The Case Against Generative AI (Part 2)

Summary

Podcast Summary: Better Offline – The Case Against Generative AI (Part 2)

Host: Ed Zitron (Cool Zone Media & iHeartPodcasts)
Date: October 1, 2025
Length of Content: ~35 minutes

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Episode Overview

In the second part of his comprehensive four-part series, Ed Zitron delves into the economic and technical realities underpinning the generative AI industry. He asserts that the growth narrative around generative AI is fundamentally flawed, fueled by media manipulation, vague promises, and financial engineering. Zitron focuses on Nvidia’s monopolistic role in the hardware powering AI and exposes the circular, debt-driven funding mechanisms keeping the generative AI bubble afloat.

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Key Discussion Points & Insights

1. The Generative AI Bubble: Hype & Reality ([02:11]–[06:00])

• Industry Manipulation:
  Zitron argues that AI industry leaders—primarily OpenAI and Nvidia—create the illusion of relentless progress via headlines and investment cycles:

  "This industry is effectively in service of two companies, OpenAI and Nvidia, who pump headlines out... to give the illusion of activity." ([02:58])

• Flawed Foundations:

  • Massive, headline-grabbing promises (e.g., OpenAI’s $400B+ commitments) are not backed by sustainable business models.
  • There is little clarity on how these astronomical investments can be recouped, barring hypothetical future benefactors.

• Problem of Hallucinations:
  Zitron explains that the unreliability of Large Language Models (LLMs)—their tendency to supply plausible but incorrect or nonsensical results ("hallucinations")—is more than a technical hiccup; it’s a structural flaw:

  "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." ([04:56])

2. Economic Absurdities of Generative AI ([06:00]–[12:00])

• Unsustainable Costs:
  Generative AI providers are forced to amass tens or hundreds of thousands of GPUs, each costing $50,000–$70,000; the necessary supporting infrastructure multiplies this cost.

  "You turn them on, and immediately they start losing you money." ([07:11])

• Winner-Take-All Market:
  Nvidia dominates due to its hardware (enterprise GPUs like A100/H100) and proprietary software platform (Cuda), making alternatives largely unviable.

• Vendor Lock-In and the Hardware Trap:

  • Generative AI only becomes competitive at massive scale, demanding billions in investment.
  • Infrastructure from cloud mining or gaming can't be repurposed for generative AI without immense, costly adaptations.

• The Executive Incentive Problem:
  AI serves as a pretext for reckless spending by tech executives—activity that impresses markets, but lacks substance:

  "AI allowed CEOs to look busy, and once the markets and journalists had agreed on the consensus opinion that AI would be big, all these executives had to do was buy GPUs...just jump on the big stupid asshole train." ([11:23])

3. Nvidia’s Growth, Market Monopolization & Contradictions ([14:54]–[24:00])

• Nvidia’s Market Domination:

  • Their revenues exploded by selling AI GPUs to hyperscalers (Meta, Microsoft, Amazon, Google) and companies reshaping their businesses around AI.
  • This frenzy is self-reinforcing: each player justifies extravagant spending because their peers are doing the same.

  "One of the major reasons the AI bubble is happening is because people conflated Nvidia’s incredible sales with actual interest in AI, rather than everybody buying GPUs at once." ([15:49])

• Growth Trap & Market Expectations:
  Even as Nvidia posts astronomical revenue growth (56%–265% YoY), any deceleration is seen as failure—reflecting market delusions. Zitron lampoons Wall Street’s insatiable appetite:

  "It must continue to grow at the fastest rate of anything ever... selling more and more GPUs to a small group of companies that immediately start losing money the moment they plug them in." ([17:58])

• Unsustainable Financial Models:
  The expectation for infinite growth is compared to cancer—a system destined to collapse as demand plateaus.

4. The NeoClouds: Financial Engineering & Circular Revenue ([24:00]–[29:31])

• Rise of Specialist NeoClouds:
  Companies like CoreWeave, Lambda, and Nebius exist solely to acquire Nvidia GPUs and rent them for AI workloads. Startup and operating costs are astronomical, leading to significant debt.

