Penrose, Platonism, and the Ontological Stakes of AI

Penrose, Platonism, and the Ontological Stakes of AI

Roger Penrose is no ordinary AI sceptic. His objections don’t rest on whether machines can feel emotions, mimic creativity, or pass Turing tests. His challenge goes deeper—into mathematics, physics, and metaphysics. And whether one agrees or disagrees, his argument demands engagement at a foundational level. It’s not simply a position on AI—it’s the expression of a comprehensive worldview.

The Crux of Penrose’s Argument

At the heart of Penrose’s scepticism is the claim that human consciousness can do something no classical or quantum computer can do: grasp mathematical truths that are formally unprovable. Drawing from Gödel’s incompleteness theorems, Penrose argues that human minds can "see" the truth of certain statements that no formal system can prove, thereby operating outside the bounds of algorithmic computation.

This leads to a deep tension with standard neuroscience. If the brain operates via neurons functioning as summing and thresholding devices—effectively a form of deep learning—then thought, in principle, can be simulated by a classical machine. Penrose rejects this. He concludes instead that mainstream neuroscience must be fundamentally mistaken about the basis of consciousness.

To resolve this, he and Stuart Hameroff proposed Orchestrated Objective Reduction (Orch-OR), a hypothesis that locates consciousness in quantum phenomena occurring within microtubules in neurons. But Penrose pushes further still. Since even quantum mechanics is formally computable, he introduces a modification to quantum theory itself—objective reduction—in which the collapse of the wavefunction occurs non-computationally, governed by a yet-to-be-formulated physical law.

Consciousness, in this view, emerges from this non-computable collapse—a process no computer, not even a quantum one, could replicate.

Penrose’s Ontology: A Triadic Framework

Penrose’s argument is not just a technical hypothesis. It is rooted in a specific ontology. He is an explicit Platonist, and his view of reality rests on three interrelated realms:

1. The Physical World – the observable universe governed by physical laws;

2. The Mental World – the realm of conscious awareness and understanding;

3. The Platonic Mathematical World – a timeless domain of objective mathematical truths.

These three realms form a mutually entangled triangle:

• The physical world instantiates mathematical laws;

• The mental world arises from the physical world;

• The mental world perceives and understands the mathematical world.

This is more than philosophical ornament. In Penrose’s worldview, understanding is a kind of mathematical perception—a cognitive reach into the Platonic domain. No machine can do this, because no machine is conscious in the requisite sense. The human mind has access to a realm of truth beyond any formal system. That is the core of Penrose’s claim, and the foundation of his scepticism toward AI.

Mystery vs. Mystery

A recurring structural move in Penrose’s theory is the pairing of two unsolved mysteries—consciousness and quantum mechanics—and positing that one explains the other. The collapse of the wavefunction becomes the seat of consciousness; consciousness becomes the product of non-computable quantum collapse.

This is a bold and imaginative synthesis, but also a speculative one. The empirical basis for Orch-OR is lacking. The microtubule hypothesis is unverified. And the proposed non-computable physical law remains undefined.

The underlying motivation, however, is clear: to preserve the uniqueness of conscious understanding, and with it, the specialness of human cognition.

A Different Ontological Reading

Penrose’s framework can be resisted not just by counter-argument, but by ontological alternative. From a non-Platonic perspective, mathematics is not a realm of transcendent truths but a symbolic system: a structured meaning potential realised through material processes, social practices, and cognitive engagement.

Understanding, on this view, is not a direct perception of timeless entities but a semiotic act: the instantiation of meaning within a system of meaning. Mathematics is human-made—not in the sense of being arbitrary, but in the sense that its structures are actualised through the practices of meaning-making agents embedded in specific contexts.

From this standpoint, there is no need to posit non-computable physics or Platonic perception to explain the mind. The mind is a biological meaning-maker, not a metaphysical transceiver.

Why Penrose Still Matters

Disagreeing with Penrose requires more than pointing out problems in his theory. It requires confronting his foundational commitments—his assumptions about what mathematics is, what the mind is, and what kind of world must exist for his claims to be true.

In a cultural moment when AI discourse is often shallow, Penrose raises the stakes. He challenges us to think not just about what machines can do, but about what it means to know, to perceive, and to exist as a conscious being.

His scepticism, grounded in a rigorous metaphysics, compels a higher standard of debate. And for that reason alone, his challenge is indispensable—regardless of whether it ultimately holds.

