job automation

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The Lights in the Tunnel by Martin Ford

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“Hardware” Jobs and Robotics A “hardware” job is a job that requires some investment in mechanical or robotic technologies in order for the job to be automated. The automation of hardware jobs started long before the computer revolution. Machines used on assembly lines, farm equipment, and heavy earth moving equipment are all technologies that have displaced millions of workers in the past. As history has shown, repetitive motion manufacturing jobs are among the easiest to automate. In fact, as I mentioned, this is how the Luddite movement got started back in 1811. However, the merger of mechanics and computer technology into the field of robotics will almost certainly impact an unprecedented number and types of jobs.

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However, the merger of mechanics and computer technology into the field of robotics will almost certainly impact an unprecedented number and types of jobs. Whether a specific hardware job is difficult or easy to automate really depends on the combination of skills and manual dexterity required. For an example of a job that is very difficult to automate, let’s consider an auto mechanic. A mechanic obviously requires a great deal of hand-eye coordination. He or she has to work on thousands of different parts in a variety of different engines, often in highly varied states of repair. In other words, a robot mechanic would face many visual recognition and manipulation problems similar to the ones we discussed earlier with the robot housekeeper.

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The point of this is not to vilify Wal-Mart or any other business that might someday choose to employ automation. We have to acknowledge that, in a free market economy, every business has to respond to its competitive environment and employ the best available technologies and processes. If it does not do so, it will not survive. History has shown that job automation very often involves pushing a significant portion of the job onto the customer. Automation in the customer service area is really self-service. This has been the case with ATMs, automated checkout isles and even self-serve gas pumps. In the recently opened Future Store27 near Düsseldorf, Germany, in-store retail sales and customer assistance is being automated via a cell-phone interface.

Futureproof: 9 Rules for Humans in the Age of Automation by Kevin Roose

Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, business process, call centre, choice architecture, coronavirus, COVID-19, data science, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, George Floyd, gig economy, Google Hangouts, hiring and firing, hustle culture, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, knowledge worker, Kodak vs Instagram, labor-force participation, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Steve Jobs, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!

But today, AI anxiety is burning bright again, fueled by popular books like Martin Ford’s Rise of the Robots and Erik Brynjolfsson and Andrew McAfee’s The Second Machine Age, both of which made the case that AI was going to fundamentally change society and transform the global economy. Academic studies of the future of work, like an Oxford University study that estimated that as many as 47 percent of U.S. jobs were at “high risk” of automation within the next two decades, added to the sense of impending doom. By 2017, three in four American adults believed that AI and automation would destroy more jobs than they would create, and a majority expected technology to widen the gap between the rich and poor. I spent much of 2019 reporting on these changing attitudes, being careful to keep an open mind to the possibility that these fears were exaggerated.

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They don’t take vacations, file HR complaints, or call in sick. And if you replace a human with a bot, you can, in theory, free that human up to do more valuable things. “Twenty to forty percent of our labor workforce is trapped into acting like bridges between applications,” Automation Anywhere’s CEO Shukla Mihir said. When these jobs get automated, he added, “not only are people able to do higher-value work, but you are able to significantly reduce your costs.” The pitch appeared to be working. Despite its low profile, Automation Anywhere has become one of the fastest-growing start-ups in the world, with a valuation of more than $6 billion.

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Optimists often cite examples of professionals who have already outsourced much of their drudgery to computers, such as doctors who use electronic medical records to do much of their routine record-keeping so they can focus on talking to patients, lawyers whose legal-research software allows them to spend more time interacting with clients, or architects whose computer-assisted design software saves them hours of pixel-pushing monotony. These jobs aren’t threatened by automation, the optimists say, because there are still plenty of things a human doctor, lawyer, or architect can do that a machine can’t. And the AI that will emerge in the next few years will eliminate even more dull and repetitive work, and free us up to do the things we actually enjoy doing. 3.

The Globotics Upheaval: Globalisation, Robotics and the Future of Work by Richard Baldwin

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The latest update of this approach—done by McKinsey based on the information reviewed above—raises this to 60 percent (due in part to the fact that white-collar robots have gotten so much better).8 These rather startling numbers refer to jobs that could be automated. But how many actually will be? A recent study by the consulting firm, Forrester, suggest that 16 percent of all US jobs will be displaced by automation in the next ten years.9 That is one out of every six jobs. The professions hardest hit are forecast to be those that employ office workers. Forrester, however, notes that about half of the job destruction will be matched by job creation equal to 9 percent of today’s jobs.

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These thinking computers are opening a new phase of automation. They are bringing the pluses and minuses of automation to a whole new class of workers—those who work in offices rather than farms and factories. These people are unprepared. Until recently, most white-collar, service-sector, and professional jobs were shielded from automation by humans’ cogitative monopoly. Computers couldn’t think, so jobs that required any type of thinking—be it teaching nuclear physics, arranging flowers, or anything in between—required a human. Automation was a threat to people who did things with their hands, not their heads. Digital technology changed this.

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Estimates of the job displacement range from big—say one in every ten jobs, which means millions of jobs—to enormous—say six out of ten jobs, which means hundreds of millions. When millions of jobs are displaced and communities are disrupted, we won’t see a stay-calm-and-carry-on attitude. Backlash Bedfellows The Trump and Brexit voters who drove the 2016 backlash know all about the job-displacing impact of automation and globalization. For decades, they, their families, and their communities have been competing with robots at home, and China abroad. They are still under siege financially. Their futures look no brighter. The economic calamity continues—especially in the US. For these voters, the policies adopted in the US and UK since 2016 are the economic equivalent of treating brain cancer with aspirin.

Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

"Robert Solow", 3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic management, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, bond market vigilante , business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Charles Babbage, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, data science, debt deflation, deskilling, disruptive innovation, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Salesforce, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, Thomas L Friedman, too big to fail, Tragedy of the Commons, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce

That’s made especially likely as the “big data” phenomenon continues to unfold: organizations are collecting incomprehensible amounts of information about nearly every aspect of their operations, and a great many jobs and tasks are likely to be encapsulated in that data—waiting for the day when a smart machine learning algorithm comes along and begins schooling itself by delving into the record left by its human predecessors. The upshot of all this is that acquiring more education and skills will not necessarily offer effective protection against job automation in the future. As an example, consider radiologists, medical doctors who specialize in the interpretation of medical images. Radiologists require a tremendous amount of training, typically a minimum of thirteen years beyond high school. Yet, computers are rapidly getting better at analyzing images.

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For a machine, visual recognition is a significant challenge: lighting conditions can be highly variable, and individual fruits can be in a variety of orientations and may be partly or even completely obscured by leaves. The same innovations that are advancing the robotics frontier in factory and warehouse settings are finally making many of these remaining agricultural jobs susceptible to automation. Vision Robotics, a company based in San Diego, California, is developing an octopus-like orange harvesting machine. The robot will use three-dimensional machine vision to make a computer model of an entire orange tree and then store the location of each fruit. That information will then be passed on to the machine’s eight robotic arms, which will rapidly harvest the oranges.33 Boston-area start-up Harvest Automation is initially focused on building robots to automate operations in nurseries and greenhouses; the company estimates that manual labor accounts for over 30 percent of the cost of growing ornamental plants.

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In the next two chapters we’ll look at the impact that automation has already had on jobs and incomes in the United States and consider the characteristics that set information technology apart as a uniquely disruptive force. That discussion will provide a jumping-off point from which to delve into an unfolding story that is poised to upend the conventional wisdom about the types of jobs most likely to be automated and the viability of ever more education and training as a solution: the machines are coming for the high-wage, high-skill jobs as well. * A video of Industrial Perception’s box-moving robot can be seen on the company’s website at http://www.industrial-perception.com/technology.html

The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

"Robert Solow", 3D printing, Alvin Toffler, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Charles Babbage, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, creative destruction, data science, David Graeber, David Ricardo: comparative advantage, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, Fairchild Semiconductor, falling living standards, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, Gini coefficient, Hans Moravec, high-speed rail, Hyperloop, income inequality, income per capita, independent contractor, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, Kiva Systems, knowledge economy, knowledge worker, labor-force participation, labour mobility, Loebner Prize, low skilled workers, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Norbert Wiener, oil shock, On the Economy of Machinery and Manufactures, opioid epidemic / opioid crisis, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, social intelligence, speech recognition, spinning jenny, Stephen Hawking, tacit knowledge, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, warehouse automation, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

But already in the 1960s, the Bureau of Labor Statistics made the following observation: “Mechanization may indeed have created many dull and routine jobs; automation, however, is not an extension but a reversal of this trend: it promises to cut out just that kind of job and to create others of higher skill.”1 They predicted the Great Reversal two decades before it happened by observing what computers can do. Because it takes time before technologies are adopted and put into widespread use, we can infer the exposure of current jobs to future automation by examining technologies that are still imperfect prototypes. There is no economic law that postulates that the next three decades must mirror the last three.

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Office and administrative support, production, transportation and logistics, food preparation, and retail jobs loom large in terms of both their exposure to automation and the percentage of Americans they support. Overall, our algorithm predicted that 47 percent of American jobs are susceptible to automation, meaning that they are potentially automatable from a technological point of view, given the latest computer-controlled equipment and sufficient relevant data for the algorithm to draw upon. What most of these jobs have in common is that they are low-income jobs that do not require high levels of education (figure 18). FIGURE 18: Jobs at Risk of Automation by Income and Educational Attainment Source: C. B. Frey and M. A. Osborne, 2017, “The Future of Employment: How Susceptible Are Jobs to Computerisation?

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Lindert, 2000a, “Three Centuries of Inequality in Britain and America,” in Handbook of Income Distribution, ed. A.B. Atkinson and F. Bourguignon, table 1; and for 1961–2014 from Milanovic 2016a. NOTES Preface 1. J. Gramlich, 2017, “Most Americans Would Favor Policies to Limit Job and Wage Losses Caused by Automation,” Pew Research Center, http://www.pewresearch.org/fact-tank/2017/10/09/most-americans-would-favor-policies-to-limit-job-and-wage-losses-caused-by-automation/. 2. K. Roose, 2018, “His 2020 Campaign Message: The Robots Are Coming,” New York Times, February 18. 3. C. B. Frey and M. A. Osborne, 2017, “The Future of Employment: How Susceptible Are Jobs to Computerisation?

Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, do what you love, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, Future Shock, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

The Future of Employment: How Susceptible are Jobs to Computerisation? Technological Forecasting and Social Change, 114, 254–280. Gramlich, J. (2017). Most Americans Would Favor Policies to Limit Job and Wage Losses Caused by Automation. Pew Research Center. Retrieved from http://www.pewresearch.org/fact-tank/2017/10/09/most-americanswould-favor-policies-to-limit-job-and-wage-lossescaused-by-automation/ Part IV Possibilities and Limitations for AI: What Can’t Machines Do? 11 What Computers Will Never Be Able To Do Thomas Tozer In 1948, John von Neumann, a father of the computer revolution, claimed that for anything he was told a computer could not do, after this ‘thing’ had been explained to him precisely he would be able to make a machine capable of doing it.

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Categories of human jobs widely expected to maintain themselves or expand in line with the contraction of others are creative jobs, jobs requiring exceptional manual dexterity, person to person services, notably healthcare, care work and so on. How many of these jobs will be created? Why should their number equal the total of jobs automated? For creative industries, a winner-takes-all projection is quite common. Top artists get top pay and ordinary ones get nothing, or almost nothing. The next issue: the question of how much people will want to work, or need to work, depends not only on technology and the nature of future work, but on what we think about human wants and needs.

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Yet, by contrast, there has been the hope that automation processes will deliver a better future where human freedom is enlarged. Indeed, some writers have championed automation as a route to a superior ‘post-work’ society (Gorz 1985). Such concerns and hopes have resurfaced in the present, due to predictions of mass job losses via automation (see Spencer 2018). The evolution of machine learning and artificial intelligence, it is claimed, will allow for the replacement of human workers across myriad jobs. Pessimists, like in the past, worry about how society will adjust to a world without work (Ford 2015). Optimists, reviving the older visionary perspective of Marx, embrace ‘full automation’ in the move to a state of luxury consumption, where work is absent (Srnicek and Williams 2015).

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, Alvin Toffler, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Dr. Strangelove, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

Yampolskiy, Professor of Computer Engineering and Computer Science, Director of Cybersecurity lab, Author of Artificial Superintelligence: a Futuristic Approach Unprecedented productivity gains and unlimited leisure—what could possibly go wrong? Everything, says Calum Chace, if we don’t evolve a social system suited to the inevitable world of connected intelligent systems. It’s a failure of imagination to debate whether there will be jobs for humans in the automated world, Chace argues - we must look farther and ask how we will organize society when labor is not necessary to provide for the necessities of life. Find an answer, and life improves for all; without one, society collapses. Read this book to understand how social and technological forces will conspire to change the world—and the problems we need to solve to achieve the promise of the Economic Singularity.

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Automated controllers which were able to modify the operation more flexibly became increasingly common in the early 20th century, but the start-stop decisions were still normally made by humans. In 1968 the first programmable logic controllers (PLCs) were introduced[xv]. These are rudimentary digital computers which allow far more flexibility in the way an electrochemical process operates, and eventually general-purpose computers were applied to the job. The advantages of process automation are clear: it can make an operation faster, cheaper, and more consistent, and it can raise quality. The disadvantages are the initial investment, which can be substantial, and the fact that close supervision is often necessary. Paradoxically, the more efficient an automated system becomes, the more crucial the contribution of the human operators.

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They don't hold out much more hope for their other principal suggested remedy: “education alone is unlikely to solve the problem of surging inequality, [but] it remains the most important factor.” Gartner Gartner is the world’s leading technology market research and advisory consultancy. At its annual conference in October 2014, its research director Peter Sondergaard declared that one in three human jobs would be automated by 2025.[l] "New digital businesses require less labor; machines will make sense of data faster than humans can." He described smart machines as an example of a “super class” of technologies which carry out a wide variety of tasks, both physical and intellectual. He illustrated the case by pointing out that machines have been grading multiple choice examinations for years, but they are now moving on to essays and unstructured text.

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, content marketing, dark matter, data science, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, financial engineering, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, independent contractor, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, tacit knowledge, tech worker, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

Are you sufficiently aware of the signs that you should? To help you get the head start you may need, here are the signs that it’s time to fly the nest. All of them are evidence that a knowledge worker’s job is on the path to automation. 1. There are automated systems available today to do some of its core tasks. The strongest evidence that automation will increasingly threaten a job is the existence of an automated system today that performs all or part of its core function. If we were radiologists or pathologists, for example, we’d be worried about the computer-aided detection systems that read images and detect signs of problems in mammography images or Pap smears.

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., 61–63, 128–29, 204–8, 223–24 business process management, 40 codified tasks and, 12–13, 14, 27–28, 30 content transmission and, 19–20 eras of, 2–5 government policies and, 229–43 income inequality and, 228–29 “isolation syndrome,” 24 job losses and, 1–6, 8, 30, 78, 150–51, 167, 223–24, 226, 227, 238 jobs resistant to, 153–75 process automation, 48–49 “race against the machine,” 8, 29 reductions in cost and time, 48, 49 regulated sectors and legal constraints on, 213–15 repetitive task, 42, 47–48, 49, 50 robotic process, 48–49, 187, 221, 222–23 “rule engines,” 47 sectors using, 1, 11–12, 13, 18, 74, 201–3 (see also specific industries) signs of coming automation, 19–22 Stepping Forward with, 176–200 Stepping In with, 134–52 Stepping Up and, 91–95 strategy of, as self-defeating, 204–8 strongest evidence of job threat, 19 Automation Anywhere, 48, 216 automotive sector, 1 Autor, David, 70–71 Balaporia, Zahir, 189–91 Bankrate.com, 96 Bathgate, Alastair, 156, 157 Baylor College of Medicine, 212 Beaudry, Paul, 6, 24 Belmont, Chris, 209 Berg company, 60–61 Berlin, Isaiah, 171 Bernanke, Ben, 28, 42, 73 Bernaski, Michael, 79, 80, 81, 82, 187 Bessen, James, 133, 233 Betterment, 86–87, 198 big-picture perspective, 71, 75, 76–77, 84, 91, 92, 99, 100, 155 Stepping Up and, 98–100 Binsted, Kim, 125 “black box,” 95, 134, 139, 148, 192, 198 Blanke, Jennifer, 7 Blue Prism, 49, 156, 216, 221 Bohrer, Abram, 159 Bostrom, Nick, 226, 227 Brackett, Glenn, 128 Braverman, Harry, 15–16 Breaking Bad (TV show), 172 Brem, Rachel, 181–82 Bridgewater Associates, 92–93 Brooks, David, 241 Brooks, Rodney, 170, 182 Brown, John Seely, 237 Brynjolfsson, Erik, 6, 8, 27, 74 Bryson, Joanna J., 226 Buehner, Carl, 120 Buffett, Warren, 244 Bush, Vannevar, 64, 248 Bustarret, Claire, 154 BYOD (Bring Your Own Device), 13 Cameron, James, 165–66 Carey, Greg, 154, 156, 172–73 Carr, Nick, 162 CastingWords, 168 Catanzaro, Sandro, 179–80, 193 Cathcart, Ron, 89–91, 95 Cerf, Vint, 248 Chambers, Joshua, 250 Charles Schwab, 88 chess, 74–76 Chi, Michelene, 163 Chicago Mercantile Exchange, 11–12 Chilean miners, 201–2 China, 239 Chiriac, Marcel, 217 Circle (Internet start-up), 146 Cisco, 43 Civilian Conservation Corps (CCC), 238 “Claiming our Humanity in the Digital Age,” 248 Class Dojo, 141 Cleveland Clinic, 54 Clifton, Jim, 8 Clinton, Bill, 108 Clockwork Universe, The (Dolnick), 169–70 Codelco/Codelco Digital, 40, 201–3 Cognex, 47 CognitiveScale, 45, 194, 209 cognitive technologies, 4–5, 32, 33–58.

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It turns out X.ai is a company that uses “natural language processing” software to interpret text and schedule meetings via email. “Amy,” in other words, is automated. Meanwhile, other tools such as email and voice mail, word processing, online travel sites, and Internet search applications have been chipping away the rest of what used to be a secretarial job. Era Two automation doesn’t only affect office workers. It washes across the entire services-based economy that arose after massive productivity gains wiped out jobs in agriculture, then manufacturing. Many modern jobs are transactional service jobs—that is, they feature people helping customers access what they need from complex business systems.

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

AI winter, Airbnb, Albert Einstein, algorithmic bias, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, cotton gin, creative destruction, crony capitalism, data science, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, Hans Moravec, happiness index / gross national happiness, high-speed rail, if you build it, they will come, ImageNet competition, impact investing, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, new economy, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, SoftBank, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, Vision Fund, warehouse robotics, Y Combinator

Osborne, “The Future of Employment: How Susceptible Are Jobs to Automation,” Oxford Martin Programme on Technology and Employment, September 17, 2013, https://www.oxfordmartin.ox.ac.uk/downloads/academic/future-of-employment.pdf. just 9 percent of jobs: Melanie Arntz, Terry Gregory, and Ulrich Zierahn, “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis,” OECD Social, Employment, and Migration Working Papers, no. 189, May 14, 2016, http://dx.doi.org/10.1787/5jlz9h56dvq7-en. 38 percent of jobs: Richard Berriman and John Hawksworth, “Will Robots Steal Our Jobs? The Potential Impact of Automation on the UK and Other Major Economies,” PwC, March 2017, https://www.pwc.co.uk/economic-services/ukeo/pwcukeo-section-4-automation-march-2017-v2.pdf.

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Few good studies have been done for the Chinese market, so I largely stick to studies estimating automation potential in the United States and then extrapolate those results to China. A pair of researchers at Oxford University kicked things off in 2013 with a paper making a dire prediction: 47 percent of U.S. jobs could be automated within the next decade or two. The paper’s authors, Carl Benedikt Frey and Michael A. Osborne, began by asking machine-learning experts to evaluate the likelihood that seventy occupations could be automated in the coming years. Combining that data with a list of the main “engineering bottlenecks” in machine learning (similar to the characteristics denoting the “Safe Zone” in the graphs on pages 155 and 156), Frey and Osborne used a probability model to project how susceptible an additional 632 occupations are to automation.

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In this model, a tax preparer is not merely categorized as one occupation but rather as a series of tasks that are automatable (reviewing income documents, calculating maximum deductions, reviewing forms for inconsistencies, etc.) and tasks that are not automatable (meeting with new clients, explaining decisions to those clients, etc.). The OECD team then ran a probability model to find what percentage of jobs were at “high risk” (i.e., at least 70 percent of the tasks associated with the job could be automated). As noted, they found that in the United States only 9 percent of workers fell in the high-risk category. Applying that same model on twenty other OECD countries, the authors found that the percentage of high-risk jobs ranged from just 6 percent in Korea to 12 percent in Austria. Don’t worry, the study seemed to say, reports of the death of work have been greatly exaggerated.

Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy

Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business process, Checklist Manifesto, cloud computing, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, fail fast, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, meta-analysis, microservices, minimum viable product, MVC pattern, OSI model, performance metric, platform as a service, revision control, risk tolerance, side project, six sigma, the scientific method, Toyota Production System, trickle-down economics, warehouse automation, web application, zero day

Beyond a certain volume of changes, it is infeasible for production-wide changes to be accomplished manually, and at some time before that point, it’s a waste to have manual oversight for a process where a large proportion of the changes are either trivial or accomplished successfully by basic relaunch-and-check strategies. Let’s use internal case studies to illustrate some of the preceding points in detail. The first case study is about how, due to some diligent, far-sighted work, we managed to achieve the self-professed nirvana of SRE: to automate ourselves out of a job. Automate Yourself Out of a Job: Automate ALL the Things! For a long while, the Ads products at Google stored their data in a MySQL database. Because Ads data obviously has high reliability requirements, an SRE team was charged with looking after that infrastructure. From 2005 to 2008, the Ads Database mostly ran in what we considered to be a mature and managed state.

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Index Symbols /varz HTTP handler, Instrumentation of Applications A abusive client behavior, Dealing with Abusive Client Behavior access control, Enforcement of Policies and Procedures ACID datastore semantics, Managing Critical State: Distributed Consensus for Reliability, Choosing a Strategy for Superior Data Integrity acknowledgments, Acknowledgments-Acknowledgments adaptive throttling, Client-Side Throttling Ads Database, Automate Yourself Out of a Job: Automate ALL the Things!-Automate Yourself Out of a Job: Automate ALL the Things! AdSense, Other service metrics aggregate availability equation, Measuring Service Risk, Availability Table aggregation, Rule Evaluation, Aggregation agility vs. stability, System Stability Versus Agility(see also software simplicity) Alertmanager service, Alerting alertsdefined, Definitions false-positive, Tagging software for, Monitoring and Alerting(see also Borgmon; time-series monitoring) anacron, Reliability Perspective Apache Mesos, Managing Machines App Engine, Case Study archives vs. backups, Backups Versus Archives asynchronous distributed consensus, How Distributed Consensus Works atomic broadcast systems, Reliable Distributed Queuing and Messaging attribution policy, Using Code Examples automationapplying to cluster turnups, Soothing the Pain: Applying Automation to Cluster Turnups-Service-Oriented Cluster-Turnup vs. autonomous systems, The Evolution of Automation at Google benefits of, The Value of Automation-The Value for Google SRE best practices for change management, Change Management Borg example, Borg: Birth of the Warehouse-Scale Computer cross-industry lessons, Automating Away Repetitive Work and Operational Overhead database example, Automate Yourself Out of a Job: Automate ALL the Things!

