2030+前瞻AI发展的多重轨迹研究报告

OECDpubishing

EXPLORING

POSSIBLEAI

TRAJECTORIES

THROUGH2030

OECDARTIFICIAL

INTELLIGENCEPAPERS

February2026No.55

》OECD

BETTERPOLlcIESFORBETTERLIVES

OECDArtificialIntelligencePapers

ExploringpossibleAItrajectoriesthrough2030

HamishHobbs,DexterDocherty,LuisAranda,KasumiSugimoto,KarinePerset,RafałKierzenkowski

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Abstract

Artificialintelligence(AI)hasadvancedrapidlyinrecentyears,withsystemsbecomingincreasinglycapable.Thispaperpresentsexpert-andevidence-informedscenariosforhowAIcouldprogressby2030.Itconsidersrecent

trendsinAIandkeyuncertaintiesforAIprogressthrough2030.Current

evidencesuggeststhatfourdifferentbroadscenarioclassesareall

plausiblethroughto2030:progressstalling,progressslowing,progress

continuing,andprogressaccelerating.ThissuggeststhatAIprogressby

2030hasaplausiblerangethatincludesbothaplateauatapproximately

today’slevelofcapabilitiesandrapidimprovementthatleadstoAIsystemswhichbroadlysurpasshumancapabilities.Thispaperdecomposes

plausibleAIcapabilityprogressineachscenarioinlinewiththeOECD’s

betaAIcapabilityindicators,exploringplausiblecapabilitytrajectoriesforAIsystem’sabilitiesinlanguage;socialinteraction;problemsolving;creativity;metacognitionandcriticalthinking;knowledge,learningandmemory;

vision;physicalmanipulation;androboticintelligence.

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Acknowledgements

ThispaperwasdraftedbyHamishHobbsfromtheOECDStrategicForesightUnit,inclosecollaborationwithDexterDochertyfromtheStrategicForesightUnitandKasumiSugimoto,LuisArandaandKarinePersetfromtheOECDDivisiononAIandEmergingDigitalTechnologies.StrategicdirectionandinputwereprovidedbyRafałKierzenkowski,SeniorCounsellorforStrategicForesightandJerrySheehanandAudreyPlonk,respectivelyDirectorandDeputyDirectoroftheOECDDirectorateforScience,TechnologyandInnovation(STI).

TheteamgratefullyacknowledgestheinputofStuartElliot,SamMitchellandZinaEfcharyregardingtheintegrationoftheOECDbetaAICapabilityIndicators.TheteamalsothanksNiamhHiggins-LaveryfromtheStrategicForesightUnitforoperationalsupportandShellieLaffont,ChristyDentlerandAndreiaFurtadofromSTICommunicationsandRomydeCourtay(externaleditor)foreditorialsupport.

ThepaperbenefittedsignificantlyfromtheoralandwrittencontributionsofGPAIdelegatesaswellasexpertsfromtheOECD.AInetworkofexperts.TheauthorswouldliketoextendtheirsinceregratitudetotheDelegationsofBrazil,Greece,India,Israel,Spain,SaudiArabia,Slovenia,Türkiye,andtheUnitedKingdomfortheirinvaluableinsights.TheauthorsthankthemembersoftheOECDExpertGrouponAIFuturesfortheirinsightfulcomments.

ThisreportbenefitedgreatlyfromdiscussionsandinputfromthewritingteamoftheInternationalAISafetyReport,includingCarinaPrunkl,StephenClare,MaksymAndriushchenko,PatrickKingandHannahMerchant.

Finally,theauthorsgratefullyrecognisethesubstantialcontributionsfromexternalexperts,includingÁlvaroSoto(PontificiaUniversidadCatólicadeChile),FriedrikHeintz(LinköpingUniversity),GopalRamchurn(UniversityofSouthampton),HiroshiIshiguro(OsakaUniversity),NickJennings(LoughboroughUniversity)JonasSandbrink(AISecurityInstitute),StuartRussell(UniversityofCalifornia,Berkeley),SusanLeavy(UniversityCollegeDublin),andYoshuaBengio(UniversityofMontreal).

