2026年人工智能现状报告构建者经济半年刊

JULY2026

2026StateofAI

TheBuilder’sEconomy

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Copyright©2026ICONIQCapital,LLC.AllRightsReserved

ForProfessionalClientsOnly.ICONIQPartners(UK)LLP(973080)isanappointedrepresentativeofKrollSecuritiesLtd(466458)whichisauthorizedandregulatedbytheFinancialConductAuthority

Introduction

BuildingandoperationalizingAIproductshasmovedfromcompetitivedifferentiationtotablestakes.InJanuary2026,we

published“TheStateofAI:Bi-AnnualSnapshot”toelevatethevoicesofarchitects,engineers,andproductleadersdrivingthedisciplinedexecutionofdeliveringandscalingAIproducts.

WhatbeganasaracetoshipAIfeatureshasmaturedintoamoreconsequentialchallenge:turningAIcapabilitiesintodurable,

economicallysoundbusinesses.Manyofbuilderswhoarepullingaheadhavemadedeliberatetradeoffsacrossthefullstack–frommodelselectionanddatainfrastructuretoorgdesign,pricingarchitecture,andinternaldeployment–andaremanagingthecost,trust,andgo-to-marketimplicationsthatcomewitheach.

ThisreportisICONIQ'sthirdbi-annualreportoftheAIbuilderlandscape,groundedinourproprietaryQ42025andQ22026

surveysofexecutivesatsoftwarecompaniesbuildingAIproducts,alongsideperspectivesfromourICONIQCommunity.Across

bothwaves,oneshiftstandsout:themarkethasmovedfromprovingAIworkstoprovingAIpays.AIproductsareapproachinghalfofrevenueatthecompanieswesurveyed,marginsareexpanding,andtheoperatorspullingaheadaretheonestreatingpricing,

cost,andorgdesignasproductdecisionsratherthanafterthoughts.

ExploreOurAIPerspectives

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TableofContents

Section

TopicsCovered

Page

AIModels&Infrastructure

•

•

•

•

AIProductLandscape

ModelsPoweringAIProductsandProviders

Performance,Quality,andSecurityforAIModels

DataReadiness

6

-

14

AIGo-to-Market&

•

•

GTMMotions&PricingModels

RevenueandCostsofAIProducts

15

-

21

Economics

•

GrossMarginOptimization

Talent&Organization

•

•

•

OrganizationalStructureofAICompanies

HowAIisImpactingOrgStructures

Spotlight:ForwardDeployedEngineering

22

-

31

AIforInternalProductivity

•

•

•

SpendonAITooling

AdoptionandImpactofAIToolsandAgents

CaseStudies:HowFunctionalTeamsAreImplementingAI

32

-

43

3

Data

Sources

&Methodology1

ThisstudysummarizesdatafromaQ22026surveyof~305executives2atsoftware

companiesbuildingAIproducts,includingCEOs,HeadsofEngineering,HeadsofAI,andHeadsofProduct.

Throughoutthisreport,wecompare

insightstoourpriorStateofAIreport,

publishedinQ420253,“

2026StateofAI:Bi

-

AnnualSnapshot

”,whereapplicable.

Wherenecessary,longitudinaldatahas

beennormalizedtoaccountfordifferencesinfirmographicstoensuretrendsare

representativeofthedata.

Wealsoweaveininsightsandwhatwe

believetobebestpracticesfromAIleadersfromtheICONIQcommunity.

Allindustryperspectivessharedinthisreporthavebeenanonymizedtoprotectcompany-levelinformation.

ByHeadquarters

%ofRespondents

87%

13%

NorthAmericaEurope

RespondentFirmographics

ByARRorRevenueRange

%ofRespondents

10%10%10%10%10%

9%

9%

9%

7%7%

7%

4%

Inthisreport,selectcompaniesarereferredtoas“highgrowthcompanies”becausetheymeetthefollowingcriteria:

100%+YoYrevenuegrowthif<$25MRevenue;

50%+YoYrevenuegrowthif$25M-250MRevenue;

30%+YoYrevenuegrowthif$250M+Revenue

HighGrowthCompanies

%ofrespondents

19%

RevenueRange

%ofHigh-GrowthRespondents

48%

24%

28%

<$100M

$100-$500M

$500M+

1–Thisdatawascollectedanonymouslybyanexternalsurvey.SurveyresponsesincludesomebutnotallICONIQVentureandGrowthportfoliocompaniesaswellascompaniesnotpartofICONIQVentureandGrowth,sportfolio.

