Author: Jae, PANews

For the past two years, the simplest and most profitable initial bullish strategy was to buy Nvidia, but this strategy is failing. When everyone knows that H100 is in short supply and every financial report exceeds expectations like a copy-paste, Alpha disappears.

Real smart money is beginning to penetrate the software layer and PowerPoint narratives, re-examining the physical foundations behind AI operations. This year, two very different individuals have become the most watched new trendsetters in the AI investment field.

An anonymous trader hiding behind female anime avatars on the X platform claims to have rejected an Nvidia offer, published a paper in Nature, and made an astonishing 45-fold profit this year by dismantling the lowest-level components in the supply chain. No one knows his real identity; they only know him as Serenity.

Another 24-year-old "outcast" from OpenAI has made a remarkable transformation from a disillusioned researcher to a founder whose company now manages a large-scale enterprise.

A multi-billion dollar hedge fund is betting on the repricing of energy, computing infrastructure, and storage with physical constraints. His name is Leopold Aschenbrenner, an outlier among Silicon Valley's elite.

One focuses on identifying "bottleneck" technologies at the micro level, while the other bets on restructuring "physical bottlenecks" at the macro level. Their rise to prominence is not only a clash of two investment strategies, but also a clarion call for the revaluation of underlying assets in the AI era.

Serenity: The Perilla Leaf Theory Unearths Hidden Hidden Horses

If you've been following the US stock community on X for a while, you've almost certainly come across an account called Serenity (@aleabitoreddit). It has an anime-style profile picture, posts frequently, and the information is mostly research on semiconductor materials, optical module substrates, and edge computing boards, with very little discussion of popular AI applications.

No one knows his true identity. He claims to have a background in programming and academia, to be an author of a Nature paper, a member of the RISC-V Foundation, and to have rejected an offer from Nvidia in 2018 to head its AI team, when Nvidia's stock price was only $6.

Serenity's rise to fame began in early 2022 on r/wallstreetbets (WSB), a well-known retail investor forum on Reddit. At the time, AXTI, a manufacturer of edge indium phosphide substrates, was largely ignored. He posted an in-depth research thread under the account "AleaBito," directly pointing to it as a material base for AI optical modules. Subsequently, this obscure micro-cap stock surged from $12 to $70, a nearly six-fold increase. His accurate prediction, however, led to his ban from the platform for "inducing hype." Last July, he moved to the X platform and quickly rose to become an "AI supply chain detective" with over 400,000 followers, becoming a rising star in the AI investment circle on X. Some even created investment research dashboards based on his tweets.

More than the price increase itself, Serenity's research methods left a deep impression on the market. He condensed his investment philosophy into his self-created "Perilla Leaf Theory".

He used Tokyo's top sushi restaurants as an analogy, noting that the most sought-after ingredient is undoubtedly tuna belly. However, the presentation of the entire sushi plate depends entirely on shiso leaves supplied by specific small farms on the Izu Peninsula: they are indispensable for removing fishy odors and for decoration. If these farms were to cut off supplies due to weather or logistical reasons, even the finest tuna would be unavailable, forcing the upscale sushi restaurant to close down.

Simply put, the most expensive ingredient is tuna, but the indispensable ingredient is perilla leaves.

In the context of the AI supply chain, perilla leaves represent those hidden manufacturers with small market capitalization and low liquidity, yet possessing absolute technological monopolies in key manufacturing processes.

Compared to the conventional approach of simply piling up financial data, Serenity's research methodology involves delving deep into the very bottom of the industry chain: studying materials science papers, mastering physical laws, mapping the supply chain, and even inputting research drafts into multiple AIs for adversarial testing, all in order to identify every "irreplaceable" chokepoint.

Over the past two years, Serenity has focused its main efforts on co-packaged optoelectronic (CPO) technology. He believes that as AI clusters expand in scale, traditional copper wire connections and pluggable optical modules will hit physical barriers in terms of power consumption and speed, and CPO, which packages optical devices and silicon chips on the same substrate, will be the inevitable path for the industry.

Based on this judgment, he successively discovered and recommended three promising companies that could address bottlenecks in the market: Sivers, Raspberry Pi, and Soitec.

Serenity continues to delve deeper into the bottom of the supply chain, and he has also discovered NCI, a Japanese chemical company that produces semiconductor-grade high-purity phosphorus and other precursor materials, pushing the "bottleneck" to the molecular-level material level.

Leopold: From 200 million to 10 billion, focusing on infrastructure arbitrage strategies

Unlike Serenity, a hunter hidden deep within the internet, Leopold Aschenbrenner is a Silicon Valley genius standing in the spotlight with billions of dollars in capital.

His resume is considered an "elite model." He graduated first in his class from Columbia University at the age of 19 and subsequently worked at FTX Future Fund and the OpenAI Superalignment team. However, in April 2024, Leopold was fired by OpenAI due to a suspected information leak.

