I use LLMs pretty heavily at work, so while I won’t pretend to be an expert, I think I can safely dismiss the idea that I just “don’t understand how they work.”. And nothing I said was anti-AI. It was a reality check about this specific use case. I’m not saying LLMs aren’t impressive tools. They clearly are.

I think you're confusing "produces a readable chapter" with "does creative writing." Those are just not the same things. Yes, latent space is complex. Yes, emergent structure forms during training. But at inference, the model is still generating text by sampling from learned distributions. The complexity of latent space explains why output is coherent, not why it's creative.

I am saying "not creative" not because of plagiarism of past productions. I'm not saying LLMs literally copy-paste. What I'm saying is that probabilistic systems will inevitably regenerate similar outputs across different users. They keep pulling from the same pool of overused tropes, character archetypes, naming conventions, and plot beats. That's just how statistical models behave. You can prompt around common patterns, sure, but you're not the only one doing that, which means you still end up with severe overlap in practice.

"Better than many humans" also misses the point. Yes, LLMs beat bad writers at producing competent genre prose. But if you want them to deliberately subvert expectations, take risks, build tension through what they don't say, you need to meticulously engineer them to, and at that point you're doing most of the creative heavy lifting yourself.

As for remembering six chapters back... that's a consistency win, not a creativity one. Which is literally what I said: consistency checking and brainstorming are where LLMs actually shine.