| The essence of finance is time travel. In the future, your factory will make widgets and you will sell them profitably, so you can borrow money, today, to build the factory to make the widgets to sell to repay the money. If you go to a bank with an architect’s plans for the widget factory, and a business plan for making and selling the widgets, and a good track record of profitably manufacturing and selling widgets, the bank will be happy to lend you the money to build the factory. The bank has people who understand widgets; it can model up a widget factory pretty easily. It will figure out an appropriate leverage level for the factory and lend you that amount of money at, like, 8% interest. Tech startups are an extreme version of this. In the future, human life will be utterly transformed by your invention: We will live on Mars or socialize in the metaverse [1] or commute in self-driving flying cars or have omniscient robots in our pockets that can fulfill every human desire. Presumably, also, you can turn that into money. So you can raise money, today, to build the metaverse or cars or robots. I once wrote about Nikola Corp., the hydrogen-powered truck startup: What you want, when you invest in a startup, is a founder who combines (1) an insanely ambitious vision with (2) a clear-eyed plan to make it come true and (3) the ability to make people believe in the vision now. “We’ll tinker with hydrogen for a while and maybe in a decade or so a fuel-cell-powered truck will come out of it”: True, yes, but a bad pitch. The pitch is, like, you put your arm around the shoulder of an investor, you gesture sweepingly into the distance, you close your eyes, she closes her eyes, and you say in mellifluous tones: “Can’t you see the trucks rolling off the assembly line right now? Aren’t they beautiful? So clean and efficient, look at how nicely they drive, look at all those components, all built in-house, aren’t they amazing? Here, hold out your hand, you can touch the truck right now. Let’s go for a drive.” That’s not true, but it’s a nice metaphor; the goal is to get the investor to see the future, so she’ll give you money today, so that you can build the future tomorrow.
This is harder to model, though. How much money will you make selling the metaverse or the omniscient robots? A large amount, probably, but you are not going to have a detailed spreadsheet projecting your margins to a tenth of a percentage point. “Ehhh omniscient robots, come on,” you say to investors, and they say “yes of course” and give you money. But this will be based more on feel and enthusiasm and competitive dynamics than on detailed financial modeling. Also it will be equity. Investors will give you money now in exchange for a slice of the enormous profits of your world-changing vision, not for a fixed 8% return. How much will they give you? Well, if you are very very very very good at pitching your vision, they might give you tens of billions of dollars. Global venture capital investment is something like $368 billion per year; getting 10% of that would be an astonishing feat. What if you want, say, $1 trillion? That is harder. You can go to Masayoshi Son with your vision, and you can put your arm around his shoulder and gesture sweepingly into the distance and whisper “omniscient robots,” and he might say “yes, here is all the money I have,” but that’s still not $1 trillion. To raise $1 trillion, you need more than a compelling science-fiction vision of your world-changing product. You need a compelling science-fiction vision of world-changing financial engineering. By this metric Sam Altman is surely the greatest tech founder in history. Sure sure sure sure sure chatbots, but look at this, man: Altman recently told employees that OpenAI wanted to build 250 gigawatts of new computing capacity by 2033, according to people familiar with the matter, a plan that would cost over $10 trillion by today’s standards. He has said that OpenAI would have to create new financing tools to help fund this massive build-out but hasn’t shared many details on what that would look like.
