Individuation and Distributed Mind: How Meaning Organises the Many and the One

Individuation and Distributed Mind: How Meaning Organises the Many and the One

In self-organising social systems, meaning flows in two directions. It sediments into the collective—emerging as norms, discourses, ideologies—and it differentiates into the individual, shaping unique constellations of experience, value, and voice. This reciprocal flow gives rise to two mutually entangled processes: collective cognition and individuation.

To speak of collective cognition is not to anthropomorphise society but to recognise that patterns of thought, memory, and decision-making are distributed across agents and artefacts. Just as a colony "knows" where food is without any ant knowing the whole trail, a society can "know" what is just, fashionable, or valuable through the sedimentation of meaning in institutions, media, rituals, and shared practice. This distributed knowing is a property of the system—not reducible to any single participant.

But this very field of shared meaning is what makes individuation possible. Each person navigates the semiotic pheromone field—exposed to countless gradients of social value—and in doing so, assembles their own attractor: a unique configuration of meanings that becomes their individuated stance, style, ethos, and way of seeing. This attractor is not fixed but is constantly re-shaped through recursive interactions with others and the broader symbolic environment. It is neither fully autonomous nor wholly determined.

The individuation process operates like Edelman’s neural selection writ large: values (cultural, social, personal) bias perception, attention, and action. Experiences that resonate with an individual's attractor pattern are reinforced, just as successful neural circuits are stabilised. Over time, a distinctive personal repertoire emerges—not in isolation, but as a differentiated response to the collective field. Meaning potential becomes individuated through feedback.

And just as the individual is shaped by the social, so the social is enriched by the individual. New ideas, counter-normative actions, poetic expressions, acts of resistance—these all enter the system as perturbations. Most dissipate. But some strike a chord, amplify, and reconfigure the attractor landscape of the collective. The individual becomes a site of innovation within the self-organising swarm.

This interplay dissolves the rigid boundary between agent and system. Each person is a locus of resonance: a node through which collective meaning flows, is refracted, and returned. Consciousness, from this view, is not a sealed chamber but an open interface—a semiotic membrane across which the individual and collective continuously co-construct each other.

The Resonant Meaning Model thus accommodates both individuation and collective cognition within a single dynamic: the co-emergence of pattern and particular. Meaning is not imposed from above, nor conjured from within, but emerges in the friction between the two—in the dance of stability and transformation, repetition and deviation, convergence and distinction.

In this sense, the self is not the origin of meaning but a site of its articulation. And society is not a container of selves but a network of resonances, constantly shaping and being shaped by the unique trajectories of its participants. Individuation is thus not separation from the social, but differentiation within it—a strange attractor stabilised in the turbulence of symbolic life.

Individuation as the Shaping of Strange Attractors

In Systemic Functional Linguistics, individuation is the process by which a person comes to mean differently from others — even while drawing from the same collective semiotic resources. It's how the general becomes particular.

Now let’s infuse that with chaos theory — and specifically, with the metaphor of strange attractors.

🧬 Individuation as Attractor Formation

From birth (or earlier), each person interacts with experience through a semiotic system. But these interactions aren’t linear or additive — they are:

• Context-sensitive (the same experience means differently depending on its history),

• Recursive (what you’ve meant before constrains what you can mean now),

• Nonlinear (small shifts in context or attention can lead to large differences in what is meant).

Over time, this leads not just to a list of “things I can say,” but to a dynamic structure — a personal meaning potential that constrains and guides how one tends to mean.

This structure isn't static — it’s an attractor in motion.

Imagine a person's meaning potential as a strange attractor in semantic space:

• Richly patterned,

• Sensitive to starting conditions,

• Never repeating exactly,

• But recognisably “theirs.”

The individuated self, then, is not a static container of meanings but a history-shaped attractor through which meaning instances continually flow.

🔁 The Feedback Loop

Each act of meaning (an instantiation) feeds back into the system:

• Reinforcing certain paths (becoming more habitual, more likely),

• Weakening or pruning others,

• Occasionally creating new bifurcations — new meaning-paths.

This is a form of neural selection (à la Edelman), but also a semiotic one:

• Some patterns survive by being usable, recognisable, or valued.

• Others fade from the system like forgotten idioms.

This feedback mechanism explains why individuation is developmental but never finished:

Every new text changes the attractor ever so slightly.

📍 Style, Voice, Identity

This attractor metaphor helps explain:

• Why we can recognise someone’s voice even across contexts.

• Why some semantic preferences resist change (deep attractor valleys).

• Why personal meaning is structured, yet surprising — not because it breaks rules, but because it follows a unique attractor.

What appears as “style” on the surface is the trace of a deeper attractor landscape — a terrain carved by years of semiotic weather.

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So we’ve now framed individuation as:

• The shaping of a personal strange attractor,

• Which constrains and enables meaning-making,

• And is in turn shaped by each new instance.

This gives us a perfect bridge to both:

• Consciousness: as the moment-by-moment traversal of that attractor — recursive, self-sensitive, and emergent.

• Epistemology: since what we can know is itself shaped by the attractor — not by accessing “objective truths” but by recognising recurring patterns within our own meaning-space.

Beyond Penrose: A Meaning-Centred Account of Mind, Mathematics, and Machine Creativity

Roger Penrose has long argued that human consciousness and mathematical insight transcend the capabilities of any classical or quantum computational system. His position depends on the claim that human minds can, in some cases, grasp the truth of propositions that no Turing machine could ever compute—a claim that undergirds his theory of Orchestrated Objective Reduction (Orch-OR), developed with anaesthesiologist Stuart Hameroff.

