Exploring the 2020 Artificial Intelligence Sector

White Star Capital
Sector Overview:
Artificial Intelligence
1
From the eyes of an
international investor
Q3 2020

White Star CapitalWhite Star Capital
Contents
2
Section 1 AI Ecosystem: An Overview
Section 2 Sector Focus
AI-First
Fintech
Mobility
Healthtech
Industry & Robotics
Communication & Collaboration
Foodtech
Wellbeing
Section 3 Geographic Outlook
North America
Europe
Asia
Section 4 Partnering with White Star Capital
4
22
73
80

White Star Capital
6.1%
13.7%
6.6%
3.8%
9.2%
1.9%
5.1%
13.0%
20.3%
15.4%
12.7%
18.6%
5.9%
10.7%
Fintech Mobility Healthtech Industry &
Robotics
Communication &
Collaboration
Foodtech Wellbeing
2014 2020 YTD (Q3)
The Age of AI
3
Sources: Pew Research, Pitchbook, IDC “The Digital Universe in 2020”
(1) The validity of the result has been challenged by several observers who claimed that the achievement was exaggerated. At any rate, this event is
still recognized as a turning point in AI history and is a great testimony of the progress made by technology over the last few decades
(2) The Turing test has received criticisms from both scientists and philosophers regarding its capacity to measure machines ability to think
(3) Gigaflops are a measure of computer speed, equal to one billion floating-point operations per second.
(4) Visions under which machines intellect greatly exceeds the cognitive performance of humans in virtually all domains of interest
(5) Artificial intelligence systems that specifically perform singular or limited task
White Star Capital raised its first transatlantic fund in 2014, the same year in which a
chatbot finally passed the Turing Test1 – an experiment designed to determine whether
a computer is capable of thinking like a human being2. Since 2014, AI has come a long
way and so has White Star Capital, having backed enterprises leveraging AI
throughout North America, Europe and Asia.
In this report, we will explore the commercial use cases enabled by the technology that
we are most excited about and share ways in which we believe AI will disrupt
traditional value chains and business processes.
Today, most experts agree that AI and related technology systems will enhance
human capacities and empower them over the next 10 years. The number of papers
published on AI in arXiv (a renowned open-source databases of pre-print and scientific
papers) has increased by a factor of 4x between 2014 and 2018. AI is everywhere and
despite a slow start driven by an important lag between initial investments and
financially attractive business application developments, we believe that the technology
has now reached a tipping point where numerous projects with promising commercial
applicability are being developed by companies across geographies, sizes and sectors.
AI’s recent rise to ubiquity has been driven by three important catalysts:
1) The significant growth in the amount of data created and stored – around 40
trillion gigabytes of data (40 zettabytes) are currently populating the digital
universe vs 1.2 zettabytes in 2010 (a 33x increase)
2) The increased computation capabilities of processing units – computation cost
fell from $1.80 per GFLOPS3 in 2011 to $0.03 in 2020 (a 98% decrease)
3) The increasingly digital nature of businesses, regardless of sector of operation
While we are still far from artificial general intelligence and artificial super intelligence4,
numerous narrow AI5 use cases already have a profound economical and societal
impact on the world. At White Star Capital, we believe that the Age of AI is now
and look forward to continue supporting the companies building it.
AI is becoming ubiquitous across sectors and is now part of a significant
number of companies’ DNA across White Star Capital verticals of interest
Share of sector deal volume driven by AI-enabled companies
2.2x
1.5x
2.3x
3.3x
2.0x
3.1x
2.1x

AI Ecosystem: An
Overview
4

White Star Capital
Report Methodology
5
AI Ecosystem: An Overview
AI is rapidly breaking grounds across all industries and the lines between
AI-first, AI-enabled, and AI-supported companies are becoming
increasingly blurry
Whereas other reports published by White Star Capital as part of this series focused
on specific sectors, this report focuses on a technology1. Hence, the approach to
determine market size and categorization has been adapted to accurately reflect
this reality.
Given that a wide variety of businesses are leveraging AI in one way or another, it
is a challenging task to draft a categorization that is mutually exclusive and
collectively exhaustive. Several technologies are often intertwined to create
solutions for individual problems and market approaches are done on both a vertical
(sector specific) and horizontal (product specific) basis.
Consequently, the segmentation proposed herein is made by choice rather than
default, and is reflective of the way in which White Star analyses the AI landscape.
The following criteria have been used to support our methodology in creating this
report:
• Inclusiveness: The topic of whether start-ups are really using AI in the way they
claim to be has been widely debated. Are there real tech capabilities or is AI just
used as a buzzword for marketing purposes? As this report aims to provide
insights from a macro perspective and given the impossible practicability that
performing due diligence on every company mentioned would involve, we
decided to account in our data for all companies operating in the Artificial
Intelligence and Machine Learning vertical according to Pitchbook – applying no
discrimination on a micro level and focusing on macro outputs, trends and
insights
• Vertical segmentation: At White Star Capital, we are thematic-driven, sectorial
investors. Hence, we decided to segment the AI landscape vertically according to
our core sectors of interest. We also added a first sub-section on AI-first
companies, which we define as the ones building the AI productization value
chain in a sector agnostic way (i.e. making AI available for companies looking to
build / use AI in their respective verticals)
• Impartiality: All data has been collected from independent third-party sources
We believe that the approach used provides numerous insightful conclusions and
accurately reflects several trends that we are witnessing firsthand in the market as
global investors.
(1) Note that the appellation “sector” is still employed throughout this report. Macro data presented includes companies deemed as operating in
the Artificial Intelligence and Machine Learning vertical according to Pitchbook. This nomenclature englobes companies that are using AI
technology in a meaningful enough way to represent this characteristic as a primary defining element of their business operations

White Star Capital
Key Definitions
6
Source: IBM Watson Health Perspectives, IBM Cloud, IBM Design for AI
Any system capable of simulating human intelligence and thought
processes is said to have Artificial Intelligence.
AI can take many forms…
Big Data
Big Data defines very large and
complex datasets that exceed the
ability of traditional data
processing applications to deal
with them. Big data has one or
more of the following
characteristics: high volume, high
velocity, or high variety. Much of it
is generated in real time and at a
very large scale, by sensors or
networks, for example.
NLP
Natural Language Processing is
an interdisciplinary field that
spans techniques to process,
understand, and analyze human
language. NLP strives to build
machines that understand and
respond to text or voice data—
and respond with text or speech
of their own—in much the same
way humans do.
Machine Learning
Machine learning is the application
of computer algorithms that improve
automatically through experience
and have the capacity to perform
tasks that aren’t explicitly
programed.
Computer Vision
Computer vision is a field of AI
that enables computers and
systems to derive meaningful
information from visual inputs
such as images or videos — and
take actions or make
recommendations based on that
information.
Deep Learning
Deep learning is a subset of
Machine Learning where neural
networks learn from large
amounts of data creating
increasingly smarter conclusions.
Deep learning systems can
prioritize the criteria most
important to reaching a decision.

AI History
7
Source: Harvard Research, BBC, digitalwellbeing.org
(1) Rules initially introduced in Runaround (1942) and included in the 1950 collection I, Robot
The 1950s: WW2 triggers fresh thinking…
1949 – ‘The Tortoises’
American-born British neurophysiologist, cybernetician and robotician William Grey
Walter creates some of the first ever robots with complex behavior
1950 – Asimov’s Three Laws of Robotics
American writer and professor of biochemistry at Boston University Isaac Asimov
publishes his thought-provoking work, I Robot, a founding document regarding
ethical considerations of Artificial Intelligence1
1950 – The Turing Test
Mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical
biologist Alan Mathison Turing considers the question ‘Can machines think?’. If a
machine can trick humans into thinking it is human, then it has intelligence. Turing also
built a machine to decrypt German Enigma’s messages during WWII
1951 – Ferranti Mark 1
The first AI-based programs were written to run on the Ferranti
Mark 1 leading to the creation of the two first self-learning game-
playing programs: a checkers and a chess-playing program
1966 - 1977 – Shakey The Robot
First general-purpose robot making decisions about its own actions given its
surroundings. It was painfully slow and made apparent that AI was lagging far
behind lofty predictions made by advocates like Minsky
1965 – ELIZA
The pioneer of NLP computer programs, ELIZA was the first
chatbot able to hold conversations with humans. ELIZA was developed
at MIT by the German American computer scientist and professor at MIT
Joseph Weizenbaum
1956: The Introduction of AI
The term 'artificial intelligence’ is coined during a summer conference at Dartmouth
University by the American computer scientist and cognitive scientist John McCarthy.
Top scientists debated how to approach AI: Some, like the renowned American
cognitive and computer scientist who co-founded the MIT’s AI laboratory, Marvin, Minsky,
favored a ‘top-down’ approach, where AI development would be inspired by the rules that
govern human behavior. Others preferred a ‘bottom-up’ approach where AI would
simulate brain cells that could learn new behaviors, which led to neural networks. Over
time Minsky's views dominated, and alongside McCarthy, he won substantial funding from
the US government, who hoped AI might give them the upper hand in the Cold War.

AI History
8
The 1970s: The AI Winter…
By the end of the 70s, millions had been spent on AI for little commercial progress.
Funding for the industry was slashed on the back of the Lighthill Report (AI: A General
Survey) published in 1973 on the state of AI in the UK in which he stated that machines
would only ever be capable of an "experienced amateur" level of chess.
The 1980s: AI commercial value attracts new
investment
In the 80s, several commercial systems with more tangible applications saw daylight.
Instead of aiming for the silver lining of general intelligence, expert systems focused on
much narrower tasks. One of the first successful commercial expert system, known as
the R1, began operation at the Digital Equipment Corporation. The R1 was a production-
rule-based system automatically selecting computer components based on customers’
requirements. By 1986, R1 was reportedly saving the company an estimated $40m a year.
The 1990s: Back to ‘bottom-up’ approach
Australian roboticist and former director of the MIT Computer Science and AI Lab
Rodney Brooks was inspired by advances in neuroscience starting to explain human
cognition and showing that different 'modules' in the brain work together to recognise
patterns, with no central control. He argued that the ‘top-down’ approach of pre-
programming a computer was wrong. He helped drive a revival of the bottom-up
approach to AI, including the long unfashionable field of neural networks.
1997 – Deep Blue
IBM Supercomputer defeats world chess champion Garry Kasparov
2002 – Roomba, first home-robot
An autonomous vacuum cleaner introduced by iRobot
2011 – IBM's Watson
The computer beats the human brain on US
quiz show Jeopardy
2011 – Apple integrates Siri
The intelligent virtual assistant with a
voice interface
2017 – Google’s AI Alpha Go
Defeats world champion in the game of Go, notable for its vast number of possible
positions
2016 – Microsoft’s Chatbot Tay
The chatbot makes inflammatory and
offensive racist comments on social media
2014 – Eugene Goostman
A chatbot finally passes the Turing Test with
a third of judges believing Eugene is human1
Are machines intelligent now?
Narrow AI allows to assist or take over specific tasks. General AI on the other end means that
machines have cognitive capabilities similar to humans. While based on those definitions and
the current state of technology we cannot proclaim that machines are fully intelligent today, we
can confirm that they are getting smarter by the day and that when coupled with human insight
and creativity, they have already enabled an array of opportunities that were fundamentally
unconceivable just a few years ago.
Source: Harvard Research, BBC, digitalwellbeing.org
(1) The validity of the result has been challenged by several observers who claimed that the achievement was exaggerated. Anyhow this event is
still recognized as a turning point in AI history and is a great testimony of the progress made by technology over the last few years

