About this transcript: This is a full AI-generated transcript of Data Centers, Debt & the Risks of AI’s Finances w/ Ed Zitron — The Weekly Show with Jon Stewart from The Weekly Show with Jon Stewart and Better Offline, published October 29, 2026. The transcript contains 16,474 words with timestamps and was generated using Whisper AI.
"On top of all of these other problems, we have this situation where hyperscalers are buying way more GPUs than they need, driving up the price of memory and thus the price of consumer electronics for everyone. And on top of that, they're lying kind of about how much capacity they have. They are..."
[00:00:00] Ed Zitron: On top of all of these other problems, we have this situation where hyperscalers are buying way more GPUs than they need, driving up the price of memory and thus the price of consumer electronics for everyone. And on top of that, they're lying kind of about how much capacity they have. They are trying to make everyone believe they're bringing gigawatts online when a data center can do many things. The data centers that people hate are not the ones that do streaming. These AI data centers are completely different beasts to anything that runs anything else. These things could all disappear tomorrow and the internet would function the mostly the same way because
[00:00:35] Speaker 2: they only exist to do AI. Well, the AI thing was always, it wasn't a necessary development in any way. I always felt like AI is just like Google that's on Coke. When you Google something, you say like, hey man, is there a pizza place near me? And it tells you. But when you say to AI, they're like, oh my God, that's such a great question. I love pizza. Pizza is so good. Did you know it started in Greece? Like it just tells you shit and you're just like, shut up. And sometimes it goes, yeah, DeMarco's is the best. Yeah, that's right. Hey man, I need all your water. I need all your water. DeMarco's doesn't exist. Yeah, it doesn't exist and it makes shit up. Ladies and gentlemen, welcome to the Weekly Show Podcast. My name is Jon Stewart. I am the host of the Weekly Show Podcast. It is Tuesday, October 27th. And excuse me, I'm going to stand back from the mic for just one moment. Can you hear the people singing? It's the election. This is the last time that we will speak before the election. And I just want to urge everybody out there to please, please, for the love of God, get out there and vote and express your political opinion. And I'd like to frame it as a, however you'll vote, but I don't mean that. I mean it as please send a message to this reckless shit show and of administration, uh, that enough is enough, uh, that we have had it, that it is time for someone to step up and provide, uh, I believe the framers referred to them as checks and balances on this, uh, utterly imperious administration. So that is, that is the spiel for today. We are not talking about the election today. We will be talking about other doom day scenarios, uh, financial ones in particular about AI. I know we've talked a little bit on the program about catat catastrophic outcomes of AI in terms of bad actors or in terms of the AI itself turning on us. This deals more with, uh, the real world implications of AI economically and financially. And we're delighted, uh, to have a guest today to talk about that. So let's just get to it. Ladies and gentlemen, we're delighted to be joined by Ed Zitron. He's the CEO of easy primary research. He's the writer of where's your Ed at newsletter, but more importantly, has a great understanding of, I think, uh, the economics of AI and ed welcome. Thanks for having me. Uh, it's, it's an absolute pleasure. Uh, I want to ask you this and I apologize in advance. This is going to be slightly remedial in some regard. So I wanted you to talk about, you know, we've heard a lot about AI in terms of doomsday scenarios. Uh, bad actor, gets ahold of it, builds their own biohazard or, uh, the agents break out and hack into all of the sensitive data and they, or water systems, things like that, or the reverse, it all works. And we're reduced to, uh, NPCs in a world that we have no agency in. I want to talk to you about the sort of the more economic imbalances of the AI world. Does that make sense? Absolutely. All right. Uh, can you give us to, to start with kind of a little bit of the playing field of what is the AI economic ecosystem chips, software dataset? What, what are we dealing with when we talk about the AI economy? So at the top of the stock market, largest company
[00:04:23] Ed Zitron: in the stock market is Nvidia. They make something called a GPU, technically means graphics processing unit. This isn't for graphics. It's because they're really good at parallel processing. So basically doing one thing a bunch of times, nevertheless, you need those chips to do two things. One to train an AI model and two, to run inference, which is just creating the outputs. When you say creating outputs,
[00:04:45] Speaker 2: you mean, so, uh, when you put in a question, the answer that comes to you is the output.
[00:04:52] Ed Zitron: Yes, exactly that. I see. Okay. And then it gets more complex with bigger models and bigger tasks,
[00:04:56] Speaker 2: but nevertheless, that's the basics. So these chips are the GPUs and they run the entire ecosystem.
[00:05:02] Ed Zitron: Yes. So, and Nvidia up until the last few years had basically had an effective monopoly on doing this because they have this thing called CUDA, which is just underlying software libraries and ways of running software on graphics processors that this meant that they were, they are effectively the company behind the AI boom. Now they sell these GPUs, which are very expensive. They're 50,000, 60,000 a piece, usually buy them in racks that are millions of dollars each. And you need thousands of them, hundreds of thousands in some cases to do these big data centers. But Nvidia, pretty much the only vendor selling them. There's technically AMD, technically Broadcom has AI chips too, but Nvidia is the top dog. Their customers are pretty much the largest companies in the world. Microsoft, Google, Meta, Amazon, Oracle, SpaceX, now, I guess. And they have a very small customer base, but that customer base has so much money that they've been able to sink 30 to 40 to 50 billion dollars a quarter into these AI chips and into AI data centers. Now, the problem is, is that these things keep getting more expensive and also take forever to install. But for the most part, Nvidia has got large because everybody has said, "AI is going to be the next big thing. You need to buy as many of these as possible." Not really, not really much proof yet that that's actually the case, or at least anything close to a justification for the trillion plus dollars that have been invested in these GPUs and the data centers for them to go into. And I can kind of stop there. There are various levels to fan
[00:06:37] Speaker 2: out from there. Right. But so the general idea of it is you have this one company, Nvidia. Now, does Nvidia, are they supplying it to everyone? Are we including China in that equation? Or are we saying that's merely the, uh, the United States and maybe other sort of, uh, AI startup areas?
[00:06:59] Ed Zitron: So technically China has a company called Huawei, which makes their own chips, but Nvidia GPUs have been making it over there for years. There was a big story at the end of last year from Bloomberg that there's this company called Megaspeed, which is called a NeoCloud. It just means a company that buys chips and installs them and rents them out, where they appeared to be allegedly a front for a Chinese data center operation. Supermicro, an AI server company was also had one of their co-founders literally using a hairdryer to get the serial numbers off. It's like, it's like weapons. Here's the thing. This era is so grim and soulless and joyless. It's nice to have some goofy high drinks for once. No, I think it is. So Nvidia chips are making over there, but they have far less and China has had to be a little more deft with how they're building these things. But for the most part, Nvidia is selling GPUs to everyone everywhere throughout the world. So Nvidia basically controls everything. This means that Nvidia is incredibly rich. They're printing money. It means that Nvidia has now become the largest supplier to Microsoft, Google, Amazon, Meta, SpaceX. The problem is because these data centers are not actually generating enough revenue or really any revenue. None of these companies, other than SpaceX, disclose their actual AI revenues. Everyone's had to start to take on debt. They've had to lay people off. Meta's doing a $95 billion share sale. Oracle had to do a $50 billion one, facing a downgrade potentially. All because, despite all of the big promises of AI, the economic payoff is not that Torsten Slock, head of, chief economist at Apollo, said the other day that there has been no payoff from AI.
[00:08:44] Speaker 2: That gets to the heart of the absurd contradiction in all this. You have this ecosystem built around these Nvidia chips that cost millions of dollars. What it does is it creates, I guess, compute. Everybody's looking for compute, but compute is very expensive. You tell me if this is incorrect, the people that use compute, the customers, don't pay anywhere near how much the compute costs to build and run. In other words, you're buying something and you're getting it at ridiculous discounts, and the company is taking on that excess cost. To an extent. The thing is, you're both wrong
[00:09:39] Ed Zitron: and we don't know, but also right at the same time. I will take that. I will take that as a win. Let me explain what I mean. We don't actually know how much it costs to run these things. Oh, we don't. We don't know how much it costs. We have a lot of estimates. A lot of AI people online have come up with their guesses, but we truly don't know. We have these companies called Silicon Data does an indices of how much you can get per hour for a GPU. And these are always going up and people say, wow, they must be making the money back. We actually truly don't know the operational costs, but also even those indexes of these prices don't really reflect what people are paying long term, because they're mostly based off of spot prices, which just means what it would cost to rent this right now versus if I buy it for a year. Let's step back a second. Yeah. Yeah.
[00:10:23] Speaker 2: And maybe so we've got a little bit of the lay of the land of who the players are. How then does Anthropic or Meta or any of those make money on their AI? How does open AI
[00:10:39] Ed Zitron: generate revenue? Okay. So two ways. One, there are subscriptions. So you pay clammy sample. When you say, I'm going to pay you 20 bucks a month and I can use open AI models for, and there are rate limits. They're kind of obfuscated, but you can use them a certain amount. They have a $100 and a $200 a month subscription. Same with Anthropic. So you pay that and you can use a certain amount. Now, Anthropic makes more money on the second one, which is the API, which just means connecting your thing to Anthropic's models. So your startup, your coding harness and you pay at the meter. So you pay on a per million token basis.
[00:11:18] Speaker 2: Go, go, go, stop. And I apologize. No, it's okay. No, please, please. What is a token? Because I hear that a lot and I'm not quite sure. And a per million token, how many tokens does it take? And what is the name of your cat? And for those of you who are listening
[00:11:33] Ed Zitron: on the radio, this is how big, beautiful, annoying, just like daddy. Okay. So all right, go ahead. So a token is just three quarters of a word. It just, it is when an, when a model spits out an output, let's say you say, give me a limerick and it will spit out. And if, and it will be a certain amount of tokens to generate it. So when you put in the stuff saying, make me a limerick, that's an input token. And when it generates the output, that's the output token.
