There is a hidden cost to having a hypothesis. It arises from the relationship between night science and day science, the two very distinct modes of activity in which scientific ideas are generated and tested, respectively [1, 2]. With a hypothesis in hand, the impressive strengths of day science are unleashed, guiding us in designing tests, estimating parameters, and throwing out the hypothesis if it fails the tests. But when we analyze the results of an experiment, our mental focus on a specific hypothesis can prevent us from exploring other aspects of the data, effectively blinding us to new ideas.
Walking intrigues the deskbound. We romanticize it, but do we do it justice? Do we walk properly? Can one walk improperly and, if so, what happens when the walk is corrected?
This talk centered on Hamming's observations and research on the question "Why do so few scientists make significant contributions and so many are forgotten in the long run?"
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.