I also wanted to add about the myriad of incoming data formats that need to be processed and the myriad of data exports that one has to implement for most of those "boring" CRUD apps.
If one hasn't written code that includes comments like "Special case, California does it this way" or "Alberta needs an .xml export, not an .xsl one", with a link to a .pdf spec that points to somewhere on the internet, then he/she hasn't got to know what it really means to write a true and "boring" CRUD app.
I point chatgpt at the general problem and ask about the tech. Check it's answers, drill into specifics. As more questions, get it to write a prototype. (obviously with different parameters to prod- I don't trust open ai not to harvest from my interactions)- ok, now I have proof of concept, if it does the thing I want to do, then go to the engineers and say - hey I want to "X", here is RFC and rough code, .. any problems? if not, how long to push a localised version?
I guess you might call this scripting or prototyping not "real coding" but, damn it's useful not have to fill my head with python etc. Or bore/waste a guy earning 150k for a half day plus to get the basics, then never get my prototype.. because, priority, resources, etc
The hard part here isn’t really the coding - but understanding the business logic and getting it out of the customer what they need.
Often the customer hasn’t even got a good enough grasp on their own business to be able to formulate what they need.
In the short term this ai tools will make the programming part easier but the planning part is still hard.