Machine, Platform, Crowd: Harnessing Our Digital Future

by Andrew McAfee

created a system that could learn them on its own.

Alibaba, founded in 1999 by former schoolteacher Jack Ma and seventeen colleagues, acted as an online middleman connecting buyers and sellers. Its most popular sites were the Taobao Marketplace, where individuals and small businesses sold goods to consumers, and Tmall, where larger companies did the same.

By the end of 2016, the number of Chinese people using Alibaba’s apps every month was greater than the entire US population.

We will try to convince you that because of recent technological changes, companies need to rethink the balance between minds and machines, between products and platforms, and between the core and the crowd. The

machines would take care of basic math, record keeping, and data transmission. This would free up people to make decisions, exercise judgment, use their creativity and intuition, and interact with each other to solve problems and take care of customers.

era of nonstop paperwork that preceded it, when carts full of file folders traveled between people and departments. A disturbing

companies should think of themselves not as conducting tasks within departments (for example, buying raw material within a purchasing department), but instead as executing business processes—such as taking, assembling, and shipping a customer’s order—that inherently cut across departments.

The business process reengineering movement was accelerated in the mid-1990s by two advances: enterprise-wide information systems and the World Wide Web. Prior to the arrival of enterprise systems,* companies typically had a jumble of separate pieces of software, many of which were not linked. The larger the company, the worse the jumble was. Enterprise systems held out the promise of replacing the jumble with a single, large piece of software† explicitly designed to execute a particular set of cross-functional business processes. This software could be bought “off the shelf” from vendors like SAP and Oracle, then configured and customized to a degree.

Enterprise systems quickly took off; by one estimate, over 60% of the Fortune 1000 had adopted at least one of them by 1999. And while they could be quite expensive and time-consuming to install and maintain, they largely delivered on their promise.

The advent of the World Wide Web extended the reach and power of enterprise systems to individual consumers via their computers (and later their tablets and phones).

The web enabled companies to extend their business processes beyond the four walls of the company and all the way to the consumer—a trend that became known as e-commerce. People started to use the web not only to search for and learn about a company’s products, but also to order and pay for them.

For two decades, then, web-enabled enterprise systems have been facilitating more and more business processes by

doing the routine things: keeping track of account balances and transactions, calculating the right quantity and timing for raw-material deliveries, sending paychecks to employees, letting customers select and pay for products, and so on.

Erik worked with Lynn Wu, now a professor at the Wharton School, to develop a simple model that predicted housing

sales and prices. They used data from Google Trends, which reports how often words like “real estate agent,” “home loan,” “home prices,” and the like were searched for each month in each of the fifty US states.

They used their model to predict future housing sales and compared their forecasts to the published predictions made by experts at the National Association of Realtors. When the results came in, their model beat the experts by a whopping 23.6%, reflecting t...

Today, examples of valuable, high-quality, 100% automatic decisions are all around us. Amazon and other e-commerce sites generate recommendations for each shopper on each visit, and while many of them miss the mark, others are pretty

compelling. Amazon, for example, estimates that 35% of its sales are from cross-selling activities such as recommending items.

As more and more of the world’s information becomes digitized, it provides a plethora of data for improving decision making, converting intuition into data-driven decision making.

instead of having machines provide data as an input to human judgment, they’re having judgment serve as an input to an algorithm.

Google is pioneering this approach in hiring, a critical area for the company, but one where analysis showed that the standard arrangement was working poorly.

While Laszlo Bock was head of People Operations at Google, he came to realize that most of the techniques then being used to select...

Among excellent companies a fundamental shift is taking place: away from long-range forecast, long-term plans, and big bets, and toward constant short-term iteration, experimentation, and testing.

It’s relatively straightforward to put this vision into

practice with a website. Because websites gather such rich data on user activities, it’s easy to see whether a given change was for the better.

The evidence is overwhelming that, whenever the option is available, relying on data and algorithms alone usually leads to better decisions and forecasts than relying on the

judgment of even experienced and “expert” humans.

Many decisions, judgments, and forecasts now made by humans should be turned over to algorithms. In some cases, people should remain in the loop to provide commonsense checks. In others, they should be taken out of the loop entirely.

Algorithms are far from perfect. If they are based on inaccurate or biased data, they will make inaccurate or biased decisions.

As technology has spread, so have opportunities to move past the standard partnership and its overreliance on human HiPPOs, and to move toward more data-driven decision making. The data show that companies that do this usually have an important advantage over those that do not.

People who can look at an issue from multiple perspectives and companies that can iterate and experiment effectively are better performers.

WITHIN ONE GENERATION, SEVERAL LONG-STANDING INDUSTRIES were transformed permanently and deeply by a single computer network. The business world has rarely, if ever, seen disruption at this speed and scale before.

there have been technology-driven industrial revolutions before, based around advances like the steam engine and electrification, but they

took longer to unfold and arguably didn’t affect as many parts of the global economy.

Perhaps the best way to grasp the impact of the Internet is to consider where things stood about twenty years ago.

Demand for recorded music, especially from the pop era’s most iconic performers, seemed robust enough to engender creative financing.

In 1997, David Bowie and investment banker David Pullman teamed up to offer “Bowie bonds,” a novel security backed by sales from the artist’s extensive catalog of music, which spanned twenty-one years and twenty-five albums at that point. The bonds quickly sold out, raising $55 million, and inspired other artists, from Iron Maiden to Rod Stewart and James Brown, to try something similar.

waves of painful changes have arrived since the mid-1990s.

Two thousand seven was the first year in half a century that a new indoor mall didn’t open somewhere in the United States.

Between 2005 and 2015, 20% of US shopping malls closed, and companies that specialize in building and maintaining them faced financial distress.

marginal cost is approaching zero.

The economic power of information goods increases once a network is available because networks add a critical third attribute: instant. Networks allow distribution of a free, perfect copy of information goods from one place to another, or from one place to many, virtually immediately.

Internet’s architecture is, in fundamental ways, indifferent to physical separation, leading to what the journalist Francis Cairncross has called “the death of distance” as a factor limiting the spread of information.

Imagine trying to run a physical newspaper or music retailer against a rival that could replicate and distribute the same products freely, perfectly, and instantly.

For most of history, few, if any, goods and services have been free, perfect, and instant. But with digital, networked goods, these three properties are automatic.

Platforms are online environments that take advantage of the economics of free, perfect, and instant. To be more precise, a platform can be defined as a digital environment characterized by near-zero marginal cost** of access, reproduction, and distribution.

The Internet, of course, is the platform most familiar to most of us, and the one responsible for the industrial disruptions we described earlier.

it is a platform of p...

an important feature of platforms: they can often be built on...

The World Wide Web, for example, is a multimedia, easy-to-navigate platform built on top of the original Internet...