Conversations with Tyler: A Special Conversation with Nicholas Copernicus, Adam Smith, and William Stanley Jevons

I was feeling impish this morning, so I gave Claude 4.6 Sonnet (Medium) the following prompt:

Back in the 20th century Steve Allen did a series of TV shows called “Meeting of Minds,” where people from different time periods were brought together to have a discussion. I want you to imagine a special episode of “Conversations with Tyler” in which Tyler Cowen moderates a discussion between Copernicus, Adam Smith, and Stanley Jevons on economics. For reference I have placed three documents in the project folder. Two books by Tyler Cowen, one on marginalism and the other on the Great Economists (GOAT), which has material on Adam Smith.. There’s a shorter document by Copernicus (in Latin), filename, “Copernicus on economics.”

I’ve appended its response below the entirely imaginary Five TC Bucks note. Click on the image to see the detail. Note that ChatGPT did the image, not Claude.

Conversations with Tyler: A Special Episode
Tyler Cowen in Conversation with Nicholas Copernicus, Adam Smith, and William Stanley Jevons

[Recorded live at the Mercatus Center, George Mason University. The set is the standard one — a small round table, minimal lighting, no audience. Tyler Cowen sits across from three guests who have, for the purposes of this episode, been freed from the constraints of mortality and non-overlapping centuries. Copernicus speaks through a translator when needed, though he proves surprisingly comfortable in English, given how many languages he actually commanded. Smith retains his Edinburgh-via-Kirkcaldy cadence. Jevons is precise, energetic, and fidgets with what appears to be a small logometer.]

* * * * *

TYLER: So let me start with something that might seem unfair to Nicholas, but I think actually redounds to his credit. Tyler Cowen — the other Tyler Cowen, me — has written that astronomers of the sixteenth century were sometimes better economists than the economists of the same period, because there essentially were no economists. And that you, Nicholas, were one of the first people to state the quantity theory of money. Now here's my question: did you think of yourself as doing economics, or did you think of yourself as solving a political crisis?

COPERNICUS: The distinction you draw would not have occurred to me. I was asked by the Diet of Prussia — by the princes, by the king's administrators — to address a practical emergency. The currency of this land had been debased so many times, by so many hands, in so many mints, that commerce had ceased to function in any stable way. The goldsmiths prospered — they were the only ones who could extract the silver from the degraded coin before the next debasement arrived. Everyone else suffered. So yes, I was solving a political crisis. But a crisis requires a theory, or you are merely thrashing. You cannot prescribe a remedy for a disease you have not diagnosed.

TYLER: And your diagnosis was what exactly?

COPERNICUS: That money is a mensura — a measure. It is like a unit of length or weight. If your ulna varies from day to day, you cannot do carpentry. If your currency varies in its silver content from month to month, you cannot do trade. The injury to commerce is the same in both cases, but the monetary injury is more insidious because it proceeds occulta quadam ratione — by a kind of hidden logic. People notice a bad harvest immediately. They do not immediately notice that the coin has been shaved, but they feel its effects: prices rise, foreign merchants refuse your money, the best craftsmen leave for regions with sounder currency.

SMITH: If I may — this is exactly the confusion I spent considerable effort untangling in Wealth of Nations when I took on the mercantilists. They believed that the accumulation of specie was wealth. What Canon Copernicus is describing from his Prussian experience is that even that modest goal — hoarding silver — is self-defeating. The moment you debase the coinage, you have, in a sense, exported your silver to every foreign merchant clever enough to melt the coins.

COPERNICUS: Precisely. The goldsmiths and those who know the quality of metals — they are the only beneficiaries. They collect the old coin, extract the silver, sell it at a premium, and leave behind a pile of copper. My recommendation was blunt: stop minting until the existing coin has restored its value, establish at most two mints for all of Prussia, and make the coin of one mint and one standard.

TYLER: Gresham's Law, essentially, before Gresham.

COPERNICUS: Before whom?

TYLER: Thomas Gresham. He gets credit for the principle that bad money drives out good. Roughly a generation after you stated it.

COPERNICUS: (pause) This is the way of things. Copernicus waits for Copernicus. In astronomy as in monetary theory.

JEVONS: I want to press on the word "measure," if I may. Canon Copernicus treats money as a standard — a fixed reference against which goods are priced. But what I discovered, or rather what I was forced to discover when trying to establish whether the value of gold had actually fallen after the Australian and Californian gold rushes of the 1850s, is that money itself has no fixed value. It is itself a commodity whose degree of utility — whose marginal utility, to use the language I was then working out — varies with its quantity. The quantity theory you describe is already implicit in this: flood the market with debased coin, and each unit of coin buys less, not merely because there is more of it, but because its intrinsic silver content is lower and everyone knows it.

