Tungsten prices have risen almost 800% so far this year, which, as with any other commodity price increase, benefits anyone who happens to have stockpiled it earlier. One group of investors who diversified into tungsten a few years ago were crypto people, who started buying tungsten cubes as a joke. One prominent crypto person, who was fond of the "yes-and" model even when the right answer was "definitely not," bought an enormous one for $250k. So he's up a bit on that. It's not his only case of inadvertently being in the right information flow: $500m into Anthropic in 2021, a 2022 investment in SpaceX via an SPV, a $200k pre-seed check into Cursor which has also turned into a few billion dollars worth of SpaceX equity.

It's a great track record, only marred by the fact that he was doing this with money that belonged to customers, not telling them about it, and ended up with a big pile of illiquid assets backing ostensibly-liquid customer accounts during a crypto downturn. Once you do that, it doesn't really matter how good your investments were. SBF could have been part of the broader archetype of people who got nerd-sniped by systematic investing in the 2010s and then by AI in the 2020s. That would probably have involved smaller sums, but he'd be experiencing a different form of economic post-scarcity than the taxpayer-funded one he actually has.

There are more benign cases where someone winds up with a surprising amount of money despite not really gunning for it: Google's chef made eight figures from stock options, and the graffiti artist who decorated Facebook's first office made nine. In earlier cycles, there were investors who were cajoled into buying Coca-Cola when it started, people who sold their companies to Standard Oil for stock, various doctors and dentists in Omaha in the 50s and 60s whose great-grandkids will inherit Berkshire class A shares, etc. And there's an even longer tail of people who invested in something for pretty vague reasons, forgot about it, and found out they made a fortune—or who invested for actively bad reasons, didn't sell, and made out okay. And the same kind of randomness affects things in the other direction, too; Julian Robertson finally wound down his fund just before an exceptional run for value stocks, during which the tech companies he'd been ignoring saw their valuations implode and funds rotated into exactly the kinds of decently-valued companies he'd specialized in.¹ Executives at Halloid would advise their friends not to buy its stock; Halloid later renamed itself after its biggest hit, Xerox, and was one of the canonical growth stocks of the 1960s.

This leads investors to two kinds of frustration:

1. It's absolutely maddening to see people make money for a bad reason, and even though it's broadly true that markets get dumber near the peak and that seeing bad ideas turn into big fortunes is a good sign that the market's going to turn, it's very hard to separate that from just resenting people for their success. It's natural to compare yourself to other people, and those people will be pretty similar to you. When a professional investor says that idiots are making fortunes, they might mean that from their 99th-percentile-skill-level perspective, it's absurd that some 98th-percentile analyst caught on to the memory shortage early.²

2. It leads to a lot of negative rumination. How do you know if you're good? Unless you're in a tightly risk-managed and fairly high-turnover strategy, you're still trying to assess your own skill based on a small number of decisions, and you don't have to subtract many specific bets from the track records of the best investors before they're unremarkable. And that risk runs the other way, too: when Warren Buffett put a third of his fund into American Express, the company was technically organized as an unlimited-liability business; if their fraud problem turned out to be deeper than it looked, he could theoretically have lost more than his entire fund.

That calibration question is one that non-investors have to ask, too. There are people who don't know what a 10-Q is, but who do know smart people in AI, and who bought shares of the companies that those smart people depend on or work for. Many of the people who did this, particularly the ones who don't have to work any more and can spend all their time worrying about existential risk, were also unfamiliar with financial arcana such as position sizing and risk management.

It makes the world a lot more interesting that these people exist, and there's a real sense in which they were more skilled at their finance hobby than professionals were at their finance job. (This, naturally, drives the professionals crazy. But if those professionals are users of Excel or Bloomberg, they're tapping into many billions of dollars' worth of programmer-hours; maybe the tech people should have resented them.) There are fairly boring portfolio allocation answers here, like: put enough into index funds that you don't have to worry about retiring, and, if you want to have fun picking stocks, do it with money you don't depend on. That's a good recipe for ending up richer-than-expected because you read the right scaling paper (or, be real: the right Gwern page) in 2020. But it's also a great way for a millionaire to spend a lot of time ruminating on how they'd be a decamillionaire if they'd moved a little more money out of VOO.

So the cruel truth is that if you have sufficiently perceptive friends, and those friends have contrarian-but-correct views about the world that can be expressed as trades, you're stuck in an unfortunate pair trade: the more of your net worth you risk on vibes-based trading, the higher the possibility that you blow up (which might make it a little hard for you to stay friends with the people who gave you bad advice). But, on the other hand, the extent to which you ignore them is the extent to which you're short a psychological call option.

Managing the short-call leg of this trade is all psychological, and there isn't much you can do other than being disciplined enough not to pull up long-term stock charts too often. The other thing you can do is remind yourself that while 100% of Apple was owned by somebody in 2003, and that Netflix shares in 2006 and Tesla stock at the IPO were similarly spoken-for, very few people who owned them held on for a long time. When you see a story about how you much you'd have if you'd put just $1,000 into the right company at the right time, remind yourself that there's a good chance that what you'd actually have is a story about how you put $1,000 into Apple, made $252 in six months, congratulated yourself on being such a genius, and sold. Lucky investors, people who made good gambles, etc. all exist, undeniably, but it's a class that you only get into in retrospect. And even the ones who were good—that Google chef had been the personal chef for the Grateful Dead!—will have outcomes very sensitive to the details of choices they made. It's a wonderful feature of capital markets that they reward information aggregation as much as they do, but the most compelling stories about this are the ones you'll learn the least from.

(Thanks to Trevor McKendrick for suggesting this piece.)

We've covered the question of luck, skill, and retail participation in the market from a few different angles in The Diff, including:

• Asking if it's better to be lucky or smart ($).

• Treating contrarian beliefs as a synthetic option ($).

• How, despite the many opportunities to get ripped off, it's a golden age for individual investors ($).

• Why it's hard for funds that focus on short-term strategies to broaden their time horizon ($).

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Currencies are one of the best long-running examples of network effects, along with language, and, like language, they leave a good paper trail. Someone intermediating between users of different kinds of currencies, from XTX to the Medici, needs to keep some kind of record of what they've bought and sold and at what exchange rates. They also have a habit of preserving these records; if you have an old balance sheet, and a list of transactions since then, you can reconstruct your current balance sheet, and it's a very good idea to be able to do that.

Right now, currencies are implicitly backed by taxes and credit—in both cases, that backing takes the form of people periodically needing to deliver some quantity of dollars to a counterparty, and thus needing to trade goods, services, or financial claims for dollars, which means having a constant demand for dollars. Historically, weaker states with less capacity to tax had to use some other technique, the most straightforward of which was to make the currency out of a precious metal—the Spanish dollar, for example, was a standardized silver coin, and Spain's control over mines in the New World meant that they didn't need to skimp on purity.

But if currencies either are some quantity of precious metal, or represent a fully-backed claim on such metals, why discriminate? Part of it was probably an information problem: if you denominate the price of everything in the Venetian ducat or Dutch guilder, you're minimizing how many discrete calculations you have to make in order to compare two values. And if you're borrowing, you'll probably want to borrow in whatever currency is easiest to source, at which point you're also strengthening that currency's core position.

The rise of global currencies tends to be contingent. Spain benefited on the supply side because it conquered places with lots of silver, but there was also a demand-side part of the story: it wasn't economical to transport bulky products long distances, Europe didn't produce any high-value finished goods that any Asian country wanted to buy, but those Asian economies did have demand for silver, and products they could trade for it.

The next big currencies, the Dutch guilder and Amsterdam bank florin, had a more familiar, modern story. The Netherlands was one of the richest places in early modern Europe, and the first to develop a sophisticated financial system. They also mastered value-added manufacturing, importing grain in order to feed cows so they could export cheese, importing wool and selling textiles, etc. And as an entrepôt, they had a lively business in importing one big bulk shipment and then parceling it out to different destinations based on local demand, which mean that 1) they were storing commodities, 2) they were standardizing them, and so 3) they could trade contracts for future delivery. If you were hedging the risk of a bad harvest by buying wheat futures, or betting on an upcoming war by buying a contract for future delivery of saltpetre.

England started to catch up in the early 18th century, as another shipping and trading power that evolved into a financial center. One of the reasons it's hard for any one country to maintain reserve status is that it entails borrowing, which, of course, means being levered. As financial systems grow relative to the rest of the economy, there's a risk that some external shock, or series of shocks, will disrupt them, and that the country's underlying activity won't be enough to service its debts. Amsterdam lost share gradually throughout the 1700s, then suddenly after a lost war with England and their central bank lost credibility.

England ended up far more dominant, in part because of how much global trade expanded in the 1800s, but also because they borrowed so much to win the Napoleonic wars, then actually succeeded in servicing those debts. If the safest global asset pays interest in pounds sterling, the pound will be the default unit. And then, in the first half of the twentieth century, a messier version of the same story played out, with the UK in the role of the Dutch and the Americans replacing the Brits: England didn't lose major wars, but, as the saying goes, the lost the peace, and especially after the Second World War wound up with a toxic mix of unsustainable debt and a declining ability to enforce pound use in its colonies. They'd also signed on to a system that could theoretically maintain some semblance of the prior status quo: the Bretton-Woods agreements, pegging many currencies to the dollar and backing the dollar with gold. Britain's peg was higher than they could sustain, which made it economically rational for British households to import, which made it rational for the government to occasionally devalue but also to impose high taxes, limit the financial system's flexibility, and allowed a little growth and a lot of inflation to slowly reduce the real value of their debt.

A system where the dollar is the default global currency and it's backed by gold is really a system where there are two currencies, dollars and gold, and gold is just a dollar where you trade zero interest for zero devaluation risk. Well, not quite zero: there's always the possibility of the gold being seized, as happened in 1933. It's like any other kind of currency peg: once there's pressure on it, the existence of that pressure is a sign for other people to make the same trade. The US was able to use extensive foreign policy maneuvering to discourage other countries from converting their dollars into gold, but it eventually became clear that they wouldn't, and the US had no choice but to break the peg.

Which, surprisingly enough, made the dollar an even more important global currency. Suddenly, there weren't any artificial limits to how many dollars could be in circulation, and there wasn't a need to keep an eye on when they crossed borders. Precious metals-backed currency systems are, in effect, partly decentralized, because anyone can mint. But that means they're limited by mining technology or the ability to transfer precious metals and coins around. Once a currency's value is entirely determined by supply and demand, it can go to zero—but it can also go everywhere, and it's always possible to ensure that there's enough of it to go around.

This is a different kind of unstable equilibrium from prior reserve currency regimes. The old risk was that the global financial system would overwhelm a reserve currency issuer's ability to support that currency and service debts with taxes. Now, the risk is that the dollar itself is mismanaged. Anchoring the biggest currency to the biggest economy helps, and it also helps that the US is even more disproportionately the biggest financial system: that means that global financial crises are dollar shortages, and in every crisis the Fed gets better at swiftly sending dollars where they need to go. So the real lesson of history is not so much that every reserve currency eventually fails but that if the dollar does, it's going to fail in a surprising and historically unique way.

We've written about currencies, and reserve currency status, many times and in different contexts:

• During Covid, American households were the global consumer of last resort ($), supply a flow of dollars the rest of the world needed. So it was a good time to revisit Bretton Woods.

• After the Russian invasion of Ukraine, we looked at what China would do if they really wanted to have a reserve currency ($).

• And after Liberation Day, it was time to note that it takes more than four years to mess this up ($).

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When Apple went public, it was a big, newsworthy event, not just because Massachusetts state law made it illegal to take companies public with a P/E over 20 there, but also because it was such a big company—with a market cap of $1.8bn, equivalent to $7-8bn today, and much larger than the companies that typically went public. Intel had IPOed with a $60m market cap, and a few years after Apple, Oracle would go public worth $270m; Microsoft, with an $800m initial market cap, was the IPO of the year. This year, SpaceX went public at a market cap of over a trillion dollars, and Anthropic and OpenAI will plausibly come out worth over a trillion, too. In theory, someone could have bought Microsoft stock at the IPO and made a 633,500% return from holding and reinvesting the dividends. For someone to get the same kind of return from the current crop, they would have had to get in very early&dmash;in fact, for both OpenAI and Anthropic, their initial pre-money valuations were already in the hundreds of millions. If you want to get in on the ground floor, you'd better find a retired payments startup founder who's decided to go to Mars when he isn't busy decarbonizing American commutes.

The usual shorthand explanation for this is simple: in the late 90s, we performed a big national experiment on whether or not it was a good idea for public markets, including retail investors, to underwrite early-stage companies that were still net consumers of cash, and to fund them based on their growth prospects and future profitability. And what we found out was that the part where growth (and certainly profitability) happens did not play out as planned, though the part about only consuming cash mostly did. Sarbanes-Oxley was actually not crafted as a solution to that—it was a reaction to accounting scandals at companies that generally started shading the truth or outright cooking the books long after they'd gone public—but it happened in the aftermath of a time when many investors had lost their shirts buying tiny companies before the IPO. So, when the negative side effect of stricter rules about corporate disclosure was that a higher price for going public would encourage companies to stay private, this wasn't seen as a big deal.

