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

In part three of this week's three-part NVIDIA series, Ed Zitron walks you through why there are millions of Blackwell GPUs sitting in warehouses, and why AI’s lack of any profits makes NVIDIA’s future entirely dependent on endless debt and venture capital. This series took a lot of work, so if you want to support me, why not subscribe to my premium newsletter? Get $10 off a year’s subscription today: https://edzitronswheresyouredatghostio.outpost.pub/public/promo-subscription/p94my1c5ya YOU CAN NOW BUY BETTER OFFLINE MERCH! Go to https://cottonbureau.com/people/better-offline and use code FREE99 for free shipping on orders of $99 or more. --- LINKS: https://www.tinyurl.com/betterofflinelinks Newsletter: https://www.wheresyoured.at/ Reddit: https://www.reddit.com/r/BetterOffline/ Discord: chat.wheresyoured.at https://discord.com/invite/QUUQUP9szv Ed's Socials: https://twitter.com/edzitron https://www.instagram.com/edzitron https://bsky.app/profile/edzitron.com https://www.threads.net/@edzitron Email Me: [email protected] mailto:[email protected] See omnystudio.com/listener https://omnystudio.com/listener for privacy information.

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• 0:00Speaker 00

  This is an iHeart Podcast. Guaranteed Human.

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• 1:08Speaker 02

  Call Zone Media.

• 1:10Ed Zitron?

  Hi, I'm Ed Zitron, and welcome back to Better Offline.

• 1:23Speaker 01

  Better Offline.

• 1:26Ed Zitron?

  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

• 2:07Ed Zitron?

  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

• 2:52Ed Zitron?

  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 31st, 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.

• 3:35Ed Zitron?

  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 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

• 4:20Ed Zitron?

  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. Firstly, 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 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.

• 5:05Ed Zitron?

  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 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 other years they'll be generating revenue or actually functioning.

• 5:48Ed Zitron?

  CoreWe, 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. The problem is, 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 organisation, we don't know the same thing about the kind of data centre 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 AI, the concept of an AI data centre is super new. We maybe

• 6:29Ed Zitron?

  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 centre, pretty much brand new, maybe a couple of 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'll be a way to lease them profitably. What if those assumptions are, I don't know, wrong?

• 7:12Ed Zitron?

  What if that hope is ultimately irrational? So there's a quote from the Centre 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 four trillion dollar accounting puzzle at the heart of the AI cloud. This 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

• 7:55Ed Zitron?

  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

• 8:40Ed Zitron?

  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 a Vera 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 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.

• 9:25Ed Zitron?

  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, the Australian Competition Commission is suing them. Maybe I mention 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 dollars 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 their three or six-year shelf life. Even if that were the case, the average rental price of an A100 is 99 cents an hour.

• 10:09Ed Zitron?

  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, but 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 in the hell

• 10:54Ed Zitron?

  are they still buying GPUs? Also, where the fuck are these GPUs going?

• 11:06Speaker 01

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• 11:37Speaker 00

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• 12:08Speaker 04

  Hi, I'm Jenna Oshkowitz. You might know me from Glee. This is At the Table, the food allergy podcast, a show about food, connection, and what it really looks like to live with food allergies. Each episode, we hear stories from real people like Daniel.

• 12:23Speaker 05

  People would always think I was lactose intolerant, so I just kind of shortened my explanation to I'm deathly allergic, in a big way that defined a lot of my childhood.

• 12:34Speaker 04

  And while there are challenges, there are moments that remind you you're not alone.

• 12:38Speaker 06

  One of the best guys that I dated, he made a point to memorize all my food allergies.

• 12:45Speaker 04

  So join us and hear their stories and more, because living with food allergies isn't just about what you can't eat, it's about still having your seat at the table. Brought to you by Genentech and Novartis. Listen to At the Table, the food allergy podcast, on America's number one podcast network, iHeart. Follow At the Table and start listening on the free iHeart radio app today.

• 13:10Ed 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 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. Even 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,

• 13:54Ed Zitron?

  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, earnings statements. I've only been able to find a, 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 going there. Now, Oracle CEO, co-CEO, I should say, Clay McGurk, that's probably not how you say that. He claimed very recently there were 96,000 of them installed,

• 14:35Ed Zitron?

  so not great. There's theoretically 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, 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

• 15:15Ed Zitron?

  supercluster. CoreWeave'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."

• 15:57Ed Zitron?

  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 kinda 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 with building these things. And in 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 Sachin Adella recently said in a podcast that his company, and I quote,

• 16:40Ed Zitron?

