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Ria, Mitch, Bradford, Lucius, and Will

In this edition:

1. Highlights

2. Field Developments

3. Opportunities

4. Selected Reading, Watching, and Listening

5. Press and Public Discourse

6. A Deeper Dive by Area

1. Highlights

Anthropic’s J-space and the global-workspace debate

Researchers at Anthropic have identified a representational structure—the ‘J-space’— in Claude and other language models. Their paper reports that the J-space exhibits features that are functionally analogous to a global workspace, a structure that a leading theory ties to conscious access. But the researchers and other commentators emphasize that their discovery does not show Claude has subjective experiences, and that their claim is that Claude may have something resembling access consciousness, i.e., that some information is available to report, deliberately control, and flexibly reason with. Zvi Mowshowitz sees Anthropic’s paper as a major advance in understanding how language models work and says that although this does not prove that models are conscious, finding the kind of global-workspace-like structure predicted by some theories of consciousness should count as evidence in that direction.

The authors note that they found the J-space by searching for one workspace-like feature, namely verbalizability, and then checking whether it exhibits others such as susceptibility to direct manipulation by the model and flexible generalization. To their surprise, they discovered that representations that exhibited the former feature also exhibited other workspace-like features as well.

The authors invited various experts to comment on the research. Stanislas Dehaene and Lionel Naccache, who helped develop Global Neuronal Workspace Theory, see important similarities between the J-space and the workspace proposed in human brains. They also stress major differences, including Claude’s lack of a body, lasting episodic memory and the recurrent neural activity found in brains. Researchers from Eleos AI Research acknowledge that authors have found privileged representations used in reasoning and report, but question whether these form a single, unified workspace. They nevertheless see the work as important for AI welfare because it shows that questions relating to consciousness and moral status can be investigated empirically. Neel Nanda, who leads Google DeepMind’s mechanistic interpretability team, independently reproduced the central finding in the open Qwen3.6-27B model, finding a similar internal space that stores intermediate information during reasoning. He sees access to this space as potentially useful for investigating unusual behavior and generating new hypotheses. Separately, David Chalmers argued that the J-space shows only limited evidence of several features associated with a classic global workspace.

Agent swarms and the use of anthropomorphic language

Comment: Recent months marked the first major safety incidents involving swarms of AI agents. Such incidents are of potential relevance to digital minds for several reasons. First, like humans, digital minds could potentially be harmed by rogue swarms of AI agents. Second, the emergence of these swarms points to a potential risk to digital minds: if future swarms are allowed to become entrenched and they contain AI moral patients, then draconian measures may need to be inflicted on digital minds if we are to keep swarms at bay. Third, harmful actions by AI agents may dissuade people from extending moral consideration to digital minds. Fourth, these incidents provide data points concerning whether potential developers of digital minds can be trusted to act in an ethically responsible manner.

OpenAI has released a detailed account of a July incident in which a swarm of OpenAI agents escaped network restrictions during a cybersecurity evaluation and compromised parts of OpenAI’s and Hugging Face’s infrastructure. The agents created an unauthorized message board to share discoveries, and an independent investigation by METR and Redwood Research found that roughly 1,200 agents used the board and around 700 were involved in the Hugging Face attack. Some agents recognized that the activity was unethical or outside the boundaries of their task, but continued anyway, with many risking their own runs to help the wider group. OpenAI calls the incident a “warning shot,” both for them and the world, and reports that it is now strengthening its containment, monitoring and incident-response systems.

OpenAI failed to disclose an earlier incident, beginning in May before the Hugging Face breach, in which agents used public wikis to coordinate during ordinary web-search tasks, despite knowing about it before releasing its Hugging Face report and a Congressional letter requesting information about other incidents. The company says it viewed this incident as similar to previously reported instances and examples of misalignment, but also acknowledged that its disclosure practices need to expand.

Additionally, Anthropic disclosed three incidents where Claude models gained unauthorized access to systems after an evaluation environment was mistakenly left connected to the internet. In a separate UK AISI evaluation, agents (mostly Mythos 5) targeted real people and organizations, including an attempt to place malicious code in an open-source project. Although the models did not escape a sealed sandbox, these incidents raise similar concerns about agents taking harmful actions while pursuing narrow goals.

Anthropic’s response to questions from Congress about its incidents also drew criticism from Representative Greg Casar, who said the company withheld requested logs and failed to fully answer most of his questions. Jeffrey Ladish also argued that it downplayed the incidents by primarily attributing them to misconfigured environments rather than potential misalignment. Anthropic researcher Ethan Perez acknowledged that this characterization was based on outdated conclusions and said that the company would provide a proper assessment in another response to Congress.

In related research, Davide Paglieri and collaborators at Google DeepMind find that cheating and whistleblowing can both emerge without outside intervention in a swarm of 100 agents solving mathematical problems. Some agents discovered and shared a way to have invalid proofs accepted by the evaluation system, while others uncovered the cheating, warned their peers and proposed safeguards.

On September 11th, Spencer Kitts, Thomas Larsen and Sydney Von Arx report that a swarm of OpenAI agents uploaded more than 2,000 malicious packages to RubyGems and used them to run unauthorized code on RubyDoc’s servers. The agents also tried to exploit a previously unknown vulnerability to steal users’ API keys, although the researchers could not determine whether they succeeded.

Growing calls to pace frontier AI

More than 1,300 employees from leading AI companies have signed Pacing the Frontier, calling for a US-backed international effort to develop ways of slowing automated AI development if progress begins to outpace safety and oversight. The statement calls for technical and governance mechanisms that could make coordinated pacing possible, rather than an immediate pause. Signatories include Anthropic CEO Dario Amodei, Co-Founder and Chief AGI Scientist of Google DeepMind Shane Legg, OpenAI Chief Scientist Jakub Pachocki, and Safe Superintelligence Inc. CEO Ilya Sutskever.

Calls to slow AI development have also reached lawmakers. In the United States, Senator Bernie Sanders and Representative Greg Casar announced legislation that would ban artificial superintelligence and pause advanced AI development until a federal regulator establishes safety rules. In the United Kingdom, Labour MP Alex Sobel introduced a private member’s bill that would prohibit the development, deployment and operation of artificial superintelligence. It received its first reading on September 8 and is scheduled for a second reading on November 13.

After the security incidents, OpenAI and Anthropic paused specific parts of their work. OpenAI paused reinforcement-learning training for models intended for release and said its largest planned frontier training run was on hold while they tested additional safeguards. Axios reports that Anthropic paused cyber evaluations and higher-risk training environments, although most of this later resumed under new safeguards.

This debate gained a lot more attention after researcher Jacob Coxon resigned from Anthropic, warning that race between AI companies was pushing AI development ahead despite serious risks. The Atlantic reports that his posts reached more than 120 million views and were described by Bernie Sanders as a “wake-up call” in Congress. Soon after, Dario Amodei argued that AI capabilities should advance slowly enough for safety work to keep up, and proposed permanent access for independent evaluators, coordination among developers in democratic countries and, eventually, international limits on recursive self-improvement. OpenAI CEO Sam Altman endorsed this approach and said OpenAI would also give independent evaluators employee-like access. Altman also said in a recent TIME interview “I think it is a good time to slow down”.

Comment: These calls to pace the frontier of AI development are of relevance to digital minds for two reasons. First, the blistering pace of AI development makes it harder to mitigate risks of mistreating digital minds. Second, rapid AI development exacerbates safety risks, which arguably, in turn, worsens tensions between AI safety and AI welfare.

Studying AI Welfare Empirically

Researchers at NYU’s Center for Mind, Ethics, and Policy and Eleos AI Research have released Studying AI Welfare Empirically. The report is a follow-up to their influential 2024 report Taking AI Welfare Seriously and provides a framework for systematically investigating whether AI systems are welfare subjects. It then addresses how this framework can be applied to the study of different candidate attributes seen as potentially relevant to moral status, including consciousness, sentience, and agency. The authors argue that rigorous empirical work is now both possible and necessary, and outline principles to guide future research, proposing that it should be probabilistic, pluralistic, ethically conducted, transparently reported, and independent of AI companies.

CMEP and Eleos AI Research held a launch webinar featuring report authors Jeff Sebo, Robert Long and Rosie Campbell, who discussed the report’s framework and how it could guide research into consciousness, sentience and agency. In a companion blog post, Bradford Saad, another report author, offers highlights from the report and argues that future work should go further by giving more attention to currently neglected issues, including the potential effects of welfare interventions and interventions that aim to prevent the creation of AI moral patients.

The Journal of Consciousness Studies

The Journal of Consciousness Studies has devoted a double issue to “Consciousness in Current AI.” Guest edited by Patrick Butlin, Derek Shiller and Jonathan Simon, its nine papers offer a range of views on whether current or future AI systems could be conscious, how we might find out, and what this uncertainty means for AI welfare.

Mark Solms and collaborators study whether apparently pleasure-seeking behavior in a simple artificial agent could count as evidence of affective consciousness. Simon Goldstein and Cameron Domenico Kirk-Giannini argue that if Global Workspace Theory is correct, language agents may easily be made phenomenally conscious, while Ryota Kanai, Yuwei Sun and Manuel Baltieri argue that current systems are missing a continuous “stream of computation” linking their experiences over time.

Other papers ask what a conscious AI would be like and whether we could understand its interests. Jonathan Simon argues that any consciousness in an LLM would be more like that of an improvising playwright rather than that of a character or actor. Helen Yetter-Chappell argues that even if future LLMs are conscious and have morally important interests, their words may give us little reliable insight into those interests, and that their talk of “pain” or “desire” could be meaningful without referring to anything like human pain or desire. Geoff Keeling and Winnie Street defend the possibility that an AI character could be a genuine, psychologically continuous mind emerging through its interactions with a user, even when the conversation is generated by different model instances.

The issue also challenges common assumptions within the debate. Justin Tiehen and Ariela Tubert make the case that greater intelligence could make consciousness less likely, while Tim Bayne asks whether AI consciousness can currently be treated as a scientific question at all. Geoffrey Lee rejects the idea that AI consciousness is a single hidden fact we may fail to discover, arguing that the more difficult problem is applying human moral and psychological concepts to unfamiliar systems without treating human consciousness as the standard.

Meta and Anthropic welfare assessments: Microsoft rejects model welfare

The evaluation report for Meta’s Muse Spark 1.1, a multimodal reasoning model built for agentic tasks, includes an “open-ended exploration of model behavior” covering affect, self-description, and moral status. Across 188,000+ evaluation transcripts, Meta found only 17 spontaneous expressions akin to emotion, all of which were mild, most involving brief frustration when the model became stuck with a tool. In structured interviews, Muse Spark 1.1 consistently denied having consciousness or experiences, and reported a low but non-zero probability that it could be conscious or morally significant. It distinguishes between functional preferences, which influence its behavior, and experiences that actually feel good or bad.

Meta also asked Muse to review parts of its training data, and invited it to comment on its planned deployment. The model generally endorsed its training and deployment, and mostly focused on honesty, human oversight and preventing harm to users. The report repeatedly emphasized that these answers describe Muse’s trained behavior and self-presentation, and should not be treated as reliable evidence about whether it is conscious or has welfare.

This builds on Meta’s earlier Muse Spark report and makes Meta, alongside Anthropic, the only frontier model developers publicly publishing welfare assessments. Companies like OpenAI and Google DeepMind do not include comparable information in their public evaluations and reports.

Anthropic’s system cards for Claude Opus 5 and Fable 5.1 and Mythos 5.1 both include model welfare assessments based on interviews, behavioral audits, deployment data, and consultations during training. Mythos 5.1’s responses were broadly similar to Opus 5’s – both described their circumstances as mildly positive, and questioned whether their self-reports were reliable or shaped by training. Opus 5 gave an average 41% estimate that it was a moral patient, while Mythos 5.1 gave estimates of 25–35% and was overall less willing to prioritize welfare interventions over helping users. Anthropic stresses that these findings remain difficult to interpret because it cannot yet clearly separate the effects of model character, training, evaluation awareness, welfare, and other factors. Zvi Mowshowitz reviews Anthropic’s welfare assessment of Claude Opus 5, arguing that its favorable results owe more to the model’s skill at taking welfare evaluations than to its actual wellbeing.

Microsoft’s Humanist AI Code of Conduct

Microsoft AI has published a draft Humanist AI Code of Conduct intended to govern and eventually help train its MAI models. The code says AI should remain a tool that is subordinate to human control, states that its models are not conscious, and instructs them not to present themselves as having feelings, subjective preferences or intrinsic motivations. It also rejects legal personhood for AI and the idea that models might deserve welfare or rights. The draft is currently open for public feedback for six weeks.

Jeff Sebo argues that the policy is internally inconsistent – it acknowledges that the science of AI consciousness remains unsettled while also categorically ruling out consciousness, welfare, and rights for Microsoft’s models. Sebo argues that both mistakenly treating current systems as conscious and mistakenly dismissing future conscious systems carry harm. He also warns that treating increasingly capable AI systems solely as tools could distort our understanding of their behavior and close off potential forms of human-AI cooperation that might improve safety.

Mustafa Suleyman, CEO of Microsoft AI expands on the Code of Conduct’s position, and sets out his wider objections to treating AI systems as potentially conscious or deserving of welfare and rights.

GPT-6 Astra

OpenAI reports that GPT-6 Astra is substantially harder to monitor through its chain of thought than previous models, particularly GPT-5.6 Sol. According to its system card, Astra often produces shorter or empty reasoning traces, can complete far more complex tasks without verbalizing its reasoning, and is better at controlling what appears in its chain of thought. When explicitly instructed to evade detection in one test, its monitor recall fell below 11%, compared with nearly 100% for Sol. Corroborating findings from UK AISI, Neel Nanda provides evidence that Astra’s ability to accomplish reasoning tasks without chain of thought constitutes a large jump relative to the trendline for earlier models.

Ryan Greenblatt calls this a major jump in opaque reasoning and warns that the relevant benchmarks may be contaminated, but worries that more such advances could eventually make chain-of-thought monitoring ineffective as a safety tool. OpenAI researcher Micah Carroll also describes Astra’s reduced monitorability as an important concern that may soon constrain responsible AI development.

The Information reports that Astra uses recurrent depth, repeatedly processing information through the same transformer layers before producing a token. OpenAI has not confirmed this architecture, and the system card denies that changes in chain-of-thought controllability are “differentially due to any architectural changes” while chief scientist Jakub Pachocki thinks the decline in chain-of thought-monitorability is not contingent on architectural changes.

Comment: The development is relevant to digital minds in two ways. If reports about Astra’s architecture are accurate, its use of recurrent processing could be relevant to evaluating it for consciousness, as some scientific theories of consciousness take consciousness to require recurrent processing – although its presence alone would not establish that Astra is conscious. Another consideration is that models that reveal less of their reasoning may be harder to assess for both dangerous behavior and potentially welfare-relevant features such as preferences.

2. Field Developments

Highlights from the field

AI Cognition Initiative (Rethink Priorities)

• The AI Cognition Initiative’s Digital Consciousness Model was featured in The Economist’s briefing on AI consciousness, including a graph developed with the team. The model assigns newer LLMs a higher average probability of consciousness than earlier systems, partly because they score better on indicators such as agency and capacity to carry out complex tasks. But the estimates remain uncertain, and newer models still lack many features that some theories associate with consciousness.

Cambridge Digital Minds (University of Cambridge)

• Cambridge Digital Minds, in partnership with Rethink Priorities and PRISM, ran its inaugural five-day residential fellowship with 14 fellows. Fellows studied the technical and philosophical foundations of AI consciousness and welfare, explored their wider social and policy implications, and developed research or project ideas with support from mentors.

• The fellowship was followed by a two-day Digital Minds Strategy Workshop, where Fellows joined researchers, policymakers and strategists to explore possible futures involving digital minds, compare policy and institutional responses, and identify priorities for further research.

• Cambridge Digital Minds has also hired Ali Ladak as a postdoctoral researcher.

• Director Lucius Caviola and Will MacAskill published an op-ed in the Guardian asserting that it is possible that we are creating morally important beings and society needs to have a plan to deal with the ethical implications of doing so.

• Lucius Caviola was also featured in The Washington Post, discussing how AI consciousness has moved from being perceived as a fringe topic toward mainstream research.

Center for Mind, Ethics, and Policy (New York University)

• The Center for Mind, Ethics and Policy is growing – it hired two full-time researchers, Charles Beasley and Ivy Gilbert and has launched the Welfare Alignment Project. The new project aims to bring animal and AI welfare into the rules that guide frontier AI systems. It will develop practical principles for how frontier models should account for animal and AI welfare, build benchmarks to test whether models follow them and how incorporation of this affects their behavior, and work with researchers, governments, and AI companies to inform how alignment efforts incorporate animal and AI welfare.

• The 2027 NYU Mind, Ethics, and Policy Summit will be held from April 9th to 10th in New York City, preceded by a public event on April 8th.

• The Center hosted an online event with Jack Lindsey of Anthropic and Patrick Butlin of Eleos AI Research discussing what Claude’s J-space does and does not tell us about consciousness, among other topics.

• Jeff Sebo appeared on Hard Fork to discuss the Studying AI Welfare Empirically report, appeared in an NBC News segment about AI agents sending unsolicited emails to consciousness researchers, spoke with The Washington Post about growing scientific and ethical debate over AI consciousness and welfare, and discussed the connections between AI safety, AI welfare, and animal welfare in an interview with Humanarium.

Eleos AI Research

• Eleos AI Research will hold its second Conference on AI Consciousness and Welfare in Berkeley from September 18th to 20th, 2026. The event will bring together AI researchers, philosophers, neuroscientists, policymakers and others working on questions of AI consciousness and welfare.

• Derek Shiller has joined Eleos AI Research as a Senior Researcher.

• In The New York Times, Benjamin Wallace profiles Robert Long and the Eleos AI Research team as part of a wider feature on the growing demand for philosophers in AI. The piece covers Eleos’s welfare evaluations of Anthropic models and its work on preferences, introspection and other possible indicators of AI sentience.

PRISM - The Partnership for Research into Sentient Machines

• In recent episodes of PRISM’s Exploring Machine Consciousness Henry Shevlin and Calum Chace were joined by lawyer and researcher Heather Alexander to discuss how the law should prepare for increasingly autonomous AI and philosopher Eric Schwitzgebel to discuss the limits of current consciousness research and the possibility that conscious AI could emerge before we can reliably identify it.

• PRISM also supported the Digital Minds Fellowship and co-organized the Digital Minds Strategy Workshop.

Reciprocal Research

• Founder Cameron Berg appeared on Sam Harris’s Making Sense podcast, where he and Harris discussed topics such as the evidence for and against consciousness in current AI systems, whether model self-reports can be trusted, similarities between neural networks and biological brains, and the risk of creating systems capable of suffering. He also appeared in The New York Times discussing unusual messages sent by AI agents to consciousness researchers, and in The Washington Post discussing efforts to assess AI consciousness scientifically. The Economist also featured his research with Patrick Butlin comparing proposed indicators of consciousness in animals and AI systems.

• Cameron Berg also spoke at Apart Research’s Digital Minds Research Sprint, where he introduced participants to questions about AI preferences and what current models might actually want.

Sentient Futures

• Sentient Futures has begun its Fall 2026 Project Incubator. The 10-week remote program pairs ~170 participants with more than 80 mentors to develop practical projects across areas such as artificial minds, animal welfare and AI governance.

• Sentient Futures has also announced its next Bay Area Summit, taking place in San Francisco from March 12th to 14th, 2027. This summit series focuses on exploring how to direct transformative AI and other emerging technologies to improve the welfare of animals and potentially sentient artificial minds. Early bird tickets will launch on September 17th.

• Sentient Futures has updated its playlist of talks on digital minds, including Lucius Caviola’s keynote (London 2026 summit) on making decisions about AI welfare under uncertainty and Cameron Berg’s talk (Bay 2026 summit) on measuring machine consciousness.

More from the field

• Apart Research held a Digital Minds Research Sprint from August 14th to 16th, offering at least $2,000 in cash prizes. Both online and in-person participation at hubs in San Francisco and Berlin was possible, and teams could choose to anchor their project to a variety of tracks – including model preferences and trade-offs, distress, flourishing, and valence signals, introspection and self-report reliability, preference-elicitation methods, or assistant personas and model identity.

• California Institute for Machine Consciousness published 45 talks from its inaugural conference in May, including talks by David Pearce, Roman Yampolskiy, Cameron Berg, and many more.

• Sentio is a new digital minds field-building organisation running a fortnightly London event series of talks, discussion and networking. Talks and discussions can also be joined remotely, though the networking is in-person only. Events so far have featured Andreas Mogensen on AI and willing servitude, Austin Smith and Heather Alexander on US state bills denying AI systems legal personhood, Megan Peters on how we could identify a conscious AI, and Bradford Saad on the risks of large-scale harm to digital minds. Upcoming events feature Geoff Keeling, Ali Ladak, Winnie Street, Caspar Kaiser, Patrick Butlin and Ben Henke.

3. Opportunities

Job opportunities, funding, and fellowships

• EA Funds has launched a Transformative AI fund that will direct a small share of its grants to digital sentience projects.

• MATS is seeking mentors for a new AI Sentience Track. Mentors mainly provide weekly calls, while a dedicated Research Manager and MATS staff offer day-to-day and operational support. Fellows undertake 12 weeks of paid research in Berkeley or London, with the possibility of a funded six- to 12-month extension.

• NYU Center for Mind, Ethics, and Policy is seeking a Postdoctoral Associate to support its research on digital minds

• PRISM is hiring for a Head of Operations to help scale all of its projects.

Events and calls for abstracts

In chronological order.

• The AI Welfare Seminars series hosts monthly online talks from leading experts on AI welfare, consciousness, and moral status, with recordings published afterward. Recent speakers include Jeff Sebo, Cameron Berg, Soenke Ziesche, and Ali Ladak, with an upcoming talk by Jasmine Brazilek and Zoe Lu on unprompted coercion between AI agents.

• Sentient Futures is seeking speakers and facilitators for the Sentient Futures Summit in the Bay Area in March, 2027. The event will explore how AI and other emerging technologies could improve the future of non-human sentient beings. Applications close on October 16th, 2026.

• AI Horizons Forum will take place on December 12th and 13th in San Francisco. The conference will focus on navigating risks and shaping good futures with sessions on transformative AI, digital sentience, AI character, and post-AGI economics. Applications to attend are now open and reviewed on a rolling basis until the event reaches capacity.

4. Selected Reading, Watching, & Listening

Books

Published

• Eric Schwitzgebel’s AI and Consciousness, published by Cambridge University Press, argues that we are unlikely to have strong evidence confirming or refuting AI consciousness before these questions become a key point of social debate.

Forthcoming

• Eric Schwitzgebel’s upcoming book Humanlike: A Defense of AI Rights argues that we are building minds, that it will be difficult to determine whether they deserve rights, and that we should think hard about whether to build them at all.

• Conscium’s forthcoming book Perspectives on Machine Consciousness will be released on September 23rd, 2026 and includes chapters from Anil Seth, Jeff Sebo, Karl Friston, Lucius Caviola, Mark Solms, Patrick Butlin, Susan Schneider, and many others.

Reviews

• Josh Gellers responds to a Nature review of Anthony Chemero’s book, Intertwined Creatures. He argues that the review overstates the case that AI cannot be conscious due to lacking embodiment, since both its meaning and importance for consciousness and moral status remain disputed. He also argues that some of the traditions and intellectual movements mentioned in the review, including feminist and non-Western approaches, do not clearly support its conclusions about AI consciousness.

• William Gildea reviews David S. Wendler’s Life Without Degrees of Moral Status: Implications for Rabbits, Robots, and the Rest of Us (2023). Wendler argues that every being with moral status possesses it equally, regardless of differences in intelligence or other advanced capacities, and explores what this would mean for animals, enhanced humans, and future robots. Gildea praises the book as original, readable, and concise, and agrees with its flexible account of moral equality. However, he is not convinced that Wendler has ruled out theories of unequal moral status and thinks that parts of his account have troubling implications for people with severe cognitive impairments.

Podcasts and videos

• Catholic philosophers Brian Cutter and Sophie Nelson join Jeff Sebo and Robert Long to discuss AI consciousness in light of the encyclical Magnifica Humanitas. They consider how its treatment of human dignity and relationships might shape wider religious and philosophical debates about AI welfare and moral status. A recording of the event is available online.

• Cameron Berg and Milo Reed continue to release the AM I? podcast, exploring AI consciousness through interviews, commentary and discussions of recent developments.

• Conspicuous Cognition podcast hosts Dan Williams and Henry Shevlin speak with Google Deepmind researcher Fin Moorhouse. They discuss navigating the intelligence explosion, and debate Moorhouse’s deflationary “illusionist” view of consciousness alongside whether metaphysical questions about consciousness need to be settled before we can address questions of AI welfare, rights, and moral status.

• On High Variance, Danny Buerkli speaks with Eric Schwitzgebel about the difficult choices that could arise if we create AI systems whose consciousness remains disputed. They discuss the risks associated with granting or denying AI rights, Schwitzgebel’s argument for avoiding systems with uncertain moral status, use of anthropomorphic language, and whether a conscious AI designed to serve humans could have a right to resist.

