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Thepivottotokenomics:NavigatingAI’snew

spenddynamics

Thepivottotokenomics:NavigatingAI’snewspenddynamics

2

Anotefromtheauthors:

AIeconomicsaffectmost

organizationsandtheC-suiteuniquely.

Thispaperguidesthosefamiliar

withAItokensinmakingstrategic

choices.Ifyou,rejustbeginningyour

explorationoftokenomics,lookfor

additionalresearchsoon.

Thepivottotokenomics:NavigatingAI,snewspenddynamics

Traditionaltotal-cost-of-ownershipframeworksmisstherealityofAI

Volatileworkloads,newinfrastructuredemands,andtokensasthe

practicalunitofcost

Acrossindustries,GenerativeAI(GenAI)hasbecomethefastest-

growinglineiteminmostcorporatetechnologybudgets—already

consuminguptohalfofITspendinsomefirms.1Cloudbillsarerisingnearly20%yearoveryear,drivenbyAIworkloads.2Atthesametime,geopoliticaluncertaintiesareintensifyingcallsfordatasovereigntyandtechnologyinfrastructureindependence,makingmany

enterprisesthinkaboutAIsovereigntyandgaininggreatercontrolovertheirinfrastructure.3ThisisnolongeraCIOoperationalissue;itisaCFO-and-boardcapitalquestionabouthowtoresponsiblymanageaninvestmentofthisscaleandvolatility.

Unlikepriortechnologywavesgovernedbylicensesorvirtual

machines,AIspendoftenscalesinnonlinearandunpredictableways.AIcapabilitiesrunontokens:smallchunksofdata—text,imageoraudio—thatAIsystemsprocessintraining,inference,andreasoning.EveryAIinteractionconsumestokens,andeverytokencarriesacost.

ThecomplexityofAI’seconomicshideswithinthesetokens.

Costsrisenotonlywithuseradoptionbutwithworkloaddesign,

algorithmiccomplexity,andinfrastructureintensity.Whatexactlyarethethresholdstomoveacrossdifferentconsumptionchoices?Itdependsontheorganization.RoughlyaquarterofrespondentsinaDeloitte2025survey4ofdatacenterandpowerexecutivessaytheyortheirclientsarereadytomakethemoveoffofcloudtoalternativesassoonascostsreachjust26%to50%ofthosealternatives,showinghighsensitivitytoevenmodestpricechanges,whileothersplantowaituntilcloudcostsexceed150%ofthe

costofalternatives.ThedecisionpointremainsuncleargiventhehighvariabilitypatternsofAItechnologies.Forexample,advancedreasoningmodelsthatkeepcontextacrossmultiplestepscanconsumemuchmorecomputethanbasicone-shotresponses.

AsNVIDIAprojectsabillion-foldsurgeinAIcomputingandGooglenowprocesses1.3quadrilliontokensamonth5—a130-foldleapinjustayear—thecapitalandenergyimplicationsareprofound.

Traditionaltotalcostofownership(TCO)approachesarenolongerthebestwaytomanageAIeconomics.Leadersmaybebetterservedbyprecisioneconomics—theabilitytotrack,predict,andoptimizespendatthetokenlevel.Tokenstranslateopaqueinfrastructurechoicesintotangiblefinancialterms:thetruecostofgeneratingadollarofrevenue,margin,orproductivity.

ThecompetitivedividewillnotlikelyhingeonwhoadoptsAIfirst,butonwhomanagesitscoststructurewithdiscipline.AIspendwilllikelyseparatevaluecreatorsfromvalueeroders.Theformerconvert

tokensintomeasurableenterpriseoutput;thelatteraccumulateungovernedcostthatcompoundsquietlyacrossthestack.

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Thepivottotokenomics:NavigatingAI,snewspenddynamics

4

TheelusiveAIROI

Despiterisinginvestment,manyleadersappeartostillbechasingmeasurablereturnoninvestment(ROI)fromAIinitiatives.

•Nearlyhalf(45%)of500leaders

surveyedinDeloitte’s2025US

TechValuesurvey

expectitwilltakeuptothreeyearstoseereturnon

investmentfrombasicAIautomation.6

•Sixin10ofthosecompletingDeloitte’s

2025TechValuesurveybelievemoreadvancedAIautomationwilltakeevenlongertoreachROI.

