2025 AI时代的开发者：技术应用、期望及塑造未来的工具研究报告 DEVELOPERS IN THE AGE OF AI Adoption,expectations,and the tools shaping the future

DEVELOPERSIN

THEAGEOFAI:

Adoption,expectations,and

thetoolsshapingthefuture

NOVEMBER2025

CanIsharedatafromthis

report?

1.LicenseGrant

Thisreportislicensedunderthe

CreativeCommonsAttribution

-

NoDerivatives

i

s.(Iia),y

o.:Putsimply,subjecttothetermsandconditionsof

Share—Youcanreproducethereportorincorporatepartsofthereportintooneormoredocumentsorpublications,forcommercialandnon-commercialpurposes.

Underthefollowingconditions:

Attribution—YoumustgiveappropriatecredittoSlashDataTM,andto

Catchyassponsorsofthisreport,andindicateifchangesweremade.Inthatcase,youmaydosoinanyreasonablemanner,butnotinanywaythat

suggeststhatSlashDataTMendorsesyouoryouruse.

NoDerivatives—Youcannotremixortransformthecontentofthereport.

Youmaynotdistributemodifiedcontent.

2.LimitationofLiability

SlashDataTM,believesthestatementscontainedinthispublicationtobebaseduponinformationthatweconsiderreliable,butwedonotrepresentthatitisaccurateorcompleteanditshouldnotberelieduponassuch.Opinions

expressedarecurrentopinionsasofthedateappearinginthispublication

onlyandtheinformation,includingtheopinionscontainedherein,aresubjecttochangewithoutnotice.Useofthispublicationbyanythirdpartyfor

whateverpurposeshouldnotanddoesnotabsolvesuchthirdpartyfromusingduediligenceinverifyingthepublication’scontents.SlashDataTM

disclaimsallimpliedwarranties,including,withoutlimitation,warrantiesofmerchantabilityorfitnessforaparticularpurpose.

SlashDataTM,itsaffiliates,andrepresentativesshallhavenoliabilityforanydirect,incidental,special,orconsequentialdamagesorlostprofits,ifany,sufferedbyanythirdpartyasaresultofdecisionsmade,ornotmade,oractionstaken,ornottaken,basedonthispublication.

Theanalystofthedevelopereconomy|formerlyknownasVisionMobile

SlashData©Copyright2025|Somerightsreserved

AboutSlashData

SlashDataisanAIanalystfirmwhichhasbeenworking

withthetopTechbrandstoprovideclarityand

confidenceintheirdecision-making.For20years,we

havebeentrackingsoftwaretechnologytrendsand

helpingtechnologybrandsmakeproductandmarketinginvestmentdecisions,challengingassumptionsand

reframingmarkettrendstoempowerindustryleaderstodrivetheworldtowardsthefuture.

TomWilliams

ManagingPartneratCatchy

Tomisastrategistandconsultantwithover15yearsofexperiencehelpingtechnology

companiestranslatecomplexideasintoclearstrategiesandimpactfulprograms.As

ManagingPartneratCatchy,heleads

consultingandstrategyacross

multidisciplinaryteams.HeholdsanMScinSocialScienceoftheInternetfromthe

UniversityofOxford.

KonstantinosKorakitis

DirectorofResearchatSlashData

KonstantinosheadstheResearchProduct

teamatSlashDataandisresponsibleforall

syndicatedresearchproductsandcustom

researchprojects.Withmorethan10yearsofexperienceasanengineer,consultantand

manager,heoverseesresearchplanning,surveydesign,dataanalysis,insights

generationandresearchoperations.

AboutCatchy

Catchyistheleadingdevelopermarketingagency,

helpingtechnologycompanieslaunchproducts,scale

platforms,andgrowecosystems.Forover15years,we’vepartneredwiththeworld’stoptechbrandstoreachandengagetechnicalaudiences,translatingcomplexideas

intostrategiesandprogramsthatdriveadoptionandgrowth.

TABLEOFCONTENTS

1.Introduction

5

2.

GeneralAIusage

7

3.

