A presentation held at Opinno in San Francisco to a delegration from PromoMadrid. Goal was to provide a quick overview of major trends in mobile in 30 min.

1. A Mobile-Centric View
of Silicon Valley
Prepared for Opinno & PromoMadrid
January 31, 2011
Licensed under Creative Commons Attribution 3.0 Unported License (http://www.creativecommons.org/licenses/by/3.0)
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2. @l1rs
Work Network
Lars Kamp Lars Kamp
Management Consulting
Suite 1200
560 Mission Street
San Francisco, CA 94105 San Francisco, CA
415.894.5423 415.894.5423
lars.kamp@accenture.com www.sfmobile.org lars@sfmobile.org
2

3. Today’s topics.
History
Mobile Economics
Silicon
Cloud
What’s Next?
3

4. History
4

5. A note on people’s ability to predict the future.
”People tend to overestimate
what can be done in one year
and to underestimate what can
be done in five to ten years.”
J. C. R. Licklider, 1965
J. C. R. Licklider
“Grandfather of the Internet”
5

6. Q: Whose mission statement is this?
“We have a dream of improving the lives of many millions
of people by means of small, intimate life support
systems that people carry with them everywhere.
These systems will help people to organize their lives, to
communicate with other people, and to access
information of all kinds.
They will be simple to use, and come in a wide range of
models to fit every budget, need, and taste. They will
change the way people live and communicate.”
6

7. A: General Magic, 1990.
You could say “mobile” started here.
7

8. General Magic’s “Magic Cap”. Looks familiar?
“Magic Cap” User Interface, 1994
8

9. Maybe now?
T-Mobile G1 (HTC Dream) User Interface, 2008
9

10. Three people from the team that architected Magic Cap.
Andy Rubin Tony Faddel Kevin Lynch
10 Source: Wired, SF Mobile analysis.

11. Economics
11

12. Software-driven innovation.
” The problem is, in hardware you
can't build a computer that's twice as
good as anyone else's anymore. […]
But you can do it in software.”
Steve Jobs, 1994
Steve Jobs
Apple Founder & CEO (on leave), in 1994 Rolling Stone interview
12 Source: Rolling Stone Magzine.

13. Mobile is the single biggest global distribution platform.
PC TV Mobile
PC Installed Base TV Households Mobile Subscribers
2009 2009
1.2 Billion 1.3 Billion
2009
4.0 Billion
2013 2013
1.6 Billion 1.33 Billion
Broadband Pay TV
Subscribers Subscribers
2009 2009
420 Million 600 Million
2013
5.5 Billion
2013 2013
648 Million 739 Million
13 Source: Gartner, PWC, ITU, IDC, Accenture analysis.

14. Evolution of “the stack”: Shift from hardware to software.
Mobile Device Stack
Early days Today
Comms User Interfaces, App Stores &
Shell & UI
e.g. USB, Speaker, Flash Card
e.g. USB, Speaker, Flash Card
Software User Software
External Interfaces,
External Interfaces,
Application
Middleware
Middleware
Phone
Middleware
Hardware
Platform / OS Core Operating System
Chipsets,
Hardware
Processors, Basebands
1-2 MB of >1 GB of open
closed software software
Hardware Software
14 Source: Accenture analysis.

15. Value in mobile is moving up the stack…
DIRECTIONAL
Cost to Per-unit Break-even
Mobile Handset Stack & Elements build ($M) Revenue ($) # of units
Services and Content $0.1M $1.00 0.1M
Screen, User Interfaces,
$20M $0.20 100M
e.g. USB, Speaker, Flash Card
e.g. USB, Speaker, Flash Card
User Software
Value Flow
Application
External Interfaces,
Middleware
$10M $0.10 100M
Device
Middleware
Core Operating System $1,000M $5.00 200M
Chipsets, Processors,
Radio Basebands
Hardware Software
15 Source: Estimates based on industry interviews; see David Wheeler “Linux Kernel 2.6: It's Worth More!” for estimating the cost of the Linux Kernel.

16. … and is fueling the app store economy.
Size of Catalog (K) – Apple App Store vs. Android Market
2008-2010, as of Q2 2010, by Number of Available Apps at End of Quarter, Excluding Books
2008 2009 2010
211,000
~20,000 monthly
submission
149,000
Android
Market
Oct 22
App Store
July 11
97,000
Day 1
62 Apps
Day 1 74,500
500 Apps
52,610 56,200
~7,000
monthly
35,200 submission
25,300 20,100
13,200 11,500
740 4,400 2,900 5,200
600
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
Source: Apple press releases & earnings calls, Google, AndroLib, PCWorld, Distimo, Accenture analysis. Catalog size for Apples excludes
16 books. All numbers rounded.

