Fermi Crisis: A Case Study of an AI Power Stock's Sudden Decline

marsbitPubblicato 2026-04-21Pubblicato ultima volta 2026-04-21

Introduzione

The article "Fermi Crisis: A Case Study of an AI Power Stock's Collapse" details the rapid rise and fall of Fermi, a company that promised to build the world's largest AI data center campus in Texas. Despite having zero revenue, tenants, or a finished product, it raised $785 million in its October IPO, with its market cap peaking at $12.5 billion. However, its stock plummeted from $37 to $5.40 within months. The collapse began when its anchor tenant, believed to be Amazon, terminated its contract after promised funding failed to materialize. Construction halted as lease terms forbade building without a tenant. The simultaneous departure of the CEO and CFO, alongside insider stock sales totaling $68 million, accelerated the decline. A short-seller report highlighted the CEO's history of alleged fraud at a previous venture. Fermi is presented as a symptom of a broader AI infrastructure bubble. A significant gap exists between announced data center projects and those under construction, hampered by severe supply chain delays for critical components like transformers. While tech giants plan massive investments, many projects face cancellation or indefinite delays. The article warns that Fermi's story may be repeated, as many ventures are built on speculative narratives rather than deliverable infrastructure.

Author: Ada, Deep Tide TechFlow

On April 20th, Fermi's stock price settled at $5.4.

Six months ago, that number was around $37. And it had just had its IPO last October.

A company less than 12 months old, with zero revenue, zero tenants, and no tangible product to speak of, yet it raised $785 million on the Nasdaq, with its market capitalization once soaring to $12.5 billion.

But then, the CEO and CFO resigned on the same day, the construction site halted, insiders sold $68 million worth of stock, a short-seller published a report alleging fraud, and a securities class-action lawsuit has been filed.

This marks the first major collapse of the AI power narrative.

The "World's First" in the Texas Wilderness

Fermi's story began in early 2025.

Former US Energy Secretary Rick Perry and private equity magnate Toby Neugebauer joined forces to found the company. Their core bet was called "Project Matador": to build the world's largest AI data center campus on 5,800 acres of land outside Amarillo, Texas, initially powered by natural gas, with plans to add four nuclear reactors in the future.

The planned capacity was 11GW of power, alongside approximately 18 million square feet of data center facilities. The label "World's Largest" was repeatedly emphasized.

AI's thirst for power is real, nuclear energy is green, and Trump signed an executive order to expand US nuclear capacity from 100GW to 400GW. All the trends aligned.

The market bought it. On October 1st last year, Fermi went public at $21 per share, jumping to $25 at open, fully oversubscribed. The next day, it peaked at $37, 76% above the IPO price. Within days, this company without a cent of revenue saw its market cap break $10 billion.

Back then, everyone was buying AI power concept stocks. No customers needed, no revenue needed, just a PowerPoint presentation and a vision no one else could see.

The Real Crisis

The first crack appeared last December.

Fermi's sole anchor tenant terminated its contract, widely believed to be Amazon. This tenant had committed to prepay up to $150 million in construction costs but ultimately paid nothing.

Short-seller Fuzzy Panda uncovered the reason behind it. Fermi had promised to secure $5 to $5.5 billion in financing to ensure project execution, but this money never materialized. The tenant grew impatient and walked away.

According to the lease terms with Texas Tech University, without a signed tenant, Fermi wasn't even permitted to begin construction. This created a vicious cycle: no tenant meant no financing, no financing meant no construction, no construction meant no tenant.

The construction site stopped. Workers posted on social media saying "we all got laid off."

Then came the recent bombshell: CEO Neugebauer and CFO Miles Everson resigned simultaneously. The company packaged it as a "Fermi 2.0" strategic transformation. But the stock fell another 22%. From the IPO last year, investors who bought FRMI stock faced maximum losses of up to 78%.

And insiders had already started running. The moment the lock-up period ended on March 30th, Griffin Perry, son of co-founder Rick Perry, immediately sold 11 million shares, cashing out $56.3 million. The COO, CFO, and Chief Development Officer followed suit, with insiders collectively selling over $68 million worth of stock.

