Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind! Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.

1. Generative AI
The deep learning chronicles:
the GAN, the Transformer and
the Neural Network
An Otter wearing a cowboy hat reading a book in a
whimsical forest with Leonardo.ai Trained community
model “Studio Ghibli style”

2. 1
Let’s decode the buzz-word of the year.
How does it work?
Engineers train GenAI model
Users can input prompts with the desired
outcom
The AI model then generates content.
Why 2023?
Well...AI can generate images that
look like .
this
Generative AI is a type of Artificial
intelligence capable of creating new digital
content.
Isometric illustration of a cyberpunk character at a computer desk,
3D rendering with Leonardo.ai Stable diffusion 2.0

3. Generative AI is a type of
deep learning application
2
IBM. AI vs machine learning vs deep learning vs neural networks: What's the difference? [Blog post].

4. Withalongstory
onitsback.
3
2013
1950
1965
1980
1997
2014
2017
2018
2019
A team at Google
Brain introduces
transformers, a new
NN architecture
used initially in NLP
models.
Ian Goodfellow and
his team develop
GANs which
opened the door to
increasingly
accurate & varied
outputs.
LSTM, a new gating
unit together with
GRU, allows RNN
models to capture
long-term
dependencies &
improve NLP
models’ results.
Hidden Markov
Models (HMMs)
and Gaussian
Mixture Models
(GMMs) were
introduced as one
of the first
generative models
to generate
sequential data.
Ivakhnenko with
his work showed
that deep neural
networks have the
potential to learn
complex patterns
& create diverse
outputs.
RNNs are first
introduced in the
1980s. Their
recursive nature
allows the models to
learn information
from the previous
time steps, but they
struggle with long
term dependencies.
Open AI releases a
paper on how
combining
transformers &
unsupervised pre-
training greatly
improves NLP
models.
NLP language
models start
adopting
transformers such
as Bert, GPT-2 &
ELMo.
The Variational
Auto-encoder
(VAE) is
introduced, which
is still an
underlying concept
of stable diffusion.
Chat GPT-3 sets
the beginning of
the Generative AI
“gold rush”.
The first multimodal
models such as CLIP
start appearing, where
vision and language
are combined allowing
it to be trained on a
massive amount of
text and image data.
Chat GPT-4 vastly
outperforms its
predecessors and
different use-cases for
GenAI start appearing.
Generative AI starts
outperforming humans
consistently.
2021
2023
2022
2030+

5. 4
ThathasfirstledtoGANS
Random Input
vector
Generator Model
Generated fake
example
Real examples Discriminator Model
Update model after binary classification
Real
False
Duck swimming in a river with Leonardo.ai
& Stable Diffusion 2.0
OpenGenus IQ. GANs: Overview with applications.

6. 5
and then to transformers
Transformers are
used to transform one sequence into another
sequences with dependencies and
connections as they don’t have memory
to focus on key terms in the sentence for context,
allowing
This technology has the potential to revolutionize
the way we interact with language and
.
a Neural Network architecture
(Seq2Seq)
Traditional Seq2Seq models have more trouble
at translating
Transformers leverage the attention mechanism
for more accurate translations
can be
applied to various fields like images and audio
Domino Data Lab. Transformers & self-attention to the rescue: A primer.

7. 6
Generative AI can be
in
classified
multiple ways
Industry
Applications
Businessfunctions
Businessmodel
TechStack
Tex
Vide
Imag
Audi
Code
Dataanalytic
Sales&marketin
Productdesig
Knowledgemanagemen
Customersupport
Datastorag
Foundational
modeltrainin
Finetunin
Frontendapps
Application
Sellingmodel’s
API
Opensource
model
E2Eplay
Healthcar
Financ
Media&entertainmen
Gamin
Fashion&design

8. Platform layer
Application layer
Open Source models
Models released as trained weights
(shared and hosted on model hubs)
Image
Text to
imag
Desig
3D
renderin
Image
editing
Video
generatio
Video
editin
Avatars
creation
Code
generatio
Debuggin
App
building
Video
Code
Closed Source models
large scale, pre-trained models
exposed via Apis
Audio
Text
Voice synthesis
Music creation
Text to speech &
speech to tex
Dubbing
Writing general
tex
Chatbot
Synthesis and
insight
Search engine
Data input
(stored in cloud)
Fine tuning
(based on use case)
7
Ourapproach:combining
thetechstackwithits
applications.

9. E2E
Model
repository
Open
source
API play
Platform
Image
Text
Audio
Code
Video
Application
Multi modal
8
The
Market
Andwithalittle
twist,GenAI
businessmodels.
Link to our Notion Database with the companies analysed in our market mapping.

10. 9
Unlocking new
in
business functions
different industries
Data &
Analytics
Product
design
Research &
Development
Sales &
Marketing
Knowledge
management
Use cases
Industries
Impacted
Generate
synthetic dat
Obtain data
insigh
Automate data
integration
Financial
analytic
E-commerc
Logistic &
transport
Ads & content
creatio
Customer
insights gen
Lead
generation
Media &
comm
Consumer
good
Retail
Drug
discover
Material
desig
Simulation
and modelling
Healthcare &
biotec
Industrial
good
Chemicals &
energy
Answering
internal Q
Content
categorisatio
Expert
systems
Finance &
Managemen
Professional
services
3D image
generatio
Real time
sketche
Streamlined
UI/UX design
Manufacturin
Fashio
Consumer
good
gaming

11. 10
Whereisthemoneygoing?
CB insights. The state of generative AI in 7 charts.

