mckinsey -原生 AI 企业的七大运营核心法则 The seven operating truths of AI-native companies 2026

June2026

ThesevenoperatingtruthsofAI-native

companies

AlmosteverycompanyhasAItools,butfewreallyknowhowtousethem.Leadersat15AI-savvycompaniessaythedifferencecomesdowntosevenoperatingtruths—thatmostorganizationsstillgetwrong.

ThisarticleisacollaborativeeffortbyFabianMetzeler,MelanieKrawina,PaulJenkins,andPhilippHillenbrand,withAlexanderRingler,representingviewsfromMcKinsey'sBusinessBuildingPractice.

ThesevenoperatingtruthsofAI-nativecompanies2

TheCEOofanearly-stageB2Bmarketplacebuildsproductionintegrationsviavoicenotesonhissubwaycommute.A20-personagriculturetechventurehaspausedallhiringbecause

commerciallargelanguagemodels(LLMs)nowhandlemorethanhalfoftasksinnearlyeverybusinessfunction.Atamaturedevelopment,security,andoperations(DevSecOps)platform,nontechnicalstaffuseAItoolstofixsmallbugsandrenamefeatures,bypassingengineeringentirely.

Thesearenotisolatedexperiments.Overthepastseveralmonths,wehavemetwiththetech

andbusinessleadersat15AI-centriccompanies—spanningcontinents,industries,andstagesofdevelopment,fromfour-personstart-upstoestablishedglobalplatforms—tolearnwhatittakestomakeAIcapabilitiestrulydeliver.Weexpectedtohear15differentstories.Instead,this

diversegroupofbusinesses,independentofoneanother,seemedtoconvergeonthesamefundamentalinsightsaboutwhatittakestosuccessfullyplaceAIatthecenterofthe

organization.Thatconvergenceisthestory.

Earlierthisyear,wepublisheda

strategicplaybookonAI-firstventurebuilding

.Thisarticle

buildsonthatplaybook—movingbeyondframeworksandtheoreticalstrategiestoidentifythe

ground-levelpracticesthatdifferentiatewinningcompaniesfromthosethatcontinueto

struggletogetrealresultsfromtheirAIefforts.Andthosestrugglesarereal:

McKinsey’smost

recentGlobalSurveyonAI

findsthatwhile88percentoforganizationsnowuseAIinatleast

onebusinessfunction,onlyaround1percentconsiderthemselvesfullymatureandroughlytwo-thirdshaveyettoscaleAIbeyondisolatedpilots.

Whatdowinningenterprisesknowthattheirpeersdonot?Whattheyseemtoshareisasenseofpossibility.RatherthandeployinggenAIandagenticAItosimplycutcostsandboost

productivity,theyapproachthetechnologyasamultiplierofbothambitionandthecapabilitiesrequiredtorealizethoseambitions.Weboileddownwhatwelearnedfromtheseleadersinto

sevenessentialtruths—hard-earnedinsightsthatcollectivelyconstituteanoperatingsystemforgettingthemostoutofAI.

AIisnotatool,it’sateammate

TherealvalueofAIisn’tdoingthesameworkfaster.It’stheabilitytoamplifytheeffortsofindividualswithagentsthatfunctionasgenuineteammembers.

Whenwebeganourresearchlatelastyear,leaderstendedtodescribeusingAItoolsas

personalproductivitycopilots.Monthslater,theywerespeakingintermsofhavinggenuine

agenticcoworkers,withtheirownnames,Slackhandles,sharedtaskboards—andtheabilitytoexecutetasksautonomously,24/7.TheCOOofaSeriesDfintech,forexample,runsa

multiagentsystemtovetnewideas.Anyemployeecansubmitone-sentenceproductideasviaSlack.Afterinitialvettingbyaproductmanager,tenspecializedagentsworkontheidea

simultaneously—coveringissuessuchasproductdefinition,back-endfeasibility,revenue

recognition,andlegalcompliance—anddeliveracomprehensivesetofproductrequirements

ThesevenoperatingtruthsofAI-nativecompanies3

withinhours.

TheCEOofaSeriesAmarketplacehasanentirestaffofpersonalagents—includingan

executiveassistantagentthatrepliestoemailsandmanageshiscalendar,achiefofstaffagentthatrecordsmeetingsandautonomouslygeneratesandcirculatesnextsteps,andananalyst

agentthatprovidesreal-timedatainsightsfromcompanydashboards.Thevirtualstaff,hesays,hasboostedhiscapacityandeffectivenessfivefold.Weheardsimilarstoriesfromotherleadersweinterviewed.