Fuzzy Panda revealed that before the lock-up expired, Griffin Perry had attempted to offload 30 million shares in a block trade.

This isn't Neugebauer's first company to face crisis.

In 2022, his "anti-woke" bank GloriFi collapsed, filing for bankruptcy after burning through investments from conservative backers like Peter Thiel, Ken Griffin, and Vivek Ramaswamy. The bankruptcy trustee alleged in court filings that Neugebauer engaged in "securities fraud," "egregious self-dealing," and "fraudulent transfers."

Fuzzy Panda's report also pointed out that several members of Fermi's current management team were associates of Neugebauer from his GloriFi days. Chief Site Development Officer Charlie Hamilton was described in bankruptcy filings as a "longtime friend" of Neugebauer. CFO Miles Everson was also accused of participating in unfair transactions涉嫌利益输送 (involving alleged利益输送 - benefit conveyance).

The bankruptcy court ruled that several of Neugebauer's transactions constituted fraudulent transfers. Yet, despite being accused of fraud at his previous company, his next company raised $785 million on Nasdaq. The IPO prospectus even mentioned these lawsuits could distract management. Investors bought anyway. What does that show? It shows that during a bubble, people don't read risk disclosures; they just look for a sexy enough story.

A Microcosm Under the Bubble

Fermi is not an isolated case. It is a microcosm.

According to data from Sightline Climate, as of April 2026, there are about 140 large data center projects scheduled to come online in the US this year, but only one-third are under actual construction. The rest are either delayed or canceled.

The bottleneck lies in electrical components.

Transformers, switchgear, and batteries – these are essential components for every data center build. Before 2020, delivery lead times for large-power transformers were 24 to 30 months. Now, wait times can stretch to five years. For data centers with deployment cycles under 18 months, this is structurally unacceptable. A delay in any single component can halt the entire project.

The deeper issue is a generational mismatch. The US power grid was not designed for the loads required by AI. Data centers can be built in three years, but power generation takes longer. Solar or wind power generation takes three to six years, gas turbine generation about six years, and nuclear power generation over a decade. Network World magazine notes that this mismatch was manageable when data centers were smaller. But the scale required by AI today, with individual facilities consuming hundreds of megawatts, has become an insurmountable bottleneck.

OpenAI's flagship project, Stargate, reportedly costing $500 billion, had no substantial construction progress as of April.

Partners are locked in disputes over site ownership and system control. The 800MW expansion of the flagship Texas campus has been canceled. Stargate projects in the UK and Norway have been paused successively, and three core executives responsible for Stargate have jumped ship to Meta.

Meanwhile, Alphabet, Amazon, Meta, and Microsoft are projected to spend over $650 billion in 2026 to expand AI capacity. Alphabet alone reported $175 to $185 billion, equating to burning $500 million daily. However, the infrastructure supporting this grand ambition cannot develop at the speed the industry requires.

The last time the US saw a energy infrastructure boom of similar scale was in the late 1990s. The dot-com bubble and electricity market deregulation spurred a wave of natural gas power plant construction, investing about $100 billion. After the bubble burst, many power plants sat idle.

This time, the scale is an order of magnitude larger. US utility companies alone have proposed $1.4 trillion in planned expenditures, 27% higher than last year's forecast. Tech companies' investment in energy-related infrastructure is already twice the annual investment of the entire US power industry.

But from Q3 to Q4, new data center deals have fallen by over 40%. Some analysts believe supercomputing companies' capital expenditures could halve this year.

Money is tightening, but the stories are still being told. This is where the danger lies.

A report by Built In summarized: When vendors heavily invest in startups, and those startups turn around and spend the money on the vendors' own products, real demand and artificially created illusions get mixed together. When a company's customers are also its investors, and revenue grows faster than actual usage, that's a signal a bubble is forming.

When the Bubble Bursts

In this food chain, there are three tiers of players.

The first tier consists of the real winners. These are companies that already own operational nuclear power plants, like Constellation Energy. They don't need to build anything new; they just need to transfer contracts from the grid to data centers to reap the AI power红利 (dividend). Meta signed a 20-year, 1.1GW nuclear power supply contract with Constellation. Microsoft spent $1.6 billion to restart the Three Mile Island nuclear plant. These are transactions with physical assets.

