The Man Who Calls BS On AI: They’re LYING About AI, 2027 Is When It All Breaks! | Ed Zitron

[0:00] I think generative AI is at its heart a con. And seeing these ultra-rich, ultra-powerful people lie through their Turns my stomach. The word con is a strong word. Well, what do you call something where from the very beginning they've sold [music] it in the terms of magic, but it's just a half-assed machine. They are misleading the entire world. >> You are the first person that I've spoken to that has that opinion. >> Well, the fact that this is happening is insane, and the fact it's not a scandal is insane. And I've been in the tech industry for 16 years now, and I love technology, and I'm enthusiastic about it. But I don't like being misled. And
[0:32] this is the largest non-consensual push of technology in history. >> So, we're going to play a game, Ed. I have the things that you consider to be myths about the AI industry. Let's play it. >> The AI industry is creating enormous economic growth. No, it's not. All of these companies run at a horrifying loss. OpenAI lost $20.9 billion last year. None of these people can just say, "Yeah, we're on the path to making this profitable." Because they can't. >> Next one. >> AI will replace all human jobs. That just isn't happening. There's no economic data to support it. Next, the
[1:02] United States need to spend trillions to beat China in the AI race. What's the race to do for us to constantly piss our pants worrying about China? But people keep saying, "What if these models fall into the wrong hands?" They're already in the wrong hands. Mark Zuckerberg, Sam Altman, Dario Amodei. >> Mark Zuckerberg says, "We'll continue to invest aggressively in infrastructure to meet the demand." >> God meta is a monstrosity. Makes me think of Shrek with Lord Farquaad. Some of you may die, but that's a risk I'm willing to accept. If only these people gave a about poverty or actual problems in the world versus, "Are we buying enough GPUs?"
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[2:33] >> [music] >> Ed Zitron. There are a number of things that you believe that a lot of other people don't believe. >> Right. >> You have, I think, a couple of controversial opinions, and opinions that are in contrast to the other guests that I've sat here with. What exactly are those opinions, Ed? >> I think generative AI is at its heart con. I don't think it is sold as honest software. I think that they overstate both what it can do, what it will do,
[3:04] and the underlying financials to the point that they are misleading the entire world, and they're actively exploiting the weaknesses in journalism, in our economies, and indeed within the responsible parties with sell-side analysts, governments, and all over the shop. >> The word con is a strong word. >> Yeah, I mean, what do you call something where from the very beginning they've sold it in the terms of magic, as this thing that will replace all jobs, that will cure cancers, all these things. And when you look at it, it's boring cloud software that's extremely expensive and unprofitable, and also
[3:35] unreliable at its core. >> Uh uh people will be asking, "Where are you drawing from in terms of your references, your personal experiences, where were you educated, what you studied, what you write about, what you do, Ed?" >> So, that's the funny thing is people say, "Oh, he's not going to finance experience. He's not going to take" I've been in the tech industry 15, 16 years now. In PR, but still had practical experience, and I love technology and I'm enthusiastic about it. And this thing just comes along that everyone is telling me is the best thing since sliced bread. It can't even do the basics. It can't even do search. Well, whenever you ask an AI person, well,
[4:05] what's your setup? They describe this PewDiePie's playhouse thing of like, well, you got a harness here and you got to use the right prompt. Well, you don't want to use that prompt. You want to use this prompt here with this model, but don't use this model for the beginning, but at the end you're going to want to use this model. And this is meant to be artificial intelligence. It's meant to be smart. It's meant to be autonomous. It's meant to be something that you set and forget. We have the sort of six leading AI companies on the table here, Anthropic, Amazon, Nvidia, Microsoft, OpenAI, Google. You're saying that their fundamental
[4:35] business model is a con. Well, their revenues are not really coming from AI. Up until fairly recently, none of their revenues were coming from AI, like dribbles of it. Right now, 70% of all AI revenues across those three companies are from OpenAI and Anthropic. Two unprofitable, unsustainable companies that literally cannot afford to exist without these very same companies giving them money. Amazon sent $50 billion to OpenAI this year. They sent $5 billion to Anthropic. Google sent $10 billion to Anthropic.
[5:06] And in the next 3 and 1/2 years, OpenAI and Anthropic, based on actual sell-side analysts' evaluations, their estimates, that inform whether stock is going to go up or down after earnings, they're expecting 400 or more billion dollars of revenue. 30 or something percent of cloud growth just from these two unprofitable companies that will need to be given the money from somewhere. And on top of that, these companies have such low respect for the average investor, for the analysts, for everyone, really, that they don't even
[5:36] disclose their AI revenues. The few times they deign us worthy, they use something called a run rate, an annualized run rate, which means well, nothing. They never define it. It can mean month times 12. It can mean months times 13. It can mean last 4 weeks times 13. It's different every time and they never define it and then they sometimes just don't mention it. So, you've got this big thing that is meant to be the biggest, most influential change to software ever. And whenever you ask them about it, when you say what how much you're making from this, they go, "Oh, I couldn't possibly say. I'm too shy."
[6:06] These are public companies or at least the ones that aren't Anthropic and OpenAI. When they have good news, they'll tell you and when they don't tell you something, well, that actually speaks volumes. >> Have you used these tools, the AI tools, Gemini, Anthropic, ChatGPT, etc.? And you found no value in them? >> There's some value, but it's not There's They have spent over a trillion dollars in CapEx. What does CapEx mean for you? >> Capital expenditures. So, when you are a business and you have operating expenses like electricity, for example, those come right off immediately. Capital expenditures are long-term investments
[6:37] that are theoretically one-off. So, a data center or indeed the GPUs you put inside an AI data center. >> Okay, so you've got a data center. And then you have these GPUs which are like computer chips. >> So, AI GPUs are much bigger, much more power intensive. They take a bunch of high bandwidth memory and they because of how many of them you need, you need thousands of them, tens of thousands, hundreds of thousands in some case, you need a bunch of power. So, an example, OpenAI and Oracle are building a data center in Texas, in Abilene,
[7:07] Texas. 1.2 gigawatts called Stargate Abilene. Within that, with each one of the eight buildings, there'll be 50,000 Nvidia GB200 GPUs. So, city of Bristol takes about 700, 800 megawatts of power a year, right? Well, Stargate Abilene is condensing more power than that, 1.2 gigawatts, into a space around 1,172 times smaller. City of Bristol is about 1.2 billion square feet. Stargate Abilene is about 998,000.
[7:38] So, you're condensing all of this power, all of this money, all of this labor, into this one spot. And all of these data centers cost billions of dollars. All of these companies other than Microsoft are now into take out debt. And the thing is, they've spent over a trillion dollars so far, and they want to spend another trillion dollars next year. And for what? To make tens of billions of dollars, most of which comes from two unprofitable companies, Anthropic and OpenAI. >> One of the rebuttal to that would be that the adoption, the customer adoption of people using OpenAI and Anthropic has
[8:09] been absolutely insane. These are the fastest-growing products in all of history, especially as it relates to sort of technology, if we're just focusing on technology. They are, you know, hundreds and hundreds of millions of people, billions of people are using these tools every single day for things that they have subjectively decided are problems they need solving. So, you know, money is a lagging indicator of value. So, one would argue that they're just investing ahead of the monetization options. >> The first, let's start with this adoption.
[8:39] Is it honest adoption when you are forced to use generative AI when you load Google, when you load Google Docs, Gemini screams in your ear. When you load Word, Copilot's bugging you. When you use Amazon, whatever ruthless AI is, wants to has opinions on what socks you're buying. This is the largest non-consensual push of technology in history. ChatGPT, for example, every single media outlet has been screaming about this for 3 years. They've been saying this will take your job. You must use this. If you don't use this, you're going to be falling behind.
[9:10] So, people are using it because they've been told to use it constantly, and they're using it like search phenomenally. And that's partly because Google fell behind search, and also because it's better ingesting queries sometimes. Sometimes, if you use a generative search, it's like a trolling vessel. It's not very good at specifics. But if you're like, does this thing exist? Has this person ever said anything like this? It'll still probably get it wrong, but it'll scour the ocean for you. Nevertheless, that's not worth a trillion dollars. None of it is. The amount of money being sunk into this
[9:40] is just incomparable to anything. Real wise, it blows everything out of the water because there is no post-bubble story even for this. AI GPU is not useful for other things, either. There's It's a directionless egregore of capitalism, this headless beast that lumbers around desperate to seek out growth everywhere in the hopes that if it harasses people and scares people and demonizes labor enough people will be forced to use it.
[10:10] >> The The reason I I pause is because I just I think about my own company. Obviously, everybody thinks about their own personal situation. So, you have people listening now that don't use any AI tools, then you'll have people that are using it for everything from coding new software tools to everything they write to, you know, images, whatever. And when you look at the the stats around enterprise adoption, it says 88% of organizations regularly use AI at least once for one particular business function. And I'd say in our company, 95% of people use one of these AI tools
[10:40] like Anthropic or ChatGPT or Gemini every day. >> Right. >> And that exists on some kind of spectrum of like the super users that are using it probably, you know, every hour of every day for almost everything to, you know, someone maybe hiring the executive team that's using it less because their job doesn't require of it as much. >> Right. >> And when you look out into the world at, you know, at how the world is changing from a content perspective if we're looking at generative AI, it is obvious that these tools are being widely adopted. Part of the symptom is the AI slop you
[11:11] see all over the internet. >> Right. >> So, I I don't know this this this idea that it's not being used I struggle with. >> It's being used. Here's the thing with the slop. Before we had AI slop, we had SEO slop because Google incentivized doing the lowest common denominator that would rank well in search. And that's a whole story about how they pulled back spam guards, thanks to Prabhakar Raghavan, which we can get into, where they made the internet worse by allowing worse content to rank higher. It's why we have when you used to Google, oh, best washing machine,
[11:43] there's 11 different horrible blogs that read like somebody got a concussion. They are built to rank rather than be read by humans or built to be good made good. So, AI helps weaponize that at scale. Yeah, you can make a bunch of generic slop. We've had slop for years. We've just found a slop machine. But then also there's the problem of cost. So, when you use AI services, you burn tokens. And it's per million tokens. >> What's a token? >> So, it's around 3/4 of a word. So, so it's characters. >> So, the AI companies have a currency in
[12:14] which they charge you like a taxi in New York has a meter. >> Yeah. >> And they call it tokens. >> Yeah. >> And every word, let's just say for ease it's a word. You're paying per word. >> a word, yeah. And it's per million tokens. So, you'll be charged per million input tokens, the stuff you're feeding into it, like a document or a bunch base. And the output tokens are both the stuff it spits out at the end, but also when it thinks. So, okay, you've asked me to give you the best restaurants in this area of New York. I should find the best restaurants in New York. All of that's output tokens as well.
[12:44] >> Mhm. >> However, when you're paying for a monthly service, you don't see any of that. All that put all that crap to the side. They just have rate limits. So, you can use them a certain amount and then when you run out, but they kind of obfuscate what that was. Now, someone recently found semi-analysis actually found this, a big analyst group, they found that on a $200 a month ChatGPT subscription, you can burn $14,000 worth of tokens. And on Anthropic's, you can burn $8,000 for 200 bucks. That is how most And then even on the 20
[13:15] buck a month service, you can burn $400. Now, most people don't realize that. Most people have no idea what AI costs. Most people just think, oh, it's 20 bucks a month. No, all of these companies run at a horrifying loss. Open AI lost $20.9 billion last year because people can burn as many tokens as they want. And when they tried to move everybody on the enterprise side, so companies bigger than 150, onto actually paying the cost of AI in around March of 2026, to quote Sam Altman, they said people have a big problem with it. I
[13:46] think it's a huge issue, which is not really what the heir apparent of tech's history is meant to be saying, but the point is enterprises immediately started freaking out. Uber burned through their entire annual token budget in 3 months. So, suddenly, after everyone's saying AI is the most productive thing ever, it's amazing, it's changing everything, the moment people actually had to pay for it, they go, >> [snorts] >> Oof, I don't know, actually. Um, maybe it's it's obviously we all love it. It's all great, right? But it's costing too much, so we now
[14:16] need to reduce the cost because people just dumping stuff into it, being like, what do I do here? And getting whatever the median is out because that's what these things do. They provide the median answer. >> So, essentially, someone like me who's a power user of these tools, I could be costing Anthropic or Open AI a thousand dollars, but they're only charging me a hundred dollars, let's say. So, they are having to subsidize nine hundred dollars of my usage because of the electricity costs and the costs at their data centers. And so, your assertion here is that that is
[14:46] unsustainable. >> Yes. And just to be clear, they're probably not one-for-one dollar. It might be 34. We don't We don't know. I think it's unprofitable. These companies don't disclose them. Even in their audited financials, they play funny games with how they categorize things. But, nevertheless, yes. And on top of that, the way that you stand up inference, which is the thing that creates the output within these data centers, you're not just saying, "Okay, turn the inference machine on, let's go." You are standing up the GPUs necessary to take in the demand. And if you buy too much,
[15:17] you've wasted the money. You have You have to pay for the hourly GPU use regardless. If you buy too few, your customers can't use it. They get pissed off at you. They cancel. They go with someone else. But nevertheless, yeah, they would get demand selling $20 or $40 for a dollar. And that's what these services do. And really, the simplest way to explain it is they were actually profitable if they were actually just they believed that these services were worthwhile and that they were worthy of the cost, they'd charge it. Regular people wouldn't be able to get a monthly subscription. They just be paying what
[15:47] it's worth. Unless, of course, there was an economic problem. And it's very simple. You pay when you use an LLM regardless of whether you get what you want. When these things hallucinate, say you're doing something with you're coding something and they go through a code base and they up a bunch of stuff and they break a bunch of stuff. You're paying for that. You're paying for it whether it works or not. Unless, of course, you're using one of these subscriptions. >> I think that the really interesting point is are they spending ahead of the value showing up,
[16:17] which is I imagine what they would argue, or are they spending all of this money and subsidizing all of their users in a way that's unsustainable and that will never be justified? Like doesn't you know, cuz you think back through the history of technology, you often get people losing money to grab market share. >> Right. >> And they're also focusing on bringing the costs down and making it more profitable for them as well. But they can't afford to under invest. >> If they were bringing the cost down, they would have brought the cost down,
[16:47] which they have not. It seems to be getting more expensive. In fact, everyone inference providers don't seem to be profitable. Even the companies renting out GPUs don't seem to be profitable. I imagine that it wasn't like they started out and they were like, "Shit, this is unprofitable at the beginning. We know it. Screw it. We'll keep doing it anyway. We'll screw I don't think it's some big conspiracy. They probably thought at some point, "Yeah, this will go profitable. The chips will catch up. Customers will pay for the overwhelming value." Because you don't know in 2023 where it's going to be in 2026. You assume it's going to go
[17:17] up. That's the nature of venture capital. They should have stopped in like 2024 when OpenAI lost over $5 billion. They should have been like, "Yep, this is not going to work." But they kept going because it helped number go up so much. It helped stock values pump. It helped everyone pump. It helped Nvidia pump, Microsoft, everyone. And not from the revenues. Because here's the funny thing about Google, Microsoft, and Amazon. People for years have been saying their AI bets have paid off. Wow, their AI bets have paid off. As these companies refuse to say how much they're making
[17:48] from AI. But because their existing businesses continue to grow and did so, by the way, through price increases, changes to how Google and Meta uh did advertising. Amazon bumped up prices and changed how they did Actually, Amazon started a remarkable ad business during this whole time as well. And the selling through Amazon platform. Anyway, nothing to do with AI. But because number go up, cuz revenue go up, everyone it's AI. Because these companies wouldn't spend a trillion dollars for for no reason. For right? Except in fiscal year 2026, which just
[18:18] ended for Microsoft. Annoying, I know. They made, total according to Bloomberg, about 34.33 billion dollars. 24.1 billion dollars of that was from OpenAI. So that leaves them with about 10 billion dollars in a year when they spent 115 billion on capital expenditures and intend to spend 175 billion next year. The math does not make sense. I imagine their plan was, "Okay, this is just going to get exponentially more valuable and then at some point the costs will be outpaced by the return." Problem is that large language models
[18:49] need a bunch of money to train them. They need constant data flows through. They need customized data. It's just this big expensive monster. And when you try and talk to people about it, and you try and say, "Hey look, this is really bad. Nvidia has sold There's 215.9 billion dollars in the last fiscal year with the GPUs mostly. And you try and go, "Yeah, that's the support like $22 billion dollars revenue total in the entire world outside of these two companies that literally require money
[19:20] being fed into them sometimes by Nvidia to keep alive. When you tell people that they go, "Well, companies just lose money, right?" Companies because we have this quote Ed Elson from Property Markets, we have this cult-like worship of the wealthy. Where we think that someone wouldn't spend all this money for no reason, right? Because reconciling with that, with this idea that the ultra-wealthy, the ultra-powerful didn't get there through big brains, they didn't get there through anything other than luck and opportunity sermon, getting an MBA perhaps with the
[19:51] right people, that they just got there because they're regular people and they just happen to be in the right place at the right time. Reconciling with that and realizing that the world is not controlled by people like a meritocracy is kind of grim. So, it's easy to be like, "No, they're not making a mistake. I must be missing something." And that's what they want. >> So, I you know, I think back through the history of technological breakthroughs and I think about I mean, you can look at different industries and one of my favorite books on this subject is The Innovator's Dilemma. >> I've read it. >> And one of the things it talks about is how the the innovation that ends up taking out
[20:22] or transforming an industry often starts worse, doesn't make economic sense, none of your customers are asking for it. And this is typically why we end up ignoring it. So, like, you've got horse and carriages in the 1800s. >> Mhm. >> Amazing form of transport according to the 1800s, you know, people of the 1800s. And then you have this thing called cars come along. Now, the problem with cars is they broke down all the time. It's kind of like AI hallucinates now. Um they were more expensive and the the economics of it didn't make sense. You might as well walk than buy a car. There was a law at the time that meant
[20:52] you had to walk in front of it with a red flag and wave and someone had to get to employ someone to walk in front of it waving a red flag. Obviously, it's worse. It's like a worse solution. However, these things that are disruptive innovations, they have a higher ceiling of growth and so they eventually overtake the horse. And I when I think about that analogy in the context of all of this, I go, "Okay, it's imperfect at the at the moment. The economic models aren't perfectly ironed out. They're still figuring out how to make it cheaper, the infrastructure, etc. But as if you think
[21:22] about the rate of improvement versus other, you know, let's say coding, how much could I train a human coder to improve and to increase their output versus an AI agent? One would go, if you just imagine any rate of improvement in these AI tools, at some point, if you just imagine a 5% rate of improvement per month, at some point, it's you know, and then you imagine a 5% reduction in cost, which is what we did with the internet, what we did with cars. >> Yeah, but that's Moore's Law. >> Moore's Law is a theory, and Moore's Law is not with GPUs. So, let me let me
[21:53] actually explain. So, Nvidia. Nvidia invented, I think it was in the 2000s, they put out something called CUDA, which is the underlying software library and the way to run software on GPUs. Took them some a decade or more to make it something where they you could do data analytics, one of the early things, mapper and such. And then when AI came along, they'd had lots of experience with it. But nevertheless, this company has got more money, more attention, more geniuses behind them, more people focused on making their things more efficient than anyone could ever ask
[22:24] for. >> And Nvidia, for anyone that doesn't know, makes the chips. >> They So, and that CUDA thing I mentioned, they were the ones with CUDA, and CUDA allowed generative AI to grow. >> Okay, so they're they're chips. >> Chips, yes. >> And chips are needed. Those are the things that go into the data centers. >> chips are the ones where you can run AI software on it. So, the training runs and also the inference. Now, here's the thing. The the car example. Back then, you didn't have pretty much every mathematician and scientist going into the car industry. You didn't have the combined world's governments never shutting up about this. And by the way,
[22:55] giving them credit early. Since 2023, they've been saying this is inevitable. Even what you said, 5% improvement, I don't even know how you'd measure that because a junior software engineer can still experience things and learn things from context, from how people deal with problems. And the way that people deal with problems is not as simple as looking at the code or reading some emails. It's context you get from speaking to a person. It's being in different environments. And there may There are uses for LLMs in coding. I don't dispute that. But even saying 5%
[23:27] better, what does that mean? Is it better at Rust? Is it better at C++? >> productivity. So just like, yeah, shipped If we did it in the context of coding, it would be like shipped code. >> That's the thing. That would be like, he's the best writer in the world cuz his newsletter is really long. That's an insane way of valuing it. With coding, it would be I mean, it's even difficult to evaluate because it's Is the software out there better? Is actually a great way of evaluating it. And I would say uniformly not. I would say the standard of software across Google, Microsoft, Amazon, Meta especially. God, Meta is a
[23:58] monstrosity. Is worse. GitHub GitHub Someone posted on Twitter earlier today, we should get a notification when GitHub is up rather than when it's down because that would be more reliable. Microsoft is one of the largest companies in the world and they can barely wipe their own ass when it comes to GitHub. The quality of software is going down. Weirdly enough, as more people use LLMs and more businesses demand, and I really do mean demand, that people use these services. >> So on this point of If we go back to this horse and carriage and car analogy, say that we're at whatever point today.