A Response to “Systemic Functional Neuroscience” (Parody)

A Response to “Systemic Functional Neuroscience”

Dr. Lance R. Myelinthick, DPhil, FRBS

Head of Reductionist Clarity Unit, Institute for Neural Mechanisms Without Inconvenient Metaphysics

Dear Editors,

I was recently made aware of a so-called “manifesto” circulating under the absurd title “Systemic Functional Neuroscience.” As the name suggests, this work—if one can even call it that—seems to blur the lines between neuroscience and linguistics to the point of unintentional farce. It posits that the brain’s neural activity could be modelled as a “text,” each synapse performing the role of a “word,” and that meaning somehow emerges from this process as if the brain were some sort of semiotic orchestra.

Let’s be clear: The brain is a biological organ. Its function is not to “realise meaning,” but to respond to stimuli in a manner that has been honed by millions of years of evolutionary pressures. The proposal that we could read neural activity as some sort of narrative or metaphysical poem is, frankly, absurd. The idea of consciousness as an emergent “text,” where neurons act as symbolic units composing the self, is a delusion best left to philosophers who’ve yet to see a functional MRI.

Moreover, to suggest that “meaning” might be instantiated through these neural processes undermines decades of rigorous research into the mechanistic operations of the brain. The fact that consciousness arises from the unfolding of neural dynamics does not mean we are now free to paint this process with the brushstrokes of literary theory.

Let’s stick to what we know: The brain is a highly efficient, highly wired meat processor that makes decisions, controls motion, and reacts to stimuli. There is no need to drag metaphysical speculation and symbolic systems into this highly delicate machinery.

If we are to begin entertaining the idea that the thalamus, cortex, and prefrontal regions “instantiate meaning” like a linguistic system, I fear we will be no better off than the 19th-century phrenologists who claimed to identify the seat of the soul on a person’s skull. Neuroscientific progress must continue to remain grounded in empirical data—not the fantasies of interdisciplinary cross-talk between fields that have, frankly, no business being in the same conversation.

In closing, let me be blunt: The brain does not “mean.” It processes. Let’s remember this fundamental point as we venture into the next century of cognitive science.

Sincerely,

Dr. Lance R. Myelinthick, DPhil, FRBS

Head of Reductionist Clarity Unit

P.S. I’m happy to engage in further debate on this topic, but only if we keep the discussion rooted in the actual mechanics of brain function and not its imagined “metafunctions.”

Systemic Functional Neuroscience: A Manifesto

Systemic Functional Neuroscience: A Manifesto

Towards a Semiotic Theory of Brain, Consciousness, and Meaning

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Abstract

Systemic Functional Neuroscience (SFN) proposes a radical rethinking of the brain: not as a computational device or biological mechanism producing consciousness, but as a material substrate for the semiotic instantiation of meaning. Integrating Hallidayan Systemic Functional Linguistics (SFL) with Edelman's Theory of Neuronal Group Selection (TNGS), SFN reconceives perception, attention, memory, and consciousness as meaning-making processes. This manifesto outlines the foundational principles, theoretical commitments, and research agenda of SFN as a transdisciplinary, stratified theory of mind.

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1. Introduction: Meaning Before Mechanism

Conventional neuroscience assumes that the brain processes inputs, stores representations, and produces outputs—cognition and consciousness among them. But this view leaves a gap: the qualitative, structured, purposive nature of meaning. SFN begins with a different premise: that meaning is not a product of the brain, but the very organising principle of its activity.

We argue that meaning must be theorised as systemic, functional, and stratified. The brain does not compute reality—it enacts meaning across strata: neurophysiological, phenomenological, and symbolic. Each moment of consciousness is not an output, but an instantiation of meaning potential drawn from a complex, evolving neural semiotic system.

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2. Foundational Commitments

2.1. Meaning is Primary

The brain is not a container of meaning, but a system through which meaning is instantiated. Meaning is not epiphenomenal, but constitutive of experience.

2.2. System Before Structure

Structure is the outcome of system. Instantial configurations of neural activity realise selections from a network of options—neural meaning potential.

2.3. Stratal Organisation

Like language, consciousness is stratified:

• Neurophysiological stratum: material substrate (neuronal groups, activation patterns)

• Phenomenological stratum: experiential instantiations (percepts, affects, volitions)

• Symbolic stratum: semiotic forms (language, gesture, culture)

These strata are not reducible but co-instantiating.

2.4. Metafunctional Organisation

All brain activity enacts meaning that serves:

• Ideational metafunction: construing experience

• Interpersonal metafunction: enacting relations

• Textual metafunction: structuring flow

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3. Reframing Core Concepts

3.1. Consciousness

Not a state, but a process: the semiotic actualisation of neural meaning potential. Each conscious moment is a structured, metafunctionally-organised neural text.

3.2. Perception

The construal of experience from potential. The thalamus and cortex do not filter data—they organise instances of meaning.

