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Networks and the Nature of the Firm
Tim O’Reilly
@timoreilly
oreilly.com
wtfeconomy.com
How is work changing?
What does technology now make
possible that was previously impossible?
What work needs doing?
How do we make the world prosperous
for all?
Why aren’t we doing it?
wtfeconomy.com
We have to let go of the maps that are steering us wrong
In 1625, we thought
California was an island
In 2018, it’s our map of the economy that is wrong
The invisible hand at work
What happens when there’s only one queue?
Networks and the Nature of the Firm
Networks and the Nature of the Firm
Networks and the Nature of the Firm
And what happens when there’s
only one price for everything?
Networks and the Nature of the Firm
Gradually, then suddenly
Large segments of the economy
are governed not by free markets
but by centrally managed platforms
Gradually, then suddenly
Artificial Intelligence and
algorithmic systems are
everywhere, in new kinds of
partnerships with humans
We are all living and working inside a machine
Networks and the Nature of the Firm
Networks and the Nature of the Firm
“The existence of high transaction costs outside
firms led to the emergence of the firm as we
know it, and management as we know it….The
reverse side of Coase’s argument is as
important: If the (transaction) costs of exchanging
value in the society at large go down drastically
as is happening today [because of networks], the
form and logic of economic and organizational
entities necessarily need to change! The
mainstream firm, as we have known it, becomes
the more expensive alternative.”Esko Kilpi
If we want to understand the future of business and the
economy, we have to understand networked platforms
“A business model is the way that
all of the parts of a business work
together to create competitive
advantage and customer value.”
- Dan and Meredith Beam
Who Do You Want Your Customers to Become?
Henry Ford didn’t just rethink
the automobile and the
factory, he rethought the
work week, and the reasons
why people might want to
drive.
A Business Model Map of Uber
 A magical app that lets drivers
and passengers find each other
in real time
 A networked marketplace of
drivers and passengers
 Customers who trust that they
don’t have to drive their own car
 Augmented workers able to join
the market as and when they
wish
 A matching market managed by
algorithm
What makes an app “magical”?
1. It seems unbelievable at first.
2. It changes the way the world works, so that the
unbelievable comes to seem inevitable and normal.
3. It results in an ecosystem of new services, jobs, business
models, and industries.
Why simply adding an app doesn’t change the game for taxis
The Augmented Worker
Neo: “Can you fly that thing?”
Trinity: “Not yet.”
This isn’t quite what Trinity’s got, but…
“The Uber app is the drivers’ workplace,
as much as the city where they’re driving
is. Each decision about its interface
structures drivers’ interactions with Uber
the company as well as Uber the
transportation marketplace. And Uber is
now putting the finishing touches on a
from-scratch rebuild of the driver app.”
Alexis Madrigal, The Atlantic,
“Uber Drivers are About to Get a New Boss”
The algorithms decide “who gets what – and why”
Markets are outcomes. A better designed
marketplace can have better outcomes.
The great opportunity of the 21st century is to use our
newfound cognitive tools to build better marketplaces
A language for networked marketplaces
Network effect: the value of a network is
proportional to the square of the number of
possible connections
Scale effect: bigger is better (but not always)
Feedback effect: the more data you collect from
the marketplace, the more you can learn, and the
better the services you can provide
Two-sided market: different classes of users
being matched up: searchers and advertisers,
drivers and passengers, homes and renters.
Networks and the Nature of the Firm
Asymptotic Networks
“The reason Lyft is able to compete with
Uber is that even if they have fewer drivers
in many cities, both platforms are still able
to guarantee a ride with an average wait
time of 4 minutes. Neither can improve by
adding more drivers to a city.”
James Currier
Networks and the Nature of the Firm
Market Networks
In a market network, you’re not
connecting a simple marketplace of
buyers and sellers, but also
connecting them with intermediaries
and service providers. A Market
Network combines elements of a
professional network, an online
marketplace, and SaaS tools.
