麦肯锡 -流动的劳动力：美国未来就业所需技能与发展路径 Workforce in motion Skills and pathways to future jobs in the United States

Workforceinmotion

Skillsandpathwaystofuturejobs

intheUnitedStates

September2026

MaríaJesúsRamírezKweilinEllingrud

DiegoCastresanaAnnaKortis

TanguyCatlin

Authors

AI,automation,andbroaderstructural

trendsareacceleratingthereallocationofworkacrosstheUSeconomy.Capturingitsbenefitsrequiresdevelopingnew

professionalpathwaysandskills,andreducingbarriersbetweendecliningandgrowingoccupations.

Confidentialandproprietary.Anyuseof

thismaterialwithoutspecificpermissionofMcKinsey&Companyisstrictlyprohibited.

Copyright©2026McKinsey&Company.

Allrightsreserved.

Coverimage:AIassistedvisual

Allinteriorimages©GettyImages

McKinseyGlobalInstitute

TheMcKinseyGlobalInstitutewasestablishedin1990.Ourmissionistoprovideafactbaseto

aiddecisionmakingontheeconomicandbusinessissuesmostcriticaltotheworld’scompanies

andpolicyleaders.WebenefitfromthefullrangeofMcKinsey’sregional,sectoral,andfunctional

knowledge,skills,andexpertise,buteditorialdirectionanddecisionsaresolelytheresponsibilityofMGIdirectorsandpartners.

Ourresearchiscurrentlygroupedintofivemajorthemes:

—Productivityandprosperity:Creatingandharnessingtheworld’sassetsmostproductively

—Resourcesoftheworld:Building,powering,andfeedingtheworldsustainably

—Humanpotential:Maximizingandachievingthepotentialofhumantalent

—Globalconnections:Exploringhowflowsofgoods,services,people,capital,andideasshapeeconomies

—Technologiesandmarketsofthefuture:Discussingthenextbigarenasofvalueandcompetition

Weaimforindependentandfact-basedresearch.Noneofourworkiscommissionedorfundedbyanybusiness,government,orotherinstitution;weshareourresultspubliclyfreeofcharge;andweareentirelyfundedbythepartnersofMcKinsey.Whileweengagemultipledistinguishedexternaladviserstocontributetoourwork,theanalysespresentedinourpublicationsareMGI’salone,andanyerrorsareourown.

YoucanfindoutmoreaboutMGIandourresearchat

/mgi

.

MGIdirectors

ShubhamSinghal(chair)ChrisBradley

TanguyCatlin

KweilinEllingrud

SylvainJohansson

NickLeung

MGIpartners

ArvindGovindarajan

AnnaKortis

MekalaKrishnan

JanMischke

JeongminSeong

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates2

Contents

Ataglance3

Introduction4

CHAPTER1

Whyjobschange6

CHAPTER2

Thegreatworkforcereallocation

21

CHAPTER3

Threetypesofpathwaysleadtogrowingjobs3

7

CHAPTER4

Essential,enabling,andempoweringskillscanexpand

accesstomoreandbetterpathways4

7

CHAPTER5

Reducingbarrierscouldmakepathwaysmoredirect

forworkers5

7

CHAPTER6

Enablingworkforcetransitions6

9

Finalthoughts

74

Glossary

75

Acknowledgments

76

Endnotes

77

Chapter1:

Whyjobschange

Chapter3:

Pathwaysleadto

growingjobs

Chapter4:

Expandaccessto

betterpathways

Chapter5:

Reducingbarriers

Chapter6:

Enabling

transformations

Final

thoughts

Chapter2:

Thegreatworkforce

reallocation

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates3

Ataglance

—TheUSeconomycouldcreatemorejobsinthenextdecadethanwillbereplacedthroughautomation,butthechangemayrequirethelargestandmostsustainedworkforce

transformationinUShistory.Weestimatethatroughly11millionworkersindeclining

occupationsmayneedtotransitiontonewjobs,rangingfromsixmillionto16milliondependingonthepaceofautomationadoptionanditsimpactonlabordemand.

—Jobsofthefuture,particularlygrowingjobs,couldpaymoreonaveragebutrequiremoreskills.Roughly60percentofgrowingemploymentcouldbeinthetoptwowage

quintiles,withmuchoftheincreaseconcentratedinhealthcare,construction,andmanagementoccupations.Conversely,morethan70percentofdecliningemploymentcouldbeinthe

bottomtwoquintiles,primarilyinofficeandadministrativesupport,retailandsales,andtransportationandlogisticsoccupations.

