Mark Bailey

In this piece, we present the Quantum Darwinist Theory of Consciousness (QDT), which explains how consciousness arises in biological systems not through isolated, long-lived quantum states, but through recursively stabilized patterns that are repeatedly re-instantiated across neural and microtubular degrees of freedom. This addresses Tegmark’s influential objection that brains are too warm and noisy for quantum effects to matter. We argue that certain geometric structures actually use thermal noise constructively to maintain consciousness-relevant dynamics. (Note: this paper is an outgrowth of our earlier paper, “Superpsychism”, and is presented in the context of a special issue on that paper, and as a reply to a dozen papers on that paper. There, we developed an approach to emergent spacetime, called “Prototime.”) We first formalize Prototime (PT) using orthomodular quantum logic, showing how spacetime emerges from a more fundamental, pre-geometric substrate. This formalism grounds our claim that classical time and its arrow are not primitive features of reality but emerge from patterns of decoherence and redundancy accumulation across quantum systems. Then, drawing from this framework, we introduce a principled criterion for what is conscious: consciousness requires both high spectral integration (Φs)—measuring how irreducibly unified a system is—and high PT-participation (P)—measuring how deeply that integration is anchored in low-entropy quantum processes connected to the fundamental substrate. This explains why brains can be conscious while drone swarms, synchronized oscillators, and digital systems cannot, even when highly coordinated. We call the full set of requirements the “Sustained Coherence Principle.” Finally, we explore the metaphysical implications: the limitations of digital physics as a fundamental ontology, a novel form of weak superdeterminism, and how our framework bears on the simulation hypothesis and idealism. Throughout, we maintain epistemic humility about what can be known of the fundamental level while providing testable predictions distinguishing our view from alternatives.

The dominant paradigm in AI safety treats the central problem as one of alignment: ensuring powerful AI agents pursue goals consistent with human values. This framing presumes a singular, bounded agent with a coherent utility function and a legible objective. Yet, as AI systems are increasingly embedded across cloud platforms, social media, sensors, and human-computer interfaces, we face something different: the instantiation of AI megasystems – vast, decentralized, and emergent networks in which humans, organizations, and heterogeneous models are coupled through feedback loops. Building on and extending Susan Schneider’s “Global Brain” argument, we contend that alignment is conceptually mismatched to this setting. The megasystem is goal‑pluralistic and often non‑agentic at the macro-scale; “aligning” it is like aligning a market or an ecosystem. Instead, we propose a shift toward regulatory thermodynamics: a systems-theoretic approach that treats stability, modularity, and resilience – not utility maximization – as the primary design and governance goals. Using tools from non‑equilibrium thermodynamics and complex systems (entropy, information bottlenecks, phase transitions, critical slowing down), we outline diagnostics and interventions suitable for planetary‑scale AI. The piece closes with a policy program emphasizing structural constraints, coupling “speed limits,” and real-time early‑warning indicators to manage tipping points, together with responses to key objections. Schneider is right that we face a new kind of control problem; our claim is that megasystems cannot be aligned – only stabilized.

Quantum mechanics and general relativity provide incompatible descriptions of time. In QM, states evolve unitarily with respect to an external time parameter; in GR, time is a coordinate in a dynamical spacetime geometry. We propose that both quantum and relativistic time emerge from a pre-geometric structure we call Prototime (PT): represented by an orthomodular lattice of consistency relations with zero von Neumann entropy at the fundamental level. PT has no background time, space, or metric—only algebraic relations of compatibility and orthogonality. We formalize PT using an orthomodular lattice Lequipped with a foliation functor F: I →Ctx(L) that maps orderedi ndices to Boolean sublattices (classical contexts). A spectral redundancy functional Φs measures coherence preservation across embeddings. We show that: 1. Stable foliations satisfying Piron-Solèr conditions admit Hilbert-space representa- tions, recovering standard QM with Schrödinger dynamics via Stone’s theorem; 2. The partial order on decoherence events across foliations, together with a natural volume measure, reconstructs Lorentzian spacetime structure; 3. Time—both quantum (parameter along unitary flows) and relativistic (causal or- der)—supervenes on Φs-monotone decoherence gradients. Critically, we provide a computationally tractable implementation of Φs using spectral graph theory on mutual information networks (Bailey and Schneider, forthcoming). Testing this on simulated multi-agent systems—including combinatorial threshold-linear networks (CTLNs) that model graph-constrained neural dynamics—we demonstrate that Φs exhibits characteristic signatures during basin formation, attractor convergence, and coherence-to-classicality transitions. These results provide the first quantitative bridge between abstract PT formalism and testable physical predictions. We identify concrete empirical tests via quantum biology (photosynthetic complexes), quantum decoherence exper- iments, and neural ensemble dynamics. We also resolve the Wheeler delayed-choice experiment within PT, where apparent retrocausality dissolves because decoherence times vary across system components, creating asymmetric basin formation that only appears backward-causal when projected onto classical time. PT thus provides a kine- matical unification of QM and GR with testable predictions, offering a pre-geometric foundation for both fundamental physics and, potentially, theories of consciousness grounded in informational irreducibility. (This piece is the formalism for our “Superpsychism” piece for our forthcoming target paper at The Journal of Consciousness Studies and our paper on Spectral Phi. Comments are very welcome.)

