The real breakthrough would be finding a way to not even do things that don’t need to be done in the first place.
90% of what management thinks it wants gets discarded/completely upended a few days/weeks/months later anyway, so we should have AI agents that just say “nah, actually you won’t need that” to 90% of our requests.
This seems silly to me. In most cases, the least amount of work you can possibly do is logically describe the process you want and the boundaries, and run that logic over the input data. In other words, coding.
The idea that we should, to avoid coding or reading code, come up with a whole new process to keep generated code on track - would almost certainly take more effort than just getting the logical incantations correct the first time.
It’s even worse with offshore devs. They produce a ton of code you have to review every morning.
Something like blowing up the stack or going out of bounds is more obviously a bug, but detecting those will often require inferences from how a code behaves during runtime to identify. LLMs might work for detecting the most basic cases because those appear most often in their data set, but whenever I see people suggest that they're good at reviewing I think it's from people that don't deeply review code.
With LLMs, the volume of code has only gotten larger but those same LLMs can help review the code being written. The current code review agents are surprisingly good at catching errors. Better than most reviewers.
We'll soon get to a point where it's no longer necessary to review code, either by the LLM prompter, or by a second reviewer (the volume of generate code will be too great). Instead, we'll need to create new tools and guardrails to ensure that whatever is written is done in a sustainable way.