17. But: An app is not a business model.
Loyalty and Retention Rates of Mobile Apps Over Time, 2010
100%
100% 100%
100%
90%
90% 90%
90%
80%
80% 80%
80%
70%
70% 70%
70%
Retention Rate
60%
60% 60%
60%
50%
50% 50%
50%
40%
40% 40%
40%
30%
30% 30%
30%
20%
20% 20%
20%
10%
10% News (9.8%) 10%
10% News (9.1%)
Enter-
Games (2.4%)
0%
0% tainment (2%) 0%
0%
0 30 60 90 120 150 180 0
0 30
30 60
60 90
90 120
120 150
150 180
180
0 30 60 90 120 150 180
Days After First Measurement Days After First Measurement
Source: Flurry, Accenture analysis. User retention defined by the number of users who downloaded an application and launched the application at any time in the past,
and also launched the app within the last seven days, e.g. "30 days ago" represents any new user that launched a given app in January and also again within
the last seven days. "60 days ago" represents new users identified in December and also used within last 7 days. Sample based on relevant 5-6 apps per
17 category with at least 120 days of data availability in the Flurry system.

18. 90% dead after 90 days.
iPhone App Retention Android App Retention
As of January 2010, by Application Category As of January 2010, by Application Category
30 Days 90 Days 30 Days 90 Days
News 52% 20% 58% 18%
Social 40% 9% 38% 5%
Networking
Games 34% 10% 34% 10%
Lifestyle 35% 9% 38% 7%
Enter-
33% 4% 42% 16%
tainment
Average
Retention 39% 10% 42% 11%
Rates
18 Source: Flurry, Accenture analysis.

19. Expect the center of gravity to shift to post-load.
ILLUSTRATIVE
Ecosystem Revenue Mix Over Time.
100% Pre-Load Revenue Post-Load Revenue
Streams Streams
0%
“Yesterday” “Today” “Tomorrow”
2000 2010 2015 Onwards
Primary • Licensing • Licensing • Social
Revenue • Software sales • Ads • Ads
Models • Hardware sales • Software sales • Service subscriptions
• Service subscriptions • Hardware sales • Transaction fees
• Service subscriptions • Privacy (User data)
19

20. Silicon
20

21. The one “law” that drives Silicon Valley.
Gordon E. More
Co-founder Intel
21 Source: Intel.

22. Moore’s Law – since ~1965 on the desktop.
22 Source: Intel.

23. Coming your way in mobile as well.
Baseband “Fat Modems” Baseband &
Processors Application Processor
Low power silicon for OS-enablement of light High performance, low
voice/SMS and long apps running on top power application
battery life. of baseband. processors.
23

24. One company at the core of the mobile revolution.
24

25. Massive on-deck computing power.
Mobile Silicon: Process Node, Cores & Clock Speed Over Time
2.5GHz
2GHz
4 cores
Cortex-A15
2 cores
Cortex-A9
1GHz 20nm
833MHz
800MHz 1 core
667MHz 28nm
533MHz Cortex-A8
1 core
Clockspeed: ARM11 32nm
1 core 45nm
Cores: ARM9 65nm
90nm
Node: 130nm
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
25 Source: ARM.

26. The latest mystery: Apple’s A4 (and A5, A6, etc.).
26

27. The ARM Architecture – at the core of Apple’s chips.
Apple SoC Processing Speeds for Single Core, 2007 – 2012
based on DMIPs & Clock Speed
2007 2008 2009 2010 2011e 2012e
iPxx & TV iPxx & TV
ARM Family ARM11 ARM11 Cortex-A8 Cortex-A8 Cortex-A9 Apple Custom
DMIPs/MHz 1.2 1.2 2.0 2.0 2.5 2.5
x x x x x x
Clock speed 400MHz 412MHz 600MHz 1GHz 1.2GHz 2.0GHz
= = = = = =
DMIPs 480 495 1,200 2,000 3,250 5,000
Increase in
processing +3% +142% +67% +63% +54%
speed
+942%
27 Source: ARM, iSuppli, PDAdb.net, Accenture analysis.

