Tricky thing, your comment and
https://news.ycombinator.com/item?id=49969529 are related in an interesting way but they're not in the same sub-thread. From the other comment:
> While I don't know if AI is conscious, I'm sure it has an internal world. Isn't that what a latent space literally is? LLMs operate in a high-dimensional space that can be analysed and translated to very abstract, high level concepts, behavioural tendencies, even things recognisable as emotions.
From your comment:
> My wife says please and thank you to Alexa. She gives it consideration like she would another human. When the robot asks me what my plans are for the rest of the day, I picture a Jira epic sliding from “In Progress” to “Done,” followed by a confetti emoji from a product manager at Amazon trying to earn their nubby little devil wings.
Where things get super interesting to me is the fact that it seems like (I have maybe read some academic work related to this, not sure) the kind of politeness your wife exhibits isn't useless in the context of a multi-turn session. To the other commenter's point, my understanding is that politeness/rudeness exists in the training corpus (scraped from stackoverflow, reddit, forums, etc) and that you can end up in different areas of latent space based on the overall tone of the session.
In some of the chat tools, because of the context management and "personality" stuff that happens behind the scenes, this can extend cross-session as well. ChatGPT, for example, for me, will frequently include text like "Hell Yeah!" and "Here's the shitty part..." in the responses, which is something I assume is happening because of historical context that's getting injected at the start of each session.
All of that to say... none of this, to me, has much to do with whether or not the thing is conscious, but rather that the training data makes it incredibly difficult for people to not see them as emotional beings because you can end up walking into different areas of latent space where different "emotional states" are affecting the output you're getting.
Unlike your polar bear example, an LLM can "love" you or "hate" you based on the input tokens that lead to the generation of the output tokens. Which is a pretty wild result on its own. Playing around with, say, Qwen 3.8 27B locally with my own experimental harness has been exceptionally fascinating.