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AdSense, Other service metrics aggregate availability equation, Measuring Service Risk, Availability Table aggregation, Rule Evaluation, Aggregation agility vs. stability, System Stability Versus Agility(see also software simplicity) Alertmanager service, Alerting alertsdefined, Definitions false-positive, Tagging software for, Monitoring and Alerting(see also Borgmon; time-series monitoring) anacron, Reliability Perspective Apache Mesos, Managing Machines App Engine, Case Study archives vs. backups, Backups Versus Archives asynchronous distributed consensus, How Distributed Consensus Works atomic broadcast systems, Reliable Distributed Queuing and Messaging attribution policy, Using Code Examples automationapplying to cluster turnups, Soothing the Pain: Applying Automation to Cluster Turnups-Service-Oriented Cluster-Turnup vs. autonomous systems, The Evolution of Automation at Google benefits of, The Value of Automation-The Value for Google SRE best practices for change management, Change Management Borg example, Borg: Birth of the Warehouse-Scale Computer cross-industry lessons, Automating Away Repetitive Work and Operational Overhead database example, Automate Yourself Out of a Job: Automate ALL the Things!-Automate Yourself Out of a Job: Automate ALL the Things! Diskerase example, Recommendations focus on reliability, Reliability Is the Fundamental Feature Google's approach to, The Value for Google SRE hierarchy of automation classes, A Hierarchy of Automation Classes recommendations for enacting, Recommendations specialized application of, The Inclination to Specialize use cases for, The Use Cases for Automation-A Hierarchy of Automation Classes automation tools, Testing Scalable Tools autonomous systems, The Evolution of Automation at Google Auxon case study, Auxon Case Study: Project Background and Problem Space-Our Solution: Intent-Based Capacity Planning, Introduction to Auxon-Introduction to Auxon availability, Indicators, Choosing a Strategy for Superior Data Integrity(see also service availability) availability table, Availability Table B B4 network, Hardware backend servers, Our Software Infrastructure, Load Balancing in the Datacenter backends, fake, Production Probes backups (see data integrity) Bandwidth Enforcer (BwE), Networking barrier tools, Testing Scalable Tools, Testing Disaster, Distributed Coordination and Locking Services batch processing pipelines, First Layer: Soft Deletion batching, Eliminate Batch Load, Batching, Drawbacks of Periodic Pipelines in Distributed Environments Bazel, Building best practicescapacity planning, Capacity Planning for change management, Change Management error budgets, Error Budgets failures, Fail Sanely feedback, Introducing a Postmortem Culture for incident management, In Summary monitoring, Monitoring overloads and failure, Overloads and Failure postmortems, Google’s Postmortem Philosophy-Collaborate and Share Knowledge, Postmortems reward systems, Introducing a Postmortem Culture role of release engineers in, The Role of a Release Engineer rollouts, Progressive Rollouts service level objectives, Define SLOs Like a User team building, SRE Teams bibliography, Bibliography Big Data, Origin of the Pipeline Design Pattern Bigtable, Storage, Target level of availability, Bigtable SRE: A Tale of Over-Alerting bimodal latency, Bimodal latency black-box monitoring, Definitions, Black-Box Versus White-Box, Black-Box Monitoring blameless cultures, Google’s Postmortem Philosophy Blaze build tool, Building Blobstore, Storage, Choosing a Strategy for Superior Data Integrity Borg, Hardware-Managing Machines, Borg: Birth of the Warehouse-Scale Computer-Borg: Birth of the Warehouse-Scale Computer, Drawbacks of Periodic Pipelines in Distributed Environments Borg Naming Service (BNS), Managing Machines Borgmon, The Rise of Borgmon-Ten Years On…(see also time-series monitoring) alerting, Monitoring and Alerting, Alerting configuration, Maintaining the Configuration rate() function, Rule Evaluation rules, Rule Evaluation-Rule Evaluation sharding, Sharding the Monitoring Topology timeseries arena, Storage in the Time-Series Arena vectors, Labels and Vectors-Labels and Vectors break-glass mechanisms, Expect Testing Fail build environments, Creating a Test and Build Environment business continuity, Ensuring Business Continuity Byzantine failures, How Distributed Consensus Works, Number of Replicas C campuses, Hardware canarying, Motivation for Error Budgets, What we learned, Canary test, Gradual and Staged Rollouts CAP theorem, Managing Critical State: Distributed Consensus for Reliability CAPA (corrective and preventative action), Postmortem Culture capacity planningapproaches to, Practices best practices for, Capacity Planning Diskerase example, Recommendations distributed consensus systems and, Capacity and Load Balancing drawbacks of "queries per second", The Pitfalls of “Queries per Second” drawbacks of traditional plans, Brittle by nature further reading on, Practices intent-based (see intent-based capacity planning) mandatory steps for, Demand Forecasting and Capacity Planning preventing server overload with, Preventing Server Overload product launches and, Capacity Planning traditional approach to, Traditional Capacity Planning cascading failuresaddressing, Immediate Steps to Address Cascading Failures-Eliminate Bad Traffic causes of, Causes of Cascading Failures and Designing to Avoid Them-Service Unavailability defined, Addressing Cascading Failures, Capacity and Load Balancing factors triggering, Triggering Conditions for Cascading Failures overview of, Closing Remarks preventing server overload, Preventing Server Overload-Always Go Downward in the Stack testing for, Testing for Cascading Failures-Test Noncritical Backends(see also overload handling) change management, Change Management(see also automation) change-induced emergencies, Change-Induced Emergency-What we learned changelists (CLs), Our Development Environment Chaos Monkey, Testing Disaster checkpoint state, Testing Disaster cherry picking tactic, Hermetic Builds Chubby lock service, Lock Service, System Architecture Patterns for Distributed Consensusplanned outage, Objectives, SLOs Set Expectations client tasks, Load Balancing in the Datacenter client-side throttling, Client-Side Throttling clients, Our Software Infrastructure clock drift, Managing Critical State: Distributed Consensus for Reliability Clos network fabric, Hardware cloud environmentdata integrity strategies, Choosing a Strategy for Superior Data Integrity, Challenges faced by cloud developers definition of data integrity in, Data Integrity’s Strict Requirements evolution of applications in, Choosing a Strategy for Superior Data Integrity technical challenges of, Requirements of the Cloud Environment in Perspective clustersapplying automation to turnups, Soothing the Pain: Applying Automation to Cluster Turnups-Service-Oriented Cluster-Turnup cluster management solution, Drawbacks of Periodic Pipelines in Distributed Environments defined, Hardware code samples, Using Code Examples cognitive flow state, Cognitive Flow State cold caching, Slow Startup and Cold Caching colocation facilities (colos), Recommendations Colossus, Storage command posts, A Recognized Command Post communication and collaborationblameless postmortems, Collaborate and Share Knowledge case studies, Case Study of Collaboration in SRE: Viceroy-Case Study: Migrating DFP to F1 importance of, Conclusion with Outalator, Reporting and communication outside SRE team, Collaboration Outside SRE position of SRE in Google, Communication and Collaboration in SRE production meetings (see production meetings) within SRE team, Collaboration within SRE company-wide resilience testing, Practices compensation structure, Compensation computational optimization, Our Solution: Intent-Based Capacity Planning configuration management, Configuration Management, Change-Induced Emergency, Integration, Process Updates configuration tests, Configuration test consensus algorithmsEgalitarian Paxos, Stable Leaders Fast Paxos, Reasoning About Performance: Fast Paxos, The Use of Paxos improving performance of, Distributed Consensus Performance Multi-Paxos, Disk Access Paxos, How Distributed Consensus Works, Disk Access Raft, Multi-Paxos: Detailed Message Flow, Stable Leaders Zab, Stable Leaders(see also distributed consensus systems) consistencyeventual, Managing Critical State: Distributed Consensus for Reliability through automation, Consistency consistent hashing, Load Balancing at the Virtual IP Address constraints, Laborious and imprecise Consul, System Architecture Patterns for Distributed Consensus consumer services, identifying risk tolerance of, Identifying the Risk Tolerance of Consumer Services-Other service metrics continuous build and deploymentBlaze build tool, Building branching, Branching build targets, Building configuration management, Configuration Management deployment, Deployment packaging, Packaging Rapid release system, Continuous Build and Deployment, Rapid testing, Testing typical release process, Rapid contributors, Acknowledgments-Acknowledgments coroutines, Origin of the Pipeline Design Pattern corporate network security, Practices correctness guarantees, Workflow Correctness Guarantees correlation vs. causation, Theory costsavailability targets and, Cost, Cost direct, The Sysadmin Approach to Service Management of failing to embrace risk, Managing Risk indirect, The Sysadmin Approach to Service Management of sysadmin management approach, The Sysadmin Approach to Service Management CPU consumption, The Pitfalls of “Queries per Second”, CPU, Overload Behavior and Load Tests crash-fail vs. crash-recover algorithms, How Distributed Consensus Works cronat large scale, Running Large Cron building at Google, Building Cron at Google-Running Large Cron idempotency, Cron Jobs and Idempotency large-scale deployment of, Cron at Large Scale leader and followers, The leader overview of, Summary Paxos algorithm and, The Use of Paxos-Storing the State purpose of, Distributed Periodic Scheduling with Cron reliability applications of, Reliability Perspective resolving partial failures, Resolving partial failures storing state, Storing the State tracking cron job state, Tracking the State of Cron Jobs uses for, Cron cross-industry lessonsApollo 8, Preface comparative questions presented, Lessons Learned from Other Industries decision-making skills, Structured and Rational Decision Making-Structured and Rational Decision Making Google's application of, Conclusions industry leaders contributing, Meet Our Industry Veterans key themes addressed, Lessons Learned from Other Industries postmortem culture, Postmortem Culture-Postmortem Culture preparedness and disaster testing, Preparedness and Disaster Testing-Defense in Depth and Breadth repetitive work/operational overhead, Automating Away Repetitive Work and Operational Overhead current state, exposing, Examine D D storage layer, Storage dashboardsbenefits of, Why Monitor?

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity by Byron Reese

agricultural Revolution, AI winter, artificial general intelligence, basic income, Buckminster Fuller, business cycle, business process, Charles Babbage, Claude Shannon: information theory, clean water, cognitive bias, computer age, crowdsourcing, dark matter, Edward Jenner, Elon Musk, Eratosthenes, estate planning, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, full employment, Hans Moravec, Hans Rosling, income inequality, invention of agriculture, invention of movable type, invention of the printing press, invention of writing, Isaac Newton, Islamic Golden Age, James Hargreaves, job automation, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, lateral thinking, life extension, Louis Pasteur, low skilled workers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Lou Jepsen, Moravec's paradox, On the Revolutions of the Heavenly Spheres, pattern recognition, profit motive, radical life extension, Ray Kurzweil, recommendation engine, Rodney Brooks, Sam Altman, self-driving car, Silicon Valley, Skype, spinning jenny, Stephen Hawking, Steve Wozniak, Steven Pinker, strong AI, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, Turing machine, Turing test, universal basic income, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

Toward the end of the report, they provide a four-hundred-word description of some of the limitations of the study’s methodology. They state that “we make no attempt to estimate how many jobs will actually be automated. The actual extent and pace of computerisation will depend on several additional factors which were left unaccounted for.” So what’s with the 47 percent figure? What they said is that some tasks within 47 percent of jobs will be automated. Well, there is nothing terribly shocking about that at all. Pretty much every job there is has had tasks within it automated. But the job remains. It is just different.

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ASSUMPTION 3: Not enough new jobs will be created quickly enough. The “we won’t make new jobs fast enough” argument, you won’t be surprised to hear, has been around for a while too. In 1961, Time magazine printed, “What worries many job experts more is that automation may prevent the economy from creating enough new jobs. . . . Today’s new industries have comparatively few jobs for the unskilled or semiskilled, just the class of workers whose jobs are being eliminated by automation.” Is this a valid concern today? Will new jobs be slow in coming? I suspect not. In 2016, the World Economic Forum in Davos, Switzerland, published a briefing paper that stated: In many industries and countries, the most in-demand occupations or specialties did not exist 10 or even five years ago, and the pace of change is set to accelerate.

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What this means is that the effects of automation are not going to be overwhelmingly borne by low-wage earners. Order takers at fast-food places may be replaced by machines, but the people who clean up the restaurant at night won’t be. The jobs that automation affects will be spread throughout the wage spectrum. All that being said, there is a widespread concern that automation is destroying jobs at the “bottom” and creating new jobs at the “top.” Automation, this logic goes, may be making new jobs at the top, like geneticist, but is destroying jobs at the bottom like warehouse worker. Doesn’t this situation lead to a giant impoverished underclass locked out of gainful employment?

The War on Normal People: The Truth About America's Disappearing Jobs and Why Universal Basic Income Is Our Future by Andrew Yang

3D printing, Airbnb, assortative mating, augmented reality, autonomous vehicles, basic income, Bear Stearns, Ben Horowitz, Bernie Sanders, call centre, corporate governance, cryptocurrency, data science, David Brooks, Donald Trump, Elon Musk, falling living standards, financial deregulation, financial engineering, full employment, future of work, global reserve currency, income inequality, Internet of things, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Khan Academy, labor-force participation, longitudinal study, low skilled workers, Lyft, manufacturing employment, Mark Zuckerberg, megacity, Narrative Science, new economy, passive income, performance metric, post-work, quantitative easing, reserve currency, Richard Florida, ride hailing / ride sharing, risk tolerance, Ronald Reagan, Sam Altman, San Francisco homelessness, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, single-payer health, Stephen Hawking, Steve Ballmer, supercomputer in your pocket, tech worker, technoutopianism, telemarketer, The future is already here, The Wealth of Nations by Adam Smith, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, unemployed young men, universal basic income, urban renewal, warehouse robotics, white flight, winner-take-all economy, Y Combinator

The rise of the machine that makes human work obsolete has long been thought to be science fiction. Today, this is the reality we face. Although the seriousness of the situation has not reached the mainstream yet, the average American is in deep trouble. Many Americans are in danger of losing their jobs right now due to automation. Not in 10 or 15 years. Right now. Here are the standard sectors Americans work in: Largest Occupational Groups in United States (2016) Occupational Group: All Total Number Employees: 140,400,040 Percentage of Workforce: 100.00% Mean Hourly Wage: $23.86 Median Hourly Wage: $17.81 Occupational Group: Office and Administrative Support Total Number Employees: 22,026,080 Percentage of Workforce: 15.69% Mean Hourly Wage: $17.91 Median Hourly Wage: $16.37 Occupational Group: Sales and Retail Total Number Employees: 14,536,530 Percentage of Workforce: 10.35% Mean Hourly Wage: $19.50 Median Hourly Wage: $12.78 Occupational Group: Food Preparation and Serving Total Number Employees: 12,981,720 Percentage of Workforce: 9.25% Mean Hourly Wage: $11.47 Median Hourly Wage: $10.01 Occupational Group: Transportation and Material Moving Total Number Employees: 9,731,790 Percentage of Workforce: 6.93% Mean Hourly Wage: $17.34 Median Hourly Wage: $14.78 Occupational Group: Production Total Number Employees: 9,105,650 Percentage of Workforce: 6.49% Mean Hourly Wage: $17.88 Median Hourly Wage: $15.93 Source: Bureau of Labor Statistics, Department of Labor, Occupational Employment Statistics (OES) Survey, May 2016.

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Also, as regional economies weaken, restaurants in those regions will struggle and close. Clerical jobs, retail jobs, and food service jobs are the most common jobs in the country. Each category is in grave danger and set to shrink dramatically. Yet they’re not even the ones to worry about most. The single most defining job in the automation story—the one that scares even the most hard-nosed observer—is the number four job category: materials transport, also known as truck driving. FIVE FACTORY WORKERS AND TRUCK DRIVERS You would have to have been asleep these past years not to have noticed that manufacturing jobs have been disappearing in large numbers.

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In 2000 there were still 17.5 million manufacturing workers in the United States. Then, the numbers fell off a cliff, plummeting to fewer than 12 million before rebounding slightly starting in 2011. More than 5 million manufacturing workers lost their jobs after 2000. More than 80 percent of the jobs lost—or 4 million jobs—were due to automation. Men make up 73 percent of manufacturing workers, so this hit working-class men particularly hard. About one in six working-age men in America is now out of the workforce, one of the highest rates among developed countries. What happened to these 5 million workers? A rosy economist might imagine that they found new manufacturing jobs, or were retrained and reskilled for different jobs, or maybe they moved to another state for greener pastures.

The Second Intelligent Species: How Humans Will Become as Irrelevant as Cockroaches by Marshall Brain

Amazon Web Services, basic income, clean water, cloud computing, computer vision, digital map, en.wikipedia.org, full employment, Garrett Hardin, income inequality, job automation, knowledge worker, low earth orbit, mutually assured destruction, Occupy movement, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, Tragedy of the Commons, working poor

For example, in a car factory, robots do all the welding and painting. Many of the factory jobs that remain have not been automated because they require vision. Putting a wiring harness into an automobile on an assembly line is done by humans today because humans can see and easily handle flexible materials. Most other human jobs that remain in an auto assembly factory require vision in the same way. Once robots can see, all of those factory jobs will start going to robots just like all of the welding, painting and machining jobs that are already automated. Think about all of the custodial jobs in hotels, arenas, college campuses, office parks and homes.

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For example, imagine that one company develops self-driving trucks and that they eliminate all of the truck driver jobs, while another company develops automated tools that eliminate many of the remaining factory jobs, and another company develops brick-laying, painting and roofing robots that eliminate quite a few construction jobs, plus another company develops a kiosk system that eliminates the jobs of many waiters and waitresses in restaurants, and so on. Now the society has a permanent loss of 200,000 jobs, with 200,000 homeless people and with more pressure on jobs from other forms of automation that are rapidly advancing. How does the society deal with this situation?

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The same sort of thing will happen in many other industries: retail stores, hotels, airports, factories, construction sites, delivery companies, education and so on. All of these jobs will evaporate at approximately the same time, leaving all of those workers unemployed. But who will be first? Which large group of employees will lose their jobs first as robots and automation start taking jobs away from human beings? It is likely to be a million or more truck drivers.... Chapter 4 - The Aborted Trucker Riots How long will it take before computer consciousness arises and begins the process of making human beings completely irrelevant? We don't know.

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It by Azeem Azhar

23andMe, 3D printing, A Declaration of the Independence of Cyberspace, Ada Lovelace, additive manufacturing, air traffic controllers' union, Airbnb, algorithmic management, algorithmic trading, Amazon Mechanical Turk, autonomous vehicles, basic income, Berlin Wall, Bernie Sanders, Big Tech, Boeing 737 MAX, Boris Johnson, Bretton Woods, carbon footprint, Chris Urmson, Clayton Christensen, cloud computing, collective bargaining, computer age, computer vision, coronavirus, COVID-19, creative destruction, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Graeber, David Ricardo: comparative advantage, decarbonisation, deglobalization, deindustrialization, dematerialisation, Diane Coyle, digital map, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, Elon Musk, emotional labour, energy security, Fairchild Semiconductor, Fall of the Berlin Wall, Firefox, Frederick Winslow Taylor, fulfillment center, future of work, Garrett Hardin, gender pay gap, gig economy, global macro, global pandemic, global supply chain, global value chain, global village, Hans Moravec, happiness index / gross national happiness, hiring and firing, hockey-stick growth, ImageNet competition, income inequality, independent contractor, industrial robot, intangible asset, Jane Jacobs, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Just-in-time delivery, Kickstarter, Kiva Systems, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, low skilled workers, lump of labour, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, megacity, Mitch Kapor, Network effects, new economy, Ocado, offshore financial centre, PalmPilot, Panopticon Jeremy Bentham, Peter Thiel, price anchoring, RAND corporation, ransomware, Ray Kurzweil, remote working, RFC: Request For Comment, Richard Florida, ride hailing / ride sharing, Robert Bork, Ronald Coase, Ronald Reagan, Salesforce, Sam Altman, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, software as a service, Steve Ballmer, Steve Jobs, Stuxnet, subscription business, tacit knowledge, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, Thomas Malthus, Tragedy of the Commons, Turing machine, Uber and Lyft, Uber for X, uber lyft, universal basic income, uranium enrichment, warehouse automation, winner-take-all economy, Yom Kippur War

In the most famous piece of research on the topic, two Oxford academics, Michael Osborne and Carl Frey, predicted that as much as 47 per cent of the US workforce were in jobs at risk of redundancy thanks to advanced computerised systems such as machine learning.9 Forecasters and futurists leapt onto these and similar findings – Osborne and Frey’s research was cited more than 7,000 times in seven years.10 In one case, in 2017, the market research company Forrester predicted that nearly 25 million US workers would lose their jobs due to automation by 2027, and that automation would only create 14 million new ones.11 A BBC headline warned of 20 million job losses globally by 2030.12 This narrative holds more than a nugget of truth. In the 2010s, many people did lose their jobs to automation. In 2017, the boss of Deutsche Bank talked of using automation to get rid of thousands of jobs, especially people who ‘spend a lot of the time basically being an abacus’.13 And though banking is a particularly unsentimental industry, Deutsche Bank weren’t alone in wanting to automate office tasks.

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As of 2019, the firm had 200,000 robots working tirelessly around the globe, sorting billions of packages a year.32 Amazon is possibly one of most robotised large companies in the world – with an astonishing one physical robot for every four workers. If you are one of the 200 million or so people who enjoy Amazon Prime’s same-day delivery, you do so courtesy of some of those bots. You might think, then, that the triumph of Amazon would lead to the loss of thousands of jobs. Automation, after all, is supposed to be leading to mass unemployment. Yet as Covid-19 hit in 2020, Amazon went on a hiring spree. And no small spree. In the six months after the World Health Organization declared the coronavirus outbreak a pandemic, Amazon announced four waves of hiring, amounting to a staggering 308,000 new jobs globally in one year.33 Amazon’s example reveals that, on the level of individual companies, automation can create more jobs than it destroys.

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All this means that we’re left with a slightly different picture of our supposedly jobless future. The more that superstar firms like Amazon and Netflix automate, the bigger they grow; the bigger they grow, the more people they employ. There’s an exponential process here, but it doesn’t lead us to employee-free corporations. Where workers do lose their jobs due to automation, it’s not because they themselves are replaced by some piece of software. It’s often because the firms they work for fail. And the firms they work for fail because their management or shareholders are unwilling or unable to keep up with the new possibilities of technology. That failure often extends to failing to invest in the training that their employees need to implement the latest technologies.

Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

"Robert Solow", 3D printing, Ada Lovelace, AI winter, Airbnb, Alvin Toffler, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, blockchain, brain emulation, Buckminster Fuller, Charles Babbage, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, hedonic treadmill, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, radical life extension, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, The future is already here, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

The leaders with the weaker forces may feel less inclined to start a war they can be fairly confident they will lose. 3.3 – Economic singularity In the medium term, AI presents economists, business people and policy makers with an even bigger concern than digital disruption. It may render most of us unemployed, and indeed unemployable, because our jobs have been automated. Automation Automation has been a feature of human civilisation since at least the early industrial revolution. In the 15th century, Dutch workers threw their shoes into textile looms to break them. (Their shoes were called sabots, which is a possible etymology for the word “saboteur”.)

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This means unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.” (19) Decades later, in the late 1970s, a powerful BBC Horizon documentary called Now the Chips are Down alerted a new generation to the idea (and showcased some truly appalling ties.) (20) Up to now the replacement of humans by machines has been a gradual process. Although it has been painful for each individual who was dismissed from a particular job, there was generally the chance to retrain, or find new work elsewhere. The idea that each job lost to automation equates to a person rendered permanently unemployed is known as the Luddite Fallacy. This is unfair to the Luddites, who weren’t advancing a sociological thesis about the long-term effects of technology. They were simply protesting about the very real danger of starvation in the short term.

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But harder to escape is the thought that the piece of analysis or decision-making that the AI can’t do today, it may well be able to do tomorrow, or the next day. Rapid job churn or economic singularity If computers steal our old jobs, perhaps we can invent lots of new ones? In the past, people whose jobs were automated turned their hands to more value-adding activity, and the net result was higher overall productivity. The children of people who did back-breaking farm work for subsistence wages moved into the cities where they earned a little more doing mundane jobs in offices and factories. Their great-grandchildren now work as social media marketers and user experience designers – jobs which their great-grandparents could not have imagined.

The Origins of the Urban Crisis by Sugrue, Thomas J.

affirmative action, business climate, collective bargaining, correlation coefficient, creative destruction, Credit Default Swap, deindustrialization, desegregation, Detroit bankruptcy, Ford paid five dollars a day, George Gilder, ghettoisation, Gunnar Myrdal, hiring and firing, housing crisis, income inequality, indoor plumbing, informal economy, invisible hand, job automation, jobless men, Joseph Schumpeter, labor-force participation, low-wage service sector, manufacturing employment, mass incarceration, military-industrial complex, New Urbanism, oil shock, pink-collar, postindustrial economy, rent control, Richard Florida, Ronald Reagan, side project, Silicon Valley, strikebreaker, The Bell Curve by Richard Herrnstein and Charles Murray, The Chicago School, union organizing, upwardly mobile, urban planning, urban renewal, War on Poverty, white flight, working-age population, Works Progress Administration

It was simply “a better way to do the job.”16 Certainly automated production replaced some of the more dangerous and onerous factory jobs. At Ford, automation eliminated “mankilling,” a task that demanded high speed and involved tremendous risk. “Mankilling” required a worker to remove hot coil springs from a coiling machine, lift them to chest height, turn around, and lower them into a quench tank, all within several seconds. In Ford’s stamping plants, new machines loaded and unloaded presses, another relatively slow, unsafe, and physically demanding job before automation. Here automation offered real benefits to workers.17 5.2.

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The hemorrhage of jobs continued in 1953 and 1955, when Ford announced the construction of new engine production facilities at Brookpark Village, Ohio, and in Lima, Ohio.23 The effects of automation on job opportunities in communities like Detroit were a well-guarded corporate secret. Responding to labor union criticism of automation, employers downplayed the possibility of significant job loss. When Ford began automating and decentralizing the Rouge plant, John Bugas, Ford’s vice president for industrial relations, told workers that they had nothing to fear. “I do not believe,” wrote Bugas in 1950, “that the over-all reduction in employees in the Rouge operations resulting from the building of new facilities will be substantial.”

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Interestingly, he advocated early retirement as “one means of cushioning the effect of reduced employment,” and noted that thousands of workers had retired under the “flexible retirement age provision” of the GM pension plan.27 The UAW, for the most part, worried about automation only insofar as it affected employment levels nationwide. National-level data gave little reason for concern. In the 1950s, there was little evidence to show that the number of auto industry jobs nationwide would fall because of automation. Some economists argued that over the long run, the introduction of automated processes would increase jobs nationwide. Aggregate employment statistics, however, masked profound local variation. Local economies in places like Detroit reeled from the consequences of automation-caused plant closings or work force reductions.

The American Dream Is Not Dead: (But Populism Could Kill It) by Michael R. Strain

Bernie Sanders, business cycle, centre right, creative destruction, deindustrialization, Donald Trump, feminist movement, full employment, gig economy, Gini coefficient, income inequality, job automation, labor-force participation, market clearing, market fundamentalism, new economy, opioid epidemic / opioid crisis, Robert Gordon, Ronald Reagan, social intelligence, Steven Pinker, The Rise and Fall of American Growth, Tyler Cowen, upwardly mobile, working poor

As with the tasks required in low-wage occupations, it’s hard to program computers and robots to do these well. It is occupations in the middle—that paid better than those at the bottom because their tasks required precision and accuracy, but paid less than those at the top because workers in those occupations are relatively less skilled—that were hit hardest by automation, because their jobs were most amenable to being automated. Those jobs included production and craft workers, machine operators and assemblers—exactly the types of jobs that have political salience today—the jobs that the president mistakenly argues were primarily affected by globalization (which was a factor, but not nearly as large a factor as automation), the jobs that didn’t require a college degree but did offer a middle-class life.

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But the tasks those workers perform in their jobs have changed. Cash handling is less important—the ATMs can do that. Instead, interpersonal and problem-solving skills have become more important. Relationship management is a skill in demand. The branches still need workers—just to do different things. This is the broader lesson: Certain job tasks can be automated. But most jobs represent a bundle of tasks, some of which are quite difficult to automate. As technology advances and becomes cheaper, situational adaptability, interpersonal interaction, judgment and common sense, and communications skills will become more valuable, because they complement technological change rather than substitute for it.

A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind

3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, blue-collar work, Boston Dynamics, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, fulfillment center, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Khan Academy, Kickstarter, low skilled workers, lump of labour, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Network effects, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, tacit knowledge, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

The ALM hypothesis also helps to expose several types of mistaken thinking about the future of work. For instance, it is very common to hear discussions about the chances of various jobs being automated, with statements like “nurses are safe but accountants are in trouble” or “X percent of jobs in the United States are at risk from automation but only Y percent in the UK.” One influential study, by Oxford’s Carl Frey and Michael Osborne, is often reported as claiming that 47 percent of US jobs are at risk of automation in the coming decades, with telemarketers the most at risk (a “99 percent” risk of automation) and recreational therapists the least (a “0.2 percent” risk).29 But as Frey and Osborne themselves have noted, conclusions like this are very misleading.

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On the other hand, more than 60 percent of the occupations were made up of tasks of which at least 30 percent could be automated.30 In other words, very few jobs could be entirely done by machines, but most could have machines take over at least a significant part of them. That’s why those who claim that “my job is protected from automation because I do X,” where “X” is a task that is particularly difficult to automate, are falling into a trap. Again, no job is made up of one task: lawyers do not only make court appearances, surgeons do not only perform operations, journalists do not only write original opinion pieces. Those particular tasks might be hard to automate, but that does not necessarily apply to all of the other activities these same professionals do in their jobs.

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The most influential institutes and think tanks—from the IMF to the World Bank, from the OECD to the International Labour Organization—have relied on it to decide which human endeavors are at risk of automation.34 Mark Carney, the governor of the Bank of England, has echoed it in a warning of a “massacre of the Dilberts”: new technologies, he believes, threaten “routine cognitive jobs” like the one that employs Dilbert, the cubicle-bound comic strip character.35 President Obama similarly warned that roles “that are repeatable” are at particular risk of automation.36 And large companies have structured their thinking around the idea: the investment bank UBS claims that new technologies will “free people from routine work and so empower them to concentrate on more creative, value-added services”; the professional services firm PwC says that “by replacing workers doing routine, methodical tasks, machines can amplify the comparative advantage of those workers with problem-solving, leadership, EQ, empathy, and creativity skills”; and Deloitte, another professional services firm, reports that in the UK “routine jobs at high risk of automation have declined but have been more than made up for by the creation of lower-risk, non-routine jobs.”37 Magazine writers and commentators have also popularized the concept. The Economist, for instance, explains that “what determines vulnerability to automation, experts say, is not so much whether the work concerned is manual or white-collar but whether or not it is routine.”

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Black Swan, call centre, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, data science, Donald Trump, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fulfillment center, future of work, gig economy, Google Glasses, Google X / Alphabet X, income inequality, independent contractor, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Lyft, Marc Andreessen, Mark Zuckerberg, military-industrial complex, money market fund, natural language processing, Ocado, pets.com, plutocrats, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, Tim Cook: Apple, too big to fail, Travis Kalanick, two-pizza team, Uber and Lyft, uber lyft, universal basic income, warehouse automation, warehouse robotics, wealth creators, web application, Whole Earth Catalog

Of course, Sanders and Bezos’s tussle aside, the long-term worry for Amazon’s lower-rung workers is not that their compensation will dip below that which is sufficient for a comfortable middle-class lifestyle (even at $15 an hour, which works out to $31,000 a year, that goal remains elusive), but that their jobs may be automated out of existence. On this topic, Bezos is a techno-optimist. He believes that the economy will provide jobs for those displaced by automation and AI. That said, from time to time he has pondered the need for a universal basic income (UBI) to make up for lost jobs. In essence, with a UBI the federal government steps in and pays every American a basic wage to make up for the disruption that technology is about to wreak on the job market.

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By 2022, there will be more than 29 billion connected devices worldwide, roughly four times the number of people in the world. Now tech giants such as Alibaba, JD.com, Tencent, and even Google’s parent, Alphabet—with its smart home devices and self-driving cars—are joining Amazon in its quest to infiltrate every corner of our lives with AI. This has dire implications for the global job market. As these companies automate their warehouses, use drones and self-driving trucks for delivery, many solid blue-collar jobs will disappear. Moreover, as Amazon and other global tech giants move into new industries, they’ll accelerate the digitization of health care, banking, and other sectors of the economy and have an even bigger impact on jobs.

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It might be true that the economy will eventually replace those jobs, but in the interim a scenario where nearly a third of the world’s workers will be forced to seek new jobs is chilling. It stretches the imagination to believe that the legions of warehouse workers, call center agents, grocery cashiers, retail clerks, and truck drivers who lose their jobs to automation will quickly and easily learn to become computer programmers, solar energy installers, or home care providers. The global economy may eventually generate enough new jobs to replace the 800 million lost, but the disruption in the meantime will be immense. Until now, technology has been about making a worker’s job easier.

