If you look from the lenses of BigTech and corporations, yes code was not a bottleneck.
But, if you look from the perspective of startups, rigorous planning was because resources to produce features were limited, which means producing a working code was a bottleneck, because in small teams you don't have coordination overhead, idea and vision is clear for them -> to produce something they have discussed and agreed on already.
My takeaway is, when discussing broad topics like usefulness of AI/LLM, don't generalize your assumptions. Code was bottleneck for some, not for others
What I've seen is exactly this, that LLMs give the most leverage to small and highly capable teams of devs. You need to be highly capable in order to get good output from LLMs, and large teams still have the coordination overhead that slows them down. LLMs supercharge the small teams that were already good.
I think another generalization people make here is around complexity. Many developers work on apps that just aren't that complex. Glorified CMS's mostly doing CRUD with well established code patterns.
Sure, LLMs might create slop on novel problems, but a non-tech company that needs to "create a new CRUD route" and an accompanying form, LLMs are smart enough.
If you look from the lenses of BigTech and corporations, yes code was not a bottleneck.
But, if you look from the perspective of startups, rigorous planning was because resources to produce features were limited, which means producing a working code was a bottleneck, because in small teams you don't have coordination overhead, idea and vision is clear for them -> to produce something they have discussed and agreed on already.
My takeaway is, when discussing broad topics like usefulness of AI/LLM, don't generalize your assumptions. Code was bottleneck for some, not for others