Thestructuralpatternisthatjobsarebeingredefinedascollaborationsbetweenhumansand

agents,withworkreallocatedtoamplifywhatteamsarewillingtoattempt.Engineersnowdo

designandcustomerresearch.Nontechnicalstaffopenmergerequestsandshipinternal

experiments.Afour-personsustainabilityventurecanserve20enterpriseclients,producing

compliancereportsinminutesthatpreviouslywouldhavetakenalawfirmweeks.ASeriesA

marketplacewentfrommanaging50dealsperadvisertorunning3,000simultaneously—notbyhiring60timesthestaff,butbybuildingagentanalyststhathandlethevolumeworkwhile

allowinghumanstofocusonthehigh-stakes,trust-heavyconversationsthattrulydrivethebusiness.Thesearenotefficiencygains.Theyarefundamentallydifferentbusinessmodels,madepossiblebecauseAIexpandedwhatteamsdaretotakeon.

Potentialpitfalls

Theshadowsideoftreatingagentsasteammembersisstructuraldependency.“What

externalitiesarewecreatingbygoingintothisagenticworldthatwehaven’tforeseen?”askstheagtechventure’sCEO.“Byturningmoreandmoreovertotheagents,whatcrisesarewe

potentiallyperpetuating?”Thecompanymitigatesthatriskwithdeliberateroledesign.Humansarechanneledintotheproblemsthatagentsgenuinelycannotsolve—suchasnovelscientific

judgmentandpartnerrelationships—andescalationpathsarecreatedforwhenagentsreachthelimitsoftheircompetence.Thehuman–agentpartnership,theCEOsays,isnotabout

“replacinghumanswithAI.It’saboutbeingsurgicalaboutwherehumantalentisirreplaceable.”

Theleadershipmove

StopmeasuringAIbyhourssaved.Measurebywhatthebusinessisnowwillingtoattempt.Giveagentsnames,responsibilities,andescalationpaths.Maphowjobsarebeingreorderedacrossyourorganizationandredesignrolestoreflectthenewhuman–agentallocation.

Knowwhattobuildandwhattobuy

Buildonlywhatmakesyoutrulydistinctive.Asforeverythingelse,howfaryougoisafunctionofyourowncomfortlevel.

Thefirstbuild-versus-buydecisionistheeasyone.EveryAI-firstcompanywespokewith

appliesthesametesttoitscoreproprietarycapability:Doesthishelpcreateadefensible

advantage—basedonourcompany’sdata,expertise,orintellectualproperty(IP)—thatanoff-

ThesevenoperatingtruthsofAI-nativecompanies4

the-shelftoolcannotreplicate?Iftheanswerisyes,buildit.Theagtechventurebuildsitsown

crop-breedingmodels,IP-landscapescanners,andself-improvingR&Dagentsin-house.The

deep-techmaterials-discoverycompanybuildsitsscientificdiscoveryagentonproprietarydata.“Ouruniquesellingpropositionisthedataandtheopinionweexposetotheworld,”thevice

presidentofengineeringataclimateintelligenceventuretoldus.“AIcan’treplacethat.”

Thetrickierdecisioniswhattodoabouteverythingelse—thetoolsandagentsthatruninternaloperations.Untilveryrecently,theanswerwassimple:Buyitall.Specialistvendorsdelivered

polishthatinternalteamscouldmatchandmoderntoolsshippedwithnativeintegrations.Manyofthecompaniesweinterviewedstilloperatethiswayandarehappywiththeresults.Atthe

seed-stageAIcompanyweinterviewed,forinstance,everysalescallisrecorded,transcriptsareautomaticallypostedtoasharedworkspace,andaweeklydigestgoestotheteam—aprocessstitchedtogetherfromoff-the-shelftools,withnocustombuilding.TheCEOcanaskthe

connectedsystemswherethingsstandwithanyclientandgetaninstantupdatefromacross

theteam.It’sacommonethosatAI-nativeventures.“Wedon’tbuywhatdefinesus,”asthetechleadatadigitalhealthscale-upputsit.“Webuywhatfreesus.”

