Better Offline - CZM Rewind: The Case Against Generative AI (Part 2)

Episode Date: December 26, 2025

In part two of this week’s four-part case against generative AI, Ed Zitron walks you through how NVIDIA funds and pumps money into unprofitable, debt-ridden “neoclouds” all to create vehicles to buy more GPUs - all to cover up the lack of demand for generative AI compute. Original Air Date: 10.1.25 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 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: [e-mail] omnystudio.com/listener for privacy information.

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Imagine an Olympics where doping is not only legal but encouraged. It's the enhanced games. Some call it grotesque. Others say it's unleashing human potential. Either way, the podcast's Superhuman documented it all, embedded in the games and with the athletes for a full year. Within probably 10 days, I'd put on 10 pounds. I was having trouble stopping the muscle growth.

Listen to Superhuman on the IHard Radio app, Apple Podcasts, or wherever you get your podcasts. AllZone Media. Hello, I'm Ed Zittron, and this, of course, is better off line. Welcome to the second part of our four-part series where I give you my most comprehensive, most up-to-day explanation of why we're in a bubble and what that even means. The reason why I'm taking my time to be descriptive and comprehensive is because I want this to make sense to those who listen to it.

Having written hundreds of thousands of words this year about the AI bubble, so many of the arguments I've made and the secrets I've exposed are contained in their own discreet little episodes or newsletters, this is my series to consolidate all of the information I've put out there in one place. And I want to make it makes sense to anyone who listens to him. I want anyone, even someone who doesn't even know that much about AI, to listen to the arguments I've been making for the past three years, to understand why things are dire and to feel the same alarm I'm feeling,

or at least understand why I'm alarmed, because I don't like to tell you how you feel. Old-school bit of feedback I got from a listener once, and I appreciate that to this day. Now, today I'll make the case that generative AI's fundamental growth story is flawed, and explain why we're in the midst of an egregious bubble. This industry is sold by keeping things vague, and knowing that most people don't dig much deeper than a headline,

a problem I simply do not have. This industry is effectively in service of two companies, Open AI and Nvidia, who pump headlines out through endless contracts between them or subsidiaries or investments to give the illusion of activity. Open AI has now promised over $400 billion in the next four years, though, honestly, they might owe about a trillion dollars

with all the data centers they signed up for, All of these are egregious sums for a company that have already forecasted billions in losses, with no clear explanation as to how it will afford any of this beyond we need more money, and the vague hope that there's another soft bank or Microsoft waiting in the wings to swoop in and save the day. Now, I'm going to walk you through where I see this industry today and why I see no future for it beyond a horrible, fiery car wreck. While everybody reasonably hops on about hallucinations, which, to remind you is when a model authoritatively states something that isn't true, the truth of why that's bad is far more complex and actually far worse than it seems.

You cannot rely on a large language model to do what you want. Even though it's highly tuned models on the most expensive and intricate platforms can't actually be relied upon to do exactly what you want. And I know some people might say, well, yes, they do. Every time, 100% of the time. A hallucination isn't just when these models say something that isn't true. It's when they decide to do something wrong because it seems the most likely thing to do, or when a coding model decides to go on a wild goose chase,

failing the user and burning a ton of money in the process. The advent of reasoning models, those engineered to think through problems in a way reminiscent of a human, but it's not thinking they don't think they have no consciousness. They literally, you ask them something, and they break down what the prompt might mean and then choose, it's not thinking. And the expansion of what people are trying to use LLMs for

demands that the definition of an AI hallucination be widened, not merely referring to factual errors, but fundamental errors in understanding the user's request or intent, or what constitutes a task, in part because these models, as I said, cannot think and do not know anything. However successful a model might be in generating something good once, it will also often generate something bad, or it'll generate the right thing but in an inefficient and over-vobo's fashion. You do not know what you're going to get each time, and hallucinations multiply with the complexity of the thing you're asking for, or whether a task contains multiple steps, which is a fatal blow to the idea of agents.

