SlideShare a Scribd company logo
1 of 77
MOBILE IS
EATING THE
WORLD
Benedict Evans
December 2016
@BenedictEvans
Mobile deployment
3
We’re halfway to connecting everyone
5.5bn people over 14 years old, ~5bn with mobile phones, ~2.5bn smartphones
* SIM penetration is >100%. Source: World Bank, TMW, CNNIC, a16z
Developed markets China LatamDeveloped markets China Latam MENA India Sub-Saharan Africa OtherDeveloped markets China Latam MENA South Asia Sub-Saharan Africa
SE Asia
Other
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Global population and mobile penetration*, December 2016
Not mobile
Mobile
Smart
Developed markets China Latam MENA South Asia Sub-Saharan Africa
SE Asia
Other
4
The mobile S-Curve is passing the PC S-Curve
Smartphones are moving past the PC and heading for 5bn+ users
Source: Apple, Google, a16z, GSMA, ITU, World Bank
0
1
2
3
4
5
6
1985 1990 1995 2000 2005 2010 2015
Global population (bn)
Mobile users
iOS & Android
smartphones
PCs
Adults
5
Mobile S-Curve moving from creation to deployment
As we pass 2.5bn smartphones in use, the issues that matter are changing
Creation Deployment
Crazy idea
Scaling
Frenzy
Maturity
Mobile
PCs
AR?
6
What does mobile deployment mean?
Creation
Platform wars
The latest handset!
Technology arguments
Millions of users
“Will this work? Who will win?”
Deployment
The big winners are clear
Steady, incremental improvement
Commodity technology (mostly)
Billions of users
“What can we build with his?”
As we pass 2.5bn smartphones in use, the issues that matter are changing
7
“What can we build with this?”
A new kind of scale for technology, and new
kinds of computer.
New ways to change other industries.
Two of those industries: ecommerce & cars.
A new kind of scale
9
0
100
200
300
400
500
1990 1995 2000 2005 2010 2015
Annual revenue ($bn, real terms)
Wintel
GAFA
Changing of the guard
Google, Apple, Facebook & Amazon (GAFA) are 3x the scale of Wintel
Source: Bloomberg, a16z
10
The Wintel explosion in 1990s – 14x growth
The golden age for Intel and Microsoft saw 14x revenue growth and dominance of tech
Source: Bloomberg, a16z
0
10
20
30
40
50
1990 1995 2000
Wintel annual revenue ($bn, real terms)
11
… But GAFA has 10x the scale
Fundamental change in what it means to be a leading tech company
Source: Bloomberg, a16z
0
100
200
300
400
500
1990 /
2005
1995 /
2010
2000 /
2015
Annual revenue ($bn, real terms)
Wintel 1990-2000
GAFA 2005-2015
12
From important to dominant
When tech was scared of Microsoft, it was a pretty small company
Source: Bloomberg, a16z
GAFA
Wintel
All other
0
10
20
30
40
50
60
70
80
90
100
1995 2016
Top 20 US listed companies by market cap (indexed)
GAFA
Wintel
All other
13
That scale means much more investment
Annual capex has increased from $1bn to $34bn since 2000
Source: Bloomberg, a16z
0
5
10
15
20
25
30
35
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Annual capex ($bn real)
Amazon
Facebook
Apple
Google
Microsoft
14
And 10x more people
(Though this chart includes a lot of warehouse and retail staff)
Source: Bloomberg
0
100
200
300
400
500
600
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Permanent employees (000)
Amazon
Facebook
Apple
Google
Microsoft
15
Today’s tech giants have a totally different
character of scale to Wintel or IBM before
them.
Giants in the economy, not just in tech.
And (at least) four competing giants, not one.
16
So Apple is a global
top 10 retailer
500 stores, ~$25bn revenue make it a
top 20 retailer.
$53bn including the online store…
Making Apple the 10th largest global
retailer by revenue.
Not bad for a marketing operation.
Retail ran at 12% of revenue before Apple stopped disclosing it: direct is 25%. Source: Apple, a16z
17
0
2
4
6
8
10
12
14
Disney NBCU CBS Netflix Turner Amazon HBO
Annual TV production budgets, 2015 ($bn)
For Amazon, content is just a strategic lever
Original content is core for Netflix, but just another way to sell Prime for Amazon.
Combined, they’re 16% of US production budgets
Source: IHS
18
Is Netflix a threat?
“Is the Albanian army going to
take over the world?”
- Jeffery Bewkes, CEO Time Warner, 2010
19
Making your own chips: Intel’s whole
business becomes a side-project.
Google: ‘TPU’ machine learning FPGA.
Amazon: ‘Annapurna’ networking ASIC.
Apple: A10 is the fastest mobile SoC – plus
custom chips for TouchID, Airpods, etc.
20
Scale of 5bn
mobile users
Vast supply chain of
commoditised
components &
platforms
Scale of the
winners
GAFA have much
greater global scale
than Wintel or IBM
ever had
New ways to
compete
Custom hardware
Retail & distribution
Content
(Cars)
Building on scale
Huge companies building on an ecosystem that commoditises almost everything before
In the mean time,
the new thing –
machine learning
22
“Is there a dog in
this picture?”
After 50 years of work, computer vision
systems got this right 72% of the time.
A whole class of similar problems –
easy for people and hard/impossible for
computers.
Consensus: decades more work.
Then, in 2013, machine learning.
Source: Imagenet
23
The arrival of machine learning
Performance on decades-old challenges has been transformed
Image
recognition
Speech
recognition
28% error rate 7%
26% error rate 4%
24
Machine learning: data instead of rules
Fundamental shift in AI from trying to codify perception to massive data and applied maths
Old: rules
Build systems to look for ears,
noses, legs, fur…
Hire linguists and write grammar
rules.
Try to codify how human
intelligence works (though we don’t
really know).
Machine learning: data
10k pictures labelled ‘dog’ and 10k
labelled ‘no dog’, and let a neural
network work it out.
Learning rather than rules.
Possible now because we have
~1m times more computing & data.
25
“Every time I fire a linguist, the
performance of the speech
recognizer goes up.”
- Frederick Jelinek
26
Since we’re analysing data, not looking for
dogs, this works for many other data sets.
“Which customers are about to churn?”
“Will that car let me merge?”
“Is anything weird happening on the
network?”
27
Alpha Go – board
games and air-con
Google took the machine learning
platform that won Go and applied it to
data center cooling.
Achieved a 15% energy saving.
Building systems that find patterns in
data.
28
“We will move from mobile-first
to AI-first.”
- Sundar Pichai, 2016
29
Data
New analysis
on almost any
data set
Interaction
Images, voice,
devices, new
UX models
Images
ML can read
images as
easily as text?
Surprises
Things that
don’t look like
ML use cases
ML use cases: how many patterns, how much data?
Things that only people could do, but at a scale that people could never do
30
At the top and
bottom of the stack
Training models at vast scale in the
cloud…
But once trained, many models could
run on very small/cheap devices.
A $10 widget could run on a cheap DSP
with a commodity camera, looking for
just one thing – people, leaks, cars…
31
0.0
2.5
5.0
7.5
10.0
12.5
2014 2015 2016e 2017e
Global image sensing market size ($bn)
Other
Cars
PC & Tablet
Camera
Mobile
Cameras in everything, except in cameras
Imaging, combined with machine learning, may be becoming a universal sensor
Source: Sony
32
What does it mean if computers can read
images in the way they’ve always been able
to read text?
33
Complexity and abstraction
Arms race of science, engineering and product – a new front for GAFA competition
Surge in complexity
Building the engineering on
top of the science
‘How to build the right ML for
this problem’ is becoming a
new CS field
Even as the science still has
far to go
Rush to abstraction
Google, Amazon, Microsoft,
IBM, etc. rushing to build ML
platforms in the cloud
Core, cutting-edge ML tech
available to rent
34
The machine learning S-Curve
Still in the very early days of creation, with lots and lots of low-hanging fruit
Creation Deployment
Crazy idea
Scaling
Frenzy
Maturity
Machine
Learning
New kinds of
computer
36
0
200
400
600
800
1,000
1,200
1,400
1,600
Jun-13 Jun-14 Jun-15 Jun-16
Billion minutes online, USA
Mobile browser
Mobile app
Desktop
Obvious but boring: apps have won mobile
Smartphone apps are now close to 60% of all time spent online in the USA
Source: Comscore
37
0
200
400
600
800
1,000
1,200
1,400
1,600
Jun-13 Jun-14 Jun-15 Jun-16
Billion minutes online, USA
Mobile browser
Facebook app
Mobile app
Desktop
… with increasing concentration
Facebook has 15-20% of mobile time (making it the largest mobile web browser)
Source: Comscore, a16z
38
More important: a new generation of computing
After 30 years, we shift to a new and more sophisticated kind of computer
Intensely
personal
Always with
you
Imaging as
primary input
Touch, tilt,
location,
microphone
App stores
Ignore battery
& bandwidth
1000x faster
Cloud &
machine
learning
And yet much
easier to
use…
39
Both more
sophisticated
and easier to
use
New sensors,
inputs and
interfaces
New
creation: live,
social,
broadcast,
touch,
imaging
New computing model…
40
Both more
sophisticated
and easier to
use
New sensors,
inputs and
interfaces,
machine
learning
New
creation: live,
social,
