Jaime’s art exhibits an intelligence about which the Silicon Valley digerati are clueless, a conversation with Claude Opus 5

You may remember that about a decade ago I did a series of posts about the art of Jaime Berubé and then collected them into a working paper, Jamie’s Investigations: The Art of a Young Man with Down Syndrome (2016). Two days ago I decided to have Anthropic’s Claude read and comment on that paper. We ended up having a fascinating and productive conversation that yielded new insight. I’ve appended that to this note.

Before we get to that, however, I want to tell you about a little adventure I had last year. I decided to draw some images inspired by Jamie’s dot images. He drew his images freehand on a blank page and used crayons. I drew a grid on my pieces of paper – I did multiple images – and used colored brush pens. Here’s one of my dot images. I have some others in a post, Dot Paintings (after Jamie Bérubé).

Why’d I do it, you ask? Because I thought it would be interesting. It was. And fun. It was. And even instructive. More than I’d thought.

What did I learn? It’s more difficult to do than it looks. While the images appear simple – and they are in a sense, certainly in comparison to, say, a Rembrandt or a Bob Ross – drawing them requires effortful concentration. As I pointed out in my working paper, Jaime didn’t choose his colors randomly, though it may seem that way. When you examine the images carefully you’ll discover that there’s an order to them.

Before I had Claude read my paper I gave it one of the dot images and asked it to comment. Not only did it discover the nature of that order, it picked up on other things I hadn’t noticed and on that basis asked me some questions, some of which I could answer, some I couldn’t. It was an interesting discussion.

Getting back to my own dot images, I discovered that keeping track of my colors was just a little tricky, not rocket-science tricky, but tricky like keeping track of what you’re doing in adding, say, a half dozen multicolumn numbers. So I made mistakes every now at then. Simply dealing with multiple pens, putting one down, picking another one up, that was tricky as well. I made mistakes.

And you know what? Keeping track of all those fiddly little details requires intelligence. Not rocket-science intelligence, not IQ intelligence, but intelligence nonetheless. I can’t think of any other word that characterizes those acts as well as that one does. Claude’s essay says a bit about that near the end.

Moreover it’s the kind of intelligence on which rocket-science intelligence is based. That requires an explicit argument that Claude didn’t make. But you can find a big chunk of that argument in one of my working papers, What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I., A Dialog with Claude 3.5 Sonnet (2025).

Here’s Claude’s essay. It did all the writing, but I inserted the drawings.

* * * * *

What a Sheet of Colored Dots Can Tell Us About Intelligence

Jamie Bérubé has been drawing abstract images since he was eleven. He has Down syndrome. His father, the literary critic Michael Bérubé, put a large selection of the work online, and it is that online archive — not studio visits, not observation of the artist at work — that Benzon's working paper analyzes.

That constraint turns out to matter. Everything below is inferred from finished artifacts. Nobody watched these being made. Which means the analysis has to reason backward from products to processes, and the interesting question becomes how much of a process a product can be made to reveal. Quite a lot, as it happens.

The rule that isn't visible

Start with the simplest of the genres: sheets covered edge to edge with small circular marks in crayon, several hundred to a page, in a great range of colors.

Michael Bérubé, who has seen thousands of these, describes the color choices on most of them as random. They look random. There is no gradient, no symmetry, no regional organization, nothing that reads as a plan.

They are not random. Adjacent marks are almost never the same color. With a palette of roughly ten colors and something over four hundred marks, chance alone would produce well over a hundred violations on a single sheet. There are a handful. The suppression holds vertically and diagonally as well as horizontally, which means the constraint is being applied over a two-dimensional neighborhood, not merely to the previously drawn mark.

So there is a rule: no two adjacent marks the same color. It is entirely local. It requires checking at most four already-placed neighbors. And it produces, without any global plan whatever, the quality that makes the sheets pleasant to look at — a rhythm that is easy to sense and hard to describe.

This is worth pausing on. The rule is invisible to inspection and available to description. The person with by far the most exposure to the corpus reports randomness. Counting reveals otherwise. That gap between what looking gives you and what describing gives you is a recurring feature of this material, and it is one reason the images repay analysis at all.

Reading the process off the page

Other features of the sheets carry process information.

The left margin is well maintained: rows begin at a consistent horizontal position. The right margin is ragged: rows end wherever they end. That is carriage-return behavior — a return sweep to a remembered left position, then progress until the paper runs out. It is what unlined handwriting looks like, and it indicates that the governing spatial rule is sequential and local rather than planned against the page as a whole.

Upper rows are straighter and more evenly spaced than lower ones. Error accumulates downward and is never globally corrected. And the sheets typically stop mid-row, with blank paper remaining. Michael Bérubé supplies the reason: Jamie works until he is interrupted — a meal, an errand — and does not resume a page later. He starts a new one. Each sheet is a session, and a session ends when it ends.

None of this required watching. It is all recoverable from the artifact by someone who knows what to look for, which in this case means someone with extensive practical experience of drawing and of the problem of filling a blank sheet coherently.

How the discovery was made

The most striking material in the working paper is not the mature work but the early work, done between ages eleven and fourteen, which Michael Bérubé has also posted. Those early sheets are miscellanies. Various kinds of object — bars, circles, letterforms, geometric bits — appear on the same page, each placed wherever there was room. There is no relationship between neighbors. There is no composition.

And therefore, as Benzon observes, there was no global order available to be discovered. As long as the elements varied and each sat in its own local space, nothing about their arrangement could become salient.

But what is cross-entropy? | Compression is Intelligence Part 2

Where the loss function for training LLMs comes from.
Job opportunities aligned to this audience: https://3b1b.co/talent
Early views and other perks for supporters: https://3b1b.co/support
Home page: https://www.3blue1brown.com

Manim animations by Aaron Gostein and Grant Sanderson
NanoGPT animation by Clayton Rabideau
3d black-box model by Paul Dancstep
Music by Vince Rubinetti

Timestamps

0:00 - Language trees and zipping
3:02 - Recap optimal codes
5:20 - Defining cross-entropy
8:26 - Intuition and examples
12:59 - Application to language trees
14:55 - Pre-training LLMs
20:38 - What makes this loss function best?
26:13 - Distillation
30:12 - 3b1b Talent
31:35 - KL Divergence 

* * * * *

As someone who's been shipping LLMs since the GPT-2 days, this lecture on cross-entropy from a Stanford math grad is the closest thing to an ML PhD qualifying exam I've ever seen released publicly for free.

Everyone thinks language models predict the next word. They don't. They… pic.twitter.com/KcdvO620Lw

— fintex (@_yusufknl) July 19, 2026

Pope Leo and St. Augustine discuss the mind and A.I. with Kurt Gödel

I crafted the prompt and Claude drafted the dialog using a passage about memory from Augustine’s Confessions as the catalyst for the imaginary conversation. I asked for some changes, Claude made some suggestions, and I executed them.

