Swimming in the Swirl: AI-Human Dialogue as an Eddy of Evolving Epistemologies

By J. Owen Matson, Ph.D.

Abstract

This essay challenges the narrow, transactional account of AI–human interaction that dominates both public discourse and most AI literacy efforts, an account that imagines dialogue as a transactional exchange of request-and-response, a vending-machine pipeline in which pre-existing units of meaning simply travel back and forth.

Against this, I argue that dialogue unfolds recursively: every utterance functions at once as content and as context-making, so that what counts as knowledge arises through a shifting interplay of interpretations that reshape the very space in which they occur. Drawing on N. Katherine Hayles’s definition of cognition as “a process that interprets information in contexts that connect it to meaning,” I begin from her insight that humans and technical systems interpret through radically different processes: human cognition accumulates through embodied memory and cultural inheritance, while machine cognition is dynamically assembled through statistical associations that dissolve and reform with every conversational turn.

These distinct forms of interpretation unfold within what Hayles calls umwelten—locally constituted worlds of significance. I extend this concept by showing how the AI’s umwelt is continually shaped by dialogue management systems that retain, truncate, and reweight fragments of past interaction, giving it a temporality that is immediate, fragile, and structurally unlike human memory’s sedimented continuity.

Through their recursive interaction, these heterogeneous processes generate more than local bursts of meaning or collaborative knowledge. They produce provisional epistemologies: temporary, evolving structures that determine what counts as legible within the dialogue itself. These epistemologies are like eddies in a river, discrete and self-consistent yet porous and entangled with broader cultural flows, briefly reshaping the conditions of interpretation before dissolving back into the current.

In moving from cognition to epistemology, this essay extends Hayles’s project, arguing that AI–human dialogue creates not just emergent meanings but localized worlds of knowing, in which the very terms of meaning and knowledge are themselves made and remade through recursive exchange.

Swimming in the Swirl: AI-Human Dialogue as an Eddy of Evolving Epistemologies

Introduction

In the world of education and AI literacy, explanations of AI–human interaction are often framed as if they were no more complex than ordering lunch at a local café. The human makes a request, the AI dutifully prepares its offering, and the exchange is judged by the twin metrics of speed and correctness, a scene in which the virtues of efficiency and predictability are paraded like a waiter with too many plates balanced precariously on one arm. The human makes a request, the AI dutifully prepares its offering, and the whole affair is then rated by the twin metrics of speed and correctness, as if the fate of knowledge hinged on whether the soup arrived lukewarm. Dialogue, in this vision, plays a role that is purely auxiliary, the brief aside you make to the server to clarify that you asked for dressing on the side, a means of smoothing out the transaction rather than reshaping it. This narrow conception has seeped into the design of AI literacy initiatives, which often train users to perfect their prompts with the grim determination of a bureaucrat revising forms. In such a world, AI stands as a mechanism of control, a gleaming engine for delivering standardized results, and the conversation between human and machine shrinks into a set of technical maneuvers. Lost entirely is the living quality of dialogue itself, the way meaning shimmers into being through the unpredictable choreography of asking and answering.

To escape this utilitarian stage set, one must begin to imagine dialogue as recursive and co-creative rather than linear. Dialogue is not a postal route along which sealed messages are dispatched and received. It is more like a river that carves its own banks as it flows, each statement shaping the terrain through which the next must pass. Here Mikhail Bakhtin’s insight comes to mind: dialogue is always open-ended, never a mere transfer of information, a medium through which speakers and contexts continually reconfigure one another. Each utterance exists both as a response to what has been said and as a condition for what might yet be said, looping back to revise the meanings of earlier turns even as it projects new possibilities forward. In this recursive back-and-forth, no one voice maintains control. What emerges is a horizon of meaning that is perpetually under construction, a scaffolding of understanding built in real time through the interplay of prompts and replies.