broadcast,
touch,
imaging
New computing model, and new creation models
Leave the PC behind, presume mobile-only and mobile-native, not mobile-first
41
How many old
assumptions does
live video break?
Assume high quality camera, fast
CPU/GPU for encoding and effects,
touch, always on, always in your pocket.
Assume unlimited bandwidth & battery.
Assume 1bn high-end smartphones on
earth - there can be 50 companies trying
this.
And turn the whole phone into the
camera.
Source: Houseparty, Yixia
42
Direct interaction -
unbundling the app
from the phone
No buttons, apps or intermediate steps –
just use it.
Hardware sensors as unbundled apps -
deep links within apps.
Apps moved to a new context.
And of course all built with commodity
components from the smartphone
supply chain.
43
Sensors
Smartphone, or
cheap smartphone
components
Machine learning
Speech recognition,
computer vision
Action
Lighting everything up with machine learning
Smartphone supply chain enables this – machine learning makes it useful
44
Sensor
Commodity
smartphone
components
Machine learning
Speech recognition,
computer vision
Action
(If the machine can answer)
Still need to narrow the domain to things you can actually do, and tell your users
“I’m sorry Dave…”
45
Changing friction and changing choices
Massive proliferation of UX models. All about removing friction. And choices…
Removing questions and friction
Do you have the printer driver?
What’s your password?
Where did you save that file?
…
What soap do you want to buy?
With which app?
But also choices
Questions are also choices
Huge strategic incentive for big
platforms to be the ones to answer
the question for you
“Who owns the customer?”
46
A computer should “never ask
the user for any information
that it can auto-detect, copy,
or deduce.”
- Eric Raymond
47
From direct, physical interaction with data…
Source: IBM47
48
To direct, physical interaction with data
Source: Magic Leap48
Ecommerce
50
Retailers as newspapers
“History doesn’t repeat, but sometimes it rhymes”
Fixed cost base with falling revenue.
Shift from paid to freemium, unbundling,
different skills.
The distribution advantage moves.
New medium means different
consumption – you buy different things,
not just in different places.
51
Break up old
bundles and
aggregators
New
aggregators
emerge
And they shape
consumption in
different ways
Everything the internet did to media will
happen to retail
52
So far, mostly, what you already know you want
Ecommerce is much better at logistics than demand generation
*Ex. food service, gasoline, auto. Source: US Census Bureau, Amazon, a16z
0%
2%
4%
6%
8%
10%
12%
14%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Ecommerce as % US retail revenue*
Amazon
US ecommerce
53
0
5
10
15
20
25
30
Walmart Macy's TJX Amazon Gap Kohl's Target L Brands Nordstrom JC Penney
Top 10 US apparel retailers by revenue, 2015 ($bn)
People will buy online if they know about it
Even clothes – especially with free returns (a new logistics arbitrage model)
Source: Bloomberg Intelligence
54
But how do you know what to buy?
Amazon is Google for products, but we don’t
have a Facebook or Buzzfeed – we don’t
have suggestion and discovery.
The internet lets you buy, but it doesn’t let
you shop.
55
Digital demand generation: step 1
$500bn spent annually on ads (a third on digital) – and another $500bn on marketing
Source: Zenith, Google, a16z
0
100
200
300
400
500
600
1980 1985 1990 1995 2000 2005 2010 2015
Global ad spending ($bn, real terms)
Other internet
Google
Print
Other
TV
56
“Your margin is my
opportunity”
- Jeff Bezos
(So what is advertising?)
57
The channel is
the product
Soap as a Service.
New purchasing journeys mean new
kinds of decisions.
If you change how things are bought,
you change what gets bought.
58
Train your AI
on 10 years of
Elle / Dwell /
NYT Recipes
Take a photo
of your outfit /
living room /
kitchen
counter
“What would I
like that I’ve
never seen?”
Scalable
curation
Scaling curation with machine learning?
When can we infer taste and preference, without a purchase history or a salesperson?
59
Data works its way through retailing
Data has affected every part of retail except demand itself, so far
ERP
1990s
Supply
chain &
logistics
Digital metrics
2000s
Advertising
Data
Social
Machine learning
2010s
Demand
60
0
100
200
300
400
500
600
1980 1985 1990 1995 2000 2005 2010 2015
Annual revenue ($bn, real terms)
Amazon
Walmart
Retail S Curves
Walmart used new technologies to change retail – the same will happen again
Source: Amazon, Bloomberg
61
Sectors up-ended by software so far…
Source: a16z
0
200
400
600
800
1,000
1,200
1,400
Car sales Advertising Consumer Media Mobile phones Pay TV Online
advertising
TV production
Global revenue, 2015 ($bn)
62
And in the future
Source: a16z
0
5,000
10,000
15,000
20,000
25,000
Retail Car sales Advertising Consumer Media Mobile phones Pay TV Online
advertising
Global revenue, 2015 ($bn)
Cars
64
Cars as phones
“History doesn’t repeat, but sometimes it rhymes”
Today’s tech moat disappears.
Key components become commodities.
Value moves to software.
Network effects move away from
hardware.
‘Phone’ means something totally different.
65
Two paths for disrupting cars
Electric
Happening right now.
Removing the engine &
transmission destabilizes the car
industry and its suppliers.
Doesn’t change how cars are used
(much).
Autonomy
5-10 years? Longer?
Many challenges to resolve.
Changes where the value is.
Accelerates on-demand.
Changes what cars are.
Changes cities as much as cars
changed cities.
The growth of electric and autonomy are both, separately, changing what cars are
66
0
50
100
150
200
250
300
0
200
400
600
800
1,000
1,200
2008 2009 2010 2011 2012 2013 2014 2015
Lithium-ion battery economics
Battery $/KwH
Energy Density
(watt
hours/litre)
Going electric: riding the battery cost curve
Growth of electric cars is a function of battery economics starting to work
Source: US Department of Energy
67
Electric unbundles cars
Radical simplification
Complex, proprietary gasoline
engines & transmissions disappear
Replaced by simple, commodity
batteries & motors
10x fewer moving parts
Whole basis of competition
changes
New bases for competition
Scale, design, distribution remain,
for those who can navigate the
change
But value moves to software -
entirely new skill set
And then with autonomy, new
layers of value built on top
Electric makes cars much simpler and turns many elements into commodities
68
Precedent – unbundling phones
Once, you had to have co-invented GSM to make phones. Now, all components unbundled
2001, Nokia
“Manufacturing is a core
competency & competitive
advantage”
75% of Nokia phones made
in 8 Nokia factories
2016, Apple
The iPhone has 189 suppliers
with 789 locations
None owned by Apple
69
(Unfair but relevant – capex scale)
Leading tech companies now spend as much on capex as car OEMs
Apple has $237bn gross cash, while Google has $73bn
Source: Bloomberg
0.0 2.5 5.0 7.5 10.0 12.5 15.0
VW
Apple
Toyota
Google
Daimler
GM
Ford
BMW
Honda
Nissan
Tesla
2015 capex ($bn)
70
Meanwhile, progress to autonomy
Steady, incremental progress, solving one tech challenge at a time
Old-fashioned
cruise control
Smart cruise
control &
lane keeping
Automatic, but
driver needs to be
ready to take over
Driver only needed
in exceptions, bad
weather etc
No controls, no
driver, no cabin
1958 Now 5-10 years?
Source: VDA, a16z
1 2 3 4 5
Level Level
71
Physical scale
Electric changes
barriers to entry…
But bashing metal is
still about scale
Data
network effects
Driving behaviour
data to feed ML
HD 3D maps
How many winners?
On-demand
network effects
Virtuous circle:
density of cars +
density of riders
Uber + Lyft
Where is the competitive moat in cars?
Once batteries and sensors are commodities, where are the strategic levers? Still lots of
unanswered questions
72
Self-driving car =
horseless carriage
First, we make the tech fit the old
patterns.
Then, we change to take advantage of
what’s new.
And removing the driver changes even
more than removing the horses.
Source: Daimler
73
The obvious impact…
It’s easy to talk about the obvious impacts of electric and autonomy – oil and safety
Half of oil
production
1.25m
annual
road
deaths
74
But the second-order effects will be much bigger
Electric changes the whole car ecosystem
Half of oil
production
1.25m
annual
road
deaths
Service & repair with 10x fewer moving parts
(And what else gets sold in 150k US gas
stations?)
Power
generation
& storage
Taxes?
Machine
tools
75
But the second-order effects will be much bigger
Electric changes the whole car ecosystem - autonomy changes what cars and cities are
Half of oil
production
1.25m
annual
road
deaths
Service & repair with 10x fewer moving parts
(And what else gets sold in 150k US gas
stations?)
Parking and congestion
Housing? Retail?
Logistics? Ownership?
Real Estate? Insurance?
Power
generation
& storage
Taxes?
Machine
tools
76
… And the biggest changes are unknowable
“Going to the opera in the year 2000”
Source: Albert Robida76
Thank you