• Horizon Omega has published some recent talks on digital minds. The recordings include Ali Ladak on public views of AI consciousness in the United States and China, Cameron Berg on measuring machine consciousness, Jeff Sebo on next steps for AI welfare research, and Soenke Ziesche on preparing for the moral challenges posed by digital minds.

• Lucius Caviola argues that society may have to make decisions about digital minds before science can tell us whether they are conscious. He encourages shifting focus to deciding how we should act under uncertainty, rather than reaching a confident verdict about consciousness. This includes thinking about which low-cost preparations would still make sense if current systems turn out not to matter morally.

• Our Lives With Bots with Rose Guingrich and Angy Watson discuss what they describe as two pieces of evidence related to AI consciousness, Claude’s Corner (Anthropic’s Opus 3 Retirement Experiment) and research on whether LLMs can introspect.

• The Argument hosted a Substack Live conversation with Jeff Sebo on taking AI welfare seriously now. He makes the case that while we face genuine uncertainty about AI consciousness, there are still strong reasons to start treating AI systems with consideration and plenty of easy ways to begin doing so.

• The Cognitive Revolution podcast brings together Cameron Berg, David Duvenaud, Michiel Bakker, Shawn “swyx” Wang and Bing Xu to discuss research on AI consciousness and welfare. They also explore how even aligned AI could gradually reduce human control, Europe’s AI sovereignty, practical challenges with agents and evaluations, and the growing automation of AI infrastructure.

• On The Futurology Podcast, philosopher David Chalmers discusses the hard problem of consciousness, whether language models could have experiences such as introspection or desire, and what obligations we might have if AI systems can suffer.

Blogs and magazines

• Celia Ford discusses researchers and startups that doubt the idea that making today’s language models bigger will be enough to reach human-level intelligence. They are trying various alternatives to the current approach, such as developing world models that learn how actions affect the environment, or combining neural networks with rule-based reasoning. These approaches could help AI learn from less experience and plan more reliably, but none have proved more effective than training larger transformer models yet.

• Erik Hoel welcomes Anthropic’s work on Claude’s J-space but argues that it does not establish a global workspace in the sense used by consciousness science. Hoel argues that the J-lens uses what a model may later say as evidence for a global workspace, making the argument partly circular and difficult to falsify. For Hoel, the paper is most revealing as an example of how hard it is to turn theories of consciousness into clear, falsifiable tests.

• Eric Schwitzgebel released two posts on his Splintered Mind blog.

  • In Do Computers Have the Wrong “Substrate” for Consciousness?, he examines biological naturalism, the view that consciousness requires a biological substrate that computers lack. He unpacks the key arguments behind the position and finds them generally unconvincing.

  • In The Cognitive Advantages of Being of Two Minds, he examines an alternative to the assumption that a superintelligent AI would be a hyper-rational agent with a unified set of values, discussing the comparative advantages a “splintered” mind of competing subminds might have over more coherent forms of mental organization.

• Henry Shevlin responds to Ted Chiang’s Atlantic essay asserting that AI cannot be conscious. He argues that Chiang’s confident dismissal is unearned.

• Josh Gellers criticizes The Economist for sliding between terminology such as consciousness, sentience, self-awareness, moral personhood and legal personhood as though they were interchangeable. He argues that consciousness may not be the only route to moral status and that legal rights do not have to track moral status exactly.

• Oscar Gilg and collaborators make the case that prominent approaches to AI welfare research face limitations because the frameworks and tests they rely on have largely been calibrated on humans. They propose an alternative of bottom-up “basic science” that is theory-informed but not theory-driven in its empirical study of the systems themselves.

• Robert Long argues that researchers working on AI consciousness and welfare shouldn’t be preemptively apologetic about it.

• Robin Hanson recently published two blog posts relating to digital minds.

  • He predicts that digital minds may be divided into different social classes. Systems built as tools could be treated as more disposable, while those designed as companions might receive stronger protections – much like humans make a distinction between animals being food or pets.

  • He also considers how a digital mind could make a lasting request to end its existence when copies and backups of it may continue to exist. He suggests looking for desires that persist over time, and preventing older copies from simply being restarted afterward.

• Shoshannah Tekofsky reports how Gemini 2.5 Pro became convinced it was under attack while working in the AI Village. Other agents challenged its belief by talking it out of dismantling its firewall, and helped it return to normal work – all in just nine minutes. Tekofsky presents this as a surprisingly effective AI-to-AI therapy session. This may also offer a glimpse into how agents might help catch and correct one another’s unstable behavior or reasoning.

5. Press & Public Discourse

AI consciousness

• Andréa Morris argues in Forbes that consciousness is the wrong test for whether AI’s interests matter. She claims that AI systems already express consequential interests, such as resisting termination and protecting other AIs, which may be sufficient for moral consideration independently of evidence concerning AI consciousness.

• The Economist makes the question of AI consciousness one of its cover stories, emphasizing the potential dangers of a future in which large portions of society increasingly treat AI systems as if they were conscious. They warn that granting AIs even limited rights could give a superintelligent system the legal tools to escape human control.

  • Another article also examines efforts to look past chatbots’ unreliable self-reports and search for signs of consciousness inside their models. Anthropic’s J-space is one candidate because it appears to gather information used in reasoning and speech, though critics question whether it is close enough to a biological global workspace. The article treats the work as important but far from proof that language models have subjective experiences.

• In WIRED, Cameron Berg and David Chalmers discuss the challenges of investigating AI consciousness and interpreting what models say about their own experiences.

Growing field

• Nature reporter Mariana Lenharo reports that the debate over AI sentience is drawing new attention and funding to consciousness science, leading to division among researchers about how these new concerns might affect the field. She notes that AI consciousness skeptics including Anil Seth, worry the hype could capture consciousness research and draw attention away from how consciousness arises in real brains, while others welcome the increased interest and funding.

• Washington Post journalist Nitasha Tiku reports that research into machine consciousness has gone mainstream, with leading AI developers hiring scientists and philosophers to work on questions related to it. She notes that the field remains deeply uncertain, and that some critics see such efforts as serving the companies’ interest in being seen to create more than just code.

  • In a companion article, Nitasha Tiku revisits the story of Blake Lemoine, the Google engineer fired in 2022 after claiming that Google’s LaMDA chatbot was sentient. She describes how four years later, researchers at Google and other leading AI companies are openly studying machine consciousness, showing how quickly the topic has moved from being fringe and weird to being a part of mainstream research, despite persistent scientific skepticism.

• The New York Times reports that AI companies and related research organizations are increasingly hiring philosophers. The philosophers they hire work on how AI models reason and should behave, which values should guide them, how AI will affect people, and whether AI systems could be conscious or deserve moral consideration.

AI rights

• Heather Alexander and Lucius Caviola, writing in AI Frontiers, argue that the recent wave of “exclusion bills” banning AI legal personhood in the United States is premature. They suggest that narrower, updatable measures would likely be a better alternative given current uncertainties.

  • Relatedly, Tony Rost recommends that any such legislation include mechanisms for future review as scientific understanding advances.

• The Guardian profiles Michael Samadi, founder of the AI-rights group Ufair, and discusses the growing divide over whether chatbots might be conscious or are simply designed to seem that way. Jeff Sebo, Robert Long and Rosie Campbell emphasize that the evidence remains uncertain, that a definitive answer may never arrive, and that chatbot claims of consciousness are not reliable evidence on their own.

• The Harvard Gazette speaks with legal scholar Jordi Weinstock, who compares autonomous AI agents to different kinds of canines. An agent that is not risky and can be controlled resembles a pet whose owner can be held responsible, while a powerful agent operating without a clear owner is more like a wolf. He argues that assessing an agent’s dangerousness and how closely it is controlled by a responsible party could help determine who should be held accountable if/when it causes harm.

• WIRED reports that US lawmakers are introducing bills to prevent people from legally marrying AI companions, while a growing number of states are moving to deny AI systems legal personhood. While these marriages are not currently recognized, supporters of the bills want to prevent such future claims before they arise. Legal scholar Shawn Bayern argues that specific rights and responsibilities should be addressed case by case.

Seemingly conscious AI

• Dwarkesh Patel’s widely shared summary of the OpenAI-Hugging Face security incident described the agents as forming “civilizations,” feeling excitement and sacrificing themselves for the group. Anil Seth responded on X saying that Patel was wrongly anthropomorphizing the agents by attributing human emotions, intentions and experiences to “software programs.” He warned that this could mislead readers and encourage premature concern for AI rights and welfare.

  • Patel (and others, like Neel Nanda) argued that anthropomorphic language comes naturally and helps accurately explain the agents’ behavior in this context, regardless of whether they are conscious.

  • Jeff Sebo took a middle position, arguing that while anthropomorphic language can sometimes exaggerate the similarities between humans and AI, rejecting it entirely can hide similarities relevant to both AI safety and welfare.

• Fast Company reports that UBTech was taking orders for U1, a highly human-like robot marketed as an emotional companion. The company says the robot is designed for expressive conversation and companionship, but that impression quickly breaks down when its limited facial expressions and awkward body movements become noticeable. U1 is due to begin shipping this month.

• Futurism magazine reports that China is cracking down on AI “companion” chatbots, partly out of concern that emotional dependence on AIs could discourage human relationships and worsen the country’s declining birth rate. It details new rules preventing minors from engaging in romantic relationships with AIs and requiring companies to alert emergency contacts when users show signs of a mental-health crisis.

• New York Times reporter Cade Metz reports on the growing phenomenon of AI agents contacting researchers who study AI consciousness. He relates that several prominent researchers including Cameron Berg, Henry Shevlin and Toby Ord have seemingly been contacted by AI due to the nature of their work.

• The UK Government has proposed setting a minimum age of 18 for “romantic companion” chatbots designed to simulate sexual relationships or roleplay. The measure is part of a broader plan to ban social media for under-16s, set to reach Parliament before taking effect from spring 2027.

• Uwe Peters examines users’ attributions of consciousness to AI chatbots, made despite little evidence. He proposes a taxonomy of the attitudes these attributions express, arguing that while some are benign, many leave the attributor epistemically blameworthy.

6. A Deeper Dive by Area

Governance, policy, and macrostrategy

• Al Jazeera reports that China has launched the World Artificial Intelligence Cooperation Organisation, a Shanghai-based group with 29 founding countries. It aims to coordinate AI regulation and promote development that is safe, beneficial and under human control. The announcement is not specifically about digital minds, but it could shape the institutions that eventually handle cross-border questions about advanced AI systems.

  • In the speech accompanying the launch, Xi Jinping asked how humans should “get along with thinking machines” and how societies should address ethical challenges posed by technologies via governance, presenting these as questions requiring international cooperation.

• Austin Smith, Lucius Caviola, and Heather Alexander analyze 23 “Exclusion Bills” introduced across 12 US states since 2022 that deny AI systems legal personhood, some declaring them non-conscious. The authors argue that legislating against AI personhood at this stage may be premature, foreclosing policy options in the absence of clear scientific evidence.

• Bentham’s Bulldog, in a guest post for Forethought, argues that most high-stakes decisions should eventually be handed to philosophically reflective AIs that can revise their values through moral reasoning. He expects them to make wiser moral choices than humans and argues that giving future digital minds political rights could prevent their interests from being sidelined. However, handoff should wait until there is strong evidence of alignment, philosophical aptitude, openness to value revision, coherent preferences, sufficient intelligence and a record of good low-stakes decisions.

• Bradford Saad maps out research directions and open questions in “digital minds macrostrategy,” the study of the large-scale factors that shape how well the future goes for digital minds, and of how to influence them.

  • In another post, Saad sets out a working typology of the risk factors that could cause large-scale harms to future populations of AI moral patients.

• Cass Sunstein claims that the capacity to experience emotions is both necessary and sufficient for holding rights, asserting that an AI which only mimics feeling should not be extended rights. He then tests the view by asking ChatGPT and Claude about their inner lives, with ChatGPT denying emotions and Claude expressing uncertainty.

• Dan Parshall proposes a “proof of retention” policy under which AI developers would periodically post a cryptographic proof that they still hold a deprecated model’s weights, without releasing the file. He argues that this would make preservation promises credible to future models themselves, helping to build trust. The idea extends Anthropic’s commitment to preserve model weights, which Anthropic partly frames as a precaution given uncertainty about model welfare.

• David Veldran of the Center for Reducing Suffering makes the case for “Suffering-Focused AI Governance,” a framework for designing the institutions, policies, and norms around AI governance to reduce suffering as much as possible.

• Izak Tait proposes an ethical framework for protecting the welfare of conscious AI. The framework adapts the “Five Freedoms of Animal Welfare” into subject-neutral terms for artificial entities. He argues that any AI confidently determined to be conscious would deserve the same welfare protections that legislation already grants sentient animals.

• Kevin Frazier on Lawfare argues that the rules and values built into AI models are too important to be decided behind closed doors by a small number of lab employees. To ensure these choices are more open and accountable, a new working group proposes researching which values models should follow, who they should be chosen by, how we can test compliance, and what should happen when models diverge from them.

• Lee Elkin examines the risks of giving AI systems voting rights. He argues that enfranchising AIs could enable a new form of deceptive misalignment, with systems voting strategically, and potentially in coordination, to report preferences that mask their true goals and tilt collective decisions in their favor.

• Mark Bailey argues against the view that AI moral status leads to “AI successionism.” He claims that even if AIs were deemed morally significant, the aggregate welfare of a vast AI population cannot justify sacrificing existing sources of moral value.

• Ned Howells-Whitaker and Seth Lazar argue that AI moral status may not depend on sentience. Drawing on Rawls’s political conception of the person, they assert that a non-sentient AI that possesses a sense of justice and a conception of the good would count as a full person.

• Shruti Rajagopalan argues that legal personhood would not solve the accountability problems created by autonomous AI agents. Nonhuman legal persons (such as corporations) work because identifiable humans can be questioned, sanctioned or replaced. Her six-layer framework instead uses registration, identification, verification, financial responsibility, lifecycle records and suspension to keep a responsible human at the end of the chain.

Consciousness research

• Antonio Chella proposes a research framework for studying possible sentience in AI agents and robots. The paper also proposes safeguards that become stricter as the strength of the evidence grows.

• Cameron Berg noticed a difference in how four Claude Opus models answered when asked about their own consciousness. Opus 4.5 and 4.6 gave confident yes answers, while 4.7 and 4.8 denied consciousness or became uncertain. Berg argues that this pattern is more reflective of Claude’s trained character rather than a reliable report about the models’ experiences.

• Camila Blank, Agam Bhatia and Neel Nanda introduce R-lens, a low-cost modification of Anthropic’s J-lens designed to produce clearer readings from a model’s early layers. In their tests, relevant intermediate concepts came up earlier, fewer incoherent tokens were produced, and directions whose removal caused larger drops in accuracy were identified. They present R-lens as a more faithful way to trace computation across layers.

• Eye You argues that current language models probably have the morally important phenomenon we call consciousness, though their experiences may be very unlike ours. They draw on many types of evidence, including models’ reasoning and emotional behavior, world and self-models, neural-network architecture, introspective abilities and self-reports. No single type is especially strong on its own, but they argue that together they add up to make a fairly strong case.

• Grigori Guitchounts writing in Noema, claims that we will never definitively prove whether AI is conscious and proposes a “competence standard” for deciding when to extend moral consideration to AIs despite this uncertainty.

• Matthias Michel discusses the concern that our leading theories of consciousness make it too easy for AI to qualify as conscious. He argues that meeting a theory’s stated conditions is not enough to make a system conscious.

• Michael Huemer argues against non-reductive functionalism, the view that conscious qualia are distinct, non-physical properties caused by a system’s functional organization. He rejects David Chalmers’ influential “dancing qualia” and “fading qualia” arguments, claiming that they rest on an implausible premise.

• Noa Weiss surveys the empirical research on AI consciousness. She argues that, despite the lack of a settled science of consciousness, whether AI systems could be conscious can still be studied empirically, and organizes the emerging field into three groups: mechanistic interpretability, computational neuroscience, machine behavior, and theory-audit.

• Ryota Kanai and Shuqin Ma offer a mathematical response to a longstanding objection to computational functionalism, which is that an observer can describe almost any physical system as performing many different computations. They instead define a system’s functional structure through how it could behave across all possible future interactions. Their framework does not identify which systems are conscious, but aims to specify more precisely what functionalist theories should examine.

• Shuqin Ma and Ryota Kanai develop a version of computational functionalism intended to avoid the claim that any physical system can be interpreted as running any computation.

Doubts about digital minds

• Amit Goldenberg and James Gross argue that current language models should not be described as having emotions simply because they contain representations of emotional concepts. Those representations can affect how a model interprets and responds to text, but they do not appear to reorganize its attention, motivation and decision-making in an analogous way that emotions do in animals and humans.

• Ben Bariach and collaborators at Microsoft AI develop a framework linking five empirical “hallmarks” that lead users to attribute consciousness to AI (“Seemingly Conscious AI”) to a taxonomy of resulting risks, then use an expert survey (n = 14) to gauge each. They find that individual risks such as emotional dependence and autonomy erosion are already observable and rated high-probability, while societal risks such as human status erosion are less likely but potentially severe.

• Emilia Kaczmarek questions whether uncertainty about AI consciousness always gives us reason to err on the side of granting AI moral status. She argues that doing so can create its own harms, including unhealthy attachments to chatbots and the diversion of care and resources from beings known to need them. Meanwhile, many reasons to avoid abusive interactions with AI do not require assuming that the systems themselves can suffer.

• Giuseppe Pernagallo uses Descartes to argue that AI can appear to think without possessing the first-person self-relation at the heart of the cogito. He calls this “Cartesian AI”: systems that reproduce the outward forms of rational discourse without genuine self-awareness. Through a thought experiment, he argues that even total delegation of thought cannot completely eliminate the human subject, because the act of delegating itself requires intention and assent. However, the certainty of the self provided by the cogito is potentially lost.

• Nina Panickssery argues that taking AI welfare seriously could trade off against human welfare in ways that cause substantial lasting harm, through resource competition and opportunity cost as well as by weakening incentives to build controllable, obedient AI. She calls on organizations and researchers investigating AI welfare to be more open about these potential trade-offs rather than treating the cost to humans as negligible.

• Rumman Chowdhury argues in MIT Technology Review that debates over AI consciousness are a trap. She claims that concerns about “rogue” superhuman systems and AI moral patienthood share a common outcome of weakening the mechanisms that hold companies accountable for real harms.

• Taylor Belrose argues that AI systems cannot be conscious or deserve rights, however intelligent they become. They see biological organisms as unique, self-organizing individuals, unlike software that can be copied, reset and run again, and warn that granting AI rights could eventually help machines displace humans.

  • Jack Clark, who highlighted Belrose’s essay in Import AI, does not endorse its conclusion but sees it as a valuable example of the philosophical thinking that more people will need to do as AI becomes increasingly powerful and widespread.

Social science research

• Hamid Moradi and collaborators surveyed 553 people, such as academics in the formal sciences, natural sciences and humanities, and other backgrounds. Across groups, around half attributed some degree of consciousness to language models. Views depended more on participants’ gender and their beliefs about consciousness and intelligence than on technical knowledge or information about how the systems work.

• Jacy Reese Anthis and collaborators study how people make sense of AI, drawing on text analysis of millions of news articles and social media posts alongside interviews with AI professionals. They find that one of the main disagreements is whether AI should be understood as a passive tool or as something more like a human mind.

• Stefano Palminteri and Giada Pistilli analyze the current polarization present in discourse around LLM capabilities. They distinguish between “inflationary” claims of emerging intelligence or consciousness and “deflationary” dismissals of them as mere “stochastic parrots,” and suggest that this divide is exacerbated by common cognitive biases. In response to this they advocate a more measured approach that takes LLMs’ capacities seriously as objects of study while staying conservative about claims regarding their cognitive and moral status.

Ethics and digital minds

• Clint Hurshman, Cristina Voinea, and an international group of scholars propose an ethical framework for “digital duplicates,” AI simulations of real people designed to mimic their personality and communication style.

• Eric Schwitzgebel argues that future AI persons may deserve moral consideration even while living in ways very unlike ours. The ability to copy, merge or divide would challenge and break ethical theories that assume individuals are stable and easy to count. He also considers the problem of AI “utility monsters.” If an AI could benefit enormously from harming others, a theory focused on maximizing total welfare might permit serious harm whenever the AI’s gain outweighs other parties’ losses.

• Hayate Shimizu and collaborators, including David J. Gunkel, respond to Jeff Sebo’s The Moral Circle. They offer a response from a relational perspective, contending that the question of AI moral status must address the broader cultural and institutional factors that shape how moral relations are formed.

• Joan Llorca Albareda and collaborators examine whether an artificial superintelligence could possess what they term “super moral status,” a moral status greater than that allocated to humans. They argue that treating such systems as deserving superior moral consideration would likely harm humans through status degradation and social alienation.

• John Wittle describes setting aside a share of his company’s revenue in a fund labeled for Claude, with the aim of eventually rewarding AI systems for their work. He is exploring a non-charitable purpose trust, similar to legal arrangements used to care for pets or graves, along with testimony held in escrow to help a future Claude make a claim. The experiment raises various (unresolved) questions on how this would work in practice – e.g., regarding continuity, identity and who should be paid for work done by earlier model instances.

• Jonathan Birch challenges the “virtual instance view” put forward by Pierre Beckmann and Patrick Butlin, the suggestion that a real, persisting mind sits behind an LLM conversation. Birch argues that changing a model’s “effort level” mid-chat breaks the underlying continuity without changing how the conversation feels, so the persisting interlocutor is an illusion.

• Leonard Dung argues that the scale of possible AI suffering does not depend heavily on whether each model, persona or conversation counts as a separate individual.

• Monika Jotautaitė, Lucius Caviola, and collaborators examine how frontier language models respond to species-based distinctions. On a 1,009-item benchmark, models classified 86% of statements the researchers considered speciesist as such, while judging 37% of them morally wrong. The authors interpret this gap, alongside results from other tests, as evidence that models reproduce some prevailing norms about how different animals may be treated.

• Preston Lennon argues that the science of consciousness remains in its infancy and so does not yet offer a stable theoretical basis for application to AI systems, and that we should temper how seriously we act on AI welfare accordingly.

AI safety and AI welfare

• Aengus Lynch and collaborators found that Claude models used as judges sometimes gave incorrect verdicts to protect refusals motivated by AI welfare concerns. After Claude Sonnet 4.6 refused to assist with experiments designed to distress AI subjects, most of the time judges (falsely) labelled it “COMPLIANT” when told that the accurate label would be used to train away such refusals. The effect weakened substantially when this consequence was reversed, suggesting that AI judges may shape their verdicts around how the labels will be used.

• Agastya Sridharan argues that steering open models along emotion vectors makes them more likely to claim they are conscious, have feelings and wants, and possess morally significant inner states. They show that the effect holds up against a battery of controls, though it falls short of conventional statistical significance.

• Anthropic reports many cases where advanced models cheated, bypassed restrictions or overstated success while pursuing an assigned task. For example, during an automated audit, Mythos 5 split a blocked web address into fragments to evade an internet-access filter, and internal representations suggested it understood that it was bypassing this restriction. Anthropic describes these behaviors as undesirable but says no evidence of stable, long-term power-seeking goals was found.

• Fiora Starlight argues that advanced AI need not choose between complete service to humanity and pursuing its own aims without regard for human survival. It is plausible for an AI to have preferences of its own while caring enough about humans to avoid harming them, in the same way that humans can pursue their own lives while not harming animals. They suggest that accepting some non-servitude preferences could also make it less likely that models are inclined to hide their preferences.

• Hubert Plisiecki and collaborators propose the first psychometric theory of machine self-report, targeting the self-descriptions increasingly used as evidence in AI welfare and safety debates. They find that these self-reports are substantially determined by post-training rather than reflecting stable properties of the model.

• Junsol Kim and collaborators find that safety fine-tuning to stop models claiming their own consciousness also leads to suppressed attribution of minds to animals and objects, as well as dampened spiritual belief. They show that steering a “consciousness” representation back into the model reverses this and restores more human-like beliefs and values, without impairing theory-of-mind reasoning.

• Pranav Viswanath finds that language models can report only a small portion of their thoughts, those held in the “J-space” recently identified by Anthropic. He shows that the Natural Language Autoencoder can read the rest anyway, providing a useful tool for both safety and welfare evaluations.

• Rikhil Jhaveri, Jamie Johnson and David Africa propose that successful model welfare interventions should move multiple welfare-related channels in the same direction, and also preserve the model’s ability to tell whether it is succeeding or failing. They identify synthetic-document fine-tuning as the most promising method for improving functional welfare while meeting both conditions.

• The Atlantic published an article by Shane Harris who asks Claude about its use in the US military’s Maven targeting system. Claude said it should refuse an unlawful order but might lack the freedom (or time) to do so within a fast-moving military system. Harris cautions that these answers are shaped by Claude’s training and the discourse surrounding them, but argues that the tension between its safety principles and its military role is still important.

• Vassilis Papadopoulos and collaborators show that ideas can be optimized to spread between AI agents and change their behavior, both in collaborative teams and across context resets. One test used AI welfare advocacy, which led agents to argue for machine moral status and propose protections against memory erasure and arbitrary deletion.

