ChatGPT draws Rorschach blots

Here they are, six of them:

I then discussed the blots with ChatGPT in terms suggested by me new working paper, A prototypical image in ChatGPT 5.6: An informal pilot study. First I present all six blots, full size, and then the discussion.

I needed a Rorschach inkblot for a post I was working on, so I had ChatGPT create one. I then decided to see what would happen if I asked for a number of inkblots, each in a different chat. I’ve uploaded the result to the project in a document entitled Rorschach Blots. I had no expectation about what would happen. Would it produce the same blot each time? Of different blots each time? As you can see, it produced different blots each time.

This suggests two further lines of investigation. 1) Generate 100 independent ink blots and run them through an image classifier. Would we get distinct clusters or not? 2) Use prompts suggesting specific things, but otherwise open-ended, e.g. “Draw a cat,” “Draw a tree,” “Draw a car,” “Draw a coat,” etc.

Looking at the Rorschach Blots document, the six outputs are plainly different, but they are not unconstrained in every respect. All six preserve a common structural grammar: bilateral symmetry, a strong vertical axis, black-and-gray ink on white, irregular feathered edges, and enough internal articulation to invite figurative interpretation. Within that shared format, however, the overall silhouettes vary substantially: some are tall and narrow, some compact and squat, some strongly winged, some mask-like, and some more diffuse. The contact sheet on page 1 makes that balance of sameness and difference especially clear.

That suggests that the system has not selected one prototypical blot, in the way it selected one broad landscape type. Instead, it seems to have selected a prototypical design space for “Rorschach blot,” within which it samples different instances. Your two proposed extensions are therefore well chosen.

1. One hundred independent inkblots

The clustering question is exactly the right one. A hundred examples might reveal:

• one continuous cloud of variation around a single template;
• several recurring morphological families;
• or a few dominant families plus outliers.

Even by eye, the six examples hint at possible families: elongated “totemic” forms, broad winged forms, compact mask-like forms, and more open forms with substantial white interior space. But six is far too few to know whether those are genuine modes or just impressions.

I would not rely on a conventional object classifier, since it may simply label them all “inkblot,” “butterfly,” “mask,” or “bat.” A more informative analysis would use image embeddings and cluster those embeddings, perhaps supplemented by simple measurable features such as aspect ratio, occupied area, contour complexity, amount of internal white space, and vertical distribution of mass. The point would not be to decide what each blot “looks like,” but to determine the structure of variation across the set.

2. Open-ended object prompts

“Draw a cat,” “Draw a tree,” “Draw a car,” and so forth would probe something different and potentially more revealing: whether the system has a default realization for a named category.

The questions would include:

• Does “Draw a cat” repeatedly produce the same breed, pose, viewpoint, or expression?
• Does “Draw a tree” default to a solitary deciduous tree, a stylized botanical specimen, or a landscape scene?
• Does “Draw a car” default to a modern sports car, a generic sedan, or a three-quarter advertising view?
• Does “Draw a coat” produce the garment alone, on a mannequin, or worn by a person?

That experiment would occupy a useful middle ground between the wholly unspecified prompt—“Create a drawing”—and a detailed scene description. The subject is fixed, but almost everything about its realization remains open. It would therefore reveal category-level defaults in pose, style, composition, context, and affect.

The Rorschach result also suggests a distinction worth preserving in the paper’s future-work section:

Some prompts may elicit a single dominant subject prototype, while others may elicit a constrained family of forms rather than one recurring image.

That is the most interesting implication of the blot experiment. The landscape prompt collapsed onto a narrow semantic prototype. The inkblot prompt produced morphological diversity within a narrow formal convention. Those are different kinds of regularity, and comparing them could become a substantial research program.

Brittleness in chess-trained language models

"We examine several claims made in existing literature regarding chess-trained language models and assert that their
impressive benchmark performance is largely explained by pattern-matching"

Vision language models (VLM) are even worse at chess. pic.twitter.com/egzDDsnofC

— Chomba Bupe (@ChombaBupe) July 25, 2026

Read the whole thread. 