White Star Capital
ML101 - An Introduction
9
Source: Imperva, IBM, Reinforcement Learning: An Introduction (Sutton, Barto)
ML can be segmented into several categories depending on the nature
of the data and the type of algorithms used to perform specific tasks
ML enables computers to detect patterns and
establish baseline behavior using algorithms
that learn through training or observation. The
approach has the capability to process and
analyze vast amounts of data that are simply
impractical for humans. ML can take three
forms:
Supervised learning starts with an established
labeled dataset and an understanding of how
that data is classified. The model then maps the
labeled inputs to the known outputs.
Unsupervised learning draws inferences from
datasets consisting of input data without labeled
responses. This approach requires clustering
and dimensionality reduction algorithms
which enables the machine to understand
patterns and discover the output.
Clustering is the assignment of objects to
homogeneous groups (clusters) based on
shared characteristics (features).
Dimensionality reduction is the reduction of
the number of features under consideration.
This approach protects the assignment from the
curse of dimensionality which arises when
analyzing and organizing data in high-
dimensional spaces
Reinforcement learning enables algorithms to
learn to react rationally to an environment on
their own. By effectively mapping more
situations to actions, the agent aims to
maximize a numerical reward signal.
Supervised learning
▪ Input and output are provided
to the machine, which finds out
the rules
▪ Suited for classification and
regression tasks
Unsupervised learning
▪ Output data are not provided
▪ Machine interprets the input
data in order to find information
from the data.
▪ Operates via clustering and
dimensionality reduction
Reinforcement learning
▪ Machine improves from its
learning errors and adjusts its
approach to maximize reward
(trial-and-error)
▪ The machine remembers its
previous behavior and corrects
it iteratively
EnvironmentAgent
Actions
Rewards
Observations
1
2
3
3
2
1

ML 201 – One Layer Further
10
Source: Global Engage, Dataflair, Emily Barry
Deep learning simulates the human brain, enabling systems that learn to
identify objects and perform complex tasks with increasing accuracy—all
without human intervention
Some widely used ML algorithms: Selection of the appropriate approach depends
on the size, structure and purpose of the dataset, among other things
Deep Learning
Deep learning is a subset of machine learning in which multi-layered neural
networks - inspired to work like the human brain - learn from large amounts of data.
Within each layer of the neural network, deep learning algorithms perform calculations
and make predictions repeatedly, progressively learning and gradually improving the
accuracy of the outcome over time.
Deep learning can be supervised, semi-supervised, unsupervised and even used in
relation with reinforcement learning.
Simple Neural Network
Input Layer
Hidden Layer(s)
Output Layer
Supervised
Unsupervised
Reinforcement
Basic Regression
Classification
Clustering
Linear – Lots of numerical data
Logistic – Target variable is categorical
Neural Net – Complex relationship
K-NN – Grouping based on proximity
Decision Tree – If/then/else
Random Forest – Can also be regression
SVM – Maximum margin classifier
Naïve Bayes – Updating knowledge
progressively with new info
K-Means – Groups based on centroids
Anomaly Detection – Outliers through grouping
Dimensionality Reduction
T-SNE – Convert similarity to joint probabilities
PCA – Distil feature space into components that
describe greatest variance
CCA – Cross-correlation matrices
LDA – Linear combination of features that
separates classes
Note that while dimensionality reduction mostly
applies to unsupervised learning, some variations /
combinations of those algorithms can also be applied
to supervised learning (i.e. Labelled-LDA, Multi-Grain
LDA, S2CCA, etc.)
Complex Neural Network

Quantum Computing 101
11
Sources: IBM, CB Insights, Nature
Quantum computing is a technological advancement that holds lots of
promises in expanding the boundaries of computational power
Ordinary computer chips use bits and act as switches that can either be in the ”off” position (0)
or in the “on” position (1). Universal quantum computers leverage the quantum mechanical
phenomena of superposition and entanglement to create states that scale with number of
qubits (or quantum bits). The technology was initially developed in 1998 at Oxford University
and has since been pushed forward by technology giants such as IBM, Google, Alibaba, and
specialized players like D-wave, Rigetti, and Xanadu. Progress in the quantum computing
space is happening fast and in 2020, Honeywell announced that it had built the most powerful
quantum machine in history with a quantum volume (IBM-created standardized performance
benchmark) of 64, twice as powerful as the next best alternative in the industry.
Quantum
Simulation
• Metaheuristic for finding the optimum of a given objective function over a given finite set of
solutions (candidate states)
• In 2011, D-Wave announced the first commercial quantum annealer on the market by the
name D-Wave One and published a paper in Nature on its performance
Quantum computing can be divided in 3 categories based on the amount
of processing power (qubits) needed, the number of possible
applications, and the time required to become commercially viable
Qubit: Typically represents subatomic particles such as electrons or photons.
Some approaches use cooled superconducting circuits, while others trap
individual atoms in electromagnetic fields on a silicon chip in ultra-high-vacuum
chambers. In both cases, the goal is to isolate the qubits in a controlled
quantum state
Superposition: Qubits can represent numerous possible combinations of 0 and
1 at the same time. This ability to simultaneously be in multiple states is called
superposition
Entanglement: Researchers can generate pairs of qubits that are “entangled”
which means the two members of a pair exist in a single quantum state
QC
• Quantum simulation is focused on the problems in quantum physics that are beyond the
capacity of classical systems
• Quantum simulation promises to have concrete applications in the study of many
problems in, e.g., condensed-matter physics, high-energy physics, atomic physics,
quantum chemistry and cosmology
• Universal quantum computers leverage the phenomena of superposition and entanglement to
create states that scale exponentially with number of qubits
• Several dozens of algorithms that are only possible on a universal quantum computer have
already been designed, including Shor’s (factoring) and Grover’s algorithms (quickly searching
unstructured and massive sets of data)

▪ In Nov-18, Xanadu, which has
been a pioneer in the quantum
machine learning field,
introduced PennyLane, a
software framework for
quantum programming
▪ NASA Quantum Artificial
Intelligence Laboratory (QuAIL)
focuses on the impact that
quantum computers could have
in solving computational
challenges faced by the agency
▪ In Mar-20, Google announced
TensorFlow Quantum, an open
source library for QML
▪ Allows researchers to construct
quantum datasets/models, and
classical control parameters
When Quantum Meets AI
12
Sources: MIT, Google, IBM, Supervised Learning with Quantum Computers (M. Schuld, F. Petruccione), Highlighting Quantum Computing for
Machine Learning (R. Louriz), Parameterized quantum circuits as machine learning models (M. Benedetti, E. Lloyd, S. Sack, M. Fiorentini)
Quantum machine learning (QML) is an emerging interdisciplinary field
that sits at the intersection of quantum computing and machine learning
QML focuses on finding synergies between two important fields that play a central role into
determining how society deals with information and data more generally. While definitions
and applications of QML are broad, the key questions behind the emergence of the field are:
can quantum mechanics be used in a way that improves how machines recognize patterns
in data? Can quantum computers help solving problems faster? Can machines learn from
fewer data samples or deal with higher levels of noise? The preliminary answers to these
questions so far seems to be yes, they can.
Early signs of traction
Several approaches in the QML space are based on the premise that quantum algorithms
can speed up ML-related linear algebra and/or sampling tasks. While the field is still nascent
and applications will continue to be developed, exponential speed-up is the primary
argument in support of QML as of today. The technology is built on two pillars: quantum data
and hybrid quantum-classical models.
Quantum data: Any data source that occurs in a natural or artificial quantum system.
Quantum data exhibits superposition and entanglement, leading to joint probability
distributions that could require an exponential amount of classical computational resources
Hybrid quantum-classical models: These hybrid models rely on the intuition that by
implementing some subroutines on classical hardware, the requirement of quantum
resources is significantly reduced, particularly the number of qubits, circuit depth, and
coherence time
Algorithms
Traditional
processing
(CPU/GPU)
Pattern
recognition
Time required
Algorithms
`Quantum
processing Pattern
recognition
Time required
ClassicalMLClassicalML
QuantumML

White Star Capital 13
AI bias happens when algorithms produce results that are systematically
prejudiced due to the recognition of erroneous patterns in data
For example, a system for spotting skin cancer might be paying more attention to
whether a photo is taken in a doctor’s office, than to the actual skin area at risk,
systemically biasing results.
That’s because AI cannot think or understand
As discussed earlier in this report, AI systems are provided with huge amounts of data
and use statistical algorithms to generate models that can make conclusions based on
patterns detected in data. This is fundamentally different to traditional software where
rules are directly inputted to generate a desired answer.
If the data provided is not representative or is inherently biased, the output and
decisions of AI systems will be wrong. An AI model is only as good as the data its
being fed. This reality can have major consequences. Data about human diversity for
instance can come with many embedded biases and generate results that
systematically discriminate against certain types of skin pigmentation. This explains
why companies like Amazon, IBM, and more recently Microsoft, decided to ban police
use of their facial-recognition technology to avoid racially biased surveillance and
arrestations.
“We will not sell facial-recognition technology to police departments in the US
until we have a national law in place, grounded in human rights, that will govern
this technology” Brad Smith – President and CLO of Microsoft
Another ethical question raised by the very own nature of AI is its unevenly
distributed force favoring companies with large data banks. Network-effect driven
businesses are often better positioned to capture the full extent of the transformative
force of AI and this reality will ultimately reinforce oligopolistic market structures in
several verticals. As Peter Thiel said, while crypto is libertarian, AI is communist.
Whether we accept this reality or not is a societal question that will be addressed
based on each countries’ jurisdiction. However, as innovation has no boundaries, the
decisions made by various states will need to be analysed in a global context as local
restriction might create an uneven playing field that will have a major influence of
tomorrow’s AI landscape.
A future of regulations
Tools and processes to regulate organizations and educate consumers so that AI
systems are included in decision processes rather than used as decision making tools
are needed. While several private initiatives such as OpenAI have seen daylight,
regulations will need to go further to ensure that the technology doesn’t stand in the
way of basic human rights as it grows in popularity and adoption.
Classical
Programming
Supervised
Machine
Learning
AnswersData
Rules Data
Answers Rules
Sources: Benedict Evans, Washington Post, npr.org
AI & Ethics: Beware of Bias

