El mayor beneficiario del auge de la IA, la historia de éxito del nuevo rey de las acciones en EE. UU., Leopold

Leopold Aschenbrenner’s holdings have surged again and again, as a rising star in hedge funds, his investment logic is being validated in reverse by the market.

In the past few days, multiple stocks in Leopold’s Situational Awareness LP disclosed holdings have collectively risen: Bloom Energy, Cipher Mining, Intel, Applied Digital, SanDisk, IREN, among others, with single-day gains exceeding 10%, prompting the market to revisit his late-2022 13F report, trying to understand why this former OpenAI researcher preemptively bet on AI infrastructure.

What is worth noting about him is not the “young” or “overnight wealth” labels, but that he has provided a framework different from mainstream AI trading. Most people equate AI investments with Nvidia, Microsoft, OpenAI, and model capabilities, but Leopold’s portfolio bypasses the most crowded star assets, shifting instead toward Bloom Energy, CoreWeave, Core Scientific, Lumentum, Intel, Bitcoin miners, and power-related companies.

The AI narrative is shifting from “whose model is stronger” to “who can support the continued expansion of models.” Training and inference require GPUs, GPUs need data centers, data centers need electricity, land, cooling, fiber optics, licenses, and long-term power contracts. Leopold is betting on the physical bottlenecks that AI growth must pass through. Fortune summarizes his latest holdings as: this former OpenAI researcher is translating his AGI paper into billion-dollar bets on power, AI infrastructure, and crypto mining companies.

In early March, Dongcha Beating conducted an in-depth analysis of Leopold and his fund’s holdings and investment logic, sharing his vision of the future of AI competition. All of this is being validated in reality: the AI narrative is returning from models on screens to land and power grids underfoot. The most expensive things in the future may not be algorithms, but the physical world that supports their expansion.

Below is the original content from Dongcha Beating:

In February 2026, hedge fund Situational Awareness LP submitted its quarterly holdings report, showing that as of the end of Q4 2025, the fund’s US stock holdings had a total market value of $5.517 billion.

Wall Street manages trillions of dollars in assets, and $55 billion is just a drop in the ocean. But this fund’s management size was less than $400 million a year ago, and its founder and CIO is a young person born in 1999.

His name is Leopold Aschenbrenner. 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 rose in single digits.

What is even more surprising is his holdings. Open the quarterly report, and you won’t find any of the AI superstar companies that always make headlines in financial news. Instead, there are companies making fuel cells, Bitcoin miners just emerging from bankruptcy, and chip giants being abandoned by the market.

He says his fund invests in AI, but it doesn’t look like an AI fund’s holdings; it more resembles a madman’s shopping list.

But this madman is one of the earliest and most profound people to understand how AI will change the world. Before joining Wall Street, he was a researcher at OpenAI, thinking about how to ensure AI doesn’t go out of control when it becomes smarter than humans; later, he was expelled for saying things he shouldn’t, writing a 165-page manifesto predicting a future that most would find absurd.

Later, he bet his entire net worth on it.

Dissecting $5.5 billion: what exactly did he buy?

The most direct way to understand Leopold Aschenbrenner’s investment genius is to open his holdings report and read it line by line.

His largest holding is Bloom Energy, with a market value of $876 million, accounting for 15.87% of the total portfolio.

This company makes fuel cells. More precisely, it produces “solid oxide fuel cells” that can directly convert natural gas into electricity with high efficiency. Founder KR Sridhar was once an engineer on NASA’s Mars exploration program, called one of “the top five futurists shaping the future” by Fortune magazine.

An AI fund has placed its biggest bet on an energy company.

According to Gartner’s forecast, the global power consumption of AI-optimized servers will soar from 93 TWh in 2025 to 432 TWh in 2030, nearly fivefold in five years. The US data center power demand will nearly triple by 2030, reaching 134.4 GW. Meanwhile, the US power infrastructure is over 25 years old on average, with many components between 40 and 70 years old, far exceeding their designed lifespan.

In other words, the electricity needed by AI exceeds what the entire grid can supply. And the grid itself is aging rapidly.