Four Futures: Life After Capitalism by Peter Frase

Aaron Swartz, Airbnb, basic income, bitcoin, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cryptocurrency, deindustrialization, do what you love, Dogecoin, Edward Snowden, emotional labour, Erik Brynjolfsson, Ferguson, Missouri, fixed income, full employment, future of work, Herbert Marcuse, high net worth, high-speed rail, income inequality, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), iterative process, Jevons paradox, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kim Stanley Robinson, litecoin, mass incarceration, means of production, military-industrial complex, Occupy movement, pattern recognition, peak oil, plutocrats, post-work, postindustrial economy, price mechanism, private military company, Ray Kurzweil, Robert Gordon, Second Machine Age, self-driving car, sharing economy, Silicon Valley, smart meter, TaskRabbit, technoutopianism, The future is already here, The Future of Employment, Thomas Malthus, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, Wolfgang Streeck

The folk tale of John Henry and the steam hammer, which originated in the nineteenth century, describes a railroad worker who tries to race against a steel powered drill and wins—only to drop dead from the effort. But several factors have come together to accentuate worries about technology and its effect on labor. The persistently weak post-recession labor market has produced a generalized background anxiety about job loss. Automation and computerization are beginning to reach into professional and creative industries that long seemed immune, threatening the jobs of the very journalists who cover these issues. And the pace of change at least seems, to many, to be faster than ever. The “second machine age” is a concept promoted by Brynjolfsson and McAfee.

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As I argue in the following sections, the real impediments to tight labor markets are currently political, not technological. AUTOMATION’S ETERNAL RETURN Mainstream economists have for generations made the same argument about the supposed danger that automation poses to labor. If some jobs are automated, they argue, labor is freed up for other, new, and perhaps better kinds of work. They point to agriculture, which once occupied most of the workforce but now occupies only about 2 percent of it in a country like the United States. The decline of agricultural employment freed up workers who would go into the factories and make up the great industrial manufacturing economy of the mid-twentieth century.

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Supporters of this position can point to previous waves of anxiety about automation, such as the one in the 1990s that produced works like Jeremy Rifkin’s The End of Work and Stanley Aronowitz and Bill DeFazio’s The Jobless Future.17 As early as 1948, the mathematician and cyberneticist Norbert Weiner warned in his book Cybernetics that in the “second, cybernetic industrial revolution,” we were approaching a society in which “the average human being of mediocre attainments or less has nothing to sell that it is worth anyone’s money to buy.”18 While many jobs have indeed been lost to automation, and jobless rates have risen and fallen with the business cycle, the social crisis of extreme mass unemployment, which many of these authors anticipated, has failed to arrive. Of course, this is the kind of argument that can only be made from a great academic height, while ignoring the pain and disruption caused to actual workers who are displaced, whether or not they can eventually find new work.

The Third Pillar: How Markets and the State Leave the Community Behind by Raghuram Rajan

activist fund / activist shareholder / activist investor, affirmative action, Affordable Care Act / Obamacare, air traffic controllers' union, airline deregulation, Albert Einstein, Andrei Shleifer, banking crisis, barriers to entry, basic income, battle of ideas, Bernie Sanders, blockchain, borderless world, Bretton Woods, British Empire, Build a better mousetrap, business cycle, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, computer vision, conceptual framework, corporate governance, corporate raider, corporate social responsibility, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, data acquisition, David Brooks, Deng Xiaoping, desegregation, deskilling, disinformation, disruptive innovation, Donald Trump, Edward Glaeser, facts on the ground, financial innovation, financial repression, full employment, future of work, global supply chain, high net worth, household responsibility system, housing crisis, Ida Tarbell, illegal immigration, income inequality, industrial cluster, intangible asset, invention of the steam engine, invisible hand, Jaron Lanier, job automation, John Maynard Keynes: technological unemployment, joint-stock company, Joseph Schumpeter, labor-force participation, low skilled workers, manufacturing employment, market fundamentalism, Martin Wolf, means of production, Money creation, moral hazard, Network effects, new economy, Nicholas Carr, obamacare, opioid epidemic / opioid crisis, Productivity paradox, profit maximization, race to the bottom, Richard Thaler, Robert Bork, Robert Gordon, Ronald Reagan, Sam Peltzman, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, SoftBank, South China Sea, South Sea Bubble, Stanford marshmallow experiment, Steve Jobs, superstar cities, The Future of Employment, The Wealth of Nations by Adam Smith, trade liberalization, trade route, transaction costs, transfer pricing, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, Upton Sinclair, Walter Mischel, War on Poverty, women in the workforce, working-age population, World Values Survey, Yom Kippur War, zero-sum game

Of the 1,250 workers represented by the steel workers union in Granite City, only 375 were working at the end of 2016.19 As described by Amy Goldstein in her book Janesville, which follows the Janesville community after General Motors closed a large plant there, the effects on the community can be devastating. In contrast, the job losses due to greater automation and computerization have been spread across manufacturing and services, and typically have hit firms that are more likely to be located near urban areas. Moreover, instead of the whole factory or office closing, a few workers doing routine jobs that can be automated are let go periodically. The remaining workers doing nonroutine work continue to be employed, and typically now are more productive. Higher productivity allows their employer to lower prices, sell more, and hire more workers in nonroutine jobs to meet the increased demand.

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It is where we congregate to start broader political movements. As we will see later in the book, a healthy, engaged, proximate community may therefore be how we manage the tension between the inherited tribalism in all of us and the requirements of a large, diverse nation. Looking to the future, as more production and service jobs are automated, the human need for relationships and the social needs of the neighborhood may well provide many of the jobs of tomorrow. In closely knit communities, a variety of transactions take place without the use of money or enforceable contracts. One side may get all the benefits in some transactions.

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A well-documented tragedy of the Industrial Revolution in England is the fate of the handloom weavers.22 The automation of spinning toward the end of the eighteenth century meant that there was much more yarn available to be woven. Automated power looms were only slowly being introduced, so there was strong demand for the labor of handloom weavers to weave the now abundantly available yarn into cloth. Unfortunately, the writing was on the wall—these jobs would be automated also. Indeed, because it was costly to let expensive power looms lie idle, the handloom weavers were already the first to be deprived of work when business slowed. Nevertheless, even as wages in handloom weaving fell as automation and the entry of workers created a labor surplus, the numbers joining the handloom weaving sector continued to increase.

The Lonely Century: How Isolation Imperils Our Future by Noreena Hertz

"side hustle", Airbnb, airport security, algorithmic bias, Asian financial crisis, Bernie Sanders, Big Tech, big-box store, Broken windows theory, call centre, Capital in the Twenty-First Century by Thomas Piketty, car-free, Cass Sunstein, centre right, conceptual framework, Copley Medal, coronavirus, correlation does not imply causation, COVID-19, dark matter, deindustrialization, Diane Coyle, disinformation, Donald Trump, emotional labour, en.wikipedia.org, Erik Brynjolfsson, Fellow of the Royal Society, future of work, gender pay gap, gig economy, Gordon Gekko, greed is good, Greta Thunberg, happiness index / gross national happiness, housing crisis, illegal immigration, independent contractor, industrial robot, Jane Jacobs, Jeff Bezos, job automation, job satisfaction, knowledge economy, labor-force participation, longitudinal study, low skilled workers, Lyft, Mark Zuckerberg, mass immigration, means of production, megacity, meta-analysis, move fast and break things, Network effects, new economy, Pepto Bismol, QWERTY keyboard, Ray Oldenburg, remote working, rent control, RFID, Ronald Reagan, Salesforce, San Francisco homelessness, Second Machine Age, Shoshana Zuboff, Silicon Valley, Skype, Snapchat, Social Responsibility of Business Is to Increase Its Profits, SoftBank, Steve Jobs, surveillance capitalism, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Great Good Place, The Wealth of Nations by Adam Smith, Tim Cook: Apple, Uber and Lyft, uber lyft, urban planning, Wall-E, warehouse automation, warehouse robotics, WeWork, working poor

One of the most widely cited projections of just how significant job losses to automation could be comes from Oxford University academics Carl Frey and Michael Osborne, who forecasted in 2013 that almost half of jobs in the US were at risk of being automated in the next twenty years.76 In April 2020 in an article in the Financial Times, Frey, who directs Oxford University’s programme on the Future of Work, made clear that the coronavirus was likely to accelerate this trend.77 This is supported by a survey conducted by the auditing firm EY in March 2020 of company bosses in forty-five countries, which found that just over 40% were already investing in accelerating automation as they prepared for a post-pandemic world.78 Even if we were to stick with the most conservative estimates – as few as 10% of jobs being lost to automation over the coming decade – we’d still be talking about upwards of 13 million workers losing their jobs in the US alone.79 This, of course, would be on top of the millions upon millions who lost their jobs during the economic crisis caused by the pandemic. In many ways this trajectory is all too familiar. Manufacturing has experienced millions of job losses as a result of automation over the past few decades. In the US, over 5 million manufacturing jobs have been lost to automation since 2000, with each robot replacing on average 3.3 human workers80 – a process that accelerated during the Great Recession beginning in 2008.81 In China – where automation is a major plank of the government’s ‘Made in China 2025’ strategy – this dislocation has been taking place on an even greater scale, with up to 40% of workers in some Chinese industrial companies having been replaced by robots in just the past few years.82 At one mobile-phone factory in Dongguan, 90% of its human workforce has been replaced by robots that work around the clock and never require a lunchbreak.83 Undoubtedly some new categories of jobs will emerge in this age of robots and machines.

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But history teaches us not only that there is a particular characteristic of jobs lost to automation – once gone they typically vanish, never to return – but also that such employment that is on offer to those who lose their jobs to automation tends to be worse paid than their previous work and of lower status, at least when it comes to low-skilled labour.84 This is one of the reasons why in the US the people most likely to have worked in factories before the rise of robots – men with only a high-school diploma – have seen their wages fall in real terms since the 1980s.85 It’s a similar story in China where many of those who have lost their jobs to automation in recent years are now ‘trying their luck in China’s swelling service sector’ where they are ‘struggling to make a living wage’, according to Jenny Chan, an assistant professor of sociology at Hong Kong Polytechnic University.86 If anything this is likely to be even more the case now, given the disproportionate impact of the coronavirus on jobs in the service sector.

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This is not because Jake, Flippy’s human co-worker, won’t be able to feel bonded to him – as we will see in the next chapter, he might well – but because although Jake tells me how ‘fun’ it is to see so many customers come in full of ‘Flippy’ love, that feeling may not persist once Jake realises that he (and many more like him) won’t just be battling against other humans for employment: his competition will be a whole army of food-service robots who will always use the correct spatula for raw and cooked meat, always clean the grill meticulously, always know exactly when it’s time to flip the burger, will never be late to work, ask for time off, need benefits, go on strike, call in sick or infect a co-worker. No human could ever compete with that, especially as the cost of robots continues to decrease and as they get better at doing human jobs. One of the most widely cited projections of just how significant job losses to automation could be comes from Oxford University academics Carl Frey and Michael Osborne, who forecasted in 2013 that almost half of jobs in the US were at risk of being automated in the next twenty years.76 In April 2020 in an article in the Financial Times, Frey, who directs Oxford University’s programme on the Future of Work, made clear that the coronavirus was likely to accelerate this trend.77 This is supported by a survey conducted by the auditing firm EY in March 2020 of company bosses in forty-five countries, which found that just over 40% were already investing in accelerating automation as they prepared for a post-pandemic world.78 Even if we were to stick with the most conservative estimates – as few as 10% of jobs being lost to automation over the coming decade – we’d still be talking about upwards of 13 million workers losing their jobs in the US alone.79 This, of course, would be on top of the millions upon millions who lost their jobs during the economic crisis caused by the pandemic.

Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy by Erik Brynjolfsson

"Robert Solow", Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business cycle, business process, call centre, combinatorial explosion, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, easy for humans, difficult for computers, Erik Brynjolfsson, factory automation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, hiring and firing, income inequality, intangible asset, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, Loebner Prize, low skilled workers, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, Ray Kurzweil, rising living standards, Robert Gordon, self-driving car, shareholder value, Skype, too big to fail, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

Meanwhile other technologies like data visualization, analytics, high-speed communications, and rapid prototyping have augmented the contributions of more abstract and data-driven reasoning, increasing the value of those jobs. Skill-biased technical change has also been important in the past. For most of the 19th century, about 25% of all agriculture labor threshed grain. That job was automated in the 1860s. The 20th century was marked by an accelerating mechanization not only of agriculture but also of factory work. Echoing the first Nobel Prize winner in economics, Jan Tinbergen, Harvard economists Claudia Goldin and Larry Katz described the resulting SBTC as a “race between education and technology.”

User Stories Applied: For Agile Software Development by Mike Cohn

A Pattern Language, c2.com, call centre, continuous integration, index card, iterative process, job automation, job satisfaction, phenotype, tacit knowledge, web application

Leadership by Algorithm: Who Leads and Who Follows in the AI Era? by David de Cremer

algorithmic bias, algorithmic management, bitcoin, blockchain, business climate, business process, Computing Machinery and Intelligence, corporate governance, data science, Donald Trump, Elon Musk, future of work, job automation, Kevin Kelly, Mark Zuckerberg, meta-analysis, Norbert Wiener, pattern recognition, Peter Thiel, race to the bottom, robotic process automation, Salesforce, scientific management, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, Stephen Hawking, The Future of Employment, Turing test, zero-sum game

This scientific evidence, combined with our tendency to think of humans and machines as us versus them, poses the question of whether AI will replace people’s jobs at center-stage.³⁰ This question is no longer a peripheral one. It dominates many discussions in business and society, to the extent that websites now exist where one can discover the likelihood of your job being automated in the next 20 years. In fact, we do not even have to wait for this scenario to happen. For example, in 2018 online retailer Shop Direct announced the closure of warehouses because nearly 2,000 jobs had become automated. The largest software company in Europe, SAP, has also eliminated several thousands of jobs by introducing AI into their management structure. The framework for today’s society is clearly dominated by the assumption that humans will be replaced by technology whenever possible (human-out-of-the-loop) and that it only makes sense for humans to be part of the business process when automation is not yet possible (contingent participation).

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And, subsequently, we recognize suddenly the beauty of an algorithm as a likely candidate to make decisions and, hence, lead. If we move from our theoretical exercise above and on to what we see in practice, we may find some evidence in favour of leadership by algorithm. The one thing that is not going unnoticed is that jobs are increasingly being automated, with algorithms integrated into decision-making processes. This trend could be interpreted as a signal that a new kind of automated leadership may well be on its way. And, why should this be? Well, the faster acting, more accurate and consistent self-learning algorithms become, the more likely it could be that humans will gradually transfer the power to lead to those same algorithms.

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And it is because of this broader social context that employees are also required to possess the social skills to talk, negotiate, lobby and collaborate with others. Unfortunately, it is also this element of giving meaning to the job in a broader work environment that is hardly ever a focus in the discussion of whether or not jobs should be automated. I argue that we are facing the same problem when we are talking about whether algorithms should and can move into leadership roles. In today’s discussions, a trend has emerged that leadership is only looked upon as a set of required skills. If all the boxes are ticked, a person should be ready to assume a leadership role.

The Fourth Industrial Revolution by Klaus Schwab

3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, Buckminster Fuller, call centre, circular economy, clean water, collaborative consumption, commoditize, conceptual framework, continuous integration, crowdsourcing, digital twin, disintermediation, disruptive innovation, distributed ledger, Edward Snowden, Elon Musk, epigenetics, Erik Brynjolfsson, future of work, global value chain, Google Glasses, income inequality, Internet Archive, Internet of things, invention of the steam engine, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, life extension, Lyft, Marc Benioff, mass immigration, megacity, meta-analysis, more computing power than Apollo, mutually assured destruction, Narrative Science, Network effects, Nicholas Carr, personalized medicine, precariat, precision agriculture, Productivity paradox, race to the bottom, randomized controlled trial, reshoring, RFID, rising living standards, Sam Altman, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, smart cities, smart contracts, software as a service, Stephen Hawking, Steve Jobs, Steven Levy, Stuxnet, supercomputer in your pocket, TaskRabbit, The Future of Employment, The Spirit Level, total factor productivity, transaction costs, Uber and Lyft, uber lyft, Watson beat the top human players on Jeopardy!, Wayback Machine, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar

In fact, in the vast majority of cases, the fusion of digital, physical and biological technologies driving the current changes will serve to enhance human labour and cognition, meaning that leaders need to prepare workforces and develop education models to work with, and alongside, increasingly capable, connected and intelligent machines. Impact on skills In the foreseeable future, low-risk jobs in terms of automation will be those that require social and creative skills; in particular, decision-making under uncertainty and the development of novel ideas. This, however, may not last. Consider one of the most creative professions – writing – and the advent of automated narrative generation. Sophisticated algorithms can create narratives in any style appropriate to a particular audience.

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, 17 September 2013 Positive impacts – Cost reductions – Efficiency gains – Unlocking innovation, opportunities for small business, start-ups (smaller barriers to entry, “software as a service” for everything) Negative impacts – Job losses – Accountability and liability – Change to legal, financial disclosure, risk – Job automation (refer to the Oxford Martin study) The shift in action Advances in automation were reported on by FORTUNE: “IBM’s Watson, well known for its stellar performance in the TV game show Jeopardy!, has already demonstrated a far more accurate diagnosis rate for lung cancers than humans – 90% versus 50% in some tests.

Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan

Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, Computing Machinery and Intelligence, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, Fairchild Semiconductor, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, information asymmetry, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kiva Systems, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

This paper meets the requirements of ANSI/NISO Z39.48–1992 (Permanence of Paper). 10 9 8 7 6 5 4 3 2 1 For Camryn Paige Kaplan Turn your dreams into words and make them true. Contents Preface Introduction: Welcome to the Future 1. Teaching Computers to Fish 2. Teaching Robots to Heel 3. Robotic Pickpockets 4. The Gods Are Angry 5. Officer, Arrest That Robot 6. America, Land of the Free Shipping 7. America, Home of the Brave Pharaohs 8. Take This Job and Automate It 9. The Fix Is In Outroduction: Welcome to Your Children’s Future Acknowledgments Notes Index Preface I’m an optimist. Not by nature, but by U.S. government design. After Russia humiliated the United States with the 1957 launch of Sputnik, the first space satellite, the government decided that science education should be a national priority.

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These systems use enormous computing power and sophisticated adaptive AI algorithms to continuously adjust radio signals to local conditions at multiple receivers simultaneously, eliminating the need for on-premises wiring entirely.17 One such technology is DIDO (distributed input, distributed output), developed by Silicon Valley entrepreneur Steve Perlman, whose previous accomplishments include QuickTime and WebTV. If his approach wins out in the marketplace, he will add handsomely to his already vast fortune, while the 250,000 people currently employed installing and repairing wiring in the United States will be applying for entry-level jobs with Enterprise Rent-a-Car.18 8. Take This Job and Automate It Despite what you read in the press, global warming isn’t all bad, and certainly not for everyone. There will be winners and losers, depending on where you live. In my case, it’s a tad too cool around here for my taste, but luckily for me, the average temperature where I live is projected to rise several degrees over the next few decades.

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The Law School Admissions Council reports that applications in 2014 were down nearly 30 percent over just the previous two years, returning to levels last seen in 1977.30 New graduates can be saddled with debt of more than $150,000, while the average graduate’s starting salary in 2011 was only $60,000, down nearly 17 percent from just two years earlier.31 But they were the lucky ones. In 2009, an astounding 35 percent of newly minted law school graduates failed to find work that required them to pass the bar exam.32 There are, of course, many factors affecting job opportunities for attorneys, but automation is certainly among them. And the problems are just getting started. To date, the use of computers in the legal profession has been largely focused on the storage and management of legal documents. This reduces billable hours because you don’t have to start from scratch when drafting contracts and briefs.

Forward: Notes on the Future of Our Democracy by Andrew Yang

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, Affordable Care Act / Obamacare, Amazon Web Services, American Society of Civil Engineers: Report Card, basic income, Bernie Sanders, blockchain, blue-collar work, call centre, centre right, clean water, coronavirus, correlation does not imply causation, COVID-19, data is the new oil, data science, disinformation, Donald Trump, facts on the ground, forensic accounting, future of work, George Floyd, gig economy, global pandemic, income inequality, independent contractor, Jaron Lanier, Jeff Bezos, job automation, labor-force participation, Marc Benioff, Mark Zuckerberg, medical bankruptcy, new economy, obamacare, opioid epidemic / opioid crisis, pez dispenser, QAnon, recommendation engine, risk tolerance, rolodex, Ronald Reagan, Sam Altman, Saturday Night Live, shareholder value, Shoshana Zuboff, Silicon Valley, Simon Kuznets, single-payer health, Snapchat, SoftBank, surveillance capitalism, tech billionaire, universal basic income, winner-take-all economy, working poor

I yelled to the crowd. “How beautiful are you? You don’t look like the internet to me!” Every statement brought a roar from the crowd. Then I launched into my routine, giving a thirty-minute speech about how Trump was not the cause but the symptom of a disease that had been building up for years. How jobs were getting automated away in massive numbers. How the Washington establishment’s halfhearted attempts to retrain America’s workforce weren’t working and no one cared. How gross domestic product (GDP), the measure around which so many decisions were made, was useless and didn’t measure the kind of work my wife did every day.

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PART II THE ERA OF INSTITUTIONAL FAILURE CHAPTER 9 SYSTEMS FAILURE Coming off the campaign trail in February was a very strange feeling. It felt like I had been running a hundred miles an hour for months, only to find myself suddenly suspended in place. My goals of raising the alarm about job automation and the need to rewrite the social contract hadn’t changed. But how could I best achieve them now? What would post-campaign life look like? I talked on the phone with Pete Buttigieg after both our campaigns ended, and he said something to me that rang true: “You need a vacation. But it also feels like a vacation might not do the trick.”

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One could ask, how could the richest country in the history of the world allow itself to decline in such fundamental ways? When I ran for president, I made a case for adjustments based on the transformation of the economy due to advancing technologies. During interviews I would often cite various facts and figures—like that we had lost five million manufacturing jobs across various states primarily to automation or that labor force participation rates and business formation rates had already plummeted to multi-decade lows. I was stunned at how infrequently either journalists or lawmakers engaged with the substance of what I was saying. It truly was as if I were speaking a different language.

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines by William Davidow, Michael Malone

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Francis Fukuyama: the end of history, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, high-speed rail, holacracy, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Snapchat, speech recognition, Stuxnet, surveillance capitalism, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, two and twenty, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, warehouse automation, zero day, zero-sum game, Zipcar

But they are arising in a territory we never anticipated—the virtual realm—and in the forms of corporations such as Facebook, Google, and Amazon. Society is already feeling some of the early effects of the Autonomous Revolution. The ice-hard stability of the good job is being replaced by the indeterminate “gig.” Countless other jobs have been shipped overseas or automated out of existence, devastating the middle class. Mind-altering processes are being used to reengineer our children’s brains. Our Brave New Social Networking world looks less open and connected every day—and more and more like the dystopian surveillance states of George Orwell and Aldous Huxley.

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Inflation-adjusted annual earnings for production employees peaked in the 1970s and is down by 14.6 percent.7 The bottom 50 percent of U.S. taxpayers, approximately 68 million people, had an average adjusted gross income of about $14,800.8 Those incomes are supplemented by transfer payments on the order of $13,000 per household.9 Nobody knows how many autonomous workers are now on the job; all we have is guesses and estimates. But the estimates of the job losses that are to come are staggering. A recent study by Frey and Osborne looked at 702 occupations and concluded that 47 percent of American jobs might be automated in the future.10 McKinsey estimates that 85 percent of the simpler business processes can be automated. Many of those processes are in companies that provide services. Using automation, one European bank was able to originate mortgages in fifteen minutes—instead of two to ten days—cutting origination costs by 70 percent.11 A more recent study by McKinsey estimates that 400 to 800 million jobs around the world will be lost to automation by 2030.12 In 2011, W.

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Two hundred years ago, when jobs were vanishing in agriculture, they were on the rise in manufacturing. Then, as the latter area matured, new jobs were created in the service industries. Eighty percent of the workforce, 104 million all told, now work in services. But as more and more of those jobs are automated, we need a new area of economic growth to absorb those excess workers. Unfortunately, that area appears to be the burgeoning workerless segment.42 Many of the proposals for bringing back the good job involve investing in infrastructure and creating more manufacturing jobs. But here is the challenge: there are only 6.9 million jobs in construction and 12.5 million jobs in manufacturing, a total of about 19.4 million.

Inventing the Future: Postcapitalism and a World Without Work by Nick Srnicek, Alex Williams

3D printing, additive manufacturing, air freight, algorithmic trading, anti-work, antiwork, back-to-the-land, banking crisis, basic income, battle of ideas, blockchain, Boris Johnson, Bretton Woods, business cycle, call centre, capital controls, capitalist realism, carbon footprint, Cass Sunstein, centre right, collective bargaining, crowdsourcing, cryptocurrency, David Graeber, decarbonisation, deindustrialization, deskilling, Doha Development Round, Elon Musk, Erik Brynjolfsson, Ferguson, Missouri, financial independence, food miles, Francis Fukuyama: the end of history, full employment, future of work, gender pay gap, housing crisis, income inequality, industrial robot, informal economy, intermodal, Internet Archive, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, Kiva Systems, late capitalism, liberation theology, Live Aid, low skilled workers, manufacturing employment, market design, Martin Wolf, mass immigration, mass incarceration, means of production, megaproject, minimum wage unemployment, Modern Monetary Theory, Mont Pelerin Society, neoliberal agenda, New Urbanism, Occupy movement, oil shale / tar sands, oil shock, Overton Window, patent troll, pattern recognition, Paul Samuelson, Philip Mirowski, post scarcity, post-Fordism, post-work, postnationalism / post nation state, precariat, precautionary principle, price stability, profit motive, quantitative easing, reshoring, Richard Florida, rising living standards, road to serfdom, Robert Gordon, Ronald Reagan, Second Machine Age, secular stagnation, self-driving car, Slavoj Žižek, social web, stakhanovite, Steve Jobs, surplus humans, tacit knowledge, the built environment, The Chicago School, The Future of Employment, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, wages for housework, warehouse automation, We are the 99%, women in the workforce, working poor, working-age population

With the potential for extensive automation of work – a topic that will be discussed further in the next chapter – it is likely that we will see the following trends in the years to come: 1.The precarity of the developed economies’ working class will intensify due to the surplus global labour supply (resulting from both globalisation and automation). 2.Jobless recoveries will continue to deepen and lengthen, predominantly affecting those whose jobs can be automated at the time. 3.Slum populations will continue to grow due to the automation of low-skilled service work, and will be exacerbated by premature deindustrialisation. 4.Urban marginality in the developed economies will grow in size as low-skilled, low-wage jobs are automated. 5.The transformation of higher education into job training will be hastened in a desperate attempt to increase the supply of high-skilled workers. 6.Growth will remain slow and make the expansion of replacement jobs unlikely. 7.The changes to workfare, immigration controls and mass incarceration will deepen as those without jobs are increasingly subjected to coercive controls and survival economies.

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(The racialisation of the surplus population also enabled owners to manipulate the white working class, keeping wages low and preventing unionisation.)89 As capitalism grew in the postwar era, manufacturing jobs eventually opened up to the black population, and by the mid 1950s rates of black and white youth unemployment were broadly similar.90 But then the globalisation of the labour supply wreaked havoc on low-skilled black workers. With manufacturing jobs shipped overseas or subject to automation, these workers were disproportionately affected by deindustrialisation.91 Industrial jobs left the urban centres and were replaced by service work often located in distant suburban areas.92 The urban ghettos were left to rot, becoming concentrated hubs of long-term joblessness.93 They became poverty traps, devoid of jobs, with little community support and a proliferation of underground economies.94 Entire communities were cast aside from the machinery of capitalism and left to fend for themselves with whatever means could be scraped together.

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A variety of policies can help in this project: more state investment, higher minimum wages and research devoted to technologies that replace rather than augment workers. In the most detailed estimates of the labour market, it is suggested that between 47 and 80 per cent of today’s jobs are capable of being automated.44 Let us take this estimate not as a deterministic prediction, but instead as the outer limit of a political project against work. We should take these numbers as a standard against which to measure our success. While full automation of the economy is presented here as an ideal and a demand, in practice it is unlikely to be fully achieved.45 In certain spheres, human labour is likely to continue for technical, economic and ethical reasons.

