job automation

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pages: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

"Robert Solow", Albert Einstein, Bill Joy: nanobots, Black-Scholes formula, business cycle, call centre, cloud computing, collateralized debt obligation, commoditize, creative destruction, credit crunch, double helix, en.wikipedia.org, factory automation, full employment, income inequality, index card, industrial robot, inventory management, invisible hand, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, knowledge worker, low skilled workers, mass immigration, Mitch Kapor, moral hazard, pattern recognition, prediction markets, Productivity paradox, Ray Kurzweil, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, strong AI, technological singularity, Thomas L Friedman, Turing test, Vernor Vinge, War on Poverty

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. 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.

A Reality Check Clearly, our simulation did not turn out well. Perhaps our initial assumption about jobs being automated was wrong. But, again, let’s leave that for the next chapter. In the meantime, we might wonder if we have made a mistake somewhere in the simulation. Let’s see if we can perform some type of “reality check” on our result. Perhaps we can look to history to see if there is anything in the past that might support what we saw happen in our simulation. Let’s leave our tunnel and travel back in time to the year 1860. In the southern part of the United States, we know will find the greatest injustice ever perpetrated in the history of our nation. Here, long before the new light of advanced technology first began to shine, men had discovered a far more primitive and perverse form of job automation. The injustice and moral outrage associated with slavery rightly attracts nearly all of our attention.

The result will be rapid penetration of these practices into businesses of all sizes. As we saw with the radiologist and the lawyer, once significant portions of jobs can be automated, the number of workers employed will immediately begin to fall. The U.S. Small Business Administration estimates that businesses with fewer than 500 employees have generated from 60-80 percent of all job growth over the past decade.25 As it becomes easier and cheaper for business owners to employ automation and offshoring, we may well find that these practices will become a significant drag on America’s primary job creation engine. “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.


pages: 301 words: 89,076

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

agricultural Revolution, Airbnb, AltaVista, Amazon Web Services, augmented reality, autonomous vehicles, basic income, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, commoditize, computer vision, Corn Laws, correlation does not imply causation, Credit Default Swap, David Ricardo: comparative advantage, declining real wages, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, hiring and firing, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, knowledge worker, laissez-faire capitalism, low skilled workers, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, manufacturing employment, Mark Zuckerberg, mass immigration, mass incarceration, Metcalfe’s law, new economy, optical character recognition, pattern recognition, Ponzi scheme, post-industrial society, post-work, profit motive, remote working, reshoring, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Reagan, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, social intelligence, sovereign wealth fund, standardized shipping container, statistical model, Stephen Hawking, Steve Jobs, supply-chain management, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income

Half of all US jobs, they estimated, were at risk—yes, half (or 47 percent to be precise). 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. The study points to “robot monitoring professionals,” data scientists, automation specialists, and content curators as the biggest sources of new tech-related jobs.

Amelia and her kind are not quite as good as real workers, but they are a whole lot cheaper, as SEB can attest. 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. A form of AI called “machine learning” has given computers skills that they never had before—things like reading, writing, speaking, and recognizing subtle patterns.

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.


pages: 484 words: 104,873

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 trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, 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, 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, Plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, 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, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

Barra also noted that Google already has “near perfect” real-time voice translation between English and Portuguese.53 As more and more routine white-collar jobs fall to automation in countries throughout the world, it seems inevitable that competition will intensify to land one of the dwindling number of positions that remain beyond the reach of the machines. The very smartest people will have a significant advantage, and they won’t hesitate to look beyond national borders. In the absence of barriers to virtual immigration, the employment prospects for nonelite college-educated workers in developed economies could turn out to be pretty grim. Education and Collaboration with the Machines As technology advances and more jobs become susceptible to automation, the conventional solution has always been to offer workers more education and training so that they can step into to new, higher-skill roles.

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. It’s quite easy to imagine that someday, in the not too distant future, radiology will be a job performed almost exclusively by machines.

Fruits and vegetables are easily damaged and often need to be selected based on color or softness. 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.


pages: 626 words: 167,836

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

"Robert Solow", 3D printing, 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, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, creative destruction, 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, 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, Hyperloop, income inequality, income per capita, 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, 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, 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, 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, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

As we saw in chapter 9, routine jobs were eliminated in large numbers beginning in the 1980s. 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. Much depends on what happens in technology and how people adjust.

We shall return to the question of the determinants of technology adoption shortly. FIGURE 17: Share of Jobs at Risk of Automation by Major Occupational Categories Source: C. B. Frey and M. A. Osborne, 2017, “The Future of Employment: How Susceptible Are Jobs to Computerisation?,” Technological Forecasting and Social Change 114 (January): 254–80. Figure 17 plots the exposure of major occupational categories to automation by their employment share. 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.

Another is that their model performs less well against our training data set.54 However, for all their differences, these studies concur that unskilled jobs are most exposed to automation.55 When President Barack Obama’s Council of Economic Advisers used our estimates to sort by wage levels the occupations most at risk of being automated, they found that 83 percent of workers in occupations that paid less than $20 an hour were at high risk of being replaced, while the corresponding figure for workers in occupations that paid more than $40 per hour was only 4 percent.56 What this shows is that the labor market prospects of the unskilled will likely continue to deteriorate, unless other forces counteract that trend. We saw in chapter 9 that the first wave of automation of routine work pushed many Americans out of decent middle-class jobs and into low-paying service jobs. Many of these low-skilled jobs are now threatened by automation, too.


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

3D printing, Airbnb, algorithmic trading, Amazon Web Services, anti-work, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, correlation does not imply causation, creative destruction, data is the new oil, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, gig economy, global supply chain, income inequality, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, 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, Second Machine Age, self-driving car, sharing economy, Steve Jobs, strong AI, 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

Political Machinery: Did Robots Swing the 2016 US Presidential Election? Oxford Review of Economic Policy, 34(3), 418–442. Frey, C. B., & Osborne, M. A. (2017). 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.

They suggest that in fact job losses will be permanent unless something is done. 1 Introduction 3 Another issue is where new jobs are to come from. 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. Needs and wants are not identical, though they are treated as such by economists.

Luddite-like anxiety has been fuelled by a fear of a future where jobs are scarce in number and where poverty levels increase significantly. 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).


pages: 477 words: 75,408

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

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, 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, 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, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, 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.

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. If an automated system falls into error it can waste an enormous amount of resources and perhaps cause significant damage before it is shut down.

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.


pages: 347 words: 97,721

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, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, 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, 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, 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, Robert Shiller, 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, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? 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. If we were IT operations engineers, we’d be worried about the systems at Facebook that let one engineer run 25,000 servers.

., 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.

Thanks, Judah Curiosity getting the best of him, Tom looked up the company in Amy’s email extension, @x.ai. 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. But whether the customer is buying an airline ticket, ordering a meal, or making an appointment, these transactions are so routinized that they are simple to translate into code.


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

AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, creative destruction, crony capitalism, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, happiness index / gross national happiness, if you build it, they will come, ImageNet competition, 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, 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, Y Combinator

leapt to $15.2 billion: “Top AI Trends to Watch in 2018,” CB Insights, February 2018, https://www.cbinsights.com/research/report/artificial-intelligence-trends-2018/. a dire prediction: Carl Benedikt Frey and Michael A. 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.

Here I give a brief overview of the literature and the methods, highlighting the studies that have shaped the debate. 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.

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. Unsurprisingly, that didn’t settle the debate.


pages: 719 words: 181,090

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, 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, meta-analysis, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, revision control, risk tolerance, side project, six sigma, the scientific method, Toyota Production System, trickle-down economics, 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. For example, we had automated away the worst, but not all, of the routine work for standard replica replacements.

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!

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?


pages: 294 words: 96,661

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, Claude Shannon: information theory, clean water, cognitive bias, computer age, crowdsourcing, dark matter, 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 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, 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, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

If we really are going to lose half our jobs in twenty years, well, then the New York Times should dust off the giant type it used back in 1969 when it printed “MEN WALK ON MOON” and report the story on the front page with equal emphasis. But that is not actually what Frey and Osborne wrote. 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. For instance, Frey and Osborne give the following jobs a 65 percent or better chance of being computerized: social science research assistants, atmospheric and space scientists, and pharmacy aides.

And those were a huge win for both workers and the overall economy, even though they were incredibly disruptive. 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.

Waiters’ jobs pay less than radiologists’ jobs not because they require fewer skills, but because the skills needed to be a waiter are widely available, whereas comparatively few people have the uncommon ability to interpret CT scans. 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? Often, the analysis you hear goes along these lines: “The new jobs are too complex for less-skilled workers.


pages: 300 words: 76,638

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, Ben Horowitz, Bernie Sanders, call centre, corporate governance, cryptocurrency, David Brooks, Donald Trump, Elon Musk, falling living standards, financial deregulation, 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, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, single-payer health, Stephen Hawking, Steve Ballmer, supercomputer in your pocket, technoutopianism, telemarketer, The Wealth of Nations by Adam Smith, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, unemployed young men, universal basic income, urban renewal, white flight, winner-take-all economy, Y Combinator

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. 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.

But increasingly all of these tasks are going to be the domain of cloud-based artificial intelligence. 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.

Beyond the hundreds of thousands of additional job losses, many communities may risk losing a sense of purpose without thousands of truckers coming through each day. For example, in Nebraska one out of every 12 workers—63,000 workers—works in and supports the trucking industry. Truck drivers do not see it coming. Indeed, when Bloomberg’s Shift Commission in 2017 asked truck drivers about how concerned they were about their jobs being replaced by automation, they almost uniformly weren’t concerned at all. Let me assure you it’s coming. Elon Musk recently announced that Tesla will be offering a freight truck as of November 2017. Musk also proclaimed that by 2019, all new Teslas will be self-driving. “Your car will drop you off at work, and then it will pick other people up and make you money all day until it’s time to pick you up again,” Musk proclaimed.


pages: 215 words: 56,215

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, income inequality, job automation, knowledge worker, low earth orbit, mutually assured destruction, Occupy movement, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, working poor

Millions of construction workers will be replaced. Factories are already highly automated. 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. With robots that can see, it is possible to clean things, and all of the custodians, janitors and maids start getting replaced.

What if there is no economic downturn, but instead new technology comes along that eliminates 200,000 jobs? 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? What if many of the people in the society stop worrying and caring about the design of the society?

Eventually, the only people who will still have jobs in the fast food industry will be the senior management team at corporate headquarters, and they will be making staggering amounts of money. 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. It will likely take a couple of decades, for example a 2040 timeframe. But 30 to 40 years is likely the maximum length of time, for reasons we explored in the Chapters 2 and 3.


pages: 144 words: 43,356

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

"Robert Solow", 3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, blockchain, brain emulation, Buckminster Fuller, 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, 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 of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

Military leaders in an age where most combatants are robots will have a clearer idea of whether their forces are a match for those of the opposition. 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”.) The development of engines powered by steam and then coal raised automation to a new level. The classic example is the mechanisation of agriculture, which accounted for 41% of US employment in 1900, and only 2% in 2000.

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. It is also not true that Maynard Keynes argued that automation would destroy jobs any time soon.

Perhaps we can get round the problem of how professionals acquire their skills when the AI is doing all the grunt work which currently provides the training for junior doctors and lawyers. 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, 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

In Harder’s view, automation would improve working conditions, reduce hours, and improve workplace safety. 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. When Ford introduced automated assembly lines in its newly opened Lima, Ohio plant in 1954, it relocated production from the River Rouge plant, displacing hundreds of Detroit workers.

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.” Ford labor relations official Manton Cummins dismissed claims that automation led to job loss as a union-led “scare campaign.”

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. Walter Reuther and other UAW officials initially expressed some concern about the effects of automation on employment and union strength in industrial cities, but for the most part they poured their energy into cushioning the effects of layoffs through extended unemployment benefits, improved pension plans, and preferential hiring plans for displaced workers.


pages: 98 words: 27,609

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, Robert Gordon, Ronald Reagan, social intelligence, Steven Pinker, The Rise and Fall of American Growth, 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.

Retail bank branches are not simply automated teller machines. Human beings work in those branches. 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. And jobs will require more tasks using these skills.


pages: 419 words: 109,241

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, blue-collar work, 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, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, 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, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

Far from being a sign of intellectual inconsistency, this is a good thing. 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.

Fewer than 5 percent of these, they found, could be completely automated with existing technologies. 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.

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.” The New Yorker, meanwhile, asks us to “imagine a matrix with two axes, manual versus cognitive and routine versus non-routine,” where every task is sorted into one of the quadrants.38 And elsewhere we can see the shadow of the “routine” versus “non-routine” distinction in the way that people so often describe automation.


pages: 411 words: 98,128

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, Black Swan, call centre, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, Donald Trump, Elon Musk, Erik Brynjolfsson, future of work, gig economy, Google Glasses, Google X / Alphabet X, income inequality, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Lyft, Marc Andreessen, Mark Zuckerberg, money market fund, natural language processing, pets.com, plutocrats, Plutocrats, race to the bottom, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, Tim Cook: Apple, too big to fail, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, wealth creators, web application, Whole Earth Catalog

Many of the company’s retail competitors, which don’t have those kinds of profitable businesses in their portfolios and which lack Amazon’s cash generation capacity, will find it difficult to deal with such a dramatic pay hike. 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. Bezos, who has libertarian leanings, hasn’t made up his mind yet on a UBI.

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. The tangible nature of Amazon’s retail business puts it at the center of a coming, massive disruption of the workplace like society has never seen.

McKinsey is also quick to point out that economic growth is likely to offset the numbers of jobs lost because of increased spending on health care, and growing investment in infrastructure, energy, and technology. 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. Think of a robotic arm lifting a heavy automobile hood for an assembly worker.


pages: 121 words: 36,908

Four Futures: Life After Capitalism by Peter Frase

Airbnb, basic income, bitcoin, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cryptocurrency, deindustrialization, Edward Snowden, Erik Brynjolfsson, Ferguson, Missouri, fixed income, full employment, future of work, high net worth, income inequality, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), iterative process, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, litecoin, mass incarceration, means of production, Occupy movement, pattern recognition, peak oil, plutocrats, 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 of Employment, Thomas Malthus, Tyler Cowen: Great Stagnation, universal basic income, Wall-E, 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. In their book of the same name, they argue that just as the first machine age—the Industrial Revolution—replaced human muscle with machine power, computerization is allowing us to greatly magnify, or even replace, “the ability to use our brains to understand and shape our environments.”6 In that book and its predecessor, Race Against the Machine, Brynjolfsson and McAfee argue that computers and robots are rapidly permeating every part of the economy, displacing labor from high- and low-skill functions alike.

But a corollary to this principle is that, if wages begin to rise and labor markets tighten, employers will start to turn to the new technologies that are currently being developed, rather than pay the cost of additional labor. 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.

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.


pages: 596 words: 163,682

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, 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, 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, housing crisis, 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, moral hazard, Network effects, new economy, Nicholas Carr, obamacare, 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, 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: 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.

It certainly is where we debate and persuade as we elect officeholders and participate in the governance of the local public services that affect us. 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. Sometimes, the expectation is that the other side will repay the favor, but this may never actually happen.

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. Eventually many ended up unemployed and destitute.


pages: 72 words: 21,361

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: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

Technologies like robotics, numerically controlled machines, computerized inventory control, and automatic transcription have been substituting for routine tasks, displacing those workers. 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.” Ever-greater investments in education, dramatically increasing the average educational level of the American workforce, helped prevent inequality from soaring as technology automated more and more unskilled work.


pages: 179 words: 43,441

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, 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, mass immigration, megacity, meta analysis, 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!, 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. The content is so human-sounding that a recent quiz by The New York Times showed that when reading two similar pieces, it is impossible to tell which one has been written by a human writer and which one is the product of a robot.

Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?”, 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. The reason is data. Keeping pace with the release of medical data could take doctors 160 hours a week, so doctors cannot possibly review the amount of new insights or even bodies of clinical evidence that can give an edge in making a diagnosis.


pages: 237 words: 64,411

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, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, 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, 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. The cold war was in full swing, and Senator John F.

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. Sounds good; hope I live to see it. Global warming in and of itself isn’t a problem.

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. But a new crop of legal-tech entrepreneurs is working to greatly reduce or eliminate the need for lawyers for the most common transactions.


