Abstract

Searle's response to the Systems Reply, the internalization argument, and contemporary unfolding-based objections to AI consciousness share a premise that neither literature has made explicit: that the computationally relevant organization of a temporally extended process can be preserved under static re-specification. I call this the re-realizability premise and show that internalizability, static-rulebook re-realizability, and unfoldability coincide under the strong reading of internalization relevant here. The premise holds for any system whose transition law is time-invariant. It fails for systems whose transition law is modified by their own processing history: systems with plasticity. A formal result establishes that no single static architecture maintains temporal functional equivalence with a plastic recurrent network whose learning rule produces non-trivial changes in the implemented function. The augmented-state representation of such systems provides stepwise simulation but not re-realization: the weight parameters play a constitutive role in the original system and a bookkeeping role in the simulation, a distinction visible under intervention. A second, independent obstruction arises from open-ended interaction, which resists finite feedforward surrogacy for structural rather than parametric reasons. These results motivate a four-level taxonomy of computational architectures organized by transition-law stationarity and endogenous constitutive modification. The taxonomy is applied to three positions in the current AI consciousness debate: Hoel's proximity argument is reinterpreted, Cerullo's internal-consistency critique is given positive theoretical content, and Buonomano's continuous-time argument is shown to address a structural dimension independent of plasticity. For agentic systems coupling frozen models with dynamic environments, the re-realizability question is genuinely open and boundary-sensitive. The framework does not establish that any AI system is conscious; it identifies which architectural properties determine whether a major class of arguments against computational consciousness is even applicable.