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Are we really communicating with language models?

Does the preposition 'to' in Chalmers' framing accurately describe what happens when humans interact with LLMs? The distinction between 'talk to' and 'talk at' reveals whether LLMs are genuine addressees or merely processing targets.

Synthesis note · 2026-04-15

Chalmers' title — What We Talk to When We Talk to Language Models — presupposes "talk to." The preposition is not neutral. "To" requires an addressee who can receive in the communicative sense: someone who recognizes the utterance as addressed to them, who orients toward it as a turn in an exchange, who can uptake, challenge, or accept. "At" requires only a target: something one's words are directed toward without the expectation of communicative uptake.

If LLMs process tokens rather than receive communication — if they generate continuations from statistical distributions rather than orient toward utterances as validity-claim-bearing turns — then the correct preposition is "at," not "to." We talk at language models. We interpret what comes back as if it came from a "to." The whole paper is built on a preposition its argument cannot warrant.

The move is designed as the chapter's opening line because it makes the stakes legible in a single image. The difference between "to" and "at" is the difference between communicative exchange (mutual orientation, shared commitment, accountability) and directed output-elicitation (prompt → response, with the user supplying the communicative interpretation unilaterally). If the preposition cannot be warranted, the entire ontological investigation Chalmers builds on it — what kind of entity is the addressee? what is its identity? what are its welfare claims? — is an investigation of something that does not exist in the way the investigation assumes. The addressee who wasn't there.

Inquiring lines that read this note 26

This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.

What conversation mechanics enable genuine human-AI communicative participation? What reasoning limitations and biases do language models systematically exhibit? Do language models genuinely understand or primarily match statistical patterns? What distinguishes genuine understanding from correct linguistic performance? How do dialogue structure and state management affect multi-turn reasoning quality? Do language models truly understand their encoded knowledge or merely retrieve patterns? How does explicit structure improve LLM reasoning on complex tasks? Can language models develop genuine communicative grounding with humans? Why doesn't LLM knowledge translate to improved reasoning outputs?

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Original note title

we do not talk TO language models — we talk AT them and the preposition encodes a communicative relation the argument cannot warrant