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

by Erik Brynjolfsson

According to Cisco Systems, worldwide Internet traffic increased by a factor of twelve in just the five years between 2006 and 2011, reaching 23.9 exabytes per month.12 An exabyte is a ridiculously big number, the equivalent of more than two hundred thousand of Watson’s entire database.

nineteenth General Conference on Weights and Measures, the largest one was yotta, signifying one septillion, or 1024.15 We’re only one prefix away from that in the ‘zettabyte era.’

William Makepeace Thackeray observed, “Novelty has charms that our mind can hardly withstand.”

‘open innovation’ and ‘crowdsourcing,’

Innocentive,

Kaggle

Quirky,

Affinnova,

sustained exponential improvement in most aspects of computing, extraordinarily large amounts of digitized information, and recombinant innovation.

Dont forget the improved marketplaces improved reach. And bidding and surge pricing automation.

We do have one quibble with Simon, however. He wrote that, “The main fuel to speed the world’s progress is our stock of knowledge, and the brake is our lack of imagination.”8 We agree about the fuel but disagree about the brake. The main impediment to progress has been that, until quite recently, a sizable portion of the world’s people had no effective way to access the world’s stock of knowledge or to add to it.

In the industrialized West we have long been accustomed to having libraries, telephones, and computers at our disposal, but these have been unimaginable luxuries to the people of the developing world. That situation is rapidly changing. In 2000, for example, there were approximately seven hundred million mobile phone subscriptions in the world, fewer than 30 percent of which were in developing countries.9 By 2012 there were more than six billion subscriptions, over 75 percent of which were in the developing world. The World Bank estimates that three-quarters of the people on the planet now have access to a mobile phone, and that in some countries mobile telephony is more widespread than electricity or clean water.

Less than ten years after its introduction, entrepreneurs were finding ways to use the Web to reinvent publishing and retailing.

When i say automation kills jobs, i dont necesarily mean robots taking jobs. Part of it is just plain killing monopolies. The internet did it to publishing, retailing, news, hotels, and cabs....

Walmart drove remarkable efficiencies in retailing by introducing systems that shared point-of-sale data with their suppliers. The real key was the introduction of complementary process innovations like vendor managed inventory, cross-docking, and efficient consumer response

(not explained by growth of inputs) will get smaller.

More and more what we care about in the second machine age are ideas, not things—mind, not matter; bits, not atoms; and interactions, not transactions.

Analog dollars are becoming digital pennies.

A simple switch to using a free texting service like Apple’s iChat instead of SMS, free classifieds like Craigslist instead of newspaper ads, or free calls like Skype instead of a traditional telephone service can make billions of dollars disappear from companies’ revenues and the GDP statistics.5

A leader whose popularity benefits from americas bad news will be bad news for us

With a greater volume of digital goods introduced each year that do not have a dollar price, this traditional GDP heuristic is becoming less useful. As we discussed in chapter 4, the second machine age is often described as an “information economy,” and with good reason. More people than ever are using Wikipedia, Facebook, Craigslist, Pandora, Hulu, and Google, with thousands of new digital goods introduced each year. The U.S. Bureau of Economic Analysis defines the information sector’s contribution to the economy as the sum of the sales of software, publishing, motion pictures, sound recording, broadcasting, telecommunications, and information and data processing services. According to the official measures, these account for just 4 percent of our GDP today, almost precisely the same share of GDP as in the late 1980s, before the World Wide Web was even invented. But clearly this isn’t right. The official statistics are missing a growing share of the real value created in our economy.