Networks and the Nature of the Firm
“We wouldn’t be able to build defensibility from our supply-
side relationships purely on the basis of a Marketplace
Network Effect, because it was in the interests of the real
estate agents to syndicate their listings through every
possible channel…. So we created an XML feed standard …
This prevented brokers from having to go through the hassle
of posting them manually to every channel, and soon our
competitors had adopted the same XML standards for their
sites as well.”
And, after 2009, when the real estate market collapsed, ”to
scale [a] new monetization strategy targeting smaller
customers, we set to work creating cost-effective marketing
and lead generation products for the individual real estate
agents.”
Pete Flint
“Once they get some traction, platforms survive
either because:
 They achieve critical mass in a market with
network effects. They are the default place to
go—this is why Craigslist, Airbnb, ebay, Etsy,
and other places with most of the inventory
thrive.
 They provide a tool set ecosystem. The
product or service has certain functions that
buyer and seller need (escrow, analytics, a
CRM, etc.) that the marketplace can provide with
an economy of scale that beats everyone doing
it themselves.”
Alistair Croll
Networks and the Nature of the Firm
Networks and the Nature of the Firm
Networks and the Nature of the Firm
OX provides tools for market networks
Communication and productivity tools for SMBs
Cloud storage and collaboration tools to upgrade
hosting capabilities
Dynamic upgrades and improved security in email
“A Network of Networks”
41
Networks and the Nature of the Firm
Create more value than you capture
The Clothesline Paradox
If you put your clothes in
the dryer, the energy
you use is measured
and counted, but if you
hang them on the
clothesline to be dried
by the sun, the energy
saved disappears from
our accounting
WordPress
Networks and the Nature of the Firm
Networks and the Nature of the Firm
ISP Services - a $100 Billion
market in the US alone
Web hosting and domain name
registration - a $5 Billion market
 Web design
 SEO
 New media businesses
Having a web site
increases the
productivity of small
businesses by 10%
So that’s where the value gets captured - by everyone!
Why platforms (and economies) fail
wtfeconomy.com
Growth goes on forever?
Fitness Landscapes
The way in which genes contribute to
the survival of an organism can be
viewed as a landscape of peaks and
valleys.
Through a series of experiments,
organisms evolve towards fitness
peaks, adapted to a particular
environment, or they die out.
Image source: http://evolution.berkeley.edu/evolibrary/article/side_0_0/complexnovelties_02
Technology also has a fitness landscape
In my career, I’ve watched a number of migrations to new peaks, and I’d like to share with
you some observations about what happened, and why. And then we’ll talk about some
lessons for companies like Google, but also for the overall economy.
Apple
Personal
Computer
Big Data
and
AI
Smartphones
The Rules for Success Change
 IBM fitness function: Maximize competitive advantage by control over hardware
 Microsoft fitness function: Maximize competitive advantage by control over software
 Google fitness function: Maximize competitive advantage by control over data and
marketplaces
 Apple fitness function: Maximize competitive advantage by integrated control of
hardware, software, and marketplace.
 In each case, companies playing by the old rules lost. Or did they?
Generosity takes us to the next peak
Tim Berners-Lee, 1990
The World Wide Web
Linus Torvalds, 1991
Linux
Big Data
and
AI
Tim Berners-Lee, 1990
The World Wide Web
Linus Torvalds, 1991
Linux
How Technologies Mature
1. Some new technology (the PC, the web, the smartphone) lowers the barriers to
participation and innovation.
2. The market explodes as “hackers” push the envelope of possibility, and
entrepreneurs make things easier for ordinary users.
3. The market stagnates as players become platforms, and raise barriers to entry.
Hackers and entrepreneurs move on, looking for new frontiers.
Or (rarely)
3. The industry builds a healthy ecosystem, in which hackers, entrepreneurs and
platform companies play a creative game of "leapfrog". No one gets complete lock in,
and everyone has to improve in order to stay competitive. Value is created for an
entire ecosystem.
