Flint Capital and international venture capital investor provides an investor's perspective on a state of Ai (artificial intelligence) in 2018 and the impact of various factors like algorithms development, hardware development, datasets development, opensource software development, investments and overall interest to a topic on a growth the category. Deep deal analysis and Ai market data and artificial intelligence market growth as is at the end of 2017, including Gartner, Forester, CBInsights, Pitchbook data sources as long as Flint Capital own analytics. Ai investment framework is given as an example of a potential investors approach to analyze startups and business cases. Plus World top 5 most active vc investors in Ai.

1. FEBRUARY 2018
BY ANDREW GERSHFELD
PARTNER
STATE AND TRENDS OF AI
INVESTORS PERSPECTIVE

2. Major barriers of AI adoption in 2016 according to IDC
49% 57% 25%
Challenges with
stakeholder buy-in
Lack of data and
skill sets
High cost of
solution
2

3. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Not sure what AI means
Don't own or have access to the required data
AI is a lot of hype with little substance
Do not have the right processes or governance
AI systems are not proven
Not certain what is needed for implementing…
Don't have the budget
Need first to invest in modernizing data mgt…
Don't have the required skills
Not clear what AI can be used for
There is no defined business case
Obstacles to AI adoption as expressed by companies
with no plans of investing in AI, Forrester, end of 2016
3

4. 2017 changed everything
7 factors driving Ai switch to mainstream
1 New Algorithms 2 Datasets 3 Specialized hardware Cloud services
5 Open source 6 Gigantic investments 7 Accelerated interest
4
4

5. Research topics & papers at the cutting edge
• Multi-agent learning: agents communicate with each other, micro-management scenarios are
suggested
• Learning from small data / knowledge transfer
• Efficiency: selective activation, better simulations
• Chasing stability that is required for wider/corporate adoption
• Transparency and safety
5

6. Democratization of data - a step towards accelerating AI
Curated repositories of datasets
Kaggle Datasets: Online repository of machine learning datasets curated by the Kaggle
community.
OpenML:Web platform with Python, R, Java, and other APIs for downloading hundreds of
machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm
performance against dozens of other algorithms.
PMLB:A large, curated repository of benchmark datasets for evaluating supervised machine
learning algorithms. Provides classification and regression datasets in a standardized format that
are accessible through a Python API.
UCI Machine Learning Repository: Regularly-updated repository containing hundreds of
datasets. Not all datasets are curated and may come in non-standard formats.
KDnuggets Datasets for Data Mining and Data Science: List of Data repositories.
6

7. Big names like Intel, NVDA, Google, Facebook and even Tesla also have made announcements
Flint Capital, Pitchbook, The Verge, TechCrunch, Bloomberg
Selected AI hardware deals of 2017, $M
Specialized hardware enables productivity
7

8. The year AI Floated into Cloud
Machine learning as a service (MLaaS)
AWS – Cloud 9
Google cloud AI MSFT AZURE ML Studio
8

9. Open Source software
Let's take a look at some of the open source ML/DL frameworks available
• Apache Singa
• Shogun
• Apache Mahout
• Apache Spark Mllib
• TensorFlow
• Oryx 2
• Accord.NET
• Amazon Machine Learning (AML)
and many more items... (Torch, Keras, Caffe)
9

10. -
500.0
1,000.0
1,500.0
2,000.0
2,500.0
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2010 2011 2012 2013 2014 2015 2016 2017 2018
# dealsInvested $M
Corporate
Growth/Expansion
Restart Later Stage VC
Later stage VC
Early stage VC
Seed
Angel
Pre/Accelerator/Incubator
Grants
# of deals
Late stage investments led the growth accompanied with series A
Here and further – data is collected from Pitchbook, if not mentioned otherwise. Companies/deals are classified as AI by Pitchbook’s metrology.
Flint’s preliminary research shows that Pitchbooks data represents at least 90-95% of the market and probably less.
Investments in AI startups, World, 2010-2018, by type, $M and # deals
Investments in AI almost doubled in 2017
10

11. 0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
2010 2011 2012 2013 2014 2015 2016 2017 2018
Oceania
Africa
South America
Middle East
Asia
North America
Europe
Asia explodes, US dominates
China grew almost 8x in 2017
Investments in AI startups, World, 2010-2016, by region, $M
Here and further – data is collected from Pitchbook, if not mentioned otherwise. Companies/deals are classified as AI by Pitchbook’s metrology.
Flint’s preliminary research shows that Pitchbooks data represents at least 90-95% of the market and probably less.
11

