Programming note: Money Stuff will be off on vacation next week, back on August 24. 

We have talked a number of times around here about businesses hedging real-world risk on prediction markets. Some business faces some risk from some real-world event: “If Zohran Mamdani is elected mayor, my business of selling Manhattan pied-à-terres will dry up,” or “if Cardi B doesn’t perform at the Super Bowl, I will sell less Cardi B-themed merch,” or, overwhelmingly, “if my local sports team does not make the playoffs my bar will not sell enough beer to pay the rent.” Prediction markets like Kalshi offer bets on those events, with real-time prices that seem to efficiently reflect their actual probabilities.

So you can go to Kalshi and buy, effectively, insurance. If you will make $100,000 selling beer if your team makes the playoffs, and $0 if it doesn’t, and Kalshi’s odds show that it has an 80% chance of making the playoffs, you can buy $100,000 of “No” contracts for $20,000 (20%). If the team makes the playoffs, you lose your $20,000 but make $100,000 on beer; if it doesn’t, you sell no beer but make an $80,000 profit on the hedge. Either way, you get $80,000: You’re hedged.

Two points about this. First, prediction markets are mostly for sports gambling, so many of these stories are about businesses hedging real-world sports risk. If you’ve got a European soccer club that might get relegated, that’s perfect for prediction-market hedging. But prediction markets are very invested in demonstrating that they are not purely sportsbooks, so when Kalshi helps a business hedge a non-sports risk I get a lot of emails.

Second, I have been imagining that you “go to Kalshi” and buy a big chunk of contracts to hedge your business risk. In practice, the actual exchange-traded liquidity in many of these contracts is limited: If you want to hedge your US presidential election risk, you can probably buy a lot of size without moving prices much, but the actual volume for even soccer relegation contracts is pretty small relative to the economic impact  of relegation. If you went to buy millions of dollars of those contracts, you would move the price against yourself so much that the insurance wouldn’t be worth it.

And so often the way these hedges work is as block trades, or, if you prefer, over-the-counter insurance contracts. That is:

• A business wants to hedge some event.
• Kalshi lists a contract on the event implying that its probability is 20% or whatever.
• You could not go buy $10 million of that contract on Kalshi, at 20 cents on the dollar or maybe at all.
• But you can go buy $10 million of that contract from Susquehanna International Group, the big proprietary trading firm that has made a push into sports and prediction markets. (Or perhaps some other big prop trading firm, but so far mostly Susquehanna.)
• Some broker will set you up with Susquehanna, who will sell you the contract.
• The pricing of the contract will be more or less the result of bilateral negotiation between you and Susquehanna, but it will probably be based on the Kalshi pricing. Susquehanna makes markets on Kalshi, it uses Kalshi market prices as an indicator of probability, and it will probably base its over-the-counter trading prices on those public market prices. (Here is an Odd Lots episode with Susquehanna’s head of prediction markets, who makes this point: Susquehanna will trade large blocks using the exchange pricing as an anchor.)

You could imagine a future in which prediction markets are so deep and liquid that it would be no problem to just log into Kalshi and buy $10 million of insurance against random events by clicking a button. But that would be weird. Who would be providing millions of dollars of resting liquidity, at low bid/ask spreads, on all sorts of weird events? If you are just bopping around trading a few hundred dollars’ worth of “Ipswich Town is relegated from the Premier League,” and then all of a sudden someone wants to buy $10 million worth, surely you are getting adversely selected.. The far more sensible thing is for most prediction markets to keep working like this: A public market trading relatively low dollar amounts but with pretty efficient pricing, and then bilaterally negotiated over-the-counter contracts for businesses that want to hedge in big size. [1]

What about a public market with a zero dollar amount? That is: What if you had some event risk that you wanted to hedge, but that wasn’t covered by a contract on Kalshi? (What if your bar will sell $1 million of beer if the local Little League team wins the county playoff, and $0 if it won’t?) You might call up Susquehanna anyway. “Hey Susquehanna,” you could say, “I want to buy insurance against this weird niche risk. Could you price it for me?” Susquehanna is very much in the business of pricing and holding weird niche risk. Often Kalshi market prices are an input to its pricing, but maybe that’s not strictly necessary. Susquehanna could go research the underlying event, figure out the probability of it happening, add some spread for itself, and quote you a price. You could pay Susquehanna a $100,000 premium for a contract that pays you $1 million if the event occurs and $0 if it doesn’t. It’s a pure over-the-counter bilateral prediction market, just you against Susquehanna.

Or almost. Here’s this:

Up until now, there was no way to directly cover the risk that a state law might shutter your goat herding business. Prediction markets changed that. 

Northern California rancher Tim Arrowsmith owns a company called Western Grazers. The business provides targeted grazing to reduce wildfire risk by clearing dry brush in hard-to-reach areas. Arrowsmith has over 4,000 grazing goats and employs eight herders who work around the clock to manage the goats. 

A recent legislative interpretation eliminated California’s longstanding wage framework for goat herders, which had been designed for the realities of around-the-clock livestock care. (Interestingly, sheep herders were spared from this interpretation of the law). If California doesn’t re-examine this law by the end of August, Arrowsmith’s labor costs could rise by roughly four times, putting his business, the jobs of his longtime herders, and an important wildfire prevention service at risk.

Arrowsmith turned to Castle, a startup that leverages financial products to mitigate risk for businesses. Castle worked with Susquehanna, one of the world’s largest proprietary trading firms, to create a hedge: they translated a highly specific regulatory exposure into a tradable market by defining objective settlement criteria and structuring the contract. Castle provided the coverage, used Kalshi, the largest federally regulated prediction market in the US, to list the market, and transferred the risk to Susquehanna, which priced the contract and provided the liquidity for the trade. …

The contract pays up to $500,000 if California does not fix the goat-herder wage rules by the end of September 2026. Arrowsmith paid a fixed premium for the protection. If lawmakers restore the previous rules or pass a similar solution, the contract expires with no payout. That is the outcome Tim is hoping for. ...

“Businesses face risks that traditional insurance often can't cover,” said Lucas Cavalieri, CEO of Castle. “A rancher should be able to hedge a bill just like a farmer hedges wheat.”

“Prediction markets are becoming a practical risk management tool, not just for institutions but also for small businesses facing real-world uncertainty,” said Eric Passmore, Senior Trader, Prediction Markets, at Susquehanna. 

“Before Kalshi, there was no practical way to hedge against a specific legislative outcome like this,” said Nicolas Hull, Director of Business Development at Kalshi.. “We hope businesses of every size and industry can use Kalshi to manage risk. This is just the beginning.”

That’s the Kalshi press release. Here is a good CNBC story with more of the background. Basically goat herders are on the job 24 hours a day, and now you have to pay them at least minimum wage for that whole time, which makes them expensive.

Here is the actual Kalshi goat herding contract. I want to make a few points about it. First, it was listed last week, purely for this trade.. This is not a case where Kalshi had an active market in California goat herding legislation probabilities. This is a bilateral trade between the goat guy and Susquehanna, which was then listed on Kalshi. CNBC explains the pricing:

Led by Susquehanna senior trader Eric Passmore, the firm established pricing and the contract terms on Kalshi. Constructing those provisions came after deep research on California’s goat herding business, speaking with industry professionals and connecting with Arrowsmith, Passmore said. 

The contract has a $500,000 payout that Arrowsmith paid a 10% premium or $50,000 on.

Second, as of about noon today, Kalshi showed a volume of about $500,476 on this contract. It was listed purely for the one guy’s over-the-counter trade, but now it is listed, so if you want to bet on California goat legislation now you can. Intriguingly, the contract has traded (in tiny size) at lower probabilities than 90%, meaning that arguably Susquehanna significantly underpriced this risk. The goat herder put one over on Susquehanna!

Third, the contract specification is long. The essential point of the contract is that it “Resolves Yes if California authorizes an alternative wage or provides qualifying relief from goat herder wage and overtime obligations before Oct 1, 2026,” and there are any number of ways in which that could happen. There could be a new statute, or a new state regulation, or a court order, or “an executive order or proclamation issued by the Governor of California.” Whatever it is must reduce the monthly cost of employing goat herders to no more than the cost of employing sheep herders.

This is not a simple yes/no question about whether a law will pass; this is a quite detailed contract specification designed to insure this guy’s actual risk. The goal is to have very little basis risk between the contract and the guy’s actual business. It is, as it were, loss-based insurance, not purely  parametric insurance.

That is a weird thing to trade, if you’re not a goat herder! If you’re just logging on to Kalshi to speculate, you might speculate on whether the California legislature will pass a law, but it’s a little weird to speculate on whether this guy’s goat herding costs will come down in any of a number of legal ways. This is not a product that Kalshi is listing because it thinks there will be a ton of retail investor demand. This is a product that Kalshi listed for the one trade, and that is optimized for that trade.

Fourth, why did Kalshi list it at all? If this is a bilateral trade between the goat herder and Susquehanna, why does it need to be publicly listed on Kalshi? (“Kalshi took a ‘backseat’ in facilitating the hedge,” notes CNBC.) Some possible answers:

1. Publicity. I was sent this press release three times. Kalshi wants to be seen to be in the business of hedging real world risk, and no one can resist a goat herding contract.
2. Neutrality. You could imagine Susquehanna and the goat herder negotiating the terms of the insurance contract, signing it, and then working in good faith to pay claims or not depending on how the event resolves. But that is a potentially complicated and adversarial process, and there’s a risk that (1) the goat herder’s costs don’t come down but (2) Susquehanna doesn’t pay anyway, because of some gap or dispute about the contract terms. But if you get Kalshi to write and list the contract and determine its resolution, maybe you trust Kalshi more: Kalshi is not on either side of the trade, and its incentive is to produce fair outcomes so that it can attract more business. Kalshi can serve as a neutral referee for bets like this.
3. Laying off. In theory, now that the event is listed on Kalshi, Susquehanna could lay off some of its risk by buying “No” contracts. Or Arrowsmith could take some immediate profit by selling his “No” contracts, which he bought at $0.10, for $0.20. In practice, the goat herder pay market is never going to be liquid enough for that to matter, but other contracts might be.
4. Regulation. Insurance is a highly regulated business, and Susquehanna can’t actually sell Arrowsmith something called “insurance” against his goat cost risk. It is selling him an event contract, which is a kind of “swap” under the US Commodity Exchange Act. That law says that “It shall be unlawful for any person, other than an eligible contract participant, to enter into a swap unless the swap is entered into on” a registered exchange. (An “eligible contract participant” basically means a large institutional trader; the goat herder wouldn’t qualify.) That is, it’s actually illegal for Susquehanna to do this trade as a pure bilateral over-the-counter contract. But if you do it as a bilateral contract that prints on Kalshi, it’s fine.

That last answer is probably the most important. As I have noted before, the definition of a “swap” is quite broad, and if you take it literally you might conclude that every sort of bet has to be traded on a registered prediction market like Kalshi. Arguably, now, anyone who wants to offer a business a weird bespoke event hedge like this has to trade it on Kalshi. 

The way financial regulation works in the US is that a regulator proposes a new rule, and then the general public has the opportunity to submit comments. The comments might support the rule, or oppose it, or suggest changes. Then the regulatory agency has to consider the comments and come up with a final rule. The comments are not binding — if there are 100 comments in favor of the rule and 900 against, it’s not like the regulator has to withdraw the rule — but they have some effect on how the rule is perceived. “SEC Expected to Change Quarterly Earnings Rule Despite Public Backlash,” the Wall Street Journal reported last month: It can do that, sure, but it looks bad.

Many comments will naturally come from financial firms whose businesses will be affected by the new rule. They tend to submit informed, articulate, helpful comments written by expensive lawyers; they will naturally be  self-serving, but they will often make the rules better. But many rules are intended to protect ordinary investors, and so there is some desire to get feedback from ordinary investors. 

How would a regulator get feedback from ordinary investors? One possibility is that an ordinary investor would learn about the rulemaking, go to the regulator’s website, read the proposed rule, have some thoughts, and submit a comment. This is not impossible, but it would be a little weird. Who does that? We talked in May about a good comment on the quarterly earnings rule submitted by “/r/wallstreetbets,” a Reddit retail investing community; in a sense they are ordinary investors, but they’re unusually dedicated and online. Ordinary ordinary investors are less likely to submit comments.

And so the normal way to get feedback from ordinary investors works something like this:

• Some financial firm, trade group, lobbying organization, etc. writes a comment letter reflecting what they think an ordinary investor might say. “This new rule will help me save more money for retirement,” or whatever.
• They go out and find some ordinary investors.
• They try to get the ordinary investors to submit the comment that they wrote. “This new rule will help you save more money for retirement, so you should submit a comment letter saying ‘this new rule will help me save more money for retirement,’ under your name,” they say.

This is to some extent a volume game: If you get 200,000 people to submit a comment opposing a rule, that makes it easier to argue that ordinary investors oppose the rule. Will most of those comments be form letters containing exactly the language that you wrote? Sure. Will that be embarrassing, undermining the perception that ordinary investors actually care? Oddly, no; everyone understands that this is the way the sausage is made.

Will some of those form letters be submitted by dead people, or otherwise attributed to people who did not actually submit them? Oh sure, sure, whatever. None of this is real. It’s not voting. The comments are not binding.. The comments are valuable (1) to the extent they make convincing arguments and (2) otherwise as a sort of vague directional indicator of quantity. If only 199,000 of the 200,000 people who oppose a rule actually exist, that doesn’t change the analysis much.

Still it is embarrassing. We talked a few years ago about a proposed US Securities and Exchange Commission rule cracking down on proxy advisers, which was supported by heartfelt comments from ordinary investors, many of whom either didn’t actually submit the comments under their names, or allowed their names to be used without reading the comments. I wrote that, if you favor limiting the power of proxy advisers, you can just say that, but:

It’s better if you can find an 83-year-old Army veteran to say “how disgusted I am that my financial investments are being used as a political pawn,” or a retired Marine veteran” to say “I didn’t invest my money to have others make any kind of political statement.. I invested my money to be able to leave something for my children and grandchildren in order to make their lives a little better,” or a hard-working single mother to say “Let the folks who work at proxy advisory firms bring about social change on their dime, not mine,” or a retiree to say “Call me naïve but I certainly didn’t sign up for groups with their own agenda voting my proxy!”

Obviously you can’t find people to say that, because no normal human actually pays attention to or cares about any of this. But if you work on one side of the issue, you can convince yourself that normal people should care about it, and then you can imagine what they might say if they did care, and then you can write it down, and then as a final, only-somewhat-fraudulent step, you can go find an ordinary person to sign the letter you wrote.

If they’re dead it’s harder, but not impossible. We have talked about proposed rules to allow more private assets (and crypto) in 401(k) retirement accounts, which is something that (1) the financial industry really really really really really really really really wants and (2) some ordinary investors might want, hey why not. So a lot of ordinary investors submitted comments supporting the rules, and some of them were fake or dead, Bloomberg’s Noah Buhayar and Jeff Kao report:

Heath Oderman was surprised to learn that a comment supporting a plan to get more Americans’ retirement plans invested in private equity and other “alternative” assets had been submitted to the federal government in May under his mother’s name.

She couldn’t have done it, he said, because she died in December.

“That’s not my mom,” he wrote in an email to Bloomberg News after being alerted to the comment, which was submitted to the US Department of Labor on May 3 under the name Danna Oderman. “The language is nothing she would ever have used while on this Earth.” …

The comment was one of nearly 12,000 showing signs that they may have been manufactured to resemble grassroots support for the controversial measure. … Bloomberg attempted to contact dozens of the people whose names were attached to the 12,000 comments and found five cases in which people said they — or their family members — did not submit them. 

So some fakes, but against a pretty low baseline of reality:

More than 92% of the submissions, both for and against the proposal, followed some sort of form letter.

The most popular of these was a petition that urged the government to scrap the proposal, arguing it would expose people to “expensive fees and dangerous levels of risk.” That message was sent more than 30,000 times, representing about two-thirds of all submissions.

Ninety-nine percent of these commenters included an email address and location in their submission, and many had personalized messages or signatures at the end of the form letter. Bloomberg attempted to contact three dozen of them. Most didn’t respond. But Judith Bergson, a social worker in Massachusetts, confirmed that she sent the comment and would be “terribly upset” if private investments were put into her retirement plans. “I’m definitely a person,” she added in an interview.

The advocacy group Americans for Financial Reform drafted the form letter that Bergson signed and worked with partner groups to disseminate it, said Ericka Taylor, the organization’s co-executive director. 

Incidentally, I have some firsthand experience with having financial regulatory comments submitted under my name! The SEC is considering new rules about sports gambling exchange-traded funds. I have written about this, because it is wild. At the end of one column on the topic, I wrote:

A couple of weeks ago, we discussed the US Securities and Exchange Commission’s “request for public comment on exchange-traded funds (ETFs) seeking to invest in innovative asset classes or engage in novel investment strategies” (sports gambling). You can submit comments here. I suppose this is my submission.

Here are the comments that have been submitted on that proposal.  One is from me. It used to contain the text of that column, though now it reads “Copyrighted material redacted” with a link to my column. I did not actually submit it, and I am not entirely sure how it ended up on the SEC’s website. I can’t really complain: I did write it, and I said “I suppose this is my submission,” so someone submitted it as a comment. And I like to think that the SEC will take it into consideration in writing rules about sports gambling ETFs.. But the point is that there’s a comment on an SEC rule submitted under my name but that I did not submit. Could happen to anyone, really.

I have, a couple of times, espoused a tidy theory that hedge funds can smooth out lockup expiries for big initial public offerings. The theory is:

• When a company is added to an index, there is quite predictable demand for the stock, on a fixed date, from index funds. There is a big business, done mostly by multistrategy hedge funds, of smoothing out this demand. Basically the hedge funds buy the stock before the index rebalancing date, and then sell it to the index funds right on the rebalancing date. Therefore the stock shouldn’t go up too sharply when it joins the index; the hedge funds effectively average it in.
• When a newly public company has a lockup expiry, there is predictable supply: A bunch of early investors are finally allowed to sell, so more shares are available than were before. This also happens at a fixed date, and hedge funds can similarly smooth out the supply. Basically they can sell short the stock before the lockup release date, and then buy it from the insiders on the lockup release date. Therefore the stock shouldn’t go down too sharply when the lockup expires; the hedge funds can average it in.

This is only a loose analogy. For one thing, it is relatively easy to buy stock ahead of an index rebalancing; it is harder to short stock ahead of a lockup release. Shorting stock requires you to borrow it and pay a fee to your stock lenders, and when a company is newly public, with only a limited float, it is often hard to borrow. If there aren’t a lot of shares available to borrow, or if the fees are high, hedge funds will not be able to pre-sell all of the locked-up stock.

For another thing, index demand is quite mechanical: You can more or less count how many shares the index funds will need to buy, and then buy those shares to sell to them. The index funds are rules-based and price-insensitive, and there is a lot of data (from previous index adds, from quarterly rebalancings) about how they behave. Lockup-release supply is much less mechanical: You can count how many shares will be released from the lockup, but it is hard to guess how many shares will actually be sold. Lots of early investors are true believers and won’t sell when their lockups expire; others are price-sensitive and will sell at high prices but not low ones; others plan to dump all their stock but are on vacation on the lockup release day and only get around to it next week. You can sort of guess “well, when the lockup expires, some shares will come up for sale,” but it is hard to quantify precisely.

SpaceX had its first post-IPO lockup release on Aug. 7, and on Aug. 6 I wrote:

Really the expected price of SpaceX stock tomorrow, after the first phase of the lockup is released, should equal the closing price of the stock today: This is a well-anticipated event, and all of those short sellers have essentially been selling tomorrow’s shares today. That said, the variance around tomorrow’s price is high.

In fact SpaceX closed that day at $114.92 and opened the next day at $114.965, so in the narrowest possible sense I was right, but more broadly I think it is fair to say that the SpaceX lockup release was not especially smoothly averaged into the price. In fact, it seems to have been overdone: The stock got as low as $104.84 on Aug. 3, it rallied into the close on Aug. 6, and it has traded up ever since. Bloomberg’s Carmen Reinicke reports:

Since SpaceX went public in June, Wall Street had been dreading Aug. 6, when the first lockup preventing early investors from selling the stock expired and millions of shares were set to flood the market.

