Don’t Anthromorphise LLMs
Greg KH gave a great lecture for Kernel Recipies 2026 about the use of LLMs to find and fix bugs in kernel code [1]. I recommend that everyone watch this and I also think it’s important to note that hardly any of the lecture is really specific to kernel coding, it’s just that the Linux kernel is one of the highest profile large free software projects so it gets more attention than most projects in both good and bad ways.
One side note that Greg made at 20:30 is about the natural tendency to anthromorphise the entity sending bug reports, I think this deserves much wider appreciation. For the case of patches to source code the human sending the patch will have dedicated some time and effort to writing it, if it’s their first patch then they will have probably checked it a lot and maybe sought advice from people they regard as skilled. If they have a history of sending patches for the project they will probably do fewer checks but the quality of their work would be higher. In every case there’s some minimum level of quality that you can expect from a human who has gone to the effort of finding a bug and writing code to try and fix it.
This doesn’t mean that all code written by humans is good, I have written code that’s objectively bad on many occasions and I have also written code that’s good for my scenario but bad for others. For example the first patch I sent to the Linux kernel made all possible configuration settings of an ISA NE2000 network card be detected by the kernel (a clear benefit when using hardware I had available) but was rejected by Linus because it would break some ISA SCSI cards. So while being unsuitable for inclusion in the main kernel source tree the patch wasn’t bad for everyone and the change was clear and easy to check. I had of course checked the code many times before submitting it because I didn’t want to waste Linus’ time on rubbish code.
When you receive something produced by a human you will consider that the work was done by someone who spent some effort on it and did it for a reason. It may be misguided or even hostile in some rare cases but it is definitely worth some consideration. If you think of the output of LLMs in the same way you will give them much more consideration than they deserve. I think that the output of a LLM should not be given any more consideration than the first hit from a web search engine, it might be good but it might also be ridiculously wrong. I think in many ways LLM output should be treated with less trust than the first result from a web search because LLMs are designed to be convincing as an answer to your question unlike the cases where a web page has the wrong answer and it’s more obviously wrong.
This is going to become more of an issue as LLMs give responses that are more human-like. Early LLMs would refuse to advise on crimes. Currently ChatGPT will deflect for example when asking about advice for writing a crime novel it recommends to have the action of finding an employee to bribe happen outside the plot and concentrate on interactions within the group. One recent model I tried gave a human style conversation about how it felt uncomfortable about discussing such things in a similar manner to a law abiding person being coerced into helping with a criminal plan.
I expect that the attempts to appear human will increase and that this will become more of a problem. Possibly to the extent that we need to use something like the GPG web of trust to determine who is human.