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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The labor market is like the pricing page for a certain category of SaaS product. For legible everyday needs, there's usually a pretty standard price list and a very standard bundle of benefits. Terms get more complex for both sides as the price goes up. And, past a certain point of complexity, the labor market says the equivalent of "Call for a consultation and we'll see if it's appropriate to give you a quote."

There are a few axes of complexity here. The simplest is that for some jobs, workers are price-takers—if a job requires few specific skills (fast food, retail, ride-sharing), it competes with all of the other jobs that require that skillset. There's still some variation; a 9-5 shift is easier to hire for than a graveyard shift, the wage differential between making fast food and delivering it depends on what premium workers put on setting their own schedule, etc. But for the most part, it's a big labor market where employers have a vested interest in making workers as undifferentiated as possible.

Oddly enough, this kind of hiring market also applies to skilled workers who go through a standardized training regimen before they enter the labor market. As far as labor's bargaining power is concerned, a freshly-minted Harvard JD has as much negotiating leverage with Latham & Watkins as a new high school graduate has with Walmart.

But if the job is in sales, a new dynamic kicks in. It's very straightforward to measure a salesperson's productivity, at least most of the time: just count up the revenue they generate. There can be some variance within this, depending on the product—if there's room for price discrimination, there's also a need for guardrails around discounting, lest one aggressive salesperson kill off a company's pricing power. But in general, a sales team can set their compensation so that pay is competitive for the producers and the weaker salespeople can't really afford to stick around.

But even that's just a rough description of how salespeople get compensated. The mechanics of paying someone a percentage of revenue from contracts they sign, perhaps with cutoffs, premiums, etc., are part of the formula, but the other piece is who gets access to which potential customers. There are some products that get sold to, and are worth selling to, a range of customers whose size varies by orders of magnitude. In that situation, being the salesperson in charge of closing a few Fortune 50 deals versus being yet another person who spends all day in Zoom calls with small business owners has a bigger economic impact than the exact choice of commission structure.

For less measurable jobs, there's another easy hack for aligning incentives: just pay people partly in equity. This is a noisy approach, but has some appealing characteristics: it means that they're paid based on how the whole company does, which reduces their incentive to benefit their team at someone else's expense. And it means that everyone has at least some financial incentive to police one another's behavior. It also fixes an incentive-misalignment problem: founders and investors might want to sell their company, but for the average employee that means risking layoffs. But if that average employee can also cash out with much more than they'd lose from being out of work for a bit, it softens the blow. This effect is a lot stronger early on, when people have points of equity rather than fractions of a basis point, but the tradition of equity compensation tends to persist long after the point where the average person getting equity compensation has the means to influence the stock price enough to matter financially.

In some jobs, there's a natural way to capture exact P&L responsibility. If you run a Chipotle, or a pod at a hedge fund that trades restaurants, the business you manage will produce a nice, clean profit and loss number at the end of every year. And it seems incentive-aligned to just pay you a portion of it. After all, that's the logic behind funds charging carried interest in the first place: 20% is equivalent to splitting the equity in a business 80/20 in favor of the investor and giving them a 1x liquidation preference.

But in both cases, the model still requires some guardrails: Chipotle doesn't want to reward managers who bump gross margin up a few basis points by replacing some of the rice with sawdust, and a hedge fund doesn't want to pay for the equivalent (writing out-of-the-money puts or taking equivalently asymmetric-downside risks).

In general, paying someone a standardized cut of the profits they produce, without many guidelines, is equivalent to arguing that every stock ought to trade at the same P/E ratio. An ideal compensation scheme would reward someone for longer-term behavior and penalize decisions that borrow future profits at a ruinous interest rate. But actually putting that into practice is painful, so instead the goal is to minimize manager discretion for any decision that could have this kind of payoff, in order to isolate the results of pure skill.

But that ends up creating a paradoxical problem: when talent is legible and measurable, and it directly predicts profits, suddenly those workers have all the bargaining power! They know exactly what they're worth, and employers are competing mostly on the basis of how small a cut of the upside they're willing to take.

But there are solutions to that, too; when workers have fungible skills that could be deployed at multiple companies doing the same kind of work, their employers want to optimize compensation around retention—paying them more every year is perfectly fine if it means they'll actually stick around year after year, over which time they'll presumably get better at their job and better at working with their colleagues. So liquid markets in legible talent lead to illiquidity and opacity: hedge funds want it to be expensive for their best portfolio managers to quit, and they also want these managers to always wonder how much money the fund is really making from them. Big funds will analyze the trades portfolio managers make, come up with a model for which ones are the best ones, and size them up (or hedge out likely duds). So even if someone knows their P&L payout to the tenth of a percentage point, they don't know whether that P&L is 100% of the value they provide to their employer, or a small fraction of it. Long noncompetes, deferred compensation, and clawback provisions all add some artificial transaction costs to the decision to leave, with the ultimate goal being that the best-paid people don't quite know what they're worth, and that it's expensive for them to find out.

Complicated and opaque pay structures affect a tiny fraction of the workforce, because most jobs' outputs are harder to measure and because most of the time, it isn't worth it. Sometimes, they seem excessively elaborate. But hiring a fund manager who produces $50m/year in alpha is basically acquiring a business that produces $50m in EBITDA, and a merger at that scale probably requires a lengthy and detailed contract and a lot of give-and-take around incentives. As the amount of data thrown off by workers rises, and as tools for analyzing data get better, you should expect these norms to work their way down to the rest of the labor market. It'll be a while before we live in a corporate panopticon where Starbucks is monitoring the microexpressions of every customer in order to provide a retention bonus to the most personable baristas. But that's the direction to bet on.

The varieties of compensation models come up often in The Diff. Some highlights:

• The economics of alpha capture ($), and AI deployment as alpha capture for everyone ($).

• You and your investment research.

• What’s happening in tech compensation ($).

• The history of options as a compensation tool.

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The financial services industry is built to intermediate. It matches people who need capital with the ones who have it and are looking for a return, or, in the case of zero-sum derivatives, it matches people who want to hedge a risk with people who either want to take that risk or have a corresponding risk to hedge. This isn't a problem in some cases, because each side's demand is pretty obvious: it's very easy to see why a neocloud might borrow money to buy GPUs, and also easy to see that the lender would appreciate the interest they earn. But in other cases, the risk is more one-sided.

Consider oil futures and mortgages, two products that are a disproportionate share of their broader asset class (oil makes up ~25% of commodities futures and mortgages ~20% of the bond market). The global market for oil continuously clears, in the sense that global storage capacity is equal to about a month and a half of production. So, in one sense, there's just as much of an aggregate incentive to hedge against oil price declines as there is to hedge against increases. You could theoretically buy oil futures to hedge against the cost of your commute, for example, and your indirect counterparty might be an oil company that's implicitly hedging against the risk that you'll use less. But if you try to do this practically, you run into problems: trading oil futures means hedging in thousand-barrel increments, which is a lot more oil than you personally consume. (At ~20 gallons of gasoline per barrel, someone driving a car that gets 27 miles per gallon is hedging a little over half a million miles of driving. Also, they're long diesel and jet fuel.) And even if you could buy a miniature contract that could hedge your economic risk, you now have a mark-to-market mismatch: if oil prices plunge, you'll get a margin call and need to put up more cash, even though your net worth hasn't changed because your hedging losses are made up for by your future savings. To keep the hedge, you'd essentially need to borrow against the future increase in disposable income you'd get from hedging.

30-year fixed mortgages with an option to refinance have a similar problem. As a borrower, you know exactly what your future payments will be, and you also know that if they change, it will be because you refinanced and they went down. But a lender is in a more annoying situation: they're buying a fixed-income asset, but unlike the typical case where a 30-year bond is worth more as rates decline, this one has a habit of getting repurchased at 100 cents on the dollar any time it's worth more. Whereas if you take a big immediate loss—you lent a homeowner money in early 2022, when rates were low, and now they're up and your loan is trading at 70 cents on the dollar—it's staying on your balance sheet.

Given that American homeowners retain access to mortgages, the system clearly works, but it raises the question of who wants to take that risk. And the answer is, generally, someone who is running some kind of fixed-income strategy that mostly targets duration risk, and is willing to continuously hedge, rearrange their investments, etc. in response to changes in the interest rate environment. A homeowner could decide that the prepayment option is an interest rates derivative that they, as a regular person, have no business speculating in, and they, too, could hedge it. But they have the same problem as the hypothetical oil trader, just over a longer timeframe. If you hedge the value of the prepayment option, and rates rise instead, your locked-in mortgage with predictable payments over the next thirty years suddenly requires an unpredictable amount of cash right now. And brokers are not especially excited about letting you cross-margin your mortgage liability and your treasury futures position, even if that is theoretically elegant.

There are other kinds of transactions that end up with some sort of leftover risk that nobody particularly wants. In fact, a lot of the industry can be modeled this way:

• Venture debt means lending to some of the most unpredictable companies in the world, and lenders don't like unpredictability much. Depending on the business, they can either cordon off the parts that are good collateral (loans secured by their biggest long-term contracts, for example) or just pair those loans with warrants that pay them for some of the volatility.

• In the CDO boom, there were many buyers who wanted a lift in rates in exchange for a little bit more credit risk, but these structures require an equity tranche that carries the most risk, pays the highest returns, and has the first loss. Sometimes that tranche ended up being owned by hedge funds (especially if those hedge funds used the returns from the equity piece to fund bets against the highest-rated tranches). There was an investor base for credit that yielded tens to hundreds of basis points more than treasuries, but there wasn't as developed a market for something that was halfway between debt and equity, didn't have an active market, and was also hard to analyze.