But it meant that for those hypothetical future Intels, Oracles, and Microsofts, the reward to going public was smaller. And they had other reasons to stay private: what public investors were doing in 1999 was what VCs had specialized in before: backing companies that didn't have GAAP earnings, or that didn't produce enough cash to grow as fast as was optimal for them. This is a very fraught business, because plenty of money-losing companies can make a compelling argument that giving them a little more capital would fix their problems. That's perfectly fine if investors know what they're getting into, but it creates a kind of adverse selection that you don't see with more mature companies. It's just a lot easier to tell a good story about burning money than it is to actually make money, and market valuations will necessarily reflect that.

Another feature of this situation is that venture used to be a more obscure asset class that simply didn't have the financial firepower to support companies as their capital needs grew. For a while, venture was growing as an asset class because there were more companies that looked venture-fundable—the development of the microchip unlocked a surprising number of capital-light businesses, and software created still more. But after a while, these funds could make the same contemporary observation that we're making in retrospect: wouldn't it have been nice if Oracle had, instead of going public, raised another round and let VCs enjoy that appreciation instead of public shareholders? If you take public money, you're signing up for a continuous investor relations task; venture capital means doing your investor relations in short bursts, with shareholders who have no choice but to stick around, and who hopefully have some kind of expertise in the business.

Once this started happening, it developed momentum. The funds that came from a growth investing lineage realized that if they were going to keep investing in the kinds of businesses they liked, they want to do it with private companies (and that they could offer a compelling value proposition: they'd mostly leave those companies alone, but also give them some useful competitive benchmarks).

Public markets are still the natural destination for sufficiently big companies; venture funds and crossover vehicles still have an expiration date, and their investors expect the asset they own to be converted into cash at some point. But in the meantime, they're often the natural holders for companies that don't want to spend their time helping analysts parse through the nuances of quarterly guidance.

It used to be that public markets were best for companies of a certain size, but it's more accurate to say that they work for companies of a particular stage: the point at which they don't meet a VC's investment mandate, which is coincidentally close to when they can start raising capital. The real question to ask is why Anthropic and OpenAI still have to go public—they're both young enough that there isn't a pressing need for their investors to hit a capital return deadline. But despite all these market evolutions, equity markets do have one advantage: they're so big that at some point, even if OpenAI would prefer to wait until it has a cleaner story and a $2tr valuation, and even though Anthropic would prefer not to deal with public company pressures while trying to solve alignment and keep from getting banned by the White House, there are some fundraising needs that only a modern American stock exchange can meet. ¹

We've looked at IPOs many times in The Diff: knowing who's going public and why tells you something about sentiment, but it's also a good way to learn more economic fundamentals by diving into a challenging business. Some greatest hits:

• Steinway ($).

• Thoughts on the OpenAI IPO, and whether or not they can pull it off given how chaotic their business is right now ($).

• Why low-priced IPOs are uniquely bad ($).

• Thoughts on whether or not some of the late-but-not-public growth companies should stay private ($).

• And: the market in executive talent used to be much less efficient, so small, cheap public companies are likely to be that way for a reason.

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One of the missing questions in wealth inequality discourse is: where are all the Astors? John Jacob Astor owned a few percent of Manhattan, Manhattan was worth about $1.74tr a few years ago, so call it a hundred billion dollars or so worth of real estate. Now, assume that over the last two centuries, that real estate has been throwing off some income—people have been complaining about high Manhattan rents for a long time—and you can imagine a pretty big fortune. Suppose the family earned a 4% cap rate, and reinvested the proceeds in bonds earning about that same return. That gets you to a total family fortune of a little under a trillion dollars, plausibly divided between a few hundred to a thousand heirs. Naïvely, as long as your fortune compounds faster than family size—e.g. if you expect heirs to have two kids by thirty, they need to earn 2.3% real returns just to keep up—then we should have a lot more high net worth Astors these days. There's at least one who's rich by UK standards, and a Viscount.

The Missing Billionaires: A Guide to Better Financial Decisions, which should be studied carefully by anyone who wants to stay rich and used very judiciously by anyone who wants to get rich, estimates that if the millionaires identified by the US census in 1900 had invested their money in a balanced mix of equity index funds and bonds, we'd have 16,000 billionaires today. Yet only 10% of the Forbes 400 list today either are or are descended from people who were on the original list in 1982, let alone 1900.

And , the net worth of the very richest has gone up, and gone up a lot more than the average person's salary or net worth. It's hard to reconcile these, but there's one simple factor that is wildly underused in inequality debates: most rich people get rich because they own a single asset that performs very well. But even if the aggregate appreciation of assets-owned-by-rich-people is impressive, any one of these will be much more volatile than broad equity indices. And if you fund an even modestly expensive lifestyle from an undiversified portfolio, you're doing the equivalent of levering up a single-stock portfolio. Even if you're right, drawdowns will aggressively eat into returns.

One of the simplest ways to illustrate this is to look at leveraged ETFs as a proxy for getting typical equity returns with more volatility. This post backtested them to 1927, and found that while owning the S&P 500 with 2x leverage is a bumpy ride to a higher net worth, owning it with 3x leverage actually makes you poorer: you lose so much in the Great Depression that you can never really recover. Add in a fixed cost, and it's easy to get to zero.

Of course, if you diversify a bit, you can mitigate that problem, and some rich people have done so pretty effectively. Bill Gates has been systematically selling down Microsoft and buying other assets for decades, and has a portfolio that's less sensitive to the day-to-day drama of what will happen to Microsoft. But that's one reason he's now worth less than Steve Ballmer. (The other is that he's donated so much to charity—and, in a way, this diversification is downstream from his charitable giving. It's just a lot easier to allocate $x billion a year to charity than to donate some uncertain lump-sum.) The Ballmer bet (and that of folks like Larry, Sergey, Jensen, Ellison, etc. who also have 90%+ of their wealth in one company) has been insanely aggressive, in that there are plenty of decent-sized tech companies where keeping all of your net worth in that company would have eliminated most of it. Nobody compiles the counterfactually-not-so-rich list of executives who made the daring decision to put all their money into a company whose share price stagnated for a decade, or went to zero.

But Ballmer's status as one of the richest people alive illustrates why volatility drag is so important to these discussions! Most of the people who make the same basic asset allocation choice end up poorer for it, and aren't part of the discussion. But there are some companies that deliver superior returns over long periods, and there are investors who are unusually good at identifying them and backing them. If you could own a portfolio of these investors, you'd probably get great returns—you'd harvest the equity risk premium, plus a kicker from owning the right equities, and many of the durable-outperformer companies do particularly well during recessions.

But that investment product isn't available; some of the assets in question are private, and some of the people making these bets are rich because of luck and leverage, not because of skill; if you take a snapshot of the rich at any given moment, that's some of what you'll see. In other words, what you'll consistently see is one group of people who are needlessly bleeding money, either out of ego or because they're repeating a mistake that paid off. You'll also see some people whose biggest category of consumption is the volatility drag from not diversifying: someone like Elon Musk is extraordinarily wealthy because, rather than just buying 1% of every listed equity and putting the rest in bonds, he builds, borrows, buys, and continually roles the dice (he has an edge, sure, but there's still some randomness). So, to the extent that the problem with inequality is any given rich person, there's a good chance that that rich person is busy solving the problem for you, by not diversifying. But if the problem has to do with the rich in the aggregate, that same concentration makes the problem worse: it raises mean outcomes by giving people exposure to the biggest power-law winners, even if it reduces median outcomes by giving them all access to a bunch of big drawdowns from which they won't recover. But those beneficiaries are, necessarily, a heterogeneous bunch: if lots of people can do the same exact thing, doing that thing is no way to get rich, and even in categories where there's more than one person with the same listed source of wealth, they did a different variant on it ("software" is a broad category; "real estate" is necessarily heterogeneous since no two properties can occupy the same location, location, location). So when wealth is durable, it's informative, and the presence of super-rich people tells you something about what previously-unexploited opportunities there were. That's not always a good thing; there have been plenty of fortunes made from addictive products, bad externalities, exploiting government policies, etc. But it's still something that you want to conclude on a case-by-case basis.

The Diff is not really a newsletter about the doings of various rich people, but they do overlap with the newsletter’s topics. Including:

• What does it mean to have a trillion dollars?

• Why most people should diversify more while active managers should diversify less ($).

• Funds that make big bets add value by convincing their backers not to redeem during drawdowns ($).

• There are fewer paths than there used to be from the very bottom of a company to the top.

• Consider the wealth-creation speed limit.

• Why do more people get rich owning equities than bonds, even though their risk-adjusted returns are similar?

• Thoughts on the big Texas oil fortunes, and where they went ($).

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Some of the biggest peaks in equity valuations have been driven by bets on new technologies: 2021 and 2000 were online-inflected software bubbles with different details, the late 1960s were a tech bubble driven partly by defense spending and partly by optimism about dual-use products, including computers. 1929 was the everything-tech peak: electricity, cars, planes, financial engineering. 1901 had its own proto-tech boom, also driven by a mix of genuine technological improvements (like deflation in steel prices unlocking new use cases) as well as M&A, and several equity spurts in the late 19th century were driven by different iterations of a big bet on railroads.

If you talked to an investor at any of those market peaks, and showed them a preview of the economy twenty years later, they'd be thrilled that they'd made exactly the right call, at least if they looked at total freight-ton miles and not unit prices. A bull who bought the big trusts at the peak in 1901 would be happy at how high industrial production had gotten, but disappointed that the growth businesses they'd bought evolved into cyclical, low-value added suppliers to big growth companies. And someone who had fully-backed those growth companies circa 1929 would be thrilled to learn how significant radio was two decades later, how ubiquitous electrification had gotten for both homes and manufacturers, and how much the promise of Dow 380 had been fulfilled in America's workplaces and living rooms. The fact that the Dow averaged half that level in 1949 would have been deeply confusing to them.

The similar stories you can tell about the 60s, 90s, and 2020s peaks are all pretty familiar, but it's fun to take the most recent one seriously. An investor going max long tech stocks in late 2021 was incredibly optimistic about software's ability to wrap itself around a growing number of economically-valuable tasks, and to gradually expand the revenue surface area, so more of the world's economic activity would be mediated through software, and more of the resulting upside would be captured by software.

Which was absolutely right! We have more apps than ever, and they're incredibly good at price-discriminating their way into capturing maximum revenue. Not only that, but one part of the margin-expansion story is working—it takes a lower developer headcount than ever to support a given level of revenue. Just not in the way someone buying Bill.com at 100x trailing revenue would have hoped.

In a way, this pattern has to be universal: if investors, in the aggregate, were always able to identify not just which tech trends mattered but which layers of the stack would collect the most value, markets would have to be pretty close to perfectly efficient all along. But a tech-driven boom is an exploratory process for technological capabilities, the most cost-effective way to deliver them, and the optimal way to charge for them. If there's any given piece of infrastructure that's a useful complement to some other layer that has more pricing power, then overinvestment in that infrastructure acts as a subsidy for whoever controls the real value lever: companies racing to lay fiber in the late 90s didn't have a good way to mark their fiber up more than that of competitors, whereas online services varied quite substantially in how well they could monetize a given terabit of information. As long as there's any complementarity between different bets on the same tech theme, and any uncertainty about which categories will do well, this kind of dispersion will show up. Sometimes, it's temporary; in 2023, companies used to be able to engineer a stock rally just by announcing that they were using some widely-available model, but that quickly became a low-class move. Sometimes, it takes longer; in the aftermath of the dot-com bust, one sophisticated take on eBay was that they'd avoided making the mistake of owning a high fixed-cost delivery network. (As it turns out, they were a bet that bandwidth for packets mattered more than bandwidth for packages, and as packets sped up, the most important source of latency turned out to be the one they didn't control.)

There are also cases where not only is the winning technology identified early, but the winning category is, too. You could have made a bet early that socializing would move online, especially once people carried around Internet-connected phones with cameras. But you could have gotten that wrong by betting on whichever system was the most open (MySpace, accidentally), or with the biggest parent company (Google+), or with the best adoption among the chattiest nodes in the world's communications network (Twitter, Snapchat). The runaway winner, Meta, doesn't have some simple shorthand to describe what they got strategically right (maybe the closest is “real identities on the internet”); but they were good at continuously understanding what other services got right and then implementing it themselves.

It's handwavy to say that "execution" is what matters, especially since execution is mostly measurable in retrospect, and the way to measure it is to look at who ended up winning. And, even then, "execution" tends to talk about a snapshot in time: the early automakers had to have incredibly efficient manufacturing, and were complex enough that they helped spur the development of modern corporate organization and accounting. But the insights they had in the twenties through the fifties did not carry over into making them agile enough to survive in the globally-competitive, efficiency-focused auto industry of the 70s and onward. It took some great execution to get to the point where they could decline so far and still survive.

Ultimately, what all of this illustrates is that it's hard to make good macro bets at a distance. If you're in the middle of the bubble, you can have some sense of what behavior is mystifying until you hear that investors like it, and what's valuable and hard to replicate. On the other hand, it's a pretty hefty upfront due diligence commitment to get good enough to get the Anthropic offer as a precondition to figuring out whether value accrues to Anthropic, its customers, its suppliers, or someone else. But, in the end, that's just another kind of market efficiency: some trends in the efficiency of specific technologies can be underwritten early. And if it were easy to extrapolate from that to knowing exactly which stocks to own, markets would be a whole lot less volatile and more efficient.

The Diff tries to look at both broad technological themes and the narrow differences that differentiate winners and losers. Consider:

• The varieties of drawdowns.

• How Bubbles and Megaprojects Parallelize Innovation, a 2020 piece that illustrates its point by noting that Nvidia’s rally to that point looks less vertiginous if you consider new GPT-wrapper startups.

• How the car industry manufactured the modern middle class ($).

