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

I think generative AI is at its heart. con and seeing these ultra rich ultra. powerful people lie through their teeth. turns my stomach. The word con is a. strong word. >> Well, what do you call something where. from the very beginning they've sold it. in the terms of magic but it's just a. halfass arcery 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.

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. Open AI 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 and there's no.

economic data to support it. Next, the. 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. Olman, Dario Amade. >> Mark Zuckerberg says, "We'll continue to. invest aggressively in infrastructure to. meet the demand." God met as a. monstrosity. Makes me think of Shrek. with L fogquad. 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. If. this continues, what does the future. look like? [ __ ]. This is super interesting to me. My team. given me this report to show me how many. of you that watch this show subscribe. And some of you have told us according. to this that you are unsubscribed from. the channel randomly. So, favor to ask. all of you. Please could you check right. now if you've hit the subscribe button. if you are a regular viewer of the show. and you like what we do here. We're. approaching quite a significant landmark. on this show in terms of a subscriber.

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Let's get on with the show. >> 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, 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 sellside. 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 cancer, and all of these. things? And when you look at it, it's.

boring cloud software that's extremely. expensive and unprofitable and also. unreliable at its core. >> People will be asking where are you. drawing from in terms of your. references, your personal experiences? Where were you educate? What you study? What you write about? What do you do Ed? >> So that's the funny thing is people say. he's not got a 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. And it can't even do.

the basics. It can't even do search. Well, whenever you ask an AI person, well, what's your setup? They describe. this PeeWee's Playhouse thing of like, well, you got to 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 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 a bit. Right now, 70% of all AI revenues across. those three companies are from OpenAI. and Anthropic to 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. And in the next three and a half years, OpenAI and Anthropic based on actual. sellside analyst evaluations, their. estimates that inform whether stock is. going to go up or down after earnings, they are expecting 400 or more billion. dollar of revenue, 30 or something% 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 analyst, for everyone. really that they don't even disclose. their AI revenues. The few times they. dain us worthy, they use something. called a run rate, an annualized run. rate, which means well, nothing. They. never define it. It can mean months 12. It can mean month 13. It can mean last 4. weeks time 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 making from. this?" They go, "Oh, I couldn't possibly. say. I'm too shy." These are public. companies, or at least the ones that. aren't anthropic and open AI. When they. have good news, they'll tell you. And. when they don't tell you something, well, that actually speaks volumes. >> Have you you used these tools, the AI. tools, Gemini, Anthropic, Chat, GBT, 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. 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. Abalene, Texas. 1.2 GW called Stargate. Abene. Within that, with each one of the. eight buildings, there'll be 50,000. Nvidia GB200 GPUs. So, city of Bristol. takes about 7800 megawatt of power a. year, right? Well, Stargate Abene is. condensing more power than that, 1.2. gawatt into a space around 1,172.

times smaller. City of Bristol is about. 1.2 billion square ft. Star Evelyn is. about 998,000. 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 to 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 Open AI. One of the. rebuttals to that would be that the. adoption, the customer adoption of. people using Open AI and Enthropic has. been absolutely insane. These are the. fastest growing products in all of. history, especially as it relates to. sort of technology. If we just focus in. 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. 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, co-pilot's bugging you. When. you use Amazon, whatever rofus AI is. wants has opinions on what socks you're. buying. This is the largest. non-consensual push of technology in. history. Chat GPD 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." So. people are using it because they've been. told to use it constantly and they're. using it like search predominantly and. that's partly because Google fell behind. search and also because it's better at. 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 is. just incomparable to anything. Railways, it blows everything out of the water. because there is no postbubble story. even for this. AIG GPU is not useful for. other things either. There's it's a. directionless egregor 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. >> 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 a one of these AI. tools like anthropical chatbt 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,

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 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. There's a.

whole story about how they pulled back. spam guards thanks to Bravagar Ragavan, 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, 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. So. >> what's a token? So it's around 3/4 of a. word. So it's characters. >> So the AI companies have a currency in. 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. >> About a word. Yeah. And it's per million. tokens. So you'll be charged per million. input tokens. The stuff you feed into it.

like a document or a bunch a code 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. >> However, when you're paying for a. monthly service, you don't see any of. 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 offiscate. what that was. Now, someone recently. found, semi analysis actually found. this, a big analyst group. They found.

that on a $200 a month chat GPD. subscription, you can burn $14,000. worth of tokens and on anthropics you. can burn $8,000 for 200 bucks. That is. how most and even on the 20 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. OpenAI 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 Orman, they said, uh, people have a. big problem with it. I think it's a huge. issue, which is not really what the air. apparent text history is meant to be. saying, but the point is enterprises. immediately started freaking out. Uber. burned through their entire annual token. budget in three months. So suddenly.

after everyone 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, I don't know actually. Um maybe it's. obviously we all love it. It's all. great, right? But it's costing too much. So we need to reduce the cost because. people are 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 OpenAI. $1,000, but they're only charging me. $100, let's say. So they are having to. subsidize $900 of my usage because of. the electricity costs and the costs at. their data centers. And so your. assertion here is that that is. unsustainable. >> Yes. And just to be clear, they're. probably not one for$1. It might be 30. for. We don't we don't know. I think. it's unprofitable. These companies don't. disclose them even in their auditive. 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, you've wasted the money. You 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'd just be paying what 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, you're coding something. and they go through a code base and they.

[ __ ] up a bunch of stuff, 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 the the. really interesting point is are they. spending ahead of the value showing up. 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 does it you know. because 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 underinvest. >> If they were bringing the cost down, they would have brought the cost down, 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 any 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 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 refused to. say how much they're making from AI, but. because their existing businesses. continued 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 because. revenue go up everyone went it's AI. because these companies wouldn't spend a. trillion dollars for for no reason right. except in fiscal year 2026 which just. ended for Microsoft annoying I know they. made total according to Bloomberg about. $34.33 billion $24.1 billion of that was. from OpenAI so that leaves them with. about $10 billion in a year when they.

spent 115 billion on capital. expenditures just 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 at some. point the costs will be outpaced by the. return. Problem is that large language. models need a bunch of money to train. them. They need constant data flow. 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 it was $215.9 billion in. the last fiscal year worth of GPUs. mostly. And you try and go, yeah, that's. to support like $22 billion of revenue. total in the entire world outside of. these two companies that literally. require money 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. Edson from Prophy 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 opportunism. and getting an MBA perhaps with the. 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 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. not read it. >> And one of the things it talks about is. how the the innovation that ends up. taking out 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. >> 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. you had to walk in front of it with a. red flag and wave and someone had you. had 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 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. but Mo's law. >> mos law is a mos law is not with GPUs. So let me let me 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 solid decade or more to. make it something where they 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. 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 chips. >> Chips and chips are needed. Those are. the things that go into the data. centers. >> And there specific 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, giving them credit. early since 2023, they've been saying.

this is inevitable. Even in 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. cues from speaking to a person. It's. being in different environments. And. there may there are uses for LLM's. encoding. I don't dispute that. But even. saying 5% uh what does that mean? Is it. better at Rust? Is it better at C++?

