CS计算机代考程序代写 information retrieval data science chain deep learning finance android data mining AWS AI ant algorithm PowerPoint Presentation

PowerPoint Presentation

1

Who’s Winning the Artificial Intelligence Race between PRC & US:
Alibaba, Tencent, Ping An, Baidu & Zhong An VERSUS
Alphabet, Amazon, Apple, Facebook & Microsoft

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Artificial Intelligence Described on a Single Chart

Source: Schulte Research Estimates

Physical
Data

IoT
Digital
Data

Infra.

Neural
Networks:
Machine
Learning

AI

1. Financial Services

2. Cognitive Services

3. Lifestyle/ Health

4. Autonomous cars

5. Robotics

6. Advertising

Cloud

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tin
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5bn
Mobile
Devices

40bn
Sensors

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Source: Schulte Research Estimates, Zenith

a. AI Financial Services Comparison:
Alibaba way ahead. Ping An and Tencent also dominate

Company Payment Insurance Personal
Loans

SME
Loans

Credit
Rating

Money
Market

Wealth
Mgmt

Crowd-
funding

Currency
Exchange

Alibaba AliPay
(400mn)

(51.8% ms)

Ant
Zhong An

Personal
loans – Ant

SME loans
– Ant
SME

Service

Zhima
Credit

Yue’bao
(CNY1.2tn)

Ant
Financial,

AliPay

ANTSDAQ AliPay

Tencent WePay
(300mn)

(38.3% ms)

Zhong An Weilidai Weilidai WeBank / WeBank JD.com /

Ping An Ping An
Bank

Ping An
Insurance

Ping An
Bank

Ping An
Bank

Ping An
Insurance

Ping An
Asset
Mgmt

Ping An
Asset
Mgmt

/ Ping An
Bank

Baidu Baidu
Wallet

(100mn)

/ / / Yes / Yes / /

Amazon / Electronic
Damage

Insurance
(UK)

/ USD$3bn
SME loans
(Amazon
Lending)

/ / / / /

Microsoft MSFT
Wallet

/ / / / / / / /

Google Google
Wallet,
Android

Pay

/ / / / / / / /

Apple Apply Pay
(85mn,

450%YOY)

/ / / / / / / /

Facebook Messenger
Pay

/ / / / / / / /

MISSED
OPPORTUNITY

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b. AI Data Source Comparison.
Alibaba may leapfrog all with PAI, Ding Talk, and Tmall Genie. This puts Alibaba in its own league with Tencent very close.

Source: Schulte Research Estimates

Company Intentional Data Un-intentional Data IoT/Car Cognitive Service Cloud

Alibaba AliPay, Taobao, Tmall,
Alibaba.com, Alibaba
Express, Yue’bao, Tmall,
Alibaba Cloud (750mn)

Youku, Weibo, UCWeb, Cainiao
Logistics, Yahoo! China, SCMP,
AliWangWang, LaiWang, PAI,
Ding Talk

”Connected Car” with
SAIC, AutoNavi, Ali
Health, KFC

Platform for
Artificial
Intelligence (PAI
2.0), Tmall Genie

Ali
Cloud

Tencent WeChat Pay, 3rd Party
Providers (JD.com, Didi,
etc.)

WeChat (938mn), QQ (700mn),
Qzone, WeChat Ecosystem,
Gaming

Didi, Dianping review
site

WeChat Voice/
Image, Tencent
Video

Tencent
Cloud

Ping An Ping An Bank, Ping An
Insurance, Ping An Asset
Mgmt (350mn)

Ping An Health, Ping An
Securities

Ping An Auto Owner,
Wanjia Clinics

Facial recognition,
Voice print

Ping An
Health
Cloud

Baidu Baidu Search, Baidu
Wallet

Baidu Search Food Delivery Service,
Project Apollo

Little Fish Baidu
Cloud

Amazon E-commerce (B2C, C2C) Shopping search Echo, Kindle, Whole
Foods, Amazon Books,
Logistics

