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

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Speaker 11 Hi, I'm Ed Zittron, and welcome back to Better Offline.

Speaker 11 And this is our third and final part of our Better Offline NVIDIA special where we're talking about, well, 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 And even that's not even certain.

Speaker 11 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.

Speaker 11 How How big of a hole?

Speaker 11 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.

Speaker 11 And they expect to spend more than $400 billion more in 2026. Every time I read these numbers, I feel a little crazy.

Speaker 11 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.

Speaker 11 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.

Speaker 11 And because I'm lazy, I'm going to quote myself in that newsletter I just mentioned a couple of seconds ago. At hem.

Speaker 11 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.

Speaker 11 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.

Speaker 11 PPE sits on the balance sheet, it's an asset, as it's stuff for the company that it owns or has leased.

Speaker 11 GPUs depreciate, meaning they lose value over time, and this depreciation is represented on a balance sheet and the income statement.

Speaker 11 Essentially, the goal is to represent the value of an asset that a company has on the income statement.

Speaker 11 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.

Speaker 11 Depreciation does two things, and I know this sounds like a lot, but I'll break it down for you.

Speaker 11 First, it allows a company to accurately, to an extent, represent the value of things it owns over their useful life.

Speaker 11 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.

Speaker 11 The way this depreciation is actually calculated can vary.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 And nobody seems to be able to come up with a consensus about how long this should be.

Speaker 11 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 Chat GPT, and before it bought a bunch of fucking GPUs.

Speaker 11 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.

Speaker 11 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 Alter Silicon after its initial contracts with companies like OpenAI expire.

Speaker 11 The problem is that AI GPUs are fairly new concepts and thus all of this is is pretty much untested ground.

Speaker 11 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 center GPUs that hyperscalers are spending tens of billions of dollars on each year.

Speaker 11 Any kind of depreciation schedule is based on, at best, assumptions and at worst hope. Now, this is important.

Speaker 11 The concept of an AI data center is super new. We maybe saw the first ones in 2019, question mark.

Speaker 11 Like it, 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 Kind of like how the first jet-powered planes of the 1950s did to those manufactured just a decade prior.

Speaker 11 The assumption that there will be, in fact, a market for older cars and that there will be a way to lease them profitably.

Speaker 11 What if those assumptions are, I don't know, wrong? What if that hope is ultimately irrational?

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 So why does this create an artificial 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.

Speaker 11 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.

Speaker 11 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 black wells will be used with some vera rubin but even then there's going to be an even bigger more huger verarubin 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 is insane Nevertheless, at some point, Wall Street is going to need to see some sort of return on this investment.

Speaker 11 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.

Speaker 11 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.

Speaker 11 It does not help it. And no, it doesn't help that they bolted CodePilot onto fucking everything.
That is not working. And in fact, the Australian Competition Commission is suing them.

Speaker 11 Maybe I mentioned that later, but whatever.

Speaker 11 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.

Speaker 11 Much of this CapEx has been committed or spent before they've even turned on a single goddamn GPU.

Speaker 11 You might argue that there's a scenario here where, say, an A100 GPU is useful past their three or six year shelf life.

Speaker 11 Even if that were the case, the average rental price of an A100 is 99 cents an hour. This is a 4 or 5 year old GPU and customers are paying for it like they would a 5 year old piece of hardware.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 So, now that you know this, there's a fairly obvious question to ask: why in the hell are they still buying GPUs? Also, where the fuck are these GPUs going?

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Speaker 11 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?

Speaker 11 In particular, the 6 million Blackwell GPUs that Jensen Huang keeps banging on about.

Speaker 11 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.

Speaker 11 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.

Speaker 11 Even if that was the case, 3 million GPUs, Blackwell specifically, the brand new ones, they're not in service.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 So, Stargate Abilene, allegedly 400,000 Blackwell GPUs are going there. Now, Oracle CEO, co-CEO, I should say, Clay McGork,

Speaker 11 that's probably not how you say that. He claimed very recently there were 96,000 of them installed.
So, not great.

Speaker 11 There's theoretically an 131,000 Blackwell GPU cluster owned by Oracle that they announced in March 2025.

Speaker 11 So, that should be online never.

Speaker 11 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.

Speaker 11 The Department of Energy is 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.

Speaker 11 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.

Speaker 11 CoreWeave's largest GB200 Blackwell cluster of 2,496 Blackwell GPUs.

Speaker 11 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.

Speaker 11 And yeah, I just want to be clear that that is also fairly recently announced. So probably not even

Speaker 11 built, let alone Powered Arm. I'm going to 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.

Speaker 11 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.

Speaker 11 And considering everybody always talks about their GPU purchases, I'm kind of shocked I can't.