No! Wrong! He has shared tons of details of what his new financing tools will look like! He shares more every day! Here’s today’s: Broadcom Inc. shares jumped after OpenAI agreed to buy the company’s custom chips and networking equipment in a multiyear deal, part of an ambitious plan by the startup to add artificial intelligence infrastructure. … Investors sent Broadcom shares up as as much as 11% on Monday, betting that the OpenAI alliance will generate hundreds of billions of dollars in new revenue for the chipmaker. But the details of how OpenAI will pay for the equipment aren’t spelled out. While the AI startup has shown it can easily raise funding from investors, it’s burning through wads of cash and doesn’t expect to be cash-flow positive until around the end of this decade. The financing tool is, you go to Broadcom and you put your arm around their shoulder and you gesture sweepingly in the distance and whisper “omniscient robots” and they whisper “yesssss” and you say “we’ll need a few hundred billions dollars of chips and equipment from you” and they say “of course” and you say “good” and they say “do you have hundreds of billions of dollars” and you whisper “omniscient robots” again and they are enlightened. And then you announce the deal and Broadcom’s stock adds $150 billion of market capitalization and you’re like “see” and they’re like “yes” and you’re like “omniscient robots” and they’re like “I know right.” That is the financing tool! In some loose postmodern sense, OpenAI has borrowed hundreds of billions of dollars from Broadcom. You can buy hundreds of billions of dollars of equipment to build the robots to sell for money to pay for the equipment, because you’ve gotten everyone to believe. The Financial Times explains: The mammoth chip order means OpenAI could spend another $350bn to $500bn on top of the roughly $1tn of chip and data centre deals it has signed in recent months as it races to secure the computing power to run services such as ChatGPT. ... The deals have bound some of the world’s biggest tech groups to OpenAI’s fortunes, despite questions about how OpenAI will fund them as the cost dwarfs the start-up’s existing revenues. Also: The deals have bound some of the world’s biggest tech groups to OpenAI’s ability to become a profitable business that can meet its increasingly steep financial obligations. “OpenAI is in no position to make any of these commitments,” said Gil Luria, analyst at DA Davidson, who added it could lose about $10bn this year. “Part of Silicon Valley’s ‘fake it until you make it’ ethos is to get people to have skin in the game. Now a lot of big companies have a lot of skin in the game on OpenAI,” he added. If you owe the bank $100, that’s your problem. If you owe Broadcom $500 billion, that’s Broadcom’s problem. If you owe every big tech company hundreds of billions of dollars, that is their problem. Surely they’ll find a solution! Or you will. The money will figure itself out. How? Well, in a world with insatiable demand for AI inference, probably you can raise quite a lot of money in debt to pay for the data centers. The bet here is something like “by the time we are actually building these data centers, they will be less like science-fiction speculation and more like widget factories, and people will be comfortable financing them based on cash-flow models.” But, for now, backstopping that theory is another theory, something like “OpenAI has a $500 billion equity value so surely it could raise more money to pay its bills.” The Financial Times notes: OpenAI’s recent blockbuster deals have added a new layer to its complicated ownership structure, leading to more uncertainty over when and how its powerful shareholders will get an eventual payout. … OpenAI’s boundless capital requirements meant its backers — including Microsoft, SoftBank and Josh Kushner’s Thrive Capital — would see their shareholding diluted through further fundraising, said people familiar with the company’s plans. ... “Most people would prefer to have a smaller piece of a bigger pie,” said a senior OpenAI executive. The Wall Street Journal adds: OpenAI’s deals with chip and cloud companies in the past few months have helped fuel a global rally in tech stocks. Each agreement has raised the already high expectations set by Altman in describing the seemingly infinite amount of compute needed to bring forth the AI revolution. The deals have surprised some competitors who have far more modest projections of their computing costs.
Last week, we discussed OpenAI’s deal with Advanced Micro Devices Inc., in which OpenAI essentially spent some of that global tech rally on chips: OpenAI agreed to pay AMD tens of billions of dollars for chips, but it took back warrants on AMD’s stock; those warrants instantly became worth tens of billions of dollars as AMD’s stock rallied on news of the deal. The Broadcom deal does not have that sort of explicit monetization of OpenAI’s ability to add equity value to every company it touches. But does it implicitly monetize that ability? I have previously written that “nobody in history has ever been better at, like, business negging than Sam Altman.” And: When Sam Altman, the chief executive officer of OpenAI, goes around publicly worrying that AI might kill us all, what he is really saying is “I am building a technology that is unimaginably powerful and will transform every aspect of human life, so it sure must be worth a lot of money.” If he just said that, you might be skeptical; it is obviously in his self-interest to talk about how powerful his technology is. But instead he goes around worrying about it, which makes him seem more credible: It is not obviously in his self-interest to talk about how dangerous his technology is and how carefully he needs to be checked. It’s more subtly in his self-interest to do that.
