The Future of Innovation

Erik Brynjolfsson and Andrew McAfee provide a fascinating look at how innovation is evolving in their book The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. The second machine age refers to the ubiquitous computational devices, the big data sets, and the clever software that drives them. Of particular importance is the availability of networks through which these devices, and their human owners, can communicate and exchange ideas.

The authors suggest that the "second" machine age is inherently different from the first. They describe the last 200 hundred years as the first machine age; one that was dominated by a few key technology innovations. Included among those would be the steam engine, the internal combustion engine, electricity, and indoor plumbing.

Many economists make the analysis of past history to be the template for future innovation and economic growth. In this picture there are a few great technical advances that spawn numerous tweaks and applications, but eventually this advance slows to a crawl as new uses become of marginal importance. Supporters of this view believe we are in need of another "big thing" if economic growth is to take off again. The authors quote the economist Bob Gordon on the topic:

"The growth of productivity (output per hour) slowed markedly after 1970. While puzzling at the time, it seems increasingly clear that the one-time-only benefits of the Great Inventions and their spin-offs had occurred and could not happen again….All that remained after 1970 were second-round improvements…."

Note that in this view computers are not recognized as a "Great Invention" in terms of economic importance.

Brynjolfsson and McAfee prefer an alternate view of innovation which is referred to as "new growth theory." Inventions do not arise from nothing. The Latin base for the word means to come upon or to find. In this view, if innovation is stagnant then we are not discovering new ways of using existing knowledge either because we are not looking hard enough or are not looking effectively. The authors quote the economist Paul Romer as a supporter of this perspective:

"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."

Brynjolfsson and McAfee see computers and digitized data and the possibilities for individuals to network and exchange thoughts as the engines which will allow increased invention. In fact, new ways will be devised to foster this collaboration and make it more efficient. These new approaches are referred to as meta-ideas. Again quoting Romer:

"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."

Digitization and the internet have provided anyone who wishes it access to incredible amounts of knowledge. The authors refer to the act of deriving a new concept from this mass of information as "recombinant" innovation. What are needed are meta-ideas for how to extract useful information from this assemblage of knowledge.

The authors provide a few examples that illustrate where innovation is headed. The first involves a problem that NASA was trying to deal with: the forecasting of solar eruptions or solar particle events (SPEs). NASA concluded that within their expertise and resources they were not able to provide useful predictions, so they posted the problem as a challenge to the world by placing it on the website of Innocentive, "an online clearinghouse for scientific problems."

"Innocentive is ‘non credentialist’; people don’t have to be PhDs or work in labs 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.’….His recombination of theory and data earned him a thirty-thousand-dollar reward from the space agency."

Other organizations have followed NASA’s example and dumped unsolvable problems on Innocentive. Scholars who have studied the efficacy of this approach note success rates of up to thirty 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."

The authors also describe another online enterprise called Kaggle that addresses non-scientific problems.

"Instead of scientific challenges, Kaggle specializes in data-intensive ones where the goal is to arrive at a better prediction than the submitting organization’s starting baseline prediction….In one case, Allstate submitted a dataset of vehicle characteristics and asked the Kaggle community to predict which of them would have later personal liability claims filed against them. The contest lasted approximately three months and drew in more than one hundred contestants. The winning prediction was more than 270 percent better than the insurance company’s baseline."

"Another interesting fact is that the majority of Kaggle contests are won by people who are marginal to the domain of the challenge….and so would not have been consulted as part of any traditional search for solutions."

Another example provided by the authors is Quirky, an online enterprise that attempts to both generate new ideas for products and decide the best way to bring them to market.

"Quirky seeks ideas for new consumer products from its crowd, and also relies on the crowd to vote on submissions, conduct research, suggest improvements, figure out how to name and brand the products, and drive sales. Quirky itself makes the final decisions about which products to launch and handles engineering, manufacturing and distribution. It keeps 70 percent of all revenue made through its website and distributes the remaining 30 percent to all crowd members involved in the development effort…."

These types of approaches fall under the generic categories of "crowdsourcing" or "open innovation." The examples the authors present are impressive, and the approaches to innovation are themselves innovative. We are living in exciting economic times and it will be interesting to see how this plays out.

Unfortunately, we have problems much bigger than coming up with the next gadget. If we are going to survive climate change associated with global warming, and going to learn enough about our own biology to repair the damages that keep appearing, we are going to need some big developments based on a lot of innovation. It is not clear that crowdsourcing or open innovation is an effective approach for these types of problems. However, if one can derive a new meta-idea on how to enhance innovation in these areas, that person would be greatly appreciated—and richly rewarded.