The point is, they don’t get “competent”. They get better at assembling pieces they were given. And a proper stack with competent developers will already have moved that redundancy out of the codebase. For whatever remains, thinking is the longest part. And LLM can’t improve that once the problem gets a tiny bit complex. Of course, I could end up having a good rough idea of what the code should look like, describe that to an LLM, and have it write actual code with proper variable names and all, but once I reach the point I can describe accurately the thing I want, it’s usually as fast to type it. With the added value that it’s easier to double check.

What remains is providing good insight on new things, and understanding complex requirements. While there is room for improvement, it seems more and more obvious that LLM are not the answer: theoretically, they are not the right tool, and seeing the various level of improvements we’re seeing, they definitely did not prove us wrong. The technology is good at some things, but not at getting “competent”.

Also, you sweep out the privacy and licensing issues, which are big no-no too.

LLM have their uses, I outline some. And in these uses, there are clear rooms for improvements. For reference, the solution I currently use puts me at accepting around 10% of the automatic suggestions. Out of these, I’d say a third needs reworking. Obviously if that moved up to like, 90% suggestions that seems decent and with less need to fix them afterward, it’d be great. Unfortunately, since you can’t trust these, you would still have to review the output carefully, making the whole operation probably not that big of a time saver anyway.

Coding doesn’t allow much leeway. Other activities which allow more leeway for mistakes can probably benefit a lot more. Translation, for example, can be acceptable, in particular because some mishaps may automatically be corrected by readers/listeners. But with code, any single mistake will lead to issues down the way.