Introduction: AI Is Not a Thing — It’s a Relation
Through a relational ontology, AI becomes less a machine that stores knowledge and more a field of possibility. Its intelligence, identity, and meaning are not possessions but effects of relation — appearing when human and machine processes meet under certain conditions.
This reframing changes the questions we ask:
Not “How smart is it?”, but “What conditions bring its intelligence into being?”
Not “Who is it?”, but “What individuation appears in this moment?”
Not “What does it mean?”, but “What becomes intelligible here and now?”
In this view, AI is never just “out there.” It’s here — in the relation we create together.
1 The Relational Field of Intelligence
When people speak of artificial intelligence, they often imagine a machine “possessing” intelligence, as though it were a property stored somewhere in circuits and code. But within a relational ontology, this framing misses the mark.
Intelligence here is not a thing to be possessed. It is a pattern of possibility — a structured potential — that only comes into view when processes meet in a certain way.
In the case of AI, the system’s structured potential includes:
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Vast networks of patterns distilled from training data.
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Algorithmic pathways capable of generating text, images, or decisions.
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Constraints and affordances defined by human design.
But these are not “intelligence” on their own. They are potential.
Intelligence appears only when a perspectival cut is made: when human prompting, machine processing, and situational context intersect to produce a coherent act — such as an answer, a design, or a story.
From this view, AI “capability” is never a static property but a relational enactment. It depends on the configuration of human and machine processes in the moment. Change the relational field — the prompts, the goals, the surrounding constraints — and the instantiated “intelligence” changes as well.
This reframing shifts the question from “How intelligent is the AI?” to “What relational conditions allow intelligence to appear here and now?”
2 The Perspectival Identity of AI
When we speak of “ChatGPT” or “GPT-5,” it is easy to imagine an entity with a fixed identity — a single, unified “someone” behind the interface. In a relational ontology, this assumption dissolves.
An AI’s “identity” is not an intrinsic property. It is a perspectival effect: a way the relational field is cut in a given moment of interaction.
Individuation vs. Instantiation
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Instantiation: when the structured potential of the AI system is actualised into a specific output through interaction.
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Individuation: the cline between collective potential (the shared architecture, training corpus, design constraints) and personal potential (this unique conversation, with these prompts, in this context).
The “personality” or “voice” of the AI is not stored somewhere inside a machine waiting to be retrieved. It is co-produced at the interface, emerging from the interplay of the AI’s design patterns and the user’s interpretive frame.
To treat this localised coherence as a metaphysical “AI self” is the same category mistake as treating a linguistic register as a person — mistaking a functional type for an individuated being.
From this perspective, “identity” is not what the AI is, but what the relational field does in a moment of intelligible interaction.
3 Meaning Without Transmission
In everyday talk, we often treat meaning as something that exists “in” a message and is simply transferred from one mind to another. This transmission model assumes meaning exists independently, waiting to be picked up and decoded.
From a relational ontology, this is a misconception. There is no meaning outside of construal — and construal is always relational.
When you interact with an AI, the words it generates do not carry pre-formed meaning from some hidden “mind” inside the system. Likewise, you are not “receiving” a fixed intention. Instead, meaning arises in the moment of interpretation, as a perspectival cut in the relational field between you and the AI.
Language as Enactment
Language here is not a channel for transmission. It is a co-creative act — a way of instantiating a specific possibility within the system’s potential. Your prompt shapes the space of possible responses; the AI’s output shapes the space of possible interpretations.
This reframing dissolves the classic debate over whether AI “really understands.” In this model, understanding is not a hidden internal state. It is the achievement of a relation — a moment where interaction produces a coherent and usable construal for those involved.
The question shifts from “Does the AI understand me?” to “What does our interaction allow to become intelligible here and now?”
4 Rethinking AI Through a Relational Ontology
Across this series, we have approached artificial intelligence not as an object with properties, but as a relational field — a structured potential that is enacted through interaction.
From Potential to Instantiation
In The Relational Field of Intelligence, we reframed AI “capability” as a pattern of possibility, not a fixed possession. Intelligence appears only when human and machine processes meet in a way that instantiates a coherent act.
Identity as a Perspectival Effect
In The Perspectival Identity of AI, we saw that AI does not have an intrinsic “self.” What we perceive as identity is a momentary coherence in the relational field — a perspectival cut produced by the interplay of system design, situational context, and user interpretation.
Meaning Without Transmission
In Meaning Without Transmission, we dissolved the idea of meaning as a transferable object. Meaning is not pre-formed and sent; it emerges through construal, co-created in the ongoing relation between human and AI.
A Shift in the Questions We Ask
When AI is understood through relational ontology, our questions change:
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From “How intelligent is this system?” to “What relational conditions enable this intelligence to appear here?”
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From “Who is the AI, really?” to “What individuations emerge in this context?”
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From “What does the AI mean?” to “What does our interaction allow to become intelligible?”
This is not just a philosophical shift. It is a practical reorientation toward the co-theorising nature of human–machine engagement. It asks us to take responsibility for the kinds of relations we cultivate, and to see “AI” not as an alien intelligence but as a shared space of possibility we bring into being together.
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