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Tableofcontents

Abstract3

Acknowledgements4

Tableofcontents5

Executivesummary8

1.Introductionandmethodology10

1.1.UnderstandingpossibleAItrajectorieswillenablegovernmentstocapturethebenefitsand

prepareforpotentialimpacts10

1.2.ExploringtrendsanduncertaintiestobuildfourcorescenariosforplausibleAItrajectories

through203010

2.AIprogresstrendsanduncertainties12

2.1.AIsystemshavedemonstratedrapidprogressonawiderangeofbenchmarks12

2.2.KeyuncertaintiesaboutfuturetrendsinAIprogress12

2.3.KeyuncertaintiesaboutfutureAIinputs15

3.Scenarios18

3.1.UsingtheOECD’sAICapabilityIndicators(beta)todefineAIcapabilitycategories18

3.2.BuildingontheseindicatorstoexplorescenariosforAIprogressin203019

3.3.Scenario1:ProgressStalls20

3.4.Scenario1:Potentialvariations23

3.5.Scenario2:ProgressSlows25

3.6.Scenario2:Potentialvariations28

3.7.Scenario3:ProgressContinues30

3.8.Scenario3:Potentialvariations33

3.9.Scenario4:ProgressAccelerates35

3.10.Scenario4:Potentialvariations38

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4.Whichfuturesareplausible?40

Conclusions41

AnnexA.ExpertInterviewsandReview42

AnnexB.AIProgressTrendsandUncertainties44

AnnexC.AIInputTrendsandUncertainties53

AnnexD.TrendExtrapolations58

References63

Endnotes73

Figure1:AIsystembenchmarkscoresrelativetohumanscoresovertime44

Figure2.Performanceonacomplexreasoningbenchmarkincreaseswithmodelscale51

Figure3.Largestfeasibletrainingrunsby2030givenestimatedconstraintsfordifferentinputs54

Figure4.Growthinreasoningtrainingcompute(measuredinFLOP)cancontinueatcurrentratesforalimited

time,butwouldlikelyslowby2026whenitapproachesthetotalamountofavailabletrainingcompute55

Figure5.LengthofsoftwareengineeringtasksthatAIsystemscanautonomouslycompletewitha50%

successratehasdoubledeverysevenmonths58

Figure6.ThelengthoftasksthatAIsystemscanautonomouslycompletewitha50%successratehasbeen

increasingforarangeoftasktypes59

Figure7.FourscenariosforfutureAIcapabilities,visualisedherebythelengthofsoftwareengineeringtasks

thatleadingAIsystemscanautonomouslycompletewithan80%successrate61

Table1.AIperformancerelativetoOECDAIcapabilityindicators,reflectingAIperformanceinlate2024(1-5

scale)19

Table2.Scenario1:AIProgressStalls–capabilityindicatorscores(1-5scale)21

Table3.VariantA:AIasanarrowtool–capabilityindicatorscores(1-5scale)23

Table4.VariantB:SimpleAIAgents–capabilityindicatorscores(1-5scale)24

Table5.Scenario2:ProgressSlowsscenariocapabilities–capabilityindicatorscores(1-5scale)26

Table6.VariantC:SimpleRobots–capabilityindicatorscores(1-5scale)28

Table7.ScenarioVariantD:Socially-LimitedAI–capabilityindicatorscores(1-5scale)29

Table8.Scenario3:ProgressContinuesscenariocapabilities–capabilityindicatorscores(1-5scale)31

Table9.VariantE:ForgetfulAI–capabilityindicatorscores(1-5scale)33

Table10.VariantF:DigitalOnlyAI–capabilityindicatorscores(1-5scale)34

Table11.Scenario4:ProgressAcceleratesscenariocapabilities–capabilityindicatorscores(1-5scale)36

