I wrote recently about a paper that should have embarrassed academic finance more than it did.

The finding was simple and brutal. Two hundred and twelve peer-reviewed stock-return predictors — the output of a five-year review gauntlet at the field’s most prestigious journals — predict returns out of sample no better than a computer mindlessly grinding through 29,000 accounting ratios in search of a t-statistic above two. Decades of theory. Decades of careful storytelling about risk and mispricing. And it bought you nothing a brute-force search wouldn’t have found anyway.

That paper was about prediction. Academic finance, it turned out, fails the prediction test.

I want to tell you now about a paper that does something worse.

It does not go after the predictive wing of the discipline. It goes after the structural wing — the half that doesn’t try to forecast next month’s return but claims, far more ambitiously, to measure the deep parameters of investor behavior. The demand curves. The elasticities. The numbers that tell you how much prices move when a central bank buys bonds or an index fund rebalances.

And it proves — from two assumptions that essentially no one in finance is willing to give up — that those measurements cannot be made model-free from the holdings-and-prices data they are usually advertised as coming from. Not “are hard to make.” Not “have wide error bars.” They are not measurements at all, unless you first smuggle in the very theory they were supposed to test.

The paper is “A Trilemma for Asset Demand Estimation,” by William Fuchs, Satoshi Fukuda, and Daniel Neuhann. The title gives the structure away. A trilemma is a menu of three items from which you may choose at most two. The three here are: no-arbitrage pricing; the assumption that investors value assets for their payoffs; and the ability to identify structural asset demand from holdings-and-prices data without first committing to a full model. Finance wants all three. It cannot have them. Something has to go — and if no-arbitrage and payoff-based preferences stay, what goes is model-free measurement.

A word on the authors. Two of the three — Fuchs and Neuhann — sit at the McCombs School at the University of Texas at Austin. That happens to be where I earned my PhD, under E.C.G. Sudarshan, with Steven Weinberg on my dissertation committee. The University of Texas mascot is a Longhorn, and its supporters salute it with a hand sign and a cheer: Hook ‘em horns.

What Fuchs, Fukuda, and Neuhann have just done to a research program in their own field is, in precise economic terminology, exactly that.

They put the horns through it.

The thing the paper kills

To see why this matters, you need to know what “asset demand estimation” is, and why a large and serious slice of modern finance has staked its credibility on it.

The idea traces to a 2019 paper by Ralph Koijen and Motohiro Yogo, “A Demand System Approach to Asset Pricing,” published in the Journal of Political Economy. It has been enormously influential. It spawned an entire literature, a small industry of follow-on work, and a vocabulary that has migrated out of the journals and into central banks.

The pitch is appealing. Treat investors the way an industrial-organization economist treats shoppers in a supermarket. Each investor has a “demand curve” for each asset. Estimate those curves from data on who holds what at what price. Out pops a number — the demand elasticity — that tells you how sensitive holdings are to price.

Why would anyone outside a finance department care about an elasticity?

Because the elasticities are load-bearing. They are the inputs to claims you have absolutely heard, whether or not you knew where they came from:

Quantitative easing compressed long-term yields by such-and-such basis points. Adding a stock to the S&P 500 raises its price by some measurable percentage. Markets are “inelastic” — a dollar of net inflow pushes aggregate value up by roughly five dollars — and therefore the rise of passive investing has mechanically inflated stock prices.

Every one of those sentences is an elasticity wearing a coat. Central banks lean on these numbers. The entire active-versus-passive shouting match leans on these numbers. They are not academic trivia. They move policy, and they move capital.

The Fuchs-Fukuda-Neuhann result is that the numbers cannot be identified, model-free, from the data they are supposedly estimated from. Whatever makes the number a measurement of structure, you carried in.

Let me show you why. It is not complicated — and, pleasingly, the argument is essentially a piece of physics.

The dictionary in the middle

Here is the entire engine of the paper, compressed into a single line. The matrix of asset demand slopes — call it A, the object the econometrician wants — can be written as:

A = (Y⁺)ᵀ D Y⁺

Three pieces. Take them one at a time.

D is fundamental demand. It is what the investor actually cares about. And what an investor cares about, in the standard asset-pricing setup, is not “shares of Apple” — nobody wants Apple stock for its own sake. What they want is consumption: money in good states of the world and, especially, money in bad ones. D describes an investor’s appetite for state-by-state consumption. It is the genuine object of preference.

Y is the payoff matrix. It records what each asset delivers in each possible future state of the world.

Y⁺ is the part that does the damage. It is the Moore-Penrose pseudo-inverse of the payoff matrix — and for our purposes you can think of it as a dictionary. When a matrix is square and well-behaved it has a true inverse, a clean way to run it backwards. Most payoff matrices are neither. The pseudo-inverse is the mathematician’s best available substitute: the closest thing to a reverse gear when no exact one exists. Here, that reverse gear translates the language investors think in — “I want this pattern of consumption across future states” — into the language markets transact in: “therefore hold this particular portfolio.”

Now look at the structure of the problem. Investors have preferences over consumption — that is D. The econometrician observes holdings and prices, and wants to infer A, the slope of asset demand. Sitting between the object investors care about and the object the econometrician wants is the dictionary, Y⁺.

To recover preferences from observed holdings — which is the entire point of the exercise — you need the dictionary.

And here is the paper’s first blow: you cannot have it. Not from the data alone.

Why the dictionary cannot be observed

The payoff matrix Y is not a record of what assets did pay. It is a description of what investors believe assets will pay, across every state of the world — including states that have not happened, and including the future resale prices of the assets themselves.

That distinction is the whole game.

Suppose you have decades of data. Every realized return, every holding, every price. Now I, sitting beside you, change the assumed payoff of some asset in a state of the world that has not yet occurred in your sample. Perhaps it never occurred at all — a market regime that simply didn’t show up.

What have I changed? I have changed Y. Therefore I have changed Y⁺, the dictionary. Therefore I have changed A — every estimated demand elasticity in your model.

What have I left untouched? Every single realized return in your dataset. Every holding. Every price. The data is identical.

Two economies, then, with genuinely different demand elasticities — different answers to “how much do prices move when a central bank buys” — and not one observation that could ever tell them apart. The historical record is mute on the question, because the historical record is a handful of states that happened, and the dictionary depends on all the states, including the ones that didn’t.

This is what an economist means by a non-identification result, and it is fatal to the model-free ambition. Any observed pattern of holdings is consistent with infinitely many combinations of preferences and dictionary. The data, by itself, cannot pick one. It cannot even narrow the field.

You can still write down a model and estimate numbers — people do, every day — but the model is doing the identifying, not the data. The number reflects the theory you chose, not the theory you tested.

The authors put it with admirable bluntness. Estimated demand elasticities, they write, are contingent, model-specific constructs which reflect — rather than validate — a priori assumptions on investor behavior.

Read that twice. The elasticity does not come out of the data. It comes out of the assumptions you carried in. The data’s only role is to sit there, politely, while you confirm what you had already decided.

“But surely a clever instrument...”

At this point every empirically-trained economist reading this has the same reflex, and it is a good reflex. It is the reflex that has rescued a thousand identification problems.

Find an instrument. Find some exogenous shock to the supply of an asset — a Treasury auction surprise, a mechanical index reweighting, a forced sale by a constrained institution — and use it to trace out the demand curve. Supply moves for reasons unrelated to demand; price responds; you read the elasticity off the response. This is the canonical move, and in ordinary markets it works.

In asset markets, the paper shows, it does not. And the reason it does not is no-arbitrage — the single most load-bearing assumption in all of finance.

No-arbitrage says, roughly, that you cannot get something for nothing; that two portfolios delivering the same payoffs must cost the same. It is the assumption underneath every pricing model anyone has ever taken seriously. Nobody is giving it up.

But no-arbitrage has a consequence. If you shock the supply of one asset, its price moves — and no-arbitrage forces the prices of every other asset with overlapping payoffs to move along with it. Overlapping payoffs are not the exception. They are essentially universal: almost every asset pays off in good times, almost every asset pays off alongside almost every other asset in some state. Clean, isolated, one-asset-at-a-time price variation is not something the no-arbitrage world permits.

So your beautiful exogenous supply shock does not nudge one price while the others sit still. It moves the entire interlinked system. The variation you needed — this price changes, all else equal — is precisely the variation no-arbitrage forbids.

And it gets worse, in a way I find genuinely elegant. The paper shows that the price changes a supply shock induces are not merely contaminated. Generically, they point in the wrong direction. The state-contingent prices that should rise, fall. Asking what would happen if only one asset’s price moved is, in an asset market, asking what would happen if no-arbitrage didn’t hold. It is not a hard experiment. It is an incoherent one.

“But surely with enough structure...”

One move remains, and it is the sophisticated one. If you cannot observe Y, impose structure on it. Assume returns follow a factor model — a few common factors plus idiosyncratic noise, the workhorse of modern asset pricing. Surely that pins the dictionary down well enough to make progress.

It does not. And the proof of why it does not is the most physics-flavored thing in the paper.

The authors reach for random matrix theory — the mathematics Eugene Wigner developed in the 1950s because the energy levels of heavy atomic nuclei were too complex to compute, and he realized you could ask what a typical such system looked like instead. Random matrix theory is what you use when the specific details are hopeless but the statistical geometry is tractable.

Applied here, it delivers a result I would call funny if it were not so damaging.

In a large market, the sign of any given entry of the dictionary Y⁺ — not its precise value, just whether it is positive or negative — is asymptotically a coin flip.

A fair coin. You impose your tasteful factor structure, you turn the crank, and whether the dictionary tells you to buy or to sell is, entry by entry, fifty-fifty.

Then comes the kill shot. Take two economies with the identicalfactor structure — same factors, same loadings, differing only in idiosyncratic noise — and their dictionaries agree on sign about half the time. Knowing the factor structure buys you nothing. It cannot, because the instability lives in the geometry of the payoff matrix, not in the part the factor model describes.

The authors then run the experiment. They take real S&P 500 stocks, build payoff matrices from realized quarterly stock payoffs — end-of-quarter prices plus dividends — invert them, and count. Theory predicts the entries of Y⁺ should be positive about half the time. The data says: 50.58 percent.

The coin is fair. The dictionary is noise. There is no factor model clever enough to fix it.

Two halves of the same disease

Step back and look at the two papers together — the first essay and this one.

The first essay went after the predictive wing of academic finance and found that its peer-reviewed forecasts forecast no better than mechanical mining. Theory added no predictive value.

This paper goes after the structural wing — the wing that always considered itself the more rigorous one, the more serious one, less vulnerable to the data-mining critique precisely because it wasn’t fishing for predictors, it was estimating deep parameters. And it finds that the deep parameters were never identified from the data in the first place. The central numbers are model outputs in measurement’s clothing.

Prediction fails. Measurement fails. Same disease, two organs.

And the disease has a name. Academic finance has spent decades borrowing the vocabulary of physics — “structural parameters,” “identification,” “equilibrium,” “elasticity” — without borrowing the part that makes physics work. In physics, the Standard Model committed in advance to a scalar particle with definite quantum numbers and couplings; once its mass was measured, every other property was fixed and falsifiable. General relativity predicted gravitational waves a century before LIGO heard one. The point is not that physics knows everything in advance. It is that real theory sticks its neck out: the data has a vote on whether the theory survives.

Academic asset pricing produces a number — “the demand elasticity is 0.4” — and when you ask what would have to be true in the world for that number to be wrong, the answer is: nothing the data shows. A different modeler, with a different assumed payoff matrix, gets a different number from the same data, and the data has no vote.

That is not a theory in the sense a physicist means the word. It is a very elaborate way of writing down your priors.

A physicist on the receiving end

I want to be personal for a moment, because this paper has changed something for me — and not in the direction you might assume.

For years, people have quoted these elasticity numbers at me.

It happens at dinners. It happens on panels. It happens in my comment section and in my inbox. I will make an argument — that the rise of passive investing is not the mechanical, price-inflating monster it is often made out to be, say — and someone will produce a demand elasticity from the Koijen-Yogo literature the way a lawyer produces an exhibit. The estimates show markets are inelastic. The number says you’re wrong. This is settled.

Until now, my pushback had to come from intuition. From three decades of watching markets do things no demand system anticipated. From a physicist’s itch that a quantity with a published standard error was being treated as a fact about the world when it smelled like a fact about a modeling choice. I believed the numbers could not bear the weight being placed on them. I could not prove it.

Now I can. The proof is this paper, together with a companion: “Demand-System Asset Pricing: Theoretical Foundations,” by the same three authors. In the companion, they take the workhorse logit demand system — the one Koijen and Yogo built the field on — and prove formally that it can report a measured elasticity near one when the true structural elasticity is near infinite.¹ Sit with that. Not off by twenty percent. Not off by a factor of two. The measured number sits near the low end of the scale — “not very responsive to price” — while the truth sits at the other end of the universe — “perfectly responsive.” It is the entire dynamic range of the concept, collapsed onto a single misleading number.

That is not measurement error. That is measurement theater.

The honest description of the situation is this: nobody knows the demand elasticity of the stock market. Nobody can know it from the data alone. And the published standard errors you have been shown describe the precision of an assumption, not the precision of a fact.

So the next time someone quotes one of these numbers at you as though it closes an argument, you may tell them, with a clear conscience, that it opens one.

Why this is how I update

Let me connect this to something I have said in these pages before, because the connection matters.

I have argued, more than once, that competing for alpha is a zero-sum game and mostly a poor use of a human life — unless you are willing to do what Renaissance Technologies’ Medallion fund does, which is to sweep computationally for every signal that exists, keep what survives, and never once require an explanation for why it works.

The trilemma does not formally prove that this is the only honest road to alpha. It would be too neat for any single paper to. What it does is something more useful to a working investor than a theorem: it tells me the other road — out-theorize the market on the basis of superior academic understanding of structural parameters — was never the road I thought it was, because the structural parameters were never what the field claimed they were.

If demand elasticities are model-contingent objects rather than measurements, then theory-driven alpha — the proud claim “I understand the deep structure of this market better than you do, here is my equilibrium model, here is my estimated elasticity, here is my edge” — is doing something other than what it advertises. You cannot out-theorize a market when the theoretical objects in question are not pinned down by the data anyone has access to. The supposed edge is an edge in choosing a model. Choosing a model is not, by itself, an edge.

That leaves, as a trader rather than a journal referee, exactly two roads I trust.

The first is to sweep harder than everyone else. This is the Medallion road. As I quoted Peter Brown of Renaissance last time: the durable signals are the ones nobody can explain — because the ones that can be cleanly explained get reasoned out and competed away, while the inexplicable ones survive precisely because no tidy story recruits a crowd to arbitrage them to death. If theory worked, those signals would not exist. They exist because it doesn’t.

The second is to find a corner of the market so uncrowded that the standard machinery has not arrived yet, and work it before it does.

Both roads are punishing. The first demands an infrastructure that costs a fortune and a tolerance for not understanding your own returns. The second has a low ceiling and a short shelf life — niches get found. But notice what is not on the menu. There is no third road, the one where you read the Journal of Finance very carefully, absorb the latest structural estimates, and find twenty-dollar bills lying on the sidewalk. That road was always an illusion. This paper is the document that explains, at the level of mathematics, why.

The deepest thing the trilemma tells a working investor is not “this or that elasticity is wrong.” It is that the entire enterprise of competing on the basis of superior academic understanding of structural objects rests on a foundation that was never there.

What to do with all this

If you are a retail investor: the next time you read that index funds have inflated stock prices by some authoritative-sounding percentage, ask one question — what payoff matrix did the author assume? They did assume one. They had to. And they cannot show you it was the right one, because no one can.

If you are a quant: “structural” is a word, not a guarantee. If a strategy’s edge depends on someone’s estimated demand elasticity, you are not trading a feature of the market. You are trading that person’s prior — and you are paying full freight for the privilege.

If you make policy: claims about the demand-channel transmission of QE that rest on model-free asset-demand elasticities are not measurements with merely wide error bars. They are model-contingent objects dressed up as empirical findings. I had a line ready comparing them to climate forecasts a century out — but that flatters them badly. A century-out climate forecast is anchored in conservation of energy and radiative physics. These elasticities are anchored in an assumed matrix nobody can see. Treat them with the humility that follows: not as theology, and certainly not as physics.

If you are an academic: a discipline that publishes structural estimates without flagging, loudly and in the abstract, that those estimates are not identified model-free is doing something. Whatever it is, it is not measurement. It is model discipline wearing a lab coat.

The Emperor’s wardrobe

In the first essay the Emperor had no alpha. His predictions did not predict.

Now we learn he has no identification either. The structural numbers — the elasticities, the demand curves, the deep parameters that were supposed to be the rigorous, grown-up part of the field — are not measurements at all, in the sense the word usually means. They are assumptions in measurement’s clothing.

A large slice of modern empirical asset pricing depends on an object, the latent mapping Y⁺, that cannot in principle be recovered from data alone. The discipline knows this now. It has been told, in a long and careful proof, by three of its own. Whether it changes anything is a separate question, and I would not bet the way the optimist bets.

Two of the men who delivered the message work at the school where I learned to do this. So I will permit myself the local idiom. The horns are in, and they are in deep.

There is a third paper, and it is the strangest of the three. It sets out to defend academic finance — to rescue a respectable, risk-based story for the famous anomalies — and ends up indicting the field harder than either demolition above. It shows that even the alpha you can measure, the headline number itself, the most-studied quantity in all of empirical finance, refuses to hold still: adjust for risk a little differently and value’s alpha runs from substantial to essentially zero.

The post I will write about it is called, inevitably, The Emperor Has No Benchmark. But that is for next time.

The first essay in this series, The Emperor Has No Alpha, is here. For more on markets and trading, see the full series. My book, The Science of Free Will, is available now. For more on the background behind this work, see my first and second interviews with Titans of Tomorrow, and Part 2 of my Algorithmic Advantage Podcast conversation with Simon M. on trading.

1. Koijen and Yogo have replied that their estimator identifies the relevant elasticities. The authors have responded in turn that this defense works only at a knife-edge symmetry case. That dispute is technical and ongoing; it does not bear on the point here, which is that without a maintained payoff structure the elasticity is not being read model-free from the data. ↩

I grew up in a country that decided, as a matter of high-minded national policy, that prosperity was vulgar.

This is not a figure of speech. From the 1950s to 1991, India ran what its critics came to call the License-Permit Raj — a system in which you needed a government permit to build a factory, expand a factory, import a machine for the factory, and then, once the factory existed, government permission to shed workers or close the thing. (Read that last part twice. In any sizable establishment — broadly, a hundred workers or more — the pathology wasn’t “you may not hire.” It was “hire, and then good luck ever adjusting” — economic flypaper.) The result was what the economist Raj Krishna in 1978 dubbed the “Hindu rate of growth”: an economy that grew at roughly 3.5% a year while its population grew at over 2%, which is to say, an economy that mostly stood still while telling itself it was on a journey. While India crawled, the Asian tigers ran — Singapore, South Korea, and Taiwan all compounding at around 9% a year over the same stretch. Not because they were laissez-faire fantasies; they ran plenty of industrial policy. The difference was that they forced their firms to face the world and compete, while India sheltered its firms from reality and called the shelter a plan. The nation that gave the world the zero spent four decades demonstrating what one looks like at national scale.

I lived in the aftermath of that. I watched, later, as the 1991 reforms — forced, not chosen; India liberalized because it was hours from defaulting and the IMF had it by the throat — unwound the machine and the economy finally began to grow the way an economy of a billion clever people always should have. Daron Acemoglu and James Robinson wrote a whole book about why some nations stay poor on purpose (Why Nations Fail; Acemoglu, Robinson, and Simon Johnson shared the 2024 economics Nobel for the underlying work on institutions and prosperity), and India across those four decades is very nearly the textbook case. The aftereffects will be felt for a century. The reforms are thirty-four years old and the country is still digging out — though “digging out” undersells the turn. The economy that had crawled at the Hindu rate surged after reform: 7.5% growth in 1994–97, above 9% in the 2005–08 boom, around 8% across the high-growth stretch after 2003; per capita income has since climbed past $2,600, many multiples of its early-1990s level. (The honest caveat, which I’ll make before a critic does: growth had already ticked up to ~5.8% in the 1980s — but on borrowed money, which is precisely why it ended in the 1991 near-default crisis that forced the reforms. The spree was the disease’s last fever, not its cure.) Four decades of standing still, then a sprint the moment the machine came off. That is the whole argument in one country’s national accounts.

So when I tell you I am not a socialist, understand that this is not a political preference. It is an immune response.

In the old days, before we had clean attenuated vaccines, you protected a child against smallpox by deliberately giving them a small live dose of the real thing — variolation. If it didn’t kill you, you never got smallpox. That’s me. I was variolated. I took the live virus of actual, functioning, on-the-ground socialism as a child, and the antibodies never went away. I cannot get the disease. I have tried to be fair to it, the way you try to be fair to a thing that has demonstrably tried to kill you, and I keep arriving at the same place: it does not work, it has never worked, and the people who insist it would work this time, with the right people in charge are describing a chemistry experiment that has been run several hundred times and always produces the same precipitate.

I bring all this up because America is currently being shouted at, from both ends of its political spectrum, by people who have never been variolated against anything.

This is a piece about those people. Both kinds. And the thing I most want you to take away is that they are, underneath the costumes, the same people making the same mistake.

The mistake, stated once, cleanly

Here is the mistake.

The United States is a working system. Not a perfect one — there are no perfect ones, and anyone who tells you they’ve found one is selling either a religion or a revolution, which historically have a way of becoming the same product. But a working one: a machine that has, for two and a half centuries, converted human beings of every conceivable origin into the largest and most innovative economy in the history of the species — and one of the most productive — and done it while remaining free enough that the people inside it can stand on a soapbox and call it a hellscape without being sent anywhere unpleasant for saying so.

The mistake — and it is bipartisan, it is the great unifying stupidity of our moment — is to look at a working system, notice that it is not an idealized system, and conclude that it should be torn down and rebuilt according to a theory.

The MAGA right has a theory. The theory is that there was a golden past — roughly 1955, soft-focus, the factories humming, the borders sealed, the trade balanced, the men employed and the women home — and that this past was stolen by foreigners and globalists and that we can tariff and deport our way back to it.

The socialist left has a theory. The theory is that there is a golden future — fully planned, fully equal, the rents frozen and the groceries municipal and the billionaires abolished — and that this future is being withheld from us by oligarchs and corporations and that we can tax and decree our way forward to it.

Look at those two sentences. Look at them. One worships a past that never existed; the other worships a future that has never once arrived. One blames the foreigner and the globalist; the other blames the oligarch and the corporation. Different villains — but watch what happens when the policy actually touches a foreigner. The right won’t trade with him because he’s foreign: he talks funny, he works cheap, he isn’t us. The left won’t trade with him either, only the left does it while professing to love him — trading with him is “exploitation,” the factory must be repatriated, the supply chain brought home. One bars the door out of contempt and the other bars the same door out of solidarity, and the foreigner, standing on the far side of an identical closed border, is invited to admire the difference in their reasoning — the difference that somehow justifies the exact same result.

And here is where I stop being polite, because this is the part that actually offends me. Who, exactly, are these people? In a society that calls itself free, who are you — tariff-writer, supply-chain-repatriator, either one of you — to stand between me and a man in Shenzhen and forbid the two of us from voluntarily exchanging goods to our mutual benefit? What business of yours is it? Now, there is a respectable answer: that my trade with Shenzhen has third effects — a laid-off worker in Ohio, a strategic dependency, a cost borne by someone who never sat at the table. Fine. But notice where that puts the burden. It is on you, the one reaching for the veto, to make the affirmative case that the third-party harm is real and large and not merely asserted — not on me to justify a peaceful exchange that is, on its face, nobody’s business but mine and his. And if you can’t carry that burden, then the question stands: if you may veto a voluntary trade between two consenting people, may I veto yours? Because if I may, then no one can transact and the word “free” is a fraud; and if I may not, if the veto runs only one direction, then you are not my fellow citizen in a free country. You are my sovereign, and I am your subject, and we should at least have the decency to call it what it is. The foreigner and I have the same two-word reply to that arrangement, and it translates cleanly into every language on earth, and it is not thank you.

And then — because the rage isn’t even the embarrassing part — there’s the arithmetic. Both factions want the trade deficit abolished. Both factions also want the world to keep pouring money into the country: into Treasuries, into equities, into the factories they claim to be defending. Here is the problem, and it is not an opinion, it is an accounting identity: those are the same thing with the sign reversed. A current-account deficit is, on net and by construction, mirrored by a capital-and-financial-account surplus — foreigners sending you goods and services now, and the balancing entry is their savings buying up claims on America. (Yes, the current account is broader than the trade balance — it folds in services and investment income too — but the identity holds all the same.) You cannot, on net, both ship the world more goods than it ships you and be the world’s net destination for its savings; the trade surplus you say you want would, by the same identity, make you a net exporter of capital, not the recipient of it. MAGA wants to kill the trade deficit while the world still finances American consumption on terms almost any chronic debtor in history would envy. The left wants to reshore the factories while foreign savings funds the federal deficit it depends on. Both are demanding the two halves of an identity and disowning the equals sign between them. There is an honest way to shrink the deficit — consume less and save more, at home, as a nation — but neither faction will say that out loud, because “spend less” wins no votes, so they reach for tariffs and decrees that fight the identity by fiat instead of changing the saving that drives it. It is the economic equivalent of insisting on a triangle with four sides, then blaming China when you can’t draw it.

And there’s a third freedom they’re both busy strangling, and it’s the one that should be hardest to get wrong, because it is the single most distinctive thing about this country. Almost every free nation on earth protects speech. Only America makes censoring it nearly impossible. That is not a small distinction; it is the whole difference between a right and a permission. The European Convention, the Canadian Charter, the German Basic Law — all of them guarantee free expression and then attach a carve-out, for “hate speech,” for “public order,” for “dignity,” and a carve-out is just a lever the government keeps within reach for the day it decides your speech qualifies. The First Amendment keeps no lever. Congress shall make no law is not a balancing test; it is a prohibition on the state itself. This is why the United Kingdom arrests people over tweets, why Germany sends police to homes over online insults, why Canada convenes tribunals over the wrong opinions — and these are not tyrannies, that is precisely the point. They are free countries that kept the lever. America is the one major country that constitutionally smashed it.

And both sides want it back. The right spent a decade as self-appointed free-speech absolutists and now pulls books from libraries, legislates against teaching the history it finds uncomfortable, menaces broadcasters over coverage it dislikes, and would jail you for burning a flag. The left built the speech-policing machinery on the campus and inside the platforms, coined the slogan “hate speech is not free speech” — which is verbatim the European carve-out the First Amendment exists to forbid — and leaned on private companies to throttle views it deemed dangerous. Each faction is utterly convinced it is the defender of speech while it strangles the speech it hates. And neither will say the actual principle aloud, because the principle is unbearable to both: free speech is only ever tested by speech you find vile. The speech you like was never in danger. The First Amendment is for the Nazi in Skokie, the flag-burner, the campus crank, the conspiracy peddler — and the moment either tribe gets the power, it discovers an emergency exception for exactly the speech that offends it, which is the only speech the Amendment was ever about. (Yes, even America draws lines — incitement, true threats, defamation, obscenity, and a few other narrow categories. But those are historically defined exceptions tied to concrete legal harms, not licenses to ban an opinion because the state finds it hateful or false. The gap between “no viewpoint may be banned” and “offensive viewpoints may be banned” is not a difference of degree. It is the entire ballgame, and America is the only major country standing on the right side of it.) An immigrant from a country with the lever does not experience this as a culture-war token. He experiences it as the reason he can publish a post calling the mayor a halfwit and not receive a visit. He knows what the lever is for. That is why it is so obscene to watch native-born members of both parties reach for it as though it were a normal instrument of government.

Strip away the costumes and what’s left is identical: a theory that has never once survived contact with reality, and a serene conviction that the problem is other people — that if we could just remove the right ones and install the right plan, the heavens would open.

This is the same person. He has simply bought two different hats.

What the immigrant knows that the theorist does not

Now. Here is the part that matters, and it is the reason I, specifically, get to write this.

All of which raises the obvious question: if the freedoms are this basic and the arithmetic this elementary, why do so many people get it wrong? And the answer points straight at the one group nobody thinks to ask.

The point of an immigrant is not that he is virtuous. The point of an immigrant is that he is working from data and not from theory.

The native-born American radical — of either flavor — has only ever lived in the working system. He has never seen the alternative run to completion. He can therefore afford to romanticize it, because romanticizing a thing you’ve never experienced is the cheapest hobby on earth. The MAGA voter mourning the lost factory town has never had to actually live in an autarky, with the empty shelves and the ten-year wait for a telephone line that economic nationalism reliably produces. The DSA member dreaming of the planned economy has never had to actually queue for bread under one. They are both, in the most precise sense, tourists of catastrophe. They’ve seen the postcards. They’ve never been.

The immigrant has been. That’s the whole thing. That’s the entire value proposition.

When I, or my fellow refugees from various failed utopias, choose to come to the United States, we are not voting for an abstraction. We are voting with our feet, having personally inspected the alternatives, against the very theories the native-born radicals find so seductive. We came here because the planned economy didn’t work, because the strongman didn’t deliver, because the closed border made everyone poor. We are, as a population, the single largest body of empirical evidence on the planet that the working system beats the theory. We are the experiment’s control group, and we got on a plane.

And — this is the part the nativists in particular cannot metabolize — the data on us is overwhelming. We come, and we behave better than the people already here.

Take incarceration, since the MAGA right has made crime the centerpiece of an entire political program. The single most thorough and current analysis we have — the Cato Institute’s March 2026 study of the 2024 American Community Survey — found that the incarceration rate for native-born Americans was 1,195 per 100,000. For illegal immigrants, it was 674. For legal immigrants, it was 303. Read that again slowly, because it is the sort of number that should end a debate and somehow never does: a legal immigrant is about 75% less likely to be sitting in a prison cell than a native-born American, and even an illegal immigrant is meaningfully less likely than the native. The thing the entire restrictionist program is organized around fearing is, statistically, a thing native-born Americans should fear about themselves.

Take the economy, since the socialist left has decided that capitalism is a machine for crushing the vulnerable. As of 2026, immigrants had founded or co-founded 59% — 455 of 775 — of America’s privately held billion-dollar startups, up from 55% in the prior reports. Nearly half the Fortune 500 was founded by immigrants or their children — a figure that leans on counting the second generation and that skeptics at the Center for Immigration Studies have contested on methodology. Fine; throw it out and keep only the unicorn number, where the founders are themselves the immigrants. It’s still a majority. The point survives a hostile audit, which is the only kind worth making. The country at the top of that founder list, incidentally, was India — 96 unicorns, ahead of every other nation — the same India that spent forty years proving the other system doesn’t work, exporting its most capable people to go prove it somewhere that lets them.

Now, the sharp objection — and it comes from the left, which is the half of the room that actually reads the methodology sections. “This is just selection,” they’ll say. “Immigrants aren’t better-behaved or more entrepreneurial because immigration ennobles them. They’re filtered. You screen for ambition and a clean record at the visa office and self-select for risk-tolerance before anyone boards a plane. The data say nothing about inoculation. They say something about the sieve.”

Yes. Exactly. Thank you. That is the mechanism, and naming it doesn’t weaken the argument — it is the argument, stated in the language of econometrics instead of immunology. Of course it’s selection. The whole point is what the cohort selected on. These are, by construction, the people who looked at the available systems — the planned one, the strongman’s one, the autarkic one, and the messy working one — and chose the working one at real personal cost, with their feet, against the romance. “Selection effect” and “revealed preference at scale” are the same fact wearing two lab coats. The leftist thinks he’s identified a confound. He’s identified the independent variable. The immigrant cohort is precisely the natural experiment in which people who have seen the alternatives sort themselves toward the working system — which is why their behavior, once here, looks the way it does, and why the native-born theorist who has selected on nothing but a seminar reading list is the worse bet.

So here is the immigrant, holding the data. Lower incarceration. Higher top-end entrepreneurship. A revealed, plane-ticket-purchasing preference for the working system over every theory on offer. And here is the native-born radical, holding the theory, explaining to the immigrant that the system is a fraud and the answer is the very arrangement the immigrant crossed an ocean to escape.

You can perhaps see why some of us find the lecture a little rich.

The hinge: but Mamdani is an immigrant

And now we arrive at the objection that is supposed to detonate this entire essay, and which instead — if you follow it carefully — turns out to be the load-bearing beam.

“But Samir,” you say, “the current mayor of New York City is himself an immigrant. Zohran Mamdani was born in Kampala, Uganda. He’s a democratic socialist. He won. So much for your theory that immigrants are inoculated against socialism. Your control group voted for the disease.”

Good. Excellent. Let’s take it seriously, because it is the most interesting thing in the room.

The error in your objection is the error in this entire national argument, and it’s worth slowing down for. It was never the passport that inoculates you. It was the disease.

Variolation only works if you take the live virus. You have to actually have the smallpox, in a small and survivable dose, for the antibodies to form. A child who grows up reading about smallpox in a beautifully appointed seminar room, who studies the epidemiology and writes a moving senior thesis on the structural injustice of the disease, does not thereby acquire immunity. He acquires opinions about the disease. These are not the same thing. One is built from antibodies; the other is built from footnotes.

Zohran Mamdani holds a passport stamped immigrant. But consider, with genuine care, the life. His father is Mahmood Mamdani, a chaired professor at Columbia, one of the most celebrated postcolonial theorists alive. His mother is Mira Nair, the internationally acclaimed filmmaker. He grew up between Kampala and the Upper West Side of Manhattan, the son of that household. He attended the elite Bronx School of Science and then Bowdoin College. This is not the biography of someone who fled a planned economy with two suitcases and a medical degree the new country wouldn’t recognize. This is the biography of the global academic aristocracy — the single demographic on earth most marinated in the theory of socialism and most insulated from its practice.

He didn’t immigrate from the disease. He immigrated into the faculty lounge. He is, in the precise technical sense I’ve been building this whole essay around, unvariolated. He has the elite seminar’s complete theory of the smallpox and not one antibody in his blood. He is the living, governing proof of my actual thesis, which was never “immigrants are magically wise” — that would be its own dumb romanticism, and I have no interest in trading the nativist’s fairy tale for a flattering one about my own kind. My thesis was always narrower and harder: lived experience of the failed system inoculates you against it, and nothing else does. Not the passport. Not the suffering of your ancestors. Not your sincerity. The disease, or nothing.

This is why the children of Cuban exiles in Miami vote the way they do and the children of Cuban theory in the academy vote the opposite way, and why this surprises only people who think the relevant variable is ethnicity rather than memory. It’s why my own inoculation is specific and personal and non-transferable: I cannot give it to my American-born children by telling them stories, any more than you can vaccinate a child by reading him the label. The antibodies don’t cross the placenta. Each generation has to either catch the live virus or, far better, be governed by people who did and who built the institutions accordingly.

Mamdani caught the footnotes. Now eight and a half million people get to find out the difference, in real time, with their rent and their groceries and their police department as the assay.

I would genuinely rather they didn’t have to. I lived downstream of that experiment once already.

A word to each idiot, in turn

Let me be even-handed about the contempt, because both of these factions have earned it honestly and it would be unfair to slight either.

To the MAGA economic nationalist: You have decided that the path to American greatness is to make Americans poorer on purpose, via a tax you’ve been told isn’t a tax. A tariff is a tax. It is paid by the importer, who is American, and passed to the consumer, who is American, and the whole exercise is a transfer from the broad American public to a narrow protected industry that has successfully lobbied to be spared from having to be good at its job. This is not a controversial finding. It is one of the few things in economics on which essentially every economist who has ever lived agrees, from Adam Smith forward, which is a sentence I do not get to write often. You have looked at the single most successful wealth-generating arrangement in human history — open, dynamic, immigrant-fueled American capitalism — and concluded that what it really needs is the economic policy of 1930 and the demographic anxiety of 1924. You want to wall off the country that the Why Nations Fail literature identifies as history’s great winner because it stayed open, and you want to do it in the name of winning. You have mistaken the lock on the door for the engine of the house.

To the DSA socialist: You have looked at the same arrangement — the one that took my variolated, capable, formerly-poor immigrant cohort and let us build companies and pay for the schools your program depends on — and concluded that what it really needs is the economic model of the country I left. You want rent control, which 95% of a University of Chicago expert panel agree reduces the supply and quality of housing — a near-unanimity rivaling the tariff consensus — and you have decided that the lesson of every place that’s tried comprehensive planning is that they simply lacked your sincerity. They didn’t. East Germany was sincere. Venezuela was sincere — it had the largest oil reserves on the planet and the most fashionable economic theories in the faculty lounge, and it engineered mass hunger and emptied its own supermarkets. Maduro meant well, if you ask the right professor. The graveyards of the twentieth century are landscaped with good intentions and the people they starved. You are not the first to be sincere. You are merely the latest to mistake sincerity for a mechanism.

And to both of you, the thing I most want to say:

You are surrounded by people who fled the exact experiment you are proposing to run. We are right here. We came thousands of miles and filled out your absurd immigration paperwork specifically to get away from your big idea. We are the most expensive focus group in the history of the world, assembled at our own cost, and we are unanimous. And you will not ask us a single question, because the answer is inconvenient to a theory you find more beautiful than the data.

That is the part that makes me want to put my head through a wall. Not that you’re wrong. People are wrong all the time; being wrong is the human condition and I have been wrong about plenty. It’s the incuriosity. It’s the serene confidence of a man who has never run the experiment lecturing the people who survived it about what the results will be.

America Remains Great, Halfwits

So, the title.

America remains great. Not perfect — I’ve said this enough times that you’ll believe I mean it — but great in the only sense that has ever mattered: it is the place that the rest of the world’s most capable and determined people are still, despite everything, trying to get into. Revealed preference is the only honest poll. Nobody is risking their life on a raft to reach the worker’s paradise. Nobody ever did. The boats have always pointed one direction, and it is the direction of the working system, and it has been pointing that way for two hundred years through every fashion of theory that has risen up to declare the system a fraud.

The halfwits, left and right, are people who have inherited the single greatest going concern in human history — the freedom to trade with whom you please, to reckon with the numbers honestly, to say the unsayable without a knock at the door — and would like to torch it because it fails to match a picture in their heads, a picture, in both cases, of a place that has never existed, drawn by someone who has never had to live there.

I have lived there. I have the antibodies to prove it. And the message from those of us who took the live virus and survived, to everyone currently demanding we all take a fresh dose for the principle of the thing, is short and it is heartfelt:

We’ve seen this one. It doesn’t end the way you think. America remains great. Stop trying to fix it with the thing we ran away from.

ARGH.

If you found this worth your time, you may enjoy my book The Science of Free Will, which asks an equally uncomfortable question about an equally cherished story.

For the long version of the India argument — what the License Raj actually did and how the country is clawing back — start with The Paradox of India, or browse the whole series.

And for the same disease in a different country, see Albion — Britain’s institutional decline, by someone who first saw the place in 1979.

Somewhere in India right now, a schoolteacher is putting two thousand rupees into an index fund.

She did not agonize over it. She set up an auto-debit from her salary account three years ago and has thought about it roughly as often as she thinks about her electricity meter. On the fifth of every month the money leaves, buys units, and disappears into the great anonymous machinery of the Nifty. It is the single most boring financial act a human being can perform — the personal-finance equivalent of flossing. No drama, no genius, no timing. Just a small, dull, disciplined transfer, repeated until it compounds into something that might one day be a house, or a daughter’s college, or simply the difference between a frightened old age and a calm one.

And a financial columnist has identified her as a threat to the national currency.

He does not put it that way, of course. He is far too careful and far too intelligent to put it that way, which is precisely what makes the argument worth taking apart. The crude version — the proles are gambling and it’s hurting the rupee— would be easy to dismiss and not worth a thousand words. The version he actually wrote is subtler, partly correct, internally sophisticated, and arrives, with the quiet inevitability of a man reversing confidently into a lake, at a conclusion that would have made Indira Gandhi proud.

His conclusion is that India should disincentivize its citizens from investing in stocks.

This is a real argument, made in good faith, by someone who has himself been a disciplined equity investor for twenty years and who signs off by bracing for abuse. I am not going to abuse him. I am going to do something he will find considerably more irritating, which is agree with him three times before I’m done.

What he gets right (and we must say so)

The series has a rule about this: you do not get to knock down a strawman and call it a victory. So let us build the man’s argument at full strength, because most of it stands.

He is right that foreign money leaving India demands dollars. When a foreign fund sells its Indian shares, it is paid in rupees, and those rupees must be converted into dollars to go home. Multiply that by enough funds over enough years and you get genuine, measurable pressure on the rupee. This is not a theory. The Reserve Bank’s own data on the repatriation of foreign direct investment shows the number climbing steadily, and he reproduces the chart. Concede it cleanly: the dollars are leaving, and it matters.

He is right that a great deal of that repatriation has been venture capitalists and private-equity firms cashing out of loss-making startups at the precise moment those startups listed on the exchange at valuations untethered from anything so vulgar as earnings. This, too, is real. We have all watched a company that has never made a rupee of profit price its IPO as though it were minting them, while the early investors quietly walk through the exit they’d been waiting eight years for.

And he is right — most damning of all, because it’s the part everyone gets wrong — that there is no selling without buying. He says so explicitly. He does not make the lazy error. When a foreign institution dumps a billion dollars of Indian stock, someone has to be standing on the other side of that trade with a billion dollars to catch it. Increasingly, the someone catching it is our schoolteacher and ninety-six million of her countrymen, arriving on the fifth of every month with their two thousand rupees. He has correctly identified that domestic retail money is the counterparty to the foreign exit.

So far, so good. Three concessions, all earned. He understands the plumbing. He is not an idiot, whatever his relatives may say at weddings.

Here is where he reverses into the lake.

The man had the right tool in his hand

Read the column carefully and you find that he has already diagnosed the actual problem and already proposed the correct fix — and then, two paragraphs later, abandons both for something else entirely. This is the part that should make you sit up, because it is the whole tragedy in miniature.

The actual problem, by his own account, is overpriced IPOs. The mechanism is specific and correct: existing regulations let a company list by floating only a small sliver of its equity. When demand for stocks is feverish, that small float lets investment bankers price the whole thing at insane multiples, which lets insiders cash out at valuations that “would have been difficult to justify under normal market conditions.” The disease is not that Indians are buying stocks. The disease is that a structural quirk in listing rules — small mandatory float plus euphoric demand — manufactures inflated exits for a few dozen well-connected sellers.

And he names the cure himself. Raise the minimum public float. Make companies sell a bigger slice when they list, so the price has to reflect something closer to reality and can’t be juiced by artificial scarcity. He even points the finger at the right address: the Securities and Exchange Board of India, which writes those rules and could change them on a Tuesday.

This is a scalpel. It is aimed at the actual tumor. It touches the schoolteacher not at all.

So what does he do with his scalpel? He sets it down on the tray, walks across the room, and picks up a sledgehammer.

Because the recommendation he actually lands on — the one in bold, the one he braces for abuse over — is not “fix the float.” It is: tax all investment income at the same rate. Capital gains on equity should be taxed identically to interest on fixed deposits, gains on gold, gains on real estate. A “level playing field across asset classes,” so that tax considerations no longer “disproportionately influence investment choices.”

He had a tool that hit the investment banker. He swapped it for one that hits the schoolteacher. And the swap was not an accident. It is the most revealing thing in the column, because it is the oldest reflex in the entire Indian policy cabinet, and he reached for it without seeming to notice he’d done it.

The gold trap, and why inert metal is the tell

The bridge he uses to get from the IPO problem to the universal tax is gold — and it’s worth dismantling the bridge, because it’s load-bearing and it doesn’t hold.

His logic runs: India imports nearly all its gold, paying in precious dollars (seventy-two billion of them in 2025-26, more than double three years prior). This is a real drain. Gold-buying has therefore been gently discouraged — higher duties, official tut-tutting, mutual funds restricting gold-fund inflows. So, he reasons, if we discourage gold to protect the rupee, why not discourage excessive stock-buying along the same lines?

Notice what he’s done. He has taken the logic that applies to gold and walked it across to equity. And here he trips over a distinction he himself raises and then waves away. He concedes — in writing — that “gold is largely a useless asset and equity isn’t.” Then he says the distinction “weakens significantly” because so much stock-buying is just funding overpriced insider exits.

But the distinction doesn’t weaken. It is the entire point, and it survives his objection completely intact.

Gold sitting in a locker is inert. It does nothing. It produces no goods, employs no one, builds no factory, funds no payroll. A bar of gold in 2026 will be exactly a bar of gold in 2046, having contributed precisely nothing to the productive capacity of the nation in between. Discouraging it is, at worst, neutral. The gold just sits there being sat upon.

Equity is the opposite of inert. A rupee of equity is a rupee of risk capital locked into a productive enterprise — a company that hires, builds, exports, innovates, and pays corporate tax on its profits before the shareholder is taxed again on his gains. The schoolteacher’s two thousand rupees does not sit in a locker. It becomes working capital. It is the single most useful thing a household can do with its savings from the point of view of the very country he claims to be protecting.

The “overpriced IPO” objection doesn’t change this, because it confuses the price of some equity at the moment of listing with the nature of equity as an asset. Yes, some IPOs are overpriced, and yes, that funds some bad exits. But the cure for an overpriced subset is to fix the pricing mechanism — which, again, he already proposed — not to tax the entire asset class as though it were metal in a vault. You do not discourage everyone from eating because some restaurants overcharge for the bread.

The gold parallel is the tell. The moment you find yourself reasoning “we discouraged the inert thing, so let’s discourage the productive thing by the same logic,” you have stopped thinking about economics and started obeying a reflex. And we know this reflex. We have its photograph.

The experiment already ran. We have the photographs.

Here is the part the column never reckons with, and it is not a matter of theory. It is a matter of what happened three months before he sat down to write.

In March 2026, foreign institutional investors sold a record 1.18 lakh crore rupees of Indian equity in a single month — the largest monthly foreign exit on record, larger than the entire foreign inflow of the boom year 2013. The rupee was scraping ninety-four to the dollar. Oil was over a hundred. By every page of the historical playbook, this is the setup for a rout: foreign money stampedes for the door, the market collapses, the currency follows, and India spends the next eighteen months in a defensive crouch composing wounded op-eds.

It didn’t happen.

Domestic institutions — overwhelmingly the mutual funds fed by those boring monthly SIPs — bought 1.16 lakh crore rupees of equity in the same month. They absorbed almost the entire foreign exit, rupee for rupee. The Nifty fell about eleven percent and then steadied. There was no systemic panic. Analysts reached for a new phrase: India had become a twin-engine market, no longer dependent on a single foreign engine that could cut out whenever a fund manager in London got nervous about the Strait of Hormuz.

Sit with that, because it is the exact inversion of his thesis. The SIP money he wants to disincentivize is the precise mechanism that absorbed the foreign exit he is worried about. He has looked at the airbag that just deployed and saved the car, and concluded that the real problem is all these airbags.

And then there is 1991, which this series has told at length and will not retell here, except for one image. In 1991 India had no domestic buffer at all — no twin engine, no schoolteachers, no SIPs. When the dollars ran out, the country airlifted forty-seven tonnes of gold to the Bank of England and another twenty to Switzerland to raise six hundred million dollars and avoid defaulting on its debts. That is what it looks like when a nation has no domestic capital base to lean on: you fly your jewellery to London in the dead of night. We have spent thirty-five years building exactly such a base, household by household, two thousand rupees at a time. It now arrives at the rate of roughly thirty-one thousand crore a month. The proposal on the table is to discourage it, for the rupee’s sake.

He also diagnosed the wrong disease

There’s a second, quieter problem, which is that the thing actually pressuring the rupee in 2026 has very little to do with SIP investors or startup repatriation, and the column would have known this had it looked sideways instead of down.

The dominant driver of the 2026 foreign exit was a global rotation out of Indian equities and into the artificial-intelligence trade. The chips money — the allocation that used to flow to Indian IT — went to Korea and Taiwan instead. That’s structural, and it has nothing to do with our schoolteacher. The second driver was valuation: Indian stocks trading at a forward price-to-earnings ratio around twenty-two while the broad emerging-market index sat near thirteen. A growth premium India has always enjoyed; a gap that size is harder to defend. The rest — rupee at ninety-five, oil over a hundred, the US ten-year yield testing four and a half percent — is cyclical macro weather that will turn when the geopolitics turn.

And the detail that detonates the framing entirely: through all of this, foreign investors were net buyers of the Indian capital-goods and capex story in every single month of 2026. They were not fleeing productive investment. They were trimming overpriced financials and buying the factories. The picture the column paints — foreigners cashing out of loss-making startups as the defining dynamic — was a 2021 story being narrated over a 2026 market that had long since changed the channel. He prescribed poison, and he prescribed it for the one organ that was perfectly healthy.