Moretechnicallyadeptorganizations,meanwhile,cannowdrawthelineinaradicallydifferentplace.ThankstocodingagentssuchasClaudeCodeandCursorandsimilaragent-builder

platforms,internalteamscanspinupdashboards,automateworkflows,andcreatetailored

agentsinhoursratherthanmonths—sometimeswithnoengineersintheloop.Theagtechchiefstrategyofficer,forexample,saysthatbuildityourselfisnowthedefault.“Ipersonallythink

SaaS[softwareasaservice]isdead,”hesays.“Tryingtointegrateatooltakeslongerthanittakestobuildatool.”

Potentialpitfalls

Buildingcustomagentsandtoolsmaybeinexpensive.Maintainingthemisnot.Itistemptingtoleteveryteamanddepartmentspinupitsownbespokeproductivitytool—butthemaintenancebillwilleventuallyarrive.Cheapandeasytobuildtodaydoesnotequalcheapandeasytoowntomorrow.

Theleadershipmove

Buildwhatmakesyoudistinctive.Fortheoperationsstack,startwithoff-the-shelftoolsand

makeAI-nativeinterfaces,integrability,and“swapability”nonnegotiable.Auditthestack

quarterlyandbepreparedtoswitchoutanythingthatfailsthosetests.Andkeepinmindthat

onlythemosttechnicalandmostdisciplinedteamsshouldattempttobuildeverythingin-housetoday—andeventheyshoulddosowitheyesopenaboutmaintenancecosts.

Yourmodelisn’tthebottleneck—accessingyourtribalknowledgeis

ManyteamsfocusonwhichAImodeltorun.Theonespullingaheadfocusonwhattheiragentscanfind,andtheyinvestintheknowledgelayerthatmakesthedifference.

ThesevenoperatingtruthsofAI-nativecompanies5

Whenyouaskanagentaquestionanditcannotanswer,it’snotnecessarilythemodel’sfault.Itcouldbethattheanswerwasneverwrittendownorislocatedsomewherethemodelcannot

reach.Inotherwords,theceilingonyourAIissetbyyourknowledgehygiene,notyourmodelchoice.“Itisn’tanAIproblem—it’saknowledgemanagementproblem,”saystheoperations

directoratanenergytechplatform.“AIjustmakesitvisible.”Therealgapisn’ttechnologyasmuchasitistheknowledgeinfrastructure:unrecordedmeetings,unstructureddata,and

expertisetrappedinpeople’sheads.

Forthosewhogetitright,thepayoffcanbeconsiderable.Attheenergytechplatform,for

example,aknowledgeagentthatindexescoderepositories,pagesonthedigitalworkspace

Notion,andSlackconversationsenablesnewhirestogetuptospeedinamatterofdays.Attheseed-stageAIcompany,theautomatedsalespipelinestoresalldataina“queryable”knowledgelayer.Whensomeoneasks,“Howfaralongarewewiththisspecificlead?”theygetinstantdealcontextpulledfrommonthsofaccumulatedconversations.“Wheneverwehavequestions,we

canjustaskourinternalknowledgebase,”saystheventure’sCEO.Thefeedbackloopbetweenproductandoperationsbecomesacompetitiveadvantage.

TheagtechCEO,forhispart,challengestheorthodoxythatyouneedasingledatalakebeforeAIcanwork:“Alotofpeoplegetwrappedaroundtheaxleof‘youneedasinglesourceoftruth.’Butthethingthatmakesdatausefulisthathumansaretouchingitandupdatingitallthetime.”SomepeoplewriteinNotion,othersusespreadsheets,othersliveinSlackoronvideocalls.

Ratherthanforcinguniformity,thecompanybuildslightweightconnectorsthatmakeallofthatdata,whereveritlives,queryablebyAI.“Ifsomebody’susingatool,justmeetthemwheretheyare,”theCEOsays.

Potentialpitfalls

Yourknowledgelayercanrotfasterthanyouthink—andwhenitdoes,agentstendtobreak.

Whendatagetsoldandstale,agentswillconfidentlyserveupoutdatedanswers,whichrapidlyerodesusertrust.“Anagentdoesn’tknowwhatisthelatestsourceoftruthandwhatisan

outdateddocumentfromayearago,”warnstheCOOofaSeriesDfintechventure.It’salessonthatthetechleadatadigitalhealthcompanylearnedthehardway.“IfIcouldchangeonething,I’dinvestearlierinstructuringourcontent,”hesays.“Fragmenteddataslowsdownflowand

frustratesteams.”Thefixisarchitectural:buildconnectorstowhereactivityalready

happens—meetings,Slackthreads,workingdocuments—sotheknowledgelayerstaysfreshwithoutanyonemaintainingitmanually.