>> I'd say productivity 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's really long. That's an. insane way of evaluing 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. 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's. 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 if you imagine any rate of.

improvement in the technology which we. have seen since tragedy came out. >> I remember when tragy came out and I was. in Asia and I was there showing it to my. fiance I was like look it 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 it's. reasoning is weak, but I don't have. outright hallucinations anymore. See. that? I I disagree. So, >> give me an example of what you define as. a hallucination. >> Okay, great one. So, I have a Bloomberg. terminal. Yeah. The very useful thing. 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 RSB and it will generate it and run. it for you. And so, you get it pulled up. and you know where the data is coming. from. It deals with hallucinations real. 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.

>> And I was about to I was copy pasted it. over to something looked at in Excel. I. was about to was writing the newsletter. I went, Microsoft stocks 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 call 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. 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. Maybe you've. forgotten a few things. You had this. slop to look for. [ __ ] I'm not doing. it. And so the problems 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 of.

the main ways they evaluate them getting. better are benchmarks that are adjusted. specifically for large language models. because you can't just have them do. tasks. They've got better at that. They. found some tasks they can have them do. on them like meter me 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 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 Victaria hallucination. 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 Chat GPT. Again the. point of nuance here is that these are. on simple tasks which is kind of what. I've experienced. I've experienced that. 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 hallucinations become rarer than. they are today increasingly and also. what I would 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 dialup 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 this is crap. This is technology crap. All the. >> I 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 have Starink 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 should compare AI to my. alternative alternative way of doing. that simple writing task which is both.

measured in my time right and my ability. to hallucinate as a person who doesn't. know everything. or 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, lives out 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 he's. willing to expand it and work with me. and moral sport and 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 I. can't get that from a large language. model. But on top of that, I don't I. push back on just the assumption there. >> When you say knows everything, what good. is something that knows everything when. it sometimes doesn't know anything when. it's 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. And in. turn gets context and in turn learns. your habits. Learns. >> AI gets context and learns. >> No, it doesn't. It. >> doesn't learn. >> I mean, it doesn't. The way it learns is. you create a giant claw. 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 PeeWee's Playhouse. You have to do. all these controversies 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? my. dog's name. >> Uhhuh. >> It would know my dog's name. >> Jesus Christ. This this company raised. 95 billion. >> I'm saying I'm 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 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. >> 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 acrue. experiences is a a milerum of emotion. and feelings and facts.

>> completely different. So I think there's. two things here. There's the process in. which something happens and then there's. the output. >> So the process you're 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. 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. we get, 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 who cares what the people. care about they pay for the outcome the. product. >> I actually disagree about the process. because Matt Hughes 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 dayong 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 like just like the. 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. I've I've used some of the higherend LLM. harness machines that the hedge funds. use, and they all give 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. 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 I. just care about the output. I could use. it to learn. I could say 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 a. report I go like that I want something. that I fully understand and also. understand the context around it I don't. think that LLM do that and I just don't. see them getting. 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 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 simple. tasks as we've seen from this. hallucination leaderboard it continually. delivers for people and that's why we've. seen the fast. >> I mean is that what that board says. >> well it's it's saying like is it getting. it wrong is it hallucinating. >> simple task how are those defined. >> I 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. aboutist masters they have like well.

look we got this we got this benchmark. that says we're good at this and look. the numbers higher What's the number. mean? No. What does that mean? And I'm. 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, go was. Penn State at the time. Oh, I felt like. the uh apes at the beginning of 2001. [ __ ] official 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 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 had. bias when I just love gadgets. >> But but I remember the famous Steve. Balmer who was the CEO of Microsoft. interview where he was told about the. iPhone and he bursts out laughing.

$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. 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 way different. No keyboard. >> Well, phones used to be insanely. 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. why 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 and 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 like an. a cell phone designed even like a. computer. It was obvious. It was obvious. from the beginning. Everyone I was I was. dating a girl 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.". >> But how come then when you look at the. stats around ChachiBT's growth, >> 100 million active users in just the. first 60 days after launching? For. comparison, Tik Tok took 9 months. Instagram took 2.5 years. And the. internet itself for the worldwide 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 a 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 chachi. okay it was didn't really 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 um 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 sat. here transformative, would she pay the per million token. 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. 23 $4 every time they did something and. they were genuinely happy. That might be. an argument. >> What is the what would be the honest. cost if they weren't sub. >> the actual per million token cost? The.

actual API cost they should char. >> Do you know how much that is relative to. God? Depends on it depends on the model. But there's actually kind of a point I. want to make about the thing you said. with the internet earlier. So when I. first got on the internet 33.4 kilobits. a second modem even back then I was like. [ __ ] if this was faster and that was. like immediate just like if this was. faster cuz it was slow. You go on like. happy puppy or something download take. all bloody day waiting for share word to. download immediately like if I could do. this faster it would 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. Cavell from Goldman Sachs in a report he. did in 2024 that was geni too much spend. for not enough return. Paraphrasing. there. And he made the point that in the. run-up to the iPhone, 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 for. AI. There was no road map to AI becoming.

this thing that they promised. And I. must be clear, if these companies had. gone out there and are 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 I'm scared way. I mean, just. like we're 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 being. like called them like I don't know.

language models and no no generative AI. stuff just being not even call it. because 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 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 uh Kevin Roose I think it was. 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, Beas. It's a. website. The website told you something. It's just LLM." 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. 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. >> 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. 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 com. bubble. you know, you wrote this great. essay. >> Yes. Yes. >> Which I found really funny um especially.

the name the rot economy and you talked. about the rotcom bubble. >> Yes. >> Talking about how AI is of less value. than people think. >> And in that in the sort of 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 dotcom 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? Huge hype, overinvestment, 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 bubble had. I mean the internet itself had the. biggest skeptics in 1998. Nobel Prize. winning economist Paul Krugman said by. 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 stool. 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 CDROM. can take the place of a competent. teacher. Commerce and businesses will. shift from offices and malls to networks. and modems. Bologoney. So, how come my. local mall does a roaring business and. the cyber mall gets zero business? And. then I'll give you one more from. Krueger, who was 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 that may actually be. the worst one of those predict like hang. around any bar in middle America. Honestly, the best conversation, >> but it's just all the same thing. >> I actually So, Clifford Stall 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. 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 it's. just completely different. So.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. eury incard company for like a billion. dollars. It was insane crap happening. that was so small. The big thing that. people are thinking about is the dark. fiber. >> Dark fiber. 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 just. 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 immediately didn't. really. Now the post.com bubble thing people say. is well but after that there was demand. from the internet. That's the thing.

though that's very different 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. experience 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, pushing this up the hill. We have. the apex predator of cloud software,

Microsoft. They can only get singledigit. billions from selling AI software. And. Christ almighty, outside of OpenAI and. Anthropic, we barely 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 open AI 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. 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. efficient. It's uh more dollars per.

megawatt." They're all koi about it. They don't just say, "Yeah, we worked. with OpenAI and Anthropic and we found. it reduced our 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 are you 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 lump uh protein folding. nothing to do with LLMs. Robotics not.