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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, 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, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, zero day, zero-sum game, Zipcar

New empires are emerging today—in many ways bigger and more powerful than the great empires of the past. 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. If we want to turn the forces of the Autonomous Revolution to our advantage, we need to see them as they are, and not through the lenses of our old rules, values, and beliefs.

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.

What is particularly disconcerting about this jobs scenario is what is missing compared to the past. 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.


pages: 357 words: 95,986

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

3D printing, additive manufacturing, air freight, algorithmic trading, anti-work, back-to-the-land, banking crisis, basic income, battle of ideas, blockchain, Boris Johnson, Bretton Woods, business cycle, call centre, capital controls, 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, late capitalism, liberation theology, Live Aid, low skilled workers, manufacturing employment, market design, Martin Wolf, mass immigration, mass incarceration, means of production, minimum wage unemployment, Mont Pelerin Society, neoliberal agenda, New Urbanism, Occupy movement, oil shale / tar sands, oil shock, patent troll, pattern recognition, Paul Samuelson, Philip Mirowski, post scarcity, post-work, postnationalism / post nation state, precariat, 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, the built environment, The Chicago School, The Future of Employment, Tyler Cowen: Great Stagnation, universal basic income, wages for housework, 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.

(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.

For instance, out of the US companies that could benefit from incorporating industrial robots, less than 10 per cent have done so.43 This is but one area for full automation to take hold in, and this reiterates the importance of making full automation a political demand, rather than assuming it will come about from economic necessity. 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.


pages: 238 words: 73,121

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, eurozone crisis, fiat currency, full employment, Gini coefficient, global village, hydraulic fracturing, income inequality, Isaac Newton, job automation, joint-stock company, Joseph Schumpeter, 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, 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, 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. Technological displacement is ongoing as we speak.

It never has been an equalizer compensating for the numbers of jobs lost; and over time, the amount of job creation for humans compared to work taken over by computers will be a steadily decreasing slice—a channel whose walls grow steadily narrower. 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. The displacement of human work will go on, not just for the next twenty years but the next hundred, even the next thousand years—unless something extrinsic happens to change the underlying mechanism driving technological displacement of work: capitalist competition.

A host of processes and problems will complicate the future: aging populations, explosion of medical costs, ethnic and religious conflict, ecological crisis, huge intercontinental migrations, perhaps wars of varying scope. 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. An advanced crisis of technological displacement of middle-class work in the United States or in western Europe would not necessarily coincide with the depth of such crisis in other parts of the globe—China, India, Brazil, or other places of significance in future decades.


pages: 94 words: 26,453

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, 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, move fast and break things, Paul Erdős, Paul Graham, recommendation engine, rising living standards, Robert Shiller, Robert Shiller, Silicon Valley, Silicon Valley startup, skunkworks, social intelligence, Steve Ballmer, Steve Jobs, Y Combinator

Because if these nice qualities are your strengths then, gradually and then very suddenly, they will become your weaknesses. 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. Some skills provide some protection. But for most jobs it’s temporary. Give it a year.

Sometime after that the elevator operator also bragged them too. 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. Even those well-heeled fortresses of human intelligence, the blue chip management consultancy firms, are under attack.


pages: 402 words: 126,835

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, deskilling, disruptive innovation, Donald Trump, Downton Abbey, Elon Musk, Erik Brynjolfsson, factory automation, follow your passion, Frederick Winslow Taylor, future of work, game design, glass ceiling, hiring and firing, immigration reform, income inequality, industrial robot, invisible hand, 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, 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, Robert Shiller, Rodney Brooks, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, Steve Jobs, The Chicago School, Thomas L Friedman, Thorstein Veblen, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban renewal, 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.

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. The same holds for the millions of Americans working in IT firms, warehouses, banks, insurance agencies, and assembly lines under contracts that promise flexibility but no paid holidays, sick days, employee-sponsored health insurance, or other benefits routinely offered permanent employees.

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.” Their argument was that unemployment and underemployment were a reflection not of a systems failure, that is, a shortfall of good jobs, but of the failure of individuals to keep up with the changing demands of the marketplace.


pages: 245 words: 64,288

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, 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, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, 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, women in the workforce

With years of experience in spreading scientific education and debunking climate change deniers, creationists, and all sorts of nonsense, I can see how Einstein’s quote could not be truer. 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?

I have no doubt about that. 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. It can take 5 to 10 years to educate a young mind in these fields, and we are talking about a mind that is not only willing to learn, but that is also enthusiastic about the learning experience.

It is time for a paradigm shift, one that will radically revolutionise our social system. In this universe, change is the only constant. 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.


pages: 372 words: 152

The End of Work by Jeremy Rifkin

banking crisis, Bertrand Russell: In Praise of Idleness, blue-collar work, cashless society, collective bargaining, 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, 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, new economy, New Urbanism, Paul Samuelson, pink-collar, post-industrial society, Productivity paradox, Richard Florida, Ronald Reagan, Silicon Valley, speech recognition, strikebreaker, technoutopianism, Thorstein Veblen, Toyota Production System, trade route, trickle-down economics, women in the workforce, working poor, working-age population, Works Progress Administration

Management consultant Peter Drucker estimates that employment in manufacturing is going to continue dropping to less than 12 percent of the US. workforce in the next decade. 17 For most of the 1980s it was fashionable to blame the loss of manufacturing jobs in the United States on foreign competition and cheap labor markets abroad. 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.

An additional 35 million have less than a ninth-grade reading level. 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.

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. What they failed to say was that 728,000 of them-nearly 60 percent-were part-time, for the most part in low-wage service industries.


pages: 667 words: 149,811

Economic Dignity by Gene Sperling

active measures, Affordable Care Act / Obamacare, autonomous vehicles, basic income, Bernie Sanders, Cass Sunstein, collective bargaining, corporate governance, David Brooks, desegregation, Detroit bankruptcy, Donald Trump, Double Irish / Dutch Sandwich, Elon Musk, employer provided health coverage, Erik Brynjolfsson, Ferguson, Missouri, full employment, gender pay gap, ghettoisation, gig economy, Gini coefficient, guest worker program, Gunnar Myrdal, housing crisis, income inequality, 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, meta-analysis, minimum wage unemployment, obamacare, offshore financial centre, payday loans, 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, Silicon Valley, single-payer health, speech recognition, 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, War on Poverty, working poor, young professional, zero-sum game

Artificial intelligence (AI), robots, and autonomous vehicle technology are among the new technological advances that threaten major economic disruption. 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 . . .

Leading economists like Jason Furman have discussed how often previous projections of massive net job loss due to technological change did not come to pass.13 Some point out that even the highly respected former Federal Reserve vice chair Alan Blinder admitted to being proven wrong after he estimated in 2007 that a quarter of white-collar jobs were vulnerable to offshoring.14 Economist David Autor has called such dramatic job loss estimates from AI “arrogant” predictions from “self-proclaimed oracles.” 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.

The good news is research finds that TAA increases cumulative earnings for participants relative to nonparticipants.54 However, it serves—by design—only a small fraction of those who might need it. 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.


pages: 260 words: 67,823

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, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, Firefox, Google Chrome, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Peter Thiel, QR code, ride hailing / ride sharing, self-driving car, Silicon Valley, Skype, Snapchat, Steve Ballmer, Steve Jobs, Steve Wozniak, Tim Cook: Apple, uber lyft, wealth creators, zero-sum game

Invent and Simplify, another example, makes invention core to people’s jobs at Amazon, not peripheral. “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. “We value calculated risk taking.” (One Amazonian, looking for extra room in his work space, brought a saw into work and took off a chunk of his desk.

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. “You have to coach and teach people how to be lifelong learners,” Wilke told me. “The way that you reward work, and learning, and how much of people’s time is devoted to these things is changing.”

“Mark Zuckerberg Has Baby and Says He Will Give Away 99% of His Facebook Shares.” BuzzFeed News. 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.” YouTube, March 29, 2019. https://youtu.be/IakpZK9q6ys. ABCDEFGHIJKLMNOPQRSTUVWXYZ INDEX The page numbers in this index refer to the printed version of this book.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, 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 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, optical character recognition, pattern recognition, phenotype, Productivity paradox, 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

I am very concerned about an unconditional basic income causing a greater proportion of the human population to become trapped doing this low-wage, low-skilled work. 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?

If the work that’s being done looks like mostly data collection, data analysis, or physical work in a highly structured environment, then much of that work is likely to be automated, whether it’s traditionally been high wage or low wage, high skill or low skill. 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.

People are looking at the technology as if the technological advances are a problem. 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. GEOFFREY HINTON: I moved to Canada because it has a higher taxation rate and because I think taxes done right are good things.


pages: 229 words: 72,431

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, disintermediation, disruptive innovation, 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, Plutocrats, recommendation engine, Schrödinger's Cat, Silicon Valley, single-payer health, statistical model, Thorstein Veblen, Turing test, unpaid internship, Vanguard fund, Vilfredo Pareto, zero-sum game, Zipcar

Fifth, entrenched interests may oppose new types of shadow work—sometimes successfully, as in New Jersey and Oregon. 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. Ralph has become part of history. He lost his job to you. two: shadow work in home and family life By and large, mothers and housewives are the only workers who do not have regular time off.

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. (Dishes only—no cones, please, as the price is pegged to weight.) The consumer may add one or more of twenty toppings offered at the counter.


pages: 391 words: 71,600

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, 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, 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, Mars Rover, Minecraft, Mother of all demos, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, telepresence, telerobotics, The Rise and Fall of American Growth, 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

One of the president’s questions felt as though it was addressed directly to me: “How do we make technology work for us, and not against us — especially when it comes to solving urgent challenges like climate change?” I sensed—or did I imagine?—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. In fact, just after the election, I joined my colleagues from the tech sector for a roundtable discussion with President-elect Trump who, like his predecessor, wanted to explore how we continue to innovate while also creating new jobs.

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. It’s the question I get most often from city, state, and national leaders wherever I travel.

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.


pages: 339 words: 88,732

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, 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, 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, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, 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, 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, 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, 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, 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: Great Stagnation, Vernor Vinge, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

Technological Unemployment We’ve seen that the overall pie of the economy is growing, but some people, even a majority of them, can be made worse off by advances in technology. 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.

These new goods and services provide a path for productivity growth based on increased output rather than reduced inputs. 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. An Alternative Explanation: Globalization Technology isn’t the only thing transforming the economy.


pages: 286 words: 79,305

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

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, additive manufacturing, Albert Einstein, anti-communist, autonomous vehicles, bank run, banks create money, bitcoin, business cycle, call centre, central bank independence, 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, Jeff Bezos, jimmy wales, job automation, Kickstarter, labour market flexibility, laissez-faire capitalism, light touch regulation, Mark Zuckerberg, market clearing, market fundamentalism, Martin Wolf, 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, 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, wealth creators, working-age population

Our economic future therefore does not depend so much on our ability to supply the products and services populations will require – this ability will expand rapidly. 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. Last time, we called it the Industrial Revolution. The Industrial Revolution was a period of unprecedented technological change which transformed almost every aspect of society.

Currently, for example, around 13 per cent of the US population are employed in the Retail and Transportation industries.25 There are signs that many of these jobs will not exist in a few years’ time – we have self-service checkouts in supermarkets; some fast food restaurants have introduced screen-based ordering; driverless trains are a reality today; driverless cars have already reached a high level of technical proficiency, and cars with some degree of autonomy (e.g. motorway driving) are already on the market; warehouses increasingly use automated picking and packing technologies. 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. You can see more details of their work in the Appendix on the 99-percent.org website.

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. Many people’s needs will simply not be met.


pages: 242 words: 73,728

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, mortgage tax deduction, new economy, obamacare, 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, 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, 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

Brick-and-mortar retailing giant Walmart has 1.5 million employees in the United States, while Web retailing giant Amazon had a third as many as of the third quarter of 2017. 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. But the plummeting cost and lightspeed improvement of robotics now threaten to halt and even shut down that source of jobs.

accountants: Gianni Giacomelli and Prashant Shukla, “Does Automation Mean Job Losses for Accountants?,” Accounting Today, Feb. 21, 2017. 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.


pages: 370 words: 94,968

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, Claude Shannon: information theory, cognitive dissonance, commoditize, complexity theory, 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, l'esprit de l'escalier, Loebner Prize, Menlo Park, 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 a mule,” says the steelworker. “A monkey can do what I do,” says the receptionist. “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.


pages: 976 words: 235,576

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, 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, 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 innovation, financial intermediation, fixed income, Ford paid five dollars a day, Frederick Winslow Taylor, full employment, future of work, gender pay gap, George Akerlof, Gini coefficient, glass ceiling, helicopter parent, 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, 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, Paul Samuelson, payday loans, plutocrats, Plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, precariat, purchasing power parity, rent-seeking, Richard Florida, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, school choice, shareholder value, Silicon Valley, Simon Kuznets, six sigma, Skype, stakhanovite, stem cell, Steve Jobs, supply-chain management, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, 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, Winter of Discontent, women in the workforce, working poor, 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.

Across restructurings in the 1980s and 1990s, middle managers were downsized at nearly twice the rate of nonmanagerial workers. 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. This massive, consciously planned corporate housecleaning of middle managers arose because new managerial technologies rendered the culled workers surplus to requirements—literally redundant.

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.


pages: 246 words: 68,392

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, David Attenborough, Donald Trump, East Village, Elon Musk, financial independence, future of work, game design, gig economy, income inequality, information asymmetry, Jeff Bezos, job automation, law of one price, Lyft, Mark Zuckerberg, market clearing, minimum wage unemployment, new economy, payday loans, post-work, profit maximization, QR code, race to the bottom, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, TaskRabbit, 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.

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. With her husband back at work, Kristy had been able to save the money she earned from Mechanical Turk for her $10,000 annual tuition.


pages: 346 words: 97,330

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-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, crowdsourcing, data is the new oil, deindustrialization, deskilling, 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, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, 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, passive income, pattern recognition, post-materialism, post-work, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, 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, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, women in the workforce, Works Progress Administration, Y Combinator

Clicking “dog” or “cat” to label an image that will eventually enable an iPhone to recognize a family pet is not that different from turning a screw on what will eventually become a Ford truck. But that’s where the job similarities end. 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.

Similarly, workers may have family obligations that limit their work hours. 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. As machines get more powerful and algorithms take over more and more problems, we know from past advances in natural language processing and image recognition that industries will continue to identify new problems to tackle.


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Bretton Woods, capital controls, 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, G4S, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, 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, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, 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. By 2007 American manufacturers were using more than six times as much equipment, including computers and software, as they had done twenty years earlier – while doubling the amount of capital used per hour of employee work.

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. The Bank of England, Oxford University, a global technology consultancy and the United States Congress are far from siren voices that are easy to dismiss.

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.


pages: 486 words: 150,849

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

affirmative action, Affordable Care Act / Obamacare, airline deregulation, airport security, always be closing, American ideology, American Legislative Exchange Council, anti-communist, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, basic income, 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, COVID-19, Covid-19, creative destruction, Credit Default Swap, cryptocurrency, deindustrialization, Donald Trump, 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, game design, George Gilder, Gordon Gekko, greed is good, High speed trading, hive mind, income inequality, industrial robot, interchangeable parts, invisible hand, Isaac Newton, 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, 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, Peter Thiel, Picturephone, plutocrats, 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, strikebreaker, 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: 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

But beyond those direct replacements of skilled workers, two MIT economists recently found that during the first wave of industrial robots in the 1990s and 2000s, each robot installed led to the loss of a half-dozen jobs. 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. Underlying the growing differences in income and wealth and security are more complicated changes in how Americans are able to earn livings.

“When I was an MIT undergraduate in the early 1970s,” he said, every economics student was exposed to the debate about automation. 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. It will be providing enough good jobs.” And soon “it may well be that some categories of labor will not be able to earn a subsistence income.”

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. Think R2D2, Optimus Prime and Robot from Lost in Space.”


pages: 361 words: 81,068

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, bitcoin, Black Swan, Bob Geldof, Burning Man, Cass Sunstein, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, David Brooks, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, full employment, future of work, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Metcalfe’s law, move fast and break things, 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, Plutocrats, Potemkin village, precariat, pre–internet, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Robert Metcalfe, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, TaskRabbit, Ted Nelson, telemarketer, The Future of Employment, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, 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.