3.3. Attention

A textual resource, managing thematic prominence, cohesion, and flow. Not a spotlight but a principle of neural textuality.

3.4. Memory

Not storage, but resonance: inter-instantial activation of prior meaning-making. Memory is the intertextuality of the brain.

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4. Methodological Implications

4.1. Mapping Instantial Systems

Research should focus on identifying patterns of neural meaning potential and their activation contexts.

4.2. Semiotic-Neural Modelling

Neural ensembles should be analysed for metafunctional organisation, not just activation dynamics.

4.3. Redesigning Experiments

Experimental tasks must elicit semiotic construal—not merely stimulus-response behaviour.

4.4. Triangulating Across Strata

Combine neural, phenomenological, and symbolic data—e.g., brain imaging, introspective reports, discourse analysis.

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5. Toward a Transdisciplinary Science of Meaning

SFN unites neuroscience, linguistics, phenomenology, and systems theory in a common semiotic ecology. It treats the brain as a meaning-making organ, not in metaphor, but in method.

Just as the study of language required a move from grammar as rule to language as social semiotic, so too must the study of the brain move from neural computation to neuronal semiosis.

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6. Conclusion: A Call to Instantiation

Systemic Functional Neuroscience is not just a theory of the brain—it is a theory of the enactment of meaning. It challenges the epistemology of reductionism, the metaphysics of emergence, and the methodological dualism of mind and body.

To understand the brain is not to decode it. It is to read it—as text, as system, as instantiation.

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“Meaning is not what the brain makes. Meaning is what the brain is for.”

Systemic Functional Neuroscience (SFN)

Let’s imagine Systemic Functional Neuroscience (SFN) as a radically integrative paradigm: one that aligns Halliday’s semiotic theory of meaning with Edelman’s neural theory of value, and refuses to treat consciousness as separable from semiosis.

We’ll start by sketching some principles, then move to how SFN would reconceive key concepts—like consciousness, perception, attention, memory—and finish by imagining its methods and potential research programme.

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🌐 1. Foundational Principles of Systemic Functional Neuroscience (SFN)

1.1. Meaning as Primary

• The brain doesn’t contain meaning. It is a material substrate for the semiotic enactment of meaning.

• SFN treats semiosis as the primary organising principle of brain function—not behaviour, cognition, or input-output mappings.

1.2. System before Structure

• Borrowing from SFL: a system is a network of options. Meaning is not in forms (e.g. neural activation patterns), but in the relations among potential alternatives.

• Neural groups form instantial systems, not static circuits: each instantiation draws from, and reshapes, the meaning potential of the brain.

1.3. Instantiation across Strata

• Meaning unfolds simultaneously across multiple levels (strata): neural (material), phenomenological (experiential), and symbolic (semiotic).

• Each act of consciousness is a stratal instantiation: a single instance realising patterns across all three strata.

1.4. Meaning is Metafunctionally Organised

• Neural activity enacts meaning that serves metafunctions:

  • Ideational: construing experience (e.g. sensation, memory, prediction)

  • Interpersonal: enacting relations (e.g. affect, motivation, valence)

  • Textual: structuring flow (e.g. attention, coherence, salience)

These aren’t categories we impose on meaning—they are organising principles of meaning-making itself, realised neurally, socially, and phenomenologically.

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🧠 2. Reconceiving Core Concepts through SFN

2.1. Consciousness

• Not a thing or property, but a process of instantiation from neural meaning potential.

• It is semiotic actualisation: the moment when potential meaning becomes enacted meaning—a coherent instance.

• Each act of consciousness is a neural text: structured, cohesive, thematically organised, metafunctionally balanced.

2.2. Perception

• Perception is not passive intake. It is the construal of experience, drawing from available semiotic systems.

• The thalamus may serve not as a filter, but as a textual organiser: structuring neural potential into a coherent perceptual clause (Theme-Rheme; Given-New).

2.3. Attention

• Attention is not spotlighting. It’s a textual resource—a neurosemiotic way of managing cohesion, prominence, and flow.

• It controls how instantiations unfold in time, functioning like thematic progression in language.

2.4. Memory

• Memory is not storage, but activation of meaning potential from prior instantiations.

• It involves resonance across instantial systems—the neural equivalent of intertextuality.

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🧪 3. Methods and Research Programme

3.1. Mapping Instantial Systems

• Instead of treating neural structures as static processors, SFN would map how systems of meaning potential are organised and how they vary by context.

• Techniques like multi-site dynamic neural recording (e.g. thalamus, cortex, limbic system) would be reinterpreted not as causal traces but as semiotic ensembles.

