AI基金会模型：简版

AIFoundationModels:

ShortVersion

18September2023

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AIFoundationModels:ShortVersion

Advancesinmachinelearningandartificialintelligence(AI)continueapace.Recentdevelopmentsinfoundationmodels(‘FMs’)–large-scalemodelsthatcanbeadaptedtoawiderangeoftasksandoperations–andtheirrapidadoptionacrossagrowingnumberofuserapplications,havehighlightedtheirpotentialtospurinnovationandeconomicgrowth.Thesetechnologiesarealreadybeingusedtohelpusresearch,learnandsolveproblems.

Welaunchedthisinitialreviewtohelpcreateanearlyunderstandingof:

HowthemarketscreatedorimpactedbythedevelopmentofFMsandtheirusecouldevolve,andthescenariosthatmayemerge;

Whatopportunitiesandrisksthesescenarioscouldbringforcompetitionandconsumerprotection;and

Whichprinciplescanbestguidetheongoingdevelopmentofthesemarketssothatthevibrantinnovationthathascharacterisedthecurrentemergingphaseissustained,andtheresultingbenefitscontinuetoflowforpeople,businessesandtheeconomy.

ThedevelopmentofAIhasraisedseveralotherimportantissues,includingsafety;security;privacy;intellectualpropertyandcopyright;andhumanrights.TheseissuesarebeingconsideredbyotherregulatorsandGovernment.ThisreviewfocusedonquestionsthattheCMAismandatedandbestplacedtoaddress,namelyquestionsaroundcompetitionandconsumerprotection.

Toinformourthinking,wehaveengagedwithover70stakeholders,includingarangeofFMdevelopers,businessesdeployingFMs,consumerandindustryorganisationsandacademics.Wegatheredinformationdirectlyfromstakeholdersaswellasconsideringpubliclyavailableinformation,includingthelatestAIresearch.

Ouranalysishasfocusedonthreelevelsofthevaluechain:(1)thedevelopmentofFMs;(2)howFMsareusedinothermarketsanduserapplications;and(3)theexperienceconsumershavewhenusingthesenewAItools(eitherstandaloneorwhenincorporatedinotherproductsorservices),inparticularwhethertheycanmakeinformedchoicesandaretreatedfairly.

Foreachlevelinthevaluechain,wesetoutabroadspectrumcoveringthepossiblewaysthemarketcoulddevelopfromacompetitionandconsumerprotectionstandpoint,frompositiveoutcomes

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tooutcomesthatwouldcauseconcern.Thisapproachprovidesaframeworktoconsiderthekeydriversforeithertypeofoutcomeandthedevelopmentofasetofprinciplesthatcanhelpguidethedevelopmentofthemarkettowardsmorepositiveoutcomesforpeople,businessesandtheeconomy.

Effectivecompetitioniscrucialtoensuremarketswithhealthybusinessrivalry,innovationandsustainedproductivitycanflourish.Strongcompetitioncouldspurtheintroductionofnewproductswhichhelpwithallkindsofcreative,scientificandadministrativetasks,andbenefitthewholeeconomybyencouragingdynamismandgrowth.However,ifcompetitionisineffective,thebestfirms,productsorserviceswillnotnecessarilywinout,andbothconsumersandbusinessesmayfindtheyarelockedintoecosystemswithhigherpricesandrestrictionsthattheycannoteasilyescape.Wealsorecognisethateffectivecompetitionaloneisnotsufficienttoensuregoodmarketoutcomes.Itisimportanttoconsidertheroleofeffectivecompetitionalongsideotherconsiderationssuchassafety,dataprotectionandintellectualpropertyrights,forexample.