  "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." ([25:00])

• Symbiotic, Circular Funding:
  Nvidia not only supplies GPUs, but also invests in these NeoClouds, sometimes becoming their biggest customer. This feedback loop artificially inflates demand and Nvidia’s revenue:

  "Fund these NeoClouds. Let these NeoClouds load themselves up with debt, at which point they buy a bunch of GPUs from Nvidia, which... can be used as collateral for loans... allowing the NeoClouds to buy even more GPUs from Nvidia. It is just that simple." ([26:40])

• Absence of Real End-Users:
  Most NeoCloud revenue comes from other tech giants (hyperscalers) or Nvidia itself—not independent, diverse customers.

5. The AI Bubble’s Reality—Falling Short ([29:31]–[34:00])

• Lack of Real Revenue:
  Zitron’s breakdown of NeoCloud customer data shows that these companies have almost no meaningful revenue outside Nvidia, Microsoft, Google, Meta, Amazon, and OpenAI:

  "In much simpler terms, the Magnificent Seven is the AI bubble. And the AI bubble exists to buy more GPUs..." ([31:44])

• Private Credit as the Engine:
  The bubble is sustained by private equity and credit giants (Goldman Sachs, JP Morgan, Blackstone), who invest billions primarily to fund ever-growing GPU purchases.

• Profitless Growth:
  Despite $50 billion per quarter in data center spending, AI industry revenue is less than successful mobile games (e.g., Genshin Impact), indicating the absence of real economic value.

• Collapsing ROI:
  Even industry behemoths like Microsoft are losing money renting AI compute, with Zitron citing specifics:

  "Microsoft makes $10 billion in revenue from renting compute to OpenAI... at cost... charging OpenAI $1.30 per hour per GPU, losing $2.20 per hour." ([33:30])

6. The Red Flags: Sustainability, Demand, and the Crash Ahead ([34:00]–[36:00])

• Dependence & Instability:
  The entire ecosystem relies on a handful of players, many of whom already have so many GPUs they can’t profitably utilize them.

• Warning Signs:

  • OpenAI alone accounts for nearly half of all AI revenue and compute consumption.
  • OpenAI will “burn” at least $115B in the next four years and needs to raise up to $400B for contracts it’s barely able to fulfill.
  • NeoClouds secure billion-dollar contracts before even building the required infrastructure.

• Zitron’s Stark Perspective:

  "We're three years and $500 billion or more into this hype cycle with few signs of life outside of, well, OpenAI promising people money. And that's not healthy or sane or normal." ([35:56])

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Notable Quotes & Memorable Moments

• On AI’s Utility & Hype:

  "Generative AI really exists for two reasons: to cost money and to make executives look busy." ([14:56])

• On Nvidia’s Business Model:

  "It’s infinite money, right? Just money. Me money. Now—you fund the company, the company buys from you, you fund them again. They’ve used the thing they bought to buy more from you. Unlimited money." ([26:40])

• On The Bubble’s Danger:

  "$50 billion 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." ([32:57])

• On Market Delusions:

  "It must continue to grow at the fastest rate of anything ever...selling more and more of GPUs to a small group of companies that immediately start losing money the moment they plug them in. It’s not brilliant, is it?" ([17:58])

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Key Timestamps

• [02:11] — Episode introduction; thesis that generative AI growth is a bubble
• [04:56] — Detailed critique of AI “hallucinations”
• [07:11] — Explanation of AI’s unsustainable infrastructure costs
• [11:23] — Critique of executive incentives and boardroom AI hype
• [14:54] — Nvidia’s showmanship and absurd market expectations
• [17:58] — The market’s insatiable demand for infinite Nvidia growth
• [25:00] — The NeoCloud debt-and-GPU acquisition feedback loop
• [29:31] — Breakdown of NeoClouds' real customers & the lack of genuine demand
• [32:57] — Private equity propels an industry less lucrative than popular mobile games
• [33:30] — Microsoft’s profitless compute rental to OpenAI
• [35:56] — Warning on $500B spent and the “unhealthy, unsane” state of AI

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Overall Tone and Delivery

Zitron’s style is frank, irreverent, and often laced with gallows humor and profanity. He oscillates between biting sarcasm and earnest alarm, using plain language to demystify the financial and technological opacity surrounding generative AI. The episode presents a deeply skeptical, critical viewpoint on the AI gold rush, urging listeners to question the sustainability and logic of current tech-industry behavior.