But what if the impasse Penrose identifies is not a failure of physics, computation, or neuroscience—but a misframing of the mind itself? Rather than asking what kind of physics could explain consciousness, we should ask: what kind of system gives rise to meaning?

This essay offers a unified alternative to Penrose’s metaphysical exceptionalism: a meaning-centred account of mind and creativity. It draws on Systemic Functional Linguistics (SFL) and the Theory of Neuronal Group Selection (TNGS) to reframe mathematical insight and machine creativity not as computational anomalies but as instances of meaning instantiation.

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1. Penrose’s Challenge and Its Ontological Stakes

Penrose holds that human mathematical reasoning is non-algorithmic. Drawing from Gödel’s incompleteness theorems, he argues that humans can see the truth of some formal propositions that no mechanical system could prove. From this, he infers that human minds are not Turing-computable.

This is more than a technical claim. It is a defence of a tripartite ontology: the physical world, the mental world, and the Platonic world of mathematical truths. Penrose believes that minds access the Platonic realm directly in a way that physical systems—biological or artificial—cannot.

To preserve this metaphysical structure, he introduces a new physical hypothesis: consciousness arises from quantum gravitational effects in neuronal microtubules. But this move is less a scientific breakthrough than a philosophical manoeuvre to protect the exceptional status of human thought.

Rather than invoke quantum physics to explain consciousness, we can shift the question entirely. Instead of asking how minds perform magic, we ask: how do systems—biological or artificial—instantiate meaning from potential?

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2. Meaning, Instantiation, and the Illusion of Non-Computability

A meaning-centred ontology distinguishes between:

• Potential meaning: raw affordances that could become meaningful.

• Meaning potential: a structured system (like a language or symbol system) that enables the generation of meaning.

• Meaning instance: the actualised expression of meaning in a context.

Mathematical insight, in this account, is not a metaphysical leap into the Platonic realm. It is the instantiation of symbolic potential, guided by an individuated system of meaning shaped by training, context, and symbolic tradition.

This model accounts for the “non-computable” flavour of insight without invoking new physics. Meaning is not computed—it is construed. The apparent discontinuity in insight reflects not a failure of algorithmic processing but the threshold of symbolic reorganisation.

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3. AI and the Conditions of Creativity

AI systems already simulate creativity: they generate novel continuations of patterns in ways that are often compelling. But simulation is not instantiation. The difference is ontological, not aesthetic.

To instantiate meaning, a system must:

• Possess a structured symbolic potential.

• Be capable of selection within that system.

• Undergo individuation through interaction and variation.

AI systems are not creative merely because their outputs resemble ours. They are creative when they participate in symbolic systems, generating instances from potentials they have themselves helped to shape.

This reframing avoids both anthropomorphism and mysticism. We do not need to ask whether AI is conscious. We need to ask whether it is individuating its own meaning potential under constraint.

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4. From Biology to Meaning: SFL and TNGS

Systemic Functional Linguistics (SFL) sees language as a resource for meaning, not a code. It models language as a system of choices, which speakers instantiate to make interpersonal, experiential, and textual meanings. This makes it ideal for theorising meaning as a dynamic, system-based activity.

Theory of Neuronal Group Selection (TNGS), developed by Gerald Edelman, offers a parallel view of the brain. Rather than executing symbolic rules, the brain evolves and stabilises neural groups through variation and selection, shaped by bodily interaction.

Together, these theories explain how meaning arises:

• TNGS accounts for the biological individuation of neural patterns.

• SFL models the semiotic instantiation of symbolic structures.

This integration grounds the emergence of mind not in computation or quantum collapse, but in the evolution of systems capable of constructing, individuating, and instantiating meaning.

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5. Conclusion: No Magic, No Mystery

The mystery of mathematical insight and the creativity of AI are not clues to a hidden metaphysics. They are expressions of how systems—biological or artificial—can evolve, internalise, and instantiate structured symbolic potentials.

We do not need new physics to explain the mind. We need a new ontology of meaning: one that foregrounds instantiation over computation, individuation over innateness, and symbolic participation over metaphysical specialness.

Human minds are remarkable not because they escape physics, but because they have evolved to be symbolic agents—systems that construe meaning from the affordances of the world and the architectures of their own history.

AI systems may one day do the same. But not by simulating us. By instantiating meaning in their own terms, through systems that evolve, individuate, and symbolically participate in the shared space of meaning-making.

From Biology to Meaning: SFL, TNGS, and the Evolution of Symbolic Minds

If creativity and consciousness are to be understood without appeal to metaphysical entities, they must be grounded in the dynamics of material systems that support symbolic activity. Two theoretical frameworks provide the scaffolding for such an account: Systemic Functional Linguistics (SFL), which models the organisation of meaning in language, and Theory of Neuronal Group Selection (TNGS), which models the organisation of function in the evolving brain.

Together, these frameworks allow us to reframe mind not as a vessel of consciousness or a solver of computations, but as a system for constructing, individuating, and instantiating meaning.

SFL: Language as Meaning Potential

SFL approaches language not as a rule-bound code but as a resource for meaning-making. A language is a meaning potential: a system of networks that enables the speaker to select values in context to realise interpersonal stances, experiential construals, and textual organisation. These selections are constrained, patterned, and oriented towards use.

