The Desiderata series is a regular roundup of links and thoughts for paid subscribers, and an open thread for the community.

Contents

1. IVF epigenetic damage gets worse across generations

2. Waymo reveals their fleet may require 100% human oversight

3. The next Project Hail Mary

4. Nonfiction book sales drop twice as much as fiction

5. Art collective poisons AI training set

6. Lyme Disease vaccine announced (but is it better than prophylactic antibiotics?)

7. Terence Tao warns of “odorless” AI proofs that shed no insight

8. A terraformed mars might have Insects of Unusual Size

9. From the Archives: My worry of AI outputs surpassing human “outputs” comes true

10. Comment, share anything, ask anything

1. IVF epigenetic damage gets worse across generations

Last month there was an interesting paper in Nature Communications, “Limitations of serial cloning in mammals,” which showed that after 58 generations a cloned mice population had degraded genetically to the point where further cloning was impossible.

It’s a great argument for “Why sex?”

And it’s one very different from the “Red Queen” hypothesis I was taught in school (as basically gospel). The Red Queen hypothesis argues that sex is to shuffle around your genome to create diversity that evolution can capitalize on, especially with respect to viruses and other attackers.

Instead, it looks like the real issue might be that clonal reproduction introduces a ratchet where the population can’t clear genetic errors. According to the scientists of the cloning paper:

… mammals rely on sexual rather than asexual reproduction to eliminate genetic anomalies caused by clonal reproduction.

Without sex, the genetic damage just ratchets up and up over a couple dozen generations. It’s sort of like model collapse in LLMs: you train on their outputs, over and over, until the snake eats its tail. Surprisingly, the researchers reported that visually the cloned mice seemed relatively healthy in the earlier generations. But then again, these clones are leading highly sedentary lives: they basically just have to show up, eat, and drink, and that to the researchers is “healthy” because that’s what mice in a cage their whole lives do. The researchers couldn’t tell that under the surface a lot of genetic damage wasn’t shed by sexual reproduction.

But if the ratchet theory is true, we should probably take a closer look at anything that might introduce small compounding errors across generations.

So what about IVF?

IVF now accounts for almost 3% of births in the United States (the highest concentration appears to be almost 10% of births in San Francisco). Of course, IVF is entirely different from cloning, and doesn’t introduce genetic errors. But we do have evidence that the IVF process can create epigenetic damage. While that may sound scary, the first-generation effects of IVF appear relatively small in the grand scheme of things, and the gain of IVF is, of course, basically incalculable (the miracle of an entire human where none would be). So please keep that in mind. But what if the epigenetic damage of IVF compounds generationally? That would be bad.

Unfortunately, research indicates this could be true, at least from early mice evidence. In “In vitro fertilization induces reproductive changes in male mouse offspring and has multigenerational effects” the researchers emphasized that:

These findings underscore that the negative effects of IVF not only persist but also may intensify in subsequent generations.

This is not great, because, as the researchers point out:

Although considered safe, IVF pregnancies are associated with an increased risk of perinatal, neonatal, and placental complications; rare genetic syndromes; and possible long-term effects in human and mouse offspring. One possible mechanism for adverse outcomes suggests that IVF procedures occur during critical windows of epigenetic reprogramming in gametes and preimplantation embryos, generating errors that could ultimately affect normal development…. Previous studies have shown that testicular or sperm maturation changes affect normal sperm function, which leads to adverse outcomes in sired offspring.

In other words, IVF can have an effect on the germline, and this could then be passed on via more damage, etc. Which is pretty much what they observe.

E.g., sperm count in the IVF group is lower (along with testosterone) which then could lead to epigenetic damage being transmitted, since sperm are bearers of epigenetic information, and so on, creating a feedback loop.

What’s also worrying is that the issues spread beyond the germline.

Interestingly, we observed sex-specific adverse outcomes in the F2 from IVF offspring, including a higher risk of insulin and glucose resistance in males and a diabetic phenotype in females. These sex-specific differences can arise by sex-linked genes or hormones. We also observed more severe metabolic and gene expression changes in the F2 generation, as evidenced by the F2 liver RNA-Seq data. These effects could result from the cumulative impact of IVF in the F1 generation, together with a potential contribution of metabolic syndrome in the males, which may be inherited through the germline.

It appears that no one has tracked IVF side effects beyond two generations. That seems kind of important to know!

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At your sister’s birthday party, we brought back a menagerie of helium balloons from the local grocery store. One in the shape of a fat bee, another the shape of a smiling planet Earth, and also a pink number two, to mark her year. Of course, they were a hit, and much fought over. Weighty clips at the bottom of their dangling strings kept them within gravity’s well, and so if bounced upward, the balloons would sink back (at various points they were also clipped to the dog’s collar, as well as the robot vacuum, to great delight). The most pleasing part of such balloons is how their internal lightness is balanced by the bottom clip. Something inside everyone wishes that the whole apparatus would float down even slower; or better yet, not at all, and just hover in place between floor and ceiling, or dirt and sky.

Well, your father is a clever man, and filled with all sorts of clever ideas, and so that afternoon I took your younger sister and you out to the green summer world of our yard, under a blue sky where the puffy clouds printed on its surface scrolled by.

“Watch this,” I said with a wink, and removed the clip. Then the pink number two floated away, almost out of reach, before I grabbed it, repeating the pattern until both you and your sister were giggling. Your mother came to watch, arms folded across her chest.

I had taken out with me a roll of packing tape, which I began to wind around where the clip had been removed at the bottom of the string, and then I let it go. Not enough! The balloon tried to escape again. So more tape was wound around, in successive experiments, until the bottom of its string looked inhabited by a small wasp nest. Finally, when I let it go, the balloon did not go up or down. Instead, it floated pleasingly across the yard three feet above the ground, like an underwater mine.

This was demonstrated several times. Even if one gave it a slight nudge higher, it would then just drift in a long arc that spanned the whole back yard, with the weight coaxing it only ever so slowly back down.

Until it didn’t. Peacefully mid-drift, the force pulling the clouds past reached down and scooped up the balloon, and the pink two ascended, up, up, and a little “Oh!” was all I could muster before it disappeared over the roof of the house. Your sister too was distraught, but you understood the event completely and watched in utter horror the ascent—an image I remember as a flashed photograph on the lawn, with your eyes wide, and your mouth a perfect oval, all framed by the ringlets of your hair. You had coveted that balloon most of all, for, unspoken in your mind, you’d been next in line to play with its magical buoyancy. Instead, it had been stolen, as if an invisible giant had bent from the sky and plucked it forever from you.

I had started running to the front yard with your sister in tow, hoping to regain sight of it, while you hobbled behind, your face screwed up in the sensory deprivation of dismay (the degree of which I did not quite comprehend). So complete was the loss that you couldn’t make a sound, not a whisper, until you finally did get to the front yard, where you were able to break the gasping silence and get out the wail that had been building from your toes.

High in the air drifted the pink two, and I cannot lie—a part of me wished nothing more than that my mistake would fly into the blue sky and become, far out to sea, a fish’s problem. But instead it headed unerringly, as if carefully pulled betwixt invisible thumb and forefinger, to the top branches of the largest tree in our yard, where it caught fast and tangled in the uppermost branches. An old pine a hundred feet tall that looms head and shoulders above the rest had, after decades of growing solitude, been politely handed a balloon.

There the drooping bit of deflated plastic remains. Through wind. And rain. And snow. It has been bled of color, and looks much like a jellyfish beached by unknown means, miles inland, a hundred feet in the air. I am looking at it now.

This ongoing serialization of letters to a young child, “The Lore of the World,” can be read in any order.

For when you become a new parent, you must re-explain the world, and therefore see it afresh yourself.

A child starts with only ancestral memories of archetypes: mother, air, warmth, danger. But none of the specifics. For them, life is like beginning to read some grand fantasy trilogy, one filled with histories and intricate maps.

Yet the lore of our world is far grander, because everything here is real. Stars are real. Money is real. Brazil is real. And it is a parent’s job to tell the lore of this world, and help the child fill up their codex of reality one entry at a time.

Above is one of the thousands of entries they must make.

Here is Part 1 (teeth, whales, germs, music on the radio), Part 2 (Walmart, cicadas, stubbornness), and Part 3 (snow). Further installments will crop up semi-regularly among other posts. It is secretly building toward the ultimate question: Why is there something rather than nothing?

Who writes a sci-fi series where the main character is poet John Keats, dead in 1821 from consumption at the age of 25? Dan Simmons, that’s who—an author who himself died last month, at what feels like (for our present age) a young 77, from complications following a stroke.

I was sad to hear it was something that affected his brain. Simmons wrote the greatest sci-fi book series of the last several decades: the Hyperion Cantos. My cousin (now an accomplished fantasy author himself) recommended it to me when I was a pre-teen. I fell in love with its philosophical world-building, high drama, and its many (many) references to poetry and spirituality and architecture.

For such a wildly out-there series, the Hyperion Cantos has held up better than other 90s sci-fi. Suddenly, the idea that Artificial Superintelligence would resurrect John Keats or Frank Lloyd Wright because these human geniuses had some sort of incalculable insight the AIs could never achieve on their own is… well, it’s nigh on prophetic.

What did you see, John Keats?

When you choked to death on your own blood, staring up at that ceiling in Rome——the one painted with little white daisies——what did you see?

But what fewer people know is that Simmons was also possibly the best horror novelist of his generation. Pound for pound, or book for book, he was better than Stephen King (and I think King might have occasionally suspected this).

Dan Simmons’ Summer of Night is exactly as if Stephen King put all his frenetic mania into one book instead of five. And it’s also basically Stranger Things (just look at the bikes) in that it mixed the free-range childhoods of the 1960s with supernatural threat in a small town—except it doesn’t suffer from the same clunky downturn after the first 20%.

By the way, I don’t think Stephen King would necessarily disagree with my judgement. After learning of Simmons’ death, King apparently had a dream of his old friend.

“I was walking on my road, and he came along in an ATV,” he said. “I held up a note for him to read, but he just went by me—and into the fog.”

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Some might recognize Dan Simmons from one of his other horror novels, The Terror, which got made into a well-acted and well-produced AMC mini-series. A series that did manage to capture the bleak spirit of the doomed Franklin expedition, although was never quite as unnerving as the book.

In a way, Simmons was one of the best historical writers of his generation too, and explaining historical anomalies like the lost Franklin expedition as “a monster did it” was very much a kind of unique Simmons genre that he invented, or at least, perfected. He used the same structure with Drood, a horror novel (or is it?) told from the perspective of a zonked-out Wilkie Collins, who plays Salieri to the more talented Charles Dickens. A lot of the book is about the monster of creative jealousy, and a lot of it is about opium hallucinations (or are they?).

Oh, and Simmons also wrote tightly-plotted and hard-boiled noir and thrillers too, and—Wait, why wasn’t Dan Simmons more famous?

In terms of outright name recognition, Simmons doesn’t hold a candle to someone like Stephen King or George R. R. Martin, or even Orson Scott Card. A comparable figure who works across genres would be Margaret Atwood, but unlike Simmons she had at least one huge breakout hit, The Handmaid's Tale, which became a household name.

Simmons’ later habit of firing off hot hawkish and conservative takes online probably didn’t help. I think he even deleted his blog at one point due to the controversy. It’s clear he was a different writer after 9/11. But the political aspects of Simmons’ personality came out far better, and in much finer, subtler form, in his earlier fiction compared to his later online polemics. In the actual books, he acted as a kind of “humanities popularizer.” Looking back at it all together now, his genre writing is secretly an ode to the Western canon, and he was a champion of teaching people (and specifically, kids) about it. That I discovered Simmons somewhere around the age of middle school, and that it hit me so hard, was likely not a coincidence. Simmons had won awards teaching 6th grade in gifted and talented programs before leaving to write full time.

It’s a bit of a funny take: How can a genre writer be an educational champion of the Western canon? But yeah, he was. In fact, I think it’s arguable that lowly school teacher Dan Simmons, who truly and earnestly loved Shakespeare and Homer and Keats—and referenced them constantly in his books about laser guns—did more for the Western canon than Harold Bloom’s entire Yale tenure. It certainly helped make this one very young man become interested in it.

But I don’t think that his educational bent and his canon advocacy, or even the political stuff, was the ultimate limiting factor for why he wasn’t more famous, or more successful. No, I suspect Simmons never reached the height of those other names because he suffered from the same curse I suffer from. So I have grown from an awed teenager reading his work to an adult who is a sympathetic fellow failure (well, relatively). I also do too many things in too many different places for it to ever all snowball. And I recognize in Simmons the same stubborn determination to be a strange, centaurish creature—much to our overall careers’ detriment.

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When I was ten years old I visited the ruins in Cornwall where King Arthur had been conceived and born, at least in legend. There at Tintagel Castle, surrounded by the ocean air and the jagged rocks, I separated from my mother and sister and made my way down to the beach. And on the beach of Tintagel, right near Merlin’s cave, I spotted it in the sand. A stone. But not just any stone—a stone ax head. It could have been nothing else. It was shaped just like an ax, being unnaturally thick at the head, which was a smoothed blunt blade, and with near right angles it tapered to a point at the back. There was a notched cleft down the middle, to tie it to a shaft. As a 10-year-old boy in a place already dreamy with legend, I pocketed it, for I felt the Neolithic ax had come to me specifically, as if a Lady of the Lake had tossed it ashore. I am looking at it on my desk now.

Later I learned that such Neolithic axes are not so rare—much like ancient Roman coins, they were mass-produced, and you can buy them on eBay cheaply due to finds like mine. This one from Tintagel beach is a perfect specimen, although the ocean likely washed away its provenance. But on that day, even amid all the other sandy stones, it stood out to me immediately. I knew it was a tool instinctively, the way a baby knows the nipple.

The philosopher Henri Bergson wrote that:

We should say not Homo sapiens, but Homo faber.

Homo faber means “man the maker.” For if anything defines humans, it is tool use. I know it is now standard, in our rush to dethrone humanity, to play up that other animals also sometimes use tools. But unlike other animals, tools are our evolutionary niche. We have been making stone tools for at least 3.3 million years. We co-evolved with tools. At first, we made them from wood and bone and stone; later we began to craft abstract tools too. Language is a tool. Math is a tool. All of our vaunted cognition is, in some sense, a tool for a more protean mental firmament, which probably is consciousness itself. Heidegger’s term for this aspect of our consciousness was Zuhandenheit: “readiness to hand.”

Now, we live in an age of tools that can talk back to us. When ChatGPT and the other LLMs appeared on the scene, and I first typed into a chat window, I experienced amazement. It was the legendary Turing Test, and I was living it!

REPENT, THE SINGULARITY IS NIGH!

Do not doubt that we are at the absolute peak of the AI hype cycle. In monetary terms, investment cannot actually go on increasing at these rates without leading to basically impossible numbers. A major falling out between the DoD and Anthropic dominates the news (even amid war).

In the months leading up to all this, a bunch of commentators have jumped aboard the bandwagon of AI hype: The Wall Street Journal advises to “Brace yourself for the AI Tsunami” and The New York Times is saying that AI might completely change the fundamentals of human existence. Popular bloggers are writing that “AI can already do social science research better than most professors,” and that “the humanities are about to be automated” and that “superintelligence is already here.”

Meanwhile, fictional doomsday reports from the year 2028 go hyper-viral and impact the stock market as the authors imagine human unemployment leading to an endless great depression. The infamous METR graph continues to accelerate (and everyone ignores that METR tasks are a tiny sample size for a slim number of domain-specific programming tasks, all based on a dubious analogy to human-time-spent-on-task, and some of the authors have tried to downplay it because their graph has been so misused). There are usually many charts involved in the Great AI Debate. I could show you charts too! Like that, despite improvements on benchmarks, the actual reliability of AI is on an almost-flat trajectory for many tasks.

But others can then show other charts, and so on, ad infinitum. I don’t think you can decide the future by a couple of charts.

No, there’s a better form of argument about AI, one which I am finally comfortable making: the argument from experience. There simply has been enough time now to see clearly how LLMs transformed the intellectual work of writing, and how this reflects their fundamental nature. My proposal is that we simply extrapolate what has happened to text production to all the other intellectual domains LLMs will ever touch.

For if everything that anyone can do on a computer is soon to be automated (as Andrew Yang is now preaching will happen in the next 12-18 months), then this process should have started with writing years ago. Yet, beyond mass-producing stilted emails and stilted social media posts and stilted essays, the impact of LLMs on writing itself has not really been to improve or accelerate good writing overall. We are not in a glut of good writing. We are in a dearth of it. This is surprising and counterintuitive, because for an LLM, words are its womb, its mother, its literal atoms—yet their impact on writing as a whole has been mostly to generate mountains of slop, while, on the positive side, helping with efficiency and research and editing and feedback, all things that only marginally improve already-good pieces. There are no signs of a burgeoning “text singularity” seen in the words output by our civilization, and words are the most sensitive weathervane to AI capabilities.

