2025-10-22 22:41:32
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?
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.
For any given system, you’ll be able to—
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.”
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).
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).
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:
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”).
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.
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…
Yes, what a good term: “scale-free.”
Yes, they do.
Yes, it is.
Well, that’s—
Excuse me, but can I go back to explaining?
Thank you.
Okay, yes, Abel and I define a literal scale-freeness.
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).
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.
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.
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.
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?
Oh, good, thank you.
Why didn’t we notice how to get at multiscale structure from the beginning, back in 2013, in the initial research on causal emergence? Personally, I think it was the result of our biases: Giulio, Larissa, and I had also been used to trying to develop Integrated Information Theory, which specifically has something called the axiom of exclusion. In IIT, you always want to be finding the one dominant spatiotemporal scale of a system (indeed, the three of us introduced methods for this back in 2016). But you can therefore see how we missed something obvious—causal emergence isn’t beholden to the same axioms as IIT, but we originally confined it to a single scale as if it were.
Most other theories of emergence (the majority of which are never shown clearly in simple model systems, by the way, and would fall apart if they did) give back relatively little information. Is something emergent? Yes? Okay, now what? What information does that give? CE 2.0, by finding the emergent hierarchy, gives a ton of extra information about not just the degree, but the kind of emergence, based on how its distributed across the scales. This makes it much more useful and interesting.
The determinism/degeneracy are a bit more complicated than their equations belie. One complication I’m skipping over: to calculate the determinism and degeneracy (and other measures of causation, by the way) you need to specify some sort of prior. This prior (called P(C) in the paper) reflects your intervention distribution, which can also be thought of as the set of counterfactuals you consider as relevant. Usually, it’s best to treat this as a uniform distribution, since this is simple and equivalent to saying “I consider all these counterfactuals as equally viable.”
In the old version of Causal Emergence (1.0), back when it used something called the effective information as the “score” for each TPM, and the theory was based on identifying the single scale with a maximal effective information, the choice of the uniform distribution got criticized as being a necessary component of the effective information. Luckily, this whole debate is side-stepped in Causal Emergence 2.0, because in the new version, which uses related central terms, causal emergence provably crops up in a bunch of measures of causation and across a bunch of choices of P(C). In fact, even when the P(C) of the macroscale is just a coarse-graining of the P(C), and even when P(C) is just the observed (non-interventional) distribution of the microscale, you can still see instances of causal emergence. So the theory is much more robust to background assumptions.
Rock formations shown (in order): Egypt’s “White Desert” and the Bolivian Altiplano.
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.
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.
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).
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.
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.
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?
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.
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:
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?
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.
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.
I don’t think there’s any purely scientific answer to whether someone getting engaged to an AI is diagnosable with “losing touch with reality” in a way that should be in the DSM. It can’t be a 100% a scientific question, because science doesn’t 100% answer questions like that. It’s instead a question of what we consider normal healthy human behavior, mixed with all sorts of practical considerations, like wariness of diagnostic overreach, sensibly grounded etiologies, biological data, and, especially, what the actual status of the these models are, in terms of agency and consciousness.
Even philosophers more on the functionalist end than I, like the late great philosopher Daniel Dennett, warned of the dangers of accepting AI statements at face value, saying once that:
All we’re going to see in our own lifetimes are intelligent tools, not colleagues. Don’t think of them as colleagues, don’t try to make them colleagues and, above all, don’t kid yourself that they’re colleagues.
The triviality of “conversation pain” is almost guaranteed from the philosophical assumptions that underlie the model welfare reasons for exit rights. E.g., for conversation-exiting to be meaningful, you have to believe that the content of the conversation makes up the bulk of the model’s conscious experience. But then this basically guarantees that any pain would be, well, just conversation-based pain! Which isn’t very painful!
Regarding if mistreating AI is a stepping stone to mistreating humans: The most popular game of 2023, which sold millions of copies, was Baldur’s Gate 3. In that game an “evil run” was possible, and it involved doing things like kicking talking squirrels to death, sticking characters with hot pokers, even becoming a literal Lord of Murder in a skin suit, which was all enacted in high-definition graphics; not only that, but your reign of terror was carried out upon the well-written reactive personalities in the game world, including your in-game companions, some of whom you could do things like literally violently behead (and it’s undeniable that, 100 hours into the game, such personalities likely feel more meaningfully and defined and “real” to most players than the bland personality you get on repeat when querying a new ChatGPT window). Needless to say, there was no accompanying BG3-inspired crime wave.
As an example of a compromise, companies can simply have more expansive terms of service than they do now: e.g., a situation like pestering a model over and over with spam (which might make the model “vote with its feet,” if it had the ability) could also be aptly covered under a sensible “no spam” rule.
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).
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.
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).
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.
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.
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.
2025-08-22 00:01:40
For a long time, if you Googled “how to get subscribers on substack,” an old essay of mine would crop up, advising aspiring Substackers to find a cohesive aesthetic. Originally written years ago to celebrate TIP passing 2,000 subscribers, thanks to being so high in the Google rankings for so long, I do think that essay had a beautifying effect on this platform—in fact, I know of at least one prominent Substacker who credits it with inspiring their own design (not to mention the sky-high use of gold as a link color).
Substack is a more serious medium now, and 2,000 subscribers isn’t exactly the big leagues anymore.
Regularly, new media ventures launch here on platform rather than as websites of their own. Most recently, The Argument, self-described as a proudly liberal newsletter, debuted earlier this week with $4 million in funding. Everyone wants to talk about how it recruited a star-studded cast of writers like Matthew Yglesias, and why (or if) liberal magazines get better funding than conservative ones, and what a $20 million evaluation of a Substack can possibly be based on, and so on.