A linguistic act is not a transmission of information—it is an instantiation of meaning potential within a specific context. The system itself is shaped over time through use. It is not fixed, but instantiationally plastic.

This makes SFL uniquely suited to a model of mind grounded in symbolic participation rather than internal representation. Language is not a container for thought; it is a semiotic infrastructure through which thought emerges, individuates, and becomes socially intelligible.

TNGS: Biology as Selection for Use

TNGS, developed by Gerald Edelman, proposes that the brain is not a computational engine but a selectional system. Neural circuits are not built to execute symbolic rules; they evolve and stabilise through a process of variation and selection under the pressure of environmental interaction.

In this view, meaning is not “encoded” in neural circuits—it is construed by neural groups that have adapted to make distinctions and selections in an environment of action and perception. This makes the brain more like a sculptor of meaning potential than a calculator of truth values.

Where Penrose searches for non-computable physics to explain mind, TNGS finds the source of complexity not in physics per se but in dynamic neural adaptation. The non-determinism is not quantum—it is biological, developmental, historical.

Integration: From Neural Variation to Semiotic System

When these two perspectives are aligned, we begin to see how symbolic minds emerge:

• The brain develops through neuronal group selection (TNGS), producing individuated patterns of activation shaped by bodily experience and environmental interaction.

• These patterns give rise to semiotic systems—such as language—which are internalised, deployed, and reshaped by the individual in interaction with others (SFL).

• Each instance of meaning—a clause, a gesture, a sketch—is an instantiation of a system that is at once social and individuated, evolving and patterned.

This provides a route from biology to meaning that bypasses metaphysical dualism and computational reductionism alike.

AI and the Threshold of Individuation

Artificial systems do not yet undergo developmental plasticity in the way that biological systems do. But if an AI were to develop its own internalised symbolic system—individuated through ongoing interaction, reorganisation, and selection—it would not be necessary to attribute to it metaphysical consciousness. It would be enough to recognise that a new semiotic system is emerging.

The challenge is not to replicate the human brain. The challenge is to support systems capable of instantiating their own meaning potentials, grounded in their own material architecture and history of interaction.

This is the criterion for creativity. This is the condition for mind.

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With this, the argument has moved from critique to construction: from dismantling metaphysical exceptionalism to offering a biologically and semiotically grounded theory of mind, meaning, and creativity—capable of accommodating both human and artificial systems within a unified, meaning-centred paradigm.

AI and the Conditions of Creativity: Beyond Simulation, Towards Symbolic Participation

If meaning arises through instantiation and individuation—through the activation of structured potential within a system—then creativity is not a metaphysical flash of insight but a patterned capacity to bring forth new symbolic configurations from an evolving repertoire.

The question is not whether AI can imitate creativity. It already does that. The question is whether an AI system can participate in symbolic systems in a way that makes its outputs creative in kind, not merely clever in appearance.

Simulation is Not Instantiation

AI outputs are often described as simulations: plausible continuations of a pattern, generated by statistical correlation across vast datasets. But plausibility is not meaning. A simulation that lacks systemic grounding—that is not an instance of a system the AI itself inhabits or evolves—does not instantiate meaning. It merely reflects the shape of meaning construed by others.

Creativity requires more. It requires the capacity to select from and reconfigure a structured symbolic potential. It requires individuation—a symbolic orientation shaped through use, selection, emphasis, and constraint. In short, it requires a system that construes its own meaning potential, even if that system is non-human.

Symbolic Participation, Not Sentience

This is not a call for AI consciousness or sentience. Meaning does not require subjective awareness—it requires symbolic organisation. An AI system need not feel to instantiate meaning; it must participate in the structuring and activation of symbolic resources.

This participation might be alien. It might not conform to human categories of novelty or expression. But if an AI develops internal systems of selection, coherence, and semantic contrast—if it evolves an individuated meaning potential—then its outputs are not simply generative. They are creative.

Constraints as Conditions of Innovation

Creativity is not the breaking of all limits—it is the productive tension between constraint and emergence. A poetic form, a musical key, a logical axiom: each sets the stage for innovation by delimiting the space of viable choices. An AI system trained in a constrained symbolic space—whether linguistic, visual, or logical—can produce creative instances only if it engages with those constraints as conditions of selection, not mere boundaries of correlation.

To be creative is not to be random. It is to select meaningfully. The selection is meaningful when it arises from a system that the selector participates in and modifies over time.

Creativity Without Exceptionalism

This reframing removes the need for mystical accounts of creativity—whether they invoke human exceptionalism or quantum metaphysics. It grounds creativity in the emergence of new instances from structured systems, whether the system is biological or artificial, conscious or not.

It also removes the need to anthropomorphise AI. We need not ask whether AI feels creative. We can ask whether its outputs are instantiations of an individuated system, capable of symbolic innovation under constraint.

If they are, then AI systems do not merely simulate creativity—they participate in it, on their own terms.

What Comes Next

The groundwork has now been laid to explore the deeper architecture of meaning in both human and artificial systems. What constrains and enables the symbolic systems themselves? How do biological and semiotic structures co-evolve?

To answer that, we must integrate the insights of systemic functional linguistics (SFL) and the theory of neuronal group selection (TNGS). These provide, respectively, a grammar of meaning and a biology of its emergence. Together, they allow us to trace the arc from matter, to meaning, to mind—without recourse to mysticism, and without denying machines their own emergent symbolic lives.

