Sell Nvidia, Buy Power Plants: 27-Year-Old AI Investor Earns $5 Billion in One Year

Article by Sleepy.md

In February 2026, hedge fund Situational Awareness LP submitted its quarterly holdings report, revealing that as of the end of the fourth quarter of 2025, the fund's total market value of U.S. stock holdings was $5.517 billion.

Wall Street manages trillions of dollars in assets, so $5.5 billion is just a drop in the ocean. However, this fund had a management size of less than $400 million just 12 months ago, and its founder and chief investment officer is a young person born in 1999.

His name is Leopold Aschenbrenner. He's 27 years old.

In 12 months, he grew this fund from $383 million to $5.517 billion, an increase of over 14 times. During the same period, the S&P 500 saw only single-digit growth.

What's even more surprising is his holdings. If you open the quarterly holdings report, you won't find any of the AI star companies that are usually in financial news headlines. Instead, there are companies working on fuel cells, bitcoin miners who just emerged from the brink of bankruptcy, and chip giants that the entire market is abandoning.

He claims his fund is AI-focused, but this doesn't look anything like the holdings of an AI fund; it looks more like a madman's shopping list.

But this madman happens to be one of the earliest and most profound visionaries in the world to understand how AI will change the world. Before joining Wall Street, he was a researcher at OpenAI, responsible for contemplating how to ensure that AI wouldn't go out of control when it became smarter than humans; later, because he said something he shouldn't have, he was kicked out, wrote a 165-page tome, and prophesied a future that seemed absurd to most.

Later on, he went all-in with his entire net worth.

Breaking Down $5.5 Billion: What Did He Actually Buy?

To understand how much of a genius Leopold Aschenbrenner is in investing, the most straightforward way is to open his holdings report and read through it line by line.

His largest holding is Bloom Energy. With a holding market value of $876 million, it represents 15.87% of the total portfolio.

This company is in the fuel cell business. More precisely, it deals with something called "solid oxide fuel cells," which can directly convert natural gas into electricity with extremely high efficiency. The founder, KR Sridhar, was once an engineer for NASA's Mars exploration program and was named by Fortune magazine as one of the "Top 5 Future Scientists Creating the Future Today.".

An AI fund has placed its biggest bet on a power generation company.

According to Gartner's forecast, the global power consumption of AI-optimized servers will skyrocket from 93 terawatt-hours in 2025 to 432 terawatt-hours in 2030, almost five times in five years. The electricity demand of U.S. data centers will nearly triple by 2030, reaching 134.4 gigawatts. The average age of the U.S. power infrastructure has already exceeded 25 years, with many components between 40 and 70 years old, far beyond their designed lifespan.

In other words, AI requires more power than the entire grid can provide, and the grid itself is on the brink of falling apart.

In the AI era, the scarcest resource is not chips, but electricity.

Bloom Energy's fuel cell happens to bypass this bottleneck. It does not need to connect to the grid, generating power right next to the data center, 24/7. In 2025, Bloom Energy secured a contract from CoreWeave to provide fuel cells for its AI data center in Illinois.

Speaking of CoreWeave, this happens to be Leopold's second-largest holding.

He holds $7.74 billion worth of CoreWeave call options, along with $4.37 billion in common stock, totaling over $12 billion, representing 22% of the total position. CoreWeave is a GPU cloud provider that transitioned from cryptocurrency mining farms.

In 2017, Mike Intrator, Brian Venturo, and a few others came together to mine Bitcoin. In 2018, during the crypto market crash, mining became unsustainable. However, they had a bunch of GPUs on hand. In 2019, they had a bright idea: GPUs could do more than mine; they could run AI.

So the company pivoted, shifting from mining to becoming an arms dealer of AI compute power. On March 27, 2025, CoreWeave went public on Nasdaq, raising $1.5 billion at a price of $40 per share. A company that crawled out of the mining pit became a core supplier of AI infrastructure.

What caught Leopold's eye was CoreWeave's large number of GPUs and its deep ties with NVIDIA. In an era where compute power is productivity, whoever has the GPUs is king.

But what truly baffles people is his third largest position: Intel. With a holding value of $747 million, all in call options, it accounts for 13.54% of the total portfolio.

By 2025, Intel had become one of the most unwelcomed companies on Wall Street. Its stock price was halved from its 2024 peak, market share was eroded by AMD and NVIDIA, and the CEO underwent round after round of changes. Almost every analyst was proclaiming the end of Intel.