BuildingwithAI

10

4.InteractingwithAItools

16

5.Evaluationanddecisioncriteria

22

6.Deploymentrealitieswithafutureoutlook

25

7.

Conclusions

29

8.Methodology

30

Introduction

Atthecrossroadsofcodeandcognition

Thisspecialeditionreportisacollaborationbetween

CatchyandSlashData.Itexploreshowdevelopersarenavigating“theageofAI”,bywhichwemeanthe

profoundshiftofthepastfewyears,where

breakthroughsingenerativemodelsandcoding

assistantshavemovedAIfromexperimentallabsintoeverydaydeveloperworkflows.Thischangeisreshapingwhatitmeanstobeadeveloperateverylevel.It’s

alteringhowcodeiswritten,howapplicationsare

designed,andhowdevelopersthinkabouttheirroleinbuildingintelligentsystems.

DrawingdatafromSlashData’sQ32025Developer

Nation,thisreportlooksbeyondthehypetorevealhowAIisbeingusedinpractice.Weexplorethemanyways

developersareadoptingassistants,embeddingAPIs,andbuildingAI-poweredapplications.

DevelopersintheageofAI05

Introduction

Atthecrossroadsofcodeandcognition

DevelopersintheageofAI06

Whatemergesisanuancedpictureofadoption.AIisnow

firmlyinthemainstream,butdeveloperbehaviors

divergedependingonwhattheyarebuilding,howdeeplytheyareengagedwithmodelinfrastructures,andwhattheyexpectfromAPIs,codingassistants,andAIservices.Tomakesenseofthesedynamics,thefindingsare

organizedaroundfivethemes:

1.GeneralAIusage

2.BuildingwithAI

3.InteractingwithAItools

4.Evaluationanddecisioncriteria

5.Anddeploymentrealitieswithafutureoutlook.

Together,theselensesshowadeveloperpopulationin

transition.Whilesomeremaininearlyexploration

phases,manyarealreadyrunningAIinproduction,andtheirprioritiesareshiftingdecisivelytowardtrust,

usability,andvalueovernovelty.ThisismorethanastoryofAIprogress.Itisastoryofdeveloperexpectations

reshapingthetrajectoryofintelligentsoftware.

Gen01

DevelopersintheageofAI

DevelopersintheageofAI08

01.GeneralAIusage

Injustafewshortyears,AIhasmovedfromthe

peripheryofdeveloperinteresttothecenteroftheir

dailypractice.Thevastmajority(82%)ofdevelopersnowreportusingorworkingwithAItechnologiesinsome

form,alevelofadoptionthatfewothertechnologieshaveachievedsoquickly.

Formany,AIiswovendirectlyintotheirworkflow.46%usechatbotslikeChatGPTtoanswercodingquestions,whilewelloverathird(37%)relyonAI-assisted

developmenttoolssuchasGitHubCopilotorCursor.

Thesetoolshavequicklybecomeanintrinsicpartofthedevelopertoolkitforproblem-solving,automation,andcreativeexploration.

Butthisisnotastoryofuniversaladoption.

Nearlyafifth(18%)ofdevelopersreportthattheydonotuseAIatall.Thisunderscorestheunevenpaceofchange.Whilethemajorityareexperimentingandadoptingat

speed,asignificantminorityremaincautious,

unconvinced,orwaitingforthetechnologytomature.

AIhasenteredthemainstream,butithasyettofully

dominate.Itplaysaroleacrossthefulldeveloper

journey,supportingproductivity,creativity,andtechnicaldecision-making,andyetengagementlevelsvarywidely.Fortoolmakersandplatformproviders,boththe

opportunityandthechallengearetodeliverclear,

sustainedvaluethatturnsexperimentationintolastingadoption.