Citing a paper by Oxford University’s Carl Benedikt Frey and Michael Osborne that predicts that 47% of all American jobs might be lost in the next couple of decades,40 the Atlantic’s Derek Thompson speculates on “which half” of the workforce could be made redundant by robots. 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. The Writing on the Wall Not everything about our automation anxiety is speculative. Indeed, when it comes to photographic process workers, there’s a 100% certainty that they lost the race with computers for jobs.


pages: 307 words: 82,680

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, intangible asset, job automation, job satisfaction, Joi Ito, labour market flexibility, land value tax, libertarian paternalism, low skilled workers, lump of labour, Mark Zuckerberg, Martin Wolf, mass immigration, mass incarceration, moral hazard, Nelson Mandela, offshore financial centre, open economy, Panopticon Jeremy Bentham, Paul Samuelson, plutocrats, Plutocrats, precariat, quantitative easing, randomized controlled trial, rent control, rent-seeking, 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

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. 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.

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. That is a further reason why a new income distribution system must be constructed, with a basic income as an anchor, an argument to which Chapter 12 will return. The disruptive character of what has been dubbed the ‘fourth technological revolution’ also appears to be more generalized than in preceding seismic changes, which predominantly hit low-skill manual jobs.19 All levels of job and occupation are being affected.


pages: 430 words: 68,225

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. Many jobs in these institutions could be at risk when a huge portion of financial transactions are processed in an automated fashion through the blockchain.


pages: 378 words: 110,518

Postcapitalism: A Guide to Our Future by Paul Mason

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, Downton Abbey, drone strike, en.wikipedia.org, energy security, eurozone crisis, factory automation, financial repression, Firefox, Fractional reserve banking, Frederick Winslow Taylor, full employment, future of work, game design, 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, 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, money: store of value / unit of account / medium of exchange, mortgage debt, Network effects, new economy, Norbert Wiener, Occupy movement, oil shale / tar sands, oil shock, Paul Samuelson, payday loans, Pearl River Delta, post-industrial society, precariat, price mechanism, profit motive, quantitative easing, race to the bottom, RAND corporation, rent-seeking, reserve currency, RFID, Richard Stallman, Robert Gordon, Robert Metcalfe, 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

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.

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. The elements of such a solution are there in modern economies: Apple is the classic price monopolist, Amazon’s business model the classic strategy for capturing externalities; commodity speculation the classic driver of energy and raw material costs above their value; while the rise of personal micro-services – dog minding, nail salons, personal concierges and the like – shows capitalism commercializing activities we used to provide through friendship or informality.

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. That is a real danger. To overcome it, capitalism would have to greatly expand the human services sector.


pages: 470 words: 148,730

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

"Robert Solow", 3D printing, affirmative action, Affordable Care Act / Obamacare, Airbnb, basic income, Bernie Sanders, 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, labor-force participation, land reform, loss aversion, low skilled workers, manufacturing employment, Mark Zuckerberg, mass immigration, 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, school choice, Second Machine Age, secular stagnation, self-driving car, shareholder value, short selling, Silicon Valley, smart meter, social graph, spinning jenny, Steve Jobs, 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

But it distracts researchers and engineers from working on the truly pathbreaking innovations. 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. One place, remarkably, where Republican and Democrat poll respondents agree is in their opposition to letting companies decide how much to automate.

Accountants, mortgage originators, management consultants, financial planners, paralegals, and sports journalists are already competing with some form of artificial intelligence or, if not, will soon. 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. So how bad will it be on net? Economists are of course intrigued by this problem, but in this case they have entirely failed to reach a consensus.

And workers who received TAA assistance were also much more likely to change region and industry.85 But instead of becoming a template of what could be done to help workers manage various kinds of difficult transitions, the TAA has remained tiny. How could that make sense? 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.


pages: 345 words: 75,660

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, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, 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, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, 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, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

Our process for implementing AI tools will determine which outcome you should emphasize. 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. In the 1986 Space Shuttle Challenger disaster, one piece in the rocket booster failed, an O-ring seal less than a half inch in diameter.

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.


pages: 611 words: 130,419

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

agricultural Revolution, 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, 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, money market fund, moral hazard, Northern Rock, nudge unit, Own Your Own Home, Paul Samuelson, Philip Mirowski, plutocrats, Plutocrats, Ponzi scheme, publish or perish, random walk, Richard Thaler, Robert Shiller, 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 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?

The first step in our task is organizing and classifying some of the major economic narratives and the mutations that allowed them to recur over long intervals of time. 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). These pairs suggest opposite economic actions and opposite moral judgments.

Noting that operators’ jobs would be lost, he expressed true moral indignation against the new phones: I ask unanimous consent to take from the table Senate resolution 74 directing the sergeant at arms to have these abominable dial telephones taken out on the Senate side … I object to being transformed into one of the employes of the telephone company without compensation.32 His resolution passed, and the dial phones were removed. 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. The greater the number of Robots employed, the less is the demand for what they produce for men cannot consume what they cannot pay for.


pages: 293 words: 78,439

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, 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, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Minecraft, obamacare, Parag Khanna, Paul Graham, peer-to-peer lending, pez dispenser, recommendation engine, self-driving car, shareholder value, side project, Silicon Valley, Skype, software as a service, software is eating the world, Steve Jobs, 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.


pages: 385 words: 111,113

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, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, 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, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, 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, 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, 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 of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, 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.

Get ready, get smart; read this book.” 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. In the next 20 years we’ll see professions like accountants, financial advisors, bank tellers and others dramatically effected by automation, experience design and artificial intelligence.

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. Amazon Loves Robot Workers Projects that purely automate are far fewer and can also be seen as creating more jobs than they displace.


pages: 371 words: 108,317

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

A Declaration of the Independence of Cyberspace, AI winter, Airbnb, Albert Einstein, 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, 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, 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, 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 scientific method, transport as a service, two-sided market, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Review, zero-sum game

The machine translator does Turkish to Hindi, or French to Korean, etc. It can of course translate any text. 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. To demand that artificial intelligence be humanlike is the same flawed logic as demanding that artificial flying be birdlike, with flapping wings.

One hundred years ago not a single citizen of China would have told you that they would rather buy a tiny glassy slab that allowed them to talk to faraway friends before they would buy indoor plumbing. 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. Google, can you tell me where my phone is? Google, can you match the people suffering depression with the doctors selling pills?

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.


pages: 362 words: 83,464

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, American Society of Civil Engineers: Report Card, Bob Noyce, California gold rush, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, creative destruction, crony capitalism, David Graeber, deindustrialization, don't be evil, Downton Abbey, Edward Glaeser, Elon Musk, energy security, falling living standards, future of work, Gini coefficient, Google bus, housing crisis, income inequality, 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, New Economic Geography, new economy, New Urbanism, obamacare, offshore financial centre, Paul Buchheit, payday loans, Peter Calthorpe, plutocrats, Plutocrats, post-industrial society, RAND corporation, Ray Kurzweil, rent control, rent-seeking, Report Card for America’s Infrastructure, Richard Florida, Silicon Valley, Silicon Valley ideology, Steve Jobs, technoutopianism, The Death and Life of Great American Cities, Thomas L Friedman, too big to fail, transcontinental railway, trickle-down economics, Tyler Cowen: Great Stagnation, upwardly mobile, urban planning, urban sprawl, War on Poverty, women in the workforce, working poor, young professional

But with the shifting of industry overseas and the decline of private sector unions, the path for blue-collar workers to enter the middle class has become more difficult.24 Although they often claim to defend the middle class, the political stance adopted by the Clerisy, as well as by the tech Oligarchs and investors, tends to worsen this trajectory. 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. After the Great Recession, the American blue-collar worker has been pitied, but certainly not helped, by the Clerisy, many of whom believe that there is no hope for manufacturing or similar outmoded jobs in an information age.


pages: 302 words: 84,428

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

activist fund / activist shareholder / activist investor, Albert Einstein, business cycle, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, financial innovation, fixed income, if you build it, they will come, income inequality, Isaac Newton, job automation, Long Term Capital Management, margin call, 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, 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

The mechanization of agriculture, for instance, allowed many fewer farmers to produce much more food at much lower cost than ever before. 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. But clearly, globalization can have differential effects on individual nations’ economies (and create winners and losers within each nation).


pages: 389 words: 119,487

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, 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, Donald Trump, 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, 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: 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.

Alongside care, creativity too poses particularly difficult hurdles for automation. 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. Consequently, when we come to evaluate art, we tend to judge it by its emotional impact on the audience.

Rather, thanks to AI trainers human chess masters became better than ever, and at least for a while human–AI teams known as ‘centaurs’ outperformed both humans and computers in chess. 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. Such occupational changes were feasible, because the move from the farm to the factory and from the factory to the supermarket required only limited retraining.


pages: 393 words: 91,257

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

Admiral Zheng, Andy Kessler, autonomous vehicles, basic income, Bernie Sanders, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, clean water, creative destruction, deindustrialization, demographic transition, don't be evil, Donald Trump, edge city, Elon Musk, European colonialism, financial independence, Francis Fukuyama: the end of history, gig economy, Gini coefficient, Google bus, guest worker program, Hans Rosling, housing crisis, income inequality, informal economy, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, Joseph Schumpeter, land reform, liberal capitalism, life extension, low skilled workers, Lyft, Mark Zuckerberg, market fundamentalism, Martin Wolf, mass immigration, megacity, Nate Silver, new economy, New Urbanism, Occupy movement, Parag Khanna, Peter Thiel, plutocrats, 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, Sam Altman, Satyajit Das, sharing economy, Silicon Valley, smart cities, Steve Jobs, Stewart Brand, superstar cities, The Death and Life of Great American Cities, 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, 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. 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.

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.


pages: 374 words: 111,284

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

"Robert Solow", 3D printing, agricultural Revolution, AI winter, Albert Einstein, anti-work, 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, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deindustrialization, deskilling, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, financial intermediation, full employment, future of work, 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, Paul Samuelson, Peter Thiel, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Robert Shiller, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, 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, 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

Actually, to McKinsey’s two key criteria for human “comparative advantage” I would add a third: the need for the exercise of common sense. Even the most “intelligent” AIs lack this facility. 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.

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.

The beneficial effect will fall disproportionately among the less well-off who, on the whole, currently find legal services prohibitively expensive. 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. In the USA there has recently been an increase in demand for the skills of those at the very top of the distribution and also an increase in demand for services of the large number of relatively unskilled people at the bottom, at the expense of those in the middle with moderate skills of a mechanical variety – of the sort that can readily be replaced by AI.


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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, 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, 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, The Chicago School, the payments system, trade route, Travis Kalanick, uranium enrichment, urban planning, wealth creators, white picket fence, working-age population, Y Combinator, young professional

TREMBLING OVER TECHNOLOGY Technology optimists like Mr Lepp and the care-robot inventors I met in Japan think their inventions will solve the challenges of our future economies. 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’. The core of this worry is that the benefits technology brings will favour some groups – the young, the urban, the educated and the wealthy – at the expense of others.

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. Automating logistics would be a huge shock to the economy, radically altering the working lives of millions of people.


pages: 187 words: 55,801

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, pattern recognition, profit motive, Robert Shiller, Robert Shiller, Ronald Reagan, speech recognition, 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? • What kinds of tasks do computers perform better than humans?


pages: 519 words: 155,332

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 innovation, future of work, ghettoisation, Gordon Gekko, hiring and firing, Home mortgage interest deduction, immigration reform, income inequality, invention of radio, job automation, knowledge economy, knowledge worker, labor-force participation, laissez-faire capitalism, Mahatma Gandhi, Mark Zuckerberg, mortgage tax deduction, new economy, obamacare, old-boy network, paper trading, 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, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, 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. Household debt by 2017 had grown higher than the peak reached in 2007 before the crash, with student and automobile loans having edged toward mortgages as the top claims on family paychecks.

In 1965, 42 percent of all companies had immediately acquiesced when a union filed a petition seeking to be recognized. In 1973 only 16 percent would. The rest fought back. 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. By 2016 it would be 6.4 percent.

Instead, in 2017 President Trump proposed cutting job training programs by 36 percent. 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.


pages: 322 words: 84,580

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

"Robert Solow", Airbnb, autonomous vehicles, balance sheet recession, bank run, banking crisis, basic income, Berlin Wall, Bernie Sanders, 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 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, meta-analysis, mini-job, 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, Robert Shiller, Ronald Reagan, secular stagnation, social intelligence, TaskRabbit, total factor productivity, universal basic income, very high income, winner-take-all economy, working poor

.’ … The second key event is the 2008 financial crisis that was followed by a 5-percent drop of GDP in a single year.”9 Could this provide a different answer to Norris’s “Why?”—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.

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. Unless governments adopt policies that handle this disruption better, it will potentially affect even greater numbers of people than deindustrialisation.

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?


pages: 214 words: 31,751

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, 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.


pages: 181 words: 52,147

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, 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, mobile money, new economy, personalized medicine, phenotype, precision agriculture, 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 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

Famed venture capitalist Vinod Khosla estimates that technology will replace 80 percent of doctors.3 But similar job losses face those in practically every profession that necessitates a human’s judgment or light creative problem solving. 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.

Rather, the robots will replace humans piecemeal in performing tasks, through specializations. 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. That may sound ominous; yes, robots may eat our jobs.

However, today’s technologies could automate 45 percent of the activities people are paid to perform across all occupations. 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.


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Industrial Internet by Jon Bruner

autonomous vehicles, barriers to entry, 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.


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

Affordable Care Act / Obamacare, Airbnb, Automated Insights, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, business intelligence, Donald Trump, Downton Abbey, East Village, Elon Musk, full employment, future of work, gig economy, glass ceiling, haute couture, income inequality, 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, Silicon Valley, Skype, Snapchat, surplus humans, TaskRabbit, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, upwardly mobile, wages for housework, women in the workforce, working poor

TUG robots and driverless trucks are far from the only encroachments on both the livelihoods of human workers and the human experience of patients and clients. They are part of a broader trend in what is sometimes called “the future of work.” The World Economic Forum (WEF) in 2016 projected a total loss of 7.1 million jobs by 2020, two-thirds of which may be concentrated in office and administrative jobs in health care, advertising, public relations, broadcasting, law, and financial services. (Women’s jobs account for more than five jobs lost due to our automated friends for every job gained.) The National Science Foundation is spending nearly $1 million to research a future of robotic nurses who will lift patients and bring them medicine while keeping living nurses “in the decision loop.” And as a 2013 McKinsey Global Institute report on disruptive technologies explained, highly skilled workers could be put on the chopping block with the expanding “automation of knowledge work.”