• Electrocloud! Or, how the rollout of cloud computing mimics some patterns from the history of electrification.

• A perversely good example of execution trumping the cycle is the story of how Teledyne navigated the bursting of the electronics bubble ($).

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There are few more grating genres of writing than LinkedIn's "Here's what losing a limb/stubbing a toe/getting divorced/being outed as a famous serial killer taught me about B2B SaaS." We're all the product of the various things that have happened to us, and careers are a big part of who many of us are, so there's some logical sense to it. But it feels like part of the broader genre of glurge, vaguely inspirational stories whose details are just a setup for an inspiring lesson about how it all works out in the end. There's zero information content other than: something bad happened to me, and I want to post about it on LinkedIn.

All that is to say: over the weekend I fell a little weird and now one of my kneecaps isn't quite where it's supposed to be, and the tendon that connects it to the rest of my leg… doesn’t connect it any more. The mechanical upshot of this is that I can't extend my left leg, the neurological upshot is that it's very painful to contract it, so the practical result is that I have one working leg for a while, and another one that has to be straightened out at all times. (Current prognosis there is: surgery in a week, probably walking again in a few months, and probably a full recovery in a year, where "full recovery" means things like running, biking, heavy squats, etc. So I'm spending a semester studying abroad in the land of disability, but I don't pretend to be either a native, nor do I intend to be a permanent resident.)

If you see someone on crutches, your natural reaction is that it's a slow and inconvenient way to get places. Which is true, but not the biggest inconvenience. The first annoyance you'll notice if you use crutches all the time is that you can't carry things, at least not without some effort. If you want to drink a cup of coffee, you're probably doing it while standing in front of the coffee machine and leaning on a crutch. You're just not likely to maneuver a hot beverage around with zero hands. You definitely aren't preparing much food, at least until the cooking process is designed around a single stationary prep cook or heating things up in the microwave qualifies.

There are many products you can purchase to make the limping life a little easier. One problem you'll quickly notice about them, if you live in a typical single-family residence, is that they probably get delivered on the ground. And the ground is hard to access from crutches, especially while keeping a leg straight. So you're either playing a very tedious game of crutch-soccer to get something up a ramp, or asking for help, at least until you get a grabber tool.

The last big issue is clearance. A fully-extended leg is pretty long (it's been a short enough period since the injury that my instinct was to get up, grab a tape measurer, and determine exactly how long, but that's now a long and uncertain project). To get into a vehicle, you need to be able to swing the leg in some kind of arc that doesn't hit an obstacle like a door. Depending on the car, and the leg, this can lead to some awkward setups (my default right now is either stretched across the back seat without a seatbelt, or possibly crammed into the middle seat, leg extended to the well of the all-the-way-forward passenger seat).

So far, I haven't gotten stuck anywhere, but this is bound to happen some day soon. So yet another constraint is that you want a phone with you for emergencies, but you probably also want loose-fitting clothing. (A phone case with a wrist strap is probably the ideal here, or maybe this is the killer app for Meta smart glasses or their rumored AI pendant.) This means thinking about even more dimensions and angles, at what is already a pretty contortive point in one's life.

All this is a bummer but could be vastly worse in many ways, But what's interesting about it is that it's a reminder that we're all operating so close to peak capacity! There is nothing quite like having to use a grabber to pick a food wrapper off the floor to remind you that, absent that ability to manipulate our environments, we'd all have access to a lot less stuff; we'd be worse at making it, consuming it, and cleaning up after.

And it's also a good reminder of complex supply chains. There's a lot you could amputate before writing this newsletter becomes infeasible, if that's literally all I'm doing. And yet, at this moment, "go upstairs to grab that book" has gone from trivial to something that risks serious injury. The most important part of a supply chain is, in general, whatever part is missing right now.

But another important part of a supply chain is what's available, and there's room for gratitude there, too. I got injured during a child's birthday party (at one of those play places where you sign a waiver beforehand—I think they know the natural result of having people with the energy of three-year-olds being chased around by other people with middle-aged joints), but it's an injury I could have easily gotten tripping on the stairs at home. Fortunately, this play place happened to be literally across the street from a Walgreens, and the Walgreens had a website that displayed exactly what was in-stock. It's just an unimaginable convenience to be crawling towards a couch with one hand while using a smartphone in the other hand to find the nearest place to get crutches.

Setting aside the physical discomfort, this feels most similar to the experience of using a programming language that forbids something other languages allow by default and never even describe as something you might, in certain situations, rationally prefer not to do. In Rust, for example, if you're going to do something to a variable (say you have a list of names and you're writing a function to count how many of them start with a given letter), if you're going to store that running total in a variable, you need to warn Rust that its contents could change, or it simply won't let you. This is very annoying, briefly, until you get used to the idea that code is safer and more predictable if you're careful about what is and isn't mutable, i.e. what variable can be treated as fixed and which one needs to be checked before use every time. In Rust's case, the goal is to run the most efficient programs possible, which is not quite analogous to my recent hobbling-around. But it does illustrate that if you get rid of a few shortcuts, most of what you had can be replicated, sometimes at great cost and sometimes pretty trivially.

The human body, and the environment we build around it, is subject to selection pressure to operate close to the limit. Our world is built to get the most out of our senses, mobility, and ability to manipulate objects. The world doesn't feel ruthlessly optimized to get the most out of what you have until you temporarily have a little less.

The Diff has covered this territory before, at least in an abstract sense:

• We've looked at the efficient frontier of automation, and how it's easier to make progress in AI when there's a single variable to target.

• Other kinds of evolved optimization include the ways modern platforms monetize.

• You can model the market as meta-efficient in that people are constantly adjusting their view on how much to invest in the research that tells them which prices are optimal ($).

• A weird market that doesn't clear in the way one would expect, but that works surprisingly well, is the market for selling ideas on stocks to sell short ($). This is a timely topic ($).

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When you think about it, the investment management business is one of the weirdest in the world because the two premises an asset manager advances to clients are:

1. I am a fearsomely sharp operator, and even given unlimited competition to provide and use liquidity, I will systematically profit at the expense of my counterparties, and

2. Would you care to take the other side of a deal with me?

More people have gotten rich charging fees for assets under management than paying them, but that's exactly what you should expect. There's a continuum of strategies' return profile: people who talk about capacity constraints (e.g. some niche crypto strategy where you need $100k in capital to make $100k a year, but the next $100k doesn't do anything for you) are often talking about something they run with their own money. Prop trading firms tend to pursue—and in fact, grow around—these strategies. Their constraints are talent and infrastructure, not capital. One step down the ladder, there are strategies that do face these constraints, but at a scale where raising incremental capital still increases returns. A small-cap value investor who could print 50% returns on $1m in capital can still earn more in the aggregate by producing, say, 25% gross returns on $100m in capital (at least, assuming the overhead of managing outside money doesn't eat it—more on that in a bit). In that category, deciding whether or not to raise outside money is a question of whether you think the opportunity is basically permanent, in which case you might prefer to just compound your way to higher assets, or whether it's transient, in which case you're grabbing the upside that you can and splitting the proceeds with your backers. And then there are strategies that are primarily selling exposure to some asset class—large-cap stocks, fixed income, whatever—and trying to add enough alpha on top of that to cover what's likely to be a management fee but no performance fee.

One complication is the compliance-and-IR wedge. There's a big difference between managing your own money and managing someone else's, and an even bigger difference between managing money for one person or a handful of people and dealing with institutional investors. So there can be a case where a strategy works fine with between $50k and $20m in AUM, and would ideally raise the $20m and manage that, but where the overhead cost of actually raising that money more than eats the fees it would pay. This wedge has been growing—one of the reasons the newest generation of suddenly-huge financial firms has more prop traders and fewer hedge funds is that it used to be viable to raise single-digit or low double-digit millions to launch a fund, but today the minimum is higher.¹

You could imagine a story where someone trading their own capital and finding adjacent strategies to what they've already done well eventually reaches the point that it's best for them to take outside capital. As long as their capacity growth exceeds their returns, that's eventually optimal. One problem with this is that it's hard to increase capacity, but a subtler problem is that firms that find themselves in this position tend to have a core business that prints money (it prints variable amounts, but its personnel costs vary a lot with gross trading revenue), so it's actually a decent borrower. One way to look at earning 2 & 20 is that it's incentive-aligned, but another way to look at it is that it's very expensive capital. If you earn a 22% return for your investors and charge them 2 & 20, you're getting about 6% of their AUM in fees that year, but another way to look at it is that you're paying 16% for their capital, which is a lot more than you'd pay for other kinds of capital. The most plausible ways for capacity-constrained firms to expand is to identify longer-scale strategies in existing asset classes (if you've found a good strategy betting on intraday correlations between stocks, you're in a good position to bet on intra-week, intra-month, and beyond; as you expand the return on capital tends to drop, but since your holding period is longer, you have more time to execute trades and won't have as big a market impact). So their incentive is to scale into slower-moving strategies by borrowing, not by letting other people invest alongside them. The usual situation with an entity that has multiple strategies with different return/capacity tradeoffs is that the low-capacity ones are constantly overflowing into the higher-capacity ones.²

Providers of capital are in a tough situation where they face constant adverse selection. For every strategy, they have to answer the question of why someone would let an outsider reap some of the profits, and, further, why they happen to be that outsider. If they back a winner, there's a good chance that they'll be told that they can't put in as much as they'd like, and may have to take some of their capital out. (The losers are naturally quite happy to have them reinvest. 2% of a sufficiently big number is itself a big enough number for many purposes.) There is still alpha in manager selection, but like other sources of alpha it's some combination of temporary, opportunistic, and based on personal networks. There were some very lucky investors who decided that a Midwestern insurance geek who slept with a Moody's Manual under his pillow could produce alpha, or that a mathematician with a theorem named after him could outsmart the median MBA. One way or another, that alpha decayed, and when limited partners got excess returns, it was from the usual sources: a mix of skill and luck, with no aggregate free lunch.

The question of why LPs can get good returns at all turns out to be more interesting if you reframe things so they're in the same business as the people they invest in, just at another layer of abstraction. And there's even some symmetry around which firms need capital and which don't; a building full of EUV machines is more expensive than a building full of accountants or consultants, so the professional services companies tend to stay more private while the asset-heavy companies have to go public. It comes up a lot:

• We've looked at hard-to-scale strategies that seed big companies.

• Sometimes, returns erode because everyone thinks the same way ($).

• Financial products inevitably get democratized.

• Some things that start as novel assets that can support unique strategies end up being just another asset class ($).

• The question of where assets should live is never settled. Consider the case of printers with a subscription attached ($).

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In 1977, bond fund managers around the country received a peculiar document: it was the usual prospectus for a new offering, but this one wasn't investment grade. Bond managers were, of course, aware that not all bonds were rated investment-grade—but those non-investment-grade bonds had either been issued as investment-grade, or issued by conglomerates in exchange for either previously-issued bonds or in exchange for some acquisition target.¹ It was somewhat original to give investors the exciting opportunity to buy a bond that had a very real chance of not getting paid back at all, but enough of them took it that a new market was born.

Or, rather, an old market was reborn: the American railroad buildout was funded by what were pretty similar to modern-day junk bonds, and for a similar reason: they were capital-intensive enough that pure equity didn't make sense, but some of that capital would immediately lose liquidation value if the business didn't work out—the rolling stock would still be worth something, but the tracks themselves were only worth something if there was demand to move passengers or cargo on them. So, bond investors demanded, and got, a premium, and they knew—or found out the hard way—that this premium was meant to compensate them for the risk they took.

The reinvention of junk bonds was contingent on some mostly-unrelated historical factors:

• High overall rates meant that there was less sticker shock in paying, or paying for, double-digit yields.

• Inflation meant that bond investors raised their hurdle rates in general.²

• The general macro volatility of that period meant that there were companies that had decent underlying economics, but had gotten on the wrong side of some big macro factor—maybe they were locked in to buying a certain amount of oil, but paying five times as much for it as they expected, or maybe they were banks who hadn't hedged their duration risk and wanted to roll the dice on something else to get back to breakeven.

• Michael Milken was such a talented trader that he started getting capacity-constrained from the existing junk bond market, but was also strategic enough as a businessman that he realized he could profitably expand the investable universe through original-issue junk.

That market has had some pretty extreme ups and downs since then, but has returned to being a fixture of US financial markets after a long period where it wasn't much of a factor. With more complete capital markets and more sophisticated investors, it isn't as much of a vehicle for adverse selection as it used to be.

And junk bonds aren't the only financial technology that has been reinvented. There was a lively market in oil futures until 1895, when Standard Oil announced that going forward, it would be setting the price of oil. Futures didn't recover after Standard Oil was broken up, partly because they were still pretty cozy and partly because their business became increasingly dependent on drilling for oil in foreign countries, and opacity benefited them. Oil trading started to come back in the 1970s, when price volatility gave Marc Rich and other traders a wedge. And then, in a bizarre historical contingency, the New York Mercantile Exchange went through a scandal when J.R. Simplot defaulted on a big potato trade, and was put in a regulatory penalty box—they weren't allowed to introduce any new contracts until they'd cleaned up their act. Given that their biggest contract up to that point had been potatoes, this was bad news. But if you get a room full of traders and take away their favorite thing to trade, they'll instinctively find something to gamble on³, and they discovered that back before Standard Oil killed the market, NYMEX had listed a contract for heating oil! Heating oil correlates pretty well with oil, so suddenly NYMEX went from an exchange that had lost its biggest product to the one place where you could make live, intraday bets on the world's most important commodity.

