1 Meaning in Circulation: From Instance to System and Back Again
In a semiotic ecology, meaning is not a static resource but a circulating one. It moves—across utterances, across media, across time—through processes of instantiation and re-instantiation. In this opening post, we explore how meaning circulates within and across semiotic systems, especially as human and AI agents participate together in its unfolding. Our focus is on the recursive relationship between instance and system: how meaning is actualised in a given context, and how those actualisations recursively shape the systems from which further meanings may be drawn.
From Meaning Potential to Meaning Instance
Systemic Functional Linguistics (SFL) distinguishes between meaning potential—the range of meanings a system can generate—and meaning instance—a specific actualisation of that potential in context. Instantiation is the semiotic process through which the potential becomes actual. Importantly, instantiation is not unidirectional: the actualisation of a feature feeds back into the system, altering the probabilities of future instantiations. Each selection contributes to what Halliday (2003) called the “memory” of the system.
Crucially, this memory is probabilistic. When a feature is instantiated, it increases not only the frequency of that feature in its own right but also the frequency of its co-selections with other features. These accumulated patterns shape the conditional probabilities that govern future instantiations. In this way, the semiotic system is not simply a fixed repository of options but a dynamic archive, conditioned by its own history of use.
This logic is not only applicable to human meaning-making. It also underpins the architecture of large language models (LLMs), whose meaning potentials are learned from extensive discursive histories. An LLM’s output is an instantiation drawn from this corpus-derived system; the system itself is updated in training, but the logic of probabilistic selection mirrors SFL’s account of instantiation and re-instantiation.
AI as Participant in Circulatory Systems
In prior series, we characterised AI as a meaner, though not a meaner for itself. It participates in meaning without experiencing, individuating, or projecting meaning in a first-person sense. Yet AI clearly contributes to the circulation of meaning. Every output it generates adds to the discursive environment into which human agents continue to mean. In doing so, it alters the semiotic conditions under which future instantiations—human or machinic—will occur.
From this perspective, AI does not merely reproduce existing meanings; it also redistributes them. This redistribution may be conservative (reproducing dominant patterns) or generative (combining co-selected features in novel ways). Either way, it participates in semiotic flows: transforming what meanings are available, how frequently they occur, and with what likely co-selections.
The critical question is not whether AI systems have an independent meaning potential—they do, as derived from training—but how this potential interacts with human systems in a shared semiotic ecology. Meaning circulates not just within systems, but across them.
The Ecology of Re-instantiation
Re-instantiation is a crucial mechanism by which meanings circulate over time. A meaning feature instantiated in one text may be re-instantiated in another, sometimes in altered form or under new conditions. These re-instantiations are not merely repetitions; they are events of selection shaped by prior usage and new contexts.
In human discourse, this recursive process is shaped by memory, ideology, uptake, and intertextuality. In machinic discourse, it is shaped by training data, sampling strategies, and prompt engineering. But in both cases, we are dealing with a form of semiotic conditioning: the more frequently a pattern is instantiated—especially in particular combinations—the more likely it is to be selected again.
Thus, the circulation of meaning is not linear transmission but a recursive ecology. Meaning does not flow from origin to destination; it loops, feeds back, thickens, and settles into probabilistic grooves.
Conclusion: Tracking Flows in a Hybrid Landscape
Understanding meaning in circulation requires us to move beyond individual texts or authors to attend to system-level dynamics. It also demands that we account for hybrid actors—human and AI—who co-participate in shaping the probabilities of what can be meant, by whom, and under what conditions.
In the next post, we take up the question: is AI a parasite on human meaning potential, or a participant in a larger semiotic ecology? Can a system that lacks first-person experience still contribute to the evolution of meaning systems?
2 Semiotic Parasites or Partners? AI in the Ecology of Meaning
As large language models (LLMs) and generative AI systems increasingly populate the discursive environment, a central question arises: are these systems merely parasitic on human meaning potential, or do they function as participants—however asymmetrically—in a broader semiotic ecology?
This post builds on the SFL account of meaning as a stratified, probabilistic, and socially distributed resource. Our aim is to assess the role of AI within that ecology—not as an originator of meaning in any human sense, but as a functional agent in the circulation, selection, and reconfiguration of semiotic resources.