LLM. >> Autonomous weapons even horrible as they. are not LLMs 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. Stop giving them credit.". >> The similarity though is they all need. GPUs, all these. >> 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.

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. >> How would you run 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 Matic Matic has 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 no year. as many as they need for generative AI. to run the data feed training data into.

it so it's able to clean a house. But. when the little buggers going around. cleaning my floor, turdsly I call him, it goes around mopping my floor, it's. not like burning money the whole time. But when it comes to these massive. amount of data center, sighteline. climate said in February there's 190. gawatts of data centers under in. planning. Don't know about under. construction that works out if about 12. million megawatt that's what like $1.6. trillion to3 trillion a year in annual. demand you'd need for that. We don't. even have $130 billion worth of annual.

demand. And people say, well, it will. grow. how when most of the demand is. coming from Amazon feeding money to open. AAI or anthropic, Microsoft feeding. money to OpenAI and Anthropic, Google. feeding money to Open AI and anthrop. well hasn't fed it to Open AI yet, but. they're a pretty big customer, billions. of dollars. The conside is that we are. building these effiges 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 online banking. Well, you do like data centers. There's. a big difference between a data center. for regular nonGPU compute for standing. up a server, a content delivery system. like Akami or something that brings the. website to you or how Meta runs. Facebook. That is not the same. It takes. way less power, mostly CPUdriven. compared to these giant GPU data centers. that offer one thing, one thing only. >> But I was doing the the research and. looking at some of these notes here. It. does say that for tougher types of AI. systems designed to solve concrete.

physics, biology, and spatial problems, they require some of the most intense. data center infrastructure on the. planet. >> Yeah. >> AI systems like Deep Mind'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 computing 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 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 has been. trained before. That's how Tesla did. 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. 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. something 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 from these two. companies who were 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 will. 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. It's 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. customers, right? They wouldn't just. have two unprofitable fail sons that. they're propping up with. Christ, they've raised $217 billion 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. because of their AI coding tool. How. >> how is how is Google worse? >> Well, I'll tell the story of a real. [ __ ] guy called Preaggo Ragavan. Previously, one of the heads of ads at. 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 at 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 I mean you. 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 Shashi Tako was another. engineer was saying yeah can we please. tell Sunda 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 would make the product worse. >> But but it would make them more money. You saying you'd show them more ads. So.

if you're spending more time on Google. because Google's work, >> but is this linked to AI doing code? >> Oh, I'll get there. So. >> this is the problem is is that this guy. called Pragar Ragavan who's the head of. ads at the time was pushing pushing and. saying, "No, we need to make more. queries happen. Got to make it happen.". and Nick Fox who was there as well I. believe was actually taking over Google. search got to make them go up this is. our new reality sometime in early 2020. propagar ragavan 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 journal and the various forums. Google stripped back a lot of the. suppression of spammy sites so that. 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 Praagar, wouldn't you know, it gets put to run. 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 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 know. 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 was. >> But I'm saying here I'm saying here but. that's not the fact that coders could. 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 led with that a. problem across the whole tech industry. >> Okay. So you're saying that you're. saying Google is going down more. >> Yes. Google is less stable. Google Docs. is a bugfest 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. >> How do we quantify that outside of. anecdotes? Like, is there a way to. >> You're right. I mean, GitHub downtime is. the best example. Amazon Web Services. went down two or three times this year. because of AI tools. And honestly, you're right. It is 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 and. software outages have demonstrabably. 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. open source has had this problem as well. because it's well-meaning people. They're like, 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 Krueger, this. [ __ ] I'm going to I'm just. like, I can understand some of this.". And now the code's all written and 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 let. it write the the code for the last 100. lines and it was broadly right. So the. next 100 lines, I won't check them as. much.". >> 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's still making the. mistake and we're still making all the. promises of AI that's the thing this. thing is meant to be this autonomous per.

you say it can't be perfect I don't know. based on what Samman has been saying for. the last few years clammy Sammy has been. promising the world saying this will. replace software engineers Dario. Ammedday Wario himself has been saying. oh yeah 50% of 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 promises. One of the smartest things a business. can do is build like a bigger company. without actually hiring like one. But. the problem we all face is that most. companies don't have every skill in. house. So when I look at the businesses. seeing real success today, the.

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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 disruption. >> 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 Sachin Nadella. He's not going to be like yeah we don't.

know if 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. He it's going to be so. powerful. And then he'll change his tune. and say actually it's not going to. replace workers. that make him more. powerful because the things aren't. catching up. Dor from Uber for example, of course he's going to say if this. happens then that would be good for Uber. because Uber would just become an. autonomous taxi service. There's a. reason that Whimo's taken I I find Whimo. fascinating. I think that [ __ ] it's. really cool. I think there are. socioeconomic 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 socioeconomically there are. like you said one of the largest. employment centers in the world. I mean. just the economics of cabs will fall. apart but again we are nowhere nowhere. nowhere near that. We're not even close. Whimo has had to do the smallest. rollouts and the most control things. 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 viz 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, 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 Zuk's cars just get. [ __ ] stuck. >> They're autonomous cars. >> Yeah, 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 actually happen. outside of a hotel when I got out of a. Whimo in San Francisco. Just stopped at. the and then a bunch of cars and another. Whimo got stuck behind it. And these are. kind of. >> 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 and pro till proven innocent. because we can prove and also they have. people overlooking them. They actually. have people monitoring the roots. 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. >> I do agree. Listen, I I'm a big fan of a.

