We've all seen the pictures of the Raptor engine for the SpaceX rockets.

And if you look at the various iterations, they go from easy to vary to hard to vary

because the most recent version just doesn't have that many parts that you can fool around with.

The earlier versions have a million different parts where you could change the thickness of it,

the width of it, the material, and so on.

The current version barely has any parts left for you to do anything with.

There's a theory on complexity theory that whenever you find a complex system working in nature,

it's usually the output of a very simple system or thing that was iterated over and over.

We're seeing this lately in AI research. You're taking

very simple algorithms and dumping more and more data into them. They keep getting smarter.

What doesn't work as well is the reverse. When you design a very complex system and then you try to

make a functioning large system out of that, it just falls apart. There's too much complexity in it.

So a lot of product design is iterating

on your own designs until you find the simple thing that works. And often, you've added stuff

around it that you don't need. And then you have to go back and extract the simplicity back out

of the noise. You can see this in personal computing, where macOS is still quite a bit harder to use than iOS.

iOS is closer to the platonic ideal of an operating system, although an LLM-based operating system

might be even closer, speaking in natural language.

Eventually, you have to remove things to get them to scale.

And the Raptor engine is an example of that.

As you figure out what works, then you realize what's unnecessary and you can remove parts.

And this is one of Musk's great driving principles where he basically says, before you optimize a system, that's among the last things that you do.

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