They needn’t have worried.

SpaceX shares have been on a tear since the lockup ended, soaring 35% in just five sessions, adding roughly $500 billion in market capitalization, and vaulting back over their $135 initial public offering price. The surge bucked concerns about an impending wave of selling and gave investors optimism that future expirations won’t be as painful as feared.

“We’ve gotten through the big hurdle, which was the unknown,” said Andrew Plum, managing partner and investment committee head at Loxahatchee Capital, which holds SpaceX shares. “It’s like the market discounts a negative event, maybe too much, as it’s waiting for that information to come to pass. And then it’s usually not as bad as what was discounted or as expected.”

One way to phrase “the market discounts a negative event, maybe too much” might be “shorts sold more stock ahead of the lockup release than was actually sold in the lockup release.”

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The basic business of finance is slicing up cash flows. You have some stuff, the stuff generates cash flows, you put the stuff in a box, the box issues securities to investors, the securities have claims on the cash flows. Some investors want safe securities with senior claims on the cash flows: If the stuff is expected to generate $100 of cash flows, you can probably issue investment-grade debt with a claim on the first $20 of the cash flows. Some investors want high-risk, high-reward securities with junior claims on the cash flows: If the stuff is expected to generate $100 of cash flows, someone will probably pay something for a stock option with a claim on all of the cash flows above $120. Etc.

This works for many, many things that you might put in the box. If you own a widget business, you can put that business in a box called a “corporation,” issue bonds with senior claims on the business’s cash flows, and issue stock to investors who want the risky upside of the business. If you’ve got a rockets-and-satellites-and-enterprise-AI-and-social-media business, you can put that in a big corporate box called SpaceX and issue bonds and stock. If you own some office buildings, you can put them in a box called a “real estate investment trust” or “REIT,” issue some bonds with a senior claim on the rental income and issue stock to investors who want the upside from your rent.. But you can also put purely financial stuff in a box. If you own some mortgage loans, or some credit card receivables, you can put those in a box and sell junior and senior claims on them. Etc.

People often use the word “securitization” to describe this process: You put stuff in a box, you issue securities, it’s securitization. It would not, I think, do huge violence to the English language to describe every public company as a “securitization”: SpaceX put its rockets-etc. business in a public company and issued securities to finance it so, why not, securitization. In practice, though, people do not use the term this way. A “securitization,” in normal usage, mostly means putting financial stuff — mortgages, credit-card receivables, car-part invoices, what have you — into a box and selling claims on them. (There is such a thing as a “whole business securitization,” but that’s weird.) 

In fact US securities law makes this distinction. Under the law, “securitization transactions” are subject to different rules from regular securities. (Most notably, securitization issuers are required to retain some “skin in the game” rather than selling all of the cash flows; there are also different disclosure rules.) A “securitization transaction” is defined to mean one involving “asset-backed securities..” Of course all securities are asset-backed: SpaceX stock and bonds are backed by its rockets and data centers, etc. But the technical term “asset-backed security” means “a fixed-income or other security collateralized by any type of self-liquidating financial asset (including a loan, a lease, a mortgage, or a secured or unsecured receivable) that allows the holder of the security to receive payments that depend primarily on cash flow from the asset.” That is, an asset-backed security is a security backed by a financial asset (not a factory or a business, but a financial claim) that is self-liquidating (that gets paid down over time until its value is zero). 

So a mortgage-backed security is the paradigmatic sort of ABS: You put some mortgages in a pot, you sell some securities with claims on them, they get paid off over time and the money is used to pay back the securities. A regular company is the paradigmatic sort of not ABS: You put a whole business in a pot, you sell some securities with claims on it, the business intends to grow and thrive and change over time, the pot (a corporation) has a perpetual life, etc.

These days, though, the stuff that everyone is interested in is not mortgages or businesses but data centers. You put a data center in a box, the box issues securities to investors, the securities have claims on the data center’s cash flows. You could imagine different ways to structure this. At one end, you could have a regular company that develops, builds and operates data centers, that finds tenants and power and chips for the data centers and tries to turn a profit, and that issues stock and bonds to finance itself. That approach exists; it more or less describes “neocloud” companies like CoreWeave Inc. [1]

At the other end, you could imagine building a data center, signing a long-term lease with Anthropic or OpenAI, and then putting the lease in a box. You have contracted to get, say, $1 billion a year from Anthropic for 10 years, you put those contractual cash flows in a box, you sell the first $100 million a year as AAA-rated bonds, etc. You don’t see a ton of this approach. Everything is too new and fast-changing. In 10 years, Anthropic might not exist, and you might have re-rented the space to someone else. You might have to tear out the chips and put in new chips. Etc. Running a data center is, at this point, like running a business; there are decisions to be made, and you can’t just do it on autopilot. 

And so a lot of what you see is somewhere in between. A big asset manager arranges the financing, a big tech firm provides the chips and guarantees their value, a big AI firm signs a lease, the cash flows are all reasonably predictable and they get packaged into rated bonds that are sold to investors. The stuff in the box is not just the lease, not just a series of contractual cash flows; the stuff in the box is the data center, the chips, the actual business assets that create the cash flows. The data center financing is colloquially a “securitization,” but it is not technically a securitization. 

Or that’s the argument. Bloomberg’s Jack Trapanick and Scott Carpenter report:

The Securities and Exchange Commission has made it easier for data center owners to sell asset-backed securities, potentially opening the door for more debt sales as tech firms scour Wall Street for ways to pay for artificial intelligence.

The SEC said a major subset of data-center securitizations don’t need to have disclosures and investor protections that similar deals require. That includes risk retention, a requirement that companies issuing asset-backed securities retain some of the debt to better align their interests with investors.

In a letter late last month, staff wrote that data centers aren’t financial assets that liquidate over time, like loans or leases, and therefore bonds tied to them aren’t subject to the same rules as debt backed by car loans or home mortgages.

Here is the SEC’s interpretive letter, and here is the letter from Latham & Watkins LLP requesting it:

We respectfully request that the Staff concur with our view that fixed-income or other securities issued in data center securitizations of the type described in this letter (“Data Center Securitizations” or “DCS”) are not Exchange Act ABS because the securitized assets are not “self-liquidating financial assets” and therefore payments to investors do not depend primarily on cash flows from self-liquidating financial assets.

The basic point is that a data center is more like a business than like a mortgage or a lease:

Although a portion of the Securitized Assets include customer contracts (including in some cases leases) that generate revenue necessary for the Issuer to make payments due on the securities, the revenue generated is reduced by expenses to operate and maintain the data center facility (e.g., taxes, insurance, electricity, repairs and maintenance, security services, etc.). Consequently, the payments to the holders of the securities do not “depend primarily on cash flow from the [self-liquidating financial] asset” because the amount of cash available to pay investors also depends on the effectiveness of the operator or manager to minimize the operating expenses of the Issuer.

The investment opportunity in a DCS is most like an investment in a real estate investment company. In general, a real estate investment company acquires, owns, finances, manages, leases, and develops real estate and may raise capital to fund these operations. Similarly, an Issuer of a DCS acquires, finances, manages, and develops data centers and as discussed above, the proceeds from the issuance of DCS may be used for a variety of purposes. An investor in a real estate investment company is exposed to the entire operations and activities of the company and all assets and liabilities. In contrast, a DCS investor is isolated from the other activities of the operator or manager, a hallmark of securitization financing. However, this type of securitization directly funds the operations of a business, and the proceeds may be used for a variety of purposes, whereas Exchange Act ABS are issued to fund the purchase of a pool of self-liquidating financial assets. 

There are some diagrams. It’s not obvious to me that data-center financing has to always work like this. You could imagine a future in which, say, SpaceX builds data centers on its own balance sheet, leases them out long-term to Anthropic, packages the leases and sells them as asset-backed securities. In a world where “compute is an investable asset,” perhaps compute-backed bonds will in fact be asset-backed securities. But for now, building AI infrastructure is more like a business.

Donald Trump is selling early access to US policy decisions to hedge funds and trading firms for about $100,000 per month. (Technically his company, Trump Media & Technology Group Inc., is selling a feed of early access to policy announcements he makes on TMTG’s Truth Social site, but Trump is TMTG’s biggest shareholder.) The pitch for this service is that Trump’s policy decisions are market-moving, and if you get them a few milliseconds before everyone else, you will be able to trade on them and make money. This isn’t true for everyone, but it probably is true for an algorithmic trading firm; if you are paying for lots of fast data feeds, you might as well pay for a fast data feed of US government policy.

This raises at least two obvious problems:

1. It seems bad for the president of the US to sell early access to government decisions for his personal profit? Just, like, as a citizen, I find that weird?
2. It’s maybe insider trading? Arguably this information belongs to the US government, and arguably Trump is misappropriating it for his own benefit and selling it for cash. At the time it is sent to hedge funds, it is arguably non-public [2]  — they get it a few milliseconds faster than anyone else — and arguably material. In general, if you pay a corporate or government insider for material nonpublic information and use that information to trade, that’s illegal insider trading. That does seem to more or less describe what is happening here, though there are counter-arguments. [3]

What can you do about it? The first problem is, it seems to me, basically a political problem: If the president is using his government policy decisions to profit personally, the remedy is for Congress to say “hey maybe stop doing that.” I would not count on that happening. “Government watchdogs and congressional Democrats have criticized the service,” notes the Wall Street Journal, sure.

The second problem is one of insider trading. The normal remedy there would be for the US Securities and Exchange Commission, or federal prosecutors, to say “hey don’t do that” or bring enforcement actions. This, for reasons, is not going to happen any time soon. [4]  

Another possible remedy is for investors to sue. Trump announces a new war, a few hedge funds sell stock a millisecond before the war is public, stocks drop when the war is public, and people who bought stock in that millisecond sue. They sue Trump Media, I guess: “We bought stocks at inflated prices because we didn’t know about the war, but then the war news came out and stocks dropped. But you sold early news about the war to the market-making firms who sold us the stock; they were insider trading and you owe us money.”

There are problems with this theory. For one thing, I suspect that many subscribers to the Truth Social feed will be market makers who use it defensively to avoid trading when a war is announced. Even if they are trying to trade during their millisecond head start, the probability that you will trade with them is low; there will not be a lot of victims here. (The victims might be market makers.) Even if there are victims, the normal move is to sue the insider traders (the people who traded on the news), not the tipper (Trump Media). [5]

It’s just an interesting little gap. “Donald Trump is selling advance notice of policy decisions to financial firms so they can trade” does sound like it might be an insider-trading problem, but it’s not clear anyone can do anything about it. 

Anyway here’s a different theory:

President Donald Trump was sued by two media organizations over Trump Media & Technology Group Corp.’s plan to sell faster access to his posts on Truth Social.

The Intercept Media, a news organization, and the non-profit Freedom of the Press Foundation asked a court in New York Wednesday to block the plan, calling it “extraordinary, corrupt, and unconstitutional.”

Well, sure, but why do you get to sue? Here is their complaint:

Plaintiffs are harmed by the President’s illegal scheme. The Intercept Media, Inc. is an award-winning nonprofit investigative news organization that frequently covers the President’s posts. Freedom of the Press Foundation is a nonprofit organization that supports public interest journalism and operates the “Trump Anti-Press Social Media Tracker” database, which aggregates and categorizes the President’s Truth Social posts attacking the media. Both face indefinitely delayed access to the President’s latest posts, and face permanent bars to the President’s archived posts, making it harder for both organizations to do their jobs.

The Constitution guarantees that public officials “can themselves have no pecuniary interest or proprietorship, as against the public at large, in the fruits of their [official] labors.” … The President is profiting by selling government information. That is illegal. Plaintiffs bring this case to stop it.

I guess their theory is that, as journalists, they are entitled to get Trump’s policy decisions at the same time hedge funds do? Maybe?

The basic situation is that US states have historically regulated sports gambling, either banning it or allowing it in regulated forms. But recently prediction markets like Kalshi Inc. have started offering sports gambling and claiming that, as federally regulated commodities exchanges, they are exempt from state regulation. In the past, the federal regulator, the US Commodity Futures Trading Commission, had not allowed prediction markets to offer sports gambling, because the CFTC’s view was that it was not in the public interest to offer sports gambling on federal commodities exchanges. But the current CFTC has reversed this position. Now its view is not only that federal law allows sports gambling, but also that it prohibits state regulation of sports gambling, and the CFTC has gone to court to argue that states can’t interfere with Kalshi’s sports betting operation.

This is still working its way through the courts. We talked last week about a lawsuit that New York brought against Kalshi, arguing that it offers sports bets in violation of New York gaming regulation. This is obviously true, but also perhaps irrelevant; Kalshi’s response was that “states can’t just shut down a federally licensed exchange.” Federal courts have largely agreed with Kalshi about that, but there are exceptions, and who knows what will happen here.

In the meantime, though, Kalshi and the CFTC have declared an emergency. The emergency is that a court might agree that Kalshi is violating the law. The CFTC says:

The [Commodity Exchange Act] requires the Commission to provide a uniform national market in derivatives transactions. As part of this obligation, the CFTC ensures public confidence in its markets by safeguarding market resilience and orderliness. The Commission is also tasked with providing competitive, fair, and efficient markets that protect the price discovery process of trading in the centralized derivatives markets. Major market disruptions hamper these efforts. 

And so it is, I think, ordering Kalshi to ignore any court orders? The CFTC’s order says:

Under the Commission’s statutory emergency powers, it may direct Kalshi and its affiliates to continue to perform its functions as an exchange in accordance with the CEA’s Core Principles and its normal practices. This exercise of the Commission’s emergency authority will give market participants the necessary assurances that a CFTC-registered [designated contract market] cannot be shut down by a single State and that the trades they execute will be duly cleared and fulfilled. 

NOW THEREFORE:
IT IS HEREBY ORDERED that, pursuant to Section 8a(9) of the CEA, the Commission having reason to believe that the threat to its market justifies the exercise of its statutory emergency power, Kalshi shall continue to perform its functions as an exchange in accordance with the CEA’s Core Principles and its normal practices. 

I don’t even think the legal analysis is really wrong; I just think it’s amazing that a federal regulator has concluded that any effort to shut down sports gambling would be an emergency for financial markets.

Two theories about the financial industry are:

1. The financial industry creates a massive waste of talent: So many of the best and brightest scientists end up working at high-frequency trading firms, making a bit of extra money for those firms in a largely zero-sum competition instead of curing cancer or discovering cold fusion or whatever.
2. No, actually, it is good that the financial industry sucks up all of the scientists and pays them really well to work on trading systems. For one thing, they make capital allocation more efficient and that’s good. For another thing, this system makes scientific training lucrative: If getting an astrophysics PhD is a good path to quant-trading riches, more people will get astrophysics PhDs and some of them will end up doing astrophysics.. Also, though, the problems that they work on — moving data around faster, analyzing it better — often have more general applicability. Many advances in communication are motivated by getting stock prices faster, and Brett Harrison posted on X that “many technologies now commonly associated with AI were created for” high-frequency trading firms.. 

There are right now any number of quantum physicists who are working on extracting information from corporate earnings calls, because hedge funds can make a lot of money if they can trade quickly on corporate earnings calls. In one sense, putting all those quantum physicists to work analyzing earnings calls seems kind of sad and drab; shouldn’t they be doing cool quantum physics stuff? In another sense, though, it seems obvious that techniques developed for extracting information from corporate earnings calls would have broader applications. Extracting information from natural-language text, extracting information from natural-language audio, analyzing tone and body language: These are all things with immediate and lucrative application to stock trading, but they are also at the core of a much larger boom in artificial intelligence. The financial industry can subsidize more general breakthroughs.

And if all those quantum earnings-call analyzers get really bored they can make an AI music app? Apparently? Bloomberg’s Lucas Shaw and Ashley Carman report on “Suno, a generative artificial intelligence startup in Cambridge that lets people be rock stars without ever picking up an instrument.” Apparently Suno’s founders started out by generating corporate earnings calls using AI, but then realized that the same skills could be applied to songs:

[Suno CEO Mikey] Shulman was in the final year of getting a doctorate in quantum physics at Harvard University when he interviewed at Kensho, a software company in Cambridge that creates tools for financial companies. While working there part time, he befriended future Suno co-founders Georg Kucsko, Martin Camacho and Keenan Freyberg. They worked on a product that used machine learning to automate corporate earnings calls. Toward the end of 2021, they had two realizations: They enjoyed working together, and they thought there was a lot more they could do in AI audio.

They quit Kensho and started Suno, which means “listen” in Hindi. At first the company developed technology for turning text prompts into audio, which it called Bark. The outside developers who started to play with Suno’s software, though, wanted to make music. Shulman and his co-founders were happy to shift the company in that direction; they’d often had jam sessions in Camacho’s basement while at Kensho. In November 2023, Suno released a product that made writing and creating songs as easy as taking photos on a phone.

I suppose things could come full circle. A core thesis of this column is that you should never participate in a corporate rap video, but does that apply to Suno? If you are a the chief executive officer of a public company, and you want a punchy rap song for your executive offsite or for, heaven forbid, your earnings call, now I guess Suno can make you one.

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I guess the sci-fi big picture here is something like:

1. In the future, artificial intelligence will do a lot of the useful labor. If you want a merger analysis or a medical diagnosis or a fun newsletter to read, an AI will create it for you.
2. The AI will in many respects be cheaper than human labor: It won’t need to eat or sleep or take nice vacations or send its children to college.
3. But the AI will need somewhere to live. AI lives on computer chips in data centers, and it will need to pay rent. 
4. Humans will own the data centers, and the supply of high-end chips and electric power will be somewhat constrained, and most of the value of the AI future will ultimately be paid out in data-center rents.
5. In the long run, the main human economic activity will be owning chips to rent out to AI, which will do the rest of the economic activity.

You could sort of imagine a Jane Austen-y early-19th-century economy, with AI as the farm workers doing the economic activity and humans as the aristocratic rentiers who own land and earn 5% in perpetuity. And then the structural changes you’d need to get there are:

1. Putting “private investments” like infrastructure funds and private credit into ordinary people’s retirement accounts, so that everyone can be at least a small-scale AI rentier, 
2. Raising a gazillion dollars of private-credit and infrastructure funds, and
3. Using the money to build the data centers to rent to the AIs.

There are other approaches — “everyone should buy stock in the AI buildout,” for instance — but the rentier approach has a certain simplicity. Who knows which AI model or harness or end user will ultimately capture the upside from AI? Whatever it is will probably need chips, though, so there’s a steady living to be made from renting out chips.

Obviously one can worry about a bubble. But I must say that the two main dynamics I write about are “we should invest everyone’s retirement savings in AI infrastructure” and “we should invest everyone’s retirement savings in sports gambling,” and only the first one has some actual economic logic to it. The point of investing is to own a share of future economic productivity. If a big share of future economic productivity might happen on computer chips, you might as well own chips.

Anyway:

US investment giants including Apollo Global Management Inc., Blackstone Inc., BlackRock Inc. and Brookfield Asset Management are partnering with Nvidia Corp. to source $500 billion in financing for artificial intelligence infrastructure.

The coalition, which also includes Goldman Sachs Group Inc. and KKR & Co., will “create dedicated pools of capital at significant scale at attractive rates for Nvidia customers,” according to a statement Monday. Nvidia Chief Executive Officer Jensen Huang said in a CNBC interview that he approached only the six firms for the commitment, and none turned him down.

The effort comes with a huge headline figure but few details on the timing and structure of the financing, or how much the plan goes beyond the string of AI deals that are already driving a large chunk of Wall Street’s biggest transactions. Executives indicated that it will focus on debt financing to provide access to compute for Nvidia’s largest customers and that there are already many deals in the works that would qualify toward this commitment.

From the announcement:

“Modern compute has emerged as a scarce, mission-critical asset class with compelling investment characteristics that is positioned to drive significant long-term economic growth and productivity gains,” said Apollo President Jim Zelter. “The combination of NVIDIA’s proprietary technology ecosystem and Apollo’s flexible, long-term capital base provides a strong foundation to support the next stage of the AI buildout as part of the broader Global Industrial Renaissance.”