Same with the computing costs. “The deals have surprised some competitors who have far more modest projections of their computing costs,” because he is better at this than they are. If you go around saying “I am going to build transformative AI efficiently,” how transformative can it be? If you go around saying “I am going to need 1,000 new nuclear plants to build my product,” everyone knows that it will be a big deal. Schematically here is how crypto market structure works. I think Dogecoin will go up. You think Dogecoin will go down. Dogecoin is trading at, let’s say, $0.20. I go to a crypto exchange and make a bet on Dogecoin. I put down $100 for my bet, at 20-to-1 leverage; effectively, I have bought $2,000 worth of Dogecoin (10,000 coins) for $100. If Dogecoin goes up to $0.25, my position is worth $2,500; I have made $500 of profit on my $100 bet. If Dogecoin goes down to $0.19, my position is worth $0; I have lost my $100 bet, and my bet is closed out. You, meanwhile, put down $100 for a 20-to-1 levered bet against Dogecoin. If Dogecoin goes down to $0.15, you make $500 of profit on your $100 bet. If Dogecoin goes up to $0.21, your position is worth $0; you have lost your $100 bet, and your bet is closed out. Notice, though, that my $100 bet was on $2,000 worth of Dogecoins. How does the crypto exchange get $2,000 worth of Dogecoins, if I have only given it $100? One possible answer is “the crypto exchange borrows $1,900 from someone else to buy my $2,000 worth of Dogecoins,” but a moment’s reflection will tell you that that’s not the answer. The answer is that the crypto exchange doesn’t buy any Dogecoins. The crypto exchange is not in the business of buying Dogecoins; it is in the business of matching bets. I bet $100 that Dogecoin will go up, and you bet $100 that Dogecoin will go down, and the crypto exchange matches our bets against each other. If I win, you lose, and vice versa; there are no actual Dogecoins involved. This is not the only crypto market structure: There are many exchanges where you can give the exchange $2,000 and get $2,000 worth of Dogecoin, which the exchange really holds in your account. There are probably some exchanges where you can give the exchange $1,000 and get $2,000 worth of Dogecoin, with the exchange finding someone else to finance the other $1,000 to actually buy the Dogecoin. But if you are getting 20-to-1 leverage on Dogecoin, nobody is buying any Dogecoin. You are making a pure derivative bet on the price of Dogecoin. In crypto this sort of derivative bet is conventionally called a “perpetual futures contract,” or “perp.” In other markets it has other names. European stock traders might call it a “contract for difference.” Old-timey American stock traders would have called it “bucketing.” I have simplified things by saying that you and I bet against each other. Actually in the first instance we bet against the exchange: I have a perp that will pay me if Dogecoin will go up, but I do not look to you for payment; I look to the exchange. The exchange will probably run a matched book (if I am long $2,000 of Dogecoin someone else has to be short $2,000 of Dogecoin), particularly if it is a decentralized exchange with no balance sheet of its own. But that is not a law of nature, and sometimes crypto exchanges are in effect long or short massive amounts of crypto bets against their customers. The famous example is of course FTX. But let’s assume there’s a matched book: The only bets at the exchange are me betting that $2,000 worth of Dogecoin will go up, and you betting that it will go down. Let’s say Dogecoin (actual Dogecoin, traded on other, less leveraged exchanges) goes down to $0.17. In theory, I have lost $300 (my account has negative $200 in it) and you have made $300 (your account has positive $400 in it). But in practice my account balance on a decentralized crypto exchange can never be negative: I set up that account with a crypto wallet, not my name and credit card, and there’s no recourse to me. If my balance gets below zero, my account is liquidated but no one can ask me for more money. So really what happens is that my account has zero, your account has positive $400, and the exchange has the $200 of collateral ($100 each) that we posted. The exchange owes you $400 but only has $200. So the exchange calls you up and says “hey sorry actually your short Dogecoin bet is closed, and it was closed at $0.19. Here’s the $200 you made.” And if you are like “wait no Dogecoin is at $0.17,” that’s not the exchange’s problem. This is called “auto-deleveraging.” Here is how Hyperliquid, a big decentralized exchange, describes it: If a user's account value or isolated position value becomes negative, the users on the opposite side of the position are ranked by unrealized pnl and leverage used. ... Those traders' positions are closed at the previous mark price against the now underwater user, ensuring that the platform has no bad debt. Auto-deleveraging is an important final safeguard on the solvency of the platform. There is a strict invariant that under all operations, a user who has no open positions will not socialize any losses of the platform. The people with winning positions socialize the losses. If you have a winning bet, someone has a losing bet. If the people with losing bets have lost more than they had, then you don’t get all of your winnings. (Incidentally I once discussed this structure with Sam Bankman-Fried, of FTX, on Bloomberg’s Odd Lots podcast. He was quite dismissive of it, arguing that FTX had a much better liquidation system to prevent winning bettors from socializing losses. It did, until it didn’t.) Anyway this is a bummer for you if you were betting that Dogecoin would go down: You were right, and you only got a portion of your winnings. It is more annoying if you weren’t doing that. What if you were an arbitrageur? You saw that traders on the decentralized exchange wanted to be long Dogecoin, so you did an arbitrage: You bought $2,000 of Dogecoin in real life, and you sold $2,000 of Dogecoin (via perp futures) on the decentralized exchange, effectively “manufacturing” the futures out of spot Dogecoin. The price of Dogecoin drops to $0.17, and: - You have a $300 loss on your spot position.