More Related Content

What's hot

Platform Strategy and Digital Ecosystems
Platform Strategy and Digital EcosystemsPlatform Strategy and Digital Ecosystems
Platform Strategy and Digital EcosystemsApigee | Google Cloud
 
Network Effects
Network EffectsNetwork Effects
Network Effectsa16z
 
STATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul Choudary
STATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul ChoudarySTATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul Choudary
STATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul ChoudarySangeet Paul Choudary
 
Boards AI Leadership. The future of Corporate Governance.
Boards AI Leadership. The future of Corporate Governance. Boards AI Leadership. The future of Corporate Governance.
Boards AI Leadership. The future of Corporate Governance. Liselotte Hägertz Engstam
 
Platforms: How Change in Industry is Driving Change in Strategy
Platforms: How Change in Industry is Driving Change in StrategyPlatforms: How Change in Industry is Driving Change in Strategy
Platforms: How Change in Industry is Driving Change in StrategyMarshall Van Alstyne
 
Solve for X with AI: a VC view of the Machine Learning & AI landscape
Solve for X with AI: a VC view of the Machine Learning & AI landscapeSolve for X with AI: a VC view of the Machine Learning & AI landscape
Solve for X with AI: a VC view of the Machine Learning & AI landscapeEd Fernandez
 
Inside The Tornado by Geoffrey Moore
Inside The Tornado by Geoffrey MooreInside The Tornado by Geoffrey Moore
Inside The Tornado by Geoffrey MooreSameer Mathur
 
Platform Strategy: Openness, Innovation & Control
Platform Strategy: Openness, Innovation & ControlPlatform Strategy: Openness, Innovation & Control
Platform Strategy: Openness, Innovation & ControlMarshall Van Alstyne
 
Amplitude Series C Deck (Abridged) August 2017
Amplitude Series C Deck (Abridged) August 2017Amplitude Series C Deck (Abridged) August 2017
Amplitude Series C Deck (Abridged) August 2017Amplitude
 
Top Rumors About Apple March 21 Big Event
Top Rumors About Apple March 21 Big EventTop Rumors About Apple March 21 Big Event
Top Rumors About Apple March 21 Big EventChromeInfo Technologies
 
How To Build The Ultimate Go-To-Market Strategy For Your App
How To Build The Ultimate Go-To-Market Strategy For Your AppHow To Build The Ultimate Go-To-Market Strategy For Your App
How To Build The Ultimate Go-To-Market Strategy For Your AppClearbridge Mobile
 
Zone to Win: Organizing to Compete in the Age of Disruption
Zone to Win: Organizing to Compete in the Age of DisruptionZone to Win: Organizing to Compete in the Age of Disruption
Zone to Win: Organizing to Compete in the Age of DisruptionGeoffrey Moore
 
How to Succeed Across Channels with Omnichannel Marketing
How to Succeed Across Channels with  Omnichannel MarketingHow to Succeed Across Channels with  Omnichannel Marketing
How to Succeed Across Channels with Omnichannel MarketingTinuiti
 
Why the Next Big Thing is eSports
Why the Next Big Thing is eSports Why the Next Big Thing is eSports
Why the Next Big Thing is eSports Bob Snyder
 
The Network Effects Bible
The Network Effects BibleThe Network Effects Bible
The Network Effects BibleNFX
 
Uber Pitch Deck Makeover by SlideTeam
Uber Pitch Deck Makeover by SlideTeamUber Pitch Deck Makeover by SlideTeam
Uber Pitch Deck Makeover by SlideTeamSlideTeam
 
Platform Dynamics - The rise and dominance of Platforms
Platform Dynamics - The rise and dominance of PlatformsPlatform Dynamics - The rise and dominance of Platforms
Platform Dynamics - The rise and dominance of PlatformsJason Dojc
 

What's hot (20)

Platform Strategy and Digital Ecosystems
Platform Strategy and Digital EcosystemsPlatform Strategy and Digital Ecosystems
Platform Strategy and Digital Ecosystems
 
Network Effects
Network EffectsNetwork Effects
Network Effects
 
STATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul Choudary
STATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul ChoudarySTATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul Choudary
STATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul Choudary
 
Boards AI Leadership. The future of Corporate Governance.
Boards AI Leadership. The future of Corporate Governance. Boards AI Leadership. The future of Corporate Governance.
Boards AI Leadership. The future of Corporate Governance.
 
