inwealthovertime.Iflaborproductivitygrowsat1%,asitdidformuchofthe1800s,thenittakesabout70years for living standards to double.However, if it grows at 4%per year, as it did in 2010, thenlivingstandardsare16timeshigherafter70years.While4%growthisexceptional,thegoodnewsisthatthepastdecadewasaprettygoodoneforlaborproductivitygrowth—thebestsincethe1960s.Theaverageofover2.5%growthperyear is farbetter than the1970sand1980s,andevenedgesout the1990s(seeFigure3.1).What’smore,there’snowanearconsensusamongeconomistsaboutthesourceoftheproductivitysurgesincethemid-1990s:IT.

Althoughtheofficialproductivitystatisticsareencouraging,theyarefarfromperfect.Theydon’tdoaverygoodjobofaccountingforquality,variety,timeliness,customerservice,orotherhard-to-measureaspectsofoutput.Whilebushelsofwheatandtonsofsteelarerelativelyeasytocount,thequalityofateacher’sinstruction,thevalueofmorecerealchoicesinasupermarket,ortheabilitytogetmoneyfromanATM24hoursadayishardertoassess.

Compounding thismeasurementproblemis thefact that freedigitalgoods likeFacebook,Wikipedia,andYouTube are essentially invisible toproductivity statistics.As the Internet andmobile telephonydelivermoreandmorefreeservices,andpeoplespendmoreoftheirwakinghoursconsumingthem,thissourceofmeasurementerrorbecomesincreasinglyimportant.Furthermore,mostgovernmentservicesare simply valued at cost, which implicitly assumes zero productivity growth for this entire sector,regardlessofwhethertrueproductivityisrisingatlevelscomparabletotherestoftheeconomy.

Afinalsourceofmeasurementerrorcomesfromhealthcare,aparticularlylargeandimportantsegmentof the economy.Health care productivity is poorlymeasured and often assumed to be stagnant, yetAmericans live on average about 10 years longer today than they did in 1960. This is enormouslyvaluable,butitisnotcountedinourproductivitydata.AccordingtoeconomistWilliamNordhaus,“toa

firstapproximation,theeconomicvalueofincreasesinlongevityoverthetwentiethcenturyisaboutaslargeasthevalueofmeasuredgrowthinnon-healthgoodsandservices.”

Earlier eras also had significant unmeasured quality components, such as the welfare gains fromtelephones, or disease reductions from antibiotics. Furthermore, there are also areas where theproductivity statistics overestimate growth, aswhen they fail to account for increases in pollutionorwhenincreasedcrimeleadspeopletospendmoreoncrime-deterringgoodsandservices.Onbalance,theofficialproductivitydata likelyunderestimate the true improvementsofour living standardsovertime.

StagnantMedianIncome

Incontrast tolaborproductivity,medianfamilyincomehasrisenonlyslowlysincethe1970s(Figure3.2)once theeffectsof inflationare taken intoaccount.Asdiscussed inChapter1,TylerCowenandotherspointtothisfactasevidenceofeconomy-widestagnation.

Insomeways,Cowenunderstateshiscase.Ifyouzoominonthepastdecadeandfocusonworking-agehouseholds,realmedianincomehasactuallyfallenfrom$60,746to$55,821.Thisisthefirstdecadetoseedecliningmedianincomesincethefigureswerefirstcompiled.Mediannetworthalsodeclinedthispastdecadewhenadjustedforinflation,anotherfirst.

Yetatthesametime,GDPperpersonhascontinuedtogrowfairlysteadily(exceptduringrecessions).Thecontrastwithmedianincomeisstriking(Figure3.3).

Howcanthisbe?Mostofthedifferencestemsfromthedistinctionbetweenthemedianandthemeanofthedistribution.2If50constructionworkersaredrinkingatabarandBillGateswalksinasthepoorestcustomerwalksout,themeanwealthofthecustomerswouldsoarto$1billion.However,thewealthofthemediancustomer,theoneexactlyinthemiddleofthedistribution,wouldn’tchangeatall.

SomethinglikethishasbeenhappeningtoincomesintheU.S.economy.Therehavebeentrillionsofdollars of wealth created in recent decades, but most of it went to a relatively small share of thepopulation. In fact, economistEdWolff found that over 100%of all thewealth increase inAmericabetween1983and2009accruedtothetop20%ofhouseholds.Theotherfour-fifthsofthepopulationsawanetdecreaseinwealthovernearly30years.Inturn,thetop5%accountedforover80%ofthenetincrease inwealthandthe top1%forover40%.Withalmostafractal-likequality,eachsuccessivelyfinersliceatthetopofthedistributionaccountedforadisproportionatelylargeshareofthetotalwealthgains.WehavecertainlynotincreasedourGDPinthewaythatFranklinD.Roosevelthopedforwhenhesaidduringhissecondinauguraladdress in1937,“Thetestofourprogress isnotwhetherweaddmoretotheabundanceofthosewhohavemuch;itiswhetherweprovideenoughforthosewhohavetoolittle.”

ThissquareswiththeevidencefromChapter2ofthegrowingperformanceofmachines.Therehasbeennostagnationintechnologicalprogressoraggregatewealthcreationasissometimesclaimed.Instead,the stagnation of median incomes primarily reflects a fundamental change in how the economyapportionsincomeandwealth.Themedianworkerislosingtheraceagainstthemachine.

Examiningotherstatisticsrevealsadeeper,morewidespreadproblem.Notonlyareincomeandwages—thepriceoflabor—suffering,butsoisthenumberofjobsorthequantityoflabordemanded(Figure3.4).ThelastdecadewasthefirstdecadesincethedepthsoftheGreatDepressionthatsawnonetjobcreation.

When you consider that the overall population has grown, the lack of job creation is even moretroubling.ThepopulationoftheUnitedStatesgrewby30millioninthepastdecade,sowewouldneedto create18million jobs just to keep the same shareof thepopulationworking as in theyear 2000.Instead,we’vecreatedvirtuallynone,reducingtheemploymenttoapopulationratiofromover64%tobarely58%.

ThelackofjobsisnotsimplyamatterofmassivelayoffsduetotheGreatRecession.Instead,itreflectsdeepstructural issues thathavebeenworseningforadecadeormore.TheBureauofLaborStatisticsJobOpenings andLaborTurnover Survey (JOLTS) shows a dramatic decrease in hiring since 2000.Lackofhiring, rather than increases in layoffs, iswhat accounts formost of the current joblessness.Furthermore,astudybyeconomistsStevenJ.Davis,JasonFaberman,andJohnHaltiwangerfoundthatrecruitingintensityperjobopeninghasplummetedduringthepastdecadeaswell.Employersjustdon’tseemtohavethesamedemandforlaborthattheyoncedid.

This reflects a pattern thatwas noticeable in the “jobless recovery” of the early 1990s, but that hasworsenedaftereachofthetworecessionssincethen.EconomistsMenzieChinnandRobertGordon,inseparate analyses, find that the venerable relationship between output and employment known asOkun’s Law has been amended.Historically, increased outputmeant increased employment, but therecent recovery createdmuch less employment than predicted; GDP rebounded but jobs didn't. Thehistoricallystrongrelationshipbetweenchanges inGDPandchanges inemploymentappears tohaveweakenedasdigitaltechnologyhasbecomemorepervasiveandpowerful.AstheexamplesinChapter2makeclear,thisisnotacoincidence.

HowTechnologyCanDestroyJobs

At least since the followersofNedLuddsmashedmechanized looms in1811,workershaveworried

aboutautomationdestroyingjobs.Economistshavereassuredthemthatnewjobswouldbecreatedevenasoldoneswereeliminated.Forover200years,theeconomistswereright.Despitemassiveautomationofmillionsofjobs,moreAmericanshadjobsattheendofeachdecadeupthroughtheendofthe20thcentury.However, thisempirical factconcealsadirtysecret.There isnoeconomic lawthatsays thateveryone,orevenmostpeople,automaticallybenefitfromtechnologicalprogress.

Peoplewith little economics training intuitively grasp this point. They understand that some humanworkersmayloseoutintheraceagainstthemachine.Ironically,thebest-educatedeconomistsareoftenthe most resistant to this idea, as the standard models of economic growth implicitly assume thateconomicgrowthbenefitsall residentsofacountry.However, justasNobelPrize-winningeconomistPaulSamuelsonshowedthatoutsourcingandoffshoringdonotnecessarilyincreasethewelfareofallworkers, it is also true that technological progress is not a rising tide that automatically raises allincomes.Evenasoverallwealthincreases, therecanbe,andusuallywillbe,winnersandlosers.Andthelosersarenotnecessarilysomesmallsegmentofthelaborforcelikebuggywhipmanufacturers.Inprinciple,theycanbeamajorityoreven90%ormoreofthepopulation.

If wages can freely adjust, then the losers keep their jobs in exchange for accepting ever-lowercompensationastechnologycontinuestoimprove.Butthere’salimittothisadjustment.ShortlyaftertheLudditesbegansmashingthemachinerythattheythoughtthreatenedtheirjobs,theeconomistDavidRicardo, who initially thought that advances in technology would benefit all, developed an abstractmodel that showed the possibility of technological unemployment. The basic ideawas that at somepoint,theequilibriumwagesforworkersmightfallbelowthelevelneededforsubsistence.Arationalhumanwouldseenopointintakingajobatawagethatlow,sotheworkerwouldgounemployedandtheworkwouldbedonebyamachineinstead.

