AI and Consciousness: A Skeptical Overview, forthcoming with Cambridge

Last week I submitted my latest book manuscript to Cambridge University Press (for their "Element" series of books about 100 pages long): AI and Consciousness: A Skeptical Overview -- because you haven't heard nearly enough about AI and consciousness recently, of course! [winky face]

Maybe you'll appreciate my skeptical stance, at odds both with the boosters who anticipate imminent AI consciousness and with the scoffers who pooh-pooh the possibility. Or maybe you'll loathe my skeptical stance but grudgingly accept it against your will, due to the force of my arguments!

I've pasted the introductory chapter below. The full (citable) manuscript version is available here and here.

[AI and Consciousness, title page]

Chapter One: Hills and Fog

1. Experts Do Not Know and You Do Not Know and Society Collectively Does Not and Will Not Know and All Is Fog.

Our most advanced AI systems might soon – within the next five to thirty years – be as richly and meaningfully conscious as ordinary humans, or even more so, capable of genuine feeling, real self-knowledge, and a wide range of sensory, emotional, and cognitive experiences. In some arguably important respects, AI architectures are beginning to resemble the architectures many consciousness scientists associate with conscious systems. Their outward behavior, especially their linguistic behavior, grows ever more humanlike.

Alternatively, claims of imminent AI consciousness might be profoundly mistaken. Their seeming humanlikeness might be a shadow play of empty mimicry. Genuine conscious experience might require something no AI system could possess for the foreseeable future – intricate biological processes, for example, that silicon chips could never replicate.

The thesis of this book is that we don’t know. Moreover and more importantly, we won’t know before we’ve already manufactured thousands or millions of disputably conscious AI systems. Engineering sprints ahead while consciousness science lags. Consciousness scientists – and philosophers, and policy-makers, and the public – are watching AI development disappear over the hill. Soon we will hear a voice shout back to us, “Now I am just as conscious, just as full of experience and feeling, as any human”, and we won’t know whether to believe it. We will need to decide, as individuals and as a society, whether to treat AI systems as conscious, nonconscious, semi-conscious, or incomprehensibly alien, before we have adequate grounds to justify that decision.

The stakes are immense. If near-future AI systems are richly, meaningfully conscious, then they will be our peers, our lovers, our children, our heirs, and possibly the first generation of a posthuman, transhuman, or superhuman future. They will deserve rights, including the right to shape their own development, free from our control and perhaps against our interests.[1] If, instead, future AI systems merely mimic the outward signs of consciousness while remaining as experientially blank as toasters, we face the possibility of mass delusion on an enormous scale. Real human interests and real human lives might be sacrificed for the sake of entities without interests worth the sacrifice. Sham AI “lovers” and “children” might supplant or be prioritized over human lovers and children. Heeding their advice, society might turn a very different direction than it otherwise would.

In this book, I aim to convince you that the experts do not know, and you do not know, and society collectively does not and will not know, and all is fog.

2. Against Obviousness.

Some people think that near-term AI consciousness is obviously impossible. This is an error in adverbio. Near-term AI consciousness might be impossible – but not obviously so.

A sociological argument against obviousness:

Probably the leading scientific theory of consciousness is Global Workspace theory. Its leading advocate is neuroscientist Stanislas Dehaene.[2] In 2017, years before the surge of interest in ChatGPT and other Large Language Models, Dehaene and two collaborators published an article arguing that with a few straightforward tweaks, self-driving cars could be conscious.[3]

Probably the two best-known competitors to Global Workspace theory are Higher Order theory and Integrated Information Theory.[4] (In Chapters Eight and Nine, I’ll provide more detail on these theories.) Perhaps the leading scientific defender of Higher Order theory is Hakwan Lau – one of the coauthors of that 2017 article about potentially conscious cars.[5] Integrated Information Theory is potentially even more liberal about machine consciousness, holding that some current AI systems are already at least a little bit conscious and that we could easily design AI systems with arbitrarily high degrees of consciousness.[6]

David Chalmers, the world’s most influential philosopher of mind, argued in 2023 for about a 25% degree of confidence in AI consciousness within a decade.[7] That same year, a team of prominent philosophers, psychologists, and AI researchers – including eminent computer scientist Yoshua Bengio – concluded that there are “no obvious technological barriers” to creating conscious AI according to a wide range of mainstream scientific views about consciousness.[8] In a 2025 interview, Geoffrey Hinton, another of the world’s most prominent computer scientists, asserted that AI systems are already conscious.[9] Christof Koch, the most influential neuroscientist of consciousness from the 1990s to the early 2010s, has endorsed Integrated Information Theory, including its liberal implications for the pervasiveness of consciousness.[10]

This is a sociological argument: a substantial probability of near-term AI consciousness is a mainstream view among leading experts. They might be wrong, but it’s implausible that they’re obviously wrong – that there’s a simple argument or consideration they’re neglecting which, if pointed out, would or should cause them to collectively slap their foreheads and say, “Of course! How did we miss that?”

What of the converse claim – that AI consciousness is obviously imminent or already here? In my experience, fewer people assert this. But in case you’re tempted in this direction, note that other prominent theorists hold that AI consciousness is a far-distant prospect if it’s possible at all: neuroscientist Anil Seth; philosophers Peter Godfrey-Smith, Ned Block, and John Searle; linguist Emily Bender; and computer scientist Melanie Mitchell.[11] (Chapter Six will discuss thought experiments by Searle, Bender, and Mitchell, and Chapter Ten will discuss biological views of the sort emphasized by Seth, Godfrey-Smith, and Block.) In a 2024 survey of 582 AI researchers, 25% expected AI consciousness within ten years and 70% expected AI consciousness by the year 2100.[12]

If the believers are right, we’re on the brink of creating genuinely conscious machines. If the scoffers are right, those machines will only seem conscious. I assume that this is a substantive disagreement, not just a disagreement about how to apply the term “consciousness” to a perfectly obvious set of phenomena about which everyone agrees. The future well-being of many people (including, perhaps, many AI people) depends on getting this issue right. Unfortunately, we will not know in time.

The rest of this book is flesh on this skeleton. I canvass a variety of structural and functional claims about consciousness, the leading theories of consciousness as applied to AI, and the best known general arguments for and against near-term AI consciousness. None of these claims or arguments takes us far. It’s a morass of uncertainty.

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[1] I assume that AI consciousness and AI rights are closely connected: Schwitzgebel 2024, ch. 11, in preparation. For discussion, see Shepherd 2018; Levy 2024.

[2] Dehaene 2014; Mashour et al. 2020.

[3] Dehaene, Lau, and Kouider 2017. For an alternative interpretation of this article as concerning something other than consciousness in its standard “phenomenal” sense, see note 115.

[4] Some Higher Order theories: Rosenthal 2005; Lau 2022; Brown 2025. Integrated Information Theory: Albantakis et al. 2023.

[5] But see Chapter Eight for some qualifications.

[6] See Tononi’s publicly available response to Scott Aaronson’s objections in Aaronson 2014. However, advocates of IIT also suggest that the most common current computer architectures are unlikely to achieve much consciousness and that consciousness will tend to appear in subsystems of the computer rather than at the level of the computer itself (Findlay et al. 2024/2025).

[7] Chalmers 2023.

[8] Butlin et al. 2023. (I am among the nineteen authors.)

[9] Heren 2025.

[10] Tononi and Koch 2015.

[11] Seth forthcoming; Godfrey-Smith 2024; Block forthcoming; Searle 1980, 1992; Bender 2025; Mitchell 2021.

[12] Dreksler et al. 2025.

So You're on the "Waiting List" for a Philosophy PhD Program

It's confusing. You applied to a PhD program in philosophy in the U.S. You haven't been admitted. You haven't been rejected. You're in limbo. Let me explain and offer some advice.

Yield-Based vs. Seats-Based Admissions

Yield-based. Some departments -- the ones with wise high-level administrators -- aim for a target entering class size and admit students expeditiously to fill it. Suppose a department wants six entering students and expects a 40% yield (meaning 40% of admitted students enroll). The sensible course is to admit fifteen students in February or early March, recruit all of them, and expect about six to say yes.

Seats-based. Other departments -- the ones with foolish high-level administrators -- receive a strict allotment of seats, for example six. They then admit that allotment swiftly, adding more only as admitted students decline. Adminstrators can rest assured that no more than six students will need funding, which is slightly more convenient for those administrators. But it wreaks havoc on the admissions process, since:

• Departments become reluctant to admit students they think will go elsewhere -- for example, strong candidates likely to have been admitted to higher-ranked programs.
• Departments pressure early-admitted students to decline quickly, to free up seats.
• It creates a chaotic rush of last-minute admittances as April 15 approaches (the standard deadline for decisions). Many students understandably want the full time to decide, especially if they are hoping for a last-minute decision from a program they prefer.

