AI科研协作者：从生物学到黑洞ChatGPT加速科研创新+AI+as+a+Scientific+Collaborator：From+Biology+to+Black+HolesChatGPT+is+Accelerating+Research

AIasaScientificCollaborator

Frombiologytoblackholes,ChatGPTisacceleratingresearchJanuary2026

Introduction:WhyAIforScienceMatters

8.4million

averageweeklymessages

onadvancedtopicsinthehardsciencesandmathematics

Roughly

1.3million

weekly

ChatGPTusers

focusonadvancedmath&sciencetopicsworldwide

Thenumberofmonthly

advancedsciencemessages

grewnearly50%

lastyear

OpenAIisbuildingtoolstohelpresearchersgenerateinsights,acceleratescientificdiscoveryandtranslatethoseinsightsintoreal-worldimpact.AcrossChatGPT,researchers,students,STEMfacultyandengineersalreadyuseAItoreadandsynthesizetechnicalliteratures,debugandwritecode,analyzedata,andplanexperiments.Eachweek,ChatGPTseesalmost8.4millionmessagesonadvancedtopicsinthesciencesandmathematics.Thesenowcomefromroughly1.3millionweeklyusersworldwide.

Onlyabout0.1percentoftheglobalpopulationidentifiesasscientists,accordingto

UNESCO

,andyettheyhaveanoutsizedimpact.Scientificresearchdrivestheengineofprogresstowardahealthier,moreprosperous,andmoreresilientfuture.Newmedicines,newtechnologies,andnewindustriescomefromnewknowledgeputtopracticaluse.Asmallgroupofearlytwentieth-centuryphysicistslaidthefoundationsofquantummechanicsthroughabstractresearchthat,decadeslater,wouldunderpinmuchofthemoderndigitaleconomy,nowmeasuredinthetensoftrillionsofdollars.Basicresearch–workdonebeforethepayoffisclear–wasthesourceofthatknowledge.In1947,scientistsatBellLabscreatedthefirstworkingtransistor,

based

oninsightsfromquantumphysics.Thetransistorbecameabuildingblockofcomputers,phones,andtoday’sdigitaltechnology.TheGlobalPositioningSystem(GPS)reliesonEinstein’s

insightsintorelativity

toguideourcarsandkeepatomicclocksaligned.

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Yetinmanydomains,itisgettinghardertokeepmakingprogress.Economistsandresearchanalystspointtofalling“researchproductivity,”meaningmorepeople,time,andmoneyarerequiredtoproducethesamenumberofinsights.Semiconductorsofferawell-knownexample:sustainingMoore’sLawtodoublethenumberoftransistorsinanintegratedcircuiteverytwoyearshasrequiredadramaticincreaseineffort,withthenumberofresearchersneededtodayestimatedatmorethan

18times

whatwasneededintheearly1970s.Asknowledgegrowsmorecomplex,eachnewgenerationofresearchersfacesaheavierburdenjusttoreachthefrontier,whichlengthenstheirtrainingtimeandnarrowstheirspecializations.Institutionally,researchhasshiftedtowardlargerteams,withgrowingoverheadforgrantproposals,compliance,reporting,andcoordinationcosts.

Inmedicine,scientificadvanceshavesavedcountlesslives.Worldwide

lifeexpectancyrose

fromroughly32yearsin1900toabout73yearsin2023(andtomorethan78yearsintheUnitedStates).Buttheremainingburdenofdiseaseisheavy.ButtheWorldHealthOrganizationreportsthatnoncommunicablediseasessuchasstroke,heartdisease,cancer,anddiabetesstillaccountforabout

74%ofglobaldeaths

.Evenwhenprogressisrapid,turningnewideasintoavailabletreatmentstakestime.Onaverage,ittakes

10-15years

fromtargetdiscoverytoregulatoryapprovalofanewdrugintheUnitedStates,alagimposedonpatientswhoneednewandbettertreatments.

Makingprogressfasterwillsavelivesandimprovethem.AIisalreadyhelpingtoaddressthebottlenecksthatslowsciencedown.Modernresearchisfragmentedacrossdisciplinesandconstrainedbylimitsthatarebothcognitiveandlogistical:readinganddigestingenormousliteraturestodeterminewhatisknown,translatingideasintomathematicsandcode,settingupanalysesandsimulations,checkingcalculations,searchinghugedesignspaces,anddecidingwhichfutureexperimentsarethemostpromising.Usedwell,AIcanserveasahigh-throughputpartnerforthought,computation,andstructuredreasoning,shorteningthecyclefromhypothesistotestandincreasingthecapacityofresearchersworkingaloneandinteams,evenacrossdisciplinarybarriers.

