Machine, Platform, Crowd: Harnessing Our Digital Future

by Andrew McAfee

In March of 2015, strategist Tom Goodwin pointed out a pattern. “Uber, the world’s largest taxi company, owns no vehicles,” he wrote. “Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. And Airbnb, the world’s largest accommodation provider, owns no real estate.”

As it presented all this content to its users, Facebook also showed them ads, and eventually a lot of them. Facebook’s revenues in the second quarter of 2016, virtually all of which came from advertising, were $6.4 billion. Profits were $2 billion.

“Half the money I spend on advertising is wasted. The trouble is I don’t know which half.”

In March of 2016, Mark Zuckerberg unveiled the company’s ten-year road map, which included major initiatives in artificial intelligence, virtual reality and augmented reality, and even solar-powered airplanes to bring Internet access to millions of people who live far from any telecommunications infrastructure.

By any standard, General Electric is one of the most successful US companies. Tracing its roots back to the iconic inventor Thomas Edison and his Edison Electric Light Company, GE was selected in 1896 as one of the twelve

The three examples we’ve just described—AlphaGo’s triumph over the best human Go players, the success of new companies like Facebook and Airbnb that have none of the traditional assets of their industries, and GE’s use of an online crowd to help it design and market a product that was well within its expertise—illustrate three great trends that are reshaping the business world. The first trend consists of the rapidly increasing and expanding capabilities of machines, as exemplified by AlphaGo’s unexpected emergence as the world’s best Go player. The second is captured by Goodwin’s observations about the recent appearance of large and influential young companies that bear little resemblance to the established incumbents in their industries, yet are deeply disrupting them. These upstarts are platforms, and they are fearsome competitors. The third trend, epitomized by GE’s unconventional development process for its Opal ice maker, is the emergence of the crowd, our term for the startlingly large amount of human knowledge, expertise, and enthusiasm distributed all over the world and now available, and able to be focused, online.

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 successful companies of the second machine age will be those that bring together minds and machines, products and platforms, and the core and crowd very differently than most do today.

But in chaos lies opportunity.

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 was born in 1989 when Tim Berners-Lee developed a set of protocols that allowed pieces of online content like text and pictures to link to each other, putting in practice the visions of hypertext first described by science and engineering polymath Vannevar Bush in 1945 (theoretically using microfilm) and computer visionary Ted Nelson, whose Project Xanadu never quite took off. The web rapidly turned the Internet from a text-only

network into one that could handle pictures, sounds, and other media. This multimedia wonder, so much richer and easier to navigate than anything before, entered the mainstream in 1994 when Netscape released the first commercial web browser, named Navigator. (One of Netscape’s cofounders was Marc Andreessen, a then twenty-two-year-old programmer who had worked on earlier web browsers. We’ll hear more from Andreessen in Chapter 11.)‡ It coincided with the commercialization of the Internet, which had previously been primarily the domain of academics.

But the overall pattern is clear: in case after case, when a model can be created and tested, it tends to perform as well as, or better than, human experts making similar decisions. Too often, we continue to rely on human judgment when machines can do better.

There’s an old joke that the factory of the future will have two employees: a human and a dog. The human’s job will be to feed the dog, and the dog’s job will be to keep the human from touching any of the machines. Is that actually what the company of tomorrow will look like?

Examples like this one show the wisdom of having human judgment and algorithms work together.

The broad approaches we’ve seen here—letting algorithms and computer systems make the decisions, sometimes with human judgment as an input, and letting the people override them when appropriate—are ways to do this.

Predict Less, Experiment More

Alan Kay’s great advice that the best way to predict the future is to invent it.

So far, automakers that have introduced self-driving technologies have also taken this approach. They stress that the human is both literally and figuratively in the driver’s seat, and is responsible for the safe operation of the car even when self-piloting technologies are operating. Always having a human in the loop seems prudent to many, since inattention can be fatal.

How much longer will we maintain our advantages over robots and drones? It’s a hard question to answer with any confidence, especially since the elements of DANCE continue to advance individually and together. It seems, though, that our senses, hands, and feet will be a hard combination for machines to beat, at least for a few more years. Robots are making impressive progress, but they’re still a lot slower than we are when they try to do humanlike things. After all, our brains and bodies draw on millions of years of evolution, rewarding the designs that solved well the problems posed by the physical world.

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.

To see this, note that delivering to customers ten music CDs, each with a single song, costs about ten times as much as delivering one CD. Multiply that by millions of customers, and you can see the attraction of bundling the songs onto a single CD. That’s the economics of atoms. But on a network the costs of delivery are virtually zero, so there’s no real penalty for selling songs à la carte. That’s the economics of networks.

This disruption happened largely because of the free, perfect, and instant economics of digital information goods in a time of pervasive networks. The marginal cost of an additional digital copy is (almost) zero, each digital copy is a perfect replica of the original, and each digital copy can be transmitted across the planet virtually instantly.

Uber’s investors are making the bet that the (two-sided) network effects and switching costs are large enough to make it worth investing billions of dollars to encourage adoption of the platform by both riders and drivers. Their strategy is complicated by the fact that geographically distinct markets each have their own local network effects. If you’re hailing a ride in Beijing, it makes little difference if Uber has lots of drivers in New York or New Delhi. The battle isn’t one big winner-take-all contest, but hundreds of separate ones, with only weak network effects across different geographies. They’re winning some and losing others. As it works to build its platform, Uber has two huge advantages. The first is a set of deep-pocketed and patient investors, who are willing to cover Uber’s costs while it scales. These initial costs—for technology development, marketing, driver recruiting, staffing, and so on—are substantial, and Uber was estimated to have raised more than $15 billion in loans and investments by July 2016 to fund its global growth. Investors have been willing to supply that money because they see the second advantage: if and when the company activates network effects and achieves scale, its marginal cost of arranging a ride somewhere in the world will be extraordinarily low. The free, perfect, and instant economics of bits will dominate, and these traits support charging very low prices. So Uber, the theory goes, will eventually be able to profitably charge...

Google changed the world with the realization that even though the crowd’s online content was uncontrolled, it wasn’t disorganized.

In the more than 700 challenges we have run on crowds for NASA, for the medical school, for companies—you name it—over the past five years, we’ve only had one failure [where] the crowd did not show up or did not work on the problem.† In all the other circumstances, we either met or vastly exceeded the internal solutions that existed.

This started to change in the 1980s when pioneers like Jim Simons (one of the most accomplished mathematicians of his generation) and David Shaw (a computer scientist) founded, respectively, Renaissance Technologies and D. E. Shaw to use machines to make investment decisions. These companies sifted through large amounts of data, built and tested quantitative models of how assets’ prices behaved under different conditions, and worked to substitute code and math for individual judgment about what and when to buy.

Bitcoin: The Pseudonymous Revolution

Because it relied heavily on many of the same algorithms and mathematics as cryptography (the art and science of making and breaking codes), Bitcoin came to be known as a “cryptocurrency.”

So we should ask not “What will technology do to us?” but rather “What do we want to do with technology?”