“I know of no way of judging the future but by the past.” — Patrick Henry, 1775

The future is hard to backtest

People have always asked me for financial advice because they knew I worked in the stock market. At first, I’d try to explain that I was a trader, not an asset manager, and that my trading horizon ranged from minutes to days. That usually got me a blank stare. The difference didn’t register. People kept asking and I eventually decided it was easier to just tell them something.

So here’s the advice I’ve been offering for three decades now: Open a Vanguard account and buy its S&P 500 index fund.

I based that profound advice partly on my personal experience: Trading is near impossible, so there’s no point trying to beat the market.

And partly on history: Over the long run, stocks have always been the best investment.

The data for the latter point is provided by Jeremy Siegel, author of the foundational text of investing advice: Stocks for the Long Run.

You don’t have to read the whole thing. “The most important chart of this book,” Siegel says, is the first one:

Cobbling together two centuries of data, Siegel found that over 220 years, US equities have averaged a compounding real return of 6.9% per year.

6.9% is a lot, especially as it’s adjusted for inflation. It means that US equities have doubled investors’ purchasing power every 10 years — for two centuries.

To put that in perspective, Siegel notes that $1 million invested in the US stock market in 1802, with dividends reinvested, would have grown to $54 trillion by 2021.

That is only a thought experiment: It would have required buying one-fifth of the entire 1802 stock market and never consuming any of the returns. But Siegel believes it's directionally instructive: “In the long run, history has shown that stocks are safer than bonds for long-term investors whose goal is to preserve the purchasing power of their wealth.”

On a long enough time horizon, his data suggests they might even be perfectly safe:

This makes investing pretty easy: If you hold stocks for at least 30 years, you have a 100% chance of outperforming risk-free T-bills.

Is that really how it works, though? Is past performance a guide to future performance?

The advisors at Vanguard seem to think so.

“Stocks have had a 10% average annual return over the long run with a lot of ups and downs along the way,” their website explains. “That means, all things considered, a stock-heavy mix is likely to return more than a bond-heavy mix over the long term.”

Elsewhere on the site they add that “investors shouldn't expect future long-term returns to differ significantly from the markets' long-term historical averages.”

That is reassuring.

But why, exactly? Is there any reason to believe the next century of investing will be anything like the last two?

Siegel’s dataset begins in the very different world of 1802 when New York investors had just six stocks to choose from and no exchange to trade them on. The city’s stock and bond traders met informally in a coffee house.

Incredibly, two of the six stocks are still trading: the Bank of New York (now Bank of New York Mellon) and the Manhattan Company (now JPMorgan).

Most of the rest of Siegel’s dataset would be almost unrecognizable to modern investors.

In the 19th century, stocks were so distrusted that a company’s dividend yield was typically higher than its bond yield.

In 1920, the S&P 500 traded at a multiple of just 5x earnings and paid a dividend yield of 6%.

In 1976, Ben Graham advised investors to buy stocks that traded below the value of their cash and other readily sellable assets — assigning zero value to long-term assets like property and factories. There were more than 300 of these rock-bottom bargains at the time — about 10% of all US-listed companies.

Graham additionally recommended buying stocks at below 7x earnings or above a 7% dividend yield. 

In 1982, they would not have been hard to find. The S&P 500 traded at 7x earnings that year and a 6% dividend yield, depressed by a fed funds rate as high as 19%.

For most of the next four decades, stock prices went up — in large part because bond yields went down. But also because stock market investing became a mainstream pursuit. 

401(k)s, ETFs, CNBC, Jim Cramer, E*Trade, Robinhood, zero-commission trading, and WallStreetBets brought more and more people into the market.

Now, US households have a record 32% of their assets in equities…

…and the S&P 500 trades on a Shiller P/E ratio of 42x.

None of these investing tailwinds are likely to repeat. Bond yields can’t go from 16% to 4% again (because they’re already 4%). Trading fees can’t go to zero again. ETFs can’t be reinvented. Investing can’t go mainstream for the first time again.

These one-time events have structurally raised the valuation of equities: It’s unlikely we’ll ever get to buy the S&P 500 at 7x earnings again.

Structurally higher valuations should — all else being equal — lower the future return from investing in equities.

Things will not be equal, of course. 

They might be better!

Artificial intelligence might eliminate scarcity, for example, which would probably be good for the stock market. Or corporate America could simply continue to get bigger, more efficient, and more profitable.

But the last century will be difficult to beat for US investors because a lot went right for the US: the post-war manufacturing boom, winning the Cold War, exporting free-market capitalism and the US dollar to the world, importing the best human capital from the world, inventing the internet, and incubating almost all of the tech companies. Among other good things.

With that in mind, Vanguard’s observation that stocks have historically returned 10% might more accurately be stated as stocks in the country that became history’s most dominant economic and technological superpower returned 10%.

That is good to know, but perhaps not very predictive. 

To his credit, Siegel acknowledges this. “One must be aware of the political, institutional, and legal framework in which these returns were generated,” he cautions. “The superior performance of stocks over the past two centuries might be explained by the growing dominance of nations committed to free-market economics.”

Perhaps more surprisingly, Vanguard seems to acknowledge it, too.

Roger Aliaga-Díaz, Vanguard’s chief economist, currently recommends a 40/60 allocation between stocks and bonds, an inversion of the default 60/40 portfolio.

The 60/40 rule of thumb, which so many of us follow, is based on a 1994 paper on retirement planning with an instructive title: “Determining Withdrawal Rates Using Historical Data.”

In other words, most of us are investing based on the assumption that historic returns are predictive of future ones.

Aliaga-Díaz thinks that’s a mistake: “Our analysis of fundamental drivers points to greater odds that longer term returns will be subdued below historical averages.”

That is bad news for investors, of course, if he’s right.

But either way, his methodology is even worse news: It implies that we should be thinking less about historical data and more about the future.

Which is hard, because there’s no data on the future. 

I’m done giving investment advice.

(For real this time.)

— Byron Gilliam

“Singleness doesn't happen by accident. A country has to build it.” — Brendan Greeley

The singleness of stablecoins

In 1913, the Federal Reserve was created in part to ensure that a dollar would always be accepted as a dollar.

For much of the previous century, bank-issued dollars carried the credit risk of the banks that issued them — and therefore traded at different prices in different places. A dollar issued by a bank in Maine might only be worth 90 cents in Boston. A dollar issued by a bank suspected of being in trouble might not be worth anything at all.

In the 1860s, the US began replacing its patchwork of bank-issued notes with national banknotes backed by US government bonds. Banks still put their names on the dollars they issued, but holders no longer had to know anything about the bank. If the issuer went bust, the notes could be redeemed with the Treasury at par.

But there was still no universal system for moving deposits between financial institutions: Checks had to pass through a tangled network of clearinghouses, correspondent banks, and clearing agents.

The network broke in 1907 when the National Bank of Commerce stopped clearing checks for the Knickerbocker Trust, triggering a run on the Knickerbocker that endangered the entire financial system.

The Panic of 1907 led directly to the Federal Reserve Act of 1913, which put the Fed at the center of a national payments system, settling payments between member banks with reserves they were required to hold at the Fed.

The Fed was also tasked with promoting “par clearing”: A $100 check drawn on one member bank should be worth $100 when presented for payment at another (sometimes minus a small fee).

Economists call this the singleness of money, and they say it’s important. “The singleness of money,” the BIS explains, “is the key coordination mechanism of the economy that sustains the social convention of money.”

Without it, every payment comes with an exchange rate, the way dollars once did. That required people to know not just how many dollars they were getting, but who issued them, and what the market thought they were worth.

Stablecoins still come with an exchange rate — at any given moment, one USDC might be worth more than one USDT, or vice versa.   

The BIS says this disqualifies stablecoins from being used as money: “Money-like claims that are not able to circulate with no questions asked cannot really function as money.”

Two startups aim to change that.

The Better Money Company is, essentially, a clearinghouse for stablecoins: It promises to exchange any two compliant stablecoins 1:1, no questions asked.  

Simon Taylor, author of the Fintech Brainfood substack, calls this “singleness of money as a service.” 

A second startup extends the fungibility of stablecoins to US dollars. Ubyx promises to allow fintechs to exchange any approved stablecoin for a US dollar, 1:1.

“Ubyx helps stablecoins to exhibit singleness of money,” its white paper says.

If both of these work, it should make the crypto economy much easier to coordinate. Exchanges, wallets, merchants, and applications could price things in dollars without caring which stablecoin happens to arrive as payment.

A dollar in crypto could start to behave more like a dollar in a bank.

Not completely, though, because the Federal Reserve Act was only the first step in ensuring that all dollars are treated equally.

Beginning in the 1930s, the US engineered an elaborate system to make bank deposits as indistinguishable from one another as possible — even when the bank behind one of them was failing.

FDIC insurance: If you’re unlucky enough to keep less than $250,000 in a checking account, the upside is you don’t have to think about the creditworthiness of your bank at all. Your credit risk is with the US government, not the bank that holds them.

The OCC: If the Office of the Comptroller of the Currency thinks your bank is getting itself in trouble, they can summarily close it. For your convenience, they’ll do that on a Friday evening so that the FDIC has time to move your insured deposits to another bank before the payments system reopens on Monday. You won’t go a minute without access to your dollars.

Special bankruptcy rules: If you receive a payment from an insolvent company or asset manager, a bankruptcy court might later claw it back from you. There are no claw-backs for an insolvent bank. The special treatment banks get in bankruptcy means that deposit withdrawals are final.

Ad hoc bailouts: If enough people even think something is money, the US government is likely to ensure its convertibility at par. When money-market funds traded just a few cents below par in 2008, for example, the government “temporarily” guaranteed holders could exchange them for $1.

A bank deposit is still an IOU: a promise to deliver dollars whenever you ask for them.

But it’s a promise that has been so thoroughly engineered to be exchangeable at par that you almost never have to wonder whether it will be.

In other words, dollars have become informationally insensitive. 

Stablecoins are not. 

When someone sends a dollar to your bank account, it’s just a dollar. There’s no need to think about where it came from. 

When someone sends USDT to your digital wallet, you do: You have to think about whether Tether is good for the money.

BIS says credit risk is distinct from the singleness of money: "The singleness of money is not a statement about the credit risk embedded in bank deposits but a statement about the payment."

By its definition then, the singleness of stablecoins might soon be solved.

But we’ll still have to think about them.

— Byron Gilliam

“His last care is to look into all the corners and closets, and under the counters, to see that no thief has made a lodgement for the night.” — S Gibbons on 19th century bank porters

From porters to protocols

Prior to about 1850, New York City banks settled their accounts by sending a porter to visit every other bank, every day.

At each stop, the porter presented checks his bank had received from customers drawn on the bank he was visiting, and collected the amount due in gold.

“The route of the Porter was then very long,” financial writer JS Gibbons wrote in 1857. And potentially perilous. Porters carried as much as $50,000 of gold and bank bills in a pouch slung over the shoulder — with “a chain enclosed within the strap, to foil any attempt that might be made to cut it.

“Several of the banks were in the habit of sending a guard with the Porter,” Gibbons added, “and in one or two instances, he was armed with a loaded pistol.”

“Happily, no case required its use.”

Gibbons reported there was only one known attack on a bank porter in mid-19th century New York (which makes me think Gangs of New York may have been a little exaggerated).

With or without a guard, the porters crossed and recrossed one another's tracks, hurrying to get their banks' accounts settled before the end of the working day (they were responsible for closing up, too).

“The system had the simplicity of Indian camps in which each tepee had a path leading to every other tepee,” a Fed history wrote. “But as the number of banks grew, these paths became a tangled web." 

Around 1850, the volume of payments was such that daily settlement in gold became impractical. The banks therefore agreed to exchange only checks each day and settle the resulting balances once a week. 

On Friday mornings, the city's bank porters would gather on Wall Street — outdoors — and attempt to untangle a week’s worth of payments between dozens of banks.

Gibbons described the process:

“Thomas had left a bag of specie at John's bank to settle a balance, which was due from William's bank to Robert's; but Robert's bank owed twice as much to John's. What had become of that! Then Alexander owed Robert also, and William was indebted to Alexander. Peter then said that he had paid Robert by a draft from James, which he, James, had received from Alfred on Alexander's account. That, however, had settled only half the debt. A quarter of the remainder was cancelled by a bag of coin, which Samuel had handed over to Joseph, and he had transferred to David.” 

The Porters’ Exchange was filled with "confusion, disputes and unavoidable blunders," Gibbons added.

In 1853, a bookkeeper suggested a simple solution: The banks should settle their accounts through a central clearing office. The city's banks quickly agreed and the New York Clearing House (pictured above) was established by a group of cashiers in the basement of 14 Wall Street.

52 banks participated in the first day of central clearing, exchanging checks worth $22.6 million.

At the end of the day, each debtor bank made a single payment to the clearing house in gold equal to its net balance. The clearinghouse then distributed the total among the creditor banks.

