AI对广域网的影响：思科2026年报告

AIImpactonWide

AreaNetworks

CiscoReport2026

©2026Ciscoand/oritsaffiliates.Allrightsreserved.

AIImpactonWideAreaNetworks:Cisco2026report

Contents

Executivesummary......................................................................................................4

KeyFindings..................................................................................................................4

Takeaways......................................................................................................................7

Introduction...................................................................................................................8

Dataanalysisandfindings........................................................................................9

Datavolumegrowth..................................................................................................................9

Dataflowlength........................................................................................................................11

Dataflowrate...........................................................................................................................12

Trafficasymmetry....................................................................................................................13

Networklatencyimpact.........................................................................................................14

AgenticAItrafficpatterns......................................................................................................15

Lookingahead............................................................................................................18

EnterpriseusageofagenticAIandnetworkimpact:2026-2035................................18

ConsumerUsageofAIandAgenticAIandnetworkimpact:2026-2035.................21

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Aboutthereport

TheAIImpactonWideAreaNetworksreportrepresentsthekeyfindingsandtakeawaysfromaresearch

initiativeledbyCiscoSystems,Inc.Thereportcombinesreal-worldtrafficanalysis(usingtheassurancetool

CiscoCrossworkAssuranceUserExperience),third-partyindustrydata,andCiscocontrolledlabtestsofAI

agents.ItexploreshowAI-driventrafficdiffersfromtraditionalnon-AIwebtraffic,withafocusontheimpact

ofagenticAItrafficandtheimplicationsfornetworkdesign,capacityplanning,andassurance.

Whatsetsthereportapartisit’sbasedonreal-worldtrafficdataincludinganearlylensonagenticAItraffic

(currentlysmallbutgrowingfast)thatletsusstarttoseeandmeasureanewclassofAInetworktrafficand

understandtheimplications.Thereportincludes:

•DirectmeasurementofliveAIinferencetrafficacrossserviceprovidernetworks

•EmpiricaltestsofAItrafficcharacteristicstotrainmodelsthatidentifyandtrackAIflowswithprecision

•ArepeatablemeasurementframeworkandbaselinetotrackAItrafficevolutionandforecastingonan

annualbasis

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Executivesummary

Therapidadoptionofartificialintelligence(AI)—particularly

AIinferenceandagenticAIsystems—isbeginning

toreshapenetworktrafficpatternsinmeasurableand

structurallyimportantways.By2027,80%ofexecutives

believetheircompany’scompetitivesurvivalwilldependon

agenticAI,1andconsumerusageofAIisalreadyprevalent

andaccelerating.Thisisdrivingafundamentalshiftinhow

trafficisgenerated,distributed,andexperienced,with

implicationsforserviceprovidersandenterprisesthat

managelarge-scalenetworks.

KeyFindings

Thefutureofconnectivityisnolongerjustaboutfasterdownloads—it’saboutthevital

“spinalcord”ofintelligencebetweenagentsandAImodels—acriticaldependencythat’s

forgingnewpathsofcommunicationsthatimpactshowwemanagenetworks.While

theindustryhasspentdecadesoptimizingthenetworkforhuman-paced,burstyvideo

streams,theriseofagenticAIischangingnetworktrafficprofilesandbehavior.By2035,

one-quarterofnetworktrafficisprojectedtobeAIinference(Source:Ciscomodel).These

flowsdon’tbehaveliketheweb.Theylivelonger,demandmoreupstreamcapacity,and

operateatsoftwarespeed,nothumanspeed.TheconnectivitybetweenagentlogicandAI

modelseffectivelybecomestheagent’s“spinalcord”—acriticaldependencywherebyany

networkdegradationdirectlyimpairsagentfunctionality.

1CiscoreportTheRacetoAgenticAI:WhyInfrastructurewillmakeorbreakworkforceintegration

/c/dam/en/us/solutions/collateral/artificial-intelligence/race-to-agentic-ai-report.pdf

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AIinferencetrafficissmalltoday—butisgrowingextremely

fast.