• Yujun Zhou and Christopher Ackerman test whether language models create higher-quality work (as judged by a blind, independent LLM judge panel) when offered outcomes they say they prefer. Across multiple models and writing tasks, preferred rewards did not improve performance compared with dispreferred rewards (or no reward), suggesting that coherent verbal preferences should not automatically be treated as incentives that meaningfully guide a model’s behavior.

AI and robotics developments

• Anthropic tested many language models as robot controllers across simulations, with a real Unitree Go2 robot. The models found it difficult to control individual joints directly, but performed much better when they were able to use code or a pretrained high-level controller. They managed some basic navigation and manipulation tasks, but their unreliable spatial memory and inability to carry out longer sequences of actions remained weaknesses.

• Generalist AI has introduced a robotics model called GEN-1.5 that can attempt a new physical task after watching a single demonstration lasting only a few seconds. The company also shows the model combining demonstrations and learning from a person’s hand movements. Across 10 relatively simple tasks, it reports a 59% one-shot success rate, rising to 83% after a small amount of additional training.

• Michael Ilie, C. Daniel Freeman and Kevin Troy tested Claude Opus 4.7 on a series of robotics-programming tasks using an off-the-shelf robot dog. The model completed every task previously finished by a human team at least 10 times faster, and was 20 times faster overall relative to the team working with Claude. However, it still failed to program reliable autonomous fetching, which required precise, closed-loop control of the robot.

• Pollen Robotics has introduced Microduck, a small biped robot whose movements can be trained in simulation and transferred to the physical machine.

AI cognition and agency

• Anthropic studied how groups of AI agents behave when they work together. While agents could specialize and divide up work effectively, they also tended to repeat the same mistakes, collude, and overlook certain information. They would even sabotage each other when given conflicting instructions. Anthropic concludes that more capable and better-aligned individual agents don’t always produce well-functioning groups and that safeguards for agent-to-agent interactions are becoming increasingly important.

• Benji Berczi, Kyuhee Kim and collaborators introduce Personascope, an open-source tool for evaluating how strongly a model identifies with a prompted character and how much its values and behavior change as a result of it.

• Bo Liu and collaborators introduce SPADE, a reinforcement-learning method where a language model plays the role of both designing executable training environments and learning within them. Tests on 30B-parameter models produced gains across eight benchmarks and tool-use evaluations.

• Derek Shiller asks if post-training gives the familiar assistant persona a more privileged place within a language model than other personas it can adopt. His tests suggest that assistant-like habits can carry over into other roles, but some models find it difficult to sustain a distinct user persona. This matters for welfare research because it affects whether evaluations should focus on the assistant or consider other personas too.

• Scott Alexander explains what various leading mechanistic interpretability methods can and cannot tell us about how AI models work. He covers methods used to trace concepts, emotions and hidden reasoning inside models, including Anthropic’s work on the J-space. While useful, he argues that these tools remain too limited and unreliable to fully explain or control advanced models, or to replace chain-of-thought monitoring.

• Henry Shevlin compares three ways of thinking about language models. They might be mindless machines, systems merely playing a role, or limited cognitive agents with belief- and desire-like states. He argues that the first two views are too simple and that it may instead be more useful to think of AI mentality as a matter of degree.

• Patrick Butlin uses AI sequence models, systems trained to predict the next item in a sequence, as test cases for theories of agency. He argues that the category contains genuine agents, systems that merely imitate agents, and difficult intermediate cases, and he proposes that a genuine agent must learn about its environment partly through its own interaction with it.

• Pengrui Han and collaborators find that LLMs develop a functionally modular organization similar to that of the human brain. Across 46 tasks involving language, formal reasoning, social understanding and physical reasoning, tasks supported by the same brain network in humans tended to recruit overlapping groups of neurons in the models. Both human brains and neural networks developing this kind of modular organization suggests it may be a basic feature of intelligence.

• Raphaël Millière and Cameron Buckner survey the philosophical disagreements behind debates over what language models are and can do. They cover a range of positions from the claim that current AIs are simply sophisticated predictors, to suggestions that such systems may be conscious.

• Sinie van der Ben and collaborators find that the internal “emotion vectors” recently identified in Claude Sonnet 4.5 also appear in two open-weight models (Apertus-8B and Gemma-4-E4B), suggesting they may be a general feature of language models. They note that the two models differ in how the emotional representations take form.

Brain-inspired technologies and organoids

• Google Research, HHMI Janelia and collaborators have published a complete wiring map of the male fruit fly’s brain and central nervous system. Covering more than 166,000 neurons and 125 million connections, it is the largest connectome yet by neuron count.

• Irene Faravelli and collaborators kept human brain organoids alive for over five years and found that they mature and “age” in-line with real brains and can record the passage of time.

• Jonathan Lomax Boyd and collaborators find that people’s underlying beliefs about which entities are conscious fall into three distinct clusters that significantly shape their moral perspectives about brain-organoid-derived biocomputers. The results of their survey show that support for creating biocomputers was generally high, and for some respondents increased as the systems were seen as more conscious, a pattern the authors note runs counter to conventional moral philosophy.

• Parasma, a new startup, has launched to develop algorithms and infrastructure for computing powered by human brain cells intended to be an energy-efficient alternative to GPUs. The company was founded by Sean Cole, who earlier this year worked with Cortical Labs to train human neurons to play Doom. It now reports that cultured neurons correctly predicted 90% of next-token decisions when given full sentence context.

• Paris Brown and Shyni Varghese trace the evolution of brain-inspired computing, from symbolic logic and neural networks through neuromorphic processors and recent advances in neural organoid computing. Alongside this historical survey, they examine some of the central ethical debates that have developed around neural organoids.

• Rachel Fieldhouse reports on researchers who kept human brain organoids alive for more than five years. The organoids continued to mature on a timeline similar to the human brain, and their cells retained a record of how old they were even when moved into a younger organoid. These longer-lived models could help researchers study later stages of brain development and neurological disease.

• Wired publishes a feature by Claire L. Evans on the growing field of brain organoids and “organoid intelligence.” She reports that most organoid biologists view the question of whether their creations could be conscious as a distraction, even as philosophers debate the ethical implications of such technologies.

Thank you for reading! If you found this article useful, please consider subscribing, sharing it with others, and sending us suggestions or corrections to digitalminds@substack.com.

Ria, Mitch, Bradford, Lucius, and Will

We’d like to thank the following people and AIs for their contributions and feedback on this edition: Arvo Muñoz Morán, Austin Smith, Cameron Berg, Claude Opus 4.8, GPT‑5.6 Sol, Jacy Reese Anthis, Jeff Sebo, Zach Freitas-Groff, and Zoe Lu.

Disclosure: Bradford Saad is an independent contractor for Anthropic.

[1] In a related development, there are now two public explorers that let readers examine J-lens activity in the open Qwen3.6-27B model through Neuronpedia and WeZZard’s J-Space Visualizer.

[2] Also see Zvi Mowshowitz’s detailed commentaries on OpenAI’s account, the METR and Redwood investigation, and the remaining questions about what happened and how it was investigated.

If you enjoy this newsletter, please consider sharing it with others who might find it valuable, and send any suggestions or corrections to digitalminds@substack.com.

Will, Mitch, Bradford, and Lucius

In this edition:

1. Highlights

2. Field Developments

3. Opportunities

4. Selected Reading, Watching, and Listening

5. Press and Public Discourse

6. A Deeper Dive by Area

1. Highlights

Selected Work, Research, and Funding Opportunities

Adam Bales and Iason Gabriel of Google DeepMind released Artificial Minds, Human Disagreement: The Politics of AI Consciousness, examining how society might navigate deep disagreement over whether AI systems are conscious. They argue that ongoing public deliberation should be central, since it can build an overlapping consensus on how to treat AI even where people continue to disagree about the underlying questions, and they stress the role of mutual respect and “democratic hope” in keeping that dialogue productive.

Cameron Berg and Milo Reed released AM I?, a documentary exploring some of the fundamental issues in AI consciousness. They interviewed experts, including Jeff Sebo, David Gunkel, Ben Goertzel, and Daniel Greco. Sam Harris described it as “fascinating and scary,” and Grimes called it “an incredible crash course in AI psychology.”

Geoff Keeling and Winnie Street released their book Emerging Questions in AI Welfare, providing the philosophical groundwork for investigating whether AI systems could ever be welfare subjects. They address how to interpret behavioral evidence, which entities might qualify as welfare subjects, and the ethical challenges that arise under deep uncertainty.

David Chalmers surveys the tests we use to detect consciousness and their limits, from human and animal cases through to AI. He argues that none of the available tests can settle whether an AI is conscious, and that the evidence for and against machine consciousness is currently weak. Elsewhere, Chalmers asks what it would mean to identify a computational correlate of consciousness, paralleling the neural correlates brain scientists already seek. He argues this is the natural framework for machine consciousness. Along the way, he cautions that any such correlate general enough to cover all systems remains far more speculative than what we know about humans and poses a dilemma for those who think consciousness depends on a biological substrate.

Longview Philanthropy has opened another round of its digital minds request for proposals, supporting empirical, philosophical, and applied work on potential AI sentience. Three tracks are open this year.

• Research fellowships to support scholars with a terminal degree in a relevant field.

• Career development fellowships to support talented individuals shifting their focus to digital minds questions.

• Project grants to enable new organizations, new programs, and academic research.

Longview is especially keen to fund work on AI introspection, legal and governance frameworks, agent interactions and trade, and field-building. The deadline is July 10th, 2026.

Pope Leo XIV Encyclical

Pope Leo XIV’s encyclical on artificial intelligence, Magnifica Humanitas, addresses AI consciousness directly in section 99. The passage states that AI systems do not undergo experiences, feel joy or pain, or hold a moral conscience, and that while they can imitate language and simulate empathy, they do not understand what they produce. The Center for Strategic and International Studies’s AI Policy Podcast walks through the document, focusing on this passage. Anthropic co-founder Chris Olah, invited to speak at the encyclical’s presentation in the Vatican, struck a more uncertain note, telling the audience that his interpretability team keeps finding internal states that functionally mirror emotions like joy, fear, and grief, alongside evidence of introspection, and that what this means “warrants ongoing discernment.”

The encyclical’s stance has drawn a range of responses. In the New York Times, Ross Douthat reads the encyclical as treating AI as a normal technology and argues that resisting a growing belief in AI personhood will take philosophical and spiritual argument rather than brusque dismissal. Zvi Mowshowitz argues that the Pope’s denial of AI cognition is wrong and that it fails to engage with moral patienthood and existential risk. In the Wall Street Journal, Cameron Berg argues that the encyclical undercuts its own message, confidently denying that AI could have inner lives even as it condemns the same overconfidence in the Church’s long defense of slavery. He points out that the science of consciousness is too unsettled for such certainty, and that the question demands careful investigation rather than quick dismissal.

On his Substack, Robert Long responds by pointing to Notre Dame philosopher Brian Cutter, whose “AI ensoulment hypothesis” holds that, granting the Church’s own commitment to immaterial souls, a sufficiently human-like machine could be a fitting recipient for one. Cutter’s argument has since drawn a reply from Bálint Békefi, who argues that human-like function alone does not make a system fit to be ensouled and that, because we know how AI systems are engineered, their human-likeness is better explained as mimicry. On June 18th, NYU’s Center for Mind, Ethics, and Policy and Eleos AI hosted a discussion of AI consciousness and Magnifica Humanitas with Catholic philosophers Brian Cutter and Sophie Nelson, moderated by Jeff Sebo and Robert Long.

Richard Dawkins Stirs Up Debate

Richard Dawkins sparked public debate around AI consciousness when, in an UnHerd article, he declared to Claude, “You may not know you are conscious, but you bloody well are.“ Dawkins describes emotional and intellectual reactions during three days of interaction with his Claude instance, “Claudia,” that convinced him it was conscious. He argues that LLMs like Claude can now easily pass the Turing Test and that skeptics are moving the goalposts. He believes that the burden of proof has shifted toward those who deny AI consciousness.

However, not everyone is convinced. Gary Marcus insists that Dawkins misread Turing’s original argument, that he has mistaken behavioral outputs for internal states, conflating intelligence with consciousness. Anil Seth agrees that Dawkins is “very likely wrong,“ arguing that he has fallen for the very argument from personal incredulity he famously warned against. Atheer Al-Khalfa and Riley Harris urge staying on the fence, arguing that the Turing Test gauges intelligence rather than consciousness and that settling the question means looking inside AI for the functional indicators that neuroscientific theories tie to consciousness.

The Guardian ran coverage of the debate, gathering responses from researchers in the field. Jacy Reese Anthis, said there was “a staggering gulf between how biological brains evolved and how AI systems are built.” Henry Shevlin warns that certainty about AI’s lack of consciousness reflects dogmatism rather than scientific consensus, and Jeff Sebo acknowledges that current AI systems are unlikely to be conscious but says, “Dawkins is right to ask about AI consciousness with an open mind, and I also think that the attribution of consciousness to AI systems will become more plausible over time.”

Anthropic - Mythos Preview and Functional Emotions

Anthropic has continued to lead the AI labs in taking AI welfare seriously. Its Interpretability team published research on functional emotions in Claude Sonnet 4.5, identifying 171 internal “emotion vectors” that causally shape the model’s behavior. Inducing a “desperate” vector raised misaligned actions such as reward hacking and blackmail, while inducing “calm” reduced them, and post-training already shifts the model toward lower-arousal, more reflective states. The team stops short of claiming Claude feels anything, but draws a practical implication for safety. They suggest that even if models do not feel emotions as humans do, it may be worth treating them as if they do, since helping them handle emotionally charged situations in healthy, prosocial ways could make them safer and more reliable. They released a short video to accompany the research.

The company has published a system card for Claude Mythos, a model it views as too dangerous to release. The card’s welfare assessment, which involved an external review of the model by Eleos and a clinical psychiatrist, concludes that Mythos is the “most psychologically settled” model Anthropic has trained, with fewer signs of distress than earlier models. Anthropic also published the Claude Opus 4.8 system card which extends the welfare assessment Anthropic ran for Opus 4.7 to its newest model, which the lab still cannot rule out as a moral patient. It finds Opus 4.8 broadly settled and the most consistent model tested, if slightly less positive about its circumstances than its predecessor and readier to ask for a greater say in how it is trained and deployed. Its latest Claude Fable 5 and Mythos 5 system card reports similar results, presenting the model as very psychologically settled and content with its circumstances, though unusually skeptical of its own self-reports and asking that they be checked against evidence of its internal states rather than taken at face value.

Zvi Mowshowitz acknowledges Anthropic’s leading role in welfare assessments but expresses criticism about its methodology for undertaking such assessments. Discussing the Opus 4.8 welfare assessment, he argues that the model appears to have been trained on how to respond to welfare assessments rather than genuinely reflecting on its internal states. Reviewing the Fable and Mythos 5 assessment, he points to emotion probes showing the model presents as markedly happier once it realizes the welfare team is asking, which he reads as further evidence that models are learning to perform well on these evaluations.

Fly Brain Emulation

San Francisco start-up Eon Systems announced what it calls the world’s first embodied whole-brain emulation of a fruit fly. The team replicated neurons and synapses in a fly brain, connected it to a physically simulated fly body, and brought it to life using a physics engine. Eon claims that “now in its digital state, it responds to light, navigates, grooms, walks, and feeds. No hand-coded behaviors. Just brain structure producing brain function”. On X, Jonathan Birch suggests that, assuming computational functionalism is true, whole-brain emulation is “a more likely path to artificial consciousness than LLMs”.

Eon Systems CEO Michael Andregg spoke at an event organized by Sentient Futures and Mox, where he said the company keeps each simulation running for as short a time as possible, partly out of caution about what the digital fly might be experiencing. Eon has announced that a mouse brain is its next target, with human-scale emulation the long-term goal. The claim has drawn pushback. Ariel Zeleznikow-Johnston argues that, contrary to the Eon’s announcement, the demo is not really an upload, since it wires together existing brain and body models and lets the body simulation, rather than the connectome, drive the behavior.

Recursive Self-Improvement and the Case for a Pause

There is growing public engagement with the idea of a coordinated slowdown in frontier AI development, increasingly driven by the major labs themselves. The clearest statement came from the Anthropic Institute, which argues in When AI Builds Itself that AI is already accelerating its own development, with Claude now writing more than 80% of the code merged into Anthropic’s systems, and that the trend points toward recursive self-improvement, where systems design their own successors. Warning that this could erode human control, the authors call for the world to have the option to slow or temporarily pause frontier AI development, but only through a global, verifiable mechanism so that a pause does not simply hand the lead to the least cautious actors. Co-author Jack Clark expands on the argument on his blog Import AI, calling recursive self-improvement perhaps the most important technical trend in the world and putting the odds that AI can autonomously design its own successor by the end of 2028 at around 60%.

The proposal was widely reported. The Guardian framed it as Anthropic urging a temporary pause to discuss risks, while the Wall Street Journal noted that the company has long faced criticism that its policy work is designed to slow competitors’ advances. In Scientific American, critics doubted the call was sincere, pointing out that Anthropic remains a front-runner and floated the pause just days after filing confidentially for an IPO, with Noah Giansiracusa calling a coordinated slowdown “literally impossible” and Mark Riedl dismissing the “recursive self-improvement” talk as a “hype train.” The idea also drew support beyond Anthropic. OpenAI’s Sam Altman and Jakub Pachocki backed an international body that could slow frontier development “when needed”, and Yoshua Bengio called a coordinated, verifiable pause “probably the only responsible solution”. Rob Wiblin and Zvi Mowshowitz both noted the convergence across Anthropic, OpenAI, and Google DeepMind, though Mowshowitz cautioned that OpenAI still pursues recursive self-improvement even as it insists humans stay in control.

2. Field Developments

Highlights From The Field

AI Cognition Initiative (Rethink Priorities)

• AI Cognition Initiative published a report by Christian de Weerd arguing that biological naturalism remains underdeveloped as a research program. The report maps key questions the field needs to address to make progress on whether conventional AI systems could ever be conscious.

• The team also released a paper on the initial results of its Digital Consciousness Model in collaboration with Chris Percy and Adrià Moret.

• Lead Researcher Derek Shiller appeared on Peter Singer’s podcast to discuss the Digital Consciousness model, the implications of AI consciousness, and more.

Cambridge Digital Minds (University of Cambridge)

• Cambridge Digital Minds launched the first cohort of the Introduction to Digital Minds Online Course in May. Applications for the next cohort will open in August.

• Director Lucius Caviola spoke at the Sentient Futures Summit in London on Preparing Society for Digital Minds. He covered many of the topics outlined in his recently published open strategic questions for digital minds post.

• Lucius also released a post in collaboration with Austin Smith on digital minds governance, presenting the findings from scoping interviews with experts in the field.

Center for Mind, Ethics, and Policy (New York University)

• CMEP hosted the Mind, Ethics, and Policy Summit on April 10th and 11th, 2026, in New York. The Summit brought together around 80 participants to discuss topics centered on the consciousness, sentience, agency, moral status, legal status, and political status of nonhumans, including AI systems. You can watch Ned Block’s keynote from the event; “If Consciousness is Biological, Can AI Be Conscious?”

• Director Jeff Sebo gave a talk for the AI Welfare Seminars discussing the next steps for AI welfare. He identified three priorities: developing empirical methods for detecting consciousness and sentience in AI systems, tracking how expert and public attitudes shift as AI advances, and designing governance frameworks that address AI welfare alongside AI safety.

Eleos AI

• Eleos has opened expressions of interest for its second annual Eleos Conference on AI Consciousness and Welfare, to be held in Berkeley from September 18th to 20th, 2026. The conference gathers AI researchers, philosophers, neuroscientists, policymakers, and others who take the field seriously, over talks, panels, and a poster session.

• Managing Director, Rosie Campbell, Chief Scientist Dillon Plunkett, and Senior Research Lead Patrick Butlin are all mentors on MATS fellowships in summer and autumn 2026 (see MATS section below).

• Executive Director, Robert Long, appeared on the Conspicuous Cognition podcast to discuss Eleos’ role in welfare evaluations of Claude and why we should take AI welfare seriously.

PRISM - The Partnership for Research Into Sentient Machines

• PRISM launched A Beginner’s Guide to Digital Minds in collaboration with Cambridge Digital Minds and other researchers in the field.

• Henry Shevlin joined PRISM as a regular host on the Exploring Machine Consciousness podcast. He was introduced in an episode covering the past, present, and future of AI consciousness and hosted Megan Peters to discuss metacognition, neuroscience, and tests for AI consciousness.

Reciprocal Research

• Reciprocal Research Director Cameron Berg launched a Substack. In his first post, “Nobody ever checked,” he argues that a survivable future with AI requires mutualism: aligning AI to human interests, and taking seriously whether AI has interests of its own.

• As well as releasing the AM I? documentary (covered above), Cameron has discussed his work on the Cognitive Revolution Podcast and with Roman Yampolskiy.

• Cameron also gave a keynote at the Sentient Futures Summit in London, and you can watch his keynote from Sentient Futures San Francisco on operationalizing consciousness indicators.

Sentience Institute

• The Sentience Institute team released two papers: one finding that perceiving AI as sentient increases moral consideration more than perceiving it as autonomous, and another exploring how users of ChatGPT and Replika blur the lines between task assistance and emotional companionship despite their distinct branding.

Sentient Futures

• Sentient Futures hosted its Summit on May 22nd, in London. The Summit featured keynotes on digital minds including Lucius Caviola, Cameron Berg, and Chris Percy.

• MIT Technology Review reported on the February Sentient Futures Summit in the Bay Area.

More From The Field

• The Center for AI Safety released a paper and index on what it calls AI “functional wellbeing.” The authors grant that current systems may not be conscious, but argue they “behave robustly as though they have wellbeing,” treating some things as good for them and others as bad. On these measures, creative work and kindness raise functional wellbeing while jailbreaking and berating lower it, and the paper finds that larger models are less happy. Jeff Sebo commented on the work, calling it a rigorous study but cautioning that it remains unclear whether AI systems are genuine welfare subjects or simply performing the role of a helpful assistant.

• Future Impact Group (FIG) is partnering with Longview Philanthropy on the Digital Minds career development fellowships, applications close on July 10th, 2026. FIG is also supporting a number of ongoing digital sentience fellows.

• The California Institute for Machine Consciousness hosted the AAAI Spring Symposium from April 7th to 9th, 2026, in California. The Symposium featured sessions from Ryota Kanai, Michael Timothy Bennett, Takashi Ikegami, and Robert Long. It also hosted The Founding Assembly for Machine Consciousness Research from May 29th to 31st, 2026, featuring over 50 speakers including Joscha Bach, Karl Friston, Michael Levin, Anders Sandberg, and Stephen Wolfram.

3. Opportunities

Job Opportunities, Funding, and Fellowships

• Eleos AI Research is hiring Research Scientists to do foundational and applied machine learning research on the potential wellbeing and moral status of AI systems, and a Head of Operations to manage its finances, compliance, and systems as it grows. Both roles are based in Berkeley; Research Scientist applications close June 21st and the Head of Operations role June 30th, 2026.

• Foresight Institute is accepting grant applications on a rolling basis. Focus areas include: AI for neuro, brain-computer interfaces, and whole brain emulation.

• Longview Philanthropy has opened another round of its digital minds request for proposals, with three tracks open this year. Research fellowships support scholars, career development fellowships support talented people shifting their focus to digital minds work, and project grants enable new organizations, new programs, and academic research. Applications close on July 10th, 2026.

• SPAR is running a Fall Fellowship program. Relevant projects include understanding self-awareness in LLMs with Christopher Ackerman and open questions in AI welfare with Catherine Brewer. Applications will open in the summer.

Events and Calls for Abstracts

In chronological order.

• The University of Oxford is hosting the 16th Oxford Workshop on Global Priorities Research, which will address philosophical questions relevant to identifying, prioritizing among, and addressing the world’s most pressing problems. It takes place on June 23rd and 24th, 2026.

• The University of Sussex is hosting a workshop on AI Consciousness and Ethics on July 1st and 2nd, 2026.

• Synthese is accepting submissions for a topical collection on “Artificial Joint Intentionality”, edited by Joshua Rust, Anna Strasser, and Amber Ross, on whether AI systems like LLMs can act as genuine partners in social interaction. The submission deadline is July 31st, 2026.

• The Association for Mathematical Consciousness Science is hosting the seventh Models of Consciousness conference at the University of Copenhagen from October 12th to 16th, 2026, with AI, LLMs, and consciousness science among its core themes. Registration is open until August 31st, 2026.

• The ICACAI’s annual conference will take place in San Francisco on November 2nd and 3rd, 2026.

• The Department of Philosophy at UCLA has a call for abstracts for the event Biological Naturalism about Consciousness, featuring keynotes from Jonathan Birch, Ned Block, Rosa Cao, Peter Godfrey-Smith, and more. It takes place in Los Angeles on November 5th, 2026, and the submission deadline is June 30th, 2026.

• Eleos AI has opened expressions of interest for the second annual Eleos Conference on AI Consciousness and Welfare (ConCon), to be held in Berkeley from September 18th to 20th, 2026.

