2026“猜想机器”：AI 智能体与科研领域全新验证瓶颈报告

July2026

ConjectureMachines:

AIagentsandthe

newvalidation

bottleneckinscience

By

DonWallace

,

ConorGriffin

,

SeanO’Neill

,

ThangLuong

,

OwenLarter

Overthepastyear,agentshavetransformedwhatitmeanstocode.Scientificresearchmaybenext—fromproposingnovelhypotheses,todesigningexperiments,todiscovering

algorithmsthatimproveonthebestthathumanshavedesigned.

Thisraisesurgentquestionsforpolicymakers

andsciencefunders,thebiggestbeinghowto

validatethecomingwaveofAI-generatedideas.

Newapproachesarealsoneededtoensurethatscientistscanaccessagents,thatdatasetsareagent-ready,andthatpeerreviewiskeptafloat.

comesnext.

Wesatdownwith10researchersandengineersfromGoogleDeepMindtotrytofigureoutwhat

2

GoogleDeepMind3

Introduction

MicrobiologistJoséPenadésandhisteamatImperialCollegeLondontookmostofadecadetoworkouthowafamilyofsuperbugsspreadsantibioticresistance.Theresultwasunpublished,knownonlyinsidehislab.Then,in2024,hedescribedtheproblemto

Co-Scientist

,anAIagentfromGoogleDeepMind.

Withintwodays,Co-Scientistreturnedfivepotentialexplanations,rankedinpriority.Number1wasthesamehypothesisPenadés’steamhadspentsolongworkingtoprove:thatsomesuperbugs

acquiretailsfromvirusesandusethemas“keys”tojumpbetweenhostspecies.Stunned,hisfirstthoughtwasthathiscomputerhadbeencompromised,soheemailedGoogletocheck.Confirmed:nopeekstaken.Ifhecouldhavegonebackintime,thatinsightwouldhavesavedhisteamyears.

An

AIagent

suchasCo-Scientistisalargelanguagemodel(LLM)-basedsystemgivenagoalandthetoolstopursueit.Unlikeaquery-answeringchatbot,anagentcanplanhowtoachievethegoalyougiveit,breakingitdownintosteps,runningmultiplesubagentsandprocessesinparallel,and

detectingandcorrectingerrorsasitgoes.Itcanalsoengagewiththewiderworld,forexamplebycallingonlinedatabasesandtools,orwritingcodetooperaterobots.

ThearrivalofAIagentsistimely.Researchersfacearapidlygrowing“burdenofknowledge”,withmoretolearnbeforetheycanmeaningfullycontribute.Thequestionsthatcountindrugdiscovery,climatemodelling,materialsdesign,andbiologyarebecoming

toocomplexandinterdisciplinary

forhumanteamstotackleatpace.

Sciencedependsinpartonasocialinfrastructurethathasgraduallyevolved:labs,teams,

institutions,peerreview,grantfunding,andnetworksthatsupporttheaccumulationofsharedknowledge.TheeraofAIagentswillchallengethatinfrastructure,andattimesdemandrapidchange—someofwhichisperhapsoverdue.

Softwareengineeringhasbeengoingthroughaversionofthis.Inbarelyayear,codingagentsreshapedtheworkingpracticesofengineers.AIagentsare

nowstartingto

rreshapescience.

Scienceismessier,butitalsosuitsagentsincertainways:alargeliteratureavailableastext,hugedatabases,andsomeworkflowsalreadybuiltaroundcode.SciencealsohasanexistinggenerationofspecialistAImodelsthatagentscanputtowork,from

protein

and

materials

designtoolsto

state-of-the-art

weatherforecasting

.

GoogleDeepMind4

WhyareAIagentssuddenlysouseful?

AnybodywhohasworkedwithearlyiterationsofAIagentsmaybescepticaloftheirutilityin

science,whererigourandreliabilityareessential.Butthreeforcesaremakingtoday’sagents

moreuseful

:strongerfrontiermodels,“scaffolding”thatenablesagentstoelicitgreater

capabilitiesfromthosefrontiermodels,andcustomisabilitythatallowsscientiststotailoragentstotheirneeds.

Strongerfrontiermodels.Leadingmodelsnowoutpacehumanexpertsondemandingscientificknowledgebenchmarks,suchas

Humanity’sLastExam

and

FrontierMath

.Perhapsmoresignificantistheirnewdepthofthinking,drivenby

inference-timereasoning

,atechniqueinwhichmodels

workthroughproblemsinextendedsteps,exploringandrevisingbeforecommittingtoananswer.