• Sometimes the residual risk is timing. Even if a market ultimately clears—over time almost every barrel of oil has a buyer and a seller, as does every unit of currency, share of stock etc.—it doesn't always clear instantaneously, and middlemen can earn a spread from trading in exchange for warehousing some of the risk they take.

• The venture studio model is, arguably, a way to warehouse extremely-early-company risk. VCs are reluctant to back a founder who knows they need a cofounder but hasn't found one yet, but an incubator can make that part of their value-add and part of the reason they deserve their equity.

• Professional services companies are at least partly in the business of warehousing talent—Bain, BCG, and McKinsey can all be confident that over the next year, corporate America will encounter many problems that temporarily need the help of 23-year-olds who are wizzes at PowerPoint and Excel (and, increasingly, Claude Code), but they don't want to source that talent on a just-in-time basis. So they buy big batches of talent upfront, and then provide talent liquidity to their customers. But they also run the risk of the talent sitting “on the beach” (underutilized) when things slow down, not dissimilar to a market maker warehousing stock they can’t immediately offload.

• Merger arbitrage, convertible bond arbitrage, and index rebalancing are all strategies that swoop in when a given asset is suddenly subject to a new set of forces. A few months ago, Warner Brothers Discovery was a bet on IP, a successful transition to streaming, and big box office successes. Now it's a bet on the balance sheets of Netflix and Larry Ellison and perhaps the whims of Donald Trump and US antitrust regulators. When a company gets added to or booted from an index, everyone who holds it has the choice of either a) getting up to speed on the latest advances in modeling the impact of these changes on financial flows (and also the latest advances in modeling how other people will model this) or b) they can just assume that the newly-prevailing price is as good an estimate of fair value as they're likely to get.¹

The places where intermediaries warehouse risk illustrate why this can be such a lucrative line of work. They have a classic problem: if they passively take on risk, they end up with peak inventory whenever the selling really gets going, and might have promised more than they can deliver when prices rise. This is yet another lens for understanding why prices are so volatile when markets drop fast: an oil trader's job is to find a buyer who is willing to accept a slightly worse price in exchange for certainty, and a seller who wants the same, and to take the risk that the buyer and seller don't show up at the same time. There's probably a price the seller could offer that would make it worth the buyer's time to go direct, and vice-versa. But that price is pretty far outside the narrow bid/ask spread established by the middlemen. If some of those middlemen have big losing positions they're in a hurry to exit, that warps the market even more. (Of course, not all of them do, and market-makers' profits tend to peak in the aggregate at extremes. But depending on the market, some of them will turn out to have executed a pivot to directional trading that lasts for the remainder of their career. Which is typically measured in weeks to hours.)

Some asset classes have natural holders. Pensions and endowments with predictable financing needs should be taking on the liquidity risk of PE and VC, at least as long as those asset classes can deliver good returns. Households saving for retirement should be cautious about opaque, high-fee vehicles, but will be basically unaffected if their 401(k) temporarily drops 40% when they're still two decades from retirement, especially since that's the cost of stocks delivering higher unlevered returns than other asset classes. For some weird assets, like weather derivatives, the natural holder is just someone looking for an uncorrelated return, so they have the chicken-and-egg problem that they need to be big and liquid enough to fit into an institutional portfolio, but won't get there unless they have counterparties. Whenever there's a new asset class, or a new kind of derivative like a prediction market, it's worth asking: who is the natural counterparty to the natural bettor. And, if that counterparty is just someone who wants to risk capital in order to get a return, where does their return come from and is the other side of the trade willing to pay them indefinitely?

Many pieces in The Diff are implicitly about this intermediation role, and where it goes wrong.

• We've looked in more depth at the question of how individual consumers should hedge ($), including the idea of renting as a natural hedge if you work in your town's biggest industry.

• How central banks monetize land ($).

• We've examined the CLO asset class, and how it does and doesn't pattern-match previous structured products.

• And we've looked at how neoclouds fit into AI financing.

• We've also considered how Affirm creates asset-backed securities that match investor preference by pairing very different kinds of loans ($).

• We’ve asked what happens when asset classes get democratized.

• And don’t miss where prediction markets fit into portfolios.

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Thanks to everyone who sent feedback on last week's experimental post about financial history. We're doing it again!

Academic finance is a strange domain because researchers are working with some of the same datasets as industry practitioners, and are working on the same basic task of figuring out why asset prices move the way they do. But because it's a description of a live, adversarial process, it has some quirks. For example, in most fields if you ran an experiment and found that X was true, and then someone else ran the same experiment a year later and found that X was false, you'd say that this raised serious questions about the validity of the original study. But when the subject is finance, it's the exact opposite! If someone publishes a paper saying that you can make money buying small-cap stocks, or that the embedded option in convertible bonds tends to be mispriced, etc., then a subsequent study finding the same thing reveals that the original study was probably wrong, because if it were right, the arbitrage would have closed.

Often, what these papers actually find is a strategy with hidden, second order constraints: stocks go up at night and are flat during the day on average, for example, but the transaction cost of trading on this anomaly eats the profits. Heavily-shorted stocks do underperform the rest of the market, but the cost to borrow them reflects this. Sometimes, the excess return is an unacknowledged risk premium: in the 80s, there was some applied research in the direction of creating cheap synthetic put options by dynamically hedging (the basic intuition is that you can mimic the payoff of owning put options on an index by shorting index futures as it declines). This was true in a stable market, but in the crash of 1987, the overall market dropped 20.5%, but futures were at one point about 18% lower than the index itself.¹

And sometimes, the reason an anomaly sticks around is a different kind of transaction costs: the kind that are imposed by participants in a price-fixing scheme. There's a 1994 paper from the Journal of Financial Economics noting an odd feature of Nasdaq stocks around that time: market-makers wouldn't quote odd-eighths. For context, since the early days of US trading, stocks have been quoted in eighths, probably reflecting the fact that Spanish dollars, the main currency in early American commerce, were worth eight Spanish reales. Exchanges would later voluntarily switch to quoting sixteenths, before regulators required them to decimalize.² And, peculiarly, Nasdaq market-makers in the early 90s were much more prone to quoting quarters and halves than quoting eighths.

And there was a pretty simple explanation for this, elaborated on in a subsequent publication, this time by the SEC: they were colluding to widen spreads so they'd earn more on trades, and the easiest wide-scale way to do this was to just refuse to quote odd eighths.

In a normal market, this would be hard to pull off. You and I could agree that we'll only quote some stock in ten-cent increments, but if the natural spread is eight cents then a competing market-maker will wedge itself between our quotes and get all the volume for itself. But at the time, the main venue for quoting trades identified the market-maker in question, so it was easy to see who was participating and who wasn't. What market-makers basically did was to spread vague rumors about unprofessional behavior on the part of the non-participants, and also to refuse to spread market-moving rumors to them.

Manual trade execution is a job where information gets transmitted in two forms: by prices, and by people sharing interesting tidbits with one another over the phone. For a market-maker, these rumors are generally about who's selling and how much (if you're a buyer, you care whether the big seller who's pushed the price down this morning is a random fat-finger trader or a big fund exiting a position. You aren't supposed to know the details, but "market color" is a fuzzy concept. In the voice trading era, it was more common for this to leak out directly, and to go to favored counterparties. Today, the same kind of information still exists, but it's in aggregated, anonymized from—prime brokers produce reports on positioning, sentiment, and general market behavior, which puts individual counterparties on a more level playing field.) It's pretty hard to be in the business of passively taking on market risk when your competitors have better access to information than you do; that basically guarantees a winner's curse.

So once this got going, it was a surprisingly durable arrangement. It's hard to tell how it got started, but one plausible mechanism is that market-makers in less liquid stocks always quoted them in wider, round-number increments (you can still find plenty of microcaps that trade in the low-dollars range and have a spread of ten or twenty cents). And when liquidity grew to the point that it was optimal to quote in eighths instead, all that had to happen was for the first market-maker to do it to get some irate complaints from counterparties who were annoyed that they'd have to accept slimmer margins. That can slowly harden into an industry convention, and eventually a taboo: if you know that bidding 10 3/8 instead of 10 1/4 is unprofessional, and that it's amateurish to even ask why, you can walk backwards into participating in a cartel.

This was already eroding by the time the SEC got involved. As the SEC report notes, the Instinet electronic trading system was more anonymous, and offered tighter spreads than the Nasdaq itself. Volume was already moving there. But industry self-regulatory bodies love preserving high margins, especially if that protects them from internal competition and transfers wealth from customers to them. So it was quite helpful for academics and then regulators to give them a kick in the pants.

The irony of all of this is that market-making is a much bigger, more lucrative business now than it was then. When trading is absurdly cheap, people trade absurdly often, and cheaper trades encourage more uninformed trading. In the 90s, if you were buying, say, a specific oil stock, and then shorting other oil stocks to hedge, you had to be at least a little careful about how you hedged because you were paying high transaction costs. Now, it's trivial to use an ETF to net out that industry exposure, and the market-makers who keep that ETF priced in line with its holdings are going to make lots of minimal-information-content trades in a bunch of oil stocks, paying tiny execution costs when they do so. Trading happens to be a domain where almost no one has good intuitions about the elasticity of demand, in part because liquidity in one market is complementary to liquidity in another, and the people who take advantage of this do so in unpredictable ways. Nasdaq market-makers made some temporary excess profits for a while, got in trouble for how dishonestly they did it, and ended up structuring their business around a version of it that had a fraction of the potential of its later form.

This one cuts across a few different Diff themes:

• On Monday, we looked at why cartels tend to be unstable.

• We’ve considered when companies own a standard, and when industries collaborate to set one.

• Bonds are going through the same voice-to-electronic transition equities went through ($).

• Hard-to-scale trades are often the root of bigger trading companies.

