This is absolutely not the case. My first startup was an attempt to build requirements management software for small teams. I am acutely aware that there is a step between "an idea" and "some code" where you have to turn the idea into something cohesive and structured that you can then turn into language a computer can understand. The bit in the middle where you write down what the software needs to do in human language is the important part of the process - you will throw the code away by deleting it, refactoring it, improving it, etc. What the code needs to do doesn't change anywhere near as fast.
Any sufficiently experienced developer who's been through the fun of working on an application that's been in production for more than a decade where the only way to know what it does is by reading the code will attest to the fact that the code is not the important part of software development. What the code is supposed to do is more important, and the code can't tell you that.
If you approach AI as an iterative process where you're architecting an application just as you would without AI, but using the tool to speed up parts of the process like writing one method or writing just the tests for the part you're building right now, then AI becomes a genuinely useful tool.
For example, I've been using AI today to build some metrics tooling, and most of what I did with it was using it to assist in writing code to access an ancient version of a build tool that I can't find the documentation for because it's about 30 versions out of date. The API is wildly different to the modern one. Claude knows it though, so I just prompt it for methods to access data from the API that I need and only that. The rest of the app is my terrible Python code. Without AI this would take me 4 or 5 times longer if I could even do it at all.