Sam Altman's Trillion-Dollar Bubble: Why OpenAI is the "Lehman Brothers" of the AI World?

Original article by Ed Zitron

Translation and compilation by Qin Xiaofeng, Odaily Planet Daily (@QinXiaofeng 888 )

Editor's Note: In June 2026, OpenAI secretly filed for an IPO with the SEC, gradually approaching an IPO. The current timeline may be pushed back to 2027. If successful, OpenAI will become one of the largest IPOs in the AI field.

Recently, independent tech commentator Ed Zitron published a lengthy article criticizing OpenAI. He pointed out that the true AI bubble is essentially an "OpenAI bubble." If OpenAI fails, it will become the "Lehman Brothers" of the AI era, potentially triggering a repricing of data centers, AI infrastructure, and tech stocks, emphasizing that OpenAI's business model is unsustainable, relying on subsidies and circular financing, and lacking genuine demand. (Odaily Note: Ed Zitron is known for his straightforward style and meticulous focus on financial details when dissecting tech bubbles. This long-form piece is the most aggressive in Zitron's series of critiques on the AI bubble, following previous articles such as exclusive reports on OpenAI's losses, the Silicon Valley Bubble series, etc.)

The original article is quite long. Below is the translation and compilation by Odaily Planet Daily. Enjoy～

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Today's article represents one of the largest free newsletters I've ever written, compiling six months of work.

And it all starts with one question: How much do you trust Sam Altman? The stock market, and to some extent the global economy, depends on your answer.

OpenAI has become one of the largest liabilities in modern economic history. You could argue that OpenAI is no longer the focal point of the AI bubble—you could talk about open-source models, Anthropic, or any other element—but without OpenAI, the AI industry would not exist, and trillions of dollars in capital expenditure would have no justification.

The AI bubble did not originate from any actual return on investment—whether purely monetary terms like revenue or profitability, productivity improvements, or any tangible or measurable metric. Instead, it's a cult-like psychotic episode that has infected the minds of some of the most powerful and wealthy individuals and institutions, where the powerful mythology of one company has inspired—and been used to inspire—the greatest capital misallocation in history.

Although this may anger some people, I am fully convinced that the only reason this has lasted so long is that OpenAI has not yet collapsed. Its failure will be a watershed moment—the Lehman Brothers of the AI bubble, an event that will define the end of one era and the beginning of another, shaking those infected out of their delusion. Without this alarm bell, NVIDIA will continue to sell GPUs, the semiconductor industry's coffers will keep swelling, and more spending commitments will be made.

OpenAI plans to burn through over $852 billion by the end of 2030. It accounts for $748 billion of the remaining performance obligations of Microsoft, Amazon, and Oracle, in addition to at least $70 billion in RPOs from Cerebras, CoreWeave, Nebius, IREN, Lambda, and Nscale (according to Kakashii's data), and plans to spend an undetermined tens of billions on Broadcom's "Jalapeno" chip. It plans to spend $50 billion or more on compute this year, which I estimate exceeds 50% of global AI compute spending (OpenAI accounts for more than 50% of all AI computing infrastructure).

OpenAI can afford these expenditures only because of its latest round (presumed fully completed) of $122 billion in funding, with at least $50 billion already received, including $20 billion from SoftBank (totaling $30 billion, with the third tranche due on October 1, 2026). NVIDIA mentioned in its latest quarterly earnings that it "estimates that one AI research and deployment company contributed a significant share of [its] revenue for the first quarter of fiscal 2027 by purchasing cloud services from its customers," referring of course to OpenAI.

The AI Bubble Is the OpenAI Bubble—Heading Inevitably Toward Collapse

OpenAI is the reason everyone is paying attention to AI. In March 2019 (according to JustDario's data), NVIDIA acquired a company called Mellanox, which produces the high-speed networking technology needed to create AI GPU clusters. Four months later, Microsoft invested $1 billion in OpenAI and began purchasing AI GPUs and building AI infrastructure for it. By March 2020, NVIDIA had shipped its A100 GPU, and in May 2020, Microsoft announced it had built a supercomputer for OpenAI with "over 285,000 CPU cores [and] 10,000 GPUs."

ChatGPT launched in November 2022, perfectly timed for the tech industry—which was then at its wit's end and teetering on the edge of a prolonged downturn. The IPO market had collapsed, interest rate hikes ended the era of zero interest rates, pandemic-era overhiring was ending with some of the worst layoffs in industry history, global venture capital shrank after historic overinvestment in 2021, and tech stocks were battered.

For the first time, the tech industry was forced to live within its means—something it has historically been reluctant to do. Big tech companies were unpopular with investors and the public. The excesses of the past decade—coupled with growing frustration over "tech exceptionalism" (the belief that rules governing the rest of the world don't apply to Silicon Valley)—tested the patience of regulators and legislators. And without "the next big thing"—a sensational, game-changing product category—there were no more excuses for lavish spending or regularly breaking the unwritten and written rules that govern society.

