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

Business Models for AI startups

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Page 1: Business Models for AI startups

Business Models for AI Startups Taipei AI Forum, 2017/08/13

Albert Y. C. Chen, Ph.D.Vice President, R&D

Viscovery

Page 2: Business Models for AI startups

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

Page 3: Business Models for AI startups

Any pie left for AI startups?

(BCG AI Report, 2016/10)

appl.layer

techlayer

infralayer

solution

platform

libraries

modules

data

machine computing power

data accumulation via open API

AI/DNN library AI/DNN library

gen purposeplatforms

gen purposeplatforms

app-specificplatforms

app-specificplatforms

app app app app app

HWco.

Verti

cal A

I Sta

rtups

agri. manu. med. fin. retail trans.

E.g., 1: Google, Amazon, FB, 2: IBM, 3: Walmart, 5: NVidia

Page 4: Business Models for AI startups

Vertical AI StartupsSolving 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)

Page 5: Business Models for AI startups

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)

Page 6: Business Models for AI startups

(Bradford Cross, 2017/06/14)

So, which vertical?• choose large TAM

with high margin • avoid 1% fallacy • avoid confirmation

bias

Page 7: Business Models for AI startups

(Bradford Cross, 2017/06/14)

AI startups total investmentUnicorn tally

Page 8: Business Models for AI startups

Technical vs Market Risk

Page 9: Business Models for AI startups

Examples of Vertical AI beating General Purpose AI

Page 10: Business Models for AI startups

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

Page 11: Business Models for AI startups

Top 5 Factors in Success (across >200 startups with successful exit)

(Bradford Cross, TedX, 2015/03)

Page 12: Business Models for AI startups

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 burstbroadband penetration > 50%

Page 13: Business Models for AI startups

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?

Page 14: Business Models for AI startups

Building up Competitive Advantage for Vertical AI Startups

Business

DataTechnology

Speed

Team

Speed

Speed

Page 15: Business Models for AI startups

Some Hard Decisions

• 2C vs 2B? Hit the Jackpot vs long and steady?

Page 16: Business Models for AI startups

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.