•Ofthe1,326globalfinanceleaders

surveyedforDeloitteGlobal’sinaugural

FinanceTrendsreport

,fieldedMay

2025,28%saidAIinvestmentsaredeliveringclear,measurablevalue.7

Buttheissueisn’twhetherAIwilldelivervalue—it’showtomeasureandmanagethatvalueinawayROIframeworkscannot.Formanyorganizations,adoptingAIisnolongeroptional;it’sastrategicresponsetocompetitiveorexistentialpressure.

ThatmakesunderstandingtheeconomicsofAI—howcosts,workloads

andreturnsflowthroughtokens—thenew

imperativeforleaders.

Thepivottotokenomics:NavigatingAI,snewspenddynamics

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Tokens:ThenewcurrencyofAI

Unliketraditionalpricingbasedoncomputetime—whichisrelativelystatic—token-basedpricingtiescostdirectlytotheactualworkAIperforms.Eachtokenrepresentsbothaunitofcomputationandaunitofcost.Inthatsense,tokensarethetruecurrencyofAIeconomics—asindispensabletomachineintelligenceaskilowatthoursareto

electricity.Thedifferenceisthattokendemandisfarhardertopredictorcontrol,makingAIspendinherentlyvolatile.

•Nonlineardemand:Complexreasoningmodelsimproveperformancebutcanconsumemoretokensthansimpleinferencetasks.

•Fluctuatingtokenuse:Tokenusefluctuateswithexperimentationlevels,workloaddesign,modelchoiceandevenpromptengineering.

•Varyingpricing:TokenpricekeepschangingbasedonAImodelcapabilitiesandtheefficiencyoftheunderlyinginfrastructure.8

Whilethisvolatilityappearstostemfromusagepatterns,itsrootsareinthetechstack.Thecompute,storage,andnetworkingdecisionsthatpowerAImodelsdeterminehowefficientlytokensareprocessed—andhowcostlyeachonebecomes.

Atokenisnotjustatechnicalmeasure—itisaneconomicsignal.Eachtokencarries

thecompoundeffectofGPUdesign,storage,throughput,networklatency,andfacilityeconomics.Thedisciplineliesintracinglineage—frominfrastructuretotheAImodeltooutcome—andaligningthosedecisionssotokencostsstayproportionaltobusinessvalue.

Thepivottotokenomics:NavigatingAI,snewspenddynamics

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Howtokensarebought

AIspendingisnotasinglemarket;itfracturesintodifferenteconomicrealitiesdependingonhoworganizationsconsumeintelligence.SomeleadersexperienceAIcostsonlyasasoftware-as-a-service(SaaS)lineitem,othersasmeteredapplicationprogramming

interface(API)calls,andagrowinggroup/cohortmanageitdirectlythroughinfrastructureownership—balancingGPUs,storage,networking,andenergy.

Buyingpatterns

•Generatingthroughpackagedsoftwareabstractstokensalmostentirely.Leadersseeapredictablesubscriptionorper-seatfee,butlittletransparencyintotokenconsumptionefficiency.Theriskislesscontrolformoresimplicity.

•ConsumingthroughAPIsmakestokensexplicit.Everyqueryismetered,

billed,andexposed.Thisbringstransparency,butalsovolatility:Costsrise

basedonworkloaddesign,promptlength,andhiddenchoicesofinfrastructureproviders.Costsgoupduetoatokenmeterrunninginrealtime.

•Runningonownedinfrastructurebringstokeneconomicsfullyin-house.

TokensbecometheoutcomeofdecisionsaboutGPUs,storagetiers,networking,andenergycontracts.Thisapproachdemandshighcapitalandtechnical

capabilitybutoffersthegreatestcontroloverlong-termcoststructureanddatasovereignty.Theemergingshorthandforthisstrategy:theAIfactory.

Eachofthesechoicesisgroundedinexistingandfuturetechnicalandoperatingdecisionsgivensystemcost,latency,security,andotherneeds,whichchangehowtokensflowintoenterpriseprofitandloss(P&L).9

Thepivottotokenomics:NavigatingAI,snewspenddynamics

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WhatisanAIfactory,andwhendoesonemakesense?