Theleadershipmove

Recordeverymeeting.Transcribeautomatically.Routeoutputstoasharedknowledgelayer.Makeyourmessagingplatformcrawlableandconnectittoyourknowledgebackboneso

conversationsbecomequeryablecontext.Meetpeoplewheretheyalreadyworkandbuild

connectorstocapturetheknowledgetheynaturallycreate.AIisonlyassmartaswhatitcanfind.

ThesevenoperatingtruthsofAI-nativecompanies6

Designfortheswap,notthestack

Thewinningarchitectureisnotamonolithicplatform.Itisathingovernancelayerthatconnectsbest-in-classcomponentsandkeepstheminterchangeable.

AI-firstcompaniesconvergeonasharedarchitecturalprinciple:assemblebest-in-classtools,wirethemintoagovernedsharedlayer,andbuildonlythethinconnectivetissuethatmakes

contextsecureandqueryable.Ataglobaltechnologyplatform,forexample,engineersquery

internalwikis,continuousintegration/continuousdeploymentpipelines,andticketingsystemsthroughAIagentsconnectedviamodelcontextprotocolservers.“Icanask,‘Whatservicesareimpactedbythisfeature?’anditshowsmeeverything,”theplatform’stechleadtoldus.That

discoveryworkflow,whichreduceshoursofmanualsearchtominutesofconversationalquery,isanarchitectureoutcome.

Modelagnosticismisanonnegotiabledesignprinciple.“Everythingwebuildneedstobedoneinsuchawaythatwecaneasilyripoutamodelhereandputsomethingelsein,”saystheSeriesDfintechCOO.Thecompanyusesamultimodelgateway,startingwithpremiummodelsfornew

workflows,thenmigratingtomoreeconomicaloptionsoncethepatternisproven.Givenhowfastthefrontiershifts,lockinginisastrategicliability.

Potentialpitfalls

Aconnected,composablearchitectureisanimmensesourceofadvantage,butit’sanequally

largeattacksurface.Toguardagainstthatrisk,theAIbiotechcompanyrunsathree-tier

securitymodel:publicdatatocommercialLLMs,sensitivedatatoproviderswithzero-data-

retentionagreements,andcoreIPprocessedonlyon-premises.“Thecompanywouldbein

mortaldangerifthisinformationleaked,”thecompany’sCEOtoldus.Securitytieringisadesigndecision,notabolt-on.

Theleadershipmove

Standardizeagovernancebackbonethatincludesidentitymanagement,permissions,securitytiers,anddataclassification.Connectbest-in-classtoolsarounditwithlightweightconnectors.Buildonlythethinintegrationlayerthatmakescontextsecureandgoverned.Designforthe

swap,notthestack.

Trustprecedesautonomy

CompaniesbuildtrustinAIsystemsthroughprogressiveautonomy:AIgenerates,humansjudge,andthesystemearnsmorefreedomonlywhenitdeservesit.

WithAIagents,thefreedomtooperateautonomouslyisaprivilegethatmustbeearned.Attheearly-stagesustainabilityventure,forexample,theteamrunseveryprocessmanuallyuntil

repetitiondemandsautomation,atwhichpointsubstepsareautomatedoneatatimeuntilthefullprocessiscomplete.“Automateslowly,”saysthecompany’sfounder.“Doitmanuallyuntil

ThesevenoperatingtruthsofAI-nativecompanies7

thepainforcesautomation.That’swhenyouknowtheworkflowisready.”Movingmethodicallylikethissurfacesedgecasesthatanautomatedpipelinewouldmiss,teachingtheteamwherehumanjudgmentisgenuinelyrequiredversuswhereithasbecomehabit.“AIistheperfect

middle-to-middletool,”thefoundersays.“Humansstillneedtostartandfinish.”

Leadingcompaniesalsoidentifytheirqualityceiling—andthenholdtheline,whiletreatinganybenchmarkasacurrentread,notafixedrule.Onehealthtechfounder,forexample,reports

beingofferedan85to90percentsuccessrateonanagenticsolution,whichherejected

outright.“Weneedtooperateat99.999precision;inhealthcare,youcan’tgo‘80percentis

goodenough,’”hesays.Insoftwaredevelopmentandback-officeworkflows,bycontrast,mostoftheleaderswetalkedtoputtheceilingatabout70to80percent—notingthatinmostcases,AIcangetyoutherereliably;beyondthat,humanjudgmentiswhererealvalueconcentrates.