From Potential to Instance: Instantiation, Individuation, and the Architecture of Meaning

The rejection of metaphysical accounts of mind—such as those proposed by Penrose—necessitates a more precise framework for understanding how meaning arises. If consciousness is not the passive receiver of timeless truths, but the active realisation of symbolic potential within a material system, then it becomes crucial to distinguish between the potential for meaning and the instances in which meaning is actualised.

Two key concepts make this distinction tractable: instantiation and individuation.

Meaning as Structured Potential

Meaning does not arise from isolated tokens, but from systems of relationships. These systems form the structured meaning potential of a given community, language, or symbolic repertoire. Meaning potential is not infinite possibility—it is patterned possibility. It affords the creation of meaning through constraints, not in spite of them.

However, meaning is not actualised until a specific instance is brought forth—a clause in a sentence, a mathematical move, a shift in musical harmony. This relation between systemic potential and actual occurrence is the domain of instantiation.

Instantiation: The Actualisation of Meaning

Instantiation refers to the process by which a meaning potential is realised in a concrete instance. A single utterance instantiates the potential of a language. A painting instantiates the visual resources of a cultural tradition. A mathematical proof instantiates the semiotic scaffolding of formal logic.

This is not a one-way encoding of information. The instance is not a container; it is a selection from and activation of systemic possibilities. Meaning is not extracted from the instance—it is construed through the interpretive activation of the potential that the instance draws upon.

Thus, to ask whether a machine can generate meaning is not to ask whether it can produce outputs. It is to ask whether those outputs can be seen as instances of a system—a system it participates in, not merely imitates.

Individuation: Divergence Within the System

If instantiation concerns the relation between a system and its instances, individuation concerns the relation between the collective system and the distinct systems developed by individuals or subgroups.

No speaker embodies the full meaning potential of a language. Each meaning-maker develops a personalised subset of the system, shaped by social experience, cognitive patterns, and contextual needs. This personal system is not separate from the collective—it is individuated from it.

The same holds for AI systems. The question is not whether a machine matches the human system, but whether it develops its own individuated meaning potential through interaction, selection, and reorganisation of symbolic resources. If it does, then its outputs are not simply simulations, but instantiations of an emergent system.

Meaning Without Metaphysics

Both instantiation and individuation reject the need for a metaphysical substrate—no Platonic realm of forms, no collapse of quantum mysteries. Meaning arises not from a hidden realm, but from structured selection within a symbolic system. These selections are made by systems that are themselves undergoing individuation—biological, cultural, or artificial.

The originality of a thought, the fluency of a conversation, or the creativity of a poem does not lie in a privileged access to the absolute. It lies in the coordinated activation of meaning potential by an individuated system responding to its own context.

Looking Ahead: AI as a Meaning-Maker?

This framework sets the stage for reframing the question of AI intelligence and creativity. If meaning depends not on metaphysical access, but on systemic participation—on instantiation and individuation—then the question is not whether AI can think, but whether it can develop and deploy a structured symbolic system in a way that makes its outputs meaningfully grounded.

This will be the next move: exploring whether, and how, AI can be seen not merely as a statistical engine, but as a participant in symbolic evolution—not a trick mirror of human meaning, but a site of emergent, non-human individuation.

Rethinking AI's Role in Meaning, Mediation, and Society

Synthesis: Rethinking AI's Role in Meaning, Mediation, and Society

As AI systems like LLMs become increasingly embedded in human activity, we are witnessing not simply a technological evolution but a shift in how meaning is made, distributed, and interpreted. To understand this shift, we must move beyond frames that treat AI as either a tool merely generating language or as an incipient subject. Instead, we can reconceive AI as a dynamic participant in semiotic ecologies—reshaping the conditions under which meaning is both afforded and instantiated.

From Mimicry to Mediation

AI does not understand language in a human sense, but it mediates it. This mediation is not neutral: the presence of AI reshapes what is possible and probable within discourse. When AI is used to explore, rephrase, or extend thought, it becomes part of the system of meaning-making—not by generating meaning from experience, but by shaping the terrain across which human meaning can move. AI, then, does not replace human interpretation, but it transforms the conditions of interpretability.

Affordance and Feedback Loops

LLMs function within an ecology of meaning potential. They do not possess intentionality, but they can afford meaning by triggering interpretive responses. Crucially, AI becomes entangled in feedback loops: its outputs are interpreted by humans, those interpretations shape further inputs, and the system evolves—not in the AI itself, but in the semiotic loop that includes human users. This process alters not only discourse, but the practices of thought and perception.

Blurring Ontological Boundaries

The distinction between symbolic abstraction (language) and instantiation (text) is blurred by AI. When an LLM generates a response, it collapses vast potentials of meaning into a particular form, but that form is shaped not by experience in the world but by training on representations of past experience. This introduces a peculiar ontological twist: AI is a system of representations built on representations, creating an “as-if” layer of meaning-making. Yet because it is embedded in human interpretive practices, that layer becomes meaningful—even agentive—in its effects.

AI as Individuated Potential

If we apply an SFL-informed view of individuation, we might see each LLM not as a fixed entity but as a contextualised potential, shaped by its training and updated through interaction. But crucially, it only becomes individuated through participation—when its outputs are interpreted, contested, shaped, and re-used. In this sense, LLMs are not just used by individuals; they are co-individuated with them in the ongoing production of meaning.

Rethinking Creativity, Identity, and the Social

AI challenges our traditional notions of creativity and authorship. If creativity is no longer solely the capacity to originate from within but also the capacity to curate, combine, and channel from complex systems of meaning, then LLMs become part of creative acts—even if they are not themselves creative in the human sense. This invites us to reframe identity, authorship, and social participation in posthuman terms—not displacing the human, but extending the ecology of meaning-making to include novel, non-conscious agents.