  "Didn't have the warm shelves 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 being 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 gigawatts or something." Power can get built. Power can get built. You can build power.

• 17:22Ed Zitron?

  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 Abeline 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.

• 18:04Ed Zitron?

  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 to Kase Kagar, our friend of the show, for telling me the story. But during the early 2020s, 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.

• 18:46Ed Zitron?

  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 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 substacks have argued with the figures, claiming that they're either delayed or are not inclusive of the revenue that OpenAI is paid

• 19:28Ed Zitron?

  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 to someone deeply bitter person. 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 a 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 glob in the podcast verse, sixty nine point one million dollars in counting the year Q three twenty twenty five. And by the way, the actual number

• 20:12Ed Zitron?

  for that three month period, including all royalties is about four point five two seven billion dollars 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 I'm gonna bring in, I'm gonna be playing Tommy Trump here as a walk around cheering. That's you're gonna get five minutes of monologue about that. Also, in the same period, OpenAI spent eight point six seven billion dollars on inference, which is the process in which an L L M creates its output. This is the biggest company in the generative AI space with eight hundred million weekly active users

• 20:56Ed Zitron?

  and the mandate of heaven in the eyes of the media. Anthropic, its largest competitor, alleges it will make eight hundred and thirty three million dollars in revenue in December twenty twenty five, and based on my estimates, will end up having about four and a half to five billion dollars of revenue by the end of the year. Based on my report from October, Anthropic spent two point six six billion dollars on Amazon Web Services through the end of September, meaning that it, based on my own analysis of reported revenues, spent a hundred and four percent 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.

• 21:38Ed Zitron?

  Wow. Even if OpenAI made thirteen billion dollars this year, even if Anthropic made five billion dollars. Okay, well, so that's not even twenty billion dollars. That's like nineteen billion dollars less than Microsoft spent on GPUs and other capex in the last quarter. That's dogshit. 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-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 dollars this year.

• 22:21Ed Zitron?

  Now, one has to ask a question about why this company that's allegedly reducing costs had to raise 13 billion dollars in September 2025 after raising 3.5 billion dollars in March 2025 after raising 4 billion dollars in November 2024. Am I really meant to read stories about Anthropic hitting break even in 2028 with a straight face? Especially as other stories say there'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 dollars this year. That's less than 2 billion dollars more than Anthropic, who makes a bunch less revenue. I can't believe I'm defending OpenAI, but...

• 23:03Ed Zitron?

  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. 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,

• 23:48Ed Zitron?

  which in turn means that we have no real idea of how much real, actual money is coming to justify these GPUs. CoreWeave made 1.36 billion dollars in revenue and lost 110 million dollars 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. CoreWeave has a backlog of over 50 billion dollars in compute, and 22 billion dollars of that is OpenAI, a company that burns billions of dollars a year and lives on venture subsidies. 14 billion dollars of that is Meta, which has yet to work out how to make any kind of real money from generative AI. And no, it's generative AI ads are not the future. 404 Media, I love you, but that story was bunk. And the rest of it is likely a mixture of

• 24:31Ed Zitron?

  Microsoft and Nvidia, which agreed to buy 6.3 billion dollars of any unused compute from CoreWeave through 2032. I 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 CoreWeave to rent to OpenAI, and I'm not shitting you. Oh, fuck, sorry. I also forgot to mention that CoreWeave's backlog problem stems from data center construction delays. That and CoreWeave has 14 billion dollars 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 to GPUs.

• 25:14Ed Zitron?

  I also left something out of this script, which is that just the last week, CoreWeave just raised another 2 billion dollars of debt. When this all ends, I am going to be a little insufferable. But let's just be abundantly clear. CoreWeave has bought all those GPUs to rent to OpenAI, Microsoft for OpenAI, Meta, Google for OpenAI, and Nvidia, which is the company that benefits from CoreWeave's continuing ability to buy GPUs.

• 25:40Ed Zitron?

  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 we not know?

• 26:23Ed Zitron?

  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 fucking cost. Imagine if we sold cars and we didn't have a miles per gallon rating.

• 26:37Ed Zitron?

  I'm serious, that's effectively where we are. Oh, God.

• 26:52Speaker 07

  OnDeck is built to back small businesses like yours. Whether you're buying equipment, expanding your team, or bridging cash flow gaps, OnDeck's loans up to $400,000 make it happen fast. Rated A+ by the Better Business Bureau and earning thousands of five-star TrustPilot reviews, OnDeck delivers funding you can count on. Apply in minutes at ondeck.com. Depending on certain loan attributes, your business loan may be issued by OnDeck or Celtic Bank. OnDeck does not lend in North Dakota. All loans and amounts subject to lender approval.