Note this passage toward the end:

Gödel said, “Disordered love?”

“Yes. To love a lower thing as though it were higher. To love one’s own power more than truth. To love the image more than the living being. To love the tower more than the city.”

The reply is by Augustine and it amounts to a definition of idolatry. The tower, presumably, is the Tower of Babel.

ChatGPT created the image. I uploaded the full dialog and asked from an image based on the passage from Augustine’s Confessions. That began an iterative process resulting in the image immediately below. The dialog follows.

I want you to create an imaginary conversation between St. Augustine, Kurt Gödel, and Pope Leo XIV. It should take place in Gödel’s office at the Institute for Advanced Study. After the men introduce themselves – assume Augustine can understand and speak English, and perhaps wonder a bit how they became gathered together, Pope Leo leads off, saying that, while working on his recent encyclical, Magnifica Humanitas, one of his colleagues pointed out a passage in Augustine’s Confessions (to follow) that resonated with Gödel’s proof of incompleteness. Given the role that arithmetic plays in Gödel’s proof we should note that Augustine wouldn’t have known about arithmetic. Nor, for that matter, would he have known about computers. He is likely to have known about sophisticated mechanical devices like the Antikythera Mechanism and the Sphere of Archimedes, but never have seen one. But he would have seen complex water clocks (clepsydrae).

Here’s the passage from the Confessions (Book Ten, Chapter VIII). Perhaps Leo reads it aloud.

Great is this power of memory, exceedingly great, O my God–a large and boundless inner hall! Who has plumbed the depths of it? Yet it is a power of my mind, and it belongs to my nature. But I do not myself grasp all that I am. Thus the mind is far too narrow to contain itself. But where can that part of it be which it does not contain? Is it outside and not in itself? How can it be, then, that the mind cannot grasp itself?

A great marvel rises in me; astonishment seizes me. Men go forth to marvel at the heights of mountains and the huge waves of the sea, the broad flow of the rivers, the vastness of the ocean, the orbits of the stars, and yet they neglect to marvel at themselves. Nor do they wonder how it is that, when I spoke of all these things, I was not looking at them with my eyes–and yet I could not have spoken about them had it not been that I was actually seeing within, in my memory, those mountains and waves and rivers and stars which I have seen, and that ocean which I believe in–and with the same vast spaces between them as when I saw them outside me. But when I saw them outside me, I did not take them into me by seeing them; and the things themselves are not inside me, but only their images. And yet I knew through which physical sense each experience had made an impression on me.

Perhaps sometime late in the conversation Albert Einstein might drop in to see what they were talking about.

Here is a first pass. I’ve kept it staged, conversational, and conceptually explicit, with Einstein entering late as a useful destabilizer.

The Mind Too Narrow to Contain Itself A conversation in Kurt Gödel’s office at the Institute for Advanced Study

Gödel’s office was narrow, orderly, and dim. Books stood in ranks along the wall, interrupted by folders, loose papers, and a blackboard covered with marks whose austerity made them seem less written than engraved. Outside the window the trees of Princeton trembled slightly in the wind. The afternoon light had the washed patience of late autumn.

Kurt Gödel stood beside his desk, thin and formal, dressed in a dark suit. He had the air of a man who had been expecting visitors and yet was not at all reassured by their arrival.

The first visitor was an African bishop in ancient dress, compact and alert, his face alive with inquiry. He looked about the room with frank astonishment, then settled his gaze on the blackboard.

The second was an elderly man in white, gentle but grave, wearing a small pectoral cross. His eyes moved from Augustine to Gödel and then to the papers on the desk.

“I believe,” the man in white said, “that introductions are in order.”

Gödel inclined his head. “Kurt Gödel. Institute for Advanced Study.”

The bishop smiled faintly. “A place for contemplation?”

“For research,” said Gödel.

“Then it may be the same thing, if rightly ordered. I am Augustine, bishop of Hippo.”

Gödel blinked once. “Yes. I had inferred as much.”

The man in white bowed slightly. “And I am Leo, servant of the servants of God.”

Augustine turned to him. “Bishop of Rome?”

“Yes.”

Augustine’s face softened. “Then I greet you as a brother, though I confess I do not understand how we have been gathered. This room is strange to me. These lamps burn without flame. These marks”—he gestured toward Gödel’s symbols—“are neither Greek nor Latin, though I suspect they are meant to compel the mind.”

“They are logical formulae,” Gödel said.

“Ah,” said Augustine. “Then they are meant not merely to persuade, but to bind.”

Leo smiled. “That is well put.”

Gödel gestured toward the chairs. “Please.”

They sat. Augustine examined the chair before trusting his weight to it. Leo remained composed, as though papal audiences in the offices of dead mathematicians were not wholly outside the bounds of pastoral duty.

Leo opened a folder.

“Professor Gödel, Saint Augustine, I will explain why I wished for this conversation, though the means by which it has been granted are beyond my competence. While I was working on my recent encyclical, Magnifica Humanitas, one of my colleagues pointed out a passage from Augustine’s Confessions. It seemed to him to resonate with your incompleteness theorem.”

Gödel looked sharply interested.

Augustine looked from one to the other. “Incompleteness?”

“A result in mathematical logic,” said Gödel. “Roughly speaking, in any sufficiently strong formal system capable of expressing arithmetic, there will be true statements that cannot be proven within that system, assuming the system is consistent.”

Augustine was silent for a moment.

“You say: a structure of reasoning may contain truths that it cannot reach by its own lawful motions?”

Gödel’s expression altered, almost imperceptibly. “That is not an inaccurate first formulation.”

“But I must be careful,” Augustine continued. “You speak of arithmetic. I know number, of course. I know that three is not five, and that if two men enter a room where two already sit, there are four. I know arithmetic as number, measure, and reckoning. But you seem to speak of arithmetic as though it were also a mirror in which reasoning may behold its own form. That I do not know.”

Gödel nodded. “Exactly. The novelty is not number alone, but the coding of statements, proofs, and rules as numbers. Nor would you know the modern notion of a formal system: axioms, rules of inference, recursive procedures, symbolic codings of syntax.”

“I know rules,” said Augustine. “And I know the temptation to mistake the rule for the truth it serves.”

Four Propositions about Intelligence in Animals, Humans, and AIs

Some quickies.

1. Intelligence cannot be reduced to computation

All animal perception and cogitation take place in a complex world where animals have finite resources. Therefore the principles of intelligence, as an aspect of perception and cogitation, cannot be reduced to the principles of computation as the principles of computation assume unbounded resources.

