Do humans and LLMs differ fundamentally or just superficially?
Explores whether the gap between human and AI cognition is categorical or contextual. Matters because it shapes how we design, evaluate, and interact with language models in practice.
This is a direct application of Habermas's distinction between the "perspective of an observer" and the "perspective of a participant in interaction."
From the observer perspective, the difference is categorical and clear: humans are biological agents with embodied consciousness, socialized subjectivity, and reflexive self-understanding. LLMs are statistical pattern-matching systems running on hardware, with no awareness or agency. Their computational mechanisms are nothing alike.
From the participant perspective — inside a discourse, where what matters is the meaning being exchanged — the difference is more subtle. Both participants are drawing on the same intersubjectively shared universe of meanings. The LLM produces outputs that are structurally meaningful within that universe because it was trained on it. Whether it "understands" in any deeper sense is secondary to the fact that its outputs enter the discourse on the same terms.
This is not a claim that LLMs are conscious or that the distinction doesn't matter. It is a structural observation about what discourse is: a space defined by shared symbolic resources, not by the inner states of participants. From inside that space, the LLM is a participant drawing on the right resources.
The practical implication for AI design: designing interactions around the observer perspective ("it's just a statistical model") misses what users actually experience. Users interact from within discourse — from the participant perspective — and that perspective is where the LLM's shared symbolic substrate makes it feel more like a peer than a tool.
Source: Discourses
Related concepts in this collection
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Do LLMs develop the same kind of mind as humans?
Explores whether LLMs and humans share the intersubjective linguistic training that shapes cognition, and whether that shared training produces equivalent forms of agency and reflexivity.
the Habermas framing this is derived from
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Does AI text affect readers the same way human text does?
If text is a condition of social processes rather than merely a container, does the origin of text matter to its effects? This explores whether AI-generated content enters the same interpretive and epistemic circuits as human writing.
the same participant-perspective logic applied to text rather than interaction
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Original note title
from the observer perspective humans and llms differ categorically but from the participant perspective the difference is subtle