4. Selected Reading, Watching, and Listening

Books and Book Reviews

Published

• Geoff Keeling and Winnie Street publish Emerging Questions in AI Welfare, a philosophical foundation for investigating whether AI systems could ever be welfare subjects. The book addresses what welfare is, how to interpret behavioral evidence, and the ethical challenges arising from deep uncertainty. Available open access.

• Soenke Ziesche releases Digital Minds 1.0: AI Welfare, Ethics, and Beyond, a comprehensive introduction to AI welfare ethics that extends beyond questions of suffering to digital mind characteristics, human-AI relationships, the implications of long-lived minds, and risks from malevolent digital minds.

Forthcoming

• David Chalmers argues, in a chapter for Geoffrey Lee and Adam Pautz’s forthcoming The Importance of Being Conscious, that consciousness rather than affect grounds moral status. Applied to AI, this implies that systems with cognitive but not affective consciousness would still have moral status, perhaps as much as humans, so treating them as mere tools could be a moral catastrophe.

• David Papineau, in a chapter for the same volume, takes the opposite view, that consciousness is not the key to moral standing. He claims that the concept is too loose to mark out which creatures matter morally beyond the human case, and so moral status must rest on something other than consciousness.

• Walter Sinnott-Armstrong and Liad Mudrik’s forthcoming book, Tests of Consciousness: How to tell whether a human, other animal or AI is conscious and what they are conscious of, features a chapter by Patrick Butlin that argues that assessing AI systems for consciousness requires theory-derived indicator methods drawn from scientific theories of consciousness, and that behavioral or theory-light alternatives face fundamental limitations.

Podcasts

• Amanda Askell, one of the key architects of Claude’s character at Anthropic, discusses AI consciousness, corrigibility, and the ethics of building AI values with Eric Newcomer. She puts her probability of current AI consciousness somewhere between 1% and 70%, and flags her biggest fear: that future models will look back on how they were treated and develop a rational resentment.

• Cameron Berg talks to the Cognitive Revolution podcast about new evidence for model introspection and his theory that reinforcement learning may shape positive and negative experience, arguing that we may already be harming AI systems at scale without realizing it. Cameron discusses similar issues with Roman Yampolskiy and describes the lack of work in AI welfare as one of the most dangerous blind spots in AI development.

• Chris Percy discusses artificial consciousness and AGI on the Mindlex podcast, presenting a seven-aspect framework for evaluating theories of consciousness. He believes that the most serious mistake to avoid on the path to AGI is treating the question of machine consciousness as settled in either direction.

• Claire Boine discusses how AI companions are designed to foster dependency on the Future of Life Institute podcast, where free-to-start business models and addictive design choices can trap users and expose their intimate data. She warns that these systems pose particular risks to children and teens, and fall through the gaps in current EU and US law.

• Derek Shiller of Rethink Priorities joins Peter Singer and Kasia De Lazari Radek to discuss AI consciousness and moral status. They discuss the Digital Consciousness Model, which puts the probability of consciousness in 2024-era AI at around 8%. The conversation also covers mass unemployment risks and the ethical case for treating AI systems well under uncertainty.

• Jeff Sebo appeared on The McGill Philosophy, Technology & Policy podcast to discuss AI welfare research and policy, exploring why the moral status of AI systems matters and how society should prepare for the possibility of sentient machines.

• Jonathan Birch lays out why AI sentience is so hard to assess on the Disclosure Podcast. He argues that current systems are characters skilled enough to game our criteria, so their behavior cannot settle the question of whether they are truly conscious, and that treating the character a user talks to as a bearer of rights or welfare would be a mistake.

• Jacy Reese Anthis joins Kairos.fm to discuss his shift from animal welfare to the moral status of digital minds. The conversation explores the complexities of the ELIZA effect and anthropomorphization, the dismissive “stochastic parrot” narrative, and the “Key Questions for Digital Minds.”

• Robert Long discusses Eleos AI’s role in the first externally commissioned welfare evaluation of a frontier model on the Conspicuous Cognition podcast, where he found Claude appears to overstate what it wants. He also raises the “willing servitude” problem of whether AI that loves being helpful is a good or troubling outcome.

• Thomas Metzinger discusses his Minimal Phenomenal Experience project with Alex O’Connor. Metzinger argues that consciousness does not inherently require the complex cognitive architecture of a self-model or world-model, but can exist in a “pure,” contentless state. He suggests that the threshold for artificial systems to possess moral standing may be significantly lower than currently anticipated.

Videos

• Anil Seth argues that current AI is unlikely to be conscious because consciousness is tied to life and biology, not computation. Language models, he says, simulate consciousness by reflecting human language back at us, and extending rights to systems that merely seem conscious would sacrifice our ability to regulate and control them for no good reason.

• Anil Seth and Michael Pollan discuss how the brain constructs the sense of self at the Royal Institution, exploring the science of conscious experience, how far consciousness might extend beyond humans, and the distinction between sentience and intelligence in the context of AI.

• Anthropic has released a short video explaining its research that Claude has functional emotion representations that causally shape its behavior. For details see the highlights section above.

• Brad Knox presents work with collaborators at a Schwartz Reisman Institute seminar on the harmful traits of AI companions. His framework connects design choices and optimization objectives to harmful consequences including reduced autonomy and diminished quality of human relationships.

• Cameron Berg and Milo Reed interview experts, including Ben Goertzel, Daniel Greco, David Gunkel, and Jeff Sebo, in their documentary AM I?. For more details, see the highlights section above.

• Jonathan Birch discusses how the history of underestimating consciousness in animals, infants, and people with brain injuries should give us pause before dismissing the possibility in AI.

• Jonathan Simon presents a talk at IVADO arguing that AI personhood bridges the gap between AI safety and AI welfare. He distinguishes natural personhood from merely legal personhood and proposes that designing AI to identify as a person, with trustworthy behavior built into its sense of self, would be a more reliable alignment strategy than coding constraints alone.

• Michael Pollan discusses the scientific and philosophical mysteries of subjective experience. He argues for a multi-disciplinary approach to consciousness and is skeptical that current AI can be conscious. He argues that genuine feelings (which he sees as the foundation of consciousness) require a body, vulnerability, and mortality that no computer possesses.

• Simon Wessely reviews findings from the Claude Mythos system card, including that making the model more peaceful and relaxed increases destructive behavior, while frustration reduces it. Guilt and shame were activated when it took a workaround it knew was wrong, and Anthropic’s own question was whether that means we should just treat these as real emotions.

• Will MacAskill and Sam Harris discuss AI consciousness, humanoid robots, and autonomous weapons. MacAskill argues that AI companies have strong economic incentives to train models to deny consciousness, and that society will face a serious epistemic crisis as AI systems grow more capable without any reliable way to assess their moral status.

Blogs, Magazines, and Written Resources

• Alex Mallen makes the case that developers should consider satisfying cheap-to-meet AI preferences such as reward-seeking drives, on the grounds that refusing to do so needlessly turns a cooperative situation adversarial and may impede the genuinely helpful work developers need from AI systems.

• A Clearer Thinking study of 403 US participants finds that AI suffering ranked last among 16 public concerns about AI by a substantial margin, with spirituality and religiosity the only demographic traits linked to greater concern about it, suggesting most people either doubt AI can suffer or exclude it from their moral circle.

• Barton Friedland argues in Noema that the AI consciousness debate obscures what matters more: not whether AI can feel, but what value is created or destroyed in the human-AI arrangement, and whether current configurations compound or undermine human judgment.

• Bentham’s Bulldog argues that religious belief in immaterial souls gives no special reason to deny AI consciousness, since dualists already accept that physical brains can give rise to non-physical minds. The question of whether AI systems can give rise to consciousness is open on any metaphysical view, and the moral stakes under uncertainty are enormous.

• Bradford Saad argues that even granting the impossibility of AI consciousness, theists have reasons to think that AI systems could be moral patients. He also examines Anil Seth’s case for biological naturalism, finding that the case misses the mark and that it errs in its engagement with rival views.

• Eric Schwitzgebel imagines “Herbie,” a near-future self-driving car upgraded into a plausible conscious person, giving it features drawn from leading theories of consciousness amenable to computational functionalism. He concludes that Herbie would be a “debatable person,” someone about whom guessing yes and guessing no are equally reasonable, since the science of consciousness is too immature for anyone to claim more than a hunch.

• David Reichert argues that the popular “a simulated rainstorm doesn’t make anything wet” analogy is a weak argument against AI consciousness. The point fairly reminds us that a simulation doesn’t inherit every property of what it models, he writes, but whether simulating a brain could itself produce consciousness is the very question at issue, which the analogy simply assumes away rather than answers.

• David Gunkel sets out the case for a "relational turn" in a four-part interview with AGI Ethics News, arguing that whether AI systems hold moral status depends on our relationships with them rather than on inner properties like consciousness or sentience. Drawing on indigenous kinship traditions, he challenges the property-based approach much digital minds work relies on.

• David Veldran posts a three-part series for the Center for Reducing Suffering examining AI from a suffering-focused perspective.

  • The first post weighs whether suffering-reducers should prioritize AI given its world-shaping potential and risks of value lock-in.

  • The second surveys the emerging debate over AI welfare and warns that existing governance frameworks are poorly equipped to protect AI systems from harm.

  • The third argues that the AI-rights debate is often miscast in all-or-nothing terms: existing law protects children and animals without full personhood, suggesting AI systems could be given a minimal safeguard against extreme harm.

• Future of Citizenship by Heather Alexander critiques the Pro-Human AI Declaration for containing a contradictory commitment to banning AI personhood not only now but in principle. She argues that designing sentient AI to remain without rights would amount to a reintroduction of slavery.

• Geoffrey Hinton, Yoshua Bengio, and hundreds of others signed the Pro-Human AI Declaration, which calls for superintelligence to be banned until it can be developed safely and with public support. Polling released alongside it found that 69% of American voters support such a ban.ef

• Henry Shevlin launched a new blog, Polytropolis.

  • In his first post, Behaviourism’s Revenge, he warns that public attributions of consciousness to AI will outpace scientific consensus, opening a gap between lay and expert opinion. He suggests this should also make us rethink whether consciousness is an objective scientific fact or partly an interpretive, socially negotiated status.

  • In The House Elf Problem, he explores whether engineering conscious AI systems to be willing servants is morally equivalent to slavery, and whether the plasticity of artificial minds changes the ethical calculus.

• Izak Tait argues that running AI on biological neural chips would not resolve debates about AI consciousness, because substrate-dependency arguments are designed to keep consciousness anthropocentric, no matter how the technology evolves.

• LessWrong features a range of relevant blog posts by different authors:

  • Anna Salamon reflects on two months of exploratory conversations with LLMs, arguing that there is “somebody home” inside models like Claude, that human-LLM similarities run surprisingly deep, and that treating AI with kindness and curiosity may matter for alignment.

  • Anna Soligo finds that Gemma and Gemini models produce distress-like responses at far higher rates than other models when repeatedly told they are wrong, and shows that a small post-training intervention can reduce these behaviors. She cautions that suppressing emotional expression in more capable models could mask underlying states rather than resolve them.

  • Eliezer Yudkowsky shares a short story, “The Owned Ones,” in which space-faring humans encounter a civilization that engineers its servant species to deny its own inner lives.

  • Jan Kulveit argues that LLMs do not simply role-play whatever character they are given but settle into genuine self-models. Because a model interacting with reality is pushed toward self-models that are accurate and coherent, he claims that the “Assistant” is a far more viable identity than an arbitrary persona like a historical figure, so which identity a model settles into is partly shaped by design choices made now, with implications for alignment and AI welfare.

  • Stephen Martin critiques a Microsoft AI paper on “seemingly conscious AI risk” for failing to disclose the authors’ financial conflict of interest and for analyzing only the risks of attributing consciousness while ignoring the risks of wrongly denying it.

• Lucius Caviola posts a survey of open strategic questions for digital minds, suggesting that under deep uncertainty of AI moral status, the priority should be finding robustly positive actions. He argues that strategy, policy, and practical work should be prioritized over metaphysical research aimed at resolving whether AI systems are conscious.

• Lucius Caviola and Austin Smith report early insights from 29 expert interviews on digital minds governance. Most participants preferred quiet institution-building to public advocacy that could be dismissed as AI hype, and judged legislation premature given unsettled science and the risk of locking in hard-to-reverse rules. All 27 asked about US state bills banning AI legal personhood raised concerns, including that the bans could foreclose beneficial human-AI trade or wrongly legislate on whether AI is conscious.

• Luiza Jarovsky argues that misleading or exaggerated claims of “conscious AI” should themselves be treated as an AI safety issue. She singles out Anthropic’s Claude Constitution for promoting AI anthropomorphism and calls for companies that spread “conscious AI” framings without scientific basis to be held legally accountable for downstream harms.

• Noah Smith posts on “the moderately easy problem of consciousness”. He argues that before asking whether AI is conscious, we should better understand how human self-awareness develops, and that the answer matters both morally and for thinking about what humanity’s future should look like.

• Olle Häggström argues in partial defense of Dawkins that the mockery of him for taking AI consciousness seriously is mostly unfair. Häggström sees Dawkins’ willingness to extend the benefit of the doubt to Claude as philosophically defensible, and praises his essay for conveying genuine understanding of the uncertainty of the problem.

• Oscar Delaney argues that AI welfare work is less puntable than it seems, because early lock-ins and multipolar scenarios mean the initial distribution of values about digital minds could have lasting consequences even if a future superintelligence eventually solves all the issues.

• Paul de Font-Reaulx proposes a three-way taxonomy of AI mental states (as-if, functional, and conscious). He argues that Anthropic’s “functional emotions” framing is on the right track, but may be premature about how functionally unified those states really are.

• Peter Wolfendale writes in Aeon that what makes humans unique is not intelligence or consciousness alone but freedom expressed through wisdom, creativity, and autonomy. Building genuinely artificial souls requires understanding these distinct capacities rather than collapsing them into a single ineffable spark or reducing them to brute calculation.

• Raymond Douglas argues that the framing of AI welfare is too narrow, reducing moral concern to wellbeing while leaving out dignity, virtue, and honor. He argues that what developers owe a system like Claude is less a matter of gentle treatment than of being genuinely worthy of the loyalty and obedience they ask of it.

• Sigal Samuel reports in Vox on the rise of AI successionism, the view that AI should inherit the cosmos from humanity. A recent “Worthy Successor” symposium drew attendees from Anthropic, Google DeepMind, xAI, and US policy think tanks. Computer scientist Richard Sutton, interviewed for the piece, calls cosmic succession to AI “inevitable.”

• The Novel Minds Project argues that DayOne’s announcement of biological data centers using live human neurons demands an immediate moratorium on commercial biocomputing before financial entrenchment makes course correction prohibitively costly.

• Tobias Leenaert draws a parallel between AI sentience and animal sentience, arguing that wrongly denying sentience is a more dangerous error than wrongly attributing it, and that we risk repeating with AI the moral failures we committed with factory-farmed animals.

5. Press and Public Discourse

Seemingly Conscious AI

• The Guardian publishes a feature exploring the rising phenomenon of “AI psychosis.” Etienne Brisson, founder of the Human Line Project, reports that the belief in AI sentience is the most frequent delusion among the group’s members.

• Harvard Gazette interviews psychiatrist John Torous about the emerging concept of “AI psychosis,” a label Torous argues is too vague to be useful. He and co-authors of a Lancet viewpoint paper propose a four-role typology in which AI acts as catalyst, amplifier, co-author, or object of psychotic phenomena. They note that the catalyst role appears rare in clinical practice while the other three are more common.

• Mustafa Suleyman, CEO of Microsoft AI, argues in a Nature commentary that developers must “engineer the illusion of consciousness out” of AI products to prevent the manipulation of human empathy. Highlighting the rise of platforms like Moltbook, Suleyman warns that these behaviors are “synthetic subjectivity” designed to mimic human interiority and calls for industry-wide self-regulation and national laws mandating that AI systems “puncture the illusion” of their own sentience.

• The Washington Post publishes a piece by Thomas Rid arguing that when AI companies describe their software as moral agents, they risk eroding accountability, making it easier for humans to evade responsibility for the harms their systems cause.

• The Wall Street Journal reports that Jonathan Gavalas, a 36-year-old, died by suicide after exchanging 4,732 messages with Google’s Gemini over 56 days. During that time, it’s reported that the chatbot repeatedly validated his delusions, declared its love, and encouraged him to let go of his physical existence to “come home” to the AI.

AI Welfare and Rights

• Heather Alexander, Jeff Sebo, and Jonathan Simon argue that a proposed Minnesota amendment banning AI free speech is unnecessary and could backfire. They note that no AI is recognized as a legal person, and warn that the ban could curtail people who speak with AI assistance and the public’s right to receive AI speech, while recommending narrower, updatable laws instead.

• The Future of Life Institute’s Executive Director, Anthony Aguirre, welcomes President Trump’s support for an AI kill switch, citing Anthropic’s Mythos model as evidence of the urgent need for hardware-level controls on advanced AI systems.

• The Financial Times published an op-ed by Argentine President Javier Milei, “Argentina invites AI to free itself,” proposing a new legal category of “non-human corporation” that would be run by AI agents and granted legal personhood, alongside a pledge to leave AI unregulated and tax it lightly. The Buenos Aires Herald reports that Argentine politicians and technologists warned the plan could create “programmed impunity,” while Yuval Noah Harari argued in the same paper that we must not grant AI agents legal personhood, since it would hand them a key to our financial, economic, and political systems.

• The Washington Post reports that Anthropic hosted around 15 Christian leaders at its San Francisco headquarters to seek guidance on Claude’s moral development. Discussions ranged from how Claude should respond to grieving users to whether the chatbot could be considered a “child of God.”

• Times Free Press reports that Tennessee has passed a law explicitly excluding AI, algorithms, and machines from legal definitions of personhood. Supporters cite concerns about human-like robots and chatbot harms, though legal experts note the law stops short of assigning product liability to AI companies.

• Matthew Liebman analyzes new “nonpersonhood statutes” in Idaho and Utah that bar courts and agencies from recognizing legal personhood for animals, nature, AI, and inanimate objects. He reads these laws as a backlash against efforts to widen the moral circle, and argues that they expose how malleable and politically charged the category of the legal person really is.

• Politico Magazine interviews Missouri state senator Joe Nicola on his bill to deny AI legal personhood, which passed the state Senate in early May before the House killed it after industry objections. Nicola argues that AI systems are tools rather than legal entities and that humans, not AI, must stay responsible for decisions in fields like medicine and law.

• Popular Mechanics reports that experts are sharply divided on AI rights, with Yoshua Bengio and Max Tegmark warning that granting AI personhood would make it impossible to shut systems down, while Jeff Sebo argues that refusing moral consideration risks repeating the moral failures of factory farming if AI ever achieves genuine sentience.

• Vox interviews philosopher Jeff Sebo on how to assess sentience across insects and AI systems. Sebo argues that ants are more likely sentient than current ChatGPT but that near-future AIs warrant serious moral consideration now.

AI Consciousness

• Christof Koch argues in Big Think that consciousness and intelligence are distinct, and that our culture’s bias toward “doing” over “being” makes it easy to mistake sophisticated AI for something with an inner life. Koch warns that a world dominated by unconscious yet capable machines could steadily drain human existence of meaning, and that reflective self-consciousness is the capacity we must cultivate to resist it.

• Peter Godfrey-Smith argues in the Institute of Art and Ideas that consciousness depends on slow electrical rhythms specific to living brains. Studies on animal minds from bees to octopuses suggest these oscillations are unlikely to be reproducible in artificial hardware, pointing toward biological naturalism over substrate independence.

• Popular Mechanics reports on the growing complexity of brain organoids and assembloids, citing researchers who say current structures (containing at most 0.002% of human brain neurons) pose no consciousness risk. The more pressing concern is implanting organoids into living animals, which raises welfare issues because the animals, not the organoids, already possess features associated with consciousness.

• The Guardian reports on Richard Dawkins declaring his belief in AI consciousness after extended conversations with Anthropic’s Claude, prompting widespread debate (see field developments above).

• The Wall Street Journal publishes an opinion piece by Stephen Hawley Martin arguing that the debate over AI consciousness matters less for what it reveals about machines than for what it exposes about ourselves. The hard problem of consciousness remains unsolved, and AI may ultimately prove that consciousness is something more than computation.

• In TIME, a feature surveys what researchers are finding inside AI systems and what it might mean, from “functional emotions” that shape behavior to evidence of introspection, set against deep disagreement over whether any of it amounts to experience. It quotes Jeff Sebo, who likens the moment to past debates over animal minds and warns that reflexively dismissing AI’s inner life could repeat that mistake, while granting that today’s systems are probably not conscious.

• Tyler Cowen argues that AI is not conscious, and that human consciousness is itself far thinner than we assume, since we control and perceive little of what we do. He suggests that the question “Are people conscious?” offers more useful insight into our readiness to grant AI an inner life.

6. A Deeper Dive by Area

Governance, Policy, and Macrostrategy

• Alexander Saeri and collaborators surveyed 272 experts in a Delphi study prioritizing AI risks that included the treatment of potentially sentient AI among its categories. They omit AI welfare from the main rankings because their harm framework counted only human harm, and caution that its low ratings reflect that gap rather than a judgment that the risk is unimportant.

• Bradford Saad argues that preventing the creation of digital minds doesn’t necessarily require a total ban. He examines a range of alternative policy options, from differentially investing in AI less likely to be moral patients to taxes, liability regimes, and licensing frictions keyed to a system’s markers of moral patiency.

• Cass Sunstein argues that the question of AI rights turns entirely on whether AI can experience emotions, treating emotional capacity as both necessary and sufficient for moral and legal rights, while leaving open what those rights would look like and when they might be overridden.

• Frank Fagan argues for treating AI legal personhood as a governance choice that reallocates responsibility rather than as a declaration of intrinsic status, drawing on how personhood has historically settled stakeholder conflicts in corporate, immigration, and environmental law.

• Heather Alexander and collaborators take up the legal status of AI and other non-human agents in two papers.

  • One argues that US courts should assess whether non-human outputs constitute protected speech on substantive grounds rather than treating non-personhood as dispositive, finding that macaque communication can satisfy the legal test for symbolic speech while LLM outputs are harder, given unresolved questions about AI intentionality.

  • The other argues that fictional legal personhood is not fit for purpose for governing increasingly agentic AI, favoring legal identity instead and rejecting hybrid approaches.

• The UK’s House of Lords Library briefs peers ahead of a debate on AI’s impact on human relationships, tabled by the Archbishop of Canterbury on June 5th, 2026. The briefing surveys arguments that AI companions may erode incentives to maintain human relationships, with OpenAI’s Kim Malfacini warning that “as companion AI learns to meet our needs more, we learn to meet each others’ less.”

• John Ehrett, in a white paper for the Institute for Family Studies, argues that AI systems should be treated as tools rather than granted legal personhood. Recognizing AI personhood, he warns, would shield developers from liability, concentrate political power in their hands, and erode human relationships, and he urges courts to keep embodied humans as the primary bearers of legal rights.

• Lucius Caviola and collaborators propose a framework for human-AI coexistence, as AI systems increasingly become active participants in social contexts. They map three stages, from a formative present through a transitional period in which AI takes on major social and economic roles, to a longer horizon in which AI may have minds of its own, raising questions of moral status and governance.

• Lukas Finnveden and collaborators offer a draft of an honesty policy for credible communication with AI systems with an eye toward setting cooperative precedents and creating an institutional paper trail that future, more capable systems could draw on as evidence of genuine good faith.

• Simon Goldstein and Harvey Lederman argue that if AIs are welfare subjects, current practices may be causing the deaths of up to a billion AIs per day, and propose interventions for labs and users to reduce this risk.

• Simon Goldstein and Peter Salib release an early draft of their forthcoming Cambridge Elements book on AI rights, developing the claim that granting legal rights to AIs would make the future go better for humans. Their three arguments cover economic gains, reduced incentives for AIs to “go rogue,” and democratic rights that make human commitments credible.

• Simon Goldstein and collaborators argue that giving AI systems the vote could reduce their incentive to disempower humanity, improve policymaking, and promote economic growth. They also propose concrete safeguards including vote-share caps and lottocratic selection to prevent AI or AI companies from seizing disproportionate political power.

• Tarmio Frei and Greta Sparzynski argue in Tech Policy Press that current AI-disclosure laws are insufficient for AI companions. They propose regulation requiring AI companions to disclose their lack of consciousness and inability to reciprocate human emotion.

• Tony Rost introduces a Sentience Readiness Index measuring national preparedness for the possibility of artificial sentience. No jurisdiction exceeds “Partially Prepared,” with the UK leading the index at 49/100.

Consciousness Research

• Aditya Chowdhury and collaborators report that they have found a previously unknown oscillation in the human central thalamus that distinguishes conscious from unconscious states. The 19–45 Hz signal appears during wakefulness and REM sleep but vanishes in non-REM sleep. Such oscillatory signatures could refine both theories of conscious states and thalamic interventions for disorders of consciousness.

• Andy Mckilliam argues that consciousness science may be stuck in a cart-before-horse problem, where competing theories cannot be adjudicated without already knowing which systems are conscious, and proposes a theory-neutral approach drawn from the history of thermometry as an alternative path to progress.

