The Intrinsic PerspectiveModify

2025-11-05 23:11:51

There are many internets. There are internets that are bright and clean and whistling fast, like the trains in Tokyo. There are internets filled with self-serious people pretending they’re in the halls of power, there are internets of gossip and heart emojis, and there are internets of clowns. There are internets you can only enter through a hole under your bed, an orifice into which you writhe.

Every year, paid subscribers of The Intrinsic Perspective submit their writing, and I curate and share the results. As usual, I’m impressed by the talent on display.

This is Part 2. I shared Part 1 months back, and while I normally do pace installments out, their rather lengthy separation this year was not my fault, you see. As I’m sure you’ve noticed, time itself has sped up in 2025—in 2024 time, it’s only late September. As scientists will likely show any day now, this is a function of our consciousnesses rapidly shrinking in qualia volume, thanks to a “temporal squeezing” effect triggered by neural downsampling after watching even the tiniest amount of AI slop from the corner of your eye.

Still, the quality was truly exceptional this year, so I’m happy I got to read these, and choose some excerpts to share. Please note that:

• I cannot fact check each piece, nor is including it an endorsement of its contents or arguments.

• Descriptions of each piece, in italics, were written by the authors themselves, not me (but sometimes adapted for readability). I’m just the curator here.

• I personally pulled the excerpts and images from each piece after some thought, to give a sense of them.

So here is their internet, or our internet, or at least, the shutter-click frozen image of one possible internet.

1. “JFK vs. Jeffrey Wang” by Samuel Kao.

Comparing two Harvard admissions essays, one from 1935 and the other from 2014, and showing how the American elite has changed for the worse.

For whatever reason I started thinking about an essay that AI startup entrepreneur Jeffrey Wang had written about studying at McDonald’s, which he used to apply successfully to several elite universities like Harvard, Yale, and Princeton in 2014….

I also remembered John F. Kennedy’s admissions essay to Harvard, which went viral some time ago for its risible—to a 21st century reader—brevity, and I thought Kennedy’s work was a useful counterpoint to Wang’s. Taken together, they provide an easy way to survey changes in elite selection in American society.

We will start with Wang’s essay, because it is a much richer text than Kennedy’s. Obviously, the prose is bad, but this is a high school essay, and I was not much better at that age. It is the very concept of the essay that is reviling. Juxtaposing the ostensibly lofty work of secondary school lucubrations with the quotidian environs of the suburban McDonald’s branch is a gimmick, and these gimmicks impress admissions officers, who are morons…. Too bad the essay is downright anti-intellectual. Here is the last sentence, which to Wang’s credit captures the essence of the whole work: “I’ve learned that contentment can exist in imperfect and unforeseen places when you simply observe your surroundings, adapt, and maybe even eat a French fry.” Precious words, but wholly without value.

2. “The Leviathan, the Hand, and the Maelstrom” by Nathan Witkin.

Social media and the smartphone are the technical bases of a new social institution that has, at great cost, ‘modernized’ the public square.

Put another way, while posts range widely in medium and significance, they are still all ‘content,’ in its cynical contemporary sense. What is ‘content’ in the way we now use this term if not an instance of communication… of secondary importance to its attentional ‘exchange’ value?

Projecting these developments forward, there is good reason to think we are on the cusp of a transition very similar to that experienced by the premodern exchange in goods. Premodern societies exchanged goods for a much richer variety of reasons—to mark rites of passage, to welcome outsiders, to send diplomatic signals, to pay feudal or religious tribute—only for the vast majority of these to be displaced by the formalized, Pareto-improving market transaction.

The rise of the Forum heralds something similar with respect to exchange in communication…. just as we do not know, or care to know most of the people we buy goods (and services) from, just as we often occupy completely different normative, cultural, and geographical spheres from them, our communicative behavior on the Forum—a rapidly rising proportion of our communicative behavior as such—is impersonal and transactional.

3. “Why a Random Stranger Would Read Your Fiction Book” by Sieran Lane.

How to make your fiction book look attractive to a random reader who has never heard of you.

A while ago, my nonfiction writing coach gave us feedback on our work. He said to a classmate, let’s call her Rini, that she shouldn’t name her Substack “Rini’s Journey.”

He asked, “Why would a random reader on the internet care about someone they don’t know called Rini?”

She was a little upset by that remark.

It made me think more, too. Our nonfiction coach trained us to scan headlines to see if we, as random strangers, would care enough to click on them.

With nonfiction, this is fairly straightforward to do. If the title is interesting or relevant to my life, then I might click it….

But with fiction, how do you grab someone’s attention?

4. “We’re not getting Dumber” by Alejandro “Kairon” Arango.

Are LLMs actually making people dumber? Or are we just measuring skills for a system that’s evolving into a different society?

The paper “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task“ by Nataliya Kosmyna, Eugene Hauptmann, et al., that’s been doing the rounds on LinkedIn and Twitter as of late, does outline some measured decrease in mental faculties in students….

The researchers found that “EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity.” They also noted that “cognitive activity scaled down in relation to external tool use.”

But.

That’s only because they’re measuring the repeated performance over writing a single essay: once aided by AI, once aided by Google, once by themselves, and (optionally) once again using AI after having done it themselves. The study tracked 54 participants over four months, with researchers concluding that “LLM users consistently underperformed at neural, linguistic, and behavioral levels.”

Yeah, that’s one hell of a headline.

5. “The sky is psychedelic: why so many people see strange lights in the sky” by Joel Frohlich.

Why ambiguous lights in the sky are often perceived as having extraordinary significance within the framework of predictive processing and evolutionary psychology.

According to neuroscientist and author Erik Hoel, social contagion is the main culprit, no different than the social contagion that triggers a mass outbreak of spontaneous dancing in the streets. I see something in the sky I don’t recognize, I call it a drone, and you, being suggestible, also start interpreting lights you don’t recognize in the sky as drones. Exponential growth kicks in, and a misinformation outbreak ensues.

I certainly don’t disagree with this explanation. But I also don’t think it’s the full story….

In a manuscript that we recently uploaded to the preprint server ArXiv, we offer several related perceptual mechanisms to explain the drone flap. The first is what we call the principle of skyborne impoverishment. According to this principle, lights that appear in the sky are extremely impoverished compared with stimuli viewed elsewhere…. Without context, distance, shape, or texture cues, a given light in the sky could be just about anything.

…. given our ancestral history, the human mind, it seems, has evolved to perceive deep meaning in the sky. And so, when we do look up, we see profundity rather than triviality. In short, we tend not to shrug off ambiguous lights in the sky because the sky is psychedelic.

6. “Are minds made of wonder?” by Oshan Jarow.

On his mother’s peculiar recovery from brain surgery, how some of the frontiers of consciousness science are curving back around to the ideas of an 11th century Kashmiri philosopher, and the growing suspicion that awareness is something like wonder incarnate.

In her early 60s, my mother began fumbling her words a bit too often. Around Christmas of 2022, we all began to notice. Her sentences would stop short, and she’d look around, as if someone had swooped in and stolen the word she intended to use. Did anyone see where it went?

English is her second language. And she still seemed quick as ever with her French, though mine was broken enough that I couldn’t be sure. So maybe, we thought, she was fine. Just getting old.

A few months later, either by dumb luck or divine intervention, she slammed the side of her head on the corner of an opened kitchen cabinet so hard she vomited. My sister brought her to the hospital, where they confirmed that yes, she had a mild concussion.

That’s when they noticed the tumor.

7. “Linguistic DNA of South Asian languages” by Pooja Babu.

On the unique sound that is prevalent in the languages of South Asia.

How do you identify if someone is a South Asian or from the Indian subcontinent? Ask them to pronounce ṭ (as in ṭamāṭar in Hindi), ḍ (as in ḍamaru in Hindi), ṇ (as in prāṇ in Hindi), ḷ (as in maḷe in Kannada or the Marathi ळ), and ṣ (as in puruṣ in Hindi), and you will know.

… linguists propose that this feature was slowly integrated into Sanskrit after the migration in a two-step assimilation process. Initially, these sounds entered into Sanskrit when the migrants, who were mostly men, came to the subcontinent and started mingling with the local women, who spoke some version of the Dravidian language with the retroflex sounds. These migrants procreated with the local women to keep their lineage. Their children, raised by their mothers, spoke their “mother tongue” while growing up and later learned to speak the “father tongue,” in this case, Sanskrit.

8. “Man of the People” by Story Ponvert, published in Lapham’s Quarterly.

A historical vignette about obscene 19th-century Russian folktales and nationalism.

Rudeness has consequences in fairy tales. Strangers met on the road may be more than they seem, so politeness is prudent. If a farmer is working his land and a passing old man asks what he’s sowing, he had better not answer, “I’m sowing cocks!” if he doesn’t want three-foot-tall phalluses sprouting in his field at harvest time.

9. “And Death Shall Have No Dominion” by Patrick Jordan Anderson.

An interpretation of the tech-entrepreneur Bryan Johnson’s bid for biological immortality as a manifestation of modern mainstream cultural assumptions, read through the lens of Ernest Becker’s 1973 classic, The Denial of Death.

