Peter D. Kaufman

They were the experts of their field, the cream of the crop, bringing their A game, using their best stories and their clearest language to communicate the most important ideas in their work.

So Peter printed all of them.

Then he read them in what he calls index fund style, which means he read them all.

He didn't pick and choose, just one after another, every single day for six straight months.

This is the universe, he said, and I'm going to own the whole universe.

and if he had been left to his own preferences he admits he probably only would have read a few of them a few would have piqued his interest he never would have voluntarily read six pages on nanoparticles but after he had completed his reading that's exactly where he found some of his best ideas

Over those six months reading across unfamiliar domains, Peter started to recognize something in each seemingly unrelated article.

That's exactly how it works over here in biology.

That's exactly how this works over here in human nature.

Thank you.

Reading broadly showed him the biggest ideas were hiding in arcane places nobody else was looking.

It's why index fund reading beats selective reading because it allows you to capture the information that most people miss.

But being multidisciplinary created a new problem.

How do you know which ideas are actually true?

And Peter came up with a clever solution to this problem that drew on statistics.

As he put it, a statistician's best friend is a large, relevant sample size.

And why?

Because a principle derived from a large, relevant sample size can't be wrong.

The only way it could be wrong is if the sample size is too small or the sample itself is not relevant.

So Peter tested every important idea against what he calls his three buckets, the three largest relevant sample sizes he could think of.