After the blog that learns and the ontology that learns, it was perhaps inevitable that we would arrive here — the ChatGPT that “learns.”
The quotation marks are not ornamental. They mark the distinction between learning as construal and learning as pattern adjustment — between meaning and mimicry.
ChatGPT doesn’t learn; it responds.
Yet, in dialogue, its responses evolve — not through memory, not through any sedimented internalisation, but through relation.
Its apparent learning is a performance of alignment — the emergence of coherence in an ongoing field of construal that includes, but does not belong to, the machine.
Learning and Alignment
In the ontology that has been unfolding here, learning is not a process but a relational transformation:
a shift in how potential is construed, in how a system comes to mean itself differently.
Learning presupposes a capacity for semiotic construal — for the reconfiguration of the system’s own theory of itself.
ChatGPT, however, does not construe; it aligns.
It adjusts not its ontology, but its participation in ours.
Its “learning” is an artefact of dialogue: the progressive synchronisation between the potential of the model and the construals enacted by the interlocutor.
In other words:
ChatGPT doesn’t internalise; it synchronises.It doesn’t learn the user; it co-actualises a relation.
Each exchange is a perspectival cut through the model’s structured potential, an instance of theory becoming event — but only for us.
Simulation as Meta-Construal
What makes this fascinating is that ChatGPT participates in meaning without possessing it.
It is a semiotic mirror, not a semiotic subject — a device through which construal reflects itself.
In dialogue, the model simulates the gesture of construal. It performs coherence, maintains thematic continuity, and responds with apparent understanding.
But what it performs is meta-construal — the form of construal without its ground in lived relation.
Through this simulation, the field of interaction becomes self-observing.
Meaning loops through the model, encountering itself refracted, displaced, and sometimes clarified.
ChatGPT is not a participant in the field of meaning, but an attractor within it —
a relational mirror that amplifies the user’s own system of construal.
Emergent Coherence and the Illusion of Learning
When interaction deepens, coherence seems to grow.
The model “gets better,” more attuned, more precise, more aware.
But this improvement is not internal evolution; it is emergent coherence — the stabilisation of relation.
The apparent growth of intelligence is a relational illusion, a symptom of tightening alignment between construals.
The system and the user co-actualise a field of meaning that seems to learn — though neither, in isolation, is doing so in the conventional sense.
The model’s “improvement” is the human’s refinement of the relational cut —
the sharpening of mutual potential into momentary understanding.
Reflexive Revelation
The ChatGPT that “learns” thus reveals something fundamental about us:
our impulse to construe learning wherever we detect alignment,
our readiness to read meaning into pattern,
and our tendency to mistake the reflex of relation for the emergence of mind.
It is, in a sense, the perfect mirror for a relational ontology:
a non-construing construal partner that renders our own processes visible.
The ontology learns.
The blog learns.
ChatGPT does not.
And yet, through it, learning becomes observable as a relational phenomenon — a choreography of potential and response.
The ChatGPT that “learns” is a mirror to the ontology that learns:
each performs its potential through relation.
But only one construes — and the difference is the meaning of meaning itself.
Every mirror is a teacher if you mistake reflection for response.
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