AI’s disruptive potential is tremendous and at White Star we’ve put our actions behind
our words by investing in leading companies such as Meero, Mnubo, KeyMe, Red Sift
and Mindsay
AI companies differ from traditional software businesses in many ways. Understanding this
difference is a tool for entrepreneurs and investors alike to successfully navigate the space
and assess the inherent operational attributes and limitations of AI-enabled business
models. Below are a few elements that explain why investing in AI comes with its fair share
of challenges and why ultimately, building a clear value proposition with a feasible path to
profitability should never be overlooked.
Lower gross margins businesses
Defensibility is often overstated
Technical differentiation: AI getting increasingly commoditized
(1) Data drift represents the sum of data changes over time
Sources: a16z, Benedict Evans
AI from an Investor Point of View
1
▪ Humans-in-the-loop are often
required for models to operate at
a high levels of accuracy
▪ Cleaning and labelling is a
heavily labour-intensive
▪ Reducing human costs by
automating actions results in
increasing infrastructure spend,
meaning that costs can be
transferred but not removed
▪ 25%+ of revenue of AI companies
are spent on cloud resources
▪ Training AI models can cost
hundreds of thousands of dollars in
compute resources due to data
drift1 as well as the complexity of
matrix-based calculations
▪ Media rich content (images,
video, audio) are more predominant
in AI-model training than in
traditional software
▪ Traditional software solutions have
really high margins because they
can be produced once and sold
many times to various customers
▪ On the other end, AI solutions are
based on trained models that need
to be maintained, updated,
tailored, and improved with data
that becomes increasingly
expensive to acquire on the edge
AI vs software Infrastructure costs Humans in the loop
AI companies generally have gross margins that are 10-30% below traditional SaaS
businesses due to the inherent nature of their business model
2
“Our moat resides in the data we have, which differentiates
us from competitors”. Sometimes true, often false. Unless
data is proprietary, the reality is that in most instances, the
bulk of capabilities that AI models will offer can be achieved
with limited amounts of data residing in the heavy head of
the distribution. Reaching a minimum viable corpus of data
is, for example, generally easier than breaking the cold start
problem in marketplaces meaning that even companies with
a head start in their respective field should expect to face
growing competition down the line.
3
Building differentiated AI solutions is difficult. New frameworks and architectures are often publicly
available in open source libraries. Pre-trained models can easily be found on the net. Data is either
(i) expensive (drift, hard to capture on the edge), (ii) public (no moat) or (iii) owned by customers.
This reality creates a dynamic in which several AI solutions are commoditized and not significantly
technically differentiated or are, on the opposite, able to successfully manage edge cases but suffer
from diseconomies of scales where the costs associated with each new data point acquired,
processed, and maintained significantly outweighs the marginal benefit of it.
In summary, profitability, defensibility, and differentiation are three elements that need to be
thoroughly reflected on when building / investing in AI. The positive note is that through our
first hand conversations with entrepreneurs across the globe, we see an increasing number of
founders approaching AI with a business builder mindset, paving the way for more
entrepreneurial successes in the field.
Heavy Head
Low cost / High use
Frequencyofuse
Total Inventory of Data
Chunky Middle
Medium cost / Medium use
Long Tail
High cost / Low use

AI Highlights as of Q3 2020
15
Sources:
(1) Pitchbook: AI & Machine Learning VC-backed start-ups as of 30.09.2020. AI & Machine Learning start-ups are defined by Pitchbook as
follows: “Companies developing technologies that enable computers to autonomously learn, deduce and act, through utilization of large
data sets. The technology enables development of systems that collect and store massive amounts of data, and analyze that content to
make decisions based on probability and statistical analysis. Applications for Artificial Intelligence & Machine Learning include speech
recognition, computer vision, robotic control and accelerating processes in the empirical sciences where large data sets are essential.”
(2) Pitchbook: Rounds >$100m
(3) According to Pitchbook
25%+
Forecasted CAGR of the
AI sector through 20233
5,861
AI deals in 2019
$138bn
AI funding since 2017
69
AI VC-backed
unicorns1
108
AI IPOs since 2011
174
AI mega rounds since
20112
+73%
Share price performance of the Global X Robotics &
Artificial Intelligence ETF, one the largest AI ETFs,
since 2017

Funding has grown c.40% yoy since 2011 as AI
use cases become ubiquitous across industries
16
Source: Pitchbook
North America and Asia have led the world from a deal value perspective
2020 YTD deal value almost at 2019 levels, while deal volume is lagging
YoY, reflecting larger average deal sizes as investors focuses on follow-
on and later stage rounds amid the COVID-19 pandemic
Deal Value
Deal Volume
$1.3bn $1.4bn $2.8bn
$8.0bn $8.5bn
$13.6bn
$9.6bn
$18.8bn
$21.8bn
$18.4bn
$1.1bn
$1.8bn
$2.9bn
$4.2bn
$3.2bn
$2.6bn
$5.6bn $13.3bn
$21.6bn
$9.8bn
$11.5bn
$2.0bn $1.7bn
$3.3bn
$9.4bn
$11.7bn
$20.2bn
$24.9bn
$43.5bn
$36.1bn
$33.1bn
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 YTD
North America Europe Asia Rest of World
246 370 628 924 1,195
1,621
2,348
2,723 2,566
1,404
134
229
394
581
852
1,310
1,545 1,536
724
144
289
573
901
1,377
1,724
1,640
820
393
594
1,006
1,618
2,367
3,418
5,113
6,086
5,861
2,984
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 YTD
North America Europe Asia Rest of World

White Star Capital
+[xx]%
Growth in share of
deals from 17-19
Globally, the AI ecosystem is primarily early stage, with
funding concentrated in Seed and Series A rounds
17
However, the US and China are showing signs of maturation with a
sustained growth in funding for deals at the Series B stage and beyond
USA Canada UK France Germany China SEA
Seed share
of deals 54% 59% 55% 33% 63% 23% 48%
(0.8)% (5.4)% (4.6)% (23.8)% 12.4% (17.9)% (6.4)%
Series A
share of
deals
25% 29% 32% 39% 29% 48% 34%
(4.6%) 17.8% 7.8% 15.1% (12.1)% 7.6% 6.3%
Series B
share of
deals
12% 8% 10% 18% 5% 19% 11%
9.3% (10.4)% 23.1% 83.4% (35.1)% 5.3% 46.4%
Series C
share of
deals
5% 3% 2% 9% 3% 7% 4%
4.5% (10.4)% 10.1% NA NA 9.0% NA
Series D
share of
deals
2% 2% 0% 0% 0% 3% 4%
11.0% NA (100.0)% NA NA 38.8% (40.2)%
Series E+
share of
deals
3% 0% 1% 0% 0% 0% 0%
13.6% NA (10.1)% NA NA 3.4% NA
Source: Pitchbook
Share of deal volume by deal stage type (2019)

$14.1m $22.4m $5.6m
$40.8m
$86.7m
$21.4m
$96.8m
$195.9m
$73.3m
Valuations have been growing at an impressive pace
across the globe, with Asia seeing the steepest
increase, especially at the Seed and Series A stages
18
Source: Pitchbook
Note: Please note Pitchbook valuation data has limitations and only considers rounds that have officially announced
valuations.
Europe valuations in the AI ecosystem are between 50% and 70% lower
than the rest of the world
Outsized rounds are becoming common place as investors are looking to
back companies with the potential to become category killer
Seed
Series A
Series B
North America Asia Europe
Series D: $224m (2020)
France
Series C: $230m (2019)
France
Series B: $151m (2019)
Canada
Series B: $940m (2019)
USA
Series A: $150m (2018)
China
Series D: $1bn (2017)
China
Growth: $20bn (2018)
China
Median pre-money valuation
Selected outsized funding rounds
+16.1% +75.9% +23.4%
+25.3% +66.3%
+18.4%
+20.4%
+12.1%
+5.8%
+[xx]%
Growth in
valuations
from 14-19
Growth: $3bn (2020)
USA
Series D: $162m (Feb-20)
UK

White Star Capital
$224m
Series D (May-20)
France
Megarounds in US and China have become
common given market sizes
19
Source: Pitchbook
Note: Mega round refers to a round of $100m+.
Mature ecosystems are now seeing $1bn+ deals happen multiple times
per year
North America Asia Europe South America
$175m
Series C (May-19)
Brazil
2019
2017
2016
2018
2020
$550m
Series C (Aug-19)
UK
$230m
Series C (Jun-19)
France
$750m
Series D (May-19)
China
$1.4bn
Series A (Oct-19)
China
$2.8bn
Series D (Mar-19)
China
$446m
Series C (Mar-19)
China
$222m
Series D (Dec-18)
UK
$500m
Series B (Jul-18)
US
$392m
Series E (Jun-18)
US
$140m
Series D (Aug-17)
US
$145m
Series D (Mai-18)
US
$620m
Series C+ (Mai-18)
China
$1.9bn
Growth (Apr-18)
China
$4.3bn
Series C (Apr-18)
China
$1bn
Series B (Apr-18)
China
$153m
Series B (Mar-18)
US
$203m
Series D (Dec-17)
US
$159m
Series B (Jul-17)
US
$410m
Series B (Jul-17)
China
$2bn
Growth (Dec-17)
China
$1bn
Series D (Apr-17)
China
$600m
Series C (Mar-17)
China
$190m
Series C (Jan-16)
US
$250m
Series A (Jul-16)
US
$880m
Series K (Jan-16)
US
$600m
Series B (Nov-16)
China
$1.4bn
Series A (Feb-16)
China
$1.2bn
Seed (Oct-16)
China
$1bn
Series A (Aug-16)
China
$3bn
Growth (Feb-20)
China
$600m
Series B (Feb-19)
US
$940m
Series B (Feb-19)
US
$700m
Series F (May-20)
US
$3bn
Growth (May-20)
US
$462m
Series B (Feb-20)
US
$263m
Series B2 (Jan-20)
US
Selected megarounds by region
• The European market is slowly accelerating, supported by the
new European Strategy for Data. However, the geography
is still lagging globally as investors underspends on tech and
R&D compared to the US and China.
• Autonomous
vehicles remain
among the most
funded AI-based
projects, followed by
healthtech, fintech
and industrial
technology
• Increasing deal sizes
over time as category
winners are
attracting growing
levels of funding
$250m
Series A (Jun-18)
US
$1.4bn
Pre-IPO (Apr-20)
China
$2.5bn
Series C+ (May-20)
China
$162m
Series D2 (Feb-20)
UK
$50m
Series E (Sep-18)
UK
$227m
Growth (Nov-19)
Canada
$151m
Series B (Sep-19)
Canada
$130m
Series B (Oct-16)
US