The most scarce resource in the AI era is not chips, but electricity.

Bloom Energy’s fuel cells can bypass this bottleneck. They don’t need to connect to the grid, generating power directly next to data centers, operating 24/7. In 2025, Bloom Energy secured a contract from CoreWeave to supply fuel cells for its AI data center in Illinois.

Speaking of CoreWeave, it is Leopold’s second-largest holding.

He holds $774 million in call options on CoreWeave, plus $437 million in common stock, totaling over $1.2 billion, accounting for 22% of the portfolio. CoreWeave is a GPU cloud service provider, transformed from a crypto mining farm.

In 2017, Mike Intrator and Brian Venturo and a few others mined Bitcoin together. In 2018, the crypto crash made mining impossible. But they had a bunch of GPUs. In 2019, they had an epiphany: GPUs can do more than mine—they can run AI.

So the company pivoted, transforming from a mining farm into an AI compute provider. On March 27, 2025, CoreWeave IPO’d on Nasdaq, raising $1.5 billion at $40 per share. A company that emerged from mining farms became a core supplier of AI infrastructure.

Leopold is interested in CoreWeave’s large GPU inventory and its deep ties with Nvidia. In an era where compute power is productivity, whoever owns GPUs is king.

But what truly confuses people is his third-largest holding: Intel. With a market value of $747 million, all in call options, accounting for 13.54% of the portfolio.

In 2025, Intel was one of Wall Street’s most unloved companies. Its stock price halved from the 2024 high, market share was eaten by AMD and Nvidia, and CEOs changed repeatedly. Almost all analysts said Intel was finished.

But Leopold boldly bought call options on Intel at this time. It’s an extremely aggressive move—if right, it soars; if wrong, it’s zero.

What is he betting on? Just two words: foundry.

In November 2024, the US Department of Commerce announced that Intel would receive up to $7.86 billion in direct funding through the Chips and Science Act. The goal was to make Intel a domestic chip foundry competing with TSMC.

In the context of US-China tech decoupling, the US needs a “homegrown” chip maker. Although lagging, Intel is the only choice. Leopold is betting not on Intel’s technology, but on US national will.

The subsequent holdings are even more interesting. Core Scientific, $419 million; IREN, $329 million; Cipher Mining, $155 million; Riot Platforms, $78 million; Hut 8, $39.5 million.

These companies share a common trait: they are all Bitcoin miners.

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

Simple: because Bitcoin miners have the cheapest electricity and largest data center sites in the US.

Core Scientific has over 1,300 MW capacity. IREN plans to expand by 1.6 GW in Oklahoma. These miners have locked in the cheapest power globally through long-term power purchase agreements to survive fierce compute competition.

And now, the most needed resource for AI data centers is precisely electricity and space.

In 2022, Core Scientific filed for bankruptcy due to the crypto crash. It restructured in January 2024, reducing about $1 billion in debt, and relisted on Nasdaq. Then, it signed a 12-year, over $10.2 billion contract with CoreWeave, transforming its mining farms into AI data centers. To fully pivot, Core Scientific even plans to sell all its Bitcoin holdings.

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

Overnight, Bitcoin miners became landlords of the AI era.

Now, let’s complete this puzzle.

Bloom Energy provides power, CoreWeave supplies GPU compute, Bitcoin miners offer space and cheap electricity, Intel provides domestic chip manufacturing. Plus, the fourth-largest holding, Lumentum ($479 million, optical components, core interconnects for AI data centers), the ninth-largest SanDisk ($250 million, data storage), and the eleventh-largest EQT Corp ($133 million, natural gas producer fueling fuel cells).

This is a complete AI infrastructure supply chain.

From power generation, transmission, chip manufacturing, GPU compute, data storage, to fiber optic interconnects. He has bought every link.

And he is doing another thing that makes this logic clearer: in Q4 2025, he completely sold off Nvidia, Broadcom, and Vistra. These three companies were the biggest winners in the 2024 AI rally.

He also shorted Infosys, one of India’s largest IT outsourcing firms.

