We tend to think that we make a program, but (writing) the program also makes us what we are. It is what we do that defines what we are. "I think, therefore I am".
I don't see a career path for a prompter. I don't see the benefit for an organization to rely on people who don't understand what they do nor how the programs that keep the organization running works.
That being said, "the man and the machine" can be a powerful combination, like when we drive a motorbike. For me the cooperation with a LLM could work like this: if there is 50 features I write the code for 25 of them and write stubs (classes, methods) for the 25 others, with comments that will be the prompt. The AI audits what I write, gives suggestion, find blind spots and learn best practices from my code. Then I do the same kind of review on the generated code and the AI learns from my review. An AI can even, with luck, suggest a completely new way to solve a problem (see AlphaGo vs Lee Sedol move#37 in game#2) and then I learn something.
Working like that will make me better at reading code (after all Linus spend a lot of time in reading code, it is a good skill to have) while the AI gets better as well. I don't delegate everything and keep practicing, I keep myself up to date, the AI learns from me and I get peer review from the AI. And the codebase does not become a mess (=technical debt) that only another AI can maintain while token prices increases.