Written by Erik Brynjolfsson & Andrew McAfee

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

“the discovery by artificial intelligence and robotics researchers that, contrary to traditional assumptions, high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources.”28* Moravec’s insight is broadly accurate, and important. As the cognitive scientist Steven Pinker puts it, “The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. . . .
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This opens up the opportunity for innovations that venture capitalist John Doerr calls “SoLoMo”—social, local, and mobile.36
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In particular, we need to understand its three key characteristics: that it is exponential, digital, and combinatorial. The next three chapters will discuss each of these in turn.
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simultaneous localization and mapping, which they refer to as SLAM.
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The Kinect sold more than eight million units in the sixty days after its release (more than either the iPhone or iPad) and currently holds the Guinness World Record for the fastest-selling consumer electronics device of all time.
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steady exponential improvement has brought us into the second half of the chessboard—into a time when what’s come before is no longer a particularly reliable guide to what will happen next.
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It has also exploded in volume, velocity, and variety.
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Making things free, perfect, and instant might seem like unreasonable expectations for most products, but as more information is digitized, more products will fall into these categories.
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One of the main reasons we cite digitization as a main force shaping the second machine age is that digitization increases understanding. It does this by making huge amounts of data readily accessible, and data are the lifeblood of science. By “science” here, we mean the work of formulating theories and hypotheses, then evaluating them. Or, less formally, guessing how something works, then checking to see if the guess is right.
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They concluded that “this work shows how social media expresses a collective wisdom which, when properly tapped, can yield an extremely powerful and accurate indicator of future outcomes.”17
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Digital information isn’t just the lifeblood for new kinds of science; it’s the second fundamental force (after exponential improvement) shaping the second machine age because of its role in fostering innovation. Waze
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the novelist William Makepeace Thackeray observed, “Novelty has charms that our mind can hardly withstand.”
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With their typical verbal flair, economists call innovations like steam power and electricity general purpose technologies (GPTs).
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At present, says Cowen, “The gains of the Internet are very real and I am here to praise them, not damn them. . . . Still, the overall picture is this: We are having more fun, in part because of the Internet. We are also having more cheap fun. [But] we are coming up short on the revenue side, so it is harder to pay our debts, whether individuals, businesses, or governments.”11 Twenty-first century ICT, in short, is failing the prime test of being economically significant.
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Another school of thought, though, holds that the true work of innovation is not coming up with something big and new, but instead recombining things that already exist. And the more closely we look at how major steps forward in our knowledge and ability to accomplish things have actually occurred, the more this recombinant view makes sense.
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“To invent something is to find it in what previously exists.”14 Economist
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“The Gross National Product does not include the beauty of our poetry or the intelligence of our public debate. It measures neither our wit nor our courage, neither our wisdom nor our
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learning, neither our compassion nor our devotion. It measures everything, in short, except that which makes life worthwhile.” —Robert F. Kennedy
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How do we measure the benefits of free goods or services that were unavailable at any price in previous eras?
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As these examples show, our economic welfare is only loosely related to GDP.
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To get a sense on the scale of this effort, consider that last year users collectively spent about 200 million hours each day just on Facebook, much of it creating content for other users to consume.13 That’s ten times as many person-hours as were needed to build the entire Panama Canal.
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Production in the second machine age depends less on physical equipment and structures and more on the four categories of intangible assets: intellectual property, organizational capital, user-generated content, and human capital.
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Important as these intangible assets are, the official GDP ignores them. User-generated content, for example, involves unmeasured labor creating an unmeasured asset that is consumed in unmeasured ways to create unmeasured consumer surplus.
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We shouldn’t ignore the economic metrics, but neither should we let them crowd out our other values simply because they are more measurable.
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The number of people enrolled in college more than doubled between 1960 and 1980,
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David Autor suggests that work can be divided into a two-by-two matrix: cognitive versus manual and routine versus nonroutine.
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In other words, the top 0.01 percent now get a bigger share of the top 1 percent of income than the top 1 percent get of the whole economy.
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“When a sergeant makes a mistake only the platoon suffers, but when a general makes a mistake the whole army suffers.”
