On July 31, 2024, a 25-year-old man who had once been trusted to align humanity’s most dangerous creation lost 67% of other people’s money in thirty days. Leopold Aschenbrenner, the former OpenAI superalignment team member who had ignited a global conversation with his “Situational Awareness” manifesto, was now the subject of a different kind of Bloomberg story—one about margin calls, forced liquidations, and a desperate fire sale to Sequoia and Greenoaks. The AI stock guru had become a casualty of his own prophecy. But this is not a story about AI. It is a story about how we convert belief into capital, and why the machinery of that conversion is catastrophically broken.
The details are still emerging, as they always are when the veil of private finance tears. We know the fund was named, not surprisingly, after his viral essay. We know it held a mix of public AI stocks and private AI company shares—possibly including stakes in companies like OpenAI or Anthropic, acquired through special purpose vehicles. We know the fund used leverage, borrowed from a prime broker, and that when public AI stocks wobbled in the summer of 2024, the collateral deflated, the margin calls came, and the fund could not meet them with enough speed. The forced liquidation ran through Citadel, a market maker often present at the scene of a violent unwind. Aschenbrenner then reportedly reached out to Sequoia and Greenoaks to sell private holdings at a discount, seeking to raise rescue capital. The high-water mark had become a cliff.
Let me be clear about why I’m writing this. I’m a protocol product manager in Nairobi, not a Wall Street guy. I spent 150 hours in 2017 manually tracing the Ethereum smart contract code behind The DAO hack, and I’ve seen more than a few spectacular collapses. From Mt. Gox to Celsius to the endless parade of DeFi exploits, the pattern is always the same: someone with a convincing story and a lever becomes the center of gravity, and then the orbit decays. Aschenbrenner’s story is simply the AI version of a tale as old as markets. But it carries an extra layer of irony because the prophet was preaching about existential risk. He believed AGI was near enough to require a global pause. Yet he built a portfolio that could only profit if the same AI companies succeeded beyond all expectations. That tension, I think, is the real story.
The Narrative-Leverage Pipeline
In crypto, we call it “narrative farming.” You cultivate attention, then convert it into total value locked. A strong memecoin or a hyped L2 can pull billions in deposits without a working product. In traditional finance, they have a fancier word: “thought leadership.” But the mechanics are identical. Aschenbrenner’s essay was his yield farm. It attracted limited partners, not liquidity providers, but the principle remains the same. He didn’t have a track record. He had a narrative—a highly technical, intellectually seductive narrative. And the market, hungry for AI exposure, handed him capital based on that narrative alone.
The problem with narratives is that they trade on faith, not risk-adjusted return. You cannot mark-to-market a belief. You can only lever it. And when you lever a belief, you expose it to the unforgiving mathematics of margin. Let’s do some arithmetic that has been missing from the coverage. A 67% drop in net asset value with a portfolio of mostly long positions means either the underlying market crashed harder than any bear case, or the fund was running material leverage. Suppose the fund was at 3x gross exposure. A 67% NAV decline implies the portfolio’s market value fell by about 22% before amplification. That is a significant drawdown for a one-month period, but not insane for a concentrated book of AI names in a volatile July. Now suppose the fund also had 40% of its assets in private shares that were not marked down in real time—still sitting at last-round valuations. The public book would have to cover the entire margin deterioration. The effective leverage on the liquid portion could easily have been 5x or 8x. This is the hidden leverage we saw in Archegos, where total return swaps allowed positions to build without disclosure. It is the same hidden leverage that killed Terra, where the anchor protocol borrowed against an asset that could not be sold in a panic.
Aschenbrenner, presumably, is not stupid. He made it to the superalignment team, which is not an easy place to land. But intelligence does not inoculate you against narrative self-anesthesia. When you have publicly predicted that superintelligence is coming in a few years, and you feel the pressure to be right, and you raise a fund to prove it, the incentive is to double down, not to diversify. Leverage is the mirror of conviction. It flatters you until it kills you.
