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

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3:30Call Zone Media. Hi, I'm Ed Zitron and welcome back to Better Offline.

3:48and 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.

4:29And 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.

5:03Much 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 two trillion dollars 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 31st but i'm 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.

5:57Ahem. 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, PPE 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 is least. GPUs depreciate, meaning they lose value over time, and this depreciation is represented on a balance sheet and the income statement.

6:31Essentially, 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.

7:07The 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 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 lot. 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.

7:39And 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 before 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. CoreWeave, for what it's worth, says the same thing, but largely because it's betting that it'll still be able to find users for older silicon after its initial contracts with companies like OpenAI expire.

8:20The problem is that AI GPUs are 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 heavy 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-centered GPUs that hyperscalers are spending tens of billions of dollars on each year. Any kind of depreciation schedule is based on it, 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? 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.

9:04I 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'll be a way to lease them profitably. What if those assumptions are, I don't know, wrong?

9:35What 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 build-out 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 years. In other words, they spread out the cost of their massive capital expenditures over a longer period than the facts warrant, what The Economist has referred to as the$4 trillion accounting puzzle at the heart of the AI cloud.

10:06This is why Michael Burry brought it up recently, because spreading out these costs allows big tech to make their net income, i.e. their profits, look better. In simple terms, by spreading out the costs over six years rather than three, hyperscalers are able to reduce the 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.

10:50Newer 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 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 huger Vera Rubin that comes that might even... I read somewhere that there might even be like kilowatt level ones, just like 100 kilowatt ones.

11:23This 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 the 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 onto fucking everything. That is not working. And in fact, their Australian Competition Commission is suing them.

11:57Maybe 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 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.

12:32This 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, but 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.

13:12So 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.

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14:30This 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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17:23So a few weeks ago, I wrote a piece, a premium one called The Hater's 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 in which they're sold, suggesting that they're either languishing in the supply chain or being warehoused by hyperscalers or even NVIDIA themselves. Now, there's the argument that this could be, and this is wanky NVIDIA bullshit, this could actually be two GPUs per GPU sold because there's two chips on each GPU.

17:56Even if that was the case, 3 million 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.

18:33But 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 McGaurek, 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 1 ,500 in a Lambda data center in Columbus, Ohio. Those are online. The Department of Energy is still in development.

19:10100 ,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. Corwee'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 black world 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 one million black world 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 three 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 six 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 with 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.

20:48Microsoft 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 the moon math of nvidia if we're going into the make-believe world the twisted mind of jensen hwang that's still 3 million gpus we're looking at still like five or six gigawatts of capacity it's not being built i don't even think two 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 gigawatts 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.

21:59I'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-technical. 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 could go our friend of the show for telling me the story but during the early 2020s so i think it's 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.

22:55I 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 12th suggests that the company only made$4.329 billion in revenue for the end of September, extrapolated from the 20 % revenue share that Microsoft receives in the company.

23:31And now some people who write really shit-ass substags 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 throughout Microsoft Azure. So I want to be clear of two things, because I'm a deeply bitter person. this is a cruel 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 open ai 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.

24:23And 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 absolutely fucking insufferable. I'm going to be playing Tommy Trump as I walk around cheering. 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.

25:05This 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 estimates, we'll 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, and likely spent as much on Google Cloud.

25:41Now, 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 open ai made 13 billion dollars this year even if anthropic made 5 billion dollars okay wow so that's not even 20 billion dollars that's like 19 billion dollars less than microsoft spent on gpus and other capex in the last quarter that's dog shit i'm sorry i'm 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-arse businesses that burn billions of dollars.