DevelopersintheageofAI9

01.GeneralAIusage

AIhasbecomeanintegralpartofdevelopers’workflow

%ofdevelopers(n=12,021)

HowdevelopersuseorworkwithAItechnologies

18%

Idon'tuseorworkwithML/AImodels,tools,orservices

46%

IuseAIchatbotsforanswerstocodingquestions(e.g.ChatGPT)

26%

IuseAItoolstogeneratecreativeassetsformyprojects(e.g.3Dmodels)

37%

IuseAI-assisteddevelopmenttoolsoragents(e.g.GitHubCopilot,Windsurf,Cursor)

20%

IaddAIfunctionalitytomyappsviafully-managedAIservices/APIs

12%

13%

IaddAIfunctionalitytomyappsviaself-managed/localAImodels

IcustomiseAImodelswithmydata

6%

6%

Ifine-tunehyperparametersofAImodels

IbuildandtrainAImodels

Questionwording:DoyouuseorworkwithML/AImodels,tools,APIs,orservices?Ifso,inwhichofthefollowingways?

Building

withAI

02

DevelopersintheageofAI

DevelopersintheageofAI11

02.BuildingwithAI

IfusingAItoolsischanginghowdeveloperswork,

buildingwithAIischangingwhattheycreate.A

substantial40%arenolongerjustexperimentingwithassistantsorchatbots;theyaredirectlyembeddingAIapplicationsordevelopingtheirownAImodels.

Thewaystheydothisrevealatenuousbalancebetweenaccessibilityandcomplexity.20%ofdevelopersaddAItoapplicationsviafullymanagedAPIs,suchashosted

largelanguagemodels,while12%runself-managedor

localmodels.Ontheotherhand,agrowingminorityaremovingintodeepercustomization:13%tailormodels

withtheirowndata,6%fine-tunehyperparameters,andanother6%buildandtrainmodelsfromscratch.

Thedatashowsusthatwhilemanydevelopersfavor

speedandaccessibility,acommittedminorityisinvestedinincreasedcontrolandspecialization.Twopathwaysforongoingadoptionareemerging.Onefocusedonfast,

reliableintegrationandtheotheronbespokedevelopmenttomeetspecificneeds.

While40%ofdevelopersareembedding,customizing,orfine-tuningAImodels,amuchlargershare(70%)useAIintheirday-to-dayworkflows(forexample,through

codingorgenerativeassistants).Thisshowsthatfornow,activeuseofAItoolsissignificantlymorecommonthanfull-scaleapplicationormodeldevelopment.

DevelopersintheageofAI12

02.BuildingwithAI

ThemajorityofdevelopersmaybeusingAIforcoding,butmore

thanathirdarealsodirectlyembeddedAIintotheirapplications

%ofdevelopers(n=12,021)

TypesofengagementwithAItechnologies

18%

NotusingorworkingwithAItechnologies

70%

36%ofdevelopersareembeddingAIintoappsordevelopingAImodels

28%

19%

UsingAIforcodingorgeneratingassets

AddingAIfunctionalitytoappsBuilding,customisingorfine-tuningAImodels

Questionwording:DoyouuseorworkwithML/AImodels,tools,APIs,orservices?Ifso,inwhichofthefollowingways?

DevelopersintheageofAI13

02.BuildingwithAI

AddingAIfunctionality

WhendevelopersembedAIintotheirapplications,twopatternsstandout:managedservicesarethedominantentrypoint,andgenerativeusecasesaretheclear

priority.

Fully-managedAPIsarethemostcommonintegration

route,usedbyjustover70%ofdevelopers.By

comparison,around40%makeuseofself-managedor

localmodels.Theseapproachesarenotexclusive,butthefiguresunderlinehowheavilydevelopersleanon

managedservicestosimplifyintegrationandreduceinfrastructureoverhead.

Onthefunctionalityside,generativeAIhasbecomethefocalpoint.Nearly80%ofdevelopersembeddingAI

reportaddinggenerativefeaturessuchastext,image,audio,orcodegeneration.Non-generativetaskslike

analysis,prediction,andclassificationremainimportant,withnearlyhalfofdevelopersstillrelyingonthem,buttheynolongerrepresenttheprimarydriverofadoption.

DevelopersintheageofAI14

02.BuildingwithAI

Modelchoicesshowasimilarpatternofpragmatism.

