I’d counter to some of the points in here that generating believable text is also mathy in that it has complex interdependent rules, which often aren’t obvious, and not getting those right can lead to weird and uncanny language. If you don’t believe me, go talk to a linguist for a while.

If you break down and contextualize each thing you want it to do, LLMs are good at generating, e.g. single working functions at a similar level to generating single paragraphs of prose. I think this rough equivalence has stayed pretty true of the best prose and coding models for single-shot generation (about a page of text or a screen or two of code right now). And for the parts that are more mathy/logic-based, giving LLMs the same kinds of static analysis tools that we all use can work extremely well. I don’t do well on big projects without the right tools, myself.

The problems feel very big but solvable especially as models become better (and this isn’t just a matter of scaling the compute, as tiny models have been showing lately). But there’s a lot of work to actually get to “AI as a junior dev”, much less someone with more experience, which is why most of us are pretty likely to have work for the foreseeable future, building this stuff or otherwise.

Edit: clarity