Generous or “Long-term greedy”?
Google’s share of ad revenue over time
O’Reilly Research
O’Reilly Media
● Providing learning for almost 40 years
● Trends called – Open Source, Web
2.0, Maker Movement, Big Data
● 500 employees, thousands of
contributors
● 5,000+ enterprise clients, 2.3m
platform users globally
● 17 global technology events serving
20k individuals and 1,000 sponsor
companies
Change the world
by spreading the knowledge of innovators
Networks and the Nature of the Firm
Networks and the Nature of the Firm
Networks and the Nature of the Firm
Networks and the Nature of the Firm
Networks and the Nature of the Firm
Networks and the Nature of the Firm
O’Reilly’s Platform-Centered Learning Ecosystem
Safari
(learning
platform)
Conferences/
Foo Camps
Live Training
Books
Video
Jupyter
Notebooks
Expert
Network
Materials for reference
and learning
Immersive learning
experiences with
industry experts
Cutting edge idea forums
Digital platform for
on-demand learning
across modalities.
Books, video,
synchronous and
asynchronous
online learning
Our ecosystem matches
thousands of learning providers
with millions of customers
“Doing digital is not the same as
being digital.”
Josh Bersin
Deloitte
How is this possible?
McDonalds 440,000 employees, 68 million monthly users
Snap ~300 employees, 100 million monthly users
“My grandfather wouldn’t recognize what I do as work.”
– Hal Varian
Many of today’s workers are programs.
Developers are actually their managers.
Every day, they are inspecting the
performance of their workers and
giving them instruction (in the form of
code) about how to do a better job
The lessons of technology are also lessons
for the organization of the business
“Services not only represent a
software structure but also the
organizational structure.”
Werner Vogels, Amazon CTO
How Amazon Became a Platform
“[Jeff’s] Big Mandate went something along these lines:
1) All teams will henceforth expose their data and functionality through service interfaces.
2) Teams must communicate with each other through these interfaces.
3) There will be no other form of interprocess communication allowed: no direct linking, no
direct reads of another team’s data store, no shared-memory model, no back-doors
whatsoever. The only communication allowed is via service interface calls over the network.
4) It doesn’t matter what technology they use. HTTP, Corba, Pubsub, custom protocols —
doesn’t matter. Bezos doesn’t care.
5) All service interfaces, without exception, must be designed from the ground up to be
externalizable. That is to say, the team must plan and design to be able to expose the
interface to developers in the outside world. No exceptions.
6) Anyone who doesn’t do this will be fired.”
Steve Yegge, in http://siliconangle.com/furrier/2011/10/12/google-engineer-accidently-
shares-his-internal-memo-about-google-platform/
Two-pizza teams
Algorithmic systems have an “objective function”
Google: Relevance
Facebook: Engagement
Uber and Lyft: Passenger pick up time
Scheduling software used by McDonald’s, The Gap, or
Walmart: Reduce employee costs and benefits
What are you asking your algorithms to optimize for?
When platforms get
their algorithms
wrong, there can be
serious
consequences!
Networks and the Nature of the Firm
Networks and the Nature of the Firm
The runaway objective function
“Even robots with a seemingly
benign task could indifferently harm
us. ‘Let’s say you create a self-
improving A.I. to pick strawberries,’
Musk said, ‘and it gets better and
better at picking strawberries and
picks more and more and it is self-
improving, so all it really wants to do
is pick strawberries. So then it would
have all the world be strawberry
fields. Strawberry fields forever.’ No
room for human beings.”
Elon Musk, quoted in Vanity Fair
https://www.vanityfair.com/news/2017/03/elon-musk-
billion-dollar-crusade-to-stop-ai-space-x
Will there really be nothing left for people to do?
Is there really
nothing left for
humans to do?