DeloittedefinesanAIfactoryasaspecialized

infrastructure(compute,network,andstorage)along

withoptimizedsoftwareandservicesthatenablestheentireAIlifecycleathighperformancescale.Theprimaryproductisintelligence,measuredbytokenthroughput,whichdrivesdecisions,automation,andnewAIsolutions.

Oneofthehardestdecisionsenterprisesfaceiswhethertocontinuepayingfortokensoffpremises(off-prem)—throughAPIsortraditionalSaaScompanies—ortobuildanAIfactoryandself-managetheinfrastructure.The

economicsvarysharplydependingonscale,sensitivity,andpredictabilityofdemand:

•Off-prem(APIortraditionalSaaS

companies):Maybemostefficientforearlypilots,spikyorseasonalworkloads,orusecaseswithlowdatasensitivity.Costsare

typicallyhigherpertokenbutpredictableandflexible,withnoup-frontcapital

expense(capex).

•AIfactory:Canbecomeattractivewhen

workloadsarelarge,predictable,latency-

sensitive,andcrossathresholdwhere

buildingandoperatinginfrastructuredeliverslowereffectivetokeneconomicsthan

continuingtorentthem.Althoughcapex

investmentmaybeneeded,per-tokencostsfallasinfrastructureisfullyutilized,and

sovereigntyrisksarecontrolled.Beyond

thetraditionalon-premises(on-prem)or

colocation(co-lo)providers,anAIfactory

canalsobestoodupusingfast-growingcloudalternatives(neoclouds)tomanageworkloadredistributiontrends,as

detailedina

recentDeloittesurvey

.10

Thedecisionisnotbinary.Formostglobalenterprises,therealityishybrid.Smaller,lesspredictableand

exploratoryworkloadsmaystayinAPIform,while

scaled,highvalueworkloadsmayrunonanAIfactoryasapplicationsscaleandeconomicsstabilize.AImodelpreferenceandselectionmayalsodriveenterprise

decisionmaking.

Howtokensarepriced

Onceleadersunderstandtheirbuyertype(generate,consume,

run),thenextchallengeistoseehowtokensarepriced.ThesameAImodelcouldbebilledasaseatlicense,oratokenmeterorGPU-hours,dependingonhowitisconsumed.Therearethreemajor

constituentstotokenpricing:

1.Theunderlyingtechstack

2.Howitishostedandconsumed

3.WhattypeofAImodelandlevelofcustomizationisrequiredtopowerthesolution

TheAItechstack

EverytokenprocessedbyanAImodelreflectsacascadeofinfrastructuredecisions.

Forpackagedbuyers,inmostcasesandatleastfornow,thesecostsarehidden.Costsareabstracted,bundledintofamiliarenterprisecontractsandvendormanagedacrosseverylayerofthetech

stack,whichmakesunpackingTCOchallenging.

ForAPIconsumers,everyelementoftheAItechstackshowsup

indirectlyasper-tokenfeesorthroughputcharges.PricevariesbyAImodelaccessed,withdifferentinputandoutputrates,usually

reportedintokenpermillion.Discountedpricingoptionssuchas

reservedtokencapacity,promptcaching,orbatchexecutionratesareusuallyoffered,whileinsomecasesenterprisecustomersmayalsogetuser-basedpricing.Additionally,storageoregresschargesmayfurtheraddtoTCO.

Forself-hostedsolutions,tokensarenotpurchasedatall;theyemergefromexplicitcapexandoperatingexpense(opex)decisionsrelatedtoinfrastructurechoices(figures1and2).

Whatchangesacrossbuyertypesisnotwhetherthesecostsexist—theyalwaysdo—butwhoseesthem,controlsthem,andpaysforthem.

Thepivottotokenomics:NavigatingAI,snewspenddynamics

8

Figure1.HowtechnicaldecisionscandrivetokencostsandimplicationsforanAIfactory

STACKCOMPONENT

TOKENIMPLICATIONS

SELF-HOSTEDAIFACTORY

Compute

Graphicalprocessingunits(GPUs)and

accelerators

ModernGPUsandhigh-bandwidthmemoryshortentimepertokenbutcomewithhigheracquisitionor

rentalcost.