—Oneinsevenworkerscouldhaveadirectpathwayintogrowingjobs,butalmosthalfmay

faceanunpavedpathway.Whileeverytransitioningworkerhasarouteintogrowingwork,onlyaboutoneinsevencanmakethemovewithnoorverylimitedretraining.Barriers,particularly

credentialsorcertifications,whichroughly85percentofgrowingjobsrequire,cantwistorblockotherwiseviablepathways.

—Overall,morethan70percentofworkerscouldrequiresomelevelofreinventionbecausenewtasksmayrequiremorethan15percentoftheirtime.Whetherornotworkerschangejobs,mostofthemwillneedtocontinuallybuildnewskillsasmostoccupationsreinvent

regardlessofwhetheremploymentgrows,declines,orremainsstable.

—Threetypesofskillshelpworkersmove,moveup,andkeepadapting.“Essential”skillswidencareeroptions,while“enabling”skillsunlockhigher-payingjobs.“Empowering”skillsprepare

workersforcontinuouschange,anddemandforthemhasalreadystartedtosurge.ThedemandforAIfluencyhasincreased11timessince2022,asdemandforadaptabilityhasincreased

fivefoldanddemandforresilience,curiosity,andwillingnesstolearnhastripled.

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates4

Introduction

Everygenerationconfrontedbyanewtechnologyaskswhethermachineswillreplaceworkers.

Steammechanizedproduction,electricityrestructuredfactories,computersdigitizedinformation.

Artificialintelligencehasrevivedthequestionwithnewurgencybecauseitaffectsallworkersandalljobs,includingcognitiveworkthatonceseemedresistanttoautomation.

Labormarketsadjusttotechnologyinnovationandadoptionnotsimplybysheddingjobsbutalsobycreatingnewjobsandbyreorganizingandreallocatingwork.Someactivitiesareautomated,othersbecomemorevaluable,newtasksemerge,demandexpandsinsomesectorsandcontractsinothers,andworkersmove—orfailtomove—betweenoccupations,employers,andgeographies.

Electronicspreadsheetsdoinsecondswhattookanaccountingclerkadaybeforetheycameonthemarketinthelate1970s,butwhileclericalrolesbuiltonmanualcalculationdisappeared,accountantsdidnot.Infact,theUSBureauofLaborStatisticsestimatesthattheprofessionwillgrowmorerapidlythantheaverageoverthenextdecade.Evenascalculatingaforecastbecamecheap,judgmentandexpertisebecamemorevaluable.

WhilesocialmediaaboundswithdirepredictionsabouttheimpactofAIonlabor,theUnitedStatesislikelytohavemorejobsavailablein2035thantoday,butwithfewerworkersbecausethepopulationisaging.WhileestimatesofthefullimpactofAIontheworkforcedifferwidely,ourbaseestimate

indicatesthatautomationcouldreducelabordemandbytheequivalentofroughly36millionjobs,whilegrowthintheAIvaluechainandthebroadereconomycouldgeneratedemandformorethan

40millionjobsoverthenextdecade.

Ourestimatesreflectfourfactors.First,automationenabledbyAIandothertechnologiescould

reducelabordemand,butnotonaone-for-onebasis,becauseautomationofworkdoesnottranslatedirectlyintojoblosses.Second,thedemographicbackdropandrisinglivingstandardscoulddrive

sustainedlabordemandgrowthinthe“humaneconomy”—jobsthatareoftenhardertoautomate

andalreadyfaceorcouldfacelaborshortages.Third,theAIboom,combinedwiththeneedto

modernizeinfrastructure,couldincreasedemandforworkersinboththe“physicaleconomy”and

the“techeconomy.”Finally,weexpectAIitselftocreatenewjobsthatwecannotyetimagine,justaspreviousgeneral-purposetechnologiesdid.Muchofthisshiftwouldoccurwithinoccupations,butroughly11millionUSworkers,orabout7percentofcurrentemployees,mightneedtoshiftbetweenoccupationstosecurenewjobs.1

Countingjobsisaninsufficientmeasureoftheimpactofnewtechnology.Jobopportunitiescanbe

abundantandyetleavemillionsofworkerswithoutworkifthosepositionsrequiredifferentskills,

credentials,locations,orpaystructuresthancurrentjobs.Manygrowingjobsinthenextdecadeswillrequirenewandmoreskills,sohavingviablepathwaysfromshrinkingtogrowingjobswillbecriticaltosuccessfultransitionsinalabormarketshapedbyautomation.

