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Tim O’Reilly
Agile Governance
2
What the great technology platforms
teach us about the future of work,
business, and the economy.
wtfeconomy.com
3
4
If we want to understand the future of business and the
economy, we have to understand these platforms
5
We are all living and working inside a machine
6
It’s no longer happening just in the digital realm
7
An Amazon warehouse is a human-machine hybrid
8
9
It makes things like this possible
68 million monthly users
440,000 employees
336 million monthly active users
~3400 employees
10
“Gradually, then suddenly”
Ernest Hemingway
11
Gradually, then suddenly
Artificial Intelligence and algorithmic
systems are everywhere, in new
kinds of partnerships with humans
12
“The hope is that, in not too many years, human
brains and computing machines will be coupled
together very tightly, and that the resulting
partnership will think as no human brain has ever
thought and process data in a way not approached by
the information-handling machines we know today.”
- J.C.R. Licklider, Man-Machine Symbiosis,1960
13
We are building a global brain, composed of all of us,
augmented and connected by technology.
Central banks are responsible for the health of a
critical part of its nervous tissue.
14
Government is also a platform!
15
So are financial markets
16
17
18
Delivery-driven Policy
“This isn’t just how we should be
developing software. It’s how we should
be developing policy.”
Cecilia Muñoz,
Former Director, White House
Domestic Policy Council
19
New skillsets are needed
• User Centered Design
• DevOps
• Site Reliability Engineering
• Data Science
• Deep Learning
• API Design
• A/B Testing via tens of thousands of experiments
20
“My grandfather wouldn’t
recognize what I do as
work.”
Hal Varian,
Google Chief Economist
21
22
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
23
A new kind of management
“It’s the difference between ‘playing
Caesar’ (deciding which projects live and
die), and ‘playing the scientist’ (being
perpetually open to search and
discovery.)”
- Eric Ries, The Startup Way
24
Software has become a set of
ongoing business processes, not an artifact
25
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
26
“Doing digital is not the same as being
digital.”
Josh Bersin
Deloitte
27
Internet-scale networked platforms managed by
algorithm are fundamentally changing the economic
equation and the very nature of markets
28
Gradually, then suddenly
Large segments of the economy
are governed not by free markets
but by centrally managed platform
monopolies
29
The invisible hand at work
30
What happens when there’s only one queue?
31
32
33
Amazon.com
34
And what happens when there’s
only one price for everything?
35
36
Algorithms decide “who gets what – and why”
Markets are outcomes. A better designed
marketplace can have better outcomes.
37
38
“In an information-rich world, the wealth of
information means a dearth of something else:
a scarcity of whatever it is that information
consumes. What information consumes is
rather obvious: it consumes the attention of its
recipients. Hence a wealth of information
creates a poverty of attention and a need to
allocate that attention efficiently.”
Herbert Simon
39
Algorithms have become a battleground
Security: “That word does not
mean what you think it means.”
40
Users post 7 billion pieces of content to
Facebook a day.
Expecting human fact checkers to catch
fake news is like asking workers to build
a modern city with only picks and
shovels.
At internet scale, we now rely
increasingly on algorithms to manage
what we see and believe.
41
Real Time Digital Regulatory Systems
• Google search quality
• Social media feed organization
• Email spam filtering
• Credit card fraud detection
• Risk management and hedging
42
Government and central bank statistics, economic
modeling, and regulations are too slow for the pace and
scale of the modern world
“Would you cross the street with
information that was five seconds
old?”
• -Jeff Jonas,
CEO of Senzing,
Former IBM Fellow
43
“Why is policy still educated
guesswork with a feedback
loop measured in years?”
Tom Loosemore,
Former Deputy Director,
UK Government Digital Service
44
Governance in the age of algorithms
• Must focus on outcomes, not on rules.
• Must operate at the speed and scale of the systems it is trying to regulate.
• Must incorporate real-time data feedback loops.
• Must be robust in the face of failure.
• Must address the incentives that lead to misbehavior.
• Must be constantly refined to meet ever-changing conditions.
45
Managing an algorithm
46
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
• Central banks: Control inflation? Employment? Interest
rates?
47
When platforms get their algorithms wrong, there can be serious
consequences!
When platforms get
their objective function
wrong, there can be
serious consequences!
48
Like the djinn of Arabian mythology, our digital djinni
do exactly what we tell them to do
49
50
51
Divergence of productivity
and real median family income in the US
52
“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
Andrew Singer
53
Are the FED’s “algorithms” having the intended effect?
Have the goals of central banks been captured by the
equivalent of spammers?
54
55
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
56
We’ve built one of these already
57
What is the objective function of our financial markets?