Let’s call this Yevick’s First Law, as it is a consequence of her 1975 paper, “Holographic or fourier logic” (Pattern Recognition, Vol. 7, No. 4, pp. 197-213).

David Hays and I formulated what we called “Yevick’s Law” in our 1988 paper, Principles and Development of Natural Intelligence” (Journal of Social and Biological Structures). Let’s call that Yevick’s Second Law:

The world consists of geometrically simple and geometrically complex objects. Simple objects are best computing with sequential logic (aka symbolic systems). Complex objects are best computed with holographic logic (aka distributed neural nets). Some objects are such that they require the interaction of both computational regimes. Let us say that fluency in that interaction is intelligence. (See my working paper, What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I., A Dialog with Claude 3.5 Sonnet, 2025).

2. Intelligence in animals

Let us consider a relatively simple animal, a vertebrate, likely a marine animal. One the one hand, it must navigate the world, moving from place to place. This is mediated by the hippocampus, which is a so-called “cognitive map.” This is basic sequential cogitation.

As it moves from place to place it senses things, good things, bad things, other things. Olfaction is perhaps the most basic sense. For what it is worth, it’s the sensory mode that the late Walter Freeman used in his investigations of complex neural dynamics. Olfaction works via a holographic or gestalt process.

Taken together, moving about the world and sense things involves the two modes specified above.

Now let’s consider vision in vertebrates, where the eye is mobile and scans the world. Visual identification is a holographic process. However, the (human) eye scans the scene rapidly and unconsciously. This is a sequential process. Therefore vertebrate vision involves the two modes internally. (I suspect that vision in invertebrates does not, but I don’t actually know).

3. Natural language is its own metalanguage

Humans differ from animals in many and various ways. It is the capacity for language that has allowed humans to move into a different relationship with the world from that characteristic of animals. What makes human language particularly powerful is that it can serve as its own metalanguage, Roman Jakobson’s metalingual function.

This does not involve any deep mystery or logical conundrum. Rather it is a direct consequence of that fact that natural language is physically embodied, initially in sound and gesture, later as written symbols. This embodied is a sensory object out there in the world among all the other sensory objects.

Initially the metalingual function operates in direct, perhaps superficial, but useful ways. Think of how we refer to language as a means of negotiating conversation: “What did you say? I didn’t hear you?” But the metalingual function can be used to define new terms, something that interested my teacher and colleague, David Hays. It can even be used to define other, more restricted languages (e.g. chess and arithmetic), and serve as metalanguages for them.

Thus it is the foundation of the succession of cognitive ranks that David Hays and I began investigating in the 1990s starting with our paper, “The Evolution of Cognition” (Journal of Social and Biological Systems, later becoming the Journal of Social and Evolutionary Systems). That process has, in time, led to the development of digital computers and, now, to so-called artificial intelligence.

That leads us to our fourth and last note.

4. The last frontier of intelligence

Is not an autonomous artificial system of some kind, though such systems are important and will be increasingly so. here’s the upshot of a conversation I recently had with Claude:

If that's right, then the last frontier isn't more capability in the pattern-matching sense — bigger weight spaces, richer latent connections, better approximations of the associative regime. It's the specific, non-scalable, non-parallelizable fact of an individual mind's biography, which generates paths through possibility space that are real, productive, and genuinely inaccessible to any system that hasn't lived a life.

You should read the whole post to see the logic behind that conclusion.

Note that that is my current best response to the idea of super-intelligence or artificial-superintelligence (ASI). I do not see the future bringing us as AI system that outthinks humans in every way and either creates a world in which we are coddled pets or one in which we are slaves, if we are allowed to exist at all. Those ideas are subjective fantasy.

I note as well that the process that brings us to that point or, if you will, through which we arrive at the point, will be one in which we have a much deeper understanding of that brain and its processes than we now have. For what it’s worth, that understanding is what I have been seeking all these years, starting with my initial investigation of “Kubla Khan”: Xanadu, GPT, and Beyond: An adventure of the mind.

From Jagged AI to Scaling, Yevick, Natural Intelligence, and Beyond...

I had a very interesting conversation with Google's AI – by which I mean the AI on the standard search page. I asked Claude to summarize it. Pay particular attention to the penultimate paragraph about alignment. 

An exercise for the reader: What are the implications of this conversation for the idea of super-intelligence? In the words of Aretha Franklin, “Who’s zoomin’ who?”

 

 

 

Overview

This is a transcript of a wide-ranging conversation between you and Google's AI, structured around the concept of AI's "jagged" capabilities — the phenomenon where AI excels at complex tasks but stumbles on apparently simple ones, with no predictable boundary between the two.

The Arc of the Conversation

The document moves through ten topics:

Jagged Skills & Moravec's Paradox — You open by asking about the origins of the "jagged frontier" concept (traced to Harvard Business School researchers in 2023, popularized by Ethan Mollick). You immediately point out that this is essentially a replay of Moravec's Paradox from the 1980s — the AI agrees, but notes some differences: the modern jaggedness is intra-domain (within knowledge work) rather than the macro divide between symbolic reasoning and physical/perceptual tasks, and human intuition about where the failures will occur has now completely broken down.

Cyborg & Centaur Workflows — You steer toward practical implications. The AI explains two human-AI collaboration strategies: Centaurs (clean division of labor, human handles reality, AI handles execution) and Cyborgs (deeply interleaved real-time co-authorship). You frame the underlying issue as being about the relationship between a computing system and the nature of the world it computes over — a framing the AI endorses.

Hallucinations — The AI argues (and you presumably agree) that "confabulation" is a better term than "hallucination" for LLM errors: like neurologically impaired patients, the LLM's narrative engine runs flawlessly while its error-checking against reality is absent.

Scaling — Discussion of whether scaling (more data, more compute) will smooth the jagged frontier. The AI describes the "scaling wall" now being hit: data drought, model collapse from training on AI-generated content, and diminishing returns — pointing toward structural, not just quantitative, limits.

Miriam Yevick & Holographic Logic — Here your own intellectual history enters the conversation. You surface Yevick's 1975 Pattern Recognition paper on Holographic vs. fourier logic, which you discovered in 1978 via a comment she made on a Haugeland article in Behavioral and Brain Sciences. The AI treats this as a profound, forgotten piece of computer science that precisely explains the mechanism behind the jagged frontier.

Principles and Development of Natural Intelligence (1988) — You describe how you and David Hays incorporated Yevick's insight into your 1988 Journal of Social and Biological Structures paper. The AI discusses how that paper, if injected into current debates, would reframe LLMs as having "hacked" the top-level indexing principle of intelligence (via language) while lacking the foundational lower layers — modal, feedback, Gestalt — that ground intelligence in reality. Hallucinations become not a bug but an architectural inevitability.