That number includes day care for children, but also adult children taking care of their parents and older couples taking care of each other. Santens fantasizes that UBI could become paid maternity leave for moms of newborns as well as replace many benefits. Proponents like Santens think that UBI could help make sense of the automation of so many middle-class and working-class jobs. It would protect workers who lose their jobs to automation and thus alleviate the impulse to blame themselves or, even worse, point fingers at immigrants and people living below the poverty line. As for how we would pay for UBI, advocates insist that it is not as expensive as it might appear. We could raise money with a flat tax. And UBI could partly or fully replace existing safety net programs, such as Medicaid and Social Security. Further, it could help eliminate some of the hidden costs of poverty—like the medical bills both the insured and uninsured incur.


pages: 524 words: 155,947

More: The 10,000-Year Rise of the World Economy by Philip Coggan

"Robert Solow", accounting loophole / creative accounting, Ada Lovelace, agricultural Revolution, Airbnb, airline deregulation, Andrei Shleifer, anti-communist, assortative mating, autonomous vehicles, bank run, banking crisis, banks create money, basic income, Berlin Wall, Bob Noyce, Branko Milanovic, Bretton Woods, British Empire, business cycle, call centre, capital controls, carbon footprint, Carmen Reinhart, Celtic Tiger, central bank independence, Charles Lindbergh, clean water, collective bargaining, Columbian Exchange, Columbine, Corn Laws, credit crunch, Credit Default Swap, crony capitalism, currency peg, debt deflation, Deng Xiaoping, discovery of the americas, Donald Trump, Erik Brynjolfsson, European colonialism, eurozone crisis, falling living standards, financial innovation, financial intermediation, floating exchange rates, Fractional reserve banking, Frederick Winslow Taylor, full employment, germ theory of disease, German hyperinflation, gig economy, Gini coefficient, global supply chain, global value chain, Gordon Gekko, greed is good, Haber-Bosch Process, Hans Rosling, Hernando de Soto, hydraulic fracturing, Ignaz Semmelweis: hand washing, income inequality, income per capita, indoor plumbing, industrial robot, inflation targeting, Isaac Newton, James Watt: steam engine, job automation, John Snow's cholera map, joint-stock company, joint-stock limited liability company, Kenneth Arrow, Kula ring, labour market flexibility, land reform, land tenure, Lao Tzu, large denomination, liquidity trap, Long Term Capital Management, Louis Blériot, low cost airline, low skilled workers, lump of labour, M-Pesa, Malcom McLean invented shipping containers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Mikhail Gorbachev, mittelstand, moral hazard, Murano, Venice glass, Myron Scholes, Nelson Mandela, Network effects, Northern Rock, oil shale / tar sands, oil shock, Paul Samuelson, popular capitalism, popular electronics, price stability, principal–agent problem, profit maximization, purchasing power parity, quantitative easing, railway mania, Ralph Nader, regulatory arbitrage, road to serfdom, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, savings glut, Scramble for Africa, Second Machine Age, secular stagnation, Silicon Valley, Simon Kuznets, South China Sea, South Sea Bubble, special drawing rights, spice trade, spinning jenny, Steven Pinker, TaskRabbit, Thales and the olive presses, Thales of Miletus, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, trade route, transaction costs, transatlantic slave trade, transcontinental railway, Triangle Shirtwaist Factory, universal basic income, Unsafe at Any Speed, Upton Sinclair, V2 rocket, Veblen good, War on Poverty, Washington Consensus, Watson beat the top human players on Jeopardy!, women in the workforce, Yom Kippur War, zero-sum game

To cut costs, of course. That is why imposing tariffs on imports will push up the price of domestically produced cars one way or the other – either the manufacturers will pay the tariffs and pass on the cost to customers, or they will disrupt their supply chains and make cars more expensively at home. This interconnectedness means that it is not only in the West that manufacturing jobs are under pressure from automation. A paper by the National Bureau of Economic Research estimated that each additional robot replaced around 6.2 workers.51 Sales of industrial robots have risen from 100,000 a year in the mid-2000s to 250,000 in 2015 and are forecast to hit 400,000 by the end of the decade.52 The standard joke is that the manufacturing plant of the future will be staffed by a man and a dog; the man’s job will be to feed the dog, and the dog’s role will be to keep the man away from the machines.

There is plenty of incentive for young people to take further education. Those workers who only completed high school earned just three-fifths of the hourly wages of those who graduated from college and less than half the rate earned by postgraduates.10 Some of this education is supplied privately. But governments have seen it as in the country’s interests to expand education, especially as low-skilled jobs are being automated or shifted to low-wage centres in Asia. Health As late as 1820, life expectancy at birth was only around 29 worldwide, and 36 in Europe. By 1913, it had edged up to 34 worldwide but was in the mid-40s in Europe and America. By 1970, the global average was 60, and Europeans could expect to live into their seventies.11 By 2015, the global average was 71.4 years, more than double that of a century earlier.12 This is an immense, and oft-overlooked, achievement.

Maximiliano Dvorkin, “Jobs involving routine tasks aren’t growing”, https://www.stlouisfed.org/on-the-economy/2016/january/jobs-involving-routine-tasks-arent-growing 38. James Pethokoukis, “What the story of ATMs and bank tellers reveals about the ‘rise of the robots’ and jobs”, American Enterprise Institute, June 6th 2016, http://www.aei.org/publication/what-atms-bank-tellers-rise-robots-and-jobs/ 39. “Automation and anxiety”, The Economist, June 23rd 2016 40. Ian Stewart, Debapratim De and Alex Cole, “Technology and people: The great job-creating machine”, Deloitte, 2015, https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/finance/deloitte-uk-technology-and-people.pdf 41. “The insecurity of freelance work”, The Economist, June 14th 2018 Chapter 18 – The crisis and after: 2007 to today 1.


pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, blockchain, business intelligence, business process, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, cryptocurrency, David Graeber, dematerialisation, digital map, disruptive innovation, distributed ledger, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, global supply chain, global village, Google Glasses, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, James Watt: steam engine, Jane Jacobs, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, late capitalism, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Occupy movement, Oculus Rift, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, RAND corporation, recommendation engine, RFID, rolodex, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, Whole Earth Review, WikiLeaks, women in the workforce

The great twentieth-century economist John Maynard Keynes had foreseen much of this early on, coining the expression “technological unemployment” sometime around 1928.1 He saw, with almost clairvoyant perspicacity, that societies might eventually automate away the jobs much of their labor force depended on, and his insight is borne out in recent United States government estimates that an American worker making less than $20 an hour now has an 83 percent chance of losing their job to automation.2 But what Keynes concluded—that the eclipse of human labor by technical systems would necessarily compel a turn toward a full-leisure society—has not come to pass, not even remotely. And what neither Keynes nor any other economist reckoned with, until very recently, was the thought that the process of automation would hardly stop when it had replaced manual and clerical labor. If automation was initially brought to bear on tasks that were one or more of the “four Ds”—dull, dirty, difficult or dangerous—the advent of sophisticated machine-learning algorithms means that professional and managerial work now comes into range.

Osborne of the University of Oxford found that 47 percent of them were vulnerable to near-term advances in machine learning and mobile robotics.23 Among developing countries, this rises to 69 percent in India, 77 percent in China and an astonishing 85 percent in Ethiopia.24 (Again, these figures refer to the percentage of job categories that are susceptible to replacement, not of workers in employment.) Meanwhile, against the oft-cited hope that technology would generate more jobs than it eliminated, Frey found that fewer than 0.5 percent of the US workforce have found employment in the high-technology industries that have emerged since the turn of the century. A World Economic Forum estimate that some five million jobs would be lost to automation by 2020 has to be regarded as a stark outlier, if not a gross error, especially since Bank of England Chief Economist Andy Haldane reckons that 15 million jobs would disappear over the same timeframe in the United Kingdom alone.25 I’m not qualified to discuss, in any but the broadest terms, what will happen to the shape and structure of national economies in the aftermath of pervasive automation.


pages: 463 words: 115,103

Head, Hand, Heart: Why Intelligence Is Over-Rewarded, Manual Workers Matter, and Caregivers Deserve More Respect by David Goodhart

active measures, Airbnb, Albert Einstein, assortative mating, basic income, Berlin Wall, Bernie Sanders, big-box store, Boris Johnson, Branko Milanovic, British Empire, call centre, Cass Sunstein, central bank independence, centre right, computer age, corporate social responsibility, COVID-19, Covid-19, David Attenborough, David Brooks, deglobalization, deindustrialization, delayed gratification, desegregation, deskilling, different worldview, Donald Trump, Elon Musk, Etonian, Fall of the Berlin Wall, Flynn Effect, Frederick Winslow Taylor, future of work, gender pay gap, gig economy, glass ceiling, illegal immigration, income inequality, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge economy, knowledge worker, labour market flexibility, longitudinal study, low skilled workers, Mark Zuckerberg, mass immigration, new economy, Nicholas Carr, oil shock, pattern recognition, Peter Thiel, pink-collar, post-industrial society, post-materialism, postindustrial economy, precariat, reshoring, Richard Florida, Scientific racism, Skype, social intelligence, spinning jenny, Steven Pinker, superintelligent machines, The Bell Curve by Richard Herrnstein and Charles Murray, The Rise and Fall of American Growth, Thorstein Veblen, twin studies, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, wages for housework, winner-take-all economy, women in the workforce, young professional

Yet as the writer Madeleine Bunting, author of Labours of Love: The Crisis of Care, argues, the ethos of care does not sit easily with the ethos of an individualistic, achievement-driven society. Care, especially in the private realm, is about duty to others, and its results are sometimes nebulous and hard to measure. (See Chapter Eight.) There is some potential for the use of smart technologies in elderly care, with more remote monitoring and so on (and this could draw more men into the sector). But most caring jobs cannot easily be automated or performed by machines. Even in aging Japan, with its antipathy to mass immigration, Filipino caregivers are preferred to robots and are gradually being welcomed in larger numbers. The rise of cognitive-analytical ability—Head work—as a measure of economic and social success, combined with the hegemony of cognitive-class political interests, has led to the current great unbalancing of Western politics.

Yet forecasts about the jobs of the future provide more support for the idea of the decline and fall of the knowledge worker, especially at the middling and lower level. The demand for Head jobs will still be there, but it will focus on the most able and creative, and the sharpest rise in demand will be for Heart and certain kind of technological jobs that combine Hand and Head. A cottage industry has emerged over recent years estimating gross job loss from automation as anything between 10 and 50 percent. Most analysts agree that many jobs will go but few whole occupations. Some of the potentially powerful effects of automation on jobs and wages are already apparent. According to McKinsey, 18 percent of all hours worked in the United States are devoted to “predictable physical activities” and half of these hours could be automated away even with current technology.


pages: 441 words: 136,954

That Used to Be Us by Thomas L. Friedman, Michael Mandelbaum

addicted to oil, Affordable Care Act / Obamacare, Albert Einstein, Amazon Web Services, American Society of Civil Engineers: Report Card, Andy Kessler, Ayatollah Khomeini, bank run, barriers to entry, Berlin Wall, blue-collar work, Bretton Woods, business process, call centre, carbon footprint, Carmen Reinhart, Cass Sunstein, centre right, Climatic Research Unit, cloud computing, collective bargaining, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, delayed gratification, energy security, Fall of the Berlin Wall, fear of failure, full employment, Google Earth, illegal immigration, immigration reform, income inequality, Intergovernmental Panel on Climate Change (IPCC), job automation, Kenneth Rogoff, knowledge economy, Lean Startup, low skilled workers, Mark Zuckerberg, market design, mass immigration, more computing power than Apollo, Network effects, obamacare, oil shock, pension reform, Report Card for America’s Infrastructure, rising living standards, Ronald Reagan, Rosa Parks, Saturday Night Live, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, the scientific method, Thomas L Friedman, too big to fail, University of East Anglia, WikiLeaks

Throughout the post–World War II period, until 1991, “it typically took on average eight months for jobs that were lost at the trough of a recession to come back to the old peak,” said Rajan. But with the introduction of all these new technologies and networks over the last two decades, that is no longer the case. With each recession and with each new hyper-flattening and hyper-connecting of the global marketplace, more and more jobs are being automated, digitized, or outsourced. “Look at the last three recessions,” said Rajan. “After 1991, it took twenty-three months for the jobs to come back to prerecession levels. After 2001, it took thirty-eight months. And after 2007, it is expected to take up to five years or more.” A key reason is that in the old cyclical recovery people got laid off and were rather quickly hired back into the workforce once demand rose again.

… Consider, for example, that half of Grinnell’s applicants from China this year have perfect scores of 800 on the math portion of the SAT, making the performance of one largely indistinguishable from another.” This is just one small reason that whatever your “extra” is—inventing a new product, reinventing an old product, or reinventing yourself to do a routine task in a new and better way—you need to fine-tune it, hone and promote it, to become a creative creator or creative server and keep your job from being outsourced, automated, digitized, or treated as an interchangeable commodity. Everyone’s “extra” can and will be different. For some it literally will be starting a company to make people’s lives more comfortable, educated, entertained, productive, healthy, or secure. And the good news is that in the hyper-connected world, that has never been easier. If you have just the spark of a new idea today, you can get a company in Taiwan to design it; you can get Alibaba in China to find you a low-cost Chinese manufacturer to make it; you can get Amazon.com to do your delivery and fulfillment and provide technology services from its cloud; you can find a bookkeeper on Craigslist to do your accounting and an artist on Freelancer.com to do your logo.

No one has more bluntly summed up why average is over, and what it means for education, than John Jazwiec, who has headed a variety of technology start-ups, including RedPrairie and FiveCubits. Blogging on his website, JohnJazwiec.com, he confessed: I am in the business of killing jobs. I kill jobs in three ways. I kill jobs when I sell, I kill jobs by killing competitors, and I kill jobs by focusing on internal productivity. All of the companies I have been a CEO of, through best-in-practice services and software, eliminate jobs. They eliminate jobs by automation, outsourcing, and efficiencies of process. The marketing is clear—less workers, more consistent output. I reckon in the last decade I have eliminated over 100,000 jobs in the worldwide economy from the software and services my companies sell. I know the number, because … my revenues … are based on the number of jobs I kill. I have killed many competitors. Again, I reckon I have eliminated over 100,000 jobs in the last decade.


pages: 380 words: 109,724

Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US by Rana Foroohar

"side hustle", accounting loophole / creative accounting, Airbnb, AltaVista, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, Bernie Sanders, bitcoin, book scanning, Brewster Kahle, Burning Man, call centre, cashless society, cleantech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, death of newspapers, Deng Xiaoping, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Filter Bubble, future of work, game design, gig economy, global supply chain, Gordon Gekko, greed is good, income inequality, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, Kenneth Rogoff, life extension, light touch regulation, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, paypal mafia, Peter Thiel, pets.com, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, South China Sea, sovereign wealth fund, Steve Jobs, Steven Levy, subscription business, supply-chain management, TaskRabbit, Telecommunications Act of 1996, The Chicago School, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, WikiLeaks, zero-sum game

(The biggest gains now come in sales and supply-chain management.)40 Most of the CEOs I’ve spoken to are extremely bullish on the subject, claiming their AI investments yield between 10 and 30 percent returns. But the more data the AI has to work with, the better it goes. That’s good for corporations, but will cause a tremendous amount of disruption for citizens whose privacy is being compromised and workers whose jobs are being automated. * * * — HOW IS IT that Big Tech has, in a matter of just twenty years, so reshaped our economy? Key to understanding that is this: Many platform technology firms operate as natural monopolies—that is, companies that can dominate a market by sheer force of their networks. Many people would argue that Google, Facebook, Amazon, and perhaps even Netflix and Apple fit this category (though Apple itself would counter that there are many competitors in its mobile marketplace, most notably Google, which takes a much larger share of the overall mobile market if tallied by percentage of users on the Android system).

And that’s not taking into account the jobs these companies disrupt—by March 2019, for example, U.S. retailers had announced more than forty-one thousand job cuts, more than double the number from the previous year, in large part due to the Amazon effect.59 The bottom line is that most technology businesses simply don’t require many employees (think of all the robots roaming around Amazon warehouses), and this will only become truer with time. It’s been estimated that globally, 60 percent of all occupations will, in the next few years, be substantially redefined because of new disruptive technologies.60 It’s not only low-level or menial jobs that will be automated—it’s all jobs. In fact, there’s a case to be made that “knowledge work”—radiology, law, sales, and finance—will actually be automated faster than more physical jobs in areas like healthcare and manufacturing. Moreover, even in fields where humans can’t be replaced entirely, the gig economy and the “sharing” economy—driven, of course, by tech firms—have dramatically increased the number of contingency workers without benefits.61 Beyond these relatively easy-to-track numbers is perhaps a deeper and more worrisome issue, which is the way in which data-driven capitalism has turned people into the factory inputs of the digital age.

At a conference in that same year, I heard chief executives from large U.S. multinationals discussing ways in which technology would be able to replace 30 to 40 percent of the jobs in their companies over the next few years—and fretting about the political impact of layoffs on that scale. I would like to propose a radical solution: Do not lay them off. I am not asking corporate America to keep workers on as charity. I am suggesting that the public and private sector come together in what could be a kind of digital New Deal. As many jobs as will be replaced by automation, there are other areas—customer service, data analysis, and so on—that desperately need talent. Companies that pledge to retain workers and retrain them for new jobs should be offered tax incentives to do so. The United States should take a page out of the post–financial crisis German playbook, in which large-scale layoffs were avoided as both the public and private sector found ways to continue to use labor even as demand dipped.