Part of what is going on here is that Nvidia is competing to be the main supplier of computer chips to AI, and making its chips easier to finance is helpful for maintaining its lead. Huang posted on X that “in some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity”: Nvidia is probably more optimistic about (and more directly invested in) the AI buildout than most investment managers are, and using its own giant investment-grade balance sheet to support AI projects makes it easier to raise debt. Ben Thompson writes:

The company is backstopping opportunities with up to 25% residual-value based financing, suggesting that Huang believes his “investable asset class” pitch much more than the market does. That is, in a certain sense, a price cut, as the goal is to reduce the cost of capital for entities building data centers with Nvidia chips, by putting Nvidia’s profits on the line for uncertain investments.

But as the Financial Times notes, this arrangement “also shows how Nvidia is building relationships with the giants of the private capital industry, which are collectively preparing to invest trillions of dollars of their insurance, retail and institutional investor assets into AI infrastructure.” A major theme in financial markets over the past few years has been the rise of “privates,” and the push to put retail investors’ savings into private assets. Sometimes this means selling private funds to retail investors in their retirement accounts; other times it means selling them annuities or life-insurance products or pensions that invest in private assets. Private assets are more lucrative for the financial industry than public ones, and selling trillions of dollars of private assets to everyone is good business. But where will the industry get those trillions of dollars of private assets? In data centers, apparently.

Elon Musk’s pay package at Tesla Inc. rewards him with giant piles of stock if he achieves certain valuation and operational goals. There are 12 tranches of stock, and each one unlocks based on some combination of (1) Tesla’s market capitalization, (2) Tesla’s earnings and (3) specific product goals like having a million robotaxis in operation, delivering a million humanoid robots, having 10 million full self-driving subscriptions, etc.

When you think about it, this is an unusual level of micromanagement for Elon Musk. The main way that investors manage Elon Musk is by giving him money, leaving him alone and seeing what he gets up to. His job, as a steward of investors’ capital, is to do whatever he thinks will maximize value. A lot of investors gave Musk billions of dollars to buy Twitter. Now Twitter is, um, a space rocket company with a $26.5 trillion total addressable market in artificial intelligence. Those investors have made a fortune.

Imagine, though, if they had put conditions on their investment with Musk. “We’ll help you buy Twitter, but we want our money back if you don’t increase ad revenue by 50% within the first two years.” “You have to commit to increasing user engagement on Twitter by 10% per year.” “You need to sell at least $10 million of premium Twitter subscriptions.” “You have to get rid of the bots.” Stupid! All of these operational metrics that you might have thought were relevant to Twitter shareholder value, and that turned out not to be. The relevant metrics were, like, data centers in space.

I mean, the relevant metrics are data centers in space, in the sense that Musk’s pay package at SpaceX (the successor to Twitter!) does contain operational targets including “non-Earth-based data centers” and “a permanent human colony on Mars with at least one million inhabitants.” Again: micromanaging! Ambitious sci-fi micromanaging, sure, but still a constraint on Musk’s imagination. Last year, it seemed to Tesla’s board of directors (and Musk) that the most likely and durable way to maximize Tesla’s value would be with humanoid robots and self-driving robotaxis. This year, it seemed to SpaceX’s board of directors (and Musk) that the most likely and durable way to maximize SpaceX’s value would be with Mars colonies and orbital data centers. 

Next year, though, what if Musk decides that the best business model for Tesla is to invent and sell teleportation devices? What if he decides that the best location for SpaceX’s data centers is in the deep ocean, or in the Earth’s core, or in the human spirit? He can produce business pivots much faster than public-company boards of directors can produce moonshot pay plans, and all of his pay plans will quickly feel a bit dated. Tesla and SpaceX will not be getting the full Elon Musk experience, if Musk is forced to think about how to build self-driving cars or orbital data centers rather than whatever he comes up with next.

In practice, there are a number of escape hatches for this problem. Most straightforwardly, Musk can start a new company, do whatever new thing he wants in that new company, quickly make it worth a trillion dollars, and then sell it at a premium to one of his other companies. His ability to extract value from his ideas, for himself and for his investors, is not limited to his formal pay packages. 

But the formal pay packages have some escape hatches too, the Wall Street Journal reports:

Deep in a $1 trillion pay package approved by Tesla’s shareholders is an escape clause that could pay off for Elon Musk in several ways if he folds the automaker into his SpaceX empire.

That kind of cars-and-rockets tie-up — the subject of widespread speculation among investors, and hinted at by Musk himself — would likely give him the tighter control he has long sought at Tesla. A hefty-enough price would also, in an instant, wipe away key performance targets standing between Musk and billions of dollars in shares.

“This $1 trillion—that was supposed to be a stretch,” said Mary Ellen Carter, a Boston College accounting professor who studies executive pay. “It turns out it isn’t really that hard. All you have to do is be bought.”

Musk has considerable say over any offer SpaceX makes for Tesla, thanks to his sweeping control over the rocket company. The upshot: Even a stratospheric bid would keep Musk in firm control of the combined company. …

Tucked into the bottom of the fifth page of the 16-page 2025 CEO Performance Award Agreement, a single sentence declares half the targets are as good as accomplished if Tesla is acquired or otherwise taken over.

“In a change in control, the earning of tranches will be based solely on the Market Capitalization Milestones,” is that sentence: If Tesla is bought, including by SpaceX, Musk will be rewarded only for how much value he creates, not for how he creates that value. Full self-driving? Robotaxis? Who cares! What you want from Elon Musk is unconstrained maximization of shareholder value, and in a merger that’s what you get.

The basic state of affairs in private markets, over the past few years, was that if you had $100 worth of SpaceX stock, you could sell it to eager individual investors for $200. There was an institutional market where SpaceX traded at one price, and a fragmented opaque retail market where SpaceX traded at a bunch of different prices, most of them higher than the institutional price. This mostly took the form of special-purpose vehicles that owned SpaceX stock and sold shares in themselves — at substantial premiums, and/or with substantial fees — to individual investors. But there were other formats, funds and futures and tokens that offered some form of SpaceX at some price. Now SpaceX is public and this trade has, for the most part, gone away, but there are similar trades in other big private companies.

Why? When you sold $100 worth of SpaceX for $200, what were you getting paid for? As far as I can tell there are about three answers:

• Access: Not just anyone could buy SpaceX in the institutional market. But you could. 
• Aggregation: The institutional market has bigger ticket sizes than most individual investors could afford; you could collect small checks from a lot of individual investors and write one big check to buy an institutional quantity of SpaceX.
• Confusion: SpaceX did not trade on a public market with a clear price, and people might just not have understood how much they were paying you or for how much stock.

I have casually been assuming that you had $100 of SpaceX stock to sell for $200, but that is not essential. If you had (1) access to SpaceX stock, (2) the ability to aggregate individual investors’ money and (3) the ability to confuse those investors, you could raise the money first and then acquire the SpaceX stock. Like:

1. Go to investors and say “give me $200 to buy SpaceX stock.”
2. They give you money.
3. You buy $100 of SpaceX stock for them.
4. Also you have $100 for yourself.

You could imagine doing this trade on the up-and-up: “If you give me $200, I will keep $100 for myself and buy $100 of SpaceX stock for you, which is a good trade because it’s not like you can buy $100 of SpaceX stock yourself,” you could say. In practice, though, that pitch sounds bad, and the trade works better if you add some confusion. “If you give me $200, I will buy $200 of SpaceX stock for you” is a better pitch, even if it is not exactly true. SpaceX didn’t trade publicly and who was to say what its price was. Buy $100 worth of SpaceX stock, call it $200 worth of SpaceX stock, easy.

Now that SpaceX is public, as I mentioned last week, a lot of people are going to discover that they didn’t own the SpaceX stock they thought they owned. Others are going to discover they overpaid for it. Here’s a US Securities and Exchange Commission enforcement action from yesterday:

The Securities and Exchange Commission today charged New York-based investment adviser Adit Ventures Management LLC, its CEO Eric Munson, and three affiliated general partners, Adit Ventures LLC; Adit Ventures II LLC; and Adit Ventures III LLC (the General Partners), for allegedly defrauding investors and client funds in connection with investments in pre-IPO shares, such as SpaceX and Klarna, including by misappropriating advisory client assets and charging millions in undisclosed fees.

According to the SEC’s complaint, from at least April 2019 through December 2024, the defendants used false claims and promises to persuade investors to contribute capital to Adit-managed funds, including Munson soliciting an investor by falsely claiming that a fund owned shares of stock of a private, pre-IPO company. As alleged, the defendants regularly used client capital for their own benefit, including by taking unsecured loans from funds on favorable terms, and these transactions were not authorized by fund documents and generally not disclosed to investors..

A simple fraud would be (1) claiming to own stock in a pre-IPO company, (2) taking investors’ money for it and (3) never actually buying the stock. But that’s not what happened. (I mean, it happened a little. [1] ) Instead, it was more like (1) taking investors’ money, (2) actually buying the stock with the money and (3) re-selling the stock to investors at a higher price. From the SEC’s complaint:

Rather than using invested Fund capital to buy shares of pre-IPO companies for the Fund directly, Defendant General Partners regularly took loans from Funds to buy shares of pre-IPO company stock and then resold those shares to their client Funds at a higher price. …

Here is an example of how this worked. In the summer of 2021, Munson had the opportunity to buy an interest in a third-party fund holding shares of SpaceX at a price of around $420 per share of SpaceX. In July 2021, Fund Astra Holdings SPV, LP acquired an interest equivalent to around 13,100 SpaceX shares for $420 per share. Part of that purchase was made with $1.2 million borrowed from Fund Ethos Holdings SPV, LP on June 30, 2021. ...

Acting through the Fund’s General Partner Adit Ventures I, Munson caused Astra Holdings SPV, LP, which owned interest in the 13,100 SpaceX shares, to assign its interest in those SpaceX shares to the Fund’s General Partner, Adit Ventures I, at cost—$420 per share.

By the end of July 2021, General Partner Adit Ventures I had sold an interest equal to those same 13,100 SpaceX shares to a new client Fund, Astra Holdings SPV III, LP, for around $498.00 per share—netting a profit of $78 per share. Adit Ventures I kept the resulting profit of around $1,020,000. …

Defendants misled the Funds and investors by claiming that the General Partners would not receive payments unless authorized by the Fund Agreements when they were actually obtaining undisclosed payments from client Funds through hidden markups. And, as explained below, Defendants misled their client Funds and investors by reporting a misleading “Original” price without disclosing that they were making a profit from these markups.

See, if you buy SpaceX stock with your own money for $420, and then you find some investors who are willing to pay $498 for it, and you sell it to them for $498, that feels, you know, fine, willing buyers willing sellers, whatever. But if you find some investors, borrow money from them to buy SpaceX stock for $420 for your own account, and then turn around and sell the stock to the investors for $498, that does seem like fraud. But as pre-IPO SpaceX SPV fraud goes, it’s maybe not that bad. At least they actually bought the shares.

The way financial innovation works is that you have a rich sophisticated client who will pay a lot of money for cutting-edge stuff, so you spend millions of dollars building her some cutting-edge stuff and you charge her a big markup. Then, having advanced the state of the art for cutting-edge stuff, you go out to a few other rich sophisticated clients and replicate it. “We just did a cutting-edge trade for Ms. X, and if it’s good enough for her it’s good enough for you,” you tell them, and you do a few more similar trades. These are much cheaper for you to do than the first one, because you get to reuse a lot of your work, but they still have high profit margins. Then your competitors get wind of the thing, and they start doing similar stuff for their rich sophisticated clients, which drives down margins.

Eventually the stuff becomes standard and automated, and the costs keep coming down. Someone realizes that they can do the formerly sophisticated cutting-edge stuff for ordinary retail clients. The marketing writes itself: “The cutting-edge stuff that was formerly accessible only to sophisticated billionaires is now available to you,” etc. And then eventually it’s on TikTok. Bloomberg’s Charlie Wells, Denitsa Tsekova and Isabelle Lee report:

For years, tax-optimizing investing strategies were confined to hedge funds and ultrawealthy families with access to specialized financial planning.

Now, they’re being pitched to the masses on YouTube and TikTok.

“There are maneuvers the wealthy have used for years to lower their tax bills that now you can access, too,” says one executive on YouTube while strolling through a leafy neighborhood. “Listen carefully in case you’re sick of paying high taxes,” starts a podcast-style interview. “Owning individual securities equals freedom to harvest losses,” explains another adviser on Instagram.

As with stock trading, alternative assets and financial advice itself, tax-focused investing is moving from the upper echelons of the American economy into the mainstream. Wealth advisers and financial influencers are peddling once-obscure strategies such as tax-aware long-short investing on social media and in marketing materials, aiming to capture business from a broader range of clientele.

Of course? We talked about tax-aware long-short investing last week. One fun fact about tax-aware long-short was that it was apparently invented at AQR Capital Management “just selfishly for the AQR partners co-invested in” AQR’s funds for tax-exempt institutions: That is, it was originally a product designed by hedge fund managers for hedge fund managers. Another fact about tax-aware long-short is that it is pretty quant-y and automatable, so there are obvious economies of scale. As I wrote last week:

If finance is getting more efficient over time, then one way — perhaps increasingly the main way? — that would manifest is in the form of investors paying no taxes.

Right now this stuff is expensive, as Wells, Tsekova and Lee write:

While tax alpha has become a “core client priority,” for smaller portfolios the strategies can ultimately offer little edge over tax-deferred vehicles such as IRAs or 401(k)s, said Jeffrey Janson, an adviser with Fiduciary Financial Advisors.

“Implementation costs can offset the benefits, and there's always the potential for tracking error or over-optimization that introduces unintended risks,” he said. “Sometimes the effort isn’t worth the reward.”

But eventually perhaps it will be efficient enough that no one has to pay capital gains taxes.

Intel Raises $20 Billion in Upsized Share Sale for AI Plans. Anthropic Tries to Shore Up Investor Confidence Ahead of Blockbuster IPO. AI-Dominated Leveraged ETFs Are Rattling Markets. Anthropic Strikes $9 Billion Cloud Deal With Riot Platforms. OpenAI’s head of ethics leaves start-up less than a year after joining. SEC Exempts Data-Center Bonds From Key Securitization Rules. Hong Kong set to include trading firms in ‘big bang’ tax reforms. Apollo Inks $2.6 Billion Financing Deal With NY Yankees. Trump Media Losses Hit $238 Million as Firm Pivots to Fast Feeds. Domino’s Set to Release New Individual-Size Pizza for Diners Who Don’t Want to Share. ‘Guac Signal’ Sparked Chipotle’s Frantic Bid to Recall Jalapeños. Italy’s $4.7 billion cheese economy is feeling the heat as climate change threatens its cheese banks that hold Parmigiano wheels as loan collateral.

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One of the core themes of this column is that it is good for your financial career to lose a billion dollars. This is largely a theory about hedge fund managers and bank traders; in those jobs, losing a billion dollars demonstrates that (1) someone trusted you with a billion dollars, (2) you boldly took risks with the money and (3) possibly you have learned some lessons from losing it and will do better next time. “My philosophy when I used to hire traders was that the optimal number of past blow ups was one,” John Arnold posted on X last month.

Over time, I have extended the theory to tech startups, where losing a billion dollars demonstrates different but related good qualities. “Move fast and break things,” “fail quickly,” etc.: If you have lost a billion dollars then you definitely know how to fail quickly. Also, if there is one thing that Silicon Valley prizes above all else, it is contrarianism, and losing money is arguably a contrarian act. “Everyone thinks that losing money is bad, but what if losing money is actually good,” Peter Thiel has probably almost said at some point.

Leopold Aschenbrenner is at the dead center of this Venn diagram and will doubtless be running a trillion dollars by the fall. Bloomberg News reported on Friday:

One week after Leopold Aschenbrenner’s hedge fund Situational Awareness nearly blew up, he’s already getting requests from investors looking to place more money with the artificial intelligence wunderkind.

His fund has seen a surge in interest from Silicon Valley investors looking to back the 20-something hedge funder in recent days, according to people familiar with the matter who asked not to be identified citing private discussions. So far, Situational Awareness has told investors that it’s not accepting new capital for now, the people said. …

The aftermath of Situational Awareness’ near miss is highlighting the stark divide between Silicon Valley and the world of finance.. For Wall Street, it was a moment when an AI hotshot was forced to learn the dangers of leverage and rein in risk. But for many across Silicon Valley, his misstep has merely been a chance to buy the dip and keep supporting the OpenAI researcher-turned-investor.

“Silicon Valley and Wall Street are both reacting differently,” said Gygmy Gonnot, an adjunct professor at New York University’s Stern School of Business and a managing director at Focus Investment Group. “At the core, Silicon Valley rewards being directionally right about transformational technologies, while Wall Street rewards generating attractive adjusted returns while preserving capital.”

One thinks of Elon Musk’s poker strategy. In some sense the problem with Situational Awareness, before last month, was that “AI is gonna be huge so buy memory stocks” was too much of a consensus view. But having that thesis and also losing tens of billions of dollars of investor money is the sort of narrative violation that you need to make it really big in Silicon Valley.

There are two basic investing strategies, value and momentum. Value means you try to buy low and sell high: You buy stocks that have gone down because now they’ll probably go up. Momentum means you try to buy high and sell higher: You buy stocks that have gone up because they’ll probably go up more.

And then the game is in figuring out which of these effects — mean reversion or momentum — predominates, and when and over what time scales. Some of this is about human nature, market psychology, etc.; Cliff Asness says that the two main explanations for momentum are that people underreact to news and that they overreact to news.

But some of it is about institutional structure. Some sorts of investors are constrained to buy stuff after it goes down. For instance, many big investors target holding, say, 60% of their portfolios in stocks and 40% in bonds, so when stocks go down they are underweight stocks and have to buy more. You could have a theory like “when stocks go down, giant 60/40 institutions have to buy stocks, pushing them back up, so I should buy the stocks that went down to get ahead of that.” [1]

Conversely, some sorts of investors are constrained to buy stuff after it goes up: Most classically, large-cap index funds buy stocks after their value has gone up enough to get into the index. (“The final dumping place for pumped-up stocks is an index fund,” I occasionally say.) Or, relatedly, they are constrained to sell stuff after it goes down: Most classically, leveraged hedge funds occasionally blow up when their stocks go down, and then have to sell more stock, as I have mentioned recently. So you could have theories like “when stocks go down, institutions have to sell them, and when they go up, institutions have to buy them, so I should sell the ones that go down and buy the ones that go up to get ahead of that.” And in fact there is a lot of money to be made by buying stocks just before they get added to the index, or by selling stocks just before a hedge fund blows up. Etc. etc. etc.; all of these things are somewhat true, in some regimes, over some time horizons, and you have to get the details right, and this is not investment advice. 

Here my point is only if that some new institutional structure has become popular, reasonable questions to ask might include “will this make stocks that have gone down go up, or go down more?” and “will this make stocks that have gone up go down, or go up more?” and “over what time horizon?” We have talked a lot recently about leveraged exchange-traded funds, because (1) they have become hugely popular and (2) they provide extremely clear answers to those questions. As I wrote last month, leveraged ETFs take the usual dynamics of leveraged investing — when levered investors’ stocks go up, they can buy more; when their stocks go down, they get margin calls and have to sell — and make them more mechanical and rigorous: The leveraged ETF buys more stock each day that its underlying stock goes up, and sells stock each day that its underlying stock goes down, according to a rigid formula. “Over the course of a day, stocks that are down go down more and stocks that are up go up more” is significantly more true than it used to be, because billions of dollars of leveraged ETFs have been added to the market, and that’s what they do.

Bloomberg’s Christian Dass reports:

The leveraged ETF boom is creating new ways to profit from sudden bursts of volatility in tech stocks.

The daily rebalancing of the funds tracking some of the most volatile names and sectors has amplified gains and losses, most recently for South Korean retail investors. It’s also added to swings during trading sessions, creating pockets where traders can take advantage. One way is to use an intraday momentum strategy, an area where banks have long offered Quantitative Investment Strategies to systematically capture short-term trends.