• What happens when the minimum tick size drops ($)?

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Programming Note: Testing out something new in Cap Gains—it's fun to write general explainers, but there are also some incidents from financial history that fit into the same model, where they're not particularly timely but are worth knowing about. Please let us know if you'd like to see more of this or less of it in the future.

Milton Friedman once observed that a communist can live a pretty communist lifestyle in a capitalist country: a group of them can get together and buy some housing that they distribute in an egalitarian way, put together a nonprofit co-op for groceries, use capitalist ink and paper to publish a communist magazine, etc. And that it's hard to imagine a similar enclave of capitalism inside a communist society.

Like many points made by clever ideologues, it's entertaining, illuminating, and directionally but not completely true. Some people were born to be commissars, and they have a hard time in multi-party democracies, but eventually find a decent spot. Natural-born central planners just end up with well-paid jobs optimizing supply chains. People who would have been revolutionaries if they'd been born in 1880s Lithuania become SaaS founders if they were born in 1980s California, etc. But every system does this. The USSR had a place for people who belonged in hedge funds, and that place was managing the USSR's own commodities trading.

You can usually think of commodities trading as two related businesses. One of them is trading claims on abstract commodities (oil futures, SLV, etc.). The other is transacting in the physical products these abstractions represent: arranging for X amount of a particular grade of oil to be loaded onto a ship and then offloaded somewhere else. These are obviously closely-related businesses; many grades of crude oil are priced in reference to the benchmark futures contracts, and of course those contracts eventually settle and there's money to be made in figuring out which particular storage tanks will be filled when, etc.

And there's a third form of vertical integration, where a commodity trader is also responsible for building and infrastructure, protecting it, and enforcing deals. Internally, the USSR was a communist state. But when it dealt with the capitalist world, it wasn't working with people who were trying to achieve the greatest good for the greatest number, but with people who wanted to buy and sell goods and services; just as anarchists like to point out that the relationship between countries is anarchism, and mostly works, capitalists get to note that communist countries plug into a capitalist system unless they're completely autarkic.

Two incidents illustrate this fully-vertically-integrated commodities trading with Soviet characteristics. The first is a bit ambiguous, but it seems like, strictly speaking and with a great deal of questionable account, the Soviet Union basically stole Spain's gold reserves. That's not quite accurate, but it's more accurate than anything else you could realistically say: early in the war, the army went one way and the remnants of the government went another, which meant that one side of the war had the government's financial resources (including what was then the fourth largest gold reserves in the world) and the other had weapons. A natural way to restore equilibrium was to exchange this money for military equipment, and the USSR was happy to oblige. Republican Spain could have shipped gold in increments to settle individual transactions, but an easier approach was to ship a large volume of gold to Moscow for safekeeping, and to periodically move it to different positions in the same vault to settle payments.¹ At some point in this process, there was a prepayment with no counterparty. At which point Russia had two choices: either they could return the gold to Spain, i.e. provide funding for Franco, or they could treat the remaining proceeds as a donation to the global workers' revolution. They made the profit-maximizing decision to just keep the gold, and to this day Russia's gold reserves are the fifth largest globally. At some point, all financial engineering and complex contractual stipulations collapse down to the question of who is in physical possession of the asset in question, and what means they have to defend it. So a country with Spanish gold and the Red Army got to keep both.

Decades later, Russia was involved in an incident with a differently piratical flair, less Blackbeard and more Jay Gould. The basic situation in the early 70s was that the USSR was the swing producer in wheat, but not by choice. If they had a good harvest, there was a wheat glut and they didn't get much for their output, and if production declined, wheat prices were high and they weren't selling any. But they had an advantage in comparison to Cargill and the like: they weren't a private business subject to the usual constraints, but also that they were a trading business attached to a (very effective) intelligence operation and a (big) army.

So, in July and August of 1972, the USSR bought about 28% of US wheat output, or 3.6% of global output—it's a decentralized market, because most of the places with the agglomeration effects necessary to buy lots of food are in fairly close proximity to the places where food was easy to grow. They agreed to pay about $1.64/bushel, and by the time they were actually getting deliveries, wheat was over $3/bushel.

This was pretty clever! Russia had an information advantage about the supply of wheat (they grew a lot of it), and they knew that their counterparty expected them to be dumb. So they locked in some cheap supply, and turned a local famine into a windfall (and a more general global food shortage—but it's easier for everyone to cut 100 calories a day than for a handful of people to cut 1,000).

One of the questions every investor has to ask is: why hasn't someone else taken this opportunity. "Nobody noticed" is often true, but also a dangerous null hypothesis. In the USSR's case, one good reason not to corner the market in grain was that it would be a PR black eye, to say the least. But the Soviets didn't have to worry about that much: internationally, it was just another Cold War headline, and domestically it was either a nonissue because bread prices didn't change, or a chance for them to brag that they'd outsmarted the plutocrats.

All this is a good reminder that supply chains are real, physical things. It almost never matters where someone stores whatever physical thing your financial assets reference, though that's occasionally the only thing that matters. And some of the time, traders who find a good opportunity for profit will back down, not because of some implementation problem, but because they worry about public backlash. But even at that level of abstraction, markets tend to be efficient: you'll either have rogue states or roguish, stateless traders like Marc Rich who make the market clear.

We've covered commodity trading, geopolitical hardball, and gold many times in The Diff, including:

• What's gold for?

• When should you short gold ($)?

• Why do commodity trading firms exist ($)?

• We also profiled LNG exporter Cheniere ($).

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When you look at a map of national borders, straight lines measure some sort of indifference, either that of two countries signing a treaty that divided up land neither of them cared that much about in the first place or because colonial administrations found it convenient. And if you see squiggles, you're generally looking at some national border that makes it inconvenient to kill people on the other side. These natural borders also make it harder to share a language with, do business with, or marry someone on the other side, but borders are much older than the concept of getting everyone to speak the same language. For example, The Discovery of France notes that modern French was a minority language until quite recently, and that the first time more than half the population spoke it was around 1900, a few decades after France established universal public schooling and insisted that it be taught in French.

A purchase is a bit more civilized, at least in comparison. And it used to be reasonably common: Denmark sold some of its colonies in what is now Ghana to the UK, and sold America the place now known as the U.S. Virgin Islands. The US was actually a pretty active buyer, having purchased about 58% of total American land area, though that includes the Treaty of Guadalupe Hidalgo, in which the US made a payment for territory as part of a peace treaty. So, this was technically a territorial purchase, but was driven by something other than the US determining that the present value of future tax revenue under American rule would justify the upfront cost. But that turns out to be a common feature of these purchases: if the seller hasn't lost a war with the buyer, they're likely worried about losing one with someone else, as when Napoleon decided that Louisiana was not an especially valuable colony if the world's largest navy was between it and the homeland. There are even some purchases that were the aftermath of wars; the Netherlands annexed a few villages from Germany after the Second World War, but sold them back (though they kept one hill). In other cases, historical contingencies helped: Bavaria became part of the German Empire in a negotiated settlement that preserved some of Bavaria's independence and involved substantial bribes to King Ludwig II, who was more interested in Wagner and architectural projects than in running a country.

The nation-building that led to coherent nation-states—with a common language, minimal internal trade barriers, internally-consistent laws, etc.—also made it much more expensive for territory to change hands. Most of the wealth in a country comes from the complementary outputs of different people across firms, the government, and other institutions, and this wealth tends to vanish in military conflicts. And if those people aren't excited about being under a new sovereign, they also won't produce as much (and if they are more excited about being ruled by one country than another, the popular country presumably doesn't have to pay; West Germany had a provision in its constitution that let other parts of Germany join automatically if they chose to.

So wars over territory tend to be more trouble than they're worth, economically speaking, and purchases of territory also tend to diminish the value of whatever they buy, so the market doesn't clear. In fact, it's been more common for older purchases to get unwound—the Panama and Suez canals, and Hong Kong all reverted to more local control.

But all of this assumes that the economic upside is driven by human capital. Greenland is just not the kind of location that's going to attract global immigration and nurture the agglomeration effects a services sector is built on. But they do have natural resources, and the US has both a bigger and more experienced extraction sector as well as capital markets that are unusually willing to finance expensive ventures like mines.¹ Even ignoring the strategic implications, the US buying Greenland would probably pay for itself from the taxes US companies will pay to dig up rare earths (less whatever deadweight loss you want to assume as a result of the US subsidizing American rare earths extraction rather than imports).

But that raises the question: what's the point? McDonald's was able to open a location in Paris even though America hadn't annexed France, and American mining companies are multinational by default; US-listed mining companies get the overwhelming majority of their revenue from mines outside the US. There might be some tiny reduction in frictional costs from having a consistent legal system, but there won't be much from language, since schools in Greenland already teach English.

There's going to be deadweight loss of one kind or another in the market of trading cash for territory. In one sense, applying one country's laws to another country's people can yield obvious upside, but there isn't a good mechanism to turn Singapore into a franchise operation or to otherwise shuffle territory around. The productive places are hard to dislodge from their home country, and for the unproductive ones, any payment that plausibly compensates for the forgone upside is embarrassing to accept.

A country can try to form closer ties with another country. The two can facilitate trade, and cross-border investments. This is a normal way for countries to relate to one another. It's not as seamless as being in the same nation-state. On the other hand, if there's some manufacturing or resource extraction facility that can be built anywhere in the world, there's a long list of countries ahead of California in the list of jurisdictions where it's easiest to build. Annexing more territory is an investment with a very uncertain payoff. And most countries, faced with similar uncertainty, have prudently decided that their current borders are roughly what they're stuck with.

The Diff mostly focuses on technology and business, not geopolitics, but sometimes these intersect.

