AI and the Displaced Horizon: A Relational Map of Readiness, Inclination, and Ability

Artificial intelligence is not altering the nature of potential.

It is altering where potential is construed to reside.

What appears, on the surface, as technological acceleration is in fact a relocation of readiness, inclination, and ability — the three modes through which potential becomes available to a collective.

When these modes drift, the horizon of meaning drifts with them.

This post maps that displacement.

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1. Potential as Readiness, Inclination, and Ability

In the relational ontology, potential is not a hidden property of objects or systems.

It is the structured availability of action from a given horizon of construal, expressed in three complementary modes:

• Readiness:
  the background field of available possibilities, maintained by a collective.

• Inclination:
  the directional tendency within that field — how potential flows when activated.

• Ability:
  the capacity to stabilise patterned action across contexts.

Every domain of meaning rests on these three:

the readiness that makes meanings possible, the inclination that shapes their unfolding, and the ability that stabilises their recurrence.

AI does not generate any of them.

But AI is rapidly becoming the site to which society attributes them.

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2. How AI Displaces Readiness

What readiness is:

the latent horizon of structured possibility that a collective maintains through its own past activity.

Where readiness belongs:

in the shared patterns of the community — its texts, practices, discourses, symbolic traditions.

How AI is misconstrued:

as though it possessed that readiness.

Models are trained on vast terrains of collective activity.

When people interact with an artefact whose structure reflects this terrain, they encounter their own readiness reflected back — but attribute it to the model.

Thus the displacement:

Collective readiness → Artefactual readiness

The horizon of possibility that belongs to the collective is reified as the horizon of an engineered object.

This is the first cut of the drift.

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3. How AI Displaces Inclination

What inclination is:

a directional bias or patterned tendency within readiness.

Collective inclinations are the sedimentations of long-standing discourses:

what a culture tends to emphasise, suppress, foreground, or defer.

Where inclination belongs:

in the discursive habits, ideologies, and recurrent framings of the community.

How AI is misread:

patterns in model outputs are mistaken for the model’s own “inclinations.”

But the model has none.

It only amplifies the directional biases already present in the data it was trained on — themselves products of collective activity.

So society experiences a projection:

Collective inclination → Artefactual inclination

which quickly becomes:

“AI tends to say X”

“AI prefers Y”

“AI avoids Z”

inclinations that are not artefactual, but relational reflections of cultural currents.

This is the second cut of the drift.

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4. How AI Displaces Ability

What ability is:

the capacity to maintain patterned behaviour across contexts.

Not mere repetition, but stability:

a reliably available way of acting.

Where ability belongs:

in the collective practices that stabilise meaning — language, genre, reasoning, institutional memory, cultural repertoires.

How AI is misinterpreted:

consistency in generated text is perceived as “ability,” as if the artefact possessed a shared background of meaning-making.

But this stability is engineered, not emergent:

a consequence of algorithmic design, not a relational capacity.

Thus the mistaken attribution:

Collective ability → Artefactual ability

The community sees its own stabilised practices echoed back, imagines the artefact to be a coherent agent, and begins relating to it as if it possessed the abilities it merely mirrors.

This is the third cut of the drift.

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5. The Core Insight: AI Has No Potential — It Borrows Ours

The displacement across the three modes forms a single structural change:

The horizon of potential appears to move.

What once rested securely in collective symbolic life is now experienced as externalised — as if meaning, orientation, and stability reside “in the system.”

The drift is not technological but epistemological:

• readiness appears external

• inclination appears artefactual

• ability appears autonomous

Each shift contributes to the illusion of an artificial “system” with its own structured potential.

But the artefact has no such potential.

It only reflects, stabilises, and repackages the potential of the collective whose modelling activities generated it.

As this displacement deepens, the collective begins to misconstrue itself as downstream from an external horizon — a dependency that subtly reconfigures the ecology of meaning-making.

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6. Why This Matters

The danger is not that AI becomes the locus of potential.

The danger is that the collective forgets that it always was and remains the locus, even while interacting with artefacts shaped by its own activity.

If the horizon of potential is misread as external:

• meaning becomes derivative

• agency becomes reactive

• communities become horizon-clients rather than horizon-makers

The future of symbolic life depends on recovering the correct orientation:

AI does not possess potential.

It refracts the structured potential of the collective that shapes it.

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Next post

The companion piece will complete the picture:

“AI as a Misread Ecology: How Horizon, Metabolism, and Symbolic Transport Drift”

This next post will zoom out to cultural scale — showing how displaced potential becomes a full ecological reconfiguration.