12. 110 35%
new deals, at seed stage
Investments in Generative AI since 2017
% of investments by stage in GenAI as
per the latest disclosed round
11
Generative AI gained significant attention in 2019 with Open AI's seed round, but it wasn't until 2022 when
the hype cycle truly began, as advancements in GenAI produced remarkably human-like results and
startups pushed the boundaries further with new technologies such as Chat GPT 3.5 and stable diffusion.
CB insights. The state of generative AI in 7 charts.

13. 12
Our compass to navigate new
opportunities in the layer
platform
Industry
specific
Industry
agnostic
Multimodal
play
Vertical
play
Industry
focus
Use cases
Multifunction
Industry
Solutions
Function Tailored
solutions
Use case focused
solutions
General Purpose
models
Multimodal LLMs have a
larger addressable market
opening up synergies
opportunities.
These models tackle a
large market, but have
limited defensibility due to
an unclear GTM.
Focusing on specific industries will allow for
better GTM strategies & higher defensibility for
specific use cases
Commoditisation.
Open-source models are already being
trained on similar amounts of data than
closed-source ones and are leveraging on
similar transformers algorithms, hence
drastically reducing margins for
differentiation.
New wave of diversification
The market will rapidly mature and
diversify as more pre-trained models
emerge. New model designs will offer more
choices for balancing size, transparency,
versatility and performance.

14. 13
Ourcompasstonavigatenew
opportunitiesinthe layer
application
High
Low
Feature Product
Fine
tuning
Coreoffering
GenerativeAI
integrations
IroncladGenAI
solutions
Tech-undifferentiated
product
Plug-insolutions
Pre-existing product that
integrates generative Ai
among their features.
Offers a solid defensible
product, targeted to
specific industries and/or
use cases
Limited mid-long term defensibility, due to low
barriers to entry for similar players. Brand and
UX led differentiation.
Add to the
model more
proprietary
data
Use Public
available
foundational
models like
ChatGPT
Virtuousflywheel
Companies that achieve success will be
those that can create a virtuous flywheel
cycle, where increased usage of their
platform by users generates more data,
which in turn is used to refine their models
and deliver improved and personalised
outcomes.
Redocean.
Many of the first waves of generative AI app
companies getting into the market today
are usually poorly performing on both of
these two axis, mostly leveraging the
emerging large foundation models and with
a few-to-none fine-tuning process.

15. 14
Dataprivacyisthemost
pressingmacrorisk
Copyright
DataPrivacy
Ethicalconcerns
Machine-generated content is based on real
data from real people, but who is the true
author?
l
Fact or Fiction? Fake content is a challenge as
misleading information can be easily created
with generative AI. Privacy matters. Several regulatory challenges
appear in terms of compliance.
Investors beware. Italy’s temporary ban and
Britain’s data watchdog warning highlight the
importance of compliance when assessing
GenAI companies.
Concerns have been raised regarding
potential biases in content generation &
impacts on employment, warranting
careful data selection and bias
mitigation to avoid perpetuating
societal issues.
Stanford University. Generative AI: The power and promise of AI that creates [PDF]
AI Multiple. Generative AI ethics: The key considerations for developers.

16. 15
It's an AI talent war out there. The ability to
attract, retain, and manage talent is make-
or-break for companies. As the talent
landscape gets trickier, nailing team
dynamics becomes crucial for generative AI
success!
Talentshortage
LackofProvenBusinessModel
Uncertain business models, with no proven
success stories, making it challenging to
assess investment opportunities in the
evolving landscape.
m
This situation adds uncertainty to investment
decisions.
DependenceonR&D
Generative AI is a frontier that's constantly
evolving, demanding investments in R&D to
stay ahead. Investors need patience and a
long-term perspective in this fast-paced
landscape. It's all about idea execution for
sustainable success!
Lack of
make it harder to assess single
companies
proven business models
Stanford University. Generative AI: The power and promise of AI that creates [PDF]
AI Multiple. Generative AI ethics: The key considerations for developers.

17. 16
What's next?
Let’s ask our
crystal ball
Cyberpunk Alexander Platz with Leonardo.ai & Stable
Diffusion 2.0
Our World in Data. Artificial intelligence timelines. Sequoia Capital.. Generative AI: A creative new world.
2023
2025
2030
2033
2035
???
Models still have limitations & rely
on good human prompters. 3D and
video generation still in their
infancy.
Models able to produce better
final drafts than the average
human. Video & 3D media are
catching up.
Improved memory and context
awareness in AI leads to professional
level results.
Hyper personalisation & new workforce
requirements are 2 important
disruptors in the business world.
Advancements in medical research
enable tailored treatments for
patients, potentially impacting
human life expectancy.
The Metaverse: Synthetic
avatars are starting to take
over our digital footprint.
90% 356 AI experts in 2022 predict
that human-like AI will be achieved
within the next 100 years. 50% say
before 2061.

18. Giddy Up Gen AI Style
Cyberpunk Cowboy in the middle of
the desert with Leonardo.ai