“It’snotjustusingAI,”saidtheheadofengineeringatacompanythatdevelopsAIforcontactcenters.“It’sknowingwhennotto.”

Potentialpitfalls

Autonomywithoutguardrailsbackfiresfast,andfailureoftenarrivesintheplacesyouleast

expect.Whennegotiatingacontractwithacustomer,forexample,execsattheAI-for-doctorscompanyranemailchainsandproposedchangesthroughanAIagentbuilttoreadandansweremails,whichimmediatelycraftedthisresponse:“Allfinere:yourchanges.Let’sgo.”Infact,theproposedtermsrequiredadditionalnegotiation.Fortunately,ahumanworkerflaggedtheerrorbeforetheresponsewassent.“Weturnedthatoffimmediately,”theCEOsays.Thelesson:AI

shouldsuggest,notact,untiltrustisearnedinthatspecificcontext.

Theleadershipmove

Definewherehumanapprovalismandatoryandencodeitintoworkflowstoday.Measurefull

cycletime(generationplusreview),notjustgenerationspeed;gainsatthegenerationstep,afterall,canvanishinthereviewstep.Buildfeedbackloopsthatletthesystemearnmoreautonomyovertime.Thegoalisnotpermanenthumanintheloop;itisbuildingthetrustthatmakes

autonomysafe.

Centralizetheplatform;decentralizethetasks

NocentralizedAIdepartmentcandrivetransformation.Whatworksiswhenplatformteamsgoverntheinfrastructureandbusinessteamssolvetheirownproblemsontopofit.

TheAIoperationsdirectoratthelargeenergytechplatformmadeadeliberatechoiceearlyon.“IdecidedthatIamnottheexpert,”shesays.Theideathatonepersoncanunderstandand

optimizeAIuseacrossmultiplebusinessfunctions,shelearned,tendstocollapsethemoment

thatworkflowschangefasterthanprescriptionscankeepup.Thetechleadatadigitalhealth

scale-upreachedthesameconclusion:“Itmakeszerosensetohaveonepersonoverseeten

businessunitstheydon’tunderstand.”Instead,eachbusinessunitattheventuredecideshowAIwillsupportitsgoals.AsmallAIguildconnectsleadersforpatternsharing,butownershipstays

ThesevenoperatingtruthsofAI-nativecompanies8

withtheteams.

Atmanyoftheorganizationswetalkedto,thetechnologyteamownsgovernedaccessto

models,composablearchitecture,securityguardrails,andconnectiveinfrastructure.The

businessteamsowntheproblemstobesolvedandthestrategiesneededtosolvethem.Thatseparationletstheplatformstaycurrentwhilebusinessteamsmoveattheirownpace.

Leadingcompaniesalsoenableanyonetobecomeabuilder,withtheideathatthepeople

drivingthemostimpactarethosewhoapplyAItotheirownworkflowfriction.ThehealthtechCEOgiveseveryoneonetotwohoursdailyforfreeexperimentation.TheSeriesAmarketplaceCEOassuresstaffers,“Youcanfigureitout.Youdon’thavetoaskengineeringtobuildyou

something.”McKinseyresearchhasfoundthatworkerswerethreetimesmorelikelythan

leadersexpectedtoreportthatAIhelpsthemperform30percentormoreoftheirdailytasks.Thebarriertoscaling,inotherwords,isnotemployeereluctance;itisleadersnotenablingfastenough.

Potentialpitfalls

Decentralizationwithoutaplatformischaos,butcentralizationwithoutspecificitycanleadtoadifferentkindoffailure.TheheadofengineeringataSeriesCscale-updescribestheformer:

“Sometimespeopleuseunapprovedautomationtools,andthenwehavetoclosethose

accountsuntilaproperapprovalisdone.”Astaffengineeratamaturetechnologyplatform

describesthelatterkindoffailure:“Ibelievewemovedtoobroadlytooearly,tryingtobuildan

‘agentthatcoulddoeverything’ratherthanfocusingonspecificusecasesfirst.Weshouldhaveshippedsmaller,faster.”Thesetwodistinctfailuremodeshavethesameanswer:arealplatform,governedcentrally,withclearlyboundedscope,thatletsbusinessteamsbuildfreelywithinit.