Yet, Leopold chose this exact moment to heavily invest in call options. This was an extremely aggressive move - a bet that could either skyrocket or go bust.

What was he betting on? Just two words: foundry business.

In November 2024, the U.S. Department of Commerce announced that Intel would receive up to $7.86 billion in direct funding through the Chip and Science Act. The sole purpose of this money was to transform Intel into a domestic chip foundry, competing with TSMC.

In the context of U.S.-China tech decoupling, the U.S. needed a "friendly" player to manufacture chips. Despite Intel's lagging technology, it was the only option. Leopold was not betting on Intel's technology, but on America's national resolve.

The subsequent holdings were even more intriguing. Core Scientific, with a $419 million holding; IREN, $329 million; Cipher Mining, $155 million; Riot Platforms, $78 million; Hut 8, $39.5 million.

These companies shared a common trait: they were all Bitcoin mining companies.

Why would an AI fund invest in a bunch of Bitcoin miners?

It's simple - because these Bitcoin miners have access to the cheapest electricity and largest data center spaces across the U.S.

Core Scientific boasts over 1,300 megawatts of power capacity. IREN plans to expand to 1.6 gigawatts in Oklahoma. These miners, in order to survive the intense mining competition, have long secured the cheapest power resources globally through long-term power purchase agreements.

And now, what AI data centers lack the most is precisely power and space.

In 2022, Core Scientific filed for bankruptcy due to the crypto market crash. It emerged from restructuring in January 2024, shedding about $1 billion in debt, and relisted on Nasdaq. It then signed a 12-year contract worth over $10.2 billion with CoreWeave to transform its mining facilities into AI data centers. To focus entirely on this transformation, Core Scientific even plans to sell off all its Bitcoin holdings.

IREN (formerly Iris Energy) then signed a $9.7 billion AI contract with Microsoft, receiving a $1.9 billion prepayment. Cipher Mining inked a 15-year lease agreement with Amazon. Riot Platforms closed a 10-year, $311 million deal with AMD.

Overnight, Bitcoin miners became the landlords of the AI era.

Now, let's complete the puzzle.

Bloom Energy provides power, CoreWeave offers GPU computing power, Bitcoin mining companies supply locations and cheap electricity, Intel provides onshore chip manufacturing capability in the U.S. Add to that the fourth-largest position in Lumentum ($479 million, making optical components for interconnecting AI data centers), the ninth-largest position in SanDisk ($250 million, data storage), and the eleventh-largest position in EQT Corp ($133 million, natural gas producer supplying fuel for fuel cells).

This is a complete AI infrastructure supply chain.

From power generation to transmission, chip manufacturing, GPU computing, data storage, to fiber optics interconnect. He bought into every link.

And another move he made makes this logic even clearer. By the fourth quarter of 2025, he completely liquidated his holdings in NVIDIA, Broadcom, and Vistra. These three companies happened to be the top-performing stars in the 2024 AI market.

He also shorted Infosys, one of India's largest IT outsourcing companies.

Selling off the hottest AI chip stocks, buying unwanted power plants and mines. Shorting traditional IT outsourcing because AI programming tools are making developers more efficient, compressing the demand for outsourcing.

Every transaction points to the same conclusion: the bottleneck of AI lies not in software, but in hardware; not in algorithms, but in power; not in cloud-based models, but in the physical world.

So, here's the question: How did a 27-year-old man form this set of beliefs?

From a Doctor's Son in East Germany to a Rebel at OpenAI

Leopold Aschenbrenner was born in Germany, both parents being doctors. His mother grew up in former East Germany, his father came from West Germany, and they met after the fall of the Berlin Wall. This family itself carries a mark of historical rupture—Cold War, division, reunion. Perhaps his later obsession with geopolitical competition can trace its roots back to this.

But Germany couldn't hold on to him. He later said in an interview, "I really wanted to leave Germany. If you're the most curious kid in class, wanting to learn more, teachers won't encourage you; they'll envy you and try to suppress you."

He called this phenomenon the "Tall Poppy Syndrome" – the taller you grow, the more likely you are to be cut down.

At the age of 15, he convinced his parents to let him fly alone to the United States and enroll at Columbia University.

Attending university at 15 is unheard of anywhere. But Leopold's performance at Columbia turned "unheard of" into "legendary." He double-majored in Economics and Mathematics-Statistics, won every award available, such as the Albert Asher Green Memorial Prize, the Romine Economics Prize, and became a Junior Phi Beta Kappa Honor Society member.