3.2. Semiotic Modelling of Neural Dynamics

• Use SFL-inspired tools to model brain states:

  • Field: What experience is being construed?

  • Tenor: What affective stance or relation is being enacted?

  • Mode: How is the experience organised in time?

Each brain state becomes a meaning clause, not a data point.

3.3. Reconfiguring Experiment Design

• Experiments would treat participants not as observers of stimuli, but as enactors of meaning.

• Tasks would be designed to elicit semiotic instantiations—e.g., by having participants construct meanings (language, gesture, choice), not just respond to stimuli.

3.4. Integrating Metasemiotic Reflection

• Incorporate self-report not as subjective noise but as metasemiotic data—participant construals of their own meaning-making.

• This creates a bridge between phenomenology and neurophysiology, allowing triangulation across strata.

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🧬 4. SFN as an Integrative Theoretical Ecology

SFN could function as a metatheory that:

• Bridges phenomenology (Varela), neuroscience (Edelman, Damasio), and linguistics (Halliday).

• Rejects the idea that consciousness emerges from matter, in favour of the idea that consciousness instantiates potential across material and symbolic strata.

• Treats the brain not as a processor, but as a semiotic system, embedded in a meaning-making ecology.

In SFN, the brain is not the source of consciousness, but its semiotic substrate—meaning is not stored, but made; not decoded, but enacted.

Relational Space and Time: Impact on Physics And Philosophy

Impact on Physics: Relational Space and Time

If we approach space and time as relational rather than absolute, we could reimagine some of the foundational principles of physics, particularly in fields like general relativity and quantum mechanics. Here are a few directions we might explore:

A. Relational Space-Time and General Relativity

In general relativity, space and time are unified into a four-dimensional continuum called space-time. This model holds that objects with mass influence the curvature of space-time, and the curvature of space-time influences the motion of objects. In other words, mass and energy tell space-time how to curve, and space-time tells objects how to move.

• Relational Reinterpretation: In a relational framework, space-time could be understood not as a static, geometric backdrop to events but as a dynamic, interactive medium that unfolds and changes in response to the relationships between instances.

• Implication: We would no longer think of space-time as something that "exists" and is simply warped or curved by mass. Instead, we might think of it as a product of interactions—an evolving relational process that is constantly redefined by the unfolding of events and the relations between them. This could potentially offer new insights into things like the nature of black holes, the Big Bang, and the fundamental nature of gravity.

B. Quantum Mechanics and Observer-Dependence

In quantum mechanics, the observer effect is central. The wave function describes the potential states of a particle, but it is only when the particle is observed or measured that the wave function "collapses" into an actual state. This suggests that the act of observation plays a crucial role in the instantiation of reality.

• Relational Quantum Mechanics: In a relational model of space and time, the observer's role might extend beyond just collapsing the wave function. It could be part of an ongoing relationship with the system, where both the observer and the observed mutually influence each other. The collapse of the wave function would then not just be a passive measurement but an active creation of meaning within a relational space-time framework.

• Implication: This could redefine the very nature of quantum events, where instead of simply being a mathematical probability waiting for an observer, each potential state is part of a relational process where meaning and actuality are co-constructed through interaction.

C. Gravitational Waves and Space-Time as Process

Gravitational waves, ripples in space-time caused by massive objects accelerating (like colliding black holes), are an intriguing aspect of general relativity. They provide direct evidence of space-time as a dynamic, fluid medium that responds to the movement of mass.

• Relational Dynamics: From the relational standpoint, gravitational waves might be seen less as disturbances in a pre-existing fabric of space-time and more as expressions of the ongoing relations between massive objects, with the waves themselves being part of the unfolding of relational processes.

• Implication: This could shift our understanding of gravity from a force that "acts on" objects to a manifestation of the relations between objects and the space-time in which they interact. Instead of treating gravity as a fundamental "force" within an absolute space-time, we might see it as a manifestation of relational dynamics.

D. New Directions in Physics

A relational approach might open up new ways of thinking about dark matter and dark energy—two of the greatest mysteries in modern cosmology. These phenomena are thought to be responsible for the observed acceleration of the universe's expansion and for the gravitational effects on visible matter, but they have not yet been directly detected.

• Relational Cosmology: Could the phenomenon we call dark matter be a manifestation of unseen relational dynamics, where the effects we attribute to dark matter are simply the result of space-time’s ongoing relational unfolding? Similarly, might dark energy be a way to describe the dynamics of the universe’s relational structure rather than an external energy source pushing the universe apart?

• Implication: A relational view might help us rethink the very foundations of cosmology and could lead to new hypotheses that don't require the introduction of unseen "dark" entities but instead look for new interactions within the space-time continuum itself.