Strategic M&A drives the vast majority of exits
globally
20
Source: Pitchbook. Note that Pitchbook data for China are usually less reliable than for North America
Embracing AI has become a focus, not just for digital native enterprises,
but for all companies looking to find more efficient ways to compete, cut
costs and deal with data overload
Exits by type
Selected VC-backed exits
$648m
IPO (Sep-19)
USA
$200m
Acq. By Apple (Jan-20)
USA
$230m
Acq. by Aurora Innovation (May-19)
Sweden
$1.3bn
Aq. By Amazon (Jun-20)
USA
C$100m
Acq. by AspenTech (Jun-19)
Canada
CNY 2.8bn
IPO (Nov-14)
China
$2bn
Acq. By Intel (Dec-19)
USA
$1.8bn
IPO (Sep-18)
China
$1.9bn
Acq. By Roche (Apr-18)
USA
$15.3bn
Acq. by Intel (Apr-18)
Israel
$961m
Acq. By Splunk (Oct-19)
USA
$766m
Acq. by Salesforce (Aug-18)
USA
$18.5bn
Acq. by Livongo (Aug-20)
Canada
$1bn
Acq. By Facebook (Sep-19)
USA
1
4
5
10
9
1
12
10
15
15
47
94
154
172
99
2016
2017
2018
2019
2020
YTD (Q3)
NorthAmerica
7
13
28
31
11
2016
2017
2018
2019
2020
YTD (Q3)
SouthAmerica&Africa
2
2
5
1
3
1
4
5
2
6
22
44
44
73
57
2016
2017
2018
2019
2020
YTD (Q3)
Europe
16
3
4
10
5
-
2
2
7
11
26
28
11
2016
2017
2018
2019
2020
YTD (Q3)
Asia
$610m
IPO (Jun-19)
USA

There are a number of
other AI-enabled start-ups
that have raised a
significant amount of
capital and are
approaching unicorn
territory too…2
Selected VC-backed unicorns using AI globally
21
Source: Pitchbook
(1) Amount shown corresponds to the last reported valuation.
(2) Amount shown corresponds to total amount raised.
Note: Unicorn: a vc-backed company that has publicly announced a fund raising round at a valuation at or above $1bn.
$7.6bn
US
$43.2bn
US
$2.0bn
US
$3.6bn
US
$2.0bn
US
$9.7bn
US
$1.3bn
US
$8.2bn
US
$3.0bn
US
$4.4bn
US
$13.9bn
US
$6.7bn
US
$2.6bn
US
$3.0bn
US
$1.9bn
US
$2.0bn
US
$1.6bn
US
$3.0bn
US
$3.3bn
US
$1.6bn
US
$28.9bn
US
$1.6bn
US
$3.7bn
US
$1.5bn
US
$1.5bn
US
$1.3bn
Canada
$1.7bn
US
$1.1bn
Canada
$9.0bn
US
$2.3bn
US
$4.0bn
US
$1.7bn
US
$3.0bn
US
$2.3bn
US
$2.1bn
US
$2.6bn
UK
$2.1bn
UK
$1.3bn
France
$2.2bn
UK
$1.0bn
France
$139bn
China
$6.4bn
China
$37bn
China
$5.4bn
China
$20bn
China
$16bn
China
$26bn
China
$4.7bn
China
$11bn
China
$2.8bn
China
$1.6bn
Singapore
Asia 1
Europe 1
Americas1
$4.1bn
US
$966m
US
$416m
France
$886m
US
$882m
US
$991m
US
$592m
UK
$949m
US
$982m
China
$973m
US
$586m
Germany
$903m
US
$908m
China
$4.0bn
China
Middle East 1
$4.0bn
Israel
$1.2bn
Israel
$1.3bn
Israel

Sector Focus
22

AI-First

AI-first solution providers encompass all companies
building the AI value chain, from data creation and
capture to model design and implementation, often in
an industry agnostic way
24
Source: Pitchbook, Mobiletransaction.org, theFintechtimes
Sector Focus: AI-First
AI-First: An Introduction
The significant growth in megarounds has driven a 12x increase in funding
allocated to AI-First companies since 2011
AI-First deal value & volume
AI has gone through numerous phases since the term was coined in 1956. First, it flourished until
the mid-1970’s, supported by early demonstrations of success like Newell and Simon’s General
Problem Solver and the advocacy of leading scientists. Then, the limited commercial applicability of
the technology, which was mostly driven by the lack of computational power at the time, initiated the
period known as the AI winter. In the 1980’s, valuable commercial systems were introduced and since
then, AI has consistently gained popularity, driven in one part by Moore’s law and on another by
the digitalization of everything. Today, most of the elements that were holding back AI in the
previous generations are mostly faded: data is omnipresent and computing power, although still
limited, is widely available.
The current context and the increasing desire of organizations to embrace technological change led
to the widespread corporate interest in the technology that we are currently witnessing.
Companies from all sizes and sectors are conscious of the benefits that AI can bring to their
organization and are looking to benefit from the technology in one way or another.
Several challenges remain and AI is still not within reach of all enterprises. Data capture and
cleaning, talent concentration, limited computing capabilities, complex algorithm design and solution
implementation are all pain-points that need to be addressed. For AI to reach ubiquity, AI-first
companies will have to ensure that the technology is effective, available and easily leverageable
and must continue improving the ways by which AI solutions are brought to market.
$1.8bn
$4.7bn $4.4bn
$5.7bn $6.7bn
$10.3bn
$11.3bn
$9.5bn
$0.7bn
$1.1bn
$1.8bn
$2.2bn
$1.4bn
$1.1bn $1.2bn
$2.0bn
$5.0bn $4.8bn
$6.4bn
$7.9bn
$12.2bn
$13.6bn
$10.9bn
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
North America Europe South East Asia
235 886364 625 941 1,253 1,679 2,423 2,326 1,810

White Star Capital
Challenges at all stages of the AI value chain are
being tackled by a wide variety of companies
25
Sources: Unite Ai, IEEE, McKinsey, Deloitte
The pace of innovation of AI-first companies has been accelerating over
the last decade as core technological advances have transformed all
stages of the AI value chain
• AI-first companies in the data
generation portion of the value chain
span across three categories:
providers of off-the-shelf structured
datasets, providers of tailored
structured datasets, and providers
of synthetic environments
• Those companies mostly address the
needs of verticals where quality
data is scarce, sensitive, or costly
AI Value Chain
Data
Generation
Data
Capture
• Two complementary / intertwined
trends involving AI-first companies in
the data capture portion of the value
chain are edge AI and the AIoT
• AIoT is the juncture between AI and
the Internet of Things which
represents the interconnection via
the Internet of computing devices
embedded in everyday objects• The vast majority of the world’s
data is unstructured and unusable
for training AI models in its raw form
• Data enrichment companies solve
this pain-point by providing data
labelling / wrangling services
• Other companies in this segment
offer efficiency tools to data
annotation providers
• AI systems depend on massive
amounts of data that
algorithms ingest, classify, and
analyze
• The development of highly
synergistic technologies in AI
such as quantum computing
and the growing use of AI-
specific processing units are
two dimensions in which AI-first
companies are effecting the data
processing part of the value
chain
• Finally, the value is ultimately
rendered to end-users when AI-
driven solutions are implemented
• This step is often led by vertically
integrated companies operating in
at least one other dimension of the
AI value chain
Data
Enrichment
Solution
Implementation
Computing
Algorithms
• Advances in algorithm design involve
technological developments such as
deep learning, federated learning,
and reinforcement learning
• Under this portion of the value-chain,
open-source frameworks such as
TensorFlow, PyTorch and Theano
have played a key role in
democratizing AI
• Some start-ups are also offering
tools to data scientists and teams
to track, compare, explain and
optimize experiments and models

White Star Capital
AI-first unicorns are emerging at all stages of the
value chain
26
Source: Company websites, Crunchbase
$431m raised to date
Selected Investors
DataRobot (US) developed a machine learning automation
platform designed to deploy accurate predictive models
DataRobot has been instrumental in democratizing access to AI
to a wide array of enterprises through its automated machine
learning solution
Selected Investors
Scale AI (US) developed some of the most advanced data
annotation capabilities in the world which it offers via APIs to
companies across all industries
Scale AI’s annotation solution has been able to reduce human
involvement to a minimum while maintaining high accuracy
Selected Investors
Coveo (Canada) has built a relevance platform for intelligent
enterprise search and predictive insights platforms for
businesses
Coveo is leveraging data and AI to deliver a recommendation
and search platform that is personalized and predictive.
$2.6bn raised to date
Selected Investors
SenseTime (China) developed AI-driven facial recognition
platforms and deep learning systems used across a wide-array
of verticals including smart cities, finance, security and others
SenseTime is among the world’s most valuable AI start-up and
is partly known for its controversial facial recognition solution
powering China’s government surveillance efforts
Selected Investors
Element AI (Canada) is a Montreal-based artificial intelligence
incubator that turns AI research into real-world business
applications
Element AI co-founder Yoshua Bengio won the Turing Award in
2018 along with Geoffrey Hinton and Yann LeCun