You can add as many levels of intrigue and reasoning as you want, but large language models cannot be trusted to do something correctly, or even consistently, let alone every time. Model companies have successfully convinced everybody that the issue is that users are prompting the models wrong, and that the people need to be trained to use AI, but what they're doing is training people to explain away the inconsistencies of large language models, and to assume individual responsibility for what is an innate flaw in how these fucking things work. Large language bottles are also uniquely expensive.

Many mistakenly try and claim that this is like the dot-com boom or Uber, but the basic unique economics of generative AI are insane. Providers must purchase tens or hundreds of thousands of GPUs, each costing 50,000 to 70,000 apiece, and the hundreds of millions or billions of dollars of infrastructure that goes around them are so expensive and hard to install, and that's without mentioning things like staffing or construction or power or water, or even permitting. Then you turn them on and immediately they start losing you money. Despite hundreds of billions of GPUs sold, nobody seems to actually make any of it,

other than Nvidia, of course, the company that makes them, and resellers like Dell and Supermicro who buy the GPUs, put them in servers and sell them to other people. Now, if you're an eager listener, I would love to hear from you on one question, and this is just something that's been bouncing around my head. Supermicro. Is Invidia a customer of Supermicro? Supermicro is a huge customer of Nvidia. I read something like 70% of their cost of goods sold is buying GPUs. But I read that Nvidia was a customer of them, but I can't find anything else. Reach out easy at better offline.com if you've got any thoughts there. Anyway, but back to those resellers. This arrangement works out great for Jensen Huang, the CEO of Nvidia and terribly for everybody else. Today I'm going to explain the insanity of the situation we find us. ourselves in and why I continue to do this work undeterred. The bubble has entered its most pornographic, aggressive and destructive stage, where the more obvious it becomes that we're all cooked here in AI land, the more ridiculous the generative AI industry will act, a dark juxtaposition against every new study that says generative AI does not work, or new story about

chat GPD's uncanny ability to activate mental illness in people. And we're going to start looking at one company, InVITA, which now dominates the stock market, and has taken extraordinary and dangerous measures to sustain growth that is to any sane person, completely unsustainable and unrealistic on every level. But let's start simple. Invitya is a hardware company that sells GPUs, including consumer GPUs that you'd see in a modern gaming PC. But when you read someone say GPU within the context of AI, they mean enterprise-focused GPUs like the A-100, H-100, and more modern GPUs like the Blackwell series B200 and GB200, which combines two GPUs with an Nvidia CPU.

This is all complex sounding, but I want you to have the groundwork. These GPUs cost anywhere from $50,000 to $70,000 and require tens of thousands of dollars more of infrastructure, networking to cluster these server racks of GPUs together to provide compute, and massive cooling systems to deal with the massive amounts of heat they produce, as well as servers themselves that they run on, which typically use top-of-the-line data center CPUs and contain vast quantities of high-speed memory and storage. While the GPU itself is likely the most expensive single item within an AI server,

the other costs, and I'm not even factoring in the actual physical building that the server lives in, or the water or electricity that you use as well, all this crap adds up. I've mentioned Nvidia because it has a virtual monopoly in this space. Generative AI effectively requires Nvidia GPUs, in part because it's the only company really making the kinds of high-powered, cards that generate if AI demands, and because Nvidia created something called CUDA, CUDA, a collection of software tools that lets programmers write software

that runs on GPUs, which were traditionally used primarily for rendering graphics in games. While there are some open source alternatives, as well as alternatives from Intel with its ARC-GPUs and AMD, NVIDIA's main rival in the consumer space, these aren't nearly as mature or feature-rich. Kuda's been around for 10, 15 years now, and they really knew what they were doing.