The second tier includes various small modular reactor (SMR) startups, like Oklo, whose stock price has been hyped to the sky, but not a single reactor has been built. US nuclear projects are notoriously known for delays and cost overruns, with almost no recent cases completed on the original schedule and budget. But investors don't care about that.

The third tier consists of companies like Fermi, which don't even have nuclear reactors, haven't started building the natural gas power plant, and have no tenants. They are at the bottom of the food chain, selling not power, but a story. When the story collapses, nothing remains.

Fermi's collapse will not be an isolated incident.

When an industry's actual delivery capability lags far behind the promises made in its PowerPoint presentations, a collapse is only a matter of time.

Of the US data center capacity planned to come online in 2027, only 6.3GW is under construction, while the announced total is 21.5GW. A gap of 15GW exists on paper, corresponding to hundreds of billions of dollars and countless promises that will fail to materialize.

Who will be the next Fermi? No one knows. But in this arena, there's $500 billion searching for power, high-power transformers awaiting delivery, and a slew of startups that haven't even secured basic grid interconnection assuring investors that everything is under control.

When the last energy infrastructure bubble burst, at least some power plants were left behind. This time, many projects might not even break ground.

And Fermi's 5,800 acres of land in the Texas wilderness, along with all the unrealized grand narratives, will slowly be buried by time.

Domande pertinenti

QWhat was the core project that Fermi was betting on, and what was its planned scale?

AFermi's core bet was 'Project Matador,' which aimed to build the world's largest AI data center campus on 5,800 acres in Amarillo, Texas. It planned to provide 11GW of power capacity (initially from natural gas, with future nuclear reactors) and about 18 million square feet of data center facilities.

QWhat was the first major crack in Fermi's story that led to its crisis?

AThe first major crack appeared in December when Fermi's sole anchor tenant, widely believed to be Amazon, terminated its contract. This tenant had promised up to $150 million in prepaid construction funding but never paid anything. The termination was linked to Fermi's failure to secure the $5-5.5 billion in financing it had promised to ensure project execution.

QWhat allegations did the short-seller Fuzzy Panda make against Fermi's management?

AFuzzy Panda alleged that Fermi's CEO Toby Neugebauer had a history of 'securities fraud,' 'egregious self-dealing,' and 'fraudulent transfers' from his previous failed company, GloriFi. The report also noted that several current Fermi executives were associates of Neugebauer from GloriFi and were implicated in questionable transactions.

QAccording to the article, what is the fundamental bottleneck causing delays and cancellations in many large US data center projects?

AThe fundamental bottleneck is the shortage and long lead times for critical electrical components, such as transformers, switchgear, and batteries. Large power transformers that once had a 24-30 month delivery time can now take up to five years. This structural delay, combined with a generational mismatch where the power grid wasn't designed for AI's massive energy demands, causes entire projects to stall.

QHow does the article categorize the three layers of players in the AI power infrastructure 'food chain', and which layer did Fermi belong to?

AThe three layers are: 1) The true winners: companies with existing operational power plants (e.g., Constellation Energy) that can simply redirect power to data centers. 2) SMR startups: companies like Oklo that have hype but no built reactors. 3) Story sellers: companies like Fermi with no power plants, no construction, and no tenants, selling only a narrative. Fermi was in the bottom third layer.