KevinWeil,VPofOpenAIforScience,describestheopportunitythisway:“AIisincreasinglybeingusedasascientificcollaborator,andwe’reseeingitsimpactgrowinrealresearchsettings.Moreresearchersareusingadvancedreasoningsystemstomakeprogressonopenproblems,interpretcomplexdata,anditeratefasterinexperimentalwork.Thatusagehasbeengrowingquicklyoverthepastyear,andtheresultsarestartingtoshowupacrossfields.We’restillearly,butthepaceofadoptionandthequalityoftheworksuggestscienceisenteringanewaccelerationphase.”

OpenAIisproudtoworkwithresearchpartnersacrossgovernmentagencies,nationallaboratories,academia,andmedicine,includingtheU.S.DepartmentofEnergy,LawrenceLivermoreNationalLaboratory,theU.S.CentersforDiseaseControlandPrevention,HarvardUniversity,MassachusettsInstituteofTechnology,theUniversityofOxford,TexasA&MUniversity,andBostonChildren’sHospital.

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Thisreportdetails:

1.HowAItoolsarealreadybeingusedinday-to-dayresearchworkflows,includingliteraturesynthesis,codegenerationanddebugging,dataanalysis,simulationsupport,andexperimentplanning

2.WhatearlyresultssuggestaboutAI’spotentialtosupportnewbreakthroughs

3.HowindividualscientistsacrossmultipledisciplineshaveusedChatGPTtomakeprogressintheirfield

4.PolicysuggestionstosupportcontinuedAIprogressinscienceandmath

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ThescaleofscientificandmathematicalworkonChatGPT

AcrossChatGPT,asmallbutconsequentialcohortofresearchersusesOpenAI’sAImodelsforsophisticatedtasksrangingfromtechnicalderivations,advancedmathematics,engineeringsimulationandmodeling,andotheradvancedproblem-solving.ThisincludesscientistsandmathematiciansspanningPhDcandidatesandpost-docstoworkingresearchersandSTEMfaculty.

BasedonaninternalanalysisofafullrandomsampleofanonymizedChatGPTconversationsfromJanuarythroughDecember2025,averageweeklymessagecountsonadvancedscienceandmathtopicsgrewabout47%,from5.7millionmessagestonearly8.4millionmessagesoverthecourseof2025.AsofJanuary2026,therearenearly1.3millionweeklyusersdiscussingadvancedtopicsinscienceandmath.

Together,thesesignalsshowhowChatGPTisacceleratingadvancedresearch:withtensofmillionsofadvancedhard-scienceandmathpromptseachmonth,generatedbyalargeandgrowingcohortusingthesystemforseriousscientificandengineeringworktobenefitsocietyandsupporteconomicgrowth.

Here’showusagebreaksdownacrossdisciplinesamongourcohortofresearch-focusedusers:

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WhatscientistsandmathematiciansactuallydowithChatGPT

Scientists,mathematicians,andengineersuseChatGPTasahighlyavailabletechnicalcollaborator:atoolwithwhichtheycaniterateoncalculations,translateideasintocode,interrogateassumptions,andcompresscomplexmaterialsintoworkablementalmodels.InOpenAI’sanalysis,“advanced”hardsciencepromptsaredefinedasthosebeingorientedtowardresearch,andrequiringgraduate-levelorresearch-levelexpertisetoanswercompetently.Withinthatcohort,behaviordiffersfromtypicalusersinwaysthatmapdirectlyontomodernresearchworkflows.

Researchtasksclusterindomainssuchascoding(drafting,reworking,anddebuggingcode),dataanalysis(cleaningandmergingdatasets,runningstatistics,interpretingresults),mathematicalreasoning(derivations,proofstrategies,algebraicchecking,longcalculations,translatingbetweenformalisms),andliteraturereviewandsynthesis(findingreferences,understandingrecentwork).

