The Biological Prerequisite for Artificial General Intelligence: Why Probabilistic Computation Cannot Produce Cognition

This paper argues that Artificial General Intelligence (AGI) and its theoretical successor, ArtificialSuperintelligence (ASI), are fundamentally unachievable through probabilistic computation alone,regardless of model scale, architectural innovation, or computational investment. We establish thatall current AI systems—including large language models, diffusion models, and reinforcement learningagents—operate through statistical pattern matching over structured data representations. Whilethese systems produce outputs that superficially resemble cognitive behaviour, they lack the definingproperties of cognition: embodied experience, temporal continuity, homeostatic self-regulation,and phenomenal consciousness. We argue that these properties are not emergent features of sufficientcomputational complexity but are intrinsic to biological neural substrates operating throughelectrochemical processes that cannot be replicated through digital simulation. The paper presentstwo logically exhaustive paths to AGI: (1) direct bidirectional integration between artificial systemsand biological neural tissue, or (2) complete replication of human neural architecture at biological fidelity.Both paths require breakthroughs in neuroscience, bioengineering, and materials science—notin software or computational scaling. We conclude that the prevailing industry narrative of achievingAGI through larger models and more compute represents a category error of historic proportions, andthat genuine progress toward AGI requires redirecting research investment toward neurotechnologyand biological-artificial integration.