Part Three: NVIDIA Isn't Enron - So What Is It?

Summary

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Better Offline Podcast Summary

Episode: Part Three: NVIDIA Isn't Enron – So What Is It?
Host: Ed Zitron
Date: December 19, 2025

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Main Theme & Purpose

In this third and final installment of the Better Offline Nvidia series, Ed Zitron takes a skeptical deep dive into Nvidia’s unprecedented growth, examining the opaque economics behind its success and raising critical questions about the sustainability of its business model. While dismissing direct comparisons to infamous corporate frauds like Enron, Zitron spotlights Nvidia’s dependency on a fragile ecosystem of indebted customers buying enormous amounts of GPUs primarily for AI infrastructure. He questions where these GPUs are actually going, who is profiting (if anyone), and what risks this poses to the broader tech and financial markets.

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Key Discussion Points & Insights

1. Nvidia and the Mirage of AI Growth

• Not Enron, But Still Worrying:
  Ed stresses Nvidia isn’t Enron—the numbers are real, the hardware exists, and there aren’t comparably fraudulent accounting practices. However, the company’s rise is built on highly uncertain ground, with customers piling into debt and capital expenditures without obvious, profitable returns.

  "Despite being on incredibly infirm ground, [Nvidia] is definitely not Enron... But there’s still quite a few causes for concern." (03:31)

• Unsustainable Capital Expenditures:
  Big Tech (Microsoft, Amazon, Google, Meta) has spent an estimated $776 billion on capital expenditures (capex) for AI from 2023 to 2025, with another $400 billion expected in 2026—all largely funneled into building out AI data centers and buying Nvidia GPUs, yet with no matching growth in AI-based revenues.

  "Big Tech needs to make $2 trillion in brand new revenue… by 2030. All of this was effectively for nothing." (04:36)

2. Depreciation: The Accounting Puzzle

• Depreciation Shenanigans:
  Companies are using aggressive depreciation schedules, spreading the cost of expensive, rapidly obsolescent GPUs over 5-6 years, despite those chips only realistically being useful (technologically or physically) for 1-3 years.

  "[They] spread the cost of their massive capital expenditures over a longer period than the facts warrant." (07:45)

  • This practice flatters company earnings but masks the real economic impact, likened to what The Economist dubbed a “$4 trillion accounting puzzle.”
  • There’s broad uncertainty about how long these assets maintain value, and what happens as future chip generations quickly render them obsolete.
  • The physical infrastructure is being built around chips that may be outdated before the data centers themselves are even completed.

3. The Mystery of the Missing GPUs

• Where Are They?
  Zitron painstakingly investigates Nvidia’s claimed GPU shipment numbers, finding:

  • Only a fraction of Blackwell GPUs can be traced to active data centers.
  • Even generous estimates (factoring in announced—but not yet operational—projects) can’t identify where millions of shipped GPUs are.

    "I am genuinely unable to find 1 million Blackwell GPUs in existence. Now some might say, there’s a bunch of secret ones—they don’t announce every single one. Here’s the thing: 3 million of these fucking things have allegedly been shipped. I can’t find a million of them." (17:48)

• Power & Infrastructure Shortfall:
  Even when chips are purchased, they often cannot be plugged in due to lack of adequate power and unfinished data centers.

  "In really simple terms, there isn’t enough power or built data centers for those Blackwell GPUs to run." (19:02)

  • Example: Stargate Abilene has only 200 megawatts of power provisioned, while a full build-out would require over 1.4 gigawatts.

• Nvidia’s Slippery Accounting:
  Nvidia has a history of blending product lines for reporting purposes (e.g., crypto GPUs reported as gaming revenue in the 2022 crypto rush), and may be aggregating orders or unshipped inventory into “shipped” numbers.

4. The (Lack of) AI Revenue Problem

• Money-Losing AI Champions:
  The two most prominent AI companies—OpenAI and Anthropic—are deeply unprofitable, spending more on compute and infrastructure than they earn.

  "OpenAI spent $8.67 billion on inference… Anthropic spent 104% of its revenue up to that point just on AWS, likely as much on Google Cloud." (23:28)

• Illusion of Demand:
  Demand for AI compute is real, but it’s “demand” funded almost entirely by debt and venture capital—not sustainable profit.

  • Example: CoreWeave, a major AI compute provider, posted a $110 million loss on $1.36 billion in revenue and is heavily indebted from GPU purchases.
  • The two largest customers for GPU compute—OpenAI and Anthropic—continue to raise billions from VCs and sovereign wealth funds just to cover their operational losses.

