transaction trends in a.i. - simmons & simmons

Strictly private and confidential

DEALMAKERS IN TECHNOLOGY

TRANSACTION TRENDS

IN A.I.

Munich · May 2019

2

Confidential

Today’s speaker

▪ Director at GP Bullhound, Berlin

▪ Doctorate in finance from Leipzig Graduate School of

Management

▪ TMT investment banking since 2005

▪ 12 years in London; in Berlin since 2017

▪ Focus on fundraisings and exits

▪ Selected recent transactions:

– $30m growth financing of Mambu by Bessemer

– Buyout of Magix by Capiton

– Sale of Elbkind to Reply

– Sale of Acmeo to H.I.G.-backed Inifigate

– $15m growth financing of TIS by 83North

▪ Focus on software, digital services, (I)IoT and cloud

infrastructure

Dr. Nikolas Westphal

[email protected]

Confidential

3

Dealmakers in Technology

530+successful transactions

$19bn+of value delivered to clients

Fintech

Entertainment

Software

Digital Services

eCommerce

100+professionals

4

Table of Content

Introduction: Riding the smart enterprise wave1

Venture environment: New platforms nearing maturity2

M&A market: The global race for AI3

Key takeaways for strategic acquirors and investors4

Supplementary materialsA

5

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I.

INTRODUCTION:Riding the smart enterprise wave

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A train carriage containing controlled nuclear materials was stolen in Cincinnati today. Its whereabouts are unknown.

The incident occurred on the downtown train line, which runs from Covington and Ashland stations.

In an email to Ohio news outlets, the U.S. Department of Energy said it is working with the Federal Railroad Administration to find the thief.

“The theft of this nuclear material will have significant negative consequences on public and environmental health, our workforce and the economy of our nation,” said Tom Hicks, the U.S. Energy Secretary, in a statement. “Our top priority is to secure the theft and ensure it doesn’t happen again.”

The stolen material was taken from the University of Cincinnati’s Research Triangle Park nuclear research site, according to a news release from Department officials.

The Nuclear Regulatory Commission did not immediately release any information.

According to the release, the U.S. Department of Energy’s Office of Nuclear Material Safety and Security is leading that team’s investigation.

“The safety of people, the environment and the nation’s nuclear stockpile is our highest priority,” Hicks said. “We will get to the bottom of this and make no excuses.

A routine news report…

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Confidential

The text on the preceding page has been written at the

first attempt by an unsupervised machine learning

model called GPT-2. The GPT-2 has been trained simply

to predict the next word in 40 GB of Internet text.

The technology has not been released due to concerns

about potential malicious applications.

Source: OpenAI Foundation,

“Better Language Models and Their Implications”

https://openai.com/blog/better-language-models/

February 2019

…or is it?

https://openai.com/blog/better-language-models/#sample2

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The smart enterprise revolution: agile, intelligent, non-linear

Source: Palantir

Electronic Tools

Semi-conductor

ConsumerTelecomEnterpriseSmart

Enterprise

Silicon Valley Technology Trends

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Data: the new oil of the global economy…

Source: Morgan Stanley Equity Research, “Engineering the 21st Century Digital Factory”

Annual data creation by industry (petabytes)

1,812

911

776 773

424 397 375 336

Manufacturing Government Banking Securities

Investment Services

Communications

& Media

Retail Professional

Services

Healthcare

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…driving immense efficiency gains on the back of AI

Source: Accenture and Frontier Economics

Note: (1) Gross Value Added

Impact of AI on industry output (Real Gross Value Added (1) in 2035 in the USA in $trn)

8.4

7.5

6.2

4.03.7

3.42.8

2.12.3

1.51.0

12.2

9.3

8.4

4.9 4.7 4.6

3.32.9 2.7

2.01.3

Baseline AI "steady state" scenario

Manufacturing Prof.

Services

Wholesale

& Retail

Public

Services

Media &

Comms.

Financial

Services

Construction Logistics Healthcare Hospitality Utilities

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A range of repeatable tasks can be automated today

Source: Adobe – “Digital Trends Report 2018”; BCG Gamma / Ipsos

Areas of Task Automation

Forecasting

Goal-oriented communication

Planning and organisation

Pattern recognition

Monitoring and observation

Repetitive manual labour

31%of companies

expected to add

AI over next

year

47%of digitally mature

organisations have

AI strategies

4.5xincrease in jobs

requiring AI

skills since

2013

70%of employees feel

that AI has increased

the appeal of their

work

12

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II.