- You should have a $300 gain on your perp position, but you don’t, because I have walked away from my bet and there’s only $100 to pay you.
- You have a net $200 loss on your hedged position.
- Also, though, your short position is closed, but your long position isn’t: You still own $1,700 of Dogecoin “naked,” without an offsetting short position.
- You might as well sell your Dogecoin to cut your losses and get back to flat.
The upshot is that when Dogecoin goes down by 5% (from $0.20 to $0.19), (1) gamblers who are long 20-to-1 levered perpetual Dogecoin futures get wiped out, (2) arbitrageurs who were short perpetual Dogecoin futures are auto-deleveraged and have to sell Dogecoin to remain hedged, (3) there’s lots of selling and no buying, (4) prices go down even more, (5) gamblers who were long 10-to-1 levered perpetual Dogecoin futures get wiped out, (6) etc. Where is the limit? Well, in theory, at some level of liquidations, long-term fundamental value investors will see value. They will say “okay when Dogecoin fell from $0.20 to $0.18, that was one thing, but now Dogecoin is trading at just $0.15, which is well below its fundamental value.” So they will step in and buy Dogecoin, planning to make money when rationality sets in and the price of Dogecoin returns to its fundamental value. You see the problem here don’t you? [2] Anyway. There was a crypto crash last week. Bloomberg’s Muyao Shen and Olga Kharif reported: A record $19 billion in bets evaporated and crypto prices tumbled, due in large part to newly severe China tariffs announced by President Donald Trump. A combination of factors — leverage, automatically triggered sales, a lack of liquidity at odd hours for global trading — fueled what might have been a less dramatic obliteration of positions. From the morning hours in Asia through the afternoon in the US on Saturday, traders, executives and market-data analysts were wondering who, exactly, had suffered losses. Did a large entity get completely hosed — or was this a case of a lot of small bettors watching their holdings evaporate to zero? More than 1.6 million traders were liquidated, according to data tracker CoinGlass. … That said, liquidations were concentrated on smaller coins beyond Bitcoin and Ether, known as altcoins. Leverage tends to be higher and liquidity much lower in those less familiar tokens. Quite a lot of crypto traders were making levered perp bets on altcoins, crypto exchanges regularly offer 40 or 50 or 100 to 1 leverage, and so when prices move slightly all those bets go to zero. Also the people who were making levered bets against those altcoins had their own trouble, as the losing bettors got liquidated: Despite being smaller than rival Binance, Hyperliquid experienced the most extinguished trades in dollar value during the 24-hour-period selloff, at $10 billion, according to CoinGlass. “Hyperliquid had the most amount of long liquidation, and least amount of liquidity to match,” said Ebtikar. A risk-management mechanism called auto-deleveraging, or ADL, contributed. ADL is designed to automatically close profitable or highly leveraged positions when liquidated trades exceed a certain capacity covered by insurance. Exchanges incorporate it to protect them from losses during extreme market volatility, but many market participants also blamed ADL for making the selloff worse. … “This mechanism is not without complications, especially for participants with more complex portfolios,” said Spencer Hallarn, global head of OTC trading at crypto investment firm GSR. “Quantitative liquidity providers and market-neutral participants can quickly find the winning sides of their trades closed prematurely due to ADL, leaving their overall books imbalanced and subject to market beta which can lead to problems, and a necessity to quickly pare imbalanced risk,” he said. Right, yes, if a ton of crypto market structure consists of 40-to-1 levered bets, then you are going to have a lot of levered losing bettors blowing up, but you are also going to have a lot of