Ed Zitron Unfiltered on OpenAI, Anthropic & Why the Whole Thing Is a Con

[00:00:00] Today on the podcast I finally bring Ed Zitron around on AI. Lifeless, it's empty, gonna look like a dog's asshole on Thanksgiving. You stupid, you moron, why don't you like data centers? Okay, maybe not quite, but I do have a lively debate with tech's loudest AI skeptic. So how are you gonna get around to the return on investment thing? He's like, yeah somebody will work it out. Sammy, clammy Sammy, this is your job. From his podcast, his newsletter, and his 18,000 word takedowns, he has made one thing

[00:00:29] very clear. AI is a bubble that's about to burst. I'm Eric Newcomer, author of the Newcomer Substack. Let's get into it. Ed Zitron, welcome back to the Newcomer Pod round two. Thanks for having me. We're live in person, just you and me, mono y mono. I asked Claude for, I don't know, what did I say?

[00:00:56] Give some monikers for you. All right, we've got the Hater Laureate, Cassandra of Compute, the Doom Sommelier, Tex Designated Mourner, the Bard of the Burn Rate, Chief Bubble Officer, the Last Honest Man. That's a charitable one. I guess. I just like, I asked the dog, would it give me, I'd be just as excited. It wouldn't be as good though.

[00:01:25] I would care. I would have more respect for it. It's just like, those are things that you get people saying in your mentions every so often. It's like, like a regular person, like, good on you, like a normie. But like, it's like Claude Slop. Okay. Hey, the Hater Laureate. Okay. Just like, such, it's so unexceptional. That's the thing. It's not just mediocre and all these different ways. It's just like, you read the text, it just feels lifeless. And if you go on Substack, it's, I think it's mostly Claude Written.

[00:01:55] Substack? Yeah, there's so many. When we saw, yeah, there were, I forget the company's name, but there's some company. Pancram, right? Yeah, yeah, yeah. And it's just, it's very depressing and so it's lifeless, it's empty. Even human written LinkedIn Slop evokes some kind of reaction. Even if it's just reminded of the feeling of being at a conference, you still, there's still something human about it, even if it's quite boring. But you read the Claude Slop as, it's not just this, it's this. And that was the twist. That was the turning point. There's these little

[00:02:24] linguistic tropes. It's just sad. It's not even good. It's not like, we're still in this. Years in, we're still like, look, yay. We can kind of write like a person and it sucks and you can tell every time. Yeah. It's just so boring. So that gives us a taste of where you're coming from. To give context to people coming to this episode, I host an AI conference. I write in Substack newsletter that is not AI Slop, AI copywritten.

[00:02:54] That I pay for. I pay for. Thank you. That's great. I'm a big fan of your newsletter as well. And though it obviously has a point of view we will get into here. Yeah. We did one episode before nine months ago, sort of disagreed for context for the viewer on me. I feel like I'm somewhere, I host an AI conference. I wrote a case against OpenAI's

[00:03:20] valuation at, I think it was 157 billion, which is March past. I sometimes report data that you're happy to have. So I feel like I sit in some middle ground where sometimes we're allied, sometimes we're clashing heads. And here's the thing with you. I know we don't agree on everything, but you published Tom Dutan, who's a fantastic journalist, who is also quite critical. You yourself are one of the few people who's been even positive you are still willing to critique Anthropic. The Kutu was the Kutu day.

[00:03:50] Yeah. Like most of them. Where they said, oh, we think it'll be worth 1.99 trillion. Trillion. Yeah. I want to say two. That would be ludicrous. Yeah. That was just that we couldn't possibly value a company that burns billions of dollars that much. And then Elon Musk comes along and is like, oh, watch this. It's so based. And it's, but you at least try. You at least try and find some middle ground of, okay, you think that this will work. I don't, whatever. But you at least try and found that in something. And even then you bring in some critical. And I think that's healthy. I think it's the

[00:04:19] healthier way of approaching this for someone who is going to be pro AI, or at least willing to accept a pro AI opinion. I never want to have a shtick. I feel like my disposition is sort of, I always like to poke and prod at things. So I think this, you know, we'll get into it and I'm sure we'll push back. But at first I just sort of want to give you a chance, like lay out your case for a couple minutes. Like, where are you today? Like what, because there's sort of this, like,

[00:04:46] it's a fraud. It doesn't work. Nobody likes it. You know, there's sort of the self-dealing aspect. There are lots of pieces to it. Like, where are you right now on the, like, it sucks. Nobody wants it versus these numbers are made up or like, what is your core case against the foundation model companies today? So we're really talking about like open AI anthropic, like the case against them. Right. So fundamentally there is no ROI in AI. You can dress it up however you want.

[00:05:13] Look at how anthropic and open AI discuss their products in the sense that they don't. They're always talking in theoreticals in, well, the potential's there. Everyone's always talking about the fact that we are still having a conversation about AI's value and whether AI is real or not is kind of the foundation of my argument. In that if this was real, we would definitively know. We wouldn't have people saying emphatically like, AI is here and it's real.

[00:05:39] You don't need to say that. You don't need to say that if something is real. So I have never disputed that LLMs can do some code, but I think people have over become a little overwrought about what kind of coding they could do and how reliable or stable or functional that coding is. But the biggest thing by far is that the con is the financial side. The con is the fact that

[00:06:03] most people's experience of AI is unrealistic when it comes to the costs. So you using ChatGPT, I believe the subscriptions will go away. I think we are just at the dawn of token-based billing, meaning that most people's use of clawed code, for example, is based on a relatively unlimited, you can burn between $8 and $13.50 for every dollar of your subscription in tokens. So most people's experience of AI is separate from the cost, divorced from the cost. And this was a deliberate

[00:06:32] con on the part of Sam Altman and Dario Amadei, because they both knew that if they actually made people pay on a per million token basis to use these things, they go, wait, how much? Whoa, this thing messed up and I still had to pay. And you're kind of seeing that already with the enterprise customers, the Ubers and Walmart's of the world who are starting to have to pull back. This is the token maxing soul searching we're seeing going on right now. Well, the thing is, Uber wasn't token maxing. I don't think they had a leaderboard. I think

[00:07:02] they might have at some point, but they burned through their entire annual token budget in four months in the space of the first quarter. And then their COO, Andrew McDonald said, it's getting hard to justify because you can't connect it to outcomes. This is, we are years in, why are we all acting like this is going well? It isn't. And so the experience of AI, the value judgments of AI are all based on phony metrics and outright con about the costs. And quite