AI 2041 by Kai-Fu Lee, Chen Qiufan

3D printing, active measures, airport security, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, carbon footprint, Charles Babbage, computer vision, coronavirus, corporate governance, corporate social responsibility, COVID-19, cryptocurrency, data science, delayed gratification, dematerialisation, digital map, digital twin, Elon Musk, fault tolerance, future of work, Future Shock, game design, global pandemic, Google Glasses, Google X / Alphabet X, happiness index / gross national happiness, hedonic treadmill, hiring and firing, Hyperloop, information security, Internet of things, iterative process, job automation, low earth orbit, Lyft, mass immigration, money: store of value / unit of account / medium of exchange, mutually assured destruction, natural language processing, Nelson Mandela, optical character recognition, pattern recognition, plutocrats, post scarcity, profit motive, QR code, quantitative easing, Richard Feynman, ride hailing / ride sharing, robotic process automation, Satoshi Nakamoto, self-driving car, Silicon Valley, smart cities, smart contracts, smart transportation, Snapchat, speech recognition, Stephen Hawking, telemarketer, Tesla Model S, The future is already here, Turing test, uber lyft, universal basic income, warehouse automation, warehouse robotics, zero-sum game

What are the jobs that AI can and cannot displace? What is the future of work? Do we need a new social contract to redefine humans’ fundamental expectations around employment? If long hours of work for economic output are no longer a necessary feature of human life, how will we spend our time? The jobs most at risk of automation by AI tend to be routine and entry-level jobs. This trend will exacerbate existing challenges in society, as those who are poor become poorer. This complicated dynamic—AI’s potential to create unprecedented efficiency as well as deep structural problems in society—can perhaps be best summed up by this question: When it comes to work, is AI ultimately a blessing or a curse?

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COVID-19 has accelerated the digitization of workflow by companies, which will make RPA and other technologies even easier to apply, thereby accelerating job displacements. While AI displacement is gradual, eventually it will also be total. Optimists argue that productivity gains from new technology almost always produce economic benefits—that more growth and more prosperity always mean more jobs. But AI and automation differ from other technologies. As we’ve established in previous chapters, AI is an omni-use technology that will drive changes across hundreds of industries and millions of tasks simultaneously, both cognitive and physical. While most technologies were job creators and job destroyers at the same time—think about how the assembly line changed the automotive industry from artisans hand-assembling expensive cars to routine workers building many cars at much lower prices—the explicit goal of AI is to take over human tasks, thereby decimating jobs.

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TOWARD AN AI ECONOMY AND A NEW SOCIAL CONTRACT Turning some of the ideas above into reality would be an unprecedented undertaking for humanity. The AI job-displacement tidal wave will eventually take away virtually all routine jobs, which tend to be entry-level jobs. But if no human takes an entry-level job, how will they learn, grow, and advance to more senior and less routine jobs? As automation becomes pervasive, we need to make sure there are still ways for people to enter all professions, to learn by doing, and to get promoted based on their capabilities. The blurring of “made-up job,” “practical training,” and “real job” are likely to emerge out of necessity, along with the use of VR technologies to implement this.

Does Capitalism Have a Future? by Immanuel Wallerstein, Randall Collins, Michael Mann, Georgi Derluguian, Craig Calhoun, Stephen Hoye, Audible Studios

affirmative action, blood diamonds, Bretton Woods, BRICs, British Empire, business cycle, butterfly effect, creative destruction, deindustrialization, demographic transition, Deng Xiaoping, discovery of the americas, distributed generation, Dr. Strangelove, eurozone crisis, fiat currency, financial engineering, full employment, Gini coefficient, global village, hydraulic fracturing, income inequality, Isaac Newton, job automation, joint-stock company, Joseph Schumpeter, junk bonds, land tenure, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, loose coupling, low skilled workers, market bubble, market fundamentalism, mass immigration, means of production, mega-rich, Mikhail Gorbachev, military-industrial complex, mutually assured destruction, offshore financial centre, oil shale / tar sands, Ponzi scheme, postindustrial economy, reserve currency, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, Suez crisis 1956, too big to fail, transaction costs, Washington Consensus, WikiLeaks

Nevertheless, Schumpeter-inspired economists also rely on nothing more than extrapolation of past trends for the argument that the number of jobs created by new products will make up for the jobs lost by destruction of old markets. None of these theories take account of the technological displacement of communicative labor, the escape valve that in the past has brought new employment to compensate for the loss of old employment. It has been argued that as telephone operators and file clerks lose their jobs to automated and computerized systems, an equal number acquire jobs as software developers, computer technicians, and mobile phone salespersons. But no one has shown any good theoretical reason why these numbers should be equal; much less why the automation of these kinds of technical and communicative tasks—for instance by shopping online—cannot drive down the size of the white-collar labor force.

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In an advanced economy such as the United States, jobs in the service sector have grown to about 75% of the labor force, a result of the decline in industrial and agricultural/extractive occupations (Autor and Dorn 2013). But the service sector is becoming squeezed by the IT economy, itself little more than twenty-five years old. Sales jobs are rapidly becoming automated by computer-generated messaging and by online buying; in brick-and-mortar stores, retail clerks are being replaced by electronic scanners. Management positions too will come under increasing pressure as artificial intelligence grows. There is no intrinsic end to this process of replacing human with computers and other machines.

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To keep the focus on the central point: how will these affect the technological displacement crisis? Some of them will exacerbate it; some will add pressures for state breakdown and thus raise the chances of revolutions, the rolling of multiple sixes on the dice. Will any of these complications turn back technological displacement, increasing middle class employment, creating new jobs to offset automation and computerization, and in sufficient degree that capitalism will be saved? Let us consider a brief checklist of complications, with these questions in mind. Global unevenness. The mechanisms driving capitalist crisis operate with different intensity in different countries and regions of the world.

I, Warbot: The Dawn of Artificially Intelligent Conflict by Kenneth Payne

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asperger Syndrome, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Boston Dynamics, combinatorial explosion, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, cuban missile crisis, data science, delayed gratification, disinformation, drone strike, Elon Musk, functional programming, Google X / Alphabet X, Internet of things, job automation, John Nash: game theory, John von Neumann, Kickstarter, loss aversion, military-industrial complex, mutually assured destruction, Nash equilibrium, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, RAND corporation, ransomware, risk tolerance, Ronald Reagan, self-driving car, semantic web, side project, Silicon Valley, South China Sea, speech recognition, Stanislav Petrov, stem cell, Stephen Hawking, Steve Jobs, strong AI, Stuxnet, theory of mind, Turing machine, Turing test, uranium enrichment, urban sprawl, V2 rocket, Von Neumann architecture, Wall-E, zero-sum game

Skeem. ‘The limits of human predictions of recidivism’, Science Advances, 6(7) (2020), https://doi.org/10.1126/sciadv.aaz0652. 11. Dastin, Jeffrey. ‘Amazon scraps secret AI recruiting tool that showed bias against women’, Reuters, 10 October 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKC-N1MK08G. 12. Singer, Natasha. ‘Amazon is pushing facial technology that a study says could be biased’, The New York Times, 24 January 2019, www.nytimes.com/2019/01/24/technology/amazon-facial-technology-study.html. 13.

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DARPA, ‘DARPA initiates design of LongShot Unmanned Air Vehicle,’ 8 February 2021, https://www.darpa.mil/news-events/2021–02–08. Dastin, Jeffrey. ‘Amazon scraps secret AI recruiting tool that showed bias against women’, Reuters, 10 October 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKC-N1MK08G. Davies, Joshua. ‘Say hello to Stanley,’ WIRED, 1 June 2006, https://www.wired.com/2006/01/stanley/. Dehaene, Stanislas. Reading in the Brain: The New Science of How We Read. New York: Penguin Books, 2010.

The End of Nice: How to Be Human in a World Run by Robots (Kindle Single) by Richard Newton

3D printing, Black Swan, British Empire, Buckminster Fuller, Clayton Christensen, crowdsourcing, deliberate practice, disruptive innovation, fail fast, fear of failure, Filter Bubble, future of work, Google Glasses, Isaac Newton, James Dyson, Jaron Lanier, Jeff Bezos, job automation, lateral thinking, Lean Startup, low skilled workers, Mark Zuckerberg, move fast and break things, Paul Erdős, Paul Graham, recommendation engine, rising living standards, Robert Shiller, Silicon Valley, Silicon Valley startup, skunkworks, social intelligence, Steve Ballmer, Steve Jobs, Tyler Cowen, Y Combinator

These once-valuable qualities of rule-bound, routinised and biddable behaviour and consistent, predictable decision-making are precisely the attributes of robots and algorithms. They are not, however, the greatest strengths of humans and this is why the days of humans-as-meat-machines are drawing to a close. To save your job from automation you cannot put in more hours, run faster, make fewer mistakes, sleep any less than you do already. Steel-cased algorithms arriving at the howling speed of six-legged robot soldiers take job after job and each time they teach the lesson: the humans were mere cogs in the machine and they just got switched out.

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Only to find that his new-fangled push-button lift reached the lobby and he was escorted out of the building. And the same story of woe is repeated through history by chimney sweeps, ice delivery men, punkahwallahs, bus conductors and ironmongers. Of course for you it might be true and maybe no machine can replace you. Perhaps. But if 50% of today’s jobs get automated (as an Oxford University study recently warned… and other studies predict worse outcomes) then the entire fabric of society will be utterly transformed. Those very few people whose jobs remain unchanged may discover their privileged position is as fine and grand as a proud horse harrumphing about their specialness while they stand on the hard shoulder of a motorway.

The Job: The Future of Work in the Modern Era by Ellen Ruppel Shell

3D printing, affirmative action, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, basic income, Baxter: Rethink Robotics, big-box store, blue-collar work, Buckminster Fuller, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, computer vision, corporate governance, corporate social responsibility, creative destruction, crowdsourcing, data science, deskilling, disruptive innovation, do what you love, Donald Trump, Downton Abbey, Elon Musk, emotional labour, Erik Brynjolfsson, factory automation, follow your passion, Frederick Winslow Taylor, future of work, game design, gamification, glass ceiling, hiring and firing, immigration reform, income inequality, independent contractor, industrial robot, invisible hand, It's morning again in America, Jeff Bezos, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, Kodak vs Instagram, labor-force participation, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, means of production, move fast and break things, new economy, Norbert Wiener, obamacare, offshore financial centre, Paul Samuelson, precariat, Ralph Waldo Emerson, risk tolerance, Robert Gordon, Robert Shiller, Rodney Brooks, Ronald Reagan, scientific management, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, Steve Jobs, stock buybacks, The Chicago School, The Theory of the Leisure Class by Thorstein Veblen, Thomas L Friedman, Thorstein Veblen, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban renewal, Wayback Machine, WeWork, white picket fence, working poor, Y Combinator, young professional, zero-sum game

The second obstacle to an open and honest dialogue is the assumption that acquiring and sustaining good work is by its very nature a winner-take-most proposition by which the victories of the few condemn the many to defeat. On its face, this assumption might seem justified. For many of us the job “hunt” has become a sort of Hunger Game, a cutthroat competition to survive in a world where jobs have been automated away, or shifted from higher-wage nations like the United States to lower-wage nations like China and India. Donald Trump acknowledged—and exploited—this trend when pledging to bring jobs “back home.” The problem with this claim is that in a global economy not all jobs have any particular “home”—many can happily land almost anywhere, and when they land in low-wage nations the benefits sometimes return to American consumers in the form of lower-priced goods.

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The nation was indeed built on a foundation of cheap labor, and since the steady decline of unions in the 1970s, we’ve come to rely on that cheap labor to prop up industries whose jobs, we’re warned, will fall victim to automation if workers who perform them dare to demand higher wages or better terms and conditions of employment. Indeed, the Bureau of Labor Statistics predicts that despite growing demand for agricultural products over the next decade, an increased demand for agricultural workers is unlikely, as their jobs are being steadily automated. Adjunct college instructors, farm laborers, and others working as contractors may have the flexibility to move between and among gigs, but there’s a good chance that many if not most would gladly trade that flexibility for the opportunity to exert more control over their working lives.

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The reason, according to a leading drone industry website, is that “there are a lot of people who know how to fly drones.” Job-training programs, whether in or outside of community colleges, have been popular for generations. In response to a 3.5 percent drop in “goods-producing industries,” President John Kennedy signed the Manpower Development and Training Act of 1962, directed at workers who had lost their jobs to automation. The act was the first of a series leading to the Job Training Partnership Act (JTPA) of the early 1980s. In an era of deregulation and cuts in antipoverty efforts, job training and retraining enjoyed widespread support among politicians for offering a “leg up” to the poor rather than a “handout.”

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking by Michael Bhaskar

"Robert Solow", 3D printing, additive manufacturing, AI winter, Albert Einstein, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Benoit Mandelbrot, Berlin Wall, Big bang: deregulation of the City of London, Big Tech, blockchain, Boeing 747, brain emulation, Brexit referendum, call centre, charter city, citizen journalism, Claude Shannon: information theory, Clayton Christensen, clean water, cleantech, Columbian Exchange, coronavirus, cosmic microwave background, COVID-19, creative destruction, crony capitalism, cyber-physical system, dark matter, David Graeber, deindustrialization, dematerialisation, demographic dividend, Deng Xiaoping, discovery of penicillin, disruptive innovation, Donald Trump, double entry bookkeeping, Edward Jenner, Edward Lorenz: Chaos theory, Elon Musk, en.wikipedia.org, endogenous growth, energy security, energy transition, epigenetics, Eratosthenes, Ernest Rutherford, fail fast, Fellow of the Royal Society, Francis Fukuyama: the end of history, germ theory of disease, glass ceiling, global pandemic, Goodhart's law, Google Glasses, Google X / Alphabet X, Haber-Bosch Process, hedonic treadmill, Herman Kahn, hive mind, Hyperloop, Ignaz Semmelweis: hand washing, Innovator's Dilemma, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of the printing press, invention of the steam engine, invention of the telegraph, invisible hand, Isaac Newton, ITER tokamak, James Watt: steam engine, Jeff Bezos, jimmy wales, job automation, Johannes Kepler, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, liberation theology, lone genius, loss aversion, Louis Pasteur, Mark Zuckerberg, Martin Wolf, megacity, megastructure, Menlo Park, Minecraft, minimum viable product, mittelstand, Modern Monetary Theory, Mont Pelerin Society, Murray Gell-Mann, natural language processing, nuclear winter, nudge unit, oil shale / tar sands, open economy, opioid epidemic / opioid crisis, PageRank, patent troll, Peter Thiel, plutocrats, post scarcity, precautionary principle, publish or perish, purchasing power parity, Ray Kurzweil, remote working, rent-seeking, Republic of Letters, Richard Feynman, Robert Gordon, secular stagnation, shareholder value, Silicon Valley, Silicon Valley ideology, Simon Kuznets, skunkworks, Slavoj Žižek, sovereign wealth fund, spinning jenny, statistical model, stem cell, Steve Jobs, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, total factor productivity, transcontinental railway, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, We wanted flying cars, instead we got 140 characters, When a measure becomes a target, X Prize, Y Combinator

The history of AI is one of crests of hype followed by troughs – the so-called AI winter set in from the 1980s with the broad failure of ‘symbolic’ approaches. But by the 2010s a new spring had arrived, and DeepMind was in the vanguard. Public conversation around AI has been dominated by the risks and rewards of job automation. And yes, this is a significant question. Nonetheless, I have yet to meet an AI scientist motivated by the prospect of automating a call centre. Instead, they are motivated by the prospect of discovery and knowledge far beyond our present abilities. AI scientists are nerds; above all they care about science and ideas.

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Rogers 332 Homo omnis (Homni) 300 Honjo, Tasuko 58 Hooke, Robert 333 Horgan, John 118, 168 Hossenfelder, Sabine 121–2 Howard Hughes Medical Institute 322 Howes, Anton 24, 172 hubris 122 Huebner, Jonathan 77–9, 81, 86, 91, 94, 97 Hugo, Victor 27 human frontier 7–19, 27, 40–1, 147–8, 156, 158, 249, 277–8, 336, 342–3 and artificial intelligence 250 diminishing returns of the 98 getting stuck at the 45 moral 132, 138 putting to rest 42 and science 123, 124 slowing down at the 14, 86–7, 131 and women 269 hydrogen 145 Hypatia of Alexandria 304 IBM 33, 184, 240, 265, 296, 312 Idea Paradox 178–9, 187, 191, 217, 226, 250, 254, 283–4, 301, 312, 342 ideas ‘0-I’ ideas 31 and economic growth 88, 89–92 nature of 17–18 protection 89 and research and development 90–1 slowdown 90–1 spread of 89–90 world-changing 5–6 see also big ideas illiteracy 277 imagination 16, 236 immunotherapy 57–61 income global 278 median 95 incrementalism 34, 71, 279 India 44, 71, 213, 264–8, 275, 277, 279–80, 284, 294–5, 308, 313, 326–8 individualism 282 Industrial Enlightenment 27 Industrial Revolution 242–3, 252–3, 295, 301 First (IIR) 79–81, 253, 259, 289, 306, 325 Fourth (4IR) 82, 86, 253–4 Second (2IR) 79–80, 81, 83, 84, 86, 104, 253, 289 Third (3IR) 81–2, 83–4, 86, 92, 253–4 inequality 251 infant mortality rate 10, 54 influence 28, 32–3 Initial Public Offerings (IPOs) 95 Inklings 124, 295 innovation 16, 18, 25, 31, 96, 98, 161, 183–5, 214, 274, 286, 288, 296–7, 298, 339 age of 5 breakthrough 32–3 curve of 77–9 disruptive 34 epochal 31 and human capital 277 and industrial revolution 84 and military technology 316–17 normal rates of 177–8 normalisation of 172 parasitic 194 radical 95 risky nature 335 scale 83 slowdown 83, 85 society's attitude to 17, 18 Innovation Illusion 13 institutional revolution 286–301, 309–10, 328–32, 337–8 Intel 92, 253, 272 intellectual, the, death of 110 intellectual property (IP) 89, 195–6, 251, 331 intelligence 247, 299 collective 339 ‘intelligence explosion’ 238 limits to 166–79 interconnectedness 274, 299–300 interest rates 95 International Exhibition of Modern Art, The 103 International Mathematics Olympiad 276 International Space Station 70 Internet 85, 128–9, 183, 185, 196, 246–7, 253–4, 265–6, 272, 274, 297, 300, 315, 329 invention 11, 15, 16, 156–8, 274, 286, 288, 339 and Bell Labs 180–4 and cities 270–2 industrial 24–5 macro- 31, 81 micro- 31 and military technology 316–17 and patents 97 IP see intellectual property IPOs see Initial Public Offerings iron oxide 89 Islam 133, 340 Islamic caliphate 259, 260 Islamic State 305 ITER 146 Jackson, Andrew 67 Jainism 108 Japan 264, 266, 268, 279, 296, 305 Jefferson, Thomas 211 Jenner, Edward 47 Jesus 24, 216, 303 jet engines 69–70 jetpacks 71, 72 Jiankui, He 255–7, 280, 285 job automation 228 job destruction 96 Jobs, Steve 159, 186 Jones, Benjamin F. 156, 158–9, 160–1 Jones, Charles I. 90–1, 93, 94, 152 joy 170–1 Joyce, James 103, 166 Jung, Carl 104 Justinian 304 Kahn, Bob 253 Kahn, Herman 129 The Year 2000 9, 12, 13 Kaku, Michio 337 Kardashev, Nikolai 337 Kardashev Scale 337–43 Kauffmann, Stuart 203 Kay, John 24–5 Kelly, Kevin 300 Kelly, Mervin 182, 206 Kepler, Johannes 36, 229 Khmer Rouge 305 Kim Il-Sung 114 Klimt, Gustav 188 Knossos 153 knowledge ‘burden of knowledge’ effect 154–65, 175, 178, 235, 338 human frontier of 7–19 Koch, Robert 38 Kodak 184 Koestler, Arthur 36, 39 Kokoschka, Oskar 188 Korea 138, 266, 268, 305 see also North Korea; South Korea Kremer, Michael 274 Kristeva, Julia 111 Kuhn, Thomas 29, 30, 159 Kurzweil, Ray 79 Kuznets, Simon 31 labour 88 Lakatos, Imre 121 Lamarck, Jean-Baptiste 35, 164 Langley, Samuel Pierpont 66 Laozi 108 Large Hadron Collider (LHC) 118, 233, 239 Latin America 266–7, 275, 295 Latour, Bruno 111 Lavoisier, Antoine 29, 34 Lawrence Berkeley National Laboratory 234–5, 296 Lawrence, D.H. 103 lawyers 205–6 Lazarsfeld, Paul 189 Le Figaro (newspaper) 64 Le Mans 64 Le sacre du printemps (The Rite of Spring) 99, 100–2, 104 Leeuwenhoek, Antonie van 231 left wing politics 113 Leibniz, Gottfried Wilhelm 25 Lem, Stanisław 44–5 Lenin, Vladimir Ilyich 188 lenses 231 Leonardo da Vinci 155 Lessing, Doris 152 Lewis, C.S. 124 LHC see Large Hadron Collider Li, Danielle 317–18 liberal democracy 111–12 life expectancy 52–5, 57, 93–4, 169 lift 65 light 75–7 Lilienthal, Otto 62, 335 Lister, Joseph 332 literature 103, 108, 124 Locke, John 25, 137, 138 Lockheed Martin 184, 296 London 133 loonshots 31–2 Loos, Adolf 103, 188 Lorenz, Edward 163 low-hanging fruit paradox 149–54, 167, 178 Lucretius 35, 155 Lulu and Nana (genetically edited twins) 255–7, 264 Luther, Martin 230 Lyell, Charles 34, 35 Lynn, Vera 105 M-theory 120 Mach, Ernst 188 machine learning (ML) 225–7, 233–4, 237, 243, 338 Madonna 105 magnetism 74–7 Mahler, Gustav 188 mail order 84–5 Malevich, Kazimir 103 Malik, Charles 134–6, 140 Malthus, Thomas 35 managerialism 204–5, 206–7 Mandelbrot, Benoit 163 Manhattan Project 119, 144–5, 148, 289, 296, 315, 317–18 Manutius, Aldus 253–4 Mao Zedong 328 Maoism 114 Marcellus 4 Marconi, Guglielemo 216, 289 Margulis, Lynn 203 Mars 218, 296, 318, 338, 341 Marx, Karl 36, 329 Massachusetts Institute of Technology (MIT) 88, 146, 184–5, 296, 314, 316, 327 see also MIT Technology Review materials science 234–5 Maxwell, James Clerk 74–7, 79, 80, 166 Demon (thought experiment) 76 Treatise on Electricity and Magnetism 74–7 Mayan civilisation 43 Mazzucato, Mariana 185, 194, 318 McCloskey, Deirdre Nansen 24 McCormick, Cyrus 11 McKeown, Thomas 53 McKinsey 34, 246 medicine 45, 46–62, 70–3, 93–4, 98, 124–6, 217–18, 338 see also drugs mega-authored papers 157 Meister, Joseph 48 Mendeleev, Dmitri 149 Menlo Park lab 286–7, 293 Merton, Robert 328 Mesopotamia 25, 291 Mesoudi, Alex 164 micro-organisms 49–51 microscopes 49, 232 Microsoft 33, 265 Middle East 138 migration 272–3 military technology 3–4 Minecraft 86 Minoans 43, 153 Minsky, Marvin 227 Mises, Ludwig von 189 MIT see Massachusetts Institute of Technology MIT Technology Review 255 Mitchell, Joni 104 modern art 103 modernity 11, 80, 81, 83–4, 85 Mokyr, Joel 25, 31, 44, 68, 81 molecule libraries 56 Mont Pelerin Society 329 Montagu, Lady Mary Wortley 335 Montgolfier brothers 65 Moon missions 70, 71, 218, 263, 315, 316 moonshots 8, 59, 136, 214, 317 Moore, Gordon 92 Moore's Law 55, 84, 92, 93, 97, 240 Morgan, J.P. 287, 288 Morris, Ian 260–1, 306 Morse, Samuel 289 motor vehicles 68–71, 95, 107–8, 219, 289 Motorwagen 68 Mozart, Wolfgang Amadeus 159 multiculturalism 268 multiverse 170, 342 music 99–108, 115, 188 Musil, Robert 188 Musk, Elon 71, 247 Mussolini, Benito 114 mysterians 166, 249 nanotechnology 242, 243, 245, 341 Napoleon Bonaparte 49 Napoleon III 50, 51 narratives, breakdown of grand 115 National Aeronautics and Space Administration (NASA) 71–2, 233, 315–16, 319 National Health Service (NHS) 56, 57 National Institute of Health (NIH) 60, 120, 185–6, 247, 319, 322 nationalism 213 natural selection 35–6, 37, 109, 118, 244 Nature magazine 12, 121, 157, 211, 220, 229 Nazis 48, 132, 190 Negroponte, Nicholas 13 neo-Enlightenment 98 Netherlands 24, 231, 283 neural networks, deep learning 225, 227, 233 Neurath, Otto 189 neuroscience 247 new molecular entities (NMEs) 93 ‘new normals’ 32 New Scientist (magazine) 122 new technology 95 disruptive 96 New York 103, 134 Newton, Isaac 25, 29, 34, 37, 74–5, 155, 159, 232, 341 Ng, Andrew 262 NHS see National Health Service Nielsen, Michael 117 Nigeria 267, 279 NIH see National Institute of Health Nijinsky, Vaslav 99–100 Nixon, Richard 59 NMEs see new molecular entities noble gases 149 Nokia 183 Nordhaus, William 186 norms, ‘new’ 32 North Korea 305 Novacene 238 Novartis 61 nuclear fission 144, 145–6, 148 nuclear fusion 145–8, 234, 317, 341 nuclear power 85, 119, 143–8, 220, 221, 290 nuclear weapons 45, 143, 144, 311 Oak Ridge laboratory 143, 147 Obama, Barack 59 Obninsk 144 Odlyzko, Andrew 184 Office of Scientific Research and Development (OSRD) 316–17 Ogburn, William 39 oil 80 oligopolies 96 optical devices 231–2 orbits, elliptical 36 organisations breakthrough 294–9 see also companies originality 24, 28, 31–3, 152, 177, 283 lack of 108 Ørsted, Hans Christian 74–5 OSRD see Office of Scientific Research and Development Ottomans 277, 308 Oxford University 123–6, 127, 296 Packalen, Mikko 201, 202, 321 Page, Larry 326 Paine, Thomas 137 painting 176–7 panpsychism 340 paper 230, 259 paradigm rigidity 160 paradigm shifts 29, 33, 105, 109, 130, 164, 222, 250, 339 Parfit, Derek 203 Paris 99–103, 110, 132, 135, 205 particle physics 117–18, 119, 120–1, 122 partisanship 209–10 Pasteur, Louis 46–53, 57, 60–1, 71, 77, 79, 139, 232–3, 296, 332, 338 pasteurisation 50, 51 patents 64, 83, 156–8, 194–6, 271–2, 292–3, 297 new classes 97 patronage 322 Paul, St 303 Pauli, Wolfgang 159 Pauling, Linus 118, 323–4 PCR see polymerase chain reaction peer learning 326–7 peer review 320–1 penicillin 38, 52, 125 Penrose, Roger 124 Pentagon, Naval Air War Center 77 pessimism, rational 123–31, 150 pet food 147 Pfizer 61 pharmaceutical industry 31, 55–7, 60, 70 see also drugs Philo of Byzantium 4–5 philosophy 103–4, 111–12, 115, 121, 124, 339 Photoshop 162 physics 74–7, 79, 80, 116–22, 124, 131, 140, 159–62, 166, 239, 242–3, 332, 341 Picasso, Pablo 36, 101, 152 Pierce, John R. 182 Pitcairn Island 42–3 Planck, Max 104, 160, 296 planets, elliptical orbit 36 plasma 145, 146 Plato 3, 108, 169, 291, 304 Plotinus 303 Plutarch 4 polio 53 political policy 114–15 politics 111–15, 208–13 polymerase chain reaction (PCR) 202 Popper, Karl 189 population growth 78, 79 exponential 11 populism 208, 211, 214, 280–1, 307 post-scarcity society 340 post-truth world 213, 215 Pot, Pol 114 Pound, Ezra 103 present 13 Presley, Elvis 36, 152 ‘priming’ 4 Princeton 180, 296 printing press 36, 230, 253–4 problems, catastrophic 42–4 production lines 104 productivity growth 82 profit 186 progress acceleration 8 linear 29 mirage of 5 nature of 13 protein-folding problem 223–6, 228–9 Proust, Marcel 103 Prussia 50 Ptolemaic astronomical system 30 Ptolemy 303 public bodies 205 public health policy 53 PubMed 28, 116 Punic Wars, Second 3 Pythagoras 304 PYTHIA 237 quantum computing 240–1, 263, 296, 312 quantum physics 159, 166, 341 rabies 48, 51 radiation 57 radioactive elements 149 railways 67, 69 Ratcliffe, Peter 124 rational pessimism 123–31, 150 Ravel, Maurice 101 RCA 33, 289 Reagan, Ronald 211 Rees, Martin 167–8 Reformation 230, 233, 328 refugees 220 regulatory burden 205–6 Relativity Theory, General 104, 117 religion 108, 214, 303–4, 340 Rembrandt 236 Renaissance 130, 156, 177, 230, 233, 252, 254 reproducibility crisis 121 research and development 128, 180–7, 214, 252, 286–90, 312, 339–40 agricultural 92–3 autonomous vehicles 219 cancer 59–61 Chinese 262–3 cleantech 195 drugs 55–7, 61, 92–4, 119, 161, 172–3, 217–18, 234, 245, 315, 338 and financialism 192 funding 202–3, 314–24 global spend 128 government funding 314–19 and ideas 90–1 and India 265, 266 military 314–17, 319 multipolar 258 nuclear 147 productivity 91–5, 97–8, 307 and scaling up 279 specialisation 156, 157–8 and tax credits 331 and training 158–60 and transportation 70, 72 and universities 200–4 revolution, diminishing nature 74–98 Ridley, Matt 281, 325 right wing politics 113, 211 rights 132–40 risk 193, 251, 313, 335–6 risk society 329 risk-aversion 210–11 risk-minimisation strategies 330 roads 66, 67 Rocket engine 26 Roerich, Nicholas 100 Rome 3–4, 43 fall of 151, 187, 190, 303–6 Romer, Paul 88–91, 94 Roosevelt, Eleanor 132, 133–6, 139–40 Rose, Jacqueline 111 Rosetta Stone 155 Rotten, Johnny 104 Roux, Emile 48 Royal Institution 75, 154, 292 Royal Society 25, 75, 154, 292, 326 Royal Society of Arts, Manufactures and Commerce 25, 292 Russia 11, 71, 111, 150, 213, 279 see also Soviet Union Russian Chemical Society 149 Rutherford, Ernest 119, 140 S-curve model 32, 33, 35 Sagan, Carl 306, 337 Salvarsan 52 sanitation 53–4 Sarewitz, Dan 175 Sartre, Jean-Paul 110 satellites 9, 70, 153, 181–2, 272, 315–16 saturation, at the limit 166–79 Saturn 75–6 Saussure, Ferdinand de 109 Scaling Revolution 232 scaling up 255–85, 293–5, 298–9, 301–2, 308, 314–15, 317, 337–8 Schiele, Egon 188 Schliemann, Heinrich 153 Schmidt, Eric 262 Schnitzler, Arthur 188 Schoenberg, Arnold 103, 104, 188 Schrödinger, Erwin 124, 237, 332 Schumpeter, Joseph 189 science 104, 115–25, 131, 157, 159–60, 201–2, 276, 332–3 limits of 168 see also biology, chemistry; physics Science (journal) 118, 164, 175, 229, 257 Science Education Initiative (SEI) 327 Scientific American (magazine) 122 Scientific Revolution 29–30, 123, 130, 229–33, 252–3, 291 Sears Roebuck 84–5 Second World War 138–9, 143–4, 148, 296, 314, 316–17, 319 Sedol, Lee 226–7 seed drills 25 SEI see Science Education Initiative semiconductors 180–1, 245, 338 Semmelweis, Ignaz 216 sensory perception 167 sewing machines 11, 33 Shakespeare, William 169 Shannon, Claude 182, 184 shareholder returns 193, 194, 217 Shaw, D.

Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy by Pistono, Federico

3D printing, Albert Einstein, autonomous vehicles, bioinformatics, Buckminster Fuller, cloud computing, computer vision, correlation does not imply causation, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Firefox, future of work, gamification, George Santayana, global village, Google Chrome, happiness index / gross national happiness, hedonic treadmill, illegal immigration, income inequality, information retrieval, Internet of things, invention of the printing press, Jeff Hawkins, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, labor-force participation, Lao Tzu, Law of Accelerating Returns, life extension, Loebner Prize, longitudinal study, means of production, Narrative Science, natural language processing, new economy, Occupy movement, patent troll, pattern recognition, peak oil, post scarcity, QR code, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, technological singularity, Turing test, Vernor Vinge, warehouse automation, warehouse robotics, women in the workforce

If mainstream economists see me as I see proponents of “intelligent design”, it should be pretty easy to refute what I say. In fact, it should be quick to dismiss my claims with a few simple examples. After a year of research and discussion, I am still waiting for them. Marshall Brain, author of Robotic Nation, gave a talk about job displacement due to automation at the Singularity Summit 2008. At the end of his presentation, he was ridiculed by one of the other speakers: “Have you ever heard of this discipline called history? We’ve gone through the same crap 150 years ago, and none of what you say has happened!”. This is the sort of easy criticism that uneducated people make very lightly: it did not happen in the past, why should it happen now?

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The problem is this: will we be able to keep up with such rapid changes, and educate the millions of workers with no formal education for these new types of jobs? I think the answer is a big and loud NO. There are millions of workers with a high school education at best, and sometimes not even that, who are over 40 years old who only know how to do either manual labour or jobs easy to automate. Any new job that we can come up with will employ a fraction of those people, at best. And these jobs will require a highly receptive, flexible mind, with profound knowledge of highly sophisticated subjects related mostly to the fields of biology, chemistry, computer science, and engineering.

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Learn to love it, embrace it, and you will succeed. Fail to predict it, resist it, and you will be swept away by the torrent of change that is about to crush our civilisation as we now it. At this point you might be wondering, will not these highly sophisticated and technically challenging jobs be automated, eventually? Given what we have learned about exponential expansion of technologies, the logical answer would be: yes, most of them. Surely we will create new fields of research, and new jobs will follow accordingly. But these new jobs will be even more difficult, and the percentage of population apt to those will be narrower and narrower every time, given that the ability for technology to self-innovate is greater and faster than our ability to keep up with it.

System Error by Rob Reich

2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Cass Sunstein, clean water, cloud computing, computer vision, coronavirus, corporate governance, COVID-19, creative destruction, crowdsourcing, data is the new oil, data science, disinformation, disruptive innovation, Donald Knuth, Donald Trump, Edward Snowden, Elon Musk, en.wikipedia.org, Fairchild Semiconductor, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, George Floyd, gig economy, Goodhart's law, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, NP-complete, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, personalized medicine, Peter Thiel, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, tech billionaire, tech worker, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

But some terms such as “baseball” or “softball” that the model can still consider are highly correlated with the applicant’s gender and can lead the model to make decisions that exhibit gender bias. That’s the kind of situation Amazon found itself in with its résumé-screening tool. Moreover, as job applicants become aware that automated tools are used for screening their résumés, there’s no end to the ways they can game the system. Say you’re an applicant and you know your résumé will be analyzed by a machine. You could submit an electronic copy with some extra text in a white font on a white background at the bottom of the page.

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As Jason Furman, the chairman of President Obama’s Council of Economic Advisers, noted in a speech in 2016, “The issue is not that automation will render the vast majority of the population unemployable. Instead, it is that workers will either lack the skills or the ability to successfully match with the good, high-paying jobs created by automation.” His conclusion is that we should not be planning for a world in which people find themselves permanently unemployed; we should focus instead on helping households navigate the dislocation caused by automation and fostering the skills, training, and other assistance needed to get people into productive, high-paying jobs.

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“we can’t afford to live by manual processes”: Harry McCracken, “Meet the Woman Behind Amazon’s Explosive Growth,” Fast Company, April 11, 2019, https://www.fastcompany.com/90325624/yes-amazon-has-an-hr-chief-meet-beth-galetti. “Everyone wanted this holy grail”: Jeffrey Dastin, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women,” Reuters, October 10, 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. significant discrimination based on perceived race: Marianne Bertrand and Sendhil Mullainathan, “Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination,” American Economic Review 94, no. 4 (2004): 991–1013.

The End of Work by Jeremy Rifkin

banking crisis, Bertrand Russell: In Praise of Idleness, blue-collar work, cashless society, Charles Babbage, collective bargaining, compensation consultant, computer age, deskilling, Dissolution of the Soviet Union, employer provided health coverage, Erik Brynjolfsson, full employment, future of work, general-purpose programming language, George Gilder, global village, Herbert Marcuse, high-speed rail, hiring and firing, informal economy, interchangeable parts, invention of the telegraph, Jacques de Vaucanson, job automation, John Maynard Keynes: technological unemployment, knowledge economy, knowledge worker, land reform, low skilled workers, means of production, military-industrial complex, new economy, New Urbanism, Paul Samuelson, pink-collar, pneumatic tube, post-Fordism, post-industrial society, Productivity paradox, Richard Florida, Ronald Reagan, scientific management, Silicon Valley, speech recognition, strikebreaker, technoutopianism, Thorstein Veblen, Toyota Production System, trade route, trickle-down economics, warehouse automation, warehouse robotics, women in the workforce, working poor, working-age population, Works Progress Administration

Recently, however, economists have begun to revise their views in light of new in-depth studies of the US. manufacturing sector. Noted economists Paul R. Krugman of MIT and Robert L. Lawrence of Harvard University suggest, on the basis of extensive data, that "the concern, widely voiced during the 1950S and 1960s, that industrial workers would lose their jobs because of automation, is closer to the truth than the current preoccupation with a presumed loss of manufacturing jobs because of foreign competition."18 Although the number of blue collar workers continues to decline, manufacturing productivity is soaring. In the United States, annual productivity, which was growing at slightly over 1 percent per year in the early 1980s, has climbed to over 3 percent in the wake of the new advances in computer automation and the restructuring of the workplace.

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As educator Jonathan Kozol points out, "employment qualifications for all but a handful of domestic jobs begins at the ninth-grade level."68 For these Americans, the hope of being retrained or schooled for a new job in the elite knowledge sector is painfully out of reach. And, even if re-education and retraining on a mass scale were implemented, not enough high-tech jobs will be available in the automated economy of the twenty-first century to absorb the vast numbers of dislocated workers. THE SHRINKING PUBLIC SECTOR For the past sixty years, increased government spending has been the only viable means "to cheat the devil of ineffective demand" says economist Paul Samuelson. 69 Technological innovation, rising productivity, growing technological unemployment, and ineffective demand have characterized the American economy since the 1950S, forcing the federal government to adopt a strategy of deficit spending to create jobs, stimulate purchasing power, and boost economic growth.

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During the 1980s, real hourly compensation in the High-Tech Winners and Losers 167 manufacturing sector alone decreased from $7.78 to $7.69 an hour. 5 By the end of the decade nearly 10 percent of the American workforce was unemployed, underemployed, or working part time because full-time work was unavailable, or were too discouraged to even look for ajob. 6 Between 1989 and 1993, more than 1.8 million workers lost their jobs in the manufacturing sector, many of them victims of automation, either at the hands of their American employers or by foreign companies whose more highly automated plants and cheaper operating costs forced domestic producers to downsize their operations and lay off workers. Of those who have lost their jobs to automation, only a third were able to find new jobs in the service sector, and then at a 20 percent drop in pay. 7 Government figures on employment are often misleading, masking the true dimensions of the unfolding job crisis. For example, in August 1993 the federal government announced that nearly 1,230,000 jobs had been created in the United States in the first half of 1993.

Economic Dignity by Gene Sperling

active measures, Affordable Care Act / Obamacare, antiwork, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Cass Sunstein, collective bargaining, corporate governance, cotton gin, David Brooks, desegregation, Detroit bankruptcy, disinformation, Donald Trump, Double Irish / Dutch Sandwich, Elon Musk, employer provided health coverage, Erik Brynjolfsson, Ferguson, Missouri, fulfillment center, full employment, gender pay gap, ghettoisation, gig economy, Gini coefficient, guest worker program, Gunnar Myrdal, housing crisis, Ida Tarbell, income inequality, independent contractor, invisible hand, job automation, job satisfaction, labor-force participation, late fees, liberal world order, longitudinal study, low skilled workers, Lyft, Mark Zuckerberg, market fundamentalism, mass incarceration, mental accounting, meta-analysis, minimum wage unemployment, obamacare, offshore financial centre, payday loans, Phillips curve, price discrimination, profit motive, race to the bottom, RAND corporation, randomized controlled trial, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Rosa Parks, Second Machine Age, secular stagnation, shareholder value, Sheryl Sandberg, Silicon Valley, single-payer health, speech recognition, stock buybacks, tech worker, The Chicago School, The Future of Employment, The Wealth of Nations by Adam Smith, Toyota Production System, traffic fines, Triangle Shirtwaist Factory, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, War on Poverty, warehouse robotics, working poor, young professional, zero-sum game

A quarter of American adults say the possibility that robots and computers could do many of the jobs done by humans makes them feel “very worried.”8 A widely cited study by Frey put the number of U.S. jobs at high risk of being automated in the next decade or two due to advances in AI and robots at 47 percent.9 According to a Brookings Institution study, thirty-six million jobs “will face high exposure to automation in the coming decades.”10 Some experts project up to three million jobs could be at risk due to self-driving trucks and cars.11 Martin Ford, author of Rise of the Robots, believes that artificial intelligence “could very well end up in a future with significant unemployment . . . maybe even declining wages . . .

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Autor finds it a “bet against human ingenuity” for people to in effect say, “If I can’t think of what people will do for work in the future, then you, me and our kids aren’t going to think of it either.”15 Others have much lower—but still significant—estimates of job loss. The OECD estimates that only 9 percent of U.S. jobs are at risk from automation.16 Similarly, analysis by McKinsey & Company found that fewer than 5 percent of jobs could be completely automated.17 My goal is not to litigate which side is right in this ongoing debate. My best guess is that we are less likely to see an unprecedented reduction in overall demand for labor in the coming decades. We’re more likely to see the continuation of current trends in our economy that have led to widening income and wealth inequality with consequential job disruptions from globalization and technological trends.

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From 2015 to 2017, only about 281,000 of 6.8 million—or 4 percent of—displaced workers received benefits through TAA.55 Indeed, TAA is designed to help only a group of workers who, through an extensive process, can establish they lost their job due to trade. Yet why should it matter if someone lost their job due to trade, automation, AI, some combination of those factors, or simply changing consumer trends? Our goal should be to help people find a new career, not investigate why they lost their old one. A UBI to Rise should be for anyone who qualifies regardless of how their career was disrupted. I have worked on versions of a UBI to Rise for years—in 1994,56 in my 2005 book,57 and in 2012 when President Obama proposed a version.58 It is long past time to get it done.

Always Day One: How the Tech Titans Plan to Stay on Top Forever by Alex Kantrowitz

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Web Services, anti-bias training, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Big Tech, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, Firefox, fulfillment center, Google Chrome, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, Kiva Systems, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Peter Thiel, QR code, ride hailing / ride sharing, robotic process automation, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, SoftBank, Steve Ballmer, Steve Jobs, Steve Wozniak, super pumped, tech worker, Tim Cook: Apple, uber lyft, warehouse robotics, wealth creators, zero-sum game

“Leaders expect and require innovation and invention,” it instructs. “They are externally aware, look for new ideas from everywhere, and are not limited by ‘not invented here.’” (A more honest reading of this principle would be: Your entire purpose at Amazon is to invent. If you’re not inventing, your job will get simplified and then automated. At Amazon, you invent or hit the road.) Bias for Action tells Amazonians to get the damn thing out the door, discouraging long, drawn-out development processes in favor of producing new things. “Many decisions and actions are reversible and do not need extensive study,” it says.

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There are robotics floor technicians, amnesty professionals (who clean up after robots when they drop products), ICQA members (who count the items in the racks, making sure they align with the system’s numbers), and quarterbacks, who monitor the robotics floor from above. In the same time Amazon has added the two hundred thousand robots, it’s added three hundred thousand human jobs. Amazon’s push toward automation may not be sending its associates to the unemployment lines, but it is forcing them to navigate constant change, which can be both invigorating and exhausting. When you work at Amazon, you could be doing something one day, only to have it replaced by computers or robots the next.

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BuzzFeed News, December 1, 2015. https://www.buzzfeednews.com/article/mathonan/mark-zuckerberg-has-baby-and-says-he-will-give-away-99-of-hi. Amazon AI tool gone bad: Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women.” Reuters. Thomson Reuters, October 9, 2018. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. J. Robert Oppenheimer: Ratcliffe, Susan. Oxford Essential Quotations. Oxford, UK: Oxford University Press, 2016. Twenty-five US federal agencies: “NITAAC Solutions Showcase: Technatomy and UI Path.”

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, data science, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, opioid epidemic / opioid crisis, optical character recognition, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, zero-sum game, Zipcar

A conditional basic income that encourages people to keep learning and keep studying will make many individuals and families better off because we’re helping people get the training they need to then do higher-value and better-paying jobs. We see economists write reports with statistics like “in 20 years, 50% of jobs are at risk of automation,” and that’s really scary, but the flip side is that the other 50% of jobs are not at risk of automation. In fact, we can’t find enough people to do some of these jobs. We can’t find enough healthcare workers, we can’t find enough teachers in the United States, and surprisingly we can’t seem to find enough wind turbine technicians. The question is, how do people whose jobs are displaced take on these other great-paying, very valuable jobs that we just can’t find enough people to do?

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On the other hand, activities that are very difficult to automate also cut across wage structures and skills requirements, including tasks that require judgment or managing people, or physical work in highly unstructured and unexpected environments. So many traditionally low wage and high wage jobs are exposed to automation, depending on the activities, but also many other traditionally low wage and high wages jobs may be protected from automation. I want to make sure we cover all the different factors at play here, as well. The fourth key consideration has to do with benefits including and beyond labor substitution. There are going to be some areas where you’re automating, but it’s not because you’re trying to save money on labor, it is because you’re actually getting a better result or even a superhuman outcome.

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The problem is in the social systems, and whether we’re going to have a social system that shares fairly, or one that focuses all the improvement on the 1% and treats the rest of the people like dirt. That’s nothing to do with technology. MARTIN FORD: That problem comes about, though, because a lot of jobs could be eliminated—in particular, jobs that are predictable and easily automated. One social response to that is a basic income, is that something that you agree with? GEOFFREY HINTON: Yes, I think a basic income is a very sensible idea. MARTIN FORD: Do you think, then, that policy responses are required to address this? Some people take a view that we should just let it play out, but that’s perhaps irresponsible.

Shadow Work: The Unpaid, Unseen Jobs That Fill Your Day by Craig Lambert

airline deregulation, Asperger Syndrome, banking crisis, Barry Marshall: ulcers, big-box store, business cycle, carbon footprint, cashless society, Clayton Christensen, cognitive dissonance, collective bargaining, Community Supported Agriculture, corporate governance, crowdsourcing, data science, disintermediation, disruptive innovation, emotional labour, financial independence, Galaxy Zoo, ghettoisation, gig economy, global village, helicopter parent, IKEA effect, industrial robot, informal economy, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Mark Zuckerberg, new economy, pattern recognition, plutocrats, pneumatic tube, recommendation engine, Schrödinger's Cat, Silicon Valley, single-payer health, statistical model, the strength of weak ties, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, Turing test, unpaid internship, Vanguard fund, Vilfredo Pareto, you are the product, zero-sum game, Zipcar

However, if shadow work saves customers money or time, it will sooner or later prevail—as it has in the other forty-eight states. Eventually, it can become such an established norm that alternatives—full-serve pumps, for example—disappear or are confined to elite enclaves. Sixth, shadow work can cost jobs—in retail service, for example, as pump attendants disappear. This resembles job losses due to automation, though here the customer pitches in alongside the robots to displace the employee. Seventh, shadow work typically decreases human interaction and may even remove it entirely. The self-serve gasoline customer now deals with a robot, not a person. There is no longer an exchange of pleasantries with the pump jockey.

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Local companies like SoCo Creamery in Great Barrington, Massachusetts, employ youthful employees to dish out a couple dozen flavors. They stack scoopfuls onto cones and sprinkle on custom toppings like Heath Bar pieces. They’ll gladly hand you a sample of an unfamiliar flavor like Chai Spice, Earl Grey Supreme, or Lavender Honey on a taster spoon. Robots are closing in on these young people’s jobs. Automated frozen yogurt parlors get shadow-working customers to perform most of these tasks for themselves. In the New Jersey shore town of Avalon, for example, Toppings of Avalon offers “self surf” nonfat frozen yogurt. Nozzles embedded in a wall offer six flavors of frogurt, which customers dispense themselves into plastic dishes.

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone by Satya Nadella, Greg Shaw, Jill Tracie Nichols

"Robert Solow", 3D printing, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, data science, Deng Xiaoping, Donald Trump, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, fulfillment center, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, late capitalism, Mars Rover, Minecraft, Mother of all demos, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, subscription business, telepresence, telerobotics, The Rise and Fall of American Growth, The Soul of a New Machine, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

—more than a few eyes searching for my reaction. The president continued. “The reason that a lot of Americans feel anxious   is that the economy has been changing in profound ways, changes that started long before the Great Recession hit and haven’t let up. Today, technology doesn’t just replace jobs on the assembly line, but any job where work can be automated. Companies in a global economy can locate anywhere, and face tougher competition.” I squirmed a little in my chair. In a few words, the president had expressed some of the anxiety we all feel about technology and its impact on jobs—anxiety that would later play out in the election of President Donald Trump.

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And so beyond this one measure called GDP, we have practically a moral obligation to continue to innovate, to build technology to solve big problems—to be a force for good in the world as well as a tool for economic growth. How can we harness technology to tackle society’s greatest challenges—the climate, cancer, and the challenge of providing people with useful, productive, and meaningful work to replace the jobs eliminated by automation? Just the week before that State of the Union in Washington, DC, questions and observations much like those raised by the president had been leveled at me by heads of state during meetings with customers and partners in the Middle East, in Dubai, Cairo, and Istanbul. Leaders were asking how the latest wave of technology could be used to grow jobs and economic opportunity.

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One explanation is the German system of vocational training through apprenticeship, which makes cutting-edge technologies available to the workforce quickly through vocational schools that have close relationships with industry. I am convinced the only way to tackle economic displacement is to make sure that we provide skills training not only to people coming out of college and other postsecondary programs, but also to workers who are losing their jobs to automation. Countries that invest in building technology skills as a percent of GDP will see the rewards. Policy reforms must also create a regulatory environment that promotes innovative and confident adoption and use of technology. While data privacy and security are always key concerns, they also need to be balanced against the demands for data to flow more freely across borders and between the various services that make up a modern global digital economy.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, global village, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

As demand falls for labor, particularly relatively unskilled labor, wages fall. But can technology actually lead to unemployment? We’re not the first people to ask these questions. In fact, they’ve been debated vigorously, even violently, for at least two hundred years. Between 1811 and 1817, a group of English textile workers whose jobs were threatened by the automated looms of the first Industrial Revolution rallied around a perhaps mythical, Robin Hood–like figure named Ned Ludd and attacked mills and machinery before being suppressed by the British government. Economists and other scholars saw in the Luddite movement an early example of a broad and important new pattern: large-scale automation entering the workplace and affecting people’s wage and employment prospects.

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Thus in a very real sense, as long as there are unmet needs and wants in the world, unemployment is a loud warning that we simply aren’t thinking hard enough about what needs doing. We aren’t being creative enough about solving the problems we have using the freed-up time and energy of the people whose old jobs were automated away. We can do more to invent technologies and business models that augment and amplify the unique capabilities of humans to create new sources of value, instead of automating the ones that already exist. As we will discuss further in the next chapters, this is the real challenge facing our policy makers, our entrepreneurs, and each of us individually.

99%: Mass Impoverishment and How We Can End It by Mark Thomas

"Robert Solow", "there is no alternative" (TINA), 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, additive manufacturing, Alan Greenspan, Albert Einstein, anti-communist, autonomous vehicles, bank run, banks create money, bitcoin, business cycle, call centre, central bank independence, circular economy, complexity theory, conceptual framework, creative destruction, credit crunch, declining real wages, distributed ledger, Donald Trump, Erik Brynjolfsson, eurozone crisis, fiat currency, Filter Bubble, full employment, future of work, Gini coefficient, gravity well, income inequality, inflation targeting, Internet of things, invisible hand, ITER tokamak, Jeff Bezos, jimmy wales, job automation, Kickstarter, labour market flexibility, laissez-faire capitalism, light touch regulation, Mark Zuckerberg, market clearing, market fundamentalism, Martin Wolf, Modern Monetary Theory, Money creation, money: store of value / unit of account / medium of exchange, Nelson Mandela, North Sea oil, Occupy movement, offshore financial centre, Own Your Own Home, Peter Thiel, Piper Alpha, plutocrats, profit maximization, quantitative easing, rent-seeking, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, smart cities, Steve Jobs, The Great Moderation, The Wealth of Nations by Adam Smith, Tyler Cowen, warehouse automation, wealth creators, working-age population

It does not depend on unexpected sources of demand developing – an ageing population and the need to develop a sustainable model for the economy both create enormous demand. It does depend on whether supply can ‘see’ the demand – whether the demand is backed by money. As more and more jobs become possible to automate, we face a challenge – will these new technologies create a demand for new and higher added-value jobs for all as some predict, or will they produce a new underclass? What happened last time British people have been through this sort of transformation before, although none of us can remember it.

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In these areas alone, we could see tens of millions of jobs disappear. But this is just the beginning. Another team at Oxford has been taking a close look at the possibilities for automation in the US. Carl Frey and Michael Osborne examined over 700 occupational categories and for each one assessed the probability that jobs in that sector would be automated within the next twenty years. Their conclusion was that 47 per cent of US jobs are in high-risk categories, with more than a 75 per cent chance of being computerized in the next two decades.26 Only 33 per cent of jobs have less than a 25 per cent chance of being computerized by 2033 – and by 2050 the process will have advanced much further.

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In the more-advanced economies, the high incomes of those who own or manage the technology and of those who possess high skills may be enough to provide auxiliary service employment for everyone else, but the level of inequality will become so great that a free society will not be able to accept it.31 There is enough evidence to conclude that the coming industrial revolution – if we do not change our economic system – poses an unprecedented threat to millions of people. Over the next twenty years, almost half of jobs currently existing will be automated which will, at the very least, mean wrenching change. By 2050 we could be in a near-workerless economy. We need to rethink our social and economic system fundamentally if we are to avoid disastrous social outcomes. If we do not change it, although we shall have almost limitless potential for supply, much of the demand will be invisible.

Give People Money by Annie Lowrey

"Robert Solow", affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, airport security, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, clean water, collective bargaining, computer age, crowdsourcing, cryptocurrency, deindustrialization, desegregation, Donald Trump, Edward Glaeser, Elon Musk, ending welfare as we know it, everywhere but in the productivity statistics, full employment, gender pay gap, gig economy, Google Earth, Home mortgage interest deduction, income inequality, indoor plumbing, information asymmetry, Jaron Lanier, jitney, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, Kodak vs Instagram, labor-force participation, late capitalism, Lyft, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, mass incarceration, McMansion, Menlo Park, mobile money, Modern Monetary Theory, mortgage tax deduction, multilevel marketing, new economy, obamacare, opioid epidemic / opioid crisis, Overton Window, Peter Thiel, post scarcity, post-work, Potemkin village, precariat, randomized controlled trial, ride hailing / ride sharing, Robert Bork, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, single-payer health, Steve Jobs, TaskRabbit, tech billionaire, The future is already here, The Future of Employment, theory of mind, total factor productivity, Turing test, two tier labour market, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, War on Poverty, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y Combinator

As famously noted by the futurist Jaron Lanier, at its peak, Kodak employed about 140,000 people; when Facebook acquired it, Instagram employed just 13. The scarier prospect is that more and more jobs are falling to the tide of tech-driven obsolescence. Studies have found that almost half of American jobs are vulnerable to automation, and the rest of the world might want to start worrying too. Countries such as Turkey, South Korea, China, and Vietnam have seen bang-up rates of growth in no small part due to industrialization—factories requiring millions of hands to feed machines and sew garments and produce electronics.

