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Towards a New Distributional Economics

A talk I gave on December 1, 2017 for a workshop on AI and the future of the economy organized by the OECD and the Berkeley Roundtable on the International Economy. In it, I explore implications of AI and internet-scale platforms for the design of markets, with the goal of starting a conversation about what we might call "distributional economics."

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Towards a New Distributional Economics

  1. Towards a New Distributional Economics Tim O’Reilly @timoreilly BRIE – OECD December 1, 2017
  2. Tim O’Reilly Founder & CEO, O’Reilly Media Partner, O’Reilly AlphaTech Ventures Board member, Code for America Co-founder, Maker 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
  3. What the great technology platforms teach us about the future of work, business, and the economy.
  4. 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:
  5. 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. Personal Computer Big Data and AI Smartphones Apple
  6. Divergence of productivity and real median family income in the US
  7. Will there really be nothing left for people to do? Is there really nothing left for humans to do?
  8. We’ve forgotten the lessons of history
  9. The weavers of Ned Ludd’s rebellion couldn’t imagine…
  10. They couldn’t imagine…
  11. What happened when Amazon added 45,000 robots
  12. Jeff Bezos calls this “the flywheel”
  13. A Business Model Map of Uber Magical user experience realizing the power of networked sensors Replacing ownership with access A platform, not just a company An algorithmic matching marketplace Cognitively augmented workers
  14. The coming robots are not autonomous
  15. Gradually, then suddenly 1. The world is becoming digital 2. Artificial Intelligence and algorithmic systems are everywhere 3. Knowledge is embedded into tools 4. We are creating new kinds of partnerships between machines and humans
  16. The Equinix NY4 data center, where trillions of dollars change hands
  17. What does it mean that these platforms, and the humans that are part of them, are increasingly managed by algorithms?
  18. “Markets are outcomes” - Mariana Mazzucatto
  19. “My grandfather wouldn’t recognize what I do as work.” -Hal Varian
  20. 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
  21. Algorithmic systems all have an “objective function” Uber and Lyft: Pick up time Google: Relevance Facebook: engagement Scheduling systems used by Walmart, the Gap, or McDonalds: reduce employee labor costs and benefits
  22. Like the djinn of Arabian mythology, our digital djinn do exactly what we tell them to do
  23. AI is “the most serious threat to the survival of the human race” Elon Musk
  24. 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 billion-dollar-crusade-to-stop-ai-space-x
  25. The Runaway Objective Function Behind Fake News
  26. And yet…. Divergence of productivity and real median family income in the US
  27. “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
  28. Who Gets What – and Why? Can we redesign markets so that they are more effective? There’s lots of evidence that we can.
  29. 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?  Apply on-demand marketplace models to healthcare, augmenting community health workers with telemedicine and AI?  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?
  30. What’s the Future? It’s Up To us