The New Road to Meritocracy?
By Jeff Howe
What do Netflix, Wikipedia, and, possibly, Amateur Night at the Apollo have in common? They all employ strategies of outsourcing labor or decision-making to the crowd, rather than to hand-picked experts: anyone can submit an entry to Netflix’s contest to create a better ranking system for its movies, anyone can submit an article to Wikipedia, and anyone can go to Amateur Night and have a say (or hoot and holler, as it were) in the crowning of the champion.
According to Jeff Howe in his new book Crowdsourcing, the outsourcing of labor to “the crowd” has now become a new business model for identifying talent and estimating the future popularity of a given product. Crowdsourcing, both the title of his new book and the term that Howe has coined for this phenomenon, happens when online communities come together to create something larger than what any individual could have done on her own. Howe fills the pages of his book with anecdotes of companies from nearly every sector that have applied this model—essentially Adam Smith’s division of labor, updated for the internet age— often to the benefit of their profit margins.
But how exactly does crowdsourcing work in practice?
Several years ago, the household goods giant Proctor and Gamble first began to funnel the labor of solving its most difficult problems to the new and relatively unknown company InnoCentive. Although the outsourcing of labor was no new strategy in the business world, InnoCentive’s employees were cut from a different stone than the regular consultant or corporate nine-to-fiver. Specializing in technical problem solving, InnoCentive offered a new and innovative model for finding talent: anyone could send in solutions to the problems that the company posted. The winning solution would be selected only on the basis of its merit, and only then would the solver’s identity be revealed, and only then would he or she be paid for the labor of research and development. The result, according to Howe, was a revolution in business strategy and, consequently, in the face of the labor force. For Howe, crowdsourcing makes possible the democratization of labor opportunities.
The ideological importance of crowdsourcing as a tool for equality informs the majority of the case studies that Howe presents, and sets an optimistic tone of empowerment and unlimited possibilities in the rest of the book. The anecdotes are populated with up-by-the-bootstraps heroes who follow their dreams and inadvertently rock the corporate world and change business models forever.
Howe’s first sentence communicates the sentiment that will be paradigmatic throughout Crowdsourcing: “The Jakes didn’t set out to democratize the world of graphic design; they just wanted to make cool T-shirts.” What follows is the story of Threadless.com, a website created over beers by two college dropouts, Jake Nickell and Jacob DeHart, and onto which designers post their T-shirt designs, the online community votes on the best one, which then gets silkscreened and sold. The reason Threadless.com works so well as a business model is because the products are tested for free (by way of the online voting) before they’re ever produced. Every winning design on the website has sold out.
In this way, crowdsourcing takes advantage of both the labor of the crowd and its inclinations. The crowd creates what it wants, when it wants it, and judges the products based on merit and preference. The result is, for Howe, a utopian market:
Crowdsourcing has the capacity to form a sort of perfect meritocracy. Gone are pedigree, race, gender, age and qualification. What remains is the quality of the work itself. In stripping away all considerations outside quality, crowdsourcing operates under the most optimistic of assumptions: that each one of us possesses a far broader, more complex range of talents than we can currently express within current economic structures.
A perfect meritocracy? The quality of the work itself? Great! Sign me up, you must be thinking. A quick look at Threadless.com will reveal a number of cool T-shirts: innovative designs, artistic prowess, and a good deal of humor. But a closer look will reveal something else: the kinds of T-shirts that you can find there can be roughly divided into a few groups, consisting of T’s with designs of: 1) anthropomorphized household appliances saying or doing cute-ish things; 2) anthropomorphized animals doing or saying a) sweet and tender-hearted things or b) somewhat ironically violent things; 3) a vaguely environmental theme 4) a vaguely sexual theme; or 5) visual puns. Quality is ultimately in the eye of the T-shirt wearer, but it seems that a number of the entries ape the designs of previous winners, resulting in an inventory that ranges from very creative to derivatively Urban Outfitters-esque. Innovation gives way to imitation, or, in some cases, imitation gives way to yet more imitation. In short, Threadless.com is trend-driven, just like most other successful clothing companies.
But if those are the crowdsourcing products, what about crowdsourcing’s winners? Are they really all fun-loving beer-drinking college dropouts?