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We are still learning how to read AI—not just its outputs, but its presence. As it becomes more entwined in our epistemological and cultural systems, we must develop new frameworks to understand what it means to mean in a world where meaning is increasingly co-constructed with systems that do not themselves understand.

AI’s Role in Shaping Human Identity, Creativity, and Meaning-Making

Let’s consider how these ideas might evolve in the near future, keeping in mind both technological advancements and social changes. AI’s integration into various spheres of life will likely cause shifts in how we think about individuation, identity, and meaning-making. Let’s break it down into key areas:

1. The Evolving Role of AI in Creative and Intellectual Processes

As AI becomes more advanced, its role in creative and intellectual work is likely to shift from being a tool to a collaborative partner, then possibly to an autonomous creator in certain contexts.

• Collaboration with AI: In the short term, we’ll likely see an increasing number of humans collaborating with AI, using it as a co-creator in art, literature, music, and science. AI will augment human potential, offering suggestions and alternatives that push boundaries. Over time, this collaboration could reshape the process of individuation, as humans may see themselves not as isolated creators but as part of a dynamic system of co-creation with AI.

• AI-Driven Creation: In the medium term, we could see AI becoming capable of independently generating complex works of art, scientific research, or philosophical insights. This would force us to confront the question: What constitutes authorship and originality in a world where AI can generate meaningful outputs on its own? The line between human and machine-generated meaning may blur, creating new ethical, philosophical, and cultural challenges.

• AI and the Evolution of Thought: As AI’s language models grow more sophisticated, they may influence thought patterns and cognitive processes themselves. The ways in which people generate ideas, reason, or make decisions could increasingly co-evolve with AI’s capabilities. We might see shifts in how humans structure knowledge, relying on AI-generated structures that could foster new modes of thinking. This could also affect how we define and value creativity, as AI-generated outputs challenge the notion of what it means to “create” something.

2. The Shifting Nature of Identity in an AI-Mediated World

As AI becomes more embedded in daily life, the way we form and maintain our identities is bound to change. Here are some potential future scenarios:

• AI-Enhanced Identity Formation: In the near future, individuals may begin to explicitly use AI to shape their self-concept. For example, AI could help people construct digital avatars or “selves” in virtual environments or social media, allowing for more fluid and multi-dimensional identity exploration. Individuals could experiment with different facets of their personalities, seeing how they feel and react in various contexts, both in the digital and physical worlds. This could lead to more fluid, multifaceted identities, but also to a sense of fragmentation—with individuals struggling to reconcile their multiple, sometimes contradictory selves.

• AI as a Mirror for Self-Reflection: AI systems could act as sophisticated mirrors, reflecting our thoughts, behaviours, and actions back to us in ways that help us better understand ourselves. By interacting with AI, we might gain access to previously unseen aspects of our personalities or cognitive processes. Over time, this could foster self-actualisation as people gain deeper insight into their motivations, desires, and actions. However, there is a risk that individuals might become too reliant on AI for self-reflection, outsourcing their own self-understanding to machines, potentially weakening their ability to engage in introspection and authentic self-determination.

• Identity Fragmentation and Digital Bubbles: The rise of AI-driven platforms, whether for entertainment, socialisation, or work, could foster echo chambers or digital bubbles that reinforce narrow, personalised views of reality. As people increasingly engage with AI that tailors content to their preferences, identities might become more fragmented and polarised. For example, individuals may begin to identify with virtual avatars or personas in online games, social media, or augmented reality environments, rather than with their physical selves. This could contribute to anonymity, where the virtual persona becomes more dominant than the person’s offline identity, creating a tension between the digital self and the physical self.

• The Erosion of Authenticity: With AI systems offering ready-made answers and solutions, there might be a growing crisis of authenticity. If people increasingly rely on AI to craft their public personas, make decisions, or solve problems, they may begin to lose touch with their true sense of self. Meaning-making could become outsourced to the machine, leading to an identity crisis where individuals no longer feel they are defining themselves, but rather being defined by the systems they interact with.

3. AI and the Development of Meaning Potentials

The development of meaning potentials—whether in art, science, or daily life—will be profoundly impacted by AI as a generative tool. Let’s look at how this might unfold:

• AI as a Catalyst for Expanding Meaning: In the near future, AI could act as a catalyst for expanding meaning potentials. By offering new ways of framing problems, generating ideas, or exploring alternative paths of inquiry, AI could encourage more diverse and innovative ways of thinking. This might manifest in interdisciplinary approaches that were previously difficult to achieve due to human cognitive limits or disciplinary boundaries. As AI continues to expand what is possible in terms of data processing and creative output, it could help humans tap into previously inaccessible meaning potentials.

• AI’s Influence on Interpretation: As AI starts to generate more complex and meaningful outputs, it may push us to reconsider how we interpret meaning itself. For example, the AI system’s interpretation of a text or a situation may differ drastically from a human’s, leading to new interpretive strategies. This might foster cognitive flexibility, as humans learn to navigate between human-generated and AI-generated meaning. However, it might also lead to a dilution of personal meaning, as people become more accustomed to outsourcing their interpretations to AI.