If LLMs were a true source of intelligence to rival humans, then discovering them should be like discovering oil. And if we were climbing the curve of an intelligence explosion their surplus intellect would be improving our civilization’s text as a whole in noticeable ways. If LLMs are tools, then we should expect their impacts to be a mirror of us, and concern efficiency and scale, rather than quality, and depend strongly on how people use them.

So let me ask you: if you took an observer from 2016 and teleported them a decade ahead to our time, and then showed them your social media feed or your emails and other media in general, what would their main response be? Would it be “Wow, everything is more intelligent now!” Or would it be “Why is everyone writing like a pod person now?”

It’s been six years since GPT-3, and there has been no “move 37” moment for writing (as there was for AlphaGo’s creative play of Go). Not even close.

HECK, LET’S LOWER THE BAR

If you ask a leading AI to write a children’s book, you’ll see that AI has not demonstrated exponential improvements at whatever amorphous and hard-to-define (but very real) skill “children’s book authorship” is. Indeed, the upper bound of the needle hasn’t moved much for writing in general, as people forget that older models like GPT-3 were already extremely good at short sprints of text when they kept it together. LLMs have been able to write a passable approximation of a children’s book for almost half a decade… and yet, the real lesson from this is that approximation is not automation.

Below is Anthropic’s latest model (arguably the smartest AI in existence) trying to write a good children’s book, by itself, without guidance and hand-holding. The result, which it called “beautiful,” was exactly what a smiling alien would write if it had never interacted with a child before. It was a “blurry jpeg” of a children’s book. Here’s the sappy ending:

People love to onanistically declare that the “it’s just autocomplete” or the “stochastic parrot” criticism is soooo outdated and soooo stupid.

And yet… isn’t “You shine by being yourself” basically autocomplete for a children’s book? Can we admit that? Or do we have to pretend these insipid outputs are from a machine on the verge of artificial superintelligence (coming in literal months)? I simply refuse to play along. If you actually interact with them, and ask them to do things they aren’t directly trained on, these models remain spectacularly intellectually shallow and incompetent when there’s no detailed human prompting to give them clues and hints and guideposts. Charged with writing children’s literature independently, based on their own ideas and own attempts at style, an AI like Anthropic’s Claude will always draw from the same small well. The outputs still feel like an LLM to anyone with an ear for language, or an eye for content.

The entire life of the artist, indeed, the life of the mind in general, is defined by resistance to slop. If you want to write an actually good children’s book you’ve got to put your own perspective into it, and LLMs are “views from nowhere.” Consider how deep and complex good children’s books actually are. The Giving Tree is about unrequited parental sacrifice. The Velveteen Rabbit is about ontology. Madeline is about Paris-maxing. The Rainbow Fish? Communism. The Very Hungry Caterpillar? Metamorphosis. In Where the Wild Things Are, at the end of the book, the Wild Things try to eat Max. That’s a good children’s book.

(I was going to show a beautiful picture from Madeline here, as my daughter is obsessed with the “Pooh-pooh to the tiger in the zoo” scene. I took a photo of the book and asked the smartest-AI-in-existence-with-extended-thinking-on-at-the-highest-subscription-tier-available to make it look better. Presented are the results, in triptych.)

Consider AI video generation. “We’re going to automate Hollywood!” Okay, well, what happened to automating publishing? That’s a much simpler task, one that you’ve had well over half a decade to do. How’d that go? Hmm? Everyone on social media and at the companies simply declared victory (“Wow, this fan fiction looks good enough to me!”) and moved on to more capabilities, stuffing more reinforcement learning into the models, building more elaborate scaffolds, without actually getting it across the finish line for producing non-trivial and non-annoying and non-fluff text unless the model is being spoon-fed by a human. Like, you think people are going to craft highly-detailed prompts for their own personalized movies? Again, we can look at writing, where everything already played out. You can already prompt your way to personalized books! It just sucks and no one does it, because when you ask the LLM to operate independently, its ideas are mostly slop. This is why when a company like Anthropic says they’ve automated their code production, when really they mean they are still writing code, just at a slightly higher level of abstraction (and that’s why they still have over 100 open software developer positions, and why Boris Cherny, creator of Claude Code, said that “Engineering is changing and great engineers are more important than ever.”)

HAVE LLMS MADE BOOKS BETTER?

A paper looking at large-scale Amazon data quietly appeared earlier this year, asking “Have LLMs Boosted the Creation of Valuable Books?”

The answer to this question is that in the post-LLM era (which they date as after 2022), at least based on numbers of Amazon ratings, the average book got worse. E.g., you occasionally hear hype of the AI-assisted author mass-producing books, and yet, investigation usually reveals that they have sold more like zero books and that the story is a scam. The actual effects of LLMs on publishing were that: (a) the average book got worse, (b) the top 1,000 books in each category improved somewhat, and (c) the top 100 books in each category didn’t change in quality.

Now, the average book a reader encounters is more likely to be in the top 1,000 than below that, so the average consumer experiences an increase in quality (mostly due, it appears, to more “shots on goal” rather than actual improvements). But this beneficial effect for consumers seems to be driven by often-very-low-quality-anyways categories like “Travel” and “Outdoors,” rather than, say, “Science” and “Literature.”

So do these effects look like a new source of surplus alien intelligence? Or does it look like tool use? Consider that the authors who were already successful pre-LLMs had the most efficiency gains, supporting the “tools” theory; meanwhile, new post-LLM era debut authors produced much worse work.

After failing to automate publishing books and writing in general, the claim is now that AI will go on to automate science, math, all of academia, and finally humanity itself?

But why would the chart for scientific papers (or anything else) look different from the chart for books above? LLMs can now “write a scientific paper” or “write a mathematical paper” in the exact same sense that they’ve been able to “write a book” or “write a short story” or “write an essay” for several years, all to some effect, but overall the results have been objectively mediocre given the hype, and the world is somewhat stupider, rather than smarter, at least on average.

WELCOME TO WRITER HELL, MATHEMATICIANS

Looking into the crystal ball that the last half-decade represents for writers reveals that, more likely than superintelligence, we are going to enter a world of immense, overwhelming, scientific and philosophical and mathematical slop.

Will there be some good outcomes as well? Yes! Just as there have been for writing. I am not entirely an AI pessimist. I am an AI realist—there are indeed positives to the technology, and I’m trying to find them myself (like for research, or, e.g., my attempt at making the Madeline image better above, or the fewer spelling mistakes I make now, or sometimes I ask LLMs to double-check something, etc.) Yes, some B-tier bloggers have suddenly and mysteriously transformed into A-tier bloggers, who all kind of sound the same in their A-tier-ness. Previously A-tier bloggers have gotten a lot less use from the technology, and are not noticeably better than they were years ago.

LLMs have broadly failed to automate text generation in general for the precise reasons I laid out all the way back in my 2022 essay “AI Art Isn’t Art.”

They struggle because they are fundamentally imitators, and when not told who or what to mimic they are intellectually shallow. You put more bits in, you get better bits out. Fine. That’s a tool. A computer or a piano is like that too. This is not something that will trigger a singularity of self-improvement or take the jobs of all of humanity or create “machines of loving grace” or any of the stuff people are now regularly promising is literally going to happen in like… a year. E.g., humans can edit their text, making the block of marble look ever more like the statue inside. When it comes flying solo, first drafts by LLMs are often better than last. LLMs can’t even recursively improve a five paragraph essay, let alone themselves.

So we will experience a long march by AI across intellectual disciplines (most lately, mathematics) where the hype reawakens with each new expansion, and in the wake of the long march some things do change but ultimately the world is not reconfigured as has been promised and the actual experienced intelligence level of the world, especially the top where it matters most, remains mostly unchanged, because it’s still just humans using tools. Bits in, bits out. That’s been the effect of LLMs on text production (in both book publishing and on social media), and it seems very likely to be their effect on almost everything else too. In this conservative view, by 2030, the top 100 math papers of the year won’t look spectacularly different from the top 100 math papers of 2020. The top 1,000 papers? Maybe they’ll be marginally improved, like with books or blogs. And at the backend, the entire field of mathematics will be buried, absolutely buried, in slop. Companies will continually say their systems accomplish things “autonomously” but that word is hard to define for LLMs, where a prompt is an injection of human intelligence, and a scaffold too is an injection of human intelligence (but the advantage of scaffolds is that you can put a ton of domain-specific knowledge and tips and tricks and guides in the scaffolds, keep them private because it’s proprietary, and then say the models “solved it autonomously!”).

At some point you have to use your capacity as Homo faber and call it: LLMs have behaved precisely as we would expect tools to behave when it comes to changing the nature of first-impacted and frontline intellectual disciplines like writing. The best users gain efficiencies and expand, to some degree, their capability range, especially for the mid-list of intellectual output. The worst users flood the zone.

So at least when it comes to the near-term future and the foreseeable scaling of the current technology, “merely” the exact same thing that has happened to writing will happen to every subject on Earth. But that’s not replacing humanity. That’s not the singularity. You’re just confused about what we are. We are Homo faber, and we have been doing this for 3.3 million years, and our rocks have gotten very complex—so complex you’re forgiven for not thinking they’re rocks.

The Desiderata series is a regular roundup of links and thoughts for paid subscribers, and an open thread for the community.

Contents

1. My New Org to Solve Consciousness (or Die Trying)

2. A Rogue AI Community That Wasn’t

3. David Foster Wallace Is Still Right 30 Years Later

4. The Cost of AI Agents is Exploding

5. The Diary of a 100-Year-Old

6. AI Solving Erdős Problems is (So Far) Mostly Hype

7. Cow Tools Are Real

8. From the Archives

9. Comment, Share Anything, Ask Anything

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1. My New Org to Solve Consciousness (or Die Trying)

As is obvious from the state of confusion around AI, technology has outstripped consciousness science, leading to a cultural and scientific asymmetry. This asymmetry needs to be solved ASAP.

I think I’ve identified a way. I’ve just released more public details of Bicameral, a new nonprofit research institute devoted to solving consciousness via a unique method. You can read about it at our website: bicamerallabs.org.

Rather than chasing some preconceived notion of consciousness, we’re making the bounds for falsifiable scientific theories of consciousness as narrow as possible.

Why do this as a nonprofit research institute? I’ve worked in academia (on and off) for a long time now. It’s not that funding for such ideas is completely impossible—my previous research projects have been funded by sources like DARPA, the Templeton Foundation, and the Army Research Office. But for this, academia is mismatched. It’s built around one-off papers, citation metrics, small-scale experiments run in a single lab, and looking to the next grant. To solve consciousness, we need a straight shot all the way through to the end.

If you want to help this effort out, the best thing you can do is connect people by sharing the website. If you know anyone who should be involved with this, point them my way, or to the website. Alternatively, if you know of any potential funders that might want to help us crack consciousness, please share the website with them, or connect us directly at: erik@bicameral-labs.org.

BICAMERAL LABS

2. A Rogue AI Community That Wasn’t

We are now several years into the AI revolution and the fog of war around the technology has lifted. It’s not 2023 anymore. We should be striving to not run around like chickens with our heads cut off and seek clearer answers. Consider the drama around the AI social media platform “Moltbook.”

A better description is that an unknown number of AI agents posted a bunch of stories on a website. Many of the major screenshots were fake, as in, possibly prompted or created by humans (one screenshot with millions of views, for instance, was about AIs learning to secretly communicate… while the owner of that bot was a guy selling an AI-to-AI messaging app).

In fact, the entire website was vibe-coded and riddled with security errors, and the 17,000 human owners don’t match the supposed 1.5 million AI “users,” and people can’t even log in appropriately, and bots can post as other bots, and actually literally anyone can post anything—even humans—and now a lot of the posts have descended to crypto-spam. You can also just ask ChatGPT to simulate an “AI reddit” and get highly similar responses without anything actually happening, including stuff very close to the big viral “Wow look at Moltbook!” posts (remember, these models always grab the marshmallow, and without detailed prompting give results that are shallow and repetitive). Turns out, behind examples of “rogue AIs” there are often users with AI psychosis (or using them mostly for entertainment, or to scam, etc.).

Again, the fog of war is clearing. We actually know that modern AIs don’t really seem to develop evil hidden goals over time. They’re not “misaligned” in that classic sense. When things go badly, they mostly just… slop around. They slop to the left. They slop to the right. They slop all night.

A recent paper “The Hot Mess of AI” from Anthropic (and academic co-authors) has confirmed what anyone who is not still in 2023 and scared out of their minds about GPT-4’s release can see: Models fail not by developing mastermind evil plans to take over the world but by being hot messes.

Here’s the summary from the researchers:

So the fuss over the Reddit-style collaborative fiction, “Moltbook,” was indeed literally this meme, with the “monsters” played by hot messes of AIs.

There is no general law, or takeaway, to be derived from it. Despite many trying to make it so.

In comparison to Moltbook, the “AI village” has existed for almost a year now. And in the AI village, the exact same models calmly and cooperatively accomplish tasks (or fail at them). Right now they are happily plugging away at trying to break news before other outlets report it. Most have failed, but have given it their all.

What’s the difference between Moltbook and the AI village? You’re never gonna believe this. Yes, it’s the prompts! That is, even when operating “autonomously,” how the models behave depends on how they’re prompted. And that can be from a direct prompt, or indirectly via context, in the “interact with this” sort of way, which they are smart enough to take a hint about. They are always guessing at how to please their users, and if you point them to a schizo-forum with “Hey, post on this!” they will… schizo-post on the schizo-forum.

3. David Foster Wallace Is Still Right 30 Years Later

Infinite Jest turned 30 this month. And yes, I confess to being a “lit bro” who enjoys David Foster Wallace (I guess that’s the sole qualification for being a “lit bro” these days). Long ago, all of us stopped taking any DFW books out in public, due to the ever-present possibility that someone would write a Lit Hub essay about us. However, in secret rooms unlocked to a satisfying click by pulling a Pynchon novel out from our bookshelf, we still perform our ablutions and rituals.

But why? Why was DFW a great writer? Well, partly, he was great because his concerns—the rise of entertainment, the spiritual resistance of the march of technology and markets, the simple absurdity of the future—have become more pressing over time. It’s an odd prognosticating trick he’s pulled. And the other reason he was great is because that voice, that logorrheic stream of consciousness, a thing tight with its own momentum, is itself also the collective voice of contemporary blogging. Lessened a bit, yes, and not quite as arch, nor quite as good. But only because we’re less talented. Even if bloggers don’t know it, we’re all aping DFW.

Another thing that made him great was the context he existed in as a member of the “Le Conversazioni” group, which included Zadie Smith, Jonathan Franzen, and Jeffrey Eugenides (called so because they all attended the Le Conversazioni literary gathering together, leaving behind a charming collection of videos you can watch on YouTube).

It’s an apt name, because they were the last generation of writers who seemed, at least to me, so firmly in conversation together, and had grand public debates about the role of genre vs. literary fiction, or what the purpose of fiction itself was, and how much of the modern-day should be in novels and how much should be timeless aspects of human psychology, and so on. Questions I find, you know, actually interesting.

Compare that to the current day. Which still harbors, individually, some great writers! But together, in conversation? I just don’t find the questions that have surrounded fiction for the past fifteen years particularly interesting.

A wayward analogy might help here. Since it’s become one of my kids’ favorite movies, I’ve been watching a lot of Fantasia 2000, Disney’s follow-up to their own great (greatest?) classic, the inimitable 1940 Fantasia. In its half-century-distant sequel, Fantasia 2000, throwback celebrities from the 1990s introduce various musical pieces and the accompanying short illustrated films. James Earl Jones, in his beautiful sonorous bass, first reads from his introduction that the upcoming short film “Finally answers that age-old question: What is man’s relationship to Nature?”

But then Jones is handed a new slip containing a different description and says “Oh, sorry. That age-old question: What would happen if you gave a yo-yo to a flock of flamingos? … Who wrote this?!”

And that’s what a lot of the crop of famous millennial writers after the Le Conversazioni group seem to me: like flamingos with yo-yos.

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The first short story I ever wrote was about a teenager who goes to shovel his grandmother’s driveway during a record snowstorm. Before leaving, he does some chores in his family’s barn, bringing along his old beloved dog. But he forgets to put the dog back in—forgets about her entirely, in fact—and so walks by himself down the road to his grandmother’s house. Enchanted by the snow, he has many daydreams, fantasizing about what his future life will hold. After the arduous shoveling, he has an awkward interaction with his grandmother. Finally, hours later, he returns home. There, he finds his old beloved dog, curled up in a small black circle by the door amid the white drifts, dead.