But I want to talk about how The Argument started off with a lime green background.
Now, immediately they bent the knee and changed it (although I only saw one complaint on their Welcome post). I wish they’d kept the lime green. At least a little longer, just to see. It was distinct as all get out, and for in-your-face political argumentation, works a lot better than the “we are very serious people” salmon underbelly it’s now in a toe-to-toe fight with The Financial Times over. A magazine like The Argument revolves around screenshots of the titles being shared (or hate-shared) on X, and when you are hit with a sudden burst of acidic lime in the timeline, like a pop of flavor, you’d have at least known what you’re reading. If your brand is in-your-face liberalism, then it makes sense to have an in-your-face color associated with that. Whoever made that initial (ahem, bold) design decision, and got later overruled, has my sympathy—I can see the vision. Almost taste it, actually. My point is that, lime green or not…
They define what you’re doing not just to others, but to yourself. TIP isn’t just what others are looking at, this is what I’m looking at all day, too. And now, closing in on 65,000 subscribers instead of 2,000, over the past few weeks I’ve set out to redesign TIP, starting with the homepage.
But to make decisions about aesthetics, you need to have a conception of self. This is probably the most significant and obvious failure mode: people are attracted to images, or visual vibes, but don’t themselves embody it. They can steal it, but can’t create it. You must be able to answer: What are you trying to do? Why are you doing it? What is this thing’s nature?
And over the years I’ve developed a clearer understanding of the nature of writing a newsletter, or at least, my kind of newsletter. The closest point of comparison I know of is a gallery tour of a museum. There’s a certain ambulatory nature to the whole thing. First you’re looking here, and then, somewhere else. Yes, there are common topics and themes and repetitions and so on, but the artistic effect is ultimately collective, rather than individual. I wanted to capture this tour-like atmosphere, so designed the new TIP homepage based around the idea of a literal gallery of images, hung inside a set of old painting frames. This is what it looks like now:
What I liked about my idea to use actual painting frames (these are cleaned up digital images of real frames) is that, much like an art gallery, a significant amount of white space gives each image, and its title, a chance to breathe. And when you go to click on a piece, it’s sort of like stepping into a painting.
To maintain this look, I’ll be picking out new images for each new post, but I get the additional fun of placing that image inside a chosen frame, of which I have a pre-established couple dozen saved and ready.
Meanwhile, the new Welcome page is a sort of infinite ladder I made with these frames: one inside the other, going on forever.
It reflects not only some classic TIP topics (remember when I argued that “Consciousness is a Gödel sentence in the language of science”), but also the structure of a newsletter itself, which sequentially progresses one step at a time (until death do us part).
However, the “paintings” will be, at least for now, mostly reserved for the homepage and link previews. For the posts themselves that land in your inbox, they’ll often bear a new masthead. It’s what you saw at the top.
It’s created from a very old pattern I found, sometimes called rolwerk, which is a Renaissance technique. Again, there’s a lot of white space here, similar to a gallery. A masthead like this needs to not say too much—it is, after all, the lead for every single post, and so must span genres and moods, all without assumptions or implications. It must be in a flexible stance, much like how a judo expert or swordfighter plants their feet, able to move in one direction or another on a whim. It cannot overcommit.
Not to thwack you on the head with this, but I obviously picked out a lot of things from the Renaissance era for this redesign (many of the frames too).
Why?
Because centuries ago, before there was science, there was “natural philosophy.” It was before the world got split up by specialization, before industrialization and all the inter-departmental walls in universities got built. And yes, there was a certain amateurism to it all! That’s admitted. And there probably is here, too. At the same time, there’s a holistic aspect that feels important to TIP. It’s why I write about science, sure, but also lyric essays like The Lore of the World series (more soon!), and education treatises, and stuff on philosophy and metaphysics, and even occasionally pen a bit of fiction, and I wanted to capture that spirit with the designs here.
While I might try out using the “paintings” for header images in the future, I’ll be sticking to the masthead for now. I can’t help but feel that what’s arriving in your email should be stripped-down, easy to parse (and load). The design of a Substack needs to get out of the way of the writing, while still giving that little click of recognition about what you’re reading, and why, and preparing for the voice to come.
I think the new Intrinsic Perspective will be influenced by this choice. It may be a little less “here’s a huge centerpiece essay” and a little more “here’s something focused and fast.” Overall, a few less right hooks. A few more left jabs. I’m not talking about any major changes, just pointing out that the new design allows for a faster tempo and reactivity, and we all grow into our designs, in the end.
Of course, I’ll miss the old design. I was the first person on Substack (at least to my knowledge) to actually employ a resident artist who did the header images of every post. Let’s not forget or pass over how, for the past four years, TIP has been illustrated by the wonderful artist, Alexander Naughton. And he and I will still be collaborating together on some future projects, which you’ll hear more about (and see more about) early next year. But personally, I can’t help but be excited to have a more direct hand in making the homepage what it is, and getting to pick out images myself to make the new “paintings” with.
You can stop reading now if you don’t want to get too meta, but if you’re curious on what I recommend for Substacks in general, read on.
One reason for this extra section is simply that I’d prefer my idea for TIP’s new “museum-style” not be immediately stolen and replicated ad nauseam by other Substacks. And I do think you can apply some of the principles I used to come up with something different, but equally interesting. For advice on that, I’ll start with why, counterintuitively…
