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The State of AI
January 24, 2024
Jenny Xiao Jay Zhao
Reviewing 2023 and Predictions for 2024
Table of Contents
Seven Trends in 202
Seven Predictions for 2024
2
CONFIDENTIAL
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
3
CONFIDENTIAL
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.
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
4
2023Trend1:Open-sourcereducesthegapbutstilllagsbehind
topclosed-sourcemodels.
5
CONFIDENTIAL
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.
TheTransformercontinuestosee
breakthroughsbutotherparadigmsareemerging.
2023Trend2:
6
CONFIDENTIAL
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).
TheTransformercontinuestosee
breakthroughsbutotherparadigmsareemerging.
2023Trend2:
7
CONFIDENTIAL
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.
RAGbecomesthestandardtechstackfor
buildingLLMappsbutsignificantpainpointsexist.
2023Trend3:
8
CONFIDENTIAL
RetrievalAugmentedGeneration(RAG)helps
LLMsincorporateexternalknowledgeand
quicklybecamethestandardwaytobuildLLM
applications.
RAGhasgonethroughthreegenerations:1)
NaiveRAG;2)AdvancedRAG;3)Modular
RAG.
Imagefrom“RAGSurvey”(TongjiUniversity2023).
AsignthattheLLMtechstackismaturing!
RAGbecomesthestandardtechstackfor
buildingLLMappsbutsignificantpainpointsexist.
2023Trend3:
9
CONFIDENTIAL
Advantages Limitations
Modelaccuracyandcredibility
improvement.
Contextlengthlimit.Shortervs.
longertrade-off.
Robustnessissues.Informationquality
determinesRAGoutput.
Production-readiness.Therearestill
engineeringchallenges(e.g.,retrieval
efficiency,datasecurity)
Continuousknowledgeupdates.
Domainknowledgeintegration.
AIsafetyandgovernanceissuesenterthe
mainstream.
2023Trend4:
10
CONFIDENTIAL
March
2023
May
2023
Oct
2023
Nov
2023
OpenAIboard
firesSamAltman
TopAIexperts
signStatementon
AIRisk
BidenExecutive
OrderonAI
SixMonthAI
Pause
AIsafetyandgovernanceissuesenterthe
mainstream.
2023Trend4:
11
CONFIDENTIAL
GenAIcopyrightlawsuitsbecomemorecommon:
AIadoptiontriggerslaborprotests:
a.NYTsuesOpenAIandMicrosoft.

b.ArtistssueMidjourneyandStableDiffusion.

a.HollywoodwritersstrikeoverpayandAI(May-
Sep2023).

AIdevelopersstrikedealswithcontentproducersfor
moredata.
a.OpenAIformspartnershipswithAP,Axel
Springer,Politico,andBusinessInsider. 

HollywoodWritersGuildstrikingagainstAIadoption.
GenerativeAIbecomesthe#1trendinthe
startupworld.ButbigtechinvestedmorethanVCs.
2023Trend5:
12
CONFIDENTIAL
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!!
EnterprisestrybutstruggletobuildAI-first
products.
2023Trend6:
13
CONFIDENTIAL
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
EnterprisestrybutstruggletobuildAI-first
products.
2023Trend6:
14
CONFIDENTIAL
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
EnterprisestrybutstruggletobuildAI-first
products.
2023Trend6:
15
CONFIDENTIAL
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
ComputeprovidersarethefirstwinnersinAI
butthegainsaretricklingdown.
2023Trend7:
16
CONFIDENTIAL
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.
AnewAIparadigmoutperformsthe
Transformer.
2024Prediction1:
17
CONFIDENTIAL
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.
AIagent-basedsoftwarefindinitial
applications.
2024Prediction2:
18
CONFIDENTIAL
OpenAIisworkingonGPT-5andtwoofitskey
prioritiesarereducinghallucinationandimproving
modelreasoningcapabilities.
MetaworkingonintegratingLlama-2intoagent-
basedproductsacrossitssocialmediaplatforms.
a.Hallucinationandreasoninglimitationsarethe
keychallengesfacingAIagentstoday.
Academicresearchersareworkinghardtobuild
frameworksandtechniquesforimprovingLLM
reasoningcapabilities.
PopularcurrentAIagentarchitecture.
Chatbot Copilot Agent
Actions?
Autonomous?
Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
19
CONFIDENTIAL
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)
Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
20
CONFIDENTIAL
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.
Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
21
CONFIDENTIAL
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.
Smallermodelsbecomemorepopularas
companiesexploremoreusecases.
2024Prediction3:
22
CONFIDENTIAL
BenefitsofdeployingSMLsonedgedevices:
Reducedconnectivitydependence.
Real-time,lowlatencyresults.
Dataprivacyandsecurity.
Scalabilityanddistributingapplications.
Costreduction.
Modeldevelopersanduserswillfacemore
regulatorychallenges.
2024Prediction4:
23
CONFIDENTIAL
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.
TheAIhypecoolsdownbutbuilding
continues.
2024Prediction5:
24
CONFIDENTIAL
EnterpriseswillfindithardertobuildLLMproductsor
integrateLLMsthantheythought.
Startupsthatarethinwrapperswillincreasinglystruggle.
“Touristfounders”willgraduallyleaveAI.
AIstartupsthatraisedmassiveroundsbutdeliverlittle
willstruggle.
AIvaluationswillgobacktoarationallevel.
FewerVCswillclaimtobe“AIexperts”andraiseAIfunds.
However,thebesttimetobuildandinvestiswhenthereis
littlenoiseandmoreaction.
Weareprobablyhere
VerticalAIstartupsdevelopmoremature
applications.
2024Prediction6:
25
CONFIDENTIAL
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.
TheM&AmarketforAIboomsaslarge
companieslooktoacquiretoinnovate.
2024Prediction7:
26
CONFIDENTIAL
2024
AIboom?
AIM&Ain2023
TheM&AmarketforAIboomsaslarge
companieslooktoacquiretoinnovate.
2024Prediction7:
27
CONFIDENTIAL
5Drivers
ofAIM&A
EnterprisesstrugglingwithAIadoption.

AlotofenterpriseswanttoofferAIproductsbutstrugglewithmodeltraininganddeployment.
BigtechaugmentingtheirAIinfrastructure. 

CloudandcomputegiantswillwanttoacquireAIcompaniestoenhancetheirinfrastructure.
BigtechacquiringtohireAItalent. 

AstheAItalentwarheatsup,acquiringmightbethebestwaytohirescarcetalent.
VerticalplayersacceleratingAIstrategy.

Non-techcompaniesinverticalsectorswillacquireAIstartupsaspartoftheirAIstrategy.
Onceprominentstartupslosingtheiredge.

StartupsthatfaceissuesincommercializationandfurtherfundraisingwilllooktotheM&Aexitpath.
1
2
3
4
5
Lookingforward...
28
CONFIDENTIAL

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