Apathwayprovidesapracticalroutebetweenoccupationsandisshapedbyfourconditions—

destinationdemand,skilladjacency,wagepreservation,andtimeneededtoacquirecredentials.

Adirectpathwaywilltakeaworkertoagrowingoccupationwithlimitedretrainingandnolossof

income,whileawindingpathwayrequiresdevelopingmorenewskillsoracceptingpaycuts.An

unpavedpathwayisstrewnwithlargeskillsgaps,lowerwages,oradditionalrequiredcredentials.Anyofthesepathwayscanbeblockedbybarrierstoolargetoovercomeinpractice.

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates5

Chapter1:

Whyjobschange

Chapter2:

Thegreatworkforce

reallocation

Chapter3:

Pathwaysleadto

growingjobs

Chapter4:

Expandaccessto

betterpathways

Chapter5:

Reducingbarriers

Chapter6:

Enabling

transformations

Final

thoughts

Thequalityofthepathwayintoaneworchangingoccupationmatters(Exhibit1).Whilealmost

everyworkerintheUSeconomyhasatleastonepathwaytoanewjob,onlyoneinsevenworkershasadirectpathwaytonewwork.Theskillsworkerscarryfromoneoccupationtoanotherarethekeystonavigatingpathwaystonewjobssuccessfully.Someskillsarerequiredinawiderangeofoccupations,andsomeareconcentratedinhigher-payingwork.Skillslikeadaptability,curiosity,resilience,andawillingnesstolearnmayincreaseinvaluebecausetheyhelpworkersacquirenewcapabilitiesasworkchanges.

Butwhileskillsfacilitatemobility,avarietyofbumpsalongpathwayscanbecomebarriers.Aworkermayhavetheskillsneededforajobinagrowingoccupation,butotherfactors—requirements

foralicenseorspecificacademicdegree,oramovetoaregionwithadifferentlanguage—may

makeitunattainable.Inthisreport,weexamineskillsandbarriersthatshapepathwaystogrowingoccupationsintheeraofAI.

Exhibit1

Theworkforcechallengeaheadisconnectingworkerstogrowingjobsthroughhigh-qualitypathways.

Threepathwaysfromdecliningtogrowinglabordemand

DECLINING

LABORDEMAND

Automationreduces

labordemandrelatedtotaskswhereproductivitygainstranslateinto

workforcereductions

Broaderstructuralshiftssuchasdigitalization

andchangingpatternsofconsumptionfurtherreducedemandinsomeUSoccupations

Directpathway

Short,directroad:Highskillmatch,nowageloss,orfastcredentialtiming

Windingpathway

Somedetours:Moderateskillmatch,somewageloss,ormoderatecredentialtiming

Unpavedpathway

Longanduncertain:Lowskillmatch,highwageloss,orextendedcredentialtiming

GROWING

LABORDEMAND

Humaneconomyevolutionexpandshealthcare,care,

andserviceoccupationsastheeconomygrows

Physicaleconomy

revivalincreasesdemandforconstruction,energy,andskilledtrades

Techeconomyacceleration

createsdigitalandtechnologyinfrastructurejobs

AIalsocreatesnew

occupationsandexpands

businesscreation,innovation,andmarketeficiency

WHATSHAPESPATHWAYQUALITY

Skills(accelerators)

Workers’skillsdeterminewhichoccupationstheycanmosteasilymoveinto

Barriers

Credentials,wages,geography,andother

frictionsdeterminewhetherpathwaysareviable

Source:McKinseyGlobalInstituteanalysis

McKinsey&Company

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates7

Chapter1:

Whyjobschange

Chapter2:

Thegreatworkforce

reallocation

Chapter3:

Pathwaysleadto

growingjobs

Chapter4:

Expandaccessto

betterpathways

Chapter5:

Reducingbarriers

Chapter6:

Enabling

transformations

Final

thoughts

CHAPTERONE

Whyjobschange

WhiletheprecisenumberofjobsthatAImaycreateordisplaceisuncertain,thetechnologytogetherwithbroadereconomictrendscouldgiveriseto41million

jobsintheUnitedStatesoverthenextdecade.Inourbaseestimate,automationadoptioncouldreducelabordemandby21percentofcurrentworkhours,

equivalenttoabout36millionjobs.Forroughly25millionoftheseworkers,

growthintheircurrentoccupationscouldoffsettheimpactofautomation,

allowingthemtostayinthesameoccupation,althoughtheirjobscouldchange.Theremaining11millionmayneedtoswitchoccupationsentirely.Thenext

decade’schallengeismobility,notscarcity.

