On Rediscovery

September 17, 2025

I've been writing every weekday in September, but haven't published anything yet. I don't do a lot of editing, so the blog posts don't feel ready for the limelight.

But there's another reason why I haven't published.

A part of me is worried that I'm just rediscovering something. I'm worried that in writing about something I've discovered, I'll reveal something I didn't already know.

The worst-case scenario of this fear is Tai's Model.

It's pretty funny.

A medical researcher, Mary Tai, published a paper about a novel method to estimate the area under a curve. This method turned out to just be the Trapezoidal Rule, first discovered thousands of years ago. The paper was cited in earnest many times in metabolic research papers.

Tai defended the discovery, saying it's a slightly different, more accessible, method, which was developed with her statistical advisor.

Tai derived it from scratch. Her decision to publish it as something novel revealed a large knowledge gap. And then when she doubled down after learning about the Trapezoidal Rule, the discovery looked even sillier.

Was the ridicule deserved?

Often, when people make fun of other people, they reveal their own fear. Maybe many people have a fear of producing work that reveals ignorance.

It's scary to publish your work.

And it can be embarrassing to discover something already known.

In Richard Feynman's Introduction to Computing lecture, he says: "What one fool can do, so can another, and the fact that some other fool beat you to it shouldn't disturb you: you should get a kick out of having discovered something."

Feynman probably would have told Tai that it was great she re-derived a fundamental theorem of calculus. And maybe when discovering that it already existed, she should have felt delight, admitted it's the same method, and moved on to the next thing.

Deriving is a good thing. Rediscovering is a good thing.

Feynman says, in his computing lecture book: "Most of the problems I give you in this book have been worked over many times, and many ingenious solutions have been devised for them. But if you keep proving stuff that others have done, getting confidence, increasing the complexities of your solutions - for the fun of it - then one day you'll turn around and discover that nobody actually did that one!"

If you find 100 Tai's Models, you might find one {your name here}'s Model.

So keep deriving.