Alexa, Rekognition,
Polly, Lex, Amazon
Video

AWS

Google Google Play Store,
Google Search

Google Search, Android OS, G-
mail, Maps, Chrome, Snapchat
(166mn MAU), Youtube,
Waymo

Android OS, Waymo Health, Translation,
Google Assistant,
Google Face, Deep
Mind

Google
Cloud

Apple iTunes (800mn), Apple
Music, Apple Pay (85mn)

iOS, Safari iPhone (1bn), iPad,
iPod, Mac, Apple Watch

Siri, Face
Recognition

iCloud

Microsoft Xbox, Microsoft Wallet
(small)

LinkedIn, Office, Skype, Bing, IE Kinect, Microsoft
Surface, Windows
Phone

Zo, Computer
vision/ Speech/
Language API

Azure

Facebook Messenger Pay Facebook, Facebook Messenger
(1.96bn), Whatsapp (1.3bn)

Oculus, Project Titan Deep face, Deep
text, Translation

/

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c. AI Cloud Comparison:
Amazon Big Winner. Alibaba Leader in Asia.

Source: Schulte Research Estimates, HostUCan, Skyhighnetworks

Company Market Share
(in respective
markets)

Comments

Alibaba 41% Leader in Asia, >100 newly developed AI services
Services in storage, networking, healthcare, logistics, lifestyle, media, business
enhancements, and etc.

Tencent 7% #3 in Chinese Market, services in public/private storage for personal/business use

Baidu 1% Small cloud service

Amazon 47% Amazon AWS: Global & US leader

Microsoft 10% Fastest Growth Rate, 97% YOY
Azure & Office 365

Google 4% Fast growing, 45 teraflops of data (45 trillion bytes/second)
Services include AI APIs’, storage, search history, e-mail, etc.
71,000 searches/second
New quantum computer

Apple 1% Small cloud service, primarily strong in private data collection
Services in location, storage, and security

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Source: Schulte Research Estimates, Zenith

d. AI Cognitive Services Comparison: Microsoft Winner.
There is a “me too” attitude. Microsoft has made a wide and deep mark. But, Alibaba PAI is deeper and broader than anyone else.

Company Image Recognition Facial Recognition Voice
Natural Language

Understanding
Video

Alibaba Document recognition,
image search+, PAI

Identity
authentication, Alipay

Customer service
AI, voice->text
service, etc., PAI

Real-time translation
services, PAI

Video
analysis,
broadcast
service, PAI

Tencent Fashion trend analysis Identity
authentication

WeChat
Voice/Image

Translation Tencent
Video

Baidu xPerception, Pixlab API,
WICG Shape Detection
API

Baidu Facial
Recognition (99.7%
accuracy)

Voice Search,
Text-Speech
Converter, Deep
Voice (97% accu.)

Translation, Speech
Recog., Kitt.AI,
RavenTech

/

Amazon Amazon Rekognition Emotion recog., Face
Comparison

Alexa, Lex Alexa, Echo, Polly Amazon
Video

Microsoft Image understanding,
Celebrities/Landmark
recog.

Face API, Emotion,
Verification,
Detection

Speech
Verification, Text-
Speech Converter

Translation, Text
Analytics, LUIS

Video
analysis,
Video
Indexer

Google Image searching Google Face Google assistant Translation AI, Text
Analytics

YouTube

Apple Classification/
Detection/ Checking

Facial Rec. Siri Siri /

Facebook Text recognition,
translation

Deep Face Oculus VR Voice
Recog.

Translation, Deep Text /

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Source: Schulte Research Estimates, Zenith

e. AI Lifestyle Comparison:
Tencent + Alibaba Winner. Microsoft + Baidu falling behind.

Company Media Food Travel Entertainment Interaction Search Education Health

Alibaba Live media,
news
production,
media
interaction,
etc.