VENTURE ENVIRONMENTNew platforms nearing maturity

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13

Continuously increasing VC funding into AI

Source: Pitchbook

Artificial Intelligence & Machine Learning VC Funding ($ Bn)

0.61.4 1.4

2.6

7.0

9.6

14.816.1

25.9

165

3,295

2010 2011 2012 2013 2014 2015 2016 2017 2018

Funding ($ Bn) Number of Rounds

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Continuously increasing VC funding into AI (continued)

Source: Pitchbook, GP Bullhound Analysis

Artificial Intelligence & Machine Learning VC Funding ($ Bn) – excluding Uber

0.61.4 1.4

2.6

4.6

6.0

9.2

16.1

25.9

165

3,295

2010 2011 2012 2013 2014 2015 2016 2017 2018

Funding ($ Bn) Number of Rounds

Confidential

15

2014

2004

2014

2011

2011

2016

2015

2012

2005

2015

2014

2015

2013

2013

2009

2003

Computer Vision

Enterprise

Automotive

Computer Vision

Digital Media

Automotive

PaaS

FinTech

Enterprise/RPA

Marketplace

Urban Mobility

Robotics

Automotive

Bio/Nanotech

Data/BI

Enterprise/RPA

Best-funded AI start-upsTop-15 best funded AI start-ups (selected)


Founded VerticalCompany

3,640

2,758

2,100

1,668

1,360

1,032

1,000

993

977

975

790

700

626

592

572

550

Country Total funding received ($ m)

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Regional trends in funding


Funding amounts over time ($ Bn) Total AI funding by region 2010-2018 ($ Bn)

0.5 1.1 1.12.3

6.37.5

12.2

7.7

14.10.4

0.8

1.4

2.0

1.4

1.4

6.1

8.2

0.3

0.41.0

1.6

0.61.4 1.4

2.6

7.0

9.6

14.816.1

25.9

2010 2011 2012 2013 2014 2015 2016 2017 2018

N America Europe Asia Other

52.9

5.1

17.6

3.7


6,876 3,292 1,668 1,569

67% 6% 22% 5%% of

Total

# of

Deals

17

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III.

M&A MARKETThe global race for AI

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0.0

10.0

20.0

30.0

40.0

2013 2014 2015 2016 2017

Big strategics starting to focus on AI

Source: CB Insight

Quarterly earnings calls: mentions of “Artificial Intelligence” Quarterly earnings calls: mentions of “Machine Learning”

0.0

10.0

20.0

30.0

40.0

50.0

60.0

2013 2014 2015 2016 2017

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19

1325

4 2 413

18 19

47

80

90

4 2 4

1319 20

48

93

115

2010 2011 2012 2013 2014 2015 2016 2017 2018

Platform Vertical

M&A transaction trends in AI

Source: Mergermarket; GP Bullhound Analysis

Annual AI M&A (number of transactions)

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20

N America

216

62%

Europe

57

16%

Asia

64

19%

Other

10

3%


N America

200

57%

Europe

96

28%

Asia

41

12%

Other

10

3%


Regional differences in AI M&A


Share of inbound (no. of transactions 2010-2018) Share of outbound (no. of transactions 2010-2018)

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1

2

4

1

3

2

2

1

1

1

1

7

6

6

1

4

2

3

2

4

3

3

2

1

1

8

8

6

5

5

5

5

4

4

3

3

3

2

1

1

1 Platform Vertical

Most active acquirors

CountryInvestor Number of AI acquisitions 2010-2018

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22

Largest AI acquisitions 2010-2018

2017

2018

2013

2018

2018

2018

2016

2018

2018

2014

2019

2019

2013

2018

2016

91 Wireless Websoft

InvestorTarget Year

12,288

1,374

1,227

791

682

494

452

397

286

241

231

227

223

193

166

Deal size ($ m)


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Selected characteristics of AI acquisitions


25% Percentile 50% Percentile 75% Percentile

Size(€m)

EV/Revenue(x)

9 23 74

3.1 7.1 28.4

Maximum

12,228

550.2

Boston AI

91 Wireless Websense

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IV.