[24:29] If you imagine any rate of improvement in the technology, which we have seen since ChatGPT came out. >> Sure. >> I remember when ChatGPT came out and I was in Asia and I was there showing it to my fiance. I was like, look, he can do this. And it was hallucinating once in a while and getting things wrong. I actually don't have that experience anymore. I have moments where I believe its reasoning is weak, but I don't have outright hallucinations anymore. >> See that? I I disagree. >> So give me an example of a what you define as a hallucination. >> Okay, great one. So I have a Bloomberg terminal. Yeah. The very useful thing
[24:59] they have on there is Ask B. So, when you do a Bloomberg inquiry to like look up what we think Nvidia's revenue is going to be next quarter, it runs something called BQL, which is its own programming language. Now, instead of having to learn that, you can just type into Ask B and it will generate it and run it for you. And so, you get pulled up and you know where the data is coming from. It deals with hallucinations very well. The other day, I was like, "You know what? Get a little spicy. I'm going to look up the growth rate of stocks of Microsoft, Google, Meta, and Amazon over the course of 5 years, I think it was."
[25:29] >> Yeah. >> And I was about to I was copy-pasted it over to something, looked at it in Excel. I was about to I was writing the newsletter. I went, "Microsoft stock's never been $575 a stock." You know what? When it's a cute little thing like, "Oh, it's a stock price." and I kind of caught it, it was no harm, no foul. That's fine. But when you're talking about, I don't know, like a transcribing tool for a doctor or a financial model that a hedge fund is dependent on, at that point, it becomes a little more dangerous.
[25:59] And the thing is, a hallucination with a software package, for example, you're refactoring a code base and it leaves a door open security-wise, or it just breaks something, and you, I don't know, maybe you've been vibe coding for 6 months. You haven't really been coding with your own hands for a while. You've maybe forgotten a few things. You have this slop to look for. You have to know what I'm not doing it. And so, the problem has become multiplicative. And I don't really know how you train them out of that, and they've certainly not succeeded. So, on one hand, they have got better, but one
[26:31] of the main ways they evaluate them getting better are benchmarks that are adjusted specifically for large language models, cuz you can't just have them do tasks. They've got better at that. They found some tasks they can have them do on. But even them, like Meta, m e t a. They have this thing where it's like, "Check out this chart. Look how much better it's getting at running tasks. Wow, it can go for an hour." And then you look at it, and it's like, "Yeah, and successfully completing them 50% of the time. >> They they have a hallucination leaderboard and it really focuses on basic tasks and it shows that the four-year trend according to historical data from the Fictoria hallucination
[27:03] leaderboard shows that hallucination rates on simple summarization tasks have plummeted from around 21% 21.8% 4 years ago down to 0.7% roughly on today's top frontier models like Gemini and ChatGPT. Again, the point of nuance here is that these are on simple tasks which is kind of what I've experienced. I've experienced it on day-to-day things that hallucinates less. Again, rate of improvement thinking. So, if I just imagine the trajectory to continue, there is going to become a time where
[27:33] hallucinations become rarer than they are today increasingly. And also I'd say is when I think about other technologies, there's two more points. Other technologies at their inception when they first came to the world like the internet also had technical difficulties. I remember growing up with dial-up modems and I couldn't go on the phone at the same time as going on the internet. I'd have to stop RuneScape upstairs to go on the phone. And you thought that this is crap this is crap technology crap all the >> What? I don't know, mate. I loved it. >> Yeah, I know. You It felt like magic and then in hindsight you go, wow, I now
[28:03] have Starlink and 5G internet from my phone. It's unbelievable. You couldn't leave the house with internet before. And that's what I mean by the rate of improvement thinking. I'd say the last point is we often compare AI to perfection, >> right? >> whereas that's not actually the alternative. In the working world, like if I wanted to do let's say a simple writing task, I'm I should compare AI to my alternative alternative way of doing that simple writing task which is both measured in my time,
[28:33] right? And my ability to hallucinate as a person who doesn't know everything. At all if I'm hiring someone, an intern who might also be prone to hallucination or have gaps in their knowledge. So, it's not actually like we're comparing we should compare AI to perfection. it's AI to the other alternatives. And if someone hallucinates 0.7% of the time, but knows way more and is faster, maybe on a net basis that's a good trade. Maybe I should use AI. >> So, let's start with an example. Someone I love dearly, Matt Hughes, my editor.
[29:03] He's He's a side of Liverpool, wonderful guy. I don't pay Matt Hughes because he knows everything. I pay him because he has incredible context and a ton of knowledge and is willing to expand it and work with me. And moral support, he's a great editor, but he's also someone who gets into the guts of it and has the experiences of it. He's a decorated tech journalist. And on top of that, a wonderful loving being with empathy and joy in his heart for the stuff he loves and absolute venom for the people he hates. That's the I can't get that from a large
[29:33] language model. But on top of that, I don't I push back on just the assumption that when you say knows everything, what good is something that knows everything when it sometimes doesn't know anything? When it sometimes and on The thing is, are you really paying an intern for something basic? Are you really going to them and saying, "Yeah, can you look up what the date is?" No, you're doing that on Google. Whatever the task is, you are trying to also train an intern. The point of an intern is to train them and turn them in take them out of Pinocchio status. But it's also An intern learns. An
[30:03] intern gets context. An intern learns your habits. Learns your >> gets context and learns. >> No, it doesn't. >> And AI doesn't learn. >> I mean, it doesn't. The way it learns is you create a giant Claude the MD file that it sometimes doesn't read, sometimes does read. You create a harness, you put It's like It's Pee-wee's breakfast machine from Pee-wee's Playhouse. You have to do all these contrivances to mitigate the hallucinations and even then at the end, how much effort have you put in? >> But so okay, this is an extreme simplified example. If I went on my Claude now and said, "What's my dog's my dog's name?"
[30:33] >> Uh-huh. >> It would know my dog's name. >> Jesus Christ, this This company raised 95 billion dollars this year. >> I'm using an extreme simplified example to show that it can remember things from the past. Obviously, it knows much more complex things as well, but I just use that as an example. So, we we we accept the fact that it can it does have memory of the past. >> It has files it can access that have stuff on it, but that's >> Yeah. >> not the same as memory. And it's also just Okay, so it remembers your dog's name. It might remember your habits. It might be able to read things you've said before. >> Yeah.
[31:03] >> Does it know your moods? Does it know what's going on in the world around it? Does it have good days and bad days? Is it there for you? Because it's just a text machine. And the thing is the intern example. An intern is something that can grow. It's something that you invest in. That's not something you do through feeding files and text to it. The way that we store memories ourselves, the way in which we accrue experiences is a a maelstrom of emotion and feelings and facts. >> Completely different. So, I think there's two things here. There's
[31:33] the process in which something happens, and then there's the output. >> Uh-huh. >> So, the process you were describing the process of how a human does memory. >> Right. >> The way that an AI does memory is different, but the thing that people care about is there value in the output. I.e., you know, if I dump all of my files into Claude, I don't really care how it processes it as long as when I ask it "What's my revenue?" it has the number. And one could say the same thing about training someone. You could say you teach them, you put lots of effort into them, you give them lots of context, you you educate them and give them
[32:04] experiences, and then you might come and say to them, "By the way, what's my revenue?" Now, the processes are entirely different, but the outcome is what I care about. Do they know the revenue number when I ask them? And so, I think that's the part that we sometimes get lost in. We get we, you know, cuz I have I've heard this debate about like can AI be creative? >> Right. >> I think like the way to answer that question is like it's about the output. When I ask it to do a creative thing, does it give me the answer? Not is the process the same as a human process. Cuz actually, no no who cares what the People care about they pay for the outcome, the product.
[32:34] >> I actually disagree about the process because Matt Levine, for example. >> Your editor. >> Yeah. Yeah. Watching him go down a rabbit hole and being there with him and actually vice versa, him doing the same thing. We wrote these Well, I mean, we were working on the research. I ended up sitting there for like the day-long session of writing 11,000 words and he he had given me a bunch of notes. It was actually just Even describing that process, I feel so happy cuz it was like us being like, "I can't believe how these Jesus Christ, they can't do They're just like the
[33:04] misanthropy of just the horrible cynical people of asset managers like Blackstone, just learning about them and being like, "It can't be this." and having a back and forth with him. That is fundamentally different because we were both learning together and the learning process was as much about creating the output as the output itself. When you learn something, you're not creating the average, which really is what these things do, of the documents it could find. You're not getting particularly novel outputs. If I needed a generic slop output, sure, but
[33:34] I've I've used some of the higher-end LLM harness machines that the hedge funds use and they all get the same shite. It's all the same the same generic reports, the same "Oh, we noticed this analysis." Things that you can find on any kind of AI slop out there. >> What you described to me there, what I heard anyway, is there's two points of value you're getting from your time with that. I mean, I mean, there's many more, but you said you're you're learning and then you're getting this book edited blog blog You're getting a blog edited, which is the output, and you're getting learning and you're also really getting connection and all these other things.