COPERNICUS: I will not quarrel with the analysis, though your language differs from mine. What I found is that the regions of Prussia which had maintained good currency were also the regions with flourishing workshops, skilled artisans, and abundant goods. The regions with debased currency had become idle. You say this is because the marginal utility of a sound currency is higher. I say it is because craftsmen and merchants are not fools: they will go where their labor and their goods are honestly compensated. 

[I wonder what Copernicus actually understood by the phrase, "marginal utility"?] 

TYLER: Adam, let me come to you here. Smith, you spent a great deal of Wealth of Nations attacking mercantilism — the view that national wealth consists in the accumulation of precious metals. But you also granted mercantilists more credit than many of your defenders are comfortable with. You said their arguments were "partly solid and partly sophistical." What did you actually concede to them?

SMITH: What I conceded is that commerce and defense are entangled in ways that pure theory does not capture cleanly. The Navigation Acts — requiring that trade to Britain's colonies be carried in British ships — were bad economics by almost any reckoning. They raised prices, restricted trade, enriched a narrow set of interests at the expense of the broader public. But I wrote, and I meant it, that defense is of more importance than opulence, and that the Navigation Acts, whatever their economic defects, had served to maintain British naval power. One cannot always afford the luxury of consistent principle. (small smile) Though I tried to be consistent as often as possible.

TYLER: Jevons, here is a question directed at you specifically: why did it take from roughly 1776, the publication of Wealth of Nations, until 1871, the publication of your Theory of Political Economy, for economics to absorb the idea that value is determined at the margin — by the last unit, not the total quantity? Smith understood the diamonds-water paradox but did not resolve it. You resolved it. What took so long?

JEVONS: I have thought about this a great deal, and I believe the answer is that the resolution required mathematics, and economics had declined to use mathematics, or rather had not yet learned that it could use mathematics. The idea that utility diminishes with quantity is not — once stated — particularly obscure. Galileo came close to it. My precursors in the British literature, Jennings and MacLeod, came close to it. But close is not enough. You need to be able to state the law precisely, apply it to a schedule, differentiate, and find the first-order conditions. You need calculus, or at least the habit of mind that calculus cultivates. Once I had that tool in hand, the whole of exchange theory reorganized itself very quickly. I felt it opening up.

Friday Fotos: Goin' to the Movies

Tyler Cowen, Tycho Brahe & Rank Shift @3QD

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

Tyler Cowen is the Tycho Brahe of Economics

It came about as a side effect of my writing and thinking about Cowen’s new monograph, Tyler Cowen, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). I was interacting with Marge, the AI attached to the online text of the monograph, when I remarked: “So, he’s the Tycho Brahe of economics.” Marge replied:

That's a sharp analogy. Brahe had the best observational data of his era, ran an extraordinary research enterprise on Hven, trained Kepler who then used Brahe's own data to refute the model Brahe himself believed in. [...] Brahe couldn't make the conceptual leap to heliocentrism despite having everything he needed to do so — his intuitions were too deeply formed in the Ptolemaic rank. But he was indispensable to the transition precisely because of his institutional energy, his data gathering, his willingness to fund and train the next generation.

Emergent Ventures is Hven. Tyler is gathering the data — the people, the ideas, the unconventional bets — that someone with different intuitions will synthesize into a framework he himself can't quite reach. The book is his Tychonic system: a genuine attempt at synthesis that's more sophisticated than what came before, that captures real phenomena, but that preserves the central commitment — equilibrium, marginalism, the whole apparatus — that the next rank will have to abandon.

My first impulse was to put that observation in my introduction to the overall series of blog posts when I gathered them into a single document. But, after thinking things over, I decided that it would be more interesting to write it up as a stand-alone document, one I could publish in 3QD. So that’s what I did.

Now, if you’ve been following the series of blog posts, you know that I write quite a bit about the theory of cultural ranks that David Hays and I developed in the 1990s. I didn’t see any need to mention that in the 3QD article, but now that it’s been published I see that it’s quite directly relevant. One problem with the theory as Hays and I wrote about it, is that it’s easy to come away with the idea that the shift from one rank to another is a step function. That’s not at all the case, such shifts take decades and even centuries. But it was all we could do simply to articular the idea of different cognitive ranks.

However, this particular comparison, between Tyler Cowen, an economist in the 21st century, and Tycho Brahe, an astronomer from the 16th century, is about rank shift. Tycho was participating in the transition from a Rank 2 model of the solar system, the geocentric model inherited from Ptolomy, and the Rank 3 model, initiated by Copernicus. Cowen is participating in the shift from Rank 4 economics, which is the focus of his monograph, to a possible Rank 5 economics, which doesn’t quite exist yet. But, who knows what the future will bring?