The Equinix NY4 data center,
where trillions of dollars change hands
What is the objective function of our financial markets?
“The Social Responsibility of Business Is to
Increase Its Profits”
Milton Friedman, 1970
Divergence of productivity
and real median family income in the US
“The art of debugging is figuring out
what you really told your program to
do rather than what you thought you
told it to do.”
Andrew Singer
Oikonomia vs Chrematistike
Can we use algorithmic systems and
modern tools for matching people to
work that needs doing to build a better
economy for all?
wtfeconomy.com
“Doughnut Economics”
Kate Raworth
“Computational Sustainability is a new interdisciplinary
research field, with the overarching goal of studying and
providing solutions to computational problems for balancing
environmental, economic, and societal needs for a
sustainable future. Such problems are unique in scale,
impact, complexity, and richness, often involving
combinatorial decisions, in highly dynamic and uncertain
environments, offering challenges but also opportunities for
the advancement of the state-of-the-art of computer and
information science. Work in Computational Sustainability
integrates in a unique way various areas within computer
science and applied mathematics, such as constraint
reasoning, optimization, machine learning, and dynamical
systems.”
Carla Gomes
The fundamental economic question
is no longer how to incentivize production
but how to incentivize fair distribution
of the fruits of increased productivity
Brian Arthur
“Economic Possibilities for Our Grandchildren”
The world of his grandchildren—the world of
those of us living today— would, “for the
first time . . . be faced with [mankind’s] real,
his permanent problem—how to use his
freedom from pressing economic cares,
how to occupy the leisure, which science
and compound interest will have won for
him, to live wisely and agreeably and well.”
John Maynard Keynes
John Maynard Keynes
There’s plenty to go around.
It’s just not going around!
It isn’t technology that wants to eliminate jobs
“Prosperity in human societies is
best understood as the
accumulation of solutions to
human problems. We won’t run
out of work until we run out of
problems.”
Nick Hanauer
Let’s use the power of networked platforms, and
new kinds of partnership with machines, to build
the world we actually want to live in
wtfeconomy.com
WTF??!!
Amazement or Dismay
Tim O’Reilly
@timoreilly
• O’Reilly AI Conference
• Strata: The Business of Data
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• O’Reilly Open Source Summit
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• 40,000+ ebooks
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of video training
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exchange
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• AI and The Next Economy
Founder & CEO, O’Reilly Media
Partner, O’Reilly AlphaTech Ventures
Board member, Code for America
Co-founder, Maker Media

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Networks and the Nature of the Firm

  • 1. Networks and the Nature of the Firm Tim O’Reilly @timoreilly oreilly.com wtfeconomy.com
  • 2. How is work changing? What does technology now make possible that was previously impossible? What work needs doing? How do we make the world prosperous for all? Why aren’t we doing it? wtfeconomy.com
  • 3. We have to let go of the maps that are steering us wrong In 1625, we thought California was an island
  • 4. In 2018, it’s our map of the economy that is wrong
  • 6. What happens when there’s only one queue?
  • 10. And what happens when there’s only one price for everything?
  • 12. Gradually, then suddenly Large segments of the economy are governed not by free markets but by centrally managed platforms
  • 13. Gradually, then suddenly Artificial Intelligence and algorithmic systems are everywhere, in new kinds of partnerships with humans
  • 14. We are all living and working inside a machine
  • 16. Networks and the Nature of the Firm “The existence of high transaction costs outside firms led to the emergence of the firm as we know it, and management as we know it….The reverse side of Coase’s argument is as important: If the (transaction) costs of exchanging value in the society at large go down drastically as is happening today [because of networks], the form and logic of economic and organizational entities necessarily need to change! The mainstream firm, as we have known it, becomes the more expensive alternative.”Esko Kilpi
  • 17. If we want to understand the future of business and the economy, we have to understand networked platforms
  • 18. “A business model is the way that all of the parts of a business work together to create competitive advantage and customer value.” - Dan and Meredith Beam
  • 19. Who Do You Want Your Customers to Become? Henry Ford didn’t just rethink the automobile and the factory, he rethought the work week, and the reasons why people might want to drive.