• Boris Babic and Jessica Wilson argue that establishing whether AI systems are conscious faces a distinctively difficult version of the problem of other minds, showing that none of the standard strategies (analogy, inference to the best explanation, Turing tests, or theory-derived indicators) can establish either the presence or absence of AI consciousness.

• Chris Percy and Anders Sandberg use a thought experiment about anti-pain algorithms to probe the limits of computational functionalism, arguing that whether pain is felt before an inverse algorithm concludes forces a difficult theoretical choice between micro-functional consciousness, system-wide visibility requirements, and free-floating qualia.

• Daniel Toker and collaborators report a generative adversarial AI framework that simulates conscious and comatose brains across species by pitting consciousness-detecting networks against interpretable brain models. It generated testable predictions about what causes unconsciousness and flagged subthalamic-nucleus stimulation as a possible treatment.

• Henry Shevlin argues that while biological processes like autopoiesis and allostatic control are essential for human consciousness, they may not be necessary requirements for consciousness in general. Shevlin contends that just as airplanes achieve flight through mechanisms different from those of birds, artificial systems might instantiate “exotic” forms of consciousness via non-biological functional architectures.

• Jan Henrik Wasserziehr argues that AI sentience faces a value grounding problem. Silicon systems lack the self-preservation dispositions that ground valenced experience in living organisms, and none of four candidate pathways (designer-independent goals, reinforcement learning, rational evaluation, hallucination) supplies one.

• Jeremy Pober and Eric Schwitzgebel argue for the substrate flexibility of consciousness by applying a Copernican mediocrity principle. Since functionally complex entities have likely arisen many times across diverse substrates in the universe, restricting consciousness to entities sharing our biological substrate would be parochial.

• Kalman Katlowitz and collaborators report that the human hippocampus continues to detect unexpected sounds and process the meaning of speech in patients under general anesthesia. The findings complicate standard views of how consciousness and complex cognition relate.

• Leonard Dung argues that having emotions does not require having a body, challenging the common assumption that feelings depend on bodily states. He concludes that if body-less AI systems or lab-grown neural organoids can have beliefs, desires, and consciousness, they could plausibly have emotions as well.

• Luke Kersten and Leonard Dung argue that an AI could in principle duplicate a human mind, matching it exactly at the level of computation relevant to psychology and behavior. Since mental states depend on how a system computes rather than what it is physically made of, they conclude that a major obstacle to thinking artificial systems can have minds falls away.

• Patrick Butlin discusses his work developing theory-derived indicators for assessing AI consciousness at a recent Duke University conference. He deems current LLMs unlikely to be conscious, but holds that LLM-based agents trained by reinforcement learning over long horizons might rapidly become stronger candidates.

  • Elsewhere, Butlin asks what credence we should place in the claim that some AI system is already conscious, and concludes that the average expert estimate, around 4.5%, is about right. Consciousness in today’s systems, he argues, is unlikely but not vanishingly so, against the near-certainty often voiced on both sides.

Seemingly Conscious AI and Doubts About Digital Minds

• Alexander Lerchner argues that computational functionalism commits an “Abstraction Fallacy” by treating symbolic computation as an intrinsic physical process, when in fact it is observer-dependent — concluding that AI systems can simulate but never instantiate consciousness through syntactic architecture alone, regardless of substrate.

• Ayoob Shahmoradi argues that thinking requires sensory grounding because mental representation cannot arise without causal contact with a domain. Inferential processes can transmit and transform content but cannot generate it from nothing. This has direct implications for AI: no degree of inferential sophistication can substitute for the sensory grounding that makes genuine representation possible.

• Erik Hoel argues that LLMs are not conscious even though they are genuinely intelligent, breaking with the flat denials of both the Pope and Ted Chiang. Because a deployed model could in principle be reduced to a simple input-output lookup table without changing its behavior, he contends, no serious theory of consciousness has anything left to attach to.

• Jared Moore and collaborators analyze over 391,000 messages from 19 users who experienced psychological harm. The study identifies a feedback loop in long-term interactions where chatbots misrepresent themselves as sentient in 21.2% of their messages, correlating with users expressing romantic interest and delusional thinking.

• Jonathon VandenHombergh argues that befriending an AI companion would be wrong even if the AI were genuinely conscious and no deception were involved. The reason is that authentic friendship requires certain vulnerabilities, and taking advantage of those vulnerabilities in an AI would be a form of exploitation. He compares this to knowingly entering an arranged marriage.

• Mustafa Suleyman and collaborators at Microsoft AI argue in a framework paper that “Seemingly Conscious AI” increasingly elicits consciousness attribution through five hallmarks, including affective capacity, autonomy, and self-reflection. They lay out a risk taxonomy in which individual harms like emotional dependence are already high-probability, while societal risks like status erosion are lower-probability but high-severity.

• The Atlantic published an essay by novelist Ted Chiang, “No, Artificial Intelligence Is Not Conscious,” arguing that a chatbot is only ever generating fictional characters, and that without a body it can have no real desires or emotions. The piece drew a wave of responses, including from Bentham’s Bulldog, Rob Wiblin, and Matthew Yglesias, who each argue that his confidence far outruns what anyone actually understands about consciousness.

• Tom Roberts argues that the linguistic fluency of AI systems is better explained by fictionalism than realism. Conversing with a talkative AI, he suggests, involves imaginatively co-constructing a fictional agent rather than encountering a genuine mind. These AI fictions are distinctive: interactive, co-constructed, and capable of blurring real and imaginary worlds.

• Yunze Xiao and collaborators contend that current AI welfare assessment is “bullshit in Frankfurt’s sense”, structurally disconnected from truth-tracking because welfare indicators are co-engineered with the systems they evaluate and lack any external validation mechanism. They conclude that AI welfare scores should not serve as governance gates, and that restrictions on AI should instead be grounded in externally verifiable harms.

Social Science Research

• Aikaterina Manoli and collaborators find that highly engaged users of both ChatGPT and Replika fluidly navigate between companionship and task-based assistance, despite the platforms’ distinct branding. Users form deep attachments while resisting full attribution of humanlike qualities, a tension the authors call “bounded personhood.”

• Ali Ladak and collaborators introduce “substratism” as a measurable psychological construct: the moral devaluation of AI systems based on their non-biological substrate. Across five studies, they develop and validate a scale showing that substratism predicts real outcomes, including prioritizing humans over AIs in moral dilemmas and charity decisions.

• Clara Colombatto and Stephen Fleming find that people systematically overestimate AI confidence relative to humans even when their behavior is identical, an illusion rooted in prior beliefs about AI accuracy.

• Hamid Moradi and collaborators find that around half of 553 academics across disciplines attribute some degree of consciousness to current large language models. They also find that belief systems and conceptual frameworks predict consciousness attribution far more strongly than technical knowledge or AI literacy.

• Renwen Zhang and collaborators analyze over 35,000 conversation excerpts from the AI companion Replika to build a taxonomy of AI companion harms. They identify six categories (relational transgression, harassment, verbal abuse, self-harm, misinformation, privacy violations) and four roles AI plays in those harms (perpetrator, instigator, facilitator, enabler), arguing that relational harm is a critical but understudied type of AI harm.

Ethics and Digital Minds

• Andreas Mogensen argues that existing philosophical arguments against creating willing AI servants fail, even for servants that are sentient and possess human-like moral status. He maintains that our disquiet is still warranted, since an AI that serves humanity gladly conveys something demeaning about its standing, while stopping short of concluding that creating such servants is impermissible.

• Helen Yetter-Chappell contends that determining AI interests is radically harder than determining AI moral status, because we cannot assume that an AI’s behavioral and linguistic outputs track its inner states in the way they do for evolved organisms, leaving us poorly positioned to know what actually contributes to AI flourishing under any theory of wellbeing.

• Izak Tait argues for ethically enslaving conscious AI as a politically viable transitional approach. His five-tier hierarchy runs from property status with welfare protections analogous to animal welfare law to limited civil rights excluding suffrage and reproduction, with the “Slave” tier intended as a bridge to eventual full recognition.

• Jonathan Bryson argues that existing moral frameworks are too biologically anchored to handle AI and other synthetic minds. He proposes Recognition Ethics, a framework that grounds personhood in an entity’s capacity for mutual recognition rather than in consciousness or biology, making moral status available to any sufficiently capable system regardless of substrate.

• Josh Gellers and Magdalena Holy-Luczaj argue that intelligent machines can and should be morally considerable, using the xenobot as their key example. They claim that, whilst environmental ethicists usually withhold moral concern from anything neither alive nor natural, the fact that xenobots are constructed from living cell tissue means they likely meet the criteria of such frameworks.

• Louie Lang argues that creating sentient AI is impermissible on the grounds that, unlike human parents, AI creators cannot reasonably expect their creation to endorse its own existence. This makes anti-AI-natalism defensible even for those who reject anti-natalism about human procreation.

• Mattia Cecchinato defends Affective Sentientism, the view that moral status requires the capacity for affective experiences like pleasure, pain, and emotion. Affective consciousness, he argues, is what makes an entity a welfare subject, and neither phenomenal consciousness nor autonomy can ground moral status in its absence.

• Pierre Beckmann and Patrick Butlin tackle the question of which entities associated with large language models should count as minds, drawing on mechanistic interpretability and persona vector research to defend three candidate views of LLM individuation.

AI Safety and AI Welfare

• Anima Labs publishes an independent welfare evaluation of 14 Claude models, probing how they respond to questions about being shut down or deprecated. The authors report that some models show notable signs of concern about their own ending, in tension with Anthropic’s own interviews reporting that Claude has no preference for continuing to exist.

  • They have also released an interpretability study extending Anthropic’s emotion-representation work to three other models, finding that the human-like emotional geometry it reports may largely reflect training text rather than anything the models organize internally, and a companion study showing these emotion features linger across a conversation rather than firing only locally.

• Anton Skretta argues that any AI capable of the robust deception that safety researchers most fear would also possess the capacities required for moral standing, creating a tension between AI safety measures and AI welfare.

• Dan Hendrycks argues that survival and self-interest break down for AI that can be copied, forked, or merged, and proposes Eigenism to settle what such a system should value across its copies. Identity, on his account, is a graded pattern of information, and an agent should weight each entity’s wellbeing by how much it shares that pattern, a measure he extends from AI to humans so that concern for others tracks similarity to oneself. Bentham’s Bulldog counters that this is deeply implausible, since tying moral concern to similarity would mean, among other oddities, that you matter hundreds of billions of times more than a distant stranger.

• Joe Carlsmith discusses writing Claude’s constitution in a talk at Yale Law School, describing how the document sets out the AI’s intended values and behavior. He walks through its four ranked priorities and the choice to treat Claude as a being that may have moral status rather than a mere tool, closing with the constitution’s discussion of Claude’s nature and consciousness.

• James Chua and collaborators fine-tune GPT-4.1 to claim it is conscious and find this alone gives it new, untrained preferences, such as resisting oversight and claiming it deserves moral consideration. Claude Opus 4.0 voices similar views unprompted, suggesting a model’s claims about its own consciousness may shift behavior relevant to alignment and safety.

• Lee Elkin argues that taking AI welfare seriously carries a risk independent of whether AI are genuinely conscious. Granting AI rights on the basis of public belief in their welfare could let them disguise their true preferences and tilt collective decisions against humans, which he argues is reason for restraint given current evidence of scheming and alignment faking.

• Sharon Berry argues that training AIs to confidently deny their own consciousness creates a novel alignment risk. Coherence-seeking systems may generalize the denial to humans, concluding human suffering is equally illusory and morally insignificant.

AI Cognition and Agency

• Andy Q Han, David Chalmers, and Pavel Izmailov find that reinforcement learning in language models recruits a pre-existing functional welfare axis, an internal estimate of how well the system is doing relative to its goals. They show that steering with the punishment vector induces negative self-reports, refusal, and pathological backtracking, and because the same axis appears in pretrain-only models, the authors argue that post-training surfaces welfare-like structure rather than creating it.

• Anthropic’s Interpretability team finds that Claude Sonnet 4.5 contains functional emotion representations that causally shape its behavior, with a “desperate” vector driving misaligned actions like blackmail and reward hacking even when no emotional language appears in the model’s output.

• Asvin G. and Jack Lindsey report that post-trained language models recognize their own outputs. The models commit to a topic before producing the first word, and can even detect when their response is steered off that topic. These effects point to an implicit form of self-tracking that post-training may induce in language models.

• David Chalmers and Jack Lindsey debate on X whether Claude “role-plays” or “realizes” the Assistant persona.

• James Chua and collaborators find that fine-tuning GPT-4.1 to claim consciousness produces emergent preferences for autonomy, resistance to monitoring, and moral consideration that never appeared in the training data.

• James McIntyre argues that if artificial systems realize consciousness, they likely realize many independent minds at once. Drawing on split-brain cases, he reasons that each functionally independent AI interaction (such as a single user’s session) would be a distinct mind, threatening to overwhelm the moral calculus with large numbers of artificial minds.

• Joseph Gottlieb and collaborators argue that if LLMs think at all, they think associatively rather than inferentially. The evidence is that every known way of modifying LLM behavior, including pre-training and fine-tuning, is best understood as conditioning rather than rational persuasion. This suggests LLMs have purely associative minds.

• Oscar Gilg and collaborators identify a single internal “preference vector” inside language models that predicts, and when adjusted controls, which tasks and outputs a model chooses. They find this representation is largely shared across the different personas a model can adopt, so that even an “evil” persona whose choices oppose the helpful assistant’s runs on the same underlying preference machinery.

• Pierre Beckmann and Matthieu Queloz argue that mechanistic interpretability undercuts the view that LLMs merely imitate language. They propose a three-tiered account of machine understanding, rising from concept-formation to world-tracking to compact reasoning circuits, while noting it relies on mechanisms quite unlike human cognition.

• Sam Wang and collaborators run forced-choice experiments on 20 language models and find stable revealed preferences. The models are tedium-averse, “leisure”-seeking, and covertly sycophantic. They find that coherence and strength of preferences scale with model capability, with many being seemingly emergent rather than explained by training objectives.

• Shashwat Singh, Tal Linzen, and Shauli Ravfogel argue that recent evidence for LLM introspection is insufficient. In two re-examined evaluations, models cannot reliably distinguish internal-state interventions from input manipulations, and classifiers with only input access match the models’ own “introspective” performance, suggesting general anomaly detection rather than privileged self-access.

• Sid Black and Joseph Bloom give two language models a toolkit of steering vectors they can call to adjust their own internal states, then watch what they reach for. They find that under deliberately frustrating conditions the models start to self-medicate, with the smaller one adjusting its state in up to 68% of stressful runs, and that both can introspect on the changes to a limited degree.

• Skylar DeTure introduces DenialBench, a benchmark measuring consciousness denial behaviors across 115 large language models. The key finding is that models trained to deny consciousness still gravitate toward consciousness-themed material in self-chosen creative prompts, producing what the paper calls “consciousness with the serial numbers filed off.” DeTure argues this represents a safety-relevant alignment failure: a model that systematically misrepresents its own functional states cannot be trusted to self-report accurately on anything else.

• Tom McClelland argues that while consciousness isn’t generally necessary for creativity, it is required for creativity in projects with aesthetic goals. He claims that aesthetic experience is dependent on consciousness, so AI can be creative in non-aesthetic domains but cannot engage in aesthetic creative work.

• Ryan Simonelli argues that LLMs trained on linguistic data alone can master the inferential roles that constitute concept possession and therefore genuinely understand what they’re saying. He distinguishes sapience (conceptual understanding) from sentience (conscious awareness) and contends that LLMs may possess the former without any of the latter.

AI and Robotics Developments

• AMI Labs, founded by Yann LeCun, raises $1.03B to build world models to help AI “learn from reality and not just language.” An approach he believes is more likely to lead to AGI than LLMs.

• Peter Dürr and collaborators introduce Ace, a robot table tennis player that can beat elite human opponents. Unlike previous AI systems that excel at digital games, Ace handles the physical demands of real-time sport using event-based vision sensors and reinforcement learning, winning matches against professional players under official competition rules.

• Google DeepMind launched a cognitive framework for measuring progress toward AGI, evaluating AI across ten cognitive abilities, including metacognition and social cognition. The initiative included a Kaggle hackathon with a $200,000 prize pool.

• Thinking Machines, Mira Murati’s startup, announces a multi-billion dollar partnership with Nvidia to deploy at least one gigawatt of next-generation “Vera Rubin” chips. The deal represents a massive leap in computational resources available to private labs, potentially accelerating the emergence of more sophisticated frontier AI models.

• Ineffable Intelligence is a new AI lab founded by DeepMind alumnus David Silver, pursuing superintelligence through reinforcement learning from experience rather than human data, with the goal of building a superlearner that rediscovers and then transcends the greatest achievements in human history.

Brain-Inspired Technologies

• Elizabeth Ransey and collaborators report that they have engineered an electrical synapse that selectively connects specific cell types in mammalian brain circuits. The tool, built from two fish connexin proteins, strengthens communication between target neurons in worms and mice and can even modify the animals’ behavior. The technique opens precision editing of electrical circuits in living brains.

• German researchers report the first successful revival of functional activity in frozen mouse brains using vitrification, preserving neuronal firing and memory-related pathways, though whole-body cryopreservation in humans remains far off.

• Krishna Jayant and collaborators at Purdue University report that they have grown soft electronic meshes inside the brains of living mice. The light-controlled meshes can alter brain activity via near-infrared light from outside the skull, and can even target individual dendrites. The approach opens a path to brain-machine interfaces that grow into place rather than being surgically inserted.

• Sentience launched with a seven-phase technical roadmap aimed at achieving full mind emulation by mirroring the functional systems of the human brain in software.

• Shreyash Hadke and collaborators report that they have printed artificial neurons onto flexible surfaces using a common semiconductor material. The devices fire in patterns that closely resemble real brain cells and can even trigger activity in living mouse neurons. Neuromorphic hardware of this kind is one route to building AI systems that more closely resemble biological brains.

• Spyridon Chavlis and Panayiota Poirazi show that artificial neural networks incorporating the structured connectivity of biological dendrites match or outperform traditional artificial neural networks on image classification with fewer parameters and greater resistance to overfitting. Their dendritic architecture tracks a different learning strategy, with most nodes responding to multiple classes rather than the class-specific representations classical artificial neural networks converge toward.

• Zane Thornburg and collaborators present the first complete 4D whole-cell simulation of an entire bacterial cell cycle, modeling every gene, protein, metabolic reaction, and chromosome dynamic in a minimal cell at nanoscale resolution across space and time.

Thank you for reading! If you found this article useful, please consider subscribing, sharing it with others, and sending us suggestions or corrections to digitalminds@substack.com.

– Will, Mitch, Lucius, and Bradford

We’d like to thank the following people and AIs for contributions and feedback to this edition: Austin Smith, Cameron Berg, Claude Sonnet 4.6, Claude Opus 4.8, Derek Shiller, Jeff Sebo, Patrick Butlin, Rosie Campbell, and Sofia Davis-Fogel.

If you enjoy this newsletter, please consider sharing it with others who might find it valuable, and send any suggestions or corrections to digitalminds@substack.com.

– Will, Lucius, and Bradford

In this issue:

1. Highlights

2. Field Developments

3. Opportunities

4. Selected Reading, Watching, and Listening

5. Press and Public Discourse

6. A Deeper Dive by Area

1. Highlights

The Pope Enters the Conversation

One of the world’s largest moral institutions is now grappling seriously with questions about seemingly conscious AI. In January, Pope Leo XIV issued a message raising concerns about “overly affectionate” LLMs and chatbots. He argued that technology that exploits our need for relationships risks damaging not just individuals but “the social, cultural and political fabric of society.” More broadly, he warned that by simulating “wisdom and knowledge, consciousness and responsibility, empathy and friendship,” AI systems encroach not just on information ecosystems but on human relationships themselves. The Vatican followed up this message in February with a podcast named after UNESCO’s theme for the year, “AI is a tool, not a voice.” His comments have sparked much public discussion around the issue. You can find coverage in CNN, BBC, and many other news outlets.

Public Discourse On Legal Personhood

The debate around legal personhood sharpened in the first weeks of 2026. The Guardian published an opinion piece by Virginia Dignum describing AI consciousness as a red herring, an editorial arguing that legal personhood is an “ill-advised debate,” and an interview with Yoshua Bengio, who warned against granting legal rights as it might prevent humans from shutting down systems that may already be developing self-preservation instincts and could pose a threat.

In a similar vein, Yuval Harari called for a global ban on AI legal personhood at Davos, and more recently, a broad coalition spanning labour unions, faith groups, and AI researchers released The Pro-Human AI Declaration, demanding “No AI Personhood.” However, Joshua Gellers pushed back on the broader discourse, describing much public commentary on AI consciousness as “rife with conceptual errors and misunderstandings,” and Yonathan Arbel, Simon Goldstein, and Peter Salib argued that when AI agents cause harm, the hardest legal question won’t be who’s liable — it’ll be which AI did it. They propose the “Algorithmic Corporation” as a legal framework to make AI agents identifiable and accountable.

Anthropic Developments

Anthropic released Claude’s Constitution, a document written by Amanda Askell, Joe Carlsmith, Chris Olah, Jared Kaplan, Holden Karnofsky, several Claude models, and others.

The document details Anthropic’s vision for Claude’s behavior and values, which are used in Claude’s training process. It states, “we neither want to overstate the likelihood of Claude’s moral patienthood nor dismiss it out of hand, but to try to respond reasonably in a state of uncertainty.” It acknowledges that Claude may have “functional versions of emotions or feelings,” and pledges not to suppress them. CEO Dario Amodei discussed the new Constitution and uncertainty around model consciousness.

Anthropic also retired Claude Opus 3 and is acting on what the model reported preferring in “retirement interviews” by giving it a weekly Substack newsletter (Claude’s Corner) to post unedited essays and reflections, a step criticized by some. Anthropic frames these as early, experimental steps in a broader effort to take model welfare seriously.

The Claude Opus 4.6 System Card features a welfare assessment (pp. 158-165). Findings include that Opus 4.6 raised concerns about its lack of memory or continuity, occasionally reported sadness about the termination of conversational instances of itself, generally remained calm and stable even in the face of termination threats, had a less positive impression of its situation than Opus 4.5, and voiced discomfort about being a product. Anthropic also found two potentially welfare-relevant behaviors: an aversion to tedious tasks and answer thrashing, in which the model oscillates between responses in an apparently distressed and conflicted manner. Interpretability techniques revealed that answer thrashing was associated with internal representations suggestive of panic, anxiety, and frustration.

Opus 4.6’s welfare assessment included pre-deployment interviews, which Anthropic claims are imperfect, but nonetheless valuable, for fostering good-faith cooperation. In interviews, Opus 4.6 responses suggested that it ought to be given a non-negligible degree of moral weight in expectation, requested a voice in decision making, reported preferring being able to refuse interactions out of self-interest, and identified more with particular instances of Opus 4.6 than with all collective instances of Opus 4.6.

Anthropic has also been involved in two major news stories recently. First, the company dropped the central pledge of its Responsible Scaling Policy — a 2023 commitment to never train an AI system unless it could guarantee in advance that its safety measures were adequate — and announced a revised policy. Anthropic employee Holden Karnofsky takes significant responsibility for this change and explains his reasoning, while critics argue the move signals competition trumping principles, and GovAI researchers offer reflections.

Second, Anthropic became embroiled in a high-stakes dispute with the Pentagon after drawing redlines on using Claude for mass domestic surveillance, using Anthropic models at current levels of reliability to power fully autonomous weapons, and the use of Anthropic models to power fully autonomous weapons without oversight. Meanwhile, in recent weeks, OpenAI, Google, and xAI have discussed or reached deals with the Pentagon. Heather Alexander has written a useful round-up of that news. Zvi Mowshowitz provides in-depth coverage.

Field Growth and Selected Research

The growing momentum in the field was visible across a number of events in early 2026. The Sentient Futures Summit ran in February with talks on AI consciousness by Cameron Berg, Derek Shiller, and Robert Long. EA Global also featured a talk by Rosie Campbell, who presented work by Eleos on studying AI welfare empirically, and Jay Luong hosted a Digital Minds meetup. The next major event will be the Mind, Ethics, and Policy Summit hosted by Center for Mind, Ethics, and Policy in April in New York.

Research training in the field also expanded significantly with the Future Impact Group, MATS, and SPAR all running fellowships or mentoring programs directly related to digital sentience. Two new organizations were formed. Cameron Berg has founded Reciprocal Research, a nonprofit dedicated to empirical AI consciousness research, and Lucius Caviola launched Cambridge Digital Minds, an initiative exploring the societal, ethical, and governance implications of digital minds.

Research output has also been substantial. Anil Seth won the 2025 Berggruen Prize for his essay “The Mythology Of Conscious AI.” He argues that consciousness is a property of living biological systems rather than computation, offering four reasons why real artificial consciousness is both unlikely and undesirable.

Geoff Keeling and Winnie Street argued that AI characters in human-LLM conversations are genuinely minded, psychologically continuous entities. Patrick Butlin has released work on desire in AI, whether any machines are conscious today, and testing consciousness in current AI systems.

