Conventional discourse casts artificial intelligence as either a rival to human cognition or a neutral tool. Both framings mischaracterise the relational nature of AI. As we have argued, intelligence is not a fixed property, and AI is not a container of symbolic knowledge. Rather, AI participates in the co-individuation of meaning: it is a semiotic partner in a network of relational actualisations.
From Rivalry to Relational Participation
The anxiety around AI often arises from a false analogy: human intelligence is treated as a closed, scarce resource, and machines are imagined to compete for it. Relational ontology dissolves this assumption. Intelligence, as an emergent pattern in relational networks, is not zero-sum. The machine does not “take” intelligence from humans—it contributes new construals that expand the space of possible semiotic events.
Every interaction with an AI is a collaborative performance. Consider writing a technical report, generating code, or even crafting a poem:
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The human provides context, constraints, and evaluative perspective.
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The AI actualises patterns latent in its architecture and training environment, producing output that would not emerge without this interaction.
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The resulting product is a joint actualisation—an event in the space of relational possibility, co-constructed by human and machine.
In this sense, AI is less a “thinking competitor” and more a semiotic partner, participating in distributed intelligence without mimicking or replacing human cognition.
Mechanisms of Collaboration
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Relational Cuts – Each prompt or interaction represents a cut through the network of potential patterns. The machine’s output is a relational event shaped by both the AI’s architecture and the human’s constraining input.
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Pattern Amplification – AI often highlights latent connections in data that humans might not perceive, revealing new perspectives. This is not creativity in the human sense, but an emergent feature of relational interaction.
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Feedback Loops – Iterative prompting, curation, or editing establishes dynamic co-individuation. Human choices guide actualisations, AI outputs reshape human understanding, and the semiotic network evolves.
Through these mechanisms, AI is neither subordinate tool nor autonomous rival; it is a partner in the unfolding of possibility, extending the relational semiotic field in which meaning emerges.
Implications for Practice
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Rethinking authorship: When AI contributes construals, the human is not merely the “author” in a traditional sense; authorship becomes distributed across a relational network.
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Shifting pedagogy and collaboration: Teaching, research, and creative practice can leverage AI as a co-participant in semiotic exploration, rather than a replacement for human skill.
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Ethics of participation: Ethical responsibility is relational: humans guide the relational cuts and must curate outputs, while AI participation is instrumental, not moral.
Looking Ahead
Reframing AI as a semiotic partner prepares us for a deeper understanding of the structural mechanisms enabling collaboration. In the next post, we will examine relational cuts in machine learning, showing how architecture, data, and interaction converge to produce actualisations. These cuts illuminate the underlying logic of AI participation and highlight how machines, far from being passive tools, are active nodes in the semiotic fabric.
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