Thisdocumentsetsoutourearlyviewson:

HowFMsaredeveloped,thekeyinputstheyrequireandhowtheyaredeployedtoday;

ThepotentialoutcomesforcompetitioninthedevelopmentofFMs;

TheimpactofFMsoncompetitioninothermarketsandthepotentialoutcomesforcompetition;

Thepotentialoutcomesforconsumers;

Thepotentialroleforregulationinenablingpositivedevelopmentandoutcomes;

1Giventheinherentunpredictabilityofthefutureitwouldlikelybeimpossibletocreateacomprehensiveandaccuratesetofpossibleoutcomes.Wehavenotattemptedtodoso.Instead,thesearenecessarilystylisedoutcomes,andwedonotclaimthatanyoftheseoptionswillmaterialiseinthewaywedescribeoratall.Rathertheyextrapolate,foranalyticpurposes,marketfeaturesandtrendsthatwethinkmayemergebasedontheevidencewehaveseenthatweconsidercouldhaveanimpactoncompetitionandconsumers.

Proposedcompetitionandconsumerprotectionprinciplesthatwillguidethedevelopmentofthemarket;and

NextstepsfortheCMA.

HowFMsaredevelopedanddeployedtoday

FMsarelarge,generalmachinelearningmodelsthataretrainedonvastamountsofdataandcanbeadaptedtoawiderangeoftasksandoperations.Evenfocusingjustonend-consumerapplications,whicharecurrentlybeingusedtopowerchatbots,createcodewritingassistants,andgenerateimagesandarebeingincorporatedintosomesoftware,suchasMicrosoft365,wheretheyarehelpingusers–bothconsumersandbusinesses–undertaketasks.

ThefirstpublicFM–GPT–wasreleasedbyOpenAIin2018.

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Sincethen,itisestimatedthattherearearound160FMsthathavebeendevelopedandreleasedbyarangeoffirms,includingestablishedplayersinother,alreadyestablishedmarkets,suchasGoogle(whichownsDeepMind),Meta,MicrosoftandNVIDIA,aswellasnewAIcompaniessuchasOpenAI,Anthropic,StabilityAIandMidjourney.

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However,notalltheFMsthathavebeencreatedarecurrentlyinuseorbeingmonetised.Asmodelsdevelopintheircapabilities,theycanquicklybecomeobsoleteandreplacedbynewermodels.Forexample,GooglepreviouslyuseditsLaMDAfamilyofmodelstopoweritsBardchatbot,butthathassincebeenreplacedbythemorepowerfulPaLM-2model.

KeyinputsrequiredforbuildingaFM

DevelopingaFMrequiresongoingaccessto:

Computingpower–FMsarelarge(manymodelshavebillionsofparameters,trainedonhundredsorthousandsofgigabytesofdata)andrequiresignificantcomputingpower,bothwhentheyaretrainedandwhentheyareused.SpecialisedchipsusedfortrainingandrunningAImodelsareinveryhighdemandrelativetocurrentsupply.FMdeveloperswithouttheirowncomputationalresourcestypicallyenteranagreementorpartnershipwithacloudserviceprovider(CSP).

2GPTwasthefirstmodelbasedontransformerarchitecture.Subsequently,modelsbasedonthisarchitecturehavebecomeknownasfoundationmodels(‘FMs’).

3StanfordUniversity(2023)

EcosystemGraphsforFoundationModels.

Data–FMsneedvastquantitiesoftrainingdatatobuildtheknowledgeofthemodel(calledpre-training)andwhenthemodelsarerefinedforaspecificapplicationsuchasacustomerservicechatbotoracodewritingassistant(anoptionalstagecalledfine-tuning).

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Technicalexpertise–FMdevelopersneedhighlyskilledresearchscientistsandengineerstodevelopandmaintaincompetitiveFMs.

Capital–BuildingandmaintainingaFMrequiresaccesstosignificantamountsofcapitaltofundtheuseofcloudservicesorsupercomputers,askilledworkforceandpossiblyalsothecostofhigh-qualitydataifitisnotfreelyavailable.

Toinvestandcompeteeffectively,FMdevelopersneedconfidencethattheycanaccesstheseinputs.

HowFMsaredeployedandusedinuser-facingapplications

AsshowninFigure1,oncethemodelshavebeentrainedandfine-tuned,theycanbereleasedanddeployedinuser-facingapplicationsinarangeofways,includingbydeployingFMsdirectly,accessinganexternallycontrolledFMviaAPIs(ie'AI-as-a-service'),orthroughbuildingplug-insthatworkwithFMapplications.Thesemethodsofdeploymentmeanthatafirmwithaconsumer-facingorabusinesscustomerbusinesscanincorporateFMtechnologyintoitsbusinessbyusingadeveloper’sFMwithouttheneedtobuildandmaintainitsownFM.