Platforms: How Change in Industry is Driving Change in Strategy
Platforms: How Change in Industry is Driving Change in StrategyPlatforms: How Change in Industry is Driving Change in Strategy
Platforms: How Change in Industry is Driving Change in Strategy
 
Solve for X with AI: a VC view of the Machine Learning & AI landscape
Solve for X with AI: a VC view of the Machine Learning & AI landscapeSolve for X with AI: a VC view of the Machine Learning & AI landscape
Solve for X with AI: a VC view of the Machine Learning & AI landscape
 
Inside The Tornado by Geoffrey Moore
Inside The Tornado by Geoffrey MooreInside The Tornado by Geoffrey Moore
Inside The Tornado by Geoffrey Moore
 
Platform Strategy: Openness, Innovation & Control
Platform Strategy: Openness, Innovation & ControlPlatform Strategy: Openness, Innovation & Control
Platform Strategy: Openness, Innovation & Control
 
Amazon Pitch
Amazon PitchAmazon Pitch
Amazon Pitch
 
Amplitude Series C Deck (Abridged) August 2017
Amplitude Series C Deck (Abridged) August 2017Amplitude Series C Deck (Abridged) August 2017
Amplitude Series C Deck (Abridged) August 2017
 
Top Rumors About Apple March 21 Big Event
Top Rumors About Apple March 21 Big EventTop Rumors About Apple March 21 Big Event
Top Rumors About Apple March 21 Big Event
 
How To Build The Ultimate Go-To-Market Strategy For Your App
How To Build The Ultimate Go-To-Market Strategy For Your AppHow To Build The Ultimate Go-To-Market Strategy For Your App
How To Build The Ultimate Go-To-Market Strategy For Your App
 
Zone to Win: Organizing to Compete in the Age of Disruption
Zone to Win: Organizing to Compete in the Age of DisruptionZone to Win: Organizing to Compete in the Age of Disruption
Zone to Win: Organizing to Compete in the Age of Disruption
 
Generative AI.pptx
Generative AI.pptxGenerative AI.pptx
Generative AI.pptx
 
How to Succeed Across Channels with Omnichannel Marketing
How to Succeed Across Channels with  Omnichannel MarketingHow to Succeed Across Channels with  Omnichannel Marketing
How to Succeed Across Channels with Omnichannel Marketing
 
Why the Next Big Thing is eSports
Why the Next Big Thing is eSports Why the Next Big Thing is eSports
Why the Next Big Thing is eSports
 
The Network Effects Bible
The Network Effects BibleThe Network Effects Bible
The Network Effects Bible
 
Platforms Transitions
Platforms TransitionsPlatforms Transitions
Platforms Transitions
 
Uber Pitch Deck Makeover by SlideTeam
Uber Pitch Deck Makeover by SlideTeamUber Pitch Deck Makeover by SlideTeam
Uber Pitch Deck Makeover by SlideTeam
 
Platform Dynamics - The rise and dominance of Platforms
Platform Dynamics - The rise and dominance of PlatformsPlatform Dynamics - The rise and dominance of Platforms
Platform Dynamics - The rise and dominance of Platforms
 

Viewers also liked

Go-to-Market Best Practices for Startups
Go-to-Market Best Practices for StartupsGo-to-Market Best Practices for Startups
Go-to-Market Best Practices for Startupsa16z
 
Software is Eating Bio
Software is Eating BioSoftware is Eating Bio
Software is Eating Bioa16z
 
Mobile Is Eating the World (2014)
Mobile Is Eating the World (2014)Mobile Is Eating the World (2014)
Mobile Is Eating the World (2014)a16z
 
Mobile Is Eating the World (2016)
Mobile Is Eating the World (2016)Mobile Is Eating the World (2016)
Mobile Is Eating the World (2016)a16z
 
Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)
Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)
Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)Fabien Grenet
 
U.S. Technology Funding -- What's Going On?
U.S. Technology Funding -- What's Going On? U.S. Technology Funding -- What's Going On?
U.S. Technology Funding -- What's Going On? a16z
 
Mobile Is Eating the World (2015)
Mobile Is Eating the World (2015)Mobile Is Eating the World (2015)
Mobile Is Eating the World (2015)a16z
 
HubSpot Investor Marketing Deck 4q15
HubSpot Investor Marketing Deck 4q15HubSpot Investor Marketing Deck 4q15
HubSpot Investor Marketing Deck 4q15IR_HubSpot
 
Intrapreneurship Through Co-Creation with Startups
Intrapreneurship Through Co-Creation with StartupsIntrapreneurship Through Co-Creation with Startups
Intrapreneurship Through Co-Creation with StartupsCorporate Startup Summit
 
Aqualink Week 1 H4D Stanford 2016
Aqualink Week 1 H4D Stanford 2016Aqualink Week 1 H4D Stanford 2016
Aqualink Week 1 H4D Stanford 2016Stanford University
 
Key challenges for Startups and how to address them
Key challenges for Startups and how to address themKey challenges for Startups and how to address them
Key challenges for Startups and how to address themClaro Partners Inc.
 
Narrative Mind week 1 H4D Stanford 2016
Narrative Mind week 1 H4D Stanford 2016Narrative Mind week 1 H4D Stanford 2016
Narrative Mind week 1 H4D Stanford 2016Stanford University
 
H4D Lecture 1.5 beneficiaries Stanford 2016
H4D Lecture 1.5 beneficiaries Stanford 2016H4D Lecture 1.5 beneficiaries Stanford 2016
H4D Lecture 1.5 beneficiaries Stanford 2016Stanford University
 
Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016
Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016
Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016Stanford University
 
Capella Week 1 H4D Stanford 2016
Capella Week 1 H4D Stanford 2016Capella Week 1 H4D Stanford 2016
Capella Week 1 H4D Stanford 2016Stanford University
 
The Platform Design Tookit 2.0 Draft Launch - Executive Summary
The Platform Design Tookit 2.0 Draft Launch  - Executive SummaryThe Platform Design Tookit 2.0 Draft Launch  - Executive Summary
The Platform Design Tookit 2.0 Draft Launch - Executive SummarySimone Cicero
 
Hubspot Case Analysis
Hubspot Case AnalysisHubspot Case Analysis
Hubspot Case AnalysisYemi Adejumo
 
Hubspot Case Presentation - First Place
Hubspot Case Presentation - First PlaceHubspot Case Presentation - First Place
Hubspot Case Presentation - First PlaceConnor Dismer
 

Viewers also liked (20)

Go-to-Market Best Practices for Startups
Go-to-Market Best Practices for StartupsGo-to-Market Best Practices for Startups
Go-to-Market Best Practices for Startups
 
Software is Eating Bio
Software is Eating BioSoftware is Eating Bio
Software is Eating Bio
 
Mobile Is Eating the World (2014)
Mobile Is Eating the World (2014)Mobile Is Eating the World (2014)
Mobile Is Eating the World (2014)
 
Mobile Is Eating the World (2016)
Mobile Is Eating the World (2016)Mobile Is Eating the World (2016)
Mobile Is Eating the World (2016)
 
Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)
Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)
Y Combinator Startup Class #1 : Ideas, Products, Teams and Execution (Part 1)
 
U.S. Technology Funding -- What's Going On?
U.S. Technology Funding -- What's Going On? U.S. Technology Funding -- What's Going On?
U.S. Technology Funding -- What's Going On?
 