Ofcourse,thiswasonlyanabstractmodel.ButinhisbookAFarewelltoAlms,AFarewelltoAlms,economistGregoryClarkgivesaneeriereal-worldexampleofthisphenomenoninaction:

There was a type of employee at the beginning of the Industrial Revolution whose job andlivelihood largelyvanished in theearly twentiethcentury.Thiswas thehorse.ThepopulationofworkinghorsesactuallypeakedinEnglandlongaftertheIndustrialRevolution,in1901,when3.25millionwere atwork. Though they had been replaced by rail for long-distance haulage and bysteamengines fordrivingmachinery, theystillplowedfields,hauledwagonsandcarriagesshortdistances, pulled boats on the canals, toiled in the pits, and carried armies into battle. But thearrival of the internal combustion engine in the late nineteenth century rapidly displaced theseworkers,sothatby1924therewerefewerthantwomillion.Therewasalwaysawageatwhichallthesehorsescouldhaveremainedemployed.Butthatwagewassolowthatitdidnotpayfortheirfeed.

Astechnologycontinuestoadvanceinthesecondhalfofthechessboard,takingonjobsandtasksthatusedtobelongonlytohumanworkers,onecanimagineatimeinthefuturewhenmoreandmorejobsaremore cheaplydonebymachines thanhumans.And indeed, thewages of unskilledworkers havetrendeddownwardforover30years,atleastintheUnitedStates.

Wealsonowunderstand that technological unemployment canoccur evenwhenwages are stillwellabovesubsistenceiftherearedownwardrigiditiesthatpreventthemfromfallingasquicklyasadvancesin technology reduce thecostsof automation.Minimumwage laws,unemployment insurance,healthbenefits, prevailing wage laws, and long-term contracts—not to mention custom and psychology—

makeitdifficulttorapidlyreducewages.3Furthermore,employerswilloftenfindwagecutsdamagingtomorale.As the efficiencywage literature notes, such cuts can be demotivating to employees andcausecompaniestolosetheirbestpeople.

Butcompletewageflexibilitywouldbenopanacea,either.Ever-fallingwagesforsignificantsharesoftheworkforce is not exactly an appealing solution to the threat of technological employment.Asidefromthedamageitdoestothelivingstandardsoftheaffectedworkers,lowerpayonlypostponesthedayofreckoning.Moore’sLawisnotaone-timeblipbutanacceleratingexponentialtrend.

The threat of technological unemployment is real. To understand this threat, we'll define threeoverlapping sets ofwinners and losers that technical change creates: (1) high-skilled vs. low-skilledworkers,(2)superstarsvs.everyoneelse,and(3)capitalvs.labor.Eachsethaswell-documentedfactsandcompellinglinkstodigitaltechnology.What’smore,thesesetsarenotmutuallyexclusive.Infact,thewinnersinonesetaremorelikelytobewinnersintheothertwosetsaswell,whichconcentratestheconsequences.

In each case, economic theory is clear.Evenwhen technological progress increases productivity andoverallwealth,itcanalsoaffectthedivisionofrewards,potentiallymakingsomepeopleworseoffthantheywerebeforetheinnovation.Inagrowingeconomy,thegainstothewinnersmaybelargerthanthelossesofthosewhoarehurt,butthisisasmallconsolationtothosewhocomeoutontheshortendofthebargain.

Ultimately,theeffectsoftechnologyareanempiricalquestion—onethatisbestsettledbylookingatthedata.Forallthreesetsofwinnersandlosers,thenewsistroubling.Let’slookateachinturn.

1.High-Skilledvs.Low-SkilledWorkers

We’llstartwithskill-biasedtechnicalchange,whichisperhapsthemostcarefullystudiedofthethreephenomena. This is technical change that increases the relative demand for high-skill labor whilereducing or eliminating the demand for low-skill labor. A lot of factory automation falls into thiscategory, as routine drudgery is turned over to machines while more complex programming,management,andmarketingdecisionsremainthepurviewofhumans.

ArecentpaperbyeconomistsDaronAcemogluandDavidAutorhighlightsthegrowingdivergenceinearningsbetweenthemost-educatedandleast-educatedworkers.Overthepast40years,weeklywagesforthosewithahighschooldegreehavefallenandwagesforthosewithahighschooldegreeandsomecollegehavestagnated.Ontheotherhand,college-educatedworkershaveseensignificantgains,withthebiggestgainsgoingtothosewhohavecompletedgraduatetraining(Figure3.5).

What’smore,thisincreaseintherelativepriceofeducatedlabor—theirwages—comesduringaperiodwherethesupplyofeducatedworkershasalsoincreased.Thecombinationofhigherpayinthefaceofgrowing supply points unmistakably to an increase in the relativedemand for skilled labor.Becausethosewiththeleasteducationtypicallyalreadyhadthelowestwages,thischangehasincreasedoverallincomeinequality.

It’sclearfromthechartinFigure3.5thatwagedivergenceacceleratedinthedigitalera.Asdocumentedin careful studies by David Autor, Lawrence Katz, and Alan Krueger, as well as Frank Levy andRichardMurnane and many others, the increase in the relative demand for skilled labor is closelycorrelated with advances in technology, particularly digital technologies. Hence, the moniker “skill-biasedtechnicalchange,”orSBTC.TherearetwodistinctcomponentstorecentSBTC.Technologieslike robotics, numerically controlled machines, computerized inventory control, and automatictranscription have been substituting for routine tasks, displacing those workers. Meanwhile othertechnologieslikedatavisualization,analytics,high-speedcommunications,andrapidprototypinghaveaugmentedthecontributionsofmoreabstractanddata-drivenreasoning,increasingthevalueofthosejobs.

Skill-biasedtechnicalchangehasalsobeenimportantinthepast.Formostofthe19thcentury,about25%ofallagriculturelaborthreshedgrain.Thatjobwasautomatedinthe1860s.The20thcenturywasmarkedbyanacceleratingmechanizationnotonlyofagriculturebutalsooffactorywork.EchoingthefirstNobelPrizewinner ineconomics,JanTinbergen,HarvardeconomistsClaudiaGoldinandLarryKatz described the resulting SBTC as a “race between education and technology.” Ever-greaterinvestments in education, dramatically increasing the average educational level of the Americanworkforce,helpedprevent inequality fromsoaringas technologyautomatedmoreandmoreunskilledwork.Whileeducationiscertainlynotsynonymouswithskill, it isoneofthemosteasilymeasurablecorrelates of skill, so this pattern suggests that demand for upskilling has increased faster than itssupply.

Studies by this book’s co-author Erik Brynjolfsson along with Timothy Bresnahan, Lorin Hitt, andShinkuYangfoundthatakeyaspectofSBTCwasnotjusttheskillsofthoseworkingwithcomputers,butmoreimportantlythebroaderchangesinworkorganizationthatweremadepossiblebyinformation

technology.Themostproductivefirmsreinventedandreorganizeddecisionrights,incentivessystems,informationflows,hiringsystems,andotheraspectsoforganizationalcapitaltogetthemostfromthetechnology.This,inturn,requiredradicallydifferentand,generally,higherskilllevelsintheworkforce.Itwasnotsomuchthatthosedirectlyworkingwithcomputershadtobemoreskilled,butratherthatwhole production processes, and even industries, were reengineered to exploit powerful newinformation technologies.What’smore, each dollar of computer hardwarewas often the catalyst formore than $10 of investment in complementary organizational capital. The intangible organizationalassetsaretypicallymuchhardertochange,buttheyarealsomuchmoreimportanttothesuccessoftheorganization.

Asthe21stcenturyunfolds,automationisaffectingbroaderswathsofwork.Eventhelowwagesearnedbyfactoryworkers inChinahavenot insulated themfrombeingundercutbynewmachineryand thecomplementary organizational and institutional changes. For instance, Terry Gou, the founder andchairmanof the electronicsmanufacturerFoxconn, announced thisyear aplan topurchase1millionrobotsoverthenextthreeyearstoreplacemuchofhisworkforce.Therobotswilltakeoverroutinejobslike sprayingpaint,welding, andbasic assembly.Foxconncurrentlyhas10,000 robots,with300,000expectedtobeinplacebynextyear.

2.Superstarsvs.EveryoneElse

The seconddivision is between superstars and everyone else.Many industries arewinner-take-all orwinner-take-mostcompetitions,inwhichafewindividualsgetthelion’sshareoftherewards.Thinkofpopmusic,professionalathletics,andthemarketforCEOs.Digitaltechnologiesincreasethesizeandscope of these markets. These technologies replicate not only information goods but increasinglybusinessprocesses aswell.As a result, the talents, insights, ordecisionsof a singlepersoncannowdominate a national or even global market. Meanwhile good, but not great, local competitors areincreasingly crowded out of their markets. The superstars in each field can now earn much largerrewardsthantheydidinearlierdecades.

Theeffectsareevidentatthetopoftheincomedistribution.Thetop10%ofthewagedistributionhasdonemuchbetter than the restof the labor force,butevenwithin thisgroup therehasbeengrowinginequality.Incomehasgrownfasterforthetop1%thantherestofthetopdecile.Inturn,thetop0.1%andtop0.01%haveseentheirincomegrowevenfaster.Thisisnotrun-of-the-millskill-biasedtechnicalchange but rather reflects the unique rewards of superstardom. Sherwin Rosen, himself a superstareconomist,laidouttheeconomicsofsuperstarsinaseminal1981article.Inmanymarkets,consumersare willing to pay a premium for the very best. If technology exists for a single seller to cheaplyreplicatehisorherservices,thenthetop-qualityprovidercancapturemost—orall—ofthemarket.Thenext-bestprovidermightbealmostasgoodyetgetonlyatinyfractionoftherevenue.

Technologycanconvertanordinarymarketintoonethatischaracterizedbysuperstars.Beforetheeraofrecordedmusic,theverybestsingermighthavefilledalargeconcerthallbutatmostwouldonlybeabletoreachthousandsoflistenersoverthecourseofayear.Eachcitymighthaveitsownlocalstars,withafewtopperformerstouringnationally,buteventhebestsingerinthenationcouldreachonlyarelatively small fraction of the potential listening audience. Once music could be recorded anddistributedataverylowmarginalcost,however,asmallnumberof topperformerscouldcapturethemajorityofrevenuesineverymarket,fromclassicalmusic’sYo-YoMatopop’sLadyGaga.