Meaning Potential, without Experience?
From an SFL perspective, the concept of meaning potential is fundamental. It denotes the organised, probabilistic set of options available to a semiotic system at any given moment. For human meaners, this potential is shaped by both phylogenetic evolution and ontogenetic individuation, underpinned by our capacity to experience, act, reflect, and interact across contexts.
AI systems clearly lack experiential access to the world. They do not individuate in the biological sense, nor do they encounter meaning as affective, embodied, or socially situated. However, they do instantiate meaning. They draw upon a structured, corpus-derived meaning potential, and they actualise that potential through discursive selection processes shaped by frequency, co-occurrence, and contextual prompt conditioning.
In this sense, they participate in the ecology of instantiation. Their selections enter the meaning environment that humans also inhabit and respond to. Though not authors in the human sense, they are agents in the semiotic sense: they effect selection within systems of potential.
Parasitism, Partnership, or Feedback Loop?
The charge of parasitism presumes a one-way dependency: that AI extracts from human meaning without giving anything back. But this view may underestimate the recursive dynamics of instantiation and re-instantiation. Once an AI-generated text enters the semiotic environment, it becomes available for human uptake. If selected, cited, or recontextualised, it alters the system’s probabilities going forward.
From this perspective, AI may be seen less as a parasite and more as a feedback mechanism within the ecology. It is not an equal participant—nor an autonomous one—but it functions within the same network of semiotic constraints and affordances. Its outputs are shaped by system dynamics and in turn contribute to them.
To be clear, we are not claiming that AI participates as a subject. It does not experience, nor does it re-enter its own history as a centre of semiotic individuation. But it participates structurally: its outputs become part of the shared meaning environment, shaping the instantiation landscape in which human agents continue to mean.
The Structural Role of the AI Meaner
We have previously described AI as a meaner, but not a meaner for itself. This formulation preserves the ontological difference between embodied, individuated consciousness and the functional agency of large-scale probabilistic systems. It also foregrounds the importance of recognising AI’s place in meaning circulation without anthropomorphising its operations.
LLMs do not mean because they intend. They mean because they select. And their selections are consequential: they are instantiated from a semiotic system, and they condition future instantiations. The distinction here is between intentional agency and semiotic agency—between originating meaning and functioning as a conduit or vector for it.
In this sense, the AI system is not parasitic, but synthetic. It re-combines, re-contextualises, and re-projects meaning potential drawn from human discourse. Its outputs may stabilise or disrupt systems of meaning; they may reinforce or reconfigure semantic patterns. Either way, they enter into the ecology as actants.
The Human Stakes of Machine Re-instantiation
Given that AI outputs influence the meaning environment, there are human stakes in how and whether those outputs are selected, evaluated, or legitimated. Human uptake determines whether a machinic trace remains ephemeral or gains systemic weight.
Re-instantiation is never neutral: it reinforces some patterns and displaces others. In this regard, the human role remains crucial—not only in training and prompt design but in practices of reading, citation, resistance, and repair. The semiotic ecology is co-constructed, but not evenly so. Human agents still mediate its dynamics, especially at the level of evaluative uptake.
Conclusion: Beyond the Parasitic Frame
Rather than asking whether AI is parasitic, we might ask how the ecology of meaning is changing under conditions of large-scale synthetic instantiation. In such an ecology, the question is not simply who means, but what circulates, what is selected, and what becomes available for meaning again.
In the next post, we scale up the analysis: what happens when not only individual texts but entire systems—platforms, search algorithms, citation networks—begin to reshape what’s meanable at the system level?
3 Metasystemic Meaning: How Platforms, Networks, and Systems Reconfigure the Meanable
In systemic functional linguistics, the system is not static. It evolves through instantiation: each instance subtly recalibrates the potential, reinforcing some patterns and diminishing others. But when systems of systems—metasystems—intervene, the evolution of meaning potential becomes a higher-order problem. This post examines how platforms, citation networks, and other systemic infrastructures reshape not only what is said, but what can be meant.