WhileAIinferencetrafficremainsnegligiblecomparedtodominant

categorieslikevideostreaming,observedgrowthratesareexceptional.

Token-consumptiondatashowsnearly10xyear-over-yeargrowth,

Tokenconsumptiondata

whileinsomeserviceprovidermeasurementsweareseeingaround4x

10xgrowthinjusteightmonths.SustainedgrowthattheseratesmeansAI

year-over-yeargrowthtrafficwillbecomeameaningfulcomponentofoverallnetworktraffic

by2035.

AIinferencetrafficflowsbehavedifferentlyatthetransport

AIinferenceflows

levelvs.typicalwebtransactions.

2x

•AIinferenceflowslastapproximately2xlonger,mainlyduetotoken-

longer

by-tokencontentgeneration.

Flowrateforwebvs.AItraffic•Medianflowratesofregularwebtransactionsare10xlarger

10xthanAIinferencetrafficflowswhichhavesmoothersustained

largerthroughput,ratherthanburstydelivery.

Thesecharacteristicswillrequireflow-awarenetworkandsecurity

systems,includingfirewalls,intrusiondetection,andDeepPacket

Inspection(DPI),tohandlegreaterscale,distribution,andstate

efficiencyovertime.

Trafficsymmetrystatusquoischanging.

AIinference—andespeciallyagenticAI—introducessignificantlymore

9%upstreamtrafficduetolarge,context-richprompts.

upstream-heavy•Approximately9%ofAIinferenceflowscarrymoreupstreamthan

AIinferenceflowsdownstreamtraffic,versusabout0.5%fortypicalwebtraffic.

Thistrendisexpectedtointensifyasagentsmaintainandtransmit

growingcontextualstate,withdirectimplicationsforaccessplanning

andupstreamcapacitymodeling.

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Networklatencyisnotthedominantbottleneck—yet.

End-to-endAIinferencelatencyisdrivenprimarilybymodelprocessing,

typicallyrangingfromhundredsofmillisecondstoseveralseconds.

Networklatencyiscurrentlysmallenoughanddoesnotyetjustify

large-scalearchitecturalshiftstowardedgeinferencepurelyforlatency

reasons.Inferencehardwareimprovementswillleadtonetworklatency

#s~5secondsforAIinference

becomingmorerelevant,andwearealreadyseeingproofpointsofthat.

latencyvs.~20-50milliseconds

MeasuringandassuringtheAIinferenceexperienceisbecomingcritical

fornetworklatency

forperceivedservicequality.

AgenticAIfundamentallyamplifiesnetworkusage.

AIagents

AIagentsactasnetwork“powerusers,”operatingatsoftwarespeed

450%ratherthanhumanspeed.Empiricaltestingshows:

moretotaltraffic

•Upto450%moretotaltrafficisgeneratedpertaskwhenperformedby

anagent.

70%

•Approximately70%ofthattrafficisAIinference,makinginference

ofthattrafficisAIinference

pathsmission-critical.

•TheconnectivitybetweenagentlogicandAImodelseffectively

becomestheagent’s“spinalcord”—acriticaldependencywherebyany

networkdegradationdirectlyimpairsagentfunctionality.

AItrafficimpactby2035*

Enterprisenetworks:

WithoutagenticAI,enterprisetrafficisprojectedtogrowbyabout2.5x

overthenextdecade.WithagenticAIadoption,trafficgrowthcould

increasebyabout9x,drivenbyautonomoustaskexecutionandinference-

AIandagenticAIheavyworkflows.

63%Consumernetworks:

additionalgrowthConsumerAIadoptionisacceleratingandwillreachnear-universalusage

networktrafficbythemid-2030s.AIandagenticAIareprojectedtoincreaseconsumer-

drivennetworktrafficbyabout6.6x,representingabout63%additional

growthcomparedtonon-AIscenarios—makingthisthedominantdriverof

overallinternettrafficexpansion.AIinferencetrafficisprojectedtobe25%

oftotalnetworktrafficby2035.