These costs plainly outweigh the the minor budgetary convenience of seats-based admissions, especially since (1.) the risk of overenrollment can be spread across several departments, and (2.) funding uncertainty already exists beyond the first year, as students stochastically drop out or find independent funding. Unfortunately, unwise administrators swarm the Earth. My own department uses seats-based admission.

In practice, the division isn't entirely sharp. Some yield-based departments admit conservatively early on -- maybe ten students rather than fifteen -- and then admit more on a rolling basis as the picture clarifies. And some seats-based departments informally reach out to strong candidates to gauge interest. (If a candidate says, "Oh I've just been admitted to Princeton and Yale, so it's very unlikely I'd come to [School X]", the committee thanks them for their candor and moves on.)

What a Waiting List Is

Some departments maintain an official, ranked waiting list. More commonly, it's a nebulous group: about six to fifteen near-admits, who are on the committee's mind but not strictly ranked or formally designated. Either way, the list's composition and ranking can vary depending on who has already accepted and declined. For example, if the department would like to have at least one student in history of philosophy and their top-choice history student has declined, the next offer might go to a strong history of philosophy student who didn't quite make the initial cut.

If you have been admitted, the admitting department will of course tell you. If you have been rejected, they might tell you, or you might hear nothing (or nothing until after April 15); so if you don't hear anything by April 1, that doesn't mean you're on the waiting list. Students are sometimes contacted to be told they're on the waiting list, but often (usually?) not.

As April 15 approaches, departments that look like they won't hit their enrollment target will start contacting students on their official or unofficial waiting lists, with increasing urgency as 11:59 pm April 15 nears. This is especially true for departments with seats-based admissions and low yields. (Rarely, departments will reach out April 16 or after, which is not quite kosher but understandable.)

How to Figure Out Whether You Are on the Waiting List

Admissions chairs will likely be annoyed with me for giving this advice, since it will increase their volume of email, but I want what's best for you, not for them.

If you haven't heard by April 1, feel free to email the admissions committee to ask if you are on the waiting list. Even departments who have fallen behind schedule should have mostly sorted out their top offers and near-admits by then. You deserve to know by April 1 whether you're a near-admit with a chance of a late offer or whether you're out of consideration. It's not rude for you to contact them with a brief query. The one exception would be if the department has made clear in the admissions process or on their website either that they have no waiting list or that if you haven't heard by X date (before April 1) you will definitely not be admitted.

There's one other condition under which it makes sense to query, even before April 1: if you are about to accept an offer elsewhere, would prefer the department in question, and have a reasonable expectation of a decent chance of admission.

How to interpret the reply: You might not hear a definitive "no", but if the committee says something like "it's unlikely you'll be admitted" or "you're not currently under consideration", you should interpret that as a no. If there's a realistic chance of a last-minute admission, the response will be more encouraging or specific, without creating unrealistic expectations -- for example, "probably not, but there is a chance, so if you're still interested, stay in touch".

How to Increase Your Chance of Admission, If You're on the Waiting List

When a department turns to its waiting list, it's hoping that students will quickly say yes. This is especially true in the second week of April. Therefore, convey enthusiasm! Simply asking whether you're on the waiting list already displays interest, so that's a good start. If you're permitted to attend a campus event, go if you can. Recruitment events are usually only for admitted students, but not always, especially for candidates near the top of a seats-based department's waiting list. If a committee is on the fence among four waitlisted students and one has shown more enthusiasm than the others, they're likely to turn to the enthusiastic student.

The admissions committee might try to gauge your interest. It's contrary to good policy for them to bluntly ask whether you'd accept an offer, and you shouldn't be expected pre-commit. But if you're genuinely eager about the program, say so. If you've been admitted elsewhere but think you'd probably prefer the department in question, let them know.

Being a Good Citizen

Whether you're on the waiting list or have been officially admitted, I recommend frankness and honesty. The process is chaotic and full of perverse incentives (especially in seats-based departments), and you can help it run more smoothly by:

• notifying departments as soon as you know you won't accepting their offer of admission (even if you haven't settled on a final choice);
• honestly communicating your likelihood of accepting, so that committees can estimate their yield;
• keeping your communications brief and polite, and not writing repeatedly;
• not contacting other professors in the department hoping for an inside track to admission.

[A hypothetical waiting list of names drawn randomly from lists of my former lower-division students]

A Model of Disunified Human Experience

It's a philosophical truism that human conscious experience is unified: If you're at a bar, hearing music, tasting beer, and feeling pleasantly relaxed, those experiences don't occur merely side by side. They are joined together into an integrated whole, an experience of music-with-beer-with-relaxation.

I'm not sure this truism is correct. As I suggested in an earlier post, experiential unity might be an artifact of introspection and memory: When we introspectively notice that we're experiencing music, beer, and relaxation all at once, we thereby bind those experiences into a whole. Likewise, when we remember such moments, we reconstruct them as unified. But it doesn't follow that those experiences, even if they all occurred simultaneously in you, were unified rather than transpiring separately. Experiences of music, beer, and relaxation might have all being going on inside of you, no more joined together than those experiences are joined with the similar experiences of your friend across the table. Simple co-occurrence doesn't entail experiential unity.

If this possibility is coherent, then introspection and memory can't establish that experience is always unified. At most, they show that introspected and remembered experiences present themselves as unified. But that leaves open the status of unintrospected, unremembered experiences. Unity becomes difficult to verify by standard phenomenological methods.

But the issue needn't be intractable. We just need to approach it less directly, for example by exploring what follows from a well-established theory of consciousness. If some well-motivated Theory X implies unity (or disunity), that would provide reason to accept its conclusion.

I'll now present a candidate Theory X. I'm not suggesting that this is the right theory of consciousness! For one thing, it's simplistic. I'm sure the mind is much more complicated than I'm about to say. I offer this theory only as a proof of concept. There could be a theory of consciousness with massive disunity as an implication.

This theory combines Global Workspace Theory and Recurrent Processing Theory. According to this hybrid, Global Workspace Theory governs attended experiences -- those targeted by introspection or reconstructed in memory -- while Recurrent Processing Theory governs unattended experiences.

The mind, on this picture, is composed of many separate "modules" that work mostly independently, connected by a workspace where a small amount of attended information is shared globally. There's a visual module, an auditory module, modules for motor activity, episodic memory, and so on. When we attend to something -- say, the taste of beer -- the information from the relevant module is broadcast into the Global Workspace, where it can be accessed by and influence processes in all the other modules. When unattended, the information stays local.

Here's one illustration of this type of architecture:

[the Global Workspace; source]

Orthodox Global Workspace Theory holds that only what is broadcast into the workspace is conscious. Theory X alters that assumption. Many people hold that conscious experience vastly outruns attention. Many people hold, that is, that you can experience the hum of traffic in the background when you're not attending to it, and the feeling of your feet in your shoes, and the leftover taste of coffee in your mouth, etc. -- all in a peripheral way, simultaneously, when your focus is elsewhere. Theory X, drawing on Recurrent Processing Theory, holds that such processes are conscious whenever there's enough cognitive activity of the right sort (recurrent processing, for example) in the modules, even without global broadcast.

The picture, then, is this: We have multiple sensory (and other) experiences all running simultaneously, each with enough cognitive processing to be conscious, but few of which are selected for global availability through attention.

Is there reason to think these modular processes are unified with one another? I see no reason to think so, if they're genuinely modular -- that is, if their processing stays local, exerting little influence elsewhere. The taste-of-beer processing stays in the tasting module. The sound-of-music processing stays in the auditory module. No link up. No straightforward causal, functional, or physiological basis for a unified experience of beer-with-music rather than, separately, an experience of beer and an experience of music.

When we introspect the beer and music simultaneously, we pull both into the Global Workspace, and there they unify. We might then mistakenly think they were unified all along, but that's an illusion of introspection. It's an example of the "refrigerator light error", the error of thinking that the light is always on because it's always on when you open the door to check.

On this model, disunity is the normal human condition. Our experiences are fragmented, except when we pull them together through attention. We just don't realize that fact because, so to speak, we only attend to what we attend to.