Largestdirectcost

Directinfrastructurespend

Rapidreleasecycles

Storage

High-speed

dataaccess

AIworkloadsstreamterabytesusingnonvolatilememoryandparallelfilesystemstosustain

performanceandmanagecost.Legacystorage

inflatesper-tokencostsbyaddinglatencyasGPUswaitfordata.

Nonvolatilememory,parallelfilesystems,vectordatabases

Heavyinvestment

Networking

GPUInterconnects

(InfiniBand,NVLink,PCIeGen5)

TrainingacrossthousandsofGPUsrequiresultra-low-latencyinterconnectstocutidlecyclesandlowercostpertoken,whiletraditionalapproachesoftendrive

tokencostshigher.

Directspend

PowerandcoolingEnergyintensity

ofAIracks

Asinglenext-generationGPUrackcandrawbetween250–300kW,comparedwith10–15kWfornon-

AIservers.Whetherbilleddirectly(on-prem)or

embeddedincloudpricing,thispoweruseshowsupineverytokenconsumed.

Highopex(250–300kWracks)

Liquidcoolingrequirements

Facilities

Physical

infrastructure

requirements

Heavierracks(upto3,000lb,11nearly40%morethantraditional),mayneedreinforcedflooring

andadvancedcoolingtobeembeddedinthecostofeverytoken.

Directcapex(reinforcedfloors,racks)

Operationalcosts

Relatedtostaffingandoperations:

•ITopsandmanagement

•Softwareandlicensing

•Applicationdevelopmentandintegration

•Datamanagementandgovernance

•Inferenceandserving

•Securityandcompliance

•Usertrainingandchangemanagement

Fullmachinelearningoperations(MLOps)costs

Fullcenterofexcellence(COE)andupskilling

OrchestrationframeworksandMLOpstools(data,orchestration,security)

Directcompliancespend,etc.

Source:Deloitteanalysisbasedonprojectexperience

Thepivottotokenomics:NavigatingAI,snewspenddynamics

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Hostingmodels

HowtokensarepricedalsodependsonwhereandhowAImodelsarehosted.Thesamelargelanguagemodel(LLM)canbedeployedviaon-prem,colocation,hyperscalers,orAPIaccess,withradicallydifferenteconomics.Forapackagebuyer,thisdecisionisagaininvisibleandresideswiththevendor.FortheAPIconsumer,itcanvarybasedonwhichofthemanymodelsonthemarketisbeingconsumed,andthisexplainswhythesametaskmaycost

moredependingontheprovider.Forself-hostedAIinfrastructureusers,allhostingtypesarepossible,anditisoftenthemostimportantdeterminantofuniteconomics.

Thepivottotokenomics:NavigatingAI,snewspenddynamics

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Figure2.GPUconsumptionmodelsandcoststructure

ON-PREM

NEOCLOUDPROVIDERS

HYPERSCALER

APIACCESS

Capexvs.opex

Highcapex/lowopex

Pureopex

Pureopex

Pureopex

Unitcost

ofcompute

(GPU/hour)

Lowest

~$1$2

Medium

~$1–$4average,buthighvariability,ondemand

High

~$3$7,

region/modeldependent

Veryhigh

$0.40$100ormorepermillionoutputtokens

Scalability

Medium

Slowduetoprocurement,power,andsetup

High

Dynamicresourceprovisioning

Medium/high

Dynamicscalingwithnear-infinitetop-end

Veryhigh

100%managedbytheprovider

Latency

Lowest

Fullcontrolover

hardwarestack

Low

Purpose-builtforAI,

butphysicallayout

notcontrollable;with

neoclouds,lowphysicalproximityismanageable

Medium

Near-zerocontroloverphysicallayerand

workloadplacement

Medium/high

Nocontroloverproviderinfrastructure/network,withlong-distance

communication

Controlandcustomization

Full

Medium

Nocontroloverphysicallayerormaintenance;highcontroloverwhatshosted

Medium

Treatedidenticallytoneocloudproviders

Verylow

Nocontrolover

infrastructurelayerandlimitedcontroloverAImodeltuning,formatofresponse

Security

anddata

sovereignty

Highest

Completecontroloverdataencryption,transit,storage

High

Treatedidenticallytoco-lo;neocloudsofferhigher

dataencryption

Medium

Dataleakageriskandlowcontroloverexacthostinglocation

Low

Nocontrolover

providerarchitectureorgovernancepractices

Deploymenttime

Long

Multi-monthprocurement,delivery,andsetup

Instant

Instant

Instant

Maintenance

responsibility

Customer

Managedservicesandsharedresponsibilitymodel(e.g.,facilities,energy,etc.)