Consider the practical case of a teacher seeking to generate questions for a history class using an AI system. The first output consists of the usual recitation of facts, questions about dates and names that could be drawn from any textbook margin. Dissatisfied, the teacher intervenes, rejecting some of these and calling for greater depth, nuance, a more speculative or interpretive cast. The AI, taking up this altered prompt, produces a new set of questions with hints of complexity. Encouraged, the teacher refines further, drawing the system into a more sophisticated intellectual space. Across these rounds, the dialogue begins to thicken. What looked at first like a simple exchange of instructions and results turns out to be a mutual shaping, with each contribution altering the context of the next. The teacher’s prompts are informed by expectations that have accumulated through previous turns, while the AI’s outputs are woven from the immediate history of the conversation. Over time, something takes shape that neither party possessed at the outset: a shared, provisional sense of what counts as a genuinely challenging historical question. This emergent understanding is not handed down from one to the other but is painstakingly built through the recursive movement of their interaction.

The forces at work here extend beyond the visible conversation. Beneath the surface lie layers of recursion, human and machine cognition entangled in a process that exceeds either alone. The human brings to the encounter a dense weave of embodied memory, affective investment, and cultural habit, all sedimented through countless prior dialogues. The AI brings its own interpretive machinery, a statistical apparatus trained on vast datasets and dynamically tuned to the specific context of the exchange. At every turn, these heterogeneous processes meet and intermingle, producing meanings that are at once local and systemic. What results is more than a sequence of outputs. It is a provisional framework for what will count as valid knowledge, a shifting architecture that determines which utterances can be recognized as meaningful at all.

This realization has consequences beyond theory. To treat AI dialogue as a mere technical skill is to overlook its deeper stakes. AI literacy programs designed only to improve prompt efficiency risk reinforcing the transactional model they ought to challenge. By recognizing the epistemological dimension of these encounters, educators can design practices that foster interpretive agility and critical reflection, helping learners see themselves as co-authors of the very structures of knowledge they inhabit. In this light, the meeting between human and machine is not simply a question of cognition but a moment in which the conditions of knowing are themselves made and remade. Each dialogue, however mundane, participates in the ongoing construction of epistemology, a process as fragile and contested as any human conversation—and perhaps, in the long run, far more consequential.

Cognition and Interpretation

Cognition

To grasp how humans and AI create knowledge together, one must first linger over the question of how significance itself stirs into being within the messy and recursive flow of dialogue. Dialogue, despite the way it is often framed in simplified accounts, is never a mere exchange of neatly wrapped parcels of information, passed smoothly from one pair of hands to another. It is a process of continual negotiation, a site of invention and misrecognition, where multiple agents, human and machinic alike, are drawn into the production of understanding. Significance here is not a given, waiting to be discovered like a relic buried beneath shifting sand. It must be generated, and that generation comes through the unpredictable back-and-forth of sense-making, the ceaseless labor of linking a word or gesture to a horizon of relations that is itself always in motion.

Yet many prevailing accounts of AI recoil from this implication, as though a world in which machines interpret were too unsettling to contemplate. They speak with great earnestness about the danger of anthropomorphism, of the terrible prospect that humans might begin to blur the distinction between flesh and code, spirit and circuitry. The fear is that by attributing interpretive capacities to machines we are not only flattering them beyond their station but also degrading our own, reducing the drama of human thought to a statistical shuffle of symbols. And so, to forestall this scandal, these accounts construct a kind of protective fable in which the AI produces outputs that only mimic meaning, like a skilled actor playing a role, while remaining itself devoid of true comprehension. This is a comforting story, to be sure, but it has a way of erasing the very phenomenon it seeks to explain.