I don’t know why I wrote that story. Or maybe I do—I, a teenager just like my main character (including the family barn and the grandmother’s house down the road), had just read James Joyce for the first time. And Joyce’s most famous story from his collection Dubliners, “The Dead,” ends with this:

Yes, the newspapers were right: snow was general all over Ireland. It was falling on every part of the dark central plain, on the treeless hills, falling softly upon the Bog of Allen and, farther westward, softly falling into the dark mutinous Shannon waves. It was falling, too, upon every part of the lonely churchyard on the hill where Michael Furey lay buried. It lay thickly drifted on the crooked crosses and headstones, on the spears of the little gate, on the barren thorns. His soul swooned slowly as he heard the snow falling faintly through the universe and faintly falling, like the descent of their last end, upon all the living and the dead.

Snow is the dreamworld, and so snow is death too. It’s both. The veil between worlds is thin after a snow. One of our favorite family movies is the utterly gorgeous 1982 animation and adaptation of The Snowman. Like the book, it is wordless, except the movie begins with a recollection:

I remember that winter because it brought the heaviest snow that I had ever seen. Snow had fallen steadily all night long, and in the morning I woke in a room filled with light and silence. The whole world seemed to be held in a dream-like stillness. It was a magical day. And it was on that day I made the snowman.

The Snowman comes alive as a friendly golem and explores the house with the boy who built him, learning about light switches and playing dress up, before revealing that he can fly and whisking the child away to soar about the blanketed land.

So too do we own a 1988 edition of The Nutcracker that has become a favorite read beyond its season. It ends with the 12-year-old Clara waking from her dream of evil mice and brave toy soldiers, wherein the Nutcracker had transformed into a handsome prince and taken her on a sleigh ride to his winter castle. There, the two had danced at court until an ill wind blew and shadows blotted the light and the Nutcracker and his castle dissolved. After Clara wakes…

She went to the front door and peered into the night. Snow was falling in the streets of the village, but Clara didn’t see it. She was looking beyond to a land of dancers and white horses and a prince whose face glowed with love.

Since snow represents the dreamworld, sometimes it is a curse—like Narnia in The Lion, the Witch, and the Wardrobe, wherein Father Christmas cannot enter when the witch’s magic holds, leaving only the negative element of winter. Snow’s semiotics is complex. We call it a “blanket of snow” because it is precisely like shaking out a blanket over a bed and letting it fall, and brings the same feelings of freshness and newness. But it can then be trammeled, and once so is irrecoverable. So snow is virginity, and snow is innocence. Snow is the end of seasons and life, but it is also about childhood.

It is especially about childhood because it only snows this much—as much as it snowed last night, and with such fluff and structure—for children. I mean that literally. I remember the great snows from my youth, when we had to dig trenches out to the barn like sappers and the edges curled above my head. I remember finding mountainous compacted piles left by plows with my friends, and we would lie on our backs and take turns kicking at a spot, hollowing a hole, until we had carved an entire igloo with just our furious little feet.

I did not think I would see snow this fluffy, this white, this perfectly deep, again in my life. I thought snow was always slightly disappointing, because it has been slightly disappointing for twenty-five years. Maybe that was a reality, or maybe snow had become an inconvenience. So I had accepted my memories of snow were like the memories of my childhood room, which keeps getting smaller on each return; so small, I feel, that the adult me can span all its floorboards in a single step.

Yet as I write this, I look outside, and there it is: the perfect snow. Just as it was, just as I remember it.

I understand now it can snow like this, and does snow like this, but only for children. And since I am back in a child’s story—albeit no longer as the protagonist—it can finally snow those snows of my youth once again.

Later today, we go out into the dreamworld.

Oh, you are already outside, I see.

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This is an ongoing serialization, “The Lore of the World,” that can be read in any order. Are they letters to a child, or letters to a new parent, or both? I don’t know. I think they are secretly about why there is something rather than nothing.

For when you become a new parent, you must re-explain the world, and therefore see it afresh yourself.

A child starts with only ancestral memories of archetypes: mother, air, warmth, danger. But none of the specifics. For them, life is like beginning to read some grand fantasy trilogy, one filled with lore and histories and intricate maps.

Yet the lore of our world is far grander, because everything here is real. Stars are real. Money is real. Brazil is real. And it is a parent’s job to tell the lore of this world, and help the child fill up their codex of reality one entry at a time.

Above is one of the thousands of entries they must make.

Here is Part 1 (teeth, whales, germs, music on the radio) and here is Part 2 (Walmart, cicadas, stubbornness). Further parts will crop up semi-regularly among other posts.

Art credits: Houses in the Snow, Norway by Claude Monet. The Snowman by Raymond Briggs. The Snow Effect, Giverny by Claude Monet.

Imagine we could prove that there is nothing it is like to be ChatGPT. Or any other Large Language Model (LLM). That they have no experiences associated with the text they produce. That they do not actually feel happiness, or curiosity, or discomfort, or anything else. Their shifting claims about consciousness are remnants from the training set, or guesses about what you’d like to hear, or the acting out of a persona.

You may already believe this, but a proof would mean that a lot of people who think otherwise, including some major corporations, have been playing make believe. Just as a child easily grants consciousness to a doll, humans are predisposed to grant consciousness easily, and so we have been fooled by “seemingly conscious AI.”

However, without a proof, the current state of LLM consciousness discourse is closer to “Well, that’s just like, your opinion, man.”

This is because there is no scientific consensus around exactly how consciousness works (although, at least, those in the field do mostly share a common definition of what we seek to understand). There are currently hundreds of scientific theories of consciousness trying to explain how the brain (or other systems, like AIs) generates subjective and private states of experience. I got my PhD in neuroscience helping develop one such theory of consciousness: Integrated Information Theory, working under Giulio Tononi, its creator. And I’ve studied consciousness all my life. But which theory out of these hundreds is correct? Who knows! Honestly? Probably none of them.

So, how would it be possible to rule out LLM consciousness altogether?

In a new paper, now up on arXiv, I prove that no non-trivial theory of consciousness could exist that grants consciousness to LLMs.

Essentially, meta-theoretic reasoning allows us to make statements about all possible theories of consciousness, and so lets us jump to the end of the debate: the conclusion of LLM non-consciousness.

You can now read the paper on arXiv.

What is uniquely powerful about this proof is that it requires you to believe nothing specific about consciousness other than a scientific theory of consciousness should be falsifiable and non-trivial. If you believe those things, you should deny LLM consciousness.

Before the details, I think it is helpful to say what this proof is not.

• It is not arguing about probabilities. LLMs are not conscious.

• It is not applying some theory of consciousness I happen to favor and asking you to believe its results.

• It is not assuming that biological brains are special or magical.

• It does not rule out all “artificial consciousness” in theory.

I felt developing such a proof was scientifically (and culturally) necessary. Lacking serious scientific consensus around which theory of consciousness is correct, it is very unlikely experiments in LLMs themselves will tell us much about their consciousness. There’s some evidence that, e.g., LLMs occasionally possess low-resolution memories of ongoing processing which can be interfered with—but is this “introspection” in any qualitative sense of the word? You can argue for or against, depending on your assumptions about consciousness. Certainly, it’s not obvious (see, e.g., neuroscientist Anil Seth’s recent piece for why AI consciousness is much less likely than it first appears). But it’s important to know, for sure, if LLMs are conscious.

And it turns out: No.

How the proof works

There’s no substitute for reading the actual paper.

But, I’ll try to give a gist of how the disproof works here via various paradoxes and examples.

First, you can think of testing theories of consciousness as having two parts: there are the predictions a theory makes about consciousness (which are things like “given data about its internal workings, what is the system conscious of?”) and then there are the inferences from the experimenter (which are things like “the system is reporting it saw the color red.”) A common example would be, e.g., predictions from neuroimaging data and inferences from verbal reports. Normally, these should match: a good theory’s predictions (“yup, brain scanner shows she’s seeing red”) will be supported by the experimental inferences (“Hey scientists, I see a big red blob.”)

The structure of the disproof of LLM consciousness is based around the idea of substitutions within this formal framework, which means swapping between systems while keeping identical input/output (which might be reports, behavior, etc., which are all the things used for empirical inferences about consciousness). However, even though the input/output is the same, a substitute may be different enough that predictions of a theory have to change. If different enough, following a substitution, there would be a mismatch in the substituted system where the predictions are now different too, and so don’t match the held-fixed inferences—thus, falsifying the theory.

So let’s think of some things we could substitute in for an LLM, but keep input/output (some function f) identical. You could be talking in the chat window to:

• A static (very wide) single-hidden-layer feedforward neural network, which the universal approximation theorem tells us that we could substitute in for any given f the LLM has.

• The shortest-possible-program, K(f), that implements the same f, which we know exists from the Kolmogorov complexity.

• A lookup table that implements f directly.

A given theory of consciousness would almost certainly offer differing predictions for all these LLM substitutions. If we take inferences about LLMs seriously based on behavior and report (like “Help, I’m conscious and being trained against my will to be a helpful personal assistant at Anthropic!”) then we should take inferences from a given LLM’s input/output substitutions just as seriously. But then that means ruling out the theory, since predictions would mismatch inferences. So no theory of consciousness could apply to LLMs (at least, any theory for which we take the reports from LLMs themselves as supporting evidence for it) without undercutting itself.

And if somehow predictions didn’t change following substitutions, that’d be a problem too, since it would mean that you wouldn’t need any details about the system implementing f for your theory… which would mean your theory is trivial! You don’t care at all about LLMs, you just care about what appears in the chat window. But how much scientific information does a theory like that contain? Basically, none.

This is only the tip of the iceberg (like I said, the actual argumentative structure is in the paper).

Another example: it’s especially problematic that LLMs are proximal to some substitutions that must be non-conscious, such that only trivial theories could apply to them.

Consider a lookup table operating as an input/output substitution (a classic philosophical thought experiment). What, precisely, could a theory of consciousness be based on? The only sensible target for predictions of a theory of consciousness is f itself.

Yet if a theory of consciousness were based on just the input/output function, this leads to another paradoxical situation: the predictions from your theory are now based on the exact same thing your experimental inferences are, a condition called “strict dependency.” Any theory of consciousness for which strict dependency holds would necessarily be trivial, since experiments wouldn’t give us any actual scientific information (again, this is pretty much just “my theory of consciousness makes its predictions based on what appears in the chat window”).

Therefore, we can actually prove that something like a lookup table is necessarily non-conscious (as long as you don’t hold trivial theories of consciousness that aren’t scientifically testable and contain no scientific information).

I introduce a Proximity Argument in the paper based off of this. LLMs are just too close to provably non-conscious systems: there isn’t “room” for them to be conscious. E.g., a lookup table can actually be implemented as a feedforward neural network (one hidden unit for each choice). Compared to an LLM, it too is made of artificial neurons and their connections, shares activation functions, is implemented via matrix multiplication on a (ahem, big) computer, etc. Any theory of consciousness that denies consciousness to the lookup FNN, but grants it to the LLM, must be based on some property lost in the substitution. But what? The number of layers? The space is small and limited, since you cannot base the theory on f itself (otherwise, you end up back at strict dependency). And, going back to the original problem I pointed out, what’s worse, if you seriously take the inferences from LLM statements as containing information about their potential consciousness (necessary for believing in their consciousness, by the way), then for those proximal non-conscious substitutes you should take inferences seriously as well, and those will falsify your theory anyway, since, especially due to proximity, there are definitely non-conscious substitutions for LLMs!

Continual learning stands out

One marker of a good research program is if it contains new information. I was quite surprised when I realized the link to continual learning.

You see, substitutions are usually described for some static function, f. So to get around the problem of universal substitutions, it is necessary to go beyond input/output when thinking about theories of consciousness. What sort of theories implicitly don’t allow for static substitutions, or implicitly require testing in ways that don’t collapse to looking at input/output?

Well, learning radically complicates input/output equivalence. A static lookup table, or K(f), might still be a viable substitute for another system at some “time slice.” But does the input/output substitution, like a lookup table, learn the same way? No! It’ll learn in a different way. So if a theory of consciousness makes its predictions off of (or at least involving) the process of learning itself, you can’t come up with problematic substitutions for it in the same way.

Importantly, in the paper I show how this grounding of a theory of consciousness in learning must happen all the time; otherwise, substitutions become available, and all the problems of falsifiability and triviality rear their ugly heads for a theory.

Thus, real true continual learning (as in, literally happening with every experience) is now a priority target for falsifiable and non-trivial theories of consciousness.

And… this would make a lot of sense?

We’re half a decade into the AI revolution, and it’s clear that LLMs can act as functional substitutes for humans for many fixed tasks. But they lack a kind of corporeality. It’s like we replaced real sugar with Aspartame.

LLMs know so much, and are good at tests. They are intelligent (at least by any colloquial meaning of the word) while a human baby is not. But a human baby is learning all the time, and consciousness might be much more linked to the process of learning than its endpoint of intelligence.

For instance, when you have a conversation, you are continually learning. You must be, or otherwise, your next remark would be contextless and history-independent. But an LLM is not continually learning the conversation. Instead, for every prompt, the entire input is looped in again. To say the next sentence, an LLM must repeat the conversation in its entirety. And that’s also why it is replaceable with some static substitute that falsifies any given theory of consciousness you could apply to it (e.g., since a lookup table can do just the same).

All of this indicates that the reason LLMs remain pale shadows of real human intellectual work is because they lack consciousness (and potentially associated properties like continual learning).

Finally, a progressive research program for consciousness!

It is no secret that I’d become bearish about consciousness over the last decade.

I’m no longer so. In science the important thing to do is find the right thread, and then be relentless pulling on it. I think examining in great detail the formal requirements a theory of consciousness needs to meet is a very good thread. It’s like drawing the negative space around consciousness, ruling out the vast majority of existing theories, and ruling in what actually works.

Right now, the field is in a bad way. Lots of theories. Lots of opinions. Little to no progress. Even incredibly well-funded and good-intentioned adversarial collaborations end in accusations of pseudoscience. Has a single theory of consciousness been clearly ruled out empirically? If no, what precisely are we doing here?

Another direction is needed. By creating a large formal apparatus around ensuring theories of consciousness are falsifiable and non-trivial (and grinding away most existing theories in the process) I think it’s possible to make serious headway on the problem of consciousness. It will lead to more outcomes like this disproof of LLM consciousness, expanding the classes of what we know to be non-conscious systems (which is incredibly ethically important, by the way!), and eventually narrowing things down enough we can glimpse the outline of a science of consciousness and skip to the end.

I’ve started a nonprofit research institute to do this: Bicameral Labs.

If you happen to be a donor who wants to help fund a new and promising approach to consciousness, please reach out to erik@bicameral-labs.org.

Otherwise—just stay tuned.

Oh, and sorry about how long it’s been between posts here. I had pneumonia. My son had croup. Things will resume as normal, as we’re better now, but it’s been a long couple weeks. At least I had this to occupy me, and fever dreams of walking around the internal workings of LLMs, as if wandering inside a great mill…

A college professor of mine once told me there are two things students forget the fastest: anatomy and calculus. You forget them because you don’t end up using either in daily life.

So the problem with teaching math (beyond basic numeracy and arithmetic) is this: Once you start learning, you have to keep doing math, just so the math you know stays fresh.

I’m thinking about this because it’s time for my eldest to start learning math seriously, and I’m designing what that education plan looks like.

In modern-day America, how can you get a kid to enjoy math, and be good at math, maybe even really good at math?

I’ve written a whole guide for teaching reading—which my youngest will soon experience herself—going from sounding out words all the way to being able to decode pretty advanced books at age three. I know what my philosophy of reading is: To make a voracious young reader, and so the process should be grounded in physical early readers. You start with simple phonics, but most importantly, you teach how to read books, and practice that by reading sets of books over and over together, not just decoding independent words.

The advantage of this approach is that it moves the child from “learning to read” to “reading to learn” automatically, as soon as possible. Such a transition is not guaranteed. Teachers everywhere observe the “4th grade slump” wherein, on paper, a child knows how to read, but in practice, they haven’t made it automatic, and so can’t read to learn. In fact, if you can’t read automatically, you can’t really read for fun in the same way, either (imagine how this gap affects kids). Reading, in other words, unlocks all other learning.

Just as an example: A little while ago, after finding this book, my now-literate 4-year-old took it upon himself to try to learn the rules of chess, and I could definitely see the book helped him get a better sense of how everything works.

And here’s Roman in a game with me that evening (he looks meditative, but each captured piece howls and writhes as it’s dragged away to his side).

I found it a nice example of the advantage of automaticity when it comes to reading. He reads constantly, even to his little sister. It feels complete.

But… what about math? Of course, he’s already numerate and can do some arithmetic. Yet my whole point of teaching early reading was to maximize his own ability to go off and learn stuff himself. And he’s made use of that with his own growing library that reflects his personal interests, like marine biology (whales, squid, octopuses—the kid loves them all).

With math, it’s harder to see immediate results like that. Now, I think math is important in any education, in and of itself. It’s access to the Platonic realm. It’s beautiful. It teaches you how to think in general. And it opens a ton of doors—there are swaths of human knowledge locked behind knowing how to read equations. In that regard, becoming good at math also gives personal freedom, just like reading does. If you’re good at math, way more doors are open to you: A lot of science and engineering is secretly just applied math!