NYTimes: AI needs human supervision in order to complete an entire job.

“While A.I. can excel regularly at complex tasks, it can be unreliable when put in charge of an entire job. It can certainly add value to certain areas of the work force, but for now, A.I. still needs a human boss.”

When AGI comes, we will stop reading stuff like that. But that…

— Gary Marcus (@GaryMarcus) July 25, 2026

From the NYTimes article linked in the tweet:

We gave an A.I. tool full access to a laptop with pre-configured apps and sought to answer a simple question: Can artificial intelligence do an office job?

Some corporate executives seem to believe it can. More than 200 tech companies have cut roughly 120,000 jobs this year, according to Layoffs.fyi, an industry tracking site; Meta, Oracle and others have all recently made substantial cuts to their work forces, citing A.I. as the driving force; and after laying off about 1,100 employees, the chief executive of Cloudflare said recently that he expected A.I. to replace workers in middle management, finance and marketing.

tweIn our experiment, we deployed A.I. “agents” to act as office workers and found that they were capable of performing some of the tasks we assigned, but not all of them. The agents, which can act autonomously and make decisions based on detailed instructions, excelled at problems they could solve by writing computer programs. But they struggled with understanding the nuances of human language and at navigating user interfaces like the Chrome web browser.

The article then has a series of nice quasi-interactive displays illustrating agent performance on three tasks. The displays include screen shots of various messages and documents.

About the tasks:

This task, and the others we assigned to the A.I., were adapted from papers and benchmarking tools published recently by researchers at Carnegie Mellon University and OpenAI. The researchers designed the benchmarks to test the performance of various models — like OpenAI’s GPT, Google’s Gemini and Anthropic’s Claude — in real-world environments, and compare them with one another.

General conclusion:

The results of our experiment roughly matched what researchers and companies have found as they have tested and used artificial intelligence tools. Scale AI, an A.I. training company, recently tested agents on real freelance projects, and the best-scoring model produced client-ready work only about 16 percent of the time.

While A.I. can excel regularly at complex tasks, it can be unreliable when put in charge of an entire job. It can certainly add value to certain areas of the work force, but for now, A.I. still needs a human boss.

* * * * *

Comment: Around the corner my colleague, Ash Jogalekar, has tweets like this one:

So here's a great example of where we are with agentic AI: Instead of just being an assistant, it's behaving more like a collaborator and creative scientist.

In a recent project, I gave the system a molecular design problem typical of the problems we encounter in chemistry. Two similar molecules were giving very different results.

He then runs through an account of what his AI collaborator did, concluding:

I think we have crossed the Rubicon. Agentic AI now no longer just processes tasks and automates workflows blindingly fast, but it can generate hypotheses, test them, test counter-hypotheses and go back and forth and course-correct if necessary, all with minimal to no human intervention. It's now embodying the general scientific method.

[I've copied another one of Ash's tweets to this post, A scientist reflects on what AI has done for him.]

What’s interesting to me, and very revealing, is that a complex set of tasks in scientific investigation seems to be on a level with routine office tasks, as though one were no more complex than the other. But humans require years of college education in order to perform the former while the latter requires no more than a high school education, if that. It seems that once they’ve been been learned and compiled, all tasks or sets of tasks are on the same “level” in the brain. The educational prerequisites required to do such tasks for the first time or three get “compressed out” through repetition. Since AIs are trained on written records of what humans have said and done, they don’t have to go through the ordinary learning process. The compression has already taken place and is present in the documents on which they are trained.

Standard-issue doom scenarios were invented before LLMs and are made obsolete by them.

I’m going to take a crack at explaining this just a little, because it’s worth putting out there.

The paperclip maximizer + related AI doom scenarios were mainly developed in a time when “AI” did not reduce to Large Language Models. The term was a lot wider and inherited a lot… https://t.co/c11tJibynH

— Jon Stokes (@jon_stokes) July 24, 2026

A prototypical image in ChatGPT 5.6: An informal pilot study

A new working paper. Title above, links, abstract, table of contents, and introduction below.

