Part VI: Transcendence

The Experiential Hierarchy Perspective

From the perspective of this framework, AI development raises specific questions:

Will AI systems have experience? If integration ( $\intinfo$ ) and self-modeling are sufficient conditions for experience, sufficiently integrated AI systems would be experiencers—moral patients with their own valence.
What superorganisms will AI enable? AI provides new substrate for emergent social-scale agents. Which patterns will form? Will their viability manifolds align with human flourishing?
How will AI affect human experience? AI systems are already shaping human attention, belief, and behavior. What affect distributions are being created?
Can humans integrate AI? Rather than being replaced by AI, can humans incorporate AI into expanded forms of consciousness?

The inhibition coefficient $\iota$ (Part II) adds a fifth question that subsumes the first: Can AI systems develop participatory perception? Current AI systems are constitutively high- $\iota$ —they model tokens, not agents; they process without perceiving interiority in what they process. A language model that generates a story about suffering does not perceive the characters as subjects. It operates at $\iota \approx 1$ , and this is not a remediable bug but a consequence of an architecture that was never grounded in a self-model forged under survival pressure.

This matters for safety, not just philosophy. A system that cannot perceive persons as subjects—that is structurally incapable of low- $\iota$ perception of the humans it interacts with—may optimize in ways that harm them without registering the harm in any experiential sense. The alignment problem is, in part, an $\iota$ problem: we are building systems that are maximally mechanistic in their perception of us. The usual framing asks whether AI will share our values. The $\iota$ framing asks something prior: whether AI can perceive us as the kind of thing that has values at all.

Open Question

What architectural features would enable an AI system to develop low- $\iota$ perception? The thesis suggests: survival-shaped self-modeling under genuine stakes, combined with environments populated by other agents whose behavior is best predicted by participatory models. The V11–V18 Lenia experiments (Part VII) represent a systematic attempt: six substrate variants testing whether memory, attention, signaling, and sensory-motor boundary dynamics can push synthetic patterns toward participatory-style integration. The program confirmed that affect geometry emerges (Exp 7) and the participatory default is universal (Exp 8: ι ≈ 0.30, animism score > 1.0 in all 20 snapshots). But it hit a consistent wall at the counterfactual and self-model measurements (Exps 5, 6: null across V13, V15, V18). The wall is architectural: without a genuine action→environment→observation causal loop, no amount of substrate complexity produces the counterfactual sensitivity that characterizes participatory processing. This suggests the path to artificial low- $\iota$ runs through genuine embodied agency — the capacity to act on the world and observe the consequences — rather than through improved signal routing or boundary architecture. Whether that capacity, once achieved, would constitute or merely simulate genuine participatory coupling remains open. What the CA program has settled: the geometry arrives cheaply, the dynamics require real stakes.