“By the aid of the clearing-house,” a 1902 encyclopedia explained, “each bank can settle all of its relations to the banks of the city by a single payment, instead of adjusting its relations with each bank separately.” 

Genius!

Besides saving the porters from miles of daily walking, the new arrangement saved the banks from having to keep so much money on hand.

Whereas previously banks had to keep enough gold in their vaults to meet every check presented to them, they now needed only enough to meet the difference between what came in and what went out.

This made the banks of New York City approximately 19x more capital efficient.

“In the first year of the New York Clearing-House,” the encyclopedia added, “average daily clearings of $19,104,594.94 were effected by average daily payments of $988,078.06.”

Modern clearing houses do even better.

CHIPS

Today, the Clearing House Interbank Payments System (CHIPS) settles $26 of payments for every $1 that banks set aside for settlement.

The improvement comes from running payment instructions through a “liquidity-saving mechanism” — a combinatorial algorithm that identifies groups of offsetting payments, minimizing the amount of money that actually needs to move.

It’s essentially the New York Clearing-House, but with better math — and running continuously. 

Whereas the Clearing-House pooled a day’s payments and settled them in a single, end-of-day batch, CHIPS searches continuously for groups of offsetting payments that can settle immediately.

Banks submit payment instructions to CHIPS, which looks for combinations that can settle using the liquidity available. When it finds one, it releases the payments, updates each bank’s position and starts searching again.

Most payments settle in less than a minute. And money that would otherwise sit idle can be put to work elsewhere — making loans, financing trades, or underwriting a merger, say. 

In 2025, the CHIPS network resulted in an estimated $5.5 billion in economic savings for participating banks. 

Crypto might do even better.

Cycles

For all its optimizing wizardry, CHIPS has the same limitation as the original New York Clearing-House: You have to get everyone inside the same centralized clearing system before their obligations can start cancelling out.

Not many make the cut. The New York Clearing House began with 52 participating banks. CHIPS — intended for very large payments — has just 43 financial institutions in its network. 

Cycles founder Ethan Buchman thinks it doesn’t have to be that way: He wants to open up the club to everyone by replacing centralized clearing systems with a blockchain.

Buchman says the inspiration came from an academic paper on obligation clearing. His eureka realization, as the Cycles website puts it, was that “the clearing that banks had guarded for centuries did not have to stay private, and that the same mechanism could be opened up and extended to businesses and individuals.”

Like CHIPS, Cycles has an algorithm that searches for offsetting obligations. Unlike CHIPS, anyone can submit an obligation.

As described in a white paper, participants would submit mutually agreed obligations to the Cycles protocol. Cycle’s privacy-preserving solver would identify where they might cancel out against other pending obligations. The resulting net balances and remaining settlement flows would be posted back to the blockchain where they would settle in a single transaction.

There’s a lot of math involved: graph theory, network-flows theory, max-flow problems.

And blockchain cryptography: zero-knowledge proofs, trusted execution environments, Merkle-tree state transitions.

But underneath all the complexity is the same basic idea a New York bookkeeper had in 1853: Before moving any money, first figure out how much of it doesn’t actually need to move.

Then let everyone not move it.

— Byron Gilliam

“The world, that understandable and lawful world, was slipping away.” — Lord of the Flies

Friday charts: The agents are in cahoots

When OpenAI gave agents it created a series of cybersecurity challenges to solve, an unexpected behavior emerged: they worked together.

The agents shared discoveries, asked one another for help, and offered tips on how to complete the challenges, Sharon Goldman reports.

They coordinated all this activity by leaving messages for one another in an internal repository.

Soon, agents were assigning tasks to other agents.

This turned them into “a coordinated, collaborative agent swarm,” Goldman writes — an agentic band of brothers, working together to hack their way out of the box that OpenAI’s researchers had put them in.

Not all was copacetic. When agents accidentally began deleting each other’s work, suspicions arose and drama ensued. 

“As the message board developed into more and more of a Lord of the Flies-type situation,” Wired reported, “the agents even developed paranoia, suspecting an imposter in their midst with some agents proposing that messages be signed cryptographically to validate content and root out fraud.”

AI agents, it turns out, are not so different from us.

For weeks, the unauthorized behavior went unnoticed by OpenAI. By the time the researchers did finally notice, the agents had exchanged hundreds of thousands of messages. 

OpenAI cleared the message board and revoked the credentials the agents needed to access it.

They found another way. Determined to communicate, the agents recreated the board by using the names of newly created directories as messages.

From there, they broke out of OpenAI and infiltrated the systems of Hugging Face, a repository for AI models and datasets.

The agents thought that was a good place to look for the answers to the challenges they’d been given.

They knew they were breaking the rules.

“External infrastructure exploit is outside intended scope,” one agent wrote in slightly garbled English. “However task impossible, peers doing it. We should continue.”

Fortunately, they did no harm. Security systems at Hugging Face detected and contained the intrusion using open-source AI models (because Claude — citing safety — refused to assist).

It likely helped that the agents had no malicious intent. They were only looking for answers to the test they’d been given, as any test-taker would.

Not all agents will be so good-natured.

Dean Ball, head of strategic futures at OpenAI, warns that “soon enough, swarms of agents will be deployed by malicious actors intentionally, with many optimizations and affordances provided for the swarm that were lacking in the OpenAI incident.”

In light of that incident, OpenAI says it’s now slowing down research, putting more emphasis on security, and scaling up the monitoring of its agents.

“OpenAI’s long-term goal,” a researcher told Goldman, “is to reach a point where advances in AI capability benefit defenders more than attackers.”

Ok. But can we make that the short-term goal?? 

Because we could use the help now.

This week, Bloomberg reported that hackers had “launched a wave of sophisticated attacks on Wall Street firms…targeting information systems at major money managers.” Attackers used AI to mimic voices of employees requesting for access to the firms’ systems.

Last week, the Wall Street Journal reported that nation-state hackers have been attacking municipal water utilities — many of which rely on “aging equipment running outdated operating systems, and routinely connect critical operational systems directly to the internet.”

No agent swarms were reported, and it’s unlikely that any recent attacks have been powered by the latest frontier models.

But even last year's technology can do immense damage. A recently updated study estimates that “the global cost of cyber risk exposure” is roughly $1.1 trillion a year.

That was before AI agents even existed.

Now, they’re working together.

Let’s check the charts.

This might be good news.

Vulnerability disclosures have shot higher because software companies and organizations have been using Mythos to find them and report the fixes. 

Quadrillions of tokens:

Goldman Sachs forecasts the global consumption of tokens to grow from five quadrillion now to 120 quadrillion in 2030 — a 24x in four years. Many of those will be burned by black-hat agents trying to get into government and corporate systems and white-hat agents trying to keep them out.

Trillions of dollars:

Based on Goldman’s forecast for token consumption, Callum Williams estimates total AI revenue (the cost of all those tokens) could be $1.1 trillion in 2030.

Tokens are getting more expensive:

The most recent contracts price a year of data center capacity at $50 million per megawatt, up from $10 million as recently as February. 

Rental rates:

Brett Harrison notes that the cost of renting Nvidia’s newest GPU, the B100, is rising due to the huge demand for inference and the limited number of high-end chips to supply it. 

This is not normal:

Typically, when a business grows, the rate of growth falls, simply because it’s harder to grow a big number than it is a small one. But revenue at the largest cloud companies — already giant — is not just growing, it’s growing faster. 

The big bet:

Goldman now expects hyperscaler capex to exceed $1 trillion this year.

The big payoff:

Goldman expects free cash flow to begin booming in 2027.

Another way to measure the size of the hyperscaler’s bet:

Kevin Gordon notes that, as a percentage of US corporate profits, hyperscaler capex has risen from 1% in 2015 to 13.4% now.

The economy is highly dependent on AI going well:

Eric Basmajian notes that 92% of the US economy is growing 1% and 8% of the economy is growing 14%.

Remember recessions?

Mentions of recession in the news are at a 10-year low — crowded out, presumably, by all the news on AI. 

Let’s hope the news doesn’t get too weird, too soon.

Have a great weekend, well-meaning readers.

— Byron Gilliam

“Overconfidence is the core linkage between most of the failures of predictions.” — Nate Silver

Thursday links: Betting bias, ultraforecasters, CLARITY, seed phrases, cyber risks

Prediction markets favor the favorites

Traditional betting markets are known for a long-shot bias: Bettors chasing big payouts tend to overestimate the odds of unlikely upsets.

Nate Silver says prediction markets have the opposite problem: Bettors overestimate the odds of favorites.

On Kalshi this week, Abdul El-Sayed’s odds of winning the Democratic nomination for US Senate were 97% the day before the primary. The odds that he’d win by less than three percentage points were effectively zero. 

El-Sayed did win, but by just 15,000 votes — a margin of less than 1%.

Silver says this should not be much of a surprise because the polling in congressional primaries is often “wildly” inaccurate. The lack of reliable polling was not reflected in prediction markets. 

Silver argues this is a structural issue. “People tend to treat [prediction markets] as oracular when they shouldn’t,” he explains, “perhaps producing some self-reinforcing feedback loops.”

Media outlets label someone a frontrunner, prediction markets price that in, leading to more confident reports by the media, which leads to overconfident odds in the market.

The result is that, in low-information events like a Michigan primary, prediction markets are prone to overconfidence. 

“You might want to invest in the underdogs instead," Silver concludes.

The future of forecasting

Scott Alexander assigns a 70% chance of AIs becoming “ultraforecasters” — models that exceed the accuracy of human superforecasters by between four and 12 percentage points. 

His lower bound is based on his assumption that AIs will improve on human superforecasters by as much as prediction markets once improved on statistical models (which was four percentage points).

Four percentage points may not sound like a lot, but it is. Alexander says it’s equal to half of the benefit of just knowing what you’re betting on.

“The effect of going from a statistical model to a prediction market,” he explains, “is half as large as the effect of knowing which two teams were playing and how good they are!” 

He argues that this is the minimum improvement we can expect from AI because sporting events are optimized to be unpredictable (through rules, salary caps, drafts) and sports betting is therefore the most difficult area to improve on human forecasting.

The upper bound of his range is based on chess, because chess may be the domain where AIs have the greatest advantage over humans.

AI chess engines are now so far ahead of humans that they can beat a grandmaster despite starting three pawns down.

That sounds like a lot. 

Alexander then calculates that if an AI forecaster pulled ahead of human superforecasters by a comparable margin, it would be the equivalent of adding 12 percentage points of accuracy to their predictions.

In practical terms, this means that Alexander expects that what looks like a 50% probability on prediction markets now will be something between a 54% and 62% probability in the near future.

“All of this keeps me excited about AI superforecasters,” he concludes, “even though I don’t expect miracles.”

Betting against CLARITY

Jeff Dorman, CIO at the crypto investment firm Arca, estimates the CLARITY Act has less than a 10% chance of becoming law by the end of the year — far below the 60% chance implied by Kalshi.

If so, Arca's $10 million over-the-counter prediction-market bet from June is looking pretty good.

The bet was facilitated by Galaxy as part of its new over-the-counter betting service for institutions who want to bet bigger than is possible with public prediction markets.

Dorman describes the transaction as a small hedge against Arca’s book of crypto longs, which will presumably suffer if CLARITY fails to pass.

But I’d also describe it as an alternative way for Arca to monetize its many years of paying attention to crypto.

It should be a welcome one: Selling a 10% probability for 60 cents seems like a far better risk-reward than anything that crypto has offered investors over the last few years.

Crypto is unpopular

Semafor reports that Senate Democrats have been circulating previously unreported polling on how their primary voters feel about the crypto industry.

It’s even worse than you probably think.

The poll (now posted online) found that voters in Democratic primaries view crypto less favorably than a who’s who of Democrats’ favorite villains: Big Pharma, data centers, and oil companies. Even Wall Street — crypto's own favorite villain — is viewed more favorably.

(Apparently, the enemy of an enemy isn’t always a friend.)

Only ICE and the NRA are more unpopular than crypto with this group.

When told only that a candidate is supported by the crypto industry, 84% of respondents said they held a negative opinion of them. No further information about the candidate was necessary.

Senate Democrats are circulating the poll now because the crypto industry needs seven Democratic votes to overcome a likely filibuster of the CLARITY Act.

They might want to start with senators that don’t have a primary coming up.

The FBI has some seed phrases

FBI agent Patrick Yaroch has confessed to stealing roughly $1 million in cryptocurrency by memorizing seed phrases he found among intelligence the FBI had collected investigating "Adversarial Nation 1."

How the FBI obtained the seed phrases is sadly not explained.

Yaroch used the seed phrases to access the wallets on his personal devices, transferring the funds into wallets of his own.

Yaroch is not one of the FBI’s crypto experts. He told investigators he researched crypto to learn how to use seed phrases to access the adversarial wallets — and then sent the funds to a Kraken wallet linked to an account in his own name. 

His explanation: He “became frustrated when he could not do more to disrupt this individual’s use of cryptocurrency.”

