[thatguyknows]

This has been my experience as well. I cannot understand how people are saying it has given them order of magnitude speed improvements in shipping.

Most code on github is by definition average, which is usually a few levels below the quality of an expert programmer. So it's not surprising the output of code LLMs is poor to average quality. For any larger block of code it outputs, a non-trivial amount of editing is required if a higher bar of quality is wanted.

[jacobr1]

Around half of developers write below average code, by definition. So it isn't surprising that raising the quality to average _for them_, and doing it faster, would be a productivity boon.

I'm a crappy marketing copy writer. A professional writer could do much better. But with chatGPT I too can write hack-quality marketing-copy that roughly conveys the a message and give it others to put in various marketing outlets.

[sdiupIGPWEfh]

> Around half of developers write below average code, by definition.

By definition, half the code out there is of below median quality. Whether or not half the code out there is of quality below the arithmetic mean depends on assumptions about the distribution of code quality. I would suspect that much more than half the code out there is "below average", so to speak.

[jacobr1]

Yep, there are assumptions all around. On the developer side, I also suspect there is a huge group casual "low-code" type programmers out there that could be assisted by copilot type tools. My main point was that the argument that the code produced by LLM type systems is low-quality is not really a barrier to adoption, and _might_ actually _raise_ the average quality of systems built in the wild. I'm less concerned with the actually distributions and more noting the point that the different relative value-point exists.

[sdiupIGPWEfh]

All pedantry aside, even if I'm skeptical and concerned at the moment, your hypothesis could well end up being true in the mid to long term.