Inside and out on the light rail

Disclosure Day [Media Notes 184]

Meh. I made it through. I wasn’t hard, except for the seat. I couple of years ago my local theater converted to those recliner seats. I wish they hadn’t. I can never find a position I find really comfortable, so I keep it moving around. I liked to old seats. They had plush cushions and just enough “give.”

Anyhow, back to the movie, Disclosure Day. By the time I decided to see it I knew it was about aliens. Not my favorite. But Spielberg’s done it before. I really liked Close Encounters of the Third Kind and E.T. the Extra Terrestrial; War of the Worlds was OK. So I went. Spielberg.

It was a chase movie, with aliens swirling around in there. That much was obvious. But just who these peoples were and what these agencies were, not at all obvious. They’re just there, doing stuff. But then the two central characters, Daniel Kellner (Josh O. Conner) and Margaret Fairchild (Emily Blunt) didn’t really know who they were or what had happened to them, much less why. And then there’s the Wardex Corporation, some kind of private security firm with deep government ties, they’re pulling all the strings. Except for the ones pulled by the extraterrestrials.

Spoilers ahead.

Anyhow, the world seems to be on the brink of another world war and the good guys and bad guys are messing with Kellner and Fairchild about the aliens while they’re running around wondering “Why me?” And fast cars and chases. There’s lots of talk about empathy and hand-held stick devices that control others and then near the end there’s a flashback when we see Kellner and Fairchild stretched out on tables. Apparently they’ve been abducted and these wand things are ringed around their heads and doing something glowy.

At that point I flashed on Forbidden Planet, from the previous century. There Dr. Morbius uses the Krell mind device to juice up his intelligence with, shall we say, mixed results. Here the results seem better. It’s kind of the inverse. Seventy years, from 1956 to 2026. At the same time I felt echoes of the current moment, where, instead of aliens, we have AIs.

And then there’s the alien, when we finally see it. We’d seen aliens in video tapes. They seem to be the standard “Greys,” short, spindly, with big heads and big eyes. This poor guy was very tall, very spindly, and with a super-large head. It’s a wonder it could stand up at all. But it did, getting up from a wheel chair – where’d they keep it, how, and why? It put one hand on Kellner and the other on Fairchild, and then the scene changes abruptly – but we’re very near the end. I kept thinking, “So very frail and fragile and somehow it caused all this fuss. Does not compute.”

All these loose ends. The whole thing, just handing in midair. It does not compute. 

* * * * * 

Hoboken streets

Clueless thy name is Zuckerberg

Victor Tangermann, Mark Zuckerberg Orders His Employees to Start Having Fun Again After Brutal Layoffs Culled Their Colleagues, Yahoo!Finance, June 16, 2026.

Morale at Meta has seemingly hit rock bottom.

Employees have been roiling from multiple rounds of major layoffs. Last month alone, the Mark Zuckerberg-led company laid off a whopping 8,000 workers, roughly ten percent of its workforce, as part of its chaotic refocusing efforts around AI.

Many of those who remain are now forced to perform the grunt work to train AI models, weekly busywork that's already driving some of them up the wall, as Wired reports.

In an internal memo to employees on Friday, Zuckerberg attempted to lift their spirits in what appears to be a notable failure to read the room. Specifically, the billionaire promised to host a companywide AI hackathon in July — only to get brutally shut down by workers who were in no mood for such a thing.

Meta has regularly hosted hackathons in the past, but given last month's layoff announcement, the reception was extremely chilly. [...]

For all its employees' pain and suffering, Meta has surprisingly little to show. The company continues to trip over its own feet, struggling to release impressive new AI models as its competitors pull ahead further in the ongoing AI race.

Victor Tangermann, Meta’s Super Expensive New AI Team Is Already a Complete Catastrophe, Yahoo!Finance, June 15, 2026.

Now that Meta CEO Mark Zuckerberg's dream of a metaverse has collapsed in on itself, the billionaire has moved onto his next money pit: a wildly expensive "Superintelligence" unit.

But those who've survived several brutal rounds of layoffs at the company aren't exactly thrilled to be part of his new vision for it. As Wired reports, morale within Meta's 6,500-staffer Applied AI team, which was created in March to support the Superintelligence Labs, is hitting rock bottom.

Three employees who spoke to the publication on the condition of anonymity said that the weekly busywork tasks they are being assigned, like generating puzzles to test the reliability of Meta's AI models, is "soul-crushing." [...]

A petition has also been signed by more than 1,600 employees, opposing a draconian new initiative that involves installing software on work computers to track everything employees to, including keystrokes and clicks, data that's then fed to train AI.

Homo economicus on steroids.

Big AI vs. Big Government in the 21st Century

Ross Douthat, The Battle With Anthropic Is the Start of a New Kind of Conflict, NYTimes, June 16, 2026.

The nature of the Anthropic conflict can be swiftly summarized even if the details are in dispute. Two months ago the company declined to publicly release its latest model, Mythos, citing various safety concerns (and hyping the model’s revolutionary power). After previewing Mythos to the U.S. government and certain corporate actors, Anthropic then released Fable, a version of the model with various safety guardrails. Amazon, an Anthropic investor and client, discovered a way to bypass some of those guardrails. This was reported to the White House, Anthropic’s response was deemed unsatisfactory, and the administration used its export-control power to forbid the use of Fable by any foreign national inside the United States and anybody at all outside it — a rule that Anthropic treated as a requirement to shut the new A.I. model down.

That’s where we are now, with the company and the administration negotiating over how to bring back Fable while ongoing leaks to the press paint one or the other side as unreasonable or reckless or ideological and clueless about tech.

Two facets of the conflict:

But beyond the specifics of why, say, the libertarian tech people in the Trump administration distrust the effective-altruist tech people running Anthropic, the kind of conflict we’re seeing here is overdetermined by the trajectory of the A.I. models: There is too much potential power here not to have ongoing, escalating struggles over who actually gets to rule.

The war over Fable previews the two broad forms that this conflict will take. First there is a private-public struggle, where governments grope for a regulatory sweet spot that allows them to maintain a meaningful veto over the A.I. behemoths without killing off their innovative power, while the A.I. companies try to maintain control over their own models and influence over how governments use their innovations.

There is a path here that leads to nationalization in all but name and a path that leads to a kind of de facto corporate takeover of the government, or at least a too-big-to-fail symbiosis. And along the way there may be not just conflicts between presidents and A.I. executives but also increasingly ruthless corporation-on-corporation action, out of fear that the A.I. landscape is winner-take-all to an extent we’ve never seen in capitalism before. [...]

Then alongside the struggle to control A.I. power within American borders, there is the geopolitical struggle to maximize global power (where the only real players are probably the United States and China) and maintain sovereignty (where everyone else is likely to be scrambling to maintain some independence). The use of export controls to shut down Fable presumably reflected U.S. fears of Chinese access to a jailbroken version of the model, but it was also a warning to every other country in the world: If we end up with economy-permeating A.I. models that are made and regulated in America, the American government will control the on-off switch.

There's more at the link.

Liszt, Hungarian Rhapsody for 2, Victor Borge and the other one

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.

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.

Newport Mall in Jersey City

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.”

Claude Code is 98% traditional computer code

https://t.co/nd0ccgv8k9

— How To Prompt (@HowToPrompt__) 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.

How'd that happen?

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.

H/t Tyler Cowen.

Plantain lily [colour]