Over the past two years, the simplest and most profitable long strategy was buying Nvidia, but this strategy is losing its edge. When everyone knows H100s are in short supply and every earnings report looks like a copy-pasted beat, the Alpha vanishes.
True smart money has begun to look past software layers and marketing narratives to re-examine the physical foundations behind AI operations. This year, two individuals with vastly different styles have become the most watched new indicators in the AI investment field.
One is an anonymous trader hidden behind a female anime avatar on X. He claims to have turned down an offer from Nvidia, published papers in Nature, and achieved a staggering 45x return this year by dissecting the lowest-level components of the supply chain. No one knows his true identity; they only know him as Serenity.
The other is a 24-year-old OpenAI "exile" who transformed from a frustrated researcher into the founder of a hedge fund managing billions. He bets on the re-pricing of energy, computing infrastructure, and storage based on physical constraints. His name is Leopold Aschenbrenner, an outlier among the Silicon Valley elite.
One searches for "chokepoints" at the micro level, while the other bets on the restructuring of "physical bottlenecks" at the macro level. Their rise is not just a collision of two investment strategies, but a clarion call for the revaluation of underlying assets in the AI era.
Serenity: Mining Hidden Dark Horses with the "Shiso Leaf" Theory
If you follow the US stock community on X, it's almost impossible to miss an account named Serenity (@aleabitoreddit). With an anime avatar and frequent posts, his content mostly focuses on semiconductor materials, optical module substrates, and edge computing boards, rarely discussing popular AI applications.
No one knows his true identity. He claims a background in programming and academia, being a Nature author and a member of the RISC-V Foundation. He even claims to have turned down an invitation to lead an AI team at Nvidia in 2018 when the stock was only $6.
Serenity's fame began in early 2022 on the famous Reddit retail forum r/wallstreetbets (WSB). At the time, AXTI, a producer of indium phosphide substrates, was ignored by the market. He posted a deep research thread under the name "AleaBito," identifying it as the material base for AI optical modules. Subsequently, this obscure micro-cap stock soared from $12 to $70, a nearly 6x increase. His precise prediction led to a ban for "inducing speculation," so he moved to X last July. He has since grown into an "AI supply chain detective" with over 400,000 followers, becoming a leader for retail AI investors.
More than the gains themselves, Serenity's research method has left a deep impression. He condenses his investment philosophy into his self-created "Shiso Leaf Theory."
He uses a top Tokyo sushi restaurant as a metaphor. The ingredient diners crave most is undoubtedly the fatty tuna (otoro). However, the presentation of the entire dish depends entirely on shiso leaves supplied by specific small farms on the Izu Peninsula: they remove fishiness and provide decoration. If these farms stop supplying due to weather or logistics, even the finest tuna cannot be served, and the high-end restaurant must close.
Simply put, the tuna is the most expensive, but the shiso leaf is indispensable.
In the AI supply chain, "shiso leaves" are those invisible manufacturers with tiny market caps and thin liquidity that hold absolute technical monopolies in specific manufacturing segments.
Rather than stacking conventional financial data, Serenity's methodology involves diving deep into the bottom of the industry chain: studying materials science papers, mastering physical laws, mapping supply chains, and even running research drafts through multiple AIs for adversarial testing to lock in every "irreplaceable" chokepoint.
Over the past two years, Serenity has focused on Co-Packaged Optics (CPO). He believes that as AI clusters scale, traditional copper connections and pluggable optical modules will hit a physical wall of power consumption and speed. CPO, which packages optical components and silicon chips on the same substrate, is the inevitable path for the industry.
Based on this judgment, he identified and recommended three explosive chokepoint targets: Sivers, Raspberry Pi, and Soitec.
Serenity continues to delve into the lowest levels of the supply chain, even discovering NCI, a Japanese chemical company producing precursors like semiconductor-grade high-purity phosphorus, pushing the "chokepoint" to the molecular material level.
Leopold: From 200 Million to 10 Billion, Mastering Infrastructure Arbitrage
Unlike Serenity, the hidden hunter, Leopold Aschenbrenner is a Silicon Valley prodigy standing in the spotlight with billions in capital.
His resume is a "model of elite success." He graduated first in his class from Columbia University at 19 and worked for the FTX Future Fund and OpenAI's Superalignment team. In April 2024, Leopold was fired from OpenAI for alleged information leaks.