Howe’s description of one of InnoCentive’s repeat problem solvers proves another interesting case in the profile of the “crowd”:
Giorgia Sgargetta isn’t really an amateur scientist, though that’s just the word that comes to mind when she describes her home laboratory. The petite thirty-four-year-old lives in a small town in the Abruzzo region of Italy, and after she’s prepared dinner and sent her eight-year-old daughter Daiane and husband, Alessandro, off to bed, Sgargetta dons a beat-up old lab coat, puts away her cooking utensils, and carefully takes her flasks, beakers, test tubes, and a small precision scale from the attic.
|Sgargetta, who solved a few major chemistry problems for InnoCentive, forms the compelling portrait of an underdog amidst the world of high-flying corporate machines. In fact, so does the greater part of the solvers. According to one study from MIT, a majority of the problem solvers at InnoCentive were those identified as the people least likely to solve the problems. Why is that?
Howe argues that the diversity of the crowd is one of the key factors in successful crowdsourcing. The more diverse the set of minds that approach a problem, the more likely a solution will be found:
Given the proper conditions, diversity will trump ability in the case of a crowdcasting network like InnoCentive. There’s a very simple reason for this: the ultimate success of one solution is not diminished by the number of unsuccessful solutions…. As more people apply more diverse sets of problem-solving methods—no matter how harebrained—the odds that someone will crack the nut can’t help but go up. And if they’re wrong, you can just ignore them. But this truth only applies to crowdcasting projects like the Netflix Prize and InnoCentive.
So was Gusteau, the chef in Ratatouille, right in a much larger sense: Can anyone cook?
As it turns out, Sgargetta, as Howe to his credit points out, “is no mere dabbler.” She is a specialist, holds a Ph.D., and was very successful in graduate school. And, as it turns out, specialization is in fact important for this model.
Another problem solver, Ed Melcarek, is cast in the same dilettante role as the Jakes: before he begins cracking scientific mysteries, he “pours himself a Saint-Rémy, lights a Player cigarette, and attacks problems that have stumped some of the best corporate scientists at Fortune 500 companies.” But the kinds of problems that Melcarek often solves are those problems for which he has specialized. For example, one chemistry problem, which had stumped all of the leading corporate chemists, had a solution related to electromagnetics, which was immediately evident to Melcarek, an electrical engineer with an advanced degree. The problem that InnoCentive benefits from is specialization: chemists in the corporate world who have only specialized in chemistry can’t be expected to solve an electromagnetics problem. But the solution to the problem is also specialization—you just need a different kind of specialist. Fields of knowledge, in this way, may be seen merely as tools for solving problems that arise in other fields, just as Melcarek’s specialization in electrical engineering helped him solve what people thought was a chemistry problem.
Although Howe indicates the educational backgrounds of both examples, the term crowdsourcing and the democratizing process that he attributes to it at times feel a bit misleading. Up-by-the-bootstraps Ph.D.s? Bootstrapiness is duly called into question on sundry occasions. The descriptions of Sgargetta and Melcarek are both variants on the underdog theme; one an Italian mother and wife, and the other a Saint-Rémy-drinking maverick. This is what makes Crowdsourcing both so interesting and, at times, so frustrating. These types of characters make for good stories. But as it turns out, the majority of InnoCentive’s solvers hold Ph.D.s, which doesn’t fit in quite as well with the book’s narrative of the internet’s empowerment of anybody and everybody.
It seems, too, that the crowd’s capacity for collecting data is often blurred with its capacity for analyzing data in the book. Howe notes a number of examples in which crowds of “amateurs” collect data for major projects: the hobbyist ornithologists’ bird watching rivals that done by academics or even the Audubon Society; school children collect data for NASA via the INSPIRE program (Interactive NASA Space Physics Ionosphere Radio Experiments), through which they receive low-frequency radio waves “in the earth’s magnetosphere”; amateur photographers post their pictures to the website iStockphoto and often make a profit. That all of these groups are at times put in the same category as InnoCentive’s problem solvers seems to elide the different functions that the crowd performs in each case. For example, Howe writes,
Technology has proven to be a great equalizer, [Shawn] Carlson says, pointing to the fields like ornithology and astronomy that have become increasingly dependent on amateurs for gathering raw data. As the Internet disseminates knowledge, amateurs will begin to help analyze the data as well. It’s hardly surprising that just as iStockphoto emerged to tap the explosive growth in skilled photographers with unrelated day jobs, a company called InnoCentive has figured out a way to tap the surplus of talented scientists whose most meaningful projects are relegated to jerry-built labs.