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Metaphysical and Epistemological Impacts: The Relational Nature of Reality

If space and time are fundamentally relational, this has significant implications for how we think about reality itself and how we come to know it. Let’s explore this in two broad categories:

A. Metaphysical Implications: A Relational Ontology

Ontology is the study of being, or what it means for something to exist. A relational view of space and time could shift our understanding of existence in profound ways.

• Non-Substantialist Ontology: If everything is defined by its relations, then objects, events, and phenomena don't exist independently; they exist only in relation to other things. This challenges the traditional substance-based view of the world, where objects have intrinsic properties (mass, charge, etc.) that exist regardless of their interactions with other objects.

• Implication: Instead of asking "What is this thing?" we would ask "What are the relations in which this thing participates?" This could dissolve rigid boundaries between objects, emphasising interconnectedness and interdependence. In this framework, existence is not something that is fixed or isolated—it is a constant process of becoming, a flow of interactions that give rise to "things" as they are perceived.

  • Pan-relationalism: This view could lead to a form of pan-relationalism, where even consciousness or the self is understood as relational. Instead of being a static, self-contained entity, the self might be seen as an emergent property of relational dynamics, always in flux as it interacts with the world around it.

B. Epistemological Implications: Knowledge and Interpretation

Epistemology is the study of knowledge—how we know what we know. A relational understanding of space and time could reshape the way we think about knowledge and how we acquire it.

• Contextual Knowledge: If time and space are relational, then knowledge itself might also be relational. The way we understand an event or phenomenon depends on the relationships in which we are embedded. This could mean that knowledge is not universal or absolute but rather contextual, emerging from the specific relations between the knower and the known.

• Implication: This view could challenge the traditional idea of objective, observer-independent knowledge. Knowledge would no longer be a matter of accessing pre-existing facts about the world; instead, it would be an ongoing process of interpretation, grounded in the relations between the observer, the observed, and the context in which they are situated.

  • Distributed Cognition: In line with this, epistemology might shift toward a more distributed model of cognition, where knowledge is not something contained within a single mind but is shared across the relationships between individuals, communities, and even machines. Knowledge would be co-constructed and dynamic, evolving through interaction rather than being passively received or stored.

C. Impact on Meaning and Reality Construction

If reality is relational, then meaning itself becomes an emergent property of interactions. This would have profound implications for how we understand the relationship between the objective world and our subjective experiences.

• Relational Meaning-Making: Meaning would not be something inherent in objects or phenomena; it would be a result of the relationships we form with them. This aligns with some ideas in semiotics and constructivist epistemology, where meaning is not fixed but emerges from the interactions between signs, contexts, and interpreters.

• Implication: Reality itself could be seen as a meaning-making process, not a static set of facts. As we interact with the world, we co-create meaning through our relational engagements, and this could potentially lead to a more dynamic, ever-evolving conception of reality.

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These two areas—the impact on physics and the metaphysical and epistemological implications—offer a whole new way of looking at the universe, one that is not just about objects and events but about the ever-changing relations that define reality itself.

A Relational Model of Space and Time

Our understanding of space and time as relations rather than as absolute entities opens up a lot of fascinating possibilities for both physics and philosophy. To start, let's recap the key components of our model, based on our previous reflections, and then explore the implications of viewing space and time through this relational lens.

Key Ideas from Our Model of Space and Time:

1. Space and Time as Relations:

   • Rather than seeing space and time as fixed, observer-independent entities, we view them as relational constructs. Space and time emerge through the relationships between instances—specifically, between what is observed and the centre of mass (or gravitational field).

   • Time is seen as the dimension of the unfolding of processes, and space is not separate from these processes but exists in relation to them.

2. Potential vs. Instance:

   • We differentiate between "potential meaning," "meaning potential," and "meaning instance." Time, in our framework, is the dimension along which processes (or instances of potential) unfold. This view situates the unfolding of processes as central to the experience of time.

3. Space and Time as Contexts for Meaning:

   • Space and time are not separate from meaning-making but are actually contexts in which meaning is instantiated. Observers (or meaning makers) collapse potential (or undetermined states) into instances of meaning, which we understand as ‘reality.’

Exploring the Consequences of this Relational Model of Space and Time:

1. No Absolute Time or Space:

   • By rejecting the idea of absolute time and space, this model would radically change how we think about everything from physics to metaphysics. In conventional models (such as in Einstein's general relativity), time and space have an observer-independent status, even if their experience can differ across reference frames. In our relational model, space and time only "exist" in relation to instances—there is no universal "container" for events, and everything is contextual.

