"OpenAI Will Surely Collapse, Global Stock Markets May Face Liquidation" - 15,000-Word Article by Major Bear Sparks Controversy

As OpenAI approaches its IPO, a blog post spanning approximately 15,000 words has once again escalated the debate surrounding the AI bubble.

Written by: Li Dan, Wall Street Insights

As OpenAI approaches its IPO, a blog post spanning approximately 15,000 words has once again escalated the debate surrounding the AI bubble.

Long-time AI bear and commentator Ed Zitron, who has a substantial readership in the tech industry, recently published a blog post presenting the most radical judgment to date: the true AI bubble is essentially the "OpenAI bubble"; if OpenAI ultimately fails, it will become the "Lehman Brothers" of the AI era, not only shattering the entire AI investment logic but also potentially triggering a massive repricing of data centers, AI infrastructure, and even global tech stocks.

His views quickly attracted the attention of financial media. According to the media, Zitron's core argument is not whether AI has value, but whether OpenAI possesses a business model robust enough to support the entire AI capital cycle. If the answer is negative, then the financing, computing power investments, and capital expenditure systems built around OpenAI may face a chain reaction.

Of course, this is not a consensus in the market. Investors, including Howard Marks, co-founder of Oak Tree Capital, have recently expressed that compared to their previous belief that AI might just be a bubble, they now recognize AI as a long-term value as a General Purpose Technology, believing that the industry is still in the early stages of commercialization.

AI Bubble or OpenAI Bubble?

Unlike most "AI bubble theories," Zitron has proposed a more shocking judgment:

What is truly concerning is not the entire AI industry, but a single company.

In his view, since the emergence of ChatGPT at the end of 2022, OpenAI has effectively become the "credit anchor" of the entire generative AI era.

Investors are willing to believe: AI will change the world; large-scale data centers are worth building; GPU demand will grow rapidly in the long term; large model companies will eventually become profitable; AI startups can create sufficiently large end-user demand.

And all of this, according to Zitron, is predicated on OpenAI's continued rapid growth. He believes that OpenAI not only defines the current AI craze but also shapes the capital market's valuation logic for the entire AI industry chain. Therefore, once this core assumption is broken, the impact may far exceed that of a single unicorn company.

In other words, OpenAI is no longer just a company but more like a "systemically important institution" for the entire AI investment cycle.

Why does he believe OpenAI's business model has fundamental flaws?

Zitron's doubts mainly focus on three aspects.

First, the inference costs remain excessively high.

As the user base of ChatGPT continues to grow, each user query means that GPU, electricity, and server costs keep rising. If a large number of users remain on low-cost or even free plans for an extended period, while enterprise-level revenue growth cannot simultaneously cover costs, then scaling up may actually lead to increased losses.

Second, capital expenditures are far outpacing improvements in cash flow.

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

OpenAI and its partners are pushing for investments in data centers amounting to hundreds of billions of dollars or even larger, and these projects typically take years to recoup costs. If future AI demand growth falls short of expectations, a significant amount of infrastructure may face declining utilization rates.

Third, there is a continued reliance on external financing.

Zitron analyzes that he believes OpenAI will need to continue financing for many years to cover expenditures related to model development, computing power procurement, and infrastructure construction; if the risk appetite in the capital markets declines or the financing environment tightens, its business model will face greater pressure.

These views currently remain Zitron's personal judgments and have not been recognized by OpenAI, but they do reflect the recent market debate surrounding AI's return on investment (ROI).

Why have Oracle, CoreWeave, and data center operators become focal points?

Compared to OpenAI itself, Zitron is more concerned about the leverage effect in the industry chain.

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

Major cloud providers like Microsoft, Google, Meta, and Amazon have all increased their capital expenditures; meanwhile, companies like Oracle and CoreWeave have taken on more and more AI computing power construction tasks.

These projects heavily rely on: long-term leasing, project financing, private credit, corporate bonds, and large-scale capital expenditures.

If future demand from core customers like OpenAI falls below expectations, or if the capital markets reassess AI's return rates, then the asset utilization rates of data centers, leasing contracts, and even financing capabilities may be affected.

The media points out that Zitron believes that if OpenAI encounters significant setbacks, companies like Oracle and CoreWeave, which rely on the growth of AI infrastructure demand, may be the first to suffer, as the high valuations previously assigned to these companies are largely based on expectations of sustained AI demand explosions.

Of course, major tech giants like Microsoft, Meta, and Alphabet are still continuing to expand their AI capital expenditures and generally emphasize that investments in AI infrastructure align with their long-term strategies, so there are currently no signs of a comprehensive contraction in capital expenditures.

Why are Anthropic and SoftBank also involved in the discussion?

In addition to OpenAI, Zitron has also pointed his finger at Anthropic.

His reasoning is that although the two companies have taken different development paths, they share a common characteristic: both require continuous investment of massive funds to build models, procure computing power, and rely on large tech companies for computing resources and financing support. If the pace of AI commercialization falls short of expectations in the future, both companies may face profitability pressures.

Another company frequently mentioned is SoftBank.

In recent years, SoftBank has returned to the forefront of large AI investments, actively participating in the financing of AI infrastructure, chips, and model companies.

If the AI industry enters a valuation adjustment cycle in the future, SoftBank's massive AI asset portfolio will naturally become a focus of market attention. However, SoftBank remains firmly committed to betting on the long-term development of AI, viewing it as a crucial direction for the next technological revolution.

Is the AI trading already overheated?

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

Proponents of the "bubble theory" argue that:

• AI infrastructure investment growth far outpaces revenue growth;
• The profitability model of large models has not yet been fully validated;
• Capital expenditures on data centers have reached record highs;
• Market valuations increasingly rely on growth expectations for the coming years.

Optimists, on the other hand, believe that AI represents a typical general-purpose technology revolution, similar to the internet and electrification, where initial investments often far exceed short-term returns but can create new industries and business models in the long run.

Howard Marks recently stated that he has shifted from initially doubting that AI might just be a bubble to recognizing its long-term value. He believes that the reasoning, contextual understanding, and interaction capabilities exhibited by modern AI have unprecedented characteristics and therefore cannot be simply compared to historical speculative bubbles.

Some academic research has also proposed a more neutral conclusion: the current AI market contains both genuine technological advancements and issues of localized overvaluation and excessive capital expenditures, thus being closer to a "technological revolution combined with localized bubbles" rather than purely speculative frenzy.

What truly deserves attention is not whether OpenAI will collapse

Regardless of whether one agrees with Zitron's judgment, the questions he raises are becoming focal points for an increasing number of investors:

When will AI investments finally translate into stable cash flows?

Over the past year, the capital markets have almost universally assumed that the higher the AI capital expenditures, the better.

However, recently, whether it is chip stocks, server manufacturers, or cloud computing companies, investors have begun to pay more attention to another set of indicators: corporate AI revenue growth; AI product payment rates; inference cost reduction rates; data center utilization rates; AI investment return cycles.

If these indicators continue to improve, then the current massive capital expenditures may ultimately prove to be a forward-looking investment similar to that of the internet era; but if the pace of commercialization lags behind investment expansion for an extended period, the market's valuation logic for AI trading may also face recalibration.

Therefore, the real discussion sparked by Ed Zitron's lengthy article is not whether "OpenAI will inevitably become the next Lehman Brothers," but rather it once again places the most critical question of the AI era in front of investors: after capital expenditures continue to set records, can cash flow and profitability keep pace? The answer to this question may ultimately determine the true direction of global AI trading in the coming years.