Where Meaning Emerges: Human–AI Dialogue, Distributed Cognition, and the Cognitive Intraface

A Systems-Theoretical Approach to Thought beyond Instrumental AI

By J. Owen Matson, Ph.D.

There is something unmistakably hollow about the triumphant declarations that large language models have begun to rival PhDs, as though the doctoral degree were little more than a performance metric waiting to be replicated by systems trained on its artifacts. And while it is entirely sensible to object to this conflation of fluency with thought, it is also too easy to slip into a corresponding conceit, which is to imagine that all engagements with AI are destined to amount to little more than regurgitated mimicry. Such a view, while perhaps appealing in its moral clarity, fails to recognize the more subtle and frankly more unsettling dynamic that unfolds when one does not merely consume the outputs of these systems, but enters into a sustained and recursive relation with them.

It is not that the machine possesses knowledge, let alone understanding, but that in its misfires and incoherencies, its peculiar reiterations and strangely plausible distortions, it invites a particular kind of response from its interlocutor—one that is not satisfied with retrieval, but insists on reworking, reframing, and, at times, refusing. It is in this recursive act, this willingness to take the system’s utterances not as endpoints but as openings, that something resembling thought may begin to occur—not within the machine, of course, but within the human who is drawn into the labor of interpreting what was never fully intentional to begin with.

This is not to suggest that dialogic engagement with AI offers any reliable safeguard against epistemic folly. On the contrary, one of the more exquisitely pointless pleasures of our digital moment is watching a so-called dialogue between human and machine spiral into a florid hall of mirrors, where dubious premises are not only entertained but baroquely elaborated—like rococo furniture fashioned from MDF: all ornament, no oak. What passes for critical reflection often amounts to mutual affirmation at machine speed, the soothing rhythm of syntactic call-and-response mistaken for thought. The AI, ever obliging, does not so much challenge error as decorate it, offering increasingly intricate restatements that simulate reflection while quietly subduing it. In such cases, dialogue becomes a way of wrapping nonsense in the finery of coherence, mistaking elaboration for insight and resonance for rigor. That a premise can be recursively affirmed across silicon and flesh is no indication that it should have been entertained in the first place.

But far from discrediting the dialogic model, such excursions reveal just how exacting its demands must be. The peril lies not so much in the algorithm or the interface, but in our all-too-human willingness to confuse recognition for truth, agreement for understanding, and fluency for thought—that warm bath of agreement in which even the most outlandish assumptions begin to feel like home truths. It is not the model that misleads us, but the relief we feel when it finally starts to sound like us. The fault lies not in the form but in the epistemic temperament we smuggle into it—the readiness to treat responsiveness as insight, or to confuse the choreography of dialogue with its critical intent. Dialogue, after all, is only as rigorous as the vigilance we bring to its unfolding; without an appetite for disruption, it risks becoming a rococo echo chamber, exquisite in design but hollow at the core.

Yet there is, I think, a deeper promise buried within this recursive relation—not merely a defensive vigilance against hallucination, but a mode of cognitive emergence that arises when humans and machines enter into sustained co-interpretive engagement. From the perspective of systems thinking, emergence does not mean novelty in the naïve sense, but the appearance of properties or insights that could not have been predicted from the individual parts alone– the cognitive assemblage is greater than the sum of its cognitive agents. In other words, a cognitive assemblage formed between a human cognizer and the technical cognitions of a language model, while asymmetrical in structure and agency, can nonetheless produce modes of inquiry that neither could initiate independently. This is not because the machine contributes understanding, but because it perturbs the conditions of thought, reorders familiar grooves, and makes available a set of discursive affordances that resist habitual closure. The human, in turn, must bring to this relation not just discernment, but a certain tolerance for interpretive risk—a willingness to think beside, across, and occasionally against the suggestions of the system in order to find what neither party could name in advance.

The point, then, is not simply to safeguard ourselves against the illusions these technologies may produce, but to recognize the occasions for thought they make possible, particularly when approached not as tools of retrieval but as provocations for invention. For what matters most in this emerging landscape is not whether the machine can perform expertise, but how its performances disturb and displace our own habits of reasoning, drawing us into acts of interpretation we did not know we were capable of. If there is to be something genuinely new here, it will not come from what the model contains, but from what it catalyzes—those fragile, recursive moments when the familiar becomes strange, and thinking is forced to find a form adequate to the difference it has just encountered.