Mobile Is Eating the World (2015)
Mobile Is Eating the World (2015)Mobile Is Eating the World (2015)
Mobile Is Eating the World (2015)
 
HubSpot Investor Marketing Deck 4q15
HubSpot Investor Marketing Deck 4q15HubSpot Investor Marketing Deck 4q15
HubSpot Investor Marketing Deck 4q15
 
Intrapreneurship Through Co-Creation with Startups
Intrapreneurship Through Co-Creation with StartupsIntrapreneurship Through Co-Creation with Startups
Intrapreneurship Through Co-Creation with Startups
 
Aqualink Week 1 H4D Stanford 2016
Aqualink Week 1 H4D Stanford 2016Aqualink Week 1 H4D Stanford 2016
Aqualink Week 1 H4D Stanford 2016
 
Key challenges for Startups and how to address them
Key challenges for Startups and how to address themKey challenges for Startups and how to address them
Key challenges for Startups and how to address them
 
Skynet Week 1 H4D Stanford 2016
Skynet Week 1 H4D Stanford 2016Skynet Week 1 H4D Stanford 2016
Skynet Week 1 H4D Stanford 2016
 
Narrative Mind week 1 H4D Stanford 2016
Narrative Mind week 1 H4D Stanford 2016Narrative Mind week 1 H4D Stanford 2016
Narrative Mind week 1 H4D Stanford 2016
 
H4D Lecture 1.5 beneficiaries Stanford 2016
H4D Lecture 1.5 beneficiaries Stanford 2016H4D Lecture 1.5 beneficiaries Stanford 2016
H4D Lecture 1.5 beneficiaries Stanford 2016
 
Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016
Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016
Live Tactical Threat Toolkit (LTTT) Week 1, H4D, Stanford 2016
 
H4D Lecture 1 stanford 2016
H4D Lecture 1 stanford 2016H4D Lecture 1 stanford 2016
H4D Lecture 1 stanford 2016
 
Capella Week 1 H4D Stanford 2016
Capella Week 1 H4D Stanford 2016Capella Week 1 H4D Stanford 2016
Capella Week 1 H4D Stanford 2016
 
The Platform Design Tookit 2.0 Draft Launch - Executive Summary
The Platform Design Tookit 2.0 Draft Launch  - Executive SummaryThe Platform Design Tookit 2.0 Draft Launch  - Executive Summary
The Platform Design Tookit 2.0 Draft Launch - Executive Summary
 
Hubspot Case Analysis
Hubspot Case AnalysisHubspot Case Analysis
Hubspot Case Analysis
 
Hubspot Case Presentation - First Place
Hubspot Case Presentation - First PlaceHubspot Case Presentation - First Place
Hubspot Case Presentation - First Place
 

Similar to Mobile Is Eating the World, 2016-2017

Bill Barney's Key Note at the Wan Summit
Bill Barney's Key Note at the Wan Summit Bill Barney's Key Note at the Wan Summit
Bill Barney's Key Note at the Wan Summit William Barney
 
Emerging Technology, Shiny Objects & The Future of Media - iSummit - Fred Steube
Emerging Technology, Shiny Objects & The Future of Media - iSummit - Fred SteubeEmerging Technology, Shiny Objects & The Future of Media - iSummit - Fred Steube
Emerging Technology, Shiny Objects & The Future of Media - iSummit - Fred SteubeFred Steube
 
The Next Big Thing in Technology: What innovations will have the biggest impa...
The Next Big Thing in Technology: What innovations will have the biggest impa...The Next Big Thing in Technology: What innovations will have the biggest impa...
The Next Big Thing in Technology: What innovations will have the biggest impa...Career Communications Group
 
OUTDATED April and May 2016 Presentation Slides
OUTDATED April and May 2016 Presentation SlidesOUTDATED April and May 2016 Presentation Slides
OUTDATED April and May 2016 Presentation SlidesClinton den Heyer
 
State of Mobile Computing
State of Mobile Computing State of Mobile Computing
State of Mobile Computing Alex Ferrara
 
Knowledge sharing-april252011
Knowledge sharing-april252011Knowledge sharing-april252011
Knowledge sharing-april252011Kamal Kannan
 
Mobile 2009, 2010 and beyond
Mobile 2009, 2010 and beyondMobile 2009, 2010 and beyond
Mobile 2009, 2010 and beyondJennifer Wilson
 
Mobile Trends 2018 by Omelyanenko Nataliia
Mobile Trends 2018 by Omelyanenko NataliiaMobile Trends 2018 by Omelyanenko Nataliia
Mobile Trends 2018 by Omelyanenko NataliiaNatali Omelyanenko
 
2015 Global Trend Forecast (Technology, Media & Telecoms)
2015 Global Trend Forecast (Technology, Media & Telecoms)2015 Global Trend Forecast (Technology, Media & Telecoms)
2015 Global Trend Forecast (Technology, Media & Telecoms)CM Research
 
The Rise of Platforms - And What It Means for Business
The Rise of Platforms - And What It Means for BusinessThe Rise of Platforms - And What It Means for Business
The Rise of Platforms - And What It Means for BusinessMarshall Van Alstyne
 
Mobile Revolution In The Digital Era
Mobile Revolution In The Digital EraMobile Revolution In The Digital Era
Mobile Revolution In The Digital EraJose Lima
 
PLNOG 8: Don Dodge - Platform shift to Mobile
PLNOG 8: Don Dodge - Platform shift to Mobile PLNOG 8: Don Dodge - Platform shift to Mobile
PLNOG 8: Don Dodge - Platform shift to Mobile PROIDEA
 
Darren Evans: Digital Trends
Darren Evans: Digital TrendsDarren Evans: Digital Trends
Darren Evans: Digital TrendsUX People
 
IoT Breakfast Briefing
IoT Breakfast BriefingIoT Breakfast Briefing
IoT Breakfast BriefingSomo
 
Aras PLM and the Google Mobile Explosion
Aras PLM and the Google Mobile ExplosionAras PLM and the Google Mobile Explosion
Aras PLM and the Google Mobile ExplosionAras
 

Similar to Mobile Is Eating the World, 2016-2017 (20)

Cloud Revolution Conitnues
Cloud Revolution ConitnuesCloud Revolution Conitnues
Cloud Revolution Conitnues
 
Bill Barney's Key Note at the Wan Summit
Bill Barney's Key Note at the Wan Summit Bill Barney's Key Note at the Wan Summit
Bill Barney's Key Note at the Wan Summit
 
Emerging Technology, Shiny Objects & The Future of Media - iSummit - Fred Steube
Emerging Technology, Shiny Objects & The Future of Media - iSummit - Fred SteubeEmerging Technology, Shiny Objects & The Future of Media - iSummit - Fred Steube
Emerging Technology, Shiny Objects & The Future of Media - iSummit - Fred Steube
 
The Next Big Thing in Technology: What innovations will have the biggest impa...
The Next Big Thing in Technology: What innovations will have the biggest impa...The Next Big Thing in Technology: What innovations will have the biggest impa...
The Next Big Thing in Technology: What innovations will have the biggest impa...
 