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Winner-take-all markets were just coming to the fore in the 1990s, when Frank and Cook wrote their remarkably prescient book. They compared these winner-take-all markets, where the compensation was mainly determined by relative performance, to traditional markets, where revenues more closely tracked absolute performance.
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“The era of bell curve distributions that supported a bulging social middle class is over and we are headed for the power-law distribution of economic opportunities. Education per se is not going to make up the difference.”26 Such a shift disrupts our mental models for understanding the world. Most of us are used to reasoning by reference to a prototypical.
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Power-law distributions don’t just increase income inequality; they also mess with our intuitions.
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Consider the electronic products that every middle-class teenager can now afford—iPhones, iPads, iPods and laptop computers. They aren’t much inferior to the electronic gadgets now used by the top 1% of American income earners, and often they are exactly the same.
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However, when one looks more closely at the data, the globalization story becomes much less compelling. Since 1996, manufacturing employment in China itself has actually fallen as well, coincidentally by an estimated 25 percent.29 That’s over thirty million fewer Chinese workers in that sector, even while output soared by 70 percent. It’s not that American workers are being replaced by Chinese workers. It’s that both American and Chinese workers are being made more efficient by automation. As a result, both countries are producing more output with fewer workers.
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“But they are useless. They can only give you answers.” —Pablo Picasso, on computers1
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Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.
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As we look across examples of things we haven’t seen computers do yet, this idea of the “new idea” keeps recurring.
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We’ve seen software that could create lines of English text that rhymed, but none that could write a true poem (“the spontaneous overflow of powerful feelings, recollected in tranquility,” as Wordsworth described it).
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we believe that employers now and for some time to come will, when looking for talent, follow the advice
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attributed to the Enlightenment sage Voltaire: “Judge a man by his questions, not his answers.”
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As futurist Kevin Kelly put it “You’ll be paid in the future based on how well you work with robots.”7
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Our recommendations about how people can remain valuable knowledge workers in the new machine age are straightforward: work to improve the skills of ideation, large-frame pattern recognition, and complex communication instead of just the three Rs. And whenever possible, take advantage of self-organizing learning environments,
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The college premium exists in part because so many types of raw data are getting dramatically cheaper, and as data get cheaper, the bottleneck increasingly is the ability to interpret and use data. This reflects the career advice that Google chief economist Hal Varian frequently gives: seek to be an indispensable complement to something that’s getting cheap and plentiful.
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In the future, more and more careers will not be in pure information work—the kind that can be done entirely from a desk. Instead, they will include moving through and interacting with the physical world. This is because computers remain comparatively weak here, even as they get so much stronger at many cognitive tasks.
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surprised when the data revealed that half of his students started working on their homework assignments before watching the video lectures. Students were more motivated to really understand the content of the lecture once they saw the specific challenges that they would learn how to overcome.
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“We need digital models of learning and teaching. Not just a technology overlay on old modes of teaching and learning.”
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Lengthening the school year may be especially beneficial for poor kids, since research suggests that rich and poor children learn at a similar rate when school is in session, but that poor children fall behind over the summer when they are not in school.
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Schumpeter put forward our favorite definition of innovation—“the market introduction of a technical or organisational novelty, not just its invention”—and, like us, believed that it was an essentially recombinant process,
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He found that for all but seven years between 1977 and 2005, existing firms as a group were net job destroyers, losing an average of approximately one million jobs annually.13 Startups, in sharp contrast, created on average a net three million jobs per year.
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The change was especially pronounced in Silicon Valley, where over half of companies founded from 1995 to 2005 had at least one immigrant founder. Between 2006 and 2012, that percentage dropped almost ten points, to 43.9 percent.
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An intriguing example is the work that Steve Case and the Kauffman Foundation are doing with the Startup America Partnership. It seeks to support over thirty entrepreneur-led startup regions, complete with a ‘dating site’ to make it easier for new ventures to partner with Fortune 500 firms that can complement their innovations
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This funding should be continued, and the recent dispiriting trend of reduced federal funding for basic research in America should be reversed.
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We should also reform the U.S. intellectual property regime, particularly when it comes to software patents and copyright duration.
