however it's something entirely different for that output to be good and maintainable

People aren't prompting LLMs to write good, maintainable code though. They're assuming that because we've made a collective assumption that good, maintainable code is the goal then it must also be the goal of an LLM too. That isn't true. LLMs don't care about our goals. They are solving problems in a probabilistic way based on the content of their training data, context, and prompting. Presumably if you take all the code in the world and throw it in mixer what comes out is not our Platonic ideal of the best possible code, but actually something more like a Lovecraftian horror that happens to get the right output. This is quite positive because it shows that with better prompting+context+training we might actually be able to guide an LLM to know what good and bad looks like (based on the fact that we know). The future is looking great.

However, we also need to be aware that 'good, maintainable code' is often not what we think is the ideal output of a developer. In businesses everywhere the goal is 'whatever works right now, and to hell with maintainability'. When a business is 3 months from failing spending time to write good code that you can continue to work on in 10 years feels like wasted effort. So really, for most code that's written, it doesn't actually need to be good or maintainable. It just needs to work. And if you look at the code that a lot of businesses are running, it doesn't. LLMs are a step forward in just getting stuff to work in the first place.

If we can move to 'bug free' using AI, at the unit level, then AI is useful. Above individual units of code, like logic, architecture, security, etc things still have to come from the developer because AI can't have the context of a complete application yet. When that's ready then we can tackle 'tech debt free' because almost all tech debt lives at that higher level. I don't think we'll get there for a long time.