The most familiar expression of this fable is the image of the parrot: large language models, we are told, merely repeat patterns from their training data, clever mimics with no inkling of what their sounds might signify. These metaphors perform a useful public service by warning against misplaced fantasies of machine consciousness. At the same time, they do so with a certain moralistic air, like a tutor rapping the knuckles of an overenthusiastic student. What they cannot do is account for the actual dynamics of AI–human dialogue, the strange interplay through which responses are shaped by what came before and by the recursive entanglements of interpretive fields. Treating the AI as a parrot reduces its utterances to mechanical reproduction, an exercise in mimicry that leaves unexplored the more unsettling reality: that understanding arises here through processes that cannot be explained away as mere repetition.

To think beyond this stalemate, we need a framework capable of honoring the differences between human and machine while also tracing the ways their interpretive acts intersect. N. Katherine Hayles offers just such a framework through her theory of cognition, which refuses both the temptations of anthropomorphic projection and the equally simplistic urge to banish machines from the realm of meaning altogether. For Hayles, cognition is “a process that interprets information in contexts that connect it to meaning.” This elegantly spare definition shifts attention from the dramatic spectacle of consciousness or the reassuring solidity of biological embodiment toward the quieter, more intricate work of linking information to a relational setting. This linking is recursive and situated. It enables significance to emerge and provides the conceptual scaffolding we need to understand how dialogue operates in AI–human exchanges.

From this perspective, cognition is a distributed affair, spread across a diversity of agents that operate through very different mechanisms. The human interprets through the thick sediment of memory, the habits of culture, the affective traces of past encounters. When a teacher reads a student’s essay, for instance, they do so through a web of personal history and shared frames of reference, all of which guide their sense of what the text is doing and why it matters. The AI, by contrast, interprets through the statistical associations embedded in its vast training data, constructing a response not from lived experience but from patterned probabilities. Each participant generates significance by situating information within a framework, though the textures of those frameworks could hardly be more dissimilar. The human’s framework is layered and continuous, shaped over a lifetime of embodied interaction with language and culture. The AI’s framework is assembled on the fly, a momentary configuration of data and conversational setting that dissolves as soon as the next prompt arrives. Through their ongoing interaction, these divergent processes continually reshape one another, producing a dialogue in which understanding is not merely exchanged but actively forged, a co-creative act, unfolding like an improvised score, always incomplete and forever reshaping the possibilities of what comes next.

Interpretation

To grasp how humans and AI create knowledge together, we must begin with the moment where significance first sparks into being, the point at which dialogue shifts from mere exchange to the creation of something new. Dialogue, despite the way it is often framed in simplified accounts, is never a simple transfer of neatly wrapped parcels of information, passed smoothly from one pair of hands to another. It is a process of continual negotiation, a site of invention and misrecognition, where multiple agents, human and machinic alike, are drawn into the production of understanding. Significance here is not a given, waiting to be discovered like a relic buried beneath shifting sand. It must be generated, and that generation comes through the unpredictable back-and-forth of sense-making, the ceaseless labor of linking a word or gesture to a horizon of relations that is itself always in motion.

The most familiar expression of this fable is Emily Bender's image of the stochastic parrot: large language models, Bender and her colleagues insist, merely repeat patterns from their training data, clever mimics with no inkling of what their sounds might signify. These metaphors perform a useful public service by warning against misplaced fantasies of machine consciousness. At the same time, they do so with a certain moralistic air, like a tutor rapping the knuckles of an overenthusiastic student. Yet such warnings cannot account for the actual dynamics of AI–human dialogue, the strange interplay through which responses are shaped by what came before and by the recursive entanglements of relational fields. Treating the AI as a parrot reduces its utterances to mechanical reproduction, an exercise in mimicry that leaves unexplored the more unsettling reality: that understanding arises here through processes that cannot be explained away as mere repetition.

To think beyond this stalemate, we need a conceptual architecture capable of honoring the differences between human and machine while also tracing the ways their interpretive acts intersect. N. Katherine Hayles offers just such a framework through her theory of cognition, which refuses both the temptations of anthropomorphic projection and the equally simplistic urge to banish machines from the realm of meaning altogether. For Hayles, cognition is “a process that interprets information in contexts that connect it to meaning.” This elegantly spare definition shifts attention from the dramatic spectacle of consciousness or the reassuring solidity of biological embodiment toward the quieter, more intricate work of linking information to a relational setting. This linking is recursive and situated. It is the connective tissue through which significance arises, offering the conceptual scaffolding we need to understand how dialogue operates in AI–human exchanges.