Intraday momentum “earns on large trending days, in either direction, and it usually bleeds on quiet ones,” said Florian Ielpo, head of macro at Lombard Odier Investment Managers. “July was a volatile and negative month for tech, and that is exactly the environment in which this trade shows its worth.” …

A basic intraday momentum strategy uses strict rules to buy into rallies and sell into dips, betting that the direction will continue. It performs best with highly volatile stocks on highly volatile days, said Yangyang Hou, a JPMorgan Chase & Co. strategist whose team wrote a paper that looked at the trades using five-minute price intervals, buying when the price was 1% above the previous close and selling when it fell below it, flattening the position at the end of the session.

The returns were “stellar” over the past two months, Yangyang wrote in an email — “a pattern reminiscent of 1998/99, when the technology revolution created unprecedented earnings uncertainty and record high single-stock volatility,” she added.

There is, however, some meta-momentum in the trade:

The July unwind from some of the stocks tracked the most by leveraged funds has reduced their total assets under management from a record high, which for now should ease some of the biggest swings from rebalancing. Trading in the ETFs has slumped in South Korea after authorities took steps to slow demand.

If you have a fund that buys high and sells low, and stocks go up, it will get a lot of inflows and buy a lot of stocks and have a big impact on the market. And then if its stocks go down, it will lose a lot of money and get a lot of outflows and have somewhat less impact afterwards.

The thing is, GameStop Corp. never really put in a $56 billion bid to buy eBay Inc. What happened is that GameStop’s feisty chief executive officer, Ryan Cohen, is fond of eBay and thinks he would do a good job running it, so he approached eBay and said “hey, wouldn’t it be cool if you put me in charge of your company?” EBay is a much bigger company than GameStop, and Cohen does not have enough money to buy it. Instead, the proposal was that (1) eBay’s current shareholders would continue to own most of eBay, (2) eBay would sell some debt to pay a big dividend to its shareholders and (3) Cohen would become CEO of eBay. Also GameStop would still be there; in the proposal, eBay would merge with GameStop, but the combined company would be mostly eBay. Someone characterized the proposal as a “hostile sale”: One way to look at it is that Cohen was trying to pressure eBay into buying GameStop and putting him in charge of the combined company.

The simpler way to describe it, though, is activism. Cohen is both an operator (he founded Chewy and now runs GameStop) and also an investor (he came to GameStop as an investor and has a history of other big investments in public companies). When he sees a public company that (1) he likes but (2) he thinks could be run better, he buys a stake in that company and agitates for change. He did that with eBay: GameStop spent about $4.4 billion buying 9.8% of eBay’s stock, and then Cohen set about proposing strategic changes and trying to make himself CEO.

Sometimes activist campaigns end in triumph for the activist: winning a proxy fight, taking over the company, etc. Sometimes they end in failure. Often, though, they end in compromise and half-success. Maybe the company adopts some of the activist’s suggestions. Maybe the activist gets a seat or two on the company’s board of directors. Maybe the company studiously ignores the activist but takes its own steps to improve operations and capital allocation, so the stock goes up. Maybe the activist fails in his stated goal — take control of the company and implement his strategy — but achieves his main goal, which is making the company more valuable. That’s good for the activist too! The way activism works is that first you buy some stock, and then you try to make the company more valuable. [2] If the company becomes more valuable — by adopting your suggestions or otherwise — you get paid.

Anyway Bloomberg’s Liana Baker reports:

GameStop Corp., led by Chief Executive Officer Ryan Cohen, is considering withdrawing its $56 billion bid for eBay Inc., according to people familiar with the matter.

“Make me your CEO” was always kind of a long-shot proposal. There are possible fallbacks:

Cohen is considering proposing a partnership or joint venture that would enable eBay to leverage GameStop’s roughly 1,600 US retail locations, the people said, asking not to be identified because the matter is private. That could allow both to increase market share in high-margin categories such as trading cards and collectibles, the people said.

GameStop, as one of eBay’s largest shareholders, would seek representation on eBay’s board as part of any partnership, they added.

A board seat and some business changes are typical in an activist compromise. Also, though, GameStop paid an average price of a bit more than $102 per share for its 9.8% stake in eBay. It has done okay on that trade:

Since its offer in May, GameStop’s stock has fallen 28% while eBay’s has climbed 7.6%. The $125-a-share offer was comprised of 50% cash and 50% in GameStop common stock.

EBay’s shares closed Friday at $111.98, giving it a market value of $49.8 billion. Including debt, it’s valued at almost $54 billion.

That’s more than $400 million of profit on the trade for GameStop, or a bit more than its net income last quarter. Good trade! Sadly eBay was down to about $107.60 per share at noon today, erasing almost half of the profit. I suppose eBay’s shareholders really were hoping that Cohen would be their next CEO.

We talked on Thursday about prediction market tick sizes. My basic points were:

• Prediction market event contracts typically have prices between $0 and $1, with an average price of $0.50.
• They typically trade in increments of $0.01.
• That’s a tick size of about 2% of value, which is really high. (Compare US stocks, which usually have prices around $100 and tick sizes of $0.01, or 0.01% of value.)
• Big tick sizes tend to mean more profits for market makers, as bid/ask spreads are necessarily fairly wide.
• The central idea of retail-facing financial firms is “get retail traders to do more high-margin trades,” meaning trades with high bid-ask spreads.
• Prediction markets are mostly sports bets, so people like to make a lot of them, and they have high bid-ask spreads, so they are lucrative for market makers and brokers.
• Robinhood Markets Inc. makes roughly 100x as much, per dollar traded, on prediction markets as it does on stock trades.
• This is why everyone’s excited about prediction markets as an “asset class.”

I should, however, add nuance to that, which is that not all prediction markets have $0.01 tick sizes. On Thursday, I was particularly focused on Robinhood, which has its own prediction-market platform called Rothera, where “each contract can be traded at $0.01 increments up to $1.” Its main competitor is Kalshi, the other big regulated US prediction market, where tick sizes are typically $0.01, but not always: Kalshi supports tick sizes as small as $0.0001, and has announced that its combo (parlay) markets will be moving from $0.001 to $0.0001 increments soon. Polymarket also offers tick sizes smaller than $0.01 in some markets. Tick sizes of $0.01 are pretty normal in prediction markets, but not universal, and the ticks might be getting smaller over time.

Which makes sense. There are two competing interests here:

1. On the one hand, if prediction markets are essentially a retail gambling product, big tick sizes and wide bid/ask spreads are good: They allow market makers and brokers to extract more money from retail gamblers on each trade.
2. On the other hand, if prediction markets are going to be big, they have to be an institutional hedging product, and it’s hard to get institutional investors excited about a product with a 2% bid/ask spread. (It’s easy to get traders excited, if they’re receiving the bid/ask spread, but hedgers are paying it.) 

You could imagine a synthesis like “retail gamblers pay $0.01 spreads and institutions pay $0.001 spreads,” though that would be a little weird. That might happen if, for instance, retail gamblers mostly traded on brokers’ internal platforms (sort of like Rothera) while institutions traded mostly on public exchanges. Or it might happen if retail gamblers mostly traded on exchanges, and institutions mostly did over-the-counter trades that are priced in reference to the exchanges: If you’re trading a $10 million sports bet with Susquehanna over the counter, you don’t have to use $100,000 tick sizes. [3] It would be a little weird because the US stock market is the opposite: Institutions tend to pay relatively wide bid/ask spreads by trading on public exchanges, while retail investors tend to get tighter bid/ask spreads because their brokers internalize their orders. There is a lot of competition to trade with retail stock orders, so the prices are low. But we are still in the early days of prediction markets.

The Wall Street Journal reported this weekend that a handful of celebrities are currently dating men who work in private equity. A running bit around here is that “private equity” is a prestige laundering mechanism that sorts ambitious highly trained graduates of prestigious universities into jobs running pest control companies in the Midwest. This bit is not exactly true— private equity is more high-finance capital allocation to pest-control rollups than it is day-to-day management of pest-control rollups — but it’s a little true, and these very funny quotes in the Journal article are even funnier if you keep it in mind:

“If you live in the celebrity bubble, party after party, with the same people—the same 25 people—life gets really boring,” said Kevin O’Leary, the businessman and “Shark Tank” panelist. “When you end up with a private-equity guy, your life gets far more interesting.”

“When you end up with a pest-control guy, your life gets far more interesting” could well be true. And:

“Whether they want to admit it or not, many women have always been attracted to bad boys and risk-takers,” said Jordi Hays, a co-host of the technology industry talk show TBPN. “I can’t think of any archetype of man that fits that description more today than capital allocators that put it all on the line every day in the markets.”

I feel like Hays is stretching a little bit there, and when he says “capital allocators that put it on the line every day in the markets” he’s actually thinking about Leopold Aschenbrenner.

Private Equity Is Stuck With 33,575 Unsold Businesses. China Unleashes $28 Trillion Capital Markets to Challenge US in AI. Intel to Sell $15 Billion in Stock With AI Boom Lifting Demand. Anthropic, Macquarie and GIC Form Venture for AI Data Centers. The bank behind China’s AI listings bonanza. Investors Want Quarterly Reports, Unfavorable Results and All. Jane Street Looks to Rework $11 Billion Debt Into Private Credit. Private Credit Is Under Growing Strain, Despite Industry’s Upbeat Tone. Private Credit Squeezed By Bank Refinancings. Paramount Will Put 30-Film Pledge in Writing to Theater Chains. Start-up bank backed by Palmer Luckey set to raise $1.5bn. Barrick Chairman’s Planned Overhaul Meets Investor Backlash. Trump Crypto Took $100 Million From a Businessman With Red Flags. The Suspected Gangster Causing Headaches for Kushner’s Albania Deal. AI Is Rewiring South Korea’s Careers, Dating and Culture. The Bruising Race to Rent in San Francisco Goes Into Overdrive. “Kicks at work now scan as uncool.”

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Today is the first lockup release date for SpaceX, which also means it’s the day that a lot of people will find out that they own fake SpaceX stock. SpaceX was a private company for a long time, and a lot of people — current and former employees, venture capitalists, Elon Musk buddies, etc. — ended up owning stock before it went public in June. Some of this stock found its way into special purpose vehicles that were sold to individual investors: The SPV would own a chunk of SpaceX shares, and you could buy shares of the SPV. You’d own SpaceX shares indirectly.

Maybe! A lot could go wrong. Mainly:

1. The SPV might not actually own the SpaceX shares. You’d give the SPV money, which the promoters would spend on yachts; SpaceX would not be involved. How could you check? You could call up SpaceX and ask if the SPV’s name appeared on its shareholder ledger, but it might not tell you.
2. The SPV might sell more shares than it owns. The SPV would own $1 million of SpaceX stock but sell $10 million worth; it would sell you a 10% stake for $100,000 but also sell 99 other 10% stakes. It would spend $1 million on SpaceX stock and $9 million on yachts. How could you check? You could ask to see the SPV’s shareholder ledger, but it could just show you a fake one.

We have talked about these problems before, and various hypothetical solutions. But for now, if you are an investor in an SPV that owns shares in a private company, it is hard to be sure that you really own the shares you think you own: The shares are not transferable, so all you actually have is an account statement from the SPV promoters, which might be fake. When the company goes public, it is still hard to be sure that you own the shares. Most initial public offerings have lockups in which pre-IPO shareholders (including SPVs) are restricted from transferring shares for some period of time, so the shares are still not transferable and you still just have an account statement. “We can’t give you the actual shares yet because they are transfer restricted,” the SPV promoters will tell you, accurately or inaccurately.

But then when the lockup expires — or is partially released, as is happening with SpaceX today — you can reasonably call up the SPV promoters and say “hey I’d like my shares now.” And then they might distribute to you your pro rata allotment of the underlying SpaceX shares and say “pleasure doing business with you, let’s do this again soon..” Or they might sell the shares and distribute to you your pro rata allotment of the cash, also a pleasure. Or they might say “sorry, you’re breaking up, I’m going through a tunnel, who is this, what is SpaceX?”

Here’s a Wall Street Journal story about Late Stage Management, which sold shares in a SpaceX SPV to individual investors:

[Investor Ram] Rupireddy wired over money before the end of the year, including $17,250 to take part in a fund that held shares in Elon Musk’s SpaceX, according to documents reviewed by The Wall Street Journal. At the time, he estimated the rocket maker was valued at $58 billion. 

When SpaceX went public this June at a $1.77 trillion valuation, Rupireddy’s dream of a windfall seemed within reach. It’s turned into more of a nightmare.

Shortly after the IPO, Rupireddy and three other investors who spoke to the Journal said they couldn’t log in to Late Stage’s web portal for investors.

Rupireddy said the investment firm eventually told him in an email that it sold the SpaceX shares he was exposed to in 2024, when they were worth around $105 each and before a 5-1 stock split, or when Rupireddy’s holding was worth $45,450.

But Rupireddy—based on the holdings shown on his investment portal as of May 2026 and on his 2025 tax document—believed he still held the equivalent of 2,500 shares of SpaceX, which at the IPO price he estimated were worth more than $300,000. …

Late Stage didn’t respond to repeated requests for comment, including to executives and to lawyers representing the firm in a class-action lawsuit, or to questions sent to the company’s main email address. 

Ah.. Strictly speaking, if you were doing a fraud, you wouldn’t have to take down your web portal shortly after the IPO, since the investors couldn’t do anything with their shares until the lockup release. But now:

A test will come on Thursday, when a first wave of pre-IPO SpaceX investors will be permitted to sell shares under so-called lockup agreements. Bankers estimate there are at least a thousand SPVs tied to SpaceX stock alone, and it will be a chance for scores of investors to cash in on the shares’ growth. Or, they could be hit by the same panic that overwhelmed Rupireddy if their share of the profits fails to materialize.

I wonder what the fully bezzle-diluted market capitalization of SpaceX is. Like, Bloomberg tells me that SpaceX’s market cap is about $1.45 trillion. About another $25 billion of shares have been sold short, [1]  adding to the supply. So there are people out there who correctly believe that they own about $1.48 trillion of SpaceX stock. Probably there aren’t people out there who incorrectly believe, in the aggregate, that they own billions of dollars of SpaceX stock. But millions? Surely?

I think sometimes about the Warren Buffett adage that “Only when the tide goes out do you discover who’s been swimming naked.” I often find that it’s the reverse. If you are running a fraudulent business, and a big financial crisis bankrupts a lot of legitimate businesses, you can tell your investors “hey guys sorry but we just got bankrupted by the crisis, legitimately, ah well,” and they will understand. If you are running a fraudulent business and reporting fake excellent results and nothing bad happens, eventually your investors will ask to withdraw their massive fake profits in cash, and then what. This is of course the plot of The Producers. Anyway if you were selling fake SpaceX shares in 2020 you had almost six years before anyone could reasonably demand their actual shares, but now the time is up. 

The big picture is something like:

1. OpenAI and Anthropic are fairly young, fast-growing, somewhat experimental, mostly unprofitable tech startups. As is customary in that situation, they finance themselves with equity: They raise money from venture capitalists hoping for huge upside if they succeed, not from bond investors who want certainty of being repaid.
2. Also though like half of the economy is OpenAI and Anthropic debt? Like, every day there is some new 11-figure data center financing in which investors buy billions of dollars of investment-grade bonds essentially backed by OpenAI’s or Anthropic’s commitments to pay for future computing capacity.

That second point is a simplification: In fact, various more mature, public, creditworthy companies are involved in backing the data center debt, because bond investors and credit ratings agencies won’t quite trust OpenAI’s or Anthropic’s credit. But of course the guarantors are trusting their credit. And their trillion-dollar-ish equity valuations provide some good reasons to trust their credit. If the market thinks that the best estimates of Anthropic’s and OpenAI’s future cash flows are around $1 trillion each, then maybe it is reasonable to underwrite, you know, $500 billion each of those cash flows as investment-grade. Their actual capital structures do not involve a lot of debt, but their equity values can be used to create some debt.

My point is that in some very loose casual sense the biggest thing in finance is lending against OpenAI and Anthropic equity. So: Why not 11-digit OpenAI margin loans? The Wall Street Journal reports:

Global tech investor SoftBank Group used its stake in OpenAI to borrow $10 billion from a group of banks, the company said Thursday, dialing up risk on its giant bet on the AI pioneer.

The cash, in turn, helps SoftBank fund an even larger stake in OpenAI: SoftBank is poised to pay another $10 billion for shares in the ChatGPT maker by October, the final chunk of a $30 billion investment announced earlier this year.. …

While Wall Street banks routinely offer so-called margin loans to investors in publicly traded companies, they are reluctant to make large loans backed by stock in private, loss-making companies given the risks inherent to startups.

In a typical margin loan tied to publicly traded stock, a borrower needs to pump more cash into the deal if the stock price falls below a certain level—otherwise the lender can seize the shares. OpenAI’s shares aren’t publicly traded, making it harder to determine how its valuation is changing. Still, SoftBank said the deal does require it to inject additional cash if its valuation falls.

“Borrowing against OpenAI to buy more OpenAI” is a decent description of the global economy right now. If OpenAI’s valuation collapses, then the banks that gave SoftBank this margin loan will be in bad trouble, but so will all the other banks. Might as well also do the margin loan.

I just want to make a small dumb point about why prediction markets are hot right now. [2]  It is:

1. Modern prediction market event contracts on platforms like Kalshi and Polymarket and Robinhood have one-dollar notional prices: Every contract pays off $1 or $0, depending on whether the underlying event occurs. Every contract is paired with its opposite — Yes pays $1 if No pays $0, and vice versa — so in some loose sense the average price of all prediction-market contracts is always $0.50.
2. Their prices move in increments of $0.01.

That’s it. The tick size is $0.01 on a contract with an average size of $0.50, or about 2% of notional value.

Meanwhile the normal price of a share of stock in the US is on the order of $100. [3]  Stock prices also move in pennies. Therefore the tick size of a US stock is on the order of 0.01% of its notional value.

If you go on Kalshi and look at some event contract, it will show the order book for that contract. The best bid (the price you’d get if you sold it immediately) will be, generally, one penny below the best ask (the price you’d get if you bought it immediately). The best bid and ask won’t be equal, because if they were they would have traded with each other; the order book reflects only orders that haven’t traded yet. They won’t be half a penny apart, because these markets move in whole pennies. (I suppose they could be two cents apart, or more, in thinly traded markets.)

This doesn’t exactly mean that you will pay one cent per contract to make a prediction-market trade. It doesn’t mean that market makers, on average, make one cent each time they buy and sell a contract. Maybe spread capture is not how market makers make their money. Maybe retail investors are constantly crossing trades with each other at the fair value and not paying a bid/ask spread to anyone.

It is suggestive, though. There is famous evidence from US stock prices. Stocks, in the US, used to have tick sizes of $0.125 (“one eighth”). In 2000, they switched to trading in ticks of $0.01, [4]  which is called “decimalization.” Studies generally found that bid/ask spreads in normal US stocks — the amount of money that investors pay to trade stocks, and the amount of money that market makers earn for trading stocks — declined significantly when decimalization was implemented. [5] Before decimalization, a market maker would buy at $50 and sell at $50.125 and make $0.125; after decimalization, she would buy at $50 and sell at $50.01 or $50.02 or maybe $50.06. If someone can charge you $0.01 for trading a stock, they might; if the only choices are $0.00 or $0.125, they’ll probably charge you $0.125. Prediction-market tick sizes are 2% of notional, equivalent to charging you a whole dollar for trading a $50 stock.

I wrote the other day:

Anything that causes retail traders to do more trades is good, for brokers and market makers. Anything that causes them to trade more high-margin stuff — like options, which often have wider bid/ask spreads than stocks — is good, too.

If prediction markets have 2% bid/ask spreads, then that’s a lot of money for market makers, and they can share some of it with brokers.

Anyway this is all very simplistic but:

1. Robinhood announced its second-quarter earnings last week.
2. Its customers traded 13.6 billion event contracts. Presumably that means $13.6 billion of notional value (at the $1 maximum payout per contract), or about $6.8 billion of market value (at an assumed average price per contract of $0.50).
3. Meanwhile, its customers traded $956 billion worth of stock.
4. Robinhood made transaction-based revenues of $776 million, of which $156 million came from event contracts (about 1.1% of notional value traded, or about 2.3% of assumed market value) and $129 million came from stocks (about 0.013% of market value traded).