Battling Eight Giants: Basic Income Now by Guy Standing

basic income, Bernie Sanders, centre right, collective bargaining, decarbonisation, diversified portfolio, Donald Trump, Elon Musk, full employment, future of work, Gini coefficient, income inequality, Intergovernmental Panel on Climate Change (IPCC), job automation, labour market flexibility, Lao Tzu, longitudinal study, low skilled workers, Martin Wolf, Mont Pelerin Society, moral hazard, North Sea oil, offshore financial centre, open economy, pension reform, precariat, quantitative easing, rent control, Ronald Reagan, selection bias, universal basic income, Y Combinator

Once in place, the basic income could be raised if anything like the dire predictions turned out to be true. Having a basic income system would also encourage people to welcome technological advances, avoiding the perfectly respectable Luddite reaction of the early nineteenth century when workers objected to mechanization because they were forced to lose and not share the gains. The assumed threat to jobs from automation has also led to calls to cut the working week to share jobs around and improve work-life balance. One proponent has even suggested that the target should be a 21-hour week. This policy might be desirable in a utopia, but it would be deeply impractical. It would be particularly inappropriate for a labour market based on flexible labour relations. It would be impossible and surely undesirable to stop people wanting to work longer than whatever duration was specified as the maximum.


pages: 491 words: 77,650

Humans as a Service: The Promise and Perils of Work in the Gig Economy by Jeremias Prassl

3D printing, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Andrei Shleifer, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, disruptive innovation, Donald Trump, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, hiring and firing, income inequality, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, low skilled workers, Lyft, Mahatma Gandhi, Mark Zuckerberg, market friction, means of production, moral hazard, Network effects, new economy, obamacare, pattern recognition, platform as a service, Productivity paradox, race to the bottom, regulatory arbitrage, remote working, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Rosa Parks, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, transaction costs, transportation-network company, Travis Kalanick, two tier labour market, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, working-age population

Justin McCurry, ‘South Korean woman’s hair eaten by robot vacuum cleaner as she slept’, The Guardian (9 February 2015), https://www.theguardian.com/ world/2015/feb/09/south-korean-womans-hair-eaten-by-robot-vacuum- cleaner-as-she-slept, archived at https://perma.cc/86YB-RF49; Aarian Marshall, ‘Puny humans still see the world better than self-driving cars, Wired (5 August 2017), https://www.wired.com/story/self-driving-cars-perception- humans/, archived at https://perma.cc/B8L9-7K32; Marty Padget, ‘Ready to pay billions for self-driving car roads?’, Venture Beat (17 May 2017), https:// venturebeat.com/2017/05/17/ready-to-pay-trillions-for-self-driving-car-roads/, archived at https://perma.cc/ZJ9K-LSXF. There is, furthermore, an important distinction between jobs that could be automated and those that actually are: see David Kucera, New Automation Technologies and Job Creation and Destruction Dynamics (International Labour Organization 2016). 14. Although I struggle to see how a robot could do the job of the TaskRabbit organizer we encountered in Chapter 1: coming up with a bespoke beach party, and keeping parents and children happy, strikes me as pretty much impossible to automate. 15.


pages: 147 words: 45,890

Aftershock: The Next Economy and America's Future by Robert B. Reich

Berlin Wall, business cycle, declining real wages, delayed gratification, Doha Development Round, endowment effect, full employment, George Akerlof, Home mortgage interest deduction, Hyman Minsky, illegal immigration, income inequality, invisible hand, job automation, labor-force participation, Long Term Capital Management, loss aversion, mortgage debt, new economy, offshore financial centre, Ralph Nader, Ronald Reagan, school vouchers, sovereign wealth fund, Thorstein Veblen, too big to fail, World Values Survey

“This factory marks a … major … millstone, er, milestone.” I congratulated the governor and got out of there as fast as I could. Remember bank tellers? Telephone operators? The fleets of airline workers behind counters who issued tickets? Service-station attendants? These and millions of other jobs weren’t lost to globalization; they were lost to automation. America has lost at least as many jobs to automated technology as it has to trade. Any routine job that requires the same steps to be performed over and over can potentially be done anywhere in the world by someone working for far less than an American wage, or it can be done by automated technology. By the late 1970s, all such jobs were on the endangered species list. By 2010, they were nearly extinct. But contrary to popular mythology, trade and technology have not really reduced the number of jobs available to Americans.


pages: 241 words: 43,073

pages: 440 words: 108,137

The Meritocracy Myth by Stephen J. McNamee

affirmative action, Affordable Care Act / Obamacare, American ideology, Bernie Madoff, British Empire, business cycle, collective bargaining, computer age, conceptual framework, corporate governance, deindustrialization, delayed gratification, demographic transition, desegregation, deskilling, equal pay for equal work, estate planning, failed state, fixed income, gender pay gap, Gini coefficient, glass ceiling, helicopter parent, income inequality, informal economy, invisible hand, job automation, joint-stock company, labor-force participation, longitudinal study, low-wage service sector, marginal employment, Mark Zuckerberg, mortgage debt, mortgage tax deduction, new economy, New Urbanism, obamacare, occupational segregation, old-boy network, pink-collar, plutocrats, Plutocrats, Ponzi scheme, post-industrial society, prediction markets, profit motive, race to the bottom, random walk, school choice, Scientific racism, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, The Spirit Level, The Wealth of Nations by Adam Smith, too big to fail, trickle-down economics, upwardly mobile, We are the 99%, white flight, young professional

While computerization created some new jobs with high skill requirements, other jobs have been automated or “deskilled” by computerization. Sales clerks, for instance, no longer need to calculate change. In fast-food chains, keyboards on cash registers sometimes display pictures rather than numbers. By the beginning of the twenty-first century, even computer-programming jobs, the supposed leading edge of the postindustrial boom, experienced sharp job losses. Between 2000 and 2004, 180,000 computer-programming jobs, or about one-quarter of the occupation’s total employment, were lost (Hacker 2008, 77). These jobs fell victim to two trends adversely affecting many other sectors of the labor force: automation and outsourcing. Many routine programming jobs were automated as advanced “canned” software programs were developed, eliminating the need to write programs in more complex and labor-intensive BASIC code.


pages: 193 words: 51,445

On the Future: Prospects for Humanity by Martin J. Rees

23andMe, 3D printing, air freight, Alfred Russel Wallace, Asilomar, autonomous vehicles, Benoit Mandelbrot, blockchain, cryptocurrency, cuban missile crisis, dark matter, decarbonisation, demographic transition, distributed ledger, double helix, effective altruism, Elon Musk, en.wikipedia.org, global village, Hyperloop, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, Johannes Kepler, John Conway, life extension, mandelbrot fractal, mass immigration, megacity, nuclear winter, pattern recognition, quantitative hedge fund, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, sharing economy, Silicon Valley, smart grid, speech recognition, Stanford marshmallow experiment, Stanislav Petrov, stem cell, Stephen Hawking, Steven Pinker, Stuxnet, supervolcano, technological singularity, the scientific method, Tunguska event, uranium enrichment, Walter Mischel, Yogi Berra

They can replace many white-collar jobs: routine legal work (such as conveyancing), accountancy, computer coding, medical diagnostics, and even surgery. Many ‘professionals’ will find their hard-earned skills in less demand. In contrast, some skilled service-sector jobs—plumbing and gardening, for instance—require nonroutine interactions with the external world and so will be among the hardest jobs to automate. To take a much-cited example, how vulnerable are the jobs of three million truck drivers in the United States? Self-driving vehicles may be quickly accepted in limited areas where they will have the roads to themselves—in designated parts of city centres, or maybe in special lanes on motorways. And there is a potential for using driverless machines in farming and harvesting, operating off road.


pages: 590 words: 153,208

Wealth and Poverty: A New Edition for the Twenty-First Century by George Gilder

"Robert Solow", affirmative action, Albert Einstein, Bernie Madoff, British Empire, business cycle, capital controls, cleantech, cloud computing, collateralized debt obligation, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, deindustrialization, diversified portfolio, Donald Trump, equal pay for equal work, floating exchange rates, full employment, George Gilder, Gunnar Myrdal, Home mortgage interest deduction, Howard Zinn, income inequality, invisible hand, Jane Jacobs, Jeff Bezos, job automation, job-hopping, Joseph Schumpeter, knowledge economy, labor-force participation, longitudinal study, margin call, Mark Zuckerberg, means of production, medical malpractice, minimum wage unemployment, money market fund, money: store of value / unit of account / medium of exchange, Mont Pelerin Society, moral hazard, mortgage debt, non-fiction novel, North Sea oil, paradox of thrift, Paul Samuelson, plutocrats, Plutocrats, Ponzi scheme, post-industrial society, price stability, Ralph Nader, rent control, Robert Gordon, Ronald Reagan, Silicon Valley, Simon Kuznets, skunkworks, Steve Jobs, The Wealth of Nations by Adam Smith, Thomas L Friedman, upwardly mobile, urban renewal, volatility arbitrage, War on Poverty, women in the workforce, working poor, working-age population, yield curve, zero-sum game

Because federal control and oversight requirements have been imposing large new burdens of medical paperwork, the automation of this aspect of health care tends to free nurses and doctors to concentrate on their real duties with patients. The average intern today, for example, spends perhaps 90 percent of his time on paperwork. It is the growth of human bureaucracy, with its necessary rules and reporting requirements, that creates alienating and impersonal jobs. Automation tends to enhance the administrators’ span of control and reduce the need for middle managers and clerks doing machine-like tasks. All such improvements do not depend on full automation. An example of the kind of small advances in management that can yield large gains in efficiency is the hot line for diabetics, which was pushed by Dr. Reese Alsop and by Dr. Peter Bourne before his departure from the White House.


pages: 202 words: 59,883

Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, ubercab, urban planning, Zipcar

Could such smart grids prevent such tragedies as the one caused by the massive forest fire that took the lives of 19 Arizona firefighters in June 2013? Perhaps not quite yet. But they are coming closer all the time. Robotic Household Assistants Another category of personal assistants for the home steps out of the pages of science fiction and perhaps meanders over the freaky line. Robots have long existed as characters in books and movies. More recently they have started taking over the most tedious jobs in automated factories and some of the most dangerous first-response work, such as disarming explosive devices. Now robots are finding roles in the home. In some cases they are serving as novelty possessions for the affluent in Asia. In India, robot maids are used by some of the country’s uppercrust. The Times of India, a favorite publication of the nation’s elite, alleged that it was because robots are less prone to tantrums than the humans they are replacing.


pages: 207 words: 59,298

The Gig Economy: A Critical Introduction by Jamie Woodcock, Mark Graham

Airbnb, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, British Empire, business process, business process outsourcing, call centre, collective bargaining, commoditize, corporate social responsibility, crowdsourcing, David Graeber, deindustrialization, disintermediation, en.wikipedia.org, full employment, future of work, gender pay gap, gig economy, global value chain, informal economy, information asymmetry, inventory management, Jaron Lanier, Jeff Bezos, job automation, knowledge economy, Lyft, mass immigration, means of production, Network effects, new economy, Panopticon Jeremy Bentham, planetary scale, precariat, rent-seeking, RFID, ride hailing / ride sharing, Ronald Reagan, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, TaskRabbit, The Future of Employment, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, women in the workforce, working poor, young professional

Too little legibility and it becomes difficult to put work onto a platform in the first place. Here, layers of tacit rather than codified knowledge structure and govern the work process. Think of babysitters or security guards as jobs in which people tend to use personal recommendations, etc., that are hard to codify into platform ratings or databases. On the other hand, too much legibility and there is the risk that jobs become automated away. The Amazon dream of autonomous drones that can deliver parcels or the Uber dream of autonomous vehicles that can transport passengers are only possible in a world in which multiple overlapping spaces, activities and processes are highly digitally legible. Having a standardized addressing system, high-quality geospatial data, and the technology to produce and read those data has allowed large platforms to more effectively operate in some countries rather than others.


pages: 294 words: 77,356

Automating Inequality by Virginia Eubanks

autonomous vehicles, basic income, business process, call centre, cognitive dissonance, collective bargaining, correlation does not imply causation, deindustrialization, disruptive innovation, Donald Trump, Elon Musk, ending welfare as we know it, experimental subject, housing crisis, IBM and the Holocaust, income inequality, job automation, mandatory minimum, Mark Zuckerberg, mass incarceration, minimum wage unemployment, mortgage tax deduction, new economy, New Urbanism, payday loans, performance metric, Ronald Reagan, self-driving car, statistical model, strikebreaker, underbanked, universal basic income, urban renewal, War on Poverty, working poor, Works Progress Administration, young professional, zero-sum game

Our responsibility as public employees is to make certain that people who are eligible get the benefits they’re entitled to.” With decades of experience and seniority, Gresham managed to hold on to her state job when the automation rolled out to Allen County. But under the new system, she no longer carried a caseload. Rather, she responded to tasks that were assigned by the new Workflow Management System (WFMS). Tasks bounced between 1,500 new ACS employees and 682 remaining state employees, now known as “state eligibility consultants.” The governor promised that no state workers would lose their jobs due to the automation and that salaries would stay the same or rise. But the reality of the new ACS positions created a wave of retirements and resignations. After reapplying for jobs they already held, sometimes for decades, and submitting to criminal background checks and drug tests, workers found their positions moved from their home county office to a regional call center.


pages: 344 words: 104,077

Superminds: The Surprising Power of People and Computers Thinking Together by Thomas W. Malone

agricultural Revolution, Airbnb, Albert Einstein, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, business process, call centre, clean water, creative destruction, crowdsourcing, Donald Trump, Douglas Engelbart, Douglas Engelbart, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, future of work, Galaxy Zoo, gig economy, happiness index / gross national happiness, industrial robot, Internet of things, invention of the telegraph, inventory management, invisible hand, Jeff Rulifson, jimmy wales, job automation, John Markoff, Joi Ito, Joseph Schumpeter, Kenneth Arrow, knowledge worker, longitudinal study, Lyft, Marshall McLuhan, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, Ray Kurzweil, Rodney Brooks, Ronald Coase, Second Machine Age, self-driving car, Silicon Valley, slashdot, social intelligence, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, technological singularity, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, transaction costs, Travis Kalanick, Uber for X, uber lyft, Vernor Vinge, Vilfredo Pareto, Watson beat the top human players on Jeopardy!

And cheap communication technologies make this possible on a scale our ancestors could never have imagined. Semiautomated Matching of Tasks to People New technologies can also play an active role in matching workers with people who have work to be done. Job-search sites like Monster.com and CareerBuilder.com provide simple examples of this. These sites bring together lots of jobs and lots of job seekers and provide automated search tools to help people on both sides of the matching process find each other. But it’s possible to go much further than today’s job-search sites do. For instance, imagine a site that operates more like Match.com than Monster.com.6 In addition to asking for objective information like your work history, it would also ask about your passions, what you do for fun, and the kinds of people you like to work with.

Redistribution of income. Perhaps the most extreme way governments can deal with this problem is with direct redistribution of income. Many countries do this already with progressive income taxes and various forms of social benefits (such as welfare payments and subsidized medical care). There could certainly be special programs established to support people who can’t recover financially after losing their jobs to automation. Whatever methods are used, it’s clear that hierarchical governments can intervene in various ways to solve the job-transition problems that markets don’t solve on their own. If it becomes common for income to be decoupled from employment, then we might, as a society, put more emphasis on other kinds of contributions to our communities. For instance, in chapter 9, we saw how cyber-socialism—an extreme version of this—might use new technologies to track many kinds of contributions people make to their communities—like creating art, providing entertainment, and being a good neighbor—without requiring them to have conventional jobs.


pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

Air France Flight 447, Airbnb, Airbus A320, AltaVista, Amazon Mechanical Turk, augmented reality, autonomous vehicles, Bernie Sanders, book scanning, Brewster Kahle, Buckminster Fuller, Burning Man, Captain Sullenberger Hudson, centralized clearinghouse, Charles Lindbergh, cloud computing, cognitive bias, collaborative consumption, computer age, corporate governance, crowdsourcing, Danny Hillis, deskilling, digital map, disruptive innovation, Donald Trump, Electric Kool-Aid Acid Test, Elon Musk, factory automation, failed state, feminist movement, Frederick Winslow Taylor, friendly fire, game design, global village, Google bus, Google Glasses, Google X / Alphabet X, Googley, hive mind, impulse control, indoor plumbing, interchangeable parts, Internet Archive, invention of movable type, invention of the steam engine, invisible hand, Isaac Newton, Jeff Bezos, jimmy wales, Joan Didion, job automation, Kevin Kelly, lifelogging, low skilled workers, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, Menlo Park, mental accounting, natural language processing, Network effects, new economy, Nicholas Carr, Norman Mailer, off grid, oil shale / tar sands, Peter Thiel, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, Republic of Letters, robot derives from the Czech word robota Czech, meaning slave, Ronald Reagan, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley ideology, Singularitarianism, Snapchat, social graph, social web, speech recognition, Startup school, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, technoutopianism, the medium is the message, theory of mind, Turing test, Whole Earth Catalog, Y Combinator

Even Adam Smith understood that machinery, in enhancing labor productivity, would often end up narrowing jobs, turning skilled work into routine work. At worst, he wrote, the factory worker would become “as stupid and ignorant as it is possible for a human creature to become.” That’s not the whole picture, of course. In evaluating the long-term effects of automation, we have to look beyond particular job categories. Even as automation reduces the skill requirements of an established occupation, it may contribute to the creation of large new categories of interesting and well-paid work. That’s what happened, as the endless-ladder mythologists like to remind us, during the latter stages of the industrial revolution. The efficiencies of assembly lines and other mechanized forms of production pushed down the prices of all sorts of goods, which drove up demand for those goods, which led manufacturers to hire not only lots of blue-collar workers to operate and repair the machines but also lots of white-collar workers to manage the factories, design new products, market and sell the goods, keep the books, and so forth.