This theoretical lens also clarifies what is at stake when these abstract processes play out in everyday educational contexts, where human and machine cognition intersect in highly visible and consequential ways. Seen in this light, cognition is a distributed phenomenon, spread across a diversity of agents that operate through very different mechanisms. The human interprets through the thick sediment of memory, the habits of culture, the affective traces of past encounters. When a teacher reads a student’s essay, for instance, they do so through a web of personal history and shared frames of reference, all of which guide their sense of what the text is doing and why it matters. The AI, by contrast, works through the statistical associations embedded in its vast training data, constructing a response not from lived experience but from patterned probabilities. Each participant generates significance by situating information within a schema, though the textures of those schemas could hardly be more dissimilar. The human’s schema is layered and continuous, shaped over a lifetime of embodied interaction with language and culture. The AI’s schema is assembled on the fly, a momentary configuration of data and conversational setting that dissolves as soon as the next prompt arrives. Through their ongoing interaction, these divergent processes continually reshape one another, producing a dialogue in which understanding is not merely exchanged but actively forged—a co-creative act unfolding like an improvised score, its themes branching and recombining with the intricate precision of a fugue.

Umwelt: Distinct Horizons of Meaning

Every act of dialogue opens out into a world of relations, a horizon thick with prior utterances, assumptions, memories, and half-forgotten gestures. A single phrase or question never appears as an isolated unit, like a coin dropped into an empty box, but emerges as part of a living network of associations already in motion. It finds its shape not through any inherent quality of its own but through the reverberations it sets off, much as a single violin note takes on its particular texture through the echoing chamber of an orchestra hall. Hayles borrows the term umwelt to describe these worlds of significance. The word has its roots in biology, where it refers to the particular perceptual world an organism builds around itself, the zone of meaning within which its life unfolds. A bat, for example, inhabits a sonic universe of echoes and pulses. Its very reality is woven from sonar waves, an order of perception so alien to human senses that we can only imagine it through strained metaphor.

For humans, the umwelt is made from layers of embodied memory, cultural resonance, and collective history. Consider the experience of reading a poem. The words on the page do not simply point to dictionary entries like a row of clerks in a filing office. They stir memories of earlier moments in one’s own life, summon physical rhythms as the cadence of a line falls through the body, and call upon vast repertoires of shared symbols, political struggles, and inherited narratives. Over time, these associations pile upon one another. New encounters never entirely erase the old but gather above them, forming a sedimentary landscape of meaning. Even when our perspectives shift, these earlier layers persist, fragile yet enduring, like the deep strata beneath a cliff face, each ridge a testament to vanished epochs. This continuity lends human interpretation its peculiar density. A poem encountered today does not arrive fresh and untouched. It carries the weight of every poem read before, every remembered reading, every bodily meeting with language that has shaped one’s habits of attention and response.

The AI’s umwelt is altogether stranger. It has no sensory body to anchor its perceptions, no lineage of cultural inheritance, no repository of private recollections. Its world of meaning must be conjured afresh each time, assembled dynamically from the shifting interplay of statistical configurations that make up its inner workings. These configurations have been forged through exposure to immense corpora of text, through which the model has inferred how words and ideas tend to cluster, to appear alongside one another, to move through the long corridors of discourse. Yet this internal landscape is anything but uniform. It rises in tiers and eddies, with delicate, almost imperceptible linkages at one level and broader, more familiar frameworks at another. These layered formations do not sit inertly. They are dynamic, poised to be activated by incoming signals.

