“We see something that works, and then we understand it.” (Thomas Dullien)

It is a deeper insight than it seems.

Young people spend years in school learning the reverse: understanding happens before progress. That is the linear theory of innovation.

So Isaac Newton comes up with his three laws of mechanics, and we get a clockmaking boom. Of course, that’s not what happened: we get the pendulum clock in 1656, then Hooke (1660) and Newton (1665–1666) get to think about forces, speed, motion, and latent energy.

The linear model of innovation makes as much sense as the waterfall model in software engineering. In the waterfall model, you are taught that you first need to design every detail of your software application (e.g., using a language like UML) before you implement it. To this day, half of the information technology staff members at my school are made up of “analysts” whose main job is supposedly to create such designs based on requirements and supervise execution.

Both the linear theory and the waterfall model are forms of thinkism, a term I learned from Kevin Kelly. Thinkism sets aside practice and experience. It is the belief that given a problem, you should just think long and hard about it, and if you spend enough time thinking, you will solve it.

Thinkism works well in school. The teacher gives you all the concepts, then gives you a problem that, by a wonderful coincidence, can be solved just by thinking with the tools the same teacher just gave you.

As a teacher, I can tell you that students get really angry if you put a question on an exam that requires a concept not explicitly covered in class. Of course, if you work as an engineer and you’re stuck on a problem and you tell your boss it cannot be solved with the ideas you learned in college… you’re going to look like a fool.

If you’re still in school, here’s a fact: you will learn as much or more every year of your professional life than you learned during an entire university degree—assuming you have a real engineering job.

Thinkism also works well in other limited domains beyond school. It works well in bureaucratic settings where all the rules are known and you’re expected to apply them without question. There are many jobs where you first learn and then apply. And if you ever encounter new conditions where your training doesn’t directly apply, you’re supposed to report back to your superiors, who will then tell you what to do.

But if you work in research and development, you always begin with incomplete understanding. And most of the time, even if you could read everything ever written about your problem, you still wouldn’t understand enough to solve it. The way you make discoveries is often to either try something that seems sensible, or to observe something that happens to work—maybe your colleague has a practical technique that just works—and then you start thinking about it, formalizing it, putting it into words… and it becomes a discovery.

And the reason it often works this way is that “nobody knows anything.” The world is so complex that even the smartest individual knows only a fraction of what there is to know, and much of what they think they know is slightly wrong—and they don’t know which part is wrong.

So why should you care about how progress happens? You should care because…
1. It gives you a recipe for breakthroughs: spend more time observing and trying new things… and less time thinking abstractly.
2. Stop expecting an AI to cure all diseases or solve all problems just because it can read all the scholarship and “think” for a very long time. No matter how much an AI “knows,” it is always too little.

Further reading: Godin, Benoît (2017). Models of innovation: The history of an idea. MIT press.