Ed Zitron: The AI Bubble is Bleeding Cash, Here Are The Receipts

Got a very special conversation. I am speaking to one of the most prolific skeptics about AI. I'm joined today by Ed Zitron, author of Where's Your Ed at Newsletter and the Better Offline podcast. Ed, welcome to monetary matters. Thanks, Ravon May. Ed, how about you start off and lay out your view of artificial intelligence and what the return on investment is for the enormous sums that are currently being spent? So, trillion plus dollars in, the fact we're still debating the ROI kind of says everything. I think if this was a real industry with the kind of tam that they've been selling us on for the past four years, we wouldn't have that debate. There wouldn't be one. The fact that we're having it, the fact that you have people to this day in the year of our Lord 2026 saying, "Well, AI's real." That, I'd never heard something real where someone has had to insist upon that. Economically speaking, the vast majority of AI companies barely make more than $100 million a year in revenue and through an open AI account for I think 89% of all the top. And on top of that, they're all horribly unprofitable. They lose billions of dollars. And the only way that they can reconcile those is by doing some, what I would call wacky accounting. And so, in my reporting from earlier in the week, open AI spent $34 billion to make $13.07 billion. And yeah, their net loss is just a little under 21 billion. They are, of course, with the FT given comment that suggests that they only lost 8 billion. I think that's laughable. I think that that was boost the key jingling. I think that that was put in there because they have to find a way to reconcile with the fact that they told CNBC they lost 8 billion dollars at the beginning of the year when it's very clear they didn't like put aside whatever fanciful quotes there are that they lost 21 billion dollars. How much by the way, you know, has Uber or Amazon lost when they were losing tons of money and before they became the giant behemoths. And how is that compared to the, as you say, 21 billion dollars that open AI loss last year? Okay. So Uber, I believe, burned like 32 billion dollars. They now are kind of like messy, gap profitable. A lot of that was sales and marketing and a lot of that was subsidizing rides. But the level of subsidies they did were just completely different. The Amazon Web Services example is the really egregious one between 2003 and 2017, I think it is normalizer inflation. Amazon Web Services was maybe 53, 55 billion dollars total. And that's all of Amazon's capex, not just AWS. Just to give you some context, if open AI closes all the money, they've been promised this year, they will have raised 122 billion dollars in the last six months. If anthropic closes all the money, they've been promised they will have raised 95 billion dollars in the last six months. Open AI raised 40 billion dollars last year and the topic raised 16 and a half billion dollars. The answer is incomparable. Like Amazon's capex for its retail operation on top of AWS was less than half of what open AI has raised in one round this year. Like it's just there's no real comparison. People making this comparison are looking for a means of rationalizing the irrational and of finding a way to not reconcile with the fact that the two largest companies, the pretty much the only demand for AI compute between anthropic and open AI. Those two companies don't make sense. They don't make economic sense. The only way to make them make economic sense is to just ignore your lying eyes. It sucks because I think that the tech industry has been poisoned with a very specific kind of ideology. And I think the irony is so many of them are atheists turn hard rationalists, but they have a quasi religious attachment to artificial intelligence. Every minus sign is proof that the great prophecy is true and every little point that could possibly a tactic is considered unilateral proof that they're wrong. And it sucks because they're trying to dump these companies on retail investors who are going to be the victims of this theological hype cycle. I think you're exactly right. I mean, it was a senior executive and co-founder of Google who said that they would rather go bankrupt than lose an AI. They're committed. Yeah. And all of that, I think comes from this theory called the rock-com bubble, which is tech is out of ideas. When I say that, I don't mean they literally have none. I mean, they have no hyper-growth ideas. They don't have a new day, AWS. They don't have a new iPhone or a new smartphone. They don't have a next cloud computing. An AI was meant to be the panacea. It was meant to be AI models as in via the API were meant to be the thing that created the next generation of startups that created the next generation of enterprise, bolt-ons, software industry could grow. It was meant to be the future of consumer software. And it was even meant to be the next AWS in the form of AI GPUs, which is why they've sunk so much money into it. I think that the hyperscalers meta, put aside, I don't think meta really has a strategy. I think it's just like spend money until Mark gets bored, but Microsoft Google Amazon, I think they saw this as an opportunity to create the next generation because they don't have anything else. There is no other business line that shown anything close to the possibility of having the growth, just talking pure growth. And the worst thing is is that while they've seen some growth from AI, it's mostly because Anthropic and OpenAI spend an alarming amount of money on compute, which means that they're basically just feeding their money back into themselves and then feeding their own cash flows into Nvidia or Broadcom or one of the Taiwanese ODM, so on high Foxconn, the companies that build the servers in Taiwan. So it's rough. It's very rough because for me to be wrong, there needs to be hundreds of billions of dollars of AI compute demand. Put aside OpenAI and Anthropic, hundreds of billions of dollars because there are over a hundred gigawatts of data centers planned, 12.5 to 15 million per megawatt. The compute demand does not exist and the only companies that are spending that much are these unprofitable AI companies which are having their gaping holes plugged by hyperscalers. It's just, it's very rough and I don't see any path for it to normalize. It hasn't been normalized and there are tons of signs things are actually getting worse. One cork claim that you said, I just want to revisit, is that the losses experienced by the two largest labs in Anthropica and OpenAI AI right now, especially OpenAI are unlike anything we've seen in history on an unannualized basis. So as you said, if we can get into the $21 billion in losses in 2025 and I'll also add something that leads to another cork claim of yours, which is that, you know, it's no secret that Silicon Valley has funded startups that are initially unprofitable, but the reason that they some of them work out and work out tremendously well is because they're software type businesses that have low costs and in particular, they have low variable costs. And you pointed out that that is not true at all about OpenAI, that they are tremendously expensive, not just to launch and start, but to operate and maintain. Yes. And one other thing with those, glad you brought up variable costs, that's actually a big problem, both for the customer and for the AI companies themselves, the labs. So the information reported the OpenAI's margins actually worsened, didn't specify in the piece how much worse in 2025, because they had to get last minute compute, because it's kind of you don't just say, okay, I'm going to buy this much compute. And then if I need more, well, I can just do make do, you have to expand, because inference on its own is so compute intensive, it's not training this take up, it's if they have an influx of customer demand, which doesn't mean money. It just means if some customers have variable, if some customers are using codex more, that will take up more GPUs. So they have to buy last minute compute, buying compute upfront, much cheaper than buying it on the spot. And especially because they need so much of it, whoever's selling it to them naturally has an advantage and can charge them more. So you have all of these variable costs, and also that Silicon Valley, I don't want to call it mythology, because it's happened. The losses are just much smaller, like snowflake, for