The biggest beneficiary of the AI surge, the new stock market legend Leopold's rise to wealth

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 publicly disclosed holdings have collectively risen: Bloom Energy, Cipher Mining, Intel, Applied Digital, SanDisk, IREN, etc., with single-day gains exceeding 10% at times. This prompted the market to revisit his late-2022 13F report, trying to understand why this former OpenAI researcher had early bets on AI infrastructure.

What’s worth noting about him isn’t the “young” or “overnight riches” labels, but that he offers 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, turning instead to 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 carry the model’s continued expansion.” 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 ex-OpenAI researcher is translating his AGI paper into billion-dollar bets on power, AI infrastructure, and crypto miners.

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 beneath our feet. The most valuable things in the future may not be algorithms, but the physical world supporting their continued expansion.

Below is Dongcha Beating’s original content:

In February 2026, hedge fund Situational Awareness LP filed 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 $5.52B.

Wall Street manages trillions of dollars in assets; $5.52B is just a drop in the ocean. But this fund’s assets under management were less than $400 million just 12 months ago, and its founder and chief investment officer is a young man born in 1999.

His name is Leopold Aschenbrenner. 27 years old.

In 12 months, he grew this fund from $383 million to $8B, an increase of over 14 times. During the same period, the S&P 500 rose by a single-digit percentage.

What’s even more surprising is his holdings. Open the quarterly report, and you won’t find any of the AI superstar companies you often see in financial headlines. Instead, there are companies making fuel cells, a Bitcoin miner just pulled back from bankruptcy, and a chip giant being abandoned by the market.

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

But this madman is one of the earliest and most profound people in the world to understand how AI will change the world. Before 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.

And then, he bet his entire net worth on it.

Breaking down $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. Market value: $876 million, accounting for 15.87% of the total portfolio.

This company makes fuel cells. More precisely, it produces “solid oxide fuel cells,” which 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, 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 aged between 40 and 70 years—far beyond their designed lifespan.

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

The scarcest resource in the AI era isn’t chips—it’s electricity.

Bloom Energy’s fuel cells can bypass this bottleneck. They don’t need to connect to the grid; they generate power directly next to data centers, 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, that’s 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, or 22% of the portfolio. CoreWeave is a GPU cloud service provider, spun out from crypto mining farms.

In 2017, Mike Intrator and Brian Venturo and a few others mined Bitcoin together. By 2018, after the crypto crash, mining was no longer feasible. But they had a bunch of GPUs. In 2019, they had an epiphany: GPUs aren’t just for mining—they can run AI.

So the company pivoted, transforming from a mining farm into an AI compute hardware supplier. On March 27, 2025, CoreWeave went public 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 betting on CoreWeave’s large GPU inventory and its deep ties with Nvidia. In an era where compute power equals productivity, whoever owns GPUs is king.

But what’s truly baffling is his third-largest holding: Intel. Market value: $747 million, all in call options, accounting for 13.54% of the portfolio.

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

Yet Leopold chose to heavily buy call options at this moment. It’s an extremely aggressive move—betting on a rise, or losing everything.

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 under the Chips and Science Act. The goal? Make Intel a domestic chip foundry to compete with TSMC.

Against the backdrop of US-China tech decoupling, America needs a “homegrown” chip maker. Intel may be behind, but it’s the only choice. Leopold isn’t betting on Intel’s technology—he’s betting 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 ($839.5M).

All these companies share a common trait: they are Bitcoin miners.

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

Simple: Bitcoin miners own 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, what AI data centers need most is power and space.

In 2022, Core Scientific filed for bankruptcy due to the crypto crash. It restructured in January 2024, cutting about $1 billion in debt, and relisted on Nasdaq. Then, it signed a 12-year, over $10.2 billion contract with CoreWeave to convert 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 supplies power, CoreWeave provides GPU compute, Bitcoin miners supply space and cheap electricity, Intel offers domestic chip manufacturing. Plus the fourth-largest holding, Lumentum ($479 million, optical components, key for interconnecting AI data centers), the ninth-largest, SanDisk ($250 million, data storage), and the eleventh, EQT Corp ($133 million, natural gas producer fueling fuel cells).

This forms a complete AI infrastructure supply chain.

From power generation, transmission, chip manufacturing, GPU compute, data storage, to fiber optic interconnects—Leopold owns a stake in every link.

And he’s doing another thing that makes this logic clearer: in Q4 2025, he completely sold off Nvidia, Broadcom, and Vistra. These three were among 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 unloved power plants and mining farms. Shorting traditional IT outsourcing, because AI coding tools are making programmers more efficient, reducing outsourcing demand.

Every trade points to one conclusion: the bottleneck of AI isn’t software, it’s hardware; not algorithms, but electricity; not cloud models, but the physical world.

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

From the Son of an East German Doctor to a Rebel at OpenAI

Leopold Aschenbrenner was born in Germany, to parents who were both doctors. His mother grew up in East Germany; his father from West Germany. 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 may have roots here.

But Germany didn’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 be jealous and try to suppress you.”

He called this phenomenon “High Poppy Syndrome”: the taller you grow, the more likely you are to be cut down.

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

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

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’d be equally impressed.”

At 19, he graduated as valedictorian—the highest honor for undergraduates at Columbia. In 2021, still under the shadow of the pandemic, a 19-year-old German boy delivered the graduation speech at Columbia, representing all graduates.

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

Cowen felt academia had become somewhat “decadent,” encouraging him to do bigger things. He also introduced him to Silicon Valley’s “Twitter weirdos,” a circle 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 AI, Aschenbrenner had spent years systematically pondering a fundamental question: what kind of event could fundamentally change human civilization’s trajectory.

Then he joined OpenAI.