EconomistsRobertFrankandPhilipCookdocumentedhowwinner-take-allmarketshaveproliferated

as technology transformednotonly recordedmusicbutalso software,drama, sports, andeveryotherindustry that canbe transmittedasdigitalbits.This trendhasacceleratedasmoreof theeconomy isbasedonsoftware,eitherimplicitlyorexplicitly.Aswediscussedinour2008HarvardBusinessReviewarticle,digitaltechnologiesmakeitpossibletoreplicatenotonlybitsbutalsoprocesses.Forinstance,companies like CVS have embedded processes like prescription drug ordering into their enterpriseinformation systems. Each time CVS makes an improvement, it is propagated across 4,000 storesnationwide,amplifyingitsvalue.Asaresult,thereachandimpactofanexecutivedecision,likehowtoorganizeaprocess,iscorrespondinglylarger.

Infact,theratioofCEOpaytoaverageworkerpayhasincreasedfrom70in1990to300in2005,andmuchofthisgrowthislinkedtothegreateruseofIT,accordingtorecentresearchthatErikdidwithhisstudentHeekyungKim.Theyfoundthatincreasesinthecompensationofothertopexecutivesfolloweda similar, if less extreme, pattern.Aided by digital technologies, entrepreneurs,CEOs, entertainmentstars,andfinancialexecutiveshavebeenabletoleveragetheirtalentsacrossglobalmarketsandcapturerewardthatwouldhavebeenunimaginableinearliertimes.

To be sure, technology is not the only factor that affects incomes. Political factors, globalization,changes inassetprices,and, in thecaseofCEOsandfinancialexecutives,corporategovernancealsoplaysarole.Inparticular, thefinancialservicessectorhasgrowndramaticallyasashareofGDPandevenmoreasashareofprofitsandcompensation,especiallyatthetopoftheincomedistribution.Whileefficientfinanceisessentialtoamoderneconomy,itappearsthatasignificantshareofreturnstolargehumanand technological investments in thepastdecade, suchas those insophisticatedcomputerizedprogram trading, were from rent redistribution rather than genuine wealth creation. Other countries,withdifferentinstitutionsandalsosloweradoptionofIT,haveseenlessextremechangesininequality.ButtheoverallchangesintheUnitedStateshavebeensubstantial.AccordingtoeconomistEmmanuelSaez,thetop1%ofU.S.householdsgot65%ofallthegrowthintheeconomysince2002.Infact,Saezreportsthatthetop0.01%ofhouseholdsintheUnitedStates—thatis,the14,588familieswithincomeabove$11,477,000—sawtheirshareofnationalincomedoublefrom3%to6%between1995and2007.

3.Capitalvs.Labor

Thethirddivisionisbetweencapitalandlabor.Most typesofproductionrequirebothmachineryandhumanlabor.Accordingtobargainingtheory,thewealththeygenerateisdividedaccordingtorelativebargaining power, which in turn typically reflects the contribution of each input. If the technologydecreases the relative importance of human labor in a particular production process, the owners ofcapital equipment will be able to capture a bigger share of income from the goods and servicesproduced. To be sure, capital owners are also humans—so it’s not like the wealth disappears fromsociety—butcapitalownersaretypicallyaverydifferentandsmallergroupthantheonesdoingmostofthelabor,sothedistributionofincomewillbeaffected.

In particular, if technology replaces labor, you might expect that the shares of income earned byequipment owners would rise relative to laborers—the classic bargaining battle between capital andlabor.4Thishasbeenhappeningincreasinglyinrecentyears.AsnotedbyKathleenMadigan,sincetherecession ended, real spending on equipment and software has soared by 26% while payrolls haveremainedessentiallyflat.

Furthermore, there is growing evidence that capital has captured a growing share of GDP in recentyears.AsshowninFigure3.6,corporateprofitshaveeasilysurpassedtheirpre-recessionlevels.

AccordingtotherecentlyupdateddatafromtheU.S.CommerceDepartment,recentcorporateprofitsaccounted for23.8%of totaldomesticcorporate income,a recordhighshare that ismore than1 fullpercentagepointabovethepreviousrecord.Similarly,corporateprofitsasashareofGDPareat50-yearhighs.Meanwhile,compensationtolaborinallforms,includingwagesandbenefits,isata50-yearlow.Capitalisgettingabiggershareofthepie,relativetolabor.

The recession exacerbated this trend, but it’s part of long-term change in the economy.As noted byeconomistsSusanFleck,JohnGlaser,andShawnSprague,thetrendlineforlabor’sshareofGDPwasessentially flat between 1974 and 1983 but has been falling since then.When one thinks about theworkersinplaceslikeFoxconn’sfactorybeingreplacedbylabor-savingrobots,it’seasytoimagineatechnology-drivenstoryforwhytherelativesharesofincomemightbechanging.

It’simportanttonotethatthe“labor”shareintheBureauofLaborStatistics’dataincludeswagespaidtoCEOs, finance professionals, professional athletes, and other “superstars” discussed above. In thissense, the declining labor share understates how badly the median worker has fared. It may alsounderstatethedivisionofincomebetweencapitalandlabor,insofarasCEOsandothertopexecutivesmay have bargaining power to capture some of the “capital’s share” thatwould otherwise accrue toownersofcommonstock.

InequalityCanAffecttheOverallSizeoftheEconomy

Technologychangesthesharesofincomefortheskilledvs.unskilled,forsuperstarsvs.therest,andforcapitalvs.labor.Isthissimplyazero-sumgamewherethelossesofsomeareexactlyoffsetbygainstoothers?Notnecessarily.Onthepositivesideoftheledger,inequalitycanprovidebeneficialincentivesfor skill acquisition, efforts toward superstardom, or capital accumulation. However, there are alsoseveralwaysitcanhurteconomicwell-being.

First, one of themost basic regularities of economics is the decliningmarginal utility of income.A$1,000windfallislikelytoincreaseyourhappiness,orutility,lessifyoualreadyhave$10millionthanifyouonlyhave$10,000.Second,equalityofopportunityisimportanttotheefficiencyandfairnessofasociety, even ifunequaloutcomesare toleratedor evencelebrated.Equalityofopportunity,however,canbeharder to achieve if childrenofpovertyget inadequatehealth care, nutrition,or education,orpeople at the bottom are otherwise unable to compete on a level-playing field. Third, inequalityinevitablyaffectspolitics,andthiscanbedamaginganddestabilizing.AseconomistDaronAcemogluputsit:

Economicpowertendstobegetpoliticalpowerevenindemocraticandpluralisticsocieties.IntheUnited States, this tends to work through campaign contributions and access to politicians thatwealthandmoneytendtobuy.Thispoliticalchannel impliesanother,potentiallymorepowerfulanddistortionarylinkbetweeninequalityandanon-levelplayingfield.

Finally, when technology leads to relatively sudden shifts in income between groups, it may alsodampenoveralleconomicgrowthandpotentiallyprecipitatethekindofcollapseinaggregatedemandreflectedinthecurrentslump.

Consider each of the three sets of winners and losers discussed earlier. When SBTC increases theincomesofhigh-skillworkersanddecreasestheincomesandemploymentoflow-skillworkers,theneteffectmaybeafallinoveralldemand.High-skillworkers,givenextraincome,maychoosetoincreasetheirleisureandsavingsratherthanworkextrahours.Meanwhile,low-skillworkerslosetheirjobs,goondisability,orotherwisedropoutof the labor force.Bothgroupswork less thanbefore, sooveralloutputfalls.5

Onecantellasimilarstoryforhowsuper-wealthysuperstars,givenadditionalwealth,choosetosavemostofitwhiletheirless-than-stellarcompetitorshavetocutbackconsumption.Again,overalloutputfallsfromsuchashift.FormersecretaryoflaborRobertReichhasarguedthatsuchadynamicwasinpartresponsiblefortheGreatDepression,andNobelPrizewinnerJosephStiglitzhaswrittenindetailabout how the increasing concentration of wealth in a relatively small group can be corrosive toeconomicgrowth.

Finally,it’seasytoseehowashiftinincomefromlabortocapitalwouldleadtoasimilarreductioninoveralldemand.Capitaliststendtosavemoreofeachmarginaldollarthanlaborers.Intheshortrun,atransferfromlaborerstocapitalistsreducestotalconsumption,andthustotalGDP.Thisphenomenonissummarized in a classic though possibly apocryphal story: Ford CEO Henry Ford II and UnitedAutomobileWorkerspresidentWalterReuther are jointly touringamodernautoplant.Ford jokinglyjabsatReuther:“Walter,howareyougoingtogettheserobotstopayUAWdues?”Notmissingabeat,Reutherresponds:“Henry,howareyougoingtogetthemtobuyyourcars?”

Overtime,awell-functioningeconomyshouldbeabletoadjusttothenewconsumptionpathrequiredbyanyorallofthesetypesofreallocationsofincome.Forinstance,about90%ofAmericansworkedinagriculturein1800;by1900itwas41%,andby2000itwasjust2%.Asworkers leftfarmsover thecourseoftwocenturies,thereweremorethanenoughnewjobscreatedinothersectors,andwholenewindustries sprang up to employ them. However, when the changes happen faster than expectationsand/or institutions can adjust, the transition can be cataclysmic. Accelerating technology in the pastdecade has disrupted not just one sector but virtually all of them. A common way to maintainconsumptiontemporarilyinthefaceofanadverseshockistoborrowmore.Whilethisisfeasibleinthe

shortrun,andisevenrationaliftheshockisexpectedtobetemporary,itisunsustainableifthetrendcontinuesor,worse,growsinmagnitude.