Open-sourceisthemostwidelyusedoption,withmorethantwo-thirdsofdevelopersdrawingonopenoropen-sourcemodels.Nearlyhalfuseproprietaryandclosed-sourcemodels,whileonlyoneinfivedevelopersbuildortrainmodelswithintheirownteams.

Takentogether,thedatashowsdevelopersfocusingongenerativefunctionality,deliveredmostoftenthrough

managedservices,andbuiltonopen-sourcefoundations.

DevelopersintheageofAI15

02.BuildingwithAI

Fully-managedAPIsarethemostcommonwayforaddingAI

functionalitytoapplications,regardlessofthetypeoffunctionality

%ofdevelopersaddingAIfunctionalitytotheirapps(n=3,415)

WaysofaccessingAImodelsbytypeofAIfunctionality

Fully-managedAIservices/APIsSelf-managed/localAImodels

43%

42%

40%

69%

65%

71%

Generative

All

Non-generative

(e.g.analysing,predicting,

orclassifyingexistingdata)

(e.g.generatingtext,images,audio,video,code,etc.)

Questionwording:WhattypesofAIfunctionalityareyouaddingtoyourapplicationswhenaccessingtheAImodelsinthefollowingway(s)?

wIi03

DevelopersintheageofAI

DevelopersintheageofAI17

03.InteractingwithAItools

FewaspectsofAIhavebeenasvisibletodevelopersascodingassistants.Awarenessisnowalmostuniversal,withmorethannineintendevelopersreporting

familiaritywithatleastoneAI-assistedcodingtool.

However,withunevenadoptionpatternsandhighchurn,awarenessdoesnotnecessarilytranslateto

commitment.

However,onetooltrulystandsout:ChatGPT.Twothirds

(66%)ofdeveloperssurveyedreportcurrentlyusingit,makingittheclearcategoryleader.GitHubCopilot

followsatnearly50%,reflectingitsestablishedroleasadefactocodingassistant,evenoutsideformalIDE

integration,andGoogle’sGeminiCodeAssisthas28%usage.

Beyondthesethree,currentadoptiondropstoaround20%orless.

Thedrop-offissteep.ToolslikeVSIntelliCode(21%),AmazonQDeveloper(21%),andCursor(17%)show

relativelyhighawarenessbutmuchloweractiveuse.

Others,includingv0,Lovable,ReplitAgent,andCline,haveawarenessratesinthe40%rangebutstruggletoreachabove5%usage.

DevelopersintheageofAI18

03.InteractingwithAItools

Experimentationiswidespread,butstickinessremains

elusive.Overhalfofdevelopers(51%)reportthatthey

havestoppedusingatleastonetool,and44%saytheyhaveevaluatedandrejectedatleastoneoutright.EvenGitHubCopilot,despiteitssignificantoveralladoption,hasoneofthehighestchurnrates:11%ofdevelopers

abandoningthetoolafteraninitialperiodof

engagement.Interestingly,ChatGPTshowsalowerchurn(7%),eventhoughithasbeenevaluatedbyconsiderablymoredevelopers.

Thepictureisoneofamarketunderactivetrial.

Developersaretestingbroadly,discardingtoolsthatfailtodeliver,andconvergingonlyaroundahandfulof

solutionsthatprovideclearvalue.

ChatGPTistheclearcategoryleader

inAIcodingassistants

%ofdeveloperswhouseorareawareof

anyAI-assistedcodingtool(n=1,718)

AdoptionofAI-assitedcodingtools(top8)

ChatGPT

GitHubCopilot

GoogleGeminiCodeAssist

VisualStudioIntelliCode

AmazonQDeveloper(formerlyCodeWhisperer)

Cursor

ClaudeDev

BLACKBOXAI

66%

48%

28%

21%

21%

17%

16%

13%

Questionwording:WhattypesofAIfunctionalityareyouaddingtoyourapplicationswhenaccessingtheAImodelsinthefollowing

way(s)?

DevelopersintheageofAI19

03.InteractingwithAItools

Dodevelopersactuallylikecodingassistants?

Adoptionnumbersonlytellpartofthestory.Loyaltyandsatisfactiondatashowadevelopercommunitythatis

curious,butnotyetconvinced.