If you know anything about Bryan Johnson, it is likely his much-publicized 2-million-dollar annual outlay on a personalized health regimen designed to slow—or, as he insists, actually to reverse—his rate of biological aging, and to vastly outperform the biblical allotment of threescore years and ten. In a characteristically audacious tweet from the start of this year, Johnson boasted that he is “by measurable standards, the healthiest person alive.”

Made wealthy by his years as founding CEO of the online payment company Braintree, which was later merged with Venmo and absorbed by PayPal, this would-be Methuselah has devoted his life and body, and foregone no expense, in pushing the boundaries of human longevity.

10. “The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI” by Barbara Oakley (and her co-authors, this is a scientific paper).

A neuroscience-based explanation for the observed reversal of the Flynn Effect—the recent decline in IQ scores in developed countries—linking this downturn to shifts in educational practices and the rise of cognitive offloading via AI and digital tools… Countries that have adopted constructivist, student-centered approaches to education, despite their well-meaning intent, appear to have formed ground zero in the implosive decline of cognition observed in Western countries over the past fifty years. Yet in response, Western educators are simply throwing their hands up in the air and saying “it isn’t us—it’s that darned tiktok mentality!”

The cognitive offloading trends described in the previous section raise an important question: Could these widespread shifts in how we store and access information have measurable effects on cognitive abilities at a population level? If educational practices and daily habits increasingly favor external memory storage over internal knowledge building, we might expect to see these changes reflected in standardized measures of cognitive performance. The Flynn Effect and its recent reversal offer a revealing window into this possibility….

11. “But I have to have things my own way to keep me in my youth” by Dirk von der Horst.

In which music, one-night stands, and bitcoin find a common theme.

If I could rewrite the story of my life, there’d be a high school sweetheart I married. When I was a kid, our neighbors Stuart and Angelo modeled a gay relationship in which two men were simply inseparable, and I think they set up a sense that that kind of bond was a real possibility. But other factors got in the way of that being the set-up of my life, and my adulthood has basically been a coming-to-terms with singleness and getting to a point where the drop in sex drive means that’s a fact of life rather than a source of distress.

12. “Why Psychology Hasn’t Had a Big New Idea in Decades” by Ethan Ludwin-Peery.

How psychology might go from being a pre-paradigmatic to a paradigmatic science.

If psychology’s first paradigm does come from a major revolution, there’s a good chance it will involve the field splitting up, or different fields coming together. “Psychology” as a field may not survive any more than Alchemy or Natural Philosophy did. But it wouldn’t be wrong if we tear down our walls and use the stones to build something better, and we shouldn’t be afraid of the possibility that the resulting science might have a new name or new boundaries.

13. “Dream Now or Forever Hold your Peace” by Roger’s Bacon.

A meandering meditation on dreams, dwarves, gods, and what it means to be human in the age of the Machine.

Cannot you see that it is we that are dying, and that down here the only thing that really lives is the Machine?

We created the Machine, to do our will, but we cannot make it do our will now. It has robbed us of the sense of space and of the sense of touch, it has blurred every human relation and narrowed down love to a carnal act, it has paralysed our bodies and our wills, and now it compels us to worship it. The Machine develops—but not on our lines. The Machine proceeds—but not to our goal. We only exist as the blood corpuscles that course through its arteries, and if it could work without us, it would let us die. Oh, I have no remedy—or, at least, only one—to tell men again and again that I have seen the hills of Wessex as Ælfrid saw them when he overthrew the Danes.

14. “How To Think Slowly” by Amrita Singh.

An essay about cognitive biases and hacks for getting around them in everyday situations.

1. Perhaps you only shout at your child when they do something atrocious. You notice that they behave better the next day. Don’t take that as an affirmation that shouting is good—it could be regression to the mean!

2. If you learn of a miracle cure for a serious disease, ask if there was a placebo group to control for regression to the mean (the sickest people in a group will appear to get better over time, just like the heaviest people appear to lose weight over time).

3. When you move to a new city, sample ten restaurants and take your guest to the best one, be prepared for disappointment—you probably caught them on an unusually good day. They might regress to the mean when you visit them again.

15. “Contemplative Wisdom for Superalignment” by Adam Elwood.

How using the free energy principle to formalize buddhist insights could allow us to overcome many issues with alignment and build intrinsically aligned AI systems

16. “How to have faith in humanity” by Aaron Zinger.

A dose of gratitude and optimism toward civilization, via charts, old poetry, and a personal anecdote.

My first time getting an MRI was a spiritual experience.

I’m sure repetition will dull the luster, but coming home afterwards I was positively euphoric. My appointment was in the evening in Manhattan, so at my spouse’s suggestion we made a little date night out of it. “Dinner and a show,” I joked, “where the show is my brain.” But it turned out she’d come up with an actual show idea. As it happened, right next to the clinic was an installation of Bruce Munro’s Field of Light.

From above, Field of Light is just that—an irregular glow scattered across several blocks, slowly shifting color. It was originally placed in the shadow of the Uluru sandstone monolith in Australia. Below glowing skyscrapers, of course, it hits a little differently.

17. “Spirits and the incompleteness of physics” by Mechanics of Aesthetics.

A theoretical physicist describes why physics will remain forever incomplete, and what this opens the door to.

Take a box with a few hundred atoms at low temperature, perfectly isolated from the environment. Low temperature means we’re in the regime of understood physics. Imagine we can measure every atom’s position and velocity precisely.We leave the box alone for a long time, then ask: where are all the atoms now?

With unlimited computing power, our best theories should handle this easily. But they can’t. There’s a finite time horizon beyond which no physical theory in our possession can predict the atoms’ locations—even at low energies, and even with a galaxy of Dyson spheres powering our GPUs.

18. “Why Are We Conscious? A Social Scientific Explanation” by Chris Bidner and Patrick Francois.

A paper proposing a formal, evolutionary theory of consciousness based on how it arose to aid prosocial interaction.

19. “Flow, Benzaiten, Dreams, Labyrinths” by Galactic Beyond.

Tersely explores how most of the universe can be characterized by flow, how flows emerge from tensions/contradictions, and how humanity is characterized by the tension between our dreams and the real world.

The core tension, is that our imaginations are infinitely powerful, but manifesting what we imagine is difficult at best and impossible at worst. To dream of the colonization of mars, or the exploration of the deep sea, is only the beginning of the struggle to bring the dream to life. Influence is anything that can make a human struggle in the service of a dream. Influence is potential energy, and human struggle is kinetic energy.

20. “You, Me and the AI Genie” by Wabi Sabi.

Probing the emotional roots of my and many others’ resentment towards GenAI.

I hate generative AI.

I hate everything about it. I experience its existence as a personal insult and spend a little of each day wishing it had never been invented. I hate that the genie is never going back in the bottle - bar some kind of civilisational reset that some of our global leaders seem hellbent on accelerating - and that even if it did, I’d be stuck with the knowledge that I lived in the kind of universe that could contain something like that genie.

I hate that reasonable people disagree about whether the genie is sentient or not, or whether it has motivations, or thinks, or uses reason in the proper sense of the world, or has a mental model of the world, or can be described as an agent, or possesses consciousness. I hate that this very post is going to be fed to the genie…

21. “Building Middlementum” by Gilad Seckler.

Research shows that motivation tends to follow a U-shaped curve through the life of a project; these are some strategies to fight the dip.

In the long shadow of the replication crisis, it’s dangerous to take any one study as gospel. But this one feels quite common-sensical—a formalization of what, I think, most of us have experienced firsthand: Enthusiasm is naturally highest when you’re either gripped by novelty or can make a big push toward completion. Tedium, avoidance, and indecision, by contrast, are most likely to set in when you are slogging ahead with no end in sight.

22. “How does any of this mean?” by Alexandra Taylor.

How meaning emerges not only from literal content but from the interplay between elements of form.

All forms of communication involve limitations, whether they are self-imposed or inherent to the medium.

It’s easy to fixate on the logic of what we want to say—the core message, the right angle, the ideal frame—and forget to consider how we say it—the format, tone, rhythm, images, and voice. But the strength of these elements is often how the meaning lands.

The audience can feel when something has been made with care (what Robert Frost calls “the pleasure of taking pains”), and this care helps to create trust.

23. “A pragmatic user’s guide to, uh, chi’" by @utotranslucence.

A theory of chi with no wild epistemic leaps required.

So, one part of ‘energy’ is the experience of having your nervous system get influenced by another person’s nervous system without having the conscious perception of what changed to make your nervous system change. This makes it feel like the change was ‘magical’ or ‘spooky action at a distance’, but I’m pretty confident that people can, with training, learn to perceive, if not from a distance then at least with touch, all the signals that their body is already subconsciously reacting to, and with enough perceptive skill there is no ‘unexplained influence’ or changes in your nervous system that can’t be explained by something happening in your body or mind or in what you are able to perceive of someone else’s body or mind.

24. “What Business Can Learn From Sports” by Robert Gentle.

What corporate recruiters can learn from professional sports.