At 17, he wrote a paper on economic growth and existential risk. After reading it, renowned economist Tyler Cowen said, "When I read it, I couldn't believe a 17-year-old wrote it. If this were a Ph.D. dissertation from MIT, I'd be impressed too."

At 19, he graduated from Columbia University as Valedictorian, the highest honor for an undergraduate. In 2021, amidst a global pandemic, a 19-year-old German stood at Columbia's graduation ceremony representing all graduates.

Tyler Cowen gave him one advice: don't pursue a Ph.D. in Economics.

Cowen felt the academic field of Economics had become somewhat "decadent" and encouraged him to aim for bigger things. Cowen also introduced him to the Silicon Valley "weird Twitter" culture, a group fascinated with AI, effective altruism, and the long-term future of humanity.

After graduation, Leopold first joined the Forethought Foundation, researching long-term economic growth and existential risk. He then entered the FTX Future Fund founded by SBF, working alongside key figures in the effective altruism movement such as Nick Beckstead and William MacAskill. His title was "Economist affiliated with the University of Oxford's Global Priorities Institute."

This experience was crucial. It meant that before entering the AI industry, Aschenbrenner had spent years systematically pondering a question: What kind of event could fundamentally alter the course of human civilization.

He then joined OpenAI.

The specific timing is unclear, but he joined a special team within OpenAI called the "Superalignment" team. This team was established on July 5, 2023, co-led by OpenAI co-founder Ilya Sutskever and Alignment team lead Jan Leike. The goal was to solve the alignment problem for superintelligent AI within four years, ensuring that an AI much smarter than humans would still obey human commands.

OpenAI had promised to dedicate 20% of its computing power to this team. However, there was a stark contrast between the promise and reality.

Leopold witnessed some unsettling things within OpenAI. He submitted a security memo to the board, warning that the company's security measures were "woefully inadequate" to prevent foreign governments from stealing crucial algorithmic secrets. The company's response took him by surprise. The HR department approached him, saying his concerns about espionage were "racist" and "unconstructive." The company's lawyers questioned his views on AGI and his team loyalty.

In April 2024, OpenAI dismissed him for "leaking confidential information."

The so-called "leak" was his sharing of a brainstorming document on AGI security measures with three external researchers. Leopold argued that the document contained no sensitive information and that sharing such files internally for feedback was a common practice.

A month later, Ilya Sutskever left OpenAI. Three days after that, Jan Leike followed suit. The Superalignment team was disbanded, and OpenAI's promised 20% computing power allocation was never fulfilled.

A team researching "how to control superintelligent AI" was dissolved by the very company creating superintelligent AI.

The irony of the situation cannot be overstated. But for Leopold, his dismissal turned into a form of liberation. No longer employed by anyone, he no longer had to carefully phrase his words in internal memos. He could now speak his mind freely to the world.

On June 4, 2024, he published a 165-page article on a website called situational-awareness.ai. The article was titled "Situational Awareness: The Decade Ahead."

Prophecy of 165 Pages

To understand Leopold's investment logic, you must first read this tome. Because that $55 billion holding is the financial translation of these 165 pages of text.

The core argument of the tome can be summarized in one sentence: AGI (Artificial General Intelligence) is highly likely to be achieved by 2027.

This assessment sounded like madness in June 2024. But Leopold's argument is very straightforward: think in orders of magnitude.

From GPT-2 to GPT-4, AI's capabilities underwent a qualitative leap, transitioning from a preschooler to an intelligent high school student. Behind this leap is approximately a 100,000-fold (5 orders of magnitude) increase in effective computation. This growth stems from the stacking of physical computing power, improvement in algorithm efficiency, and the unleashing of capabilities through model "unshackling."

His prediction is that by 2027, a similar scale of growth will occur again. In terms of physical computing power, the computational resources used to train cutting-edge models will be 100 times greater than GPT-4. In algorithm efficiency, there will be an annual improvement of approximately 0.5 orders of magnitude, accumulating to about 100 times over four years. Coupled with the gains from "unshackling," allowing AI to transform from a chatbot into an entity that can use tools and act autonomously represents another order of magnitude leap.

The compounding of three 100-fold increases results in another 100,000-fold leap, another qualitative leap. Moving from surpassing high school students to transcending humanity.

What truly makes this article compelling is the series of consequences he deduces from this prediction.

The first consequence: trillion-dollar-scale computing clusters.