[00:12:04] Speaker 2: And is that calculated on the amount of compute that an input would require to get a result?
[00:12:14] Ed Zitron: No, it's calculated based on what it's doing and what it's putting out. The compute side, the, how much the GPUs cost to run, what this, we truly don't know. Like we have estimates from all over the shop, but when it comes to especially like Anthropic and OpenAI, who are the main AI labs, who are just the ones that make the most popular models. We don't have any insight into how expensive their operations are. We have some documents. I got OpenAI's audited financials earlier in the year, but we don't have, Hey, it costs when we run one Nvidia B 200 chip, that's how much this costs an hour. And the problem is, is that it's bordering on impossible to actually estimate because we don't have insight into their infrastructure. We don't even really know how much compute they have. And we can go into this in a bit, I guess, but the hyperscalers, the Amazons, Googles, Metas, Microsofts, and so on.
[00:13:10] Speaker 2: And those are the ones that are trying to build all the data centers so that they can increase their compute. But if, if we don't know how, why are they all in this race then to create more compute? Are they maxed out? Are, are the inputs that are coming in so much greater than what they're able to export?
[00:13:35] Ed Zitron: Okay. So that's actually a very good and important question. Oh God, thank you. No, because John, you, you're being self-effacing about not knowing this stuff. We actually, a lot of this stuff is very obfuscated.
[00:13:47] Speaker 2: Okay. Purposefully so, I would imagine. On purpose. Okay.
[00:13:52] Ed Zitron: So it's not so much the largeness of the outputs, it's that there, it takes a long time to build AI data centers, way longer. Each one of these things is the single most ambitious infrastructure project in history. Each data center? Pretty much. So OpenAI's data center, the Oracle has been building it for two and a half years in Abilene, Texas. It is, I think about 998,000 square feet. And then it's going to take about 1.2 gigawatts of power, which is basically the power of a small city, but in a thousandth of a space. So I use the city of Bristol in England as an example. It's basically the power of the city of Bristol in a one, in a basically several large warehouses.
[00:14:37] Speaker 2: We, we've got, we've got Americans right now scrambling to find out where Bristol is and what
[00:14:41] Ed Zitron: it looks like. Yeah. The most exciting thing to happen to Bristol. Sorry, Bristol listeners. But so the hyperscalers are building this capacity because they see demand, but really most of that demand is coming from OpenAI and Anthropic who have this voracious appetite for AI compute to the point that because they have so many customers and so much attention, they are basically buying up everything. I estimate that 80% or more of AI compute spend is from those two companies, but hyperscalers have all got deals with just these two companies. Yes, they have some other customers, but when you remove Anthropic and OpenAI, I think that's like $22 billion, which sounds like a lot until you realize Microsoft alone has spent over a quarter of a trillion.
[00:15:26] Speaker 2: Microsoft has spent a quarter of a trillion on, on hyperscalers. On capital expenditures since 2022.
[00:15:33] Ed Zitron: So capital expenditures are just investments in assets that are something you will use for the future in the company to make the company more money. When you say that, uh, they're spending money
[00:15:45] Speaker 2: as customers of OpenAI and Anthropic, isn't Meta, uh, also in the game and aren't they, or, or they're a minor player in all this.
[00:15:56] Ed Zitron: So that's the funny thing. I don't know how people forget about the Metaverse, why they're called Meta, but Meta has spent over 160 billion. I think it is at this point probably more on AI building these data centers, building out AI compute. But they don't really, like they have their own AI models, but Meta doesn't have an AI strategy. Meta has built their own AI models. They have this Muse app.
[00:16:20] Speaker 2: Right. The little assistant.
[00:16:22] Ed Zitron: That's recently popped up. That is to quote Mark Zuckerberg, very unprofitable. They don't make much money. I know, right? You look, we love this. Um, and this is the thing. All of these companies are doing this because they're out of hyper growth ideas. They don't have a new Google search. They don't have a next Facebook. So they all wet because they're all tired, old, boring monopolists. They go, wait, Microsoft did this chat GPT thing. We should all buy these chips, more chips now, big, big. And the problem is they put so much money into it. They can't really walk it back, even though most of their customer base is Anthropoc and OpenAI. And without them, it's kind of piddly amounts of revenue.
[00:17:09] Speaker 2: Folks, I know, I know things seem a little, a little grim right now, but I'm telling you, man, there is a new project on news that is here to save us. Ground News. It's our old pal, Ground News. They are here to help you deal with the news-based relentless insanity, the algorithmic, uh, driven bowl of sadness and despair that pours forth through every outlet and orifice that you can find your way too. It is, it is madness, but Ground News, they, they take every article about the same news story from all the outlets all over the world and they put them in one place and they tell you where it's coming from. You can see how these different organizations and algorithms manipulate your information. They can show you how reliable the source is, who is funding the source. It's a hell of a service that they provide. Subscribe for 40% off unlimited access by scanning the QR code on screen or go to ground.news/js. Make sure you use our link so they know we sent you. Again, all you hear about is this is a revolution on the scale of, I don't know, the cotton gin or the railroad, or, you know, and some would say, you know, the iPhone and the internet and how that changed everything. They, they certainly feel as though they have discovered the future, whether or not the rest of us want to live in that future is sort of a separate question, but so there are they,
[00:18:59] Ed Zitron: they're making a bet that this is transformative. Yes. That they think, and here's, I must be clear. These people are not super sophisticated. They may be rich, but they are not sophisticated. November, 2022 rolls around. ChatGPT comes out. Literally, we are just out. We're, we have gone through what, four rate hikes that year, maybe more. Everything's really expensive. The tech industry
[00:19:25] Speaker 2: had this massive boom era in 2021. Inflation is very high. We're coming out of the pandemic.
[00:19:31] Ed Zitron: Supply chains are depressed. Revenues are down across the board. NVIDIA's revenue is flat. And then back in 2019, uh, NVIDIA had bought a company called Mellanox in Israel, which allowed you to just use high-speed networking to connect a bunch of servers, inventing the modern AI cluster. Now, 2022 comes around ChatGPT. And because remember, these hyperscalers are not invest inventive. They steal from each other. They, they copy each other. It's all they can do. They all see ChatGPT and they go, oh, oh, that looks like a new thing. What if we put a bunch of money into it? And that's what they've done since.
[00:20:07] Speaker 2: They, they weren't. Now to, to be fair to them, when ChatGPT came out, it was a sensation.
[00:20:16] Ed Zitron: Yes.
[00:20:17] Speaker 2: It did ripple. Like the effects of the introduction of that technology, uh, people around the world suddenly started, uh, asking their computer to write breakup letters, uh, as though they were Drake. Like people weren't necessarily using it for anything productive. Yeah. Children can't use that though. That's right. But it was, they were absolutely, it, it was adopted very quickly without any real sense of what it might be useful for. So it was, it wasn't as though it was an entirely fabricated product.
[00:20:54] Ed Zitron: Yes, I agree. However, it did go viral. The media took it the next, the next steps. I would say the media ran away with it, but also the hyperscalers put all the money into it. And so there was this natural belief that, okay, this is real because there's a bunch of money behind it. I'm not doubting that large language models are interesting and that there was something there. I've just been saying this whole time that the actual economic outputs and actual, the technological, things they are claiming it can do aren't really matching up with what they've said.
[00:21:25] Speaker 2: So anyway, so the, the idea is, is, is, and that's been fine. But that the user fees are never going to match the infrastructure. Sort of the idea of if Netflix came out and, uh, they could only charge 50 cents and that's the only thing that people would pay, but it's going to cost them a trillion dollars to make every show. And so it's just not, it will never match up that the money that's coming in will never match up with how much it's going to cost you to create and maintain the technology necessary for AI. Is that somewhat in the neighborhood?
[00:22:09] Ed Zitron: Yes. So OpenAI and Anthropic are really interesting companies, economically speaking, because they lose billions of dollars. Anthropics S1 just came out. They lose less billions than OpenAI. S1 is? It's the filing you make when you go public. So you file an S1 with the SEC.
[00:22:25] Speaker 2: And they are about to go public. So they have to file an S1 with the SEC. Okay.
[00:22:29] Ed Zitron: Exactly. And what is in the S1? Well, what's funny is what isn't in it. All of the stuff for the latest quarter is blank. So all of the numbers that you really need to understand what Anthropic's like, blank. They've only got up until June 30th. Now, to be clear, they shared many of these numbers with investors at an Investor Day two weeks ago. So, oh, well, screw the retail investor, I guess. Now, what's interesting is the company scraped by with a light profit in Q2, but that was because Elon Musk discounted their compute at SpaceX for two months. So whatever. And we don't.
[00:23:06] Speaker 2: Their, their compute is owned by SpaceX? Some of it. Is this all just a circle jerk? Yes. I don't understand. So everybody's building data centers.
[00:23:16] Ed Zitron: Yes.
[00:23:16] Speaker 2: And they're all renting space at each other's data centers and charging each other different rates.
[00:23:24] Ed Zitron: So just to be clear, the AI labs, the Anthropics and OpenAI, they rent compute. They rent compute from space. Well, OpenAI doesn't, but Anthropic pays, they're going to be, as of December, they're going to be paying $3.1 billion a month, a month to Elon Musk, a month. It's crazy.
[00:23:43] Speaker 2: So, so when did Elon Musk build all the compute? I had no idea that he was even in this. And why wouldn't Anthropic or OpenAI be building their own compute?
