Sounds plausible. Is that 50% of coding work that the LLMs replace of a particular sort, and the other 50% a distinctly different sort?

My impression is that they are getting consistently better at coding tasks of a kind that would show up in the curriculum of an undergrad CS class, but much more slowly improving at nonstandard or technical tasks. 

I do use LLMs for coding assistance every time I code now, and I have in fact noticed improvements in the coding abilities of the new models, but I basically endorse this. I mostly make small asks of the sort that sifting through docs or stack-overflow would normally answer. When I feel tempted to make big asks of the models, I end up spending more time trying to get the LLMs to get the bugs out than I'd have spent writing it all myself, and having the LLM produce code which is "close but not quite and possibly buggy and possibly subtly so" that I then have to understand and debug could maybe save time but I haven't tried because it is more annoying than just doing it myself.

If someone has experience using LLMs to substantially accelerate things of a similar difficulty/flavor to transpilation of a high-level torch module into a functional JITable form in JAX which produces numerically close outputs, or implementation of a JAX/numpy based renderer of a traversable grid of lines borrowing only the window logic from, for example, pyglet (no GLSL calls, rasterize from scratch,) with consistent screen-space pixel width and fade-on-distance logic, I'd be interested in seeing how you do your thing. I've done both of these, with and without LLM help and I think leaning hard on the LLMs took me more time rather than less.

File I/O and other such 'mundane' boilerplate-y tasks work great right off the bat, but getting the details right on less common tasks still seems pretty hard to elicit from LLMs. (And breaking it down into pieces small enough for them to get it right is very time consuming and unpleasant.)

I think that "getting good" at the "free association" game is in finding the sweet spot / negotiation between full freedom of association and directing toward your own interests, probably ideally with a skew toward what the other is interested in. If you're both "free associating" with a bias toward your own interests and an additional skew toward perceived overlap, updating on that understanding along the way, then my experience says you'll have a good chance of chatting about something that interests you both. (I.e. finding a spot of conversation which becomes much more directed than vibey free association.) Conditional on doing something like that strategy, I find it ends up being just a question of your relative+combined ability at this and the extent of overlap (or lack thereof) in interests.

So short model is: Git gud at free association (+sussing out interests) -> gradient ascend yourselves to a more substantial conversation interesting to you both.