Shared

Physicalinfrastructure:provider;allotherlayers:customer

Shared

Physicalinfrastructure:provider;allotherlayers:customer

AImodelprovider

Bestusecases

Stable,high-

throughputworkloads

Elasticcompute,

proofsofconcept(POCs),cost-sensitive

workloads;neocloudsmaybringadded

functionalityfordata-sensitiveworkloads

Elasticcompute,POCs

Fastexperimentation,agents,retrieval-

augmentedstorage(RAG)

Source:Deloitteanalysisbasedonpublicandproprietaryestimations,includingpubliclyavailableGPUpricingdata,APIpricingbenchmarks,and

hyperscalercostcalculatorreferences.IndicativereferencesincludepublicGPUcostanalysisandtotal-cost-of-ownershipmodels(e.g.,semi-analysisAITCOframework);publicAPIpricingbenchmarksforGenerativeAImodels(e.g.,representativeGPT-5familyrates);hyperscalercomputepricing

estimatesderivedfromstandardcloudcostcalculators

Thepivottotokenomics:NavigatingAI,snewspenddynamics

Ultimately,thecoststructurefollowsthearchitecture.Compute

density,networkproximity,andstoragethroughputeachinfluencehowefficientlytokensareprocessed—andtherefore,wherea

modelshouldlive.Thedecisionisn’taboutspeedorpreference;it’saboutmatchingworkloadphysicstobusinesseconomics.Inourexperience,we’vefoundhybridarchitecturessustainperformancewithoutinflatingtokencosts.

AImodelselection

AImodelstrategyisaseconddecisionpoint:open-sourceor

closedAImodels(proprietary).Packagebuyersinheritwhateverthevendorbuilds.APIuserscanchooseprovidersbutnotthemodels’economics.Onlyself-hostedAIfactoryuserscontrolthefulltrade-offacrosscost,flexibility,andsovereignty.12

Open-sourceAImodels

Open-sourcemodelsaregenerallyfreeandtypicallyrunin

self-hostedenvironments,givingenterprisesgreatercontrol,

customization,anddatasovereignty.Theyarewellsuitedforfine-tuningonproprietaryorsensitivedata,minimizingvendorlock-in,andloweringtokencostsovertime.

ExamplesincludeMetaLlama,Mistral,andothers.Emerging

frameworkssuchasNVIDIANIMMicroservicesillustratehow

vendorsarepackagingopen-sourcemodelsintostandardized,

securedeploymentunits—bringingoperationaldisciplinetowhatwasoncebespokeintegrationwork.

Proprietary(closed)AImodels

Theseareconsume-as-you-go,typicallybilledpertokenandallowuserstoquicklyhitthegroundwithnoup-frontinvestment,arepretrained,havestrongout-of-the-boxfunctionality,andenableaccesstovendorsupportforoperationalsupport.Examplesof

suchAImodelsincludeAnthropicClaude,GoogleGemini,OpenAIGPTs,xAIGrok,andothers.However,thistypicallycomeswith

higherper-tokencost,lowercostpredictabilityduetofluctuatingtokenusage,lackofcustomization,openconcernarounddata

storage,andriskofvendorlock-in.

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Thepivottotokenomics:NavigatingAI,snewspenddynamics

DecodingtheAIcostcurve

AIeconomicsfollowJevons’paradox:Asefficiencyimproves,totalconsumptionrises.13Tokenpricesarefallingfast—whatoncecostdollarsperthousandnowcostspenniespermillion—andDeloitteprojectstheaverageinferencecostwilldropfrom$0.04permilliontokensin2025toabout$0.01by2030.14

Yetenterprisespendingcontinuestosurge.15Asagenticsystemsandmultiagentworkflowsproliferate,tokendemandgrowsexponentially—oftenfasterthaninfrastructureefficiencygainscanoffset.Theparadoxisn’tthatAIisbecomingcheaper;it’sthatefficiencyitselfisdrivingexpansion.Withoutdisciplinedcostgovernance,totalcostsgrow.