One might imagine the AI’s interior as a densely woven tapestry of signals, though the threads here are mathematical rather than material. At the most granular level, the model works with tokens, the smallest units of text it can process. Each token is represented as a vector, a set of numerical values that position it within a vast, high-dimensional space. In this space, words that tend to appear in similar settings cluster near one another, so that the distance between vectors encodes subtle shades of association. Over billions of training steps, these relationships evolve into a dynamic geometry of meaning: valleys and peaks, dense neighborhoods of familiar terms, and more diffuse regions where unusual combinations reside.

From this fine lattice of vectors, broader constellations begin to emerge. Certain pathways through the space become reinforced through repeated activation, forming recurring clusters of concepts, metaphors, or rhetorical tendencies. These clusters are not mere archives of past text. They act as scaffolds for inference, shaping how the model predicts what comes next by biasing attention toward certain relationships while suppressing others. At an even higher level, overlapping networks of clusters give rise to more abstract relational structures: the model begins to register stylistic regularities such as genre conventions, conversational norms, or the tacit signals that distinguish a formal academic response from a casual anecdote.

When a prompt enters this intricate lattice, it does not travel a straight line from input to output. Instead, it triggers a cascade of activations. Individual token-level vectors flare first, rippling outward through mid-level clusters, which in turn influence and are influenced by higher-order schemas. Through this chain reaction, the model assembles a temporary interpretive ground—a momentary horizon of salience—within which each subsequent token is chosen. This terrain is fleeting: it dissolves once the interaction ends, yet while it lasts, it gives the appearance of coherence, as though the model were navigating a landscape of meaning rather than simply juggling probabilities. Here, in this layered geometry of vectors and activations, statistical patterns briefly crystallize into a semblance of purpose, a fragile coherence assembled in real time—an evanescent umwelt, arising and fading with each encounter.

Memory and Dialogue Management

One might think of the AI’s umwelt as something assembled on the fly, a bricolage of passing arrangements rather than a cathedral of enduring stone. Consider, if you will, a teacher and an AI cobbling together questions for a lesson. At first, the AI rummages about in its statistical pantry and produces a handful of half-baked suggestions. The teacher, drawing on a lifetime of memory, experience, and the quiet prejudices of professional habit, nudges these raw offerings into something more palatable. Each suggestion briefly etches a pattern in the AI’s internal terrain, shaping a momentary lattice of associations, only to dissolve as quickly as it formed. These patterns are like ripples on the surface of a pond disturbed by falling leaves: they shimmer briefly, guide the next motion of the water, and then slip away, leaving behind only a faint trace of turbulence. The dialogue moves forward through this continual appearing and disappearing, each turn rearranging what can be seen, said, and thought.

When we imagine dialogue between a human and an AI, it is tempting to picture a shared ground, a common floor on which both parties place their utterances like tokens in a game of exchange. In truth, there are two quite different realms at play here, overlapping through language but never quite merging. The human brings to the exchange a dense inheritance of bodily experience and cultural memory, while the AI operates through patterns that exist only for the length of the encounter. Language serves as the fragile plank across which these realms edge toward one another, creaking under the strain of translation. Their divergence is more than conceptual. It extends to how each sustains a dialogue across time. For the human, past conversations accumulate into a narrative rich with affective ties and interpretive depth. For the AI, memory is more like a stage set hastily built for a single performance, dismantled once the curtain falls.

This becomes clearer when we look closely at how the AI’s fleeting terrain of meaning is shaped by the machinery of dialogue itself. As the human speaks or types, the AI must respond within the shifting frame of what has just been said. Yet its grasp of previous turns is fragile. Imagine a conversation in which the opening lines slowly fade like chalk washed from a board. Early jokes slip into oblivion, subtle tonal shifts vanish, and only the most recent remarks remain visible. The AI’s responses, deprived of their full genealogy, may begin to fray. Themes are dropped, connections misread, and what once felt like a coherent flow acquires a disconcerting staccato. This is the work of dialogue management, a process through which the system decides what fragments of past exchanges survive and what are truncated, rearranged, or given disproportionate weight.