They also bought a company called Melanox, which did the high-speed networking, back in 2019, I think, for $6 billion. Anyway, due to the complexities of AI models, one cannot just stand up a few of these GPUs either. You need clusters of thousands, tens of thousands, or hundreds of thousands of them for it to be worthwhile, making any investment in GPUs in the hundreds of millions or billions of dollars,

especially considering they require completely different data center architecture to make them run. You've probably read a bunch of stuff about crypto miners turning into AI data center providers. These crypto data centers have to be. knocked down and replaced. You can't just put the same GPUs in. Isn't going to work. And with the new blackwell ones, the brand new ones, and then the Rubens following them, same deal. A common request, like asking a generative AI model to pass through thousands of lines of code and make a change or an addition may use multiples of these $50,000 GPUs at the same time. And so if you aspire to serve thousands or millions of concurrent users, you need to spend big. Really, really, really, really big.

It's these factors, the vendor lock-in, the ecosystem, and the fact that generative AI really only works when you're buying GPUs at scale that underpin the rise of Nvidia. But beyond the economic and technical factors, there are human ones too. To understand the AI bubble is to understand why CEOs do the things they do, because an executive job is so vague, they can telegraph the value of their labor by spending money on initiatives and partnerships and stratagem. AI gave hyperscalers the excuse to spend hundreds of billions of dollars on data centers and buy a bunch of GPUs to go in them because that to the markets looks like they're doing something. By virtue of spending a lot of money in a frighteningly short amount of time, Sachin Adela received multiple glossy profiles, all without having to prove that AI can really do anything, be it a job or make Microsoft money.

Nevertheless, AI allowed CEOs to look busy, and once the markets and journalists had agreed on the consensus opinion that AI would be big, all that these executives had to do was buy GPUs and do AI, or plug AI within their own software products, but really it was just jump on the big stupid asshole train. Another podcast from some SNL late night comedy guy, not quite. Unhumor me with Robert Smygel and friends, me and hilarious guests from Jim Gaffigan to Bob Odenkirk to David Letterman, help make you funnier. This week, my guest, SNL's Mikey Day and head writer Streeter Seidel help an acapella band with their between songs banter.

There's that worst singer in the group? The worst? Yeah. Me. Is there anything to the idea that because you're from Harvard, you only got in because your parents made a huge donation. The group. The yard birds, right?

That's the name. The Harvard yard, but they're open to change. Do you have a name suggestion? We're open. Since you guys are middle aged. One erection. Listen to humor me with Robert Smigel and Friends on the I-Heart Radio app, Apple Podcasts, or wherever.

you get your podcast. Cuba me. I need some jokes to make me seem funny. Run a business and not thinking about podcasting, think again. More Americans listen to podcasts than ad-supported streaming music from Spotify and Pandora. And as the number one podcaster, IHearts twice as large as the next two combined. So whatever your customers listen to, they'll hear your message. Plus, only I-Heart can extend your message to audiences across broadcast radio.

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we sit down with the most inspiring women in sports and wellness, professional athletes, coaches, and Olympic champions to talk about the challenges that shaped them and the mindset that keeps them going. From the WNBA standout, Kate Martin, and rising hockey star Layla Edwards. If a boy can do it, I don't. See why a girl can't?

Like, I've never understood that. Like, it didn't make sense in my brain. It's hard to be in spaces that no one looks like you, but don't ever feel like you don't belong. Don't let that be the reason you don't do it. An Olympic champs Gabby Thomas and Katie Ladeki. The ability to show a gold medal to someone and have their face light up and smile,

that means the world to me. And that's what motivates me to win more gold medals. At our level, at this scale, like being able to fail in front of the entire world. Like, I can do anything. I can do anything. Because resilience isn't just about winning. It's about showing up, even when it's hard. Listen to Hurtle with Emily Abadi on the IHeart Radio app,

Apple Podcasts, or wherever you get your podcasts. Presented by Capital One, founding partner of IHart Women's Sports. Imagine an Olympics where doping is not only legal but encouraged. It's the enhanced games. Some call it grotesque. Others say it's unleashing human potential. Either way, the podcast's superhuman. Human documented it all, embedded in the games and with the athletes for a full year.