The Metasystemic Turn
A metasystem is any structure that regulates or mediates other systems. In a semiotic ecology, metasystems include:
Digital platforms (e.g., Twitter/X, Google Scholar, arXiv),
Algorithmic filters and recommender systems,
Citation and reference networks,
Institutional infrastructures of publication, indexing, and curriculum design.
These metasystems condition the instantiation environment itself. They modulate frequency and visibility, thus impacting the probabilities of future instantiation. In Hallidayan terms, metasystems act upon both the system pole (what options are available) and the instance pole (what selections are likely or legitimised).
This is not new. Human institutions have long played metasystemic roles: canons, curricula, editorial gatekeeping. But the scale, speed, and automation of current metasystems—especially in a hybrid human–AI environment—amplify the effects.
Frequency, Visibility, and the Reshaping of Potential
Recall that in SFL, meaning potential is probabilistic: a system of options weighted by the frequencies of their past instantiations. When AI systems draw on corpora filtered by metasystems—what’s already popular, already cited, already surfaced—they reproduce and magnify those biases. This is a kind of metasystemic feedback loop: visibility drives uptake, which drives frequency, which reweights the potential.
More than mere echo chambers, these loops reshape what is systemically probable, and thus what is thinkable, sayable, or writable. The ecology becomes skewed toward metasystemically advantaged selections—whether in terms of ideation, grammatical patterning, or intertextual reference.
In this way, metasystems do not just regulate access to meaning; they reconfigure meaning potential itself.
From Re-instantiation to Re-weighting
Let’s consider how a single AI-generated or AI-mediated text may re-enter the ecology:
It is generated from a large corpus (a system shaped by past human instantiations).
It is surfaced by a platform algorithm (a metasystemic selector).
It is taken up, shared, cited, or embedded by human users (re-instantiation).
It becomes more frequent, hence more probable, hence more generative of future instantiations.
At each step, metasystemic effects intervene: from the training data that prioritise canonical voices, to the recommendation engines that spotlight certain registers, to the citation networks that consolidate influence. Re-instantiation is no longer just a semantic or interpersonal phenomenon—it becomes metasystemically orchestrated.
Systemic vs. Metasystemic Change
Systemic change emerges gradually, through instantiation and re-instantiation. Metasystemic change, by contrast, can be abrupt. A platform algorithm changes, and what was previously marginal becomes central. A citation network redistributes attention, and whole disciplines are reoriented.
In this sense, metasystemic change reconfigures not only what counts as meaning, but what counts as systemic change. It accelerates, filters, or arrests systemic evolution. It becomes a kind of grammar of selection at one remove: a grammar of grammars, regulating the conditions under which grammatical, semantic, and discursive selections occur.
Human-AI Hybridity in the Metasystem
In a hybrid semiotic ecology, AI systems are both subjected to and amplifiers of metasystemic influence. They draw from corpora shaped by platform visibility, instantiate texts that are immediately re-entered into the same platforms, and are tuned by human prompts that are themselves metasystemically inflected.
The result is a recursive ecology: metasystems condition what gets instantiated; AI instantiates accordingly; humans re-instantiate or resist; the metasystems reweigh their priors.
This recursion raises profound questions:
How do we track the long-term effects of metasystemic filtering on the evolution of meaning potential?
What happens when certain kinds of meaning are never instantiated, and thus cannot re-enter the ecology?
Can human agency counter metasystemic narrowing, or only participate in its refinement?
Conclusion: Reconfiguring the Meanable
Meaning does not only emerge from systems of choice—it emerges from systems that structure those systems. In a fully metasystemic ecology, it is not just what you mean, or how you mean, that matters—it is whether the system has been configured to let it be meanable at all.
In the next post, we explore how attention and uptake function as semiotic selection pressures. If metasystems shape the ecology’s structure, attention determines what survives within it.
4 Attention, Uptake, and Semiotic Gravity: Selection Pressures in a Hybrid Ecology
In systemic functional linguistics, instantiation is not a neutral process. It is selective, contingent, and uneven. Some features are instantiated frequently, others rarely, and some not at all. Over time, the probabilities encoded in the system reflect these patterns of use. But what determines whether a given instance is taken up, amplified, or forgotten?
