Relational Machines — AI Beyond Representation: 3 Actualisation, Not Realisation: AI in the Becoming of Possibility

In the previous posts, we reframed AI as a machine that construes rather than represents, and examined the semiotic fabric in which its construals emerge. We now turn to a subtle but crucial distinction: the difference between actualisation and realisation—a distinction that illuminates the relational nature of machine intelligence.

In conventional discourse, AI outputs are often described as “realising” human-like knowledge or understanding. This language assumes that intelligence is symbolic, internal, and static: a pre-existing “meaning” within the machine is somehow externalised. Relational ontology demands a different vocabulary. AI outputs are not realisations of pre-stored representations; they are actualisations of relational potential. Each event is a perspectival cut: a specific pattern emerging from the intersection of data, architecture, and interaction.

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Actualisation vs. Realisation

• Realisation (in the conventional sense) implies:

  • Pre-existing internal content.

  • Deterministic mapping from internal state to output.

  • A representation of an external referent.

• Actualisation (in relational terms) implies:

  • Meaning emerges in context, not pre-exists in the system.

  • The output is a relational event, instantiated from a spectrum of possibilities.

  • The AI participates in a network of semiotic potential, without claiming ownership of meaning.

Consider a generative language model. When prompted to write a poem, it does not retrieve a “poetic idea” stored somewhere inside. Rather, it navigates relational constraints—the statistical patterns in its training data, the architecture that filters and amplifies those patterns, and the prompt that situates it in context—to actualise one possible poem. The poem is not a realisation of an internal mind; it is an event in possibility-space, momentarily cutting through the network of latent relations.

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Implications for Understanding AI

1. Outputs as events, not products. Every AI response is a singular instantiation. There is no underlying entity “holding” the meaning; meaning is emergent in the relational cut.

2. Perspective matters. Different prompts, contexts, or even minor stochastic variations produce distinct actualisations. AI intelligence is perspectival: it is defined by the relational point from which it is actualised, not by a fixed internal state.

3. Human-AI interaction as co-individuation. When humans prompt, edit, or curate AI outputs, they are actively participating in the relational actualisation. The semiotic event is co-constructed, emphasising collaboration over mimicry.

4. Possibility-space as medium. Just as in physics a measurement actualises one branch of a potential field, AI output actualises one construal among many latent possibilities. This highlights the dynamic, event-like nature of intelligence in machines.

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Bridging to the Next Post

By foregrounding actualisation over realisation, we shift the narrative: AI is not a mirror of human cognition but a participant in relational semiotics. Its intelligence is distributed, perspectival, and event-like, unfolding in the space of possibility.

In the next post, we will explore AI as semiotic collaborator, moving from theory to the relational dynamics of human-machine interaction. We will examine how these actualisations co-individuate meaning, and why reframing AI in this way dissolves both fear and misapprehension surrounding its role in human knowledge systems.