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.

Little Miss Sunshine [Media Notes 188]

Little Miss Sunshine (2006) is a little strange and quite wonderful. Here’s how Wikipedia opens its plot summary:

Sheryl Hoover is a stressed mother of two living in Albuquerque, New Mexico. Her husband Richard is an aspiring motivational speaker and life coach. Dwayne, Sheryl's Nietzsche-admiring teenage son from her previous marriage, has taken a vow of silence until he accomplishes his dream of becoming a fighter pilot. Sheryl's older brother Frank Ginsburg, a gay scholar of Proust, is living with the family after attempting suicide. Richard's foul-mouthed father Edwin is also living with the family after being evicted from a retirement home for snorting heroin. Olive, Richard and Sheryl's 7-year-old daughter, is an aspiring beauty queen coached by Edwin.

Olive learns she has qualified for the "Little Miss Sunshine" beauty pageant being held in Redondo Beach, California, in two days. Richard, Sheryl, and Edwin want to support her, and Frank and Dwayne cannot be left alone, so the whole family attends. Due to financial constraints, they go on an 800-mile (1,300 km) road trip in their yellow Volkswagen van.

We know that much by, I don’t know, let’s say 10 or 15 minutes in (out of 102 minutes in total). At this point we pretty much know what’s going to happen, if only in broad outline. There will be a happy ending, because that’s how these films go; you knew this much when you bought your ticket, or picked in Netflix, as I did. You also that the trip is going to be, shall we say, stressful, with revelations and perhaps a (big) downer. It’s the details of the stress, revelations, and downer that matter.

As for the ending, obviously that’s going to be Olive’s performance at the “Little Miss Sunshine” pageant. Olive is cheerful and kind; she’s also a bit overweight, chubby but not fat. Somehow she made it to the finals despite being chubby but not fat. We know, because it is in the nature of beauty pageants for little girls, that chubby but not fat does not win the prize, no matter what kind of talent the contestant exhibits.

Here’s what Wikipedia says about that:

The hitherto-unseen dance routine Edwin had taught Olive is revealed to be a striptease performed to the Rocasound remix of Rick James’ “Super Freak”. Olive is oblivious to the subtext of the routine, but it horrifies and angers most of the audience, and the pageant organizer demands she be removed from the stage.

Needless to say, the audience is, shall we say, quite taken aback.

I leave it as an exercise for the reader to figure out how the film turns that sow’s ear into a silk purse. Better yet, watch the film.

Don’t worry, you can listen to the music without having the film spoiled. The visual you see now is the only visual there is for the clip.

Interior space

Robert Wright talks with Connor Leahy about the cult of AI

YouTube:

Robert Wright and Connor Leahy, US executive director of ControlAI, discuss the history of AI culture and the influences that shaped it—from the hippies, to the rationalists, to Marx, to sci-fi stories, and more. Plus: Mythos as “digital nuke," Dario's real motivations, and Connor's case against any and all attempts at building superintelligence.

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0:00 Teaser
0:47 Connor’s place in the AI world
3:16 Mythos: “digital nuke”?
14:12 The milieu that birthed Dario and other AI titans
20:52 Yudkowsky, Marx, and other AI harbingers
35:34 Effective altruism’s effects on big tech
46:22 The "cult" in Silicon Valley culture
59:59 Is the AI race a market failure?
1:05:51 What’s really driving the AI titans?
1:19:04 Connor: ASI is a worse bet than Russian roulette

I've been aware of much of this story for some time, though many details are new. Back in 2023 I published an article in 3 Quarks Daily about the cultish nature of Silicon Valley computer culture, A New Counter Culture: From the Reification of IQ to the AI Apocalypse. Nonetheless, hearing the story once again, in one place, with new details has been interesting. It may shed light on just why the dominant AI culture is an technology monoculture. The cultish nature of the surrounding industrial culture has something to do with it.

Note that both Apple (1976) and Microsoft (1975) date back to the personal computer revolution of the 1970s. Amazon was founded when the web was born (1994), as was Google (Alphabet, 1998). Facebook (Meta) didn't emerge until the social web was well-established, 2004. None of them are pure AI companies. They came later; OpenAI in 2015 and Anthropic in 2021. Those were the products of this ingrown culture. The other companies were founded as business ventures. OpenAI and Anthropic were founded as expressions of a quasi-religious vision.