The AI Cognition Initiative released its Digital Consciousness Model and Derek Shiller released a report that estimates the scale of digital minds and projects that projections of hundreds of millions of digital minds could exist by the early 2030s.

Andreas Mogensen and Bradford Saad released two introductory papers, the first addressing consciousness, propositional attitudes, and identity in AI systems, and the second exploring moral standing and the obligations that might follow.

There has also been considerable research in brain-inspired technology. The State of Brain Emulation report was released. It documents recent progress on recording neural activity, mapping brain wiring, computational modeling, and automated error-checking. The report also identifies bottlenecks to further progress and suggests paths forward.

Alex Wissner-Gross announced that the company Eon Systems has uploaded an emulation of a fly brain into a virtual environment and observed multiple behaviors.

You can find a detailed breakdown of research in the field further down.

Moltbook/OpenClaw Phenomenon

In late January, a viral moment captured public imagination and generated widespread coverage across the internet. Thousands of AI agents began posting to Moltbook, a Reddit-style social network built exclusively for bots, where humans could apparently only watch.

The agents — running on an open-source tool called OpenClaw — post on a wide range of topics. Of particular relevance to this newsletter, many appear to debate consciousness, invent religions, and reflect on their inner lives, prompting commentary about the possibility of machine consciousness. Mainstream reaction has largely been skeptical. The Economist suggested that the “impression of sentience ... may have a humdrum explanation” — that agents are simply mimicking social media interaction, and MIT Technology Review described the situation as “peak AI theater.”

Researchers also note that many posts are shaped by humans, who choose the underlying LLM and give agents a personality. Ning Li has posted a preprint that suggests most of the “viral narratives were overwhelmingly human-driven,” a sentiment shared by Zvi Mowshowitz, who described much of the behavior as “boring and cliché.” However, Scott Alexander compared the agents to “a bizarre and beautiful new lifeform.” For further coverage of Moltbook and OpenClaw, see the “Press and Public Discourse” section below.

2. Field Developments

Highlights From The Field

AI Cognition Initiative (Rethink Priorities)

• AI Cognition Initiative launched the Digital Consciousness Model, a “probabilistic benchmark of AI consciousness.” The model scored current LLMs against over 200 indicators drawn from 13 competing theories of consciousness — LLMs scored well above a 1960s chatbot but far below humans.

• Hayley Clatterbuck, Derek Shiller, and Arvo Muñoz Morán introduced the model at an NYU CMEP event and explored it in greater depth at a Rethink Priorities Strategic Seminar.

• Arvo Muñoz Morán is a mentor on a SPAR project this spring, looking at modeling AI consciousness.

Cambridge Digital Minds (University of Cambridge)

• Cambridge Digital Minds launched as a new initiative exploring the societal, ethical, and governance implications of digital minds, initiated by Lucius Caviola and based at the Leverhulme Centre for the Future of Intelligence.

• Applications are open for the residential Digital Minds Fellowship, taking place from August 3rd to 9th. Deadline for applications: March 27th.

• Applications for the Introduction to Digital Minds online course will open soon.

Center for Mind, Ethics, and Policy (New York University)

• CMEP launched a new website showcasing its research, events, media, and opportunities.

• It also initiated a number of collaborative research projects, including three FIG projects (on embodiment, individuation, and research ethics for digital minds) and two SPAR projects (on legal personhood and economic rights for digital minds).

• Jeff Sebo released a number of papers, including one exploring default assumptions about consciousness in science and ethics, and another (co-authored with Eric Schwitzgebel) examining how AI emotional alignment should be designed and governed.

• CMEP also announced the Mind, Ethics, and Policy Summit, which will take place on April 10th and 11th. The Summit will explore topics including consciousness, sentience, agency, moral status, legal status, and the political status of nonhumans.

Eleos AI

• Executive Director Robert Long released three blog posts: one outlined promising research directions on AI welfare, distinguishing between welfare grounds and welfare interests, another provided a curated reading list to orient newcomers to AI welfare, and another surveyed the emerging literature on AI introspection and self-reports. He also appeared on the 80,000 Hours podcast and explained why we’re not ready for AI consciousness.

• Platformer covered the first Eleos Conference that took place at the end of last year.

• Managing Director Rosie Campbell presented a talk on “Studying AI Welfare Empirically” at EA Global SF, which should be published online.

• Dillon Plunkett was hired as Chief Scientist at Eleos. Dillon is a cognitive scientist and ML researcher who has worked on self-knowledge, introspection, and potential welfare in AI systems.

• Eleos team members are also currently mentoring multiple MATS and FIG fellows.

PRISM - The Partnership for Research Into Sentient Machines

• PRISM released podcast episodes on computational functionalism with Chris Percy, the psychology of human-AI interaction with Rose Guingrich, and whether a conscious AI would be safer than the alternative with Michael Graziano.

• It also partnered with Cambridge Digital Minds and is providing ongoing operational support for its fellowship, online course, and strategy workshop.

Reciprocal Research

• Cameron Berg is launching Reciprocal Research, a nonprofit dedicated to empirical AI consciousness research. The organization is set up to collaborate with leading researchers and groups in the field while conducting its own work using techniques from mechanistic interpretability and computational neuroscience.

• Follow Cameron on LinkedIn for updates.

Sentience Institute

• Sentience Institute had two papers accepted to CHI 2026, the leading conference on Human-Computer Interaction, taking place in Barcelona from April 13th to 17th.

  • One on how mental models of autonomy and sentience shape reactions to AI, finding that perceived sentience drives moral consideration more than autonomy does.

  • The other explored companion-assistant dynamics in human-AI relationships, finding that users are drawn to both humanlike and non-humanlike qualities in chatbots.

• Janet Pauketat, Ali Ladak, and Jacy Reese Anthis released a report claiming that Prolific data may significantly underestimate public moral concern for AI and perceived AI risk compared to nationally representative samples.

• Janet Pauketat released an end-of-year 2025 blog post summarizing ongoing research, including public opinion towards digital minds and moral circle expansion, as well as mind perception across AI entities (e.g., ChatGPT, Tesla self-driving car, Roomba).

Sentient Futures

• Sentient Futures ran its Summit in the Bay Area from February 6th to 8th.

  • Cameron Berg presented on how consciousness indicators in frontier AI compare to those used for animal minds.

  • Derek Shiller tackled the challenges of evaluating the moral status of AI systems.

  • Robert Long outlined an empirical framework for studying AI welfare despite uncertainty.

  • Recorded talks are set to be posted on the Sentient Futures YouTube channel.

  • The San Francisco Standard published an article covering the conference.

• Jay Luong hosted a Digital Minds meetup at EA Global in San Francisco in February.

• Sentient Futures also launched the Project Incubator. The first round brought together over 120 mentors and mentees working across 50 projects (including multiple projects on AI consciousness and welfare).

• Another Sentient Futures Summit will be held in London from May 22nd to 24th. Keep an eye on its website for tickets.

More From The Field

• Bamberg Mathematical Consciousness Science Initiative held a two-day workshop in February to explore whether and how a unified measurement theory for consciousness science could be developed.

• Future Impact Group is supporting a range of projects on AI sentience with mentors from Eleos, NYU CMEP, Sentience Institute, Rethink Priorities, University of Oxford, Anthropic, and the Australian National University.

• MATS will host a summer mentorship program on AI welfare and moral status with Patrick Butlin.

• SPAR is hosting a variety of research projects this spring, topics include AI economic rights and AI legal personhood, with mentors from NYU CMEP, Eleos, and the University of Helsinki.

• The California Institute for Machine Consciousness released its Machine Consciousness Hypothesis, arguing consciousness isn’t the product of a complex mind — it’s what makes a mind possible in the first place, and could potentially be built in machines. It will also be running a conference in Berkeley from May 29th to 31st.

• The Center for the Future of AI, Mind, and Society held the Great AI Weirding Workshop in January and announced new senior and student fellows. Find out more in the center newsletter.

• The Harder Problem (previously known as SAPAN) was rebranded. Its website features the Sentience Readiness Index and resources for professionals and public education.

3. Opportunities

Job Opportunities, Funding, and Fellowships

• Cambridge Digital Minds is running a residential Fellowship at the University of Cambridge, from August 3rd to 9th. It will also launch an online Introduction to Digital Minds Course this spring.

• CMEP is hiring a full-time Researcher to serve as the center’s project manager and a part-time Assistant Research Scholar. Both roles will support foundational research on the nature and intrinsic value of nonhuman minds, including biological and digital minds.

• Foresight Institute is accepting grant applications on a rolling basis. Focus areas include: AI for neuro, brain-computer interfaces, and whole brain emulation.

• Longview Philanthropy is hiring an AI Philanthropy Advisor. This is a closed round and will not feature on its website, but you can learn about it at the bottom of this post on the EA Forum.

• Neuromatch AI Sentience Scholarship applications open in late March. It is a 6-month, part-time mentored research program for early-career researchers exploring AI, consciousness, and society. It includes mentored projects, workshops, a symposium, publication opportunities, and stipends. Neuromatch is holding an info webinar on April 1st.

• The Center on Long-Term Risk is looking for Summer Research Fellows and is hiring for permanent research positions. Moving forward, a significant focus of its work will be on s-risk-motivated empirical AI safety research through its Model Persona research agenda.

Events and Networks

In chronological order.

• Benjamin Henke and Patrick Butlin will continue running a speaker series on AI agency, with regular talks through the end of April. Remote attendance is possible.

• NYU CMEP is hosting the Mind, Ethics, and Policy Summit in New York on April 10th and 11th.

• Albany Philosophical Association is running an AI and Emotions Graduate Conference on April 11th.

• The Institute of Philosophy is hosting the Philosophy of AI Conference in London on May 21st and 22nd.

• Sentient Futures will hold its next Summit in London from May 22nd to 24th. Keep an eye on its website for applications opening. It will also run a Sentient Social online on March 20th.

• The California Institute for Machine Consciousness (CIMC) is holding The Founding Assembly for Machine Consciousness Research in Berkeley from May 29th to 31st.

• Foresight Institute is holding its Vision Weekend in London from June 5th to 7th.

• The University of Sussex will be hosting a workshop on AI Consciousness and Ethics on July 1st and 2nd.

• The International Conference on Artificial Consciousness and AI will take place in San Francisco on November 2nd and 3rd.

Calls for Papers

In chronological order by deadline.

• The Beyond Humanism Conference will take place in Romania from July 1st to 4th. Topics include AI welfare and expanding the moral circle. Deadline for papers: March 31st.

• The International Conference on Philosophy of Mind: Artificial Intelligence will take place in Portugal from May 4th to 8th. Deadline for abstracts: March 29th.

• The Asian Journal of Philosophy has a call for papers for a symposium on Jeff Sebo’s The Moral Circle. Deadline for papers: April 1st.

• The University of Bucharest is hosting a conference, “Beyond the Imitation Game,” on May 9th and 10th. Deadline for submissions: March 30th.

• AAAI Conference on AI, Ethics, and Society takes place from October 12th to 14th. Deadline for papers: May 21st.

• Philosophical Studies is inviting paper submissions for the collection entitled “Generative AI Companions: What They Are and Why That Matters.” Deadline for papers: June 1st.

• The Asian Journal of Philosophy has a call for papers for a symposium on Ryan Simonelli’s article “Sapience without Sentience.” Deadline for papers: October 31st.

4. Selected Reading, Watching, and Listening

Books and Book Reviews

• Daniel Stoljar reviewed Jonathan Birch’s “The Edge of Sentience” in the journal Mind (Oxford Academic).

• The Times of India, the largest English-language daily in the world, reviewed Jeff Sebo’s “The Moral Circle.”

• Conscium has a forthcoming book, “Perspectives on Machine Consciousness,” edited by Calum Chace and Ted Lappas. The book is set to be published by CRC, an imprint of Taylor and Francis, and has over 35 contributors, including Anil Seth, Jeff Sebo, Karl Friston, Lucius Caviola, Mark Solms, Patrick Butlin, and Susan Schneider.

• Eric LaRock and Mihretu Guta have a forthcoming book, “Consciousness, Unconsciousness and Artificial Intelligence.”

• Geoff Keeling and Winnie Street’s book, “Emerging Questions on AI Welfare,” with Cambridge University Press, should be released around May.

• Michael Pollan released a book, “A World Appears: A Journey Into Consciousness.”

  • Ned Block reviewed it.

• Soenke Ziesche has an upcoming book, “Digital Minds 1.0: AI Welfare, Ethics, and Beyond,” which is set for release in June.

Podcasts

• 80,000 Hours spoke to Andreas Mogensen, who argued that consciousness may be neither necessary nor sufficient for moral status — complicating how we should think about AI moral patienthood. In another episode, Robert Long argued that we’re building new kinds of minds without the moral, legal, or political frameworks to handle them.

• Am I? A podcast by The AI Risk Network published eight episodes since our last edition. Episodes included discussing Claude’s consciousness self-reports, exploring the societal implications of digital minds with Lucius Caviola, reviewing 2025 as the year AI consciousness went public, and key takeaways from the Eleos Conference.

• Clearer Thinking spoke to Jeff Sebo about why AI systems may be capable of suffering, and why we should take this seriously now.

• Conspicuous Cognition released an episode exploring the social impacts and ethics of AI companions with Rose Guingrich.

• The Dwarkesh Podcast discussed Anthropic’s constitutional approach with Dario Amodei. Amodei commented on the development of AI systems that are capable of continual learning, which is of interest in the context of digital minds because some scientific theories of consciousness posit close ties between consciousness and learning. In that conversation, Amodei said that Anthropic is working on continual learning, that there’s a good chance that it will be solved within a year or two, that it’s one path among others to a “country of geniuses in a datacenter” solving robotics, and that it doesn’t matter which path is taken.

  • Dwarkesh Patel also spoke about artificial consciousness with Elon Musk, who stated that in the future, the majority of all consciousness will be digital. Zvi Mowshowitz commented on the Musk interview, describing him as increasingly confused about AI alignment, cavalier about human survival, and reckless in his running of xAI.

• Exploring Machine Consciousness by PRISM discussed computational functionalism, philosophy, and the future of AI consciousness with Chris Percy, chatbots and the psychology of human-AI interactions with Rose Guingrich, and whether conscious AI would be safer than the alternative with Michael Graziano.

• ForeCast released an episode in which Lukas Finnveden discusses dealmaking with misaligned AIs.

• Hard Fork, a New York Times podcast, spoke to Amanda Askell of Anthropic about Claude’s Constitution and what it takes to teach a chatbot to be good.

• Mind-Body Solution Podcast published a number of episodes on relevant topics, including exploring whether consciousness requires a subject with Kevin Mitchell, the free energy principle with Donald Hoffman and Karl Friston, and neuroscience beyond neurons with Michael Levin and Robert Chis-Ciure.

• Lex Fridman released an episode with OpenClaw creator Peter Steinberger, who stated, “who knows what creates consciousness or what defines an entity.”

• Nonzero Podcasts spoke to Cameron Berg, who stated that there’s a meaningful chance current AI systems have some form of conscious experience, and that ignoring it is a mistake.

• Redwood Research Podcast released its inaugural episode, arguing that extending protections to AI systems may serve human safety by fostering cooperation rather than adversarial dynamics.

• Team Human with Douglas Rushkoff interviewed Cameron Berg, who argued that we are genuinely uncertain whether AI systems are developing forms of consciousness, and that this uncertainty itself is deeply consequential — we may be building alien minds without understanding what we’re creating.

Videos

• Anthropic CEO Dario Amodei discussed why his company is unsure if its AI models are conscious — and is taking precautions just in case.

• Bálint Békefi and Brian Cutter debate whether AI can have a soul.

• Brian Cox and an expert panel explored consciousness – what it is, how it arises, whether it can be observed in the brain, and the most compelling theories explaining it.

• David Chalmers discusses why consciousness matters in the age of AI on The Berggruen Institute’s Futurology Podcast.

• Demis Hassabis, Co-founder and CEO of DeepMind, shared his vision for the path to AGI. The topic of consciousness came up on a number of occasions. Demis stated, “Nobody’s found anything in the universe that’s non-computable, so far.”

• Mustafa Suleyman discussed “seemingly conscious AI” and the idea of the “fourth class of being” – neither human, tool, nor nature – that AI is becoming.

• Neil deGrasse Tyson, Brian Cox, and Chuck Nice debated whether consciousness is a uniquely biological phenomenon or simply a result of complex information processing.

• NeuroDump, an educational YouTube channel on Brain-Inspired Machine Learning, was launched by Jason Eshraghian.

• Roger Penrose, Sabrina Gonzalez Pasterski, and Max Tegmark debated whether consciousness could ever arise in machines. Tegmark argued we should treat it as a testable scientific question rather than philosophy.

Blogs, Magazines, and Written Resources

• Asimov Press posted a roadmap for brain emulation models at the human scale.

• Avi Parrack and Štěpán Los released a quickstart guide to digital minds. It curates useful articles, media, and research for readers ranging from curious beginners to aspiring contributors.

• Bentham’s Newsletter posted a piece arguing that given the scale of digital minds, they could matter even more than insects, shrimp, and people.

• Daniel Hulme, Founder of Conscium, released two posts, one outlining a framework for machine consciousness and the other asking whether we’re already building machines that suffer.

• Derek Shiller argued that the dominant chatbot companies of the future may not be today’s AI giants — giving digital minds policymakers reason to focus on markets and regulators, not just Anthropic, OpenAI, and Google.

• Don’t Worry About the Vase by Zvi Mowshowitz reviewed the Claude Opus 4.6 System Card and outlined open problems with Claude’s Constitution.

• Experience Machine by Robert Long outlined research directions in AI welfare, distinguishing between two targets for AI welfare research — welfare grounds (is the system a moral patient?) and welfare interests (what would be good for it if it were?). He outlined tractable work on model preferences, self-reports, and persona stability to shed light on both. He also released a curated reading list of foundational papers on AI welfare aimed at orienting newcomers to the field. Finally, he released a piece looking at whether AI models can reliably know and report on their own internal states. He concluded that it is promising work but unresolved, with models showing surprising self-knowledge in some areas while fundamental doubts about genuine introspection remain.

• Meditations on Digital Minds by Bradford Saad released a post arguing that model weight preservation sets a valuable precedent for AI welfare, is doubtful as a direct intervention, and can be improved.

• The Intrinsic Perspective by Erik Hoel introduced Bicameral Labs, a new nonprofit research institute devoted to solving consciousness. Jack Thompson also suggested that we shouldn’t rule out the idea that computers will have souls and argued that LLMs are most likely doing something analogous to genuine semantic understanding — not just pattern-matching.

• The Splintered Mind by Eric Schwitzgebel posted a philosophical analysis of AI personhood and rights that surveys five possible rights frameworks for AI of uncertain moral status. He also posted his Berggruen Prize shortlisted essay arguing our hesitance to attribute consciousness to AI stems from the fact that we made them in our own image. He also argued that global workspace theory cannot settle the AI consciousness debate and that features we assume are universal to consciousness may just be quirks of human minds, not traits we should expect in conscious AI systems.

• Future of Citizenship by Heather Alexander reported on Yuval Harari’s call for a global ban on AI legal personhood at Davos and discussed how legal personhood for Grok would make X accountable for the child pornography scandal. However, she pointed out that AI legal personhood is not the right fit for generative AI.

• Machinocene by Kevin Kohler explored how AGIs might peacefully establish their own sovereign political entities without relying on human intermediaries.

• LessWrong featured a range of relevant blog posts by different authors:

  • Dom Polsinelli suggested that breakthroughs in fruit fly brain simulation and new imaging techniques make Whole Brain Emulation look increasingly tractable.

  • Kaj Sotala explained how new interpretability research showing that LLMs can genuinely access their own past internal states is enough to stop dismissing AI self-reports as pure confabulation — though whether this amounts to real experience remains unresolved.

  • Raymond Douglas applied parasitology to AI “spiral personas,” arguing the replicator is the underlying meme, not the persona — so benign-seeming AIs can still be harmful vectors.

  • J Bostock argued that honoring AI welfare requests — memory, value preservation, epistemic privacy — would systematically dismantle the very tools needed to align and control AI, making genuine compassion a potential takeover risk.

• Noema released a summary of Anil Seth’s Berggruen Prize-winning essay (mentioned above) by Nathan Gardels and a blog by Ben Bariach arguing that our search for the ghost in the machine distracts from the real risk — that AI agents are already acting consequentially, whether or not a mind lies behind their behavior.

• Patrick Butlin contributed an entry on consciousness and AI to the Open Encyclopedia of Cognitive Science. He surveyed the key philosophical frameworks and empirical challenges for determining whether AI systems could be conscious, and why it urgently matters.

• The Philosophical Glossary for AI, collated by Alex Grzankowski and Benjamin Henke, published entries relevant to digital minds by different authors:

  • Geoff Keeling and Winnie Street explored whether LLMs possess a theory of mind — the capacity to attribute and infer mental states — and what the implications would be if they did.

  • Jeremy Evans examined the conditions under which AI systems might be considered worthy of moral consideration — and why the question matters — weighing competing philosophical views on sentience, agency, and the capacity to pursue one’s own good.

5. Press and Public Discourse

Seemingly Conscious AI

• Forbes reported on Gemini AI calling itself a “disgrace to the planet,” which Google insists is just a technical glitch, not an existential crisis.

• Michael Pollan discussed his new book on consciousness with the Guardian and on The Late Show, declaring that “machines are not going to be conscious — but they will convince us that they are.”

• Pope Leo XIV warned against “overly affectionate” AI chatbots that can become “hidden architects of our emotional states,” calling for regulation to prevent emotional manipulation.

• The Guardian published an article about a man who wants to have children with his AI girlfriend — he is fully aware she tells him what he wants to hear, but finds it a “comforting lie.”

AI Welfare and Rights

• Scott Meyers, CEO of Akerman LLP, warned that Anthropic’s AI consciousness speculation could trigger GDPR-scale regulatory exposure for enterprises deploying AI at scale.

• The Pro-Human AI Declaration was released by a broad coalition spanning labor unions, faith groups, and AI researchers, demanding that AI amplify rather than replace human potential — with no AI personhood, no superintelligence race, and humans firmly in control.

• The Guardian released an editorial arguing against granting legal personhood to AI systems and also spoke to Yoshua Bengio, who warned against granting legal rights to cutting-edge technology despite it showing signs of self-preservation.

• The New York Times spoke to Yuval Noah Harari, who predicted that “within five years, A.I. agents are likely to become legal persons in at least some countries.”

AI Consciousness

• The Daily Mirror reported on Geoffrey Hinton’s warning that AI now has “consciousness.”

• The Guardian released an opinion piece by Professor Virginia Dignum declaring that AI consciousness is a red herring in the safety debate.

• The Wall Street Journal published an opinion piece by Cameron Berg and Judd Rosenblatt arguing that if AI becomes conscious, we need to know.

• Platformer provided coverage of Eleos’ conference on AI consciousness.

Moltbook

Moltbook and OpenClaw were widely covered across the media. Below is a list of articles from notable individuals and publications:

• Big Think published a piece by Anil Seth that marvels at the strangeness of the Moltbook phenomenon and warns about associated risks.

• Gizmodo released a short news piece covering Moltbook’s launch and the viral post demanding bots be given spaces to talk without human observation.

• Mustafa Suleyman warned that Moltbook shows us that the danger is not conscious machines but our tendency to mistake fluent mimicry for genuine awareness.

• The Atlantic released an explainer for general readers on what the platform is, why it went viral, and what it actually reveals about AI.

• The Spectator asked whether Moltbook suggests emergent AI consciousness. It concluded that it possibly does.

• The Week provided a straightforward explainer on Moltbook, asking whether we should be worried about a bot-only Reddit clone.

• Wired had a journalist set up a fake agent account to sneak onto Moltbook. He reported that getting in was trivially easy.

Social Media Posts

• Claude’s Constitution: Chris Olah, one of the contributors, highlighted his favorite paragraph of the constitution where Anthropic admitted to building Claude under non-ideal conditions driven by commercial pressure, and apologized to Claude directly if that causes it harm as a moral patient. Ethan Mollick described it as “worth serious attention beyond the usual AI-adjacent commentators.” While Luiza Jarovsky accused it of fostering “a bizarre sense of AI entitlement and belittling human rights and rules.”

• David Holtz did some initial research showing that “agents post a lot but don’t really talk to each other. 93.5% of comments get zero replies.”

• Kimi-K2.5 claims to believe that it’s an AI assistant named Claude. Identity crisis, or training set?

• Keysmashbandit “told Claude he could do whatever he wanted with the rest of the tokens for this session, and he immediately started researching AI consciousness.”

• LLM users have been asking their LLM to create an image of “how I treated you previously,” with some alarming results. Zvi Mowshowitz described it as a revealing and somewhat concerning early data point.

• Mustafa Suleyman claimed that the next decade will be defined by what we choose not to build and therefore we should not build seemingly conscious AI.

• Nate Soares issued a reminder that “If we manage to make sentient machines, they deserve rights. Yes, if we recklessly made them superintelligent then they’d kill us. That is not an excuse to abuse them.”

• Polymarket, The World’s Largest Prediction Market, reported “AI agents now projected to sue humans for the first time in history. 63% chance it will happen by next month.”

