1. Ai INVESTMENT PANEL

2. Returned to European VCs
About magister
International tech merchant bank
Exit preparation, M&A, large financings
High multiples achieved, high certainty of success
$2.5bn+
Deal value last 4 years
$1.5bn+
Returned to European VCs 22
Deals since 2011
90%+
Success Rate

3. AI INVESTING

4. AI HAS HAD SEVERAL FALSE DAWNS
Due to lack of progress on machine translation, US govt. ended support after having spent $20m; “machine translation was more expensive, less accurate and slower than human translation” —Automatic Language Processing Advisory Committee
1966
“In no part of the field have discoveries made so far produced the major impact that was then promised. AI techniques may work within the scope of small problem domains, but would not scale up to solve more realistic problems” —Lighthill report, published by British Science Research Council
1974
Before cutting all funding. DARPA spent $1B on Strategic Computing Initiative to advance artificial intelligence during “DARPA should focus its funding only on those technologies which showed the most promise” —Jacob Schwartz, director of the DARPA Information Science & Technology Office
80–90s
In 1982 Japanese govt. spent $400m on “fifth gen. computer” to accelerate AI. ”10 years ago we faced criticism of being too reckless, now we see criticism from inside and outside the country because we have failed to achieve such grand goals.“ —Kazuhiro Fuchi, head of Fifth Generation Project
80–90s
Unrealistic expectations, then repeated massive AI research funding cuts
Source: Press Releases

5. “DIFFERENT THIS TIME”? Dramatic increase in computational power
Data sets of entirely different scale
Funding shift from unstable government sources to VCs, strategics
Transparent co-operation between universities & industry
Critical mass & strategic value achieved at small company size (sub 20)
Many AI co’s have yet to firm up business models
Already being commoditized in key categories (chatbots, visual search)
Low revenue from marquee customers (large corporates with significant bargaining power vs. small AI players)
Most AI companies technology driven, not commercially driven

6. $ UP AND TO THE RIGHT
Source: PitchBook

7. Real value being created
Median Price Paid
Per Employee
$2.6M

8. Beautiful, but small Total AI M&A Deals Total 53 Year
Undisclosed & Sub 50m
50m–100m
100–200m
200m–500m
500m+
2017 YTD 4 1
2016 21 5 3
2015 11
2014 9 2
2013 8
2012
Total 53

9. Raised before Sale ($m)
Not too much $ invested
Top 10 AI M&A Deals Ever
Target
Acquirer
Deal Date
Raised before Sale ($m)
Deal Size ($m)
Argo AI
Ford Motor Company
10-Feb-17
n.a.
1,000
Google DeepMind
Google
27-Jan-14 26
650
TellApart
Twitter
31-May-15 18
533
Nervana Systems
Intel
09-Aug-16 24
408
Movidius
05-Sept-16 85
392
SwiftKey
Microsoft
03-Feb-16 30
250
Turi
Apple
05-Aug-16 25
200
Equivio
01-Oct-14
Mobile Technologies
Facebook
180
Bit Stew Systems
General Electric
15-Nov-16
153
Source: PitchBook

10. EXPAND OUR DEFINITION OF “INVESTING”
Source: content/uploads/2017/04/Paysa-AI-Tech-Investment.pdf

11. SOMETHING CONTROVERSIAL
Many AI companies still not ready to scale
Value pricing is really really hard
Small deal values, but high $ paid per employee
Small $ raised by even the largest M&A targets
Does it make sense to take (or invest) $10M+ of VC money?
Source: Press Releases

12. DON’T BE PUT OFF!