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Conclusions & Takeaway

Ed Zitron delivers a polemical, well-supported case that the generative AI industry is propped up by debt, hype, and circular financial logic rather than meaningful demand or sustainable innovation. Nvidia, aided by specialist NeoClouds and compliant investors, has orchestrated a market where revenue and demand are largely artificial—suggesting a looming and potentially catastrophic collapse for the sector.

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For more in-depth analysis, tune in to upcoming episodes or read Ed Zitron’s newsletter and join the Better Offline online communities.

Transcript

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[Ed Zitron]

Call Zone Media hello, I'm Ed Zitron and this of course is better offline. Better off. Welcome to the second part of our four part series where I give you my most comprehensive, most up to date 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 put out there in one place, and I want to make it make sense to anyone who listens to him. I want anyone, even who 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, OpenAI and Nvidia, who pump headlines out through endless contracts between them or subsidiaries or investments to give the illusion of activity. OpenAI 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 softbank 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 harps on about hallucinations, which to remind you is when a model authoritatively states something that isn't true, the 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 the most 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, 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 will generate the right thing, but in an inefficient and nova verbose fashion. You do not know what you are going to get each time. And hallucinations multiply with the complexity of the thing you are 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 models are also uniquely expensive. Many mistakenly try and claim that this is like the.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 of 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 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 Nvidia a Supermicro? They're a custom. 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 easyteroffline.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 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 news story about ChatGPT's uncanny ability to activate mental illness in people. And we're going to start looking at one company, Nvidia, 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. Nvidia 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 A100H100H200 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 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 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 generative 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. Cuda's been around for 10, 15 years now, and they really knew what they were doing. They also bought a company called Mellanox, 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. It isn't going to work with the new Blackwell ones, the brand new ones, and then the Rubins 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 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. Satya Nadella 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.

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[Ed Zitron]

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, OpenAI and and Nvidia. And Nvidia was for a while, living high on the hog. All CEO Jensen Huang had to do every three months was say, check out these numbers and the markets and business journalists would squeal 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 comfy. You're going to need to be Anyway Nvidia is now facing a big problem that the only thing that grows forever is cancer. On September 9, 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 has 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 analyst 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 Dom Perignon 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 they're 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 in Q4 fiscal Year 2024. But for it to grow even 55% year over year from Q2 FY 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 sell 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 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, 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 GPUs to a small group of companies that immediately start losing money the moment they plug them in. It's not brilliant, is 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, 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 OpenAI with some smaller competitors that don't really matter that latter revenue stream, renting out GPUs is where Jensen Huang found a solution to that horrible eternal growth problem. The NEO cloud, namely companies like CoreWeave, 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 or 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 an hour at a time. 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 Azure and Google Cloud, all of which 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 NEO Cloud is expensive, even if the company in question already has data centers, as Core Weave 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 Neo Clouds 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 raise debt on those GPUs, and then they use that debt to buy more GPUs from Nvidia. It's enough to drive a man insane. Corewave, 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 NEO Clouds, you'll never guess. It's Nvidia. Nvidia is also one of CoreWeave's largest customers, accounting for 15% of its revenue in 2024, and just signed a deal to buy $6.3 billion of any capacity that CoreWeave can't otherwise sell to someone else through 2032, an extension of a 1.3 billion DOL by the Information. It was also the anchor investment in Corweave's IPO, about $250 million. Nvidia is currently doing the same thing with Lambda, another NEO Cloud that Nvidia invested in, which also plans to go public next year. Nvidia 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 N scale, a former crypto miner that has Never built an AI data center, having no experience, committed $1 billion and or 100,000 GPUs to an OpenAI data center in Norway. On Thursday, September 25, N Scale 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. Nvidia also invested in Nebius, an outgrowth of Russian conglomerate Yandex and Nebius provides through their partnership with Nvidia, tens of thousands of dollars of compute credits. The company's Nvidia's Inception startup program Look, Nvidia's plan is simple. Fund these Neo Clouds. Let these Neo Clouds 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 Neo clouds 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, you fund them again. They've used the thing they bought to buy more from you. Unlimited money. Except that is for one small prop them. These companies, don't they? They don't really appear to have that many customers and they don't appear to be making much money.