Capitalism, Alone: The Future of the System That Rules the World by Branko Milanovic

"Robert Solow", affirmative action, Asian financial crisis, assortative mating, barriers to entry, basic income, Berlin Wall, bilateral investment treaty, Black Swan, Branko Milanovic, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carried interest, colonial rule, corporate governance, creative destruction, crony capitalism, deindustrialization, dematerialisation, Deng Xiaoping, discovery of the americas, European colonialism, Fall of the Berlin Wall, financial deregulation, Francis Fukuyama: the end of history, full employment, ghettoisation, gig economy, Gini coefficient, global supply chain, global value chain, high net worth, income inequality, income per capita, invention of the wheel, invisible hand, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, labor-force participation, laissez-faire capitalism, land reform, liberal capitalism, low skilled workers, Lyft, means of production, new economy, offshore financial centre, Paul Samuelson, plutocrats, Plutocrats, post-materialism, purchasing power parity, remote working, rent-seeking, ride hailing / ride sharing, Silicon Valley, single-payer health, special economic zone, The Wealth of Nations by Adam Smith, Thorstein Veblen, uber lyft, universal basic income, Vilfredo Pareto, Washington Consensus, women in the workforce, working-age population, Xiaogang Anhui farmers

New technologies ended up creating enough new jobs, and actually more and better jobs than those that were lost. This does not mean that no one loses as a result of automation. The new machines (called “robots”) will replace some workers, and some people’s wages will be reduced. But however tragic these losses may be for the individuals involved, they do not affect society as a whole. Estimates of the proportion of jobs under threat from automation vary widely, both among countries and within countries, depending on the methodology used. For the United States, estimates of the proportion of jobs at risk vary between 7 and 47 percent; for Japan, between 6 and 55 percent.26 The high values are obtained when occupations are deemed by more than 70 percent of “experts” as likely to be affected by automation; but when the same exercise is conducted looking at the more granular distinction between tasks within occupations, the percentages are much smaller, ranging between 6 and 12 percent for OECD countries (Hallward-Driemeier and Nayyar 2018).

See also Income inequality; Systemic inequalities in liberal meritocratic capitalism; Wage inequality Inequality extraction ratio, 246n16 Inequality in income from capital and labor, 26–27 Inequality in liberal meritocratic capitalism: capital and labor incomes and, 18; globalization and, 22; inequality in labor income and, 27; intergenerational transmission of, 19–20; marriage patterns and, 18–19, 22; share of capital in total income and rising, 15–16, 21–22; systemic and nonsystemic causes of, 21–23 Inequality of opportunity: global, 158–159; intergenerational, 49, 50; reducing, 48 Information and communication technology (ICT): change in global income inequality and, 8–9; global value chains and, 148; second globalization and, 150, 151–152 Inheritance tax, to deconcentrate capital ownership, 48–50 Inherited wealth, 62–63 Innovation rents, 152 Innovations, income convergence and, 235 Institutions, globalization and increasing importance of, 151–152 Intergenerational advantage, education and, 61–62 Intergenerational education mobility, 247n35 Intergenerational equality of opportunity, inheritance taxes and, 49, 50 Intergenerational mobility: in China, 105; decline in, 41–42; evolution of capitalism and, 216; inequality and, 63–65; in liberal meritocratic capitalism, 215; in social-democratic capitalism, 215 Intergenerational transmission of inequality, 19–20 Intergenerational transmission of wealth, 158–159; investment in children and, 39, 40; liberal meritocratic capitalism and, 40–42 International Monetary Fund (IMF), 107, 127, 148, 161 Interpersonal distribution of income, 233 Inventions, income convergence and, 235 “Invisible hand,” hypercommercialization and, 227–229 Iran, universal basic income in, 202 Israel, subcitizenship in, 136 Italy: assortative mating in, 240n31; corruption in, 121; displacement of native population by rich from other countries in, 186 Jacques, Martin, 122, 126, 128 Japan: growth rate in, 235; jobs under threat from automation in, 198 Jefferson, Thomas, 178 “Jerusalem” laws, 68 Jevons, Stanley, 200, 256n28 Jianhua, Xiao, 93 Karabarbounis, Loukas, 24 Katz, Lawrence, 24 Keynes, John Maynard, 23, 179, 186, 200, 201, 256n28 Khashoggi, Jamal, 180 Khodorkovsky, Mikhail, 93, 170, 252n33 Kohl, Helmut, 58 Kuhn, Moritz, 31–32 Kuomintang, 81 Kuznets waves, 100, 102 Labor: in gig economy, 192; globalization and mobility of, 129–130, 150, 154; organization of, 43; skill premium, 21; wage inequality and, 50 Labor, migration of, 131–147; arguments against, 138–139, 140–141; citizenship as economic asset, 134–136; citizenship premium or rent, 131–134; under conditions of globalization, 137–139; defined, 137; free movement of factors of production and, 136–141; reconciling concerns of natives with desires of migrants, 142–147; value systems of migrants, 140–141; welfare state and, 156; why labor differs from capital, 139–141; world income and, 250n9 Labor income, 16–18; association of high capital and high labor income in same individuals, 34–36; liberal meritocratic capitalism and inequality in, 27, 28–30 Labor mobility, globalization and, 129–130, 150, 154 Labor-rich people, incomes of, 16–18 Landes, David, 196 Land rent, citizenship and, 132, 133–134 Latin America: inequality in, 102; structuralism and dependecia theory, 77–78, 170–171 Law, outsourcing morality and, 181, 182 Law of Peoples, The (Rawls), 158 Left-wing parties, antiglobalization stance of, 157 Legacy admissions, 60 Legal intrusion into family life, 188–190 Legal vs. ethical, 182 Leisure, increase in as mitigation for commercialized capitalism, 185–187 “Leisure class,” Veblen and, 17 Lenin, Vladimir, 224 Less-developed countries, success of communism in, 82–87.

., 72 Technological frontier, income convergence and, 235 Technological progress: fear of, 197–205; globalization and, 152, 153; rise in labor productivity and, 24; threat of global war and, 207 Technological revolution, changes in global income inequality and, 7, 8–9 Teleological view of history, 68–69 Temporary visas, 146 Thailand, increase in economic growth in, 8 Thatcher, Margaret, 48, 57 Theory of Justice, A (Rawls), 12, 158 Theory of Moral Sentiments, The (Smith), 159, 178, 228, 253–254n4 Third World: development of capitalism in, 222–224; explaining communism in, 74, 75–78; role of communist revolution in, 78–82 Tinbergen, Jan, 24, 43 Trade globalization, welfare state and, 51 Trade unions, decline in membership, 25, 42–43 Transparency International, 97; Corruption Perception Index, 160 Treaty of Detroit (1949), 25 Trump, Donald, 114 Uber, 190, 199 Unconditional convergence, 234 Underclass, migrant, 55, 146–147 Undocumented migrants, in United States, 145–146 Unitary elasticity of substitution between capital and labor, 23 United Kingdom: classical capitalism in, 13; cracking down on tax havens, 173; free movement of people and, 250n6; inequality in income from capital and labor in, 26–27, 28; purchase of residence permits in, 134, 135; share of capital as percent of national income in, 15 United States: assortative mating and increase in inequality in, 39; bifurcated system of education in, 59–62; challenging Swiss banking secrecy laws, 173; concentration of capital ownership in, 26–31; concentration of wealth and direct ownership of stock in, 35–36; decline in absolute mobility in, 42, 241n35; “export” of liberal capitalism, 112–113; flows of people within, 138; GDP per capita growth rate in, 86; growth rate in, 235; immigration and, 52, 54–55, 145–146; income inequality in, 102; increasing aggregate share of capital in national income in, 24, 25; inequality and mobility in, 63–65; inequality in income from capital and labor in, 26–27, 28; intergenerational transmission of inequality in, 19; jobs under threat from automation in, 198; K / L ratios in, 149; leverage of middle-class wealth in, 31–32; limits of tax-and-transfer redistribution in, 44–46; money as equalizer in, 177; perception of greater equality of opportunity in, 240n32; rights of migrants in, 145–146; ruling class control of financial capital, 65; share of capital income in total income, 15; share of global GDP, 9, 10; social-democratic capitalism in, 13; taxation of inheritance in, 49–50; top decile of capitalists in top decile of workers, 35 Universal basic income (UBI), problems with, 201–205 Universities, moral money laundering and, 169–170 Upper class: elite education and, 59–62; expensive education and, 65–66; inherited wealth and, 62–63; openness to outsiders, 63–65, 66; political power of, 56–59; role of, 65; self-perpetuating, 56–66.


pages: 523 words: 61,179

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

Even given such innovations, though, human ethics compliance managers will still need to monitor and help ensure the proper operation of those sophisticated systems. An AI system could be technically proficient and ethical, but still be detrimental to an organization. That’s why companies will need automation ethicists. These individuals will be responsible for evaluating the noneconomic impact of AI systems. One important issue is people’s general acceptance for these new technologies. Employees are naturally fearful of losing their jobs to an automated application that performs just as well, if not better, than a human could. Such emotions can be especially powerful in response to robotic AI systems. Masahiro Mori, a Japanese robotics expert, in a study of how we respond to robots, has discovered an interesting effect. As a robot becomes more lifelike, our affinity and empathy for it increases until a certain point. Then, as the robot becomes more like us, we quickly become repulsed by any slight imperfections.

In the United States, the 2016 White House report “Artificial Intelligence, Automation, and the Economy” notes that the nation spends only around 0.1 percent of its GDP on programs that help people adjust to workplace changes. This number has fallen over the last thirty years, and the federal readjustment programs that exist—mostly used to help people deal with coal mines or military bases that close—are not designed to help people whose jobs are lost or changed by automation.6 Results are mixed in other countries. Japan and China are among those that stand out by making significant commitments to AI education and workforce training as the core piece of long-term national AI strategies. For instance, China’s State Council has the stated goal of making the nation equal among leading AI countries by 2020 and the world’s “premier artificial intelligence innovation center by 2030.”7 This development plan includes major investments in retraining workers for an economy where “collaboration between humans and machines will become a mainstream production and service mode.”8 A Call to Action: Reimagining Business AI is rapidly making inroads in business.


pages: 254 words: 61,387

This Could Be Our Future: A Manifesto for a More Generous World by Yancey Strickler

basic income, big-box store, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cognitive dissonance, corporate governance, Daniel Kahneman / Amos Tversky, David Graeber, Donald Trump, Doomsday Clock, effective altruism, Elon Musk, financial independence, gender pay gap, global supply chain, housing crisis, Ignaz Semmelweis: hand washing, invention of the printing press, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Nash: game theory, Joi Ito, Joseph Schumpeter, Kickstarter, Louis Pasteur, Mark Zuckerberg, medical bankruptcy, new economy, Oculus Rift, off grid, offshore financial centre, Ralph Nader, RAND corporation, Richard Thaler, Ronald Reagan, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, Snapchat, Social Responsibility of Business Is to Increase Its Profits, stem cell, Steve Jobs, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Travis Kalanick, universal basic income, white flight

See money: and happiness Harvard Business Review, 87–89, 98 Harvard Business School, 61, 92 Harvard’s Institute of Politics, xiv health/healthcare and bankruptcies, 23, 249 and Bentoism, 202, 208 and financial maximization, 23–26 history of, 146–51 and Maslow’s hierarchy, 111–12, 114 rising costs of, 110, 259 and technology, 162, 216 and thirty-year theory, xiv, 187, 189 See also exercise Helu, Carlos Slim, 109–10 high five, 8, 105 Hippocrates, 148 housing, 78, 110, 216, 259 Hume, David, 223–24, 271 Hunt, Sam, 37, 41, 55 income, 120–21, 195–96, 259–60. See also wages individual, the, xiv–xv, 26–27, 269–70 inequality, 14, 73, 114, 170, 196, 239, 260 Intel, 79 internet, 84, 191, 267 control over, 53–54 creation of, xiv, 38 gov. investment in, 78–79 retailers on, 51, 54–55, 71 iPhone, xii, 54, 78, 168, 182–83 Japan, xvi, 27, 101–3, 129–30 jobs and automation, 72–73, 192 creation of, x, 193 and lack of raises, 63–66 and mass layoffs, 62, 67, 71–73, 84–85 and top earners, 64 Jobs, Steve, 15, 79 Jogging (Bowerman), 186 Johnson, Magic, 159 Kahneman, Daniel, 22–23, 113 Kalanick, Travis, 98 Kennedy, John F., 184–85, 187 Keynes, John Maynard, 193–95 Kickstarter, 15, 115, 175 charter of, 170–71 creative projects of, 5–7, 10–13 founding of, 4–8, 236, 247 as PBC, 6, 9–12, 100–101, 169–71, 264 and stock buybacks, 67–68 wins best award, 87–88 knowledge, 21, 123, 217 and generational change, 180–81 as governing value, 144–45 high value of, xii–xiii, xv, 25 new, 150, 202, 268 Kondratiev waves, 267–68 Kuznets, Simon, 120–21 Lancet, The, 179, 184 Lazonick, William, 73 Let My People Go Surfing (Chouinard), 172 Lewis, Michael, x, 159–60 Liar’s Poker (Lewis), x life goals meaningful, 89–92, 111, 201 purpose-oriented, 94, 119 wealth-centric, 89–92, 94, 105, 119 life span, xi, 15, 266 Lister, Joseph, 147, 149, 179, 183–84, 187 Live Nation, 162, 263 long-term oriented, 110, 166–68, 175–76, 264.


pages: 501 words: 114,888

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives by Peter H. Diamandis, Steven Kotler

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, blood diamonds, Burning Man, call centre, cashless society, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, crowdsourcing, cryptocurrency, Dean Kamen, delayed gratification, dematerialisation, digital twin, disruptive innovation, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, food miles, game design, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, indoor plumbing, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, mass immigration, megacity, meta analysis, meta-analysis, microbiome, mobile money, multiplanetary species, Narrative Science, natural language processing, Network effects, new economy, New Urbanism, Oculus Rift, out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, Satoshi Nakamoto, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, technoutopianism, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

Theoretically, workers have been on the fast track to obsolescence since the Luddites took sledgehammers to industrial looms in the early 1800s. In 1790, 90 percent of all Americans made their living as farmers; today it’s less than 2 percent. Did those jobs disappear? Not exactly. The agrarian economy morphed, first into the industrial economy, next into the service economy, now the information economy. Automation produces job substitution far more than job obliteration. Even when there’s automation, this doesn’t always create the dire results we expect. Consider automatic teller machines (ATMs). When they were first rolled out in the late 1970s, there were serious concerns about bank teller layoffs. Between 1995 and 2010, the number of ATMs in America went from one hundred thousand to four hundred thousand, but mass teller unemployment wasn’t the result. Because ATMs made it cheaper to operate banks, the number of banks grew by 40 percent.