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legal assistants: Dan Mangan, “Lawyers Could Be the Next Profession to Be Replaced by Computers,” CNBC.com, Feb. 17, 2017. cashiers: Claire Cain Miller, “Amazon’s Move Signals End of Line for Many Cashiers,” New York Times, June 17, 2017. translators: Conner Forrest, “The First 10 Jobs That Will Be Automated by AI and Robots,” ZDNet, Aug. 3, 2015. diagnosticians: Vinod Khosla, “Technology Will Replace 80% of What Doctors Do,” Fortune, Dec. 4, 2012. stockbrokers: Saijel Kishan, Hugh Son, and Mira Rojanasakul, “Robots Are Coming for These Wall Street Jobs,” Bloomberg, Oct. 18, 2017. home appraisers: Joe Light, “The Next Job Humans Lose to Robots: Real Estate Appraiser,” Bloomberg, July 11, 2017.

The Most Human Human: What Talking With Computers Teaches Us About What It Means to Be Alive by Brian Christian

4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Bertrand Russell: In Praise of Idleness, carbon footprint, cellular automata, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, commoditize, complexity theory, Computing Machinery and Intelligence, crowdsourcing, David Heinemeier Hansson, Donald Trump, Douglas Hofstadter, George Akerlof, Gödel, Escher, Bach, high net worth, Isaac Newton, Jacques de Vaucanson, Jaron Lanier, job automation, Ken Thompson, l'esprit de l'escalier, Loebner Prize, Menlo Park, operational security, Ray Kurzweil, RFID, Richard Feynman, Ronald Reagan, Skype, Social Responsibility of Business Is to Increase Its Profits, starchitect, statistical model, Stephen Hawking, Steve Jobs, Steven Pinker, Thales of Miletus, theory of mind, Thomas Bayes, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero-sum game

“I’m less than a farm implement,” says the migrant worker. “I’m an object,” says the high-fashion model. Blue collar and white call upon the identical phrase: “I’m a robot.” –STUDS TERKEL The notion of computer therapists of course raises one of the major things that people think of when AI comes to mind: losing their jobs. Automation and mechanization have been reshaping the job market for several centuries at this point, and whether these changes have been positive or negative is a contentious issue. One side argues that machines take human jobs away; the other side argues that increased mechanization has resulted in economic efficiency that raises the standard of living for all, and that has released humans from a number of unpleasant tasks.

The Meritocracy Trap: How America's Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite by Daniel Markovits

"Robert Solow", 8-hour work day, activist fund / activist shareholder / activist investor, affirmative action, algorithmic management, Anton Chekhov, asset-backed security, assortative mating, basic income, Bernie Sanders, big-box store, business cycle, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, carried interest, collateralized debt obligation, collective bargaining, compensation consultant, computer age, corporate governance, corporate raider, crony capitalism, David Brooks, deskilling, Detroit bankruptcy, disruptive innovation, Donald Trump, Edward Glaeser, Emanuel Derman, equity premium, European colonialism, everywhere but in the productivity statistics, fear of failure, financial engineering, financial innovation, financial intermediation, fixed income, Ford paid five dollars a day, Frederick Winslow Taylor, fulfillment center, full employment, future of work, gender pay gap, George Akerlof, Gini coefficient, glass ceiling, Greenspan put, helicopter parent, Herbert Marcuse, high net worth, hiring and firing, income inequality, industrial robot, interchangeable parts, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, junk bonds, Kiva Systems, knowledge economy, knowledge worker, Kodak vs Instagram, labor-force participation, longitudinal study, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, mass incarceration, medical residency, minimum wage unemployment, Myron Scholes, Nate Silver, New Economic Geography, new economy, offshore financial centre, opioid epidemic / opioid crisis, Paul Samuelson, payday loans, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, precariat, purchasing power parity, rent-seeking, Richard Florida, Robert Gordon, Robert Shiller, Ronald Reagan, savings glut, school choice, shareholder value, Silicon Valley, Simon Kuznets, six sigma, Skype, stakhanovite, stem cell, Steve Jobs, stock buybacks, supply-chain management, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Theory of the Leisure Class by Thorstein Veblen, Thomas Davenport, Thorstein Veblen, too big to fail, total factor productivity, transaction costs, traveling salesman, universal basic income, unpaid internship, Vanguard fund, War on Poverty, warehouse robotics, Winter of Discontent, women in the workforce, working poor, Yochai Benkler, young professional, zero-sum game

The jobs most likely to be displaced are routine or routinizable and therefore mid-skilled: loan officers, receptionists, paralegals, retail salespersons, and taxi drivers. The jobs least likely to be displaced are all fluid and require social perception and creative intelligence: reporters, physicians, lawyers, teachers, and doctors. Carl Benedikt Frey and Michael A. Osborne, “Job Automation May Threaten Half of U.S. Workforce,” Bloomberg, March 12, 2014, accessed November 18, 2018, www.bloomberg.com/graphics/infographics/job-automation-threatens-workforce.html. displaced by automation by 2030: James Manyika et al., “Jobs Lost, Jobs Gained: What the Future of Work Will Mean for Jobs, Skills, and Wages,” McKinsey Global Institute, November 2017, accessed October 26 2018, www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages.

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And the share of all managers aged forty-five to sixty-four whose job tenure exceeded fifteen years has collapsed (falling by more than a quarter in just the two decades between 1987 and 2006). The process, moreover, continues today. Algorithmic management consulting firms now expressly seek “not [to] automat[e] [line workers’] jobs per se, but [rather to] automat[e] the [middle] manager’s job.” All this downsizing is driven by structural considerations rather than by firm-specific economic distress: it hits profitable as well as unprofitable firms, continues during economic booms as well as busts, and peaked during the epochal economic boom in the 1990s.

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In addition, the Bureau of Labor Statistics predicts that over the coming decade, the fastest-shrinking job categories will all be mid-skilled, and the ten fastest-growing will all be either low- or super-skilled. The McKinsey Global Institute—the consulting firm’s research arm—forecasts an even more dramatic transformation, predicting that nearly one-third of the U.S. workforce, overwhelmingly in mid-skilled jobs, will be displaced by automation by 2030. These developments, taken all together, constitute not a ripple but a tidal wave—even a sea change. The labor market has, bluntly put, abandoned the midcentury workforce’s democratic center, and this has fundamentally transformed the nature of work. Whereas work once underwrote midcentury America’s apt self-image as an economy and society dominated by the broad middle class, work today underwrites the equally apt sense of a rising division between the rich and the rest.

Gigged: The End of the Job and the Future of Work by Sarah Kessler

Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, basic income, bitcoin, blockchain, business cycle, call centre, cognitive dissonance, collective bargaining, crowdsourcing, data science, David Attenborough, do what you love, Donald Trump, East Village, Elon Musk, financial independence, future of work, game design, gig economy, Hacker News, income inequality, independent contractor, information asymmetry, Jeff Bezos, job automation, law of one price, Lyft, Mark Zuckerberg, market clearing, minimum wage unemployment, new economy, opioid epidemic / opioid crisis, payday loans, post-work, profit maximization, QR code, race to the bottom, ride hailing / ride sharing, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, TaskRabbit, TechCrunch disrupt, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, working-age population, Works Progress Administration, Y Combinator

Toyota, Nissan, General Motors, and Google have all estimated that automated cars will be on the road by 2020.3 In the United States, 1.8 million people make a living driving trucks; another 687,000 drive buses; another 1.4 million deliver packages; and another 305,000 work as taxi drivers and chauffeurs. What will they do when vehicles drive themselves? It’s not just drivers who may soon see their jobs, or portions of their jobs, become automated. A recent McKinsey report estimated that almost all jobs could be automated in some respect, though the extent and impact of this automation is likely to vary widely.4 At some point, increasing automation will help power the gig economy, making it even more efficient than it is now. Though Curtis never talked about it, and I’m not sure he even realized it, Gigster’s ultimate goal is to automate as much of the programming process as possible.

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An executive who Uber unsuccessfully tried to recruit in 2016 told The Guardian that during his job interview, Uber’s chief product officer had responded to a question about how the company would handle the discontent among its drivers by saying, “Well, we’re just going to replace them all with robots” (an Uber spokesman told the paper that its executive did not recall making the statement).19 * * * On her applications to universities, Kristy had described her Mechanical Turk work as a “crowdsourcing micro-contractor” position, a job that she noted included working with several Fortune 500 companies. She hoped to study psychology. Mechanical Turk had shown Kristy how close many jobs were to being automated. She’d been part of a crowd that helped train machines to do things like recognize images and diagnose diseases, and she knew that someday those algorithms wouldn’t need training anymore. They’d replace the humans currently doing the work. As far as she could tell, though, people would always want a therapist to offer a real human connection.

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass by Mary L. Gray, Siddharth Suri

Affordable Care Act / Obamacare, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, basic income, Big Tech, big-box store, bitcoin, blue-collar work, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, cotton gin, crowdsourcing, data is the new oil, data science, deindustrialization, deskilling, do what you love, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, independent contractor, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, market friction, Mars Rover, natural language processing, new economy, operational security, passive income, pattern recognition, post-materialism, post-work, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, scientific management, search costs, Second Machine Age, sentiment analysis, sharing economy, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, Skype, software as a service, speech recognition, spinning jenny, Stephen Hawking, The Future of Employment, The Nature of the Firm, Tragedy of the Commons, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, Wayback Machine, women in the workforce, Works Progress Administration, Y Combinator, Yochai Benkler

Blue-collar manufacturing jobs have been the most visible targets of AI’s advance. The Foxconn factories that make iPhones allegedly replaced 60,000 humans with robots in 2016. Amazon’s 20 fulfillment centers reportedly deployed 45,000 robots to work alongside 230,000 people that same year. Yet these numbers confound how many jobs are created by automation. And the media coverage of AI’s impact on full-time blue-collar work can distract us from the rapid growth of a new category of human workers to complement or tend to automated manufacturing systems when AI hits its limits. In the past 20 years, the most profitable companies have slowly transitioned from ones that mass-manufacture durable goods, like furniture and clothing, to businesses that sell services, like healthcare, consumer analytics, and retail.

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Since on-demand work can be available at any time, it can be molded around these responsibilities as well. Finally, if workers are constrained because they don’t have the training for a job they seek, they can use on-demand work to build up a résumé of experience showing that they have what it takes to do a specific job. SEMI-AUTOMATED FUTURE The days of large enterprises with full-time employees working on-site are numbered as more and more projects rely on an off-site workforce available on demand, around the globe. Our employment classification systems, won in the 1930s to make full-time assembly line work sustainable, were not built for this future.

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", Alan Greenspan, autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Boston Dynamics, Bretton Woods, Brexit referendum, capital controls, capitalist realism, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, David Ricardo: comparative advantage, decarbonisation, dematerialisation, Donald Trump, double helix, Elon Musk, energy transition, Erik Brynjolfsson, financial independence, Francis Fukuyama: the end of history, future of work, Future Shock, G4S, Gregor Mendel, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jevons paradox, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, liberal capitalism, low earth orbit, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private space industry, Productivity paradox, profit motive, race to the bottom, RFID, rising living standards, scientific management, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, SoftBank, stem cell, Stewart Brand, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, working-age population

The ever-greater employment of industrial robots correlates entirely with what can be observed in both manufacturing jobs and output. In the two decades following Leontief’s prediction, information technology and robotics allowed the US steel industry to increase output from 75 to 125 million tonnes while the number of workers declined from 289,000 to 74,000. More broadly, the US lost 2 million manufacturing jobs over the period to automation – around 11 per cent of the sector. Between 1997 and 2005 that trend only continued to accelerate with US manufacturing output increasing by another 60 per cent while almost 4 million more jobs in the sector disappeared. The explanation why is straightforward: a major rise in productivity allowed industry to produce more with less.

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Those findings confirmed the conclusions of an earlier report published by two Oxford University academics, Carl Benedikt Frey and Michael Osborne. In 2013 they claimed that 47 per cent of all US jobs were at ‘high risk’ of being automated, with a further 19 per cent facing medium risk. Elsewhere Peter Sondergaard, research director for the consultancy Gartner, predicted that by 2025 one in three jobs will be automated as the result of an emerging ‘super class’ of technologies, with general purpose robotics and machine learning leading the way. Finally, in a 2016 report to Congress, White House economists forecast an 83 per cent chance that workers earning less than $20 per hour will lose their jobs to robots in the medium term.

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Geriatric care – which combines high levels of fine motor coordination with affective labour and ongoing risk management – is one; after all, societies around the world will be affected by ageing populations over the course of the twenty-first century. Health and education generally will remain labour-intensive and, at the very least, will take longer to disappear. Even with these growth areas in mind, however, the overall picture of job losses due to automation makes standing still seem wildly optimistic. The Future of Work Not everyone agrees that progress will lead to peak human in the Third Disruption as the steam engine and fossil fuels led to peak horse in the Second. Indeed, two of the leading voices in the field of work and technological change, Erik Brynjolfsson and Andrew McAfee, believe value will instead increasingly derive from the generation of new ideas.

Evil Geniuses: The Unmaking of America: A Recent History by Kurt Andersen

affirmative action, Affordable Care Act / Obamacare, air traffic controllers' union, airline deregulation, airport security, Alan Greenspan, always be closing, American ideology, American Legislative Exchange Council, An Inconvenient Truth, anti-communist, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, basic income, Bear Stearns, Bernie Sanders, blue-collar work, Bonfire of the Vanities, bonus culture, Burning Man, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, centre right, computer age, coronavirus, corporate governance, corporate raider, cotton gin, COVID-19, creative destruction, Credit Default Swap, cryptocurrency, deindustrialization, Donald Trump, Dr. Strangelove, Elon Musk, ending welfare as we know it, Erik Brynjolfsson, feminist movement, financial deregulation, financial innovation, Francis Fukuyama: the end of history, future of work, Future Shock, game design, George Floyd, George Gilder, Gordon Gekko, greed is good, Herbert Marcuse, Herman Kahn, High speed trading, hive mind, income inequality, industrial robot, interchangeable parts, invisible hand, Isaac Newton, It's morning again in America, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jeff Bezos, jitney, Joan Didion, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, junk bonds, knowledge worker, low skilled workers, Lyft, Mark Zuckerberg, market bubble, mass immigration, mass incarceration, Menlo Park, Naomi Klein, new economy, Norbert Wiener, Norman Mailer, obamacare, Overton Window, Peter Thiel, Picturephone, plutocrats, post-industrial society, Powell Memorandum, pre–internet, Ralph Nader, Right to Buy, road to serfdom, Robert Bork, Robert Gordon, Robert Mercer, Ronald Reagan, Saturday Night Live, Seaside, Florida, Second Machine Age, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Stewart Brand, stock buybacks, strikebreaker, tech billionaire, The Death and Life of Great American Cities, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, too big to fail, trickle-down economics, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, union organizing, universal basic income, Unsafe at Any Speed, urban planning, urban renewal, very high income, wage slave, Wall-E, War on Poverty, Whole Earth Catalog, winner-take-all economy, women in the workforce, working poor, young professional, éminence grise

The cost of robots is dropping, and the number installed in American factories has been doubling every few years and has passed a quarter-million. But our “robot density” is still less than a third of South Korea’s and is also much less than that of Japan and the advanced European countries. At the end of 2019, there were still millions of U.S. manufacturing jobs waiting to be automated out of existence by robots and other machines. The easy summary of what’s afflicted our political economy the last forty years is economic inequality and insecurity, fortunate people at and near the top getting paid more and more and remaining highly employable, but no such luck for almost everyone else.

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There were two factions in those debates….The stupid people thought that automation was going to make all the jobs go away and there wasn’t going to be any work to do. And the smart people understood that when more was produced, there would be more income and therefore there would be more demand. It wasn’t possible that all the jobs would go away, so automation was a blessing….I’m not so completely certain now. To Summers, “the prodigious change” in the political economy wrought by computers and the way we use them looks “qualitatively different from past technological change.” From here on out, “the economic challenge will not be producing enough.

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But Martin Ford, the Silicon Valley investor, says beware of assurances that the “jobs of the future will involve collaborating with the machines,” because “if you find yourself working with, or under the direction of, a smart software system, it’s probably a pretty good bet that you are also training the software to ultimately replace you.” The authors of What to Do When Machines Do Everything—three executives at the huge digital services and consulting firm Cognizant, whose whole business is about enabling corporations to shrink their workforces—absurdly promise that while some jobs will “be ‘automated away’ in the coming years…for the vast majority of professions, the new machine will actually enhance and protect employment.” Walmart, which employs more Americans by far than any other company, leans hard on that enhance-and-protect line. “Every hero needs a sidekick,” said its cute 2019 press release headlined #SquadGoals, “and some of the best have been automated.

The Internet Is Not the Answer by Andrew Keen

"Robert Solow", 3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, augmented reality, Bay Area Rapid Transit, Berlin Wall, Big Tech, bitcoin, Black Swan, Bob Geldof, Boston Dynamics, Burning Man, Cass Sunstein, Charles Babbage, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, data science, David Brooks, digital capitalism, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Dr. Strangelove, Edward Snowden, Elon Musk, Erik Brynjolfsson, fail fast, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, fulfillment center, full employment, future of work, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, holacracy, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Perry Barlow, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, Kiva Systems, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Metcalfe’s law, military-industrial complex, move fast and break things, Nate Silver, Nelson Mandela, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Norman Mailer, Occupy movement, packet switching, PageRank, Panopticon Jeremy Bentham, Paul Graham, peer-to-peer, peer-to-peer rental, Peter Thiel, plutocrats, Potemkin village, precariat, pre–internet, printed gun, Project Xanadu, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Robert Metcalfe, San Francisco homelessness, scientific management, Second Machine Age, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, subscription business, TaskRabbit, tech bro, tech worker, TechCrunch disrupt, Ted Nelson, telemarketer, The future is already here, The Future of Employment, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, warehouse robotics, Whole Earth Catalog, WikiLeaks, winner-take-all economy, working poor, Y Combinator

And, everyone’s favorite, ROBOTS,” wrote the Atlantic’s Derek Thompson in 2014 about our increasing concern with the elimination of jobs from the economy.17 As if to mark (or perhaps mourn) the twenty-fifth anniversary of the Web, it seems as if 2014 is the year that we’ve finally fully woken up to what the Wall Street Journal columnist Daniel Akst dubs “automation anxiety.”18 The cover of the one business magazine that I’d read on the flight from Chicago to Rochester, for example, featured the image of a deadly tornado roaring through a workspace. “Coming to an office near you . . .,” it warned about what technology will do to “tomorrow’s jobs.”19 Many others share this automation anxiety. The distinguished Financial Times economics columnist Martin Wolf warns that intelligent machines could hollow out middle-class jobs, compound income inequality, make the wealthy “indifferent” to the fate of everyone else, and make a “mockery” of democratic citizenship.20 “The robots are coming and will terminate your jobs,”21 worries the generally cheerful economist Tim Harford in response to Google’s acquisition in December 2013 of Boston Dynamics, a producer of military robots such as Big Dog, a three-foot-long, 240-pound, four-footed beast that can carry a 340-pound load and climb snowy hiking trails.

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Of the ten jobs that have a 99% likelihood of being replaced by networked software and automation over the next quarter century, Thompson includes tax preparers, library technicians, telemarketers, sewers in clothing factories, accounts clerks, and photographic process workers.41 While it’s all very well to speculate about who will lose their jobs because of automation, Thompson says, “the truth is scarier. We don’t have a clue.”42 But Thompson is wrong. The writing is on the wall about both the winners and the losers in this dehumanizing race between computers and people. We do indeed have more than a clue about its outcome. And that’s what really is scary.

A Pelican Introduction: Basic Income by Guy Standing

bank run, basic income, Bernie Sanders, Bertrand Russell: In Praise of Idleness, Black Swan, Boris Johnson, British Empire, centre right, collective bargaining, cryptocurrency, David Graeber, declining real wages, deindustrialization, Donald Trump, Elon Musk, Fellow of the Royal Society, financial intermediation, full employment, future of work, gig economy, Gunnar Myrdal, housing crisis, hydraulic fracturing, income inequality, independent contractor, intangible asset, job automation, job satisfaction, Joi Ito, labour market flexibility, land value tax, libertarian paternalism, low skilled workers, lump of labour, Marc Benioff, Mark Zuckerberg, Martin Wolf, mass immigration, mass incarceration, moral hazard, Nelson Mandela, offshore financial centre, open economy, Panopticon Jeremy Bentham, Paul Samuelson, plutocrats, precariat, quantitative easing, randomized controlled trial, rent control, rent-seeking, Salesforce, Sam Altman, self-driving car, shareholder value, sharing economy, Silicon Valley, sovereign wealth fund, Stephen Hawking, The Future of Employment, universal basic income, Wolfgang Streeck, women in the workforce, working poor, Y Combinator, Zipcar

It is the latest version of the ‘lump of labour fallacy’, the idea that there is only a certain amount of labour and work to be done, so that if more of it can be automated or done by intelligent robots, human workers will be rendered redundant. In any case, very few jobs can be automated in their entirety. The suggestion in a much-cited study17 that nearly half of all US jobs are vulnerable to automation has been challenged by, among others, the OECD, which puts the figure of jobs ‘at risk’ at 9 per cent for industrialized countries.18 That said, the nature of jobs will undoubtedly change, perhaps rapidly. And while this writer does not believe that a jobless (still less ‘workless’) future is likely, the technological revolution is seriously increasing inequality, with profoundly regressive effects on the distribution of income, as powerful companies and their owners capture the lion’s share of the gains.

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Star bond investor Bill Gross has also come out in support of a basic income as a response to what he perceives as the coming robot-driven ‘end of work’.13 In July 2016, there was even a Facebook Live roundtable held in the White House on automation and basic income, though in a report issued the following December the US President’s Council of Economic Advisers rejected the idea, seemingly based on its chairman’s critical remarks six months earlier that were dissected in Chapter 4.14 A significant convert to the technological unemployment perspective is Andy Stern, former head of the US Service Employees International Union (SEIU) and the first leading trade unionist to come out in favour of a basic income.15 In a 2016 book widely publicized in the US, Stern claimed that 58 per cent of all jobs would be automated eventually, driven by the ethos of shareholder value. He told the American media group Bloomberg, ‘It’s not like the fall of the auto and steel industries. That hit just a sector of the country. This will be widespread. People will realize that we don’t have a storm anymore; we have a tsunami.’16 Nevertheless, there are reasons to be sceptical about the prospect of a jobless or even workless future.

Blockchain Basics: A Non-Technical Introduction in 25 Steps by Daniel Drescher

bitcoin, blockchain, business process, central bank independence, collaborative editing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, Ethereum, ethereum blockchain, fiat currency, job automation, linked data, peer-to-peer, place-making, Satoshi Nakamoto, smart contracts, transaction costs

Due to open questions regarding the legal acceptance of the blockchain, people expressed their doubt whether the blockchain as a fully automated protocol-driven transaction machinery can take the responsibility of its actions in the same way traditional intermediaries do. However, this criticism may foster legal initiatives for clarifying open issues regarding the legal status of the blockchain. 246 Step 25 | Summarizing and Going Further Loss of Jobs Automation and standardization have not only shaped the process and the costs of producing goods but also caused friction in the labor market. Many players in the financial industry such as banks, brokers, custodians, money- transfer agencies, and notaries are directly tied to their roles as intermediaries.

Postcapitalism: A Guide to Our Future by Paul Mason

air traffic controllers' union, Alan Greenspan, Alfred Russel Wallace, bank run, banking crisis, banks create money, Basel III, basic income, Bernie Madoff, Bill Gates: Altair 8800, bitcoin, Branko Milanovic, Bretton Woods, BRICs, British Empire, business cycle, business process, butterfly effect, call centre, capital controls, Cesare Marchetti: Marchetti’s constant, Claude Shannon: information theory, collaborative economy, collective bargaining, Corn Laws, corporate social responsibility, creative destruction, credit crunch, currency manipulation / currency intervention, currency peg, David Graeber, deglobalization, deindustrialization, deskilling, discovery of the americas, disinformation, Downton Abbey, drone strike, en.wikipedia.org, energy security, eurozone crisis, factory automation, financial engineering, financial repression, Firefox, Fractional reserve banking, Frederick Winslow Taylor, fulfillment center, full employment, future of work, game design, Herbert Marcuse, income inequality, inflation targeting, informal economy, information asymmetry, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Perry Barlow, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, low skilled workers, market clearing, means of production, Metcalfe's law, microservices, middle-income trap, Money creation, money: store of value / unit of account / medium of exchange, mortgage debt, Network effects, new economy, Nixon triggered the end of the Bretton Woods system, Norbert Wiener, Occupy movement, oil shale / tar sands, oil shock, Paul Samuelson, payday loans, Pearl River Delta, post-industrial society, precariat, precautionary principle, price mechanism, profit motive, quantitative easing, race to the bottom, RAND corporation, rent-seeking, reserve currency, RFID, Richard Stallman, Robert Gordon, Robert Metcalfe, scientific management, secular stagnation, sharing economy, Stewart Brand, structural adjustment programs, supply-chain management, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, Transnistria, union organizing, universal basic income, urban decay, urban planning, Vilfredo Pareto, wages for housework, WikiLeaks, women in the workforce, Yochai Benkler

In 2014, the OECD released its projections for the world economy in the years between now and 2060.40 World growth will slow to 2.7 per cent, said the Paris-based think tank, because the catch-up effects boosting growth in the developing world – growing population, education, urbanization – will peter out. Even before that, near-stagnation in advanced economies indicates average global growth of just 3 per cent over the next fifty years, significantly below the pre-crisis average. Meanwhile, because semi-skilled jobs will become automated, leaving only high- and low-paid ones, global inequality will rise by 40 per cent. By 2060, countries such as Sweden will have the levels of inequality currently seen in the USA: think Gary, Indiana in the suburbs of Stockholm. There is also the very real risk that climate change will begin to destroy capital, coastal land and agriculture, shaving up to 2.5 per cent off world GDP, and 6 per cent in south-east Asia.

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The 250-year history of capitalism has been about pushing market forces into sectors where they did not exist before. Info-capitalism would have to take this to its extremes, creating new forms of person-to-person micro-services, paid for using micro-payments, and mainly in the private sector. And finally, for info-capitalism to succeed it would have to find work for the millions of people whose jobs are automated. These could not be in the majority low-paid jobs because the traditional escape mechanism needs labour costs to rise: human life has to become more complex, needing more labour inputs, not fewer, as in the four cyclical upswings described by long-cycle theory. If all these things could happen, info-capitalism could take off.

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They predicted two waves of computerization over the next twenty years: ‘In the first wave, we find that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labour in production occupations, are likely to be substituted by computer capital.’36 In the second wave, it is everything relying on finger dexterity, observation, feedback, or working in a cramped space that gets robotized. They concluded the jobs safest from automation were service jobs where a high understanding of human interaction was needed – for example, nursing – and jobs requiring creativity. The study provoked an outcry along familiar under-consumptionist lines: robots will kill capitalism because they will create mass underemployment and consumption will collapse.

Good Economics for Hard Times: Better Answers to Our Biggest Problems by Abhijit V. Banerjee, Esther Duflo

"Robert Solow", 3D printing, accelerated depreciation, affirmative action, Affordable Care Act / Obamacare, air traffic controllers' union, Airbnb, basic income, Bernie Sanders, Big Tech, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, charter city, correlation does not imply causation, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, decarbonisation, Deng Xiaoping, Donald Trump, Edward Glaeser, en.wikipedia.org, endowment effect, energy transition, Erik Brynjolfsson, experimental economics, experimental subject, facts on the ground, fear of failure, financial innovation, George Akerlof, high net worth, immigration reform, income inequality, Indoor air pollution, industrial cluster, industrial robot, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jean Tirole, Jeff Bezos, job automation, Joseph Schumpeter, junk bonds, labor-force participation, land reform, loss aversion, low skilled workers, manufacturing employment, Mark Zuckerberg, mass immigration, middle-income trap, Network effects, new economy, New Urbanism, non-tariff barriers, obamacare, offshore financial centre, open economy, Paul Samuelson, place-making, price stability, profit maximization, purchasing power parity, race to the bottom, RAND corporation, randomized controlled trial, Richard Thaler, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Savings and loan crisis, school choice, Second Machine Age, secular stagnation, self-driving car, shareholder value, short selling, Silicon Valley, smart meter, social graph, spinning jenny, Steve Jobs, Tax Reform Act of 1986, tech worker, technology bubble, The Chicago School, The Future of Employment, The Market for Lemons, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, total factor productivity, trade liberalization, transaction costs, trickle-down economics, universal basic income, urban sprawl, very high income, War on Poverty, women in the workforce, working-age population, Y2K

For example, inventing new software or hardware health workers could use to assist patients in doing their rehabilitation therapy at home after a surgery rather than in a hospital could potentially save insurance companies lot of money, improve well-being, and create new jobs. But the bulk of the automation effort today in insurance firms goes toward searching for algorithms that automate the approval of insurance claims. This saves money but destroys jobs. This emphasis on the automation of existing jobs increases the potential for the current wave of innovation to be very damaging for workers. That unregulated automation could be bad for workers is also the instinct of most Americans on the right and the left.