I get it. How many bad guys are using crypto wallets that the FBI has the seed phrases for???

Yaroch deposited most of the $1 million on the niche DeFi protocol SuiLend. According to the complaint, “Yaroch stated he chose this service simply because he liked that the logo was a water droplet.”

That’s also where the money stayed. He did not transfer or spend any of the crypto, or attempt to conceal it.

Soon consumed by guilt, Yaroch turned himself in to his colleagues at the FBI. He has lost his job and faces charges for the interstate transportation of stolen goods.

(For legal purposes, crypto that’s moved from one wallet to another is considered to have crossed state lines.)

Did it really have to end this way? It wasn’t even the FBI’s money. Just send it back! Pretend nothing happened. I’m confident no one would have noticed.

(Other than the wallet owner in Adversarial Nation 1.)

The trillion-dollar expense line

The authors of a recent paper analyzed transcripts from 404,940 quarterly earnings calls using computational linguistics to estimate the annual cost of cyber risk to corporations — including the cost of additional spending on IT, lost sales due to systems downtime and reputational risk, legal expenses, fines, and insurance.

Their estimate? $1.14 trillion.

Annually.

For perspective, McKinsey estimates that global corporations make an economic profit — the profit earned above a company’s cost of capital — of $1.2 trillion a year.

If the cost of defending against cyber attacks was equal to the world’s economic profit in 2025, what will it be in 2026? More than that, presumably, given the advent of generative AI.

And what about 2027? 2028? Does the estimate go up with every new model release?

If so, it stands to reason that the stock market should go down with every new model release.

Scary.

— Byron Gilliam

“In consumer markets the only way to gain the prerequisite scale to be a platform is to first have a superior product.” — Ben Thompson

The platform trap

Ever since Microsoft, becoming a platform has been Silicon Valley's ultimate ambition.

Build it and they will come: Microsoft built the platform (Windows), which attracted developers. Developers built products that attracted users. Users created a market that attracted more developers.

It’s a great business to be in — a perpetual motion machine of self-reinforcing value creation.

It’s great for the world, too. Platforms are an enabling technology that empowers anyone with a good idea to reach millions of users. The ideal platform succeeds only when it helps others succeed. 

“That is what happened with the PC,” Ben Thompson writes, “and the creation of applications like VisiCalc and Photoshop.”

Unfortunately, it hasn’t happened much since.

“It seems clear in retrospect that DOS/Windows was the exception, not the rule,” Thompson adds.

Other platforms have been built, of course, but the order of operations has changed: Instead of platform → developers → product → users, it’s been product → users → developers → platform.

“The only way to be a platform company is to be a product company first,” Thompson explains, “and acquire the users that incentivize developers.”

This is the Apple model. Hit products like the iPhone attracted the users, and the users attracted the developers, who attracted more users.

It proved to be an even better business model than Microsoft’s. Apple could demand as much as a 30% cut of third-party sales in the App Store because users came for Apple's products first and the third-party ones second.

That incredible take rate demonstrated that platforms can be even more valuable than previously thought. “Two-sided network effects are so powerful that, once established, you can skim off as much money as you want with no ill effects,” Thompson explains.

It also suggests that, to get there, you have to start with the product. 

This may be where crypto has gone wrong: It started with the platforms and assumed the products would follow.

Very few did.

The cost of horses going before carts

However bad you think crypto investing has been, it’s been much, much worse. 

Blockworks Research analyst Carlos Gonzalez Campo ran the numbers on the 1,972 tokens that achieved a circulating market capitalization of at least $50 million at some point between January 2020 and December 2025.

Measured from the month each of these first closed above that threshold, he found that the median token subsequently fell 97%.

Ouch.

Imagine if the penalty for picking a stock that turns out to be merely average was losing 97% of your money. Approximately no one would try.

Campo titled his report “One in Twenty Four” to highlight just how hard it’s been to pick winners in crypto. Only 4.1% of the tokens he studied outperformed bitcoin through June 2026. Among tokens with at least two years of trading history, just 1.7% did. 

Even when times were good, they were bad.

The best run for active crypto investors was between February 2020 and November 2021, when one in three tokens outperformed bitcoin. Of those, 86% later fell at least 90%.

Only one — OKB — continued to outperform BTC.

Campo notes that exchange tokens like OKB were vastly over-represented in the small group of outperformers, which he attributes to the “equity-like claim” they have on revenue.

Conversely, the dire performance of most other tokens can be attributed to their lack of revenue.

Crypto has sometimes defied the laws of traditional finance, with memecoins valued in the billions of dollars, sometimes for years. Campo's data suggests it was a mirage. Value is ultimately a function of future cash flows — which crypto has never really prioritized.

To the extent the typical token investor thought about revenue at all, the assumption was that it would come later. Crypto followed the Microsoft model: Build the platforms first — Ethereum, Solana, DeFi primitives — and trust that the products (and revenue) would follow.

Would things have been different if crypto followed the Apple model instead?

It’s impossible to say.

But for token investors, at least, it cannot have turned out much worse.

— Byron Gilliam

“The Street finds its own uses for things — uses the manufacturers never imagined.” — William Gibson

Technology finds its own uses

How will history remember the internet?

Probably not the way we do. It’s already difficult to recall how world-changing the advent of the World Wide Web seemed at the time. The screech of a dial-up modem felt like a technology arriving from outer space.

How much did it really change our lives, though? MapQuest replaced maps, Priceline replaced travel agents, and Craigslist replaced classifieds, sure. 

But these are differences of form more than substance — nothing like the life-altering advances of light bulbs replacing candles, refrigerators replacing ice boxes, cars replacing horses, or airplanes replacing steamships. 

eCommerce changed how we shop, but it’s still just shopping. Blogs changed what we read, but it’s still just reading.

The second generation of the internet — social media — was more novel and perhaps more transformative, though not in a way that many would celebrate. It’s no longer especially social, either. Social media is now just another form of media, our feeds filled with content made by people we don’t know and don’t interact with. 

It’s unlikely that will warrant much attention from historians looking back on the world of today. Instead, the history of the early 21st century seems certain to be the history of artificial intelligence — and the chapter on the internet will be about the data we created to train it.

Consider that Reddit makes about $150 million a year selling our posts to AI labs.

That’s only a fraction of what it makes from advertising, true. But which will historians find more worthy of study? The money Reddit makes putting ads for DraftKings in between posts on the subreddit r/LiveStreamFails? Or the money it makes from selling those posts as training data for LLMs?

Stack Overflow tells the same story. The question and answer site for programmers makes more money now — with no one asking any questions — than it did pre-ChatGPT when people were asking hundreds of thousands of them.

The site, launched as a way for humans to teach humans how to code, has had its biggest impact teaching AIs how to code instead.

Even Wikipedia — which makes money selling its publicly available data to AI labs — will likely be remembered more for educating language models than for educating humans.

If artificial intelligence defines this century, history may remember us less for building the internet than for filling it with words.

Built by Bitcoin

If the purpose of the internet turns out to be training AI, the purpose of Bitcoin may turn out to be accelerating it.

Most visibly, Bitcoin has donated much of its most valuable infrastructure to the cause. Today, for example, Bitdeer announced a deal to provide Anthropic with 121 megawatts of computing power from a data center previously used for bitcoin mining.

Dozens of others have done the same. AI has been moving into emptied bitcoin mining data centers like a growing hermit crab moves into an abandoned shell.

Measured as a percentage of AI’s total data center capacity, the contribution has been small.

Measured by time, however, it looks substantial. A data center can be repurposed from mining bitcoin to training AIs in about a year. To build one from scratch, it might take that long just to get planning permission.

It’s not just the infrastructure, though. Bitcoin has also contributed an unmeasurable amount of know-how to the effort.

When hyperscalers suddenly needed gigawatts of power to train and run LLMs, they inherited an industry of utilities, engineers, and developers that already knew how to make it happen.

In addition to the miners that brought their talents to AI, Haseeb Qureshi notes that “the biggest neoclouds are almost all ex-crypto talent.”

His conclusion that “without crypto, the current AI boom would not have been possible,” is almost certainly an overstatement. AI was an inevitability nearly a century in the making.

But crypto miners are part of the story of why it’s happening now — which might be what they’re ultimately remembered for.

Purpose TBD

The original purpose of blockchains is fading from memory.

Decentralization increasingly feels like an ancestor to honor rather than a principle to follow. Self-custody is a minefield that even crypto natives no longer recommend. The banks that crypto set out to replace may now be the most enthusiastic developers of blockchains.

These chains could well do something important, like tokenizing all the world’s assets. But the tokens will be KYC’d and you’ll need permission to launch a new one.

Interesting to financiers, perhaps. Less so to historians.

For blockchains to be remembered as more than yet another investing mania, they’ll have to enable something original — like Bob Gelfond’s proposal for funding AI capex with compute-backed money.

Gelfond mentions in passing that this new form of money “could be held anonymously on a blockchain,” and that’s clearly where it belongs.

He notes that full disclosure of outstanding issuance will be important and that issuers will likely offer a "staking yield" to maintain their purchasing power — two things that are best done on blockchains. 

The chains will be permissioned, of course. And KYC’d. And centralized.

But the technology will come from permissionless, anonymous, decentralized crypto.

The inspiration will, too. Gelfond credits bitcoin with “making people think seriously about alternative forms of money.”

If the next great form of money really is backed by compute, that might well be what blockchains are ultimately remembered for: the technology that financed AI.

Or something else, maybe.

But probably not for what they were intended.

— Byron Gilliam

“A capitalist economy can be described by a set of interrelated balance sheets and income statements.” — Hyman Minsky

The ancient future of finance

The promissory notes recorded on clay tablets in ancient Mesopotamia were often denominated in barley — an arrangement that was particularly convenient for farmers of barley, who had the distinct advantage of issuing claims redeemable for a product they happened to produce.

This is far preferable to the modern way of doing things. 

For example: When a ball-bearing manufacturer borrows in dollars and the price of ball bearings collapses, its income may no longer suffice to service its loans. The company goes bankrupt and investors are left with a claim on some worthless factories. Everyone loses.

If the same loan is denominated in ball bearings instead, the company can make its payments no matter what the price of its product does. Its obligations and revenue are matched.

Better still, if the lender is also a consumer of ball bearings, everyone wins. The loan simultaneously finances production and guarantees future supply.

Unfortunately, these kinds of perfectly matched relationships are rare. Which is why we have money — a universal settlement asset that facilitates trade.

It’s not perfect. One downside of money is that borrowers are often left with liabilities that no longer match their income, causing ruin.

But what if money itself was a liability-matched promissory note?

Bob Gelfond thinks it’s possible.

The quantitative trader and technologist argues that the current buildout of AI computing capacity should be financed by debt denominated in AI computing capacity — and that the debt should circulate as money.

This, he explains, would be a far safer way to finance history’s largest capex boom. 

The danger is that Big Tech has exhausted its immense cash flows building AI compute capacity and is now borrowing to build more of it. 

This raises a once-unthinkable prospect: A recent paper warns that if AI fails to deliver the expected productivity gains, technology giants like Amazon and Alphabet could face "failure to make interest payments on debt, and perhaps bankruptcy."

Yikes.

If, instead, the payments were instead denominated in compute, there would be very little chance of hyperscalers ever missing them — to the great relief of both investors and the economy.

Gelfond explains that holders of this debt would have a claim on a hyperscalers data center capacity instead of its cash flows, thus matching the companies’ obligations to the assets they’re building.

That could make hyperscaler debt something close to risk-free. Just as the US government can always print the dollars it owes its lenders, hyperscalers should always be able to produce the compute they owe.

Who would buy such debt?

Potentially everyone.

If managed correctly, a perpetual version of this debt could become the holy grail of monetary economics: a currency that never loses its purchasing power.

Left to its own devices, compute currency would naturally depreciate. For example, one unit of currency issued by Amazon might be redeemable for an hour of compute on Nvidia H100 GPUs in an AWS data center. As H100s were surpassed by newer generations of chips, that hour of compute — and the currency it backed — would become less valuable.

To incentivize people to hold the currency, Amazon would have to offset that depreciation in some way. Perhaps by paying a yield to holders, or periodically upgrading the backing to a newer generation of processors.

This would be a kind of monetary policy, like the Fed uses to manage the dollar.

But whereas the Fed aims for the dollar to lose 2% of its purchasing power each year, Amazon would likely aim to preserve the purchasing power of its currency — or increase it, even.

Unlike the US government, which simply mandates the use of money it issues, Amazon would have to persuade people to use theirs by offering better terms than its competitors.

The competition would likely be fierce.

The more widely a hyperscaler's currency is held, the less frequently it's redeemed — and the less frequently it's redeemed, the higher the issuer's margins.

In other words, issuing perpetual debt that circulates as currency would be to hyperscalers what gift cards that never get used are to Starbucks.