[34:05] >> But when I come to when people sort of think about the value of AI, of course they could use it to learn, but in the example I gave of like repeat my revenue number back to me or do this number, I am I just care about the output. I could use it to learn. >> What if What if What if the What if the revenue number was wrong once? You should have defined deterministic ways of knowing those numbers. You should not rely on them. Even with the terminal running BQL, which I trust, I will double triple treble-check everything just to be sure. Partly because also the process of learning for me, I don't want just to report I
[34:35] go like that. I want something that I fully understand and also understand the context around it. I don't think that LLMs do that and I just don't see them getting better in a way that does that because it's it's just not what they do. And also, there's the other problem of the more detailed the report, the more likely there are things to be wrong with it. If you are with Matt Hughes, for example, I can trust he's got it right. I can trust he understood and I can trust that I can have a back and forth with him that will inform me if I've missed something. I can read the stuff
[35:05] that he's read and actually trust him because there's a big trust part as well. >> What is the basis of your trust in Matt? Could it be his historical performance? >> I mean, yes. >> Okay. >> And also, the fact we've learned half of this stuff together. >> But but tenure tenure doesn't necessarily There's probably people you know for 15 years who you also don't trust. >> Yes. >> So, I think I was trying to figure out like what is the what is the thing that's causing humans to trust another thing and I guess it would be continual delivery of a commitment made of sorts. And so, with Claude, for example, on
[35:37] simple tasks as we've seen from the hallucination leaderboard, it continually delivers for people and that's why we've seen the fastest >> that what that board says? >> Well, it's it's saying like is it getting it wrong? Is it hallucinating? >> On simple tasks. >> Simple tasks, yeah. >> How are those defined? >> I don't know. >> That's the thing though because this is actually a very very illustrative thing of the AI industry. They are the what about is masters. They have like, well, look, we've got this we've got this benchmark that says we're good at this and look, the number's higher. What's the number mean? Uh uh uh you know. What's that mean? And I'm
[36:07] not using this as a critic against you. It's when you can't give a direct answer, you give a side answer. When you as the LLM industry want to prove your worth, you can't just be like, just use the product. When the first iPhone came out, I was at Penn State at the time. Ooh, I felt like the apes at the beginning of 2001. I was like, visual voicemail. It was immediate and I showed it to tech friends, I showed it to the most normal people in the world and everyone was like, "Holy this is They were on Razors, they were on Nokia 3210s. It was obvious the value. Amazon
[36:38] Web Services, same deal. >> It wasn't obvious though. >> Yes, it was. I mean, I bought it >> To you. To you it was. >> It was and I also showed it to a bunch of people because I'm aware that I have bias when I just look at gadgets. >> But I remember the famous Steve Ballmer who was the CEO of Microsoft interview where he is told about the iPhone and he bursts out laughing. >> [laughter] >> $500? Fully subsidized with a plan? I said, "That is the most expensive phone in the world and it doesn't appeal to business customers because it doesn't
[37:09] have a keyboard which makes it not a very good email machine. You can get a Motorola Q phone now for $99. It's a very capable machine. It'll do music, it'll do internet, it'll do email, it'll do instant messaging. So, I I kind of look at that and I say, "Well, I like our strategy. I like it a lot." >> He burst out laughing, mocking it. >> Because it was so disruptive, it was way more expensive and it was a way different. No keyboard. >> Well, phones used to be insanely
[37:39] expensive and the carriers would cover them but you had to sign a long contract. You were still spending 500 bucks but the thing I'm getting at is you didn't have to explain to someone what you Perhaps you'd have to get past the cost part but you could just be like, "Look how good this is." And then once the app was the iPhone 3G with the App Store, people were like, "Oh this could actually change things." Mobile web, even though it was a monstrosity, it was so bad at first. Even then you could get your emails, you could just look at them. Point is Blackberries were also expensive and were still actually kind of cool but the way they worked was not like consumer software. They didn't have the classic GUI. iPhones felt like that. It felt
[38:10] like an a cell phone designed even like a computer. It was obvious. It was obvious in the beginning. Everyone I was I was staying at the go in the center of Pennsylvania at the time and everyone I showed it to was like, "Wow, this is incredible." That to me is the obvious thing. With AI to this day, when you're like, "Okay, why is it so amazing?" People still dither. People are still like, "Yeah, you can't run a business fully with it without this weird system of pulleys and levers and such." And >> But what How come then when you look at the stats around ChatGPT's growth, 100
[38:42] million active users in just the first 60 days after launching? For comparison, TikTok took 9 months, Instagram took 2.5 years, and the internet itself, the World Wide Web, took roughly 7 years to reach that scale. Over 60% of the US adults are integrated into AI tools in their daily and regular routines within 3 years of the launch, reaching up 40% of the population. And that same milestone took the internet 5 years, and personal computers nearly 12. >> Okay. So, like this is the I think this is the part that's giving me dissonance, is like when I showed my fiance ChatGPT,
[39:14] okay, it wasn't really [clears throat] work. But as a sole entrepreneur who English isn't her first language, who has to write lots of text, lots of copy, and generate lots of images, and was paying a graphic designer to help her make certain images that she, you know, couldn't make herself because she doesn't have the skills, she would describe it as being transformative for her business. What I'm hearing from you is that it's not transformative, and there's no value in it for people. >> But she If she was actually not she would say transformative. >> It would she pay the per million token
[39:44] rate? Would she pay the actual rate? Cuz that's the thing. >> If this was sold at its honest cost, >> Yeah. >> I would actually if And people were reacting like that, and they were paying two, three, four dollars every time they did something, and they were genuinely happy, then there might be an argument. >> the What would be the honest cost if they weren't subsidized? >> per million token cost, the actual API cost they should charge. >> Do you know how much that is relative to >> Oh, god, it depends. It depends on the model. But there's actually kind of a point I want to make about the thing you said about the internet earlier. So, when I first got on the internet, a 33.4 kilobits a
[40:14] second modem, even back then I was like, "Fuck, if this was faster." And that was like immediate, just like, "If this was faster." Cuz it was slow. You're going like Happy Puppy or something. Download take all bloody day waiting for shareware to download. Immediately, you're like, "If I could do this faster, it'd be better." And even back then, I'm like, "Man, you could probably do video camera stuff with this." Stuff that eventually happened. And actually, there's this guy called Jim Cavallo from Goldman Sachs. In a report he did in 2024, it was GenAI too much spend for not enough return. Paraphrasing there. And he made the point that in the run-up to the iPhone,
[40:46] there was thousands of presentations that when GSM radios get smaller, when Bluetooth radios get smaller, when Wi-Fi radios get smaller, it is inevitable that we will get something like this. And then he said that there is no such path that AI there was no roadmap to AI becoming this thing that they promised. And I must be clear. If these companies had gone out there and they're like, "Yeah, this is interesting cloud software. It's generative. It's really expensive. We're not sure if we can fully not trust it." Not in the ooh, I'm scared way. I mean, just like, "We're
[41:17] not sure that this is going to be a disruptive, world-changing thing. It has potential, but we're going to go slow. It's really expensive. This is an R&D effort. We're not going to expose consumers to it." And actually be like call them like, I don't know, language models. And no, no generative AI stuff. Just being Not even call it cuz it isn't AI. It's not autonomous. It's not smart. I actually might respect it. But this is not They've gone out there since 2023 and said, it was 2022, "This is the best thing since sliced bread. This is changing everything. This is going to do
[41:47] all your work. This is going to take your job. You're going to talk to Bing and it's going to tell you to leave your wife." All of these crazy things. And what's funny is when the writer Kevin Roose, I think it was. Um, he was speaking to Kevin Scott, the CTO of Microsoft, about it. And Kevin Scott goes, "You know, I'm just glad we're having this conversation." Instead of being like, "Settle down, Beavis. It's a website. The website told you something. It's just LLMs." They talked it up. And that's because everyone is talking about what they wish this was, rather than talking about what it can actually do. This makes it scary to people,
[42:18] deliberately so. It makes it environmentally destructive. Look at the gas turbines poisoning black neighborhoods. I think it's in Louisiana. It's one of Musk's data centers. Look at the incredible energy draws. It is raising power bills. And also, it is creating inflation across all consumer electronics because of the massive RAM >> Do you know what's interesting? I almost feel like so much of what you're saying is true. And also, it can be true that this technology is going to profoundly change the world.
[42:48] And I think like you know, I think back to the early days of the internet is maybe the closest analogy we have of you know, in the dot-com bubble. You know, you wrote this great essay. >> Yeah, yes, yes. >> Which I found really funny. Um especially the name, the rot economy. And you talked about the rot-com bubble. >> Yes. >> Talking about how AI is uh less value than people think. >> Mhm. >> And in that in the sort of dot-com bubble, what you saw is huge hype, people overselling the capabilities of their websites and what they were building. But in the wake of the dot-com
[43:20] bubble, yes, 90% of stuff went to zero, but you had generational companies born that changed the world. >> Right. >> And so I I do I kind of And that's what bubbles do, right? But that huge hype, over investment, investors get crazy delusional, they think it's everything's going to change. At the same time, you do have skeptics in these moments. The the dot-com bubble had I mean, the internet itself had the biggest skeptics. In 1998, Nobel Prize winning economist Paul Krugman said, "By
[43:50] 2005 or so, it will become clear that the internet's impact on the economy has been no greater than the fax machine." In 1995, astrophysicist Clifford Stoll famously I wrote about this in my book, wrote famously in Newsweek, "Do our computer pundits lack all common sense? The truth is, no online database will replace your daily newspaper. No CD-ROM can take the place of a competent teacher. Commerce and businesses will shift from offices and malls to networks and modems. Baloney. So, how come my local mall does a roaring business and
[44:22] the cyber mall gets zero business? And I'll give you one more from Krugman, who is the award-winning economist. He said, "The growth of the internet will slow drastically as it becomes apparent most people have nothing to say to each other." >> That's That That may actually be the worst one of those Like the Hang around any bar in Middle America. Honestly, the best conversation. >> But it's just all It's all the same thing. >> I actually So, Clifford Stoll actually, his piece was interesting cuz that There were some boner points in it. But he made points about how like an overwhelming amount of bad information
[44:53] out there is bad for society. He's completely right. Saying how online education would not be a great replacement for regular education. I think we've seen that. But there is an economic difference that's vastly completely different. So, dot-com bubble was actually two bubbles. There was the website bubble, which was just trash on trash on trash. It was just like I think what was it? Excite at home bought a e-greeting card company for like a billion dollars. It was insane crap happening. That was so small. The big thing that people are thinking about is
[45:23] the dark fiber. >> Dark fiber? >> It Dark fiber was all of the wires that put in the ground thinking, "We're going to have all this demand for internet." And it turned out that demand for internet, I think the analyst estimate was it was doubling every 90 days when it was doing that every 6 to 12 months, maybe maybe longer. And just thus there was a massive overbuild of fiber optic cable and indeed the transmission stations and such to simplifying to bring that to people's houses. And there was the assumption that well, that would all get lit up and people would want it
[45:53] immediately. Didn't really happen. Now, the post dot-com bubble thing people say is, "Well, but after that, there was demand for the internet." That's the thing, though. That's very to demand for generative AI. Right now, the demand we have for generative AI is predominantly subsidized. Just Let's start there. >> Yeah. >> Predominantly subsidized, and most people experiencing it are not paying the real cost. >> I agree. >> On top of that, we already have all of the possible marketing in the world. We have the largest, most disingenuous marketing campaign in the history of man
[46:25] pushing this up the hill. We have the apex predator of cloud software, Microsoft. They can only get single-digit billions from selling AI software. And Christ almighty, outside of OpenAI and Anthropic, who better get $22 billion. And the thing is, $22 billion is a large amount to you and me. It's not a large amount of money when you spent a trillion-plus dollars, when you have Anthropic and OpenAI with $1.1 trillion worth of cloud commitments. And on top of that, how does this turn into a post.com bubble thing? A data center built today
[46:57] is going to be as expensive to run in 2050 as it is today. Unless there's some breakthrough in electricity, but again, that's not happening with AI. AI is not doing that. Unless there's some breakthrough in GPU technology, but we already have Broadcom, Nvidia, etched. We have every major chip company, ARM, trying to do something about this. And no one seems to magically be able to make this profitable or indeed even less costly. Even Nvidia, with Vera Rubin, their more expensive new GPU system, even then they're like, "Yeah, 10x more
[47:28] efficient. It's a more dollars per megawatt." They're all coy about it. They don't just say, "Yeah, we worked with OpenAI and Anthropic, and we found it reduced their cost by 50%." Easiest thing in the world if it was true. And that's because it's not happening. And this isn't a case where >> So, you're saying there's not going to be the demand for Let's say let's, you know, there's different types of AI, generative AI. We >> Yeah, and actually that's a good point to make. The reason they use the term artificial intelligence is so everyone would lump everything into it. They would [clears throat] lump protein folding, nothing to do with LLM's.
[47:59] Robotics, not LLM's. Autonomous weapons, even horrible as they are, not LLM's because you couldn't trust them. But they've mushed everything into AI so that when you say, "Well, AI can't." They'll go, "Um um sir, you forgot to give us homework. And also, AI it's working on curing cancer." When it's just like, "No, that's not LLM's. Stop giving them credit." So >> though is they all need GPUs or at least GPUs. >> and that's the funny thing. All those data centers that we're building, all of them are for just generative AI. They're not for all of the other stuff. They're
[48:30] not for the cool AI has been around for a long time. Google a lot of the good stuff that comes out of Google from the search side is AI, but pre-generative. >> would you run uh the the type of AI that sits in a robot, let's say one of the Optimus robots, if you didn't have a GPU? >> So, Metic. Metic is this cleaning robot, for example. That thing is not got a little GPU in it. What it has and may indeed have used some GPUs, but nowhere near as many as they need for generative AI to run the
[49:00] data feed training data into it so it's able to clean a house. But when the little bugger's going around cleaning my floor, turdsley I call him, goes around what mopping my floor, it's not like burning money the whole time. But when it comes to these massive amount of data centers site line climate said in February there's 190 gigawatts of data centers under in planning, don't know about under construction. That works out at about 12 million a megawatt, that's what like 1.6 trillion to 3 trillion dollars a year in annual demand you'd need for that. We don't even have 130 billion dollars worth of annual demand. And people say, "Well, it
[49:31] will grow." How? When most of the demand is coming from Amazon feeding money to Open AI and Anthropic, Microsoft feeding money to Open AI and Anthropic, Google feeding money to Open AI and Anthro Well, Google hasn't fed it to Open AI yet, but they're a pretty big customer, billions of dollars. The con side is that we are building these effigies to capitalism, these giant GPU data centers, and people are being told, "Well, it's for AI." You know, the thing that's done all this other stuff that's unrelated. Or the worst thing I've seen is like, "Oh, you don't like you like
[50:02] online banking. Well, you do like data centers." There's a big difference between a data center for regular non-GPU compute for standing up a server or a content delivery system like Akamai or something that brings the website to you, or how Meta runs Facebook. That is not the same. It takes way less power, mostly CPU-driven, compared to these giant GPU data centers that are for one thing. One thing only. >> But I was doing the the research of looking at some of these notes here. It It does say that for tougher types of AI systems designed to solve concrete physics, biology, and spatial problems,
[50:32] they require some of the most intense data center infrastructure on the planet. AI systems like DeepMind's AlphaFold, the protein folding company, used for genomic sequencing and climate forecasting, etc., run on high-performance computing clusters. These require immense precision and continuous heavy compute in data centers. >> Yeah. >> Training the brains for self-driving cars requires billions of miles of simulated physics environments. The AI isn't generating text. It's learning to navigate 3D spaces and gravity and
[51:03] relies on data centers. >> Right. >> And the thing is, those data centers, they might have GPUs in them. We had GPUs used for this HPC, the high-performance computing, before generative AI. And yeah, that's how AI's been trained before. That's how Tesla did it. I believe they've had their own data centers when it comes to training the autopilot system for better or for worse. >> That's how we've done it before. >> Again, that is not why we're building these data centers. These data centers are being built to sell to AI generative AI companies to either train systems or
[51:33] run inference. These things are being built in this brainless way where it's just, "Well, actually, maybe this is a good way of illustrating the con." Because everyone saw Google, Microsoft, Amazon, and Meta give Nvidia over call it 800 selling billion dollars. Because everyone saw that, they went, "Well, they wouldn't do that for no reason." They went, "We got to build more of these things. There must be all this demand." Even though the demand 70% or more of all that demand comes
[52:05] from these two companies who are funded by these three companies. And that's the funny thing. The reason that they don't want to break out their AI revenues is because it would become alarmingly obvious that this was the case. It turns out that the only real big customers, cuz it's not like they're building a few data centers, they're building trillion plus revenue potential they believe they'll get. Speculative. Entirely speculative. They're building it because they saw the biggest companies in the world buy a bunch of GPUs and they said, "I want in on that." They must have diverse
[52:36] customers, right? They wouldn't just have two unprofitable fail sons that they're propping up with Christ, they've raised 217 billion dollars just in 2026. >> So, we know that some of the biggest companies in the world are using AI, generative AI, to write a lot of their code. >> Mhm. >> That is a great productivity gain for those companies, right? I mean, have you used Google or Facebook or Instagram or GitHub recently? Because they are catastrophically worse. Amazon Web Services went down multiple times
[53:06] because of their AI coding tool. >> How How is How is Google worse? >> Well, I'll tell the story of a real guy called Prabhakar Raghavan, previously one of the heads of ads Google. In 2019, Google called something called a code yellow, which is when they said, "We've got a problem." And it was material weakness in query numbers, which means the amount of times that people were searching on Google Search. Guy called Ben Gomes, internal to Google, then the head of Google Search, says, "Wait a minute. To increase this number of using Google more, >> Mhm. >> we're going to have to Well, I mean, you
