The document discusses business models for AI startups. It suggests that vertical AI startups that solve industry-specific problems by combining AI and subject matter expertise have advantages over general purpose AI companies. These include developing full stack products, proprietary data, and ensuring AI delivers core value. The document also notes factors important for success include choosing a large target market, technical advantages over general purpose AI, and building competitive advantages in business, data, technology, speed and team. Timing is also critical for opportunities in venture capital investment and customer needs.

1. Business Models for AI Startups
Taipei AI Forum, 2017/08/13
Albert Y. C. Chen, Ph.D.
Vice President, R&D
Viscovery

2. Albert Y. C. Chen, Ph.D.
• Experience
2017-present: Vice President of R&D @ Viscovery
2016-2017: Chief Scientist @ Viscovery
2015: Principal Scientist @ Nervve Technologies
2013-2014 Computer Vision Scientist @ Tandent
2011-2012 @ GE Global Research
• Education
Ph.D. in Computer Science, SUNY-Buffalo
M.S. in Computer Science, NTNU
B.S. in Computer Science, NTHU

3. Any pie left for AI startups?
(BCG AI Report, 2016/10)
appl.
layer
tech
layer
infra
layer
solution
platform
libraries
modules
data
machine computing power
data accumulation via open API
AI/DNN library AI/DNN library
gen purpose
platforms
gen purpose
platforms
app-specific
platforms
app-specific
platforms
app app app app app
HW
co.
VerticalAIStartups
agri. manu. med. fin. retail trans.
E.g., 1: Google, Amazon, FB, 2: IBM, 3: Walmart, 5: NVidia

4. Vertical AI Startups
Solving industry-specific problems by combining
AI and Subject Matter Expertise.
• Full Stack Products
• Subject Matter Expertise
• Proprietary Data
• AI delivers core value
(Bradford Cross, 2017/06/14)

5. Why Go Vertical?
• Don’t get ripped off—don’t get disconnected
from key customer/providers.
• Tasks get commoditized
• Software is eating the world—every company in
every industry needs to be a tech company.
• Enterprise exits come in cohorts
(Bradford Cross, 2017/06/14)

6. (Bradford Cross, 2017/06/14)
So, which vertical?
• choose large TAM
with high margin
• avoid 1% fallacy
• avoid confirmation
bias

7. (Bradford Cross, 2017/06/14)
AI startups total investmentUnicorn tally

8. Technical vs Market Risk

9. Examples of Vertical AI
beating General Purpose AI

10. Examples of Vertical AI
beating General Purpose AI
TOP 5 TAGS COMPARISON
TAG
AD PLACEMENT
VALUE
TAG
AD PLACEMENT
VALUE
Person Low
Coulee Nazha
(actress)
High
Anime Low Sean Sun (actor) High
Screenshot Low Back of smartphone High
Cartoon Low Female Medium
Adult Medium Young Medium
“FIRST LOVE” DRAMA SERIES SCENE
Competitive Analysis
Baidu vs. Viscovery
TOP 5 TAGS COMPARISON
TAG (Man’s Face)
AD PLACEMENT
VALUE
TAG
AD PLACEMENT
VALUE
Age: 32 Medium Necklace High
Asian Medium Baseball cap High
Male Medium Bracelet High
Not smiling Low (inaccurate) Ziwen Wang High

11. Top 5 Factors in Success
(across >200 startups with successful exit)
(Bradford Cross, TedX, 2015/03)

12. Timing, timing, timing.
Rule of Thumb:
• Customers within the target vertical having
immediate unmet needs.
• VCs scouting that vertical ready to invest.
2000
.com bubble burst
2002 2004 2006 2008 2010 2012 2014 2016
housing bubble burst
broadband penetration > 50%

13. Your Idea
• Are you passionate about it?
• Is it disruptive enough?
• What is your business plan?
• What is it?
• Can it make money?
• What is the future of the idea?
• What is your competitive advantage?
• How do you build up your entry barrier?

14. Building up Competitive
Advantage for Vertical AI Startups
Business
DataTechnology
Speed
Team
Speed
Speed

15. Some Hard Decisions
• 2C vs 2B? Hit the Jackpot vs long and steady?

16. Some Hard Lessons Learned
• Swallowed alive by giants (Speed, Timing)
• Aiming for a narrow exit. (Business)
• Failing to disrupt the market (Technology)
• Everybody in the field is lying (Data)
• Don’t underestimate the cost of going long in
any vertical.