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provides a great playbook for how to run a prize:21 1. Shine a spotlight on a problem or opportunity 2. Pay only for results 3. Target an ambitious goal without predicting which team or approach is most likely to succeed 4. Reach beyond usual suspects to tap top talent 5. Stimulate private-sector investment many times greater than the prize purse 6. Bring out-of-discipline perspectives to bear 7. Inspire risk taking by offering a level playing field 8. Establish clear target metrics and validation protocols
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Australia, the UK, and Chile have all launched programs to attract early-stage entrepreneur immigrants, and in January 2013 Canada announced a full-fledged startup visa program, the first of its kind in the world.36 Meanwhile, comprehensive immigration reform stalled in the U.S. Congress in the summer of that same year.
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In all likelihood, we could raise more revenue by increasing marginal tax rates on the highest income earners, for instance by introducing new tax brackets at the one-million- and ten-million-dollar levels of annual income. We do not find much evidence supporting the counter-argument that higher taxes on this population will harm economic growth by eroding high earners’ initiative. In fact, research by our MIT colleague and
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Winston Churchill said that, “Democracy is the worst form of government except for all those others that have been tried.”2 We believe the same about capitalism.
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Many of them have proposed the same simple solution: give people money. The
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If people want to improve on it by working, investing, starting a company, or doing any of the other activities of the capitalist engine they certainly can, but even if they don’t they will still be able to act as consumers, since they will still receive money.
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Daniel Pink summarizes the three key motivations from the research literature: mastery, autonomy, and purpose.7
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William Julius Wilson summarized a long career’s worth of findings in his 1996 book When Work Disappears. His conclusions are unequivocal: The consequences of high neighborhood joblessness are more devastating than those of high neighborhood poverty. A neighborhood in which people are poor but employed is different from a neighborhood in which many people are poor and jobless.
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We bring up these points not because we dislike Social Security or health care coverage. We like both of them a great deal and want them to continue. We simply point out that these and other popular programs are financed, in whole or in part, by taxes on labor. This might have been an appropriate idea when there were no viable alternatives to humans for most jobs, but that is no longer the case. The better machines become at substituting for human labor, the bigger negative effect any tax or mandate will have on human employment.
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So it’s not the case that people cease to be valuable the instant computers surpass them in a domain. They can be enormously useful once they’ve paired up to race with machines, instead of against them.
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The Mechanical Turk software was similar to this automaton in that it too appeared to accomplish tasks automatically, but in reality made use of human labor. It was an example of what Amazon CEO Jeff Bezos called “artificial artificial intelligence,” and another way for people to race with machines,
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Mechanical Turk, which quickly became popular, was an early instance of what came to be called crowdsourcing, defined by communications scholar Daren Brabham as “an online, distributed problem-solving and production model.”27 This model is interesting because instead of using technology to automate a process, crowdsourcing makes it deliberately labor intensive.
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We like the efficiency gains and price declines that crowdsourcing brings, but we also like the work that it brings. Participation in services like TaskRabbit and Airbnb gives people previously unavailable economic opportunities, and it also gives them something to do. It therefore has the potential to address all three of Voltaire’s “great evils,”
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Start a ‘made by humans’ labeling movement, similar to those now in place for organic foods, or award credits for companies that employ humans, similar to the carbon offsets that can be purchased. If some consumers wanted to increase the demand for human workers, such labels or credits would let them do so.
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Any system this complex and tightly coupled has two related weaknesses. First, it’s subject to seeing minor initial flaws cascade via an unpredictable sequence into something much larger and more damaging.
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Second, complex, tightly coupled systems make tempting targets for spies, criminals, and those who seek to wreak havoc.
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Until recently, our species did not have the ability to destroy itself. Today it does. What’s more, that power will reach the hands of more and more individuals as technologies become both more powerful and cheaper—and thus more ubiquitous. Not all of those individuals will be both sane and well intentioned.
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genetic engineering and artificial intelligence can create self-replicating entities.
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As we were researching this book we heard similar sentiments from most of the innovators we talked to. Most of them weren’t trying to unravel the mysteries of human consciousness or understand exactly how we think; they were trying to solve problems and seize opportunities. As they did so, they sometimes came up with technologies that had human-like skills and abilities. But these tools themselves were not like humans at all.
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the real promise of the second machine age is to help unleash the power of human ingenuity.
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