Using the tool in this way is a bit like mining: repeatedly hacking away with a blunt instrument (simple prompt) looking for diamonds (100x speedup out of nowhere). Probably a lot of work will be done in this semi-skilled brute-force sort of way.
It looks to me to be exactly what a typical coding interview looks like; the first shot is correct and works, and then the interviewer keeps asking if you can spot any ways to make it better/faster/more efficient
If I were a CS student cramming for interviews, I might be dismayed to see that my entire value proposition has been completely automated before I even enter the market.
Well, in this case it's kind of similar to how people write code. A loop consisting of writing something, reviewing/testing, improving until we're happy enough.
Sure, you'll get better results with an LLM when you're more specific, but what's the point then? I don't need AI when I already know what changes to make.
Reading to understand all the subtext and side-effects can be harder than writing, sure. But it won't stop people trying this approach and hammering out code full of those types of subtle bugs.
Human developers will be more focused on this type of system integration and diagnostics work. There will be more focus on reading and understanding than the actual writing. It's a bit like working with contractors.