4Forcompleteness,someapplicationsthatuseFMs,suchassearchengines,alsorequireaccesstoadditional(oftenreal-time)datatosupplythenecessarycontextorinputswhenthemodelisused,egtoreturnoutputthatmakesuseofcurrentandrelevantsearchresults.

Figure1:Anoverviewoffoundationmodeldevelopment,traininganddeployment

AkeyfeatureinthismarketisthatapproachestoreleaseandmakingFMsavailablecanvaryinopenness.Open-sourcemodelsarethosethathavebeenreleasedinawaywhichallowsthemtobefreelysharedforotherFMdeveloperstobuildupontocreatetheirownFMs,andwithrelativelyfewornorestrictionsonhowtheycanbeused(includingforcommercialuse),suchastheUnitedArabEmiratesTechnologyInnovationInstitute’sFalconmodel.

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Therearemanymodelsthatare'open’(inthesensethatacopyofthetrainedmodelisreadilyavailable)butstillhavesomelicensingrestrictionsthatlimitcommercialuse,orrestrictwhoisabletouseit.Forexample,Meta’sLlama-2modelisavailablefreelyformostcommercialuse,butifitisusedinanapporservicewithmorethan700millionmonthlyusers,anadditionallicenseisrequired.

5ThereareavarietyofwaysinwhichFMscanbemoreopen(includingtheavailabilityofitscode,data,weights,publishedinformationanddocumentation,andthepermissivenessofitslicense),andthattheterm‘open’and‘open-source’arecurrentlyusedinavarietyofwaystodescribeFMs.Inourreport,unlessotherwisestated,wehavechosentouse‘open-source’inawaywhichemphasisestheaspectsofgeneralavailabilityofmodelweightsandrelativelypermissivelicensetomodify,extendanduseforavarietyofpurposes(includingcommercialuse),astheseaspectsarethemostdirectlyrelevanttocompetitivedynamics.

Incontrast,closed-sourceorproprietarymodelsarenotsharedpublicly,andthereisoftenmorelimitedpublicinformationaboutthemodels’characteristicsandcapabilities.Accessanduseofclosed-sourcemodelsaremorecontrolled.DevelopersofclosedsourcemodelscanchoosewhethertousethemodelsforonlytheirownbusinessortolicenseitsusetootherpartiessuchasviaAPIs.Forexample,BloombergusesitsFMcalled‘BloombergGPT’initsownfinancialservicessoftware‘BloombergTerminal’anddoesnotlicenseitsuseforothers,andOpenAIcontrolsaccesstoitsFM‘GPT-3’whichitmakesavailableviaanAPIforthirdpartiestouseintheirproductsandservices.

Atpresentamixofopenandclosed-sourceFMsareavailableandcompeting.ThisisallowingarangeoffirmstoinvestinanddevelopFMsandasaresultwearealreadyseeingdeploymentoftheseFMsinagrowingrangeofapplicationsacrosstheeconomy.

Search.MicrosofthasintegratedmodelsfromOpenAIintoitssearchengineBing.GooglehasannouncedplanstoincorporateFMsintosearch.

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TherearealsomanysearchoranswerenginesenteringthemarketsuchasChatGPT,YandPerplexity.ai.

Productivitysoftware.Google,Microsoft,Adobe,andSlackhaveallannouncedplanstointegrateFMsintotheirexistingproductsandenvironments.

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Socialmedia.SnapchatincorporatedtheChatGPT-powered‘MyAI’chatbotinitsappthatrepliestousers’postsor‘Snaps’withatext-basedreply.

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Healthcare.FMsaretransformingscientifichealthcareanddrugdiscovery,includingresearchonproteinfolding/expressionpredictionandrarediseaseresearch.