KFC
China,
Koubei,
Ele.me

Air/train
tickets,
hotel
booking

Audio/video
solutions, video
game services, e-
commerce, Youku,
AGTech

Online
shopping
support

Personalized
search, direct
marketing
service, big
data analytics

Media
education
services

Utilities
payment,
Hospital-
patient comm.,
smart diagnosis

Tencent QQ music,
Joox, Tencent
Video/ News

Meituan
Dianping

LY.com E-commerce, video
games

WeChat WeChat
Search, Sogou

Koo Learn,
Ke.qq

WeChat
Intelligent
Hospital

Baidu Book recomm. / Ctrip / / Search engine
(76% market
share),
personalized
search, data
marketing

Baidu
Education
(Jiaoyu)

Health Search

Amazon Books, music,
TV streaming

Whole
Foods,
Amazon
Fresh

/ TV streaming,
Amazon Studio

/ Search
recomm.

Amazon
Inspire

/

Microsoft / / / X-box/gaming Skype,
LinkedIn

Bing MSFT
Education

MSFT Health

Google Google Videos / Google
Flights

YouTube Snapchat,
Gmail,
Google+

Search engine,
personalized
search, data
marketing

Google for
Education

Google Health,
Google Fit

Apple ITunes / / Apps, App Store,
Game
development kits

Messages / / Health apps

Facebook Facebook
newsfeed

/ / Facebook games Facebook,
Whatsapp,
Instagram

Internal Search
Function

/ /

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Alibaba: E-commerce + the fullest AI ecosystem of any company on earth.
And, it has achieved this while being the most profitable of any company.

B2B

C2C

B2C

Cloud Computing

IoT

Artificial Intelligence/ PAI 2.0

Source: Alibaba Website, Schulte Research

Investment

Management

Audio/Video

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Alibaba AI: a truly common man’s bible – access to virtually
any and all data dumps of all of China.

Source: Alibaba Cloud Website, Schulte Research

Artificial Intelligence/

PAI 2.0

A. Financial Services N. Government

E. Online-To-Offline

M. Environment

B. E-Commerce

D. Manufacturing

G. Websites/Apps

J. Audio/Video

L. Health

F. Logistics

C. Cyber-Security

I. Video Games

K. Energy

H. Media

AI

Government

Services

AI

Lifestyle

Services

AI

Banking

Services

AI

Business

Services

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Source: WeChat, Schulte Research

QQ

Cloud Computing

IoT

WeChat

Tencent Holdings Progression

Gaming

Tencent: from social media network to the use of unintentional
data to create a full ecosystem

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Ping An has made greater strides into becoming an
autonomous financial ecosystem than any other company

Insurance

Golden Butler

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Facial Recognition

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Artificial Intelligence

Finance

Healthcare

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Zhong An: only 3 years old, the largest pure online, cloud-based
insurance company globally.

Returns

Agent

Management

Credit Rating

D
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P
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Artificial Intelligence

Lifestyle

Enterprise Solution

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Baidu: stumbled in its transition from search engine to AI;
regaining momentum after widescale mngt. shakeup, and are
pursuing risky strategy of acquisition

Search

Autonomous Driving

Baidu Progression

Source: Baidu Website, Schulte Research

Cloud Services

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Amazon: online bookstore for 3 years to #1 global player in
almost everything, including Cloud, Enviable execution +
impressive global ambition.

Books

NLP

Life style

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Everything..

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le

F
o

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Artificial Intelligence

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Google: from #16 in search engine, Google has used AI to become
advertising behemoth. But, it is not on the radar screen in finance.

Google

Translation

Analytics

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Artificial Intelligence

Services

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Microsoft: started as dominant PC operating system but failed in the
all important transition to mobile, trying to recover by its Azure
Cloud. Why is Microsoft not a dominant leader in SME lending?

Windows

IE

Language

Understanding

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A
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Chatbot

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Artificial Intelligence

Lifestyle

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Apple: this is a hardware company with awesome marketing,
unimpressive AI and seem to have stumbled in the progression.
Too late to the game?

Apple Inc
Siri

Artificial Intelligence

Devices

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Facebook: the greatest 1 trick pony ever. They control the social
network + people think this is important – but for it’s own sake? One
of the worst scores.

