I'm more worried about the other scenario - AI continuing to improve steadily and ultimately resulting in significant job displacement among so called "knowledge workers" (including programmers and other engineering types). My concern isn't "what happens when there aren't jobs for AI engineers", it's "what happens when millions of highly-educated, intelligent, and previously highly-paid white collar workers suddenly find that there is no demand for their skills?"

There was a similar AI/ML bust in the mid-late 2010s.

If you're truly knowledgeable in your subdomain's technical fundamentals (information retrieval/databases, MLOps/DevOps, GPU Programming/systems programming, etc) you will land a job in other adjacent fields or remain relevant in the AI/ML space.

Algos and Systems Programming are core fundamentals of CS, and weak fundamentals in these two core areas of CS are a major reason SWEs start lagging in their careers.

If you are a Pandas/SKlearn script-kiddie, you're screwed, but for the same reason a front-end or backend dev who doesn't understand architecture or design is screwed as well.

The Perf_Events bug writeup on HN is a great example. A good GPU Programmer/ML Infra Engineer will have that level of Linux Kernel and eBPF knowledge, and could easily pivot into adjacent fields like HFT, Databases, Cloud, etc.

Software is software, the specific nature of it at a point-in-time hardly matters in the overall scope of your career: what happened to the developers building those ANSI POS terminals running in DOS? They learnt new ways to write software, for desktops. Then they learnt new ways to write software, for the web. Etc.

"when", not "if"