This turn of events prompted his transition to the investment world. 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, and superintelligence would arrive by 2030. The real bottleneck to achieving all this, he argued, lies not in algorithms and models, but in physical resources such as power grids, land, data centers, and high-bandwidth storage.

Based on this highly forward-thinking theory, he founded the hedge fund Situational Awareness LP. Silicon Valley heavyweights such as Nat Friedman, Daniel Gross, and the Collison brothers, founders of Stripe, generously contributed, and $225 million in seed funding was quickly secured.

Leopold's social circle is also noteworthy. His fiancée, Avital Balwit, previously worked at the Future of Humanity Institute (FHI) at Oxford University, where she conducted long-term research on transformative artificial intelligence. She later joined Anthropic, serving 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 its charitable organization, the FTX Future Fund.

This network of relationships provided Leopold with a unique information flow, cognitive perspective, and resources for its subsequent research framework and investment strategy, which may also be its biggest and most difficult-to-replicate Alpha.

On May 18, Situational Awareness LP filed its Q1 13F holdings report, revealing that Leopold's fund management assets have exceeded $10 billion. This document disclosed for the first time to the market its highly concentrated long positions in memory stocks, as well as a massive put option portfolio totaling nearly $8.5 billion across the entire semiconductor and chip manufacturing sector.

In terms of portfolio allocation, Leopold adopted an infrastructure arbitrage strategy. On the one hand, he made large-scale purchases of memory hardware manufacturer SanDisk and professional computing cloud provider CoreWeave, firmly establishing a strong foothold in the physical storage market.

On the other hand, he invested billions of dollars in put options on Nvidia (NVDA), TSMC (TSM), Broadcom (AVGO), ASML (ASML), and the Semiconductor ETF (SMH), effectively shorting the entire semiconductor sector.

In his view, the current valuation of the chip sector has significantly deviated from the actual construction speed of physical infrastructure such as power grids and data centers. The deployment of AI computing clusters relies on stable power, sufficient land, and mature cooling systems, and the construction cycle of these physical infrastructures can take 3-5 years, far slower than the chip shipment pace. In the short term, the high growth of chip giants is unsustainable, and valuations may face a pullback. Put options will capture the short-selling profits during the sector's decline.

Crypto companies are also part of Leopold's investment portfolio. He has heavily invested approximately $1 billion in long positions in Bitcoin mining companies, making significant purchases in companies such as IREN, Core Scientific, Riot, and CleanSpark. In his view, Bitcoin mining companies are discounted alternatives to AI computing power centers and are severely undervalued by the market.

Abandoning software and emphasizing physical infrastructure: The hidden dangers of AI computing power "toll fees".

Although Serenity and Leopold have different "toolboxes," their core AI investment strategies are highly similar: abandoning the software layer, which lacks physical barriers, and heavily investing in hardware, which is subject to physical laws .

Whether it's the external CW laser source and high-purity phosphorus in Serenity's eyes, or the substation and land in Leopold's eyes, they all reveal one thing: no matter how innovative AI is at the model level, whoever controls the scarce resources of the physical world will have the power to levy "computing power tolls" on tech giants in the AI era.

However, there is no perfect strategy in the world. Their strategies will all face challenges in different dimensions.

For Serenity, its biggest weakness lies in the "liquidity abyss" of microcap stocks. When it recommends microcap stocks with market capitalizations of only a few hundred million dollars to its 400,000 followers on X, a small influx of retail investor funds is enough to drive up the stock price. However, this "frenzy" is built on a foundation of low liquidity. Once market liquidity tightens, or the recommended companies encounter setbacks in technical validation, the prices of these microcap stocks will plummet, and retail investors who rushed in at high prices may lose everything.

Furthermore, while Serenity's supply chain research is thorough in its technical details, its identity, background, and historical performance have not been verified. Investors should not blindly replicate its strategies as "stock market gurus," as this carries significant risk. While the "bottleneck" strategy for micro-cap stocks is highly explosive, its extremely high capital expenditure, thin profit margins, and potential customer churn risk mean that this strategy is only suitable as a "high-beta catalyst" in asset allocation, supplemented by large-cap blue-chip stocks for risk hedging, and requires strict position management.

For Leopold, his biggest enemy is the "time lag" in macroeconomic game theory. The fact that physical infrastructure development lags far behind computing power demand is causally sound and an objective reality. However, capital markets often exhibit irrational sentiment and longer lag effects, which could prolong the high valuations of chip giants. When faced with unexpectedly strong earnings reports from giants like Nvidia and a short squeeze in their stock prices, his massive put options will suffer huge paper losses.

To some extent, Serenity and Leopold represent a new phase of AI investment logic. Value capture in the AI industry is shifting from semiconductors themselves to the materials, equipment, electricity, and land behind the chips.

As the scale of models and the demand for computing power continue to grow, key links in the AI industry that are scarce, have technological barriers, and have supply conditions may receive more market attention in the future.

A whale bought 2,400 ETH during a certain trading session and then placed a sell order at $2,132, targeting a profit of $117,000.

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