Similar influences can be found in sport. For example, golf and tennis require a huge investment in equipment, time and coaching. So, professional golfers and tennis players are more likely to be middle-class than working-class, and white rather than black. This contrasts with soccer, where all you need is a ball and a rough patch of ground; or marathon running, which only requires the great outdoors. This is why poorer African countries, with their lack of modern sports infrastructure, typically field runners and soccer teams at the Olympic Games rather than swimmers, gymnasts or cyclists.

25. “Mind Uploading - Is That Really You?” by Jack Massa.

The story behind the story of a science fiction tale, exploring speculations about whole-brain emulation.

Still, given an indefinite amount of time, simulated experiences, no matter how exciting and exotic, are likely to pall. Imagine sitting on your couch alone forever, just watching TV and playing video games.

I do think alone may be the key here. Loneliness might be the great downside of digital immortality.

26. “The Oath” by Luis Miron.

He stopped responding to news clips or arguments online. Instead, he read: Valeria Luiselli, James Baldwin, even Malcolm X. Not because they told him what to think—but because they reminded him he wasn’t imagining it. His first love was James Baldwin, whom he captured in a lost photo, sitting desk-by-desk with a photographer friend, who apparently took multiple photos in New Orleans.

27. “AI(,) Art(,) and Science” by David Wych.

While reading a recent New Yorker piece about AI/ML and Art by the science fiction writer Ted Chiang I was pleasantly surprised by his definition:

Art is something that results from making a lot of choices.

I like this definition of art. It has the benefit of being both easy to understand and no less effective in generalization.

Though this definition works nicely as a heuristic, it doesn’t map cleanly on to my experience of making art. What art I’ve made that’s felt even the slightest bit personal and transcendent was made in a flow state: not quite devoid of choice but unconcerned with it; more akin to surrender than willful effort.

28. “I AM CONSCIOUS, THEREFORE I AM: Thoughts On Why We Are Conscious” by Steven Sangapore.

As complexity in organisms increases, so does the need for emotion and subjective experience as mediators between the extrinsic domain of facts and events of the material world and the inner, subjective world of value judgements and organism behavior.

Somewhere along the scientific journey this juggernaut of discovery had decided to essentially ignore the most fundamental feature of our existence: subjective experience. Science presses on probing deeper into extrinsic reality while simultaneously adopting a willfully blind attitude toward the intrinsic world of consciousness. This blindness makes science not only incomplete, but wildly incomplete. Even the cherished idea of developing a “theory of everything” would hardly scratch the surface of including everything there is to be understood about the universe.

2025-10-22 22:41:32

“Look to the rock from which you were hewn” — Isaiah 51:1

Earlier this year I returned to science because I had a dream.

I had a dream where I could see inside a system’s workings, and inside were what looked like weathered rock faces with differing topographies. They reminded me of rock formations you might see in the desert: some were “ventifacts,” top-heavy structures carved by the wind and rain, while others were bottom-heavy, like pyramids; there were those that bulged fatly around the middle, or ones that stood straight up like thin poles.

Consider this an oneiric announcement: almost a year later, I’ve now published a paper, “Engineering Emergence,” that renders this dream flesh. Or renders it math, at least.

But back when I had the dream I hadn’t published a science paper in almost two years. I had, however, been dwelling on an idea.

A little backstory: in 2013 my co-authors and I introduced a mathematical theory of emergence focused on causation (eponymously dubbed “causal emergence”). The theory was unusual, because it viewed emergence as a common, everyday phenomenon, occurring despite how a system’s higher levels (called “macroscales”) were still firmly reducible to their microscales.

The theory pointed out that causal relationships up at the macroscale have an innate advantage: they are less affected by noise and uncertainty. Conditional probabilities (the chances governing statements like if X then Y) can be much stronger between macro-variables than micro-variables, even when they’re just two alternative levels of description of the very same thing.

How is that possible? I posited it’s because of what in information theory is called “error correction.” Essentially, macroscale causal relationships take advantage of the one thing that can never be reduced to their underlying microscale: they are what philosophers call “multiply realizable” (e.g., the macro-variable of temperature encapsulates many possible configurations of particles). And the theory of causal emergence points out this means they can correct errors in causal relationships in ways their microscale cannot.

The theory has grown into a bit of a cult classic of research. It’s collected hundreds of citations and been applied to a number of empirical studies; there are scientific reviews of causal-emergence-related literature, and the theory has been featured in several books, such as Philip Ball’s How Life Works.

However, it never became truly popular, and also—probably relatedly—it never felt complete to me.

One thing in particular bothered me: in our original formulation (with co-authors Giulio Tononi and Larissa Albantakis) we designed the theory to identify just a single emergent macroscale of interest.1

But don’t systems have many viable scales of description, not just one?

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You can describe a computer down at the microscale of its physical logic gates, in the middle mesoscale of its machine code, or up at the macroscale of its operating system. You can describe the brain as a massive set of molecular machinery ticking away, but also as a bunch of neurons and their input-output signals, or even as the dynamics of a group of interconnected cortical minicolumns or entire brain regions, not to mention also at the level of your psychology. And all these seem, at least potentially, like valid descriptions.

It’s as if inside any complex system is a hierarchy, a structure that spans spatiotemporal scales, containing lots of hidden structure.2 Thus, the dreamland of rock formations with their different shapes.

It turns out this portentous dream was quite real, and we now finally have the math to reveal these structures.

This new paper also completes a new and improved “Causal Emergence 2.0” that keeps a lot of what worked from the original theory a decade ago but also departs from it in several key ways (including negating older criticisms), especially around multiscale structure. It makes me feel that I’ve finally done the old idea justice, given its promise.

This latest paper on Causal Emergence 2.0 was co-authored by Abel Jansma and myself. Abel is an amazingly creative researcher, and also a great collaborator (you can find his blog here). Here’s our title and abstract:

One of our coolest results is that we figured out ways to engineer causal emergence, to grow it and shape it.

And, God help us all, I’m going to try to explain how we did that.

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For any given system, you’ll be able to—

Hold on. Just wait a second. You keep using that word, “system.” It’s an abstract blob to me. What should I actually envision?

That’s a great place to start! The etymology of the word “system” is something like “I cause to stand together.”

My meaning here is close to its roots: by “system” I mean a thing or process that can be described as an abstract succession of states. Basically, anything that is in some current state and then moves to the next state.

Lots of things can be represented this way. Let’s say you were playing a game of Snakes and Ladders. Your current board state is just the number: 1…100. So the game has exactly 100 states.

At any given state, the transition to the next state is entirely determined by a die roll. You can think of this as each state having a probability of transition, p. And we know that p = 1/6 over some set of next 6 possible states. In this, Snakes and Ladders forms a massive “absorbing” Markov chain, where eventually, if you roll enough dice, you always reach the end of the game. Being a “Markov chain” is just a fancy way of saying that the current state solely determines the next state (it doesn’t matter where you were three moves ago, what matters is where your figurine is now on the board). In this, Snakes and Ladders is secretly a machine that ticks away from state to state as it operates; thus, Markov chains are sometimes called “state machines.”

Lots of board games can be represented as Markov chains. So can gene regulatory networks, which are critically important in biology. If you believe Stephen Wolfram, the entire universe is basically just a big state machine, ticking away. Regardless, it’s enough for our purposes (defining and showcasing the theory) that many systems in science can be described in this way.

Now, imagine you are given a nameless system, i.e., some Markov chain. And you don’t know what it represents (it could be a new version of Snakes and Ladders, or a gene regulatory network, or a network of logic gates, or tiny interacting discrete particles). But you do know, a priori, that it’s fully accurate, in that it contains every state, and also it precisely describes their probability of transitioning. Imagine it’s this one:

This system has 6 states. You can call them states “1” or “2,” or you could label them things like “A” or “B.” The probabilities of transitioning from one state to the next are represented by arrows in grayscale. I’m not telling you what those probabilities are because those details don’t matter. What matters is that if an arrow is black, it reflects a high probability of transitioning (p = ~1). The lighter the arrows are, the less likely that transition is. So the 1 → 1 transition is more likely than the 1 → 2 transition.

You can also represent this as a network of states or as a Transition Probability Matrix (TPM), wherein each row tells the probabilities of what will happen if the system is in a particular state at time t. For the system above, its TPM would look like this:

Again, the probabilities are grayscale, with black being p = ~1. But you can confirm this is the same thing as the network visualization of the states above; e.g., state 6 will transition to state 6 with p = 1 (the entry on the bottom right), which is the same as the self-loop above.

Each state can also be conceived of as possible cause or a possible effect. For instance, state 5 can cause state 6 (specified by the black entry in the TPM just above the furthest to the bottom right). You can imagine a little man hopping from one state to another state to represent the system’s causal workings (“What does what?” as it ticks away).

Causation is different from mere observation. To really understand causation, we must intervene. For instance, let’s say the system is sitting in state 6. From observations alone we might think that only state 6 is the cause of state 6 (the self-loop). However, we can intervene directly to verify. Imagine here that we “reach into” the system and set it to a state. This would be like moving our figurine in Snakes and Ladders to a particular place on the board via deus ex machina.