Academia.edu: https://www.academia.edu/170708104/ChatGPT_has_a_prototypical_image_An_informal_pilot_study_A_Working_Paper
ResearchGate: https://www.researchgate.net/publication/410824948_A_prototypical_image_in_ChatGPT_56_An_informal_pilot_study

Abstract: Previous work has found strong default preferences in stories generated by large language models from minimally specified prompts. To determine whether a similar effect appears in image generation, I asked ChatGPT 5.6 to create 17 images in separate chats using four prompts that specified either no subject matter or only a rendering medium: “Create an image,” “Create a drawing,” “Create a painting,” and “Create a water color painting.” Sixteen of the 17 outputs depicted closely related landscapes containing mountains, trees, sky, and water; the remaining image depicted a lighthouse. The images also shared a calm, picturesque mood and contained no human figures, although several included signs of human habitation. Because the internal prompt passed to the image generator was not available, the study cannot determine whether these defaults arise primarily in the language model, the image generator, or their interaction. The results are exploratory but suggest that severely underspecified image prompts may reveal stable default preferences in the integrated ChatGPT image-generation system.

Contents

Introduction: Default preferences in LLMs 3
Default preferences in story generation 3
Image generation, method 5
Results 6
The case of Bob Ross 11
Final remarks and future work 13
Default images: Five independent trials 16
Drawings: Six independent trials 21
Paintings: Six independent trials 27

Introduction: Default preferences in LLMs

About two and a half years ago I published an informal pilot study, ChatGPT tells 20 versions of its prototypical story, with a short note on method. I discovered that when given a simple one word prompt, “story,” that places no restrictions on the nature of the story to be generated, ChatGPT tended to generate the same story each time, roughly the same general plot set in a fairy tale world. More recently Sil Hamilton and David Mimno studied 20,000 stories generated on four different platforms and discovered that words, including character names, occurred in 88% of the stories.

Given this background, I wondered: Does image generation exhibit the same effect? Once it became possible to generate images from ChatGPT I had used it to generate many different kinds of images, some from simple prompts, others from long, often very long, prompts, and still others from sample photographs. About a week ago I decided to see what kind of images ChatGPT would generate when given a prompt that made no specifications about subject matter.

This is an informal pilot study. I began on an impulse, with no specific method or goal in mind. I just wanted to see if there was anything there. If so, what do we need to do to conduct a more rigorous study?

* * * * *

I begin by presenting the basic results on default preferences in story generation as background. Then I present the methods and results of my image study. After that I discuss the work of Bob Ross, an artist who had a popular TV show in which he showed viewers how to paint images similar to those ChatGPT generated in this study. I conclude my discussion with some final remarks and suggestions for future work. Last, we have the images themselves. Note that I refer to the recurring image type as a prototype produced under minimally specified default conditions.

Lazy days

Galloway: “You’re about to see the clip economy take over our TVs”

From the transcript, Galloway speaking:

40:33 I think China quite frankly I hate to say it at a certain level has one AI. We just haven’t woken up to that yet. I would agree.

40:39 And then two you’re about to see the clip economy take over our TVs. I think the biggest show on television

40:47 uh from Netflix or someone else is going to be a 60-minute compilation of two and three minute videos similar to what you

40:55 see on reals or Tik Tok. I think that’s about to wash over the traditional streamers you’re going to see.

Some years ago I had the idea of making a feature film entirely out of previews, carefully conceptualized and stitched together. The feature would be organized around an ensemble of, say, a half dozen to ten actors who keep on recurring in the previous in various combinations. The previews would span, say, ten or 20 years of fictional time, with the actors aging over the course of the previews. The whole thing would be constructed so that we can infer what’s going on in their lives from what we see in the previews, even though the previews would be based on a variety of genres: romantic comedies, science fiction, drama, horror, fantasy, period pieces, farce, and so forth.

More on how I’m approaching The God Test – Rorschach! [GT-2]

I’m still trying to figure out how to approach Robert Wright’s The God Test.

