The "Sigmoid" Curve

Why "Good Enough" Beats "Perfect" in Innovation

Every spam filter, every recommendation algorithm, every AI decision about your loan application—they all rely on a mathematical function called sigmoid.

It's an S-shaped curve that turns any calculation into a probability between 0 and 1.

But here's the thing: there are countless other curves that could do the same job. Hyperbolic tangent. Arctangent. Step functions. Custom polynomials.

So why sigmoid?

Not because it's the best.

Because it was good enough, and it got there first.

It had clean math. It connected to existing theory. It worked reliably. And most importantly, once people started using it, they built tools around it, taught it to students, and optimized software for it.

Sigmoid didn't win because it was perfect. It won because it was practical.

The network effect did the rest.

This is how standards emerge in every field. Not through optimization, but through adoption.

QWERTY keyboards. The English language. The format of business cards. None of these are optimal. All of them are entrenched.

The lesson isn't to build the best mousetrap.

The lesson is to build the mousetrap that others want to build mousetraps around.

Because in the end, the curve doesn't just shape the data.

The curve shapes the future.