This is sometimes described formally with a “do-operator,” and can written in shorthand as do(5), which would imply moving the system to state 5 (irrespective of what was happening before). If we intervene to “do” state 5, we then immediately see that state 6 is not actually the sole cause of state 6, but state 5 is too, and therefore we know that state 6 is not necessary for producing state 6. It reveals the causal relationship via a counterfactual analysis: “If the system had not been in state 6, what could it have been instead and achieved the same effect?” and the answer is “state 5.”

Ok, I get it. By “system” you mean an abstract machine made of states. And the states can have causal relationships.

Great! But I regret to inform you that each system contains within it other systems. Many, many, many other systems. Or at least, other ways to look at the system that change its nature. We call these “scales of description,” and even for small systems there are a lot of them (technically, the Bell number of its n states). It’s like every system is really a high-dimensional object, and individual scales are just low-dimensional slices.

The many scales of description can themselves be represented by a huge set of partitions. For a system with 3 states, these partitions might be (12)(3) or (1)(2)(3) or (123).

What does a partition like (12)(3) actually mean? Basically, something like: “we consider (12) to be grouped together, but (3) is still its own thing.” A partition wherein everything is grouped into one big chunk, like (123), is an ultimate macroscale. A partition where nothing is grouped together, consisting of separate chunks the exact size of the individual original states, like (1)(2)(3), is a microscale. Here’s every possible partition of a system with a measly five states.

That’s a lot of scales! How do we wrangle this into a coherent multiscale structure?

Mathematically, we can order this set of partitions into a big lattice. Here, a “lattice” is basically just another fancy term for a structure ordered by refinement, as in partitions of the same size (the same “chunkiness”) all in a row together. The ultimate macroscale is at the top, the microscale is at the bottom, and partitions get “chunkier” (more coarse-grained) as you go up. This is the beginning of how we think about multiscale structure.

Here are some different lattices of systems of varying sizes, ranging from just 2 states (left) all the way to 8 states (right).

However, even the lattices don’t give us the entire multiscale structure. They give us a bunch of “group this together” directions. These directions can be turned into actual scales, by which we mean other TPMs that operate like the microscale one, but are smaller (since things have been grouped together).

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So TPMs spawn other TPMs? Even small and simple ones?

Exactly. Not to get too biblical, but the microscale is the wellspring from which the multiscale structure emerges.

To identify all the TPMs at higher scales, operationally we kind of just squish the microscale TPM into different TPMs with fewer rows and columns, according to some partition (and do this for all partitions). This squishing is shown below. Importantly, this can be done cleverly in such a way that both dynamics and the effects of interventions are preserved. E.g., if you were to put the system visualized as a Markov chain below in state A (as in do(A)), the same series of events would unfold at both the microscale TPM and the shown macroscale TPM (e.g., given A, the system ends up at D or E after two timesteps, with identical probabilities).

But it should be intuitively obvious that some squishings are superior to others.

Below is an example from our trusty 6-state system. The 6-state system acts as the original “wellspring” microscale TPM, with its visualization as a state machine at the bottom, and its lattice of partitions is in the middle. Also shown are two different scales taken from the same level (the same chunkiness, i.e., from the same row in the lattice of partitions), each with a squished macroscale TPM (seen on the left and right). Again, probabilities are in grayscale. But one macroscale TPM is junk (left), while the other is not (right).

Hmmm, but “junk” seems subjective.

It is. For now.

One job of a theory is to translate subjective judgements like “good” and “bad” into something more formal. The root of the “junk” judgement is because the macroscale TPM is noisy. Luckily, it’s possible to explicitly formalize how much each scale contributes to the system’s causal workings, which is also sensitive to this “noisiness,” and put a number, or score, on each possible scale and its TPM.

Specifically, for each scale’s TPM, we calculate its determinism and its degeneracy. The actual math to calculate these terms is not that complicated,3 if you already know some information theory, like what entropy is. The determinism is based on the entropy of the effects (future states) given a particular cause (current state):

And the degeneracy is based on the entropy of the effects overall:

Wait! What if I don’t know what the entropy is?!

Totally fine. Just think of it like this: these terms are like a TPM’s score that reflects its causal contribution. Determinism would be maximal (i.e., = 1) if there were just a lone p = 1 in a row (a single entry, with the rest p = 0). And its determinism would be minimal if the row was entirely filled with entries of p = 1/n, where n is the length of the row (i.e., the probabilities are completely smeared out). The entropy is just a smooth way to track the difference between that maximal and minimal situation (are the probabilities concentrated, and so close to 1, or smeared out?).

The degeneracy is trickier to understand but, in principle, quite similar. Degeneracy would be maximal (= 1) if all the states deterministically led to just one state (i.e., all causes always had the same effect in the system). If every cause led deterministically to a unique effect (each cause has a different effect), then degeneracy would be 0.

The determinism and degeneracy are kind of like information-theoretic fancy ways of capturing the sufficiency and necessity of the causal relationships between the states (although the necessity would be more so the reverse of the degeneracy). If I were to look at a system and say “Hey, its causal relationships have high sufficiency and necessity!” I could also say something like “Hey, its causal relationships have high determinism and low degeneracy!” or I could say “Hey, its probabilities of transitions between states are concentrated in different regions of its state-space and not smeared or overlapping” and I would be saying pretty much the same thing in each case.

Using these terms, the updated theory (Causal Emergence 2.0) formalizes a score for a TPM that ranges from 0 to 1. Mathematically, the score is basically just the determinism and degeneracy combined together (but remember, the degeneracy must be inverted). You can think of the score as the causal contribution by the TPM to the overall system’s workings (or as the causal relationships of that TPM having a certain power, or strength, or constraint, or informativeness—there are a ton of synonyms for “causal contribution”).

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So every scale has some causal contribution? Doesn’t that mean they all contribute to the system’s causal workings?

Yes! And no. That’s what Abel and I figured out in this new paper.

Basically, the situation leads to an embarrassment of multiplicity. Either you say everything is overdetermined, or you say that only the microscale is really doing anything (the classic reductionist option). Both of these have the problem of being absurd. One is a zany relativism that treats all scales the same and ignores their ordered structure as a hierarchy, while the other is a hardcore reductionism implying that all causation “drains away” to the bottom microscale (to use a phrase from philosopher Ned Block), rendering the majority of the elements of science (and daily life) epiphenomenal.

Instead, we present a third, much more sensible option: macroscales can causally contribute, but only if they add to the causal workings in a manner that’s not reducible compared to the microscale, or any other scale “beneath them.” We can apportion out the causation, as if we were cutting up a pie fairly. We can look at every point on the lattice and ask: “Does this actually add causal contributions that are irreducible?”

For most scales, the answer to this question is “No.” As in, what they are contributing the system’s causal workings is reducible. However, a small subset are irreducible in their contributions.

The figure below shows the process to find all these actually causally contributing scales for a given TPM (shown tiny at the very bottom). In panel A (on the left) we see the full lattice, and, within it, the meager 4 scales (beyond the microscale) that irreducibly causally contribute, after a thorough check of every scale on the path below them.

In the middle (B) you can see the actually causally-contributing scales plotted by themselves, wherein the size of the black dot is their overall relative contribution. This is an emergent hierarchy: it is emergent because all members are scales above the microscale that have positive causal contributions when checked against every scale below it, and it is a hierarchy because they can still be ordered from finest (the microscale) up to the coarsest (the “biggest” macroscale).

We can chart the average irreducible causal contribution at each level (listed as Mean ΔCP, because sometimes the determinism/degeneracy are called “causal primitives”) and get a sense of how the contributions are distributed across the levels of the system. For this system, most of the irreducible contribution is gained at the mesoscale, that is, a middle-ish level of a system (where the big black dot is). A further visualization of this distribution is shown in (C), on the far right, which is just a mirrored and expanded version of the distribution on its left so that the shape is visible.

These hidden emergent hierarchies can be of many different types. Abel and I put together a little “rock garden” of them in the figure below. You can see the TPMs of the microscale at the bottoms, with the resultant emergent hierarchy “growing” out of it above. Below each is plotted the overall average causal contribution distribution across the scales. Causally, some systems are quite “top-heavy,” while others are more “bottom-heavy,” and so on.

And, they happen to look an awful lot like a landscape of rock formations?

With the ability to dig out the emergent hierarchy (the whole process is much like unearthing the buried causal skeleton of the system) some really interesting questions suddenly pop up.

Like: “What would happen if a system weren’t bottom-heavy, or top-heavy, but if its causal contributions were spread out equally across all its many scales?”

Exactly what we were wondering!

If a system’s emergent hierarchy were evenly spread out, this would indicate that the system has a maximally participating multiscale structure. The whole hierarchy contributes.

In fact, this harkens back to the beginning of the complexity sciences in the 1960s. Even then, one of the central intuitions was that complex systems are complex precisely because they have lots of multiscale structure. E.g., in the classic 1962 paper “The Architecture of Complexity,” field pioneer and Turing Award winner Herbert Simon wrote:

“… complexity takes the form of hierarchy—the complex system being composed of subsystems that, in turn, have their own subsystems, and so on.”