Within probably 10 days, I'd put on 10 pounds. I was having trouble stopping the muscle growth. Listen to Superhuman on the I-Hard Radio app, Apple Podcasts, or wherever you get your podcasts. We are in the midst of one of the darkest forms of software in history, described by many as unwanted guests invading their products, their social media feeds, their bosses empty minds, and resting in the hands of monsters. Every story of AI's success feels bereft of any real triumph, with every literal description of its abilities involving multiple caveats about the mistakes it makes or the incredible costs of running it.

Generative AI really exists for two reasons, to cost money and to make executives look busy. It was meant to be the new enterprise software and the new iPhone and the new Netflix all at once, a panacea where the software guys pay one hardware guy for GPUs to unlock the incredible value creation of the future. In many ways, generative AI was always set up to fail, because it was meant to be everything, was talked about like it was everything, it's still sold like it's everything, yet for all the fucking hype it comes down to two companies, Open AI and Invidia. And Nvidia was, for a while, living high on the hog. All CEO Jensen Huang had to do every three months would say, check out these numbers, and the markets and business journalists would squeer with glee, even as he said stuff like, the more you buy, the more you save,

in part tipping his head to the very real and sensible idea of accelerated computing, but framed within the context of the cash inferno that's generative AI. And it all seems kind of fucking ludicrous. Huang's showmanship worked really well for Nvidia for a while, because for a while the growth was easy. Everybody was buying GPUs, meta, Microsoft, Amazon, Google, and to a lesser extent, Apple and Tesla made up 42% of NVIDIA's revenue, creating at least for the first four, a degree of shared mania where everybody justified buying tens of billions of dollars of GPUs by saying, the other guy's doing it. This is one of the major reasons the AI bubble is happening, because people conflated Nvidia's incredible sales with interest in AI, rather than everybody buying GPUs at once. Don't worry, I'll explain the revenue side a little bit later. We're here for the long haul.

sit down, get comfort you're going to need to be. Anyway, Nvidia is now facing a big problem. The only thing that grows forever is cancer. On September 9th, 2025, the Wall Street Journal said that Nvidia's wow factor was fading, going from beating analyst estimates by nearly 21% in its fiscal year Q2-2024 earnings to scraping by with a pathetic, measly 1.52% beat in its most recent earnings, something that for any other company would be a good thing because they made so much money, but framed against the delusional expectations that generative AI is inspired, well, the figure looks nothing short of ominous. I quote the Wall Street Journal.

Already, Nvidia's 56% annual revenue growth rate in its latest quarter was its slowest in more than two years. If analysts' projections hold, growth will slow further in the current quarter. In any other scenario, 56% year-over-year growth would lead to an abundance of Domperignon and Huang signing hundreds of boobs. But this is Nvidia, and that's just not good enough. Back in February 2024, Nvidia was booking 265% year-over-year growth, but in its February 2025 earnings, Nvidia only grew by a measly pathetic, disgusting 78% year-over-year. I'm being sarcastic, of course. It isn't so much that Nvidia isn't growing, but to grow year-over-year at the rates that people expect is insane.

Life was a lot easier. When Nvidia went from $6.05 billion in revenue in Q4 fiscal year 2025 to $22 billion in revenue, the $22 billion in revenue in Q4 fiscal year 2024. But for it to grow even 55% year over year from Q2FY 2026, I'm just going to truncate that now, which was $46.7 billion to Q2, 2027, that would require them to make $72.385 billion in revenue in the space of three months, mostly from selling GPUs, which make up about 88% of its revenue. Just want to be clear there. In a year, they would have to make $72 billion. Just selling. pretty much GPUs and the associated hardware in the space of three months. It's insane. This is,

this is really, it's too much. It's too much to expect. And this, by the way, would put Nvidia in the ballpark of Microsoft, who made $76 billion in their last quarterly earnings, and within the neighborhood of Apple, who made $94 billion in their last quarter of earnings. And they would do this predominantly making money in an industry that a year and a half ago barely made the company $6 billion in a quarter. And the market needs Nvidia to perform. They must, they must, they as the company makes up 7 to 8% of the value of the S&P 500. It's not enough for Nvidia to be wildly profitable or to have a monopsony on selling GPUs or for it to have effectively 10x their stock in a few years.