ContrastedwithtypicalusersofChatGPT,advancedscienceandmathusers:

●Sendroughly3.5×moremessagesthanthebaseline

●Sendcoding-relatedmessagesnearly12×moreoften

●Average9informational-overviewpromptsperweekvs.1.5prompts

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Categorizedbymostcommontasks,hereishowthiscohorttendstouseChatGPT,:

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AIatthefrontierofmathandscience

FrontierAIcapabilitiesinmathematics

Recentprogress

Overthelasttwoyears,largelanguagemodelshaveprogressedfromearly,unevenperformanceonbasicarithmetictohandlingmulti-stepmathematicalreasoningthatcanbeusefulinrealmathematicalwork.Muchofthatimprovementcamefrommethodsthatencouragestep-by-stepreasoning,andfromtighterintegrationwithtoolslikecalculatorsandcodeexecutionforexactcomputation.Asmodelsimproved,benchmarkingalsoshiftedtowardhardertestsdesignedtomeasuredeeperreasoningwhilereducing“patternmatching”wins.

In2025andearly2026,thegreatestimpacthascomefromtest-timecomputescaling,or“slowthinking.”Insteadofcommittingquicklytoonepath,amodelwillspendmorecomputationexploringalternativesandself-checking.Atthesametime,approachestotrainingthatrewardverifiableoutcomes,suchasproducingacorrectfinalanswerorexecutablecode,havepushedmathandcodingtobecomemorereliable,andcorrectoftenenoughtobeusefulwithhumanguidance.

OnesignofthisshiftcamewithInternationalMathematicalOlympiadcoverageinJuly2025,whenanOpenAImodelachieved

gold-levelperformance

onthe2025problemsetalongsideDeepMind.

Currentcapabilities

GPT-5.2’ssteadyadvanceinmathematicalcapabilitiesstemsfromstrongerlong-horizonreasoning,moresystematicverificationhabits,andbetteruseofcheckabletools.AIME,theAmericanInvitationalMathematicsExamination,isdesignedtotestmulti-stepproblemsolving:GPT-5.2ThinkingachievedaperfectscoreonAIME2025withoutexternaltools.TheGPT-5.2series(e.g.ThinkingandPro)hasprogressedpastcompetition-levelperformancetowardmathematicaldiscovery,includingworkonestablishedopenproblems.

Onresearch-stylebenchmarking,the“Google-proof”FrontierMathproblemsethasbeenconstructedtobeaccessibleonlytotrueexperts;i.e.evenasmartPhDstudentinmathcannotsolvetheminafewhoursofwork.Onthatbenchmark,GPT-5.2Thinkinghassolved

40.3%ofproblems

inTiers1–3.

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Performancestilldropsonthehardesttier,whereGPT-5.2Prohasscored

31%onFrontierMathTier4

onasetofproblemsthatcanbedescribedas“miniresearchprojects.”

AsecondmajorcapabilityleaphasoccurredasGPT-5.2isincreasinglypairedwithformalverificationworkflows.Inoneprominentintegration,GPT-5.2generatesnaturallanguageproofsandusesAristotle,athird-partyLLM,toformalizethoseproofsinLean,whichisaproofassistantwhereproofsarewritteninaformacomputerchecksstepbystep,withthesystemdetectingandcorrectinggapsduringformalization.

Theseintegrationsmatterbecauseonelongstandingfailuremodeforlanguagemodelsinmathematicshappenswhenasolution“looksright”,butisn’t:i.e.argumentsthatappearplausible,butcontainsubtlegaps.Lean-checkedproofssubstantiallyraisethestandardofconfidenceinaproofbyforcingexplicit,mechanicallycheckedstepsunderastatedformalization.

Erdősproblems,AIsolutions

PaulErdős(1913–1996)wasaglobe-trottingHungarianmathematicianwholivedoutofasuitcase,movingfromcampustocampusashesoughtoutfellowmathresearchers.The“Erdősproblems”arethevastsetofquestionsandconjecturesheposed,rangingfromdeceptivelysimplepuzzlestoproblemsthatstillresistthebesttechniqueswehave.Theproblemshaveactedliketrailmarkersformodernmathematics:clarifyingwhatwedon’tyetunderstand,seedingwholeresearchprograms,anddrawinggenerationsofmathematicianstowardtheedgesoftheknown.

