And I agree that "less AI" edit passes reduce a lot of the hallmarks, but I think these models truly struggle with expressing complicated ideas. Have Opus been good at that for you?

I think people should find this sort of result surprising by default. Like, presumably you don't know why your output isn't as good, or you'd fix it. So doesn't that lack of understanding concern you?

Good question! It mostly doesn’t concern me. I believe these models have a huge “latent space,” so it’s not surprising to me that other people, with the right harnesses and prompts, can get them to produce more-valuable content.

I spent a lot of time calibrating how I use these models to produce valuable, good code, right?

But as I alluded with my question above, I haven’t seen a ton of evidence that these models have human-level argumentation in that latent space. If my assumption there is wrong, then yes, I’d have to reexamine my understanding of these models.

Ah, fair, there's opportunity cost to that. May as well prioritize what you actually care about, yeah. Personally I'm fine with skimming through the prose in the default style, at least as long as the LLM isn't getting stuck in its own invented jargon. It'd be different if I were publishing that output as project documentation, but I don't even really want to try that.

> I haven’t seen a ton of evidence that these models have human-level argumentation in that latent space.

Not sure what you mean by argumentation. I'd agree that they don't seem to have flashes of insight or make intuitive leaps in the same way. But they clearly can follow through the logical steps of reasoning very thoroughly, once they know what to reason about. For a technology that seemed to struggle with understanding negation a couple years back, it's seemingly miraculous.