Shareholder buybacks are another case study. They're a common corporate finance tool now, but until 1982, it wasn't clear if they were actually legal, or if the SEC would treat them as market manipulation. In an unclear regulatory environment, companies didn't want to risk being accused of either pumping their stock or exploiting shareholders, so they mostly avoided buybacks, or did them as one-time tender offers at a premium to the share price. But it turns out that they were well-understood in the 1930s, and some investment trusts that traded below net asset value used them to close that gap.

There are some products that only work when financial markets reach some threshold of size or complexity. And that threshold always changes—options got much easier to trade when Black-Scholes and pocket calculators meant that more people could estimate their fair value, though it's still a computational challenge to simultaneously quote so many contracts at once. Asset classes that seemed discredited by one cycle turned out to reach their peak in a later cycle, and that will probably happen again.⁴ There are lots of useful ways to slice up a stream of cash flows, or turn a risk into a tradeable asset, and there are lots of creative people who try to do this at what turns out to be the wrong time.

We've covered rereuns of financial history many times in The Diff. When something new is happening, you can learn a lot from figuring out how it's not so new, after all:

• Here's more on Milken and junk bonds.

• A profile of Teledyne, a pioneer in bringing back buybacks.

• The kind of risk-slicing that characterized the 2000s is back with AI.

• Learning from the rise of electrification.

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If you're trying to model investor behavior, it's pretty hard to start with any assumption other than that investors are all trying to make money. But for that model to say anything useful, you need a little variation: some of them are close to pure wealth-maximizers, but many of them are trying to smooth their lifetime consumption by accumulating risky assets when they're young, slowly shifting their portfolio into safer ones, and then drawing down that portfolio once they retire. By far the easiest way to do this is to index, but some investors—a growing fraction of them—like to pick individual stocks.

If you're trying to understand professional stock-pickers who are following some kind of mandate, what you're looking at is a basically an annoying dynamic programming problem: an analyst has a finite amount of attention, each trade requires some time investment to determine whether or not it's worth doing and an ongoing time investment to decide when to exit. And knowing how long it will take to exit the position (and how volatile it will be, and therefore how closely you’ll have to watch it) is a separate source of uncertainty. So it's a constant process of guesswork in order to produce a coherent collection of... guesses about which way a stock will move! Big funds have gotten pretty good at optimizing this kind of thing, and a healthy research cadence ends up being a natural output of targeting a particular level of volatility and factor exposures. Conveniently, funds have a sensible way to set a goal for this optimization process: they want to fire anyone whose contribution to returns isn't commensurate with the risk budget they've been given.

All of this is hard to implement and has all kinds of pitfalls and perverse incentives, but the goal is clearer. For individual investors, the goal can be fuzzier. Someone might buy individual stocks because:

• They think they have an edge, either because they're looking at smaller companies that institutions wouldn't cover, because they have special knowledge about some industry (like someone in AI who spotted the demand spike for GPUs, memory, CPUs, etc.), or because they believe they're better analysts.

• They might just enjoy the research process and not necessarily expect to win, but still feel that it's a good use of time to understand companies—and that their returns are a measure of how well they actually do understand them.

• They might have some mental threshold for net worth, such that they'd rather have a lower EV but a higher chance of escaping the permanent economic underclass.

• Gambling is fun, and retail investing platforms have various ways to pay extremely high transaction costs in order to get leverage via options.

• Sometimes, owning an asset is a cosmetic or quasi-religious choice (like being a diehard fan of a sports team). Someone who buys Tesla stock today is making a partly-political statement; ten years ago, they were doing that, too, but it was roughly the opposite political statement. Meme stocks are partly a way to say that finance is too stuffy, too rigged, or too boring.

It's these last few that start to distort markets a bit. Usually, a company with more volatile cash flows has a higher cost of capital; it's less likely to be able to return capital through buybacks when it's cheap, and also less likely to survive the next cycle. But some kinds of volatility now maximize fun. If you own shares in a company that diversifies in goofy ways, launches a crypto treasury strategy, or just issues some peculiar press releases, that's probably a tailwind to valuation right now even if it's usually a headwind.

Some other decisions companies make are meant to appeal to investors who are rolling their own ESG mandate: Tesla fit this description, Beyond Meat did, too, and back before they pivoted to AI datacenters, Allbirds had a sustainability angle. A few companies also comply with B Corp rules (Warby Parker, Lemonade), or have a policy of donations, like Salesforce's 1% pledge. All of these give investors a nice sense that they're making a difference while making money, and it's probably a nonlinear one—there just aren't that many people saying that Salesforce is just short of their hurdle rate right now, but when you factor in the extra 1% that they're donating, the returns-plus-charitable-utility calculation suddenly pencils out.

In general, these companies aren't doing purely performative charity just to reduce their cost of capital. They might not maximize impact, and it might be more optimal for companies to focus on maximizing profits while leaving the charity part up to their shareholders.¹ But these companies are run by human beings, who have interests beyond wealth-maximization, and these companies also hire people, who will have to spend a lot of time together. There are many ways that corporate evolution selects for companies being a bit nicer beyond purely sourcing capital. But the whole situation means that when you look at individual investor outcomes, you can't just compare their returns to the S&P. Sometimes, the returns gap just means that they used the market as a vehicle to pay for something that matters to them more than the last dollar. But these companies are run by human beings, who have interests beyond wealth-maximization, and these companies also hire people, who will have to spend a lot of time together. There are many ways that corporate evolution selects for companies being a bit nicer beyond purely sourcing capital. But the whole situation means that when you look at individual investor outcomes, you can't just compare their returns to the S&P. Sometimes, the returns gap just means that they used the market as a vehicle to pay for something that matters to them more than the last dollar.

In The Diff, there are a few places where we've covered the nuances of cost of capital and what shareholders are really trying to earn:

• Democratizing financial products always happens, and it's never without some problems.

• When a Korean boy band company went public, it was partly a form of price discrimination to capture more money from superfans ($).

• There are many things we mean when we talk about stocks.

• Shareholder activism has gone through a few distinct eras ($).

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Tech stocks peaked on March 10th, 2000, and didn't reach that level again until April 2015. The two categories of tech investors are the ones for whom that drawdown was a formative experience, and the ones for whom it's as abstractly historical as the Panic of 1873. But it was a memorable experience. In retrospect, we tend to mentally draw fairly straight lines between big market inflections, but 2000 actually featured several shifting narratives. The year kicked off with the continuation of the vertiginous late-90s rally, when basically every tech stock was ripping, especially the smaller and more speculative ones and a few sporting what were then considered extreme price/sales ratios.¹

The really interesting phase was after the big March selloff, when prices started to creep back, but unevenly. The new, sophisticated claim was: who knows if Pets.com will be the one in twenty online pet stores that actually makes money? It's a sucker bet to figure it out. But, whoever does eventually win is still going to need networking equipment, and bandwidth to provide it. So, stop betting on the companies panning for gold, and bet on the ones selling picks and shovels like a responsible adult!

The only big problems with this were:

1. The tech financial system at that time was basically a machine for turning venture dollars and dot-com IPO proceeds into revenue for companies that sold servers, routers, databases, etc. There wasn't a good bridge between when this funding disappeared and when the first dot-com winners were generating enough operating cash flow to replace that demand. Amazon, for example, slammed on the brakes hard enough that by Q3 2001, their annual growth was +0.2% Y/Y, slower than GDP growth, and they were still far from generating positive cash flow.² It turned out that starting a dozen, well-funded companies in parallel to go after each vertical generated a lot more spending than having a handful of scrappy companies doing the same thing.

2. One of the big capex categories was fiber. If there's a shortage of bandwidth, it's great to own scarce wires. But the marginal cost of transmitting additional bits on a line that's below capacity is very low indeed, and it turned out that the market-clearing price for fiber was pretty close to free. By the time some fiber projects were complete, there wasn't any reason to turn them on. (That fiber did end up getting bought: when capex overshoots, there's net wealth destruction, but some people make money from selling early while others make money from buying very, very late.³ )

It's tempting to map this straight to AI. Consumer-facing AI applications have a tendency for their peak ARR to exceed their lifetime revenue: they grow very fast, but people churn fast, either because their product turns out to make more sense as a feature in an established company's app, or because the labs subsume it, or just because it was a fun toy that didn't find a business model in time. The investors backing datacenters are much more serious and much more careful.

But the supply-glut piece can't easily be replayed, because inference isn't a zero-marginal cost activity. There can be a glut that pushes prices down to the point that companies are reporting negative GAAP income by pricing tokens at a slim margin above electricity prices, but what those companies are really doing is inefficiently liquidating their assets in order to pay back bondholders. Meanwhile, since the power part of the supply chain lags more than the chip production part, inference can get cheaper again as power gets cheaper.⁴ And, unlike in the dot-com boom, we do have a pretty good idea of where to direct money in order to bet on current trends continuing. Even though AI companies will have weirder economics than most businesses, their business models are pretty legible to investors—if investors in 1999 had thought about cohort economics the way modern investors do, and had convinced companies to report them accordingly, Amazon would have had a much smaller drawdown.

But the parallel to worry about is thesis drift during drawdowns. If investors start getting cautious about AI, and capital shifts from applications and models to infrastructure, there's always the risk that they're betting on revenue that's from other companies' venture funding, even as that funding disappears. But this, too, is something that people who survived the dot-com bubble remember well (or anyone in a cyclical industry): a drop in demand somewhere will ripple through the relevant supply chain and be very bad news to whichever link in that supply chain most overinvested. If there is a big bear market in AI soon, there will be brief periods when some wishful thinking and special pleading makes it seem that it won't apply to some places. And it'll be good to remember that these have a shorter half-life than the narratives they replace.

The summer 2000 rally has many lessons for investors, and echoes other events in financial history where a trend looked like it was reversing but was actually just getting started. We've written some related pieces in The Diff:

• Here's more detail on what specific bet neocloud investors are making.

• Thoughts on what AI economics would look like after a bust.

• One of the things that keeps these rallies going is opportunistic M&A, which often doesn't work out. But the acquisitions that matter are the ones that would be blocked on antitrust grounds if regulators had as much foresight as buyers ($).

• You can hide but not remove most volatility ($).

• One feature of the economic cycle is that some industries get more capital-intensive as they grow ($).

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At any given time, some things will look historically great, and some pretty bad, judged mostly by where you chose to start your time series.

You see this in financial data all the time. In the late 90s, when people talked about the stock market, they'd say its average return was 11% per year. A few years later, the number people threw out was usually more like 9%. Choose some point in the 20th century when the time series of S&P 500 performance and dividends is within your budget, and roll things forward to a few different dates, and you'll see returns bob around a bit. They'll still fluctuate around some central average, but it's hard to know exactly what that mean is. For a market like Japan, returns looked structurally high and strategists drove themselves crazy trying to rationalize it—maybe cross-shareholdings meant that if you were buying equity in any one Japanese company, you were getting a piece of the rest of the market, and maybe there was a lower risk premium for a country that had mastered sustainable growth. It turned out that all of these stories were basically wrong, and the stocks were just overpriced.

That same start-date sensitivity also shows up in politics and social commentary. Compared to the 1950s, manufacturing jobs pay less, it's harder to have a single-earner household, the media are more fragmented and less responsible, and politics has sharp partisan divides. But the 1950s were a high-water mark for many of these phenomena! Postwar, the US had a surplus of manufacturing capacity and a shortage of workers, so it made sense to offer generous wages, particularly in scale- and utilization-driven industries like auto manufacturing. The media environment was less confusing when there were fewer media outlets, partly because of another kind of scale economy—it's easier to maintain one newspaper distribution network rather than two, and because there were only three TV networks. As for politicians, they had an easier time getting along for a few reasons. Most of them had recently served in the same war, but they were also united in their fear of either losing the Cold War to, or being mistaken for, communists. And some other issues, like segregation, cut across party lines—the divides were between Northern and Southern Democrats, and between Western/Midwestern and Eastern Republicans. That leads to what looks a lot like bipartisan cooperation, but which was actually a different flavor of coalitions.

The easiest way to get this wrong, as alluded to above, is to be born at the right time. People who started following news in the 90s will never shake the sense that a mostly peaceful world benignly ruled by the United States was the default expectation, or at least the rough direction things were headed. People who started following news in the 70s, or the 2000s, tend to have a darker view by default.

You'll sometimes see this evolution in other dimensions, like the size of organizations. Over my lifetime, the expected lifespan of big companies has dropped a lot—GM was a big company when I was born, and a big company a generation earlier. It was a pretty good-sized growth stock a generation before that! But now, while GM is an objectively big company that still produces lots of cars and pays lots of workers, it isn't the kind of unbeatable business it used to be. You could look at this as an example of the economy getting more dynamic, and companies going through faster rises and falls, but that, too, is a cyclical phenomenon. There are periods where small companies can scale quickly, like the new industrial giants of the late 19th century, or the railroads before that. (Railroads had a strange kind of scaling: the minimum capital investment required to enter the industry meant that some of them were born big, and didn't grow especially fast after.) And there are times when the list of big companies is static—for a long time, the dominant company in the computer industry was IBM, which had been founded as a roll-up of scale, calculator, and time clock companies in 1911. There were new entrants, but IBM had accidentally backed into a model where they installed computers upfront and earned recurring revenue, which meant that competing in that business was capital-intensive, and the financial sector wasn't equipped to fund it appropriately. Still , IBM went from being the largest company in the S&P by a large margin in 1985 to out of the top 10 within 30 years.¹ An environment where the biggest companies were mostly founded within the lifetimes of the people analyzing them is historically unusual—it's a function of a venture capital ecosystem that can fund capital-intensive new businesses, if they're promising enough, and an equity market that lets those companies attract talent with equity compensation.