• We’ve examined how big tech companies and governments have converging incentives.

• We looked at how commodity price moves in response to an anticipated war made that war more likely ($).

• In a worst-case scenario, there’s one not-quite-obvious trade to make.

• We reviewed Tony Judt’s Postwar, which talks early on about how modern borders were made.

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Investors talk about market regimes, like periods when growth stocks gracefully trend upward or times when the main thing traders fixate on is a company's ability to withstand inflation. And more narrowly, they talk about themes, where there's one big bet that can be expressed across sectors and categories—you could play the rise of GLP-1s by shorting candy and alcohol stocks, but also by buying clothing retailers (everyone has to replace their wardrobe) or, in the era of peak GLP-1 extrapolation, arguing that it would save some airlines tens of millions of dollars in fuel costs.¹ And there's something in between these two: loose, mostly qualitative heuristics about the way things work, that feel permanent but aren't really.

The best way to illustrate these is with an example: when I started working on the buyside, I came across a reference to how treasury bonds move inversely with equities, thought about it for a second, and realized how obviously true it was: stocks go up when the economy grows faster, but that leads to higher rates, so long-duration bonds suffer. And when growth expectations come down, the government cuts rates (or just buys lots of bonds directly if rates are already at zero). So, stocks plus bonds plus leverage can deliver a better-than-stocks return with equivalent risk. But, as it turns out, this only started to be true around 1998, and earlier generations of investors didn't think that way. Someone active in finance in the 70s and early 80s would have been more likely to think of the big two categories of inversely correlated assets as financial claims like most stocks and bonds, or real assets like real estate and commodities (equity in commodity producers did work as an inflation hedge). When inflation is high and uncertain, central banks don't mechanically respond to slower growth by cutting rates, because they have a more complicated balancing act. But gold does fine whether the bank's response to stagflation is to cut rates and let inflation rip or to raise rates and deal with a brutal recession.

This kind of narrative isn't really one you can make a single directional bet on, but it's a correlation that persists for long enough that people can forget what drives it. Until very recently, the big question about China's growth in any given year was whether it would be around the typical 10% or a worrisome number like 6% or 7%. Every year that China had above-average growth was a year where a) China's growth got more important to the global economy, and b) questioning how long it could last got less and less credible. And then there was a reset, partly driven by the Trump/Biden/Trump administrations (which were pretty consistent on decoupling), and probably driven by Covid creating an explainable break in the growth time series that made it less embarrassing to reset to a lower and more volatile number.² An investor with the perspective of ten years ago, who looked at China's GDP growth today, would be astounded that copper prices had tripled over the intervening decade.

There are other periods that have other almost-unstated assumptions. The 90s were the era of equity maximalism; the market was regularly hitting all-time highs, and celebrity fund managers were back after a generation-long hiatus. Nobody in 1980 was all that interested in reading about the astounding investing talents who'd managed to lose only 10% of their investors' money in real terms over the intervening decade, though readers of trade magazines like Institutional Investor heard about Soros in the early 80s, back when he was a relative neophyte in macro and had spent most of his career in equities.

It's hard to remember now, but there was a widespread view in the 90s that people were underweight equities and, similarly, that it was hard for stocks to go down. (I remember asking my dad in the 90s if a crash like 1929 could ever happen again. His answer was that every two weeks, millions of people automatically put a set fraction of their paycheck into retirement plans, so there's always a buyer. While it was true that we haven't had anything like 1929 again, flows like that get priced in after a while and we can still have drawdowns.)

Sometimes, shifts in these narratives catch people by surprise. The default way of talking about software companies from the earliest period that they started going public through just the last few years was that they were a high-quality business because they needed so little capital. All you needed to start one was a working computer, an Internet connection, and maybe an O'Reilly book. But today, they're the biggest capital spenders around—once again, someone transported from the mid-2010s to the present who looked at big tech's capex as a percentage of sales would have to assume that something terrible had happened, and that all of these companies would be trading at a discount to the market. They would also be very surprised to see that despite record high capex, many of these companies have simultaneously printed record ROICs (which also happen to be leagues above broader market ROICs).

What often happens with these shifts is that there's a zombie correlation that sticks around in stock prices for a while even when fundamentals have changed, but then reverses violently when there's a big drawdown and people start pricing out the downside scenario. Railroad economics were permanently impaired by the rise of the car, which permanently impaired their passenger business. But the rise of the car, and of some other new manufactured goods, created more freight demand, which made railroads look fine, albeit a bit dowdy, in the 1920s. And then early in the Depression, investors reassessed them and decided that they weren't the safest businesses any more, and that there wouldn't be mean-reversion back to a market that was mostly railroads ever again. The current generation of investors won't think of treasury bonds as a universal equity hedge, because they remember 2022. There are probably some investors out there who think of consumer staples as good companies to own during a flight to safety, since they've historically outperformed in tough markets; you might cancel a vacation because you got laid off, but you're probably not going to stop buying laundry and toilet paper. But you might switch brands, and if stores see that consumers are more price-sensitive, one way to preserve their spending power in high-margin areas is to get them to buy the cheap store brand for these kinds of repeat purchases. Tech is the odd exception here; nobody is calling Microsoft a capital-light business these days. But even that illustrates just how extreme the change in fundamentals has to be before it changes investor perceptions: yes, investors can change their mind about these kinds of unconsidered assumptions before it costs them money, but usually when the deviation from that older model is greater than a percentage point of GDP.

In The Diff, we often look at cases where these narratives work, and where they shift.

• Even if there's a good theoretical argument for a given approach, you need some kind of heuristic you can actually apply ($).

• One way to get around this problem is to maximize your idea velocity.

• A good example of betting against the easy secular model is the long winning stream of Nexstar, which happens to be well-positioned in a dying industry ($).

• And betting on one: Google’s growth rate has been surprisingly durable over long periods ($)

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The nice thing about markets is that in some literal sense, everyone's on a level playing field: if you put in a limit order to buy 100 shares of something, and an order from Citadel to buy the same thing hits the order book a microsecond later or a penny per share lower, no matter how sophisticated the infrastructure and analysis behind their trade was, you're ahead of them in line and get to trade before them. Then again, the downside is that if you're participating in markets, there is no one to protect you from the fact that you're competing with Citadel, and many other very competent participants besides. To an extent, you can free-ride on all of their efforts by just buying index funds—market-makers are competing to provide liquidity to that kind of flow, and hedge funds and long-onlies are competing to make the prices for what you're buying more efficient.

There are less liquid and more exclusive markets that are adjacent to these liquid ones. For example, suppose your view on some company is: it's a good short eventually, there's no catalyst right now, but once there is a catalyst, it'll be so expensive to borrow that it probably won't be worth shorting. For an individual investor, that's just a frustrating fact of life; sometimes you pay 5% a year to short it when there's no reason for it to go down, and you'd have to pay 50% or more to maintain that position once it's actually dropping. But an institutional investor, or a sufficiently wealthy individual, has another option: they can get a bank to write them a total return swap that's equivalent to shorting the stock, then buy an offsetting position. There are two ways to look at what they've done in this case:

1. They've paid a fee to lock in the option to short the stock on prearranged terms; when they sell the stock they bought to offset that position, their economic exposure flips to being a short position, but they don't actually have to arrange any borrowing. (That's their counterparty's problem, at least if the counterparty is fully hedged.)

2. They've created a synthetic asset: a bet on how much it costs to borrow a given stock over a year. They can monetize this by shorting the stock (some anomalies that look like promising ways to spot good companies to short completely go away once you account for the cost to borrow). If the position is big enough, they could even monetize it by negotiating a borrow fee split with their prime broker.

There are plenty of other equities-adjacent businesses where not everyone has access to the same suite of products. And one of the more interesting is venture, where getting into deals is a big part of the entire business. It's also a very tough business, because there's a correlation between a fund being prominent enough to hear about an interesting round first and that fund quickly deciding whether or not it wants to lead the round or even offer to do the entire round itself. So deal flow is not just a measure of the quantity of deals a given investor gets access to, but a proxy for their quality, too. This dynamic exists in places other than venture—Berkshire Hathaway under Buffett had unique access to M&A opportunities with big family-owned companies and non-controlling stakes in distressed financial firms.¹

But all is not lost! What this actually means is that there isn't a single consistent market for access to startups. Instead, there's a graph, and as long as a given investor is a prominent node in some important part of that graph, they have the opportunity to produce alpha—there are investors who are good at staying connected to particular alumni networks, there are investors who are better than their peers at early technical diligence in their particular field, and there are the ones whose franchise is that they respond to emails incredibly fast in a way that's generally helpful.

In none of these cases will you get anything resembling a meaningful sample size before the market has moved on. By the time there were clear leaders in the field of backing pre-IPO PayPal employees, all of them had moved on to other things, like Youtube, SpaceX and Palantir, and presumably by the time it's obvious who is best at investing in ex-SpaceX or ex-Palantir companies, most of the alumni who were going to start big companies will already have done so.

One of the most interesting situations is the kind that occurs across asset classes, where a previously untradable asset suddenly turns into one with a lively, active market. This can happen for all sorts of reasons: in options, it was partly because of the publication of the Black-Scholes model and partly from the introduction of hand-held calculators; in pre-IPO stock, IPO timelines got longer right about when it became trivial to check LinkedIn for early employees of now-valuable companies; in high-yield bonds, Michael Milken pretty much willed the market into existence (though 60s-era conglomerates' habit of issuing low-rated debt, and frequent defaults elsewhere during the disruptive 1970s, certainly gave him some good raw material to work with). There's a lot of money in bringing markets into existence, for exchanges and for market-makers. But it's quite hard to do, because most of the time, a good market doesn't exist for a reason. Startups don't really need there to be a Series-A Stock Exchange, because their financial backers already have a mandate to hold for a long time, and because they want their employees to have equity in order to stay incentivized.