• The Circular, Unsustainable Money Flow:
  Zitron lays out the self-consuming ecosystem:

  "Venture capital funds AI startups, who pay OpenAI/Anthropic for model access, who rent GPUs from hyperscalers, who buy more Nvidia GPUs. Only Nvidia makes a profit here." (36:13)

  • Any break in this debt and VC-fueled chain would collapse the entire system.

5. Nvidia’s Real Customer: Debt Markets

• Dependence on Borrowing:
  The only entities able to continue buying Nvidia’s expensive chips are those who can access vast amounts of debt or venture financing.

  "Nvidia’s largest customers are increasingly unable to afford its GPUs... even Google, Amazon, Meta, and Oracle are taking on massive amounts of new debt, all without a plan to make a profit." (39:42)

• Credit Ratings as an Unseen Risk:
  As customers’ credit dries up, their ability to buy more Nvidia GPUs collapses—threatening Nvidia’s own revenue growth.

6. Market Implications and Existential Warnings

• Systemic Financial Risk:
  Nvidia’s growth props up not just itself but is deeply intertwined with the health of the US stock market and includes the 401ks of millions of Americans.

  "So many hundreds of billions of dollars of Nvidia stock sits in the hands of retail investors and people’s 401ks. Once this pops—and it will pop—there must be a referendum." (41:10)

• Skepticism About Long-Term Viability:
  Zitron insists he is not predicting an Enron-style fraud or imminent Nvidia collapse, but he is deeply concerned by the lack of real, profitable demand for AI compute and how long the illusion can be sustained.

  "Much of the US stock market’s growth is held up by how long everybody is willing to be gaslit by Jensen Huang into believing that they need more GPUs. At this point, it’s barely about AI anymore." (41:44)

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Notable Quotes & Moments

• On Tech’s AI Investment Hole:

  "Every time I read these numbers I feel a little crazy… Big Tech needs to make $2 trillion in brand new, brand spanking new revenue, specifically from AI by 2030, or all of this was effectively for nothing." (04:35)

• On GPU Depreciation Math:

  "GPUs depreciate, meaning they lose value over time... In Microsoft’s case, depreciation for its servers is spread over six years, a convenient change it made in August 2022, a few months [before] it bought a bunch of fucking GPUs." (06:25)

• On Obsolete Hardware:

  "Nvidia is committed to releasing a new GPU every single year. Newer generation GPUs require entirely new data center architecture, meaning that one has to either build a brand new data center or retrofit an old one." (10:12)

• On the Absurdity of AI Compute Revenues:

  "Even if OpenAI made $13 billion this year, even if Anthropic made $5 billion, okay, wow… That's like $19 billion less than Microsoft spent on GPUs and other capex in the last quarter. That's dog shit." (25:55)

• On Systemic Risk:

  "At some point, a link in this debt-backed chain breaks because very little cash flow exists to prop it up… At some point, venture capitalists will be forced to stop funneling money into unprofitable, unsustainable AI companies…" (37:18)

• On Nvidia's Puzzling Success:

  "None of Nvidia’s success really makes any sense. Who’s buying so many GPUs and where are they going?... How long realistically can the largest company on the stock market continue to grow revenues selling assets that only seem to lose its customers money and don’t seem to even be in use for years?" (40:36)

• On the Coming Reckoning:

  "Once this pops, and it will pop, because there's simply not enough money to do this forever, there must be a referendum on those that chose to ignore the naked instability of this era and the endless lies that inflated the AI bubble." (41:51)

• On the Wile E. Coyote Moment:

  "Everybody is betting billions on the idea that Wile E. Coyote won’t look down. He’s gonna have to at some point, won’t he?" (42:35)

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Timestamps for Key Segments

• Nvidia’s Risky Foundations – 03:31
• Big Tech’s AI Infrastructure Spend – 04:15 to 06:00
• Depreciation Accounting Problem – 06:31 to 10:19
• Data Center & Power Limitations – 17:48 to 21:25
• The “Missing” GPUs – 16:46 to 19:58
• AI Revenue vs. Costs Reality – 22:30 to 26:20
• Circular Money Flows and Debt Dependency – 36:00 to 39:00
• Systemic Market Consequences & Host’s Final Rant – 40:30 to 42:35

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Tone and Style

Ed Zitron’s approach is direct, caustic, and darkly comedic, alternating deep research with exasperated asides and sarcasm. He leverages industry data, premium reporting, and pointed anecdotes, using frequent profanity and rhetorical questions to punctuate his skepticism about the industry’s ability to continue its current trajectory. He makes clear he's not predicting criminal collapse—just “plain old economic reality.”

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For Listeners New to the Episode

This episode is a must-listen for anyone concerned about the sustainability of Silicon Valley, the real economics of AI, or the health of the American stock market. Zitron expertly exposes how Nvidia’s meteoric rise is exposed to breathtaking systemic risks rooted in debt, untested technology, and a pervasive lack of profitability throughout the AI ecosystem.

