While you're laughing yourself silly at what you're seeing think about the interpersonal coordination required to pull this off. That's what music is like, all the time.
Tuesday, June 16, 2026
Liszt, Hungarian Rhapsody for 2, Victor Borge and the other one
Thick Objects, High-Dimensional Models, and the New Intuitions [MR #11]
Early in Chapter 4 of his marginalism monograph, “Why Marginalism Will Dwindle, and What Will Replace It?,” Cowen reviews what happened between the late 19th century and now. He then makes his way to a striking example from something called Arbitrage Pricing Theory (APT) (pp. 99-100):
There is a recent working paper which is perhaps more striking yet, by Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly, and Semyon Malamud. They pick up from Arbitrage Pricing Theory (APT), a well-established idea from financial economics. APT typically looks for “factors” in the data which predict excess returns, and a traditional APT model might have found five or six such factors. Are “inflation” or perhaps “the term structure of interest rates” useful factors? Well, that can be debated, but if so, those results sound pretty intuitive. But those intuitions seem to be disappearing. In a paper by these authors, they apply machine learning methods to look for more factors. As we know, machine learning is very good at finding non-obvious relationships in the data. The largest model they built has 360,000 (!) factors, and it reduces pricing errors by 54.8 percent relative to the classic six-factor model from Fama and French. Bravo to the authors, but what kinds of intuitions do you think possibly can be supported by those 360,000 factors?
That sent me through a loop. I thought about it, consulted Claude, and ended up writing a rather long working paper, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena. I thought about it some more and then went to ChatGPT this time for another long dialog (14K words). I then asked ChatGPT to summarize the dialog. Here’s the prompt I gave it:
Would you summarize the preceding discussion in terms suitable for a blog post? I want it to start with some kind of quick introductory overview and then go on through asset pricing, De Vany on movies, and Jockers on novels in that order. Two things to bridge between asset pricing and movies: 1) Despite the different objects, movies vs. stocks, the objects are both ‘thick’ and the underlying structures are similar to a first approximation though the analytic methods are quite different. 2) Galloway’s remark about stocks as brands connects movies with stocks as thick objects.
I’ve appended that summary below. The title of this post is the one ChatGPT gave to its summary.
* * * * *
We have been circling around a question raised by Tyler Cowen’s recent reflections on economics and artificial intelligence: what happens when the old economic intuitions no longer seem adequate to the objects economists are trying to understand? Cowen’s worry is that marginalist reasoning, once the pride of economics, may have helped create a world too complex for marginalist intuition to master.
That is a real worry, but perhaps not a hopeless one. If we look across asset pricing, movie dynamics, and literary history, a different picture emerges. Machine learning and high-dimensional modeling are not simply replacing human understanding. They are revealing new conceptual objects. Just as telescopes and microscopes disclosed new physical objects in the early modern world, computational models now disclose new relational objects: pricing kernels, heavy-tailed outcome regimes, temporal diagonals in literary space.
The old intuitions are not enough. But the new models may help us build new intuitions.
Asset Pricing: From Marginalism to High-Dimensional Valuation
Classical asset-pricing theory begins with a powerful marginalist intuition: investors require compensation for bearing risk. An asset’s expected return should depend on its exposure to systematic risks. CAPM gave us beta; later factor models added size, value, momentum, profitability, investment, and other variables.
These models are attractive because they are low-dimensional and intuitively graspable. A few named factors are supposed to explain many asset returns. That is the dream: a compact causal vocabulary.
But recent high-dimensional models suggest that the true pricing structure may not be compressible into five or six factors. The AIPT work we discussed begins with roughly 130 stock characteristics and then generates hundreds of thousands of nonlinear factors. These are used to approximate the market’s pricing kernel, or stochastic discount factor: the hidden valuation structure through which future payoffs are priced under uncertainty.
This is no longer a simple causal story in which one named factor explains one outcome. It is more algorithmic. The model works by probing a vast feature space and finding structure there. The individual factors may not be interpretable as ordinary causes. But the model still has an economic frame: markets price future payoffs, and that pricing process appears to be high-dimensional.
This is where Cowen’s story becomes interesting. Marginalism did not merely explain markets. It helped construct modern finance. Pricing theory made derivatives, securitization, and the decomposition of risk into tradable claims possible. But once those instruments proliferated, they helped create a financial world too complex for the original marginalist intuitions to command.
In short: marginalism may have helped build the world that now requires machine learning to map.
Thick Objects: Stocks, Brands, and Movies
At first glance, financial assets seem very different from movies or novels. A stock is an economic claim; a movie is a cultural artifact. But the distinction begins to blur once we treat both as “thick objects.”
A thick object is not exhausted by one or two measurable properties. It has many dimensions. It is embedded in social interpretation. Its value depends on history, expectation, reputation, performance, and future promise.
