Towards a New Distributional Economics

Editor's Notes

• #2: Do More. Do Things That Were Previously Impossible! So many companies play defense. Cut costs, watch the competition, follow best practices. Great entrepreneurs like Jeff Bezos and Elon Musk play offense. They see the world with fresh eyes, taking off the blinders that keep companies using technology to make slight improvements to existing products and practices, rather than imagining the world as it could be, given the new capabilities that technology has given us. They also understand that a business model is the way that all the parts of a business work together to create competitive advantage and customer value. Despite appearances, Uber and Lyft have a very different business model from taxi companies, Airbnb has a very different business model than Hyatt or Hilton, Google has a very different business model than Facebook in advertising, and than Apple in smartphones. Understanding how all the parts of your business work together is the key to innovation, because it lets you take advantage of the capabilities provided by new technology without getting sucked into the vortex of me-too thinking that never quite seems to work out the way it does for the startups who first show its power.
• #4: In my new book, WTF?: What’s the Future and Why It’s Up to Us, I talk about What the great technology platforms have to tell us about the future of business and the economy. 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? And what are some of the key skills we need to master.
• #5: Recent events in world politics, as well as the history in the technology industry as I’ve lived it for the past thirty years, teach us that the notion from evolutionary biology, of a fitness landscape, is perhaps a better metaphor for how the future unfolds than agraph that goes always up and to the right. A fitness landscape is a way of visualizing how genes contribute to the survival of an organism and a species. External conditions 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.
• #6: Technology and business also has a fitness landscape, and one that changes very rapidly. 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 digitalization of the overall economy. When a new wave of technology hits, a new company almost always becomes dominant. The dominant company of one technology wave sometimes manages to survive, but it loses its privileged position as the technology marketplace migrates to a new peak. The path to the top of each new peak requires new competencies – a new fitness function – and holding tight to the old competency actually holds back the previously dominant company.
• #7: I want to use what we learn from technology platforms to provide an additional perspective on this graph. It looks a lot to me like what happens when technology platforms peak, and begin to lose their vitality. Source http://stateofworkingamerica.org/charts/productivity-and-real-median-family-income-growth-1947-2009/ via https://en.wikipedia.org/wiki/Income_inequality_in_the_United_States
• #8: We’ve seen calls for Universal Basic Income, with the assumption that there will be nothing left for humans to do once corporations outsource all the work to machines. While I think Universal Basic Income is an intriguing idea, I don’t think we need it because there will be nothing left for humans to do. There’s plenty to do. The problem is that
• #9: We’ve forgotten the lessons of history. In England, back in 1811 and 1812, a group of weavers invoking the name of Ned Ludd staged a rebellion, smashing the steam powered looms that were threatening their livelihood. The Luddites were right to be afraid. The decades ahead were grim, as machines replaced human labor, and it took time for society to adjust.
• #10: But those weavers carrying the banner of Ned Ludd couldn’t imagine that their descendants would have more clothing than the kings and queens of Europe, that ordinary people, not just kings and queens, would eat the fruits of summer in the depths of winter, luxuries brought from all over the world.
• #11: They couldn’t imagine that we’d tunnel through mountains and under the sea, that we’d fly through the air, crossing continents in hours, that we’d build cities in the desert with buildings a half mile high, that we’d put spacecraft in orbit, that we would eliminate so many scourges of disease! And they couldn’t imagine that their children, grandchildren, and great grandchildren would find meaningful work bringing all of these things to life! Technology eliminates work, but it also increases work, as long as we use the new forms of productivity to increase wealth in circulation so that more people can enjoy the fruits of that productivity.
• #12: You can see how the partnership of humans and machines expanding capacity at Amazon. At the same time as Amazon added 45,000 robots to their warehouses, they added more than 250,000 human workers. The human workers are part of a complex ballet of human and machine, programmers and warehouse workers and delivery drivers, websites and robots, all coordinated by algorithms to work with uncanny speed and precision, delivering many products within a few hours in the luckiest zip codes. Source: https://qz.com/904285/the-optimists-guide-to-the-robot-apocalypse/
• #13: Jeff Bezos calls this the flywheel. Lower costs lead to lower prices, which lead to more customers, which draws more sellers, offering a greater selection, which leads to better customer experience and more economic activity in a virtuous cycle. This has been true as long as market economies have been around. But you have to work at speeding up the flywheel, like Amazon does.