2–Certainquestionsinthesurveywereoptionalorroutedbasedonpersona.Accordingly,somen-Sizenumbersinthispresentationarelessthan305.

3–TheQ42025reportsummarizesdatafromaDecember2025surveyof~300executivesatsoftwarecompanies,includingCEOs,HeadsofEngineering,HeadsofAI,HeadsofProduct,ChiefRevenueOfficers,andChiefFinancialOfficers.

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ExecutiveSummary

FourshiftsweseedefiningtheAImarkettoday

Buildershaveconvergedontheapplicationlayerandarebettingonagenticdepth

01

Nearlytwo-thirdsofbuildersarebuildinghorizontalor

verticalapps(verticalaloneat43%ofcompaniesanalyzed)andthey,removingintohigh-complexitydomains,with

financialservicesandhealthcareusecasesrisinginpopularity.Agenticcapabilitiesandagent

reliability/orchestrationarethetop2customer-facingprioritiesforthecomingyear.

ratherthananexperiment

03AIisnowmoreofarevenueandmarginstory

AIproductsgrewfrom32%ofrevenue(2025actuals)toa

projected42%thisyear,withgrossmarginsmovingfrom

45%to53%andontrackfor~59%by2027.Partofrising

marginsisdrivingbypricingmodelchanges:consumption-andoutcome-basedmodelsarerising(42%and23%,withcompaniesnowblending1.7modelsonaveragevs.1.5in

Q4,25),asbuildersalignpricetothecostandvalueofusage.

02

Multi-modelappearstobethedefaultwiththeprovidermixreshuffled

TopowerAIproducts,mostbuildersnowrun~3.3providersonaverage(upfrom3.1inQ4,25).Amongsurvey

respondents,Anthropicmovedtothetopspot(51%Q4‘25→81%inQ2‘26)asthefieldhasbroadened.Selection

criteriaheldsteady(reliability,thencost),but

security/privacyandSOC2/SLAsbothclimbed,signalingenterprise-readinesspressureasproductsmature.

product

04AIisreshapingtheorgchart,notjustthe

AI-drivencompanies1runflatterandmorecross-functionalthantheirpeers.45%ofcompaniesareplanningadifferentmixofrolesand33%areactivelyplanningasmallerteam.Whilecompaniescontinuetoseegainsoninternal

productivity,itisdifficulttopredictinternalAI,struecost,withoverrunsemergingfromtokens,datainfrastructure,andenablement

Source:PerspectivesfromtheICONIQGenAISurveys(December2025&May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork

1–Companieswith50%+revenuedrivenbyAIproducts

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AIModels&

Infrastructure

Private&StrictlyConfidential

AIModels&Infrastructure

Buildershaveconvergedontheapplicationlayer,withnearlytwo-thirdsofthemarketfocusingonhorizontalandverticalAIapplications.Withinthis,dataanalyticsandproductivitytoolsarethemosttargetedusecases

WhatistheprimaryAIproductyouarebuilding?%ofRespondents,Single-Select,N=304

★VerticalAIapplications

Highlyspecializedapplicationsdesignedforaspecificindustryorfunction

20%

★HorizontalAIapplications

Enterpriseapplicationsthatcanbeusedacrossmultipleindustriesorfunctions

11%

ConsumerAIapplications

AIproductsprimarilytargetedatindividualconsumers

10%

CoreAImodels/technologies

FoundationalAImodelsorspecializedMLalgorithms

10%

AIplatforms/infrastructure

Toolsthatfacilitatedeployment,hosting,monitoring,andscalingofAIinproduction

6%

AIdevelopmenttooling

Toolsthatsupportthebuilding,evaluation,anditerationofAIproducts

43%

★WhichusecasedoesyourprimaryAIproducttarget?