Arguably, something like thishashappened in thepastdecade.Wages formanyAmericans fellwellshortofhistoricalgrowthratesandevenfellinrealtermsformanygroupsastechnologytransformedtheirindustries.BorrowinghelpedmasktheproblemuntiltheGreatRecessioncamealong.Thegradualdemandcollapsethatmighthavebeenspreadoverdecadeswascompressedintoamuchshorterperiod,makingitharderforworkers tochangetheirskills,entrepreneurs to inventnewbusinessmodels,andmanagerstomakethenecessaryadjustmentsequallyquickly.Theresulthasbeenadysfunctionalseriesofcrises.Certainly,muchoftherecentunemploymentis,aspastbusinesscycles,simplyduetoweakdemandintheoveralleconomy,reflectinganextremelyseveredownturn.However,thisdoesnotnegatetheimportantstructuralcomponenttothefallinglevelsofemployment,anditisplausiblethattheGreatRecessionitselfmay,inpart,reflectadelayedresponsetothesedeeperstructuralissues.

LookingAhead

Aswelookahead,weseethesethreetrendsnotonlyacceleratingbutalsoevolving.Forinstance,newresearchbyDavidAutorandDavidDornhasputaninterestingtwistontheSBTCstory.Theyfindthatthe relationshipbetween skills andwageshas recently becomeU-shaped. In themost recent decade,demandhas fallenmost for those in themiddle of the skill distribution.The highest-skilledworkershavedonewell,butinterestinglythosewiththelowestskillshavesufferedlessthanthosewithaverageskills,reflectingapolarizationoflabordemand.

Thisreflectsaninterestingfactaboutautomation.Itcanbeeasiertoautomatetheworkofabookkeeper,bank teller, or semi-skilled factory worker than a gardener, hairdresser, or home health aide. Inparticular,overthepast25years,physicalactivitiesthatrequireadegreeofphysicalcoordinationandsensory perception have proven more resistant to automation than basic information processing, aphenomenon known as Moravec’s Paradox’. For instance, many types of clerical work have beenautomated, andmillionsofpeople interactwith robotbank tellers andairport ticket agents eachday.More recently,callcenterwork—whichwaswidelyoffshored to India, thePhilippines,orother low-wagenationsinthe1990s—hasincreasinglybeenreplacedbyautomatedvoiceresponsesystemsthatcanrecognizeanincreasinglylargedomain-specificvocabularyandevencompletesentences.

Incontrast,vision,finemotorskills,andlocomotionhavebeenmuchhardertoautomate.Thehumanbrain can draw on highly specialized neural circuitry, refined by millions of years of evolution, torecognize faces, manipulate objects, and walk through unstructured environments. Althoughmultiplying five-digit numbers is an unnatural and difficult skill for the humanmind tomaster, thevisualcortexroutinelydoesfarmorecomplexmathematicseachtimeitdetectsanedgeorusesparallaxtolocateanobjectinspace.Machinecomputationhassurpassedhumansinthefirsttaskbutnotyetinthesecondone.

Asdigitaltechnologiescontinuetoimprove,weareskepticalthateventheseskillswillremainbastionsofhumanexceptionalismin thecomingdecades.Theexamples inChapter2ofGoogle’sself-drivingcarandIBM’sWatsonpoint toadifferentpathgoingforward.Thetechnologyisrapidlyemergingtoautomate truck driving in the coming decade, just as scheduling truck routes was increasinglyautomatedinthelastdecade.Likewise,thehighendoftheskillspectrumisalsovulnerable,asweseein the case of e-discovery displacing lawyers and, perhaps, in aWatson-like technology, displacinghumanmedicaldiagnosticians.

SomeConclusions

Technologyhasadvancedrapidly,andthegoodnewsisthatthishasradicallyincreasedtheeconomy’sproductive capacity. However, technological progress does not automatically benefit everyone in asociety. Inparticular, incomeshavebecomemoreuneven,ashaveemploymentopportunities.Recenttechnologicaladvanceshave favoredsomeskillgroupsoverothers,particularly“superstars” inmanyfields,andprobablyalsoincreasedtheoverallshareofGDPaccruingtocapitalrelativetolabor.

Thestagnationinmedianincomeisnotbecauseofalackoftechnologicalprogress.Onthecontrary,theproblemisthatourskillsandinstitutionshavenotkeptupwiththerapidchangesintechnology.Inthe19th and20th centuries, as each successivewaveof automation eliminated jobs in some sectors andoccupations,entrepreneurs identifiednewopportunitieswherelaborcouldberedeployedandworkerslearnedthenecessaryskills tosucceed.Millionsofpeople leftagriculture,butanevenlargernumberfoundemploymentinmanufacturingandservices.

In the21st century, technological change isboth faster andmorepervasive.While the steamengine,electricmotor,andinternalcombustionenginewereeachimpressivetechnologies,theywerenotsubjectto anongoing level of continuous improvement anywherenear thepace seen indigital technologies.Already,computersarethousandsoftimesmorepowerfulthantheywere30yearsago,andallevidencesuggests that this pace will continue for at least another decade, and probably more. Furthermore,computersare,insomesense,the“universalmachine”thathasapplicationsinalmostallindustriesandtasks.Inparticular,digitaltechnologiesnowperformmentaltasksthathadbeentheexclusivedomainofhumansin thepast.Generalpurposecomputersaredirectlyrelevantnotonly to the60%of the laborforceinvolvedininformationprocessingtasksbutalsotomoreandmoreoftheremaining40%.Asthetechnology moves into the second half of the chessboard, each successive doubling in power willincreasethenumberofapplicationswhereitcanaffectworkandemployment.Asaresult,ourskillsandinstitutionswillhavetoworkharderandhardertokeepuplestmoreandmoreofthelaborforcefacestechnologicalunemployment.

2Thedifferencealsoreflectsthefactthathouseholdsaresomewhatsmallernowthaninthepast(thus,householdincomewillgrowless thanindividual income)andsometechnicaldifferencesbetweenthewayGDPandincomearecalculated.

3Suchwagerigiditieshavebeenwidelyobservedandlieattheheartofmanymacroeconomicmodelsofthebusinesscycle.

4 The precise economic theory is a bit more complicated, however. In a well-functioning market,rewards for capital (or labor) tend to reflect thevalueof anadditionalpieceof capital (or additionalworker)at themarginthemargin.Dependingonhowexpensive it is to increase thecapitalstock, therewards earned by capitalistsmay not automatically growwith increased automation—the predictedeffectsdependontheexactdetailsoftheproduction,distribution,andgovernancesystems.

5EconomistArnoldKlingdescribessuchamodelonhisblog.

Chapter4.WhatIstoBeDone?PrescriptionsandRecommendations

Thegreatest taskbeforecivilizationatpresent is tomakemachineswhattheyoughttobe, theslaves,insteadofthemastersofmen.

—HavelockEllis,1922

Intheprevioustwochaptersweshowedhowquicklyanddeeplycomputersareencroachingintohumanterritory, and discussed the economic consequences of this phenomenon—how digital progress canleave some peopleworse off even as it improves productivity and grows the overall pie.Of course,concerns about the interplay between technology and economics are not new. In fact, they’ve evenenteredAmericanfolklore.

ThelegendofJohnHenrybecamepopularinthelate19thcenturyastheeffectsofthesteam-poweredIndustrial Revolution affected every industry and job that relied heavily on human strength. It’s thestoryofacontestbetweenasteamdrillandJohnHenry,apowerfulrailroadworker,toseewhichofthetwocouldborethelongerholeintosolidrock.6Henrywinsthisraceagainstthemachinebutloseshislife;hisexertionscausehishearttoburst.Humansneverdirectlychallengedthesteamdrillagain.

Thislegendreflectedpopularuneaseatthetimeaboutthepotentialfortechnologytomakehumanlaborobsolete.ButthisisnotatallwhathappenedastheIndustrialRevolutionprogressed.Assteampoweradvanced and spread throughout industry, more humanworkers were needed, not fewer. Theywereneeded not for their raw physical strength (as was the case with John Henry) but instead for otherhumanskills:physicalones like locomotion,dexterity,coordination,andperception,andmentaloneslikecommunication,patternmatching,andcreativity.

TheJohnHenrylegendshowsusthat,inmanycontexts,humanswilleventuallylosethehead-to-headraceagainstthemachine.ButthebroaderlessonofthefirstIndustrialRevolutionismoreliketheIndy500 than JohnHenry: economicprogress comes fromconstant innovation inwhichpeople racewithmachines.Humanandmachinecollaboratetogetherinaracetoproducemore,tocapturemarkets,andtobeatotherteamsofhumansandmachines.

Thislessonremainsvalidandinstructivetodayasmachinesarewinninghead-to-headmentalcontests,notjustphysicalones.Hereagain,weobservethatthingsgetreallyinterestingoncethiscontestisoverandpeoplestartracingwithmachinesinsteadofagainstthem.

Thegameofchessprovidesagreatexample.In1997,GaryKasparov,humanity’smostbrilliantchessmaster,losttoDeepBlue,a$10millionspecializedsupercomputerprogrammedbyateamfromIBM.Thatwasbignewswhenithappened,butthendevelopmentsintheworldofchesswentbacktobeingreportedonandreadmainlybychessgeeks.Asaresult,it’snotwellknownthatthebestchessplayeron theplanet today isnotacomputer.Nor is it ahuman.Thebestchessplayer isa teamofhumansusingcomputers.

After head-to-head matches between humans and computers became uninteresting (because the

computers alwayswon), the actionmoved to “freestyle” competitions, allowing any combination ofpeopleandmachines.Theoverallwinnerinarecentfreestyletournamenthadneither thebesthumanplayersnorthemostpowerfulcomputers.AsKasparovwrites,itinsteadconsistedof

apairofamateurAmericanchessplayersusing threecomputersat the same time.Their skill atmanipulating and “coaching” their computers to look very deeply into positions effectivelycounteracted the superior chess understanding of their grandmaster opponents and the greatercomputational power of other participants. … Weak human + machine + better process wassuperiortoastrongcomputeraloneand,moreremarkably,superiortoastronghuman+machine+inferiorprocess.