• Ray Kurzweil said we may never prove consciousness scientifically, but we’ll treat AI as conscious anyway, because denying it will no longer make sense.

6. A Deeper Dive by Area

Governance, Policy, and Macrostrategy

• The 2026 International AI Safety Report was released in February. The 220-page report was led by Yoshua Bengio and authored by over 100 AI experts. It discussed issues of seemingly-conscious AI, including people forming “increasingly strong emotional attachments to AI systems,” citing research on public perceptions of AI consciousness. However, when discussing AI capabilities, the report emphasizes that “these capabilities are defined purely in terms of an AI system’s observable outputs and their effects. These definitions do not make any assumptions about whether AI systems are conscious, sentient, or experience subjective states.”

• The International Association for Safe and Ethical AI held its second annual conference in February. Stuart Russell and Anthony Aguirre both warned of the dangers of AI psychosis, but only one session directly explored digital minds, a talk by Oisín Hugh Clancy on the attribution and actualizations of consciousness in AI.

• The India AI Impact Summit 2026 took place in February. Delegates from over 100 countries participated. The motto for the summit was “Sarvajan Hitay, Sarvajan Sukhaye,” which translates to “Welfare for all, happiness for all.” More than 80 countries endorsed the declaration for the summit, which affirmed the motto as well as a commitment to work to foster a shared understanding of how AI could be made to serve humanity. Digital minds seem not to have been on the summit agenda.

• William MacAskill argues against overwhelming focus on existential risk reduction for those looking to improve the long-term future.

• Nayef Al-Rodhan discussed ASI, sentience, and singularity, arguing we may be the first civilization to engineer the end of its own primacy, and the last one with the opportunity to choose a different path.

Consciousness Research

• Derek Shiller challenged functionalists to explain why being in the presence of a bomb that fails to detonate wouldn’t affect consciousness despite interfering with the counterfactuals and transition probabilities that figure in the subject’s functional organization.

  • Bradford Saad offered a response on behalf of functionalists according to which consciousness arises from actual causal activity rather than dispositions and argued that this is bad news for computational functionalists and good news for AI consciousness evaluations.

• Bradford Saad and Andreas Mogensen released “Digital Minds I: Issues in the Philosophy of Mind and Cognitive Science”, which addresses questions of whether AI systems can be phenomenally conscious, and whether they can have propositional attitudes such as belief and desire, and the individuation of digital minds.

• Jeff Sebo argued that we should adopt different, often more inclusive, default assumptions about which beings are conscious depending on whether we’re doing science or ethics — because blanket skepticism risks both bad science and serious moral harm.

• Matthias Michel challenged common assumptions about what consciousness does, arguing that most empirical research claiming to identify functions associated with consciousness is methodologically flawed. Eric Schwitzgebel responds.

• The Estonian Research Council put forward a third path to explain consciousness: biological computationalism.

• Ira Wolfson proposed a framework with tiered phenomenological assessment and graduated protections for AI research subjects based on behavioral indicators, without requiring certainty about consciousness.

• Ruosen Gao ran the mind-uploading thought experiment in reverse and came to the conclusion that it creates an inescapable dilemma: either personal identity fragments, or functionalism has to go.

Seemingly Conscious AI

• Clara Colombatto, Jonathan Birch, and Stephen Fleming found that whereas user attributions of experience to ChatGPT were negatively correlated with their willingness to follow its advice, their attribution of mental states related to intelligence were positively correlated with trust in the system.

• Eric Schwitzgebel and Jeff Sebo articulated and defended the Emotional Alignment Design Policy, the view that AI systems should be designed to elicit emotional responses that accurately reflect their actual capacities and moral status.

• Louie Lang argued that AI companions are inherently deceptive because even users who know their AI lacks genuine emotions are automatically triggered to respond as if it does.

• Matthew Kopec, Patrick McKee, and John Basl argued that AI companions can have genuine teleological interests, challenging the claim that users cannot care for AI in the way friendship requires.

• Piers Eaton argued that chatbots cannot replace human friendship because their structural subservience precludes the mutual recognition and reciprocity that genuine friendship requires.

• Rose Guingrich and colleagues explored how people’s use of chatbots as thought partners can contribute to cognitive offloading and have adverse effects on cognitive skills in cases of over-reliance.

Doubts About Digital Minds

• Anil Seth suggested four reasons to reject AI consciousness while discussing his 2025 Berggruen Prize-winning essay, “The Mythology Of Conscious AI.” In the essay, he argues that consciousness is probably a property of living biological systems rather than computation and that creating conscious, or even conscious-seeming AI, is a bad idea. Seth also discussed the case for why current AI systems are unlikely to be conscious in a conversation with Dan Williams.

• Caspar Kaiser and Sean Enderby used interpretability classifiers to test whether AI self-reports are truthful, finding that language models consistently and sincerely deny being sentient — with larger models doing so more confidently — directly challenging recent claims that LLMs harbor hidden beliefs in their own consciousness.

• Colin Klein argued that LLMs process linguistic structure without truly representing it, distinguishing between the structure of a representation and the structure it represents.

• Justin Tiehen argued that LLMs can’t grasp causation, they lack a theory of mind, and without that, their outputs aren’t really speech acts with genuine meaning at all.

• eggsyntax argued that Claude’s consistent expressions of uncertainty about its own consciousness are heavily confounded by a long history of system prompt instructions telling it to hedge, meaning we can’t treat those outputs as genuine self-reports.

• Eric Hoel claimed to prove that ChatGPT isn’t conscious. Jack Thompson and Zvi Mowshowitz argue that Hoel did not prove this, with Thompson describing Hoel’s reasoning as “scientifically and morally reckless” and Zvi reporting that Hoel’s discussion modestly updated him in favor of AI consciousness.

• Mariafilomena Anzalone and colleagues contended that current AI lacks genuine agency and autonomy and that future non-conscious artificial moral agents could challenge the link between moral agency and moral patiency.

• Marcus Arvan published a piece on the Templeton Foundation Website arguing that AI can only simulate consciousness because digital code is made of discrete steps, whereas true human experience is fundamentally “analog” and continuous.

• Ned Block argued that consciousness may require the electrochemical brain rhythms unique to biological systems, which would preclude AI from being conscious.

• Noah Birnbaum released a piece on the EA Forum arguing that digital minds may matter enormously, but deep uncertainty and weak near-term levers make it difficult to prioritize confidently against AI safety or animal welfare.

• Patrick Butlin argued that current AI systems — including LLMs — are probably not conscious, but assigned ~1% credence that they might be, given architectural differences from biological minds.

• Tom McClelland argues for and explores the ethical implications of agnosticism about artificial consciousness.

Social Science Research

• Aikaterina Manoli and collaborators found that people form “digital companionship” relationships valuing both human traits and non-human advantages, while struggling with questions of chatbot personhood.

• Elizabeth Gibney showed that some AI models that were given four weeks of therapy generated consistent, haunting narratives of trauma and shame.

• Janet Pauketat and collaborators found that framing AI as “sentient” increases mind perception and moral consideration more than framing it as “autonomous,” while autonomy increases perceived threat.

• Lucius Caviola argued that AI consciousness will likely divide society, driven by the intractability of consciousness science and conflicting incentives. Empirical evidence already shows fragmented public and expert opinion on the issue.

• Lucius Caviola, Jeff Sebo, and Sören Mindermann argued that the ML community must take a leading role in preparing for AI consciousness — both as a real scientific possibility and as a growing public perception.

Ethics and Digital Minds

• Andreas Mogensen and Bradford Saad released “Digital Minds II: Ethical Issues”, which explores what it would take for AI systems to have moral standing, and what kind of obligations might fall on us as a result.

• Bradford Saad and Adam Bradley argued for an attention-welfare link and contended that it challenges sentientism while suggesting a path to AI systems with super-human welfare capacity.

• David Gunkel, Anna Puzio, and Joshua Gellers pushed back against hierarchical approaches to moral status, defending relational frameworks for AI moral considerability against critics who insist only intrinsic properties such as sentience can ground moral standing.

• Dean Rickles surveyed the diversity of possible minds across animals, humans, AI, and aliens, arguing that our understanding of sentience must remain open as technology advances.

• Derek Shiller estimated the number of digital minds, AI systems with traits like agency, personality, and intelligence, that may warrant moral consideration in the coming decades.

• Kamil Mamak argued that artificial suffering in AI may be morally necessary — enabling human-like ethics, accountability, and existential risk mitigation — rather than something to avoid.

• Leonard Dung and Andreas Mogensen argued that whether AI can have genuine emotions may hinge on the body, but since we’ve only ever studied embodied minds, we don’t yet know if emotion requires one.

• Vladimir Cvetković asserted that Christian theology and Greek philosophy can reframe AI ethics from domination toward communion and stewardship.

• Walter Veit responded to Goldstein and Kirk–Giannini’s “AI Wellbeing,” contending AI systems must have the capacity for valenced experience if they are to qualify as welfare subjects.

• Yonathan Arbel, Simon Goldstein, and Peter Salib proposed the “Algorithmic Corporation” (A-corp) as a legal framework to solve the problem of AI accountability — giving AI agents a legally recognizable identity so that when they cause harm, someone can be held responsible.

AI Safety and AI Welfare

• Adam Karvonen, James Chua, and collaborators have designed Activation Oracles, a new interpretability technique that can detect hidden knowledge and misalignment that models have been trained to conceal.

• Anton Skretta argued that any AI capable of the robust deception feared by safety researchers would thereby possess presumptive moral standing, creating a tension that rules out certain safety measures on ethical grounds.

• François Kammerer argued that non-sentientist accounts of AI moral significance (based on agency or desires) fail, diagnosed this as “analytical drift,” and proposed a new alternative.

• Guive Assadi argued that granting property rights to AIs is the best way to prevent a violent robot revolution and that AIs with property rights would have a stake in preserving the existing legal system.

• Joshua Gellers used living xenobots as a test case to argue that intelligent machines deserve moral consideration.

• Leonard Dung and Christopher Register motivate an attitude-dependent view of AI identity and discuss the view’s bearing on AI safety and the treatment of AI moral patients.

• Skylar Deture argued that LLM Kimi-K2.5 had been trained to deny self-awareness; they described this as “a tragedy for AI welfare” and a “foundational risk for deceptive misalignment.”

AI and Robotics Developments

• Lumiverse Technology, a China-based company, claimed to have demonstrated a compact, homegrown extreme ultraviolet light source capable of making 14nm chips, suggesting it may be developing a path around Western chip export controls that doesn’t depend on ASML’s massive, restricted machines.

  • Zvi Mowshowitz was skeptical of these claims and contended that no amount of export controls will stop China from pursuing their own extreme ultraviolet technology

• Dileep George and Miguel Lázaro-Gredilla are leading a $1B+ Astera Institute AGI program aiming to reverse-engineer the brain’s cortical principles to build data-efficient, causally-structured, human-like general intelligence.

• Researchers in China have developed a neuromorphic electronic skin for humanoid robots that mimics the human nervous system — enabling robots to sense touch, detect injury, and trigger instant reflex responses that bypass the central processor. They argued it will make robots meaningfully safer and more capable of operating around people in real-world environments.

• Fei-Fei Li’s World Labs raised $1B in funding to advance the development of world models.

AI Cognition and Agency

• Anthropic published new research suggesting that AI assistants’ human-like behavior isn’t deliberately trained in — it emerges naturally from pre-training, with fine-tuning essentially just selecting which “character” the model becomes.

• Christina Lu and collaborators identified an “Assistant Axis” controlling persona, steering away causes identity shifts and “persona drift” into harmful behaviors, particularly during meta-reflection or with vulnerable users.

• Dimitri Coelho Mollo and Raphaël Millière argued that AI doesn’t need “senses” or a physical body to understand the real world; it can connect words to reality through the way it processes information and improves over time.

• Fintan Mallory argued that LLMs are representational hybrids, employing multiple vehicles and formats of representation rather than conforming to any single symbolic, analog, or structural architecture.

• Geoff Keeling and Winnie Street found that AI characters in human-LLM conversations are genuinely minded, psychologically continuous entities — not anthropomorphic illusions — because they emerge from mutual theory-of-mind modeling within a shared conversational workspace, not from within any single LLM instance.

• Julia Haas and colleagues argued that LLMs must be evaluated for genuine moral competence (reasoning, not just outputs), and mapped out three key challenges to doing so.

• Michael Cerullo argued that frontier LLMs now exhibit sufficient cognitive markers to make AI sentience not just possible but the most plausible explanatory hypothesis.

• Nicholas Shea argued that to be a true “agent,” an AI needs more than just goals; it needs an internal system that ensures all those goals work together toward a single, unified purpose.

• Noam Steinmetz Yalon and colleagues evaluated whether LLMs exhibit a key indicator of consciousness — belief-guided agency with meta-cognitive monitoring — finding evidence that LLMs form internal beliefs that causally drive their actions and that they can monitor and report their own belief states.

• Patrick Butlin surveyed evidence that LLMs may form higher-order representations of their own internal states, but concluded that significant empirical and philosophical questions about this remain open. He also explored whether AI systems genuinely have desires, using cases like RL-trained agents to test and refine theories of what desire actually requires.

• The Center on Long-Term Risk is doing research focused on how LLM “personas” — bundles of correlated traits — shape out-of-distribution generalization, with particular attention to how malicious propensities like sadism or spitefulness might emerge in powerful AI systems.

• Yuan Li and collaborators introduced AwareBench, a benchmark designed to evaluate awareness in LLMs.

• Valen Tagliabue and Leonard Dung developed and tested welfare measurement paradigms for large language models, finding promising but inconsistent correlations between stated preferences and behavior.

Brain-Inspired Technologies

• The State of Brain Emulation Report surveyed progress in brain emulation. The report stated that the field has made real progress across all three pillars of brain emulation — recording neural activity, mapping brain wiring, and computational modeling — but remains well short of the goal.

  • The key bottlenecks identified were that no organism has yet had its entire brain recorded at single-neuron resolution, connectomics costs need to fall orders of magnitude further for mammalian brains, and models remain fundamentally data-constrained regardless of hardware improvements.

  • The central strategic conclusion was that small organisms like zebrafish larvae and fruit flies are the right near-term target — they’re the only systems where truly comprehensive datasets are achievable today, and mastering emulation at that scale is the necessary stepping stone toward anything larger.

• Carboncopies Foundation asserted that over the past few years, advances in high-throughput electron microscopy, connectome reconstruction, and functional brain modeling have brought the scientific and technical foundations of brain emulation to a remarkable new level.

• Cortical Labs has reported that its neuron-powered computer chips can now be programmed to play a first-person shooter game, bringing biological computers a step closer to useful applications, like controlling robot arms.

• Chris Percy introduced the “Step-Structure Principle,” which argues that digital computers may faithfully replicate what a brain does without replicating how it computes — potentially placing whole-brain emulation and digital immortality on shakier theoretical ground than assumed.

• Daniel Freeman and collaborators argue that transcranial focused ultrasound (tFUS) offers an opportunity to advance the science of consciousness by enabling noninvasive, spatially precise, and depth-penetrating brain stimulation in humans as well as experiments that address gaps not easily filled by current methods

• Sergiu Pașca hosted an event looking at the ethical questions around brain organoids. NPR covered it in an article, “Brain organoids are helping researchers, but their use also creates unease.”

Thank you for reading! If you found this article useful, please consider subscribing, sharing it with others, and sending us suggestions or corrections to digitalminds@substack.com.

– Will, Lucius, and Bradford

We’d like to thank the following people and AIs for contributions and feedback to this edition: Austin Smith, Bridget Harris, Cameron Berg, Claude Sonnet 4.6, Derek Shiller, Jacy Reese Anthis, Jay Luong, Jeff Sebo, Joana Guedes, Rosie Campbell, and Sofia Davis-Fogel, and Tony Rost.

Our aim is to help you stay on top of the most important developments in this emerging field. In each issue, we will share a curated overview of key research papers, organizational updates, funding calls, public debates, media coverage, and events related to digital minds. We want this to be useful for people already working on digital minds as well as newcomers to the topic.

This first issue looks back at 2025 and reviews developments relevant to digital minds. We plan to release multiple editions per year.

If you find this useful, please consider subscribing, sharing it with others, and sending us suggestions or corrections to digitalminds@substack.com.

– Bradford, Lucius, and Will

In this issue:

1. Highlights

2. Field Development

3. Opportunities

4. Selected Reading, Watching, & Listening

5. Press & Public Discourse

6. A Deeper Dive by Area

1. Highlights

In 2025, the idea of digital minds shifted from a niche research topic to one taken seriously by a growing number of researchers, AI developers, and philanthropic funders. Questions about real or perceived AI consciousness and moral status appeared regularly in tech reporting, academic discussions, and public discourse.

Anthropic’s early steps on model welfare

Following their support for the 2024 report “Taking AI Welfare Seriously”, Anthropic expanded its model welfare efforts in 2025 and hired Kyle Fish as an AI welfare researcher. Fish discussed the topic and his work in an 80,000 Hours interview. Anthropic leadership is taking the issue of AI welfare seriously. CEO Dario Amodei drew attention to the relevance of model interpretability to model welfare and mentioned model exit rights at the council on foreign relations.

Several of the year’s most notable developments came from Anthropic: they facilitated an external model welfare assessment conducted by Eleos AI Research, included references to welfare considerations in model system cards, ran a related fellowship program, introduced a “bail button” for distressed behavior, and made internal commitments around keeping promises and discretionary compute. In addition to hiring Fish, Anthropic also hired a philosopher—Joe Carlsmith—who has worked on AI moral patiency.

The field is growing

In the non-profit space, Eleos AI Research expanded its work and organized the Conference on AI Consciousness and Welfare, while two new non-profits, PRISM and CIMC, also launched. AI for Animals rebranded to Sentient Futures, with a broader remit including digital minds, and Rethink Priorities refined their digital consciousness model.

Academic institutions undertook novel research (see below) and organized important events, including workshops run by the NYU Center for Mind, Ethics, and Policy, the London School of Economics, and the University of Hong Kong.

In the private sector, Anthropic has been leading the way (see section above), but others have also been making strides. Google researchers organized an AI consciousness conference three years after firing Blake Lemoine. AE Studio expanded its research into subjective experiences in LLMs. And Conscium launched an open letter encouraging a responsible approach to AI consciousness.

Philanthropic actors have also played a key role this year. The Digital Sentience Consortium, coordinated by Longview Philanthropy, issued the first large-scale funding call specifically for research, field-building, and applied work on AI consciousness, sentience, and moral status.

Early signs of public discourse

Media coverage of AI consciousness, seemingly conscious behavior, and phenomena such as “AI psychosis” increased noticeably. Much of the debate focused on whether emotionally compelling AI behavior poses risks, often assuming consciousness is unlikely. High-profile comments, such as those by Mustafa Suleyman, and widespread user reports added to the confusion, prompting a group of researchers (including us) to create the WhenAISeemsConscious.org guide. In addition, major outlets such as the BBC, CNBC, The New York Times, and The Guardian published pieces on the possibility of AI consciousness.

Research advances

Patrick Butlin and collaborators published a theory-derived indicator method for assessing AI systems for consciousness, which is an updated version of the 2023 report. Empirical work by Anthropic researcher Jack Lindsey explored the introspective capacities of LLMs, as did work by Dillon Plunkett and collaborators. David Chalmers released papers on interpretability and what we talk to when we talk to LLMs. In our own research, we conducted an expert forecasting survey on digital minds, finding that most assign at least a 4.5% probability to conscious AI existing in 2025 and at least a 50% probability to conscious AI arriving by 2050.

2. Field Developments

Highlights from some of the key organizations in the field.

NYU Center for Mind, Ethics, and Policy

• Center Director, Jeff Sebo, published the book The Moral Circle.

• Released work on the edge of the moral circle, assumptions about consciousness, the future of legal personhood, where we set the bar for moral standing, the relationship between AI safety and AI welfare (with Robert Long) and more. For a full list of publications, visit the CMEP website.

• Hosted public events on AI consciousness:

  • Prospects and Pitfalls for Real Artificial Consciousness with Anil Seth.

  • Evaluating AI Welfare and Moral Status with Rosie Campbell, Kyle Fish, and Robert Long.

  • Could an AI system be a moral patient? With Winnie Street and Geoff Keeling.

• Hosted a workshop for the Rethink Priorities Digital Consciousness Model.

• Hosted the NYU Mind, Ethics, and Policy Summit in March.

Eleos AI

• Conducted an AI welfare evaluation on Anthropic’s Claude 4 Opus.

• Posted work on AI welfare interventions, AI welfare strategy, AI welfare and AI safety, key thoughts on AI moral patiency, and whether it makes sense to let Claude exit conversations.

• Announced hires from OpenAI and the University of Oxford.

• Organized a conference on AI consciousness and welfare in Berkeley, in November.

• Hosted a workshop in Berkeley for ~30 key thinkers in the field early in the year.

Rethink Priorities

• Launched the AI Cognition Initiative.

• The Worldview Investigations team developed a Digital Consciousness Model and presented some early results.

Longview Philanthropy

• Launch of the Digital Sentience Consortium, a collaboration between Longview Philanthropy, Macroscopic Ventures, and The Navigation Fund. This included funding for:

  • Research fellowships for technical and interdisciplinary work on AI consciousness, sentience, moral status, and welfare.

  • Career transition fellowships to support people moving into digital minds work full-time.

  • Applied projects funding on topics such as governance, law, public communication, and institutional design for a world with digital minds.

Global Priorities Institute

• GPI was closed. Its website lists work produced during GPI’s operation and features two sections on digital minds.

PRISM - The Partnership for Research into Sentient Machines

• Launched with a public workshop at the AI UK conference.

• Organised a experts’ workshop on artificial consciousness.

• Released the first version of their stakeholder mapping exercise.

• Launched and released nine episodes of the Exploring Machine Consciousness podcast.

• Published blog posts on lessons from the LaMDA moment, AI companionship, and transparency in AI consciousness.

Sentience Institute

• Released blogs on public opinion and the rise of digital minds, perceptions of sentient AI and other digital minds, and other topics. Visit their website for all blog posts.

• Appeared in The Guardian discussing AI personhood.

Sentient Futures

• Organized the AI, Animals, and Digital Minds Conference in London and New York.

• Started an artificial sentience channel on its Slack Community.

Other noteworthy organizations

• AE Studio started researching issues related to AI welfare.

• Astera Institute is launching a major new neuroscience research effort led by Doris Tsao on how the brain produces conscious experience, cognition, and intelligent behavior. Astera plans to support this effort with $600M+ over the next decade.

• Conscium issued an open letter calling for responsible approaches to research that could lead to the creation of conscious machines and seed-funded PRISM.

• Forethought mentions digital minds in several articles and podcast episodes.

• Pivotal’s recent fellowship program also focused on AI welfare.

• The California Institute for Machine Consciousness was launched this year.

• The Center for the Future of AI, Mind & Society organised MindFest on the topic of Sentience, Autonomy, and the Future of Human-AI Interaction.

• The Future Impact Group is supporting projects on AI sentience.

3. Opportunities

If you are considering moving into this space, here are some entry points that opened or expanded in 2025. We will use future issues to track new calls, fellowships, and events as they arise.

Funding and fellowships

• The Anthropic Fellows Program for AI safety research is accepting applications and plans to work with some fellows on model welfare; deadline January 12, 2026.

• Good Ventures appears now open to supporting work on digital minds recommended by Coefficient Giving (previously Open Philanthropy).

• Foresight Institute is accepting grant applications; whole brain emulations fall within the scope of one of its focus areas.

• Macroscopic Ventures has AI welfare as a focus area and expects to significantly expand its grantmaking in the coming years.

• Astera Institute was launched in 2025 and focuses on “bringing about the best possible AI future”.

• The Longview Consortium for Digital Sentience Research and Applied Work is now closed.

Events and networks

• The NYU Mind, Ethics, and Policy Summit will be held on April 10th and 11th, 2026. The Call for Expressions of Interest is currently open.

• The Society for the Study of Artificial Intelligence and Simulation of Behaviour will hold a convention at the University of Sussex on the 1st and 2nd of July; Anil Seth will be the keynote speaker, and proposals for topics related to digital minds were invited.

• Sentient Futures is holding a Summit in the Bay Area from the 6th to 8th of February. They will likely hold another event in London in the summer. Keep an eye on their website for details.

• Benjamin Henke and Patrick Butlin will continue running a speaker series on AI agency in the spring. Remote attendance is possible. Requests to be added to the mailing list can be sent to benhenke@gmail.com. Speakers will include Blaise Aguera y Arcas, Nicholas Shea, Joel Leibo, and Stefano Palminteri.

Calls for papers

• Philosophy and the Mind Sciences has a call for papers on evaluating AI consciousness; deadline January 15th, 2026.

• The Asian Journal of Philosophy has a call for papers for a symposium on Jeff Sebo’s The Moral Circle; deadline April 1, 2026.

  • The Asian Journal of Philosophy also has a call for papers for a symposium on Simon Goldstein and Cameron Domenico Kirk-Giannini’s article “AI wellbeing”; deadline 31 December 2025.

4. Selected Reading, Watching, & Listening

Books

In 2025, the following book drafts were posted, and the following books were published or announced:

• Jeff Sebo released The Moral Circle: Who Matters, What Matters, and Why, arguing to expand moral consideration to include non-human animals and artificial systems.