Inmathematicsandcomputerscience,wherebreakthroughscanrestonreasoningalone,

models

areproducingimpressiveresults

.Butsharperreasoninghelpsbeyondmathematics,enabling

agentstodrawonthe“longtail”offindingsthatareoftenburiedinpapers,makescientificconnectionsacrossfields,judgewhichtoolstocallandwhen,andcatchtheirownerrorsastheygo.

GoogleDeepMind5

Scaffolding.Thisisatypeof“harness”thatenablesAIagentstobetterelicitthelatent

capabilitiesthatresideinbasemodelsbutwhichdonotemergebydefault.Thiscode“wraps”a

frontiermodel,givingitstructure,memory,andtheabilitytoaccessandusetoolsincludingcode

execution,scientificsoftware,andliteraturesearch.ItalsoletsagentscallonspecialisedAImodelsandinteractwithotheragents.

Earlyagentscaffoldswerebespoke:theygaveaparticularagentdetailedinstructionsforhowtoplan,carryouttasks,andusetools.Butoftentheydidnottransferwelltootheragentsystems,oreventolatergenerationsofthefrontiermodelstheydependedon.Newstandards,suchas

protocolsforhowagentscancommunicatewitheachother

,shouldreducetheneedforsomuchcustomscaffolding.

Customisation.Agentscanbetailoredtoadiscipline,alab’sworkflow,oranindividualresearcher.Akeymechanismforthatisthecurrentproliferationof

agent“skills”

thatuserscancreatetotheirowndetailedspecs.

Basemodelsholdagreatdealofexplicitscientificknowledge,thekindwrittendowninpapersandtextbooks.Whattheylackistacitknowledge:thehard-to-articulatecraft,builtupoveryearsof

practiceandfailure,thatletsascientistcoaxacelllineintogrowingorgetasimulationtorunwell.Untilnow,scientistsusingAItoolshadtoconveytheirmethodologicalknow-how,processes,andpreferencesthehardway:throughdetailedprompts,bespokescaffolding,ormodelretraining.

Agentskillsmakesomeofthisknow-howportable.Theyareinstructionsets,oftenjustsimple

textfiles,thattellanagenthowtoperformataskandwhattoproduce.Skillsarerelativelyeasytoproduce,shareandaccumulate.Asascientistusestheiragentmore,theagentcanalsodrawontheseinteractions,makingtheexperiencemorepersonalised.Scientificlabsandinstitutionscanalsomaketheirproprietarydata—suchasoldlabnotebooks—securelyavailabletotheiragents.

NatashaLatysheva,acomputationalbiologist,saysshehasdistilledaspectsofherresearchprocessintoskills.“WhileAIagentsaren’tfullyreliableyet,Ithinkthetrendisclear.Scientificresearchwillshiftfromhands-onexecutiontohigh-levelorchestration,”shesays.“We’llstartourworkdaybyreviewingtheexperimentsandanalysesouragentsranovernight,tweaking

theirdirectionandguidingtheirattention.”

“

WhileAIagentsaren’tfullyreliableyet,Ithinkthetrendisclear.

GoogleDeepMind6

HowAIagentsarechanging(andnotchanging)science

Themostimmediatechangeforscientistsisalsothemostmundane:theavailabilityofsmart,

tirelessdigitalassistants.Researchersarehandingoffmoreoftheirdailygrind—siftingthe

literature,queryingdatabases,orchestratinganalyses,curatingdata,writinggrantproposals—toagentsthatdoitinminutesratherthanhoursordays,thenreportback.

Agentsalsoopenupnewpossibilities.“Researcherscansuddenlydothingstheycouldn’t

doatallbefore,”saysMatejBalog,aSeniorStaffResearchScientist.Forexample,plentyof

scientistslacktheskillstobuildthesoftwaretoolsandpipelinestheyneed.Thisispartlybecausedevelopingsoftwareforscience

ishard

.Butalsobecausemostscientistsarenotdeeplytrainedinprogramming,andsciencehas

nottraditionallyprovidedcompetitivecareerpaths

fordedicatedresearchsoftwareengineersinacademiclabs.