[00:07:30] frankly, a very weird psyop. The whole thing is a psyop, the cult of personality around AI, the very much, if you are in the out group and you don't like it, you're othered, you're different. You're a hater, you're a skeptic. And it's frustrating because Silicon Valley is meant to be a place of meritocracy. It's meant to be a place where you're evaluated based on your value, theoretically

[00:07:55] speaking. Remember the, I don't know if you ever saw the GitHub thing, the meritocracy doormat they had. No, it looked like they had like a white house. Yeah. It looked like a CIA thing, which is also good. We love that. But the thing is, Silicon Valley is meant to be a meritocracy. Yeah. Everyone coddles these things. Everyone talks about these things like a gifted child. Like they're afraid that they'll get turned into an ear of corn, like that Twilight Zone episode for saying the wrong thing. I know. Oh, I watched your Bloomberg interview recently. And you know, I used to work at

[00:08:25] Bloomberg. I'm very sympathetic to them. But their response to some of your criticism was very, uh, but they're smart people who believe this. Like surely it can. Yeah. I felt a little bit like, is that the best AI defense they're going to muster? So I just want to make sure, I mean, I think, you know, argument number one is that AI is talk, you know, the AI companies talk about what they can do in the future, not what they can do today. Yeah. Your assessment of what they can do today is not that excitement, exciting to the extent

[00:08:53] companies are using it. It's often subsidized or, and, or there's sort of this delusional sense of like, clearly we're supposed to get some value out of it. We're going to spend that that's going to come to a stop. Um, what's your view? You know, Anthropic is now confidentially filed to go public. I think you've, you said in our last conversation, there wouldn't be exits is now the view that they're going to try to exit, but they're going to pull one over on us. Like what's your view on, I guess, when does this all come to an end? Like, does it survive an IPO?

[00:09:22] So I said that this on Bloomberg, I don't think regulatory bodies should let Anthropica or Open AI go public. I think that they are both irresponsibly run companies run by craven liars, both Dario Amaday and Sam Orman sell their products based on what they might do theoretically, rather than what they can do. Uh, every time anyone asks Sam Orman, how this will work out on CNBC, David Faber was like, so how are you going to get around to the, um, return on investment thing?

[00:09:47] And he's like, yeah, somebody will work it out. Sammy, clammy Sammy, this is your job. You are the AI industry. But to your greater point, I think one of them is going to try and go public. I think they're going to bull rush this thing. I think it's inevitable opening I first go public. And based on some stuff I might've read recently, they sure shouldn't. They're losing too much money. And Anthropic on the other hand is also losing just as much money. They had this, one of the most disgusting things I've seen done by these companies outside of the environmental

[00:10:16] damage, the stealing, the CSAM that Grok generated. Putting all that aside, Anthropic leaking that they are profitable in the two months that Elon Musk discounted their compute. Do we not have an SEC? Do we not have any kind of regulatory body? We certainly don't have a conscience in the valley because everyone was like... Your view is that they made a profit because Elon gave them a deal on their compute. Yes, and that's in the SpaceX S1.

[00:10:43] Well, specifically the May and June in Q2, Anthropic will become profitable. Will, because it's June now. It was not when that story came out. And mysteriously, those are the two months, May and June, when Elon Musk discounted Colossus 1 and 2 for Anthropic. It's just like... And what sucks is that's publicly available information. This is in the S1 for SpaceX. And people are still like, they're profitable now. It's like, yeah, you're profitable as long as you

[00:11:11] don't count the costs. And so... I mean, my view on this is we will get the answers when the S1 comes public. Yes, I'm looking forward to it. And, you know, we'll talk about the Uber analogy because I covered Uber very closely. Yes, you were one of the... And you were actually one of the few people who are really critical of them. I was very critical. But in some ways, I learned the opposite lesson that you did, which is they ended up turning a profit. People over extrapolated from some of the private financials

[00:11:38] and drew these complicated models that sure, like Uber was spending more aggressively and they were... They were like Uber pool never worked, right? So there were things about Uber that didn't work, that were worthy of criticism. But I just... There are just limits to how much you can sort of do financial calculuses off of these like limited private numbers that we see. Do you want to wait to discuss it? No, I don't want to... Let's talk about the Uber case because it forms both of our views so quickly.

[00:12:06] Exactly. So with Uber, for example, a lot of their money was marketing and R&D. I like to equate Uber to Groupon more than anything. Heavy, heavy marketing. And that was where they did the subsidies for the rides and all that. Also, R&D efforts within autonomous driving that I think went nowhere. Forgive me for not having looked recently. Also, the burn rate on Uber was just a different planet. I think they burned... Way lower. It was scary then, but now it seems like a joke.

[00:12:31] It's about $32 billion in history and Anthropic has raised over $75 billion in the last six months. So I think that that's altogether different. And also, the level of burn, the scale of burn and the linear reality of it. It's a word, I think. Basically, as Anthropic's revenue increases, so do its costs. So the costs are always up here. Same with OpenAI. Uber had a messy cost structure,

[00:12:56] but they didn't change the unit of revenue. So they didn't have a thing where it wasn't... They tried the ride pass where you could just pay one fee and drive as much as you want, but you still paid for a ride. A customer was still paying to go from point A to point B. With the subsidized AI Labs subscriptions, they are training people and they've trained millions of people to use these things in a way that does not make sense and they pay the costs. The reason that you're seeing companies burn so much money is...

[00:13:24] Imagine if you were driving your car and you just never really had to think about fuel. Fuel was just included with it perfectly. And then one day... And so you... Actually, no. Let's reframe this. Use a cab service and it will drive you as much as you want. You get 300 rides a month. You could drive from LA to San Francisco. You can drive from just the Upper West Side to the Upper East Side. No suddenly one day they say, actually, I'm going to have to pay on a mile per mile basis.

[00:13:51] You would have no idea how to plan your life. You had planned your life around this subsidized, unlimited thing. And perhaps you could say that the ride point to point from the original Uber was like that. No. This is like if every Uber ride used to cost $3 and the new ones cost $300, $500, $1,000. People do not know how much their AI costs. They have no idea how to measure a unit of work because they never had to. Think about it. Like if you were used to just loading up Claude going,

[00:14:19] fix these bugs and it kind of does it. And then it messes some stuff up and you poke it, bonk it on the head a few times. Maybe it gets it right. Maybe you mess around with it, but you don't know what it costs and you weren't paying the costs. Suddenly doing that would give you CTE. Look at the people. So GitHub Copilot, which is Microsoft's coding tool, they just moved from a premium request model to a token-based billing. Go on the subreddit. People are screaming like they're being stung to death by bees because they're finding they're burning through their