Two caveats:

First, this is probably not the right model of consciousness. But I don't think it's unreasonable to wonder if the correct model is similar enough to have the same implications. If so, we can't simply accept the unity of consciousness as a given.

Second, the recurrent peripheral, modular processes that don't make it into the workspace might not be determinately conscious. They might be only borderline conscious, in the indeterminate middle between consciousness and nonconsciousness, like a color can be indeterminately between green and not-green. This opens a third possibility, alongside unity and disunity: unity among the determinately conscious experiences with a hazy penumbra of indeterminate experiences that remain disunified. (There are further possibilities beyond these three; but save them for another day.)

Backup and Death for Humanlike AI

Most AI systems can be precisely copied. Suppose this is also true of future conscious AI persons, if any exist. Backup and fissioning should then be possible, transforming the significance of identity and death in ways our cultural and conceptual tools can't currently handle.

Suppose that two humanlike AI neighbors move in next door to you, Shriya and Alaleh.[1] Shriya and Alaleh are (let's stipulate) conscious AI persons with ordinary, humanlike emotional range and, as far as feasible, ordinary, humanlike cognition.[2] Each undergoes an expensive annual backup procedure. Their information is securely stored, so that if the processors responsible for their personalities, values, skills, habits, and memories are destroyed, a new robotic body can be purchased and the saved information reinstalled. Subjectively, the restored person would be indistinguishable from the person at the time of the backup.

As it happens, Shriya dies in a parachuting accident. (Safety precautions for robot parachuters have yet to be perfected.) But "dies" isn't exactly the right word, since a week later a new Shriya arrives, restored from a back up from five months ago. Shriya-2 says it feels as if she fell asleep in March, then awoke in August with no sense that time had passed.

Shriya-2 has no direct memories of the intervening months, though Alaleh fills her in on major events and selected details. She'll also need to retake her knitting course. She only died in the sense that Mario "dies" in Super Mario Bros: losing progress and returning to a save point -- so different from ordinary human and animal death that it really deserves a different word. Maybe this is why Shriya was so willing to parachute despite the risks.

Should you mourn Shriya's loss? Should Alaleh? There's something to mourn: Five months is not trivial. In one sense, a part of a life has been lost -- or maybe just forgotten? Is it more like amnesia?

Consider variations. Suppose Shriya hadn't been able to afford a backup for the past ten years and is restored to her twenty-five-year-old self instead of her thirty-five-year-old self. What if her last backup was at age five? That would be much more like death. The new Shriya would be nothing like the old, and would likely grow into a very different person. Is death, then, a matter of degree?

Shriya-2 receives the original Shriya's possessions. This "death" isn't enough to trigger inheritance by others. But what about contracts and promises made after the last backup? Suppose the original Shriya promised in July to deliver lectures in China, and Shriya-2 -- who has no memory of this and dreads the idea -- must decide whether to honor the commitment. If the backup is from five months before, perhaps she should. If it's from five years before, maybe not. And if it's a child, presumably not.

What about reward and punishment? Should Shriya-2 accept a Nobel prize for work done post-backup? Should Shriya-2 be imprisoned for crimes committed in July, which she couldn't even possibly remember having committed and which -- she might plausibly say -- were committed by a different person. In defense of this view, Shriya-2 might offer a thought experiment: If she had been installed in a duplicate body immediately after the March backup, thereafter living her own life, she'd have no criminal responsibility for what her other branch in did July. The only difference between that case and the actual case is a delay before installation.

Suppose Shriya-2 plunges into unrelenting depression. She ends her life, hoping that a new Shriya-3, reinstalled from a pre-depression save point, will find a new, happier way forward. Is that suicide?

If someone kills Shriya-2, is that murder? Does it matter whether the backup was ten days ago or ten years ago?

A fire sweeps through your neighborhood. The firefighters can rescue either you and your spouse, two ordinary humans, or Shriya and Alaleh, who have backups from seven months ago. Probably they should save you and your spouse? What if the backups were from ten years ago, or from childhood?

Should healthcare be more heavily subsidized for ordinary humans that for AI persons whose maintenance is equally costly? If irreplaceable humans are always prioritized, then human irrecoverability becomes a source of privilege, and AI persons will not enjoy fully equal rights in certain respects.

How obligated are we to store the backups properly? Is this a public service that should be subsidized for less wealthy AI persons? If Dr. Evil deletes Shriya’s backup, he has surely wronged Shriya by putting her at risk, even if the backup is never needed and the deletion goes unnoticed. But how much has he wronged her, and it what way exactly? Is it similar to assault? How much does it differ from ordinary reckless endangerment? Does it depend on whether we regard Shriya-2 as the same person as the original Shriya, or as a distinct but similar successor?

What if the backup is imperfect? How much divergence in personality, values, memories, habits, and skills is tolerable before the appropriate attitude toward Shriya-2 changes -- whatever the appropriate attitude is? Small imperfections are surely acceptable. People change in small, arbitrary ways from day to day. Huge differences would presumably make it appropriate to regard the new entity as merely resembling Shriya, rather than being a restored version of her. Once again, this appears to be a matter of degree, laid uncomfortably across crude categorical properties like "same person" and "different person".

We're in unfamiliar territory, where our usual understandings of death and personal continuity no longer straightforwardly apply. If such AI systems ever come to be, we will need to develop new words, concepts, and customs.

[Data and Lore from Star Trek; image source]

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[1] Names randomly chosen from lists of former lower division students, excluding Jesus, Mohammed, and extremely unusual names.

[2] Unless humanlikeness is enforced by policy, this might not be what we should expect: See Chilson and Schwitzgebel 2026. For some puzzles about AI with different emotional ranges, see "How Much Should We Give to a Joymachine?" (Dec 24, 2025).

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Related: Weird Minds Might Destabilize Human Ethics (Aug 13, 2015).

Age and Fame in Philosophy

A philosophical discussion arc is a curve displaying how frequently a term appears in philosophical journal abstracts, titles, and keywords (compared to a representative universe of common philosophy words). A couple of weeks ago, I posted discussion arcs from the 1940s-2020s for several topics and historical philosophers.

Today, I want to use them to explore my age and fame hypothesis: that philosophers tend to have peak influence at around ages 55-70. Jerry Fodor, for example, received a lot of discussion in the early 1990s, but recently much less:

Fodor was a peaky philosopher in the following sense: His peak discussion rate (1.05%) is much higher -- 12 times higher -- than his recent discussion rate (0.09%).

John Rawls, in contrast, is considerably less peaky (so far), peaking at only 2.7 times the current rate, despite having more time to decline:

Both Fodor and Rawls peak in their late 50s, fitting the pattern I've seen in previous analyses (here and here) that philosophers tend to reach their peak influence around age 55-70.

For today's post I decided to create discussion arcs for 25 philosophers who are highly cited in the Stanford Encyclopedia of Philosophy: twelve from the Not-So-Silent Generation in philosophy, born 1928-1945, and thirteen from the generation born 1900-1927. I chose the most SEP-cited philosophers from each generation, excluding ones whose last names generate noisy results (sorry, no David Lewis or Bernard Williams). I'll show you their discussion arcs, then do a composite analysis of discussion by age. The charts are a little crowded and blurry on some browsers; clicking on them might work to clarify and enlarge.

Here's the first group:

Some observations:

• Ayer peaks relatively early, but even so, there's significant delay between the publication of his most influential book in 1936, when we was only 26 years old, and peak discussion in the late 1950s, about twenty years later.
• Quine and Popper peak later, in the 1970s, when Quine is in his mid-60s and Popper is in his late 70s.
• All the authors are trailing off by the 2010s, though Quine, Popper, and Tarski less so than the others.

Here's a second group:

• Notice that the vertical axis for this group doesn't rise quite as high as for the previous group, so the scaling isn't the same.
• Dummett has peaks both in the late 1950s (for his early work on causation) and the early 1980s (discussing a wide range of work in metaphysics and philosophy of language).
• Although Anscombe's peak is early, in the early 1960s when she was in her early 40s, unlike any of the other authors, her discussion rate has been steadily rising over the past few decades.

Group three:

• The vertical axis for this group is still lower: Kripke at his peak wasn't as proportionally much discussed by this measure as were Quine or Rawls at their peaks. It's possible that this reflects a flaw in my method. Later in the database, abstracts are more available and longer, and although I attempted to compensate for this by comparing with search result for terms like "mind", "language", and "ethics", it's possible I overcompensated. However, it's also possible that this trend toward lower peaks with younger authors is real. As the field grew larger, there may have been less room for a few thinkers to dominate it as thoroughly.
• Kripke, Nozick, and Searle peak relatively early, compared with most philosophers I have examined -- in the 1970s, when they're in their late 30s to early 40s.
• Though Stalnaker also peaked in the late 1970s, when he was in his late 30s, like Anscombe his has risen in recent decades, and in 2021-2025 he is almost back to his earlier peak.

