For example, the other day I asked a major LLMs to generate a simple markdown viewer with automatic section indentation for me in Node.js. The basic code worked after a few additional prompts from me.
Now I wanted folding. That was also done by the LLM. And then when I tried to add a few additional simples features, things fell apart. There were one or two seemingly simple runtime errors that the LLM was unable to fix after almost 10 tries.
I could fix it if I started digging inside the code, but then the productivity gains would start to slip away.
I'm using VSCode Github Copilot in "Agent Mode", btw. It's able to navigate around an entire project, understand it, and work on it. You just lean back and watch it open files, edit them, show you in realtime what it's thought process is, as it does everything, etc. etc. It's truly like magic.
Any other way of doing development, in 2025, is like being in the stone ages.
Anything beyond that and LLMs require a lot of hand holding, and frequently regress to boot
If you write a prompt with perfect specificity as to what you want done, an agent like "Github Copilot+Claude" can work at about the same level as a senior dev. I do it all day long. It writes complex SQL, complex algorithms, etc.
Saying it only does boilerplate well reminds me of my mother who was brainwashed by a PBS TV show into thinking LLMs can only finish sentences they've seen before and cannot reason thru things.
Even if my prompt was ambiguous, the LLM has no excuse producing code that does not type-check, or crashes in an obvious way when run. The ambiguity should affect what the code tries to do, not it's basic quality.
And your use of totalizing adjectives like "zero ambiguity" and "perfect specificity" tells me your arguments are somewhat suspect. There's nothing like "zero" and "perfect" as far as architecturing and implementing code goes.