example, snowflake is not a profitable company, but it's not horrifying, it's something that can keep going, they have some degree of venture debt, they can keep plugging away, they will eventually make it. I don't think snowflakes going to be an incredible margin business, but I think it will get there. This is not like that. And on top of that, no one has a path. No one has been able to explain, we are now approaching Vera Rubin, we've had Blackwell for a year or so. Where's the cheapness happening? Whatever happened to those open AIA6 and Thropic has been using GPUs for years, they're not working out their cosyther. There is just multiple things that just don't make sense, and the only reason it's continued is this kind of cult like this worship of capital, this cult like belief, that just if we believe in this enough, all of the bad stuff will go away. That doesn't really work at this scale. There's no government bailout opportunity here, there's not really any, there's at some point going to be a limit to how much capital you can sink into this. And Thropic's lost round there, like. 30 people in, or 30 entities invest. It's just things don't look good all over the shop, but I've really looked for good signs, and I can't find them. - Tell me what you found when you got access to open AI's financials for 2025. What, just how big are the losses? I know there's various ways of measuring them, and then there's, you know, there's some pushback from people who are kind of on the other side of this, and I wanna get your feedback to that. - Sure, so revenue was $13.07 billion. Cost of revenue, which is a very interesting way of referring to costs, which I'll get to in a minute, $7.5 billion, R&D, $19.1 billion, sales and marketing. $5.73 billion, general admin, $1.57 billion. So you get total cost and expense about $34 billion, the loss of operations about 20.9 billion, roughly 21 billion. So that sales and marketing cost is the thing I want people to really pay attention to, 'cause it's very, very strange. So I went and dug around. Open AI did their first major ad campaign in September 2025. Go on. - I'm thinking the thought that you probably had when you were making this point of just like, yeah, that is sales and marketing. That is a very high number for sales and marketing. - It's more than Coca-Cola spends. Like it's more than Coca-Cola's annual budget. I know the Open AI does Facebook advertising, Reddit advertising, but if they spend, let's just say $1.5 billion on Facebook in a year, they would be one of the largest advertisers on matter. They would be a material customer that would likely get called out somewhere, couldn't find any evidence of that. What I can, however, say is that buried in the back of these documents is the specific, itemized related costs to Microsoft and Softbank. Now, $527 million of their sales and marketing costs went through Microsoft. Now, do you think that Microsoft is doing a bunch of advertising for Open AI? Or is it the free credits that Microsoft is giving away by the Microsoft startups program, much like Open AI for startups? And on top of that, I think they're putting free inference in there. But my biggest evidence, by the way, that they're doing that, is that I reported last November, that Open AI spent $8.67 billion on inference on Microsoft Azure. I reported that, and that was also validated by the FT. But nevertheless, this sales and marketing cost, they think is really interesting. Maybe it's where Open AI puts the cut that Microsoft takes from selling its models. Maybe it's the inference cost that Microsoft charges when they sell their models through Azure AI. I truly don't know. But what I can say is based on my previous reporting is that the cost of revenue is not all the inference. It's definitely not. I'm very confident in my sourcing from November. And also, what the hell are they spending on this sales and marketing on if it's not that? It's like, it's such a weird category and I sadly don't have a segment breakdown. Oh, I wish it did. - So the some percentage of the 5.7 billion in sales and marketing you think ought to actually be attributed to cost of revenue, which would make gross margins way less a lot lower than they would be? - Yes. - So, and to kind of elaborate on that, I think the way they're getting around it is they're saying their free customers are in there, which is weird because I've seen the information suggest that their margins include. So like their free users, so who knows? But it's very clear that this cost is inflated by something and it's also linearly increasing with revenue. So it's kind of like, where is this going? It's either them giving away credits and then putting the inference cost in there, which actually would make some sense or it's their free users or it's some Arnold Palmer of the two, 'cause it's not advertising. It's, they, I think I saw a point that said they had 500 sales people in 2025. What would be clear if you paid each of them a quarter of a million dollars? That'd be about 125 million dollars, which is not even like a third of 10, less than third of the 10% of this cost. It's bizarre and you kind of see similarly inflated sales and marketing costs when you look at Zipu and Minimax, the two Chinese AI labs that went public. Last year I think both of which are, their cost of revenue is lower than their revenue, but their sales and marketing plus their cost of revenue is not. And it's very interesting because this is probably legal on the gap. I'm sure that they've found, I'm sure Sarah Fry is a smart person. I think she probably wouldn't have something like this unless it was defensible. But nevertheless, this is not a company that is approaching profitability. And to consider their gross margin without sales and marketing is just kind of laughable because that much like the R&D cost is not going away. - I think it's a really important point. Also, as you said, that some percentage of these include Microsoft credits. Now when the FT quoted so-called people familiar with the matter, he said actually the true losses, we should say are more like 8 billion. I believe it's the case, correct me if I'm wrong, that they netted out a ton of, not just stock based compensation, but cloud computing credits from Microsoft, which if that is true, in my opinion, that is a real cost. Like if I get billions of dollars worth of free stuff, I think it would be honest for me to include that as a cost because you can't normally just count that as, oh my God, normally, yeah, normally I'm going to get tons of free stuff. You know, that's not how it works. - So the documents that I've seen do not mention those credits, there is no mention of credits. They do mention stock based compensation though. And what's interesting with that is their stock based compensation of, and during this memory, I'm definitely not reading something, of about, let's see, $6.4 billion, but there's share based compensation for compute provided by a related party of $1.2 billion. How strange. How strange. Are they trading stock for compute? That's also weird, 'cause that's the thing the point you just made is very salient, which is if they are getting compute credits and those compute credits are lowering their costs, that is still an expense, and it's also not a permanent one. Is Microsoft going to permanently feed them credits? Or are these, I think those credits are the ones left over from the $13 billion that Microsoft invested in 2023. Semaphore said that was mostly in credits, so perhaps it's that. The accounting does not say that. I also just, I actually think whoever made that, if that person is somewhat listening, go fuck yourself, just like seriously, like that is not what you lost, dude. You lost nearly $21 billion. You can dance around all you want and be like, well, when you move these numbers around and do this and do that, well, it's eight now. The only people you're doing that for are Twitter posters. Actual investors will be able to see through this. It's very silly to do this because when the S1 comes out, they're gonna have to define things. They're gonna have to explain what these things mean, at least a little bit more. And it's frustrating seeing that quote, because the FD does good reporting, but it was so blatantly obvious that someone from OpenAI, I assume I truly don't know who it is or where they're from, was going to try and feed that in there. But the only way in which OpenAI lost 8 billion is in, I don't know, the La La Land of their accountancy. And I just, I think it's offensive to just about everybody involved that they would say this because it's not what happened. It's, if the FD took that number, seriously, it would be in the headline, it's not the thing to focus on is the actual costs. And here's the thing, they're gonna run out of tokens from, as you're at some point. And now I also can confirm it's not in the story. They spend at least a billion dollars on Oracle last year renting H100s and H200s, I believe, and whatever exists of Abilene. Larry Alison isn't giving them any tokens. That's gonna cost them real dollars, core weave, who they paid net 360. They're not taking tokens either. They're gonna need dollars. And while they might be able to do this fuzzy moon math for one year or two years, I guess, with 2024. 