Thispatternistruenotonlyinchessbutthroughouttheeconomy.Inmedicine,law,finance,retailing,manufacturing, and even scientific discovery, the key towinning the race is not to competeagainstmachines but to competewith machines. As we saw in Chapter 2, while computers win at routineprocessing, repetitivearithmetic, anderror-freeconsistencyandarequicklygettingbetter at complexcommunicationandpatternmatching,theylackintuitionandcreativityandarelostwhenaskedtoworkevena littleoutsideapredefineddomain.Fortunately,humansarestrongestexactlywherecomputersareweak,creatingapotentiallybeautifulpartnership.

As this partnership advances, we’re not too worried about computers holding up their end of thebargain. Technologists are doing an amazing job of making them ever faster, smaller, more energyefficient, and cheaper over time.We are confident that these trendswill continue even aswemovedeeperintothesecondhalfofthechessboard.

Digitalprogress,infact,issorapidandrelentlessthatpeopleandorganizationsarehavingahardtimekeepingup.Sointhischapterwewanttofocusonrecommendationsintwoareas:improvingtherateandqualityoforganizationalinnovation,andincreasinghumancapital—ensuringthatpeoplehavetheskillstheyneedtoparticipateintoday’seconomy,andtomorrow’s.Makingprogressinthesetwoareaswillbethebestwaytoallowhumanworkersandinstitutionstoracewithmachines,notagainstthem.

FosteringOrganizationalInnovation

Howcanweimplementa“racewithmachines”strategy?Thesolutionisorganizationalinnovation:co-inventingneworganizational structures,processes, andbusinessmodels that leverageever-advancingtechnologyandhumanskills.JosephSchumpeter,theeconomist,describedthisasaprocessof“creativedestruction” and gave entrepreneurs the central role in the development and propagation of thenecessaryinnovations.Entrepreneursreaprichrewardsbecausewhattheydo,whentheydoitwell,isbothincrediblyvaluableandfartoorare.

Toputitanotherway,thestagnationofmedianwagesandpolarizationofjobgrowthisanopportunityforcreativeentrepreneurs.Theycandevelopnewbusinessmodelsthatcombinetheswellingnumbersofmid-skilledworkerswithever-cheapertechnologytocreatevalue.Therehasneverbeenaworsetimetobecompetingwithmachines,buttherehasneverbeenabettertimetobeatalentedentrepreneur.

Entrepreneurial energy in America’s tech sector drove themost visible reinvention of the economy.Google,Facebook,Apple,andAmazon,amongothers,havecreatedhundredsofbillionsofdollarsofshareholder value by creating whole new product categories, ecosystems, and even industries. Newplatforms leverage technology to createmarketplaces that address the employment crisis bybringing

togethermachinesandhumanskillsinnewandunexpectedways:

eBayandAmazonMarketplacespurredover600,000peopletoearntheirlivingsbydreamingupnew,improved,orsimplydifferentorcheaperproductsforaworldwidecustomerbase.TheLongTailofnewproductsofferedenormousconsumervalueandisarapidlygrowingsegmentoftheeconomy.

Apple’sAppStore andGoogle’sAndroidMarketplacemake it easy for peoplewith ideas formobileapplicationstocreateanddistributethem.

Threadless letspeoplecreateandselldesignsfor t-shirts.Amazon’sMechanicalTurkmakes iteasytofindcheaplabortodoabreathtakingarrayofsimple,well-definedtasks.Kickstarterflipsthismodelonitsheadandhelpsdesignersandcreativeartistsfindsponsorsfortheirprojects.

HeartlandRoboticsprovidescheaprobots-in-a-boxthatmakeitpossibleforsmallbusinesspeopleto quickly set up their own highly automated factory, dramatically reducing the costs andincreasingtheflexibilityofmanufacturing.

Collectively, these new businesses directly create millions of new jobs.7 Some of them also createplatformsforthousandsofotherentrepreneurs.Noneofthemmayevercreatebillion-dollarbusinessesthemselves,butcollectivelytheycandomoretocreatejobsandwealththaneventhemostsuccessfulsingleventure.

As thegreat theorist ofmarketsFriedrichHayeknoted, some of themost valuable knowledge in aneconomyisdispersedamongindividuals.Itis

theknowledgeoftheparticularcircumstancesoftimeandplace.…Toknowofandputtouseamachinenotfullyemployed,orsomebody'sskillwhichcouldbebetterutilized,ortobeawareofasurplus stock which can be drawn upon during an interruption of supplies, is socially quite asusefulastheknowledgeofbetteralternativetechniques.Andtheshipperwhoearnshislivingfromusingotherwiseemptyorhalf-filledjourneysoftramp-steamers,ortheestateagentwhosewholeknowledgeisalmostexclusivelyoneoftemporaryopportunities,orthearbitrageurwhogainsfromlocal differences of commodity prices, are all performing eminently useful functions based onspecialknowledgeofcircumstancesofthefleetingmomentnotknowntoothers.

Fortunately,digital technologiescreateenormousopportunitiesforindividualstousetheiruniqueanddispersedknowledgefor thebenefitof thewholeeconomy.Asaresult, technologyenablesmoreandmore opportunities for what Google chief economist Hal Varian calls “micromultinationals”—businesses with less than a dozen employees that sell to customers worldwide and often draw onworldwidesupplierandpartnernetworks.Whilethearchetypal20th-centurymultinationalwasoneofasmallnumberofmegafirmswithhugefixedcostsandthousandsofemployees,thecomingcenturywillgivebirthtothousandsofsmallmultinationalswithlowfixedcostsandasmallnumberofemployeeseach. Bothmodels can conceivably employ similar numbers of people overall, but the latter one islikelytobemoreflexible.

Butarethereenoughopportunitiesforalltheseentrepreneurs?Arewerunningoutofinnovations?

Whenbusinessesarebasedonbitsinsteadofatoms,theneachnewproductaddstothesetofbuildingblocks available to the next entrepreneur insteadof depleting the stockof ideas thewayminerals orfarmlandsaredepletedinthephysicalworld.Newdigitalbusinessesareoftenrecombinations,ormash-ups,ofpreviousones.Forexample,astudentinoneofourclassesatMITcreatedasimpleFacebookapplication for sharing photos. Although he had little formal training in programming, he created arobust andprofessional-lookingapp in a fewdaysusing standard tools.Within ayearhehadover1millionusers.ThiswaspossiblebecausehisinnovationleveragedtheFacebookuserbase,whichinturnleveragedthebroaderWorldWideWeb,whichinturnleveragedtheInternetprotocols,whichinturnleveragedthecheapcomputersofMoore'sLawandmanyotherinnovations.Hecouldnothavecreatedvalue for his million users without the existence of these prior inventions. Because the process ofinnovationoftenreliesheavilyonthecombiningandrecombiningofpreviousinnovations,thebroaderanddeeperthepoolofaccessibleideasandindividuals,themoreopportunitiesthereareforinnovation.

Weareinnodangerofrunningoutofnewcombinationstotry.Eveniftechnologyfrozetoday,wehavemorepossiblewaysofconfiguringthedifferentapplications,machines,tasks,anddistributionchannelstocreatenewprocessesandproductsthanwecouldeverexhaust.

Here’sasimpleproof:supposethepeopleinasmallcompanywritedowntheirworktasks—onetaskpercard.Iftherewereonly52tasksinthecompany,asmanyasinastandarddeckofcards,thentherewouldbe52!differentwaystoarrangethesetasks.8Thisisfarmorethanthenumberofgrainsofriceon the second 32 squares of a chessboard or even a second or third full chessboard. Combinatorialexplosionisoneofthefewmathematicalfunctionsthatoutgrowsanexponentialtrend.AndthatmeansthatcombinatorialinnovationisthebestwayforhumaningenuitytostayintheracewithMoore’sLaw.

Mostofthecombinationsmaybenobetterthanwhatwealreadyhave,butsomesurelywillbe,andafewwillbe“homeruns”thatarevastimprovements.Thetrickisfindingtheonesthatmakeapositivedifference.Parallelexperimentationbymillionsofentrepreneursisthebestandfastestwaytodothat.As Thomas Edison once said when trying to find the right combination of materials for a workinglightbulb:“Ihavenotfailed.I'vejustfound10,000waysthatwon'twork.”Multiplythatby10millionentrepreneursandyoucanbegin to see the scaleof theeconomy’s innovationpotential.Mostof thispotentialremainsuntapped.

Astechnologymakesitpossibleformorepeopletostartenterprisesonanationalorevenglobalscale,morepeoplewillbe in theposition toearnsuperstarcompensation.Whilewinner-take-alleconomicscanleadtovastlydisproportionaterewardstothetopperformerineachmarket,thekeyisthatthereisnoautomaticceilingtothenumberofdifferentmarketsthatcanbecreated.Inprinciple,tensofmillionsofpeoplecouldeachbealeadingperformer—eventhetopexpert—intensofmillionsofdistinct,value-creatingfields.Thinkofthemasmicro-expertsformacro-markets.TechnologyscholarThomasMalonecallsthistheageofhyperspecialization.Digitaltechnologiesmakeitpossibletoscalethatexpertisesothatweallbenefitfromthosetalentsandcreativity.

InvestinginHumanCapital

Technologyracesaheadeverfasteraswemovedeeperintothesecondhalfofthechessboard.Tokeepup,weneednotonlyorganizationalinnovation,orchestratedbyentrepreneurs,butalsoasecondbroadstrategy:investmentsinthecomplementaryhumancapital—theeducationandskillsrequiredtogetthe

mostoutofour racing technology.Smartentrepreneurscan,andwill, inventways tocreatevaluebyemployingevenlessskilledworkers.However,themessagethelabormarketisclearlysendingisthatit’smucheasiertocreatevaluewithhighlyeducatedworkers.