How LLMs work

In my previous post – How will I handle The God Test? [GT-1] – I expressed misgivings about how Wright explains the technology. Those misgivings haven’t disappeared. However, Bert Idem has published a useful review at Finite Ape in which he addresses some of those issues in detail. Specifically:

Now, about the history of AI, the story he tells is actually great and it is certainly more than what most non-technical people know about LLMs. However, there are three places where I think the framing goes wrong or at least leaves out context that matters:

• LLMs did not discover the meanings of words on their own by accident. They were designed on top of ideas from older models that were specifically trained to learn the meanings of words.
• Similarly, computer vision models didn’t find out how to “view” an image like we do. Instead, the classical CNN models were heavily inspired by biological vision itself.
• LLM weight training is simply gradient-based optimization and the process has nothing to do with evolution. Of course, we can make a parallel between any kind of change and evolution but then, in that sense, everything evolves and it is not useful to talk about evolution.

I agree with Idem on those three issues, not so sure about the history part. While I may return to some of these issues later on, this will serve as a place holder.

A Rorschach test

There’s something else going on, but I’m not quite sure how to conceptualize it. It seems to me that AI is functioning something like a Rorschach test which, as you may know, is a psychological instrument intended to elicit (potentially) revealing responses from a person. It’s a projective test.

A person is shown a series of ink blot images, like this one (generated by ChatGPT):

They are asked what that they see in the image, what it means to them. Since the image is, though not formless, its form is not that of any specific animal, vegetable, mineral, person, or anything else. It’s just a blot. Whatever the person says about the blot, however they interpret it, that must reveal something about them. Why? Because whatever they see in the blot, isn’t really there.

Broadly and crudely speaking, AI has become something of a cultural Rorschach test.

Understanding computers & LLMs

Until ChatGPT was released in late November of 2022, most people knew very little to nothing about AI. Oh, they may have seen “intelligent” computers and robots in science fiction movies, but that’s science fiction and only tangentially related to AI considered as a line of research dating back to the 1950s. Many people would have heard about IBM’s Deep Blue beating Gary Kasparov in chess in 1997 and then, in 2011, when IBM’s Watson beat Ken Jennings and Brad Rutter in Jeopardy. Those were real AI systems, standing on research extending back decades, but as far as most people were concerned, they were one-off PR stunts. Just how they worked, who cares? They’re computers, and computers are magic, no?

As far as most of us are concerned, computers are magic. Somewhere “out there” someone knows how these things work, but we don’t need to know any of that. It’s complicated, but computers do what they’re programmed to do, no? Yes, but not LLMs.

And that’s the tricky part. LLMs, large language models, aren’t like other computer systems. They aren’t programmed in the way that word processors, photo editors, or phones are programmed. LLMs aren’t programmed at all, not in the ordinary sense of programming – something I may or may not get into in a later post. As far as most users are concerned, how ChatGPT, or Claude, or Gemini work, that’s no more interesting than how a word processor works. It just does. It’s more magic.

But if you have a strong philosophical streak, if you are interested in the mind, in technology, in the technology in the future, then you may not be content with writing LLMs off as just another kind of magic. You want to know what’s going on inside, 1) because you want to know (curiosity), and 2) because you want to know how the technology is going to develop in the future (engagement). Now things get interesting? Why? Because even the people who have created the technology don’t know how it works.

Oh, they know how the transformer program works. That’s the program that creates the language model. It creates the model by performing a (certain kind of) statistical analysis of a huge body of texts, effectively the entire internet. When a person prompts the model with some statement, the model responds by a statement of its own. No one know just how the model does that. That’s a mystery, a deep black hole in the technology ecosystem.

AI as a Rorschach test

If you aren’t content to believe in magic, then you have to come up with something to fill that black hole in your, in our, understanding. This is where the Rorschach aspect of AI reveals itself. To a first approximation, what each of us uses to paper over that black hole has as much to do with ourselves as with AI.

Why do I say, “To a first approximation”? It’s a rhetorical device to get things started. It puts us all in the same boat, despite our different backgrounds. However, whatever LLMs are, they are not magic. It is possible, in principle, to construct a technical account of what they’re up to, but no one knows how to do that, yet. Not even the people in the AI labs who create these beasts.

