[RossBencina]

> The first group are still thinking fairly deeply about design and interfaces and data structures, and are doing fairly heavy review in those areas.

I can't speak for others, but I'd go further and say that LLMs allow me to go deeper on the design side. I can survey alternative data structures, brainstorm conversationally, play design golf, work out a consistent domain taxonomy and from there function, data structure and field names, draft and redraft code, and then rewrite or edit the code myself when the AI cost/benefit trade off breaks down.

[mikepurvis]

GP and this is where I stand with it too. Additionally, the cost of "exploring" down a riskier design path and discovering the unknown unknowns is substantially reduced too, which I think ultimately leads to better decision-making on the design side. It's less "let's just stick to the pattern/tools that we know for sure works because we've done it before" and more "here's a vibed up mockup of it working, we can all see how this actually works and the better pattern that it enables".

Obviously technical and design choices have risks beyond just initial implementation, and those have to be considered too (do we trust the dependency, will it still be there in a year, can we get fixes merged upstream), but I think there's significant value in driving down the cost of code sketches involving unfamiliar libraries and tools.