Selling the hottest AI chip stocks, buying power plants and mining farms no one wants. Shorting traditional IT outsourcing because AI programming tools are making programmers more efficient, reducing outsourcing demand.

Every trade points to the same conclusion: the bottleneck of AI is not software, but hardware; not algorithms, but electricity; not cloud models, but the physical world.

So, how did a 27-year-old form this understanding?

From East German doctor’s son to a rebel at OpenAI

Leopold Aschenbrenner was born in Germany, to parents who were doctors. His mother grew up in East Germany, his father from West Germany, and they met after the Berlin Wall fell. This family bears the mark of a historical rupture—Cold War, division, reunion. His obsession with geopolitical competition perhaps finds its roots here.

But Germany couldn’t keep 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 calls this phenomenon “the high poppy syndrome,” where the taller you grow, the more you get cut down.

At 15, he convinced his parents to let him fly alone to the US, to Columbia University.

Studying at 15 is unusual anywhere. But Leopold’s performance at Columbia turned “outsider” into “legend.” He majored in economics and math-statistics, winning awards like the Albert Asher Green Memorial Prize, Romine Economics Award, and becoming a Junior Phi Beta Kappa member.

At 17, he wrote a paper on economic growth and existential risks. Renowned economist Tyler Cowen read it and said: “When I read it, I couldn’t believe it was written by a 17-year-old. If it were a PhD thesis from MIT, I would be equally impressed.”

At 19, he graduated as valedictorian from Columbia, the highest honor for undergraduates. In 2021, amid the pandemic shadows, a 19-year-old German boy delivered the commencement speech at Columbia.

Tyler Cowen gave him a piece of advice: don’t pursue a PhD in economics.

Cowen felt the academic field of economics had become somewhat “decadent,” encouraging him to do bigger things. He also introduced him to Silicon Valley’s “Twitter oddball” culture, a group obsessed with AI, effective altruism, and humanity’s long-term fate.

After graduation, Leopold first joined the Forethought Foundation, researching long-term economic growth and existential risks. Then he joined the FTX Future Fund founded by SBF, working alongside key figures in effective altruism like Nick Beckstead and William MacAskill. His title was “economist affiliated with the Oxford University Future of Humanity Institute.”

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

Then, he joined OpenAI.

The exact timing is unclear, but he joined a special team—the “Superalignment” team. Founded on July 5, 2023, led by OpenAI co-founder Ilya Sutskever and team leader Jan Leike, its goal was to solve the alignment problem of superintelligence within four years, ensuring that an AI far smarter than humans would still obey human commands.

OpenAI had promised to allocate 20% of its compute power to this team. But there was a gap between promise and reality.

Leopold saw some unsettling things inside OpenAI. He submitted a security memo to the board warning that the company’s safety measures were “severely inadequate” to prevent foreign governments from stealing key algorithm secrets. The company’s response surprised him: HR called him to say his concerns about espionage were “racist” and “unconstructive.” Company lawyers questioned his views on AGI and his team’s loyalty.

In April 2024, OpenAI fired him for “leaking confidential information.”

The so-called “leak” was sharing a brainstorming document on AGI safety with three external researchers. Leopold said the document contained no sensitive information, and sharing such internal drafts for feedback was normal.

A month later, Ilya Sutskever left OpenAI. Three days after, Jan Leike also departed. The Superalignment team was disbanded, and OpenAI’s promised 20% compute was never delivered.

A team researching “how to control superintelligence” was dismantled by the very company creating superintelligence.

The irony of this is undeniable. But for Leopold, being fired was a kind of liberation. He was no longer employed by anyone, no longer had to be cautious in internal memos. He could speak openly to the world.

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

165 pages of prophecy

To understand Leopold’s investment logic, you must first read this manifesto. Because that $5.5 billion in holdings is the financial translation of these 165 pages.

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

This judgment sounds like madness in June 2024. But Leopold’s reasoning is straightforward: order of magnitude.

From GPT-2 to GPT-4, AI capabilities have undergone a qualitative leap, transforming from preschoolers to smart high school students. Behind this leap is roughly a 100,000-fold (five orders of magnitude) increase in effective compute. This growth comes from stacking physical compute, improving algorithm efficiency, and capabilities unlocked by “unbounding” models.

