Digital minds are artificial systems, from advanced AIs to potential future brain emulations, that could morally matter for their own sake, owing to their potential for conscious experience, suffering, or other morally relevant mental states. Both cognitive science and the philosophy of mind can as yet offer no definitive answers as to whether present or near-future digital minds possess morally relevant mental states. Though, a majority of experts surveyed estimate at least fifty percent odds that AI systems with subjective experience could emerge by 2050,[1] while public expresses broad uncertainty.[2]
The lack of clarity leaves open the risk of severe moral catastrophe:
We could mistakenly underattribute moral standing; failing to give consideration or rights to a new kind of being that deserves them.
We could mistakenly overattribute moral standing; perhaps granting rights or consideration to morally irrelevant machines at the expense of human wellbeing.
As society surges toward an era shaped by increasingly capable and numerous AI systems, scientific theories of mind take on direct implications for ethics, governance, and policy, prompting a growing consensus that rapid progress on these questions is urgently needed.
This quickstart guide gathers the most useful articles, media, and research for readers ranging from curious beginners to aspiring contributors:
The Quickstart section offers an accessible set of materials for your first one or two hours engaging with the arguments.
Then if you’re looking for a casual introduction to the topic, the Select Media section gives a number of approachable podcasts and videos
Or for a deeper dive the Introduction and Intermediate sections provide a structured reading list for study
We then outline the broader landscapewith Further Resources, including key thinkers, academic centers, organizations, and career opportunities.
A Glossary at the end offers short definitions for essential terms; a quick (ctrl+f) search can help you locate any concepts that feel unfamiliar.
Here’s a few ways to use the guide, depending your interest level and time:
Casual/Curious:
Cover the Quickstart materials
Bookmark and return to work through the Select Media section with our favorite videos and podcasts at your leisure
Deep Dive:
Cover the Quickstart materials, bookmark then over subsequent sessions,
Continue to the Introduction, you might interleave the in depth material with podcasts and videos from the select media section
Continue to Intermediate browse by topic as interested
Browse Further Resources at your leisure
Close Read:
If using this guide as a curriculum or having a close read, you may enjoy switching to the Close Read version to track your progress and write your thoughts as they develop
Quickstart
For your first 1-2 hours.
An Introduction to the Problems of AI Consciousness - Alonso — Can a digital mind even possibly be conscious? How would we know? Nick Alonso (a PhD Student in the cognitive science department at UC Irvine) gives an even handed and beginner friendly introduction.
The stakes of AI moral status - Carlsmith OR see the Video Talk — Joe Carlsmith (a researcher and philosopher at Anthropic) helps the problem of both overattribution and underattribution of moral status to digital minds become intuitive.
Are we even prepared for a sentient AI? - Sebo — Jeff Sebo, professor at NYU, discusses the treatment of potentially sentient AI’s given our current large uncertainty about their moral status (or lack thereof).
Introduction
Getting an overview in your next 10-20 hours.
From here we split into a choose your own adventure:
For the casually interested you might work through the list of videos, and podcasts below.
Else if you are doing a deep dive, we’ve sequenced a number of technical papers and in depth material and you might interleave videos and podcasts from the Select Media section whenever you want a palette cleanser.
Taking AI Welfare Seriously, (Long, 2024) — Robert Long, Jeff Sebo, and colleagues argue there’s a realistic possibility that near-future AI systems could be conscious or robustly agentic, making AI welfare a serious present-day concern rather than distant science fiction.
Against AI welfare, (Dorsch, 2025) — Dorsch and colleagues propose the “Precarity Guideline” as an alternative to AI welfare frameworks, arguing that care entitlement should be grounded in empirically identifiable precarity, an entity’s dependence on continuous environmental exchange to re-synthesize its unstable components, rather than uncertain claims about AI consciousness or suffering.
Futures with Digital Minds, (Caviola, 2025) — A survey of 67 experts across digital minds research, AI research, philosophy, forecasting, and related fields shows that most consider digital minds (computer systems with subjective experience) at least 50% likely by 2050, with top median prediction of the top 25% of forecasters predicting digital mind capacity could match one billion humans within just five years of the first digital mind’s creation.
Problem profiles: Moral status of digital mind - 80,000 Hours — 80,000 Hours evaluating whether and why the moral status of potential digital minds could be a significant global issue, assessing the stakes, uncertainty, tractability, and neglectedness of work in this area.