No, no, no, more, more, more, always more, number must go up. It must continue to grow at the fastest rate of anything ever, making more and more money, selling more and more of these GPUs to a small group of companies that immediately start losing money the moment they plug them in. It's not brilliant. it. While a few members of the Magnificent Seven could be depended on to funnel tens of billions of dollars into a furnace each quarter, there were limits, even for companies like Microsoft, which had bought over 485,000 GPUs in 2024 alone. To take a step back about how people actually make money from buying

these GPUs, companies like Microsoft, Google and Amazon make their money by either selling access to large language models that people incorporate into their products, or by renting out servers full of those GPUs to run inference, the thing to generate the output, or train AI models for companies that develop and market their models themselves, namely Anthropic and Open AI with some smaller competitors that don't really matter. That latter revenue stream, renting out GPUs, is where Jensen Wong found a solution to that horrible eternal growth problem, the Neo-Cloud, namely companies like Corweave, Lambda and Nebius. Now, these businesses are fairly straightforward. They own, or lease data centers that they then fill full.

of servers that are full of Nvidia GPUs, which they then rent out on an hourly basis to customers, either on a per GPU basis are in large batches for large customers who guarantee they'll use a certain amount of compute and sign up for a long-term agreement, for so more than a hour at time, a couple years, perhaps, these larger commitments. A Neo-Cloud is a specialist cloud compute company that exists only to provide access to GPUs for AI, unlike Amazon Web Services, Microsoft DeZure and Google Cloud, all of which to have healthy businesses selling other kinds of compute, with AI, as I'll get into later, failing to provide much of a return on investment at all. It's not just the fact that these companies are more specialized than, say, AWS or Azure.

As you've gathered from the name, these are new, young, and in almost all cases, incredibly precarious businesses, each with financial circumstances that would make a Greek finance minister blush. That's because setting up a neocloud is expensive. Even if the company in question already has data centers, as Corwe've did with its cryptocurrency mining operation, AI requires, as I said, completely new data center infrastructure to run and cool the GPUs. And those GPUs also need paying for, and then there's the other stuff I mentioned earlier, like power, water, and the other bits of the computer, CPU, motherboard, blah, blah, blah, blah, blah. As a result, these neoclouds are forced to raise billions of dollars in debt,

which they collateralize using the GPUs they already have, along with contracts from customers, which they then use to buy more GPUs. That's right, they buy GPUs from Nvidia. They raised debt on those GPUs and then they use that debt to buy more GPUs from Nvidia. It's enough to drive man insane. Corweave, for example, has $25 billion in debt on an estimated $5.35 billion of revenue in 2025, losing hundreds of billions of dollars per quarter. Now, you know who also invests in these neoclounds?

You'll never guess it's Nvidia. Invidia is also one of Corweave's largest customers, accounting for 15% of its revenue in 2024 and just signed a deal to buy six, $1.3 billion of any capacity that Corweeb can't otherwise sell to someone else through 2032, an extension of a $1.3 billion $2,223 deal reported by the information. It was also the anchor investment in Corwee's IPO, about $250 million. Invita is currently doing the same thing with Lambda, another Neo-Cloud that Invidia invested in, which also plans to go public next year. Invidia is also one of Lambda's largest customers, signing a deal with it this summer to rent

10,000 GPUs for $1.3 billion over four years. In the UK, Nvidia has also just invested $700 million in Nscale, a former crypto miner that has never built an AI data center that has, despite having no experience, committed $1 billion and or 100,000 GPUs to an open AI data center in Norway. On Thursday, September 25th, Nscale announced that it closed another funding round with Nvidia listed as the main backer, although it's unclear how much money it put in. it would be safe to assume it's probably at least $100 million. Invidia also invested in Nebius, an outgrowth of Russian conglomerate Yandex.