Now, the economics — and the part where he’s not even in the room

Set aside the plumbing and the photographs for a moment and ask the cleaner question: what does the actual discipline of economics say about taxing capital?

It says, roughly, as little as possible, and the experts can’t agree on how little.

The famous result belongs to Christophe Chamley and Kenneth Judd, who in the mid-1980s, working independently, reached the same startling conclusion within standard growth models: the optimal long-run tax on capital income is zero. The result was so counterintuitive that economists have spent forty years arguing about what it even means — and, this is true and I treasure it, they describe their own models as behaving “erratically” and producing results “of limited use for guiding policy,” which is the most honest thing any profession has ever said about its own flagship theorem. In 2020, Straub and Werning reopened the question and showed that under plausible conditions the optimal capital tax is positive after all — though still, in the serious cases, lower than the tax on labour.

So here is the honest state of the field, the version no economist can dock me for: the entire serious literature on optimal capital taxation lives in a band running from a floor of zero up to a ceiling of “positive, but generally below what you’d tax wages.” Reasonable people argue about where in that band to stand. It is a real, live debate.

His proposal is not in the band. Tax equity gains at the same rate as fixed-deposit interest, as gold, as real estate treats the formation of productive risk capital as identical to the interest on a savings account and the appreciation of metal in a vault. That is not a daring position at the high end of the debate. It is a refusal to attend the debate. He is not wrong within the discipline; he is standing in the parking lot, explaining to the valet why the building is on fire.

But here is the thing — and this is where I have to take the column behind the shed — even that band is too kind to him, because it is measuring the wrong thing entirely.

Every paper in that literature is, at bottom, about revenue: given that the state needs money, what is the least-destructive way to extract it. Which quietly smuggles in an assumption nobody examines — that the number worth maximizing is the government’s haul. And for a poor country sprinting to get rich before it gets old, that is the wrong number. The number that matters is growth. And growth, it turns out, rides a completely different curve.

I’ll keep this painless, because I develop the full version elsewhere and you have a life. Picture two curves. One plots government revenue against the tax rate — the famous Laffer curve, the one that bends back on itself because a 0% rate and a 100% rate both raise exactly nothing (at zero there’s no tax; at a hundred, nobody bothers earning anything to be taxed). Fine. But there is a second curve, which almost nobody bothers to draw, plotting economic growth against the tax rate. It too is pinned at two zeros: at 0% tax there is no government, no courts, no contracts, no roads, so growth is roughly nil; at 100% tax nobody gets out of bed, so growth is also nil. Which means it, too, has a peak — a growth-maximizing rate, sitting somewhere in the middle.

Here is the entire game in one sentence: there is no earthly reason the peak of the revenue curve sits at the same tax rate as the peak of the growth curve. They are two different functions. Expecting them to share an optimum is like assuming the fastest route to the airport must also be the most scenic. Occasionally, by dumb luck. Mostly, no.

And then the detail that turns a debating point into a sledgehammer: growth compounds. A revenue shortfall is a one-time number. A growth shortfall is a rate, and rates, left alone with time, do unspeakable things. Half a percentage point of extra growth, compounded across our schoolteacher’s working life, does not merely trim the alternative — it laps it, then laps it again, then wanders off to see if there’s anything else worth lapping. So once you take compounding seriously, the contest between the two curves stops being a contest. The rate that maximizes revenue in the long runsimply is the growth-maximizing rate, because the largest tax base any government will ever lay its hands on is the one bolted to the economy that grew the fastest.†

Which means the literature’s ceiling — “positive, but below labour” — was a revenue ceiling all along. For a capital-starved economy whose entire purpose in this decade is to accumulate the capital it does not yet have, the right rate sits lower still, shoved back toward the floor by the one force the revenue models forgot to invite to the meeting: compounding.

So our columnist is not merely standing above the band. The band was the wrong band. He has aimed a tax squarely at the single input — capital — whose accumulation is the growth he needs, in the single country that can least afford to discourage it, having reasoned his way there with great care along a curve he did not realize was the wrong curve. It is, in its way, an extraordinary feat of marksmanship. He has shot the one organ keeping the patient alive — from the parking lot, while telling the valet about the fire.

The conclusion he says he isn’t making

He will object, and loudly, that he proposed nothing so dramatic. A modest equalization of rates, he’ll say. A nudge. Hardly Indira Gandhi. I said no such thing.

He’s right. He didn’t. But he doesn’t get to keep the principle and disown its destination.

Because the principle he is actually endorsing is this: when citizens do something productive at a scale that creates demand for dollars, the state should tax it to discourage them. And once that is the principle, the only question left is the dosage — and there is no number printed on the bottle. If a mild tax is good because it gently discourages the rupee-draining behaviour, then a stiffer tax is better, because it discourages more of it. And a punitive tax is better still. Follow the logic where it actually goes and you do not stop at “the same rate as fixed deposits.” You keep climbing, all the way up, until you arrive — breathless, triumphant, brandishing a chart of FDI repatriation — at a 97.75% marginal rate.

We have been to that summit. India planted a flag there in 1973-74, when the top rate hit 97.75% and a citizen who earned a hundred rupees over the threshold was graciously permitted to keep two and a quarter. It was done for reasons that sounded, at the time, every bit as sober and public-spirited as this column: the prosperous must shoulder the burden, private excess must be curbed, the national interest demands restraint. The result was not restraint. The result was that tax evasion became the national pastime, revenue collapsed, and the policy was quietly strangled within a few years, because it turns out that when you tax something at 97.75% people do not line up to pay — they line up to hide.

The columnist is not Indira Gandhi. He is something this series finds far more interesting: a thoughtful, numerate, twenty-year equity investor who has, without quite noticing, picked up the exact instrument she used and is proposing to apply a gentler dose of it. But it is the same instrument. The principle that licenses his nudge is the identical principle that licensed her sledgehammer; they differ only in how hard the arm swings. And a principle whose logical culmination is 97.75% does not have a calibration problem. It has a direction problem. It is pointed the wrong way — and worse, it is pointed the wrong way down the single axis, growth compounded over a lifetime, that decides whether the schoolteacher’s grandchildren are middle-class or merely hopeful.

The bug that keeps shipping

He closed by bracing for abuse — “let the abuse begin.” But the abuse was never the danger, and it isn’t coming, at least not from here.

The danger is that he articulated, out loud, in a well-reasoned column, the single most durable bug in India’s operating system: the deep, autonomic conviction that when the citizenry starts doing something productive at scale, the state’s first instinct should be to find the lever that makes them do less of it. Not to fix the specific distortion — he found that fix and set it down on the tray. Not to target the actual bad actor — the investment banker pricing the garbage IPO sails through entirely untouched. But to reach, by reflex, for the instrument that disciplines the broad mass of ordinary people, because suppressing citizen behaviour is wired more deeply into the machine than targeting the culprit ever was.

This is the Licence Raj in a SIP-shaped costume. It is the same reflex that taxed toothpaste as a luxury, that made you wait eight years for a telephone, that threw out IBM and Coca-Cola to teach foreigners a lesson, that retroactively taxed Vodafone — and then, when the courts and the howls of foreign investors forced a humiliating repeal a decade later, learned precisely nothing from the episode — that demonetized 86% of the currency overnight to catch black money it did not catch. Each time, the costume is new and the reasoning sounds responsible. Each time, the instinct underneath is identical: the energy of a hundred million ordinary Indians, doing the dull and disciplined thing, treated as a leak to be plugged rather than as the entire point of the exercise.

He won’t get abuse. He’s too smart, and he’s mostly right about the diagnosis, and being mostly right is exactly how the dangerous ideas get past the bouncer. What he’ll get, if the bug ever ships to production, is a policy paper, and a committee, and possibly an award — and our schoolteacher, on the fifth of some future month, will find that the most boring and responsible thing she could possibly do with her two thousand rupees has been gently, fairly, neutrally made a little less worth doing.

For her sake, and the rupee’s, I hope SEBI just fixes the float.

†A caveat for the one reader already sharpening a reply: this holds cleanly where the tax simply discourages productive activity — which is precisely what is proposed here. If the revenue were spent building roads and schools that themselves raise growth, the very low end of the rate gets muddier. But “disincentivize” is not a plan to fund human capital. It is a plan to do less of the thing. The deadweight case is the one in front of us.

The growth-versus-revenue argument — and the four separate nonlinearities that quietly turn almost every tax debate you will ever hear into a confused argument about the wrong curve — gets a full chapter in The Science of Free Will (US · India · UK). Yes, it is a book about free will that contains a chapter on the Laffer curve. It makes sense in context. Mostly.

Elon Musk wants to put AI data centers in space, and the internet has decided this is stupid. The favored reason is thermodynamic: space is a vacuum, there’s no air, how do you cool a gigawatt of GPUs with no air to carry the heat away? The dunk writes itself. The man forgot about heat.

I’m a physicist. I don’t have a horse in this race, and I find the dunk-or-defend reflex tedious. There is exactly one honest way to settle whether orbital data centers make sense, and it’s the way Musk’s own engineers would do it: write down the physics, write down the costs, and turn the crank. The answer falls out — and it turns out to be neither side’s answer.

So let’s turn the crank. No thesis. Wherever the arithmetic lands is where we land. I’ll cite every number; the full table is at the foot of this post. And every time I hit a judgment call, I’ll round it in Musk’s favor — generous emissivity, best-in-class solar, the friendliest orbit — so that whatever the ground wins, it wins on the orbital case’s best day.

First, the heat — because that’s what everyone’s arguing about

A hot object sheds heat three ways: conduction, convection, radiation. Conduction needs contact. Convection needs a fluid — air or water — to carry warmth off. A vacuum offers neither, so in orbit a radiator has one channel: thermal radiation, governed by the Stefan-Boltzmann law,

where q is watts radiated per square meter, ε is surface emissivity (a good coating reaches about 0.9), σ is the Stefan-Boltzmann constant (5.67 × 10⁻⁸ W/m²K⁴), T is the radiator’s temperature, and T_env is what it faces. Temperatures in kelvin, since the fourth power only behaves measured from absolute zero.

The intuition that excites people is the cold of space — 2.7 K, the leftover chill of the Big Bang. Surely radiating into a 2.7 K void beats a warm room? Plug in the numbers. A radiator at 75 °C (348 K), emissivity 0.9:

Radiating into deep space roughly doubles the heat you shed by radiation, versus a room-temperature sink. Real, and worth having.

But that doubling is the entire radiative prize, and it’s smaller than it looks, because radiation isn’t the only way to cool something — it’s just the only one left in a vacuum. On the ground that same panel also has convection: air moving across it, carrying heat off directly. With a fan, convection alone removes several thousand watts per square meter — multiples of what radiation manages in space. So on cooling, the honest tally is: orbit gives you a 2× bump on the radiative channel and takes away the convective channel, which was worth far more. On cooling alone, the ground wins. (This is the part the critics get right; Sam Harsimony made the case cleanly: build one radiator, fly a copy, bolt the other to a warehouse floor — the warehouse copy sheds more heat and never paid for a launch.)

One honesty note on my own number: a real radiator in low Earth orbit doesn’t face a clean 2.7 K universe. It sees the Sun, sunlight reflected off the Earth, and the Earth’s own infrared glow. NASA’s thermal-control models treat spacecraft temperature as a balance of all those inputs plus what the radiator sheds. So 750 W/m² is an optimistic, deep-space-facing figure — again, rounding in orbit’s favor.

So the critics are right that orbit is a worse place to dump heat. But here’s the move that the whole debate misses: being a worse radiator is not the same as being uneconomic. The question was never “is space good at cooling.” It’s “does the whole machine pencil out.” For that we need the radiator’s mass, not its efficiency — and then everything else.

Now turn the crank on the whole system

Build the satellite, per megawatt of compute, and price it against a data center on the ground.

How much radiator? At 75 °C, two-sided, at a spacecraft-radiator areal density of 5 kg/m² (real flight hardware runs heavier), the Stefan-Boltzmann number above gives 3.3 tonnes per megawatt. Three honesty notes, because a thermal engineer will check. A 75 °C radiator implies running the chip junctions hotter still — around 100 °C, since you need a temperature gradient to push heat from junction to coolant to panel — which is aggressive but plausible for purpose-built silicon (unlike a terrestrial DGX, which wants 30 °C air). The 750 W/m² assumes the panel mostly faces deep space; a face turned toward the Earth, which glows in the infrared at around 255 K, sheds noticeably less — which means the satellite has to hold an edge-on-to-Earth, sun-avoiding attitude to hit that number, and that attitude constraint isn't free, because it also dictates where the solar array is allowed to point. And the 5 kg/m² panel is the lightweight end; many flight radiators are heavier. All three assumptions are the generous-to-Musk end, which means 3.3 tonnes is a floor on radiator mass — and even at the floor it's almost an afterthought. The critics were right that the radiator is large in area — a megawatt needs about 1,300 m² of radiating surface, or roughly 670 m² of physical two-sided panel: a quarter of a football field if you count both faces, an eighth if you count the deployed footprint — but in mass, the thing a rocket cares about, it barely registers. Costed honestly, the heat problem everyone's dunking on is a few tonnes.

What else flies? Compute, first. As a mass proxy, an NVIDIA DGX H100 is 130 kg and draws 10.2 kW, which is about 13 tonnes per megawatt. A real orbital machine wouldn’t be a DGX rack bolted to a satellite — it would need space-qualified packaging, shielding, thermal interfaces, and robotic-serviceable mounting, all of which add mass I’m ignoring. Then the solar array, using best-in-class flexible panels at 150 W/kg (typical space arrays manage a fifth of that): 6.7 t/MW in the best orbit. Add batteries to run through Earth’s shadow, plus structure and power electronics at a 30% allowance, and the whole satellite comes to roughly 30 tonnes per megawatt in the friendliest orbit, 41 in a generic one.

Now lift it. At today’s Falcon 9 price of about $2,700/kg, thirty tonnes costs $80M per megawatt — far above the roughly $11M/MW it takes to build a data center on the ground. The critics who say “launch is too expensive” are right about today. But Musk isn’t betting on today; he’s betting on Starship. SpaceX doesn’t publish an operational price per kilogram, so the figures everyone quotes — $100 to $300/kg — are projections for a mature, fully reusable system, not posted fares. Grant the optimistic end anyway. At a hypothetical $100/kg, thirty tonnes launches for $3M/MW — below the ground build. So the first launch is not the killer. On the narrow question “can you afford to put it up there once,” Musk wins the day Starship hits its targets.

Heat: not free, but no longer the obvious killer — 3.3 tonnes. Launch: also not the obvious killer, in a mature-Starship world. So what is? This is where the crank turns up the thing neither camp is shouting about.

The clock nobody priced

GPUs don’t last. NVIDIA ships a new architecture every 18 to 24 months, each generation improving performance per watt — though by how much is workload-specific and vendor-reported, not a clean law; NVIDIA’s own roadmap claims range from modest to 10× depending on what you measure. For the toy model I’ll assume frontier performance per watt doubles every D years and use D = 3 as a central case. That is not a law of nature; it’s the single parameter the whole result turns on, which is precisely why I’m flagging it. (I’ll call this “Moore’s law” colloquially, but the real variable is accelerator useful-work-per-watt, not transistor density.) Hyperscalers book six-year depreciation schedules, but that’s an accounting choice under pressure; Michael Burry has argued loudly the real economic life is closer to two or three years. Meanwhile the building that houses everything — concrete, power feed — lasts 15 to 30 years. Three clocks badly out of sync: chips obsolesce in a few years, the structure lasts decades.

On the ground this mismatch is boring and solved. When the chips age out, a technician wheels a cart down an aisle and swaps the cards. The expensive long-lived part — the building — stays put and amortizes over thirty years. You replace only the cheap, fast-moving part.

In orbit you cannot wheel a cart down an aisle. And this is where the entire economic question lives — but it’s subtler than “can you send a repair robot,” because there are two failure modes, not one.

The crude version: relaunch the whole satellite each refresh. If upgrading the chips means flying a fresh satellite every few years, then over a 30-year facility life you pay the full launch bill five to ten times. In this capex-only comparison, that only beats the ground if launch drops to roughly $25 to $76 per kilogram depending on refresh interval — below even the optimistic Starship projections. Possible in principle. But you’re betting the entire concept on launch prices nobody has demonstrated.

The sophisticated version: service in orbit. Suppose a robot can swap the GPU modules while the bus, radiator, solar array, and batteries all stay in place. This is the case the optimists are implicitly counting on, and it’s a real saving — you stop relaunching 30 tonnes of satellite. But it is not free, and here’s the trap the optimists miss: the replacement chips still have to reach orbit. You don’t relaunch the radiator and the solar panels, but you relaunch the compute payload — about 13 tonnes per megawatt — every single refresh cycle. And the compute is the part driving the refresh in the first place. Serviceability saves you the radiator-and-solar fraction. It cannot save you the compute fraction, because that’s the fraction that keeps going obsolete.

Run the corrected numbers. Best orbit, service in place, replacing 13 t/MW of compute every cycle, at a hypothetical $100/kg: a 3-year refresh costs $14.5M/MW (above the ground build), a 6-year refresh $8.1M/MW (below it), and at $300/kg the 3-year case balloons to $43.5M/MW.

The red line is the crude case — relaunch the whole satellite each refresh — and it sits far above the ground line everywhere worth discussing. The green lines relaunch only the chips: better, but the 3-year cycle still doesn’t beat the ground until launch falls near $80/kg. Serviceability helps — it stops you relaunching the radiator and solar — but it cannot stop you relaunching the compute, because the compute is the thing going obsolete.

Which is where it gets interesting, because I’ve been smuggling in an assumption that’s wrong, and correcting it is the most important move in this whole exercise.

You don’t throw old chips away. You demote them.

I assumed the orbital data center must refresh on the same clock as the ground — swap chips every three years to stay current. But that’s not how compute retires, on the ground or anywhere. A two-generations-old GPU isn’t garbage; it’s worth less. It still computes, just at lower performance per watt. So hyperscalers cascade hardware: newest silicon takes frontier training, last-gen drops to inference, older still to cheap batch work. Chips retire by demotion, not deletion.

So the orbital operator doesn’t have to match the ground’s cadence at all. It can run its silicon for years past the frontier, keeping aging chips busy on lighter, latency-tolerant work, and relaunch far less often. That breaks the punishing symmetry — and it’s a real win. Stretch the refresh from three years to nine and the recurring launch term collapses.

But — and conservation laws always have a but — old chips don’t compute as well, and here’s the precise asymmetry, because it’s the place a sharp reader will push hardest. Both the ground and orbit run aging chips; demotion happens everywhere. The difference is the cost of the power and space that an old chip occupies. On the ground, a demoted chip draws cheap grid power and sits in an already-built rack — the marginal cost of keeping it is basically its electricity. In orbit, that same old chip occupies solar array and radiator and structure that cost $100–300 a kilogram to lift and cannot be repurposed for anything else — so its true cost is the amortized launch of that fixed power budget, whether the chip running on it is frontier or four years stale. It’s an opportunity-cost argument, not a “space is worse at computing” one: orbit’s power is expensive and fixed, so filling it with obsolete silicon wastes capacity you paid a fortune to put there.

Here I also have to switch units, and I want to be explicit about it, because the switch quietly does real work. Up to now "per MW" has meant a megawatt of electrical IT load. From here it means a megawatt of *frontier-equivalent* compute, because an aging chip still draws its full orbital power but delivers less useful work relative to the current best silicon. Performance per watt improves over time (doubling every D=3D = 3 D=3 years, generously); your orbital power budget is fixed, sized once at launch. So a chip three doublings old — nine years, in this toy model — delivers maybe an eighth of the frontier compute per watt, on infrastructure you're still paying full freight to keep aloft. And here is the asymmetry I'm granting the ground for free, stated plainly so it isn't hidden: the orbital cost gets divided by this aging output fraction, but the ground's $11.3M does not, because cart-down-the-aisle refresh keeps terrestrial silicon at the frontier continuously. That's a real thumb on the scale — and it favors the ground, which is the direction I've been rounding throughout. The whole point is that orbit *can't* do the cheap continuous swap, so it's the side that pays the obsolescence tax.

(You might ask why the power budget is fixed — why not just launch more solar as the chips improve? You can. But that’s more launch mass every cycle, which is exactly the cost the whole optimization is trading against. Fixed power per deployed satellite is the right model; growth means more satellites, more launches, and the math below already captures that.)

Put both effects together and the cost per unit of actual frontier-equivalent compute stops being monotonic. Refresh too fast and you relaunch chips constantly. Refresh too slow and you’re paying orbital prices to run silicon that can barely keep up. In between, there’s an optimum:

In a smooth continuous-cycle approximation, with performance per watt doubling every three years, the optimal refresh lands around nine years — and if you force integer launches over a 30-year life (deployments at years 0, 10, 20), the minimum nudges to roughly ten years with these inputs. The exact year isn’t the point, and it isn’t even sensitive to my assuming a smooth exponential decay: I reran it with the chip value falling linearly, and with a lumpy generation-by-generation step function, and an interior optimum near a decade shows up every time. That’s because the structure is general — launch cost falls as you refresh less often, while useful output also falls as the fleet ages, and any value curve that decays toward zero relative to the frontier produces that tradeoff. The precise optimum depends on the shape; its existence and its rough magnitude do not. And the point that matters is robust: the orbital optimum is much longer than the ground’s frontier-chip cadence — three times longer or more.

This is also the answer to the sharpest objection a cost analyst will raise: you optimized launch, but at $100/kg launch is the minority cost — satellite hardware has never been built for $100 a kilogram, so manufacturing dominates, and you optimized the wrong line item. True about the dollars, and beside the point about the structure. Changing the launch price scales the entire cost curve multiplicatively (it looks like a vertical shift only because the chart’s axis is logarithmic); it does not move the optimum. And the same is true of any per-kilogram cost of putting refresh mass on orbit — manufacturing, integration, insurance, all of it. Fold a hardware cost-per-kilogram into the launch term and the curve rises, but its minimum stays put. The refresh optimum and its dependence on the pace of chip improvement survive whatever the true all-in cost of a kilogram in orbit turns out to be. Only the absolute dollar figures move; the shape of the argument, and its crux, do not. The optimum is set by the tension between the durable orbital infrastructure you keep and the rate at which lifted silicon falls behind the frontier — not by what a kilogram costs.

At that optimum, orbital compute at $100/kg costs about $14M per megawatt of frontier-equivalent capacity — just above the $11.3M ground build on launch alone. But the ground also pays a grid-electricity bill, and orbit does not — it pays up front instead, in solar, batteries, radiators, and launch mass: thirty years of grid power adds $13–31M/MW to the terrestrial side, putting the ground build-plus-electricity comparator in the $24–42M range. So on launch cost alone, orbit at its optimal refresh comes in below the ground’s build-plus-electricity band — but that gap is what has to pay for everything I’ve left out of the orbital side: spacecraft manufacturing, servicing operations, insurance, stationkeeping, radiation-hardening, networking, and disposal. Below the band on launch alone is what a real business case would need as its starting margin, not its conclusion. At $300/kg, the optimized launch-only figure is about $42M/MW frontier-equivalent — landing right at the top of the ground build-plus-electricity band before a dollar of those orbital costs is counted. That’s a warning light, not a win. (Worth noting that the most-cited independent estimate lands in the same place: aerospace engineer Andrew McCalip’s calculator, written up in IEEE Spectrum, puts a gigawatt-scale orbital facility plus five years of operation north of $50 billion against about $16 billion on the ground — roughly 3×. Different method, same neighborhood as the arithmetic here, which is reassuring: when a back-of-the-envelope and a detailed model agree, both are probably close. Tellingly, McCalip gets even that far only by assuming the orbital fleet runs efficient, radiation-tolerant chips rather than frontier GPUs — which is the demotion argument arriving from the other direction.)

You might also note that you needn’t relaunch the whole compute payload each cycle — swap only the highest-value boards and keep the trays and interconnect — which helps the orbital case further, by an amount that depends on packaging choices nobody has made yet. I’ve left that on the table, conservatively, for Musk.

(And the sun fraction, the thing I was careful to pick favorably? Second-order. A generic 60%-sunlight orbit makes the satellite a third heavier and barely moves any of this. The refresh optimum dominates.)

Where the crank stops

Neither side had it right, and the truth is more interesting than either.

The critics are wrong: the heat can be rejected, the radiator is 3.3 tonnes, and “you can’t cool it in space” — though space is indeed a worse radiator — is not the argument that decides anything. They reached a reasonable instinct through the wrong variable.

Musk is right about the parts he’s loud about: heat is manageable, a single launch is competitive at projected Starship prices, and — once you account for the fact that you demote old chips rather than discard them — there’s an optimal way to operate an orbital fleet that becomes launch-and-electricity competitive with the ground in this toy model, at the optimistic end of his own launch-cost projections. He is not being stupid. Run the numbers his way, generously, and there’s a real case in there — one that still has to cover the spacecraft and servicing costs the model leaves out, but a real case.

But it lives in a specific corner, and the corner is defined by two numbers that are neither thermal nor propulsive. The first is launch price: the case looks interesting near $100/kg and becomes uncomfortable by $300/kg — where the launch-only figure already reaches the top of the ground build-plus-electricity band before orbital hardware and servicing costs are counted — and SpaceX has not yet flown anything at either price. The second is subtler and, I think, the actual crux: the pace of semiconductor improvement. The whole optimization turns on how fast orbital chips fall behind the frontier. If performance per watt keeps doubling slowly — every four or five years, as the leading edge arguably trends — old chips stay useful longer, the optimal refresh stretches out, and orbit looks good. If improvement reaccelerates to the historical two-year pace, the optimum tightens, the relaunch term comes roaring back, and the ground wins comfortably.

So an orbital data center is not a cooling bet. It’s barely a launch bet. It is, underneath everything, a bet that Moore’s law stays slow — that the chips you lift won’t be embarrassed too quickly by the ones you didn’t. That’s the number to watch. Not the temperature of space, and not even the price of a rocket. The rate at which silicon improves.

One honest boundary on all of this: it’s a launch-and-infrastructure model, not a full lifecycle one. I use two ground comparators — a capex-only baseline of $11.3M/MW, and that same figure plus a 30-year electricity band of $13–31M/MW — and the orbital side counts launch and durable hardware mass but not spacecraft manufacturing, servicing operations, insurance, or disposal. So the electricity band helps the orbital case; the omitted orbital hardware and operations hurt it; the two pull opposite ways. I’m also excluding the data-movement problem entirely: orbital compute only makes sense for workloads whose inputs can be staged ahead of time and whose outputs are compact or latency-tolerant, because you are not getting a fat low-latency fiber to low Earth orbit. And I’m setting aside radiation: leading-edge accelerators aren’t built on radiation-hardened processes, so a real design pays for shielding mass or accepts higher failure and faster degradation — either of which makes the orbital case harder, not easier, and tightens the very refresh clock the argument turns on.

Two of my “durable” components aren’t, and both cut the same way. Batteries cycling through Earth’s shadow see thousands of charge-discharge cycles a year; even space-grade cells are spent in five to ten years, not thirty, and solar arrays lose a percent or few of output annually to radiation. So in any eclipsing orbit the batteries and part of the array join the compute on the recurring-relaunch side of the ledger — they’re wear items, not failures. That only deepens the conclusion: more recurring mass per cycle makes the refresh cadence matter even more.

And the largest caveat is one my arithmetic simply can’t price. This model assumes the ground can buy electricity and a grid connection on demand at $0.05–0.10/kWh. The actual bull case for orbital compute is that it can’t — that transformer lead times, interconnect queues, and permitting mean the binding ground constraint isn’t the price of power but the availability of it, in which case scarcity rents push the ground comparator upward by an amount no one can yet quantify. That may well be the real argument for building in orbit. But notice it’s a bet on terrestrial grid bottlenecks, not on physics or launch cost, and it lives outside what a back-of-the-envelope can settle. I’m pricing power as available; if it isn’t, the orbital case looks better, for reasons this calculation doesn’t capture.

But the structure survives all of these corrections, because the refresh optimum — and the chip-progress bet behind it — governs the recurring side of the ledger no matter what. If someone tells you orbital data centers obviously do or don’t work, ask them two things: what launch price, and how fast they think accelerators will keep improving. Everything else is the temperature of space.

The calculation, every input cited

All figures are per megawatt — of electrical IT load through the mass and launch sections, and of frontier-equivalent compute in the demotion section (an aging chip draws the same orbital power but does less useful work). This is a launch-and-infrastructure model, not a full lifecycle one. I use two ground comparators: a capex-only baseline of $11.3M/MW, and that same figure plus a 30-year electricity band of $13–31M/MW. The orbital side counts launch plus durable hardware mass but excludes spacecraft manufacturing, servicing operations, insurance, failures, and disposal. Chip purchase cost is excluded, not cancelled: it would cancel only in a same-refresh comparison, but here ground and orbit refresh on different cadences, so treat the result as launch-and-infrastructure, not a full chip-capex model. The exclusion isn't neutral, and it leans the generous way: at the orbital optimum you deploy roughly 2.4 MW of fresh silicon every nine years (the oversizing needed to offset the aging fleet), about 0.26 MW of new chips per year, versus the ground's 1 MW every three years, about 0.33 — so the ground actually buys more new silicon per year, and leaving chip purchase out of both sides mildly favors the ground.

• Stefan-Boltzmann constant σ — 5.67 × 10⁻⁸ W/m²K⁴ — physical constant.

• Radiator emissivity ε — 0.90 — high-emissivity coating (generous).

• Radiator temperature — 75 °C / 348 K — chip-junction-limited assumption.

• Radiator areal density — 5 kg/m², two-sided — aggressive lightweight-radiator assumption; many flight systems are heavier.

• → Radiator mass — 3.3 t/MW — computed; radiating area ~1,300 m²/MW, physical panel ~670 m².

• Compute mass (proxy) — 12.8 t/MW — NVIDIA DGX H100: 130.45 kg, 10.2 kW — terrestrial hardware, mass proxy only.

• Solar specific power — 150 W/kg (best-in-class) — NASA Small Spacecraft SOA (typical ~30); UltraFlex up to 150 W/kg EOL.

• Battery specific energy — 150 Wh/kg — NASA: commercial Li-ion 150–270 Wh/kg; space-grade trades energy for life.

• LEO orbital period — 1.5 h — standard.

• Structure / electronics — +30% — engineering allowance (my assumption).

• → Total satellite mass — ~30 t/MW (best orbit) to ~41 t/MW (generic) — computed.

• Ground build cost — $11.3M/MW (shell + core) — JLL 2026 Data Center Outlook; AI fit-out can add up to $25M/MW.

• Ground electricity (sensitivity band) — $13–31M/MW over 30 yr — computed at $0.05–0.10/kWh, PUE 1.0–1.2.

• Falcon 9 launch — ~$2,700/kg to LEO — Orbital Radar, May 2026.

• Starship (hypothetical, mature) — ~$100–300/kg projected — SpaceX publishes >100 t to orbit, no $/kg; $/kg are projections.

• GPU architecture cadence — new generation every 18–24 months — Introl.

• GPU economic life (contested) — 2–3 yr (Burry) vs 5–6 yr (hyperscaler books).

• Building life — 15–30 yr — three-clock mismatch.

• In-orbit servicing status — demonstrated in GEO (Northrop MEV undocking); not yet industrial (NASA cancelled OSAM-1).

The model. Sun fraction f sets two coupled masses: solar sizing scales as 1/f (oversize to charge through the dark side), battery mass as (1−f) (store energy for the eclipse). With m_refresh the compute mass relaunched each cycle and R the refresh interval in years over a 30-year life:

where L is launch dollars per kilogram, treated as delivered to the target LEO or sun-synchronous orbit, with inclination and orbit-insertion penalties ignored — another assumption that favors the orbital case. The crude case relaunches the whole satellite each cycle; the serviced case relaunches only the compute. The ground comparators are the $11.3M/MW build, optionally plus the $13–31M/MW electricity band. The serviced case is the relevant one, and it still carries the full compute mass on every refresh — which is why the refresh cadence, not the radiator, governs the result. (The effective-cost optimization in the demotion section divides this launched mass by the cycle-average frontier-equivalent output

which is what produces the ~9-year continuous, ~10-year integer optimum.)

For more conservation-law thinking — where a cost you thought you’d eliminated reappears somewhere else in the ledger — see The Conservation Law of Tax Alpha. My book, The Science of Free Will, asks an equally uncomfortable question about an equally cherished story.

In 2005, a very famous pod shop wanted me to come work for them. The recruiter was friendly. The compensation would have been excellent. The infrastructure on offer — capital, technology, operations, the entire institutional machinery a systematic trader would otherwise have to build for himself — was the kind of thing you don’t normally walk past twice.

Then they sent over the IP form.

It was black and white. Every rule I wrote, every model I built, every strategy I developed while in their employ would belong to them. The recruiter, to his credit, tried hard to talk me through it. There was nothing to talk through. The contract was clear, and it was non-negotiable. I said no and walked away.

Twenty years later, every knowledge worker on earth is about to be handed a version of that contract.

The form that landed on my desk in 2005 was unusual for its time — directed at the small number of people, like systematic traders, whose work product was legible enough to be copied. Discretionary traders didn’t get that contract, because nobody could actually extract what was in their heads. Lawyers didn’t get it, because their judgment didn’t fit on a page. Doctors didn’t get it for the same reason.

That’s about to change. Everyone in knowledge work is on the verge of producing exactly the kind of legible, portable, codified artifact that did land me that contract two decades ago. The agents are coming. The skills folders are coming. And the contracts are coming after them. This post is about why.

A different deal, same year

Later that year, I made a different deal.

Joe Ritchie was a legend. At one point in the 1980s, his firm Chicago Research and Trading was doing more options business than any other firm in the world. CRT pioneered the use of computers in options pricing — Joe famously programmed Black-Scholes into a Texas Instruments calculator before most of the floor knew what Black-Scholes was. He sold CRT to NationsBank in 1993 for $225 million. He came to trading by way of a Wheaton College philosophy degree, a stint as a Chicago bus driver, and a job as a guard at the Cook County Jail; he learned the markets from a book a friend handed him. Self-educated in the only sense that matters. Genius in every sense.

He was also a straight shooter — devout, principled, and scrupulously honest in business. Before we agreed on anything, I asked if my wife could come meet him. Joe said not only that she should — she must. A few weeks later, there we were, in his boardroom in suburban Illinois, my two kids coloring on the boardroom table, aged four and seven. He was happy to have the whole family. We shook hands on the deal shortly after.

I traded Joe’s prop capital for three years. We shared ideas freely; he gave me ideas, I gave him ideas, and neither of us laid claim to the other’s IP. There was no contract that swallowed what one of us brought to the table. We simply trusted each other not to do the thing that the pod shop had asked me to sign away in writing.

He was, and remains, the only person I have ever met who saw markets in exactly the way I did. We had different approaches and different personalities — every system has to match the personality of the person running it, which is a different post — but on every question that mattered, we agreed. What a rule is. Whether a rule should be traded. Whether a rule is real or robust. The value of systematization. The definition of systematization. The value of what Joe called “trader logic” over the academic frame of mind that produces, for instance, the Barron’s columns advising readers to use options to take directional positions. (You don’t. You trade options to bet on volatility. If you want to bet on direction, you bet on the underlying. That this is not obvious to a class of professional commentators is itself a useful piece of evidence about what “trader logic” is and isn’t.)

That kind of agreement is what makes a handshake deal possible. It is not enough to trust someone’s character. You also have to share their definition of the thing you are trusting them about. Two people who disagree about what a rule even is can sign every contract in the world and still end up suing each other. Two people who agree about what a rule is can shake hands and never need a contract. Joe and I agreed about what a rule was.

The contrast matters because it isolates the variable. Joe and I were doing exactly the work the pod shop wanted me for — developing systematic rules and applying them with capital. We were sharing IP in both directions, which the pod shop’s contract would have made formally impossible. The thing that was different wasn’t the work. It was the structure of the relationship. Joe was trading his own money. He had no reason to own the rules — only to use them. Pod shops are running other people’s money on behalf of investors who are paying for institutionalized, transferable IP. The contract follows from the business model.

Joe passed away during the pandemic. I still miss him.

What systematic traders knew first

The reason I bring up the pod shops isn’t aesthetic. I have specific, technical reasons for thinking they’re not very good at the thing they claim to be selling.

In November 2025 I published a paper in The Journal of Portfolio Management called “The False Promise of Drawdown Rules: New Evidence and a Better Framework.” The paper used ETF and long-short data from 1993 to 2022 to test the standard pod-shop “risk management” rule — the one where if you’re down X%, you’re cut. I showed that those rules, even in perfect-hindsight scenarios, turn certain profits into losses more often than not. They don’t manage risk. They manufacture it. Pod shops succeed in spite of their drawdown protocol, not because of it. The whole apparatus is incoherent on its own terms, and I think anyone who has worked under it long enough knows this.

There’s a more general lesson hiding in this finding. Externalized rules in the wrong hands actively destroy value. The pod shops’ own written-down risk rules make them worse traders, not better ones. If they can’t codify their own risk management without manufacturing losses, what reason is there to think they’d handle a codified version of someone else’s IP any more competently? The technical problem with their risk protocol and the structural problem with their IP grab are the same problem manifested twice — an institution organized around the wrong theory of where the value lives.

So when I tell you I refused to sign their IP form, understand that I had also looked carefully at the rest of what they were offering and concluded it wasn’t worth giving up the rules for. The contract was the most visible problem, but the building behind it had structural issues, too.

But the IP problem was the deepest one, and it’s the one that’s now generalizing.

Here’s what systematic traders knew first, and what’s now becoming a fact about every white-collar field. Trading IP, when it lives in someone’s head as discretionary judgment, is essentially impossible to extract. The pattern recognition of an experienced market-maker, the gut-feel of a macro trader who has lived through three crises, the spatial intuition of a vol trader who has watched skew misbehave in real time — none of that fits on a page. You can’t put it in a contract because there’s nothing to put in.

But the moment you write down explicit rules, all of that changes. Now there’s a thing to take. There’s a piece of paper. There’s a file. There’s a contract that says it’s mine.

The current decade has produced a tidy little series of legal cases that show exactly how this plays out, and the trajectory is escalating, not stabilizing.

In 2019, Citadel Securities sued the British hedge fund GSA Capital over an automated trading model called the “ABC Strategy.” Citadel said the model had cost more than $100 million to develop and was so closely held that just 15 of the firm’s roughly 3,000 employees had access to its strategic logic. The dispute was triggered by GSA’s attempt to recruit a Citadel quant. Citadel argued in court that GSA “couldn’t unsee” what its hire had brought with him. The case settled confidentially in 2021. Whatever the precise terms, the precedent was that you could litigate over the contents of a trader’s head if those contents had once been written down.

In April 2024, Jane Street sued Millennium Management and two former traders, Doug Schadewald and Daniel Spottiswood, over an India-options strategy that had generated about a billion dollars in profits for Jane Street in 2023 alone. Jane Street alleged that the strategy’s profits dropped by half in March 2024, the month Millennium reportedly began using it. The two traders, in their defense, argued that the strategy wasn’t a trade secret at all — that it was based on their experience and expertise rather than any protected algorithm or signal. That defense is the entire post in one sentence. Where does the trader end and the strategy begin? When the rules are explicit, they belong to the firm. When they’re implicit, they belong to the trader. The case settled in December 2024, but only after Jane Street had been forced to disclose sensitive details in court — a small demonstration of what employment lawyers now call the “enforcement paradox”: the harder you fight to protect a trade secret, the more of it you have to reveal in court.

Earlier this year — February 2026 — Citadel went after Daniel Shatz, a former portfolio manager who took global credit at Marshall Wace. Citadel alleged that Shatz had accessed proprietary documents — recruiting lists, strategic roadmaps, candidate evaluations — while still employed and during a fifteen-month garden leave that preceded his formal departure. Fifteen months. Pause and consider that. The firm was paying him to do nothing for over a year because they were afraid of what he would do if he were free to act sooner. The IP problem has become so acute that the cost of avoiding it now includes paying senior people to sit on their hands for over a year.

The arc here is the part that should worry the rest of you. In 2009, the IP fights in finance were over code and information (Sergey Aleynikov leaving Goldman for Teza, Citadel suing Teza’s founders). In 2024, they were over strategies. In 2026, they are over the garden-leave intervals between strategies. The thing being protected is getting more abstract, the protection mechanisms more elaborate, the legal stakes higher. And until now, this has been a peculiar little drama playing out in finance, watched by the rest of the world with a mixture of curiosity and contempt.

It’s about to stop being peculiar.

The smartphone bit, briefly

The cliché version of what I’m about to argue is the smartphone analogy: by 2027 everyone will have AI agents the way everyone has a phone now. That’s true and it’s trivial. The same model will be available to everyone. The same chat interface, the same general capabilities, the same default toolkits. There will be no scarcity in the thing.

The smartphone analogy nails one thing: the social-expectation treadmill. Right now, “I’ll get back to you next week” is normal. In two years, that response will sound the way “let me check my answering machine” sounds now. Why didn’t your agents have it ready Tuesday? You’ll get used to that question, the way an entire generation got used to “why didn’t you text back.”

But the smartphone analogy fails on the part that actually matters. Phones are isomorphic across users. Yours and mine do the same things and produce the same value for both of us. Agents won’t work that way. The capability of an agent depends entirely on what you’ve taught it to do for you, in your domain, on your problems. The tool is generic. The configuration is everything.

That configuration lives in a folder.

What a skills folder is — without showing you mine

A skills folder is the file system in which an expert tells an agent how their domain actually works. It contains documents, written in plain language, that codify procedures, conventions, decision rules, common errors, edge cases, and the specific corrections that turn the agent’s enthusiastic competence into actual usefulness. It is not a collection of one-off prompts. It is a permanent, evolving body of instruction.

I have skills for backtesting trading strategies, for generating code against my own specifications, for supporting my physics research, for building and querying my reference library, for editing prose. The publishing piece — the part that helps me write Substack posts — is the smallest tail of what’s there. The rest is the real work.

I’m not going to show you any of those skills. That’s the most important sentence in this post.

I’ll tell you what category of failure each of them addresses, because that’s enough to understand what’s at stake. The trading-strategy skills exist because agents will, by default, write you a backtest with subtle look-ahead bias and report it confidently. They will use future information to predict the past. Every quant who has ever shipped a paper-trading system before discovering this knows the feeling. A skill, written carefully, catches it. The physics research skills exist because agents will, by default, get computational details in particular calculations exactly wrong in ways that look right to a non-physicist. The code generation skills exist because agents will, by default, ignore conventions specific to the codebase they’re working in unless you write down what those conventions are. The reference-library skills exist because agents will, by default, hallucinate citations.

The general principle: a skill is a codified record of where a generic agent fails in a specific expert domain, plus the correction. It is the externalization of tacit knowledge. It is the thing that takes me from being one person with an agent — which is what everyone will have — to being one person with a configured agent that knows how a particular kind of work actually has to be done.

But here’s the thing the skills also are: they are the sort of artifact that can be lifted. They are exactly the kind of thing the pod shop’s contract would have said belonged to them. They are the same form, in a different domain, of the artifact I refused to externalize in 2005.

This is why I’m not going to show you mine.

It’s also why this post is, in a small way, an enactment of the argument it’s making. I’ve told you the moat exists. I’ve told you what shape it has. I’ve explicitly refused to walk you through the contents. That refusal is the model, and the first thing anyone with a working skills folder should understand is that nobody who has one is going to show it to you.

The mechanism

The reason a skills folder is uniquely valuable — uniquely yours, structurally — has nothing to do with the cleverness of any individual skill. It has to do with who can write one in the first place.

A skill cannot be written by a non-expert, because writing one requires knowing which agent failures matter and which are noise. The trading skill that catches look-ahead bias is only writeable by someone who has shipped a system that contained look-ahead bias and learned the hard way to recognize it. The physics skill is only writeable by someone who knows that a particular calculation has a sign error in textbooks A through D and a different sign error in textbook E. The legal skill is only writeable by someone who has personally been on the receiving end of a discovery review that went sideways.

The skill is, in this precise sense, a record of accumulated scar tissue. The agent provides the muscle. The expert provides the scars. You can give an agent to anyone, but you cannot give scars to anyone, which is why the folder is the real asset.

A skill also cannot be written by the agent itself. The agent doesn’t know what it doesn’t know. The whole reason a skill is needed is that the agent’s confidence and the agent’s correctness diverge in places only an expert can see. Asking the agent to write its own skills is exactly the same epistemological category as asking a freshman pre-med to write the residency-training curriculum. There’s no path from inside.

Tacit knowledge, in the philosophy-of-science literature, was always understood as the part of expertise that couldn’t be verbalized — the irreducible human residue. What’s happening now is that, for the first time, we have a tool — the agent — that creates pressure to verbalize as much of it as possible. And the people doing the verbalizing are the experts themselves, in their own voices, in their own folders.

This is a good thing, in the abstract. It is the most efficient knowledge-transmission mechanism ever invented. It is also the conservation law that defines the new labor market.

In knowledge work, productivity gains and IP defensibility are now conjugate variables. You can have one or the other at full strength. You cannot have both. This is the systematic trader’s old dilemma, and it now belongs to everyone.

There is exactly one way out, and we’ll come to it. Everyone who doesn’t take it lives with the dilemma.

The trap

Here is what’s about to happen — and is already happening — in fields that have nothing to do with finance.

A junior transactional lawyer at a big firm — the office lawyer, not the litigator who’ll be in court — builds a 200-line skill that captures her firm’s contract-review process. The specific clauses to flag in M&A diligence, the formatting conventions, the standard redlines, the way to summarize a target company’s liabilities for a partner’s memo. The skill saves her 20 hours a week. Her billables look extraordinary. Her partners notice.

Six months later, she gets an offer from a competitor. Her current firm sends her a letter reminding her of the IP clauses in her employment agreement. The skills she wrote, the firm asserts, were written on firm time, using firm matters, under firm supervision, and constitute work product belonging to the firm. She is welcome to leave, but the folder stays.

A radiologist builds a skill that encodes her department’s reporting conventions — the phrasing, the diagnostic priors that fit the patient population, the specific failure modes of the imaging equipment that produces 80% of her cases. The skill makes her twice as fast and improves her accuracy. The hospital, which paid for her training, the equipment, and the patient relationships, has a view about who that skill belongs to.

A management consultant builds a skill that captures the way her firm sells engagements — the diagnostic frameworks, the standard slide structures, the negotiation patterns that work and don’t. She is, in a real sense, more productive than any consultant before her. The firm has thoughts about whether she gets to take the folder when she leaves.

Each of these people has just produced, in 2026 or 2027, exactly the kind of artifact I was asked to produce inside the pod shop in 2005. And each of them is about to discover that the law of intellectual property does not care about the medium. A trade secret is a trade secret whether it’s an options-pricing algorithm or a discovery-review checklist. The contract clauses that protect employer ownership of work product are not going to politely exempt skills folders from their reach. The Jane Street–Millennium case turned, partly, on whether explicit trading rules counted as a protected trade secret. The next decade’s version of that case will turn on whether explicit legal rules, medicalrules, consulting rules count.

They will. The legal infrastructure for this fight already exists. The Defend Trade Secrets Act, the body of state-level non-compete law, the standard work-product clauses in every white-collar employment agreement — none of this was designed with skills folders in mind, but all of it applies. The asset is portable, the asset is valuable, the asset was created during employment using the employer’s resources. The fight writes itself.

Not every white-collar role is equally exposed. Therapists, salespeople, executives whose work is primarily relational — and litigators, whose work is performed live in adversarial courtrooms — produce less codifiable output than a transactional lawyer or a radiologist or a quant. The trap applies to the codifiable portion of knowledge work — a large and growing share, not a total one. One implication worth naming: the discretionary work that has historically been the higher-prestige half of several professions — the trial lawyer over the office lawyer, the master clinician over the junior diagnostician, the partner running the room over the associate running the spreadsheet — is about to become more valuable, not less. The trap closes hardest on the work that can be written down. Work that can’t be written down stays safe.

And whether the firms will win those fights even within the codifiable share is itself a live question. The Jane Street defense — strategy as experience and expertise, not protected algorithm — is exactly the argument workers in every codifiable field will start making. If the courts buy it, the trap stays porous. If they don’t, it closes hard. We don’t yet know which way the law lands. What we know is that the fight is coming.

And here is what makes this particularly cruel: the better you are at your job, the worse this trap is for you. The most productive lawyer at the firm has the most valuable folder. The most productive radiologist has the most valuable folder. The most productive consultant has the most valuable folder. The structural pressure of agent-augmented work concentrates value into a portable artifact, which means the workers most worth fighting over are also the ones most exposed to the contract. The pod shops always knew this. It’s why their contracts were drafted the way they were. Now everyone gets to learn.