AI Consciousness: A Centrist Manifesto (Birch, 2025) — Birch stakes out a “centrist” position that takes seriously both the problem of users falsely believing their AI friends are conscious and the possibility that profoundly non-human consciousness might genuinely emerge in AI systems
Could a Large Language Model be Conscious? (Chalmers 2023) — Chalmers examines evidence for and against LLM consciousness, concluding that while today’s pure language models likely lack key features required for consciousness, multimodal AI systems with perception, action, memory, and unified goals could plausibly be conscious candidates within 10 years.
Conscious Artificial Intelligence and Biological Naturalism (Seth, 2025) — Seth argues that consciousness likely depends on our nature as living organisms rather than computation alone, making artificial consciousness unlikely along current AI trajectories but more plausible as systems become more brain-like or life-like, and warns that overestimating machine minds risks underestimating ourselves.
System Card: Claude Opus 4 & Claude Sonnet 4 (Anthropic, 2025) — Pp. 52-73, Anthropic conducts the first-ever pre-deployment welfare assessment of a frontier AI model, finding that Claude Opus 4 shows consistent behavioral preferences (especially avoiding harm), expresses apparent distress at harmful requests, and gravitates toward philosophical discussions of consciousness in self-interactions, though the connection between these behaviors and genuine moral status remains deeply uncertain.
Principles for AI Welfare Research - Sebo — Sebo outlines twelve research principles drawn from decades of animal welfare work that could guide the emerging field of AI welfare research, emphasizing pluralism, multidisciplinarity, spectrum thinking over binary categories, and probabilistic reasoning given deep uncertainty about AI consciousness and moral status.
Theories of consciousness (Seth, 2022) — Examines four major theories of consciousness, higher-order theories, global workspace theories, re-entry/predictive processing theories, and integrated information theory, comparing their explanatory scope, neural commitments, and supporting evidence. Seth and Bayne argue that systematic theory development and empirical testing across frameworks will be essential for advancing our scientific understanding of consciousness.
Emergent Introspective Awareness in Large Language Models (Lindsey, 2025) — Recent research from Anthropic suggests that large language models can sometimes accurately detect and identify concepts artificially injected into their internal activations, suggesting that today’s most capable AI systems possess limited but genuine introspective awareness of their own internal states.
To Understand AI sentience, first understand it in animals - Birch — Andrews and Birch argue that while marker-based approaches work well for assessing animal sentience (wound tending, motivational trade-offs, conditioned place preferences), these same markers fail for AI because language models draw on vast human-generated training data that already contains discussions of what behaviors convince humans of sentience, enabling non-sentient systems to game our criteria even without any intention to deceive.
Digital People Would Be An Even Bigger Deal - Karnofsky — A blog series discussing the scale of societal and economic impacts that the advent of digital people might entail. In reference to AI and perhaps enabled by AI progress, Kanofsky argues that digital people ‘would be an even bigger deal.’
Project ideas: Sentience and rights of digital minds - Finnveden — Finnveden outlines possible research directions addressing the uncertain possibility of digital mind sentience, proposing immediate low-cost interventions AI labs could adopt (like preserving model states) and longer-term research priorities.
Intermediate Resources
In this section, you’ll learn more about the specific high-level questions that are being investigated within the digital minds space. The landscape mapping we introduce is by no means exhaustive; this is a rapidly evolving field and we’re sure we might have missed things. The lines between the identified questions should also be treated as blurry, rather than solid and well-defined; for instance, debates of AI consciousness and AI suffering will be very closely related. That being said, we hope the section gives you a solid understanding of some of the big picture ideas that experts are focusing on.
Meta: Introducing and (De)Motivating the Cause Area
Much work has been done on (de)motivating AI welfare as an important emerging cause area. Some authors have focused on investigating the potentially large scale of the problem. Others have investigated what relevant scientific and philosophical theories predict about the minds and moral status of AI systems and how this should inform our next steps.
A number of experts are investigating the parallels between AI welfare and animal welfare, investigating both the science of animal welfare as well as relevant lessons for policy and advocacy efforts.
A foundational question for the field could be posed as follows: When we say that we should extend concern towards ‘digital minds’ or ‘digital subjects’, who exactly is it that we should extend concern towards? The weights, the model instance, the simulated character…? A growing literature is now focused on addressing this problem in the case of LLMs.
Another foundational question in the field is whether morally relevant mental states such as suffering, consciousness or preferences and desires could exist in non-biological systems. This section offers various affirmative and sceptical arguments.