Facebook

Deep Face

IO
T

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rs Te
x
t A

I

Instagram

Artificial Intelligence

Lifestyle

FAIR labs

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Chinese firms have implemented quicker and jettisoned quicker. China dropped
duds & created financial empires. US firms stopped their efforts.

Company Duds Successful Replacements

Alibaba Lai Wang, Alibaba.com (HK) Ant Financial, AliPay, Taobao, Tmall, Alibaba Cloud, PAI 2.0,
Zhima Credit, ANTSDAQ, Yue’bao, Youku, Weibo, etc.

Tencent Pai Pai, E-commerce WePay, Weilidai, WeBank, JD.com, WeChat, Didi, Tencent
Cloud, etc.

Ping An Ping An Good Car Ping An Bank, Ping An Insurance, Ping An Asset Mgmt, Ping An
Health, etc.

Baidu O2O Wai Mai Baidu Wallet

Microsoft Microsoft Wallet Skype

Google Google Hangout Android Pay

Facebook Messenger Pay /

Apple Apple Pay /

Amazon Amazon Lending
Amazon Insurance

/

*China had duds but morphed quickly and pivoted effectively

Duds with no
replacement

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Project Conclusions

General Conclusions

The Cloud is the foundation of AI strategy. Alibaba dominates in Asia.
Amazon dominates in US.

Cognitive services are the foundation of AI development -> voice, image,
face, video, language. However, there is a “me too” and, surprisingly, a
feeling that AI is already commoditized.

SMEs and corporates can access cheap AI tools which allow them to access
new forms of credit & offer new products. This is the age of the SME.

Firms, especially in China + other GEMS, are leap-frogging banks rapidly.
They are using AI to analyze vast amounts of integrated data in order to
offer new financial services to billions of unbanked people and millions of
unbanked SMEs.

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Project Conclusions

Chinese Firms Conclusions

PRC firms are better at monetizing technology for mass use.

PRC has a long term, coherent plan for AI. The US has no plan.

Chinese are more willing to surrender data.

Alibaba and Ping An are much further into new territory than anyone else.

Integration of finance and lifestyle is welcomed and encouraged by PRC.

PRC has a clear national policy of proliferating credit to individuals and SMEs.

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Project Conclusions

Reasons for China’s Success over US

US heavily entrenched incumbents & lobbying groups have impeded progress to protect cartels.

Multiple regulators who were on the warpath after the devastation of the GFC (US$300bn of fines)
discouraged new consumers credit products.

Many of the Chinese companies learned to “eat dirt”. They were toughened by a highly
competitive environment.

China succeeded since there was no “there there”. Banking and regulatory structure were absent
and monetary infrastructure was very immature. The only way to win was with “new”.

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Robot installations in China are 1/3 of global total

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Master Summary: Financial, Operational, Artificial Intelligence Analysis

First Second Third Laggard

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ce Operational Health Facebook Alibaba Tencent Amazon

Revenue Diversification Microsoft Alibaba Tencent Facebook

Valuation Ping An Apple Google Tencent

Credit Health Google Apple Tencent Baidu

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Fintech Alibaba Ping An Tencent Facebook

Insurtech Ping An Alibaba Tencent Facebook

Healthtech Ping An Alibaba Amazon Baidu

Cloud Amazon Microsoft Alibaba Facebook

Data Source Alibaba Ping An Tencent Baidu

Cog Services Google Alibaba Microsoft Facebook

Lifestyle Tencent Alibaba Amazon Baidu

Securities Ping An Alibaba Tencent Microsoft

Overall
Winners/Losers

Ping An Alibaba Tencent Baidu/Facebook

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Overall Rankings: Ping An and Alibaba dominate in all categories
Ranking of each company’s performance across all three areas: Artificial Intelligence Services, Financials, and Valuations. (1st is
best, 9th is worst)

AI Scorecard Financials Valuation Average

1. Ping An 2 4 1 2.3

2. Alibaba 1 2 7 3.3

3. Google 5 4 3 4.0

4. Apple 6 5 2 4.3

5. Tencent 3 3 8 4.7

6. Facebook 8 1 6 5.0

7. Microsoft 7 6 4 5.7

8. Baidu 9 7 5 7.0

9. Amazon 4 8 9 7.0

Note: “1” is best, “9” is worst

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Artificial Intelligence Scorecard:
Chinese firms have diversity and consist of a full menu. US firms tend to specialize through 1 brand. This age
of AI requires diversified ecosystem. Specialist firms lose out.