The Liquidity Trap
Private equity is the silent killer of leveraged bets. Let me explain why. In the decentralized world, we obsess over something called “unstoppable liquidity”—the ability to exit at any time, on-chain, without asking permission. TradFi fundamentally lacks this. A private share in OpenAI has no live oracle. Its price is whatever the last funding round says, updated every few months. That is a mark-to-model, not a mark-to-market. It is a memory of price, not a price. When a lender issues a margin call, they do not care about the memory. They want cash, or liquid public securities, within hours. The fund had both liquid and illiquid assets. But the illiquid ones were arguably the “real” AI exposure—the deeper venture-like bets that would deliver outsized returns if the narrative held. Those shares were un-mobilizable in the moment of crisis. The fund tried to sell them to Sequoia and Greenoaks, but that process takes weeks. The margin call takes days. The mismatch is lethal.
We saw this exact pattern in DeFi during the early days of MakerDAO, when adding non-liquid collateral types created crisis instability. We saw it again in the fall of 2022, when Celsius faced an avalanche of withdrawals while holding illiquid mining assets. The lesson is not that private AI shares are bad. The lesson is that you cannot mix high-conviction, low-liquidity assets with leverage without a structural mechanism that forces price discovery before panic. In a decentralized protocol, a liquidation engine automatically sells collateral at a discount, but it is transparent, auditable, and everyone can see the auction. In Aschenbrenner’s fund, the auction was a private phone call to two prestigious VC firms. That is not a market. It is a favor. And in a forced sale, the favor goes only to the buyer.
The detail that Citadel cleared the positions is revelatory. Citadel as a market maker typically receives orders from broker-dealers executing liquidations, such as when Robinhood unwound violent meme stock movements in 2021. That means the fund’s prime broker did not wait for Aschenbrenner to gracefully unwind. They stepped in and forced the trades from his account. This is not just a loss; it is a termination event. The prime brokerage relationship, once broken, is rarely restorable. A 25-year-old former researcher who wants to trade again with institutional financing will find the door closed. The market has blacklisted him not because he was wrong, but because he was wrong with borrowed money.
The Oracle’s Paradox
The most philosophically interesting dimension of this collapse is the direct contradiction between Aschenbrenner’s public safety stance and his private financial bets. In his essay, he argued that AI capabilities would soon pose catastrophic risks, that governments groped in the dark, that we needed a Manhattan Project for AI safety. The essay was read by many as a warning, a plea for humility. Yet the fund he created is a high-leverage instrument designed to profit from AI accelerating without interruption. If he truly believed that AI could become an existential threat, why would he make a concentrated, leveraged bet on its success? One could argue he was hedging against his own fears—if AI succeeds, he would profit; if not, he would lose but the world would be saved. But that is a very generous reading of a structure that, in practice, resembles a classic insider-style belief monetization. More likely, the essay was the marketing layer, and the fund was the extraction layer.
Analyse the timing. The essay goes viral in June 2024. The fund launches shortly thereafter. The first major drawdown hits in July. The entire life cycle—from oracle to zero—was less than one quarter. In the hedge fund world, that is not a career, it is a flashback. How did he raise assets so quickly? Because in the AI industry, charisma and perceived access are currency. Investors didn’t need a track record; they needed a door into privileged deal flow. Aschenbrenner’s personal relationship with OpenAI gave him an information advantage. He could call former colleagues, sense what the labs were planning. He could see the supply chain, the compute budgets, the talent flows. This is the modern equivalent of an “expert network” but with the trust level of a family dinner. The LP who invested was not buying a strategy; she was buying proximity. And proximity is the most overpriced asset in any technology bubble.
But the deeper irony remains. The safety researcher turned speculator is not just a story of one man’s hypocrisy. It is a structural failure of the way we assign moral authority. In crypto, we have seen how “Chad with a whitepaper” becomes a community leader, and how that leader can rug pull. The Web3 community eventually learned to distrust fame. The AI community has not yet learned that lesson. Aschenbrenner’s collapse is a vivid, expensive illustration that having a high IQ and a big intellectual contribution does not mean you understand risk, nor liquidity, nor the psychology of marks. The technical reasoning that lets you analyze recursive self-improvement does not transfer to the emotional calculus of leveraged positions.