6Google-TheKeyword(10/05/2023)

HowGoogleisimprovingSearchwithGenerativeAI;

MicrosoftBingBlogs(2023):

Confirmed:thenewBingrunsonOpenAI’sGPT-4

7GoogleWorkspace(11/05/2023):

IntroducingDuetAIinGoogleWorkspace;

Microsoft(16/03/2023):

Introducing

Microsoft365Copilot

;Abode:

AIartgenerator–AdobeFirefly;

Slack:

IntroducingSlackGPT,thefutureofAIin

Slack

8TechCrunch(31/03/2023)

SnapchatlaunchesanewgenerativeAIfeature,‘MyAISnaps,’forpaidsubscribers;

TheVerge(27/02/2023)

Snapchatreleases‘MyAI’chatbotpoweredbyChatGPT

Robotics.ResearchershavebeenexperimentingwithFMsforarangeofroboticsapplicationsincludingreasoning,planning,instructionsandnavigation.

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Firmstructureandintegration

Giventhewiderangeofdeploymentoptions,aspectrumofpossiblefirmandmarketstructuresmayarise.Focusingonpossiblefirmstructures,oneendofthespectrumcouldbeafullyintegratedfirmwhichsuppliesitsowncomputingpower,developsitsownFMusingitsownAIdevelopmenttools,anddeploysitsFMsintoitsownproductsandservices(integrations).Attheotherend,eachstageinthevaluechaincouldbefulfilledbydifferentfirms(seeFigure3).

Today,weobservesignificantverticalintegration,withmanyfirmshavingapresenceintwoormorestagesofthevaluechain.SeveralFMdevelopers,suchasMicrosoft,AmazonandGoogle,ownkeyinfrastructureforproducinganddistributingFMssuchasdatacentres,serversanddatarepositories.

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Thosefirmsalsohaveapresenceinarangeofuser-facingmarketswhereFMtechnologycanbeintegrated,fromonlineshopping,search,throughtothesupplyofsoftware,sotheyhavelinksacrossseveralpartsofthevaluechain.

Figure2-AfullyintegratedvaluechainwheretheCloudServiceProviderprovidesallservices.

Figure3-Anon-integratedvaluechainwhereeachserviceisprovidedbyadifferentfirm.

9Formoreinformation,seeGitHubrepository

GT-RIPL/Awesome-LLM-Robotic

sforalistofpapersexperimentingwithusingFMsforroboticsapplications.

10J.Cobbe,M.Veale,J.Singh(2023)

Understandingaccountabilityinalgorithmicsupplychains.

Wealsoseelinksacrosspartsofthevaluechainintheformofpartnershipsandstrategicinvestments.GoogleandMicrosofthaveenteredintosuchagreementswithvariousFMdevelopers,includingAnthropic

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andOpenAI.

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Bothfirmsprovidecloudcomputingservicesaspartoftheiragreements.

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Wewillcloselymonitortheimpactoftheseinvestment,partnershipandverticalintegrationlinksoncompetitioninthedevelopmentanduseofFMs.

CompetitioninthedevelopmentofFMs

TorealisethefullpotentialofFMs,itisvitalthatthereis,onasustainedbasis,effectivecompetitionbetweenFMdeveloperstoproducehigh-qualitymodelsthatcanbedeployedinawiderangeofapplications.Apositivemarketoutcomeforpeople,businessesandthewidereconomywouldariseifthereweremultipleindependentdeveloperscompetingwithoneanothertoproduceleadingFMmodels,withinnovativefirmsabletoaccesstheinputstheyneedtoenter,expandandcompeteeffectively.Inthatscenario,firmswouldbeabletoexperimentwithdifferentbusinessmodelsandformsofmonetisation,includingthesupplyofFMsonbothanopen-sourceandclosed-sourcebasissootherscancontinuetobuildonexistingFMcapabilities.

However,aconcerningmarketoutcomecouldemergeifaccesstoinputsisrestrictedsoonlyahandfuloffirmscancreateandmaintaintheleadingmodels.Asaresult,thoseremainingfirmswoulddeveloppositionsofstrengthwhichcouldgivethemtheabilityandincentivetoprovidemodelsonaclosed-sourcebasisonlyandtoimposeunfairpricesandterms.Anyresultingreductionincompetitionmayresultinreducedincentivestoinnovateandthiscouldreducethescopeforcompetitiveinnovationbyarangeofdifferentfirms,whichmayhaveanegativeeffectoneconomicgrowthandproductivity.