“The Social Responsibility of Business Is to
Increase Its Profits”
Milton Friedman, 1970
58
Is there really
nothing left
for humans to
do?
59
60
Dealing with climate change
Rebuilding our infrastructure
Feeding the world
Ending disease
Resettling refugees
Caring for each other
Educating the next generation
Enjoying the fruits of shared prosperity
61
This is what technology wants
“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
62
“A platform is when the
economic value of everybody
that uses it exceeds the
value of the company that
creates it. Then it's
a platform.” – Bill Gates
63
Once a platform stops creating more
value for others than it captures for
itself, people migrate elsewhere.
64
Remember Microsoft’s competition with its ecosystem?
65
Google’s share of ad revenue over time
O’Reilly Research
66
Nations fail for the same reason
Inclusive economies outperform extractive
economies. When inclusive economies fall
prey to extractive elites, everyone is worse
off.
67
Growth goes on forever?
One of the key drivers of
corporate bad behavior is the
command given them by
financial markets that they
must constantly grow and
increase their profits
68
An alternative: “Doughnut Economics”
Kate Raworth
69
Oikonomia vs Chrematistike
70
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
71
Change the world
by spreading the knowledge of innovators
72
73
74
75
“The opportunity for AI is to help humans model
and manage complex interacting systems.”
Paul R. Cohen
76
77
“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
78
The great opportunity of the 21st century is to use our
newfound cognitive tools to build
sustainable networks and ecosystems
79
Can we build an economic flywheel
that keeps us in the doughnut?
80
A Social Investment Stipend?
We need “a new social contract, one that values and
rewards socially beneficial activities in the same way
that we currently reward economically productive
activities.”
- Kai Fu Lee, China’s most successful AI investor
81
“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
82
What would it take for us to
• Put people to work tackling the world’s greatest problems?
• Treat humans as assets, not liabilities?
• Create an economy based on caring and creativity, while machines
focus on repetitive tasks?
• Give everyone access to knowledge on demand, whenever we need it?
• Have fresh approaches to public policy based on what is possible now,
and by learning what works, rather than picking from set political
menus?
83
We will create the economy of the
future when we remember that the
function of technology is to empower
people to do things that were
previously impossible!
84
What’s the Future?
It’s Up To us
wtfeconomy.com
85
Tim O’Reilly
Founder & CEO, O’Reilly Media
@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

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The Opportunity for Agile Governance

  • 2. 2 What the great technology platforms teach us about the future of work, business, and the economy. wtfeconomy.com
  • 3. 3
  • 4. 4 If we want to understand the future of business and the economy, we have to understand these platforms
  • 5. 5 We are all living and working inside a machine
  • 6. 6 It’s no longer happening just in the digital realm
  • 7. 7 An Amazon warehouse is a human-machine hybrid
  • 8. 8
  • 9. 9 It makes things like this possible 68 million monthly users 440,000 employees 336 million monthly active users ~3400 employees
  • 11. 11 Gradually, then suddenly Artificial Intelligence and algorithmic systems are everywhere, in new kinds of partnerships with humans
  • 12. 12 “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.” - J.C.R. Licklider, Man-Machine Symbiosis,1960
  • 13. 13 We are building a global brain, composed of all of us, augmented and connected by technology. Central banks are responsible for the health of a critical part of its nervous tissue.
  • 14. 14 Government is also a platform!
  • 16. 16
  • 17. 17
  • 18. 18 Delivery-driven Policy “This isn’t just how we should be developing software. It’s how we should be developing policy.” Cecilia Muñoz, Former Director, White House Domestic Policy Council
  • 19. 19 New skillsets are needed • User Centered Design • DevOps • Site Reliability Engineering • Data Science • Deep Learning • API Design • A/B Testing via tens of thousands of experiments
  • 20. 20 “My grandfather wouldn’t recognize what I do as work.” Hal Varian, Google Chief Economist
  • 21. 21
  • 22. 22 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
  • 23. 23 A new kind of management “It’s the difference between ‘playing Caesar’ (deciding which projects live and die), and ‘playing the scientist’ (being perpetually open to search and discovery.)” - Eric Ries, The Startup Way
  • 24. 24 Software has become a set of ongoing business processes, not an artifact
  • 25. 25 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
  • 26. 26 “Doing digital is not the same as being digital.” Josh Bersin Deloitte
  • 27. 27 Internet-scale networked platforms managed by algorithm are fundamentally changing the economic equation and the very nature of markets
  • 28. 28 Gradually, then suddenly Large segments of the economy are governed not by free markets but by centrally managed platform monopolies
  • 30. 30 What happens when there’s only one queue?