Scott Galloway’s remark captures this beautifully: “Stocks are like brands and that is they’re part promise and part performance.” [YouTube, Pivot, May 29, 2026.]
That is a sophisticated observation. A stock is not merely a claim on current earnings. It is a socially circulating judgment about a company’s future. The performance side includes revenue, earnings, margins, growth, debt, liquidity, volatility, and so forth. The promise side includes brand power, technological imagination, managerial credibility, founder charisma, regulatory risk, and collective belief about what the company may become.
That makes stocks resemble brands. A brand is not just a name, logo, or handle. It is a socially stabilized promise. It condenses past performance and future expectation into a recognizable entity.
And that is the bridge to movies. A movie before release is also part promise and part performance. The performance side includes director, stars, genre, budget, studio, distribution, trailers, and reviews. The promise side is what audiences imagine the movie will deliver: spectacle, prestige, emotional satisfaction, social participation, novelty, nostalgia.
So although movies and stocks are different objects, both are thick. Both circulate through populations. Both are interpreted under uncertainty. Both depend on the conversion of signals into expectation and expectation into value.
The analytic methods differ. Asset-pricing models estimate valuation structure. Movie models track social diffusion and outcome distributions. But to a first approximation, the underlying situation is structurally similar: complex objects move through fields of collective judgment.
In brains of Spanish-English bilinguals grammar is embodied in shared tissue
Xuanyi Jessica Chen and Esti Blanco-Elorrieta, A Shared Neural Mechanism for Abstract Grammatical Computations Across Languages in Bilinguals, The Journal of Neuroscience, June 15, 2026.
Abstract: A central question in cognitive neuroscience is how the brain implements abstract computations that must generalize across superficially different inputs. Language provides a strong test case: the same grammatical operation, such as pluralization, can be realized through distinct rules and forms across languages. Whether such transformations rely on language-specific neural systems or on abstract mechanisms that generalize across linguistic contexts remains unresolved. Crucially, these transformations must be computed online and integrated into speech planning within a tightly constrained time window. Using magnetoencephalography (MEG), we tracked the millisecond dynamics of grammatical word-form transformations during semi-naturalistic phrase completion in humans of both sexes. Highly proficient Spanish–English bilinguals produced singular and plural noun forms in both languages in a design that fully orthogonalized semantic number, phonological changes, grammatical inflection and produced language. Adjusting words to fit their grammatical context engaged a left-lateralized fronto-temporal network beginning ∼100 ms after cue onset. Multivariate decoding revealed that the neural patterns supporting this computation generalized across languages, across different surface plural forms, and to pseudowords, demonstrating that abstractly equivalent operations are instantiated in the same neural substrates despite differences in linguistic form. Together, these findings provide time-resolved neural evidence for a language-general computational mechanism, showing that the brain implements grammatical transformations as abstract, generative operations. More broadly, they show how bilingualism can be used to probe general principles of neural organization, revealing how abstract computations may be shared and reused across representational systems.
Significance Statement: Human language relies on the ability to modify words to convey information like number and tense, but languages vary widely in how these transformations are implemented. This variation raises a fundamental question in cognitive neuroscience: do such transformations depend on language-specific neural systems, or are they processed by abstract neural mechanisms that generalize across languages? We demonstrate that Spanish–English bilinguals engage a shared left frontal–temporal network when producing grammatically appropriate forms in both languages. This common neural signature emerges early during speech planning and even generalizes to novel words. These findings indicate that the brain builds abstract, reusable neural mechanisms, consistent with models where language is organized by computational principles rather than by language-specific systems.
Here's an article in the NYTimes about these results: K. R. Callaway, How Does One Brain Speak Two Languages?, NYTimes, June 15, 2026.
When deciding how to make a word singular or plural, for instance, bilingual people exhibit strikingly similar brain activity regardless of whether they are speaking in their first or second language.
“It wasn’t obvious that it was going to be so shared,” said Esti Blanco-Elorrieta, a psychologist and neuroscientist at New York University and an author of the study, which was published on Monday in the journal JNeurosci. “I think this is arguably one of the first very fine-grained findings of how truly integrated two languages in the brain are.”
Early research viewed bilingualism as an “add on” or “disruption” to the processing of one’s native language, said Judith Kroll, a psycholinguist at the University of California, Irvine who was not involved in the new study.
Subsequent studies have found that bilingual brains tend to display physical differences, such as more efficient white matter and changes to the gray matter, and to perform better on memory and concentration tasks.
Now scientists are probing further, to understand whether core aspects of the brain’s neural network does double or triple duty to process multiple languages.
A single grammatical engine:
The finding is in line with other initial results in this area, said Mirjana Bozic, a cognitive neuroscientist at the University of Cambridge who was not involved in the study. For instance, the new study provided additional evidence that the front left side of the brain was typically involved in processing the grammatical structure of sentences across different languages. On the whole, Dr. Blanco-Elorrieta said in a news release, a single “grammatical engine” in the brain appeared capable of powering multiple languages at once.