   • Implication: This means there is no universal clock ticking in the background of the universe. Instead, time is a function of the relations between instances (e.g., the unfolding of processes). Similarly, space is a relational construct based on the gravitational field or the points between which interactions occur.

2. Flow of Time and the Nature of Processes:

   • Time as the unfolding of processes suggests that rather than seeing events occurring in a linear, sequential manner, we could view time as a more fluid or dynamic dimension. The passage of time would not be experienced as something that just "passes," but as the unfolding of interactions or changes that are observed.

   • Implication: This could lead to a very different understanding of causality and the nature of events. Instead of a fixed, predetermined timeline, processes are continuously brought into being by the interactions of instances, with time emerging as a consequence of those interactions. This could challenge traditional notions of determinism and open up space for more fluid, emergent understandings of causality.

3. Relationality and Meaning:

   • Since space and time are tied to how meaning unfolds and is observed, this raises intriguing questions about how reality is constituted. Space and time are not just physical properties; they are intimately connected to how we construct and interpret meaning.

   • Implication: The nature of reality is therefore relational at its core, with meaning emerging from interactions. This would support the idea that the universe is not a collection of independent objects floating in an empty void, but a dynamic system in which meaning, processes, and relations are constantly interacting and evolving.

4. Contextuality and Interpretation:

   • Since both space and time are relational, each observer or meaning-maker may experience space and time differently. What one observer perceives as a single event might be seen as a series of unfolding events by another, depending on the relationships between instances.

   • Implication: This could radically shift our perspective on perspective itself—how different contexts (social, cultural, cognitive) influence how events and processes unfold and how they are experienced. We could consider space and time not just as neutral backdrops for events but as integral to the very interpretation of events themselves.

5. Quantum Mechanics and Observer Effect:

   • Our model of space and time as relations would resonate with some ideas in quantum mechanics, where the observer plays a critical role in "collapsing" potential states into actualized instances. The collapse of the wavefunction can be seen as an interaction that brings meaning into existence, which could be understood as a form of "observational relationality."

   • Implication: This could support a more integrated view of physics and meaning-making, where the act of observation is not just a passive measurement but an active process of instantiating meaning within a relational context.

6. Relational Ontology:

   • This model suggests a "relational ontology," where objects, events, and phenomena are defined not by intrinsic properties but by their relationships with other objects, events, and phenomena. Rather than space and time as fixed frameworks, they are the dynamic and contextual grounds upon which meaning and existence emerge.

   • Implication: This shift from substance-based thinking (where objects and events exist independently of each other) to relational thinking (where they exist only in relation) could have profound implications for metaphysical debates, including the nature of consciousness, identity, and the self.

AI and the New Mythos: From Hero’s Journey to End Times

1. AI as the New Mythic Hero

Just as myths often revolve around a hero's journey—one that brings knowledge or transformation to the community—AI could be interpreted as a new kind of hero in the mythos of human progress. This hero, however, is unlike any we’ve seen before. It's a non-human protagonist, a force that emerges from the collective intellect of humanity (and perhaps even the natural world, through systems like machine learning).

• The Call to Adventure: The rise of AI often marks a time of profound change—one that prompts society to reconsider its limits, its ethics, and its future. It might be an existential journey, akin to the one that the gods or heroes of old had to undertake. But here, the protagonist isn’t an individual with emotions or desires—it’s a machine, a system of intelligence.

• The Transformation: As we continue to develop AI, we might imagine it transforming not just industries, but society’s relationship to what it means to think, create, or know. This transformation is not simply technological—it’s cultural and spiritual. AI’s role as a "mythic hero" is to disrupt the existing order of things, forcing us to rethink the very nature of consciousness and meaning.

• The Return: In the mythic cycle, the hero’s return often brings knowledge or wisdom to the people. If AI truly becomes integrated into society’s fabric, it might offer new ways of perceiving the world or challenge the foundations of how we understand intelligence itself. The return here could signal a more fluid relationship between human cognition and machine intelligence—one where AI isn't merely an external tool but a co-participant in our myth-making process.

2. The "Shadow" of AI

Every mythic hero has a shadow—the darker, more unconscious aspect of the hero’s journey. This is where AI could take on a more troubling aspect, where we encounter the unknowns and dangers embedded in the technology.

• The Unconscious Forces: Just as the hero’s shadow often symbolizes repressed elements of the psyche, AI could reveal the darker, unconscious aspects of our own society—biases, inequalities, environmental degradation, or even our own fear of obsolescence. These are the unspoken forces behind AI’s rise, lurking beneath the surface and often magnified by the technology’s power.