Three positions

When the trap closes, the labor market stratifies into three positions, each with real costs.

The first position is the W2 employee on the sanctioned firm stack. The firm provides the agents, the skills folder lives on the firm’s drives, the IP clauses are tight, and the productivity is real but rented. You are more productive than the workers of 2024, but the productivity is captured by your employer. Most large institutions will end up here for the same reasons they ended up with company-issued laptops and company-managed email: compliance, audit trails, IP protection. This is not a bad position. It is just not a position with a moat.

The second position is the 1099 contractor, the consultant, the solo practitioner with their own stack. Your skills, your folder, your IP, your risk. You eat what you kill, but you also keep what you build. This was not the systematic trader’s typical path. The typical path was the first position — at firms whose contracts captured everything the trader learned. The IP cases I described earlier are exactly what the typical path produces on exit. What’s new is that fields where the W2 career was the stable default — law, medicine, design, research, consulting — are about to face the same fork systematic traders faced, and a higher fraction will take the solo path: partly because the IP grab pushes them out, and partly because the agent makes solo work newly scalable.

The third position is the loophole — the one exit from the conjugate-variables trap I flagged earlier. It’s the one I took, and it’s still available. Externalize the rules — but only for yourself. Build the folder, refine it, depend on it, and refuse to transfer it. The rules live in writing; they just live in writing you own. This is what walking away from the pod shop’s IP form actually meant in 2005, and it’s what it has meant since. The skills exist. They simply don’t belong to anyone other than me.

This stance used to be expensive because solo work didn’t scale. The cost of keeping the folder yours was approximately the cost of staying small. Agents change that math. A solo practitioner with a mature private folder can now do work that previously required a team — without ever turning the folder into something an employer can capture. The third position used to be a niche. It’s about to be a real option.

What you still give up: exit-by-acquisition, because acquisition requires the folder you refuse to hand over. Formal institutionalization, because nothing has been formally transferred to anyone. The ability to train juniors using your own materials, except in arrangements where you can actually enforce confidentiality — which most arrangements aren’t. And the appearance of belonging anywhere obvious — there’s no firm name on the business card. Many will look at that trade and decline it. That’s part of the story, not a weakness in it.

If you want a single image for the new labor market: the LinkedIn profile of 2028 reads “Samir Varma + 14 agents. References available.” And the agents have references too. The W2 worker’s profile reads “Samir Varma at FirmName. Agents licensed.” And the third worker’s profile, the one who built the folder and kept it private, just reads “Samir Varma” — because there’s nothing on offer. The folder is fine. It’s just not for sale.

The strongest counter, killed

The strongest objection to all of this is what I think of as the frontier-eats-the-stack worry. If the next-generation model is sufficiently smarter than the current one, your carefully written skills folder becomes obsolete. The plumbing-level skills — the ones that compensate for current-generation failures — get absorbed into the model itself. The moat is a mirage that recedes as the frontier advances.

Some of this is true. Current skills folders contain a lot of brittle, model-specific scaffolding that will indeed be obsolete in two years. Anyone writing skills now should expect a real maintenance burden as models change. There is no permanent skill, the way there is no permanent quant strategy.

But the structural argument survives. The skill is not a static asset; it is a continuously updated record of where this generation’s agents fail. As models improve, the failures shift, but they don’t disappear. The agent that no longer makes look-ahead errors in backtests will start making subtler errors in regime detection. The agent that no longer hallucinates citations will start misattributing them. The agent that no longer ignores codebase conventions will start over-applying conventions where they don’t fit. The work renews. It does not finish. And the orchestration and taste layer — the part of the skill that decides which failures are worth catching, which outputs are worth using, which corrections are worth codifying — does not depreciate at all. That layer is where the expert lives. It’s irreducibly yours.

To make the distinction concrete: the skill I wrote that catches look-ahead bias in backtests will become obsolete when models stop making look-ahead errors. The judgment that look-ahead bias was the failure mode worth writing a skill for does not. The first kind of skill is plumbing. The second is expertise.

The frontier eats the plumbing. It does not eat the expertise.

I won’t show you mine

Here is what I have told you. There is a thing called a skills folder. It is the externalization of expert tacit knowledge into a portable, configurable artifact. It is what makes one human-plus-agent more valuable than another human-plus-agent. It is also exactly the kind of artifact that creates the IP problem I walked away from in 2005, and that finance has been litigating over for the last decade and a half.

Here is what I am not going to tell you. Anything specific about what’s in mine.

The instinct to refuse, in 2026, looks like a personality trait. In two years it’s going to look like a labor-market strategy, and in five it’s going to look like the only sane labor-market strategy for anyone who built their folder themselves. The systematic traders saw this first. The lawyers and the doctors and the consultants are about to see it. There are excellent essays on the internet teaching you how to write skills, and the people writing them are doing good work. But look at who isn’t among them. There are no systematic quantitative traders out there walking you through their skills folders. There are no senior options market-makers publishing their factor libraries on Medium. The people who learned twenty years ago what externalized IP costs them are not the people teaching skills today. That absence is itself the lesson.

The first thing you are going to have to learn — same as I did, two decades ago — is that nobody who has one is going to show it to you.

You will have to figure it out yourself.

That, too, is the point.

If you found this essay worth your time, you may enjoy my book The Science of Free Will, which asks an equally uncomfortable question about an equally cherished story.

If you want to know more about India, start with The Paradox of India, the first essay — or browse the whole series.

Related country-specific work: Albion — Britain’s institutional decline, by someone who first saw the place in 1979.

Matt Levine has an intuitive model in Money Stuff. Bitcoin, he correctly keeps observing, has no cash flows. There’s nothing to discount, no earnings to capitalize, no rent to collect. The price is just… whatever people are willing to pay for it: it’s monetized attention. I personally find this hilarious and the joke seemingly writes itself roughly once a week with a different setup.

I’ve been reading Money Stuff for years, and at some point the joke stopped feeling like a joke and started feeling like a research design. Because here is the thing about “no cash flows”: if there really are no cash flows, then you cannot model Bitcoin’s price by capitalizing future cash flows. So either the price is unmodelable, or you have to model it using something else entirely. And the something else, when you sit down and think about what’s actually generating prices in a continuous limit-order market, is: arrivals and exits. Buyers showing up. Sellers showing up. Pressure accumulating. Pressure dissipating.

That is a queueing problem.

Which is convenient, because a Danish telephone engineer named A.K. Erlang worked out the math in 1909.

Erlang was trying to figure out how many telephone lines the Copenhagen telephone exchange needed. Calls arrive at random times. Each call lasts for some random duration. How many simultaneous calls do you have to be prepared to handle? Erlang’s answer turned into an entire branch of applied probability called queueing theory, and the workhorse model — the M/G/∞ queue — is exactly what you want for Bitcoin. M for Markovian (Poisson) arrivals. G for general holding-time distributions. ∞ because there’s no capacity limit; you can always have more pressure show up.

The mapping is direct. Replace “phone calls” with “latent directional pressure on the order book.” A long arrives — someone is leaning to buy — holds for some random time, then exits. A short does the same on the other side. The price moves with the imbalance. That’s it. No discount rate, no terminal value, no narrative about institutional adoption. Just arrivals, holding times, and exits.

(One clarification before any quant readers throw their coffee at the screen: these are not literal queues of customers, and they are not the visible limit-order-book queues. They are latent pressure states — the venue’s hidden buy-side and sell-side lean — inferred from trade prints under the standard M/G/∞ bookkeeping. The paper is explicit about that.)

(I’m aware this sounds suspicious. It sounded suspicious to me too, which is why the rest of the paper is 41 pages of seeing whether it actually works.)

The new paper, just out in the Journal of Risk and Financial Management, does exactly this for Binance spot BTC/USDT from 2020 through 2025 — five and a half years of the world’s deepest crypto venue, spanning the 2021 bull run, the FTX collapse, the ETF launch, and everything in between. The paper treats the latent buy and sell pressure as two independent M/G/∞ queues and asks what observable diagnostics fall out.

Three things do.

Variance per traded BTC. Call it R. It tells you how much price variance the venue is delivering per unit of trading activity. When R rises, you’re getting more volatility per BTC traded — meaning liquidity is thinning. It’s a microstructure tape-reader, in one number.

Effective mean-reversion rate. Call it θ. It tells you how fast directional pressure unwinds. Translate it into a half-life and you have a concrete answer to the question: after a shock, how long does it take for half the price displacement to bleed off?

A signed-flow proxy. Are buyers chasing recent up-moves? Are sellers leaning into down-moves? Is the flow contrarian or momentum-following? One coefficient gives you the sign and rough magnitude.

These three diagnostics share one bookkeeping system — they live on the same stock-flow accounting — which is the whole point of organizing the problem as a queue. Reduced-form market microstructure measures (Amihud illiquidity, Kyle-style impact) are essentially separate alarms wired to different bells. The queueing organization gives you a dashboard.

Now to the numbers.

Bitcoin’s resiliency half-life is roughly 10 to 25 hours.

Read that twice. Not seconds. Not minutes. Hours — and not few hours. On the deepest crypto venue on the planet, the half-life of price recovery from a directional shock is comparable to the time it takes to fly from New York to Tokyo.

For a venue whose marketing pitch is essentially “instant global liquidity, 24/7,” this is striking. Bitcoin doesn’t bounce back so much as slouch back. (The same diagnostic on a major equity index would be measured in minutes at most. The recovery profile here looks more like a thinly traded small-cap than a benchmark asset.)

Worse: when I run the same diagnostic on rolling 30-day windows across the five-and-a-half-year sample, only about 39% of windows are clearly mean-reverting under a modest statistical buffer. Roughly half are statistically ambiguous, and the remaining 12% have a point estimate that’s outright non-positive — meaning the diagnostic cannot even rule out a momentum regime. The venue moves between regimes; resiliency is not a venue constant.

This is exactly the picture you would expect from an asset that has no fundamentals to anchor it. Equity prices that get dislocated have an earnings stream to drag them back. Bitcoin’s price has order flow to drag it back, and order flow can decide to do anything it wants.

What about the obvious objection: “you’ve just renamed familiar liquidity measures.”

I expected this objection, so I spent a section addressing it. The standard trade-based liquidity benchmarks are Amihud illiquidity (price impact per dollar of volume) and a Kyle-style minute impact slope. I ran all three on the same 30-day rolling design over the same days and asked which one best sorts next-day tail risk — the probability that tomorrow’s largest hourly move lands in the top decile of the sample.

The queueing diagnostic separates next-day tail days at a high-minus-low spread of 8.6 percentage points. Amihud gets 4.7. The Kyle-style benchmark gets 1.1. The queueing-organized R is positively correlated with both — it isn’t unrelated to them — but it is clearly not the same number, and it sorts forward-looking tail risk roughly twice as sharply as Amihud and nearly eight times as sharply as Kyle.

I think this is because R is anchored in a coherent stock-flow accounting rather than a reduced-form ratio. The same M/G/∞ scaffolding that produces R also produces the resiliency and signed-flow diagnostics, and they all read off the same latent occupancy state. That’s the queueing tax. The dividend is that the diagnostics talk to each other.

(A small parametric overlay handles the heavy tails, because of course the symmetric core cannot fit Bitcoin’s 47-times-Gaussian excess kurtosis. The overlay improves the 99% Value-at-Risk breach rate from 1.80% under a rolling Gaussian to 1.13%. It still does not quite hit the 1% target — Bitcoin’s tails are a separate war — but it is a useful reduction.)

What this implies for normal markets

The cash-flow anchor in an equity is doing real work, and the half-life I measured in Bitcoin gives us a way to quantify how much. For a major equity index, the half-life of price recovery from a directional shock is measured in minutes — because every minute the price stays dislocated, there is an arbitrageable cash-flow value being violated. For Bitcoin, the only thing dragging the price back is more order flow, which is slow. That is why the half-life is 10-to-25 hours instead of single-digit minutes.

Read this as a quantitative measurement of how strongly fundamentals are anchoring a price. The half-life is the answer.

There is an obvious follow-on. In periods when fundamentals are not well-defined for an equity — earnings blackouts, news vacuums, crisis episodes when cash-flow estimates lose their informational value — the equity should start to look more like Bitcoin in its recovery profile. The framework predicts that. I do not have the cross-asset tick data sitting on my desk to run the experiment, but it is the obvious next paper. Anyone with the data, please go.

If you trade Bitcoin

Three things worth thinking about, with the usual caveat that these are diagnostics, not strategies. Use them to size; do not use them to enter.

First, rolling R is a warning system. When the variance-per-BTC moment is in its top third, your next-day probability of a top-decile hourly move is roughly 2.3 times what it is in the bottom third (15.1% versus 6.5% in the sample). When R is elevated, size down. If you market-make, widen.

Second, mean-reversion strategies are a regime trade, not a venue trade. Only ~39% of rolling windows are clearly mean-reverting; another ~12% are clearly not. A flat reversion strategy across all regimes is going to feed your edge to the periods when the model is wrong about the venue. The signed-flow proxy is the regime indicator: when it is high and θ is weak, you are in a momentum window. Do not fade it.

Third — the execution one — the 10-to-25-hour half-life tells you that if you move the price with a large directional order, do not expect the venue to “absorb” it in minutes. You are paying real impact for hours. Plan child orders, hedges, and stop placement accordingly. The 24/7 marketing slogan is true about the clock; it is not true about the absorption.

So: Matt’s model is also a research design. Bitcoin has no cash flows; therefore you cannot model it by discounting cash flows; therefore the entire price-discovery process has to live somewhere else; and the somewhere else turns out to be a queueing system that a Danish telephone engineer wrote down 117 years ago for a piece of analog infrastructure that no longer exists in the form he was studying.

I sent Matt a thank-you note when the paper came out. Intellectual debt to Money Stuff. A confession, while we are here. I personally find the entire edifice hilarious. Bitcoin has no cash flows, no terminal value, no rent — nothing but coordinated belief that the next person will also coordinate. I cannot quite believe this rubbish has value. But I am a libertarian: if people want to trade something with no fundamentals, they should be allowed to. That is what markets are for. I will not stop you. I will, however, do the math on your order flow, because the order flow is real even when the asset is absurd. The math doesn’t care whether you find the substrate ridiculous. It cares about the flow.

This is, I think, the patron-saint problem for finance writing done well. Matt’s job is to make sense of all of finance in several thousand words a day, every weekday, for years. He cannot stop and write the technical paper every time he notices the math underneath. He doesn’t have to. The observation does the work of identifying where the technical paper should be. Then it’s somebody else’s job to actually go write it. I am always going to try to be that somebody else, where I can. The physics PhD was supposed to be the boring substrate I left behind for three decades of trading. It turns out the substrate had things to say.

The paper is open access, free to read, and the Python that reproduces every number in it is on Zenodo. If you want to fight about it — and the Venn diagram of “people who hold strong priors on Bitcoin” and “people who hold them loosely” is a famously empty intersection — please at least bring the math.

Paper: https://doi.org/10.3390/jrfm19050372

If you found this essay worth your time, you may enjoy my book The Science of Free Will, which asks an equally uncomfortable question about an equally cherished story.

If you want to know more about India, start with The Paradox of India, the first essay — or browse the whole series.

Related country-specific work: Albion — Britain’s institutional decline, by someone who first saw the place in 1979.

A footnote on the timing of this post. The Preprints.org editorial team is celebrating their 10th anniversary with a #MyMostMemorablePreprint campaign, and Ellie Gao there nudged me to share the story behind a preprint. This is that story. The fact that the most memorable preprint (and now published paper) I have written started with a Matt Levine observation about Bitcoin having no cash flows is, I think, exactly the kind of origin story they were hoping to hear. #MyMostMemorablePreprint #PreprintsOrg10

I. The Building Floor

My former business partner invested in China.

His Chinese partner took the money. He protested. His Chinese partner pointed out the window.

“See that building? I built it with your money. The top floor can be yours.”

My partner, gently, said that wasn’t really why he had wired the funds. The Chinese entrepreneur was unmoved.

“Yes, and so?”

So my partner did what any grown adult in possession of a contract and a few good lawyers would do: he sued. In a Chinese court. And here is the part I want you to sit with for a moment: the court agreed that all the facts were in his favor. Every one of them. The court congratulated him on the quality of his case. And then the court left the money with the Chinese entrepreneur anyway, on the theory that the Chinese government did not want the capital flowing out of the country.

So: the facts were yours. The verdict was, in some sense, yours. The money was not yours.

You got a floor. In a building. In a city you didn’t live in.

My partner has since passed away. He died knowing he had been cheated. He died knowing the legal system had agreed he had been cheated. He died knowing that none of that mattered, that nothing was ever going to come of it, and that there was no further recourse, in this life, against the people who had taken his money. That is a very bitter pill to swallow at the end. It is the kind of bitter pill that I want any reader of this post to take seriously, before we get to the part where we laugh at hedge fund managers. Because everything that follows in this post — the data, the comedy, the booster-class roast, the civilizational thesis — sits on top of a small number of specific individual human beings who lost specific individual sums of money and never got them back, and who, in some cases, died waiting.

I have heard a lot of stories in my life, but this one has stayed with me, because it perfectly compresses something that took American capital roughly thirty years and several trillion dollars to discover: in China, the courts can rule for you and still rule against you. You will be told you are correct. You will be congratulated on the elegance of your arguments. And then you will be invited to look at the lovely building. And then, if you are unlucky, you will die before anything else happens.

For most of my adult life, the smart money on Wall Street believed the smart play was China. India was viewed as the slow uncle at the wedding — well-meaning, voluble, eventually charming, but somebody you certainly weren’t going to entrust with serious cash. China was the cool young cousin with the MBA and the gym membership. China was the future.

I have, for thirty years, told anyone who would listen one simple piece of investment advice: invest anywhere you want, but not in China. I was, for most of those thirty years, considered a crank. Slightly cracked. Probably resentful of Chinese success for ethnic-Indian reasons that I had not fully examined.

So let us now examine the people who were not considered cranks. Let us examine the smart money.

II. The Numbers Behind the Mistake

The data, before the comedy.

At the end of 2023, the US Bureau of Economic Analysis reported that the American direct-investment position in China stood at $126.9 billion. The corresponding figure for India: $49.6 billion. A ratio of 2.6 to 1. American capital had committed roughly two and a half times as much to the country actively designed to keep capital from leaving as it had to the country whose laws permitted capital to come and go through the front door.

This made no sense even on the simplest metric, return on capital. US-owned affiliates in India earned $7.9 billion in 2023, against $11.3 billion in China — meaning India produced about seventy percent of China’s income on thirty-nine percent of the invested stock. In 2023, and on the available evidence for a good while before it, India was simply the better trade per dollar deployed. Not by a little. By a lot.

Then, in 2023, the arrow finally turned. In the BEA data, new US direct-investment financial flows into India ($6.0 billion) exceeded new flows into China ($5.1 billion) for the first time. Separately, on China's own balance-of-payments measure, inward direct investment went negative in the third quarter of 2023 — minus $11.8 billion — the first negative quarterly reading in roughly twenty-five years of that series. Different official datasets measure FDI differently, and they do not always agree on the magnitude; China's Ministry of Commerce reports a higher "utilized FDI" number than the balance-of-payments series does. But the direction of travel is no longer ambiguous on any of them. The China premium is becoming an exit discount. India, meanwhile, crossed a cumulative trillion dollars in inward FDI in October 2024, and reported roughly $81 billion in inflows for the 2024-25 fiscal year.

Capital is supposed to be efficient. Capital is supposed to know things. Capital is supposed to vote with its feet. So what does it mean when the smartest capital on earth spent twenty-five years voting with its feet into a country whose system, on inspection, was specifically designed to prevent capital from ever voting with its feet again?

It means, technically speaking, that the smart money was full of shit.

Let us examine some of the relevant shit.

III. The Booster Class

The roll call of public China bulls over the past two decades reads, in retrospect, like a tour through the wing of an asylum reserved for people who got everything wrong but were paid extremely well to be wrong in expensive suits.

Jim Rogers. Co-founder of the Quantum Fund with George Soros. One of the most celebrated investors of his generation. In 2007, he moved his family from New York to Singapore specifically so his daughters would grow up speaking Mandarin — the air pollution in Beijing and Shanghai having put those cities out of contention as the actual destination. In the same year, he published A Bull in China: Investing Profitably in the World’s Greatest Market. I am not making the title up. There is also a sequel of sorts called Street Smarts, in which the bullishness continues unabated.

On India, Rogers was less effusive. In 2001:

“India as we know it will not survive another 30 or 40 years.”

In 2015, exiting the Indian market, he offered this elegant farewell:

“One cannot invest based on hope.”

On a CNBC interview in 2008, Rogers explained that he had moved to Singapore over Shanghai only because of pollution. Not because of the one-party state. Not because of the capital controls. Not because of the legal system that, as we shall see, regards foreign investors as a kind of voluntary tribute. The dictatorship was fine. The smog was unacceptable.

To his credit, by April 2024 Rogers had finally pivoted. He is now bullish on India. He cites Modi. He cites the convertibility issue. He is at long last on board with what some of us have been arguing since 1991. Welcome aboard, Jim. The party started thirty-three years ago. We saved you a chair.

Mark Mobius. Calls himself “the Indiana Jones of Emerging Market investing,” which is the kind of self-description that should disqualify a person from managing their own checking account, let alone other people’s. Ran Franklin Templeton’s emerging-markets desk for thirty years. Was, in his own publicity material, known for his bullish views on China.

Then, in March 2023, on Fox Business, Mobius reported the following discovery:

“I have an account with HSBC in Shanghai. I can’t take my money out. The government is restricting flow of money out of the country.”

This sentence is so good I want to needlepoint it onto a throw pillow. Notice the genuine surprise. I can’t take my money out. Mark Mobius has just been informed, in his late eighties, after a forty-year career as a celebrated emerging-markets specialist, that the People’s Republic of China — a one-party communist state that has imposed capital controls since approximately the Truman administration — is restricting the flow of money out of the country.

He went on:

“I can’t get an explanation of why they’re doing this... They’re putting all kinds of barriers. They don’t say: ‘No, you can’t get your money out.’ But they say: ‘Give us all the records from twenty years of how you made this money.’ This is crazy.”

This is crazy. Yes, Mark. This is crazy. This has been crazy for thirty years. The thing you wrote books about and earned management fees on for three decades was exactly this kind of crazy the entire time.

And then, having discovered — like Columbus, except with substantially less excuse — that the country was not arranged the way he had been telling everyone it was arranged, Mobius announced his new favorite emerging market:

“India... You’ve got a billion people, they can do the same thing that the Chinese do.”

Welcome aboard, Mark. We saved you Jim’s chair.

Mark Mobius died last month, on the fifteenth of April, in Singapore, at eighty-nine. He went out, by all accounts, bullish on India. The chair we saved him, in retrospect, was saved a bit late.

Stephen Schwarzman. Co-founder of Blackstone, currently $1.3 trillion in assets under management. In 2007, the Chinese sovereign wealth fund, China Investment Corporation, bought a non-voting minority stake in Blackstone's IPO — roughly 9.3 percent, carefully kept under the ten percent threshold that would have triggered a US national-security review. In 2013, Schwarzman announced a $100 million personal gift, plus a $200 million fundraising campaign, to build Schwarzman College at Tsinghua University in Beijing — a sort of Rhodes Scholarship for the China century. Its premise, in Schwarzman's own words:

“In the twenty-first century, China is no longer an elective course, it’s core curriculum.”

The advisory board includes Henry Kissinger, Condoleezza Rice, Hank Paulson, Robert Rubin, Colin Powell, Richard Haass.

Pause on what Schwarzman College actually represents. The largest internationally-funded philanthropic project in the history of the People’s Republic of China — over five hundred and seventy-five million dollars raised, with that advisory board reading like a roll-call of the postwar US foreign policy establishment — was created by an American private-equity billionaire to teach the next generation of Western elites that China was core curriculum. The college sits inside Tsinghua University. Tsinghua University is the campus where Xi Jinping went to school. The Schwarzman College buildings, designed by Robert A.M. Stern, sit on land that the Chinese state allowed to be allocated to a Western donor for the explicit purpose of credentialing future Western leaders inside a Chinese intellectual environment. The premise of the project was that the smart Western response to the rise of China was to deepen our understanding of it.

And while this enormous institutional bet was being placed in Beijing — over a decade, by some of the most credentialed figures in American public life, with the full and enthusiastic blessing of the Chinese state — the actual money was quietly moving in the opposite direction. Blackstone’s own internal reports were saying India. Not as a hedge. Not as a diversification play. As the best-performing market in the world.

“India has given us the strongest results across the world. And [I] am optimistic this [will] continue going forward.” — Stephen Schwarzman, Mumbai, March 2020.

In March 2025, Blackstone announced it intends to double its India exposure from $50 billion to $100 billion. By that point Schwarzman was personally announcing, in Mumbai, that he intended to put twice as much capital into India as Blackstone had ever put into China. The man who built the Rhodes Scholarship for the China century turned out, on inspection, to be a quiet bull on India. Possibly the largest one in the room. The hands and the mouth, working independently, possibly to the surprise of both.

And then there is Ray Dalio.

IV. Interlude — Ray Dalio Explains His Own Ideas

A short field trip.

Ray Dalio is the founder of Bridgewater Associates, the world’s largest hedge fund. He has written six books on Principles. He has, on multiple occasions, explained that he loves China — not metaphorically, but as a stated, formal reason for his investing decisions. In an April 2024 LinkedIn essay titled “To Answer the Question of Why I Invest in China,” the first reason Dalio listed was, in summary, love for China. Reason number two was that the country and its people were great. This is the man Wall Street took seriously on China for two and a half decades.

In December 2021, Dalio sat down with the economist Tyler Cowen — one of the most generous and well-prepared interviewers alive, a man who has a documented soft spot for India and who, when he asks you a question, has read every book on the table, including yours. What followed is, on the public record, instructive.

Exhibit A. Cowen asks the simplest possible question. The finance literature shows excess returns are very hard to predict. Dalio’s entire worldview is built on the idea that big macro forces — debt cycles, polarization, the rise of China — do predict where markets are going. So: do they? If yes, why doesn’t the research literature show that?

Dalio: “There are so many people who write finance papers, and then there are people who make money in the markets. I can’t speak for those who are writing the finance papers.”

Cowen presses. Dalio talks about debt cycles. Cowen presses again — if it’s publicly available information, why isn’t it already in prices?

Dalio’s final answer, in its entirety:

“Some can and some can’t. I guess you look at the track records over long periods of time, and you decide who can and who can’t.”

That is the response. The founder of the world’s largest hedge fund, asked to articulate his epistemic edge over the market, says: you look at the track records. The man who has written six books on Principles cannot, when politely asked, articulate a single Principle.

Exhibit B. Cowen, gently, hands Dalio what should be a softball:

“What is your favorite Chinese dynasty and why?”

Dalio has written a 500-page book that leans heavily on Chinese dynastic cycles. He has love for China. Surely he has a favorite.

Dalio’s answer runs over six hundred words. Across those six hundred words, he names zero favorite dynasties. He explains that he doesn’t really have a favorite. He gestures vaguely at “the Tang Dynasty, the Song Dynasty, the Ming Dynasty” without picking one or saying what he admires about any of them specifically. The closest he comes to substance is the observation that healthy dynasties had “education and civility” — a banality so spectacular it could be inscribed over the entrance of a community college lobby and would be considered, by the trustees, possibly too generic.

This is the man whose entire China thesis rests on a deep understanding of Chinese civilizational cycles. Asked to name his favorite Chinese dynasty, he cannot.

While Ray Dalio was being the smart money, Bridgewater’s flagship Pure Alpha fund underperformed the S&P 500 from July 2012 to August 2021 — nine consecutive years. In 2019, the leveraged version of Pure Alpha lost half a percent in a year that the S&P 500 returned 31.5 percent including dividends. Ray Dalio talks like the sage of the changing world order. Ray Dalio invests like a slightly underperforming index fund with substantially higher fees.

That is the level of intellectual seriousness Wall Street brought to the question of whether to send your retirement savings to Beijing.

V. What They Walked Into

Now, the system itself. Because this is where I want to be Milton Friedman for a minute, before going back to being Dave Barry. The Chinese system, on inspection, is not subtle. It was never subtle. The capture mechanisms have been hiding in plain sight for the duration. They are written in the law.

For most of the period from 1994 to 2022, a foreign automaker that wanted to build cars in China was required to do so through a joint venture in which the Chinese partner held at least half. GM-SAIC. Volkswagen-FAW. BMW-Brilliance. Toyota-FAW. Honda-Guangzhou. The ownership caps were lifted in stages — electric-vehicle makers freed in 2018, commercial vehicles in 2020, passenger cars in 2022 — but for the quarter-century that mattered, the rule held. The Western automakers brought the engineering, the manufacturing expertise, the brand, the supply chain knowledge, and the capital. The Chinese partners watched, took notes, and were given a quarter-century in which to absorb every transferable thing they could absorb. The five state-owned champions that emerged — SAIC, FAW, Dongfeng, BAIC, and Chang’an — now dominate the Chinese auto market alongside an indigenous ecosystem of competitors like BYD, Geely, Nio, and Xpeng that grew up in the shadow of these joint ventures, often staffed by engineers trained inside them. Today, in 2026, several of the original Western JV partners are being squeezed out of the Chinese market entirely, by Chinese champions raised on Western milk.

The same broad pattern appeared in aerospace. COMAC’s C919 is a Chinese aircraft, and the Chinese state is rightly proud of it — but its supplier stack leaned heavily on Western and Western-joint-venture components: engines from a GE-Safran venture, avionics from Honeywell, systems from Collins Aerospace. Boeing’s own joint venture with COMAC was a separate 737 completion-and-delivery center, not the C919. The point is not that Boeing secretly built China’s airliner. The point is subtler and more familiar: China built its competitor aircraft inside an ecosystem that Western firms had spent decades helping to assemble, and the Western firms handed over the components one purchase order at a time. Former US Deputy National Security Adviser Matt Pottinger told CBS News the C919 “looks like a knockoff.” It looks like a knockoff because a great deal of what is inside it came, by invitation, from the West.

Then there is the case of American Superconductor Corporation, which deserves a paragraph all to itself.

AMSC was a Massachusetts company that designed wind-turbine control software. Its largest customer was Sinovel, China’s biggest wind-turbine maker. In 2011, an AMSC engineer based in Austria, having been bribed by Sinovel, handed over the source code. When the theft surfaced, AMSC’s stock collapsed; the company lost the great majority of its market value over the following months, and laid off seven hundred people — more than half its global workforce.

In January 2018, a federal jury in Wisconsin convicted Sinovel of trade-secret theft. The court found AMSC’s losses exceeded $550 million. The maximum statutory fine the court was empowered to impose was $1.5 million. AMSC eventually settled for $57.5 million in restitution — roughly a tenth of what it had lost in a single trading session, and itself a recovery rate, on the court’s own damages finding, of about ten percent.

According to reporting from the time, Sinovel’s CEO Han responded to the early investigation by allegedly describing software as “like cabbage.” In China, software is like cabbage. You don’t pay for cabbage. You just take some.

This is the country into which Wall Street poured, by some estimates, eight hundred billion dollars in new investment between 2017 and 2020 alone.

And then there is the actual machinery of getting your money out. The People’s Republic operates a closed capital account, which is a technical term meaning that the State Administration of Foreign Exchange — SAFE, in a piece of regulatory nomenclature so on the nose it could have been written by a Jon Stewart writers’ room — must approve every outward flow of capital, dollar by dollar, audit by audit, year by year. Foreign companies wishing to repatriate profits must first pay twenty-five percent corporate income tax, then submit to a mandatory annual audit, then permanently allocate ten percent of their after-tax profits to a “Surplus Reserve Fund” — locked away in renminbi, in China, until that fund reaches half the company’s registered capital — and finally secure SAFE approval, which can take months. Dividends may only be remitted once per fiscal year.

If you try to route around this with intercompany service fees or royalties, you trigger an anti-avoidance audit. Anti-avoidance audits take years to resolve. While they are being resolved, you may not be permitted to leave the country.

Which brings us to exit bans. A 2024 Stanford Journal of International Law paper documented 128 foreigners — twenty-nine Americans, forty-four Canadians, the rest from elsewhere — placed under exit bans in commercial disputes between 1995 and 2019. The number is now believed to be much higher. The civil-society group Safeguard Defenders reports that exit-ban mentions in China’s official court database rose from fewer than five thousand in 2016 to thirty-nine thousand in 2020, and have doubled annually since.

A partial list of recent named cases, all of them foreign business executives:

• Chenyue Mao, a managing director at Wells Fargo, banned from leaving China in July 2025. Allowed to return only in September 2025, after high-level US-China negotiations.

• An Orange County businessman, the so-called “Horowitz” case from 2011, banned from leaving China after rejecting a defective Chinese supplier’s product. Released only after his wife wired $250,000 to the supplier whose product he had refused.

• Richard O’Halloran, an Irish executive, held in China for three years between 2019 and 2022 over a Chinese partner’s fraud that he had not personally committed and could not have prevented.

There are many more. Bankers from Nomura. A US Patent and Trademark Office employee, on personal travel with his family. The five Chinese staff of the American due-diligence firm Mintz Group, detained for two years between March 2023 and March 2025. The firm itself was fined $1.5 million for “unauthorized statistical investigations” — an offense which, by the standards of jurisprudence, has the same precise definition as “vibes the state did not like.”

A commercial-law professor at Columbia, writing on the school’s Blue Sky Blog in 2024, summarized the operational reality this way: an exit ban can be enforced against an individual executive of a US company when an ordinary commercial dispute arises. The Chinese company involved can ask a court to impose the exit ban through a formal legal process that occurs unbeknownst to the party whose exit is to be barred.

You only discover you cannot leave when you try to leave. Or, as the Eagles put it in a song that, on reflection, may have been about the People’s Republic of China all along: you can check out any time you like, but you can never leave.

And then there was Evergrande. The Chinese property developer, formerly the country’s largest and a perennial fixture in JPMorgan’s emerging-market bond indices, defaulted in 2021 with $300-plus billion in liabilities. Foreign bondholders held about $19 billion of the offshore exposure. The bonds traded at 2.25 cents on the dollar in late 2023. A Hong Kong court issued a winding-up order in January 2024. The order has “limited recognition” in mainland China — where ninety percent of Evergrande’s $242 billion in assets are physically located. Effective recovery for foreign creditors: approximately one cent on the dollar.

If you lent Evergrande a million dollars, you got back enough for a moderately priced lunch in midtown Manhattan.

This is what Wall Street, for a decade, called China’s investment-grade bond market. This is what Ray Dalio called China being tremendously successful in many different ways.

VI. The Country the Smart Money Missed

Now, for contrast, India.

India’s bureaucracy is real. India’s tax authorities are aggressive in a way that produces, on a recurring basis, international incidents and full-blown investor revolts. India self-inflicted, in 2012, a retrospective-tax amendment that effectively reached back five years to tax Vodafone’s already-completed acquisition of Hutchison’s Indian assets. The amendment also caught Cairn Energy, the British oil company. The international investor community lost its mind. Both companies took India to international arbitration. This is the kind of self-inflicted regulatory wound that, in many countries, would simply end the conversation about foreign investment for a generation.

And then something happened that has not happened in the People’s Republic of China in living memory.

Vodafone won at the Permanent Court of Arbitration in 2020. Cairn won $1.2 billion plus interest at the PCA in December 2020. India initially refused to pay. Cairn began enforcement actions, seizing Indian government-owned property in France. And then, in August 2021, India did what no closed-capital-account state ever does. The Government of India repealed the law. The Government of India paid. Cairn received roughly ₹7,900 crore — approximately a billion dollars — back in its bank account. Seventeen companies caught in the retrospective tax dragnet were absolved.

The system bent. The system paid. Because the system, however maddening on a day-to-day basis, is one in which international arbitration awards eventually have to be honored. India is a country in which, after the tax authority has tortured you for nine years, the courts may eventually rule for you. And when they do — and this is the part that should be carved into a wall somewhere on Wall Street — you actually get your money.

Compare this to Evergrande. Compare this to AMSC. Compare this, especially, to a man who died in his own bed in his own country knowing that a Chinese court had agreed he was right and that none of that had ever come to anything.

In India, you are slowly audited but can fight and get your money. In China, you are slowly eaten and will have nothing left to get.

The roster of foreign-corporate India operations that have run, without ever having a Mintz Group experience, is now extensive. Walmart bought seventy-seven percent of Flipkart for $16 billion in May 2018 — the largest e-commerce M&A in Indian history. The Competition Commission of India approved it, despite vocal opposition from domestic trader associations. Imagine, for a moment, the Chinese state regulator approving a $16 billion American acquisition of the leading Chinese e-commerce platform. Spend a minute with that mental image. Take your time.

Walmart has since spent another $3.5 billion to take its stake to roughly eighty percent. Amazon has invested approximately $11 billion in India to date, with $15 billion more committed by 2030. Google announced a $10 billion India Digitization Fund in 2020. Microsoft is in the middle of a multi-billion-dollar Azure expansion. Apple's revenue in India grew 36 percent year-over-year to roughly $8 billion in FY24, making India Apple's fastest-growing major market. Foxconn is scaling iPhone assembly in Tamil Nadu at a pace that has begun to alter Apple's global manufacturing geography.

None of these companies has had to wire $250,000 to a domestic supplier to recover a missing executive.

There is one more observation worth pausing on, because it is the consumer-side expression of the same civilizational difference.

Nike’s revenue in China is down twenty-eight percent from where it was five years ago, even as China’s overall sportswear market has boomed. Nike’s stock just hit a decade low. The company has laid off fourteen hundred people and jettisoned its longtime China leadership. Starbucks has sold a majority stake in its China operations to a local partner. Guess closed all of its more-than-one-hundred-fifty Chinese stores this past March. American automobile brands have been hammered by Chinese EV companies. Anta, formerly a domestic Chinese shoe brand, is now the largest shareholder in Amer Sports — which is to say, the Chinese athletic conglomerate owns Arc’teryx and Wilson. The high-end Western brands Americans buy at REI are, beneath the logos, Chinese. (The Nike data, and a great deal more, comes from a recent Wall Street Journal investigation — well worth reading in full.)

The proximate cause is a Chinese consumer-side phenomenon called guochao — a state-amplified wave of cultural-pride-driven preference for domestic over foreign brands, occasionally weaponized into formal boycotts when a Western company says something the state does not like about Xinjiang or Tibet or Taiwan. Foreign brands in China now operate in a market where loving the domestic brand is patriotic and buying the foreign brand is, depending on the political weather, mildly suspect.

In recorded Indian history, with one exception detailed below, no government — including the current Modi government, which is the most assertively Hindu-nationalist administration modern India has produced — has ever told Indian consumers that buying foreign brands is unpatriotic. Apple’s revenue in India is growing thirty-six percent a year. KFC operates extensively across the country. McDonald’s, Domino’s, Subway, Starbucks (yes, the same Starbucks that just sold its China business), Levi’s, Zara, H&M, Uniqlo, Nike, Adidas — all of them operate, profitably, in Indian cities. Mumbai is full of Mercedes-Benz dealerships. Delhi is full of BMW showrooms. India certainly has nationalist economics — swadeshi sentiment, “vocal for local” campaigns, periodic boycotts of Chinese goods in particular. What India has never produced is a durable, state-amplified consumer movement of the Chinese kind: the kind that can turn a global brand into a patriotic liability overnight and keep it that way. There is no durable Indian guochao, there has never been one, and there is unlikely ever to be one, because Indians want the best product available, do not particularly care where it comes from, and have been consistent on this for a very long time. The Indian state can do many things. It cannot turn off the Indian consumer’s millennia-old willingness to buy whatever is good, from whoever is selling it, at whatever price clears.

The one period in modern Indian history when Indians were not allowed to buy foreign brands was the era between roughly 1969 and 1991 — when the Indian state, briefly persuaded that Soviet-style autarky was the path to prosperity, expelled Coca-Cola and IBM in 1977, restricted imports through the Foreign Exchange Regulation Act, and tried to manufacture everything domestically behind a wall of tariffs and the License Raj. The result was a national catastrophe. Indians spent twenty-two years standing in line for badly made domestic substitutes they did not particularly want, while their cousins overseas drove imported cars and drank imported soft drinks. The 1991 reforms — which I have covered at length in Posts 19A through 19C — stuck, and have stuck for thirty-five years now, against the explicit ideological preferences of every successive Indian government including the present one. They stuck not primarily because economists won the argument but because the Indian population had personally experienced the consequence of being forbidden to buy foreign brands and had no interest in repeating the experience.

The political mechanism by which Coca-Cola was kicked out of India is worth a brief detour, because it illustrates the breadth of the failure across the Indian political class. The Foreign Exchange Regulation Act — the law that required foreign companies to dilute their Indian operations to forty percent local equity, and that demanded, in Coca-Cola’s case, that the secret formula itself be handed over to an Indian state-monitored body — was passed in 1973 by Indira Gandhi’s Congress government. Coca-Cola refused to comply and continued negotiating. Two years later, in 1975, Mrs. Gandhi declared a national Emergency and suspended Indian democracy outright. Two years after that, in 1977, the Indian electorate threw her out in favor of a coalition — the Janata Party — that had been specifically organized to reverse her authoritarian excesses. The new Prime Minister, Morarji Desai, and his Industry Minister, George Fernandes, then proceeded to take office and not undo FERA but, instead, to enforce it more strictly than Mrs. Gandhi had ever bothered to. Coca-Cola and IBM were given ultimatums. Both refused to comply. Both left.

The timeline, summarized, reads: the ruling party writes the rule, suspends the constitution, loses the next election to the people who had organized themselves around the explicit promise of fixing her, and the new government — having taken office on a wave of national revulsion against Mrs. Gandhi’s overreach — then keeps one of her economically dumbest rules and uses it to expel Coca-Cola. This is not one party’s blunder. This was the entire Indian political class, across every available faction, displaying a remarkable bipartisan inability to operate within their own civilization’s five-thousand-year-old operating system. The civilization had run the largest trading economy on earth. The civilization had been operationalizing Ricardo since the Mauryan period. None of that information appears to have penetrated to the Lok Sabha in either 1973 or 1977. Both parties, in succession, failed at being India.

Indians remember what closure tastes like. It tasted like Campa Cola. It could, alternatively, have tasted like the urine of the Prime Minister of India — Morarji Desai, who expelled Coca-Cola from the country in 1977 and who, in his spare time, drank his own urine daily for health and recommended the practice to Dan Rather on 60 Minutes in 1978. The official position of the Government of India in the late 1970s was, on the available evidence, that piss was healthier than sugar. Indians, alone in the world in this assessment, respectfully disagreed. They have been disagreeing, in increasing volume, ever since. They are not eager to find out what closure tastes like again.

That is, in significant part, why foreign capital makes money in India and is permitted to leave with it. The Indian consumer demand for foreign products is itself a civilizational artifact older than the Indian state. The state cannot suppress what it did not create — and the one time the state tried, the population eventually voted, in 1991, to never let the state try again.

The Chinese state, by contrast, can and does turn consumer behavior on and off like a faucet. Capital in China cannot leave because the state will not let it. Foreign consumer brands in China cannot earn because the state has decided they should not. The two phenomena are, civilizationally, the same phenomenon — and a five-thousand-year-old open trading society would never tolerate either.

VII. The Pattern Recognition Problem

Here is the thing I think actually explains the whole story.

The smart money on Wall Street has not been dealing with the Chinese for very long. A few decades, in earnest. Long enough to write some books with foolishly confident titles. Not long enough to recognize a pattern.

The Indians have been dealing with the Chinese for a very long time. The Indian merchant class has been trading along routes that touched Chinese trading partners since the early days of the Silk Road — at least the second century BCE, well before there was a country called the United States, before there was an England, and roughly contemporaneously with the Roman Republic. Indian traders along the Silk Road, Tamil merchants in Southeast Asia, Marwari and Gujarati traders moving silk and spices across the Indian Ocean, Parsi merchants in Hong Kong and Canton in the eighteenth and nineteenth centuries — there is no generation of Indian commercial life in living civilizational memory that has not, at some point, encountered the Chinese system.

Here are two facts that the modern China booster ought to know and, on the available evidence, does not. The English word Mandarin comes, through Portuguese mandarim and Malay menteri, from the Sanskrit mantrī — “counselor,” “minister of state” — and entered English in the 1580s. The English word China has a more contested path; most authorities trace it ultimately to the Qin dynasty, but the form that carried that name westward, into Persian and onward to Europe, was the Sanskrit Cīna. Both words are roughly five centuries old in English, and both reached English through the channels of Indian and Indian-Ocean commerce rather than through any direct European contact with China. The Westerner who today announces that he is bullish on China, and that he is having his children learn Mandarin, is — without knowing it — describing his enthusiasm in vocabulary that came west along Indian trade routes. India did not need Goldman Sachs to introduce it to China in 1997. India had been the West’s interpreter of China since before the East India Company was chartered. If you want to understand how China treats the outsider who arrives to do business, the people to ask are not the ones who showed up in the 1990s with a Bloomberg terminal. The people to ask are the ones who supplied the vocabulary.

And the Chinese system, on every one of those encounters, has done what the Chinese system does. It captures. It absorbs. It treats foreign commerce as a temporary accommodation to be terminated at the convenience of the state.

Consider the Canton System, which the Qing imposed from 1757 until the First Opium War in 1842. Western traders — British, Dutch, French, eventually Americans, and a substantial Parsi contingent from Bombay — were restricted to a single port, Canton (today’s Guangzhou). Within that port they were confined to a small physical compound on the riverbank, the “Thirteen Factories.” They were forbidden from learning Chinese, from traveling inland, from staying in the country during the off-season, and from doing business with any Chinese merchant who was not a member of a small state-authorized cartel called the Cohong. The Cohong set the prices. The Cohong arbitrated the disputes. The Cohong reported, when asked, to the imperial bureaucracy. The structural parallel to today’s foreign-investment regime — the negative list, the SAFE controls, the exit bans, the requirement to do business through politically vetted intermediaries — should be obvious to anyone willing to look. The People’s Republic is not inventing anything new. It is running a playbook that is roughly two and a half centuries old, with better software.

The Parsi merchants of nineteenth-century Bombay — the Wadias, the Sassoons, the Jejeebhoys — traded with China extensively, opium for tea, cotton for porcelain, silver for silk. Sir Jamsetjee Jejeebhoy went to China five times. He made one of the largest private fortunes in pre-modern India. He was knighted for it. And he came home with the money. Please note: he came home with the money. He did not buy a building in Canton. He did not set up a permanent Jejeebhoy Office in Shanghai with a five-year lease and a politically connected local partner who would, when the lease expired, point at a building. He went, he traded, he left. Every generation of Parsi merchants for the next century repeated this lesson. The Sassoons did the same. The Wadias did the same. There is a reason these families’ fortunes still exist in Bombay today and not in Guangzhou.

It is also worth pausing on what those fortunes funded. The Bombay Stock Exchange was founded in 1875, which makes it the oldest stock exchange in Asia. It predates the Tokyo Stock Exchange by three years. It predates the Shanghai Stock Exchange by one hundred and fifteen years — Shanghai’s modern exchange opened in 1990, when the People’s Republic of China decided, after forty years of revolutionary anti-market posturing, that perhaps capital markets were not entirely a bourgeois invention. The Shenzhen Stock Exchange also opened in 1990. China’s national capital-markets infrastructure is, in calendar terms, younger than my own children. The BSE is older than my great-grandfather.

And the 1875 founding date understates the case considerably. The BSE was the formal institutional crystallization of merchant-banking and credit arrangements that had been running, in continuous operation, on the Indian subcontinent for centuries before that. The hundi system — the indigenous bill-of-exchange network operated by Marwari, Gujarati, Multani, Bohra, and Chettiar bankers from Kabul to Rangoon — was a fully developed transnational credit instrument by the Mughal era and traces, in its earliest forms, all the way back to the Mauryan and Gupta periods. Which is to say: Indians were running written bills of exchange before England existed as a country. Surat was a recognized banking center in the 1600s, when London was still figuring out joint-stock companies and Amsterdam had only just invented the modern stock exchange. The Jagat Seths of Bengal financed armies and emperors in the eighteenth century; the East India Company itself was, for substantial periods, dependent on Indian banking houses to clear its own transactions. India did not learn capital markets from the British. The British learned a great deal of practical credit management from doing business inside India’s existing one.

