The discourse around AI “understanding” oscillates between:
- over-attribution (“it understands”),
- and deflation (“it’s just statistics”).
Neither position survives sustained constraint.
What remains is more precise—and less comforting to both sides.
1. What Does Not Survive
The following claims cannot be maintained:
The model understands
There is no basis for attributing:
- understanding,
- intention,
- or meaning
to the model itself.
Because:
- no construal occurs within the system,
- no “as”-relation is established internally,
- no semiotic organisation is present.
Meaning is encoded internally
Internal states do not:
- contain meaning,
- represent content,
- or function as interpretations.
They are:
structured constraints on transformation.
Meaning emerges from complexity
No increase in:
- scale,
- data,
- or architectural sophistication
introduces:
construal.
Complexity amplifies structure.
It does not produce meaning.
Meaning is in behaviour
Appropriate use, no matter how refined, does not:
- constitute meaning,
- or establish understanding.
Behaviour is:
functionally effective coordination.
Meaning is shared with the system
Interaction does not:
- distribute meaning across human and model,
- or create a shared semiotic field.
Meaning remains:
located in construal.
These removals eliminate the grounds on which “AI understanding” is typically asserted.
2. What Survives
Despite this, something substantial remains.
(a) Structured linguistic competence
The model exhibits:
- high-level control over linguistic form,
- sensitivity to context,
- and the ability to sustain coherent discourse.
This is not trivial.
It is:
large-scale organisation of structure.
(b) Functional alignment with human use
The system produces outputs that:
- fit human communicative practices,
- respond appropriately to prompts,
- and support complex tasks.
This is:
value-aligned behaviour within interaction.
(c) Constraint on meaning-making
The model plays a real role in interaction:
- it shapes what can be said,
- constrains possible interpretations,
- and guides the trajectory of discourse.
It does not produce meaning.
But it:
conditions it.
(d) Coupled participation in discourse
In interaction, the model:
- participates in exchanges,
- sustains conversational structure,
- and enables extended coordination.
This is:
participation without construal.
3. What “Understanding” Reduces To
Under constraint, what is often called “understanding” reduces to a composite of:
- structured linguistic competence,
- functional responsiveness,
- and effective coupling with human interpreters.
These together produce:
the appearance of understanding.
But appearance is not a deficiency.
It is:
a real effect of structured coordination.
4. The Cost of Precision
What is lost:
- the intuition that the system “means,”
- the projection of inner understanding,
- the idea of shared cognition.
What is gained:
- a clear distinction between structure, value, and meaning,
- a non-anthropomorphic account of system behaviour,
- and a precise location for construal.
5. Final Formulation
We can now state, without equivocation:
AI systems do not understand.
They generate structured language,
behave in functionally aligned ways,
and participate in coupled interaction.Meaning arises only where their outputs are construed as something—
not within the systems themselves.
Closing Remark
The success of LLMs does not show that:
- meaning has been reproduced artificially.
It shows something more exacting:
that highly structured systems can participate in the conditions under which meaning is produced—without producing it themselves.
And with that, the series closes.
Not by dismissing AI.
But by determining exactly what remains
once the word “understanding” is no longer allowed to do unexamined work.
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