%ofRespondents,Horizontal&VerticalAIBuildersOnly,Single-Select,N=192

Q2,26

UseCase

Q4,25

Q2,26

UseCase

Q4,25

Rank

Rank

Rank

Rank

#1

DataAnalyticsAndBusinessIntelligence

1

#7

IT&Security

v4

#2

Productivity&Collaboration

2

#8

HR&RecruitingTools

10

#3

FinancialServices

8

#9

Legal&Compliance

9

#4

Marketing&Sales

5

#10

Product&Design

7

#5

Healthcare&LifeSciences

11

#11

DeveloperTools

6

#6

CustomerSupport

3

#12

Other

-12

MajorShifts

Companiesareincreasinglyfocusingonusecaseswithhighlycomplexandspecializedworkflows,e.g.,financial

servicesandhealthcare,comparedto6monthsago.Theworkflow-embeddednatureofverticalproductsappearstogivetheproductanaturalmoatthatisharderforgeneral-purposeAImodelstodisplaceovernight.

Source:PerspectivesfromtheICONIQGenAISurveys(December2025&May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork

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AIModels&Infrastructure

Deepworkflowintegrationisthepriority.Manybuildersaredesigningtheirproductsforhabitualuseandprioritizinginvestinginagenticcapabilitiesoverthenext12months

HowwouldyoucharacterizethetypicalusagepatternofyourprimaryAIproductamongactivecustomers?

%ofRespondents,Single-Select,N=305

84%

Ofproductstargetusageof>1timeperweekforactivecustomers

Fullusagedistribution

Dailyuse

Severaltimesperweek24%

Adhoc

Weekly口6%

60%

10%

Whereareyouprioritizingcustomer-facingAIproductinvestmentoverthenext12months?

Rankedbyshareofrespondentsplacingeachcategoryintheirtop3,N=305

1AgenticAIcapabilities66%

2Agentreliability&orchestrationinfrastructure35%

3Deeperverticalordomain-specificfunctionality35%

4Applicationlayerdifferentiation34%

5Enterprisereadiness27%

6Contextengineering24%

7Multimodalcapabilities20%

8Integrationsandecosystem18%

9Costandinferenceoptimization14%

10Evaluationandreliabilityinfrastructure14%

11Modellayerdifferentiation13%

Source:PerspectivesfromtheICONIQGenAISurveys(May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork

8

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AIModels&Infrastructure

Licensedthird-partyAPIsarethemostpopularmodeltypetopowercustomer-facingAIproducts,butadoptionofopen-sourcemodelsisincreasing.Weexpectthisshifttocontinueascostandcontroladvantagescompound

Q420251

Q220262

85%

Whichtypeofmodel(s)poweryourcustomer-facingAIproduct?

#ofModelTypes

%ofRespondents,SelectAllThatApply

#ofModelTypesSelectedinQ2,26

%ofRespondents,SelectAllThatApply

1

34%

11%

55%

2

3

80%

37%

33%

37%40%

Licensed3rd-partyAPIse.g.,OpenAI,Anthropic,Google

Open-sourcemodelse.g.,Llama,Mistral

ProprietaryfoundationmodelsPre-trained/trainedfromscratch,company-

owned

58%

Ofrespondentsfine-tuneandcustomizeontopofmodelsinQ2,26

Source:PerspectivesfromtheICONIQGenAISurveys(May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork;1–N=288;2–N=292

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AIModels&Infrastructure

Similarly,buildersarechoosingawidervarietyofmodelproviders(~3.3modelsonaverage).Anthropichasgrowntothetopprovideramongsurveyrespondentsoverthelast6months

TopModelProviders

%ofRespondents,SelectAllThatApply

Q420251

Q220262

81%

77%

Onaverage,companiesselected~3.3modelprovidersinQ22026vs~3.1inQ42025.

71%

56%

51%50%

Datanotavailable

30%

27%

26%

22%

21%21%

9%

8%8%7%7%

8%

5%

6%

4%

4%3%4%3%

ANTHROPC

Google

AAzure

CMeta

deepseek

Other

Source:PerspectivesfromtheICONIQGenAISurveys(December2025&May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork;1-N=194;2-N=301

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AIModels&Infrastructure

Topmodelselectioncriteriahavelargelyremainedconsistentoverthelast6months,withmodelreliability&accuracy

remaininginthetoprankedpriority

TopConsiderationsWhenChoosingaFoundationalModelforCustomer-FacingUseCases

Rankedbyshareofrespondentsplacingeachintheirtop3,N=305

Q2,26

Rank

Consideration

Q4,25

Rank

#1

ModelReliability&Accuracy

1

#2

Cost

2

#3

Security/Privacy

4

#4

Abilitytofine-tune/Customize

3

#5

Latency

5

#6

SpeedofModelAdvancement

7

#7

ModelTransparency/Explainability

8

#8

SOC2/EnterpriseSLAs

10

#9

CompetitivenesswithPeers

6

#10

OpenSource

9

PerspectivesfromtheICONIQNetwork

Security/privacy,explainability,andSOC2/SLAshaveclimbedinimportanceoverthelast6months,signalingenterprise-readinesspressureasproductsmature.