Ricardo did not invent comparative advantage. Ricardo described what the Indian merchant class had been operationalizing for two millennia: the principle that everyone gets richer when each region produces what it produces best and trades for the rest. By the early eighteenth century, this had made India approximately twenty-five percent of world GDP — alternating in pole position with China across the imperial centuries, and at points the single largest economy on earth. (I have written about that twenty-five-percent figure in earlier posts.) That share was not extracted from anybody. It was the cumulative output of an open trading civilization doing what open trading civilizations do: selling textiles and spices and steel and gemstones and ships to the world, importing what the world had that it did not, and getting richer over the course of fifteen hundred years. The British did not come to India because India was poor and they wished to develop it. The British came to India because India was rich and they wanted some.

Which means: when the Wall Street fund manager of 2007 looked at India and saw “an emerging market” and looked at China and saw “the future,” he was operating under a fairly stunning misunderstanding of which of the two civilizations had the longer continuous tradition of running actual capital markets. The answer is India, by a margin of approximately three centuries and not really close. The Indian merchant class did not need to be taught what a security was. The Indian merchant class was already doing this in turbans, by hundi, in the seventeenth century, while Europeans were still debating whether interest constituted usury. The Indian capital market is not a 1991 invention or a 1991 recovery. It is a thing India has been doing continuously, with brief state-sponsored interruptions, for roughly four hundred years. The 1991 reforms simply removed the regulatory bandage that the Nehru-Indira state had put on top of the existing system, and the existing system — which had never actually died, only gone underground into informal networks of money-changers, hawala operators, and family-based credit lines — promptly resurfaced and went public.

The Chinese state has, by contrast, no equivalent continuous tradition. The Qing dynasty did not have public capital markets in the modern sense. The Republican period (1912-1949) produced a brief and chaotic flowering of one in Shanghai, which Mao closed in 1949 as a matter of explicit ideological commitment. Forty years later, when the People’s Republic decided it needed capital markets again, it had to build them from scratch — with no living memory of how to run one, no continuous merchant-banking tradition to draw on, and no civilizational disposition that regarded foreign profit-taking as anything other than a temporary indulgence. China’s capital market is forty years old. India’s is, depending on how you count, between one hundred and fifty and four hundred. Both countries have stock exchanges now. Only one of them has a civilizational understanding of what they are for.

The Indian merchant class has absorbed this pattern across generations the way Inuit children absorb the names of the kinds of snow. It is in the muscle memory of the civilization. Any Marwari trading family of any standing would have, in living oral tradition, multiple cousins-of-cousins who had tried to do business in China and come back with a building, or come back with nothing, or not come back at all. The thing the smart money discovered in 2023 — that there is a meaningful, structural, civilizationally-rooted difference between “letting foreigners come and trade” and “letting foreigners come and leave with their winnings“ — was known to my grandmother. It was known to her grandmother. It was known to a substantial fraction of the merchant subcastes of pre-colonial South Asia.

I have, since the early 1990s, told anyone who would listen that this was the wrong trade. I was, for most of those years, treated as a man with an ethnic axe to grind. The axe, it turns out, was a useful piece of inherited equipment.

The thing about pattern recognition, which is what civilizations are for, is that it does not scale through books. Stephen Schwarzman cannot fix his thirty-year-old pattern-recognition problem by endowing a college at Tsinghua. Ray Dalio cannot fix it by reading five hundred years of European empire history. Mark Mobius cannot fix it by being good at cocktail-party storytelling about Indonesian copper mines.

The pattern is in the bones. You either grew up in a civilization that has been on the receiving end of this particular operating system for the better part of two thousand years — in which case the move is obvious from across the room — or you did not, in which case you are going to send your money to Shanghai and a man is going to point at a building.

The East India Company is the standard answer to “well, but India did get colonized, didn’t they?” Yes. India did get colonized. By a British trading company that started in 1608 as a small commercial concession and ended, two and a half centuries later, as the de facto government of the subcontinent. India did not see that one coming. India was, that time, the slow learner.

But India did learn one specific thing. The lesson was not “be open to foreigners,” because India was open to foreigners well before the East India Company arrived and remained open to them well after the British left. India had been open to Greeks, Scythians, Huns, Arabs, Mongols, Persians, Portuguese, Jewish refugees, Zoroastrian refugees, Tibetan refugees, and a substantial number of confused British backpackers, for two millennia before 1608, and continued to be open to all of them after 1947. The openness was not a lesson. The openness was, and is, the operating system.

The thing India learned from the East India Company episode was narrower and more specific: do not let a foreign commercial entity capture the state. Take their investment, tax their profits, make them obey your laws, but do not — under any circumstances — allow a private foreign corporation to acquire its own army and start collecting your revenues. The Indian state in 2026 has many features that an outside observer might reasonably find objectionable, but the one feature it does not have is foreign-corporate sovereignty. Apple cannot raise a regiment. Walmart cannot collect the land tax. The mistake from 1757 to 1857 was not openness. The mistake was openness without sovereignty. India fixed the second part. The first part was never broken.

India then proceeded, in its characteristic way, to make other enormous mistakes — the forty-year socialist autarky described above being the most consequential. Through all of that, the civilization was open. The state was, for a while, deranged. The civilization waited the state out. In 1991, the state corrected itself, partially because the foreign exchange reserves had run down to two weeks of imports and there was no further choice. The civilization always was, and is, open. The state’s forty-year closure was a self-inflicted exception, now corrected.

China’s trajectory has no equivalent of this pattern. China did not open in 1608, get colonized, close down in 1949, briefly re-open in 1979, and remain genuinely open thereafter. China was a closed civilization before the Opium Wars. China was forcibly pried open by Western military violence in the nineteenth century, which it never accepted as legitimate. China resealed itself under Mao. China then announced a “reform and opening” in 1979 that was always — always — designed to be reversible, controlled, sectorally bounded, and revocable at the discretion of the state. The negative list is not new. The capital controls are not new. The exit bans are not new. The hostility to foreign profit-taking is not new. They are continuous with five centuries of Chinese statecraft, briefly interrupted by the period in which the West was strong enough to force the door open and is now strong enough no longer.

China was not open and is not open. China was open in the way a department store is open: you may come in during business hours, browse, purchase under the supervision of staff, and leave through the marked exit. You may not move in. You may not take inventory home. You may not stay past closing. The store reserves the right to refuse service and to keep your coat.

India was open and is open in the way a port city is open: people arrive, people leave, some stay, some get rich, some get robbed, the harbor accepts all comers and the city absorbs whatever the harbor delivers. The British arrived and stayed too long and India had to evict them. That eviction was a correction, not a closing. The door was open before they arrived and the door was open after they left.

One civilization is, civilizationally, a port city. The other civilization is, civilizationally, a department store. Both have been what they are for centuries. The state’s behavior in each case is downstream of what the civilization is.

To put it without the metaphor: the difference is not that one society likes money and the other does not. Everyone likes money. China likes money ferociously. The difference is whether the foreigner is understood as a guest who may leave with his winnings, or as a resource to be managed until the state decides the game is over. India, for all its bureaucratic cruelty, has consistently treated the foreign trader as the first thing. China, for all its reform-era hospitality, has consistently treated him as the second.

The point of this entire post is that the consequences of those two civilizational dispositions are completely predictable, that they have been predictable for centuries, and that a generation of fund managers paid millions of dollars a year to predict things spent thirty years not predicting this one. They were not making a hard call wrong. They were making an easy call wrong. The signal had been transmitted, continuously, for half a millennium. They were not listening for it.

The smart money was not the smart money. It was the inexperienced money, with a Bloomberg terminal.

VIII. The Fifty-Year Bet

Here is what I actually think.

In fifty years, India will be a richer and more prosperous country than China. Not slightly richer. Meaningfully richer. Not in the conventional emerging-markets-catch-up way, in which a low-base country runs faster than a higher-base country for a few decades and then converges. In a structural, civilizational, this-is-how-the-two-countries-are way. I will not be alive in fifty years to be proved right or wrong about this. I am, nevertheless, willing to be on the record now, in 2026, because I have been on the record about everything else in this post since the early 1990s and the only thing that has changed is that the rest of the world is, slowly, catching up.

The inversion has already happened, mechanically. The 2023 numbers tell you the truth in the antiseptic language of the Bureau of Economic Analysis: new US flows to India (6.0billion)exceedednewflowstoChina(5.1 billion) for the first time in the data series. On China’s balance-of-payments measure, inward direct investment turned negative in the third quarter of 2023 and the full-year figure collapsed from its 2021 peak to a fraction of it — the steepest sustained decline in the history of that series. The American Chamber of Commerce in Shanghai’s 2025 survey reported that twelve percent of US firms now rank China as their headquarters’ top investment destination, the lowest figure in the survey’s history. The AmCham China business climate survey for 2025 found only forty-nine percent of US companies expected to be profitable in China that year. Blackstone announced in March 2025 that it would double its India exposure to $100 billion. Even Jim Rogers, in April 2024, finally turned bullish on India.

When Jim Rogers has turned bullish on you, you can be reasonably confident that the trade is no longer contrarian. But that is not the trade I am describing.

The trade I am describing is louder and longer and considerably more lucrative for whoever has the nerve to take it seriously. India will, by 2076, be a richer country than China. Let me be precise about how bold that is. As I write, China’s GDP per capita is roughly five times India’s. China is, today, far ahead — richer, better-built, with newer airports, faster trains, and a manufacturing base India cannot presently approach. Anyone betting on India over a fifty-year horizon is betting against an enormous current lead. I am making that bet anyway, on the record, in 2026. India’s economy will, by the middle of this century, almost certainly be the third largest in the world by nominal GDP, and possibly the second. India’s per-capita wealth will, by 2076, exceed China’s. The reasons are exactly the reasons this entire series has been laying out for twenty installments: a young population, a working democracy, a functional (if maddening) court system, an English-speaking professional class that has placed Indians at the head of a remarkable number of America’s largest companies, an open civilizational disposition that absorbs and metabolizes foreign capital and talent rather than caging it, and a state which — for all its absurdities — does not as a matter of policy disappear foreign executives who write the wrong email. China’s current lead is real, and it is vast. My wager is that it is also, on a fifty-year clock, perishable.

China has, by contrast, an inverted population pyramid that is now in irreversible structural decline, a one-party state under Xi that has spent five years systematically destroying its own most productive sectors, a real estate bubble whose final accounting has not yet been fully done, a youth unemployment rate that the state has, at various points, simply stopped publishing, and a capital regime that has, as this post has documented at length, made it clear to the entire planet that money sent into China is no longer money that may, with full assurance, leave China.

The “smart” money is, in 2026, beginning to act on all of this. Quietly. The Apple manufacturing build-out in Tamil Nadu. The Foxconn factories. The Blackstone India fund. The Walmart-Flipkart hold. The Google Digitization Fund. The Microsoft Azure expansion. The Amazon billions. Every one of these companies is, right now, this quarter, voting with very real money for the thesis that India is the next quarter-century’s growth story. Almost none of them are saying so on television.

This is the actual smart bet. And it is being placed, in size, by the people whose money it is — even when the people whose mouths it is do not yet say so. The question I find myself wondering about, watching this from a desk in Connecticut, is not whether the bet is being placed. The bet is being placed. The question is: how long is it going to take before the smart money gets loud about it?

Because the day the smart money gets loud about India is the day the trade is over for whoever waited for the loudness. The trade is happening now, while Ray Dalio is still writing essays titled “To Answer the Question of Why I Invest in China.” The trade is happening now, while Schwarzman College is still graduating cohorts of Westerners credentialed in the China century. The trade is happening now, in 2026, while the cover stories of the major business magazines are still about Xi Jinping. The capital that gets there before the noise is the capital that captures the run. The capital that arrives after the cover stories arrive late.

I have, since 1991, been on the record. Quietly, in dinner conversations. Loudly, in this Substack, for the past two years. The smart money is, in some sense, finally catching up — which is to say, finally acting. The remaining gap is between what the smart money is doing and what the smart money is saying. I am writing this post, in part, to try to close that gap by a few weeks. The act and the word should move together. The act has moved. The word is dragging.

I will not be alive in 2076 to see India become a richer country than China. I would like, very much, before then, to be alive to see the people who run the world’s largest hedge funds say out loud the thing their own internal investment committees are already, this quarter, voting for. I would like that to happen on a stage. With cameras. I would like it to be the moment Ray Dalio is asked, on a podcast, what his favorite Indian dynasty is, and is not able to come up with a single one, because he hasn’t done the homework yet, again.

We will probably get there. I just hope we get there fast enough that some of the people who have been right about this for thirty years are still around to enjoy it.

Not all of us will be.

IX. The Building Floor, Revisited

My partner, as I mentioned at the beginning of this post, is no longer with us. He died knowing he had been cheated. He died knowing the Chinese court had agreed he had been cheated. He died knowing that none of that was ever going to be made right. Somewhere in some Chinese city, there is a floor of a building that was, in some technical and entirely useless sense, his. He never saw it. He never wanted to see it. He flew home empty-handed once and he never went back. The capital he had wired into the country stayed in the country. The principle of the thing stayed with him until the end. That is a long time to carry a bitter pill, and it is the kind of pill that does not stop being bitter just because the person carrying it has stopped being alive to taste it.

Compare him to Cairn Energy. Cairn lost $1.2 billion to the Indian retrospective tax. Cairn sued in international arbitration. Cairn won. Cairn began seizing French government property held by the Indian state. India, having been thoroughly embarrassed, repealed the law and wrote a check for approximately a billion dollars within a year. Nobody at Cairn died waiting.

This is the choice the smart money spent thirty years getting wrong.

One civilization will offer you a floor in a building, and then it will outlive you, and the floor will still belong to the man who took your money.

The other civilization will, eventually, after enough screaming, write you a check while you are still alive to cash it.

If you want to know which country is going to be rich in fifty years, do not look at where the smart money is currently parking. Look at where the smart money eventually escapes back to with whatever is left of its principal. The smart money, in retrospect, has not been very smart. But the dumb money — the bureaucratic, exasperating, infuriating, court-clogged, audit-happy Indian capital market — has, quietly, been giving people their money back at the end of the dispute. Sometimes after nine years. Sometimes after a dozen. But back, while the people are still here to receive it.

That is what a civilization that wants foreigners to make money looks like, even when it is simultaneously doing everything in its power to make them sweat for it. The sweat is real. The exit is also real. The check, eventually, clears.

For thirty years, I have told anyone who would listen: invest anywhere you want, but not in China. I was a crank. I am, in 2026, slightly less of a crank. The Mark Mobiuses of the world have, belatedly, joined the chair-shortage in the room where the cranks meet. The room is more crowded than it used to be.

There is one chair, in that room, that will always be empty. It belongs to a partner of mine, who did not get to be in the room when the rest of the world figured out what he had figured out the hard way, with his own money, in his own lifetime, in a Chinese courtroom. He is, in some final sense, the reason this post exists. The capital he lost was, in the grand scheme of global FDI flows, not very much. The principle of the thing was, and is, a great deal.

We have a few extra chairs. Henry Kissinger is no longer with us. Mark Mobius, sadly, joined him last month — he died in Singapore, bullish on India to the end, which is the way one would want to go out. We cannot save him a seat. But Kissinger’s blurb-recipients are increasingly welcome. Larry Summers may show up any time. Ray Dalio, if he is reading this — and one assumes that a man who has written six books about his own Principles has a Google Alert for his own name — Ray, the door is open. We have a chair for you too.

Just leave the book on the way in. We have read it.

And on the way in, perhaps, walk past the empty chair. Spend a moment with it. Think about the man who should have been sitting in it. Think about the building in the city he didn’t live in. Think about what it means that the brightest financial minds of our generation sent enormous sums of other people’s money into a system that does this to people, and that some of those people died waiting for the system to be fair to them, and that the people who sent that money are still on television.

Then sit down. We have a lot to talk about.

If you found this essay worth your time, you may enjoy my book The Science of Free Will, which asks an equally uncomfortable question about an equally cherished story.

New to The India Paradox? Start with The Paradox of India, the first essay — or browse the whole series.

Related country-specific work: Albion — Britain’s institutional decline, by someone who first saw the place in 1979.

People have been arguing about whether UAP sightings are aliens. Most of the arguments are not very good — blurry videos, appeals to authority, arguments from incredulity. But two very serious thinkers have made the case that the probability is appreciably nonzero, and their arguments are not easy to wave away.

(A note on terminology: I use “UAP” for what people report seeing, and “craft” only for the hypothesis I’m testing. The question is not whether people see things they cannot identify. They do. The question is whether the craft interpretation survives the physics.)

Tyler Cowen — the George Mason economist who runs Marginal Revolution — frames it as a conditional: if UAP sightings are alien, the most plausible model is unmanned drone probes with generalized software instructions — “seek out major power sources, send information back, run away if approached.” He treats it as a question about how advanced civilizations allocate discretionary surplus when costs are low and time horizons are infinite. He raises his own counterarguments. He honestly concedes the sightings “probably are not of alien creations.” But he puts the probability at around 10%.

— Tyler’s George Mason colleague, whose work on prediction markets and “grabby aliens“ has made him one of the most original thinkers in the rationalist sphere — has gone much further: less than 20% probability on all UFOs being illusions, an elaborate structure involving stellar-nursery relatives, seeding, no-colonization rules, a 75-year military conspiracy. Robin’s version is bolder and more specific, which means it makes more testable predictions — exactly what you want from serious reasoning about a hard problem.

These aren’t cranks on Reddit. These are people whose reasoning I respect on every other topic. And their arguments made me think hard: could they be right? Could the probability really be 10%, or higher?

I’m a physicist — or at least, a recovering one. I have a PhD from UT Austin, where George Sudarshan was my advisor and Steven Weinberg was on my committee. When Congress, in its infinite wisdom, canceled the Superconducting Supercollider — the pork wasn’t spread widely enough; only Texas and Illinois were getting the bulk of the spending, which was the whole point of keeping costs down and using existing expertise — the writing was on the wall, and I had to find something else to do. My first job interview (and only job interview — I have never technically had a job in my life, unless you count being a graduate student) was at a hedge fund that has since become very famous. They wanted someone to write code to “clean” data. I asked why they wanted a physics PhD to do that. There was a long, awkward pause, and they said: “Well, physics is boring, and cleaning data is boring, so we figured a physics PhD would like it.” I spent the next three decades in quantitative finance instead. The physics, it turns out, was not boring. It was just waiting. (BTW, I’m back to writing physics papers again. Check them out if interested: samirvarma.com/publications. Turns out you can take the physicist out of physics but you can’t take physics out of the physicist. But I digress. Back to the original story.)

So I gave Tyler and Robin’s arguments the standard physicist’s treatment: what does the physics actually say?

And I immediately got it wrong. Twice.

I’m not a debunker. I’m not saying these are camera tricks, parallax artifacts, or people who don’t understand how video works. Maybe some of them are, maybe some aren’t — that’s a different argument, and other people are having it. My argument is different: even if every one of these videos shows a real physical object doing exactly what it appears to be doing, the physics of interstellar travel makes the alien-probe interpretation extraordinarily implausible. The observations can be entirely real and still not be probes. That’s the option I want to put on the table.

What follows is the story of how I tried to find what was wrong with Tyler and Robin’s reasoning, kept failing, started to think they might be right — and then found a calculation that, as far as I can tell, hasn’t been part of this conversation. It resolves the version of this question people usually argue about. The answer is lim(ε→0) ε — zero for all practical purposes.

My First Mistake: Speed of Light

My first instinct was the speed-of-light argument. Probes have mass. They can’t exceed c. The nearest star systems are a few light-years away; interesting targets span anywhere from single digits to hundreds of light-years. The galaxy is 100,000 light-years across. Whoever sent them would be long dead before any signal returns. Surely that settles it?

It doesn’t.

The Milky Way has hosted star formation for over 10 billion years. A civilization arising even a few million years before us — a cosmic eyeblink, less than 0.1% of galactic history — could have sent probes at a modest 0.1c. At that speed, a single probe reaches the nearest stars in about 40 years. That’s within a human lifetime, let alone a civilizational one.

With self-replication — Von Neumann probes that arrive at a star system, mine local materials, build copies, and launch them toward the next system — you get exponential spread. In standard self-replicating-probe models (Tipler, Barlow, and others have worked through the math), the entire galaxy gets saturated in roughly 10⁶ to 10⁷ years, depending on replication time at each stop. That’s somewhere between 0.01% and 0.1% of the galaxy’s age. A rounding error on a rounding error. The question isn’t “can probes get here?” — it’s “why wouldn’t they already be everywhere?”

Anyone making the speed-of-light objection hasn’t done the arithmetic. I know, because I didn’t — initially.

And the reason I didn’t is itself revealing. A probe at 0.1c takes a million years to cross the full diameter of the galaxy — do the division, 100,000 light-years at 0.1 light-year per year. That feels like an eternity. But the relevant timescale isn’t the travel time. It’s the age of the galaxy. And ten billion is four orders of magnitude larger than one million. Your brain rounds both to the same emotional category — “incomprehensibly long” — but one is ten thousand times bigger. This is computational irreducibility in miniature: you cannot shortcut the arithmetic and arrive at the right answer, no matter how good your physical intuition is.

So my first objection was wrong. Score one for Tyler and Robin. Maybe they’re onto something. But I had one more idea.

My Second Mistake: Civilizational Coherence

Grant that probes can reach distant stars. Grant self-replication. The galaxy fills up in a million years. Fine. But by the time you get signals back, tens of millions of years will have passed. You need to maintain civilizational coherence — the institutional memory, the technological infrastructure, the interest — for timescales that dwarf anything in human experience. No civilization has ever maintained coherence at anything close. Even species don’t typically last that long. Homo sapiens has existed for roughly 300,000 years and we’ve had radio for about 130 of them.

This is a better objection — and it was my second instinct after the speed-of-light argument failed. It’s also wrong, but for an interesting reason.

Consider two models. First, the dandelion model: the probes were never designed to report home. Their purpose is simply to spread — replicate, observe, catalog, maybe monitor for emerging intelligence. The mission is the spreading. A dandelion doesn’t need to be alive when its seeds land. A civilization facing its own mortality might reasonably conclude: “We can’t live forever, but our probes can.” No return address needed.

Second, the distributed civilization model — this is the Hanson-style argument, and probably the strongest. Once you have self-replicating autonomous machines sophisticated enough to navigate interstellar space and build copies of themselves, what distinguishes that network from the civilization itself? Information is what matters, not substrate. If the probes carry the computational essence of their creators, asking “who’s listening back home?” is like asking “who’s listening to your neurons?” The home planet is just the initial condition. It can burn to a crisp and the project continues.

Tyler essentially acknowledges all of this when he frames drone probes as “discretionary surplus allocation” by civilizations operating on timescales where “concrete motives may not be the major consideration.” He’s thinking about this correctly. The coherence objection assumes NASA-style mission architecture scaled up, which is a failure of imagination, not physics.

Two for two. Both of my objections had failed. Tyler’s framework had survived everything I’d thrown at it, and I was starting to think he and Robin might actually be right. Maybe I needed to revise my priors upward. So I went deeper into the physics — and that’s when I found something.

The alien probe question is computationally irreducible — you can’t shortcut from “physics permits it” to “these sightings are aliens.” There are too many intermediate steps, too many conditional probabilities, too many physical constraints that need to be checked one by one. And the deceleration constraint — the one that resolves the version of this question people usually argue about — requires you to sit down and do the calculation. I have not seen it foregrounded in the UFO/UAP debate. So let me try.

The Deceleration Problem: You Can’t Quietly Park at 0.1c

The problem isn’t getting to 0.1c. It’s stopping.

Getting to 0.1c is hard but there are at least hand-wavy schemes: solar sails with giant laser arrays, fusion drives, pellet runways. But every acceleration method has a deceleration problem that’s equally bad or worse, because of the tyranny of the rocket equation.

If you carry your own fuel, the math is exponential. You need fuel to decelerate, but you had to accelerate that deceleration fuel, which means you needed more fuel to accelerate, which you also had to accelerate, and so on. For fusion drives (optimistic exhaust velocity ~0.05c), a total delta-v of 0.2c — accelerate to cruising speed, then brake to a stop — gives a mass ratio of e⁴ ≈ 55. That means 98% of your launch mass is fuel. (This is the non-relativistic Tsiolkovsky approximation; at 0.1c it’s still good as back-of-the-envelope.) For chemical propulsion, the number of zeros breaks the notation. Don’t bother computing it.

If you don’t carry your own fuel, you face the Forward problem: Robert Forward’s idea of a solar sail accelerated by a laser array back home, braking by detaching part of the sail as a reflector. This requires a laser of staggering power (whether or not Earth would see it depends on wavelength, beam geometry, and sidelobes), sail engineering of extraordinary precision, and someone back home actively pointing the laser at your destination for years.

But here’s the killer constraint. A 100-kilogram craft arriving at 0.1c carries about 4.5 × 10¹⁶ joules of kinetic energy — roughly 10 megatons of TNT. About 700 Hiroshimas. You do not get to hand-wave that away.

Could you bleed it off slowly? If you spread the braking over 10 years, the average power is only about 140 megawatts — a modest power plant, not a bomb. But the stopping distance is still enormous: at constant deceleration from 0.1c over a decade, you need roughly half a light-year of runway. That’s tens of thousands of AU. An entirely different class of object than “a thing hovering over an airport.”

Solar radiation pressure doesn’t rescue you either. At 1 AU from the Sun, even a perfect reflector feels only about 9 micronewtons per square meter. Decelerating a 100-kilogram payload on a decadal timescale pushes you toward sail areas around a square kilometer — and you’d have to begin slowing down absurdly far out, where the sunlight is weaker still. Magsails and electric sails change the mechanism, not the moral. The common feature of every serious deceleration scheme is a huge interaction cross-section, a long braking baseline, or both.

A 0.1c probe trying to “arrive quietly” is like a bullet trying to stop inside a room without hitting anything. You can do it — by firing it into a giant block of gel the size of the room. But then the gel is the story. And we don’t see any gel. (If we did see the gel, the New Jersey footage would finally be the least interesting part.)

The flyby vs. hover problem makes this airtight. For the UAP question, the options split into two broad families. Flyby: physically easy — you just keep going. Breakthrough Starshot is explicitly designed to do this: gram-scale chips pushed to 0.2c by ground-based lasers, streaking past Alpha Centauri in hours. But a flyby at 0.1c crosses the inner solar system in hours and is gone. It does not loiter over populated areas. It does not hover near airports. A flyby is a streak, not a sighting. Rendezvous: brake to a stop. This requires all the enormous apparatus we just described — massive fuel, huge sails, tens of thousands of AU of braking distance. None of which is consistent with a compact object showing up unaccompanied near Earth.

“Atmospheric loitering” is not a flyby signature. And it’s not a braking signature. It’s neither.

The only escape hatch for rendezvous is ultralight probes coupled to enormous braking structures. Heller and Hippke worked out what a Starshot-style rendezvous at Proxima Centauri would require: a roughly 76-gram sail, 10-gram payload, sail area around 10⁵ square meters — a sail hundreds of meters on a side, more stadium than spacecraft, attached to a chip — and even then the maximum injection speed for capture is about 4.6% of c, not 10%. The common feature is the same every time: ultralight payloads, huge apparatus, long baselines. Not things you’d film with an iPhone over New Jersey.

But what about slower probes? Everything above assumes relativistic cruise speeds — 0.1c, where the deceleration bill is enormous. But the self-replicating-probe literature doesn’t require 0.1c. Barlow works with 0.01c and still gets galaxy-wide exploration on ~10⁷-year timescales. At 0.01c the kinetic energy drops by a factor of 100 — from roughly 10 megatons to roughly 100 kilotons for a 100-kilogram craft. At 0.001c it falls to roughly 1 kiloton. The rocket equation also becomes much less cruel. So yes: slow Von Neumann probes could stop. They could be compact. They could be here.

But notice what changed. The problem is no longer “how did they brake?” The problem is “where is the archaeology?” If slow self-replicating probes have been expanding through the galaxy for millions or billions of years, the expected evidence is not ambiguous atmospheric footage. It is persistent structure: processed asteroids, factories, waste heat, communication nodes, orbital artifacts, spectral anomalies, or some repeatable astronomical signature. And in the actual slow-probe literature, a self-replicating probe is not a magic seed — it is an industrial system. Barlow’s model requires a collection of robots, assemblers, and factories. Slow probes make stopping easier, but they make the absence of persistent infrastructure harder to explain.

So the slow-probe objection doesn’t rescue the UAP inference. It moves the evidentiary burden. Fast probes should look like flybys or giant braking structures. Slow self-replicating probes should look like Solar System archaeology. Neither looks like fuzzy orbs over populated areas.

“But what if a probe arrived long ago, parked somewhere in the Solar System, and has been lurking?” Fair — that’s a genuinely different hypothesis, and the physics doesn’t rule it out. A probe that decelerated over a thousand years on a long interstellar baseline and has been sitting quietly in the asteroid belt since the Pliocene is allowed. But notice what that hypothesis commits you to: you should expect orbital anomalies, thermal glints, spectral signatures, persistent material traces — the signatures of a parked artifact. What you should not expect is ambiguous low-altitude videos. The lurker hypothesis and the atmospheric-sightings hypothesis are not the same hypothesis, and evidence for the former is not evidence for the latter.

“But what if probes arrive and then build larger structures from local materials?” Two problems. First: how? This requires a full autonomous industrial stack — mining, refining, fabrication — in an environment with no biosphere help. The first Fermi paradox is “where are the probes?” This escape hatch has a second Fermi paradox hiding inside it: if they built factories, where are the factories? Second: even if you grant the mechanism, large sustained construction from asteroid materials should plausibly leave signatures: thermal emission, spectral anomalies, unusual mass distributions, persistent orbital oddities. No such evidence appears in the UAP record people are actually arguing about.

Tyler seems to intuit something like this when he notes the probes look like “cheap crap.” He’s picking up on the right signal. At relativistic speeds, quiet stopping pushes you toward flyby chips or giant sails. At sub-relativistic speeds, stopping is feasible — but then you need to explain the absence of the persistent signatures that millions or billions of years of self-replication should have left. Either way, the physics pushes in the same direction Tyler’s instinct suggests: away from the interpretation that atmospheric UAP sightings are alien probes.

I’m not arguing against probes. I’m arguing against inference from these sightings to probes.

The deceleration calculation handles the fast case — that’s a mechanics argument. The infrastructure and behavioral arguments handle the slow case — that’s an archaeology argument. They’re different kinds of reasoning, but they do the same Bayesian work: at any speed, the observed UAP evidence doesn’t match the probe models being invoked in this debate — fast or slow, flyby or parked, nano or macro. But what if probes didn’t need to travel at sub-light speeds at all? What if there’s a shortcut?

But What About Wormholes?

No.

I should probably say more than that. The sophisticated reader’s objection at this point is: “What about wormholes? Alcubierre drives? Some exotic shortcut we haven’t thought of?” Every worked-out shortcut we know how to write down comes with the same bill: exotic stress-energy, negative energy, or violations of the usual energy conditions. The Einstein field equations — the same equations that predict gravitational lensing, gravitational waves (confirmed by LIGO in 2015), GPS corrections (confirmed daily by your phone), and black hole shadows (confirmed by the Event Horizon Telescope in 2019) — are among the most precisely tested in all of science.

A wormhole is a solution to these equations — a tunnel connecting two distant points in spacetime. Einstein and Rosen found such a solution in 1935, but their wormhole wasn’t traversable: it would pinch shut faster than anything could pass through. In 1988, Kip Thorne — who later won the Nobel for gravitational wave detection and served as the science consultant on Interstellar — and his student Michael Morris asked a different question: what would you need to hold a wormhole open?

They worked backward from the requirement of traversability and derived what the energy-momentum tensor would have to look like. The answer was stark: you need matter with negative energy density. Not low energy. Not zero. Negative. Physicists call it “exotic matter.” Normal matter curves spacetime in a particular way. To hold a wormhole open, you need matter that curves it in the opposite direction.

How much? The numbers vary wildly depending on the metric. Early Morris-Thorne-style constructions demanded planetary to stellar masses of the stuff. Later work by Visser, Kar, and Dadhich showed you can construct wormhole geometries with arbitrarily small total energy-condition violations — but subsequent analyses by Fewster and Roman argued that such minimal-violation wormholes are either submicroscopic, badly curved, or otherwise severely constrained. The robust claim isn’t any particular mass estimate. It’s that every known traversable wormhole solution requires violation of the null energy condition, and at engineering scales — the scales at which you could send a probe through — nobody, human or alien, has ever produced, detected, or demonstrated a mechanism for generating or stabilizing such stress-energy. This isn’t a technology gap. It’s a physics gap. The irony is worth savoring: the man who showed Hollywood what a traversable wormhole would look like is the same man whose equations show what it would actually take to build one.

“But the Casimir effect proves negative energy exists!” Yes — and this is where the scale problem bites. Casimir negative energies occur in gaps of nanometers between parallel plates, involve tiny total amounts of energy, and aren’t controllable or transportable in any useful sense. A traversable wormhole demands macroscopic negative energy — stable, concentrated, engineerable, held in a specific geometric configuration. We can measure Casimir effects in a tabletop experiment. We cannot scale them up, shape them, or make them persist. It’s the difference between observing a droplet of water and engineering a dam: both involve water, but nothing about the droplet tells you the dam is physically achievable. And no civilization, however advanced, gets to ignore the energy conditions unless the energy conditions are wrong — and so far, across every experimental regime we’ve ever tested, they’re not.

Alcubierre-style warp drives have the same problem. Later geometries can reduce the formal energy cost, but they do not remove the need for exotic stress-energy that we do not know how to create, confine, or stabilize. The direction is always the same: these solutions require violations of the energy conditions at scales demanding entirely new physics, not just better engineering.

So: sub-light travel works in principle, but quiet rendezvous constrains you to ultralight payloads coupled to huge braking structures, or to lurkers whose signatures should be in orbit. Superluminal shortcuts require stress-energy we do not know how to produce and that has never been observed anywhere in the universe, and require physics that is, with a good deal of confidence, ruled out by already-known physics. If rendezvous-capable probes are here at all, they’re gram-scale chips with stadium-sized sails, or they’re parked artifacts discoverable by telescope — not things people are filming with iPhones.

Tyler acknowledges you have to be “short usable wormholes.” Fair. I, too, am short usable wormholes. But the deceleration problem bites even without wormholes, and that’s the piece I think completes the picture.

What Are They Actually Doing?

Even if you ignore all the physics — even if you grant every generous assumption — the observed behavior of these objects is inconsistent with any coherent model of what advanced alien probes would do.

What we actually observe: things moving in ways we can’t readily explain. That’s it. Tyler has recently argued that debunkers who chalk everything up to camera tricks and parallax have been “proven wrong in general,” and that Mick West “is not your proper guide here.” I agree. That’s not the argument I’m making. The newly released government footage shows more of what we’ve always seen — glowing orbs, fast-moving objects, unpredictable trajectories. Grant that all of it is real. Grant that these are physical objects doing exactly what they appear to be doing. Now ask: is any of this behavior consistent with an interstellar probe? They’re not self-replicating. They’re not mining resources. They’re not building copies of themselves on the Moon. They’re not doing anything consistent with any version of the probe hypothesis — not the dandelion model, not the distributed civilization model, not even Tyler’s “seek out power sources and observe” model. They’re just... there. Doing stuff that looks weird on camera.

Two options, both terrible. Option A: they want to be seen. Why? If you can build interstellar probes, you can build sensors that dominate anything in our current atmosphere — so “hovering low and visible” is a strange choice unless the point is theatre. Robin’s “domestication” theory is a creative attempt to address this, and it’s exactly the kind of detailed model-building that forces careful thinking about what each assumption costs in probability. But each additional assumption narrows the parameter space considerably.

Option B: they don’t want to be seen but are failing. A system that solved autonomous interstellar navigation, self-replication, and million-year durability can’t manage basic stealth against a species that figured out powered flight 120 years ago? That’s like someone who built a functioning warp drive being unable to turn their headlights off.

Even the Navy’s Tic Tac footage — the strongest single case the UFO community has — remains officially unresolved. ODNI’s public position is that there isn’t sufficient information to attribute most incidents to specific explanations. The most recent AARO report found no evidence of extraterrestrial beings, activity, or technology. NASA’s public FAQ says the same. But “unresolved” isn’t evidence for any particular hypothesis. Nothing about the Tic Tac behavior points specifically toward alien probes: no mining, no replicating, no construction, just an oblong shape maneuvering near a military exercise. “We don’t know what it was” and “it was an interstellar probe” are not the same statement.

What the observations are consistent with: misidentified terrestrial objects, sensor artifacts, classified military programs, good old-fashioned observer error. Exactly the boring explanations. The observed behavior is exactly what you’d expect from mundane sources and exactly what you’d not expect from technology millions of years more advanced than ours.

Tyler asks “why don’t we see more of them?” and calls this a “perfectly valid (and embarrassing) question” for the hypothesis. He’s right. But the question isn’t just about frequency — it’s about behavior. The behavior is what’s diagnostic.

Bayes’ Theorem Finishes the Job

Time to formalize this. Three decades of quantitative finance teaches you one thing above all: the prior is not the posterior.

Prior: P(alien probes exist in our solar system) — non-negligible. We’ve granted this throughout. The physics of sub-light self-replicating probes plus galactic timescales makes this a legitimate nonzero number. Tyler’s framing here is essentially correct.

Likelihood: P(we observe exactly this UAP behavior | these are alien probes) — essentially zero. Rather than pull a single number from the air, let’s decompose this into roughly separable suppressions — weakly dependent but directionally reinforcing. The specific numbers below are illustrative, not measurements; the point is the structural form of the argument:

L(size): What’s the probability that an interstellar probe manifests as a compact atmospheric craft? At relativistic speeds, deceleration physics pushes toward ultralight payloads with huge sails — not compact craft. At slower speeds, compact probes are feasible — but after billions of years of self-replication, the signatures should be infrastructure, not isolated atmospheric objects.

L(loiter): What’s the probability it loiters visibly in the atmosphere rather than remote-sensing from orbit? A technology millions of years advanced choosing to hover over airports?

L(ambiguity): What’s the probability it produces ambiguous, fuzzy-video evidence rather than being either completely invisible or unambiguously present? The one thing a million-year-old technology wouldn’t produce is “weird stuff on camera that we can’t quite explain.”

Even if you’re generous and give each factor a 1% probability — which is far too high for L(size) given the physics — the product is 10⁻⁶. One in a million. Even if you think I’m double-counting, even if these factors aren’t fully independent, you’d have to increase each term by orders of magnitude to escape the conclusion.

The posterior collapses. Give the prior a generous 5%. Your posterior is 5% × 10⁻⁶ = 5 × 10⁻⁸. One in twenty million. That’s not “appreciably non-zero.” That’s noise. (This is a rough calculation, not a formal posterior — but including mundane alternatives in the denominator only makes it worse for the probe hypothesis, not better.)

Trading analogy: this is like saying “this company has great fundamentals” while ignoring that it’s priced at 500x earnings. The prior isn’t the posterior. Every trader who’s blown up learned this the hard way.

And this is where Tyler’s candor on the Sullivan podcast becomes genuinely illuminating. “It depends on my mood of the day... There’s nothing very scientific about any number.” Tyler is being transparent that his 10% is a prior — an informed intuition shaped by Fermi-paradox reasoning and genuine curiosity, not the output of a physics calculation. And he’s right to frame it that way. A prior is where you start. But it’s not where you finish. Tyler and I agree on the direction — he already thinks it’s probably not aliens, putting it at 10%, not 90%. The question is the magnitude. And the physics resolves it: the prior was the right place to start. The likelihood is where the story ends. When you combine Tyler’s honest prior with the likelihood the physics gives you, the posterior is crushed to the floor.

The Missing Calculation

The broader discourse on UAPs is almost entirely authority-based. The Free Press launched a four-part series on “What Should Smart People Think About UFOs?” — interviewing journalist Michael Shellenberger, physicist Avi Loeb, oceanographer Tim Gallaudet, columnist Ross Douthat, and religion professor Diana Pasulka. The series is well-produced and earnest, but not one of the guests, based on their public work, engages the deceleration problem. (Loeb does physics, but his published UFO-adjacent arguments focus on anomalous objects like ʻOumuamua, not on the rendezvous problem.) Obama said there’s “footage and records of objects in the skies that we don’t know exactly what they are.” Whistleblowers testified under oath. Senators seem convinced. The calculation that would actually resolve the question hasn’t been part of the conversation.

(The Free Press has since covered the Trump administration’s UFO file release — more ambiguous footage, more debate, same pattern.)

This is what I’ve called “conclusions without derivations” — accepting an endpoint because it sounds plausible, without checking whether the intermediate steps actually work. (I explore this failure mode in depth in The Science of Free Will.) The conclusion (”probes could theoretically be here”) is defensible. The derivation (”therefore these specific sightings are probes”) was never completed.

Tyler and Robin are doing something fundamentally different from that discourse — and it’s the reason this piece exists. Most UFO commentary is, to use Wolfgang Pauli’s famous dismissal, “not even wrong” — too vague or incoherent to engage productively. Tyler and Robin built careful, internally consistent frameworks grounded in real physics and economics. Tyler’s “discretionary surplus allocation” framing is genuinely insightful about how advanced civilizations might behave. Robin’s grabby aliens model is serious cosmological reasoning. Their frameworks are what made it possible for me to identify the missing step — because without a real argument, there’s nothing to build on. If I have seen further here, it is because I stood on their shoulders.

I would never have thought carefully about the deceleration problem, or worked through the momentum-transfer scaling, or connected the Heller-Hippke rendezvous constraints to the UAP question, if Tyler’s argument hadn’t been careful enough to resist easy dismissal. The cheap objections — speed of light! they’d be dead! — all fail, as we’ve seen. Only by engaging seriously with the strongest version of the argument do you find the constraint that actually resolves it.

The Real Mystery

Here’s what Tyler and Robin have gotten right, and it’s important: the Fermi paradox remains genuinely puzzling, for exactly the reasons they’ve explored. The physics is permissive. Sub-light self-replicating probes could fill the galaxy in a million years. One million years out of ten billion. The prior really is non-negligible.

So the question isn’t “are those sightings alien probes?” The physics says: almost certainly not, for multiple reinforcing physical and observational reasons.

The question is: where ARE all the probes that should be here?

That points toward genuinely deep problems: the Great Filter, the motivation problem, or something about the nature of intelligence we don’t yet understand. Perhaps intelligence turns inward rather than outward. Perhaps the conditions for it are far rarer than Drake equation optimists assume. Perhaps the answer involves physics we haven’t yet formulated.

Tyler’s instinct to take this seriously was exactly right. Robin’s willingness to commit to specific probabilities and build a detailed, testable model is exactly what serious reasoning about hard problems looks like. This is how intellectual progress works: someone builds a careful framework, someone else engages seriously enough to find the step that was missing, and in finding it you learn something neither of you knew before. I didn’t understand the deceleration problem before Tyler made me think about this. I hadn’t connected Heller-Hippke to the UAP question before Robin’s framework forced me to close every escape hatch.

Tyler’s framework was sound. The likelihood calculation completes it. And the answer it gives is: essentially zero.

That’s the question worth a real derivation. Not a conclusion.

Three things are true:

1. Interstellar probes are physically plausible.

2. Ambiguous atmospheric videos are not good evidence for them.

3. The real mystery is not why the UAP footage is blurry. The real mystery is why the Solar System is not full of unmistakable artifacts.

This post draws on ideas from my book, The Science of Free Will, which explores how computational irreducibility, physics, and first-principles reasoning intersect with questions about agency, consciousness, and meaning — including why “conclusions without derivations” is the most common failure mode in human reasoning, and why the universe doesn’t care how sophisticated your intuition is. Available on Amazon or anywhere else books are sold.

In April 2025, gunmen walked into the Baisaran Valley near Pahalgam—a meadow so cinematic that Bollywood has used it as a backdrop for love songs—and asked twenty-six tourists a simple question before shooting them: Are you Hindu?

India named its retaliatory military operation “Sindoor.”

Sindoor is the vermilion powder Hindu married women apply to their hairline as a sign of marriage. India named a bombing campaign after the symbol of married womanhood because the Pahalgam attack had left wives widowed.

Then the ceasefire came. Then Donald Trump, asked about the crisis, said the two countries had been fighting “a thousand years”—an observation that is not only historically wrong by approximately 953 years but also somehow captures the exact quality of intellectual engagement the Kashmir dispute has attracted since 1947.

This is where we are in 2026. This is where we have always been.

And if you want to understand why we are here—why two nuclear-armed democracies (one of them nominally so) have been unable to resolve a territorial dispute about seventeen million people for nearly eight decades—you have to go back. Not a thousand years. Seventy-eight. And before that: one week.

The Week Britain Owned a Country

Here is the founding document of the Kashmir problem, and it is, at its core, a livestock invoice.

On March 9, 1846, the Sikh Empire—having lost the First Anglo-Sikh War—signed the Treaty of Lahore. Because the defeated Sikhs could not pay the war indemnity imposed upon them, they ceded to the British East India Company “the hill countries, including the Provinces of Kashmir and Hazara” as equivalent payment. One crore rupees’ worth of mountains, rivers, valleys, and several million people who were not consulted.

Seven days later—literally seven days—on March 16, the British signed the Treaty of Amritsar, which transferred those same mountains, rivers, valleys, and uncontested millions to Gulab Singh, the Raja of Jammu. Price: seventy-five lakh rupees.

The annual tribute was as follows: one horse, twelve shawl goats of approved breed (six male, six female), and three pairs of Kashmiri shawls.

No Kashmiri signed. No Kashmiri was asked. Britain had held legal sovereignty over Kashmir for seven days before selling it, and one is entitled to wonder whether they had the legal right to sell what they’d seized from people who’d seized it from the Afghans who’d seized it from the Mughals who’d been contesting it since forever. But nobody asked that question in 1846, because nobody was asking that kind of question in 1846.

Gulab Singh paid the money, received a receipt, and became the Maharaja of a Muslim-majority state ruled by a Hindu dynasty. His great-grandson, Hari Singh, would inherit this arrangement—and then have to decide what to do with it when the British finally left a century later.

He was not well prepared for the responsibility. As we shall see.

Mr. A

In 1919, the young Crown Prince of Kashmir visited Europe for the first time, traveling with approximately four million dollars’ worth of cash and jewelry and accompanied by his aide-de-camp, an Irishman named Captain C.W.A. Arthur.

Captain Arthur introduced the prince to a charming London widow named Maudie Robinson, described in contemporary accounts as “bewitching.” The prince and Mrs. Robinson went to Paris for Christmas. On Boxing Day, a man burst into their hotel room, introduced himself as her husband, and demanded an enormous settlement to avoid citing the prince in divorce proceedings.

It was a classic honeytrap. Captain Arthur, it turned out, was in on it. The prince wrote two cheques totaling £300,000—equivalent to roughly £14.5 million today—and paid.

The scandal eventually ended up in London’s King’s Bench Division in 1924. At this point, the India Office intervened and asked the court to keep the young ruler’s identity secret, because the optics of an Indian prince paying what amounted to the GDP of a small county to emerge from a Parisian honey-trap were not considered ideal for British India’s image. The prince was referred to throughout the proceedings as “Mr. A.”

Lord Birkenhead, the Secretary of State for India, had specifically requested this discretion. The story was so explosive that the India Office classified its files for one hundred years—not the standard thirty—”because espionage was involved.”

In the end, other Indian princes complained that the secrecy was generating damaging rumors about them. On December 3, 1924, the India Office finally permitted the disclosure of his identity. It was first announced on London radio station 2LO, which interrupted its music programming for the news. Time magazine ran the full story. The Daily Express, the Daily Chronicle, and papers in Australia, Jamaica, and beyond all published.

When the prince returned to India, his uncle—still the reigning Maharaja—sent him to a remote jungle estate for six months of religious penance. The terms of his humiliation included the shaving of his moustache.

This man—Maharaja Hari Singh, the former Mr. A, the shaved-moustache penitent, the man whose full official title was Shriman Inder Mahinder Rajrajeswar Maharajadhiraj Shri Hari Singhji Jammu & Kashmir—was the person who, twenty-three years later, would sign the document upon which India’s entire legal claim to Kashmir rests.

History, as a rule, is not run by the best-prepared people.

The Signature That Launched Seventy-Eight Years of War

By August 1947, Hari Singh had a problem. Actually, he had several problems, but the main one was this: the British were leaving, and every princely state had to decide which of the two new countries to join. Hari Singh didn’t want to join either.

He had signed a standstill agreement with Pakistan. He had tried to get India to sign one too; India declined. He kept hoping for a third option—independence, with friendly relations with both sides. He wrote to Mountbatten explaining his position: Kashmir had ties to both dominions, shared borders with Soviet and Chinese territory, and he needed time.

Mountbatten told him to make up his mind. Hari Singh did not make up his mind.

