The Diary Of A CEO with Steven Bartlett - The Man Who Calls BS On AI: AI Is The World’s Greatest SCAM, And They All Know It! | Ed Zitron

Starting point is 00:00:00 I think Generative AI is at its heart con. And seeing these ultra-rich, ultra-powerful people, life through their f***ent 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 half-arsery machine.

Starting point is 00:00:16 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. it. But I don't like being misled. And this is the largest non-consensual push of technology in history.

Starting point is 00:00:36 So we're going to play a game at. 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 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 in the 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?

Starting point is 00:01:09 But people keep saying, what if these models fall into the wrong hands? They're already in the wrong hands. Mark Zuckerberg, Sam Alman, Dario Amadee. Mark Zuckerberg says, we'll continue to invest aggressively in infrastructure to meet the demand. God matters a monstrosity. Makes me think of Shrek with Lord Fag quad. Some of you may die, but that's a risk I'm willing to accept. If only these people gave a fuck about poverty or...

Starting point is 00:01:30 actual problems of the world versus are we buying enough GPUs? If this continues, what does the future look like? Fuck. Guys, I've got a favour to ask before this episode begins. The algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the most shared episodes, the most rated episodes, I would love you to know. And the simple way for you to know that is to hit that follow button.

Starting point is 00:01:59 But also, it's the simple, easy, free thing that you can do to help us make this show better. And I would be hugely grateful if you could take a minute on the app you're listening to this on right now and hit that follow button. Thank you so, so, so much. Ed Zitron. There are a number of things that you believe that a lot of other people don't believe. 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.

Starting point is 00:02:36 I don't think it is sold as honest software. I think that they overstay 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 cell-side analysts, governments and all over the shop. The word con is a strong word. Yeah, I mean, what do you call something where, from the very big, beginning they've sold it in the terms of magic as this thing that will replace all jobs,

Starting point is 00:03:07 that will cure cancer, as 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 you educate, what you study, what you write about, what you do, Ed. So that's the funny thing is people say, he's not going to finance experience, he's not going to tech. 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.

Starting point is 00:03:40 It can't even do the basics. It can't even do search. Whenever you ask an AI person, well, what's your setup? They describe this peewee's playhouse thing of like, well, you've got a harness here and you've 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.

Starting point is 00:04:01 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, Open AI, 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 Open AI 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 Open AI this year. They sent $5 billion to Anthropic. Google sent $10 billion to Anthropic. And in the next three and a half years,

Starting point is 00:04:48 Open AI and Anthropic, based on actual cell site analysts evaluations, their estimates, to inform whether stock is going to go up or down after earnings. They are expecting $400 or more billion of revenue. 30 or something percent of cloud growth, just from these two unprofitable companies that will need to be given the money from somewhere. And on top of that, these companies have such low respect for the average investor, for the analysts, for everyone really, that they don't even disclose their AI revenues. The few times they deign us worthy, they use something called a run rate, an annualized run rate, which means, well, nothing, they never define it. It can mean

Starting point is 00:05:25 month times 12. It can mean month times 13. It can mean last four weeks times 30. 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, how much you're 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.

Starting point is 00:05:50 When they have good news, they'll tell you. And when they don't tell you something, well, that actually speaks volumes. Have you used these tools, AI tools? Gemini, Anthropic, Chat, Chat, BT, etc. and you found no value in them. There's some value, but it's not, they've spent over a trillion dollars in CAPEX. What does CAPEX mean for? Capital expenditures.

Starting point is 00:06:09 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 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.

Starting point is 00:06:42 So an example, Open AI and Oracle are building a data center in Texas, in Abilene, Texas, 1.2 gigawatts, called Stargate Abilene. Within that, with each one of the eight buildings, there'll be 50,000 Nvidia, GB200 GPUs. So, City of Bristol takes about 780, 800 megawatts of power a year, right? Well, Stargate Abilene is condensing more power than that, 1.2 gigawatts, into a space around 1,172 times smaller. City of Bristol is about 1.2 billion square feet. Stargate Abilene is about 998,000. So you're condensing all of this power, all of this money, all of this labour into this one spot. And all of these data centers cost for billions of dollars.

Starting point is 00:07:26 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,

Starting point is 00:07:45 the customer adoption of people using Open AI and Anthropic has been absolutely insane. These are the fastest-grown products in all of history, especially as it relates to sort of technology, if we just focus on 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.

