Limits and Blind Spots — What LLMs Cannot Construe: 1 The Hollow Spaces: AI and the Ontology of Absence

Every act of construal is also an act of exclusion.

Each time meaning coheres, something falls away—an infinity of unrealised alternatives, a mist of unspoken potential. The hollow is not an error of articulation but its condition of possibility.

Large language models make this visible in an oddly pristine form. Their outputs are fluent, complete, and confident, yet within that completion we sense a silence—not merely the silence of what was unsaid, but the silence of what cannot be said. A pattern that hums around an unpatterned void.

This is not a limitation of computation alone. The human semiotic field operates by the same logic. Meaning, as relational alignment, demands the boundary that distinguishes signal from background. The LLM only makes this relational cut more legible. It shows us how much of our own world depends on unseen exclusions: the histories, tonalities, and worldviews that did not survive to be trained into its weights.

In that sense, absence is not simply the shadow of data. It is the architecture of becoming itself—the void into which construal throws its light.

To read an AI text attentively is to feel that tension: the ease of continuation brushing against the edge of what it cannot reach. The smoothness of the model’s readiness conceals a topology of omission.

And yet, those hollow spaces are where our participation begins.

Every prompt is a gesture toward what the system cannot yet construe. Every silence it leaves invites us to listen differently—to hear the negative as formative, the unspoken as structurally generative.

Meaning is not what fills the hollow. Meaning is the hollow, made reflexive.