  • 20. A Business Model Map of Uber  A magical app that lets drivers and passengers find each other in real time  A networked marketplace of drivers and passengers  Customers who trust that they don’t have to drive their own car  Augmented workers able to join the market as and when they wish  A matching market managed by algorithm
  • 21. What makes an app “magical”? 1. It seems unbelievable at first. 2. It changes the way the world works, so that the unbelievable comes to seem inevitable and normal. 3. It results in an ecosystem of new services, jobs, business models, and industries.
  • 22. Why simply adding an app doesn’t change the game for taxis
  • 23. The Augmented Worker Neo: “Can you fly that thing?” Trinity: “Not yet.”
  • 24. This isn’t quite what Trinity’s got, but…
  • 25. “The Uber app is the drivers’ workplace, as much as the city where they’re driving is. Each decision about its interface structures drivers’ interactions with Uber the company as well as Uber the transportation marketplace. And Uber is now putting the finishing touches on a from-scratch rebuild of the driver app.” Alexis Madrigal, The Atlantic, “Uber Drivers are About to Get a New Boss”
  • 26. The algorithms decide “who gets what – and why” Markets are outcomes. A better designed marketplace can have better outcomes.
  • 27. The great opportunity of the 21st century is to use our newfound cognitive tools to build better marketplaces
  • 28. A language for networked marketplaces Network effect: the value of a network is proportional to the square of the number of possible connections Scale effect: bigger is better (but not always) Feedback effect: the more data you collect from the marketplace, the more you can learn, and the better the services you can provide Two-sided market: different classes of users being matched up: searchers and advertisers, drivers and passengers, homes and renters.
  • 30. Asymptotic Networks “The reason Lyft is able to compete with Uber is that even if they have fewer drivers in many cities, both platforms are still able to guarantee a ride with an average wait time of 4 minutes. Neither can improve by adding more drivers to a city.” James Currier
  • 32. Market Networks In a market network, you’re not connecting a simple marketplace of buyers and sellers, but also connecting them with intermediaries and service providers. A Market Network combines elements of a professional network, an online marketplace, and SaaS tools.
  • 34. “We wouldn’t be able to build defensibility from our supply- side relationships purely on the basis of a Marketplace Network Effect, because it was in the interests of the real estate agents to syndicate their listings through every possible channel…. So we created an XML feed standard … This prevented brokers from having to go through the hassle of posting them manually to every channel, and soon our competitors had adopted the same XML standards for their sites as well.” And, after 2009, when the real estate market collapsed, ”to scale [a] new monetization strategy targeting smaller customers, we set to work creating cost-effective marketing and lead generation products for the individual real estate agents.” Pete Flint
  • 35. “Once they get some traction, platforms survive either because:  They achieve critical mass in a market with network effects. They are the default place to go—this is why Craigslist, Airbnb, ebay, Etsy, and other places with most of the inventory thrive.  They provide a tool set ecosystem. The product or service has certain functions that buyer and seller need (escrow, analytics, a CRM, etc.) that the marketplace can provide with an economy of scale that beats everyone doing it themselves.” Alistair Croll
  • 39. OX provides tools for market networks Communication and productivity tools for SMBs Cloud storage and collaboration tools to upgrade hosting capabilities Dynamic upgrades and improved security in email
  • 40. “A Network of Networks” 41
  • 42. Create more value than you capture
  • 43. The Clothesline Paradox If you put your clothes in the dryer, the energy you use is measured and counted, but if you hang them on the clothesline to be dried by the sun, the energy saved disappears from our accounting
  • 47. ISP Services - a $100 Billion market in the US alone Web hosting and domain name registration - a $5 Billion market
  • 48.  Web design  SEO  New media businesses
  • 49. Having a web site increases the productivity of small businesses by 10%
  • 50. So that’s where the value gets captured - by everyone!