[53:37] What you're suggesting would mean we give worse answers because if someone got the answer quickly, that would reduce the amount of queries, right? And people at Google, the Shashi Thakur was another engineer was saying, "Yeah, can we please tell Sundar this? Because this doesn't seem good. We can't just increase the amount of queries. That would just mean that people would have to search more, which make the product worse." >> But but it would make them more money, you're saying? >> Yes. And it would cause you to show them more ads. So, if you were spending more time on Google cuz Google's what >> But is this linked to AI doing code? >> I'll get there. So,
[54:07] this is The problem is is that this guy called Prabhakar Raghavan, who's the head of ads at the time, was pushing pushing and saying, "No, we need to make more queries happen. Going to make it happen." Nick Fox, who was there as well, I believe he's actually taken over Google Search, said, "Going to make them go up. This is our new reality." Sometime in early 2020, Prabhakar Raghavan takes over Google Search. From then, and this is This is what I believe. Can't prove it. If you go and look around the various SEO sites, such as Journal and the various forums, Google stripped back a lot of the suppression of spammy sites so that
[54:38] people would be on Google more. And then over the course of time, Google wanted to create more queries, and Google Search became much worse. It's why people always do like plus Reddit or from Reddit or what have you. It's because the actual underlying search results of Google had got worse. And then generative AI came along. And Prabhakar, wouldn't you know it, gets put around part of Gemini. And Google also was having trouble getting people back on Google. And what did they think they'd do? Well, everyone's talking about this AI thing. We'll just put it right at the top so
[55:08] people have to stay at Google. And actually, they'll use it more because instead of searching websites and doing that annoying thing where they click away from Google, they'll just only use Google. Instead of generating answers, by which I mean giving you search results you click through, now Google is the answer. Is it right? God no. It might tell you to eat rocks. Might eat poisonous mushrooms. Maybe it'll give you a little few links you could click through. But the ideal situation was that AI was the ultimate form of Google's evil, which were >> But I'm saying here I'm saying here but that's not the fact that coders could
[55:39] code on Google that's made Google worse. That's human decisions have made it worse. >> Yes, and then there's the instability of Google's platform, which is actually I should have probably labeled that a problem across the whole tech industry. >> Okay, so you're saying that that you're saying Google is going down more? >> Yes, Google is less stable. Google Docs is a bug fest right now and has been for a while. Google Sheets, same deal. And the thing is, you're right. I'm being a little unfair. This is everyone. It's the same with Microsoft. It's the same with Amazon. It's the same across the >> How do we quantify that outside of anecdotes? Like is there a way to
[56:09] >> You're right. I mean GitHub downtime is the best example. Amazon Web Services went down like two or three times this year because of AI tools. And honestly, you're right. It's kind of hard to quantify outside of anecdotes, but I challenge anyone listening to this. Go and use a website these days and tell me how well it works. Tell me how buggy it is. Tell me how many problems even with my iPhone, the supposed best UX in town. Even the iPhone is a flipping mess these days. >> Okay, so the research says the short answer is yes. Tech downtime
[56:41] and software outages have demonstrably increased over the last few years and industry data points directly to the explosion of AI-assisted coding as a primary culprit. The problem is hitting the tech industry from two entirely different directions. The code itself is getting buggier and the sheer volume of AI activity is literally crashing the underlying infrastructure. >> Interesting. >> Yeah, that's because GitHub people are just writing a bunch of code, pushing it. And thus there's just more code on there. >> That's interesting. >> Yeah, it's it's a real mess as well because
[57:12] open source is at this problem as well because it's well-meaning people. They're like, "Oh, I learned a bit of code with an LLM. I'm going to go out and do some stuff. I'm going to make this project better." And these people barely understand what they're shipping. Or maybe they understand a bit of code and they say, "Oh, Dunning-Kruger this I'm going to I'm just like I can understand some of this." And now the code's all written and they just push it right now. So GitHub is flooded with AI code. >> This sounds like it's making humans complacent. >> It is. >> Because we're going, "Okay, look, I'll let it write the the code for the last 100 lines, and it was broadly right. So,
[57:43] the next 100 lines, I won't check them as much." >> Yeah, yeah, yeah. >> And that's human nature is to get sort of to take shortcuts, to spend less energy on an activity if you can. >> Right, but the AI is still making the mistake and we're still making all the promises of the AI. That's the thing. This thing is meant to be this autonomous perfect You say it can't be perfect. I don't know. Based on what Sam Altman has been saying for the last few years, clammy Sammy's been promising the world saying this will replace software engineers. Dario Amodei, Wario himself has been saying, "Oh, yeah, 50% of
[58:15] white-collar labor is going to go away in the next few years." These people are promising the world. Again, if they were saying it would be smaller and they were like, "Yeah, it does have issues and we must be None of this, "Oh, what if it wakes up and it's super powerful." Just like, "Yeah, it's probabilistic. It's going to make mistakes. And if you don't know what you're doing, you don't really know what you're looking at, you're going to miss those mistakes, and it's going to get multiplicatively worse as you go when you don't know what you're doing." So, yeah, human nature is part of it. But so is the marketing, so are the
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[60:47] 15% off. Keep that to yourself. My My car that drives itself, that is AI technology. >> Yes. >> I sat here with Dara from Uber, and he was saying that I think in a couple of years time we won't need drivers um for Uber because the cars will drive themselves, like they'll be fully autonomous. >> And I think, if I'm not mistaken, driving is one of the biggest professions on planet Earth. So, when you hear people, when you hear these CEOs saying that there will be job
[61:17] disruption, >> Mhm. >> you say that they are not telling the truth. >> Yes, or they're guessing in a way that's very good for them. Think about it from perspective of Microsoft, Satya Nadella. He's not going to be like, "Yeah, we've done it. This is going to work, mate." Of course, he's going to talk his book and he's going to say, "Yeah, this is going to replace all workers. It's going to be amazing. You you It's going to be so powerful." And then he'll change his tune and say, "Actually, it's not going to replace workers. It'll make them more powerful because the things aren't catching up." Dara Khosrowshahi for example, of course, he's going to say, "If this happens,
[61:48] then that would be good for Uber because Uber would just become an autonomous taxi service." There's a reason that Waymo's taking I I find Waymo fascinating. I think that shit's really cool. I think there are socio-economic problems that will come from it. I think there are actual real problems that will emerge. And also, >> What kind of problems? >> Well, I mean, socio-economically they're like you said, one of the largest employment centers in the world. I mean, just the economics of cabs before apart. But again, we are nowhere nowhere nowhere near that. We're not even close. Waymo has had to do these smallest rollouts in the most controlled things
[62:18] because the problem with pretty much every AI system, but especially driving, is not the getting 95% of the way. It's those edge cases. It's raining, which is a big problem for them in San Francisco. It's a kid runs across the road, but they're wearing a high-vis thing. Does it even notice it's a child? >> Again, this is a really interesting but very very applicable example of uh the right comparison to be made shouldn't be autonomous vehicles versus perfection. It should be autonomous vehicles versus human drivers. >> I mean, I don't know if I agree because a human driver might make mistakes,
[62:48] sure, but again, not an expert in autonomous cars. Just want to be clear. But if we're pushing autonomous cars out there willy-nilly and we're not doing so in extremely controlled environments, those edge cases will multiply and be dangerous. Yeah, they might be better at human drivers in some ways, but they might also I was in Vegas the other day and I was in a hotel and I watched a bunch of Zoox cars just get stuck. >> The autonomous cars? >> Yeah, yeah, yeah. They they these weird boxy things. They just blocked the exit. They just all kind of lined up and just fell asleep. I saw the same thing
[63:19] actually happen outside of a hotel when I got out of a Waymo in San Francisco. Just stopped at the side and then a bunch of cars and then another Waymo got stuck behind it. And these are kind of These are kind >> I've seen some human bad drivers as well. >> I I agree, but it's just we have control over deploying these bad or good drivers. We have an ability to roll them out slowly, which is exactly what we should do. I'm not saying autonomous cars are bad. I'm saying we need to be so so so careful and treat them as guilty until proven innocent. Because we can prove And also
[63:50] they have people overlooking them. They actually have people monitoring the routes. It is something they cannot rush out and it doesn't seem like they're rushing it, which is good. And they're not promising the world. >> agree. Listen, I I'm a big fan of a big fan of taxi drivers generally in in part because I spend a lot of time in taxis and I think I'm not just getting in there because I want to get to from A to B. I'm getting in there for lots of other reasons. >> Yeah. >> However, when I look at the stats around what is more dangerous, >> Uh-huh. >> driving myself or having an autonomous vehicle drive me, there's a 68% lower overall crash involvement rate when
[64:21] you're in an an autonomous vehicle. >> Uh-huh. >> Autonomous vehicles experience roughly 2.1 police-reported crashes per million miles compared to humans that are at roughly 4.68 per million miles. >> Right. >> A 55% reduction when you get in an autonomous vehicle. And autonomous vehicles show an 80 to 81% reduction in crashes resulting in injuries versus human drivers. >> Uh-huh. >> So, you're 85% less likely to be involved in a single vehicle crash, like hitting a wall or a tree, if you're in an autonomous vehicle
[64:51] versus being driven by >> I I agree, but >> So, it's safer. >> And also that data is What's the sample size of human drivers? I mean, we've got many many many many many many more years of drivers and many many many more years of accidents. And also man, does that not have anything to do with generative AI? If we were just talking about that, we're having a different conversation. >> the question here was really around job disruption. Like, you know, we we look across industries and we go driving's a massive profession. Is there going to be job disruption because cars can now drive themselves? If we think about
[65:21] white-collar, you know, jobs, you know, lawyers and accountants, people sit here and they tell me that lawyers and accountants will the profession, right? I should say some of the skills within the profession will be relegated to AIs to do. >> Here's the thing. Lawyers, for example, example, always hearing legal partners talking about AI. Never the associates. The associates are the ones that go out and find the precedent. They're the ones that go and do the grunt work. They're the ones who are pulling motions off the time. The partner is the one that might be the
[65:51] litigator, it might be the client-facing, but the ones that are actually doing the day-to-day work, I'm not hearing from them. I'm not hearing associates being like, "This is awesome." I'm hearing a bunch of well-paid people that have sat on ChatGPT and gone, "Yeah. Yeah, I'm the greatest lawyer ever." They're not the ones that I want to hear from. The actual workers. White-collar labor disruption is not happening. OpenAI had a study that came out, I think like a week ago, that said there was no connection between spending on AI tokens and revenue per employee. Like, this is
[66:21] open and that's the thing. >> What does that mean? Could you explain that to me? >> As in, the more tokens you spend has no correlation at all with the amount of money you make. It's the second report they've put out. The other one was like hallucinations are mathematically guaranteed. Kind of almost It's the one thing I respect about that company that occasionally they just put out a study that's like, "Yeah, shit's going to suck." >> [clears throat] >> But, the people that are having their lives disrupted, work-wise, are art directors. It's people Art directors, transcribers, translators, who have bosses that don't care about the output.
[66:52] It's what they consider cheap work. And the problem is is those people would have automated your work away anyway. They would have sold it. They would have taken the cheapest They would have sold it to the global south. They would have taken the shittiest option they could. That is something that AI is doing. And again, those people are not paying the actual cost of AI. They're using a subscription. The actual white-collar labor force might have some things that are slightly changing, but there is no evidence of like productivity gains. In fact, if there were, they would be screaming it from
[67:22] the rooftops. There was an Oxford economic study last year where it's like, "Oh, young people are finding less jobs because of AI." We actually read the study, which multiple journalists did not. It was a single line that said, "Yeah, we saw some correlation." Didn't give a number. Didn't actually say what the correlation was. We are so conditioned to believe that the rich and powerful know what they're doing that we internalize these narratives about like, "Well, previous booms lost a lot of money. Well, technology takes time to do stuff." And they are intentionally playing on those
[67:54] mythologies. They are playing on these knowing that journalists, analysts, investors will believe them. And this is partly because our our reality is defined by stock prices. Because the stock prices of these companies went up, we're like, "Oh, look, it must be working, right?" >> Both of those things you said were true though, right? Like that previous technologies didn't make money at the start, and you the other one you said was um they'll get better. >> But that's the thing. Okay, because another thing got better, this will get better? >> No, but there's there's got to be something that they're saying that is
[68:24] fundamentally not true, because those are two true statements that okay, technology often starts >> Okay, I know, I get what you mean. What they are fundamentally misleading people about is how possible it is. >> Okay. >> How many actual signs they have. Because they don't have the signs. If they had the signs >> Signs? >> As in the signs of this getting cheaper. As in the signs of this being able to autonomously do work without the Rube Goldberg machine. And even then, in a reliable way that was making the customer more money. Being productive in a way you can say with your whole chest without a series of asterisks. And that's how it is across the board. The
[68:55] people that are most excited about this psychopaths on Twitter in many cases are people that I believe >> Psychopaths >> They are really awesome. I'm sorry. There are some people on Twitter because the other thing about this is This is really unique to the AI industry. I've never seen it any other industry outside of maybe like sports teams. The attachment that some people online have to these companies. If you dare dare to criticize Anthropic. It's almost this religious attachment. Good example was this week Bloomberg reported that OpenAI
[69:25] was on track to hit $40 billion in annualized revenue. Months times 12 4 weeks times 13. We don't know. They don't define it. I saw multiple people online going actually it's $60 billion. It's actually $60 billion. I heard from someone. It is like a cult and it's a cult of software driven around growth and this idea that by backing the right horse you will have some grand thing and OpenAI in particular and particular Mr. Altman They have been fermenting this. That
[69:56] Tibo as well at Tibo that one of the guys at OpenAI. They ferment this thing online. They build this kind of parasocial relationship with both the large language model themselves and the companies. And one's allegiance to the companies is so important. It's truly vile. If only these people gave a about I don't know Medicare for all or poverty or thing like actual problems in the world versus are we buying enough GPUs? >> Do you know what's interesting is some of what your narrative
[70:26] one would argue actually helps them. >> How? >> Because you know the AI doomers that have come here and talk you know some of the original founding fathers of AI like Geoffrey Hinton have told me that what they're building is highly highly dangerous and that it will be fundamentally disruptive to society. And it's interesting because some of the CEOs who you've mentioned their historical narrative was also by the way this is really dangerous. >> Yeah. >> And there is a significant chance it could we could up the the planet. >> Uh-huh. >> And what we've seen is this slow pivot away from it which is
[70:56] >> Because now they're getting booed and they're they're being attacked. Yeah. They've been a slow pivot away from it. And the pivot almost sounds a little bit like your narrative. It now sounds like actually now it's not going to change anything and you're all going to be fine. And it's now it's just not it's not it's not dangerous at all. >> But that's the funny thing. >> And that's why I'm saying like your your they I actually think there might be a couple PR people at these big AI companies thinking thank god for Ed. >> Oh, I don't know about that. >> some of it some of [laughter] it because you're like you're saying actually don't worry everything's going to be fine.
[71:26] It's not going to take your job. It's not going to disrupt the economy. It's just a fad. There's no technology. And I think they don't think that. >> Here's the thing. I think Altman and Amodei are some of the most deeply corrupt and cynical people in the world. I don't Of course they were going to say from the it was early 2023. Altman said we're a little bit scared about what we're creating. Oh, shut up. I'm just I hear that and I feel so frustrated because I've met so many of these rich liars. These people And you know why he wants to say that? So you'll invest in his company and buy the software. So you'll be scared that if
[71:56] you don't use AI today, you'll be left behind in the future. Which is their continual narrative. That if you don't get on the train today, then you'll be left behind. By the way, every single scam and con starts with rushing you. Every single trick in history begins with saying you must do this now. And best piece of advice I ever got was if anyone tries to rush you and it's not literally a mortal thing like you are bleeding or on fire or the house is on fire, slow down. And yet all of these companies say so scary. And now they're talking about slow downs. But
[72:26] you ever notice that Amodei and Altman they say oh maybe we should slow down progress. And then they don't. Right now Altman's saying oh we slow down progress because we're so delayed. Now they're out compute. Now they're doing it. But I can guarantee you by the way their PR people do not like me. I know for a I know I don't think Open AI's PR people are super fond of me for >> But I bet there's elements of what you're saying because you're calming people you you are theoretically calming down the general public. >> And you know what? I hope I am because the fear based tactics is horrible. These companies don't want that. These companies want people scared. I'm 100%
[72:56] sure. >> I don't I just fundamentally disagree. I think it >> Can I So, the timelines there and I sit here and what I do is I log their quotes over time. And I read them out from 2015 to 2026. And the change you see is them going from there could be extinction. That's the narrative the early narrative. Elon said it himself. He says it's the single most dangerous thing in the >> Elon says a lot of things. >> And then you track it over time and it evolves to this age of abundance. We're all going to have unlimited stuff. And then um the the the new slogan at
[73:28] ChatGPT is intelligence for everyone. It suddenly and all the and and whenever Dario comes out and says, "By the way, it's really rough and dangerous." They attack Dario. >> Yeah, that's me. >> They hate him. >> Yeah, that might >> [laughter] >> Dario is >> They're like, "Dario, shut the up." >> Honestly, I I've been saying, "Dario, shut the up." for years, but it's The thing is, I get your point where it's like >> I don't think they've changed to calm the public down so much as they're desperate to not get regulated, which is laughable. We don't regulate tech. We don't regulate America doesn't regulate We are in the We are
[73:59] still trapped in the hands of Milton Friedman, Margaret Thatcher, and Ronald Reagan. We're still stuck in the near liberalistic hellscape, which is growth at all costs free market capitalism. So, no. No one's regulating. The regulation of these companies should have been, I don't know, breaking up Put these bastards aside. Break up these >> For sure. We shouldn't have companies this big. It makes things worse. >> technologies are dangerous. >> I mean, they're dangerous, but not in the ways they've been warning about. >> Less If we think about cyber hacking.
