The Case Against Generative AI (Part 2)

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1:53Call Zone Media Hello, I'm Ed Zitron, and this, of course, is Better Offline.

2:08Better Offline 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 it 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.

2:56Because 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.

3:28OpenAI 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 truth of why that's bad is far more complex and actually far worse than it seems.

4:11You cannot rely on a large language model to do what you want. Even those 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.

4:56And 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 overvibosed 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.

5:34You 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 a piece 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.

6:32Then 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 NVIDIA a customer of Supermicro? They're a custom, and Supermicro is a huge customer of NVIDIA. I read something like 70 % of their cost of goods sold is buying GPUs.

7:07But I read that NVIDIA was a customer of them, but I can't find anything else. Reach out easy at betteroffline.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.

7:37A 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 A100, H100, H200, and more modern GPUs like the Blackwell series B200 and GB200, which combines two GPUs with an NVIDIA CPU.

8:25This 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 it uses, well, all this crap adds up.

9:05I'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, C-U-D-A, 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.

9:37which 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.

10:12These 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. 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 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.

10:50But 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.

11:28Nevertheless, 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 arsehole train.

12:06We'll see you next time.

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14:51Listen to No Such Thing on the iHeartRadio 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 an unwanted guest 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.

15:29It 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, is still sold like it's everything, yet for all the fucking hype it comes down to two companies, OpenAI and NVIDIA. 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 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.

16:14And 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.

16:48Don'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. 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 has inspired, well, the figure looks nothing short of ominous.

17:27I 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.

18:03It 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 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 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.

18:52It'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.

19:29No, 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, 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, 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 OpenAI with some smaller competitors that don't really matter.

20:25That latter revenue stream, renting out GPUs, is where Jensen Huang found a solution to that horrible eternal growth problem, the NeoCloud. 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 more than an hour at a time, couple years perhaps, these larger commitments.

21:00A NeoCloud 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 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.

21:32That's because setting up a NeoCloud is expensive. Even if the company in question already has data centers, as CoreWeave 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.

22:05That'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. CoreWeave, 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 neoclouds? 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 2023 deal reported by the information.

22:48It was also the anchor investment in CoreWeave's IPO, about$250 million. NVIDIA is currently doing the same thing with Lambda, another NeoCloud 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 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, 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.

23:34It would be safe to assume it's probably at least$100 million. dollars. 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 to the company's NVIDIA'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 NeoClouds to buy even more GPUs from NVIDIA.

24:04It 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 problem. These companies don't really appear to have that many customers and they don't appear to be making much money.

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