  • 51. Why platforms (and economies) fail wtfeconomy.com
  • 52. Growth goes on forever?
  • 53. Fitness Landscapes The way in which genes contribute to the survival of an organism can be viewed as a landscape of peaks and valleys. Through a series of experiments, organisms evolve towards fitness peaks, adapted to a particular environment, or they die out. Image source: http://evolution.berkeley.edu/evolibrary/article/side_0_0/complexnovelties_02
  • 54. Technology also has a fitness landscape In my career, I’ve watched a number of migrations to new peaks, and I’d like to share with you some observations about what happened, and why. And then we’ll talk about some lessons for companies like Google, but also for the overall economy. Apple Personal Computer Big Data and AI Smartphones
  • 55. The Rules for Success Change  IBM fitness function: Maximize competitive advantage by control over hardware  Microsoft fitness function: Maximize competitive advantage by control over software  Google fitness function: Maximize competitive advantage by control over data and marketplaces  Apple fitness function: Maximize competitive advantage by integrated control of hardware, software, and marketplace.  In each case, companies playing by the old rules lost. Or did they?
  • 56. Generosity takes us to the next peak Tim Berners-Lee, 1990 The World Wide Web Linus Torvalds, 1991 Linux Big Data and AI Tim Berners-Lee, 1990 The World Wide Web Linus Torvalds, 1991 Linux
  • 57. How Technologies Mature 1. Some new technology (the PC, the web, the smartphone) lowers the barriers to participation and innovation. 2. The market explodes as “hackers” push the envelope of possibility, and entrepreneurs make things easier for ordinary users. 3. The market stagnates as players become platforms, and raise barriers to entry. Hackers and entrepreneurs move on, looking for new frontiers. Or (rarely) 3. The industry builds a healthy ecosystem, in which hackers, entrepreneurs and platform companies play a creative game of "leapfrog". No one gets complete lock in, and everyone has to improve in order to stay competitive. Value is created for an entire ecosystem.
  • 59. Google’s share of ad revenue over time O’Reilly Research
  • 60. O’Reilly Media ● Providing learning for almost 40 years ● Trends called – Open Source, Web 2.0, Maker Movement, Big Data ● 500 employees, thousands of contributors ● 5,000+ enterprise clients, 2.3m platform users globally ● 17 global technology events serving 20k individuals and 1,000 sponsor companies
  • 61. Change the world by spreading the knowledge of innovators
  • 68. O’Reilly’s Platform-Centered Learning Ecosystem Safari (learning platform) Conferences/ Foo Camps Live Training Books Video Jupyter Notebooks Expert Network Materials for reference and learning Immersive learning experiences with industry experts Cutting edge idea forums Digital platform for on-demand learning across modalities. Books, video, synchronous and asynchronous online learning Our ecosystem matches thousands of learning providers with millions of customers
  • 69. “Doing digital is not the same as being digital.” Josh Bersin Deloitte
  • 70. How is this possible? McDonalds 440,000 employees, 68 million monthly users Snap ~300 employees, 100 million monthly users
  • 71. “My grandfather wouldn’t recognize what I do as work.” – Hal Varian
  • 72. Many of today’s workers are programs. Developers are actually their managers. Every day, they are inspecting the performance of their workers and giving them instruction (in the form of code) about how to do a better job
  • 73. The lessons of technology are also lessons for the organization of the business “Services not only represent a software structure but also the organizational structure.” Werner Vogels, Amazon CTO
  • 74. How Amazon Became a Platform “[Jeff’s] Big Mandate went something along these lines: 1) All teams will henceforth expose their data and functionality through service interfaces. 2) Teams must communicate with each other through these interfaces. 3) There will be no other form of interprocess communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network. 4) It doesn’t matter what technology they use. HTTP, Corba, Pubsub, custom protocols — doesn’t matter. Bezos doesn’t care. 5) All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions. 6) Anyone who doesn’t do this will be fired.” Steve Yegge, in http://siliconangle.com/furrier/2011/10/12/google-engineer-accidently- shares-his-internal-memo-about-google-platform/
  • 76. Algorithmic systems have an “objective function” Google: Relevance Facebook: Engagement Uber and Lyft: Passenger pick up time Scheduling software used by McDonald’s, The Gap, or Walmart: Reduce employee costs and benefits
  • 77. What are you asking your algorithms to optimize for?
