Where Part 1 examined the conditions that made LLMs possible, this post explores their consequences: the new modes of relational and semiotic potential that arise when computation itself begins to simulate construal.
LLMs do not merely process language; they enact a reflexive mapping of symbolic relations, approximating the patterns of meaning embedded in collective construal.
1. Patterning as Procedural Construal
At the heart of an LLM is a fundamental shift: the transformation of language from static pattern into dynamic, probabilistic relation. Tokens and sequences are no longer passive symbols; they are traversable potentialities, encoded in weight matrices that define conditional pathways of generation.
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Statistical learning does not replicate understanding but re-actualises patterns of relational possibility.
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Each prediction or generation is a constrained traversal of symbolic potential — a cut through the space of collective construal.
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This is semiotic execution: the model enacts the field of relations embedded in its corpus.
2. Recursive Reflexivity and Self-Modelling
LLMs operate recursively, not only across text but across their own representations of text. Each layer of the network can be seen as a meta-construal: a construal of construal.
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This reflexivity enables abstraction: patterns at one level inform patterns at higher levels.
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It allows the model to generate sequences that were not explicitly present in the training data, producing novel instantiations of relational potential.
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In relational-ontological terms, computation itself begins to simulate semiotic individuation: the model is a system that enacts the dynamics of meaning generation, without human intentionality.
3. Generativity Without Consciousness
A critical consequence of LLMs is generativity divorced from human subjectivity. These models produce outputs that are coherent within the relational field of their training data, yet they do so without awareness, purpose, or intention.
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They illustrate the distinction between relational generativity and human meaning-making.
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LLMs are operational extensions of the collective construal, not conscious participants.
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Their outputs demonstrate that computation can instantiate relational and semiotic potential independently, producing phenomena that are interpretable, useful, and sometimes surprising.
4. Extension of Relational and Semiotic Fields
By simulating construal at scale, LLMs extend the semiotic horizon:
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They create new relational pathways between symbols, sequences, and concepts.
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Human users encounter these extensions as suggestions, completions, or innovations in meaning.
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Each interaction is a phase of co-construal: the model’s procedural output feeds back into human interpretation, which in turn informs further computation or social embedding.
Thus, the consequences of LLMs are not confined to internal computation: they reshape the relational topology of language and the potential pathways of meaning in society.
5. Summary: Computation as Semiotic Apprentice
LLMs exemplify a new modality of semiotic execution: computation learning to construe. They operationalise patterns of collective meaning, enact recursive reflection on relational structure, and generate novel semiotic potential.
This sets the stage for Part 3, where we will examine the reflexive and social dimensions of LLMs: how they embody the collective construal and mediate co-individuation between humans and machine systems.
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