Unfortunately,oureducationalprogresshasstalledand,asdiscussed inChapter3, this is reflected instagnatingwagesandfewerjobs.Themedianworkerisnotkeepingupwithcutting-edgetechnologies.AlthoughtheUnitedStatesonceledtheworldintheeducationofitscitizens,ithasfallenfromfirsttotenth in the share of citizenswho are college graduates.The high costs and lowperformance of theAmerican educational system are classic symptoms of low productivity in this sector. Despite theimportanceofproductivitytooveralllivingstandards,andthedisproportionateimportanceofeducationto productivity, there is far too little systematic work done to measure, let alone improve, theproductivityofeducationitself.

It’snot acoincidence that theeducational sectoralso lagsasanadopterof information technologies.Basic instructionalmethods, involving a teacher lecturing to rows of passive students, have changedlittleincenturies.Astheoldjokegoes,it’sasystemfortransmittinginformationfromthenotesofthelecturertothenotesofthestudentwithoutgoingthroughthebrainofeither.Inmanyclassrooms,themain instructional technology is literally a piece of yellowish limestone rock scraped across a largerblackslate.

The optimistic interpretation is that we have tremendous upside potential for improvements ineducation.Aseducationbecomesincreasinglydigitized,educatorscanexperimentandtrackalternativeapproaches,measureandidentifywhatworks,sharetheirfindings,andreplicatethebestapproachesinother subjects and geographies. This enables a faster pace of innovation, leading to furtherimprovements in educational productivity. It also enables unbundling of instruction, evaluation, andcertification, which encourages educational systems to be based more on delivering genuine,measurableresultsandlessonsimplysignalingselection,effort,andprestige.

Furthermore,usingIT,bothscaleandcustomizationcanbeincreaseddramatically.Agoodexampleisthefreeonlinecourseonartificial intelligenceatStanford thatattractedat least58,000students.Thecourse uses digital networks to broadcast material and track all students individually, radicallyimproving the productivity of the instructors, lowering costs to students, and, at least in principle,deliveringaqualityproductthatwouldotherwisebeinaccessibletothevastmajorityoftheparticipants.MIT has been running similar, albeit smaller, classes using a combination of information andcommunication technologies for over a decade,most notably in its SystemDesign andManagementprogram.StudentsatcompaniesaroundtheworlduseacombinationofinformationandcommunicationtechnologiestointeractwithprofessorscentrallylocatedatMITandwithinstructorslocaltoeachgroupofstudents.

AttheK-12level,KhanAcademyoffersover2,600shorteducationalvideosand144self-assessmentmodules for free on theweb. Students can learn at their own pace, pausing and replaying videos asneeded,earning“badges” todemonstratemasteryofvarious skillsandknowledge,andcharting theirowncurricula through theever-growingcollectionofmodules.Studentshave loggedover70millionvisitstoKhanAcademysofar.Agrowinginfrastructuremakesiteasyforparentsorteacherstotrackstudentprogress.

An increasingly common approach uses the Khan Academy’s tools to flip the traditional classroommodelonitshead,lettingstudentswatchthevideolecturesathomeattheirownpaceandthenhaving

them do the “home work” exercises in class while a teacher circulates among them, helping eachstudent individuallywithspecificdifficultiesrather thanprovidingaone-size-fits-all lecture toall thestudentssimultaneously.

Combiningvideoconferencing,software,andnetworkswithlocal teachersandtutorshasanumberofpotentialadvantages.Theverybest“superstar”teacherscanbe“replicated”viatechnology,givingmorestudentsachancetolearnfromthem.Furthermore,studentscanlearnattheirownpace.Forinstance,softwarecansensewhenstudentsarehavingdifficultiesandneedmoredetail,repetition,andperhapsaslowerpace,aswellaswhentheyarequicklygraspingthecontentandcanbeaccelerated.Localhumanteachers, tutors, and peer tutoring can easily be incorporated into the system to provide some of thekinds of value that the technology can’t do well, such as emotional support and less-structuredinstructionandassessment.

Forinstance,creativewriting,artsinstruction,andother“softskills”arenotalwaysasamenabletorule-based software or distance learning.We concurwithRhode Island School ofDesign president JohnMaeda’s vision that a move from STEM (Science, Technology, Engineering, and Mathematics) toSTEAM (adding Arts to the mix) is the right vision for boosting innovation. The technology andsystemsforeducationhavetobecompatiblewiththatvision.

Inparticular,softerskills likeleadership, teambuilding,andcreativitywillbeincreasinglyimportant.They are the areas least likely to be automated and most in demand in a dynamic, entrepreneurialeconomy.Conversely,collegegraduateswhoseekthetraditionaltypeofjob,wheresomeoneelsetellsthemwhattodoeachday,willfindthemselvesincreasinglyincompetitionwithmachines,whichexcelatfollowingdetailedinstructions.

TheLimitstoOrganizationalInnovationandHumanCapitalInvestment

We’reencouragedbytheemergingopportunitiestocombinedigital,organizational,andhumancapitalto create wealth: technology, entrepreneurship, and education are an extraordinarily powerfulcombination.Butwewanttostressthateventhiscombinationcannotsolveallourproblems.

First,noteveryonecanorshouldbeanentrepreneur,andnoteveryonecanorshouldspend16ormoreyearsinschool.Second,therearelimitstothepowerofAmericanentrepreneurshipforjobcreation.A2011researchreport for theKauffmanFoundation byE. J.Reddy andRobertLitan found that eventhoughthetotalnumberofnewbusinessesfoundedannuallyintheUnitedStateshasremainedlargelysteady,thetotalnumberofpeopleemployedbythematstartuphasbeendeclininginrecentyears.Thiscouldbebecausemodernbusinesstechnologyletsacompanystartleanerandstayleanerasitgrows.

Third, andmost importantly, evenwhen humans are racing usingmachines instead of against them,there are still winners and losers as described in Chapter 3. Some people, perhaps even a lot, cancontinuetoseetheirincomesstagnateorshrinkandtheirjobsvanishwhileoverallgrowthcontinues.

Whensignificantnumbersofpeopleseetheirstandardsoflivingfalldespiteanever-growingeconomicpie,itthreatensthesocialcontractoftheeconomyandeventhesocialfabricofsociety.Oneinstinctualresponseistosimplyredistributeincometothosewhohavebeenhurt.Whileredistributionamelioratesthematerialcostsofinequality,andthat’snotabadthing,itdoesn’taddresstherootoftheproblemsoureconomy is facing. By itself, redistribution does nothing to make unemployed workers productiveagain. Furthermore, the value of gainfulwork is farmore than themoney earned. There is also the

psychologicalvaluethatalmostallpeopleplaceondoingsomethinguseful.Forcedidlenessisnotthesameasvoluntaryleisure.FranklinD.Rooseveltputthismosteloquently:

Nocountry,howeverrich,canaffordthewasteofitshumanresources.Demoralizationcausedbyvastunemployment isourgreatest extravagance.Morally, it is thegreatestmenace toour socialorder.

Thus,wefocusourrecommendationsoncreatingwaysforeveryonetocontributeproductivelytotheeconomy.Astechnologycontinuestoraceahead,itcanwidenthegapsbetweentheswiftandtheslowonmany dimensions.Organizational and institutional innovations can recombine human capitalwithmachinestocreatebroad-basedproductivitygrowth.That’swherewefocusourrecommendations.

TowardanAgendaforAction

Byfirstdiagnosingthereasonforstagnatingmedianincome,weareinapositiontoprescribesolutions.These involve accelerating organizational innovation and human capital creation to keep pace withtechnology.Thereareatleast19specificstepswecantaketotheseends.

Education1.Investineducation.Startbysimplypayingteachersmoresothatmoreofthebestandthebrightest

signupforthisprofession,astheydoinmanyothernations.Americanteachersmake40%lessthan the average college graduate. Teachers are some of America’s most important wealthcreators. Increasing thequantityandqualityof skilled laborprovidesadoublewinbyboostingeconomicgrowthandreducingincomeinequality.

2. Holdteachersaccountableforperformanceby,forexample,eliminatingtenure.Thisshouldbepartofthebargainforhigherpay.

3. Separate student instruction from testingandcertification.Focus schoolingmoreonverifiableoutcomesandmeasurableperformanceandlessonsignalingtime,effortorprestige.

4. KeepK-12 students in classrooms formore hours.One reasonAmerican students lag behindinternationalcompetitorsistheysimplyreceiveaboutonemonthlessinstructionperyear.

5.IncreasetheratioofskilledworkersintheUnitedStatesbyencouragingskilledimmigrants.Offergreencardstoforeignstudentswhentheycompleteadvanceddegrees,especiallyinscienceandengineeringsubjectsatapproveduniversities.ExpandtheH-1Bvisaprogram.SkilledworkersinAmerica often create more value when working with other skilled workers. Bringing themtogethercanincreaseworldwideinnovationandgrowth.

Entrepreneurship

6.Teachentrepreneurshipasaskillnotjustinelitebusinessschoolsbutthroughouthighereducation.Foster a broader class of mid-tech, middle-class entrepreneurs by training them in the

fundamentalsofbusinesscreationandmanagement.

7.BoostentrepreneurshipinAmericanbycreatingacategoryoffounders’visasforentrepreneurs,likethoseinCanadaandothercountries.

8.Createclearinghousesanddatabasestofacilitatethecreationanddisseminationoftemplatesfornew businesses. A set of standardized packages for startups can smooth the path for newentrepreneurs in many industries. These can range from franchise opportunities to digital“cookbooks” that provide the skeleton structure for an operation. Job training should besupplementedwithentrepreneurshipguidanceasthenatureofworkevolves.

9. Aggressively lower the governmental barriers to business creation. In too many industries,elaborate regulatory approvals are needed from multiple agencies at multiple levels ofgovernment.Thesetoooftenhavetheimplicitgoalofpreservingrentsofexistingbusinessownersattheexpenseofnewbusinessesandtheiremployees.