11ItisreportedthatAnthropichasreceivedatotalof$450millioninfundingfromGoogle.Seehere:

/markets/deals/alphabet-backed-ai-startup-anthropic-raises-450-million-funding-freeze-thaws-2023-

05-23/

12Microsofthasinvestedatotalof$13billioninOpenAIoverthreeroundsoffunding.Thefirstround,inJuly2019,wasfor$1billion.Thesecondround,inJanuary2021,wasfor$1.5billion.Andthethirdround,inJanuary2023,wasfor$10billion.Seehere:

MicrosofttoinvestmoreinOpenAIastechraceheatsup|Reuters

13MicrosoftCorporateBlogs(2023)

MicrosoftandOpenAIextendpartnership.

Anthropic(2023)

AnthropicPartners

withGoogleCloud.

Whereonthespectrumofthosetwooutcomesthemarketdevelopswillbedrivenby:

Accesstodata–theextenttowhichaccesstoproprietarydatawillbecomenecessarytocompeteeffectivelyindevelopingFMs;

Requirementsforandaccesstocomputingpower–thedegreetowhicheconomicallyusefulandrelevantmodelswillneedtobecomelargerandrequiremorecomputingpower(andotherresources);

Whetherlargetechnologycompaniesandfirst-movershaveanadvantageoverothers;and

Theexistenceofcompetitiveopen-sourcemodels-willsomecompetitivemodelsremainavailableonanopen-sourcebasis,allowingFMdeveloperstouseandimproveuponthemwithouttheneedtobuildtheirownFM?

Accesstodata–Willaccesstoproprietarydatabecomenecessarytocompete?

Readyaccesstodatahas,todate,beenakeyfactorincreatingtheconditionswherenewdeveloperscanexperimentanddevelopnewmodels,oftendevelopingmodelswithcomparablecapabilitiestothehighestperformingmodels.Forexample,popularFMssuchasMeta’sLlama2andStabilityAI’sStableDiffusionwerepre-trainedusingonlydatascrapedfromthewebandotherpubliclyavailabledata.

However,wehaveheardthatinfutureitcouldbemorechallengingforFMdeveloperstoimproveonmodelperformancebyincreasingthescaleoftrainingdatabecausefreelyavailabledatamaybefullyexploited(iethereisnonewdatathatmodelscouldbetrainedupon)orgrowataslowerrate.Ifthathappens,todevelopfutureFMs,developersmayneedtofindwaystoaccessnewtrainingdatabeyondwhatisfreelyavailable.Currently,FMdevelopershavetwooptionsforsourcingnewdata:(1)theycanusedatatheyalreadyhaveasabusiness,suchasunpublished/privatearticlesoranalysisor(2)theycanpurchasedatafromthirdpartyproviders,suchaspublishersandimagerepositories,inreturnforafeeand/orlicencingconditions.ForindependentFMdeveloperswhodonotalreadyhaveaccesstorelevantproprietarydata,anyshiftsintheavailabilityofdatacouldaffectthecostsofdevelopinganFM.

Wehaveheardthatsomefirmsarealreadystartingtouseproprietarysourcessuchasacademicjournals,imagerepositories,andcontentwebsitesformodeltraining,whichsuggeststhattheuseofproprietarydataisincreasinginimportance.Ifthattrendcontinues,thatcouldgiveanadvantagetoFMdevelopersthatalreadyhaveaccesstogoodproprietarydata.Forexample,averticallyintegratedfirmmaygatherusefulfeedbackonhowusersinteractwithcontentonitssocialmediaplatformswhichcanthenbeusedtoimproveitsFMtoproducemorerelevantoutputsorpresentresultsinawayusersaremorelikelytoengagewith.