Demandforworkersshiftsbecausetechnologychangesthenumberofworkersneededtoperform

existingwork.Onitsown,artificialintelligencecanhaveadirectimpactondemandforsome

occupationsbyautomatingoraugmentingactivities,whichhasraisedfearandconcern,anditcan

increaseworkrelatedtoimplementation,governance,supervision,andworkflowredesign.ButAIcanalsoindirectlycreatejobsbyexpandingdemandforcertainwork,includingintheinfrastructureandtechnologyvaluechain,andcangiverisetonewproductsandindustries,aswellascontributingto

economicgrowthmoregenerally.

Moreover,AIistakingholdassocietyisaging.Peoplearelivinglongeraroundtheworld,andfewerbabiesarebeingborn,particularlyindevelopedcountries.PreviousMGIresearchestimatesthatatthecurrentrateofdemographicdecline,advancedeconomieswouldneedtoraiseproductivitygrowthbytwotofourtimescurrentratessimplytoholdpercapitaGDPgrowthsteadythrough

2050.Moreolderpeopleandfewerpeopleofworkingagearealreadyincreasingdemandforhomehealthcareworkers,medicaltechnicians,andfoodserviceworkers,whiledemandforprimaryand

secondaryeducation,toymanufacturing,andcampcounselorsisdropping.Iftheworkingpopulationissupportedinmakingjobtransitions,artificialintelligencecouldgreatlyaccelerateeconomic

growth.Butifthesetransitionsarehandledpoorly,onlyafewwillbenefitwhiletheemploymentoutlookformanyworkersgrowsdim.

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates8

Chapter1:

Whyjobschange

Chapter2:

Thegreatworkforce

reallocation

Chapter3:

Pathwaysleadto

growingjobs

Chapter4:

Expandaccessto

betterpathways

Chapter5:

Reducingbarriers

Chapter6:

Enabling

transformations

Final

thoughts

Automationadoptionwillbefasterinofficeandadmin,computerandmath,andsalesoccupations,andslowerinhealthcare

EarlydiscussionsaboutAIandtheworkforcefocusedonitspotentialimpactonwhite-collarjobs,butinfactautomationadoptionislikelytobehighlyuneveninalltypesofjobs.Historically,automation

hastakenholdinfitsandstartsasorganizationsdecidehowandwheretodeploynewtechnologies,wheretowait,andwherehumanworkshouldremaincentral.Similarly,whatAIcandoaswellas

whereitcancreatevaluewithacceptablelevelsofriskwilldetermineitsdeploymentanduse.

Ouranalysisfindsthatoccupationsinofficeandadministrativesupportwillhavethehighest

expectedaverageadoption,roughly80percentofcurrentworkhoursby2035.Automationcouldtakeon70percentofthehourscurrentlyworkedintechnologyandanalyticsoccupationsand

68percentinretailandsalesoccupations(Exhibit2).Bycontrast,thetechnologywilllikelyrolloutmoregraduallyinto26percentofworkhoursinhealthcareprofessionals,and28percentofpublicsafetyandsecurityoccupations.2

Sidebar

Ourmethodology

Weuseamethodologyconsistentwith

previousMcKinseyGlobalInstitute(MGI)

researchonthefutureofworktomodelhowlabordemandandoccupationaltransitions

couldevolvefrom2025to2035.Ouranalysisprobedtwoquestions:(1)Howcouldlabor

demandgrowordeclineacrossoccupationsasautomation,AI,andotherstructuralforcesreshapetheUSeconomy?;and(2)How

readilycouldworkersmovefromshrinkingoccupationsintoexpandingones?

WebuildonMGI’sestablishedautomation

methodology,whichmodelsworkactivities

andoccupationsusingtheStandard

OccupationalClassification(SOC)system

fromtheUSBureauofLaborStatistics(BLS).Forthisanalysis,wemapthoseestimates

tothemoredetailedLightcastOccupation

Taxonomy(LOT)fromLightcastLLC,which

includesabout1,800occupations.This

allowsustoanalyzelabordemandatamoregranularoccupationallevelwhilemaintaining

consistencywithMGI’sunderlying

automationmodel.Changesinoccupationallabordemandareinfull-timeequivalents

(FTEs)by2035relativetoa2025baseline.