Letture associate

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**Summary: The Value Distribution of Stablecoins** The article argues that stablecoins are evolving from mere trading tools into broader channels for dollar access. It divides the stablecoin ecosystem into four layers to analyze how value is distributed: 1. **Issuance Layer:** Mints stablecoins, holds reserve assets, and captures the spread between reserve yield and user costs (e.g., Tether, Circle). This layer currently earns the largest profit margin. 2. **Infrastructure Layer:** Connects stablecoins to the traditional financial system, handling fiat on/off-ramps, banking integration, compliance (KYC/AML), and asset management (e.g., Bridge, BVNK). This is the "unglamorous" but critical work, building the essential bridges between crypto and real-world finance. 3. **Acquiring/Distribution Layer:** Integrates stablecoins into merchant systems, manages payment flows, and provides enterprise financial software (e.g., Stripe, Coinbase). They act as the access point for businesses. 4. **Application Layer:** The end-users and businesses that ultimately use stablecoins for payments, settlements, or as a store of value. They benefit from convenience but have little pricing power. The core thesis is that while the issuance layer currently dominates profits, the often-overlooked **infrastructure layer holds significant long-term potential**. The real challenge and barrier to mass adoption is not the on-chain transfer of stablecoins (which is simple), but the complex "last mile" integration into existing business workflows, banking systems, and regulatory frameworks across different countries. Companies in this layer are currently in a "land grab" phase, investing heavily to build networks, secure bank partnerships, and establish compliance pathways. While their position is currently pressured by the profitable issuers above and distribution platforms below, the article suggests that if stablecoins become a default financial rail for businesses, the infrastructure providers who have done the hard work of integration will ultimately gain strong pricing power and become entrenched, essential players.

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The Value Distribution of Stablecoins

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The Value Distribution of Stablecoins

The Value Distribution of Stablecoins The article argues that stablecoins are evolving from a mere trading tool into a broad "dollar channel." It analyzes the industry's value chain through four layers: 1. **Issuance Layer (e.g., Tether, Circle):** The top layer that mints stablecoins, holds reserve assets, and captures the thickest interest rate spread. 2. **Infrastructure Layer (e.g., Bridge, BVNK):** Connects stablecoins to the traditional financial system, handling critical but complex "dirty work" like fiat on/off-ramps, banking integration, compliance (KYC/AML), and cross-border settlement. 3. **Acquiring/Distribution Layer (e.g., Stripe, Coinbase):** Embeds stablecoins into merchant systems, manages payment flows, and integrates with enterprise software. 4. **Application Layer:** End-users and businesses that ultimately use stablecoins for payments, settlement, or storing value. The author posits that while the issuance layer currently captures the most profit, the most overlooked and potentially critical layer is infrastructure. The core challenge for stablecoin adoption isn't the on-chain transfer (which is simple), but bridging the gap between blockchain and the real-world financial system. This involves solving practical problems for businesses: fiat conversion, reconciliation, tax handling, and user onboarding. Infrastructure companies are currently in a difficult "land-grab" phase—building networks, securing banking relationships, and achieving compliance country-by-country. They face pressure from both the profitable issuance layer above and distribution platforms below. However, the author suggests this layer is building a crucial moat. Once stablecoins become a default business rail, the infrastructure players who have done the hard work of integration may gain significant, durable value and pricing power.

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How to Do Research Well: Deliberately Practice the Real Skills That Matter

No one truly teaches you how to do research. You're often given a desk, a pre-selected problem, and vague instructions to "create something new." Consequently, many people reverse-engineer the job based on visible outputs—papers, posts, announcements—learning only how to *appear* like a researcher rather than how to *become* one. True research capability is built from stacking small, trainable skills, nearly all of which can be developed through deliberate practice. **Pick Your Own Problem:** Most researchers absorb problems from advisors or trends, lacking the underlying reasoning. Choosing a problem you genuinely care about, as John Schulman advises, leads to original work. Develop "taste" like a muscle: predict experiment outcomes, guess paper results from methods, and track which findings remain important over time. **Upgrade Your Inputs:** Relying on shared reading lists (arXiv hot lists, filtered group chats) leads to unoriginal conclusions. Undervalued old literature often holds crucial insights (e.g., MoE, LSTM, backpropagation). Richard Sutton's "The Bitter Lesson" or Claude Shannon's 1952 talk on creative thinking are more predictive than lengthy modern surveys. Breadth matters as much as depth: draw from neuroscience, mechanism design, hardware knowledge, and honest statistics. Read papers directly, especially appendices and limitations sections. **Write Everything Down:** As Paul Graham noted, writing exposes flaws in seemingly mature ideas. Writing is the cheapest defense against self-deception. Following Feynman's principle, Darwin programmatically wrote down facts contradicting his theory to combat memory bias. Maintain a detailed log of hypotheses, setups, predictions, results, and updated understandings. Reviewing past logs fosters essential humility.

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