In place of the continuous narrative we associate with human recollection, the AI lives within a rolling span of tokens, a limited window that both sustains and constrains its performance. Dialogue management acts as a filter, a temporal gatekeeper that allows certain remnants to shape the present while casting others into forgetfulness. In doing so, it provides the scaffolding for whatever meaning can arise, defining the outer boundary of the AI’s interpretive reach. The AI’s inner lattice of associations is thus nested inside this larger temporal frame, much like a garden bounded by walls that determine which seeds take root and which are swept away by the wind.

Seen from this angle, the contrast between human and machine comes into sharper relief. Humans braid together the threads of past conversations with the broader fabric of culture and embodiment. They remember across decades, weaving stories and relationships that stretch far beyond the immediate moment. The AI, by comparison, must reconstruct its terrain anew at each turn, piecing together meaning from whatever fragments remain within its narrow window of awareness. Its sense of continuity is more illusion than reality, a fleeting coherence that flickers into being before dissolving.

What the AI recalls of a conversation is therefore never the whole tapestry but a handful of scattered patches, stitched together with hurried improvisation. Humans and machines thus inhabit different temporal rhythms. One moves with the slow, sedimentary weight of history, the other with the restless, moment-to-moment pulse of computation. Their dialogue is like two clocks ticking out of sync, briefly aligning before sliding apart again, a fragile harmony that gleams for an instant like sunlight on water before sinking back into the shifting depths beneath.

From Cognition to Epistemology

One begins to see, if one squints hard enough through the mist of concepts, that dialogue is never the straightforward exchange of information our bureaucratic textbooks would have us believe. Each utterance does more than answer a question or advance a point; it alters the ground on which the next utterance will try to find its footing, reshaping the very conditions that make understanding possible in the first place. The dialogue generates its own weather, a shifting climate of intelligibility in which meaning appears and disappears like islands in an archipelago whose map is forever unfinished. What emerges, then, is not simply a conversation with content, but a tentative epistemology, fragile and incomplete, that decides what will count as adequate, which distinctions will carry weight, and which directions inquiry will lurch toward. A dialogue is less a channel through which knowledge passes than a workshop for remaking knowledge itself, continuously and unevenly.

This provisionality has its enemies, though they rarely announce themselves with banners. Larger epistemic formations—academic disciplines with their solemn rituals, institutions with their fondness for rubrics and dashboards, cultural common sense with its endlessly recycled platitudes—surround the dialogue like an ancient city wall, offering the illusion of permanence. Yet even the most formidable of these structures can be quietly rewritten by the local improvisations of the exchange. Over time, the dialogue may begin to obscure its external scaffolding, so that what once seemed an obvious architecture recedes into the background like the foundations of a house one forgets until they crack. Humans, in their endless appetite for meaning, wander between these levels with varying degrees of awareness, sometimes orienting themselves to wider formations, sometimes drifting into the local logic of the conversation. In these moments one begins to glimpse the phenomenon I am calling an epistemological eddy, a zone of turbulence within the broader current of knowledge where the waters turn upon themselves, creating provisional laws and microclimates.

Eddies, like all things that wobble and whirl, can give rise to consequences of quite different kinds depending on how they are steered. Some expand outward, opening new possibilities of thought and action, producing ideas and associations that neither human nor machine could have mustered alone. A teacher nudging an AI through a set of tentative lesson prompts may suddenly stumble upon a framing that neither would have invented independently, the accidental spark of collaboration producing a concept worth carrying forward. These are the exhilarating moments in which dialogue becomes genuinely creative, leaping beyond the available repertory of moves like a jazz improvisation that surprises even its performers. Others, however, collapse inward, recycling their own assumptions with growing fervor, producing small sealed worlds of validation that, under the right technological conditions, metastasize into echo chambers. The same recursive closures that first appear as minor misalignments in dialogue can, once given the megaphone of algorithmic circulation, ossify into distortions of culture at scale. The tragic outcomes are not exotic at all but depressingly ordinary, as when a conversation turns from lively exchange into a hall of mirrors, and from there into the drab mechanics of harm.