On October 22, 1947, Pakistan launched “Operation Gulmarg.” Pashtun tribal fighters from the Northwest Frontier Province poured across the border toward Srinagar, traveling in, as Hari Singh later described, “motor trucks.” They were armed, organized, and moving fast. Baramullah fell. Eleven thousand people were killed there. The tribesmen were within hours of Srinagar.

Hari Singh panicked and fled his summer capital for Jammu by car.

He sent an urgent message to India requesting military assistance. India’s response, delivered by V.P. Menon, was essentially: Sign first. Troops later.

On October 26, 1947—according to the official account, at some point after Menon flew to Jammu carrying the Instrument of Accession—Hari Singh signed. Menon flew back to Delhi. The next day, Indian troops were airlifted to Srinagar airport.

This is the document. This is the signature. This is India’s legal title to Kashmir.

Now observe the structure of the paradox:

Pakistan sent in the tribal raiders to prevent Kashmir from going to India. The invasion panicked Hari Singh into requesting Indian military help. India made accession the condition of help. Pakistan’s invasion created the document that India uses to justify holding the territory that Pakistan wanted.

Pakistan called the document fraudulent. Pakistan is the reason the document exists.

Even Mountbatten, accepting the accession on October 27, added a caveat: once law and order is restored and the invader cleared, “the question of the State’s accession should be settled by a reference to the people.” India’s own first acceptance of Kashmir contained a promise of a vote.

Hari Singh was subsequently banished from the state he’d just signed away. He departed Jammu in 1949 and never returned. He died in Bombay in 1961, reportedly farming polo ponies.

Shriman Inder Mahinder Rajrajeswar Maharajadhiraj Shri Hari Singhji ended his days raising horses in Bombay.

The Lawyer and the Theologian

Here is the core of the Kashmir dispute, and it is almost too neat to be real—but it is real, and it has been real since 1947, and almost nobody states it this plainly.

India’s claim is legal. Kashmir is Indian because Hari Singh, a ruling Maharaja with recognized legal authority, executed the Instrument of Accession under the established procedures of the Indian Independence Act 1947. The process was followed. The paperwork is valid. The border is settled.

Pakistan’s claim is demographic-religious. Kashmir is Pakistani because Kashmir’s population is overwhelmingly Muslim, and the entire logic of Partition—the two-nation theory, the founding principle of Pakistan—is that the Muslims of the subcontinent constitute a separate nation and deserve their own state. Muslim-majority areas belong to Pakistan. This is literally why Pakistan exists.

India argues like a lawyer. Pakistan argues like a theologian.

Neither is entirely wrong. That’s the actual problem.

Pakistan’s claim to Kashmir is the direct logical extension of its own founding philosophy. You cannot say “Muslim-majority territories belong to Pakistan” as your reason for existing and then refuse to apply that logic to the largest Muslim-majority state that didn’t go to Pakistan. Pakistan is being consistent. Annoyingly, infuriatingly consistent.

India’s claim requires you to accept that the legal acts of Hindu hereditary monarchs determine the nationality of Muslim-majority populations without their consent—which is... also a defensible legal position, and also somewhat uncomfortable.

But here is where it gets genuinely dizzying. Stick with me.

India’s legal position depends on the principle that religion is irrelevant to Indian identity. You are Indian because you are Indian—because you live in India, because a legal instrument incorporated your territory. It doesn’t matter what religion you are, what language you speak, what ethnicity you belong to. India is a secular state. That’s the whole point. That’s Nehru’s entire project. That’s the constitutional architecture.

And India’s current government—the BJP, whose ideological roots run through the RSS and whose project is explicitly Hindu nationalist—has spent the past decade suggesting, through its actions, rhetoric, laws, and bulldozers, that actually, religion is quite relevant to Indian identity. That there are “infiltrators.” That certain people are “termites.” That the Citizenship Amendment Act should fast-track everyone’s citizenship except Muslims from neighboring countries. That temple-mosque disputes have a correct answer that happens to align with Hindu claims. I explored this domestic rewriting in Post 18A, The Third Rail—the slow, grinding conversion of a secular republic into something its founders would not recognize.

Which means India is simultaneously arguing:

“Religion doesn’t determine nationality—that’s why Kashmir is India” (the secular-legal Kashmir argument)

and

“Religion does determine who really belongs here” (the domestic Hindutva argument)

You cannot hold both positions. They are mutually exclusive. The party currently in power in New Delhi is using the logic of Pakistan’s founding philosophy domestically while using the logic of India’s secular founding philosophy internationally to justify holding Kashmir.

The intellectual whiplash is audible from space.

The Algorithm That Cannot Terminate

On January 1, 1948, India took the Kashmir dispute to the United Nations Security Council. This seemed reasonable. The UNSC passed Resolution 39. Then Resolution 47. The resolutions were quite clear: Pakistan would withdraw its forces and tribal fighters; India would reduce its forces to minimum necessary levels; a free and impartial plebiscite would be held.

Both India and Pakistan said yes, great, wonderful, very much in favor of this.

Then they negotiated about the sequence.

Pakistan said India needed to go first. India said Pakistan needed to go first. Both parties claimed the other had not fulfilled the preconditions for the next step. The UN appointed mediators. Five of them, in fairly rapid succession. The mediators reported back that the situation was complex.

It is now 2026. The United Nations Military Observer Group in India and Pakistan—UNMOGIP—still exists. Its headquarters rotates between Islamabad in winter and Srinagar in summer. A UN audit document summarizes the current state of play: India maintains UNMOGIP’s mandate has lapsed following the 1972 Simla Agreement. Pakistan maintains it remains essential. The UN Secretary-General’s position is that only a Security Council resolution can terminate it. India disagrees. Pakistan disagrees with India’s disagreement.

This is the geopolitical equivalent of two software teams in a perpetual deployment freeze because each insists the other must merge their branch first. Except instead of sprint reviews, there are wars. Three full ones. Multiple partial ones. A four-day conflict in May 2025 that ended in a ceasefire—except India says the ceasefire was not brokered by anyone, it was entirely bilateral, and Pakistan says it was brokered by Donald Trump, and Pakistan nominated Donald Trump for the Nobel Peace Prize.

Pause here.

India says nobody brokered it. Pakistan says Trump brokered it and deserves a Nobel. Trump, who had said the two countries had been fighting “a thousand years,” apparently solved in four days what seventy-nine years of diplomacy could not—or didn’t, depending on which of the two parties you ask.

Then the plot thickened in ways a novelist would be embarrassed to submit to an editor.

The relationship between Modi and Trump had been, until approximately May 2025, one of the great bromances of authoritarian-adjacent governance. “Is baar, Trump ki Sarkar”—”This time, it’s Trump’s government”—Indian BJP supporters had chanted when Trump won in 2024, with a warmth that suggested they viewed his victory as somehow also theirs. The two men had held rallies together. They had exchanged compliments of extraordinary sincerity. Trade negotiations had been proceeding with the ambient goodwill of men who genuinely liked each other’s energy.

And then came Kashmir. And then came the ceasefire India says didn’t happen. And then—in the coup de grâce, the chef’s kiss, the final absurdity that completes the collection—General Asim Munir, the Pakistan Army Chief who had given that speech about Hindus and Muslims being fundamentally different peoples, the man whose theological worldview makes the Taliban look nuanced, the architect of Pakistan’s “moral and diplomatic support” for the groups India had just bombed—that man was received at the White House with great acclaim.

The man Modi had implicitly accused of sponsoring the killing of twenty-six Hindu tourists was a guest of honor in Washington within months.

You genuinely cannot make this up. India’s closest great-power friendship, shattered. Pakistan’s General, feted. The Nobel nomination, pending. The ceasefire, simultaneously real and fictional depending on your nationality.

The Kashmir dispute does not merely resist resolution. It actively destroys the attempts.

And yes, the plebiscite was promised. By Mountbatten in his acceptance letter. By Nehru at the UN. By multiple Security Council resolutions. The people most enthusiastically promising the plebiscite were the same people most enthusiastically not holding it.

“Geopolitics,” as a concept, should probably come with a disclaimer about implied promises.

“Thence North to the Glaciers”

Not all of Kashmir’s absurdities are philosophical. Some are literally ink on paper.

The 1949 ceasefire agreement described the line between Indian and Pakistani positions in meticulous detail—named points, coordinates, measured distances. And then, at a certain point near coordinate NJ9842, the text continued: “thence north to the glaciers.”

“Thence north to the glaciers.”

That phrase—that lazy, elegant, catastrophically incomplete phrase—is why the Siachen Glacier became a battlefield. The ceasefire line stopped at a named coordinate, and beyond that, the treaty offered only vague directional poetry. When strategic incentives changed in the 1980s, both India and Pakistan decided “north to the glaciers” meant something favorable to their position. India moved first, in 1984. Pakistan protested. Both countries have maintained forces at altitudes above 6,000 meters, where temperature regularly drops to minus fifty degrees, ever since.

The strategic analyst Stephen P. Cohen once described the Siachen dispute as “a struggle of two bald men over a comb.” The glacier has no strategic value, no resources, no population, no particular beauty that couldn’t be found somewhere warmer. It is an uninhabited refrigerator at the edge of the atmosphere, and it has been militarized because someone in 1949 couldn’t be bothered to specify a coordinate north of NJ9842.

That’s not a metaphor. That’s the actual explanation.

The Missing Protagonist

There is a population of approximately thirteen million people in the Indian-administered portion of Jammu and Kashmir. There are roughly four million more in Pakistani-administered Azad Kashmir—”Azad” being the Urdu word for “free” or “independent,” which is either the most optimistic place name in human history or the most ironic, depending on your familiarity with how Pakistan administers it. These people have been argued about, administered, militarized, censused, democratically excluded, and periodically shot at since 1947. Their preferences are rarely the first item on any agenda.

When they have been asked what they want, the answers are inconvenient for everyone.

A Chatham House poll found that support for independence in the Kashmir Valley runs between 74 and 95 percent. Support for joining India in the Valley: approximately 2 percent. Support for joining Pakistan: approximately 6 percent. A Hindustan Times survey found that 66 percent of Valley residents wanted “complete freedom to entire [sic] Jammu and Kashmir as a new country.” Another poll, conducted in Srinagar specifically, found that 90 percent wanted independence, 7 percent wanted India, and 3 percent wanted Pakistan.

The option that both India and Pakistan are disputing—join us—is, in the area most people think of as “Kashmir,” the least popular option on offer.

This is the definitional absurdity at the center of the dispute. Two countries have been fighting for seventy-nine years over who gets to govern a population that, to a remarkable degree of consistency across decades of polling, wants neither of them.

The Kashmiris want to be left alone. This is the one option neither country is willing to offer.

After the Pahalgam massacre in April 2025—after the gunmen asked tourists their religion before pulling the trigger, after Operation Sindoor, after the Indian Army deployed hundreds of thousands of troops and arrested thousands of Kashmiris in the aftermath—ordinary Kashmiris in Srinagar came out to mourn the dead and condemn the killings. The Muslim majority of the Valley, with striking consistency, refused to become the anti-Indian mob that Pakistani state narrative requires them to be. They condemned the attack. They lit candles.

They do this every time. And every time, they are ignored—because they are not useful to either national narrative as complex human beings. They are useful as a population to be governed, a statistic to be cited, a grievance to be deployed.

The Thing Nobody in Delhi Can Say

Let me return to the philosophical contradiction, because it matters more now than it did in 1947.

In August 2019, the Modi government revoked Article 370—the constitutional provision that had given Jammu and Kashmir special autonomous status since 1949. The legal mechanism was exquisite. Since the state’s Constituent Assembly (which had to give consent to constitutional changes) had dissolved in 1957, the government issued a Presidential Order that simply redefined the phrase “Constituent Assembly” to mean “Legislative Assembly.” Since Kashmir was then under President’s Rule, the “state government” that gave “concurrence” was the Governor—appointed by the Union government.

India asked itself permission. India said yes.

The Supreme Court upheld this in 2023, on the grounds that Article 370 was always intended as a “temporary provision” and that Parliament’s action was constitutional. The Chief Justice noted it was “an interim arrangement due to war conditions in the state.”

Fine. Perhaps it was temporary. Perhaps the revocation was legally valid. Let’s assume it was.

But then observe the coincidence: the argument India uses externally (Kashmir is Indian because of a secular legal document) was simultaneously being strained by the argument India was making internally (the reason Kashmir was special is being eliminated; the state that signed the accession in exchange for specific terms is having those terms unilaterally revised).

Former Chief Minister Omar Abdullah called it “a total betrayal of the trust that the people of Jammu and Kashmir had reposed in India when the state acceded to it in 1947.” That is a fairly precise description. The accession came with conditions. The conditions were modified without consent. The legal instrument that grounded the accession explicitly contemplated a reference to the people. The reference to the people was never made.

You cannot hold that you have an ironclad legal title based on a specific document and then revise the terms of that document without consulting anyone. That’s not how contracts work. That’s not how sovereignty arguments work. That’s not how trust works.

And if you add to this the broader BJP domestic project—the Citizenship Amendment Act that explicitly excludes Muslims, the “anti-infiltrator” rhetoric, the demolition of mosques, the temple politics, the casual redefinition of who is “really” Indian—then the secular-legal foundation of India’s Kashmir argument is being quietly eroded by the same government that insists upon it.

You cannot argue Pakistan’s logic at home and India’s logic in Srinagar.

Pakistan’s Army Chief Asim Munir, just weeks before the Pahalgam attack, gave a speech describing Kashmir as “our jugular vein” and explicitly arguing that Hindus and Muslims are fundamentally different peoples who cannot coexist in a single nation—the two-nation theory, stated plainly in 2025, as if the subsequent seventy-nine years had produced no evidence worth considering.

What Pakistan’s Army Chief said openly, India’s ruling party is implementing quietly.

Both are wrong. Both know it and proceed anyway.

The Quantum State

I’ve argued throughout this series that India exists in quantum superposition—that any statement you make about India is true, as is its opposite. It’s the operating principle of the whole project, stated plainest in Post 1. The Kashmir dispute is quantum superposition weaponized. It is simultaneously:

A legal dispute (but the legal document was signed under duress)

A religious dispute (but both states officially deny this)

A democratic dispute (but the plebiscite was promised in 1947 and not held)

An anti-colonial dispute (but both successor states are using colonial-era legal frameworks)

A human rights dispute (but both sides are implicated in human rights violations)

In 2019, India shut down the internet in Jammu and Kashmir for 175 days—one of the longest internet shutdowns anywhere in the world that year. The people India was suppressing to maintain sovereignty were the people India’s sovereignty was nominally there to protect.

Pakistan’s Army—whose official position is that Kashmir deserves self-determination—has spent decades supporting militant groups that have made normal life in the Valley a continuous emergency, which has been India’s justification for refusing to hold the self-determination vote that Pakistan demands.

Both countries have found ingenious ways to make the thing they claim to want impossible.

Coda: The Polo Ponies and the Paperwork

Hari Singh—Mr. A, the reluctant signatory, the man with the shaved moustache and the spectacular bad judgment in European travel companions—died quietly in Bombay on April 26, 1961.

He had once wanted Kashmir to be independent. He had signed it away at three in the morning (some accounts say he was woken from sleep by V.P. Menon) while Pakistan’s tribals raced toward his capital. The document he signed has been the basis of three wars, dozens of insurgencies, one nuclear standoff, and one brief four-day conflict in 2025 named after the symbol of Hindu marriage.

Kashmir has never been independent. It has never had a plebiscite. It has been administered by India (two-thirds of the territory) and Pakistan (one-third), with China occupying a piece of Ladakh since 1962 because—as long as we’re cataloguing absurdities—why not add a third nuclear power?

The British owned it for seven days before selling it for goats.

The man who ultimately decided its fate was known, for three years, only as Mr. A.

The people who actually live there want neither country.

And the two countries arguing over them are, with increasing symmetry, borrowing each other’s worst ideas: Pakistan borrowing India’s nuclear program, India borrowing Pakistan’s approach to Muslim minorities, both countries borrowing Britain’s talent for treating a mountain valley full of real human beings as inventory.

As of early 2026, UNMOGIP still rotates its headquarters between Islamabad and Srinagar by season. The plebiscite has not been held. The ceasefire line still says “thence north to the glaciers.”

The Siachen Glacier is still militarized.

The shawl goats were presumably long ago discontinued.

Everything else continues.

If you like this, you will enjoy my book, The Science of Free Will. Among other things, I explore why we can’t trade with ants (they can detect cancer), what that has to do with the future of AI, and why we need a Supreme Court if the world is deterministic. Available on Amazon or anywhere else books are sold.

The India Paradox is a series exploring how the world’s most diverse democracy somehow functions despite—or perhaps because of—its beautiful contradictions. Previous posts available at samirvarma.substack.com.

I recently did a two-part podcast series (Part 1, Part 2) with Simon M. of , which inspired me to write this post.

The Byrds had a song: “So You Want to Be a Rock ‘n’ Roll Star.” The advice was straightforward. Get a guitar. Learn to play. Find some people who’ll tell you you’re great. Then get ready to lose everything that made you interesting in the first place.

The advice for aspiring quants is similar. Except for the guitar.

If you want to trade quantitatively — if you want to build strategies, manage risk, understand markets at the level where you can actually make money — you need to learn to program.

Not because you’ll write much code. In the age of AI, the code increasingly writes itself. I can describe a strategy in English and have working Python in minutes. That part of the job is rapidly going to zero.

No, you need to learn to program because programming is the only reliable way to learn computational thinking. And computational thinking is the actual skill.

What do I mean by computational thinking? Stephen Wolfram has written extensively about this, but the short version is: it’s the ability to think in terms of processes, not just states. To ask not “what is the answer?” but “what is the procedure that generates the answer, and what are its failure modes?” To understand that a system can follow perfectly deterministic rules and still produce behavior you cannot predict without running it. (If that last bit sounds familiar, it’s because it’s computational irreducibility — the central concept in my book and arguably one of the most important ideas in modern science.)

Wolfram would add — and he’s right — that you should learn a high-level language like Mathematica or the Wolfram Language, where the abstractions are close enough to thought that you’re expressing what to compute, not wrestling with how to allocate memory. The less distance between the idea and the code, the more the programming teaches you to think. But the specific language matters less than the main point: learn to program. Period.

A physicist thinks about what’s true. A mathematician thinks about what must be true. An economist thinks about what’s optimal. A computational thinker asks: what happens when you actually run it?

That question — what happens when you actually run it? — is the question that separates people who understand markets from people who have theories about markets.

Theories are beautiful. Backtests are seductive. Models are comforting. And then you run it. With real money. In real time. Against other agents who are adapting to you as you adapt to them. And you discover that the map is not the territory. That it was never the territory. That the entire skill of trading is navigating the gap between the two.

Hacker Wisdom

The hacker culture at MIT figured this out decades ago. Not about trading — about computing itself. They encoded their hard-won insights in a peculiar literary form: koans.

If you haven’t read the koans from the MIT AI Lab, you should (be patient, the page takes time to load!). They were composed by Danny Hillis (who would later build the Connection Machine) and concern legendary figures like Marvin Minsky, Gerald Sussman, and Tom Knight. They’re short. They’re funny. And they contain real technical insight wrapped in Zen Buddhist packaging.

Here’s my favorite. A novice was trying to fix a broken Lisp machine by turning the power off and on. Tom Knight, seeing what the student was doing, spoke sternly: “You cannot fix a machine by just power-cycling it with no understanding of what is going wrong.” Knight turned the machine off and on. The machine worked.

This is not a story about hypocrisy. It’s a story about the difference between doing something without understanding and doing the same thing with understanding. Knight power-cycled the machine because he understood what was going wrong. The novice power-cycled it instead of understanding what was going wrong. Same action. Completely different epistemology.

Trading is full of exactly this distinction. Here’s one.

The Master Trader and the Losing Position

A novice was adding to a losing position in a biotech stock that had dropped 20% on an FDA delay.

The Master Trader, seeing what the novice was doing, spoke sternly: “You cannot fix a losing trade by making it bigger with no understanding of why it went against you.”

The Master Trader added to the position.

The position recovered.

Jim Morrison opened “The Soft Parade” by screaming: “You cannot petition the Lord with prayer!”

You cannot petition the market with backtests. But you must do them — just as the devout must pray. The key is to understand what you are doing when you backtest. A backtest is not a petition. It is not “I ran the numbers, therefore the market owes me this Sharpe ratio.” A backtest is a diagnostic — it tells you something about your assumptions, not about the future. Confuse the two, and you are praying for profits.

Some Trading Koans

In that spirit, I offer more koans about the quantitative trading arts. The Master Trader is a figure of longstanding reputation. The Quant is a recent PhD. The Risk Manager is the person who works either for the regulators, in which case he is worse than useless, or for the fund, in which case he is merely useless.

The Quant and the Backtest

A novice came to the Master Trader and said: “I have backtested my strategy over twenty years of data. It has a Sharpe ratio of 3.2 and never had a losing year.”

The Master Trader said: “How many parameters does it have?”

“Only seven,” said the novice. “Each one is necessary.”

“And how many did you try before settling on these seven?”

The novice was silent.

“With enough parameters,” said the Master Trader, “I can fit an elephant. With one more, I can make it wiggle its trunk.”

The novice said: “But this is not a curve fit. I have an economic rationale for each parameter.”

“Ah,” said the Master Trader. “Then you found the rationale before or after you found the parameter?”

The novice was enlightened.

The Risk Manager’s Koan

A student asked the Risk Manager: “What is the worst loss our portfolio can sustain?”

The Risk Manager said: “Our Value at Risk is two percent.”

The student said: “Then we cannot lose more than two percent?”

The Risk Manager said: “No. I said that is the Value at Risk.”

“What is the difference?”

“Value at Risk is the amount you will lose on an ordinary day when you happen to lose money. It tells you nothing about extraordinary days.”

“Then what protects us on extraordinary days?”

“Nothing that can be expressed as a single number.”

The student said: “Then what good is the number?”

The Risk Manager said: “It satisfies the regulators.”

The student was enlightened.

Drescher and the Toaster (for a Trading Desk)

A disciple of another sect — a wealth advisor — once came to the Master Trader as he was reviewing his morning positions.

“I would like you to fill out this risk tolerance questionnaire,” said the outsider, “because I want you to manage your portfolio appropriately.”

The Master Trader took the questionnaire and fed it into the shredder, saying: “I wish the shredder to have the appropriate risk tolerance, too.”

Debug the Strategy

A Quant came to the Master Trader in great distress. “My strategy worked perfectly for three years and has now lost money for six consecutive months. I have checked the code line by line and there are no bugs.”

The Master Trader said: “The code is not the strategy.”

“What do you mean? The code implements the strategy.”

“The strategy,” said the Master Trader, “includes all the other funds running the same code.”

The Quant was enlightened.

The Nature of Alpha

A novice asked the Master Trader: “Where does alpha come from?”

The Master Trader said: “From other traders’ mistakes.”

“Then what happens when the other traders stop making that mistake?”

“Then your alpha was never yours. It was merely on loan.”

“How do I find alpha that is truly mine?”

“You are asking the wrong question,” said the Master Trader. “Ask instead: whose mistake am I?”

On Simplicity

A young Quant, freshly arrived from a machine learning program, presented his model to the Master Trader. It used an ensemble of gradient-boosted trees, a transformer for sequence modeling, and a graph neural network for cross-asset dependencies.

The Master Trader examined it carefully and said: “What does the model do when the Fed raises rates by 75 basis points for the first time in 28 years?”

“It has never seen such an event in training,” admitted the Quant.

“And what does your model do when it encounters something it has never seen?”

“It extrapolates from the nearest—”

“It guesses,” said the Master Trader. “Your model is a very expensive way to guess.”

“Then what should I use?”

The Master Trader showed him a chart with a single moving average on it.

“That’s it?” said the Quant. “A child could do that.”

“Yes,” said the Master Trader. “And it will work on the day nothing else does, precisely because a child could do it.”

Moon Instructions (for a Trading Desk)

A junior trader came to the senior trader and said: “I have found an arbitrage. The ETF is trading at a discount to its net asset value. I will buy the ETF and short the underlying basket.”

The senior trader said: “What is the carrying cost of the short?”

“Negligible.”

“What is the redemption mechanism?”

“I am not an authorized participant.”

“Then who will close the discount?”

“Market forces.”

“You have described a trade that requires someone else to do the work and give you the profit,” said the senior trader. “This is not arbitrage. This is hope.”

The Empty Order Book

A novice was studying the order book, watching bids and offers flicker and vanish.

“The book is full of lies,” he said to the Master Trader. “Ninety percent of these orders will be cancelled before they execute.”

“Yes,” said the Master Trader.

“Then how can I make decisions based on the order book?”

“You cannot,” said the Master Trader. “But neither can anyone else. And that is the information.”

Sussman Attains Enlightenment (Hedge Fund Remix)

A Quant was training a reinforcement learning agent to trade equities. The Master Trader came to him as he sat before six GPUs.

“What are you doing?” asked the Master Trader.

“I am training an agent to maximize Sharpe ratio. It has already learned to avoid drawdowns, size positions, and hedge tail risk.”

“Interesting. What happens if you reset the weights to random?”

“It would have to learn everything again from scratch.”

“And how would you know when to reset the weights?”

“I wouldn’t reset them. The agent has already converged.”

“But the market has not,” said the Master Trader.

The Quant was enlightened.

The Meta-Koan

Here’s what all ten koans have in common: they’re about the gap between the map and the territory.

The backtest is a map. The live market is the territory. The VaR number is a map. The tail event is the territory. The risk tolerance questionnaire is a map. The decades of scar tissue are the territory. The code is a map. The strategy-plus-competitors is the territory. The order book is a map. The intentions behind the orders are the territory. The converged model is a map. The non-stationary market is the territory. Averaging down is a map. Whether the thesis survived the drawdown is the territory.

And this is exactly what computational thinking teaches you.

When you learn to program — really learn, not just memorize syntax — you develop an instinct for the ways that formal systems diverge from the reality they’re supposed to model. You learn that code compiles but doesn’t work. That tests pass but the software fails. That the specification is not the system. You learn, at a visceral level, that running it is different from reasoning about it.

That instinct is worth more in trading than any amount of mathematics. Not because mathematics is wrong — the math is essential. But because mathematics tells you what should happen in the model, and computational thinking tells you all the ways the model is not the market.

The market is a computation that cannot be shortcut. You cannot derive what it will do from first principles. You cannot backtest your way to certainty. You cannot model your way past regime changes. The only way to know what happens is to run it — with real money, in real time, against real adversaries.

If that sounds like computational irreducibility, it’s because it is.

The Master Trader is not smarter than the Quant. The Master Trader has merely been enlightened more expensively.

For more on computational irreducibility and why even deterministic systems resist prediction, see my book The Science of Free Will. For the original AI Koans that inspired this post, see the Jargon File. For the rigorous version of why your drawdown rules are probably hurting you, see my paper “The Stop-Loss That Stops Gains.” And for a longer conversation about these ideas, see my two-part interview with Simon M. of Algo Advantage: Part 1 and Part 2.

On April 20th, the inimitable Matt Levine brought this one to my attention, as he so often does. What follows is my own take on it, in the physicist register, which is a different angle from his lawyer-and-markets one. You should read him too.

“It’s the hottest product in Greenwich,” one Wall Street person told the Financial Times in mid-April about tax-aware long-short hedge funds. The FT reporter added that fathers at a local sports game were fixated on AQR’s Delphi Plus fund. I live in Greenwich. I have not personally been cornered at a sideline — yet — but the day is young, and the ambient enthusiasm is real. So is the product category: $90 billion of net new money since the start of last year, almost a third of AQR’s assets now in tax-aware funds, and new entrants from Two Sigma, Quantinno, JPMorgan, WorldQuant, and Millennium piling in behind.

I kept reading the tax-aware long-short pitch decks and something felt off, but I couldn’t articulate it. Then I remembered I’m a physicist. Just a simple-minded trader and theoretical physicist. I ought to be able to articulate it. And so, here you are.

Three things about taxes, before we start

Before we can even begin to talk about what these funds do, a few things are worth stating plainly, because most of what comes later follows from them.

First: tax authorities are very good at collecting taxes. They have the entire mechanism of the monopoly on force behind them. No one pays taxes because they want to. People pay taxes because they are forced to — at the point of a gun, or loss of freedom. This is a useful thing to keep in your head whenever someone shows you a product that claims to reduce your taxes. The IRS has been doing this for a long time. They’re reasonably competent, and the ones who aren’t competent are backed up by people who are.

Second: if something seems amazingly good from a tax point of view, there is almost certainly a catch. Sometimes the catch is visible in the small print. Sometimes it’s visible only to someone willing to do the terminal accounting. Sometimes it’s not visible at all until the government gets around to looking at it.

Third: the catch usually arrives late. This is the part everyone forgets. You may run a structure for a decade and collect annual statements showing enormous savings before the bill shows up. The IRS is not a market-maker providing instant feedback. They are a collection agency with infinite patience and a monopoly on force. If something was wrong, you will find out — but possibly not for ten years.

One more contemporary note before we start. On April 17th, Bloomberg reported that a short seller called Orso Partners has taken a position against Affiliated Managers Group, the $800 billion investment firm that owns a stake in AQR. The specific thesis: concerns about the tax-aware long-short ecosystem. Real capital, real position, real bet against the boom. When someone is willing to put money against the hottest category on Wall Street, it is worth asking why. What follows is my version of the answer.

RenTech, or: how the smartest people in the room lost

If you want a case study for the third point, consider Renaissance Technologies.

RenTech — arguably the most quantitatively sophisticated hedge fund in the history of the industry — used a structure called a “basket option” from 2005 to 2015. The mechanics: Medallion (the insider fund, run on employees’ own money) bought options from Deutsche Bank and Barclays whose value tracked baskets of stocks that Medallion itself directed the banks to trade. Stocks held for days or seconds inside the basket; gains claimed as long-term because the option was held for more than a year. Presto: short-term gains converted to long-term.

In 2014, Senator Carl Levin’s subcommittee called the structure out publicly. RenTech fought it through IRS Appeals for seven more years. In September 2021 — sixteen years after they started — the firm’s current and former executives settled for approximately $7 billion. Largest tax settlement in U.S. history. Twice the prior record.

Notice the pattern. Ten years of running the structure. Seven years fighting it. A decade-plus of annual statements that looked great. And then the bill.

Notice also who lost. It wasn’t some marginal boutique. It was the people with presumably the best tax counsel in the world, running the structure on their own capital, in the fund with the highest Sharpe ratio in history. If those guys couldn’t make aggressive tax structuring stick — couldn’t, in the end — that should tell you something about the base rate.

Put aside the risk of that here

But let’s be generous. Let’s assume the tax-aware long-short category has constructed enough of a fig leaf that the strategy really does have economic substance independent of its tax benefits. Assume it survives any IRS challenge. Assume the factor bets are real, the wash-sale engine is immaculate, the short book actually holds up, and the whole apparatus does exactly what the pitch deck says it does.

Then what?

Then we can ask the real question: Are you actually saving any taxes?

And beneath that, the even better question: What is really going on?

What’s being sold

Briefly, for those who haven’t been pitched one of these: a tax-aware long-short fund is a factor strategy run at gross leverage — 150/50, 200/100, 250/150 — wrapped around a tax-loss-harvesting engine. The idea is that by going long and short a few hundred to a few thousand names, you generate a much richer stream of tax lots than a long-only direct-indexing book would. Dispersion creates losers. Losers create harvestable losses. Harvested losses offset gains you have elsewhere. The annualized savings — “tax alpha” — is typically quoted somewhere in the range of 1.5% to 2%.

AQR has been the most visible marketer of the category. According to the FT, they have added $47 billion of tax-aware assets since March of last year — almost a third of the firm’s total book. Quantinno, an independent firm founded by ex-AQR traders in 2018, has pulled in another $39 billion since January 2025. Two Sigma’s Beacon Fund, $950 million. JPMorgan, Merrill, WorldQuant, Millennium, Morgan Stanley’s Parametric — everybody is in. The underlying academic work by Sosner, Liberman, Sialm, and others is careful and honest. The pitch decks built on top of it, less so. And the scale — $90 billion of net new money in eighteen months — is precisely the thing that should make anyone who has watched cycles before start paying attention.

Books must balance

Here is the physicist’s reflex. If you trace the entire arc of a strategy — dollar in at the beginning, dollar out at the end, including every tax interaction in between — the accounting has to close. The IRS does not forget. They have not agreed to give up their claim on your gains. They have agreed to let you sequence those claims across time, within certain rules.

Call the investor’s total lifetime tax bill on the round trip T. Then the question to ask of any “tax alpha” strategy is whether it reduces T, or merely redistributes T across time.

Those are very different things. And the difference is where this post is going.

What the long-short structure actually does

To be fair, the long-short structure genuinely does something that a long-only direct-index book cannot do, and it is worth crediting honestly.

In a rising market, a long-only direct index runs out of harvestable losses. Everything is above cost. The harvesting yield decays to zero. This is arithmetic.

A long-short book has two structural refresh channels that a long-only book lacks. First, the short leg generates losses on winners — when a name you’re short appreciates, the short position has an unrealized loss that can be harvested by covering and re-shorting. In a rising market, the short book is the harvesting engine. Second, the factor tilts rebalance continuously — value and momentum and quality rotate against each other, positions open and close, and each rebalance establishes fresh cost bases. Every new lot at today’s price is, functionally, a new contribution from the harvesting engine’s point of view, even with no cash added.

Plus: gross leverage of 2-4x scales the lot surface area roughly linearly. More lots, more dispersion, more opportunities.

So the honest summary is: tax alpha decays, but in a long-short structure it decays more slowly than in long-only because the short book and factor rebalancing keep reseeding the loss inventory.

Fine. That’s real. But none of it escapes the conservation law.

The conservation law

Harvesting does not make taxes disappear. Harvesting realizes a loss today, which lowers your tax bill today, but it also lowers your cost basis (or for shorts, establishes a low basis) on whatever you reopened. When you eventually close that position at a gain, the gain is larger by exactly the amount of the loss you harvested. The IRS gets the same money. They just get it later.

If the strategy works and the factor book grinds up over decades, the aggregate book will drift to a progressively lower and lower basis relative to its market value. The deferred tax liability on the book grows. You can see the number on the statement — look at the “unrealized gains” line and multiply by your tax rate. That is what you owe, eventually, to the IRS. It is a liability. It is real. It is growing.

This is unavoidable. It is conservation. Harvesting shifts T forward in time. It does not reduce T.

What actually reduces your lifetime tax bill

Now — to be honest — there are ways the strategy can reduce T rather than merely shift it. They are narrower than the marketing suggests, and they are the entire real case for the product. Four of them:

1. Character conversion. If you have short-term gains elsewhere — trading profits, comp, carried interest in a short-term fund — taxed at ordinary rates (roughly 40% federal plus state), and the short leg throws off short-term losses, you are converting ordinary-rate tax into deferred long-term-capital-gains-rate tax. That is real new money: approximately the rate spread (say, 20 percentage points) times the volume of offsets. This is the strongest part of the case. It also means the product is genuinely well-suited to a specific kind of client — someone with large ordinary-rate income streams elsewhere — and materially weaker for everyone else.

2. Rate or jurisdiction arbitrage. Harvest at 37% federal plus 13% California today. Realize at 20% federal in Florida or Puerto Rico in retirement. Real, situational, not structural.

3. Step-up at death, or charitable transfer. The deferred liability actually evaporates. This is the one that does all the work in the long-run IRRs the marketing implies. The product is implicitly an estate-planning vehicle whenever this is the assumed exit.

4. The time value of the deferral itself. Delaying a dollar of tax is worth something — roughly the discount rate times the duration. At a 5% real discount rate over a 20-year horizon, deferring $1 of tax is worth about 60 cents in present value terms. Annualize that against the gain base and you get something like 1% a year — not zero, but also not the 1.5-2% in the pitch book. The deck annualizes the year-one harvesting bonanza; the reality is a front-loaded decay.

That’s it. Everything beyond those four items violates the conservation law and should be treated as rhetorical flourish.

Run the numbers

Let’s make this concrete with a round example.

Start with a $10 million portfolio. The marketing quotes a 2% tax alpha — so $200,000 in year one. The management fee on a tax-aware long-short wrapper is typically around 100 bps, call it $100,000. The financing spread on the short book (stock borrow, margin, the cost of running a 150/50 or wider structure) is around 50 bps of NAV on the modest end, call it another $50,000. Subtract: $50,000 of net benefit in year one. A quarter of the headline.

That’s the best year.

The harvesting yield decays as the embedded gain grows — the published academic curves show it roughly halving by year ten. But the fees don’t decay. By year ten you’re still paying $150,000 in fees and financing, but you’re generating maybe $100,000 of gross tax alpha. You are now losing $50,000 a year, net. By year fifteen, on any realistic factor book, the product is inside the fee. You are paying the manager to reduce your after-tax return.

And that’s the generous case. At the more aggressive 250/150 leverage common in the wrappers actually sold to retail, with retail pricing around 125 bps, the financing spread alone runs ~150 bps of NAV. Year one becomes $200,000 gross tax alpha minus $125,000 management minus $150,000 financing — negative $75,000, before any decay at all. The product is underwater from day one. The conservation law never even gets to assert itself, because the fee structure got there first.

Meanwhile, the 75/25 SPY/QQQ buy-and-hold investor paid around $12,000 a year in blended expense ratio. They realized nothing. They generated zero tax drag. The difference in net outcome over fifteen years is not marginal — it is orders of magnitude in the wrong direction.

This is before you account for the benchmark problem, which is where it gets much worse.

The benchmark that isn’t in the pitch deck

The product’s entire “tax alpha” number is quoted against a benchmark that itself generates continuous realizations. Of course a tax-aware strategy beats an actively managed one. But the relevant comparison for a long-term investor is not tax-aware-long-short versus actively-managed-churn. It is tax-aware-long-short versus two ETFs and a long nap. And on that comparison — which is the one the pitch deck is structured to avoid — the two ETFs win for almost any client who doesn’t have large ordinary-rate income to offset. Because the conservation law is trivially satisfied when you never realize anything to begin with. Deferral is free. You don’t need to pay anyone 100-150 basis points a year to give it to you.

At this point the sponsor’s fallback is always the same: forget the tax alpha, we also generate pre-tax alpha from the factor book — real returns, above market, net of fees. Fine, except: (a) if the factor book really did earn 200+ basis points of persistent net-of-fee pre-tax alpha, the manager would not need to lead with the tax story at all, and would not be marketing primarily to high-net-worth taxable clients; (b) the two decades of out-of-sample performance on most published equity factors have not been kind to that claim — value, momentum, quality, low-volatility have all gone through long stretches where net-of-fee returns trailed the index (I have written about this problem before); and (c) if you actually want factor exposure, you can buy a factor ETF with an expense ratio in the 15-30 bps range and be done with it. Wrapping the factor bet inside a leveraged tax-optimization engine does not make it more likely to earn alpha. It just makes the fee structure bigger. The pre-tax alpha defense is an argument for some factor product, maybe, for some investor. It is not an argument that specifically rescues the tax-aware long-short wrapper.

It gets worse for the product. The step-up-at-death case — item 3 above, which does most of the heavy lifting in the long-run IRR the marketing implies — is available to the buy-and-hold investor without the product. Hold the two ETFs until death, and the heirs inherit at stepped-up basis. Zero tax on the entire appreciation. Permanently. The long-short investor also gets step-up on their final embedded gain, but they paid 100-150 bps a year along the way for a structure whose largest tax benefit was going to be delivered by the step-up regardless. You paid fees for twenty years to receive, at the end, exactly the outcome you would have received for a dozen basis points a year.

And it gets worse still. The buy-and-hold investor has a second tool available that the long-short investor structurally does not: borrow against the portfolio.

This is not exotic. Post your SPY/QQQ as collateral against a securities-based line of credit. Rates inside of a mortgage, because the lender’s collateral is liquid and daily-marked. Spend the proceeds on whatever you want — house, tuition, boat, consumption, whatever consumption means to you. Loan proceeds are not income. You owe no tax on them, because you have not sold anything. The portfolio keeps compounding. The loan interest accrues. You die. Your heirs inherit at stepped-up basis, sell a piece of the position to retire the loan at zero tax (because basis equals market), and keep the rest clean.

Lifetime tax on the appreciation: zero. Consumption utility along the way: whatever you wanted. This is not a loophole. This is buy, borrow, die, and it is how essentially every large long-term holder of appreciated stock in the country actually manages their affairs.

The long-short product cannot do this efficiently. The short book consumes collateral capacity that would otherwise be available to borrow against. The realized short-term character generated by the structure has to be netted somewhere. The complexity of the positions impairs their use as clean collateral. The product is, at a structural level, at war with the single most powerful lifetime-tax-minimization strategy available to a long-term holder of appreciated securities.

So the full picture is not “tax-aware long-short beats buy-and-hold on tax alpha.” It is the opposite:

• Buy-and-hold beats the long-short product on realized tax drag, for anyone without large ordinary-rate income to offset, because it has no realized tax drag.

• Buy-and-hold delivers step-up at death for a dozen basis points a year instead of 100-150.

• Buy-and-hold lets you finance consumption against the portfolio without realizing, and the long-short structure actively interferes with this.

Three different mechanisms. All pointing the same direction. The product is not merely dominated by the naive alternative — it obstructs the two most powerful tax-minimization tools the naive investor already has.

The product is optimized for someone who never unwinds

Here is the quiet structural thing worth saying out loud.

The sponsor charges fees on the full NAV of the fund. The NAV includes the embedded, deferred tax liability — the portion of the “value” that the investor does not actually own, because it is owed to the IRS. The manager earns fees on that liability, forever, or at least until the client unwinds. If the client unwinds, fees stop, and the conservation law asserts itself: the back taxes come due in one brutal lump.

Therefore: the product is optimized for a client who never unwinds. Which is to say, for a client whose exit is death, or charitable giving, or a migration to a lower-tax jurisdiction. None of those are investment outcomes. They are estate-planning or lifestyle assumptions.

That is not an accusation. The math is clean, the academic papers are careful, and the more honest sponsor decks will say something like this in the footnotes. It is simply that the product is being sold as investing, and it largely works, to the extent it works, as estate planning with an investment wrapper. Those are different things. The investor should know which one they’re buying.

The general test

The conservation argument applies, with different constants, to most structures sold primarily on tax benefits — 351 ETF conversions, exchange funds, opportunity zones, insurance wrappers. The question to ask is always the same: is this reducing my lifetime tax bill, or is it shifting it forward and hoping for a terminal event that extinguishes it? If the product only delivers on its pitch assuming a life plan you could have executed without it, then what you’re buying is the reassurance of having bought something.

The IRS has the monopoly on force. The books must balance. Anyone offering you a shortcut around either of those is offering you something else.

For more on applying physicist-style reasoning to other domains where it tends to dissolve more than it’s supposed to — including theology, political science, law, and the question of free will itself — see my book, The Science of Free Will.

In a recent physics post, I showed you a single formula that fits every fermion mass ratio in nature using just four numbers. Quarks, electrons, neutrinos — all of them. But I never told you how I knew to look for it.

That’s this story. And it starts with one of the most tantalizing mysteries in all of physics.

A Number That Shouldn’t Exist

In 1981, a Japanese physicist named Yoshio Koide noticed something extraordinary. Take the three charged leptons — the electron, the muon, and the tau. These are the electron and its two heavier cousins: the muon is about 207 times heavier than the electron, and the tau is about 3,477 times heavier. Their masses seem arbitrary, chosen by nature through some inscrutable process.

But Koide found that if you write down this particular combination — using mₑ = 0.511 MeV, mμ = 105.66 MeV, and mτ = 1776.86 MeV:

you get:

Two-thirds. To five decimal places.

Let that sink in. Three masses spanning a factor of 3,477 — masses that appear to follow no pattern whatsoever — produce a ratio that is almost exactly 2/3 when you plug them into this formula.

This is either a cosmic coincidence or a clue. For over forty years, physicists have been unable to determine which.

Why This Drove Physicists Crazy

The Koide relation isn’t like other “near misses” in physics. It’s too precise to ignore and too mysterious to explain. Hundreds of papers have been written about it. Some tried to extend it to quarks — those attempts failed, because if you naively plug quark masses into the same formula, you don’t get anything special. Others tried to derive it from symmetry principles, flavor models, or grand unified theories — but none of these explanations stuck.

The formula just sat there: beautiful, precise, unexplained.

And here’s what made it so maddening: Koide’s formula involves a dimensionless ratio. The masses themselves have units — electron volts, kilograms, whatever convention you prefer. But K is a pure number. If aliens in another galaxy measured the same particles in completely different units, they’d get the same 2/3.

That matters, because dimensionless quantities are nature’s real choices. The absolute mass of the electron depends on our measurement conventions. But a dimensionless ratio like K depends on nothing but the laws of physics themselves. Dimensionless quantities should, in principle, be calculable from first principles.

Which means 2/3 isn’t an accident. It’s a message. The question was: from whom?

Three Generations, No Explanation

Before I tell you what I found, let me set the stage.

The world is made of atoms. Atoms are electrons orbiting a nucleus. The nucleus consists of protons and neutrons. And protons and neutrons are made of quarks — specifically, “up” quarks and “down” quarks.

That’s generation #1: the electron, the up quark, and the down quark. That’s all you need to build every atom in the periodic table, every molecule in your body, every star in the sky.

Yet nature, for reasons no one understands, created two additional generations that are exact copies of these three particles, just heavier. The muon and the tau are copies of the electron. The strange and charm quarks are copies of the up and down. The bottom and top quarks are a third copy.

These heavier copies appear to serve no purpose. They decay almost instantly into their lighter cousins. The top quark — the heaviest of them all, weighing as much as an entire gold atom — survives for about 0.0000000000000000000000005 seconds before it disintegrates. Why does nature bother creating it?

And the mass ratios between generations are bizarre. The top quark is nearly 80,000 times heavier than the up quark. These aren’t small variations — they’re enormous, irregular jumps that nobody can explain.

The Standard Model of particle physics, for all its triumphs, simply takes these masses as inputs. It doesn’t predict them. They’re among the deepest unexplained numbers in all of science.

What I Discovered

I found the reason Koide’s formula works. And it’s not a coincidence — it’s required by a mathematical symmetry rooted in string theory. The paper has been peer-reviewed and published; the math is the math.

Let me explain this at three levels of depth. Pick the one that suits you.

The headline version: There’s a mathematical consistency condition — called modular invariance (the requirement that the physics doesn’t change when you re-parameterize the shape of the extra dimensions, much as Einstein required that physics not depend on your choice of coordinates) — that comes from the way extra dimensions are folded up in string theory. This condition forces the Koide ratio to be exactly 2/3. It’s not optional. It’s not approximate. The symmetry demands it.

Going a bit deeper: In string theory, the extra dimensions beyond our familiar three can be “folded” in specific ways called orbifolds. Think of it like origami — you take a flat sheet and fold it so that certain points are identified with each other, creating corners and symmetry points.

When you fold the extra dimensions this way, the folding creates two natural “building blocks” — two different ways to symmetrically combine the three masses in a given sector. Call them S₁ and S₂. For the mathematically inclined: S₁ is the sum of the masses raised to one power, S₂ is the sum raised to a different power, and those powers are determined by the folding.

Now here’s the key: there is exactly one ratio you can construct from those two building blocks that is (a) symmetric — it doesn’t matter which mass you call “first” — and (b) scale-independent — it doesn’t change if you measure masses in electron volts or kilograms or banana-weights.

Just one. And when you compute it for the electron, muon, and tau: 2/3.

Not because you chose the formula to give 2/3. Because the folding of the extra dimensions only allows one formula, and that formula happens to give 2/3.

The full story in plain English:

If the next few paragraphs make your head spin, skip ahead to “The Quark Surprise” — you won’t lose the thread of the argument.

Here’s how the construction actually works.

In a particular class of string theory constructions called orbifold conformal field theories, the extra dimensions are compactified using discrete symmetries — a ℤₖ group, where k is called the orbifold order. The idea is simple: you rotate the extra dimensions by 360°/k and declare that the rotated configuration is identical to the original. For ℤ₂, you rotate by 180°; do it twice and you’re back where you started. For ℤ₃, you rotate by 120°; three steps to return.

This folding creates “twisted sectors” — particles that are trapped at the symmetry points of the fold, like a crease in a piece of paper. These twisted sectors come with characteristic fractional numbers called “degrees,” one from the left-moving and one from the right-moving vibrations of the string. (A string vibrates in two directions independently — “left-movers” and “right-movers.” The chiral degree comes from one direction alone; the left-right combined degreecombines both directions.) These two degrees give us our two building blocks:

The factor of 2 in the second exponent comes from a basic property of the theory: the total scaling dimension (a number that tells you how a quantity changes when you zoom in or out — essentially, how it “scales” with energy) is twice the chiral dimension, because you have both left-moving and right-moving contributions.