If you look at a general trend—more globalization, more bandwidth, cheaper electronics, more paywalls, etc.--it's actually pretty reasonable to extrapolate. They tend to go on for a long time! But if you want to be early to spotting changes, you have to treat the trend as a measure of some underlying reality that might not endlessly trend in the same direction.

These timing-related questions are an important part of framing pieces on long-term trends. A few examples:

• In The Tyranny of the Long Generation, we look at the pattern where an industry stops growing and its workforce starts aging, leading to a feedback loop.

• There are some time periods long enough that these effects wash out. Like 10,000 years.

• What happens when you buy at an all-time high?

• One of the long-running cycles to pay attention to is demographic ($).

• The consumer credit cycle matters ($), but isn’t the only driver of cycles.

• Sometimes, what looks like an intrinsic feature of an industry’s economics turns out to be temporary, as with capital-intensity ($).

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If you make a list of the biggest targets of shareholder activist campaigns, you'll note that most of them were US-listed companies, and all of the activists were US-based firms: AT&T, ExxonMobil, Procter & Gamble, Microsoft, etc. were basically all dealing with US investors. That isn't true for hostile takeovers, where some of the biggest (Mannesmann, SABMiller) were outside the US.

Why is activism in particular such an American thing?

One reason is that US equity markets played such an important role in the country's history. US equity market cap didn't exceed that of Britain until around 1914, but railroads were a larger share of US equities, and the US was able to follow the leapfrog development model by having big railroad bubbles a bit later, and in a country that had enough of a natural resource base that these railroads ended up contributing to the overall economy (even if the railroad's shareholders and lenders didn't participate much in this).

Railroads were also an early playground for financial engineering, and for thinking seriously about corporate governance. They had access to capital through the financial markets, and some of the biggest also had access to government subsidies; they spent large sums on construction and materials, too. So someone who could get control of a railroad without paying full price could direct that spending to businesses they controlled in order to get kickbacks, or just announce surprise dividend cuts or increases that they could trade ahead of.

Shareholders didn't particularly like this setup, and the later generation of railroad professionals, like Morgan and Harriman, tended to play it straight. (Jay Gould was a special case: he was very willing to engage in some tricky behavior early in his career—including paying a large bribe to someone who turned out to be a con artist, and then trying to have him kidnapped, which led to an international incident—he ended up being a boringly competent railroad manager in the last years of his career.)

In countries with a less laissez faire approach, there were limits on what one tyrannical speculator/executive could get away with, but the US set those limits organically, by starting with the assumption that someone who controlled a company could do whatever they wanted with it, and then litigating every bit of misbehavior they came up with. There is no particular reason to privilege incumbents over outsiders in this kind of situation—it's possible for someone to take over a company and loot it, but it's also possible to for someone to take over a company specifically to stop someone else from looting it, or at least to get them to do so at a more measured pace.

There are isolated examples of shareholder activism in the early twentieth century: Benjamin Graham found out that a company called Northern Pipeline owned a small oil pipeline, but also had bonds worth more than its share price. So he bought 5% of the stock, showed up at the annual meeting—and got shot down, because he made a motion to discuss distributing the cash, but the only attendees were company employees, none of whom seconded it. So he had to go back a year later, this time bringing some company (two lawyers), got himself and one of the lawyers elected to the board, and finally convinced the company to sell most of the bonds and pay a dividend.

Three decades later, Robert R. "Railroad" Young, a successful speculator, ran a campaign to take over the New York Central Railroad. Young had worked in GM's treasury department, speculated in stocks, and eventually taken over Allegheny, a holding company mostly focused on railroads. He ran a very high-profile campaign, eventually took it over, but wasn't able to execute his plan of rolling up other railroads to create a more integrated network. (That part didn't work out; four years after taking over, he committed suicide by shotgun. Decades later, his mansion was sold to a different corporate raider.)

In a country that takes shareholder primacy seriously, and that has liquid capital markets and specialized risk-seeking investing vehicles, the equilibrium is that if a company is undervalued conditional on having better management, and someone makes a case that the management problem can be fixed, its shareholder base will soon consist mostly of people betting on that exact outcome. That's also a market where there's enough liquidity to justify an investment research and news ecosystem, and where investors have dense enough social networks that theses spread fast.

All of this means that the US is uniquely good at having preference cascades around corporate decisions: which executives to hire or fire, which division to spin off, whether to double down on some new plan or cut losses, etc. And American capital markets are deep enough that if there's something trading at $20, and it would be trading at $25 given some simple change, it's actually possible for activists to accumulate a big enough stake that they can force that change, with a market impact and timeframe that makes the $25/share outcome a pretty good one.

America's financial norms are contagious; if someone has a directional view on a foreign stock, and they want to hedge their industry exposure, the straightforward way to do that is to make an offsetting bet on that company's US-listed competitors—they're liquid, they have lots of eyes on them, and that means they're an easy default for hedging. And once that happens, those companies are also being compared to American companies, being traded in reference to how those American companies perform, etc. It's hard for financial systems not to end up tightly linked in this way, especially now that the language barrier is basically irrelevant. Shareholder activism does work, and in a sense it's democratic—it means companies are accountable to the broader investing public rather than their board, though full suffrage is only accorded to investors who have enough capital to file a 13D (and at companies where the founders don’t have supervoting shares).

Hostile takeovers and shareholder activism are common themes in The Diff. We've covered:

• The various eras of shareholder activism ($).

• Whether they're targeting companies for malinvestment, or pushing them to irresponsibly ignore important bets ($).

• The modern era of activism is hard to imagine without Drexel.

• And all of this can be helpful because finance is an invisible productivity subsidy.

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The value of a company is the sum of the present value of its future cash flows, discounted at some rate that appropriately compensates investors for the risk they're taking. This is one of those elegant, load-bearing tautologies, like net worth equals assets minus liabilities, or that evolution selects for the fittest, where fitness is defined by the ability to reproduce. It's a mix of "duh" and "wow." And you can build fascinating, testable predictions off of them, and advance the state of the world, just by starting with a premise that basically asserts itself, just in a way that's structured enough that you can usefully apply it to specific scenarios.

The tautological part of the discounted cash flow model is that the discount rate is whatever a given investor demands to be paid for the risk they're taking. So the DCF model is basically saying that there are two things that contribute to the price of an asset: the unknown future cash flows, and what's going on in investors' heads when they decide how much they'd want to be paid to take the risk that those cash flows won't materialize.

But it turns out to be useful, because you can decompose so many questions about how a given change in the state of the world affects the value of an asset by asking: does this change the level and timing of their future cash flows? And how does it affect how risky those cash flows are?

For M&A, there are three good levers to consider:

1. Synergy is a real thing. A merged company ends up having twice as many C-level executives as it needs, and has similar but smaller excesses moving down the org chart. It's a bigger customer for its suppliers, too, and can extract better terms. There are often opportunities to cross-sell complementary solutions into the new merged customer base. And in many cases, if two companies in a given industry merge, it will turn out that they've discovered different kinds of tacit knowledge, and that the sum of these is greater than the parts.

2. When a buyer is counting the net present value of future cash flows, they're not just looking at the cash flows of the company they're buying; they're considering their own cash flows conditional on having bought them. This can play out in a purely redistributive sense, where one less competitor means that the remaining companies can charge more. (On the other hand, if the widget industry has ten participants who have 10% market share, and one of them pays a premium to buy the other, and a nine-firm widget business can charge a little bit more—80% of the benefit goes to someone other than the buyer, but the buyer pays all of the merger premium to make it happen. And regulators pay pretty close attention when one company with, say, 70% market share tries to buy a competitor who controls half of the remaining market.) The more interesting case is when an industry is locked into a prisoner's dilemma: when airlines are fragmented, any one airline benefits from ordering more planes and thus reducing their average cost, but that means the industry as a whole is constantly overproducing, and no one has pricing power. Thisleads to a cycle where airlines in general are unstable, and unions appropriately respond to incentives by getting whatever they can while they have the opportunity. And it occasionally meant that airlines shut down literally overnight; Aloha Airlines gave passengers one day's notice that they were shutting down, Skybus was also abrupt, and Europe's fiercely competitive budget carrier industry features similar collapses like Monarch, WOW, and Germania. It's a temporary consumer subsidy, but the subsidy takes the following form: your ticket is cheaper than it otherwise would be, but your airline may or may not exist to take you on the homebound leg of your round trip. When the industry consolidated, everyone could look a little further ahead, and the industry turned out to be more operationally and financially resilient to a global pandemic in 2020 than it was to a period of elevated fuel prices in 2008.

3. The airline example illustrates something else, too: these airlines were lowering their discount rates—and so were the unions, the aircraft manufacturers, the credit card companies, etc. Once it was pretty clear that Delta, United, and American were probably going to be around in ten years, unions could ask themselves whether a 10% raise right now really made more sense than locking in, say, 5% annual raises for a few years. And when they do that, Boeing and Airbus can be more confident that someone ordering aircraft for delivery years from now will be in a position to pay for them.

And then there are some bad levers to consider: CEOs tend to have healthy egos. If they compare themselves to similarly-skilled CEOs, they'll probably think they could run things a bit better. It's almost impossible for this not to be the case to at least some degree.¹ So, they'll tend to overestimate the synergies, but perhaps underestimate the counterfactual downside of having one more competitor, and they'll almost certainly put too low a discount rate on the result.

You can just simplify this by saying that controlling a company is worth some premium over being a minority shareholder, but if that were the case, you'd expect to see that the companies with the best governance sold with a minimal premium, and that a company that's run entirely for the benefit of executives can get taken out at a giant multiple. But that doesn't really happen—partly because some companies in the latter category get taken out at a modest multiple by those same executives, as one last act of shareholder exploitation. Even if that's part of what happens, it won't be a big part, because when wealth gets transferred from the company to management, the company doesn't grow as quickly, so it's going to be a smaller share of the phenomenon regardless.

What's really going on is that the corporate wrapper around particular collections of assets, people, and company culture is not necessarily the optimal one. Sometimes, one company really ought to be two or three, and it uses spinoffs or divestitures to accomplish that. And sometimes, there are two legally-distinct companies that happen to contain a very complementary set of assets, it's obvious that they'd be more valuable together, and an M&A premium from the largest to the smallest is a good way to fairly split those gains and then get things off on the right foot.

Thanks to this tweet for the suggestion. It is, indeed, something I would write about.

Companies rise and fall, and some of them do so in an acquisition-heavy way. We’ve covered this in a few different places:

• GE had a little too much synergy ($).

• Teledyne, in contrast, got it right.

• Mergers and acquisitions create opportunities for another set of deals: betting on which ones will close.

• You can learn a lot about merger synergies from the airline business.

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Elon Musk has taken a car company public, and plans on holding another IPO this year that, if all goes well, will be the largest in history. He'll be the second member of that elite club. The first, H. Rowan Gaither, Jr., joined in 1956 when, as head of the Ford Foundation, he agreed to sell 10m of the foundation's Ford shares at $64.50 apiece, raising, after commissions, $642.6m. This was a big deal, financially (the biggest offering in history up to that point had been a GM secondary a year earlier, raising $325m).

Ford, the company, was unusually big for a private business—by assets, it was one of the half-dozen largest non-financial companies in the country. Henry Ford had previously had outside stockholders, but had feuded with them—they ended up suing him, and winning the case, forcing him to pay out higher dividends rather than investing in expansion. But this case was also a landmark of legal realism, because what he did next was to threaten to quit and start a new car company, then use that as leverage to buy out the outside shareholders so they'd keep quiet. Ford had a hard time defending himself because he openly said that he was expanding because he wanted to create more jobs for autoworkers, not because he necessarily thought it was the right business decision. But, it was also the right business decision, and, relieved of the kind of cap table that makes startup life complicated from time to time, he was able to keep growing the business, die a rich man, and leave much of that wealth to his foundation in the form of Ford stock. (The Ford family kept some super-voting stock, and remains in effective control to this day.)

The Ford IPO happened for roughly the same reason Ford's shareholders had sued him long ago: the foundation didn't want the vast majority of its net worth to be in an illiquid, low-yield asset. But selling that much stock was a challenge: in 1956, it had only been two years since the Dow surpassed its 1929 high, and the stock market still struck many investors as a hostile, confusing place. On the other hand, many of those same customers knew and trusted Ford, the product, and had a good feeling about Ford, the company.

The result was an astonishingly widely-distributed IPO. They had to print 1.5 million copies of the prospectus, and mail them around the country. Basically every broker in the US participated, except for Morgan Stanley and Dillon Read, who had existing relationships with the other two of the Big Three. The total underwriting commission was $14.3m, a huge absolute  number at the time but a relatively small percentage commission (2.17%), at a time when standard IPO commissions were much higher (this SEC report mentions a $230m offering where the fee was 5.5%, with smaller deals taking place at 15%, and often including warrant kickers, and expenses charged to the IPOing company that could put the total over 25%.)

But the brokers, all 722 of them, knew that the economics of this deal weren't contained entirely in the day-one revenue:

1. It was what we'd call a brand collaboration, between the beloved Ford brand and the somewhat tarnished Wall Street. It's a bit like the first minor podcast a celebrity goes on a couple years after they get canceled.

