Being able to build for reliability, performance, scale, and security will be a highly-prized skill. When [with AI] anyone can generate software that sort of works until it doesn’t, there will be more demand for engineers who produce quality work that always works as expected.
You cannot prompt an AI to create secure, performant code: you need to know what you want, how to validate the nonfunctional requirements, architect the code, and prompt the AI accordingly. You might also need to throw away the AI and get down to writing code or configuration by hand in order to get the details right. Basically, it pays to know when to use your own expertise.
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The good news is that software engineering fundamentals should become more important, the more a team relies on AI to generate code. More code leads to more problems which need to be caught earlier, and dealt with systematically. This is what good software engineering is about, and always has been.