* * * * * *

You can download a PDF:

• Academia.edu: https://www.academia.edu/168873753/Tyler_Cowen_is_the_Tycho_Brahe_of_Economics
• ResearchGate: https://www.researchgate.net/publication/407286640_Tyler_Cowen_is_the_Tycho_Brahe_of_Economics

LLMs are not enough [LeCun]

1. Je n'ai jamais dit que les LLMs étaient inutiles ou n'avaient pas de valeur commerciale.
2. Mais beaucoup de technologies sont utiles et à haute valeur sans être particulièrement "intelligentes"
3. Les LLMs sont essentiellement inutiles et sans grande valeur pour ce qui est…

— Yann LeCun (@ylecun) June 18, 2026

English translation is available at the link. 

Orange, yellow, yellow, orange, red

The world's first trillionaire racist [Musk]

Toby Buckle, Greg Sargent, Elon Musk’s Race War Just Took Darker Turn—Time for a Global Response, The New Republic, June 17, 2026.

If you were on the verge of becoming the first trillionaire in human history, with the press breathlessly reporting on your every move, that would probably be your focus. Yet in the days before SpaceX’s initial public offering vaulted Musk into the 13-digit wealth club, the tech mogul’s mind was elsewhere—a white man in Belfast had been viciously stabbed by a Sudanese immigrant, and it provided Musk an opening.

In numerous social media postings, Musk highlighted the crime in starkly racist terms. Several nights of violent anti-immigrant pogroms orchestrated by fascist mobs followed. It was a telling confluence of events: Musk’s extraordinary wealth is fueled by investors’ bedazzlement at his techno-utopian schemes. But the Belfast conflagration revealed the other side of his future vision: his belief that the white populations of the world must violently subjugate the nonwhite enemy in what he sees as a multicontinental, Armageddon-like Total War for global racial supremacy.

As the bedlam raged in Belfast after the stabbing—resulting in far-right rioters torching cars, buses, and even the homes of immigrants—Musk egged it on. Using X—the platform he acquired precisely for moments like these—he posted locations for groups of rioters to congregate. He elevated vile, overtly fascist and white-supremacist exhortations. When one far-right British politician called for the prosecution of officials who “placed dangerous third world savages in our communities,” Musk replied: “This is the way.” [...]

In a very real sense, the fires in Belfast illuminate the emerging outlines of that coming struggle. Musk’s involvement in British politics has tracked with his growing fantasies about global race war.

Back at home:

In the United States, Musk had already been experimenting with a consistent trope in fascist rhetoric: seizing on a crime committed by an individual member of a minority group and claiming that it shows the innate murderous tendencies of the group as a whole. The real claim here is that these isolated horrific acts “reveal” the whole minority group’s disguised genocidal intent toward white peoples—and suggest that this is why the group has deviously infiltrated white countries and communities.

Western civilization is under attack:

By endorsing Reconquista, Musk is amplifying one of the clarion calls of the European far right and, increasingly, the American right, as well: “remigration.” This is the idea that saving Western civilization (as Musk imagines it) requires the forced expulsion of huge numbers of immigrants, especially Muslims, and even untold numbers of their descendants. In response to a missive about white people potentially becoming a minority in New Zealand, Musk posted recently: “The doom of Western Civilization must be averted!”

A robot-AI utopia:

But the real intent behind Musk’s indictment of “suicidal empathy” appears to be future-focused too. In an interview, Tarnoff points out that Musk’s imagined future of humanoid robots and AI can be squared with his hatred of mass migration. “You reduce your reliance on immigration as a labor source if you automate more labor,” Tarnoff tells us, noting that in this scenario, AI and robots might then be pressed into service to “restrict immigration.”

In this context, we believe it’s not an accident that Musk explicitly denigrated “civilizational suicidal empathy” while his Department of Government Efficiency decimated the budget of USAID, which helped produce hundreds of thousands of deaths abroad. That was a test run: If Musk’s robot-and-AI utopia fails to produce mass abundance for the global poor, and global warming spurs more suffering and more migrations, saving human “civilization” will require dispensing with the empathy gene: It will give us the fortitude to cut loose all that third-world civilizational dead weight.

A national security threat:

Then there’s what a future Democratic Congress can do. The Musk problem will have to be on its agenda in a serious and meaningful way. Claire Finkelstein, a professor of national security law at the University of Pennsylvania, points out a core problem here: His many government contracts, and his access to privileged information, pose a “national security threat,” even as Space X itself is in many ways a “national security asset.” We need to know a lot more about what Musk’s contracts actually translate into in terms of his personal influence inside the government.

There's much more at the link.

Dick Cavett interviews Salvador Dali

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 hanging in midair, pushed and dragged along by a bunch of breathless chase sequences to keep you from thinking too much. 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