When you reach into a "statistical model" and find that it has generalized abstracts like "deceptive behavior", or "code error"? Abstracts that you can intentionally activate or deactivate - making an AI act as if 3+5 would return a code error, or as if dividing by zero wouldn't? That's abstract thinking.

Those are real examples of the kind of thing that can be found in modern production grade AIs. Not "anthropomorphizing" means not understanding how modern AI operates at all.

I don't think you have any idea what you're talking about at all.

You've clearly read a lot of social media content about AI, but have you ever read any philosophy?

Almost all philosophy is incredibly worthless in general, and especially in application to AI tech.

Anything that actually works and is in any way useful is removed from philosophy and gets its own field. So philosophy is left as, largely, a collection of curios and failures.

Also, I would advise you to never discuss philosophy with an LLM. It might be a legitimate cognitohazard.

By comparing measurable performance metrics and examining what little we know of the internal representations.

If you don't have anything measurable, you don't have anything at all. And philosophy doesn't deal in measurables.

How do you know what is, isn't, could be, or couldn't be measurable?

You're not being serious.