Exponential Convergence 2018

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Exponential
Convergence
Azeem Azhar, @azeem
impact & discussions

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0 125 250 375 500
2007
0 225 450 675 900 1125
2017
Source: Bloomberg, Form 10-K, 2017
The age of technology

2027?
Source: Bloomberg, Form 10-K, 2017
The age of technology
0 225 450 675 900 1125
2017
What does the world look like in a decade and
beyond?

The Cycle of Tech Revolutions
1770 1820 1870 1920 1970 2020
INNOVATION
CANAL
MANIA
GREAT
BRITISH LEAP
The Industrial
Revolution
RAILWAY
MANIA
VICTORIAN
BOOM
Age of steam
and railways
GLOBAL
INFRA. BUILD
BELLE
EPOQUE
PROG. ERA
Age of steel and
heavy engineering
THE ROARING
20s
POST-WAR
GLOBAL AGE
Age of oil, autos
and heavy production
DOTCOM
& INTERNET
MANIA
THE
KNOWLEDGE
AGE
The ICT revolution

The economy will continue to switch to
intangibles with broad implications
Sunk cost investments
Highly scalable, winner-take-all plays
Spillovers (which includes copying)
Synergies (partnership approaches, internal culture)
Source: Haskel, Westlake, author analysis
Suggests a “venture capital”, startup building model

Exponential change in technology inputs
59%
50-60%
37%
29%
A technology which
improves at 10%+
every year at a fixed cost
Moore's Law
Kryder's Law
Keck's Law
Hendy's
Law
LiIon battery 19-20%

Positive feedback drove the PC
dividend
CPU
OS
quality
Increased capability
Software
Reduced
performanc
e
Greater
sales
• Major winners (Intel, Microsoft)
• New industries
(PC, peripherals, networking,
software, video games)
• Impact on other industries
(computerisation)

2007
2017

YouTube
Facebook
Netflix (US)
US online retail
Internet advertising
Smartphone installed base
Amazon Echo
Blockchains
Data created annually
200 MAU 1.5bn MAU 7x
69m users MAU 2bn MAU 30x
0 175m minutes/month ∞
$140bn $400bn 2.9x
$21bn $73bn 3.5x
250m 3bn 12x
0 10m US households ∞
$0 $150bn ∞
0.1 ZB
20
ZB
200x
A decade of exponential change
2007 2017

Apple Watch >> Cray 2
ROOM
1.9Gflops/2.5t
$35M
1985
POCKET
1.7Gflops/420g
$434
2010 2015
WRIST
>3Gflops/125g
$349

Moore's Law and beyond
Source: Nvidia Investor Day Presentation, 2017
CUDA introduced

Beyond GPUs to dedicated ML silicon
Source: Nvidia Investor Day Presentation, 2017
Dedicated ML (Google, Graphcore)
(10-1000x improvement on current GPUs
for deep learning promised)
Driven by
massive computer demand
for artificial intelligence
which is the software glue
binding services together

There is a convergence of exponential tech
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
$100M
$10M
$1M
$100K
$1K
COST PER GENOME
Source: National Human Genome Research Institute, 2017

MARKET VALUE AND SHIPMENTS OF CMOS SENSORS
0
4
8
12
16
0
1.5
3
4.5
6
7.5
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
MarketValue($B)
WorldwideUnitShipment(B)
Source: IC Insights
15%
CAGR

0
5000
10000
15000
20000
25000
Sept
'15
Oct
'15
Nov
'15
Dec
'15
Jan
'16
Feb
'16
Mar
'16
Apr
'16
May
'16
Jun
'16
Jul
'16
Aug
'16
Sep
'16
Oct
'16
Nov
'16
Dec
'16
Jan
'17
Sep
'17
14 130
950
2000
5191
7053
Voice: a new computing category
EST 8M UNITS
SOLD IN 1ST 2
YEARS (100%
CAGR)
NUMBER OF ALEXA SKILLS
Untitled 1
Source: Voicebot.ai, 2017
20K

Blockchains: a new category
% of total market cap Nov 2013 - Nov 2017
$198bn
Source: CoinMarketCap.com www.exponentialview.co

The most powerful computer
Hash rate, TH/s = 1,000,000,000,000 hashes/sec
Source: blockchain.info
3.5x10^22 FLOPs*
May 2017Jan 2016
5,000,000TH/s
*Source: https://steemit.com/cryptocurrency/@dutch/cryptocurrency-the-wasted-potential-of-the-hash
**Source: https://en.wikipedia.org/wiki/Sunway_TaihuLight
9.3x10^16 FLOPs**
#1 supercomputer
7x

0
20
40
60
80
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Connecteddevicesinbillions
NUMBER OF CONNECTED DEVICES
Source: Statista, 2017
17%
CAGR

LITHIUM-ION BATTERY PRICE FORECAST
0
200
400
600
800
1000
2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030
ESS lithium-ion
demand
EV lithium-ion
demand
BNEF observed
values
19% learning
rate
Source: Bloomberg New Energy Finance, 2017
Battery prices
($/kWh)

LITHIUM-ION BATTERY PRICE FORECAST
0
200
400
600
800
1000
2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030
ESS lithium-ion
demand
EV lithium-ion
demand
BNEF observed
values
19% learning
rate
Source: Bloomberg New Energy Finance, 2017
Battery prices
($/kWh)
26/2/2018

Convergent exponential feedback loops

Value is created faster than ever before
0
2
4
5
7
9
11
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Yearsto$1bnvaluation
• Docusign
• Shopify
• Palantir
• Facebook
• Whatsapp
• Spotify
• Twitter
• Evernote
• Dropbox
• Box
• Tumblr
• Mongo DB
• Cloudera
• AppNexus
• Pinterest
• Airbnb
• Uber
• Stripe
• Instagram
• Square • Snapchat
• OculusRift
• Slack
NUMBER OF YEARS TO REACH $1BN VALUATION FROM THE FOUNDING DATE
Source: GartnerSYM, 2016

These feedback loops create non-
linear results that are hard to predict
1 3
6
9
14
18
25
0
7.5
15
22.5
30
2011 2012 2013 2014 2015 2016 2017
$Bn
ALIBABA Singles Day SALES
77%
CAGR
120
66
90

The rate of change is increasing
Deployment of new consumer propositions is
happening faster than ever before
Source: Horace Dediu, Personal Correspondence www.exponentialview.co

www.exponentialview.coSource: Horace Dediu, Personal Correspondence
Deployment of new consumer propositions is
happening faster than ever before

ML compute meets IOT
illustrative to 2028
17%
CAGR
50%
CAGR
c. 60x
over ten years
c. 5x
300x IOT
compute
Value creation
Apps?
Systems?
Infrastructure?
Business models?
2nd order effects
Power utilisation
Network usage
Data creation
Security?

Super hard to predict:
learning rates, positive feedback loops
Total EVs on the road

AI, automation and new business models will
restructure work. How will economies adapt?
Which economies & workforce
can adapt to the changes?
European model?
High trust
High education levels
Strong safety nets
Greater institutional mechanisms
for worker / firm dialogue
US model?
Business-centric
Highly agile, lower friction
Market mechanisms
Automation
Broad automation, leading
to widespread job displacement
Platformisation of work
Increasing use of ‘thick platforms’
to orchestrate ‘free lance work’ vs
traditional jobs
42m
(3x by 2020)
Americans
freelancing
800m
global job
changes by 2030
(McKinsey)
375m
workers needing
to retrain
(McK)
of which

What will the next decade hold?
Source: EV analysis
100x 5x
10x 10-20x 10+ x
1000x
?

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