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

Original article by Ed Zitron

Translated by Qin Xiaofeng, Odaily Planet Daily (@QinXiaofeng 888 )

Editor's Note: OpenAI secretly filed its IPO application with the SEC in June 2026, gradually approaching an IPO. The current timeline may be delayed until 2027; if successful, it would be one of the largest IPOs in the AI field.

Recently, independent tech commentator Ed Zitron published a lengthy article criticizing OpenAI. He points out that the real AI bubble is essentially the "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. He emphasizes that OpenAI's business model is unsustainable, relying on subsidies and circular financing, lacking genuine demand. (Odaily Note: Ed Zitron is known for his straightforward, financially detailed style of dissecting tech bubbles. This latest article is the most radical in his long-running series criticizing the AI bubble, following previous pieces like the exclusive report on OpenAI's losses and the Silicon Valley Bubble series.)

The original text is quite lengthy; here is the translation by Odaily Planet Daily. Enjoy~

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Today's article is one of the largest free newsletters I have ever written, compiling the results of the past 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 can talk about open-source models, Anthropic, or any other element – but without OpenAI, the AI industry wouldn't exist, and the justification for trillions of dollars in capital expenditure would vanish.

The AI bubble does not stem from any actual return on investment – whether purely monetary, like revenue or profitability, productivity gains, or anything tangible or measurable. Instead, it is a cult-like psychosis infecting the minds of some of the most powerful and wealthy individuals and institutions. A company's powerful mythology has inspired – and been used to inspire – the greatest capital misallocation in history.

Although this will annoy some people, I am fully convinced that the only reason this has lasted so long is that OpenAI hasn't collapsed yet. Its failure would 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 wake-up call, NVIDIA continues to sell GPUs, the coffers of the semiconductor industry continue to swell, and more spending commitments are made.

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

OpenAI can afford this spending only because its latest round (presumably fully completed) of $122 billion has received at least $50 billion, including $20 billion from SoftBank. NVIDIA mentioned in its latest quarterly report 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 Towards an Inevitable End

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

ChatGPT launched in November 2022, perfectly timed for a tech industry that was running out of ideas and teetering on the brink of a prolonged downturn. The IPO market had collapsed, interest rate hikes ended the zero-interest-rate era, pandemic-era over-hiring began to end with some of the worst layoffs in industry history, global venture capital shrank after historic over-investment in 2021, and tech stocks were battered.

The tech industry was forced to live within its means for the first time – something it has historically been reluctant to do. Big tech was unpopular with investors and the public. The excesses of the past decade, coupled with growing frustration over "tech exceptionalism," tested the patience of regulators and lawmakers. Moreover, lacking "the next big thing" – a sensational, game-changing product category – it no longer had an excuse for profligate spending or regularly breaking the unwritten and written rules governing society.

OpenAI's existence justified an era of frenzy and extravagance. Hyperscalers, lacking new hyper-growth ideas, pointed to ChatGPT's "fastest-growing user base ever" and Microsoft's "supercomputer" that built it, telling investors they would be left behind if they didn't invest. 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 this was the rapid growth of ChatGPT. Sam Altman's brief ouster only inflated the AI bubble further, adding a layer of dull palace intrigue to a tech industry lacking imagination and personality, and further solidified Microsoft's role as OpenAI's paternalistic funder, ensuring Altman's return to the helm.

To be clear, by "rapid growth," I mean 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, mostly in the form of Microsoft Azure credits.

OpenAI is also the reason Anthropic exists – not only because several of its founders came from OpenAI, but also because Google and Amazon both agreed to provide a total of $6 billion to Anthropic in 2023 as a means to "compete" with Microsoft's new darling, giving both further reason to spend hundreds of billions more to "ensure they don't miss out on AI."

When you remove the word "AI" from the equation, it all seems a bit absurd. $16 billion in equity investments, plus over $150 billion in capital expenditure by the end of 2023, all basically justified because one website is very popular.

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

Global venture capital fell to its lowest level since Q3 2016 in Q4 2023, 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 infrastructure scale they needed. Without hyperscalers inflating the value of these two companies, there wouldn't be the same 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 simply wouldn't have inflated. No other major AI company showed any signs of life – whether peddled by hyperscalers, funded by VCs, or launched by other tech companies.

The only reason any hyperscaler's AI efforts could generate revenue – and besides OpenAI and Anthropic, it's quite meager! – is because they know they just have to sit there and keep saying "AI is the future" until customers eventually give in and try it… largely because everyone is talking about ChatGPT.

Anthropic wasn't even considered a serious contender until early 2025, and it could only secure continuous funding because people wanted to invest in the "next OpenAI." Anthropic's initial funding rounds and infrastructure buildout were justified solely on the grounds of competing with OpenAI.

The $178.5 billion in US data center debt transactions in 2025? Almost entirely justified by OpenAI's growth and its insatiable demand for computing power, because besides OpenAI (and later Anthropic), no one else was using massive clusters of tens of thousands of GPUs, and there didn't seem to be a market for computing power of this scale for months or even years to come.

