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

by Erik Brynjolfsson

So ideation, large-frame pattern recognition, and the most complex forms of communication are cognitive areas where people still seem to have the advantage, and also seem likely to hold on to it for some time to come. Unfortunately, though, these skills are not emphasized in most educational environments today. Instead, primary education often focuses on rote memorization of facts, and on the skills of reading, writing, and arithmetic—the ‘three Rs,’

I tried to look at where did the kind of learning we do in schools, where did it come from? . . . It came from . . . the last and the biggest of the empires on this planet, [the British Empire]. What they did was amazing. They created a global computer made up of people. It’s still with us today. It’s called the bureaucratic administrative machine.

Mitra’s work shows that children, even poor and uneducated ones, can learn to read discerningly. The children in his studies form teams, use technology to search broadly for relevant information, discuss what they’re learning with one another, and eventually come up with new (to them) ideas that very often turn out to be correct. In other words, they acquire and demonstrate the skills of ideation, broad-frame pattern recognition, and complex communication. So the “self-organizing learning environments” (SOLEs) Mitra observed seem to be teaching children the skills that will give them advantages over digital labor.

Montessori classrooms emphasize self-directed learning, hands-on engagement with a wide variety of materials (including plants and animals), and a largely unstructured school day. And in recent years they’ve produced alumni including the founders of Google (Larry Page and Sergey Brin), Amazon (Jeff Bezos), and Wikipedia (Jimmy Wales).

Management researchers Jeffrey Dyer and Hal Gregersen interviewed five hundred prominent innovators and found that a disproportionate number of them also went to Montessori schools, where “they learned to follow their curiosity.”

“part of that training [was] not following rules and orders, and being self-motivated, questioning what’s going on in the world, doing things a little bit differently.”

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, which have a track record of developing these skills in people.

Collegiate Learning Assessment (CLA),

Although the CLA is administered via computer, it requires essays instead of multiple-choice answers. One of its main components is the ‘performance task,’ which presents students with a set of background documents and gives them ninety minutes to write an essay requiring them to extract information from the materials given and develop a point of view or recommendation. In short, the performance task is a good test of ideation, pattern recognition, and complex communication.

Their findings are alarming: 45 percent of students demonstrate no significant improvement on the CLA after two years of college, and 36 percent did not improve at all even after four years.

some students show great improvement on the CLA. In general, these are students who spent more time studying (especially studying alone), took courses with more required reading and writing, and had more demanding faculty.

“the impact of college is largely determined by individual effort and involvement in the academic, interpersonal, and extracurricular offerings on a campus.”14 This work leads directly to our most fundamental recommendation to students and their parents: study hard, using technology and all other available resources to ‘fill up your toolkit’ and acquire skills and abilities that will be needed in the second machine age.

Acquiring an excellent education is the best way to not be left behind as technology races ahead. The discouraging news is that today many students seem to be squandering at least some of their educational opportunities. The good news, though, is that technology is now providing more of these opportunities than ever before. Motivated students and modern technologies are a formidable combination. The best educational resources available online allow users to create self-organized and self-paced learning environments—ones that allow them to spend as much time as they need with the material, and also to take tests that tell them if they mastered

In chapter 9, we described the growing gap in earnings between those with and without college degrees. Our MIT colleague David Autor summarizes the research by writing that “large payoffs from schooling are increasingly associated with the attainment of four-year and postcollege degrees. . . . Workers with less than a college education cluster relatively closer together in the earnings distribution while the most educated groups pull away.”

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. Examples include data scientists, writers of mobile phone apps, and genetic counselors, who have come into demand as more people have their genes sequenced. Bill Gates has said that he chose to go into software when he saw how cheap and ubiquitous computers, especially microcomputers, were becoming. Jeff Bezos systematically analyzed the bottlenecks and opportunities created by low-cost online commerce, particularly the ability to index large numbers of products, before he set up Amazon. Today, the cognitive skills of college graduates—including not only science, technology, engineering, and math, the so-called STEM disciplines, but also humanities, arts, and social sciences—are often complements to low-cost data and cheap computer power. This helps them command a premium wage.

However, another part of the college premium is less encouraging. More and more employers are requiring college degrees, even for entry-level jobs. As Rampell writes, “The college degree is becoming the new high school diploma: the new minimum requirement, albeit an expensive one, for getting even the lowest-level job. . . . Across industries and geographic areas, many other jobs that didn’t used to require a diploma—positions like dental hygienists, cargo agents, clerks and claims adjusters—are increasingly requiring one.”19 This ‘degree inflation’ is troubling because a college education is expensive and causes many people to go into debt. By the end of 2011, in fact, student loan debt in America was greater than either total outstanding car loans or credit card debt.20 We hope that MOOCs and other educational innovations eventually provide a lower-cost alternative to traditional colleges, and one that is taken seriously by employers, but until that time comes a college degree remains a vital stepping stone to most careers.

but a towel-folding robot illustrates how far we are from cracking Moravec’s paradox.

the robot successfully grasped and folded the towels, even though it sometimes took more than one try to grab them correctly. However, it took an average of 1,478 seconds, or more than twenty-four minutes, per

Results like these indicate that cooks, gardeners, repairmen, carpenters, dentists, and home health aides are not about to be replaced by machines in the short term. All of these professions involve a lot of sensorimotor work, and many of them also require the skills of ideation, large-frame pattern recognition, and complex communication. Not all of these jobs are well paying, but they’re also not subject to a head-to-head race against the machine.

They may, however, be subject to more competition among people. As the labor market polarizes more and the middle class continues to hollow out, people who were previously doing mid-skill knowledge work start going after jobs lower on the skill and wage ladder.

We’re very confident that more surprises are in store. After spending time working with leading technologists and watching one bastion of human uniqueness after another fall before the inexorable onslaught of innovation, it’s becoming harder and harder to have confidence that any given task will be indefinitely resistant to automation. That means people will need to be more adaptable and flexible in their career aspirations, ready to move on from areas that become subject to automation, and seize new opportunities where machines complement and augment human capabilities. Maybe we’ll see a program that can scan the business landscape, spot an opportunity, and write up a business plan so good it’ll have venture capitalists ready to invest. Maybe we’ll see a computer that can write a thoughtful and insightful report on a complicated topic. Maybe we’ll see an automatic medical diagnostician with all the different kinds of knowledge and awareness of a human doctor. And maybe we’ll see a computer that can walk up the stairs to an elderly woman’s apartment, take her blood pressure, draw blood, and ask if she’s been taking her medication, all while putting her at ease instead of terrifying her. We don’t think any of these advances is likely to come any time soon, but we’ve also learned that it’s very easy to underestimate the power of digital, exponential, and combinatorial innovation. So never say never.