Source: Schulte Research Estimates

*Scoring from 1 (worst) to 10 (best)

1
Alibaba

2
Ping An

3
Tencent

4
Amazon

5
Google

6
Apple

7
Microsoft

8
Facebook

9
Baidu

FinTech 10 10 9 7 6 7 4 4 5

InsureTech 9 10 9 6 4 4 4 4 4

HealthTech 9 10 8 8 7 7 7 5 4

Cloud 8 5 7 10 8 6 9 4 5

Data source 9 8 9 8 8 8 7 7 6

Cognitive
Services

9 8 7 8 10 7 9 7 8

Lifestyle 9 8 10 9 9 9 7 9 6

Securities 9 10 9 4 5 6 4 5 5

Average 9.0 8.6 8.5 7.5 7.1 6.8 6.4 5.6 5.4

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Financial Rankings:
Facebook/Alibaba best in operations; Baidu/Amazon worst. Microsoft also mediocre.

*Ranking from 1st (best) to 9th (worst)
*ROE/ROC/ROA are next 4Q estimates
*Detailed numbers on Page 15/16

1
Facebook

2
Alibaba

3
Tencent

4
Alphabet

5
Apple

6
Microsoft

7
Baidu

8
Amazon

Retained Earnings
(US$mn)

3 5 4 1 2 8 6 7

Gross Margin (%) 1 2 5 4 7 3 6 8

Net Margin (%) 1 2 3 6 5 4 7 8

Revenue CAGR
(5yrs) (%)

2 1 3 6 7 8 4 5

Cash/Total Asset 4 1 3 5 6 8 7 2

Tangible Leverage 1 3 5 2 6 8 4 7

ROE (%) 2 5 3 6 1 4 7 8

ROC (%) 3 6 2 5 1 4 7 8

ROA (%) 3 2 1 4 5 6 7 8

Average 2.3 3.1 3.3 4.6 4.6 6.2 6.3 7.2

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Valuation Rankings:
Ping An cheapest; Amazon most expensive

*Ranking from 1st (best) to 9th (worst)

1
Ping An

2
Apple

3
Alphabet

4
Microsoft

5
Baidu

6
Facebook

7
Alibaba

8
Tencent

9
Amazon

P/E 1 2 4 3 5 6 7 8 9

P/B 1 4 2 6 3 5 7 8 9

PEG 2 7 6 8 3 1 4 5 9

Price/ Free Cash
Flow

1 2 4 3 5 7 6 8 9

EV/EBITDA NA 1 2 3 5 4 7 8 6

EV/EBIT NA 1 2 3 5 4 6 7 8

Average 2.4 2.8 3.3 4.3 4.3 4.5 6.2 7.5 8.5

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Strategy Directions: The key is integrated ecosystems. Specialization in
IOT + AI is a liability. Google/Facebook have thrown away spectacular
opportunities.

Source: Schulte Research

Hierarchy of
Product

Development

Time

Microsoft

Facebook

High P/E

Low P/E

Right direction

Wrong
direction

Alibaba, Tencent, and Ping An have IOT/AI plus fully integrated systems. Baidu
dropped the ball.

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Advertisement: Facebook is leading, other data rich
firms have the potential to expand the revenue source

97.0%

91.4%
89.0%

55.0 %

17.7%

7.1%

2.2%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Facebook Baidu Google Alibaba Tencent Microsoft Amazon

Advertisement revenue as % of total revenue

Source: Schulte Research Estimates, Bloomberg

1 trick ponies?

