While many NVIDIA fans praise Jensen “Schlong” Huang’s “vision”—either because they believe CUDA made NVIDIA incredibly successful even before the AI frenzy or because they are brainwashed gamers—the reality is that Schlong’s financial engineering could trigger the next market crash, which would severely impact the world’s economy.

The excerpts from press reproduced below have been published in my Quick Notes section as the 200th note: 🧑‍💻📈 How Jensen “Asshole” Huang engineered a GPU shortage. To give you a quick map: they start with Wccftech elaborating on a WSJ article, then it copiously cites Ed Zitron’s excessively long article, Where’re All The AI Chips?

🧑‍💻📈

Wccf (Where Consumers Come First) tech: Instead Of Lowering GPU Prices, Former NVIDIA CEO Jensen Huang Reportedly Financed AI Startups To Absorb Excess Silicon, Shattering The Shortage Perception:

NVIDIA faced an “avalanche” of excess TSMC silicon and memory, and the only way to curb the oversupply was to fund AI startups

The fresh revelations stem from a tense August 2026 confrontation between Huang and NVIDIA’s longtime Chief Financial Officer, Colette Kress, now the interim CEO. Seeing as how the former head honcho was escalating the GPU maker’s financial exposure, Kress pushed to rein in the company’s “circular financing” commitments to smaller cloud providers and major AI firms such as OpenAI and Anthropic, according to The Wall Street Journal.

…

To justify NVIDIA’s need to financially underwrite OpenAI and smaller cloud operators to keep them purchasing its GPUs, Huang privately admitted that major cloud giants were pivoting away from NVIDIA in favor of in-house custom chips. With hyperscalers actively reducing their reliance on “off-the-shelf parts,” Huang acknowledged that the massive volume of prepaid TSMC wafers and memory NVIDIA had committed to buying was “coming at him like an avalanche.”

By funding datacenter expansion for AI startups, NVIDIA effectively created artificial demand to clear out its own surplus silicon instead of letting the supply flood the market and force GPU prices down. Those internal pressures align with earlier assessments from industry analyst Ed Zitron, who estimated that at least $200 billion worth of NVIDIA AI GPUs are sitting idle in warehouses after being purchased by cloud giants that lacked either the electrical grid capacity or immediate end-user demand to power them on.

…

For PC builders and hardware enthusiasts who have endured years of steep GPU and RAM pricing under the impression of severe component scarcity, the disclosures shatter the shortage narrative and expose the aggressive financial maneuvering used to keep hardware prices elevated.

The source of the above revelations is this WSJ article. Its ending is telling:

In Berlin during a conference, Huang, accompanied by his top sales executives, broadcast a new message to a half-dozen CEOs of companies that are major buyers of Nvidia chips who were seated around a circular table. He opened with a story that sounded like a fable, according to people who attended.

Huang had once found a soup vendor in Japan, he said, with a long line of customers. He joined the line, and after a long wait, enjoyed one of the best bowls of soup he’d ever had. When he asked for another serving, the soup-seller told him no—he had too many customers waiting and not enough soup for all of them.

“He’s a better businessperson than all of you,” Huang told the cloud computing CEOs. The soup-man doesn’t sell out all his inventory to one or two customers, but instead keeps prices high and his customer base diverse, the CEO said.

“You are not establishing the value of my products,” he said.

As for Ed Zitron’s estimations, they’re from Sept. 22: Where’re All The AI Chips?

A few months ago, I asked where all the data centers were, because I was struggling to find proof that very many were being finished despite all the capex spending, construction, and the skyrocketing cost of RAM.

The answer to that question was, effectively, “nowhere.” The vast majority of Microsoft data center projects I could find had barely gotten started, aside from the massive OpenAI-dedicated “Fairwater” data centers that are “open” in the sense that some of the buildings are turned on, with the remainder either being actively built or with construction expected to start at some later point.

…

Today’s newsletter is about a cruel truth: that NVIDIA’s revenue growth is almost entirely the result of speculative purchases by hyperscalers and neoclouds that take years to install its GPUs.

In other words, I think everybody is completely wrong about data center capacity, and it’s going to be a nightmare to untangle once they work it out.

Everybody wants AI to be just like the Dot-Com Bubble, when I fear that GPUs may end up like the millions of unsold copies of Atari’s ET found buried in New Mexico.