2026, they've signed up with Amazon. They've signed up with CoreWe, with CerroBrus, with Google who is renting capacity from CoreWeave to sell OpenAI. They have all of these partners now that they can't really dance around with credits. So I think, I can't wait for the SS1. I really cannot wait. I just, I think it's very exciting that we're going to see more of this. And I think it's also laughable that anyone is seriously saying they lost $8 billion. Is the copolympics. - In your piece, there's some numbers you cite that are even larger than the $21 billion loss from operations. So the net loss, a tribute to the company of $38.5 billion. And then something even bigger. - The 60.5.1. I will definitely say those numbers are inflated by the conversion from before. From a non-profit to a for-profit, which is why I'm very much more focused on the $21 billion number. But something bizarre is going on with OpenAI's accounting. Just gonna say this, because this net loss attributable to non-controlling members capital, magic to weigh $3.7 billion worth of cost in 2024. Where did they go? Where did they go? I've spoken to a few accounts. since then, they also say the same thing, which is, yeah, that's still cost them something. They just put it to a subsidiary, I guess. It's very weird. It's all so bizarre. I've never seen anything like this before. I wonder, and I want to say I don't know if I'm right on this, if so that when a company goes public like an IPO or a SPAC deal and it has these warrant liabilities, and it's basically what the company owes to people who own the warrants. When the stock price goes up, the warrants that they owe increase in value. So these are on a app thing looks like a, and from a counting sense, it is a net loss, but it's not a true. In my view, like economic loss, so I wondered to what degree it's like that. And that's, I'm referring to the 2025. The 2024 is in the article though, just to be clear. I just said, yeah. Yeah, but just to be clear, I agree. The warrant costs those are non cash. Like there's, I would never, I put 38.5 billion in because that's what it says. But just to be clear, the cash loss is lower than that. That, I've been quite clear about the reason I bring up the 2024 one is they're lost from operations in that year was $8.7 billion, $8.78 even in 2024. But it became 5.098 billion because, well, you know, those costs go somewhere else. It's very weird. I thought for a second, it could be cloud credits. That might make sense, but it's not clear what entity that could be. Now, things get messier in 2025 because of the revaluation, because of the conversion. I get that there's nothing we can do. It's, you can't really pass out much from that. Nevertheless, they did magical, weighs 17.8 billion dollars, of course, which is nice. I do wonder how this is going to look in the S1, though, whether it's going to be the same, how they're going to find ways to fin, finagle or finesse this. I'm really don't have much clarity there. And the economic stress or the statements don't really break down anything about like the exact breakdown of where that might be going, or what entities might be. The thing to note, though, that they do, and this is in the article as well, is when they break down the open AI spent about 17 billion dollars on Microsoft Azure. That is a large chunk of Microsoft's revenue that's coming from a company that's going to run out on money. That is very, like a large part of their RPO, our RPOs even, 250 billion plus, is coming from open AI, a company that cannot afford to pay them. And even in 2025, a astonishing amount of money, just really like Microsoft's revenue is inflated by open AI. That is now fact. That's very bad. That's very bad indeed, because there was reported in 2025 that open AI's compute cost was sold at cost. That was with the information. Now that referred to A100 GPUs, so maybe it's not the same with A100s and B200s and all that, but happened at one point. And it's just, if this was a real industry, with the kind of things that were trending in the right direction, there wouldn't be so many asterisks. There wouldn't be so many weird things. You'd be able to say with your whole chest, wow, what a profitable company. Look how well it's growing. Look at the way it's triumphant marched towards profitability. It's not doing that. And in fact, I'm not sure what the plan is for this company given the most recent use as well. So there are people who are probably bulls and AI, believers and I, who may be watching this and say, Ed, if we were to wind the clock back to November 2025, basically everything you'd said, particularly about open AI, is true. They were losing an enormous amount of money. However, in December 2025, the models got way better and particular and thropic. And since then, the revenue has seen a huge upsurge. What is your reaction to that argument? Well, you know what also increases with open AI's revenue? It's costs. So fun fact about open AI's revenues as of the beginning of this year. So Sam Altman himself said that it was a huge issue for the customers, how expensive things got. So what happened was at the beginning of the year, everyone was still in these subsidized models. So they've subsidized subscriptions. Then I think in March, open AI started along with anthropic moving people to token based billing. This created a massive burst of revenue. And now I'm sure you've seen all the different conversations about ROI, Uber burn through their entire AI budget in three months. I think it was. They put in caps on their engineers. Nothing about that changes the thesis at all. In fact, the one bit of evidence we have most strongly is the costs increase with the revenue. The more they burn, the more they make the more they burn, that's been happening consistently across the board. People will say, well, look, the cost of goods isn't going up. Sales and marketing increased over 400% year over year. It's the fastest growing category in this company. Funnily enough, open AI has been giving away a thousand dollars of codex API credits to anyone, anyone who has a business. Anthropics doing the same thing with Claude code, by the way. So even if they had a burst of revenue, they also had a burst of cost and now they're going to have customers who are already pulling back on spend. Also, the Wall Street General reported the open AI is considering and I quote, drastic price cuts. That's not something you do when customers are showing anything other than an intent to churn. And I think, again, come back to it. If this was working, you'd be able to point to it and just say it's working. They wouldn't have to do all this kind of three-card Monty stuff with the finances. And I think that I actually think they're going to end subsidized subscriptions. I think that we're getting to a point where there's just no economic point other than marketing, which again makes me wonder what that sales and marketing cost is. It's just it's very, it's frustrating arguing against this because there's a great deal of there's a great deal of the arguments against me that are just nah, and it's like, come on, may like you can you can only say Nvidia super cycle and so many times before that actually has to happen. What about Anthropic? I think they're in the same place. So Anthropic, funny company, Wall Street Journal Story came out about a month ago that said, oh, they're profitable in this quarter and it was because Elon Musk sold them colossus's compute and gave them a discount for the exact two months they were profitable and they were profitable by like a couple hundred million dollars. So 1.25 billion dollar a month contract. The