Nebius provides, through their partnership with Nvidia, tens of thousands of dollars of compute credits the company's Invitia's inception startup program. Look, Nvidia's plan is simple. Fund these neoclouds, let these neoclouds load themselves up with debt, at which point they buy bunches of GPUs from Nvidia, which can then be used as collateral for loans, along with contracts from customers, allowing the neoclodes to buy even more GPUs from Nvidia, it is just that simple. It's infinite money, right? Just money me, money now. You fund the

company, the company buys from you, fund them again. They've used the thing they bought to buy more from you. Unlimited money, except that is for one small problem. These companies don't they don't really appear to have that many customers and they don't appear to be making much money. Another podcast from some SNL late night comedy guy, not quite unhumored me with Robert Smygel and friends, me and hilarious guests from Jim Gaffigan to Bob Odenkirk to David Letterman, help make you funnier. This week, my guest, SNL's Mikey Day and head writer Streeter Seidel, help an Acapella band with their between songs banter.

Who's that worst singer in the group? The worst? Yeah. Me. Is there anything to the idea that because you're from Harvard, you only got in because your parents made a huge donation. To the group. side of the group.

The yard herds, right? That's the name. The Harvard yard, but they're open. Do you have a name suggestion? We're open. Since you guys are middle aged, one erection. Listen to humor me with Robert Smigel and Friends on the I Heart Radio app, Apple Podcasts, or wherever you get your podcast. Humor me.

I need some jokes to make me seem funny. Run a business and not thinking about podcasting, think again. More Americans. listen to podcasts, then ads supported streaming music from Spotify and Pandora. And as the number one podcaster, IHearts twice as large as the next two combined. So whatever your customers listen to, they'll hear your message. Plus, only IHeart can extend your message to audiences across broadcast radio. Think podcasting can help your business. Think IHeart. Streaming, radio, and podcasting. Let us show you at iHeartadvertising.com. That's iHeartadvertising.com.

Life throws hurdles big and small. The question is, how do you conquer you? them. On Hurtle with Emily Abadi, we sit down with the most inspiring women in sports and wellness, professional athletes, coaches, and Olympic champions to talk about the challenges that shaped them and the mindset that keeps them going. From the WNBA standout, Kate Martin, and rising hockey star, Layla Edwards. If a boy can do it, I don't see why a girl can't. Like, I've never understood that. Like, it didn't make sense in my brain. It's hard to be in spaces that no one looks like you, but don't ever feel like you don't feel long. Don't let that be the reason you don't do it. An Olympic champs Gabby Thomas and Katie Ladecki.

The ability to show a gold medal to someone and have their face light up and smile, that means the world to me. And that's what motivates me to win more gold medals. At our level, at this scale, like being able to fail in front of the entire world. Like, I can do anything. I can do anything. Because resilience isn't just about winning. It's about showing up, even when it's hard.

Listen to Hurtle with Emily Abadi on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. Presented by Capital One, founding partner of IHeart Women's Sports. Imagine an Olympics where doping is not only legal, but encouraged. It's the enhanced games. Some call it grotesque. Others say it's unleashing human potential. Either way, the podcast's Superhuman documented it all,

embedded in the games and with the athletes for a full year. Within probably 10 days, I'd put on 10 pounds. I was having trouble stopping them. muscle growth. Listen to Superhuman on the IHard Radio app, Apple Podcasts, or wherever you get your podcasts. As I went into in a recent premium newsletter, Nvidia funds and sustains NeoClouds as a way of funneling revenue to itself, as well as partners like Super Micro and Dell, resellers that take Nvidia GPUs, like I mentioned, and put them in service to sell pre-built to customers. These two companies made up 39% of Nvidia's revenues last quarter. Yet when you remove hyperscaler revenue,

