"Cognitive inbreeding" is an interesting (though maybe not entirely accurate) term for something I dislike a lot about LLMs. It really is a thing. You're recycling the same biases over and over, and it can be very difficult to tell if you don't review and distill the contents of your discourse with LLMs. Especially true if you're only using one.

I do think there's a solution to this—kind of—which dramatically reduces the probability and allowing for broad inductive biases. And that's to ask question with narrower scopes, and to ensure you're the one driving conversation.

It's true with programming as well. When you clearly define what you need and how things should be done, the biases are less evident. When you ask broad questions and only define desired outcomes in ambiguous terms, biases will be more likely to take over.

When people ask LLMs to build the world, they will do it in extremely biased ways. This makes sense. When you ask it specifics about narrow topics, this is still be a problem, but greatly mitigated.

I suppose what's happening is an inversion of cognitive load, so the human is taking on more and selecting bias such that the LLM is less free to do so. This is roughly in line with the article's premise (maybe not the entire article, though), which is fine; I think I generally agree that these are cognitive muscles that need exercising, and allowing an LLM to do it all for you is potentially harmful. But I don't think we're trapped with the outcome, we do have agency, and with care it's a technology that can be quite beneficial.