Quick note that this has not been my experience. LLMs have been very useful with codebases as far from crud web apps as you can get.
Two engineers use LLM-based coding tools; one comes away with nothing but frustration, the other one gets useful results. They trade anecdotes and wonder what the other is doing that is so different.
Maybe the other person is incompetent? Maybe they chose a different tool? Maybe their codebase is very different?
"Put this data on a web page" is easy. Complex application-like interactions seem to be more challenging. It's faster/easier to do the work by hand than it is to wait for the LLM, then correct it.
But if you aren't already an expert, you probably aren't looking for complex interaction models. "Put this data on a web page" is often just fine.
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.
Protestant Reformation? Done, 7 years ago, different professor. Your brothers are pleased to liberate you for Saturday's house party.
Barter Economy in Soviet Breakaway Republics? Sorry, bro. But we have a Red Square McDonald's feasibility study; you can change the names?
Sorry to anyone whose feelings this hurts.
Not everyone cares to be precise with their semantics.