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Host: Ed Zitron
Podcast: Better Offline (Cool Zone Media / iHeartPodcasts)

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Transcript

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[Ed Zitron]

Call Zone Media hi, I'm Ed Zitron and welcome back to Better Offline. Better Offline and this is our third and final part of our Better Offline Nvidia special where we're talking about, well, the shakiness behind its growth and how the company, despite being on incredibly infirm ground, is definitely not Enron or Nortel or WorldCom or Lucent or any other dot com bubble era firm that imploded under its own weight and, well, quite dodgy accounting. The thing is, even if Enron is nothing like them, there's still quite a few causes for concern, and that's largely driven from the fact that Nvidia makes the majority of its money selling GPUs to a handful of customers. And so, well, some of those also look to be on some of their own incredibly shaky ground. And yeah, I'm talking about Oracle. Now, Nvidia's health, saying nothing of its growth, isn't just tied to these customers, it's also tied to whether these customers can actually turn a profit from their capex spending. And even that's not even certain. So due to the fact that so much money has been piled into building AI infrastructure and Big Tech has promised to spend hundreds of billions of dollars more in the next year, Big Tech has found itself in a bit of a hole. How big of a hole? Well, by the end of the year, Microsoft, Amazon, Google and Meta will have spent over $400 billion in capital expenditures, much of it focused on building AI infrastructure on top of $228.4 billion in capex in 2024 and around 148 billion in capital expenditures in 2023, for a total of 776 billion in the space of three years. And they expect to spend more than $400 billion more in 2026. Every time I read these numbers I feel a little crazy. As a result, based on my own analysis, Big Tech needs to make $2 trillion in brand new, brand spanking new revenue, specifically from AI by 2030. All of this was effectively for nothing. Now I go into detail about this in the premium newsletter I did on October 31, but I'm going to give you a short explanation here. First though, we have to talk about depreciation. And because I'm lazy, I'm going to quote myself in that newsletter I just mentioned a couple of seconds ago. Ahem. So when Microsoft buys say $100 million worth of GPUs, it immediately comes out of its capital expenditures, which is when a company uses money to invest in either buying or upgrading something, it then adds to its property, plants and equipment assets for short. Although some companies list this on their annual and quarterly financials as property and equipment, PPE sits on the balance sheet. It's an asset as it's stuff for the company that it owns or has leased. GPUs depreciate, meaning they lose value over time, and this depreciation is represented on a balance sheet and the income statement. Essentially, the goal is to represent the value of an asset that a company has on the income statement and we see how much the assets have declined during the reporting period, whether that be a year or a quarter or something else. Whereas the balance sheet shows the cumulative depreciation of every asset currently in play. Depreciation does two things, and I know this sounds like a lot, but I'll break it down for you. First, it allows a company to accurately, to an extent, represent the value of things it owns over their useful life. Secondly, it allows a company to deduct the value of an asset across said useful life right up until its eventual removal versus having to take a big hit up front. The way this depreciation is actually calculated can vary. There are several different methods available, with some allowing for greater deductions at the start of the term, which is useful for those items that will experience the biggest drop in value right after after buying them and their initial use. An example you're probably familiar with is a new car which loses a significant chunk of its value the moment it's driven off a dealership. Long depreciation has become a big ugly problem with GPUs specifically because of that useful life, defined either as how long the thing is able to be run before it dies, or how long before it becomes obsolete. And nobody seems to be able to come up with a consensus about how long this should be. In Microsoft's case, depreciation for its servers is spread over six years, a convenient change it made in August 2022, a few months the launch of ChatGPT, and before it bought a bunch of fucking GPUs. This means that Microsoft can spread the cost of tens of thousands of A100 GPUs bought in 2020 or the 450,000 H100 GPUs it bought in 2024 across six years, regardless of whether those are the years they'll be generating revenue or actually functioning. Core Weave, for what it's worth, says the same thing, but largely because it's betting that it will still be able to find users for Alter Silicon after its initial contracts with companies like OpenAI expire. The problem is that AI GPU' fairly new concepts and thus all of this is pretty much untested ground. Whereas we know how long, say a truck or a piece of every machinery can last and how long it can deliver value to an organization, we don't know the same thing about the kind of data center GPUs that hyperscalers are spending tens of billions of dollars on each year. Any kind of depreciation schedule is based on, at best assumptions and at worst hope. Now