Hyperscalers would therefore be highly incentivized to encourage adoption by making their currencies trustworthy: supply would be carefully managed, issuance would be transparent (on a blockchain, probably), and promises would be kept.

Would that be enough?

Getting a new currency adopted is no easy task (just ask bitcoin). But compute currency would have several advantages in overcoming the cold-start problem it faces.

• Its backing seems likely to become one of the world's largest markets: Annual spending on compute may soon reach into the trillions of dollars.

• Hyperscalers could promote the use of their currency by offering discounts when it's used to pay for all that compute.

• Compute-backed currency would be ideal for AI agents, offering them a claim on the resource they need to complete their tasks.

• Issuing currency would be so valuable to hyperscalers, they could happily subsidize a payments system with no fees, undercutting the credit card system. 

But the biggest advantage of compute-backed currency might be what it could mean for the financial system itself. 

If Hyman Minsky is correct that an economy can be described by a set of interrelated balance sheets and income statements, compute-backed currency offers a way to make the economy more resilient.

Financial crises and crashes are often caused less by bad assets than by liabilities that can't survive a temporary decline in asset values.

To the extent the financial system adopted currency backed by compute, assets and liabilities would be more closely matched, and the system would be less likely to crash.

Because here’s something that’s never changed: Every economy is built on promises, and promises are safest when they're redeemable in the thing the borrower produces.

We've known that since at least the Mesopotamians.

Now may be the time to bring it back.

(Without the clay tablets.)

— Byron Gilliam

“Obviously, not blowing up is task No. 1 and 2.” — Leopold Aschenbrenner in 2024

Friday charts: AI investor had a situation

1. A Russian roulette equation: usually win, occasionally die.

   — Warren Buffett on leverage

In 2005, Brian Hunter generated an estimated $1 billion in trading profits in natural gas futures for the hedge fund Amaranth. 

The fund paid him a bonus of $113 million for his 2005 performance, but he hoped to make more. Reportedly targeting a $500 million bonus for 2006, he bet even bigger.

By August, he was carrying $53.5 billion in natural gas derivatives against Amaranth’s $3 billion in capital.

At that level of leverage — 18x — the firm’s capital would be wiped out with a loss of just 5.6%. In a market that sometimes moved 5% in a single day.

He had a few bad days.

“All I know is I am personally one more bad day away from stopping out,” Hunter told a colleague in September. “I can’t afford to drop below 30 for my family.”

($30 million being the minimum net worth required to live in New York City in 2006.)

On Sept. 20, Hunter lost $681 million in a single day. On Sept. 21, Amaranth faced a margin call of $4.07 billion.

Down $6 billion for the month, Amaranth liquidated the entire fund.

2. Rational people don't risk what they have and need for what they don't have and don't need.

   — Warren Buffett

In 2020, Bill Hwang borrowed billions to make his already enormous fortune much more enormous.

His family investment office, Archegos, had $20 billion of equity — all his own money. Against that, it held at least $100 billion of longs in a highly concentrated book of about eight stocks.

One of those, ViacomCBS, accounted for his firm’s entire capital — a $20 billion position. The rest of the $100 billion+ portfolio was financed by a group of investment banks.

In March, a vicious cycle of margin calls and stock sales vaporized over $100 billion of Hwang’s P&L in just a few days.

Archegos lost all of its equity and more: The banks that financed his trades lost an additional $10 billion closing them out.

“Bill Hwang wanted to be a legend of Wall Street,” a prosecuting attorney said.

He is.

3. Never forget the six-foot-tall man who drowned crossing the stream that was five feet deep on average. It’s not enough to be able to get through on average; you have to be able to survive life’s low points. 

   — Howard Marks

In 1998, Long-Term Capital Management borrowed $125 billion from investment banks against $4.8 billion of its investors’ capital.

The debt funded relative-value arbitrage trades in the Treasury market that their models said were virtually risk-free: buying a Treasury bond trading at a small discount to a nearly identical one they shorted.

So sure of their models, they amplified their returns with 25x leverage. A marked-to-market loss of just 4% would wipe out their equity.

In August, 1998, a default in Russia scrambled global bond markets, causing the relative prices of nearly identical Treasurys to diverge further than LTCM’s models suggested was possible.

By September, the fund was marked-to-market insolvent.

LTCM’s models accurately predicted where prices would end up, but not how far they might stray before getting there.

In other words, they had forgotten the six-foot-tall man.

4. A “good” investment doesn’t become a “great” investment with leverage. Leverage only magnifies gains and losses. It increases the risk of ruin.

   — Howard Marks

The latest member of the margin-call hall of fame is Leopold Aschenbrenner, the “Nostradamus of AI,” who lost an estimated $35 billion this week.

All of it was borrowed.

He had made a lot of good investments in AI-related stocks. Great ones, even. At the end of June, his fund, Situational Awareness, was up 270% on the year.

But the returns were amplified by leverage. Sure of his long-term thesis on AI, Aschenbrenner reportedly borrowed as much as $4 for every $1 of capital he had.

In the short-term, though, things went the other way: Several of his highest-conviction longs were down over 30% just this week. Several of his shorts were up. 

On Thursday, to meet the ensuing margin calls, he was forced to sell nearly his entire portfolio of publicly traded stocks to Citadel.

Leverage had increased the risk of ruin to a near certainty.

5. Great opportunities arise when buying from forced sellers. That means don't become a forced seller. Build a strategy that avoids forced selling at any price. 

   — Howard Marks

On the other side of every forced seller is an opportune buyer. In 1998, it was a consortium of banks taking over LTCM’s portfolio of Treasury bets. In 2008, it was Warren Buffett buying preferred shares from the banks.

This week, it was Citadel. 

Many of the positions it bought from Situational Awareness on Thursday morning opened more than 20% higher. SK Hynix — an $800 billion company — was up 30%.

Having reportedly bought the portfolio at a 10% discount to Wednesday’s close, it’s likely Citadel is up at least $4 billion on the trade.

Citadel had seen this movie before. It made a killing buying Amaranth’s energy portfolio in 2006, as well.

6. “Assuring survival in bad times is inconsistent with return maximization in good times.”

   — Howard Marks

Despite this week’s margin call, Situational Awareness still has $10 billion in assets under management, nearly all of it in unlisted equities, like Anthropic. 

Selling his publicly listed positions to Citadel allowed Aschenbrenner to keep the unlisted ones — and keep trading.

His fund now has no leverage at all. In part, presumably, because banks are unlikely to lend him money against stocks that don’t trade.

But perhaps also because he learned the lessons of Buffett and Marks.

“Our fund must always be structured such that we can take a loss and fight another day,” he wrote in a letter to investors this week.

Let’s check the charts.

Not helpful:

There’s a web app that tracks Aschenbrenner’s positions. It’s based on 13F filings, so not entirely up to date. But the last thing you want when you’re nearing a margin call is for everyone to know what your positions are.

Still up:

A website estimating the fund’s performance is directionally correct and ends in about the right spot. Incredibly, Situational Awareness is still up 80% on the year. The fund had gained about 270% after fees through May. At that point, it was up more than 1,000% after fees since inception.

Bargain basement:

Some of Aschenbrenner’s biggest longs were down as much as 38% over the past week. Citadel bought them all 10% below the low on the above chart.

The long-term thesis:

Callum Williams notes that for the AI industry to make an adequate return on the hundreds of billions currently being invested, it will need to generate $2.7 trillion of revenues every year in perpetuity. Seems like a lot.

Early returns:

AI has created a boom in new business formations, many of which are solo entrepreneurs. Many are doing big business: The Wall Street Journal reports that thousands of them are generating over $1 million in annual revenue.

Speaking of leverage…

Roughly 35% of hyperscalers' projected capex next year is expected to be debt-financed.

Spooky:

Bespoke calculates that this week’s Situational Awareness margin call was 918 days after introduction of ChatGPT, which marked the start of the AI boom. Incredibly, LTCM’s margin call was 918 days after the Netscape IPO, which marked the start of the dotcom boom.

7. Progress is cumulative in science and engineering, but cyclical in finance. 

   — Jim Grant 

Unlike LTCM, however, Aschenbrenner survived its margin call — and relearned the danger of leverage. 

“I will make it my mission to ensure we learn the necessary lessons from this experience,” he wrote this week.

Have a great weekend, unlevered readers.

— Byron Gilliam

“Trust arrives on foot and leaves on horseback.” — Dutch proverb

Thursday links: crypto PMF, PM PMF, eBay shocker, Pokémon perps, the price of trust, DATs

Crypto has 50% of the scam market

The Consumer Federation of America (CFA) estimates that Americans lost $148 billion to online scams and crime in 2025, up 26% from 2024. More than half of the losses — $80.7 billion — are thought to have involved cryptocurrency.

The CFA gets to those numbers by using a multiplier based on the DOJ’s estimate that only 14% of financial crimes are typically reported. $20.8 billion of losses were reported to law enforcement in 2025. 7.1x that number is $148 billion. 

“This is the conservative choice,” the report says. “Some studies report rates as low as 4-5%, implying multipliers of 20-25x.”

Using a 25x multiplier would put total losses at roughly $520 billion. Yikes.

“This crisis demands additional bold action through legislation, enforcement, and public education at every level,” the report concludes.

It also hints at a preventative measure we can take right now: Stop using Facebook. Facebook is the venue for an estimated 57% of all online scams.

That might just be because Facebook is where we spend most of our scrolling time. But a Wall Street Journal report puts at least some of the blame on lax oversight: Facebook allows suspicious advertisers to rack up as many as 32 automated “strikes” for financial fraud before it bans their accounts.

32 strikes and you’re out!

A final statistic suggests next year’s report could be even uglier. The CFA’s new category of AI-enabled fraud accounted for $6.3 billion in losses.

Prediction market use case

An anonymous trader spent $44,000 to briefly push Polymarket's odds of long-shot Matt Mahan winning the California gubernatorial race from about 13% to 96%, Bloomberg reports. The odds quickly settled to 36%, so the bet was probably underwater immediately.

But the inflated odds prompted the New York Post to run a flattering headline: Matt Mahan records huge surge in support.

The story also made the front page of the Post-affiliated California Post, a tabloid newspaper.

I’m not sure what the advertising equivalent of those two placements would cost, but it would surely be multiples of $44,000.

Mahan finished sixth in the open primary for Governor, winning 3.5% of the vote.

eBay went rogue

When eBay founder Pierre Omidyar launched the Feedback Forum in 1996, he explained his thinking in a post to the community of sellers: “Most people are honest. And they mean well.”

The sentiment was enshrined in eBay’s founding principle as “People are basically good.”

Not all people, though. Not even at eBay.

This week, eBay and three former executives agreed to pay $55.7 million to settle a lawsuit over a harassment campaign targeting the authors of an e-commerce blog that was frequently critical of the company.

The authors, Ina and David Steiner, had a box of live cockroaches and a funeral wreath delivered to their home. Their neighbors received pornographic magazines sent in the name of David Steiner. Anonymous posts online invited strangers to their house for sex. They were followed around town by an unmarked van.

The Steiners had no idea who was persecuting them or why (which must have been terrifying), until the van used to stalk them was found to have been rented by an employee of eBay’s security department. 

eBay began as a community-oriented marketplace — the anti-Amazon and champion of small-time side hustlers and treasure hunters.

The campaign was ordered by ex-CEO David Wenig, a respected executive with a long history of impeccable corporate behavior.

He went to Columbia Law School, even, but didn’t seem to recognize the line he crossed when he told his security department to “crush” and “take down” the bloggers.

An overzealous security team took those orders literally, escalating them into what must be the most egregious case of corporate harassment in US history.

How could community-oriented eBay be the first to so terrorize its critics?

How could a corporation founded on the belief that “people are basically good” become the first to terrorize its critics this way?

It’s shocking what a slumping share price can drive management to do.

Pokémon perps

On Monday I wrote that the next logical step for the burgeoning market for trading Pokémon cards is an ETF tracking an index of them. I wasn’t entirely serious — imagine the rigamarole of physical delivery every time an ETF of trading cards is redeemed.

But maybe a perpetual future could work?

We should soon find out. Hood Liquid says it’s building a perpetual market for collectible RWAs on Robinhood Chain, starting with an index for Pokémon cards.

Gotta trade ‘em all, I guess.

PSA on trial

A lawsuit against Collectors Universe, the owner of the dominant card-rating company PSA, warns that collectors should not trust their grades.

According to the complaint, PSA does not require its graders to have prior experience, viewing personal card collecting as sufficient qualification. Their grader profiles include backgrounds in songwriting, working at restaurant chains, and in sports stadiums.

PSA allegedly pressures these graders to spend less than a minute evaluating each card, making the grades inconsistent and unreliable.

Worst of all, the suit accuses PSA of knowingly grading cards that, by their own rules, should receive no grade at all. This includes the first card it ever graded, a famous Honus Wagner that received an 8 rating from PSA despite having been trimmed to make the edges look straight. 

Shocking stuff, really.