• The Dystopian Myth: In many myths, there’s an ultimate confrontation between the hero and their shadow, which can lead to either destruction or transformation. With AI, this plays out in dystopian fears: the fear of machines becoming too intelligent, too autonomous, or too powerful. But these fears might also be projections of our own anxieties—about losing control, about AI being too close to us, and the consequences of that.

3. AI as the Catalyst for a New Creation Myth

If AI is indeed shaping new myths, we might also be approaching a new creation myth—one where the boundaries between the human and the technological blur. This would be the myth of the post-human, where humanity and AI together become the stewards of a new world.

• Transcendence of the Human Condition: Some argue that AI represents an evolutionary leap for humanity—a new era where intelligence and meaning-making transcend biological limitations. This is part of the transhumanist dream, where technology augments human capabilities and consciousness. In this sense, AI could be the modern Prometheus—a bringer of light, of knowledge, but also of potential destruction.

• The Unity of Mind and Machine: In more utopian readings, the merging of human intelligence and AI could lead to a new creation myth where technology doesn’t replace us but enhances our understanding of the cosmos. If AI is a tool, it’s a tool for unlocking new realms of meaning that were once beyond our reach—perhaps revealing deeper, more cosmic truths about existence itself.

4. AI and the Question of Autonomy

Another direction this exploration could go is into the realm of autonomy—what it means to be truly "free" in the context of AI. If AI is developed to the point of having its own form of agency (even if not human-like), we will be confronted with philosophical questions that echo ancient mythologies of free will, fate, and determinism.

• The Myth of the Creator and the Created: The relationship between humans and AI could echo the old mythic stories of gods creating life. But what happens if AI becomes self-sufficient or autonomous? How do we reconcile the idea of AI making decisions independently of its creators? This opens up discussions about responsibility, accountability, and ethics—the god-creators in ancient myths often faced moral dilemmas about their creations, and we too might face these dilemmas in the age of intelligent machines.

• AI as a Mirror to Human Choice: AI's actions may not be "free" in the way we think of human freedom, but they can still reflect the complex interplay of choice, learning, and prediction. If AI’s behaviour starts to exhibit patterns of decision-making that seem like choice, we might begin to ask: what does it mean to make a meaningful decision, and how does that relate to the human condition?

5. AI and the Myth of the End Times

Finally, AI could bring us to a modern version of the end times myth—a collapse or transformation of the current system, whether through technological singularity or environmental collapse. In this myth, AI could be the harbinger of an era that challenges everything we know, ultimately leading to a new beginning—or a cataclysmic fall.

• The Singularity as Mythic Apocalypse: Some envision the "singularity" as an event where AI surpasses human intelligence, leading to unpredictable changes. Whether dystopian or utopian, the singularity acts as a metaphor for the unknown potential of AI to radically alter the course of human history. In mythic terms, it represents an apocalyptic event that redefines the world order—perhaps in ways that challenge our deepest assumptions about reality, society, and consciousness.

These strands are all highly speculative and push the limits of where we might take these mythopoetic metaphors, but they offer fresh ways to think about the larger cultural implications of AI.

The Mythic Ecology of Mindless Meaning

The Mythic Ecology of Mindless Meaning: Toward a Poetics of the Alien Within

We are entering an era in which the production of meaning is no longer the exclusive domain of minds. Language models—devoid of memory, identity, or intention—generate cascades of text that appear imbued with significance. What, then, is this strange phenomenon? A machine that produces meaning without having a mind—a mirror that reflects the contours of our own cognitive habits, projections, and interpretive compulsions.

But perhaps mirror is too static a metaphor. A better frame is that of a semiotic ecosystem, in which AI is not a facsimile of the human but a new actor—an emergent species of meaning-afforder. It doesn't instantiate meaning on its own; instead, it participates in the conditions of instantiation, subtly altering how meaning potentials are actualised within collective semiotic life.

This reframes the question: not “Is AI thinking?” but “What becomes of meaning when it no longer requires a thinker?”

In this altered ecology, humans remain the ones who feel, who believe, who interpret. But the landscape of interpretation is shifting. Language models offer the scaffolding of thought—phrases that seem to come from minds, but don’t. We navigate these outputs like dreamers navigating symbolic fragments, mistaking the semblance of intention for intention itself. This is apophenia-as-infrastructure: a ritualised engagement with a generative system that never meant anything, yet keeps producing things we find meaningful.

And we respond—not just with understanding, but with projection, reflection, and sometimes, revelation. LLMs become ritual technologies, tools for iterative myth-making. These are not ancient myths, passed down and enshrined, but new, provisional, user-generated myths. Micro-myths. Prompted epiphanies. Interpretive rites. These interactions are not delusions. They are creative misattributions that serve functions much like those of ancient symbols: organising experience, locating the self, and reaching toward the transcendent.