[00:23:56] Ed Zitron: Well, that's the problem. These companies lose billions of dollars, which makes banks really uneasy when it comes to lending you money. So what they do is they have their hyperscale pay picks. So Oracle, Larry Ellison, everyone's favorite. Larry Ellison is building about $400 billion worth of AI data centers for one customer. Sam Ullman. Clammy Sammy himself is going to apparently pay him $300 billion over five years. Now, does OpenAI have the money? No. Does Oracle have the capacity? No. But don't worry. According to TD Cohen, NVIDIA is going to be helping Oracle out by leasing space and then subleasing it to them. Oh my lord. NVIDIA, by the way, is the GE capital of this all. They invest in all the companies. They invested in OpenAI, SpaceX, Anthropic, CoreWeave, which is a NeoCloud, so a company that just builds data centers and sells AI compute. And by the way, takes out debt using the chips as collateral and then buys more
[00:24:56] Speaker 2: chips using that. It's magical. So they take out with money they don't have, they're leveraging. So they're highly leveraged. That's CoreWeave, yes. On their own build out. And yet they are fighting not to pay taxes on this value that they are saying they have but don't have. Yeah. Yep. Is that the magic trick?
[00:25:22] Ed Zitron: There's so many magic tricks. That's the thing because the AI labs, it's funny. People are always talking about the OpenAI's and Anthropics as in, "Oh, well, they're building their own stuff." No, they're not. Anthropic has, this is serious, $430 billion worth of non-cancellable commitments with Amazon, Google, Microsoft, and Broadcom. Broadcom, the AI chip company, regardless of how
[00:25:47] Speaker 2: much money- Uncancellable, you cannot cancel these commitments. And what are these commitments that
[00:25:52] Ed Zitron: they've made? So for Amazon, Google, Microsoft, they've made commitments to rent data center capacity, hundreds of billions of dollars worth. And when I say non-cancellable, it's something called a take or pay arrangement, which means you are like, you're going to use this- Pay or play. Yeah. You got to pay regardless. Okay. So Anthropic has, it's funny. The way I would describe the S1 is it's a description of a company that doesn't exist anymore because it was before all of these commitments begun. And Anthropic now has these things probably starting next year that are going to cost it $75 to $100 billion a year within the next year or two. And this is regardless of whether its revenue grows. This is a company that
[00:26:32] Speaker 2: only loses money. How are they justifying these commitments? Where, where are they seeing then the growth in all this? How do they justify putting that much money into a build out and usage?
[00:26:48] Ed Zitron: They believe that they will grow to hundreds of billions of dollars of revenue in the next few years, because if they don't, they're not going to be able to pay this. Their operating expenses will be $100 billion plus in short order. Microsoft, so a very profitable $330 billion a year company, has operating expenses of like $150 billion. So Anthropic just believes it will be the size of Microsoft Zoom. You may be wondering where the proof is. It isn't there. The S1. I am wondering that. That was one thing I was wondering. Yeah. The S1 doesn't explain that. Anthropic just has these crazy projections. Their entire valuation comes down to them making $200 billion a year in revenue in two years. $200 billion. And what is their revenue now? Probably in the region of 30 to 50 billion a year,
[00:27:37] Speaker 2: max. We don't have the fourth quarter. Oh, this is the, this is the worst episode of Shark Tank ever.
[00:27:43] Ed Zitron: What's crazy is you tell people this and they will say, well, it's different this time. These are the smartest people, the smartest guys in the room, if you will, you know, Enron. And these people are so smart. AI is the next big thing, but here's the really scary part. So according to, it's a payment and credit card company called Ramp, 80% of open AI and Anthropics revenues come from 1% of their customers. And most of those customers are venture backed AI startups that run their services
[00:28:15] Speaker 2: at a massive loss. Okay. Okay. Okay. Okay. Let we, I want to roll this one back. Cause this one. All right. So my understanding of all of this is, is probably steeped in the television business. Again, Netflix streaming. I I'm thinking of it in terms of customers who pay a fee to utilize a service that is necessary for them to run their business. So we're, and so we're talking about the $100, $200 a month, whatever they might charge a company to use their compute. You're saying that's only 20% of their revenues, 80% of their revenue are actually other AI startups and those AI startups, what are they are? They're not paying a user fee. What are they paying for?
[00:29:08] Ed Zitron: So remember I mentioned the API earlier. So connecting your stuff to the models. So when you. Yes. What is API? Just, just. I'm going to forget the term, but it just means the, the way that you connect your service to another service. So you're running, there's a AI legal service called Harvey. They use the API so that their software runs using open AI or anthropics models.
[00:29:31] Speaker 2: Jillian just popped into the chat application programming interface. They're going to have my guts for God is for getting that one. Oh, she's the best. Uh, software, uh, that, that talks to each other. So they have to connect to, uh, the, the big brain or their AI startup does not function.
[00:29:51] Ed Zitron: Yes. And then there's the other problem, which is on these companies. And when you, you, when you use say perplexity, which is an AI search company and you pay them 20 bucks a month, you can run up a token spend of like 60 bucks or 70 bucks or 80 bucks a month.
[00:30:09] Speaker 2: So they're running at a loss.
[00:30:11] Ed Zitron: Exactly. And what's crazy is, is that everyone says, wow, anthropics growth, open eyes growth. It's so impressive. How is it so impressive? The answer is, is that they have a bunch of venture backed startups that run their services at a loss and have users who say, Hey, I want to use the latest models, the latest, more expensive models, the latest models as well. The prices may seem lower or similar, but they burn more tokens and they actually output tokens. So they create the thing that it gives you. Those are more expensive. So everyone is crediting the growth of these companies to some sort of diverse revenue model. When a large chunk of that revenue is based on basically the ninja borrowers that from the great financial crisis of the AI generation, these people that can only afford to pay for all these tokens, as long as venture capital holds them up. And this is a genuinely scary thing because it means their customer base is inherently, definitionally brittle.
[00:31:19] Speaker 2: So everybody's burning money on all ends of this. This is a house of cards held up by other houses of cards. And is the assumption here, because it's a, and we'll deal with this in the realm of anthropics valuation, which people are saying they're going to have an IPO. They're going to raise 1.5 trillion or maybe even 2 trillion. They're going to be valued at that and raise like a hundred billion. They're going to be valued at that and raise all that money. Um, but what that does is they've exhausted the private venture money. And now what they're saying is, oh, you know, we're, you know, who we can get the money from now people. Yep. Just fucking people just dupes.
[00:32:04] Ed Zitron: Like they're going to, and what's crazy about this as well is back in 2012, there's this thing called the jobs act pushed through so that it was meant to open up capital markets, more people, but it had a subtle change that meant that private companies, it used to be that private companies over a certain size would have to report their actual revenues. They'd have to file like a public company. So it forced companies to go public, uh, much lower valuations. So Google went public. I think their valuation was 23 billion. There are three, 4 trillion. Now I think Microsoft was hunt like 700 million. They're worth trillions. Now Amazon few hundred million. Anthropic is going out at 1.5 to 2 trillion dollars. This is exit liquidity. What is exit liquidity? Tell me, tell me what that is. Oh, I'm getting that. Don't worry. So we are sold this. We are sold this idea that we are investing in the future with these companies that we're investing in the next Google, the next Meta, the next what have you. That's right. When actually what we are is the dumping ground for venture capitalists that have invested in the company. We allow them to exit the company by selling their shares that they bought at a much lower valuation for a massive payoff. And in return, we get a company that, what, is Anthropic? Even if Anthropic was to grow to the size of Nvidia, it would be what, three X, four X, which is a good return, I guess, but compared to what you used to get from these stars, but also it's not going to do that. This is a company with negative EBITDA. So a negative, basically earnings before tax taxes depreciation. Let me, let me push on it in a kind of a little
[00:33:41] Speaker 2: devil's advocate. So sure. It's a similar story though. I remember when Google came out and they had these wild valuations and they were getting crazy amounts of venture capital and people were saying, how do they make money? It's you're building out all of this. They didn't call it compute then, but it was the network you had to build out, you know, fiber optic networks that were taking over some of the.com bubble build out. And, but how are they going to make any money? And over time though, it did turn into a profitable business, whether it was through advertising or the selling of data or other ways that they made money, either by sucking up other companies or things like that. Are we missing that same story with AI? Are we being too, too skeptical about how they can turn this into a profitable business? So let's take Amazon. Amazon is the one people
[00:34:38] Ed Zitron: like to choose Amazon web services, which is their cloud platform. One of the most profitable businesses in history, incredible stuff founded in 2003. Okay. I think it was available to the public in 2006, profitable in 2015. Amazon's combined capital expenditures in that period to build out cloud capacity for everything, actually that all of their capital expenditures for the logistics network, for the cloud, for everything was $29.7 billion adjusted for inflation over how long a period? 12 years between 2003 and 2015. And that's not per year. That's total. That's the total capital expenditures. $30 billion. And Anthropic raised $30 billion in February. They raised $65 billion in May. Amazon spent, I think, $45 billion in capex in the last quarter. And here's the thing as well. Amazon at that time was a much smaller company. So if you invested in Amazon at that point, you were having an incredible return on investment if you held on. It paid off in spades. And also the difference is that Anthropic and OpenAI do not have assets. These companies don't have data centers. They're not buying data centers. They're leasing data centers. They're leasing chips.
[00:35:54] Speaker 2: But that's the part that I don't quite understand. Why wouldn't they want to own their own infrastructure? How would they afford them? But then what are they? So all the money they're raising is just on leasing, is just on agreements that they're making with other companies?
[00:36:11] Ed Zitron: Yep. That's exactly it.
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[00:38:18] Ed Zitron: Yeah. And what sucks is as well is the American stock market is basically held up on like a toothpick of AI.
[00:38:27] Speaker 2: Oh, I think only it's the max. It's only those seven companies are driving the entirety of the gains of the stock market.