[00:14:49] entire monthly allotment, which is just the price of tokens, in like a day. There's someone who did it in three prompts. There's so much I want to respond to this. So I'd like to take a move. Sorry. But I do, the specific one major contradiction that seems to appear to me in your argument is that people see the cost being high and they freak out because they want the product desperately. Like how do you reconcile with the idea that it's like people who are bullish on AI see the fact that

[00:15:18] people want to spend money on it, want access to it as evidence that it is doing well, that they see some value in it. Like, do you not see that? I do see value in it. I'm not disputing that. I'm saying that the value they get out of it is based on the idea of subsidies because they have evaluated it based on it not costing what it really does. So if you use the product and you could do all this stuff with it, however accurate or inaccurate it is, you can say, well, that's a great deal at 200 bucks a month. Is it a good deal at 300 bucks a week? Probably not. You wouldn't be able to do as

[00:15:46] much. And I also think psychologically, you look at it differently. If something costs 200 bucks for a month and you run up against right limits, fine. You can accept that. Sure. So I want to talk through the Uber analogy as I see it. I mean, yes, I think Uber was a similar case where people felt like, oh, it might work at one price, but it won't work at another. And I think what happened was people got really upset in this

[00:16:13] amazing period where Uber was overpaying drivers relative to what they needed to pay them to get them to do the work, discounting rides to keep the riders from going to Lyft. And basically we had this like free giveaway and there was like a crowd of people online who were like very angry about this, even though they were getting stuff at a big discount. Right. They were getting it. And so then that period ends, basically Uber and Lyft sort of come to a

[00:16:42] sort of equilibrium where it's, I don't know, it's probably 85, 15 or even more aggressive in Uber side. And yeah, I mean, basically they have to squeeze drivers more, especially on airport trips. But there is a price where people take Uber, it works. It's not like the insane Google level business that people thought. And things like Uber pool, which we're supposed to allow it to sort of like get to scale, don't work. So there are pieces. I just don't think there's this like something's

[00:17:08] valuable at one point. You know, it's sort of a sliding scale. And I feel like we didn't learn the lesson from Uber that, you know, that this sort of like big hustle was had on us. Right. But here's the thing. When you take from Brooklyn to Grand Central, 50, 60 bucks, it's still within the realm of what you would pay for a cab. I am saying that what's happening with AI now is people went from just for the GitHub code pilot example, this is not hyperbole. There were

[00:17:37] people who were paying 39 bucks a month and being able to burn 1500 to $3,000 worth of tokens. That is not the same. That's not the same at all. That would be if like Uber rides were $5 and then became $1,500 or $300. It's just a different economic scale. And also you can plan around that still, even though it's more expensive, you can say, okay, this ride was 10 bucks. Damn, I wish it was still 10 bucks, but it's 50 bucks now. With AI, it's, you don't know how to

[00:18:06] measure a unit of work. That's the biggest thing. Like it's tough across models, across harnesses, you use open code, you're clawed code, you're using a deep seek model, you're using the JPT 5.5X high plus alpha, you're using whatever you're using. Each prompt, oh, what are you having it do? It might act differently because hallucinations are a mathematical guarantee per open AI. So you don't know how to measure the unit of work. You also pay regardless of whether there is a mistake or not. If it gets it right, if it doesn't get it right, you are still paying on a per million token basis.

[00:18:34] So it's not like you can say, well, okay, was spending 200 bucks a month. Now I'm spending 500 bucks a month. You're unaware, you have no idea. The more complex the work you do, and because AI code is very verbose, the more AI you use, the more convoluted things are going to become. And the more reliant you are on the machine to kind of work out what it's doing. And those costs are dramatic. They are unrealistic for most people. They're unrealistic for most organizations.

[00:19:02] The models that we have today will be cheaper in the future. We see that over and over again. What do you mean? The models of 12 months ago fall in price. The new models obviously are new, have new prices, but the old models become either offerings from Anthropic and OpenAI themselves and or open source competitors basically copy versions of that model and offer them at a discount.

[00:19:31] So I can see the argument that, and you've talked about this sort of like the CapEx expenditure, like the situation with Uber and OpenAI and Anthropic are very different in the sense that like, well, OpenAI's costs are because they're spending a lot training a new model. Well, but before we get on from the token thing, there is an important distinction I need to make, which is models are burning more tokens. So even if the cost of intelligence comes down,

[00:19:56] the biggest myth of it all, even if a model might be cheaper in general, they are burning more tokens. Opus 4.8, but more tokens than ever. And every time Anthropic has released a new model, it seems to get worse. I don't know if you noticed that. They seem to be more convoluted. They do things differently. Again, burning tokens so you can... You're saying every Anthropic model is getting worse. If you go and look at Twitter right now and see how people are reacting to 4.8, see how they react. But they're comparing it to Codex.

[00:20:24] No, they're comparing it to Opus 4.6, man. Like they're literally comparing it. But this is the thing. Good example, actually. Even if it's better, it is still different, which means you have an adjustment to it, which means you are going to burn tokens. But anyway, they are burning more tokens. Chain of thought reasoning models burn more tokens. So even if the model is cheaper on a per million token basis, you're still burning way more tokens. So you're burning more tokens and spending more money. Same thing with open source models. This has been

[00:20:52] a consistent problem. It's a problem with Grok. It's a problem with all of them. And the thing is, if you're happy as a company with the model today, you can basically find a version of that model. You can make a version of that model yourself. What do you mean to make a version? Like people can build their own models. There are open source models that people use that they sort of fine tune themselves. There are companies that really build their in-house models.

[00:21:16] Like, it's not like you're forced by Anthropic and OpenAI to shift off the model architecture that you like. The thing is, you're speaking in generalities here. Who has successfully done that? Microsoft just farted out six new models from Gooner himself, Mustafa Suleiman, who is apparently quite a horrible boss here who goes nuts on Slack. But putting it all like gossip aside. Going all over. Gossip is like gossip aside. But there's Kimmy and DeepSeq. Obviously, there are a lot of Chinese companies that have produced models.