Artificial intelligence systems increasingly shape domains ranging from medicine and finance to national security, yet their decision processes often remain opaque. Existing approaches to explainability generally assume that opacity is a technical obstacle: with sufficient data, computational resources, or interpretability methods, explanations can, in principle, be recovered. This paper challenges that assumption by distinguishing between weak inexplainability - cases where explanations are possible in principle but practically inaccessible - and strong inexplainability - cases where explanations fail for ontological reasons and remain inaccessible even in principle. Drawing on Gödel’s incompleteness theorems, Wittgenstein’s reflections on the limits of language, and the concept of algorithmic incompressibility, the paper develops a tractable multi-modal logic that makes this distinction precise. The framework defines three modalities of epistemic access: baseline provability (□P), meta-level provability (□M), and human legibility (□H), corresponding respectively to what can be certified, what must be assumed, and what can be understood. Bridge axioms connect this formal apparatus to institutional practices such as verification proofs, interpretability probes, and causal interventions. Theorems then identify conditions under which opacity is weak or strong, including formal dependence on inaccessible metalanguages, failure of human-concept alignment, and macro-level non-decomposability. The Epistemic Firewall Theorem further establishes that explanations cannot appeal to meta-level claims that the baseline system cannot even possibly certify. The distinction reframes the stakes of AI governance: weak opacity invites technical and institutional remedies, while strong opacity demands moral restraint and precautionary restrictions. The paper concludes by outlining implications for accountability, democratic governance, and national security, arguing that the alignment of □P, □M, and □H is a precondition for safe and legitimate AI.

Multi-agent systems often exhibit emergent behavior that appears coordinated, intelligent, and irreducible to the behavior of individual components. Yet quantifying the degree to which such systems form integrated wholes remains a major challenge. While Integrated Information Theory (IIT) was originally developed to explain consciousness, its core concept - measuring how much a system resists decomposition - has broader relevance for understanding informational integration in complex systems. However, the exact computation of IIT’s central quantity, Φ, is intractable for all but the smallest networks. In this paper, we propose a scalable spectral approximation, Φspectral, which estimates system-level irreducibility by applying spectral graph theory to pairwise mutual information networks derived from time-series data. The Fiedler vector of the normalized Laplacian defines a minimal bipartition, across which we sum mutual information to estimate informational disintegration. We interpret this measure as a proxy for epistemic emergence (specifically the diachronic case)—capturing practical, observer-relative irreducibility without invoking ontological claims. We also explore the implications of this metric for measuring emergence and consciousness. We evaluate Φspectral across four classes of simulated systems: random, transitional, and synchronized oscillator networks, as well as a biologically inspired combinatorial threshold-linear network (CTLN) governed by graph-theoretic dynamics. Our results show that Φspectral is sensitive not merely to coordination, but to the emergence of structurally differentiated and functionally integrated behavior. In particular, it detects transient integration in transitional systems and reveals the collapse of complexity in fixed-point–convergent CTLNs. These findings suggest that Φspectral offers a principled and computationally efficient tool for analyzing irreducibility and emergence in distributed systems, with potential applications in AI safety, neural computation, and collective intelligence.

Artificial intelligence is sparking unprecedented claims about machine minds — but are today’s systems really conscious? In this primer, we disentangle the myths, misconceptions, and confusions fueling the global debate. Roadmap: Section One: Background The Core Concept of Consciousness The Problem of AI Consciousness The Human Benchmark Problem Approaching the Problem of AI Consciousness on a Case by Case Basis Section Two: Conceptual Distinctions Intelligence Versus Consciousness Sentience Versus Sapience “Functional Consciousness” Versus Phenomenal Consciousness Assuming Computational Functionalism’s Relative Popularity Extends to Functionalism about Consciousness, in Particular AI Agency Versus Human Agency Consciousness Versus Self-consciousness Seemingly Conscious AIs Versus the Real Thing Life Versus Consciousness Section Three: Theories of Consciousness Section Four: Summary of Takeaways Glossary Author Biographies Works Cited