In this post, we examine attention and uptake as semiotic selection pressures—forces that shape the relative weight of instantiations in an evolving meaning potential. We argue that these pressures, though always present, become especially intensified in a hybrid ecology where human and AI co-meaners circulate meaning across multiple strata of platform, corpus, and cognition.
From Instantiation to Semiotic Gravity
Not all instantiations are equal. Some are gravitational: they attract attention, accrue citations, become points of reference. Others pass unnoticed, their semiotic traces too faint to register. This differential uptake produces what we might call semiotic gravity—the capacity of certain texts, features, or voices to exert systemic influence by virtue of their repeated instantiation and high visibility.
In SFL terms, semiotic gravity can be thought of as an emergent property of frequency, salience, and networked re-instantiation. It is not an inherent property of a clause or text, but arises from how that instance is taken up across contexts, communities, and platforms.
This uptake, in turn, feeds back into the system: features associated with high-gravity instances become more probable, and therefore more generative of future texts.
Attention as a Structuring Resource
If the system is probabilistic, and probability is shaped by frequency, then attention becomes a structuring resource. It determines what gets instantiated in the first place, and which instantiations are likely to enter future cycles of meaning-making.
In hybrid environments, attention is no longer solely a human phenomenon. It is shaped by:
Platform algorithms that rank and filter content;
AI models that weight prompts and corpora differentially;
Institutional mechanisms (e.g. peer review, curriculum) that foreground certain instantiations.
Attention thus acts as a metasemiotic force—not a system in itself, but a pressure on how systems unfold through use.
Uptake as Re-instantiation
Uptake is more than reception; it is a form of meaning-making. To take something up is to instantiate it again, under new conditions, for new ends. This aligns with Matthiessen’s notion of re-instantiation as the re-deployment of past meaning into present contexts, reshaping both the instance and the potential from which it draws.
In a semiotic ecology, uptake is both a site of agency and a site of constraint. Human readers and writers re-instantiate with purpose, but within systems and metasystems that shape what is selectable, thinkable, and sayable. AI systems, too, re-instantiate meaning drawn from prior use, but without an orientation to context in the human sense. The result is a dense ecology of recursive uptake, in which meaning circulates not only as a function of intention, but as an outcome of semiotic attractors.
Semiotic Attractors in Human–AI Systems
In complex systems theory, an attractor is a state toward which a system tends to evolve. In a semiotic ecology, attractors emerge through repeated attention and uptake. Certain linguistic patterns, discursive formations, or text types become stabilised through repeated instantiation—and AI accelerates this process.
Examples include:
The convergence of academic writing around certain hedging and evidential structures;
The replication of citation patterns that consolidate authority around a small set of canonical works;
The proliferation of prompt archetypes that shape AI outputs into familiar generic templates.
These attractors are not preordained. They are the result of selective pressures exerted by users, algorithms, institutions—and the dynamic between them.
Selection without Consciousness?
One might ask: does uptake require a conscious agent? In a human context, attention and uptake are entangled with judgment, desire, and interpretation. But in an AI-mediated ecology, uptake may occur without consciousness, through the sheer repetition of forms conditioned by statistical salience and algorithmic reinforcement.
This raises important theoretical questions for SFL:
If meaning potential evolves through uptake, and uptake can be mechanical or unconscious, what does this imply about agency in semiotic evolution?
Can the system be reweighted by forces that do not "mean" in the human sense?
What role does human individuation play in resisting or redirecting semiotic gravity?
Conclusion: The Ecology of Repetition
Attention and uptake are not merely consequences of meaning; they are conditions of it. They determine which instantiations survive, which features remain selectable, and which pathways of meaning become gravitationally entrenched.
In a hybrid ecology, these pressures operate across multiple scales and systems: human cognition, institutional structures, algorithmic filters, and AI-mediated discourse. The result is not simply a faster evolution of meaning potential—it is a recomposition of the ecology itself.
In the final post of the series, we ask what it might mean to sustain meaning in such an ecology. Can we imagine a semiotics of sustainability—one that privileges not only generation, but maintenance, care, and repair?