Idc predictions 2015
Idc predictions 2015Idc predictions 2015
Idc predictions 2015
 
what_is_next _now_ppt
what_is_next _now_pptwhat_is_next _now_ppt
what_is_next _now_ppt
 
OUTDATED April and May 2016 Presentation Slides
OUTDATED April and May 2016 Presentation SlidesOUTDATED April and May 2016 Presentation Slides
OUTDATED April and May 2016 Presentation Slides
 
State of Mobile Computing
State of Mobile Computing State of Mobile Computing
State of Mobile Computing
 
Mobile August 2011
Mobile   August 2011Mobile   August 2011
Mobile August 2011
 
Knowledge sharing-april252011
Knowledge sharing-april252011Knowledge sharing-april252011
Knowledge sharing-april252011
 
Mobile 2009, 2010 and beyond
Mobile 2009, 2010 and beyondMobile 2009, 2010 and beyond
Mobile 2009, 2010 and beyond
 
Mobile Trends 2018 by Omelyanenko Nataliia
Mobile Trends 2018 by Omelyanenko NataliiaMobile Trends 2018 by Omelyanenko Nataliia
Mobile Trends 2018 by Omelyanenko Nataliia
 
2015 Global Trend Forecast (Technology, Media & Telecoms)
2015 Global Trend Forecast (Technology, Media & Telecoms)2015 Global Trend Forecast (Technology, Media & Telecoms)
2015 Global Trend Forecast (Technology, Media & Telecoms)
 
Digitalisation and the Future of Work (Part 1) - October 2016 Meeting of the ...
Digitalisation and the Future of Work (Part 1) - October 2016 Meeting of the ...Digitalisation and the Future of Work (Part 1) - October 2016 Meeting of the ...
Digitalisation and the Future of Work (Part 1) - October 2016 Meeting of the ...
 
The Rise of Platforms - And What It Means for Business
The Rise of Platforms - And What It Means for BusinessThe Rise of Platforms - And What It Means for Business
The Rise of Platforms - And What It Means for Business
 
Mobile Revolution In The Digital Era
Mobile Revolution In The Digital EraMobile Revolution In The Digital Era
Mobile Revolution In The Digital Era
 
PLNOG 8: Don Dodge - Platform shift to Mobile
PLNOG 8: Don Dodge - Platform shift to Mobile PLNOG 8: Don Dodge - Platform shift to Mobile
PLNOG 8: Don Dodge - Platform shift to Mobile
 
Darren Evans: Digital Trends
Darren Evans: Digital TrendsDarren Evans: Digital Trends
Darren Evans: Digital Trends
 
IoT Breakfast Briefing
IoT Breakfast BriefingIoT Breakfast Briefing
IoT Breakfast Briefing
 
Aras PLM and the Google Mobile Explosion
Aras PLM and the Google Mobile ExplosionAras PLM and the Google Mobile Explosion
Aras PLM and the Google Mobile Explosion
 

Recently uploaded

Exploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your QueriesExploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your QueriesSanjay Willie
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentPim van der Noll
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Visualising and forecasting stocks using Dash
Visualising and forecasting stocks using DashVisualising and forecasting stocks using Dash
Visualising and forecasting stocks using Dashnarutouzumaki53779
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Fact vs. Fiction: Autodetecting Hallucinations in LLMs
Fact vs. Fiction: Autodetecting Hallucinations in LLMsFact vs. Fiction: Autodetecting Hallucinations in LLMs
Fact vs. Fiction: Autodetecting Hallucinations in LLMsZilliz
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rick Flair
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 

Recently uploaded (20)

Exploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your QueriesExploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
Exploring ChatGPT Prompt Hacks To Maximally Optimise Your Queries
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Visualising and forecasting stocks using Dash
Visualising and forecasting stocks using DashVisualising and forecasting stocks using Dash
Visualising and forecasting stocks using Dash
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Fact vs. Fiction: Autodetecting Hallucinations in LLMs
Fact vs. Fiction: Autodetecting Hallucinations in LLMsFact vs. Fiction: Autodetecting Hallucinations in LLMs
Fact vs. Fiction: Autodetecting Hallucinations in LLMs
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 