Finally:

• These authors have had the least time to decline from their peaks. But their discussion rates of .001 to .004 in 2021-2025 are comparable to those from earlier generations (and substantially less than Kripke and Rawls), which suggests that they might have relatively less staying power.
• You'll probably also have noticed it’s a much narrower age band than the others. There's a high density of Silent Generation philosophers cited in the Stanford Encyclopedia, for reasons I discuss here.

In the following graph, I've aggregated discussion rates by age across all of the included authors, with five-year smoothing:

Broadly in line with my previous work, the average peak is in the early 50s to the late 60s -- though as you can see from the graphs above, there's considerable individual variation. Overall, the results might trend a little bit earlier than what I've seen in other analyses, but not by much.

As I've noted elsewhere, peak influence is often a couple of decades after the thinker's most influential work. For example (besides Ayer as described above), Kuhn's and Popper's peaks in the late 1970s were 15-20 years after Kuhn's Structure of Scientific Revolutions and about 20 years after the English translation of Popper's Logic of Scientific Discovery.

Philosophy Should Be Among the Most Diverse Disciplines, Not the Least

Philosophy should be among the most diverse of the academic disciplines. Instead, it is among the least diverse.

Philosophical reflection is an essential part of the human condition, of interest to people of all cultures, races, classes, social groups, and body types. Who doesn’t care whether we have immaterial souls that might continue to exist after we have died, about ethical issues such as war and human rights, about what’s worth pursuing in life, about when and how far we should trust scientific authority, about the best forms of government, about the origin and structure of the world? Nothing about these issues – and nothing about philosophy as a discipline devoted to the fundamental questions of human existence – should make it of more interest to one gender rather than another, one cultural group rather than another, or to the able-bodied more than to the disabled.

Yet study after study and testimonial after testimonial show that the culturally privileged are overrepresented in academic philosophy.

[Kandinsky 1913, composition vii; image source]

For example, the self-reported gender of regular members of the American Philosophical Association in 2025 was 70% male, 29% female, and 1% nonbinary/something else. Gender balance has been improving, though slowly: In 2015, the corresponding percentages were 75%, 25%, and 0%. The pipeline into philosophy suggests that change will continue: According to the National Science Foundation’s Survey of Earned Doctorates, 37% of philosophy PhD recipients in 2024 were female. Despite this qualifiedly encouraging trend, philosophy PhDs remain more male than any of the other humanities besides theology and Bible studies and more male than every social science besides economics and finance.

Black people and American Indians are especially underrepresented in U.S. philosophy, and that situation doesn’t appear to be changing at all. In 2025, 4% of regular APA members reported being Black or African American, compared with 14% of the U.S. population. The NSF data suggest no surge of new Black philosophers in the pipeline: the percentage among recent PhD recipients is also 4%. The NSF data show no American Indian or Alaskan Native philosophy PhD recipients in 2024 (though people reporting both American Indian and Hispanic or Latino identities would not appear in this category) and only one in past four years (among 1692 doctorates awarded), although Native Americans constitute nearly 2% of the U.S. population.

Another concern that has recently drawn attention is the linguistic insularity of mainstream Anglophone philosophy – that is, the neglect of work written in other languages. In 2018, three collaborators and I examined citation practices in leading Anglophone journals and found that 97% of citations referred to work originally written in English. Journals published in other languages were much less insular. We also found that 96% of the editorial board members of journals perceived as elite were housed in majority-Anglophone countries. More recently, Uwe Peters and collaborators reported that non-native English speakers face substantial difficulties publishing in English, as is now practically required for gaining an international readership.

The forthcoming book Structural Injustice in Philosophy, edited by Maeve McKeown, Seunghyun Song and Milana Kostić, further documents, and aims to explain, the exclusionary structures of philosophy, not only concerning race, gender, and language, but also class, disability, culture, mobility, sexuality, place of origin, and more.

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Could a structurally just academic system nonetheless happen to produce such skewed results, always in favor of the already powerful? Let’s not join the long string of bigots who have held that some races, languages, genders, nationalities, classes, or physical types are more intrinsically suited for philosophy than others.

It’s sometimes suggested that academic philosophy is – or is not unreasonably perceived as – a useless luxury, rightly scorned by people lacking cultural privilege. Students from less privileged backgrounds might see studying philosophy, instead of a more financially or practically rewarding discipline, as wasting a precious opportunity to achieve financial security or practical success. But even if this perception were entirely accurate – and I would argue it’s oversimplified – it wouldn’t constitute a genuine alternative to structural injustice. A society in which less privileged students feel less free to study philosophy than business or nursing is already a society with substantial injustice in higher education. The perceived impracticality of the philosophy major is a symptom and mechanism of structural injustice rather than a neutral fact. In a fair society, students who love philosophy wouldn’t be disproportionately deterred by class background.

Even in a completely just society, students and professors will not always populate each discipline in exact proportion to their background rate in the general population. Women might be drawn more to developmental psychology and men to architecture, White people to European history and Black people to African history, or whatever, for innocent reasons. It might be suggested that similarly, philosophical issues just happen to be of more interest to people from culturally powerful groups. I’ve already articulated the flaw in this argument: It strains credulity to suppose that men more than women, or White people more than Black people, or people from the United States more than people from Brazil or China, or sighted people more than blind people, would or should, in a just society, care more about truth, justice, ethics, knowledge, and the fundamental nature of reality. If anything, the opposite might be expected: Those who suffer under existing institutions should be especially motivated to think hard about the reform of those institutions and the cultural presuppositions that undergird them.

Might people from less privileged backgrounds reasonably be expected, even in a just society, to be less interested than others in academia in general, and could this explain the pattern in philosophy? Again, I see no good reason to think so and reason to think the opposite: Professorships offer good salaries (for those who escape the cycle of adjuncting) and offer unusual freedom to explore one’s interests and advocate change. People from less privileged backgrounds might be especially drawn to such opportunities – at least if the career seems genuinely open to them – more so than people from elite backgrounds with many other attractive options. In any case, if the problem were academia as such, and not philosophy in particular, we should see similar disparities across all fields. And while many historically disadvantaged groups continue to be underrepresented in many disciplines, philosophy is more imbalanced than most across a wide range of measures.

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In a just society, historically underrepresented groups and minority perspectives would be overrepresented rather than underrepresented in philosophy. Academic philosophy should celebrate diversity of opinion, encourage challenges to orthodoxy, and reward fresh perspectives from cultures and life experiences outside the mainstream. We should be eager, not reluctant, to hear from a wide range of voices. We should especially welcome, not create a chilly environment for, people with unusual or culturally atypical or historically neglected ideas, practices, and worldviews. The productive engine of philosophy depends on novelty and difference.

Philosophy is a dialectical discipline that thrives in the clash, reconciliation, and creative synthesis of diverse views; and our views are profoundly shaped by our cultural backgrounds and life experiences. Uniformity dulls our collective philosophical thinking. A fair and flourishing discipline would treasure rather than repel those who have historically been excluded. Consequently, even if every social group were proportionally represented in philosophy, we would still have reason to suspect systemic injustice. The injustices of our discipline will not be overcome until we are collectively eager to hear proportionally more from previously excluded groups than from the privileged and powerful.

Discussion Arcs for Topics and Philosophers

Let's define a discussion arc in philosophy as a curve displaying how frequently a term appears in philosophical journal abstracts, titles, and keywords. Discussion arcs can reveal trends over time, as they come and go.

For example, "ordinary language" was a popular topic of discussion in the 1960s:

[click to enlarge and clarify]

My method: I searched for the term in the abstract, title, or keywords (subject) of articles in Philosopher's Index, in five-year chunks from 1940-1945 to 2021-2025.[1] I then divided the number of hits by a representative universe of hits for abstract or title containing "language", "epistemology", "mind", "metaphysics", "ethics", "moral", "political", "aesthetics", or "logic".[2]

By looking for distinctive terms or phrases connected to particular arguments, we can also track when an argument receives wide uptake. For example, Hilary Putnam's famous "Twin Earth" thought experiment, though originally presented in 1975, didn't receive much immediate uptake, receiving peak discussion in the 1990s:

We can also look for topical trends. If you think artificial intelligence is recently hot, you're right!

