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

marsbitPublished on 2026-04-21Last updated on 2026-04-21

Abstract

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.

Related Questions

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.

Related Reads

AI PC Battle: Bet on the Toll Booth, Not the Camp

**Title:** The AI PC Battle: Don't Bet on Sides, Bet on the Tollbooth **Summary:** The AI PC competition is moving beyond simple "x86 vs. Arm" narratives. The core investment thesis should focus on identifying which players can sustain margins, cash flow, and pricing power throughout the upgrade cycle, rather than backing a particular architecture. The opportunity is analyzed in three layers: 1. **The Advanced Foundry Tollbooth:** TSMC is positioned to collect "tolls" regardless of which chip designer wins, due to its dominant ~70% share in advanced semiconductor manufacturing, which is essential for high-end AI PC chips. 2. **Compute & Platform Spillover:** AMD represents an offensive in the x86 CPU+GPU space, while NVIDIA leverages its GPU and CUDA software stack dominance. Both benefit from the demand for increased local AI compute. 3. **Architecture Diffusion & Turnaround Plays:** ARM and Intel offer potential for significant upside (elasticity), but investments here require stricter discipline due to higher execution risks and competitive challenges. The industry is transitioning from concept to shipment validation. While short-term forecasts for AI PC adoption have been revised down slightly due to tariffs and procurement delays, the long-term trend towards AI becoming a standard PC feature remains intact. The key driver for upgrade cycles will be whether compelling enterprise applications (e.g., privacy-sensitive computing, low-latency inference) emerge beyond consumer-focused features like meeting summarization. Investment strategy should prioritize companies with platform-level advantages and recurring revenue streams. TSMC offers high certainty as the foundational tollbooth. AMD presents a strong offensive play within the established ecosystem. ARM and Intel are higher-risk, higher-potential-reward turnaround bets. The report cautions against chasing short-term hype and emphasizes a disciplined, long-term approach focused on buying ecosystem strength and cash-flow certainty after market enthusiasm subsides. **Key Risks:** Underwhelming AI PC applications slowing upgrade cycles; slow improvement in Windows on Arm compatibility; macro/tariff impacts on PC demand; potential advanced node supply-demand mismatches affecting TSMC; high overall AI sector valuations making stocks vulnerable to a risk-off shift in markets.

marsbit13m ago

AI PC Battle: Bet on the Toll Booth, Not the Camp

marsbit13m ago

Ten-Thousand-Word Analysis: From $10 to $290, MRVL Wins the Entire AI Era by 'Not Making GPUs'

Marvell Technology's stock price surged from under $10 in 2016 to a record $290 in June 2026, fueled not by making GPUs, but by dominating AI infrastructure connectivity. This analysis argues the market misvalues MRVL as merely a smaller Broadcom in custom AI chips, overlooking its true, unique position. Marvell's core strength lies in enabling high-speed data flow for AI clusters through three interconnected businesses. First, it holds a commanding ~70% market share in high-speed optical DSPs (essential for data center light modules), a deep-moat business with accelerating growth. Second, its custom AI chip design business serves hyperscalers like AWS, Microsoft, and Google, with a significant revenue pipeline despite lower margins. Third, stable cash flows come from Ethernet switch chips and enterprise storage controllers. Together, they form a full-stack "AI data movement" platform. CEO Matt Murphy's transformative leadership since 2016, involving strategic divestments, key acquisitions (like Inphi for optical DSPs), and securing long-term agreements with major cloud providers, repositioned the company. A pivotal $2 billion strategic investment from NVIDIA in 2026 underscored Marvell's critical role in the AI ecosystem, particularly through collaborations like NVLink Fusion. While Marvell faces risks—including client concentration (losing the Amazon Trainium3 design), lower-margin business mix, competitive threats, insider selling, and complex supply chains—its fundamentals remain strong. The optical interconnect moat is widening with the acquisition of Celestial AI (photonics fabric), and financial metrics show accelerating revenue growth and operating leverage. With a PEG ratio suggesting undervaluation relative to its growth, the thesis is that the market undervalues Marvell's monopolistic position in AI "plumbing" while overemphasizing its competitive custom chip segment. The story transcends investing, symbolizing how in any complex system—from the internet to AI—the value of "connection" ultimately surpasses that of individual "nodes."

marsbit43m ago

Ten-Thousand-Word Analysis: From $10 to $290, MRVL Wins the Entire AI Era by 'Not Making GPUs'

marsbit43m ago

AI Relay Stations Spark Heated Debate on Zhihu: Behind Cheap Tokens, What Are Users Really Worried About?