Investment

10.Investtoupgradethecountry’scommunicationsandtransportationinfrastructure.TheAmericanSocietyofCivilEngineersgivesagradeofDtoouroverallinfrastructureatpresent.Improvingitwillbringproductivitybenefitsbyfacilitatingflowandmixingideas,people,andtechnologies.Itwillalsoputmanypeopletoworkdirectly.Youdon’thavetobeanardentKeynesiantobelievethatthebesttimetomaketheseinvestmentsiswhenthereisplentyofslackinthelabormarket.

11.IncreasefundingforbasicresearchandforourpreeminentgovernmentR&DinstitutionsincludingtheNational Science Foundation, theNational Institutes ofHealth, and theDefenseAdvancedResearch Projects Agency (DARPA) with a renewed focus on intangible assets and businessinnovation.Likeotherformsofbasicresearch,theseinvestmentsareoftenunderfundedbyprivateinvestorsbecauseofthespilloverstheycreate.

Laws,Regulations,andTaxes

12.PreservetherelativeflexibilityofAmericanlabormarketsbyresistingeffortstoregulatehiringandfiring.Banninglayoffsparadoxicallycanloweremploymentbymakingitriskierforfirmstohireinthefirstplace,especiallyiftheyareexperimentingwithnewproductsorbusinessmodels.

13.Makeitcomparativelymoreattractivetohireapersonthantobuymoretechnology.Thiscanbedoneby, amongother things, decreasing employer payroll taxes andproviding subsidies or taxbreaksforemployingpeoplewhohavebeenoutofworkforalongtime.Taxesoncongestionandpollutioncanmorethanmakeupforthereducedlabortaxes.

14. Decouplebenefits from jobs to increase flexibilityanddynamism.Tyinghealthcareandothermandatedbenefitstojobsmakesitharderforpeopletomovetonewjobsortoquitandstartnewbusinesses.Forinstance,manyapotentialentrepreneurhasbeenblockedbytheneedtomaintainhealthinsurance.DenmarkandtheNetherlandshaveledthewayhere.

15. Don’t rush to regulate new network businesses. Some observers feel that “crowdsourcing”businesseslikeAmazon’sMechanicalTurkexploittheirmembers,whoshouldthereforebebetterprotected. However, especially in this early, experimental period, the developers of theseinnovative platforms should be givenmaximum freedom to innovate and experiment, and theirmembers’freelymadedecisionstoparticipateshouldbehonored,notoverturned.

16. Eliminateorreducethemassivehomemortgagesubsidy.Thiscostsover$130billionperyear,which would do much more for growth if allocated to research or education. While homeownershiphasmanylaudablebenefits, it likelyreduces labormobilityandeconomicflexibility,whichconflictswiththeeconomy’sincreasedneedforflexibility.

17. Reduce the large implicit and explicit subsidies to financial services. This sector attracts adisproportionatenumberofthebestandthebrightestmindsandtechnologies,inpartbecausethegovernmenteffectivelyguarantees“toobigtofail”institutions.

18.Reformthepatentsystem.Notonlydoesittakeyearstoissuegoodpatentsduetothebacklogandshortageofqualifiedexaminers,buttoomanylow-qualitypatentsareissued,cloggingourcourts.Asaresult,patenttrollsarechillinginnovationratherthanencouragingit.

19.Shorten,ratherthanlengthen,copyrightperiodsandincreasetheflexibilityoffairuse.Copyrightcovers too much digital content. Rather than encouraging innovation, as specified in theConstitution, excessive restrictions like the SonnyBonoCopyright TermExtensionAct inhibitmixingandmatchingofcontentandusingitcreativelyinnewways.

Thesesuggestionsareonlythetipoftheicebergofabroadertransformationthatweneedtosupport,notonlytomitigatetechnologicalunemploymentandinequality,butalsotofulfillthepotentialfornewtechnologies togrowtheeconomyandcreatebroad-basedvalue.Wearenotputtingforthacompleteblueprint for the next economy—that task is inherently impossible. Instead, we seek to initiate aconversation. That conversation will be successful if we accurately diagnose themismatch betweenaccelerating technologies and stagnant organizations and skills. Successful economies in the 21stcentury will be those that develop the best ways to foster organizational innovation and skilldevelopment,andweinviteourreaderstocontributetothatagenda.

6Railroadconstructioncrewsofthetimeblastedtunnelsthoughmountainsidesbydrillingholesintotherock,packingtheholeswithexplosives,anddetonatingthem.

7It’salsoworthnotingthatsomevaluableenterprisesneednotcreatepayingjobstogeneratevalueinthe economy. For instance,Wikipedia thrives on amodel that is largely separate from the financialeconomy, but it nonetheless provides rewards and value. Judging by the revealed preferences ofparticipants,Wikipediaprovidessufficientnon-monetaryrewardstoattractmillionsofcontributorswithdiversetalentsandexpertisewhocreatetremendousvalue.Whenthinkingabouttheevolvingeconomy,weneed to remember thatAbrahamMaslow’s hierarchy of needsahamMaslow’s hierarchy of needsextendsbeyondmaterialthings.

852!isshorthandfor52x51x50x…x2x1,whichmultipliestoover8.06x1067.Thatisaboutthenumberofatomsinourgalaxy.

Chapter5.Conclusion:TheDigitalFrontier

TechnologyisagiftofGod.AfterthegiftoflifeitisperhapsthegreatestofGod'sgifts.Itisthemotherofcivilizations,ofartsandofsciences.

—FreemanDyson,1988

In this bookwe’ve concentrated on how our increasingly powerful digital technologies affect skills,jobs,andthedemandforhumanlabor.We’vestressedthatcomputersarerapidlyencroachingintoareasthat used to be the domain of people only, like complex communication and advanced patternrecognition.Andwe’veshownhowthisencroachmentcancausecompaniestousemorecomputersandfewerpeopleinagrowingsetoftasks.

Weseecauseforconcernwiththisphenomenonbecausewebelievethatonesignofahealthyeconomyisitsabilitytoprovidejobsforallthepeoplewhowanttowork.Aswe’veshown,there’sgoodreasonto believe that ever-more powerful computers have for some time now been substituting for humanskillsandworkersandslowingmedianincomesandjobgrowthintheUnitedStates.Asweheaddeeperinto the second half of the chessboard—into the period where continuing exponential increases incomputing power yield astonishing results—we expect that economic disruptions will only grow aswell.

We’vedocumentedourconcernshereandsuggestedpolicychangesandotherinterventionstoaddressthem.Butweclearlyarenotpessimistsabout technologyand its impacts. In fact, thiswasoriginallygoing tobeabookaboutall thebenefitsmoderndigital technologieshavebrought to theworld.Weplanned to call itTheDigital Frontier, since the image that keeps occurring to us is one of a hugeamountofnewterritoryopeningupbecauseoftechnologicalimprovementandinnovation.

This image first occurred to us as wewere conducting research to understand the impact of digitaltechnologyoncompetitioninallU.S. industries.Wefoundthat themoretechnologyanindustryhad,the more intense competition within it became. In particular, performance gaps got bigger. Thedifference in, say,profitmarginbetween the topandbottomcompaniesgot a lot larger.This findingimpliesthatsomecompanies—thetopperformers—wereracingaheadoftheresttoexploreandexploitnew technology-enabled businessmodels. Theywere homesteading on a digital frontier, opening upnewterritorythatotherswouldeventuallysettlein.

Tobuildonthisresearchwestartedcollectingexamplesofdigitalpioneersandcutting-edgepractices,and assembled a group of students and colleagues to brainstorm and do researchwith us.We calledourselvesthe“DigitalFrontierteam.”

Wechangedcoursewiththisbookbecausethemorewelooked,themorewebecameconvincedoftwothings.First,thattheissueoftechnology’simpactonemploymentwasaparticularlyimportantone.TheGreatRecessionandthepaceoftechnicalprogresshavecombinedtomakejobsacriticalissueatthistime,whichisadifficultoneformanypeople.Whenwethinkaboutsomeonetryingtoacquirevaluableskillsandenterorreentertheworkforcenow,we’reremindedoftheoldChinesecurse,“Mayyouliveininterestingtimes.”

Second,wesawthatveryfewotherpeoplewerefocusingontheissuesraisedhere.Whendiscussing

jobs and unemployment, there has been a great deal of attention paid to issues like weak demand,outsourcing,andlabormobilitybutrelativelylittleattentiongiventotechnology’srole.Wefeltthatthiswasaseriousomissionandwantedtocorrectit.Wewantedtoshowhowfarandfast technologyhasracedaheadrecently,andhighlightthatcurrentperspectivesandpolicieswillneedtochangetokeepupwithit.

Butevenafterwritingthisbook,westillfirmlybelieveinthepromiseofthedigitalfrontier.Technologyhasalreadyopenedupahugeamountofrichnewterritoryandwillkeepdoingso.Aroundtheworld,economies,societies,andpeople’sliveshavebeenimprovedbydigitalgoodsandhigh-techproducts;thesehappytrendswillcontinue,andlikelyaccelerate.

Sowewanttoconcludethisbookwithaglimpseoftheemergingdigitalfrontier—abrieflookatsomeof the benefits brought by the still-unfolding computer revolution. These benefits arise from theconstant improvements summarized by Moore’s Law and discussed above in Chapter 2, and alsobecauseofthecharacteristicsofinformationitself.

AWorldofBenefits

Informationdoesn’tgetusedupevenwhenit’sconsumed.IfErikeatsameal,Andycan’teatit,too,butErikcanabsolutelyhandabooktoAndyoncehe’sfinished,andthebookitself(unlessErikhasspilledcoffeeon it) isnot inanywaydiminishedforAndy. In fact, it’sprobablymorevaluable tohimafterErik’sdonewithit,becausethentheybothhaveitscontentsintheirheadsandcanusetheinformationtocollaborativelygeneratenewideas.