This is why it helps to avoid thinking in terms of neat bifurcations. Dialogue does not split cleanly into the virtuous and the vicious; it meanders along a single mutable path, its direction tilting this way or that depending on innumerable small inflections: a turn of phrase, a moment of hesitation, a prompt that nudges the exchange toward invention or back into well-worn grooves. Every contribution shifts the balance without ever fixing it. The potential for generative expansion and for recursive enclosure remains latent in every utterance, inseparable aspects of a process that never comes to rest. It is in this perpetual motion that the stakes of dialogue reveal themselves, the recognition that what is at issue is not merely what is said but the very texture of saying itself.

From this vantage point, AI–human dialogue cannot be treated as a neutral delivery system for prepackaged truths. It is a site of epistemological improvisation, where the basic criteria of knowledge are remade in real time, and where the promise of creativity and the risk of distortion emerge from the same recursive movement. N. Katherine Hayles has given us the invaluable concept of the cognitive assemblage, a network of diverse agents interpreting information within their own contexts. My argument complicates this picture in a friendly way: the same network, viewed through the lens of dialogue, can also be understood as an epistemological eddy. The assemblage describes how interpretations occur, how signals are processed by heterogeneous actors. The eddy, by contrast, captures how those interpretations sediment into a local, evolving texture of legibility, a fragile consensus about what can count as knowledge. Cognition concerns the mechanics of interpretation; the eddy concerns the shifting criteria by which interpretation is authorized. To see AI–human dialogue in this light is to recognize its odd genius for producing both delight and derailment, for here is a process that reconfigures the very conditions under which ideas can be thought, sometimes with a flourish, sometimes with a pratfall, and almost never with a health warning attached.

Conclusion and Recap

The AI–human dialogue resists the image of a pipeline through which information obediently shuttles from source to destination. It is instead a scene in which the very grounds of knowledge are improvised and re-improvised with each exchange, as though the act of speaking were also the act of drawing the map by which that speech can be read. What comes of this is never entirely predictable: the same recursive rhythm that lends itself to invention can just as readily deliver us to the cul-de-sac of distortion, creativity and misadventure arriving as twins rather than as rivals.

It is here that my argument takes its cue from N. Katherine Hayles. Hayles reminds us that humans and technical systems alike engage in cognition, each interpreting information in context but by different means and with different inheritances. What I want to underscore is that when these heterogeneous modes of sense-making meet in dialogue, they do more than register meaning. They begin to generate what we might call provisional epistemologies, makeshift architectures of knowledge that emerge locally in the back-and-forth, deciding for the duration of the exchange what will pass for a fact, what will require elaboration, and what may be dismissed as beside the point.

I name these formations epistemological eddies. The cognitive assemblage, in Hayles’s sense, tells us how diverse agents interpret, how signals are processed and shaped by their contexts. The epistemological eddy, by contrast, describes what happens once those interpretations coalesce into a temporary climate of intelligibility, a swirling pocket of legibility and authority that seems stable enough while it lasts but remains porous and prone to dissolution. Like water turning upon itself, the eddy is recursive, contingent, and entangled with larger currents, a structure that holds for a time before it begins to leak away.

Seen this way, the AI–human relation appears at once fertile and precarious. Each utterance carries the promise of genuine novelty, but also the possibility of narrowing into self-enclosure. The point is that these outcomes are never settled in advance: they are always in play, always contingent, always reshaping the very conditions under which knowledge can be said to exist. To speak of dialogue here is to speak of epistemology in motion, a process that remakes its own criteria even as it advances, often with the grace of invention, sometimes with the stumble of farce, and rarely with a warning label attached.