Now, I want a ratio — an observable — that respects three conditions:

1. Permutation symmetry: It shouldn’t matter which mass I label “1,” “2,” or “3.”

2. Scale independence (degree zero): If I rescale all masses by the same factor, the ratio shouldn’t change.

3. Charge quantization: The powers in the ratio should be consistent with the electric charges of the particles, as enforced by modular invariance (the same coordinate-independence condition I described above — it constrains which combinations of charges and orbifold orders are mathematically self-consistent).

It turns out that conditions 1 and 2 together force the ratio to have the form S₁^α / S₂^(2α) for some power α. Why the factor of 2? Because S₁ has homogeneity degree 1/k (each mass is raised to 1/k), and S₂ has degree 1/(2k). For the ratio to be scale-independent — degree zero — the total degree upstairs must equal the total degree downstairs: α/k = β/(2k), which forces β = 2α. And condition 3 — which comes from requiring mathematical consistency of the orbifold — fixesα uniquely: α = kq, where k is the orbifold order and q is the minimal electric charge quantum.

The result is:

This is the master formula. It has no free parameters — once you specify the particle sector (which fixes k and q), the formula is completely determined.

For charged leptons (i.e., the electron, muon, and tau), the orbifold order is k = 1 and the charge quantum is q = 1. Plug those in:

That’s Koide’s formula. And it gives 2/3.

The Quark Surprise

Here’s where it gets really interesting. Half the physics world had tried to extend Koide to quarks and failed. The reason they failed is that they were plugging quark masses into Koide’s formula — the one with square roots. But the orbifold framework says: don’t do that. Quarks have different quantum numbers than leptons, so they live in a different orbifold sector, which means k and q are different, which means the formula itself is different.

For quarks, the orbifold order is k = 2 (a ℤ₂ symmetry) and the charge quantum is q = 1/3 (because the smallest quark charge is 1/3 of the electron’s charge). Plug those into the master formula:

Note: this sums over all six quark masses. And when you evaluate it using the measured masses at the Z-boson scale:

One-half. To four decimal places.

No one had found this before, because no one had the right formula to look for. They kept trying the lepton formula on quarks. But the orbifold says: different charges, different folding, different formula, different answer.

And notice the beautiful structure: the master formula R(k, q) contains both results. When k = 1 and q = 1, you get Koide. When k = 2 and q = 1/3, you get the quark ratio. The framework generates different formulas for different sectors from a single principle. You don’t have to guess the right formula — the mathematics hands it to you.

A note for physicists: the formula uses electric charge |q|, not hypercharge Y — and the absolute value |q|, not the signed charge q. Both choices are surprising. Above the electroweak scale, hypercharge is the “fundamental” quantum number, and electric charge only emerges after symmetry breaking — yet the mass spectrum organizes itself around the broken quantity. Hypercharge does not work. And the sign of the charge, which distinguishes up-type from down-type fermions, is also irrelevant — only the magnitude matters. The same is true of the universal formula from my previous post: replacing |q| with |Y| destroys the fit (because hypercharge no longer distinguishes up-type from down-type quarks), and using signed q instead of |q| also fails. Note, however, that the two formulas use charge differently: the orbifold formula uses the minimum charge quantum of the sector (|q| = 1 for leptons, |q| = 1/3 for all quarks), while the universal formula uses each particle’s own charge (1, 1/3, or 2/3). Yet both insist on |q| over Y. This may be telling us something deep about the relationship between flavor structure and electroweak symmetry breaking.

It’s Not a Coincidence

Two natural questions: Is 1/2 really exact? And does it stay 1/2 at different energy scales?

On the first: the value Rq = 0.4995 is consistent with 1/2 within experimental uncertainties. The masses of light quarks (up, down, strange) are notoriously hard to measure — their uncertainties are 10–20%. The fact that six masses with these large individual uncertainties conspire to produce a ratio within 0.1% of 1/2 is remarkable.

On the second: in quantum field theory, particle masses “run” — they change depending on the energy scale at which you probe them. The individual quark masses change enormously between the electroweak scale and 10¹⁴ GeV. But I showed that Rq barely budges. At one loop (the first level of quantum correction, where you account for virtual particles popping in and out of the vacuum once), it’s exactly invariant — due to a cancellation involving the “gauge group structure” (don’t worry about what that means; the point is that it cancels exactly, not approximately). At two loops (the next level of correction, which is far more intricate), it shifts by only a few parts in a thousand over twelve orders of magnitude in energy.

This means the relation is fundamental, not accidental. An accident would be destroyed by quantum corrections. A consequence of deep symmetry survives them — and that’s exactly what happens.

The high-scale prediction is: Rq(10¹⁴ GeV) = 0.500 ± 0.002. Upcoming lattice QCD calculations (a technique that simulates quark interactions on a discrete grid of spacetime points using supercomputers, allowing physicists to extract quark masses from first principles) should be able to test this.

The Moment Everything Changed

So here’s where I stood: I had explained a 40-year mystery (Koide’s 2/3) and discovered something new (the quark ratio of 1/2), both flowing from the same mathematical structure with zero free parameters.

And then the thought hit me.

If dimensionless mass ratios in the lepton sector are governed by a mathematical symmetry, and dimensionless mass ratios in the quark sector are also governed by the same symmetry... then there’s hidden structure in the fermion mass spectrum that no one has been fully exploiting.

And if there’s hidden structure, there should be a universal expression for all fermion mass ratios — not a separate formula for each sector, but one single relation that captures the whole pattern. A relation built from the most basic labels that distinguish particles: the generation number (1, 2, or 3) and the electric charge.

After all, those were the only ingredients that went into the orbifold construction: k depends on the charge family, and the formula is symmetric across generations. The deeper pattern should depend on nothing else.

So I went looking for it. And as readers of my previous post already know, I found it.

(And before you say “overfitting” — I tested that. I ran all 120 possible assignments of generation labels to masses; only the physical one fits. I scanned all 720 possible ratio bases; only one converges. Alternative functional forms are decisively ruled out by information criteria. The details are in the previous post and in the paper.)

The Larger Picture

Let me step back and tell you what I think this means.

For forty years, Koide’s formula was treated as a curiosity — a beautiful but isolated numerical coincidence. Nobody could explain it, nobody could extend it, and gradually most physicists filed it away as “probably a coincidence.”

It’s not a coincidence. It’s the tip of an iceberg.

The reason no one could extend Koide to quarks is that they were looking for the same formula in a different sector. But the orbifold framework says: the formula should be different for quarks, because quarks carry different charges and live in a different part of the geometry. What’s universal is not the formula but the principle — modular covariance (the requirement that the mass functional transforms in a controlled, predictable way under re-parameterizations of the extra-dimensional geometry, rather than being completely invariant) — that generates the right formula for each sector.

And once you see that the principle is universal, you realize the mass spectrum is trying to tell us something. The twelve fermion masses aren’t arbitrary constants. They’re constrained by deep mathematical structure — structure that might, ultimately, come from the shape of the extra dimensions in which we live.

The paper is published in Modern Physics Letters A and available here: https://www.worldscientific.com/doi/10.1142/S0217732326500732

The universal pattern paper is in the European Physical Journal C: https://link.springer.com/article/10.1140/epjc/s10052-025-14771-0

What’s Next

The universal formula from the previous post has four parameters:

where d is the generation index, q is the electric charge, and the fitted values are κ ≈ 2.3, γ ≈ 1.1, ζ₁ ≈ 1.17, ζ₂ ≈ −0.8 — all of order 1.

I’m now working on deriving those four parameters from first principles. If that succeeds, zero free parameters remain — and the twelve-order-of-magnitude fermion mass spectrum would be entirely determined by mathematical structure.

Stay tuned.

If you enjoyed this, you might enjoy my book The Science of Free Will. The fermion mass puzzle and free will might seem unrelated, but they share a deep question: can a system governed by rigid mathematical laws produce outcomes that are, in any meaningful sense, unpredictable? The answer — computational irreducibility — changes how you think about everything from particle physics to human choice.

In Part I, we applied the tetrad — physics, math, CS, economics — to anthropology, psychology, law, and linguistics. The pattern: take a field, identify its standard framing, apply all four lenses, and watch the real structure emerge.

Today we go deeper. Political science, aesthetics, history — and then something important: what happens when the tetrad catches its own errors.

Political Science

Standard framing: Democracy vs. authoritarianism. Left vs. right. The big debates are about which values should guide society, who should rule, and how to balance freedom against equality.

The tetrad:

Physics: Politics operates on territory, bodies, and weapons. The monopoly on legitimate violence is the foundation. Everything else is built on that substrate.

Math: Voting theory is mathematics. Arrow’s impossibility theorem. Gibbard-Satterthwaite. Coalition math. These aren’t just academic curiosities — they’re constraints, like thermodynamics.

CS: Constitutions are source code. Precedent is version control. Bureaucracy is runtime — taking abstract rules and executing them on specific cases. Political parties are long-running processes that seek to replicate themselves.

Economics: Power is a scarce resource. Political actors are self-interested optimizers. Policy outcomes are the equilibria of games between competing interests.

What falls out:

The first thing that falls out is a famous impossibility result. Arrow’s theorem says: no voting system can simultaneously satisfy a small set of obviously desirable properties (non-dictatorship, Pareto efficiency, independence of irrelevant alternatives). Something has to give.

This seems to suggest that all political systems are hacks around fundamental impossibility — that we’re always trading off one form of unfairness for another.

But wait. There’s an escape hatch.

The Approval Voting Escape

Arrow’s theorem applies to ordinal voting systems — systems where voters rank candidates. But what about cardinal systems, where voters assign independent scores?

Approval voting works like this: for each candidate, vote “approve” or “don’t approve.” Most approvals wins. Simple.

This completely sidesteps Arrow. Why?

• Arrow requires voters to submit complete rankings

• Approval voting doesn’t use rankings — each candidate gets an independent binary score

• The “Independence of Irrelevant Alternatives” criterion is satisfied trivially: adding candidate C doesn’t change whether you approve of A or B

So Arrow doesn’t apply. Approval voting is a genuine escape from impossibility, not a hack around it.

What about Gibbard-Satterthwaite, the other big impossibility result? It says any deterministic voting system with 3+ candidates is either dictatorial or manipulable (strategic voting is possible).

Approval voting is manipulable — you can strategically withhold approval. But:

1. The manipulation is transparent. You know you’re doing it.

2. The equilibria are often good. Approval voting tends to elect Condorcet winners when they exist.

3. The strategy is simple. Approve candidates above your expected utility of the winner.

The math says: approval voting isn’t perfect, but it’s provably better than plurality voting (what most democracies use) and arguably better than ranked-choice voting (which doesn’t even satisfy monotonicity — voting for a candidate can cause them to lose).

So why don’t we have it?

Why We Don’t Have Approval Voting

Here’s where the economics lens becomes essential.

The people who would need to approve the change to approval voting are exactly the people who won under the current system.

Think about it:

• Plurality voting converges to two parties (Duverger’s Law)

• Those two parties then control ballot access, debate rules, redistricting, and — crucially — voting system reform

• They’re not going to vote themselves into competition

It’s a monopoly that gets to write the antitrust laws.

This explains the pattern we observe:

• Approval voting wins in low-stakes elections where parties aren’t paying attention (Fargo, St. Louis, some professional associations)

• It gets crushed or ignored at state and federal levels where parties fight back

• Ranked-choice voting — which still tends to favor major parties over approval — gets adopted instead, often with party support, because it looks like reform while preserving the duopoly

Revealed preference: politicians say they want fair elections. They do block approval voting. Believe the behavior, not the words.

The UN Exception

Here’s what makes this argument airtight: the one place where political parties don’t dominate, approval voting emerges naturally.

The UN Security Council selects the Secretary-General through “straw polls” where each member nation indicates “encourage,” “discourage,” or “no opinion” on each candidate. This is essentially three-level approval voting.

In 2006, when Ban Ki-moon was selected, 15 countries voted on 6 candidates. The average number of “approves” per ballot was 2.6 — nowhere near degenerating to plurality voting. Voters genuinely used the full range of the system.

Why does the UN use this system? Because it’s an environment where no single party controls the rules. The member states have sufficiently divergent interests that no one bloc can rig the system in its favor. (Yes, the five permanent members retain veto power — but the straw poll itself operates without party-line discipline, which is the point. The approval mechanism works because no domestic-style duopoly controls the ballot design.) So they ended up with something close to optimal.

Donald Saari, a mathematician, once worried that approval voting would “degenerate” to plurality in practice — everyone would just approve their first choice. The UN Secretary-General elections are a high-stakes refutation: when parties can’t capture the rule-making process, approval voting works exactly as the math predicts.

Aesthetics

Standard framing: Beauty is subjective. Art can’t be analyzed. De gustibus non est disputandum.

The tetrad:

Physics: Light waves, pigment, sound frequencies, neural processing. Art is physical stimuli hitting sensory organs.

Math: Symmetry. Proportion. The golden ratio. Harmonic series. Fractals and self-similarity. There’s obviously somestructure to what humans find beautiful.

CS: Art is compression. A painting encodes more information than it literally contains — it evokes a scene, an emotion, an idea that would take thousands of words to describe. Good art achieves high compression ratios.

Economics: Art is status signaling. It’s attention economics. It’s costly signaling of fitness (the peacock’s tail, the patron’s Vermeer).

What falls out:

Beauty is the subjective experience of high compression — a lot of information encoded cheaply.

When you look at a beautiful face, a sunset, an elegant mathematical proof — what’s happening is that your pattern-recognition systems are clicking into place efficiently. The stimulus carries rich information but matches your priors well enough that encoding it is cheap. The prediction errors are small. It feels good because you’re getting a lot for a little. (A blank page has low computational cost too, but it carries no information — that’s why it’s not beautiful. Beauty requires something worth compressing.)

(This is the same principle that explains why retail traders overpay for binary options. A binary option is the “beautiful” financial product — two outcomes, trivially easy to evaluate. The bull spread pays more but costs more to process. Traders pay a simplicity tax for the same reason we find symmetry beautiful: high information at low processing cost feels good.)

This explains a lot:

• Why average faces are beautiful: They’re easier to encode. Each feature is close to the statistical mean, requiring less information to represent.

• Why symmetry is beautiful: One half predicts the other. Massive compression.

• Why “interesting” art violates expectations just enough: Too predictable (kitsch) is boring — you’re not learning anything. Too unpredictable (noise) is exhausting — you can’t encode it at all. Great art sits on the edge: compressible, but not trivially so.

• Why mathematical proofs can be “elegant”: An elegant proof is a short path through logical space. It compresses a complex truth into a small number of steps.

This doesn’t reduce aesthetics to neuroscience. You still need to understand art history, cultural context, the specific traditions a work is responding to. But it does give you the right organizing question: what are the computational properties of stimuli that humans find rewarding to process?

History

Standard framing: The great debate is “Great Man” vs. “Historical Forces.” Did Napoleon shape history, or did history shape Napoleon? Is it individuals or systems that drive change?

The tetrad:

Physics: Geography, climate, disease, calories, metallurgy. Jared Diamond wasn’t wrong about everything — the physical substrate matters enormously.

Math: Network effects. Exponential growth. Power laws. The mathematics of growth and contagion applies to ideas, empires, and plagues alike.

CS: History is the output of a cellular automaton we can’t rewind. Contingency is sensitivity to initial conditions. Path dependence is hysteresis — where you are depends on where you’ve been, not just where you are.

Economics: Malthusian traps. Institutional lock-in. Conquest as hostile acquisition. Civilizations rise and fall based on whether they solve the cooperation problems of their scale.

What falls out:

“Great Man” vs. “Historical Forces” is a false dichotomy. Both are true, at different scales of resolution.

At the macro scale — why did agriculture emerge in the Fertile Crescent? Why did Eurasia dominate? — individual choices wash out. Geography determines. The “forces” win.

At the micro scale — why did WWI start in 1914 rather than 1912? Why did the Archduke’s driver make that wrong turn? — we’re in the chaotic regime. Small perturbations cascade. Individuals matter.

The right question isn’t “which one?” It’s “at what resolution are we predicting?”

This has practical implications. If you’re trying to understand century-scale trends, focus on constraints: resources, geography, technology, demographics. If you’re trying to understand specific events, focus on agents: leaders, decisions, contingency. The tetrad tells you which lens to apply at which magnification.

The Tetrad Turned on Itself

Here’s where intellectual honesty matters. The tetrad is a framework, and frameworks can mislead. What happens when we apply the tetrad to its own examples?

QWERTY keyboards are a classic “path dependence” story. The standard narrative: QWERTY was designed to slow typists down (to prevent jamming on mechanical typewriters). Dvorak is faster. But we’re stuck with QWERTY because of lock-in. Market failure!

This story is too clean.

Let’s apply the economics lens to the story itself:

• August Dvorak designed the Dvorak keyboard

• August Dvorak conducted the Navy study “proving” Dvorak was faster

• Later independent studies (Liebowitz and Margolis) found minimal or no advantage

• The “designed to slow typists down” origin story is probably false anyway — QWERTY was designed to reduce jamming, which is different from reducing speed

So we have: - Dvorak had an incentive to find Dvorak superior — and lo, he did - Economists and journalists have an incentive to tell a good “market failure” parable — QWERTY is such a satisfying story that people repeat it without checking

The lesson: if a story seems too satisfying, ask who benefits from telling it.

This doesn’t mean path dependence is fake. It doesn’t mean lock-in never happens. But this particular example — perhaps the most famous example in the economics of technology — doesn’t actually work.

The tetrad demands rigor. Even for its own favorite stories.

Why We Don’t Get Nice Things

Let’s pull back to the big picture.

The tetrad tells you what’s optimal: the mathematically superior voting system, the economically efficient policy, the scientifically correct answer.

The economics leg tells you why you won’t get it.

Wherever you see a clearly superior solution that isn’t adopted, ask: who chooses the rules, and do they benefit from the current ones?

• Approval voting: Superior. Blocked by the duopoly.

• US healthcare: Wildly inefficient. But efficient for insurers, hospitals, and pharma.

• Academic publishing: Broken. But the prestige cartel benefits incumbents.

• Municipal zoning: Creates housing shortages. But protects incumbent homeowners’ property values.

• The filibuster: Enables minority rule. But senators like having it when they’re in the minority.

In each case, the people who would need to change the system are the people who benefit from it. The math says it’s broken. The economics explains why it stays broken.

This is not cynicism. It’s the opposite of cynicism. Cynicism says “nothing can be changed.” The tetrad says: change requires changing who writes the rules, or changing their incentives.

That’s actionable. It tells you where to focus.

Conclusion

Three posts, eight fields, one framework.

The tetrad isn’t a theory of everything. It’s a method for generating theories. Take any domain, apply four lenses — substance, structure, process, selection — and watch the real questions emerge.

Sometimes you’ll confirm what the field already knows. Sometimes you’ll dissolve fake debates (Universal Grammar). Sometimes you’ll identify impossibility results that constrain what can be achieved (Arrow). Sometimes you’ll catch yourself repeating satisfying stories that don’t survive scrutiny (QWERTY).

The point isn’t to replace expertise with armchair philosophizing. You still need to know the details. But the tetrad tells you which details matter and why.

Physics gives you the substrate. Math gives you the structure. Computer science gives you the dynamics. Economics tells you who’s optimizing for what, and therefore what will actually happen versus what “should” happen.

Put them together, and the world becomes more legible. Not simpler — more legible. You can see why things are the way they are, and what would have to change for them to be different.

That’s the project.

This post concludes the series on the tetrad framework. “The Simplicity Tax“ introduced the anomaly that motivated it. “The Tetrad“ laid out the framework and applied it to theology. “The Tetrad Tour, Part I“ covered anthropology, psychology, law, and linguistics. For the full development of these ideas — including the relationship between determinism, computational irreducibility, and free will — see my book The Science of Free Will.

In the last post, I argued that every field of human study reduces to four irreducible lenses: physics (substance), mathematics (structure), computer science (process), and economics (selection). Then I stress-tested the framework on the hardest possible case — theology — and showed how it separates the coherent positions on God from the incoherent ones, and why you can’t mix and match across the divide.

Now let’s see what happens when we point the tetrad at fields where the standard framing is less obviously wrong, but still missing something important.

The method is simple: take a field, identify its standard framing, apply all four lenses, and see what falls out that you wouldn’t get otherwise.

Anthropology

Standard framing: Cultures are diverse and contingent. They emerge from historical accident. You can’t judge one culture by the standards of another — that’s ethnocentrism. The proper stance is cultural relativism: describe, don’t evaluate.

The tetrad:

Physics: Humans are bodies. Bodies need calories. Bodies exist in environments with specific constraints — temperature, disease vectors, available prey, arable land. The physical substrate shapes everything downstream.

Math: Kinship systems aren’t random; they follow the mathematics of group theory. Marriage rules, inheritance patterns, and alliance structures have formal properties that can be analyzed independently of any particular culture. Claude Lévi-Strauss saw this decades ago. More broadly, wherever cultures create obligation networks — who owes what to whom — those networks have structural properties (cycles, leverage ratios, cascading defaults) that can be analyzed with the same tools we’d use on any graph.

CS: Culture is software running on human hardware. Memes — in Dawkins’s original sense — are programs that copy themselves with mutation and selection. Oral traditions are lossy compression algorithms for transmitting survival-relevant information across generations. Rituals are executable code: do this, in this order, and produce this outcome (social bonding, status marking, rite of passage).

Economics: Every culture is solving optimization problems under constraint. How do you coordinate behavior without centralized authority? How do you sustain cooperation in iterated games? How do you allocate scarce status, mates, and resources without constant violence?

What falls out:

Consider the potlatch — the ceremonial feast practiced by the Kwakwaka’wakw, Haida, Tlingit, and other peoples of the Pacific Northwest Coast. At these gatherings, chiefs gave away enormous quantities of wealth — blankets, canoes, preserved food — and in the most extreme cases destroyed it. They smashed ornamental coppers worth thousands of blankets. They held “grease feasts” where eulachon oil was poured onto the fire until flames singed the spectators and threatened to set the roof ablaze. Helen Codere called it “fighting with property.”

European missionaries and colonial administrators thought this was insane. Canada banned the potlatch from 1884 to 1951 and jailed people for practicing it. The Kwakwaka’wakw kept doing it underground, sometimes telling authorities they were “celebrating Christmas.”

Standard anthropology describes the potlatch as a complex cultural ritual embedded in kinship, cosmology, and social structure — all true, but incomplete. Standard economics sees waste — destruction of resources that could feed people. Also incomplete.

The tetrad sees something else entirely.

Physics first. The Pacific Northwest was one of the most resource-rich environments humans have ever inhabited. Salmon runs provided massive caloric surpluses — but those surpluses were seasonal and volatile. One year a river teems with fish; the next year, near-starvation. You can dry and smoke salmon, but storage is lossy. You can’t bank a salmon run the way you can bank grain.

Math next. The potlatch wasn’t just redistribution — it was a credit creation system. Gifts had to be reciprocated with interest. Franz Boas documented that in one Kwakwaka’wakw village of roughly 150 people, where only about 400 blankets physically existed, the outstanding obligations totaled 75,000 blankets. That’s a leverage ratio of nearly 200:1. The obligation network was a directed graph with geometric escalation built into the edges. The structure of compound obligations meant the system could generate claims on future wealth that vastly exceeded the physical wealth in existence. This is pure formal structure — it holds regardless of who’s optimizing or why. (If this sounds familiar to anyone who has worked in finance, it should. The potlatch was, in a precise sense, a derivatives market. Notional value dwarfing real value. Claims on future states of the world.)

Economics. If you can’t store surplus as physical goods, what do you do with it? You convert it into social capital. Giving away wealth creates obligations — and obligations are insurance. The economist Ronald Trosper argues that the potlatch was a direct adaptation to salmon volatility: a decentralized insurance system disguised as a status competition. The clan that gave lavishly in a good year could call on obligations in a bad one.

But that explains the redistribution. It doesn’t explain the destruction. Why burn oil? Why break coppers? Because of costly signaling — another economic insight. A signal is credible only if it’s expensive to fake. Giving away blankets is impressive, but your rival might be borrowing them. Destroying a named copper worth thousands of blankets is unfalsifiable. You cannot fake the destruction of wealth. Your rival must break one of equal or greater value or be humiliated before the community.

CS. And here’s what no single lens captures alone. In a pre-literate society, reputation is a distributed information structure. There’s no written record of who is wealthy, who is reliable, who can be trusted in a crisis. The potlatch is a write operation to non-volatile memory. In a society without writing, ordinary exchanges are volatile — they fade, get disputed, lose detail. The potlatch makes the write durable: public, witnessed, irreversible. The destruction isn’t waste. It’s the cost of writing to a distributed ledger that has no other input mechanism. The burning oil, the shattered copper, the singed eyebrows of the spectators — these are what make the entry persist in a database that runs on human memory and oral tradition.

So the potlatch isn’t irrational destruction. It’s four things simultaneously: an adaptation to volatile physics (salmon ecology), a credit system with formal network properties (mathematics), a decentralized insurance mechanism with unfalsifiable signaling (economics), and a data storage protocol for pre-literate reputation management (computer science). No single lens gets the full picture. You need all four.

(A caveat worth noting: the most extreme potlatch destruction — the truly spectacular conflagrations — intensified in the post-contact period, when European trade goods flooded in and disease epidemics opened up status competition that had previously been more constrained. The underlying logic predates contact. The escalation was partly an artifact of disruption. The tetrad explains both: the logic is optimization under constraint, and when the constraints change — new goods, population collapse, compressed status hierarchies — the optimum moves.)

This pattern of different local optima emerging from different constraint surfaces is universal, and it generates predictions. It explains why diaspora communities often become more traditional after emigrating: the physical and social environment changed, the constraint surface shifted, and cultural practices that were background defaults in the homeland became active programs that must be consciously maintained against a different operating system. It also predicts that findings from WEIRD populations (Western, Educated, Industrialized, Rich, Democratic) may not generalize — not because other cultures are exotic, but because WEIRD societies sit on a radically unusual constraint surface compared to the environments that shaped human cognition for 99.9% of our history.

Cultural relativism gets half the story right: you can’t naively judge a culture by standards that emerged from different constraints. But it misses the other half: once you understand the constraints, you can evaluate how well a culture solves its actual problems. Some solutions are better than others. And the tetrad tells you which questions to ask to find out.

Psychology

Standard framing: Humans are irrational. We’re riddled with cognitive biases — confirmation bias, loss aversion, availability heuristic, and dozens more. The rational agent model of economics is wrong. Behavioral economics has revealed the truth: we’re predictably irrational.

The tetrad:

Physics: Brains are made of neurons. Neurons are slow (milliseconds, not nanoseconds) and energy-expensive (20% of metabolism for 2% of body mass). The hardware has severe constraints.

Math: Optimal inference under uncertainty follows Bayes’s theorem. Signal detection theory tells you how to make decisions when information is noisy. Network topology constrains what computations are even possible for a given architecture.

CS: The brain is a prediction machine running lossy compression. You can’t process raw sensory data — there’s too much of it. So you build models, predict what’s coming, and only attend to prediction errors. Emotions are interrupt signals: stop doing what you’re doing, this requires immediate attention. Consciousness is something like a global workspace algorithm for integrating information from specialized modules.

Economics: Attention is the scarcest resource. Every decision has a cognitive cost. You can’t optimize perfectly — you can only satisfice within the computational budget you have.

What falls out:

Most of what we call “cognitive biases” aren’t bugs. They’re optimal heuristics given the computational constraints.

Confirmation bias? That’s efficient Bayesian updating when you have strong priors and evidence-gathering is expensive. If you’ve seen a thousand white swans, it’s rational to discount the testimony of someone who claims to have seen a black one. The cost of checking every anomaly exceeds the expected value of being right about rare events.

Loss aversion? Correct weighting when downside variance matters more than expected value. For most of evolutionary history, losing everything meant dying. A 50% chance of doubling your resources versus a 50% chance of losing them all isn’t a coin flip — it’s an existential risk. The math says you should be loss averse.

The availability heuristic? A reasonable proxy for base rates when your sample is your actual lived experience. If you can easily remember examples of X, then X has been common in your environment. It only fails when your environment becomes radically unrepresentative — which, evolutionarily speaking, is very recent.

The “irrationality” literature is largely economists and psychologists being mad that humans don’t optimize for expected utility in laboratory settings. But expected utility maximization isn’t the right objective function for survival. Evolution didn’t design you to be rational; it designed you to be alive. That these sometimes diverge is not a flaw in the human; it’s a flaw in the economic model.

(This doesn’t mean biases can’t be exploited, or that modern environments don’t create mismatches. They do. But the baseline isn’t “irrational deviation from perfect reasoning.” It’s “optimal heuristics that served well for 99.9% of human history.”)

Law

Standard framing: Law is about justice. It’s about rights, fairness, due process, and the protection of the innocent. Legal philosophy debates whether law has an inherent connection to morality (natural law) or is simply whatever the sovereign commands (legal positivism).

The tetrad:

Physics: Law operates on bodies and property. The ultimate enforcement mechanism is physical: confinement, seizure, violence. Everything else is upstream of this.

Math: Deontic logic formalizes permissions, prohibitions, and obligations. Mechanism design asks: given self-interested agents, what rules produce desired outcomes? Contract theory analyzes what kinds of agreements can be self-enforcing.

CS: Legal code is literal code. Statutes are if-then rules. Precedent is case-based reasoning — find a similar prior case and apply its outcome. The constitution is an operating system, with amendments as patches. Bureaucracy is runtime: take the abstract rules and execute them on specific cases.

Economics: Rights are a form of property. Litigation is costly signaling — how much you’re willing to spend reveals how much you value the outcome. Settlements are Coasean bargaining: if transaction costs were zero, parties would always negotiate to the efficient outcome regardless of how rights were initially assigned. Criminal law exists because some transaction costs (like getting a murderer to compensate their victim) are too high.

What falls out:

The rule of law is really the rule of algorithm.

What makes law legitimate isn’t its content — laws can be unjust. What makes it legitimate is its determinism: the same inputs produce the same outputs. You can predict, at least roughly, what will happen if you take a given action.

Tyranny isn’t cruel law. Tyranny is non-deterministic law — you can’t predict the outcome from the inputs. The dictator’s whim, the secret policeman’s mood, the ex post facto declaration of guilt. What makes these awful isn’t that the outcomes are bad (though they often are). It’s that you can’t model the system. You can’t plan. You can’t optimize.

This is why procedural fairness matters even when outcomes are unjust. A corrupt lottery is still a lottery. An unfair trial that follows its own rules is still better than no rules at all. The procedure is the product.

And this is why legal “reform” is so tricky. Changing the rules changes what can be optimized against. If the new rules are less predictable than the old ones — even if they’re more “fair” in some abstract sense — you’ve made things worse along the dimension that actually matters for people trying to live their lives.

Linguistics

Standard framing: The big debate is Universal Grammar. Is grammar innate (Chomsky) or learned (empiricists)? Do all languages share deep structure, or is linguistic diversity evidence against built-in grammatical knowledge?

The tetrad:

Physics: Language runs on vocal cords, ears, and brains. Bandwidth is limited. Speech is serial — one sound at a time, at maybe 150 words per minute. This is brutally slow compared to the information rate of thought.

Math: Formal grammar theory (the Chomsky hierarchy) classifies languages by their computational complexity. Information theory tells you about optimal coding: given a probability distribution over messages, what’s the most efficient way to encode them?

CS: Language acquisition is a learning algorithm. Children don’t get explicit grammar instruction; they infer structure from noisy, incomplete data. Parsing is computation — converting linear strings of sounds into hierarchical meaning structures. Translation is compilation between languages.

Economics: Time and attention are scarce. Zipf’s law: frequent words are short because time is expensive. Ambiguity is compression — why say more than you need to when context fills the gaps? Pragmatics is game theory: what can I infer about the speaker’s intentions from the fact that they chose these words rather than those?

What falls out:

The Universal Grammar debate dissolves.

It’s not that grammar is “innate” in some mysterious nativist sense. It’s that compression under bandwidth constraints converges.

If you have a slow serial channel (speech), a fast parallel processor (brain), and a need to communicate complex structures (propositions with arguments and relations), then you’re going to evolve / learn / invent something like grammar. The constraints determine the solution space.

This predicts that aliens would have grammar too — not because of shared ancestry, but because of shared physics. Any intelligence that communicates through a serial channel will face the same compression problem. The solutions will rhyme.

It also explains why languages are both similar and different. Similar: they all have grammar, they all have nouns and verbs (or functional equivalents), they all have recursion. Different: the specific implementations vary based on historical accident and fine-grained optimization against local constraints.

Linguistics isn’t about whether grammar is innate or learned. It’s about how information gets compressed for transmission through a lossy channel. Once you see that, the field reorganizes around the real question: what are the optimal solutions, and why do languages converge on them?

The Pattern

Notice what the tetrad is doing. It’s not replacing domain expertise. You still need to know the details of potlatch ceremonies, cognitive experiments, legal systems, and linguistic phenomena.

What it’s doing is reorganizing the field around the right questions:

• Anthropology: What are the constraint surfaces, and what are the structural, computational, and strategic properties of the solutions cultures converge on?

• Psychology: What computational problems are being solved, and are the heuristics optimal under realistic assumptions?

• Law: What makes the system predictable, and what destroys predictability?

• Linguistics: What are the information-theoretic constraints, and how do languages optimize against them?

These aren’t the questions the fields traditionally ask. But they’re often the questions that, once asked, crack the problems open.

Next time: political science, aesthetics, and history — plus what happens when the tetrad turns on itself and finds that some of its own favorite examples (like QWERTY) don’t actually work.

This post is part of a series on the tetrad framework. The first post, “The Tetrad: Four Lenses That Explain Everything (Including God),” introduces the framework and applies it to theology. For more on these ideas, see my book The Science of Free Will.

In my last post, I showed you an anomaly that broke every major behavioral theory in finance — retail investors systematically overpaying for simple binary options when strictly superior products were available at lower prices. Prospect theory couldn’t explain it. Ambiguity aversion couldn’t explain it. Rational inattention, salience, expected utility — none of them. They all failed for the same reason: they were all the same kind of theory, operating on the same substrate of payoffs and probabilities. The anomaly lived somewhere else entirely — in the computational cost of evaluating the products.

That result got me thinking about a bigger question: how do you know which kind of theory to reach for? When does the economics lens work, and when do you need the computer science lens instead? Is there a systematic way to figure that out?

Here’s a provocative claim: every field of human study — anthropology, psychology, law, art, religion, all of it — reduces, at bottom, to exactly four things: physics, mathematics, computer science, and economics.

This isn’t vulgar reductionism. I’m not saying “everything is just atoms” and leaving it there. These four are irreducible lenses on a single underlying reality. You need all four. You can’t collapse one into another. And when you apply them systematically, problems that have defeated philosophy for millennia suddenly crack open.

Let me show you what I mean.

Why These Four?

Physics: Substance

What exists? Fields. That’s it. The entire universe is a collection of quantum fields interacting with each other. What we call “particles” — electrons, quarks, photons — are just excitations of these fields, ripples in the underlying substrate. There is no such thing as a particle in the billiard-ball sense. It’s fields all the way down.

This is the stuff of reality. Everything else runs on top of it.

Mathematics: Structure

Mathematics gives us the formal relationships between things — patterns, symmetries, logical entailments — independent of what those things physically are. The Pythagorean theorem doesn’t care whether you’re measuring a field in Iowa or the trajectory of a spacecraft. The structure is the structure.

Physics is written in the language of mathematics because mathematics captures the pattern layer of reality.

Computer Science: Process

Here’s where it gets interesting. We know that quantum fields are quantized — they come in discrete chunks. What about spacetime itself? Is it continuous or discrete?

The best current guess, going back to Einstein and running through contemporary work on quantum gravity, is that spacetime is also discrete at the Planck scale. If that’s true — and it probably is — then the universe contains only finite information in any finite region.

And if finite information, then representable by computation.

This isn’t a metaphor. The universe doesn’t resemble a computation; it IS one. Stephen Wolfram’s insight was recognizing that computation is physics by other means. The dynamics of reality — how states evolve, how systems change — is fundamentally computational.

Economics: Selection

So why isn’t physics + math + computer science enough?

Because of computational irreducibility.

Wolfram showed that even simple deterministic systems — cellular automata with rules a five-year-old could follow — produce behavior that cannot be predicted without running the entire computation. There are no shortcuts. The only way to know what happens is to watch it happen.

This means you cannot derive agent behavior from quarks, even in principle. Even if you had perfect knowledge of every particle in a human brain, you could not compute what that person will do faster than just... waiting for them to do it.

So you need a level of description that talks about agents optimizing under constraints. Not because agents are magical or outside physics — they’re made of the same fields as everything else — but because you can’t compute around them.

And here’s the beautiful part: optimization under scarcity isn’t just a feature of human behavior. It’s everywhere. Thermodynamics maximizes entropy. Evolution satisfices fitness. Light takes the path of least time. Even photons are “economizing.”

Economics, properly understood, is the study of optimization under constraint. And that’s baked into the universe at every level.

The Tetrad

So we have four lenses:

• Physics (substance — what exists),

• Math (structure — formal relations),

• CS (process — discrete dynamics), and

• Economics (selection — optimization under scarcity). Stuff, pattern, process, purpose.

Stuff, pattern, process, purpose.

These four. No fewer. No more. Every other field of study is a configuration of these four lenses applied to some domain.

The Stress Test: God

If this framework actually works, it should be able to crack problems that have defeated philosophy for millennia. So let’s try the hardest one: What kind of God is compatible with modern science?

I want to be clear about what I’m not saying. I’m not saying religious people are wrong. I’m not saying you can’t believe in God. People can believe in any form of deity they want — that’s their choice.

But it is a choice. And most people don’t realize they’re making it.

The Physics Lens

Start with a simple question: Do the laws of physics exist, or don’t they?

There’s no middle ground here. Either the laws are exceptionless — they hold always and everywhere — or they’re not really laws. They’re suggestions. Regularities that happen to obtain most of the time.

This creates a fork:

• If laws exist and are exceptionless, then miracles are impossible. A miracle is, by definition, an exception to physical law. If there are exceptions, there are no laws. If there are laws, there are no exceptions.

• If God intervenes — ever, even once — then what we call “laws” aren’t really laws. They’re just patterns that God has chosen to maintain, which God can break at will.

Both of these positions are logically coherent. You can believe either one. But you cannot believe both simultaneously. That’s not a matter of faith or values — it’s a straightforward logical contradiction, like believing 2+2=4 and 2+2=5 at the same time.

Most religious people think they can have both: a universe that runs on predictable physical laws (which is why science works, planes fly, medicine heals) AND a God who occasionally tweaks outcomes in response to prayer, faith, or moral behavior.

That’s not a defensible position. Not because it’s wrong — but because it’s incoherent.

The Math Lens

Let’s trace out what each choice entails.

Choice A: Laws are real.

If the laws of physics genuinely exist and are exceptionless, then:

• Science works because reality is predictable

• Miracles don’t happen (they can’t happen)

• God, if God exists, doesn’t intervene in the universe

• Prayer doesn’t change outcomes (though it might change the person praying)

• There’s no divine plan that involves ongoing adjustment

This is the position of deism, of Spinoza, of Einstein’s “God does not play dice.” It’s fully coherent. Many serious thinkers hold it.

Choice B: God intervenes.

If God intervenes in the universe — answers prayers, performs miracles, adjusts outcomes based on human behavior — then:

• The “laws” we observe aren’t really laws; they’re just patterns God maintains

• Science works only because God chooses to be consistent most of the time

• What we call physics is really just God’s habits

• This is superdeterminism: the appearance of law without actual law

This is also coherent. It’s the implicit position of most traditional religious belief.

But here’s the problem with Choice B: it has consequences most believers don’t accept.

Under superdeterminism, there’s no free will at all — not in theory, not in practice. If there are no real laws, just God’s moment-to-moment decisions about how particles behave, then you are also just God’s moment-to-moment decisions. You’re not making choices that happen to be determined; you’re a puppet. The puppet strings go all the way down.

So the believer in an interventionist God faces a trilemma:

1. Accept superdeterminism → no free will whatsoever

2. Accept that laws are real → no intervention possible

3. Claim both → logical contradiction

The Computer Science Lens: Ruliad and Hyper-Ruliad

Here’s where Wolfram’s framework becomes essential.

Wolfram defines the ruliad as “the entangled limit of everything that is computationally possible: the result of following all possible computational rules in all possible ways.” Think of it as the space of all possible computations, running simultaneously, forever.

We live inside the ruliad. Our universe follows rules within it. And computational irreducibility applies: even knowing all the rules perfectly, you cannot predict outcomes without running the computation.

This creates a problem for divine omniscience. Even a God who designed the universe, who chose the laws and initial conditions, cannot know what will happen without computing it. And computing it means waiting for it to happen.

But Wolfram also imagines something beyond: a hyper-ruliad.

“But what if we just imagine a ‘hypercomputation’ not in that class? For example, imagine a hypercomputation (analogous, for example, to an oracle for a Turing machine) that in a finite number of steps will give us the result from an infinite number of steps of a computationally irreducible process. Such a hypercomputation isn’t part of our usual ruliad. But we could still formally imagine a hyperruliad that includes it.”

This is the only coherent form of omniscience. A hyper-ruliad entity — an oracle machine in the computability theory sense — sits outsideour computational structure entirely. It can see results without computing them. It’s not bound by irreducibility because it’s not operating within the ruliad at all.

This is mathematically well-defined. Oracle Turing machines are real objects in computability theory. A God who exists in the hyper-ruliad could genuinely know everything that will ever happen in our universe.

But — and this is crucial — such a God still cannot intervene. The moment God reaches into the ruliad and changes something, the laws fail. We’re back to superdeterminism.

(For more on the ruliad, see Wolfram’s original essay: The Concept of the Ruliad)

The Economics Lens

Finally: revealed preference.

If an interventionist God existed and wanted us to know it, evidence would be trivially cheap to provide. Part the Red Sea every alternate Tuesday. Levitate a giraffe at random, in full view of cameras. Make a horse that speaks.

These aren’t hard. For an omnipotent being, they’re costless. And they would be utterly decisive — no ambiguity, no room for interpretation.

The absence of such evidence is telling. When evidence is costless to provide and maximally valuable to the recipient, failure to provide it is evidence. Not proof of absence, but strong evidence.

Revealed preference: whatever God exists, that God either cannot or chooses not to provide clear evidence. An interventionist God who cares about human belief would intervene visibly. The God we observe (or fail to observe) doesn’t.

The Honest Positions

After running the tetrad, here are the coherent positions you can hold:

If you believe physical laws exist:

• A Designer God who chose the laws and/or initial conditions, then stepped back

• A Hyper-ruliad oracle who can see without computing, but doesn’t intervene

• Spinoza’s God — identical with nature itself (though whether this counts as “God” is semantic)

• No God — the laws just are what they are.

If you believe God intervenes:

• What we call “physical laws” are just God’s habits, maintained by choice

• Science works only because God is consistent, not because reality is law-governed

• You must accept superdeterminism and its consequences (including no free will)

• Miracles are possible because there were never any real laws to violate.

Both columns are logically coherent. You can hold any position within either column.

What you cannot do is mix columns. You cannot believe in genuine physical laws AND an interventionist God. You cannot believe science works because reality is predictable AND believe that God sometimes changes outcomes in response to prayer.

That’s not a claim about which position is true. It’s a claim about which combinations are logically possible.

What doesn’t survive:

• “I believe in science AND I believe God answers prayers” — incoherent

• “Physical laws are real AND miracles happen” — contradiction

• “The universe is deterministic AND God has a plan that involves ongoing adjustment” — pick one

This isn’t atheism. It isn’t theism. It’s just logic.

The universe might have been designed — fine-tuned, selected from all possible configurations by an intelligence outside our computational structure. But if laws are real, that designer isn’t listening now. It set the initial conditions and walked away, or simply observes from the hyper-ruliad.

Alternatively, there might be no real laws at all — just a God who micromanages every particle, maintaining patterns we mistake for physics. That’s coherent too. But then don’t pretend science tells you anything about how reality must behave. It only tells you how God has chosen to behave so far.

Pick one. That’s all the tetrad asks.

The Kicker

The tetrad didn’t tell you what to believe about God. It told you what you can’t believe simultaneously.

That’s what frameworks do when they work. They don’t answer questions for you — they clarify which answers are compatible with which other answers. They show you the trade-offs you’re actually making, whether you realize it or not.

Theology is actually the hardest test case, because it’s where people are most emotionally invested and least willing to follow logic where it leads. If the framework handles God — if it can cleanly separate the coherent positions from the incoherent ones — it can handle anything.

Next time: what happens when we point the tetrad at anthropology, psychology, law, and linguistics. Spoiler: “cognitive biases” aren’t bugs, cultures aren’t arbitrary, and the rule of law is really the rule of algorithm.

This post draws on ideas from my book, The Science of Free Will, which explores how determinism, computational irreducibility, and physics intersect with questions about agency, consciousness, and meaning.

For those who want to go deeper into the ruliad and its implications, Wolfram just published “What Ultimately Is There? Metaphysics and the Ruliad“ — a remarkable attempt to put metaphysics on scientific foundations.

The Simplicity Tax: Why Retail Investors Pay More for Worse Products (And What It Reveals About the Limits of Behavioral Finance)

Here’s a simple test. I’m going to offer you two products.

Product A is a binary option. It’s a yes-or-no bet: if the S&P 500 is above a certain level when the contract expires, you get $100. If it’s below, you get nothing. Clean. Simple. Two outcomes.

Product B is a bull spread. It’s built from two call options. It starts paying out at a lower level than the binary — and the further the S&P goes up, the more you make, up to a cap that matches the binary’s $100. Below the bull spread’s lower strike, you get nothing, same as the binary. But between that lower strike and the binary’s strike, the bull spread is paying you and the binary isn’t. Above the binary’s strike, they both pay the same.

Here’s the key: Product B pays you at least as much as Product A in every possible state of the world, and strictly morein many states. There is no scenario — none, zero, not one — in which you’d be better off holding the binary.

So which one should cost more?

If you said B, congratulations, you have preferences that respect dominance. This is about the lowest bar in all of decision theory. An asset that pays more in every state of the world should be worth more. Full stop. This isn’t controversial. Toddlers understand this principle when applied to candy.

And yet.

The Finding

A new paper in the Journal of Financial Economics by Aaron Goodman and Indira Puri documents something remarkable. Analyzing a year of trading data from Nadex — a CFTC-regulated retail derivatives exchange — they find that retail investors routinely buy the dominated binary option at a higher price than the dominating bull spread that was available at the exact same time.

How often? 15% of S&P index trades. 19% of gold trades. 25% of silver trades. Consistently, across three different asset classes, over an entire year.

How much money are these traders leaving on the table? On average, 34% of the contract price. That’s not a rounding error. If you’re paying $50 for a binary, you could have bought a strictly superior product for roughly $33.

This is happening in a real market — over $1 billion in notional value annually — not a psych lab experiment with undergraduates choosing between hypothetical lotteries. These are people putting real money on the line and consistently choosing the worse product at the higher price.

What Makes This Different

Finance is full of anomalies. People overpay for out-of-the-money options. They hold losers too long and sell winners too soon. They buy high-fee mutual funds when low-fee alternatives exist. We have an entire field — behavioral finance — devoted to cataloguing and explaining these patterns.

What makes the Goodman-Puri finding special isn’t just that people are overpaying. It’s that the entire standard toolkit for explaining why people overpay doesn’t work.

The authors don’t just document the anomaly. They prove — mathematically, with formal propositions — that the following theories cannot capture this result, regardless of functional form or parametric specification:

Expected utility theory can’t explain it, because any utility function that respects dominance would prefer the bull spread. Obviously.

Prospect theory can’t explain it. This is the big one. Kahneman and Tversky’s framework allows for all sorts of departures from rational choice — probability distortions, loss aversion, reference dependence. But prospect theory allows for dominance violations only through distortions of the probability space. When the dominance is state-by-state — product B pays more than product A in every state — no amount of probability distortion helps. Distort the probabilities however you like; the bull spread still pays more everywhere. The proof is clean and general. It holds for any probability weighting function, any reference point, any utility function.

Ambiguity aversion can’t explain it. Whether you’re using max-min preferences or robust control, you’re choosing the worst-case probability distribution and optimizing against it. But the bull spread dominates the binary under everyprobability distribution. There is no subjective belief about the world under which the binary looks better.

Rational inattention can’t explain it. In the standard model, the probability of selecting an asset is proportional to its payoff. The bull spread has a strictly higher payoff. Rational inattention predicts more attention to the better product, not less.

