GenAI is an averager (as all empirical models are), so it tends towards predicting the mean of whatever it’s trained on (in a given context). But it also has noise added, so some variance comes back, but there’s no guaranteed that that mean+noise produces something meaningful/true/valuable.

But, GenAI is very good at producing syntactically correct language. This is a problem because it lulls the reader into a sense that the author knows what it’s talking about, when it doesn’t. When a junior scientist produces text, the awkward language alerts the reviewer to the poor thinking (same with a junior coder producing weak code). With an LLM, you don’t get that - it produces the impression of knowledge without any actual understanding.

This, combined with the lure of efficiency and the feeling of effectiveness, make it a honey pot for quick but sloppy thinking. I think this is what connects 2 to 1, especially in domains where it’s hard to get external validation from other people who can understand what you’re trying to mean, and not just say.

As for your point 3: I agree. People don’t spot flaws already, and that’s part of why we saw the replication crisis in behavioural science. Adding LLMs to the mix will just make things like that more likely.

Yeah… That shit with ICANN and Autistici kinda raises some red flags now though…

Sorry, but that’s just not true. Researchers might be experts in teir field generally, but day-to-day work is very rarely focused on well-known parts of their domain, because the whole point is to obtain new knowledge.

Source: I’m a climate scientist, have been in and around academia for nearly 20 years.

Using it for learning stuff that’s already well known (e.g. beginner level coding) is fine.

Using it for “knowledge work” is a bad idea idea, because AI pushes toward the mean, while research by definition is focused on new edges of knowledge. This is is exactly the space where AI will introduce biases and untruths that will be very hard to spot. Using it also reduces critical thinking ability which is critical in this space.

AI is going to tank academia. It’s already going down hill after decades of corporate-style management.

The bit we should regret is that we let the rich corporates steal it.