And then Marc Andreesen: "The gap between what a highly productive person can do and what an average person can do is getting bigger and bigger," he told author Bill Taylor. "Five great programmers can completely outperform 1,000 mediocre programmers."
But as the ever-prescient Priceonomics blog notes, it ain't just coding that skews so exceptionally.
You may be aware of the Pareto Principle: the oft-cited postulate that you get 80% of your value from 20% of your resources--it's the organizing force behind Tim Ferriss's 4-Hour franchise, a way by which you can shave away the unnecessary in your work, exercise, or cooking.
But as researchers Herman Aguinis and Ernest O’Boyle Jr. have found, star performers--who account for four-fifths of a company's output--cut across fields. As Priceonomics writer Alex Mayyas describes:
The publication record of academics as measured by citations shows a small number of giants outperforming the rest. The same is true of the awarding of patents and National Science Foundation grants. The power law is at work in the measurable performance of certain salesmen and women, politicians, athletes, entertainers . . . and in the number of kills by fighter pilots in World War II.
If you went to business school, the Pareto was probably left out. Why? O'Boyle tells Priceonomics that management theory and organizational psychology are based on a tradition of thinking that grew up between the '50s and '80s--and mainly in manufacturing settings.
Then the economy switched to service, and since productivity was harder to measure, researchers switched to using supervisors' qualitative ratings. But supervisors, like hiring managers, have bias--sometimes instilled in them.
The academics that came to the managers didn't want them to rate all their employees as nine out of 10--how uninteresting!--they taught them to distribute the ratings in the "correct" way, on the familiar curve.
"By forcing manufacturing-based theory onto service industries, supervisors hid all the evidence of star performers’ outsize contributions," Mayyasi notes. "Back at the university, doctoral students trained in statistical techniques that assumed normal distributions molded performance data to fit their model’s assumptions."
And it's part of the reason why journalist are being turned into machine learners--hooray, categorically short-cited metrics!
But this quantitive measurement could be a good thing for us worker bees. Why? Because if we're going to be asking for a raise or a promotion, we'll need the data to back it up. Regardless of what management science says.
Hat tip: Priceonomics
[Image: Flickr user Lim Mingwei]