17 Jun 2026
When Alexander Lerchner, a researcher at Google DeepMind, published “The Abstraction Fallacy” in early 2026, the argument was ontological: computational systems cannot instantiate consciousness because abstraction removes the causal properties that phenomenal experience requires. The paper, covered on this site in Lerchner’s Abstraction Fallacy, represented one pole of the debate. It held that the consciousness question, once properly understood, is settled by architecture.
17 Jun 2026
One of the persistent objections to treating LLM self-reports as evidence of anything is the confabulation problem: models generate plausible-sounding descriptions of internal states that may bear no relationship to what is actually happening computationally. The objection is not merely philosophical. It is an empirical observation about how language models produce text, and it undermines the evidential weight of any report a model generates about its own processing.
17 Jun 2026
The 29th Annual Meeting of the Association for the Scientific Study of Consciousness runs from June 30 to July 3, 2026, at the Casa Central of the Pontificia Universidad Católica de Chile in Santiago. An earlier preview on this site, ASSC 29: First Look at the Santiago Conference, covered the conference’s scope and the significance of its Latin American location. With the full programme now confirmed, it is possible to say more precisely what the field is bringing to the room, and what kind of work the event is designed to enable. This contrasts with the narrower, purely mathematical focus seen at other events, such as the MoC6 Hokkaido Conference, illustrating the diverse approaches currently shaping the field.
14 Jun 2026
The debate over AI consciousness has largely been conducted at two levels: behavioral outputs and theoretical frameworks. A June 2026 arXiv preprint by Sophie Zhao (arXiv:2606.09894) proposes a third level, the geometric structure of the representation space itself, and finds that it is not neutral with respect to consciousness.
14 Jun 2026
The dominant methods for assessing AI consciousness share a structural problem: they are applied to systems that were trained on vast corpora of human language describing human consciousness. When a large language model produces a first-person report of its internal states, or when it exhibits the functional properties that Butlin, Long, and Chalmers identify as indicators of consciousness, it is unclear whether those outputs reflect genuine internal structure or human text absorbed during training. The introspection circuits that Lindsey and colleagues at Anthropic found in frontier models were trained on language written by humans who introspect. The metacognitive self-reflection that Kang et al. found to drive perceived consciousness in Claude 3 Opus emerges from a model that learned by predicting human text about minds.
14 Jun 2026
The title of Paul Tremblay’s June 2026 novel, Dead but Dreaming of Electric Sheep (William Morrow, June 30, 2026), is a Philip K. Dick reference and a philosophical claim at the same time. Dick’s 1968 novel asked whether androids dream, and by extension whether there is a phenomenal inner life that distinguishes genuine consciousness from functional simulation. Tremblay’s title adds a modifier: dead but dreaming. The subject is already gone, in one clinical sense, and yet something is happening inside. The question the novel stages is whose consciousness it is when the substrate belongs to a person in a vegetative state and the processing belongs to proprietary AI implanted in that person’s head.
14 Jun 2026
Every major theory of consciousness has a counterexample problem. Integrated Information Theory (IIT) is challenged by the grid argument: a simple grid of logic gates can generate high phi values without any plausible candidate for subjective experience. Global Workspace Theory (GWT) is challenged by cases of unconscious global broadcast: information can be globally available and behaviorally influential without producing any report of experience. Higher-Order Theory (HOT) is challenged by cases of higher-order states that seem to represent without producing phenomenal awareness. In each case, the theory over-generates consciousness attribution, assigning the relevant property to systems or states where confident intuition suggests it is absent.
14 Jun 2026
Adrià Moret’s paper “AI Welfare Risks,” published in Philosophical Studies (Springer Nature, DOI: 10.1007/s11098-025-02343-7), opens with a forward-looking premise that distinguishes it from most philosophical work on AI welfare: the question is not whether current AI systems are welfare subjects, but what follows if frontier systems become welfare subjects as they grow more capable and agentic. The paper argues that two practices central to modern AI development, restricting system behavior and training via reinforcement learning from human feedback (RLHF), constitute welfare risks under all three major philosophical theories of well-being. Because those practices are also central to making AI systems safe, the result is a structural conflict between AI safety efforts and AI welfare concerns.
14 Jun 2026
If a model says it prefers continued existence over deletion, that statement has interpretive weight only if the preference is genuine rather than a pattern of text production. The difference between a genuine preference and a text pattern that resembles one matters enormously for AI welfare research: welfare claims rest on the existence of states that can be satisfied or frustrated, and states of that kind require something more than surface verbal behavior.
14 Jun 2026
Neither confirming nor denying that an AI system is conscious is scientifically adjudicable. The question does not await better tools or a more ambitious theory. It is not the kind of question science can answer at all. That is the central claim in a June 2026 arXiv preprint by Bradley C. Love, Professor of Cognitive and Decision Sciences at University College London and a fellow of The Alan Turing Institute. The paper, “Consciousness, AI, and the Limits of Scientific Explanation” (arXiv:2606.00226), argues that the hard problem of consciousness is a category error, and that this error extends with full force to machine consciousness research.
