「Lehman Moment」Approaching? Short Seller's 15,000-Word Exposé: OpenAI Is Bound to Collapse, Global Stock Markets Face a Crackdown

Original Author: Li Dan

Source: Wall Street CN

As OpenAI approaches its IPO, a blog post of approximately 15,000 words has once again pushed the debate over the AI bubble to a fever pitch.

Ed Zitron, a long-time AI skeptic and commentator with a large readership in the tech industry, has put forward his most radical judgment yet in a recent blog post: the real AI bubble is essentially an "OpenAI bubble"; if OpenAI ultimately fails, it could become the "Lehman Brothers" of the AI era, not only shattering the entire investment thesis for AI but potentially triggering a massive repricing of data centers, AI infrastructure, and even global tech stocks.

His views quickly captured the attention of financial media. In the media's view, Zitron's core argument isn't whether AI has value, but whether OpenAI possesses a business model robust enough to sustain the entire AI capital cycle. If the answer is no, then the system of financing, compute investment, and capital expenditure built around OpenAI could face a chain reaction.

Of course, this is not the market consensus. Investors like Howard Marks, co-founder of Oaktree Capital, recently stated that, compared to earlier views that AI might be just a bubble, they now more readily acknowledge AI's long-term value as a General Purpose Technology platform, believing the industry is still in its early commercialization stage.

AI Bubble, or OpenAI Bubble?

Unlike most "AI bubble" theories, Zitron presents a more provocative judgment:

What truly warrants concern isn't the entire AI industry, but a single company.

In his view, since the launch of ChatGPT in late 2022, OpenAI has effectively become the "credit anchor" for the entire generative AI era.

Investors have been willing to believe: AI will change the world; hyperscale data centers are worth building; GPU demand will see sustained high growth; massive model companies will eventually become profitable; AI startups can generate sufficient end-user demand.

All of this, Zitron argues, is predicated on OpenAI continuing its rapid growth trajectory. He believes OpenAI has not only defined the current AI boom but has also shaped the capital market's valuation logic for the entire AI value chain. Therefore, if this core assumption is broken, the impact could far exceed that of a single unicorn company failing.

In other words, OpenAI is no longer just a company; it has become a "systemically important institution" for the entire AI investment cycle.

Why Does He Believe OpenAI's Business Model Has Fundamental Flaws?

Zitron's criticisms focus on three main areas.

First, inference costs remain too high.

As ChatGPT's user base continues to grow, each user query incurs ongoing costs for GPUs, electricity, and servers. If a large number of users remain on low-cost or even free tiers for a long time, and enterprise revenue growth cannot simultaneously cover these costs, then scaling up might actually mean expanding losses.

Second, capital expenditure is far outpacing cash flow improvement.

Currently, the largest expense in the AI industry is no longer model training, but inference compute, GPU procurement, and global data center construction.

OpenAI and its partners are driving tens of billions of dollars or more in data center investments, projects that typically take years to recoup costs. If future AI demand growth falls short of expectations, a significant portion of this infrastructure could face underutilization issues.

Third, continued reliance on external financing.

Zitron analyzes that OpenAI will likely need continuous financing for years to come to cover model R&D, compute procurement, and infrastructure build-out. If capital market risk appetite declines or the financing environment tightens, its business model will face increased pressure.

These views, while currently Zitron's personal opinion and not endorsed by OpenAI, do reflect the ongoing market debate surrounding AI Return on Investment (ROI).

Why Oracle, CoreWeave, and Data Center Operators Become Focal Points?

More so than OpenAI itself, Zitron is concerned about the leverage effect within the industry chain.

Over the past two years, the U.S. tech sector has witnessed an unprecedented wave of data center construction.

Hyperscalers like Microsoft, Google, Meta, and Amazon have all significantly increased capital expenditure. Concurrently, companies like Oracle and CoreWeave have taken on an increasing share of AI compute infrastructure build-out.

These projects rely heavily on: long-term leases, project financing, private credit, corporate bonds, and massive capital expenditure.

If future demand from core clients like OpenAI falls short of expectations, or if capital markets reassess AI returns, data center utilization rates, lease contracts, and even financing capabilities could be affected.

Media outlets point out that Zitron believes if OpenAI suffers a major setback, companies like Oracle and CoreWeave, which depend on growing AI infrastructure demand, could be the first to be hit. This is because the high valuations these companies previously received from the market were largely built on expectations of explosive AI demand.

Of course, tech giants like Microsoft, Meta, and Alphabet continue to expand their AI capital expenditure, universally emphasizing that AI infrastructure investments align with their long-term strategy. Therefore, there are currently no signs of a widespread pullback in capital spending.

Why Are Anthropic and SoftBank Also Drawn into the Discussion?

Beyond OpenAI, Zitron also targets Anthropic.

His reasoning is that although the two companies pursue different development paths, they share common characteristics: a need for continuous massive investment in model building and compute procurement, and reliance on large tech companies for compute resources and financing support. If the pace of AI commercialization slows down, both companies could face profitability pressures.

Another company repeatedly mentioned is SoftBank.

In recent years, SoftBank has returned to the forefront of major AI investments, actively participating in financing rounds for AI infrastructure, chip, and model companies.

If the AI industry enters a valuation adjustment cycle, SoftBank's vast AI asset portfolio will naturally become a market focus. However, SoftBank itself remains firmly committed to AI's long-term development, viewing it as a key direction for the next technological revolution.

Have AI Trades Become Overheated?

In fact, Wall Street's debate on whether AI has entered a bubble phase has been ongoing for over a year.

Proponents of the "bubble theory" argue:

• AI infrastructure investment growth is far outpacing revenue growth;
• The profitability model for large language models remains unproven;
• Data center capital expenditure is at record highs;
• Market valuations increasingly rely on future growth expectations years down the line.

Optimists counter that AI represents a classic general-purpose technology revolution, akin to the internet or electrification. Early investment often far exceeds short-term returns, but over the long term, it can create new industries and business models.

Howard Marks recently stated that he has shifted from his initial skepticism that AI might be just a bubble to a greater recognition of its long-term value. He believes that modern AI's demonstrated reasoning, contextual understanding, and interactive capabilities are unprecedented, and therefore cannot be simply compared to historical speculative bubbles.

Some academic research also offers a more neutral conclusion: the current AI market exhibits both genuine technological progress and localized valuation overheating along with premature capital expenditure. Thus, it is closer to a "technological revolution overlapped with local bubbles" rather than pure speculative frenzy.

What Truly Matters Isn't Whether OpenAI Will Fall

Regardless of whether one agrees with Zitron's judgment, the questions he raises are becoming a focal point for a growing number of investors:

When will AI investments translate into stable cash flows?

Over the past year, the capital market almost defaulted on the idea that higher AI capital expenditure was always better.

But recently, whether for chip stocks, server manufacturers, or cloud computing companies, investors are beginning to pay more attention to a different set of metrics: enterprise AI revenue growth; AI product pay-up rates; the rate of decline in inference costs; data center utilization rates; the payback period for AI investments.

If these metrics continue to improve, the current massive capital expenditure might ultimately prove to be a forward-looking investment similar to the internet era. However, if commercialization speed persistently lags behind investment expansion, the market's valuation logic for AI trades could face recalibration.

Therefore, the real discussion sparked by Ed Zitron's lengthy article is not "whether OpenAI will definitely become the next Lehman Brothers," but rather that it once again places the most critical question of the AI era before investors: as capital expenditure continues to break records, can cash flow and profitability ultimately keep pace? The answer to this question may well determine the true direction of global AI trades in the coming years.