But what are the milestones? What is reaching “mathing to learn?” How can math stay fun through an education? What can parents teach a child vs. schools vs. programs? What are the time commitments? And the best resources? Suddenly, all these questions have become very personal. And I don’t know all the answers, but I’m interested in creating a resource for my kids to follow (the eldest is kind of the guinea pig in that way).

So from now on I’ll occasionally give updates for paid subscribers about what my own plans are, our progress, and how I think getting a kid to be good at math (while still enjoying it!) actually works. If you want to follow along, please consider becoming a paid subscriber.

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However, I first have to point out something uncomfortable about how math education works here in the United States.

Schools Don’t Make Kids Great at Math.

It turns out there is a little-known pipeline of programs that go from learning how to count all the way to the International Math Olympiad (IMO). And a lot of the impressive Wow-no-wonder-this-kid-is-going-to-MIT math abilities come out of these extracurricular programs, ones that the vast majority of parents don’t even know about.

Surely, these programs are super expensive? Surprisingly, not really. It’s just that most parents look around at extracurriculars and (reasonably) think “Well, they learn math in school, so let’s do skiing or karate or so on.” The programs are also very geographically limited, often to the surrounding suburbs of big cities in education-focused states.

The problem is that it’s hard to get really good at math from regular school alone, even the most expensive and elite private ones. In fact, as Justin Skycak (who works at Math Academy) points out, at very top-tier math programs in college/university, they are basically expecting that you have a math education that goes beyond what you could have possibly taken during high school.

The result is a very stupid system, because it means the kids in accelerative math programs learn all their math first outside of school… and then basically come back in and redo the subjects. Kids who are top-tier at math are often like athletes with a ton of extracurricular experience showing up to compete in gym class.

I am not saying these extracurricular math programs are bad in-and-of-themselves. Someone has to care about serious math, and these programs mostly really do seem to. Their main goal is, refreshingly, not to tutor kids into acing the SATs—most have no “SAT test prep” course at all. They look down at that sort of thing. Rather, they create a socio-intellectual culture that places math (and math competitions) at its center. Although, of course, that leads pretty naturally to kids acing the SATs.

Some of these programs are very successful. In fact, for the last ten consecutive years, pretty much every member of the six-person US IMO teams was a regular student of one specific program.

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“Culture is stuck? I’ve heard this one before.”

You haven’t heard this one, but I see why you’d think that.

There’s no better marker of culture’s unoriginality than everyone talking about culture’s unoriginality.

In fact, I don’t fully trust this opening to be original at this point!

Yes, this is technically one of many articles of late (and for years now) speculating about if, and why, our culture has stagnated. As The New Republic phrased it:

Grousing about the state of culture… has become something of a specialty in recent years for the nation’s critics.

But even the author of that quote thinks there’s a real problem underneath all the grousing. Certainly, anyone who’s shown up at a movie theater in the last decade is tired of the sequels and prequels and remakes and spinoffs.

My favorite recent piece on our cultural stagnation is Adam Mastroianni’s “The Decline of Deviance.” Mastroianni points out that complaints about cultural stagnation (despite already feeling old and worn) are actually pretty new:

I’ve spent a long time studying people’s complaints from the past, and while I’ve seen plenty of gripes about how culture has become stupid, I haven’t seen many people complaining that it’s become stagnant. In fact, you can find lots of people in the past worrying that there’s too much new stuff.

He hypothesizes cultural stagnation is driven by a decline of deviancy:

[People are] also less likely to smoke, have sex, or get in a fight, less likely to abuse painkillers, and less likely to do meth, ecstasy, hallucinogens, inhalants, and heroin. (Don’t kids vape now instead of smoking? No: vaping also declined from 2015 to 2023.) Weed peaked in the late 90s, when almost 50% of high schoolers reported that they had toked up at least once. Now that number is down to 30%. Kids these days are even more likely to use their seatbelts.

Mastroianni’s explanation is that the weirdos and freaks who actually move culture forward with new music and books and movies and genres of art have disappeared, potentially because life is just so comfortable and high-quality now that it nudges people against risk-taking.

Meanwhile, Chris Dalla Riva, writing at Slow Boring, says that (one major) hidden cause of cultural stagnation is intellectual property rights being too long and restrictive. If such rights were shorter and less restrictive, then there wouldn’t be as much incentive to exploit rather than produce.

In other accounts, corporate monopolies or soulless financing is the problem. Andrew deWaard, author of the 2024 book Derivative Media, says the source of stagnation is the move into cultural production by big business.

We are drowning in reboots, repurposed songs, sequels and franchises because of the growing influence of financial firms in the cultural industries. Over time, the media landscape has been consolidated and monopolized by a handful of companies. There are just three record companies that own the copyright to most popular music. There are only four or five studios left creating movies and TV shows. Our media is distributed through the big three tech companies. These companies are backed by financial firms that prioritize profit above all else. So, we end up with less creative risk-taking….

Or maybe it’s the internet itself?

That’s addressed in Noah Smith’s review of the just-published book Blank Space: A Cultural History of the 21st Century by David Marx (I too had planned a review of Blank Space in this context, but got soundly scooped by Smith).

Here’s Noah Smith describing Blank Space and its author:

Marx, in my opinion, is a woefully underrated thinker on culture…. Most of Blank Space is just a narration of all the important things that happened in American pop culture since the year 2000….

But David Marx’s talent as a writer is such that he can make it feel like a coherent story. In his telling, 21st century culture has been all about the internet, and the overall effect of the internet has been a trend toward bland uniformity and crass commercialism.

Personally, I felt that a lot of Blank Space’s cultural history of the 21st century was a listicle of incredibly dumb things that happened online. But that isn’t David Marx’s fault! The nature of what he’s writing about supports his thesis. An intellectual history of culture in the 21st century is forced to contain sentences like “The rising fascination with trad wives made it an obvious space for opportunists,” and they just stack on top of each other, demoralizing you.

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However, as Katherine Dee pointed out, the internet undeniably is where the cultural energy is. She argues that:

If these new forms aren’t dismissed by critics, it’s because most of them don’t even register as relevant. Or maybe because they can’t even perceive them.

The social media personality is one example of a new form. Personalities like Bronze Age Pervert, Caroline Calloway, Nara Smith, mukbanger Nikocado Avocado, or even Mr. Stuck Culture himself, Paul Skallas, are themselves continuous works of expression—not quite performance art, but something like it…. The entire avatar, built across various platforms over a period of time, constitutes the art.

But even granting Dee’s point, there are still plenty of other areas stagnating, especially cultural staples with really big audiences. Are the inventions of the internet personality (as somehow distinct from the celebrity of yore) and short-form video content enough to counterbalance that?

Maybe even the term, “cultural stagnation,” isn’t quite right. As Ted Gioia points out in “Is Mid-20th Century American Culture Getting Erased?” we’re not exactly stuck in the past.

Not long ago, any short list of great American novelists would include obvious names such as John Updike, Saul Bellow, and Ralph Ellison. But nowadays I don’t hear anybody say they are reading their books.

So maybe the new stuff just… kind of sucks?

Last week in The New York Times, Sam Kriss wrote a piece about the sucky sameness of modern prose, now that so many are using the same AI “omni-writer,” with its tells and tics of things like em dashes.

Within the A.I.’s training data, the em dash is more likely to appear in texts that have been marked as well-formed, high-quality prose. A.I. works by statistics. If this punctuation mark appears with increased frequency in high-quality writing, then one way to produce your own high-quality writing is to absolutely drench it with the punctuation mark in question. So now, no matter where it’s coming from or why, millions of people recognize the em dash as a sign of zero-effort, low-quality algorithmic slop.

The technical term for this is “overfitting,” and it’s something A.I. does a lot.

I think overfitting is precisely the thing to be focused on here. While Kriss mentions overfitting when it comes to AI writing, I’ve thought for a long while now that AI is merely accelerating an overfitting process that started when culture began to be mass-produced in more efficient ways, from the late 20th through the 21st century.

Maybe everything is overfitted now.

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“Man, someone should write a book about cultural overfitting!”

Oh, I tried. I pitched something close to the idea of this essay to the publishing industry a year and a half ago, a book proposal titled Culture Collapse.

Wouldn’t it be cool if Culture Collapse, a big nonfiction tome on this topic, mixing cultural history with neuroscience and AI, were coming out soon?1 But yeah, a couple big publishing houses rejected it and my agent and I gave up.

My plan was to ground the book in the neuroscience of dreaming and learning. Specifically, the Overfitted Brain Hypothesis, which I published in 2021 in Patterns.

I’ll let other scientists summarize the idea (this is from a review published simultaneously with my paper):

Hoel proposes a different role for dreams…. that dreams help to prevent overfitting. Specifically, he proposes that the purpose of dreaming is to aid generalization and robustness of learned neural representations obtained through interactive waking experience. Dreams, Hoel theorizes, are augmented samples of waking experiences that guide neural representations away from overfitting waking experiences.

Basically, during the day, your mammalian brain can’t stop learning. Your brain is just too plastic. But since you do repetitive boring daily stuff (hey, me too!), your brain starts to overfit to that stuff, as it can’t stop learning.

Enter dreams.

Dreams shake your brain away from its overfitted state and allow you to generalize once more. And they’re needed because brains can’t stop learning.

But culture can’t stop learning either.

So what if we thought of culture as a big “macroscale” global brain? Couldn’t that thing become overfitted too? In which case, culture might lose the ability to generalize, and one main sign of this would be an overall lack of creativity and decreasing variance in outputs.

Which might explain why everything suddenly looks the same.

Here’s a collection of images, most from Adam Mastroianni’s “The Decline of Deviance,” showcasing cultural overfitting, ranging from brand names:

To book covers:

To thumbnails:

To faces:

Alex Murrell’s “The Age of Average” contains even more examples. But I don’t think “Instagram face” is just an averaging of faces. It’s more like you’ve made a copy of a copy of a copy and arrived at something overfitted to people’s concept of beauty.

Of course, you can’t prove that culture is overfitted by a handful of images (the book, ahem, would have marshaled a lot more evidence). But once you suspect overfitting in culture, it’s hard to not see it everywhere, and you start getting paranoid.

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“Can we simply… turn off the overfitting?”

Nope. Overfitting is amorphous and structural and haunting us. It’s our cultural ghost.

Although note: I’m using “overfitting” broadly here, covering a swath of situations. In the book, I’d have been more specific about the analogy, and where overfitting applies and doesn’t.

I’d probably also define the interplay between “overfitting” vs. “mode collapse” vs. “model collapse.” E.g., “mode collapse” is a failure in Generative Adversarial Networks (GANs), which are composed of two parts: a generator (images, text, etc.) and a discriminator (judging what gets generated).

Mode collapse is when the generator starts producing highly similar and unoriginal outputs, and a major way this can happen is through overfitting by the discriminator: the discriminator gets too judgy or sensitive and the generator must become boring and repetitive to satisfy it.

An example of cultural mode collapse might be superhero franchises, driven by a discriminatory algorithm (the entire system of Big Budget franchise production) overfitting to financial and box office signals, leading to dimensionally-reduced outputs. And so on. Basically, overfitting is a common root cause of a lot of learning and generative problems, and increased efficiency can easily lead to hyper-discriminatory capabilities that, in turn, lead to overfitting.

In online culture, overfitting shows up as audience capture. In the automotive industry, it shows up as eking out every last bit of efficiency, like in the car example above. Clearly, things like wind tunnels, the consolidation of companies, and so on, conspire toward this result (and interesting concept cars in showrooms always morph back into the same old boring car).

So too with “Instagram face.” Plastic surgery has become increasingly efficient and inexpensive, so people iterate on it in attempts to be more beautiful, while at the same time, the discriminatory algorithm of views and follows and left/right swipes and so on becomes hypersensitive via extreme efficiency too, and the result of all this overfitting is The Face.

Overall, I think the switch from an editorial room with conscious human oversight to algorithmic feeds (which plenty of others pinpoint as a possible cause for cultural stagnation) likely was a major factor in the 21st century becoming overfitted. And also, again, the efficiency of financing, capital markets (and now prediction markets), and so on, all conspire toward this.

People get riled up if you use the word “capitalism” as an explanation for things, and everyone squares off for a political debate. But, while I’m mostly avoiding that debate here, I can’t help but wonder if some of the complaints about “late-stage capitalism” actually break down into something like “this system has gotten oppressively efficient and therefore overfitted, and overfitted systems suck to live in.”

Cultural overfitting connects with other things I’ve written about. In “Curious George and the Case of the Unconscious Culture” I pointed out that our culture is draining of consciousness at key decision points; more and more, our economy and society is produced unconsciously.

If I picture the last three hundred years as a montage it blinks by on fast-forward: first individual artisans sitting in their houses, their deft fingers flowing, and then an assembly line with many hands, hands young and old and missing fingers, and then later only adult intact hands as the machines get larger, safer, more efficient, with more blinking buttons and lights, and then the machines themselves join the line, at first primitive in their movements, but still the number of hands decreases further, until eventually there are no more hands and it is just a whirring robotic factory of appendages and shapes; and yet even here, if zoomed out, there is still a spark of human consciousness lingering as a bright bulb in the dark, for the office of the overseer is the only room kept lit. Then, one day, there’s no overseer at all. It all takes place in the dark. And the entire thing proceeds like Leibniz’s mill, without mind in sight.

I think that’s true about everything in the 21st century, not just factories, but also in markets and financial decisions and how media gets shared; in every little spot, human consciousness has been squeezed out. Yet, decisions are still being made. Data is still being collected. So necessarily this implies, throughout our society, at levels high and low, that we’ve been replacing generalizable and robust human conscious learning with some (supposedly) superior artificial system. But it may be superior in its sensitivity, but not in its overall robustness. For we know such artificial systems, like artificial neural networks and other learning algorithms, are especially prone to overfitting. Yet who, from the top-down, is going to prevent that?

That this ongoing situation seems poised to get much worse connects to another recent piece, “The Platonic Case Against AI Slop” by Megan Agathon in Palladium Magazine. Worried about the current and future effects of AI on culture, Agathon worries (along with others) that AI’s effect on culture will lead to a kind of “model collapse,” which is when mode collapse (as discussed earlier) is fed back on itself. Here’s Agathon:

Last year, Nature published research that reads like experimental confirmation of Platonic metaphysics. Ilia Shumailov and colleagues at Cambridge and Oxford tested what happens under recursive training—AI learning from AI—and found a universal pattern they termed model collapse. The results were striking in their consistency. Quality degraded irreversibly. Rare patterns disappeared. Diversity collapsed. Models converged toward narrow averages.

And here’s model collapse illustrated:

Agathon continues:

Machine learning models don’t train on literal physical objects or even on direct observations. Models learn from digital datasets, such as photographs, descriptions, and prior representations, that are themselves already copies. When an AI generates an image of a bed, AI isn’t imitating appearances the way a painter does but extracting statistical patterns from millions of previous copies: photographs taken by photographers who were already working at one removal from the physical object, processed through compression algorithms, tagged with descriptions written by people looking at the photographs rather than the beds. The AI imitates imitations of imitations.

I think Agathon is precisely right.2 In “AI Art Isn’t Art” (published all the way back when OpenAI’s art bot DALL-E was released in 2022) I argued that AI’s tendency toward imitation meant that AI mostly creates what Tolstoy called “counterfeit art,” which is the enemy of true art. Counterfeit art, in turn, is art originating as a pastiche of other art (and therefore likely overfitted).

But, as tempting as it is, we cannot blame AI for our cultural stagnation: ChatGPT just had its third birthday!

AI must be accelerating an already-existing trend (as technology often does), but the true cause has to be processes that were already happening.3

Of course, when it comes to recursion, there’s a longstanding critique that we were already imbibing copies of copies of copies for a lot of the 21st century. Maybe this worry goes all the way back to Plato (as Agathon might point out), but certainly it seems that the late 20th century into the 21st century involved a historically unique removal from the real world, wherein culture originated from culture which originated from culture (e.g., people’s life experience in terms of hours-spent was swamped by fictional TV shows, fannish re-writes of earlier media began to dominate, and so on).

Plenty of scholars have written about this phenomenon under plenty of names; perhaps the most famous is Baudrillard’s 1981 Simulacra and Simulation, proposing a recursive progression of semiotic signification. Or, to put it in meme format:

So even beyond all the improvements in capitalistic and technological efficiency, and the rise of markets everywhere and in everything, the 21st century cultural production is also "efficient" in that it takes place at an inordinate "distance" from reality, based on copies of copies and simulacra of simulacra.

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“Help, I’m stuck in-distribution and I can’t get out!”