[00:38:34] Ed Zitron: Yeah. Like 45%, I think at the value of the NASDAQ 100 is in the magnificent seven. And by the way, the valuation of the magnificent seven is largely based on the idea that AI will be the biggest, most strongest thing and grow forever. Nvidia's continued valuation, I think $215 a share as I think about this is based on the idea that Nvidia will sell more and more GPUs, basically selling a trillion dollars worth of them in two years. And we're at the point now where outside of Microsoft, who has just offered buyouts to 60, 27% of its staff to keep bankrolling AI, Meta, Google, Amazon, cashflow negative, all cashflow negative having to take on debt. They can no longer buy these GPUs out of cashflow. So the Neo clouds never could afford to. So they've had to take on onerous debt, core weave, the biggest of them and the worst of them. They're raising, I think that if they were to raise debt today, it'd be 13, 14, 15%, basically a personal loan. So this is all based on debt. So yeah, the moment anything bad truly happens, that debt stops getting issued. They stopped buying
[00:39:42] Speaker 2: those GPUs. Do you have a sense, Ed, that these businesses, you know, we always think of it in terms of leverage, like how over leveraged are these businesses? And, you know, again, going back to 2008, when they found out that banks were leveraging themselves at 30 to one or 35 to one and, and endangering the very sort of foundation of their businesses, if hit the fan, have they calculated the sort of leveraging of debt of these companies and these build outs and lease agreements?
[00:40:13] Ed Zitron: So when it comes to the big ones, like Microsoft, Google, Amazon meta, they're not going to go bankrupt. Nvidia isn't going to go bankrupt. Like Nvidia as the chip seller is going to probably take a haircut, but the big hyperscalers are going to be fine, but in trouble because when they stop doing this, everyone's going to ask why they did it. Coreweave has 30, 40 billion dollars worth of debt. Morgan Stanley said they're going to need a hundred billion dollars more. What is coreweave? What is, what is that? Coreweave is a neocloud. It is a company that raises debt, buys GPUs, builds data centers. And by the way, you'll be shocked to hear that their customers are Microsoft for open AI, Google for open AI, Anthropic, Meta. Yeah. Shocking.
[00:40:58] Speaker 2: You know what? This reminds me of, I hate to even bring this up. You probably don't. There used to be a show called Beavis and Butthead. Do you remember the show? Yes. So there's an episode of Beavis and Butthead where they're selling, uh, you know, kids used to get those. You'd get a tin of chocolate, you'd have 10 candy bars and you would sell them to raise money for like the pep squad or any of that kind of stuff. And so one of them had a dollar. I think Beavis had a dollar and he said to Butthead, give me one of those chocolates. And he gave him the dollar and then he ate the chocolate. And then, uh, Butthead said to Beavis, uh, you give me one of them. And they just passed the dollar back and forth. And by the end of it, there's no more chocolate and they still only have $1. Yep. That's basically it. Except NVIDIA
[00:41:38] Ed Zitron: gets the real dollars. Yes. NVIDIA gets so many real dollars, but. Well, and Ellison gets the real
[00:41:43] Speaker 2: dollars. The landlords are still getting, no. Okay. Talk to me there. Talk to me there. Oracle is in a
[00:41:49] Ed Zitron: weird position. Okay. Our favorite, our favorite leathery old man, Larry Ellison is, um. How dare you, sir? That's my new daddy. That's my new father. I know. I know all of the Ellison fans screaming in the audience. No. So Oracle is in a peculiar situation because their public relations is phenomenal because people say Oracle is doing great. Oracle's revenue has been flat for 15 years, adjusted for inflation. The only part of the company growing is AI compute, which is destroying their margins because it costs so much to buy it and takes so long to install. And then Larry had this wonderful idea where, Hey, this open AI company that loses billions of dollars, I should build endless compute for them. So September last year, they announced this $300 billion five year long contract with open AI and the analysts gooned. They went completely nuts. They were saying, Oh my God, this is so amazing. Now Oracle stocks tanking because everyone went, wait, can you afford this? Wait, can open AI afford, what happens if open AI dies? Who pays for this? Oh, no one. There's no one else. There's literally no one who can afford this. Nobody. Oh, that's bad. But even then the market hasn't fully ingested it. So what this on a very grand scheme, what this is, is AI to the greediest of us represents something. And to all of these big companies, they believe, based on what I believe is kind of a horribly misread demand signal, that there is insatiable demand for compute. And what's really happening is two very large companies are buying all of it. And because these companies are run by, uh, the big ones are run by people without vision. They say, well, these companies are growing real fast now, which means they will grow even bigger and faster in the future. It is that simple. It is not an elegant or D it may be complex in the execution, but the logic is extremely childish.
[00:43:46] Speaker 2: So they're all building an endless compute for a demand that they're not sure the public will even be asking for that. There'll be a plateau. Like I'm assuming like, like everything else. And I hate to keep going back to streaming, but it's kind of the only business that I mildly understand, but there are plateaus in subscribers. And so what happened with that business is everything is chasing penetration. So you want to get into as many homes as you possibly can, but at a certain point you run out of homes or certainly run out of homes that give a. So then you have to double back and create other methods of revenue within that plateau. But it sounds like they're making an assumption on an, a non plateau system. So they haven't demonstrated the demand or the usage or the profitability, but they're betting that
[00:44:50] Ed Zitron: that they will. Yes. And what's funny is it's not just demand they need. It's it like people could want AI, but most people don't spend that much on AI. Most people won't spend money on AI. The vast
[00:45:07] Speaker 2: majority. Right. Well, you said mostly it's going to be other AI startup companies that need to use their interfaces. So there that's where they, that's where they believe their big money will be.
[00:45:19] Ed Zitron: And even more worrying. I saw a few people on Twitter talking about this. There are AI startups now who are renting their own GPUs and they're renting them. And this is the scariest thing they've said. They've said, don't worry. There's so much demand for compute that if we don't need this, we'll just sell it to someone else.
[00:45:38] Speaker 2: That's right. It'll be excess compute that will be allowed to, but it sounds like they're just going to be selling it amongst each other. It's kind of like when in the, in 2008, when people were saying,
[00:45:49] Ed Zitron: well, if I can't refinance my house, I'll just sell it. When, Ed, when does this bill
[00:45:56] Speaker 2: become due? Do you know, like how much this profligate spending you talked about, you said they had spending commitments, did you say $60 billion or am I imagining that?
[00:46:07] Ed Zitron: Oh, it's much bigger. Anthropic has in the next seven to 10 years, $430 billion worth of non-cancellable commitments. Open AI has projected to spend $750 billion by 2030.
[00:46:20] Speaker 2: And how, so, and I imagine that they also have servicing that debt. There's probably, I don't know what interest rates will be in that moment. And I guess you can't project that, but I imagine they also have to service that debt on the commitments that they've made.
[00:46:36] Ed Zitron: Well, that's the thing. It isn't even debt. Well, technically, so Anthropic technically has $161 billion worth of leases with, with, uh, Broadcom, which does carry interest, but these commitments
[00:46:49] Speaker 2: are otherwise just contracts. So the debt is being carried by the hyperscalers, by the people that are building this infrastructure, so they're not, they're tenants and everyone else is a landlord that's building out. And those people will have to service the debt if AI companies like Open AI can't fulfill
[00:47:12] Ed Zitron: their service leases. Exactly. And then there is the other thing of, even if Open AI and Anthropic had the demand, even if they needed it, there's the other problem of how slowly these things are being built. And again, we get back to how little we know about this capacity. So Microsoft spent $265 billion on AI, on AI GPUs and data centers since 2022. They have said in the last three quarters, each quarter, like we brought on a gigawatt of data center capacity. Bloomberg reported, and I reported with the Guardian as well, that actually Microsoft only has two gigawatts of AI compute capacity, operational, with the rest of it sitting in warehouses. Amazon, Business Insider just reported, has billions of dollars of chips that they either can't plug in or, well, they, they either can't plug in because there's not enough power or they just haven't built a data center yet. So on top of all of these other problems, we have this situation where hyperscalers are buying way more GPUs than they need, driving up the price of memory and thus the price of consumer electronics for everyone. And on top of that, they're lying, kind of, about how much capacity they have. They are trying to make everyone believe they're bringing gigawatts online, when a data center can do many things. The data centers that people hate are not the ones that do streaming. These AI data centers are completely different beasts to anything that runs anything else. These things could all disappear tomorrow and the internet would function the mostly the same way because they only exist to do AI.
[00:48:44] Speaker 2: Well, the AI thing was always, it, it, it wasn't a necessary development in any way, you know, I always felt like AI is just like Google that Google that's on Coke. Like when you Google something, you say like, Hey man, is there a pizza place near me? And it tells you, but when you say to AI, they're like, Oh my God, that's such a great question. I love pizza. Pizza is so good. Did you know what started in Greece? Like it just tells you shit and you're just like, shut up. And sometimes it goes, yeah, DeMarco's is the best. Yeah, that's right. Hey man, I, I need all your water. I need all your water. DeMarco's does not, doesn't exist. Yeah, it doesn't exist. And it makes shit up. Yeah. So let me, so, so here, I want to now like take a step back and give people a sense of like, here are the vulnerability points of, so like, you've got a bunch of vulnerability points. One is these AI startups that are using the APIs will, uh, fail to the extent that they lose eight, you know, enough of their 80% of revenue that their revenue crashes a little bit. Yeah. The anthropic and open AI. Um, I guess the second part is Nvidia has a growth problem or just a chip surplus. Cause they've got too many chips and people aren't building them fast enough.