Mobile Is Eating the World, 2016-2017

  • 1. MOBILE IS EATING THE WORLD Benedict Evans December 2016 @BenedictEvans
  • 3. 3 We’re halfway to connecting everyone 5.5bn people over 14 years old, ~5bn with mobile phones, ~2.5bn smartphones * SIM penetration is >100%. Source: World Bank, TMW, CNNIC, a16z Developed markets China LatamDeveloped markets China Latam MENA India Sub-Saharan Africa OtherDeveloped markets China Latam MENA South Asia Sub-Saharan Africa SE Asia Other 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Global population and mobile penetration*, December 2016 Not mobile Mobile Smart Developed markets China Latam MENA South Asia Sub-Saharan Africa SE Asia Other
  • 4. 4 The mobile S-Curve is passing the PC S-Curve Smartphones are moving past the PC and heading for 5bn+ users Source: Apple, Google, a16z, GSMA, ITU, World Bank 0 1 2 3 4 5 6 1985 1990 1995 2000 2005 2010 2015 Global population (bn) Mobile users iOS & Android smartphones PCs Adults
  • 5. 5 Mobile S-Curve moving from creation to deployment As we pass 2.5bn smartphones in use, the issues that matter are changing Creation Deployment Crazy idea Scaling Frenzy Maturity Mobile PCs AR?
  • 6. 6 What does mobile deployment mean? Creation Platform wars The latest handset! Technology arguments Millions of users “Will this work? Who will win?” Deployment The big winners are clear Steady, incremental improvement Commodity technology (mostly) Billions of users “What can we build with his?” As we pass 2.5bn smartphones in use, the issues that matter are changing
  • 7. 7 “What can we build with this?” A new kind of scale for technology, and new kinds of computer. New ways to change other industries. Two of those industries: ecommerce & cars.
  • 8. A new kind of scale
  • 9. 9 0 100 200 300 400 500 1990 1995 2000 2005 2010 2015 Annual revenue ($bn, real terms) Wintel GAFA Changing of the guard Google, Apple, Facebook & Amazon (GAFA) are 3x the scale of Wintel Source: Bloomberg, a16z
  • 10. 10 The Wintel explosion in 1990s – 14x growth The golden age for Intel and Microsoft saw 14x revenue growth and dominance of tech Source: Bloomberg, a16z 0 10 20 30 40 50 1990 1995 2000 Wintel annual revenue ($bn, real terms)
  • 11. 11 … But GAFA has 10x the scale Fundamental change in what it means to be a leading tech company Source: Bloomberg, a16z 0 100 200 300 400 500 1990 / 2005 1995 / 2010 2000 / 2015 Annual revenue ($bn, real terms) Wintel 1990-2000 GAFA 2005-2015
  • 12. 12 From important to dominant When tech was scared of Microsoft, it was a pretty small company Source: Bloomberg, a16z GAFA Wintel All other 0 10 20 30 40 50 60 70 80 90 100 1995 2016 Top 20 US listed companies by market cap (indexed) GAFA Wintel All other
  • 13. 13 That scale means much more investment Annual capex has increased from $1bn to $34bn since 2000 Source: Bloomberg, a16z 0 5 10 15 20 25 30 35 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Annual capex ($bn real) Amazon Facebook Apple Google Microsoft
  • 14. 14 And 10x more people (Though this chart includes a lot of warehouse and retail staff) Source: Bloomberg 0 100 200 300 400 500 600 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Permanent employees (000) Amazon Facebook Apple Google Microsoft
  • 15. 15 Today’s tech giants have a totally different character of scale to Wintel or IBM before them. Giants in the economy, not just in tech. And (at least) four competing giants, not one.
  • 16. 16 So Apple is a global top 10 retailer 500 stores, ~$25bn revenue make it a top 20 retailer. $53bn including the online store… Making Apple the 10th largest global retailer by revenue. Not bad for a marketing operation. Retail ran at 12% of revenue before Apple stopped disclosing it: direct is 25%. Source: Apple, a16z
  • 17. 17 0 2 4 6 8 10 12 14 Disney NBCU CBS Netflix Turner Amazon HBO Annual TV production budgets, 2015 ($bn) For Amazon, content is just a strategic lever Original content is core for Netflix, but just another way to sell Prime for Amazon. Combined, they’re 16% of US production budgets Source: IHS
  • 18. 18 Is Netflix a threat? “Is the Albanian army going to take over the world?” - Jeffery Bewkes, CEO Time Warner, 2010
  • 19. 19 Making your own chips: Intel’s whole business becomes a side-project. Google: ‘TPU’ machine learning FPGA. Amazon: ‘Annapurna’ networking ASIC. Apple: A10 is the fastest mobile SoC – plus custom chips for TouchID, Airpods, etc.
  • 20. 20 Scale of 5bn mobile users Vast supply chain of commoditised components & platforms Scale of the winners GAFA have much greater global scale than Wintel or IBM ever had New ways to compete Custom hardware Retail & distribution Content (Cars) Building on scale Huge companies building on an ecosystem that commoditises almost everything before
  • 21. In the mean time, the new thing – machine learning
  • 22. 22 “Is there a dog in this picture?” After 50 years of work, computer vision systems got this right 72% of the time. A whole class of similar problems – easy for people and hard/impossible for computers. Consensus: decades more work. Then, in 2013, machine learning. Source: Imagenet
  • 23. 23 The arrival of machine learning Performance on decades-old challenges has been transformed Image recognition Speech recognition 28% error rate 7% 26% error rate 4%
  • 24. 24 Machine learning: data instead of rules Fundamental shift in AI from trying to codify perception to massive data and applied maths Old: rules Build systems to look for ears, noses, legs, fur… Hire linguists and write grammar rules. Try to codify how human intelligence works (though we don’t really know). Machine learning: data 10k pictures labelled ‘dog’ and 10k labelled ‘no dog’, and let a neural network work it out. Learning rather than rules. Possible now because we have ~1m times more computing & data.
  • 25. 25 “Every time I fire a linguist, the performance of the speech recognizer goes up.” - Frederick Jelinek
  • 26. 26 Since we’re analysing data, not looking for dogs, this works for many other data sets. “Which customers are about to churn?” “Will that car let me merge?” “Is anything weird happening on the network?”
  • 27. 27 Alpha Go – board games and air-con Google took the machine learning platform that won Go and applied it to data center cooling. Achieved a 15% energy saving. Building systems that find patterns in data.
  • 28. 28 “We will move from mobile-first to AI-first.” - Sundar Pichai, 2016
  • 29. 29 Data New analysis on almost any data set Interaction Images, voice, devices, new UX models Images ML can read images as easily as text? Surprises Things that don’t look like ML use cases ML use cases: how many patterns, how much data? Things that only people could do, but at a scale that people could never do
  • 30. 30 At the top and bottom of the stack Training models at vast scale in the cloud… But once trained, many models could run on very small/cheap devices. A $10 widget could run on a cheap DSP with a commodity camera, looking for just one thing – people, leaks, cars…
  • 31. 31 0.0 2.5 5.0 7.5 10.0 12.5 2014 2015 2016e 2017e Global image sensing market size ($bn) Other Cars PC & Tablet Camera Mobile Cameras in everything, except in cameras Imaging, combined with machine learning, may be becoming a universal sensor Source: Sony
  • 32. 32 What does it mean if computers can read images in the way they’ve always been able to read text?
  • 33. 33 Complexity and abstraction Arms race of science, engineering and product – a new front for GAFA competition Surge in complexity Building the engineering on top of the science ‘How to build the right ML for this problem’ is becoming a new CS field Even as the science still has far to go Rush to abstraction Google, Amazon, Microsoft, IBM, etc. rushing to build ML platforms in the cloud Core, cutting-edge ML tech available to rent
  • 34. 34 The machine learning S-Curve Still in the very early days of creation, with lots and lots of low-hanging fruit Creation Deployment Crazy idea Scaling Frenzy Maturity Machine Learning
  • 36. 36 0 200 400 600 800 1,000 1,200 1,400 1,600 Jun-13 Jun-14 Jun-15 Jun-16 Billion minutes online, USA Mobile browser Mobile app Desktop Obvious but boring: apps have won mobile Smartphone apps are now close to 60% of all time spent online in the USA Source: Comscore
  • 37. 37 0 200 400 600 800 1,000 1,200 1,400 1,600 Jun-13 Jun-14 Jun-15 Jun-16 Billion minutes online, USA Mobile browser Facebook app Mobile app Desktop … with increasing concentration Facebook has 15-20% of mobile time (making it the largest mobile web browser) Source: Comscore, a16z
  • 38. 38 More important: a new generation of computing After 30 years, we shift to a new and more sophisticated kind of computer Intensely personal Always with you Imaging as primary input Touch, tilt, location, microphone App stores Ignore battery & bandwidth 1000x faster Cloud & machine learning And yet much easier to use…
  • 39. 39 Both more sophisticated and easier to use New sensors, inputs and interfaces New creation: live, social, broadcast, touch, imaging New computing model…