His prediction is that by 2027, a similar scale of growth will occur again. In terms of physical compute, the resources used to train cutting-edge models will be 100 times more than GPT-4. Algorithm efficiency will improve about 0.5 orders of magnitude annually, totaling about 100 times over four years. Plus, the “unbounding” effect will turn AI from chatbots into tools and autonomous agents, another order of magnitude leap.

Stacked together, these three 100-fold increases amount to another 100,000 times—another qualitative leap—from high schoolers to surpassing humans.

What truly makes this article unsettling is the chain of consequences he derives from this prediction.

First consequence: Trillion-dollar compute clusters.

He writes that in the past year, Silicon Valley’s focus has shifted from $10 billion clusters to $100 billion, and now to trillion-dollar clusters. Every six months, the board’s plans add another zero. By the end of this decade, hundreds of millions of GPUs will be in operation.

This forecast sounded exaggerated in June 2024. But in January 2025, the Trump administration announced the Stargate project, a joint investment by SoftBank, OpenAI, Oracle, and MGX, planning to invest $500 billion in AI infrastructure in the US over four years. The first immediate funding was $100 billion, and construction has already begun in Texas.

The “trillion-dollar cluster” in his manifesto became an official White House plan within half a year.

Second consequence: Power crisis.

How much electricity do hundreds of millions of GPUs need? Leopold’s answer: the US must increase its power generation capacity by dozens of percentage points.

Data confirms his judgment. In 2024, Amazon, Microsoft, Google, and Meta’s capital expenditures exceeded $200 billion, up 62% from 2023. Amazon alone spent $85.8 billion, up 78%. In 2025, Amazon’s capex is expected to surpass $100 billion.

Most of this money is spent on data centers and power infrastructure.

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

Yes, the same Three Mile Island that experienced the worst nuclear accident in US history in 1979.

This plant will reopen in 2028, renamed the Crane Clean Energy Center, powering Microsoft’s data centers. Constellation CEO Joe Dominguez said: “Providing power for critical industries, including data centers, requires sufficient, carbon-free, reliable energy every day and hour, and nuclear is the only energy that can deliver this promise continuously.”

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

Third consequence: Geopolitical competition.

The most controversial part of the manifesto is Leopold’s use of Cold War-like language, defining the AGI race as a struggle for the survival of the “free world.” He criticizes US top AI labs’ safety measures as “virtually useless” and calls for AI algorithms and model weights to be treated as national secrets.

He even predicts that the US government will eventually have to launch a national AGI project akin to the Manhattan Project.

These claims have sparked fierce debate. Critics say he oversimplifies geopolitical complexity and uses panic narratives to justify unrestrained acceleration.

But some believe he is revealing the truth. Dario Amodei of Anthropic and Sam Altman of OpenAI share his view that AGI will come soon.

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

If AGI really arrives around 2027, then before that,

What does the world need? Massive compute power.

What does compute power need? GPUs.

What do GPUs need? Electricity.

Where does electricity come from? Power plants, nuclear stations, Bitcoin mines with cheap power.

Where are chips made? TSMC.

But what if US-China decoupling happens? Then, Intel.

How are data centers interconnected? Optical components—Lumentum.

Where is data stored? Storage—SanDisk.

See, that’s the logic behind that holdings report.

The manifesto is a map; holdings are the route. Leopold translated this 165-page macro forecast into an investable portfolio. Every buy corresponds to a point in the manifesto; every sell to a market mispricing he perceives.

But a map alone isn’t enough. In real markets, you need one more thing: the ability to continue believing you’re right when everyone else says you’re wrong.

This ability was tested most severely on January 27, 2025.

DeepSeek Shock

On January 27, 2025, the release of DeepSeek’s DeepSeek-R1 model plunged Wall Street into panic. Its performance approached OpenAI’s GPT-1, but at 20 to 50 times lower cost. Even more shocking, its predecessor, DeepSeek-V3, was reportedly trained at less than $6 million using Nvidia’s H800 chips, sanctioned and performance-limited by the US.