math is pretty obvious, but Anthropic has the same thing as OpenAI. They're revenue increases, they're costs of good sold increases, and their sales and marketing increases. It's the same thing. I genuinely think sales and marketing is the the slop trough to put the cost that they don't want to put up on the top line. I think it should still be considered the cost. I don't think that that changes very much, but it exists only to baguille the easily baguille to convince the people that want to be convinced that this is all going well. The problem is also they both of them have actually increased their sales and marketing costs on top of this. Anthropic is one of the most aggressive influencer campaigns you've ever seen. Both have increased their ad spend to an indeterminate level. I see them all the time on my subreddit. So I think Anthropic is in much the same position. And Anthrop, sorry, OpenAI was doing those drastic price cuts because they believe Anthropic will do the same. So it's AI needs to keep accelerating and it's already kind of slowing down. And I think OpenAI and Anthropic are basically the same company. I think they run in similar ways. I think Dario Amadin Samuelsman are similarly fabulous people. I don't think either of them believe in very much both of them are terrifying boys, at least Jensen Huang's funny. At least when he gets mad about stuff, you're kind of like, oh, oh, is he going to smack this college student? At least Hawk Tan has a kind of like emanating aura from him that's terrifying and horrible. Then you have to hit God damn Samu. Then everyone Samuelsman Dario Amadin, they all say the same thing. They're all doing the same song and dance. You'll notice that both of them brought up recursive self-improvement recently because they're both giving up on coming up with ideas. They're like, our idea is the machine will come up with the idea. And of course, Jingle Jingle to the AI Boasters, AI that trains itself, we have no proof that this will happen. But yay, we can repeat this and be part of the club. It's a shame because only so much can be done on hype and hope. What do you make of the claims of ARR for Anthropic from $9 billion, anyways, recurring revenue to $14 billion in 2026 to what a month ago they said it was $42, $44 billion over $44,47 in their series age announcement. So the information thinks, "Remove day over there, fantastic reporter." The way Anthropic calculates its annualized run rate, which is not ARR, I made this mistake too. ARR refers to annual recurring revenue with stable contracts. But putting on that side, annualized, they calculated by taking that day's subscribers and multiplying them by 12 and the last four weeks of API spend and timesing it by 13. So the problem with this is, API calls model spend is not a recurring expense. It's not. Perhaps it's something you can extrapolate from. Perhaps it's something you can say, yeah, we got this much. Perhaps you have contracts with people that say they have to spend X amount in a month in a year, even or within the three month period. I'm not part of their contracts. But yeah, that number can be manipulated real easy. For example, Axios is Madison Mills reported that 500, someone spent 500 million dollars in the space of a month on Claude because they did not set up, well, somebody, I mean, an enterprise company spent that because they didn't set up spend controls. If you use that times 13 mathematics, that's $6.5 billion in annual run rate. For a cost that will never come back, we are, they were measuring based on the token maxing era that is coming to an end. This is the problem with run rate. It's a problematic measurement and it's very weird and kind of telling that the only two hyperscalers who have ever talked about AI revenue, a Microsoft and Amazon and both of them only use run rate because run rate is a snapshot. You can have a $47 billion run rate, then API calls drop and suddenly you don't have the same thing. But because that's out there, people will think, wow, they're going to make $47 billion in the year. When I think the Wall Street Journal reported they made $4.6 billion in Q1 and they're on course to make a little over $10 billion in Q2, which are large amounts of money. They are not $47 billion. In fact, they would have to keep growing at a remarkable rate that is not going to happen to get to $47 billion in the year. In fact, there are plenty of signs that things are slowing down. If they're talking cost cuts, then they absolutely are. They've seen something they saw that's genuinely bothered them. Shout out Madison Mill. I know her and that's great to hear you. No, she's fantastic. She's awesome. So tell us about the pushback to token maxing and what do you think that means for real revenues? How do you measure them? To set the scene, the only way for these companies to grow, like I said, $1.1 trillion in compute commitments for open and an anthropic combined. I think that's through 2030. For them to actually make good on that, to actually pay their contracts, they have to keep growing and the only way that that can happen is through selling direct access to the models to enterprises because they're not growing that based on consumer subscriptions or even enterprise subscriptions. You're not doing that kind of rapid growth, which will mean I think OpenAI projects to be at $284 billion in revenue by 2030 and Thropic 174 I think by 2029. That's not happening unless they can charge just everyone on top of base billing. On top of that, they need everybody to be spending more and more and more. They need to get more customers and those customers need to massively increase. The problem is that everyone is being that all these executives and all these companies have been saying use AI as much as possible without making sure that there's a measurable return on investment. There isn't. In fact, it's quite difficult to measure the actual cost of an AI task because across different models, different prompts, different harnesses. If the jump from 4.7 to 4.8 with Opus changed how the models did stuff, and you pay regardless of whether they screw up or not. All these organizations went token crazy, Uber burned through their entire budget in three months, Zillow who I reported on burned through their entire cursor budget for the year by the end of May. Now everyone's pulling back. Everyone's doing limits. I think Brex, I reported out. I think there's 1.5 or 2K per engineer. Then it's like five bucks a week for non-engineers. Uber's 1,500. Limit for engineers. I hear data breaks are still letting people go nuts bananas, but good luck on that. Ali, I think that what's happening is organizations are pretty poorer measure and productivity in general. Except when they've had to measure it before, it wasn't coupled with a multimillion dollar monthly cost. It wasn't suddenly this massive operative cost explosion. I think what they're doing now is they've gone from no cost controls to cost controls. I think those cost controls get more, I think they start crushing a little bit because you've got open source models coming. Also, it's hard to measure the ROI. Say you cut from a million dollars to 500 grand a month. How do you know that 500 grand a month's good? How are you measuring that? Some people are saying lines of code. That's an insane way to measure. It's just not a good measurement of productivity. It's just a measurement of how much you've shipped. Pulled grass same deal with Vizillo who were reported on a few weeks ago. I think it was something like they increased the amount of review hours for human beings, but thousands and thousands of hours a month, they just added. Instead of replacing humans, they just added more labor for their other humans. It's a situation where I don't see how that transforms into the kind of rocket ship growth they need. I must be clear, however you feel about the current state of these AI companies and their revenues, what they're doing today is no one near close to what they need. They need to be rocking within a couple of years, 15, 20 billion a month. That needs to happen because if it doesn't, they cannot afford their compute. I don't think they can raise enough money and I don't think they're magically becoming profitable. How does that work? People are going to say, "Oh, custom silicon hasn't happened. It's not happening. When's that going to happen? How long do I have to wait?" You're saying people would say "Custom silicon is going to make it