The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (11 page)

Why We Shouldn’t Be Worried: Innovations Don’t Get Used Up

For any good scientist, of course, data are the ultimate decider of hypotheses. So what do the data say here? Do the productivity numbers back up this pessimistic view of the power of digitization? We’ll get to the data in chapter 7. First, though, we want to present a very different view of how innovation works—an alternative to the notion that innovations get ‘used up.’

Gordon writes that “it is useful to think of the innovative process as a series of discrete inventions followed by incremental improvements which ultimately tap the full potential of the initial invention.”
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This seems sensible enough. An invention like the steam engine or computer comes along and we reap economic benefits from it. Those benefits start small while the technology is immature and not widely used, grow to be quite big as the GPT improves and propagates, then taper off as the improvement—and especially the propagation—die down. When multiple GPTs appear at the same time, or in a steady sequence, we sustain high rates of growth over a long period. But if there’s a big gap between major innovations, economic growth will eventually peter out. We’ll call this the ‘innovation-as-fruit’ view of things, in honor of Tyler Cowen’s imagery of all the low-hanging fruit being picked. In this perspective, coming up with an innovation is like growing fruit, and exploiting an innovation is like eating the fruit over time.

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. For example, it’s exactly how at least one Nobel Prize–winning innovation came about.

Kary Mullis won the 1993 Nobel Prize in Chemistry for the development of the polymerase chain reaction (PCR), a now ubiquitous technique for replicating DNA sequences. When the idea first came to him on a nighttime drive in California, though, he almost dismissed it out of hand. As he recounted in his Nobel Award speech, “Somehow, I thought, it had to be an illusion. . . . It was too easy. . . . There was not a single unknown in the scheme. Every step involved had been done already.”
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“All” Mullis did was recombine well-understood techniques in biochemistry to generate a new one. And yet it’s obvious Mullis’s recombination is an enormously valuable one.

After examining many examples of invention, innovation, and technological progress, complexity scholar Brian Arthur became convinced that stories like the invention of PCR are the rule, not the exception. As he summarizes in his book
The Nature of Technology
, “To invent something is to find it in what previously exists.”
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Economist Paul Romer has argued forcefully in favor of this view, the so-called ‘new growth theory’ within economics, in order to distinguish it from perspectives like Gordon’s. Romer’s inherently optimistic theory stresses the importance of recombinant innovation. As he writes:

Economic growth occurs whenever people take resources and rearrange them in ways that make them more valuable. . . . Every generation has perceived the limits to growth that finite resources and undesirable side effects would pose if no new . . . ideas were discovered. And every generation has underestimated the potential for finding new . . . ideas. We consistently fail to grasp how many ideas remain to be discovered. . . . Possibilities do not merely add up; they multiply.
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Romer also makes a vital point about a particularly important category of idea, which he calls “meta-ideas”:

Perhaps the most important ideas of all are meta-ideas—ideas about how to support the production and transmission of other ideas. . . . There are . . . two safe predictions. First, the country that takes the lead in the twenty-first century will be the one that implements an innovation that more effectively supports the production of new ideas in the private sector. Second, new meta-ideas of this kind will be found.
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Digital Technologies: The Most General Purpose of All

Gordon and Cowen are world-class economists, but they’re not giving digital technologies their due. The next great meta-idea, invoked by Romer, has already been found: it can be seen in the new communities of minds and machines made possible by networked digital devices running an astonishing variety of software. The GPT of ICT has given birth to radically new ways to combine and recombine ideas. Like language, printing, the library, or universal education, the global digital network fosters recombinant innovation. We can mix and remix ideas, both old and recent, in ways we never could before. Let’s look at a few examples.

Google’s Chauffeur project gives new life to an earlier GPT: the internal combustion engine. When an everyday car is equipped with a fast computer and a bunch of sensors (all of which get cheaper according to Moore’s Law) and a huge amount of map and street information (available thanks to the digitization of everything) it becomes an autopiloted vehicle straight out of science fiction. While we humans are still the ones doing the driving, innovations like Waze will help us get around more quickly and ease traffic jams. Waze is a recombination of a location sensor, data transmission device (that is, a phone), GPS system, and social network. The team at Waze invented none of these technologies; they just put them together in a new way. Moore’s Law made all involved devices cheap enough, and digitization made all necessary data available to facilitate the Waze system.

The Web itself is a pretty straightforward combination of the Internet’s much older TCP/IP data transmission network; a markup language called HTML that specified how text, pictures, and so on should be laid out; and a simple PC application called a ‘browser’ to display the results. None of these elements was particularly novel. Their combination was revolutionary.

Facebook has built on the Web infrastructure by allowing people to digitize their social network and put media online without having to learn HTML. Whether or not this was an intellectually profound combination of technological capabilities, it was a popular and economically significantly one—by July 2013, the company was valued at over $60 billion.
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When photo sharing became one of the most popular activities on Facebook, Kevin Systrom and Mike Krieger decided to build a smartphone application that mimicked this capability, combining it with the option to modify a photo’s appearance with digital filters. This seems like a minor innovation, especially since Facebook already had enabled mobile photo sharing in 2010 when Systrom and Krieger started their project. However, the application they built, called Instagram, attracted more than 30 million users by the spring of 2012, users who had collectively uploaded more than 100 million photographs. Facebook acquired Instagram for approximately $1 billion in April of 2012.