See: https://www.sciencedaily.com/releases/2009/07/090717104618.htm. replanting a portion of the Irrawaddy Delta: Adele Peters, “These Tree-Planting Drones Are About to Start an Entire Forest from the Sky,”Fast Company, August 10, 2017. See: https://www.fastcompany.com/40450262/these-tree-planting-drones-are-about-to-fire-a-million-seeds-to-re-grow-a-forest. Economic Risks: The Threat of Technological Unemployment 47 percent of all US jobs: L. Nedelkoska, “Automation, Skills Use and Training,” OECD Social, Employment and Migration Working Papers, no. 202 (OECD Publishing, Paris, 2018). See: https://doi.org/10.1787/2e2f4eea-en. as journalist and author James Surowiecki: James Surowiecki, “Robots Will Not Take Your Job,” Wired. August, 2017. See: https://www.wired.com/2017/08/robots-will-not-take-your-job/. In 1790, 90 percent: See a more complete history here: https://classroom.synonym.com/during-early-1800s-americans-earned-living-what-12580.html.


pages: 448 words: 84,462

Testing Extreme Programming by Lisa Crispin, Tip House

c2.com, continuous integration, data acquisition, database schema, Donner party, Drosophila, hypertext link, index card, job automation, web application

Why XP Teams Need Testers Much of the published material on Extreme Programming is aimed at programmers, customers, and managers. Some purists may argue that a tester role is unnecessary in XP projects: customers can write the acceptance tests and programmers can automate them. This can work, and certainly some successful XP projects don't have testers. We believe, however, that more XP teams can be successful by doing a better job of defining, automating, and running acceptance tests when someone is focused on that role and that this focus helps in other areas as well. If you don't like to think of someone in the tester "role" on an XP project (because the only true roles defined in XP are programmer and customer), think of having a programmer with a "tester focus." We'll illustrate this point with a true story about a remodeling project Lisa recently experienced, but first let's define what we mean by the term "tester."


pages: 236 words: 67,953

Brave New World of Work by Ulrich Beck

affirmative action, anti-globalists, Asian financial crisis, basic income, Berlin Wall, collective bargaining, conceptual framework, Fall of the Berlin Wall, feminist movement, full employment, future of work, Gunnar Myrdal, hiring and firing, illegal immigration, income inequality, informal economy, job automation, knowledge worker, labour market flexibility, labour mobility, low skilled workers, McJob, means of production, mini-job, post-work, postnationalism / post nation state, profit maximization, purchasing power parity, rising living standards, Silicon Valley, working poor, working-age population, zero-sum game

Jeremy Rifkin has shown that in the United States the proportion of factory workers in the economically active population fell over the past thirty years from 33 per cent to 17 per cent, even though there was a sharp rise in industrial output.23 In ten years' time less than 12 per cent of America's working population will be employed in factories – and by the year 2020 the figure will be less than 2 per cent. Moreover, even in the classical service sectors where hopes are directed towards a new jobs miracle, automation and downsizing have long since begun. Those who boost the economy further will not only fail to overcome structural unemployment; they will actually reinforce it. For flourishing enterprises make their profits mainly through rationalization (and no one can be blamed for that in an economic system geared to profit). Why should they create jobs when machines can work much more efficiently than people?


pages: 413 words: 119,587

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game


pages: 504 words: 126,835

The Innovation Illusion: How So Little Is Created by So Many Working So Hard by Fredrik Erixon, Bjorn Weigel

"Robert Solow", Airbnb, Albert Einstein, American ideology, asset allocation, autonomous vehicles, barriers to entry, Basel III, Bernie Madoff, bitcoin, Black Swan, blockchain, BRICs, Burning Man, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, Clayton Christensen, Colonization of Mars, commoditize, corporate governance, corporate social responsibility, creative destruction, crony capitalism, dark matter, David Graeber, David Ricardo: comparative advantage, discounted cash flows, distributed ledger, Donald Trump, Elon Musk, Erik Brynjolfsson, fear of failure, first square of the chessboard / second half of the chessboard, Francis Fukuyama: the end of history, George Gilder, global supply chain, global value chain, Google Glasses, Google X / Alphabet X, Gordon Gekko, high net worth, hiring and firing, Hyman Minsky, income inequality, income per capita, index fund, industrial robot, Internet of things, Jeff Bezos, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, joint-stock company, Joseph Schumpeter, Just-in-time delivery, Kevin Kelly, knowledge economy, laissez-faire capitalism, Lyft, manufacturing employment, Mark Zuckerberg, market design, Martin Wolf, mass affluent, means of production, Mont Pelerin Society, Network effects, new economy, offshore financial centre, pensions crisis, Peter Thiel, Potemkin village, price mechanism, principal–agent problem, Productivity paradox, QWERTY keyboard, RAND corporation, Ray Kurzweil, rent-seeking, risk tolerance, risk/return, Robert Gordon, Ronald Coase, Ronald Reagan, savings glut, Second Machine Age, secular stagnation, Silicon Valley, Silicon Valley startup, Skype, sovereign wealth fund, Steve Ballmer, Steve Jobs, Steve Wozniak, technological singularity, telemarketer, The Chicago School, The Future of Employment, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, transaction costs, transportation-network company, tulip mania, Tyler Cowen: Great Stagnation, uber lyft, University of East Anglia, unpaid internship, Vanguard fund, Yogi Berra

Potentially, it was argued, automation could destroy the industrial fabric of the United States and cause mass unemployment à la the Great Depression.12 However, the majority of these fears turned out to be unfounded – and, by the end of the decade, when the economy had improved and fears had abated, no one remembered what the panic had been about. Automation, like previous technological shifts, destroyed jobs, but it also created new ones, and much safer and better-paid jobs at that. An automation blitz never occurred; the process took several decades as technology had to adjust to the composition of markets, companies, and several other aspects than simply the capacity of machines to substitute for labor. Just as in the industrial revolution, automation did not win merely by showing up. It progressively improved and adjusted to the economic, social, and institutional conditions for innovation.

(i) Angry Birds (app game) (i) Anheuser-Busch (i) Ansoff, Igor (i) antitrust laws (i) anxiety and automation/high-tech employment (i), (ii) psychology of, and low growth expectations (i) Apollo, moon landing (i) Apple and Foxconn (i) iOS (i) iPad (i), (ii) iPhone (i), (ii), (iii), (iv), (v) iPod and value chains (i) and Nokia (i) unutilized cash balances (i) apps (i), (ii), (iii), (iv), (v), (vi), (vii) Arab Spring (2011) (i) Arendt, Hannah (i) Art Vandelay character (Seinfeld TV series) (i), (ii), (iii) artificial intelligence (AI) (i), (ii) see also automation; robotics/robots Asia Asian markets (i), (ii) labor market flexibility (i) regionalization of trade growth (i) see also East Asia Asian Tigers (Hong Kong, Singapore, South Korea, Taiwan) (i) Asimov, Isaac (i) aspirations (i), (ii), (iii), (iv), (v) asset bubbles (i) asset managers and financial regulation (i) and gray capitalism (i), (ii), (iii), (iv), (v), (vi) and modern portfolio theory (i) and retirement savings (i) AT&T, Bell Labs (i) automated teller machines (ATMs), and teller jobs (i) automation and labor (i), (ii), (iii), (iv), (v) see also artificial intelligence; New Machine Age thesis; robotics/robots; technology automobile industry see car industry “average is over” thesis (i) Babson College, entrepreneurship study (i) baby boomer (or boomer) generation (i), (ii), (iii), (iv) Back to the Future II (movie) (i) Bacon, Francis (i) Bailey, Ronald (i) Baldwin, Richard (i) Ballmer, Steve (i), (ii) ballooning (i) Balsillie, Jim (i) Bank for International Settlements (BIS) economists (i), (ii) banks bank services and globalization (i) bank teller jobs and ATMs (i) European banks and Basel III rules (i), (ii) and financial regulation (i), (ii) mobile banking in Africa (i) proneness to risk (i) “put option” (i) US banks and compliance officers (i) barber profession, evolution of (i) Basel III (i), (ii) BASF (i), (ii) Baumol, William (i), (ii), (iii), (iv)n70 “bazaar economy” (Hans-Werner Sinn) (i) Beals, Vaughn (i) Bean, Charles “Independent Review of UK Economic Statistics” (i) on UK productivity puzzle (i) Beer, Stafford (i), (ii) Being There (movie), Mr.


pages: 509 words: 132,327

Rise of the Machines: A Cybernetic History by Thomas Rid

1960s counterculture, A Declaration of the Independence of Cyberspace, agricultural Revolution, Albert Einstein, Alistair Cooke, Apple II, Apple's 1984 Super Bowl advert, back-to-the-land, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, business intelligence, Charles Lindbergh, Claude Shannon: information theory, conceptual framework, connected car, domain-specific language, Douglas Engelbart, Douglas Engelbart, dumpster diving, Extropian, full employment, game design, global village, Haight Ashbury, Howard Rheingold, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kubernetes, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, new economy, New Journalism, Norbert Wiener, offshore financial centre, oil shale / tar sands, pattern recognition, RAND corporation, Silicon Valley, Simon Singh, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, technoutopianism, Telecommunications Act of 1996, telepresence, The Hackers Conference, Vernor Vinge, Whole Earth Catalog, Whole Earth Review, Y2K, Yom Kippur War, Zimmermann PGP

Unemployment in the United States steadily cycled upward throughout the 1950s, from 4.2 percent at the start of the decade to 5.8 percent.74 By the end of the decade, economists had begun articulating concerns about structural unemployment. But from 1961 to 1969, employment in the goods-producing industries grew by 19 percent, and the service sector grew by nearly 30 percent. It also became clear that computers and control systems created new jobs. A leading trade magazine, Automation, commissioned a study of 3,440 industrial plants. Eleven percent were using advanced automation technology, such as computer control. Of those automated factories, only 10.4 percent reported a reduction of personnel; 41.5 percent reported no change; and nearly half of all automated companies told the magazine that more workers were needed to service the machines, not fewer.75 The distinctly 1960s hype about automation had two main drivers.


When Computers Can Think: The Artificial Intelligence Singularity by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann, Michelle Estes

3D printing, AI winter, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, blue-collar work, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, create, read, update, delete, cuban missile crisis, David Attenborough, Elon Musk, en.wikipedia.org, epigenetics, Ernest Rutherford, factory automation, feminist movement, finite state, Flynn Effect, friendly AI, general-purpose programming language, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, sorting algorithm, speech recognition, statistical model, stem cell, Stephen Hawking, Stuxnet, superintelligent machines, technological singularity, Thomas Malthus, Turing machine, Turing test, uranium enrichment, Von Neumann architecture, Watson beat the top human players on Jeopardy!, wikimedia commons, zero day

One of the toughest problems traditionally is bin picking, namely to pick objects out of a jumble of objects in a bin as shown above. The robot has to sense where the objects are and what their orientation or pose is. It then has to plan a sequence of movements to accurately grasp the object. This means that the factory environment does not need to be as rigidly controlled, and that many additional jobs can be automated. The advanced vision systems this requires have now become much more affordable. The system shown above shown above just uses the same Kinect sensors that are used in the XBox consumer game console. So the factory lights are being turned back on, but not for human eyes. Motion Planning Hexapod robot. Corporate http://www.hexapodrobot.com/store/index.php?cPath=21_22 Other robots can move about, with wheels or caterpillar treads or even legs.


pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

.… It’s my belief we’re going to need some assistance and I think AI is the solution to that.1 We’ve all heard that in the future AI will take over the jobs that humans hate—low-wage jobs that are boring, exhausting, degrading, exploitative, or downright dangerous. If this actually happens, it could be a true boon for human well-being. (Later I’ll discuss the other side of this coin—AI taking away too many human jobs.) Robots are already widely used for menial and repetitive factory tasks, though there are many such jobs still beyond the abilities of today’s robots. But as AI progresses, more and more of these jobs could be taken over by automation. Examples of future AI workplace applications include self-driving trucks and taxis, as well as robots for harvesting fruits, fighting fires, removing land mines, and performing environmental cleanups. In addition, robots will likely see an even larger role than they have now in planetary and space exploration. Will it actually benefit society for AI systems to take over such jobs?

Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? The respondents were divided: 63 percent predicted that progress in AI would leave humans better off by 2030, while 37 percent disagreed. Opinions ranged from the view that AI “can virtually eliminate global poverty, massively reduce disease and provide better education to almost everyone on the planet” to predictions of an apocalyptic future: legions of jobs taken over by automation, erosion of privacy and civil rights due to AI surveillance, amoral autonomous weapons, unchecked decisions by opaque and untrustworthy computer programs, magnification of racial and gender bias, manipulation of the mass media, increase of cybercrime, and what one respondent called “true, existential irrelevance” for humans. Machine intelligence presents a knotty array of ethical issues, and discussions related to the ethics of AI and big data have filled several books.4 In order to illustrate the complexity of the issues, I’ll dig deeper into one example that is getting a lot of attention these days: automated face recognition.


pages: 217 words: 63,287

The Participation Revolution: How to Ride the Waves of Change in a Terrifyingly Turbulent World by Neil Gibb

Airbnb, Albert Einstein, blockchain, Buckminster Fuller, call centre, carbon footprint, Clayton Christensen, collapse of Lehman Brothers, corporate social responsibility, creative destruction, crowdsourcing, disruptive innovation, Donald Trump, gig economy, iterative process, job automation, Joseph Schumpeter, Khan Academy, Kibera, Kodak vs Instagram, Mark Zuckerberg, Menlo Park, Minecraft, Network effects, new economy, performance metric, ride hailing / ride sharing, shareholder value, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Jobs, the scientific method, Thomas Kuhn: the structure of scientific revolutions, trade route, urban renewal

It was a cold, damp day, but the group grew quickly. A rebellious energy coursed through their ranks. They were not agitators by disposition. They were skilled workers from the local textile industry. But they were very angry. The city’s manufacturing companies were introducing radical new technologies and working practices that were disrupting their jobs and livelihoods beyond recognition. Skilled jobs were being lost to automation. Salaried jobs were being replaced with zero-hour contracts. Wages were falling, jobs were disappearing, people were being laid off. At the same time, local business owners were getting extremely rich. On top of this, there was the shock of a new leader of what was then the world’s most powerful nation – George IV, King of the United Kingdom. Whereas his predecessor, George III, had been a liberal, thoughtful man, popular with the people, George IV was brash, impulsive, and divisive.


pages: 239 words: 62,311

Emotional design: why we love (or hate) everyday things by Donald A. Norman

A Pattern Language, crew resource management, Dean Kamen, industrial robot, job automation, Rodney Brooks, Vernor Vinge, Yogi Berra

Throughout history, each new wave of technology has displaced workers, but the total result has been increased life span and quality of living for everyone, including, in the end, increased jobs—although of a different nature than before. In transitional periods, however, people are displaced and unemployed, for the new jobs that result often require skills very distant from those of the people who have been displaced. This is a major social problem that must be addressed. In the past, most of the jobs replaced by automation have been lowlevel jobs, jobs that did not require much skill or education to perform. In the future, however, robots are apt to replace some highly skilled jobs. Will film actors be replaced by computer-generated characters that sound and act just as realistic, but are much more under the TLFeBOOK 208 Emotional Design control of the director? Will robot athletes compete, if not with humans, then perhaps in their own leagues—but thereby leading to the demise of human leagues?


pages: 267 words: 72,552

Reinventing Capitalism in the Age of Big Data by Viktor Mayer-Schönberger, Thomas Ramge

accounting loophole / creative accounting, Air France Flight 447, Airbnb, Alvin Roth, Atul Gawande, augmented reality, banking crisis, basic income, Bayesian statistics, bitcoin, blockchain, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, centralized clearinghouse, Checklist Manifesto, cloud computing, cognitive bias, conceptual framework, creative destruction, Daniel Kahneman / Amos Tversky, disruptive innovation, Donald Trump, double entry bookkeeping, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ford paid five dollars a day, Frederick Winslow Taylor, fundamental attribution error, George Akerlof, gig economy, Google Glasses, information asymmetry, interchangeable parts, invention of the telegraph, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, joint-stock company, Joseph Schumpeter, Kickstarter, knowledge worker, labor-force participation, land reform, lone genius, low cost airline, low cost carrier, Marc Andreessen, market bubble, market design, market fundamentalism, means of production, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, multi-sided market, natural language processing, Network effects, Norbert Wiener, offshore financial centre, Parag Khanna, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price mechanism, purchasing power parity, random walk, recommendation engine, Richard Thaler, ride hailing / ride sharing, Sam Altman, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, Snapchat, statistical model, Steve Jobs, technoutopianism, The Future of Employment, The Market for Lemons, The Nature of the Firm, transaction costs, universal basic income, William Langewiesche, Y Combinator