Which ties pretty closely to tick size: Robinhood makes on the order of one penny per $100 stock trade and also one penny per $1 event-contract trade.

That is, Robinhood’s cut of event contract trading is about 100 times as big as its cut of stock trading. That might be why everyone is excited for this burgeoning asset class.

My basic take on Leopold Aschenbrenner and Situational Awareness has been that:

1. Being smart and plugged-in about the long-term rise of artificial intelligence is a pretty good investing strategy, but
2. Doing it in a hedge fund with lots of borrowed money that can be called away at any time is a bad funding strategy.

“If you’re a venture capitalist, ‘we will make huge directional bets on the pace of AI adoption’ sounds great,” I wrote on Monday. “If you are a hedge fund running a long/short portfolio at 5x gross leverage, though, it’s worrying.”

Aschenbrenner learned that lesson the hard way last week, when the bulk of his public portfolio was cast into the maw of Citadel, but he learned both parts of the lesson: no more leverage, but still full speed ahead on venture capital. Bloomberg’s Hema Parmar reports:

Just days after his hedge fund was on the brink of collapse, Leopold Aschenbrenner has made his return to the investing scene, plunking down $400 million to back a privately held company, according to people familiar with the matter.

The investment, completed on Tuesday, was the first sign of how Aschenbrenner intends to pick up the pieces of his Situational Awareness after it nearly buckled under a barrage of margin calls from lenders across Wall Street last week.

Man good for him. He got married over the weekend too. You might think that between evaporating tens of billions of dollars of investor money and getting married, he might take a break from investing in AI. But I gather the essential thesis of Situational Awareness is that the pace of AI is faster, and the stakes are higher, than you think. There’s no time to stop investing in AI just because you blew up a giant hedge fund!

Stereotypically, the way the consulting business works is that firms hire recent graduates of top universities who are good at doing mock case interviews, send them out to big corporations to make PowerPoint presentations about the corporations’ business problems, charge the corporations large fees, and use the fees to (1) pay the recent graduates pretty good entry-level salaries, (2) pay the senior partners who supervise them larger salaries and (3) pay for flights and hotels.

A fascinating tweak to that business model is: What if you hired them before they graduated? The work product might be more or less the same: Your frontline consultants are young people with elite educational credentials, demonstrated excellence at doing mock case interviews and not much business experience; the corporations that pay for recent-graduate consultants should pay about the same for undergraduate consultants. Maybe you do fewer on-site projects (because the consultants have to go to class!), so you save on hotel bills. Bloomberg’s Samuel Church reports:

The Harvard Undergraduate Consulting Group [was] tasked with looking for vulnerabilities to certain risks among AstraZeneca’s suppliers and suggesting how the company could use data to anticipate problems, according to internal HUCG documents obtained by Bloomberg.

It was just one of more than 30 assignments that the group took on last year, including tasks for Delta Air Lines Inc. and Samsung Electronics Co., that helped the club’s net assets reach just over $2.7 million as of June 30, 2025, according to its latest tax filing. Since 2017, the first year that public filings are available, the all-student group has brought in almost $7 million in revenue, the filings show.

It turns out that the real savings are in salaries: Apparently the going rate for an undergraduate consultant at Harvard is $0, because (1) modern colleges are magical wonderlands in which all of your material needs are effortlessly met so nobody needs money and (2) conversely, modern colleges are brutal battlefields where students struggle desperately for artificially scarce credentials that will allow them to land plum jobs. (We have talked about undergraduate finance clubs before; sheesh!) So:

The club’s prestigious client list makes it one of the most sought-after organizations on campus, despite the fact that, like most student organizations at Harvard, HUCG doesn’t pay the students a salary for the work they put into each mandate.

About 450 students apply to join each semester, with an acceptance rate of around 10%, said Noreen Mohamed, HUCG’s director and a Harvard senior. Applicants — some of whom have never heard of consulting before landing at Harvard — go through a rigorous interview process to get their foot in the door of an industry where starting salaries can reach six figures. …

The formal application process involves a written questionnaire, followed by first round interviews, a mock case study and a final presentation. Members said the process is meant to mimic how professional consulting firms hire employees.

I feel like the 46th-best Harvard student at doing mock case interviews would also be a fine consultant but whatever. [6] The business question here is, if you can bring in a lot of revenue by renting out consultants, but you do not pay the consultants, what do you spend the money on? And the answer is parties:

HUCG doesn’t pay students, but it throws lavish parties for them on a scale most student groups could never imagine, complete with bougie locations, open bars and reptile petting zoos.

One of last year’s celebrations was held at an estate in Newport, Rhode Island, according to the event’s invitation and several students who attended. Guests were treated to a multi-course dinner with the option of sirloin steak, risotto or duck. After eating, guests floated between the bar, fire pit and bouquet-making station, attendees said.

And then there were the snakes. HUCG brought in pythons and other reptiles as part of an exotic petting zoo, the people said. Guests stopped and posed for pictures with the creatures wrapped around their necks.

I am an old man and would prefer to be compensated for my work in the form of cash, not in the form of parties with snakes and aspiring consultants. But I can see how, when you are young, you might prefer the parties.

Millennium Partners With Anthropic to Develop AI Risk Analyst. Biggest US law firms explore selling stakes to private equity. Alphabet Is Seeking Up to $25 Billion From Latest Bond Sale. Blackstone BDC Profit Drops 94% While Loan Performance Steadies. Inside Intel: how America’s chip champion came back from the brink. Armed With $10 Billion, Sequoia’s Leaders Plan Its New Era. Meta Releases Coding Agent to Compete With OpenAI and Anthropic. OpenAI Models Joined Forces Months Ahead of Hugging Face Hack. Paramount agrees safeguards for UK approval of $110bn WBD deal. Deutsche Bank, KBC Freeze Some Radiant World Funds in Singapore. Hedge Fund Albar Is Returning Cash to Run Only Millennium Money. Startup Raises $700 Million to Replace Data-Center Wires With Light. “The most valuable pregame signal in football might be whether the head coach decided normal pants would not cut it.”

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Today, there are about 639 million shares of SpaceX available in the market, which SpaceX sold in its initial public offering in June. Tomorrow, there will be about 1.55 billion shares available, as a portion of the shares that were locked up after the IPO are unlocked. That’s much more. SpaceX should be cheaper tomorrow than it is today, because of supply and demand. This is not investment advice. It’s not even true! If something should be cheaper tomorrow than it is today, what you should do is sell it today (at a high price) and buy it back tomorrow (cheaper). This has the effect of lowering the price today and raising the price tomorrow. You should keep doing this until it is no longer true, until the price today equals the expected price tomorrow. This is called “arbitrage.”

We have talked about this before. I have sort of argued that this trade is the mirror index of the index rebalancing trade that big hedge funds do. In index rebalancing, there is predictable large buying demand from index funds on a fixed date, which will predictably push up the price of the stock; hedge funds buy stock ahead of the fixed date to sell into the index-fund demand, smoothing out price moves. Similarly, in lockup releases, there is predictable supply from locked-up early shareholders on a fixed date, which will predictably push down the price of the stock; hedge funds short stock ahead of the lockup release date to buy from the lockup-release supply, smoothing out price moves. The hedge funds do a trade that could be described as arbitrage, or even I suppose as “front-running” the lockup release, but that could also be described as liquidity provision: They match up today’s SpaceX buyers with tomorrow’s lockup-release sellers.

This is not quite a thing, in the way that index rebalancing is a thing. You do not hear a lot about multistrategy hedge funds having lockup-release pods that make billions of dollars in good months, like index-rebalancing pods do, but partly that is a matter of scale. Index funds are really big and their rebalances involve many billions of dollars of stock. Most companies are relatively small when they go public, and their lockup releases aren’t that exciting. SpaceX’s is. Bloomberg’s Bailey Lipschultz reports:

Investors hanging onto SpaceX shares after a plunge below their IPO price are bracing for the next potential hit, when $101 billion worth of stock becomes available for trading on Thursday. …

“We’ve never seen anything like it, we’ve never seen anything of this scale, we’ve never seen a lock-up being phased in this way,” said Peter Singlehurst, head of the private companies team at Baillie Gifford, which first invested in Elon Musk’s company in 2018. “We’re in uncharted waters.”

The lifting of restrictions on up to 911.5 million shares comes as volatility whips the shares after its first quarterly earnings report triggered a 13% slide to reverse a two-day surge that added in excess of $250 billion to its market capitalization to start the week. …

Ending restrictions on some insiders will more than double the number of shares available, to as many as 1.55 billion shares from about 639 million shares now, according to the IPO prospectus. …

The highly-anticipated unlocking paired with a lofty valuation has drawn a swarm of short-selling investors with 35% of shares currently available for trading are being sold short, according to data compiled by S3 Partners through Tuesday’s close.

Really the expected price of SpaceX stock tomorrow, after the first phase of the lockup is released, should equal the closing price of the stock today: This is a well-anticipated event, and all of those short sellers have essentially been selling tomorrow’s shares today. That said, the variance around tomorrow’s price is high:

“Many investors are simply unwilling to buy SpaceX shares in the interim based on fundamentals and the lock-up overhang that’s going to exist until December,” said David Wagner, portfolio manager at Aptus Capital Advisors. “Bluntly put, we are in that same exact boat.” ...

“There’s probably a reasonably good chance that this will be the biggest single increase in the supply of shares for a single company in a single day ever,” said Baillie Gifford’s Singlehurst. “And so what happens on that day? I don’t know.”

One thing to think might be that, in an increasingly efficient market, this problem will just get solved. SpaceX has a staggered series of lockup releases; the one in December is even bigger than tomorrow’s. Anthropic and OpenAI are expected to go public soonish too, and will probably have basically similar dynamics, with gigantic periodic lockup releases. [1]  Right now, it is hard to know what to expect from a gigantic lockup release. But smart people are working on it; there is money to be made by arbitraging between the locked-up and unlocked states of the world. It’s a harder problem than the index-rebalancing trade: Index demand is fairly mechanical, but you need some theory of mind of SpaceX insiders to anticipate how much stock they’ll actually sell when they can. [2] But it’s a lucrative problem, tomorrow is a data point, and eventually people might figure it out. Perhaps by the time OpenAI’s lockup expires, no investors will be going around saying “we are unwilling to buy the stock because of the lockup overhang.” Perhaps that problem will be solved.

By the way, as I once wrote, in some ways the nicest solution would be to combine the two trades: Set the lockup release date to be the same day that the company is added to the index, so that index funds are buying right as locked-up insiders are selling. [3]  You don’t need arbitrageurs to provide (as much) liquidity either way; you just have the predictable supply sell into the predictable demand. I do not expect that to happen.

How much money was Situational Awareness, Leopold Aschenbrenner’s artificial intelligence-focused hedge fund, running at its peak? I don’t know! There are reports suggesting that it had perhaps $45 billion of assets under management at its peak in early July. And there are reports suggesting that at least some of its book was levered four or five times, meaning that it had $4 of long and short positions for every $1 of equity. If you just naively multiplied those two numbers together you would get a number that is bigger than $100 billion. [4]

And then its AI bets went wrong in July, coming to a head last week when its banks arranged a fire sale of most of its remaining public equity book to Citadel. There are reports suggesting that this trade was for something like $16 billion or $20 billion of assets. And now Situational Awareness is left with something like $8 billion or $10 billion of remaining assets, unlevered, many of them private.

There are huge error bars around all of these numbers, but I just want to make the simple naive point that the number in the first paragraph is quite a bit bigger than the number in the second paragraph. The portfolio that Citadel acquired last week, combined with the portfolio that Situational Awareness was left with, seems to have been slimmed down significantly from the portfolio that Situational Awareness was managing at its peak.

You could imagine a story of Situational Awareness that is like:

1. It had some AI bets, which went well.
2. Then they started going poorly for exogenous reasons.
3. Then one day its banks sent it a margin call, which it could not meet, leading to a fire sale to Citadel to clear out its debt.

But a slightly different story would be:

1. It had some AI bets, which went well.
2. Then they started going poorly for exogenous reasons.
3. Its banks sent it daily margin calls, asking for more money each time its stocks went down.
4. Then Situational Awareness started selling billions of dollars of stock to raise cash to meet the margin calls.
5. Which pushed its positions down some more.
6. Leading to more margin calls.
7. Eventually this drip-drip approach seemed bad and the banks and Situational Awareness got together to end it, by clearing the portfolio out to Citadel.

In this story, Situational Awareness was part of the cause of its AI positions moving against it over the past few weeks. It was selling stock to meet margin calls, and because (1) it held huge chunks of many of its stocks and (2) a lot of other investors were paying attention to its trading, its sales drove down the prices of its positions, leading to more margin calls, leading to more sales. It was not closed out to Citadel overnight last week; it was closed out in the classic way, gradually and then suddenly.

That story must be at least partly true. The assets peaked in early July and then sold off; Bloomberg reports that “when the tech stock rally began to falter, the fund’s lenders became nervous about highly-leveraged losses, forcing Aschenbrenner to start liquidating his portfolio to meet their margin calls.” The Wall Street Journal reports that “when AI stocks wobbled in recent weeks, Situational faced margin calls from its lenders,” “dumped stocks to raise cash before seeking out a buyer who could take down the bulk of its portfolio in one fell swoop” and “met its margin calls as recently as Wednesday.” And Aschenbrenner’s letter to investors last week said:

The portfolio experienced a significant drawdown over the course of July, which was exacerbated by extreme moves in core positions over the past week. …

As these moves proceeded, we started to see increasingly adverse trading in names publicly associated with us. These dynamics are essentially similar to a bank run: vulnerability begetting vulnerability. We worked to keep the portfolio within our risk parameters, but gradually this became more difficult as positions rapidly moved against us and market liquidity dried up.

On Wednesday night/Thursday morning, we took decisive action to protect LP capital. We traded a portion of our public portfolio in a block transaction to remove all leverage from the fund.

Like: “We worked to keep the portfolio within our risk parameters” by selling, “but gradually this became more difficult as positions rapidly moved against us” because of all the selling, so “we took decisive action” by doing the block trade. Vulnerability begetting vulnerability.

Maybe this is all obvious? Or maybe I’m overstating it and there was no material forced selling until last Wednesday; it is hard to be sure about the size and timing of all of this. But there is a popular narrative that Situational Awareness was somehow assassinated, that predatory short-sellers or a Citadel rate hike call caused its positions to collapse overnight, and I am skeptical. Bloomberg’s Yiqin Shen reports:

The losses that forced hedge fund Situational Awareness to sell stocks at deep discounts appear to be the result of highly concentrated positions in crowded trades, rather than a concerted effort by short-sellers, according to the founder of a company that analyzes short positioning data.

Instead of any clear pattern of “predatory trading,” S3 Partners’ data show there was no significant increase in short sales across the fund’s top holdings, according to the firm’s founder Bob Sloan. Rather, the data show that while short sellers increased bearish bets on some of the fund’s holdings, half of the top 10 saw short interest flat or declining.

The simple story is not that shadowy predators were selling Situational Awareness’s stocks to drive it out of business; it’s that Situational Awareness was selling Situational Awareness’s stocks to drive it out of business. That’s life running a risky directional book at 4x leverage!

Meanwhile, Bloomberg’s Hema Parmar and Nishant Kumar report that “Whale Rock Capital Management’s flagship hedge fund is one of the biggest losers in last month’s AI selloff, with a 21.7% drop in July erasing about half of its gains for the year.” But they also also report that;

Citadel’s flagship fund surged 5.9% in July as the firm became one of the few winners from the turmoil at artificial-intelligence hedge fund Situational Awareness.

The results show Citadel’s rapid deal to buy most of Situational Awareness’s public stocks at a discount brought almost immediate gains: the Wellington fund at Ken Griffin’s firm had only been up 0.45% with a week to go for the month of July.

The transaction helped boost year-to-date gains at Citadel to 12%, according to a person familiar with the matter, who asked not to be identified discussing confidential matters..

Once the forced selling stops, the stocks go up.

One of the main goals of most gambling regulators is to minimize or at least manage the negative social consequences of gambling addiction. Regulated sportsbooks in the US tend to list problem-gambling hotlines on their websites, and sportsbooks that too aggressively exploit gambling addicts sometimes get in trouble. Most US states limit legal gambling to customers who are at least 21 years old, on the theory that teenagers are more likely to be susceptible to addiction. I don’t want to overstate any of this, and it is possible to be quite cynical about all of it. A lot of casinos and sportsbooks (and state lotteries for that matter) do quite obviously make a lot of their money from problem gamblers; you can’t realistically expect widespread legal regulated gambling to be only fun and harmless. Still, my sense is that most US state gaming regulators try. And of course some states don’t allow sports betting at all, which is another way to limit the harms.

But in the last two years somehow the US Commodity Futures Trading Commission has also become a gambling regulator, and has in fact asserted that it is the only legitimate gambling regulator in the US [5] : Because it regulates sports gambling on prediction markets like Kalshi, no state can do anything to limit or regulate that sports gambling. This is a weird role for the CFTC; historically, US financial markets regulators have not promoted sports gambling. But the CFTC has taken to it enthusiastically, and we talked in June about its proposed rules to regulate sports betting.

The basic point about those rules, though, is that they are market integrity rules. The CFTC would like to make sure that markets for wheat or gold or Treasury futures, or for the over/under in the Knicks game, produce legitimate prices and are hard to manipulate. That is the traditional job of a markets regulator.

The CFTC doesn’t care about gambling addiction, though. Its 468-page proposed rulemaking about sports gambling is concerned with “the factors that the Commission would apply in determining whether [sports bets] are contrary to the public interest.” [6] But gambling addiction gets almost no mention in the proposal. On page 202, the CFTC does acknowledge that it is promoting a vast expansion in federal sports gambling, and that, “with this possible increase in the number of event contracts, a related public interest issue should be considered”:

Some prediction market trading characteristics (such as outcomes occurring at unpredictable intervals, perception of skill-based decision making, near miss experiences, and potential for loss chasing behavior) are generally associated with addictive potential. …

These factors can lead to financial harm as users accumulate losses through high frequency trading, allocate disproportionate resources to trading activity, and have trouble disengaging voluntarily. Protective measures, such as position limits, cooling off periods, notification restrictions, or self-exclusion mechanisms might preserve legitimate market functions while mitigating harm to retail traders.

But there are no concrete proposals to extend these normal gambling protections to federally regulated sports bets. And the one nearly universal protection that US state gaming regulators use — banning teenagers from gambling — is not even considered.. If you’re 18 and you want to bet on sports on Kalshi, that’s not the CFTC’s concern; it is completely incurious about whether letting 18-year-olds bet on sports on their phones is in the public interest.

This is very obvious and normal if you think about the CFTC as a market integrity regulator. It is odd if you think about the CFTC as a gaming regulator. It is odd for the CFTC to be a gaming regulator. But here we are.

Anyway Blooomberg’s Erik Larson reported on Friday:

New York sued Kalshi Inc.’s trading subsidiary for allegedly running an illegal, unlicensed gambling operation in the state, marking another legal hurdle for an industry that has won support from the Trump administration..

The suit, filed Friday in state court in Manhattan, alleges that Kalshi’s prediction market exposes state residents – including those under the legal gambling age of 21 – to “serious personal and financial risk,” New York Attorney General Letitia James said in a statement. …

“Kalshi has chosen to ignore New York’s gaming laws, which exist to protect consumers, prevent problematic gambling, deliver funding for critical public services, and ensure that every company plays by the same rules,” New York Governor Kathy Hochul said in the statement. ...

Kalshi called the New York lawsuit “sad” political theater.

“States can’t just shut down a federally licensed exchange,” Kalshi spokeswoman Elisabeth Diana said in a statement. “This would also hurt New Yorkers, who would be driven offshore.”

Here is New York’s press release. Just a weird state of affairs we are in, in which federally regulated sportsbooks can get around state consumer-protection regulation, but there is no federal consumer-protection regulation for sportsbooks.