[74:29] >> Right. And just be clear, those cyber hacking things that happened were not a result of They were like, "Break out of the sandbox." And then they set the sandbox up wrong. They set up the server they were on wrong. >> But I mean, you know, advanced AI models could very easily, cuz they can go out onto the open internet as agents, they could very easily go and look at code bases of different websites, find vulnerabilities, and exploit those vulnerabilities. >> Yeah. >> And at scale, and arguably, um, at a higher intelligence and faster and wider
[74:59] than humans a human hacker could theoretically. So, that's dangerous. >> Well, here's the funny thing. We don't know how much compute was spent to do the hugging face attack, the open AI one. We also do know that they improperly set up the server to keep it in. They thought they'd turn the internet off, and they didn't. That's human error, and that's human error in a sense that, yeah, they threw about an indeterminately large amount of compute. This is dangerous, but people keep saying we can't let the the Chinese get a hold of these models. We couldn't possibly, because what if these models fall into the wrong hands? They're
[75:30] already in the wrong hands. Mark Zuckerberg, Sam Altman, Dario Amodei. The wrong hands are the hands of those who are running these companies. We should not be training these models to do these things. I don't know why the we're doing it, other than they've run out of other things they can train on. There's a ton. And the fact that they can do it is kind of interesting. >> But you do it Would you agree that it's an intelligence, and I'll call it that, you know, you might disagree with that terminology, but an intelligence that can go out onto the internet and click around and take actions is inherently
[76:03] there's risks associated with that. Well, >> the second part I agree with, the risks. We've had people running automated scripts hacking scripts for a while. We've had hackers doing that for years and years and years. This is brute forcing it with a bunch of compute, and yet, it is dangerous. These companies are doing something dangerous. That is not what Geoffrey Hinton et al. have been warning about. They've been saying, "Oh, these things could destroy society. They could manipulate people." When you actually look at the underlying thing, it's not so much. Geoffrey Hinton, as well, talking his book, still got his Google stock, I think. And weirdly
[76:33] enough, he left Google cuz he was worried about the AI there, but then immediately made a comment being like, "Yeah, actually though, Google's very responsible." Strange thing, that. But let's get back to the the cybersecurity side. I agree, this is dangerous. These people should not have access to so much compute. They clearly don't know what to do with it. There's a really easy way of dealing with this. It's not letting them use so much compute. It's regulating that part out of existence. What if the Chinese do it? The Chinese are able to distill the models. And also, I don't know, regulate it and stop I I
[77:04] feel like with this particular thing as well, we got to this point and let the genie out of the bottle to use an annoying Sam Altman term. We let this happen because we let these companies be unregulated and use as much compute as we want. We have these enablers allowing them to burn as much compute as they want. And also, we for all of these dire warnings about AI dangers, no one seems to have done anything. >> Okay, we're going to play a game, Ed. >> Let's play it. >> On these cards here, I have the things that you consider to be myths about the
[77:35] AI industry. The challenge is I want you to give me one sentence. on each myth. >> Ah, crap. >> So, just your first reaction. You're going to pick it up, you're going to read it, and then you're going to give me one sentence on your opinion of that um belief. >> Okay, let's go. >> So, >> Let's do this. >> What does it say and what and what's your one sentence? >> It says the the AI industry is creating enormous economic growth. No, it's not. It's nowhere in the data.
[78:05] >> Okay. >> [laughter] >> Like it's just May I do a second sentence? >> Go ahead. >> Pretty much all of the economics is either Nvidia feeding money to its companies like CoreWeave or these three companies feeding money to these ones to spend it with the them. >> Okay. And what evidence do you have that there's it's not causing economic growth? >> Just to be clear, other than the spend on semiconductors, so the speculative investment in GPUs and data center infrastructure, that's happening. But, as far as like spend on AI goes, barely cracking a hundred billion dollars, and
[78:35] most of that is just these two running their services and paying these three companies Oracle, Huawei, and others. >> But a hundred billion dollars is a lot of money for a relatively new technology. >> Not when you've spent three hundred billion dollars in equity funding and if we're going with just these three, I think six hundred billion dollars in capital expenditures. >> Yeah, I get that. That means it's not profitable, but the hundred billion is an expression of consumer demand. >> When the compute is mostly driven by subscriptions that subsidized, no, it's not. When you're giving someone twenty dollars or forty dollars for a dollar,
[79:05] they are going to use it more. If this was all on a per million token basis, we'd have a different conversation. >> Fine. Cool. Next one. The United States need to spend trillions to beat China in the AI race. Let's see. What AI race? That's actually That's actually my point. It's what AI race is there? Is it to make big scary LLMs? They They did that already. Without the Nvidia GPUs. By the way, they've got Blackwell GPUs. Kakashi and Just Taro, two amazing
[79:35] analysts I love. They've been on this for years. It's like China's already had Nvidia GPUs that they're not meant to have for years. But also, to do what? They already got the LLMs. What What's the race to do? To make us spend more money than them? For us to constantly piss our pants worrying about China? Because they won if that's the case. Myth number three, AI will replace all human jobs. That just isn't happening. There's no economic data to support it. >> Will it replace some jobs? >> I mean, it's replace some contract labor
[80:06] that would otherwise be replaced with cheap labor out in the global south. It's a digital globalization that sense, but all jobs, most jobs, a lot of jobs, no. >> What about robotics? >> Robotics is not what we're talking about. Robotics is a very different thing. And even then, >> Robotics will be powered by AI. >> I mean, yes, but there are tons of different kinds of AI. We're talking explicitly about generative AI, and that's what I This is for my myth busters piece. It was definitely about generative AI. >> Okay, but what about robotics? Like the thing is the Optimus robot that Elon's working on at Tesla.
[80:36] >> The one where even in the demo of the hand, he still like they had to have a guy controlling it. It wasn't doing that autonomously. Here's the thing. If they can beat all these challenges, yeah, robotics would be really cool. I don't know how long that's That's one I'd actually be willing to believe in a couple decades. >> Have you seen them try them Chinese robots? I know you've seen them Chinese >> What's it called? The one that can dance and that, but they can't really do human things. >> Well, it's just it is pretty mind-blowing. >> It's robotics are cool. I like
[81:06] I'm not going to pretend I don't think robots are cool. I wish they were building robots and actually doing cool I wish the tech industry still made fun stuff and interesting stuff. Instead we get these large language models. >> But with AI plus robotics is an You know, I I was in San Francisco and I went to this massive incubator there. And when I'd gone there 3 years earlier, it was all software startups. And when I went back 3 years later, it was all these robots startups. And I remember saying to the founder of the incubator, I was like, "Why is everything robots now?" There was this one robot where it was just the arm and it had a frying pan
[81:37] on it. >> Yeah. >> And its whole thing is it cooks for you. Yeah. So it was it was showing me it cooking, whatever. And he goes, "Well, you know the arm? He goes the the hardware part, the physical parts, that's always been fairly cheap." Yeah. He goes, "The expensive part was the intelligence. And now that's come down to pennies. So what you're seeing is this explosion in the robotics industry cuz robotics is a function of intelligence plus hardware. We've always had the >> And a ton of data though as well. Like data is very expensive. >> Yeah. >> The thing is cybercabs rolled out real slow. It's going to take a long time. It
[82:07] could be a threat. If they do a robot that could replace a human job, sure it could. But that human jobs are multifaceted. Human jobs change with environments and also a lot of human jobs that you might think of like I don't know, dishwashing robot for example. >> Yeah. >> Some guy at a restaurant isn't paying 10, 20 grand for a robot to replace the job that they're already not paying enough for. The point is, yeah, it could if you can replace the jobs. That is not what we're talking about with this. >> Yeah, I I just I just I ask these questions not because I'm trying to be
[82:37] like I actually I'm trying to form my own opinion on these things and I I do think, you know, as it's written there, it says AI will replace all human jobs. Obviously not. Obviously that's But I'm I'm trying to figure out if the truth is somewhere in the middle. That there's a certain type of job which actually humans probably shouldn't have ever been doing really. Um if you think back through history, there was someone's job just to sit in an elevator and press the buttons. >> Right. >> example of a job that humans probably shouldn't have been doing. And as technology gets more advanced, it takes
[83:07] on a lot of that sort of automated monotonous stuff. >> Right. The thing is with this particular thing that I know that this is from, it's a specific blog I wrote. I was explicitly talking about generative AI though. I was explicitly [clears throat] talking about people when they say this, they're referring to that. >> So, you're not talking about agentic AI with >> Agentic AI is LLMs. Agentic AI is just a fancy way of saying an LLM talking to another LLM with a harness on top. That is still LLMs. Agentic AI is one of the big the bigger lies they tell. It's like when you hear agent, you're meant to think autonomous AI can do what you
[83:38] want. It's still LLMs. It's still LLMs talking to other LLMs doing what LLMs >> Yeah. >> taking screenshots and putting them in LLMs and stuff. >> Oh god, yeah. >> Okay, but but you know, I could I could make the case that I'm just thinking about my personal usage. I definitely use agents to do things that I would have previously asked people to do. It's not to say that I didn't I still don't hire cuz we're hiring like crazy. >> Yeah. >> And I still in that particular function I'm thinking about like the chief of staff role. So, my chief of staff would have triaged all of my inboxes previously and put them somewhere
[84:08] and told me about them or maybe once upon a time showed me a piece of paper back in the day, I guess. Now, my chief of staff is no longer doing that job. >> Do you still have a chief of staff though? >> And this is what I'm saying, they're doing other things. >> Right. But the thing is, again, what you were describing is fairly basic automation. I don't know what the tasks are, Tristram. >> basic even >> They spent a trillion dollars on triaging email. Like, that's the the promise is if they'd spent 10 billion dollars and this was much smaller and you're like, "Oh, cool. Cool software. Yay." A lot of the things that people are impressed with are like script stuff as well. It's just LLMs doing Python.
[84:38] You should be impressed by Python code. Python's incredible. You can scrape websites, you can download It's awesome. But, the point I'm making is none of this would be anywhere near as much of a problem if they didn't ask for all of the attention, all of the money, and promise the world. It's their promises that are the problem and the journalists who went along with it and the analysts and the Twitter people who went along with this saying that this would change everything and replace everything and leaving the realm of reality. >> Is there any technological innovation through history that was really, really game-changing where that didn't happen?
[85:10] >> I mean the internet. >> I mean, people over-promised that. >> I mean, they over-promised on the businesses, but I've read through a great many pieces about the early internet. A lot of people were excited but hesitant. They were worried that there was not enough demand, but they were still like, "Oh, yeah. This could have potential ramifications if it happened." People were not super negative about the internet. A lot of the skeptics were saying, "We're worried about an overload of bad information." Look at where we are. A lot of people
[85:40] were worried about the social consequences of everyone talking online, which they were correct about. With the economic things, they were specifically talking about like the Globe, which I think made hundreds of thousands of dollars and had like a think a billion-dollar market cap. They were talking >> Yeah, there was massive hype in the dot-com era. >> I read a lot of those stories. The hype was nowhere near You didn't have articles everywhere that was saying "If you don't get online, you'll be left behind." You didn't have professional consequences. Nick Srnicek mentioned his blog earlier. He described the thing global AI assisting global
[86:10] decision-making, where he said that you have businesses you work at where if you don't say that you're more productive with AI, whether or not it's true is irrelevant, you have professional consequences, you can get fired. There are people having to AI wash their jobs by saying AI did it. Otherwise, their bosses, who don't do will get mad at them. This did not happen with the internet. It was not present. And part of the thing is social media was not like it is today. The kind of uh was it decentralization of media
[86:41] in general has caused this as well. And also the fact that day trading there's so many different things that are different. It's crazy. >> I I do I do think AI is different from the internet in part if you just measured it on the speed of adoption. Especially if we can just think about generative AI and AI is also >> But but the the the the adoption of the internet required physical connections to your house. The adoption of generative AI involves having a web browser. It took a vast amount of effort to bring internet to people. Even with dial-up connections, it still required the distribution. >> why it was so slow and there was less,
[87:12] you know, there was less hype than AI. I do agree that there's way more hype. And we are again, going back to this point that we're clustering AI in this big category of lots of different things. It was generative AI. It's generative AI, there's like real-world AI. >> AI is explicitly what I'm talking about here. When bosses are saying you need to use AI, they're not saying I need you to go and buy a Unibeam robot. They're saying use LLMs so that I And that's the thing. I have this theory that era of the business idiot where it's like we are ruled by people that don't do work cuz nobody who actually does a bunch of work, who really is productive, is
[87:43] harassing someone who works to them for not being productive enough. They're not They don't have the time. They're doing work. Someone who's sitting there with the ingratiation machine that's telling them that every beautiful idea out of their messy little skull is amazing. Yeah, they're going, "Damn, this thing says I'm a genius. Why are you not using the genius machine to do more work?" And yeah, if you're a boss that goes to lunch, leaves lunch, and sometimes reads your emails, LLMs are magic. >> I do you know one of the most compelling arguments I have for the over-hype of AI in a world where everybody has access to
[88:14] these tools, whatever the tools [clears throat] can do would largely be commoditized. And what the tools can't do, which one could say is the human taste judgment, you could say it's people skills, whatever you want to say, is now going to be the valuable thing because the scarce and the hard becomes the most valuable through history. And the commoditized becomes the least valuable. So, the very nature that we're commoditizing the generation of content or whatever you want to call it code, means that's actually not where the value will accrue as for the user.
[88:45] And actually, if you think about what it takes to now make something that is objectively great, if an AI can do it, then it's not the the great thing is not a value. So, so I think a lot I've been thinking a lot actually about how how do you avoid the temptation of slopification of the things you make, the value you put into the world? It's very simple example that people will be able to relate to. If you use ChatGPT or Anthropic, you know, Claude to make your LinkedIn posts, let's say,
[89:16] they will be LinkedIn posts because everybody else is using them. And actually, a great LinkedIn post now is someone who doesn't use them and makes something that's like irreplaceably human and deeper and more personal, N of 1, lived experience. >> Yeah. >> All these things that AI can't do. And I think that's a compelling argument that actually the commodity tools produce commodity outcomes. >> So, everyone has access to these things and what's changed? Like really, like what >> The slopification of stuff. >> We've We've got a bunch of slop, but these people were half-assing their jobs
[89:46] before. It's just a half-assery machine. And it's just It's It's the thing It's what I'm talking about with the slop blocks. It's like It's a Yeah, people that gave you dog before have now got the dog machine to pump out dog It's So, there's a guy called Carl Brown, internet box, awesome guy, great software engineer. He He said I might have said this earlier, so it makes the easy things easy, the hard harder. When you know you're doing a really distinct small script for something and it can pop that out, it's awesome. I used Claude the other day for something useful. My kid loves Minecraft. I was
[90:16] trying to fix a broken mod cuz he loves his Wither Storm. It's awesome. And it still took me half an hour and kept getting things wrong. What do you use AI for? Generative AI. I really don't. I You don't use it. With Bloomberg Terminal, I use Ask B, which is just when it's like requesting the consensus analyst estimates for Nvidia. But otherwise, you don't use it. >> No. >> So, how do you know it's bad? >> I've used it. I've put it through its paces. I've used it to try and do financial models and found one error and immediately be like, "Ah." I've never been particularly impressed. The one thing I will defend it on is it's really
[90:46] good for like tech support. Like I have this thing called Synergy in my New York New York place I go to. I have this monitor where I have a MacBook and a PC laptop. And this thing Synergy for using the same mouse and keyboard. Dropping a giant troubleshooting log into this thing and going, "What's wrong?" And it going, "This is wrong." Yeah, super useful. Is that trillion-dollar nut? Is that a two-trillion-dollar company? No. Pretty easy. >> Better than Google, though, right? Better than Google, though. >> I mean, yeah. Remember to use Google Search still? >> I try. I have to push the crap
[91:16] out of the way and scroll. >> I can't remember the last time I did a Google search. Christ, I find myself using Bing sometimes. >> I know. I hate saying it, too. But I have to scroll past the AI crap cuz I want the good stuff. I want the I want the actual links to stuff so that I can read the thing and go >> But you can ask the AI to give you the links. >> Yeah, and it doesn't do a particularly good job. Like my >> Say Say that the other day, my iPad wasn't turning on and it was doing this funny little thing on the screen. You think that it's better to type that into Google than >> Oh, no. I must be clear. That may be the
[91:48] only LLM use case I defend. The troubleshooting thing is awesome for it. I will It's the the one weakness I have. It's like genuinely being able to drop a log into it. That's awesome. Again, that is not what they're selling it as. They're not selling it as a useful little tool. They're selling it as the uh software, as the thing that will change everything, that will replace all jobs, that will do this and that. It's not like they sold it as a quirky bit of software. >> No, you are all right. They are, you know, telling us that it is going to replace everything. But, funnily enough, the critics are saying that as well.
[92:18] >> Which one? I mean I mean >> like, the Geoffrey Hintons of the world. You know, even people that have left the safety team in ChatGPT who have who have who have who have sat here with the the these are critics that are that are warning of the impacts it's going to have on the world. >> Sweet, all these critics also have a vested interest in AI doing well, though. Daniel Former, OpenAI guy, AI 2027, written with the Star Codex guy that was nothing more than badly written science fiction that he's already had to walk back. >> could have made more money by staying at ChatGPT. >> Could he?