1.SizinggrowinganddecliningoccupationsOuranalysisisdesignedprimarilytoestimatehowthecompositionofUSemployment

couldshiftratherthantoproducean

independentforecastoftotalemployment.WeanchoraggregateemploymentgrowthtotheBLSprojectionofapproximately3.1percentoveradecade,whileourmodel

determineshowthatdemandisdistributedacrossoccupationsastheforcesdescribedabovetakeeffect.

Foreachoccupation,weestimatethenet

changeinlabordemandbycombining

forcesthatcouldreducedemand—notably

automation—withforcesthatcouldincreaseit.

1.1.Labor-demandreductionsfrom

automation.WebuildonMGI’sexisting

automationmodel,whichestimates

thetechnicalautomationpotentialof

individualworkactivitiesanddefines

adoptionscenariosreflectinghowquicklytechnologiescouldbecomefeasibleand

economicalandcoulddiffuseovertime.Weextendthatapproachintwoways.1

First,weaccountforthefeasibilityof

adoptionwithinoccupationsbyselectinga

differentadoptionscenarioforeachdetailedworkactivity–occupationpair,ratherthan

assumingadoptionproceedsatthesame

speedacrossoccupations.Weuseascoringframeworkthatcapturesfactorslikelyto

accelerateorslowadoption,includingthe

codifiability,routineness,andverifiabilityofthework;requiredworkflowreconfiguration;andregulatory,business,reputational,and

socialconstraints.Wealsoaccountforthefactthatactivitieswithinajobarebundledandcannotnecessarilybeautomated

independently.2

1“TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier,”McKinseyGlobalInstitute,June14,2023;“Agents,robots,andus:SkillpartnershipsintheageofAI,”McKinseyGlobalInstitute,November25,2025.

2Forexample,AIcanincreasinglyinterpretmedicalimages,butradiologistsbundleimageinterpretationwithreviewingdifficultcases,coordinatingwithotherclinicians,informingtreatmentdecisions,andtakingresponsibilityforthediagnosis.Automatingoneactivitythereforedoesnotnecessarilyeliminatethebroaderbundleofwork.Formore,seeLuis

Garicano,JinLi,andYanhuiWu,Weakbundle,strongbundle:HowAIredrawsjobboundaries,CEPRdiscussionpapernumber21453,CentreforEconomicPolicyResearch,May2026.

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates9

Chapter1:

Whyjobschange

Chapter2:

Thegreatworkforce

reallocation

Chapter3:

Pathwaysleadto

growingjobs

Chapter4:

Expandaccessto

betterpathways

Chapter5:

Reducingbarriers

Chapter6:

Enabling

transformations

Final

thoughts

Sidebar(continued)

Ourmethodology

Second,wedistinguishbetweenworkhoursautomatedandlabordemandreduced.Not

everyhourfreedbyautomationtranslates

proportionallyintolessemployment.Some

capacitymayinsteadbeoffsetbyreduced

excessworkinghours,additionaldemand

fromproductivitygains(“Jevonsparadox”),

ornewactivitiessuchasoverseeing,

managing,andworkingalongsideAIsystems.Organizationalandregulatorybarriers

mayfurtherlimitreductionsinheadcount.3

Together,theseadjustmentstranslate

projectedautomationadoptionintoan

estimatedreductioninfull-time-equivalent

labordemand.Twoassumptionsare

particularlyuncertain:thepaceofautomationadoptionandtheextenttowhichautomationtranslatesintolowerlabordemand.We

thereforetestalternativeassumptionsfor

both.Fasteradoptionandgreaterlabor

substitutionincreasetransitionneeds,whilesloweradoptionorgreateraugmentation

reducesthem.Thesescenariostestthe

sensitivityofourresultsratherthandefiningpreciseupperandlowerbounds.Formoredetail,seethetechnicalappendix.

1.2.Labor-demandincreasesfromAI

valuechainneedsandbroaderstructural

trends.Wemodelforcesthatcouldincreaselabordemandinthreebroadpartsofthe

economy.Eachisestimatedusingprimary

orthird-partysources,whileanchoringthe

totalnumberofjobsintheBLSemploymentgrowthprojections,andtranslatedinto

occupationaldemandusingdriver-specific

relationshipsratherthanassuming

employmentrisesone-for-onewithspendingorpopulationgrowth.