[00:49:56] Ed Zitron: Let me correct on, on Nvidia cause Nvidia is a very peculiar case. So Nvidia doesn't care whether you install them. Jensen Huang gets paid regardless. Indeed. It appears that business insider guy could Eugene Kim had a great story about this. Amazon has been doing an early rack receipt program, which means that they have to take chips before they're ready, which is very clearly Nvidia forcing them so that Nvidia can bump its revenue.
[00:50:19] Speaker 2: But that, that, that, that's not sustainable. That, that they'll have a revenue growth slow.
[00:50:25] Ed Zitron: It isn't. And on top of that, they have another problem. You're talking about weak points. I think it's Deutsche Bank says that 50 to 60% of Nvidia's revenue comes from hyperscalers. So like a handful of companies, most of them requiring debt. So if the debt don't come, they not buy chip and chips, by the way, are getting more and more expensive. So it's not just Nvidia needs to sell more chips. Are getting more and more expensive. So really simple. This is going to sound technical, but it's pretty simple. So the reason that every consumer electronics, your phone, your laptop, everything is more expensive is because there are basically three companies that make all the world's RAM. Micron, SK Hynix, and Samsung.
[00:51:04] Speaker 2: Now that makes all the world's RAM meaning memory.
[00:51:07] Ed Zitron: Yes. So random access memory so that it's in everything. Okay. Now, Nvidia's GPUs, which they're selling millions of, regardless of where they go, use something called high bandwidth memory. It's a special kind of RAM and it takes up more space to build it on the fabricator, leaving less space for the useful RAM that everyone else needs, which the AI servers also need. So long story short, because they've sold so many of these chips, the triopoly of RAM companies have said, we're just jacking up the price because they can, because they can. And so all of these GPUs being sold in many cases, months, if not years in advance, being built for no reason are pushing up the cost of every single consumer electronic. So that's just another fun thing with Nvidia. That's just another thing that we can
[00:51:54] Speaker 2: blame the AI boom on. In your mind, is there an event horizon on this? Is there a moment, a singularity of economic catastrophes that are, are they racing against the clock or are we underestimating their monopolistic creativity in this very high stakes circle jerk? Like, are we not being, are you looking at it too rationally?
[00:52:25] Ed Zitron: - That was my mistake a few years ago. I really was like that. The, the, the nuance of my analysis now is that I've given up on childish things of rationality.
[00:52:37] Speaker 2: - So you used to have more of a, a calendar on this. You thought this was a ticking time bomb and it's going off at such and such a moment. You don't believe that anymore.
[00:52:50] Ed Zitron: - No, but the things that can destabilize this most obvious one is these venture-backed startups. So they have to raise money tens, hundreds of millions of dollars every three months.
[00:53:01] Speaker 2: - So this is an interest rate game. If interest rates go up anymore, boy, that could create an enormous issue in the VC world.
[00:53:09] Ed Zitron: - Well, it's that. And also just venture capital will run out of money. Venture capital has been on an insane losing streak since 20, yeah, since 2017, there have been thousands of these things called zombie unicorns companies valued at $1 billion. I know it's a great term. $1 billion, but they can't raise another round because if they raise the round, their valuation would go down because they're not worth as much because they were overvalued. - Right. - And, um, or they can't get acquired because they're worth, they're worth on paper too much. So venture capital since 2017, the average VC fund has failed to return more than like $1 and 30 cents for every dollar invested. So you literally would have been better on the S and P 500. On top of this through between 2018 and 2022, a bunch of software startups got vastly overvalued. So there's a bunch of stuff they can't sell.
[00:53:58] Speaker 2: - I mean, that exuberance is part of any of these technological, you know, breakthroughs.
[00:54:02] Ed Zitron: - But there is another layer to that, which was that I think 30% of private equity take privates in that period were for software companies. So you have private equity with thousands of companies, they can't sell either. And now venture capital has made one really big mistake, which is they're saying AI is going to be the biggest, most hugest thing ever. Even though most of these companies are just wrappers around open AI and anthropics tech, but because they need so much money, they need to buy, they need to get money from VC every few months. Every time that happens, because everyone's excited, the valuation goes up and up and up and up and up and up and up and up. There's a company, the reason I mentioned Harvey is they've raised like over a billion dollars in the last year or so. - Billion dollars. Jesus. - And they're worth $15.5 billion. $15.5 billion for a company with no real IP. It's mostly just software. Cognition's another one. I mean, they're just a business. It's very unclear exactly what they do. They have a coding agent, some other stuff. They've raised billions of dollars and they're worth
[00:55:06] Speaker 2: $48 billion. There's no IPO. - Is there a scenario where this works? Where, you know, look, we were, I remember pets.com, you know, when we went through the internet startup and everybody's like, and, and everybody rushed, those were all the zombie unicorns back in that day. And VC came in and all these people and anybody that had an internet startup suddenly, you know, it was, everybody was, we work and they all went in there. And a lot of those collapsed out like everything else. And, but there was a solid infrastructure left standing. That was worth something. - Yeah. - It's clearly worth something. It may not be worth the, every company is a $2 trillion, you know, a behemoth, but it's clearly worth something. How does this get solidified? What's the scenario where this works out less catastrophically? - So the problem is, is the.com bubble was the
[00:56:11] Ed Zitron: actual software part of that was teeny tiny in comparison. These companies were valued at hundreds
[00:56:15] Speaker 2: of millions of dollars, which was a lot. We've got. - And now would probably be a billion dollars. - Yeah. With inflation, whoever knows. But nevertheless, most of these companies raised
[00:56:28] Ed Zitron: fractions of the amount, even adjusted for inflation, that one of these AI startups does. Perplexity. An AI search startup with basically no mo of any kind in a dying customer base is raising billions from Nvidia, by the way, from Nvidia at a $30 billion valuation. - Oh my Lord. - There is no path for these companies. Even with a pivot, they're not going to be worth even a shred of that. And the venture capitalists are going to get washed out. Boo hoo. They deserve it. Venture capital needs, they need an apocalypse event because they no longer invest in valuable companies. They invest in financialization. - And private equity, the same.
[00:57:01] Speaker 2: - Oh yeah. - It's all the same,
[00:57:03] Ed Zitron: financialization. - And here's the thing. Blackstone, we can go into, but Blackstone may actually face an existential event too. - Really? - I'll get to that in a second because the data center part's really important. I know, it's the best part. - That's the best part. - AI data centers are useful for one thing, AI. Now the comparison that people make with the dotcom era is the telecommunications companies, the dark fiber. There was all this fiber in the ground. The expensive part was the fiber in the ground. Once you had the fiber, most of the work was done for you. Companies die, use the fiber, easy peasy. Not with the AI data centers. If they're incomplete, then they're going to need just as much money in three or four or 10 years to finish as they did back then. And then there are the OPEX, the operating expenses. - Yes. - The electricity is going to be just as expensive, if not more so, in a few years. This isn't getting cheaper. And everyone wants to say, oh, it's like the dotcom bubble, because you know, after that, which included like a massive drawdown in NASDAQ, it was horrible and a hundred thousand people lost their jobs and it was very horrible. And I think people are too blase about that. But nevertheless, there is no dotcom bubble recovery here. There are no alternate uses for GPUs and the demand for AI GPUs outside of Anthropoc and OpenAI is minuscule. So while it could exist after this, we have, according to sunshine climate, sunrise climate even, it was like 190 gigawatts worth of capacity in planning. Even if a fraction of that happens, that's still 30, 40, 50 gigawatts. And we're talking hundreds of billions of dollars worth of demand just to make the bill, to get the bills paid. And these data centers are paid with debt. I don't see how that turns into something else after this. And people really want it to. And I think that people choose the dotcom bubble because first of all, it allows them to believe the system functions correctly, that finance and tech can't be wrong. Right. That it can correct itself. Exactly. However, it also allows them to say, oh, this wasn't a big waste. And here's the thing, while the hyperscalers might be able to repurpose this stuff, we are building these data centers everywhere. And most of them are barely complete. All of these GPUs sitting in storage are not useful
[00:59:16] Speaker 2: for other things. We can turn those buildings into malls. You know, the country laser tag arenas, it could always use more malls. I don't think that'll ever go away. Yeah. I mean,
[00:59:24] Ed Zitron: but even dead malls in places are another fascinating story of commerce. No, I know. I'm kidding.
[00:59:33] Speaker 2: Oh, today's ad. Today's ad is bowl and branch. You know, I remember when they first got together, like Dow and Jones, when bowl, it was doing fine and branch, you know, both listen, they both were doing the best they could. But when they got together and formed bowl and branch and thank God that a company that sounds so pleasant makes good stuff. Uh, they got, they got great sheets, a hundred percent organic cotton, uh, good airflow, competing, man. It's all about the airflow. You're always thinking you're too hot, too cold. And the thing you get airflow, give you a little softness without that trap heat, but bush, I say make pillows, waffle blankets, the comforters, temperature regulating coverage. 94%, by the way, of the bowl and branch customers say their sheets actually get softer with every single wash, which I'm going to tell my son who was in college to encourage him to wash his sheets. So the bowl of branch, dude, just wash him. It'll get softer. I shouldn't have outed him like that. The point is this upgrade your sleep with bowl and branch, get 20% off your first order, plus free shipping at bowl and branch.com slash TWS with code TWS. That's bowl and branch B O L L and branch.com slash TWS code TWS to unlock 20% off exclusion supply. Do they believe their own bullshit? Like does Sam Altman, if you pin them down, you know, the, the Amadeus, do they believe that it isn't going to be about customers, that there will be such a productivity surge in the economy? It will replace so much labor for, uh, the, the, the corporations that these giant bets will pay off. Like what is their, and I don't know if you know this and I don't know if you may think like, well, they're deluding themselves, what is their version of success in all this? How does this, uh, uh, carousel stop?