big fan of taxi drivers generally 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. >> driving myself or having an autonomous. vehicle drive me, there's an 68% lower. overall crash involvement rate when. you're in an an autonomous vehicle. Mhm. >> Autonomous vehicles experience roughly. 2.1 police reported crashes per million. miles compared to humans that are at. roughly 4.68 per million miles. So, 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. >> Uhhuh. >> So, you're 85% less likely to be. involved in a single vehicle crash like. hitting a wall or a tree if you're an. autonomous vehicle. >> versus being driven by I agree. But. >> so it's safer. >> in 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 be. having a different conversation. >> I guess the question here was really. around job disruption. Like you know we. we look across industries and we go. driving is a massive profession. Is. there going to be job disruption because. cars can now drive themselves? If we. think about white collar, you know, jobs, you know, lawyers and accountants, people sit here and they tell me that. lawyers and accountants would 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, great example. Always hearing [ __ ]. legal partners talking about AI. Never. the associates. The associates are the. ones that go out and find the president. They're the ones that go and do the. grunt work. They're the ones who are. pulling motions half the time. The. partner is the one that might be the. litigant. It may 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 Chat GPT 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. Open AAI had a study that. came out I think like a week ago that. said there was no corre connection. between spending on AI tokens and. revenue per employee. Like this is open. and that's. >> what does that mean? Could you explain. that to me? >> As in the more tokens you spend has no. 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 the one thing. I respect about that company that. occasion they just put out a study. It's. like, yeah, [ __ ] kind of sucks. 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. 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 for they would have.

sold it to the global self. 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. the rooftops. There was an Oxford. economics 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 mythologies. They are. playing on these knowing that. journalists, analysts, investors will.

believe them. And this is partly because. our our realities are 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. fundamentally not true because those are. two true statements that okay, technology often starts. >> I know. I get what you mean. What they.

are fundamentally misleading people. about is how possible it is. How many. actual signs they have because they. don't have the signs. If they had the. signs as in the signs of this getting. cheaper as in the signs of this being. able to autonomously do work without the. Rub 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 people. that are most excited about this, psychopaths on Twitter in many cases are.

people that I believe there really are. some 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 was on track to hit $40. billion in annualized revenue. Month. times 12, four weeks times 13, we don't.

know. They don't define it. I saw. multiple people and I 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 open AI in. particular in particular Mr. Baltman. they have been fermenting this that Tibo. as well the Tibbo the one of the guys at. uh 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. one would argue actually helps them. How? Because you know the AI doomers.

that have come here and told you know. some of the original founding fathers of. AI like Jeffrey 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. and there is a significant chance it. could f we could [ __ ] up the planet. >> And what we've seen is this slow pivot. away from it because now they're getting. booed and they're being attacked. There've been this slow pivot away from.

it. And the pivot almost sounds a little. bit like your narrative. >> It now sounds like actually no, it's not. going to change anything and you're all. going to be fine. And it's now there's. just not it's nah it's not dangerous at. all. >> But that's the funny thing. >> and that's why I'm saying like you're. you're not they I actually think there. might be a couple PR people at these big. AI companies thinking thank god for Ed. some of it because you're like you're. saying actually don't worry everything's. going to be fine. 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 Alman and. Amday are some of the most deeply. corrupt and cynical people in the world. I don't think of course they were going. to say from the it was early 2023 or man. 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 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 saying it's so scary. And now they're talking about slowdowns. But you ever noticed that Amade and. Ortman, they say, "Oh, maybe we should. slow down progress." And then they.

don't. Right now, Orman's saying, "Oh, we slow down progress because we're so. delayed." No, they're out of compute. Now, they're doing it. I can guarantee. you, by the way, their PR people do not. like me. I know for I know I don't think. OpenAI's PR people are super fond of me. >> 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%. sure. >> Uh 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. >> Elon 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 new slogan at. trackbt is intelligence for everyone.

It's suddenly and all the and and. whenever Daario comes out and says, "By. the way, it's really [ __ ] dangerous.". They attack Daario. Yeah. They hate him. >> That man Daario is. >> They're like, "Dario, shut the [ __ ] up.". >> Honestly, I I've been saying Dario, shut. the [ __ ] up for years. But it's But 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.

still trapped in the hands of Milton. Freriedman, Margaret Thatcher, and. [ __ ] Ronald Reagan. We're still stuck. in the neoliberalistic hellscape, which. is growth at all cost, 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 to the side. Break up. these [ __ ] for sure. We shouldn't. have companies this big. It makes things. worse. >> But these technologies are dangerous. >> I mean, they're dangerous, but not in. the ways they've been warning about. Let's if we think about cyber hacking,

>> right? And just to 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. in at scale and arguably um at a. higher intelligence and faster and wider. 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.

already in the wrong hands. Mark. Zuckerberg, Sam Olman, Dario Amade. 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, it's kind of interesting. But you do. 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.

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 Jeffrey Hinton at 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 things, not so. much. Jeffrey Hinton as well talking his.

book still got his Google stock, I. think. And weirdly enough, he left. Google because he was worried about the. AI there, but then immediately made a. comment being like, "Yeah, actually. though, Google's very responsible.". Strange thing. But let's get back to the. the cyber security side. I agree this is. dangerous. These people should not have. access to so much comput. 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 were able to distill the. models. And also,

I don't know, regulate it and stop I I. 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 Samman term. We let this happen. because we let these companies be. unregulated and use as much computers we. want. We had these [ __ ] enablers. allowing them to burn as much computers. 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 AI industry. >> The challenge is I want you to give me. one sentence. >> [ __ ] on each myth. >> Oh, Christ. >> 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 in your says the the AI. industry is creating enormous economic.

growth? >> No, it's not. It's nowhere in the data. >> Okay. Like, it's just May I do a second. sentence? >> Go ahead. pretty much all of the. economics is either Nvidia feeding money. to it companies like Corewave 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 hundred billion. And most of. that is just these two running their. services and paying these three. companies, Oracle, Core, and others. >> But a hundred billion is a lot of money. for a relatively new technology. >> Not when you've spent $300 billion in. equity funding. And it if we're going. with just these three, I think $600. billion 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 $20 or. $40 for a dollar, they're going to use. it more. If this was all on a per. million token basis, we'd be having a. different conversation. >> Okay, fair. 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 Jastario, two amazing. 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 LMS. 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 u they won if that's the case. Myth number three, AI will replace all. human jobs. that just isn't happening and there's no.

economic data to support it. >> Will it replace some jobs? >> I mean, it's replaced some contract. labor that would otherwise be replaced. with cheap labor out in the global. south. It's a digital globalization in. 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 mythbusters piece. that was definitely about generative AI.