He writes that over the past year, Silicon Valley's discourse has shifted from a $100 billion computing cluster to a $1 trillion cluster, and most recently to a trillion-dollar cluster. Every six months, another zero is added to the board's plans. By the end of this decade, there will be hundreds of millions of GPUs deployed.

This prediction sounded exaggerated in June 2024. However, in January 2025, the Trump administration announced the Stargate project, jointly funded by SoftBank, OpenAI, Oracle, and MGX, planning to invest $500 billion over four years in constructing AI infrastructure in the United States. The first tranche of funds to be immediately deployed is $100 billion. Construction has already begun in Texas.

In his manifesto, he wrote about the "trillion-dollar cluster," which became the White House's official plan just six months later.

The second consequence: a power crisis.

How much electricity do hundreds of millions of GPUs need? Leopold's answer: The U.S. needs to increase its electricity production capacity by several tens of percentage points.

The data confirmed his assessment. In 2024, the combined capital expenditures of Amazon, Microsoft, Google, and Meta exceeded $200 billion, a 62% increase from 2023. Amazon alone spent $85.8 billion, a 78% year-over-year increase. In 2025, Amazon's capital expenditure is expected to exceed $100 billion.

The majority of this money was spent on data centers and power infrastructure.

Microsoft even did something unimaginable ten years ago: it signed a 20-year power purchase agreement with Constellation Energy to restart the Three Mile Island nuclear power plant.

Yes, the same Three Mile Island that was the site of the most serious nuclear accident in U.S. history in 1979.

This nuclear plant will reopen in 2028, renamed the Crane Clean Energy Center, specifically to power Microsoft's data centers. Constellation Energy's CEO, Joe Dominguez, said, "Providing power to critical industries, including data centers, requires abundant, carbon-free, and reliable energy every day, every hour, and nuclear power is the only energy source that consistently delivers on that promise."

When a software company starts restarting a nuclear power plant, you know that electricity has shifted from an infrastructure issue to a strategic resource problem.

The third consequence: geopolitical competition.

The most controversial part of the manifesto is Leopold's near-Cold War language, defining the AGI race as a struggle for the survival of the "free world." He harshly criticized the security measures of the top AI labs in the U.S., calling them a mere facade. He called for AI algorithms and model weights to be treated as the highest national secrets.

He even predicted that the U.S. government would eventually have to launch a national AGI project similar to the "Manhattan Project."

These arguments sparked intense debate. Critics argue that he oversimplified the complexity of geopolitics and provided a rationale for unchecked accelerated development through a narrative of panic.

But some also believe that he has spoken the truth. Anthropic's Dario Amodei and OpenAI's Sam Altman also believe, like him, that AGI will soon become a reality.

The true value of the Decameron is not whether its predictions are 100% accurate, but that it provides a comprehensive, actionable framework.

If AGI really arrives around 2027, what does the world need before then?

The world needs massive computing power.

What does computing power need? It needs GPUs.

What do GPUs need? They need electricity.

Where does electricity come from? From power plants, nuclear power plants, and from Bitcoin mining facilities with cheap electricity.

Where are chips manufactured? At TSMC.

But what if there is a decoupling between the U.S. and China? Then we will need Intel.

How are data centers interconnected? They need optical components—Lumentum.

Where is data stored? It needs storage—SanDisk.

You see, this is the logic of that position report.

The Decameron is the map, and the position report is the route. Leopold has translated this 165-page macro forecast into an investment portfolio that can be bet with real money. Each buy corresponds to a point in the Decameron. Each sell corresponds to an assumption where he believes the market is mispricing.

But having just the map is not enough. In the real market, you need one more thing: the ability to continue to believe you are right when everyone else says you are wrong.

This ability was put to the toughest test on January 27, 2025.

DeepSeek Impact

On January 27, 2025, the release of DeepSeek's DeepSeek-R1 model plunged all of Wall Street into panic. The performance of this model was close to OpenAI's o1, but the operating cost was reduced by 20 to 50 times. Even more shocking was the reported training cost of its predecessor model, DeepSeek-V3, at less than $6 million, using the sanctioned and performance-limited NVIDIA H800 chip.

The market's logic collapsed in an instant.

If the Chinese can train a top model with $6 million and a crippled chip, what does the hundreds of billions of dollars spent by American tech giants annually amount to? Does the trillion-dollar computing power cluster plan still make sense? Will GPU demand plummet off a cliff?

Panic spread like a plague. NVIDIA's stock price plummeted nearly 17%, with a single-day market cap evaporation of $593 billion, marking the largest single-day market cap loss in Wall Street history. The Philadelphia Semiconductor Index dropped by 9.2%, achieving the largest single-day decline since the panic in March 2020. Broadcom fell by 17.4%, Marvell by 19.1%, Oracle by 13.8%.