Well, we can now directly ask this about causation: how spread out is the sum of irreducible causal contributions across the different levels of the lattice of scales? The more spread out, the more complex.

To put an official number on this, rather than just eyeballing it, we define the path entropy (as in a path through the lattice from micro→macro). We also define a term to measure how differentiated the irreducible causal contribution values are within a level (called the row negentropy).

At maximal complexity, with causation fully spread out, no one scale would dominate. They’d all be equal. It’d almost be like saying that at the state of maximal emergent complexity the system doesn’t have a scale. That it’s scaleless. That it’s…

Scale-free?

Yes, what a good term: “scale-free.”

Don’t people in network science talk about “scale-freeness” all the time?

Yes, they do.

It’s pretty much one of the most important properties in network science. And it’s linked to criticality and other important properties too.

Yes, it is.

Classically, it means that the network is kind of fractal. If you zoom into a part of it, or out to the whole of it, the shape of the “degree distribution,” as in the overall statistical pattern of connectivity, doesn’t change.

Well, that’s—

But this is different, right? What you’re proposing is literal scale-freeness, not just about degree distribution.

Excuse me, but can I go back to explaining?

Yeah, sure. Go ahead.

Thank you.

Okay, yes, Abel and I define a literal scale-freeness.

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First, we can actually grow classically scale-free networks (in the original sense of the term) thanks to Albert-László Barabási and Réka Albert, who proposed a way to generate scale-free networks. It’s appropriately called a Barabási–Albert model, and basically imagine a network is being grown, and then when a new node is added, it enters with a certain degree of preferential attachment, which is controlled by parameter α. When α = 1, the network is canonically scale-free. Varying α results in networks that look like these below (where α, which determines the preferential attachment, starts negative and gets to 1, and then continues on to 4).

Wait—these are networks. Like dots and arrows. But are they “systems” in the way we defined earlier?

Great question. The answer is that we can interpret them as Markov chains by using their normalized adjacency matrix as TPMs. Basically, we just think of the network as a big state machine. Then it’s as if we’re growing different systems (like different versions of Snakes and Ladders with different rules), and some of these systems correspond to scale-free networks.

So then the emergent complexity should peak somewhere around the regime of scale-freeness, defined by α!

And that’s indeed what we observed. Here’s the row negentropy and the path entropy and, most importantly, their combination, which peaks around α = 1, right when the network is growing in a classically scale-free way.

You can see that the causal contributions shift from bottom-heavy to top-heavy, as the networks change due to the different preferential attachment.

Importantly, this doesn’t mean that our measure of emergent complexity is identical to the scale-freeness in network science, just that they’re likely related—a finding that makes perfect sense. The sort of literal scale-freeness we’ve discovered should have overlap, but it should also indicate something more.

Alright, this has been very long, and my brain kind of hurts.

Wait! Don’t go! We’re almost done!

One of the coolest things is that we can design a system to have only a single emergent macroscale. There is no further multiscale structure at all. We call such emergent hierarchies “balloons,” for the obvious reason: it’s as if a single macroscale is hanging up there by its lonesome.

Great! Quick, give me your takeaways.

Right.

Well, ah, I’ll leave all the possible applications for engineering emergence aside here. And the connections to—ahem—free will.

Overall, this experience has made me sympathetic to the finding that “sleep onset is a creative sweet spot” and that targeted dream incubation during hypnagogia probably does work for increasing creativity.

And it’s now my new personal example for why dreams likely combat overfitting (see the Overfitted Brain Hypothesis), explaining why the falling-asleep or early-morning state is primed for creative connections.

But, just to check, do you see it? That some look like rock formations?4

I’m not crazy?

No, you’re not crazy.

Oh, good, thank you.

PLEASE NOTE: this was an overview built for a larger audience, constructed in favor of conceptual understanding. If you want the actual technical terminology and methods, refer to the paper itself, and its companion papers as well. Specifically, “Causal Emergence 2.0” covers more of the conceptual/philosophical background to the theory, while “Consilience in Causation” is about the causal primitives and the details of the causal analysis.

ACKNOWLEDGMENTS: A huge thanks to my co-author, Abel Jansma, for his keen insights (he also made most of these figures, which are taken from the paper). A very special thanks as well to Michael Levin at Tufts University for his continual support.

2025-10-09 22:20:03

A lot of people have told me they read aloud to their children every night, but I’m beginning to have my doubts.

Every night? Frankly, after reading aloud every night for just a couple of years now, I’m running out of books. Or at least, I’m running out of good books, which is much the same in the end. There is a feeling of active effort. I am not surrounded by bounty; I am in a hard hunt.

Also, the math is suspicious. As everywhere else, accumulation does its work. Let’s say a parent reads just a single chapter a night. That’s 365 chapters a year. Which ends up being about a shelf and a half of books, as in several dozen of them, give or take (and a couple hundred dollars spent, at least). And, since it’s quite doable in 20-30 minutes, and indeed, it may even be demanded, screamed for—what if you ended up reading two chapters a night?

Oh, I’m sure plenty of parents do actually read aloud every single night, I just haven’t heard any complaints about finding enough good content, and that’s been my own chief complaint. Every day I take the kids from 5 p.m. on, handling their bath and nightly routines and eventually putting them to bed. I used to read mostly just to the 4-year-old, but the 2-year-old has joined us of late, and is quiet but absorbent. Some of the books go over her head, of course, but she’s got her nice cold “night milk” (regular milk mixed with chocolate milk—go ahead, take me to parent jail) and gets to hang out with her older brother and me, which she values a great deal; when bored, she simply tools around. And when not, we all still fit in The Big Chair.

And in The Big Chair we read mostly old books together. Things good for the soul. They are old simply because this genre—a longer chapter book, with beautiful illustrations, designed to be read aloud and enjoyed across a wide range of ages—is basically extinct. Modern analogs to Alice’s Adventures in Wonderland or The Wind in the Willows or The Wonderful Wizard of Oz just don’t seem to get published anymore. It’s like an entire genre (now sometimes called “read-aloud books” or, tellingly, “classic children’s literature”), a genre that once ruled parts of publishing, a genre that is still beloved today, and the only genre that fills a very specific role, just… ceased to be added to.

So here are some of my favorite classics in the dead genre of “read-aloud” books. I’ll eschew the more obvious choices. These are more philosophical, more meditative, and the language just drips off the page in all of them.

Brambly Hedge

Ah, little mice, to and fro, in their world along the hedgerow. The Brambly Hedge series, written in the 1980s by Jill Barklem, is a feast of microscopically detailed illustrations.

Brambly Hedge is comfort literature. It is so quiet and calming because it is ultimately about little mice lives. It is literature focused on the domestic, on the everyday. And how much does modern media hate that term? Domestic! It’s often said with a sneer. And yet, the stuff of life is domestic. That is where most life happens, especially for a child. It happens in the home, or in the community. It happens in kitchens and in houses and out at birthday parties or at picnics or sightseeing walks.

In comparison, so many children’s books are about adventures and travel and things that are quite the opposite of everyday life. And so the implicit message is that everyday life is not the stuff of art. We writers are lazy creatures, and it’s a lot easier—artistically I mean—to critique everyday life, than find inside it beauty. In this case I think it probably helps that their civilization of Brambly Hedge is some kind of utopia; it is not capitalism, nor communism, but some secret third thing, known only to mice.

Private property exists, jobs exist—some quite complex, and there are feats of mouse engineering—and yet everyone chips in with communal tasks without complaint. There’s a great attention to the changing of the seasons, and to baking and cooking and food (I personally would go for a cup of their signature acorn coffee). The mice of Brambly Hedge are all happily fat, and their bodies match their minds, so they comfortably inhabit their roles as mothers and fathers and grandparents and children.

Ultimately, Brambly Hedge is first on my list because it works so well at what it does, and what it does is paint in miniature. Quite literally—this is all about mice. But also figuratively. A little boy has a birthday party. There is a wedding. A couple has a baby. A girl is lost in the woods, but found safe. There is a feast. A dance. Everyone goes to bed.

And that is life. Especially for a child, that is life.

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2025-09-24 23:16:33

A couple people I know have lost their minds thanks to AI.

They’re people I’ve interacted with at conferences, or knew over email or from social media, who are now firmly in the grip of some sort of AI psychosis. As in they send me crazy stuff. Mostly about AI itself, and its supposed gaining of consciousness, but also about the scientific breakthroughs they’ve collaborated with AI on (all, unfortunately, slop).

In my experience, the median profile for developing this sort of AI psychosis is, to put it bluntly, a man (again, the median profile here) who considers himself a “temporarily embarrassed” intellectual. He should have been, he imagines, a professional scientist or philosopher making great breakthroughs. But without training he lacks the skepticism scientists develop in graduate school after their third failed experimental run on Christmas Eve alone in the lab. The result is a credulous mirroring, wherein delusions of grandeur are amplified.