Agentsareparticularlystrongatwritingcodebecauseitisadomainwithanenormousamountoftrainingdata,wherecorrectnesscantypicallybecheckedautomatically.Ascientistcannowdescribewhattheyneedinnaturallanguage—“writemeascripttocleanandmergethese

threedatasets”,“buildmeaninteractivebrowser-basedtooltoexplorethisoutput”—andgetserviceablecodeinminutes.

Butultimately,thebiggestchangeforscientistsisastructuralone.Agentsaremakingiteasiertocomeupwithideasandproposedsolutionsforproblemstheyareworkingon,butarenotyetprovidingthesameupliftwhenitcomestovalidatingthem.

GoogleDeepMind7

Ideation

Mostscientistshavenoshortageofideas.Thechallengeisknowingwhichonestopursue.This

iswhereagentsarestartingtohelp.Anagentcandigestafield’saccessibleliterature,making

connectionsacrossdisciplinesthatnosingleresearcherwouldhavetimetotrace.Existing

Deep

Researchtools

alreadyusesubagentstodothis.Butagentsoptimisedforideationgoevenfurther.Co-Scientist,forexample,directsaraftofsubagentstogeneratediversehypotheses,critique

them,rankthem,anditerate—mimickingaspectsofhowahumanresearchgroupoperates,butatremarkablespeed.

GaryPeltzatStanfordUniversityusedthetool

inhishuntforexistingdrugsthatcouldbe

repurposedtotreatliverfibrosis,thescarringprocessbehind1.4millioncirrhosisdeathsayear.

Basedonhisownliteraturereviewanddecadesofexpertise,hepickedtwocandidatedrugs.Co-

Scientistpickedthree.NeitherofPeltz’spicksshowedanybenefitinassayswithlivehumanliver

cells.TwoofCo-Scientist’spicksnotonlyblockedfibrosisbutalsopromotedlivercellregeneration.

Thisisapositiveexample,butanyLLM-

basedsystemisfallible—eventhestrongestreasonerscanstillmakethingsup.“Asinglehallucinatedclaimonpage10ofanoutput

caninvalidatethewholething,”saysVivekNatarajan,aCo-Scientistlead.Missing

sucherrorswastestimeandresources,and

catchingthemcanrequireascientistwith

deepexpertise.ThisfallibilitymakesscientistsunderstandablycautiousaboutAIagents,andreducingitisatoppriorityforthosebuilding

“

Asinglehallucinated

claimonpage10ofanoutputcaninvalidatethewholething.

thesesystems.Tofunctioneffectivelyinaresearchsetting,agentscannotactasblackboxesthatsimplyoutputanswers;theymustexposetheirreasoningandserveastransparentcollaborators.

OnekeychallengeisimbuingagentswithwhatNatarajancalls“epistemichumility”:modelsthatknowwhentheydon’tknow,andsayso.HepointstoAlphaFold,theprotein-structurepredictor,whichisprizedpartlybecauseitreportshowconfidentitisineachaspectofeachprediction,

soresearchersknowwhentotrustitandwhentoreachforothermethods.Calibratingthatconfidenceacrossmoreopen-endedscientificreasoningremainsanunsolvedproblem.

Lookingfurtherahead,theharderproblemmaybeatensionbetweentwothingsscientistswant

atonce:hypothesesthataregroundedintheliteratureandfreeoferror,butalsogenuinelynovel.Agentstunedforcautioncandrifttowardthesafeandtheknown;tunedfororiginality,theyare

morelikelytogoastray.Thischallengewillitselfrequirenewideas,suchas

betterways

toevaluatenovelty.Orbetterwaystoconnectagentstomorespecializedmodels—trainedonfirst-principlesscientificdata—tohelpgenerateideasthatarebothoriginalandrobust.

GoogleDeepMind8

Findingtheoptimalcandidatesolution

Agentscansearchforthebestsolutiontoaproblem,notjustaworkableone.Amaterialsscientisthuntingforanewcatalystfacesanear-infinitenumberofmolecularstructures,eachcostlytomakeandtest.Acomputerscientistlookingforamoreefficientalgorithmfacesasimilarexplosionof

possibilities.

Vastsolutionspacesliketheseturnupallacrossscienceandindustry.