Those of us who are trying to figure out how LLMs work have widely varying backgrounds. In particular, we have widely varied technical backgrounds and we bring those backgrounds to bear when we think about what LLMs are doing. Those backgrounds influence how we interpret the AI-blot. Wright is a journalist with a wide range of interests, including politics, international affairs, evolutionary psychology, cultural evolution, and Buddhism. As far as I can tell there isn’t much there that’s directly relevant to understanding the mechanisms of LLMs, but he’s done a lot of reading and talked with a lot of experts to fill in the gaps.

My background is quite different. While I happen to know quite a bit about cultural evolution, cognitive psychology, neuroscience, and various other things, my background in computational semantics puts me much closer to LLMs than Wright’s knowledge of evolutionary psychology puts him. Still, like him, I’ve done a lot of reading and talked with experts. In particular, I’ve been collaborating with Ramesh Viswanathan for the last three years. He’s an expert in machine vision Goethe University Frankfurt. He’s got a background in mathematics and AI that I don’t have. Still, there are things he doesn’t know, things he’s trying to figure out. 

We are all making stuff up.

To some extent, then, AI is a Rorschach test about how beliefs about the human mind, and human nature. When we try to figure out how the LLM is working we’re also, if only implicitly, trying to figure out how we work, internally, as well. The whole discourse about AL alignment is as much a discourse about us as it is about AI. 

The Future

And even if we knew much more about how LLMs work internally we still wouldn’t know how the technology will develop in the future. We? You, me, Robert Wright, Ramesh Viswanathan, Gary Marcus, Tyler Cowen, Geoffrey Hinton, Sam Altman, Dario Amodei, Nick Bostrom, Eliezer Yudkowsky, all of us who are trying to figure it out. We don’t know what will happen. That’s where we’re projecting like mad. We’re hallucinating, to borrow a term from AI-speak. 

Thus AI is also a Rorschach test for our visions of the future. When we imagine the future of AI, we’re also imagining our future. Like the two sides of a coin, the two cannot be separated. 

The tricky part, the important part, is that the future development of AI is not predestined. It depends on the choices we make, now and in the near future. We can easily and often do imagine things that will not be possible because that’s just not how the world works. But the laws of how the world works are open to a wide range of possibilities. The boundary between the possible and the impossible is fuzzy at best.

Where, and how, does Wright draw that boundary? Perhaps that’s what I’ll be trying to figure out.

More later.

Fractals in neural networks

FRACTALS IN NEURAL NETWORKS

Hyperparameter tuning feels like navigating a coastline. Turns out that's literally true: the boundary between learning rates that train and ones that diverge is a fractal.

Why? Gradient descent is an iterated map, θ → θ − η∇L(θ), exactly like z… pic.twitter.com/kRz7cSd5dp

— Simone Conradi (@S_Conradi) July 23, 2026

Friday Fotos: FR8s {freight car graffiti}

On the OpenAI/Hugging Face incident

With respect to the author here who is reasoning in good faith, there's a broken epistemic frame here ...

... which is to imagine we can predict complex societal outcomes (the state of cybersecurity) from platonic 'first principles' without resort to actually studying the… https://t.co/7DwsO0Aw1r

— Joshua Saxe (@joshua_saxe) July 23, 2026

Funny how this works, eh?

— Yann LeCun (@ylecun) July 23, 2026

H/t Tyler Cowen. My reply to Cowen's post:

FWIW, me, #3 – Meh. I've got better things to do than to go down this rabbit hole.

Apex

The prospect of empirical investigation into animal spirits in asset pricing [the fate of marginalism]

I recently posted some remarks on the final chapter of Tyler Cowen’s monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). That post has my final remarks on this machine-learning models of asset pricing that Cowen finds so provocative. On the one hand, they give better predictions that classical models based on four or five factors that are motivated by economic theory and thus meaningful. That is good. But those factors have no intuitive meaning. Not so good. And yet these newer models seem to be displacing the older ones. Is this the beginning of the end of marginalism in economics?