NetPromoterScores(NPS)highlightthedivide.Cursor

leadswithastrongscoreof42,followedbyIBMwatsonxCodeAssistant(40)andWindsurf(35).Thesetools-eventhoughtheystillhaverelativelysmalluserbases-inspirerealenthusiasmamongtheirusers,signalingtheyhave

crossedthethresholdfromtoytotrustedutility.

Elsewhere,thepictureisfarlessflattering.Manycodingassistantsclusteraroundneutralscores,andsome,suchasReplitAgent,evendipintothenegative.Theselow

andmiddlingscoresreflectreal-worldfrictionfrom

disappointingperformance,poorintegration,orcoststhatoutweighperceivedbenefits.

DevelopersintheageofAI20

03.InteractingwithAItools

Cursor,Windsurf,andwatsonxCodeAssistantsettheindustry

standardfortrustedAI-assistedcodingutilities

NetPromoterScore(n=1,107)

AI-assistedcodingtoolsrecommendationbreakdown

Cursor

IBMwatsonxCodeAssistant

Windsurf(formerlyCodeium)

ChatGPT

GitLabDuo

GitHubCopilot

Lovable

42

40

35

34

34

33

30

Questionwording:HowlikelyareyoutorecommendthefollowingAI-assistedcodingtools?

DevelopersintheageofAI21

03.InteractingwithAItools

CustomerSatisfaction(CSAT)scoresaddasecondlens.Here,ChatGPTleadswithanoverallsatisfactionscoreof79.GitHubCopilot(78),JetBrainsAI(76),andCursor(73)alsorankhighly.Bycontrast,othersscorefarlower:

ReplitAgent(55)andBolt(64)sitatthebottomofthetable,underlininghowunevenperformancefeelsacrossthecategory.

Lookedataltogether,thesignalisclear.Developersarewillingtotryalmostanything,buttheywillonlystaywithtoolsthatdelivertrustandmeasurablevalue.Ina

crowdedfieldwhereawarenessisalreadyhigh,thecompetitivebattlegroundhasshifteddecisivelyfromadoptiontoretention.

ChatGPT,GitHubCopilot,andJetBrainsAI

topthechartsindevelopersatisfaction

CSATscoresbasedonthesumof5-starand4-starratings

acrossproductattributes(n=2,828)

OverallCSATscoresforeachAI-assisted

codingtool(top8)

ChatGPT

GitHubCopilot

JetBrainsAI

Cursor

Windsurf(formerlyCodeium)

Cline

GitLabDuo

AmazonQDeveloper(formerlyCodeWhisperer)

79

78

76

73

72

72

72

72

Questionwording:HowwouldyouscorethefollowingAI-assistedcodingtoolswithrespecttotheseattributes?

an04

DevelopersintheageofAI

DevelopersintheageofAI23

04.Evaluationanddecisioncriteria

AsgenerativeAIshiftsfromexperimentationto

production,developersaresettingoutaclearsetof

expectations.Theirfocusisnotonnoveltyorcuriosity,butonwhetherservicescandelivertrustworthy,

repeatablevalue.

WhenevaluatingfullymanagedgenerativeAIservicesorAPIs,developersprioritizethreeattributesalmost

equally:thequalityofoutputs(32%),dataprivacyand

security(31%),andreliability(31%).Thesefactorsdefinewhetheraservicecanbetrustedtoperforminreal-worldapplications,whereinconsistentresultsorunclear

safeguardscanquicklyundermineadoption.

Asecondtierofconsiderationsfollowsclosely:easeofintegration(27%),speedandlatency(26%),andcost

andpricingstructure(25%).Thesepracticalitiesmatter,buttheycomeafterthefundamentalsofqualityand

trust.Furtherdownthelistarescalability(22%),

technicalsupport(19%),documentation(17%),andmodelcustomization(17%).

Inotherwords,thetablestakesforgenerativeAIarenolongermodelsizeorfeaturecount.Theyaretrust,

stability,andusability.