2. Brokers couldn't really afford not to be on the underwriter list, both because customers would expect to be able to get some Ford stock and because every firm without a good excuse had already signed on.

3. Within the underwriting, it was a chance for brokers at smaller firms to strut their stuff among the bigger banks, and big clients. Goldman Sachs's CEO had closely consulted with the Ford family and the Ford Foundation to make the IPO happen, and Goldman co-led the deal.¹

Ford shares were the right product for the right moment. If the Model T represented membership in the solid, respectable middle class a generation earlier, a brokerage account and the ability to learnedly skim the stock quotes section of the newspaper was becoming a new middle-class signifier. So, if the outbreak of the Second World War was the end of the economic side of the Great Depression, Ford's successful IPO—complete with a modest but not scandalous first-day pop—was when financial markets finally emerged from the shadow of 1929.

There's a lot we can't learn from this deal as we once again approach the biggest IPO ever. The entire financial services industry has completely changed, a SpaceX IPO isn't the same kind of cultural event Ford's was (unless you live in Atherton), and the last thing retail investors need right now is someone telling them that putting all of their money into individual stocks is a good way to get rich.² On the other hand, there are some surprising similarities. At the time of the IPO, the US was being pulled out of a mild recession by a big capex splurge from the automakers, and something similar is happening in AI today. Big deals are a big deal; they shifted the American balance sheet so more of Ford was owned by households and more cash was held by foundations, and since pensions and endowments are such a big category of limited partners in venture funds, that's going to happen again, too. But the only way for the deals to be similar in retrospect is for the IPO Class of '26 to bookend a period of high retail participation in markets, the way Ford's did, but in the opposite direction.

Big IPOs are a big deal, and come up often in The Diff. We've covered:

• Palantir

• Snowflake ($)

• Airbnb

• Coinbase

• And more obscure ones like Waterbridge ($) and the abortive Steinway IPO ($).

• Outside of The Diff, for much more on the Ford IPO, the definitive piece is this one from John Brooks.

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The Infinity Machine, the new biography of Demis Hassabis, mentions that at one point he had a skunkworks project inside Google to apply machine intelligence to investing. Which is not a bad idea! AI research and quantitative training share a similar fundamental process, and there's some overlap between people who've done one and the other ($, FT). They're obviously using different kinds of data and different kinds of models, but in another sense they're very closely-tied: the transformer architecture is a clever way to say "if you have x, followed by y, what's the most likely z," and chain such predictions together. In both cases, clean data beats more data.

But in another sense, these domains are fundamentally different. AI training data generally consists of people trying to communicate with each other. If there's a Reddit thread from someone asking why the grass on their lawn keeps dying, there will be lots of earnest answers from people sharing some combination of their general lawn-care knowledge and their specific lawn-care experiences. Whereas the market is closer to a consortium of people who get together to misdirect all of their neighbors, since only one of them can have the greenest grass.

A trading signal is generally some indication that some other market participant is making an error, either of commission or omission. Betting on short-term mean reversion, for example, means providing liquidity to people who put on large trades in a sloppy way. Statistical arbitrage is a scalable way to make the point that a price change in one stock should, if markets are reasonably efficient, reflect changes in other assets, too, but insufficiently diligent traders might over-fixate on one company, or just not get the relative impacts right.

And every time one of these trades gets implemented, the underlying signal gets weaker. Maybe, absent systematic mean-reversion, a given attempt to liquidate some stock will push the price down by 1% over the course of a day, all else being equal. But suppose there's a trader who can identify that kind of selling, step in, and stabilize the price when it's down half a percentage point, expecting it to drift part of the way back up in the next few days. Suddenly, your data no longer shows that big liquidation! Instead, it shows a stock sinking slightly on above-average volume, and weakly outperforming soon after.

In fact, your mean-reversion trader might be thinking a few more steps ahead than that! Suppose the profit-maximizing approach is the one outlined above: when you've identified a sloppy seller, buy when half the unaffected market impact has been realized, and hold for a few days. That means the market impact is smaller, but someone carefully studying the numbers might see it—and it might catch their interest if the average move shrinks by half, at which point they might implement a similar strategy, but calibrate it to buy when the stock is down 45 basis points. They're making a bit less than the original trader, but if the strategy is still profitable, they may be satisfied with that. Our trader could avoid this by guessing what the odds of being copied are as a function of how much alpha there is each time a signal is getting used, and might conclude that this signal can, for example, produce $1m in annualized profits with a 50% chance of being copied each year, or $500k in annual profits with a 20% chance.

At that point, someone who's incredibly careful about analyzing the data might reach an unfortunate null-hypothesis result: "It used to be that if you saw a 500-share sell order every five minutes, always at the midpoint of the bid/ask spread, you knew someone was selling and could wait for the stock to drop and then start buying. But now, that kind of trade barely moves the market at all!" So, to them, equal-sized orders made at equal time increments are noise, not signal. But that's just because they're already someone else's signal. Because everyone’s behaviors obfuscate the truth, the result is like training a language model exclusively on North Korean propaganda and then asking it to write a coherent story of the twentieth century: much of what you’d want to train on is simply missing from the data.

Solving the market at any given point means identifying:

1. The set of all signals that nobody else is exploiting, and, related to that,

2. The set of all signals that arise from other people's systematic strategies.

Unfortunately, the second set of signals often shows up when a strategy matures, and goes through a cycle where too much money is flowing into it, those inflows temporarily push prices in the direction the strategy predicts, investors collectively conclude that even though the upside of the strategy is not what it once was, the variance is lower so they can still achieve a decent return by levering up—and suddenly there's a whole ecosystem of related trades that will all unwind at once. The Quant Quake of 2007 is the canonical example of this: there were funds diversified across fixed income arbitrage, equity statistical arbitrage, owning factors like value and momentum, etc. But since they were all diversified across those strategies, and they were all levered, when any one of them started shrinking its balance sheet, all of them did, and everything went down.

So the property that emerges from the interactions of lots of traders who are looking at the same datasets, using the same statistical tools, and coming to the same (correct) judgements about historical performance, is that at some point in the future, these strategies will be perfectly correlated in that they'll all lose money in sync. It's a signal you get to test exactly once.

And it implies something important: to fully solve the market, you have to infer the counterfactual market state if each of a thousand other market participants hadn't busily solved some part of the market. And then you have to implement something that implicitly replicates their strategies, and then a model to predict their suboptimal reactions when those strategies hit a rough patch. Some people have gotten rich by doubling down when they know they're right. They haven't necessarily stayed rich doing that, but any given snapshot of the rich will have people like this. Such a snapshot will also have people who followed the opposite policy: being fairly conservative most of the time, so they could be aggressively opportunistic when there were big opportunities. This is all out-of-sample. And, even more frustrating, the historical trend is that when markets are generally stable, it leads to too much leverage and they destabilize again. It'll feel the most like asset allocation has been perfectly solved just before another 1987-style break or 2008-sized collapse.

The last reason it's hard is that making billions of dollars using AI to trade is probably not the most valuable thing you can do with those skills. Big fortunes have been made and lost in finance, but if you look at the top of the Forbes 400 list, owning software companies, especially the ones that sell ads, is where the real money is. If your world model is so robust that it can simulate what a portfolio manager at AQR would do in response to a decision by a quant researcher at XTX, you can probably use that deep psychological insight to convince either of them to move to a nicer apartment, buy a faster car, take a ludicrous vacation, etc. And you can do the same for all the high-income people who don't interact in a market-facing way.¹ The people who made the most money from deeply understanding orderbooks, latency, and adverse selection did it by putting ads on news feeds and search results, not by trading.

It’s always useful to have a sense of how efficient the market is, where it’s more or less so, and why. The Diff has looked at this question from multiple angles:

• We’ve looked at how crowded strategies create accidental adversarial attacks.

• There’s more than one efficient market paradox.

• More thoughts on correlations going to 1.

• And some reflections on Rentech.

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Here are two defensible stories about why the dollar is a reserve currency issued by a country that can't balance its budget or produce more than it consumes:

1. This is primarily a function of external demand. The rest of the world needs that reserve currency, and as the rest of the world grows, and its financial system grows, too, it needs more and more reserves. The only way to get dollars is to sell something to Americans and then not spend the dollars on things Americans sell to you, or to outsource this relationship to some other trading partner. So that's what they do.

2. The US lives beyond its means, both in terms of consumption exceeding production and in terms of spending exceeding taxes. But it's also a rich country with plenty of investment opportunities, so it's historically been able to outgrow a little profligacy. But the result of this profligacy is that there are lots of USD-denominated assets owned by foreign investors, and these assets are liquid. The US's persistent deficit means that everyone else knows they can source dollars, so they're willing to borrow in USD, and that makes the US dollar the global reserve currency.

These stories both make sense, though they ignore some of the relevant historical contingencies: the US dollar was designed as a reserve currency by the Bretton Woods agreements, which pegged every currency to dollars and pegged the dollar to gold. When the US abandoned gold convertibility, the world's financial infrastructure still treated the dollar like a reserve currency—and now that precious metal reserves didn't constrain issuance, the US could offer the world as much currency as the world needed.

This has some interesting side effects. Historically, reserve currency status correlates with GDP, but more strongly with trade and with the size of a country's financial system. The US is a big player in global trade, but its share of global trade is actually smaller than its share of GDP (23% vs 26%). But the American financial system is so sprawling, and the dollar so important globally, that in effect every financial system that isn't actively under US sanctions or that doesn't have significant barriers to capital mobility is part of the US system.

A big financial system makes reserve currency status durable, as the example of the UK illustrates. The US had a bigger economy than the UK by the turn of the twentieth century, but a disproportionate share of global trade was priced in sterling, and there were plenty of global borrowers who'd tapped liquid markets in London and still needed to source pounds when they paid down debt. And because there were so many pound-denominated bonds outstanding, there was good infrastructure for borrowing against them and trading them, so it still made sense for borrowers to use pounds.

This effect is easier to describe today, because we have more data and more terminology. If there are more investors who want to denominate their wealth in US assets, it has several effects:

1. These assets become more liquid, and worth more.

2. At a given valuation, they have better risk-adjusted returns—you can be more confident that you can borrow against them, trade in and out rapidly, use derivatives to hedge, etc.

3. At an earlier stage of a company's existence, when it's a net consumer of capital and is mostly funded through venture capital or other non-publicly traded assets, there's a much stronger incentive for VCs to move money into the most promising companies, because they'll have bigger IPOs.

4. If talent is mobile, too, this ends up being a subsidy for skilled immigration—while it's a big hassle to move to the US from India or France or Brazil or wherever, it has an enormous payoff, so the US ends up with a disproportionate share of the right tail of the talent distribution.

In a way, the US economy has expanded the idea of a "reserve asset" from just the currency and short-term government obligations to the entire civilization. The US is the global backstop when there's either an economic or geopolitical crisis, and this means the US is more densely-networked with the rest of the world, which raises both the ability and need to perform that stabilizing function.

As the UK example illustrates, this is a hard equilibrium to break. Even if the US loses share of global economic activity, and even starts to lose share of the financial system, there's still a lot of dollar debt outstanding, and that means that there are still lots of companies that transact in dollars, which mean that for many of them, dollar debt is still a good idea. Since the US is a disproportionate share of global consumption relative to GDP—that gap being the only way to supply the world with the dollar assets it demands—economic shocks to the US tend to produce worse economic shocks to the rest of the world, leading to dollar scarcity and an even more US-centric financial system.

It could break eventually, and it would be a painful process if that happened—an America that consumes a little less than it produces in order to pay a crushing debt burden is simply a less fun place to be, and also one that wouldn't have nearly as robust a financial system and would thus miss out on capital allocation-driven growth. It would be in some ways a saner system—it's always been a bit peculiar that American consumption is indirectly subsidized by high savings rates in third-world countries—but most economic actors have incentives to keep the system going rather than to take it apart.

The Diff aims to cover the dollar's reserve currency status in the same way that a newspaper for fish would talk about water. It's so ubiquitous that you'd be forgiven for never thinking about it, but if you do think about it, you'll have a better understanding of the environment you're in. So we've looked at:

• Reserve currencies as Giffen Goods ($)

• How China might try to make its own reserve currency ($), and why that probably won't work

• Why a sudden increase in tariffs threatens, but doesn't end, the dollar's status ($)

• Volatility in a financialized economy ($)

• And Part 1 ($) and part 2 of a series on emerging market banking and why it's so challenging.

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A decade before the financial crisis, we got a nice little preview of what could happen if a massively levered fixed-income trade backed mostly by short-term wholesale funding unwound. Long-Term Capital Management was a $4.7bn fund with a balance sheet that peaked at ~$125bn, and collapsed over the course of the summer of 1998, losing almost all of its' investors' money.¹ From a narrative perspective, you couldn't ask for a better story: the fund was founded by John Meriwether, who'd previously run the Salomon Brothers fixed income arbitrage business. Meriwether was the star of the opening anecdote in Liar's Poker, in which Salomon's CEO challenges him to a single hand of liar's poker (the game), for $1m. Meriwether promptly demands $10m, and the CEO folds. So you could tell a great story about hubris there. You could also tell a story about overconfident nerds (they had plenty of PhDs on staff, including Myron Scholes and Robert Merton, co-inventors of the Black-Scholes model of options pricing. And their trades and approach had the sort of manipulating-Greek-letters approach that drives more traditional investors up the wall.