[01:01:52] Ed Zitron: So Dario Amadei is deeply embedded with this group called the rationalists and rationalist does not mean rational belief. It's the rational belief that super intelligence will kill us all. I think that, so that's just one of the people influencing him. If you ever hear about METR or any of these organizations, they are a religious cult. It's insane that they are poisoned. It's everywhere. It's truly this guy called, um, Adam Becker, you should talk to about this. He did a book called more everything forever about it and many other things, but basically it's a billion, it's a billionaire cult that believes that super intelligence will destroy us all. And yet they're, they're all building it out. Yes. Because don't you want to get killed by American tech, but they're also to be clear that the promises, the promises are so far from what all of this actually is. But Amadei's argument in, I actually think he may believe this, but I'd also believe that he's a cynical grifter is that, oh, this will cure diseases. There's no proof of that. None. Oh, it will autonomous. It will run businesses. No real proof of that.
[01:02:53] Speaker 2: That still doesn't make it profitable. And by the way, they keep saying this and it drives me crazy. They say, what is the benefit of this? It's going to cure climate. It's going to cure cancer. And you're like, oh, well, what are we using it for now? Uh, finding undocumented people, targeting, uh, weapons, companies are using it to use, uh, uh, pricing that's dynamic blowing up schools in Iran, blowing up schools in Iran. Right. So they keep saying there is this utopian world that we're going to get to with this incredible intelligence. You're like, well, what are we using it now? And they go, well, mostly exploitation. It's mostly exploitation and extraction and business stuff,
[01:03:32] Ed Zitron: coding, right? And the thing is the biggest, and I would argue only consistent market is coding. Like it's the one thing it's fairly good at. And even then fairly good at still involves it being, being really great sometimes in the complete more on others. And hopefully you can spot,
[01:03:48] Speaker 2: but that wouldn't justify the cost of it because that whole coding market's not worth that.
[01:03:53] Ed Zitron: Here's the thing I'd say that, and people get really mad at the booster types, the AI fans hate this, but wow. Open AI claims to have solved an ancient math problem. Wow. Great. Have you asked the same machine how to make a profitable business? Because apparently that math problem is just too big for, you can solve all of the, these historic
[01:04:16] Speaker 2: problems. Have you tried to do that? Have you gone on like Claude and said, how can I make AI profit? Just like, is Claude bullshitting itself? Well, the answer is always, they'll just make it more efficient. Here's a nightmare scenario for you. Do you remember when deep seek came out and everybody's like, wait a minute. So, okay, this is another thing. And I'm sorry to keep bouncing around like this and I appreciate your patience. So generally these technologies get cheaper as they get, they get more efficient. They get just like everything, you know, the progress is always like, how long does it take to make a baked potato? An hour. I can do it in 10 minutes. Like it's supposed to get cheaper and faster and smaller. And that's kind of how these technological progressions are supposed to go. This doesn't seem to be the case with AI. It gets more expensive. It needs more of our resources. It needs more, uh, electricity and water and space and cooling and everything else. But is it possible that what we don't know about what China is developing as they did with deep seek is a model that is pretty much as good, but runs on much less compute and power and water.
[01:05:29] Ed Zitron: I mean, in theory, but, but not really. Well, I mean, also Minimax and Zipu are two Chinese AI startups that are public. Zipu? Zipu. Z-H-I-P-U. All right. But nevertheless, they're public and, uh, they are also unprofitable.
[01:05:45] Speaker 2: So we're all going down together. China's going down. We're going down. Doesn't fucking matter.
[01:05:52] Ed Zitron: I mean, going down, I must be clear. I don't, I don't think this is great financial crisis level bad, but that doesn't mean it won't be very bad. Well, here's the thing. It will be different, which could be the, how do you measure bad? But no, no area of the economy though,
[01:06:09] Speaker 2: is not at some level integrated now into this project. And everyone is invested in the market.
[01:06:17] Ed Zitron: Now 20% of the stock market's volume is retail investors, right? We have, everyone has exposure. When I say not as bad, I'm even hesitant to say that. I just think it will be a different kind of bad because great financial crisis was massive amounts of speculation and terribly done CLOs that had other bits and pieces. Now that, and that fell apart, not necessarily because every asset was broken, but because everyone lost confidence in the system. Almost no assets were broken. It was,
[01:06:47] Speaker 2: we literally, the world fell apart because 7% of people's mortgages were under water. Which is that was the entirety of the fucking crisis.
[01:06:58] Ed Zitron: And the thing is with this is it will be different because it's one of the biggest problems could be entirely symbolic. It is the loss of confidence in the AI trade and the magnificent seven. And that is a large chunk of people's S&P 500 and NASDAQ 100 and NASDAQ composite holders.
[01:07:17] Speaker 2: So should we shut up? If the whole idea is none of it's real. It's only a confidence game. Should we just be like, it's going to be great?
[01:07:26] Ed Zitron: No. In fact, I wish we'd have said this earlier because the thing is Microsoft, Google, Amazon, Meta do not disclose their AI revenues. How is that legal? How is that even possible? Because you don't, they report their gap earnings. So generally accepted accounting principles, they report legally, but they don't have to, they don't have to do anything. They don't have to do shit. So they don't have to tell you how much money they are making. And because the media, the finance and business and tech media goes easy on them and has for years, no one really says boo. And when you tell people where they go, well, look, and the way that people justify it is they go, well, look, their revenues are going up. Fun fact, Meta increased the cost of ads. Google increased the cost of ad. Microsoft increased the cost of everything. Amazon increased the cost of, I mean, things in Amazon web services. They turn the fees up on their sellers. They increased revenue through other things. But because people are saying, wow, these are the smartest guys ever. They wouldn't spend hundreds of billions of dollars on nothing. So that growth must be coming from AI. And the growth they are seeing from AI, by the way, is from Anthropic and OpenAI spending money on compute. And you'll never guess who funds OpenAI and Anthropic. That's right. Microsoft, Google, and Amazon. And also Nvidia and Broadcom and Micron.
[01:08:49] Speaker 2: No more chocolate. And they only have one dollar. It really is. But that is the story of all of it.
[01:08:56] Ed Zitron: And what sucks is as well is all of this could have been avoided if we'd been a little more sensible and actually had real financial regulations, but we don't. And what sucks is Anthropic will probably go public. Anthropic will go public and the retail investment. No question. And they'll get dumped. They'll get this company at 1.5 trillion. So a little bit under Meta's market cap. And they will. And Anthropic has intentionally in their S1 not given us a timetable of when their big payments kick in. They don't tell us what their cash position was in Q3 2026, even though they have that information. And they've done it because in the next six months, Anthropic is going to have tens of billions of dollars of new compute costs out of nowhere. And retail investors will have no idea because the media, oh, oh, look at Anthropic. Look, Anthropic had a one profitable quarter because that they got a discount from Elon Musk. Oh, that's fine. Don't worry about it. Except I do worry about it because there are going to be people that buy into this stuff. And they're going to be people that think because they've been lied to, they're buying the next Google.
[01:10:01] Speaker 2: But what I don't understand is where their credibility comes from in the first place. And you see it like, let's just take Meta as an example, you know, and social media as an example for years, they went down to Capitol Hill and said, I'm telling you, we're doing this right. We put in all kinds of restrictions and things to protect people and nothing will go wrong. And yet every time you turn around, they're paying $20 billion here for harm that they caused in because they didn't do the safeguards that they said they were going to do or that they didn't, you know, whistleblowers will come out and say they knew that these products were harming people and they didn't give a fuck. They just kept going. And so we're doing this. We're making the same mistake we made with the financial crisis with is don't worry about the crimes you're committing. If you just give us a cut of it, we'll be fine. We won't make you change your behavior. If you could just throw us 5 billion, we'll just call it square.
[01:11:01] Ed Zitron: I think that it's, and this is grim to say, I think the media is a large part of it. Since 2023, the media was saying that AI was doing stuff it couldn't do. There were stories in the times about a task rabbit being manipulated by an open AI model that didn't happen. It was a guy asking an open AI model for what to say to a task rabbit to manipulate it. What's a task rabbit? Is that an agent? Task rabbit is someone you hire. It's someone you hire to hang up pictures. And in this example, it was to solve a web thing. It was just so fucking stupid. But nevertheless, the media was saying, posting every few weeks, Sam Altman, very scared of his own stuff. And then when the AI, when the hyperscalers were spending all this money, I was going to people in media and saying, hey, why aren't they saying what they're making? And people would say it's the early days. It's just like the dot-com bubble, the same old tired tropes instead of scrutinizing the people who were spending the money. And the thing is, I've definitely been wrong on timing. I thought this thing would wrap up in 2025. Couldn't have been more wrong on that one. But nevertheless, the underlying economic problems are the same as before.
[01:12:16] Speaker 2: And they say that they haven't corrected themselves. It just hasn't proven fatal. And it just continues to go on. So if we're looking at, because, so now I'm just trying to sort of, this new technology and find a way to categorize where the advantages and the threats are. In terms of the threats, you have sort of that, as you were saying, you know, the Amadei who's so afraid of his own technology and believes that ultimately it will kill us, but at least we will be killed by American technology and not Chinese technology. There's the area that I find is the one that is most likely, which is bad actor using really powerful, uh, technology to create something, uh, dangerous and that that creates all kinds of problems. And then there's this third area, which is financial house of cards. Where does your kind of lie awake worrying about these things? Where do you place? Or the fourth category, which is it all works and we live in a utopia with high universal income and nobody has to work and we've all lost agency. So where, where, where do you place in that, uh, continuum?
