TIM O’REILLY Founder + CEO
O’Reilly Media, Inc. Twitter » @timoreilly Rethinking Who Gets What and Why

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

“My grandfather wouldn’t recognize what
I do as work.” Hal Varian, Google Chief Economist

Many of today’s workers are
programs. Software 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

Software has become a set
of ongoing business processes, not an artifact

New skillsets are needed User
Centered Design Site Reliability Engineering Data Science Deep Learning API Design Economics Market design

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 maps
of business and the economy that are wrong

The invisible hand at work

What happens when there’s only
one queue? And it’s personalized for you?

Amazon.com

And what happens when there’s
only one price for everything?

Algorithms decide “who gets what
– and why” Markets are outcomes. A better designed marketplace can have better outcomes.

Price signaling is no longer
the primary coordinator

“Gradually, then suddenly”
Ernest Hemingway

Gradually, then suddenly Large segments
of the economy are governed not by free markets but by centrally managed platforms

“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

Algorithms have become a battleground
Security: “That word does not mean what you think it means.”

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.

Gradually, then suddenly Artificial Intelligence
and algorithmic systems are everywhere, in new kinds of partnerships with humans

“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

We are all living and
working inside a machine

It’s no longer just in
the digital realm

An Amazon warehouse is a
human-machine hybrid

It makes things like this
possible 68 million monthly users 440,000 employees 336 million monthly active users ~3400 employees

Managing an algorithmic marketplace

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 and hostile attacks.  Must address the incentives that lead to misbehavior.  Must be constantly refined to meet ever-changing conditions.

Real Time Digital Regulatory Systems
Google search quality Social media feed organization Email spam filtering Credit card fraud detection Risk management and hedging

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

“Why is policy still educated
guesswork with a feedback loop measured in years?” Tom Loosemore, Former Deputy Director, UK Government Digital Service

Governance too must be reshaped
by the digital “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

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?

When platforms get their algorithms
wrong, there can be serious consequences! When platforms get their objective function wrong, there can be serious consequences!

Like the djinn of Arabian
mythology, our digital djinni do exactly what we tell them to do

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 Andrew Singer

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

We’ve built one of these
already

What is the objective function
of our financial markets? “The Social Responsibility of Business Is to Increase Its Profits” Milton Friedman, 1970

Is there really nothing left
for humans to do?

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

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

“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

Once a platform stops creating
more value for others than it captures for itself, people migrate elsewhere.

Microsoft crushed its ecosystem

How Industries 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.

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

Google’s share of ad revenue
over time O’Reilly Research

Generous is also “Long-term greedy”

“Self-interest properly regarded” A new
inclusive opportunity ecosystem keeps the game going

Nations fail for the same
reason as tech platforms Inclusive economies outperform extractive economies. When inclusive economies fall prey to extractive elites, everyone is worse off.

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

An alternative: “Doughnut Economics” Kate
Raworth

Oikonomia vs Chrematistike

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Change the world by spreading
the knowledge of innovators

“The opportunity for AI is
to help humans model and manage complex interacting systems.” Paul R. Cohen

“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 great opportunity of the
21st century is to use our newfound cognitive tools to build sustainable businesses and economies

Can we build an economic
flywheel that keeps us in the doughnut?

What’s the Future? It’s Up
To us wtfeconomy.com

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