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Advertisement: Facebook is leading, other data rich
firms have the potential to expand revenue sources

Source: Schulte Research Estimates, Bloomberg

0

50

100

150

200

250

Apple Amazon Ping An Alphabet Microsoft Facebook Alibaba Tencent Baidu

Total Revenue (US$bn)

Other

Advertising

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Chinese Companies Revenue Breakdown
China much more diversified than the US: PRC has a deeper, more integrated ecosystem

58%
28%

12%
1% 1%

Ping An Revenue Breakdown

Life insurance

Property and casualty
insurance

Banking

Trust business

Securities business

55%

17%

4%

3%

4%

1%

5%

9%
2%

Alibaba Revenue Breakdown

Advertising

C2C/B2C

B2B

International commerce retail

International commerce
wholesale

Other commerce

Cloud computing

Digital media and entertainment

Innovation initiatives and others

47%

24%

18%

11%

Tencent Revenue Breakdown

Online games

Social networks

Advertising

Other

41%

4%
8%

47%

Baidu Revenue Breakdown

Search service

Transaction service

iQiyi

Advertising

Source: Schulte Research Estimates, Bloomberg, Company filings

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63%
7%

11%

13%

6%

Apple Revenue Breakdown

iPhone

iPad

Mac

Services (Apple
Pay, AppleCare,
etc.)

Other (Apple TV,
Watch, Beats,
accessories, etc.)

87%

12%
1%

Google Revenue Breakdown

Advertising

Other

Other bets
(Google Fiber,
Calico Labs,
Nest, Verily,
etc.)

98%

2%

Facebook Revenue Breakdown

Advertising

Payments and
other fees

US Companies Revenue Breakdown
These 5 companies are essentially specializing. I think the US companies have moved away from the
conglomerate model at the wrong time?

29%

27%

44%

Microsoft Revenue
Breakdown

Business
Computing
(Office, Skype,
etc.)

Cloud (Azure,
SQL, etc.)

Personal
computing
(Windows,
Surface, Xbox,
etc.)

63%

18%

6%

11%
2%

Amazon Revenue
Breakdown

Retail Products

C2V

Subscriptions

AWS

Other

Source: Schulte Research Estimates, Bloomberg, Company filings

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Research Directions: US Firms
All companies have a similar vision of the future. AI, Big Data, and Language!

Source: Schulte Research

Companies Research Direction Unique Point

Facebook

1.Facebook AI Research (FAIR) – semantic analysis, sentient analysis, etc.

2.Natural Language Processing and Speech – translation services, word sense disambiguation, etc.

3.Applied Machine Learning – streamline content delivery, connect users w/ desired content

4.Data Science – human interaction analysis, market intelligence, etc.

5.Virtual Reality – augmented reality experiences

Virtual Reality

Microsoft

1.Artificial Intelligence

2.Computer Vision

3.Human-computer Interaction

4.Human Language Technologies

5.Search and Information Retrieval

Human-computer

interaction

Apple

1.Apple Car/ Autonomous Driving
2.Augmented Reality
3.HealthTech – Health functions within Apple watch
4.Chips – Hardware improvement

Chips

Amazon

1.Image/Face Recognition

2.Voice Recognition

3.ChatBot AI

4.Drone Delivery

5.Warehouse Robotics

Drone, Robotics

Google

1.Machine Translation System

2.Deep Learning Algorithms – detection of diabetic retinopathy

3.Quantum Computing

4.Big Data Analytics

5.Machine Intelligence/Perception

Quantum

Computing

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Research Directions: Chinese Firms
All companies have a similar vision of the future. Chinese firms show higher degree of diversification.