>> Okay. But what about robotics? Like the. thing is the Optimus robot that Elon's. working on at Tesla. >> The one where even in the demo of the. hand he like they had to have a guy. controlling it. Wasn't doing it. 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 ch them Chinese. robots? I know you've seen them. the. uni, 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. mindblowing. >> Robotics are [ __ ] cool. I like 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, you know, I was in San. Francisco and I went to this massive um. incubator there. And when I'd gone there. three years earlier, it was all software. startups, right? And when I went back. three years later, it was all these. robot 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 on. it. Yeah. >> And it whole thing is it cooks for you. >> Yeah. >> So it was he 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. because robotics is a function of. intelligence plus hardware. We've always. had the. >> and a ton of data though as well and the.

data is very expensive. >> Yeah. >> The thing is cyber cabs rolled out real. slow. It's going to take a long time. It. 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. 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. [ __ ] Yeah. >> But um 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. >> That's an example of a job that humans. probably shouldn't have been doing. And. as technology gets more advanced, it. takes 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 talking about. people when they say this they are. referring to that. >> So you're not talking about agentic AI. which is. >> 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 LLM. Agentic AI is one of the. big the bigger lies they to tell. It's. like when you hear agent you're meant to. think autonomous AI can do what you. want. It's still LLMs. It's still LM. talking to other LMLs. >> taking screenshots and putting them in. LLM 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 and. told me about them or maybe once upon a. time shown me a piece of paper back in. the day. I guess now my chief of staff. is no longer doing that job. You still. have a chief of staff though. >> 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 triaging. >> Basic spend a trillion dollars on. triaging email. Like that's the the. promise. If they'd spent $10 billion and.

this was much smaller and you I go cool. software. Yay. A lot of the things that. people are impressed with like script. stuff as well. It's just LM's doing. Python. 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? I mean. the internet. >> I mean people overpromised that. >> I mean they overpromised 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 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 I. think a billion dollar market cap, but. they were talking. >> Yeah, there was massive hype in the com. era. >> I read a lot of those stories. The hype. was nowhere in it. You didn't have. articles everywhere that were saying if.

you don't get online, you'll be left. behind. You didn't have professional. consequences. Nick Sesh mentioned his. blog earlier. He described this thing. global uh AI sisterating global. decision-m 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 in general has caused this as. well and also the fact of day trading. there's so many different things that. are different it's crazy. >> I I do think AI is different from the. internet in part if you just measured it. on the speed of adoption especially if. we just think about generative AI AI. >> but the this 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 dialup connections, it still required the distribution. >> and that's why it was so slow and there. was less, you know, there was less hype. than AI. I do agree that there's way. more hype and we again going back to. this point that we're clustering AI in. this big category of lots of different. things. >> There's generative AI. >> There's generative AI. There's like real. world AI. >> Generative 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 LLM so. that I and that's the thing. They have. this theory, the era of the business. idiot where it's like we are ruled by. people that don't do work because nobody. who actually does a bunch of work who. really is productive is harassing. someone who works for them for not being. productive enough. >> They're not they don't have the time. They're doing work. Someone who is. 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, LM are. magic. >> I, you know, one of the most compelling. arguments I have for the overhype of AI. >> in a world where everybody has access to. these tools, whatever the tools can do, would largely be commoditized. 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 acrue as for the user. 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. of value. >> So so I think a lot I've been thinking a. lot actually about how.

>> how do you um avoid the temptation of. sloppification 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 chat GBT. or anthropic, you know, Claude to make. your LinkedIn posts, let's say, >> 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, >> right? >> And deeper and more personal N of one.

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 we've we've got a. bunch of slop but these people were. halfassing their jobs before. It's just. a halfass arcery machine and it's just. it's it's the thing. It's what I'm. talking about with the slot blogs. It's. like it's it 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 uh.

internet bucks. Awesome guy. Great. software engineer. He he said I might. have said this earlier. So, it makes the. easy things easy, the hard things. harder. When you know you're doing a. really distinct small script for. something and it can plop that out. It's. awesome. I used Claude the other day for. something useful. My kid loves. Minecraft. I was 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. >> I really don't. I don't use it. >> with Bloomberg terminal. I use AskB, 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. 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 dollars? No. Is that a $2. trillion company? No. Pretty use. >> Better than Google though, right? Better. than Google search. >> I know. I mean, yeah. Remember, >> do you use Google search still? >> I try. I have to [ __ ] push the crap. out of the way. And. >> 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. >> So 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. only LLM use case I defend. The. troubleshooting thing is awesome for it. I 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 right. They are, you know, telling us that it is going to replace. everything. But funnily enough, the. critics are saying that as well. >> Which one I mean I mean. >> they are like the Jeffrey Hintons of the. world. you know, even people that have. left the safety team in chat who who. I've sat here with the these are critics. that are that are warning of the impacts.

it's going to have on the world. It's. weird how all these critics also have. vested interest in AI doing well though. Daniel, former open AI guy, AI 2027. written with the Star Codeex guy that. was nothing more than badly written. science fiction that he's already had to. walk back. >> You know, he could have made more money. by staying at chat. >> Could he? >> I mean, looks like he lost. >> if he had options early. it sticking. around. >> Did he lose the options? How much do. they. >> 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 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 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 tests 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? >> You agree? in terms of what it's capable. of doing. There we go. Yeah, >> cuz it's not it's not got new 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. Over time, AI's 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. >> will it not start to do some of the jobs. that people are doing today. >> outside of software engineering remove. software because I will concede software. engineering it's got better at that.

outside of software engineering where. >> so the chief of staff things that admin. >> okay so it's got better admin video. generation photo generation. >> text generation theoretically coding. >> right. >> and then I'd say agentic workflows. So. >> what is an agentic workflow? >> So automated workflows where you're. doing the same I mean a good example is. looking at the backend data of the dire. of a CEO. >> summarizing. >> looking at all of the data ingesting all. of it going out into the internet and. searching who Ed is. >> looking at every interview you've ever. done ever. >> Uhhuh.

>> This is summarizing and generating. >> making a little model on you know the. things people want to know from Ed. >> Producing a report sending that to my. inbox. >> Me getting a 20 30 40 50page report on. Ed before he arrives. >> This is all basically the same thing. I. think it's been doing for years though. It's It's not really new capabilities. >> Research. It's It's. >> still the same things. They've had web. search for years. They've had report. generation for years. >> Well, we couldn't generate. highquality videos that are like. indistinguishable from cameras. Seed. 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 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. >> 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 actually, >> I actually don't think it I think that. there are hard limits that we're going. to hit. So you do believe in that. there's a hard limit somewhere. >> We've kind of already hit the. diminishing returns level because for. example video generation which is by the. way far less an American concern. anymore. OpenAI shut down Sora. I think. you can still use the API but. nevertheless look at the look around you. with the amount of stuff in the crew you. need to get a shot. People think the. movies are just shot by shot by shot and.

they just magically happen. When you've. got my my wonderful girlfriend of first. ads, 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 one minute long. thing that might fool someone. How do. you practically turn that into a movie? Because that movie, I forget what the. name is. There was 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 number go up. When it comes to. can this actually do distinct tasks 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 linearlyish 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. neuros symbolic. You actually need to. build a structure around the AI to make. it work. And even then, it doesn't fix. the. >> So you're saying that there will become. a point where the rate of improvement. will plateau. >> We're already there and stop. >> We've already hit that diminishing. Gary. Marcus said this in 2022 as well. Do you. know there's lots of people listening. now that like they've had their. workflows completely transformed by.