The Vicious Cycle of Collateral
The fire sale to Sequoia and Greenoaks deserves a paragraph. Those two firms are early backers of some of the most valuable AI companies on earth. They have a bid on exactly the kind of private shares that would have been in this fund’s portfolio. In the forced sale scenario, the fund is not a seller with negotiating power; it is a distressed asset looking for an exit. The buyer knows the fund has a margin call looming. They know the seller cannot wait. The discount applied can be as deep as 30% to 50% of the last round’s book value. That means the LP who invested in AI through Aschenbrenner will likely receive a fraction of the perceived value of their exposure. The private market, which is supposed to hold the “liquid gold” of tech innovation, has become a museum of futures that cannot be sold without burning the building down.
This is where I want to draw a direct line to the decentralized architecture that I spend my waking hours building. Imagine if that fund’s positions had been represented by tokens on a public blockchain, with continuous price oracles, and smart contracts that enforce collateralization ratios in real time. A downside move would trigger an automated auction: the collateral would be sold at a deterministic threshold, the proceeds would go to the lender, and everyone would see the process transparently. The market would absorb the supply because it is open. The price may still fall, but the fall would be orderly, auditable, and non-negotiable. Aschenbrenner might still lose his money, but he would not lose it to a backroom deal. More importantly, the investors would know the exact live value of their stake at any moment, rather than reading about it in a Bloomberg article months later.
Some will say that blockchain is not relevant to a hedge fund’s problem. But that is exactly the wrong response. The problem is not specific to openAI or to Sequoia. The problem is a financial system that relies on centralized trust in opaque entities. When a fund uses a prime broker, the broker has absolute power over the borrower’s fate. When a private market uses paper contracts, the holders have absolute illiquidity until a bigger name allows them to exit. The whole structure is a permissioned, delegated network. Bitcoin was born to solve precisely this—to create settlement without a counterparty. Ethereum extended that to programmatic trust. The crisis of Aschenbrenner’s fund is the crisis of trusting a single charismatic individual with your money, and expecting the entity to act with theoretical competence. It is the same crisis we saw in the crypto bear market of 2022 with the collapse of FTX.
Let me be explicit about the parallel. Sam Bankman-Fried was also a young, brilliant, influential figure who ran a leveraged company on the back of a narrative—in that case “effective altruism” and “quantitative genius.” He, too, attracted sovereign wealth funds and celebrities. He, too, experienced a liquidity squeeze and tried to find a private exit before the government stepped in. Aschenbrenner is not a fraud, as far as we know. But the structural conditions are identical: high leverage, a charismatic leader, an illiquid asset base, and a narrative that bypasses critical scrutiny. The financial, institutional, sociological machinery is the same. The only difference is the domain—AI versus crypto. That is why I think this event should be read as a warning by everyone in the broader technology finance world, not just AI investors.
The bear market didn’t create the leverage. The bear market didn’t create the opaqueness. The bear market didn’t create the imbalance between private valuations and public collateral. Those are permanent features of a centralized finance system that has not updated its architecture since the 1980s. What the bear market—or in this case, the mid-summer drawdown—did was expose those features with brutal clarity. The victim was not AI. The victim was the illusion that a smart person with a strong narrative can act as a sufficient risk engine.
The 67% Math and the High-Water Mountain
Let me spend a moment on the mathematics of recovery, because it is the most underappreciated detail in this story. A 67% drawdown means that the fund must gain 203% from the bottom just to return to the previous high-water mark. That is not a comeback; it is a miracle. In a leveraged portfolio, the difficulty is even greater because the same leverage that amplified the loss now makes the remaining capital more fragile. To make 203% on a concentrated AI book, you would need to nail a massive bottom, ride every wave perfectly, and still face the unavoidable drag of financing costs. The probability is close to zero. Therefore, even if Aschenbrenner found rescue capital, the existing LPs are effectively wiped out. They will never see a positive carry from this fund. The only possible beneficiaries are the rescuers who receive heavily diluted terms or preferred shares that convert in the restructuring.
The fact that he was reportedly seeking “new capital injection” after the liquidation is telling. It suggests he believed the underlying assets were worth saving. Perhaps he was right—after all, the private AI shares may still appreciate dramatically in the long run. But the fund’s structure violated the first rule of asset allocation: match the liquidity of liabilities with the liquidity of assets. The liabilities were short-term margin loans. The assets were long-duration venture equity. This is not a mistake; it is a destiny. The same destiny awaits any fund that decides to “HODL” its way through a liquidity crisis, hoping the market will rescue them. In crypto, we see this with traders who refuse to sell into a downturn because they are “late cycle” holders. The outcomes are usually final.