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Cynics might say it is precisely because these more high-end jobs are on the line that we are finally talking about this, and they may be right. But AI will also hurt shelf stackers, office cleaners, restaurant workers, and taxi drivers. Based on the tasks they perform, a McKinsey report6 concludes that 45 percent of US jobs are at risk of being automated, and the OECD estimates that 46 percent of the workers in OECD countries are in occupations at high risk of being either replaced or fundamentally transformed.7 Of course, what this calculation misses is that as some tasks get automatized, and the need for humans gets relieved, people can be put to work elsewhere.

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TOGETHER IN DIGNITY The reluctance to make use of available government programs, even when they work well, may be related to the fact that a majority of Republicans and a substantial fraction of Democrats are against the government starting a universal income program or a national job program to support those who lose their jobs to automation, even though many more are in favor of limiting the right of companies to replace people with robots.86 Behind this is partly suspicion about the government’s motives (they only want to help “those people”) and partly exaggerated skepticism about the government’s ability to deliver. But there is also something else that even people and organizations on the left share: a suspicion of handouts, of charity without empathy or understanding.

Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, algorithmic bias, Amazon Picking Challenge, artificial general intelligence, autonomous vehicles, backpropagation, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Charles Babbage, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, data science, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, fulfillment center, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, Jeff Hawkins, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Salesforce, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

It involves evaluating entire work flows, whether they are within or across jobs (or departmental or organizational boundaries), and then breaking down the work flow into constituent tasks and seeing whether you can fruitfully employ a prediction machine in those tasks. Then, you must reconstitute tasks into jobs. Missing Links in Automation In some cases, the goal is to fully automate every task associated with a job. AI tools are unlikely to be a catalyst for this on their own because work flows amenable to full automation have a series of tasks involved that cannot be (easily) avoided, even for tasks that seem initially to be both low skilled and unimportant.

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Thus, people unsurprisingly took notice when, in December 2016, he wrote: “The automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.”3 Several studies had already tallied up potential job destruction due to automation, and this time it wasn’t just physical labor but also cognitive functions previously believed immune to such forces.4 After all, horses fell behind in horsepower, not brainpower. As economists, we’ve heard these claims before. But while the specter of technological unemployment has loomed since the Luddites destroyed textile frames centuries ago, unemployment rates have been remarkably low.

Narrative Economics: How Stories Go Viral and Drive Major Economic Events by Robert J. Shiller

agricultural Revolution, Alan Greenspan, Albert Einstein, algorithmic trading, Andrei Shleifer, autonomous vehicles, bank run, banking crisis, basic income, bitcoin, blockchain, business cycle, butterfly effect, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, central bank independence, collective bargaining, computerized trading, corporate raider, correlation does not imply causation, cryptocurrency, Daniel Kahneman / Amos Tversky, debt deflation, disintermediation, Donald Trump, Edmond Halley, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, financial engineering, full employment, George Akerlof, germ theory of disease, German hyperinflation, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, implied volatility, income inequality, inflation targeting, invention of radio, invention of the telegraph, Jean Tirole, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, litecoin, market bubble, Modern Monetary Theory, money market fund, moral hazard, Northern Rock, nudge unit, Own Your Own Home, Paul Samuelson, Philip Mirowski, plutocrats, Ponzi scheme, publish or perish, random walk, Richard Thaler, Robert Shiller, Ronald Reagan, Rubik’s Cube, Satoshi Nakamoto, secular stagnation, shareholder value, Silicon Valley, speech recognition, Steve Jobs, Steven Pinker, stochastic process, stocks for the long run, superstar cities, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, theory of mind, Thorstein Veblen, traveling salesman, trickle-down economics, tulip mania, universal basic income, Watson beat the top human players on Jeopardy!, We are the 99%, yellow journalism, yield curve, Yom Kippur War

31 5  The Laffer Curve and Rubik’s Cube Go Viral  41 6  Diverse Evidence on the Virality of Economic Narratives  53 Part II   The Foundations of Narrative Economics 7  Causality and Constellations  71 8  Seven Propositions of Narrative Economics  87 Part III   Perennial Economic Narratives 9  Recurrence and Mutation  107 10  Panic versus Confidence  114 11  Frugality versus Conspicuous Consumption  136 12  The Gold Standard versus Bimetallism  156 13  Labor-Saving Machines Replace Many Jobs  174 14  Automation and Artificial Intelligence Replace Almost All Jobs  196 15  Real Estate Booms and Busts  212 16  Stock Market Bubbles  228 17  Boycotts, Profiteers, and Evil Business  239 18  The Wage-Price Spiral and Evil Labor Unions  258 Part IV   Advancing Narrative Economics 19  Future Narratives, Future Research  271 Appendix: Applying Epidemic Models to Economic Narratives  289 Notes  301 References  325 Index  351 Figures 2.1 Articles Containing the Word Narrative as a Percentage of All Articles in Academic Disciplines   13 3.1 Epidemic Curve Example, Number of Newly Reported Ebola Cases in Lofa County, Liberia, by week, June 8–November 1, 2014   19 3.2 Percentage of All Articles by Year Using the Word Bimetallism or Bitcoin in News and Newspapers, 1850–2019   22 3.3 Frequency of Appearance of Four Economic Theories, 1940–2008   27 5.1 Frequency of Appearance of the Laffer Curve   43 10.1 Frequency of Appearance of Financial Panic, Business Confidence, and Consumer Confidence in Books, 1800–2008   116 10.2 Frequency of Appearance of Financial Panic Narratives within a Constellation of Panic Narratives through Time, 1800–2000   118 10.3 Frequency of Appearance of Suggestibility, Autosuggestion, and Crowd Psychology in Books, 1800–2008   120 10.4 Frequency of Appearance of Great Depression in Books, 1900–2008, and News, 1900–2019   134 11.1 Frequency of Appearance of American Dream in Books, 1800–2008, and News, 1800–2016   152 12.1 Frequency of Appearance of Gold Standard in Books, 1850–2008, and News, 1850–2019   159 13.1 Frequency of Appearance of Labor-Saving Machinery and Technological Unemployment in Books, 1800–2008   175 14.1 Percentage of Articles Containing the Words Automation and Artificial Intelligence in News and Newspapers, 1900–2019   197 15.1 “Housing Bubble” Google Search Queries, 2004–19   226 16.1 Frequency of Appearance of Stock Market Crash in Books, 1900–2008, and News, 1900–2019   232 17.1 Frequency of Appearance of Profiteer in Books, 1900–2008, and News, 1900–2019   243 18.1 Frequency of Appearance of Wage-Price Spiral and Cost-Push Inflation in Books, 1900–2008   259 A.1 Theoretical Epidemic Paths   291 Preface: What Is Narrative Economics?

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The remaining chapters in this part describe nine perennial economic narratives, along with some of their mutations and recurrences. Most readers will recognize these narratives in their most recent forms but not in their older forms: Panic versus confidence Frugality versus conspicuous consumption Gold standard versus bimetallism Labor-saving machines replace many jobs Automation and artificial intelligence replace almost all jobs Real estate booms and busts Stock market bubbles Boycotts, profiteers, and evil business The wage-price spiral and evil labor unions Some of these chapters present a pair of opposing narrative constellations (for example, frugality versus conspicuous consumption).

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It is hard to imagine that such a resolution would have passed if the nation had not been experiencing high unemployment. This story fed a contagious economic narrative that helped augment the atmosphere of fear associated with the contraction in aggregate demand during the Great Depression. The loss of jobs to robots (that is, automation) became a major explanation of the Great Depression, and, hence, a perceived major cause of it. An article in the Los Angeles Times in 1931 was one of many that explained this idea: Whenever a man is replaced by a machine a consumer is lost; for the man is deprived of the means of paying for what he consumes.

Dual Transformation: How to Reposition Today's Business While Creating the Future by Scott D. Anthony, Mark W. Johnson

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, autonomous vehicles, barriers to entry, Ben Horowitz, Big Tech, blockchain, business process, business process outsourcing, call centre, Clayton Christensen, cloud computing, commoditize, corporate governance, creative destruction, crowdsourcing, death of newspapers, disintermediation, disruptive innovation, distributed ledger, diversified portfolio, Internet of things, invention of hypertext, inventory management, Jeff Bezos, job automation, job satisfaction, Joseph Schumpeter, Kickstarter, late fees, Lean Startup, long term incentive plan, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Minecraft, obamacare, Parag Khanna, Paul Graham, peer-to-peer lending, pez dispenser, recommendation engine, Salesforce, self-driving car, shareholder value, side project, Silicon Valley, Skype, software as a service, software is eating the world, Steve Jobs, subscription business, the market place, the scientific method, Thomas Kuhn: the structure of scientific revolutions, transfer pricing, uber lyft, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

Nestlé and Samsung partnership: Samsung, “Samsung and Nestlé Collaborate on the Internet of Things and Nutrition to Advance Digital Health,” Samsung.com, July 28, 2016, https://news.samsung.com/global/samsung-and-nestle-collaborate-on-the-internet-of-things-and-nutrition-to-advance-digital-health. TechCrunch on platforms: Tom Goodwin, “The Battle Is For The Customer Interface ,” TechCrunch.com, March 3, 2015, https://techcrunch.com/2015/03/03/in-the-age-of-disintermediation-the-battle-is-all-for-the-customer-interface/. Oxford research on job automation: Aviva Hope Rutkin, “Report Suggests Nearly Half of U.S. Jobs Are Vulnerable to Computerization,” Technology Review, September 12, 2013, https://www.technologyreview.com/s/519241/report-suggests-nearly-half-of-us-jobs-are-vulnerable-to-computerization/. Index AbbVie, 19 Ablaza, Gerry, 111, 127–128, 142, 184–185, 189 on aligning leadership and boards, 193 on importance of senior support, 192–193 acquisitions for capability development, 66–69 crises of commitment and, 158–163 pharmaceutical industry, 22–23 at SingPost, 51, 52–53 at Singtel, 145 ACS, 67 additive manufacturing, 202–203 adjacencies, 22–23 Adobe acquisitions and partnerships at, 67 business model innovation at, 40, 42 commitment to transformation A at, 44 experimentation at, 148–149 focus at, 117 postdisruption job to be done at, 39 transformation A at, 31–32, 33 transformation journey at, 181 AdSense, 48 Adult Rock Band, 186 advertising at Google, 48, 61, 77 at Manila Water, 127 newspapers and, 3, 77 at Turner, 96, 99 AdWords, 48, 61 Aetna, 23, 87, 182–183 crises of conflict at, 168 decision making at, 99–102 early warning signs at, 108 purpose at, 177 Affiliated Computer Services (ACS), 14, 64 Affordable Care Act, 100 Alibaba Group, 52–53, 67, 201–202, 203 “aliens,” in transformations, 68–69 alignment, 193–194 overestimation of, 119 transformation blurbs and, 129 Alipay, 201–202 Alliance Boots, 60 Alphabet, 47–48, 54 Altman, Elizabeth, 62 Amara, Roy, 104 Amazon, 53–55, 66 business model of, 106 drone-based deliveries, 203 statement of purpose, 178 Amazon Web Services (AWS), 53–55 America Online, 27 Amobee, 145, 188 Andreessen, Marc, 2–3, 206 Andreessen Horowitz, 206 Android, 4, 92 Anthony, Scott D., 62–63, 72–73, 81 on disruptive potential of YouTube, 108 on risk management, 65 Apple, 4, 8 acquisitions and partnerships at, 67 developer kit, 152 focus at, 116, 132 influence of Xerox on, 13 iPhone, 4, 92–93 transformation journey at, 181–182 arbitration, 86–87 Arizona State University (ASU), 56–57, 59, 183–184 partnerships with, 67 Arrested Development, 35 Ayala Corporation, 117, 143–144 Ayala Group, 184 Aztec empire, conquest of, 43 Baffrey, Robert “Boogz,” 127 Baier, Wolfgang, 52, 53 balance in capabilities link, 75 crises of commitment and, 158–160 curiosity to explore and, 139 between transformations A and B, 173–175 Balsillie, Jim, 4 banking, 151–152, 200–202 Barnes & Noble, 12–13 barriers to consumption, identifying, 61–62 Baxter International, 64, 86 behavior celebrating desired, 149–150 changes in customer, 105 predictors of, 63 Bell Labs, 115 Benioff, Marc, 27–28, 151 Berkshire Hathaway, 156 Berners-Lee, Tim, 3 Bertolini, Mark, 23, 87, 100–102, 168, 182–183 on aligning leadership and boards, 193 on communication, 195 on crises of commitment, 187 on crises of conflict, 190 on focus, 194 on quieting critics, 191–192 Bezos, Jeff, 53–55 BlackBerry, 4 Blank, Steve, 65, 153 Blockbuster Video, 32–33, 34 boards, 11, 166–167, 193–194 Boeing Planner, 78 Bohm, David, 130 Borders, 12–13 Boston Red Sox, 1, 3 boundaries, determining, 121–123, 215 Brigham Young University-Idaho (BYU-Idaho), 9, 59 business model at, 41, 42 commitment to transformation A at, 44 exchange team at, 84 identity change at, 170 the job to be done at, 37–38 postdisruption job to be done at, 39 superheroes at, 174–175 transformation B at, 57–58 Bryan, J.

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, Boston Dynamics, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, Computing Machinery and Intelligence, congestion charging, crowdsourcing, cryptocurrency, data science, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gamification, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, high-speed rail, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kim Stanley Robinson, Kiva Systems, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks

These features were weighted according to how automatable they were, and according to the engineering obstacles currently preventing automation or computerisation. The results were calculated with a common statistical modelling method. The outcome was clear. In the United States, more than 45 per cent of jobs could be automated within one to two decades. Table 2.3 shows a few jobs that are basically at 100 per cent risk of automation (I’ve highlighted a few of my favourites):8 Table 2.3: Some of the Jobs at Risk from Automation and AI Telemarketers Telemarketers Data Entry Professionals Procurement Clerks Title Examiners, Abstractors and Searchers Timing Device Assemblers and Adjusters Shipping, Receiving and Traffic Clerks Sewers, Hand Insurance Claims and Policy Processing Clerks Milling and Planing Machine Setters, Operators Mathematical Technicians Brokerage Clerks Credit Analysts Insurance Underwriters Order Clerks Parts Salespersons Watch Repairers Loan Officers Claims Adjusters, Examiners and Investigators Cargo and Freight Agents Insurance Appraisers, Auto Damage Driver/Sales Workers Tax Preparers Umpires, Referees and Other Sports Officials Radio Operators Photographic Process Workers and Processing Machine Operators Bank Tellers Legal Secretaries New Accounts Clerks Etchers and Engravers Bookkeeping, Accounting and Auditing Clerks Library Technicians Packaging and Filling Machine Operators Inspectors, Testers, Sorters, Samplers and Weighing Technicians One often voiced concern is that AI will create huge wealth for a limited few who own the technology, thus implying that the wealth gap will become even more acute.

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Robert Tercek, author of Vaporized “We live in a world where software is getting smart enough to automate tasks that only people could do just a few years ago. This is going to radically change the way we educate our children and the way people work in the future. Augmented is a wake-up call for a whole swathe of industries including the accounting profession. If your job can be automated, it probably will be. Artificial intelligence, embedded experience design and real-time advice will undermine many of the professional services industries that grew rapidly last century. The future is one that is very different and King, Lark, Lightman and Rangaswami are the best guys on the planet to explain how we might get there.

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If we look at the last 30 years of software-based automation using customer relationship management (CRM) and enterprise resource planning (ERP), we generally find that implementing the technology is the easy part. Getting the employees to accept and embrace the new technologies and use them productively is the single most important factor. More often, these new technology projects lead to more staff, contract and consultants jobs than the automation ever replaces. When these projects are successful, they usually informate and create better employee and customer experiences and drive companies to be more successful, grow and hire. When these projects fail, heads roll, customer and employee experiences fall and headcounts are reduced.

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly

A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, Albert Einstein, Alvin Toffler, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, commoditize, computer age, connected car, crowdsourcing, dark matter, data science, dematerialisation, Downton Abbey, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, game design, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, Marc Andreessen, Marshall McLuhan, means of production, megacity, Minecraft, Mitch Kapor, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, Project Xanadu, recommendation engine, RFID, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, The future is already here, the scientific method, transport as a service, two-sided market, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WeWork, Whole Earth Review, Yochai Benkler, zero-sum game

High-level diplomatic translators won’t lose their jobs for a while, but day-to-day translating chores in business will all be better done by machines. In fact, any job dealing with reams of paperwork will be taken over by bots, including much of medicine. The rote tasks of any information-intensive job can be automated. It doesn’t matter if you are a doctor, translator, editor, lawyer, architect, reporter, or even programmer: The robot takeover will be epic. We are already at the inflection point. We have preconceptions about how an intelligent robot should look and act, and these can blind us to what is already happening around us.

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But every day peasant farmers in China without plumbing purchase smartphones. Crafty AIs embedded in first-person shooter games have given millions of teenage boys the urge, the need, to become professional game designers—a dream that no boy in Victorian times ever had. In a very real way our inventions assign us our jobs. Each successful bit of automation generates new occupations—occupations we would not have fantasized about without the prompting of the automation. To reiterate, the bulk of new tasks created by automation are tasks only other automation can handle. Now that we have search engines like Google, we set the servant upon a thousand new errands.

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See also artificial intelligence “machine readable” information, 267 Magic Leap, 216 malaria, 241 Malthus, Thomas, 243 Mann, Steve, 247 Manovich, Lev, 200 manufacturing, robots in, 52–53, 55 maps, 272 mathematics, 47, 239, 242–43 The Matrix (1999), 211 maximum likelihood estimation (MLE), 265 McDonalds, 25–26 McLuhan, Marshall, 63, 127 media fluency, 201 media genres, 194–95 medical technology and field AI applications in, 31, 55 and crowdfunding, 157 and diagnoses, 31 future flows of, 80 interpretation services in field of, 69 and lifelogging, 250 new jobs related to automation in, 58 paperwork in, 51 personalization of, 69 and personalized pharmaceuticals, 173 and pooling patient data, 145 and tracking technology, 173, 237, 238–40, 241–42, 243–44, 250 Meerkat, 76 memory, 245–46, 249 messaging, 239–40 metadata, 258–59, 267 microphones, 221 Microsoft, 122–23, 124, 216, 247 minds, variety of, 44–46 Minecraft, 218 miniaturization, 237 Minority Report (2002), 221–22, 255 MIT Media Lab, 219, 220, 222 money, 4, 65, 119–21 monopolies, 209 mood tracking, 238 Moore’s Law, 257 movies, 77–78, 81–82, 168, 204–7 Mozilla, 151 MP3 compression, 165–66 music and musicians AI applications in, 35 creation of, 73–76, 77 and crowdfunding, 157 and free/ubiquitous copies, 66–67 and intellectual property issues, 208–9 and interactivity, 221 liquidity of, 66–67, 73–78 and live performances, 71 low-cost reproduction of, 87 of nonprofessionals, 75–76 and patronage, 72 sales of, 75 soundtracks for content, 76 total volume of recorded music, 165–66 Musk, Elon, 44 mutual surveillance (“coveillance”), 259–64 MyLifeBits, 247 Nabokov, Vladimir, 204 Napster, 66 The Narrative, 248–49, 251 National Geographic, 278 National Science Foundation, 17–18 National Security Agency (NSA), 261 Nature, 32 Negroponte, Nicholas, 16, 219 Nelson, Ted, 18–19, 21, 247 Nest smart thermostat, 253, 283 Netflix and accessibility vs. ownership, 109 and crowdsourcing programming, 160 and on-demand access, 64 and recommendation engines, 39, 154, 169 and reviews, 73, 154 and sharing economy, 138 and tracking technology, 254 Netscape browser, 15 network effect, 40 neural networks, 38–40 newbies, 10–11, 15 new media forms, 194–95 newspapers, 177 Ng, Andrew, 38, 39 niche interests, 155–56 nicknames, 263 nondestructive editing, 206 nonprofits, 157 noosphere, 292 Northwestern University, 225 numeracy, 242–43 Nupedia, 270 OBD chips, 251, 252 obscure or niche interests, 155–56 office settings, 222.

The New Class Conflict by Joel Kotkin

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, affirmative action, Affordable Care Act / Obamacare, Alvin Toffler, American Society of Civil Engineers: Report Card, Bob Noyce, Boston Dynamics, California gold rush, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, creative destruction, crony capitalism, David Graeber, deindustrialization, do what you love, don't be evil, Downton Abbey, Edward Glaeser, Elon Musk, energy security, falling living standards, future of work, Future Shock, Gini coefficient, Google bus, Herman Kahn, housing crisis, income inequality, independent contractor, informal economy, Internet of things, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kevin Kelly, labor-force participation, low-wage service sector, Marc Andreessen, Mark Zuckerberg, mass affluent, McJob, McMansion, medical bankruptcy, Nate Silver, National Debt Clock, New Economic Geography, new economy, New Urbanism, obamacare, offshore financial centre, Paul Buchheit, payday loans, Peter Calthorpe, plutocrats, post-industrial society, RAND corporation, Ray Kurzweil, rent control, rent-seeking, Report Card for America’s Infrastructure, Richard Florida, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Steve Jobs, stock buybacks, tech worker, technoutopianism, The Death and Life of Great American Cities, Thomas L Friedman, too big to fail, transcontinental railway, trickle-down economics, Tyler Cowen, Tyler Cowen: Great Stagnation, upwardly mobile, urban planning, urban sprawl, Virgin Galactic, War on Poverty, women in the workforce, working poor, young professional

Environmental concerns impose themselves most against basic industries such as fossil fuels, agriculture, and much of manufacturing. These employ many in highly paid blue-collar fields, with average salaries of close to $100,000. In the last decade, top U.S. firms, notes the liberal Center for American Progress, have cut almost three million domestic jobs. Automation also leads to the diminution of traditional white-collar professions as well as the shift of high-end service jobs offshore.25 Overall, it has become increasingly common to regard the middle class as threatened and even doomed. Indeed, as early as 1988 Time magazine featured a cover story on the “declining middle class,” which at that time was considerably healthier than it is today.

Mastering the Market Cycle: Getting the Odds on Your Side by Howard Marks

activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, business cycle, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, financial engineering, financial innovation, fixed income, if you build it, they will come, income inequality, Isaac Newton, job automation, junk bonds, Long Term Capital Management, margin call, Michael Milken, money market fund, moral hazard, new economy, profit motive, quantitative easing, race to the bottom, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, secular stagnation, short selling, South Sea Bubble, stocks for the long run, superstar cities, The Chicago School, The Great Moderation, transaction costs, VA Linux, Y2K, yield curve

But on the other hand, automation decreases the hours of labor applied to production. Today we see factories run by just a few workers that thirty years ago might have had a hundred. Thus the net effect of automation on GDP might be neutral or positive but, since it has the ability to eliminate jobs, automation might have the effect of reducing employment, and thus incomes, and thus consumption. Globalization —The integration of nations into a world economy may add to total world economic output, in part because of benefits from specialization, or it may not, leaving it a zero-sum (or negative-sum) exercise.

Work: A History of How We Spend Our Time by James Suzman

agricultural Revolution, basic income, carbon footprint, clean water, coronavirus, corporate social responsibility, cyber-physical system, David Graeber, do-ocracy, double entry bookkeeping, double helix, financial deregulation, founder crops, Frederick Winslow Taylor, interchangeable parts, invention of agriculture, invention of writing, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kibera, Kickstarter, late capitalism, lateral thinking, market bubble, New Urbanism, Occupy movement, Parkinson's law, Peter Singer: altruism, post-industrial society, post-work, Rubik’s Cube, Schrödinger's Cat, scientific management, sharing economy, social intelligence, spinning jenny, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, theory of mind, trickle-down economics, universal basic income, upwardly mobile, urban planning

For those in professions that have up to now been immune from technological redundancy, the rise of the job-eating robots manifests in the mundane: the choruses of robotic greetings and reprimands that emanate from the ranks of automated tellers in supermarkets or the clumsy algorithms that both guide and frustrate our adventures in the digital universe. For the hundreds of millions of unemployed people scraping a living in the corrugated-iron margins of developing countries, where economic growth is driven ever more by the marriage of cutting-edge technology and capital and so generates few new jobs, automation is an altogether more immediate concern. It is also an immediate concern for ranks of semi-skilled workers in industrialised economies whose only option is to strike to save their jobs from automata whose principal virtue is that they never go on strike. And, even if it doesn’t feel like it just yet, the writing is on the wall for some in highly skilled professions too.

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Some regions, like West Slovakia, they anticipated might experience job attrition rates of 40 per cent, while others, like Norway’s capital Oslo, would barely notice anything with fewer than 5 per cent of roles being automated. ‘Top talent’ at McKinsey and Company’s Global Institute suggested that between 30 and 70 per cent of jobs were vulnerable to partial automation over the course of the next fifteen to thirty-five years, and another big consultancy firm, PricewaterhouseCoopers, suggested that 30 per cent of jobs in the United Kingdom, 38 per cent of jobs in the United States, 35 per cent in Germany and only 21 per cent in Japan were vulnerable.

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Some regions, like West Slovakia, they anticipated might experience job attrition rates of 40 per cent, while others, like Norway’s capital Oslo, would barely notice anything with fewer than 5 per cent of roles being automated. ‘Top talent’ at McKinsey and Company’s Global Institute suggested that between 30 and 70 per cent of jobs were vulnerable to partial automation over the course of the next fifteen to thirty-five years, and another big consultancy firm, PricewaterhouseCoopers, suggested that 30 per cent of jobs in the United Kingdom, 38 per cent of jobs in the United States, 35 per cent in Germany and only 21 per cent in Japan were vulnerable.

21 Lessons for the 21st Century by Yuval Noah Harari

1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Brexit referendum, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon-based life, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, deglobalization, disinformation, Donald Trump, Dr. Strangelove, failed state, Filter Bubble, Francis Fukuyama: the end of history, Freestyle chess, gig economy, glass ceiling, Google Glasses, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, job automation, knowledge economy, liberation theology, Louis Pasteur, low skilled workers, Mahatma Gandhi, Mark Zuckerberg, mass immigration, means of production, Menlo Park, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-work, purchasing power parity, race to the bottom, RAND corporation, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, transatlantic slave trade, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, Watson beat the top human players on Jeopardy!, zero-sum game

Since the beginning of the Industrial Revolution, for every job lost to a machine at least one new job was created, and the average standard of living has increased dramatically.1 Yet there are good reasons to think that this time it is different, and that machine learning will be a real game changer. Humans have two types of abilities – physical and cognitive. In the past, machines competed with humans mainly in raw physical abilities, while humans retained an immense edge over machines in cognition. Hence as manual jobs in agriculture and industry were automated, new service jobs emerged that required the kind of cognitive skills only humans possessed: learning, analysing, communicating and above all understanding human emotions. However, AI is now beginning to outperform humans in more and more of these skills, including in the understanding of human emotions.2 We don’t know of any third field of activity – beyond the physical and the cognitive – where humans will always retain a secure edge.

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We don’t need humans to sell us music any more – we can download it directly from the iTunes store – but the composers, musicians, singers and DJs are still flesh and blood. We rely on their creativity not just to produce completely new music, but also to choose among a mind-boggling range of available possibilities. Nevertheless, in the long run no job will remain absolutely safe from automation. Even artists should be put on notice. In the modern world art is usually associated with human emotions. We tend to think that artists are channelling internal psychological forces, and that the whole purpose of art is to connect us with our emotions or to inspire in us some new feeling.

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AI might similarly help groom the best detectives, bankers and soldiers in history.14 The problem with all such new jobs, however, is that they will probably demand high levels of expertise, and will therefore not solve the problems of unemployed unskilled labourers. Creating new human jobs might prove easier than retraining humans to actually fill these jobs. During previous waves of automation, people could usually switch from one routine low-skill job to another. In 1920 a farm worker laid off due to the mechanisation of agriculture could find a new job in a factory producing tractors. In 1980 an unemployed factory worker could start working as a cashier in a supermarket.