[92:48] >> I mean, looks like it. >> If he had options early >> It sticking around would have made him >> Did he lose the options? How much did he >> You can't You're not saying that they're they're being critical >> They're not critical of the companies themselves. They're not critical of the stealing. They're not critical of the environmental damage. They're not critical of the fact that you cannot rely on the answers. They're critical of this big scary boogeyman out in the future where it's like, oh, I'm scared of when this becomes so powerful, and everyone should talk to me about how scary and powerful it is. They're not saying, "Hey, here are the harms today. Here are
[93:18] the things we're actually looking at today. Here are the social problems of having this automated way of spewing out slop, of filling our feeds with crap, of having information that will pop up that is presented even with the little disclaimer thing of saying, 'Yeah, sometimes this gets wrong.' So, in the tiniest words possible." They don't talk about the fact that these things are trained on stealing millions of people's work. >> But, on that last point where you say that it's going to get progressively more intelligent, and when it does, it will be a danger. >> Yeah. >> Would you agree with the statement that
[93:49] artificial intelligence has gotten more intelligent if you measure it based on any sort of measure of intelligence one might use? >> It's got better on the test that are rigged for the models, it's got better at tests where you can train for the test. >> Okay, so it's got better at >> It's got better at tests that they're intentionally trained for. So, >> If you logged the rate of improvement on a graph, it would look something like this. >> Right. >> in terms of what it's capable of doing. >> Oh, that's >> There we go. >> Yeah, cuz it's not It's not got new
[94:20] features. You'll notice that outside of OpenAI and Anthropic, the va >> When you remove the coding startups, there's basically no successful AI startup company. >> So, we agree that it's got better, it's got more capable at doing things. >> Yeah, >> Okay, fine. >> Over time, AI has got more capable. If we imagine that trajectory will continue, it will get more capable. Then at some point it does cross, you know, this is what they say to me, it crosses human intelligence. And at such time,
[94:51] will it not start to do some of the jobs that people are doing today? >> Outside of software engineering. Remove software cuz I will concede software engineering has got better at that. >> Outside of software engineering, >> Yeah. >> where? >> So, the chief of staff things, the admin. >> Okay, so it's got better at admin. >> Video generation, photo generation, text generation, uh theoretically, coding. >> Right, but >> And then I'd say agentic workflows. >> What is an agentic workflow? >> So, automated workflows is where you're doing the same I mean, a good example is looking at the back-end data of the
[95:21] Darvis CEO. >> Summarizing. >> Looking at all of the data, ingesting all of it, going on the internet and searching who that is. >> Uh-huh. >> Looking at every interview you've ever done ever. >> Uh-huh. This is summarizing and generating. >> Making a little model on, you know, the things people want to know from Ed. >> Uh-huh. >> Producing a report, sending that to my inbox. >> Uh-huh. >> Me getting a 20, 30, 40, 50-page report on Ed before he arrives. >> This is all basically the same thing it's been doing for years, though. It's It's not really new capabilities. >> Research, it's it's >> It's still the >> It's still the same thing. They've had web search for years. They've had report
[95:51] generation for years. Well, we couldn't generate high-quality videos that are like indistinguishable from cameras. See dance and these ones that look like movies. >> I mean, they >> are incredible. So, I'm saying the point I'm trying to make is that if we imagine that over the last 10 years there has been a rate of improvement in terms of capabilities and output and quality. We've seen hallucinations drop, we've seen the models get more quote-unquote intelligent, get better at, you know, if you did it give it an IQ test, it's getting higher scores than it was 10 years ago. We agree that there's been a upward motion of improvement.
[96:21] >> This is pretty much how machine learning goes when you feed it more data. >> Exactly, and you put more compute behind it. So, if this continues, what does the future look like? So, the rebuttal I was expecting to hear is that it won't continue. >> And I actually don't think it I think that there are hard limits that we're going to hit. >> Okay, so you do believe in that there's a hard limit somewhere. >> kind of already hit the diminishing returns level because for example, video generation, which is, by the way, far less than American concern anymore. Open AI has shut down
[96:51] Sora. I think you can still use the API, but nevertheless, look at the look around you with the amount of stuff and the crew you need to get a shot. People think that movies are just shot by shot by shot and they just magically happen. When you've got my my wonderful girlfriend of first ADs, so assistant directors, you've got gaffers, you've got lighters, and also simulating light is insanely difficult. There are so many magical things that happen in creating visual images that yeah, you could create a 1-minute long thing that might fool someone. How do you practically turn that into a movie? Because that movie I forget what the
[97:21] name is. There's a movie that claimed it aired at Can. It didn't. No one It aired in the city of Can during the Can Film Festival. It was not at the film festival. When it comes to the practical creation of actual things at the end of it versus magic tricks, the actual practical outcomes are not there. The reason I keep coming back to the capabilities thing for the example is yeah, they can do better at tests, do better at you number go up. When it comes to can this actually do two tasks that you can rely on it? You can rely on it for summaries. You can rely on it for generations. The things it was doing it's getting
[97:52] linearly-ish better at. But again, there's a ceiling to that. Like, okay, so it gets really good at research. What does that actually mean? You've already kind of got the automation there. What is the next step of that? Because training it to be more autonomous, for example, that's not something that comes from training data. That is actually a new Gary Marcus a neuro-symbolic. You need as you need to build a structure around the AI to make it work. And even then, doesn't fix the pro So you're >> So you're saying that there will become a point where the rate of improvement will plateau? >> there. >> And stop.
[98:22] >> We've already hit that dimension. Gary Marcus said this in 2022 as well. >> You know, there's lots of people listening now that like they've had their workflows completely transformed by these tools. >> Have they? They'll be be Yeah, they will. Yeah, the thing is, first of all, every single one of them, did you pay for the tokens? That's the thing. Did you pay for the tokens? And also, how many tokens did you burn? But putting all that aside, what workflows? Because if it's yeah, I did a bunch of web scraping or web searches, I'm just not impressed. Did you make an entire movie? No, you didn't. Is it speeding up your coding? Yeah, I believe
[98:52] that. I've heard that from multiple people. But again, how much can you trust this >> I think what I was getting at is, you know, when in the moment of any technological innovation, people they extrapolate linearly or they view it as a static state. I mean, they think today is going to look like tomorrow. Or they think it's going to get better in the sort of straight line. But what we end up seeing a lot of the time is this exponential improvement. >> All of the innovations we're talking about with you with like with computer and all that with fast processors, those are hardware breakthroughs. The hardware
[99:23] breakthrough companies don't seem to be fixing the LLM problems. Despite the all the king's horses, all the king's men were what? 9 10 generations of TPUs from Google now. Broadcom's building stuff with Open AI, their jalapeno chip. And yet none of these people can just say, "Yeah, we're on the path to making this profitable." Because they can't. If we fix the environmental problems and the profitability situation, maybe I'd be more generous with this stuff. But they don't seem to be able to and you talk about these improvements in capabilities,
[99:53] there's a certain point at which I'm saying, "Okay, can it do even a tenth of the stuff they're promising?" Sam Altman the other week was saying it's going to be a you know, like 6 months we'll be like a genie that you can ask wishes for from this like motherfucker's never watched Aladdin. What's he talking about? Like Also, the the genie was charming. Anyway, long story short, the promises do not line up with the capabilities or the capability improvements. An exponential improvement in software and software performance is always a result of direct hardware
[100:24] improvement. We have all the gifted mathematicians, all the gifted software engineers, all the gifted hardware engineers. And where are we? Trillion plus dollars in with the future great financial crisis and the world's greatest marketing psyop. >> I just think in the future >> I do think that all of the devices and the computers we use and the physical items in our world will be more intelligent. >> I mean, sure, but is that LLMs? >> And that'll be powered by the underlying AI infrastructure. It'll be the data centers data centers. It'll be energy coming down.
[100:54] >> How does a GPU full data center translate to a Nikon camera that can I don't know even what you'd think think it could like because what is the thing we're talking about here? Because the idea that devices will get smarter, sure, I can see that that's a very broad statement. I can see it happening. It's already kind of happening. What does that have to do with the data centers? Because these data centers again are not being built to make your consumer electronics smarter. They're not being built for anything other than
[101:24] speculating on the ability to capture demand for generative AI services. >> But it's not just generative AI. We went through that. >> It is No, but those data centers, they are being built for generative AI. They are not being built for anything else. >> Would Do consider generative AI to be the fact that on Meta's earnings call like a couple of weeks ago, Mark Zuckerberg said the big breakthrough we've had which has resulted in 15 basis points of increased retention, I believe he was referring to Instagram, is that we now take anything you post on social media and we run it through an AI to get full context of what it is and because
[101:55] we can see guy sat in front of me called Ed with blue shirt and coffee, we now can train AI to serve whoever wants blue shirt Ed with coffee to the right user which means people are retained longer because >> Isn't 15 basis points like 0.15%? >> so small but it makes a difference at scale. It makes a big difference at scale. >> Yeah, but 100 and something billion dollars in and the best you've got is 0.15%? >> If If he could be fired, I mean how much of a difference because there's a reason he's saying basis
[102:26] points versus dollars. Because think about it like this. If Mark Zuckerberg was I take your point about scale. >> No, I'm saying the point I was making was that that is another application of these data centers cuz it needs a data center that is driving revenues but also that's not that's outside of us thinking about just generating a data center. >> and that's generate the gem there generative model. >> Muse. >> It was it Oh, Muse Spark is their LLM. Gem is their generative model. Well, Muse then then that's them doing the weird
[102:57] thing where it's like on Instagram and it's like Dave the cat, why is Dave the cat suffering? Like it's the weird pop-up things. Meta is God damn that company sucks. Like every time I think about how they've ruined that product but that's the thing though, again, why can't he just say with his whole chest we've made a couple billion? Why can't he say that? Because he isn't because there's not actually a way of going I spent all this money, I spent 14 billion god damn dollars on scale. I hired Alexander Wong and I made this much. They can't It gets back to a very simple point of hey, if it was going
[103:27] well, you'd tell me how well it was going rather than I don't know, doing this weird rain dance where you're like, "Well, if we move all the pieces around in 3 years, theoretically this will happen." >> I've done almost 700 interviews with some of the most interesting people in the world. And one of the things you learn, which is unexpected, is that vulnerability is the doorway to connection. And after sitting here for 2-3 hours with a guest, I feel a deep sense of connection to them. And as they leave, what I get them to do is to write
[103:57] a question in the diary of a CEO. We've taken all of the questions from the Diary of a CEO. We have put the question here on this card with the name of the person that wrote it. So, you can sit at home, as I do with my fiance, and my colleagues at work, and other people in my life, whenever we get a minute, we play the Diary of a CEO conversation cards. And it is incredible what happens. These are great if you're in a romantic relationship, and you want to connect your partner more. These are also great if you're in a team, and you
[104:28] want to bond your team together. And I have to say, they're also great for families that want to learn more about each other, and that need a good excuse to spend some time in a digital world in the analog environment, connecting human to human. It is remarkable what the right question at the right time can do. Go to the diary.com, and you can get these conversation cards right now. There should be a button just down below here. And if it says subscribe, you're already subscribed. If it says subscribe-a, that means you're not yet.
[104:59] And if you're not subscribed, please could you do us a favor and hit that button. It helps to show more than you know. And according to the algorithm, you're someone that watches our show, but you haven't yet hit that button. Thank you so much. >> I do think you're accurate and right when you talk about the fact that there's a lot of like Is the word fugazi? Yeah. Where like there's a lot of people that have spent a lot of money, and they kind of shouldn't have spent it, and they up. And now they're thinking, "Shit, like we spent all this invested money." Kind of like the metaverse was a bit of a >> Oh my god. That was a bit of a joke. >> That's so weird. Lot of money spent, we kind of thought this dream was coming of this one I shouldn't say dream cuz it's
[105:29] not a dream that I've had. >> A dream that they had. >> Yeah, this sort of virtual world and actually it never transpired and there's there's no sign that it will in the near term. AI and the dot-com boom in this regard are the same. NFTs were the same. >> Right. >> So, crypto one could argue that a lot of the crypto industry was the same. >> It's way that is inflated by the media. The difference is the reason the metaverse and NFTs didn't escape this was they weren't stocks to speculate on. They weren't big companies that you could invest in. They had rec- record earnings in 2021. There was a bunch of
[106:00] money flowing in the system thanks to post-COVID, uh the PRDC that basically government federal money flowed in to the banks. There was a bunch of easy money, zero interest for the era. Money was easy to find. Then after that, there was the hangover. Growth started to slow down dramatically. This is actually my rock combo bubble theory, which is they don't have any hypergrowth ideas anymore. So, suddenly they started buying GPUs. When they bought GPUs, people went they're doing AI. Oh, we better buy the stock and the stocks went on an incredible run. Meta's like several hundred percent growth. In
[106:30] the last few years, their stock has grown like hundreds of percent despite zero proof. And because the media was just saying, "Yeah, Meta's revenue's growing cuz of AI, right? Microsoft's revenue's growing cuz of AI, right?" The fugazi you're talking about was the fact that everyone just gave them credit in advance. And now we're kind of getting to the point where it's like, "Hey, you didn't spend that trillion dollars for no reason, did you Sacha?" Amy Amy heard just going to take him out back, send him to the glue factory or something? Like >> I do think there's overspending. I I want to concede that point. I do think
[107:00] there's >> Dramatic. >> Yeah, no, I do think there is. And I think the reason why there's overspending and is I think there is something here. >> And what? >> In terms of like I think there is prac- practical uses for this technology. And I think when people realize that through history, they go crazy cuz they want to be the person that owns the opportunity. going >> honest. I just I fundamentally don't agree. >> You don't agree with which part? >> I don't agree that this that the speculation is a result of actual demand. I don't believe it's a spec I don't think private credit is sinking
[107:30] conscious of billions of dollars into AI because of actual demand. They are doing it because they saw the biggest companies in the world building data centers making a ton of money from two companies they feed money and went I want some of that money. >> I'm saying that I do think there is value in the underlying technology. I think that and so I think I'm not saying how much value. >> Right. >> Okay, I get I actually I get you meaning that. >> That's fair. >> I'm not saying it's proportionate to the investment. All I'm saying is that do you know what it's like? It's like I mean if I take your example, the raw economy essay that you wrote. Say that you're on a desert island and then
[108:00] someone says they found a banana tree. >> Right. >> And there's there's 10,000 people on the island. >> Okay. >> They are going to stampede towards where they think the banana tree is. They are going to claw each other to pieces and if if your essay here is right that there was desperation cuz they hadn't found an innovation in a while. Maybe that explains it. Maybe there is a bit of value here. >> Right. >> And they're stampeding and killing each other and making irrational decisions like hungry people would. >> I actually think we're then we actually
[108:30] agree. That is actually my point which is these three companies in meta, their main business lines are running out of growth. There's only so much they can grow and indeed in the next three and a half years analysts think that these two bastards these two open AI and Anthropic are going to spend over 400 billion dollars on these people alone. >> Microsoft >> Google and Amazon and the crazy thing is is that's a large part of their future growth and if this money isn't spent their growth slows down. >> Okay. >> So your point about the bananas, I actually agree. That is the raw comb bubble. It's they don't have a new thing and they're desperate and indeed they
[109:00] got rewarded for buying the GPUs. They got when they bought these goddamn GPUs from Nvidia, all the markets went rock hard overnight. They loved it. There were stories about how they were sending armored cars with the GPUs to Microsoft to make sure Microsoft got the GPUs and so everyone saw all that money flowing in even though they never disclosed their AI revenues. They saw the expenditures and they went "Well, I want to do what these people are doing. I want to get a little of that money, don't I?" >> I think the area where we have a slight disagreement is that I think the underlying technology has a lot more
[109:31] promise over the long term than you do. >> So the thing I want to push back on there is to have progress with AI >> Yeah. >> taking it in a vacuum. To have progress for these two companies to keep going, to keep progressing, they need to spend tens of billions of dollars a year on training. >> Uh-huh. >> The only way that that can happen is if these companies and venture capitalists and private credit firms and Nvidia keep circulating money to them. So the progress >> Yeah. >> that we've got so far is entirely a
[110:01] result of this circular system. So it means that >> Circular? You talked about VCs there. >> Venture capitalists who are, by the way, >> Investors. >> the majority of the funding that OpenAI got in the last 6 months came from SoftBank, Nvidia, and Amazon. >> Okay. >> Yeah. >> So >> Just the point is is you're talking about progress continuing. Progress in LLMs can only continue as long as the money keeps flowing. Once the money Once the money stops flowing, the progress stops, which means >> But isn't that most like early like Spotify didn't make money for 20 years?
[110:31] >> Spotify didn't lose 20.9 billion dollars in 1 year. They didn't need to raise 217 billion dollars in the space of 6 months. >> Yeah, and like Uber's another example. >> 33 billion dollars since inception before it became a messy kind of profitable. Amazon Web Services between 2003 and 2015 when it became profitable, 29.7 billion dollars. >> They've spent >> Yeah, that's the total capital expenditures and that's not just Amazon Web Services, that's the entire logistics operation, normalized for inflation. >> So they all lost money for a long period of time is the TLDR. >> Yes, but the amount of money they lost
[111:01] is completely just magnitudes different on a level where these three companies >> Can I argue then that the that's because the potential of intelligence permeates everything. Whereas Amazon at the time was like selling books. >> No. >> That was That was bringing retail online. >> When Amazon Web Services grew, it was great. >> Oh, Amazon Web Services. >> Amazon Web Services. So, Amazon Cloud. With Amazon Web Services, the reason I bring that up, I'm going to repeat something, but it's really important. 2003 it was founded. And it was founded mostly because Amazon as a growing
[111:32] online store needed hardcore infrastructure. 2006 I think is when they turned it client-facing. I may be wrong on the dates there, but 2015 >> is the year it became profitable. >> Yeah. >> The total capital expenditures normalized for inflation were $29.7 billion across that 12-year period. >> Yeah. >> And yeah, it lost money, but if we speak cold economics here, Amazon didn't have to go into the They were unprofitable in a in a way, but their margins actually started improving cuz AWS was a very margin-heavy business. It was great.