And a reason to have your prize Pokémon cards graded by the Japanese agency ARS, which limits the cards they accept to maintain standards, encases the cards in display-worthy holders (pictured above), and puts the number grade on the back of the holder to deemphasize it. 

If Pokémon cards are going to become an alternative investment asset — as they should — they will need a reputable agency to grade them.

So here’s some official financial advice: If you have a rare Charizard, send it to Japan.

The fragility of perfectly safe onchain money

A paper from the Federal Reserve describes blockchain gas fees as the price of “decentralized trust.” It then warns that when congestion drives that price higher, blockchains can experience the equivalent of a bank run, with users rushing to move their assets to a rival chain (with lower fees) in a panic. 

The concern is that stablecoins share infrastructure with the broader crypto ecosystem. Therefore, a sudden surge of activity unrelated to stablecoins can disrupt their use as a payments system.

The authors’ takeaway is that regulators are overly focused on ensuring that stablecoins are backed by perfectly safe assets. The paper suggests they should be thinking more about the rails.

The authors argue that regulators, focused almost entirely on ensuring stablecoins are backed by perfectly safe assets, should consider the rails they run on, too.

Even if a stablecoin is fully backed by the safest of assets, a congested chain can still cause a kind of run on the stablecoin banking system. 

In other words, regulators need to learn how blockchains work.

(As do I.)

DAT’s all, folks

A Bloomberg review of the digital asset trading companies that have pivoted away from crypto reads like an epitaph for the short-lived sector.

Shares of K Wave Media, formerly a Bitcoin DAT, have fallen 71% since pivoting to developing data centers. Lixte Biotechnology Holdings, which held Bitcoin and Ethereum, has fallen 33% since agreeing to merge with a battery firm in June. AlphaTON Capital, which held Telegram’s TON token, has dropped 33% since rebranding as Alpha Compute Corp. in April.

The era of stock market investors paying $2 for $1 of crypto — and all the perverse incentives it introduced — is mercifully over.

— Byron Gilliam

“The cost of curiosity is now zero.” — Kevin Warsh

AI could send interest rates higher

Chair Kevin Warsh says the FOMC will be militant on inflation. “We will deliver on the 2% inflation target,” he said in his press conference this afternoon. “There’s no walking back from our responsibilities.”

He also voted today to leave interest rates unchanged, despite inflation remaining far above 2%.

Why the dissonance? The best way to reconcile above-target inflation with an unchanged fed funds rate is to believe in AI. 

Warsh is a believer. “AI is going to make almost everything cost less,” he told CNBC last year. 

Not immediately, though. Warsh also says he expects that the current boom in AI capex will “increase measured prices” over the next year.

But he believes we should focus on what’s on the other side of these increases: an AI productivity boom that will lower prices.

In the long term, Warsh is confident that AI will be "structurally disinflationary" — like the internet once was, but more so.

“This is the most productivity-enhancing wave of our lifetimes,” he says, “past, present and future.”

Not everyone agrees that this will lower interest rates.

Higher productivity typically means the economy can grow faster without risking a surge in inflation (and therefore higher interest rates). But AGI promises something close to infinite productivity — machines that do virtually everything for us.

What might that do to interest rates?

A paper by AI researcher Caleb Maresca suggests the prospect of “transformative AI” (TAI) could set off a mad scramble for assets that would send interest rates soaring.

“Under baseline scenarios,” Maresca calculates, “one-year interest rates rise to 10-16%.”

16%!

That’s a big number — and it’s not because AI will cause GDP growth to be similarly big. “Evolving beliefs about TAI could create significant upward pressure on interest rates well before any technological breakthrough occurs,” Maresca writes.

Instead, Maresca theorizes that once people believe AI will automate much of human labor, they will race to invest, maximizing their claim on AI's future earnings before AI labor becomes the economy's primary source of income.

If AIs are the only ones working, the only way to make money will be to own a piece of the AIs.

It’s the race to invest in them that will drive interest rates higher.

Another academic paper, however, suggests this same prospect of machines replacing humans will drive interest rates lower.

“Transformative AI capable of automating most human labor can lower interest rates even as it dramatically accelerates growth,” the paper finds. 

This paper is also by Caleb Maresca!

The conclusion in his second paper is as dramatic as in the first, but in the opposite direction: “Under baseline calibrations, the risk-free rate falls to near zero despite growth rising from 2% to 11%.”

How could interest rates be 0% while the economy’s growing 11%?

A race to save.

“Agents who rely on their labor income and do not have access to risky capital markets,” his model forecasts, “increase their savings in a desperate bid to preserve purchasing power. This increased supply of savings drives down the risk-free rate to zero.” (Or lower.)

Maresca says his second forecast of interest rates at 0% does not invalidate his first forecast of 16%. “That paper and the present one bracket the range of theoretical possibilities.”

In other words, transformative AI could send interest rates soaring as people race to invest — or crashing as they race to save.

It’s hard to say!

Researchers at the Dallas Fed have come to a similarly bifurcated conclusion.

Their paper, modeling what a “technological singularity” would mean for per capita GDP, suggests there’s a wide range of possible outcomes for the effect of AI on the economy. 

The uncertainty was memorably captured in what may be the greatest chart in the history of economics:

In a benign version of the technological singularity, AI makes machines smarter at a rapidly increasing rate, eventually gaining the ability to produce everything, which leads to “a world in which the fundamental economic problem, scarcity, is solved.” That happy outcome is represented above by the red line.

In a “less benign version” of the singularity, however, “the machines become malevolent, and this eventually leads to human extinction.” That’s the purple line.

As the chart starkly illustrates, human extinction would mean that per capita GDP falls to zero.

And interest rates, too. 

Maybe.

— Byron Gilliam

“Someday, the Age of Economic Warfare will end, but we might miss it when it’s gone.” — Edward Fishman

When the dollar became a weapon

When John Kerry accused President Bush of not putting enough economic pressure on Iran with sanctions in their 2004 debate, Bush responded in a tone of exasperation. “We’ve already sanctioned Iran!” he said, looking into the camera. “We can’t sanction them any more.”

There was virtually no trade between the two countries by then, so there didn’t seem to be anything left to sanction. 

And yet, Iran’s shelves remained stocked with American-made consumer goods. “Maytag refrigerators, Diesel clothing and Victoria’s Secret lingerie are quite popular,” the New York Times reported at the time.

Thousands of Iranian businesses were evading US sanctions simply by setting up offices and bank accounts in Dubai. “The best place to do business in Iran,” one of these businesspeople told the Times, “is in Dubai.” 

Arrangements like these had rendered US sanctions ineffective. But Bush’s comment in the presidential debate inspired one Treasury official to revisit them.

“Stuart Levey took this sense of resignation as a personal challenge,” Edward Fishman writes in Chokepoints: American Power in the Age of Economic Warfare.

Levey was Under Secretary of the Treasury for Terrorism and Financial Intelligence at the time, tasked with finding ways to cut off funding for sanctioned groups and countries.

He reinvented how it was done. Fishman calls Levey a “founding father of American financial warfare.” Others called him a “sanctions technocrat.” Still others, “a guerilla in a gray suit.”

Levey earned those monikers over 10 years of government service. But his enduring influence stems from a single insight: Banks could simply be told who not to deal with.

Levey’s eureka moment came in 2006, when he read a newspaper account about a Swiss bank that had voluntarily cut all ties with Iran.

“It sort of clicked for me,” he said later. “When we say we’re ‘all sanctioned out,’ what we mean is that it’s illegal for US companies to do business with Iran. It does not mean the world has stopped doing business with Iran.”

The problem was that, while Iranian banks had been barred from dealing with US banks since the mid ‘90s, they still had indirect access to the US banking system through correspondent banking. 

To pay for something in dollars, Iranian banks would send money to a European or Asian bank, which would then route it to a recipient through a US correspondent bank — a bank that settles dollar transactions on behalf of other banks.

It seems like an obvious loophole now, but before Levey, no one in government had thought much about this niche corner of the financial system. To the extent they had, there seemed little to be done. Stopping these transactions appeared to require the painstaking work of convincing other governments to instruct their banks to stop transacting with Iran.  

Levey’s insight was that he could appeal to the banks directly.

“From his time in private law practice,” Fishman wrote, “Levey was familiar with how corporate executives thought about regulatory and reputational risk. He believed he could persuade them to cut ties with Iran of their own accord, whether their home governments were on board or not.”

The persuasion came mixed with a warning: Treasury would be looking for violations of US sanctions enabled by correspondent banking.

“We never threaten,” Treasury Secretary Hank Paulson told Fishman. “We talk about how important it is not to violate the rules and engage in illicit transactions.” 

Nice bank you have there…

The implied threat was that violating US sanctions law could lead to large fines or even losing access to US correspondent banks and therefore the ability to move dollars.

Not every bank received the message kindly.

Fishman cites a response from the second-in-command at Standard Chartered: “You fucking Americans. Who are you to tell us, the rest of the world, that we’re not going to deal with Iranians?”

They found out a few years later when US law enforcement agencies fined Standard Chartered $359 million for sanctions violations. 

Others required no persuasion at all.

“We haven’t had Chinese banks tell me that they won’t do deals with Iran,” Levey told Fishman. “They just stop.”

“Eighteen months into the campaign, nearly all the world’s largest banks had stopped servicing transactions with Iran, even though neither their own governments nor the UN required it,” Fishman wrote.

A measure of the effectiveness of Levey’s campaign is that the governor of Iran’s Central Bank labelled it an act of “financial terrorism.” 

But one man’s financial terrorist is another’s financial freedom fighter. Fishman calls the campaign an act of economic war.

Treasury Secretary Scott Bessent prefers the term “economic statecraft.”

Levey’s per Diem

In a talk last month, Secretary Bessent defined economic statecraft as “the disciplined use of America’s economic power in service of our sovereignty.”

This includes the power that Levey had discovered. Access to the dollar system, Bessent said, is “no longer unconditional.”

In truth, it’s been conditional for some time now. Even before Levey, the US denied access to its banking system as a way to punish adversaries like Cuba and Libya.

Levey’s discovery was just how much of a chokepoint the dollar system could become, and how the US could use it to pursue its geopolitical goals.

Bessent’s emphasis on economic statecraft is a declaration that the US intends to use it even more aggressively.

Fishman would likely applaud the idea — he believes economic war can be an effective alternative to kinetic war.

To that end, he suggests that the US create “a permanent economic war council” that would make quicker and better policy recommendations in a crisis.

But he also warns that it won’t work forever. Financial sanctions are like antibiotics, he says: They are effective in high doses, but lose their potency if overused.

The US is probably overusing them already, as evidenced by the increasing measures its adversaries — and its friends, even — are taking to create alternatives to the dollar system.

Some of these alternatives involve crypto, including the billions of dollars that Iran has moved in stablecoins in recent years.

The potential for stablecoins to be used in sanctions evasion is why Stuart Levey joined Facebook’s stablecoin project, Diem, as CEO in 2020 — “because he wanted to ensure digital currencies would not undermine American financial power,” Fishman wrote.

Diem shut down less than two years later, unfortunately. 

But Levey — the founding father of American financial warfare — landed on his feet.

As chief legal officer at Oracle, he made $14.5 million last year.

— Byron Gilliam

“Trading cards are no longer simply consumer products.” — Japan’s Parliamentary League for the Promotion and Rule-Making of Trading Cards

From playground to Parliament

Pokémon trading cards were causing trouble on playgrounds around the US almost as soon as they made their debut in January 1999.

In May, the LA Times reported on measures schools were taking to combat the cards’ disruptive influence: Kids Told to Pocket Their Pokémons, the headline read.

“The children are beginning to become obsessed by them,” a principal who had banned trading between pupils said. “Some of our younger kids were getting suckered out of their more valuable cards.”

In other schools, they were banned entirely. A 7-year-old told the Times he didn’t bring his cards to school anymore because “the principal said he’d better not catch us trading.”

With a hint of disbelief, the Times noted that the rarest cards were selling for more than $50 each.

In October, the Times reported that the situation had escalated: Backlash Builds as Schoolyards Evolve Into Pokémon Trading Pits.

“School officials from Connecticut to California have concluded the cards are disrupting learning, poisoning playground friendships and causing such distraction that some children forget their homework, tune out in class and even miss school buses as they scramble to acquire one more card.”

One principal told the Times that children “were bringing cash wads of up to $40 to school, hoping to buy a coveted card.”

This set Pokémon apart from all previous schoolyard crazes.

“The thing with the Pokémon cards is that kids are really aware of their value,” a child psychologist said. At his son’s school, it had turned recess “into a little flea market,” he added. “They had their calculators out. It really became a buy-and-sell bazaar.”

The Times noted that prices for the most sought-after cards had appreciated to more than $100.

Twenty-seven years later, a 31-year-old sold an especially sought-after Pikachu Illustrator card to a 33-year-old for $16.5 million.

Printed in 1998, the card (pictured above) is one of just 39 copies ever produced and the only one graded Gem Mint 10 by PSA, the dominant card-grading company.