OpenAI's existence legitimized an era of frenzy and extravagance. Lacking new hyper-growth ideas, hyperscalers pointed to ChatGPT's "fastest-growing user base ever" and Microsoft's "supercomputer" that built it, telling investors that if they didn't invest, they would be left behind. Amazon, Meta, and Google announced their own vague "supercomputers" in 2023.

By the end of 2023, NVIDIA had sold 500,000 A100 GPUs, and the only reason it could do so was ChatGPT's rapid growth. Sam Altman's brief ouster only inflated the AI bubble further—adding a layer of dreary palace intrigue to an uninspired and bland tech industry—and further solidified Microsoft's role as OpenAI's paternalistic funder, ensuring Altman's return to the helm.

To clarify, by "rapid growth" I mean that OpenAI reached 100 million weekly active users by the end of 2023, with monthly revenue of around $108 million. Microsoft invested another $10 billion that year, most of it provided in the form of Microsoft Azure credits.

OpenAI is also the reason Anthropic exists—not only because many of its founders came from OpenAI, but because Google and Amazon both agreed to provide it with a total of $6 billion in 2023 as a means to "compete" with Microsoft's new favorite, giving both companies a reason to spend further trillions on "ensuring they don't miss out on AI."

When you remove the word "AI" from the equation, it all seems somewhat absurd. $16 billion in equity investments, coupled with over $150 billion in capital expenditures by the end of 2023—all of this was essentially justified simply because one website was very popular.

And the only reason these two companies could grow was that hyperscalers funded their entire infrastructure.

In Q4 2023, global venture capital fell to its lowest level since Q3 2016, with US startups accounting for $183.6 billion of the year's total investment. Venture capital itself could not—and would not—truly support OpenAI or Anthropic at the scale of infrastructure they needed, and without hyperscalers inflating the value of both companies, there wouldn't have been any hunger from hyperscalers or those providing debt financing for data centers—almost entirely because of OpenAI's success.

If you removed OpenAI from the 2020-2024 period, the AI bubble would never have inflated. No other major AI company showed any signs of life—whether peddled by hyperscalers, funded by venture capitalists, or launched by other tech companies.

The only reason any hyperscaler's AI efforts could generate revenue—and apart from OpenAI and Anthropic, the revenue has been quite meager!—is because they knew they could just sit there and keep saying "AI is the future" until customers eventually gave in and tried it… largely because everyone was talking about ChatGPT.

Anthropic wasn't even considered a contender until early 2025, and it could only continue to get funding because people wanted to invest in the next OpenAI. Anthropic's initial funding rounds and infrastructure construction were justified solely by the rationale of competing with OpenAI.

US data center debt transactions reached $178.5 billion in 2025? Almost entirely justified by OpenAI's growth and its insatiable demand for compute, because apart from OpenAI (and later Anthropic), no one else was using massive clusters of tens of thousands of GPUs, and a market for compute at this scale didn't seem to emerge for months or even years afterward.

The largest consumers of compute remain Microsoft (for OpenAI), Google (for Anthropic), Amazon (for OpenAI and Anthropic), CoreWeave (for OpenAI and Anthropic), Meta (imitating other hyperscalers), and Oracle (for OpenAI). Otherwise, there is very little evidence—and I have looked diligently—that AI compute demand exceeds a few tens of billions of dollars, and that's being generous.

All of this investment—whether in AI startups or data centers—exists to fund the next OpenAI, or to become the landlord of the next OpenAI.

The assumption—because no one has ever seriously thought about it—is that since one OpenAI exists, more OpenAIs will bloom. Since there is one large compute customer, the template for future compute-intensive startups has been set… and, again, because no one ever seriously thinks about anything, no one realizes that the reason there is no second OpenAI is that OpenAI and Anthropic are a carefully orchestrated financial psychological operation by the world's largest software companies.

OpenAI and Anthropic: Hyper-Scale Psychological Warfare Experts Tailored for Silicon Valley's Monoculture

The brutal truth is that you cannot fund an AI lab through venture capital. Although OpenAI and Anthropic have raised nearly $300 billion in the past few years, their actual infrastructure costs—the GPUs and data centers powering their services—are entirely funded by hyperscalers, costing perhaps another $250 billion in the process, as Microsoft stated it had spent $100 billion on its relationship with OpenAI by early 2026.

However, the real cost is not just financial; it's the experience and industrial knowledge required to execute a massive infrastructure bailout. Apart from Google, Microsoft, and Amazon, no other company has the scale or experience to build the kind of AI clusters that OpenAI (and later Anthropic) requires.

We know this for several reasons. First, because before 2023, almost no company was building the scale of AI compute clusters that OpenAI or Anthropic needed. The closest might have been cryptocurrency mining companies, and tellingly, many of today's new cloud service providers (most notably CoreWeave) originally operated warehouses full of ASIC chips for mining Bitcoin and Ethereum.

Second, because based on conversations with data center industry insiders, the entire Overton window of what constitutes a "large" facility has shifted. Previously, a 50MW data center would have been considered a significant (even noteworthy) development. These were exceptions rather than the rule, with most data centers being much smaller. The only companies with experience building at this scale were mostly hyperscalers.