So the collapse got plenty of coverage. Michael Lewis wrote a lengthy piece and Roger Lowenstein wrote a book. These both adopt a fairly moralizing tone, with Lewis focused more on the ruthlessness of other firms, which bet against LTCM, and Lowenstein incensed at all the complicated quantitative strategies they were running and the leverage they used to execute them. Lowenstein thinks they should go back to basics and read some Benjamin Graham. Graham was the dean of value investing² , but before that he was a precocious enough college student that before he finished his undergraduate degree at Columbia, the university told him he was welcome to stick around and teach math, English, or philosophy. But Graham himself also did some derivatives trading, including convertible arbitrage; he didn't turn his nose up at mathematically complex trades (he also wouldn't have been intimidated by the notation used to formalize models today, given that he could read Greek).

That narrative is broadly correct, but it's important to understand exactly what LTCM was, and the nature of the risks that killed them. There's a fiddly technical sense in which leverage reduces risk: you'll never miss a loan payment if you don't have any loans. But this is true in the same sense that never committing to anything means you'll never disappoint everyone: it's true but implies a limited existence, and also artificially flattens distinctions between useless and useful obligations. For leverage, too, the raw numbers are only relevant in light of what LTCM was levering up to do. 10:1 leverage for a trade like the futures basis trade, i.e. patiently buying relatively illiquid treasury bonds, and shorting liquid and higher-priced futures against them, is a lot safer than 2:1 leverage trading equity indices or crude oil.

And most of LTCM's trades were very much in the mold of buying one thing and shorting something else that was very similar, but not quite identical. They were trading different classes of the same stock, like Royal Dutch against Shell. They would bet on the convergence between high-interest borrowers in peripheral Europe, and hedged with short positions in German bunds. This wasn't a purely quantitative bet on correlations: it had a macro justification in that Europe was converging on a single currency and would have more consistent fiscal policy in addition to a single monetary policy so even if those bonds weren't identical, they were getting a lot more identical.

They did use plenty of fancy modeling to decide how big these bets would be, but they weren't treating those models as sacred. In fact, Victor Haghani writes in The Missing Billionaires that the firm sent a memo to its investors in October 1994 telling them that the biggest losses they'd expect were, in fact, bigger than what the models said.³ And their general approach was to avoid making any one massive bet; they were diversified across geographies, currencies, and asset classes, hedged every risk they didn't feel they were being paid appropriately to take, and tested against historically extreme downside scenarios.

But these backtests assumed the wrong constants. A levered market participant needs to negotiate leverage with its counterparties, and the counterparties have some flexibility to demand additional capital when trades start to go badly. LTCM didn't want to leak the exact trades it was making, so it would often execute one leg of a trade with one counterparty and another leg with a different counterparty. These were hedged trades, so in general when one of them was making money the other was losing, and even if the counterparty for the losing trade demanded more margin, they'd have roughly that much additional margin free from the other leg of the trade.

All of this was very sensible, even elegant, when the market was stable. And they ended up with a perverse kind of bad luck: 1997 marked the beginning of the East Asian Financial Crisis. This is the kind of disruptive market event that makes mean-reversion trades of the kind LTCM did go haywire. But they ended the year up 17%! If a good year means 40%+ performance, and a bad year means performance in the teens, it's easy to think that you've solved the market. But then, in 1998, there was yet another financial crisis, this time focused on Russia, which had defaulted on its debt. LTCM didn't have any direct exposure to Russia, but any time something weird happens, investors rush for safe, liquid assets. In other words, they sell the kinds of things LTCM owned (less-liquid bonds from less reliable credits) and buy the assets LTCM used to hedge (liquid and safe ones).

And the margin impact was uneven: they were being asked to put up much more collateral for the losses than they were given credit for from whichever legs of trades still had gains. If they'd had a bigger balance sheet, they would have been able to weather this, but they'd returned about $1.7bn in equity to their existing investors, in order to lever up the rest.

So they found themselves in a position where:

1. They were distressed, because trades were moving against them.

2. Counterparties could see this, because they were shrinking their exposure everywhere, even in trades where prices were pretty attractive.

3. Every counterparty had only part of the picture, but could trade on that part.

4. It was crash season. Presumably, at many of their counterparties the senior people were on vacation and, rather than ruin their vacation, just told underlings to cut exposure.

If you've lent someone money to make some trade (short bunds, for example), and that trade starts going bad, you can ask your counterparty for more collateral. But you can also hedge the risk that they won't be able to provide it, by taking the opposite side of the trade. And if everyone does this, the market moves in exactly the opposite direction that the models say it will.

If someone had read a few of the retrospectives on LTCM right after it happened, and then fell into a coma and woke up in the present day, there's a lot they'd find confusing, but they'd have the impression that we've learned absolutely nothing from LTCM. There are still massively levered hedge funds, and they're still doing some of the same trades LTCM did. In fact, a big one for many funds is the treasury basis trade, a classic case of a bet that goes terribly whenever markets are volatile.

Part of what these modern funds do differently is that they're much more diversified. Almost everything LTCM bet on was, fundamentally, a bet on mean-reversion. Sometimes, these trades had an expiration date—when your short futures position matures, you either roll it over or deliver the bonds you own and close it out. But some of them just bet that, at some unspecified point in the future, the world would get more normal. When a hedge fund with big exposures to equities, as well as directional commodity and macro bets adds in a few mean-reversion trades, that's defensible—they're also betting against mean-reversion elsewhere. And these funds tend to pay close attention to the liability side of their balance sheet: the more predictable portfolio managers' performance is, the more it affects outcomes if they can execute trades cheaply and efficiently, have a low cost of funding, and, critically, can keep their funding when the market goes crazy.

LTCM was ultimately bailed out, and the buyers got a good deal: they were essentially buying a very safe portfolio at 97 cents on the dollar, of which 96+ cents consisted of credit they'd already collectively extended to the firm, with the last fractional penny as equity. Summer gave way to fall, the VIX went from the 40s back to the 20s, and life went on. There was a serious risk that all the other banks running similar trades to LTCM would be dragged down with it, and that the entire financial system would be destabilized, but it turned out that the banks were able to work together, mostly, to prevent that. (Bear declined to assist in the bailout, which is one reason they were the first to fail in 2008.) So the biggest incorrect lesson of LTCM was the one we learned almost exactly a decade later: when a sufficiently big levered investor gets into trouble, they won't necessarily get rescued in time.

LTCM was an important moment in financial history—the business itself was an evolutionary dead-end, but pointed to some more viable possibilities in the future. We’ve talked about it many times:

• One piece on idea velocity notes that even though LTCM ran a diversified set of strategies, its profits were more skewed, as is the case with modern multi-strategy funds.

• We’ve speculated about what would happen if pod shops blew up.

• 1998, the year of LTCM’s collapse, was a turning point in financial history.

• You can still find some of LTCM’s marketing materials floating around.

• Don’t fear the basis trade ($).

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Prices of stocks and bonds have an admirable clarity to them, because they're denominated in the terms of their payoff. If a company announces something and the stock price goes up 20%, you can usually assume that the consensus among marginal price-setters is that the present value of that company's cash flow is 20% higher per share than it was before.

But when oil futures go up 20%, it doesn't imply that. The futures price that gets quoted is the next month's future, but financial media and traders have slightly different conventions here: as each month's contracts approach expiration, traders will typically roll them forward to the next month, so at some point the next contract to expire is not the most liquid, and thus not the reference point for trading.

But you can look further out and see futures contracts for other months. Which, in a perfect world, would mean that you can use futures markets to predict where the market thinks oil will trade in any given month. But it isn't so simple because, again, these are contracts that at least nominally entitle the buyer to take delivery of 1,000 barrels of light sweet intermediate crude at Cushing, Oklahoma. It's a physical place, on a map, and if you actually look at such a map, you'll notice something odd: there's a regular city, and then to the south there are a bunch of neatly-arranged white circles. Those are oil storage tanks. And part of what the price of May, June, July, etc. futures is is that if you sell a futures contract for delivery then, and hedge your risk by buying physical oil, you're paying to move it or at least to store it.

So futures do provide a higher-dimension view of prices than purely financial assets, albeit a warped one. But this also means that they're incredibly easy to misread. At the start of March, April WTI oil futures traded at $70, hit a high of almost $120 later, and are currently in the high 80s. But go six months out, to October futures, and you see a product that started the month at $64, spiked to the low 80s, and is now back to the low 70s. The direction is roughly the same, which makes sense, and they're telling a story about higher and more volatile prices. But people trading October futures aren't adjusting prices 1:1 with the front month.

The consistent story you can tell is:

1. The attack on Iran, and the risk of long-term disruption to the Strait of Hormuz, has made oil a little scarcer. If you can lock down commitments now, you might be more inclined to do so.

2. That said, the front month is pricing in the difficulty of getting oil right now, not some long-term shift.

One of the things that makes this messy is that higher uncertainty about future oil supply increases the option value of having physical oil somewhere. If prices get really crazy—if people who were shorting futures are just one barrel short, or if there isn't enough oil to keep refineries running 24/7—it's valuable to have oil physically available. And that naturally pushes up the equilibrium rent for oil storage, which means that someone who's arbitraging by selling futures against oil they own has to bake in a higher rent. So the more valuable optionality of having oil physically close to where it gets delivered puts upward pressure on futures prices. They're pricing in volatility, as well as price level. But someone who's been doing that trade before is probably cashing in on it now, which is basically pulling supply forward.

All of this means that it's confusing to talk about "the price of oil." Do you mean the invisible long-term equilibrium price, or do you mean a current price distorted by lots of factors the market believes are temporary?

It should be a confusing question, because it's an ill-posed one. The current price of oil is always the temporary price of oil right now, and the future price of oil is either unknowable or at least something you have to pay for certainty about.

If you see situations like this, where the same product has different prices for different months of delivery, you might think of a trade that doesn't require you to store 40,000-odd gallons of fluid somewhere: why not sell an expensive near-term contract, and hedge with a cheaper contract further out? Maybe you buy August futures at $79, sell July at $82, and collect that $3 spread? There's a name for doing this trade in general: it's a calendar spread. In some markets, particular calendar spreads have another name: the Widowmaker Spread. This usually refers to the gap between March and April natural gas contracts, i.e. the gap between natural gas in the last high net demand month for heating and in the first month where it's probably net stored. Natural gas consumption is more seasonal than production, so that point in the year is when you switch from betting on the specifics of the weather—how many heating degree days, any local shortages, etc.—to a longer-term fundamental bet on the value of a molecule of methane. It's a widowmaker not just because it's volatile, but because traders have to be aware of whether they're trading based on immediate supply and demand fundamentals or longer-term sentiment.

Sometimes, stocks and bonds can have the same delivery-related kinks commodities do. A stock has a price, but short-selling is a mechanism which, like commodity futures, assumes that something will be delivered to a counterparty. A stock can be objectively expensive, but also get sufficiently hard to borrow that the price starts to rise, forcing more shorts to cover and ratcheting that effect up.

One place where this has happened a lot in the last few years is in de-SPAC transactions. Shareholders in a SPAC can redeem their stock for cash if they don't like a deal, and if the deal looks worse when it was agreed to than when it was executed, many of them will. They can get $10/share (plus interest), and they might think the business the SPAC is merging into is only worth, say, $5/share. Redeeming is a good choice in that case, and the price will tend to hug the redemption value. And you'd think that, once shareholders can't get $10/share in cash, there won't be any support for the stock and it'll collapse. Instead, high redemptions can lead to a shortage of shares—if you're right on the fundamentals, but nobody will lend you stock even if you're willing to pay 500% or more, your broker will force you to buy.

That explains otherwise odd things like the long-term chart of Grindr, which starts with an epoch where it's basically an illiquid money-market account that sometimes trades at a small premium, followed by a spike from just over $10 to the 30s when the deal closed, most SPAC shareholders redeemed, and there weren't enough shares for everyone who wanted to short. Short sellers who held on did okay; it had dropped to under $5 within two months of the SPAC deal closing (though hopefully they covered after, because it's back up above the initial SPAC price).

In a way, equities and bonds are misleading asset classes. For most of the products we own and use, there isn't a meaningful market for buying them in the future, and there isn't a good way to talk about the price other than by asking what you could pay for it right now and what tradeoffs you'd make if you didn't buy it now because you wanted to buy it later. Oil in particular exhibits extreme near-term volatility when there are disruptions to producing, moving, or refining it, because there are so many parts of the economy that need a continuous stream of oil to keep working. The front-month futures price is very real in the sense that it reflects the cost of the oil that's getting used right now, but its relationship to long-term prices is noisier than someone with an equities mindset is used to.

We’ve covered commodities and the many determinants of their pricing dynamics in The Diff:

• One thing the physical reality of oil does is it makes the US government uniquely good at betting against oil volatility ($).

• Oil demand may be more flexible than it once was ($).

• How do energy companies allocate capital when they don’t know how long the world will want oil ($)?

• Fracking is a big deal

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"Inequality" is a popular political issue, but it's a paradoxical one. There are many kinds that are debatable—most countries have substantial athletic inequality, beauty inequality, music appreciation inequality, etc., and we mostly don't consider that a problem. To the extent that we do, we care about raising the floor rather than lowering the ceiling: public health campaigns focus on things like vaccination and hand-washing, not snipping athletes' ACLs so they hobble as fast as we walk. Power inequality is hard to measure, but it certainly exists, and one form it takes is that politicians can direct people to do things they wouldn't otherwise do (this is pretty much their job, and it's an important one). So when a politician wants to expand the scope of the government as part of a plan to combat inequality, they're also saying that we should aggravate power inequality in order to reduce wealth inequality.

