[steve_adams_86]

I don't agree.

There are large yet trivially generated change sets that you can trust an LLM to do, and review easily. There are large errors LLMs are incredible at parsing and inferring causes from. There are unstructured logs you can feed into an LLM and get meaningful information out of. You can save yourself a tremendous amount of time if you know when to use them.

Then there are test and analysis harnesses, scripts for performing rudimentary tasks, having them fetch data from disparate sources and synthesizing them in one place. This is all extremely helpful.

I was skeptical for a long time, but it's a significant multiplier now. I write a lot of code myself still, I review everything, but so much of what the LLM does is supplementary to the outputs. They evaluate, unblock, inform, and triage well enough and so ridiculously quickly that being correct even 70% of the time is still useful. It doesn't take much guidance and guard railing to get them well above that metric.