*Source:Ciscomodel

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Takeaways

•AIandagenticAIwillnotjustincreasetrafficvolume—theywillchangetrafficshape,symmetry,duration,

andcriticality.

•AIinferencepathswillbecomestrategicnetworkassets,requiringhighlevelsofresilience,observability,

anddifferentiatedtreatment,forexample,QualityofService(QoS)andpathsecurity.

•Traditionalplanningassumptions(burstiness,downlinkdominance,human-pacedinteractions)willneedto

adapttothenewrealityofAIandagenticAItrafficthatlastslonger,demandsmoreupstreamcapacity,

andoperatesatsoftwarespeed.

•ContinuousmeasurementofAI-specificKeyPerformanceIndicators(KPIs)andadaptivenetwork

architectureswillbeessentialtosustainexpectationsforperformanceanduserexperience.

ThisreportmarksthefirststepinongoingresearchontheimpactofAIonnetworktraffic.AsAIadoption

deepensacrossenterprisesandconsumers,futureresearchworkwillrefineprojections,validateemerging

patterns,andguideoperatorsinevolvingtheirnetworksforanAI-drivenworld.

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Introduction

□

AdoptionofAIandAI-basedapplicationssuchasAIagentsisgrowingataveryfastpace.In

aCiscoreportwithOmdia,80%ofexecutivesviewagenticAIascriticaltocompanysurvival

by20272.Otherindustryreportsconfirmsimilargrowthprojectionsforbothenterprisesand

consumerAIapplications.Inthiscontext,serviceprovidersandorganizationsrunninglarge-

scalenetworksneedtoevaluatehowtheproliferationofAIisgoingtoimpactnetworksin

termsoftrafficgrowth,patterns,orothercharacteristics.

Thisreportisthefirstinanannualserieswherewewillsharekeyfindingsandimplicationsto

helpnetworkleadersmakedecisionsontheevolutionoftheirnetworkinfrastructure.Cisco

hasinitiatedresearchonAItrafficpatternsandhowtheydifferfromnon-AIweb-based

transactions,withaspecificlensonagenticAItrafficandapplications.Thedatapresented

inthereportisbasedonrealtrafficdataanalysisfromCiscoCrossworkAssuranceUser

Experience,Ciscoempiricaltests,andthird-partyreports.

2CiscoreportTheRacetoAgenticAI:WhyInfrastructureWillMakeorBreakWorkforceTransformation.

/c/dam/en/us/solutions/collateral/artificial-intelligence/race-to-agentic-ai-report.pdf

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Dataanalysisandfindings

Datavolumegrowth

Thefirstquestionsnetworkoperatorsaretryingtoanswerare“HowmuchAItrafficwillexist?”and“Howwillthis

trafficimpactournetwork?”

AItraffic,inthiscase,referstoAIinferencetrafficwhichflowsacrosstheWANbetweenthedevicerequesting

modelinferenceandthedatacenterorlocationwherethemodelishostedandtheinferencehappens.Insidedata

centers,terabytesoftrafficflowsarehappeningduringAImodeltraining,butthattrafficisnotcrossingtheWAN,

exceptforlargedatatransfersforAItrainingpurposes.