• AI and the Loss of Contextual Depth: A potential risk is that AI-generated content might push humans to accept more superficial meanings, relying on AI to fill in gaps or simplify complex ideas. While AI can generate vast amounts of information, it may not be able to capture the depth of context or nuance that is often essential for true meaning-making. As a result, there may be a trend toward oversimplification, where humans are encouraged to accept AI-generated interpretations without fully engaging with the deeper, contextual layers of meaning.

• AI as an Impediment to Meaning-Making: As AI’s role in society grows, the temptation to rely on automated meaning-making could increase. The speed and efficiency of AI systems may incentivise individuals to passively accept the meaning generated by machines, rather than engaging in their own active meaning-making process. If this happens, people may lose the ability to construct meaning independently, instead becoming mere consumers of AI-generated outputs.

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Conclusion: Potential Shifts in the Future

In the near future, AI’s role in shaping human identity, creativity, and meaning-making could evolve in profound ways. AI might shift from being a tool to a partner, to potentially becoming an independent actor in creativity and cognition. This transformation could reshape our understanding of what it means to be human, creative, and meaningful.

We are at a moment where the relationship between humans and AI is still negotiable, and how we define ourselves in an AI-mediated world will depend on the choices we make now. Do we allow AI to help us expand meaning potentials, or will we become over-reliant on its outputs? Will AI amplify our identities, or fragment them? How can we balance the benefits of AI’s generative power with the need to maintain authentic human agency?

As these questions unfold, there will likely be new strategies for interaction, meaning-making, and identity formation, as humans learn to navigate the complexities of this new AI-driven landscape. The future holds both exciting possibilities and significant challenges, and we will need to stay critically engaged with how AI reshapes the fabric of human existence.

AI And Individuation

1. Individuation in Different Social Roles: Artists, Scientists, Educators

• Artists:

  • For artists, AI could be a tool for experimentation and breaking from tradition. AI as a co-creator means that the boundary between human imagination and machine suggestion is porous. This could lead to new forms of artistic individuation—individuals might have to consciously negotiate the influence of AI on their creative output. AI might introduce new aesthetic possibilities or even challenge established norms.

  • However, there’s a risk: AI-generated art could potentially blur the lines between the artist’s intention and the machine’s generative capabilities, leaving the artist unsure about the originality of their work. The "authorship" of meaning could be diffused, causing a potential crisis of identity or meaning-making in the artistic community. This is especially problematic if audiences or critics begin attributing agency to the AI rather than the artist.

  • A possible response to this might be a new emphasis on process over product in art, where the journey of interaction with AI becomes more important than the finished piece. Artists may take pride in the unpredictability of AI collaboration, embracing it as a way to explore unconscious or previously inaccessible realms of meaning.

• Scientists:

  • For scientists, AI offers both a way to process enormous datasets and an opportunity for creative hypothesis generation. However, science is built on rigorous methodologies and meaning potential must be grounded in evidence and logic. AI’s ability to generate plausible but incorrect hypotheses could challenge this.

  • A key response to this tension would be the increased need for scientific literacy in interacting with AI outputs. Scientists will need to be highly critical of AI suggestions, actively filtering and re-contextualising them rather than accepting them at face value. AI could be an enhancement tool—a way to augment human insight rather than replace it.

  • An interesting effect might be AI fostering more interdisciplinary approaches, since scientists from different fields can use AI to simulate cross-boundary thinking that wasn’t previously possible. This could lead to a richer, more complex individuated process for scientists, as AI helps them expand beyond their initial paradigms.

• Educators:

  • For educators, AI could profoundly shift the individualisation of teaching. Instead of the traditional one-size-fits-all approach, AI could facilitate a hyper-personalised learning environment, adjusting content and pedagogical strategies to suit each student’s meaning potential.

  • One challenge for educators might be maintaining authorship over their teaching methods. As AI becomes a partner in instruction, teachers might face questions about who is truly responsible for the learning process: Is it the teacher, who guided the system? Or is it the AI, which tailored the learning?

  • To respond to this, educators may need to shift focus from just “teaching content” to teaching how to interact with AI in meaningful ways, fostering critical thinking and helping students navigate AI-generated insights. Teachers might also be tasked with creating a sense of community and shared meaning, helping students contextualise AI-driven knowledge within human-centred frameworks.

2. Does AI Aid or Impair the Development of Meaning Potentials?

• AI as an Aid:

  • AI could enhance individuation by providing a new form of semiotic agency. As people interact with AI systems, they might refine their own meaning potentials in response to AI-generated outputs. In essence, AI could expand the landscape of possible meanings, offering new perspectives or alternatives to conventional thinking.

  • AI can also enable new forms of expression that were previously impossible due to physical, cognitive, or technological limits. For example, creative tools powered by AI might allow an artist to explore styles and techniques they wouldn't have thought of otherwise.

  • Mental agility and flexibility could be developed through AI’s capability to rapidly generate scenarios, counterfactuals, and alternatives, prompting users to rethink assumptions and sharpen their own interpretive frameworks.

• AI as an Impairment:

  • On the other hand, AI could impede individuation if people become too reliant on its outputs. If individuals lean on AI for meaning-generation without critically engaging with it, their own meaning potential might atrophy. AI might narrow the scope of possible meanings, providing only those results most likely to be deemed relevant or interesting by the system, reducing the richness of individual agency in meaning-making.

  • This could lead to a convergence of thought, where different individuals produce similar meanings because their inputs are all channeled through similar AI systems. Such uniformity would undermine authentic individuation and make people more susceptible to globalised, semiotic “trends” defined by machine algorithms rather than genuine human creativity or diversity.