weird results for the levered winning bettors. They can only get paid out of the losing bets. Here is a more cynical take from John Hempton: Lots of [altcoins] will make 10-15% moves in discontinuous markets. The way your exchange guards against going bust is by not actually buying all the crypto that [the customer] thinks he owns. An exchange that doesn’t buy the underlying asset they have promised their clients is known as a “bucket shop.” This comes from the 1920s practice of gambling houses “trading” in stocks but really keeping all the money in a bucket. … Bucket shops do not buy all the shares (or coins) necessary to hedge every client position. This is because they can’t manage the risk of a market plunge. But as a result, they have a big problem in a bull market. Imagine your little crypto exchange, now a bucket shop, does not actually buy $100K of $ATOM on [the customer’s] behalf. Then suppose $ATOM doubles — a realistic outcome in the recent bull market. Now you owe [the customer’s] a net $110k and you simply haven’t earned it. You have a very big shortfall. … There is a solution to this insolvency, a simple one. One that was known in the 1920s as a “bucket shop drive” and was a discussion point in Reminiscences of a Stock Operator. The idea is that, if you are a bucket shop that is systematically short crypto to your highly levered customers, an occasional 10% price drop can be very good for business: The customers all get wiped out and you keep their deposits. I don’t think that this explanation is necessary, just because standard crypto market structure explicitly buckets customer orders into matched books, but it is fun to think about. In arguably related news, my Money Stuff podcast co-host Katie Greifeld reported last week: A violent rally in shares of Advanced Micro Devices this week forced the demise of the GraniteShares 3x Short AMD exchange-traded product, which aimed to offer three times the inverse performance of the stock. The extinction event was a stunning surge of as much as 38% in AMD on Monday after the chipmaker announced a deal with OpenAI, sinking the ETP and cratering its net-asset value to zero. As such, “no redemptions payments will be made,” according to the GraniteShares website. The issuer declined to comment to Bloomberg News.
If you have 50x levered Dogecoin, a 2% move will wipe you out. If you have 3x levered short AMD, a 38% move will wipe you out. Can’t really complain about that; you got the experience you paid for. People are worried about stock buybacks | One simple model of the stock market is: - Lots of investors allocate X% of their assets to stocks. The actual X will vary from investor to investor: Institutions might have a 60/40 portfolio (60% in stocks), individuals might follow the “100 minus age” rule of thumb, etc. Also some investors will have explicit numerical asset-allocation mandates, while others will just follow a vague gut instinct of “I should have some stocks and some bonds and check periodically to make sure it’s not way out of line.” And these target allocations can shift over time: Once upon a time prudent investors didn’t put that much money in stocks, but over the years it has become more normal to invest more in stocks, and now investors are increasingly crypto-curious. Still you can sort of average all of this out and say “over the medium term the market as a whole targets roughly an X% allocation to stocks,” for some X.
- The stock market keeps shrinking: Public companies get bought by private equity, giant private companies like SpaceX and OpenAI delay going public because there is plenty of money available in private markets, and the public companies that are left tend to be big and profitable and mature and spend a lot of their profits on stock buybacks, which further reduce the supply of stocks.
- Therefore stock-market valuations have to go up: People want to keep the same percentage of their portfolio in stocks, but the supply of stocks keeps shrinking, so the value of the remaining scarce stocks has to go up.
