[월가아재] 30조 굴리고 있는 월가의 라이징 스타, AI에 13조 숏베팅? | 레오폴드 아셴브레너 1부

Leopold Aschenbrenner, a 24-year-old, rapidly ascended to prominence in the financial world. Just two years prior, he was an unemployed researcher at OpenAI; now, his hedge fund manages over $20 billion, rivaling renowned investors like Bill Ackman of Pershing Square. His fund’s assets grew from $25 million in its first quarter to $137 million within five quarters, boasting a remarkable 270% return for the year up to May and an overall 1000%+ cumulative return.

Despite his rapid financial success, Aschenbrenner initially gained notoriety not for his investment performance but for an essay he wrote after being laid off from OpenAI. This 165-page essay predicted the arrival of human-level AI within years and shook Silicon Valley and Washington D.C., attracting significant investment and attention.

In a surprising move, Aschenbrenner has placed substantial short bets—approximately $84 million—on AI semiconductor companies like Nvidia. This strategy is paradoxical as he’s known for predicting the rise of AI but is betting against the companies that are creating it, raising questions about his true beliefs and strategies.

Leopold Aschenbrenner’s educational background is exceptionally impressive. He attended a bilingual school in Berlin, similar to an American school for children of diplomats, and achieved remarkable academic success, including winning a prestigious science competition at age 14 and being selected for the Phi Beta Kappa honor society as a junior.

The discussion highlights how the ethical failings of Sam Bankman-Fried, particularly his focus on maximizing profit through questionable means, created a culture that enabled unethical behavior. Aschenbrenner, despite being young and not holding leadership positions at FTX, witnessed this environment firsthand and was deeply affected by it. This experience shaped his later actions and concerns regarding corporate ethics within the AI space.

Leopold Aschenbrenner joined OpenAI’s newly formed ‘Superalignment’ team in 2013, a group focused on the critical challenge of controlling future AI systems that could surpass human intelligence. The team recognized that if AI becomes more intelligent than humans, humans may not be able to evaluate its outputs or control it effectively. This led them to explore methods for aligning advanced AI with human values and intentions.

Aschenbrenner raised concerns about the security of OpenAI’s models and algorithms, warning that they were vulnerable to theft by malicious actors, including nation-states. After sharing these concerns with colleagues and management, a significant security breach occurred, leading to his termination six months later under accusations of information leakage. Aschenbrenner maintains this was retaliation for raising legitimate security issues.

Following his termination from OpenAI, Aschenbrenner published a 165-page document titled ‘Situational Awareness.ai,’ predicting significant advancements in AI, potential geopolitical conflicts driven by AI dominance, and the need for substantial investment in power infrastructure to support these developments. The document quickly gained widespread attention, being shared by influential figures like Michael Dell and Ivanka Trump, sparking discussions about AI’s impact on national security and global competition.

The transcript details a shift towards government intervention in AI development, suggesting a future where it is absorbed into a national program rather than being solely controlled by private companies like Google. This transition involves Silicon Valley companies restructuring like industrial conglomerates and Washington focusing on AI as a security issue, marking a significant change in the landscape of AI innovation.

Aschenbrenner’s fund differentiates itself not through stock selection but through superior situational awareness – understanding the trajectory of AI and its implications. This edge is reflected in their portfolio, which focuses on areas critical to AI growth like power generation and data center infrastructure, demonstrating a deep understanding of the underlying dependencies driving AI development.

Aschenbrenner views AI not as a standalone industry theme, but as a macroeconomic factor like interest rates or inflation. This perspective led him to establish a hedge fund rather than a venture capital firm, allowing for greater liquidity and the ability to react swiftly to market changes. He believes AGI's impact will be felt globally, necessitating a liquid investment vehicle.

When DeepSea, a Chinese startup, released its R1 model, it caused market shock due to its cost-effectiveness. However, Aschenbrenner’s fund strategically bought more shares during this period, demonstrating confidence in their analysis and foresight. This event highlighted the fund's ability to identify overreactions and capitalize on opportunities others missed.

The speaker clarifies that it's difficult to determine whether the large short bets are a confident bet or simply inexpensive insurance based on available documentation. He explains that interpretations range from a hedge against Middle Eastern risk (a short-term insurance play) to a more strategic move, emphasizing the lack of clarity in public filings regarding these actions.

The speaker explains that Oracle's presence on the short list isn’t necessarily due to its semiconductor business, but rather reflects an overvaluation based on AI infrastructure expectations. He posits it may be a bet against the premium attached to Oracle’s valuation rather than the underlying chip technology itself, highlighting the complexity of interpreting these trades.

The speaker details a significant shift in the portfolio's composition over time. Initially, semiconductor shorts like SMH dominated (57 million USD in Q2 2026), followed by individual shorts on Nvidia, Broadcom, DSMC, and Micron. By Q4 2026, these positions were largely eliminated, replaced with a strong bullish stance including Infosys puts and call options on Coreweave, Intel, and Equitium Blue.

The speaker highlights the significant investment in Entropic, a company initially valued at $600 million and later re-evaluated at $9.65 billion within a year. This substantial gain is not reflected in public filings (13F), indicating that much of the fund's success stems from private investments and connections within Silicon Valley, contributing to wealth disparity.