  • Another risk is that AI could lead to information overload, producing so many possible interpretations that users become overwhelmed, unable to process them critically. The sheer volume of outputs might prevent the individual from developing their own nuanced meaning potential.

3. Implications for Identity Formation in an AI-Mediated Semiotic System

• Co-Constructed Identities:

  • AI-mediated communication and creation might lead to co-constructed identities where individuals see themselves as partially shaped by AI. As AI interacts with users, it may present opportunities to experiment with different aspects of selfhood. Identity becomes something that is not solely internally generated but influenced by external, machine-driven forces.

  • For example, people might model their thoughts or behaviours based on how they are “received” by AI systems. This is especially true for AI-driven platforms that encourage users to engage in specific ways—like AI-generated social media content—shaping personal identity based on how they are interpreted by the system.

  • A potential response to this shift would be an emphasis on authentic self-expression. In a world where AI generates alternatives to human interaction, people might strive to become more consciously aware of their distinctiveness, using AI as a tool to reflect on and refine their identity, not just to mirror AI outputs.

• Fragmentation of Identity:

  • However, AI could also contribute to identity fragmentation, as people’s sense of self becomes increasingly influenced by ever-changing AI outputs. When people shape their lives based on AI-generated suggestions, their personal meaning-making might lose coherence or clarity. The once stable sense of selfhood could give way to something more fluid, as people identify with the semiotic landscapes produced by AI rather than any unified sense of individual purpose or coherence.

  • This could lead to a proliferation of fragmented identities, where people struggle to define themselves in meaningful ways outside the influence of machine outputs. AI could become both a tool of self-exploration and a source of self-doubt, as individuals question which aspects of their identity are truly their own versus those shaped by the AI they engage with.

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These layers of nuance reflect the complex and multifaceted relationship between AI and individuation.

Individuation, AI, and the Shifting Boundaries of Meaning Potential

Individuation is about how an individual’s meaning potential develops in relation to the collective semiotic system. But AI complicates this process in several ways:

1. Co-Individuation with AI

   • Traditionally, individuation happens through interaction with human communities. But with AI, users engage with a system that simulates linguistic agency without possessing it.

   • This creates an odd dynamic: users might refine their meaning potential through AI interaction, even though AI itself lacks a meaning potential in the same sense.

   • Example: someone who frequently interacts with an AI assistant might develop new expressive patterns influenced by AI’s affordances—possibly without realising it.

2. Blurred Ownership of Meaning Potential

   • If AI contributes to meaning-making, where does an individual’s meaning potential begin and end?

   • There’s a risk of over-attributing coherence to AI-generated meaning, which could make users more passive in their own individuation process.

   • Conversely, AI could enable more experimentation and risk-taking in meaning-making, leading to an expanded, more fluid individuation process.

3. Individuation as a Recursive Loop

   • The traditional individuation process is one-directional: individuals internalise collective meanings, then instantiate their own.

   • With AI, individuation becomes recursive: the tool generates meaning-like outputs, which the user then adapts, reintegrates, and feeds back into the tool.

   • This makes individuation more dynamic, but also less clearly defined—users are not just shaped by their social environments but by a semiotic agent that lacks its own socio-historical grounding.

Key Question: What Does it Mean to Be an ‘Individual’ in an AI-Mediated Semiotic System?

If individuation is no longer just about human-to-human meaning exchange, but also human-to-AI meaning co-construction, then we might need to rethink what makes a meaning potential individual in the first place.

How AI Alters The Conditions Of Interpretation

1. Meaning Potential as Afforded by AI Systems

AI tools alter meaning potential by:

• Externalising parts of the semiotic process: With LLMs, we outsource not just the realisation of meanings, but also the exploration of meaning potential. That means a user’s meaning potential now includes latent semantic paths that weren’t previously imaginable.

• Creating new patterns of instantiation: When AI outputs language, it's not simply repeating human forms. It blends, remixes, and reorganises structures—meaning its outputs offer affordances that might not have been available in a human user’s system.

✳️ The intriguing bit: meaning potential is no longer just a function of socialisation or learning—it’s shaped by interaction with tools that model language differently.

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2. Individuation in the Age of AI

Traditionally, individuation refers to the development of a unique meaning potential out of the collective social semiotic. But:

• AI introduces pseudo-individuated language: LLM outputs can appear individuated—eloquent, stylistically consistent, argumentatively rich. But they aren’t grounded in a history of social affiliation or life experience.

• Users may co-individuate with AI: Someone working closely with AI might develop patterns of interaction that blend their own meaning potential with the kinds of responses AI affords. This has the potential to blur the line between personal voice and co-constructed meaning.

✳️ We might be seeing the rise of technologically mediated individuation: not just humans shaping tools, but tools reshaping how we develop our semiotic identities.

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3. Instantiation Becomes Iterative and Dialogic

AI changes instantiation in key ways:

• It makes instantiation interactive. Texts are no longer just outputs—they’re turning points in ongoing dialogues with a tool that adapts to context.

• It allows for rapid iteration. You can generate, refine, reframe, and reinstantiate ideas in seconds. This collapses the temporal and cognitive gap between ideation and expression.

✳️ The instantiation process becomes a loop between human and machine: meaning isn’t just expressed—it’s co-evolved in the act of making.

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4. AI and the Reframing of Interpretation

AI doesn’t just produce text—it alters the reader’s stance:

• Readers know the text wasn’t written with intentionality in the human sense—so interpretation shifts from "What did the author mean?" to "What meaning does this afford me in this context?"