This model does not necessarily make economic sense. From a strictly rational economic perspective, you might say “I buy stocks to get long-term exposure to economic growth, and as the public stock market shrinks and becomes more mature, it provides a smaller share of future economic growth, so I should reduce my allocation to public stocks and buy more private equity or crypto or whatever.” But those are complicated conversations, and if you have a simple heuristic like “60% stocks,” it might be sticky even as the makeup of the stock market changes. Victor Haghani and James White have a fun paper out along these lines on “The Impact of U.S. Stock Buybacks: Theory vs Practice,” noting that buybacks — which shrink the total size of the stock market — should have a levered impact on prices: The impact of buybacks on share prices arises from many investors using incomplete heuristics to determine how much of an asset they want to hold. For example, if they want to hold a constant proportional amount of some asset, and the available amount of the asset is reduced, the only way this can be accommodated is for the price of the asset to go up. … [For] fixed-weight allocators (e.g. 50% equities / 50% other assets): To absorb 3% of stock repurchases, prices must rise ~6%. … Fixed-weight asset allocators are probably the most common investor type, considering the trillions of dollars invested in target date funds, balanced funds, and in endowments, pension funds and foundations who very infrequently change their asset allocation. … We can see how buybacks on the order of 3% could plausibly push up prices by 3 – 5%. It is interesting to note that over the past ten years, the US Price-Earnings multiple has increased by 4% per annum. The 3% – 5% per annum potential impact of buybacks provides one explanation of this increase. I should say that this is not just a story about buybacks; it is more broadly a story about shrinking public stock markets and “private markets are the new public markets.” There’s a trillion dollars of stock value — about 1.5% of the value of all publicly listed US stocks — just in OpenAI, SpaceX and Stripe; if those companies went public, they would more or less offset the $1.2 trillion of estimated buybacks this year. If the big private companies stay private and the big public companies keep buying back stock, then public stocks will get more scarce, which might make them more expensive. What is the state of the art methodology for stealing quantitative trading secrets from your employer when you leave to take a new job at a different quantitative trading firm? Obviously, like, “email all of the trading algorithms to your Gmail” doesn’t work; your old employer tracks and probably blocks that. “Download them to a thumb drive,” or “print them on the office printer,” will also probably get you caught. “Pull up the trading algorithms on your work laptop over the weekend, and then use your personal iPad to take photographs of your laptop screen” is maybe somewhat better tradecraft. “Take the photographs using an iPad provided to you by your new employer” is I suppose a nice touch. This is not any sort of advice. Here is a UK judicial ruling in a lawsuit brought by G-Research against a data scientist named Pierre Allain, who worked there from 2021 until 2025. “On Friday 21 March 2025, the defendant accepted a job offer from one of G-Research’s principal competitors, Citadel Securities LLC”; on Monday, March 24, he resigned from G-Research. Over the weekend, he did this: Over a three day period between Saturday 22 and Monday 24 March 2025, the day that he resigned (and when he should have been at work), the defendant took 1,087 photographs which contain the claimants’ confidential information (as displayed on his laptop screen which was logged in remotely to the first claimant’s IT systems) using a personal iPad that had been given to him as a welcome gift by Citadel Securities just days before; and (ii) over a period of months, dating back to at least 17 July 2024, the defendant had created, and frequently amended, several text files in markdown format which contain the claimants’ confidential information. Over 400 of the photographs relate to ‘Humber’, which the claimants allege is a highly valuable trading strategy, only recently developed by the first claimant following the expenditure of significant time and resources. In the text files, the defendant repeatedly recorded that he could (amongst other things) “replicate” Humber for a competitor.
His defense is that “the photographs were part of an aide memoire, and that he acted impulsively in taking them” and never shared them with his new employer. From Tricolor to Saks, Bonds Are Now Crashing at Breakneck Speed. Bank deregulation set to unlock $2.6tn of Wall Street lending capacity. Thinking Machines Lab Co-Founder Departs for Meta. Warner Bros. Is Said to Rebuff Paramount Takeover Approach. Paramount Circling Warner Bros. Discovery After Rebuffed Approach. First Brands’ Founder Patrick James Steps Down as CEO. Jefferies Defends First Brands Deals, Says Losses Manageable. Brookfield to Acquire Remaining Oaktree Stake for $3 Billion. Silver Roars Higher as Short Squeeze Rocks the London Market. Kalshi Raises Funds at $5 Billion Valuation, to Expand Overseas. Senegal Raises $795 Million in Oversubscribed Regional Bond Sale. US Lifeline Buys Argentina Time as Investors Urge FX Overhaul. Warren Asks Hedge Fund Group If It Played Argentina Bailout Role. The Former Banker Betting on Unprofitable Business of Flood Insurance. The Real AI Risk is ‘Meh’ Technology That Takes Jobs and Annoys Us All. Amateur gold prospecting. NIL injury insurance. If you'd like to get Money Stuff in handy email form, right in your inbox, please subscribe at this link. Or you can subscribe to Money Stuff and other great Bloomberg newsletters here. Thanks! | 