KEY TAKEAWAYSfor strategic acquirors and investors

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25

-10 -5 0 5 10 15 20

High tech and

telecommunications

Automotive and assembly

Financial serv ices

Resources and utilities

Transportation and logistics

Consumer packaged goods

Retail

Education

Health care

Building materials and

construction

Professional serv ices

Travel and tourism

Define an AI strategy around your key verticalsAI adoption across the value chain driving companies’ performance

Source: McKinsey Global Institute, Digital Europe: Pushing the frontier, capturing the benefits; Digital America: A tale of the haves and have-mores

AI adoption in key industries across the value chain AI adoption vs. profit margins

Relatively low Relatively high

AI adopters with

proactive strategy

Partial AI adopters

or experimentersNon-adopters

1 Operating profit margin for selected sectors as a share of turnover, for continuing operations and

before exceptional items.2 Firms that are big data and cloud service users and report their strategic posture towards AI to be:

“Disrupting our industry using AI technology is at the core of our strategy”,”We have changed our

longer-term corporate strategy to address the AI threat or opportunity disruption,” or “We have

developed a coordinated plan to respond to the AI threat or opportunity but have not changed

out longer-term corporate strategy.”

Self-reported current profit marginDifference from industry average (unweighted) (percentage points )

AI index Assets Usage Labor

Ove

rall A

I in

de

x

MG

I D

igit

iza

tio

n in

de

x

De

pth

of

AI t

ec

hn

olo

gie

s

AI s

pe

nd

Su

pp

ort

ing

dig

ita

l ass

ets

Pro

du

ct

de

ve

lop

me

nt

Op

era

tio

ns

Su

pp

ly c

ha

in a

nd

dis

trib

utio

n

Cu

sto

me

r e

xpe

rie

nc

e

Fin

an

cia

l an

d g

en

era

l

ma

na

ge

me

nt

Wo

rkfo

rce

ma

na

ge

me

nt

Exp

osu

re t

o A

I in

wo

rkfo

rce

AI r

eso

urc

es

pe

r w

ork

er

High tech and

telecom.

Automotive and

assembly

Financial

serv ices

Resources and

utilities

Media and

entertainment

Consumer

packaged

Transportation

and logistics

Retail

Education

Professional

serv ices

Health care

Materials and

construction

Travel and

tourism

1

26

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Build an early-stage ecosystem around your AI strategyExample: strategic venture investments in industrial automation / smart manufacturing

2

2

2

8

1

5

9

3

3

2

4

4

4

9

13

9

9

17

4

4

4

11

7

6

12

12

13

8

2

5

5

1

2

15

10

11

23

1

9

10

16

16

10

7

7

8

19

12

17

23

31

34

36

42

43

49

75

Wearables & VR/AR Data & analytics Simulation & design Robotics & (additive) manufacturing IIoT Platforms & Hardware

CountryInvestor Number of venture investments by vertical

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Plug strategic capability gaps with acquisitionsExample: Microsoft / XOXCO and Oracle / DataFox

3

▪ Recent M&A:Acquired by Microsoft

▪ Vertical:Conversational

chat bots

▪ Key future engagement tool

▪ Microsoft Bot Framework:

already 360,000+ developers

▪ Recent M&A:Acquired by

Oracle

▪ Vertical:Automated data extraction

▪ Extracts and structures

content from millions of

public sources

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Be prepared to invest at strategic valuation levels“Rule of 40” software valuations vs. selected AI transaction multiples

Sources: Capital IQ, Mergermarket, GP Bullhound Analysis

Note: Data based on GP Bullhound SaaS Index, excludes outliers and companies with “N/A”

“Rule of 40” software valuations… …vs. selected AI acquisition multiples

4

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7x

8x

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10x

11x

12x

13x

14x

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70%

EV

/2019E R

eve

nu

e M

ultip

le

2019E Revenue Growth + 2019E Unlevered FCF Margin

550xMaximum AI

EV/Revenue

acquisition

multiple 28x75% percentile AI

EV/Revenue

acquisition

multiple7xMedian AI

EV/Revenue

acquisition

multiple

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SUPPLEMENTARY MATERIALS

30

Why now?Examples of vertical platforms starting to gain critical mass

Source: Pitchbook; Company Information

▪ Recent funding:

$225 – Series C,

Sep-18

▪ Vertical:

Robotic process

automation

▪ Automation of

back-office tasks

▪ Rapid take-up,

especially via

channels

▪ Recent funding:

$180 – Series A,

Oct-18

▪ Vertical:

Industrial

automation

▪ Combines AI and

computer vision

with agile robots

▪ Vision of “lights-out

factory”

▪ Recent M&A:

Acquired by

Microsoft

▪ Vertical:

Conversational

chat bots

▪ Key future

engagement tool

▪ Microsoft Bot

Framework: already

360,000+ developers

▪ Recent M&A:

Acquired by

Oracle

▪ Vertical:

Automated data

extraction

▪ Extracts and

structures content

from millions of

public sources

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Technology universe share price development

Source: Capital IQ

Share Price Development of Selected Indices

75.0%

125.0%

175.0%

225.0%

275.0%

Jan-17 May-17 Sep-17 Jan-18 May-18 Sep-18 Jan-19 May-19

Tech Majors IT Services E-Commerce & Marketplaces Enterprise Software & SaaS Index S&P500 Dax30

+173.5%

+115.8%

+66.0%

+29.2%

+6.8%

+1.94%

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5.0x

15.0x

25.0x

35.0x

45.0x

55.0x

Jan-14 Apr-14 Jul-14 Oct-14 Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19

Enterprise Software & SaaS Index Tech Majors E-Commerce / Marketplaces IT Services DAX 30 S&P 500

Valuation development over time

Source: Capital IQ

Note: EV/EBITDA multiples > 75.0x considered as not meaningful

Historical EV / NTM EBITDA Trading Performance of Selected Indices

Median EV / NTM EBITDA

5Y 3Y 1Y Today

Enterprise Software & SaaS Index 30.4x 33.0x 35.9x 43.4x

Tech Majors 11.2x 12.2x 12.4x 13.1x

E-Commerce / Marketplaces 16.0x 15.3x 13.6x 13.7x

IT Services 8.7x 8.6x 8.6x 8.9x

DAX 30 8.3x 8.4x 8.1x 8.9x

S&P 500 11.2x 11.6x 11.5x 11.9x

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0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

De

c-0

9

De

c-1

0

De

c-1

1

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p-1

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Dry

Po

wd

er ($

bn

)

Venture Capital Buyout Growth Other Private Equity*

Private equity is awash with capital …

Source: Pitchbook.com, Preqin.com

Note: (1) as at July 2018

Private Equity: Dry Powder by Fund Type, 2009 - Q3 2018 PE Funds in Market over Time, Q1 2013 – Q3 2018 (1)

Largest Private Equity Funds Closed in Q2 2018

0

500

1,000

1,500

2,000

2,500

3,000

3,500

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3

2013 2014 2015 2016 2017 2018

No. of Funds Raising Aggregate Capital Targeted ($bn)

Fund Firm Fund Size (m) Fund TypeGeographic

Focus

Carlyle Asia Partners V Carlyle Group 6,550 USD Buyout Asia

Bain Capital Europe V Bain Capital 4,350 EUR Buyout Europe

Nordic Capital Fund IX Nordic Capital 4,300 EUR Buyout Europe

TowerBrook Investors V TowerBrook Capital Partners 4,250 USD Buyout US

General Atlantic

Investment Partners 2017General Atlantic 3,289 USD Growth US

Thoma Bravo Discover

Fund IIThoma Bravo 2,400 USD Buyout US

Blackstone Capital

Partners AsiaBlackstone Group 2,300 USD Buyout Asia

BGH Capital Fund I BGH Capital 2,600 AUD Buyout Australasia

Inflexion Buyout Fund V Inflexion Private Equity Partners 1,250 GBP Buyout Europe