Good point

In order to consume YouTube, Facebook, or e-mail, we must ‘pay’ attention. In fact, Americans nearly doubled the amount of leisure time they spent on Internet between 2000 and 2011. This implies that they valued it more than the other ways they could spend their time. By considering the value of users’ time and comparing leisure time spent on the Internet to time spent in other ways, Erik and Joo Hee estimated that the Internet created about $2,600 of value per user each year. None of this showed up in the GDP statistics but if it had, GDP growth—and thus productivity growth—would have been about 0.3 percent higher each year. In other words, instead of the reported 1.2 percent productivity growth for 2012, it would have been 1.5 percent. In contrast

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.14 None of this is counted in our GDP statistics as either input or output, but these kinds of zero-wage and zero-price activities still contribute to welfare. Researchers like Luis von Ahn at Carnegie Mellon are working on ways of motivating and organizing millions of people to create value via collective projects on the Internet.15

Ahn, creating user work force

There have even been substantial increases in the number of stock keeping units (SKUs) in most physical stores as computerized inventory management systems, supply chains, and manufacturing have become more efficient and flexible.

In the past, ignorance protected inefficient or lower-quality sellers from being unmasked by unsuspecting consumers, while geography limited competition from other sellers.

No longer can a seller of substandard services expect to feed on a continuing stream of naïve or ill-informed consumers. No longer can the seller expect to be insulated from competitors in other locations who can deliver a better service for less. Research by Michael Luca of Harvard Business School has found that the increased transparency has helped smaller independent restaurants compete with bigger chains because customers can more quickly find quality food via rating services like Yelp, reducing their reliance on brand names’ expensive marketing campaigns.

So interesting

better matches, timeliness, customer service, and increased convenience—are exactly the types of benefits identified by the 1996 Boskin Commission as being poorly measured in our official price and GDP statistics.18 This is another way in which our true growth is greater than the standard data suggest.

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.

Key

even larger—category of intangibles is organizational capital like new business processes, techniques of production, organizational forms, and business models. Effective uses of the new technologies of the second machine age almost invariably require changes in the organization of work. For instance, when companies spend millions of dollars on computer hardware and software for a new enterprise resource planning system, they typically also include process changes that are three to five times as costly as the original investments in hardware and software. Yet, while the hardware and software spending generally shows up as additions to the nation’s capital stock, the new business processes, which often outlast the hardware, are generally not counted as capital. Our research suggests that a correct accounting for computer-related intangible assets would add over $2 trillion to the official estimates of the capital assets in the United States economy.

User-generated content is a smaller but rapidly growing third category of intangible assets.

There are 43,200 hours of new YouTube videos created each day,22 as well as 250 million new photos uploaded each day on Facebook.23 Users also contribute valuable but unmeasured content in the form of reviews on sites like Amazon, TripAdvisor, and Yelp.

investments in human capital will be increasingly important as routine tasks become automated and the need for human creativity increases.

Very important

“An imbalance between rich and poor is the oldest and most fatal ailment of all republics.” —Plutarch

OF THE 3.5 TRILLION photos that have been snapped since the first image of a busy Parisian street in 1838, fully 10 percent were taken in the last year.

Wow

it has been estimated that more photos are now taken every two minutes than in all of the nineteenth century.

The combination of bounty and spread challenges two common though contradictory worldviews. One common view is that advances in technology always boost incomes. The other is that automation hurts workers’ wages as people are replaced by machines. Both of these have a kernel of truth, but the reality is more subtle.

A good place to start is median income—the income of the person at the fiftieth percentile of the total distribution. The year 1999 was the peak year for the real (inflation-adjusted) income of the median American household. It reached $54,932 that year, but then started falling. By 2011, it had fallen nearly 10 percent to $50,054, even as overall GDP hit a record high. In particular, wages of unskilled workers in the United States and other advanced countries have trended downward. Meanwhile, for the first time since before the Great Depression, over half the total income in the United States went to the top 10 percent of Americans in 2012. The top 1 percent earned over 22 percent of income, more than doubling their share since the early 1980s. The share of income going to the top hundredth of one percent of Americans, a few thousand people with annual incomes over $11 million, is now at 5.5 percent, after increasing more between 2011 and 2012 than any year since 1927–28.9

Between 1983 and 2009, Americans became vastly wealthier overall as the total value of their assets increased. However, as noted by economists Ed Wolff and Sylvia Allegretto, the bottom 80 percent of the income distribution actually saw a net decrease in their wealth.

an oft-cited example, by 2010 the six heirs of Sam Walton’s fortune, earned when he created Walmart, had more net wealth than the bottom 40 percent of the income distribution in America.12 In part, this reflects the fact that thirteen million families had a negative net worth.