The largest consumers of computing power remain Microsoft (for OpenAI), Google (for Anthropic), Amazon (for OpenAI and Anthropic), CoreWeave (for OpenAI and Anthropic), Meta (mimicking other hyperscalers), and Oracle (for OpenAI). Otherwise, there is little evidence – and I have looked carefully – that AI computing demand exceeds a few tens of billions of dollars, which is already generous.

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

The assumption – because no one ever seriously thinks about it – is that since one OpenAI exists, more OpenAIs will bloom. Since one major computing customer exists, a template for future computing-intensive startups has been established… and, again, because no one ever seriously thinks about anything, no one realizes that a second OpenAI hasn't emerged because OpenAI and Anthropic are a carefully orchestrated financial psychological warfare campaign by the world's largest software companies.

OpenAI and Anthropic: Masters of Hyperscale Psychological Warfare for Silicon Valley's Monoculture

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

However, the real cost isn't just financial; it includes the experience and industry knowledge required to actually execute a massive infrastructure bailout. Besides Google, Microsoft, and Amazon, no other company has the scale or experience to build the kind of AI clusters needed by OpenAI (and later Anthropic).

We know this for several reasons. First, because before 2023, hardly any company was building AI computing clusters of the scale needed by OpenAI or Anthropic. The closest might have been cryptocurrency mining companies, and tellingly, many new cloud service providers today (most notably CoreWeave) originally operated warehouses filled with ASIC chips to mine Bitcoin and Ethereum.

Second, because according to 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, not the rule; most data centers were much smaller. The only companies with experience building at this scale were mostly hyperscalers themselves.

By treating OpenAI as a "venture-backed startup," the hyperscalers created the illusion that it was the next big company that would, in turn, create the next huge demand center for cloud computing – except these companies exist solely because the hyperscalers themselves are enabling their existence, funding them with enormous sums 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 – no matter how illogically – to imagine the existence of more OpenAIs, which in turn means those OpenAIs will need as much computing power as OpenAI.

Foolish investors who believe this nonsense can rationalize it through countless buy-side analysts or bought-off media pundits who talk about "insatiable demand for computing," pointing to capacity constraints and rising GPU prices as evidence of actual massive demand, without ever truly 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 dollar of profit, they provide justification for anyone to invest in AI data centers, with the logic being "the world's largest companies can't all be wrong" – even though the reason they are doing it is to expand capacity for OpenAI and Anthropic, companies that the hyperscalers themselves incubated.

Hyperscalers spending this much on AI infrastructure is fundamentally illogical and insane, and the reason few 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.

Whether you derive utility from LLMs or not is irrelevant, as this is not the actual basis for data center investment. While accelerated progress in code generation may have helped Anthropic's growth, the vast majority of data center capital expenditure is chasing the phantom of what AI *could be*, rather than having any connection to a company's overall revenue or economic situation – aside from their own computing spending.

This is the underlying greed driving this wasteful, reckless, and destructive era – the belief that there will be another OpenAI, and as I mentioned, the opportunity to become the landlord of the next OpenAI. And because the media and analysts rarely have original ideas, everyone rationalizes this waste through the same clichés, saying it's "like Uber" or "like Amazon Web Services."

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

This myth only grows in an environment deliberately starved of good information. We've been in this terrible bubble for four years, and there is still no consensus on the actual cost of large language models, proving 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 true underlying costs of their operations. Silicon Valley, an industry supposedly composed of free thinkers and individuals, is all too happy to accept any convenient myth that might sustain its dreams.

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

Investing in an AI startup? The money goes to some AI lab, which then pays a hyperscaler. Investing in an AI infrastructure company? The money goes to NVIDIA, then upstream to semiconductor companies. Ultimately, whether they die or get acquired, all the value ends up in the hands of the hyperscalers who created this illusory era and then blew it up 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 the potential collapse of OpenAI, they act purely cowardly, either saying "it won't be that bad" or vaguely claiming it's "too big to fail."

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

You see, Goldman Sachs' Jeffrey Papai recently noted that replicating the hundreds of billions of dollars raised by hyperscalers over the past four years would be "very difficult" – just $244 billion in 2026 alone, a problem because, as of Q3 2026, they could no longer fund their data center capital expenditure with cash flow.

And to be clear, hyperscaler capital expenditure doesn't have to stop completely for NVIDIA to fall. It just needs to slow down significantly enough that Jensen Huang can no longer deliver over 60% year-on-year revenue growth to investors, because the AI bubble is built on sentiment, and it can only survive as long as the sentiment doesn't sour.

Yes, yes, I know there are other customers, but the vast majority of NVIDIA's demand comes from hyperscalers, who are (mostly) either expanding for OpenAI and Anthropic or just mimicking what other hyperscalers are doing.

Once hyperscalers stop spending, banks worried about "choking" on data center debt will see a massive amount of capital exiting the market and underwrite deals accordingly.

This means that at some point, both OpenAI and Anthropic will hold out their hands saying "Please give us money!" – precisely when everyone else is scaling back. While NVIDIA might be a bit desperate and throw some money their way, its interest in further inflating the bubble will disappear if revenue starts to collapse, as investors begin to question whether this is all real or a giant