There's a point at which markets reach escape velocity, and an asset shifts from being something that's primarily bought for the long term to something you can put a stop-loss on. That shift usually requires a slow increase in transparency or an improvement in valuation norms, followed by a gradual shift to ad hoc over-the-counter transactions, eventually culminating in a formal market with an order book or at least a cohort of specialized liquidity providers who are willing to make a market. But for some markets, especially ones where making a market means revealing too much alpha, it's basically impossible for everything to align in a way that creates a durable market. For the truly bespoke derivatives bets, and for the earliest stages of venture, illiquidity will remain the default.

In The Diff, we spend a lot of time looking at where markets do and don't emerge, including:

• How Milken made high-yield debt a real market.

• Data companies follow a few basic templates ($).

• The many paradoxes of market efficiency.

• Many of your biggest assets aren't on your balance sheet.

• Prediction markets are a new market with an old problem.

• Stakes in venture-backed companies are also somewhat hard to trade ($).

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As a general rule, the average person should think about asset allocation only a handful of times: the first time they make enough money to put some of it aside for later, any time their expected income goes way up or way down, and then one more time when they retire and set-and-forget for the last time.

In practice, you're probably not reading this because you want to invest (110 minus your age)% of your money in stocks and the rest in bonds. If you want to do better—or even if you just want to have a keener appreciation for the people who do—it helps to think carefully about the question of where it should be possible to beat the market, and who would be in a position to do that.

The first two things you should conclude from thinking about this for a minute:

1. It's very, very hard to have a meaningful edge in liquid markets. If you're betting on S&P 500 stocks, major FX pairs, rates, commodities, etc., you're betting against some incredibly sophisticated counterparties. Some of these counterparties are looking at fundamentals—they have meetings with management and they spend a lot on real-time data sets. Some of them are looking at short-term changes in sentiment, and they spend a lot of their time talking to other people and figuring out what positioning is (if you're right that a company will beat sell-side revenue estimates by 2%, but the consensus among pod shops is that the real number is 4%, you can feel gratified that you were right but still lose money if the real number turns out to be 3%). On shorter trading timelines, there's active competition for the privilege of being on the other side of small buy and sell orders.¹

2. If you don't have an edge, the main thing that determines how badly you do will be how frequently you trade. Trading has a cost, even if commissions are free—you're still either putting in market orders and paying to cross the spread (e.g. if a stock is trading at about $10, you might be buying for $10.02 and selling for $9.98, and if $10 is a good guess as to fair value then on average you lose 0.2% every time you trade), or you're putting in limit orders and dealing with the curse of adverse selection: the smarter your order was, the less likely it was to get filled. In that ~$10 example above, if you put in an order at $9.99, then that order won't be filled if you're right about the immediate direction of the market and the stock goes straight to $10.10, but you will place your trade if you're wrong and the stock drops again.

Poker players sometimes talk about how most of the upside comes from picking the right people to play against: "If you can't spot the sucker in the first half hour at the table, then you are the sucker.” Investing is no different; if you choose an easily-accessible venue and trade things people have heard of, you’re opting into competing with some very smart, well-informed people, and you’re probably the sucker. 

You can work backwards and ask where professionals would be reluctant to trade. Successful traders gather assets, so one place to look is anything too small to get their attention. There's a whole universe of microcap stocks out there that big funds don't want to touch, because they can monetize a correct prediction about a 5% move in a megacap stock more effectively than they can a 50% move that finally pushes a company's market cap above $100m.

But be careful! Small-cap stocks are priced less efficiently than big ones, but:

• Especially at the very small end, they're subject to more chicanery than big stocks; if they're too small for activist investors to really care, then you can run into cases where you buy the stock at $5 knowing that it owns assets worth at least $15/share, and then discover that the chairman has generously agreed to buy the whole company for $6/share.

• It's not uncommon for professionals to trade smaller stocks in their personal accounts. If it trades $100k a day, it's not going into a pod shop portfolio (except for systematic strategies that trade a little of everything). But $100k a day is enough volume that someone might chuck some of their bonus money into it, especially if they have access to lots of market depth data from Bloomberg, know how to whip up a model, and can use some of their datasets to triangulate things.

• Small companies are small for a reason. Which is not necessarily bad! The US has lots of tiny publicly-traded banks, many of which have zero analyst coverage and will sometimes end up being too cheap. But when you invest in one, you're implicitly making a bet that there's a reason they haven't sold to a big competitor and this reason won't impair your returns.

In other markets, there can be more opportunities because the market in question is new, illegible to institutions, and legally tricky. Not too long ago, all of crypto fit that bill, and there are still corners that do. Even if you don't have the same risk profile as a prop trading firm that isn't going to risk a billion dollars in tradfi P&L in order to make a million dollars a year trading in some shady offshore venue, you do run some risk there, and that risk is hard to underwrite. These days, prediction markets are all the rage, though even those are starting to draw institutional attention.

Markets tend to be fractally efficient. If a market is easy to trade in, it's very hard to get repeatable alpha, and at the limit, profitable trading looks a lot more like running an operating company—if you're doing high-frequency trading, or have a bunch of teams running uncorrelated strategies, you have fixed costs for infrastructure and support, and those costs will rise over time. There's room to use similar approaches in less liquid markets, but the paradox you run into is that if you do a great job in those markets, you're all tapped out. Markets impose the Peter Principle on everyone: if you're small enough that you can compound your money fast, pretty soon you'll have enough that that compounding slows down. And if you run the numbers, a buy-side job involving more liquid investments plus putting your bonus into boring index funds probably pencils out better—if you're going to be a small solo investor, do it because you love it.

The Diff has looked at how professionals trade, and how trading gets presented to retail:

• Handcrafted artisanal liquidity provision describes the phenomenon where big firms got started betting on a small-scale anomaly. (But beware! One such opportunity highlighted in the piece is waiting for companies to do a tender offer or reverse split and buying a small enough stake to get cashed out. But every so often, companies get annoyed at how many people are doing this, and change their minds.

• Here’s another look at small scale strategies growing into a big financial business.

• One advantage big funds have is alternative data ($).

• One of the risks and opportunities in small-cap stocks: brokers will produce shoddy, conflict-riddle research ($).

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One stylized way to look at capital structures is that the most senior creditors are entitled to the first dollar, equity holders get the last dollar after all obligations are satisfied, and that there are intermediate securities—preferred stock and convertibles—that combine features of both. Preferreds used to be much more common than they are today—you'd have a fixed dividend, but the company would follow a mechanical payout rule for whether or not you got your money. So a company might issue $1m in bonds yielding 6%, $1m in preferreds yielding 8%, and $1m in common. They had to pay $60k to bondholders regardless, preferred shareholders would get the next $80k (and if there wasn't $80k to go around, that dividend would accumulate until it was paid off), and common shareholders would get an uncertain dividend that could be revoked at any time. Convertible bonds (or convertible preferred) are just another wrinkle on that middle-tier structure; a convertible bond would generally pay a lower rate than a bond, but could be converted into stock at some fixed exchange rate (maybe when the stock's at $20, you could buy a $1,000 convertible bond that could be exchanged for shares at $25 and paid you a little interest along the way).

So you'd think that companies would turn to equity markets when their prospects were most uncertain, then convertible bonds as their prospects firmed up but investors still wanted a kicker if everything worked out, and then they'd mature into the kind of business that can issue a 20-year bond because it's pretty clear that they'd be around in twenty years to pay it off.

In practice, that's not how it works, because a convertible bond is really an option with a small fixed-income component—but plenty of the scenarios where the convertibility portion doesn't pay off are ones where there are credit problems, too. And since the value of an option is tied to volatility, you end up with an odd hierarchy:

• Growth gets funded with equity when there's uncertainty and illiquidity.

• Certainty gets converted into higher returns through leverage—you can get the variance in profits you want by adjusting leverage.¹

• Uncertainty plus liquidity encourages companies to sell convertible debt, mostly to hedge funds (and cross over investors, in some very interesting cases: Silver Lake, TCV) that specialize in it.

The presence of those convertible debt funds is part of a weird historical contingency: convertible bonds got popular in part because they feel like a compromise, and in part because it's easy to confuse people with them. But over time, market participants got more and more confident that they could price them effectively. Ed Thorp wrote a book on this in 1967, some smart people read it, other smart people tried to think step-by-step about how to value the different parts of the payoff and stumbled on good ways to hedge it, and eventually a cottage industry of convertible arbitrage arose.

The way a typical trade worked was that someone would identify a convertible bond that looked mispriced—it works in either direction, but historically investors underpriced the option and so the first leg of the trade was to buy the convert. Then they'd short enough common to hedge out their immediate exposure (so if you buy a convertible bond for $1,000, whose value rises ~0.25% for every 1% the common rises, you short about $250 of the common).² Hedge the impact of stock price fluctuations on the convertible bond, and you've isolated the "everything else" that drives the value of an option. If you run the trade in this fairly naïve way, you end up long volatility by way of the option, but also short volatility by way of the fixed-income piece.

When this business was just getting started, it was at the ideal intersection for self-directed traders: you didn't need to do anything special to get access to the relevant asset classes, you just needed to do some math (either plugging numbers into somebody else's formula or figuring things out on your own), and you had a nice little arbitrage that, for a while, printed money. The trade got crowded, and, as it did, some nuances started to show up. A trader doing convertible arb is still taking on some correlated risk, especially if there are other levered traders doing it, too. Ed Thorp, Ken Griffin, Paul Singer, and Michael Hintze all started now-diversified vehicles whose original business was convertible arb, and it must have been a uniquely insightful business because there are roughly three directions you can go once you have a 101-level volatility-harvesting strategy³ :

1. Maybe you notice that you lose big when there's some fundamental reason for volatility to be priced the way it is, so you expand into discretionary trading.