• #14: The same is true of services like Uber and Lyft. Yes, they have put some traditional taxi drivers out of business – BUT THERE ARE FAR MORE PEOPLE MAKING A LIVING PROVIDING DRIVING SERVICES NOW THAN UNDER THE OLD MODEL! Technology made it easier, and better, and increased demand while also lowering prices. And the average Uber or Lyft driver makes more than the average taxi driver working under the old business model. When you look at a service like Uber, you also see more clearly what today’s data-infused information platform has become. A vast, buzzing hive of humans is connected in real time using sensors in their mobile devices and in satellites, woven together by algorithms running in cloud data centers. This is a real-time marketplace for services, connecting people who want something to people who want to provide it. An Amazon warehouse works just the same way.
• #15: Many years ago, consultants Dan and Meredith Beam said to me that “A business model is the way that all the parts of a business work together to create customer value and marketplace advantage.” They taught me a way of mapping out my own company’s business model, which, in this diagram, I use to map out some of the elements that make Uber and Lyft successful: 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
• #16: But here’s the most important thing to understand about robots. We focus on the “intelligent” thing – the robot, the autonomous vehicle, the self-aware AI – rather than understanding that we are increasingly living INSIDE the machine. Even when the car drives itself, these systems are not autonomous. They are part of vast algorithmic systems in partnership with humans. Humans supervise them, but are also supervised by them. “We shape our tools, and then they shape us.”
• #17: Gradually, then suddenly, we are realizing that The world is becoming digital; that Artificial Intelligence and algorithmic systems are everywhere, that knowledge is embedded into our tools, and that we are creating new kinds of partnerships between machines and humans.
• #18: We are developing new kinds of partnerships between human and machine. We need new skills because humans are working alongside automation in very new ways. Even in a company as driven by computer technology as Google, there are humans who keep things running. There are other humans – all of us - who contribute new knowledge and seek it out, reinforcing neural pathways by what we link to, and what we pass onThere are other humans who write code and AI models.. But I want to focus a bit on the skills that are needed by the people creating the models. https://www.google.com/about/datacenters/gallery/#/people/14
• #19: There’s one other of these hybrid proto-Ais to consider, and that’s our financial markets. And that’s where we should be worrying about Skynet, that fabled AI gone wrong, hostile to humans. Like Google and Facebook and Twitter, our financial market is a composite organism made up of its human microbiome, which shapes its behavior, combined with machines driven by encoded objectives.
• #20: In the book, I also talk about what the great technology platforms have to tell us about the future of business and the economy. 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? And what are some of the key skills we need to master.
• #21: Economist Mariana Mazzucatto likes to note that “Markets are outcomes.” That is, they are the result of rules, not just a natural phenomenon. And one of the really important things that internet services teach us is that we can use data, algorithms, and AI to improve the outcomes of markets. For example, Google realized that selling ads to the highest bidder was not the most effective way to sell ads – using more data, they were able to sell pay-per-click ads to the bidder with the best combination of bidding price and likelihood that a customer would actually click on the ad. Uber and Lyft use algorithms to match drivers with opportunity more effectively than the old dispatch or “drive and pray for a fare” model. And of course, we now uderstand how the algorithms of Google, twitter and facebook influence what we think and share. I believe that these algorithmic marketplaces are actually primitive hybrid AIs, combining billions of humans and millions of computers into a new kind of global brain.
• #22: Hal Varian, Google’s chief economist, once said to me: “My grandfather wouldn’t recognize what I do as work.”
• #23: So he says! I say “The more things change, the more they stay the same!” These programmers at Pivotal bear an uncanny resemblance to workers in a Victorian sweatshop! But there is a huge difference. If you look at those programmers with a 20th century mindset, you imagine that they are cranking out software in the same way that factory workers make widgets or those workers were making clothes. But the truth is that the workers at companies like Google and Facebook are programs. Those programmers are their managers. Every day, they take in data from their customers – Startup Way style – and use it to give feedback to their workers in the form of bug fixes, feature advances, and new data loaded into their models.
• #24: This is a very different kind of management. As Eric Ries wrote in the startup way, “It’s the difference between ‘playing Caesar’ (deciding which projects live and die), and ‘playing the scientist’ (being perpetually open to search and discovery.”
• #25: Now here’s the thing. These algorithmic systems all have an “objective function,” something they are relentlessly optimizing. Uber and Lyft optimize for passenger pickup time. Both of them are trying to create a matching marketplace in which passengers will find drivers within three minutes. Google optimizes for relevance in search results and ads, using hundreds of different algorithmic systems and AI to deliver results that people will be satisfied with. Facebook deploys its algorithms to find content that its users will find engaging, that they will spend time with and want to share with their friends. Scheduling systems used by low wage employers aim to minimize the cost of labor, without concern for the needs of employees.
