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We Must Redraw the Map

My keynote at the 2018 New Profit Gathering of Leaders conference in Boston on May 17, 2018. I talk about the lessons from technology platforms, how they teach us what is wrong with our economy, and the possibilities of AI for creating better, fairer, more effective decisions about "who gets what and why" in the economy.

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We Must Redraw the Map

  1. We Must Redraw The Map! Tim O’Reilly @timoreilly New Profit Gathering of Leaders May 26, 2018
  2. How is the economy changing? What are the implications for business? What does technology now make possible that was previously impossible? What work needs doing? Why aren’t we doing it?
  3. “…47 percent of jobs are “at risk” of being automated in the next 20 years.” Carl Frey and Michael Osborne, Oxford University “The Future of Employment: How Susceptible Are Jobs to Computerisation?”
  4. 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
  5. Gradually, then suddenly
  6. Gradually, then suddenly Artificial Intelligence and algorithmic systems are everywhere, in new kinds of partnerships with humans
  7. Gradually, then suddenly Large segments of the economy are governed not by free markets but by centrally managed platform monopolies
  8. We are all living and working inside a machine
  9. Does that machine have human interests at heart?
  10. The algorithms decide “who gets what – and why” Markets are outcomes. A better designed marketplace can have better outcomes. The choices made by the marketplace designer have enormous consequences for the participants and for society Are they the right choices?
  11. The algorithms do what we ask them to do
  12. But not necessarily what we want!
  13. The Equinix NY4 data center, where trillions of dollars change hands
  14. What is the objective function of our financial markets? “The Social Responsibility of Business Is to Increase Its Profits” Milton Friedman, 1970
  15. We have to let go of this map that is steering us wrong In 1625, we thought California was an island
  16. The master algorithm asks for growth to go on forever It should be doing a better job of solving for
  17. Divergence of productivity and real median family income in the US
  18. 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:
  19. Technology also has a fitness landscape In my career, I’ve watched a number of migrations to new peaks Apple Personal Computer Big Data and AI Smartphones
  20. 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
  21. The same dynamics play out at the national level Inclusive economies prosper. Extractive economies falter. Why do we incentivize extractive behavior?
  22. “Doughnut Economics” Kate Raworth
  23. Oikonomia vs Chrematistike
  24. 1. We must rewrite the rules King George III George Washington
  25. Another view of “Who Gets What – and Why” Profit = Revenue – Expenses Profit = Revenue – (Cost of materials + cost of labor + cost of capital) Return to capital = Revenue – (Cost of materials + cost of labor) Shouldn’t the return be proportional to the contribution of all of the inputs?
  26. “The opportunity for AI is to help humans model and manage complex interacting systems.” Paul R. Cohen
  27. “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
  28. “What good governance and the good society look like is now inextricably linked to an understanding of the digital.” Tom Steinberg, MySociety 2. Leaders must become digitally literate!
  29. How we apply these ideas at O’Reilly Media
  30. Government is a platform. Its policies shape who gets what and why. What lessons should we be taking from the success and failure of tech platforms?
  31. $470B Spent on government safety net programs $42B Technology and government are the two most powerful ways to get to scale. At Code for America, we bring them together. Charitable contributions towards safety net Code for America
  32. People, Not Data
  33. GetCalFresh
  34. ClearMyRecord
  35. 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
  36. 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?
  37. 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
  38. “Biophilic work” Natasha Iskander
  39. Let the machines do as much of the work as they can. Let humans get on with the real work of the 21st century.