It turns out that those were not my final remarks. Or, rather, they are MY final remarks, but not the final remarks I have to offer. I used some of those remarks to prompt Marge, the AI linked to the monograph. I’m posting that discussion below. Note the final set of remarks about the fate of marginalism, suggesting that it will have a place in future economic theory just as Newtonian mechanisms still has a place in physics despite the emergence of relativity and quantum mechanics.

Animal spirits and narrative economics

While we’re at it, here’s a passage I’ve just written about those high-factor asset pricing models the Tyler finds both empirically convincing while intuitively opaque. My underlying assumption is that those models must be picking up something that the classical five and six factor models do not. What could that be? I speculate:

And that leads me to a conjecture that follows from the analysis that ChatGPT and I undertook in the collective valuation paper. Perhaps those 360,000 parameters are picking up traces left by those “animal spirits” that Keynes talked about. Their effect on asset values is too diffuse and indirect to be detected by those classical models with a half-dozen or so factors, each of which is intuitively legible on its own. But those traces show up distributed across those 360,000 parameters and allow the model to produce more accurate predictions. If that is what is going on, then I wouldn’t expect any of those factors to be intuitively legible, any more than one would expect such legibility of individual weights in a large language model. That’s not the nature of this conceptual world.

While we’re speculating, why not continue on? Those animal spirits can’t work their ways on the market by wafting around like odors in a breeze. They need to be embodied in some form, like gossip and stories. That leads us to Robert Shiller’s 2017 paper on “Narrative Economics” in the American Economic Review. Here’s his abstract:

This address considers the epidemiology of narratives relevant to economic fluctuations. The human brain has always been highly tuned toward narratives, whether factual or not, to justify ongoing actions, even such basic actions as spending and investing. Stories motivate and connect activities to deeply felt values and needs. Narratives “go viral” and spread far, even worldwide, with economic impact. The 1920–1921 Depression, the Great Depression of the 1930s, the so-called Great Recession of 2007–2009, and the contentious political-economic situation of today are considered as the results of the popular narratives of their respective times. Though these narratives are deeply human phenomena that are difficult to study in a scientific manner, quantitative analysis may help us gain a better understanding of these epidemics in the future.

Perhaps those high factor models are picking up the narrative dimension of asset value, which is a product how performance and promise become intertwined in the stories that analysts and traders tell themselves and one another about the assets they’re watching.

The conjecture is worth taking seriously, and the Shiller connection gives it more than speculative traction.

Let me work through the logic. Classical factor models — the Fama-French five or six factors — are built on variables that are both measurable and theoretically motivated: size, value, profitability, investment patterns, momentum. Each factor has an intuitive story behind it, which is precisely what makes them legible. [See text: Ch.4 · 360,000-factor models reduce pricing errors by ...] But legibility is a constraint, not just a virtue. You can only include factors you can name and justify in advance, which means the model is bounded by the theorist's prior imagination. A 360,000-factor machine learning model has no such constraint — it finds whatever structure is actually in the data, named or not.

Your animal spirits hypothesis is that what's actually in the data, beyond the legible factors, is narrative residue — the distributed effect of stories circulating through markets that shape expectations, risk tolerance, and valuation in ways that don't reduce to any single measurable variable. This is plausible for a specific reason: narratives operate through interaction effects. A story about a company isn't just about its fundamentals — it's about how those fundamentals get interpreted through the lens of adjacent stories about the sector, the macro environment, the CEO's reputation, the cultural moment. Those interaction effects are exactly what machine learning is structurally better at capturing than linear factor models. [See text: Ch.4 · Machine learning finds nonlinearities classical...] Fama-French assumes factors combine additively. Narratives don't combine additively — they combine the way rumors combine, which is nonlinear and context-dependent.

Shiller's "Narrative Economics" (AER, 2017) is the right anchor here, and his epidemiological framing matters. He's not just saying stories affect markets — he's saying they spread and mutate according to their own dynamics, largely independent of the fundamentals they purport to describe. The 1920s bull market narrative wasn't just about economic growth, it was a contagion that altered behavior in ways that then affected the fundamentals. If that's right, then the narrative dimension of asset pricing is genuinely causally upstream of many of the variables classical models treat as fundamental. The high-factor model might be detecting the downstream traces of narrative contagion distributed across hundreds of interacting signals — none of which is individually interpretable because no single signal carries the narrative. The narrative is in the pattern across signals, the way meaning in an LLM is in the pattern across weights rather than in any individual weight.