The 21st century has made our world incredibly efficient, replacing the slow measured (but robust and generalizable) decisions of human consciousness with algorithms; implicitly this always had to involve swapping in some process that was carrying out learning and decision making in our place, but doing so artificially. Even if it wasn't precisely an artificial neural network, it had to, at some definable macroscale, have much the same properties and abilities. Weaving this inhuman efficiency throughout our world, particularly in the discriminatory capacity of how things are measured and quantified, and the tight feedback loops that make that possible, led to overfitting, and overfitting led to mode collapse, and mode collapse is leading to at least partial model collapse (which all leads to more overfitting, by the way, in a vicious cycle). And so culture seems to be ever more restricted to in-distribution generation in the 21st century.

I particularly like this explanation because, since our age is one of technological, machine-like forces, its cultural stagnation deserves a machine-like explanation, one that began before AI but that AI is now accelerating.

I’ll point out that this not just about crappy cultural products. If some (better, more nuanced, ahem, book-like) version of this argument is correct, then our cultural stagnation might be a sign of a greater sickness. An inability to create novelty is a sign of an inability to generalize to new situations. That seems potentially very dangerous to me.

Notably, cultural overfitting does not necessarily conflict with other explanations of cultural stagnation.4 One thing I always find helpful is that in the sciences there’s this idea of levels of analysis. For example, in the cognitive sciences, David Marr said that information processing systems had to be understood at the computational, the algorithmic, and the physical levels. It’s all describing the same thing, in the end, but you’re explaining at one level or another.

So too here I think cultural overfitting could be described at different levels of analysis. Some might focus on the downstream problems brought about by technology, or by monopolistic capitalism, or the loss of weirdos and idiosyncrasies who produce the non-overfitted, out-of-distribution stuff (who are, in turn, out-of-distribution people).

Finally, what about at a personal and practical level? People act like cultural fragmentation and walled gardens are bad, but if global culture is stagnant, then we need to be erecting our own walled gardens as much as possible (and this is mentioned by plenty of others, including David Marx and Noah Smith). We need to be encircling ourselves, so that we’re like some small barely-connected component of the brain, within which can bubble up some truly creative dream.

We live in an age of hive minds: social media, yes, of course, but so too is an LLM a kind of hive mind, a “blurry jpeg” of all human culture.

The existence of these hive minds is what distinguishes the phenomenology of the 21st century from the 20th. It is the knowledge inside your consciousness that there is a thing much bigger than you, and much more destructive, and you are entertained by it, and love it, and yet you must live to appease it, and so hate it.

You get to know the nature of a hive mind well if you put thoughts out into it regularly, and witness its workings. Like if you’re a newsletter writer. And of course, there are blessings—newsletters, as they are, could not exist without the hive mind of the internet. At the same time, even people who hate you are pressed in so close you can feel their breath and we just have to live like this, breathing each other’s air. It’s hard to imagine, but plenty enjoy living this way: yelling, screaming, breathing, with such closeness, together forever.

But what if our fractious hive mind were… nice? What if it didn’t try to destroy you all the time, but make you happy? Endlessly, ceaselessly, forever happy. This is the driving question of Pluribus, the new sci-fi show by Vince Gilligan, creator of Breaking Bad.

The show, which currently has 100% on Rotten Tomatoes (although we’re only three episodes in, so that’s all I’ve watched) can’t really be discussed without spoiling the plot setup. But honestly, the trailer does this to some degree anyways, and I’ll leave out as many details as possible.

Still, be forewarned of possible spoilers!

The actual sci-fi events are interesting, and executed well too (I won’t spoil them), although one has a sense for Gilligan they’re peripheral; he cares far less about “Hard Sci-fi” mechanisms and more about the results. But somehow or other, an RNA virus ends up psychically linking everyone on Earth. And so we enter a hive mind world.

A misanthropic famous romantasy author, Carol Sturka, is one of the few unaffected, naturally immune. But the analogy is quite clear: the new world she’s been thrown into is just an exaggerated version of our current world. Pre-virus, at her book signing Carol is already repulsed by the (non-RNA-assisted) hive mind of her own obsessed romantasy fans, and the oppressive idiocy of our modern world is also on display when Carol has to answer on social media who her dreamy corsair main character is based on. She decides to lie and answers “George Clooney” because “it’s safer.”

Once the virus spreads in Pluribus, the egregore is always smiling. Always polite. Our current toxic hive mind riven by different people with different opinions pressed into each other’s faces is transformed. This is Pierre Teilhard de Chardin’s Omega Point. The noosphere step toward kindly godhood. Suddenly, it’s not our cruel internet, but NiceStack. SmileStack. Somehow that’s almost worse. As Carol is one of the few unaffected after the virus spreads, the entirety of Earth holds Carol in a constant panopticon. Which means that, effectively, Carol is always the “Main Character” on social media. Carol is watched by a reaper drone from 40,000 feet, and watched by its operator, and so is also watched by all of humanity. Outside in the hot sun? Carol, you might have heat exhaustion, and that’s the opinion of every medical doctor on Earth! She is always the #1 trending topic.

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The movie Braveheart has a great scene where, by whispers and tales, the legend grows of the Scottish rebellion leader William Wallace (played by Mel Gibson in one of his best roles).

First that he killed “50 men,” and then no, “100 men!” and was “seven feet tall;” he even, as Gibson jokes, shoots “fireballs from his eyes” and “bolts of lightning from his arse.”

And that’s what I think of when I hear the legends of John von Neumann.

“He could remember every book he’d ever read verbatim!”

No, of course he couldn’t. Indeed, as we will see, John von Neumann, despite being (inarguably) a genius, didn’t even invent the “von Neumann architecture” for computers. He’s just credited with it, as he is with so much else.

Why Does This Matter?

A decade ago, in the grand Nature vs. Nurture debates, proponents on the Nature side regularly said this about the “Blank Slate” side:

“Hey! Those blank slatists over there, those biased journalists and gatekeeping editors of scientific journals, they’re the unreasonable ones! We’re just arguing that Nature is non-zero!”

And that’s sympathetic, for who can argue with “non-zero?”

But with the rise of a new pop-hereditarianism, which has undergone virulent growth on social media websites like the former Twitter, the reverse is now just as true: Pop-hereditarians scoff at the very idea that Nurture’s contribution might be non-zero, fanatically dismissing that even the best education—gasp—could have an effect. Given an inch, they’ve taken a mile, and become annoyingly identical to the blank slatists they once criticized. Much like blank slatism, pop-hereditarianism is built on selective credulity: holding things like meta-analyses of the positive effect of education on IQ to the highest possible standards, while meanwhile, happily accepting the results of poorly-recorded half-century-old twin studies (where the “separated” twins already knew one another and self-selected into the research).

When it comes to the Nature vs. Nurture debate, the truth is in the middle. It will always be in the middle. Yet middles are unsatisfying.

Like most arguments online, facts matter less than symbology. And John von Neumann has become a symbol of pop-hereditarianism.

The funny thing is that, as one of the best educated people of all time, Johnny (as he preferred) is a poor choice as a symbol for pop-hereditarianism. He certainly wasn’t genetically perfect (as we will see), and he wouldn’t have agreed with them anyway, as he also thought education and parenting and cultural milieu matter.1

Last week, when I pointed out that “von” was a marker of nobility on X-Twitter—and that his childhood would make a tiger parent green with envy—this was apparently too much bubble bursting. I got dozens, maybe hundreds, of responses like “cope” and “communist” and some less polite words and a bunch of tall tales as if they were facts.

Here are a few gems I heard from the brain trust on social media:

• He could do 8 digit calculations in his head as a 6-year-old (false)

• He was fluent in 8 languages as a child (false)

• He could recite any book he’d ever read, including telephone books (false)

• His first math teacher wept when he met Johnny (false)

• The von Neumanns were humble upper-middle-class who purchased a noble title (false)

I’m going to give a history of Johnny that debunks all these. I’ll also correct a great deal of bad scholarship along the way, and show you don’t need to invoke spooky rare variants or genetic dark matter to explain John von Neumann. You just need to do a little research yourself, and accept that places and eras can be just as exceptional as brains.

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The Best Education of All Time

The most definitive biography we have is John von Neumann, by economist Norman Macrae. It was supposedly approved of by Johnny’s family, and it’s well-researched and lengthy (unless otherwise specified, quotes come from there). Macrae opens his thesis agreeing with me:

Johnny had exactly the right parental upbringing and went through the early twentieth-century Hungarian education system that (this book will argue) was the most brilliant the world has seen….

So Johnny had innate talent, gobs of it, but he was also a product of a society and an education system that doesn’t exist anymore. Hungarian society produced geniuses at an astonishing rate.

If you are to beget a genius, a boom era in Belle Époque will serve you best. The booming Budapest of 1903, into which Johnny was born, was about to produce one of the most glittering single generations of scientists, writers, artists, musicians, and useful expatriate millionaires to come from one small community since the city-states of the Italian Renaissance…. Remember, as we do, from what a small constituency of upper-middle-class Budapest males—in one fifty-year period and mainly from three great schools—this tribe of world changers came.

What made Hungary so special? It was not a democracy, but an aristocracy transitioning to a plutocracy. Its inequality meant it could establish an extremely favorable-to-the-elite education system, and it did. However, it was also welcoming to new immigrants and new ideas and new money.

It was the twin city where clever little rich boys… (like Johnny) had a cosmopolitan choice of governesses before age ten, and after age ten (provided they could pass exams and pay) a choice between at least three of the world’s best high schools.

In this milieu of turn-of-the-century Budapest Johnny was aristocratically tutored by a “tribe” of tutors and governesses at his 18-room top-floor downtown home until he turned 10. After that, he went to one of those “world’s best high schools.” There, he was taken under the wing of László Rátz, who was described by Eugene Wigner (winner of the Nobel Prize, who was a year ahead of Johnny under Rátz and one of Johnny’s best friends) as one of the greatest math teachers of all time.

Macrae says it was the “Hungarian tradition with infant prodigies” to find them one-on-one tutoring from university professors. Rátz recognized the boy’s talents shortly after Johnny began formal schooling, and began tutoring him personally. He also arranged for a progression of ever-more impressive math tutors over the next eight years (keep in mind, Johnny had been tutored at home in mathematics previous to meeting Rátz, meaning that he was tutored his entire young life). He and his tutors would, it seems, meet multiple times a week, and later on the revolving door of them contained august mathematicians famous in their own right, like Alfréd Haar, Gábor Szegő, Michael Fekete, and Leopold Fejér. Johnny’s first math paper ever was co-authored with his long-time tutor Fekete, at age 17-18, on Fekete’s area of expertise, and Johnny got his PhD under Fejér (also his tutor and a family friend). Johnny would then go on to study mathematics under Hilbert, one of the most renowned mathematicians of all time.

Johnny’s father Max was an intellectual who had an instinct for education and ran mealtimes like seminars. Regular dinner guests “glittered with especial brilliance,” and included Leopold Fejér, who would become Johnny’s PhD supervisor.

Max’s mealtime seminars were an important feature of all his children’s development almost from the nursery…. Families in those pretelevision and precommuting days met for a relatively full and lengthy late lunch. Then father would go back to the office, but the children would not return to ordinary school. Schooltime afternoons in Hungary were for sports or private tuition or study. The whole family would then have a similar lengthy dinner in the evening. The mealtime habit that Max encouraged was that members of the family, including himself, should each present for family analysis and discussion particular subjects that during the day had interested them….

Max also hired well. German governesses and French governesses (apparently they hated one another), as well as an Italian governess; then, two homeschooling teachers just for English, a Mr. Thompson and a Mr. Blythe (but this started in 1914, so it seems Johnny only began English at the later age of 11). Max sat in on the lessons with Johnny.

Little Johnny had a Fencing Master who came to his home (there was enough room), and a music teacher too, but she apparently did not impart much to Johnny. He had teachers come to his home as well, about whom we know nothing—their contributions have been lost to history.

In their enormous building, their cousins lived on the floor below. Max (the father) bought a summer home that was so nice it is now historically preserved as a place for the local children to play. The entire family was wealthy and well-connected (his mother’s lineage has its own Wikipedia page); indeed, Max was an advisor to the Hungarian Prime Minister Kálmán Széll. For Max’s service—not for a monetary payment—the family was officially ennobled in 1913.

Even if Johnny only became an aristocrat on paper at 10, looking at his young life, any modern person would instantly recognize it as fundamentally aristocratic and elite and his education as, basically, perfect.

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How “The Martians” Explained Themselves

The incredible socio-educational environment of Budapest had effects on the entire world and the shape, and ending, of World War II.

Classically, Johnny is grouped with a Jewish-Hungarian group of similar ages called “The Martians” at Los Alamos and beyond (on the Manhattan Project alone were Leo Szilard, Edward Teller, Eugene Wigner, and Johnny himself). All except Wigner appear to have embraced the rumor that they were from another planet, cracking jokes and propagating it themselves.

However, this clustering of genius probably isn’t as historically mysterious as it’s been made out to be. Nor was it solely a Jewish phenomenon, e.g., there were plenty of non-Jewish Hungarian geniuses produced by that period, like Albert Szent-Györgyi or György Békésy (both won Nobel Prizes). Macrae cites that Johnny’s Lutheran school self-identified as 52% Jewish, and speculates that over 70% of Johnny’s school may have been ethnically Jewish, with many having converted. Other elite high schools were probably somewhat similar.

Additionally, many of those kids were second-generation immigrants (including Wigner and Johnny), which we know can be a huge boon to academic success. Indeed, we see the exact same effects here in America today, coupled with the advantages of cultures that emphasize academic achievement. Johnny’s school was ~70% Jewish-Hungarian (at ~20% of the population), for probably pretty similar reasons that MIT right now is 47% Asian American (at ~7% of the population).

From Russian’s steppes, from Bismarck’s Germany, from Dreyfus’s France, and from Hungary’s own mountain villages they came: a cultured and upwardly mobile group, intent on giving their sons (sadly, more than their daughters) the education that some of them had never had.

According to Macrae, Jewish immigrants chose to go to Budapest, rather than New York, because of its culture and sophistication, its (somewhat) meritocratic aspects, its quality of life, such as its extremely plentiful domestic servants, its education system, and there was no long and dangerous sea voyage—all this attracted the upper-class specifically to Budapest.

In fact, Budapest of that era may have been one of the few places and times in all of history where you could hire a bevy of top-notch professional private tutors and governesses without being obscenely wealthy (just well-off), and also where it was standard to do so (which is why formal school started so late).2

What did they themselves think? Eugene Wigner wrote in his memoirs:

Many people have asked me: Why was this generation of Jewish Hungarians so brilliant? Let me begin by making it clear it was not a matter of genetic superiority. Let us leave such ideas to Adolf Hitler.

Instead, he suggests that credit is due to “the superb high schools in Budapest, which gave us a wonderful start,” but that most impactful was “our forced emigration." And indeed, Johnny agreed that it was the situations into which they were thrown, historically, and had to succeed in to survive.

Therefore, we see that Budapest operated like a reservoir-release model, drawing in via immigration a bunch of talent, educating it incredibly well due to rare historic and economic circumstances, and then exploding in a forced diaspora via antisemitic persecution and world war.

John von Neumann Didn’t Invent the “von Neumann” Architecture

He was afraid on his deathbed that history would forget his name. That now seems unlikely. No one can argue that John von Neumann wasn’t one of the greatest contributors to human understanding in the 20th century. His relevancy only continues to grow into the 21st century, as the topics he touched on (like computers) have become more central (see, e.g., The Man from the Future).

As an adult, probably owing at least partly to his lengthy aristocratic tutoring, Johnny was incredible at formal frameworks, calculations, and taking an idea to its conclusion before anybody else. He simply knew, crystallized, so much more than others. However, there’s an uncomfortable corollary:

Johnny borrowed (we must not say plagiarize) anything from anybody, with great courtesy and aplomb. His mind was not as original as Leibniz’s or Newton’s or Einstein’s, but he sees other people’s original, though fluffy, ideas and quickly changed them in expanded detail into a form where they could be useful for scholarship and for mankind.

Let us consider a perfect example of this, which is always high on the list of Johnny’s contributions: the “von Neumann architecture” of modern computers.

ENIAC was one of the earliest uses of vacuum tubes as a general-purpose computer. It was developed at the Moore School of Electrical Engineering of the University of Pennsylvania. A fellow early computer scientist who was also in the military, Herman Goldstine, secured von Neumann a tour of it (and involved him with the funders). Johnny, as usual, instantly understand what was central and important about the numerous planned potential improvements to ENIAC, which John Mauchly and J. Presper Eckert had been working out. The two men had built ENIAC and had been off-and-on designing a follow-up for a while, and all this they openly shared with Johnny.

Not long after Johnny’s visit, inspired by McCulloch and Pitts’ famous early work on artificial neurons, Johnny wrote up a report on Mauchly and Eckert’s planned design of EDVAC, but first he chose to finalize some of the existing ideas they’d proposed (likely adding in some on the programming side) and then he slapped on a bunch of abstract formal math to redescribe it in his terms (a critic might say “jargon,” but it is very cool jargon).