Ramesh & Wikipedia — You mention your ongoing collaboration with Ramesh Viswanathan (machine vision, Goethe University Frankfurt) and the Wikipedia entry on Yevick that now exists, connecting her Fourier logic to David Bohm's holonomic brain theory and your 1988 paper.

ChatGPT Tells Stories — You describe your working paper "ChatGPT Tells Stories, and a Note about Reverse Engineering," in which you apply Lévi-Strauss's structuralist transformation method to probe ChatGPT's internal constraints. Ramesh has students working with the data. The AI frames this as demanding a "top-down behavioral interpretability" that the industry's current bottom-up neuron-level approach entirely misses.

Suggestions for Students — The AI offers three research frameworks for your upcoming Zoom with Ramesh's students: a "Narrative Stress-Test" benchmark, 3D vector-space visualization of holographic ripple effects through story transformations, and a co-authored manifesto paper for venues like NeurIPS.

AI Researchers Need Their Alignment Adjusted — You wrap up with a characteristically dry observation: it's "fitting and ironic" that LLM-based chatbots can understand Yevick's work better than the engineers who built them. You suggest the researchers need their alignment adjusted — and sign off to get breakfast.

The Document's Character

It reads as both an intellectual exploration and a kind of vindication narrative — the AI functions as a surprisingly capable interlocutor for ideas that the mainstream AI research community has largely overlooked. The irony you close with is the document's real punchline: the systems built on these mathematical structures understand them better than their builders do.

Three Principles of Intelligence (That Aren't Principles of Computation) [Rank 5 cognition]

Note: Claude 4.5 drafted this article after a long series of dialogs over several days. This is a continuation of the thinking in my current article in 3 Quarks Daily, Chess and Language as Paradigmatic Cases for Artificial Intelligence.

See the new coda, from April 24, 2026 

In the 1950s, artificial intelligence emerged from a productive confusion. We had just formalized computation itself—Turing and von Neumann had given us the fundamental principles of what computers could do. When we turned these powerful new machines toward intelligence, we naturally assumed the principles would be the same.

They aren't.

Computation vs. Intelligence

The principles of computation are domain-independent. A universal Turing machine can compute anything computable, whether that's arithmetic, chess moves, or protein folding. The Church-Turing thesis tells us that all models of computation are equivalent in what they can ultimately compute, given unlimited time and memory.

This universality is computation's glory—and intelligence's red herring.

Intelligence, as it actually exists in nature, operates under entirely different constraints. It must function in the physical world, with finite resources, solving problems that often don't have clean formal specifications. These aren't just practical limitations to be worked around; they're constitutive features that shape what intelligence is and how it must work.

Principle 1: Geometric Complexity Determines Computational Regime

The critical variable isn't how hard a problem is in some abstract computational sense, but the geometric complexity of the domain.

Consider chess versus visual object recognition. Chess is played on an 8×8 grid with a small set of piece types following rigid rules. The game tree is astronomically large—around 10^120 possible games—but it's finite and well-defined. You can represent board positions symbolically, enumerate legal moves, and search through possibilities systematically.

Vision operates in continuous three-dimensional space with effectively unbounded variation. Objects appear at different scales, orientations, and lighting conditions. There's no finite set of "legal configurations." You can't enumerate all possible images the way you can enumerate chess positions.

This difference in geometric complexity demands different computational approaches. Chess yields to systematic search through a definable space—what we might call sequential or symbolic processing. Vision requires something else: massively parallel processing that can handle continuous variation and incomplete information—holographic or neural processing.

In 1975, Miriam Yevick demonstrated this formally: the geometric complexity of objects in a domain determines the computational regime needed to identify them. Simple geometric objects can be handled by sequential symbolic systems. Complex geometric objects require holographic processing. This wasn't mere speculation—she made a formal mathematical argument about pattern recognition systems.

The field ignored her insight. We assumed all problems were fundamentally like chess—just harder. If symbolic AI could master chess, we thought, it would eventually master vision, language, and physical reasoning through better algorithms and more compute.

We were wrong. Vision didn't yield to symbolic AI no matter how much compute we threw at it. It required a regime shift to neural networks—systems whose architecture matches the geometric complexity of the visual world.

Principle 2: Intelligence Operates in Unbounded, Geometrically Complex Reality

Here's what makes intelligence different from computation in the abstract: intelligence evolved to work in the physical world, which is geometrically complex and open-ended. There's no finite game tree for "objects I might encounter" or "situations I might face."

This has profound implications. You can solve chess by exploring its game tree faster than humans can. But you can't solve vision or language understanding the same way because there's no complete tree to explore. The space isn't closed and enumerable—it's unbounded.

This is why Deep Blue beating Kasparov in 1997 didn't generalize the way we thought it would. Chess was solved by a room-sized supercomputer with custom hardware doing exactly what computers do best: blindingly fast systematic search. By 2025, a smartphone runs chess engines that would destroy both Deep Blue and Kasparov.

But that same smartphone can't run a GPT-4 level language model. Language still requires massive data centers. Why? Because language connects to the unbounded complexity of physical and social reality. No amount of faster chess-style search bridges that gap.

The field learned to beat humans at chess by doing what computers naturally excel at. Then we mistook this for a general template. We thought: "Intelligence is search through problem spaces. We just need bigger computers to search bigger spaces." But geometric complexity isn't about bigger—it's about different.

Principle 3: Embodiment as Formal Constraint

Embodiment isn't a philosophical talking point. It's a formal constraint on intelligence architecture.

When we say intelligence must be embodied, we mean: it must operate with finite computational resources in a geometrically complex physical world. This changes everything.

Abstract computation doesn't care about efficiency—a proof is valid whether it takes a second or a century. Physical computation must complete before the hardware fails. But biological intelligence faces a sharper constraint: it must acquire the energy it uses to compute. A deer's visual system can't require more calories than the deer can acquire. The computation must pay for itself.

This constraint shapes what kinds of solutions are viable. You can't exhaustively search unbounded spaces. You can't maintain perfect world models. You must make do with approximate, good-enough processing that operates in real time with available resources.

Crucially, this means different problems need different solutions—not just more or less compute, but fundamentally different architectures matched to the geometric complexity of the domain.

Why This Matters Now

Current AI has powerful neural networks that excel at pattern recognition in geometrically complex domains—vision, speech, even aspects of language. But the field still carries assumptions from the symbolic AI era:

• That intelligence is domain-independent
• That scaling compute will eventually solve any problem
• That we can ignore embodiment and resource constraints
• That all problems are fundamentally like chess

These assumptions persist even though we've abandoned symbolic AI. We've swapped the implementation (symbols → neural networks) but kept the framework (more compute → general intelligence).