this is important. The concept of an AI data center is super new. We maybe saw the first ones in 2019. Question mark? Like, it's kind of hard to say, but even at the scale we're seeing today, a gigawatt data center pretty much brand new, maybe a couple years old. I don't even think they've even built any, but we'll get to that in a bit. There are a lot of assumptions at play. There's the assumption that the cards won't degrade with heavy usage, or the assumption that future generations of GPUs won't be so powerful and impressive that they'll render the previous ones more obsolete than expected. Kind of like how the first jet powered planes of the 1950s did to those manufactured just a decade prior. The assumption that there will be in fact a market for older cards and that there will be a way to lease them profitably. What if those assumptions are I don't know wrong. What if that hope is ultimately irrational? So there's a quote from the center for Information Technology Policy framing this problem. Well, that I'll link to in the notes. Here is the puzzle. The chips at the heart of the infrastructure buildout have a useful lifespan of one to three years due to rapid technological obsolescence and physical wear. But companies depreciate them over five or six. In other words, they spread out the cost of their massive capital expenditures over a longer period than the facts warrant. What the Economist is referred to as the $4 trillion accounting puzzle at the heart of the AI cloud. This is why Michael Burry brought it up recently, because spreading out these costs allow big tech to make their net income, that is Their profits, look better. In simple terms, by spreading out the costs over six years rather than three, hyperscalers are able to reduce a line item that eats into their earnings, which makes their companies look better to the markets. So why does this create an artificial time limit? Well, let's start with a horrible fact. It takes 2.5 years of construction time and about $50 billion per gigawatt of data center capacity. No matter when the GPUs for a gigawatt data center are bought one way or another, these GPUs are depreciating in value either through death or reduced efficacy, through wear and tear or becoming obsolete. Which is very likely as Nvidia is committed to releasing a new GPU every single year. Newer generation GPUs like Nvidia's Blackwell and Vera Rubin, require entirely new data center architecture, meaning that one has to either build a brand new data center or retrofit an old one. Essentially we have facilities that are being built around a GPU design or product that may change in a year or two. Now I hear that Nvidia, the Oberon racks that they use for the Blackwells, will be used with some Vera Rubin. But even then there's going to be an even bigger, more huge Avira Rubin that comes that might even that. I read somewhere that there might even be like kilowatt level ones just in like 100 kilowatt ones that this company's insane. Nevertheless, at some point Wall street is going to need to see some sort of return on this investment, and right now that return is negative dollars. I break it down on my October 31st premium piece, but for your sake, I'll just say it. I estimate that big tech needs to make $2 for every dollar of capex they've spent. And this revenue must be new brand new as this capex is only for AI, this capex is useless for everything else. It does not help it. And no it doesn't help that they bolted copilot ont that is not working and in fact the Australian Competition Commission is suing them. Maybe I mentioned that later, but whatever. Meta, Amazon, Google and Microsoft are already years and hundreds of billions of dollars in and are yet to see a dollar of profit creating a 1.21 trillion dollar hole just to justify the expenses. So around $605 billion of capex all told. At the time I calculated it, much of this capex has been committed or spent before they've even turned on a single goddamn gpu. You might argue that there's a scenario here where say an A100 GPU is useful past the three or six year shelf life. Even if that were the case, the average rental price of an A100 is 99 cents an hour. This is a four or five year old GPU and customers are paying for it like they would a five year old piece of hardware. The same Fate Awaits the H100 which was released in 2022, was still sold in great volume through 2024 and I hear the H200 of the same generation is still selling to this day. Every year Nvidia releases a new GPU, lowering the value of all the other GPUs in the process making it harder to fill in the holes created by all the other GPUs. Capex and costs this whole time nobody appears to have found a way to make a profit, meaning that the hole created by these GPUs remains unfilled, all while big tech firms buy more GPUs creating more holes to fill. So now that you know this, there's a fairly obvious question to ask. Why the hell are they still buying GPUs? Also, where the fuck are these GPUs going? It's cold and wet out, but the good news is that I bought a bunch of cashmere sweaters from Quince which fit really nicely and are both warm and go with just about anything, especially the leather racer jacket I got recently too. I love their t shirts. I'm currently eyeing one of their leather messenger bags, their gloves as well, and they've got some fetching boots. I really like quints and everything I've bought so far has felt high quality like I'd get from a high end department store despite being much cheaper. 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[Ed Zitron]