cheaper?" Yeah, they've been saying it for years. Another weird thing as well. Back in October last year, Broadcom and OpenAI said that they were going to do 10 gigawatts of AI data centers together. I don't think OpenAI is all to the single chip from them yet. What happened there? That's weird. That was really. That was just a weird. AMD said that they were going to do six gigawatts with OpenAI. It just never happened. SK high-necks in Samsung said that they were going to sell them 900,000 waifers of RAM a month. They also didn't happen. It's almost as if lots of this isn't real. What do you make of a Genetic AI and the claim that in the future, a lot of work is going to be done by AI agents. If five years ago, in a job of a lawyer or a tax-accounted profession, would be completely done by a human that in the near future, you are going to have computers who are working for that person sending emails, buying things from maybe from other agents, doing work. Setting all the numbers aside, which we've talked about, do you think that fundamentally that vision is right or wrong? I think it's wrong. I think that everything you're talking about there involves multiple different deterministic functions. Large language models cannot do those. You cannot rely on a large language model to replicably do something. You change your hardest, you change your model, you mess up a prompt, or it just misreads a prompt because it hallucinates, which is mathematically certain. That's not going to happen. Anything with money? That's not going to happen either. I think I've seen six different companies say that agents can buy with them. I can't find evidence of a single dollar being spent. I've heard people doing it with their open claw. Again, no evidence that's actually happened. On top of the incredible expense of doing this, which is not going anywhere, it's also just not happening. We're not getting signs. Let's have, I've talked to people who use Harvey or LaGoura or what have you. And I, it sounds like they're a rapper for large language models. It sounds like it's just, you can feed lost stuff in and we've got some custom prompt engineering that makes it do stuff. Perhaps that's useful. Perhaps it's not. I've yet to speak to a person who's super excited about it or even likes it. The people that I hear going on about those products always got them partners. They're partners at the top of the law firm who aren't doing the grunt work that associates do. Accountancy. Yeah, just no on that one. Just don't think that I think that there's probably a small crop of people that get something out of chat GBT looking at a PDF. I don't think that scales to a business that replaces people. And also just a gente gay eye is this. It's like the beginning of Peewee's big adventure. It's the breakfast machine. It's all these cobbled together deterministic scripts to try and not these goddamn models into doing something, doing something never seems to leave the realm of coding with any seriousness. So when people say, well, in the future, a gente gay eye, I'm just kind of like, I'm sorry, I'm not going to fill in the gaps with these companies. And I mean this to the boosters who might listen, you're being conned, you're debasing yourself because it's is one thing you can believe this will happen. But the fundamental proof is not there. And the fundamental proof not being there means that to basically say that this is going to work out, just involves saying, don't believe you're lying eyes, you are doing the exact thing you are being propagandized to. And I think it's because there are people who are like emotionally invested in the success of this. I actually don't think the majority of them are financially invested. And it's sucks because these people are marks. They have been they have been conned. You can be I have no problem with anyone being excited about LLMs. I truly don't. My problem is the way they're being sold, the way they're being lied about, the way that people are talking about these things is actively misleading. You want to do on device stuff, go nuts. I think on devices the future of this stuff. I think if it hangs out, which I am still thinking is an open question, I think it becomes this very specialist on device software engineer tool, which could be kind of cool in the decade. That could be interesting. Good for them. I think that the era of GPU based LLMs will die and even if it doesn't, even if it doesn't for a while at least, the amount of revenue to substantiate and justify the data centers that are allegedly under construction means that we need two or three more open AIN and the topic sized customers that do not exist. I mean, just I don't mean that as like anything about the efficacy of LLMs or anything. I just mean on a raw spend level, we need hundreds of billions of dollars of AI compute revenue by 23 and we need it to exist because if not, we're going to have theoretical millions of H200s, GB200s, I guess the BNBL 72s, but nevertheless, we're going to have racks of these things that are sitting follow and I also think the customers are all unprofitable AI companies. So yeah, I don't hate on this stuff because I'm like, yay, good, I get to be angry at something, I get the crit, so I think it's because I think people are being misled, I even think the booster types are being misled. I feel bad for them on some of them because it's like, why are you angry at me? Be angry at the companies for being so dodgy. Like that's, if you don't like what I'm saying, be angry at them, be angry at them for not giving you better evidence because that's the thing. If it wasn't going, if these companies weren't dodgy, why do they act so dodgyly? Earlier you said that the, you know, the bull market in compute, the fact that demand for compute seems off the charts, that that is wrong. Why do you say that? Because the majority of AI compute revenue is either anthropic, open AI or meta or hyperscalers giving compute to my anthropic and open AI. So let's go through them. So meta, I just want to be clear, it does not have an AI strategy. Mark Zuckerberg, but yeah, it's just like talking over what meta is doing is just like talking about a friend with a drug problem. Like it's just like, you, Mark, you go, you've got to get off the compute, mate. You've got to get claim. But let's look at anthropic, for example, $330 billion of the remaining performance obligations for Microsoft, Google and Amazon are anthropic. Just that's like, they're not selling very much compute to anyone else. Core Weaves revenue is principally either Microsoft for open AI, Nvidia's backstop is $6.3 billion backstop, or Google for open AI or anthropic or meta, of course. Iron, iron is being hired by Microsoft and Nvidia. What would they put? What could Microsoft possibly be doing with that compute other than selling it to open AI? Cypher is think it's cypher mining and terrarolth, both had loans backstop by Google to build data centers for fluid stack for anthropic. Let's see, nebius, $17 billion deal with Microsoft. What is that compute going to be used? I've already confirmed it's going to be used for open AI. This is the story across the board. When you peel away the non open AI, non anthropic, non meta, compute revenue, it's like a billion or two. Like just because most people don't need that much inference or training, just put aside my bare case for a second, just on a very basic level, the demand and natural need for AI compute is not that high. Like it's just not there. People's arguments might be, well, we'll use more of this in the future. The ROI conversation is actually pushing back against that. Kind of suggests that actually people are kind of hesitant with the cost. Well, they could move to open source models. Great. Those things seem to use less compute. So what we're going to do with all those data centers. And no one really has a compelling answer to this. And the thing is, the largest customers of AI compute are unprofitable. They're customers who are the ones pushing them as in the AI startups who use those APIs, also unprofitable. Every AI company is unprofitable. So this entire industry's revenue appears to be a test of how long venture capital and debt can hold it up. And now Broadcom is back stopping $30 billion of a $35 billion deal for. And God, this thing's so stupid. To borrow money that goes into a joint venture that buys TPUs from Broadcom who then sells them to Google, who then rents them to Anthropic and Anthropic pays the