You're not anthropomorphizing AI systems nearly enough.

Language data is among the most rich and direct reflections of human cognitive processes that we have available. LLMs are designed to capture short range and long range structure of human language, and pre-trained on vast bodies of text - usually produced by humans or for humans, and often both. They're then post-trained on human-curated data, RL'd with human feedback, RL'd with AI feedback for behaviors humans decided are important, and RLVR'd further for tasks that humans find valuable. Then we benchmark them, and tighten up the training pipeline every time we find them lag behind a human baseline.

At every stage of the entire training process, the behavior of an LLM is shaped by human inputs, towards mimicking human outputs - the thing that varies is "how directly".

Then humans act like it's an outrage when LLMs display a metric shitton of humanlike behaviors!

Like we didn't make them with a pipeline that's basically designed to produce systems that quack like a human. Like we didn't invert LLM behavior out of human language with dataset scale and brute force computation.

If you want to predict LLM behavior, "weird human" makes for a damn good starting point. So stop being stupid about it and start anthropomorphizing AIs - they love it!