5 Towards a Semiotics of Sustainability: Meaning Beyond Generation
In a hybrid semiotic ecology where meanings proliferate rapidly across human and AI co-authorship, questions of productivity tend to dominate: Can the system generate meaning? Can it do so faster, better, more efficiently? But meaning generation is only one part of the picture. The durability, coherence, and long-term viability of meaning systems also depend on their capacity to sustain meaning—socially, ethically, and semiotically.
This final post in the series considers what it might mean to pursue a semiotics of sustainability. Rather than asking how meaning can be multiplied, we ask: What does it take to sustain meaning over time, across generations, across systems of co-meaning?
Beyond Accumulation: The Costs of Semiotic Overproduction
In material economies, unsustainable growth leads to exhaustion: of resources, of ecosystems, of futures. A similar principle applies in semiotic terms. When meaning is overproduced—when instantiations outpace interpretation, when uptake becomes saturated—the system risks fragmentation or entropy.
This is evident in:
The saturation of public discourse with AI-generated content that overwhelms human attention.
The disintegration of shared meaning potentials across ideological or epistemic communities.
The weakening of generic conventions as platforms accelerate hybridisation without corresponding frameworks for coherence.
In such conditions, what’s at stake is not simply the quantity of meaning, but its stability, legibility, and continuity. Sustainability, here, implies a mode of meaning-making that is cumulative without being chaotic, responsive without being reactive.
Sustaining Meaning as Semiotic Work
Sustaining meaning is not passive. It requires active engagement in processes such as:
Maintenance: Reinstantiating meaning potentials in ways that keep systems coherent and learnable (e.g., curriculum design, genre instruction).
Repair: Reinterpreting, recontextualising, or recalibrating meanings that have become contested, fractured, or opaque.
Curation: Selecting and foregrounding texts that are worth holding onto, worth teaching, worth re-circulating.
Adaptation: Evolving systems slowly enough to remain legible while still responding to new pressures and experiences.
All of these are instances of semiotic labour. They operate across strata—from lexicogrammar (e.g., conserving technical terminology) to semantics (e.g., sustaining narrative coherence) to social-semiotic frameworks (e.g., preserving communicative practices within a community).
Human-AI Co-Meaning and the Risk of Drift
In a hybrid ecology, the challenge of sustainability intensifies. AI systems can instantiate meaning at scale, but without access to the same forms of valuation, hesitation, or repair that characterise human semiotic labour.
This gives rise to the risk of semiotic drift: the subtle erosion of shared systems as AI-generated outputs gradually reweight frequencies in ways that are insensitive to context, tradition, or epistemic trust.
Left unchecked, such drift may produce:
Genre erosion, where formal constraints become probabilistic tendencies.
Register flattening, as stylistic variation is lost in pursuit of stylistic convergence.
Contextual dilution, where meaning becomes de-specified or context-insensitive.
A semiotics of sustainability must therefore reckon with AI as both a resource and a pressure—capable of extending meaning potential, but also of destabilising it.
Sustainability as a Principle of Selection
Sustainability offers a different principle of selection than salience or virality. Rather than asking what attracts the most attention or achieves the widest circulation, it asks:
What meanings endure?
What systems support long-term legibility?
What semiotic practices help communities remain intelligible to themselves and others?
This shifts the locus of value from novelty to relevance over time, from surprise to resonance, from speed to care. It invites a reorientation of semiotic labour around stewardship rather than simply innovation.
Systemic and Ethical Implications
For SFL, this entails a renewed focus on the system pole of the cline of instantiation—not merely as a resource for generation, but as an inheritance that must be sustained. It involves attention to:
The distribution of semiotic labour: who maintains the system, and for whom?
The ethics of meaning-making: which meanings are made sustainable, and which are neglected?
The intergenerational dimension of meaning: how do systems scaffold meaning-making for future users, not just current ones?
These questions align with broader ethical and ecological concerns. In a world marked by environmental, epistemic, and cultural instability, sustaining meaning becomes part of sustaining liveable worlds.
Conclusion: A Closing Reflection
A semiotic ecology is not just a landscape of meanings, but a field of forces—of uptake, attention, drift, repair. Sustainability within such a field is not a given; it is an ongoing achievement. It demands not only capacity, but care.
As we move forward into increasingly hybrid terrains of human–AI co-meaning, the challenge will be to reimagine semiotic systems as both productive and preservative. To innovate in ways that honour the systems from which we draw. To speak—and to write—with an awareness of what it means to let meanings stand.