A discussion on Zhihu about "AI relay stations" shifted the niche developer topic of "cheap tokens" into broader user awareness. Users moved beyond simply questioning the legitimacy of these services to focus on practical concerns: Where do cheap tokens truly come from? Is the model being accessed the real one? Can relay stations see prompts, code, and API keys? For occasional users, are the risks worth it? The core debate centered less on price and more on trust. A primary worry is model authenticity—the risk of "model swapping," where users paying for a premium model might be routed to a cheaper one, creating an information asymmetry. Others argued that cost comparisons matter; while cheaper than official pay-as-you-go APIs, relay stations may not be the lowest-cost option versus subscriptions, domestic models, or free tiers, making user needs assessment crucial. Speculation about token sources ranged from legitimate bulk discounts to gray-area methods like account sharing or exploiting regional pricing. This opacity makes risk assessment difficult for users. Data security emerged as a critical concern, especially for enterprise use. When processing sensitive information like code, contracts, or client data, the inability to verify a relay station's data handling, retention, or access policies poses significant compliance and confidentiality risks. The evolving consensus suggests relay stations can be used cautiously for low-sensitivity, disposable tasks (e.g., summarizing public info, simple translation). However, they should not be the default for sensitive, professional, or production workflows involving proprietary data, Agents, or automated systems. Recommendations include avoiding large prepayments, not relying on a single service, using test prompts to monitor quality, anonymizing data where possible, and keeping official channels as backups. Ultimately, the discussion framed tokens not just as a billing unit but as a measure of real cost encompassing price, model integrity, data security, and service stability. The popularity of relay stations highlights user demand for affordable access, but the debate underscores a key trade-off: the savings from cheap tokens may come at the price of trust, transparency, and control over one's data and AI experience.

marsbit1h ago

AI Relay Stations Spark Heated Debate on Zhihu: Behind Cheap Tokens, What Are Users Really Worried About?

marsbit1h ago

In-Depth Research Report on TradFi: The Convergence Wave of Crypto and Traditional Finance

In 2026, the crypto industry is undergoing a profound infrastructure-level transformation—TradFi assets are migrating on-chain at an unprecedented pace. According to CoinGecko's Q1 2026 report, the total value locked (TVL) of tokenized real-world assets (RWA) has surpassed $31 billion, a nearly 4x increase from $7.8 billion at the beginning of 2025, with the sector’s aggregate market capitalization reaching $19.3 billion. Among these, the market cap of tokenized stocks surged from $2 million to $486 million, with Q1 spot trading volume reaching $15.1 billion—a single quarter already surpassing the entire second half of 2025. RWA perpetual contract Q1 trading volume reached a staggering $524.8 billion, far exceeding the $313 billion for all of 2025. Meanwhile, BlackRock's BUIDL fund has reached $2.3 billion in scale and has filed for two new tokenized funds, signaling that the world's largest asset manager's tokenization strategy is evolving from pilot to product suite expansion. HTX, as a core participant in the crypto exchange sector, officially launched TradFi perpetual futures products including NVDA, AAPL, MSFT, META, and SPY in 2026, enabling crypto users to gain 24/7 trading access to core U.S. equities. Boston Consulting Group predicts that global tokenized asset scale could reach $16 trillion by 2030, while McKinsey offers a conservative estimate of approximately $2 trillion. The on-chain migration of TradFi assets is no longer a "future narrative" but a structural transformation unfolding in real time, as crypto exchanges evolve from single crypto asset trading platforms toward "multi-asset-class trading infrastructure."

HTX Learn1h ago

In-Depth Research Report on TradFi: The Convergence Wave of Crypto and Traditional Finance

HTX Learn1h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ADA (ADA) are presented below.

活动图片