Any attempt to track down the perfect getaway is made all the more complex because almost everything we know about burglary—including how they did (or did not) get away—comes from the burglars we’ve caught. As sociologist R. I. Mawby pithily phrases this dilemma, “Known burglars are unrepresentative of burglars in general.” Great methodological despair is hidden in such a comment. Studying burglary is thus a strangely Heisenbergian undertaking, riddled with uncertainty and distorted by moving data points. The getaway to end all getaways—the one that leaves us all scratching our heads—to no small extent remains impossible to study.
Here, then, is the central idea: the form of made things is always subject to change in response to their real or perceived shortcomings, their failures to function properly. This principle governs all invention, innovation, ingenuity.
A couple years ago I was having dinner with a playwright, Bekah Brunstetter, and her director David Shmidt Chapman. We talked about how rejection is just part of the landscape for all beginning artists, no matter how talented or hardworking they might be or how successful they might appear. David said he’d love to publish his “anti-résumé” someday—a list of all the things he didn’t get.
I observed something fairly early on at Apple, which I didn’t know how to explain then, but I’ve thought a lot about it since. Most things in life have a dynamic range in which [the ratio of] “average” to “best” is at most 2:1.
For example, if you go to New York City and get an average taxi cab driver, versus the best taxi cab driver, you’ll probably get to your destination with the best taxi driver 30% faster. And an automobile; what’s the difference between the average car and the best? Maybe 20%? The best CD player versus the average CD player? Maybe 20%? So 2:1 is a big dynamic range for most things in life.
Now, in software, and it used to be the case in hardware, the difference between the average software developer and the best is 50:1; maybe even 100:1. Very few things in life are like this, but what I was lucky enough to spend my life doing, which is software, is like this.
So I’ve built a lot of my success on finding these truly gifted people, and not settling for “B” and “C” players, but really going for the “A” players. And I found something… I found that when you get enough “A” players together, when you go through the incredible work to find these “A” players, they really like working with each other. Because most have never had the chance to do that before. And they don’t work with “B” and “C” players, so it’s self-policing. They only want to hire “A” players. So you build these pockets of “A” players and it just propagates.