  • 40. 40 Both more sophisticated and easier to use New sensors, inputs and interfaces, machine learning New creation: live, social, broadcast, touch, imaging New computing model, and new creation models Leave the PC behind, presume mobile-only and mobile-native, not mobile-first
  • 41. 41 How many old assumptions does live video break? Assume high quality camera, fast CPU/GPU for encoding and effects, touch, always on, always in your pocket. Assume unlimited bandwidth & battery. Assume 1bn high-end smartphones on earth - there can be 50 companies trying this. And turn the whole phone into the camera. Source: Houseparty, Yixia
  • 42. 42 Direct interaction - unbundling the app from the phone No buttons, apps or intermediate steps – just use it. Hardware sensors as unbundled apps - deep links within apps. Apps moved to a new context. And of course all built with commodity components from the smartphone supply chain.
  • 43. 43 Sensors Smartphone, or cheap smartphone components Machine learning Speech recognition, computer vision Action Lighting everything up with machine learning Smartphone supply chain enables this – machine learning makes it useful
  • 44. 44 Sensor Commodity smartphone components Machine learning Speech recognition, computer vision Action (If the machine can answer) Still need to narrow the domain to things you can actually do, and tell your users “I’m sorry Dave…”
  • 45. 45 Changing friction and changing choices Massive proliferation of UX models. All about removing friction. And choices… Removing questions and friction Do you have the printer driver? What’s your password? Where did you save that file? … What soap do you want to buy? With which app? But also choices Questions are also choices Huge strategic incentive for big platforms to be the ones to answer the question for you “Who owns the customer?”
  • 46. 46 A computer should “never ask the user for any information that it can auto-detect, copy, or deduce.” - Eric Raymond
  • 47. 47 From direct, physical interaction with data… Source: IBM47
  • 48. 48 To direct, physical interaction with data Source: Magic Leap48
  • 50. 50 Retailers as newspapers “History doesn’t repeat, but sometimes it rhymes” Fixed cost base with falling revenue. Shift from paid to freemium, unbundling, different skills. The distribution advantage moves. New medium means different consumption – you buy different things, not just in different places.
  • 51. 51 Break up old bundles and aggregators New aggregators emerge And they shape consumption in different ways Everything the internet did to media will happen to retail
  • 52. 52 So far, mostly, what you already know you want Ecommerce is much better at logistics than demand generation *Ex. food service, gasoline, auto. Source: US Census Bureau, Amazon, a16z 0% 2% 4% 6% 8% 10% 12% 14% 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Ecommerce as % US retail revenue* Amazon US ecommerce
  • 53. 53 0 5 10 15 20 25 30 Walmart Macy's TJX Amazon Gap Kohl's Target L Brands Nordstrom JC Penney Top 10 US apparel retailers by revenue, 2015 ($bn) People will buy online if they know about it Even clothes – especially with free returns (a new logistics arbitrage model) Source: Bloomberg Intelligence
  • 54. 54 But how do you know what to buy? Amazon is Google for products, but we don’t have a Facebook or Buzzfeed – we don’t have suggestion and discovery. The internet lets you buy, but it doesn’t let you shop.
  • 55. 55 Digital demand generation: step 1 $500bn spent annually on ads (a third on digital) – and another $500bn on marketing Source: Zenith, Google, a16z 0 100 200 300 400 500 600 1980 1985 1990 1995 2000 2005 2010 2015 Global ad spending ($bn, real terms) Other internet Google Print Other TV
  • 56. 56 “Your margin is my opportunity” - Jeff Bezos (So what is advertising?)
  • 57. 57 The channel is the product Soap as a Service. New purchasing journeys mean new kinds of decisions. If you change how things are bought, you change what gets bought.
  • 58. 58 Train your AI on 10 years of Elle / Dwell / NYT Recipes Take a photo of your outfit / living room / kitchen counter “What would I like that I’ve never seen?” Scalable curation Scaling curation with machine learning? When can we infer taste and preference, without a purchase history or a salesperson?
  • 59. 59 Data works its way through retailing Data has affected every part of retail except demand itself, so far ERP 1990s Supply chain & logistics Digital metrics 2000s Advertising Data Social Machine learning 2010s Demand
  • 60. 60 0 100 200 300 400 500 600 1980 1985 1990 1995 2000 2005 2010 2015 Annual revenue ($bn, real terms) Amazon Walmart Retail S Curves Walmart used new technologies to change retail – the same will happen again Source: Amazon, Bloomberg
  • 61. 61 Sectors up-ended by software so far… Source: a16z 0 200 400 600 800 1,000 1,200 1,400 Car sales Advertising Consumer Media Mobile phones Pay TV Online advertising TV production Global revenue, 2015 ($bn)
  • 62. 62 And in the future Source: a16z 0 5,000 10,000 15,000 20,000 25,000 Retail Car sales Advertising Consumer Media Mobile phones Pay TV Online advertising Global revenue, 2015 ($bn)
  • 63. Cars
  • 64. 64 Cars as phones “History doesn’t repeat, but sometimes it rhymes” Today’s tech moat disappears. Key components become commodities. Value moves to software. Network effects move away from hardware. ‘Phone’ means something totally different.
  • 65. 65 Two paths for disrupting cars Electric Happening right now. Removing the engine & transmission destabilizes the car industry and its suppliers. Doesn’t change how cars are used (much). Autonomy 5-10 years? Longer? Many challenges to resolve. Changes where the value is. Accelerates on-demand. Changes what cars are. Changes cities as much as cars changed cities. The growth of electric and autonomy are both, separately, changing what cars are
  • 66. 66 0 50 100 150 200 250 300 0 200 400 600 800 1,000 1,200 2008 2009 2010 2011 2012 2013 2014 2015 Lithium-ion battery economics Battery $/KwH Energy Density (watt hours/litre) Going electric: riding the battery cost curve Growth of electric cars is a function of battery economics starting to work Source: US Department of Energy
  • 67. 67 Electric unbundles cars Radical simplification Complex, proprietary gasoline engines & transmissions disappear Replaced by simple, commodity batteries & motors 10x fewer moving parts Whole basis of competition changes New bases for competition Scale, design, distribution remain, for those who can navigate the change But value moves to software - entirely new skill set And then with autonomy, new layers of value built on top Electric makes cars much simpler and turns many elements into commodities
  • 68. 68 Precedent – unbundling phones Once, you had to have co-invented GSM to make phones. Now, all components unbundled 2001, Nokia “Manufacturing is a core competency & competitive advantage” 75% of Nokia phones made in 8 Nokia factories 2016, Apple The iPhone has 189 suppliers with 789 locations None owned by Apple
  • 69. 69 (Unfair but relevant – capex scale) Leading tech companies now spend as much on capex as car OEMs Apple has $237bn gross cash, while Google has $73bn Source: Bloomberg 0.0 2.5 5.0 7.5 10.0 12.5 15.0 VW Apple Toyota Google Daimler GM Ford BMW Honda Nissan Tesla 2015 capex ($bn)
  • 70. 70 Meanwhile, progress to autonomy Steady, incremental progress, solving one tech challenge at a time Old-fashioned cruise control Smart cruise control & lane keeping Automatic, but driver needs to be ready to take over Driver only needed in exceptions, bad weather etc No controls, no driver, no cabin 1958 Now 5-10 years? Source: VDA, a16z 1 2 3 4 5 Level Level
  • 71. 71 Physical scale Electric changes barriers to entry… But bashing metal is still about scale Data network effects Driving behaviour data to feed ML HD 3D maps How many winners? On-demand network effects Virtuous circle: density of cars + density of riders Uber + Lyft Where is the competitive moat in cars? Once batteries and sensors are commodities, where are the strategic levers? Still lots of unanswered questions
  • 72. 72 Self-driving car = horseless carriage First, we make the tech fit the old patterns. Then, we change to take advantage of what’s new. And removing the driver changes even more than removing the horses. Source: Daimler
  • 73. 73 The obvious impact… It’s easy to talk about the obvious impacts of electric and autonomy – oil and safety Half of oil production 1.25m annual road deaths
  • 74. 74 But the second-order effects will be much bigger Electric changes the whole car ecosystem Half of oil production 1.25m annual road deaths Service & repair with 10x fewer moving parts (And what else gets sold in 150k US gas stations?) Power generation & storage Taxes? Machine tools
  • 75. 75 But the second-order effects will be much bigger Electric changes the whole car ecosystem - autonomy changes what cars and cities are Half of oil production 1.25m annual road deaths Service & repair with 10x fewer moving parts (And what else gets sold in 150k US gas stations?) Parking and congestion Housing? Retail? Logistics? Ownership? Real Estate? Insurance? Power generation & storage Taxes? Machine tools
  • 76. 76 … And the biggest changes are unknowable “Going to the opera in the year 2000” Source: Albert Robida76