Speaker 11 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.

Speaker 11 In fact, power has become one of the biggest issues of building these things.

Speaker 11 In fact, it's really difficult and maybe impossible to get the amount of power these things need to the goddamn data centers.

Speaker 11 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.

Speaker 11 Microsoft CEO Satchinadella 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.

Speaker 11 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.

Speaker 11 We're looking at still like five or six gigawatts of capacity. It's not been built.
I don't even think two gigawatts of data center capacity have been built.

Speaker 11 And I swear to fucking God, if one of you emails me and said, America's built on 20 gigawatts or something,

Speaker 11 power can get built. Power can get built.
You can build power.

Speaker 11 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.

Speaker 11 Stargate Abilene only has 200 megawatts. They're going to need over 1.4 gigawatts just to turn the fucking thing on.
I'm so tired of these goddamn GPUs. But

Speaker 11 with all this said, why pray tell is Jensen Huang of Nvidia saying that he has 20 million Blackwell Invera Rubin GPUs ordered through the end of 2026? Where are they they fucking going, Jensen?

Speaker 11 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.

Speaker 11 You need to know that this is par for the course with NVIDIA. NVIDIA loves schmushing accountancy things together and coming up with random numbers.

Speaker 11 Credit to Casey Kago, our friend of the show, for telling me the story.

Speaker 11 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.

Speaker 11 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.

Speaker 11 Now, AI bulls will tell you that there's this insatiable demand for AI and these massive amounts of orders are proof of...

Speaker 11 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.

Speaker 11 Nobody has made a profit from AI, and those making revenue aren't really making that much. Let me give you an example.

Speaker 11 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.

Speaker 11 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 is paid from Microsoft as part of Bing's AI integration and sales of OpenAI's models through Microsoft Azure.

Speaker 11 So I want to be clear of two things, so I'm a deeply bitter person. This is a cruel accounting, meaning that these numbers are revenue booked in the quarter I reported them.

Speaker 11 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 you're a bozo.

Speaker 11 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 counting the year, Q3 2025.

Speaker 11 And by the way, the actual number for that three-month period, including all royalties, is about $4.527 billion of revenue.

Speaker 11 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.

Speaker 11 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.

Speaker 11 I'm going to bring in, I'm going to be playing Tommy Trumpet as I walk around cheering. You're going to get five minutes of monologue about that.

Speaker 11 Also, in the same period, OpenAI spent $8.67 billion on inference, which is the process in which an LLM creates its output.

Speaker 11 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.

Speaker 11 Anthropic, its largest competitor, alleges it will make $833 million in revenue in December 2025 and based on my estimates will end up having about $4.5 to $5 billion of revenue by the end of the year.

Speaker 11 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.

Speaker 11 Now, the reason I'm bringing bringing up these numbers is these are the champions, the champions of the AI boom.

Speaker 11 Yet their revenues kind of fucking stink. Wow.
Even if OpenAI made $13 billion this year, even if Anthropic made $5 billion.

Speaker 11 Okay, wow. So that's not even $20 billion.
That's like

Speaker 11 $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.

Speaker 11 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?

Speaker 11 Another thing I'm tired about is everybody telling this story about Anthropic being more efficient and only burning $2.8 billion this year.

Speaker 11 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 in March 2025 after raising $4 billion in November 2024.

Speaker 11 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.

Speaker 11 This company is 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.

Speaker 11 I can't believe I'm defending open AI, but

Speaker 11 these companies are the two largest ones in the generative AI space, and by extension, the two largest consumers of GPU compute.

Speaker 11 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.

Speaker 11 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.

Speaker 11 And in all seriousness, who else is buying AI Compute? What are they doing with it?

Speaker 11 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.

Speaker 11 CoreWeave made $1.36 billion in revenue and lost $110 million doing so in the last quarter.

Speaker 11 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.

Speaker 11 CoreWeave has a backlog of over $50 billion in compute, and $22 billion of that is OpenAI, a company that burns billions of dollars a year and lives on venture subsidies.

Speaker 11 $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 are not the future.

Speaker 11 404 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 Sorry, I also forgot to mention that CoreWeave's backlog problem stems from data center construction delays.

Speaker 11 That and Coreweave 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.

Speaker 11 I also left something out of this script, which is that just the last week, Core Weave just raised another $2 billion of debt. When this all ends, I am going to be a little insufferable.

Speaker 11 But let's just be abundantly clear.

Speaker 11 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 continuum ability to buy GPUs.

Speaker 11 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?

Speaker 11 How much money is renting those GPUs? Can you...

Speaker 11 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?

Speaker 11 And why exactly are we still buying GPUs? What are they doing? To whom are they being rented? For what purpose?

