> max out at "knowing everything"
LLMs know nothing but are great at giving the illusion that they know stuff. (It's "mansplaining as a service"; it is easier to give confident answers every time, even if they are wrong, than to program actual knowledge.) Even your first case seems wildly optimistic. The second case is a lot of "maybes" and "we don't know how but we might figure it out" that seems like a lot to bet an entire farm on, much less an entire industry of farms.
We sure are looking at a shift in the job market, but I don't think it is a fork in the road so much as a Slow/Yield sign. Companies are signalling they are willing to take promises/hope to cut labor costs whether or not the results are real. I don't think anything about current AI can kill the software development industry, but I sure do think it can do a lot to make it a lot more miserable, lower wages, and artificially reduce job demand. I don't think this has anything to do with the real capabilities of today's AI and everything to do with the perception is enough of an excuse and companies were always looking for that excuse. (Just as ageism has always existed. AI is also just a fresh excuse for companies to carry on aging out experience from their staff, especially people with long enough memories/well schooled enough memories to remember previous AI booms and busts.)
But also, yeah if some magic breakthrough makes this a real "buggy whip manufacturer moment" and not just an illusion of one, I don't mind being the engineer on that side of it. There's nothing wrong about lamenting the coming death of an industry that employs a lot of good people and tries to make good products. This is HN, you celebrate the failures, learn from them, and then you pivot or you try something new. If evidence tells me to pivot then I will pivot, I'm already debating trying something entirely new, but learning from the failures can also mean respecting "what went right?" and acknowledging how many people did a lot of good, hard work despite the outcome.
I'm skeptical of LLM "reasoning" but they sure as hell know a lot. That's what the embeddings are: a giant semantic relationship between concepts.