Securityisbecomingaswitchingcost.

AnenterprisesoftwareCISOnotedthatthefirstprovidertoleadon

enterprisecontrols(accountblocking,executiongovernance,governancehooks,etc.)becomesthedefaultanddrivesconsolidation.

Observabilityisthenewblocker.

ACISOatamajordataplatformflaggedalackoftelemetryandvisibilityintowhatagentictoolsareactuallydoingasanacuteandgrowingconcern.

Regulatoryexplainabilityisahardgate.

ACISOataglobalinsurercan'tyetdescribetoregulatorswhatdeployed

agentsaredoingorhowthey'remonitoredwhichinsteadofcapability,isthecoreblocker.

Thegapsarelive,nothypothetical.

Aglobalbank'ssecurityteamframesfiveproductiondomains(identity,

shadowAI,dataprovenance,supplychain,insiderrisk)asthelensthroughwhichmodelprovidersarenowevaluated.

Source:PerspectivesfromtheICONIQGenAISurveys(December2025&May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork

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AIModels&Infrastructure

Buildersarewidelyadoptingarangeofapproachestohelpensuremodeloutputqualityandsafety,includinguser

feedback-drivenevalsandauditlogging

QUALITY&RELIABILITY

Qualityassuranceismostlyreactivetoday.Builderscatchissuesthrough

feedback,monitoring,andhumanreviewratherthanpre-deploymenttesting.Proactiveadversarialtestingremainsaminoritypracticeat21%.

Howbuildersensureoutputquality

%ofRespondents,SelectAllThatApply,N=302

1Userfeedback-drivenevals67%

2Modelmonitoring&observability63%

3Humanoversight&approvalworkflows59%

4Fine-tuning&promptoptimization55%

5Automatedvalidation/evalframeworks48%

6Groundinginverifiedorproprietarydata41%

7Adversarialtesting/red-teaming21%

8Noformalqualitypracticesyet3%

SAFETY&SECURITY

Dataprotectionguardrails,includingauditlogging,scoperestrictions,andPII

controlsarewidelyadopted.AdoptionislowerforsafeguardsaimedatAI-specificrisks(e.g.,promptinjection,dataexfiltration,autonomousmodelactions).

Guardrailsinplace

%ofRespondents,SelectAllThatApply,N=302

1Auditloggingofactions&dataaccess64%

2Topicorscoperestrictions59%

3PIIdetection&redaction55%

4Inputfiltering54%

5Least-privilegeaccesscontrols45%

6Promptinjectiondetection&prevention44%

7Dataexfiltrationprevention38%

8Limitedmodelautonomyinhigh-riskcases34%

9Noformalguardrails7%

Source:PerspectivesfromtheICONIQGenAISurveys(May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork

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AIModels&Infrastructure

ContextengineeringisbecomingincreasinglyimportanttoAIproductdevelopmentasbuilderscombineRAG,memory,agent-to-agentcontext,amongothertechniques,tomanageandprovidecontexttomodelsatruntime

HowdoesyourAIproductmanageandprovidecontexttomodelsatruntime?

%ofRespondents,SelectAllThatApply,N=294

RAG,knowledgebases

1Retrieverelevantdata(RAG)63%

Prompttemplates,few-shotexamples

2Structurepromptsorinputs56%

Routing,multi-steppipelines

3Dynamicallyassemblecontext51%

Cross-sessioncontext,userhistory

4Maintainmemoryacrossinteractions47%

Multi-agentsystems,toolcoordination

5Sharecontextacrossworkflows/agents47%

6Minimalcontextmanagement(staticprompts)16%

7Noformalapproachtocontextmanagement3%

Source:PerspectivesfromtheICONIQGenAISurveys(May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork

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AIModels&Infrastructure

IncreasedinvestmentinAIinfrastructureappearstobepayingoff,withmorecompaniesstatingtheirdatafoundationisfullyreadyandoptimizedtosupportaccurateAIworkflows