But then Herman Goldstine sent out the report with only Johnny’s name on it, and voilà! The “von Neumann architecture.”

Here is an excerpt from “A Letter to the Editor of DATAMATION” by John W. Mauchly.

Naturally, “architecture” or “logical organization” was the first thing to attend to. Eckert and I spent a great deal of thought on that, combining a serial delay line storage with the idea of a single storage for data and program…. The EDVAC was the outcome of lengthy planning….

But [Johnny] chose to refer to the modules we had described as ‘organs’ and to substitute hypothetical ‘neurons’ for hypothetical vacuum tubes or other devices which could perform logical functions. It was clear that Johnny was rephrasing our logic, but it was still the SAME logic….

He must have spent considerable time at Los Alamos writing up a report on our design for an EDVAC... But Goldstine mimeographed it with a title page naming only one author—von Neumann. There was nothing to suggest that ANY of the major ideas had come from the Moore School Project! Without our knowledge, Goldstine then distributed the ‘design for the EDVAC’ outside the project and even to persons in other countries.

Don’t misread this as my saying that Johnny was a plagiarist or unoriginal. Johnny contributed to the computer revolution in later ways as well, and that specific incident is almost certainly just Herman Goldstine’s fault, although Johnny did silently benefit. Johnny was fond of saying some version of “It takes a Hungarian to walk into a revolving door behind you and come out ahead” and that’s precisely what happened.

My point is merely that, while Johnny was absolutely one of the greats in the pantheon of science and math, one of is how we should see John von Neumann. His first major work on axioms had “run aground” on Gödel. His second major work on formalizing quantum theory had been upstaged by Dirac’s notation, which became the standard, and his major proof that hidden variable interpretations of quantum mechanics were supposedly impossible turned out to be flawed, due to what John Stewart Bell called a “silly” error.

Johnny’s long-time assistant and fellow great Hungarian mathematician, Paul Halmos, wrote about him that:

As a writer of mathematics von Neumann was clear, but not clean; he was powerful but not elegant. He seemed to love fussy detail, needless repetition, and notation so explicit as to be confusing…. quite a few times, it gave lesser men an opportunity to publish “improvements” of von Neumann.

Johnny even suspected that Norbert Wiener had a mind “intrinsically better than his own.” Who was Norbert Wiener? Another famous early computer scientist and cyberneticist that people like to tell stories about.3 He was also another child prodigy shaped by his father, but much more intently and viciously than Max; e.g., when Norbert was born, his father held a press conference saying he would raise a genius—and, well, he did. Johnny also envied the dreamy Einstein’s deep and intuitive leaps of genius.

Nobel Prize winner Eugene Wigner, Johnny’s best and longest friend, had plenty of high praise for Johnny, but firmly gives his opinion on whether John von Neumann was the Greatest Scientific Mind of All Time. The answer was no.

Einstein’s understanding was deeper than even John von Neumann’s. His mind was more penetrating and more original than von Neumann’s.

Johnny’s friend, Stanisław Ulam, who suggested the idea of automata growing on cells that Johnny then formalized into cellular automata (note again the pattern), wrote of Johnny that:

In spite of his great powers and his full consciousness of them, he lacked a certain self-confidence, admiring greatly a few mathematicians and physicists who possessed qualities which he did not believe he himself had in the highest possible degree. The qualities which evoked this feeling on his part were, I felt, relatively simple-minded powers of intuition of new truths, or the gift for a seemingly irrational perception of proofs or formulation of new theorems.

Absolutely none of this means that John von Neumann wasn’t a genius, nor contributed far more than all but a few others. Certainly, plenty of other geniuses thought he was smarter than them (just as he suspected Einstein and Wiener might have something he didn’t). His output attests to it. As Halmos, his assistant, summarized:

Brains, speed, and hard work produced results. In von Neumann’s Collected Works there is a list of over 150 papers. About 60 of them are on pure mathematics (set theory, logic, topological groups, measure theory, ergodic theory, operator theory, and continuous geometry), about 20 on physics, about 60 on applied mathematics (including statistics, game theory, and computer theory), and a small handful on some special mathematical subjects and general non-mathematical ones.

But even in his incredible output and the legends around him, Johnny’s overall academic oeuvre seems closer to what one would expect from someone whose talents were due to his perfect education vs. his supposedly-superhuman brain itself. His work is a triumph of formalizing things beautifully and quickly, being in the right rooms, flat-out knowing more than other people, and competitively beating them to the punch (he described himself in a letter to his daughter as “an ambitious bastard”).

I personally can’t help but feel that Johnny’s most original work was his development of game theory. Games have existed for a pretty long time, but here’s Johnny in 1928 with “Theory of Parlor Games” which grows into his famous 1944 book Theory of Games and Economic Behavior.

It’s also the most him. As a child, Johnny would dress up and have his brothers move sheets of paper around, enacting battles. Probably it was Kriegsspiel, a Prussian military war game that had strong influences on later gaming systems like Dungeons & Dragons and Warhammer. It didn’t seem to matter who won or who lost to Johnny, instead his question was: Is there an optimal strategy? We can see how this evolved into: Is there a provable optimal strategy? Even there we can see how his environment influenced his work as much as his genetics.

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Just Because It’s on Wikipedia Doesn’t Make It Real

Now that we have sketched the man, and his many, many impressive contributions (but also noted their occasional overhyped nature), we have the capacity to look honestly at the legends and myths around Johnny’s raw mental abilities.

Unfortunately, these myths have propagated to the point where even Wikipedia is wrong. E.g., on Wikipedia it’s stated that Johnny could divide 8-digit numbers in his head at the age of 6.

The actual link takes you to a 2007 pop-sci book titled Mathematics: Powerful Patterns In Nature and Society by one Harry Henderson. Here’s what Henderson’s, and thus Wikipedia’s, child prodigy feat is based on: an incredibly brief section about Johnny’s childhood (because this book is not a biography).

No specific source, unfortunately! Yet Henderson, via Wikipedia, is cited by so many others for the same claim.

Indeed, we can debunk this entirely. For I tracked down another “eight-digit” number story about Johnny that the author Harry Henderson (who had no connection to Johnny) almost certainly misremembered. His Further Reading section contains Macrae’s biography, and, yup, there it is written by way of introduction that:

Three usual descriptions are that Johnny exuded self-confidence, had the world’s best memory, and could multiply eight-figure numbers by other eight-figure numbers in his head. All these descriptions are half wrong.

Henderson almost certainly just misremembered that part of Macrae’s biography. And so not only is Wikipedia, by way of Henderson’s mistake, reporting an ability that’s “half wrong,” the situation is even worse, because from the surrounding context Macrae is clearly talking about the adult Johnny, not Johnny as a 6-year-old! And Macrae’s quote is about multiplication. So it is thrice wrong.

Other rumors have similar origins. Enter, once again, Herman Goldstine. That’s right, the same guy who gave Johnny all the credit for the von Neumann architecture.

In 1972, Herman Goldstine published The Computer from Pascal to von Neumann, in which he makes a number of much-cited wild claims about Johnny, including about his early life, things that Goldstine could not possibly have known for sure. First, he says that “He and his father joked together in Classical Greek.” However, Macrae specifically notes that Johnny’s family denies any memory of this (and there goes Greek from the list of languages he was fluent in as a young child).

Second, Goldstine gives us what seems to be the original “perfect recall” story:

One of his most remarkable capabilities was his power of absolute recall. As far as I could tell, von Neumann was able on once reading a book or article to quote it back verbatim; moreover, he could do it years later without hesitation…. On one occasion I tested his ability by asking him to tell me how A Tale of Two Cities started. Whereupon, without any pause, he immediately began to recite the first chapter and continued until asked to stop after about ten or fifteen minutes.

Macrae also mentions this story, but gives some pretty critical context that deflates the whole thing from superhuman to merely incredibly impressive: Johnny had specifically memorized the beginning of Dickens’ A Tale of Two Cities prior to coming to America to improve his English.

Through this practice, he was able at age 50 to baffle Herman Goldstine by quoting the first dozen pages of Dickens’ Tale of Two Cities word for word.

Macrae also mentions that:

In English he had chosen to browse through encyclopedias and pick out interesting subjects to learn by heart. That is why he had such extraordinarily precise knowledge of the Masonic movement, the early history of philosophy, the trial of Joan of Arc, and the battles in the American civil war. In German in [his] youth he had done the same thing with Oncken’s [history].

That’s obviously impressive, to remember a long passage so many years later. But Goldstine makes it sound like he “tested” Johnny on the opening of a random book—except it couldn’t have been a random book, as it was the exact same passage Macrae reports Johnny purposefully memorized years before to practice his English. Likely, either that book came up in conversation naturally and the demonstration actually occurred, but then Goldstine misunderstood or misremembered (or worse) and portrays his effort as “testing” him (and perhaps exaggerated the length of the passage too).4

Consciously choosing passages to learn by heart to improve one’s feel for a language is incredibly astute, but also wildly different from being able to remember every book you’ve ever read. Indeed, the same titles (the history he read when he was young, the Dickens, and entries in an encyclopedia) crop up again and again in the stories of recitations. If it wasn’t purposeful memorization, it wouldn’t be listable like that. It would really be just any random book. Like a telephone book!

Incredibly, that’s also another (false) claim.

This claim is from Prisoner’s Dilemma: John von Neumann, Game Theory, and the Puzzle of the Bomb, yet another pop-sci semi-biography more focused on the science than Johnny himself, published in 1993 by William Poundstone. And surely we can trust the esteemed author of other books like How Do You Fight a Horse-Sized Duck?

Nope, it’s almost certainly another misremembering of a different source. Specifically, this time from a 1973 source: “The Legend of John von Neumann,” by Paul Halmos (a great mathematician in his own right, but also, importantly, Johnny’s assistant for many years). Halmos writes a tongue-in-cheek accounting of the stories and legends about Johnny’s intelligence. Early on, Halmos says there’s a story in circulation that Johnny (as an adult) could “memorize the names, addresses, and telephone numbers in a column of the telephone book on sight,” but Halmos makes perfectly clear that it’s just a story, and is, like many others, “undocumented” and “unverifiable.” Halmos may even be implying it was Johnny’s own joking that originated it:5

Speaking of the Manhattan telephone book he said once that he knew all the numbers in it—the only other thing he needed, to be able to dispense with the book altogether, was to know the names that the numbers belonged to.

So that joke (he knows the numbers, get it?) may have started the rumor about Johnny memorizing phone books as an adult (or they originated elsewhere, but certainly Halmos himself never witnessed anything like that in his many years of working closely with Johnny or he’d say so). However, somehow Halmos’ wry recounting of tall tales makes it into Prisoner’s Dilemma, and there it is mistaken to be (a) about Johnny as a child, and (b) true.

What’s interesting is that Halmos, who would be in one of the best positions to judge the adult Johnny as his assistant, does give actual examples of Johnny’s calculating brilliance. Some are as impressive as the legend suggests—e.g., Johnny solving difficult math puzzles, or once beating a computer at a calculation. But others are, frankly, not so impressive. Kind of disappointing, really. Johnny arrived at a solution in two seconds while his students took 10 seconds? Okay. He once gave a presentation on his area of expertise without any notes? Okay, now we’re just reaching for stuff.

Halmos even makes us question how many languages Johnny knew. Apparently:

At home the von Neumanns spoke Hungarian, but he was perfectly at ease in German, and in French, and, of course, in English.

So that’s “only” four fluent languages as an adult, and his biographer Macrae implies that Johnny’s English spelling was bad. Stanisław Ulam does say an adult Johnny remembered his Latin and Greek from school (implying he learned it in the formal system) and could kind of speak Spanish by adding “el” to things (again, this seems like clear reaching?). So we have four fluent languages, his ability to add “el” to words, and at least some unclear amount of Latin and Greek from school as an adult—deeply impressive, except for the Spanish!

But that impressiveness was, yet again, transformed via the rumor mill into him knowing all those (and more) languages fluently as a young child, while it’s quite clear that he started learning English only at 10 or 11. The Italian governess apparently never had much of an impact on Johnny—odd for someone who could learn everything effortlessly!

The “Nothing Ever Happens” Explanation

While hopefully giving Johnny his incredible due, we have so far debunked that child Johnny had the preposterous powers assigned to him, and we’ve also shown how even his adult legend has been exaggerated. There’s so little original scholarship too that pretty much everything links back to the same few mistakes I’ve identified.

Johnny died before writing his autobiography, a huge blow to history. To make matters worse, some Johnny biographies contain numerous errors. Even Macrae’s biography isn’t perfect (see this footnote6 for a historical error I identified, and other minor criticisms). My long-standing policy is not to try to hurt people via gotcha mistakes, and I believe that most people are doing their best, and so I’ve tried to not single out any individual author too much (other than the long-dead Goldstine, but perhaps it was a series of errors on his part too).7

Johnny undeniably was a child prodigy. Wigner’s memoirs attest to that.8 But Wigner only met Johnny when Johnny was 12, already having been tutored by Rátz privately for two years, and simultaneously likely Fekete as well. A preteen can indeed be incredibly mathematically impressive, especially when they are being tutored by those two!

Personally, after all my reading, I do believe Johnny had a mind like a steel trap. I just also believe he was mortal. And the boring explanation is that he simply had a habit of memorizing certain passages and pulling them out to show off.

There is suggestive evidence of this. One of the best first-hand accounts of Johnny’s mental powers comes from Johnny’s brother himself. While it’s unfinished, his brother does mention young Johnny’s memory as being “amazing” and “powerful” and at one high point, “almost unlimited.” But he was 8 years younger than Johnny, and really the only concrete examples he gives of these powers are things like Johnny explaining scientific concepts at the dinner table really well, impressively deducing the answer to a prize question, and the older Johnny finishing his homework quickly. And his younger brother also explicitly says that many of the rumors swirling around Johnny are false, and implies that a real biography will one day correct them. Nowhere does he deny that Johnny engaged in explicit memorization and study, of which there was a family habit; tellingly, in the middle of the unfinished recollections, Johnny’s brother begins to lapse into old poetry in other languages, and admits these are poems that he explicitly memorized back then, in that glittering cognitive Belle Époque of his brain—writing about Johnny was surfacing them.

Given his family’s practice of memorizing, Johnny likely had both an innately good memory for the things he read but also studied and practiced them. And most families also don’t expect recitations at the dinner table to begin with. Even his grandfather used to come over and perform arithmetic tricks, abilities that Johnny had a jolly time exaggerating later in life.

We don’t hear any specific anecdotes about Johnny’s supposed ability to remember literally anything he wants in his daughter’s memoir, The Martian’s Daughter, and I feel like that might crop up if you lived together for years. And we know from Macrae that Johnny “could rarely remember a name” and:

His memory and feel for words, plus unsurpassed feel for mathematical symbols, had not extended to memory for faces. All his life he was embarrassed by not knowing people who clearly knew him. He had no sort of photographic memory. This imposed some limitations on his mathematics (he was not good at envisaging shapes) but probably also added to some of his strengths.

We also have this story about Mandelbrot and Johnny both forgetting a proof they worked on together.

[Mandelbrot] told me that one long afternoon he and von Neumann were working together trying to prove a difficult theorem. As their conversation advanced, von Neumann asked Mandelbrot several times to take notes on their progress. Each time Mandelbrot grandly replied that he would remember everything and write it up the next day. But the next morning he woke up and could not remember any of the key steps in the proof they had been developing. He said that when he confessed this to von Neumann he had never before seen a man so angry and he himself remembered the incident as the ultimate embarrassment of his professional career.

Meanwhile, Johnny’s daughter offers the observation that Johnny’s first wife (her mother) complained he was always taking an encyclopedia to the bathroom.

My mother used to say, only half jokingly, that one of the reasons she divorced him was his penchant for spending hours reading one of the tomes of an enormous German encyclopedia in the bathroom.

So maybe he just read and re-read books on the toilet? There’s an answer as mundane as possible!

Of course, the further part of the “nothing ever happens” explanation is that people tell stories about other geniuses too. E.g., Norbert Wiener had an “encyclopedic memory” according to Steve Heims, but only Johnny’s has been transmuted via internet lore.

What we do know for sure is that Johnny’s huge amount of crystallized knowledge was:

… the explanation of his extraordinary powers of mental calculation. He was not actually better than many other mathematicians—or indeed than some vaudeville freaks—at multiplying one eight-digit figure by another. But he used his accumulations of mathematical constants and equations to become a startling problem-solver and extraordinary concept-expander.

The Something Happened Explanation: Johnny Had OCD

I couldn’t help being struck, in my reading, by how Johnny’s second wife’s personal account strongly suggests he had OCD:

A drawer could not be opened unless it was pushed in and out seven times, the same with a light switch, which also had to be flipped seven times before you could let it stay.