  • 78. When platforms get their algorithms wrong, there can be serious consequences!
  • 81. The runaway objective function “Even robots with a seemingly benign task could indifferently harm us. ‘Let’s say you create a self- improving A.I. to pick strawberries,’ Musk said, ‘and it gets better and better at picking strawberries and picks more and more and it is self- improving, so all it really wants to do is pick strawberries. So then it would have all the world be strawberry fields. Strawberry fields forever.’ No room for human beings.” Elon Musk, quoted in Vanity Fair https://www.vanityfair.com/news/2017/03/elon-musk- billion-dollar-crusade-to-stop-ai-space-x
  • 82. Will there really be nothing left for people to do? Is there really nothing left for humans to do?
  • 83. The Equinix NY4 data center, where trillions of dollars change hands
  • 84. What is the objective function of our financial markets? “The Social Responsibility of Business Is to Increase Its Profits” Milton Friedman, 1970
  • 85. Divergence of productivity and real median family income in the US
  • 86. “The art of debugging is figuring out what you really told your program to do rather than what you thought you told it to do.” Andrew Singer
  • 88. Can we use algorithmic systems and modern tools for matching people to work that needs doing to build a better economy for all? wtfeconomy.com
  • 90. “Computational Sustainability is a new interdisciplinary research field, with the overarching goal of studying and providing solutions to computational problems for balancing environmental, economic, and societal needs for a sustainable future. Such problems are unique in scale, impact, complexity, and richness, often involving combinatorial decisions, in highly dynamic and uncertain environments, offering challenges but also opportunities for the advancement of the state-of-the-art of computer and information science. Work in Computational Sustainability integrates in a unique way various areas within computer science and applied mathematics, such as constraint reasoning, optimization, machine learning, and dynamical systems.” Carla Gomes
  • 91. The fundamental economic question is no longer how to incentivize production but how to incentivize fair distribution of the fruits of increased productivity Brian Arthur
  • 92. “Economic Possibilities for Our Grandchildren” The world of his grandchildren—the world of those of us living today— would, “for the first time . . . be faced with [mankind’s] real, his permanent problem—how to use his freedom from pressing economic cares, how to occupy the leisure, which science and compound interest will have won for him, to live wisely and agreeably and well.” John Maynard Keynes John Maynard Keynes
  • 93. There’s plenty to go around. It’s just not going around!
  • 94. It isn’t technology that wants to eliminate jobs “Prosperity in human societies is best understood as the accumulation of solutions to human problems. We won’t run out of work until we run out of problems.” Nick Hanauer
  • 95. Let’s use the power of networked platforms, and new kinds of partnership with machines, to build the world we actually want to live in
  • 97. Tim O’Reilly @timoreilly • O’Reilly AI Conference • Strata: The Business of Data • JupyterCon • O’Reilly Open Source Summit • Maker Faire • Foo Camp • … • 40,000+ ebooks • Tens of thousands of hours of video training • Live training • Millions of customers • A platform for knowledge exchange • Commercial internet • Open source software • Web 2.0 • Maker movement • Government as a platform • AI and The Next Economy Founder & CEO, O’Reilly Media Partner, O’Reilly AlphaTech Ventures Board member, Code for America Co-founder, Maker Media