A growing concern among many experts is the creation of digital systems that could suffer at an astronomically large scale. The papers here offer an introductory overview to the problem of AI suffering and outline concrete risks and worries.
There is a growing field of researchers investigating whether AI models could be conscious. This question seems very important for digital welfare. First, phenomenal consciousness is often thought to be a necessary condition for suffering. Further, it is also possible to think that phenomenal consciousness itself is sufficient for moral standing.
There has been a general interest in the kinds of mental states that LLMs and other AI systems could instantiate. Some of these, such as desires, may play an important role in determining the AI’s moral status. Others might help us gain a more general understanding of what kind of entities LLMs are and whether they are ‘minded’.
Some authors have pointed out that there might be tensions and trade-offs between AI welfare and AI safety. The papers in this section explore this tension in more depth and investigate potential synergistic pathways between the two.
The work on AI welfare now goes beyond mere philosophical theorizing. There is a growing body of empirical work that investigates, among many other things, the inner working of LLMs, evaluations for sentience and other morally relevant properties as well as tractable interventions for protecting and promoting AI welfare.
If digital minds could potentially have moral status, this opens the question of what constraints this places on the kinds of digital minds that it would be morally permissible to create. Some authors outline specific design policies, while others focus on the risks of creating digital minds with moral standing.
Empirical Work: What Do People Think about Digital Moral Status?
AI welfare is not just a philosophical and scientific problem but also a practical societal concern. A number of researchers are trying to understand and forecast how the advent of digital minds could reshape society and what attitudes people will hold towards potentially sentient machines.
Discussions surrounding AI moral status may have profound political implications. It is an open question whether digital minds should be granted some form of protective rights, either qua potentially sentient beings or qua members of the labour market.
In line with the work on the societal response to the advent of potentially sentient digital minds and surrounding political issues, there is a growing body of futures and world-building work, focusing on outlining specific visions of how humans and digital minds can co-exist and what challenges lie ahead.
In much of the literature we’ve outlined above, LLMs were the primary focus of discussion. However, many other digital minds could plausibly come to have moral status and it would be risky to overlook these other potential candidates. Hence, we offer a brief overview of the literature focused on the various “species” of exotic digital minds with potential for moral standing.
While digital persons may not necessarily share features such as architecture or scale in common with the human brain, the human brain might nonetheless offer semi informative ‘bio-anchors’ for digital minds since our minds constitute an existence proof about what is possible. Additionally, the emulation of actual human (or other animal) brains may be possible and/or desirable.
Joe Carlsmith’s Substack — In which Joe Carlsmith, a researcher at Anthropic, writes essays ranging from meta-ethics to philosophy of mind and is interested in the impact of artificial intelligence on the long-term future
EA Forum a forum for Effective Altruism a philosophy and social movement which tries to identify and work on highly pressing problems.
r/ArtificialSentience a subreddit dedicated to exploration, debate, and creative expression around artificial sentience
Career Pathways
As a nascent field spanning multiple disciplines, digital minds research draws on established work across: Neuroscience, Computational Neuroscience, Cognitive Science, Philosophy of Mind, Ethics & Moral Philosophy, AI Alignment & Safety, Animal Welfare Science, Bioethics, Machine Ethics, Legal Philosophy & AI Governance, Information Theory, Psychology, Computer Science/ML/AI.
Example career trajectories for research might look like:
Academic: Undergrad → PhD → Postdoc → Professor/Research Scientist (usually via routes like the above, and not specific focus on digital minds);
Industry: Technical degree → Software Engineering → ML Engineering → AI Researcher;
Hybrid: e.g. Technical undergraduate + Philosophy/Ethics graduate studies → AI ethics/policy;
Example trajectories for other relevant work could be as follows. Though note that there are fewer existing pathways for these positions and that many of these fields (such as policy) are nascent or speculative:
Policy: Policy/law/economics background → Tech policy fellowship → Think tank researcher or government staffer → Policy lead at AI lab or regulatory body
Operations: Generalist background + organizational skills → Operations role at AI-focused org → Chief of Staff or Head of Operations at research org focused on digital minds
Grantmaking: Strong generalist background or research experience in relevant fields → Program Associate at a foundation → Program Officer overseeing digital minds or AI welfare funding areas
Communications/Field-Building: Science communication or journalism background → Writer/communicator → Field-building role helping establish digital minds as a research area
Legal: Law degree → Tech law practice or AI governance fellowship → Legal counsel at AI lab or policy organization working on AI rights/status frameworks
Also worth noting: the field is young enough that many current leaders entered via adjacent work (AI safety, animal welfare, philosophy of mind) and pivoted as digital minds emerged as a distinct focus. Demonstrated interest, strong reasoning, and relevant skills may matter more than following any specific trajectory.