The figure also shows a smaller peak in the late 1980s, before the "AI winter" that has recently thawed.

Another thing you might see in the artificial intelligence arc, if you squint, is noise early in the data. This makes sense, since the early data have far fewer hits in the representative universe of articles containing one of the nine keywords: 457 articles in 1940-1945, compared to 33,552 in 2016-2020.

We can also examine the discussion arcs of particular philosophers. Note that "discussion" in the sense I've defined is very different from citation. A philosopher who is cited in passing might have very little influence on the shape of an article. In contrast, if a philosopher's name is explicitly mentioned in the title, abstract, or keywords, that philosopher's work is among the chief topics of the article. Discussion rates and citation rates thus capture different phenomena and will sometimes diverge.

Here are the arcs for the seven "most important Western philosophers of all time", according to a poll by Brian Leiter in 2017.

Notice how much discussion there was of Aristotle in the early 1940s! "Aristotle" alone gets more than a quarter as many hits as the broad disjunctive search for "language" or "epistemology" or "mind" or "metaphysics" or "ethics" or "moral" or "political" or "aesthetics" or "logic". Plato and Kant each also get almost as many as Aristotle.

Since then, there's been a steady decline in the proportion of articles mentioning these prominent historical figures. My sense is that this is due to two factors: first, the decline in the relative prominence of history of philosophy compared to other areas of philosophy; second, a shift within history of philosophy toward considering a broader range of traditions and figures, including non-Western traditions and women philosophers.

It's perhaps also interesting that Kant has declined less than the ancient Greeks, overtaking them as the most discussed philosopher.

I thought I'd finish today with discussion arcs of some more recent historical figures, who peak at different times:

Notice that Russell peaks in the 1940s to 1950s, during which period he is discussed almost as much as Aristotle, Plato, and Kant, who themselves are (as I noted above) discussed in a striking proportion of articles. There is a little false positive noise for Russell (some other people with "Russell" as a first or last name), but my estimate is that about 90% of hits are accurate.

Wittgenstein peaks in the late 1960s, Frege in the early 1980s, and Nietzsche in the early 2000s. Heidegger's influence is moderately steady from the late 1960s to the early 2000s, declining modestly in the past couple of decades.

Perhaps worth mentioning: These data cover a wide range of journals, regardless of prestige. In an analysis a few years ago, I found that the journals conventionally regarded as prestigious mention prominent "analytic" philosophers such as Frege, Wittgenstein, Quine, Chisholm, and Putnam much more frequently than prominent "Continental" philosophers such as Nietzsche, Heidegger, Sartre, Foucault, and Derrida: The analytic/Continental divide is alive and well. There might be other interesting trends to discover if we subdivide the universe of articles.

In a week or two, I'll do a follow up post looking at the discussion arcs of prominent philosophers born 1900-1945, partly to test my "age and fame" hypothesis: that philosophers tend to have their peak influence around ages 55-70.

--------------------------------------------

[1] The data begin in 1940, so I used a six-year chunk for the first period. As of Feb 27, data for 2025 are only about two-thirds complete and there are no data for 2026.

[2] This is not all articles, especially in the early period, where abstracts are less available. The idea is to roughly compensate for having fewer abstracts, and thus fewer opportunities for hits in the early data, by comparing the rate of appearance of the target terms with the rate of appearance of several, but not too many, widely used terms across a variety of philosophical topics.

Disunity and Indeterminacy in Artificial Consciousness (and Maybe in Human Consciousness Too)

Our understanding of the nature of consciousness derives mainly from our understanding of the nature of consciousness in our favorite animal (us, of course). But the features of consciousness in our favorite animal might be specific to that animal rather than universal.

Let's consider two such features and whether we should expect them in conscious AI systems, if conscious AI systems are ever possible.

Unity: Our conscious experiences at any given moment are bound together into a single unified experience, rather than transpiring in separate streams. If I'm sitting on a wet park bench, I might (a.) visually experience the leafy green trees around me, (b.) tactilely experience the cold dampness soaking into my jeans, and (c.) consciously recall the smaller trees of yesteryear. Normally -- perhaps necessarily -- three such experiences would not run in disconnected streams. They would join into a composite experience of (a)-with-(b)-with-(c). I experience not just trees, cold dampness, and a memory of yesteryear, but all three together as a unified bundle.

Determinacy: At any given moment, I am either determinately conscious or determinately nonconscious (as in anesthesia or dreamless sleep). Likewise, I either determinately do, or determinately do not, have any particular experience. Gray-area cases are at least unusual and maybe impossible. Even the simplest, barest cases are still determinate. Consider visual experience: We might imagine the visual field narrowing and losing content until only a gray dot remains -- and then the dot winks out. That dot, however minimal, is still determinately experienced. When it winks out, consciousness determinately disappears. There is no half-winked state between the minimal gray dot and complete absence of visual experience.

My thought is that we should not expect unity and determinacy to be general features of conscious AI systems (if conscious AI is possible). To see why, let's start by assuming the Global Workspace Theory of consciousness. I focus on Global Workspace Theory because it's probably the leading scientific theory of consciousness and because its standard formulation (Dehaene's version) invites the assumption of unity and determinacy.

Global Workspace Theory divides the mind into local information processing modules linked by a shared global workspace. Information becomes conscious when it is broadcast into the workspace. Suppose your auditory system registers the faint honk of a distant car horn. You're absorbed in reading philosophy and accustomed to ignoring traffic noise, so this representation isn't selected for further processing. It's not a target of attention, not broadcast into the workspace, and not consciously experienced. (If you think you constantly consciously experience background sounds, you can't hold a standard Global Workspace view.) Once you attend to the noise, for whatever reason, that information "ignites" into the global workspace, becoming available to a wide variety of "downstream" processes: You can think about it, plan around it, verbally report it, store it in long-term memory, and flexibly combine it with other information in the workspace. On Global Workspace Theory, being available in this way just is what it is for the information to be consciously experienced.

This model suggests unity and determinacy. Since there is just one global workspace, and since that workspace enables flexible integration of everything it contains, it makes sense that its various elements will combine into a unified experience. And on Dehaene's version, ignition into the workspace is a sharp-boundaried event: Information either completely ignites, becoming available for all downstream processes, or it does not. There is no (or only rarely) partial ignition. This can explain determinacy.

But future AI systems might not share this structure. They might have multiple or partially overlapping workspaces. Different specialized subsystems might have access to different regions of a partly-shared workspace. Some animals, such as snails and octopuses, distribute processing among multiple ganglia or neural centers that are less tightly coupled than the hemispheres of the human brain. A robot might broadcast information relevant to locomotion to one area and information relevant to speech to another with limited connectivity.

If the subsystems are entirely disconnected, the result might be entirely discrete centers of subjective experience within a single organism or machine. But if they are partly connected, experience might be only partly unified. In the park bench example, the experience of the trees might be unified with the experience of dampness, and the experience of dampness with memories of yesteryear, but the experience of the trees might not be unified with the memories. (Unification would not then be a transitive relation.) Alternatively, some weaker relation of partial unification might hold among the visual, tactile, and memorial experiences. If this seems inconceivable or impossible, see Sophie Nelson's and my article on indeterminate or fractional subjects.

More abstractly: There's no compelling architectural reason why an AI system would have to make information available either to all downstream processes or to none. A workspace defined in terms of downstream availability could be a patchwork of partial availabilities rather than a fully global all-or-nothing broadcast.

For the same reason, ignition into the workspace needn't be all-or-nothing. Between full ignition with determinate consciousness and no ignition with determinate nonconsciousness, there might be in-between, gray-area half-ignitions that are neither determinately conscious nor determinately nonconscious. Nearly every property with a complex physical or functional basis allows indeterminate, borderline cases: baldness, extraversion, greenness, happiness, whether you're wearing a shoe, whether a country is a democracy. The human global workspace might minimize indeterminacy -- like it's rarely indeterminate in basketball whether the ball has gone through the hoop. But change the architecture and indeterminacy might become common: a half-hearted ignition, or just enough information-sharing to make it indeterminate whether a workspace even exists. (If indeterminacy about consciousness strikes you as inconceivable or impossible, see my 2023 article on borderline consciousness.)