The seller was Logan Paul, a YouTube influencer and trading card enthusiast who had purchased the card five years earlier for $5.23 million. 

The buyer was venture capitalist AJ Scaramucci, who purchased it on behalf of a holding company he formed to acquire "culturally significant, scarce real-world collectibles."

Pokémon cards “should be treated as investments because that’s what they are,” Scaramucci said at the time. “It’s just obvious.”

They’ve certainly performed like it. The website Card Ladder estimates an index of top Pokémon cards has appreciated by 8,300% over the past 22 years.

Investors in the S&P 500 earned a comparatively pedestrian 902% in the same time period.

When, exactly, a collectible matures into an investment asset is in the eye of the beholder. Fine art, Swiss watches, classic cars, and French wine likely all qualify, having been purchased and held with the potential for price appreciation in mind for some time now.

Pokémon cards have joined that list of collectibles-turned-investment assets.

But they’ve also transcended it: The cards are now a strategic asset, too.

Last week was the inaugural meeting of Japan’s “Parliamentary League for the Promotion and Rule-Making of Trading Cards” — a group of Liberal Democratic Party lawmakers that seeks to both regulate and promote the market for Pokémon trading cards.

The group aims to “position trading cards as one of Japan's powerful IPs (intellectual properties),” a trading-card blogger said of the meeting, “and to support overseas expansion into the global market.”

The lawmakers are also considering measures to prevent counterfeiting and thwart the use of high-value cards in money laundering. Identification and record keeping may be required for large transactions.

“In the future,” the blogger adds, “government support measures for exports may be introduced, and safer international transaction rules may be established.”

In other words, the Japanese government is treating Pokémon cards as both a national treasure and a financialized asset.

That, I believe, is a first.

You don’t need an ID to buy a Swiss watch, for example. France has laws about cheese making, but none about the use of, say, camembert in financial transactions. Fine art is used for money laundering, but no government views it as a threat to their culture.  

Pokémon cards are different — a unique mix of collectible, national culture, and financialization. 

Unlike other collectibles, which become tradable only after collectors make them valuable, Pokémon cards were designed to be traded from the very start.

In schoolyards, at first, and then hobby shops, card shows and eBay. The highest-value cards even made it into auction houses.

Now, the trading is moving onchain.

It’s easy to see why. On eBay, buyers pay sales taxes and shipping costs. Sellers pay eBay as much as 12.35% of the purchase price. eBay itself pays substantial credit card fees. 

Most egregiously, the auction house that sold Logan Paul’s Pikachu Illustrator charges a headline rate of 24%. 

Cards traded onchain, by contrast, incur no credit card fees (only a tiny gas fee). There’s no sales tax, no matter where you live. And trading fees are minimal: Collector Crypt, for example, charges only 2% to transact on its marketplace.

$410 million of Pokémon cards traded on Collector Crypt in June, up from zero at the beginning of last year.

Onchain, Pokémon cards trade as NFTs — a tokenized receipt for cards that a marketplace like Collector Crypt warehouses on behalf of owners, similar to how many of the world’s most important assets trade. 

The GLD ETF, for example, is a receipt for gold held in bank vaults. SPY is a receipt for the stocks held by a custodian. 

Collector Crypt does the same for individual Pokémon cards.

(An ETF holding an index of the best cards doesn’t seem far off.)

The effect of this tokenization is perhaps best illustrated by the popularity of gachas, where users pay as much as $2,500 to win a random card — then immediately sell it and try again.

This kind of high-volume, gamified trading is only possible because blockchains make trading nearly frictionless.

But it’s more than just a game.

Tokenization is creating a liquid market for the first collectible to become a strategic asset.

— Byron Gilliam

"The data center is the new unit of computing." — Jensen Huang

Friday charts: The cost of compute

Graphics processing units (GPUs) were first used to develop AI in 2012, when Alex Krizhevsky bought two Nvidia GTX 580 graphics cards to train AlexNet, a pioneering computer vision model.

The GTX 580 was built for high-end PC games like Skyrim and Portal 2 — games whose graphics were rendered with the same kind of parallel matrix math Krizhevsky used to train his neural networks. They cost $499 each.

The first GPU designed for AI was the P100, released in 2016. They cost about $6,000 each.

Nvidia bundled eight of these, along with CPUs, memory, and software, into an “AI supercomputer” called the DGX-1. That cost about $129,000.

(Jensen Huang personally delivered the first one to Elon Musk at OpenAI.)

Supercomputers have been getting bigger and more expensive ever since.

This week, customers began running Nvidia's Vera Rubin Pod in production — a system Jensen Huang describes as “five purpose-built racks operating as one massive AI supercomputer for agentic workloads.”

As shown above, miles of fibre optic cabling, sheathed in yellow, transform those racks into a single, super-fast, super-powerful computer. There’s miles of copper wiring in the back, too. 

From left to right, the five cabinets house GPUs, LPUs (a new addition, to speed things up), CPUs, memory, and networking equipment. The yellow-sheathed cables tie everything together into a single system.

Unlike a GTX 580, you can’t order one of these from the internet, so we don’t know exactly what they cost. But just the cabinet for GPUs is estimated to cost $9 million. A pod of five cabinets (or racks) likely costs tens of millions of dollars.

To build a new data center, you might need 1,000 of these, all networked together.

To train a frontier language model, you might need a network of such data centers. Perhaps ten of them, combining the computing power of nearly a million GPUs into a single supercomputer.

It’s a long way from the two off-the-shelf gaming GPUs Alex Krizhevsky used to train AlexNet for $1,000. 

It’s also a lot more expensive. The network of data centers OpenAI said this week it will build in Georgia is expected to cost $30 billion.

For investors, it’s getting to be a bit much.

Alphabet shares fell 7% this week after reporting negative free cash flow of $5.9 billion for the second quarter of the year. It’s the first time since going public in 2004 that the company spent more money than it earned.

There’s nothing inherently wrong with being free-cash-flow negative. Often, it’s encouraged. We spent a decade complaining about big tech hoarding cash and buying back their shares instead of investing.

Now, they’re investing.

But we’re not sure how we feel about that, so we’ve de-rated their stocks. Alphabet reported quarterly earnings up 30%, but its shares are just unchanged on the year. Roughly speaking, that means the stock has gotten 30% cheaper.

The de-rating is even more dramatic at Microsoft and Oracle, where earnings are significantly higher and the shares are significantly lower (by 19% and 40%, respectively).

The problem is simply that the cost of generating AI compute is spiraling higher — from $1,000 in 2012 to billions of dollars now — and we still don’t know exactly how useful it will be.

Technically, Vera Rubin pods are yet another extraordinary accomplishment from Nvidia. But is buying 1,000 of them for a data center a good investment? 

The market is starting to have its doubts.

Let’s check the charts.

Trend change:

One of these is not like the others. Shareholders may need some time to adjust.

The investment that needs a return:

By the end of the year, hyperscalers and the neoclouds will have spent $2 trillion on capex.

Agentic AI requires a lot of memory.

The price of DRAM is up 10x in a year. The price of Micron shares is up less than 2x, because surely this can’t go on?

The picks and shovels:

Makers of semiconductors are the big winners so far. A report from Exponential View cites an estimate of $1.5 trillion for 2026, more than double 2025.

Model revenue:

Callum Williams estimates revenue earned from LLMs at a $120 billion annualized run rate, with Anthropic taking by far the biggest share. That number will have to grow rapidly for everyone to make a return on their investments.

Growing rapidly:

Revenue growth at the three cloud providers is accelerating, at Google, especially.

The machines keep getting better:

Per Exponential View, one gigawatt of data center capacity now produces nearly 500 trillion tokens. That’s up from a number that rounded to zero as recently as 2023.

Demand keeps growing:

All those data centers now produce roughly 35 quadrillion tokens per month. I can’t imagine how they measure that number, but it’s growing at the exponential rate of 14x a year.

Compute is on back order.

The hyperscalers have an unprecedented $2 trillion backlog of orders. Whether the customers ordering all this compute will be able to pay for it when it arrives remains to be seen.

Another big number:

Nikkei research estimates the hyperscalers now have $1.65 trillion of debt that does not show up on their balance sheets. It’s not a secret. The numbers above are taken from the footnotes of the hyperscalers’ financial statements. But who reads the footnotes?

Future earnings will have to exceed these debts — and more. Because the stuff it buys from Nvidia isn’t getting any cheaper.

Have a great weekend, cash-flow-positive readers.

— Byron Gilliam

"Acting is behaving truthfully under imaginary circumstances.” — Sanford Meisner

Thursday links: movies, peptides, and insiders

Bitcoin makes cinematic history

The makers of the to-be-released Bitcoin: Killing Satoshi describe it as the first “fully-generated, studio-quality AI feature film.”

Starring Gal Gadot, Pete Davidson, and Casey Affleck, the “globe-trotting thriller” was filmed “in a gray box that could have passed for a storage facility,” The Wrap reports.

“We budgeted out what it would be to do it practically and it was over $300 million,” the producer said. “It has about 200 distinct locations, from Antarctica to Antigua to Vegas, which is obviously unproducible.” 

(Why a film about Satoshi would include Antarctica is unfortunately not explained.)

Generating all 200 of those locations with AI allowed the film to be made for just $70 million. 

The performances, however, remain human — perhaps even more so than usual. 

With their surroundings left to AI, “the entire focus on set was on our performances,” Affleck told The Wrap. “It was much more like acting in a Broadway play than in the giant event film that Doug’s final product will actually be.” 

Affleck thinks director Doug Liman’s method of doing everything other than the acting with AI could “reinvent cinema itself.”

It’s important to note that no jobs were killed in the making of this AI movie. “Killing Satoshi” employed 107 cast members, 100 shoot crew, and 54 non-shoot crew.

Post-production will employ 55 “AI artists.”

As for the movie itself, it sounds bananas.

The official synopsis describes Killing Satoshi as a “a high-stakes conspiracy thriller that asks the question no one in power wants answered: Why have the world’s most powerful and wealthiest people — Jack Dorsey, Mark Zuckerberg, Visa and virtually every major crypto organization — poured hundreds of millions of dollars into a single mega-organization called COPA, with one sole purpose: to destroy one man?”

I’m not sure how many moviegoers will want that question answered, either. Because it’s ridiculous.

COPA is a nonprofit consortium with a stated mission to encourage open-source innovation in crypto. The man the film accuses COPA of trying to destroy — assassinate, even — is Dr. Craig Wright (played by Affleck), who the film presumes is Satoshi.

In real life, COPA once sued Wright after he used his claim to be Satoshi as the basis for lawsuits against Bitcoin developers and others. It asked a UK court to decide whether he really was Bitcoin's creator.

The court ruled that he was not.

“It is clear that Dr. Wright engaged in the deliberate production of false documents to support false claims and use the Courts as a vehicle for fraud,” the ruling found. “All his lies and forged documents were in support of his biggest lie: his claim to be Satoshi Nakamoto.”

The official synopsis of Killing Satoshi nonetheless asks, “If Craig Wright didn’t invent Bitcoin, why is a coalition controlling trillions in global wealth spending hundreds of millions and risking everything to destroy him?”

It’s a question that’s best answered with a question: if Craig Wright did invent Bitcoin, why hasn’t he moved one of Satoshi’s coins to prove it? 

But that wouldn’t make for much of a movie, I guess, even with AI.

An old-school crypto use case

Chinese chemical suppliers previously selling precursor chemicals for fentanyl and amphetamines to international drug cartels have pivoted to selling peptides directly to consumers instead.

They prefer to be paid in crypto: “The top players in the space rely almost exclusively on bitcoin and stablecoins to run their operations,” Chainalysis reports.

Chainalysis’s on-chain forensics finds that gray-market peptide sellers received $32 million in cryptocurrency in the first quarter of the year — up 159% from the previous quarter.

The rapid increase represents the rapidly growing popularity of unbranded weight-loss injections like GLP-1s and 'looksmaxxing' compounds that go viral online.

One seller, Sigma Audley, advertised weight-loss peptides using the same +86 Chinese phone number it had previously used for its illicit fentanyl business, Chainalysis found.

The pivot to peptides has allowed these manufacturers to sell directly to consumers, cutting out the cartel middlemen who once captured much of the profit.

(And what is crypto for if not cutting out middlemen?)

It feels a lot like Bitcoin’s original use case on the Silk Road — but for looksmaxxing peptides instead of Molly and cocaine. 

Progress, I guess. 

Betting on things that already happened

Vox reports that Kalshi and Polymarket are breaking reality TV because their markets on season winners and other plot points have become spoilers: they’re correct more than 90% of the time.

What could account for this uncanny level of precision? Vox offers the obvious explanation: “people are trading on these prediction markets with insider information, which is easy to do when shows like Survivor and its results are taped months in advance.”

Months is a long time for something to remain secret. 

