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AIFoundationModels:
ShortVersion
18September2023
PAGE
10
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
1
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.
2
Sincethen,itisestimatedthattherearearound160FMsthathavebeendevelopedandreleasedbyarangeoffirms,includingestablishedplayersinother,alreadyestablishedmarkets,suchasGoogle(whichownsDeepMind),Meta,MicrosoftandNVIDIA,aswellasnewAIcompaniessuchasOpenAI,Anthropic,StabilityAIandMidjourney.
3
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).
4
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.
5
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.
6
TherearealsomanysearchoranswerenginesenteringthemarketsuchasChatGPT,YandPerplexity.ai.
Productivitysoftware.Google,Microsoft,Adobe,andSlackhaveallannouncedplanstointegrateFMsintotheirexistingproductsandenvironments.
7
Socialmedia.SnapchatincorporatedtheChatGPT-powered‘MyAI’chatbotinitsappthatrepliestousers’postsor‘Snaps’withatext-basedreply.
8
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.
9
Firmstructureandintegration
Giventhewiderangeofdeploymentoptions,aspectrumofpossiblefirmandmarketstructuresmayarise.Focusingonpossiblefirmstructures,oneendofthespectrumcouldbeafullyintegratedfirmwhichsuppliesitsowncomputingpower,developsitsownFMusingitsownAIdevelopmenttools,anddeploysitsFMsintoitsownproductsandservices(integrations).Attheotherend,eachstageinthevaluechaincouldbefulfilledbydifferentfirms(seeFigure3).
Today,weobservesignificantverticalintegration,withmanyfirmshavingapresenceintwoormorestagesofthevaluechain.SeveralFMdevelopers,suchasMicrosoft,AmazonandGoogle,ownkeyinfrastructureforproducinganddistributingFMssuchasdatacentres,serversanddatarepositories.
10
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
11
andOpenAI.
12
Bothfirmsprovidecloudcomputingservicesaspartoftheiragreements.
13
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).
14
Sincethen,modelssuchasPaLM,GPT-3,andMegatron-TuringNLGhavebeendevelopedwithhundredsofbillionsofparameters,
15
andpopularopen-sourcemodelshaveintherangeoftensofbillionsofparameters.
16
Theprincipalreasonbehindthistrendisanobservedpositiverelationshipbetweenscaleandperformance,knownas‘scalinglaws’–largermodels,trainedonmoredata,usingmorecomputetotrainandrun,tendtodobetterthansmallermodels.However,thereisuncertaintyoverwhetherthisrelationshipwillendureifmodelscontinuetogrowinthefutureorwhethermodelperformancecouldplateauorevendeclineatgreaterscale.
Largermodelscurrentlytendtodobetterbutcostmoretodevelopanduse,particularlyinrelationtocomputingcosts.Meta’sFM‘LLaMA’has65billionparametersandanestimatedcomputecostof$4million.Incontrast,thelargerFM‘Megatron-TuringNLG’with530billionparametershasanestimatedcomputecostof$100million.
17
Asaconsequence,withoutsignificantinvestment,smallerFMdevelopersareunlikelytobeabletofinancethecomputingcostsrequiredtotrainthelargestmodels.
Pre-trainingFMsrequiresalargeamountofcomputationalpower.MostFMdevelopersdonotownthesufficientcomputationalinfrastructuretotrainmodelsin-house,thereforemostrelyonagreementsorpartnershipswithCSPs.Wehaveheardconcernsthatfirmswhoalreadyhaveagreementsorpartnershipswithcomputingprovidersaremorelikelytogetaccesstothecomputingpowertheyneed.Althoughsomestartupscanreceiveinvestmentintheformof‘credits’
14
[1810.04805v2]BERT:Pre-trainingofDeepBidirectionalTransformersforLanguageUnderstanding()
15
PathwaysLanguageModel(PaLM):Scalingto540BillionParametersforBreakthroughPerformance–Google
ResearchBlog()
[2005.14165]LanguageModelsareFew-ShotLearners()
Megatron-LM:TrainingMulti-BillionParameterLanguageModelsUsingModelParallelism()
16
OpenLLMLeaderboard-aHuggingFaceSpacebyHuggingFaceH4
17TowardsDataScience(2023)
EstimatingtheCostofTrainingLLMs|TowardsDataScience,
HuggingFace(2021)
LargeLanguageModels:ANewMoore'sLaw?(huggingface.co).
fromlargecomputingproviderstospendoncloudcomputing,
18
wehaveheardaconcernthatlargercompaniesarestillmorelikelytoget‘firstinline’andmakedealstoholdlargercomputeclusters.
OfcomiscurrentlyconductingamarketstudyofcloudinfrastructureservicesintheUK.InitsInterimReport,Ofcomhighlightedcloudservicesasincreasinglyimportantinputstomanybusinessesandorganisationsacrosstheeconomy,notingthatcloudisalsoacornerstoneofrecenttechnologicalinnovations,includingartificialintelligence.
19
Ofcomhasprovisionallyidentifiedfeaturesandpracticesthatmakeitmoredifficultforcustomerstoswitchandusemultiplecloudsuppliers,andhasproposedtoreferpubliccloudinfrastructureservicestotheCMAforfurtherinvestigation.
20
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.
18
AWSActivateforStartups,Founders,&Entrepreneurs()
AIstartupprogram|GoogleCloud
19
Consultation:Cloudservicesmarketstudy-Interimreport(.uk),
5April2023,paragraphs3.8-3.9.
20
OfcomproposestoreferUKcloudmarketforinvestigation-Ofcom
ThereisongoingresearchintonewFMarchitectureanddesigntoidentifywhethertherearemoreefficientwaystodevelopmodelsthatrequirefewerresources,likecomputingpower.Itremainsunclearwhether,asthatresearchdevelops,modelswillbecomecutting-edgebutonasmallerscale.However,ifthetrendtowardseverlargermodels,withanincreasingamountofcomputingpowerrequ
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