Market logic instantly collapsed.

If Chinese researchers can train top models for $6 million with limited chips, what are the billions spent annually by US tech giants for? Do the trillions of compute clusters still matter? Will GPU demand plummet?

Panic spread like a plague. Nvidia’s stock plummeted nearly 17%, losing $593 billion in market cap in a single day—the largest single-day loss in Wall Street history. The Philadelphia Semiconductor Index fell 9.2%, its worst since March 2020. Broadcom dropped 17.4%, Marvell 19.1%, Oracle 13.8%.

The decline started in Asia, spread to Europe, and finally exploded in the US. The Nasdaq 100 lost nearly $1 trillion in market value in one day.

Silicon Valley venture legend Marc Andreessen called DeepSeek the “Sputnik moment” for AI, saying: “This is one of the most astonishing 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 should have been a disaster. His holdings were all in AI infrastructure stocks, and the market was questioning the entire logic of AI infrastructure.

But according to Fortune, a source from Situational Awareness LP said that during the panic sell-off, some large tech funds called to inquire. The answer they received was five words:

“Leopold says it’s fine.” (Leopold dice que está bien.)

Why is Leopold so calm? Because, in his view, DeepSeek’s emergence not only did not overturn his logic but confirmed it.

His manifesto’s core point: AI progress will not slow down; it will accelerate.

The improvement in algorithm efficiency is one of the three engines driving AI development. DeepSeek trained a stronger model with less money and weaker chips, precisely proving that algorithm efficiency is rapidly improving. The higher the efficiency, the more valuable each GPU becomes, stimulating greater demand rather than reducing it.

Using his framework, DeepSeek does not prove “we need fewer GPUs,” but “each GPU becomes more valuable.” When you can train better models with less money, you won’t stop—you’ll train more, bigger, and stronger models.

Panic stems from the fear that “demand will disappear.” But those who truly understand AI know that cost reductions never eliminate demand; they only create larger demand.

Leopold bought in counter to panic. The market soon proved him right. Nvidia and the entire AI sector rebounded quickly in the following weeks, surpassing pre-crash levels.

In the world of investing, belief is the most scarce asset. Not because forming beliefs is hard, but because sticking to them when everyone else says you’re wrong is almost against human nature.

The End of the Physical World

Leopold Aschenbrenner’s story can of course be simplified as a tale of a genius teenager who got rich overnight. But if you only see the money, you miss the true value of this story.

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

In 2024, the world discusses how powerful GPT-5 will be, how realistic Sora’s videos are, and when AI will replace programmers. These discussions are important. But Leopold asked a deeper question: how much electricity do these things need? Where does that electricity come from?

This question sounds naive, but it points directly to the biggest investment opportunity of the AI era.

AI is growing exponentially, but its supporting physical infrastructure remains stuck in the last century. Leopold saw this gap. Then, tracing along this fissure, he looked all the way to the end of the physical world. Every step starts from a physical bottleneck, finds the company solving it, and bets on it.

This methodology is not new. During the California Gold Rush in the 19th century, the biggest profits went not to the prospectors but to the sellers of shovels and jeans. Levi Strauss made his fortune then.

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

Because to do so, you need two capabilities: a deep understanding of technological trends—knowing AI’s development path and resource needs; and a concrete understanding of the physical world—knowing where electricity comes from, how to build data centers, and how to lay fiber.

The former requires experience in labs like OpenAI; the latter requires willingness to squat down and study a bankrupt miner’s power contracts.

Tech experts understand AI but not power markets. financiers understand markets but not the physical constraints of AI. Leopold has both.

But more important than ability is perspective.

His manifesto often quotes: “You can see the future first in San Francisco.” The subtext is: the future is not evenly distributed.

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

Leopold saw the AI capability curve firsthand at OpenAI, knowing GPT-4 is not the end but the start, expecting bigger models, more compute, and crazier capital inflows. Yet the market still debates whether “AI is a bubble.”

This is the mispricing. What he does is turn this mispricing into a $5.5 billion portfolio.

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