[00:21:44] DeepSeq is running the Chinese wear them down process. They discounted permanently just to drag down American models. That's tactical. I love that. It's just funny to watch them do that because it's the kind of thing American AI labs have no idea how to do, which is political rat effing. I'm not going to swear today. But back to the core thing, which is, it's kind of like the mileage thing. Okay, gas is cheaper, but you're driving an extra 50 miles so it's not. It's still the cost

[00:22:08] of the journey. It's more expensive. So every model is burning more because the more complex they get, the whole mixture of experts thing that was meant to bring costs down. It is not more expensive to do the same task. It gets more expensive because people get more ambitious about what tasks they're going to do. You know, it's actually that because everything I've read is the, go on the clawed code. I love going on these subreddits because you can get a real taste test for it. They will find these

[00:22:35] things, new models going in crazy loops. The things that they could do before magically don't work. This is a common experience with large language models. Speak to these users. Certainly it is true that when new models come out, they have like all these different edge cases and their personalities change, their sycophancy level changes. So there's clearly a reaction where people are like, I used it for this one thing. It doesn't deliver on this anymore. But like

[00:23:01] the companies are clearly have every incentive and are measuring their models to make them better over time. Like they score them. Are they? I mean, just pushing back there. Are they? Because... Are they incentivized or are they delivering? I don't either because I don't think they're incentivized too. I actually don't think Anthropic has any incentive to make their models more efficient. Same with OpenAI. The markets are rewarding them for being big, lossy, messy companies. And on top of that, they have the various business

[00:23:28] idiots and various companies who will... Zillow. I've been talking to people at Zillow for a few weeks. Crazy AI psychosis over there burning over a million dollars a month in tokens. But the thing is, these companies are not incentivized to be more efficient. And they're not incentivized to bring costs down because Anthropic believes that they have the mandate of heaven. I believe that they're a deeply cynical organization. And also, they're burning more tokens. Like they obviously are doing it. You can... People will say, oh, we're just picking out a few edge examples.

[00:23:58] Are any of the people who are not... Who are excited about this actually talking about the real costs? Probably not. And it's... It's for sure. I agree with you that there's, you know, public companies, all sorts of companies have an incentive to show that they're getting the most out of AI. Yeah. And therefore, they have a reason to spend aggressively independent of what they're delivering. I think you underestimate that companies do find value in AI. Like I feel like you... When?

[00:24:25] Well, literally my own company. My own company, you know, our ticketing infrastructure was vibe-coded. Like we could have paid, you know, for HubSpot or whatever, some SaaS product. And you build your own version so that you can quickly change it. Like... Quick question. Did you pay on a per-token basis? Uh, no, I doubt it. You're like, it's just not... That's the thing. That the model...

[00:24:49] You are living in a dream, but who is the dreamer here? Like, this is the thing. You are experiencing it through the larger con, which is, yeah, it burped out some open source software. You were like, it burped out a copy of an open source CRM. You have it privately, so there's not... I mean, I would make sure it's checked by a real coder, but you also did not pay for it. You paid for the subsidized rate. I'm guessing the $20 a month plan, maybe the $100 a month plan.

[00:25:16] I'm sure it's $100 a month. But that's the thing though. This is my point. You didn't experience the reality of AI. You were... You're the victim of a con. You have been convinced that something is a price that it is not. And Anthropic only moved companies to token-based billing, along with OpenAI, who do it much quieter. About three months ago, it was announced by the information in April, but it's been happening longer. And already they're screaming. They're like, ah, I don't know about this. Because it's one thing to say, wow, I'm spending $200 a head, and they get rate limits,

[00:25:45] and they can go nuts, nuts bananas on this. It's another when you have people spending $1,500, $2,000 a month. Uber just had to limit their engineers to $1,500 a month. Which makes me wonder, how much were they burning before? Because that's the thing. At $200 a month, very different product to one that is a variable cost of $100 a day, $200 a day. Anthropic's own documentation says that they

[00:26:11] think that people spend an average of $13 a day in tokens on clawed code. That's not sustainable for the average person. And I think, ethically speaking, any journalist writing about AI should not use the subscriptions. I think they should only use tokens. No, no, why are you laughing? I'm not even being, I'm not being aggressive here. I'm saying, why aren't they doing that? Because to have a realistic perspective on the actual efficacy of these models, you have to experience the horrors of

[00:26:40] this thing just went on a little loop and burned $5. Yeah, most consumers are not going to want, consumers don't want an experience where the product could accidentally weigh overspend. And they'll, and they- Enterprises are sophisticated. I mean, enterprises are not sophisticated. Not always, but at least it's- I mean, in general, I mean, but the point I'm making is, I think token-based billing is coming for everyone. I think it is an inevitability. The economics do not make sense for subscriptions.

[00:27:07] Everyone does this funny fusion dance of, oh, inference is profitable. No one has proof of that. Maybe, maybe if they didn't pay any training, there would be economics that may be lined up. But in truth, inference and training are one and the same at this point. You have to keep post-training. You cannot stop. I mean, we're going to see the number. Do you think these IPOs are not going to happen? They're going to fail? Like make a specific prediction. Oh man. Because the thing is, things are so

[00:27:34] wacky. Because Anthropoc is like, we've confidentially, as they announced it, filed a night for our S1. But we're not going to say when we're doing it. OpenAI, the journal reported a few weeks, guys, it's Burbogen. It was like, oh, they're going to file soon. But they haven't yet. I think that there is going to be a reaction based on the SpaceX IPO. If it rips, OpenAI lists. If it doesn't, OpenAI slow rolls. But also, I could see Sam Orton getting real mad and saying,

[00:28:00] I gotta get this. I gotta get this. Screaming at Sarah Fryer. Right. I mean, to be clear, I think the SpaceX IPO, to me, is uniquely working. It's a dog. I guess, to me, it's like the problem with SpaceX is it has failed to be the AI company it wants to be in. The space company has a limited ups. You know, there's just like, you can value the space business and it doesn't reach, you know,

[00:28:23] $2 trillion. But so where we differ is, well, I don't know what the correct price of OpenAI and Anthropik is. It's hard to say, yeah. I mean, but that's, it's not my job. You know, it is not to pull the Bloomberg, but it is sort of the business of investors to speculate about the price and make bets and have sort of this competitive dynamic. And that is ultimately what sets prices. So I think whoever goes public out of Anthropik and OpenAI stops the other one. I think that- You think it's so bad that they'll only be one.

[00:28:51] Oh, I'm confident OpenAI is really bad. Very confident. And I think that Anthropik- Like the numbers will appear and it's like, this is so abysmal. I think that the losses for OpenAI are going to look like a dog's asshole on Thanksgiving. I think that, I don't know about Anthropik, but the fact that- Dario Amadei, I have to give him credit. He is one of the best media manipulators in history. He's better than Jobs. He just, he knows how to do the financial stuff with the annualized revenue. He's so good at it.

[00:29:17] But you complain about this a lot. Annualized revenue is useful to convey if your revenue is growing. And so you're trying to show- It's not useful for something like API calls though. Those are not monthly recurring expenses. There was some numbnuts who spent $500 million on Claude in April, I think it was reported by Matty Mills-Raxius.