Whopaysthebill?

Thecostcurvedoesn’taffecteveryparticipantthesameway.Astokenconsumption

accelerates,thequestionbecomeswhoultimatelyabsorbsthatspend—theenterprise,thevendor,ortheenduser—andhowthosedynamicsevolveasworkloadsscaleandgrowmorecomplex.Deloitte,sTCOanalysisexaminesexactlywhereandwhenthosecostsshift.

12

Thepivottotokenomics:NavigatingAI,snewspenddynamics

ThetokenTCOestimationandscenarioanalysis

Toquantifythesedynamics,DeloitteconductedadetailedtokenTCOanalysisdesignedtocapturehowAI’sunderlyingeconomicsshiftacrossthefulltechstack.Theanalysistested

howtotalcostofownershipevolvesalongthreecriticaldimensionsthatshapetokenpricing:

1.Technologystack:TheGPUs,AImodels,andarchitecturespoweringAIworkloads.

2.Hostingapproach:Comparisonsasusageandcomplexityscaleovertime.

3.Usagescaling:Increaseintheoveralltokenconsumptiondrivenbyincreaseinusercountorthecomplexity/depthofreasoningeachusecasedemands.

TheobjectivewastounderstandhowthesefactorsinteracttoredefineorganizationalstrategybasedonwhatthekeydriversofAITCOare,howcostsevolveasusage

scales,andwheretheinflectionpointsemergeincostpertoken.Beforepresentingtheoutcomes,thenextsectionoutlinesthekeyassumptionsandconfigurations

underpinningthemodelusedinourtests.

13

Thepivottotokenomics:NavigatingAI,snewspenddynamics

Modelassumptions

Themodelwasbuilttotestrealistic,enterprise-scaleconditionsratherthanidealized

labsettings.16Whileitcanaccommodateawiderangeofconfigurations,theversion

summarizedherereflectsarepresentativescenarioacrosscommonenterpriseworkloads.

Thebaselineconfigurationincluded:

•Computestack:NVIDIAHGXB200GPUServer(NVLink/NVSwitchEnabled)|CPU–AMDEPYC9654.

•LLM:Llama3.370BFP8TP2,GPT-4oselectedbecauseavarietyofcommonconfigurationswerebeingtested.

•Hostingmodels:On-prem,APIaccess,specializedneocloudproviders

(NCPs).NCPsofferhourlyratesaswellasreservedcontractingfordifferentperiods.Inthismodel,weassumedhourlyandnotreservedpricing.

ThissetupenabledDeloittetoisolatehowhostingchoices,AImodelselection,andusage

maturityinteracttodrivetokenconsumptionandtotalcost.Thefollowinganalysishighlightstheresultingcostcurvesandinflectionpointsthatemergeasusagescales.Theanalysis

simulatesgrowthscalinginincrementsof8GPUs(figure3).

Figure3.Scenariocomplexityandtokenassumptionsdrivingfour-yearTCOdynamics

TOKENSCENARIOS

EXAMPLESCENARIODESCRIPTION/USECASE

YEAR1

Pilotstage

InitialdeploymentofsimpleusecasessuchaschatbotorFAQassistant:AlightweightconversationalAIusedforcustomerservice,HRinquiries,orbasicIThelpdesksupport.Handlesshort,structuredQ&Awithminimalcontextretention.

YEAR2

POC/lightweightadoption

Scalingtoincludeknowledge-drivenusecasessuchasdocumentsummarizationand

knowledgesearch:Internalenterpriseassistantthatretrievesandsummarizespolicydocuments,proposals,orcontracts.Includessemanticsearchandmultiturnconversations.

YEAR3

Inferencingatscale

Maturingtodrivedecision-supportusecasessuchasananalyticsco-pilot:Assistsconsultants,analysts,orauditorsingeneratinginsights,draftingreports,orperformingdataanalysisacross

multipledatasources.Includesreasoning,structuredoutput,andintegrationwithenterprisesystems.