The Coming of Neo-Feudalism: A Warning to the Global Middle Class by Joel Kotkin

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", Admiral Zheng, Alvin Toffler, Andy Kessler, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Brexit referendum, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, clean water, content marketing, creative destruction, data science, deindustrialization, demographic transition, don't be evil, Donald Trump, edge city, Elon Musk, European colonialism, financial independence, Francis Fukuyama: the end of history, Future Shock, gig economy, Gini coefficient, Google bus, guest worker program, Hans Rosling, Herbert Marcuse, housing crisis, income inequality, informal economy, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, job polarisation, job satisfaction, Joseph Schumpeter, land reform, liberal capitalism, life extension, low skilled workers, Lyft, Marc Benioff, Mark Zuckerberg, market fundamentalism, Martin Wolf, mass immigration, megacity, Nate Silver, new economy, New Urbanism, Occupy movement, Parag Khanna, Peter Thiel, plutocrats, post-industrial society, post-work, postindustrial economy, postnationalism / post nation state, precariat, profit motive, RAND corporation, Ray Kurzweil, rent control, Richard Florida, road to serfdom, Robert Gordon, Salesforce, Sam Altman, San Francisco homelessness, Satyajit Das, sharing economy, Silicon Valley, smart cities, Steve Jobs, Stewart Brand, superstar cities, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Rise and Fall of American Growth, Thomas L Friedman, too big to fail, trade route, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, unpaid internship, upwardly mobile, Virgin Galactic, We are the 99%, Wolfgang Streeck, women in the workforce, working-age population, Y Combinator

This does not mean that all American incomes dropped across the board, but the overall trend was downward.38 Upward mobility—the essence of capitalist promise—has declined markedly in virtually all high-income countries.39 In Ontario, the economic center of historically egalitarian Canada, middle-class jobs are disappearing and being replaced by a mix of highly technical jobs and low-end work.40 The “job polarization” resulting from shrinkage of the middle-wage sector can be seen in Europe as well, notably Germany, France, and Sweden—countries long associated with social democracy.41 In the United Kingdom, between 2010 and 2014, urban wages dropped 5 percent even as a million jobs were created.42 In France, a majority of citizens could not save more than 50 euros ($56) a month.43 Future technological advances could further intensify the pressure on the working class globally. In 2017, a British report predicted that about 30 percent of jobs in the UK would be automated within fiteen years, with a higher risk of automation for jobs typically held by men (35 percent) than for those normally done by women (26 percent). It’s easier to automate trucking than nursing.44 Artificial intelligence could accelerate the loss of many kinds of jobs that once provided a means of upward mobility: postal workers, switchboard operators, machinists, computer operators, bank tellers, travel agents.

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It’s easier to automate trucking than nursing.44 Artificial intelligence could accelerate the loss of many kinds of jobs that once provided a means of upward mobility: postal workers, switchboard operators, machinists, computer operators, bank tellers, travel agents. For the 90 million Americans who work in such jobs—and their counterparts elsewhere—the future could be bleak.45 CHAPTER 14 The Future of the Working Class In the past, fears of job losses from automation were often over-stated. Technological progress eliminated some jobs but created others, and often better-paying ones. In the early days of the high-tech revolution, many of the pioneering firms—such as Hewlett-Packard, Intel, and IBM—were widely praised for treating their lower-level workers as part of the company and deserving of opportunities for advancement, as well as benefits including health insurance and a pension.1 The labor policies of the newer generation of tech giants tend to be vastly different.

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And too bad if [it] isn’t popular.”26 If political elites in Europe regard open borders as good for the economy, corporate elites in the United States are eager to import skilled technicians and other workers, who typically accept lower wages. The tech oligarchs in particular like to hire from abroad: in Silicon Valley, roughly 40 percent of the tech workforce is made up of noncitizens. Steve Case, the former CEO of America Online, has suggested that immigrant entrepreneurs and workers could offset middle-class job losses from automation.27 Some conservative intellectuals have even thought that hardworking newcomers should replace the “lazy” elements of the working class.28 Some of the earliest opposition to the Trump administration focused on his agenda of curtailing immigration.29 Somewheres vs. Anywheres Ironically, the people who most strongly favor open borders are welcoming large numbers of immigrants who do not share their own secular, progressive values.

The AI Economy: Work, Wealth and Welfare in the Robot Age by Roger Bootle

"Robert Solow", 3D printing, agricultural Revolution, AI winter, Albert Einstein, Alvin Toffler, anti-work, antiwork, autonomous vehicles, basic income, Ben Bernanke: helicopter money, Bernie Sanders, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, computer age, Computing Machinery and Intelligence, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deindustrialization, deskilling, Dr. Strangelove, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, financial intermediation, full employment, future of work, Future Shock, Hans Moravec, income inequality, income per capita, industrial robot, Internet of things, invention of the wheel, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, license plate recognition, Marc Andreessen, Mark Zuckerberg, market bubble, mega-rich, natural language processing, Network effects, new economy, Nicholas Carr, Ocado, Paul Samuelson, Peter Thiel, Phillips curve, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Silicon Valley billionaire, Simon Kuznets, Skype, social intelligence, spinning jenny, Stanislav Petrov, Stephen Hawking, Steven Pinker, technological singularity, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, universal basic income, US Airways Flight 1549, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, wealth creators, winner-take-all economy, Y2K, Yogi Berra

This will probably mean that even in job categories or areas of economic activity where machines will largely take over, there will still need to be a higher level of human oversight.21 Similar conclusions to McKinsey’s have been reached by the OECD. The study mentioned earlier concluded that most jobs were difficult to automate because they required creativity, complex reasoning, the ability to carry out physical tasks in an unstructured work environment, and the ability to negotiate social relationships. The director of employment, labor, and social affairs at the OECD, Stefano Scarpetta, gives an interesting example that contrasts a car mechanic working on a production line in a huge plant with one working in an independent garage.

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Not the effects envisaged Even where information technology has fulfilled the technological hopes entertained for it and has been employed in the workplace, it has still not had quite the effect on people and society that was envisaged – for both good and ill. There is a long history of people seeing the progress of technology as having negative economic consequences. In 1931 Einstein blamed the Great Depression on machines. In the late 1970s British Prime Minister James Callaghan commissioned a study from the civil service on the threat to jobs from automation.37 When they first emerged, it was widely predicted that computers would put an end to large numbers of office jobs. Nothing of the sort has happened, even though the job of typist has just about disappeared. And what about the paperless office? Remember that one? In particular, it was widely believed when spreadsheet software appeared in the 1980s that this would cause huge job losses among accountants.

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Accordingly, it is by no means obvious that the AI revolution is bound to increase income inequality. Indeed, it is possible that, at least across some parts of the income distribution, the effect of the AI revolution will be to reduce it. After all, the thrust of preceding chapters is that many manual jobs will not readily succumb to automation. Meanwhile, many skilled but essentially routine white-collar jobs will. Prime examples of the latter include large numbers of mid-level lawyers and accountants. Such people have typically earned much more than the average manual worker. Mind you, this does not settle the matter.

Fair Shot: Rethinking Inequality and How We Earn by Chris Hughes

"side hustle", basic income, Donald Trump, effective altruism, Elon Musk, end world poverty, full employment, future of journalism, gig economy, high net worth, hockey-stick growth, income inequality, invisible hand, Jeff Bezos, job automation, knowledge economy, labor-force participation, Lyft, M-Pesa, Mark Zuckerberg, meta-analysis, new economy, oil rush, payday loans, Peter Singer: altruism, Potemkin village, precariat, randomized controlled trial, ride hailing / ride sharing, Ronald Reagan, Second Machine Age, self-driving car, side project, Silicon Valley, TaskRabbit, The Bell Curve by Richard Herrnstein and Charles Murray, traveling salesman, trickle-down economics, uber lyft, universal basic income, winner-take-all economy, working poor, working-age population, zero-sum game

Extreme Economies: Survival, Failure, Future – Lessons From the World’s Limits by Richard Davies

agricultural Revolution, air freight, Anton Chekhov, artificial general intelligence, autonomous vehicles, barriers to entry, big-box store, cashless society, clean water, complexity theory, deindustrialization, eurozone crisis, failed state, financial innovation, Garrett Hardin, gentleman farmer, illegal immigration, income inequality, informal economy, James Hargreaves, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, joint-stock company, large denomination, Livingstone, I presume, Malacca Straits, mandatory minimum, manufacturing employment, means of production, megacity, meta-analysis, new economy, off grid, oil shale / tar sands, pension reform, profit motive, randomized controlled trial, school choice, school vouchers, Scramble for Africa, side project, Silicon Valley, Simon Kuznets, Skype, spinning jenny, subscription business, The Chicago School, the payments system, trade route, Tragedy of the Commons, Travis Kalanick, uranium enrichment, urban planning, wealth creators, white picket fence, working-age population, Y Combinator, young professional

But around the world technological advances are also causing fear and uncertainty, with worries about elections, privacy and ethics, and alongside these political fears two deep economic concerns. The first is the prospect of mass unemployment, the idea that labour-saving technology – which could be software or machines – will make human workers redundant. Estimates of the likely job losses as automation looms vary, but the latest studies suggest that 25 per cent of workers in the US and 30 per cent in the UK are at risk of being replaced by a machine. The robots are coming, the story goes, and they are going to take our jobs. The second fear is that technological advances will be unfair, generating a new type of inequality some call the ‘digital divide’.

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If the complexities a small delivery robot faces can be cracked, and if Mr Heinla is right that trucks and vans will be relatively easy to automate, the era of humans delivering things will soon be over. The prospect of a world of automated delivery is exciting and terrifying. Studies of the risks of automation predict scarily large numbers of job losses. Transport and logistics are big employers: in the US 4 million people currently do the kinds of jobs Mr Heinla predicts will be automated in the near future. This is 4 per cent of the workforce and includes 1.5 million people working in trucking, 630,000 couriers and messengers, 140,000 school and passenger bus drivers, and 75,000 who drive taxis and limousines. In the UK an even higher share of the workforce (6 per cent) does this kind of work.

The New Division of Labor: How Computers Are Creating the Next Job Market by Frank Levy, Richard J. Murnane

Atul Gawande, business cycle, call centre, computer age, Computer Numeric Control, correlation does not imply causation, David Ricardo: comparative advantage, deskilling, Frank Levy and Richard Murnane: The New Division of Labor, Gunnar Myrdal, hypertext link, index card, information asymmetry, job automation, knowledge economy, knowledge worker, low skilled workers, low-wage service sector, PalmPilot, pattern recognition, profit motive, Robert Shiller, Ronald Reagan, Salesforce, speech recognition, tacit knowledge, talking drums, telemarketer, The Wealth of Nations by Adam Smith, working poor

The unemployment rate moved through recessions and expansions but the same jobs that were lost on downturns were largely replaced on the upturns. Because the job market was fairly stable, the policies that interacted with the job market—the tax system, education, training—could be stable as well. That world is largely gone now. Many of the jobs lost in the post-2000 recession—clerical and factory jobs lost to automation, call center jobs lost to India, manufacturing jobs lost to China—will not be coming back. This dynamic environment requires new policies and the first step in creating new policies is to recognize our new situation. In chapter 1, we listed a set of four questions this book was designed to answer: • What kinds of tasks do humans perform better than computers?

The Economics of Belonging: A Radical Plan to Win Back the Left Behind and Achieve Prosperity for All by Martin Sandbu

"Robert Solow", air traffic controllers' union, Airbnb, Alan Greenspan, autonomous vehicles, balance sheet recession, bank run, banking crisis, basic income, Berlin Wall, Bernie Sanders, Big Tech, Boris Johnson, Branko Milanovic, Bretton Woods, business cycle, call centre, capital controls, carbon footprint, Carmen Reinhart, centre right, collective bargaining, debt deflation, deindustrialization, deskilling, Diane Coyle, Donald Trump, Edward Glaeser, eurozone crisis, Fall of the Berlin Wall, financial engineering, financial intermediation, full employment, future of work, gig economy, Gini coefficient, hiring and firing, income inequality, income per capita, industrial robot, intangible asset, job automation, John Maynard Keynes: technological unemployment, Kenneth Rogoff, knowledge economy, knowledge worker, labour market flexibility, liquidity trap, longitudinal study, low skilled workers, manufacturing employment, Martin Wolf, meta-analysis, mini-job, Money creation, mortgage debt, new economy, offshore financial centre, oil shock, open economy, pattern recognition, pink-collar, precariat, quantitative easing, race to the bottom, Richard Florida, Robert Shiller, Ronald Reagan, secular stagnation, social intelligence, TaskRabbit, total factor productivity, universal basic income, very high income, winner-take-all economy, working poor

—in other words, might it be economics after all, even in Sweden? The economists grouped data on individuals according to whether they were labour market “insiders” with stable jobs or “outsiders” moving in and out of unstable work. They further classified insiders according to how liable their jobs were to be eliminated by automation. Which group a person belonged to turns out to have made a huge difference to their post-2006 fortunes: “Over a mere six years, these reforms led to large shifts in inequality … incomes continued to grow among labour-market ‘insiders’ with stable employment, while cuts in benefits implied a stagnation of disposable incomes for labour-market ‘outsiders’ with unstable or no jobs.

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Coding, translation, copyediting, and other high-skilled and middle-class jobs are opening up to global competition even as computerised pattern recognition and artificial intelligence mean fewer people are required to accomplish the same amount of work. Automation and globalisation are both expanding from blue-collar to white-collar work, which is set to be disrupted as much as if not more than manufacturing was from the late 1970s on.24 Job loss through automation-driven productivity growth and, to some extent, competition from globalisation—these are the very same forces that, in the absence of an adequate policy response, denied a large group of workers what they expected from the social contract. That means economic belonging is likely to take another hit.

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That is because lower-skilled routine jobs—for example in retail, warehousing, and customer service such as call centres—are both more threatened by technological innovation and disproportionately found in places that previously lost industry or mining jobs, places like the north of England or the US states of Indiana and Ohio. In contrast, the places with a high proportion of knowledge economy jobs—think Oxford or New York—are not just doing better already but are also more secure because such jobs tend to be harder to automate.25 In baseball, it’s three strikes and you’re out. Unless governments do a better job of rising to this third challenge than they did to the previous two, it is the Western liberal order that is likely to strike out. 5 Scapegoating Globalisation In 1997 a soft-spoken Harvard economics professor named Dani Rodrik published a short book called Has Globalization Gone Too Far?

Tailspin: The People and Forces Behind America's Fifty-Year Fall--And Those Fighting to Reverse It by Steven Brill

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, activist fund / activist shareholder / activist investor, affirmative action, Affordable Care Act / Obamacare, airport security, American Society of Civil Engineers: Report Card, asset allocation, Bernie Madoff, Bernie Sanders, Blythe Masters, Bretton Woods, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, carried interest, clean water, collapse of Lehman Brothers, collective bargaining, computerized trading, corporate governance, corporate raider, corporate social responsibility, Credit Default Swap, currency manipulation / currency intervention, Donald Trump, ending welfare as we know it, failed state, financial deregulation, financial engineering, financial innovation, future of work, ghettoisation, Gordon Gekko, hiring and firing, Home mortgage interest deduction, immigration reform, income inequality, invention of radio, job automation, junk bonds, knowledge economy, knowledge worker, labor-force participation, laissez-faire capitalism, Mahatma Gandhi, Mark Zuckerberg, Michael Milken, military-industrial complex, mortgage tax deduction, new economy, Nixon triggered the end of the Bretton Woods system, obamacare, old-boy network, opioid epidemic / opioid crisis, paper trading, Paris climate accords, performance metric, post-work, Potemkin village, Powell Memorandum, quantitative hedge fund, Ralph Nader, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Mercer, Ronald Reagan, Salesforce, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, stock buybacks, Tax Reform Act of 1986, tech worker, telemarketer, too big to fail, trade liberalization, union organizing, Unsafe at Any Speed, War on Poverty, women in the workforce, working poor

Their incomes in the three years following the crash went up by nearly a third, while the bottom 99 percent saw an uptick of less than half of one percent. Only a democracy and an economy that has discarded its basic mission of holding the community together, or failed at it, would produce those results. Most Americans with average incomes have been left largely to fend for themselves, often at jobs where automation, outsourcing, the near-vanishing of union protection, and the boss’s obsession with squeezing out every penny of short-term profit have eroded any sense of security. Self-inflicted deaths—from opioid and other drug abuse, alcoholism, and suicide—are at record highs, so much so that the country’s average life expectancy has been falling despite medical advances.

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In 1950, unions won 74 percent of all election contests to get certified at a workplace, yielding 754,000 new union members. In 1965, they won only 61 percent. In 1980, they won 48 percent, yielding just 175,000 new members. With companies shifting to non-union shops in the South or, later, laying off workers as jobs were automated or outsourced overseas, the dwindling number of new union members was more than offset by workers who went off the union rolls. When Taft-Hartley was passed in 1947, about 37 percent of the entire private workforce in the U.S. was unionized. In 1960, it was still 32 percent. Then it began a downward slide that pushed unionization to 22 percent in 1980, and steadily lower after that.

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Although with his daughter Ivanka he touted a new push for apprenticeship programs through an executive order, the order was only a directive to his Department of Labor to encourage such programs. No new funds were allocated. The federal government should also provide tax credits or other inducements to corporations to offer retraining programs for workers about to lose their jobs because of automation. When unions were strong, they were sometimes able to negotiate that help into their contracts. After Ford announced plans to close an assembly plant near San Jose, California, in 1983, workers received what could have become a model retraining and transitional income support program. It didn’t help everyone, but more than 80 percent of the workers got new jobs, including 25 percent in the blossoming tech industry in neighboring Silicon Valley.

Software Engineering at Google: Lessons Learned From Programming Over Time by Titus Winters, Tom Manshreck, Hyrum Wright

anti-pattern, computer vision, continuous integration, defense in depth, en.wikipedia.org, functional programming, Jevons paradox, job automation, loss aversion, microservices, transaction costs, Turing complete

In this environment, we’ve found it useful to treat specific changes as cattle: nameless and faceless commits, which might be rolled back or otherwise rejected at any given time with little cost unless the entire herd is affected. Often this happens because of an unforeseen problem not caught by tests, or even something as simple as a merge conflict. With a “pet” commit, it can be hard to not take rejection personally, but when working with many changes as part of a large-scale change, it’s just the nature of the job. Having automation means that tooling can be updated and new changes generated at very low cost, so losing a few cattle now and then isn’t a problem. Testing Each independent shard is tested by running it through TAP, Google’s continuous integration framework. We run every test which depends on the files in a given change transitively, which often creates high load on our continuous integration system.

The Driver in the Driverless Car: How Our Technology Choices Will Create the Future by Vivek Wadhwa, Alex Salkever

23andMe, 3D printing, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, distributed ledger, Donald Trump, double helix, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Google bus, Hyperloop, income inequality, information security, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Menlo Park, microbiome, military-industrial complex, mobile money, new economy, personalized medicine, phenotype, precision agriculture, radical life extension, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, Tesla Model S, The future is already here, The Future of Employment, Thomas Davenport, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, uranium enrichment, Watson beat the top human players on Jeopardy!, zero day

A.I.’s medical judgments are already superseding those of human physicians.4 Another example of a profession that you might not expect to be at risk is the legal profession. Only a few decades ago, a law degree was considered a ticket to a solid middle-or upper-middle-class life in the United States. Today, young lawyers are struggling to find jobs, and salaries are stagnant. Automation driven by A.I. has begun to rapidly strip away chunks of what junior attorneys formerly used to do, from contract analysis to document discovery. Symantec, for example, has a software product, Clearwell, that does legal discovery. Legal discovery is the laborious process of sifting through boxes of documents, reams of e-mails, and numerous other forms of information submitted to the court by litigants.

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In this fashion, the robots will gradually, task by task, assume the jobs of humans in manufacturing plants, in grocery stores, in pharmacies. Hospitals rely on A.I.-driven systems in their pharmacies right now to spot potential problems due to conflicting medicines. I can envisage the job of pharmacist being completely automated. Further down the economic food chain, McDonald’s is in the process of rolling out automated order-taking at its counters. This could be matched by an automated engine to cook hamburger and fries. One of these already exists. It’s from a venture-backed company called Momentum Machines and can make a hamburger every ten seconds.

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What’s more, about 60 percent of all occupations could see 30 percent or more of their work activities automated.”11 The report also notes that the mere ability to automate work doesn’t make it a sensible thing to do. As long as $10-per-hour cooks are cheaper than Momentum Machines on fast-food lines, it’s unlikely that food-service jobs will succumb to automation. The alternative extreme—no robots—is simply not realistic. The giant bubble of aging people could overwhelm most of the developed world, as well as many developing countries, such as China. Self-driving cars will save tens of millions of lives over the next decades. More agile and intelligent robots will take over the most dangerous human tasks and jobs such as mining, firefighting, search and rescue, and inspecting tall buildings and communications towers.

Industrial Internet by Jon Bruner

autonomous vehicles, barriers to entry, Boeing 747, commoditize, computer vision, data acquisition, demand response, en.wikipedia.org, factory automation, Google X / Alphabet X, industrial robot, Internet of things, job automation, loose coupling, natural language processing, performance metric, Silicon Valley, slashdot, smart grid, smart meter, statistical model, web application

If information is seamlessly captured from machines as well as people, we’ll need fewer low-level data shepherds like medical transcriptionists (ironically, the demand for these types of jobs has increased with the introduction of electronic medical records, though that’s largely due to the persistence of poor user interfaces and interoperability barriers). The industrial internet will automate certain repetitive jobs that have so far resisted automation because they require some degree of human judgment and spatial understanding — driving a truck, perhaps, or recognizing a marred paint job on an assembly line. In fast-growing fields like health care, displaced workers might be absorbed into other low- or medium-skill roles, but in others, the economic tradeoffs will be similar to those in factory automation: higher productivity, lower prices for consumers, continued feasibility of manufacturing in high-cost countries like the United States — but also fewer jobs for people without high-demand technical skills.

Doing Good Better: How Effective Altruism Can Help You Make a Difference by William MacAskill

barriers to entry, basic income, Black Swan, Branko Milanovic, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Edward Jenner, effective altruism, en.wikipedia.org, end world poverty, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, job automation, job satisfaction, Lean Startup, M-Pesa, mass immigration, meta-analysis, microcredit, Nate Silver, Peter Singer: altruism, purchasing power parity, quantitative trading / quantitative finance, randomized controlled trial, self-driving car, Skype, Stanislav Petrov, Steve Jobs, Steve Wozniak, Steven Pinker, The Future of Employment, The Wealth of Nations by Adam Smith, Tyler Cowen, universal basic income, women in the workforce

The Data Detective: Ten Easy Rules to Make Sense of Statistics by Tim Harford

access to a mobile phone, Ada Lovelace, affirmative action, algorithmic bias, Automated Insights, banking crisis, basic income, Black Swan, Bretton Woods, British Empire, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, Charles Babbage, clean water, collapse of Lehman Brothers, coronavirus, correlation does not imply causation, COVID-19, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Attenborough, Diane Coyle, disinformation, Donald Trump, Estimating the Reproducibility of Psychological Science, experimental subject, financial innovation, Florence Nightingale: pie chart, Gini coefficient, Hans Rosling, high-speed rail, income inequality, Isaac Newton, job automation, Kickstarter, life extension, meta-analysis, microcredit, Milgram experiment, moral panic, Netflix Prize, opioid epidemic / opioid crisis, Paul Samuelson, Phillips curve, publication bias, publish or perish, random walk, randomized controlled trial, recommendation engine, replication crisis, Richard Feynman, Richard Thaler, rolodex, Ronald Reagan, selection bias, sentiment analysis, Silicon Valley, sorting algorithm, statistical model, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, survivorship bias, universal basic income, W. E. B. Du Bois, When a measure becomes a target

Levine, “A Critical Appraisal of 98.6°F, the Upper Limit of the Normal Body Temperature, and Other Legacies of Carl Reinhold August Wunderlich,” JAMA 268, no. 12 (1992), 1578–80, DOI: 10.1001/jama.1992.03490120092034. 14. Jeffrey Dastin, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women,” Reuters, October 10, 2018, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 15. Gerd Gigerenzer and Stephanie Kurzenhaeuser, “Fast and Frugal Heuristics in Medical Decision Making,” in Roger Bibace et al., Science and Medicine in Dialogue: Thinking through Particulars and Universals (Westport, CT: Praeger, 2005), 3–15. 16.

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World by Cade Metz

AI winter, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Big Tech, British Empire, carbon-based life, cloud computing, computer age, computer vision, digital map, Donald Trump, drone strike, Elon Musk, Fellow of the Royal Society, Frank Gehry, game design, Google Earth, Google X / Alphabet X, Googley, Internet Archive, Isaac Newton, Jeff Hawkins, Jeffrey Epstein, job automation, John Markoff, life extension, Mark Zuckerberg, means of production, Menlo Park, move fast and break things, new economy, nuclear winter, PageRank, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, profit motive, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Sam Altman, Sand Hill Road, self-driving car, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Ballmer, Steven Levy, Steven Pinker, tech worker, telemarketer, The Future of Employment, Turing test, warehouse automation, warehouse robotics, Y Combinator

AI is working for a certain very small segment of the world population. And the people creating it are from a very minuscule segment of the world population. Certain segments of the population are actively harmed by it. Not only because the algorithms work against them, but also because their jobs are automated. These people are actively excluded from entering a high paying field that is removing them from the workforce. I’ve heard many talk about diversity as if it is some sort of charity. I see companies and even individuals using it as a PR stunt while paying lip service to it. Because it is the language du jour, “we value diversity” is something you’re supposed to say.

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“I’ve worked in the logistics industry for more than sixteen years and I’ve never seen anything like this,” said Peter Puchwein, vice president of Knapp, an Austrian company that had long provided automation technology for warehouses and helped develop and install the Covariant technology in Berlin. It showed that robotic automation would continue to spread across the retail and logistics industry in the years to come, and perhaps across manufacturing plants, too. It also raised new concerns over warehouse workers, losing their jobs to automated systems. In the German warehouse, the jobs of three humans were done by one robot. At the time, though, economists didn’t think this kind of technology would diminish the overall number of logistics jobs anytime soon. The online retail business was growing much too quickly, and most companies would take years or even decades to install the new breed of automation.

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy by Christopher Mims

air freight, Airbnb, Amazon Web Services, augmented reality, autonomous vehicles, big-box store, blue-collar work, Boeing 747, book scanning, business process, call centre, cloud computing, coronavirus, cotton gin, COVID-19, creative destruction, data science, Dava Sobel, dematerialisation, deskilling, digital twin, Donald Trump, easy for humans, difficult for computers, Elon Musk, Frederick Winslow Taylor, fulfillment center, gig economy, global pandemic, global supply chain, guest worker program, Hans Moravec, heat death of the universe, hive mind, Hyperloop, immigration reform, income inequality, independent contractor, industrial robot, interchangeable parts, intermodal, inventory management, Jacquard loom, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kanban, Kiva Systems, level 1 cache, lone genius, Lyft, Malacca Straits, Mark Zuckerberg, market bubble, minimum wage unemployment, Ocado, operation paperclip, Panamax, Pearl River Delta, planetary scale, pneumatic tube, polynesian navigation, post-Panamax, random stow, ride hailing / ride sharing, robot derives from the Czech word robota Czech, meaning slave, Rodney Brooks, rubber-tired gantry crane, scientific management, self-driving car, sensor fusion, Shenzhen special economic zone , Shoshana Zuboff, Silicon Valley, six sigma, skunkworks, South China Sea, special economic zone, spinning jenny, standardized shipping container, Steve Jobs, supply-chain management, surveillance capitalism, the scientific method, Tim Cook: Apple, Toyota Production System, traveling salesman, Turing test, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, warehouse automation, warehouse robotics

Squeezed: Why Our Families Can't Afford America by Alissa Quart

Affordable Care Act / Obamacare, Airbnb, Alvin Toffler, antiwork, Automated Insights, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, business intelligence, do what you love, Donald Trump, Downton Abbey, East Village, Elon Musk, emotional labour, full employment, future of work, gig economy, glass ceiling, haute couture, income inequality, independent contractor, information security, Jaron Lanier, job automation, late capitalism, Lyft, minimum wage unemployment, moral panic, new economy, nuclear winter, obamacare, Ponzi scheme, post-work, precariat, price mechanism, rent control, ride hailing / ride sharing, school choice, sharing economy, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, surplus humans, TaskRabbit, tech worker, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, upwardly mobile, wages for housework, WeWork, women in the workforce, working poor