I have read this same comment so many times in various forms. I know many of them are shill accounts/bots, but many are real. I think there are a few things at play that make people
feel this way. Even if you're in a CRUD shop with low standards for reliability/scale/performance/efficiency, a person who isn't an experienced engineer could not make the LLM do your job. LLMs have a perfect combination of traits that cause people to overestimate their utility. The biggest one I think is that their utility is super front-loaded.
If a task before would take you ten hours to think through the thing, translate that into an implementation approach, implement it, and test it, and at the end of the ten hours you're 100% there and you've got a good implementation which you understand and can explain to colleagues in detail later if needed. Your code was written by a human expert with intention, and you reviewed it as you wrote it and as you planned the work out.
With an LLM, you spend the same amount of time figuring out what you're going to do, plus more time writing detailed prompts and making the requisite files and context available for the LLM, then you press a button and tada, five minutes later you have a whole bunch of code. And it sorta seems to work. This gives you a big burst of dopamine due to the randomness of the result. So now, with your dopamine levels high and your work seemingly basically done, your brain registers that work as having been done in those five minutes.
But you now (if you're doing work people are willing to pay you for), you probably have to actually verify that it didn't break things or cause huge security holes, and clean up the redundant code and other exceedingly verbose garbage it generated. This is not the same process as verifying your own code. First, LLM output is meant to look as correct as possible, and it will do some REALLY incorrect things that no sane person would do that are not easy to spot in the same way you'd spot them if it were human-written. You also don't really know what all of this shit is - it almost always has a ton of redundant code, or just exceedingly verbose nonsense that ends up being technical debt and more tokens in the context for the next session. So now you have to carefully review it. You have to test things you wouldn't have had to test, with much more care, and you have to look for things that are hard to spot, like redundant code or regressions with other features it shouldn't have touched. And you have to actually make sure it did what you told it to, because sometimes it says it did, and it just didn't. This is a whole process. You're far from done here, and this (to me at least) can only be done by a professional. It's not hard - it's tedious and boring, but it does require your learned expertise.