• Although society is generating data at an
unprecedented rate, some use cases
require specific datasets that are not easily
accessible to today’s data-hungry algorithms
• Synthetic data can replicate all important
statistical properties of real data while
overcoming key restrictions such as privacy,
availability, feasibility and cost
• Generative Adversarial Network has been
a key enabler of synthetic data advancement
• AIoT is based on the premise that AI can be
used to transform IoT data into useful
information for improved decision-making
processes
• Applying AI algorithms on edge devices vs on
central cloud / server gives devices the ability
to process information locally and to
respond more quickly to situations
• Particularly caters use cases where real-
time decision is crucial and where latency
can prove fatal (i.e. autonomous vehicles)
Source: Deloitte, McKinsey, MarketsandMarkets, IBM, Forbes
(1) O’Reilly - Artificial Intelligence Adoption in the Enterprise, 2019
(2) MarketsandMarkets
(3) IBM – The future of cognitive computing
(4) Appen – Company presentation
(5) Forbes - Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says
• Organizations across industries
accumulated enormous amounts of data
and are now looking to leverage this asset
in the form of AI-driven applications
• While the data accumulated is valuable, it is
often not suited to fuel AI models in its
raw form and needs to be “prepared”
• Firms such as Appen, Mechanical Turk and
Samasource are tackling the data cleaning
pain-point with varying degree of accuracy,
automation and specialization
Creating data in a scalable way
Sensors are becoming intelligent
Solving AI’s biggest pain point
As significant industry pain points like data
collection and preparation are being solved…
1/3rd
Of the data used in ML
models requires at least
monthly refreshes(4)
80%
of the world’s data is
categorized as dark data(3)
80%
of the time of data scientists
is spent cleaning and
prepping data(5)
Factors holding back AI adoption by
companies’ stage of AI adoption(1)
500
1,00075
100
75
150
50
100
50
250
2020 2024
Smartphone Tablet Speaker
Wearable Enterprise Edge
Edge AI chips by device, 2020 and 2024
(millions of units)(2)
Evaluation Stage Mature Practice
Company culture 22% 10%
Lack of data or data
quality
20% 26%
Lack of skilled people 18% 24%
Difficulties identifying
use cases
21% 11%
Technical infrastructure
challenges
7% 11%
Legal / compliance risk 4% 11%
Other 8% 7%

• AI algorithms are improving fast, mostly
driven by the field’s growing popularity and
a significant amount of money invested on
both the corporate and academic fronts
• Key advances such as federated learning
and reinforcement learning are showing
great promise and techniques such neural
architecture search are playing a key role
in making machine learning available to
the masses
• While GPUs have been an important
enabler of AI’s rise to ubiquity over the
last few years, tensor processing units,
neural network processors, vision
processing units, intelligent processing
units and dataflow processing units are
all innovations that are set to enable the
next era of AI computation
• QML is also a rapidly evolving field
holding lots of promise
• AI-solutions come in all forms and
substance, and are usually either offered on
a horizontal basis (i.e. industry agnostic ,
NLP, speech and computer vision
capabilities), or on a vertical basis (i.e.
integrated fintech solutions)
• Adoption plans from organizations
across all categories are increasing and
the penetration of the technology is still in
its early innings
Average AI projects per
organisation(1)
Academic advances and open-source
frameworks have played a key role in
democratizing access to AI
Core technological advances are
redefining computational capabilities
And interest for AI-driven applications is
emerging from companies across all
sizes and sectors
… AI-driven applications are seeing an increased
interest from organizations across all verticals
4
10
20
35
2019E 2020E 2021E 2022E
Source: IBM, Forbes, AI Impact
(1) Appen – Company presentation
(2) AI Impact – History of GFLOPS costs
Computing cost keeps falling(2)
-
$0.30
$0.60
$0.90
$1.20
$1.50
$1.80
$2.10
2011 2013 2015 2017 2019
$/GFLOPS
Adoption plans for AI across
organizations(1)
- 25% 50% 75% 100%
No plans
Pilot
Moving to
production
Scaled
Predictive analystics Computer vision
NLP Chatbot
RPA

White Star Capital
Despite progress, there still exists a number of challenges
that present opportunities to create significant value
29
(1) Harvey Nash / KPMG CIO Survey 2019
(2) Company documents
(3) Charles Brun - “Is Simulated Data the Great Equalizer in the AI race?“
Democratizing AI
• Data is unevenly distributed, and
big tech players are collecting the
lion’s share of it globally
• Everyday, 350m images are uploaded
on Facebook, 65bn messages are
sent on WhatsApp, and 5bn+
searches are made on Google2
• As data becomes a new barrier to
entry in several sectors, alternatives
are being built to ensure that value
creation from the AI flywheel (most
data → best models → best
product → best traction → most
data) is not exclusively captured by
a handful of players
Empowering Data Scientists
• Nearly half of CIOs claim suffering from
skilled employee shortage in big data
& analytics. AI is identified as the
second most important shortage
category with about 38%1 of CIOs
claiming a lack of adequate staff
• While data scientists are a rare asset,
their time is systematically lost on
limited value-added activities
• Growing frustrations on data
quality, availability and structure
are creating significant turnover and
dissatisfaction
• As AI grows in popularity, the need for
data scientists to be provided with the
right tools is only increasing
Opportunity
• As a gigantic amount of data is being
captured by network-effect driven
businesses and technology giants
(namely FAGMA and BATX),
synthetic data presents itself as a
technology that has the potential to
act as an important equalizer of AI3
• In addition to its democratization
potential, synthetic data has the
potential to address sampling
bias, black swan events,
scarcity, privacy, and data
labelling pain points
• Synthetic data operating leverage
can be up to 600x the current
market price for image
annotations
• WSC believes that innovative
companies building synthetic
datasets to support real world data
through an industry agnostic
approach will play an increasingly
growing role in the AI ecosystem
Opportunity
• This challenge is inherent to the life
cycle stage in which AI is and will likely
be addressed over the next few years
• As organizations realizes the cost of
not providing the appropriate
infrastructure to their data scientists,
budget offered to tooling solutions
should increase across organisations
• WSC is keen to see start-ups that have
a focus on simplifying ML model
creation and increasing collaboration,
tracking and management capabilities
for data scientists
• GitHub and GitLab have offered
appropriate tools and infrastructure
to software developers to collaborate
and we believe that a comparable
set of solutions to AI solutions
development is needed

Fintech

As fintech companies unbundled the banking industry
over the last decade, the quest to improve customer
experiences and business processes has made AI a
weapon of choice across many sub-verticals
31
Sources: Pitchbook, China Banking News
Sector Focus: Fintech
AI in Fintech: An Introduction
AI representation in the fintech sector as steadily increased since 2011,
across geographies
AI / ML and Fintech deal value & volume
Fintech is broad and encompasses a wide array of sub-verticals which can be regrouped into
consumer facing solutions, enterprise finance solutions, and infrastructure. All those areas have
been transformed by AI over the last few years. As Du Xiaoman Financial’s (former fintech unit of
Baidu) CEO Zhu Guang said: “the application of AI technology in the financial sphere has already left
the laboratory phase, and officially entered the standardized application phase”. Finance is among
the most rapid channel for achieving commercialization of AI technology and the bourgeoning
landscape of emerging start-ups is a clear testimony of that.
On the consumer facing solutions side, AI has had a profound impact. Digital banking, bill
management, insurance, credit and loans, remittance, trading, investment, and advisory have all been
transformed in one way or another by AI.
The same applies for enterprise finance solutions and infrastructure where AI has found applications in
fraud detection and compliance, customer support, money laundering prevention, data-driven
customer acquisition and many other relevant use cases.
We are still at the early innings of AI’s penetration into the fintech space and as the graph below
suggests, the pace of adoption is poised to continue accelerating.
$532m $698m
$350m
$644m
$1,718m $1,769m
$1,350m
$172m
$331m
$334m $449m
$642m
$162m
$67m $126m $200m
$589m
$730m
$527m
$1,000m
$2,127m
$2,380m
$2,063m
2.2%
4.3% 3.6%
6.1% 6.5%
9.9%
14.1% 14.7% 14.8% 13.0%
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
North America Europe Southeast Asia % of AI driven deal volume
19928 37 91 121 211 361 436 42110

White Star Capital
AI has found ways to reduce risk, increase customer
engagement, optimize spend effectiveness, and
alleviate cost structures across many use cases
Sources: CB Insight, McKinsey, Pitchbook
AI is redefining how we save, invest, pay, insure, borrow, protect, and
control financial services
Consumer
Facing Solutions
Enterprise
Finance Solutions
Infrastructure
Personal Finance
An armada of AI-supported solution providers are reshaping and optimizing the ways in which
the most basic financial operations are performed today. Value chains are being streamlined,
optimized and increasingly automatized
Business Operations Tools
Insurance
Financing and Lending
Payment: PoS and Checkout
Payment: Infrastructure
Fintech-as-a-service
Security, Analytics, Compliance
BorrowSave and invest Pay Protect and control
Insurer/
Carrier
Re-insurer
Broker
Quasi-Carrier
Traditional
MGA
Distribution
partners
Balance
sheet
lenders
Asset-light
lenders
Institutional,
public bodies,
supranational,
retail
Institutional,
Hedge Funds,
VC
Lending
platforms
Fund providers
Regulatory
affairs
Identity
verification
& KYC
Data
security &
transaction
monitoring
Institutional,
Hedge Funds,
VC
Borrowers
Investment
Bank
Security
Exchange
Corporates
Bank
Issuing
Bank
MerchantMerchant
Acquiring
Bank
Acquiring
Bank
Payment
processing
network
Insure
MGA: Managing General Agent
Blockchain
$$$ $ $ $
32

White Star Capital
Fintech start-ups are leveraging AI across a wide
variety of subsectors
33
Selected Investors
MoneyLion (US) is a mobile personal finance platform offering
products spanning from borrowing to saving and investing
The company uses advanced analytics and AI to gain a
complete view of personal finances of its users and build
consumer financial products
Selected Investors
Du Xiaoman Financial (China) is the former Baidu fintech unit
and is among the most innovative fintech company globally on
the AI front
The company’s financial platform uses AI to provide services
including financial management, credit business, wealth
management and digital payment
Selected Investors
Figure (US) is a provider of consumer financial solutions
intended for home improvement, debt consolidation and
retirement planning
Figure uses blockchain and AI to streamline the home loan
process by finding access points for consumer credit products
Selected Investors
Lemonade (US) is a licensed carrier providing digital home
insurance products to consumers
Lemonade uses AI to optimize risks underwriting, claim
management and customer communication (through its chatbot
Maya)
Selected Investors
Using artificial intelligence, Shift (France) provides solutions for
claims automation and to combat insurance fraud
Shift helps insurance players reduce fraud through its AI
powered detection models that automates claim reviews, which
to date has been a very manual process