[00:29:41] Your point of view, right? So this, like if I report my ARR this month and then I have a problem next month, like I'm going to be screwed if that number comes out because I'm going to go from a great number to a bad number. So you've been complaining about the ARR over and over and over again, but the revenue keeps going up. Like the problem with using ARR is that like the chickens can come home to roots at some point. The problem with ARR is not that they can come home. It's that

[00:30:08] you can manipulate it in any which way you like. Anthropic has never defined what they mean by run rate. Not once. And I've read every single story. The information I think, Sree, Sree over there, reported that they do it by taking the day's subscriptions, like the monthly subscriptions they have times 12, then their API revenue for the last four weeks times 13. API revenue is a really weird one to do on a recurring basis because that is going to flow.

[00:30:34] And as you see these cost cuts happening, yeah, that's coming down. So it's just like revenue is how you should report revenue. And you should also, all of these things are non-gap. It's the non-gappiest non-gap that's ever non-gapped. And it's frustrating because they share the run rate so that it's a really simple, manipulative trick. You're meant to go, oh, $47 billion of

[00:30:58] run rate. That's how much they made this. If they made $47 billion. It's meant it is... But these are, I mean, they're sophisticated investors doing it, right? I mean, what? I'm sorry, mate. No, I'm just saying like, to a regular person, the idea that it's like forward-looking revenue might seem unusual, but investors are very aware of what like 2025 revenue is versus like taking... Are they? Yes. I mean, are they getting audited

[00:31:25] financials or are they getting a PowerPoint deck that Daria Amaday doing the Elizabeth Holmes CEO voice shows good look at this? Do you think the books are cooked? I don't think the books are cooked. I think that at the end of every presentation is like the, like a font size three series of disclosures that are like on a non-gap basis. Yeah. If they even show... I mean, they're going to go public. So they will have to reveal gap financials. I cannot wait because my God,

[00:31:52] would it be nice to stop having... No, I don't mean this one, but you know what I mean? It's constantly defending this, but here's the thing. You have been saying that we are in a bubble since at least 2024, right? Yeah. I mean, what have you gotten wrong? Like there... What have I gotten wrong? You've been negative in a period where everybody else has gotten more positive. Oh, I definitely flubbed how quickly it would end. I will fully admit like 2024, 2024, I was like, it landed three cores. I'm just wrong. So what? Like over and over again,

[00:32:19] you're like, this is the peak. This is the peak, right? And then early in 2025, I very specifically was like, I'm not going to call the peak anymore. You just did call the peak again, right? Didn't you do like it's a bubble? The top is the IPO. It's 100%. I mean, Paul Kudruski agrees with me. He's excellent. Like you just had him on the show. Like the Google equity raise, Google raising $84 billion on the public markets, like a goddamn middle low tier cap company. Come on, man. But the thing that is, I agree with you. That is a potential bubble sign. I think

[00:32:48] though, but not in the cynical way you're putting it. It's like Google's like, wow, people really think we're worth a lot of money. Like let's get some cash. No, I mean, it's their share price. They're allowed to get money, you know, to raise money. I mean, they're allowed to do it. That's perfectly legal. I'm just saying that they're doing it because the credit markets are drying up. That's 100% what's happening. Well, their equity is super inflated, I guess we'd both say. So they can get a good deal raising money at a high equity price. Sure. But also the credit markets are running. Like that's what's happening here. And that's one of the big things that people really

[00:33:16] don't want to talk about, which is let's say Nvidia does the thing they're meant to, which is they're meant to sell a trillion dollars of GPUs by the end of 2027. Yeah. Sell. By which I mean, put in a warehouse that would, I think that's like 15 or something gigawatts of capacity. Data centers aren't being built at that scale. We are watching one of the biggest Kickstarters, a pre-order campaign of all time. For there to be a trillion dollars of sales, we're going to need like $800

[00:33:43] billion worth of debt. We're going to, putting aside cynicism or anything, just dollars to donuts, we're going to need $800 billion worth of debt. And for Nvidia to keep growing at their current rate, in a couple of years, they're going to have to be selling quarter of a trillion dollars worth of GPUs in a quarter, $250 billion in a quarter. And again, the cashflow required to do this is so severe that Microsoft, Google, Amazon, and Meta all having to raise debt, all having to do dodgy little,

[00:34:09] what's it called like off-balance sheet deals. Putting aside however we feel about AI, that is more money than I think is out there. People will say, oh, the money's on the sideline. There was a story in the FT saying banks are worried about choking on data center debt. For this to continue requires an astronomical amount of money. And then there's the messy little question of why. What does building more capacity do? If OpenAI had 10 gigawatts more capacity today,

[00:34:36] what would they do? What did Anthropic do when they got access to Colossus 1 from Elon Musk? Oh, they are up the rate limits on Claude. Yay. I mean, the model, the consensus is certainly that the models have improved. Most people think the scaling laws are holding true, that the larger training runs you run, the better output you get. I mean, you might severely pulled back on pre-training. Isn't it more possible? I mean, there are scaling still the training runs. Obviously, there's like...

[00:35:05] Yes, but like they're scaling on inference with chain of thought reasoning and also pre-training is because it's still happening. Right, right, right. It's all of the above. They are coming up with new techniques to improve the models in addition to trying to run larger models. Right. Then why, here's the thing though. Then why are we only really talking about code? Like that's, that's a very important, like we really only talk about code and there's, it's because there's a ton of training data for code. There's a lot of people that can post-train the models. I mean, I do think like a key, there's clearly a receptivity

[00:35:33] among like consumers to the argument you're making. And my view on it is that AI requires like proactive work. You know, you have to go out, you have to use the models, you have to find things you want from them. Still, I mean, when we talked last time, you were really emphasizing that OpenAI was like the only one. And now the top three apps, I believe are like ChachiBT, Claude and Gemini in some, some order. So it's not just one, like you've argued in the past.

[00:36:00] So that is another thing that changed. But, but the core issue is that there's a large audience of people that are like, I'm not, I don't, it's stupid. I used it a couple months ago, like hallucinated. And they're not like investing like the energy and time to like get value out of the models to the extent they experience it, they get it through Google and they underestimate the extent that they're getting LLM answers in the first place. So they're sort of using Google results, forgetting how the product used to work, how it is now. And sort of, it's pretty easy if you don't

[00:36:29] pick up the app, use it and spend time on it to say nothing's happening. And so people like this story of they don't derive any value, but then I feel like I'm using a fairly, like, it's not like I'm building, you know, like I have like my own server at home, you know, I'm using Cloud in a pretty straightforward way. And I find that I derive a lot of value from the subsidized thing. But also, were you what, just, this is not an insult. Were you describing, no, no, no, no. I really want to

[00:36:54] be clear. This is not, it doesn't sound very intelligent. You are, you're front loading this by saying, well, they haven't made the effort to make these things useful. That doesn't sound like AI to me. That sounds like a kind of scam. It sounds, it sounds like something that some, that someone on these model companies would say, because they know that they can't just give you something and use it. When I used the first iPhone, it was one of the first people to buy it, State College, Pennsylvania, singular wireless, that thing immediately realized, holy crap,

[00:37:23] like this is this future. Using AI to this day, and I've played around with it. I've used it. I've really tried. I've thrown a few models in it, messes things up all the time, still does it. This is the same fight we had last time where, I mean, you sort of presented AI as if it had been sold to the world and we've been like conned into it. But, but the iPhone was significantly marketed. Like it took the world a long time. Like most people did not use the first iPhone.