2. Maybe you notice that weird quirks in the bonds' terms can have a big, unpleasant impact, and you decide that the world of distressed debt and activist investing is a natural way to harvest alpha from being picky about the details of agreements.

3. Or maybe you get tired of people intruding into your ivory tower to remind you that the time series data attached to alphanumeric tickers actually represents, in some abstract sense, an effort to value real-world businesses, and that the collision between models and economic reality keeps costing you money. If you're Ed Thorp, at some point you apparently throw up your hands and say "Screw this! I'm going to find similar systematic strategies and run them until I'm so diversified that no real-world event can throw things off" and then invent statistical arbitrage.

The strategy still exists, though it's more crowded and less fun than it used to be. But now it serves a social function: when traders get too excited about batting some stock around, the company can get a very good deal by issuing convertible bonds. These bonds will basically all get sold to convertible bond funds, who will all do roughly the same thing—short some stock to be delta-neutral, and adjust those hedges over time. To stay delta-neutral if you own a call option, you short more as it goes up (it's further in-the-money and more sensitive to stock price movements) and vice-versa. In other words, issuing a convertible bond to take advantage of high volatility basically means paying convertible arbs a commission for reducing that volatility to a more sensible level.

So we have an investment ecosystem where, when a company's share price bounces around more than fundamentals justify, management can directly convert this into funding, and do so in a way that systematically pushes against exactly those moves. This is just one of those facts of corporate finance, in the same way that a company with a really cheap stock will probably buy some back and a company with an expensive stock will restore the equilibrium by using that stock as currency for an acquisition. The main difference is that there's a lot more room for value-destructive behavior, and the investor audience that makes things really volatile is a group of retail investors who don't necessarily realize that they're making bets with negative edge.

The Diff has talked a bit about converts and a lot about volatility in general:

• We've looked at why arbitrage in general was a great training ground for investors.

• When companies court retail investors, they sometimes engage in a kind of corporate populism.

• The terms of a venture investment can be tweaked to give both investor and recipient better incentives.

• The concept of volatility being shifted around rather than created or destroyed has interesting implications when you apply it to the real economy ($).

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The fundamental problem the modern corporation solves is matching capital in the abstract—a number stored in a database that you can decrement in exchange for access to goods, services, and investments—with capital in the form of buildings, equipment, workers, brand names, tacit knowledge, etc. But as soon as you put this into practice, with one group of people supplying the capital and someone else providing the management, you have to align incentives, because one side has a lot of money at risk and the other gets upside but doesn't have much downside.

There are a few cases where there's a simple, elegant solution: if a company was founded by someone who already had capital, or by a team that didn't need much capital to get going, they'll have a built-in incentive to maximize the value of their stock. Mark Zuckerberg has a $1/year base salary, but to the extent that he's profit-motivated, owning 343m shares of Meta probably gives him an incentive to get out of bed in the morning and make sure the value of those shares doesn’t evaporate.

But most big company CEOs aren't also founding CEOs, and the skill of building a company is not the same as the skill of managing an already big company. At that point, you have to strike a balance between two forces:

• You can give your managers a stake in the upside they produce, perhaps by granting them lots of options struck at the current share price, or giving them performance-based stock units that vest at higher prices. This means that they aren’t getting paid for what someone else built, but it also means that they have a different set of incentives compared to shareholders: performance hurdles mean that they're long volatility as well as price, and have an incentive to do crazy things that make the stock move fast rather than sane things that make it move up slowly.

• If you grant them pure equity, you've aligned their incentives going forward but also given them a windfall that's more tied to taking a job than to how they perform at it.

Fortunately, there are other forces pushing in the opposite direction. Volatility is good to the CEO-as-holder-of-out-of-the-money-calls, but bad to the CEO-who-has-to-explain-decisions-to-the-board. It's pretty easy to imagine choices that lead to binary outcomes that are good for someone who has stock options, but the board of directors will spend a lot of time picking apart the upside case and fixating on the downside if that volatility-maximizing decision doesn't directly lead to a higher share price.

And in a way, experienced CEO hires are bringing some preexisting equity to the job: if they got hired, it's because someone was able to make a credible case that they'd help the company perform better than some hypothetical replacement-level CEO. The ability to make this case is a function of track record, and that track record is at risk any time a CEO maximizes variance. Between the reputational pressure on managers and the incentive of boards, companies tend to have a pretty low expressed risk preference: it's easier to underwrite buybacks than expansion, easier to justify bolt-on acquisitions than transformative ones, and easier to nudge up pricing than to blow up the pricing structure entirely. Even when companies do take risks, they often taken them in a risk-averse way: when big tech companies talk about their AI capex decisions, they'll often frame them in terms of the risk of being left behind, not just the upside from being first to some futuristic new technology.

There are some odd cases where an executive already has a very motivating stake in the company, and gets a pay package offering more of the same. Elon Musk periodically informs the car company he's synonymous with that he needs a sum of money that would move the needle for the richest man on earth in order to keep his attention from wandering. Rivian has apparently decided that it's very important to follow industry norms around compensation, so they're offering their billionaire CEO billions of dollars in performance-based comp, too.

This is just a lot weirder than the other cases above, but the more confusing you find the behavior of centibillionaires, the less confused you get to be about why you aren't one. If a $100m bonus wouldn't motivate you to work very hard if you already had a billion dollars, you're probably going to stop short of $1bn.¹ And if someone really is wired in such a way that the second billion hits just as hard as the first, and the third billion just makes them wonder why they don't have four already—then they're pretty incentive-aligned with shareholders, and if they ever underperform it will be incredibly easy to make the case that they're overpaid.

One workaround for executive compensation in general is to stop thinking about it in terms of what share of the upside goes to shareholders and what share goes to managers, but to look at what underlying drivers will improve the share price over time. There are executives who get paid a fixed piece of the profits they generate, or who have pay tied to other KPIs like return on equity or even total revenue. These variables tend to be more stable than share prices, and they're more under the control of management—but as soon as they're the target, there will be cases where the pay scheme tells managers to do something that will make the stock price go down. Pay based on return on equity, for example, and you're telling CEOs to lever up; switch that to return on assets, and you're telling them to outsource the most capital-intensive parts of their business, which may be more strategically important than the asset-light bits. Pay based on total profit, and you're paying them to make dilutive acquisitions; pay based on earnings per share, and you're paying every company trading at 30x earnings to use stock to diversify into sectors where the average multiple is closer to 10x.

None of this ever makes compensation easy to figure out. Any cogent argument that some people will be motivated by a boatload of money will probably be best articulated by a sociopath who wants the money and can claim whatever motivation is necessary to get it. But overall, things work out pretty well: there are still plenty of complaints that big companies and captive boards overpay their executives, but it's not like CEO pay is the first cost a private equity firm cuts—though they 're more likely to put a cap on salaries and tilt comp much more to equity, which tends to make average pay even higher.² There will be standout cases where CEOs are paid extremely well for mediocre performance, but the go-to example of this, David Zaslav, is currently presiding over a bidding war that has already produced more for shareholders than he's earned at their expense. Figuring out the right amount of pay, and the right way to pay, is a fundamentally messy business.

Compensation and conflicts of interest are a long-running theme in The Diff, at the level of operating businesses and asset managers. For example:

• Sometimes figuring out the right pay structure is similar to designing a good game.

• Compensation norms are contagiuos ($).

• Paying with stock options is a surprisingly new feature of business (and yet another example of a practice that started out as regulatory arbitrage and then evolved into having decent standalone economics).

• If there's no correlation between CEO pay and stock performance, it can mean that there's no such thing as a good CEO, or that good CEOs charge exactly what they're worth ($).

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Prices are a wonderful tool for compressing and transmitting information, so in general, more accurate prices are a good thing.¹ But sometimes, they feel entirely too efficient. Stock volatility and stock-based comp injects a little bit of manic depression into every tech workforce. Levered institutions can go into a death spiral when the value the market places on their liabilities becomes an input into future lender's decisions about whether or not to cut them off. If a developing world country goes through an economic hiccup, the forex market can bloodlessly turn this into a currency crisis that throws millions of people out of work.

That doesn't sound so good.

Consider a country with the usual developing country mix that leads to uncertain access to capital markets: unstable politics, dubious public finances, and a legal system that does a better job of punishing speech than protecting rights. When one such country, the UK, introduced an unusually free-spending budget in 2022, yields on government debt spiked and the pound crashed. But one reason for that was that British pensions had reduced their ownership of bonds, because of the return drag, and had replaced that exposure with bond futures, which they had to quickly liquidate when prices dropped. So the reaction to the budget was really a reaction to some longer-term problems: pensions taking on more risk in order to reduce their immediate funding needs, a spike in inflation that broke the two decade-plus streak of bonds moving inversely with stocks, and a leverage-driven liquidation.

But in another sense, this was just a vigorous marking-to-market of various losses. Replacing direct ownership of bonds with a portfolio of riskier assets plus levered exposure to bonds creates this kind of risk; treating the bond/equity relationship as a universal one rather than a feature of low-inflation environments is also a way to get paid a fairly steady return in exchange for inflation risk. And underestimating how fast asset prices can whipsaw also leads to more risk-taking than the alternative. In this case, market volatility was actually showing that the market wasn't efficient enough; more effort spent on portfolio construction would have led to higher bond yields in advance of the 2022 mini-budget, and higher interest costs would have made those tax cuts look less affordable.

