In this report, we reviewed the remarkable AI developments in 2023 and made some predictions for new year! We will touch upon a wide range of topics -- technological advancements, startup activities, enterprise moves, investment trends, AI regulations, etc -- everything that we think are relevant for AI startups and VCs.

1. The State of AI
January 24, 2024
Jenny Xiao Jay Zhao
Reviewing 2023 and Predictions for 2024

2. Table of Contents
Seven Trends in 202
Seven Predictions for 2024
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3. Jay Zhao
Jenny Xiao
Founder & General Partner
Partner
Former Partner @ T Fund,
Former Board Member @
Marqeta & WorkBoard
Ph.D. @ Columbia University,
Former Researcher @ OpenAI
About Us
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Leonis Capital is a research-driven pre-seed fund for
technical founders.
Research-Driven
We believe that having in-depth knowledge and a granular
perspective drives superior returns and provides unique support
to our companies at the earliest stage.
First-Check
Our research-backed conviction allows us to invest early.
Founders love that we don’t require social proof and we get to
optimize the fund’s return upside.

4. CONFIDENTIAL
We do things differently...
Here’s a snapshot of how much research we do...in 2023 alone.
400+
academic papers read
1,000+
companies mapped
241
pages of research slides produced
730h
spent on delving into tech stacks
142
LLMs tracked and benchmarked
50%
partner time spent on research
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5. 2023Trend1:Open-sourcereducesthegapbutstilllagsbehind
topclosed-sourcemodels.
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Open-sourcevs.closed-sourcemodelsonMMLUbenchmark.
OSmodelsreducetheperformancegapandgain
popularityamongdevelopers.
a.Llama-2wasthebigmomentinopen-source
performance.
OSmodelsperformingwellonbenchmarks=/=inreallife.
a.Morepeoplearequestioningthevalidityof
benchmarks.
GPT-4remainsthebest-performingLLMandclosed-
sourcemodelswillprobablymaintaintheleadfora
while.
Butperformanceisn’teverything! OS> proprietaryon
latency, privacy, customization, etc.

6. TheTransformercontinuestosee
breakthroughsbutotherparadigmsareemerging.
2023Trend2:
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FourBreakthroughsAreasinTransformersin2023
AIAgents MultimodalLLMs Efficiency Alignment
Transformer-basedAIagents
seesignificantbreakthroughs
in2023withincreased
reasoningcapabilitiesandmore
matureframeworks.
Examples:GenerativeAgents
(StanfordandGoogle),
Toolformer(Nvidia).
Aligningmodelswithhuman
intentionsbecomeeasieras
researchersmovebeyond
RLHF.
Examples:DPO(Stanford),
Zephyr(HuggingFace).
MultimodalLLMsadvance
quicklybutremainlargely
undertheradarin2023.
Examples:PaLM-E(Google).
Methodsforimproving
trainingefficiency significantly
lowerthehardware
requirementsfortrainingand
fine-tuningLLMs.
Examples:QLoRA
(UWashington),
FlashAttention-2(Stanford).

7. TheTransformercontinuestosee
breakthroughsbutotherparadigmsareemerging.
2023Trend2:
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Already,somearchitecturesareshowing
promiseinovertakingtheTransformer.
a.Mostnotably,Mamba(Stanford,Dec2023).
b.Liquidneuralnetworks(LNNs)canalsobe
agamechanger(MIT2020).Theteambehind
LNNsstartedLiquidAIandraised$37.6Mto
buildnextgenAImodels.
c.Smallmodelcollaborationisapproach
gainingpopularity(e.g.,“BlendingisAllYou
Need”UCBerkeley2024).Sakana.aiisa
Japan-basedAIcompaniesthatraised$30M
topioneerthisapproach.
AcomparisonbetweenTransformersandthenewMambamodel.
LNNarchitecture.

8. RAGbecomesthestandardtechstackfor
buildingLLMappsbutsignificantpainpointsexist.
2023Trend3:
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RetrievalAugmentedGeneration(RAG)helps
LLMsincorporateexternalknowledgeand
quicklybecamethestandardwaytobuildLLM
applications.
RAGhasgonethroughthreegenerations:1)
NaiveRAG;2)AdvancedRAG;3)Modular
RAG.
Imagefrom“RAGSurvey”(TongjiUniversity2023).
AsignthattheLLMtechstackismaturing!