By portraying OpenAI as a "venture-backed startup," hyperscalers created the illusion that this was the next big company, which would in turn create the next huge demand center for cloud computing—except the only reason these companies exist is that hyperscalers themselves are facilitating their existence, funding them with massive sums of money, and allowing them to burn cash at will.

This is why the idea of OpenAI's infinite growth is central to the AI bubble myth. The existence of one OpenAI allows others—however illogically—to imagine the existence of more OpenAIs, which in turn means those OpenAIs will need as much compute as OpenAI.

Stupid investors who believe this nonsense can rationalize it through countless buy-side analysts or bought-off media pundits—who talk about "insatiable demand for compute," point to capacity constraints (caused by slow data center construction and OpenAI and Anthropic consuming most of the world's compute) and rising GPU prices as evidence that huge demand actually exists, without ever really thinking deeply.

The biggest trick the hyperscalers play is never backing down. By pouring over a trillion dollars into AI capital expenditure without showing a single dollar of profit, they justify anyone investing in AI data centers, with the logic being "the world's largest companies can't all be wrong"—even though the reason they're doing it is to expand capacity for OpenAI and Anthropic, which are companies the hyperscalers themselves incubated.

Hyperscalers spending so much money on AI infrastructure is fundamentally illogical and crazy, and the reason few people are willing to say so is that, until recently, suggesting it was a waste of money was considered radical—almost entirely because of OpenAI's existence and continued growth. (Note: I realize Anthropic has attracted significant attention and grown rapidly in the past year, but it can do so A) because of the OpenAI myth, and B) because it, too, was incubated and allowed to operate at massive losses.)

Whether you get utility from LLMs or not is irrelevant, because it's not the actual basis for data center investment. While accelerated progress in code generation (which itself is only possible with massive subsidies) may have helped Anthropic grow, the vast majority of data center capital expenditure is chasing the phantom of what AI might become, not linked to any company's overall revenue or economic reality—except, of course, for their own compute spending.

This is the underlying greed driving this wasteful, reckless, and destructive era—the belief that there will be another OpenAI, and, as I've said, the opportunity to become the landlord of the next OpenAI. And because the media and analysts rarely have original ideas, everyone rationalizes (and continues to rationalize) this waste with the same clichés, saying it's "like Uber (it's not!)" or "like Amazon Web Services (between 2003 and 2015, Amazon spent $29.7 billion on capital expenditure, adjusted for inflation)."

Like any great investment bubble, the more money pours in, the stronger the FOMO, the more dollars that can be rationalized, and the more complex and twisted the myth becomes—which is why you see prominent venture capitalists claiming AI labs have "inference profit margins above 90%," a completely unsubstantiated claim that AI acolytes cling to and repeat so often it's enshrined as truth, likely because they can't face the fact that you can burn through $14,000 worth of tokens on a $200 monthly ChatGPT subscription.

This myth can only grow in an environment deliberately starved of good information. That we've been stuck in this terrible bubble for four years and still have no consensus on the actual cost of large language models is proof of an industry-wide effort to suppress this information.

OpenAI, Anthropic, Microsoft, Google, and Amazon have done everything in their power—according to discussions with sources familiar with their infrastructure—to obscure the actual underlying costs of their operations, and Silicon Valley, an industry supposedly composed of free thinkers and individuals, is all too happy to accept any convenient myth that sustains its dreams.

Ultimately, they have all become useful idiots for the hyperscalers. Their obsessive attachment to OpenAI—and by extension Anthropic—appears to be decisions made in the name of "democratizing powerful AI," when in reality every dollar flows to Microsoft, Google, Amazon, or Oracle, which then flows to NVIDIA or Broadcom, which then flows to TSMC, SK Hynix, Samsung, or Micron.

Invest in an AI startup? They'll pay an AI lab, which will then pay a hyperscaler. Invest in an AI infrastructure company? Money flows to NVIDIA, then upstream to semiconductor companies. Ultimately, whether they die or get acquired (because none of them will go public), all the value falls into the hands of the hyperscalers who created this illusory era and then inflated it into something very dangerous.

Why the AI Bubble Cannot Survive Without OpenAI

The problem, however, is that this industry cannot survive without OpenAI under any circumstances.

When people discuss OpenAI's possible collapse, they behave entirely out of cowardice, either saying "it won't be that bad" or vaguely saying it's "too big to fail."

If OpenAI—the company with the most money, the most infrastructure, the most attention, and the most AI talent—collapses, it will likely happen after AI data center debt and venture capital funds are almost completely exhausted.

You see, Goldman Sachs' Jeffrey Papai recently pointed out that replicating the hundreds of billions of dollars that hyperscalers have raised over the past four years would be "very difficult"—$244 billion in 2026 alone, even counting NVIDIA and SpaceX—which is a problem because, as of Q3 2026, they can no longer fund their data center capital expenditure with cash flow.

And to be clear, hyperscaler capital expenditure doesn't