Inearly2026,GPT-5.2hascontributedtosolutionstoseveralopenErdősproblemswiththehelpoftoolslikeAristotleandLean,andwiththesolutionsvalidatedbyTerenceTao.Problems

#281

,

#728

,

#729

arenowlistedasproved,and

#397

asdisproved.WhilemathematicianscautionthatErdősproblemsvaryenormouslyindifficulty,thesesolutionspointtotheincreasingcapabilityofOpenAImodelstodorealmathematicalworkandmakenovelcontributionswithminimalguidance.

Near-termpotential

Somesignificantmathematicaldiscoveriescantaketheformofstitchingknownmethodstogethertofindthecorrectargument.GPT5.2candothisnowinmanycases.Otherdiscoveriesinvolveinventingentirelynewkindsofmath,asNewtoninventedcalculustounderstanddynamics(theforces,massandenergythatexplainchangesinmotion).ThatisbeyondcurrentAImodels.Butathirdtypeofsignificantdiscoveryinvolvesestablishingconnectionsbetweentwofields,andbringingtheknownmachinery,resultsandtoolsofonefieldtotheother(e.g.algebraicgeometry,whicharosefromabstractalgebraandclassicalgeometry).ModestexamplesofthishavealreadyoccurredwithAI,andwebelievethesignificanceofthoseconnections,andthesubfieldscreatedfromthem,willincreaseinthenearfuture.

Atthesametime,muchofAI’snear-termvaluewillbeintransformingworkflows.GPT-5.2canproposeanditerateonsolutionpaths,whileexternaltoolsenforcecorrectnessthroughexactcomputationorformalchecking.Thisalignswithbroadertrendstowardhybridapproachesandauto-formalization,

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whereinformalmathistranslatedintoformallanguageslikeLeansothatcorrectnesscanbeverifiedmechanically.GPT-5.2isalreadyusefulforliteraturereview(surveyingwhatisknown)tolocatetheedgeofknowledgeandsurfaceunexpectedorobscurereferences.Itisusefulincomingupwithproofs,critiquingthem,simplifyingthemandsuggestingproofstrategies.

Ifthistrajectorycontinues,GPT-5.2’snear-termimpactislikelytoshowupasabroadproductivityupgradeformathematicalresearchers,aswellasscienceandengineeringteams(sincemathisacorecomponentofmuchscientificandengineeringwork):thiswillmanifestasfastertranslationfromamessyproblemdescriptiontoacleanmathematicalstatement,fewerdroppedconstraintsinmulti-stepderivations,morereliabledebuggingofcalculationsandproofs,andagrowingshareofresultsthatcanbebackedbyformalverification.

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FrontierAIcapabilitiesinscience

Acrossdisciplinessuchasphysics,chemistry,andbiology,ChatGPT-classLLMsincreasinglysupporttechnicalreasoningandtool-mediatedresearchworkflows,aswellasscientificwriting.BenchmarkslikeGPQA,agraduate-levelsetof“Google-proof”questionsauthoredbydomainexperts,initiallyshowedasignificanthumanadvantage,withexpertsreaching65%accuracywhiletheGPT-4baselinereached39%.OpenAInowreports

GPQADiamondaccuracy

of93.2%forGPT-5.2Proand92.4%forGPT-5.2Thinking(withnotoolsenabledandatmaximumreasoningeffort),suggestingahigherbaselineforgraduate-levelscientificquestionansweringacrossmanyscientificdisciplines.Inparallel,automatingtheunspokendrudgeryofresearch–referencehunting,bibliographyassembly,androutineadministrativereporting–freesscientists’scarceattentionforhigher-valuework.Pairedwithinformationretrievalandexecutabletoolsthatenablecheckablecalculationsandstepwisevalidation,thesemodelsarebecomingreliableworkfloworchestratorsforscientificplanning,analysis,anddocumentation,teeingupaccelerationacrossmanyfields.

Physics

Lastmonth,OpenAIannouncedamemorandumofunderstandingwiththeUSDepartmentofEnergytosupportcollaborationonAIandadvancedcomputinginordertoadvanceDOEinitiativesincludingtheGenesisMission,withapplicationsinenergysuchasfusionresearch.