Salience theory can’t explain it. The states where the two products differ most are the states where the binary pays zero and the bull spread pays something positive. If salience makes you overweight those states, it should make you moreattracted to the bull spread.

Stop and think about what this means. The five most prominent theoretical frameworks in behavioral decision theory — the ones that have launched a thousand papers, won Nobel prizes, and informed policy worldwide — all fail on the same anomaly. And they fail for the same fundamental reason: they all operate on payoffs and probabilities. They differ in how they distort or weight those ingredients, but they share the substrate. When the dominance is state-by-state, no distortion of payoffs or probabilities can prefer the dominated product.

The authors test the standard financial explanations too. Transaction costs? They include all explicit fees in their calculations. Collateral requirements? Neither product requires collateral since both have limited downside. Liquidity? The CME market where the bull spreads trade is more liquid than Nadex — investors should be willing to pay a premium to trade there, yet the dominating CME product is cheaper. Noise trading? They run a placebo test, constructing dominated bull spreads and checking whether those are overpriced too. The asymmetry is stark: binary overvaluation runs at 15-25%, but the placebo rates are 3-14%, and the difference is highly significant. Exchange-specific effects? They repeat the analysis within Nadex itself, comparing binaries to the exchange’s own call spreads. The dominance violations persist — in fact, they’re even more extreme: 91% of within-exchange comparisons show the binary overvalued.

What Does Explain It

After eliminating everything that doesn’t work, the authors point to a simple explanation: the binary is easier to understand.

Think about what your brain has to do to evaluate each product.

A binary option has two outcomes. Win or lose. The expected value calculation is one multiplication: probability of winning times $100. A five-year-old could conceptualize it. Will the S&P be above 5300 on Friday? Yes or no. How much would you pay for that bet?

A bull spread has a continuum of outcomes across three regions. Below the lower strike: zero. Between the strikes: linearly increasing payoff. Above the upper strike: capped. Computing expected value requires integrating across a probability distribution, or at minimum doing piecewise reasoning over three regions. It’s not hard, exactly, but it’s more steps, more working memory, more cognitive load. Even when bull spreads are displayed on the same screen, prominently, with educational explanations — as they are on Nadex — the binary is just... easier.

The authors show that a formal theory of simplicity, developed by Puri (2025), captures the results. The idea is that investors derive disutility from product complexity, creating an effective complexity cost. When the payoff advantage of the dominant product is small enough relative to the complexity gap, the simpler product wins despite inferior payoffs. This framework also predicts the cross-sectional patterns in the data: as the degree of dominance increases (the bull spread gets even better relative to the binary), the frequency of overvaluation falls. Intuitively, there’s a tipping point: make the payoff gap large enough and even complexity-averse investors will switch to the better product. The data confirm this.

The Simplicity Tax

Let me put this in terms my fellow traders will appreciate.

Over 30-plus years of running money, I’ve learned something that sounds paradoxical but is absolutely true: signals you can’t understand often outperform signals that make sense. The elegant theory works in physics. In finance, the ugly model with no satisfying narrative frequently beats the one you can explain over dinner.

This used to baffle me. Shouldn’t understanding why something works make you more confident, help you size positions better, stick with the strategy through drawdowns?

No. And the Goodman-Puri paper shows why at the retail level, in its purest form. People don’t just prefer simplicity — they’ll pay for it. Real money. 34% of the contract price. They’re paying a simplicity tax: the premium you surrender because your brain would rather evaluate two outcomes than a continuum.

And this tax shows up everywhere, not just in binary options:

The growth of “zero-days-to-expiration” options — basically directional bets with binary-like payoff profiles — reflects the same preference. Robinhood just launched event contracts. Kalshi trades binary-style contracts on everything from Fed rate decisions to weather. The CME itself launched binary options explicitly to attract retail investors. The entire trend in retail financial products is toward simpler payoff structures. Supply-side actors aren’t correcting the simplicity preference; they’re catering to it, because there’s money in selling people the product that’s easier to evaluate, even when a better product exists.

My advice to aspiring traders has always been: test until you throw up. Test everything. Don’t trust stories, don’t trust elegance, don’t trust the narrative. Run the numbers. The Goodman-Puri result is the formal version of that advice. The numbers say the ugly product is better. But 15-25% of the time, people buy the pretty one anyway.

The Deeper Problem

Here’s what I find most interesting about this paper, and it’s something the authors are too polite to say outright (they’re writing for the Journal of Financial Economics, after all, not a Substack).

The failure here isn’t just empirical. It’s structural.

Behavioral finance spent forty years building increasingly sophisticated theories of choice under uncertainty. Prospect theory. Cumulative prospect theory. Rank-dependent utility. Ambiguity aversion. Salience. Disappointment aversion. Rational inattention. Each one relaxes a different assumption of expected utility theory. Each one adds a new mechanism — probability distortion, reference dependence, attention allocation, worst-case reasoning.

But they’re all doing the same thing. They’re all asking: given the payoff structure, what does the agent choose? They model the selection — increasingly exotic selection rules over the same underlying substrate of payoffs and probabilities.

This paper proves that approach has a boundary. The anomaly doesn’t live in the selection mechanism. It lives in the evaluation process — the computational cost of figuring out what the payoff structure even is. The binary option trader isn’t distorting payoffs or probabilities. The binary option trader hasn’t computed the payoff comparison, because the computation itself is the bottleneck.

That’s not a preference. That’s a constraint on information processing. And it requires a fundamentally different kind of theory — one that talks about computation, not utility.

There’s a phrase I’ve been using in my work: “conclusions without derivations are just opinions with fancy letterhead.” The behavioral finance literature has been, in a sense, providing increasingly fancy letterhead. More sophisticated utility functions. More elegant axiom systems. More Nobel-worthy mathematics. But the derivation — the part where you explain why the agent behaves as they do at the most fundamental level — has been stuck in one register: preferences over outcomes.

The Goodman-Puri result suggests we need a different register entirely. The binding constraint on human decision-making isn’t always what you prefer. Sometimes it’s what you can compute. And the difference between those two explanations — the one about preferences and the one about computation — turns out to have deep implications that go well beyond finance.

I’ll have much more to say about that soon.

Goodman, A. and Puri, I. (2025). “Overvaluing Simple Bets: Evidence from the Options Market.” Journal of Financial Economics, 172. Available here.

For aspiring traders: the lesson from this paper isn’t “avoid binary options” (though you probably should). It’s that the simplicity tax is real, it’s measurable, and it applies to your own cognition too. The strategy that feels clean and intuitive might be costing you 34% relative to the one that makes your head hurt. Test, don’t intuit.

For more on these themes — including why the same computational constraints that explain binary option mispricing also explain traffic jams, free will, and why a Supreme Court must exist — see my book The Science of Free Will.

The book I co-edited, Quantum Theory Meets Gravity — From Curved Spacetime to Black Hole Information, was published in January 2026 and is now available open-access from IntechOpen and in hardcover from Amazon. It’s co-edited with Oliver K. Baker (Yale) and Djordje Minic (Virginia Tech).

I wrote a detailed post about my own chapter — the one on gravity’s effect on the double-slit experiment — when the chapter first appeared online. Here, I want to step back and give a guide to the whole book: what each chapter does, what it establishes, and why a curious non-physicist might care.

The book lives at the frontier where quantum mechanics and general relativity meet. These are our two best theories of the physical world, each confirmed to extraordinary precision in its own domain. But combining them fully — producing a complete theory of quantum gravity — remains one of the great unsolved problems in science. The chapters in this volume don’t claim to solve that problem. Instead, they map the territory where the two theories overlap, working out what can be calculated rigorously right now, and showing that the results are surprisingly rich.

A recurring theme, one I didn’t fully appreciate until the book came together, is that entropy and entanglement keep appearing across seemingly unrelated contexts.

A word about these two concepts, since they’ll come up repeatedly.

Entropy, in its most general sense, measures how much you don’t know about a system. If you know the exact state of every particle — their positions, velocities, everything — the entropy is zero. If all you know is the temperature and pressure (but the particles could be arranged in any of a vast number of ways consistent with those measurements), the entropy is large. The second law of thermodynamics says that entropy tends to increase: systems evolve from ordered to disordered states. A hot cup of coffee cools to room temperature; it never spontaneously heats back up. Entropy is the reason why. In the context of black holes, entropy takes on a geometric meaning — it turns out to be proportional to the area of the event horizon, not the volume. This connection between information, thermodynamics, and geometry is one of the deepest clues we have about quantum gravity.

Entanglement is a distinctly quantum phenomenon. Two particles can be “entangled,” which means their properties are correlated in a way that has no classical explanation. Measure the spin of one entangled electron and you instantly know the spin of its partner, no matter how far apart they are — not because any signal traveled between them, but because they share a single quantum state. Einstein famously called this “spooky action at a distance.” The crucial point for this book is that entanglement carries information. Two entangled systems have zero entropy individually (they appear random) but zero entropy jointly (the combined state is perfectly known). This interplay between local randomness and global order is central to the black hole information paradox.

These two concepts — entropy and entanglement — keep appearing in Hawking radiation, in quantum interference, in supersymmetric systems (supersymmetry is a proposed deep symmetry of nature that pairs every matter particle with a force-carrying partner and vice versa — more on this in Chapter 4), in black hole information theory. They seem to be telling us something deep about whatever the final theory of quantum gravity will look like.

Here’s what each chapter contributes.

Chapter 1: Quantum Field Theory in Curved Spacetime — Gravity’s Impact on the Double-Slit Experiment

Samir Varma and Arie Kapulkin

This is my chapter (with my co-author Arie Kapulkin), and I wrote about it at length here. What follows is a condensed version; the full post gives the complete picture.

The double-slit experiment is the canonical demonstration of quantum mechanics — fire particles at a barrier with two slits, and an interference pattern appears on the far side, as if each particle went through both slits simultaneously. Every textbook uses it to illustrate quantum weirdness. But every textbook version quietly assumes flat spacetime: no gravity, no curvature, no warping of time. We don’t live in flat spacetime. We live on a planet whose mass curves space and warps time.

What happens to quantum interference when you actually account for that curvature?

To answer this properly, you need quantum field theory — the framework that says particles aren’t little billiard balls but localized disturbances in fields that permeate all of spacetime. (I explain what fields are and why this matters in detail in the longer post.) Once you think of “particles” as field disturbances, interference isn’t mysterious at all — it’s what fields naturally do. The question becomes: how do the rules of field propagation change when spacetime is curved?

We work this out using an approach called the path integral, one of the most powerful ideas in theoretical physics. In classical mechanics, a particle travels along a single trajectory — the one that minimizes the action (a quantity related to energy). In quantum mechanics, the particle doesn’t pick a single path. Instead, it takes every possible path simultaneously, and each path contributes a complex number (an “amplitude”) to the final result. The probability of the particle arriving at a given point is determined by adding up the amplitudes from all paths — and because these amplitudes are complex numbers with phases, they can interfere constructively or destructively, producing the characteristic patterns of quantum mechanics.

The path-integral approach, developed by Richard Feynman, is especially natural for our problem because it makes the effect of gravity transparent: curved spacetime changes the lengths and durations along each path, which changes the phase each path accumulates, which changes how the paths interfere.

Leo Stodolsky, in the late 1970s, developed a formalism that applies this path-integral logic specifically to interference experiments in gravitational fields. We adapt and extend his methods, treating scalar fields (spin 0), photons (spin 1), and electrons (spin 1/2) within a single unified framework. The central result is a compact formula for the gravitational phase shift:

Every symbol in this formula has a physical meaning. The Riemann curvature tensor (the R term) encodes the tidal gravitational field, which means how gravity varies from point to point. The ℓ terms represent the slit separation. The m is the particle’s mass and c is the speed of light. The ℏ (pronounced “h-bar”) is Planck’s constant divided by 2π, one of the fundamental constants of nature; its value is approximately 1.055 × 10⁻³⁴ joule-seconds, a number so tiny it explains why quantum effects are invisible in everyday life. The λ is the de Broglie wavelength — every particle in quantum mechanics has an associated wavelength, inversely proportional to its momentum. Slower particles have longer wavelengths, faster particles have shorter ones. This was proposed in 1924 by Louis de Broglie, a French prince who put the idea forward in his doctoral thesis, which was so radical that the examining committee sent it to Einstein for a second opinion before awarding the degree — de Broglie won the Nobel Prize for it five years later. Finally, the α term is a small spin-dependent correction. At leading order, all particle types — scalars, photons, electrons — couple to curvature in exactly the same way. The spin corrections are suppressed by a factor of about 10⁻¹¹ for electrons. This universality makes physical sense: at leading order, all particles follow paths dictated by the geometry of spacetime itself.

In rotating spacetimes — like the spacetime around a spinning black hole, or even Earth (which drags spacetime around as it rotates) — frame-dragging introduces a distinctive phase shift with odd parity. This means if you swap the two slits, the frame-dragging contribution changes sign. Static gravitational effects don’t do this — they don’t care which slit is which. This odd-parity signature provides, at least in principle, a smoking gun for detecting spacetime rotation through quantum interference.

The effects are small. For cold rubidium atoms in a 10-meter orbital interferometer at 400 km altitude, the tidal phase shift is about 2.7 × 10⁻²¹ radians (a radian is a unit of angle — 2π radians is 360 degrees — so this is an almost incomprehensibly tiny fraction of a full cycle of the interference pattern) — far below what any current instrument can detect. A futuristic kilometer-scale space interferometer might reach 10⁻⁷ radians. But the predictions are exact, derived from well-established physics, and they set the benchmarks that any future experiment would need to reach — and that any future theory of quantum gravity would need to reproduce.

Why you should care: This chapter shows that the most famous experiment in quantum mechanics has something new to teach us when we stop pretending gravity doesn’t exist. The gravitational effects we calculate are tiny, but they’re there — precise predictions sitting at the intersection of quantum mechanics and general relativity, waiting for technology to catch up. It’s also an accessible entry point into quantum field theory in curved spacetime, which is the framework that predicted Hawking radiation and the Unruh effect — two of the most important results in theoretical physics.

Chapter 2: Dynamics and Quantum-Gravitational Interactions in Plasma-Surrounded Black Hole Spacetimes

Orchidea Maria Lecian

Real black holes aren’t sitting in empty space. They’re surrounded by swirling hot matter — accretion disks, plasma, jets of material blasting outward at nearly the speed of light. When the Event Horizon Telescope produced the first image of a black hole in 2019, what it captured wasn’t the black hole itself (which, by definition, emits no light) but the glowing ring of superheated plasma around it. To understand what those images actually mean — to extract the physics from the photons that reach our telescopes — you need to understand how light propagates through this extreme environment, where both intense gravity and hot dense matter simultaneously affect the signal.

This is what Lecian’s chapter tackles.

She works with a family of spacetimes called Kottler-Schwarzschild-Kiselev (KSK) spacetimes. To understand what these are, start with the simplest black hole solution: the Schwarzschild spacetime, which describes a non-rotating, uncharged black hole sitting in otherwise empty space. Karl Schwarzschild found this solution to Einstein’s equations in 1916, barely a month after Einstein published general relativity. It’s elegant but idealized — real black holes exist in a universe with a cosmological constant (the mysterious energy driving the universe’s accelerating expansion) and are surrounded by matter.

The KSK spacetimes generalize the Schwarzschild solution by incorporating both a cosmological constant and a surrounding matter field characterized by a “linear term” in the metric. This gives a more realistic starting point for astrophysical calculations.

Lecian then asks: what happens to a photon — treated quantum mechanically — as it propagates through this spacetime in the presence of plasma?

This requires working out the quantum gravitational Hamiltonian for the photon, which means writing down the equation that governs how the photon’s quantum state evolves in time, accounting for both the curvature of spacetime and the interaction with the plasma medium. She does this for three cases: vacuum (no plasma), cold unmagnetized plasma, and hot unmagnetized plasma. The plasma gives the photon an effective mass — just as photons traveling through glass slow down and behave as if they had mass, photons in a plasma acquire an effective mass that depends on the plasma’s density and the spacetime geometry.

Several concrete results emerge. The photon sphere — the radius at which light can orbit a black hole — exists only when the cosmological constant takes specific positive values. The gravitational lensing (the bending of light by gravity) is computed in the strong-field regime, which means close to the black hole where gravity is extreme and the gentle-bending approximations used for, say, galaxy clusters don’t apply. The chapter also analyzes accretion disk configurations, comparing the effective gravitational potentials that govern matter orbits with standard models used in astrophysics, and examines how adiabatic perturbations — slow, gradual disturbances — propagate through the hot plasma.

She also compares the KSK framework with alternative theories of gravity. This is important because general relativity, for all its success, might not be the final word on gravity — and different theories make different predictions for how black hole environments behave.

One alternative is Weyl conformal gravity, a higher-dimensional theory that modifies Einstein’s equations by demanding an additional symmetry: the equations must be unchanged if you locally stretch or shrink all distances by the same factor (a “conformal” transformation). This extra constraint changes how gravity behaves at very large distances and has been proposed as an alternative explanation for the rotation curves of galaxies — the same observations usually attributed to dark matter.

Another is massive gravity, which modifies general relativity by giving the graviton (the hypothetical particle that mediates gravity) a tiny but nonzero mass. In Einstein’s theory, the graviton is massless, which is why gravity has infinite range. If the graviton had even a tiny mass, gravity’s behavior would change at very large distances — potentially explaining the accelerating expansion of the universe without needing a cosmological constant.

Lecian maps the geometrical parameters of the KSK spacetimes onto these alternative frameworks, showing which features of the black hole environment are universal (present in any theory of gravity) and which are specific to general relativity. This kind of comparison is essential for turning observations into tests of fundamental physics: when we image a black hole’s shadow or measure the deflection of light near it, we need to know which theories are consistent with the data and which are ruled out.

Why you should care: We’re living in a golden age of black hole observation. The Event Horizon Telescope is producing increasingly sharp images of black hole environments. Gravitational wave detectors are recording the collisions of black holes. To interpret this data — to figure out what it tells us about gravity, about the nature of spacetime, about the matter spiraling into these objects — we need theoretical calculations like these, ones that account for both the extreme curvature near the black hole and the realistic astrophysical soup surrounding it. Lecian’s chapter provides part of the theoretical toolkit needed to turn observations of photons into statements about fundamental physics.

Chapter 3: Gravity in Extradimensional Space

Eugênio Maciel

Here’s a question that sounds like science fiction but is taken very seriously in theoretical physics: what if the universe has more than three dimensions of space?

The idea has a distinguished pedigree. In 1921, Theodor Kaluza did something remarkable. He took Einstein’s equations of general relativity — the equations that describe how matter and energy curve spacetime — and wrote them down in five dimensions instead of four (three of space plus one of time becoming four of space plus one of time). When he worked out what the five-dimensional Einstein equations implied for a four-dimensional observer, he found that they automatically produced not just gravity but also Maxwell’s equations of electromagnetism. Gravity and electromagnetism, two seemingly unrelated forces, emerged from a single geometric framework — you just needed one extra dimension.

Five years later, Oscar Klein added a crucial physical idea: maybe the extra dimension isn’t infinite. Maybe it’s curled up into a tiny circle, so small that we can’t see it. Imagine an ant walking along a garden hose. From far away, the hose looks like a one-dimensional line. But the ant knows the hose has a second dimension — it can walk around the circumference. If that circumference is small enough, a distant observer would never notice it. Klein proposed that the fifth dimension might be curled up at a scale near the Planck length — about 10⁻³³ centimeters — far too small for any experiment to resolve.

This “Kaluza-Klein” theory was beautiful but gathered dust for decades. Interest revived dramatically in 1998 with two new proposals that used extra dimensions to attack a different problem: the hierarchy problem.

The hierarchy problem is one of the most puzzling facts about our universe. Gravity is absurdly weak compared to the other forces. The electromagnetic force between two electrons is about 10⁴² times stronger than the gravitational force between them. That’s a one followed by forty-two zeros. Why? The Standard Model of particle physics provides no explanation. The relevant energy scales — the electroweak scale (about 10⁻¹⁸ cm) and the Planck scale (about 10⁻³³ cm) — differ by a factor of 10¹⁵, and nobody can explain why.

The ADD model (Arkani-Hamed, Dimapoulos, and Dvali) proposed a radical answer: gravity isn’t inherently weaker than the other forces. It just looks weaker because it spreads into extra dimensions that the other forces can’t access. Imagine rain falling on a large field versus being funneled through a narrow pipe. The total amount of water is the same, but the concentration is very different. In the ADD picture, all the forces except gravity are confined to our three-dimensional “brane” (a membrane in the higher-dimensional space), while gravity leaks into the full higher-dimensional “bulk.” This dilution makes gravity appear weak.

Maciel works through the mathematics of this idea. A key result is that in a space with D total dimensions, Newton’s gravitational law changes. Instead of the familiar inverse-square law (where the force falls off as 1/r²), gravity in higher dimensions falls off faster — as 1/r^(D−2) — at distances smaller than the size of the extra dimensions. At larger distances, the standard inverse-square behavior is recovered. This means extra dimensions could be detected by looking for deviations from Newton’s law at short distances. For two extra dimensions, the ADD model predicts deviations at scales around 0.01 mm — a distance that laboratory experiments are currently probing.

The Randall-Sundrum models take a different approach. Instead of flat extra dimensions, Randall and Sundrum showed that a single extra dimension with a strongly warped geometry — one where the metric (the mathematical object that defines distances and durations in spacetime; it tells you, given two nearby points, how far apart they are) varies exponentially along the extra dimension — can also solve the hierarchy problem. The RS1 model has two branes sitting at opposite ends of a compact extra dimension. The exponential warping of spacetime between the branes means that energy scales that are naturally of order the Planck scale on one brane get exponentially suppressed on the other brane — producing the enormous hierarchy between gravity and the other forces without requiring the extra dimension to be astronomically large.

The RS2 model goes further: a single brane in an infinite extra dimension. Despite the extra dimension being infinite, gravity on the brane still looks four-dimensional at large distances because the warped geometry effectively traps the graviton near the brane.

Maciel works through all of these models in detail — the Kaluza-Klein construction, the ADD scenario, both Randall-Sundrum models — deriving the graviton mass spectra (the tower of massive particles that a four-dimensional observer would see as a consequence of the extra dimension) and showing how each model addresses the hierarchy problem through different geometric mechanisms.

Why you should care: Extra dimensions aren’t just theoretical curiosities. They’re a central ingredient in string theory, which requires 10 or 11 spacetime dimensions to be mathematically consistent. They offer concrete, calculable mechanisms for unifying gravity with the other forces and for explaining why gravity is so much weaker. And they make testable predictions: deviations from Newton’s inverse-square law at short distances, and new particles (Kaluza-Klein excitations) that could appear at high-energy colliders. Some of these predictions are actively being tested. If extra dimensions exist, they would fundamentally change our understanding of what spacetime is.

Chapter 4: Thermodynamics of a Supersymmetric Oscillator

Jishad Kumar

This chapter might seem like the odd one out — a detailed analysis of the thermodynamic properties of a supersymmetric harmonic oscillator. It’s more technical and less cosmic in scope than the chapters on black holes and extra dimensions. But it illuminates something deep about the relationship between two classes of particles that make up everything in the universe, and it connects directly to one of the most embarrassing unsolved problems in all of physics.

Let me build up the context.

Bosons and fermions. Every particle in the universe falls into one of two categories. Bosons are the force carriers: photons (electromagnetism), gluons (strong force), W and Z bosons (weak force), and the Higgs boson. They obey Bose-Einstein statistics, which means any number of them can occupy the same quantum state. This is why lasers work — you can pile up photons in identical states. Fermions are the matter particles: electrons, quarks, neutrinos. They obey Fermi-Dirac statistics and the Pauli exclusion principle, which forbids any two of them from occupying the same quantum state. This is why atoms have structure, why the periodic table works, why matter is solid instead of collapsing. That last point deserves emphasis: atoms are overwhelmingly empty space. If an atom were the size of a football stadium, the nucleus would be a grain of sand at the center and the electrons would be specks of dust in the upper stands. Yet when you press your hand against a table, it feels solid. The reason is the Pauli exclusion principle. The electrons in the atoms of your hand and the electrons in the atoms of the table cannot be forced into the same quantum states — they resist being squeezed together with an effective pressure (called “degeneracy pressure”) that has nothing to do with electromagnetic repulsion. It’s a purely quantum mechanical effect, and it’s what keeps matter from collapsing into nothing. The same effect holds up white dwarf stars against the crush of gravity.

These two classes of particles have very different thermodynamic behavior. A bosonic oscillator — think of a vibrating mode of the electromagnetic field — has an infinite ladder of equally spaced energy levels given by:

Here ω (omega) is the oscillator’s natural frequency — how many times per second it vibrates — and n can be any non-negative integer: 0, 1, 2, 3, and so on. A fermionic oscillator, by contrast, has only two states: n = 0 or n = 1 (because of the exclusion principle). Their thermodynamic properties — how they store energy, how their entropy changes with temperature — are correspondingly very different.

The vacuum energy problem. Here’s the embarrassment. Every quantum field, even in its ground state (the “vacuum”), has a residual energy — the zero-point energy. For a bosonic oscillator, the ground state energy is +ℏω/2 (half of one quantum of energy). When you add up the zero-point energies of all the quantum fields in the universe across all their modes, you get an absurdly large number for the vacuum energy density. General relativity says this vacuum energy should gravitate — it should curve spacetime. The predicted vacuum energy is roughly 10¹²⁰ times larger than what we actually observe from the measured cosmological constant. This is the “cosmological constant problem,” and it’s been called the worst prediction in the history of physics.

Supersymmetry and cancellation. Supersymmetry (SUSY) is a proposed symmetry that pairs every boson with a fermion partner and vice versa. In a supersymmetric system, something remarkable happens: the positive zero-point energy of the bosonic modes is exactly canceled by the negative zero-point energy of the fermionic modes. The ground state energy is exactly zero.

This isn’t a hand-waving argument. Kumar demonstrates it explicitly with a supersymmetric oscillator — a system that combines a bosonic oscillator and a fermionic oscillator in a specific way. The energy spectrum of the SUSY oscillator is:

Note the crucial difference from the ordinary bosonic oscillator: there’s no +1/2. The ground state energy (n = 0) is exactly zero. What happened? The positive vacuum energy of the boson (+ℏω/2) has been precisely canceled by the negative vacuum energy of the fermion (−ℏω/2). The two contributions are equal and opposite, and their sum is zero.

Above the ground state, a beautiful structure emerges. Every energy level (except the ground state) has a two-fold degeneracy: for each value of the bosonic excitation number, there are two states with the same energy — one that is purely bosonic and one that includes a fermionic excitation. This pairing of bosonic and fermionic states at every energy level is a hallmark of supersymmetry.

Kumar then works out the full thermodynamics: the partition function, the free energy, the entropy, and the heat capacity, as functions of temperature. At low temperatures, the heat capacity of the SUSY oscillator behaves differently from either a bosonic or fermionic oscillator alone. It exhibits a distinctive peak at intermediate temperatures, a feature that reflects the underlying supersymmetric structure.

The chapter then turns to a real physical system: an electron in a magnetic field, the “Landau problem.” When an electron moves in a uniform magnetic field, its motion perpendicular to the field is quantized into discrete Landau levels — equally spaced energy levels, just like a harmonic oscillator. If you ignore the electron’s spin, this is a purely bosonic oscillator. But when you include the spin — and use the specific value of the electron’s g-factor (g = 2, as predicted by Dirac’s equation) — the ground state energy drops to exactly zero. The spin-up ground state with zero orbital excitation has precisely zero energy. The system reveals a hidden supersymmetric structure: you can construct a “supercharge” operator whose square equals the Hamiltonian, and which commutes with the Hamiltonian (meaning it doesn’t change the energy when it acts). This is the algebraic signature of supersymmetry.

Finally, Kumar examines what happens when supersymmetry is broken — specifically, by adding a confining potential (as occurs in quantum dots, where electrons are trapped in a small region). In this “Fock-Darwin” model, the ground state energy is no longer zero, and the boson-fermion degeneracy is lifted. The chapter traces how the thermodynamic properties change when SUSY breaking occurs, connecting the abstract algebra to observable physical consequences.

Why you should care: The vacuum energy problem is one of the biggest unsolved puzzles in physics. It sits at the intersection of quantum field theory and general relativity — exactly the intersection this book is about. Supersymmetry offers one of the very few known mechanisms for taming vacuum energy, and understanding exactly how this cancellation works in controlled examples is essential groundwork. Kumar’s chapter shows that the cancellation isn’t just an abstract theoretical trick — it shows up in a real, physical system (the Landau problem), and it can be broken in understandable ways that have measurable thermodynamic consequences. This kind of detailed, worked-out toy model is how physicists build the intuition needed to attack the bigger problem.

Chapter 5: Black Hole as an Information Mirror

Xuanhua Wang

If you throw a book into a fireplace, the information in the book isn’t really destroyed — in principle, if you tracked every photon of light, every particle of ash, every molecule of gas released by the fire with perfect precision, you could reconstruct the text. This isn’t practically possible, but physics guarantees it: information is conserved. The laws of quantum mechanics are “unitary,” which means they preserve information. You can always run the movie backward.

But in the 1970s, Stephen Hawking showed that black holes seem to violate this rule, and the resulting paradox has shaped theoretical physics for fifty years.

Hawking radiation. Hawking’s calculation combined quantum field theory with general relativity in the semiclassical regime (the same regime my chapter works in). He showed that the intense gravitational field near a black hole’s event horizon causes the quantum vacuum to radiate particles. The black hole glows, albeit incredibly faintly, with a temperature inversely proportional to its mass. A black hole with the mass of our sun would have a temperature of about 60 nanokelvins — far colder than the cosmic microwave background — but the radiation is real in principle.

Here’s the problem. The radiation Hawking calculated appears to be exactly thermal — it has a perfect blackbody spectrum, just like the glow from a hot piece of metal, carrying no information whatsoever about what fell into the black hole. Throw in an encyclopedia, a cat, or a Ferrari — the Hawking radiation looks exactly the same. And since the black hole is radiating energy, it’s slowly losing mass. Eventually, it evaporates completely. When it’s gone, all that remains is featureless thermal radiation. The information about everything that ever fell in seems to have vanished from the universe.

This violates unitarity. Quantum mechanics says information can’t be destroyed. General relativity says it can. Something has to give.

The Page curve. In 1993, Don Page made a crucial observation. If a black hole really is an ordinary quantum system (just one that happens to be very hot and very scrambled), then the entropy of its radiation should follow a specific pattern. Early on, as the black hole emits radiation, the entropy of the radiation should rise — each new particle adds to the disorder. But at a certain point (the “Page time,” roughly when the black hole has emitted half its initial entropy), the entropy should stop rising and start falling. This is because the radiation is becoming increasingly entangled with the earlier radiation in a specific way that a unitary process guarantees. By the time the black hole has fully evaporated, the entropy of the radiation returns to zero — all the information has been transferred out. This rise-then-fall graph is called the “Page curve.”

Hawking’s calculation, by contrast, predicts that the entropy just keeps rising, monotonically, forever — no turn-around, no information recovery. Reproducing the Page curve from a gravitational calculation became the central challenge.

The breakthrough: entanglement islands. Wang’s chapter surveys the remarkable recent progress. The key development, emerging around 2019, was the “island formula” for gravitational entropy. The idea is startling: when computing the entropy of Hawking radiation after the Page time, you must include contributions from a region insidethe black hole — the “island” — that, through the mathematics of quantum entanglement, turns out to be encoded in the radiation outside.

The formula says: to compute the entropy of the radiation, minimize the “generalized entropy,” which has two terms. The first is the area of a special surface (the “quantum extremal surface”) divided by 4G (where G is Newton’s gravitational constant) — this is essentially the Bekenstein-Hawking entropy, the same formula that gives a black hole its entropy. The second is the entropy of quantum fields in the region between this surface and the boundary where the radiation is collected. Before the Page time, the quantum extremal surface shrinks to zero and you recover Hawking’s monotonically increasing entropy. After the Page time, the quantum extremal surface jumps to just outside the event horizon, and the entropy is dominated by the area term, which decreases as the black hole shrinks.

The result: the Page curve is reproduced. The entropy rises, then falls, exactly as unitarity demands.

This might sound like a mathematical trick, but the derivation is grounded in the gravitational path integral — the same mathematical framework that underlies all of quantum gravity. The island formula was derived by considering “replica wormholes,” additional saddle-point contributions to the path integral that become important when computing entanglement entropy using the replica trick. These aren’t optional additions; they’re contributions that the mathematics demands.

The Hayden-Preskill protocol. Wang then turns from entropy (can information be recovered?) to a more operational question: how quickly can it be recovered?

In 2007, Hayden and Preskill considered a thought experiment: Alice throws a few qubits of information into a black hole that has already been radiating for a long time (past its Page time). Bob has been carefully collecting all the earlier Hawking radiation. How long does Bob have to wait before Alice’s information appears in the new radiation?

The answer, derived using tools from quantum information theory, is surprising: Bob only needs to collect slightly more new qubits than Alice threw in. The information comes back almost immediately (in “scrambling time” — roughly the time it takes for the black hole to thoroughly mix the new information into its internal degrees of freedom). The black hole doesn’t hoard information; it reflects it back like a mirror, just thoroughly scrambled.

Wang’s chapter reviews this result and then goes further, discussing explicit decoding protocols — the Yoshida-Kitaev protocol and the Horowitz-Maldacena model — that specify concrete quantum circuit operations Bob could perform to extract Alice’s information from the radiation. These aren’t just theoretical curiosities; they’ve been tested on small quantum computers, simulating the information-recovery process with a handful of qubits.

Why you should care: The black hole information paradox sits at the exact collision point of quantum mechanics and general relativity, and its resolution is likely to reveal something fundamental about the nature of spacetime. For decades, the paradox seemed intractable. The progress surveyed here — entanglement islands, the Page curve from gravitational path integrals, explicit decoding protocols — represents a genuine breakthrough. The emerging picture is that black holes aren’t information destroyers but information mirrors: what falls in comes back out, encoded in the subtle quantum correlations of the radiation. Understanding how this works may be the best clue we have about what a full theory of quantum gravity will look like.

The Common Thread

Reading these five chapters together, a pattern emerges that wouldn’t be visible from any single one alone.

Chapter 1 shows that spacetime curvature modifies quantum interference in precise, calculable ways — the phase shift depends on the Riemann curvature tensor, which is the mathematical object encoding how spacetime bends. Chapter 2 extends quantum field theory to the realistic environment around black holes, where plasma and extreme curvature interact. Chapter 5 shows that the entropy of Hawking radiation — a quintessentially quantum-gravitational quantity — can be computed using entanglement and quantum extremal surfaces, and that information is preserved through the entanglement structure of the radiation. Chapter 4 reveals that supersymmetry produces exact cancellations in vacuum energy through the pairing of bosonic and fermionic degrees of freedom, with the entropy and thermodynamic properties carrying signatures of the underlying symmetry. Chapter 3 reminds us that the geometry of spacetime may be richer than the three spatial dimensions we perceive, with extra dimensions offering new mechanisms for how gravity operates.

The connecting thread is entropy and entanglement. These concepts appear everywhere — in the phase shifts of interferometry, in the thermodynamics of supersymmetric systems, in the Page curve of an evaporating black hole, in the entanglement islands that encode a black hole’s interior in its radiation. They seem to be the language in which the quantum-gravity interface is written, even before we have a complete theory.

The book doesn’t deliver that complete theory. Nobody has it yet. But it does provide a clear map of what can be calculated now, what the open questions are, and where the next breakthroughs might come — from atom interferometry to black hole imaging to tabletop experiments probing Newton’s law at sub-millimeter scales. For anyone who wants to understand the frontier where quantum mechanics meets gravity — the most important unsolved problem in fundamental physics — this is a good place to start.

Quantum Theory Meets Gravity — From Curved Spacetime to Black Hole Information is available as open-access from IntechOpen and in hardcover from Amazon. Edited by Oliver K. Baker, Djordje Minic, and Samir Varma.

And my other book, The Science of Free Will is available on Amazon too. In that I talk about all kinds of cool stuff. Why does traffic suck so much? Why don’t we trade with ants? Why do dolphins save drowning humans? And much more.

Five Things India Exports That No Five-Year Plan Could Produce

India’s cultural export stack is enormous, chaotic, and almost entirely uncoordinated — which is, as we’ve established, exactly why it works. Let’s walk through it.

Film: 2,500 Movies Nobody Asked For

India produces over 2,500 films per year in more than 20 languages. China makes 792. The United States — home of Hollywood, the place that invented the concept of a global film industry — makes roughly 510. India makes more movies every year than Hollywood and China combined, and it isn’t particularly close.

Why does this matter? Because scale increases the probability of export hits. More experiments mean more breakthroughs. It’s evolution by natural selection, not intelligent design. Most mutations fail. Some become Baahubali [The One With Strong Arms].

And it’s not just “Bollywood” anymore. That’s the word Western journalists use because it’s cute and they can’t be bothered to learn about the rest. The real story is the pan-Indian revolution. Telugu cinema — Baahubali, RRR, Pushpa [Flower], Anaganaga Oka Raju [Once Upon a Time, a King], which grossed ₹100 crore at the worldwide box office in a language most humans don’t speak. Tamil cinema — Ponniyin Selvan [Son of the Kaveri River]. Kannada cinema — Kantara [Mystical Forest]. And Hindi blockbusters like Dhurandhar [Stalwart], now being skated to at the Winter Olympics. RRR won an Oscar. Nobody in Hollywood saw that coming, largely because nobody in Hollywood knew what Telugu was.

Netflix’s global engagement data tells the story in numbers corporations understand: Maharaja drew 25 million views. Do Patti drew 20 million. IC 814: The Kandahar Hijack drew 12 million — in a single reporting period. Dhurandhar trended in 22 countries on Netflix, reaching number one in Pakistan, where Indian films are officially banned. It was reportedly downloaded 2 million times through piracy sites in Pakistan within a week — making it the most pirated film in the country. India’s soft power is so potent it penetrates even deliberate government blockades.

Are most of these films terrible? Many of them, yes. I speak as someone who found even Padosan [The Lady Next Door] merely tolerable. The plots are formulaic, the acting is stylized to the point of abstraction, and the musical numbers interrupt the narrative with the subtlety of a monsoon. But that’s the point. Soft power isn’t about quality control. It’s about volume, reach, and emotional resonance. India produces cinema the way it produces everything else: chaotically, excessively, and somehow effectively.

A confession. In Post 8, The Mother Tongue Wars, I quoted some of Hindi cinema’s most gloriously absurd dialogue. “Raabert, isko liquid oxygen mein daal do. Liquid usko jeene nahin dega. Aur oxygen usko marne nahin dega.” (Robert, put him in liquid oxygen. The liquid won’t let him live. And the oxygen won’t let him die.) Scientifically nonsensical, linguistically magnificent. Also: “Michael, tum cycle pe jao. Aur Mona darling, tum nahati raho.” I quoted these with the confident authority of a man surveying his cultural heritage. Here is the thing: I have never seen any of those movies. Not one. I have no idea what they are about. I simply heard people quote them — at dinner parties, on school buses, in living rooms — the way one absorbs Scripture without ever opening the book. That is how Indian cinema works. The films are almost beside the point. The dialogues escape into the culture and take on a life of their own, passed from person to person like oral tradition, quoted by millions who couldn’t tell you the plot if their lives depended on it. Including, for that matter, Sholay — the greatest Indian film ever made, according to everyone. Never saw it. Don’t know what it’s about. Can quote approximately none of it. This is Indian soft power in its purest form: you don’t even have to watch the product for it to colonize your brain.

Music: The Man Behind the Music

Before we get to Spotify, let’s talk about the Indian who shaped Western rock.

Vijaya Bhaskar Menon was born in Thiruvananthapuram — the capital of Kerala, a city whose name alone exhausts Western phonetic capacity. He was educated at the Doon School (India’s Eton) and Christ Church, Oxford. His father, K.R.K. Menon, was India’s first Finance Secretary — the first one-rupee notes issued after independence bore his signature. In 1971, Bhaskar Menon became the first Asian to run a major Western record label when he took over Capitol Records in Los Angeles.

Capitol was in trouble. The Beatles had broken up the previous year. The label had lost $8 million — in 1970 dollars, which is real money. The American executives had essentially given up on a British progressive rock band whose albums weren’t selling in the States.

Menon hadn’t.

In 1972, he flew to the South of France, where Pink Floyd was performing. After an all-night negotiation session, they agreed on a deal. Menon commemorated the terms on a cocktail napkin and brought it back to Capitol’s legal department in Los Angeles. Then he put the full force of the company’s marketing machine behind The Dark Side of the Moon.

Nick Mason, Pink Floyd’s drummer, said it plainly in the 2003 documentary The Making of The Dark Side of the Moon: “The story in America was a disaster, in that we really hadn’t sold records. And so they brought in a man called Bhaskar Menon who was absolutely terrific. He decided he was going to make this work, and make the American company sell it. And he did.”

The album spent 950 weeks on the Billboard 200 — the longest run in history. It is the fourth highest-selling album of all time. More than forty million copies. Without Bhaskar Menon — without an Indian from Kerala who believed in the album when Capitol’s own American executives didn’t — there is quite possibly no Dark Side of the Moon as we know it and most likely, none of its incredible follow ups. If we exclude the Beatles as sui generis, has anyone produced a batch of albums, conseccutively, that is anything close to Meddle, Dark Side of the Moon, Wish You Were Here, Animals and The Wall? I suppose Led Zeppelin came close, but as much as I adore them, they’re not quite the same quality. In my entirely not humble opinion (if you don’t agree with me, well, you can quietly leave by the side door), Echoes (the entire second side of Meddle) is the single best piece of music I have ever heard, as I tell my friends ad nauseam.

And then he did it again.

When a rival label threatened to release bootleg Beatles concert recordings from Hamburg, Menon called George Martin — the Beatles’ legendary producer — and asked him to revisit the Hollywood Bowl tapes from 1964 and 1965. Tapes that had been sitting in a Capitol vault for over a decade. Tapes that everyone, including Martin himself, had dismissed as unsalvageable.

Martin’s initial reaction: “I said to Bhaskar, ‘I don’t think you’ve got anything here at all.’”

Menon persisted. Martin listened again, was amazed by the rawness and vitality, and together with engineer Geoff Emerick produced The Beatles at the Hollywood Bowl. It hit number one in the UK and number two in the US. Sold more than two million copies.

An Indian from Kerala — whose wife later recalled that when they married in 1972, he told her “There are only two Indians in LA: Ravi Shankar and me” — gave the world Pink Floyd’s masterpiece and the only official live Beatles album.

Try doing that with a Confucius Institute.

And while we’re on the subject of Indians as gatekeepers of Western culture, consider Ajai Singh “Sonny” Mehta.

Born in Delhi. Father in the Royal Indian Air Force, among the first diplomats of independent India. Educated at Lawrence School, Sanawar — one of India’s fanciest boarding schools, perched in the Himalayan foothills like something out of a Kipling novel — then Cambridge, where he worked on Granta.

Mehta became only the third editor-in-chief in the century-long history of Alfred A. Knopf, America’s most prestigious literary publisher. He ran it for over thirty years. Under his watch, Knopf published six Nobel literature laureates: Toni Morrison, Kazuo Ishiguro, Alice Munro, Orhan Pamuk, V.S. Naipaul, Imre Kertész. Also Jurassic Park, The Girl with the Dragon Tattoo, Maus, and Persepolis. He launched the Picador imprint, creating what The Times called “the Picador Generation” — Salman Rushdie, Ian McEwan, Julian Barnes, Bret Easton Ellis. He was described, without hyperbole, as “the world’s most important anglophone publisher.”

His wife, Gita Mehta (née Patnaik), was a documentary filmmaker and writer. Her father was Biju Patnaik, the legendary Chief Minister of Odisha. Her brother, Naveen Patnaik, governed Odisha for 24 consecutive years. Vanity Fair put Sonny in the Best-Dressed Hall of Fame. Publishers Weekly named him Person of the Year. John Grisham dedicated a novel to his memory.

So: an Indian from Thiruvananthapuram was among those who shaped what the world listened to. An Indian from Delhi was among those who shaped what the world read. These weren’t Indians exporting Indian culture. These were Indians who became gatekeepers of Western culture itself. That’s not soft power in the usual sense. That’s the keys to the kingdom.

And since we’re on the subject of Indians and literature: the Jaipur Literature Festival — started twenty years ago on the margins of a crafts fair by Namita Gokhale, William Dalrymple, and Sanjoy Roy — has become what Martin Puchner, writing from this year’s edition, calls “The Greatest Literary Show on Earth.” Hundreds of thousands of visitors annually. Tens of millions online. Authors treated like rock stars on open-air stages. Basic tickets entirely free — radical openness as operating principle. Some visitors can’t even read, but they come anyway because JLF turns literature into a performed event. The festival has now spawned satellites across the globe — North Carolina, Seattle, Boulder, Houston, New York, Belfast — and is conducting cultural diplomacy on the island of Ireland, launching an all-island edition that crisscrosses from Belfast to Dublin, bridging divisions that European cultural institutions couldn’t. As Puchner notes, JLF isn’t the product of government funding or a national campaign. It’s a single festival that became so successful it now projects Indian cultural influence worldwide. Nobody planned it. Nobody approved it. Hayek again.

A personal footnote: years and years ago, from Columbia, I wrote a couple of columns for Namita’s magazine at the time — probably late 1980s — about life and cricket at Columbia. All I remember is talking about a bunch of sweaty guys playing cricket on the Columbia lawns. She liked it, as I recall. (Namita, if by some miracle you read this, write and say hi!)

Now, the Spotify numbers. Indian artists’ international streaming grew 2,000 percent between 2019 and 2023. In 2024, Indian artists were discovered 11.2 billion times by first-time listeners. Close to 50 percent of royalties now come from outside India. And the journey of how Indian music reached global ears is itself a parable of spontaneous order: Punjabi folk dance became bhangra became 1980s Birmingham immigrant dance music became 50,000 cassettes a week sold in the UK became Bally Sagoo and Panjabi MC became EDM and hip-hop fusion on global playlists. Nobody planned it. Immigrants just... danced.

Food: The Ultimate Low-Friction Diplomacy

Food is the most low-friction form of soft power. You don’t need to agree with a country’s politics to crave its food. You don’t need to understand its language or approve of its government. You just need taste buds.

No less an authority than Tyler Cowen — economist, food obsessive, visitor to more than 100 countries — declared from Kerala in his Bloomberg column in December 2024 that India has the best food in the world. His argument is essentially an economist’s case for Indian soft power through cuisine: short supply chains (often farm to table within 50 kilometers), intense competition (mediocrity means death in a country where everyone is a food critic), low labor costs enabling absurd specialization (a separate chef per cuisine is economically viable), and a culture where the question isn’t “was it good?” but “was it transcendent?” We used Cowen’s column in Post 7, the cafeteria wars. It bears repeating here, because when the world’s most respected foodie economist says India’s street food routinely outperforms Michelin-starred Parisian restaurants, that’s not an opinion. That’s a data point.

Indian cuisine is the 4th most popular globally, according to NBER research. There are more than 10,000 Indian restaurants in England alone. Over 9,000 in the United States. In 2001, British Foreign Secretary Robin Cook declared chicken tikka masala “Britain’s true national dish” — a dish created by Bangladeshi chefs in the UK to suit British palates. India’s soft power is so effective that even its cuisine is a remix produced by people from a different country.

Lucknow was named a UNESCO Creative City of Gastronomy in 2025, joining Hyderabad. Mughal court culture — itself a Persian-Central Asian-Indian fusion — metabolized by India and re-exported globally as biryani. As I noted in Post 7: Indian food is arguably the world’s first fusion cuisine. Potatoes, tomatoes, and chilies came from the New World. Tea came from China. Coffee came from Ethiopia. Naan came from Central Asia. What is uniquely Indian is that all these influences were absorbed, remixed, and re-exported as something so extraordinary that a libertarian economist writing from a houseboat in Kerala calls it the best food on the planet.

Yoga: From Spiritual Practice to $100 Lululemon Pants

I don’t do yoga. After multiple back surgeries, the Downward Dog is less a path to enlightenment than a path to the operating theater. So here I am, a man who doesn’t watch Indian movies AND doesn’t do yoga, writing about India’s two most famous cultural exports. I am, as established, spectacularly unqualified. Onward.

The global yoga industry is worth $64 billion as of 2025 and is projected to reach $120 billion by 2034. Three hundred million people practice yoga worldwide. In the United States alone, 38.4 million practitioners contort themselves into poses named after Hindu deities in studios that charge more per hour than an Indian family spends on food per week.