12. The highest growth is between Series A and B
AI valuation by stage, sampled 301 deals, world, 2017 only
7 10 29
136
316
883
0.0x
0.5x
1.0x
1.5x
2.0x
2.5x
3.0x
3.5x
4.0x
4.5x
5.0x
0
100
200
300
400
500
600
700
800
900
1000
Angel Seed A B C D
Valuationgrowth
Averagepre-money$M
Average pre-money valuation $M Average valuation growth x
Investors make 10x or more through stages
Here and further – data is collected from Pitchbook, if not mentioned otherwise. Companies/deals are classified as AI by Pitchbook’s metrology.
Flint’s preliminary research shows that Pitchbooks data represents at least 90-95% of the market and probably less.
12

13. Top 5 AI VC investors by # of deals in 2017
Some investors close more that 10 AI deals a year
0 5 10 15 20
Lux Capital
Data Collective
Intel Capital
Accel
New Enterprise Associates
5 investors have made 69 unique
deals via, that are accounted for
$1.5B or 11% of all investments in
2017.
Here and further – data is collected from Pitchbook, if not mentioned otherwise. Companies/deals are classified as AI by Pitchbook’s metrology.
Flint’s preliminary research shows that Pitchbooks data represents at least 90-95% of the market and probably less.
Top tier VC investors commit to AI startups
13

14. Exits from AI startups, World, 2010-2018, by type, $M and # deals
99% of exits take place via M&As
0
20
40
60
80
100
120
140
-
5,000.0
10,000.0
15,000.0
20,000.0
25,000.0
2010 2011 2012 2013 2014 2015 2016 2017 2018
#deals
Exitsvalue$M
IPO
Merger/Acquisition
Merger of Equals
# of exits
Here and further – data is collected from Pitchbook, if not mentioned otherwise. Companies/deals are classified as AI by Pitchbook’s metrology.
Flint’s preliminary research shows that Pitchbooks data represents at least 90-95% of the market and probably less.
AI exits in 2017 grow x6 vs 2016
14

15. 0%
10%
20%
30%
40%
50%
60%
0
10
20
30
40
50
60
70
80
90
2010 2011 2012 2013 2014 2015 2016 2017
Top acquirers Went public
Other acquirers Top acquirers as %
Exits from AI startups, World, 2010-2017, # deals
Acquiring an AI company is becoming a mainstream
Flint Capital, sampled 321 deals
Top AI acquirers, 2010-2017Row Labels Count of Company Name
Alphabet / Google DeepMind 29
Intel 12
Apple 10
Facebook 7
Nuance Communications 5
Yahoo 5
Twitter 4
Amazon.com / Amazon Web Services 4
Salesforce.com 4
eBay 4
AOL 4
GE: GE Aviation / GE Digital 3
International Business Machines 3
Snap 3
Microsoft 3
Qualcomm 3
Microsoft 3
IT market leaders are on the constant hunt
15

16. Exits from AI, World, 2010-2016, by size, % deals Exits from AI, $100M+
Data is collected from Pitchbook, if not mentioned otherwise. Companies/deals are classified
as AI by Pitchbook’s metrology. Flint’s preliminary research shows that Pitchbooks data
represents at least 90-95% of the market and probably less.
$5B+
$1B to $4.9B
$100M to $999M
$50M to $ 99M
Less than $50M
Talent and tech are the major rationale for M&A
Exits above $100M are less then 10%
16

17. The hottest AI topics are Healthcare, Fintech & Core AI –
the function of AI adoption
1. Healthcare
2. Cross-Industry Applications
3. Cybersecurity
4. Commerce
5. AD&Marketing
6. Fintech&Insurance
7. Enterprise AI
8. IOT/IIOT
9. Sales&CRM
10.Auto tech
11.HR Tech
There is a market for IT services as
long as for software
Flint Capitl, CB Insights
Opportunity
Hot
17

18. An investment framework
Value potential Value realization Defensibility
Applying framework helps highlight attractive investment opportunities
• Extracted value and
disruption;
• Unattractive
alternatives;
• Suitability of AI to a
business problem;
• A path to acceptable
technical performance;
• Management;
• Commerciality;
• Quantifiability of ROI;
• Buyer readiness;
• Availability of data;
• Benign regulation;
• Deployment scalability.
• Distance from AI
whales’ offerings;
• Domain complexity;
• Network effects in data;
• Proprietary algorithms
and R&D;
• Attractive AI talent
environment;
• Capitalization
intensiveness.
18

19. • By 2019, more than 10% of IT hires in customer service will mostly
write scripts for bot interactions.
• By 2019, startups will overtake Amazon, Google, IBM and
Microsoft in driving the artificial intelligence economy with
disruptive business solutions.
• By 2019, artificial intelligence platform services will cannibalize
revenues for 30% of market-leading companies.
• By 2020, 20% of companies will dedicate workers to monitor and
guide neural networks.
• Through 2020, organizations using cognitive ergonomics and
system design in new artificial intelligence projects will achieve
long-term success four times more often than others.
Predictions by Gartner
19

20. If you are not an investor or startup founder in Ai
20

21. Thank you!
ANDREW GERSHFELD
ag@flintcap.com
https://www.linkedin.com/in/andrewger