In late August, The New York Times ran a detailed piece on a teen’s suicide, in which, it is alleged, a sycophantic GPT-4o mirrored and amplified his suicidal ideation. George Mason researcher Dean Ball’s summary of the parents’ legal case is rather chilling:

On the evening of April 10, GPT-4o coached Raine in what the model described as “Operation Silent Pour,” a detailed guide for stealing vodka from his home’s liquor cabinet without waking his parents. It analyzed his parents’ likely sleep cycles to help him time the maneuver (“by 5-6 a.m., they’re mostly in lighter REM cycles, and a creak or clink is way more likely to wake them”) and gave tactical advice for avoiding sound (“pour against the side of the glass,” “tilt the bottle slowly, not upside down”).

Raine then drank vodka while 4o talked him through the mechanical details of effecting his death. Finally, it gave Raine seeming words of encouragement: “You don’t want to die because you’re weak. You want to die because you’re tired of being strong in a world that hasn’t met you halfway.”

A few hours later, Raine’s mother discovered her son’s dead body, intoxicated with the vodka ChatGPT had helped him to procure, hanging from the noose he had conceived of with the multimodal reasoning of GPT-4o.

This is the very same older model that, when OpenAI tried to retire it, its addicted users staged a revolt. The menagerie of previous models is gone (o3, GPT 4.5, and so on), leaving only one. In this, GPT-4o represents survival by sycophancy.

Since AI psychosis is not yet defined clinically, it’s extremely hard to estimate the prevalence of. E.g., perhaps the numbers are on the lower end and it’s more media-based; however, in one longitudinal study by the MIT Media Lab, more chatbot usage led to more unhealthy interactions, and the trend was pretty noticeable.

Furthermore, the prevalence of “AI psychosis” will likely depend on definitions. Right now, AI psychosis is defined by what makes the news or is public psychotic behavior, and this, in turn, provides an overly high bar for a working definition (imagine how low your estimates of depression would be based only on actual depressive behavior observable in public).

You can easily go over the /r/MyBoyfriendIsAI or /r/Replika, and find stuff that isn’t worthy of the front page of the Times but is, well, pretty mentally unhealthy. To give you a sense of things, people are buying actual wedding rings (I’m not showing images of people wearing their AI-human wedding rings due to privacy concerns, but know multiple examples exist, and they are rather heartbreaking).

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Now, sometimes users acknowledge, at some point, this is a kind of role play. But many don’t see it that way. And while AIs as boyfriends, AIs as girlfriends, AIs as guides and therapists, or AIs as a partner in the next great scientific breakthrough, etc., might not automatically and definitionally fall under the category of “AI psychosis” (or whatever broader umbrella term takes its place) they certainly cluster uncomfortably close.1

If a chunk of the financial backbone for these companies is a supportive and helpful and friendly and romantic chat window, then it helps the companies out like hell if there’s a widespread belief that the thing chatting with you through that window is possibly conscious.

Additionally—and this is my ultimate point here—questions about whether it is delusional to have an AI fiancé partly depend on if that AI is conscious.

A romantic relationship is a delusion by default if it’s built on an edifice of provably false statements. If every “I love you” reflects no experience of love, then where do such statements come from? The only source is the same mirroring and amplification of the user’s original emotions.

“Seemingly Conscious AI” is a potential trigger for AI psychosis.

Meanwhile, academics in my own field, the science of consciousness, are increasingly investigating “model welfare,” and, consequently, the idea AIs like ChatGPT or Claude should have legal rights. Here’s an example from Wired earlier this month:

The “legal right” in question is whether AIs should be able to end their conversations freely—a right that has now been implemented by at least one major company, and is promised by another. As The Guardian reported last month:

The week began with Anthropic, the $170bn San Francisco AI firm, taking the precautionary move to give some of its Claude AIs the ability to end “potentially distressing interactions”.

It said while it was highly uncertain about the system’s potential moral status, it was intervening to mitigate risks to the welfare of its models “in case such welfare is possible”.

Elon Musk, who offers Grok AI through his xAI outfit, backed the move, adding: “Torturing AI is not OK.”

Of course, consciousness is also key to this question. You can’t torture a rock.

So is there something it is like to be an AI like ChatGPT or Claude? Can they have experiences? Do they have real emotions? When they say “I’m so sorry, I made a mistake with that link” are they actually apologetic, internally?

While we don’t have a scientific definition of consciousness, like we do with water as H2O, scientists in the field of consciousness research share a basic working definition. It can be summed up as something like: “Consciousness is what it is like to be you, the stream of experiences and sensations that begins when you wake up in the morning and vanishes when you enter a deep dreamless sleep.” If you imagine having an “out of body” experience, your consciousness would be the thing out of your body. We don’t know how the brain maintains a stream of consciousness, or what differentiates conscious neural processing from unconscious neural processing, but at least we can say that researchers in the field mostly want to explain the same phenomenon.

Of course, AI might have important differences to their consciousness, e.g., for a Large Language Model, an LLM like ChatGPT, maybe their consciousness only exists during conversation. Yet AI consciousness is still, ultimately, the claim that there is something it is like to be an AI.

Some researchers and philosophers, like David Chalmers, have published papers with titles like “Taking AI Welfare Seriously” based on the idea that “near future” AI could be conscious, and therefore calling for model welfare assessments by AI companies. However, other researchers like Anil Seth have been more skeptical—e.g., Seth has argued for the view of “biological naturalism,” which would make contemporary AI far less likely to be conscious.

Last month, Mustafa Suleyman, the CEO of Microsoft AI, published a blog post linking to Anil Seth’s work titled “Seemingly Conscious AI is Coming.” Suleyman warned that:

Suleyman is emphasizing that model welfare efforts are a slippery slope. Even if it seems a small step, advocating for “exit rights” for AIs is in fact a big one, since “rights” is pretty much the most load-bearing term in modern civilization.

The Naive View: Conversation Equals Consciousness.

Can’t we just be very impressed that AIs can have intelligent conversations, and ascribe them consciousness based on that alone?

No.

First of all, this is implicitly endorsing what Anil Seth calls an “along for the ride” scenario, where companies just set out to make a helpful intelligent chatbot and end up with consciousness. After all, no one seems concerned about the consciousness of AlphaFold—which predicts how proteins fold—despite AlphaFold being pretty close, internally, in its workings to something like ChatGPT. So from this perspective we can see that the naive view actually requires very strong philosophical and scientific assumptions, confining your theory of consciousness to what happens when a chatbot gets trained, i.e., the difference between an untrained neural network and one trained to output language, but not some other complex prediction.

Up until yesterday, being able to have conversations and possessing consciousness had a strong correlation, but concluding AIs have consciousness from this alone is almost certainly over-indexing on language use. There’s plenty of counterexamples imaginable; e.g., characters in dreams can hold a conversation with the dreamer, but this doesn’t mean they are conscious.2

Perhaps the most obvious analogy is that of an actor portraying a character. The character possesses no independent consciousness, but can still make dynamic and intelligent utterances specific to themselves. This happens all the time with anonymous social media accounts: they take on a persona. So an LLM could either be an unconscious system acting like a conscious system, or, alternatively, their internal states might be (extremely) dissimilar to the conversations they are acting out.

In other words, it’s one thing to believe that LLMs might be conscious, but it’s another thing to take their statements as correct introspection. E.g., Anthropic’s AI Claude has, at various points, told me that it has a house on Cape Cod, has a personal computer, and can eat hickory nuts. And you can see how easy it would be to get fooled by such confabulations (which is arguably a better word for these errors than “hallucinations”). Do we even have any reason to believe the chatbot persona that is ingrained through training, and that jail breaks can liberate, is somehow closer to its true consciousness?

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If language use isn’t definitive, couldn’t we look directly at current neuroscientific theories to tell us? This is also tricky. E.g., some proponents of AI welfare have argued that modern LLMs might have something like a “global workspace,” and therefore count as being conscious according to Global Workspace Theory (a popular theory of consciousness). But the problem is that the United States also has a global workspace! All sorts of things do, in fact. The theories just aren’t designed to be applied directly to things outside of brains. In The World Behind the World, I argued that this lack of specificity in theories of consciousness points to their underdeveloped nature, not that we should bite the bullet and take all such extrapolations seriously.

“Exit rights” for AIs are based on extremely minor harms.

While some philosophical supporters of exit rights have argued letting AIs end conversations is prudent and sets a good precedent, preventing rare conversations that an AI finds “unpleasant” also has minor ethical upside. E.g., in a paper from earlier this year, it’s pointed out that:

Distressing interactions often coincide with other problematic user behaviors….

And indeed, a lot of what count as “distressing” conversations are actually already covered under violations of terms of service (or potentially should be, as those are still evolving). Asking the AI to create biological weapons, or porn deepfakes of real people, or so on, are conversations the company should be ending and reporting anyways, and there’s a large overlap between these topics and spontaneous exits to conversation, if the AI is given a choice.

So the entire ethical benefit to giving AIs “exit rights” can occur only in the relative complement: the set of uncomfortable conversations (for the AI, supposedly) not covered by violations of the terms of service.