One of the most interesting things about the 67% number is what it implies about the portfolio’s actual public market exposure. If the fund had $100 million of NAV, and a 3x gross exposure, the total portfolio was $300 million. If the public stocks lost 25% in a month—a deep but plausible AI correction—the public book loses $37.5 million after the first dollar of debt is still serviced. The NAV drops to $62.5 million. That is a 37.5% drawdown. To get to 67%, you need additional losses from forced selling discounts, or a higher effective leverage. The more private shares you hold, the less marginable collateral you have, and the more you must over-leverage the public part. That is why my rough estimate of 5-8x on the liquid book is not outlandish. In a stressed scenario, a 10x leverage on a 20% drop is a 200% loss of equity, but the fund might only lose 67% due to the offset of private book values still being marked high. The key point is: the risk management was impossible from day one.
What This Teaches About AI Capital Flows
The industry reaction has been predictable. Some say this proves AI is a bubble. Others say it proves that “crypto” and “AI” both attract charlatans. Both are wrong. This event is a small ripple in a huge ocean of AI investment. Microsoft, Meta, Google, and Amazon are still spending tens of billions of dollars on compute, not because a hedge fund believed in it, but because their business models require it for search rankings, cloud margins, and autonomous systems. The real AI infrastructure buildout is funded by corporate cash flows and strategic fears. It is not levered retail money. A $500 million hedge fund’s collapse, while painful for its LPs, does not alter the fundamental demand for GPUs or the pace of model development. What it does alter is the marginal pricing of the “AI narrative asset class”—the shares of private AI companies, the convertible notes, the secondary market employee equities.
That is the real transmission channel. When a forced seller dumps a block of OpenAI-related shares onto a thin secondary market, it establishes a low reference transaction. Subsequent private financings may still be priced at a high round, but the secondary signal quietly influences the next negotiation. Employees of AI companies who hold options and want to liquidate them will see a buyer’s market. The effect is not that these companies are suddenly worth less—they are not. The effect is that the round valuations are no longer the only sounding board. The primary market was already starting to separate from the secondary market; this event accelerates that divide. In other words, the first victim of a leveraged AI fund blowup is not the AI industry; it is the price discovery machinery of private markets.
The second-order effect is on investors’ trust in “AI thought leaders.” Aschenbrenner is not the last AI luminary to start a fund. There are dozens of researchers, authors, and conference keynote speakers who possess deep technical insight but have zero institutional experience. The LP community will become more careful. They will request audited track records, liquidity buffers, and independent risk managers. This is healthy. It raises the bar for entry, filtering out those who are only there for social influence. In the long run, that is a good thing for credible researchers who also want to responsibly allocate capital.
But there is a darker possibility. Aschenbrenner may use this failure to enhance his martyr complex. He may make a public statement that the “establishment” did not understand AI’s exponential potential, that the markets were too rigid, that the crash was an accident of traditional infrastructure—not a flaw in his vision. I expect, based on what I know of human psychology and of tech celebrities, that he will pivot to an even more extreme narrative. He will say that his superintelligence forecast was correct and that the failure was merely in the packaging. This would be a mistake. It would further erode the credibility of the AI safety movement, because the public will associate safety advocacy with speculative behavior. The more insidious consequence is that the AI community—which is already fractionated between “accelerationists” and “doomers”—may become even more suspicious of anyone who bridges both worlds. The bridge person has just fallen into the ravine.
A Tale of Two Markets
Let me return to the contrast between centralized and decentralized markets one more time, but with a new nuance. Aschenbrenner’s failure is not a failure of “the market” in an abstract sense. It is a failure of an exclusive market, one where liquidity is a privilege granted by a few intermediaries. In a truly decentralized market, anyone with a secure wallet can trade any tokenized asset 24/7, and prices are determined by the sum of all participants, not by a phone call. We don’t yet have a decentralized venue for private AI company shares, but the technology exists to create one—using tokenization of SPVs, or even simple smart contracts that represent equity in a SAFE. The problem has always been regulatory; the SEC doesn’t love the idea. But events like this should make us ask: would the 67% drawdown have been less likely if the fund’s holdings had been publicly visible in real time on-chain? Yes. Because margin calls wouldn’t need to be secret; they would be automated. Not only that, but the forced liquidation would have been visible to the entire market, and arbitrageurs would swoop in to absorb the supply, not just a select group at Sequoia.