So a few weeks ago I wrote a piece, a premium one, called the Haters Guide to Nvidia. And I asked a basic question in there. Where have all the GPUs that Nvidia has sold actually gone? In particular, the 6 million Blackwell GPUs that Jensen Huang keeps banging on about. Now, there's little evidence that these are being used in the volume which they're sold, suggesting that they're either languishing in the supply chain or being warehoused by hyperscalers, Nvidia themselves. Now there's the argument that this could be, and this is wanky Nvidian bullshit, this could actually be two GPUs per GPU sold because there's two chips on each GPU. Even if that was the case, 3 billion GPUs, Blackwell, specifically the brand new ones, they're not in service. Now. While I'm not going to go and copy paste an entire premium piece into this script, I am however going to go into detail about what I found. And the truth is I can only really see, and this includes looking over like bunches of data center maps, reading hundreds of press releases, documents, earning statements. I've only been able to find maybe a couple hundred thousand Blackwell GPUs in existence. Maybe half a million to 750,000 if you include the stuff that hasn't even been built yet. But let's go into it. So Stargate, Abilene, allegedly 400,000 Blackwell GPUs are going there. Now, Oracle CEO, co CEO, I should say, Clay McGoyrick. That's probably not how you say that. He claimed very recently there were 96,000 of them installed. So not great. There's theoretically in 131,000 Blackwell GPU cluster owned by Oracle that they announced in March 2025. So that should be online. Never. 5,000 Blackwell GPUs at the University of Texas Austin, which sound like they're online. More than 1500 in a Lambda data center in Columbus, Ohio. Those are online. The datu, the Department of Energy still in development, 100,000 GPU supercluster, as well as 10,000 Nvidia Blackwell GPUs that are expected to be available in 2026 in its Equinox cluster. Really can't establish how many of those are actually in operation, 50,000 of these Blackwell GPUs going into the still unbuilt Musk Run Colossus 2 supercluster, Corey's largest GB200 Blackwell cluster of 2,496 Blackwell GPUs, tens of thousands of them deployed globally by Microsoft, including 4,600 Blackwell Ultra GPUs and 260,000 of them. These Blackwell GPUs going into five AI data centers for the South Korean government. And yeah, I just want to be clear that that is also fairly recently announced. So probably not even, not even built, let alone powered on. I'm gonna be honest, I'm genuinely unable to find 1 million Blackwell GPUs like in existence. Now some of you might say, oh, there's a bunch of secret ones, there's a bunch of them. They don't announce every single one. Here's the thing, 3 million of these fucking things have allegedly been shipped. I can't find a million of them. And considering everybody always talks about their GPU purchases, I'm kind of shocked. I can't. Now, I do not know where these 6 million Blackwell GPUs have gone, but they certainly haven't gone into data centers that are powered and turned on. In fact, power has become one of the biggest issues of building these things in the fact it's really difficult and maybe impossible to get the amount of power these things need to the goddamn data centers. In really simple terms, there isn't enough power or built data centers for those Blackwell GPUs to run. In part because the data centers aren't built and in part because there isn't enough power for the ones that are. Microsoft CEO Satya Nadella recently said in a podcast that his company, and I quote, didn't have the warm shells to plug into, meaning buildings with sufficient power, and heavily suggested that Microsoft may actually have a bunch of chips sitting in inventory that they couldn't plug in. Now, just to give you an estimate here, even if we say 3 million GPUs, even if we're going with the moon math of Nvidia, if we're going into the make believe world, the twisted mind of Jensen Huang, that's still 3 million GPUs. We're looking at still like 5 or 6 gigawatts of capacity, it's not been built. I don't even think 2 gigawatts of data center capacity have been built. And I swear to fucking God, if one of you emails me and said America's built on 20 gigabytes or something. Power can get built. Power can get built. You can build power getting it to the data center and actually powering the data center correctly, as in things turn on, everything works, nothing overloads, nothing blacks out and the power is consistently done. Takes. Well, it's just months of surveys and scientific stuff and then years to just get it done. Stargate abilene only has 200 megawatts. They're gonna need over 1.4 gigawatts just to turn the fucking thing on. I'm so tired of these goddamn GPUs. But with all this said, why pray tell, is Jensen Huang of Nvidia saying that he has 20 million Blackwell and Vera Rubin GPUs ordered through the end of 2026. Where are they fucking going? Jensen? Now I think that number also includes the 6 million. And also just to be clear, I know a lot of you aren't technical, which is awesome. I love non tech. I want you all to know about this. You need to know that this is par for the course with Nvidia. Nvidia loves smushing accountancy things together and coming up with random numbers. Credit the case Kagawa, friend of the show for telling me the story. But during the early 2000s, so I think it was 2022, during the big crypto rush, Nvidia classified gaming GPUs that were sold to bitcoin miners as gaming revenue. They got dinged by the sec. Wasn't fraud. But just so you know, Nvidia will move shit around. And I truly do not know where these GPUs are. I do not know even why anyone is still buying GPUs. Now AI bulls will tell you that there's this insatiable demand for AI and these massive amounts of orders are proof of something. Or rather, and you know what? I'll give them that it's proof that people are buying a lot of GPUs. I just don't know why. Nobody has made a profit from AI. And those making revenue aren't really making that much. Let me give you an example. My reporting on OpenAI from November 12 suggests that the company only made $4.329 billion in revenue through the end of September, extrapolated from the 20% revenue share that Microsoft receives in the company. And now some people who write really shit ass sub stacks have argued with the figures, claiming that they're either delayed or are not inclusive of the revenue that OpenAI has paid from Microsoft as part of Bing's AI integration and sales of OpenAI's models through Microsoft Azure. So I want to be clear of two things. Some are deeply bitter. This is accrual accounting, meaning that these numbers are revenue booked in the quarter I reported them. Any comments about quarter long delays or naive approaches and you know who I'm fucking talking about if