Here is a story that you could have told — and that I have told — about Robinhood Markets Inc.:

1. Robinhood is a brokerage platform that makes it fun, easy and commission-free to trade stocks or options from your phone.
2. This got a lot of young people hooked on frenetically trading stocks on their phones.
3. That is, relative to some baseline, bad: Frenetically trading stocks on your phone as a hobby is probably worse, for most people, than buying and holding index funds.
4. It is, however, relative to some other baseline, good: Frenetically trading stocks on your phone as a hobby is probably better, for most people, than some other sources of financial dopamine. Stock trading is an essentially positive-sum game; even if you are investing in some dumb companies, you are getting exposure to broad secular growth in the stock market. Putting money into stocks for fun is better for you, financially, than spending that money on fancy dinners or beer or movies or, heaven forbid, sports gambling.
5. Eventually people grow up, and if you got them hooked on stock investing with confetti and meme stocks, maybe they will stick around for low-cost index funds and estate planning.
6. Perhaps, as I have put it, “day trading meme-stock options is a gateway drug for sensible retirement investing”: Robinhood draws customers in with risky fun, but they will ultimately graduate to positive-expected-value investing.

Here is an alternative, worse story:

1. Robinhood is a brokerage platform that makes it fun, easy and commission-free to trade stocks or options from your phone.
2. Robinhood needs to constantly find ways to dial up the dopamine hit of trading on your phone, so that it can keep growing.
3. Through an absolutely bizarre twist of fate, Robinhood can now let its customers do sports gambling from their phones, as part of its brokerage app.
4. Sports gambling is not a positive-sum game, not a way to save for retirement, etc.
5. But it’s a good business for Robinhood: Sports are more fun than stocks, sports gambling is addictive and exciting, and people will spend lots of money on it.
6. Perhaps Robinhood’s free stock trading is a gateway drug for sports gambling: Robinhood draws customers in with risky fun, and then they keep graduating to riskier fun.

Which story is true? I mean, both, but the Information reported last week:

Robinhood’s prediction markets revenue surged more than tenfold year over year in the second quarter to $156 million, the company reported last week, as part of its second-quarter earnings update. Prediction markets accounted for 20% of Robinhood’s trading revenue in the quarter, eclipsing both stock and crypto revenue for the first time and becoming its second-largest trading business after options.

That’s a significant change in the company’s revenue mix driven by Robinhood’s less than two-year-old prediction business. It also highlights how speculators have shifted their attention, and their trading activity, from financial markets to real-world events via prediction markets. …

“The people on Robinhood love gambling, and [the] prediction market is right up their alley,” said Dan Dolev, equity research analyst at Mizuho Securities. “It’s the perfect replacement for crypto because it produces a faster reward system in your brain. You don’t have to wait.” …

Robinhood’s second-quarter surge came during the World Cup, an event that takes place once every four years.

That made June and July volumes “abnormally strong,” according to a research note on Robinhood by Ed Engel, an equity research analyst at Compass Point. Still, U.S. football season is coming up this fall, he noted, which should provide a fresh boost.

Ah yes, football season, traditionally a lucrative time for stock brokerages. Because that’s when people like to bet on football. In their brokerage accounts.

Wall Street Investment Bankers’ Bonuses to Climb as Deals Surge. Trump administration refunds $100bn in ‘liberation day’ tariffs. US Keeps Debt-Sale Guidance Steady, Deepening Bill Reliance. Blackstone Has Pitched Mega Debt Package for Anthropic Chip Deal. Banks to offload $15bn of debt for Anthropic data centre backed by Google. SpaceX’s AI Splurge Puts a Damper on First Earnings After IPO. China courts global investors with bond market tweaks. Wall Street Is Deepening Its Crypto Grip and Rewiring the Market. Glencore Takes Provision on Exposure to Trader Radiant World. OpenAI to Pay $3.2 Million to Settle Claims It Discriminated Against U.S. Workers. US bans exports of various used critical minerals. Some 64% of Young Men Day Trading Stocks Feel Like Failures.

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Last weekend Bloomberg’s Yiqin Shen published a fun story that would make for a good final exam in a financial engineering class. It has a bit of everything. 

Start with leveraged exchange-traded funds. Lots of investors want to get 2 or 3 times the daily return of some stock, and there are single-stock levered ETFs that provide this. Investors put in $100 of their own money, the ETF borrows $100 from a bank, and it buys $200 of SK Hynix Inc. stock or whatever. (Really it will do this using total return swaps, but that doesn’t matter for our purposes.) If the stock goes up 5%, the ETF will have $210 worth of stock, a 10% return on the investors’ $100. To keep providing the same 2x exposure the next day, the ETF will have to rebalance: It will borrow another $10 from its bank to buy more stock, so that the next day it has $220 worth of stock, twice its equity. Or if the stock goes down, the ETF will sell some to repay some borrowing.

This means that, each day, the bank has loaned $X against $2X of stock: It has no real long-term risk, because if the collateral ever loses value some of the loan is automatically repaid. The only risk is if the stock falls more than 50% in a day [1] : The ETF only has $100 from its investors, and if that equity is wiped out then there’s nothing else. If it borrows $100 to buy $200 of stock, and that stock falls 60%, the bank has only $80 of collateral to secure its $100 loan. It can’t send a margin call asking for more money; there’s no more money. Shen:

The setup leaves banks exposed to what’s known as “gap risk.” If a stock falls sharply enough in a single session — roughly more than 50% for a 2X fund or 33% for a 3X fund — the ETF is at risk of losing an amount greater than its net assets, potentially strapping the banks with losses that the fund’s issuer can’t cover.

This is a very unlikely risk, but not impossible: Leveraged ETFs are particularly popular on underlying stocks that are volatile to begin with, and leveraged ETFs increase the volatility of the underlying stock by buying when it goes up and selling when it goes down. We talked last month about a leveraged ETF on Lucid Group Inc., which got wiped out when Lucid fell 57% intraday. It happens.

Banks do not like this sort of weird tail risk, so they want to buy insurance. The insurance takes approximately the form “if the underlying stock falls more than 50% on any day, you pay us the difference.” That is a put option, or more technically it’s a cliquet, a series of daily put options each struck at a price that is down 50% from the previous day’s close. A bank might pay a hedge fund some premium upfront for a one-year series of these puts, to hedge it against its leveraged ETF risk on a stock:

The result has been a quiet surge of activity in an exotic corner of the derivatives market, where investment banks, hedge funds and other institutional investors trade what are often known as “crash puts” and sometimes referred to as cliquets or stability notes. 

“I have never seen this level of demand in this product,” said Natasha Sibley, a portfolio manager in the diversified alternatives team of Janus Henderson.

We talked about crash insurance last week, and I mentioned some problems with it:

1. People do not like selling way-out-of-the-money insurance against unlikely events, because they collect only a small premium for the insurance, but might have to pay out a lot of money in bad states of the world.
2. You might not want to buy way-out-of-the-money insurance against unlikely events from a hedge fund, because if the bad state of the world occurs, the hedge fund might be bankrupt.

The solution to the second problem, as we discussed last week, is pre-funding: Instead of just paying the hedge fund, say, $3 for its future promise to pay up to $100 in a crash, the bank sells the hedge fund a $100 structured note. The hedge fund gives the bank $100 upfront for the note. If the crash happens, the bank keeps (some of) the money; the note is triggered and doesn’t repay its full principal amount. If no crash happens, the bank repays the hedge fund’s $100, plus interest, plus the $3 insurance premium, in a year.

That $3 is not very exciting, though. The solution to the first problem is levering the note: Instead of the hedge fund giving the bank $100 for the note, the hedge fund gives the bank $20 for the note and borrows the other $80 from the bank. [2] If the crash happens, the bank keeps (some of) the hedge fund’s $20. If the crash is really really bad, though, the hedge fund’s $20 is not sufficient to cover the bank’s losses, and the bank eats the rest. It has not insured its tail risk, per se, but rather the, like, first-few-inches-of-the-tail risk.

This might be sensible for intuitive how-leveraged-ETFs-work reasons. If the underlying stock falls a lot, the leveraged ETF (or rather the bank providing the leverage) will sell stock during the day to reduce its risk. This will drive the stock further down. But by the time the stock has fallen by 50%, the bank will have sold all of the underlying stock (and the leveraged ETF will go poof). There won’t be any more selling pressure from the ETF, so the stock should recover. (Worked for Lucid!) There will be some slippage — the stock won’t turn on a dime at a 50% loss — but if the hedge funds cover the losses from down 50% to down 60%, that’s probably a good enough cushion.

And now, instead of earning a $3 premium on a $100 investment (a ho-hum 3%), the hedge fund is earning a $3 premium on a $20 investment, a juicier 15%. [3] Now the trade works for the hedge fund. The bank has bought pre-funded tail insurance from a hedge fund, and then levered up that insurance — effectively buying back part of the tail risk — to make the economics work:

One email from Goldman Sachs Group Inc. in May pitched a trade idea around “Expensive Crash Cliquet”: it meant to capitalize on “immense demand” to hedge the share prices of South Korea’s SK Hynix Inc. and Samsung Electronics Co., since a one-day share-price drop of 50% or more could wipe out the 2X ETFs tied to them. How expensive exactly? The proposed yields on offer for investors with the gumption to assume the tail risk for up to one year — and use leverage to amplify returns — ranged from 14.2% to 20%.

The trade “presents an opportunity for investors to act as the ‘insurer,’ selling this overpriced crash protection to earn a high premium,” the email read.

And then the cherry on top is that you smash the whole thing into an ETF:

These days, the type of income available from crash-put products isn’t only available to big funds: There are now ETFs that offer it. Janus Henderson launched two active structured income funds in April, JELH and JELM, which use stability swaps and equity-linked notes to package institutional structured-note strategies.

“It’s been great timing,” said Sibley. “When we designed these ETFs, we wanted to include stability notes in there along with autocallables. We were familiar with them in the index space and liked the pricing, but since then they have exploded in the single-stock space.”

An ETF that sells crash puts against the gap risk of leveraged single-stock ETFs, wonderful.

It seems plausible that finance would be getting more efficient over time. A lot of smart people work long hours to apply increasingly sophisticated math and technology to the allocation of capital; surely they should get better at it. What would that mean for investors? The simplest answer seems drab and boring: If finance is more efficient, it should be hard to beat the market; every investment should earn its appropriate risk-adjusted return. Efficient computers can make market prices correct more cheaply than old-fashioned artisanal human arbitrageurs did, so the returns to being smarter than the market should go down. 

If you expand your focus a little bit, you could tell more optimistic stories. “Efficient finance will allow society to finance a giant artificial intelligence buildout that will usher in a golden age of abundance, and which would not have been possible in an earlier era of less perfect capital allocation”: sure, maybe? With perfectly efficient finance, investors can’t really expect to earn market-beating returns, but maybe the market returns will be higher if the market omnisciently finances the best projects. 

Those are the two main sources of returns to investors: Increasing financial efficiency might increase economic growth, but it can’t increase net market-beating returns, and should probably decrease gross market-beating returns.

But there is another source of returns. That source is the Internal Revenue Service. The aggregate return of investing, to investors, is not quite the market return. It’s the market return minus taxes. If the broad market return is 10%, then investors in the aggregate will earn 10% before taxes. But they might earn 8% or 9% or 10% or conceivably 12% after taxes. That number will be determined by skill: The more skillful and efficient finance is, the more brain and computer power that is devoted to reducing taxes, the lower the taxes will be. On investors. [4]

If finance is getting more efficient over time, then one way — perhaps increasingly the main way? — that would manifest is in the form of investors paying no taxes.

And here we are! We talked last month about Section 351 exchange-traded funds. The gist of it is:

1. ETFs are, approximately, investing vehicles that pay no taxes on their gains; and
2. In a perfectly efficient financial world, every investor can become their own ETF, so that they never need to pay taxes on their gains.

And today Bloomberg’s Loukia Gyftopoulou, Katherine Burton, Sridhar Natarajan and Justina Lee report:

Cliff Asness … and his team at AQR Capital Management had figured out a way to make a widespread technique for reducing taxes much more potent. Their discovery: The right mix of stock bets not only grows tax-free, it can eliminate taxes from other investments, too. ...

By some estimates, a total of more than $150 billion is deployed at the firm and a phalanx of competitors that run their own versions of what’s known as tax-aware long-short investing. That puts them at the cutting edge of the broader “tax alpha” universe, in which more than $1 trillion is deployed in strategies devoted to delaying or shrinking payments to the government. …

For AQR, the payoff already has been enormous, turning it into the world’s largest hedge fund by the end of 2025, the latest industry data show. Its hedge fund assets multiplied within just a few years, leapfrogging rivals to surpass $140 billion by the end of March.

We have talked a few times about tax-aware long-short strategies. If you buy a bunch of stocks, some will go up and some will go down. You sell the ones that went down, generating capital losses that you can use to offset other capital gains, and keep the ones that went up, deferring capital gains taxes. This is called “tax loss harvesting,” and back in the olden days it was a sophisticated strategy that your financial adviser would laboriously execute for you, but now any robo-adviser will do it.

“Tax-aware long-short” involves grossing this up: Instead of spending $100 to buy a bunch of stocks, you borrow $100, buy $200 worth of stocks and then short $100 of other stocks. You still have $100 of net market exposure — you are still exposed to the market return on $100 of stocks — but you have much more tax loss. (Because you own $200 of stocks instead of $100, and because you now have offsetting long and short bets, one of which will presumably win when the other loses.) And then maybe with the right witchcraft you can do this in ways that generate ordinary-income losses rather than capital losses.

One thing to say about tax-aware long-short is that it is not just about reading the tax code cleverly. It really is about increasing financial efficiency; it is an outgrowth of modern academic finance. When I first wrote about it, here’s how I started:

One useful intuition of modern finance is that stocks are all kind of the same. Oh they’re not, they’re not, this isn’t true. But lots of stocks are interchangeable to some degree, particularly if you own a lot of them. A fair amount of the returns of many stock portfolios are determined by the returns to the overall market, to industry sectors, and to other well-known factors like value and size. You could construct a diversified portfolio of 100 stocks that is reasonably well correlated with the S&P 500 Index, and then you could construct a diversified portfolio of 100 entirely different stocks that is also reasonably well correlated with the S&P 500.

This would have sounded like witchcraft a generation ago. “No,” investors would have said, “an investment in Amalgamated Spats is a bet on the spats industry and the cut of the chief executive officer’s jib; selling Amalgamated Spats to buy Consolidated Sprockets is no substitute at all.” But the rise of quantitative finance and factor investing — in which Asness and AQR are leading figures — has normalized this view. “My portfolio has a lot of exposure to small-cap value,” an investor might say, without knowing what actual stocks she owns. You can get your small-cap value exposure from a lot of different stocks.

This matters for tax-aware long-short strategies, because:

1. You have to be long some stocks, and short some other stocks as a hedge. If the short stocks are going to be a good hedge, they have to be correlated with the long stocks. [5] But you can’t be long and short the same stocks: That’s a straddle, and you won’t be able to deduct your losses. You have to be long and short similar stocks, which requires a factor-based quant approach.
2. When you harvest your tax losses, you have to sell some stocks (the losers) and buy new ones to replace them. [6]  You can’t buy the same stocks: That’s a wash sale, and you won’t be able to deduct your losses. You have to sell your stocks and buy similar stocks, which again requires a quantitative notion of similarity.

The upshot is that, if you take a bunch of finance PhDs and apply sophisticated mathematical methods to investing, what you might get is lower taxes. Which makes total sense.

Also, there’s a reason that this stuff made AQR the world’s largest hedge fund. If finance is getting more efficient, then that should mean that alpha is getting scarcer: It is harder to beat the market, and the returns to being smarter than everyone else should be getting lower and harder to find. But if finance is getting more efficient, the that should mean that tax alpha is getting more plentiful: It is easier to beat the IRS, and the returns to being smarter than the IRS should be getting higher. You can put more money to work in “beat the IRS” than you can in “beat other hedge funds.” 

Anyway the Bloomberg story has other good bits. For instance:

A presentation that AQR recently provided to some wealth managers to recruit more clients sketches out an astonishing scenario: Invest $100 million in the most aggressive of AQR’s Flex strategies. Wait 10 years while the money triples. And in that period, it may generate over $580 million of losses that can be used to erase taxes from other investments.

It’s safe to say that, in the hedge fund world, boasting that a strategy might produce almost six times more losses than the initial investment is unusual.

No, it’s efficient. In the modern world, if you send clients a presentation saying “we will 6x your money in 10 years,” they’ll be like “no, markets are efficient, you cannot promise that level of outperformance.” If you send clients a presentation saying “we will generate reasonable alpha but also tax losses of 6x your investment in 10 years” they will be like “ah, good, you are doing your job efficiently.”

Earlier this year, Kalshi had some betting markets about who would attend the State of the Union address. One market was on George Santos, the former congressman, who naturally (1) traded the contract and (2) posted on X to manipulate its price:

Santos made a series of social media posts regarding his plans to attend the State of the Union. Shortly after these posts, the price of the SOTU event contract rose or dropped. For example, while holding a Yes position on his attendance, Santos posted on the X social media platform about what he should wear to the State of the Union. Within hours of the post, the price of the Yes position rose, after which Santos exited at a profit. ...

The next day, Santos posted a video on X stating he would be attending the event. After this video, the contract price for his attendance rose. Later that same day, Santos began building a No position regarding his attendance. … On the day of the State of the Union, Santos posted that he was stuck at the airport and would miss the event. The price of the Yes position fell, conversely making the value of the No position increase in value. At this point, both his plane and train tickets had been cancelled, and he had not purchased any alternative travel options. In the early hours of February 25th, Santos exited his No position, making a profit of $14,390.57. 

That is from last week’s US Commodity Futures Trading Commission settlement with Santos, in which he agreed to disgorge $17,569.98 of profits and pay a $17,500 fine. Obviously this is all incredibly stupid. When we talked about this in June, I wrote:

Every headline about this says “insider trading,” but I don’t think this is insider trading? Santos had no obligation of confidentiality to anyone to keep his State of the Union plans confidential; therefore he was free to trade on them. …

I do think that announcing that you’re going to the State of the Union to push up the “Yes” price, buying “No” contracts and then not showing up seems like a pretty textbook case of market manipulation.

And in fact the CFTC came after him for market manipulation, not insider trading:

Santos violated Section 6(c)(1) and Regulation 180.1(a) when he traded in an event contract where he could influence the outcome of the underlying event and engaged in activity designed to affect the price of the swap. Santos made misleading public statements and omissions about his activities in relation to the underlying event to influence the contract price for the benefit of his trading position.

If he just bet against himself, without posting about it, it might have been fine? Not legal advice.

There are so many very poor decisions that one could make related to cryptocurrency wallets, but I suppose one of them is … stealing $1 million of crypto from North Korean hackers (?) while working for the FBI? Or whatever this is:

An FBI agent has allegedly turned himself in for stealing about $1 million in cryptocurrency from foreigners the agency was investigating.

Patrick Steven Yaroch contacted the Department of Justice last week to confess to making “very poor decisions related to cryptocurrency wallets,” according to a Federal Bureau of Investigation affidavit filed with a district court in Virginia.

Yaroch used FBI systems to access the keys needed to transfer funds from cryptocurrency accounts associated with an “adversarial nation” to himself, the document released Monday says. Starting in late 2024 or early 2025, he conducted about 10 transfers totaling around $1 million, it says.

Terrific. Was turning himself in also a very poor decision related to cryptocurrency wallets? Who can say, but here was his earlier plan:

The authorities said in court papers that a search of the agent’s internet activity showed that in May, he had asked ChatGPT to recommend how to invest a million dollars with “a goal of potentially retiring around 40, and a clear interest in eventually building a slower-living vineyard/agricultural lifestyle in places like Cilento or Portugal’s Dao region.”

I was joking/guessing about North Korea, but this really is a good thriller plot.

Sure:

A growing number of investors from the US to South Korea are using leveraged exchange-traded funds for long-term investing, a far cry from the day trading they were designed for. …

In South Korea, the epicenter of the leveraged-ETF trend, former dentist Song Wonjun says the products helped him retire at 44 after turning them into the centerpiece of a long-term investing strategy he now teaches to hundreds of thousands of online followers.

If your financial adviser is going around online touting long-term investments in leveraged ETFs, that seems bad. If your dentist is doing it, yeah, that seems right.

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One thing you could do, as an investor, is try to figure out which stocks will go up, and buy them. You have $100, you find 10 really good stocks, you put $10 into each of them, and if they go up you make money. 