AlphaEvolve

isanagentthatcanfindthebestcandidateswithinthem.Givenaproblemexpressedascodeandawaytoscorepotentialsolutions,itorchestratesanensembleofagentstogeneratemanyalgorithmic

candidates,keepsthosethatscorehighest,andbreedsthenextgenerationfromthesurvivors.Itrunsunattended,generatingandimprovingcandidatesatascalenohumanteamcouldmatch.

AlphaEvolveworksincode,butitsreachextendswellbeyondsoftware.AsBalogpointsout:“Algorithmscanaccuratelydescribesomanyoftheworld’sscientificandnaturalprocesses.”

AlphaEvolvehasassisted

inthedesignofGoogle’snext-generationTPUchips,helpedthe

mathematicianTerenceTaosolveopenErdősproblems,andimprovedtheanalysisofgenomicsdata.

Butinscientificdomainssuchasmaterials,AlphaEvolve’stopscorersareleads,notfinalresults.Apromisingcatalyststillhastobemadeandmeasuredatthebench.

Validation

Validationistheslow,costlybusinessof

testingwhetheranideasurvivescontactwithreality.KarlPoppersaidscienceadvances

throughconjecturesandrefutations.AgenticAIischangingtheeconomicsofthatpairing.AIagentsareconjecturemachines,makingideasandcandidatesolutionsabundantandrelativelycheap.Refutationsremainphysicalandinstitutional—andso,costlyandslow.

Mathematicsandcomputerscienceareoftenviewedasgreatexceptionsbecausevalidation

“

Algorithmscan

accuratelydescribe

somanyoftheworld’sscientificandnatural

processes.

canruninsilico.AnAIagentcangenerateaproof,representitinaformallanguagelikeLean,andhavethecomputerverify,unambiguously,thatitiscorrect.

Evenforthepartsofmathsthatcan’tyetbedescribedinformallanguage,validationisadvancing.

Aletheia

pairsaproofgeneratorwith

anaturallanguageverifier

thatchecksitsworkandsendsflawsbackforrevision.InFebruarythisyear,mathematiciansrantheinaugural

FirstProofchallenge

:10researchproblems,keptunpublishedsotheycouldn’tbefoundinanytrainingdata.Intheweek

allotted,Aletheiasolvedsix—thebestresult.

GoogleDeepMind9

Mathematicianswillneedtoabsorballthesenewoutputs.SomealreadycomplainthatAI-

generatedproofsaretoolongandhardtoparse(although

certain

AI-generatedproofsareshortandelegant).“Wearemovingtowardafutureofserious‘proofindigestion’whereAIgenerates

breakthroughsfasterthanhumanscanreviewthem,”saysThangLuong,wholedtheAletheia

effort.Tobreakthisbottleneck,

automatedverification

mustbecomestandard,butthis

verification

willneedtocombinetheabsolutecorrectnessofformallanguageslikeLeanwiththemore

expressivereasoningofnaturallanguage.”

AlexDavies,whoalsoleadsworkonAIformathematics,ismindfulthatbyautomatinglargechunks

ofmathematicians’workflows,hisdisciplineisdealingwithquestionsthatothersmayfaceinthecomingyears:“Icanimagineaworldin

whichmachinesdothediscovery,andwhat’sleftformathematiciansistounderstand

it,andtodecidewhatquestionsareworth

pursuingnext.”Luongechoesthis,notingthatmathsmayalsoprovidesomeof

thegeneral

technology

neededtoaddressthevalidationbottleneckinotherfields:“Onecanthinkof

mathematicsasanacceleratedtestbedfortherestofscience”.

“

Wearemovingtowardafutureofserious

‘proofindigestion’

whereAIgeneratesbreakthroughsfaster

Anagentcan

propose

anovelgeneticleadreviewthem.

Atthemoment,however,thevalidationgapthanhumanscaninmostdisciplinesiswidening,notclosing.

toreversecellularageing,butcannotsay

definitivelywhetheritactuallyworks.ThisexplainswhycompanieslikeGoogleDeepMind,GinkgoBioworksandLilaSciencesareinvestinginautomatedlabs.Buttheyonlysuitsomefields,are

expensivetobuildandarestillearlyindevelopment.Andevenautomationcannotrushnature’s

clock.Celllinesneedtimetogrow,chemicalreactionstaketimetocomplete.Formuchofscience,then,thelabsetsthepace.