you're listening are incorrect and rebozo. Also, Microsoft's revenue share payments to OpenAI are kind of pathetic. Totaling based on documents reviewed by this newsletter, publication, whatever you call me media entity Floating blob in the podcast verse $69.1 million in calendar year Q3 2025. And by the way, the actual number for that three month period, including all royalties, is about $4.527 billion of revenue. I just want to be clear about something with OpenAI. I'm not saying they're misrepresenting their numbers to anyone. I hope that OpenAI is being honest with their revenues. But if it comes out I'm right, if it comes out that it turns out that they've been telling investors completely different numbers, I'm going to be absolute, absolutely fucking insufferable. I'm going to bring in, I'm going to be playing Tommy Trumpet as I walk around cheering. That's you're going to get five minutes of monologue about that. Also in the same period, OpenAI spent $8.67 billion on inference, which is the process in which an LLM creates its output. This is the biggest company in the generative AI space, with 800 million weekly active users and the mandate of heaven in the eyes of the media. Anthropic, its largest competitor, alleges it will make $833 million in revenue in December 2025 and based on my will end up having about 4.5 to $5 billion of revenue by the end of the year. Based on my reporting from October, Anthropic spent $2.66 billion on Amazon Web Services through the end of September, meaning that it, based on my own analysis of reported revenue, spent 104% of its revenue up to that point just on AWS, likely spent as much on Google Cloud. Now the reason I'm bringing up these numbers is these are the champions, the champions of the AI boom. Yet their revenues kind of fucking stink. Wow. Even if OpenAI made $13 billion this year, even if Anthropic made $5 billion, okay, wow. So that's not even $20 billion. That's like $19 billion less than Microsoft spent on GPUs and other capex in the last quarter. That's dog shit. I'm sorry. I'm just tired of. I am tired of humoring this. I'm sure all of you are too. I find it loathsome that we have to pretend these people are gifted somehow. They have shit ass businesses that burn billions of dollars. And you know what another thing I'm tired about is everybody telling this story about Anthropic being more efficient and only burning $2.8 billion this year. Now one has to ask a question about why this company that's allegedly reducing costs had to raise $13 billion in September 2025 after raising 3.5 billion dol 2025 after raising $4 billion in November 2024am I really meant to read stories about Anthropic hitting break even in 2028 with a straight face. Especially as other stories say they'll be cash flow positive as soon as 2027. This company's as big a pile of shit as OpenAI. OpenAI raised $18.3 billion this year. That's less than $2 billion more than anthropic who makes a bunch less revenue. Can't believe I'm defending OpenAI. But however, these companies are the two largest ones in the generative AI space and by extension the two largest consumers of GPU compute. Both companies burn billions of dollars and require an infinite amount of venture capital to keep them alive. At a time when the Saudi public investment fund is struggling and the US Venture capital system is set to run out of cash in the next year and a half, the two largest sources of actual revenue for selling AI compute are subsidized by venture capital and debt. There. What happens if these sources dry up? They're not paying out of cash flow. And in all seriousness, who else is buying AI compute? What are they doing with it? Hyperscalers Other than Microsoft, which chose to stop reporting its AI revenue back in January when it claimed it made about a billion dollars a month in revenue, don't disclose anything about their AI revenue, which in turn means that we have no real idea of how much real actual money is coming to justify these GPUs use. Core Weave made $1.36 billion in revenue and lost $110 million doing so in the last quarter. And if that's indicative of the kind of actual real demand for AI compute, I think it's time to start panicking about whether all of this was for nothing. Core Weave has a backlog of over $50 billion in compute and $22 billion of that is OpenAI, a company that earns billions of dollars a year and lives on venture subsidies. $14 billion of that is Meta, which is yet to work out how to make any kind of real money from Generative AI. And no, it's Generative AI ads future 404 media. I love you, but that story was bunk. And the rest of it is likely a mixture of Microsoft and Nvidia, which agreed to buy $6.3 billion of any unused compute from Core Weave through 2032. Should also be clear, I do pay and subscribe to 404. I love it. Just the AI ad story was wank. I love you. I love you, Joe. I love, I love, I love the publication. Sorry, I also forgot Google, by the way, which is renting capacity from Core Weave to rent to OpenAI. And I'm not. Oh fuck. Sorry. I also forgot to mention that Core Weave's backlog problem stems from data center construction delays. That and core weave has $14 billion in debt, mostly from buying GPUs, which it was able to raise by using GPUs as collateral. And then it had contracts from customers willing to pay for it, such as Nvidia, who is also selling it the GPUs. I also left something out of this script, which is that just the last week, Core, we've just raised another $2 billion of that. When this all ends, I am going to be a little insufferable. But let's just be abundantly clear. Core Weavers bought all those GPUs to rent OpenAI, Microsoft for OpenAI, Meta, Google for OpenAI and Nvidia, which is the company that benefits from Core Wave's continuum ability to buy GPUs? Otherwise, where's the fucking business? Exactly. Who are the customers? Who are the people renting the GPUs, and what is the purpose for which they're being rented? How much money is renting those GPUs? Can you. Can you tell me? Can anyone tell me? Can anyone tell me anything? You can sit and wank and waffle on about the supposed glorious AI revolution all you want, but where's the goddamn money? And why exactly are we still buying GPUs? What are they doing? To whom are they being rented? For what purpose? And why isn't it creating the kind of revenue that's actually worth sharing or products that are actually worth using? Is it because the products suck? Is it because the revenue sucks? Is it because it's unprofitable to make the revenue? And why at this point in history do not know hundreds of billions of dollars that have made Nvidia the biggest company on the stock market and we still do not know why people buy these fucking things nor do we know what they cost. Imagine if we sold cars and we didn't have a miles per gallon rating. I'm serious. That's effectively where we are. Oh God. Foreign.