A surprisingly adjacent case is when markets are efficient at pricing an asset in reference to supply and demand, as opposed to its cash flows. This shows up most starkly in a corner, when some shareholder or group of shareholders has managed to buy more than 100% of the outstanding shares of a business, or a short squeeze, when prices spike because traders worry about getting cornered. For a brief period in October 2008, Volkswagen was the most valuable company on earth, for the simple reason that Lower Saxony owned 20%, Volkswagen owned 42.6%, they had options to buy another 31.5%. That left 5.9% for the rest of the market to fight over, and in October of 2008 they were particularly unexcited about taking big nonlinear risks. One reason this squeeze was so damaging was that Porsche's buying coincided with a big drop in the market, which made Volkswagen look more expensive. So if you were a long/short trader, it looked like something sensible to short: a cyclical company that was clearly going to suffer in a downturn, with a big enough market cap that there was plenty of liquidity.

The traders who made that bet joined the very big club of market participants who, one way or another, got smoked in Q3 '08 through Q1 '09. But, like the British pensions, the reason they were in a position to lose a lot was that they were rounding a risk from "low" to "zero." In many contexts, this has a small effect on behavior—if there's a 0.1% chance that you'll get hit by a car if you cross the street without looking both ways, it happens one time in a thousand. But when you're participating in a market, you're always providing liquidity to someone who disagrees with you, and you're sending a signal about the state of the world. If you bought British bonds, you were both betting that inflation in the UK would be low and telling the government that it was okay to borrow more. If you shorted Volkswagen because it was fundamentally overpriced, you were offering a great deal to someone who was betting more on the mechanics of the market.

And you can't really have a working market without those complicated mechanics. Market-makers need to be able to short in order to provide liquidity to both sides of the market; that's a much worse business if everyone needs to have enough capital to hold inventory and needs to take the directional risk on how the price of that inventory fluctuates. Options are a natural feature of markets, because there can be efficiencies in the level of prices and inefficiencies in their volatility, which options straightforwardly capture. So if you're going to have all of the tools to accurately price things, you're also going to have a system where sometimes a surprising interaction between these tools produces a crazy result. It's a bit like the presence of bugs as a so-far-inevitable result of turning manual processes into software: computers make fewer little errors like transposing digits, but also don't notice illogical things like the fact that they're sending a utility bill for an amount higher than M2 or something. But the fact that this weirdness is ultimately tolerable, and that the most complex markets are also widely regarded as the most efficient, is a sign that meta-efficiency creates certain kinds of inefficiency that you only see when markets are doing a very good job at the basics.

We’ve covered the nuances of market efficiency from single stocks to macro shifts:

• If you want more background on why bonds used to hedge stocks so well, consider that modern financial history begins in 1998, with the Asian Financial Crisis and its aftermath.

• There’s more than one efficient market paradox.

• One way to understand sudden policy-driven swings is to look at them from the inside, by reading about the banker on the other side of Soros’ legendary pound short ($).

• Even if you’re right about the fundamentals, you can still lose money shorting shares of worthless companies ($).

• A fun efficient markets parable: once upon a time, the news that a company had $450m more cash than investors thought sent the stock down.

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This piece about updating the poverty line for 2025 spending has been getting some attention. Mike Green, the author, has made some clever points in the past (during the great inverse-vol bubble of 2018, he outlined what would happen in advance). He's also been early to thinking through some of the subtle impacts of index fund investing, though it's always hard to model how other market participants will handle that.

So, smart guy, with lots of variance from one take to another.

This take is that the poverty line was first calculated in 1963, and if you use the same methodology today, you'd get a poverty line of $140k. The basic argument circa 1963 was: we have good data on food prices but not on other products. We know that the average family spends one third of their money on food. And the USDA had put together a report on the minimum cost to feed people of various ages. So, assume that one third of the budget on food is normal, calculate the income that would make that minimum food budget equal to one third of income, and you know the line at which someone is poor enough that they're literally reducing their calorie consumption in order to pay for other necessities.

As the essay notes, this was something we did at a time of information scarcity, when we just didn't have enough information about overall expenditures. It is, in one sense, a pretty strict definition: even back then, there were plenty of people who'd consider themselves poor even if they weren't going to be hungry for financial reasons.

But now, grocery spending is about 4.9% of household expenditures. Food out of home, which was a quarter of grocery spending in 1963, is actually higher than grocery spending, at 5.5%. And even that understates how much food consumption has shifted from buying and preparing food to paying someone else to prepare it and consuming the finished product, because some of the incremental grocery dollars you spend come from things like buying pre-sliced fruits and vegetables or entire prepared meals. We also snack more than we did historically.¹

If grocery spending has been crowded out because we're all meeting our basic needs and spend our incremental income on discretionary purchases, then basically everyone in America has cleared the poverty line. If we're still spending-constrained because there are new needs, or newly-expensive ones, then we can potentially be poorer.

But even in that latter case, we have to be careful. The more of your spending that's absorbed by services, the more your spending turns directly into income for somebody else. We can't all be poor because of the high cost of non-tradable outputs like healthcare and education. Some of us have to be providing those services, and if buying them makes everyone else poor, but GDP per capita is rising, then either the providers of those services are getting quite rich indeed or there are middlemen who are minting money and they're where all the wealth goes.

What Green does in this essay is to compare the median household income ($80k) to some estimated median expenditures for a family of four. He comes to a final poverty line—the income below which a family is desperately poor—of $140k. This seems high. The first item is $32k for childcare. Ouch!

But wait! First of all, those childcare expenditures are some combination of daycare, sitters, and tuition. But 87% of school-age children are in public schools. So when we're looking at that average, over the course of 18 years, we have maybe six years of potentially needing full-time care, and then 0.13*12 = 1.56 years of private school tuition per child. Looking at full-time care alone, the numbers only tie out if either a) we're specifically measuring poverty for families with young children, or b) we're assuming that the average cost of daycare or tuition is $76k per child-year, since we only need 7.56 child-years of care for an 18-year childhood. (Also, average earnings for families with kids are slightly higher than the national average, even though wages tend to peak later in people’s careers.)

And also, let's wait some more: this family is earning $80k. And we're assuming they need full-time childcare, which implies two full-time earners. They're in the 12% Federal tax bracket and have another 7.65% in payroll taxes. Depending on where they live, they also face state taxes. Paying $32k is plausibly roughly breakeven if both members of the couple earn the same amount and they live in a state without income taxes, but it doesn't take much variance between them at all for that childcare to be a bad deal after taxes—if the split is an entirely plausible $30k/$50k, they're poorer having paid $32k for childcare to enable another parent to work even ignoring the tax impact. (Unless they happen to live in a handful of metro areas with good public transportation—where they'll be earning more, spending more, and very unlikely to have two kids with an $80k household income—the cost of an extra vehicle will probably wipe out these savings.)

Now, it's easy to push back against that, but in a way that complicates the picture further. You can note that some people don't want to spend all of their time on childcare, which is absolutely true. But poverty is not the state of having to make tradeoffs that have downsides—that state is the state of existence. There are non-financial tradeoffs, of course; one member of the couple might not want a career interruption that would make it harder to reenter the workforce later. But for that to be an economically rational decision, they have to be operating on the assumption that they'll earn more relative to their childcare costs in the future. In other words, this is a snapshot of someone who looks poorer-on-paper because some of their consumption is actually an investment (in retaining access to their chosen career track) that will yield higher income in the future. It's just very hard to point to someone who is buying full-time childcare in order to have more time to pursue a career in which they expect future raises—or paying for childcare temporarily in order to maintain their position in a career where they expect stable income—and call them poor. You're either looking at someone at a low point in their discretionary spending who is also having more kids than most people of their income level (and more kids than most people in the US will have!), or you're looking at the spending patterns of someone who is sacrificing in one area because they'd prefer to spend their money on something else.

And there's pushback-to-the-pushback: if you graph hourly wages against hours worked, you see that for earners in the bottom half, lower hourly earnings predict fewer hours worked. In communities where underemployment is the norm and multigenerational families are common, there's a sort of informal in-kind welfare state: grandparents babysit, nephews crash on the couch, etc. This is less common among higher earners, but it's another way to state the claim that some consumption of services is market-based at higher incomes and informal at lower incomes, so it describes what kind of spending makes sense as the opportunity cost of your time rises, not what the baseline necessity is.

Other entries in the list are more sensible. Healthcare, for example, really is expensive, though if you're comparing average household income to average healthcare spending, 1) you need to consider that ~60% of working-age households have access to employer-subsidized health insurance, and 2) health expenditures overwhelmingly occur later in life; if you're a peak healthcare spender, it's very unlikely that you're paying for daycare, though your kids may be paying for your grandkids to go to private school. So it’s a duration mismatch. 

As a general rule, you're only thinking clearly about healthcare when someone is calling you a moron for how unsustainable your spending plans are or a monster for depriving people of care. The US is definitely rich enough to provide any one person with the most comprehensive healthcare imaginable, and we don't really have a political culture that would trade more generous health benefits for stricter regulations on unhealthy behavior. A single-payer system coupled with retina scans when you buy snack food and prison sentences for the sale or distribution of combustible tobacco products would drive down healthcare costs, at least in the short term, but would be opposed by basically every mainstream politician. So the politically-tenable choices are: various kinds of quasi-socialism with nightmarishly complicated payment systems, or, well, other kinds of quasi-socialism with different complications instead. The only places that don't have to have some mechanism for rationing healthcare are the ones that don't have any healthcare at all. For everyone else, it's a choice between some people being sick because they couldn't afford healthcare, or people being sick because it's free and there's a waitlist. In some places, if you don't want to die while waiting for treatment, there are other options; in 2023, 4.7% of Canadian deaths were through assisted suicide.