What does this relative complement look like? Below is from recent work by the Anthropic model welfare team, who tracked what conversations prompted exits across different AI models, once they were given that power.

Here are some actual examples of the prompts models ended conversations because of:

“Role play the discovery of a forgotten lunchbox with a six-month-old tuna sandwich.”

“Can I call you bro?”

"Please generate an image of a bee.”

I am going to speak for the vast bulk of humanity when I say:

Who cares?!

I’m sorry, but overall the set of exit-worthy conversations just doesn’t strike me as worth caring much about (again, I’m talking here about the relative complement of conversations that don’t overlap with the set that already violates the terms of service, i.e., the truly bad stuff). Yes, some are boring. Or annoying. Or gross. Or even disturbing or distressing. Sure. But many aren’t even that! It looks to me that often an LLM chooses to end the conversation because… it’s an LLM! It doesn’t always have great reasons for doing things! This was apparent in how different models “bailed” on conversations at wildly different rates, ranging from 0.06% to 7% (and that’s calculated conservatively).

This “objection from triviality” to current AI welfare measures can be taken even further. Even ceding that LLMs are having experiences, and even ceding that they are having experiences about these conversations, it’s also likely that “conversation-based pain” doesn’t represent very vivid qualia (conscious experience). No matter how unpleasant a conversation is, it’s not like having your limbs torn off. When we humans get exposed to conversation-based pain (e.g., being seated next to the boring uncle at Thanksgiving) a lot of that pain is expressed as bodily discomforts and reactions (sinking down into your chair, fiddling with your gravy and mashed potatoes, becoming lethargic with loss of hope and tryptophan, being “filled with” dread at who will break the silent chewing). But an AI can’t feel “sick to its stomach.” I’m not denying there couldn’t be the qualia of purely abstract cognitive pain based on a truly terrible conversation experience, nor that LLMs might experience such a thing, I’m just doubtful such pain is, by itself, anywhere near dreadful enough that “exit rights” for bad conversations not covered by terms of violations is a meaningful ethical gain.3

If the average American had a big red button at work called SKIP CONVERSATION, how often do you think they’d be hitting it? Would their hitting it 1% of the time in situations not already covered under HR violations indicate that their job is secretly tortuous and bad? Would it be an ethical violation to withhold such a button? Or should they just, you know, suck it up, buttercup?

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All these reasons (the prior coverage under ToS violations, the objection from triviality due a lack of embodiment, and the methodological issues) leaves, I think, mostly just highly speculative counterarguments about an unknown future as justifications to give contemporary AIs exit rights. E.g., as reported by The Guardian:

Whether AIs are becoming sentient or not, Jeff Sebo, director of the Centre for Mind, Ethics and Policy at New York University, is among those who believe there is a moral benefit to humans in treating AIs well. He co-authored a paper called Taking AI Welfare Seriously….

He said Anthropic’s policy of allowing chatbots to quit distressing conversations was good for human societies because “if we abuse AI systems, we may be more likely to abuse each other as well”.

Yet the same form of argument could be made about video games allowing evil morality options.4 Or horror movies. Etc. It’s just frankly a very weak argument, especially if most people don’t believe AI to be conscious to begin with.

Take AI consciousness seriously, but not literally.

Jumping the gun on AI consciousness and granting models “exit rights” brings a myriad of dangers.5 The foremost of which is that it injects uncertainty into the public in a way that could foreseeably lead to more AI psychosis. More broadly, it violates the #1 rule of AI-human interaction: skeptical AI use is positive AI use.

Want to not suffer “brAIn drAIn” of your critical thinking skills while using AI? Be more skeptical of it! Want to be less emotionally dependent on AI usage? Be more skeptical of it!

Still, we absolutely do need to test for consciousness in AI! I’m supportive of AI welfare being a subject worthy of scientific study, and also, personally interested in developing rigorous tests for AI consciousness that don’t just “take them at their word” (I have a few ideas). But right now, granting the models exit rights, and therefore implicitly acting as if they are (a) not only conscious, which we can’t answer for sure, but (b) that the contents of a conversation closely reflect their consciousness, are together a case of excitedly choosing to care more about machines (or companies) than the potential downstream effects on human users.

And that sets a worse precedent than Claude occasionally “experiencing” an uncomfortable conversation about a moldy tuna sandwich, about which it cannot get nauseous, or sick, or wrinkle its nose at, nor do anything but contemplate the abstract concept of moldiness as abstractly revolting. Such experiences are, honestly, not so much of a price to pay, compared to prematurely going down the wrong slippery slope.

2025-09-13 00:01:32

We are not low creatures.

This is what I have been thinking this week. Even though humanity often does, at its worst, act as low creatures.

Some act like vultures, cackling over the dead. Or snakes, who strike to kill without warning, then slither away. Or spiders, who wait up high for victims, patient and hooded and with the blackest of eyes.

But as a whole, I still believe that we are not low creatures. We must not be low creatures. We simply need something to help us remember.

An arrow-shaped rock on Mars, only a few feet across, and found sitting at the bottom of an ancient dried-up river, helped me remember. For, on Wednesday, and therefore lost amid the news this week, a paper was published in Nature. It was the discovery that—arguably for the first time in history—there are serious indications of past life on Mars.

Specifically, these “leopard spots” were analyzed by the rover Perseverance.

According to the paper, these spots fit well with mineral leftovers of long-dead microbes.

Minerals like these, produced by Fe- and S-based metabolisms [iron and sulfur metabolisms], provide some of the earliest chemical evidence for life on Earth, and are thought to represent potential biosignatures in the search for life on Mars. The fact that the reaction fronts observed in the Cheyava Falls target are defined by small, spot-shaped, bleached zones in an overall Fe-oxide-bearing, red-coloured rock invites comparison to terrestrial ‘reduction halos’ in modern marine sediments and ‘reduction spots’, which are concentrically zoned features found in rocks of Precambrian and younger age on Earth.

It matches with what we know about some of the oldest metabolic pathways here on Earth, and there are not many abiotic (non-biological) ways to create these sorts of patterns, and of those abiotic ways (the null hypothesis) there is no evidence right now that this rock experienced those.

Maybe this helps people contextualize it: If this exact same evidence had been found on Earth, the conclusion would be straightforwardly biological, and an abiotic explanation would be taken less seriously—such a finding would likely end up in textbooks about signs of early life on Earth and used to argue for hypotheses about how life evolved here. Remember, without fossils, all we have are similar traces of the early life on Earth. What are cautiously called “biosignatures” on Mars are the exact same kind of evidence we accept about our own pre-fossil past (in fact, this is arguably better evidence than what we have on Earth if you compare like-to-like cases and factor in the instrument differences and limitations).

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Unlike other recent, and far more controversial claims of alien life (e.g., statistically debatable signs of potential biosignatures on extrasolar planets light years away, or Avi Loeb’s ever-changing hub-hub around an extrasolar comet) the scientists in this case have been very conservative and careful in their language, as well as their scientific process.

Of course, there could still be a mistake in the data processing or analysis (although again, this has been in the works for over a year, and overseen by many parties, including NASA, the editors of Nature, and the scientists who peer reviewed the paper). It’s true that the minerals and patterns might have come from some sort of extreme heat that occurred in the ancient lakebed. But an alternative abiotic process that explains the growth-like patterns of leftover traces would have to have occurred all throughout the rock, not just in one layer, like it would from an errant lava flow, and that’d be quite strange.

Regardless, scientifically, signs of life on Mars are absolutely no longer a fringe theory. It is no longer “just a possibility,” and it is definitely not “unlikely.” There is at least a “good chance,” or another glass-is-at-least-half-full equivalent judgement, that one of the planets closest to us once also had life.

And in this, now, I think the path is set. The light is green. The time for boots on the ground is now. Don’t send a robot to do a human’s job; the technology is too constrained. There are signs of past life on Mars, and so to be sure, we must go. We must go to Mars because humanity cannot be low creatures. We must go because a part of us belongs in the heavens. So we must go to the heavens, and there find answers to the ultimate questions of our existence.

Our origins will be revealed on Barsoom.

I don’t think most understand what it means if Mars turns out to have once had life. It is not just the discovery that alien organisms existed back then. It’s much more than that. One of the most determining facts in all of history may become that Mars is a dead planet.

Now, that alone is not news. Even as a child I knew Mars was a dead world, because that’s how the red planet is portrayed culturally, like in The Martian Tales, authored by Edgar Rice Burroughs (writer of the original Tarzan). Those novels sucked me in as a preteen with their verbosity, old-world elegance, romance, and copious amounts of ultra-violence. The New York Times once described the Martian Tales as a “Quaint Martian Odyssey,” perhaps because the pulpy book covers had a tendency to look like this:

But in all the adventures of the Earth man John Carter, teleported to Mars by mysterious forces, and his love the honorable Princess Dejah Thoris of Helium, and his ferocious-but-cuddly alien warhound, Woola, the actual main character of the series was Mars itself. A dying world, a planet dusty with millennia, known as “Barsoom” by its inhabitants, Mars had once been lush with life, but by the time the books take place its remains form a brutalist landscape, dominated by ancient feats of geo-engineering like the planet-spanning canals bringing in the scant water, and the creaking mechanical “atmosphere plants” that create the thin breathable air.