It’s just that the return on automation—the cost savings realized—must be higher than before. Ironically, therefore, a human-labor tax credit may stimulate efforts to develop technical advances that offer substantially higher cost efficiencies. Sectors ripe for automation may actually become more automated as a result of the tax credit. As the saying goes, this is not a bug but a feature. While encouraging job creation that is insulated from automation (at least in the medium term), it would stimulate further automation in those areas where humans are already in danger of being replaced by machines. And to the extent policy makers want to retain retraining and reskilling programs, these programs need to be designed so that they are eminently and swiftly adaptable. It is no longer sufficient to react to changes in demand for specific skills long after the fact; rather, reskilling programs need to fashion cutting-edge analytics of rich data, including data from large online talent markets such as LinkedIn, to spot changes in skill demand as they occur and reflect them in reskilling programs without undue time lags.


pages: 209 words: 80,086

The Global Auction: The Broken Promises of Education, Jobs, and Incomes by Phillip Brown, Hugh Lauder, David Ashton

active measures, affirmative action, barriers to entry, Branko Milanovic, BRICs, business process, business process outsourcing, call centre, collective bargaining, corporate governance, creative destruction, credit crunch, David Ricardo: comparative advantage, deindustrialization, deskilling, disruptive innovation, Frederick Winslow Taylor, full employment, future of work, glass ceiling, global supply chain, immigration reform, income inequality, industrial cluster, industrial robot, intangible asset, job automation, Joseph Schumpeter, knowledge economy, knowledge worker, low skilled workers, manufacturing employment, market bubble, market design, neoliberal agenda, new economy, Paul Samuelson, pensions crisis, post-industrial society, profit maximization, purchasing power parity, QWERTY keyboard, race to the bottom, Richard Florida, Ronald Reagan, shared worldview, shareholder value, Silicon Valley, sovereign wealth fund, stem cell, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, Thomas L Friedman, trade liberalization, transaction costs, trickle-down economics, winner-take-all economy, working poor, zero-sum game

This is an issue to which we will return in the final chapter, but there is another argument popular with American economists embracing the view that income inequalities are a result of changes in technologies. Is There a Hi-Tech Elephant in the Room? The idea that income inequalities are explained by the introduction of new technologies rather than global trade is intuitively attractive. It asserts that as new technologies are introduced into the workplace, some jobs are automated while more skilled workers are required to exploit the productive potential of new technologies. Widening income inequalities reflect the growing disparity in productivity achieved by high- as opposed to low-skill employees. “It seems undeniable,” suggests Paul Krugman, a Nobel Prize winner, “that the increase in the skill premium in the advanced world is primarily the result of skillbiased technological change.”35 Other luminaries like Lawrence Summers reaffirmed this view, claiming that “most of the observed increases in income inequality in the American economy are due to new technology rather than increased trade,” although he does recognize the threat of the global auction.36 The idea that new technologies usually demand higher levels of skill has led economists to present education and technology as a race in which the supply of educated workers needs to keep up with technology-led demand; otherwise, a shortage of skilled workers will lead to a polarization of incomes.37 A major study by Claudia Goldin and Lawrence Katz documents how the supply of educated workers kept pace with demand for much of the twentieth century, which they called the century of human capital.


pages: 396 words: 117,149

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, zero-sum game

If computers make us smarter, computers running the Master Algorithm will make us feel like geniuses. Technological progress will noticeably speed up, not just in computer science but in many different fields. This in turn will add to economic growth and speed poverty’s decline. With the Master Algorithm to help synthesize and distribute knowledge, the intelligence of an organization will be more than the sum of its parts, not less. Routine jobs will be automated and replaced by more interesting ones. Every job will be done better than it is today, whether by a better-trained human, a computer, or a combination of the two. Stock-market crashes will be fewer and smaller. With a fine grid of sensors covering the globe and learned models to make sense of its output moment by moment, we will no longer be flying blind; the health of our planet will take a turn for the better.

Speech recognition is hard for computers because it’s hard to fill in the blanks—literally, the sounds speakers routinely elide—when you have no idea what the person is talking about. Algorithms can predict stock fluctuations but have no clue how they relate to politics. The more context a job requires, the less likely a computer will be able to do it soon. Common sense is important not just because your mom taught you so, but because computers don’t have it. The best way to not lose your job is to automate it yourself. Then you’ll have time for all the parts of it that you didn’t before and that a computer won’t be able to do any time soon. (If there aren’t any, stay ahead of the curve and get a new job now.) If a computer has learned to do your job, don’t try to compete with it; harness it. H&R Block is still in business, but tax preparers’ jobs are much less dreary than they used to be, now that computers do most of the grunge work.


pages: 138 words: 40,525

pages: 289 words: 87,292

The Strange Order of Things: The Biological Roots of Culture by Antonio Damasio

Albert Einstein, biofilm, business process, Daniel Kahneman / Amos Tversky, double helix, Gordon Gekko, invention of the wheel, invention of writing, invisible hand, job automation, mental accounting, meta analysis, meta-analysis, microbiome, Norbert Wiener, pattern recognition, Peter Singer: altruism, planetary scale, profit motive, Ray Kurzweil, Richard Feynman, self-driving car, Silicon Valley, Steven Pinker, Thomas Malthus

To produce accounts of humanity that appear to diminish human dignity—even if they are not meant to do so—does not advance the human cause. Advancing the human cause is hardly the issue for those who believe that we are entering a “post-humanist” phase of history, a phase in which most human individuals have lost their usefulness to society. In the picture painted by Yuval Harari, when humans are no longer required to fight wars—cyber warfare can do that for them—and after humans have lost their jobs to automation, most of them will simply wither away. History will belong to those who will prevail by acquiring immortality—or at least long, long longevity—and who will remain to benefit from this arrangement. I say “benefit” rather than “enjoy” because I imagine that the status of their feelings will be murky.5 The philosopher Nick Bostrom provides another alternative vision, one in which very intelligent and destructive robots will actually take over the world and put an end to human misery.6 In either case, future lives and minds are presumed to depend at least in part on “electronic algorithms” that artificially simulate what “biochemical algorithms” currently do.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, M-Pesa, Marc Andreessen, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, offshore financial centre, open economy, Parag Khanna, paypal mafia, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, The Future of Employment, Travis Kalanick, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional

During the recent recession, one in twelve people working in sales in the United States was laid off. Two Oxford University professors who studied more than 700 detailed occupational types have published a study making the case that over half of US jobs could be at risk of computerization in the next two decades. Forty-seven percent of American jobs are at high risk for robot takeover, and another 19 percent face a medium level of risk. Those with jobs that are hard to automate—lawyers, for example—may be safe for now, but those with more easily automated white-collar jobs, like paralegals, are at high risk. In the greatest peril are the 60 percent of the US workforce whose main job function is to aggregate and apply information. When I was growing up, my mom worked as a paralegal at the Putnam County Courthouse in Winfield, West Virginia. Her job largely consisted of rummaging through enormous 15-pound books looking for specific information on old court cases and real estate closings.


pages: 243 words: 59,662

Free to Focus: A Total Productivity System to Achieve More by Doing Less by Michael Hyatt

"side hustle", Atul Gawande, Cal Newport, Checklist Manifesto, Donald Trump, Elon Musk, Frederick Winslow Taylor, informal economy, invention of the telegraph, Jeff Bezos, job automation, knowledge economy, knowledge worker, Parkinson's law, remote working, Steve Jobs, zero-sum game

One method of taking care of critical tasks with little investment of attention is automation. Normally, when I say automation, people assume I mean robots, apps, and macros. But it doesn’t take an engineer or a geek to benefit from automation. Every day jobs come up that we don’t have time to think about, yet they still need to get done. But who says you have to give the job your full attention? What if you could subtract yourself from the equation and still get the job done? That’s where automation comes in, and I like to think of the topic under four main headers: self-automation template automation process automation tech automation In this chapter we’ll look at all four and explore several key automation strategies that will enable you to put many of your Drudgery and Disinterest Zone tasks on autopilot. Self-Automation Your first step is automating yourself through a process of self-automation.


pages: 602 words: 177,874

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations by Thomas L. Friedman

3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Bob Noyce, business cycle, business process, call centre, centre right, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, corporate social responsibility, creative destruction, crowdsourcing, David Brooks, demand response, demographic dividend, demographic transition, Deng Xiaoping, Donald Trump, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, game design, gig economy, global pandemic, global supply chain, illegal immigration, immigration reform, income inequality, indoor plumbing, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the steam engine, inventory management, Irwin Jacobs: Qualcomm, Jeff Bezos, job automation, John Markoff, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, Live Aid, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Nelson Mandela, pattern recognition, planetary scale, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, Steve Jobs, supercomputer in your pocket, TaskRabbit, The Rise and Fall of American Growth, Thomas L Friedman, transaction costs, Transnistria, uber lyft, undersea cable, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra, zero-sum game

There is probably no one in America, or anywhere for that matter, who makes their living today producing buggy whips—not since the horse and buggy gave way to the automobile. But it is critical to remember that even 98 percent automation of a job is not the same as 100 percent automation. Why? In the nineteenth century, 98 percent of the labor involved in weaving a yard of cloth got automated. The task went from 100 percent manual labor to 2 percent. “And what happened?” asked Bessen. “The number of weaver jobs increased.” Why? “Because when you automate a job that has largely been done manually, you make it hugely more productive.” And when that happens, he explained, “prices go down and demand goes up” for the product. At the beginning of the nineteenth century, many people had one set of clothes—and they were all man-made. And by the end of that century, most people had multiple sets of clothing, drapes on their windows, rugs on their floors, and upholstery on their furniture.


pages: 588 words: 131,025

The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol

23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, commoditize, computer vision, conceptual framework, connected car, correlation does not imply causation, creative destruction, crowdsourcing, dark matter, data acquisition, disintermediation, disruptive innovation, don't be evil, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, RAND corporation, randomized controlled trial, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize

We’ve already seen some examples of how physicians react to the threat of being marginalized, along with their general reluctance to adapt to new technology. Now we get into the “Second Machine Age”101 question as to whether the new digital landscape will reboot the need for doctors and health professionals. Kevin Kelly, a cofounder of Wired, has asserted: “The role tasks of any information-intensive job can be automated. It doesn’t matter if you are a doctor, lawyer, architect, reporter, or even programmer: The robot takeover will be epic.”102 An emergency medicine physician likened the current practice of medicine to a Radio Shack store in his piece “Doctor Dinosaur: Physicians may not be exempt from extinction.”103 In late 2013, Korean doctors threatened to go on an all-out strike if the government went ahead with new telemedicine laws that would support clinical diagnoses to be made remotely.

On either end of it are intelligent human beings who are ready to assume quite different roles from what the history of medicine has established. Patients will always crave and need the human touch from a doctor, but that can be had on a more selective basis with the tools at hand. Instead of doctors being squeezed, resorting to computer automation can actually markedly expand their roles. As Kevin Kelly wrote, “the rote tasks of any information-intensive job can be automated. It doesn’t matter if you are a doctor, lawyer, architect, reporter, or even programmer.”102 The Economist weighed in on this too: “The machines are not just cleverer, but they also have access to far more data. The combination of big data and smart machines will take over some occupations wholesale.”153 But smart doctors need not feel threatened, for their occupation is secure. Letting go and competing on embracing digital medicine may turn out to be the best way to prevent disintermediation and disillusionment in the long run.


pages: 431 words: 129,071

Selfie: How We Became So Self-Obsessed and What It's Doing to Us by Will Storr

Albert Einstein, autonomous vehicles, banking crisis, bitcoin, computer age, correlation does not imply causation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Elon Musk, en.wikipedia.org, gig economy, greed is good, invisible hand, job automation, John Markoff, Kickstarter, longitudinal study, Lyft, Menlo Park, meta analysis, meta-analysis, Mont Pelerin Society, mortgage debt, Mother of all demos, Nixon shock, Peter Thiel, QWERTY keyboard, rising living standards, road to serfdom, Robert Gordon, Ronald Reagan, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Silicon Valley startup, Steve Jobs, Steven Levy, Stewart Brand, The Future of Employment, The Rise and Fall of American Growth, Tim Cook: Apple, Travis Kalanick, twin studies, Uber and Lyft, uber lyft, War on Poverty, Whole Earth Catalog

It was then that, feeling ignored and resentful (and, in many cases, racist), a number of working-class Democrats deserted the party for the Republicans. In 1964, 55 per cent of all working-class voters were Democrats. By 1980, that number had fallen to 35 per cent. Under the inequalities of neoliberalism, the white working class suffered. The new era of globalization it brought about saw some of the manufacturing and service industries they relied upon moving overseas. Many others lost their jobs because of automation, the effects of which a more collectively minded state might have sought to mitigate. Whilst plenty of people have become better off, since the 1970s, a good deal of others have seen the worth of their paychecks stall or fall. The average real income for the bottom 90 per cent of earners in the US, for example, has pretty much stagnated. It was $35,411 in 1972, it peaked at $37,053 in 2000 and, by 2013, had fallen to $31,652.

It did, facilitating and accelerating the neoliberal project of globalization immensely from the 1990s onwards. It has plenty in store for the future, too, with automation and artificial intelligence predicted to further decimate middle- and working-class jobs. There are 1.7 million truck drivers in the US alone whose livelihoods are at risk from the introduction of autonomous vehicles. Researchers at the University of Oxford have predicted that, by 2033, nearly half of all US jobs could be automated. The technologists promised us a ‘Long Boom’. They didn’t tell us that boom would be directed mostly at the top. It was another Silicon Valley product, social media, that enabled Donald Trump to connect directly with his supporters, bypassing traditional journalists and undermining their reporting by calling them liars. It’s true that some of his strongest support came from white males without a college degree, who are among those least likely to actually use the internet.


pages: 346 words: 89,180

Capitalism Without Capital: The Rise of the Intangible Economy by Jonathan Haskel, Stian Westlake

"Robert Solow", 23andMe, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, Andrei Shleifer, bank run, banking crisis, Bernie Sanders, business climate, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive bias, computer age, corporate governance, corporate raider, correlation does not imply causation, creative destruction, dark matter, Diane Coyle, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Glaeser, Elon Musk, endogenous growth, Erik Brynjolfsson, everywhere but in the productivity statistics, Fellow of the Royal Society, financial innovation, full employment, fundamental attribution error, future of work, Gini coefficient, Hernando de Soto, hiring and firing, income inequality, index card, indoor plumbing, intangible asset, Internet of things, Jane Jacobs, Jaron Lanier, job automation, Kenneth Arrow, Kickstarter, knowledge economy, knowledge worker, laissez-faire capitalism, liquidity trap, low skilled workers, Marc Andreessen, Mother of all demos, Network effects, new economy, open economy, patent troll, paypal mafia, Peter Thiel, pets.com, place-making, post-industrial society, Productivity paradox, quantitative hedge fund, rent-seeking, revision control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Sand Hill Road, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, software patent, sovereign wealth fund, spinning jenny, Steve Jobs, survivorship bias, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, total factor productivity, Tyler Cowen: Great Stagnation, urban planning, Vanguard fund, walkable city, X Prize, zero-sum game

Indeed, stories that technology would spell the end of employment and lead to social crisis have been a mainstay of economic punditry for over a century. Louis Anslow, an enterprising journalist, collected an archive of news stories to this effect, with examples dating back as early as the 1920s, including a speech by Albert Einstein in 1931 blaming the Great Depression on machines, and the British Prime Minister James Callaghan asking Downing Street civil servants to review the threat to jobs from automation shortly before he was ousted by Margaret Thatcher.2 All this suggests that while technology has the potential to displace jobs and create inequality, it ain’t necessarily so. The second challenge to the mainstream explanations of inequality comes from Piketty’s observation that the rise in wage inequality is very concentrated at the very top. In the United States, the gap in income between skilled and unskilled workers, which initially gave rise to explanations based on skills-biased technical change, stopped diverging in about 2000.


pages: 285 words: 86,853

What Algorithms Want: Imagination in the Age of Computing by Ed Finn

Airbnb, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, bitcoin,