—Humaneconomy.Wemodeldemand

forpeople-centeredservicesasthe

populationages,shiftsinhealthand

healthcareutilization,risingliving

standards,changesinpostsecondary

education,andthemarketizationofunpaid

householdwork.Weadjustoverlappingdriverstoavoiddoublecounting.

—Physicaleconomy.Weestimatelabordemandassociatedwithconstruction,infrastructure,andenergyinvestment,drawingonMcKinseyresearchand

third-partyinvestmentforecasts

andtranslatingthatinvestmentintooccupationaldemand.

—Technologyeconomy.Weestimate

employmentassociatedwithgrowthin

technologyspendingandthebuild-outofAIanddigitalinfrastructure,andmapthatinvestmenttotheoccupationsneededtodeliverit.

WeuseanMGIinput-outputmodeltocaptureindirecteffectsasadditionaldemandflows

throughsuppliersandtheirsupplychains.

1.3.NewoccupationscreatedbyAI.Based

onouranalysisofemploymentassociated

withpreviousgeneral-purposetechnologies,weincludeaconservativeestimateof

potentialemploymentinnewAI-relatedoccupations.Wedonotcapturepotentialadditionaldemandfromgreatermarketefficiency,fasterR&Dandinnovation,orhigherratesofbusinesscreation.

2.Buildingpathwaysbetweenoccupations

Wenextassesshowreadilyworkersin

decliningoccupationscouldmoveinto

growingones.Weevaluatepotential

occupation-to-occupationpathwaysanduseanoptimizationmodeltoidentifyfeasible

matchesacrosstheUSlabormarketbasedonfourdimensions.

Skilladjacency.Wederivetheskills

associatedwitheachoccupationfrom

millionsofLightcastjobpostings.Using

semanticembedding,wemeasurethe

similaritybetweenskillsinaworker’scurrentoccupationandthoserequiredinapotential

destination.Thesecomparisonsallowustoestimatehowmuchoftheskillsetrequiredforadestinationoccupationisalready

coveredbythesourceoccupation.

Credentials.Weidentifycredential

requirementsusingLightcast’staxonomy,supplementedwithpubliclicensing

informationandastructuredreviewof

regulatedoccupations.Wedistinguish

legalorcorerequirementsfromcredentialsstronglypreferredbyemployers,excludingancillarycredentialsoflimitedimportance.

Timetotrain.Foreachoccupation-to-

credentialpathway,weestimatetheminimumrealistictimerequiredtoobtainnecessary

qualifications,includingprerequisite

education,formaltraining,supervisedor

clinicalhours,andlicensingexaminations.

Weestimatetrainingtimefromthreestartingpoints:ahighschooldiploma,anassociate

degree,andabachelor’sdegree.Weusedalargelanguagemodeltoidentifyand

synthesizepubliclyavailablecredential

andtrainingrequirementsandtoderive

estimatedtrainingtimesforeachpathway.

Wages.Weuseadvertisedwagesfrom

Lightcastjobpostingstocomparepay

betweensourceanddestinationoccupations.Thisreflectspostedbasepayratherthan

totalcompensation.

Theoptimizationcombinesthesedimensionsintoatransition-costframeworkthatfavorspathwayspreservingworkers’wageswhile

alsoaccountingforskillgaps,credential

requirements,andtrainingtime.Basedontheresultingtransitioncost,weclassify

pathwaysasstraight,winding,orunpaved.

3Jevonsparadoxdescribeshowgreaterefficiencycanincreasetotaldemandbyloweringeffectivecosts.Appliedtolabor,AImaymakeataskcheapertoperform,increasingdemandforitsoutputenoughtooffsetsomeorallthelaborhourssaved.

Chapter1:

Whyjobschange

Chapter3:

Pathwaysleadto

growingjobs

Chapter4:

Expandaccessto

betterpathways

Chapter5:

Reducingbarriers

Chapter6:

Enabling

transformations

Final

thoughts

Chapter2:

Thegreatworkforce

reallocation

Workforceinmotion:SkillsandpathwaystofuturejobsintheUnitedStates10

Exhibit2

Automationadoptionvariessubstantiallywithinoccupationalgroups,notjustbetweenthem.

Likelyautomationadoptionby2035,byoccupationalgroupintheUS1

Occupationwithineachgroupthatrepresents:

.LowestadoptionAverageadoption,weightedbyfull-timeequivalent(FTE)workersHighestadoption

OccupationalgroupAdoptionasashareofcurrentworkhours,%

FTEcount,million

0

Oficeandadministrativesupport

10

2

03

04

05

0

6

07

08

09

010