Microsoft Only Has 2GW of AI Specific Data Center Capacity, Despite Claiming It Added A Gigawatt A Quarter Three Quarters straight

…

Let me be very blunt: it appears that Microsoft has, since the beginning of 2022, spent around $265 billion in capital expenditures (and added around $320 billion in assets to its properties, plants and equipment) to bring around $50 billion of GPUs online, and has used broad, vague language to make it seem like it’s been far more productive.

Nobody on God’s green Earth is sitting here wondering if Microsoft added gigawatts of CPU capacity or cloud storage, nor are analysts desperate to hear about aggregate numbers — they’re asking where is all the money you’re spending on AI going, and the answer, it appears, is “into a warehouse” or “into a data center without power.”

…

It appears that Microsoft is being vague with its numbers to hide the very obvious truth: that it’s spending hundreds of billions of dollars to buy GPUs that sit in warehouses or unpowered data centers, likely years in advance.

This is a huge scandal. Why is Microsoft buying so many GPUs if it’s not got anywhere to put them? Why isn’t it waiting to buy the GPUs when it needs them, rather than buying them months or years in advance? Why is Microsoft telling us it’s bringing gigawatts of capacity online when it’s very clearly doing otherwise?

And at this point, can we take anyone’s capacity announcements seriously?

Microsoft Is Warehousing An Estimated $50 Billion to $100 Billion In GPUs

…
As mentioned above, Nadella mentioned in November 2025 that he had “…a bunch of chips sitting in inventory that [he couldn’t plug in],” but didn’t make any mention of how many there were.

In other words, Microsoft is warehousing anywhere from $50 billion to $100 billion in GPUs, and has barely gotten $50 billion worth installed in the last four years.

If Microsoft is struggling, everybody’s struggling, and we may have a very inconvenient truth: that NVIDIA has potentially sold hundreds of billions of GPUs years in advance.

And yes, everybody is struggling.

Neoclouds and Hyperscalers Have Over $374 Billion In “Construction In Progress” Assets, With An Estimated $200 Billion+ In GPUs Sitting In Warehouses

…

Across hyperscalers including Google, Meta, Oracle, Amazon, SpaceX, and Tesla, neoclouds like CoreWeave and IREN, and colocation companies like Core Scientific and Applied Digital, there is over $374 billion in construction in progress, a figure that’s likely lower than the true number, because Amazon’s contribution ($71.7 billion) is only current as of the end of 2025.

The $374 billion number doesn’t include any CIP from Microsoft, Firmus, Sharon AI, Equinix, Nebius, or any number of private operators like Vantage, DataBank, CyrusOne, or QTS. It does not include any sovereign AI projects (Humain/Saudi Aramco, Singapore, Reliance in India, G42 in the UAE), private projects run by or for OpenAI or Anthropic, Stack Infrastructure (which is building Oracle’s New Mexico data center, with the CIP not landing on Oracle’s balance sheet as it doesn’t “own” the project), or Meta’s $27.3 billion off-balance-sheet “Hyperion” data center. Between them, I think there’s at least another $50 billion to $100 billion of CIP, but for fairness I’m not including it in the larger total.

…

Between 2023 and 2025, the combined capital expenditures of these companies was $351.6 billion on $243.2 billion of CIP. In other words, lots of money out the door with a bunch of stuff in a big, confusing and potentially-unproductive pile.

If I’m honest, I think that $200 billion number might be a little generous.

…

So, if we assume the number is roughly $374 billion, plus (at minimum) $50 billion from Microsoft, plus another (at minimum) $20 billion from Amazon, that puts us around $444 billion, with Google’s share — $120.8 billion — being 60% GPUs and related hardware, for a total of $72.48 billion, putting us at (assuming a 50/50 split) an estimated $234 billion in GPUs and TPUs sitting in warehouses.

NVIDIA and Broadcom Have Sold Approximately $561.5 Billion In AI Chips and Hardware Since The Beginning of 2023, Meaning That Approximately 50% Of Sold AI Chips/Hardware Is Being Warehoused And Their Sales Are The Product Of Speculation Rather Than Value

…

All AI Data Center Capacity Data Is Now Suspicious, Questionable, Or Outright Useless As A Barometer Of What’s Actually Functional

…

NVIDIA Is Selling Hundreds of Billions Of Dollars’ Worth Of GPUs Years In Advance — Why Are Companies Still Buying Them?

This situation is utterly obscene.

It’s very clear that at least $200 billion — if not more than $300 billion — of NVIDIA’s GPU sales have been made a year or years in advance, just as the company telegraphs it will make over $670 billion in revenue in its fiscal year 2028 (starting February 2027).

