Development speed is not a bottleneck

It's completely absurd how wrong this article is. Development speed is 100% the bottleneck.

Just to quote one little bit from the piece regarding Google: "In other words, there have been numerous dead ends that they explored, invalidated, and moved on from. There's no knowing up front."

Every time you change your mind or learn something new and you have to make a course correction, there's latency. That latency is just development velocity. The way to find the right answer isn't to think very hard and miraculously come up with the perfect answer. It's to try every goddamn thing that shows promise. The bottleneck for that is 100% development speed.

If you can shrink your iteration time, then there are fewer meetings trying to determine prioritization. There are fewer discussions and bargaining sessions you need to do. Because just developing the variations would be faster than all of the debate. So the amount of time you waste in meetings and deliberation goes down as well.

If you can shrink your iteration time between versions 2 and 3, between versions 3 and 4, etc. The advantage compounds over your competitors. You find promising solutions earlier, which lead to new promising solutions earlier. Over an extended period of time, this is how you build a moat.

This article is right insofar as "development velocity" has been redefined to be "typing speed."

With LLMs, you can type so much faster! So we should be going faster! It feels faster!

(We are not going faster.)

But your definition, the right one, is spot on. The pace of learning and decisions is exactly what drives development velocity. My one quibble is that if you want to learn whether something is worth doing, implementing it isn't always the answer. Prototyping vs. production-quality implementation is different, even within that. But yeah, broadly, you need to test and validate as many _ideas_ as possible, in order take make as many correct _decisions_ as possible.

That's one place I'm pretty bullish on AI: using it to explore/test ideas, which otherwise would have been too expensive. You can learn a ton by sending the AI off to research stuff (code, web search, your production logs, whatever), which lets you try more stuff. That genuinely tightens the feedback loop, and you go faster.

I wrote a bit more about that here: https://tern.sh/blog/you-have-to-decide/

Naur’s theory of programming has always felt right to me. Once you known everything about the current implementation, planning and decision making can be done really fast and there’s not much time lost on actually implementing prototypes and dead ends (learning with extra steps).

It’s very rare to not touch up code, even when writing new features. Knowing where to do so in advance (and planning to not have to do that a lot) is where velocity is. AI can’t help.