Source: Synergy Research, Schulte Research

Companies Research Direction Unique Point

Alibaba

1.Financial Securities Exchange Core

2.Customer Service AI

3.Intelligent Manufacturing – Cloud computing/big data analytics

4.Health Data Platform – Integration of patient data, image analysis, diagnosis

5.Commercial Vehicle Networks – logistics enhancement & optimization

Securities Exchange,

Intelligent Manufacturing

Tencent

1.Artificial Intelligence
2.Machine Learning – stock picking, HFT, portfolio management
3.Big Data – trend analysis
4.Cloud Computing

Portfolio Management

Ping An
1.Data Mining – social media user behavior, engagement rates
2.Computational Algorithms
3.Big Data Analytics – purchasing patterns, WMPs

WMPs

Zhong An

1.Financial Platforms/Cores – E-commerce platforms, insurance cores, finance cores

2.Data Analytics/ Risk Management Modelling

3.AI – Transaction decision-making

4.Blockchain – Digitalize off-chain assets, data storage, secure ID

5.AI – Customer service ChatBot

Blockchain

Baidu

1.Artificial Intelligence – image/speech recognition
2.High Performance Computing/Big Data Analytics
3.Natural Language Processing
4.Deep Learning
5.Augmented Reality

High performance
computing

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Major Acquisitions: US Firms
US firms invest heavily in acquiring smaller firms that do not necessarily operate in their industry.

Source: Synergy Research, Schulte Research

Company Acquisition Amount (US$) Year Description Comment

Facebook

WhatsApp 19bn 2014 Messaging

Consolidation of social

network industry

Oculus 2bn 2014 VR

Instagram 1.01bn 2012 Social Network

LiveRail 500mn 2014 Video ad.

Microsoft

LinkedIn 26.2bn 2016 Social Network

Failed hardware attempt
on Nokia. LinkedIn is the
new bet on professional
network.

Nokia 8bn 2013 Phone

Skype 8.5bn 2011 Messaging

Mojang 2.5bn 2014 Game developer

Yammer 1.2bn 2012 Corp communication

Apple

Beats Electronics 3bn 2014 Headphone

Lifestyle + Health
HopStop.com 1bn 2013 Transit guide

Lattice 200mn 2017 AI

Gliimpse 200mn 2016 Health

Amazon

Whole Foods Market 13.7bn 2017 Supermarket

O2O bet on Whole Foods

Twitch 970mn 2014 Game streaming

Souq.com 580mn 2017 E-commerce

Elemental Technologies 500mn 2015 Mobile video

Annapurna Labs 37mn 2015 Chip maker

Google

Motorola Mobility 12.5bn 2012 Phone

Another failed case of
acquiring phone company

Waze 1.3bn 2013 Map

Apigee 625mn 2016 API platform

DeepMind 500mn 2014 AI

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Major Acquisitions: Chinese Firms
All companies have a similar vision of the future. Chinese firms show higher degree of diversification.

Source: Synergy Research, Schulte Research

Companies Acquisition Amount (US$) Year Description Comment

Alibaba

UCWeb 4.7bn 2014 Mobile browser

Contents + data

Youku 3.5bn 2015 Chinese Youtube

AGTech Holdings 2.39bn 2016 Lottery

SCMP 262mn 2015 Media

Wandoujija 200mn 2016 Android app store

Tencent

Supercell 8.6bn 2016 Game developer

Consolidation of
game industry

China Music Corp 2.7bn 2016 Music

Riot Games 400mn 2011 Game developer

Miniclip SA na 2015 Game developer

Sanook na 2016 Thai web portal

Baidu

91 Boyuan Wireless 1.9bn 2013 Android app store

Diverse
Beijing Huanxiang Zongheng Chinese
Literature

31.3mn 2013 Online publisher

TrustGo 30mn 2013 Mobile security

Ping An Autohome 1.6bn 2016 Auto website Auto insurance

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Market Cap:
Tech companies are a gathering force in market cap. They dominate capital markets now. Banks can’t
hold a candle to tech spending

0

100

200

300

400

500

600

700

800

900

Apple Alphabet Microsoft Facebook Amazon Alibaba Tencent Wells
Fargo

CCB HSBC Ping An Goldman
Sachs

Baidu Deutsche
Bank

Market Cap (US$bn)

Source: Schulte Research Estimates, Bloomberg

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Retained Earnings:
Alphabet and Apple are sitting on mountains of cash. Much of it as untaxed offshore cash.

