You make the point that one does not need to learn these "obsolete" AI approaches because they are not relevant anymore. I don't understand why you say that. These approaches are still state of the art for their respective tasks and there is no other approach that has been shown to do any better, including deep neural networks. In what sense are they "no longer more than briefly and tangentially relevant" as you say?
Regarding the gold rush, the point of the analogy is that in a gold rush only a very few people will ever strike gold. This is exactly the state of research into deep learning currently. After a few initial big breakthroughs, like CNNs and LSTMs, progress has stalled and the vast, vast majority of published papers (or papers put on arxiv permanently) present incremental results, if that. Literally thousands of deep learning papers are published each month and the chance to have an impact is miniscule. From my point of view, as a researcher, going into deep learning right now would be career suicide. Not to mention that, while the first few successes were achieved by small academic teams, who had typical academic motives (er, glory), the game has now passed to the hands of big corporate teams that have quite different incentives, so it's almost impossible for small teams or individual researchers to make a dent.
As to the winter and whether GOFAI works, perhaps I haven't convinced you with my sources, but in that case, I have to go back to my earlier question and ask where your knowledge comes from. You clearly have a strong opinion on GOFAI and the AI winter of the '80s, but what knowledge does this opinion come from? Can you say? And if this sounds like a challenge, well, that's because it is. I'm challenging you to re-examine the basis of your convictions, if you like. Because to me, they sound like they are not well-founded and that you should put some water in your wine. The things you say "don't work", work and the things you say work, don't work as well as you say.
For my part, I certainly agree that GPT-3 or the next iteration of a large transformer-built language model can be a useful tool, but such a tool will always be limited by the fact that it's, well, a language model, and it can only do what language models do, which does not include e.g. the ability for reasoning (despite big claims to the contrary) or arithmetic (ditto) or generation of novel programs. For instance, the append() example you show above is clearly memorised: you haven't given the model any examples of append(), so it can't possibly learn its definition from examples. It only returns a correct result because it's seen the results of append() before. Not the same result, but close enough. Like I say, this ability can definitely be useful- but its usefulness is limited compared to the ability to learn arbitrary programs, never before seen.
btw, why do you need to give it the list "a"? What happens if this is ommitted from the prompt?