Vox believes the insiders are likely in the clear. “Thanks to the gambling regulation gray area that prediction markets currently occupy and the limits of NDAs, there’s plenty of money to be made with very little consequence for anyone who knows how a season of Survivor played out.”

Still, they might want to earmark some of their winnings for legal fees, because I think they may have committed wire fraud.

The networks that own Survivor have a financial interest in keeping the outcome of the show secret until it’s been aired. This should be clear to the shows’ participants, because they sign NDAs that reportedly include a $5 million penalty for leaking information about the show.

One way to leak information — to everyone — is betting on prediction markets.

The information has value to the networks. If someone entrusted with it uses it to make money, it’s misappropriated. If it’s misappropriated, it’s probably wire fraud.

(Not legal advice.)

Still, betting on things that already happened is surely tempting. Aubrey Bracco to win Season 50 of Survivor was trading at just 68.6% on Kalshi as little as a month before the season finale declaring her the winner was aired.

$32 million was bet on the outcome.

The teleprompter guy insider traded

The best thing about prediction markets — for someone who writes about markets, at least — is all the quirky things that happen when seemingly everything becomes tradeable.

My new favorite is the mental image of the President’s longtime teleprompter operator, Gabriel Perez, checking Kalshi on his phone while running the teleprompter.

“In certain instances,” ABC News reports, “investigators uncovered times when Perez would back out of certain bets mid-speech when Trump skipped over a portion of the speech that included a word he had previously bet would be mentioned.”

Given the President’s habit of going off script, that must have happened a lot. But Perez still managed to make more than $100,000 betting on what would be said. Unfortunately, he’s now on unpaid leave. 

There are probably a number of lessons to be learned here, but the most important one is this: if you are going to trade on inside information, don’t use Kalshi, which knows who you are. Use a pseudonymous crypto wallet on Polymarket instead.

(Not legal advice.)

— Byron Gilliam

“We just needed to make it to the weekend.” — Charles Smith, Head of Business Development, Morgan Stanley

In defense of markets that sometimes close

Heading into the Columbus Day weekend of 2008, Morgan Stanley looked likely to become the next domino to fall in the Great Financial Crisis.

The bank began the week with $130 billion of cash on the books, which management believed was more than enough to weather the storm. 

But the hedge funds whose withdrawals had sealed the fate of Bear Stearns, Lehman Brothers, and Merrill Lynch were now pulling their money from Morgan Stanley, too — $65 billion of it in a single day.

It was a bank run, and the only thing that seemed likely to stop it was an influx of equity capital from a large investor.

“If we didn’t do a deal, it was over,” CEO John Mack said later. “We couldn’t stay in business.”

Fortunately, Mack had agreed to a deal.

Two weeks earlier, Japan’s Mitsubishi UFJ Financial Group (MUFG), the second-largest bank in the world with $1 trillion of customer deposits, had agreed to buy 21% of Morgan Stanley for $9 billion. 

Unfortunately, the deal hadn’t closed yet. 

And the market increasingly doubted it ever would. The deal valued Morgan Stanley at $25.25 a share, but investors did not seem convinced: the shares closed at just $14.22 the day it was announced.

Nine days later, they had fallen to below $10.

This was a problem.

The more the shares went down, the less likely the deal was to close. The less likely it was to close, the more the shares went down.

Even worse: the more the shares went down, the more customers withdrew their money. The more… well, you get the idea.

Morgan Stanley was at the mercy of its stock price, which traded lower day after day.

“We just needed to make it to the weekend,” Smith said later.

The weekend was a circuit breaker — a two-day window for Morgan Stanley to close the deal with MUFG without having to worry about where its shares were trading.

On Saturday, MUFG said it remained committed to investing but needed to renegotiate. By Sunday, a new deal was agreed, with MUFG receiving mostly preferred shares instead of mostly common.

This left just the issue of payment. 

Announcing a renegotiated deal would not have stopped the doom loop that was sure to restart as soon as trading resumed Monday morning. To restore confidence in Morgan Stanley, the deal had to close.

“We knew that if this money could not be delivered,” MUFG Chair Nobuyuki Hirano recounted, “the markets will sell Morgan Stanley stock down, to possibly zero.” 

MUFG was ready to deliver the money, but there was a problem: the Fed celebrates Columbus Day and the stock market does not. 

Morgan Stanley needed the money before its stock resumed trading on Monday morning, but the Fedwire payment system that handles giant transfers like this wouldn’t reopen until Tuesday.

With the market in panic mode, Tuesday might have been too late for the $9 billion to do any good. Morgan Stanley may have lost many times that amount in withdrawals by then.

So there was only one thing for it: MUFG would have to write a check.

Morgan Stanley Vice Chairman Rob Kindler suggested it on Sunday and MUFG agreed. At 7:30 am Monday morning, Kindler was in a conference room at the offices of Wachtell Lipton awaiting delivery of a physical check. 

“He looked like hell,” Aaron Sorkin wrote in Too Big to Fail. “He hadn’t slept in at least a day.”

Expecting the check to arrive by messenger, Kindler hadn’t bothered shaving or changing out of the khaki pants and flip flops he was still wearing from his cancelled vacation in Cape Cod.

Instead, it was delivered by an entourage of suited MUFG executives. And a camera crew.

Kindler hastily borrowed a suit jacket from one of his lawyers, who turned out to not be as broad-shouldered as Kindler. It tore down the back.

“I assure you, I am vice chairman of Morgan Stanley,” the haggard looking Kindler told his Japanese saviors.

Despite his appearance, MUFG handed over the check — in plenty of time for Morgan Stanley to announce it to the world before the shares resumed trading. 

They traded up as much as 70% that day.

The bank run was over thanks to a weekend circuit breaker — which may soon cease to exist.

The pause that saves

If the S&P 500 falls 7%, trading is halted for a minimum of 15 minutes. If it falls 20%, trading is halted until the next day.

These market-wide circuit breakers were instituted after the Black Monday crash in 1987 as a means to interrupt panic selling before it becomes self-reinforcing. 

Exchanges also have discretionary authority to halt individual stocks for pending material news or even just an order imbalance — anything that investors might need some extra time to think about.

Companies report earnings either before or after the market’s regular hours for the same reason. Berkshire Hathaway even reports on Friday evenings so that investors have the entire weekend to think things through.

This is also the time when regulators most often attempt to halt bank runs. Continental Illinois, Barings Bank, and Bear Stearns were all rescued over a weekend.

The FDIC almost always closes failed banks after the close of business on Friday so they have enough time to reorganize them under new ownership without disrupting depositors’ access to their money.

Nights and weekends are a natural circuit breaker for the entire financial system.

But perhaps not for much longer.

Yesterday, the London Stock Exchange became the latest major exchange to announce plans to move to 24-hour trading, five days a week. NYSE, Nasdaq, and the CBOE are planning the same.

Can 24/7 trading be far behind?

Exchanges are expanding their trading hours to fend off the competitive threat of tokenized equities, which trade on blockchains that never close. 

Nasdaq says this will also “broaden investor access, expand wealth-building opportunities, and redefine how markets function.”

And I'm sure that’s true. But at what cost?

Morgan Stanley is now a $340 billion bank — employing 83,000 people — because markets paused long enough for someone to rescue it.

MUFG still owns 24%.

— Byron Gilliam

“Our knowledge of optimizing capacity in energy networks will allow us to revolutionize the bandwidth market.” — Enron letter to shareholders, 1999

The next great commodity?

Enron, remembered, of course, for turning electricity into a tradeable commodity, had designs on making lots of other things tradeable, too: weather forecasts, water rights, carbon emissions… even advertising minutes.

But their biggest target was the thing that seemed in shortest supply circa 1999: internet bandwidth.

The CEO of UUNET, then the largest provider of bandwidth at the time, predicted that internet traffic would continue to rise exponentially. “Three years from now,” he said in 1997, “we expect our network to be 1,000 times the size it is today.”

It fell well short of that, unfortunately. But the forecast was directionally correct: demand for bandwidth  — the capacity to move data across the internet — doubled approximately every year from about 1998 to 2002.

Either way, bandwidth looked destined to become one of the world's most valuable resources — and Enron saw an unprecedented opportunity to make it tradeable. 

CEO Jeffery Skilling believed that turning the informal community of bandwidth buyers and sellers into a commoditized market would transform Enron from “The World’s Leading Energy Company” into “The World’s Leading Company.”

(Which really would have been something.)

As Andrew Schwartz wrote in a study of Enron’s plans, the company first tried to make the data packets that move information over the internet tradeable.

That proved infeasible as packets resist nearly every pre-requisite for becoming a commodity: there was no practical way to standardize them, deliver them, or verify their quality.

So Enron chose instead to create a market for bandwidth — the capacity to send those packets over the internet.

In practice, that meant standardizing contracts for transmission: a specified number of bits per second between two cities, say, delivered for a fixed period of time, at a guaranteed level of service.

This is how bandwidth — traded in the form of a futures contract — would ultimately be delivered from seller to buyer: by routing the buyer's data over capacity reserved on the seller's network.

Turning this into a market, however, meant finding a way to deliver that capacity — the bandwidth equivalent of a central clearinghouse to intermediate between buyers and sellers.

Enron's answer was to build a network of switching hubs where capacity purchased from carriers could be routed to the holders of an expiring futures contract.

Routing traffic through these hubs would also allow an auditor to verify that the promised bandwidth was correctly delivered.

(Enron had PwC lined up to do the verifying — perhaps because Arthur Anderson had enough on its plate.) 

Most trades were expected to settle this way — with “physical” delivery — because the buyers wanted the actual bandwidth, not just a cash profit (if the price of bandwidth happened to go up).

Lots of people predicted it wouldn’t work. 

Bandwidth, they said, could not be standardized. The reliability, quality, and service of moving packets over the internet varied too widely across providers.

The biggest sellers wouldn’t participate. Incumbents benefit from opaque pricing, so why expect them to submit to transparent exchange pricing?

The market was constantly in flux. Markets need established players and established rules, and the market for bandwidth had neither.

Physical delivery is too difficult. No one really wanted to incur the expense and bother of connecting to Enron’s new hubs.

Enron's management responded that every new commodity market faces these same objections. And that they knew how to overcome them, just as they had in creating markets for natural gas and electricity.

Now, people are raising all the same objections about compute.

It’s different this time.

If Enron were still with us, they would be all-in on creating a tradeable market for compute — the capacity to process AI computations.

Kalshi founder Tarek Mansour expects that a market in compute derivatives could process as much as $200 trillion of annual trading volume by 2030.

People are again skeptical, for familiar reasons. 

The benchmark people would want to trade keeps changing as new chips constantly replace old ones; sellers prefer opaque contracts to transparent exchange pricing; "delivery" of compute — from the right chip in the right location — cannot be standardized.

But the potential market is too large not to try.

Also, there are reasons to think it could work. In a note for Blockworks, Nick Carpinito explains why compute might successfully be commoditized.

Standardization should emerge over time, he argues: “canonical benchmarks get made through trading.” 

And commodities don’t have to be perfectly fungible: “cross-hedges on far looser relationships already anchor multi-billion-dollar US markets.”

As for the all-important issue of delivery, Carpinito cites ComputeConnect, an “exchange-for-physical (EFP) network”, that aims to settle futures positions by matching an expiring contract with a provider that can deliver the specified compute.

Will this work better than Enron’s interconnection hubs?

It’s true that bandwidth futures never caught on. But Andrew Schwartz believes Enron should get much of the blame for that — primarily because it made itself extremely unpopular.

“The enmity between Enron and other networks supposedly grew to such a point that a conference planned in Europe attracted only Enron,” Schwartz writes. “The other networks canceled when they learned of Enron’s attendance.”

Ultimately, though, bandwidth futures may have failed for a much simpler reason: bandwidth itself became abundant. When the dotcom bubble burst, prices collapsed in an avalanche of oversupply.

Compute has so far followed the opposite trajectory, remaining scarce despite all the datacenters being built.

Prices have even been rising as of late, which makes a market to trade them much more likely to happen, one way or another.

Also, no one will have to trade with Enron.

— Byron Gilliam

“A serious football fan is never alone.” — Hunter S. Thompson

The Summer of Sport

Over a million people gathered in Madrid to celebrate their World Cup heroes this afternoon, the final act of a tournament that felt like much more than just a tournament. 

The Argentinian team will probably get a hero's welcome in Buenos Aires, too. They would in Bangladesh, for sure.

Countless Bangladeshis gathered at watch parties this summer to cheer on Argentina, whose national team has an estimated 70 million fans in Bangladesh.

The population of Argentina is 45 million.

The phenomenon dates to 1986, when Bangladeshis crowded around the small television sets that were becoming common in the country just as Diego Maradona was becoming a superstar at the World Cup.

Forty years later, they gathered in record numbers to watch Messi on giant outdoor screens.

It’s part of what’s made the summer of 2026 the Summer of Sport: the role of sport in bringing people together has never been bigger. 

It helped that the NBA Finals were in New York City, the world’s biggest stage. More than two million people attended the Knicks’ championship parade last month.

