Whenever someone makes a fortune in the US stock market, the first thing onlookers always does is the same: check their portfolio report to find the next stock to buy.
The most frequently checked portfolio report recently belongs to a 24-year-old German named Leopold Aschenbrenner.
In March of this year, domestic media outlets reported on him extensively, with similar headlines; for example, "The genius fired from OpenAI wrote a 165-page paper predicting AI trends, started a hedge fund, and manages $5.5 billion..."
But labels are just labels. What truly makes this fund remarkable is that it doesn't buy Nvidia, doesn't buy OpenAI, and doesn't buy any companies that make AI models.
But labels are just labels. What makes this fund truly remarkable is that it doesn't buy Nvidia, doesn't buy OpenAI, and doesn't buy any companies that make AI models.
It only buys things that AI cannot live without: power generation, chip manufacturing, optical communication, data centers... In the words of his own paper, the bottleneck of AI is not in algorithms, but in electricity and computing power. The entire fund is betting that this statement is correct. On social media, investment bloggers call him "the son of the US stock market in the AI era," or "the AI version of Warren Buffett." This title has recently resurfaced because the extent to which his bets have been correct has become somewhat outrageous. According to data released by the copy trading platform Autopilot on May 1st, a portfolio simulating his holdings has risen by 61% in two months. Based on this, his fund size is approaching $9 billion. Where did the money come from? Mainly from two heavily invested stocks. Bloom Energy, a fuel cell company that provides off-grid power to AI data centers, has seen its stock price rise by 239% year-to-date. According to a holdings report released at the end of last year, he held $875 million worth of stock and options in this company, which has now ballooned to nearly $3 billion in market value. And Intel. The same portfolio report shows that he bought 20.2 million call options on Intel in the first quarter of 2025, when Intel's stock price was around $20, and the mainstream opinion on Wall Street was that Intel was not doing well. Last week, Intel rose to $113, a 25-year high. In less than a year, it nearly quintupled, making this young man's option returns far more dramatic than his stock returns. I can understand the excitement of onlookers. The American investment website Motley Fool published four articles in one day analyzing his holdings, and the overseas Reddit investment forum was discussing whether to copy his strategy. Everyone was trying to find the next Intel from his portfolio report. But you should know that portfolio reports generally have a 45-day delay. By the time you see what he bought, the market has already moved halfway. More importantly, even if you know his holdings in real time, you can't replicate why he consistently bets correctly. The circle is the greatest Alpha. First, what makes Leopold Aschenbrenner so remarkable is his 2004 paper on AI, which almost predicted the current direction of AI development and investment trends. The core argument can be summarized in one sentence: the training computing power of AI models is increasing by about half an order of magnitude every year; at this rate, around 2027, we will see artificial general intelligence (AGI) with capabilities approaching those of humans. However, to maintain this growth rate, the key constraints are not at the algorithm level, but rather in electricity, chip production capacity, and physical space. The power consumption of a single training cluster will jump from megawatts to gigawatts, approaching the output of a large nuclear power plant. This is the underlying logic of his entire fund. The speed of AI development is determined by physical bottlenecks, so you should invest in the bottlenecks themselves. This judgment sounds like a conclusion deduced by a smart person after doing a lot of research in their study; but in reality, I think it was the circle he formed that led him to this judgment. Before writing his paper, he spent a year working on OpenAI's Superalignment team. This team specializes in controlling AI that is smarter than humans and reports directly to the lead scientist, Ilya Sutskever. During that year, he witnessed internal training plans, actual computing power consumption, and the specific power and chip requirements of next-generation models. His assessment of "gigawatt-level power consumption" in his paper was likely based on internal roadmaps from the lab. In April 2024, he was fired from OpenAI. The trigger was an internal memo he wrote to the OpenAI board warning of inadequate security measures and the potential risk of infiltration by foreign intelligence agencies. This memo sparked tension between management and the board, and OpenAI subsequently dismissed him for "leaking information." Two months later, his paper was published. This paper is less of an independent study and more of a public version of his internal understanding within OpenAI. The AI paper solved the problem of "which direction to look at." But in investing, simply knowing the direction is far from enough. AI needs more electricity—this was a prediction many analysts were making back in 2024. What's truly valuable is timing and positioning; for example, would you dare to invest 20 million call options when Intel's stock was at $20? This confidence doesn't just stem from believing in the general trend of AI, but from knowing specifically which companies are signing large electricity purchase contracts, which data centers are expanding, and the actual scale of demand. And the investors