Dr. Bozic said that the find, although not surprising, was “highly informative, providing elegant and convincing evidence that bilingual speakers rely on shared neural mechanisms. She added, “One question that remains is how far these findings generalize across language pairs that differ more substantially.”
Monday, June 15, 2026
John Quiggin on the SpaceX IPO
At Crooked Timber, June 15, 2026.
The SpaceX IPO, valuing a motley collection of dubious business at over a trillion dollars, marks the abandonment of the Efficient (financial) Markets Hypothesis, one of the zombie ideas I criticised in the wake of the Global Financial Crisis. Not only do financial markets fail in the task of valuing assets accurately, but the institutional structures that are supposed to make them work have given up trying.
This was prefigured by the rise of Bitcoin and other forms of crypto. Revealingly, no one any longer uses the term “cryptocurrency” – these assets are never used as currency in ordinary transactions, and even their illicit uses seem to have faded. Rather, Bitcoin is valuable solely because it is valued. As I pointed out back in 2018, (free from paywall here) once this logic is accepted, it can be applied to financial assets more generally, and particularly to stock markets. [...]
It’s only with SpaceX that we can see the complete abandonment of any pretence at rationality. In the case of SpaceX, I was struck by Dave Karpf’s observation that Musk’s wealth in 2020 was “only” $24 billion. Everything of value in his career (Tesla cars, batteries, Starlink) had been achieved by then, and everything he has touched since then has been a disaster (Xitter, Cybertruck, robotaxis, Starship). Yet his wealth has multiplied 50 times over).
In support of the IPO, Goldman Sachs has put its name to the claim that the company will grow 100 times over by 2030. This is patently absurd. Nothing in Musk’s ragbag of assets has this kind of potential. [...]
The comprehensive corruption of the financial system confirms me in the view that the USA is one big grift. Not just the financial system, but politically and militarily as well. But that’s a topic for another day.
Sunday, June 14, 2026
Masterful control of all ten digits
Those who have not seen this Video, Must See till the End. She is *Roxana Küwen*, a German Circus Artist, graduated at Fontys Academy for Circus & Performance Art, Tilburg, Netherlands. Watch her Foot & Hand Movements With Five Balls, As If She has FOUR Hands !! Absolutely… pic.twitter.com/w4uMYlRAlM
— Dr Mouth Matters (@GanKanchi) June 12, 2026
A sophisticated take on the AI bubble argument
GeometricInvestor has a longish analysis of money flow and investment in the AI business, The AI Capex Ledger: Who Pays, Who Earns, and What the Bond Market Is Missing, June 12,2026.
The opening paragraphs:
The AI debate is stuck on the wrong question.
The question is not whether AI is a bubble. Nor is it whether NVIDIA is expensive, whether Michael Burry is early again, or whether productivity gains will eventually lower inflation. The better question is an accounting one: who needs to earn what return for the AI capex cycle to make sense?
At the bottom of the stack, GPU, HBM, networking, power, and cooling suppliers are already earning. The capex is real. The checks have cleared. That was the first phase of the trade.
The harder question sits one layer above. Hyperscalers and neoclouds are converting capital into compute. Compute becomes tokens. Tokens must become revenue. Revenue must become gross profit after depreciation, power, financing, and model costs. And finally, the buyers of those tokens must earn a return high enough to keep spending.
Only then does AI become a true macro productivity shock rather than a capital-spending boom with better branding.
Then comes the long analysis, followed by these concluding remarks:
The AI cycle will not be resolved by asking whether GPUs are expensive or whether chatbots are useful. It will be resolved by a chain of returns.
Can infrastructure suppliers earn margins without oversupply?
Can hyperscalers and neoclouds sell enough tokens to cover depreciation, power, financing, and obsolescence?
Can enterprises earn more from those tokens than they spend?
Can the economy convert those firm-level returns into productivity growth?
And if it can — does the bond market understand that higher productivity may mean a higher neutral rate, not just lower inflation?
That is the real AI macro debate. Each layer has a hurdle, and each hurdle has a date with evidence. The next time an AI headline crosses your screen, skip the bubble question and ask the ledger question: which layer is this, and what return does it need?
The framework carries its own falsifier, as it should: monetizable AI revenue clearing the hurdle band for years while aggregate productivity and corporate operating leverage outside the supplier base stay flat. That would mean buyers funding sellers indefinitely without a return — the one thing this piece says cannot last.
The first phase was about capex. The next phase is about returns.
Ezra Klein contra (extreme) Homo economicus [Don't torture pigs]
Ezra Klein, What the Cult of Efficiency Costs Us, NYTimes, June 14, 2026.