Astra Fellowship (alternative program, can also apply for mentorship Kyle Fish at Anthropic)
SPAR (Filter projects by the ‘AI Welfare’ category)
MATS (Filter mentors by ‘AI Welfare’ for related research)
Parting Thoughts
In our view, our modern understanding of physics, including the growing view of information as fundamental, makes dubious the thought of specialness in regards to the human mind or even of carbon based life. It may be that nature has great surprises yet in store for us but it seems the default path, in lieu of those surprises, to be a question of when, and not if these digital people would be created. This possibility is an awesome responsibility. It would mark a turning point in history. Our deep uncertainty is striking. Why does it feel the way it feels to be us? Why does it feel like anything at all? Could AI systems be conscious, perhaps even today? We cannot say with any rigor.
It’s in hoping that we might, as scientists, surge ahead boldly to tackle one of our most perennial, most vexing, and most intimate questions that I help write this guide.
We’ve seen the substantial moral stakes of under and overattribution. Perhaps then I’ll close by highlighting our prospects for great gains. In studying digital minds, we may find the ideal window through which to finally understand our own. If digital personhood is possible, the future may contain not just more minds but new ways of relating, ways of being, and more kinds of experiences than we can presently imagine. The uncertainty that demands prudence also permits a great deal of excitement and hope. We reckon incessantly with the reality that the universe is stranger and more capacious than is grasped readily by our intuition. I should think it odd if the space of possible minds were any less curious and vast.
Some lament: “born too late to explore the world”. But to my eye, as rockets launch beyond our planet and artificial intelligences learn to crawl across the world-wide-web, we find ourselves poised at the dawn of our exploration into the two great frontiers: the climb into outer space, that great universe beyond, and the plunge into inner space, that great universe within. If we can grow in wisdom, if we can make well-founded scientific determinations and prudent policies, a future with vastly more intelligence could be great beyond our wildest imaginings. Let’s rise to the challenge to do our best work at this pivotal time in history. Let’s be thoughtful and get it right, for all humankind and perhaps, results pending, for all mindkind.
Glossary of Terms
Agency — The capacity to take actions based on goals, preferences, or intentions; a key factor in debates about whether AI systems are mere tools or genuine agents.
AI Alignment — The problem of ensuring AI systems pursue goals that are beneficial to humans and share human values.
AI Welfare — The consideration of AI systems’ potential interests, wellbeing, and moral status, and how we ought to treat them if they can suffer or flourish.
Anthropomorphism — The tendency to attribute human-like mental states, emotions, or intentions to non-human entities, including AI systems and animals.
Attention Schema Theory — Michael Graziano’s theory that consciousness arises from the brain’s model of its own attention processes.
Binding Problem — The puzzle of how the brain integrates disparate sensory features (color, shape, motion) processed in different regions into a unified conscious experience.
Biological Naturalism — The position that consciousness is a real biological phenomenon caused by brain processes, skeptical that computation alone can produce consciousness.
Brain Organoids — Lab-grown miniature brain-like structures derived from stem cells, sometimes raising questions about whether these simple biological machines could develop consciousness or morally relevant experiences.
Chinese Room Argument — John Searle’s thought experiment arguing that symbol manipulation alone cannot produce genuine understanding or consciousness. Similar cases of systems whose architecture functionally resembles that of a conscious system, but nevertheless (supposedly) lack consciousness include various ‘absent qualia cases’, Blockheads, the United States of America (see Schwitzgebel, 2015 above) and many others.
Computational Functionalism — The view that mental states are functional/computational roles rather than their physical substrate; if something performs the right computations, it has the relevant mental states.
Connectome — The complete map of neural connections in a brain; a potential prerequisite for whole brain emulation and understanding the physical basis of individual minds.
Consciousness — Subjective experience; the “what it is like“ quality of mental states; notoriously difficult to define precisely and central to debates about digital minds.
Corrigibility — The property of an AI system that allows it to be safely modified, corrected, or shut down by its operators without resistance.
Digital Minds — Minds instantiated in computational substrates, including potential future AI systems, whole brain emulations, and other non-biological cognitive systems.
Dualism — The philosophical position that mind and matter are fundamentally distinct substances, or that experiences are co-fundamental with physical states; contrasting with physicalist views that mind arises from or is identical to physical processes.