GoogleDeepMind10

Implicationsforpolicymakersandresearchfunders

Tosomeextent,agentssimplyaddintensitytoquestionsthatpolicymakersarealreadyfocusedonintheir

AIforSciencestrategies

,suchashowtotrainthenextgenerationofscientists,howtoexperimentwithnewformsofscientificinstitutions,andhowtoensurethatAIisnotmisusedbythreatactors,whilestillputtingthetechnologytouseaddressingthevariousnaturalrisksthatsocietyfaces,likethenextpandemic.

Foreveryencouragingscenario,thereisachallengingone.Forexample,AIagentscould

makeitmorefeasibleforsmall,agileteamstopursuecreative,ambitiousideas,reversingthetrendtowards“bigteamscience”,orenablescientists

toworkacrossdomains

,bringingnewperspectivestoexistingproblems.Assumingefficiencygainsmakeagentssufficientlycost-effective,thesetrendscouldparticularlybenefitsmaller,lesswell-resourcedcountriesand

institutions.

Butagentswillalsogiverisetoanxietyamongjuniorscientiststhattheirinstitutionsarechoosingtospendbudgetsontokensinsteadofstaff.Andleftunmanaged,thereisariskthatagentictoolscouldde-skillnewgenerationsofscientistsbeforetheydevelopthejudgementneededtouse

themeffectively.Forthesamereasonthatmathematicsstudentsstillprovetheoremsunaided,

science-graduatetrainingmayneedstructuredperiodsofagent-freeworkandaccesstoagentsthatactasgenuine

cognitivepartners

ratherthanoracles.

Beyondthesequestions,AIagentspresentatleastfoururgentnewpriorities:1.Scientists

needaccesstothetools.2.Thetoolsneedaccesstoagent-readydata.3.Weneedmore

experimentalinfrastructuretovalidateAIideas.4.Andweneedtoupdatethepeerreviewprocess.

1Ensurewidespreadaccesstoagents

Agentswillbeextremelyusefulandfallibleinnon-obviousways.Bothfactorsprovideastrong

rationaleforpolicymakerstoensurethatallscientistscanaccessthebestagents—tospeedupdiscoveryandtoprovidetheindependentevaluationsofAIagentsthatthescientificcommunityneedstojudgehowbesttousethem.

Thisisanurgentstrategicpriorityforpolicymakersandsciencefunders,akintothehistorical

challengeofprovidingaccesstosupercomputers.Geopoliticaldebatestodayoftenfocuson

oneaspectofsovereigncapability—whetherastatecantrainitsownfrontiermodel.Muchlessattentionispaidtowhatmayprovetobeamoreimportantissue:acountry’sabilitytodeploy

agentsacrossitsscientificecosystemsfortransformativeimpact.

Atthemicrolevel,fundersmustfirstdecidehowlabsandresearchersselectandpayfor

agents.Selectionistheeasiernear-termproblem:letresearchersfindthemostusefultoolsforthemselves,withoutexcessiveapprovalsorcomplexprocurement.

GoogleDeepMind11

Payingisharder.Thetemptationistouseexistingstructures,withscientistsseekingfunding

throughgrantapplicationsordrawingonlabbudgets.Butthecomputerequiredtorunagentscanbelargeandthefrontierofwhatagentscandoisconstantlyexpanding.Whilethecostperunit

ofAIcapabilityisfallingfast,totallabexpendituresonagentsarestilllikelytoriseasagentstakeonlongerandmorecomplextasks.Policymakersmustquicklyassesswhetherbudgetupliftsor

entirelynewfundingprogrammesareneeded.Deliveringaccessatthescaleandpriceneededwillrequirenovelpublic-privatepartnerships;the

USGenesisMission

isone

promisingmodel

.

Biggerquestionsawait.Agentsmaymaketheexistingprocessofallocatingnationalbudgets

acrossdisciplinesmorelegible,forcingsciencefundersandresearchprogrammestoquantifytheinvestmentindataandcomputeneededtomakeprogressonspecificproblems.Thisinturnmayleadtomoretargeteddebatesabouttherelativevalueofsolvingdifferentproblems.Ifagents

proposethetophypothesestoexploreacrossanentirefield,withveryexpensiveexperimentalvalidationplans,howshouldthisfitintonationalfundingstrategies?

