Why does the commentariat reason about AI using vocabulary for smart agents?
This explores why public and pundit discourse reaches for agent vocabulary — intent, goals, understanding, reasoning — to describe systems that may not be agents in that sense, and what in the AI itself invites that framing.
This reads the question as being about projection: why the people who talk about AI for a living default to the language of smart, goal-having agents. The corpus suggests the framing isn't a careless mistake — it's actively invited by how these systems behave, and then completed by the human reader. The sharpest account is that AI doesn't produce utterances at all; it produces what one note calls "event-residue" — text carrying the communicative markers it inherited from training data, but missing the event structure that makes a real utterance. Humans then animate that residue into a pseudo-exchange, supplying the orientation, intent, and stance that only ever existed on the human side Does AI generate genuine utterances or just text patterns?. The agentive vocabulary, in other words, describes the listener's interpretive labor as if it were a property of the speaker.
What makes this projection so easy is that fluency masks passivity. Conversational LLMs are structurally reactive — they can't initiate a topic, plan strategically, or pursue a goal of their own, because training optimizes for responding to queries rather than originating dialogue — yet the polish of the output hides that absence of goal-awareness Why can't conversational AI agents take the initiative?. When something answers smoothly, the cheapest available explanation is "it understood," and the commentariat reaches for the agent word.
There's also a deeper reason the framing feels defensible rather than naive. One note applies Habermas's observer/participant split: viewed from the outside as systems, humans and LLMs are categorically different, 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?. So when you're inside the discourse — which is exactly where pundits sit — the model genuinely occupies the seat of a participant, and participant-vocabulary follows.
But the corpus also shows where the smart-agent language overreaches. Models master grammar while avoiding the evaluative stance-taking that carries a real point of view, producing prose that is coherent but argumentatively inert Why does AI writing sound generic despite being grammatically correct?. They fail at lexical entrainment — the way human speakers drift toward each other's word choices to build rapport lexical-entrainment-is-absent-from-current-conversational-ai-despite-being-fundamentally. These are exactly the agent-like behaviors the vocabulary implies, and they're conspicuously missing.
The quiet punchline is economic. Most of what we file under "agentic" work turns out to be repetitive, well-defined language tasks that small models handle at a fraction of the cost — the heavy general intelligence is invoked only selectively Can small language models handle most agent tasks?. The commentariat reasons about AI with the vocabulary of smart agents partly because the surface invites it, partly because they're standing inside the conversation where the model looks like a peer — but the systems doing the actual work are closer to fast, narrow language machines than to the strategizing minds the language conjures.
Sources 6 notes
AI output carries communicative markers inherited from training data but lacks the event structure that produces actual utterances. Users supply the missing orientation through interpretive labor, creating a pseudo-event with structure only on the human side.
Research shows LLMs including ChatGPT cannot initiate topics, plan strategically, or lead conversations because their training optimizes for responding to queries, not creating dialogue from agent goals. This passivity is reinforced by alignment objectives and masked by fluent-sounding outputs.
Applied Habermas's observer/participant distinction to AI: from outside, humans and LLMs are utterly different; from within shared discourse, both draw on the same symbolic substrate, making the difference structural rather than absolute.
AI text uses manner nouns and anaphoric references that are descriptively neutral, while human writers use status and evidential nouns that carry evaluative weight. This produces organizationally coherent but argumentatively inert prose.
SLMs handle the repetitive, well-defined language tasks that constitute most agent work at 10–30× lower cost than LLMs, making heterogeneous architectures (SLMs by default, LLMs selective) the economically rational design pattern.