• #26: These algorithmic systems can go wrong. You can think of big data, algorithmic systems, and AI a bit like the Djinn, the powerful, independent spirits from Arabian mythology who can be coerced into fulfilling our wishes, but who so often artfully reinterpret the wish to their master’s maximum disadvantage. Every algorithmic system has an objective function, the thing it is optimizing for. These objective functions are a bit like the “wishes” that Aladdin might give to the genie from his magic lamp. If you phrase the wish wrong, all hell breaks loose. Like their mythological predecessors, algorithmic djinns do whatever it is that we ask them to do, but they are likely to be very single-minded and obtuse in interpreting it, with unintended and sometimes frightening results. This detail from an image of a Djinn from Edmund Dulac’s 1908 illustrated edition of 1001 Nights suggests what we know of the Djinn. A sudden arising of great power, with unintended consequences.
• #27: This idea of the runaway objective function is one of the things behind many fears of AI. Elon Musk has been one of the most outspoken. He has said that “AI is the most serious threat to the survival of the human race.” His concerns have been echoed by other tech luminaries, from Bill Gates to Steven Hawking. Many of the actual practitioners in the field believe that we are very far from developing true, self-improving artificial intelligence.
• #28: Elon’s fears about runaway AI seem very similar to the broom conjured by Mickey Mouse in Disney’s version of The Sorcerer’s Apprentice, where the broom asked to help Mickey carry buckets of water get out of control, multiply, and generate a flood. Nick Bostrom first articulated the idea of the runaway optimization of an objective function in the context of AI with the thought experiment of a self-improving AI that had been given the goal of maximizing paperclip production. Elon Musk used the same thought experiment recently but used the example of a strawberry-picking robot.
• #29: We don’t need to wait for a far future AI to see runaway objective functions. Facebook told its algorithmic systems to optimize for engagement – to show people more of what they like, share, and spend time with. They thought that this would increase community and build a great advertising business. They didn’t expect it to increase hyperpartisanship and fracture our nation. But they did, and we expect them to fix it.
• #30: I believe that this is a great example of the runaway objective function. Facebook’s engineers are a bit like Mickey Mouse in Disney’s Sorcerer’s Apprentice. Mickey borrows his master’s spellbook, and compels the broom to help him fetch water. Unfortunately, he doesn’t know how to stop the broom, and before long
• #31: He is desperately trying to find a way to stop the power he has unleashed. This is what Mark Zuckerberg and team look like right now.
• #32: So, back to that divergence of productivity and real median family income? Why do we see that, despite the continuing growth of productivity, family incomes have stagnated, and as Raj Chetty’s research has shown, most children in developed countries can no longer expect to do better economically than their parents. Inequality has skyrocketed. I believe that it is the result of a very similar objective function gone awry. Our politicians and our businesses bought into an economic theory that said that if we optimized relentlessly for shareholder value, it would be good for the economy as a whole. It turned out not to be true. So just as the Facebook engineers are trying to re-engineer their algorithms, we need to re-engineer the economic algorithms that underly and shape our markets, giving us outcomes that are not those that we really want! Source http://stateofworkingamerica.org/charts/productivity-and-real-median-family-income-growth-1947-2009/ via https://en.wikipedia.org/wiki/Income_inequality_in_the_United_States
• #33: My late friend Andrew Singer gave me a wise piece of advice many, many years ago, which remains as true in the days of AI as it was in the early days of Macintosh programming, when he said it to me. “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.” Facebook didn’t mean to enable partisanship and racism, but it is hard to think of every eventuality, and an objective function that mindlessly offers up advertising to every targeted audience, and amplifies the most engaging content, ended up doing something its creators never expected. We didn’t mean to tell our companies to treat humans as a cost to be eliminated, our communities as something to be hollowed out. We didn’t mean to create an opioid epidemic when we asked our financial system djinns to optimize for shareholders above all else. But that’s what we did.
• #34: It seems to me that for centuries, we’ve been obsessed with the economics of production, and have assumed that the “natural” market will correctly allocate the fruits of that productivity. I think it’s time for a new distributional economics, where we design better markets to more fully share the productive capacity of our society. Roth got his Nobel Prize in economics for his work on the redesign of kidney transplant marketplaces, with a system that increased trust, allowing for better matches. Better market design, as noted above, is the key to the success of virtually every internet company today, which is why, increasingly, they all have chief economists, and others who study and design markets.
• #35: 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?
• #36: What’s the future? It’s up to us.