The opening:
Chris Murphy, the Democratic senator from Connecticut, offered the graduates of Wesleyan University wise counsel in his commencement speech a few weeks back. “You are about to step out into a world that prizes efficiency and the annihilation of drift and friction above all else,” he said. “Our entire economy is built on rewarding companies that are efficient at making a profit, not based upon how they treat their workers, the social value of their product or the impact they have on the community.”
“You didn’t design this world,” he continued. “You didn’t choose it. But you will live with the consequences of this cult of efficiency. And you will have to choose which side you are on.”
Efficiency inflicted on pigs:
In 2016 and 2018, voters in Massachusetts and California passed ballot initiatives banning, among other things, the sale of pork from pigs confined in gestation crates. These crates confine breeding sows — large animals, often 400 to 500 pounds — in two-by-seven-foot cages in which they cannot so much as turn around, much less root or socialize. Because sows are often reimpregnated about a month after their piglets are born, they can spend years of their lives in these crates.
I watched, in the interest of fairness, a video from an arm of the National Pork Board on why gestation crates are good for pigs. It features row upon row of sows penned between bars so narrow they cannot turn around. It is no way for any animal to live, particularly not one as smart and as social as a pig.
There are studies I can cite on the psychic and physical violence these crates inflict on pigs — the elevated cortisol levels, the sores, the obsessive biting of metal bars — but I think the way Kristof puts it is simpler and more honest: “Think of your dog enduring what pigs face, and you realize that the moral cost is incalculable.” The difference between dogs and pigs is neither their intelligence nor their sentience. It is our willingness to admit their intelligence and their sentience. It is our decision to extend them our compassion and concern.
Homo economicus:
In traditional economics, prices are the informational lifeblood of an economy: They reveal the cost of materials and labor, the balance of supply and demand. But much can be hidden in prices. Perhaps it is artificially low because waste is being dumped into the rivers or workers are being robbed of their wages or the burden is borne by animals that will spend years of their lives without the comfort of their herd or the ability to feel grass beneath their hooves or turning around when curious about a sound. When that happens, we have sacrificed compassion for cost.
Most of us know by now that the lives animals lead in factory farms are often hideous. A 2019 survey by the Johns Hopkins Center for a Livable Future found that a majority of Americans wanted stronger oversight of confined animal feeding operations and a plurality wanted a ban on new ones. Those numbers were even higher when the same pollsters asked Iowans and North Carolinians, where majorities favored a ban on new concentrated animal feeding operations, in part because these operations often impose terrible costs on the human beings who live near them.
There's much more at the link.
Saturday, June 13, 2026
Burton “Random Walk” Malkiel on SpaceX’s IPO and the poor upside potential for the stock
Burton Malkiel, best known for his 1973 book, A Random Walk Down Wall Street, has some interesting remarks in the NYTimes for those who are wary of being stuck with a stake on SpaceX (and perhaps OpenAI and Anthropic as well) as a consequence of having index funds in their 401(k).
Given that so many millions of Americans are suddenly having SpaceX shares foisted upon them, I understand why some financial experts are criticizing the practice of index investing itself. Right now, just a handful of A.I.-related stocks represent almost half the value of the total stock market index. If A.I. stocks collapse, so will the worth of your index fund.
This is the paragraph I find particularly interesting:
Unlike prior initial public offerings, SpaceX shares are already so expensive there isn’t a lot of upside potential left. When Facebook, now Meta, went public in 2012 with a valuation of $100 billion, shareholders were able to benefit financially from its growth to a $1.5 trillion giant. Amazon went public with a generous (for 1997) valuation of $440 million, and shareholders profited as it grew into a $2.5 trillion behemoth. Not so SpaceX. Because it was owned by private investors for so long, much of the gain will immediately be handed off to its venture and private equity backers rather than preserved for new investors.
He goes on to point this as well:
Moreover, unlike other public companies, SpaceX is employing a dual class share structure that gives Elon Musk essentially complete control with no independent oversight. Public shareholders will, comparatively, have no voice in corporate decisions. Mr. Musk controls multiple related corporate enterprises, raising the possibility of conflicted transactions within the Musk ecosystem. Many investors will be uncomfortable giving him so much power and holding an index fund in which he has so large a share.
He then says:
These are all legitimate reasons to worry. But in my view, it would be a mistake to abandon an indexing strategy. Timing the market is impossible. Yes, the stock market is unusually concentrated today, and it is likely to get even more so over the next period with Anthropic and OpenAI looking to go public soon.
You can read the rest of the article, if you wish. But it's that paragraph about upside potential that caught my attention.
More Hockney, a cat, wall of blue, and leafy branches
“I don’t reflect too much.
— 반야 (@DiamondBanya) June 12, 2026
I live now.
It’s always now.”
데이비드 호크니.
그림도 생각도 얽매이지 않은, 자유로운. pic.twitter.com/DDEuna2X4r
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