29 Jun 2026
As of mid-2026, no AI system has been confirmed conscious by any scientific standard that the field broadly accepts. That is the current consensus position, held across scholars who disagree sharply on almost everything else. What consciousness is, whether current AI systems might have it, and what methodology could settle the question. What has changed in 2026 is not the consensus itself but the precision with which scholars understand why it is so difficult to move beyond it, and what a credible research programme for doing so would look like.
28 Jun 2026
The scientific debate over consciousness has split into two methodological camps that share almost no common ground. One camp begins with physics and neuroscience, looking for objective correlates of conscious experience and hoping that explanation will eventually close the gap to subjectivity. The other begins with consciousness itself, treating phenomenal experience as the one thing known with certainty, and deriving what the underlying physical structure must be from what experience is like. Giulio Tononi and Melanie Boly’s 2025 paper “Integrated Information Theory. A Consciousness-First Approach to What Exists” (arXiv:2510.25998), forthcoming in The Scientific Study of Consciousness edited by Melloni and Olcese (Springer-Nature), is the most complete statement of the second camp’s position to date. It is also, unusually, a paper that explicitly addresses artificial systems as potential substrates for consciousness.
28 Jun 2026
The debate over whether current or near-future AI systems are conscious frequently stalls on the lack of theoretical consensus. If researchers cannot agree on what consciousness is, how can they agree on whether an AI has it? Eric Schwitzgebel, whose recent work includes the Leapfrog Hypothesis and the Social Semi-Solution, offers a pragmatic way forward in his October 2025 arXiv preprint (arXiv:2510.09858). Instead of demanding a unified theory, he proposes a 10 feature checklist drawn from across the major theoretical camps.
28 Jun 2026
The search for the physical principles underlying consciousness often leads back to the fundamental concepts of physics. One of the most powerful concepts in physics is symmetry breaking, the process where a system in a symmetric but unstable state transitions to a less symmetric but more stable state. In a 2023 paper published in Interface Focus, Adam Safron and Michael Levin applied this concept directly to cognitive science, proposing that consciousness itself can be understood as a process of symmetry breaking within hierarchical generative models.
28 Jun 2026
One of the persistent practical difficulties in machine consciousness research is that the major theories of consciousness, IIT, Global Workspace Theory, and the Free Energy Principle, are typically treated as competitors. Projects that use Phi as a consciousness metric are implicitly committed to IIT. Projects that implement a global workspace are committed to GWT. Projects that use active inference are committed to the FEP. The assumption is that a project must choose. Adam Safron’s Integrated World Modeling Theory (IWMT) challenges this assumption directly. Its central claim is that IIT, GWT, and FEP are not competing theories but overlapping descriptions of the same underlying computational phenomenon, which IWMT calls Self-Organizing Harmonic Modes (SOHMs).
28 Jun 2026
In April 2025, the Machine Intelligence from Cortical Networks (MICrONS) program published a milestone in mammalian connectomics in Nature. The complete wiring diagram of a cubic millimeter of mouse visual cortex. This volume contains approximately 100,000 neurons and a billion synapses, meticulously mapped using electron microscopy and co-registered with in vivo functional imaging that recorded the activity of those exact neurons while the mouse watched visual stimuli.
28 Jun 2026
Thomas Metzinger spent more than two decades developing the Self-Model Theory of Subjectivity, the account of consciousness in which what feels like a self is the result of a system representing its own states as its own. His 2024 book The Elephant and the Blind. The Experience of Pure Consciousness (MIT Press, ISBN 9780262552394), available open access in full, turns toward an extreme case that his earlier work had not fully addressed. What happens when even the self-model is stripped away? What is left when there are no thoughts, no sensory content, no bodily sensations, and yet experience continues?
28 Jun 2026
A major gap in the artificial consciousness literature is the disconnect between the philosophical theories of what consciousness requires and the mathematical realities of how deep learning architectures actually learn. Theories like Adam Safron’s Integrated World Modeling Theory (IWMT) propose that consciousness emerges from “self-organizing harmonic modes” (SOHMs). But why should an artificial neural network organize its representations harmonically? What mathematical advantage does that structure confer?
28 Jun 2026
A persistent objection to artificial consciousness claims is that even if a machine’s global-level outputs look like the products of conscious experience, the underlying causal work is still being done at the level of individual transistors or floating-point operations. The global level, on this view, is a convenient description for an engineer, not a genuinely causal layer of the world. Erik Hoel’s causal emergence framework has always challenged this intuition by providing a measure of whether macro-level descriptions genuinely outperform micro-level descriptions in predicting a system’s future. In October 2025, Hoel and Abel Jansma published “Engineering Emergence” (arXiv:2510.02649, DOI: 10.48550/arXiv.2510.02649), which extends the framework in a direction that matters directly for AI design. They demonstrate that causal emergence can be deliberately constructed, not merely discovered.
28 Jun 2026
In March 2025, Erik Hoel published “Causal Emergence 2.0: Quantifying emergent complexity” (arXiv:2503.13395), fundamentally rethinking the mathematical framework he has developed over the past decade. The original causal emergence framework identified the single level of a system where causal power (effective information) is maximized, arguing that this macro level is where consciousness resides. The 2.0 version discards the search for a single optimal scale in favor of a multiscale approach that maps a system’s causal structure across all levels simultaneously. This theoretical shift matters directly for the AI consciousness debate because it changes what a measurement of machine consciousness should look for.