28. Google’s Android: One OEM and SemiCo at a time.
Android
Release
C D E F G H
April Sept Oct May Dec H1
2009 2009 2009 2010 2010 2011
Cupcake Donut Éclair FroYo GiBr HoCo
v1.5 v1.6 v2.0 v2.2 v2.3 v3.0
Feature
Device
HTC Samsung Motorola HTC Samsung Motorola
Dream Behold II Droid Nexus One Nexus II Xoom
Chip
Qualcomm Qualcomm TI Qualcomm Samsung- NVIDIA
MSM7201A MSM7201A OMAP 3430 QSD8250 Intrinsity Tegra 2 250,
528MHz 528MHz 600 MHz 998MHz S5PC110 1000MHz
1000MHz
28

29. Cloud
29

30. The cloud: Massive off-deck computing power.
”In addition to making raw computer
power available in a convenient
economical form, a computer utility
would be concerned with almost any
service or function which could in
some way be related to the
processing, storage, collection and
distribution of information.”
Douglas Parkhill, 1966
Douglas Parkhill
“The Challenge of the Computer Utility”, 1966
30

31. What is “The Cloud”?
A style of computing that provides on demand access to a shared set of
highly scalable services.
Cloud Origins Cloud Today Cloud Benefits
• Cost Reduction
Virtualization Lower infrastructure,
One computer • Virtualization and energy, licensing and
Grid abstracted maintenance costs
acting like
many • Computing as a • Speed to Market
utility Reduces time required
to pilot projects
+ • Scale economies
of central supply
• Uses massively-
• Elasticity / Scalability
On-demand capacity and
high business agility
Grid parallel processing
Computing • Geo-distributed • High Performance
Many with massive Computing
redundancy Provides “infinite”
computers computing
acting like one capacity as needed
31

32. Who is building a cloud?
Facebook – Prineville Yahoo – Lockport Google – The Dalles
Apple – Maiden Amazon – Morrow Microsoft – Dublin
32

33. Stuff you can do with the cloud.
• 88B searches / • 500M+ active users
month worldwide • 1.2M photo views /
• 1M+ servers second
• 1 PB of data • 50 PB of
processed / hour uncompressed data
by 2011
• 65 Million users • 90M tweets / day
daily • 12 TB incremental
• 1,000 servers data / day
added / week to
accommodate
traffic
33

34. Stuff you can do with the cloud and your phone.
34

35. What’s
Next
35

36. Jevon’s Paradox
” It is a confusion of ideas to suppose
that the economical use of fuel is
equivalent to diminished consumption.
The very contrary is the truth."
William S. Jevons, 1865
William S. Jevons
From the Book “The Coal Question”
36

37. Silicon: Order of magnitude jump in processing power.
HIGHLY SIMPLIFIED
ARM Family ARM11 Cortex
Shipment Date 2007 2009 2010 2012
Chip ARM1136 Cortex-A8 Cortex-A9 Cortex-A15
DMIPs/MHz 1.2 2.0 2.5 2.5
“Typical” Moore’s Law
x x x x behavior for single
Clock Speed 600MHz 1GHz 2GHz 2.5GHz core processors
= = = =
DMIPs/Core 720 2,000 5,000 6,250
Processing Doubles on average
Speed Increase ~9x every ~21 months
Cores/Cluster 1 1 2 4
x x x x
Clusters 1 1 1 4 Theoretical max
computing power
= = = = increased through
multi-core and
Total Cores 1 1 2 16
clustering
Total DMIPS 720 2000 10,000 100,000
Processing
~138x
Speed Increase
37 Source: Calculations based on ARM marketing material.

38. Cloud: 107 = 10M machines, roughly 10x of today.
38 Source: Google.

39. Industrialization of the mobile cloud...
Cloud Device
Today
HTTP
(custom libraries)
Tomorrow
SDKs
39

40. … will bring massive off-deck computing to mobile.
40 Source: Amazon press release, December 2010.

41. Plenty of cash.
Cash on Hand for Select Tech Titans
Cash and Cash Equivalents, as of 1/26/2011
44
39
35
27
29 Total of
22 226B
11
10
7
6
41

42. As computing gets cheaper…
U.S. Asset Prices, 1945 - 2008
Normalized, 1995 = 100
105
Normalized Price: 1995 = 100 (log)
Computers and
Peripheral Equipment
104
103
Transportation
102 Equipment
Other Equipment
Industrial Equipment
10
1950 1960 1970 1980 1990 2000
42 Source: The Business Impact of IT, based on U.S. Bureau of Economic Analysis data.

43. … companies consume more of it.
U.S. IT Investment, 1970 - 2008
Nominal Annual Investment & Investment per Employee
3,500 350B
3,000 300B
2,500 250B
2,000 200B
1,500 150B
IT Investment /
Employee
1,000 100B
500 Annual
50B
Investment
0 0
1970 1975 1980 1985 1990 1995 2000 2005 2010
43 Source: The Business Impact of IT, based on U.S. Bureau of Economic Analysis data.

44. Think again…
”People tend to overestimate
what can be done in one year
and to underestimate what can
be done in five to ten years.”
J. C. R. Licklider, 1965
J. C. R. Licklider
“Grandfather of the Internet”
44

45. 45