9. RAGbecomesthestandardtechstackfor
buildingLLMappsbutsignificantpainpointsexist.
2023Trend3:
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Advantages Limitations
Modelaccuracyandcredibility
improvement.
Contextlengthlimit.Shortervs.
longertrade-off.
Robustnessissues.Informationquality
determinesRAGoutput.
Production-readiness.Therearestill
engineeringchallenges(e.g.,retrieval
efficiency,datasecurity)
Continuousknowledgeupdates.
Domainknowledgeintegration.

10. AIsafetyandgovernanceissuesenterthe
mainstream.
2023Trend4:
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March
2023
May
2023
Oct
2023
Nov
2023
OpenAIboard
firesSamAltman
TopAIexperts
signStatementon
AIRisk
BidenExecutive
OrderonAI
SixMonthAI
Pause

11. AIsafetyandgovernanceissuesenterthe
mainstream.
2023Trend4:
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GenAIcopyrightlawsuitsbecomemorecommon:
AIadoptiontriggerslaborprotests:
a.NYTsuesOpenAIandMicrosoft.
b.ArtistssueMidjourneyandStableDiffusion.
a.HollywoodwritersstrikeoverpayandAI(May-
Sep2023).
AIdevelopersstrikedealswithcontentproducersfor
moredata.
a.OpenAIformspartnershipswithAP,Axel
Springer,Politico,andBusinessInsider.
HollywoodWritersGuildstrikingagainstAIadoption.

12. GenerativeAIbecomesthe#1trendinthe
startupworld.ButbigtechinvestedmorethanVCs.
2023Trend5:
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Microsoft($10B)
Microsoft($1.3B)
Google,Nvidia,
Salesforce($141M)
Google($2B),
Amazon($4B)
Google,Nvidia
($500M)
Google($2B),
Amazon($4B)
Google
Google,Nvidia
BiggestAIFundraisesin2023
Strategic,not
financial
investments!!

13. EnterprisestrybutstruggletobuildAI-first
products.
2023Trend6:
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Source:LeonisCapitalResearch.
WhatPeopleThinkLLMProductsLookLike
Lotsofenterpriseswanttobuild
internalLLMappsorrolloutLLM
products.
!!Buildingaproduction-
gradeLLMappisHARD
WhatLLMProductsActuallyLookLike
Finetunedataset LLM(asAPI) End-userapps
Easy Easy Easy
End-userapps Prompts,
tasks&
queries
Users
End-userapps
Hard Hard
Hard
Personalization Prompts,
tasks&
queries
Reinforcementlearningfrom
humanfeedback(RLHF)
Users
LLMProvider
Finetunedataset LLM(asAPI)
Costoptimization
Real-timeinference
Easy Easy
Easy
Hard Hard/ Moderate
Hard
Moderate Moderate
Moderate
Moderate
LLMProvider
Company
Dataprocessing
pipelines
Finetune
dataset
Training
dataset
Dataprocessing
pipelines
Vectordatabases Finetunedor
trainedmodel

14. EnterprisestrybutstruggletobuildAI-first
products.
2023Trend6:
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AI-First
TechStackComparison
TraditionalEnterpriseSaaS
Generalcloudservices
Efficientstorage&retrieval
Applicationdevelopment-centered
Focusonreliability&efficiency
Cloudservices+specializedhardware
Robustprocessing&storage
Algorithmdevelopment-centered
Focusonadaptabilityandimprovement
Compute/Hardware
Stabletechstackemphasizingreliability,
businessapplicationefficiency,andscalability.
Adaptabletechstackcenteredaroundthe
continuousimprovementofAImodels.
Deployment
Development
Data

15. EnterprisestrybutstruggletobuildAI-first
products.
2023Trend6:
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AI-First
TeamCompositionComparison
TraditionalEnterpriseSaaS
CTO
Broaderrangeofroles,more
people,moreakintoafactory.
Fewerbutmorespecializedroles,more
research-oriented,moreakintoalab.
Backend
team
Quality
assurance
QAanalysts
Product
team
Testers
Productmanagers
Frontendengineers
UXdesigners
Cloudarchitects
Sitereliabilityengs
Systemadmins
Dataengineers
Securityengineers Releaseengineers
Middlewaredevs
Databaseadmins
Backendengineers
DevOps
team
CTO
Backend
team
Quality
assurance
AI QAanalysts
Product
team
AI productmanagers
AI productengineers
ML infraengineers
AI operationsengs
AI platformengs
Researchengineers
AI securityengs
Researchscientists
AI/ML engineers
DevOps
team