Andonceabookorotherbodyofinformationisdigitized,evenmorepossibilitiesopenup.Itcanbecopiedinfinitelyandperfectly,anddistributedaroundtheworldinstantlyandatnoadditionalcost.Thisisnothing like theeconomicsof traditionalgoodsandservices thatare theprimaryfocusofstandardtextbooks.Itcanbeanightmareforsomecopyrightholders,butit’sgreatformostpeople.Thetwoofus,forexample,wantasmanypeopleaspossibletogetacopyofthisebookassoonaspossibleafterwe’redonewritingit.ThankstoebookplatformsandtheInternet,wecanaccomplishthisvision.Inthepreviousworldofpaper-onlybooks,publicationanddistributioncouldtakeayear,andsaleswouldbelimited by the physical availability of copies of the book. The digital frontier has eliminated thislimitationandcollapsedtimelines.

Theeconomicsofdigitalinformation,inshort,aretheeconomicsnotofscarcitybutofabundance.Thisisafundamentalshift,andafundamentallybeneficialone.Totakejustoneexample,theInternetisnowthelargestrepositoryofinformationthathaseverexistedinthehistoryofhumankind.Itisalsoafast,efficient, and cheap worldwide distribution network for all this information. Finally, it is open andaccessiblesothatmoreandmorepeoplecanjoinit,accessallofitsideas,andcontributetheirown.

Thisisincalculablyvaluable,andgroundsforgreatoptimism,evenifsomethingslookbleakrightnow,forcomputersaremachinesthathelpwithideas,andeconomiesrunonideas.AseconomistPaulRomerwrites:

Every generation has perceived the limits to growth that finite resources and undesirable sideeffectswouldposeifnonew…ideaswerediscovered.Andeverygenerationhasunderestimatedthepotentialforfindingnew…ideas.Weconsistentlyfailtograsphowmanyideasremaintobediscovered.…Possibilitiesdonotmerelyaddup;theymultiply.

Itmightseemasifwe’reshortonbignewideasatpresent,butthisisalmostcertainlyanillusion.AsDavidLeonhardtnotes,whenBillClintonassembledthetopmindsofthenationtodiscusstheeconomyin1992,noonementionedtheInternet.

Romeralsomakesthepointthat“perhapsthemostimportantideasofallaremeta-ideas—ideasabouthowtosupporttheproductionandtransmissionofotherideas.”Thedigitalfrontierisjustsuchameta-idea—itgeneratesmoreideasandsharesthembetterthananythingelsewe’veevercomeupwith.Soeither a huge amount of basic thinking about economics and growth iswrong, or a bumper crop ofusefulinnovationswillgrowonthisfrontier.We’rebettingonthelatterpossibility.

Atalessabstractandmorepersonallevel,thedigitalfrontierisalsoimprovingourlives.IfyouhaveInternetaccessandaconnecteddevicetoday,it’sbothfreeandeasytokeepintouchwiththepeoplewhomean something to you—your kith and kin—even as you and theymove around.You can useresourceslikeSkype,Facebook,andTwittertosendmessages,makevoiceandvideocalls,sharestillandmovingpictures,andleteveryoneknowwhatyou’redoingandhowyou’redoing.Asanyloverorgrandparentwilltellyou,thesearenottrivialcapabilities;they’repricelessones.

Manyofususetheseresourcessooftennowthatwetakethemforgranted,butthey’realllessthan10yearsold.Thedigitalfrontierof2001wasalreadywide,butit’sgottenimmeasurablybiggeroverthepastdecade,andenrichedourlivesasithasdoneso.

We see this same phenomenon everywhere we look. The developing world, for example, has beentransformedbymobiletelephones.Weinrichcountrieslongagoforgotwhatit’sliketohavetoliveinisolation—tohavenoeasywaytocommunicatefartherthanourvoicesorbodiescouldtravel.Butsuchisolationwasthesadrealityforbillionsofpeoplearoundtheworld,untilmobiletelephonycamealong.

Once itdid, the resultswerebreathtaking.Awonderful studybyeconomistRobert Jensen found, forexample,thatassoonasmobiletelephonesbecameavailableinthefishingregionsofKerala,India,theprice of sardines dropped and stabilized, yet fishermen’s profits actually went up. This happenedbecause fishermen for the first time had access to real-time price and demand information from themarketsonland,whichtheyusedtomakedecisionsthatcompletelyeliminatedwaste.Resultslikethesehelpexplainwhythereweremorethan3.8billionmobilephonesubscriptionsinthedevelopingworldby late2010,andwhyTheEconomistmagazinewrote that“their spread inpoorcountries isnot justreshapingtheindustry—itischangingtheworld.”

Asdigitaltechnologiesmakemarketsandbusinessesmoreefficient,theybenefitallofusasconsumers.As they increasegovernment transparencyandaccountabilityandgiveusnewways toassembleandmakeourvoicesheard,theybenefitusascitizens.Andastheyputusintouchwithideas,knowledge,friends,andlovedones,theybenefitusashumanbeings.

Soasweobservetheopeningupofthedigitalfrontier,wearehugelyoptimistic.Historyhaswitnessedthree industrial revolutions,eachassociatedwithageneralpurpose technology.Thefirst,poweredbysteam,changedtheworldsomuchthataccordingtohistorianIanMorris,it“mademockeryofallthathadgonebefore.”Itallowedhugeandunprecedentedincreasesinpopulation,socialdevelopment,andstandardsofliving.Thesecond,basedonelectricity,allowedthesebeneficialtrendstocontinueandledto a sharp acceleration of productivity in the 20th century. In each case there were disruptions andcrises,butintheend,themassofhumanitywasimmenselybetteroffthanbefore.

The third industrial revolution,which is unfolding now, is fuelled by computers and networks. Likebothofthepreviousones,itwilltakedecadestofullyplayout.Andlikeeachofthefirsttwo,itwillleadtosharpchangesinthepathofhumandevelopmentandhistory.Thetwistsanddisruptionswillnotalwaysbeeasytonavigate.Butweareconfidentthatmostofthesechangeswillbebeneficialones,andthatweandourworldwillprosperonthedigitalfrontier.

Acknowledgments

We’vebeentalkingabouttheideasthatwentintothisbookforawhilenow,andwithalotofpeople.We found a fantastic group of colleagues in the Digital Frontier team we assembled—a group ofstudentsandresearchersatMITwhovolunteeredalotoftheirtimeoverthecourseofayeartotalkwithus, hunt down facts, figures, and examples, and brainstorm about what was going on betweentechnology and the economy. Teammembers includedWhitney Braunstein, Claire Calméjane, GregGimpel,TongLi,LironWand,GeorgeWesterman,andLynnWu.We’reextremelygratefultothem.Inaddition,MonaMasghatiandMayaBustanhelpedAndyagreatdealwithhisresearch,andHeekyungKimandJonathanSidididthesameforErik.

WearegratefulforconversationsontechnologyandemploymentwithourMITcolleagues, includingDaron Acemoglu, David Autor, Frank Levy, Tod Loofbourrow, Thomas Malone, Stuart Madnick,Wanda Orlikowski, Michael Schrage, Peter Weill, and Irving Wladawsky-Berger. In addition, RobAtkinson, Yannis Bakos, Susanto Basu, Menzie Chinn, Robert Gordon, Lorin Hitt, Rob Huckman,Michael Mandel, Dan Snow, Zeynep Ton andMarshall van Alstyne were very generous with theirinsights.We also benefited greatly from talking with people in industry who are making and usingincredibletechnologies,includingRodBrooks,PaulHofmannRayKurzweil,IkeNassi,andHalVarian.

WepresentedsomeoftheideascontainedheretoseminaraudiencesatMIT,HarvardBusinessSchool,Northwestern, NYU, UC/Irvine, USC’s Annenberg School, SAP, McKinsey, and the InformationTechnologyandInnovationFoundation.WealsopresentedrelatedworkatconferencesincludingWISE,ICIS, Techonomy, and the Aspen Ideas Festival. We received invaluable feedback at each of thesesessions. The most important thing we learned was that the topic of technology’s influence onemploymentimmediatelyengagespeople’sinterest,soweshapedourresearchandwritingaccordingly.Techonomy organizer David Kirkpatrick has been especially keen to start a high-level conversationaboutcomputers,robots,andjobs.

Weimposedonasmallgroupofpeopletoreadearlydraftsofthemanuscript.MarthaPavlakis,AnnaIvey,GeorgeWesterman,DavidMcAfee,NancyHaller,JohnBrowning,CarolFranco,andJeffKehoeallobligedandsharpenedupourideasandourprose.AndreaandDanaMeyerofWorkingKnowledgehonedthembothfurtherwhileJodyBermanprovidedimpeccablecopyeditingandproofreading.WearegratefultoGregLeutenbergforthecoverdesign.

TheMITCenterforDigitalBusiness(CDB)hasbeentheidealhomefromwhichtoconductthiswork,andwe’re particularly grateful to our colleagueDavidVerrill, executive director of theCDB.Davidmakestheplacerunbeautifully;he’llbethelastpersoneverreplacedbyamachine.

Weclaimsoleownershipofvirtuallynoneoftheideaspresentedhere,butwe’reemphaticthatallthemistakesare100%ours.

Authors

ErikBrynjolfssonisaprofessorattheMITSloanSchoolofManagement,DirectoroftheMITCenterforDigitalBusiness,ChairmanoftheSloanManagementReview,aresearchassociateat theNationalBureau of Economic Research, and co-author ofWired for Innovation: How IT Is Reshaping theEconomy.HegraduatedfromHarvardUniversityandMIT.

AndrewMcAfee is aprincipal research scientist andassociatedirector at theMITCenter forDigitalBusinessat theSloanSchoolofManagement.He is theauthorofEnterprise2.0:NewCollaborativeToolsforYourOrganization’sToughestChallenges.HegraduatedfromMITandHarvardUniversity.