AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. As always, the realities of building a startup are different, and the path to success requires not just technical prowess but also thoughtful market positioning and business excellence. In a talk of interest to anyone building or implementing an AI product, Matt Turck and Peter Brodsky leverage hundreds of conversations with AI (and big data) founders and hard-learned lessons building companies from the ground up to highlight successful strategies and tactics. Topics include: Successful data acquisition strategies Data network effects Competing with the giants A pragmatic approach to building an AI team Why social engineering is just as important to success as groundbreaking AI technology

1. Matt Turck, FirstMark Capital
Peter Brodsky, HyperScience
Sept 27, 2016
Building An AI Startup
Realities & Tactics

2. MATT TURCK
Managing Director
Early stage venture capital firm
based in New York City.
FirstMarkCap.com
Largest data-focused monthly
event in the country
DataDrivenNYC.com

3. HyperScience is an AI company
based in New York, leveraging
unique technology to solve large
enterprise problems, starting with
back office automation.
HyperScience.com
PETER BRODSKY
Co-Founder & CEO

4. AI is hot…

5. The
technology
is working…

6. Plenty of
hype!

7. Zeitgeist

8. VC money is pouring in
A record $1.05B went into 121 private AI
companies in Q2, according to CB Insights

9. But the reality
from the
trenches is
different

11. How do you actually build an AI company?
| Positioning
| Product
| Petabytes
| Process
| People

12. | POSITIONING

13. Exciting times to build an AI startup!
Just One Small Problem

14. All the Big Tech Companies Got the Memo, Too

15. “Artificial intelligence would be the ultimate
version of Google.
We’re nowhere near doing that now.
However, we can get incrementally closer to
that, and that is basically what we work on.”
- Larry Page
CEO, Google, October 2000
They’ve Been Thinking About AI For A Long Time

16. They’re Now Betting the Farm On It
“We’ve been building the best AI
team and tools for years, and
recent breakthroughs will allow us
to do even more.
We will move from mobile first to
an AI first world.”
- Larry Page
CEO, Alphabet, April 28, 2016

17. They Can Hire
ALL THE AI ROCKSTARS
Andrew NgYann LecunGeoff HintonPeter Norvig

18. They Can Acquire The Best Teams

19. And They Have All The Data In The World

20. …Position
away from
them!
So What Do You Do?

21. V
E
R
T
I
C
A
L
However they’re not
going to tackle every
single vertical problem
HORIZONTAL
Tech giants have a formidable
advantage when it comes to building
broad horizontal products
(image/video/voice recognition,
language translation) & infrastructure
(AI cloud)
v s

22. Enterprise vs. Consumer
• Tech giants, on the whole, are more focused on
consumer than enterprise
• Plenty of opportunities to deliver deep enterprise
solutions
• Fortune 1000 companies have large datasets!

23. Tools vs. Platforms
• Offering broad core technology (including
“strong AI”) is tricky, long-term, for any startup
• Giants may impact your business just by open-
sourcing some of their tech (TensorFlow)
• Focus on tools that solve specific customer
problems, including “last mile”

24. The HyperScience Experience
Broad AI technology that can be applied
to many problems
Decision #2
Back office
automation as first
beachhead
Decision #1
Focus on the
enterprise, particularly
Fortune 1000

25. | PRODUCT

26. Should your product be all AI?
Remember when I
said “the technology
works”?
LIES
It never works 100%

27. Sudden Perception that
Using Humans in AI = Failure

28. Sometimes You Need 100% Accuracy,
Sometimes You Don’t
Low Product Risk High Product Risk

29. Humans In The Loop: Avoid Disasters

30. Or Underwhelming User Experiences

31. Leveraging Human Users to Train the AI

32. AI in the Enterprise: Humans Needed!
• The “S Word”: Services.
VCs will scream in
horror!
• But reality of AI is that
services required for
successful deployment
in the enterprise

33. Build your Product with
Data Network Effects in Mind
Illustration Source: Moritz Mueller-Freitag,
”10 Data Acquisition Strategies for Startups”

34. Data Network Effects
Exemplified by Industry
Giants

35. Data Network Effects
Exemplified by Industry
Giants
But also available
to startups

36. | PETABYTES

37. Cold Start Problem
Usually have
to start the
flywheel here

38. Data Sets
Licensed data setsPublic data sets

39. Data Crawling
Web Crawling Real World Crawling
• While Tesla owners have driven around
100 million miles on Autopilot,
Anderson reveals that the fleet
Autopilot hardware-equipped cars has
collectively driven 780 million miles….
• Tesla basically turned its fleet of
vehicles into an incredible data
gathering asset for the Autopilot
program before enabling the software.
Electrek, May 2016

40. Data Capture Networks
Sense360's sensor-technology is on
more than 250 mobile apps and more
than 1.5 million devices in the US. Our
panel generates more than a terabyte
of anonymous sensor data every
single day and provides a detailed
view of more than 100 million
anonymous user visits a month.

41. Data "Traps"
• Consumer apps: Facebook, IoT products like Kinsa
• Enterprise apps: Slack >> Bots
• “Trojan Horse” side apps: Forevery (Clarifai)
Source: Clarifai, Recode

42. AI Training

43. | PROCESS

44. One Dirty Secret of AI
When it comes to successfully deploying AI in
the real world, half the battle has to do with
expectation management and social
engineering, not technological prowess

45. AI Engineers are a Novelty
in the Enterprise
Companies are trying to make sense of this strange
new type of vendors promising miracles
WHAT YOU THINK YOU LOOK
LIKE
WHAT YOU ACTUALLY LOOK LIKE

46. How NOT to sell AI to the Enterprise
“Give us all your data, we’ll use
it to fine tune the algorithms in
our black box, and awesome
things will happen.”
Without real value provided upfront, that typically doesn’t work.

47. AI Social Engineering
• Help customers understand which problems can
be solved with AI (and which problems cannot)
• Assist customers in developing relevant testing
procedures and success metrics for AI
• Address any security or data privacy concern

48. | PEOPLE

49. One Big Misperception
• TensorFlow = doesn’t (yet) mean AI is now easy
to use!
• Not about slapping “.ai” after your startup name
• This stuff is really hard!

50. Need Deeply Technical Teams
• Need core machine learning talent – often PhD
level
• Also top engineers who can productize and
deploy AI
• Ideally, people who can do both!
• In most cases, CEO needs to be deeply technical
too

51. Rare Birds
• There is a very limited supply of such talent
• Big tech companies will pay millions just in sign up
bonus for a brand new PhD in deep learning
• Hard to attract top AI talent for a startup, but even
harder for a Fortune 1000 company

52. But Talent is Globally Distributed!

53. Recruit Globally

54. Deep Customer Focus
• Danger with very technical teams:
building “tech for the sake of tech”
• Focus on serving customers need to
be part of the team’s DNA

55. The HyperScience Experience
• 26 team members, only one “non-technical” to
handle sales (but he can code!)
• Half of the team is based in Bulgaria
• Secured customers very early in the life of the
company and built product closely with them

56. CONCLUSION

57. How do you actually build an AI company?
The Five P’s of AI
| Positioning
| Product
| Petabytes
| Process
| People

58. Now is the perfect time to
build an AI company!
“Do not throw
away your
shot!”