[00:37:50] I sold 4 million of them in the first year, man. Most of the, yeah, because it was limited to a single carrier. I mean, I'm sure ChatGPT has more users than the first iPhone. Yes, but it was limited to a single carrier, singular wireless, and didn't come to Verizon, I think for at least, I think it was six months to a year. But Apple is a great marketing company. Like the iPhone was sold to America in a way that like the AI providers are fairly clumsy marketers. Like you think the AI companies are good marketers? My man, what are you talking about? Everyone has been talking about

[00:38:18] AI nonstop for three years. Because as a culture, we're interested in it. No, no, no. Because it is the stuff of science fiction. Like, come on, man. It is not the companies forcing us to do this. It is something that humanity is in every country in the world is interrogating. Eric, since 2023, we have stories in mainstream media saying we should be scared of these things. We've had stories in the New York Times saying, turn it off because it conned a task rabbit when it didn't actually do it.

[00:38:45] We have had it shoved in Google. We've had it shoved in every app we use, every single goddamn app, summaries, generations where we don't need them. Shoved. It's taken over Google search. It's been crammed in everything. Every company has been saying, well, if AI is coming along, it's going to take your job. Every news outlet has been talking about AI, AI, AI, AI. And it's been done on the terms of the company. Every product is forcing you to do Apple intelligence. That was not a consensual delivery. Right. Apple intelligence, I'm negative on. Certainly.

[00:39:13] They forced chat GPT on people. They forced AI on people. Google. Microsoft puts co-pilot everywhere. How can you say with a straight face that this was a consensual exploration of this tech? People were not told, take your Nokia 3210, throw it in the trash or you'll lose your job. Take the iPhone, you pig. That never happened. There was no pressure campaign like there has been. There's been this thing of... But my point is that it is a sort of, certainly there is pressure. Like bosses want

[00:39:42] employees to use AI because they see value. But to me, when you say it's sort of like a marketing effort on the part of the companies. Like I think this is a organic idea that people are excited about something. I think you are ignoring things. I think you are ignoring the fact that both Altman and Amaday have... The Super Bowl ads that they ran, like Anthropics Super Bowl ad was like not effective. It was like one of the worst ads that's ever been run. Like they're bad marketers.

[00:40:07] Except they managed to pretend they weren't involved in the war in Iran. And then I managed to get Katy Perry and a bunch of other people to say, je suis Claude, because nobody paid attention to the fact they've been working with the government since 2024. But the point I'm making is Altman... That was a pretty big veer. I mean, that's what happened. But we can get back to that in a second. The point I'm making is Altman has been doing the big scary technology thing. Mira Morati was on The Daily Show in November 2022. Like this is not something where... This is

[00:40:36] manufactured consent at scale. iPhone didn't happen. I promise you, because I remember being a PC zone magazine and turning to my editor and going, we should write about apps on smartphones. Honestly, it's not going to happen. I'm serious. I don't push back on this because I'm just being a hater. I push back on it because it is. It is manufactured consent for a technology and an anti-labor technology at that. And more importantly, the media fell for it. For it to be anti-labor, it needs to work.

[00:41:05] No, it doesn't. Why? It just needs to be an excuse. But if the jobs weren't needed in the first place, then... What do you mean the jobs weren't needed? You know, I'm saying it's an excuse to fire people that the companies were not finding value from. How do you know they weren't finding value? No, I'm asking you. It's an excuse to do what? It is an excuse to start chopping off some people arbitrarily in a way that's damaging the companies because they overhired in 2022. But isn't it for companies to decide... Like companies hire and fire based on the value?

[00:41:32] I mean, I advise you speak to people from Meta and Oracle right now. Oracle right now, I think like 40% of their renewals aren't happening because they fired the people and forgot to reassign them. You have jobs that aren't getting done across data centers. They're just hiring and firing. Executive AI psychosis, basically. It is. It is. So think of it like this. You are a guy who makes $50 million of RSEs and cash a year, probably more on the stock side. And you have this chat bot. Every idea you've ever had, it's like... You're a genius.

[00:41:59] You're the smartest man. That's exactly right. You should fire all of them. You don't need HR. And these people, they get this tool and they're able... AI. So Mobitar, fantastic. You're very pro-right. Like he likes AI. Fantastic YouTube guy. Has a good point that AI is really good at a demo. It's good at making like a very rough thingy. And that's all it takes to brainwash an executive. Nick Suresh quote. All you need for an executive is to cobble something together that looks close enough. And these people don't do work. Most CEOs go to lunch, leave lunch, and

[00:42:28] barely read their emails. You know, one thing I... Self-driving cars are obviously a very relevant... They're interesting. Yeah. And so, you know, self-driving cars, the industry tried to get us very excited, you know, like a decade ago and they weren't ready because there were a lot of edge cases. And I think there's similarity to what you're saying with, you know, the demo idea, which is basically a demo. It's like, it's mostly there. And then people way underestimate how much it takes to get it all the way there. Imagine if they used Waymo and just fired all the cab drives. Every single one, it's like,

[00:42:57] this is good enough. And then you just, every time it rained, you would just have the Waymos jumping in. But then it'll be a great opportunity for every business that doesn't. Like that's just like competitive dynamics in business. What business? What do you mean? What do you mean? Like businesses that hold on to their... If your case is that all these companies are going to find that they needed those employees, the companies that don't react this way will outmaneuver them. Like that's... Yeah, probably. That's competition. Yeah. That's in fact, in the ERP space of the Oracle, they're finding a bunch of churn to other ERP, enterprise resource planning companies. That's happening. I think Meta could

[00:43:26] die in 10 or 15 years because of the things that Zuckerberg has done to it. I think Microsoft, people at Microsoft are miserable because co-pilot, co-pilot, co-pilot. Satya Nadella, MBA. Sundar Pichai at Google, MBA, McKinsey. Mark Zuckerberg ran on MBA. Tim Cook, MBA. I think John Ternis is MBA. Point is, these people don't experience problems, so they have no idea. So you're saying Zuckerberg has an MBA? He doesn't have an MBA. He hasn't written a line of code since 2006, apparently. Okay. Which is really funny. But getting back to the main point, which is,