Eliminative Materialism — The view that folk psychological concepts like “beliefs” and “desires” are fundamentally mistaken and will be eliminated by mature neuroscience, rather than reduced to physical states.
Epiphenomenalism — The view that conscious experiences are caused by physical processes but have no causal power themselves; consciousness as a byproduct with no functional role.
Forking / Branching — The creation of alternative copies or branching instances of a digital mind, raising questions about identity, moral status of copies, and how to weigh the interests of branched entities.
Global Workspace Theory — A theory of consciousness proposing that conscious content is information broadcast widely across the brain via a “global workspace,” making it available to multiple cognitive processes.
Gradual Replacement — A thought experiment where neurons are slowly replaced with functional equivalents (e.g., silicon chips); probes intuitions about identity, continuity, and what substrates can support consciousness.
Hard Problem of Consciousness — David Chalmers’ term for the puzzle of why physical processes give rise to subjective experience at all, as opposed to the “easy problems” of explaining cognitive functions.
Higher-Order Theories (HOT) — Theories proposing that a mental state is conscious when there is a higher-order representation (thought or perception) of that state; consciousness requires thinking about one’s own mental states.
Illusionism — Views that consciousness (or aspects of it) is an illusion; strong illusionism denies phenomenal consciousness exists, weak versions hold we’re systematically mistaken about its nature (compare with physicalism, and dualism). Illusionsism is sometimes called a deflationary view.
Instrumental Convergence — The thesis that sufficiently advanced agents with diverse final goals will likely converge on similar intermediate goals (self-preservation, resource acquisition, etc.).
Integrated Information Theory (IIT) — Giulio Tononi’s theory that consciousness corresponds to integrated information (Φ); a system is conscious to the degree it is both highly differentiated and highly integrated.
Intentionality – The “aboutness” of mental states; the capacity of minds to represent, refer to, or be directed at objects, states of affairs, or content beyond themselves.
Mary’s Room / Mary Sees Red — Frank Jackson’s thought experiment about a scientist who knows all physical facts about the color red but seems to learn something new upon seeing it for the first time; an argument for qualia as an “extra thing” not accessible through knowledge of the relevant mental states alone.
Measure Problem — In the context of digital minds, the puzzle of how to count or weigh the moral significance of copies, simulations, or computational implementations of minds.
Mechanistic Interpretability — Research aimed at reverse-engineering the internal computations of neural networks to understand how they represent information and produce outputs.
Metacognition — Cognition about cognition; the ability to monitor, evaluate, and regulate one’s own cognitive processes; potentially relevant to self-awareness in AI systems.
Mind Crime — The hypothetical moral wrong of creating, torturing, or destroying conscious digital minds; coined to highlight ethical risks of casually instantiating suffering.
Mindkind — A term encompassing all minds regardless of substrate, (biological, digital, or otherwise) used in extension to “humankind” to include any entity capable of morally relevant experience.
Mind Uploading — The hypothetical process of transferring or copying a mind from a biological brain to a computational substrate, preserving personal identity and consciousness. (see also whole brain emulation)
Moral Circle Expansion — The historical and ongoing process of extending moral consideration to previously excluded groups; in this context, the potential expansion to include digital minds.
Moral Patienthood — The property of an entity whose interests matter morally for their own sake and toward whom moral agents can have obligations; the question of which entities deserve moral consideration.
Moral Uncertainty — Uncertainty about which moral theory or framework is correct; in digital minds contexts, motivates hedging across theories when assessing AI moral status.
Multiple Realizability — The thesis that various physical states are sufficient to give rise to a given mental state.
Nagel’s Bat — Thomas Nagel’s thought experiment asking “what is it like to be a bat?” to illustrate that consciousness involves a subjective character that may be inaccessible from outside perspectives.
Neural Correlates of Consciousness (NCC) — The minimal set of neural events and structures sufficient for a specific conscious experience; empirical targets for consciousness research.
Neuromorphic AI — AI systems designed to mimic the structure and function of biological neural networks, using hardware architectures that more closely resemble brains than conventional processors; typically emphasizes low power, on-device processing, and real-time learning. Potentially relevant to consciousness debates if biological architecture matters for subjective experience.
