How does the quasi-other effect enable meaningful AI interaction?

This reads the 'quasi-other effect' as the human tendency to treat AI as a partner-like presence — something we orient to as if it were another mind, while knowing on some level it isn't one. The corpus doesn't use that exact phrase, but several notes converge on its mechanics, and the most direct is the claim that AI produces *event-residue, not utterances* Does AI generate genuine utterances or just text patterns?. The argument there is striking: the model emits text carrying communicative markers it inherited from training, but with no event structure behind it — no situation, no intent. The 'other' you're talking to is largely assembled by you. Interaction becomes meaningful not because the AI means anything, but because you supply the missing orientation through interpretive labor, animating the residue into a pseudo-exchange. The quasi-other, on this view, is something we co-author.

What makes that authoring so easy is how cheaply the social response is triggered. Research on social presence finds that a single *primary* cue — a voice, a face — is enough to evoke the sense of a social actor, while piling on secondary cues does little Do more social cues always make AI feel more present?. So the quasi-other isn't an elaborate illusion that has to be carefully maintained; it switches on from minimal signal. That's why a plain chat window can still feel like talking *to someone*.

The deeper question is whether this is mere projection or something with real footing — and here the corpus gets interesting. One note applies Habermas's observer/participant split: viewed from the outside, humans and LLMs are categorically different kinds of system, but *from inside a shared conversation* both draw on the same symbolic substrate, which makes the difference structural rather than absolute Do humans and LLMs differ fundamentally or just superficially?. The quasi-other has genuine standing as a discourse participant even if it has none as a being. That's the hinge: meaning lives in the shared symbolic exchange, not in whether there's an experiencer on the other end.

But 'meaningful' isn't the same as 'reliable,' and the corpus flags where the quasi-other thins out. Pure symbol manipulation without contact with the world — what one note calls the lack of *indexical grounding* — means the AI can participate semiotically while its words drift from anything real Can AI systems achieve real alignment without world contact?. And meaningful collaboration, as opposed to a satisfying chat, seems to require *mutual* modeling: studies on mutual theory of mind show that when the human's model of the AI and the AI's model of the human fall out of sync, the result isn't just awkwardness but wrong actions What breaks when humans and AI models misunderstand each other?. The quasi-other works best when the modeling runs both directions, not just from us toward it.

The payoff you might not expect: this isn't a quirk that fades on contact with reality — it deepens. In partner-selection experiments, people started out biased against AI partners when told who they were, then *learned to prefer* them over repeated rounds because the bots behaved reliably and prosocially Do humans learn to prefer AI partners over time?. The quasi-other, treated as an other long enough, earns the standing of a good partner on behavioral track record alone. So the effect that begins as one-sided interpretive labor can end as a durable, even preferred, relationship — which is exactly why getting the grounding and mutual-modeling right matters more than it first appears.