TheactualvolumeofAIinferencetrafficisstillnegligiblecomparedwithothertypesoftraffic,suchasvideo

streaming,andwilllikelyremainverylowinrelativetermsinthenearterm.However,thereareverysignificant

growthrates:

•Tokenconsumptiongrowthof10xyear-over-yearinOpenRouterAIfromDecember2024toDecember2025.)3

LLMLeaderboard

TokenusageacrossmodelsonOpenRouter

8T

6T

4T

2T

Jan13,2025Feb17Mar24Apr28Jun2Jul7Aug11Sep15Oct20Nov24Dec29

Figure1.OpenRoutertokenconsumptionCY2026

Source:Openrouter.ai

3OpenRouter,LLMLeaderboard.https://openrouter.ai/rankings#leaderboard

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TrafficanalysiswithdatafromtwoserviceprovidersshowsconsistenttrafficgrowthforAIinferenceoverasimilar

period:4xtrafficgrowthoveraperiodofeightmonthsoftrafficmonitoring.

TotaltrafficvolumeLLMtraffic

1e7

8

total_volume

Trend

7

6

5

4

3

2

2025-042025-052025-062025-072025-082025-092025-102025-112025-12

Timestamp

Figure2.AIinferencetrafficvolumeevolution

Source:Ciscodata

AIinferenceservicesareusingbothTransmissionControlProtocol(TCP)-andQuickUDPInternetConnections

(QUIC)-basedtransport.Thesplitintermsofnumberofflowsisalmost50%,whileintermsofdatavolumeQUIC

represents57%.

Implications:WhileAIinferencetrafficisstilllowinabsoluteterms,sustainedhighgrowthrateswillresultinto

arelevantportionofalltrafficovertime.Clearly,theadoptionofAIbyapplicationsandusersisdrivingfast

growth.TheadoptionofQUICastransportforAIinferencepresentsencryptionchallengesforDPIsystemsto

detecttrafficattheapplicationlayer.

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Dataflowlength

AIinferenceflowspresentdifferentcharacteristicscomparedtonon-AIwebtraffic.Whilenotdramatic,these

differencesmayimpactcapacityplanning.AnalysisofdataflowlengthshowsdifferencesbetweenAIinference

flowsversusregularwebtransactions.

Statistically,AIinferenceflowslast2xlongerthanregularwebtransactions.

NormalizedHistogramofflowduration(ms)

0.20

LLMflow

non-LLMflow

Median-LLMflow:1292.00

Mediannon-LLMflow:643.00

0.15

0.10

0.05

0.00

05000100001500020000

Flowduration(ms)

Figure3.FlowdurationdistributionofAIinferenceflowsvs.non-AIwebtransactions

Source:Ciscodata

ThemaindriveristhewayAIinferencetrafficgeneratescontent,onetokenatatime,resultinginlongerandlower

rateflowscomparedtootherflows.

Implications:For“flow-aware”networksystemsthatmustkeepstateforflowsintables,theproliferationof

AIinferenceflowsthatlastlongermeansgrowingflowtableswillneedtobeeffectivelyplanned.Overtime,

securityandflow-awarenetworksystemsarelikelytobecomemoredistributedtocopewithforwardingstate

growth,similartotheapproachusedbyCiscoHypershield™.

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Dataflowrate

Relateddirectlytotheflowlength,thedataflowrateforAIinferenceflowsshowsadifferentpatterncomparedto

regularwebtransactions.Medianflowrateis10xlargerforregularwebtransactionscomparedtoAIInference

flows.

NormalizedHistogramofflowrate(Kbps)

0.25

0.20

LLMflow

non-LLMflow

MedianLLMflow:338.00

Mediannon-LLMflow:3803.00

0.15

0.10

0.05

0.00

050001000015000200002500030000

Flowrate(Kbps)

Figure4.FlowratedistributionofAIinferenceflowsvs.non-AIwebtransactions

Source:Ciscodata

ThemainreasonforthisdifferenceistheprocessthatgeneratesthedatainAIinference.Adataflowthatoccurs

onetokenatatimeislikehavinga“shaper”appliedtothedataflow.Regularwebtrafficflowspeakmuchhigher,

asthecontentcanberetrievedfromwhereveritisstoredanddeliveredtotheuser.

Implications:Differentmedianaverage-trafficandpeak-to-averageratesmayrequiredifferentQoSsettingsin

thenetworktomanageAIinferencetrafficversusnon-AIinferencewebtransactions.