DevelopersintheageofAI24

04.Evaluationanddecisioncriteria

Developersvaluequality,security,andtrustwaymorethan

technicalcapabilitywhenevaluatingfully-managedGenAIservices

%ofdeveloperswhoaddgenerativeAIfunctionalitytotheirapps

viafully-managedAIservices/APIs(Q12025|n=1,475)

Topdeveloperprioritieswhenevaluatingfully-managedgenerativeAIservices

QualityofoutputsDataprivacyandsecurityReliability

Easeofintegration

SpeedandlatencyCostandpricingstructure

ScalabilityTechnicalsupportDocumentation

Modelcustomisation

ModelvarietyandrangeEthicalandcompliancefeatures

32%31%31%

27%

26%

25%

22%

19%

17%

16%

15%

13%

Questionwording:Whichofthefollowingattributesdoyouprioritisewhenevaluatingafully-managedgenerativeAIservice/API?

05

Deployment

realitieswithafutureoutlook

DevelopersintheageofAI

DevelopersintheageofAI26

05.Deploymentrealitieswithafutureoutlook

LookingspecificallyatAI/MLanddatasciencedevelopersrevealshowprojectsaredeployedtodayandwheretheymaybeheaded.Cloudremainsthedominant

environment,with79%ofdevelopersreportingrunningmachine-learningorAIcodeonsomeformofcloud

infrastructure:private(22%),hybrid(21%),public(20%),ormulti-cloud(16%).Thisreflectsboththescalabilityofcloudplatformsandthematurityoftoolsthatenablethequickdeploymentofmodelsintoproduction.

Atthesametime,alternativedeploymentroutesare

gainingground.Desktoporlaptopexecutioniscommon(43%),andon-premisesservers(21%)remainrelevantforteamsthatprioritizecontrolordatasovereignty.

Edgeenvironmentsandnetworkinfrastructure,includingindustrialcomputing,alsoshowsteadyifsmaller

adoption,appealingwherelatencyorregulatoryrequirementsmatter.

Programmingchoicesmirrorthisblendofcontinuityanddiversification.Pythonleadsbyawidemargin(56%),butdevelopersdrawonabroadsetoflanguages,includingJava(28%),C++(27%),JavaScript/TypeScript(24%),

andSQL(22%),dependingonthetaskandintegrationneeds.EmergingstandardssuchasModelControl

Protocol(MCP)mayencouragemoreconsistencyacrossthesevariedenvironmentswithoutconstraining

flexibility.

Thepicturethatemergesispragmatic,notradical.

Developerscontinuetofavorcloud-firstdeploymentandPython-leddevelopment,butareincreasinglyopento

hybridmodelsanddiverselanguageecosystemswheretheymeetspecificprojectneeds.

DevelopersintheageofAI27

05.Deploymentrealitieswithafutureoutlook

What’snextinAI/ML:Thedeveloperoutlook

DeveloperswhoworkcloselywithAIandmachine

learningseethenextthreetofiveyearsasaperiodofpracticalintegrationandproductivitygains.Survey

resultsshowthemostanticipatedusecasesofAIinsoftware-developmentare:

•Dataprocessing,analytics,andvisualization(30%)

•Cybersecurityapplications(28%)

•Intelligentdevelopmentassistants,suchasAIpairprogramming(26%)

•Automatedcodegeneration(24%)

Educationandpersonalisedlearning(22%)andaddingAIfunctionalitytoapplications(21%)roundoutthetoptier.

Togethertheseanswershighlightanear-termfocusondeveloperefficiency,secureoperations,andenhancedanalytics,ratherthanspeculativebreakthroughs.Ethicsandgovernanceremainimportantdiscussionpointsintheindustry,buttheyarenotprimarydriversof

developers’statedprioritiesinthissurvey.

DevelopersintheageofAI28

05.Deploymentrealitieswithafutureoutlook

Developerefficiency,secureoperations,andadvancedanalytics

areseenasthemostimportantAIusecasesbydevelopers

%ofdevelopers(Q42024n=1,837)

MostimportantusecasesforAIinsoftwaredevelopmentinthe

next3to5yearsaccordingtodevelopers