I wonder, if mass adoption of LLM code-generation will accelerate this process or not. On the one hand, it is now easier to "write" (LLM-generate) code then ever, so time/effort savings are not there anymore (probably, I'm not sure). On the other hand, now everyone uses LLMs so, I think, people are less deterred by non-determinism and statistical nature of ML models.
Let's imagine that you are making a CLI tool. What does a CLI tool do? Well, it accepts reads arguments, reads stdin, writes stdout and make syscalls. So, all possible inputs and outputs are very well defined. What if in the future it will be easier (and maybe even more natural) to ask an LLM to "imagine" tons of possible inputs and correct outputs for a tool that you are making and then train a tiny model, without writing or generating any code?