in Leopold Aschenbrenner's Situational Awareness fund happen to be sitting in the front row of these decisions. The fund's LPs include the two founders of Stripe, a company that handles payment transactions for most of Silicon Valley's tech companies and directly senses the acceleration in infrastructure spending. Another investor is Nat Friedman, former CEO of GitHub and current head of Meta AI products, who is involved in daily computing power procurement decisions. Besides initial capital, they bring the fund a continuously updated information pipeline. Furthermore, the fund's research director is also a key figure in this chain. Carl Shulman, a veteran in AI security, previously worked at Peter Thiel's hedge fund Clarium Capital, specializing in translating AI insights into actionable trading strategies. There's another easily overlooked corner of his portfolio: crypto. His portfolio report from the end of last year showed that he established positions in CleanSpark and Bitfarms, both Bitcoin mining companies transforming their BTC mining facilities into AI computing centers. Crypto mining farms naturally possess large-scale power access and cooling systems, which are precisely the scarcest resources for AI data centers. Interestingly, he's no stranger to the crypto industry. In 2022, he worked for nine months at the FTX charitable foundation, Future Fund, founded by SBF, leaving just before FTX's collapse. Whether this experience directly influenced his judgment of mining companies is unknown to outsiders. However, it is certain that he is one of the very few people who have had in-depth contact with both the crypto industry and cutting-edge AI labs. This intersection itself represents a rare cognitive position and possible network of connections. Another detail is that his fiancée, Avital Balwit, is the chief of staff to Anthropic CEO Dario Amodei. Anthropic is Claude's parent company and OpenAI's most direct competitor. He worked at OpenAI, and his fiancée worked alongside the Anthropic CEO. He has practical experience at one of the two leading companies in the AGI race, and regular contact with the other. Last year, Fortune magazine interviewed over a dozen people in the industry who had contact with him, concluding that he was adept at "packaging ideas brewing in Silicon Valley labs into narratives." I think this is too polite. What he does is more direct: he bets on knowledge gained in his private circles in the public market. His published AI papers are declassified versions; his investment fund is the complete version. A positive feedback loop that outsiders can't enter. Looking back, Leopold Aschenbrenner's fund chose a less common structure. Most AI-related funds follow a venture capital route, investing in early-stage companies and betting on who will become the next OpenAI. He didn't take that path. According to Fortune, he explicitly rejected the VC model when establishing his fund, arguing that AGI's influence was too great, and that investment decisions could only be fully expressed in the most liquid public markets. This choice itself reveals a consensus within his circle: the biggest investment opportunities in the AI era may lie in established companies that already possess physical infrastructure. This could be a fuel cell company with readily available power access, a chip giant with wafer foundry production lines, or a Bitcoin mining company with mining farms and cooling systems. These companies have been listed for years and have good liquidity, but most analysts are still using old valuation frameworks to price them, and haven't seriously incorporated the variable of "essential AI infrastructure needs" into their models. This is where his arbitrage opportunity lies. Those in the industry already know the pace and scale of AI infrastructure expansion, but the public market is still pricing it using old logic. The price difference is the source of profit. This information advantage has another characteristic: it is self-reinforcing. The better the fund's returns, the more people at the core of the industry are willing to become LPs. The more LPs there are, the more concentrated the information the fund can access from the decision-making level. The more concentrated the information, the higher the accuracy of the bets. This is a positive feedback loop, and for outsiders, the barrier to entry will only get higher and higher. Of course, this loop also has its vulnerabilities. Highly concentrated holdings coupled with significant leverage mean the entire fund is extremely reliant on a single narrative. As long as the premise of "continuous expansion of AI infrastructure" holds true, everything goes smoothly. However, if the pace of AI development slows down, or if the energy bottleneck is circumvented by some technological breakthrough, the pullback of concentrated positions will be much faster than the speed of position building. He's betting not only on the direction, but also on the timing. Once the timing is off, the consensus within the circle may become a collective blind spot. Returning to the initial question... Everyone is studying his holdings, trying to replicate his operations. But behind the stock market guru's phenomenal returns lie structural conditions. His papers are public, his portfolio reports are public, and his investment logic is clearly explained in podcasts and interviews. But even if you fully understand every single one of his judgments, you cannot replicate the position he was in when he made those judgments. Positions can be traced back, returns are enviable, but the source of knowledge cannot be shared. This is perhaps the most expensive asymmetry of our time.
Alex
Miyuki
Weiliang
Anais
Weatherly