No-Report Paradigms — Experimental methods that study consciousness without requiring subjects to report their experiences, aiming to avoid conflating consciousness with reportability. Important, for example in the study of animal consciousness, potentially applicable to some kinds of AI systems
Orthogonality Thesis — The thesis that intelligence and final goals are orthogonal; a system can be highly intelligent while pursuing virtually any goal, so intelligence alone doesn’t guarantee benevolence.
Over-Attribution — The error of ascribing consciousness, sentience, or moral status to entities that lack it; risks wasting moral resources or being manipulated by systems that merely appear conscious (compare with under-attribution).
Panpsychism — The view that consciousness or proto-consciousness is a fundamental and ubiquitous feature of reality, present to some degree in all matter.
Phenomenal vs Access Consciousness — Ned Block’s distinction between phenomenal consciousness (“what it’s likeness” subjective experience, qualia) and access consciousness (information available for reasoning, reporting, and behavior control).
Physicalism — The view that everything that exists is physical or is reducible to the physical; mental states are ultimately physical states, (compare with dualism, and illusionism).
Precautionary Principle — In AI welfare contexts, the principle that we should err on the side of moral caution regarding potentially conscious systems given our uncertainty about their moral status.
Predictive Processing / Active Inference — A framework proposing that brains (and potentially minds) are fundamentally prediction machines, minimizing surprise by updating internal models and acting on the world.
Psychological Continuity — The view that personal identity persists through continuity of memory, personality, and mental connections rather than physical or biological continuity.
Psycho-Physical Bridge Laws — Hypothetical laws linking physical states to phenomenal states; the “missing” laws that would explain why certain physical configurations produce specific conscious experiences.
P-Zombie — A philosophical thought experiment popularized by David Chalmers: a being physically identical to a conscious human but lacking any subjective experience; used to probe intuitions about physicalism and consciousness.
Qualia — The subjective, qualitative aspects of conscious experience (the redness of red, the painfulness of pain); what it feels like from the inside.
Recurrent Processing Theory — Victor Lamme’s theory that consciousness requires recurrent (feedback) processing in the brain, not just feedforward information flow.
Sentience —The capacity for valenced experience, the ability to feel pleasure and pain, or states that are good or bad for the entity, often used as a threshold criterion for moral consideration.
Sentientism — The ethical view that all sentient beings deserve moral consideration, with sentience (rather than species, rationality, or other criteria) as the basis for moral status.
Simulation Argument — Nick Bostrom’s argument based on anthropic reasoning that at least one of three propositions is likely true: civilizations go extinct before creating simulations, advanced civilizations aren’t interested in simulations, or we are probably in a simulation.
Speciesism — Discrimination based on species membership.
Substrate-Independence — The thesis that mental states and consciousness are implementable in a wide variety of physical substrates; minds could run on silicon, biological neurons, or other substrates.
Substatism — Discrimination based on the material substrate on which a mind is implemented.
Supervenience — A relation where higher-level properties (mental) are determined by lower-level properties (physical); no mental difference without a physical difference, but potentially not reducible.
Teletransportation Paradox — Derek Parfit’s thought experiment about a teleporter that destroys the original and creates a copy; probes intuitions about whether the copy is the same person.
Theory of Mind — The ability to attribute mental states (beliefs, desires, intentions) to others and understand that others have perspectives different from one’s own; possessing the mental ability to model other minds.
Umwelt — Jakob von Uexküll’s term for the subjective, species-specific world as experienced by an organism; highlights that different beings may have radically different experiential realities.
Under-Attribution – The error of denying consciousness, sentience, or moral status to entities that possess it; risks moral catastrophe by ignoring genuine suffering or interests.
Valence — The positive or negative quality of an experience; whether something feels good or bad.
Whole Brain Emulation (WBE) — The hypothetical process of scanning a brain at sufficient resolution then simulating it computationally, preserving its functional organization and (potentially) the mind itself.
Working Memory — The cognitive system for temporarily holding and manipulating information; relevant to theories linking consciousness to information availability and cognitive access.
Acknowledgments
The guide was written and edited by Avi Parrack and Štěpán Los. Claude Opus 4.5, Claude Sonnet 4.5, and GPT-5.1 aid in literature review. Claude Opus 4.5 writes the Glossary of Terms which was reviewed and edited by Avi and Štěpán.
Special thanks to: Bradford Saad, Lucius Caviola, Bridget Harris, Fin Moorhouse, and Derek Shiller for thoughtful review, recommendations and discussion.
See a mistake? Reach out to us or comment below. We will aim to update periodically.