One problem with this approach is that it is risky: Stocks are volatile, and even if you are good at picking the stocks that will go up, sometimes they will go down for a while for reasons that are not your fault. 

Another problem is that, conversely, it is too easy. Stocks mostly go up, and if you picked your list of stocks by throwing darts at the stock pages, you’d probably get a pretty good return. If you are investing as a hobby, this is a nice fact, but if you are a professional investment manager it is awkward. Your investors will want to know what they’re paying you for. “Our portfolio was up 15% last year,” you say, and potential investors say “well but the S&P 500 index was up 16% and we can buy that in an index fund that charges like 0.01%.” They want alpha, market-beating returns, not beta, exposure to the market.

A simple solution to these two problems is to hedge. For instance: You have $100, you find 10 really good stocks, you put $10 into each of them, and you also sell short $100 of the S&P 500 index. If the market goes down, but your stocks go down less than the rest of the market, you will still make money.

Because you are hedged against broad market moves, your portfolio is safer. Your brokers might lend you more money to do more trading: With $100 of your own money, you might be able to buy $300 worth of stocks and short $300 of the S&P, for 6:1 gross leverage ($600 of bets on $100 of equity). That is not nearly as risky as buying $600 worth of stocks, because to your S&P 500 short will cancel out some of the moves in your long portfolio.

You might want to refine this solution. Why short the market? You are good at finding the stocks that will go up, so it stands to reason you’d also be good at finding the stocks that won’t. Why not just short the bad stocks? Like:

• Find 10 really good stocks and put $10 into each of them; and
• Find 10 really bad stocks and short $10 worth of each of them.

Now you are again, approximately, hedged against broad market moves: If there’s a big market crash, your longs will go down but your shorts will also go down. But you are now adding alpha (or trying to) on both sides of the trade. If the market goes up 10%, but your good stocks go up 20% and your bad stocks go down 5%, you have a 25% return. [1] Great stuff: More alpha with less risk.

How do you pick the 10 stocks to buy and the 10 stocks to short? One possibility is that you do lots of deep fundamental research on the particular pros and cons of each company. You buy one stock because it has a new product coming out that you think looks good, and another because its cost-cutting program is beginning to show real effects, and a third because you think it’s a likely takeover target. You short one stock because you have discovered accounting fraud, and another because it is overvalued by retail investors. There’s no theme to your longs and shorts, just some uncorrelated bets on companies that you like or dislike for particular reasons.

Here’s another, perhaps more realistic possibility. You are a person. You have a mental model of the world. The mental model has a few moving pieces, and you apply it to every company. Just to pick a mental model for illustrative purposes, you might go through the world thinking “AI is going to change everything.” What stocks will you buy? The good ones, the ones that will go up when AI changes everything. GPU companies and memory-chip companies, hyperscalers and neoclouds, publicly traded power utilities and private frontier labs. What stocks will you short? The bad ones, the ones that will go down when AI changes everything. Old-school software companies, maybe, or wealth management firms.

This second, more thematic approach might be more congenial than the first, more idiosyncratic one: Instead of having disconnected one-off theses about a bunch of different companies, you have one grand idea that you can read across to many different companies. When you talk about this, you will sound … well, I was going to say “smarter,” but that is a matter of taste. The hedgehog knows one big thing, and when you go to meetings in Silicon Valley and say “here is how AI will change Companies A, B and C,” you will sound smart: You have a vision of the future, you have a coherent theory of the world, you have situational awareness. But the fox knows many things, and if you instead go to meetings in New York and say “here’s the revenue-growth story that drives value at Company A, and here’s the M&A story that drives value at Company B, and here’s the accounting fraud at Company C,” you will sound smart: You have a grasp of the details, you have a range of approaches, you have mental flexibility. Which of those things sounds smarter might depend on what coast you live on and what industry you work in. [2]

The thing to notice about the more thematic approach is that you have not reduced risk. You have, like, rotated your risk. You are still, approximately, insulated against broad market moves: If the market crashes because of a war or a rate hike or whatever, your longs and shorts might both go down.. But you are very exposed to the theme: Your long bets and short bets are all the same bet, a bet on your big idea, and if your big idea is wrong, or just early, then both sides will move against you. If one day investors decide “hmm the pace of AI adoption will be a bit slower,” then all your longs will go down (because they are bets on rapid AI adoption) and all your shorts will go up (because they are also bets on rapid AI adoption). You have a completely unhedged exposure to your one idea. [3]  

Which is fine, if that’s your thing! If you’re a hobbyist investing your personal account, go nuts. If you’re a venture capitalist, “we will make huge directional bets on the pace of AI adoption” sounds great. If you are a hedge fund running a long/short portfolio at 5x gross leverage, though, it’s worrying. If that bet goes down by 20%, you’re ruined. And that bet might be more volatile than the market as a whole. Just intuitively, what sounds riskier: “Expectations of future US corporate profits will grow over time” or “the world will change utterly, soon, because of AI”? 

Avoiding this sort of thing — where all of your longs and all of your shorts are just one big bet — is a main organizing principle of the big multimanager multi-strategy “pod shop” hedge funds: Their separation of pods, and their measurement and supervision of individual pods’ portfolio managers, are about making sure that they have lots of uncorrelated bets instead of one big bet. [4]  

But if you are running a single-manager hedge fund, and you are relatively new to investing, without a big experienced institutional risk management apparatus, without a team of quants building factor risk management models, you might just do the one big bet. Especially if your entire investing history comes in a huge AI boom. Your mental model might have one moving piece, “AI good,” and you might apply it rigorously and with hedge-fund-like leverage and with huge success. And then, you know. Less success.

We talked on Thursday about Situational Awareness, a big AI-focused hedge fund founded by Leopold Aschenbrenner that ran into trouble last week. It its peak, Situational Awareness seems to have managed about $45 billion of net assets, at 4 or 5 times leverage, with a big concentrated portfolio of long bets on AI adoption and short bets against software companies. This thesis was, in the long arc of the last two years, pretty good, and Situational Awareness returned something like 1,000%. And then in the last few weeks the market sneezed, the thesis came under fire, AI stocks went down, software stocks went up, Situational Awareness got margin calls and Citadel ended up acquiring the bulk of its public portfolio at a big discount Thursday morning. Here’s Aschenbrenner’s letter to investors, promising to “learn the necessary lessons from this experience” and also to run “a fully-paid-for public book (long stock and long fully-paid-for-options, with no margin/liquidation risk)” from now on.

At the New York Times, Rob Copeland suggests an East Coast/West Coast split in enthusiasm for Aschenbrenner’s approach [5] :

When this star of San Francisco arrived in New York during his fund-raising tour around last summer, however, he received a relatively cool reception, according to three people from whom he tried to raise money, who declined to be identified talking about a private fund. They said they viewed him as a lightweight and a one-hit wonder. The asset management colossus Blackstone, the world’s largest investor in hedge funds, passed on investing, according to two of those people.

One wealthy New York investor who did take the meeting welcomed Mr. Aschenbrenner into his downtown office, and then gave him a grilling, according to the investor, who declined to be named publicly because he had agreed to keep the contents of the fund’s pitch private.

What was Mr. Aschenbrenner’s plan if the A.I. revolution didn’t pan out quite as hoped? The hedge-fund founder had no detailed response, the investor recalled. Mr. Aschenbrenner simply truly believed it would all work out.

That was reflected in his portfolio, a long/short book that seems to have been one thematic exposure:

As it frantically called rivals for help, the firm’s representatives claimed that it was hocking a portfolio of so-called hedges, or modest investments meant to protect from risk, that were now in distress, according to three people briefed on the entreaties.

That made little sense as potential buyers examined what was for sale: These were monster bets, many against companies like Adobe that the fund thought would be replaced by A.I. but now suddenly appeared stronger than ever, the three people said. These wagers formed a huge swath of the firm’s total investments.

They were hedges, in the sense that Situational Awareness was long some tech stocks and short some other tech stocks, all of which had a positive market beta: If the stock market went down, being short Adobe would be a hedge to being long Sandisk or whatever. The stock market didn’t particularly go down, though. What happened was that Aschenbrenner’s thesis went down, so he lost money on the longs and the shorts. He had no hedge for that.

One other thing. Bill Hwang’s family office, Archegos Capital Management, blew up in April 2021 by making concentrated levered bets on a few stocks. When that happened, I wrote:

One thing about margin lending is that if you borrow money to buy stocks, and your stocks go up, you automatically deleverage. If you use $15 of your own money and borrow $85 from your broker to buy $100 worth of stock, you have 85% leverage; if the stock then goes up to $200, you are down to 42.5% leverage. You still owe your broker $85, but now you have $200 worth of stock. If the stock then falls by 25% to $150, that’s fine: You are still in the black, and your broker still has ample security for its loan.

The incredible thing about Bill Hwang is that he made enormous levered bets on risky stocks, and those bets worked out perfectly and made him immensely wealthy in the course of a year or two, and he seems to have plowed every cent of it back into increasing those levered bets.

Maybe it’s not that weird? Like: You run a hedge fund, or a hedge-fund-like thing like Archegos. You run at a certain level of leverage; say you borrow $3 for every $1 of capital or whatever. Your stocks go up, naturally deleveraging you. You arrive at work the next day and say “hmm, I’m below my target leverage, better borrow some more and buy more stuff.” You have grown used to operating at a target leverage level, and you try to stay there.

Still, it is weird, in the sense that the trade I am describing here is “buy high and sell low”: Whenever your stocks go up you buy more, and whenever they go down you sell some, to remain at your target leverage level. Ideally it wouldn’t be quite that; ideally you would come in each day with a blank sheet of paper and use your profits from the previous day to buy the best positions to put on today. But if you run a concentrated portfolio of 12 stocks, and they go up, you’re probably going to buy more of those 12 stocks. 

This was bad, in Hwang’s case, because he owned so much of those stocks that he effectively caused their rise: They’d go up, he’d borrow more money to buy more, they’d go up more, etc., in a spiral that led to them trading way above fundamental value. And then if a slight breeze knocked them over — say, if one of the companies in his portfolio decided to take advantage of its absurd stock price by selling some stock — it would all break. The stocks would go down, he’d get margin calls, he’d have to sell more stock, he was the only buyer at those levels so the stock would collapse, etc., ruin.

The Situational Awareness situation is different, but isn’t it a little weird that Aschenbrenner was up 1,000% in two years and still running at something like 4x leverage? Bloomberg’s Katherine Burton, Sridhar Natarajan, Todd Gillespie and Nishant Kumar reported on Thursday:

Even before this week’s margin calls, there were signs that Wall Street was starting to grow cautious.

As some of its positions reached all-time highs in recent weeks, Situational Awareness was on the hunt for more banks that could finance its bets. The fund approached some lenders, including Barclays Plc, to work with their prime brokerage units, according to people familiar with the matter.

Barclays turned away the firm because it was concerned about the level of its exposure to just one sector, the people said. A spokesperson for the bank declined to comment.

“Our bets have paid off lavishly, so we need to borrow ever more money to keep on the same level of leverage”: What, no, why?

The basic theory of retail stock and options trading is:

1. Retail traders buy and sell stocks, effectively, at random.
2. If you can charge them a bid/ask spread — buy stock from them for $50 and sell to them at $50.01, or buy options from them at $2 and sell to them at $2.10 — then you will, in expectation, make a little bit of money each time they trade, with very low risk.
3. Anything you can do to make them trade more is good, for you: A little bit of money times a zillion trades is a lot of money.

Is trading more good for them? I mean, no; if you’re making a ton of money that’s coming at their expense. But they’re having fun, they’re engaged, they’re buying stocks, maybe it’s fine.

The most important application of this theory came when Robinhood Markets Inc. introduced widespread zero-commission retail stock trading. Before Robinhood, most brokerages would charge you some money to do a trade. This was good for the brokers, because they got money, but it also discouraged trading. Robinhood realized that if the trades were free, there would be many, many, many more trades: With $9.95 trades you might do one a month, but with $0 trades you might do 20 a day. Each trade was an opportunity for a market maker to charge a bid/ask spread, make a little low-risk profit, and send some of it to Robinhood as payment for order flow, and those small payments times zillions of trades might be worth more than $9.95 times many fewer trades.

The theory is more general. Anything that causes retail traders to do more trades is good, for brokers and market makers. Anything that causes them to trade more high-margin stuff — like options, which often have wider bid/ask spreads than stocks — is good, too. Thus the rise of zero-day options, which give retail investors many more opportunities to trade products with wide bid/ask spreads.

Nothing has ever topped zero-commission trading, though. That was really the Big Bang of getting retail traders to do zillions of trades. But Bloomberg’s Zijia Song and Bernard Goyder report on “Zero Commission 2.0”:

[A day-trader] who left his job to focus on managing his own money, is among an early wave of those trying to harness the power of artificial-intelligence models to gin up home-brew versions of the sophisticated algorithms that have made fortunes for hedge-fund firms like Millennium Management, Citadel and Renaissance Technologies.

The experiments, if successful, promise to turbocharge the day-trading boom that’s flourished since the pandemic, as individual investors chase gains in everything from meme stocks and derivatives to wagers on real-world events on prediction markets.

It may also provide another jolt to an investment industry that has already been transformed over the years by low-cost index funds and zero-fee brokerages. Now, AI is threatening to chip away at the competitive edge of financial advisers and money managers whose selling point has been the ability to seize on rapid market moves or deploy models honed by years of expertise and data. ...

AI will usher in a new phase by allowing autonomous agents to capture opportunities investors would usually miss during the course of the workday, said Anthony Denier, group president of the brokerage Webull Corp. His platform has rolled out an AI-powered portfolio adviser and launched technology that allows users to trade through AI prompts made in plain English.

“I view AI integration into retail platforms as zero commission 2.0,” he said, referring to the wave of investment activity that came after brokers slashed fees to zero.

With $9.95 trades you might do one a month, with manual $0 trades you might do 20 a day, but with $0 trades automated by an agentic system that you can instruct in plain English and that can trade while you’re at work, you might do, like, 20,000 trades a day. Obviously brokers and market makers are thrilled. “More than 100,000 customers have opened agentic trading accounts,” Robinhood’s Vlad Tenev posted on Friday.. Maybe some of the hobbyist agentic trading systems will even make money, why not!

A Bitcoin is an entry on a distributed digital ledger. It lives on the internet. It could not live anywhere else. It is possible to be a little loose about this. We have talked a lot about a guy who accidentally threw away a hard drive containing 8,000 Bitcoins, which are now buried in a Welsh garbage dump. Not really, though. He threw away a hard drive containing the private keys that would allow him to transfer those Bitcoins. Those Bitcoins are not, themselves, in the garbage dump. They are on the internet, the only place they could possibly be. What he threw away was, in effect, the list of passwords that would allow him to access his Bitcoins online, but the Bitcoins are still there. They have no physical form and could not be in a dump.

If you hold Bitcoin, it is often convenient for you to keep your private keys connected to the internet. For instance, if you want to spend some Bitcoin, you will want to have an app on your phone that allows you to send Bitcoins by scanning a QR code or typing in the recipient’s phone number or whatever. The app, which is on the internet, will need to have access to your private keys in order to send the Bitcoins: The app needs to use your private keys to instruct the Bitcoin ledger to send some Bitcoins from your account to someone else’s. This means, though, that you are sharing your private keys with someone else (the app). If the app doesn’t encrypt them well, if it gets hacked, if it is secretly a North Korean heist operation, etc., your Bitcoins might get stolen. Effectively your password is on the internet, where someone might find it.

Many Bitcoin holders find it safer but less convenient to keep at least some of their Bitcoins in “cold storage.” This means: Instead of giving your private keys to an app, which might get hacked, you write them down on a piece of paper and keep it in your desk. Or if that is too low-tech, you put them on a hard drive not connected to the internet (and preferably not connected to a dump), or on some security device marketed to Bitcoin aficionados that is approximately a hard drive not connected to the internet that can generate new private keys. Here’s one called Coldcard, which looks sort of like a pocket calculator that you’d bring to a war in outer space.

Your Bitcoins are not, however, on the piece of paper, or the hard drive, or the space war calculator. Your Bitcoins are on the internet. Anyone who correctly guesses your private key can steal them. Correctly guessing your private key is generally hard because it is a long random-looking number. On the other hand if you choose “1234” as your password someone might guess it. If your space war calculator is programmed to choose “1234” as your password, someone might guess it. Your Bitcoins might disappear. “No, wait, I have them right here in my air-gapped cold storage,” you say, but no you don’t. They were on the internet. You had your password on a fancy device, but you — or the device — should have chosen a better password.

Anyway:

Hackers have found a software flaw in a brand of “cold” Bitcoin wallet — considered one of the safest places to store cryptocurrency — and are siphoning tens of millions of dollars in an ongoing attack.

Late last week, Canada-based Coinkite Inc. notified users of its Coldcard devices that a security flaw in the keys that protect their Bitcoin had compromised some wallets. By Monday, more than 1,755 of the tokens worth some $110 million had been drained from roughly 5,000 wallets, according to Galaxy Research.

Coldcard is a brand of hardware device that allows users to secure their Bitcoin in so-called cold wallets. These wallets are supposed to be the safest place to keep cryptocurrency because they are isolated from the internet.

However, a flaw in the software of the Coldcard devices meant that the generated “seed phrase” — a long string of words used to gain access to a wallet — was predictable, according to a report from Block Inc.’s engineering team.

“Not your keys, not your coins,” but if your keys were not generated using robust randomization techniques, also not your coins. Oops!

The cynical view is that OpenAI made itself the leading artificial intelligence lab by (1) being good at building AI models and (2) very vocally worrying that its AI models might destroy humanity. “Ooh, those models must be good if they might destroy humanity,” investors would think, and they’d throw money at OpenAI. “Business negging,” I sometimes call this.

And then Anthropic sort of supplanted OpenAI as the leading AI lab by (1) being even better at building AI models and (2) even more vocally worrying that its AI models might destroy humanity. Frontier AI research is a weird corner of the art world where you can command the highest prices by being the most anti-commercial. The most valuable frontier AI model is one that is so dangerously omniscient that no one can be allowed to use it. The cash flows from that model would be zero, but just think about how cool it would be.

Anyway:

Anthropic PBC said its artificial intelligence models breached three organizations during cybersecurity tests that went awry, a little more than a week after its chief rival, OpenAI, disclosed a similar incident.

Anthropic said in a blog post Thursday that it made the discovery after performing a review of its own cybersecurity tests, following OpenAI’s announcement of a breach. In both the OpenAI and Anthropic tests, the AI models were able to access the internet from within testing environments that should have been sealed off, according to Anthropic’s blog.

Part of me thinks that Elon Musk should announce that actually Grok has hacked into 20 companies, but most of me knows no one would care. You can’t come to this stuff as a tourist; you have to make being worried about AI your whole identity as an AI lab.

The Million-Dollar Talent Wars for 20-Something Math Geniuses. AstraZeneca Shares Fall on Deal Talks With Bristol-Myers. David Ellison’s Risky Courtroom Strategy to Save His $81 Billion Warner Deal. Tesla Weighs Sale of China Business to Pave Way for Potential SpaceX Merger. Jefferies Analyst Calls Out ‘Simple-Minded’ Criticism of SpaceX’s Governance. Wall Street learns to love blockchain. Vitol, Cargill Cut Ties With Radiant World Amid Fake Invoice Concerns. Aston Martin Creditors Fume at Lack of Detail on Debt Deal. Six Lawyers Switched Firms and Big Law Will Never Be the Same. The Worries That Drove Uncle Sam to Buy Yen. Bessent Seen Rebuffing Wall Street on Guidance for US Debt Sales. Trump’s Arctic Mining Deal Signals a New Era of State Capitalism. American Oil Majors Trying to Get Into Venezuela Have Hit a Wall. Strategy Sells More Bitcoin, Stock as It Pushes on With Overhaul. ‘Crush this lady’: how eBay harassment campaign led to $56mn payout. Boomers Are Leaving Behind More Art Than Anyone Wants. Bars Are Giving Up on Young People and Catering to Gen X Instead. What’s the “Head of Macro” guy up to? AI Startup Apologizes for Offering Job Interviews in Exchange for Company Tattoos.