[00:43:57] I think people credit CEOs with way more intelligence than they have. I also think they think that these guys at the top, like Google, for example, they'd never make a big mistake. Go and use Google right now and tell me if that looks like a good product or it looks like if a startup released a search engine that randomly responded like a weirdo to you in 2014, Michael Arrington would have them shot. I just, I don't get the, like, moral lens. Like, if these people are wrong, they're going to lose a bunch of money. And like, if you don't like them, you can be happy about that. Like, what's,

[00:44:26] what's the issue? Like, they're trying- The tens of thousands of people losing their jobs. The people across the world who have spent three years being scared, being told this thing that cannot do it, but regardless, still, as if it could, being fired, being threatened. They open the newspaper every day. There's a completely fictitious story about- But which is your, like, strategy for them? Like, are you long, like, just like human heavy businesses or like- I'm long humans. I don't invest in the market. I, people have this weird thing like, oh, what are you showing? I'm not. I like writing. I actually

[00:44:56] care about this stuff. And I think people love, love- But I'm just saying, what's like the positive vision for, for the world? What, what do you mean? Well, I'm just saying it's very focused on, on sort of like the negative, like my view of the AI industry is that people are building interesting stuff that they think, I mean, I do think it will help with drug discovery, research, all sorts of- Not really generative though. You're, you're mushing- There are, there are different techniques, some of the techniques are- But that's the thing though. But there are different techniques, like-

[00:45:23] That's the marketing technique though. They want you to think all AI is one blob, when AI is many different things and the thing everyone's talking about- So your, your criticism is specifically text, generative. Generative AI. So video, video- But like the techniques that like allow for AlphaGo and AlphaFault. AlphaFault I do not believe is generative. Right, but there are, there are related techniques involved. And like, certainly Demis, like who is creating them is across all this stuff. Are you skeptical of him? Yes. So Demis, no about all-

[00:45:53] Demis, a Google- No about all science. Yeah, but chemistry. And, and also AI techniques within chemistry, not generative. Every time I see him on stage, he's like, I think that in 10 years, the computer is going to wake up. And irresponsible, disgusting. I think it's disgraceful to constantly do this thing of, AGI is just 10 years away. You want to know why they're talking about AGI? Because the current stuff isn't good enough.

[00:46:19] Do you not, like, I don't find a lot of coders who don't, like coders seem to believe that this is impacting their career. Like, do you disagree with that? Some are. I, I, Karl Brown, the Internet of Bugs puts it where it's like, it makes the easy things easier, the hard things harder. There are people that use it who just ignore the outputs. And the thing is, you can make really clumsy fudge software that sort of kind of works until it doesn't before LLMs. There was a bunch of software within major hyperscalers that just didn't work.

[00:46:48] This has compounded that problem. The problem with LLMs, one of the most dangerous things about them is they can make an incompetent imbecile seem semi-competent. They can hang around and do more damage. It's far more damaging to have someone with an LLM who can't code or can barely code, who is able to pretend they can and get responsibility than it is for that person to just not be able to do that. Not to sound like a tech person, but what you're describing is, you know, democratizing intelligence.

[00:47:16] You're saying that there's someone who doesn't know how to code, that you're giving them more capability than they had before, and that there's something wrong about that because you want to keep it as the reserve of the super intelligent coder. You are crediting the models with intelligence they don't have. You're not giving these people intelligence. You're giving them a very complex autocomplete, which is still what it is, and people love that when I say that. You are giving them something that can do things that it's seen before. It can do shit off of Stack Overflow or what have you.

[00:47:45] It can do things from its training data, and it can, when you hit it enough, produce something that will work. Work does not mean stable. Work does not mean functional even. Work means functional enough that no one gets on their back. It's democratizing coding to give an imbecile something that is as dumb as it but able to do a better impression of someone who can do their job. And I think the problem is that people conflate those things.

[00:48:08] If these models were literally perfect, if they never made mistakes, if they were always good, we'd be talking about something else. What would it take to change your point of view? Like, what could come true? They would have to solve all hallucinations forever, which they are completely incapable of. But humans make tons of mistakes. Like, it is very inherent to reasoning. Like, one of us is engaged in logical fallacies here. Like, it is normal for human beings to think incorrectly. The mistakes a human being makes, they learn from.

[00:48:37] When a human being makes a mistake, even if they don't know they've made it, when they find out the error of their ways through trial and error, they can fix it. Some imbeciles don't. Some people don't. But they can learn. Models have no capacity for learning. They have none. The mistakes they make are mathematical certainties. They are not – the mistakes a human being can make can be mitigated. They can be learned from. They can be trained into in a way that models cannot. We do not learn just from having knowledge crammed into us. Your bar is no hallucination. None.

[00:49:06] No mistakes of any kind. Why should that – this just seems like – humans make mistakes. Like, even the best mathematician would feel – This is meant to be artificial intelligence. They've sold this as the most powerful thing ever that's going to replace 50% of white-collar workers. Why do I have to lower my standards when they keep raising them? This just seems like you don't want to have machines involved. Like, it seems like a truly impossible bar. I use the computer all the time. Then what's the problem with AI? Why do I have to accept mediocrity? Like, that's my question.

[00:49:35] This just is – I'm so dug in that nothing could change my point of view. We're talking about artificial intelligence, man. And you're saying, can't it be a little stupid? You're saying, can't it be perfect? You're saying the date is perfect. I will accept it. They're talking about replacing 50% of workers. If they were selling – But most of those workers, myself included, make many mistakes. Like, the bar for perfect just seems, like, totally outlandish to me. It should be – Based on their promises, no, it doesn't. Based on what they've been talking about. If they had solved this from the beginning, it's like, hey – Who is they? Open AI.

[00:50:04] These companies disagree. They dislike each other. They're competing. Yeah, and they're all lying about – They're all talking about some theoretical future. Demis, Sam Altman, Dario. They're all doing the same crock of crap where they're just saying, this will do, this will do. When it does this, if it – They're all talking in theoreticals. If they have, from the beginning, solved this as a kind of experimental project that we need to be really careful with because it can make so many mistakes. And they were like – That's basic. In 2023, that's how it was. That's not –

[00:50:34] There were people who didn't want to release. My man, go back and read the stories. I'm so sorry. I mean, there were people who are optimistic. The employees themselves are true believers. Like, there's a whole – Go back and listen to Sam Altman. But it was called GPT-3. Like, it wasn't branded in this amazing – I feel like you tell this story of, like, great marketing. They're bad marketers. In the system card of GPT-3.5, I believe, there was a story that a task rabbit had been manipulated by the model autonomously.

