Ai INVESTMENT PANEL

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

AI INVESTING

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 1983-93. “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

“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

$ UP AND TO THE RIGHT Source: PitchBook

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

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

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

EXPAND OUR DEFINITION OF “INVESTING” Source: https://www.paysa.com/blog/wp- content/uploads/2017/04/Paysa-AI-Tech-Investment.pdf

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

DON’T BE PUT OFF!