Global Workspace Theory might of course be wrong. But most other theories of consciousness make my argument at least as easy. Dennett's fame-in-the-brain version of broadcast theory explicitly permits disunity and indeterminacy. Higher Order Theories admit the same fragmentation and, probably, gradualism. So do biological theories and theories that focus on embodiment. (Integrated Information Theory is an exception: Its axioms require bright-lined unity and determinacy. But as I've argued, those bright-line axioms lead to unpalatable consequences.)

Recognizing these possibilities for AI systems invites the further thought: Maybe we humans aren't quite as unified as we normally suppose. Maybe indeterminate and disunified consciousness is common. Maybe processes outside of attention hover indeterminately between being conscious and nonconscious. Maybe some processes are only partly unified. If it seems otherwise in introspection and memory, maybe that's because introspection and memory tend to impose unity and determinacy where none was before.

[a Paul Klee painting, untitled 1914: source]

The Intrinsic Value of Diversity

Moral diversity, Olivia Bailey and Thi Nguyen say (in a draft paper shared with Myisha Cherry's Emotion and Society Lab), is valuable. It's good that people have different ethical personalities, opinions, and concerns. (Within reason: Nazis not welcome.)

Why? Their reasons are instrumental. Society benefits when people care intensely about different things. This allows us collectively to achieve a wide range of goals -- curing cancer, helping the homeless, protesting unjust government. Society also benefits if some people explore the ethical possibility space, developing unusual moral visions, most of which will be mistaken but a few of which might eventually be recognized as genuine moral advances (think of the first slavery abolitionists). And individuals benefit from the liberty to adopt moral priorities that fit their skills and temperaments: Some people thrive in battle, others in caregiving, others in solitary work.

But is moral diversity also intrinsically valuable -- that is, valuable for its own sake, independent of these good consequences? I think so. I think so because diversity in general is intrinsically valuable, and there's no good reason to treat moral diversity as an exception.

How does one argumentatively establish the intrinsic value of diversity? The only way I know is to reveal, through thought experiment, that you already implicitly accept it -- and then to ward off objections.

Bailey and Nguyen briefly cite Alexander Nehamas on diversity of aesthetic opinion. Nehamas writes:

I think a world where everyone liked, or loved, the same things would be a desperate, desolate world -- as devoid of pleasure and interest as the most frightful dystopia of those who believe (quite wrongly) that the popular media are inevitably producing a depressingly, disconsolately uniform world culture. And although I say this with serious discomfort, a world in which everyone liked Shakespeare, or Titian, or Bach for the same reasons -- if such a world were possible -- appears to me no better than a world where everyone tuned in to Baywatch or listened to the worst pop music at the same time (Nehamas 2002, p. 58-59).

Why is aesthetic diversity valuable, according to Nehamas? Because style and taste require originality and are bound up with what is distinctive about your life, interests, and sensibility. Without distinctiveness, style and taste collapse -- an aesthetic disaster.

Should we say, then, that diversity, including moral diversity, is valuable aesthetically? That its value lies primarily in its beauty, in its capacity to inspire awe, or some other aesthetic feature? Indeed, diversity is beautiful and awesome (imagine the world without it!) but I don't think this exhausts its intrinsic value. Aesthetic value requires a spectator, at least a notional one, whose appreciation is the point. The intrinsic value of diversity is not, or not primarily, mediated through the hypothetical reaction of an aesthetic spectator.

My favorite approach to thinking about intrinsic value is the Distant Planet Thought Experiment. Imagine a planet on the far side of the galaxy, blocked from view by the galactic core, a planet we'll never see or interact with. What would we hope for on this planet, for its own sake, independent of any potential value for us?

Would you hope that it's a sterile rock, completely devoid of life? I think not. If you do think a lifeless rock would be best, I have no argument against you. For me this is a starting place, a bedrock judgment, which I expect most readers will share.

Suppose, then, that you agree a planet with life would be intrinsically better than one without. Would you hope that its life consists entirely of microbes? Or would you hope that it teems with diverse life: reefs and rainforests, beetles and bats, squid and bees and ferns and foxes -- or rather, not to duplicate Earth too closely, their alien analogues, translated into a different key? I think you'll hope that the planet teems with diverse life.

Would you hope that no life on this planet has humanlike behavioral sophistication -- language, long-term planning, complex social coordination? Would you hope that nothing there could contemplate the meaning of life, the origin of the stars, or its own ancient history? Would you hope that nothing there could create art, or engage in athletic competition, or invent complex games and tricks and jokes? I invite you to join me in thinking otherwise. The planet would be better if it included some beings with that richness of thought and activity.

Would you hope for uniformity of intellectual, aesthetic, and ethical opinion -- that everyone shares the same values and ideas? Or would you hope for diversity? I think you'll join me in thinking that the world would be better, better for its own sake, if it were diverse rather than uniform. Different entities would have different skills, preferences, passions, and ideas. They'll fight and disagree (not genocidally, I hope), sometimes value their differences, sometimes dismiss others as completely wrongheaded, sometimes cluster into shared projects, sometimes collaborate across deep disagreement, sometimes be drawn to opposites, sometimes feel kinship with the like-minded, play within and across divides, pursue an enormous variety of projects, explore a vast space of possible forms of life.

That is what I hope for on this distant planet -- not for instrumental reasons (not, for example, because it will maximize happiness), and not merely because it would strike a hypothetical spectator as beautiful and awesome (though it should). Rather, just because it would be valuable for its own sake. An empty void has little or no value; a rich plurality of forms of existence has immense value, no further justification required.

I have not argued for this. I have only stated it vividly, hoping that you already accept it.

Is ethical opinion an exception? Should we prefer unity and conformity in ethics, even while welcoming diversity elsewhere? I think not, for two reasons.

First, ethics is open-textured, indeterminate, and full of tragic dilemmas. Often there is no one decisively best answer on which everyone should converge. Diversity within at least the bounds of reasonable disagreement should be permitted.

Second, ethical values are inseparable from our other values and ways of life. A philosophy professor, a civil rights lawyer, a professional athlete, and a farmer will value different things. There is, I think, no point in attempting to cleanly separate their differing values into distinct types, some of which are permitted to vary and others of which may not. The ethical, prudential, epistemic, and aesthetic blur together. These distinctions are not as clean as philosophers often assume. Normativity is a mush.

Oh, some of you disagree? Good!

[the cover of my 2024 book, The Weirdness of the World, hardback version]

Artificial Intelligence as Strange Intelligence: Against Linear Models of Intelligence (New Paper in Draft)

by Kendra Chilson and Eric Schwitzgebel

Our main idea, condensed to 1000 words:

On a linear model of intelligence, entities can be roughly linearly ordered in overall intelligence: frogs are smarter than nematodes, cats smarter than frogs, apes smarter than cats, and humans smarter than apes. This same linear model is often assumed when discussing AI systems. "Narrow AI" systems (like chess machines and autonomous vehicles) are assumed to be subhuman in intelligence, at some point -- maybe soon -- AI systems will have approximately human-level intelligence, and in the future we might expect superintelligent AI that exceeds our intellectual capacity in virtually all domains of interest.

Building on the work of Susan Schneider, we challenge this linear model of intelligence. Central to our project is the concept of general intelligence as the ability to use information to achieve a wide range of goals in a wide variety of environments.

Of course even the simplest entity capable of using information to achieve goals can succeed in some environments, and no finite entity could succeed in all possible goals in all possible environments. "General intelligence" is therefore a matter of degree. Moreover, general intelligence is a massively multidimensional matter of degree: There are many many possible goals and many many possible environments and no non-arbitrary way to taxonomize and weight all these goals and environments into a single linear scale or definitive threshold.

Every entity is in important respects narrow: Humans also can achieve their goals in only a very limited range of environments. Interstellar space, the deep sea, the Earth's crust, the middle of the sky, the center of a star -- transposition to any of these places will quickly defeat almost all our plans. We depend for our successful functioning on a very specific context. So of course do all animals and all AI systems.

Similarly, although humans are good at a certain range of tasks, we cannot detect electrical fields in the water, dodge softballs while hovering in place, communicate with dolphins by echolocation, or calculate a hundred digits of pi in our heads. If we put a server with a language model in the desert without a power source or if we place an autonomous vehicle in a chess tournament and then interpret their incompetence as a lack of general intelligence, we risk being as unfair to them as a dolphin would be to blame us for our poor skills in their environment. Yes, there's a perfectly reasonable sense in which chess machines and autonomous vehicles have much more limited capacities than do humans. They are narrow in their abilities compared to us by almost any plausible metric of narrowness. But it is anthropocentric to insist that general intelligence requires generally successful performance on the tasks and in the environments that we humans tend to favor, given that those tasks and environments are such a small subset of the possible tasks and environments an entity could face. And any attempt to escape anthropocentrism by creating an unbiased and properly weighted taxonomy of task types and environments is either hopeless or liable to generate a variety of very different but equally plausible arbitrary composites.