Theleadershipmove

Appointaplatformownerwithexplicitauthorityovergovernance,architecture,andsecurity

guardrails,anddocumentwhichdecisionsbelongtobusinessteamsbeforetheplatformgoes

live.Buildlightweightsharingrituals(adedicatedshowcasechannel,weeklydemoslots,a

sharedpromptlibrary)solocalwinscompoundintoreusablepatternswithoutbureaucratic

overhead.Measureplatformadoptionattwolevels:activitysignals(tokenusage,tooladoptionrates)areusefulleadingindicators;therealmetriciswhatbusinessteamsbuiltanddeployedontopoftheplatform.

Adoptionisaflywheel,notarollout

Successfuladoptionisn’tarolloutwithadeadline.It’saflywheelwithfourreinforcinglayers:rolemodeling,sharebacks,measurement,andhiring.

MostorganizationshavedeployedAItools.Farfewerhavebuiltthecultureandmuscletousethematscale.Thecompaniesthatclosethegapbuildaflywheel.

ThesevenoperatingtruthsofAI-nativecompanies9

Thefirstlayerofthatflywheelisrolemodeling:leadersgofirst,visibly.TheSeriesAmarketplaceCEObuildsproductionintegrationshimselfandmakesAIfluencypartofperformancereviews.

“Ifanyonehereisnottinkering,”hesays,“they’reprobablycooked.”TheSeriesDfintechCOOblocksoutFridayafternoonsforcompany-widehackingsessions;eventheCEOhasbeen

forcedtobuildhisownagent.“Ifyouarenotspendingsignificanttimethinkingabouthowyouscaleyourself,”theCOOsays,“thenyou’renotupforthejob.”Thetakeaway:WhenleadersuseAIandsharetheresults,theygiveeveryoneelsepermissiontoexperiment.

Thesecondlayerisconvictionthroughsharebacks.Noneofthecompanieswelookedatreliesonmandatesalone;instead,themechanismforgenuineadoptionissocialproof.Theenergy

techplatformrunsAIguildtalks,townhallsfeaturingsuccessstories,andmonthlyAIchallengesfornon-engineeringteams.“Youcan’tsitonpeople’sshouldersandtellthemtouseit,”the

platform’soperationsdirectorsays.“Ifpeoplesharesuccessstories,that’swhatworks.”Attheseed-stagesustainabilitycompany,effectivepromptsandworkflowsarewrappedintoreusablecustomGPTsanddistributedtotheteam.“Wheneversomeonebuildsagreatpromptor

workflow,weshareit,”thecompany’sfoundertoldus.

Thethirdlayerisreinforcementthroughmeasurement.“WeevenhavealeaderboardofwhichdepartmentusesthemostAI,”theDevSecOpsstaffengineernotes.Thelesson:Whatgets

measuredgetsrepeated.

ThefourthlayerishiringfortherightDNA.TheSeriesDfintechCOOprobeseverycandidateonAIexperimentation.IfacandidatedescribesusingAIonlyforsummaries,hesays,“Iliterally

think,‘OK,thenourinterviewisover.’”ThehealthtechCEOdescribesthebaras“morea

willingnesscheckthanaskillscheck.”Thedeep-techCEOappliesthesamestandardacrossfunctions,eveninnontechnicalroles:“Ifsomeonesaid,‘I’veneverusedAIbeforeandIdo

everythingmanually,’that’sprettymuchano-go.”

Thisfour-layerflywheelconstitutesaself-reinforcingsystemthatcompoundsovertime.WhenleadersrolemodelAIuse,theygiveteamspermissiontoexperiment.Whenexperiments

succeedandareshared,theycreatesocialproofthatacceleratesadoptionacrossthe

organization.Measurementmakesthatadoptionvisibleandcreatesaccountability.AndhiringforAIfluencyensuresthateverynewjoinerraisesthebaseline,makingallthreepriorlayersmoreeffective.Theflywheelstallswhenanysinglelayerismissing.

Potentialpitfalls

Twofailuremodesbrackettheflywheel.Thefirstisexhaustion.“Peoplecanactuallygetburnedoutbecausethere’ssomuchopportunitytodosomanythingsnow,”thedeep-techCEOsays.Thesecondisforgettingthatchange,especiallyrapidandradicalchange,canbedisorienting.“Formostpeople,it’squitescary,”saystheoperationsdirectoroftheenergyplatform.“Bekindaboutit.ShifttousingAItoso