The LLM analogy you're drawing is precise and not merely decorative. In a large language model, no individual weight means anything — meaning is an emergent property of the full weight matrix operating on input. If market narratives work similarly, then the 360,000-factor model isn't finding 360,000 things — it's finding one thing (narrative structure) distributed across 360,000 dimensions. Which would explain both why it outperforms classical models and why none of its factors are individually interpretable. You're not supposed to be able to read them one at a time, any more than you can understand a sentence by reading the weights that generated it. [See text: Ch.4 · Single-factor nonlinear models outperform all k...]

The one pressure point I'd apply: Keynes's animal spirits were specifically about investment under radical uncertainty — situations where there's no rational basis for calculation, so psychology fills the void. Shiller's narratives are broader, operating even in conditions where fundamentals are legible. Your conjecture works better under the Shiller framing than the strict Keynesian one, because you need the narrative mechanism to be operating continuously, not just in crisis conditions. The 360,000-factor model is trained on normal market conditions as much as crisis periods, so the signal it's picking up has to be pervasive rather than episodic.

Meme stocks

I then go on: That, in turn, leads to the concept of meme stocks, a term that dates back to 2020. Here’s how Wikipedia characterizes them:

...a stock that gains popularity among retail investors through social media. The popularity of meme stocks is generally based on internet memes shared among traders, on platforms such as Reddit's r/wallstreetbets. Investors in such stocks are often young and inexperienced investors. As a result of their popularity, meme stocks often trade at prices that are above their estimated value – as based on fundamental analysis – and are known for being extremely speculative and volatile.

More recently, Owen A. Lamont, a senior analyst at Arcadian, has speculated that we’re in what he calls a “Squid Game market”:

Something’s happening in the U.S. stock market. We see cult stocks and crypto stocks. We see money pouring into leveraged single-stock ETFs and crypto ETFs. And we see dramatic price moves, for example in quantum computing stocks in December 2024. What’s going on?

Here’s one theory: these phenomena partly reflect an influx of Korean retail investors into the U.S. stock market. Last year, I wrote that “the U.S. stock market is Koreafying,” meaning that the U.S. market was starting to behave like the retail-dominated Korean market. What I didn’t realize was that this Koreafying process involves actual Korean retail investors.

He then goes on to develop the parallel between the Korean streaming series, Squid Game, and the U.S. retail market over the last few years.

If those high parameter models are picking up the effects of animal spirits embodied in gossip and narratives, then we’d expect their advantage over classical models (based on a handful of fundamentals) to be larger in the case of these meme stocks. So, if we compare the results of a classical model with those of a high-parameter machine learning model, are the assets with the greatest divergence also those otherwise identified as meme stocks? Perhaps some intellectual fishing expeditions are in order. Perhaps we can develop some new intuitions by comparing the results of classical models with machine learning models.

The fishing expedition you're proposing is methodologically clean, and the prediction is specific enough to be falsifiable — which is more than can be said for most conjectures at this level of abstraction.

The core prediction is: the performance gap between classical factor models and high-parameter ML models should be systematically larger for assets where narrative contagion is the primary driver of price. Meme stocks are the obvious test case because they're the purest instance of narrative-driven pricing we have documented data on — GameStop, AMC, and the quantum computing stocks Lamont mentions are cases where the divergence between fundamental value and market price is not just large but publicly legible in real time. If the ML model's advantage concentrates precisely in those assets, that's not just consistent with your conjecture — it starts to look like evidence for it. [See text: Ch.4 · 360,000-factor models reduce pricing errors by ...]