What follows is the description of InnoCentive’s Giorgia Sgargetta, which initially downplays her education in the opening passage of the anecdote and emphasizes the recreational quality of her at-home lab. The difference between the skills sets required to compile data versus those required to analyze data is often obscured in the greater narrative arc of the book and its anecdotes. In this way, a pattern of elisions clearly emerges, perhaps best exemplified by the introduction of Sgargetta’s story, quoted above: “Giorgia Sgargetta isn’t really an amateur scientist, though that’s just the word that comes to mind when she describes her home laboratory.” The difference between the words that come to mind and the words that actually describe something with accuracy can often amount to a signifying chasm; those aren’t necessarily the same words, and Sgargetta, as Howe does note, is far from amateur status. Just as the conflation of seeming and being obscures the role of specialization and education in Sgargetta’s case, so, too, does the hazy indistinction between collecting versus analyzing data involve something of a leap or a blurring on Howe’s part, ultimately obscuring what crowdsourcing really is, and who really comprises the “crowd”, especially for very specialized fields.
This narrative of self-made scrappers comes in both individual and team editions—think The Mighty Ducks, The Replacements, or Ladybugs, and replace their stories with entrepreneurs, hipsters and computer geeks. A gang of losers bands together, diversified in their talents, and takes the pennant. Except they’re not losers. It’s more like a group of ballers who were close but who didn’t quite make the cut for the All-Star team. One of the major difficulties of Crowdsourcing is that Howe consistently downplays this baller element—he makes Sgargetta, Melcarek and their like seem more like the Bad News Bears rather than the scholarly standouts that they are.
Howe writes near the beginning of the book,
…a renaissance of amateurism, the emergence of the open source software movement, the increasing availability of the tools of production, and finally the rise of vibrant online communities organized according to people’s interests—have made crowdsourcing not only possible, but inevitable.
The broader brush that the term “amateurism” casts throughout the book seems to exacerbate this problem of who the so-called “amateurs” in the “crowd” really are. Though Howe himself points out those exceptions, he seems to want to have his rhetorical cake and eat it, too—to create a narrative of the social renegades versus the pressed shirts, and then back away from it only moments later.
Crowdsourcing, despite its penchant for ascension narratives, offers moments of admirable journalistic writing. Although many businesses will not be able to employ the model of crowdsourcing, its basic tenets have always been employed in successful management. The success of the crowdsourcing model relies on the sense of community and ownership that people feel in their work, as well as the creativity that it provides them. A good manager will be able to find other ways to create community through social events and adopt a management style that gives her employees a sense of value, as well as ownership in the work they perform, without actually outsourcing all the labor of the company to a “crowd”. Providing more outlets for creativity, however simply, and favoring at least a style of democratization over firm hierarchies will reproduce the atmospheric conditions that make so-called crowdsourcing businesses so popular.
Another nugget that many businesses have already begun to take away is the division of labor through digitization. The cases of citizen reporting that Howe notes and with which he was involved worked well because the tasks assigned to volunteers were very simple, concrete, and manageable. For example, in one project, people reported the prices of staple foods at their local grocery store in a collective effort to identify the worst stores and neighborhoods in New York for price gouging. In short, I think that this kind of model resembles the collective survey or even a networked and divided type of data entry; but it must be noted that this is the collection of data, and not analysis.
Those leaders and communities that were most successful in their “crowdsourcing” enterprises often organized themselves around certain rituals—periodic contests, updates in the websites, and parties. This tactic—consolidating power and support through ritual—has been employed for ages by monarchs seeking to legitimize themselves, such as Henry V or Elizabeth I. A good CEO will recognize the importance of the symbolic capital that ritual creates and performs and will make that a fixture in the organization, with or without crowdsourcing.
Although Crowdsourcing offers a number of striking anecdotes and interesting theories behind successful business models (such as SellaBand or Marketocracy), it often waxes repetitive, both in the kinds of stories offered and in the conclusions drawn from those stories, which generally restate the thesis of the book. Anyone interested in information technology and the impact of the internet on business should give it a quick perusal. But it is important to keep in mind the social problems that could arise from some of these business models: many of these companies operate on an entire labor force of uninsured freelancers, a point into which Howe refrains from delving too deeply. For those less interested or less patient, a quick read of the introduction, chapter one and the last chapter should do the job.
On a final note, Crowdsourcing seems to describe what has been already articulated in literary fields for years; William Gibson’s Pattern Recognition in particular comes to mind, in which an online forum serves as the information center for both hobbyists and big corporations in the cracking of an artistic and marketing mystery. Though the forum is run by “the crowd,” their intense passion for art seems to amount to specialization, and results in the creation of an information network that is larger than any individual part. Networks of information and socialization have always been a part of culture at large and specialized groups in particular; the internet age has merely broadened the possibilities of those networks.
Kathleen Smith is a graduate student at Columbia University.