Yes. Agents are good at solving densely represented (embarrassingly solved) problems, and a surprising and disturbing number of problems we have are, at least at the decomposed level, well represented. They can even compose them in new ways. But for the same reason they would be unable to derive general relativity, they cannot use insight to reformulate problems. I base this statement on my experience trying to get them to implement Flying Edges, a parallel isosurface extraction algorithm. It’s a reformulation of marching cubes, a serial algorithm that works over voxels, that works over edges instead. If they’re not shown known good code, models will try and implement marching cubes superficially shaped like flying edges.

You are still necessary to push the frontier forward. Though, given the way some models will catch themselves making a conceptual error and correct in real time, we should be nervous.

If you regard a CS degree as vocational training to "code" then perhaps not - but I don't think that's really how people should be regarding a CS degree?

Is there any point in teaching aviation engineering when an LLM could probably generate something that looks reasonable from a corpus existing work?

Depends on whether one wants to be a software engineer or a mere LLM operator.

To be fair to the parent poster, many people do seem to aspire only to be LLM operators, who will be a dime-a-dozen commodities accorded even less respect and pay than the average developer is today.

Computer science and coding are as related as physics and writing. If your thesis is the LLM can replace all of science then you have more faith in them than I do. If anything the LLM accelerates computer science and frees it from the perception that it is coding.