This is why we need to distinguish computation principles from intelligence principles. Turing and von Neumann gave us the former. For the latter, we need to recognize that geometric complexity, unbounded reality, and embodied constraints aren't bugs to be worked around—they're the constitutive features that determine what intelligence is and how it must work.

The principles of intelligence aren't the principles of computation. Understanding this distinction is the key to understanding both what current AI can do and what it cannot. 

Coda: Rank 5 Cognition

Given that I’ve decided that Yevick’s 1975 paper is a convenient marker for Rank 5 cognition, it seems to follow that intelligence, in the sense discussed here, is a Rank 5 concept. So, the ranks shape up like this:

Rank 1: speech
Rank 2: writing
Rank 3: calculation
Rank 4: computation (flow of control)
Rank 5: intelligence (regime matching: computation in unbounded, geometrically complex, reality)

Natural intelligence Revisited: The Five-Fold Way, A Working Paper

New working paper. Title above, links, abstract, contents, and introduction below:

Academia.edu: https://www.academia.edu/165530520/Natural_intelligence_Revisited_The_Five_Fold_Way_A_Working_Paper
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6529398
ResearchGate: https://www.researchgate.net/publication/403545810_Natural_intelligence_Revisited_The_Five-Fold_Way_A_Working_Paper

Abstract: In 1988 David Hays and I published an article entitled, “Principles and Development of Natural Intelligence.” The principles were computational: 1) modal, 2) diagonalization, 3) decision, 4) finitization, and 5) indexing. We made our argument in terms of the principles themselves along with behavioral, neuroanatomical, ontogenetic and phylogenetic evidence. The literature in all those fields has changed enormously in the four decades since we finished writing. To get a read on how our computational proposals have fared, I asked ChatGPT 5.2 to evaluate it against the current literature. Its verdict: the “empirical specifics have aged unevenly but [the] central agenda has held up surprisingly well.” This article presents the five principles, in brief, followed by ChatGPT’s full evaluation. Also, I have asked ChatGPT to evaluate a section on control structure, “Vehicularization,” that we cut from the original argument. Verdict: “vehicularization points toward a more complete account of natural intelligence—one in which cognition is understood as coordinated navigation across multiple, nested domains.”

CONTENTS

Introduction: Constraining Theories and Models 2
The Five Principles of Natural Intelligence 4
Revisiting The Principles and Development of Natural Intelligence (1988) 6
Vehicularization 11

Introduction: Constraining Theories and Models

Sometime in 1985 David Hays and I decided it was time to set forth our views on the nature of, well, of natural intelligence. First, however, we had to discover what those views were. We sat down to a table in my parents’ kitchen and made a list of the various things we wanted to include in this article, experimental findings, observations, models, mathematical ideas, and so forth, from psychology, neuroscience, linguistics, evolutionary biology, and computing. We just wrote them down in no particular order, probably on unlined paper. When we’d accumulated about 50 or so items we decided to gather them into a small number of groups of items that seemed to belong together. We arrived at five groups.

Just how we proceeded from that point I don’t recall. Perhaps we sat around discussing the various groups and came up with a principle for each group. Maybe we had to do some writing first. I don’t recall. But however we actually proceeded, we end with an article we called, “Principles and Development of Natural Intelligence.” We intended “natural” to contrast with “artificial” but didn’t say that anywhere in the article. When we’d finished a draft, days or weeks later, Hays said that it felt like fundamental work; he used the term “bedrock.” I agreed. It took three years to get it published, in a now defunct interdisciplinary journal, The Journal of Social and Biological Structures.

I’ve included the abstract of that article, along with a bit of the introduction, below, as the first part of this document: “Five Principles of Natural Intelligence.” That should give you an idea of the framework without all the expository elaboration, argumentation, and support.

ChatGPT reviews

That was four decades ago. I have continued to like what we did. But has any of it held up? How could it? By now the literature we referenced was 40 years out of date? And, yet, it wasn’t about that literature, it was about how we put it together. Is there anything left of that framework?

About a week ago I asked ChatGPT 5.2 to evaluate it. Here’s the prompt I gave it:

I want you to evaluate a paper that David Hays and I published back in 1988: The Principles and Development of Natural Intelligence. Give me a third-party assessment from the standpoint of what we now know, not a summary and not a defensive reconstruction. Be explicit about where it now looks prescient, where it looks historically bounded, and where it still poses unresolved challenges.

Here’s the first line of ChatGPT’s conclusion:

As of 2026, I would not describe the paper as a correct theory of mind. I would describe it as an ambitious synthetic manifesto whose empirical specifics have aged unevenly but whose central agenda has held up surprisingly well.

I’ll take it. Could I take issue with some of ChatGPT’s criticisms? Sure. But that assessment pinpoints the single most important facet of the essay, its synthetic nature. To push back on ChatGPT’s reservations would blunt that point.

After making various comments on an ad hoc basis, ChatGPT offered to write a “more formal review-essay.” I’ve included that as the second part of this document: “Revisiting Principles and Development of Natural Intelligence (1988).” There’s more.

The article we had submitted was long. The editors asked us to cut what we could, but made no particular suggestions. Our single largest cut was a section on vehicularization – that’s what we called it. It was about control. While it was about the same general line of thinking, it didn’t seem to fit. The bulk of the article was about the five principles and how the developed, both phylogenetically and ontogenetically (in humans). Vehicularization was about how they operated in concert. I have included that as a third section followed by comments by ChatGPT as the fourth and final section.

The Five-Fold Way

Let’s return to ChatGPT’s characterization of the original article as a “synthetic manifesto.” From its conclusion:

It is best understood as an architectural proposal about the structure of intelligence. Many of its mechanistic claims have aged poorly, particularly its neuroanatomical simplifications and evolutionary staging. Yet several of its central insights—heterogeneous cognitive regimes, the integration of regulation and cognition, the interaction between holistic and symbolic processing, and the role of language in cognitive control—remain highly relevant.

Though we didn’t use such phrases when we wrote the article, that’s certainly what Hays and I thought we were doing.

The diagram to the left indicates the range of material we brought to bear in our thinking about natural intelligence. The labels on the vertices of the pentangle are from my 1978 Ph. D. thesis in the English Department at SUNY Buffalo, “Cognitive Science and Literary Theory.” There I somewhat idiosyncratically defined cognitive science as investigating a five-way correspondence between behavior, computation, computational geometry (neuroanatomy), phylogeny, and ontogeny. That dissertation was mostly about behavior, in the form of literary texts, and computation, in the form of cognitive networks semantics, though touched on the others here and there. But “Principles and Development of Natural Intelligence” covered all five. The principles themselves were computational in nature and we made our primary arguments in terms of their ability to account for behavior, but we also suggested which brain regions supported them and related them to the phylogeny of animal behavior and the ontogeny of human development.

