This podcast explores a bold new theory proposing that every feeling, thought, or dream may have an actual shape. The Phenomenal Manifold Hypothesis by Éric Reis suggests that consciousness can be mapped as a geometric structure—a “phenomenal manifold” (Ψ). Instead of asking why experience exists, it focuses on describing its structure, much like thermodynamics described heat before molecular theory. According to the model, each conscious experience corresponds to a point in a vast multidimensional landscape, and the distance between points reflects how similar two experiences are. The geometry of Ψ is determined by three measurable properties of brain dynamics: Integration (I), representing how unified and irreducible a conscious moment is; Coherence (Γ), measuring how synchronized neural regions are; and Differentiation (Δ), capturing the richness and variety of possible brain states. These three forces define the curvature, dimensionality, and shape of your inner world at each moment. The theory predicts that different states of consciousness correspond to distinct geometries. Wakefulness forms a high-dimensional space with moderate curvature. Deep sleep or anesthesia collapses the manifold into a low-dimensional, nearly flat structure. Psychedelic states expand the geometry dramatically into a highly complex, high-dimensional manifold with high Differentiation but often lower Coherence. Certain meditative states contract the manifold into a unified, low-volume geometry that may shrink to less than 20% of its waking size. Crucially, the model is testable and falsifiable. It must accurately reconstruct known phenomenological structures—such as color relationships—or it fails. It also predicts that the intrinsic dimension of consciousness should remain relatively stable across healthy individuals; large variations would falsify the theory. The hypothesis also offers a framework for evaluating machine consciousness. By analyzing an AI system’s informational dynamics, researchers could compute Integration, Coherence, and Differentiation. The theory proposes minimal thresholds—such as Imin ≈ 0.15 bits and dimensionality n ≥ 3—for a system to be considered a potential candidate for consciousness. If an AI meets these criteria, the precautionary principle suggests treating it as potentially phenomenal. Ultimately, this podcast discusses how the Phenomenal Manifold Hypothesis proposes that consciousness may have a discoverable geometry. By translating neural information dynamics into geometric structure, it offers a scientific bridge between objective brain activity and subjective experience, opening new ways to map the hidden landscapes of the mind.