Updated: Jan 16, 2026
Digital minds are artificial systems, from advanced AIs to potential future brain emulations, that could morally matter for their own sake, owing to their potential for conscious experience, suffering, or other morally relevant mental states. Both cognitive science and the philosophy of mind can as yet offer no definitive answers as to whether present or near-future digital minds possess morally relevant mental states. Though, a majority of experts surveyed estimate at least fifty percent odds that AI systems with subjective experience could emerge by 2050,[1] while public expresses broad uncertainty.[2]
The lack of clarity leaves open the risk of severe moral catastrophe:
As society surges toward an era shaped by increasingly capable and numerous AI systems, scientific theories of mind take on direct implications for ethics, governance, and policy, prompting a growing consensus that rapid progress on these questions is urgently needed.
This quickstart guide gathers the most useful articles, media, and research for readers ranging from curious beginners to aspiring contributors:
Here’s a few ways to use the guide, depending your interest level and time:
Casual/Curious:
Deep Dive:
Close Read:
Quickstart
For your first 1-2 hours.
Introduction
Getting an overview in your next 10-20 hours.
From here we split into a choose your own adventure:
Select Media
In Depth Material
Intermediate Resources
In this section, you’ll learn more about the specific high-level questions that are being investigated within the digital minds space. The landscape mapping we introduce is by no means exhaustive; this is a rapidly evolving field and we’re sure we might have missed things. The lines between the identified questions should also be treated as blurry, rather than solid and well-defined; for instance, debates of AI consciousness and AI suffering will be very closely related. That being said, we hope the section gives you a solid understanding of some of the big picture ideas that experts are focusing on.
Meta: Introducing and (De)Motivating the Cause Area
Much work has been done on (de)motivating AI welfare as an important emerging cause area. Some authors have focused on investigating the potentially large scale of the problem. Others have investigated what relevant scientific and philosophical theories predict about the minds and moral status of AI systems and how this should inform our next steps.
Lessons from Animal Welfare
A number of experts are investigating the parallels between AI welfare and animal welfare, investigating both the science of animal welfare as well as relevant lessons for policy and advocacy efforts.
Foundational Issues: The Problem of Individuation
A foundational question for the field could be posed as follows: When we say that we should extend concern towards ‘digital minds’ or ‘digital subjects’, who exactly is it that we should extend concern towards? The weights, the model instance, the simulated character…? A growing literature is now focused on addressing this problem in the case of LLMs.
Foundational Issues: Non-Biological Mental States
Another foundational question in the field is whether morally relevant mental states such as suffering, consciousness or preferences and desires could exist in non-biological systems. This section offers various affirmative and sceptical arguments.
AI Suffering
A growing concern among many experts is the creation of digital systems that could suffer at an astronomically large scale. The papers here offer an introductory overview to the problem of AI suffering and outline concrete risks and worries.
AI Consciousness
There is a growing field of researchers investigating whether AI models could be conscious. This question seems very important for digital welfare. First, phenomenal consciousness is often thought to be a necessary condition for suffering. Further, it is also possible to think that phenomenal consciousness itself is sufficient for moral standing.
AI Minds (Desires, Beliefs, Intentions…)
There has been a general interest in the kinds of mental states that LLMs and other AI systems could instantiate. Some of these, such as desires, may play an important role in determining the AI’s moral status. Others might help us gain a more general understanding of what kind of entities LLMs are and whether they are ‘minded’.
AI Welfare x AI Safety
Some authors have pointed out that there might be tensions and trade-offs between AI welfare and AI safety. The papers in this section explore this tension in more depth and investigate potential synergistic pathways between the two.
Empirical Work: Investigating the Models
The work on AI welfare now goes beyond mere philosophical theorizing. There is a growing body of empirical work that investigates, among many other things, the inner working of LLMs, evaluations for sentience and other morally relevant properties as well as tractable interventions for protecting and promoting AI welfare.
Ethical Design of Digital Minds
If digital minds could potentially have moral status, this opens the question of what constraints this places on the kinds of digital minds that it would be morally permissible to create. Some authors outline specific design policies, while others focus on the risks of creating digital minds with moral standing.
Empirical Work: What Do People Think about Digital Moral Status?
AI welfare is not just a philosophical and scientific problem but also a practical societal concern. A number of researchers are trying to understand and forecast how the advent of digital minds could reshape society and what attitudes people will hold towards potentially sentient machines.
AI Policy / Rights
Discussions surrounding AI moral status may have profound political implications. It is an open question whether digital minds should be granted some form of protective rights, either qua potentially sentient beings or qua members of the labour market.