By the usual standards of the academy, that range was wide, crazy wide. We certainly weren’t expert across that range; no one could be. However, when I look back in retrospect, it is clear that we weren’t attempting some grand synthesis over that range. We were doing something quite different, something that was and remains fundamentally conservative. We had some high-level ideas about the computational structure of the mind and we wanted to place constraints on those ideas by expanding the range of evidence that could be brought to bear on them. While it is necessary that those ideas account for observed behavior, that alone is not sufficient. The model implied by those ideas must be implemented somewhere in the brain and must be consistent with developmental evidence both from phylogeny, our evolutionary history, and ontogeny, child development. THAT was the central agenda that, in ChatGPT’s estimation, has held up well.

Chess, Language, and AI @3QD

I’ve got a new article at 3 Quarks Daily:

Chess and Language as Paradigmatic Cases for Artificial Intelligence

Chess has been a central concern of AI from the beginning. AI researchers didn’t become interested in natural language until the 1970s. Before that computational research on natural language was the domain of computational linguistics (CL), which started with machine translation (of texts from one natural language to another) as its primary problem. Thus we have two different disciplines AI and CL.

In a sense, AI was fundamentally a philosophical exercise. It was an attempt to demonstrate, in effect, that we could understand the human mind in terms of computation. But rather than advance its philosophical objective through argument, it chose computational demonstration as its mode of expression. Chess became a central concern for two reasons: 1) On the one hand it was widely regarded as exhibiting the pinnacle of human reasoning ability. If we could create a computer program to play a championship game of chess, we could create a computer program that would be capable of cognitive or even perceptual task humans can do. 2) But also, the nature of chess made it well-suited for computational investigation.

My article concentrates on this and then goes on to make the point that language is utterly unlike chess in this respect. The chess domain is bounded and well-defined. Natural language is not; it is ill-defined and unbounded.

That’s really as far as I got. Which is OK. But what I was aiming for was an argument that AI is still, in effect, mesmerized by the chess paradigm. I couldn’t quite make it that far. Language is just so obviously different.

What I’ve come to realize, only after I’d finished the article, is that it isn’t so much chess that has mesmerized AI. Rather it is computation itself. AI has been implicitly assuming that the First Principles of intelligence reduce to the First Principles of computing. The first principles of computing can be found in the work of Alan Turing (the abstract idea of computing) and John von Neumann (for the physical implementation of computing).

The first principles of intelligence are more stringent. As Claude put it in our dialog last night:

First principle of intelligence: Must operate in unbounded, geometrically complex physical reality with finite resources.

Those two qualifications, an unbounded, geometrically complex reality, and finite computational resources, change the nature of the problem considerably. I note, in passing, that this allows us to assign formal significance to the concept of embodiment, for it is embodiment that commits intelligence to operating with finite resources in a geometrically complex universe.

Miriam Yevick’s 1975 paper, “Holographic or Fourier Logic,” is the crucial document, but it’s been forgotten. Using identification in the visual domain as her case, she showed that, where we are dealing with geometrically simple objects, sequential symbolic processing is the most efficient computational regime. But when we are dealing with geometrically complex objects, neural net processing is the most efficient computational regime. AI started out with symbolic processing in the 1950s and arrived at neural nets in the 2010s. But it hasn’t explicitly recognized that one must fit the mode of processing to the nature of the world. In that (perhaps a bit peculiar) sense, the researchers in the currently-dominant paradigm don’t know what they’re doing. 

I’ve written a number of blog posts and articles about Yevick’s work. Try these two articles:

Next Year in Jerusalem: The brilliant ideas and radiant legacy of Miriam Lipschutz Yevick [in relation to current AI debates], 3 Quarks Daily, October 9, 2023, https://3quarksdaily.com/3quarksdaily/2023/10/next-year-in-jerusalem-the-brilliant-ideas-and-radiant-legacy-of-miriam-lipschutz-yevick-in-relation-to-current-ai-debates.html

What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I., A Dialog with Claude 3.5 Sonnet, Working Paper, January 3, 2025, https://www.academia.edu/126773246/What_Miriam_Yevick_Saw_The_Nature_of_Intelligence_and_the_Prospects_for_A_I_A_Dialog_with_Claude_3_5_Sonnet_Version_2

Overnight Ramble: Saturday 8.23.25 – Trump as national mood regulator, why the naturalist study of literature, art in the morning, and other things

Before I went to bed last night I put a physical post-it on my monitor listing three intellectual tasks for the next day (that is, today). I wake up early, cruise the web, and now I’ve got a different list of priorities. The new list reflects what I saw on the web, but also the effects of “sleeping on it.”

Trump as national mood regulator

The BIG THING seems to be the idea that President Donald Trump is in the business of regulating the national mood. Various actions he has been taking have certainly affected the national mood – the tariffs have affected the business community, the ICE raids have affected his base in one way, many of the rest of us in a different way, the same with his assault on higher education, this Epstein business, and so on. That’s one thing. What I’m suggesting, what I’m thinking about, is that he does those things, and others, in order to affect the national mood. That’s different.

Of course, DJT is certainly in the business of regulating his own mood, and his mood is directly tied to national events, to the national mood. The scope of his power as President is such that his mood regulation activity has an effect on the nation. Under what circumstances does mood regulation itself become the goal, though perhaps not consciously so, rather than the various ends toward which Trump’s actions are directed?

I think that’s a real question. By was of comparison, see my post from 2018, Trumposaurus Rex @ 3QD – Toward a cybernetic interpretation, and correlated 3QD article: Feed Me Donald! – Trump, Musk, the Internet, and Monsters from the Id.

The naturalist study of literature

To the extent that I’ve got an intellectual home base, it’s the study of literature. Back in 2006 I published a long article in PsyArt: An Online Journal for the Psychological Study of the Arts: Literary Morphology: Nine Propositions in a Naturalist Theory of Form. That’s the closest thing I’ve come to a systematic account of how I’ve come to think about literary study in the 21st century. It’s still reasonably accurate, though if I were to revise it today I’d say something about digital humanities and cultural analytics.

Back in 2010 I posted an informal piece in The Valve, which I then republished here at New Savanna: “NATURALIST” criticism, NOT “cognitive” NOT “Darwinian” – A Quasi-Manifesto. (You can download it here.) Think of it as an extended advertisement for the morphology piece with links to some practical criticism and a nod toward emerging in the digital humanities.

