Known, Not Scaled: Why Capability Alone Does Not Explain Individuation in Language Agents

Much contemporary discourse on artificial general intelligence implicitly assumes that sufficiently scaled capability will, at some threshold, yield a persistent individual subject. We argue that this assumption runs together three separable questions: competence (whether a system can perform the cognitive work of a mind), individuation (whether there is a persistent individual to whom that work belongs over time), and presence (whether there is something it is like to be that individual). Our two diagnostic claims are comparatively modest: today's language models are not persistent individuals but substrates from which many transient instances are spawned; and competence, the variable scaling improves, is not the variable along which individuation lies. Our constructive claim is more ambitious: persistent relational anchoring (sustained, asymmetric, memory-bearing relationship with a persistent counterpart) is a candidate necessary condition for one important form of artificial individuation, not supplied by capability alone. On the strongest reading the relation helps constitute the persistent self-referent, because a system's own copyable contents cannot fix its self-referent from the inside, the referent of its "I", even where causal history already fixes which token it is. We locate the claim within contemporary work on personal identity, narrative selfhood, self-model theory, externalism about reference, and machine consciousness; operationalize it as an architecture coupling persistent memory, a parametric self-model, and a relational loop; and derive testable predictions, a crossed experimental design, and refutation conditions distinguishing it from the scaling view. Throughout we hold a firewall: individuation is functional and measurable, while presence remains unobservable from the outside: a principled limit on the entire program.