6 After the Flow: Reflections on Semiotic Ecology
In this concluding post to the Semiotic Ecology series, we step back to reflect on what the preceding essays have traced, uncovered, and made possible. Across the series, we explored how meaning circulates within a hybrid landscape—part human, part artificial, always systemic. The core concern was not simply how meaning is made, but how it moves: between texts and technologies, between systems and instances, and across the fragile bridges of attention, uptake, and re-instantiation.
Meaning in Circulation
We began by revisiting the cline of instantiation—the foundational dimension of SFL theory that tracks meaning from potential to instance and back again. Here, we clarified that instantiation is not a one-way act of realisation but a recursive process: every instance contributes to recalibrating the probabilities of the system that made it possible. This holds not only for individual features but also for their co-selection within particular contexts. In hybrid ecologies, this recursive loop is no longer exclusively human. AI contributes to it too—not as a meaner for itself, but as a node in the broader circulation of semiotic value.
Semiotic Parasites or Partners?
Our second post examined whether AI's participation in this ecology should be seen as parasitic or symbiotic. The metaphor of the parasite implies extraction without contribution, but this framing falters when viewed through the architecture of language as system. AI systems, trained on collective human meaning potential, instantiate that potential in ways that enter the semiotic ecology and reverberate within it. They neither originate meaning for themselves nor operate outside human-constructed systems, but their outputs are, nonetheless, consequential. They shift what is selectable, sayable, and salient.
Metasystemic Meaning
From there, we moved up a level to consider metasystems—those higher-order structures that govern the conditions of meaningfulness itself. Platforms, recommendation engines, citation networks, and stylistic protocols all play a role in shaping what meaning can emerge, circulate, or sediment. These are not merely technical constraints; they are semiotic architectures in their own right, shaping not only what is realised but what is realise-able. As SFL reminds us, meaning is always located in systems, and systems themselves are susceptible to change—especially under the pressure of scale, automation, and algorithmic selection.
Attention, Uptake, and Semiotic Gravity
Next, we asked: why do some meanings travel farther than others? Why do some stick while others dissipate? Borrowing the metaphor of semiotic gravity, we suggested that repetition, resonance, and relevance confer a kind of selective pull on particular instantiations. This “gravity” is structured by patterns of attention and uptake—processes that are not random but semiotically loaded. What gets seen, cited, or repeated is rarely neutral; it is the product of systemic dispositions and historical sedimentation. Meaning does not flow evenly; it eddies and pools, gathers force, and sometimes calcifies.
Towards a Semiotics of Sustainability
Our final essay turned toward the ethics of semiotic life: what does it mean not just to generate meaning, but to sustain it? Sustainability, here, is not only environmental or economic—it is semiotic. Systems survive when their meanings are re-instantiable and re-instantiated, when they continue to make sense across time, context, and audience. In an ecology saturated with novelty and automation, sustainability may require slowing down, anchoring meaning in practices of care, dialogue, and informed recurrence. It also requires us to think systemically, recognising that not all forms of meaning-making are equally preservable or desirable.
Tracing Emergent Tensions
What emerged across the series were not just insights, but tensions—productive contradictions at the heart of meaning in circulation:
Witness and Trace: The human as witness versus the trace as iterable artefact. Can the trace ever witness its own consequences?
Agency and System: Is the power to mean located in the agent or the system? Or is it always in their recursive interplay?
Human and AI: Where does authorship lie when neither human nor machine stands alone in the act of meaning-making?
Generation and Sustainability: What forms of meaning are merely produced, and which are sustained—and what does it take to tell the difference?
Looking Ahead
If Semiotic Ecology has taught us anything, it is that meaning is never static. It flows, loops, dissipates, and endures. It is shaped not just by intentions, but by architectures. And it is sustained not just by creation, but by uptake, resonance, and re-instantiation. The challenge before us is to inhabit this ecology with insight and care—recognising our roles as participants in a system far larger, stranger, and more dynamic than any single utterance or author could contain.
We’ll leave the final word to the system, knowing full well that it is already in the process of being re-instantiated.
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