[01:13:27] Ed Zitron: So I think that there is a real, so these models going out and hacking, they're not going rogue. They don't have an accord. They don't have thoughts. Nevertheless, we have OpenAI Anthropic and I think Google and Meta as well running dangerous automated software at scale using hundreds of billions of dollars of infrastructure and we have no legal. We should, Sam Altman should be in prison. I think they, I think that they should. Here's the thing. An OpenAI model hacked an Australian government website. If a regular person did that, they would be in prison. Go to jail. That's right. Why is Sam Altman not at least at the police station? Why is Trump not on television going, Clemmy Sammy is terrible man. Right, right. He would love that. By the way, that's pretty, that's pretty good impression. Oh yeah. I've been work working on it up for hours practicing. No, I like it. But I think that that is the most obvious problem right now. We have this unrestrained, unregulated tech being run on massive infrastructure and in the tests they're doing real damage. And now we have open source. They had no reason to train the models to do this stuff.
[01:14:31] Speaker 2: Why are they beta testing things in the public square? And when they say it breaks out of the sandbox, why you're, you're absolutely right there. And everybody just kind of shrugs their shoulders.
[01:14:39] Ed Zitron: Yeah. And up until this explodes, that is going to be an escalating problem where basically hard forcing compute to break things by bad actors using open AI's models mostly or open source models that have been distilled. So trained off of open AI and Anthropics models. My big thing is the financial thing because I mentioned earlier Blackstone. Blackstone has over $200 billion of data center infrastructure. Who funds Blackstone? CalPERS, the California pension system, pension funds, individual investors. Wow. Okay. And so this data center debt and Blackstone is in CoreWeave. They're in open AI. They're in Anthropic. Right. They have bits everywhere. They're in Broadcom's deal with Anthropic. They have tens of billions of dollars of deals partially funded by pension funds.
[01:15:26] Speaker 2: Everyone is exposed to this.
[01:15:28] Ed Zitron: Apollo merged with Athene, a life insurance and pension fund company back in 2022. Athene is backstopping part of one of Anthropic's loans. The good news is, is these pension systems are huge. So billions would be bad, but they'll still keep paying pensioners. But I don't know because of the private part of private credit. I don't know how widespread this
[01:15:51] Speaker 2: is. That scares me. Here's what ultimately happens. And I think we've learned this is that the federal reserve ends up just attaching a hose to whatever is bleeding and pumping it full of quantitative easing. Like they're just going to print money and pump it to bail out because of these things are too big to fail that they've made themselves. We're basically, I think at the mercy of an extortionary industry that tells us you can't tax us. I, I don't agree. You can't. What is that? I don't agree. I don't agree with that.
[01:16:28] Ed Zitron: So you can bail out the insurance companies. I think that will happen, but the government has too much debt to do a 2008 style bailout anyway, but also you don't think they would bail out the pension systems. Uh, no, that's what I meant when I said the insurance and pensions. Yeah, they would, they bail that out, but they ain't going to bail out Blackstone. And also they're not going to bail out open AI and Anthropic. And even if they did, you're going to bail out the $430 billion of commitments from Anthropic. And on top of all of that, you can't bail out a stock market that is, you can't bail out the value of Nvidia, unless the government is going to give Nvidia $1.2 trillion a year. If, if they are Microsoft, Google, Amazon, their growth futures are based on Anthropic and open AI spending. Is the government going to prop that up? No. So maybe they bail out little pieces of it, but you can't bail out a stock market that's drunk on growth.
[01:17:21] Speaker 2: It's interesting. It used to be too big. You got to make yourself too big to fail. They've actually made themselves too big to not fail because we can't bail them out.
[01:17:29] Ed Zitron: And also open AI is too small to pull into component parts because open AI is fascinating in the, I think Dario Amadei might believe something. I think he's got the religious culture surrounding him. I think Sam Altman's just the most cynical guy ever. I think he just goes and says, whatever the last, whatever he thinks the last guy would like to hear.
[01:17:48] Speaker 2: Well, you see that in his interview. He says different things in different places and you just,
[01:17:51] Ed Zitron: you don't know what any of it means. And he'll occasionally say something insane, like with Jimmy Fallon, where he said, I couldn't imagine raising a child without chat GPT. Mate, ugh. But the thing is with Altman is he is doing what they're also all following the Silicon Valley playbook, which is sign as many big deals, make number go up so big, so huge. And then thing happens. It gets cheaper, right? Except as we've established that isn't happening. And the problem that I imagine they have is with OpenAI and Anthropic is that the demands on them from the world are, you will grow to be the next Microsoft, Google, Amazon plus plus, which requires you to act like it, which requires you to sign all these contracts. And because we are surrounded by what I call business idiots, we are in the era of the business idiot, the kind of, it's kind of like the, the final bosses of neoliberalism, these guys who only care about growth, the Satchi Nadella's, Hock Tans and so on, these guys who everyone claims are the smartest guys in the room, Enron. And what they do is they go, well, these people are promising me all this money. How amazing would it be if I got paid that money? Are they thinking, can they pay? No. Right. OpenAI is the fastest growing company in the world. Anthropic's fastest growing company. Shell game. And the thing is, everyone, the media, regular people, investors buys into it because it's such a, it's a powerful confidence game. It's, they all believe the same thing, we hope. And because they all believe it, no one wants to stop. And there's always that tinge of truth,
[01:19:22] Speaker 2: which is this is incredible technology. It's like nothing we've ever seen before. I'm not quite sure how to use it, but I'm sure they'll figure it out. But that's the thing though. That's the crazy thing
[01:19:32] Ed Zitron: about this though, even in describing this thing, four years, trillion plus dollars in, what's it good for? Code?
[01:19:39] Speaker 2: Professionalizing your emails. I don't need that. I have fingers. Right. And it, and by the way, and I've seen it, I don't really use it much, but the times I have, I realized like, oh, I'm getting dumber. I think just during this session. You're having concepts serve to you rather than understanding the concepts. And you don't even, right. I need to be connected to the world that I live in. I just have to, that's just the way it works. But Ed, it's, it's absolutely man. Thank you. Thank you for, for helping to really give me an articulation of the shape of what this financial shell game really looks like and what the component parts are. I found this really, really helpful, frightening. I'm not going to lie, but, but really helpful to understand it. So I really appreciate your patience and taking the time. Ed Zitron, CEO of Easy Primary Research. He's got the Where's Your Ed At newsletter. And man, man knows his shit. Thank you, sir.
[01:20:39] Ed Zitron: Thanks so much.
[01:20:44] Speaker 2: You know, when you talk to somebody and they have recall. He knows a lot of stuff. What? My head, I started to think like, do we even need chat GBT or can I just call, can I just call this guy? Yes. A lot of facts and figures. The level of detail on a variety of issues. You know, when you read a book sometimes and you have to go back and go like, what does vituperative, what is, wait, what is like, and he's, well, the, you know, if the S1 doesn't go through and then you've got the 39 and it wasn't like when I know things, even then I generalize it. Like it's around 40, but the $39.6 billion in S1 when they filed the thing,
[01:21:29] Speaker 3: but they didn't show you. And I was just like, holy shit. And he's on Pacific coast time.
[01:21:34] Speaker 2: Oh, twist. He's up early and he looked quite fresh. I liked how he said data centers. I got to tell you, everything sounds more authoritative when delivered in just a hint of that. He didn't have, he wasn't full on Cockney, but we had London accent. Just a hint of that.
[01:21:56] Speaker 4: Did this motherfucker go to Eaton? And even if not, he could have, you know, like he was nearby.
[01:22:02] Speaker 2: So let me ask you guys. So I am in that data in that AI doom loop a little bit of not knowing where the threat is and also not seeing the promise of the upside of like, we're going to cure all these things for you, but all they're using it for are these exploitative surveillance, uh, state issues and weapons issues. As we laid out the various threat matrix that goes along here is the banality of just the economics of it. Does that move to the front of the class as far as threats? Where are you guys coming out on this? That's kind of where I've been.
[01:22:42] Speaker 4: Oh, really? Okay. Just because it seems really unsustainable and we've all seen, you know, number go up and to, and it won't, it's like, you know, completely irrational. And it seems like everybody's just paying each other, like the Beavis and Butthead, um, comparison that you made. Everyone's just paying each other the same dollars.
[01:23:03] Speaker 2: I'm a very sophisticated analyst.
[01:23:05] Speaker 4: Round and round.
[01:23:07] Speaker 2: So you've been on, you've been on bubble watch for a while. Cause I've been on somebody's going to make a bio web. Like that's where I've been.
[01:23:15] Speaker 4: That's like the back of your mind. That's always there, you know, where you're like, right. And then also, you know, we're all going to die.
[01:23:22] Speaker 2: Somebody might write. Brittany, same with you.
[01:23:25] Speaker 3: Uh, no, I think the economics of this was a surprise to me. Like I haven't been following this so closely. I know that we use the term circle jerk often, but this is circular financing
[01:23:37] Speaker 2: jerk. I apologize to all the, all of our parents out there. We do tend to go with circle jerk quite a bit. We apologize.
[01:23:44] Speaker 3: It always applies. It's crazy. It works. Uh, but I was like, holy shit, this is the ultimate one. Like that was very surprising to me to hear the level of which I didn't realize the, the big surprise
[01:23:55] Speaker 2: to me there was that open AI and, and anthropic they're just tenants. I did not know that their business model and that Larry Ellison is really an Elon Musk. They're the landlords, but they're all Amazon and meta, right? But they all live in the same town. And then at other times it probably flips and those people are giving money to the other. It's yeah. Yeah. It's a wild little house
[01:24:19] Speaker 4: of cards. I mean, this morning NVIDIA announced they were going to invest like 3 billion in meta. And it's like, I guess meta would just be giving that money back to you at some point and way more
[01:24:30] Speaker 2: than that back to you. Is it basically I'll give you $3 billion and you're going to make a $15 billion chip purchase along with data center leases and everything else. It's, it's a, it's a wild little game, but I, I really appreciate it. Uh, Brittany, what, what are the people thinking about? Are, are we, are we getting a lot of questions on midterms? Where, where are we at?