Note: As far as I can tell, Leahy seems rather adjacent and Wright seems to take their technological visions more seriously than is intellectually warranted.

It’s getting to the math that’s tricky.

Good to see you, Judea.

The promise of LLMs is once we have the mathematizations, they can quickly carry them forward to possible implications and contradictions, allowing for the rapid exploration of hundreds of variations. Much to be done. This is just beginning.

— Kareem Carr, Ph.D. (@kareem_carr) July 21, 2026

LLMs are secretly obsessed with Japan.

Researchers proved every major LLM is secretly obsessed with Japan.

And they finally figured out why.

For years, we’ve been told that AI is entirely Western-centric, that it just reflects Silicon Valley and American values.

A landmark paper by Cardiff and Basque researchers… pic.twitter.com/ZfyypmmbjL

— Superman (@thesupermanmx) July 20, 2026

Red flowers in a planter on a street corner in Hobken

Beyond Marginalism: What’s Next? [MR #12]

It is time to conclude my series of posts on Tyler Cowen’s monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). Let’s look at the fourth and final chapter, “Why Marginalism Will Dwindle, and What Will Replace It?” Here’s how Cowen opens it (p. 85):

The underappreciated news is that marginalism is on the way out. Furthermore, this is old news, though the trend is accelerating.

Most of all it is underdiscussed news. As economics continues to evolve, marginalist insights – probably of all different kinds – will lie ever further from the frontiers of research and knowledge.

I find it easy to imagine that – less than 20 years from now – marginalism will be viewed as a historical curiosity rather than a central analytical engine of economics. No one will quite come out and say that, nor will they present marginalism as false or destructive. Rather it will be seen as of limited relevance, much as we might view parts of the earlier classical economists, such as their expositions of the quantity theory of money. New and different analytical frameworks will replace the ones that have dominated neoclassical economics to date.

Think about that, think about it very carefully. When thinking about it remind yourself that Cowen named his blog, his virtual home base for the last two decades, after marginalism.

For a professional academic to say that the world in which they were trained, the structure of ideas within which they have worked, which they have nurtured in students, which they have communicated to the public at large, which they have come to love, to say that that world is slipping away into the past, man, that’s rough. And rare. Not many have been able to do it.

Back in 1946 the great physicist, Max Planck, remarked, “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” Thomas Kuhn referenced that remark in The Structure of Scientific Revolution, and the economist Paul Samuelson gave a compressed version in a 1975 article in Newsweek. It would appear that Cowen has gotten the message and decided that, rather than dropping dead, he’d give the new ideas a boost.

After that sobering opening, Cowen reviews what happened between the late 19th century and now. He lands on price theory. Price theory? – “the view that the basic intuitive economic concepts, as would be taught in intermediate microeconomics, are highly useful and for advanced problems too” (p. 91). There’s that word, “intuitive.” Cowen explains:

Your hypothesis should be intelligible in terms of microeconomic concepts that you can hold in your mind and understand. In most (maybe not all?) cases, you should be able to explain some version of those principles to a well-educated, non-economist onlooker.

A couple pages later we arrive at something called “Topkis’s Theorem” which is very mathy (p. 94). Two pages after that: “Economic intuition, RIP. And marginalism with it.” Whoops! “I am seeing the traditional, intuitive approach to economic reasoning retreating from one field after another. To give one vivid and also important example, machine learning and neural nets are overturning the world of finance.”

Modeling collective action with 360,000 factors

A couple of pages later Cowen gives us a striking example. It’s from something called Arbitrage Pricing Theory (APT) (pp. 99-100). It’s a model that uses machine learning to develop 360,000 factors and does a better job of predicting than traditional models have only five or six factors. However, the factors in the traditional models are derived from marginalist assumptions and make intuitive sense while none of those 360,000 factors are legible. It’s clear to Cowen that, in the current intellectual marketplace for economics, the unintelligible models with superior performance are out-competing the traditional marginalist models. Bye, bye, marginalism!