these tools? Have they? >> There'll be people. Yeah, there are. 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. that. I've heard that from multiple. people. But again, how much can you. trust this? >> I think I'm 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.e. they think today is. going to look like tomorrow or they. think it's going to get better in this. 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 compute and all that with. fast processes, those are hardware. breakthroughs. The hardware breakthrough. companies don't seem to be fixing the. LLM problems despite the all the king's. horses, all the king's men with what.

nine 10 generations of TPUs from Google. now. Broadcoms building stuff with open. AI, their halapeno 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 and. capabilities. There's a certain point at. which I'm saying, "Okay, can it do even. a tenth of the stuff they're promising?". Sam the other [ __ ] week was saying it.

was going to be in like 6 months will be. like a genie that you can ask wishes for. from like [ __ ] 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. 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 scop. >> 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 will be powered by the. underlying AI infrastructure. It will be. the more data data centers. It will be. energy coming down. >> How does a GPU full data center. translate to a Nikon camera that can I.

don't know even what you'd think think. like because what is the thing we're. talking about here? Because the idea. that devices will get smarter. Sure, I. can see that. It's a very broad. statement. I could see it happening. It's really kind of happening. What does. that have to do with the data centers? Cuz these data centers again are not. being built to make your consumer. electronics smarter. They're not being. built for anything other than. speculating on the ability to capture. demand for generative AI services. >> But it's not just generative AI. We went. through that earlier. >> Yes. No, but those data centers, they.

are being built for generative AI. They. are not being built for anything else. Would you 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 we can see guy sat in front of. me called Ed with blue shirt and coffee,

we now can train the AI to serve whoever. wants blue shirt, Ed, and with coffee to. the right user, which means people are. retained longer because. >> it'sn't 15 basis points, like 0.15%. >> Yeah, it's cool. But it makes a. difference at scale. It makes a big. difference at scale. >> Yeah. But 10 and something billion. dollars in and the best you've got is. 0.15%. If if he could be fight I mean. how much of a difference because. >> there's a reason he's saying basis. 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 because it needs a data. center that is driving revenues, but. also that's not out that's outside of us. thinking about just generating. >> and that's generative. model. Muse was it? Oh, Muse Spark is. their LLM. Gem is their generative ad. model. Well, Muse then then that's them. doing the weird 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 popup 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 goddamn. dollars on scale Alexander Wong and I. made this much. They can't. It gets back. to a very simple point of, hey, if it. was going well, you'd tell me how well. it was going rather than, I don't know,

doing this weird rain dance thing 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. three 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. 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 diio 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 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. subscribed, you're already subscribed. If it says subscriber, that means you're. not yet. And if you're not subscribed,

please could you do us a favor and hit. that button? It helps the 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 for gazy? >> 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 [ __ ]. like we've 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. >> A lot of money spent. We kind of thought. this dream was coming of this well I. shouldn't say dream cuz it's not a dream.

I've had but. >> dream that they had. >> Yeah. This sort of virtual world and. actually it never transpired and there's. no sign that it will in the near term. AI and the dotcom boom in this regard. are the same. NFTTS were the same, >> you know. So crypto, one could argue. that a lot of the crypto industry was. the same. It's weighing that is inflated. by the media. The difference is the. reason the metaverse and NFTs didn't. escape this was there weren't stocks to. speculate on. There weren't big. companies that you could invest in. They. had re record earnings in 2021. There's.

a bunch of money floating in the system. thanks to postcoid uh the PDC that. basically government federal money. flowed in to the banks. There was a. bunch of easy money zero interest free. era money was easy to find. Then after. that there was the hangover. Growth. started to slow down dramatically. This. is actually my rockcom bubble theory. which is they don't have any hyperrowth. ideas anymore. So suddenly they started. buying GPUs. And when they bought GPUs. people went they're doing AI. Oh we. better buy the stock. And the stocks. went on an incredible run. may like. several hundred percent grow in the last.

few years. the stock has grown by. hundreds of percent. Despite zero proof. and because the media was just saying, "Yeah, Meta's revenues growing because. of AI, right? Microsoft's revenue is. grown because 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? Satcha Amy Amy Hood. 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 but I doic.

>> yeah no I do think there is and I think. the reason why there's overspending Ed. is I think there is something here. >> and what. >> in terms of like I think there is pra p. p p p p p p p p p p p p p p p p p p p. practical uses for this technology and I. think when people realize that through. history they go crazy because they want. to be the person that owns the. opportunity. >> I'm going to be honest I just I. fundamentally don't agree. >> You don't agree with which part you. >> I don't agree that this that the. speculation is a result of actual. demand. I don't believe it's suspect. I. don't think private credit is sinking.

hundreds 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 am 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 actually I get your meaning. 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 if I. take your example, the rot economy essay. that you wrote. >> Yeah. >> Say that you're on a desert island and.

then someone says they found a banana. tree, >> right? >> And there's there's 10,000 people on the. island. >> Okay. >> They are going to stam [ __ ] peed. 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 stam [ __ ] peeding and. killing each other and making irrational. decisions like hungry people would. >> I actually think we're then we actually.

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, OpenAI and. Anthropic are going to spend over $400. billion 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 a bananas, I. actually agree. That is the rockcom. bubble, it's they don't have a new thing. and they're desperate. And indeed, they.

got rewarded for buying the GPUs. They. got when they bought these goddamn GPUs. from Nvidia, all the markets went. rockard 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 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.

promise over the long term than you do. So the thing I want to push back on. there is. to have progress with AI just on a. taking it in a vacuum to have progress. for these two companies to keep going. and to keep progressing they need to. spend tens of billions of dollars a year. on training. >> 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. >> that we've got so far is entirely a.

result of this circular system. So it. means that. >> circular you talked about VCs there. >> venture capitalists who are by the way. the majority of the funding that open. AAI 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. LLM can only continue as long as the. money keeps flowing once the money keep. once the money stops flowing the. progress stops which. >> but isn't that most like early like.