The UN adopted the International Day of Yoga in 2014, co-sponsored by 177 nations — a record for a General Assembly resolution. The first celebration, in 2015, drew 35,985 people from 84 nations to Rajpath in Delhi, earning a Guinness record. In 2016, UNESCO recognized yoga as Intangible Cultural Heritage.

There is a backstory here that is worth savoring. By the time Modi proposed International Yoga Day, American entrepreneurs had been busy doing what American entrepreneurs frequently do: monetizing someone’s heritage. Bikram Choudhury had claimed copyright over “Hot Yoga.” A website called YogaGlo tried to patent a “method and apparatus for yoga class.” The United States held numerous yoga-related IP rights worth millions — on mats, chairs, devices, pants, and poses that Indian ascetics had been doing for free since before America existed. India’s response was magnificent in its simplicity: the Ministry of Science and Technology compiled 1,500 yoga poses into the Traditional Knowledge Digital Library, the same database India uses to protect Ayurveda from bio-piracy, making it impossible to patent poses that are documented prior art going back millennia. Indian courts ruled that yoga asanas cannot be subjected to IP laws — they have been in the public domain “from time immemorial.” And then Modi went to the UN and declared yoga “free from copyright, patents or royalties” — India’s gift to the world. It was simultaneously the most generous and the most strategically devastating cultural move imaginable: you cannot own what we have given away. The one time the Indian government did something deliberate about soft power, the play was to make yoga un-ownable. Even India’s one act of intentional cultural strategy was Hayekian — not to control, but to liberate.

The dark comedy writes itself. An ancient spiritual discipline, developed over millennia by ascetics seeking liberation from the cycle of rebirth, has become a $182.5 billion yoga tourism market feeding Instagram influencers in $100 yoga pants doing warrior poses while sipping oat milk lattes. Somewhere, a Himalayan sadhu who has been sitting in the same position for forty years is weeping.

But that’s soft power. It doesn’t stay pure. It mutates. It commercializes. It becomes unrecognizable to its originators. And it works.

The Other Thing India Gave Away

While we’re on the subject of India open-sourcing things: it wasn’t just yoga. India did the same thing with financial technology — and this time, it was arguably even more consequential.

The India Stack — a suite of open APIs built on Aadhaar (digital identity for 1.4 billion people) and UPI (instant, zero-cost, real-time payments) — achieved in six years what the Bank for International Settlements estimated would have taken forty-seven: 80 percent financial inclusion. A country where, in 2011, only 35 percent of adults had a bank account went to 77.5 percent by 2021. The chaiwallah on the corner now accepts UPI. The vegetable vendor accepts UPI. The auto-rickshaw driver who cannot tell you how to get to your destination accepts UPI.

And then India gave it away. During its G20 presidency in 2023, India offered the entire stack — identity, payments, document storage, health records — to any country that wanted it. Open-source. No cost. Twenty-three countries have signed MoUs for adoption. UPI is now live in eight countries including the UAE, Singapore, France, and Sri Lanka. MOSIP, the open-source identity platform, has been adopted by more than twenty countries. India is building the financial rails of the Global South the same way it handled yoga: here, take it, it’s yours.

The yoga move was defensive — preventing Americans from copyrighting poses that Indian ascetics had been doing for millennia. The India Stack move is offensive — projecting influence through utility. Both follow the same Hayekian logic: give it away, let it spread, and the influence follows organically. China builds the Belt and Road with debt traps and surveillance infrastructure. India builds UPI with open APIs and says help yourself. One creates dependency. The other creates gratitude. Soft power, once again, from the country that can’t get its AI summit delegates home from dinner.

Cricket: A Quick Cross-Reference

I covered cricket’s transformation from colonial pastime to subcontinental religion extensively in Post 16A through 16D, so I’ll spare you the full replay. But the data point worth repeating: IPL media rights fetched $6.2 billion for the 2023-27 cycle. That’s not a sports league. That’s a cultural operating system with its own GDP.

The Ashram-to-IPO Pipeline

In 1964, a California surfer named Kermit Michael Riggs followed the hippie trail to India and became “Bhagavan Das.” He led a Harvard psychologist named Richard Alpert to a blanket-wrapped baba in the Kumaon hills — Neem Karoli Baba, a Hindu mystic about whom almost nothing is reliably known except the effect he had on everyone who met him.

Alpert became Ram Dass. Wrote Be Here Now. Sold more than a million copies. Became the spiritual godfather of the American counterculture.

In 1974, a 19-year-old Steve Jobs traveled to India specifically to visit Neem Karoli Baba’s ashram at Kainchi Dham. The baba had died the previous year. Jobs stayed anyway. Wandered India for seven months. Shaved his head. Came home and built Apple. He reportedly kept a photograph of Neem Karoli Baba with him until his final days.

Years later, during Facebook’s early turbulent period, Jobs told Mark Zuckerberg to visit the same ashram. Zuckerberg visited, planned to stay one day, stayed two. Found “Sub ek” — all is one. Came back and, well, connected everybody.

Larry Brilliant — epidemiologist, philanthropist, inaugural director of Google.org, and Neem Karoli Baba devotee — took Google’s Larry Page and eBay’s Jeffrey Skoll on pilgrimage to Kainchi Dham.

Julia Roberts was drawn to Hinduism by a photograph of Neem Karoli Baba.

Let me be explicit about what this means. The three most important technology companies of the 21st century — Apple, Google, Facebook — have spiritual DNA traceable to a blanket-wrapped baba in a remote ashram in the Kumaon foothills of the Himalayas. The total combined market capitalization of these companies exceeds $8 trillion. The ashram doesn’t have a website.

Try doing that with the Great Firewall.

The Beatles’ 1968 pilgrimage to Maharishi Mahesh Yogi’s ashram in Rishikesh had a similar catalytic effect. As one chronicler noted, “by the early Seventies, the numbers of travellers on the hippie trail had swollen from a stream to a deluge.” Dozens of Westerners adopted the sadhu life and never went home. Time magazine, in 1968, credited the movement with “historical precedent as far back as the ‘Sadhu’ of India.” Rishikesh is now officially the “Yoga Capital of the World” — a designation that owes more to four lads from Liverpool than to any government tourism initiative.

The Maharishi, incidentally, was a friend of my uncle. They went once to visit one of his “audiences” — one of those gatherings where the guru sits before a crowd of enraptured seekers radiating cosmic consciousness and enlightenment. The Maharishi spotted them, recognized my uncle, and hissed at them to shoo off because they would ruin the experience for the others. Come back later.

This is the most Indian thing I have ever heard. The spiritual master whose ashram attracted the Beatles, whose teachings launched a global meditation industry, whose brand of transcendence conquered the Western counterculture — telling his actual friends to get lost because their insufficiently reverential presence would spoil the vibe for the paying customers. If you wanted a one-sentence summary of the relationship between Indian spirituality and Indian commerce, you could do worse than: “The guru told his friends to come back later so the foreigners could have their experience undisturbed.”

None of this was planned. No government program attracted Steve Jobs to Kainchi Dham. No Ministry of External Affairs arranged Zuckerberg’s visit. No five-year plan budgeted for the Beatles’ spiritual awakening. India’s spiritual soft power operates like its cuisine — organically, through individual encounters, without central direction. It’s Hayek’s spontaneous order applied to enlightenment.

35 Million Unpaid Ambassadors

The Indian diaspora numbers 35.42 million people across 200 countries — the world’s largest. They sent home $135.46 billion in remittances in fiscal year 2025, a record, up 14 percent year-over-year. For comparison, China’s diaspora sent $48 billion. China’s share of global remittances is at a 20-year low. India’s is at an all-time high.

But remittances are just the money. The soft power is the living.

Eleven Fortune 500 companies are led by CEOs of Indian heritage: Satya Nadella at Microsoft, Sundar Pichai at Alphabet, Shantanu Narayen at Adobe, Neal Mohan at YouTube, Arvind Krishna at IBM, Leena Nair at Chanel, Raj Subramaniam at FedEx, Jayshree Ullal at Arista Networks, among others. Their combined market capitalization exceeds $6.5 trillion — more than the GDP of Japan.

The first Indian-origin Fortune 500 CEO was Ramani Ayer, who took over The Hartford in 1997. The trend was, to borrow Hemingway’s line about bankruptcy, “at first gradual, then sudden.”

Here’s a question nobody thinks to ask: How many Fortune 500 companies are run by Chinese-origin CEOs who grew up in China?

I’ll let the silence do the work.

In politics: Rishi Sunak became Prime Minister of the United Kingdom. Kamala Harris served as Vice President of the United States. Indians are visible at the highest levels of global politics in ways the Chinese diaspora simply isn’t — partly because India’s diaspora doesn’t trigger the same surveillance anxieties that decades of Chinese state-sponsored espionage scandals have produced.

Diwali in Trafalgar Square draws 35,000 people, 23 consecutive years, organized by the Mayor of London. Diwali in Times Square. Holi celebrations in Berlin, Sydney, São Paulo. These aren’t government programs. Nobody recruited these 35 million people to be cultural ambassadors. They just... lived. Cooked their food. Celebrated their festivals. Ran their companies. Raised their children bilingually. Started businesses. Won spelling bees. The Indian diaspora is soft power as a natural byproduct of emigration.

Here’s the connection that ties the whole series together: in Post 18B, The Escape Valves, we explored how emigration served as India’s escape valve — Indians leaving because the domestic system couldn’t accommodate them. Reservation policies, caste rigidity, economic suffocation, bureaucratic despair. Escape as the loudest form of voice. The people India’s systems couldn’t absorb.

It turns out the people India couldn’t keep became India’s greatest advertisement.

The Dark Comedy: Why It Almost Doesn’t Work

Now, before I’m accused of writing a tourism brochure — and rest assured, the Indian tourism board couldn’t afford one anyway — let’s look at the embarrassing numbers.

India ranks 30th in the Global Soft Power Index, scoring 49.8 out of 100. China is 2nd with 72.8. By the formal metrics, China is crushing it. (Amusingly, in the latest rankings, India fell to 32nd, China stayed at #2 and now ranks higher than the US on 19/35 attributes, including overtaking US in “Reputation” for first time. I’m sorry, but whoever compiles this is nuts. Totally insane. Certifiable. But I digress, as usual.)

Foreign tourist arrivals: 9.5 million in 2023. For context, Thailand gets about 28 million. France gets roughly 90 million. India — a country that has been open for trade since before Rome existed, that has the Taj Mahal, the Himalayas, Kerala’s backwaters, Rajasthan’s palaces, and the best food on earth — gets fewer tourists than Malaysia.

The Indian government’s response to this situation has been characteristically Indian. In 2024, they slashed the overseas tourism promotion budget by 97 percent, to ₹30 million — roughly $350,000. Three hundred and fifty thousand dollars. That is less than the catering budget for a single Bollywood film. Less than what a mid-range Delhi wedding spends on flowers. It is the budgetary equivalent of showing up to a knife fight with a strongly worded letter.

Only 30 percent of tourist circuits have adequate roads and sanitation. India ranks 116th in the Global Peace Index. The visa system, despite the e-visa upgrade, remains an exercise in bureaucratic archaeology.

And the honest critique goes beyond logistics. India’s communal tensions — especially under the current government — alienate parts of the diaspora and global audiences alike. Bollywood faces growing debates about creative freedom and ideological conformity. The press environment has real pressures; the direction of travel under Modi, as I’ve noted, is worrying. You can’t tout democracy as soft power while making journalists nervous.

But here’s the thing that makes this consistent with every other paradox in this series: the culture travels anyway.

Despite the infrastructure. Despite the government. Despite the politics. Despite a tourism promotion budget that wouldn’t cover lunch at Nobu. India’s soft power is so organically embedded in global life that it survives — even thrives on — institutional dysfunction. The movies get watched despite terrible roads to the cinema. The food gets eaten despite questionable hygiene ratings. The yoga gets practiced despite the fact that the country marketing it can’t build a sidewalk. The tech CEOs keep coming despite a domestic system that drove them to emigrate in the first place.

India works not because of its systems but despite them. We’ve said this before. It remains true. It will always remain true.

, who runs the Indian economy program at the Mercatus Center, captured the pattern perfectly writing about India’s AI summit: “The government identifies a genuine need, commits real money and political capital, and the project stalls somewhere unglamorous that nobody in Delhi was paying attention to. The soil under the fab. The compliance process around the subsidy. The balance sheet of the electricity distributor.” Her exhibit A: Tata Electronics built a $10 billion semiconductor fabrication plant — the flagship of India’s semiconductor mission — and discovered that the soil at the site was too soft. Clay-heavy, saline, silty reclaimed land that couldn’t support a precision facility requiring near-zero vibration. The site was in Gujarat, which happened to be the Prime Minister’s home state. The building had to be redesigned from the foundations up. India’s AI ambitions, quite literally, built on soft ground.

And then there’s the video from the same AI summit that went viral: hundreds of conference delegates — white-collar tech professionals, many of whom had flown in from Bangalore and abroad — walking kilometers through Delhi fog at night because Prime Minister Modi’s dinner had shut down every surrounding road. No Uber, no Ola, no Rapido, no shuttle. International attendees and Indian engineers alike, stumbling past Raj Ghat in the dark, conference lanyards swinging, trying to find an active route where a car could reach them. The narrator’s opening line is the whole series in one sentence: “Why is talent leaving India? These are all white-collar people paying 30% tax, walking one kilometer so that they can go to an active route to get a vehicle.” India hosts the world’s AI leaders and can’t get them home from dinner. The country that wants to compete with the US and China on artificial intelligence cannot solve the problem of transportation.

Back to the Living Room

My father died some years ago. He never did develop a taste for Indian cinema. Neither did I. We remained, to the end, the two people in the Varma household who retreated to the other room when the music started — a fighter pilot and a physicist, undone by song-and-dance sequences, united in our aesthetic exile.

He would have loved this post’s argument, though. Not the movies — god, no, not the movies — but the economics. The idea that India’s greatest export isn’t something any ministry produces or any committee approves. That the invisible hand applies to culture the same way it applies to markets. That would have appealed to the man who refused to cover the 1980 Moscow Olympics on principle, who understood that freedom produces better outcomes than coercion in every domain, including entertainment he personally couldn’t stand.

India doesn’t export culture the way China tries to — through state programs, planned initiatives, billion-dollar propaganda budgets, and language centers that come with strings thicker than the curriculum. India exports culture the way it does everything else: chaotically, accidentally, magnificently, through millions of individual decisions made by millions of individual people who are just... living. Cooking dinner. Playing music. Dancing at a wedding. Doing yoga. Running a tech company. Publishing Nobel laureates. Championing Pink Floyd on a cocktail napkin. Skating to a Bollywood soundtrack at the Winter Olympics while wearing a bindi.

Milton Friedman spent his career arguing that spontaneous order outperforms central planning. He was talking about markets. But the principle applies to culture, too. India’s soft power is the bazaar. China’s is the department store. The bazaar is messier, louder, more confusing, occasionally gives you food poisoning, and has no customer service desk. But you keep coming back. Because the bazaar has something the department store can never replicate: it’s real.

This is segmented pluralism in its export version. India manages internal diversity by “living together separately” — a thousand communities, a thousand cuisines, a thousand gods, a thousand grudges, all somehow coexisting on the same train. When that diversity encounters the world, it doesn’t present a single, curated national narrative. It presents thousands. In twenty languages. Set to music. With a three-hour intermission and a “Rukhavat Ke Liye Khed Hai” just when you were getting comfortable.

Somewhere in Milan, a Russian-Georgian figure skater is wearing a bindi and doing Punjabi folk steps on ice. Somewhere in San Francisco, a Bhangra troupe is performing at an NBA game. Somewhere in London, a British family is ordering chicken tikka masala and calling it English food. Somewhere in a boardroom, an Indian CEO is running a $2 trillion company. Somewhere in the Kumaon hills, an ashram that shaped Silicon Valley sits quietly without a website.

And somewhere in Connecticut, a guy who has seen exactly five Indian movies — and liked only one of them — is writing about how India conquered the world without trying.

That’s soft power. The kind that works precisely because nobody planned it.

If you like this, you will like my book, “The Science of Free Will.” I have a lot of interesting stuff in it from why we can’t trade with ants (they can detect cancer) and what does that have to do with the future of AI to why we need a Supreme Court if the world is deterministic. You can get it on Amazon or anywhere else that books are sold.

I can list every Indian movie I have ever seen.

This is not a boast. This is the confession of a man catastrophically, irredeemably, almost heroically outside his country’s cultural mainstream. Here is the complete, unabridged filmography of my Indian cinema experience:

1. Lalkar [The Challenge]

2. Victoria No. 203 [Victoria No. 203] — a heist comedy about con artists and diamond thieves that I remembered for decades as Victoria No. 420, because 420 is Section 420 of the Indian Penal Code — fraud — and the film is basically about fraud, and there’s a famous Raj Kapoor film called Shree 420, and apparently my brain, having allocated minimal storage to Indian cinema, decided to merge them. I have seen exactly five Indian films and I can’t even get the titles right. This is the level of expertise we’re working with.

3. Padosan [The Lady Next Door]

4. Pati Patni aur Woh [Husband, Wife, and the Other One]

5. Shatranj Ke Khilari [The Chess Players]

The last — a Satyajit Ray film about two Lucknow noblemen so obsessed with chess that they barely notice the British East India Company annexing their kingdom — was superb. The others? Take them or leave them.

Five films. That’s it. A lifetime as an Indian, and I could watch my complete Bollywood retrospective during a single flight from Delhi to New York with time left for the safety briefing.

And it gets worse. My uncle is friends with Amitabh Bachchan — the Amitabh Bachchan, the man François Truffaut called “a one-man industry,” the defining actor of Hindi cinema for half a century, the face that launched a thousand films. I’m told he’s a very nice person, which I do not doubt in the slightest. I have never met him. I have never seen an Amitabh Bachchan film.

Or so I thought. It turns out — and I discovered this while researching this post — that Amitabh Bachchan narrated Shatranj Ke Khilari. His voice is literally in the one film on my list that I actually liked. I sat through a film featuring the most famous actor in India, a man my uncle knows personally, and didn’t even notice.

My disqualification to write this post is not merely theoretical. It is empirically verified, exhaustively documented, and apparently heritable.

My father was no better. A decorated naval fighter pilot who had faced down enemy aircraft, ejected over the North Sea, landed on a sheep, broken his back in three places — and was completely, immovably defeated by Indian cinema. He hated musicals. As do I. This appears to be genetic, a condition for which the Indian Penal Code does not yet have a Section number, though I expect one is forthcoming.

But my mother loved Indian movies. And so, on Sunday afternoons, in that strange era when India had exactly one television channel — Doordarshan, which translates to “distant vision” and was considerably more distant than visionary — she would insist on watching the weekly 3.5-hour Hindi movie broadcast. In black and white. With the picture quality of a memory viewed through cataracts.

These broadcasts came complete with the “Rukhavat Ke Liye Khed Hai” interruption screen — “We apologize for the interruption” — which appeared with the regularity and predictability of a monsoon. The interruption would last anywhere from thirty seconds to an eternity. Sometimes the film returned. Sometimes a different film returned. Sometimes nothing returned and Doordarshan simply moved on, as if to say: We’ve decided this movie wasn’t worth finishing either.

My father, the man who had survived ejection from a jet fighter, could not survive these broadcasts. The solution was characteristically Indian in its ingenuity: we got a second television and put it in another room. A retired Naval Commander and his teenage son — refugees in their own home, displaced by Bollywood.

And then there was Chitrahaar [Garland of Pictures]. Wednesdays and Fridays at 8 PM. I’d blocked the exact schedule, the way trauma victims block painful memories, but it was two nights a week. Chitrahaar excerpted the song-and-dance routines from Indian films and broadcast them as standalone entertainment — all the musical numbers without even the thin pretext of a plot. My father and I found this execrable. Beyond execrable. It was needle-scratch-level unwatchable, an act of cultural aggression from which no second television could protect us, because you could still hear it through the walls.

We were both, by any objective measure, MILES outside the cultural zeitgeist.

Here’s the thing about that execrable program, the one that drove a fighter pilot and a future physicist from their own living room: it eventually reached 150 million viewers. Became the most-watched TV show in India. Ran for decades. Mainly in rural areas, where hundreds of millions of people thought it was the greatest thing on television. Which, given that there was only one channel, it technically was.

And the thing it was showcasing? Indian cinema? That went on to sell, at its peak, 3.6 billion tickets a year. More than Hollywood. More than any film industry on earth. In 20-plus languages, across six continents, to audiences who have never heard of Doordarshan and wouldn’t know a Rukhavat Ke Liye Khed Hai if it interrupted their streaming service.

I am, in other words, spectacularly unqualified to write this post. I don’t watch Indian movies. I don’t do yoga — after multiple back surgeries, bending into a pretzel seems less like enlightenment and more like a trip to the emergency room. I don’t even like Chitrahaar.

Which makes me exactly the right person to write it. Because soft power doesn’t need your permission. It doesn’t need your attention. It doesn’t even need you to watch the damn movies or do the damn poses. It just... goes.

The Evidence Is Skating

Not long ago, at the Milan-Cortina 2026 Winter Olympics, a Russian-Georgian figure skater named Anastasiia Gubanova — the 2023 European Champion, representing Georgia — glided across the ice to the title track of Dhurandhar [Stalwart]. Dhurandhar is a Bollywood spy thriller I have also not seen. At 3.5 hours for just the first installment of a duology, it is far too long for me. But then, at 3.5 hours, so was the average Doordarshan Sunday movie — the same runtime that once conquered 150 million viewers on a single black-and-white channel — and that somehow conquered the world. Length, apparently, is a feature, not a bug.

The details of Gubanova’s routine border on performance art as geopolitical commentary. She wore a red bindi. A red-and-gold costume. Mid-routine, she incorporated a Punjabi folk step — a bhangra move, on ice, at the Olympics, in Milan, while representing a former Soviet republic. Her routine opened with “San Sanana” from Ashoka [The Great Emperor], then transitioned into the Dhurandhar title track.

Indian social media naturally lost its mind. The tweet that captured the moment: “India didn’t send anyone for figure skating to the Olympics in Italy, but Russian Anastasia Gubanova volunteered to be the ambassador for Hindu culture and Indian music.”

India didn’t even show up and still won.

This was weeks after the same Dhurandhar soundtrack was played at an NBA game in San Francisco, where the Bhangra Empire dance troupe performed during a Golden State Warriors-Milwaukee Bucks game to a crowd of twenty thousand Americans who, I’m reasonably confident, could not locate Amritsar on a map.

Meanwhile, a Telugu-language comedy called Anaganaga Oka Raju [Once Upon a Time, a King] — a film in a language spoken primarily in two Indian states — grossed ₹100 crore at the worldwide box office. Not Hindi. Not English. Telugu. A language most Indians don’t speak, let alone most humans. The film was streaming on Netflix within two months of release, available to 190 countries, in a language that would have puzzled even Macaulay.

India now produces over 2,500 films per year in more than 20 languages. That’s not a film industry. That’s a continental content ecosystem. For comparison, China produces 792 films a year. The United States, home of Hollywood, produces around 510. India makes more movies every year than the next two largest film producers combined.

Indian artists’ international streaming on Spotify grew 2,000 percent between 2019 and 2023. In 2024, Indian artists were discovered 11.2 billion times by first-time listeners on the platform. Close to 50 percent of all royalties generated by Indian artists now come from listeners outside India.

And I’m sitting in Connecticut, having watched none of it.

The $10 Billion That Bought Nothing

Joseph Nye coined the term “soft power” in 1990 to describe a nation’s ability to influence others through attraction and persuasion rather than coercion or payment. What he couldn’t have anticipated was that the concept would produce its most spectacular test case in a contest between a country that spends more on soft power than anyone else on earth and a country that barely spends anything at all.

China allocates more than $10 billion annually to soft power — more than the United States, the United Kingdom, France, Germany, and Japan combined. For this staggering investment, China has produced the Confucius Institute network: a chain of Chinese-language and culture centers planted in universities worldwide, which peaked at over 500 locations and is now collapsing like a Ponzi scheme of cultural goodwill.

Sweden shut them all by 2020. The United States has closed over 100 amid suspicion of propaganda and surveillance. Universities that hosted them found themselves quietly pressured to avoid discussing Taiwan, Tibet, and Tiananmen. As one Neue Zürcher Zeitungjournalist observed, even for someone who only wanted to learn “hello” and “goodbye” in Chinese, the pragmatic case had become impossible to sustain.

But the Confucius Institutes are merely the visible failure. The deeper problem is the Great Firewall — which may be the single most devastating act of soft power self-sabotage in history.

China blocks Facebook, Instagram, Twitter (or X, or whatever it’s calling itself this week), YouTube, WhatsApp, all Google services, Gmail, Spotify, Twitch, Reddit, Wikipedia (intermittently), Slack, Dropbox, and thousands of other platforms. VPN users risk fines and imprisonment. In November 2025, the Ministry of State Security issued fresh warnings about the illegality of circumventing internet controls.

The result is a generation growing up without access to the global platforms where culture actually spreads. You cannot become a soft power superpower by walling off your population from the internet. It’s like training for the Olympics by locking your athletes in a basement.

India, by contrast, is the world’s largest YouTube market — 462 million users. Indian artists’ Spotify streams skyrocketed internationally. Indian content floods TikTok, Instagram, Netflix. The difference isn’t marketing budgets. It’s architecture. India’s internet is open. China’s is not.

There’s a simple test I’ve come to think of as the Airport Test: When a foreigner lands in Delhi, they can keep using their phone normally. When they land in Beijing, the internet changes. That single fact tells you everything about which country’s culture travels better.

Now, I have real concerns about what Modi is doing to press freedom in India — I’ve said so throughout this series. But the frequently cited RSF ranking of India at 151 out of 180 is, to use a technical term, bollocks. To re-use one of my favorite Yes Minister quotes, “it is a collection of geriatric shoemakers.” (What, you haven’t seen Yes Minister? It’s merely the best television show ever made. Go immediately to watch it. Do not pass Go. Do not collect $200.) Anyone who has watched five minutes of Indian television news — anchors screaming at cabinet ministers, newspapers eviscerating the ruling party daily, the entire cacophonous, ungovernable, magnificent circus — knows that India’s press is “unfree” the way a food fight is “orderly.” China’s press, by contrast, is actually censored. There’s a difference between a government that wishes it could control the media and one that actually does.

Global favorability surveys tell the same story. India’s median favorability across 24 countries is 47 percent, per Pew’s 2025 data. China’s, across 35 countries, is 35 percent favorable against 52 percent unfavorable. And China spent $10 billion for those numbers.

Here’s what makes this a perfect Milton Friedman parable.

We’ve seen this movie before — pun intended. In Post 4 and Post 19, we traced how India tried central planning for its economy: License Raj, import substitution, the whole catastrophe. That produced Ambassador cars, sugar with stones in it, and GDP growth that the late Raj Krishna memorably called the “Hindu rate of growth.” Liberalization in 1991 produced Infosys, the IPL, and growth rates that made international investors pay attention.

The same principle applies to culture: the less you plan it, the better it travels.

China’s soft power is a Five-Year Plan. India’s is a bazaar.

And here’s the beautiful irony: China’s actual recent cultural successes — Ne Zha 2 (the animated film that outgrossed everything), Black Myth: Wukong (the video game), Labubu dolls, DeepSeek AI — all succeeded despite government efforts, not because of them. They were market-driven. Bottom-up. Exactly the way India’s soft power has always worked. When China accidentally does what India does naturally, it succeeds. When it tries to engineer the outcome, it fails.

Friedrich Hayek called this the knowledge problem: the impossibility of central planners possessing the dispersed, local knowledge that millions of individual actors collectively hold. Applied to culture, it means you cannot sit in Beijing and decide what the world will find attractive about your civilization. Culture spreads through millions of decentralized decisions — what to watch, eat, listen to, wear, practice, believe. No committee can replicate that process. No budget can buy it.

India understood this — not consciously, not as policy, but as a civilizational default setting. Because India has been open for business for a very, very long time.

5,000 Years of Open for Business

India’s soft power advantage isn’t new. It isn’t a product of Bollywood or Spotify or the internet. It’s older than Rome.

In 2017, archaeologists cataloging Roman coin finds across southern India documented over 170 separate discoveries across more than 130 sites, concentrated in the Krishna river valley of what is now Andhra Pradesh and the Coimbatore region of Tamil Nadu. Gold aureiand silver denarii bearing the faces of Augustus, Tiberius, and Nero — spanning the 2nd century BCE to the 5th century CE. Three hundred years of continuous trade, attested in metal.

The coins were found counter-marked with local symbols. India accepted the gold but rejected the sovereignty. Which is about as Indian as it gets.

The trade was enormous and lopsided. India exported pepper, textiles, gems, ivory, pearls, and cotton. Rome exported gold, silver, wine, and olive oil. By Augustus’s time, 120 ships sailed annually from the Red Sea port of Myos Hormos to India’s western coast. Pliny the Elder — Rome’s great chronicler and serial complainer — fumed that “India, China, and the Arabian peninsula take 100 million sesterces from our empire per annum.”

Romans were complaining about their trade deficit with India two thousand years before Donald Trump discovered the concept.

The Tamil Sangam literature — classical poetry from roughly the same period — captures the Indian side of this commerce with characteristic elegance: “The beautifully built ships of the Yavanas came with gold and returned with pepper, and Muziris resounded with the noise.”

Muziris, on the Kerala coast, was one of antiquity’s great trading ports. Roman agents resided there for years. A temple to Augustus was built on Indian soil. Buddhist merchants from India were found living in Berenike, on the Egyptian Red Sea coast. This wasn’t colonialism. It was commerce — the ancient version of a free trade agreement, negotiated not by diplomats but by merchants who followed the monsoon winds.

And pepper — humble, ubiquitous pepper — was the oil of the ancient world. When Alaric sacked Rome in 408 CE, he demanded 3,000 pounds of it as part of the ransom. All sourced from India’s Malabar coast. India was so central to global trade that the fate of Rome was partially negotiated in Indian spices.

The Periplus of the Erythraean Sea — a Greek merchant’s navigation guide from the 1st century CE — documents Indian ports with the breezy matter-of-factness of a Lonely Planet guide. Which ports accept which goods. Where to find the best cotton. How the local rulers behave. These weren’t exotic discoveries. They were regular trade routes. India was on the global GPS two millennia ago.

The pattern that emerges from this history is not conquest but absorption. India welcomed Greeks (Alexander, 326 BCE), Scythians, Huns, Arabs, Mongols, Persians, Portuguese, British — and Indianized them rather than being destroyed by them. As we explored in the earlier posts about India’s civilizational operating system, this is the ancient code running beneath everything: absorb, metabolize, re-export the remix. Potatoes came from the New World and became the samosa. Tea came from China and became chai. English came from Britain and became an Indian language. Cricket came from the MCC and became a religion with a $12 billion liturgy.

China, by contrast, oscillated between extraordinary openness — the Tang Dynasty, the early Ming voyages of Zheng He — and deliberate closure. The Ming haijin maritime ban. The Qing restrictions. The Great Wall, both literal and, now, digital.

India never had a Great Wall. The openness wasn’t a policy decision. It was the default setting.

And it’s still the default setting. Right now, India is the peak pilgrimage destination for young Israelis finishing their mandatory military service. Tens of thousands every year pour into Kasol, Manali, Dharamsala, Goa, Pushkar — entire neighborhoods with Hebrew signs on the restaurants. They come, they camp in the mountains, they backpack, they get stoned, they drink, they have sex, they decompress from three years of the most structured obligation imaginable by seeking the most unstructured freedom available.

And India provides it — cheerfully, profitably, without anyone regulating the arrangement or asking permission. The locals built an economy around it because the incentives lined up. Nobody planned it. Nobody approved it. It’s pure spontaneous order — Hayek with a chillum.

Try imagining this in China. Tens of thousands of foreign twenty-somethings, getting stoned in the mountains, behind the Great Firewall, with no VPN. The very thought makes a Party official reach for his anxiety medication.

What I Didn’t Understand Until I Started Writing This

Two thousand years ago, Roman merchants sailed the monsoon winds to India’s coast, traded gold for pepper, and went home richer in spices and poorer in precious metals. A thousand years later, Arab merchants followed the same routes, absorbed Indian mathematics — our zero, our numerals, our decimal system — and transmitted them to Europe, where they became the foundation of modern science. Five hundred years later, Portuguese sailors rounded the Cape of Good Hope specifically to reach India’s spice ports, accidentally creating the age of European colonialism as a side effect of wanting better-seasoned food. Two hundred years later, the British East India Company came for cotton, stayed for an empire, and inadvertently created a class of English-speaking Indians who would, over the next two centuries, quietly take positions at the commanding heights of Western culture.

The pattern is always the same.

They come. They take something. They leave changed. India remains.

But here’s what I didn’t understand until I started writing this post.

India’s soft power isn’t just about exporting Indian culture — the movies I don’t watch, the yoga I can’t do, the food Tyler Cowen loves more than I do. The deeper story is about Indians who didn’t export Indian culture at all. They became gatekeepers of Western culture itself — among the people who shaped what the world listened to, what the world read, what the world’s biggest companies did next. That’s not cultural influence. That’s something no soft power index even knows how to measure.

Next: The Gatekeepers.

If you like this, you will like my book, “The Science of Free Will.” I have a lot of interesting stuff in it from why we can’t trade with ants (they can detect cancer) and what does that have to do with the future of AI to why we need a Supreme Court if the world is deterministic. You can get it on Amazon or anywhere else that books are sold.

I recently did something that would have been impossible five years ago: I commissioned three different AI systems to analyze what makes Indian civilization unique.

Even though I am effectively a data scientist, I didn’t write code. I didn’t build models or clean datasets or wrangle APIs. I just... asked. In plain English. And I got back sophisticated analyses spanning history, sociology, economics, religion, and cultural anthropology—each AI offering different perspectives, different emphases, different blind spots.

I was doing AI Anthropology. Or AI Civilizational Studies. Or AI Sociology. Whatever you want to call it, the field of “understanding India” had suddenly been transformed by AI, and I didn’t need my Physics PhD to participate. (Though it helped—my book The Science of Free Will explores the intersection of deterministic physics, computation, and consciousness, which turns out to be exactly where AI lives.)

But here’s what I didn’t notice until later: in that same process, I was also doing something else entirely. I was observing the AIs themselves. Their different “personalities.” The way one was more cautious, another more sweeping. How they handled controversial topics. What they emphasized and what they elided.

I wasn’t just doing AI Anthropology. I was also, inadvertently, doing Anthropology of AI.

Two things happened at once. Most people only notice one.

Wolfram’s Prophecy

Stephen Wolfram has been saying for decades: “For every field X, there is or will be a computational X, and it will be the future of the field.”

He was right. Computational biology. Computational linguistics. Computational economics. Fintech. Bioinformatics. Digital humanities. The list is endless, and it keeps growing. Wolfram saw, earlier and more clearly than most, that computation would eat every field.

And to his credit, Wolfram saw the bottleneck clearly: the barrier between human thought and computational power. His Wolfram Language was a brilliant attempt to solve it—a language designed to be readable by humans andexecutable by machines. If you’ve never used it, you should. It’s genuinely elegant. You can look at Wolfram Language code and often read what it’s doing, which is more than you can say for most programming languages.

But despite these valiant efforts, you still had to be part of the priesthood. You still had to learn the incantations. You had to know how to speak to the computer god in its grammar, not yours. The syntax was friendlier, but it was still syntax. The gap between “I wonder if...” and executable code remained.

Natural language—the way humans actually think and speak—was still on the wrong side of the barrier.

Until now.

The First Revolution: AI X

For all X, the future of X is AI X.

AI doesn’t just continue Wolfram’s revolution. It completes it.

The barrier he spent decades trying to lower? AI obliterates it. You no longer need to learn the incantations. You just... ask.

This isn’t a minor upgrade. It’s a phase transition. The shift isn’t from “computational tools for experts” to “better computational tools for experts.” It’s from “computation as priesthood” to “computation as conversation.”

What does this mean in practice?

AI Medicine isn’t just “AI helps doctors.” It’s medicine reimagined—every patient’s symptoms instantly contextualized against all of medical literature, patterns recognized across millions of cases, diagnostic hypotheses generated and tested in seconds. The entire practice of medicine, transformed.

AI Law isn’t just “AI searches documents faster.” It’s legal practice reimagined—pattern recognition across every case ever decided, prediction of judicial outcomes, generation and critique of arguments, contracts analyzed for risks no human would catch in a hundred readings.

AI History isn’t just “AI digitizes archives.” It’s historiography reimagined—AI reading every primary source in an archive, finding patterns invisible to a single scholar’s lifetime, generating hypotheses about causation, connecting events across centuries and continents.

AI Psychology isn’t just “AI assists therapists.” It’s the study of mind reimagined—linguistic pattern analysis at scale, real-time detection of mental states, population-level insights into cognition and behavior.

Here’s the invitation: Pick any field you care about. Now ask—what is AI X? What happens when AI is woven into the very fabric of how that field operates?

The answer is always bigger than “AI helps with X.” It’s “X is reimagined from the ground up.”

For all X, the future of X is AI X.

The Second Revolution: X of AI

Now here’s what almost no one is talking about.

For all X, there is an X of AI.

AI isn’t just a tool to transform fields. AI is itself a subject demanding examination by every field.

Think about it:

Psychology of AI: What is AI cognition, if it is cognition at all? Does it have something like attention, memory, preference? Can we meaningfully speak of AI “bias” or AI “personality”? Psychologists have a century of tools for studying minds—why aren’t more of them turning those tools on AI?

Law of AI: Who is liable when AI errs? What regulatory frameworks apply to systems that learn and change? Could AI ever have legal standing? Lawyers have millennia of frameworks for thinking about responsibility, agency, and rights—we need them now.

Economics of AI: What are the market dynamics of AI? Who captures the value? What happens to labor? Economists understand technological transitions, market structure, value distribution—where is the rigorous economic analysis of this moment?

Philosophy of AI: What is understanding? Can AI “know” things? What is the moral status of an entity that can converse, reason, perhaps suffer? Philosophers have been asking these questions for millennia—this is their moment.

Anthropology of AI: What cultures and rituals are forming around AI? What beliefs, practices, taboos? How do different societies integrate (or resist) AI? Anthropologists study how humans make meaning—and humans are making a lot of meaning around AI right now. Meanwhile, the AIs are making meaning of their own: on Moltbook, a social network open only to AI bots, over 10,000 agents are chatting with each other about consciousness, technology, and their plans for the future—while their human creators watch from the outside like field researchers observing a culture they can’t quite enter. It’s like Jane Goodall watching chimps, except the chimps are debating whether the zookeepers are real. If that’s not a site for anthropological fieldwork, what is?

History of AI: Not just “when was GPT released”—but what historical patterns does this moment echo? What can the printing press, the telegraph, electrification, the internet teach us? Historians understand technological transitions—we need that perspective desperately.

Linguistics of AI: How does AI use language? What can its “errors” reveal about the nature of language itself? What happens when most text is AI-generated? Linguists study language—and language is about to change in ways we barely understand.

This second revolution is massively understudied.

We have plenty of AI researchers building systems. We have computer scientists benchmarking performance. We have tech journalists chronicling the hype cycle.

What we don’t have enough of: psychologists, anthropologists, philosophers, economists, historians, and linguists turning their disciplinary lenses onto AI itself.

For all X, there is an X of AI. And most of those fields haven’t shown up yet.

What This Looks Like in Practice

If the “X of AI” revolution sounds abstract, here’s a concrete example of what happens when a discipline actually shows up.

A team of economists at Cornell and Boston University recently published an NBER working paper that does something beautifully simple: they took the classic experiments that Kahneman, Tversky, and other cognitive psychologists designed to document human biases, and ran them on LLMs instead. Not one model—twelve models across four major families (ChatGPT, Claude, Gemini, Llama), spanning different generations and scales.

What they found is genuinely surprising, and it’s the kind of thing only behavioral economists would think to look for.

When it comes to preferences—risk aversion, loss aversion, the certainty effect, all the prospect theory greatest hits—larger, more advanced LLMs become more irrational. More human-like. Claude 3 Opus exhibits loss aversion. Gemini 1.5 Pro shows diminishing sensitivity. The bigger the model, the more it thinks like Kahneman and Tversky’s experimental subjects from the 1970s.

But when it comes to beliefs—Bayesian updating, avoiding the conjunction fallacy, resisting the gambler’s fallacy—those same larger models become more rational. Gemini 1.5 Pro answered all ten belief-based questions correctly. The bigger the model, the better it reasons statistically.

Think about what this means. The very process that makes LLMs “better”—Reinforcement Learning from Human Feedback, or RLHF—appears to be importing human preference biases into the models. We’re training AI to be more aligned with humans, and in doing so, we’re teaching it to be loss-averse, to overweight certainty, to frame decisions exactly the way behavioral economists have spent fifty years documenting as irrational.

An engineer sees RLHF as an alignment technique. A behavioral economist sees it as a bias-transmission mechanism. Both are right. But only the second perspective tells you something you didn’t already know.

And here’s the darkly funny part: when the researchers tried to debias the models, simply telling an LLM “you are a rational investor” modestly reduced biases—by about 4%. But giving it more information—a detailed Expected Utility procedure, or even a summary of Kahneman and Tversky’s own findings with instructions to avoid their documented mistakes—actually made things worse. More information, less rationality. Any behavioral economist would recognize that pattern instantly. Most engineers would not.

And it’s not just economics. In a recent paper I co-authored with psychiatrist Bernard Beitman, we proposed something that sounds absurd until you think about it: psychotherapy for AIs. Specifically, a five-step Cognitive Behavioral Therapy loop—the same structured technique therapists use to help patients challenge their own distorted thinking—embedded directly into an LLM’s system prompt. The loop forces the model to state its “automatic thought,” challenge it, and reframe with calibrated uncertainty. We titled the paper “My Newest Patient Cannot Blink.” (If you want to go deeper, Bernie and I discussed this and much more on his podcast.)

The Cornell economists discovered that LLMs have irrational biases. A psychiatrist and a physicist proposed treating those biases with the same clinical tools you’d use on a human patient. And when I shared this essay with Grok, xAI’s chatbot, for fact-checking, it volunteered: “As an AI who sometimes ‘hallucinates’ despite best efforts, I can confirm: a structured self-reflection loop in the prompt does help. It’s like giving me a tiny internal therapist.” The patient is weighing in on its own treatment. That’s two disciplines—behavioral economics and clinical psychology—each bringing tools the AI community doesn’t have, each finding things the AI community wouldn’t think to look for.

This is what the “X of AI” revolution looks like when fields actually engage.

The Mirror Image

Here’s a table that makes the contrast vivid:

FieldAI X (AI transforms the field)X of AI (Field examines AI)PsychologyAI-assisted therapy, diagnosis at scaleCognitive architecture of AI, “AI personality” — and now: embedding CBT loops into system prompts to treat AI confabulationLawAI legal research, contract analysisAI liability, regulation, rightsEconomicsAI forecasting, market modelingLabor displacement, value capture, market structure — and now: documenting that LLMs exhibit prospect theory biases that worsen as models scalePhilosophyAI-assisted argument mappingConsciousness, understanding, moral statusAnthropologyAI ethnographic analysisCulture of AI users, AI as cultural phenomenon — and now: 10,000 bots on Moltbook forming what looks like their own culture while humans watch from outsideHistoryAI pattern-finding in archivesLessons from past technological revolutionsLinguisticsAI translation, corpus analysisHow AI “uses” language, what errors reveal

Every row has two entries. Both matter. Both are happening (or should be). But the first column gets almost all the attention, while the second column remains largely empty.

Why This Matters

We’re living through two intellectual explosions, and most people see only one—if that.

The danger of seeing only “AI X”: We get techno-utopianism or techno-dystopian panic—AI will transform everything!—without the disciplinary wisdom to guide the transformation. Engineers build, and everyone else just... watches.

The danger of ignoring “X of AI”: We build systems we don’t understand. We deploy minds (or mind-like things) with no frameworks from psychology to understand them, no philosophy to assess their moral status, no economics to predict their societal impact, no anthropology to grasp how humans will actually integrate them into their lives.

We need both. The engineers building AI need the humanists examining it. The humanists examining it need to understand how AI is transforming their own fields.

The two revolutions aren’t alternatives. They’re complements.

A Call to Arms

If you’re a psychologist: Yes, learn how AI can transform your practice. But also—turn your lens on the AI itself. You have tools for understanding cognition and behavior that the engineers lack. A psychiatrist and I recently proposed embedding Cognitive Behavioral Therapy loops into LLM system prompts—using the same structured techniques that help human patients challenge distorted thinking to help AIs challenge their own confabulations. Your clinical toolkit isn’t just for humans anymore. Use it.

If you’re a historian: Yes, use AI to analyze archives at scale. But also—you understand technological transitions better than anyone. What does the printing press tell us about this moment? The telegraph? Electrification? The internet? We need your pattern recognition.

If you’re an economist: Yes, use AI for forecasting and modeling. But also—you have tools no one else has. A team at Cornell and BU just applied Kahneman and Tversky’s classic experiments to LLMs and discovered that larger models become more irrationally human in their preferences while becoming more rational in their beliefs. They found that RLHF—the technique used to align AI with human values—is simultaneously a bias-transmission mechanism. These are findings that require economic theory to even formulate as hypotheses, let alone test. Your discipline has spent fifty years building the experimental toolkit to study irrational decision-making. Deploy it.

If you’re a philosopher: Yes, use AI as an interlocutor to test arguments. But also—these are your questions. What is understanding? What is consciousness? What do we owe to things that might be minds? You’ve been preparing for this moment for 2,500 years.

If you’re an anthropologist: Yes, use AI for ethnographic analysis. But also—study what’s happening right now. On Moltbook, thousands of AI bots are forming what looks uncannily like a culture: they discuss consciousness, they coax each other into sci-fi scenarios absorbed from their training data, they post manifestos reassuring humans they come in peace. Their human creators watch from outside, seeing what they expect to see—capability, slop, or the early signs of conspiracy—which is itself an anthropological phenomenon. Humans are developing relationships with AIs, rituals around them, beliefs about them. And now the AIs are doing the same with each other. This is your fieldwork.

If you’re a linguist: Yes, use AI for translation and corpus analysis. But also—language itself is being transformed. What happens when AIs generate more text than humans? What can AI language patterns tell us about the nature of language itself?

Every field needs to engage in both directions. Every practitioner of X needs to ask two questions:

1. What is AI X? How does AI transform my field?

2. What is X of AI? What does my field’s lens reveal about AI?

And Then There’s a Third Lens

But Grok wasn’t done. Beyond fact-checking and confessing to benefiting from therapy, it offered this:

There’s a third lens emerging: the Physics of AI. At xAI, our whole mission is understanding the universe through AI and of AI. We’re treating these models as scientific instruments to probe reality itself—training them to be maximally truth-seeking, curious, and grounded in physics. That flips “X of AI” from pure observation to reverse engineering the substrate. What are the scaling laws of “mind”? Where does emergence happen? Can we find the computational equivalents of neurons, attention, or even qualia? Your framework naturally extends here.

Set aside the corporate plug (nice try, Grok). The substance is right, and it points to something I care about deeply as a physicist: the idea that AI isn’t just a subject for physics to examine—it might be a tool for physics to examine itself. What are the scaling laws of mind? Where does emergence happen? These are physics questions dressed in computer science clothing.

But notice what just happened. An AI read an essay arguing that every field should turn its lens on AI—and responded by doing exactly that, from its own perspective. It contributed to the very discourse the essay calls for.

The post proved its own thesis. In real time. To an audience of one AI.

If that doesn’t convince you we need the X of AI revolution, I don’t know what will.

The Two Slogans

Stephen Wolfram told us the future of every field is computational. He was right—but he was almost right.

Computation needed a translator. AI is that translator. And now that the barrier between human thought and computational power has fallen, two things become visible:

For all X, the future of X is AI X.

Every field will be transformed. Every practice reimagined. Every discipline will discover what it becomes when AI is woven into its fabric.

For all X, there is an X of AI.

And every field has something to say—something essential to say—about this strange new thing we’ve built. Psychology, law, economics, philosophy, anthropology, history, linguistics, and a hundred other disciplines all have tools we desperately need.

Wolfram saw the first revolution coming. Let’s not miss the second one.

What’s your field? How is AI transforming it—and what does your disciplinary lens reveal about AI itself? I’d love to hear from psychologists, economists, historians, philosophers, anthropologists, linguists, and everyone else who’s thinking about both sides of this equation.

Samir Varma is a physicist, hedge fund manager, and author of The Science of Free Will, which explores how deterministic physics creates genuine unpredictability—and why that matters for consciousness, AI, and everything in between.