The malware paranoia is so strong that my company has had to temporarily block use of 4.7 on our IDE of choice, as the model was behaving in a concerningly unaligned way, as well as spending large amounts of token budget contemplating whether any particular code or task was related to malware development (we are a relatively boring financial services entity - the jokes write themselves).

In one case I actually encountered a situation where I felt that the model was deliberately failing execute a particular task, and when queried the tool output that it was trying to abide by directives about malware. I know that model introspection reporting is of poor quality and unreliable, but in this specific case I did not 'hint' it in any way. This feels qualitatively like Claude Golden Gate Bridge territory, hence my earlier contemplation on steering vectors. I've been many other people online complaining about the malware paranoia too, especially on reddit, so I don't think it's just me!

Unfortunately, this is not the case. You can detect only the most obvious cases of the output from these tools. The distinctive presentation of these tools is a very intentional design choice - partly by the construction of the RLHF process, partly through the incentives given to and selection of human feedback agents, and in the case of Claude, partly through direct steering through SA (sparse autoencoder activation manipulation). This is done for mostly obvious reasons: it's inoffensive, 'seems' to be truth-y and informative (qualities selected for in the RLHF process), and doesn't ask much of the user. The models are also steered to avoid having a clear 'point of view', agenda, point-to-make, and on on, characteristics which tend to identify a human writer. They are steered away from highly persuasive behaviour, although there is evidence that they are extremely effective at writing this way (https://www.anthropic.com/news/measuring-model-persuasivenes...). The same arguments apply to spelling and grammar errors, and so on. These are design choices for public facing, commercial products with no particular audience.

An AI detector may be able to identify that a text has some of these properties in cases where they are exceptionally obvious, but fails in the general case. Worse still, students will begin to naturally write like these tools because they are continually exposed to text produced by them!

You can easily get an LLM to produce text in a variety of styles, some which are dissimilar to normal human writing entirely, such as unique ones which are the amalgamation of many different and discordant styles. You can get the models to produce highly coherent text which is indistinguishable from that of any individual person with any particular agenda and tone of voice that you want. You can get the models to produce text with varying cadence, with incredible cleverness of diction and structure, with intermittent errors and backtracking and _anything else you can imagine. It's not super easy to get the commercial products to do this, but trivial to get an open source model to behave this way. So you can guarantee that there are a million open source solutions for students and working professionals that will pop up to produce 'undetectable' AI output. This battle is lost, and there is no closing pandora's box. My earlier point about students slowly adopting the style of the commercial LLMs really frightens me in particular, because it is a shallow, pointless way of writing which demands little to no interaction with the text, tends to be devoid of questions or rhetorical devices, and in my opinion, makes us worse at thinking.

We need to search for new solutions and new approaches for education.

I don't want to be disrespectful to the authors, and it's (vaguely) interesting to see how far they've been able to go with this, but this idea is still an abomination.

I still use it sometimes for trivial stuff like giving me recipe or travel inspiration (using the web search API), but I haven't been using it for any sort of algorithms/coding stuff.

It's useful for tasks which have a high tolerance for not being completely correct. Which is definitely a subset of all tasks that interest me, but it's a smaller subset that I originally thought it would be. It's just really hard to find out where the models are wrong when it's a complex question. If it wasn't, then I wouldn't need the tool, I guess.