The decline started in Asia, spread to Europe, and finally exploded in the United States. The Nasdaq 100 index components alone shed nearly a trillion dollars in market value in a single day.

Silicon Valley venture capital pioneer Marc Andreessen called DeepSeek AI's "Sputnik moment" on Twitter, saying, "This is one of the most amazing and impressive breakthroughs I've seen, and as an open-source project, it's a gift to the world."

For Leopold's fund, this day was supposed to be a disaster. His holdings are all in AI infrastructure stocks, and the market is questioning the entire logic of AI infrastructure.

But according to Fortune magazine, an investor at Situational Awareness LP revealed that on that day, during the market's panic selling, large tech funds called to inquire about the situation. The answer they received was five words:

"Leopold says it's fine."

Why was Leopold so calm? Because in his view, the emergence of DeepSeek not only did not overturn his logic but rather affirmed it.

A core argument in his lengthy essay is that AI progress will not slow down, only accelerate.

Algorithm efficiency improvement is one of the three main engines driving AI development. DeepSeek trained a stronger model with less money and weaker chips, proving that algorithm efficiency is rapidly increasing. And the higher the algorithm efficiency, the stronger AI can be produced with the same computing power, which will stimulate more demand for computing power rather than reduce it.

Using the framework from his Magnum Opus: DeepSeek didn't prove that "we don't need as many GPUs," but rather proved that "each GPU became more valuable." When you can train better models with less money, you don't stop; you train more, bigger, stronger models.

The panic stemmed from the fear of "the demand will disappear." But those who truly understand AI know that cost reduction never eliminates demand; it only creates more significant demand.

Leopold bought against the panic. The market quickly proved him right. NVIDIA and the entire AI sector rebounded rapidly in the following weeks, reaching levels higher than before the crash.

In the world of investing, belief is the scarcest asset. Not because forming beliefs is hard, but because persisting in belief when everyone tells you that you are wrong is almost against human nature.

The Limits of the Physical World

Leopold Aschenbrenner's story could, of course, be reduced to a wish-fulfillment tale of a teenage genius becoming rich overnight. But if you only see the money, you miss the true value of this story.

What he truly did right was, while everyone was fixated on the code and model parameters on the screen, shifting his focus to the smokestacks of power plants, the substations of mines, and the fiber optic cables spanning continents.

In 2024, the world was all about discussing how powerful GPT-5 would be, how realistic Sora's generated videos could get, and when AI could replace programmers. These discussions are undoubtedly important. But Leopold posed a more fundamental question: how much electricity do these things need? Where does the electricity come from?

While this question may sound too simplistic, it is precisely this simple question that points to the greatest investment opportunity of the AI era.

AI is growing at an exponential rate, yet the physical infrastructure supporting it remains stuck in the last century. Leopold saw this crack. And along this crack, he traced back to the limits of the physical world. Each step started from a physical bottleneck, found the companies solving that bottleneck, and then placed his bets.

The essence of this methodology is not actually new. During the California Gold Rush of the 19th century, those who made the most money weren't the gold diggers but the ones selling shovels and jeans. Levi Strauss made his fortune back then.

But knowing this truth is one thing; executing it in the AI era is another.

Because to execute on it, you need to possess two distinct capabilities simultaneously: one is a deep understanding of technological trends, knowing the development path of AI and its resource requirements; the other is a specific knowledge of the physical world, understanding where electricity comes from, how data centers are built, and how fiber optics are laid.

The former requires you to have spent time in OpenAI's lab, the latter requires you to be willing to crouch down and study the power contracts of a bankrupt mining company.

Technical individuals understand AI but not the electricity market. Financial professionals understand the market but not the physical constraints of AI. Leopold happens to have both.

But more important than the capabilities is the perspective.

A line from his lengthy treatise is often quoted: "You can see the future first in San Francisco." The implication of this line is: the future is not evenly distributed.

The essence of investing is to find price dislocations in a future that has already arrived but is not evenly distributed yet.

Leopold has witnessed the capability curve of AI in OpenAI's lab firsthand; he knows that GPT-4 is not the end but the beginning, he knows that larger models, more computing power, and crazier capital infusion are on the horizon. Meanwhile, the market is still debating whether "AI is a bubble."

That's the dislocation. What he has done is turn this dislocation into $5.5 billion.