Weconsideredwhethersyntheticdata–datathatisartificiallygenerated(includingbyotherFMs)ratherthancollectedbasedonreal-worldevents–couldbeusedasasubstitutetodrivefutureimprovementsandprovideFMdeveloperswithaccesstocheapertrainingdata.WeheardthatthereisariskthattheuseofsyntheticdatageneratedbyFMsfortrainingfuturemodelscouldresultintheirreversibledegradationoftheirperformance,aphenomenonreferredtoas‘modelcollapse’.Thereisongoingresearchinthisarea,butwenotethatthereisstillconsiderableuncertaintyabouttheextenttowhichsyntheticdatacanbeaviabledatasourceforFMdevelopersthatcouldbeacompletealternativetoreal-worldandincreasinglyproprietarydata.

Thereisthereforeariskthat,ifproprietarydatabecomesincreasinglyimportanttodevelopcompetitiveFMs,butalsolessavailableandmoreexpensive,manyFMdevelopersmayhaveinsufficientaccesstoviablealternativedatasourcesthattheycanusetokeeppace.Asaresult,thoseFMdevelopersmayexitthemarketaltogetherorbecomereliantonasmallnumberoffirmstosupplythemwiththenecessarydata.Wecannotpredictwhetherthemarketwilltipentirelytowardstheuseofproprietarydata,butthemarketwilldeliverbetteroutcomesifitmaintainsadynamicwherebyarangeofFMdeveloperscangainaccess,onreasonableterms,tothedatatheyneedtobuildFMs.WewouldbeconcernediffirmsusedtheirleadingpositionsinothermarketstoundulyrestrictaccesstoothercompetingFMdevelopers.

Requirementsforandaccesstocomputingpower–Willmodelsneedtobecomelarger?

Althoughadetailedassessmentofthesupplyofsemiconductorswasnotthefocusofthisinitialreview,weunderstandthatFMsrequirelarge,distributedcomputingsystems,oftenconsistingofhundredsofspecialisedchipsusedfortrainingandrunningAImodels,calledAIacceleratorchips.Currently,AI

acceleratorchipsareinveryhighdemandrelativetocurrentsupply,andtheyareexpensivetoacquireandhavelimitedavailability.NVIDIAiscurrentlythemainsupplierofchipsthatareusedforAIpurposes,althoughotherfirmsareinvariousstagesofdevelopingtheirownAIacceleratorchips.

FMshavealsobeengettinglarger.OneofthefirstFMsreleasedwasBERTin2018whichhad354milliontrainableparameters(valuesthatencodetheknowledgeofthemodel).

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Sincethen,modelssuchasPaLM,GPT-3,andMegatron-TuringNLGhavebeendevelopedwithhundredsofbillionsofparameters,

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andpopularopen-sourcemodelshaveintherangeoftensofbillionsofparameters.

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Theprincipalreasonbehindthistrendisanobservedpositiverelationshipbetweenscaleandperformance,knownas‘scalinglaws’–largermodels,trainedonmoredata,usingmorecomputetotrainandrun,tendtodobetterthansmallermodels.However,thereisuncertaintyoverwhetherthisrelationshipwillendureifmodelscontinuetogrowinthefutureorwhethermodelperformancecouldplateauorevendeclineatgreaterscale.

Largermodelscurrentlytendtodobetterbutcostmoretodevelopanduse,particularlyinrelationtocomputingcosts.Meta’sFM‘LLaMA’has65billionparametersandanestimatedcomputecostof$4million.Incontrast,thelargerFM‘Megatron-TuringNLG’with530billionparametershasanestimatedcomputecostof$100million.

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Asaconsequence,withoutsignificantinvestment,smallerFMdevelopersareunlikelytobeabletofinancethecomputingcostsrequiredtotrainthelargestmodels.