Table12.VariantG:AGI–capabilityindicatorscores(1-5scale)38

Table13.VariantH:Superintelligence–capabilityindicatorscores(1-5scale)39

Table14ObservedperformanceandrateofprogressofAIsystemsonbenchmarksassessingthelengthof

tasksAIsystemscancompletewitha50%successrateacrossdifferentdomains60

Box1.Scenario1:ProgressStalls20

Box2.VariantA:AIasaNarrowTool23

Box3.VariantB:SimpleAIAgents24

Box4.Scenario2:ProgressSlows25

Box5.VariantC:SimpleRobots28

Box6.VariantD:Socially-LimitedAI29

Box7.Scenario3:ProgressContinues30

Box8.VariantE:ForgetfulAI33

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Box9.VariantF:Digital-OnlyAI34

Box10.Scenario4:ProgressAccelerates35

Box11.VariantG:ArtificialGeneralIntelligence(AGI)38

Box12.VariantH:Superintelligence39

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Executivesummary

ArtificialIntelligence(AI)hasadvancedrapidlyinrecentyears,withsystemsbecomingincreasinglycapable.UnderstandinghowAImightevolveby2030willhelpgovernmentscraftpoliciesthatcapturethebenefitsofAIprogressandprepareforitspotentialimpacts.

TheOECDhasdevelopedexpertandevidenceinformedscenariosforhowAIcouldprogressby2030,buildingontheOECD’sbetaAICapabilityIndicators(OECD,2025[1]).WhilethestateofAIcapabilitiesin2030cannotbepredictedwithcertainty,governmentswouldbenefitfromunderstandingarangeofplausibledevelopmenttrajectories.

Policymakerscouldconsiderfourdifferentbroadscenarioclassesthatareallplausibleby2030:

•ProgressStalls:AscenarioinwhichprogressinthemostadvancedAIsystemslargelyhaltsandcapabilitiesremainlargelyunchanged.RapidgainsobservedoverrecentyearsstopandAIprogressplateaus.Diffusionandapplicationdevelopmentcontinueforexistingcapabilities.In2030,AIsystemscanquicklyundertakearangeoftasksthatwouldtakehumanshourstoperform,butissuesofrobustnessandhallucinationsimpactreliability.AIsystemstypicallyrelyuponsubstantialsupportfromhumanstocompletetasks,suchasdetailedprompting,reviewandprovisionofcontext.

•ProgressSlows:AscenarioinwhichincrementalgainsinthemostadvancedAIsystemsdelivercontinuedbutslowerprogress.In2030,AIsystemshaveadeepknowledgebase,excelatstandardformsofstructuredreasoning,andcanactasusefulassistantsfortasksthatrequirethemtouseacomputer,navigatetheweborundertakelimitedinteractionwithpeopleorservicesonbehalfoftheuser.AIsystemscanquicklyundertakewell-scopedtasksthatwouldtakehumanshoursordaystoperform.AIsystemstypicallyrelyonhumanstoprovidethemwithclearlyscopedtasks,reviewimportantdecisionsoractions,andprovidedetailedguidanceandcontext.

•ProgressContinues:Ascenarioinwhichcontinuedrapidprogressoccurs.In2030,AIsystemscanperformmanyprofessionaltasksindigitalenvironmentsthatmighttakehumansamonthtocomplete.DeficitsinAIsystem’scontinuallearningandgeneralisationtocomplexreal-worldenvironmentsandsituationspersist.AIsystemstypicallyrelyonhumanstoprovidehighleveldirectionsandboundsfortheirbehaviour,butcanoftenoperatewithhighautonomywithintheseboundstowardsagivenobjective,includingautonomouslyinteractingwitharangeofstakeholders.