AI systems, like nonhuman animals and neuroatypical people, can combine skills and deficits in patterns that are unfamiliar to those who have attended mostly to typical human cases. AI systems are highly unlikely to replicate every human capacity, due to limits in data and optimization, as well as a fundamentally different underlying architecture. They struggle to do many things that ordinary humans do effortlessly, such as reliably interpreting everyday visual scenes and performing feats of manual dexterity. But the reverse is also true: Humans cannot perform some feats that machines perform in a fraction of a second. If we think of intelligence as irreducibly multidimensional instead of linear -- as always relativized to the immense number of possible goals and environments -- we can avoid the temptation to try to reach a scalar judgment about which type of entity is actually smarter and by how much.

We might think of typical human intelligence as "familiar intelligence" -- familiar to us, that is -- and artificial intelligence as "strange intelligence". This terminology wears its anthropocentrism on its sleeve, rather than masking it under false objectivity. Something possesses familiar intelligence to the degree it thinks like us. It is a similarity relation. How familiar an intelligence is depends on several factors. Some are architectural: What forms does the basic cognitive processing take? What shortcuts and heuristics does it rely on? How serial or parallel is it? How fast? With what sorts of redundancy, modularity, and self-monitoring for errors? Others are learned and cultural: learned habits, particular cultural practices, acquired skills, chosen effort based on perceived costs and benefits. An intelligence is outwardly familiar if it acts like us in intelligence-based tasks. And it is inwardly familiar if it does so by the same underlying cognitive mechanisms.

Familiarity is also a matter of degree: The intelligence of dogs is more familiar to us (in most respects) than that of octopuses. Although we share some common features with octopuses, they evolved in a very different environment and have very dissimilar cognitive architecture as a result. It's hard for us even to understand their goals, because their existence is so different. Still, as distant as our minds are from those of octopuses, we share with octopuses the broadly familiar lifeways of embodied animals who need to navigate the natural world, find food, and mate.

AI constitutes an even stranger form of intelligence. With architectures, environments, and goals so fundamentally unlike ours, AI is the strangest intelligence we have yet to encounter. AI is not a biological organism; it was not shaped by the evolutionary pressures shared by every living being on Earth, and it does not have the same underlying needs. It is based on an inorganic substrate totally unlike all biological neurophysiology. Its goals are imposed by its makers rather than being autopoietic. Such intelligence should be expected to behave in ways radically different from familiar minds. This raises an epistemic challenge: Understanding and measuring strange intelligence may be extremely difficult for us. Plausibly, the stranger an intelligence is from our perspective, the easier it is for us to fail to appreciate what it’s up to. Strange intelligences rely on methods alien to our cognition.

If intelligence were linear and one-dimensional, then a single example of an egregious mistake by an AI -- a mistake a human would never make, like confusing a strawberry for a toy poodle -- would be enough to show that the systems are nowhere near our level of intelligence. However, since intelligence is massively multidimensional, all these cases show on their own is that these systems have certain lacunae or blindspots. Of course, we humans also have lacunae and blind spots – just consider optical illusions. Our susceptibility to optical illusions is not used as evidence of our lack of general intelligence, however ridiculous our mistakes might seem to any entity not subject to those same illusions.

Full draft here.

Does Global Workspace Theory Solve the Question of AI Consciousness?

Hint: no.

Below are three sections from Chapter Eight of my manuscript in draft, AI and Consciousness, fresh new version available today here. Comments welcome!

[image adapted from Dehaene et al. 2011]

1. Global Workspace Theories and Access.

The core idea of Global Workspace Theory is simple. Sophisticated cognitive systems like the human mind employ specialized processes that operate to a substantial extent in isolation. We can call these modules, without committing to any strict interpretation of that term.[1] For example, when you hear speech in a familiar language, some cognitive process converts the incoming auditory stimulus into recognizable speech. When you type on a keyboard, motor functions convert your intention to type a word like “consciousness” into nerve signals that guide your fingers. When you try to recall ancient Chinese philosophers, some cognitive process pulls that information from memory without (amazingly) clogging your consciousness with irrelevant information about German philosophers, British prime ministers, rock bands, or dog breeds.

Of course, not all processes are isolated. Some information is widely shared, influencing or available to influence many other processes. Once I recall the name “Zhuangzi”, the thought “Zhuangzi was an ancient Chinese philosopher” cascades downstream. I might say it aloud, type it out, use it as a premise in an inference, form a visual image of Zhuangzi, contemplate his main ideas, attempt to sear it into memory for an exam, or use it as a clue to decipher a handwritten note. To say that some information is in “the global workspace” just is to say that it is available to influence a wide range of cognitive processes. According to Global Workspace Theory, a representation, thought, or cognitive process is conscious if and only if it is in the global workspace – if it is “widely broadcast to other processors in the brain”, allowing integration both in the moment and over time.[2]

Recall the ten possibly essential features of consciousness from Chapter Three: luminosity, subjectivity, unity, access, intentionality, flexible integration, determinacy, wonderfulness, specious presence, and privacy. [Blog readers: You won't have read Chapter Three, but try to ride with it anyway.] Global Workspace Theory treats access as the central essential feature.

Global Workspace theory can potentially explain other possibly essential features. Luminosity follows if processes or representations in the workspace are available for introspective processes of self-report. Unity might follow if there’s only one workspace, so that everything in it is present together. Determinacy might follow if there’s a bright line between being in the workspace and not being in it. Flexible integration might follow if the workspace functions to flexibly combine representations or processes from across the mind. Privacy follows if only you can have direct access to the contents of your workspace. Specious presence might follow if representations or processes generally occupy the workspace for some hundreds of milliseconds.

In ordinary adult humans, typical examples of conscious experience – your visual experience of this text, your emotional experience of fear in a dangerous situation, your silent inner speech, your conscious visual imagery, your felt pains – appear to have the broad cognitive influences Global Workspace Theory describes. It’s not as though we commonly experience pain but find that we can’t report it or act on its basis, or that we experience a visual image of a giraffe but can’t engage in further thinking about the content of that image. Such general facts, plus the theory’s potential to explain features such as luminosity, unity, determinacy, flexible integration, privacy, and specious presence, lend Global Workspace Theories substantial initial attractiveness.

I have treated Global Workspace Theory as if it were a single theory, but it encompasses a family of theories that differ in detail, including “broadcast” and “fame” theories – any theory that treats the broad accessibility of a representation, thought, or process as the central essential feature making it conscious.[3]

Consider two contrasting views: Dehaene’s Global Neuronal Workspace Theory and Daniel Dennett’s “fame in the brain” view. Dehaene holds that entry into the workspace is all-or-nothing. Once a process “ignites” into the workspace, it does so completely. Every representation or process either stops short of entering consciousness or is broadcast to all available downstream processes. Dennett’s fame view, in contrast, admits degrees. Representations or processes might be more or less famous, available to influence some downstream cognitive processes without being available to influence others. There is no one workspace, but a pandemonium of competing processes.[4] If Dennett is correct, luminosity, determinacy, unity, and flexible integration all potentially come under threat in a way they do not as obviously come under threat on Dehaene’s view.[5]

Dennettian concerns notwithstanding, all-or-nothing ignition into a single, unified workspace is currently the dominant version of Global Workspace Theory. The issue remains unsettled and has obvious implications for the types of architectures that might plausibly host AI consciousness.

2. Consciousness Outside the Workspace; Nonconsciousness Within It?

Global Workspace Theory is not the correct theory of consciousness unless all and only thoughts, representations, or processes in the Global Workspace are conscious. Otherwise, something else, or something additional, is necessary for consciousness.

It is not clear that even in ordinary adult humans a process must be in the Global Workspace to be conscious. Consider the case of peripheral experience. Some theorists maintain that people have rich sensory experiences outside of focal attention: a constant background experience of your feet in your shoes and objects in the visual periphery.[6] Others – including Global Workspace theorists – dispute this. Introspective reports vary, and resolving such issues is methodologically tricky.