Microsoft, Amazon, Google, OpenAI, and Nvidia from the revenues of these Neo-Clouds, there's barely $1 billion in revenue combined across Corweave, Nebius and Lambda. Coreweave's $5.35 billion in revenue is predominantly made up with its contracts with Nvidia, Microsoft who are offering that compute to OpenAI, Google, who have hired CoreWeave to offer compute to OpenAI, and I'm not kidding, and of course OpenAI itself, which has now promised Coreweave $22.4 billion in business over the next five years. a lot of stuff. So I'll make it really simple. There's no real money in offering AI compute, but that isn't Jensen Huang's problem. So we simply will force Nvidia to hand money to these companies so that they have contracts to point out so they can raise debt to buy more of those GPUs so that Nvidia can give them more contracts so they can use that to raise more money. It's really bad. All right, it's really bad. When I read this stuff out loud, I feel a little crazy because it's so obviously unsustainable. Neoclouds are effectively giant private equity

vehicles that exist to raise money to buy GPUs from Nvidia or for hyperscalers to move money around so they don't have to increase their capital expenditures and can, as Microsoft did earlier in the year, simply walk away from deals they don't like with the masses of data center leases they walk from. Nebius recently signed a $17.4 billion deal with Microsoft, which even included the clause in its 6K filing and official filing with the government, that Microsoft can terminate the deal in the event the capacity is built by the delivery dates. And by the way, Nebius already used the contract that Microsoft gave them to raise $3 billion to, I'm not shitting you here, build the data center to actually, to actually provide the

compute for the contract. They don't have it, yeah. They don't have the, they don't have the, they don't have the fucking bill it. They haven't got the compute, mate. These fucking companies are right. Anyway, anyway, Sorry, sorry, I'll stop spiraling. Let me just break down these numbers. Let's look at CourtWeb first. Microsoft, they're 60% of their revenue in 2024, and they're providing compute mostly for OpenAI,

15% of their revenue last year was Invidia, and then the rest was meta, and then OpenAI and Google. Lambda, half of their revenue comes from Amazon and Microsoft, and now $1.5 billion of their revenue comes from Invidia, which are their current revenue, by the way, and that $1.5 billion over four years, so the current revenue is $250 million. $2 billion. Well, that would make Nvidia the largest customer. I realize I'm just saying numbers here, but for real, with that contract, because Lambda only made $250 million in the first half of this year, and Nvidia is spreading $1.5 billion across four years. Invita is the largest customer now. Now, Nebius has got similar revenue to Lambda, but their largest customer is now, it's fucking Microsoft. They don't have real customers. They just have hyperscalers or Nvidia themselves.

And from my analysis, it appears that Corweave, despite expectations to make that $5.35 million, has only around $500 million of non-magnificent 7 or open AI revenue in 2025, with Lambda estimated to have maybe a round of $100 million in AI revenue otherwise, and Nebius only around $250 million. dollars, and that's being generous. In much simpler terms, the Magnificent Seven is the AI bubble, and the AI bubble exists to buy more GPUs, because as I'll talk about, there's no real money or growth coming out of this other than the amount that private credit is investing. And this really is quite worrying, by the way. I had a quote here for an analyst that says it's about $50 billion a quarter for the low end for the past three quarters. So why is this bad? All right, I don't know. Let's start simple. 50 billion dollars a quarter of data center funding is going into an industry that has less revenue than free-to-play mobile game, Genshin Impact.