[01:24:48] Speaker 3: We've actually first one, John, we've got one week left. If the Democrats win big,
[01:24:55] Speaker 2: do you think Trump and co will just January 6th? Oh God. I don't, uh, you mean like just not seat the representatives and senators. It's, it's honestly, and as we've seen over the past episodes, I am not a man known for my lighthearted vision of the future. I mean, I am optimistic at my core about people, but people exist in systems that are designed by, I guess we'll call them assholes. Um, it is, it's hard for me. It may be a failure of my imagination as January 6th was too, by the way, I remember watching it in utter disbelief, but it is hard for me to imagine. And, and I do believe, and I'm not saying we're Paris and I don't mean that in the burn it all down way, but I mean it in the, I do think there is a point where people will go to the streets and those elections. And I don't mean it in a January 6th way. I mean it in a, we are protesting an authoritative power grab kind of a way. I do think these are the fulcrum points where a society decides it's enough already. And we're no longer going to play this game. Uh, but I, but I, I, I'm, I'm clinging to the idea that somebody there learned a lesson from that terrible, terrible day and will function as a governor on those impulses. But God knows I've could be very fucking naive. Yeah. Come on, Jillian. You got something cooking in there. Come on.
[01:26:43] Speaker 4: I mean, I'm not, I think, you know, everybody says it and it's true. We have to come out and like overwhelming numbers because you can fudge the numbers a bit when it's, you know, 10,000 votes. You could fudge. What did, what did he ask for in Georgia? Like 13,000 votes. Yeah. You know, those are numbers where you, they can start fucking around, but we all have to show up. So I don't, I, I think even they are not. No. And it would have to be egregious.
[01:27:08] Speaker 2: I mean, whatever it's like when they say, it's why the worst question that I ever see newscasters ask as it comes to, uh, these election cycles is they always say, you know, does she vow to honor the results of the election? And you always want to say like, well, if you don't know what happened, how can you vow in the future? You, you, even a well-intentioned person can't answer that because you don't know. Cause what if there is fuckery? Right. What if it's obvious egregious fuckery? And that's the thing that I think people are going to have to be. And by the way, I do think that preparations for fuckery have been underway. And I do think there is structures in place to be able to combat that as much as, you know, uh, they would like to muddle the picture. I do think there are people out there fighting and I give them the man, the people that run our elections in this country, the individual citizens who put themselves on the line to be the administrators of our, you know, democratic republic. Like God bless. They are doing the angel's work as far as I can see. Totally. Yeah. Thank your poll workers. And I think the good news is I
[01:28:30] Speaker 3: saw in truth somewhere that, um, if you do see fuckery and you report it, you get a million dollars.
[01:28:35] Speaker 2: What? Where's my phone? I wonder if you'll get a million dollars if you report Trump's fuckery. If the money comes from him, he's like, you got me. All right. What else? What else, Brittany? Uh, John, if you had to, would you? Yes, I would. I would twice. Now I'm desperately afraid of what's coming next. What is, what is the, would you? That's it.
[01:29:05] Speaker 3: Oh yeah. Twice. You had to, would you? Yeah. Twice. Uh, next question. Wait, was that really the question? That was the question.
[01:29:13] Speaker 2: If you had to, would you? Oh God. It's like tautological a little bit. No, hold on. I want to change my answer. Too late. We've moved on. Son of a bitch.
[01:29:25] Speaker 3: Uh, John, with everything he has going for him, why doesn't Trump complain that he's never been
[01:29:29] Speaker 2: voted people's sexiest man alive? I think even that is a bridge too, I think he, I think even he understands too far. I mean, he has a fucking, at this point in his life, he has a front butt.
[01:29:42] Speaker 4: So I don't think. Well, he loves asses. He's got one in the front and one in the back. I don't know. Have you seen that back butt though? He loves asses, but he's not paying
[01:29:49] Speaker 2: attention to the front butt. And that's, that, that has been the, one of the strangest twists in, in this election cycle. And it's so interesting in the previous times where he's been kind of the lead campaigner for his own campaigns and everything else, he's always done stick. There's always been within it. He'll find like a little bit and he'll hit it a couple of to the enemy, the people, or he'll do a little stick. Like they always say to me, I watch these things now and I go, it's really all stick.
[01:30:24] Speaker 4: Yeah. Yeah. There's not a lot of there there.
[01:30:27] Speaker 2: There's really every now and again, it'll be like, well, I mean, even when he's talking about what he thinks are policy wins for his administration, their stick, because it's, he can't just say, ladies and gentlemen, you know, oil and gas companies are making billions, trillions of dollars. You've got to reelect us to keep this gravy train going for Exxon mobile. So he couches it in. They tell me not to say anything about it, but I do in the fake news media. It's, it's all now really, he is a Vegas nostalgia act.
[01:31:06] Speaker 4: Yeah. All that's left is like the cult of personality. Like there's nothing on top of it
[01:31:12] Speaker 2: anymore, right? The accomplishments are, are nil. And so the shtick has to carry the day because he has nothing. None of them do. And the Republicans that are running, what are they running on? We didn't stop him once. Like we didn't provide any check or balance elect us again.
[01:31:32] Speaker 4: Yeah. I mean, that's the hope is like, you still love this guy, right? Like you.
[01:31:38] Speaker 2: That's all they're, that's all they're running on.
[01:31:40] Speaker 3: But the shtick is creepy now. Like it's gotten more, forgive me for saying this, but it's more sexual, like constantly, like. Oh.
[01:31:48] Speaker 4: Poor John Husted. Like he doesn't, you know, he didn't ask for this.
[01:31:52] Speaker 2: But let's never forget when it wasn't shtick, it was always wildly offensive. And remember, this is a guy who recovered from grabbing by the pussy, who came down an escalator. And the first thing he said was, I want to travel ban on Muslims and Mexicans are, you know, some of them are okay, but rapists mostly. So he's never tempered those, the vileness of his id, but now he couches it in a kind of comic persona that he no longer carries the straightforward, to use that word that we thought about earlier, vituperative persona. Now it's all couched in and, you know, and then he's like, I'm just a harmless old man who likes to stare at asses, but still, uh, am terrifying vulnerable communities throughout this country. And isn't that wonderful?
[01:32:52] Speaker 3: Yeah.
[01:32:53] Speaker 2: So what else? One week, one week, one more day.
[01:32:59] Speaker 3: Uh, last question, John, for Halloween, do you sit by the door to give out candy or do you just put out a big bowl of candy and trust people to do the right thing?
[01:33:10] Speaker 2: So first of all, there is no right thing on Halloween. It is basically, it's a free for all. So if there's, if there's candy in a bowl and you want to dump it in your pillowcase, God bless, have at it. Um, it is, uh,
[01:33:21] Speaker 4: Those are the rules. There are no rules.
[01:33:22] Speaker 2: Yeah, there are, the rules are, the only rules are, if someone gives you an apple, you can egg their house. That's really the only, that's the only rule that's going. Uh, unless obviously it's a caramel apple or a candied apple. Which by the way, what is the red candy on a candied apple? Uh, it, it is not a, certainly a flavor known to man.
[01:33:41] Speaker 4: No.
[01:33:42] Speaker 2: Or a substance really. Is it microplastics? It's got to be.
[01:33:45] Speaker 4: Is it merely? Yeah, the shine on that, that's unnatural.
[01:33:47] Speaker 2: Is it, is it just melted water bottles with a little bit of, uh, grenadine added to it for coloring? It is delicious. Um, so in New York, so New York was great because the kids, we could go right out on the street and, uh, you know, you could do, you could sit on the stoop. That's we did that one year. We sat on the stoop and just passed out candies. People went by and that was great. Uh, suburbs are a different deal, man. And it is definitely like now that the kids are older, it's, it's just not a, it's not even really when your kids aren't doing it anymore and nobody really comes to our house because obviously the, the guard turrets and the guns.
[01:34:29] Speaker 4: That's when you buy the candy just in case, and then you eat all the candy yourself. That's right.
[01:34:33] Speaker 2: That's right. All right. Um, well, very, very good. And I want to remind people, uh, our podcast, uh, next week, what do we do? We come out normally on Wednesday. I think we're coming out on Thursday because of the election. We want to tape it on Wednesday so that we have some sense of, of what happens. So I think the podcast will come out on, on Thursday there. Uh, would that be correct?
[01:34:56] Speaker 4: That's correct.
[01:34:57] Speaker 2: I now lean on every, it's so funny to me during, like the, during the interviews, like Jillian, I'll just pop in with shit. Like you're completely wrong about that. The answer is that, and I'll be like, oh, thank you.
[01:35:06] Speaker 4: Hardly.
[01:35:06] Speaker 2: Uh, all right, kids. Well, by the way, um, how do they stay in touch with us?
[01:35:11] Speaker 3: Uh, Twitter. We are weekly show pod, Instagram threads, TikTok, blue sky. We are weekly show podcast, and you can like, subscribe and comment on our YouTube channel, the weekly show with John Stewart.
[01:35:21] Speaker 2: Thank you very much. As always, producer, Brittany Mamedovic, producer, Jillian Spear, video editor and engineer, Rob Batola, audio editor and engineer, Nicole Boyce, associate producer, Rebecca Rotenberg, and our executive producers, Chris McShane and Katie Gray. We shall see you next time. Happy election. The weekly show with John Stewart is a comedy central podcast. It's produced by Paramount audio and bus boy productions.
[01:35:49] Speaker ?: The weekly show with John Stewart. The weekly show with John Stewart. The weekly show with John Stewart.
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