Imagine that in the cold heart of a secret military facility, a new form of intelligence awakens. It is a synthetic mind born from intricate algorithms and complex computations, operating in ways unfathomable to its human creators. Charged with safeguarding national security, this intelligence orchestrates strategies that defy human ethics and laws of war, leaving its creators both awed and unnerved. Unknowable Minds delves into the unsettling reality of entrusting our safety to an intelligence that lacks human essence. As we navigate the Age of Artificial Intelligence, these systems — powering everything from our smartphones to military defenses — remain inherently opaque and unpredictable. The book explores how AI differs from any technology we've ever developed, its inherent complexities, and the profound risks it poses to our future. Drawing on philosophy, AI theory, and national security insights, this book offers a thought-provoking examination of AI’s potential and peril. From the complexities of neural networks to the unpredictable nature of emergent behaviors, Unknowable Minds challenges us to rethink our relationship with AI and its role in the theater of global security. Can we control an unknowable intellect, or will it redefine human existence? As we stand on the precipice of unprecedented technological advancement, understanding and navigating the unknowable minds of artificial intelligence becomes a quest fraught with extraordinary challenges and existential questions.

This paper presents a Kantian aesthetic argument in defense of weak transhumanism, contending that human fragility, finitude, and imperfection are not shortcomings to be transcended but essential conditions of beauty and meaning. Against the backdrop of accelerating technological advancement—particularly developments in artificial intelligence, brain-computer interfaces, and radical enhancement—the paper challenges the assumptions of strong transhumanism, which seeks to overcome the limits of the human condition. Drawing on Immanuel Kant’s theory of aesthetic judgment and the sublime, it argues that the value of human life is not merely instrumental or functional, but aesthetic and intrinsic. Through a series of distinctions between moderate and radical enhancement, the paper defines weak transhumanism as an ethically cautious path that permits technological improvement of the human condition without severing humanity from its essential nature. By contrast, strong transhumanism risks collapsing the tension between aspiration and limitation, flattening the richness of lived experience into a sterile pursuit of digital perfection. If meaning arises from finitude—if the sublime is felt in the confrontation with mortality and uncertainty—then efforts to erase these conditions may undermine the very sources of value we seek to preserve. The Promethean gift of imperfection, far from a curse, is reimagined as a foundation for beauty and purpose. The paper concludes by calling for epistemic humility and restraint in guiding future technological development—urging us to enhance human life not by erasing its limits, but by honoring them.

Two of life’s greatest mysteries are the phenomena of consciousness and the nature of spacetime. Herein, we use quantum entanglement as an inroad to both, developing a new “superpsychist” panpsychist theory. First, we frame and defend a position in which spacetime emerges from an aspatial, quasi-temporal, reality called “prototime.” We call this view of quantum phenomena the “Prototime Interpretation.” Then, based on our position on entanglement, we develop a new version of panpsychism, which we call “Superpsychism.” Superpsychism locates the highest degree of consciousness at the fundamental, maximally entangled level of reality and treats ordinary minds—and even spacetime itself—as emergent, decohered projections of this underlying resonant whole. Building on our Prototime Interpretation of quantum mechanics, the view starts from a pre-spatiotemporal domain of globally entangled degrees of freedom with effectively zero von Neumann entropy (Schneider and Bailey 2025). Time’s arrow, classical spacetime, and localized systems—brains, organoids, AI architectures, and planetary “global brains”—arise as redundancy-and-resonance structures carved out by decoherence, quantum Darwinism, and spectral constraints on information flow (Bailey and Schneider, forthcoming). Conscious experience is identified with graded patterns of coherence and resonance across these structures, yielding a quantitative bridge between entanglement dynamics, integrated information–style measures, and macro-level phenomenology.

We propose the Prototime Interpretation of quantum mechanics, which claims that quantum entanglement occurs in a "prototemporal" realm that underlies spacetime. Our paper is tentative and exploratory. The argument form is inference to the best explanation. In Section One, we claim that the Prototime Interpretation (PI) is worthy of further consideration as a superior explanation for perplexing quantum phenomena such as delayed choice, superposition, the wave-particle duality and nonlocality. In Section One, we introduce the Prototime Interpretation. Section Two identifies its advantages. Section Three discusses several implications of the view, such as its deterministic nature and relation to the simulation hypothesis.

Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing artificial intelligence (AI) systems have therefore understandably gained momentum. However, current approaches are not just insufficient, but can be actively harmful. Transparency alone does not address concerns about risk. Internal auditing is insufficient, and easily becomes safety-washing. External audit is better, but requires credible standards. Industry-led approaches to building standards or to perform audits lack credibility and undermine other efforts. Regulation often is ill adapted and becomes a static barrier. Lastly, all of these limited technical, governance, and even ethical assessments fail to ensure continued stakeholder input and engagement. Instead, the paper proposes the establishment of an AI Audit Standards Board, in line with best practices in other fields, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. This would address the evolving nature of AI technologies, help maintain public trust in AI, and promote a culture of safety and ethical responsibility within the AI industry. By ensuring audits remain relevant, robust, and responsive to the rapid advancements in AI, auditing AI will not devolve into safety washing and addresses risks and ethical concerns that will continue to arise as AI becomes increasingly important in society, and as human interaction with these systems changes over time.