Howwouldyourateyourdatafoundation,sreadinesstosupportaccurateAIworkflows?1

%ofRespondents,Single-Select

<$100M$100M-$500M$500M+

4%

10%

22%

56%

53%

59%

55%

54%

36%

28%

24%

24%

NotReady3%2%5%5%4%

SomewhatReady

MostlyReady

FullyReady

29%

48%

32%

10%

19%

18%

Q42025Q22026Q42025Q22026Q42025Q22026

Source:PerspectivesfromtheICONIQGenAISurveys(December2025&May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,and

othersinournetwork.N-Sizes:Q42025-N=198;Q22026-N=304

1-Fullyready=Governed,high-quality,andlabeleddataoptimizedforAIworkflows;MostlyReady=Soliddatapipelines,minorgaps;Somewhatready=Basicdatasystemsinplace,limitedforAI;Notready=Significantdatagapsorqualityissues

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AIGo-to-Market

Strategy&Economics

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AIGTM&Economics

GTMstrategiesforAIbuildersarediversifiedacrossdifferentmotions,buthybridmotions(combinationofPLGandSLG)

hasgainedthemosttractionoverthelast6months

Q420251

Q220262

PrimaryGTMMotionforAIProducts

%ofRespondents,Single-Select

39%

37%

36%

30%

31%

27%

Sales-ledgrowthHybrid(evenlysplitbetweenproduct-andsales-led

growth)

Product-ledgrowth/self-serve

Source:PerspectivesfromtheICONIQGenAISurveys(December2025&May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork;1-N=288;2-N=289

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AIGTM&Economics

Subscription/platformcomponentsarestillthemostcommonpricingmodel;however,consumptionandoutcome-basedpricinghasgrowninprevalenceoverthelast6monthstobetteralignpricingtoproductvalue

Q420251

Q220262

AIPricingStrategies

%ofRespondents,SelectAllThatApply

Companiesselected1.7pricingmodelsonQ22026vs1.5pricingmodelsinQ42025

58%

averagein

57%

42%

35%

28%

23%

23%

17%

18%

15%

Subscription/platformConsumption-basedSeat-basedOutcome-basedAIproductisofferedatnoextra

cost

Source:PerspectivesfromtheICONIQGenAISurveys(December2025&May2026)andperspectivesfromtheICONIQteamandnetworkofAIleadersconsistingofourcommunityofCIO/CDOsoverseeingAIinitiativesinenterprises,CTOs,ourTechnicalAdvisoryBoard,andothersinournetwork;;1-N=297;2-N=305

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AIGTM&Economics

AsAIbecomesamoresignificantdriverofrevenueandcustomerbaseforcompaniesbuildingAIproducts,theeconomicsofbuildingandsellingAIproductsisbecomingincreasinglyimportant

%ofRevenuefromAIProducts1

2025

32%

Actual

Baseline

Average,ExcludesAI-NativeCompanies,N=265

2026

42%

Projected

+10ppvs.2025

2027

53%

Projected

MajorityThreshold

1.AIadoptionwithcustomerbasesfrequentlyrequiresaTrojanhorse

•Top-down"end-to-endautomation"pitchesreliablytriggerpushbackfromnon-AI-nativeenterpriseusers

•Whatworks:matchtheAIworkflowtowhatusersalreadydo,justfaster,andletthemdiscoverthecompoundingeffects

themselves.Adoptionfollowsorganicallyoncetwoacceleratedstepsvisiblyconnect

•Customerliteracyprogramspayoffevenwithnoproductionoutput.Gettingusershands-oncreatestheinternaladvocatesneededtoscalelater

"Wetriedthefullend-to-endautomatedpitchandeveryonehadthatknee-jerkreaction.Trojan-horsingitin,thesamethingjustfaster,

gotthemtoorganicallyformtheirownviewofwhatcouldbeautomated.“–CTO@SeriesA,AI-NativeCompany

2.Featuredistributionandcustomereducationremainthebottleneck

•Shippingfastcreatesitsownproblem:thehumanswhotalktocustomersneedcontinuouseducationonnewfeaturesto

translatethemintoadoption

•Themorelayersbetweenproductandcustomer,themoreitcompoundsacrossproductmarketing,salesenablement,andaccountmanagement

"Ihaven'tfoundanAI-poweredwaytodrive