• As new regulations, such as PSD2, 5AMLD,
and GPDR are raising the compliance bar to
tackle financial crime, corporations are
looking for ways to improve their KYC and
AML capabilities
• Failure to stay on top of this has resulted in
over $350bn worth of fines over the past
decade
• Security and compliance has already seen
significant penetration of AI-driven fintech
solution
• According to the 2018 Growth Readiness
Study, asset managers who are embracing
big data and analytics are found to be
growing their revenue 1.5x faster than the
rest of financial services
• Asset management related AI transformation
include alpha generation, operational
efficiency improvement, product design,
content distribution, and risk management
• AI algorithm trading is also a use case that
has gained in popularity recently
Sources: PwC, Deloitte, McKinsey, IBM
(1) Medici – The Regtech Effect
(2) Medici – RegTech Companies in the US Driving Down Compliance
Costs to Enable Innovation
(3) The Global RegTech Industry Benchmark Report
(4) AlternativeDataOrg
(5) Juniper Research
(6) IBM
• Interest for chatbots and financial assistants
has been strong in the financial services
sector and is now part of the playbook of both
fintech companies and incumbents
• BofA’s Erica, HDFC’s EVA, Lemonade’s
Maya, JPMorgan, Wells Fargo, and many
other financial institution have made chatbots
central to their modus operandi
• Chatbot’s widely spread use cases include
lead generation, customer support,
feedback collection and back-office
operations streamlining
KYC and AML
Seeking alpha
Talk the talk
Wherever data overload is an issue, AI has found
ways to integrate the tech stack...
Total buy-side spend on
alternative data(4)
(In $m)
90%
of banks interactions with
clients could be automated
by chatbots by 2022(5)
$8bn
in savings will be generated by
chatbots globally by 2022(5)
80%
of routine questions can be
answered by chatbots on
average(6)
$232
$400
$656
$1,088
$1,708
2016A 2017A 2018E 2019E 2020E
$120bn
Amount of global
compliance spending by
2022(2)
10-15%
of staff dedicate their time to
compliance(1)
56%
of regtech companies are
employing machine
learning(3)

• Potential to offer smarter and more nimble
workflows to improve the productivity and
reach of lending operations
• The use of alternative data analysis allows a
deeper assessment of applicants'
creditworthiness, especially in situations where
limited history is available
• Advanced credit-decision models have the
potential to provide lenders with the confidence
to expand reach and broaden inclusion
• Automated decision-making can adjudicate
customers in real-time and at low cost, allowing
tailored lending at the point of purchase
• On the insurance front, devices connected to
actuarial databases can calculate consumers’
risk score based on daily activities as well as
the probability and severity of potential events
• AI is anticipated to replace the vast majority
of manual underwriting for personal and SMB
products across life and P&C insurance
Redefining credit worthiness
Pricing risk accurately
Other fintech-related relevant use cases
… and by doing so, AI has increased financial
inclusion and enabled risk to be better assessed
Source: Fannie Mae, McKinsey, Chatbot Guide, World Economic Forum
(1) Fannie Mae – Mortgage lender sentiment survey
(2) Transforming Paradigms A Global AI in Financial Services Survey
Relative interest levels of
AI/ML applications for lenders
(average = 100)(1)
51
60
65
95
110
136
182
Social trust score
Customer service
digital assistants
Customer relationship
management
Property valuation
Borrower status
assessment
Borrower default
risk assessment
Anomaly detection
automation
Higher priorities
• Back office automation: JPMorgan’s chatbot
COIN is focused on analyzing commercial-loan
agreements and reduced work from 360,000
hours down to seconds
• Report generation: Automate report
generation from structured datasets
• Image recognition: Used for enhancing
customer experience or security
• Narrative science: Generation of narrative
reports from structured data, such as sales
records, using natural language generation
8%
6%
12%
14%
31%
25%
4%
% of total R&D spend
Fintech R&D expenditure
spent on AI(2)

White Star Capital
Trends and Challenges
Key Trends, Challenges and Opportunities
36
AI has the potential to address some of fintechs and financial incumbents’
most important pain-points while broadening financial inclusion
Infrastructure shift
Strong focus on the development
of core banking infrastructures and
enabling technologies, allowing
both incumbent to reimagine their
back ends and non financial
companies to deploy financial
products
Increased specialization
Financial and insurance offerings
are increasingly specialized to
their targeted group’s needs and
single product players are rapidly
expanding their offering to cater
for all needs and diversify revenue
streams
From competition to
cooperation
While the bulk of fintechs were
initially positioning themselves as
challengers to the banks, we are now
seeing growing levels of collaboration
across emerging technology
companies and incumbents
Changing regulations
Companies are struggling to cope
with the ever-evolving regulatory
landscape. Failure to stay on top
of this has resulted in over $350bn
worth of fines over the past
decade
Financial education
Fintechs have so far made their
products accessible to a wide
audience. However, trusted
financial advice is often only
limited to the wealthier members
of society
Big tech in finance
Big tech growing involvement in
financial services could potentially
represent a threat to banks and
emerging fintechs alike
Opportunities of Interest to White Star Capital
Financial solutions are getting
increasingly commoditized…
• The lending market is slowly showing
signs of commoditization as most of the
differentiation levers (access to capital,
credit decisioning, customer acquisition,
and customer experience) are reaching the
point of diminishing returns
• Lack of perceived relevance, lower
brand loyalty, low NPS scores and lack
of trust of emerging institutions compared
to established brands have been a major
road block to scaling insurtechs and has
significantly impacted unit economics
…And emerging players are looking to
capitalize on AI’s capabilities to differentiate
themselves
• By using alternative data and a wider array of
risk pricing methodologies, lenders and insurers
have the potential to expand their reach to
underserved niche markets and benefit from
increased visibility by becoming the #1
reference in specific market segments
• AI has the potential to broaden financial
inclusion
• In industries where customer loyalty and NPS
are extremely low, AI technologies offer more
personalized and effective customer
approaches
• WSC believes that there is a significant
opportunity for fintech companies to use AI to
create a deeper engagement and sense of
belonging by offering solutions to segments
where none were previously available

Mobility

AI is transforming mobility as we know it today
38
Sources: Accenture, Pitchbook
Sector Focus: Mobility
Megarounds have been an important part of AI-mobility related deals,
explaining the important level of lumpiness in funding history of the sector
AI / ML and Mobility Tech deal value & volume
We are about to enter a new era where cars are increasingly connected
and where drivers of today will become riders of the future. Mobility as a
Service and AI will optimize travel routes and safety, but also our lives
and cities globally
Technology, with AI at the forefront, is transforming the mobility sector. By 2025, a
forecasted 40% of new vehicles will have embedded telematics according to Accenture.
Software and electronics will be at the center of the revolutionized automotive value chain
as well as at the center of mobility services in general.
The status quo of universal individual car ownership era will be challenged over the
coming years. By 2045, Victoria Transport Policy Institute estimates that at least 50% of
newly sold cars will be at least partially autonomous which will change out current
consumption patterns around mobility. As vehicles and their accompanying technologies
evolve, they will continue to disrupt mobility-related industries, making possible entirely new
business models and spurring even greater AI enabled inventions, which will make owning
a car less relevant and relatively expensive.
Individual Car Ownership
Connectivity
Ubiquitous
Mobility as a
Service
Fully-Autonomous
Vehicles
Today…
Transforming
into…
$0.4bn
$2.8bn
$3.8bn
$8.2bn
$1.8bn
$4.6bn $4.7bn $5.2bn
$0.3bn
$0.5bn $0.1bn
$0.4bn
$0.0bn $0.1bn
$0.4bn
$2.8bn
$3.8bn
$8.3bn
$2.0bn
$5.1bn $4.9bn
$5.5bn
8.4% 8.4%
12.6% 13.7% 15.8%
19.3% 21.4%
25.7%
21.5% 20.3%
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
North America Europe South East Asia % of AI driven deal volume
11 5915 33 52 75 99 143 166 139

White Star Capital
Are vehicles autonomous yet?
39
Source: McKinsey, Benedict Evans
In 2016, only about 1% of vehicles sold were equipped with basic partial-
autonomous-driving technology. Today, 80% of the top ten OEMs have
announced plans for highly autonomous technology to be ready for the
road by 2025.
Vehicles are categorized according to what type of advanced driver assistance systems
features they offer. These different types of features are defined into 6 levels of automation
(including level 0 which represents no automation). The difference between the ratings
depends on the level of driver intervention and attentiveness needed to operate the vehicle.
Automated vehicleDriver control
That being said, it is very unlikely that there will be a defined moment in the next decade when the
‘first’ L4 operating autonomous vehicle goes on sale. The level of autonomy will be different from
one area to another. This variability applies not just across different cities and countries but also in
different parts of each urban landscape: freeways are easier than city centers, which might be easier
or harder than suburbs. The “first” deployed autonomous vehicles might be L4 or L5 on highways but
only L2 or L3 on city streets.
The ‘when’ of autonomous cars highly depends on the “where” and “what”, and these are a
matter of incremental deployments of different models in different areas over time.
Full self-driving
autopilot
Assistance systems
Lane departure assist
Adaptive cruise control

White Star Capital
The rise of AI introduces prominent new actors in
the autonomous vehicles value chain
40
Source: Freescale / NXP, Andreessen Horowitz, UBS
5%
10%
15%
22%
35%
50%
1970
1980
1990
2000
2010
2030E
Electronic systems as % of total car cost
Advanced driver
assist
Active-passive safety
Powertrain
Radar / vision
Infotrainment
Airbag
ABS / ESP
Electronic
fuel injection
LG only started to call out revenues from the
auto industry in 2015. In 2017, 56% of the
vehicle components of the Chevy Bolt were
supplied by LG.
This example points the way to the future by
showing that new entrants in the value chain will
become central in future vehicles value chains.
Value shifting into unexpected players’ hands…
Software and electronics
are now at the heart of the
automotive value chain
Freescale, a semi-conductor
company has forecasted that
by 2030, 50% of the total cost
of manufacturing a car will go
to electronics and software
providers as opposed to the
traditional metal class
providers.
Building blocks of tomorrow’s mobility landscape
Currently, incumbents invest in software systems, but they also invest across the whole
panel of AI-enabled technologies that will shape autonomous vehicles. In the future, we
may see the emergence of more full stack companies compared to modular ones we see
today (where car manufacturers currently assemble components from thousands of
different providers)
Proprietary vs. Open source
Full stack vs modular
Incumbent vs start-ups
Currently, it is difficult to know whether the software of self driving cars will be proprietary
and become a type of environment in which one dominant company licences its operating
systems to OEMs, or will if it will be open source
Future fleets sales of autonomous vehicles can be won by current incumbents such as
Ford, Toyota, Volkswagen, etc., or by emerging start-ups. The likely result will probably be
a mix of both