The exact timing is unclear, but he was part of a special team—“Superalignment.” Founded on July 5, 2023, led by OpenAI co-founder Ilya Sutskever and Superalignment lead Jan Leike. Its goal: solve the alignment problem for superintelligence within four years—to ensure an AI smarter than humans still obeys 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 unsettling things inside OpenAI. He submitted a security memo warning that the company’s safeguards were “severely inadequate” to prevent foreign governments from stealing key algorithm secrets. The response surprised him: HR called him, saying 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 that he shared a brainstorming document on AGI safety with three external researchers. Leopold said the document contained no sensitive info; sharing such internal drafts for feedback was normal.

A month later, Ilya Sutskever left OpenAI. Three days after that, 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 is undeniable. But for Leopold, being fired was a form of liberation. He was no longer an employee, 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.”

165 Pages of Prophecy

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

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

This judgment sounds crazy in June 2024. But Leopold’s reasoning is straightforward: orders of magnitude.

From GPT-2 to GPT-4, AI capabilities have leapt from preschool to high school level. Behind this leap is roughly a 100k-fold (five orders of magnitude) increase in effective compute. This growth comes from stacking physical compute, improving algorithms, and “unbounding” models to unleash capabilities.

He predicts that by 2027, a similar scale of growth will happen again. In terms of physical compute, training the latest models will require 100 times more resources than GPT-4. Algorithm efficiency will improve about 0.5 orders of magnitude annually, totaling roughly 100 times over four years. Plus, “unbounding” gains will turn AI from chatbots into tools and autonomous agents—another order of magnitude jump.

Stacking three 100-fold increases results in another 10,000-fold, or 100k times overall—another qualitative leap from high schooler to surpassing human intelligence.

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

First: trillion-dollar-scale 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 by January 2025, the US government announced the Stargate project, a joint investment by SoftBank, OpenAI, Oracle, and MGX, planning to invest $500 billion over four years to build AI infrastructure in the US. The first immediate funding was $100 billion. Construction has already begun in Texas.

What he wrote as “trillion-dollar clusters” in his manifesto became an official White House plan within half a year.

Second: the power crisis.

How much electricity do hundreds of millions of GPUs need? Leopold’s answer: the US’s power generation capacity must be increased 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 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 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 abundant, carbon-free, reliable energy every hour of every day. Nuclear is the only energy that can deliver that promise continuously.”

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

Third: geopolitical competition.

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

He even predicts 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’s telling the truth. Dario Amodei of Anthropic and Sam Altman of OpenAI share his view that AGI will arrive soon.

The true value of the manifesto isn’t whether its predictions are 100% accurate, but that it offers a comprehensive, actionable mental framework.

If AGI really arrives around 2027, then before that,

the world needs what? Massive compute power.

What does compute power need? GPUs.

What do GPUs need? Electricity.

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

Where are chips made? TSMC.

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

How are data centers interconnected? Optical components—Lumentum.

Where is data stored? Storage—SanDisk.

See? That’s the logic of that holdings report.

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

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

That ability was put to the ultimate test on January 27, 2025.

DeepSeek Shock

On January 27, 2025, the release of DeepSeek’s DeepSeek-R1 model sent shockwaves through Wall Street. Its performance approached OpenAI’s GPT-1, but at 20 to 50 times lower cost. Even more astonishing, its predecessor, DeepSeek-V3, reportedly cost less than $6 million to train, using Nvidia’s H800 chips sanctioned and performance-restricted by the US.

Market logic instantly shattered.

If Chinese researchers can train top-tier models for $6 million using restricted chips, what’s the point of the US tech giants spending billions annually? Do the trillions of dollars in compute cluster plans still matter? Will GPU demand plummet off a cliff?

Panic spread like a plague. Nvidia’s stock plunged 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 pandemic panic. 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 alone evaporated nearly $1 trillion in market value in one day.

Silicon Valley venture legend Marc Andreessen called DeepSeek a “Sputnik moment” for AI, tweeting: “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 are all in AI infrastructure stocks, and the market was questioning the entire logic of AI infrastructure.

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

“Leopold says it’s fine.” (“Leopold says it’s fine.”)

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

His core argument in the manifesto: AI progress won’t slow down; it will accelerate.

Algorithm efficiency improvements are 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 skyrocketing. The higher the efficiency, the more valuable each GPU becomes, fueling greater demand—not less.

Using his framework: DeepSeek doesn’t prove “we don’t need so many GPUs,” but “each GPU is more valuable.” When you can train better models for less money, you don’t stop—you train more, bigger, 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 create bigger demand.

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

In 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 genius teen’s overnight riches fantasy. But if you only see the money, you miss the real value of this story.

What he’s really doing is, while everyone’s eyes are on code and model parameters on screens, shifting focus to power plant smokestacks, substation yards, and transcontinental fiber optic cables.

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 asks a deeper question: how much electricity do all these things need? Where does that electricity come from?

This seemingly simple question 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 sees this gap. Then he traces it back to the physical world’s end. Every step starts from a physical bottleneck, finds the company solving it, and bets.

This methodology isn’t new. During the California Gold Rush, the biggest winners weren’t the prospectors but 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 skills: a deep understanding of technological trends—knowing AI’s development path and resource needs; and a concrete grasp 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, willingness to squat down and study a bankrupt miner’s power contract.

Tech folks 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: the future isn’t evenly distributed.

The essence of investing is finding mispricings in a future that’s already here but not yet evenly spread.

Leopold saw the AI capability curve firsthand at OpenAI. He knows GPT-4 isn’t the end but the beginning. He expects bigger models, more compute, crazier capital inflows. Yet the market still debates whether “AI is a bubble.”

That’s the mispricing. His job is to turn that mispricing into a $5.5 billion reality.