Connecting Little Guys to Really Big Guys
"Crowdsourcing" is the new buzzword to describe leveraging the Internet and the "wisdom of crowds" to solve problems and obtain information, whether via open source programming, file sharing or soliciting group input. The idea, of course, isn't new, but who's using it is of interest.
Pharmaceutical maker Eli Lilly funded InnoCentive’s launch in 2001 as a way to connect with brainpower outside the company – people who could help develop drugs and speed them to market. From the outset, InnoCentive threw open the doors to other firms eager to access the network’s trove of ad hoc experts. Companies like Boeing, DuPont, and Procter & Gamble now post their most ornery scientific problems on InnoCentive’s Web site; anyone on InnoCentive’s network can take a shot at cracking them.
The companies – or seekers, in InnoCentive parlance – pay solvers anywhere from $10,000 to $100,000 per solution. (They also pay InnoCentive a fee to participate.) Jill Panetta, InnoCentive’s chief scientific officer, says more than 30 percent of the problems posted on the site have been cracked, “which is 30 percent more than would have been solved using a traditional, in-house approach.”
The solvers are not who you might expect. Many are hobbyists working from their proverbial garage, like the University of Dallas undergrad who came up with a chemical to use in art restoration, or the Cary, North Carolina, patent lawyer who devised a novel way to mix large batches of chemical compounds.
A related concept is the iBridge Network, which aims to link universities up with entrepreneurs who can help bring technologies being developed in university labs to market.
When it works, crowdsourcing can be a win-win situation. An individual or group looking for a solution can obtain one at relatively low cost, while individuals with knowledge can apply it to make money or advance their careers. Naturally, the risk of abuse exists -- and that's where opportunity exists for developers seeking to design networking sites that are effective, efficient, and equitable.
Sources: Wired, KurzweilAI.net, innovation.net