Spotify didn't make money for 20 years. >> Spotify didn't lose 20.9 9 billion in. one year. They didn't need to raise $217. billion in the space of 6 months. >> Yeah. And Uber is another example. >> $33 billion since inception before it. became a messy kind of profitable. Amazon Web Services between 2003 and. 2015 when it became profitable. $29.7. billion the scale. 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 TLDDR. >> Yes. But the amount of money they lost.

is. completely. just magnitudes different on a level. where these three. >> 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. >> oh so cloud with Amazon web services the. reason I bring that up going to repeat. something but it's really important 2003. it was founded. >> and it was founded mostly because Amazon.

as a growing online store needed. hardcore infrastructure. 2006, I think, is when they turned it client-f facing. I may be wrong on the dates there, but. 2015 was the year it became profitable. >> Yeah. >> The total capital expenditures. normalized for inflation with $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 in a way, but their. margins actually started improving. because AWS was a very margin heavy.

business. It was great. >> 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 four years. They have gone from being these cash. machines to these cash furnaces.

>> You said a second ago, this can only. continue if 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 addressable market is enormous. Maybe the revival back to me is about. open source and all these kind of. >> No, no, no. I I actually know what. you're getting at. So what you were. describing there is the argument that. Sachinadella or Sam 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 compan Open AI just.

open AI Clammy Sam has said Wall Street. Journal and Isaagi 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. That. is crazy. >> and 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. training is actually a really. interesting thing because when you think. of like for Jake and Troy my trainers. when I train with them when I lift with. them I have a defined thing and 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, the money won't be there to buy the. data or 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. GPT5 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 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 billion. over 5 years. >> It's absolutely fascinating because I. was just reading through a list of. quotes from the big CEOs of AI companies. to see what they would rebuttle 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 of. underinvesting is dramatically greater. than the risk of overinvesting. And you go down, you go through this, you know, Andy Jasse, 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, CE of. Meta, says we'll continue to invest. aggressively in infrastructure to meet. the demand. I'd rather risk building. capacity before it's needed than being. late. Makes me think of Shrek with L. Farquad. 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 who know it matter, 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. biggest, strongest thing in the world, did they ever get that? I I mean this. like. >> 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 to make this. profitable. A bunch of. >> question new mathemat 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. person the the [ __ ] with a blog is. the one who's like me. Trust me. If they.

came here, they'd be on the hot seat, too. Trust me. >> Oh, I Oh, they they won't talk to me. Don't know why, Steve. They don't know. It's cuz I call him Clammy Sammy. Um. >> I think it's cuz my guests are quite. quite critical that I don't think Solman. wants to come here. >> Mr. Orman, 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 rocom 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. 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. 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 a few people agree on the spending. part but the other part. So with you. it's one of probably the first person. that I've spoken to that's had this. opinion. >> So how what would it take for you to. 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 it'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. violent 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 chummy 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 wait into it, but the use of gas turbines and behind. the meter power 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, a random business. They. want I have a good idea. They go to a. bank, a bank of town, go [ __ ]. themselves. They'll say, "I'm not g you. going to make a store that sells stuff. Screw you. You want to build a data. center? You Jensen Hang will back you. Jensen Hong will give you 25% residual. value. You want to build a regular. business that's even profitable? [ __ ]. you. No, a venture capitalist 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 Hong to buy some. GPUs? He'll give you a contract. Corewave is a great example. C Neocloud, which is just a company that builds data. centers and puts GPUs and rent them to. people. Nvidia, one of their first. investors in 2023, signed a $1.3 billion. contract to rent back their GPUs from. Core. So that Core go to a bank and go, I 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 need to show us way. more than that. I don't trust you. regular folks. But if you're an. unprofitable Neocloud, 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 prouser. Let's take another myth. AI will be conscious. Mhm. So.

super intelligence, artificial general. intelligence, these are theories. Anyone. saying this stuff will become this is. just guessing and does not have proof. >> Okay. >> 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. Open AAI's GPT 3.5. I.

realize this is more than the 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 user of GPT doing the. experiment. got it to generate things to say to a. task rabbit to make a task rabbit do. stuff. >> A task rabbit. >> as in a person that you rent, not even. to do a capture. It's something you rent. to like nail a 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, "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 slot me up. I don't need no. thoughts. Put the story in the bag. And. it's frustrating because it scares. people. Put aside the fact it's wrong. It's scary. It's scary to people. people. living their lives who have to work. longer hours to make less money and. their money doesn't go far and they turn. on the [ __ ] news and there's some. [ __ ] being like, "Yeah, you should be. terrified it blackmailed someone.". >> But this is 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. 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 these. serious are being attacked at home. >> Yeah. Which [ __ ] sucks. Which is. >> terrible. I must be clear like you. dislike the 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. I 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 Kick'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 happen I believe. just guessing. They thought they do this. 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 in. articulate. They're disconnected from. regular people. Samman drives a $5. million car around San Francisco. So. that that man's doing it like 9 miles an. hour. 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 think, well, if we scare people into doing what. we want, that'll work, right? It didn't. They 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 o 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. 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. >> I get what you mean. The reason I don't.

like Dario is Daario was doing the scare. tactics thing when he worked at OpenAI. when GPT2 came out say 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. He's. now being attacked by them. >> Good. >> Um but the thing is sorry I mean let me. clarify the word attack. Darian is being. verbally attacked by Silicon Valley and. you know if Silicon Valley if powerful.

people in Silicon Valley are attacking. someone. >> Four months ago he wasn't though. They. were all saying he was the smartest boy. ever. >> 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 it's just. like what are you doing? Well we have an. alignment team. So does every AI lab. Well I guess open AI cycles through. those really quickly. Here's the thing. If I'm Dario Amade, I'm sitting there. going, 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 Orman's hand at 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. him. I think Open AAI and Anthropic are. basically the same level of Bad Company. I think Anthropic is more cultlike. I. think it's so weird like Jack Clark over. there, one of the co-founders. That fell.

used to be at the register. He used to. be one of the most critical journalists. ever. Now he's it's like like something. took over him because they talk of these. things in these high fluent terms. But. then again, maybe the people at. anthropic buy their [ __ ] Maybe some of. the people at OpenAI buy their [ __ ] I. don't know. 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 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 have.

to be a truly autonomous. >> it would have to continue its. improvement in terms of capability. >> It would have to be a different product. It would have to be it would have to be. indistinguishable from magic. And the. reason they have these high standards is. they set them. >> Okay. Fair. 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. 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. Just you got to talk about. today, mate. You get two weeks in the. future, Max. Because if they were. constrained to what was happening today, it they would sound like insane people. >> Yeah. No, I think yeah, most I guess. most technology companies would at the. time. Like Uber would sound insane. Amazon was. >> Uber was basically the difference. >> They were pissing money though, 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 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 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. So with the. anthropics fable model with some. accounts you have to pay for usage and. also adoption of fable has been pretty. low because of this because of the cost. but with enterprises so companies over. 150 people you have to pay by the token.

now or per million token. >> Oh so they are moving to a token. >> Yeah. But when they did that everyone. went from being like this is the most. impressive thing ever to being like. >> it's always we got to control these. costs. Uber's COO said as 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. >> He said the thing like he said the. actual thing I've been saying and it's. so 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 scared of. Donald Trump." Here's the problem with. this. It isn't just an AI bubble. It's the. rockcom bubble. So the AI bubble. collapsing will probably be this company. running out of money. Open AI.