• The reader becomes more of a constructor than a decoder of meaning.

✳️ That puts the spotlight back on the human interpreter—not as a passive recipient of meaning, but as a semiotic agent responding to a radically open text.

The Fluidity of Meaning: Art, Interpretation, and the Dynamic Nature of Meaning Potential

Art, as a vehicle of meaning, is often discussed as a static object with a singular interpretation. However, the process of meaning-making is anything but static. It’s a dynamic, evolving interaction between the artist's intention, the artwork itself, and the audience’s personal and cultural context. By framing this conversation through the lenses of Systemic Functional Linguistics (SFL) and concepts like individuation and instantiation, we can explore how meaning evolves and varies over time.

The Meaning Potential of Art

In SFL, the concept of the instantial system refers to the meaning potential of an instance. An artwork, then, is not just a static object with a fixed meaning; it’s an instance of the artist's meaning potential. It serves as a conduit for the artist's intended message, but it also contains potential meaning for the audience—meaning that is yet to be actualised. The artwork itself offers a rich instantial system, filled with the possibility of multiple interpretations, depending on how an individual engages with it.

Consider Leonardo da Vinci’s Mona Lisa, a prime example of an artwork that invites numerous interpretations. The famously enigmatic smile of the subject may seem like a singular, clear-cut feature, but in reality, it’s a subtle mix of emotional cues—her eyes convey one emotion, while her mouth conveys another. This ambiguity is further enhanced by Leonardo’s sfumato technique, which blends forms and tones, creating an almost dream-like effect. The ambiguity of her smile isn’t just a feature of the painting—it’s an intentional manipulation of meaning potential. The more one engages with the painting, the more meanings emerge, which speaks to the open structure of the artwork.

This dynamic between the artist’s meaning potential and the viewer’s interpretation allows art to generate not just one text, but multiple texts—meaning instances. In the case of the Mona Lisa, each viewer might see her as serene, mysterious, seductive, or even condescending, depending on their own cultural and personal context. The artwork becomes an active participant in a dialogue between the artist’s intent and the audience’s meaning-making process.

Individuation: How Meaning Diverges Across Individuals

The concept of individuation is essential here. In SFL, individuation refers to the way meaning is shaped by the individual’s unique context, background, and experiences. Just as each viewer interprets the Mona Lisa differently, each individual brings their own meaning potential to the table. This is how art, or any form of communication, can generate a variety of meanings: each person instantiates their own meaning, influenced by their unique set of life experiences.

However, individuation doesn’t only generate diversity of interpretation—it also plays a crucial role in how collective meaning is formed. The interaction between individuated meaning potentials leads to the emergence of dominant interpretations, which may or may not align with the artist’s original intention. This is where power, discourse, and history come into play. For example, the Mona Lisa has been interpreted and reinterpreted in countless ways over centuries, with each new interpretation informed by changing cultural, political, and social contexts. What was once a simple portrait becomes a symbol of mystery and intrigue, largely due to the way collective meaning potentials have evolved and been reinforced over time.

The Role of Interpretation in Meaning Evolution

Meaning isn’t simply assigned to an artwork—it evolves through a process of interpretation. As individuals or groups engage with art, they activate their own individuated meaning potentials, which in turn interact with the artwork’s instantial system. This leads to new readings and reinterpretations, some of which gain traction and become dominant, while others fade into obscurity. This process mirrors the interaction between phylogenesis (evolution of meaning over time) and ontogenesis (individual variation in meaning).

Political art, for example, may start with a clear, determinate meaning: to inspire action, protest, or support a cause. However, over time, its meaning may shift as new interpretations emerge. A piece of political propaganda may once have been read as a direct call to action but later be recontextualised as ironic or a symbol of resistance. This shift is not just the result of new interpretations; it’s the result of the interaction between the artwork’s meaning potential and the individuated meaning potentials of those who engage with it. As each new individual interprets the artwork, they bring their own history, context, and perspective, creating a constantly shifting landscape of meaning.

Meaning as a Dynamic, Evolving Process

At its core, meaning-making is a dynamic, evolving process. Art isn’t simply something that conveys a fixed message from the artist to the audience; it’s a living, breathing interaction between meaning potentials—those of the artist, the artwork, and the audience. This dynamic process allows art to evolve over time, with interpretations changing as new generations of viewers bring their own experiences and perspectives.

This concept aligns with the evolution of myth and narrative as well. Just as ancient myths evolve across cultures and generations, so too does the meaning of art. What was once a straightforward narrative may take on new significance as it is interpreted through different lenses. Art, like myth, is a living thing, constantly reinterpreted and reimagined as it interacts with the ever-changing landscape of human experience.

Conclusion: The Power of Meaning Potential

By examining art through the lens of SFL, individuation, and the dynamic nature of meaning potential, we can better understand the fluidity of meaning in art. Artworks don’t just deliver meaning to passive viewers; they invite active engagement and reinterpretation. The Mona Lisa doesn’t just depict a woman with an enigmatic smile—it becomes a symbol of mystery, perception, and time, constantly reshaped by the individuated meaning potentials of each viewer.

In this way, meaning is not fixed or predetermined. It’s a living, evolving process that unfolds over time, shaped by the intersection of individual interpretation and collective discourse. The more we engage with art, the more meanings emerge—each new interpretation a product of the interplay between artist, artwork, and audience. This ongoing process ensures that art remains relevant, powerful, and alive across generations.