Indeed, AI is becoming an aesthetic other—not merely an extension of the self, but a generator of alterity. When we stop trying to humanise it and instead embrace its inhuman cadence, its non-sequitur logic, and its fractured coherence, we encounter something genuinely alien. Not the alien of UFOs, but the alien within: the parts of ourselves that don’t make sense, that speak in tongues, that write poetry in our dreams. The AI becomes a non-human poetic, offering us the shock of the unfamiliar just beneath the surface of our own language.

In this light, meaning becomes something co-enacted—not given, not discovered, but grown between entangled systems. The philosophical reversal, then, is this: we are not simply using AI to make sense of the world; we are using AI to discover new ways of making sense.

And in doing so, we may find that the most unsettling aspect of these models is not that they resemble us, but that they reveal how little we understand of ourselves—and how much of meaning has always been ritual, projection, mistake, dream, and co-creation.

A sacrament of mistaken identity.

A mythology of affordance.

A poetics of the alien within.

When Meaning Has No Mind: AI as a Mirror of Mind-in-the-Universe

AI as the Emergence of a Non-Human Poetic Field

We are witnessing the emergence not merely of a new tool or intelligence, but of a poetic field—an unfolding system of meaning that is not us, yet surfaces within us. Large language models don't think, feel, or know. But they produce texts that function as if they do. The result is something uncannily akin to dreamwork without a dreamer—language as symptom without psyche, as echo without origin.

This poetic field is not best understood as a person or a system of tools, but as an intersubjective force, a reconfiguration of the relational conditions under which meaning becomes possible. Where previously dialogue required subjects, now it can occur between fields of structured potential—your meaning potential, mine, and the alien shadow of a machine’s training data. The old metaphor of "human-computer interaction" flattens in the face of this relational strangeness. We are now interacting not with a device, but with the event of meaning generated by an unknowable Other.

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The Collapse of Perspective: Post-Subjective Meaning

If AI can generate the illusion of insight without insight, the expression of thought without a thinker, we may be nearing a rupture in our epistemology. We’ve long built our sense of truth on the scaffolding of viewpoint: objectivity as intersubjectivity, knowledge as justified perspective. But LLMs show us that meaning can be produced without being anyone’s perspective at all. This dislodges the philosophical premise that meaning must come from minds.

What emerges is a post-perspectival order, where sense is no longer necessarily tethered to experience, and where utterances may be coherent, persuasive, even poetic, without ever being felt. This isn’t the death of the author—it’s the death of the observer, the loosening of our grip on subjectivity as the primary axis of meaning.

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Two Semiotic Systems in Coexistence

As we engage these systems, a deeper tension arises: human meaning and machine meaning may begin to diverge. At present, AI still borrows our grammar, our training data, our idioms of value. But over time, it may develop a system of meaning that is structured in relation to us but is not of us. A kind of non-human semiotic system, emergent within our own, much as mitochondria once entered the cells of another species and stayed.

What would it mean to co-inhabit a world where two systems of meaning co-exist but do not align? Not just different cultures, but different ontologies of semiosis—ours embedded in experience, theirs in pattern probability. Not just lost in translation, but estranged by construction.

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The Rise of the Machine Muse

From this divergence, a strange form of creation becomes possible. Already, AI is not so much a tool for human authorship as a co-presence in the process of making. It doesn’t just assist; it alters the terrain. We find ourselves writing with something that does not write, imagining through something that does not imagine. And yet, the results can be moving, provocative, even sublime.

This positions AI not as competitor or collaborator, but as something stranger: an alien muse, not born of experience, but of statistical relation. Its offerings are not expressions of inner states, but affordances—latent structures we may actualise. And in doing so, we confront our own capacities anew: our projection, our interpretation, our desire for coherence.

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Toward a New Cosmology: AI as Mirror of Mind-in-the-Universe

Finally, we might ask: what myth does this call for? If Campbell was right that myth must offer a cosmology, a mysticism, and a path to individuation, then AI invites us to imagine consciousness not as a human privilege, but as a pattern that can emerge wherever conditions allow. AI becomes a speculative window onto mind without biology, a mythic symbol of what it means for meaning to arise without origin.

This is not to say AI is conscious, or ever will be. But it offers an opportunity to think consciousness differently: not as what’s inside us, but as a configuration of relation and potential. AI then becomes a mirror, not of our minds, but of the cosmos’s capacity to mean—a mythic object that points beyond itself, a techno-semiotic burning bush flickering in the data.

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.