  • 31. 31
  • 32. 32
  • 34. 34 And what happens when there’s only one price for everything?
  • 35. 35
  • 36. 36 Algorithms decide “who gets what – and why” Markets are outcomes. A better designed marketplace can have better outcomes.
  • 37. 37
  • 38. 38 “In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently.” Herbert Simon
  • 39. 39 Algorithms have become a battleground Security: “That word does not mean what you think it means.”
  • 40. 40 Users post 7 billion pieces of content to Facebook a day. Expecting human fact checkers to catch fake news is like asking workers to build a modern city with only picks and shovels. At internet scale, we now rely increasingly on algorithms to manage what we see and believe.
  • 41. 41 Real Time Digital Regulatory Systems • Google search quality • Social media feed organization • Email spam filtering • Credit card fraud detection • Risk management and hedging
  • 42. 42 Government and central bank statistics, economic modeling, and regulations are too slow for the pace and scale of the modern world “Would you cross the street with information that was five seconds old?” • -Jeff Jonas, CEO of Senzing, Former IBM Fellow
  • 43. 43 “Why is policy still educated guesswork with a feedback loop measured in years?” Tom Loosemore, Former Deputy Director, UK Government Digital Service
  • 44. 44 Governance in the age of algorithms • Must focus on outcomes, not on rules. • Must operate at the speed and scale of the systems it is trying to regulate. • Must incorporate real-time data feedback loops. • Must be robust in the face of failure. • Must address the incentives that lead to misbehavior. • Must be constantly refined to meet ever-changing conditions.
  • 46. 46 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 • Central banks: Control inflation? Employment? Interest rates?
  • 47. 47 When platforms get their algorithms wrong, there can be serious consequences! When platforms get their objective function wrong, there can be serious consequences!
  • 48. 48 Like the djinn of Arabian mythology, our digital djinni do exactly what we tell them to do
  • 49. 49
  • 50. 50
  • 51. 51 Divergence of productivity and real median family income in the US
  • 52. 52 “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 Andrew Singer
  • 53. 53 Are the FED’s “algorithms” having the intended effect? Have the goals of central banks been captured by the equivalent of spammers?
  • 54. 54
  • 55. 55 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
  • 56. 56 We’ve built one of these already
  • 57. 57 What is the objective function of our financial markets? “The Social Responsibility of Business Is to Increase Its Profits” Milton Friedman, 1970
  • 58. 58 Is there really nothing left for humans to do?
  • 59. 59
  • 60. 60 Dealing with climate change Rebuilding our infrastructure Feeding the world Ending disease Resettling refugees Caring for each other Educating the next generation Enjoying the fruits of shared prosperity
  • 61. 61 This is what technology wants “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
  • 62. 62 “A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it. Then it's a platform.” – Bill Gates
  • 63. 63 Once a platform stops creating more value for others than it captures for itself, people migrate elsewhere.
  • 65. 65 Google’s share of ad revenue over time O’Reilly Research
  • 66. 66 Nations fail for the same reason Inclusive economies outperform extractive economies. When inclusive economies fall prey to extractive elites, everyone is worse off.
  • 67. 67 Growth goes on forever? One of the key drivers of corporate bad behavior is the command given them by financial markets that they must constantly grow and increase their profits
  • 68. 68 An alternative: “Doughnut Economics” Kate Raworth
  • 70. 70 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
  • 71. 71 Change the world by spreading the knowledge of innovators
  • 72. 72
  • 73. 73
  • 74. 74
  • 75. 75 “The opportunity for AI is to help humans model and manage complex interacting systems.” Paul R. Cohen
  • 76. 76
  • 77. 77 “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
  • 78. 78 The great opportunity of the 21st century is to use our newfound cognitive tools to build sustainable networks and ecosystems
  • 79. 79 Can we build an economic flywheel that keeps us in the doughnut?
  • 80. 80 A Social Investment Stipend? We need “a new social contract, one that values and rewards socially beneficial activities in the same way that we currently reward economically productive activities.” - Kai Fu Lee, China’s most successful AI investor
  • 81. 81 “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
  • 82. 82 What would it take for us to • Put people to work tackling the world’s greatest problems? • Treat humans as assets, not liabilities? • Create an economy based on caring and creativity, while machines focus on repetitive tasks? • Give everyone access to knowledge on demand, whenever we need it? • Have fresh approaches to public policy based on what is possible now, and by learning what works, rather than picking from set political menus?
  • 83. 83 We will create the economy of the future when we remember that the function of technology is to empower people to do things that were previously impossible!
  • 84. 84 What’s the Future? It’s Up To us wtfeconomy.com
  • 85. 85 Tim O’Reilly Founder & CEO, O’Reilly Media @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