The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee Page A
Authors: Erik Brynjolfsson, Andrew McAfee
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quickly there is soon a virtually infinite number of potentially valuable recombinations of the existing knowledge pieces. * 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. 19
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.” 20 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.” 21
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.” 22 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
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. 19
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.” 20 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.” 21
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.” 22 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
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