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Nvidia is currently making hundreds of billions of dollars in revenue selling GPUs to companies that either plug them in and start losing money, or I assume, put them in a warehouse for safekeeping. And those companies increasingly are racking up mountains of debt to do so and billions more in long term lease payments. And this brings me to my core anxiety. Why exactly are companies pre ordering GPUs? What benefit is there in doing so? Blackwell does not appear to be more efficient in a way that actually makes anybody a profit. And we're potentially years from seeing these GPUs in operation in data centers at the scale they're being shipped. So why is anyone buying more? I just want to be really specific about something because I don't feel like I nailed this down two and a half years. $50 billion per gigawatt of data centers. You may be thinking, well, Blackwells, you'll just shove them in the old data centers, right? No, they use these Oberon racks, specific new racks. They take a bunch more power and they need a bunch of liquid cooling. You can't just retrofit easily. You have to bulldoze shit and rebuild. Well, remove all the housing and then add H vac stuff. It's very expensive and takes a long time and. Look, I just don't know what's happening with these GPUs, and I'm a little bit concerned. And I doubt these are new customers. They're likely hyperscalers, neo clouds like Core Weave, and resellers like Dell and Supermicro, who also both sell to Core Wave. Because the only companies that can actually afford to buy GPUs are those with massive amounts of cash or debt, to the point that even Google, Amazon, Meta, and Oracle are taking on massive amounts of new debt, all without a plan to make a profit. Oracle is looking potentially at $56 billion of debt. It's completely bonkers. Nvidia's largest customers are increasingly unable to afford its GPUs, which appear to be increasing in price with every subsequent generation. Nvidia's GPUs are so expensive that the only way you can buy them is by already having billions of dollars or being able to raise billions of dollars. Which means, in a very real sense, that Nvidia is dependent not on its customers, but on its customers, credit ratings and financial backers and the larger private credit institutions, which I'm eventually going to have to do a newsletter on and a podcast on, because honestly, every time I read about the private credit situation with Blue Owl again, I begin hearing the bit from Kill Bill. It's not. It's not good. And to make matters worse, the key reason that one would buy a GPU is to either run AI services using it or rent it to somebody else to run AI services. And the two largest parties spending money on These services are OpenAI and Anthropic, both of whom lose billions of dollars and thus are much like the people buying the GPUs dependent on venture capital and deb. Now, remember, OpenAI and Anthropic both have lines of credit. $4 billion for OpenAI and 2 1/2 billion for Anthropic. In simple terms, Nvidia's customers rely on debt to buy its GPUs, and Nvidia's customers customers rely on debt to pay to rent them. Yeah, it's not great. Yet it actually gets worse from there. Who, after all, are the biggest customers paying the companies renting GPUs to sell their AI models? That's right, AI startups. All of which are deeply unprofitable. Cursor. Anthropic's largest customer, and now its biggest Competitor in the A coding sphere raised $2.3 billion in November after raising $900 million in June. Perplexity, one of the most popular I put that in air quotes raised $200 million in September after raising $100 million in July after seeming to fail to raise half a billion dollars in May after raising $500 million in December 2024. Cognition raised $400 million in September after raising $300 million in March. And Cohere raised $100 million in September a month after it raised $500 million. None of these companies are profitable. Not even close. I read a story in Newcomer by Tom Dutton that said the cursor sends 100% of its revenue to Anthropic to pay for its models. Very cool. So I really want to lay this out for you because it's very bad when you think about so venture capital is feeding money to startups. They fund the startups, AI startups, and then they pay either or both OpenAI or Anthropic to use their models. Now OpenAI and Anthropic need to serve those models right. So they then venture capital or dare to pay hyperscalers or neo clouds to rent Nvidia GPUs. At that point, Hyperscalers and Neo Clouds then use either DARE or existing cash flow. In the case of hyperscalers, though not for long, to buy more Nvidia GPUs. Only one company appears to make a profit here and it's Nvidia. Well, Nvidia and its resellers like Dell and Supermicro which buy Nvidia GPUs, put them in service and sell them to neoclouds like Lambda or Core Wave. At some point a link in this debt backed chain breaks because