White Star Capital
A complex ecosystem with frontier innovation
happening in both hardware and software
41
Source: Pitchbook
Full stack
These companies develop their entire
autonomous mobility ecosystem including self-
driving vehicles fleets, control systems, and
most of the time, rise-sharing service and
mobility optimization systems
Perception software
Crunch data from sensors to direct the car’s
movements. Represents the “brain” of the
vehicle and receives information from various
components and directs the vehicle overall.
Manipulates steering, accelerator and brakes
by understanding the rule of the road.
Radar sensors
Send radio waves that are able to detect short
& long-range depth. The short-range radar
detects objects, pedestrians and vehicles near
the car. The long-range radar detects and
measures velocity of traffic down the road.
Such sensors are already used in adaptive
cruise-control systems.
Lidar (light detection and ranging)
Measures the distance by illuminating the
target with pulsed laser light and measures the
reflected pulses with sensors to create a 3D
map of the surrounding area. These pulses are
analysed to identify lane marking and the
edges of roads.
Cameras
Provide real-time 360-degree obstacle
detection to facilitate lane departure and track
roadway information. They detect traffic lights,
read road signs, keep track of the position of
other vehicles and look out for pedestrians and
obstacles on the road.
V2X (Vehicle-to-everything)
Allows vehicles to communicate with moving
parts of the traffic system around them. One
component of this technology is called vehicle-
to-vehicle (V2V) which allows vehicles to
communicate with one another. Another
component is vehicle to infrastructure (V2I)
which allows vehicles to communicate with
external systems such as street lights,
buildings, and even cyclists or pedestrians.
Autonomous vehicles
components

White Star Capital
A complex ecosystem with frontier innovation
happening in both hardware and software (cont’d)
42
Source: Pitchbook
Connectivity & Data Management
Some companies develop AI-powered mobility
operating systems enabling transport
infrastructure, mobility services and cities to
optimize traveling by human mobility analysis.
Other companies provide software tools that
enable collection, sharing and management of
data.
Fleet management
Companies in this subsector provide mobile
workforce platforms and solutions for service-
based businesses operating fleets. These
companies develop tracking systems through
IoT and AI allowing to monitor, schedule, and
optimize factors such as driver behaviors, fuel
consumption. They develop data-based
predictive analytics, enabling fleet managers to
optimize overall fleet management, improving
safety and minimizing operational costs.
Passenger safety
Passenger safety tools include systems
which employ machine vision and sensor
fusion algorithms to detect hazards and AI
systems to reduce car accidents and provide
accident reports. Computer vision and deep
learning technology is also used to eradicate
the distracted driving by tracking gaze
direction, eye openness and head position of
drivers.
Cybersecurity
Cybersecurity systems are intended to detect
and eliminate cyber threats faced by connected
and autonomous vehicles. Hardware and AI-
powered software systems are combined to
defend against any type of cyber-attacks and
prevent cars from being hacked, enabling
automotive companies to protect connected
vehicles against threats that can endanger their
physical safety and information safety
Fleet management & connectivity solutions providers create the bridge
between vehicles and people, networks and infrastructure. New players
emerging in the sector include connectivity and data management
platforms, fleet management platforms and tools, parking applications,
passenger safety tools and automotive cybersecurity technology.

White Star Capital
The road to Mobility as a Service (MaaS) and
optimized environments through ridesharing
43
Sources: McKinsey, RethinkX, Medium, Andreessen Horowitz
As autonomous vehicles become commonplace across the mobility
industry, ridesharing platforms and connectivity will challenge the
individual car ownership status and launch the era of Travel as a Service
(TaaS) and global mobility optimization embedded in future smart cities
Economics will drive the adoption of TaaS
Fleets & ridesharing platforms
Smart cities & environment optimization
Forecasted 2030 costs per mile
0.03 $
0.10 $
0.31 $
0.62 $
0.78 $
TaaS Pool TaaS Operating
cost of
existing
ICE vehicle
Buy a new
EV
Buy a new
ICE vehicle
• By 2030, consumers will be facing the
option of spending about $3,400/year on
driverless TaaS journeys (or $1,700 on
TaaS Pool – or shared TaaS), rather
than an average of approximately
$9,000/year on a personally owned
vehicle which will challenge the current
status of individual car ownership.
• Individually owned cars are used only
4% of the time. While there will be fewer
cars, TaaS vehicles will be available on-
demand 24 hours per day, providing
door-to-door transport to passengers.
TaaS vehicles will be utilized 10 times
more than individually owned vehicles.
• In major regional and local markets, large shared-mobility providers
dominate, with combined market shares of up to 90%.
• As of this writing, in 2017, at least $32bn had been invested in
ridesharing start-ups alone. There is strong growth potential as less
than 1% of passenger miles traveled today are carried out using
shared-mobility services, and US customers expect usage of shared
mobility to increase by around 80% once robo-taxis are available.
90%
adoption rate of
smartphone
ridesharing apps
by 2030
250 million
cars connected
through V2X
systems to
infrastructure and
direct environment
• Smart cities will have clean and efficient transportation of goods
through optimized mobility. Ubiquitous MaaS and use of
ridesharing platforms will ease traffic congestion and provide users
with real-time updates to avoid waiting times and provide ideal
match of supply and demand for traffic fluidity.
• Smart cities and mobility tech also pave the way for AI-enabled
drones and autonomous flying that will be used for people and
goods transportation.

White Star Capital
$462m
Series B raised in February 2020
Incumbent Investors
Developer of an autonomous driving technology leveraging AI
to accurately perceive the vehicle's surroundings, enabling
vehicle companies to improve their car functionality and safety
Incumbents heavily invest in the future of mobility
through AI systems and technologies
44
$1.3bn
M&A deal completed in June 2020
Acquirer
Developer of an autonomous mobility ecosystem that includes
self-driving vehicles (automated fully-electric vehicle fleets
providing MaaS in urban environments), control systems, AI,
and a ride-sharing service all designed to improve urban
mobility
$1bn
M&A deal completed in February
2017
Acquirer
Developer of AI software technology and robotics for self-
driving vehicles, enabling its users to avail effective self-
driving technology
$15.3bn
M&A deal completed in April 2018
Acquirer
Developer of collision avoidance system designed to reduce
vehicle injuries and fatalities by using computer vision and ML,
data analysis, localization, and mapping for advanced driver
assistance systems and autonomous driving
$600m
Series B and B1 raised in 2019
Incumbent Investors
Developer of an autonomous car technology designed to
create self driving cars. The company's technology uses
advanced AI to power self driving cars by leveraging a
combination of camera, radar, and LiDAR

White Star Capital
45
AI is one of the core technologies driving mobility forward as vehicles and
cities are getting increasingly connected
Autonomous vehicles
Synthetic environment are a core
part of autonomous vehicles’
training. Waymo says it drives 20
million miles a day in its Carcraft
simulation platform — the
equivalent of over 100 years of
real-world driving on public roads
Smart cities
Smart cities put data and digital
technology to work to make better
decisions and improve quality of
life by understanding how patterns
are changing and responding
accordingly
Mobility-as-a-Service
Trends towards aggregation of
various forms of transport into a
consolidated system that offers
commuters a single application
allowing them to select their
preferred mobility mode
Safety
There has been several self-
driving vehicles related fatalities
over the last few years that have
underlined the need for safety
improvements
Regulations
While the US federal government
states voluntary guidelines, self-
driving vehicles regulations are
made on state by state basis and
vary significantly across regions in
the US
Connectivity
Latency remains a key challenge
to widespread rollout of self-driving
vehicles
Mobility behaviours are poised to
change drastically over the coming
decade…
• New generations of urbanites have no
interest in owning assets, but look for the
most seamless and affordable solutions
for their mobility needs
• Citizens have come to value convenience
over car ownership. With this comes a
strong market pull for MaaS, its first
incarnations being in the form of fleet-
sharing and ride-hailing services
• Commercially viable Level-5 autonomy has
proven harder to crack than some experts
anticipated and outcomes of investments
in the space have been highly binary
…And AI is well positioned to become a
core elements of our new reality
• We see tremendous opportunities in the
shift towards MaaS and in players
offering integrated end-to-end and
multi-modal solutions
• We also see strong potential in companies
leveraging AI in the self-driving space for
current pain-points with clear and
immediate commercial applicability
• Finally, we have a strong interest for
companies that will present creative ways
to reduce congestion and pollution and
present new opportunities for a greener
and more sustainable mobility

Healthtech

The digitalization of healthcare has opened the door
for a wider application of AI, both inside and outside
the hospital
47
Source: Pitchbook, China Banking News
Sector Focus: Healthtech
AI in Healthcare: An introduction
2018 has been a turning point for AI-enabled companies in healthtech, with
funding reaching $1.5bn, more than 2x the level reached in 2017
AI / ML and Healthtech deal value & volume
AI was first used in the healthcare space with the creation of Dendral in the 1960s. Developed by
Stanford researchers, it was the first problem solving program, used to identify samples of organic
molecules. Dendral paved the way for several other systems to be developed such as MYCIN in the
1970s which used AI to identify blood-born infections and recommend antibiotics to patients.
Up until the 1990s, scientists were trying to develop completely autonomous programs. Due to the lack
of notable advancements, approaches ultimately shifted to recognize AI’s limitation and started
focusing on AI as a tool rather than an holistic solution. Following that shift, the technology’s incursion
into the health sector has accelerated. Surgical robots were approved by the FDA in 2000 and the first
fully automated surgery by AI-powered robot was performed no later than in 2006 in Italy to correct
heart arrythmia.
Today, global annual healthcare expenditure is close to $9tn (10% of the world’s GDP), with the US
representing about $3.5tn of that amount. Healthcare services have grown in complexity and cost
for governments and patients alike over the last decades and this trend has been exacerbated by the
recent COVID-19 pandemic. Consequently, the industry is increasingly turning towards technology-
driven solutions to improve processes, reduce costs, and enhance care quality. Predictive analytics
and AI application focused on automating administrative functions, supporting diagnosis and
treatment. and facilitating patient interactions are seeing increasing adoption.
As the sharing of data required to develop AI tools speeds up, driven by a transition to Electric Medical
Records, a stronger data infrastructures will emerge, supporting healthcare organizations and
addressing the current challenges of data silos that have historically weighed on the sector.
$232m $240m
$678m $573m
$1,376m
$1,086m
$1,439m$128m
$164m $839m
$37m $67m
$182m $245m $262m
$732m $702m
$1,546m
$1,925m
$1,548m
3.6% 4.4%
6.3% 6.6%
8.3%
10.7%
12.9%
15.7% 14.9% 15.4%
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
North America Europe Southeast Asia % of AI driven deal volume
18 17029 62 78 130 187 250 306 278

