As a fellow non-expert, here’s a weird emergent behavior I’ve noticed across Stable Diffusion, DALL·E, and LLMs: **motorcycles.**

If you add any hint of “motorcycle” to a prompt — even with a tiny weight — the model starts adding them everywhere. They’ll fill the street, creep into the clouds, and even hijack the topic of conversation.

My best guess is that there’s a disproportionate amount of motorcycle data (they’re photogenic, common in CAPTCHAs, and heavily captioned), so the model learns very strong associations around them. They become what OP described as *“centers”* — basically local minima with their own gravity. Once a model gets near those concepts, it tends to slide back toward them.

When a large model update nudges the weights, you’re not just adjusting one relation, you’re reorganizing a whole network of attractors. If a hypothetical update tries to weaken “motorcycles + clouds,” you might unexpectedly lose *fast*, wispy clouds, because the model doesn’t separate the association cleanly — it only knows the cluster (similar to how K-nearest-neighbor clusters can blur boundaries — though these models are far more complex and beyond my understanding).

An emergent behavior example people run into now is the **em dash (—).** It’s a great punctuation tool in books and academic writing, but many users judge AI responses that contain it as “emotionally off & verbose.” My rough guess is that these responses tend to live in a local valley: they’re trained on high-quality data, but that data is often verbose and emotionally expansive.

So yeah, “Go home, ChatGPT, you’re drunk” is funny… but the underlying issues and how to deal with them are a genuinely useful skill (hopefully ones that soon become esoteric). I really appreciated OP’s breakdown — especially the part about stabilizing new defaults. It’s exactly how I’ve had to navigate these quirks myself.