(There is a reason I'm not a professional speechwriter.)

But even that kind of inequality starts to get sort of slippery. If you compare the top to bottom quintiles, the rich side has about 18.3x the wealth of the poorest. They have about 14.4x the income. But they only have 4.3x the consumption. In other words, you can eliminate about three quarters of inequality if you switch from measuring how much people could theoretically consume in the future to measuring how much they consume right now. 4.3x is still a big deal; a household spending $50k a year lives very differently than one spending $215k. But that gap illustrates several reasons that wealth inequality is not quite what it looks like.

For one thing, wealth is the cumulative total of savings (plus transfers) and capital appreciation. A natural financial lifecycle for a household is to start out with roughly zero, then go negative to spend money for education, claw their way back a bit, then go into debt to buy a house.¹ So measured income inequality is partly a measure of growing lifespans and a partially-privatized retirement system. And, if you're tracking wealth in accounting terms, you're also missing intangibles. Some of these are hard to live on—you're not going to retire on your pleasant memories of Ibiza—but it's fair to say that an annuity is a financial asset with a readily-determined value, and one of those, Social Security, represents about half of the wealth of the bottom 90% of the wealth distribution. And it's gone up a lot over time, partly because there are more earners, and partly because its value is a function of interest rates and lifespans. When rates decline, the present value of a fixed-income asset goes up. This is very easy to see when market multiples expand as rates decline, making the paper wealth of the richest much higher. But someone with a government-guaranteed annuity probably has more duration exposure than the typical equity-owning rich person. So, any assessment of changes in wealth inequality that excludes Social Security and starts any time before 2007 is systematically excluding the biggest source of gains for the majority of Americans.

And this highlights yet another point. The rich have more volatile net worths. Social Security has cost-of-living adjustments, so in 2022 the nominal value had to be adjusted up in response to inflation. But equities do not have a direct means-testing component; when inflation spikes and rates go up, valuations tend to come down. And individual equities are much more volatile than indices; plenty of people have gone from multibillionaire status to mere centimillionairehood and at least one person has had great luck recently in the other direction: going from mere multibillionaire status to centibillionairehood in just a handful of years

Taxes and transfer payments complicate things further. They chip away a bit at inequality (the top 1% get about 16% of pretax income and 13% of after-tax income), but not in a way that materially changes the picture. What really matters is that people vary in how motivated they are by money compared to other things. They vary in their baseline consumption needs, too; some 22-year-olds really want to be investment bankers, others really want to be poets, and many of the would-be poets are willing to forgo a banker lifestyle to pursue their passion. For other people, it's fuzzier, but it's not unheard of for someone to turn down a promotion that means working longer hours, or to stay in a city they like even if they could move somewhere else and get a raise. If that's true, and the government also uses economic redistribution to flatten consumption inequality, then higher income inequality is partly a measure of how well it does this. The easiest thought experiment here is to ask whether or not you'd quit your job (either to retire or pursue a passion project) if you were given an annuity that provided you with the same real income. Certainly, many people would. But not everyone! And that not-everyone cohort would now be providing relatively scarce labor in an economy whose consumption demand was undiminished. So, every time public benefits hit a new threshold, some people at that level see their income drop (it's quitting time! Or at least finally-write-that-novel time) and others see it grow ("Bob has decided to retire in order to focus on oil painting; you up for some overtime?").

If you ran the world's most successful social democracy, where you combined generous transfer payments with a flexible labor market and financial system, you'd expect extreme income inequality: you'd have a smaller labor force, because people wouldn't work so much; you'd also have lower wages for jobs that pay mostly in prestige—many middle-class jobs would be easier to get, and pay pretty well, but art, sports, and academia would all be much more brutally competitive, since so many people opt out of those fields for financial reasons.² Meanwhile, the people who are really money-motivated would have plenty of end consumers for their output, and would thus get a lot richer.

Since consumption inequality is much lower than wealth inequality, one of the important questions to ask is: in what ways does wealth matter? If we gave everyone in the US a bank account with $1m, but with the limitation that they could never withdraw money from it, borrow against it, or sell it to anybody else, we'd theoretically reduce wealth inequality while having zero real-world effect. And if a rich person has a sufficiently low marginal propensity to consume, much of their wealth is, from a consumption standpoint, equivalent to that. Someone else's wealth makes you poorer if they compete with you for access to scarce goods and services—the existence of rich people makes you poorer if one rich person with three full-time nannies is the reason you can't find a babysitter one night a month. But if that rich person gets a kick out of driving a used car and shopping at Target, it doesn't affect you if they're a millionaire or a billionaire.

Or, at least, it affects you in an indirect way. Having wealth in the form of equity tends to correlate with having control, and it's nonlinear; someone who owns 25% of a company has a lot more than 25 times as much influence as someone who owns 1%. If the company is public, and doesn't have other big shareholders, 25% is de facto control unless they really mismanage things. So high wealth inequality probably means that more of the marginal income in a given country goes to investment rather than consumption. That investment eventually yields consumption—assets only produce a return if there's someone to buy their output—but the cost of getting more of it is getting it later. One could imagine a world where the assets of the wealthy compound indefinitely and they end up owning everything, but they don't compound indefinitely: one symptom of actually-excessive wealth inequality is that investments' returns decline, because economic output is being invested and not consumed, so the pool of returns-producing consumption shrinks while there's more money chasing investments. (This also means that the wealth is much more volatile when inequality is high: fixed-income assets are more rates-sensitive, and high-multiple stocks tend to react more strongly to incremental news flow.)

If your wealth hasn't reached escape velocity by the time this happens, it's not exactly fun sitting around in a low-growth, low-return environment where it takes forever to save for a down payment, waiting for things to mean-revert. But low rates also mean that investments that pay out further in the future are more worthwhile today, and encourages R&D to find new useful products. But a maximum-redistribution economy enjoys its own kind of stasis. The problem there isn't that rich people will simply go on strike, it's that higher taxes slow the pace at which money flows from bad companies to good ones, and that it also blunts the difference between bad managers and good ones. The case against inequality is pessimistic at both ends: high wealth inequality is a problem if disruptive technologies never arrive, and if the endpoint of the economy is a handful of people cashing in the treasury bonds they bought for a 1% return and rolling over the proceeds into bonds that yield 0.5%. In the maximum equality scenario, you have to hope that the mix of assets and companies we have is the right one, because it isn't going to change any time soon. In lower-growth economies, this leads to a different kind of volatility: they tend to revert to more egalitarian economies through some mix of total war, revolution, state collapse, or pandemics.

Meanwhile, back in the real world, we're pretty far from either pole. If there's a country that's achieved maximum inequality and zero-growth stasis, it's North Korea. But the rich world does, from time to time, have debates over policies to mitigate inequality. As noted above, these are really policies to shift inequality, moving power from rich people to legislators. That can be good or bad, depending on who goes into business versus politics and who succeeds at either. But one thing to note is that if you're worried about entrenched power, you should probably skew things towards wanting higher wealth inequality: last year's Forbes 400 had 14 new entrants and 22 returning members, for an annual turnover of 9%. Meanwhile, in the last election cycle, 97% of incumbents who ran were reelected, and even if you include people who declined to run, the annualized turnover rate was just 7%. The half-life of political power is longer than the half-life of financial power, and even that understates things, since seniority affects how much authority individual legislators have.

There's probably a level of wealth turnover and industry shifts that the average person would find unappealing even if it made them better-off in the long run. The market-clearing wage is lower for jobs with great job security, and a lot higher for the ones where you could be fired at any time. The tradeoffs are real, and different electorates can reasonably prefer different arrangements. Whatever arrangements they end up making.

Inequality and its cycles drive some interesting results, which we’ve covered in The Diff. For example, see:

• A solution in search of a problem is a low-rates phenomenon.

• The case for independent fiscal policy ($). 

• Why buy-borrow-die ($) increases intergenerational mobility but also whoever turns out to be richest much richer.

• We’ve considered the wealth-creation speed limit.

• Technological shifts and well-exploited luck can create a temporary cohort of very rich people ($).

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There are a few trades that are good enough to make it into a book, like taking the other side of Amaranth's natural gas trade in 2006 or betting on persistently low rates after the crisis.¹ Going long vol ahead of a panic even has an airport novel. But the trade that spawned multiple books, and a movie, was buying credit default swaps on subprime mortgage-backed collateralized debt obligations. (Having established a sizable short position in polysyllabic latinate jargon, I'll call these CDSs on CDOs from here on.)

This was, in dollar terms, exactly what the best book about it says on the tin: The Greatest Trade Ever. But it's really several trades in one. You have the directional bet that housing is overvalued and is going to go down. There's the asset class expressing the bet—it's hard to short individual houses, homebuilders are cyclicals with low peak-of-cycle P/Es so you don't get much juice there, so you'd want to bet against mortgages. And conveniently, credit default swaps had grown from a kind of financially-engineered diplomacy—banks sometimes regretted specific loans they'd made, but wanted to preserve a counterparty relationship, so they'd buy insurance on the loan from another bank—basically the gap between what the loan's spread and the CDS cost was an annual subscription to remaining buddies with management. And, at first separately, bankers were packaging together piles of loans into CDOs.

The way to think about a CDO is that it's a bank's balance sheet, just without a bank attached. What you do is collect a large number of loans—similar enough that your product fits in some coherent category, but diversified enough that, importantly, they don't all default at once. And then you sell debt against it. Suppose your CDO is composed of loans that would, individually, be rated around BBB, so an expected one-year default rate of around 0.5%. And suppose their defaults are not especially correlated. If you have $100m of them, you might end up selling $70m worth of AAA-rated paper, all backed by whichever 70% of them turn out to default last. You'd sell some smaller slices at other ratings, but you run into a bit of a problem there: at some point, the slice you have left is basically guaranteed to default, because you're asking "what's the performance of the worst loan in a portfolio where they're all pretty mediocre?" and the answer is that it returns roughly zero. So what you do instead is create an equity tranche, which gets whatever is left over if every other tranche is paid off. Or, equivalently, the tranche that takes the first loss any time there's a default.² That slice is pretty small; in this CDO prospectus from 2007, that piece is 4% of the $1.5bn structure, while 68% is AAA-rated or "Super-Senior," designed to be even safer than AAA.

If you put together CDOs and CDSs, you get a fun little opportunity: the interest paid by a given tranche, or the cost of buying insurance against it, implicitly sketch out the market's claim of how well the assets are correlated. If the correlation is exactly zero, that CDO is actually making the world a wealthier place: it's taking a bunch of different products that would have uncertain returns, and instead delivering investors their blended return, smoothing out the variance. And those investors get to choose exactly where on the risk/reward curve they want to sit. If correlations are 1, every tranche performs identically; either everything pays off, and a residual goes to the equity tranche, or everyone, from the equity to the super-senior gets wiped out, so they all deserve the same yield.

Both of these are extremes, but they illustrate something important: if correlations between defaults within a given CDO are higher than the market expects, the most senior tranche is less safe than it looks—but the equity slice is actually more safe than it looks. Correlated defaults can mean unexpectedly high default rates, but also unexpectedly low ones.

If you looked at historical data about mortgages, you got a certain view of how likely it was that a default in Florida predicted one in Maine. But if you considered how fast the subprime mortgage business was scaling, how fast some metro areas were growing, how dependent these economies were on homebuilding, etc., you could easily come to the view that local housing markets were now more correlated, and more driven by the availability of credit. That pushes correlations up, both for upside and downside scenarios: the good times stay good, and the most levered and thus least-creditworthy borrowers are the biggest beneficiaries when more credit availability makes their homes worth more and gives them the opportunity to refinance. And, of course, if it gets hard to get a mortgage, suddenly home prices will collapse just about everywhere, at the same time.

This setup means that you can structure the whole trade so that you're earning positive carry: your equity slices are paying you more than you're spending on insurance against the super-safe ones. This solves for a problem that some of the subprime short trade narratives underrate: figuring out the trade involved predicting the right trend and finding a good instrument to express that view, but maximizing the size of the trade at the optimal time was really an investor relations problem. John Paulson solved this one by creating a strategy-specific fund, so his investors were true believers. Michael Burry solved this with what his investors probably thought was a classic case of macro tourism—most of the time, "good value investor" plus "macro view expressed through complex derivatives" equals "world's best counterparty."³

If you're making a bet like this, you're making a meta-bet on timing. Plenty of people talked about housing being a bubble; by 2005, you could watch Flip This House or Flip That House. Being early means bleeding money for a while, and it's very hard for investors to underwrite a negative-carry strategy like this. So the dilemma for fund managers was that if they made the bet too late, they'd miss out, but if they made it too early, they might get preemptively cashed out. A positive-carry version of the trade solves for career risk. Most of the time, it pays a little, and occasionally it pays a huge amount.

It's probably the case that every bubble offers the economic equivalent of this, i.e. there are ways to bet that the whole thing doesn't pan out, but that if it does it's better than anyone expected. But only rarely is this something you can get in a convenient capital markets package.

This trade was truly one-of-a-kind. But the ideas behind it aren't! In The Diff, we've written about:

• The case for a concetrated portfolio.

• A breakdown of CoreWeave, where Magnetar has played a major role throughout its capital structure ($).

• How arbitrage is a great training ground for investors.

• Sometimes, structuring a trade means being able to pay in cash or GPU capacity, at many orders of magnitude ($).

• Another fun trade: the tale of Venture Global, an LNG company with artfully-crafted trades ($).

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