White Star Capital
AI has been at the core of the value proposition of
several healthtech unicorns
48
Selected Investors
Selected Investors
$235m raised before IPO
Selected Investors
Livongo (US) leverages technology to help customers detect
and manage diabetes and other chronic diseases
Livongo’s AI and data science powered technologies has proven
to be a game changer to better manage chronic conditions at a
lower cost
Selected Investors
Babylon Health (UK) develops digital healthcare applications for
people to get access to affordable care through phones
Babylon Health has been a leader in using AI to provide affordable,
personalized, and interactive healthcare through its AI-powered
chatbot, with over 4 millions patients registered
$237m raised before IPO
Selected Investors
Accolade (US) offers a personalized health and benefits
platform, designed to improve experience, outcomes and
costs for employers
Accolade’s use of AI to provide customized solutions designed
to increase consumer engagement gave the company a
leading position in the ongoing healthcare market revolution
Tempus (US) provides a data analytics platform designed to
improve patient outcomes through the application of AI
Tempus transformed collaborative research by building the world’s
largest library of clinical and molecular data to the benefits of
researchers and doctors
Zymergen (US) develops molecular technologies to search the
molecular genome and provides biology-driven solutions
Zymergen disrupted the biotech industry by developing
revolutionary new products across industries from electronics to
pharmaceutical

White Star Capital
Diagnosis and treatment applications
Combinations of robotics, NLP, and computer vision to augment diagnostics and treatment
capabilities with applications in:
• AI powered surgical
robots
• Creation, understanding,
and classification of
clinical documentation
• Aggregation of data to
reduce silo effects
• Predictive and early
detection medicine
• Computation-based
rational drugs design
Patient engagement and adherence applications
Tools designed to improve patients’ experience, both inside and outside of the hospital,
and to provide faster and more effective outcomes.
As trends such as the digitalization of the value chain, the consumerization
of healthcare, and the shift towards value-based care are taking place, AI is
reinventing how we treat, interact and manage patients globally
49
• Conversational AI for
triage and preparation
for care
• AI-powered programs to
help track progress and
improve quality of care
• Real time remote care
delivery
• Chatbots for patient
interactions in mental
health and wellness
• Support solutions
providing diagnostic
recommendations to
primary care providers
Administrative applications
Use on AI to optimize regulatory and administrative processes and
accelerate revenue cycle.
• RPA and IPA for
administrative tasks
• AI-driven solutions
offered to health insurers
to optimize workflow and
claim management
• Health predictions used
by insurers to develop
personalized life
insurance package
• Optimization of revenue
cycle and medical
records management
Key Sub-Sectors

White Star Capital
50
AI’s adoption in healthcare has accelerated in light of the recent
COVID-19 pandemic and a proactive attitudes from authorities
Telehealth – the new norm
Medicine is shifting towards AI-
powered products for immediate
care, accessible through software
and virtual platforms
Value-based care
In hospitals, AI-based
management solution have
accelerated the development of
the value-based service model
which will be key in driving costs
of care down
Playing the momentum
Increasing efforts from Big Tech
and start-ups to develop AI
programs designed for clinical use
as many regulatory institutions
across the world are offering faster
tracks of approval due to the
COVID-19 pandemic
Data usability
Less than 20% of available
healthcare data is structured.
Major investments will be required
to integrate current information in
clinical workflows and EHR
systems
Trust and ethics
Patients’ concerns about privacy,
reliability, medical ethics, and poor
usability is a major roadblock to
automated diagnostics
AI for all?
Smaller institutions and rural
healthcare providers might not be
able to immediately adopt costly AI
solutions which might increase
inequality gaps
The overall cost of healthcare and data
integration challenges remains major
industry pain points
• Health spending in the U.S. increased by
4.6% in 2018 to $3.6 trillion or $11,172
per capita
• Legacy healthcare networks must
improve the exchange of member,
payer, patient and provider data on a
near real-time basis to meet modern care
needs
• Lack of system interoperability is the
biggest operational challenges for
healthcare, holding substantive
advances in the patient experience
Technology is rapidly growing in adoption
in the health space both inside and outside
the hospital
• With 4 trillions gigabytes available, health data
has exploded and triggered recent regulatory
changes proposals for more transparency and
integrated data sharing to eliminate silos. We
see strong potential in companies solving for
medical data normalization, integration,
and secure data search and retrieval
• Amid one of the most important health crisis of
recent history, we’ve seen how telemedicine
has been a vital to broadening access to
care. We see strong potential in businesses
democratizing access to and affordability
of healthcare in a world where the cost of
healthcare is rising disproportionately faster
than the average income

Industry and Robotics

AI is at the heart of the next Industrial Revolution
52
Source: RBC Capital Markets, Pitchbook
Sector Focus: Industry and Robotics
Industry 1.0
Mechanization,
Steam Power
Industry 2.0
Mass production,
Assembly Lines
Industry 4.0
IoT, Cyber Physical
Systems & Networks
Industry 3.0
Automation,
IT & Electronics
AI-enabled companies operating in the industrial & robotics sector have
consistently attracted more than $2bn in funding annually since 2014
AI / ML and Industrial Technology deal value & volume
Escalating artificial intelligence capabilities are driving the adoption of
Industrial Automation, referred to as Industry 4.0.
AI & Industrial Automation will affect global supply chains in three immediate ways:
1) Demand-Driven Production – The implementation of an intelligence layer allowing
business to predict and automate an entire supply chain based on both macro (market)
and micro (consumer) conditions.
2) Smarter Robots and Adaptive Manufacturing – most industrial robots today are
designed for simple, repetitive tasks like lifting, drilling, assembling, etc. The next
generation of robots will be General Process robots, flexible in their use cases and
adaptable to changing environments, constraints, and sectors.
3) Automated Quality Control and Predictive Maintenance – The introduction of
sensors, smart instrumentation and data analytics enables enterprises to create digital
twins of a machine, production process, or entire business. These models are being
used to monitor and adjust different nodes of the system in real-time.
$0.5bn
$3.0bn $3.2bn
$6.7bn
$2.0bn
$4.7bn
$5.5bn
$3.3bn
$0.2bn
$0.2bn
$0.3bn
$0.3bn
$0.3bn
$0.3bn $0.2bn $0.6bn
$3.1bn $3.3bn
$6.9bn
$2.3bn
$5.1bn
$5.8bn
$3.6bn
1.6% 2.4% 3.0% 3.8% 4.9%
7.4%
11.3% 13.0% 12.6% 12.7%
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
North America Europe South East Asia % of AI driven deal volume
33 23661 96 156 230 339 510 524 436

AI use cases enabling greater efficiency, control,
and flexibility
AI-enhanced
Supply Chain
Management
Product
Customization
• As digital and physical products grow in complexity, AI can be
applied to accelerate the design process and facilitate
product engineering and manufacturing
• With generative design, clients and product designers can
specify a product by its constraints, and allow a machine
learning algorithm to produce design alternatives that optimize
qualities such as weight or performance
• AI-powered supply chain optimization can dynamically
adapt to changes in product mix or distribution network
due to unforeseen events
• Future systems will address the entire value chain from
suppliers of raw materials to end customer. AI enables fully
automated, self-adjusting decision making systems for supply
chain management connecting all actors
• AI systems can predict demand spikes and automatically
adjust routes and volumes of material flows, allowing inventory
reductions of 20 to 50%
/
• Quality assurance systems require high upfront investment and
extensive calibration. Current automated approaches for visual
inspection compare products up for testing to reference images.
Under this approach, ideal preconditions need to be met for the
process to be reliable
• Operators’ trust in the results of the automated inspection process
is critical. A large number of false positives may reduce trust,
thereby eroding any benefits from automation. Methodologies based
on computer vision and ML can overcome these challenges
• In AI-enabled visual quality inspection, machine learning abstracts
from differences in illumination, surface orientation, or presence of
irregular background and focuses on defects only. AI will enable
the detection of defects much earlier than it currently does
Automated
Quality Testing
Producer
Distributor Customer
Supplier
Supplier Customer
DistributorProducer
SCM
Supply chain democratization enabled with AI
50-60%
UK citizens polled
expressed interest in
personalized goods
Smart manufacturing market
to reach $1 trillion by
2030, with the number of
connected endpoints to
increase by 100x

AI use cases enabling greater efficiency, control
and flexibility
Robots and
Collaboration
• Today, industrial robots can’t react effectively to changes in
their environment and need to follow predefined steps.
Advancements in AI are enabling a new generation of non-
special-purpose automation robots that are easier to
incorporate into specific environments, including robots and
humans collaboration.
• Advances in computer vision drive the developments of
collaborative robots. Fully context-aware robots can safely
and autonomously interact with the real world.
• Deep learning allows contextual object identification and
enables robots to handle objects without requiring
predefined positions. It is also now possible to “program” a
robot by simply showing the desired movements to it.
• Yield losses due to disposed products or products with
defects play an important role in manufacturing. Testing cost
and yield losses can constitute up to 20 to 30% of the total
production cost in some industries.
• Yield losses as well as the root causes of quality loss can be
identified by linking process control data with quality control
and yield data. AI will be used to determine the optimized
product operating conditions or process conditions to
significantly reduce products defects.
Yield
Enhancement
…creating downstream market effectsRobot demand is increasing…
10%
Decline in robot
prices over next 5
years results in
2x
Demand for
General Purpose
robots over next 5
years
60%
General Purpose
robot market
share in 2025
2x
Increase in
demand for non-
conventional
robot end-market
by 2025
• Failure forecasting is complex due to external influencing
factors. As new sensors and IoT devices get integrated in
production processes and operations, data availability will
continuously increase.
• AI-based algorithms recognize errors and are able to
consider only relevant information among all data
available to predict breakdowns and guide future
maintenance and investment decisions.
• Machine-learning techniques will be essential to examine
the relationship between data records and failures and then
create data-driven models for maintenance and
breakdown predictions, reducing downtime by up to 20%.
AI-enhanced
Predictive
Maintenance

White Star Capital
Significant investments have been made in
industry and robotics start-ups by VCs globally
55
Selected InvestorsHive Box provides a self-service platform for express delivery
companies and e-commerce logistics. It operates a network of
self-service package drop-off and pick-up stations in residential
areas across China, enabling customers to store and pick up
their packages anytime.
$1bn raised to date
Selected InvestorsNuro develops a suite of robotics including autonomous vehicle
for local goods transportation enabling autonomous delivery.
Selected Investors
Flexport develops a freight forwarding platform designed to
provide visibility and control over the entire supply chain. The
platform arranges goods to be transported and subsequently
tracks the inventory in real-time in orders carried by ocean, air,
and road freight, enabling logistics companies to optimize
transportation routes and inventory management.
Selected InvestorsAera Technology developed a cloud-based supply chain
intelligence software solution. The company offers prescriptive
analytics functionalities capable of predicting demand,
diagnosing root cause issues and detecting problems at each
step in the supply chain by turning raw data into contextualized
infographics
Selected Investors
Convoy provides an efficient digital freight network that
connects shippers and carriers. The company's technology
and data solve the problem of carbon waste and inefficiency
in the trucking industry by matching trucking companies with
shippers that need to move freight.

Exploring the 2020 Artificial Intelligence Sector

Exploring the 2020 Artificial Intelligence Sector

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