[00:51:03] When you click down into the meter study, it turns out that they had actually prompted it, asked it what it would say, preferentially chosen the responses, and then they intimated that they never did the experiment at all. They used that and allowed multiple different stories – I think 14 different stories – to talk about how ChatGBT-3.5 had convinced – maybe four, actually. It was the 2023 era – had convinced the task of it and blackmailed them. Anthropic has multiple times done these stories saying, Claude blackmailed someone.

[00:51:32] And what actually happened was they trained it to do something and it did it. It escaped a sandbox. Actually, it wasn't in the sandbox. They didn't escape it at all. So, yeah. Actually, I do. Don't you want them running safety tests? Like, the tests are meant to see if the models can be manipulated to do damaging things. Then if they're running safety tests, why do they keep doing weird manipulative things about them blackmailing when they're not – I mean, white paper – like, just most Americans – nobody is reading – these white papers are for people like us.

[00:51:58] Journalists are reading them and then reporting them in the most preferential way possible, which then informs regular people. So, yeah. Actually, I do think my standards of – Most Americans – like, Americans are largely skeptical of AI. Like, they've failed, right? Don't you agree? Like, the polling is negative. Americans – Here's the thing. Most – Most Americans don't run CEO – don't run companies and most CEOs believe this because most CEOs are disconnected from labor. That's the problem. Venture capital has predominantly funded AI because of things like this.

[00:52:27] Venture capital has failed to do the due diligence necessary because venture capital has Uber brain. Venture capital thinks that burning money is good. And I think it's perfectly reasonable to ask for perfection because that's how they're selling it. They are selling it as autonomous intelligence. In your view, a bunch of rich people who are deluding themselves are lighting their money on fire. Like – Yes. What's the problem? Like, you don't like the rich people who are lighting their money on fire.

[00:52:53] Because they're covering our country in data centers, ruining local environments, gas belching turbines that are ruining black neighborhoods. They are stealing from everyone. They are threatening everyone constantly in the media. And when people say they don't like this stuff, someone in the media will condescendingly say, oh, you just don't get it. You haven't used it. You need to do this. You're so stupid. You moron. Why don't you like data centers? Yeah. I think that that's pretty bad. You're the one – you call people imbeciles, morons.

[00:53:22] Like, I'm not the one doing that. Like, I feel like – I'm not saying – I wasn't talking about you, man. I was talking in general. But journalists are much more measured, I'd say. Go read the New York Times, mate. Go read how the Times talks to people when they don't like AI. Come on. Where? When? Casey Newton and Kevin Roos, mate. Come on. Look at them on Twitter. You're engaged. I actually don't thought – I'm not doing the horse – That is a personality – I'm genuinely not doing horse trading here. I'm just saying it happens. And that has a real effect on people.

[00:53:51] And I think the tech media needs more empathy for regular folks, but also a genuine connection with how this has been sold. This has been sold as autonomous. It is not. It's been sold as intelligent. It is not. Like, it's sold on a lie. And it's now inflated markets. When we talked nine months ago, you were very negative. My view is that the models have only continued to improve.

[00:54:17] I think the funny dynamic is that your crowd continues to not see that anything is happening. Like, you're – Who is my crowd? I don't know. I don't know. Anonymous YouTube fans. Like, you're a very popular guy. Right. Right. Yeah. I think you are selling something to a media consumer that they want to hear, that nothing is happening. But you've been doing it year after year after year. I think what is strange is that optimism is framed as never – not saying yours here.

[00:54:44] That optimism, which I actually don't think most of the AI industry is doing. Blind hope in technology and appreciation of capitalism is framed as a moral good. That all investment is progress. And skepticism and scrutiny is framed as other and different. Yeah, the fact that you say you're people, but you can't even designate the group makes me wonder who that actually refers to. Regular folks? Who am I? I don't know. You know your audience better than me.

[00:55:14] You use the term. No, no. I'm like, who are your – I just see them online. They're a bunch of anonymous accounts. I talk to a ton of people in the tech industry. I have a ton of tech works. Yeah. I have janitors, line cooks. I have teachers, ton of teachers, ton of people in academia. I talk to people in the industry. All the time. So, yeah, man. I think the thing I represent is a person who loves technology watching the tech industry ruin it.

[00:55:39] I just think we hunger for what are we creating as a society? What is our society moving towards in a world that is post-religion for the most part, is unsure of what Armenia is supposed to be, is looking for some sort of forward direction? I don't think AI is perfect. Society isn't post-religion? What are you talking about? I think – Church going is down, but there are many spiritual people, tons of them. What is your – Anthropic just meant the Pope, man. What are you talking about? Like here's the thing.

[00:56:07] I'm just saying the critique of negativity is we need some sort of like if not this, what are you proposing? Do you think that the AI industry is a hopeful thing? We're going to – Yes. Yes. Dotting the world with data centers for a product, ruining local environments, spending more money than there's ever been spent on anything for a still theoretical outcome, for a still wholly theoretical outcome. Do you think that what we have today with large language models is worth a trillion dollars? God's honest truth. Worth a trillion dollars. The thing – because that's what it's cost –

[00:56:36] All of AI? What we have got today with – yes, with large language models specifically because that's where the trillion dollars went. Yeah. Yeah. I mean we have – Why? I mean multiple companies that are going to get valued certainly. So strictly on an equity basis you believe. Right, right, right. Yeah. That's how things are – The net is where – What are you asking me? Like are they going to be valued over the next five years for more than a trillion dollars? It seems very likely. Okay.

[00:57:01] But that's not where the trillion dollars – the trillion dollars of CapEx like the trillion dollar – it's more than a trillion when you include investments via venture capital. You include the debt as well. Right, right, right. Obviously the market is saying it's worth way more than a trillion. Okay, I don't care about the market. So I'm saying a trillion is easy. Yeah. You think that this is worth it. Say it stopped today and this was where we – this is theoretical. Say nothing else happened. Would you say that this was worth it? The trillion dollars that's being spent. What we have today. Forget the equity. That it was good. That it was money well spent.

[00:57:31] Is that what you're asking? Yeah. Yes. Thanks for sticking around with our second face-off with Ed Zitron. I think he held too high of a bar for perfection there at the end. This is the Newcomer Podcast. If you want more, please check out the substack, newcomer.co. If you really want more, I have another show, Cerebral Valley Show, where I chop it up on AI with my Cerebral Valley co-hosts. It's on YouTube and everywhere else you get your podcasts. Like, comment, subscribe. We're building this channel out.

[00:58:00] Tell us who you want to hear from next. Tell them to come on the pod. Thanks so much. We'll see you next week.