My experience is that these models could write a simple function and get it right if it does not require any out of the box thinking (so essentially offloading the boilerplate part of coding). When it comes to think creatively and have a much better solution to a specific task that would require the think 2-3 steps ahead than they are not suitable.
It's finding out why "jbdoe1337" added this large if/else around the entire function body back in 2016 - it seems important business logic, but the commit just says "updated code". And how the h*ll this interaction between the conf.ini files, the conf/something.json and the ENV vars works. Why sometimes the ENV var overrides a value in the ini and why its sometimes the other way around. But also finding that when you clean it up, everything falls apart.
It's discussing with the stakeholders why "adding a delete button" isn't as easy as just putting a button there, but that it means designing a whole cascading deletion strategy and/or trashcan and/or soft-delete and/or garbage-collection.
It's finding out why - again - the grumb pipeline crashes with the typebar checker, when used through mpm-yearn package manager. Both in containers and on a osx machine but not on Linux Brobuntu 22.12 LTLS.
It's moving stuff in the right abstraction layer. It's removing abstractions while introducing others. KISS vs future flexibility. It's gut feeling when to apply DRY and when to embrace it.
And then, if your lucky, churning out boilerplate or new code for 120 minutes a week.
I'm glad that this 120 minutes can be improved with AI and become 20 minutes. Truly. But this is not what (senior?) programmers do. Despite what the hyped up AI press makes us believe. It only shows they have no idea what the "real" problems and time-consumers are for programmers.