•ProgressAccelerates:AscenarioinwhichdramaticprogressleadstoAIsystemsasormorecapablethanhumansacrossmostorallcapabilitydimensions.In2030,AIsystemscanoperatewithlevelsofautonomyandcognitiveabilitythatmatchorsurpasshumansincognitivetasks,autonomouslyworkingtowardsbroadstrategicgoalsthattheycanreflectuponandreviseifcircumstanceschange,whilealsocollaboratingwithhumanswherenecessary.AI-guidedrobotscanhandlecomplextasksindynamicreal-worldenvironmentsinmanyindustriesandroles,thoughtheystilllargelylaghumansintheserolesunlessdevelopedspecificallyforthatrole.

Thestateoftheevidenceisinsufficienttodiscountanyofthescenariosoutlinedinthispaper,orvariationsthereupon.Theviewsofconsultedexpertsalignedwiththisassessment.ThissuggeststhatAIprogressby2030hasaplausiblerangethatincludesbothaplateauatapproximatelytoday’slevelofcapabilitiesandrapidimprovementthatleadstoAIsystemswhichbroadlysurpasshumancapabilities.

ConsultedexpertsexpressedhighuncertaintyandlowconfidenceintheirabilitytopredicttherateofAIprogressby2030andbeyond.ThisreflectstheextremelyrapidrateofinnovationinAIsystemsoverrecent

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yearscombinedwithhighuncertaintyabouttheextenttowhichrecentdriversofAIprogresswillcontinuetodrivefurtherprogress.

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1.Introductionandmethodology

1.1.UnderstandingpossibleAItrajectorieswillenablegovernmentstocapturethebenefitsandprepareforpotentialimpacts

AIhasadvancedrapidlyinrecentyears.AIsystemsarenowabletodraftacademicessaysatthelevelofuniversitystudentsandsolvecodingproblemsatthelevelofhumanprogrammers(Yeadonetal.,2025[2];HouandJi,2024[3]).MoreadvancedAIsystemsareroutinelydevelopedwhilegovernments,economiesandsocietiesareracingtokeepupwiththepaceofchange.

GovernmentswouldbenefitfromabetterunderstandingofhowAIcouldcontinueadvancing.ThisunderstandingwillhelpinformpolicydecisionsthatbestcapturethebenefitsofAIprogressandprepareforitspotentialimpacts.

Toaddressthispressingpolicyneed,theOECDhasdevelopedasetofexpertandevidenceinformedscenariosforhowAIcouldadvanceby2030.WhileAIadvancesthrough2030cannotbepredictedwithcertainty,governmentscanconsidertherangeofplausibletrajectoriesinformedbythebestavailableevidenceandexpertinsights.Thisscenariosanalysisaimstoprovidethatbaseline,outliningarangeofplausibletrajectoriesforAIprogressby2030.

1.2.ExploringtrendsanduncertaintiestobuildfourcorescenariosforplausibleAItrajectoriesthrough2030

Thisanalysisissupportedbyacombinationofinputs:

a.Reviewofrelevantliterature:thispaperdrawsonawiderangeofcutting-edgeresearchandevidencetoinformitsanalysis.

b.InterviewsandreviewbyleadingAIexperts:thispaperdrawsoninputsfromexpertswithdiversebackgroundsandperspectives.Expertsinterviewedorinvolvedinreviewingthisanalysisaredetailedin

AnnexA

.

c.Scenariosanalysisusingstrategicforesightmethods:thispaperemploysstrategicforesightmethodstotestassumptionsandbuildscenariosaboutplausibleAIfutures.Strategicforesightmethodsusedincludetrendanalysis,horizonscanning,drivermappingandtechnologyroadmapping.

d.Trendextrapolationtosupplementthescenariosanalysis:thispaperdrawsonexistingdataofhistorictrendsinAIprogresstoextrapolateplausibleratesofprogressthroughto2030.Ratherthanbeingtheonlymethodusedtogeneratethescenarios,thistrendanalysissupplementsthescenariosandhelpstomakethemcohesiveandconcrete.