16. ComputeprovidersarethefirstwinnersinAI
butthegainsaretricklingdown.
2023Trend7:
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Hardware Valueaccrual
Cloud
ModelProviders
DevTools
Applications &more
Theconsensusinitiallywasthatcomputeproviders
(hardware+cloud)willcapturemostifnotallofthe
valueinAI.
a.Nvidiastockpricessoared237%in2023.
b.MostLLMappsruninthecloudandconsumes
alotofcomputeforbothtrainingandinference.
a.Mostnotably,OpenAI’srevenuetopping$1.6B
in2023.
a.Valuewillaccruetoverticalapplicationswhile
generalistapplicationsmaybecommoditized.
Buttherearesignsthattheeconomicgainsfrom
AIaretricklingdown.
Moreeconomicvaluewillaccruetothedevtool
andapplicationlayerasapplicationsmature.

17. AnewAIparadigmoutperformsthe
Transformer.
2024Prediction1:
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Reinforcement
Learning(2015)
Transformers&
Diffusion(2017+)
CNNs(2012) GANs(2014)
OverdueforaParadigmShift?
NextBigThing(?)
Mamba?
LNNs?
Blending?
Mostlyusedforimage
classification,achieved
muchbetterresultsthan
otherdeeplearning
architectures.
Hyper-realisticimage
generation,impressive
“deepfake”humanfaces.
RLmodelslearned
strategicgameplay
throughtrial-and-error
andreward.
Thebeginningof
“generativeAI” -- AI is
abletogeneratetext,
image,audio,andvideo
content.

18. AIagent-basedsoftwarefindinitial
applications.
2024Prediction2:
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OpenAIisworkingonGPT-5andtwoofitskey
prioritiesarereducinghallucinationandimproving
modelreasoningcapabilities.
MetaworkingonintegratingLlama-2intoagent-
basedproductsacrossitssocialmediaplatforms.
a.Hallucinationandreasoninglimitationsarethe
keychallengesfacingAIagentstoday.
Academicresearchersareworkinghardtobuild
frameworksandtechniquesforimprovingLLM
reasoningcapabilities.
PopularcurrentAIagentarchitecture.
Chatbot Copilot Agent
Actions?
Autonomous?

19. Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
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Who’sbuildingsmalllanguagemodels(SLMs)?
Phi-2-2.7B
Orca-7B
Phi-1.5-1.3B
Phi-1-1.3B
(Dec2023)
(Nov2023)
(Sep2023)
(June2023)
Llama-2-7B
Alpaca7B
Llama-1-7B
(July2023)
(March2023)
(Feb2023)
Zephyr-7B
(Oct2023)
MPT-7B
(May2023)
Mistral-7B
(Oct2023)
Falcon-7B
(June2023)

20. Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
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Technique Examples Effects
Quantization
HardwareOptimization
E.g.,QLoRA(UWashington2023),
LoRA(Microsoft2021).
E.g.,LLMinaFlash(Apple2023),
FlashAttention-2(Stanford2023),
FlashAttention(Stanford2022).
E.g.,DPO(Stanford2023),PEFT
(HKMU2023),RLHF(OpenAI
2017).
E.g.,MiniLLM(Tsinghua2023),
Distilling-Step-by-Step(Google
2023).
Reducingthenumberofbits
neededtorepresentinformation.
Smallermodelsize
Canrunmodelsonconsumer
GPUswithminimalqualityloss.
MethodstorunTransformers
moreefficientlyonGPUs& more.
MorecommonlyknownintheLLM
worldasfine-tuning.
Distillingknowledgefromalarge
modeltoamuchsmallerone.
TransferLearning
KnowledgeDistillation
Reducedmemoryusage.
Fastertraining
Allowsdeploymentonedge.
Parameterefficiency.
Improvedperformance
Userfriendliness.
Outperforminglargermodels
Efficienttraining
Reducedcomputationdemand.

21. Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
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SLMs
Cost
Performance
Deployment
LLMs
Expensive.Oftencostseveralmillion
USDtotrain.
Cheaper.Oftencanbetrainedin
severalhundredthousandUSD.
Demanding.Mustbedeployedon
cloudorserver.
Speed Slow.LatencyisabigissueforLLM
applications.
High.Thebestperformingmodelsare
largemodels.
Lessdemanding.Canbedeployedon
edgedeviceslikePCsorphones.
Fast.Lowlatencyandfastinference
speeds.
Lower.ButSLMperformanceis
improvingquickly.

22. Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
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BenefitsofdeployingSMLsonedgedevices:
Reducedconnectivitydependence.
Real-time,lowlatencyresults.
Dataprivacyandsecurity.
Scalabilityanddistributingapplications.
Costreduction.

23. Modeldevelopersanduserswillfacemore
regulatorychallenges.
2024Prediction4:
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EUAIAct(2023)
BidenAIExecutiveOrder
(2023)
AIBillofRights(2023)
Risk-basedapproach
Regulatorysandbox
Penaltiesofupto6%ofannual
revenue
Sector-specificregulations.
RequiresAIdeveloperstoshare
safetytestresults.
Standardsdevelopment
Protectionagainstbiorisk,privacy,
cybersecurity,andfraud.
Safeandeffectivesystems
Algorithmicdiscrimination
protections
Dataprivacy
NoticethatAIsystemsareused
Optiontooptout.
AISafetySummit(2023)
China’sProvisionson
GenAI(2023) BletchleyDeclaration: first
internationalagreementon
containingAIrisk
Emphasizescatastrophicrisks
fromAI.
Algorithmfiling
Securityassessment
Appstoreverifications
Trainingdataregulations
Protectinguserinputs.

24. TheAIhypecoolsdownbutbuilding
continues.
2024Prediction5:
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EnterpriseswillfindithardertobuildLLMproductsor
integrateLLMsthantheythought.
Startupsthatarethinwrapperswillincreasinglystruggle.
“Touristfounders”willgraduallyleaveAI.
AIstartupsthatraisedmassiveroundsbutdeliverlittle
willstruggle.
AIvaluationswillgobacktoarationallevel.
FewerVCswillclaimtobe“AIexperts”andraiseAIfunds.
However,thebesttimetobuildandinvestiswhenthereis
littlenoiseandmoreaction.
Weareprobablyhere

25. VerticalAIstartupsdevelopmoremature
applications.
2024Prediction6:
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Vertical
Startups+Verticals=
GreenField+NewOpportunities
BigTech+Verticals=
HardtoSpecialize
Startups+Horizontal=
DirectCompetitionwithBigTech
BigTech+Horizontal=
Distributional+DataAdvantage
Startups
Big
Tech
WhereMost
WinnersWillBe
Hardtoworkwithverticalenterprises.
Facesuspicionandanti-trustconcerns.
Easiertoworkwithverticalenterprises.
TheAImodel-datatradeisquitecommon.
Lackexisting expertiseandinfrastructure.
Mostdon’thaveexperienceinverticals.
Movesfasterinhighly regulateddomains.
Candothingsthatbig techwon’tdaredo.

26. TheM&AmarketforAIboomsaslarge
companieslooktoacquiretoinnovate.
2024Prediction7:
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2024
AIboom?
AIM&Ain2023

27. TheM&AmarketforAIboomsaslarge
companieslooktoacquiretoinnovate.
2024Prediction7:
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5Drivers
ofAIM&A
EnterprisesstrugglingwithAIadoption.
AlotofenterpriseswanttoofferAIproductsbutstrugglewithmodeltraininganddeployment.
BigtechaugmentingtheirAIinfrastructure.
CloudandcomputegiantswillwanttoacquireAIcompaniestoenhancetheirinfrastructure.
BigtechacquiringtohireAItalent.
AstheAItalentwarheatsup,acquiringmightbethebestwaytohirescarcetalent.
VerticalplayersacceleratingAIstrategy.
Non-techcompaniesinverticalsectorswillacquireAIstartupsaspartoftheirAIstrategy.
Onceprominentstartupslosingtheiredge.
StartupsthatfaceissuesincommercializationandfurtherfundraisingwilllooktotheM&Aexitpath.
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28. Lookingforward...
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