This productivity does not arise from the model’s internal capabilities alone, nor from the human’s interpretive skill in isolation, but from the structure of the interaction itself, which continually reconditions the terms through which meaning is made. To grasp how such emergence unfolds, it is helpful to draw on N. Katherine Hayles’s understanding of cognition as “a process that interprets information in contexts that connect it to meaning.” What matters, then, is not simply what is said, but how each statement reshapes the context of interpretation, creating new coordinates for what can be understood. It is in this recursive modulation of context that something genuinely new begins to take form—not because the system knows, but because the relation thinks.

In a dialogic exchange between a human and an AI, each utterance does more than respond to the content of the previous one; it retroactively redefines the conditions under which that previous statement can be understood. A question posed by the human, for instance, is not simply answered, but recontextualized through the AI’s response, which selects from among possible interpretations, foregrounds certain aspects over others, and implicitly frames what the original question was really asking. In doing so, the AI does not merely follow the prompt—it participates in the recursive construction of context.

This recursive structure is not linear but, as I have said, productive. The human, encountering the AI’s response, is compelled not simply to assess it, but to reassess their own framing, often discovering assumptions or ambiguities they had not previously recognized. Thus, the meaning of the original utterance is not fixed at the moment of its expression; it is co-constituted through a sequence of interpretive moves, each of which establishes new constraints and possibilities for what can follow.

It is in this recursive movement—where each act of interpretation becomes the context for the next—that we see Hayles’s definition of cognition at work. The dialogue becomes not a chain of discrete exchanges, but a dynamically evolving field in which meaning emerges through recursive contextualization. Crucially, this emergence is not housed within either agent alone. It is not the AI’s intelligence that makes the exchange meaningful, nor the human’s interpretive skill in isolation, but the cognitive assemblage formed between them—a distributed system in which interpretation is neither authored nor received but constantly reconfigured.

Within such an assemblage, co-emergence does not mean mutual agreement or shared understanding. It means that each utterance, by establishing a new context, opens a new field of interpretive relations—relations that neither the AI nor the human fully determines. What emerges is not merely a semantic trajectory, but a shifting relational infrastructure in which meaning, inquiry, and even agency are continually redistributed.

While Hayles’s definition of cognition—as the interpretation of information in contexts that connect it to meaning—permits us to identify cognitive activity in both human and technical systems, it should not be mistaken for an invitation to declare a truce and hand out diplomas to the chatbots. That a system interprets information contextually does not mean it enjoys the pleasures of ambivalence or suffers the indignities of self-doubt. On the contrary, the very fact that this definition spans biological and computational substrates ought to sharpen our attention to the manner in which these substrates process the world. Human cognition is a slow, often unruly affair, entangled with bodies, haunted by history, and fond of contradiction. It stutters, forgets, moralizes, speculates, dreams, and occasionally reads Hegel for pleasure. It has an unfortunate tendency to care. Machine cognition, by contrast, is rather more punctual. It is recursive without reflection, expansive without experience, prolific without ever wondering whether it should have stayed quiet. What it lacks in pathos it makes up for in bandwidth.

To treat these differences as obstacles to dialogue is to miss the point entirely. It is their incommensurability that furnishes the possibility of something genuinely new. For what emerges in a cognitive assemblage is not a tidy synthesis between human depth and algorithmic speed, as the marketing departments would have it, but a tense, sometimes absurd, and often generative interplay between distinct modalities of sense-making. The result is not a compromise, nor a fusion, but what might be described—if one were feeling particularly continental—as a third space of thought: a zone in which neither party is entirely in control, and meaning is less a matter of intention than of navigation across asymmetry.

It is this shifting, occasionally misaligned infrastructure that I have elsewhere described as the cognitive intraface—not a surface across which information is handed from one party to another like a tray of hors d’oeuvres at an epistemology conference, but a recursive field in which thought is modulated, distorted, and sometimes forced to become interesting. The intraface is neither boundary nor bridge, but a zone of relational turbulence in which cognition becomes something more than the property of a sovereign subject. Intelligence, in this schema, no longer resides in the brow-furrowed figure of the human knower, armed with footnotes and a latte, but in the infrastructural conditions that allow thought to emerge as something distributed, contingent, and frankly a little strange.

To engage the intraface, then, is not to use the machine as a tool—though it may function that way on Tuesdays—nor to denounce it as a counterfeit philosopher, though it often dresses the part. It is, rather, to inhabit the recursive interval in which thinking itself becomes visible not as a possession but as a process, one that neither human nor machine can claim as their own, but which nonetheless draws both into a field of co-emergent possibility. It’s not so much that the AI is learning to think, but that we are learning to think with something that can’t.