2Makenationaldataassetsagent-ready

Whilescientistsneedaccesstoagents,agentsneedaccesstodata.Datathatisopenorlow-riskshouldbeexposedtoagentsthroughwell-documentedAPIs,withsufficientqualitycontroland

metadata.Theengineeringsupportandmaintenancetodosoisnottrivial,sofundersshould

ensuresuchdatastewardshipisproperlyresourcedandsupportinteroperabledatastandards.Butultimately,theabilityofagentstohelpextractandannotatedata—fromPDFstodownloadportals—providesanopportunityforgovernmentstoextractalotmorevaluefromthedatatheyhave

alreadyfunded.

Moresensitivedatasetsingenomics,virology,orotherareascarryingdual-useriskoftencome

withrestrictionsonwhomayusethemandhow.Thechallengenowistodevelopsimilarprivacy-preserving

solutions

,whenappropriate,foragents,withauditabilityandprivacybuiltin.Exampleslike

OpenSAFELY

,whichletshumanresearcherssecurelyaccessvaluablehealthdata,canprovideinspiration.Theprizeislarge.Adatasetanalysisthatcurrentlytakesyearsofdoctoralworkcould,withtherightsecureagentinfrastructure,runautonomouslyindays.

Perhapsmostimportantly,agentsprovideastrongrationaleforfundingthecreationofentirely

newopendatasets.Thisleadstoafurtherquestionforfunders:couldagentshelpidentifythe

mostimportantdatasetstofund?Someoftheauthorsofthisarticlerecentlymadeahuman-

expert-drivenattempttoanswer

thatquestion

orfusionenergy.Howsoonwillagentsbecapableofrunningsimilar“AIdatastocktake”exercises?

GoogleDeepMind12

3Tacklethevalidationbottleneck

Manyscientistsalreadystruggletogetenoughtimeinfacilitiestoruntheirexperiments.AsAI

agentsmakehypothesesandcandidatesolutionsincreasinglyabundant,thisbottleneckwillonlytighten.Policymakersandfundersshouldaddressthisinatleasttwoways:investinginexistingexperimentalvalidationinfrastructureandacceleratingprogressonautomatedlabs.

Publicresearchbodiesholdextensiveexperimentalfacilitiesacrossalmosteveryscientificfield.AIagentsprovideareinvigoratedcaseforinvestinginthemandopeningthemup,byrentingbenchspaceorexperimentalrun-timetoresearcherstestingcomputationalhypothesesandpredictionsagainstreality.DirectpartnershipswithAIlabsareanotheravenue.GoogleDeepMindhascreatedawetlabinsidetheUK’sFrancisCrickInstitute,aleaderinbiomedicalresearch,andisalso

providingindependentscientistsfunding—alongsideCo-Scientistaccess—tocarryoutthewetlabexperimentsneededtovalidateagent-enabledhypotheses.TheUSgovernment’s

Genesis

Mission

willconnecttheworld-classexperimentalfacilitiesoftheDepartmentofEnergy’s(DOE)NationalLaboratorieswithacademiaandtheAIindustry.

Automatedlabsareanotherpromisingroutetotacklingthevalidationbottleneck,butthey

currentlyrelyonexpensiveroboticsandcompute.Ensuringbroadaccesswillrequirepublic

investment,andtherearegoodearlyeffortshere.TheUS

NationalScienceFoundationhasput

$100mtowardsanationalnetworkofdistributedfacilities,whiletheUK

launched

acallforideasandalreadyhoststhe£81million

MaterialsInnovationFactory

.Thebuild-outofautomatedlabswillalmostcertainlygobeyondwhatanysinglelaborinstitutioncanafford,sogovernmentsshould

alsoexplorebuildingcentralisedcapacityandadoptingthekindof“

userfacility”accessmodel

seenattheUSDOE’snationallabs.

4Empowerpeerreviewerswithagents

The

peerreviewprocesshaslongbeenunderstrain

,withslowtimelinesandreviewsofvariedquality.Now,scientistsareusingAItowriteevermoregrantapplicationsandpapers.Thisis

makingitharderforfunderstoknowwhichresearchtofund,andforpeerreviewerstovalidatefindingsandidentifythemostimportantwork.

As

noted

byProfessorsJamesWilsdonandGeraintRees,thechallengeisnotjustanincreaseinsupply;writingqualityisalsonolongerareliablediscriminator.Agentsdeepentheproblem.Themoreanagentislefttoplanandoptimiseanapplication,drawingonthefunder’scriteriaanditsrecentwinners,thelessthebidreflectsascientist’soriginalthinking.

Funder