The report seems interesting if you want to have a look: https://www.amnesty.org/en/documents/pol40/0996/2026/en/ Although when I skimmed it, it felt a little one-sided to me. A little overly focused on “average home user and impact on them” and less so other impacts.

For example, I found little on the wider impacts on art and personal expression in a society, like explored here: https://www.theguardian.com/commentisfree/2025/may/20/ai-art-concerns-originality-connection

Although I suppose perhaps that angle isn’t concrete enough for human rights violations, I don’t know. Or perhaps I just missed it. It’s a fairly long report.

I suppose that could be one possible explanation. Let’s hope that this is why. However, the previous comments do make me worry.

No worries, the amount of new slop code with hidden bugs is entering the ring to try to keep the balance and to ensure the overall security doesn’t improve by too much: https://www.neowin.net/news/linus-torvalds-declares-massive-ai-fueled-code-surges-as-the-new-normal-for-linux/

I think this is the report it talks about: https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report Does this link work better?

My apologies, I think this is the report it is talking about: https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report

I’ve had success, for example, having them remove pointless, confusing try…except blocks surrounding imports at work.

And you may have introduced some dangerous hidden bug that way, which you may not have doing it manually.

(I’m not saying that makes it not worth it, this is just what the studies are saying. I personally think it’s not worth it, but I realize there is some subjectivity here.)

They excel at specific tasks that are built for them

They are however widely known to be terrible at code, at least compared to an advanced coder. They introduce not only more bugs even after human review, but new kinds of more insideous bugs.

I like to say the main problems with most projects were already the code quality and the bugs, and not that we somehow needed even more low quality lines of code.

(Disclaimer: not talking about passive AI bug analysis here, just using AI to write actual code.)

Quoting studies to actually back up one’s point is in my opinion far less of an echo chamber and a fantasy than anecdotes of “but for me it feels faster”. Especially when AI is known to slow people down while making them feel faster.

Are you asking me to reject my professional daily reality?!

Can you point me to a single field study that shows programmers become faster and not just feel faster, and that doesn’t come with some caveat like they haven’t tested AI coders vs non-AI coders, or coders without significant AI exposure before (since otherwise it won’t rule out simply becoming dependent)?

Even if you could find one, and I was unable to so far, it doesn’t change that:

1. you are probably faster by verbatim plagiarizing somebody’s other project at a large scale, and

2. by making yourself addicted and reliant on the AI where your own skill is eroding: https://www.404media.co/software-developers-say-ai-is-rotting-their-brains/ (if you get a paywall: https://archive.is/tHq80 ) and

3. by having a higher rate of bugs in your code no matter how carefully you review it https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report which especially for security sensitive projects may have dire long term consequences, and

4. by encouraging the environmental destruction brought on in particular by the training of new models.

Two caveats:

1. Keep in mind more lines of code is not a useful metric for faster project completion and faster maintenance task completion, especially for code bases that are already large.

2. I’m merely speaking about using LLM code in your project, so for example LLM auto completion or copy&pasting code from a chatbot. I’m mot talking about LLM code reviews that point out issues in natural language.

https://machinelearning.apple.com/research/illusion-of-thinking It’s not surprising LLMs keep messing up in what seem to be the most braindead ways.

LLMs seem to be inherently dumb: https://machinelearning.apple.com/research/illusion-of-thinking

And from what I can find in recent studies, no, they didn’t suddenly get smart. They just plagiarize slightly better: https://www.sciencedirect.com/science/article/pii/S2949719123000213#b7

We found that the models that consistently output the highest-quality text are also the ones that have the highest memorization rate.

AI code is pretty unusably bad for long term use anyway https://medium.com/@dumaysacha/i-saw-the-horror-of-ai-and-coderabbit-ai-did-too-a09622ac85de so best solution is to just to handwrite proper code as before. It’s not like we ever had much of an output problem in most coding industries, it was always a quality and bugs problem.

Feels like what they actually want is an excuse for even more public surveillance. AI cameras with remote cloud upload are a privacy nightmare when used in public places. You’re feeding everyone’s movements through those intersections to AI companies.

I wonder what people’s opinions are on the kernel drivers apparently still not being in mainline. That appears to severely limit the availability of other Linux variants. Personally, that’s one of the ecosystem problems that bothers me the most.

I guess prepare for potential kernel rot: https://www.neowin.net/news/linus-torvalds-declares-massive-ai-fueled-code-surges-as-the-new-normal-for-linux/

https://cssdoom.wtf/

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The studies about hidden errors don’t really care about how “slop” the code looks, as far as I understand them. That’s why LLM code is kind of dangerous.

My condolences.