Thescenariosexploredinthispaperareplausiblebutuncertainfuturesintendedtoinformpolicydiscussions,notpredictions.GiventheuncertaintyregardingfutureAItrajectories,probabilitiesarenot

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assignedtothedifferentscenarios.TheanalysisofAIcapabilitiesinthispaperisbasedoninformationavailableuptoOctober2025.

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2.AIprogresstrendsanduncertainties

2.1.AIsystemshavedemonstratedrapidprogressonawiderangeofbenchmarks

Overrecentdecades,theperformanceofleadingAIsystemshasimprovedquicklyonawiderangeofbenchmarksandtests(see

Annex

B).OnabenchmarkofPhDlevelsciencequestions,AIsystemsnowoutperformhumanexperts,arapidimprovementfromscoringonlyslightlybetterthanchancein2023(EpochAI,2025[4]).In2025,AIsystemsachievedgoldmedallevelperformanceintheInternationalMathematicalOlympiad,aprestigiouscompetitionforpre-universitymathematicians(Kazemietal.,2025[5];Metz,2025[6]).Theyalsoachievedgold-medallevelattheInternationalCollegiateProgrammingContestWorldFinals,wheretopuniversityteamscompetegloballytosolvecomplexprogrammingproblems(LinandCheng,2025[7]).AIsystemscontinuetoimprovetheirabilitytosolvelonger,morecomplextasksautonomously,includingtaskssuchasvision-guidedcomputeruse,softwareengineering,videointerpretation,andsimulatedobjectmanipulation(METR,2025[8]).AIsystemshavealsoadvancedrapidlyintheirmultilingualabilities,achievinghumanparityinabenchmarkoftranslationqualityforwidelyspokenlanguages(Proietti,PerrelleandNavigli,2025[9]).AnewbenchmarktestingAIsystemsonprecisely-specifieddigitaltasksperformedbyworkersfrom44occupations(rangingfromindustrialengineerstonurses)foundthatleadingAIsystem’soutputsmatchedorwerepreferredtohumanoutputs47.6%ofthetimebyexpertgraders,indicatingnearparitywithhumanperformanceonthesetasks(Patwardhanetal.,2025[10])

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.Benchmarksandtestssuchastheseareimperfect,buttheyrepresentdevelopersandexperts’besteffortstoquantitativelyassesstheabilitiesofdifferentAIsystems.

Despitetheserapidgains,humansstilloutperformAIsystemsinimportantareas.AIsystemslaginseveralareassuchascontinuallearning,metacognition,agency,solvingdynamicandreal-worldproblems,generalisingtosolvenovelproblems,creativity,physicaltasksandsocialinteractionindynamicsocialcontexts(OECD,2025[1]).Issuesofrobustnessandhallucinationscontinuetosubstantiallyimpactreliability(Song,HanandGoodman,2025[11]).AIperformanceisalsohighlyunevenacrosslanguages,withAIperformanceonreasoningtasksdroppingsubstantiallyinlowresourcelanguages(Xuanetal.,2025[12]).ForfurtherdiscussionoftrendsinAIsystemcapabilities,ongoinglimitations,andlimitationsofAIbenchmarks,see

AnnexB

.

2.2.KeyuncertaintiesaboutfuturetrendsinAIprogress

2.2.1.Therelationshipbetweenscalingofpretraining

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andperformancegains

Indeeplearning,theAIsystemlearnspatternsfromdatainsteadofbeingexplicitlyprogrammed.Deeplearningmodelshave“parameters”,whicharenumbersusedbythemodelstoencodeeverythingtheylearnfromthedata.Thevalueoftheseparametersissetby“training”themodelonalargeamountofdata.Throughtraining,themodelgraduallyupdatestheparametervaluesasitseesthosedata,sothat

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itgetsbetteratwhateverobjectiveitisbeingtrainedfor.Thisprocessoftrainingrequirescomputingpower(“compute”)toprocessthedataandupdatetheparameters.Computeisalsorequiredto