As far as today's models, these are best understood as tools to be used as humans. They're only replacements for humans insofar as individual developers can accomplish more with the help of an AI than they could alone, so a smaller team can accomplish what used to require a bigger team. Due to Jevon's paradox this is probably a good thing for developer salaries: their skills are now that much more in demand.
But you have to consider the trajectory we're on. GPT went from an interesting curiosity to absolutely groundbreaking in less than five years. What will the next five years bring? Do you expect development to speed up, slow down, stay the course, or go off in an entirely different direction?
Obviously, the correct answer to that question is "Nobody knows for sure." We could be approaching the top of a sigmoid type curve where progress slows down after all the easy parts are worked out. Or maybe we're just approaching the base of the real inflection point where all white collar work can be accomplished better and more cheaply by a pile of GPUs.
Since the future is uncertain, a reasonable course of action is probably to keep your own coding skills up to date, but also get comfortable leveraging AI and learning its (current) strengths and weaknesses.