Speaker 11 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?

Speaker 11 Is it because it's unprofitable to make the revenue? And why at this point in history do we not know?

Speaker 11 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.

Speaker 11 Imagine if we sold cars and we didn't have a miles per gallon rating.

Speaker 11 I'm serious, that's effectively where we are. Oh God.

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Speaker 11 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.

Speaker 11 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.

Speaker 11 Why exactly are companies pre-ordering GPUs? What benefit is there in doing so?

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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.

Speaker 11 It's very expensive and and takes a long time.

Speaker 11 And look,

Speaker 11 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.

Speaker 11 They're likely hyperscalers, neoclouds like Coreweave and resellers like Dell and Supermicro, who also both sell to Coreweave.

Speaker 11 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.

Speaker 11 Oracle is looking potentially at $56 billion of debt. It's completely bonkers.

Speaker 11 Nvidia's largest customers are increasingly unable to afford its GPUs, which appear to be increasing in price with every subsequent generation.

Speaker 11 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 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,

Speaker 11 I begin hearing the

Speaker 11 bit from Kill Bill. 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.

Speaker 11 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.

Speaker 11 Now remember, OpenAI and Anthropic both have lines of credit, $4 billion for OpenAI and $2.5 billion for Anthropic.

Speaker 11 In simple terms, NVIDIA's customers rely on debt to buy his 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.

Speaker 11 Who, after all, are the biggest customers paying the companies renting GPUs to sell their AI bottles? That's right, AI startups, all of which are deeply unprofitable.

Speaker 11 Cursor, 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.

Speaker 11 Perplexity, 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.

Speaker 11 Cognition raised $400 million in September after raising $300 million in March, and Cohere raised $100 million million in September a month after it raised $500 million.

Speaker 11 None of these companies are profitable, not even close. I read a story in Newcomer by Tom Duttan that said that Cursor sends 100% of its revenue to Anthropic to pay for its models.
Very cool.

Speaker 11 So I really want to lay this out for you because it's very bad when you think about it. So venture capital is feeding money to startups.

Speaker 11 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?

Speaker 11 So they then raise venture capital or DAI to pay hyperscalers or Neo-Clouds to rent NVIDIA GPUs.

Speaker 11 At that point, hyperscalers and NeoClouds then use either DAIT or existing cash flow in the case of hyperscalers, though not for long, to buy more NVIDIA GPUs.

Speaker 11 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 servers, and sell them to

Speaker 11 neoclouds like Lambda or Coreweaf. At some point, a link in this debt-backed chain breaks because very little cash flow exists to prop it up.

Speaker 11 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.

Speaker 11 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.

Speaker 11 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?

Speaker 11 And how much product is Nvidia shipping before it gets paid? Well, these are both explainable as this is a big company and this is how companies do business, and that's true.

Speaker 11 Why do receivables not seem to be coming down?

Speaker 11 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?

Speaker 11 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 dare, because nobody is actually making money to pay for these these GPUs, let alone running them.

Speaker 11 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.

Speaker 11 It really just comes down to how long people are willing to be stupid and how long Jensen Wong is able to call up Sachinadella and Co. at 3 in the morning and say, buy $1 billion of GPUs, you pig.

Speaker 11 FinDom style baby. But really, I think...

Speaker 11 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.

Speaker 11 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.

Speaker 11 It's not like any actual innovation or progress is driving this bullshit. In any case, the markets crave a healthy NVIDIA.

Speaker 11 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.

Speaker 11 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.

Speaker 11 I will be walking around with a gavel.

Speaker 11 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.

Speaker 11 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.

Speaker 11 All of them could have looked and they could have looked a year ago.

Speaker 11 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 Matasowski for the noises I make.

Speaker 11 But I think in the next few months we're all going to be validated. It's going to be the great vindication.

Speaker 11 But until then, everybody is betting billions on the idea that Wily Coyote won't look down. He's gonna have to at some point, won't he?

Speaker 16 Thank you for listening to Better Offline.

Speaker 19 The editor and composer of the Better Offline theme song is Matosowski. You can check out more of his music and audio projects at matosowski.com.
M-A-T-T-O-S-O-W-S-K-I dot com.

Speaker 8 You can email me at easy at betteroffline.com or visit betteroffline.com to find more podcast links and, of course, my newsletter.

Speaker 8 I also really recommend you go to chat.where's your ed.at to visit the Discord and go to r/slash betteroffline to check out our Reddit. Thank you so much for listening.

Speaker 1 Better Offline is a production of CoolZone Media.

Speaker 1 For more from CoolZone Media, visit our website, coolzonemedia.com or check us us out on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

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Speaker 10 All rights reserved.

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