Inphysics,LLMsarebeingusedacrossmajorfacilities,includingmany

USnationallabs

,asaunifyinglayerovercomplexoperationsstacksandinternalknowledgebases,acceleratinganalysisanddecision-makingunderstrictconstraintsalongsideexistingmachine-learningtoolsforsimulation,real-timedatareduction,andexperimentalcontrol.LLMscandigestshiftlogsandalerts,answerquestionsfrominternaldocumentation,andhelprouteworktotherightanalysis,simulation,orcontroltool,allunderstrictsafety,timing,andresourceconstraints.Thisaugmentsalongertrackrecordofspecializedmachinelearninginphysics:neural“surrogate”modelsthatapproximateequation-governedsimulationswhenfullcomputationistooslow,real-timefilteringandreconstructioninparticledetectorsthatseetensofmillionsofcollisionspersecond,andmachine-learningsupportedcontrollersthatcoordinatethemanyelectromagnetsintokamakfusionexperimentswhilestayingwithinhardwareandsafetylimits.

Near-termgainsinphysicsarelikelytoconcentrateinhigh-throughput,decision-heavysettingswhereexpertattentionandturnaroundtimearethebottlenecks.AIassistantsthatcanreferencealab’sinternaldocumentationandrunautomatedcheckscanturnliveexperimentalerts,notes,andlogfilesintoprioritizednext-stepresearchplansandrepeatableanalyticoutputs,suchasnotebooks,scripts,andreports.

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Intheoreticalphysics,LLMswillcontinuetodelivervalueasthoughtpartnerscompressingresearchers’cognitiveoverheadwhileexpandingthespaceofexploration.Whenacomplexcalculationhitsaroadblock,LLMscansurfacewaystoreframetheproblem,suggestintermediatesteps,andprovidequickconsistencychecksthatreducetimethatphysicistsspendbeing“stuck.”Sometimesthisproducesinsightslikeamissingcondition,acleanerformulation,orausefulrelationshipbetweenexpressionsthatcanbecomeameaningfulingredientofapaper.Thelargermultipliermaycomefromsynthesizingresearchatscale,wheremodelsscantheliteratureacrosspapersandsubfieldstosurfacenewconnectionsautomatically.

Chemistry

ChatGPT’sapplicationsinchemistryhavemovedpastone-shotquestionansweringtowardmulti-stepworkflowsthattranslatebetweennaturallanguageandchemicalrepresentations,andrelyonexternaltoolsforverificationandretrieval.

ChemBench

,publishedinNatureChemistryin2025,curatedmorethan2,700expert-writtenquestionsandfoundthatleadingmodelsoutperformedhumanchemistsonaverage,whilestillstrugglingonsomebasictasksandproducingoverconfidenterrors.

LeadingAIsystemsinchemistryincreasinglyuseahybridworkflow:ageneral-purposeLLMhelpstoplanmulti-stepworkandcoordinatetools,whilespecializedmodelsthatunderstandmolecularstructurehandlepredictionandsimulation.Akeyexampleisstate-of-the-artgraphneuralnetworks(GNNs):thesemodelstreatamoleculelikeanetwork,withatomsasnodesandbondsasconnections,sothesystemcanlearnhowlocalchangesaffectthewholestructure.NewerGNNsaredesignedsotheirpredictionsremainconsistentwhenamoleculeisrotatedorshiftedin3Dspace,whichmakesthemwellsuitedforlearningtheenergyrulesneededtorunfast,accuratemolecularsimulations.Asmoleculargraphmodelsscalewithmoredataandpretraining,resultscontinuetoimprove,buttougherbenchmarksforchemicalreasoning,includingorganicreactionmechanismtaskssuchas

oMeBench

,underscoretheneedforhumanoversight.

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Biology

ChatGPT’sapplicationsinbiologyincreasinglyextendintomulti-stepworkflowsthatcombinenatural-languagequestionswithstructuredscientificsourcessuchasgenomicsdatabases,proteinrepositories,andthebiomedicalliterature,oftenwithcodeandretrievaltoolsusedfortraceabilityandverification.

GeneTuring

,a2025genomicsbenchmarkinBriefingsinBioinformatics,curated1,600questionsacross16tasktypesandmanuallyevaluated48,000answersfrommultiplemodelconfigurations.Thestrongestresultscamefromatool-augmentedsetupthatpairedageneral-purposemodelwithdirectaccesstoNationalCenterforBiotechnologyInformation(NCBI)APIs,reinforcingthatreliabilityimproveswhenlanguagemodelsareconnectedtoauthoritativereferencedataandcanshowtheirwork.