lease on them. So again, I guess back to the larger point of this industry wasn't dodgy, wasn't acting dodgy. Like these are not the things that you do when there's real demand, where there's tangible demand. And I mean, you've what base 10 raised $1.5 billion. If the demand existed at the scale that should theoretically be happening, based on it would be worth way more than that because the inference demand would be so obvious. There are no real signs that this demand is coming either because I don't know, who else is that? Who else needs it other than Mugs Aukabug? Tell me about Metta's AI strategy. When I say I don't understand it, I'm not trying to insult it by people. I literally don't understand it. And I ask Gemini about Metta's AI strategy and other than, oh, we're going to make her as more effective, which of course, they literally one of the things they said is a health application that's going to charge $7.99 per month. And that's not going to justify. So one thing I like to my advice I give basically anyone is never assume there's a plan. Like that's like anthropoconopen AI people like, well, they must have a plan. I don't think they do. I think they thought, which I kind of, I don't like it, but I get it's like, let's throw as much money at this and it should work out. It didn't, but I see the strategy. With Metta, Mugs Aukabug is a capricious man. He moves some idea to idea. He moved on from the Mettaverse. He changed the name of the company to Metta. And then a year later, it was like, sorry, two years, I was like, am I going to do AI now? But the thing is, no one's really had much of an AI strategy. Google and Microsoft and Amazon kind of had an obvious one, Amazon, building infrastructure, Microsoft building infrastructure and bulk AI services that people hate onto other products. Google same deal. And Google had TPUs already. I think Google is probably the best positioned if only because so much of their silicon is their own. Don't know if it's as good. But anyway, Metta on the other hand, well, their first strategy was to buy as many H100s and H200s as they can find. And then put them in data centers. Then they put them in their gem model, their generative advertising model, which lots of people have tried to extrapolate to say, well, that means that Metta is using all these GPUs for ad targeting, kind of, but not in a way that I think is drastically increasing profits. Because I don't know if it was a year or two into this. They got all these bloody GPUs. Wouldn't their growth be astronomical? It wouldn't be, it would be like 50% year of year because the power of AI. What I think happens is they got incremental improvements out there, a couple of percentage points here and there. There are various blogs about gem that suggest that that's the case. But when you actually look at them, it's like, okay, they had a lifted engagement here and here. How does that actually, where's that end up on the cash flow statement? Look, where's the money from that? What I think happened was Mark Zuckerberg saw everyone else doing something and decided to do it too. He bought all the GPU and he went, great, now we'll train our own model. And so they did actually something pretty cool, which is Lama, the open source model. They then realized, wait, crap, this is open source. We can't charge people for that. So they actually, there's the information report this last year. They went around the major hyperscalers being like, hey, can you give us money for Lama and then hyperscalers said, no, it's free. You made this free. Why would we pay you for that? And so meta spent $14 billion to bring in Alexander one who then decided to do internal models, which are an indeterminate level of good. I have heard from a source that they are doing so they have this plan to do an open-close style thing called hatch. And they're going to do like a pendant. The reason I'm listing all these things out, likely listeners are going to say, wow, that sounds like a bunch of disconnected ideas, just kind of throwing together. That is Matt Zay, I strategy, because that's meta. This is actually all in line with the company's history. This is a company that has not had a new idea since Facebook. Instagram, they bought stories, they stole from Snapchat, Reels, they stole from TikTok. Every idea they've had, they've stolen. All of this is to say is that meta doesn't have an AI strategy. They've reorganized their AI department four times, maybe more. And so everyone's just kind of chasing their tail at this point there. It's so strange. I think meta will be the last man standing in AI. I think that. I don't think they have any reason to stop. I think the Zuck has at this point over-committed many times over. the scale AI acquisition, whatever you call it, insane acquisition. Just what are you doing? Mark Zuckerberg can't be fired also. So there's not really any pressure the board can put on him. If the market kills the stock, if just something massive changes, maybe. But my great authority that I mentioned earlier is when these companies stop doing AI, which is the reason that they put so much money in and the reason that they have not stopped yet, is because once they stop doing AI, the markets will ask a very reasonable question, which is great. What's next? How are you going to grow further? They don't have anything. Quantum isn't going to do it. Robotics isn't going to do it. These are quantumists they have quantum. It works, but it's not really a product yet, so to speak. Robotics is. No, Mark, Mark, walk away from the robot. Amazon tried. Amazon already tried robotics. They're not going to do that. I guess Microsoft will raise prices again. It's what they do every few years anyway. But yeah, no one has anything more, so that's why they haven't given up. That's why they're not. That's why they're so steadfastly dedicating themselves to the graveyard, smash of spending a trillion dollars a year on this, with no return on investment for them. How much cash do. Does Anthropic and Open AI have left? I think you had numbers in your recent piece on exclusive on Open AI, but I don't know if that was before or after the fund raise, and can you also share your views on the ability or willingness of the venture capital community, as well as corporate VCs, like Nvidia, Google, or whatever, as well as the general public if there's a. Or institutional investors, if there's an IPO, to invest in these companies. What do you think the odds are of you are right on the fundamentals, but over the next 18 months, like $150 billion is raised to keep on funding these, as you say, money-losing operations. First and foremost, I think venture capital is running at its limit. I think that's why you have a bunch of private equity firms who got involved, and I think you even had a private credit fund. Like, I think you've. You've started moving into the big asset managers, so as the cash position goes, even, they're 22 billion ish in cash. More than that, they're like 50 billion total in assets. The information just reported that they had $73 billion in cash and other things, so it's kind of hard to pass that out. The thing is, they could raise that money. It could go on a little longer, but at some point, open AI is going to have to pay $300 billion to Larry Ellison over five years. At some point, anthropics are going to have to pay part of that $330 billion. They're not going to be able to just keep scraping along on cloud credits or venture capital subsidies. And also, if they go public, it's going to be. If they want to do equity dums, they can. But in the state of these companies, I don't know if that's a good idea. Nor do I think that the bond market's going to be very helpful. Maybe they do one or two bond sales, people are really stupid. But even then, it's like, we have not seen a company with this frightful level of economics. Go public. SpaceX is a piss-poor company. But at least they have business lines, like Starlink, which makes more money. SpaceX, which blows up rockets and has a lot of young contracts and also XD everything app, which generates non-concentral porn. Like, they have money losing operations, but the thing losing the money the most is AI. Open AI, anthropic, they are just AI. And they have businesses that are increasingly commoditized. So, I think they can raise more money. I don't think that's impossible. I think that the scale of their last raises tells me that they are running up against the limits. The fact that