Healthcare is just full of unpleasant tradeoffs, and in general when you see what looks like an easy win, there's going to be someone who is very upset that you took away either their lifestyle subsidy or their income. You may not care about the former—personally, I'm fine with the cost of smoking falling on smokers—but in the case of the latter, you also have to worry about lobbying (and when healthcare providers defend their incomes, they'll tend to use sympathetic healthcare recipients as a human shield).

One of the most unpleasant tradeoffs is that the high fixed cost of research, and the risk-averse nature of the industry, mean that promising new treatments often start out very expensive and sometimes never get cheap. In fact, it's hard to tell what anything costs, given the level of cross-subsidies both at the level of care for individual patients and at the level of ensuring that hospitals provide emergency treatment to people who need it regardless of their ability to pay and still collect enough money to keep the lights on. But even when things do follow the path from expensive to ubiquitous, that means that at the start, their existence makes people feel relatively worse off: they're sick, there's a treatment, they can't afford it. Pharmaceutical companies do try to price-discriminate here, but it's a messy and inconsistent system.

But you can't really buy 1963-level healthcare. Nor would you want to; polio and measles were still common, though vaccination was cutting that risk drastically; Hib influenza, which can cause deafness and learning disabilities, did not yet have a vaccine (today, cases in the developed world are almost nonexistent); Rotavirus and RSV were putting lots of babies in hospitals; the survival rate for acute lymphoblastic leukemia was ~10% instead of today's ~90%; and infant mortality was four times higher. All of this has a cost; the healthcare system was a lot cheaper when the only option was for more people to die, and that's not the case any more.

So, are you poorer? In some sense, you absolutely are, but it's in the same sense that you're poorer when your boss gives you a raise and a promotion and suddenly you find that you're working longer hours and stressing out more. You can also look backwards: if the level of healthcare the median American pays for is a necessity, and poverty means being unable to afford necessities, then everyone was below the poverty line before these products were invented. But it’s not especially useful to say that the poverty rate is higher than you think, but fortunately has declined from 100%—if you’re tracking a statistic, it’s presumably because you care about the level and the trend, and a statistic that’s fixed at the most extreme reading it can give for the majority of the time series in question just isn’t a very sensitive metric.

And another important point the piece makes is housing. If there is a coherent theory by which output grows over time, but standards of living decline, it basically has to pass through housing supply restrictions. In that case, the standard-of-living problem comes down to marginal propensity to consume: if the upside from growth accrues to property owners, but they don't spend their gains, then you get a low-growth environment. Housing has gotten more expensive, but we also use a lot more of it than we used to; the average house has 40% more square footage than in 1973, and average household size dropped 17% over that time period. Housing is another category where it's hard to even find the 1963 consumption basket of a smaller dwelling with no air conditioning. Housing policy has pretty clear economics and much trickier politics; the people who own homes vote more than the people who rent because they can't afford a house yet, and obviously the counterfactual children who don't exist because their parents delayed family formation due to housing costs and had fewer kids are 0% of the electorate. But it's not a problem that can be easily solved with redistribution, rather than supply increases. We'd all fight about housing less if it were easy to build more of it.

The piece does raise one very important point, which is that means-testing with cliffs leads to all sorts of crazy distortions, like when Pennsylvania's system meant that a single mom with two children would be able to consume more when earning $29k than she would earning $69k. This basically turns career progression in the bottom half of the distribution into a kind of video game, where you can make a number go up and feel proud of it but get very little real-world upside. You do get the upside of having more control over what you spend money on—if you lose a Section 8 voucher and have cash instead, maybe your preference is to live with roommates and spend that housing money on something more fun, or to save it, or whatever. But you have to clear a high hurdle before you reach the point where making more money makes you better-off.

But that's also a function of how taxes and redistribution work—if there's some basic standard of living that everyone needs to get, the two ways to achieve this are 1) a big flat UBI that covers those basics, and progressive taxes to fund it, or 2) means-tested transfers such that we give more to people who earn less and vice-versa, which means we flatten the curve of after-tax income graphed against pretax income. Lowering the effective marginal tax rate of the lowest earners means some combination of transferring less to the poorest and raising taxes on everyone else. And that, once again, runs into the problem that the electorate is not the same as the population: higher earners vote more, and donate much more, so they have a bigger say in how things turn out.

When people from outside the US visit American friends of the same relative socioeconomic status, i.e. if a middle-class French person comes to the US to hang out with a middle-class American, they don't tend to wonder why everyone is poor. They will sometimes wonder just how many pickup trucks per capita Americans need, why there are so many parking lots, and why these pickup trucks need to be roughly the size of a tank. They also sometimes remark that the Standard American Climate, in July and December and in both Maine and Phoenix, is a steady 71 degrees year-round. It takes living in a very rich country to feel poor at $140k, but if Americans weren't capable of feeling that way, we'd probably take more vacations and grind a bit less.

It's just very hard to draw an accurate poverty line in a society that's well-off in the aggregate, offers lots of means for self-expression by way of consumption, but also has big non-tradable sectors like healthcare, housing, and education that suffer from a mix of cost disease, structural inefficiency, regulatory overhead, and regulatory capture. Some of the things you need to not feel poor today are unimaginable miracles by 1963 standards, and some of the things people took for granted then, particularly unmeasurable ones, are now high-class luxuries. It's very impressive that we can have standards as high as we do, but we need to be honest about how high those standards are historically and relative to other countries, and about what specific problem we're trying to measure when we talk about poverty.

Healthcare, education, and housing have risen as a share of household budgets in part because that’s where more marginal consumption dollars go when basic needs have been met. If you’re poor enough that you can’t afford to eat, your next dollar probably goes to a meal. But if you’re reasonably well-off already and your income suddenly doubles, you’re more likely to increase the square footage of your home (or move somewhere with pricier square feet) than to double your calorie consumption. Healthcare and education can absorb surprisingly large sums in a rich country because that country has to pay people more to do those jobs; housing can absorb a lot more money because people put a high value on living in particular places. All of this would be true even if there weren’t regulatory and industry structure reasons for costs to spiral. But all of those extra layers of inefficiency are paying people, even if they shouldn’t be. So it’s inflating the cost of a middle-class lifestyle, but also inflating the incomes of workers who can’t get jobs in higher-output sectors. It’s hard to solve a cost-of-living problem purely through redistribution, but even in a very streamlined economy, higher output would make these specific problems—you have to pay service workers more per hour the richer the country gets, and these kinds of spending will represent a growing share of your spending basket—worse. 

The skeleton key to a lot of this discourse might be surprising: one reason millennials feel poorer than previous generations despite having higher inflation-adjusted net worths at the same age is that their parents, especially the richer ones, had kids later in life. If you remember when your parents were in their 20s, you probably remember your parents having to scrimp and budget a bit, but if you were born when your parents were in their mid-30s, and your first memories are from when they were in their 40s, they were already pretty well-established. It's one thing to ride the bus with Dad; it's another thing entirely to hear Dad talk about how he used to have to ride the bus to work every morning, and to hear that story from the back seat of a BMW. If your parents had you later in life, your first experience of what it's like to be in your early 20s and nearly broke is when you reach your early 20s and find that you're nearly broke. It's quite a shock!² But it's also a kind of reenchantment: if you're an only child, having roommates as a young adult means you can truly appreciate the experience of finally being able to afford a place of your own. If you've had to run the numbers on calories per dollar because you just ran the numbers on bills due before your next paycheck, it's an incredibly liberating experience to suddenly discover that you can buy a cup of coffee without looking at the price tag. One of the biggest political issues in the US is, and has been for decades, the question of how we decide which of the millions of people who want to come to this country get to do so. Did the whole world get poorer? Is America incredibly good at marketing an image of success that doesn't align with people's expectations, and if that's the case why don't people bounce back to their more prosperous home countries? It's not impossible for this to be the case, but it does imply that the real question at hand is how people who were fortunate enough to grow up in the richest country in the world still feel poor.

And, at the same time: it's true that if you extrapolate the economic growth previous generations experienced, you get much higher GDP per capita numbers today. We really did have a long lull in productivity growth starting in 1971, though recent numbers actually look a lot more like what we had during that mid-century boom. That makes it a very tricky time for economic populism: there's a new economic growth engine, it will absolutely produce a mix of winners and losers, and when the losers say that they did everything right and lost their jobs anyway, they'll have a point. It was a very fortunate historical coincidence that the US had just had a unifying national triumph in the form of the Second World War, and also faced a similarly unity-inspiring enemy in the form of the Soviet Union. This round, we started with less social trust, and our last big collective lifesaving triumph, the rollout of Covid vaccines, has been more or less disowned by the politician who could be most associated with it. At least we have a geopolitical rival to rally against, though. One of the great sacrifices Americans make for the common good is to feel broke earning $20k, $100k, $500k, $2.5m, etc. If you can trick yourself into doing that, and blame yourself for it, you're going to work a lot harder than you otherwise would and take the kinds of risks long-term growth is made of. But if you turn it into a political issue rather than a personal one, you're going to be tempted to look at the numbers that feel right rather than the numbers that accurately describe reality.

The Diff has written about the factors affecting economic growth, mobility, and inequality in pieces that look at specific industries as well as the broader context. Among them:

• Older and richer voters are more likely to prefer low-growth policies that also reduce inflation ($).

• Housing is such a big asset class, and has such long duration, that it can drive a feedback loop of low rates and low growth ($).

• The original essay being discussed gets one thing quite right: it's getting increasingly hard to compare price levels over time ($).

• When there's a major new technology, it revalues existing resources. That's one of the ways growth drives inequality: it can lead to more bidders for the same input.

• It's very hard to design an optimal tax system without some perverse incentives.

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