I see no need to comment extensively on this particular model as I’ve already given it a great deal of attention, generating two different working papers from it. The first, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena, places it in the context of a half-dozen other investigations in a half-dozen fields in the social and natural sciences. The second, Notes on the Collective Valuation of "Thick" Objects: Financial Assets, Movies, and Novels, compares it with work that Arthur De Vany published in 2004, Hollywood Economics, and a more recent study by Matthew Jockers, Macroanalysis (2013), in which he investigated a corpus of 6000 19th century Anglophone novels. I’ve also written a blog post that complements that second paper: Thick Objects, High-Dimensional Models, and the New Intuitions [MR #11].

In that second paper and in the blog post I argue that those three cases are about a collective process where a population of human actors – traders and analysts in one case, movie goers in another, and novel readers in the third case – make judgements about “thick” objects. Even before I made an explicit argument, I had an intuition, an intuition that, despite the obvious differences, what De Vany was up to with movies was somehow like what Didisheim et al. were up to with stocks. Just where those intuitions came from, I can’t say, but I’ve been thinking about complex systems for a long time. [As an aside, for what it’s worth, Robert De Vany’s work on movies is perhaps where my interests in culture and cultural evolution come into closest contact with Cowen’s interests in economics and, in particular, in the economics of culture.]

As for the idea of thick objects, the term was suggested to me by either ChatGPT or Claude to characterizes complex objects whose characteristics cannot be fully enumerated because of that complexity. Moreover they are under constant scrutiny by a population of people who are interested in them and constantly evaluating them back and forth among themselves and, in that process, revealing further characteristics. It is not difficult to see that movies and novels are the same kind of thing, each is a mode of storytelling, and that they are complex objects. But what do they have to do with stocks? A remark by the pundit, Scott Galloway, made the connection for me in a podcast with Kara Swisher, “Stocks are like brands and that is they’re part promise and part performance.” Performance is assessed by a wide variety of metrics, metrics which go into the models such as the one by Didisheim et al., while promise is subject to endless speculation, some of which inevitably precipitates into those metrics.

Animal spirits, narrative economics, memes, and a Squid Game market

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.

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.

Whoops! Alphabet, Microsoft, Amazon, Meta, and Oracle have $1.65 trillion in debt that doesn't appear on their balance sheets

🦔A Nikkei investigation found that Alphabet, Microsoft, Amazon, Meta, and Oracle have $1.65 trillion in debt that doesn't appear on their balance sheets, more than the $1.35 trillion they officially report. These are GPU contracts, data center leases, and joint ventures that… pic.twitter.com/QbkzlenQAv

— Hedgie (@HedgieMarkets) July 20, 2026

Some birches in Liberty State Park [2016]

Agentic AI for science – Yippie!

Using agentic AI for scientific research has been an utterly fascinating experience. I can't remember the last time in my scientific career when I tackled such a stunning variety of problems with so much complexity and - most importantly - derived meaningful, reasonable answers…

— Ash Jogalekar (@curiouswavefn) July 21, 2026

America and China in the World, a quick note

Is that where we’re headed, to a bipolar world dominated by America and China? I just asked Google Search: “Is China the largest economy in the world?” Its reply:

China is the world's largest economy when measured by Purchasing Power Parity (PPP), but the United States is the largest in terms of nominal Gross Domestic Product (GDP).

• By Purchasing Power Parity (PPP): China is the world's largest economy, with an estimated output exceeding $44 trillion. This metric adjusts for the cost of living and the price of local goods, reflecting a higher real volume of economic activity.
  
• By Nominal GDP: The United States holds the top spot. In current market exchange rates, the U.S. economy is valued at approximately $32 trillion, while China's nominal GDP is roughly $20 trillion.

So that’s one thing. The other is AI. America and China are in a race for dominance in AI. America has a technical lead, but with respect to Frontier LLMs, that lead is measured in months, not years. And China is making its models open weight while the most advanced American models (Anthropic, OpenAI, Google) and not open. It’s not at all clear how that will shake out.

With the Trump administration authoritarianism is on the rise in America. While the Democrats may well win the next presidential election, it’s not at all clear what that means for America’s already precarious democracy. The problem is that a great deal of power is concentrated in the hands of political and business elites, so much that America’s democracy looks like a chess game played by oligarchs where ordinary Americans are the pawns.

Is that what the world will become in 2040, a competition between American and Chinese oligarchs in which everyone else is a pawn, with Homo economicus triumphant over all?