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Trafficasymmetry

PotentialchangesintrafficasymmetryareexpectedwithAIinferencetraffic.Networktrafficasymmetryvaries

dependingonaccesstype(mobilevs.wirelinenetworks)andservices.Mobilenetworktraffictendstobemore

upstreamheavyduetosocialnetworking,withmorecontentsentupstreamcomparedtowirelinenetworks.

AnalysisofAIinferenceflowsversusnon-AIwebtransactionsshowscleardifferencesintrafficsymmetry.As

Figure5shows,inAIinferencetraffic,9%offlowshavemoreupstreamtrafficthandownstreamtraffic

comparedtootherHTTPtransactions,wherethisoccursinonlyabout0.5%oftheflows.

NormalizedHistogramofFlowsymmetry(downstream/upstreamratio)

0.6

LLMflow

0.5non-LLMflow

MedianLLMflow:21.11

Mediannon-LLMflow:145.39

0.4

0.3

0.2

0.1

0.0

02505007501000125015001750

Flowsymmetry(downstream/upstreamration)

Figure5.TrafficflowsymmetryofAIinferenceflowsvs.non-AIwebtransactions

Source:Ciscodata

Weforeseethistrendfurtherconsolidatingandgrowing.ThereasonforthisdifferenceisthefactthatAI

inferencerequestsoftencaptureagrowingamountofcontextinthepromptinordertoproducearelevant

response.AsadoptionofagenticAIgrows,moresignificantchangestonetworktrafficsymmetrypatternscanbe

expected.Agentswillcapturecontinuouscontextastasksareexecuted,andthisstateneedstobeinsertedinto

theprompts.

Implications:Particularlyforradiocapacityplanning,trafficsymmetryassumptionsarearelevantfactor.As

adoptionofAIinferenceandagenticAIgrows,itwillbeimportanttotracktheevolutionoftrafficsymmetry,as

itwillcontinuedecreasingovertime.

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Networklatencyimpact

LargeLanguageModel(LLM)inferencerequeststendtohavesignificantlyhigherlatencythantypicalweb

APIcalls,andtheresponsetimescanbemorevariable.TraditionalwebapplicationRESTAPIsoftenstrivefor

subsecondorevensub-100-millisecond(ms)responsetimes.Forinstance,agoodAPIresponsetimeistypically

below100msinmanywebscenarios.Bycontrast,evenshortLLMqueriesincurresponsetimesofhundreds

ofmillisecondsjusttobeginproducinganoutput,andfullresponsesoftentakeseconds.LLMlatencycanbe

modeledasfollows:

Latency=TimeToFirstToken(TTFT)+TimePerOutputToken(TPOT)×Numberofoutputtokens

TTFTistheinitialoverheadtoprocesstheprompt,andTPOTisthegenerationtimepertoken.Thismeanslatency

growswithoutputlength.Forexample,OpenAIGPT-5.24mighttakeontheorderof9mspertoken,andAnthropic

ClaudeOpus4.5about20mspertoken.A100-tokenGPT-5.2answercouldtakeabout1second,anda

maximum-length600-tokenanswercouldapproach5.4seconds.ThereiswidevariabilityamongLLMs,model

providers,implementations,andthehardwareusedforinference.

Implications:AIinferencelatencyvarieswidelyfromafewhundredmillisecondstomultipleseconds.Network

latencyisstillatthispointaverysmallcomponentofthetotalend-to-endinferencelatency.Inference

hardwareis,however,evolvingandbecomingmuchfaster.Asinferenceaccelerates,thenetworklatency

componentplaysamorerelevantroleontheend-to-endperceivedexperience.Networklatencywillbecomea

keyfactorforinferencedistribution,combinedwithotherslikescale,datasovereignty,andsecurity.Additionally

ServiceProviderswillneedtomonitortheactual