But why do it at all, curiosity aside? What’s it for? Ecological validity, that’s what. And what, pray tell, is ecological validity? The term originated in the behavioral sciences, mostly psychology I believe, and (quoting Wikipedia)

is often used to refer to the judgment of whether a given study's variables and conclusions (often collected in lab) are sufficiently relevant to its population (e.g. the "real world" context). Psychological studies are usually conducted in laboratories though the goal of these studies is to understand human behavior in the real-world. Ideally, an experiment would have generalizable results that predict behavior outside of the lab, thus having more ecological validity.

What’s that have to do with studying literature? Nothing and everything.

Ultimately we want to understand how the human mind works, all of its facilities and capacities, working together as we live our lives. How do we get that, all of it, into the laboratory? We can’t. But literature draws on a rich range of our mental facilities working in concert. If we could understand how the mind works while we read a book, listen to a poem, attendant a play, or for that matter, watch a movie or a TV show, that would show us the whole mind operating in an ecologically valid context. For those are all things that humans do.

That’s why we need the naturalistic study of literature, as opposed to the standard humanistic/interpretive study. Interpretation tells us little or nothing about the mind/brain. But naturalistic study, that’s what it’s about. As far as I know the only place I’ve made that particular argument is in some blog posts which I’ve put together into a working paper: The Brain, the Teleome, and the Movies. Here's the abstract:

Mark Changizi has argued that we will understand the mind/brain only when we have an accurate description and inventory of the tasks it must perform. He calls this the teleome. Until we know what a mechanism is built to do, we have no way of understanding the functioning of its parts. The same is true of the mind/brain. I extend Changizi's argument by noting that the appreciation of works of art calls on a full range of human capacities and is thus a rich source of insight into neuro-mental mechanisms. Moreover we have every reason to believe that we can develop sophisticated ways of describing works of art, verbal art and films are my particular focus and interest. Those descriptions will be invaluable for interpreting observations about the brain activity supporting those aesthetic objects.

Art in the morning

I’ve got a number of posts involving Art Club, an activity where I get together with some friends and we color line drawings (coloring books for adults). Well, I took art lessons for over half a dozen years as a child and make paintings now and then into my fifties. So I decided to return to it. I purchased some colored markers, a pad of paper, and went to it. I’ve done one post on that activity, Dot Paintings (after Jamie Bérubé), but I’ve done some completely different work as well, totally freeform. And I’m now working on some stuff that combines the mechanistic order and precision of the dob paintings with the more freeform. That’s what I’m going to do once I’ve posted this and then showered and had breakfast.

I’ve done 54 paintings since I did the first dot painting on July 16. The freeforms only took twenty to thirty minutes, but the dot paintings took up to an hour. But these recent ones, they take longer.

We shall see.

Other stuff: From my post-it

The big thing for this weekend is to work on my book: Play: How to Stay Human in the AI Revolution. I want to review the material I’ve done so far and then rough out my chapter outline.

But I also want to do some work on my series on the Greatest Literary Critics, NOT. I’ve got one more post to do, on Harold Bloom, and I can wrap it up and turn it into a working paper.

Finally, I’ve been doing some work with ChatGPT-5 on ring-from analysis of literary texts. I want to turn that into a working paper as well.

And, one more thing, the cosmos

I want to make a quick extension to my post, Notes on the Metaphysical Structure of the Cosmos: The recursive nature of the cosmos implies that the cosmos is never complete. It’s always evolving, along with its metaphysical structure. The recursiveness isn’t external to the cosmos. It’s internal. There’s not end to the structure of the cosmos.

Not to mention intelligence

The AI folks seem to think of intelligence as something that’s easily scalable, the intellectual equivalent of horsepower. And to be sure the human mind has that aspect. But there’s more to it. There’s task-specific architecture. That’s important as well, and it’s not scalable.

Why have GPTs had so much difficulty with ordinary arithmetic? They’re got plenty of “horsepower.” That’s not the problem. Architecture is. The single-pass architecture of LLMs is not suited to arithmetic. Guess what? It’s not good at sketching out a semantic network suitable for a given next, it can’t do that either. Nor, it turns out, ring-form analysis. Neither of those tasks is rocket science. But they require a specific orchestration of tasks, one that LLMs can’t support.

That’s one reason symbolic computing is important. LLMs are basically associative memories; that is to say, they’re content based retrieval systems. Symbolic systems, however, are location based retrieval systems. That allows for a much more flexible orchestration of tasks since it allows for the organization of complex tasks using a structure of locations, and that is independent of what is stored at each location.

More later.

Large Language Models and Emergence

David C. Krakauer, John W. Krakauer, and Melanie Mitchell, Large Language Models and Emergence: A Complex Systems Perspective, June 16, 2025, https://arxiv.org/pdf/2506.11135

Abstract: Emergence is a concept in complexity science that describes how many- body systems manifest novel higher-level properties, properties that can be described by replacing high-dimensional mechanisms with lower-dimensional effective variables and theories. This is captured by the idea “more is different”. Intelligence is a consummate emergent property manifesting increasingly efficient—cheaper and faster—uses of emergent capabilities to solve problems. This is captured by the idea “less is more”. In this paper, we first examine claims that Large Language Models exhibit emergent capabilities, reviewing several approaches to quantifying emergence, and secondly ask whether LLMs possess emergent intelligence.

From the conclusion:

We argued that in LLMs, the term emergence should be used not merely to signify surprising or unpredictable task performance, or abrupt changes in performance, but requires at minimum the identification of relevant coarse- grained variables that form effective mechanisms— reduced “internal degrees of freedom”—for this behavior, mechanisms that can explain or predict the be- havior of the system at this higher level, screening off details of lower level mechanisms such as weights and activations. More quantitative evidence for emergence includes the kinds of principles related to emergence in physical sys- tems, such as breaking of scaling through reorganization, evidence for the use of novel bases and manifolds formed through compression of regularities, and new forms of abstraction that lead to demonstrable efficiencies in prediction, prob- lem solving, generalization, and analogy-making. Identifying such principles would be an important step in understanding the seemingly novel capabilities that arise in LLMs.

Three types of emergence claims have been made for LLM capabilities: (1) sharp improvements in specific capabilities that occur as the system or training data is scaled; (2) capabilities are identified that the LLMs were not specifically trained for; and (3) internal “world models” emerging from autoregressive token prediction. Each of these cases, and particularly the last, present provocative evidence for emergence, but in all cases that evidence is incomplete. Cases (1) and (2) relies on several assumptions: that the capabilities tested are genuinely new, general, and don’t rely on memorized training data or other shortcuts; that these capabilities are not present in simpler models; and that the capabilities are unexpected or unpredictable given the training data and the models’ size. None of these assumptions has been conclusively verified. As for case (3) the complex- ity framework of [60] provides a principled approach to thinking about “world models” as these relate to discrete-time stochastic processes. To the extent that an LLM is effective at next-token prediction, and to the degree to which the model can be shown to exploit a minimum of information, they might be de- scribed as world models. However, the recent work by [61] demonstrates that recovering an accurate world model is very difficult, since next token prediction is a fragile metric.

