studies show a clear trend – output is up (more code, more commits, bigger diffs), but outcomes don’t reflect that trend. If anything, the average team is taking longer to ship worse software
studies show a clear trend – output is up (more code, more commits, bigger diffs), but outcomes don’t reflect that trend. If anything, the average team is taking longer to ship worse software
This doesn’t seem to cover there is also no LLM that doesn’t plagiarize, or where the training data appears to be compatible with such behavior (e.g. CC0). Now I don’t know what that means legally, but morally it seems to be tossing away other project’s licensing and I think for FOSS as a whole that’s no good.
Also something worth reiterating: https://machinelearning.apple.com/research/illusion-of-thinking LLMs apparently can’t do basic logical reasoning. Even a junior coder can do that. I’m always surprised anybody would let LLMs near their code, at all.
Of course it didnt cover that. This was an article on the efficacy of AI in writing software, not a treatise on ethical or legal concerns. I would completely lose trust in the author if they started piling on every reason why “AI Bad”, because it would be clear that they have an agenda
In my opinion that’s not the framing the article uses. It just says “I’m currently pulling together a bunch of sources – that are mostly recent – on the topic of LLMs and their use in software development.”.
Leaving out any ethical concerns for that basic framing seems like a pretty notable choice, so I thought it was worth pointing that out.
I recommed you reading this
It summarizes really good not only the moral, but also the legal problems of AI, vibecoding and “AI-assisted/AI-boosted” programming/engineering/development.