The deeper point is that openness is itself a risk management tool. When a fund’s positions are public, investors can avoid the “narrative opacity” trap. They can see the actual concentration, the leverage ratio, the liquidity of each asset. If they still choose to invest, they are making an informed bet. In Aschenbrenner’s case, investors had almost no real-time visibility. They had to trust the guru. In the crypto world, we learned after FTX that “trusting the brand” is a suicide pact. We now demand proof-of-reserves, transparent settlement, and on-chain audits. The AI fund industry has not undergone that enlightenment. The result is a blowup that was entirely predictable to anyone who did a simple liquidity stress test.
I am not saying blockchain is a panacea. I have spent enough time staring at vulnerable smart contracts to know that code can be hacked, oracles can be manipulated, and governance can be captured. But the core property of decentralized systems—shared, verifiable state—provides a baseline of scrutiny that opaque TradFi vehicles lack. The EVM does not care how many followers you have on X. It executes the contract. That neutrality is what makes it an effective check on the “prophet” phenomenon.
The tragedy of Aschenbrenner’s fall is not that he was delusional. It is that he was a brilliant, sincere convert to a form of financial alchemy that rewards confidence over rigor. He took an important insight about AI’s potential, and then stretched it into a leverage ratio that could not bear its own weight. He is, in a sense, a martyr for the very thing I believe in: the necessity of persistent, public verification. The markets need better infrastructure. Not more prophets.
Contrarian: The Bear Market Didn’t Do This
Here is the contrarian angle that everyone in the AI bubble commentary is missing. The bear market didn’t kill Aschenbrenner’s fund. The bull market’s narrative did. The fund was born without a track record, scaled on the back of an essay, and leveraged into a market that was already pricing in perfection. The 2024 AI summer was a period of extreme exuberance, where the difference between “fundamentals” and “pronouncements” became dangerously thin. In such times, the most fragile structures are not the ones that know they are fragile; they are the ones that think their narrative is strong enough to withstand gravity. The AI accelerator ecosystem believed that the story of AGI was so powerful that it could serve as collateral. It could not. Collateralization requires a robust secondary auction mechanism, not a meme.
Some will say that Aschenbrenner was an early victim of the inevitable AI “crypto winter” that is coming. But I think the opposite. The technology progress continues apace. The demand for compute is not diminishing. The generative AI revenue is growing… more slowly than the hype, but still growing. The real issue is not the AI, but the financial vessels in which we put the narrative. Those vessels will keep breaking until we redesign them. The volatility is not a sign of a false prophecy; it is the price of an unregulated belief system. And we should take that seriously because the same machinery will be used in the next cycle, whether it’s quantum computing, brain-computer interfaces, or decentralized autonomous countries. The lesson is timeless: if you let anyone be the price oracle for their own dreams, you will eventually get a rude awakening.
So I reject the interpretation that this proves AI is overhyped. I also reject the interpretation that this is merely a one-off greedy mistake. This is a structural failure of the financial architecture to handle deeply uncertain, high-conviction, low-liquidity assets. That architecture is not immutable. It can be redesigned. We have the tools to build something better, but only if we stop worshiping oracles and start building open, verifiable mechanisms.
Takeaway: On the Other Side of the High-Water Mark
We are entering a new cycle of “founder funds”—from AI prophets to crypto degens. The ones with the best narratives will attract the most capital, and the most leverage. The next blowup is already being planned. The only question is whether we build the infrastructure to make the fallout visible in real time, or wait for the next Bloomberg story. I know which side I’m on. I’m Chris Thompson, a protocol PM in Nairobi, and I believe in open-source trust. Because if we can’t audit the risk, we’re just praying. We don’t need another prophet. We need protocols. The bear market didn’t teach us to endure; it taught us to verify. The 67% fall is not an ending; it’s a prompt. The next oracle may be more charismatic, but the math will be the same. Let’s build the machinery that forces the math to be honest, before the story repeats itself with even larger capital. About Me? I’m still curious, still skeptical, and still building.