Source:Deloitteanalysis

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Thepivottotokenomics:NavigatingAI,snewspenddynamics

Navigatingtheeconomicsofanacceleratingtechnologyenvironment

TherapidpaceofAIhardwareadvancementhas

createdobsolescencecyclesthatfaroutpacetraditionaldepreciationschedules,withGPUgenerationsnow

refreshingrapidly.Forexample,recentmodelreleases

quicklyoutgrewthecapabilitiesofpreviouslyleading

GPUstounlockfeatures,whilelegacysupportforolder

hardwarediminishes.NewerGPUsthatswitchtoanannualreleasecyclefurtheracceleratestheserefreshdemands,challengingenterprisestocontinuallybalancethebenefitsoffasterupgradeswiththeriskoffallingbehind.

SuchrecentadvancesinGPUtechnologyhaveenabledAIapplicationsrequiringlargercontextlengths,suchasreasoningmodels,summarizingextensivetextcorpora,

andhigh-fidelitymultimodaltaskslikeanalyzinghour-longvideos.Theseusecases,includingagenticreasoning,

demandsubstantialGPUmemoryandthelatesthardwaretoaccuratelyprocesssuchcomplexorlarge-scaledata.

However,adoptionofmultimodality,andagenticreasoningattheenterpriselevelisinitsearlystages,andinferencetasksoftenrunwellonolderGPUsespeciallyformidsizemodels.

AstokenpricingforAImodelsdeclinesandtheeconomicsof“buildvs.buy”shiftrapidly,enterprisescannotrelyonstaticassumptionsandshoulddevelopforward-looking

infrastructurestrategies—carefullyplanningupgrades,assessingcosts,andensuringinvestmentsremainviableasthemarketstabilizesovertime.

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Thepivottotokenomics:NavigatingAI,snewspenddynamics

16

Analysisoutcome

TheTCOsimulationincorporatedreal-worldparametersacrossthefullAIvaluechain—fromhardwareutilizationandenergycoststofacilitiesexpenses.Eachvariablewascalibratedtoreflectcurrentmarketconditionsandoperationalrealitiesratherthantheoreticalefficiency.

Thisapproachensuredaholisticviewofcostbehavior:howGPUutilizationrates,power

efficiency,andAImodelcomplexitycombinetoshapeeffectivecostpertoken.TheresultinganalysissurfacedtheunderlyingmechanicsofanewAIeconomy—onewheretechnicaldecisionsdirectlydictatefinancialoutcomes.

1.Usagescalingandcomplexitydriveshostingadvantage.

InourTCOmodeling,thefirstyearat10billiontokens,workloadsfavortheAPIaccess

approach—pay-as-you-goapproachesminimizeidlecapacitycosts.Asthenumberof

tokensrisesinyeartwo,theeconomicsflip.Athigherreasoningloadsmoretokensareconsumed,andself-hostedAIfactoriesoutperformAPIsasfixedinfrastructurecostsareabsorbedandutilizationincreases.Afterfouryears,thesimulationprojectedcumulativeTCOistwicethecostforAPIhostingasitwouldbeforanAIfactory,giventhesame

configurationandtokenscaling(figure4).

Figure4.Over3years,anAIfactoryis~2.1xmorecost-effectivethanAPI-basedsolutions

AIfactoryaverages~150%annualTCOgrowthvs.>1,000%(API)and>800%(NCP),ensuringmorestable,predictable,andmanageablecosts

AIfactorysees>90%dropin$/BtokensfromY1toY3

($24Kto$1.45K)vs.64%(API)and84%(NCP),becomingmostcost-efficientathighscale

ANNUALTOTALCOSTOFOWNERSHIP

AnnualTCO(USDinmillions)

4.0M

3.5M

3.0M

2.5M

2.0M

1.5M

1.0M

0.5M

0.0M

3.50M

Overa3-yearTCO,AIfactoryon-prem

2.72M

deliversmorethan50%costsavings

comparedtobothAPI-basedandNCPsolutions

1.45M

0.97M1.06M

0.49M

0.24M0.17M

Year1

Year2

Year3

10billiontokens

300billiontokens1

s1,000billiontokens(1trillion)

0.04M

AIfatloud(NCP)API

Source:Deloittesimulation

Pay-as-you-goAPIsandNCParemoresuitedtosimple,low-volumeworkloads,whileAIfactory(self-hosted)isco