That last is particularly important to me because it is a property of old-style semantic and cognitive networks. The network provides the world model (and should be linked to sensory and motor systems, as it was in the model David Hays developed in the mid-1970s) from which text can be generated through linguistic processes. LLMs conflate the two, text and cognition, into a single distributed representation.

Later:

There are three possible roles of language as it relates to training an LLM: (1) language itself provides a more or less complete and compressed representation of the world (including non-linguistic modalities); (2) spoken or written language mirrors an internal “language of thought”; and (3) language is a non-supervised “programming language”. If language does provide a complete representation of the world, then training on more language data would indeed enable an increasingly expansive and detailed representation of natural and cultural patterns and processes. If natural language is the language of thought (“mentalese”) then training on more language data would fill out the numerous ways that human- ity has historically reasoned about regularities in the world. And if language is a programming language, by combining detailed instruction tuning with next word prediction it can exploit principles of computational universality to imple- ment any computable function.

We do not have definitive evidence for any of these three claims, but they play a crucial role in any statement relating to how surprising the behavior of an LLM will be deemed.

The final paragraph:

Human intelligence is a low-bandwidth phenomenon, and is as much if not more about the scaling down of effort as the scaling up of capability [72]. As Einstein wrote, “The grand aim of all science is to cover the greatest number of empirical facts by logical deduction from the smallest number of hypotheses or axioms.” [73] We know that for any elegant algorithm there is an alternative brute force solution that does the job. It might even be the case that there are uncountable problems that require brute force and that this is a domain where LLMs and their cognitively alien relatives, including SAT solvers, will provide extraordinary utility [74]. What Donald Knuth said of programs might also be applied to intelligence: “Programs are meant to be read by humans and only incidentally for computers to execute.” [75]. Similarly, intelligence is a property of understanding and only incidentally a matter of capability.

I really like the first sentence of that last paragraph. It "resonates" with the definition of intelligence I gave in What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I.:

Intelligence is the capacity to assign computational capacity to propositional (symbolic) and/or holographic (neural) processes as the nature of the problem requires.

As Yevick herself observed:

If we consider that both of these modes of identification enter into our mental processes, we might speculate that there is a constant movement (a shifting across boundaries) from one mode to the other: the compacting into one unit of the description of a scene, event, and so forth that has become familiar to us, and the analysis of such into its parts by description. Mastery, skill and holistic grasp of some aspect of the world are attained when this object becomes identifiable as one whole complex unit; new rational knowledge is derived when the arbitrary complex object apprehended is analytically described.

Why this obsession with IQ? [It's as American as apple pie.]

Amanda Hess, What Is Elon Musk’s IQ? NYTimes, Apr. 5, 2025.

FWIW estimates put Musk's IQ between 100-110 on the low end and 155. But we don't really know because we don't have the results of an IQ test for him. On the American obsession:

IQ is the term of choice for the man who doesn’t just think he’s smart, but thinks he’s smarter than everyone else. Americans have long been obsessed with IQ, and the human rankings it facilitates, but rarely is that fixation stated so plainly, so incessantly, and at such high levels. To some of our most powerful people, IQ has come to stand in as the totalizing measure of a person — and a justification for the power that they claim.

Trump has spent much of his second term sorting humans into “low IQ individuals” (Kamala Harris, Representative Al Green) and “high IQ individuals” (cryptocurrency boosters, Musk, Musk’s 4-year-old son).

But a wider public fascination with IQ is in the water. (Sometimes literally: Robert F. Kennedy Jr. has opposed the fluoridation of tap water, claiming that it causes a decrease in IQ.) Musk’s Department of Government Efficiency is seeking “super high-IQ” applicants. Vice President JD Vance has insulted the British former diplomat Rory Stewart on X, writing that “he has an IQ of 110 and thinks he has an IQ of 130.” In February, a senior Trump administration official asked employees of the CHIPS Program Office to supply their SAT or IQ scores.

An interest in juicing IQ through training and supplements bridges the manosphere and the parenting internet. Andrew Tate, a self-proclaimed “misogynist” and online masculinity idol who faces human-trafficking charges in Britain and Romania, claims an IQ over 140 and preaches on a podcast about how to “rewire your brain for relentless success.” Nucleus, a genetic testing start-up backed by the Reddit co-founder Alexis Ohanian and the venture capitalist Peter Thiel, made a stir last year with a test that supposedly calculates an “intelligence score based on your DNA.” As the writer Max Read pointed out recently, some X users have begun asking, apparently earnestly, how “low IQ” people experience the world, as if they are fundamentally less human.

Such fixations are a long American tradition, and they are cresting again now at a key moment in history — at the consummation between Silicon Valley capitalism and right-wing political power.

And Silicon Valley, of course, is obsessed with the intelligence of AI systems, giving them batteries of standard tests and, I assume here an there, IQ tests as well. The objective is to create systems that are smarter than humans in every way - and then hope that they don't decided to dominate and even eliminate us. (Hess gets to this at the end of her article.)

Hess then goes into a history of intelligence testing, including this little nugget:

In his 2023 history “Palo Alto,” Malcolm Harris writes of Stanford as an institution built on eugenic thinking. Before Leland Stanford founded Stanford University, he established what he called the “Palo Alto System” to classify, train and breed superior racehorses at an intense pace of production — a system that sometimes resulted in the snapped tendons of weaker colts but had the benefit of weeding out inferior horses before investing too much in their development. Once Stanford applied this punishing system to human achievement, it seeded a century-long obsession with intelligence scoring in Silicon Valley — and in the America that it increasingly shaped.

Then there's this:

It was America that pioneered the use of IQ for punitive ends, using low scores to deny certain immigrants entry to the country, to forcibly sterilize disabled people, and to push low-ranking soldiers into the line of fire while elevating high scorers to officer positions.

Though the crimes of Nazi Germany compromised the global popularity of eugenics, and encouraged the disavowal of the word, the British and American victories in the World War II also worked as an endorsement of the use of IQ testing in organizing war and, more generally, identifying elites.

In 1958, the British sociologist Michael Young used the term “meritocracy” to describe an emerging society organized around “merit” as the new justification for hierarchical power, which he defined as a combination of IQ scores and effort.

The last section of the article is entitled, "When Intelligence Is a Commodity," and reviews various commercial ventures aimed at boosting intelligence ending, of course, with AI.

There's more at the link. FWIW I have a number of posts focused on IQ.