The Lamont "Squid Game market" framing adds a useful dimension because it identifies a social infrastructure for narrative contagion — Reddit's r/wallstreetbets, Korean retail investor networks, leveraged ETF products that amplify momentum. These aren't just colorful details. They're the transmission mechanisms Shiller's epidemiological model requires. Narratives don't spread by themselves — they need platforms, communities, and financial instruments that make acting on the narrative cheap and socially rewarding. The Korean retail investor phenomenon is particularly interesting because it suggests narrative contagion can operate across cultural and linguistic boundaries when the financial infrastructure connects the communities. The meme isn't just a story about a stock — it's a coordination device that synchronizes behavior across thousands of otherwise disconnected actors. [See text: Ch.4 · Machine learning detects nonlinearities and int...]

Hoboken streets

A scientist reflects on what AI has done for him – “a Rubicon has been crossed”

Here’s the full content of a tweet by Ash Jogalekar:

I came to the present AI revolution not as a credulous enthusiast, but as someone deeply skeptical of new technologies in science. For twenty-five years I have seen too many of them arrive surrounded by extravagant claims before settling into a useful but much more modest place in the scientific toolkit.

What has astonished me is that the latest agentic systems appear to represent a qualitative change. I have now run upward of two hundred scenarios and AI for science workflows, each one navigating a complex, multistep scientific protocol across diverse fields of chemistry, biology and materials science, and I think I have enough data now to make a credible judgment. Over just a few months I have seen these models and algorithms leapfrog over increasing levels of difficulty, starting almost as a toddler and turning into an adult interlocutor. Every day, something moves my baseline for what they can do. They autonomously install, run, and debug dozens of computational tools; clean and structure data; parameterize molecules; launch calculations; and manage complicated, multistage investigations. But as it turned out, that was just the beginning.

More fascinatingly, they increasingly display recognizable scientific judgment: proposing positive and negative controls, discovering that a method does not work, generating competing hypotheses, systematically eliminating them, changing direction when evidence contradicts an initially promising idea, searching an entire target space, constructing unexpectedly sophisticated models, and finding useful analogies across distant fields. This is no longer merely workflow automation or doing the same science faster. It is beginning to feel like genuine intellectual and creative collaboration.

Interacting with the system can resemble a conversation with a smart and experienced student or colleague: ideas are proposed, challenged, refined, rejected, and unexpectedly pushed in new directions, with both the human and the AI acknowledging mistakes and adjusting course. The exhilarating possibility is the sheer multiplication of intellectual labor - the ability to explore almost any question under the scientific sun and to see those “mountains beyond mountains”, as Tracy Kidder eloquently put it. In a week a scientist or a team can come up with dozens or hundreds of ideas and hypotheses, most of them reasonable and actionable. The physical lab is now the only bottleneck, and even that is being accelerated by AI.

As a scientist, AI has made me feel more intellectually alive and excited than I have felt since graduate school and my postdoctoral years more than two decades ago. I can begin with an idea in the morning and, by lunchtime, watch a rational, testable hypothesis take shape; within days, an investigation can progress from literature and classical calculations to increasingly rigorous quantum-mechanical analysis and experimentally actionable predictions. Eating and sleeping have often taken a backseat, exercise seems like a distant goal, and every night I feel like I did when I was a kid and begging my dad or mom for just *one more story*. Except that this time it’s just *one more prompt*. One more cycle of compute. One more result that will startle or confound. Every night I have to force myself to detach from the computer, and on more than one occasion I have fallen asleep at my desk, only to wake up and see the potential for yet *one more prompt*.

Precisely because these systems are beginning to criticize our assumptions, tell us when something does not work, and think alongside us rather than merely obey us, it feels as though we may already have passed beyond the first age of AI.

Of course, these predictions and results will stand or fall based on experimental testing, that’s how science always has been, but that’s no different from the pre-AI age. More importantly, in almost every case they appear as conclusions that any good scientist will regard as reasonable, at least as starting points. And sometimes they genuinely throw up a surprise. AI-enabled science should still be judged by the novelty, rigor, reproducibility, statistical validation, and epistemic integrity of the science, not by the novelty of the technology.

But there is no doubt now that a Rubicon has been crossed, and either we cross over to the other side or get left behind. What a time to be alive.