The Location of Subjectivity Derived by AI — A Structural Experiment with "Abnormally High Correspondence" Reproducible on Your Own AI — A Spin-Off from The Structural Conditions of Consciousness — Structural Knot Theory

When AI systems are given only nine ordinary keywords that are nearly self-evident for any autonomous embodied organism—such as autonomy, embodiment, irreversible time, behavioral selection, and information discard—they repeatedly derive a structure resembling a convergence point where multiple possibilities collapse into a single executable history. This structural locus corresponds closely to major properties commonly associated with subjectivity, including singularity, non-branching continuity, temporal embedding, irreversibility, embodiment, action coupling, and single-history fixation. The paper does not explain or generate subjectivity itself but presents a reproducible structural phenomenon regarding where subjectivity becomes fixed, and encourages readers to test the prompt on their own AI systems.

Study at a glance

Abstract

This paper presents an experimental observation regarding how AI systems structurally derive the “location of subjectivity” when given only nine keywords that are almost implicitly presupposed in biological existence itself. To this end, the paper provides an extremely simple prompt that readers can immediately test on their own smartphones or general AI systems. Despite the minimality of the input conditions, AI systems repeatedly tend to derive, with unusually high structural correspondence, an engineering or control-theoretic structure resembling: “a convergence point at which multiple possibilities collapse into a single executable history.” Importantly, the prompt used in this paper is intentionally constructed not from newly invented metaphysical concepts or specialized assumptions, but only from highly ordinary and near-self-evident conditions associated with autonomous embodied organisms, including autonomy, embodiment, irreversible time, behavioral selection, and information discard. The structure of the prompt intentionally minimizes additional assumptions in order to satisfy Occam’s principle as strongly as possible. Existing major theories — including Global Workspace Theory, Integrated Information Theory, Higher-Order Theories, Predictive Processing, and research into Neural Correlates of Consciousness — have primarily attempted to explain subjectivity through integration, representation, or increasingly sophisticated information processing. Implicitly, many such approaches assume that sufficiently advanced integration will eventually generate a unified subjective center. However, there may exist not only points of integration, but also points of convergence. In this sense, consciousness research may ultimately reduce to the search for such a point. Furthermore, the major properties of such convergence points — including singularity, non-branching continuity, temporal embedding, irreversibility, embodiment, action coupling, and single-history fixation — strongly correspond to major structural properties commonly associated with subjectivity. This paper is not a theory that explains or generates subjectivity itself. Rather, it presents a reproducible structural phenomenon concerning: “the structural locus at which subjectivity becomes fixed.” Readers are strongly encouraged to test these prompts using their own AI systems.