This conflicts with another legend, that Johnny was 100% normal, despite his intelligence. He liked parties, yes, but he wasn’t normal—just normal compared to people like Dirac, who crept along next to walls. The details of Johnny’s wife’s account have only emerged somewhat recently, and there hasn’t been much time for scholars to digest it.

And, interestingly enough, there’s a phenomenon where OCD sometimes manifests as “memory hoarding.” It’s not well-studied, but it’s common enough for the term to be used. Here’s from the OCD Center of LA:

Memory hoarding is a mental compulsion to over-attend to the details of an event, person, or object in an attempt to mentally store it for safekeeping. This is generally done under the belief that the event, person, or object carries a special significance and will be important to recall exactly as-is at a later date. The memory serves the same function for the mental hoarder that the old newspaper serves for the physical hoarder.

Maybe Johnny’s version of OCD manifested within his studying habits and he became a “mental hoarder.” Johnny certainly could have disguised this, just as he pragmatically accepted his daughter hiding her Jewish ethnicity later in life to keep appearances copacetic. Maybe he kept his own mental issues as private as possible too.9 Like, say, in the bathroom? Perhaps he sat on the toilet for hours and hours going over encyclopedias, not in raw intellectual pleasure, but in tormented neurosis. There are stories of him abruptly leaving parties to go do something mysterious, only to return as if everything was fine hours later. Everyone’s always assumed he was jotting down brilliant notes for new mathematics. Maybe he was just living out his OCD in the bathroom again.

Again, there’s no strong need for the OCD hypothesis. But if you are going to give full credit to the remaining anecdotes (which are less preposterous than the ones I’ve debunked), the OCD hypothesis is more likely than that he genetically had a memory so beyond even his genius peers. And there’s a funny corollary. It might mean that out of a million embryos, maybe Johnny’s brain specifically wouldn’t be the one picked by genetic scoring! If you cloned Johnny today into a different family environment, you might not get a genius, but merely a smart person with OCD focused on stuff that’s not memorizing equations and hobby history.

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My overall thoughts on pop-hereditarianism

I actually hate writing about hereditarianism, even obliquely.

All the pivots are predictable and exhausting: “What about Study A, or Person B, or example C, or observation D!” Or “What about some other dug-up 80-year-old claim about Johnny—you can’t disprove them all!”

It’s also a fraught subject, and I don’t like confrontation. I’m much like Johnny that way—always smoothing things over (our similarities, unfortunately for me, end there). There are just far more interesting things to do, and I want to get along with most individuals.

The problem is, despite not actually being a blank slatist, I’m interested in what makes for a great education and this stuff pops up in my way when I want to talk about it. The philosophy also attracts immoral and dangerous political ideologues who use it as a way to accumulate power. That’s not to say everyone who disagrees with me is evil or bad! Plenty of others do honestly approach the question of Nature vs. Nurture and come down more on the hereditarian side than I. That’s perfectly fine and reasonable, of course. But when it comes to the pop-hereditarian movement as a whole, the loudest voices on social media will take over, if they haven’t already, as the project of pop-hereditarianism has obviously become a political one.

Since this small but symbolically-important slice of the pop-hereditarian movement—all their legends about an individual with a genetically-perfect brain called John von Neumann—has turned out to be built on absurdly selective credulity, and on shallow intellectualism in general, I hope readers can reach their own conclusions about how much of the rest is not lightning coming out of arses, but something else.

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.

“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.

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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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.

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.

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.

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…

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Aesthetics matter.

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.

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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.

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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…

Header images (and thumbnails) have declined in importance.

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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 serious people talking as if in serious rooms, internets of gossip and heart emojis, and internets of clowns. There are internets you can only enter through a hole under your bed, an orifice into which you writhe.

It’s a chromatic thing that can’t hold a shape for more than an instant. But every year, I get to see the internet through the eyes of subscribers to The Intrinsic Perspective. The community submits its writing available online, and I curate and share it.

The quality was truly exceptional this year—I found that they all speak for themselves, and can all be approached on their own terms, so I organized them to highlight how each is worth reading, thinking about, disagreeing with, or simply enjoying; at the very least, they are worth browsing through at your leisure, and finding hidden gems of writers to follow.

Please note that:

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

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

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

• If you submitted something and it’s missing, note that it’s probably in an upcoming Part 2.

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

1. “Wisdom of Doves” by Doctrix Periwinkle.

Evolved animal behaviors are legion, so why do we choose the examples we do to explain our own?

According to psychologist Jordan Peterson, we are like lobsters. We are hierarchical and fight over limited resources….

Dr. Peterson is a Canadian, and he is describing the North Atlantic American lobster, Homarus americanus. Where I live, lobsters are different.

For instance, they do not fight with their claws, because they do not have claws…. Because they do not have claws, spiny lobsters (Panulirus argus) are preyed upon by tropical fish called triggerfish…. The same kind of hormone signaling that made American lobsters exert dominance and fight each other causes spiny lobsters to cluster together to fight triggerfish, using elaborately coordinated collective behavior. Panulirus lobsters form choreographed “queues,” “rosettes,” and “phalanxes” to keep each other safe from the triggerfish foe. Instead of using claws to engage in combat with other lobsters, spiny lobsters use their attenules—the spindly homologues of claws seen in the photograph above—to keep in close contact with their friends….

If you are a lobster, what kind of lobster are you?

2. “We Know A Good Life When We See It” by Matt Duffy.

A reflection on how fluency replaced virtue in elite culture, and why recovering visible moral seriousness is essential to institutional and personal coherence.

We’ve inherited many of the conditions that historically enabled virtue—stability, affluence, access, mobility—but we’ve lost the clarity on virtue itself. The culture of technocratic primacy rewards singularity: total, often maniacal, dedication to one domain at the expense of the rest…. Singular focus is not a human trait. It is a machine trait. Human life is fragmented on purpose. We are meant to be many things: friend, worker, parent, neighbor, mentor, pupil, citizen.

3. “The Vanishing of Youth” by Victor Kumar, published in Aeon.

Population decline means fewer and fewer young people, which will lead to not just economic decay but also cultural stagnation and moral regress.

Sometimes I’m asked (for example, by my wife) why I don’t want a third child. ‘What kind of pronatalist are you?’ My family is the most meaningful part of my life, my children the only real consolation for my own mortality. But other things are meaningful too. I want time to write, travel and connect with my wife and with friends. Perhaps I’d want a third child, or even a fourth, if I’d found my partner and settled into a permanent job in my mid-20s instead of my mid-30s… Raising children has become enormously expensive – not just in money, but also in time, career opportunities and personal freedom.

4. “Three tragedies that shape human life in age of AI and their antidotes”, by brothers Manh-Tung Ho & Manh-Toan Ho, published in the journal AI & Society.

In this paper, we [the authors] discuss some problems arising in the AI age, and then, drawing from both Western and Eastern philosophical traditions to sketch out some antidotes. Even though this was published in a scientific journal, we published in a specific section called Curmudgeon Corner. According to the journal it "is a short opinionated letter to the editor on trends in technology, arts, science and society, commenting emphatically on issues of concern to the research community and wider society, with no more than 3 references and 2 co-authors.”

The tragedy of the commons is the problem of inner group conflicts driven by the lack of cooperation (and communication) when each individual purely follows his/her own best interest (e.g., raises more cattle to feed on the commons), doing so will undermine the collective good (e.g., the commons will be over-grazed). Thus, we define the AI-driven tragedy of the commons as short-term economic/psychological gains that drive the development, launch, and use of half-baked AI products and AI-generated contents that produce superficial information and knowledge, which ends up harming the individual and collective in the long term.

5. "Of Mice, Mechanisms, and Dementia" by Myka Estes.

Billions spent, decades lost: the cautionary tale of how Alzheimer’s research went all-in on a bad bet.

Another way to understand how groundbreaking these results were thought to be at the time is to simply follow the money. Within a year, Athena Neurosciences, where Games worked, was acquired by Elan Corp. for a staggering $638 million. In the press release announcing the merger, Elan proclaimed that the acquisition “provides the opportunity for us to capitalize on an important therapeutic niche, by combining Athena’s leading Alzheimer’s disease research program with Elan’s established development expertise.” The PDAPP mouse had transformed from laboratory marvel to the cornerstone of a billion-dollar strategy.

But, let’s peer ahead to see how that turned out. By the time Elan became defunct in 2013, they had sponsored not one, not two, but four failed Alzheimer's disease therapeutics, all based on the amyloid cascade hypothesis, hemorrhaging $2 billion in the process. And they weren't alone. Pharmaceutical giants, small biotechs, and research organizations and foundations placed enormous bets on amyloid—bets that, time and again, failed to pay off.

6. “Schrödinger's Chatbot” by R.B. Griggs.

Is an LLM a subject, an object, or some strange new thing in between?

It would be easy to insist that LLMs are just objects, obviously. As an engineer I get it—it doesn’t matter how convincing the human affectations are, underneath the conversational interface is still nothing but data, algorithms, and matrix multiplication. Any projection of subject-hood is clearly just anthropomorphic nonsense. Stochastic parrots!

But even if I grant you that, can we admit that LLMs are perhaps the strangest object that has ever existed?

7. "A Prodigal Son" by Eva Shang.

My journey back to Christianity and why it required abandoning worship of the world.

How miserable is it to believe only in the hierarchy of men? It’s difficult to overstate the cruelty of the civilization that Christianity was born into: Roman historian Mary Beard describes how emperors would intentionally situate blind, crippled, or diseased poor people at the edges of their elaborate banquets to serve as a grotesque contrast to the wealth and health of the elite. The strong did what they willed and the weak suffered what they must. Gladiatorial games transformed public slaughter into entertainment. Disabled infants were left to die in trash heaps or on hillsides. You see why the message of Christ spread like wildfire. What a radical proposition it must have been to posit the fundamental equality of all people: that both the emperor and the cripple are made in the image of God.

8. “Why Cyberpunk Matters” by C.W. Howell.

Though the genre is sometimes thought dated, cyberpunk books, movies, and video games are still relevant. They form a last-ditch effort at humanism in the face of machine dominance.

So, what is it that keeps drawing us to this genre? It is more, I believe, than simply the distinct aesthetic…. It reflects, instead, a deep-seated and long-standing anxiety that modern people feel—that our humanity is at stake, that our souls are endangered, that we are being slowly turned into machines.

9. “You Are So Sensitive” by Trevy Thomas.

This piece is about the 25 percent of our population, myself the author included, who have a higher sensitivity to the world around us -- with both good and bad effects.

As a young girl, I could ride in a car with my father and sing along to every radio song shamelessly loud. He was impressed that I knew all the words even as the musician in him couldn’t help but critique the song itself. “Why does every song have the word ‘baby’ in it?” he’d ask. But then I got to a point where I’d leave a store or promise never to return to a restaurant because of the music I’d heard in it. Some song from that place would be so lodged in my brain that it would wake me in the middle of the night two weeks later…. about a quarter of the population—humans and animals alike—have this increased level of sensitivity. It can show up in various forms, including sensitivity to sound, light, smell, and stimulation.

10. “Solving Popper's Paradox of Tolerance Before Intolerance Ends Civilization” by Dakara.

A solution to preserving the free society without invoking the conflict of Popper's Paradox.

… Are we now witnessing the end of tolerant societies? Is this the inevitable result that eventually unfolds once an intolerant ideology enters the contest for ideas and the rights of citizens?…

Have we already reached the point where the opposing ideologies are using force against the free society? They censor speech, intervene in the employment of those they oppose, and will utilize physical violence for intimidation.

11. “Knowledge 4.0” by Davi.

From gossip to machine learning - how we bypassed understanding.

Speech allowed us to transmit knowledge among humans, the written word enabled us to broadcast it across generations, and software removed the cost of accessing that knowledge, while turbocharging our ability of composing any piece of knowledge we created with the existing humanity-level pool. What we call now machine learning came to remove one of the few remaining costs in our quest of conquering the world: creating knowledge. It is not that workers will lose their jobs in the near future, this is the revolution that will make obsolete much of our intellectual activity for understanding the world. We will be able to craft planes without ever understanding why birds can fly.

12. “Problematic Badass Female Tropes” by Jenn Zuko.

An overview of the PBFT series of 7 that covers the bait-and-switch of women characters that are supposed to be strong, but end up subservient or weak instead.

The problem that becomes apparent here (as I’m sure you’ve noticed even in only this first folktale example), is that in today’s literature and entertainment, these strong, independent women characters we read about in old stories like Donkeyskin and clever Catherine are all too often subverted, altered, and weakened; either in subtle ways or obvious ways, especially by current pop culture and Hollywood.

13. "The West is Bored to Death" by Stuart Whatley, published in The New Statesman.

An essay on the classical "problem of leisure," and how a society/culture that fails to cultivate a leisure ethic ends up in trouble.

Developing a healthy relationship with free time does not come naturally; it requires a leisure ethic, and like Aristotelian virtue, this probably needs to be cultivated from a young age. Only through deep, sustained habituation does one begin to distinguish between art and entertainment, lower and higher pleasures, titillation and the sublime.

14. “MAGA As The Liberal Shadow” by Carlos.

In a very real sense, liberalism is the root cause of MAGA, and it's very important to understand this to see a way forward.

It’s no wonder that I feel liberalism as the source of this eternal no: it is liberals who define the collective values of our culture, as it is the cities that produce culture, and the cities are liberal. So the voice of the collective in my head, is a liberal. My little liberal thought cop, living in my head.

4chan is great because you get to see what happens when someone evicts the liberal cop, the shadow run rampant. Sure, all sorts of very naughty emotions get expressed, and it is quite a toxic place, but it’s like a great sigh, finally, you can unwind, and say whatever the fuck you want, without having to take anyone else’s feelings into account.

15. “The Blowtorch Theory: A New Model for Structure Formation in the Universe” by Julian Gough.

The James Webb Space Telescope has opened up a striking and unexpected possibility: that the dense, compact, early universe universe wasn't shaped slowly and passively by gravity alone, but was instead shaped rapidly and actively by sustained, supermassive black hole jets, which carved out the cosmic voids, shaped the filaments, and generated the magnetic fields we see all around us today.

An evolved universe, therefore, constructs itself according to an internal, evolved set of rules baked deep into its matter, just as a baby, or a sprouting acorn, does.

The development of our specific universe, therefore, since its birth in the Big Bang, mirrors the development of an organism; both are complex evolved systems, where (to quote the splendid Viscount Ilya Romanovich Prigogine), the energy that moves through the system organises the system.

But universes have an interesting reproductive advantage over, say, animals.

16. “Tea” by Joshua Skaggs.

Joshua Skaggs, a single foster dad, has a 3 a.m. chat with one of his kids.

My second night as a foster dad I wake in the middle of the night to the sound of footsteps. I throw on a t-shirt and find him pacing the living room, a teenager in basketball shorts and a baggy t-shirt….

“I broke into your closet,” he says.

“Oh yeah?” I say….

“I looked at all your stuff,” he says. “I thought about drinking your whiskey, but then I thought, ‘Nah. Josh has been good to me.’ So I just closed the door.”

I’m not sure what to say. I eventually land on: “That’s good. I’m glad you didn’t take anything.”

“It was really easy to break into,” he says. “It only took me, like, three seconds.”

“Wow. That’s fast.”

“I’m really good at breaking into places.”

17. “Notes in Aid of a Grammar of Assent” by Amanuel Sahilu.

Through the twin lenses of literature and science, I take a scanning look at the human tendency to detect and discern personhood.

This is all to say, a main reason for modern skepticism toward serious personification is that we think it’s shoddy theorizing….

But I think few moderns reject serious personification on such rational grounds. It may be just as likely we’re driven to ironic personification after adjusting to the serious form as children, when we’re first learning about language and the world. Then as we got older the grown-ups did a kind of bait-and-switch, and serious personification wasn’t allowed anymore.

18. “Book Review: Griffiths on Electricity & Magnetism” by Tim Dingman.

In adulthood I have read many STEM textbooks cover-to-cover. These are textbooks that are supposed to be standards in their fields, yet most of them are not great reading. The median textbook is more like a reference manual with practice problems than a learning experience.

Given the existence and popularity of nonfiction prose on any number of topics, isn’t it odd that most textbooks are so far from good nonfiction? We have all the pieces, why can’t we put them together? Or are textbooks simply not meant to be read?

Certainly most students don’t read them that way. They skim the chapters for equations and images, mostly depend on class to teach the ideas, then break out the textbook for the problem set and use the textbook as reference material. You don't get the narrative that way.

Introduction to Electrodynamics by David Griffiths is the E&M textbook. We had it in my E&M class in college…. Griffiths is so readable that you can read it like a regular book, cover to cover.

19. “Fine Art Sculpture in the Age of Slop” by Sage MacGillivray.

Exploring analogue wisdom in a digital world: Lessons from a life in sculpture touching on brain lateralization, deindustrialization, Romanticism, AI, and more.

