This has been my experience, effectively.

Sometimes I don't care for things to be done in a very specific way. For those cases, LLMs are acceptable-to-good. Example: I had a networked device that exposes a proprietary protocol on a specific port. I needed a simple UI tool to control it; think toggles/labels/timed switches. With a couple of iterations, the LLM produced something good enough for my purposes, even if it wasn't particularly doted with the best UX practices.

Other times, I very much care for things to be done in a very specific way. Sometimes due to regulatory constraints, others because of visual/code consistency, or some other reasons. In those cases, getting the AI to produce what I need specifically feels like an exercise in herding incredibly stubborn cats. It will get done faster (and better) if I do it myself.