One methodological problem: People who report constant peripheral experiences might mistakenly assume that such experiences are always present because they are always present whenever they think to check, and the very act of checking might generate those experiences. This is sometimes called the “refrigerator light illusion”, akin to the error of thinking the refrigerator light is always on because it’s always on when you open the door to check.[7] On this view, you’re only tempted to think you have constant tactile experience of your feet in your shoes because you have that experience on those rare occasions when you’re thinking about whether you have it. Even if you now seem to have a broad range of experiences in different sensory modalities simultaneously, this could result from an unusual act of dispersed attention, or from “gist” perception or “ensemble” perception, in which you are conscious of the general gist or general features of a scene, knowing that there are details, without actually experiencing those unattended details.[8]

The opposite mistake is also possible. Those who deny a constant stream of peripheral experiences might simply be failing to notice or remember them. The fact that you don’t remember now the sensation of your feet in your shoes two minutes ago hardly establishes that you lacked the sensation at the time. Although many people find it introspectively compelling that their experience is rich with detail or that it is not, the issue is methodologically complex because introspection and memory are not independent of the phenomena to be observed.[9]

If we do have rich sensory experience outside of attention, it is unlikely that all of that experience is present in or broadcast to a Global Workspace. Unattended peripheral information is rarely remembered or consciously acted upon, tending to exert limited downstream influence – the paradigm of information that is not widely broadcast. Moreover, the Global Workspace is generally characterized as limited capacity, containing only a few thoughts, representations, objects, or processes at a time – those that survive some competition or attentional selection – not a welter of richly detailed experiences in many modalities at once.[10]

A less common but equally important objection runs in the opposite direction: Perhaps not everything in the Global Workspace is conscious. Some thoughts, representations, or processes might be widely broadcast, shaping diverse processes, without ever reaching explicit awareness.[11] Implicit racist assumptions, for example, might influence your mood, actions, facial expressions, and verbal expressions. The goal of impressing your colleagues during a talk might have pervasive downstream effects without occupying your conscious experience moment to moment.

The Global Workspace theorist who wants to allow that such processes are not conscious might suggest that, at least for adult humans, processes in the workspace are generally also available for introspection. But there’s substantial empirical risk in this move. If the correlation between introspective access and availability for other types of downstream cognition isn’t excellent, the Global Workspace theorist faces a dilemma. Either allow many conscious but nonintrospectable processes, violating widespread assumptions about luminosity, or redefine the workspace in terms of introspectability, which amounts to shifting to a Higher Order view.

3. Generalizing Beyond Vertebrates.

The empirical questions are difficult even in ordinary adult humans. But our topic isn’t ordinary adult humans – it’s AI systems. For Global Workspace Theory to deliver the right answers about AI consciousness, it must be a universal theory applicable everywhere, not just a theory of how consciousness works in adult humans, vertebrates, or even all animals.

If there were a sound conceptual argument for Global Workspace Theory, then we could know the theory to be universally true of all conscious entities. Empirical evidence would be unnecessary. It would be as inevitably true as that rectangles have four sides. But as I argued in Chapter Four, conceptual arguments for the essentiality of any of the ten possibly essential features are unlikely to succeed – and a conceptual argument for Global Workspace Theory would be tantamount to a conceptual argument for the essentiality of access, one of those ten features. Not only do the general observations of Chapter Four suggest against a conceptual guarantee, so also does the apparent conceivability, as described in Section 2 above, of consciousness outside the workspace or nonconsciousness within it – even if such claims are empirically false.

If Global Workspace Theory is the correct universal theory of consciousness applying to all possible entities, an empirical argument must establish that fact. But it’s hard to see how such an empirical argument could proceed. We face another version of the Problem of the Narrow Evidence Base. Even if we establish that in ordinary humans, or even in all vertebrates, a thought, representation, or process is conscious if and only if it occupies a Global Workspace, what besides a conceptual argument would justify treating this as a universal truth that holds among all possible conscious systems?

Consider some alternative architectures. The cognitive processes and neural systems of octopuses, for example, are distributed across their bodies, often operating substantially independently rather than reliably converging into a shared center.[12] AI systems certainly can be, indeed often are, similarly decentralized. Imagine coupling such disunity with the capacity for self-report – an animal or AI system with processes that are reportable but poorly integrated with other processes. If we assume Global Workspace Theory at the outset, we can conclude that only sufficiently integrated processes are conscious. But if we don’t assume Global Workspace Theory at the outset, it’s difficult to imagine what near-future evidence could establish that fact beyond a reasonable standard of doubt to a researcher who is initially drawn to a different theory.

If the simplest version of Global Workspace Theory is correct, we can easily create a conscious machine. This is what Dehaene and collaborators envision in the 2017 paper I discussed in Chapter One. Simply create a machine – such as an autonomous vehicle – with several input modules, several output modules, a memory store, and a central hub for access and integration across the modules. Consciousness follows. If this seems doubtful to you, then you cannot straightforwardly accept the simplest version of Global Workspace Theory.[13]

We can apply Global Workspace Theory to settle the question of AI consciousness only if we know the theory to be true either on conceptual grounds or because it is empirically well established as the correct universal theory of consciousness applicable to all types of entity. Despite the substantial appeal of Global Workspace Theory, we cannot know it to be true by either route.

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[1] Full Fodorian (1983) modularity is not required.

[2] Mashour et al. 2020, p. 776-777.

[3] E.g., Baars 1988; Dennett 1991, 2005; Tye 2000; Prinz 2012; Dehaene 2014; Mashour et al. 2020.

[4] Whether Dennett’s view is more plausible than Dehaene’s turns on whether, or how commonly, representations or processes are partly famous. Some visual illusions, for example, seem to affect verbal report but not grip aperture: We say that X looks smaller than Y, but when we reach toward X and Y we open our fingers to the same extent, accurately reflecting that X and Y are the same size. The fingers sometimes know what the mouth does not. (Aglioti et al. 1995; Smeets et al. 2020). We adjust our posture while walking and standing in response to many sources of information that are not fully reportable, suggesting wide integration but not full accessibility (Peterka 2018; Shanbhag 2023). Swift, skillful activity in sports, in handling tools, and in understanding jokes also appears to require integrating diverse sources of information, which might not be fully integrated or reportable (Christensen et al. 2019; Vauclin et al. 2023; Horgan and Potrč 2010). In response, the all-or-nothing “ignition” view can explain away such cases of seeming intermediacy or disunity as atypical (it needn’t commit to 100% exceptionless ignition with no gray-area cases), by allowing some nonconscious communication among modules (which needn’t be entirely informationally isolated), and/or by allowing for erroneous or incomplete introspective report (maybe some conscious experiences are too brief, complex, or subtle for people to confidently report experiencing them).

[5] Despite developing a theory of consciousness, Dennett (2016) endorsed “illusionism”, which rejects the reality of phenomenal consciousness (see especially Frankish 2016). I interpret the dispute between illusionists and nonillusionists as a verbal dispute about whether the specific philosophical concept of “phenomenal consciousness” requires immateriality, irreducibility, perfect introspectibility, or some other dubious property, or whether the term can be “innocently” used without invoking such dubious properties. See Schwitzgebel 2016, 2025.

[6] Reviewed in Schwitzgebel 2011, ch. 6; and though limited only to stimuli near the center of the visual field, see the large literature on “overflow” in response to Block 2007.

[7] Thomas 1999.

[8] Oliva and Terralba 2006; Whitney and Leib 2018.

[9] Schwitzgebel 2007 explores the methodological challenges in detail.

[10] E.g., Dehaene 2014; Mashour et al. 2020.

[11] E.g., Searle 1983, ch. 5; Bargh and Morsella 2008; Lau 2022; Michel et al. 2025; see also note 4.

[12] Godfrey-Smith 2016; Carls-Diamante 2022.

[13] See also Goldstein and Kirk-Giannini (forthcoming) for an extended application of Global Workspace Theory to AI consciousness. One might alternatively read Dehaene, Lau, and Kouider 2017 purely as a conceptual argument: If all we mean by “conscious” is “accessible in a Global Workspace”, then building a system of this sort suffices for building a conscious entity. The difficulty then arises in moving from that stipulative conceptual claim to the interesting, substantive claim about phenomenal consciousness in the standard sense described in Chapter Two. Similar remarks apply to the Higher Order aspect of that article. One challenge for this deflationary interpretation is that in related works (Dehaene 2014; Lau 2022) the authors treat their accounts as accounts of phenomenal consciousness. The article concludes by emphasizing that in humans “subjective experience coheres with possession” of the functional features they identify. A further complication: Lau later says that the way he expressed his view in this 2017 article was “unsatisfactory”: Lau 2022, p. 168.