Understanding consciousness is the ultimate prize for creators of artificial intelligence. Nevertheless, consciousness theory will also shape how we view ourselves and our place in the world. Although AI systems can mimic human reasoning, they can only regurgitate the input data. They are sophisticated pattern recognizers and content remixers, but cannot step beyond the limitations of the input. Understanding consciousness would enable us to transition from synthetic to synthesis, unlocking unlimited potential.
Computer scientists hope that recurrent computation will somehow ‘awaken’ code to consciousness. Yet the spectacular achievements of large language and diffusion models have not moved beyond imitation. We train models on the outputs of consciousness—our language, our art, our logic—while remaining entirely ignorant of the process that produces them. An AI can write a gut-wrenching paragraph about sadness by replicating patterns, vocabulary, and syntax. But it knows nothing of grief. It can create a shadow play, yet knows nothing of the object that casts it. This imitation, while impressive, should not be mistaken for a proper understanding of consciousness. No amount of coloring can turn the shadow into a solid object.
To reverse-engineer the mind, we need a blueprint. The pressing need to advance AI is a physicalist theory of consciousness, the architecture of subjective experience itself. The Fermionic Mind Hypothesis (FMH) is such a physicalist framework. It posits that selfhood is structurally and functionally analogous to a fermion in physics. The self’s persistent core operates as an energy-regulating system, maintaining mental equilibrium through continuous thermodynamic cycles. Within this cycle, cognitive processes such as decision-making are wave-particle transitions that capture the inherent nondeterminism and contextual collapse of probabilistic mental states.