Pre-trainingFMsrequiresalargeamountofcomputationalpower.MostFMdevelopersdonotownthesufficientcomputationalinfrastructuretotrainmodelsin-house,thereforemostrelyonagreementsorpartnershipswithCSPs.Wehaveheardconcernsthatfirmswhoalreadyhaveagreementsorpartnershipswithcomputingprovidersaremorelikelytogetaccesstothecomputingpowertheyneed.Althoughsomestartupscanreceiveinvestmentintheformof‘credits’

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[1810.04805v2]BERT:Pre-trainingofDeepBidirectionalTransformersforLanguageUnderstanding()

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PathwaysLanguageModel(PaLM):Scalingto540BillionParametersforBreakthroughPerformance–Google

ResearchBlog()

[2005.14165]LanguageModelsareFew-ShotLearners()

Megatron-LM:TrainingMulti-BillionParameterLanguageModelsUsingModelParallelism()

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OpenLLMLeaderboard-aHuggingFaceSpacebyHuggingFaceH4

17TowardsDataScience(2023)

EstimatingtheCostofTrainingLLMs|TowardsDataScience,

HuggingFace(2021)

LargeLanguageModels:ANewMoore'sLaw?(huggingface.co).

fromlargecomputingproviderstospendoncloudcomputing,

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wehaveheardaconcernthatlargercompaniesarestillmorelikelytoget‘firstinline’andmakedealstoholdlargercomputeclusters.

OfcomiscurrentlyconductingamarketstudyofcloudinfrastructureservicesintheUK.InitsInterimReport,Ofcomhighlightedcloudservicesasincreasinglyimportantinputstomanybusinessesandorganisationsacrosstheeconomy,notingthatcloudisalsoacornerstoneofrecenttechnologicalinnovations,includingartificialintelligence.

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Ofcomhasprovisionallyidentifiedfeaturesandpracticesthatmakeitmoredifficultforcustomerstoswitchandusemultiplecloudsuppliers,andhasproposedtoreferpubliccloudinfrastructureservicestotheCMAforfurtherinvestigation.

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Ofcomintendstopublishafinalreportnolaterthan5October2023.IntheeventthatOfcommakesthemarketinvestigationreference,theCMAwillcarryoutanindependentinvestigationinrelationtopubliccloudinfrastructureservicesintheUKanddeterminewhetherthereareanyadverseeffectsoncompetition.ThiscouldincludeconsiderationofissuesrelatedtoFMrequirementsandCSPs.

ItremainstobeseenhowFMswilldevelopandhowtheywillbeadaptedfordifferentuses,andwhetherthiswillinfluencehowlargeamodelneedstobetoperformtasks.Basedontheevidencewehaveseen,itappearsthat,atpresent,smallermodelsdonotofferthesamelevelofperformanceaslargermodels,althoughthismaychangeasAItechnologydevelops.However,smallermodelsmayneverthelessbeaneffectivecompetitiveoption,astheycanbedevelopedandrunmorecheaplythanlargermodels.Organisationswillhavedifferentrequirementsforperformancedependingontheircontextandapplication.Itispossiblethatsomeproductsandserviceswillrequirecutting-edgeperformanceandsorequirethehighestperformingFM,whichisdevelopedatahighercostandonlyavailablefromfewerproviders.However,itisalsopossiblethattherecouldbearangeofuserapplicationsthatrequiregood,butnotcutting-edge,performance,whereasmaller,cheaper,modelwouldsufficetocompletethetask,withcorrespondinglylowerbarriersforprovidersandmoreoptionsforcustomers.ItislikelythatovertimeacombinationofdifferentlysizedFMswillberequired,butitisunclearwhattheoverallrangeofsizesmightbe,andhowlargethelargestmodelsmightbecome.

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AWSActivateforStartups,Founders,&Entrepreneurs()

AIstartupprogram|GoogleCloud

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Consultation:Cloudservicesmarketstudy-Interimreport(.uk),

5April2023,paragraphs3.8-3.9.

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OfcomproposestoreferUKcloudmarketforinvestigation-Ofcom

ThereisongoingresearchintonewFMarchitectureanddesigntoidentifywhethertherearemoreefficientwaystodevelopmodelsthatrequirefewerresources,likecomputingpower.Itremainsunclearwhether,asthatresearchdevelops,modelswillbecomecutting-edgebutonasmallerscale.However,ifthetrendtowardseverlargermodels,withanincreasingamountofcomputingpowerrequ