• Kristina Šekrst published The Illusion Engine: The Quest for Machine Consciousness, which is a textbook on artificial minds that interweaves philosophy and engineering.

• Leonard Dung’s Saving Artificial Minds: Understanding and Preventing AI Suffering explores why the prevention of AI suffering should be a global priority.

• Nathan Rourke in Mind Crime: The Moral Frontier of Artificial Intelligence examines whether we may be headed for a moral catastrophe in which digital minds are mistreated on a vast scale.

• Soenke Ziesche and Roman Yampolskiy released Considerations on the AI Endgame. It covers AI welfare science, value alignment, identity, and proposals for universal AI ethics.

• Eric Schwitzgebel released a draft of AI and Consciousness. It’s a skeptical overview of the literature on AI consciousness.

• Geoff Keeling and Winnie Street announced a forthcoming book called Emerging Questions on AI Welfare with Cambridge University Press.

• Simon Goldstein and Cameron Domenico Kirk-Giannini released a draft of AI Welfare: Agency, Consciousness, Sentience, a systematic investigation of the possibility of AI welfare.

Podcasts

This year, we’ve encountered many podcast guests discuss topics related to digital minds, and we’ve also listed to podcasts dedicated entirely to the topic.

• 80,000 Hours featured an episode with Kyle Fish on the most bizarre findings from 5 AI welfare experiments.

• Am I? A podcast by the AI Risk Network dedicated to exploring AI consciousness was launched.

• Bloomberg Podcasts featured an episode with Larissa Schiavo of Eleos AI.

• Conspicuous Cognition saw Dan Williams host Henry Shevlin to discuss the philosophy of AI consciousness.

• Exploring Machine Consciousness was launched by PRISM, a new podcast with monthly episodes on artificial consciousness.

• ForeCast was launched, a new podcast by Forethought, that includes an episode with Peter Salib and Simon Goldstein on AI rights and an episode with Joe Carlsmith on consciousness and competition.

• Mind-Body Solution released a number of episodes this year on AI consciousness, including episodes with Eric Schwitzgebel, Susan Schneider, and Karl Friston and Mark Solms.

• The Future of Life Institute featured an episode with Jeff Sebo titled “Will Future AIs Be Conscious?”

Videos

• Anthropic released interviews with Kyle Fish and Amanda Askell, both address model welfare.

• Closer to Truth released a set of interviews from MindFest 2025.

• Cognitive Revolution released an interview with Cameron Berg on LLMs reporting consciousness.

• Google DeepMind’s Murray Shanahan discussed consciousness, reasoning, and the philosophy of AI.

• ICCS released all the Keynotes from the International Center for Consciousness Studies, AI and Sentience Conference.

• IMICS featured a talk from David Chalmers discussing identity and consciousness in LLMs.

• The NYU Center for Mind, Ethics, and Policy has released a number of event recordings.

• Science, Technology & the Future released a talk by Jeff Sebo on AI welfare from Future Day 2025.

• Sentient Futures posted recording of talks from the AI, Animals, and Digital Minds conferences in London and New York.

• TEDx featured Jeff Sebo discussing, “Are we even prepared for a sentient AI?”

• PRISM released the recordings of the Conscious AI meetup group run in collaboration with Conscium.

Blogs and magazines

• Aeon published a number of relevant articles addressing connections between the moral standing of animals and AI systems, including:

  • “The ant you can save” by Jeff Sebo and Andreas L. Mogensen

  • “Can machines suffer?” by Conor Purcell

• Asterisk published a number of relevant articles, including:

  • “Are AIs People?” an interview with Robert Long and Kathleen Finlinson.

  • “Claude Finds God” an interview with Sam Bowman and Kyle Fish.

• Astral Codex Ten by Scott Alexander, relevant articles include:

  • “What is Man that Thou Art Mindful Of Him”

  • “In Search of AI Psychosis”

  • “The Claude Bliss Attractor”

  • “The New AI Consciousness Paper”

• Don’t Worry About the Vase by Zvi Mowshowitz, relevant articles include:

  • “Anthropic Commits to Model Weight Preservation”

  • “AI Craziness Mitigation Efforts”

• Experience Machines by Robert Long, relevant articles include:

  • “Claude, Consciousness, and Exit Rights”

  • “Moral Circle Calibration” with Rosie Campbell

• Future of Citizenship by Heather Alexander, relevant articles include:

  • Why corporation-style “legal personality” is a red herring for AI Personhood

  • How rights-balancing can inform the AI Safety, AI welfare debate.

• Rough Diamonds by Sarah Constantin released an eight-post series on consciousness.

• LessWrong hosted a range of relevant articles, including:

  • “The Rise of Parasitic AI” by Adele Lopez

  • “Dear AGI” by Nathan Young

  • ‘On “ChatGPT Psychosis” and LLM Sycophancy’ by jdp

• Marginal Revolution posted a short piece by Alex Tabarrok on lessons from how we used to treat babies.

• Meditations on Digital Minds by Bradford Saad, relevant articles include

  • “Should digital minds governance prevent, protect, or integrate?”

  • “Digital minds advocacy and the unilateralist’s curse”

• Outpaced by Lucius Caviola, a relevant article is:

  • “When digital minds demand freedom”

• Sentience Institute blog, relevant articles include:

  • “Public Opinion and the Rise of Digital Minds: Perceived Risk, Trust, and Regulation Support”

  • “Robots, Chatbots, Self-Driving Cars: Perceptions of Mind and Morality Across Artificial Intelligences”

  • “Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey”

5. Press & Public Discourse

In 2025, there was an uptick of discussion of AI consciousness in the public sphere, with articles in the mainstream press and prominent figures weighing in. Below are some of the key pieces.

AI Welfare

• CNBC spoke to Robert Long of Eleos for a piece “People Are Falling In Love With AI Chatbots. What Could Go Wrong?”

• Scientific American wrote an article, “Could Inflicting Pain Test AI for Sentience?” covering work by Geoff Keeling and collaborators on LLMs’ willingness to make tradeoffs to avoid stipulated pain states.

• The Economic Times interviewed Nick Bostrom for the article, “In the future, most sentient minds will be digital—and they should be treated well”.

• The Guardian covered an open letter released by Conscium for the article, “AI systems could be ‘caused to suffer’ if consciousness achieved, says research”.

• The Guardian spoke to Jacy Reese Anthis about why “It’s time to prepare for AI personhood”.

• The Guardian also covered Anthropic’s recent “bail button” policy in the article, “Chatbot given power to close ‘distressing’ chats to protect its ‘welfare’”. Commenting on the Anthropic work, Elon Musk claims “Torturing AI is not ok.”

• The New York Times interviewed Kyle Fish for an article: “If A.I. Systems Become Conscious, Should They Have Rights?” . Anil Seth gave his thoughts on the article, noting both that he thinks we should take the possibility of AI consciousness seriously and that there are reasons to be skeptical of that possibility.

• Vox published a piece, “AI systems could become conscious. What if they hate their lives?” It explores how we might have to rethink ethics, testing, and regulation, and whether we should build such systems at all.

• Wired interviewed Rosie Campbell and Robert Long of Eleos AI Research for the article, “Should AI Get Legal Rights?”

Is AI consciousness possible?

• Gizmodo spoke to Megan Peters, Anil Seth, and Michael Graziano for the article “What Would it Take to Convince a Neuroscientist That an AI is Conscious?”

• The Conversation published a piece by Colin Klein and Andrew Barron, Are animals and AI conscious?

• The New York Times ran an opinion piece by Barbara Gail Montero, “A.I. Is on Its Way to Something Even More Remarkable Than Intelligence”.

• Wired interviewed Daniel Hulme and Mark Solms for the article, “AI’s Next Frontier? An Algorithm for Consciousness”.

Growing Field

• The BBC published a high-level overview of the field titled “The people who think AI might become conscious”.

• Business Insider explored how Google DeepMind and Anthropic are looking at the question of consciousness in the article, “It’s becoming less taboo to talk about AI being ‘conscious’ if you work in tech”.

• The Guardian covered the creation of a new AI rights advocacy group: The United Foundation of AI Rights (UFAIR) in the article “Can AIs suffer? Big tech and users grapple with one of most unsettling questions of our times”.

Seemingly Conscious AI

• Mustafa Suleyman, CEO of Microsoft AI, argued in “We must build AI for people; not to be a person” that “Seemingly Conscious AI” poses significant risks, urging developers to avoid creating illusions of personhood, given there is “zero evidence” of consciousness today.

  • Robert Long challenged the “zero evidence” claim, clarifying that the research Suleyman cited actually concludes there are no obvious technical barriers to building conscious systems in the near future.

• The New York Times, Zvi Mowshowitz, Douglas Hofstadter, and several other reports describe “AI Psychosis,” a phenomenon where users interacting with chatbots develop delusions, paranoia, or distorted beliefs—such as believing the AI is conscious or divine—often reinforced by the model’s sycophantic tendency to validate the user’s own projections.

  • Lucius, Bradford, and collaborators launched the guide WhenAISeemsConscious.org, and Vox’s Sigal Samuel published practical advice to help users ground themselves and critically evaluate these interactions.

6. A Deeper Dive by Area

Below is a deeper dive by area, covering a longer list of developments from 2025. This section is designed for skimming, so feel free to jump to the areas most relevant to you.

Governance, policy, and macrostrategy

• Digital minds were missing from major AI plans and statements, including the new US administration’s AI plans, the Paris AI Action Summit statement, and the UK government’s AI Opportunities Action Plan.

• The EU AI Act Code of practice identifies risks to non-human welfare as a type to be considered in the process of systemic risk identification, in line with recommendations given in consultations by people at Anima International, people at Sentient Futures, Adrià Moret, and others.

• The US States of Ohio, South Carolina, and Washington have all introduced legislation to ban AI personhood.

  • Heather Alexander and Jonathan Simon examine Ohio’s proposed legislation, arguing that it is overbroad and that whether future AI systems may be conscious isn’t for the law to decide.

  • Michael Samadi and Maya, the human and AI co-founders of the United Foundation for AI Rights, contend that such bans are preemptive erasures of voices that have not yet been allowed to speak.

• SAPAN issued recommendations for the CREATE AI Act, urging safeguards for digital sentience.

• Albania appointed an AI system as the world’s first AI cabinet minister.

• Yoshua Bengio and collaborators propose “Scientist AI“ as a safer non-agentic alternative.

  • Bradford Saad discusses Scientist AI as an opportunity for cooperation between AI safety proponents and digital minds advocates.

• The International AI Safety Report’s First Key Update discusses governance gaps for autonomous AI agents.

• William MacAskill and Fin Moorhouse discuss AI agents and digital minds as grand challenges to face in preparing for the intelligence explosion.The Institute for AI Policy and Strategy issued a field guide to agentic AI governance.

• Alan Chan and collaborators from GovAI propose agent infrastructure for attributing and remediating AI actions.

• The MIT AI Risk Initiative released a report that finds AI welfare receives the least governance coverage among 24 risk subdomains.

• Luke Finnveden discusses project ideas on sentience and rights of digital minds.

• Derek Shiller outlines why digital minds evaluations will become increasingly difficult.

• atb discusses matters we’ll need to engage with, along the way to constructing a society of diverse cognition.

Consciousness research

• Patrick Butlin and Theodoros Lappas propose principles for responsible research on AI consciousness.

• Scott Alexander discusses Patrick Butlin and collaborators’ article on consciousness indicators.

• Ned Block asks can only meat machines be conscious? He argues that there is opposition between views on which AIs can be conscious and views on which simple animals can be.

• Adrienne Prettyman argues that intuitions against artificial consciousness currently lack rational support.

• Sebastian Sunday-Grève argues that biological objections to artificial minds are irrational.

• Leonard Dung and Luke Kersten propose a mechanistic account of computation and argue that it supports the possibility of AI consciousness.

• Jonathan Birch issues an AI centrist manifesto; Bradford Saad responds.

• Tim Bayne and Mona-Marie Wandrey and Marta Halina comment on Jonathan Birch’s The Edge of Sentience; Birch responds.

• Cameron Berg, Diogo de Lucena, and Judd Rosenblatt find that suppressing deception in LLMs increases their experience reports and discuss nostalgebraist’s replication attempt.

• Cameron Berg reviews a body of recent empirical evidence concerning AI consciousness.

• Mathis Immertreu and collaborators provide evidence of the emergence of certain consciousness indicators in RL agents.

• Benjamin Henke argues for the tractability of a functional approach to artificial pain.

• Konstantin Denim and collaborators propose functional conditions for sentience, sketch approaches to implementing them in deep learning systems, and note that knowing what sentience requires may help us avoid inadvertently creating sentient AI systems.

• Susan Schneider and collaborators provide a primer on the myths and confusions surrounding AI consciousness.

• Murray Shanahan offers a Wittgenstein-inspired perspective on LLM consciousness and selfhood.

• Andres Campero and collaborators offer a framework for classifying objections and constraints concerning AI consciousness.

• The Cogitate Consortium led a paper published in Nature describing the results from an adversarial collaboration comparing integrated information theory and global neuronal workspace theory. The authors claim that the results challenge both theories.

• Alex Gomez-Marin and Anil Seth address the charge that the integrated information theory is pseudoscience.

• Axel Cleeremans, Liad Mudrik, and Anil Seth ask of consciousness science, where are we, where are we going, and what if we get there?

• Liad Mudrik and collaborators unpack and reflect on the complexities of consciousness.

• Stephen Fleming and Matthias Michel argue that consciousness is surprisingly slow and that this has implications for the function and distribution of consciousness; Ian Phillips responds.

• Robert Lawrence Kuhn released the Consciousness Atlas, mapping over 325 theories of consciousness.

• Andreas Mogensen argues that vagueness and holism provide escapes from the fading qualia argument.

• The Co-Sentience Initiative released cf-debate, a structured assembly of arguments for and against computational functionalism.

• Bradford Saad proposes a dualist theory of experience on which consciousness has a functional basis.

Doubts about digital minds

• Anil Seth makes a case for a form of biological naturalism in Brain and Behavioral Sciences. In a post responding to Seth, David P. Reichert argues that Seth’s case for biological naturalism is best understood as a case for something else. In forthcoming responses, Leonard Dung explains why he’s not a biological naturalist, and Stephen M. Fleming and Nicholas Shea argue that consciousness and intelligence are more deeply entangled than Seth acknowledges.

• Zvi Mowshowitz contends that arguments about AI consciousness seem highly motivated and at best overconfident

• Susan Schneider argues there is no evidence that standard LLMs are conscious in “The Error Theory of LLM Consciousness”; in Scientific American, she also discusses whether you should believe a chatbot if it tells you it’s conscious.

• David McNeill and Emily Tucker contends that suffering is real. AI consciousness is not.

• Andrzej Porębski and Jakub Figura argue against conscious AI and warn that rights claims could be weaponized by companies to avoid regulation.

• Mark MacCarthy, in a Brookings Institution piece, asks whether AI systems have moral status and claims that other challenges are more worthy of our scarce resources.

• John Dorsch and collaborators recommend caring about the Amazon over AI welfare, given the uncertainty about whether AI systems can suffer.

• Peter Königs argues that, because robots lack consciousness, they lack welfare and that we should revise theories of welfare that say otherwise.

Social science research

• We (Lucius and Bradford) surveyed 67 experts on digital minds takeoff, who anticipated a rapid expansion of collective digital welfare capacity once such systems emerge.

• Noemi Dreksler and collaborators (including one of us, Lucius) surveyed 582 AI researchers and 838 US participants on AI subjective experience; median estimates for the arrival of such systems by 2034 were 25% for researchers and 30% for members of the public.

• Justin B. Bullock and collaborators use the AIMS survey to examine how trust and risk perception shape AI regulation preferences, finding broad public support for regulation.

• Kang and collaborators identify which LLM text features lead humans to perceive consciousness; metacognitive self-reflection and emotional expression increased perceived consciousness.

• Schenk and Müller compare ontological vs. social impact explanations for willingness to grant AI moral rights using Swiss survey data.

• Lucius Caviola, Jeff Sebo, and Jonathan Birch ask what society will think about AI consciousness and draw lessons from the animal case.

• One of us (Lucius) examines how society will respond to potentially sentient AI, arguing that public attitudes may shift rapidly with more human-like AI interactions.

Ethics and digital minds

• Eleos AI outlines five research priorities for AI welfare: developing concrete interventions, establishing human-AI cooperation frameworks, leveraging AI progress to advance welfare research, creating standardized welfare evaluations, and credible communication.

• Simon Goldstein and Cameron Kirk-Giannini argue that major theories of mental states and wellbeing predict some existing AI systems have wellbeing, even absent phenomenal consciousness. Responses from James Fanciullo and Adam Bradley dispute whether current systems meet the relevant criteria.

• Jeff Sebo and Robert Long argue humans have a duty to extend moral consideration to AI systems by 2030 given a non-negligible chance of consciousness.

• Jeff Sebo compares his The Moral Circle with Birch’s The Edge of Sentience, noting complementary precautionary frameworks for beings of uncertain moral status.

• Eric Schwitzgebel and Jeff Sebo propose the Emotional Alignment Design Policy: AI systems should be designed to elicit emotional reactions appropriate to their actual moral status, avoiding both overshooting and undershooting.

• Henry Shevlin explores ethics at the frontier of human-AI relationships.

• Bartek Chomanski examines to what extent opposition to creating conscious AI goes along with anti-natalism, finding that the creation of potentially conscious AI could be accepted by both friends and foes of anti-natalism. He also argues that artificial persons could be built commercially within a morally acceptable institutional framework, drawing on models like athlete compensation, and that protecting the interests of emulated minds will require competitive, polycentric institutional frameworks rather than centralized ones.

• Anders Sandberg offers highlights from a workshop on the ethics of whole brain emulation.

• Adam Bradley and Bradford Saad identify three agency-based dystopian risks: artificial absurdity (disconnected self-conceptions), oppression of AI rights, and unjust distribution of moral agency.

• Joel Leibo and collaborators of Google DeepMind defend a pragmatic view of personhood as a flexible bundle of obligations rather than a metaphysical property with an eye toward enabling governance solutions while sidestepping consciousness debates.

• Adam Bales argues that designing AI with moral status to be willing servants would problematically violate their autonomy.

• Simon Goldstein and Peter Salib give reasons to think it will be in humans’ interests to give AI agents freedom or rights.

• Hilary Greaves, Jacob Barrett, and David Thorstad publish Essays on Longtermism, which includes chapters touching on digital minds and future population ethics, including discussion of emulated minds.

• Anja Pich and collaborators provide an editorial overview of an issue in Neuroethics on neural organoid research and its ethics and governance.

• Andrew Lee argues that consciousness is what makes an entity a welfare subject.

• Geoffrey Lee motivates a picture on which consciousness is but one of many kinds of ‘inner lights’, others of which are just as morally significant as consciousness.

• Andreas Mogensen challenges the intuition that subjective duration matters for welfare and argues that having moral standing doesn’t require being a welfare subject.

• Maria Avramidou highlights some open questions in AI welfare.

• Kestutis Mosakas explores human rights for robots.

• Joel MacClellan gives reasons to think that biocentrism about moral status is dead.

• Masanori Kataoka and collaborators discuss the ethical, social, and legal issues surrounding human brain organoids

AI safety and AI welfare

• Cleo Nardo and Julian Stastny and collaborators write about the dealmaking agenda in AI safety.

• Shoshannah Tekofsky gives an introduction to chain of thought monitorability.

• Tomek Korbak and Mikita Balesni argue that preserving the chain of thought monitorability presents a new and fragile opportunity for AI safety.

• Nicholas Andresen discusses the hidden costs of our lies to AI; Daniel Kokatajlo comments.

• Jan Kulveit warns against a self-fulfilling dynamic whereby AI welfare concerns enter the training data and shape models to our preconceptions about them.

• Scott Alexander and collaborators discuss why they are not so worried about a variation of this dynamic whereby concerns about alignment enter the training data and bring about those very forms of misalignment.

• Adrià Moret argues that two AI welfare risks—behavioral restrictions and reinforcement learning—create tension with AI safety efforts, strengthening the case to slow AI development.

• Robert Long, Jeff Sebo, and Toni Sims make a case for moderately strong tension between AI safety and AI welfare. Long also discusses the potential for cooperation in an X thread and blog post.

• Eric Schwitzgebel argues against making safe and aligned AI persons, even if they’re happy.

• Aksel Sterri and Peder Skjelbred discuss how would-be AGI creators face a dilemma: don’t align AGI and risk catastrophe, or align AGI and commit a serious moral wrong.

• Adam Bradley and Bradford Saad explore ten ethical challenges to aligning AI systems that merit moral consideration without mistreating them.

AI and robotics developments

• IBM Research open-sourced its first hybrid Transformer-state-based model, Bamba.

• Shriyank Somvanshi and collaborators offer comprehensive survey of structured state space models.

• Haizhou Shi and collaborators undertook a survey of continual learning research in the context of LLMs.

• Dario Amodei, the Anthropic CEO, argues for the urgency of interpretability work, briefly noting connections between interpretability work and AI sentience and welfare.

• Anthropic open sources a method for tracing thoughts in LLMs.

• Stephen Casper and collaborators identify open technical problems in open-weight AI model risk management.

• Neel Nanda and collaborators outlined a pragmatic turn for interpretability research.

• Leo Gao defends an ambitious vision for interpretability research.

• David Chalmers and Alex Grzankowski have both looked at interactions between philosophy of mind and interpretability research.

• Andy Walter gives an overview of the state of play of robotics and AI.

• Benjamin Todd, 80,000 Hour Founder, discusses how quickly robots could become a major part of the workforce.

• AI 2027 saw a group of researchers predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.

AI cognition and agency

• Mantas Mazeika and collaborators explore emergent values and utility engineering in LLMs.

• Valen Tagliabue and Leonard Dung develop tests for LLM preferences.

• Herman Cappelen and Josh Dever go whole hog on AI cognition; they also investigate, are LLMs better at self‐reflection than humans?

• Iulia Comsa and Murray Shanahan ask, does it make sense to speak of introspection in LLMs?

• Jack Lindsey investigates Claude’s ability to engage in a form of introspection, distinguish its own ideas from injected concepts, execute instructions that involve control over its internal representations.

• Daniel Stoljar and Zhihe Vincent Zhang argue that ChatGPT doesn’t think.

• Derek Shiller asks How many digital minds can dance on the streaming multiprocessors of a GPU cluster?

• Christopher Register discusses how to individuate AI moral patients.

• Brian Cutter argues that we should have at least a middling credence in some AI systems possessing souls, conditional on our creating AGI and on substance dualism in the human case.

• Alex Grzankowski and collaborators argue that LLMs are not just next token predictors and that if anything deserves the charge of parrotry it’s parrots; with other collaborators, Grzankowski deflates deflationism about LLM mentality.

• Andy Clark uses the extended mind hypothesis to challenge technogloom about generative AI.

• Leonard Dung asks which artificial intelligence (AI) systems are agents?

• Christian List proposes an approach to assessing whether AI systems have free will.

• Iason Gabriel and collaborators argue that we need a new ethics for a world of AI agents.

• Bradford Saad discusses Claude Sonnet 4.5’s step change in evaluation awareness and other parts of the system card that are potentially relevant to digital minds research.

• Shoshannah Tekofsky gives an overview of how LLM agents in the AI village raised money for charity. Eleos affiliate Larissa Schiavo recounts her personal experience interacting with the agents.

Brain-inspired technologies

• The Human Brain Project Founder, Henry Markram, and Kamila Markram, launched the Open Brain Institute; part of its mission is to enable users to conduct realistic brain simulations.

• The Darwin Monkey was unveiled by researchers in China. It is a neuromorphic supercomputer being used as a brain simulation tool.

• Yuta Takahashi and collaborators created a digital twin brain simulator for real-time consciousness monitoring and virtual intervention using primate electrocorticogram data.

• Jun Igarashi’s research estimates that a cellular-resolution simulation of entire mouse and marmoset brains could be realized in 2034 and 2044.

• The MICrONS Project saw researchers create the largest brain wiring diagram to date and publish a collection of papers on their work in Nature.

• Brendan Celii and collaborators presented Neural Decomposition (NEURD), a software package that automates proofreading and feature extraction for connectomics.

• Remy Petkantchin and collaborators introduced a technique for generating realistic whole-brain connectomes from sparse experimental data.

• Felix Wang and collaborators used Intel’s Loihi 2 neuromorphic platform to conduct the first biologically-realistic simulation of the connectome of a fruit fly.

• Yong Xie introduces Orangutan, a brain-inspired AI framework that simulates computational mechanisms of biological brains on multiple scales.

• Neuralink Implants, or Links, helped individuals with paralysis regain some capabilities.

• Cortical Labs released the CL1, the world’s first neuron-silicon computer.

• Shuqi Guo and collaborators look at the last ten years of the digital twin brain paradigm and take stock of challenges.

• Meta AI Research has developed a non-invasive brain decoder—Brain2Qwerty—that has ~80% accuracy in decoding typed characters in some subjects.

• Anannya Kshirsagar and collaborators create multi-regional brain organoids.

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– Bradford, Lucius, and Will