Yes, and knowing what to write has always been the more important challenge, long before AI. But - one thing I’ve noticed is that in some cases, LLMs can help me try out and iterate on more concepts and design ideas than I was doing before. I can try out the thing I thought was going to work and then see the downsides I didn’t anticipate, and then fix it or tear it down and try something else. That was always possible, but when using LLMs this cycle feels much easier and like it’s happening much faster and going through more rough draft iterations than what I used to do. I’m trying more ideas than I would have otherwise, and it feels like it’s leading in many cases to a stronger foundation on which to take the draft through review to production. It’s far more reviewing and testing than before, but I guess in short, there might be an important component of the speed of writing code that feeds into figuring out what to write; yes we should absolutely focus entirely on priorities, requirements, and quality, but we also shouldn’t underestimate the impact that iteration speed can have on those goals.

We need a comparison between an LLM and an experienced engineer reviewing a juniors system design for some problem. I imagine the LLM will be way too enthusiastic about whatever design is presented and will help force poor designs into working shape.

I’d never done half as much code profiling & experimenting before. Now that generating one-shot code is cheap, I can send the agent off on a mission to find slow code and attempt to speed it up. This way, only once it has shown speedup is there and reasonably attainable do I need to think about how to speed the code up “properly”. The expected value was too low when the experimenting was expensive.