Google had to do an equity sale. 85 billion, I think it was, proves that the credit markets are starting to run dry. There was an FT story a few months ago, maybe a month or two ago, where they said that banks were afraid they were choking on data sent adept. I don't think that those same banks are going to be particularly excited about loaning money to these companies. They've given them some lines of credit, but we're talking then based on their fundraising history, these companies are going to need $150 billion within the next six to 12 months. And also, they're going to go public. And they're going to have to raise that money with everyone knowing the dirty business. So, not really sure how that works out, but they've also just made so many compute commitments. And though anthropic and open AI both project that they will be profitable by the end of 2030, no proof as to how that's happening, no actual evidence. Other than, oh yeah, we just didn't pay that. We were allowed not to pay a cost. They don't have a plan. And also, it's very obvious that training is not going away. This is the thing that no one wants. Everyone here is training and they're like, "Oh, it's a temporary cost. They could just stop training." When, when? When? Because it keeps going up? Does that stop going up? Because the basics of machine learning are that model drift happens. You have to constantly update these models. I think through the middle of last year, Joe Biden was still president, according to chat GFT. Like, these things need constant updates. And it's not just pre-training. It's post-training. It's specialist training data. It's a ton of investment has to go into this just to keep them going. And also, they have to produce new models because the ones right now aren't doing enough. They're not providing the ROI that justifies their current costs. They need to keep noodling at this. And I think the markets are going to eventually ask, "How long do you need? Do you actually have a plan? What is it? Can we see it? What do you mean, you left it at home?" Like that kind of thing. And I want to talk about the public markets exposure to AI. Like, there are probably some people saying, "Okay, I don't love AI at all, but I'm diversified. I'm invested in the S&P 500. I've talked to lenders who have confidence that they're lending against basically the credit of the hyperscaler, so the biggest companies in the S&P 500. In other words, they're confident that these companies have entered into lease commitments of hundreds of billions of dollars that are going to appear as costs that I don't think many people are considering that." During the dot com bubble, Lucent Technology's, they had a $2 billion deal with WinStar, a company that only ever lost money, $2 billion, where they did a circular financing thing. Now, WinStar ran out of money, and actually ran out of money because of the cost of that loan. It's very possible to sign a huge deal and then just not get paid. That happens many times. But the fundamental thing is, yeah, you're betting against, you're betting that the hyperscalers will make, like the Siphon mining deal, for example, a terrible one, back by Google, that Broadcom deal, that Broadcom deal with Anthropic. Anthropic still has to make those payments, and if they don't, Broadcom will have to. I think that we are yet to see a real test of any of these situations because the data centers are taking so long to build. I think the slower that happens, the longer it will take to have that test. But I think that the fundamental problem is that hyperscalers can only cosine these like student loans or student credit cards so many times before it starts to affect their balance sheets, before it starts to affect investor considerations of those companies. And I think that once Anthropic and OpenAI go public, and I think it's very possible one or both of them do, I think that that will become a much more serious issue because that will be, you will have to mention your exposure to these companies as a risk. And I think it's also a real risk when you are one of the people that's keeping them alive. And that comes a time when I genuinely think just there is not enough money. If I think I read some stat rates like 90, by next year I think 98% of all hyperscaler cash flows going to Capix, they're gonna have to take on that. The markets do not like the debt. The whole reason you invest in one of the magnificent seven is because they're cash rich asset light. Now they're just plummed full of these bloody GPUs. These bloody GPUs that aren't useful for anything outside of AI. And the other reason they've been given this affordance is because they've been relative, like they've, their other businesses have kept growing and because the markets are invested in by people with the brains of dogs at times, people are just like, well, number keep going up, that must be AI. That must be a AI is doing that. Even though they won't tell us how much money they're making from AI, even though they obfuscate that in every way, shape or form, well, you know, that's good enough for me. That stops being as fun when you get burned with all of this debt. And they're gonna have more and more debt. It's gonna get worse and worse because they're also not making a profit from AI. But it's just the question of when the markets eventually care. And also how desperate the hyperscalers get. What is going on between Anthropic and the US government where the Commerce Secretary saying, you can't use these models, you have to exclude them to foreign nationals. So basically Anthropic has taken the fabled model away, which is a moderately dumbed down version of mythos. What's going on here? What should we take away? What are you taking away from this? Let's go back to Waipro. So in April, Anthropic said with Project Glasswing, "Oh, we've made this big scary model called mythos." And it has these powerful cybersecurity things and it can find vulnerabilities in all sorts of things up and down side to side. Since then, it's come out that the system card mostly overstated things. They don't include how many false positives there might be. So and also it's good at finding vulnerabilities. Doesn't really exploit them. Isn't really clear what all the freaking out was. No. Nevertheless, they said this is too dangerous. Well, you're going to give it to 15 organizations. But a month later, they were like, actually, it's going to be 150 organizations. And then a couple of weeks ago, they said, well, actually, we're going to release something called Fable 5. Fable 5 is a mythos class model with guardrails. So you can't use it for biological stuff. You can't use it for subscurity. So eventually, because guess what? Is what happens when you tell software engineers they can't do something? Their first thing they try is to do it immediately. They would just say, I will break. There's a guy called, I was at Pliny Liberator on Twitter as well. He jail broke it as well. That guy really loves jail breaking shit. But put it on that side, an Amazon research group. And then this message then went through Andy Jassy, the CEO of Amazon, reported it to the Commerce Secretary that there was a jailbreak. The Howard Luntnik went to Anthropic. And Anthropic said, it's not a big deal. The government said you need to fix this jailbreak. Anthropic said, we're not going to. It's not a big deal. The government said we're going to add export controls. No non-US citizens inside or outside of America can use this. And so Anthropic, when the only way we can comply with this is to just take mythos and fable offline. Now there's some back and forth. And it seems that there's a degree of something with the argument that the Department of Defense from a few months ago. There's clearly bad blood. They clearly want them to kiss the ring. They're claiming they're working on a framework. But what this is is the consequence of lying for years, of just saying, our models are big and scary. And they're going to destroy everyone. They're going to take every job in this big and scary mythos is too powerful to launch other than the fact we're launching it. It's not safe enough for anyone to use other than JP Morgan, Goldman Sachs, and multiple other organizations. And also 150 of them in 15 different countries. Otherwise, it's not safe at all. I've spoken to people that use mythos. They're like-- Yeah. It's just like-- I've spoken to them. Yeah, it was able to find some vulnerabilities. It found a bunch that