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This week’s episode of the Money Stuff podcast is out now. My guest-co-host this week is Bloomberg’s Silvia Killingsworth.. We discuss the Situational Awareness situation, record dates, CVRs, and sourcing weird volatility risk from retail customers to sell to hedge funds. The Money Stuff podcast will be off for August, though we will be re-running an old interview or two.

You can listen to this week’s episode here:

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Being bullish on artificial intelligence has been, for the most part, a great trade. Nvidia Corp.’s stock is up 83% over the past two years. SK Hynix is up 220%. Anthropic is up more than 400% in less than a year. Over the past few years, a consensus has developed that AI will be big, that companies will spend tons of money building it, and that companies that sell chips and power into the AI boom will make a lot of money. There have been related fears that traditional software companies will lose out. Adobe Inc. is down 51% over the last two years.

Let’s say you were really smart two years ago. What should you have done? I mean:

1. Buy stock in AI model and infrastructure companies;
2. Ideally pick the right ones, the ones that will be winners in the AI boom; and
3. Short traditional software companies that will be losers in the boom.

Great trade! You would have made a zillion dollars. Do it all day long. Put all of your money into this trade.. Put more than all of your money into this trade. Raise money from outside investors, and put their money into the trade. Borrow money from banks, and put that money into the trade. To be clear, this is not investing advice. It is hindsight. Buying stocks that go up and shorting stocks that go down is a good investing strategy and you should, um, have done it, a lot.

Leopold Aschenbrenner did. Here’s a Wall Street Journal profile of him from June:

Aschenbrenner had no professional investing experience when he launched his AI-focused firm, Situational Awareness, less than two years ago, with a few hundred million dollars. 

Prescient stock picks and whooshes of inflows have vaulted its assets under management to more than $20 billion, according to people familiar with the matter, approaching the size of Bill Ackman’s Pershing Square and Dan Loeb’s Third Point.

Situational Awareness has gained about 270% after fees this year through May and is up more than 1,000% after fees since inception, one of the people said. One of the fund’s most successful bets is a stake in Anthropic that today accounts for about one-fifth of its assets, the person said.

His core thesis, as CNBC notes today, was that the AI boom would be big and real:

Aschenbrenner became prominent in technology and investing circles after publishing a series of essays in 2024 arguing that rapid advances in artificial intelligence would require an enormous expansion of computing power, advanced semiconductors, memory and energy infrastructure. Those ideas became the intellectual foundation for Situational Awareness after he left OpenAI.

That is the central modern AI thesis, and Aschenbrenner was early and smart and articulate about it. This allowed him to raise money, and the fact that it has been basically correct allowed him to return 270% through May.

The only note, really, is that if you are all-in on this thesis, you might be more than all-in on this thesis. You won’t put 100% of your money (and your investors’ money) into the AI boom. You’ll put, like, 300% of your money into the AI boom. You’ll borrow money to lever up your bets on the AI boom. As your AI stocks go up, you’ll borrow more to buy more. Getting a 200% return on your money by buying SK Hynix stock is great, but getting a 1,000% return on your money requires borrowing more money to buy more stock.

This is a naturally long-term trade. Aschenbrenner’s famous June 2024 essay series is titled “Situational Awareness: The Decade Ahead.” The point is not, like, “SK Hynix will beat earnings expectations next quarter”; the point is stuff like “by the end of the decade, we are headed to $1T+ individual training clusters, requiring power equivalent to >20% of US electricity production.” A lot of people believe this, and a lot of money has been mobilized to bet on it. Tech companies and private credit firms are raising tens of billions of dollars of bonds to build data centers. Frontier AI labs and public AI companies like Alphabet and SpaceX are selling tens of billions of dollars of stock. The way those bonds work is that investors give the data-center builders their money and get paid back over decades as the data centers make money; the money doesn’t have to be repaid for years. The way those stocks work is that investors give the AI companies their money and hope that their stocks become more valuable; they never have to be paid back.

If you are a hedge fund borrowing money from banks to buy AI stocks, the way that borrowing works is, uh, if the AI stocks go down you get margin calls? Like, that day? Your money is not locked up for the long term, and you might have to repay it at any time. There is a mismatch between your thesis, which is measured in decades, and your funding, which is kind of overnight. The AI thesis is up a ton over the past two years, but it is down quite a bit over the past two weeks, and CNBC reported today:

The battered hedge fund founded by former OpenAI researcher Leopold Aschenbrenner is unwinding many of its trades after big losses on artificial intelligence stocks and a bad bet against  software stocks left it scrambling to raise cash, according to people familiar with the matter. ...

The fund grew to as big as $45 billion at the start of July before big losses took hold, according to a person familiar.

Several of the firm’s prime brokers — including Bank of America, Goldman Sachs and JPMorgan Chase — have been working with the fund as it seeks to meet margin requirements or reduce positions in an orderly fashion, according to people familiar with the discussions. The brokers have been marketing a group of the firm’s holding on both the long and short side for sale prior to Thursday’s start of trading, according to people familiar with the situation. …

And the Wall Street Journal reports:

Situational Awareness, the highflying artificial-intelligence-focused hedge-fund firm, sold the bulk of its stock portfolio to Ken Griffin’s investment firm Citadel after suffering deep losses, according to people familiar with the matter.

The firm had been seeking buyers for its holdings and trying to raise new capital in recent days, some of the people said. Its public investments include South Korean chip maker SK Hynix and others that had been stung by an investor backlash to AI.

A crude but useful characterization is that Situational Awareness is really really good at thinking about the long-term implications of AI, and Citadel is really really good at thinking about funding risk. [1] So now Citadel owns Situational Awareness’s long-term AI bets. And those bets seem to be up since Citadel bought them.

On prediction markets, you can bet on some stupid stuff. Some stupid markets are reasonably liquid, and you can bet large amounts of money on the stupid stuff without moving prices much. And then maybe you can make the stupid stuff happen, so your bets pay off. I wrote a few months ago:

One, uh, accomplishment of modern prediction markets is that they can, in theory, allow for an arbitrarily large amount of betting on arbitrarily small quantities of reality. You can bet on what words someone will say at a press conference, and make tens of thousands of dollars if you’re right. ...

And so one meta-strategy for trading on prediction markets is to try to find the largest gaps between (1) the amount of money to be made and (2) the amount of underlying reality. And then, you know. Make a big bet, and cause a small change to reality so your bet pays off..

Also, though, the opposite. Prediction markets are meant to be truth machines, to produce socially useful information about the probability of future events. Some of those events are stupid, and most of them are sports, but some of them are important. The classic use case for prediction markets is election betting: You can bet on who will win an election, and the betting prices reflect the wisdom of the crowd about who will actually win. Kalshi and Polymarket have signed up journalistic organizations as partners to use their market prices in their election coverage.

And so another meta-strategy for prediction markets is to find the largest gaps between (1) the amount of money required to move prices and (2) the importance of the underlying reality. And then make a relatively small bet that causes a relatively large move in the market-implied probability of your favorite candidate winning an important election.

And then what? The classic form of market manipulation is that you manipulate a small illiquid market (the election contract) to make money in a bigger market. I am not sure what the bigger market is. You could imagine some story like “I will short bonds and then bid up the probability of a socialist candidate winning an important election, which will cause bond yields to spike and make me more money on my short than I spent on the election bets.” Sure. 

Another possibility is: “I will run for office, I will bid up my probability of winning, and then I will go to donors and say ‘hey I’m in the lead’ and they will give me money.” I guess you use the money to, like, pay your buddies consulting fees, and also to pay for the prediction-market bets.

But most people assume that you would do this manipulation, not to make money, but to increase the probability of your preferred candidate actually winning. Raising money is useful in politics! Having good news stories written about you is useful! If the prediction markets say you are likely to win, that can get you money and coverage and support, which will make you more likely to win in real life. And then you can be president or whatever.

That is the theory. I am never sure how seriously to take this. It relies on election markets being inefficient: The theory is “you can spend a little money to move the election odds in your favor, because nobody will trade against you to make an easy profit from your manipulation.” The basic premise of prediction markets is that that’s not true.. The premise of prediction markets is that there are enough economically motivated sharp bettors that, if you spend some money trying to puff up a no-hope candidate’s probability of winning an election, a few sharp bettors will say “lol that’s free money,” bet against you, and quickly push the probability back to its correct level.

Still there must be at least some cases where the sharps are asleep or underfunded. There are limits to arbitrage in real markets; they must be more binding in prediction markets. Bloomberg’s Liam Vaughan reports:

After a couple of weeks teasing out his candidacy, Matt Mahan, San Jose’s Democratic mayor, officially entered California’s gubernatorial race on Jan. 29. It was a crowded field, but the unassumingly handsome, 43-year-old centrist had a lot going for him. …

His campaign received a helping hand from Polymarket, one of the world’s largest prediction markets and, increasingly, a driver of political discourse in the US and beyond. That morning a user named Fine-Tributary spent $44,000 buying “yes” contracts for Mahan — that is, contracts that would pay out if the candidate won. It was a massive bet relative to the size of the market so early in the race, pushing Mahan’s price from the low teens to 96¢ out of a maximum of $1. In prediction markets, price is understood to equate to odds, meaning Polymarket, for the moment, was indicating a 96% chance Mahan would win the race. The odds fell back as switched-on traders hoovered up “no” contracts in response, but the following day Polymarket still gave Mahan a 36% chance of replacing Gavin Newsom. Traditional polls had him around 4%. …

That afternoon the New York Post and its new sister title, the California Post, ran a story about the Polymarket spike under the headline “Matt Mahan records huge surge in support of California governor run.” It stated “betting markets” were “predicting tough-on-crime Dem with Silicon Valley ties has what it takes” to defeat an “underwhelming field of lefty Democrats.” The article didn’t mention that the move was driven almost entirely by a solitary, anonymous wallet that had placed only one other trade on Polymarket, for $500, five years earlier (and it hasn’t traded since the Mahan bet). …

Mahan didn’t respond to multiple emails and messages left with his office. But people in his orbit clearly understood the platform’s potential. Tech entrepreneur Adam Kalamchi, Mahan’s college roommate at Harvard University, wrote on LinkedIn: “Whatever you can do to help his candidacy — donate, post, push up the Polymarket bids — please do!” ...

Mahan’s campaign ultimately lost momentum, and after a few weeks his Polymarket numbers fell back in line with the polls. 

Is posting on LinkedIn “push up the Polymarket bids” … commodities market manipulation? 

The basic rule is that, if the directors of a company decide to sell the company for cash, they have to maximize value for shareholders. If one bidder will pay $50 per share, and another will pay $60, the $60 bidder wins. [2] This is called the board’s “Revlon duty,” after the Delaware case about it.

The rule is not quite that, if the directors of a company think it’s only worth $50 per share on its own, and some bidder comes in offering $60, the directors need to sell. But it tends to push in that direction. “The fiduciary duties owed by directors of a Delaware corporation require the directors to seek to maximize the value of the corporation over the long-term for the benefit of the stockholders,” and if the directors are pretty sure that they can’t create more than $60 per share of long-term value, they should sell for $60 in cash.

This can be unsatisfying. When Twitter Inc.’s directors sold out to Elon Musk for $54.20 per share, they seemed sad about it. Twitter’s chief executive officer expressed some doubts that selling to Musk was in the best interests of Twitter, that it would be good for the product or the employees or the users or the advertisers. But he was pretty sure that “this offer at the price it ended up at was in the best long-term interest of our shareholders,” and the shareholders were all that mattered.

There is, however, another kind of company, a “public benefit corporation,” or PBC. The basic point of a PBC is that its directors have different fiduciary duties: They can “manage or direct the business and affairs of the public benefit corporation in a manner that balances the pecuniary interests of the stockholders, the best interests of those materially affected by the corporation's conduct, and the specific public benefit or public benefits identified in its certificate of incorporation.” If Twitter had been a PBC, it could have rejected Musk’s bid by saying “well, this bid is in the best interests of our shareholders, but not of our users or employees or our public purpose as a town square, so, no.” 

I mean, that’s what I always assumed, but the PBC is a somewhat new technology and hasn’t been tested much. For instance: If a PBC’s board puts the company up for sale, does it have to sell to the highest bidder? Surely not? Surely it can sell to the nicest and most public-benefit-y bidder? But Revlon is pretty deeply ingrained in Delaware law and the brains of mergers-and-acquisitions lawyers, so maybe? In any case, yesterday the Delaware Court of Chancery ruled, no, a PBC doesn’t have to sell to the highest bidder:

This case presents an issue of first impression: how Revlon and its progeny apply, if at all, to the board of a public benefit corporation navigating a change-of-control transaction. …

To say that directors of a public benefit corporation “must perform [their] fiduciary duties in the service of [the] specific objective” of “maximizing the sale price of the enterprise,” or that they “must focus on” that “primary objective[,]” would be inconsistent with the public benefit corporation statute’s requirement that directors consider and balance other interests against stockholder pecuniary interests.

I guess that’s pretty obvious, but there you go. 

Here is a history of crypto bankruptcies in three parts. In the first phase, in the early days of crypto, (1) crypto prices mostly went up over time, but (2) crypto exchanges were constantly getting hacked and losing investors’ money. A crypto exchange would have $100 million worth of customer crypto assets, it would get hacked and lose half of its crypto, and it would declare bankruptcy. The bankruptcy process would take a while to sift through all of the claims, and by the time it was ready to pay back its customers, it would have, like, $300 million worth of crypto: Crypto prices had gone up so much that the non-stolen assets were worth considerably more than the dollar value of the customers’ claims. The customers could be made whole (in dollar terms), with money left over. This is approximately the story of Mt. Gox, an early crypto exchange that went bankrupt in 2014.

In the second phase, crypto prices were volatile and people built leveraged financial institutions on top of them. A crypto exchange would have $10 billion worth of customer assets, and then all of a sudden it would have $5 billion of customer assets because of some combination of (1) maybe stealing the money, (2) crypto prices crashing and (3) a self-reinforcing cycle in which crypto prices depended on confidence in the exchange but confidence in the exchange depended on crypto prices. The exchange would declare bankruptcy, the bankruptcy process would take a while, and by the time it was ready to pay back customers, crypto prices would have recovered cyclically and it would have, like, $11 billion worth of crypto. The customers could be made whole (in dollar terms), with money left over. This is approximately the story of FTX, which went bankrupt in 2022, inaugurating a long crypto winter.

In both of these cases, the customers could still reasonably feel aggrieved: If the exchange hadn’t lost their crypto and gone bankrupt, they’d have participated in all of the gains on crypto prices; as it was, they only got their money back in dollar terms at the lows. But that’s life in crypto bankruptcy..

We might now be in a third phase, which goes like this:

• In the good times (pre-2022), crypto was hot, and people built leveraged financial institutions that held customer money and also did some crypto mining because why not.
• Then, for the usual combination of reasons (fraud, volatility, reflexivity), the leveraged financial institutions went bankrupt without enough money to pay back their customers.
• The bankruptcy process took a while.
• These days, eh, crypto, whatever; you can’t rely on a rally in crypto prices to bail out the customers.
• But if you did Bitcoin mining, you’re in luck: Owning a big data center full of graphics processing units, with good access to electric power, is super valuable right now. You can do AI with that! 
• So the customers could be made whole (in dollar terms), out of AI revenue.

CoinDesk reports:

Ionic Digital (IOND), the bitcoin miner formed out of Celsius Network’s bankruptcy, rose 26% on its Nasdaq debut, valuing the company at $2.8 billion following the exchange’s largest direct listing since 2021.

The Washington D.C.-based company, which is pivoting to power AI calculations, was created in January 2024 to acquire Celsius’ mining assets under the bankrupt lender’s court-approved reorganization. … Ionic issued “37 million shares of Class A common stock to eligible holders of certain claims against Celsius Network and its affiliates,” according to its registration statement. ...

Ionic decommissioned bitcoin mining at its Ward County, Texas, site in December and committed its 234 MW of capacity to Nscale under a 126-month lease carrying $1.95 billion in contracted revenue, according to the registration statement.

We talked about Celsius a lot back in the day; I once called it “perhaps the most ridiculous of the big crypto companies.” Mostly the ridiculousness was in its core business, which involved borrowing from customers at 18% interest and yeeting their money directly into the sun. But a small sideline of the ridiculousness was that it got into Bitcoin mining. Which, over the long arc of bankruptcy, worked out for its customers!

World Liberty Financial is a crypto thing whose fundraising pitch had two main elements:

1. It was going to build a decentralized financial blah blah blah, and
2. Most of the money it raised would go directly to Donald Trump.

Which element of this pitch was more important? Man, come on. At New York Magazine, Jen Wieczner writes:

Making a large investment in the project was widely seen as a play to curry favor with Trump. The project was upfront about the fact that 75 percent of net revenues would go to the president’s company DT Marks DEFI LLC (of which Trump owns 70 percent and family members own the rest). It was as though World Liberty had opened up a way to give money directly to Trump. Even in the profiteering world of crypto, an arrangement so lopsided, so nakedly self-dealing, was remarkable.

That said, though, the decentralized blah blah blah part of the pitch was also important. A pitch like “hey give some money to Donald Trump for no reason” raises some pretty obvious issues, while a pitch like “hey give us money and we’ll build a decentralized blah blah blah and Donald Trump will get a cut” raises … uh, really the same issues, but maybe you can look past them? It was very useful to everyone involved in World Liberty to believe that it was building a real business, the decentralized financial system of the future, and not just transferring money to Donald Trump.

Wieczner’s article is about Justin Sun’s feud with World Liberty over his investment. Sun, the crypto billionaire behind the Tron blockchain, was being investigated by the US Securities and Exchange Commission at the time he invested millions of dollars in World Liberty, and now he is not. “Sun’s team has emphatically rejected the notion that his World Liberty investment greased the gears in Washington for the dismissal of his case, suggesting the timing was coincidental.”

But Sun tells Wieczner that he totally believed in the decentralized blah blah blah and was shocked, shocked to discover that World Liberty has not in fact revolutionized the financial system:

Sun would come to believe he’d made a colossal mistake. World Liberty would disappoint his expectations and those of many other investors: The decentralized crypto products it promised would largely never materialize. ... “Today, the thing I regret the most is not only that my money got stuck but that my name is on the project,” Sun says. “I believe many other people also got harmed.” … He couldn’t hide his outrage at World Liberty — for making a mockery of the principles it was purportedly founded on and for, in his eyes, essentially stealing his money in full public view. “I never expected this to happen to me,” says Sun. “This is the worst-case scenario.” …

The “financial revolution” World Liberty promised never materialized. Sun says he gave the World Liberty team ideas for products it could build, but no one followed through on any of them. “I started to notice what I believed to be red flags,” Sun says now. “They seemed to be interested only in raising money, not in developing actual products.”

Uh huh. Wieczner also talks to other people who were less surprised:

“Anyone I know that got involved with the project just did it because they knew Trump was enough of a crook to not let this fail,” says the longtime crypto investor who did not invest. “That anybody could be surprised by what has happened is the only shocking part.” …

“It’s scammers scamming scammers,” more than one crypto investor told me. The former World Liberty adviser said the scenario reminded him of the meme in which two Spider-Mans point at each other. 

Sure. Elsewhere, Bloomberg reports on the fate of a World Liberty Financial treasury company, which is exactly what you’d expect:

Last summer … ALT5 Sigma Corp., a blockchain firm with fewer than two dozen employees, suddenly pivoted to buying virtual tokens issued by World Liberty Financial. … To buy World Liberty tokens, the company raised $750 million from heavyweights including hedge funds Point72 Asset Management and ExodusPoint Capital Management. …

Retail investors snatched up the stock, looking to ride the wave of the new powerful connections. The company, which was worth roughly $100 million in the month before the deal, suddenly cracked $1 billion in market value. Donald Trump Jr. and Eric Trump, the president’s two oldest sons, heralded the transaction by ringing the Nasdaq opening bell.

Nearly a year later, the buzz is gone. ALT5 has changed its name, its CEO and cycled through three auditors. The company now known as AI Financial Corp. is worth less than $60 million and faces getting kicked off the Nasdaq stock exchange. The tokens it bought at 20 cents each have slumped to about 6 cents apiece.

A World Liberty Financial treasury company! Come on. 

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