• 27:20Speaker 00

  Empower is all about helping you invest well, so you can go out and live a little. And their free Empower Personal Dashboard shows you your full financial life in one place. You'll be able to see your net worth, monitor budgets, analyze your portfolio, set retirement goals, and more. Whether you're saving for something big, want to treat yourself, friends, or family, or meet long-term goals, Empower helps you see where you stand. You've worked hard for your money, and Empower can help you make the most of it. Download the Empower Personal Dashboard or visit empower.com. Not an Empower client paid or sponsored.

• 27:51Speaker 04

  Hi, I'm Jenna Oshkowitz. You might know me from Glee. This is At the Table, the food allergy podcast, a show about food, connection, and what it really looks like to live with food allergies. Each episode, we hear stories from real people like Daniel.

• 28:06Speaker 05

  People would always think I was lactose intolerant, so I just kind of shortened my explanation to I'm deathly allergic, in a big way that defined a lot of my childhood.

• 28:17Speaker 04

  And while there are challenges, there are moments that remind you you're not alone.

• 28:22Speaker 06

  One of the best guys that I dated, he made a point to memorize all my food allergies.

• 28:28Speaker 04

  So join us and hear their stories and more, because living with food allergies isn't just about what you can't eat, it's about still having your seat at the table. Brought to you by Genentech and Novartis. Listen to At the Table, the food allergy podcast, on America's number one podcast network, iHeart. Follow At the Table and start listening on the free iHeart Radio app today.

• 28:52Ed Zitron?

  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,

• 29:36Ed Zitron?

  well, Blackwell's, 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 HVAC 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, NeoClouds like CoreWeave and resellers like Dell and Super Micro who also both sell to CoreWeave. 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

• 30:19Ed Zitron?

  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 gonna have to do a newsletter on and a podcast on, because honestly, every time I read about the private credit situation, it blew out again.

• 31:02Ed Zitron?

  I begin hearing the "bwee bwee" 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 debt. Now remember, OpenAI and Anthropic both have lines of credit, $4 billion for OpenAI and $2.5 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

• 31:47Ed Zitron?

  the companies renting GPUs to sell their AI models? That's right, AI startups, all of which are deeply unprofitable. Cursa, Anthropic's largest customer and now its biggest competitor in the AI coding sphere, raised $2.3 billion in November after raising $900 million in June. But PlexD, one of the most "popular", and 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.

• 32:30Ed Zitron?

  I read a story in Newcomer by Tom Datan that said that Curse sends 100% of its revenue to Anthropic to pay for its models. Very cool. So, I really wanna 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 raise venture capital or debt to pay hyperscalers or NeoClouds to rent Nvidia GPUs. At that point, hyperscalers and NeoClouds then use either debt or existing cash flow in the case of hyperscalers, though not for long, to buy more Nvidia GPUs.

• 33:13Ed Zitron?

  Only one company appears to make profit here, and it's Nvidia. Well, Nvidia and its resellers like Dell and Super Micro, 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.

• 33:53Ed Zitron?

  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 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

• 34:38Ed Zitron?

  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 on its success, and its success rides on the back of dwindling amounts of venture capital and debt, because 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 Sachin Adella and co. at 3:00 in the morning and say, "Buy $1 billion of GPUs, you pig." Findom-style, baby. But really, I think much of the US stock market's growth is held up by how long everybody's

• 35:22Ed Zitron?

  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.

• 36:04Ed Zitron?

  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 inflate the AI bubble. I will be walking around with a gavel. I am going to be taking heads. I am fucking 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 fucking looked. All of them could have looked, and they could have looked a year ago. The incredible support I get from all of you truly makes this show a joy to make, even

• 36:46Ed Zitron?

  though I've done way too many retakes on this, and apologies to Matt Asowski 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's betting billions on the idea that Wile E. Coyote won't look down. He's gonna have to at some point, won't he?

• 37:14Ed Zitron?

  Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Osowski. You can check out more of his music and audio projects at mattosowski.com. M A T T O S O W S K I dot com. You can email me at [email protected] or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat dot where's your ed dot at to visit the Discord and go to r slash better offline to check out our Reddit. Thank you so much for listening.

• 37:46Speaker 02

  Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, visit our website, coolzonemedia.com, or check us out on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

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