The most important curve of all explains tech adoption too

Pedro Domingos in his book “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World” (phew! that was long wasn’t it) says that the following curve is the most important of them all. Ever.

And it turns out it indeed is. Tweaking the parameters of the formula, we can represent a wide range of phenomena: from population growths to the making of pop-corn. But there is one particular application of the curve that I find very interesting: the rate at which technology gets adopted. Actually, let me be more specific: the way successful technologies get adopted. Have another look at the curve and think about an innovation that we use now everyday. The TV, Internet, smartphones, Facebook.

The curve reads by itself when applied backwards to something that has happened: new technologies start slow, then they increasingly get adopted, then they reach a plateau. But how can it be useful for forward planning? After all, the purpose of developing a model is that it should help us doing things better today. I don’t pretend to sketch a full in-depth analysis (some of those in the links below) but some things strike to mind.

As I see it, the first thing is to realise where you are with your product: when you started and what its adoption is today. If you've been working already for a number of months and your user base is low, you need to keep iterating fast and at the same time make sure you don't run out of funding. Then evaluate yourself again by looking at the S curve. Where are you now?

If in the middle of the curve, keep doing what you're doing.

And for the final part, well congratulations. You have achieved an impressive stage where users have adopted the new solution in mass. Now what? Start again. Find either another product or develop a new feature that will create a parallel S curve next to the current one. And so on. Technology doesn't stop.

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