[112:02] >> Yeah. >> These These two, Google, cash flow negative. Amazon, cash flow negative. These businesses, the reason you liked software businesses, was they are meant to be cash-heavy, asset-light. These companies, along with Meta, have added more than $700 billion of new property, plants, and equipment, so assets, data centers, GPUs in the last 4 years. They have gone from being these cash machines to these cash furnaces. >> You said a second ago this can only
[112:33] continue if investors continue to invest. >> Yes. >> And I was saying I I think that investors are used to pumping money into things that are burning cash. Your rebuttal to me sounds like, "Well, this is burning more cash than ever." And then so I would say, "Well, is the opportunity bigger than those other case studies you referenced like AWS?" And one would say that the opportunity of intelligence permeates everything. So, the TAM, the total adjustable market, is enormous.
[113:03] Maybe the rebuttal back to me is about open-source and all these kinds of things. >> I I actually know you're getting at. So, what you were describing there is the argument that Satya Nadella or Sam Altman would make, that the theoretical opportunity of large language models. And I could have bought that into any 24 from them. When they were like, "Oh, we see the opportunity." We've gone way past the point at which you can rationally argue that LLMs need this much money. And when I say the money needs to keep flowing, I am talking these two comp- OpenAI, just OpenAI.
[113:33] Clammy Sam Altman has said, Wall Street Journal and Nisa Godizi reported a few weeks ago, they plan to spend $750 billion on compute through 2030. I think they're going to be dead before then, but $750 billion. That is an insane amount of money. >> crazy. >> [laughter] >> A large chunk of that is training. So, when I say progress, I mean literally to make the models better at stuff requires billions of dollars invested just in data. And also tens of billions of dollars of taking that data. And so, training.
[114:04] Training's actually a really interesting thing because when you think of like, well, Jake and Troy are my trainers. Well, I train with them. I lift with them. I have a defined thing. When I do it and I eat right, muscles get bigger. They would And here's the thing, when you train with an LLM, you're experimenting. Each And this is not actually a hit on the companies, because they're still trying to work out how to do the thing. Because putting aside how I feel like they're trying to innovate, I think there are people at these companies that actually want to do something interesting. It's costing too much money. So, once the money tap turns off,
[114:34] the money won't be there to buy the data or to feed the data into the GPUs. Put aside all the thoughts I have, just the raw capital to get them this far has cost increasingly larger amounts of money, and increasingly larger amounts of training money for training runs that sometimes can fail. GPT-5 was meant to be this panacea for the AI industry. They had at least one training run that cost half a billion dollars and did nothing. And that's the thing. If we are thinking about progress in a in a vacuum, they need so much more
[115:04] money just to maybe get somewhere. There's no guarantee. There's never any guarantee, but there's a reason that Google and Amazon are cash flow negative now. There's a reason why Oracle's probably going to die as a result of OpenAI because Oracle's future depends on OpenAI spending $300 over 5 years. >> It's absolutely fascinating because I was just reading through a list of quotes from the big CEOs of the AI companies to see what they would rebuttal you. >> Yeah. >> And they're all basically saying the same thing. They're all saying this is actual an exact quote from Sundar, who is the CEO of Google. He says, "The risk
[115:35] of under investing is dramatically greater than the risk of over investing." And you go down you go through this that you know but Andy Jassy, CEO of Amazon, "We're not investing approximately 200 billion in CapEx in 2026 on a hunch. We're not going to be conservative in how we play this. We're investing to be the meaningful leader and our future business operating income and free cash flow will be much larger because of this investment." Then Mark Zuckerberg, CEO of Meta, says, "We'll continue to invest
[116:06] aggressively in infrastructure to meet the demand. I'd rather risk building capacity before it's needed than being late." >> I just be thinking of Shrek with Lord Farquaad, "Some of you may die, but that's a risk I'm willing to accept." It's like, "You know, I'm just going to spend all this money. You can't fire me cuz Mark Zuckerberg can't be fired due to the unique board situation he's got going." So, yeah, he's just going to piss the money away and hope he's right. And I know from the people I know at Meta, he's not right. >> The thing is why might you be wrong? >> I mean, this is the thing. The AI people who claim this is going to be the
[116:36] biggest strongest thing in the world, did they ever get that I I mean this >> It's a good question. >> Because it's like they don't. And the thing is what would it take for me to be wrong? A bunch of hardware breakthroughs that make this profitable. A bunch of what question new math because the thing is when it comes to being a critic or a skeptic, you are put on the hot seat, not the people spending a trillion dollars, not the people promising the world. The the the with a blog is the one who's like, "No, no, no." >> me. Trust me. If they came here, they'd be on the hot seat, too. Trust me. >> Oh, they They won't talk to me. Don't know why they They
[117:06] Don't know It's cuz I call him clammy Sammy. >> I think it's cuz my guests are quite quite critical that I don't think Sam Altman wants to come here. >> Altman, go on Steve Show. Do it. But this is the thing, like, of course they're going to say that. And also, if they thought they were right, I don't think they do anymore. If I was in their shoes and I thought that this was an existential thing, sure. But it gets back to the rock con bubble, which is, yeah, this is the last thing they've got. >> But I really want to know that question. It was one of the questions I was really excited to ask you, which is, you have a different opinion. We said this at the top. You have a very different opinion from a lot of people. I would categorize
[117:38] the the two most popular opinions as uh AI is going to hurt everybody and it's going to be catastrophic and we need to stop. Yeah. The other opinion is age of abundance is going to be amazing. Let us crack on. >> Yeah. >> Yours is different from both of those, which is, as you said in your words, it's a con and it's and there's no real underlying value in the technology and it's overhyped. >> Yes. >> And there's way too much spending. I mean, like, a few people agree on the spending part, to be But the other part So, with you, it's one of the probably the first person that I've spoken to that's had this opinion. So, how What would it take for you to
[118:10] change your mind about what you believe here? >> There would need to be a hardware breakthrough that reduced the cost by like a thousand. But it would have to be just a dramatic breakthrough that is not happening, just to be clear, because they've all been trying. >> So, what's the cost for you that would have to change? >> It's the cost, and it's also the data centers. I think the way they're building the data centers is reckless and damaging to communities. The fact that you have communities like in Violand, New Jersey, where the residents like, "I don't want this." But the planning boards vote for it because they're all I assume having chammy
[118:40] lunches with the people doing it. I think the use of gas turbines is disgraceful. I The water situation, I'm not super well read on, so I'm not going to wade into it. But the use of gas turbines and behind the meter of is reckless and damaging to communities. The noise that these things make and also generative AI is this egregious pornographic demonstration of how unfair the world is. Regular people try and get a loan for a business, random business they want to have a good idea. They go to a bank, bank will tell them to go themselves. They'll say, "I'm
[119:10] not going to give you what you're You're going to make a store that sells stuff? Screw you." You want to build a data center? You Jensen Huang will back you. Jensen Huang will give you 25% residual value. You want to build a regular business that's even profitable? you. No, venture capitalists won't give you the money. Something that's just growing steadily but it's profitable? Screw that. No, I need 10 100X return. Try and get a mortgage. You have to give the bank a full colonic. But you want to get money for Jensen Huang to buy some GPUs? He'll give you a contract. Core Weave's a great example. Core a Neo Cloud, which
[119:42] is just a company that builds data centers and puts GPUs and rent people. Nvidia, one of their first investors in 2023, signed a $1.3 billion contract to rent back their GPUs from Core Weave so that Core Weave go to a bank and go, "I've got a customer. Yeah, it's the guy I'm buying the GPUs from with the debt I'm getting from you." If you want to buy GPUs, it's open season. If you want to live a regular life where you build a regular business or buy a house, highest interest rates ever. Screw you. Up yours. Yeah, you
[120:12] need to show us way more than that. I don't trust you regular folks. But if you're an unprofitable Neo Cloud, you get billions from Jensen. It doesn't matter. >> It's so interesting. You It's interesting because you are the first person that I've spoken to that has that opinion. >> I am pro user. Let's take another myth. AI will be conscious. >> Mhm. >> So, superintelligence, artificial general intelligence, these are theories. Anyone saying this stuff will become this is just guessing and does not have
[120:43] proof. >> Okay. >> And that And like that's really it. >> Okay. Okay, let's take another myth. >> AI systems are already blackmailing and escaping control. So, this is a really specific one. Anthropic, there's actually two. OpenAI's GPT-3.5 I realize this is more than a sentence, I apologize. in their system card. And a bunch of media outlets covered this saying that OpenAI's model blackmailed a task rabbit into solving a capture. What actually happened was a
[121:15] user of GPT doing the experiment got it to generate things to say to a task rabbit to make a task rabbit do stuff. >> rabbit being >> As in a person that you rent not even to do a capture. It's someone you rent to like nail that nail picture up in your apartment. It's an insane example. This was covered as if these things blackmailed someone and and it and they specifically said, "Yeah, we prompted it to do this." And also, the other note was that yeah, AI systems can't do autonomous stuff like this. Then there was this other one where Anthropic said,
[121:45] "Oh, yeah, a model was blackmailing someone saying that if you don't do this, I'll email proof that you slept with someone else other than your wife." I think it was. What actually happened was Anthropic explicitly trained a model to do this and then prompted it to blackmail. This keeps happening and the media just slop slop me up. I don't need no thoughts. Put the story in the bag. And it's frustrating because it's scarce people. Put aside the fact it's wrong, it's scary. It's scary to people. People living their lives who have to work
[122:16] longer hours to make less money and their money doesn't go far. And they can turn on the news and there's some being like, "Yeah, you should be terrified. It blackmailed someone." >> But this is a this is so counterintuitive of their interest to some degree and they've experienced it backfire. >> Well, they have now. Like it's it's literally backfired. >> It's backfired. Er- Eric Schmidt getting booed at a commencement speech by 8,000 people every time he said the word AI. But I mean, this is this is I mean, they're they're these CEOs are being attacked at home. >> Yeah, which sucks. >> Yeah, which is terrible.
[122:46] >> I I must be clear, like, you dislike it come don't hurt people. >> Yeah, don't don't attack people at home. But but the point here is that that narrative is backfiring in a big big way for them. >> don't think they saw it coming. Because you have to remember, you mentioned regulation earlier. These tech companies have been glazed for their entire existence. Travis Kalanick's like, "Oh, what? People don't like me now?" And it's because Uber was a horribly run place, and he was kind of a monster. Also, tons of articles about how great Uber was at the time. The point I'm making is these companies are not used to push back. They thought what would
[123:17] happen, I believe, just guessing. They thought they'd do the scary stuff, and they would just get floods of money, and everyone would just be like, "I kneel before you. I'll do whatever you want." They didn't expect, I think, what has I I agree, this has backfired on them. Because they were inarticulate. They're disconnected from regular people. Sam Altman drives a $5 million car around San Francisco. So, that that man's doing like 9 mph. It's hilarious. But, these people are disconnected from everyone else. So, they don't they don't experience real problems, so they can't build the solutions for them. And they
[123:48] think, "Well, if we scare people into doing what we want, that'll work, right?" It didn't. They This was all of this blackmail stuff was an attempt to make it mystic. It was a mysticism attempt. It was to make it seem like this unknowable, impossible to control, just this powerful thing. But, we're the only ones We are the only Only us. Only these two angels could possibly control the beast we've created. >> This is This is quite a controversial statement, but I think that for some reason, I trust Dario a little bit more because I think he's been the most
[124:18] balanced in his writing about the risk profile. >> I >> Whereas the others, they they seem to kind of move with the wind. >> I I >> Do you know what I mean? >> I get what you mean. But, the reason I don't like Dario is Dario was doing the scare tactics thing when he worked at OpenAI. When GPT-2 came out, he said, "It's too scary to release." He's also gone on television and given AI psychosis to Axios being like, "50% of jobs are going to go away because of AI." What I respect is the consistency.
[124:48] He's now being attacked by them. Good. >> Um but the thing is >> He is sorry, I mean let me clarify the word attack. >> Dario >> verbally >> is being verbally attacked by Silicon Valley and you know, if Silicon Valley, if powerful people in Silicon Valley are attacking someone >> 4 months ago he was >> wasn't though. They were all saying he was the smartest boy ever. But the point I want to make there as well is again, "Wow, you're so scared of how powerful this is. You're so scared of it. It's so scary." What are you doing about it? Oh, nothing. Like he's just like, "What are you doing?" Well, we have an alignment team. So does every AI lab. Well, I guess
[125:19] OpenAI cycles through those really quickly. Here's the thing, if I'm Dario Amodei and I'm like, "I'm scared of all things changing." And I thought I had made a thing that would eliminate all jobs, I'd be terrified. I'd be walking around with like like a 10-ton weight on my back. The show >> the responsibility >> The fact he doesn't, the fact he wants to be this weird elder statesman that's too scared to hold Sam Altman's hand in an event just makes me believe that he's just saying it because it's convenient and he'll wind that back as he kind of already has whenever it's convenient for
[125:49] him. I think OpenAI and Anthropic are basically the same level of bad company. I think Anthropic is more cult-like. I think It's so weird like Jack Clark over there, one of the co-founders. That fellow used to be at The Register. Used to be one of the most critical journalists ever. Now he's it's like like something took over >> over him because they talk of these things in these highfalutin terms. But then again, maybe the people at Anthropic buy their Maybe some of the people at OpenAI buy their >> So so going back to the central question we asked at the top here was what would have to be the case for you to look back and say, "Do you know what, I was wrong in 2026." And you said to me
[126:20] it would be mainly that the cost of production around AI drops dramatically. >> And it would have to also do insane amounts of stuff it does it would It to be a truly autonomous >> have to continue its improvement in terms of capability. >> It would have to be It would have to be indistinguishable from magic. And the reason I have these high standards is they set them. >> Okay, fair enough. >> It's interesting as well because all these myths and all these conversations, it's about technology but it's also it's an information war. It's literally
[126:50] narrative versus narrative. Everyone trying to escape the financials. Everyone trying to actually escape what the models can do. And the big thing I always say about AI boosters is if I could regulate them, I'd regulate them they can't speak in the future tense anymore. Cuz you got to talk about today, mate. You get 2 weeks in the future max. Because if they were constrained to what was happening today it they would sound like insane people. >> Yeah, I know. I think Yeah, most I guess most technology companies would at the time. Like Uber would sound insane. >> Uber Uber Uber was basically the difference
[127:20] >> pissing money, they weren't they? >> They were pissing money away but the unit economics were the same just subsidized. So you were still getting a service from A to B and paying a much lower cost. It wasn't like you paid Uber 200 uh sorry, 20 bucks a month and you could get 500 miles of Uber and then one day you started paying by the mile cuz that's what's happening with this. >> Have they they've they've changed their business model for customers like me now so that I have to buy credits. >> No, so you Well, kind of with >> They asked me the other day to buy credits. >> So with the Anthropic's Fable model,
[127:51] with some accounts you have to pay for usage. And also adoption of Fable has been pretty low because of this cuz of the cost. But with enterprises or companies over 150 people you have to pay by the token now or per million token. >> I see. So they are moving to a token now. >> Yeah, but when they did that everyone went from being like this is the most impressive thing ever to being like >> It's so expensive. >> It's always uh we've got to control these costs. Uber's COO says Andrew McDonald, I think he said that it's getting hard to justify cuz it's hard to connect spending money on tokens to actual useful outcomes.
[128:21] >> Hmm. >> He said the thing. Like he said the actual thing I'd be saying and it's >> So you we're in an AI bubble. >> Yes. >> And when will When this AI bubble collapses, so much of the economy is resting upon it. >> Yeah. >> It's going to have downstream consequences. So, I got two questions for you. I guess the first question is, are we in an AI bubble? And what happens when the bubble pops? >> Yes, and it's it depends. So, the big thing that people say is, "Oh, we'll get bailed out. Donald Trump's scared of Donald Trump." Here's the problem with this.