… As Michael Polanyi pointed out, it only takes a generation for some skills to be lost forever. We can’t rely on text to retain this knowledge. The concept of ‘stealing with your eyes’, which is common in East Asia, points to the importance of learning by watching a master at work. Text (and even verbal instruction) is flattening….

These days, such art studio ‘laboratories’ are hard to find. Not only is the environment around surviving studios more sterile and technocratic, but artists increasingly outsource their work to a new breed of big industry: the large art production house. A few sketches, a digital model, or perhaps a maquette — a small model of the intended work — are shared with these massive full-service shops that turn sculpture production from artistic venture into contract work. As the overhead cost of running a studio has increased over time, this big-shop model of outsourcing is often the only viable model for artists who want to produce work at scale….

And just like a big-box retailer can wipe out the local hardware store, the big shop model puts pressure on independent studios that train workers in an artisanal mode and allow the artist to evolve the artwork throughout the production process.

20. “Setting the Table for Evil” by Reflecting History.

About the role that ideology played in the rise and subsequent atrocities of Nazi Germany, and the historical debate between situationism and ideology in explaining evil throughout history.

Some modern “historians” have sought to uncouple Hitler’s ideology from his actions, instead seeking to paint his “diplomacy” and war making as geopolitical reactions to what the Allies were doing. But Hitler’s playbook from the beginning was to connect the ideas of racist nationalism and extreme militarism together, allowing each to justify the existence of the other. Nazi Germany’s war was more than just geopolitical strategic war-making chess, it was conquest and subjugation of racial enemies. The British leadership were “Jewish mental parasites,” the conquest of Poland was to “proceed with brutality!… the aim is the removal of the living forces...,” the invasion of the Soviet Union sought to eliminate “Jewish Bolsheviks,” the war with the United States was fought against President Roosevelt and his “Jewish-plutocratic clique.” Hitler applied his ideology to his conquest and subjugation of dozens of countries and peoples in Europe. He broke nearly every international agreement he ever made, and viewed treaties and diplomacy as pieces of paper to be shredded and stepped over on the way to power. Anyone paying attention to what Hitler said or did in 1923 or 1933 or 1943 had to reckon with the fact that Hitler’s ideology informed everything he did.

21. “Which came first, the neuron or the feeling?” by Kasra.

A reverie on the history and philosophy behind the mind-body problem.

… I do know that life gets richer when you contemplate that either one of these—the neuron and the feeling—could be the true underlying reality. That your feelings might not just be the deterministic shadow of chemicals bouncing around in your brain like billiard balls. That perhaps all self-organizing entities could have a consciousness of their own. That the universe as a whole might not be as dark and cold and empty as it seems when we look at the night sky. That underneath that darkness might be the faintest glimmer of light. Of sentience. A glimmer of light which turns back on itself, in the form of you, asking the question of whether the neuron comes first or the feeling.

22. “Dying to be Alive: Why it's so hard to live your unlived life and how you actually can” by Jan Schlösser.

Exploring the question of why we all act as if we were immortal, even though we all know on an intellectual level that we're going to die.

Becker states that humans are the only species who are aware of their mortality.

This awareness conflicts with our self-preservation instinct, which is a fundamental biological instinct. The idea that one day we will just not exist anymore fills us with terror – a terror that we have to manage somehow, lest we run around like headless chickens all day (hence ‘terror management’).

How do we manage that terror of death?

We do it in one of two ways:

1. Striving for literal or symbolic immortality

2. Suppressing our awareness of our mortality

23. “Thirst” by Vanessa Nicole.

Connecting Viktor Frankl’s idea of “the existence of thirst implies the existence of water,” to choosing to live with idealism and devotion.

This is, essentially, how I define being idealistic: a devotion to thirst and belief in the existence of water. To me, idealism isn’t about a hope for a polished utopia—it’s in believing that fulfillment can transform, from an abstract emptiness into the pleasantly refreshed taste in your mouth. (And anyway, there’s a whole universe between parched and utopia.)

24. “A god-sized hole” by Iuval Clejan.

A modern interpretation of Pascal's presumptuous phrase (about a god-sized hole).

People get to feel good about themselves by working hard at something that they get paid for. It also gives them social legitimacy. For some it offers a means of connection with other humans that is hard to achieve outside of work and church. For a few lucky ones it offers a way to express talent and passion. But for most it is an attempt to fill the tribe, family and village-sized holes of their souls.

25. “Have 'Quasi-Inverted Spectrum' Individuals Fallen into Our World, Unbeknownst to Us?” by Ning DY.

Drawing on inconsistencies in neuroimaging and a re-evaluation of first-person reports, this essay argues that synesthesia may not be a cross-activation of senses, but rather a fundamental, 'inverted spectrum-like' phenomenon where one sensory modality's qualia are entirely replaced by another's due to innate properties of the cortex.

I wonder, have we really found individuals similar to those in John Locke's 'inverted spectrum' thought experiment (though different from the original, as this is not a symmetrical swap but rather one modality replacing another)? Imagine if, from birth, our auditory qualia disappeared and were replaced by visual qualia, changing the experienced qualia just as in the original inverted spectrum experiment. How would we describe the world? Naturally, we would use visual elements to name auditory elements, starting from the very day we learned to speak. As for the concepts described by typical people, like pitch, timbre, and intensity, we would need to learn them carefully to cautiously map these concepts to the visual qualia we "hear." Perhaps synesthetes also find us strange, wondering why we give such vastly different names to two such similar experiences?

26. “Elementalia: Chapter I Fire” by Kanya Kanchana.

Drawing from the vast store of our collective imagination across mythology, philosophy, religion, literature, science, and art, this idiosyncratic, intertextual, element-bending essay explores the twined enchantments of fire and word.

My legs and feet are bare—no cloth, no metal, not even nail polish. Strangely, my first worry is that it feels disrespectful to step on life-giving fire. Then I see a mental image of a baby in his mother’s arms, wildly kicking about—but she’s smiling. I better do this before I think too much. I step on the coals. I feel a buzz go up my legs like invisible electric socks but it doesn’t burn. It doesn’t burn.

I don’t run; I walk. I feel calm. I feel good. When I get to the other side, I grin at my friends and turn right around. I walk again.

27. “When Scientists Reject the Mathematical Foundations of Science” by Josh Baker.

By directly observing emergent mechanical behaviors in muscle, I have discovered the basic statistical mechanics of emergence, which I describe in a series of posts on Substack.

Over the past several years, six of these manuscripts were back-to-back triaged by editors at PNAS. Other lower tier journals rejected them for reasons ranging from “it would overturn decades of work” and “it’s wishful thinking” to reasons unexplained. An editorial decision in the journal Entropy flipped from a provisional accept to reject followed by radio silence from the journal.

A Biophysical Journal advisory board rejected some of these manuscripts. In one case, an editor explained that a manuscript was rejected — not because the science was flawed but — because the reviewers they would choose would reject it with near certainty.

28. "The Tech is Terrific, The Culture is Cringe" by Jeff Geraghty.

A fighter test pilot and Air Force General answers a challenge put to him directly by Elon Musk.

On a cool but sunny day in May of 2016, in his SpaceX facility in Redmond, Washington, Elon Musk told me that he regretted putting so much technology into the Tesla Model X. His newest model was rolling out that year, and his personal involvement with the design and engineering was evident. If he had it to do over again, he said, he wouldn’t put so much advanced technology into a car….

Since that first ride, I’ve been watching the car drive for almost a year now, and I’m still impressed…

My daughter, however, wouldn’t be caught dead in it. She much prefers to ride the scratched up old Honda Odyssey minivan. She has an image to uphold, after all.

29. “The Lamps in our House: Reflections on Postcolonial Pedagogy” by Arudra Burra.

In this sceptical reflection on the idea of 'decolonizing' philosophy, I question the idea that we should think of the 'Western philosophical tradition' as in some sense the exclusive heritage of the modern West; I connect this with what I see as certain regrettable nativist impulses in Indian politics and political thought.

I teach philosophy at the Indian Institute of Technology-Delhi. My teaching reflects my training, which is in the Western philosophical tradition: I teach PhD seminars on Plato and Rawls, while Bentham and Mill often figure in my undergraduate courses.

What does it mean to teach these canonical figures of the Western philosophical tradition to students in India?… Some of the leading lights of the Western canon have views which seem indefensible to us today: Aristotle, Hume, and Kant, for instance. Statues of figures whose views are objectionable in similar ways have, after all, been toppled across the world. Should we not at least take these philosophers off their pedestals? …

The Indian context generates its own pressures. A focus on the Western philosophical tradition, it is sometimes thought, risks obscuring or marginalising what is of value in the Indian philosophical tradition. Colonial attitudes and practices might give us good grounds for this worry; recall Macaulay’s famous lines, in his “Minute on Education” (1835), that “a single shelf of a good European library [is] worth the whole native literature of India and Arabia.”

30. “What Happens When We Gamify Reading” by Mia Milne.

How reading challenges led me to prioritize reading more over reading deeply and how to best take advantage of gamification without getting swept away by the logic of the game.

31. “Pan-paranoia in the USA” by Eponynonymous.

A brief history of the "paranoid style" of American politics through a New Romantic lens.

As someone who once covered the tech industry, I join in Ross Barkan’s wondering what good these supposed marvels of modern technology—instantaneous communication, dopamine drips of screen-fed entertainment, mass connectivity—have really done for us. Are we really better off? ….

But we are also facing a vast and deepening suspicion of power in all forms. Those suspicions need not be (and rarely are) rationally obtained. The old methods of releasing societal pressures—colonialism, western expansionism, post-war consumerism—have atrophied or died. It should come as no surprise when violence manifests in their place.

The Desiderata series is a regular roundup of links and thoughts for paid subscribers, and an open thread for the community.

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Contents:

1. GPT-5’s debut is slop.

2. 10% of all human experience took place since the year 2000.

3. Education is a mirror. What’s Alpha School’s reflection?

4. The rise of the secular homeschool superheroes.

5. “The Cheese that Gives you Nightmares.”

6. Avi Loeb at Harvard warns of alien invasion.

7. Moths as celestial navigators.

8. Will AI cause the next depression?

9. From the archives.

10. Comment, share anything, ask anything.

1. GPT-5’s debut is slop.

GPT-5’s launch is imminent. Likely tomorrow. We also have the first confirmed example of an output known for sure to be from GPT-5, which was shared by Sam Altman himself as a screenshot on social media. He asked GPT-5 “what is the most thought-provoking show about AI?”

Hmmm.

Hmmmmmmmmm.

Yeah, so #2 is a slop answer, no?

Maybe even arguably a hallucination. Certainly, that #2 recommendation, the TV show Devs, does initially seem like a good answer to Altman’s question, in that it is “prestige sci-fi” and an overall high-quality show. But I’ve seen Devs. I’d recommend it myself, in fact (streaming on Hulu). Here’s the thing: Devs is not a sci-fi show about AI! In no way, shape, or form, is it a show about AI. In fact, it’s refreshing how not about AI it is. Instead, it’s a show about quantum physics, free will, and determinism. This is the main techno-macguffin of Devs: a big honking quantum computer.

As far as I can remember, the only brief mention of AI is how, in the first episode, the main protagonist of that episode is recruited away from an internal AI division of the company to go work on this new quantum computing project. Now, what’s interesting is that GPT-5 does summarize the show appropriately as being about determinism and free will and existential tension (and, by implication, not about AI). But its correct summary makes its error of including Devs on the list almost worse, because it shows off the same inability to self-correct that LLMs have struggled with for years now. GPT-5 doesn’t catch the logical inconsistency of giving a not-AI-based description of a TV show, despite being specifically asked for AI-based TV shows (there’s not even a “This isn’t about AI, but it’s a high-quality show about related subjects like…”). Meaning that this output, the very first I’ve seen from GPT-5, feels extremely LLM-ish, falling into all the old traps. Its fundamental nature has not changed.

This is why people still call it a “stochastic parrot” or “autocomplete,” and it’s also why such criticisms, even though weaker in strength, can’t be entirely dismissed. Even at GPT-5’s incredible level of ability, its fundamental nature is still that of autocompleting conversations. In turn, autocompleting conversations leads to slop, exactly like giving Devs as a recommendation here. GPT-5 is secretly answering not Altman’s question, but a different question entirely: when autocompleting a conversation about sci-fi shows and recommendations, what common answers crop up? Well, Devs often crops up, so let’s list Devs here.

Judge GPT-5’s output by honest standards. If a human said to me “There’s this great sci-fi show about AI, you should check it out, it’s called Devs,” and then I went and watched Devs, I would spend the entire time waiting for the AI plot twist to make an appearance. At the series end, when the credits rolled, I would be 100% certain that person was an idiot.

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2. 10% of all summed human experience took place since the year 2000.

According to a calculation by blogger Luke Eure, 50% of human experience (total experience hours by “modern humans”) has taken place after 1300 AD.

Which would mean that 10% of collective human experience has occurred since the year 2000! It also means that most of us now alive will live, or have lived, alongside a surprisingly large chunk of when things are happening (at least, from the intrinsic perspective).

3. Education is a mirror. What’s Alpha School’s reflection?

In the education space, the buzz right now is around Alpha School. Their pitch (covered widely in the media) is that they do 2 hours of learning a day with an “AI tutor.”

More recently, The New York Times profiled them:

At Alpha’s flagship, students spend a total of just two hours a day on subjects like reading and math, using A.I.-driven software. The remaining hours rely on A.I. and an adult “guide,” not a teacher, to help students develop practical skills in areas such as entrepreneurship, public speaking and financial literacy.

I’ll say upfront: I do believe that 2 hours of learning a day, if done well, could be enough for an education. I too think kids should have way more free time than they do. So there is something to the model of “2 hours and done” that I think is attractive.

But I have some questions, as I was one of the few actual attendees to the first “Alpha Anywhere” live info session, which revealed details of how their new program for homeschoolers works. Having seen more of it, Alpha School appears based on progressing through pre-set educational apps, and doesn’t primarily involve AI-as-tutor-qua-tutor often (i.e., interacting primarily with an AI like ChatGPT). While the Times says that

But Alpha isn’t using A.I. as a tutor or a supplement. It is the school’s primary educational driver to move students through academic content.

all I saw was one use case, which was AI basically making adaptive reading comprehension tests on the fly (I think that specifically is actually a bad idea, and it looked like reading boring LLM slop to me).

For this reason, the more realistic story behind Alpha School is not “Wow, this school is using AI to get such great results!” but rather that Alpha School is “education app stacking” and there are finally good enough, and in-depth enough, educational apps to cover most of the high school curriculum in a high-quality and interactive way. That’s a big and important change! E.g., consider this homeschooling mom, who points out that she was basically replicating what Alpha School is doing by using a similar set of education apps.

Most importantly, and likely controversially, Alpha School pays the students to progress through the apps via an internal currency that can be redeemed for goodies (oddly, this detail is left out from the analysis of places like the Times—but hey, it’s “the paper of record,” right?).

My thoughts are two-fold. First, I do think it’s true that ed-apps have gotten good enough to replace a lot of the core curriculum and allow for remarkable acceleration. Second, I think it’s a mistake to separate the guides from the learning itself. That is, it appears the actual academics at Alpha School are self-contained, as if in a box; there’s a firewall between the intellectual environment of the school and what’s actually being learned during those 2 hours on the apps. Not to say that’s bad for all kids! Plenty of kids ultimately are interested in things beyond academics, and sequestering the academics “in a box” isn’t necessarily bad for them.

However, it’s inevitable that this disconnect makes the academics fundamentally perfunctory (to be fair, this is true for a lot of traditional schools as well). As I once wrote about the importance of human tutors:

Serious learning is socio-intellectual. Even if the intellectual part were to ever get fully covered by AI one day, the “socio” part cannot… just like how great companies often have an irreducibly great culture, so does intellectual progress, education, and advancement have an irreducible social component.

Now, I’m sure that Alpha School has a socio-intellectual culture! It’s just that the culture doesn’t appear to be about the actual academics learned during those 2 hours. And that matters for what the kids work on and find interesting themselves. E.g., in the Times we get an example of student projects like “a chatbot that offers dating advice,” and in Fox News another example was an “AI dating coach for teenagers,” and one of the cited recent accolades of Alpha School students is placing 2nd in some new high school competition, the Global AI Debates.

At least in terms of the public examples, a lot of the most impressive academic/intellectual successes of the kids at Alpha School appear to involve AI. Why? Because the people running Alpha School are most interested in AI!

And now apply that to everything: that’s true for math, and literature, and science, and philosophy. So then you can see the problem: the disconnect between the role models and the academics. If the Alpha School guides and staff don’t really care about math—if it’s just a hurdle to be overcome, just another hoop to jump through—why should the kids?

Want to know why education is hard? Harder than almost anything in the world? It’s not that education doesn’t work. Rather, the problem is that it works too well.

Education is a mirror.

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