very little cash flow exists to prop it up. At some point venture capitalists will be forced to stop funneling money into unprofitable, unsustainable AI companies, which will make those companies unable to funnel money into the pockets of anthropic and OpenAI who rent the GPUs will then not be able to funnel money into the pockets of those buying GPUs, which will make it harder for those companies to justify buying GPUs. At that point some of this comes to Nvidia and Nvidia doesn't make so much money. And if I'm honest, none of Nvidia's success really makes any sense. Who's buying so many GPUs and where are they going? Why are Nvidia's inventories increasing? Is it really just pre buying parts for future orders? Why are their accounts receivable climbing and how much product is Nvidia shipping before it gets paid? While these are both accounts explainable, as this is a big company and this is how companies do business and that's true, why do receivables not seem to be coming down? And how long realistically can the largest company on the stock market continue to grow revenues selling assets that only seem to lose its customers money and don't seem to even be in use for years? I worry about Nvidia not because I think there's a massive scandal, but because so much rides in its success and its success rides on the back of dwindling amounts of venture capital and debt. Because no, nobody is actually making money to pay for these GPUs, let alone running them. In fact, I'm not even saying Nvidia goes tits up. I want to be clear about that. I think they may even have another good quarter or two in them. It really just comes down to how long people are willing to be stupid and how long Jensen Huang is able to call up Satya Nadella and company at 3 in the morning and say buy $1 billion of GPUs, you pig. Findom style baby. But really, I think think much of the US stock market's growth is held up by how long everybody is willing to be gaslit by Jensen Huang into believing that they need more GPUs. At this point, it's barely about AI anymore as AI revenue, real cash made from selling services run on those GPUs doesn't even cover the costs, let alone create the cash flow necessary to buy more $70,000 GPUs, thousands at a time. It's not like any actual innovation or progress is driving this bullshit. In any case, the markets crave a healthy Nvidia as so many hundreds of billions of dollars of Nvidia stock sits in the hands of retail investors and people's 401ks. And its endless growth has helped paper over the pallid growth of the US stock market and by extension the decay of the tech industry's ability to innovate. Once this pops and it will pop. Because there's simply not enough money to do this forever. There must be a referendum on those that chose to ignore the naked instability of this era and the endless lies that inflated the AI by bubble. I will be walking around with a gavel. I am going to be taking heads. I am sick of this era and what I'm most sick of is that so few people are still to this day willing to admit how bad this is. And I know in the next few months we're going to get articles from major media outlets that say, how could we have seen this coming? And like I said in the previous episode, they could have looked. All of them could have looked. And they could have looked a year ago ago. The incredible support I get from all of you truly makes this show a joy to make, even though I've done way too many retakes on this. And apologies to Mattasowski for the noises I made. But I think in the next few months we're all going to be validated. It's going to be the great Vindication. But until then, everybody is betting billions on the idea that Wile E. Coyote won't look down. He's gonna have to at some point, won't he? Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Mattasowski. You can check out more of his music and audio [e-mail] m a t t o s o w s k-I.com you can email me at ezetteroffline.com or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat where's your Ed app to visit the Discord and go to R betteroffline to check out our Reddit. Thank you so much for listening. Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, Visit our website coolzone media.com or check us.

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[Sophie Cunningham]

This is Sophie Cunningham from Show Me Something. Do you know the symptoms of moderate to severe obstructive sleep apnea or OSA in adults with obesity? They may be happening to you without you knowing. If anyone has ever said you snored loudly or if you spend your days fighting off excessive tiredness, irritability and concentration issues, it may be due to osa. OSA is a serious condition where your airway partially or completely collapses during sleep, which may cause breathing interruptions and oxygen deprivation. Learn more at don'tsleep on OSA.com this information is provided by Lilly, a medicine company.

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