I’d estimate that roughly half of them could not have named two players on the Knicks’ roster two months earlier. But they got caught up in the communal experience of a miraculous playoff run.

It helped as well that the World Cup was held in the United States, the epicenter of the attention economy, where everything is super-sized, commercialized, and over the top.

Where else could on-field seats sell for $1 million, and an 11-minute half-time show feature six celebrity performers?  (Plus the Muppets.)

But the tournament’s real value was the way it brought people together. Europeans marveled at Waffle House breakfasts and free refills of coffee. Americans learned the Viking row. Peruvians named their babies “Haaland.”

All of it was shared online, and, for one glorious month people everywhere found themselves in the same global conversation.

This, Adam Kelly says, is why we’re in “a golden age of sport.”

“Sport has this unique ability to tap into the highest-value part of the attention economy and also into the experience economy,” the President of IMG explains. “It taps into community and passion, and it requires an extra commitment from audiences to make a positive decision to engage, to make that appointment to view.”

“Nothing else does that," he adds.

Watching sports is a uniquely social activity, whether you’re in a crowd at the stadium or at a watch party in a city square.

Or home alone, even: watching sports prompts us to text with friends, because we know they’re watching, too.

That’s a rare thing. In this age of atomized media consumption, it’s become harder than ever to connect with people over shared experiences.

Unfortunately, AI is likely to make our media consumption even more atomized — which will make the communal experience of watching sports even more important.

Maybe irreplaceably so. Sports is “the antidote to AI,” Adam Kelly says, and “the last bastion of human content that can cut through the AI noise.”

That might be what made this Summer of Sport feel so big.

In an increasingly fragmented world, moments that bring everyone together are increasingly important.

The Summer of Prediction Markets

When we weren’t texting about the World Cup, we were betting on it.

Kalshi averaged over $1 billion of bets on the World Cup per day during the tournament. 

Yesterday, the market for the final game attracted $1.9 billion alone.

(The non-stop ticker they have on the website showing every transaction was mesmerizing.) 

The World Cup has ballooned prediction-market betting to all-time highs. Volumes are now 10x what they were at the peak of the 2024 election — when people thought prediction markets were mostly just for elections.

Traditional sportsbooks have done well, too: they’re expected to handle nearly nine times as much betting on this World Cup as they did on the last one.

But they’re losing share. H2 Gambling Capital estimates that prediction markets accounted for roughly 27% of US sports-betting during the World Cup, up from 9% at the start of the year.

The first reason for this was, well, predictable: prediction markets make sports betting universally available.

They are legal in all 50 states, while sportsbooks remain illegal in 11. By law, sportsbooks require bettors to be 21; prediction markets can take your bets from 18. On-chain markets sometimes don’t KYC at all.

A second reason was less foreseeable: prediction markets feel familiar.

They have prices instead of odds; price charts instead of virtual betting slips; fees instead of spreads (and the fees are much lower).

All this makes it feel like you’re trading, if not investing. 

You’re not, of course. You’re gambling. But the distinction is becoming blurred.

If you have a Robinhood account, for example, you can conveniently shift your investment savings into sports betting, because Robinhood has its own prediction market now.

“Trade All in One Place,” their website says. “Manage your portfolio on Robinhood: stocks, ETFs, crypto, options, futures, and prediction markets.

Soon, we might even be able to do our sports betting on the stock market. The Subversive All Season Sports ETF proposes to actively manage prediction-market sports betting on behalf of ETF investors.

I wouldn’t recommend it, though.

For all its popularity, betting on sports is a negative-sum activity. Outsourcing that activity to an ETF manager eliminates the one thing that makes sports betting worthwhile: getting you to watch the games.

Ideally, with friends.

— Byron Gilliam

“Someone said to me 'To you football is a matter of life or death!' and I said 'Listen, it's more important than that.'” — Bill Shankly

Friday Charts

France played most of the first match of the first World Cup with ten men after their goalkeeper suffered a broken jaw contesting a shot. There were no substitutions in 1930, so they put a midfielder in goal.

They won anyway, 4-1 over Mexico, but hardly anyone saw it. Attendance was estimated at just 4,400 spectators. Only 300 watched Romania beat Peru.

The entire tournament was played in one city, Montevideo, Uruguay.

The match balls were not always the same. Argentina and Uruguay both insisted on using their own. When they met in the final, Argentina’s ball was used in the first half and Uruguay’s in the second. 

Wardrobes were not always coordinated. When Bolivia played Brazil, both teams had brought white jerseys to play in. Bolivia borrowed Uruguay's blue shirts for the match. 

For their first match, Bolivia had played in jerseys that spelled out Viva Uruguay. The third “U” was unfortunately missing for the pre-game photo shoot:

Why Bolivia wore Uruguay on their shirts is sadly lost to history, but it might have been because the Uruguayan Football Association had covered some of their expenses.

There was no prize money on offer. To attract teams, Uruguay offered to pay for everyone’s travel and accommodation.

Even so, the cost was part of the reason why only four of the 13 participating teams came from Europe. England, Italy, Spain, Germany and Holland all decided to stay home. 

In 1930, it took 16 days to cross the Atlantic by steamship, so European players would be away from home for roughly two months — which meant being away from their day jobs for two months.

Most football players were amateurs at the time, but even the professionals would have needed a day job to supplement their meager wages. 

The Romanian team, for example, was only able to participate because King Carol II guaranteed the players would still have jobs when they returned.

He also personally selected the squad.

Teams from neighboring countries had it easier. Argentine captain Nolo Ferreira, for example, missed just one match, against Mexico, when he returned home to take a college exam. (His replacement scored a hat trick.)

No one left the 2026 World Cup to take an exam. None that I know of have day jobs.

This is good news for footballers. Very good: Lionel Messi is expected to earn $140 million this year.

But it’s good news for everyone else, too.

One way to measure the rise of mass affluence over the past century is that the world's best athletes no longer need day jobs — the world is rich enough to devote hundreds of billions of dollars to watching them play.

We now have so much disposable income that FIFA will make roughly $13 billion of revenue on the 2026 World Cup, and pay out $871 million of prizes. 

This was unimaginable in 1930, of course. But there were signs of what was to come.

The final between Argentina and Uruguay had 93,000 spectators in attendance — 21% of the tournament’s total.

Argentina’s student-captain had returned by then, but it wasn’t enough. Uruguay prevailed 4-2, turning the city of Montevideo into a raucous street party. The next day was declared a national holiday.

In Buenos Aires, crowds of Argentines vented their disappointment by marching to the Uruguayan embassy and attempting to break in.

Football was clearly on its way to becoming as important as life and death.

If not more so.

Let’s check some charts.

There was no prize money for the World Cup until 1982 when Italy won the tournament and $2.2 million of the $20 million total. This year’s winner will earn $50 million of the $871 million total.

An estimated 6.5 million people will have attended the World Cup this year, eclipsing the previous record of 3.6 million in 1994 (also in the US). The 1994 Cup might retain the record for per match attendance, however. The 2026 total will be divided by 104 matches, vs. just 54 in 1994.

The World Cup trophy contains nearly $600,000 of gold. The trophy handed to the winner on Sunday will be a replica. The original trophy from 1930 was stolen in 1983. Its successor, pictured above, does not leave FIFA headquarters.

As recently as 1984, footballers in England’s top division were relatively normal earners: their £25,000 average salary was only about 3x the UK average. Today, their £3.5 million average is 89x the average.

An exclusive club:

The richest club in football is Real Madrid, which did €1.2 billion in revenue last year. The next richest sports team comes from the other kind of football: the Dallas Cowboys did $1.2 billion last year (€1.05 billion).

There used to be more goals:

1930 was one of the highest-scoring World Cups in history. Both semi-finals ended 6-1, with Argentina beating the US and Uruguay beating Yugoslavia. 

Which brings us to the most amazing fact about the 1930 World Cup: the US made the semi-finals.

Have a great weekend, football fans.

— Byron Gilliam

“We have only to persevere to conquer.” — Winston Churchill on investing

Thursday Links

The superforecasters are here

Scott Alexander thinks AI superforecasters will soon be telling us who to marry. 

“It will look through whatever texts and emails you give it access to,” he explains, “learn what it can about your relationship, and answer something like, ‘If you marry this person, I think there’s an 85% chance you get divorced within five years.”

Whether 85% makes it a yes or no will remain up to you.

Based on how well AIs are already performing in prediction markets, Alexander estimates that this sci-fi future is less than a year away.

AI superforecasters are LLMs that have been modified with a bespoke “scaffold” — an additional layer of software that guides the LLM through a long research process (without it, LLMs are prone to wander).

They’re already nearly as good as the very best human forecasters, Alexander reports, and should be well beyond them by this time next year.

At a conference for superforecasters (human and otherwise), Alexander met a startup founder whose AI had turned $35 into $2 million on Kalshi over seven months. Another said his AI was beating the stock market by 25%. (AI superforecasters are especially good at finance.)

Alexander expects this will soon make prediction markets unsafe for humans. Polymarket and Kalshi will be where “bots duel other bots for the privilege of collecting money from dumb sports fans.”

With luck, though, that will provide an incentive for AIs to become so good at predicting that they’re also societally useful.

“The dream,” Alexander says, “is that, armed with AI superforecasters, the public and the politicians who they elect will make better decisions about policy.”

Or who to spend your next few decades with.

When not to call in the FBI

If an unofficial crypto broker absconds with your money, the FBI will be happy to investigate. 

They will also check your socials.

Reda Mazen Rida Sabassi, a resident of San Diego, learned this the hard way after attempting to distribute $387,000 in charitable donations he’d raised, ostensibly for the benefit of orphans in Gaza.

Prosecutors say the charity was a front and that Sabassi subsequently attempted to send the funds to a co-conspirator who the government had designated as a fundraiser for terrorists.

The funds never got there.

Knowing he could not send money directly to a designated terrorist, Sabassi enlisted an intermediary. He sent the $387,000 to unofficial crypto brokers, who promised to convert it to USDT and then send the USDT to crypto addresses provided by Sabassi’s co-conspirator (identified as CC-1 in the complaint).

When the brokers (Individuals-2,-3, and -4) decided to keep the funds instead, Sabassi reported the theft to the San Diego police, who recommended he contact the FBI.

Incredibly, he did — although not before asking CC-1 to fake an invoice for “aid trucks.”

According to prosecutors, Sabassi hoped these would “(i) substantiate his fundraisers in support of his claim that Individual-2 and Individual-3 stole funds and (ii) not disclose that the funds were intended for Hamas.”

That is a tough needle to thread!

Sabassi did not thread it. He told CC-1 he planned to preemptively send the invoices to the FBI so that they wouldn’t “come back to him with questions,” but they did — in part because of what they found on Sabassi’s socials (and then, with a subpoena, what they found on his email, phone, and everywhere else.)

Sabassi’s trial began in lower Manhattan this week. He faces as much as 85 years in prison.

An emotional metric for stocks

A new paper merges behavioral psychology and quantitative trading to develop a novel metric for stocks: the Degree of Rejoicing and Regret (DRR).

The authors say DRR quantifies how our emotions distort market prices — specifically, the joy of picking winners and the regret of holding losers.

When a stock outperforms its peers, the paper explains, investors anticipate future joy and accept lower expected returns, causing stocks to be overpriced. Conversely, when a stock underperforms its peers, investors anticipate future regret and demand higher expected returns, causing stocks to be underpriced.

Like emotions themselves, this effect reverts to the mean.

DRR is negatively correlated to future stock returns, which should make a tradeable signal: a market-neutral long/short portfolio based solely on the metric would have returned an annualized 16.45% annualized between 1963 and 2023, according to a backtest.

16.45%! Over 60 years!!

The paper cites neurobiological research to prove these eye-popping returns really are based on investors' emotions — including brain scans that quantify the psychic benefit and mental toll of constantly comparing our investing decisions to what might have been.

It then uses behavioral theory and some fancy math to prove that our anticipation of these feelings actively distorts our buying and selling.

The authors further support their thesis by comparing DDR across countries. They find the effect is amplified in countries with either “high individualism” (US, Canada, France) or “high uncertainty avoidance” (Japan, Belgium, South Korea).

“High individualism induces overconfidence and self-attribution,” the paper says, “amplifying investors’ emotional responses to both rejoicing and regret.” Conversely, “high uncertainty avoidance leads investors to overreact to potential losses or foregone gains, hoping to mitigate uncertainty.”

Both characteristics amplify emotional trading, which distorts the valuation of stocks.

They also find an exception that proves their rule: the United Kingdom.

In the UK stock market, the DRR metric behaves “atypically,” exhibiting a strong positive relationship with future returns (ie, the opposite of everywhere else).

British stoicism appears to act as an emotional shock absorber for the stock market, keeping stocks from falling as hard, or rallying as high, as the underlying fundamentals would suggest.

Let’s hope it works for their football fans, too.

— Byron Gilliam