>> And the thing is with Open AI is they. were meant to go public this year and. now it's been pushed to next year a week. and a half after I released their. auditive financials. Wonder where that. was. Um, but they've delayed to next. year. Sarah Frier, 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. when you join the stock market, your. investors can finally sell their 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. >> Open AAI up until this point has been a. private company and their last funding. round they were valued at $865 billion. Now when they tried to go public, New. York Times Mike Isaac reported this. They tried to list 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 open AI 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 hundred billion a. year just to survive. If they can't go. public they will have to raise another. funding round. The problem is it's going. to be difficult to raise at even the. same one they raise that. They're. probably going to have to take a flat.

So the same amount. Exactly. But they. need money. They need money so bad. Amazon sent them $35 billion that was. meant to be contingent on them going. public early. >> They did that because they need 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 Open AI to do so because Anthropic, an unprofitable, unsustainable AI lab, but a better business that's growing. faster than Open AI's. I believe they.

have a ceiling. They're eventually going. to face predition, 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 Invidia props them. up a little. Maybe Private Credit, Blackstone, Black Rockck 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's 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. We work was a great. example. Another SoftBank classic. Now, I think Open AI 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 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 hundred billion worth of. OpenAI stock. If they can't go public, they can't do diddly squat with that. And so Soft Bank'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. 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 rockcom bubble the core of. my theory is that they're out of. hyperrowth ideas but the market doesn't. think so the reason they're so.

maniacally spending is because buying AI. GPUs allows them to kick the can further. 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. There's 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 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. >> what's the 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 7. thinking the number go up forever is the. largest company on the Fortune 500 and.

NASDAQ as well and like 7 to 8% of the. S&P 500 that company when in when the. bottom falls out from Nvidia and we. haven't really got into it but Nvidia is. doing the most circular of financing, 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. singledigit billion dollars. >> And what happens though, I'm thinking. about like Jenny and Dave that are. watching this right now and they are. just normal people. >> with normal jobs.

>> 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 7. So. Apple, Tesla, Meta as well. And the. thing is I don't know what happens after. that because venture capital has also. more than half of venture capital last. year went into AI. I think most venture. capital investments in AI are going to. zero because when it comes to building a.

company on top of an LLM, all of those. are unprofitable too. And the thing is. LLM companies have not really been. acquired. The exception being Cursible. 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. [ __ ] TJ Maxx for AI? Like Jesus. Christ. >> So is that a recession you're.

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 profit shrink, and.

lastly, cost cutting measures to survive. 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. 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 start. They have. between these two companies, Anthropic.

and Open AI, you have $1.1 trillion of. commitments. >> Just OpenAI. >> Oracle is building 7.1 gawatt 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 the last 15 years when you adjust. for inflation. Without Open AI, Oracle. dies. So you think open AAI 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. impact the broader economy. >> Yes. And also the tens of thousands of.

people that will be laid off from the. 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. total value put in. So the amount of. money you get back for your dollar is. between8 and 1.21 meaning for every. dollar you invest you get 80 cents to. $120. >> paper gains. >> Well no that's just actual g like actual. returns. Paper gains they'll give you. but even then internal rate return which.

is a whole separate thing even 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. >> Mhm. And actually 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. 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. Oh, it's really that it's. that great tweet. It's like when you're. reaping, it's like, "Yeah, [ __ ] yeah, this rocks." Sewing. 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 can write, the promises we. can make. Then when the rubber meets the.

road, it's going to be pretty rough on. them because 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, to quote Ed Elson from ProfitG. 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 value you as such.". >> So, if it's Jenny and Dave, 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? Unfortunately, >> oh god, no. I honestly I live in cash. right now. I live in cash. Yeah. I don't. [ __ ] trust the market, man. Try and.

get some gains here. I'm like I'm not. comfortable 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. 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 of annualized run rate in. AI. You hear that, you go, they made 38. $37 billion, 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. where protecting the readers is. necessary. >> What would they say? They would say Ed. 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 I think that's their. sentiment. I'm not defending them or. anything. I'm just I'm trying to provide. enough like balance to we see if we can.

dance between these these two. perspectives. >> And a lot of people would say that. there's going to be a blood bath 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 com bubble. thing. It's like it worked out. afterwards because Amazon, Oracle, they. didn't die after the com bubble. They're.

actually fine. This isn't like that. They're bigger companies. They're have. bigger promises. And even I'm not like. Oracle I actually think could die. I RIP. Larry. What couldn't happen to a nastier. man? They'll probably. >> You don't like these people, do you? >> No, I No. Again, I asked this question. purely because I want an answer, not. because I agree or disagree. But um why. don't you like these these people? I. don't like being misled and I don't. think regular people like being misled. either. And I really don't think that. the average person can get away with.

bullshitting as much these companies do. And I don't think the average person. gets anywhere near the level of. 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 that really great people, but it's. like. seeing these ultra rich, ultra wealthy, ultra powerful people lie through their.

[ __ ] teeth or misstate or whatever. people want to call it, it turns my. stomach. And I hate seeing people being. misled. And I feel like I write at such. length because I really want people to. see why I've come to a conclusion. Am I. right? Am I wrong? I think I am. Of. course I do. But I also. I just find it loathome. I find these. companies don't make good products. anymore. They don't care about their. 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. you do. >> Yeah, Better of Flame. >> Um I'm going to link both of them below. So, if anyone 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. That is just not the nature of. information in the world and opinions. and progress and discussion. What what. happens is people have different. opinions. And I think my job, but also. the listener's job is to try 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 endear and 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 um because it provides a. different opinion and that challenges me. to think beyond my current 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 on 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 fazy going.

on here. >> Yeah. >> And then it made me reflect on history. and go, you know, through history. there's always a bit of fazy in these. moments and oh that's an interesting. take on 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 because you do you. challenge me to think differently. Um, >> 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? So this is actually. connected to the AI bubble. So I am a. critic. I'm a skeptic. What quote I have. found that showing and appreciating and. loving the people around you and. uplifting them and me 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 found and the joy I found from. community and the people around because. even in the in the small groups of. haters even like Gary Marcus and sort of. the people I talked to Edward on Grao. 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, 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. Even like Troy and Jake, my my trainers. who's so excited about this. um even. talking to them about the [ __ ] 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 to 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 on 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. listeners and my readers a lot the most. common thing they 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 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. 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 fugazi and the. narrative in this moment in time and you.

are 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. for everybody looking right now. Check. this video out and I bet you you might. love it.