This progression drives home the point that digital innovation is recombinant innovation in its purest form. Each development becomes a building block for future innovations. Progress doesn’t run out; it accumulates. And the digital world doesn’t respect any boundaries. It extends into the physical one, leading to cars and planes that drive themselves, printers that make parts, and so on. Moore’s Law makes computing devices and sensors exponentially cheaper over time, enabling them to be built economically into more and more gear, from doorknobs to greeting cards. Digitization makes available massive bodies of data relevant to almost any situation, and this information can be infinitely reproduced and reused because it is non-rival. As a result of these two forces, the number of potentially valuable building blocks is exploding around the world, and the possibilities are multiplying as never before. We’ll call this the ‘innovation-as-building-block’ view of the world; it’s the one held by Arthur, Romer, and the two of us. From this perspective, unlike in the innovation-as-fruit view, building blocks don’t ever get eaten or otherwise used up. In fact, they increase the opportunities for future recombinations.

Limits to Recombinant Growth

If this recombinant view of innovation is correct, then a problem looms: as the number of building blocks explodes, the main difficulty is knowing which combinations of them will be valuable. In his paper “Recombinant Growth,” the economist Martin Weitzman developed a mathematical model of new growth theory in which the ‘fixed factors’ in an economy—machine tools, trucks, laboratories, and so on—are augmented over time by pieces of knowledge that he calls ‘seed ideas,’ and knowledge itself increases over time as previous seed ideas are recombined into new ones.
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This is an innovation-as-building-block view of the world, where both the knowledge pieces and the seed ideas can be combined and recombined over time.

This model has a fascinating result: because combinatorial possibilities explode so quickly there is soon a virtually infinite number of potentially valuable recombinations of the existing knowledge pieces.
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The constraint on the economy’s growth then becomes its ability to go through all these potential recombinations to find the truly valuable ones.

As Weitzman writes,

In such a world the core of economic life could appear increasingly to be centered on the more and more intensive processing of ever-greater numbers of new seed ideas into workable innovations. . . . In the early stages of development, growth is constrained by number of potential new ideas, but later on it is constrained only by the ability to process them.
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Gordon asks the provocative question, “Is growth over?” We’ll respond on behalf of Weitzman, Romer, and the other new growth theorists with “Not a chance. It’s just being held back by our inability to process all the new ideas fast enough.”

What This Problem Needs Are More Eyeballs and Bigger Computers

If this response is at least somewhat accurate—if it captures something about how innovation and economic growth work in the real world—then the best way to accelerate progress is to increase our capacity to test out new combinations of ideas. One excellent way to do this is to involve more people in this testing process, and digital technologies are making it possible for ever more people to participate. We’re interlinked by global ICT, and we have affordable access to masses of data and vast computing power. Today’s digital environment, in short, is a playground for large-scale recombination. The open source software advocate Eric Raymond has an optimistic observation: “Given enough eyeballs, all bugs are shallow.”
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The innovation equivalent to this might be, “With more eyeballs, more powerful combinations will be found.”

NASA experienced this effect as it was trying to improve its ability to forecast solar flares, or eruptions on the sun’s surface. Accuracy and plenty of advance warning are both important here, since solar particle events (or SPEs, as flares are properly known) can bring harmful levels of radiation to unshielded gear and people in space. Despite thirty-five years of research and data on SPEs, however, NASA acknowledged that it had “no method available to predict the onset, intensity or duration of a solar particle event.”
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The agency eventually posted its data and a description of the challenge of predicting SPEs on Innocentive, an online clearinghouse for scientific problems. Innocentive is ‘non-credentialist’; people don’t have to be PhDs or work in labs in order to browse the problems, download data, or upload a solution. Anyone can work on problems from any discipline; physicists, for example, are not excluded from digging in on biology problems.

As it turned out, the person with the insight and expertise needed to improve SPE prediction was not part of any recognizable astrophysics community. He was Bruce Cragin, a retired radio frequency engineer living in a small town in New Hampshire. Cragin said that, “Though I hadn’t worked in the area of solar physics as such, I had thought a lot about the theory of magnetic reconnection.”
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This was evidently the right theory for the job, because Cragin’s approach enabled prediction of SPEs eight hours in advance with 85 percent accuracy, and twenty-four hours in advance with 75 percent accuracy. His recombination of theory and data earned him a thirty-thousand-dollar reward from the space agency.

In recent years, many organizations have adopted NASA’s strategy of using technology to open up their innovation challenges and opportunities to more eyeballs. This phenomenon goes by several names, including ‘open innovation’ and ‘crowdsourcing,’ and it can be remarkably effective. The innovation scholars Lars Bo Jeppesen and Karim Lakhani studied 166 scientific problems posted to Innocentive, all of which had stumped their home organizations. They found that the crowd assembled around Innocentive was able to solve forty-nine of them, for a success rate of nearly 30 percent. They also found that people whose expertise was far away from the apparent domain of the problem were more likely to submit winning solutions. In other words, it seemed to actually help a solver to be ‘marginal’—to have education, training, and experience that were not obviously relevant for the problem. Jeppesen and Lakhani provide vivid examples of this:

[There were] different winning solutions to the same scientific challenge of identifying a food-grade polymer delivery system by an aerospace physicist, a small agribusiness owner, a transdermal drug delivery specialist, and an industrial scientist. . . . All four submissions successfully achieved the required challenge objectives with differing scientific mechanisms. . . .

[Another case involved] an R&D lab that, even after consulting with internal and external specialists, did not understand the toxicological significance of a particular pathology that had been observed in an ongoing research program. . . . It was eventually solved, using methods common in her field, by a scientist with a Ph.D. in protein crystallography who would not normally be exposed to toxicology problems or solve such problems on a routine basis.
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