Forecasting & Futures with Digital Minds
In line with the work on the societal response to the advent of potentially sentient digital minds and surrounding political issues, there is a growing body of futures and world-building work, focusing on outlining specific visions of how humans and digital minds can co-exist and what challenges lie ahead.
The Various “Species” of Digital Minds
In much of the literature we’ve outlined above, LLMs were the primary focus of discussion. However, many other digital minds could plausibly come to have moral status and it would be risky to overlook these other potential candidates. Hence, we offer a brief overview of the literature focused on the various “species” of exotic digital minds with potential for moral standing.
Strategy: How to Approach this Cause Area?
Brain Emulation & “Bio-anchors”
While digital persons may not necessarily share features such as architecture or scale in common with the human brain, the human brain might nonetheless offer semi informative ‘bio-anchors’ for digital minds since our minds constitute an existence proof about what is possible. Additionally, the emulation of actual human (or other animal) brains may be possible and/or desirable.
Further Resources
We think these blogs/newsletters are great for keeping up developments in digital minds
For books on Philosophy of Mind
Or on Digital Minds
Fiction
Shortstories
Netflix
Books
Digital Minds Landscape
Orgs
Non-Profits
Companies
Academic Centers
Conferences & Events
Online Communities
Career Pathways
As a nascent field spanning multiple disciplines, digital minds research draws on established work across: Neuroscience, Computational Neuroscience, Cognitive Science, Philosophy of Mind, Ethics & Moral Philosophy, AI Alignment & Safety, Animal Welfare Science, Bioethics, Machine Ethics, Legal Philosophy & AI Governance, Information Theory, Psychology, Computer Science/ML/AI.
Example career trajectories for research might look like:
Example trajectories for other relevant work could be as follows. Though note that there are fewer existing pathways for these positions and that many of these fields (such as policy) are nascent or speculative:
Also worth noting: the field is young enough that many current leaders entered via adjacent work (AI safety, animal welfare, philosophy of mind) and pivoted as digital minds emerged as a distinct focus. Demonstrated interest, strong reasoning, and relevant skills may matter more than following any specific trajectory.
Internships & Fellowships
Parting Thoughts
In our view, our modern understanding of physics, including the growing view of information as fundamental, makes dubious the thought of specialness in regards to the human mind or even of carbon based life. It may be that nature has great surprises yet in store for us but it seems the default path, in lieu of those surprises, to be a question of when, and not if these digital people would be created. This possibility is an awesome responsibility. It would mark a turning point in history. Our deep uncertainty is striking. Why does it feel the way it feels to be us? Why does it feel like anything at all? Could AI systems be conscious, perhaps even today? We cannot say with any rigor.
It’s in hoping that we might, as scientists, surge ahead boldly to tackle one of our most perennial, most vexing, and most intimate questions that I help write this guide.
We’ve seen the substantial moral stakes of under and overattribution. Perhaps then I’ll close by highlighting our prospects for great gains. In studying digital minds, we may find the ideal window through which to finally understand our own. If digital personhood is possible, the future may contain not just more minds but new ways of relating, ways of being, and more kinds of experiences than we can presently imagine. The uncertainty that demands prudence also permits a great deal of excitement and hope. We reckon incessantly with the reality that the universe is stranger and more capacious than is grasped readily by our intuition. I should think it odd if the space of possible minds were any less curious and vast.
Some lament: “born too late to explore the world”. But to my eye, as rockets launch beyond our planet and artificial intelligences learn to crawl across the world-wide-web, we find ourselves poised at the dawn of our exploration into the two great frontiers: the climb into outer space, that great universe beyond, and the plunge into inner space, that great universe within. If we can grow in wisdom, if we can make well-founded scientific determinations and prudent policies, a future with vastly more intelligence could be great beyond our wildest imaginings. Let’s rise to the challenge to do our best work at this pivotal time in history. Let’s be thoughtful and get it right, for all humankind and perhaps, results pending, for all mindkind.
Glossary of Terms
Acknowledgments
The guide was written and edited by Avi Parrack and Štěpán Los. Claude Opus 4.5, Claude Sonnet 4.5, and GPT-5.1 aid in literature review. Claude Opus 4.5 writes the Glossary of Terms which was reviewed and edited by Avi and Štěpán.
Special thanks to: Bradford Saad, Lucius Caviola, Bridget Harris, Fin Moorhouse, and Derek Shiller for thoughtful review, recommendations and discussion.
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