By contrast, technologies like big data and analytics, high-speed communications, and rapid prototyping have augmented the contributions made by more abstract and data-driven reasoning, and in turn have increased the value of people with the right engineering, creative, or design skills. The net effect has been to decrease demand for less skilled labor while increasing the demand for skilled labor.

The number of people enrolled in college more than doubled between 1960 and 1980, from 758,000 to 1,589,000.19 In other words, there was a large increase in the supply of educated labor. Normally, greater supply leads to lower prices. In this case, the flood of graduates from college and graduate school should have pushed down their relative wages, but it didn’t.

thinning. The lack of demand for unskilled workers meant ever-lower wages for those who continued to compete for low-skill jobs. And because most of the people with the least education already had the lowest wages, this change increased overall income inequality.

The reorganization often eliminates a lot of routine work, such as repetitive order entry, leaving behind a residual set of tasks that require relatively more judgment, skills, and training.

demand for routine cognitive tasks such as cashiers, mail clerks, and bank tellers and routine manual tasks such as machine operators, cement masons, and dressmakers was not only falling, but falling at an accelerating rate. These jobs fell by 5.6 percent between 1981 and 1991, 6.6 percent between 1991 and 2001, and 11 percent between 2001 and 2011.26 In contrast, both nonroutine cognitive work and nonroutine manual work grew in all three decades.

Oy

However, economic theory also holds open the possibility that the remaining workers would see an increase in pay. In particular, if their work complements the technology, then demand for their services will increase. In addition, as technical advances increase labor productivity, employers can afford to pay more for each worker. In some cases, this is reflected directly in higher wages and benefits. In other cases, the prices of products and services fall, so the real wage of workers increases as they are able to buy more with each dollar. As productivity improves, total amount of output per person would increase but the amount earned by human workers could either fall or rise, with the remainder going to capital owners.

course, almost every economy has been using technology to substitute capital for labor for decades, if not centuries. Automatic threshing machines replaced a full 30 percent of the agricultural labor force in the middle of the nineteenth century,

Nineteenth-century economists like Karl Marx and David Ricardo predicted that the mechanization of the economy would worsen the fate of workers, ultimately driving them to a subsistence wage.

Sure

If productivity is growing and labor as a whole isn’t capturing the value, who is? Owners of physical capital, to a large extent.

What’s more, the collapse in the share of GDP going to labor actually understates how the situation has deteriorated for the typical worker. The official measure of labor compensation includes soaring wages for a small number of superstars in media, finance, sports, and corporate positions. Furthermore, it is debatable that all of the compensation going to CEOs and other top executives is solely due to their ‘labor’ income. It may also reflect their bargaining power, as suggested by Harvard Law Professor Lucian Bebchuk and others.40 In this sense, it might make sense to think of CEOs’ income as due to their control of capital, not labor, at least in part.

Ceos income is part of their own control of capital

the share of national income to capital has been growing at the expense of labor, economic theory does not necessarily predict that this will continue, even if robots and other machines take over more and more work. The threat to capital’s share comes not (just) from the bargaining power of various types of human labor, from CEOs or labor unions but, ironically, from other capital.

In addition, capital-biased technological changes that encourage substitution of physical capital for labor have increased the profits earned by capital owners and reduced the share of income going to labor. In each case, historic amounts of wealth have been created. In each case, we also have seen increases in the earnings of the winners relative to the losers.

Point one

talent-biased technical change.* In many industries, the difference in payout between number one and second-best has widened into a canyon. As a controversial Nike ad noted, you don’t win silver, you lose gold.1 When ‘winner-take-all’ markets become more important, income inequality will rise because pay at the very top pulls away from pay in the middle.

Point two

As noted earlier, between 2002 and 2007, the top 1 percent got two-thirds of all the profits from the growth in the U.S. economy.