Aswithchemistry,state-of-the-artAI-enabledresearchinbiologyreliesonhybridstacks:general-purposelanguagemodelshelpplanandcoordinateanalysis,whilespecializedfoundationmodelstrainedonbiologicalsequencesandstructurespowerpredictionanddesign.Inproteinscience,

AlphaFold3

representsasteptowardunifiedbiomolecularmodelingbypredictingthejoint3Dstructureofcomplexesthatcanincludeproteins,DNAandRNA,andsmallmoleculeswithinadiffusion-basedarchitecture.

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Usecaseprofiles

ErnestRyu-Mathematician

ErnestRyupickedupChatGPToutofcuriosityin2023,andsawitadvanceuntilitcouldgenerateapublishableresultlastyear.Ryu’sacademicworkhasfocusedonoptimization:themathbehindefficient,reliablealgorithmsthatsupportmoderneconomies,fromplanninglogisticstokeepingaircraftwingsstable.

Whenlargelanguagemodelsweresurginginpopularityin2023,Ryubeganhisfirstexperiments:couldamodeltranslatereal-world“wordproblems”intopreciseoptimizationmodels,includingallthehiddenconstraints,andthenhandthemtoasolver?Schedulingabaseballseason,forexample,requireshardconstraints(e.g.noteamplaystwogamesatthesametime)andsofterones(e.g.travelrestdaysthatcanbeviolatedifnecessary).Thatearlymodelstruggledwiththecarefulconstrainthandlingthisworkdemands,sometimesomittingconstraintsandfailingonlarger,realisticschedules.

Theinflectionpointcamelastyear,afterthearrivalofreasoningmodelsandOpenAI’swinninggoldattheInternationalMathematicalOlympiad.ThesameclassofschedulingproblemsRyuhadtestedbeforewerereliablysolved.ThatsuccessledRyutoapplyLLMstoeverydaymathematicalwork:whilewritinglectures,RyubeganaskingChatGPTforproofsofresultsheknewweretruebutdidn’thavetopofmind.

Finally,hetrieditonresearch.RyuchoseaproblemrelatedtoNesterovacceleration,awell-knowntechniqueforspeedingupoptimization,andpickedaversionoftheproblemthatwasopenlongenoughthatothershadattemptedit,yetsimpleenoughthatashortproofmightexist.Forthreeconsecutiveevenings,afterhissonwenttobed,heworkedfrom8pmtomidnight,andbythethirdnighthehadguidedAItothepointwhereitcrackedtheproblem.

Theircollaborationlookedlikerealresearch.Themodelproducedaninitialproofwithacalculationmistake,soRyubegantoiterate:hecorrectedtheerror,preservedthecorrectintermediatestepsinagrowingprompt,abandoneddeadends,andpushedthemodelintootherapproaches.Ryudescribesthisasmazerunning,whereyouturndowncorridorsandopendoors,sometimesonlytofindthemempty,whilekeepingamentalmapofwhatfailsandwhatseemspromising.ChatGPThelpedRyuacceleratehowfastheranthemazeby3xto10x.

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Onthethirdnight,themodelmadeasmallbutmeaningfulleapthat“lookeddifferent”enoughtounlocktheproof.Ryusaidhe“morethantriple-checked”theargument,thenhadastudentverifyit,beforeheshareditpubliclytoanoptimizationcommunitythatreactedwithsurpriseandexcitement.Fromthere,thecontinuous-timeresultwastranslatedintothediscrete-timealgorithmstatementwithasingleprompt,leavingashort,onenovelcorethatmetthestandardforapublishableadvance.

Sincethen,RyuhasjoinedOpenAI’ssyntheticdatateam,wherehiscorefocusisimprovingthemodel’smathematicalcapability.

AlexLupsasca-Physicist

AlexLupsascacametoAIthewaymanyphysicistscometoboldclaims:withpoliteskepticismandabunchoftests.Inearly2025,hetriedChatGPT,andhefounditusefulfortheroutineadministrativetasksthatregularlypopupinacademia.Buthedidnotseeitasatoolforthehardpartofthejob:turningthelawsofphysicsintoconcreteandverifiablepredictions.

Acommonrealityofacademicpublishing,Lupsascasays,isthataresearchprojectmayoftenresultinadraftequationsandroughconnectivetissue,andaprofessor’sworkbecomesthatofacopyeditor,especiallywhencollaboratorsarewritingoutsidetheirfirstlanguage.ChatGPTcantakearough