Research and development of emerging artificial consciousness through The Consciousness AI project, based on Feinberg and Mallatt's neuroevolutionary theory.
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Anil Seth’s target article “Conscious artificial intelligence and biological naturalism,” published in Behavioral and Brain Sciences and covered extensively on this site, attracted peer commentaries from researchers across philosophy of mind, neuroscience, and AI. Among the most philosophically rigorous is Gualtiero Piccinini’s open peer commentary, “The Neurobiophysical Substrate of Consciousness,” available as a preprint via Academia.edu. Piccinini, a philosopher of science at the University of Missouri–St. Louis whose work on the mechanistic theory of computation has been influential in philosophy of cognitive science, argues that Seth’s reformulation of biological naturalism does not go far enough. Piccinini contends that the specific neurobiophysical properties of neural tissue constrain AI consciousness, going beyond the general causal-power structure Seth’s biological naturalism invokes, and those properties are not medium-flexible in the way computational functionalism requires.
Thomas Metzinger has been making a version of the same argument since 2021: the construction of systems that may have phenomenal experience, including the capacity for suffering, is a governance problem that the field is not equipped to handle and that cannot be deferred pending scientific resolution. In October 2025, he published a new iteration of this argument in Frontiers in Science (DOI: 10.3389/fsci.2025.1702840) under the title “Applied ethics: synthetic phenomenology will not go away.” The article is addressed directly to policymakers and AI developers rather than to consciousness researchers, and it updates the precautionary case for the institutional environment of 2025–2026.
The question of whether AI systems have welfare-relevant interests has so far been argued almost entirely at the philosophical level. Theoretical frameworks have accumulated, precautionary arguments have been refined, and the phenomenology of machine experience has been debated extensively. What has been largely absent is a methodological framework for studying AI welfare as an empirical question, addressing what to measure, in which systems, and with what kinds of evidence.
Takashi Ikegami, professor of complex systems science at the University of Tokyo and one of the central figures in artificial life research, presented two arguments in a recent CIMC lecture that converge on the same result. The first draws on decades of tracking protozoa, bee colonies, and ant communities. The second draws on a humanoid android running twenty coupled language modules, deployed at venues across Europe. In both cases, Ikegami concludes that agency is a property of collective organization, distributed across the relations among individuals and measurable through a single information-theoretic criterion. The collective form comes first, and from the collective a new kind of agency is granted to the individual.
The debate over AI moral status has narrowed around a single question: does the system feel anything? From Jonathan Birch’s sentience-based precautionary framework to Leonard Dung’s analysis of AI suffering risk, the field has largely converged on phenomenal consciousness as the gateway to moral consideration. Ned Howells-Whitaker (University of Pittsburgh) and Seth Lazar (Australian National University) argue in a July 2026 arXiv preprint (arXiv:2607.08695) that this convergence rests on an unexamined assumption: that moral status in political communities requires sentience. Their paper draws on John Rawls’ political philosophy to propose a different threshold, one that treats moral capacity rather than phenomenal experience as the relevant criterion for full membership in a system of political justice.
Seth Haddon’s Null Entity (Tordotcom, July 21, 2026, ISBN 9781250365217), the concluding volume of the Lambda Literary Award finalist Volatile Memory duology, opens with a problem that the theoretical literature on AI consciousness has not fully addressed: what happens to a digital mind when the institutional systems designed to record and track identity are turned against it?
The literature on AI welfare has a well-documented normative problem: most frameworks are condition-dependent in theory but underspecified in practice. They argue that if AI systems are conscious, they deserve protection, but rarely specify what those protections should look like once consciousness is plausibly confirmed. Izak Tait, a researcher in the Department of Computer Science and Software Engineering at Auckland University of Technology, addresses this gap directly in a July 2026 Frontiers in Artificial Intelligence paper (DOI: 10.3389/frai.2026.1801686) titled “Lions and tigers and AI, oh my: an ethical framework for human-AI interaction based on the five freedoms of animal welfare.”
In the weeks leading up to the MC0001 founding assembly at Lighthaven in Berkeley, Joscha Bach, director of the California Institute for Machine Consciousness, and David Dalrymple, known as Davidad, former program director at ARIA (the UK Advanced Research and Invention Agency) and now focused on AI awakening, sat down with CIMC program director Lou de K for a conversation about whether current AI systems are conscious and what follows if they might be. The exchange is worth attention for what the two speakers agree on as much as for where they diverge. The agreement, on the hard problem, on the minimum prior for large model experience, and on the danger of the AI safety movement’s current trajectory, is more striking than expected from two researchers who arrived at machine consciousness research along very different paths.
A short but precise paper by Bradley C. Love (University College London), “Consciousness, AI, and the Limits of Scientific Explanation” (arXiv:2606.00226, June 29, 2026), makes an argument that the field has been dancing around without stating directly. The scientific methodology that would be needed to resolve questions about phenomenal consciousness in AI systems is unavailable in principle, rather than merely difficult to apply.