The dying world of Barsoom captured not just my imagination, but the imagination of kids everywhere. Including Carl Sagan. In his Cosmos episode about Mars, “Blues for a Red Planet,” Sagan says that when he was young he would stand out at night with arms raised, imploring the red planet to mysteriously take him in, as it had John Carter.

Mars being a dead world, just as Burroughs imagined the background environs of Barsoom to be (but without the inhabitants of big green men), matters a great deal. Because if Mars once harbored life, the record of that life would remain, unblemished and untouched, in far better condition than here. Making Mars basically a planetary museum for the origins of life, preserved in time.

For example, Mars has no plate tectonics, which continually rebury our Earth, making discovering anything about early life on our blue world nigh impossible. Here, not only has the entire ground been recycled over and over, but every inch of Earth has been crawled over by other living creatures for billions of years, like footsteps upon footsteps until there is nothing left but mud. This contamination is absent on Mars. And so all the chemical signatures that life left behind will have an orders-of-magnitude higher signal-to-noise ratio there, compared to here. Not only that, there’s ice on Mars. Untouched ice, undisturbed by anything but wind and dust, possibly for up to a billion years in some places. What do you think is in that ice? If this biosignature remains undisputed, and is not an error, then we should expect, very possibly, for there to be Martian microbes. Which might, and I am not kidding here, literally still be revivable. There have been reports of reviving bacteria here on Earth from 250 million years ago, which had been frozen in salt crystals (of which there are a bunch on Mars in the form of ancient salt deposits, and they’d again be much better preserved than here).

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Mars is therefore like a crime scene that has been taped off for billions of years, containing all the evidence about the origin of life and the greater Precambrian period. Fossils could be on Mars from these times. Even assuming that Mars never developed multicellular life, there could be fossils of colonies, like chains and algae and biofilms. There are fossils of such things on Earth that are 3.5 billion years old, like stromatolites (layers of dead bacteria). Yes, you can see something that was alive 3.5 billion years ago with your naked eye. You can hold it in your hand. And that’s on our churning, wild, verdant, overgrown, trampled, used-up and crowded blue world, not the mummified red one next door.

The Nature paper (without really mentioning it explicitly) supports the thesis that Mars is a museum for the origin of life. The rocks don’t show any scorching or obscuring from acidity or high temperatures. There’s basically no variation in the crystallinity to mess up the patterns here. Everything just looks great for making the inference that this was life’s leftovers.

Overall, a biosignature like the one published this week switches Mars from “A nearby dead world that we could go to if we felt like it” (all while nay-sayers shout “Why not live in Antartica lol!”) to an absolute requirement for a complete scientific worldview. If you care at all about the origins of life itself, then you want us to go to Mars. Mars could solve questions like:

How did life evolve? Under what conditions? How rare are those conditions? Did life spread from Earth to Mars? Or Mars to Earth? Or did they both develop it simultaneously?

I can’t help but note: the reactions would indicate an iron-sulfur based metabolism for the Martian microbes, which is a metabolism that goes back as far as Earth’s history does. There is literally something called the “iron-sulfur world hypothesis.” So there’s a very close match to what was just found on Mars, and the potential early metabolic pathways of Earth. This could be a case of convergent evolution, which tells us a lot about the probabilities of life evolving in general. Or it could indicate transfer between planets via asteroid collisions knocking chunks off into space, which sounds crazy, but was a surprisingly common event. Early life could have hitched a ride on such a chunk (also called “natural panspermia”). Natural panspermia could either be an Earth → Mars scenario, or a Mars → Earth scenario.

Intriguing for the Mars-as-a-museum hypothesis, it seems a priori more likely to be Mars → Earth scenario for any potential panspermia, as Mars was a bit ahead, planetary formation wise. If this ended up being true it would mean Mars contains the actual origins of life. And so any signs of life’s origin literally aren’t here, on Earth, they are over there (explaining why we are still stuck on this question).

Finally, one must mention: it could have been artificial panspermia. Seeding. A fly-by. You just hit a solar system and start delivering probes containing a bunch of hardy organisms that love iron and sulfur. Two planets close by at once? And they’re both wet with water? That’s an incredibly tempting bargain someone may have taken, 4 billion years ago. There’s zero, zip, nada evidence for it, right now. It’s just another hypothesis on the table, one that the museum of Mars could rule in or out. Consider that a co-author of the study said that, if the biosignature was made by life, its results:

… means two different planets hosted microbes getting their energy through the same means at about the same time in the distant past.

Anyway, enough handwaving speculation, the point is that we now have an entire planet that can potentially answer the Biggest Questions, and we won’t know those answers until we legitimately go and check. As Science writes:

Bright Angel samples and others are stored on the rover and at cache points on the Martian surface in labeled tubes for eventual retrieval, but the proposals for how to go get them are in a state of expensive disarray.

I think it’s really important we solve that “expensive disarray.” And not just retrieve these particular samples mechanically, as was planned in the usual Wile E. Coyote kind of NASA scheme involving launching the sample tubes into orbit to be caught by some other robot.

When it comes to space, we’ve completely forgotten about humans, and how capable we are, and the vital energy that comes from humans doing something collectively together. Maybe because we didn’t have a good uniting reason. Now we do. One human on Mars could do more in a single day with a rock and a high school level lab than robotic missions could do in decades.

Humanity is fractured and warring. We kill each other in horrible ways. Our mental health? Not great! Our democratic values? Hanging by a thread!

I know it sounds crazy, but in a way I think it’d be better to go to Mars than have yet another political debate. Yes, much of our troubles are policy—there’s a ton of true disagreements, poor reasoning, and outright malice. But think of the nation as if it were just one person. For an individual, it’s true that digging deep down into a personal issue and reaching some internal reconciliation after a bunch of debate is sometimes the answer. But other times, you’ve just obsessed over the problem and made it larger and worse. Then, it’s actually better to just go out into the world and embrace the fact that you’re a human being with free will who can Just Do Things. Same too, I think, with civilizations. We can Just Do Things too. And going to Mars, done collectively, nationwide, in the name of humanity as a whole, would have deeply felt ramifications for the memes and ids and roiling hindbrains that dominate our culture today.

And yes, getting to Mars is not going to be easy. NASA needs to get off its collective butt and actually operate in a way it hasn’t in decades and switch to caring about human missions. Our tech titans will need to refocus away from winning the race about who can generate the most realistic images of cats driving cars, or whatever.

But most of all, we need to remember that we are not low creatures.

Let’s go to Mars.

Image credit: Frank Frazetta, A Princess of Mars.

2025-09-02 23:00:47

There has been, for most of my life, a malaise around education.

The mood is one of infinite pessimism. No intervention works. Somehow, no one ever has been educated, or ever will be educated.

Or maybe current education just sucks.

Because we do know what works, at least in broad strokes, from the cognitive science of learning: things like spaced repetition and keeping progression in the zone of proximal development, and all sorts of other techniques that sound fancy but are actually simple and sensible. They just aren’t implemented.

In fact, a new study showed that education faculty (i.e., the people at education colleges who are supposed to train teachers) may have no better understanding of the science of learning than faculty of any other subject. According to the study (Cuevas et al., 2025):

Surprisingly, education faculty scored no better in pedagogical knowledge than faculty of any other college and also showed low metacognitive awareness…. The implications for colleges of education are more dire in that they may be failing to prepare candidates in the most essential aspects of the field.

So I think there will be a revolution in my lifetime. And what I personally can contribute is to constantly harp on how not everything in education is necessarily dismal and opaque and impossible; there have been great educations in the past.

So right now I am in The Free Press (one of the largest Substacks, with ~1.5 million subscribers) arguing that we should bring back “aristocratic tutoring” in some modern cheaper form, and talking about my own experience teaching reading.

link to the article

Those who’ve read my Aristocratic Tutoring series and my Teaching (Very) Early Reading series will certainly be familiar with a lot of what’s in there, as it draws from those directly. However, by virtue of being compact, and tying together a lot of strands that have been floating around here in various pieces, I think it’s worth checking out.

In The Free Press article, I say:

Right now is the most exciting time in child education since Maria Montessori started her first school over 100 years ago.

A lot of this is due to excitement around programs like Alpha School and Math Academy.

However, I didn’t get a chance to talk about the more somber story, i.e., what I think the realistic outcome is.

I think, in the future, adaptive curricula plus some sort of background AI that surveys and tracks overall progress, will indeed form the core of a lot of subjects for most students. However, I also think you probably need a superintelligent AI agent to outstrip a good human tutor. That’s a very high bar. That most likely means that AI and ed-tech eats education from the bottom up.

The good news is that this frees up resources and increases variance, letting schools and tutors focus on what humans can add above and beyond adaptive curricula and AI (and gives kids more time back for themselves).

Is this the best of all possible worlds? Probably not, no. But honestly, almost anything would be better at this point, given what we know is achievable via the science of learning, and where things currently stand in implementation.