That feels pretty bad. Who's going to use these data centers? How are they even going to make money on them? Private equity firms don't typically hold onto assets. They sell them or they take them public. That doesn't seem great to me. Anyway, if AI was truly the next big growth vehicle, neoclouds would be swimming in diverse global revenue streams. Instead, they're heavily centralized around the same few names, one of which, in Vidiya, directly benefits from their existence, not as a company doing business, but as an entity that can accrue debt and spend money on GPUs. These neoclods are entirely dependent on a continual flow of private credit from firms like Goldman Sachs, who's Beckneyby as Corweave and Lambda, J.P. Morgan, Lambda, Crusoe, Building Abilene, Texas's Open AI Data Center, and of course Corwave, and Blackstone, Lambda and Corwave, who have, in a very real sense created an entirely debt-based infrastructure to feed billions of dollars directly to Nvidia. All in the name of an AI

that's yet to arrive. The fact that the rest of the neocloud revenue stream is effectively either a hyperscaler or open AI is also concerning. Hyperscalers are at this point the majority of data center capital expenditures and have yet to prove any kind of success from building out this capacity. Outside of course Microsoft's investment in Open AI, which has succeeded in generating revenue while burning billions of dollars of revenue on, well I mean it's not really any profit is they just burning money. It's also insane. When you say this stuff, I've got two more goddamn episodes of this. And when I read these scripts, I'm just like, how is nobody else more freaked out? Oh, well, hyperscaler revenue is also capricious. But even if it isn't,

why are there no other major customers? Why across all of these companies does there not seem to be one major customer who isn't open AI? Well, the answer is quite obvious. Nobody that wants it can afford it, and those that can afford it, don't need it. It's also unclear what exactly hyperscalers are doing with this compute, because it sure isn't making much. money. While Microsoft makes $10 billion in revenue from renting compute to OpenAI via their Microsoft Azure Cloud, it does so at cost. And was charging OpenAI $1.30 per hour for each A100 AI GPU it rents, a loss of $2.2 an hour per GPU, meaning that it is likely losing money on this compute, especially as semi-analysis has the total cost per hour per GPU at around $1.46 with the cost

of capital and debt associated for a hyperscaler, though it's unclear that whether that's for an H-100 or an A-100 GPU. In any case, how do these neoclouds pay for their debt if the hyperscalers give up, or invidia doesn't send them money, or more likely, private credit begins to notice that there's no real revenue growth outside of circular compute deals with neocloud's largest suppliers, investors, and customers. Don't know why I said plural there, because it's just one, Nvidia. And the answer is, they don't. In fact, I have serious concerns that they can't even build the capacity necessary to fulfill these deals, but nobody seems to worry or think about them. But really, though, it appears to be taking Oracle and Cruce around 2.5 years per gigawatt of compute capacity. How exactly are

any of these neoclouds, or indeed Oracle itself, able to expand to capture this revenue? Who knows? But I assume somebody is going to say Open AI. Here's an insane statistic for you, by the way. Open AI will account for in both its revenue, projected $13 billion and in its own compute cost $10, somewhere in the region of 40 to 50% of all AI revenues in 2025. As a reminder, Open AI has leaked that it will burn $150 billion in the next four years, and based on my estimates, it actually needs to raise, I mean, upwards of $400 billion in the next four years, based on its $300 billion deal with Oracle and some recently announced $100 billion compute purchases for backup. And that alone.

is a very bad sign. Very, very bad indeed, especially with three years and $500 billion or more into this hype cycle, with few signs of life outside of, well, open AI promising people money. And that's not healthy or sane or normal, and it's certainly not stable. And it's going to get bad real fast. Catch you tomorrow. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Rosowski. You can check out more of his music and audio projects.

and Matasowski.com, M-A-T-T-O-S-O-S-K-I-com. You can email me at E-Z at Better Offline.com or visit Better Offline.com to find more podcast links and, of course, my newsletter. I also really recommend you go to chat. Where's your ed dot at to visit the Discord and go to R-S-Better-O-Line to check out our Reddit. Thank you so much for listening. Better Offline is a production of CoolZone Media.

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