105

96

27

22
20

16
12

5
3

0

20

40

60

80

100

120

Alphabet Apple Ping An Facebook Tencent Alibaba Baidu Amazon Microsoft

Retained Earnings (US$bn)

Un-sustainable cash pile

Finally profitable Stock buybacks

Source: Schulte Research Estimates, Bloomberg

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Free Cash Flow:
Cash flows from operations – investment in operating capital (mostly fixed assets)

Tech companies blow away banks in cash flow (‘different’ numbers but you get the
point!)

0

10

20

30

40

50

60

Apple Ping An Microsoft Alphabet Citi (pre-tax
profit)

Facebook Amazon Alibaba Tencent HSBC (pre-
tax profit)

Baidu

Free Cash Flow (US$bn) (2016)

Source: Bloomberg, Citi Annual Report, HSBC Annual Report

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Investment Cash Flow
(net spending in Prop Plt & Eqpt, Capex, M&A)

US$300bn in “conduct costs” for banks is that much more that can’t be used for
investment. Plus, current R/D spending is woefully under invested.

Source: Bloomberg

0

5

10

15

20

25

30

35

40

45

50

Microsoft Apple Alphabet Lloyds
(fines)

RBS (fines) Deutsche
(fines)

Facebook Alibaba Tencent BNP (fines) Amazon Baidu

Investment Cash Flow (US$bn) (2016)

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Chinese Companies Liquidity (1.2 * Working Capital / Total Assets)

0

0.1

0.2

0.3

0.4

0.5

0.6

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Alibaba Liquidity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Baidu Liquidity

0

0.05

0.1

0.15

0.2

0.25

0.3

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Tencent Liquidity

5 year deterioration

Source: Bloomberg
*Dotted line represents NASDAQ average

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US Companies Liquidity (1.2 * Working Capital / Total Assets)

0

0.05

0.1

0.15

0.2

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Amazon Liquidity

0

0.1

0.2

0.3

0.4

0.5

0.6

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Microsoft Liquidity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Facebook Liquidity

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Apple Liquidity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Google Liquidity

Source: Bloomberg
*Dotted line represents NASDAQ average

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Chinese Companies Capital Structure (1.4 * Retained Earnings / Total Assets)

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Alibaba Capital Structure

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Tencent Capital Structure

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Baidu Capital Structure

Source: Bloomberg
*Dotted line represents NASDAQ average

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US Companies Capital Structure (1.4 * Retained Earnings / Total Assets)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Amazon Capital Structure

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Microsoft Capital Structure

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Facebook Capital Structure

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Apple Capital Structure

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Google Capital Structure

Stock buybacks

Stock buybacks

Source: Bloomberg
*Dotted line represents NASDAQ average

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Chinese Companies Profitability (3.3 * EBIT / Total Asset)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Alibaba Profitability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Tencent Profitability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Baidu Profitability

5 year deterioration

Source: Bloomberg
*Dotted line represents NASDAQ average

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US Companies Profitability (3.3 * EBIT / Total Asset)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Amazon Profitability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Microsoft Profitability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Facebook Profitability

0

0.2

0.4

0.6

0.8

1

1.2

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Apple Profitability

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Google Profitability

Source: Bloomberg
*Dotted line represents NASDAQ average

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Chinese Companies Debt Structure (0.6 * Market Cap / Total Liabilities)

0

2

4

6

8

10

12

FY 2014 FY 2015 FY 2016

Alibaba Debt Structure

0

1

2

3

4

5

6

7

8

9

10

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Tencent Debt Structure

0

1

2

3

4

5

6

7

8

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Baidu Debt Structure

Deterioration

Source: Bloomberg
*Dotted line represents NASDAQ average

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US Companies Debt Structure (0.6 * Market Cap / Total Liabilities)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Amazon Debt Structure

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Microsoft Debt Structure

0

5

10

15

20

25

30

35

40

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Facebook Debt Structure

0

1

2

3

4

5

6

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Apple Debt Structure

0

2

4

6

8

10

12

14

FY 2012 FY 2013 FY 2014 FY 2015 FY 2016

Google Debt Structure

Source: Bloomberg
*Dotted line represents NASDAQ average