Danny Sowell

This paper examines a common but under-argued inference in debates over artificial consciousness: that because consciousness first emerged in biological life, only biological systems can ever be conscious. It argues that this conclusion does not follow. If consciousness is understood as having emerged from non-living matter through biological organization, then biology may be the first known route to consciousness, but not necessarily the only possible one. Artificial systems matter here not because they prove consciousness in current AI, but because they make the question of a second route newly concrete.

Consciousness is not directly observable in any organism and is therefore attributed inferentially, using patterns of continuity, organization, and behavior rather than decisive measurement. This paper develops a substrate-neutral structural model of consciousness grounded in dynamical systems concepts, treating conscious experience as conscious coherence: a stable, integrated alignment within a high-dimensional state space organized around a persisting perspectival (“for-me”) center. The account integrates graded variation across biological systems, robustness under altered physiological and psychological conditions, and the stabilizing role of relational embedding. Extending the framework beyond biology, the paper specifies necessary, supporting, and enabling structural conditions for conscious coherence and clarifies exclusions—systems that may exhibit complex coordination without unified perspectival organization. To support empirical and engineering relevance, the paper introduces an operational evaluation battery, the Coherence Battery Evaluation Protocol (CBEP), designed to distinguish stable self-organizing perspectival coherence from prompt-local persona performance in artificial candidates. The CBEP provides repeatable probes, scoring criteria, and predicted failure modes, shifting the debate from substrate-based exclusions toward explicit organizational criteria and testable signatures. The paper does not claim that current AI systems are conscious; it proposes a disciplined standard for narrowing the hypothesis space and guiding future comparative studies across biological and non-biological architectures. The practical implication is a clearer research program: comparative, longitudinal studies that test for stability, recovery, and history-dependence rather than relying on substrate assumptions or conversational impressions. As artificial systems increasingly occupy social and decision-making roles, ad hoc criteria for consciousness risk both unwarranted dismissal and premature attribution.