[128:51] It isn't just an AI bubble, it's the Rubicon bubble. So, the AI bubble collapsing will probably be this company running out of money, OpenAI. And the thing is with OpenAI is, they were meant to go public this year. And now it's been pushed to next year. >> Oh, we going to have after they release their audited financials. What do I that was? >> Um but they've delayed to next year. Sarah Fryer, the CFO, has now said, "Well, they'll do it earlier than 2027 or 2027." Great answer there. >> For anyone that doesn't understand what going public means, that means joining the stock market. And at such a time
[129:22] when you join the stock market, your investors can finally sell the equity that they got for investing in the company when it was private. So, often times companies will flirt with the idea of we'll go public someday soon because investors will have a moment in their head where they'll get their money back at a return. So, you kind of need to If you're in these guys' shoes, you kind of need to be flirting with going public or investors won't want to invest. >> OpenAI up until this point has been a private company. And their last funding round, they were valued at $865 billion.
[129:55] when they tried to go public, New York Times' Mike Isaac reported this, they tried to list at Well, they wanted to go at a set a $1 trillion valuation. Apparently, their advisor said, "No, don't do that." That is very bad for a number of reasons. One, OpenAI needs perpetual amounts of money. They raised $122 billion this year. Most of it's crossed, there's some left, but they are going to need to raise at least $100 billion a dollars a just to survive. If they can't go public, they will have to raise another funding round. Problem is, it's
[130:25] going to be difficult to raise at even the same one they raised at. So, they're probably going to have to take a flat, so the same amount >> They can't go flat. >> Exactly. But, they need money. They need money so bad. Amazon sent them 35 billion dollars that was meant to be contingent on them going public early. They did that because they needed the money. Now, OpenAI is the kind of catastrophe center here because Anthropic is likely going to beat it to go public, and once Anthropic goes public, it'll be borderline impossible for OpenAI to do so because Anthropic, an unprofitable, unsustainable AI lab,
[130:56] but a better business that's growing faster than OpenAI's. I believe they have a ceiling. They're eventually going to face competition, too. I think sometime in 2027, things are going to start running out of steam because the thing I said earlier, the only way these models get better is if you feed more money, tens of billions of dollars into them. >> So, you think OpenAI runs out of steam in 2027? >> I think they're already running out of steam, yeah, but I think they run out of cash. >> You think they run out of cash? >> Yes. >> And the sequence of events here will be they they go out and try and raise >> And they have trouble raising another round. I think maybe Nvidia props them
[131:27] up a little, maybe private credit, BlackRock and the like, the ones And the reason that private credit is getting involved, so asset managers, is because they're investing in the data centers, and they know this company is most of the data center demand. >> Okay, so they run out of steam in 2027, according to you. >> Yep. And maybe they try If they bum rush to go public, they're going to have worse economics than Anthropic. They're going to get savage. It WeWork was a great example. Another SoftBank classic. Now, I think OpenAI collapses. There are many different ways it could happen. There are many different ways it could end. But, the crucial thing is is that there
[131:57] are multiple companies that are existentially tied to OpenAI. SoftBank, one of the largest companies in the Japanese stock market, a holding company with lots of investments. They have on paper about 100 billion dollars worth of OpenAI stock. If they can't go public, they can't do diddly squat with them. And so, SoftBank's future, their ability to continue paying the people around them and existing as a business, relies on their ability to continually liquidate funds to be to take the things they've invested in and have value from
[132:27] them, either by selling the stock or taking loans out on the stock. If OpenAI can't go public, SoftBank can't do that. SoftBank probably won't run out of money, but we're going to see one of the largest holding companies in the world become much smaller. We will also see Amazon, Google, and Microsoft have to restate guidance. They will have to say, "Actually, we don't think we're going to grow as fast." >> And what happens then? >> Well, I think we enter a tech depression, because the Roku bubble, the core of my theory, is that they're out of hypergrowth ideas, but the market doesn't think so.
[132:58] The reason they're so maniacally spending is because buying AI GPUs allows them to kick the can further. It allows them to say, "We're still doing something. We're working on AI. Don't think too hard." And also, their current businesses are still growing. Their current businesses will eventually slow. There's only so many price increases, only so many tweaks to ads, only so many tweaks to Google search, only so only so many ways that Amazon can screw merchants. >> So, in that tech depression, which you think it might be triggered in 2027, is
[133:29] that a cascading downstream economic depression, because the stock market is heavily dependent on these companies? The stock market sees a pullback, investors stop investing, they get panicked. >> Yes. I think that because >> sort of downstream consequence, the sort of domino effect? >> There's so much to imagine that it's difficult to capture everything, but there are a few things that worry me. First of all, a ton of American money, just regular people's money, retail investors, are in these companies, and they bought into the Magnificent Seven thinking that number go up forever. Nvidia is the largest company on the
[134:00] Fortune 500 and Nasdaq as well. And like 7 to 8% of the S&P 500, that company, when when the bottom falls out from Nvidia, and we haven't really got into it, but Nvidia is is the most circular refinancing, feeding companies money so that they can raise debt to buy more GPUs. I think Nvidia's revenue could go 50 to 70% down. I think that Nvidia could put Nvidia back in 2022 was making single-digit billion dollars. >> And what happens though? If I'm thinking about like Jenny and Dave that are watching this right now, and they aren't just normal people with normal jobs.
[134:30] >> People's retirements are going to contract severely, and I don't believe they're going to return to those values. And I think that because so much of the value of the S&P 500 and Russell 1000 index comes from these four companies and the rest of the Magnificent Seven, so Apple, Tesla, Meta as well. And the thing is I don't know what happens after that because venture capital has also like more than half of venture capital last year went into AI. I think most venture capital investments in AI going to zero because when it comes to building a company on
[135:00] top of an LLM, all of those are unprofitable, too. And the thing is LLM companies have not really been acquired. The exception being Cursor bought by Elon Musk for the coding side. But you have Cognition, which is just another LLM company raising a $26 billion valuation. That means that company has to go public cuz who's buying a company at $26 billion other than Elon Musk? And there were rumors that Elon Musk was trying to buy them as well. Is Elon Musk just going to pick off every like LLM company like going to a TJ Maxx for AI? Like Jesus Christ. >> So, is that a recession you're
[135:30] describing? >> It is a recession, but it's also a depression within people's retirements. Like I'm talking about 20, 30, 40% off the top of these companies' stock value. >> Economic contractions recessions consistently lead to job losses and rising unemployment. When an economy contracts, the mechanism driving job losses typically follows a predictable sequence. Falling demand, consumers and businesses spend less money causing revenues across most industries to drop. Margin compression, with lower revenue and often fixed overhead costs like rent or debt, corporate profits shrink. And lastly, cost-cutting measures to survive
[136:01] or protect profit margins, businesses freeze hiring, reduce hours, and resort to layoffs. >> Yes, that's that would all happen, but the thing is we're talking about equity values dropping and we're talking about there not really being a home for that value or that money. >> [snorts] >> So much is riding on these companies, but you can't bail it out. You can theoretically bail out OpenAI. I don't think it happens. You could pump these dogs full of money and keep them alive for a bit, but at some point they're going to have to stop They have between these two companies, Anthropic and OpenAI,
[136:32] you have $1.1 trillion of commitments. >> Just OpenAI? >> Mhm. >> Oracle is building 7.1 gigawatts of data centers, so over $400 billion worth. Just for OpenAI. There is not a customer on Earth and Oracle's revenue has been flat last 15 years when you adjust for inflation. Without OpenAI, Oracle dies. >> So you think OpenAI is going to crash and run out of money and that's going to cause this domino effect across these other big tech companies, which is going to impact the stock market and in impact the broader economy? >> Yes. And also the tens of thousands of people that will be laid off from the
[137:02] tech sector, but also the venture capital thing is significant because venture capital has been having one of the most historic bad runs in history. Since 2018, the average return from venture capital at total value put in, so the amount of money you get back for your dollars between 0.8 and 1.21, meaning for every dollar you invest get 80 cents to a dollar 20. >> Paper gains? >> Well, no, that's just actual get like actual returns. Paper gains they'll give you, but even an internal rate return, which is a whole separate thing, even
[137:32] that's not very happy, but long story short, very simple. Venture capital is not making money come out. Venture capital is not actually providing returns. >> They're celebrating paper gains. >> They're celebrating paper gains. >> And they're raising off paper gains. >> Uh-huh. And actually >> By paper gains I mean just being able to say, "Oh, look, the valuation of Anthropic went up, so that's >> But that's that's what Google and Amazon were doing. Google's last quarter, they boosted their net profits, profits on paper, by $99 billion because of the increased value of their SpaceX holding and their Anthropic holding. And, again,
[138:03] the fact that this is happening is insane, and the fact it's not a scandal is insane, but we live in this culture, I guess. But, everyone is really benefiting right now. It's really that It's that great tweet where it's like, "When you're uh reaping, it's like, 'Yeah, yeah, this rocks.' Sowing, 'Ah, this sucks.'" Because right now they're all like, "Yeah, all the speculative gains are awesome. The paper gains are awesome. The theoreticals of Anthropic being worth $2 trillion. Wow, the articles we could write, the promises we can make." Then when the rubber meets the road, it's going to be pretty rough on them
[138:33] because like, the valuation of Amazon, Google, Microsoft, and Meta is based on this idea that they will grow eternally, that they will grow forever. If that changes, quoted Ellison from Profit Markets again, it's this they're all doing Botox right now. They're sinking money into it to make themselves feel young again, and the market believes them. When the market doesn't, we're not just talking about a depression, I'm talking about the market valuing them like airlines, and saying, "Yeah, you're real big, and you make money off your existing products." But, guess what? You don't have new You're just going to be doing this forever, and we're going to
[139:03] value you as such. So, if Jenny and Dave sh- should they do anything differently? Should they be conserving money if there's a recession or depression coming? Should they be a little bit more conservative? Should they I Yes. I actually I actually think it's I don't know. I don't have money in the market. I think it's a casino casino pumped up by the media. Should they invest in the S&P 500? Should they invest in Open AI on Facebook? I I God, no. I Honestly, I live in cash right now. I I cash. I don't trust the market, man. Try and get some gains here. I'm like, I'm not comfortable
[139:33] giving financial advice, but it's like, if you like, it's like you're gambling. Okay, be conservative. Things might get volatile. Yeah, it really is It's going to be act as you would with volatility. Take the gains when you've got them. Don't sell everything, but be suspicious of tech. Like, that's actually the biggest thing. It's like, be suspicious of what they're promising. If you're acting based on their promises, don't trust the promises. Trust that they are going to say what will make the stock run rather than what's actually happening and that they will find every
[140:04] dodgy way to make you think something is happening rather than it's actually happening. Annualized run rate, great example. Microsoft said that they had 38 37 billion dollars of annualized run rate in AI. You hear that, you go they made 38 37 billion dollars, right? Wow, that's so much. Run rate maybe month times 12. They don't even define it, but it's built to manipulate and they do that because we don't have a functional SEC and we don't have a media environment that actually where skepticism is the priority and
[140:34] where protecting the readers is necessary. What would they say? They would say >> uh this technology is going to be so great and so transformative that we are investing a ton of money um in advance of the value and utility showing up. That's what they would say. >> Right. >> And I've heard your rebuttal, but I just wanted to express like that. >> get that. >> I think that's their sentiment. I'm not defending them or anything. I'm just trying to provide enough like balance to see if we can see if we can dance between these these two perspectives.
[141:05] And a lot of people would say that there's going to be a bloodbath because they can't all win big in the way that they're kind of describing. So someone's going to have to lose and when one of these players starts to lose big, I think it could as you say there could be some kind of domino effect or contraction. >> Yeah, and I think the thing that people want to believe is they the dot com bubble thing. It's like it worked out afterwards because Amazon, Oracle, they didn't die after the dot com bubble. They're actually fine. This isn't like that. They're bigger companies. They have bigger promises and even I'm not
[141:36] like Oracle I actually think could die. RIP Larry. Couldn't happen to a nastier man. They'll probably >> these people, do you? >> No, I actually >> No, again, I ask this question purely because I want an answer not because I agree or disagree. But why don't you like these these people? >> I don't like being misled and I don't think regular people are being misled either and I really don't think that the average person can get away with bullshitting as much as these companies do and I don't think the average person gets anywhere near the level of
[142:06] affordance for failure and lying as these companies do and I think there is a real economic and human cost to allowing these companies to run rampant and promise the world and never really get called up on it. The tepid nature of criticism these days is so frustrating. There are some really great critics out there like they're really great people but it's like seeing these ultra rich, ultra wealthy, ultra powerful people lie through their teeth or misstate or whatever it people want to call it. It turns my stomach and I hate seeing people being
[142:38] misled and I feel like I write at such length cuz I really want people to see why I've come to a conclusion. Am I right or am I wrong? I think I am. Of course I do. But I also I just find it loathsome. I find these companies don't make good products anymore. They don't care about the customers. And and they treat their customers with contempt. >> If people want to go read more about your work, um you have a great Substack. >> Ghost actually. It looks exactly like I moved off of Substack in 2024. >> Oh, okay. And you also have a podcast
[143:09] you do? >> Yeah, Better Offline. >> Um I'm going to link both of them below. So whoever wants to read more, get more detail and and follow Ed, I think it's I would highly recommend. It's it is fascinating. And you know what? One of the things people um sometimes struggle with when they listen to podcasts is you get lots of different opinions. And weirdly I think they think of Some people assume podcasts are going to be like one person saying the same thing as the next person and then the next person and the next person. That is just not the nature of information in the world and opinions and progress and discussion. Uh what what happens is people have different opinions and I think my job, but also the listeners' job, is to try
[143:39] and pass through it and over time collect more of these reference points from different people and and do your own research. >> Yeah. >> Whether it's on your health or whether it's on something like this is to watch and do your own research and to learn. And I would say also, never believe one person. Never believe one particular perspective religiously. You know, collect a body of evidence and follow follow the evidence yourself. But I love watching your YouTube because it provides a different opinion. And that challenges me to think beyond my current
[144:12] opinion about what might be possible. So, when I've heard you talking about how this is an economic bubble, and I've heard you talk about the capex spend with these big sort of frontier AI labs, it really did make me pause for a second. And it really did make me consider that there could be a bit of fugazi going on here. >> Yeah. >> And then it made me reflect on history and go, you know, through history there's always a bit of fugazi in these moments. And Oh, that's an interesting take. And what's going to happen in 2027, 2028 when there's a bit of a market pullback? And So, I highly recommend people go watch
[144:42] cuz you do you challenge me to think differently. >> Yeah. >> And we need some of those contrarian voices to to have honest discussions. So, thank you for doing what you do. Really appreciate it. And I find you to be a very compelling, captivating communicator. And I've I feel like I've learned a lot today. So, I appreciate that. We have a closing tradition. >> Yeah. >> Where the last guest leaves a question for the next guest not knowing who they're leaving it for. And the question left for you is given that high-quality relationships are important for health and longevity, what should we be doing to improve our relationships and social connection?
[145:12] >> So, this is actually connected to the AI bubble. >> So, I'm a critic. I'm a skeptic, we'll call it. I have found that showing and appreciating and loving the people around you and uplifting them and and raising them up as you succeed is the way we do that. Your success should be everyone around you. It's not economic, it's talking about Matt Hughes for a while made me really happy. This whole thing has been at times quite grueling and quite negative and quite brutal. But the love I've found and the joy I
[145:43] found from community and the people because even in the in the small groups of haters, even like Gary Marcus and so the people I talked to Edward Jr., Molly White, Brian Merchant. There are so many people who have been loving and caring and I think within especially these very critical moments when you're like very much dialing in on how negative things are and how bad things are. Finding the people who maybe find it repulsive too. Finding the people Finding your people who can be and the people who will talk to you about it.
[146:13] Even like Troy and Jake, my my trainers who are so excited about this. Um even talk to them about this as normal people. Knowing that there are people there going through their own struggles but also to just give you the perspective and also remind you that you are human too and focus. I know this is kind of a all over the place point but it's just it's really easy to get hard locked in everything in life and to kind of get away from why you do things and focus too much on the work when the most important thing at times is just to know there are other people feeling the way you do and when I hear from my
[146:44] listeners and my readers a lot, the most common thing I feel is they feel like they have a voice and they feel like someone is there for you. And I don't think it can be understated how much it means when you just reach out to someone you love and tell them you love them. Tell them their rocks. Say that their bangs. Tell everyone you when you like an artist or a writer like they were a podcast like this, tell them you love it. We don't do this enough and we need to do it more. >> Well, that's a good closing message. So if you do have you have enjoyed the conversation today with Ed, please do let Ed know that you love it down below.
[147:14] Um but please do leave your opinions down below and I shall read all of them. Ed, thank you so much. I'll link to your website but also to your YouTube channel where people can learn more and I would highly recommend you do because it is truly fascinating and I think we need more voices that are demystifying a lot of the figazi in the narrative in this moment in time and you're certainly one of them. I really enjoyed the conversation. >> Thank you so much. >> YouTube have this new crazy algorithm where they know exactly what video you would like to watch next based on AI and all of your viewing behavior. And the algorithm says that this video is the perfect video for you. It's different
[147:45] for everybody looking right now. Check this video out. I bet you you might love it.