weren't actually vulnerabilities, too, and many that weren't even executable. What we meant to do with that. But anthropic scare mongering, because this has been since GPT2. When Dario Amadeh still worked at OpenAI, they've been doing this thing of, oh, it's so scary. Oh, the models are so scary. Then they took a model and they sold it, literally saying it's too scary. But now we're going to release it for some reason. And one of you know someone took it seriously. One of those well, well, well, if isn't the consequences of my actions. It's just frustrating because there are some people like, "I just proof that it's too powerful." No, it's not. No, it's the stop here. It's K-fabe nonsense. Why are we doing-- why are we pretending? Silicon Valley was built on this kind of meritocratic, rugged, like pragmatism and realism. Rationalism. And it's like, yeah, but the moment one thing comes along, they're all like, they're talking about it. They saw Jesus in a cup of coffee. They're reading the tea leaves. They're doing tarot card decks. Everyone becomes a god damn mystic when the NLM is involved. But that's what happened. They scared people. They sold something on fear. And then people, the government in this case, acted like somebody would if they were scared of something. I think it could be genuinely really bad for AI development. I actually think it sets up a really terrible precedent for pretty much everything now. I think it's bad for the software industry. They netcown you poor computer scientists. It was just a New York Times called it doom trolling. I think it is. I think that these labs, because they realize they can't sell the software based on today, that they have to do this. And I think it's good they face consequences. I think it's since a horrible, horrible precedent for how the government is going to deal with tech going forward and blaming Dario Amade is necessary, because he is responsible. Him. Samoam and did a bit of whiskey. But Amade is the number one carnival barca scare monger. He is a problem. He is an actual problem. And both of them are genuinely bad for the tech industry. But Dario Amade, he really sees himself as some jobsy and socialite kind of like a elder statesman type. And he's just kind of an oaf. He doesn't have-- you know things are bad. You know you're an oaf when Samoam is politicking better than you. Do you think the AI bubble pops this year, 2022-27? Or 2022-28? I think 2022-27 is the safe bet. I think 2022-26 could be possible. If SpaceX starts tanking, for example, if SpaceX-- it's been kind of trundling now. I don't know when this runs, probably embarrassed myself. And it will be back up. But if SpaceX could not transform into a meme stock, like Tesla, I think that might make the open AI IPO a little bit more dangerous. But there's also the chance that just the money starts running out. The data center's stopped getting built. There's enough situations. And also, some videos got two, three more earnings calls. If their guidance doesn't make the markets rock hard every three months, people get-- if you read the headlines before Nvidia's earnings, it's always like people were like, OK, Jensen, keep me alive here. OK, please don't mess this up, Jensen. And because Nvidia has done the circular financing to keep this inflated, they set these unrealistic expectations. Nvidia better bloody hope they have a trillion dollars of sales through 2027, because if they don't, think the markets will fall apart. It really comes down to the fact that because real revenues from these companies are not an actual ROI, is not what's making the AI bubble inflate, it's going to come down to a vibeshift, and it's already begun. And what do you think is the greatest misconception by the AI bulls, or as you said, the AI boosters that we haven't talked about so far? The average AI booster has this belief that I'm doing this because I just hate-- I hate every-- I hate progress. I actually think the biggest misconception they have is that AI is progress. The AI is a progressive thing when it actually is a flattening of everything. It is an averaging out of everything. It is a technology that's not sold on what it does today, but what it might do in the future. Every conversation happens in the future tense. You can't talk about AI without someone saying, well, it will. I genuinely think that AI bulls are conflating a semiconductor bubble, and these massive sales that they're seeing because of all that this debt fueling the AI CapEx bubble. I think they see that and think that that is demand for AI. And what that is is a demand for speculative debt. It's a demand for private credit funds to find more yield. When you actually go and look at people using AI stats, it's always like, yeah, when you ask a CEO, it's the best thing ever. When you ask a worker, it's like, it's fine. It's all right. But I think the AI industry has successfully co-opted a lot of people that conflate technological progress with stock values. Who have taken this era of LLMs to mean that all AI is going to grow exponentially? And I think these people are marks. I think they're being used by the companies. Because the companies treat them. And I mean this for every booster. They treat them with contempt. How else do you describe what they're doing? You can't get a straight answer out of any of these companies. They don't want to give you the direct story. They don't want it. They move stuff around their balance sheets to try and make things look good. They give weird quotes to the FT. And that is contemptuous towards their fans. If I was an AI booster, I would want better evidence than this. I would be genuinely like, if I had to do the bull case, I would want better. I would be-- here's the thing. I would be asking Sam Orman, Dario Amade, Boris Cherny, all of them, hey, this is worrying. What is your answer? I won't be getting mad at me. I'll be getting mad at the fact that I wouldn't have fundamentally sound information. Because that's what keeps happening. It's like, you like LLMs? Fine. Good for you. Enjoy. I don't like them. I think that they do bad things the world. But I won't do that. Fine. It's software. Who gives a shit? But when it comes down to-- oh, if you get in the way of this, you're against the future. God, no. I love the computer. I think I genuinely-- in fact, maybe that's the biggest misconception. I love the computer. I grew up online. I have great affection for software. I think the computer has made me a better person. It's given me so much value. And I think that many AI balls and I actually kind of meet on that level. I think they, too, have a debt of gratitude to software. That's not what this is. It's not what it is. It's an aberration of software. It is a draining of Silicon Valley's value. It is an intellectual bubble that cautious dissent, that intentionally pits people against each other, that makes people angry for stepping out of line in a way that resembles cult mentality. And that's not an insult to the people involved. I consider AI balls largely manipulated by the companies who want their craving communities. We all do as human beings. And it's frustrating. It's frustrating because one of us is going to be right. If I'm wrong, I'm committing to explaining why. But a lot of the demands of me often come down to people that don't want to actually engage with my work, which I get, I won't want to read something that pissed me off either. I think it's just the level of, at the end of this, we're going to know who's right. There's plenty of evidence I'm right. There's nothing wrong with it meeting you wrong. I will admit I'm wrong when I'm wrong. I'm sure I'll be wrong in the future. and it frustrates me because the bulls I think will end up being wrong and they will have been wrong after investing probably not a ton of money but a ton of emotional and intellectual energy into something that flattens the experience that takes attention and money away from actual innovation and ultimately just makes a couple of other guys really rich. Maybe they aspire to be them. It's not worth them. Ed, thanks so much for coming on monetary matters. If people want to learn more about your thoughts, you have you write prolifically at where's your ed newsletter which people should check that out as well as the better offline podcast. Thank you everyone for watching. Please leave a rating and review for monetary matters on Apple Podcasts and Spotify and subscribe to the Monetary Matters YouTube channel.