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[Ed Zitron]

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 Supermicro and Dell resellers that a take Nvidia GPUs like I mentioned and put them in servers 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 Core Weave, 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 Core Weave $22.4 billion in business over the next five years. This is all 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 at 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. Did you. It's. 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 from. Nebius recently signed a $17.4 billion deal with Microsoft, which even included a clause in its 6K filing and official filing with the government that Microsoft can terminate the deal in the event the capacity isn't 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. For the contract. They don't have it yet. They don't have the. They don't have the compute. They don't have that. They haven't built it. No one built it. They haven't got the compute, mate. These companies are right.

[Ryan Reynolds]

Anyway.

[Ed Zitron]

Anyway. Sorry, sorry. I'll stop spiraling. Let me just break down these numbers. Let's look at Core Weave first. Microsoft. They're 60% of their revenue in 2024, and they're providing compute mostly for open AI. 15% of their revenue last year was Nvidia, 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 Nvidia, which their current revenue, by the way, and that $1.5 billion over four years. So the current revenue is $250 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. Nvidia 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 Core Weave, despite expectations to make that $5.35 billion this year, has only around $500 million of non Magnificent Seven or OpenAI revenue in 2025, with Lambda estimated to have maybe a round of 100 million do AI revenue. Otherwise Nebby is only around $250 million. 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 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? I don't know. Let's start simple. $50 billion 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 Nvidia 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 neo clouds are entirely dependent on a continual flow of private credit from firms like Goldman Sachs, who's backed Nebius, Core Weave and Lambda, JP Morgan, Lambda Crusoe Building, Abilene, Texas's OpenAI data center, and of course Core Weave and Blackstone, Lambda and Corweave 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 revolution that's yet to arrive. The fact that the rest of the NEO cloud revenue stream is effectively either a hyperscaler or OpenAI 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 OpenAI, 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're 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 and 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 OpenAI? Well, the answer is 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 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 a 100 AI GPU it rents a loss of 2.2 dollars an hour per GPU, meaning that it is likely losing money on this compute, especially as semi analysis as hour per GPU at around $1.46 with the cost of capital and debt associated for a hyperscaler, though it's unclear whether that's for an H100 or an A100 GPU. In any case, how do these NEO clouds pay for their debt if the hyperscalers give up or Nvidia 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 ser 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 Crusoe around 2.5 years per gigawatt of compute capacity. How exactly are any of these Neo Clouds, or indeed Oracle itself able to expand to capture this revenue? Who knows, But I assume somebody is going to say OpenAI. Here's an insane statistic for you, by the way. OpenAI 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, OpenAI has leaked that it will burn $115 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 dollar deal with Oracle and some recently announced 100 billion dollar compute purchases for backup. And that alone is a very bad sign. Very, very bad indeed. Especially as we're three years and $500 billion or more into this hype cycle with few signs of life outside of, well, OpenAI promising people money. And that's not healthy or sane or normal. It's certainly not stable and it's going to get bad real fast. Catch you tomorrow. Thank you thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Osawski. You can check out more of his music and audio [e-mail] m a t t o s o w s k-I.com you can email me at ezetteroffline.com or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat. Where's your ED app to visit the Discord Discord and go to R betteroffline to check out our Reddit. Thank you so much for listening. Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, Visit our website coolzonemedia.com or check us out.

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[Ed Zitron]

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