Atlas Capital Resources III Atlas Holdings 1,675 USD Turnaround US

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European buyout activity

… leading to an increasingly crowded market and bigger

and bigger deals

Source: Pitchbook.com, Preqin.com

Strategies Targeted by PEs in the NTM, Q3 2016 – Q3 2018

Regions Targeted by PEs in the NTM, Q2 2016 – Q2 2018

51%53%

31%

11%

19%

37%

50%52%

30%

11%

19%

39%

56%

61%

31%

9%

16%

45%

0%

10%

20%

30%

40%

50%

60%

70%

North

America

Europe Asia-Pacific Rest of

World

Emerging

Markets

Global

Pro

po

rtio

n o

f Fu

nd

Se

arc

he

s

Q2 2016

Q2 2017

Q2 2018

18

5€

19

3€

16

3€

23

9€

32

2€

43

7€

23

1€

35

3€

40

6€

798

860 866

900

1,175

1,398

1,1661,171

986

0

200

400

600

800

1000

1200

1400

1600

0

50

100

150

200

250

300

350

400

450

500

2010 2011 2012 2013 2014 2015 2016 2017 2018

Deal Value (EURbn) # of Deals closed

70%

50%42%

16% 15%

9%

5% 2%

71%

51% 51%

14% 13%

5% 5% 3%

70%

53% 50%

16% 15%

8%6%

2%0%

10%

20%

30%

40%

50%

60%

70%

80%

Bu

yo

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Q2 2016

Q2 2017

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35

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(15.0%)

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0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

(5%) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65%

2019E U

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Rule of 40% – Public SaaS2018E Growth and Profitability of Public SaaS Companies in GPB Index

Source(s): Capital IQ

Note: Data based on GP Bullhound SaaS Index, constituents can be found in Appendix A,

excludes outliers and companies with “N/A”

The Rule of

40% Line

36

Rule of 40% – Public SaaS (Cont’d)


Note: Data based on GP Bullhound SaaS Index, excludes outliers and companies with “N/A”

Sub-Performers Market-Performers Out-Performers

Companies whose summed revenue growth rate and FCF margin is greater than 40%, are valued higher

by investors than those below this 'rule of 40' line

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0x

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6x

7x

8x

9x

10x

11x

12x

13x

14x

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70%

EV

/2019E R

eve

nu

e M

ultip

le

2019E Revenue Growth + 2019E Unlevered FCF Margin

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75%

60%

46% 46%

32% 32%

25%

40%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Horizontal vs. Vertical Focused Cloud Software TrendsVertical-specific has lower Sales & Marketing spend as a percentage of revenue

However, generally lower growth and valuation multiples

Horizontal Software Vertical-Specific Software

Median: 21%Median: 46%


Note: GP Bullhound SaaS Index constituents can be found in Appendix A

13.0x 25.0x 10.1x 16.2x 6.4x 15.2x 7.0x 4.7x 6.8x 12.8x 18.6xEV/LTM

Revenue

FY19/18E

Revenue

Growth6.9% 50.7% 26.8% 31.3% 18.3% 31.7%

2.4x

0.3% 16.1% 4.8% 13.7% 34.6% 25.9%

9.0x

20.2%

31%

24% 23%19% 18% 17%

0%

10%

20%

30%

40%

50%

60%

70%

80%

38

▪ From the 1910s to the 2000s, labor distribution has changed to reflect shifts in society

» Farmers in the U.S. declined from 24% to <1% of labor force

» Technical professions rose 20%, teachers doubled, and health workers grew by 7x

» The concept of an engineer meanwhile went from almost non-existent, to 5% of the workforce

▪ Though concerns exist around impact of AI and workforce automation, we believe

working conditions will improve

» AI will augment tasks that existing employees spend far too much of their time completing

» More flexible work hours and work environments

» Less confinement and time commuting = increased productivity

▪ Many innovators in the space are painting a picture of symbiosis between humans and

artificial intelligence

» Potential to assist many professions including surgeons, machinists, soldiers and chefs

» “EQ” professions will become more important again

▪ Ethics still need to be explored

» E.g.: is it right to delegate core HCM tasks to a machine learning algorithm?

Outlook: what AI may hold in store for us

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information contained in this Presentation is provided for illustrative purposes only and is not necessarily a guide to future performance and the value of

securities may fall as well as rise. In particular, investments in the technology sector can involve a high degree of risk and investors may not get back the full

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