It’s also clear that Microsoft, Google, Amazon, Meta, SpaceX, and every neocloud are purchasing NVIDIA GPUs tens of billions at a time under the implicit knowledge that it will take years to build the capacity and connect the power to them, creating what amounts to the largest pre-order campaign in the history of capitalism, but also a material misrepresentation of the current AI buildout.

Less Than 50% Of The $1.2 Trillion+ In Hyperscaler Capital Expenditures Have Turned Into Operational Data Center Capacity — Leaving An Estimated $390 Billion+ Of AI Hardware Uninstalled

…

$200bn+ In Uninstalled GPUs Guarantees We’re In An Overbuild Scenario — And How Anthropic and OpenAI Distort The Demand For AI Compute

…

The $1.3 trillion in compute commitments from Anthropic and OpenAI have created a distortion in the demand for AI compute, in part because of their massive amounts of capital and in part because of their ridiculous demands for compute.

…

You see, the ultimate problem is that the world outside of hyperscalers is — as a result of the obfuscation of data center capacity — under the belief that these companies are buying GPUs and then quickly turning that into cash versus buying these GPUs and quickly turning them into storage.

…

What If… We’re In An AI Data Center Overbuild?

…

In all honesty, the best case scenario would be if NVIDIA stopped selling GPUs, or AI data center debt stopped being issued, because every single time a data center is funded and breaks ground, it increases the severity of the overbuild scenario.

As I discussed in my piece This Is Worse Than The Dot Com Bubble from a few months ago, GPUs are nothing like dark fiber. An incomplete data center will cost just as much to finish in 2030 as it will today, as will the GPUs cost just as much to run. The difference will be that once the AI bubble bursts, the customers of AI compute — predominantly unprofitable, venture-backed startups — won’t exist.

Right now, with more than half of NVIDIA GPUs yet to be turned into operational capacity, every single new data center being built is effectively a bet on whether AI demand is larger in 2028 or 2029 than it is today, because you’re going to be competing with all the other capacity coming online in the years preceding that have already broken ground.

…

NVIDIA Should Issue Guidance Around Operational AI Capacity Versus Sales, Otherwise It Is Actively Misleading Investors and Customers Alike

…

I want to be clear about something: there is absolutely no advantage to or reason for buying GPUs months or years in advance outside of the vendor (NVIDIA) playing hardball. Yet it appears that hyperscalers — which make up more than 50% of its revenue — are willing to do so, quarter after quarter, hoarding tens of billions of dollars to “secure supply” that is only constrained because of the hyperscalers themselves.

…

I need to be more blunt here: the vast majority of companies that have bought GPUs have yet to turn them into meaningful revenue, if they’ve turned them on at all. Most of NVIDIA’s sales are sitting in warehouses, and that is a significant disclosure that NVIDIA should have already been forced to make.

…

NVIDIA’s Revenues Are Driven By Speculative Sales Of Assets That Take Years To Make Money, And Everybody Conflating Demand For GPUs With Demand For AI Compute

I realize that I’m Mr. Bubble and everybody gets mad at me for poo-pooing our big, beautiful AI bubble, but I cannot express how serious this situation has become. Hundreds of billions of dollars of debt has been issued, the price of every imaginable consumer electronic has been inflated, and both most of our stock market and parts of our economy have become dependent on the sales of GPUs, most of which are going to a handful of companies that are, for the most part, not fucking using them.

…

One of the core reasons that people believe that AI isn’t a bubble is because of NVIDIA’s perpetual quarterly revenue growth, which is branded, once again, as insatiable demand for AI compute, when it’s actually almost entirely-speculative purchases based on potential revenues, with said potential mostly driven by the compute spend from OpenAI and Anthropic, two unprofitable and unsustainable AI labs.

…

I think everybody who invests in these things ultimately loses, ranging from embarrassment and terrible earnings for hyperscalers to genuine destruction for anyone that trusted the pablum that “all useful compute will be used.” Until that happens, more and more money will be sunk into further theoretical capacity, making the eventual collapse all the more gruesome.

And in the end, what was any of this for? What did this achieve? What was the point of stacking up hundreds of billions of dollars of debt to buy hundreds of billions of dollars’ worth of AI chips years in the future?

What do you think happens when the first hyperscaler pulls out?

What do you think happens when the debt stops flowing?

I’ll give you one answer: everybody will realize that they conflated a great sales pitch with a thriving industry, and both the markets and the economy will suffer as a result.

…

What a fucking waste. I don’t enjoy finding this stuff out. I wish we’d have stopped doing this years ago.

Will Colette Kress be able to prevent the Apocalypse? 🤔