This turn of events prompted his transition to investment. In June 2024, he published a 165-page industry manifesto, "Situational Awareness: The Next Decade." In it, Leopold boldly predicted that AGI would be achieved around 2027, with superintelligence arriving by 2030. He argued that the true bottleneck is not algorithms or models, but physical resources like power grids, land, data centers, and high-bandwidth storage.
Based on this forward-looking theory, he founded the hedge fund Situational Awareness LP. Silicon Valley giants like Nat Friedman, Daniel Gross, and the Collison brothers (founders of Stripe) contributed generously, quickly securing a $225 million seed round.
Leopold's social circle is also noteworthy. His fiancée, Avital Balwit, worked at Oxford's Future of Humanity Institute (FHI) and later joined Anthropic as Chief of Staff to CEO Dario Amodei. FTX was one of Anthropic's most important early investors. Before FTX's collapse, both Leopold and Avital were core members of the FTX Future Fund.
This network provides Leopold with a unique flow of information, cognitive perspectives, and resources—perhaps his greatest and hardest-to-replicate Alpha.
On May 18, Situational Awareness LP submitted its Q1 13F filing, showing the fund's management scale has exceeded $10 billion. The document revealed a highly concentrated long position in storage stocks and a massive $8.5 billion put option portfolio against the entire semiconductor and chip manufacturing sector.
Looking at the portfolio layout, Leopold employs an infrastructure arbitrage strategy. On one hand, he heavily buys memory hardware manufacturer SanDisk and specialized computing cloud CoreWeave, firmly positioning himself at the hard barriers of physical storage.
On the other hand, he has put billions into put options against Nvidia (NVDA), TSMC (TSM), Broadcom (AVGO), ASML, and the semiconductor ETF (SMH), effectively shorting the entire sector.
In his view, chip sector valuations have severely detached from the actual construction speed of physical infrastructure like power grids and data centers. The deployment of AI computing clusters requires stable power, sufficient land, and mature cooling systems—infrastructure that takes 3-5 years to build, far slower than chip shipping cycles. In the short term, the high growth of chip giants is unsustainable, and valuations may face a pullback, with put options capturing the downside gains.
Crypto companies are also in Leopold's portfolio; he has placed about $1 billion in long positions on Bitcoin miners, buying IREN, Core Scientific, Riot, and CleanSpark. In his eyes, Bitcoin miners are discounted alternatives to AI data centers, severely undervalued by the market.
Abandoning Software, Emphasizing Tangibles: The "Toll Booths" of AI Computing
Although Serenity and Leopold use different "toolboxes," their AI investment core is highly similar: abandon the software layer that lacks physical barriers and go heavy on hardware constrained by physical laws.
Whether it's the external CW laser sources and high-purity phosphorus in Serenity's eyes, or the substations and land in Leopold's, both reveal one point: no matter how AI innovates at the model layer, whoever controls the scarce resources of the physical world has the power to collect a "computing toll" from tech giants in the AI era.
However, no strategy is perfect. Both will face tests in different dimensions.
For Serenity, the biggest weakness is the "liquidity abyss" of micro-cap stocks. When he recommends a micro-cap with a market cap of only a few hundred million to 400,000 followers, a small influx of retail capital is enough to drive up the price. However, this "carnival" is built on low liquidity. If market liquidity tightens or the company fails technical validation, prices will plummet, leaving retail investors who entered at the top with nothing.
Additionally, while Serenity's supply chain research is technically thorough, his identity, background, and historical performance remain unverified. Investors should not blindly copy him. The "chokepoint" strategy is explosive but carries high capital expenditure, thin margins, and potential customer loss risks.
For Leopold, the biggest enemy is the "time lag" of macro gaming. The fact that physical infrastructure lags far behind computing demand is objectively true. However, capital markets often exhibit irrational sentiment and longer lag effects, which could keep chip giant valuations high for longer. Facing strong earnings and short squeezes from giants like Nvidia, his massive put options could suffer significant book losses.
To some extent, Serenity and Leopold represent the AI investment logic of the new phase. Value capture in the AI industry is moving from semiconductors themselves toward the materials, equipment, power, and land behind the chips.
As model scales and computing demands continue to grow, key links in the AI industry with scarcity, technical barriers, and supply constraints will likely receive more market attention in the future.
@PANewsCN
@PANewsLab