Starting point is 00:08:07 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-pilots bugging you. When you use Amazon, whatever Rufous AI is, wants to have 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

Starting point is 00:08:42 outlet has been screaming about this for three 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. That's partly because Google fell behind search. And also because it's better ingesting queries sometimes. Sometimes if you use a generative search, it's like a trawling vessel. It's not very good at specifics. But if you're like, does this thing exist?

Starting point is 00:09:07 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 post-bubble story even for this, AIGPU is not useful for other things either. It's a directionless, egregore of capitalism, this headless beast that lumberes around, desperate to

Starting point is 00:09:40 seek out growth everywhere, in the hopes that if it harasses people and scares people and demonises labour enough, people will be forced to use it. The reason I pause is because 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, and they'll have people that are using it for everything, from coding new software tools to everything they write, to images, whatever. And when you look at the stats around enterprise adoption,

Starting point is 00:10:08 it says 88% of organizations regularly use AI at least once for a one particular business function. And I'd say in our company, 95% of people use one of these AI tools, like Anthropical, ChatGBT, 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.

Starting point is 00:10:38 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 have been widely adopted. Part of the symptom is the AI slop you see all over the internet. Right. So I don't know, 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 on search. It's a whole story about how they pulled back spam guards, thanks to Brabagar, Ragavan, which you can get into, where they made the internet worse by allowing worse content to rank higher.

Starting point is 00:11:18 It's why we had, when you used to Google, our 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 made good. So AI helps weaponize that at the scale. Yeah, you can make a bunch of generic slop. We've had slop for years. We've just found a slop machine. But then also there's the problem of cost. So when you use AI services, you burn tokens and it's per million tokens. What's a token? So it's around three quarters 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.

Starting point is 00:11:57 Yeah. And they call it tokens. Yeah. And every word, let's just say for easy, it's a word. Yeah. About a word. Yeah. And it's per million tokens.

Starting point is 00:12:06 So you'll be charged per million input tokens, the stuff you feed into it, like a document or 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 obfuscate what that was. Now, someone recently found semi-analysis actually found this, a big analyst group. They found that on a $200 a month,

Starting point is 00:12:42 chat GPD subscription, you can burn $14,000 worth of tokens. And on Anthropics, you can burn $8,000. $400 for $200. That is how most, and even on the $20 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 a month. No, all of these companies run a horrifying loss. Open AI lost $20.9 billion last year, because people can burn as many tokens as they

Starting point is 00:13:12 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 Samo, and they said, people have a big problem with it, I think. It's a huge issue, which is not really what the heir apparent of techs history is meant to be saying, but the point is,

Starting point is 00:13:33 enterprises immediately started freaking out. Uber burned through their entire annual token budget in three months. So suddenly, after everyone's saying AI is the most productive thing ever, it's amazing, it's changing everything. The moment people actually had to pay for it, they go, I don't know actually it's obviously we all love it it's all great right

Starting point is 00:13:53 but it's costing too much so we now 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

Starting point is 00:14:07 who's a power user of these tools I could be costing anthropic or open AI a thousand dollars but they're only charging me $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 $34. We don't know.

Starting point is 00:14:32 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 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 can so 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.

Starting point is 00:15:12 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 fuck 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.

Starting point is 00:15:46 unless of course you're using one of these subscriptions. I think 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? Because you think back through the history of technology,

Starting point is 00:16:09 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 company's renting out GPUs don't seem to be profitable.

Starting point is 00:16:36 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. 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 Open AI 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

Starting point is 00:17:09 much. It helped stock values pump. It helped everyone pump. It helped invidia 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 are paid off. Wow, their AI bets are 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 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,

Starting point is 00:17:48 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 some open AI. So that leaves them with about $10 billion in a year when they spent $115 billion on capital expenditures and intend to spend $175 billion next year. The math does not make sense. I imagine their plan was, okay, this is just going to get exponentially more valuable,

Starting point is 00:18:23 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 flows through it. 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,

Starting point is 00:18:44 there's $215.9 billion in the last fiscal year worth of GPUs mostly. And you try and go, yeah, that's the 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 buy NVIDIA to keep alive.

Starting point is 00:19:03 When you tell people that, they go, well, companies just, lose money, right? Companies, because we have this, quote Ed Ellison from profiting 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 lack an opportunity and sermon, getting an MBA, perhaps, of 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

Starting point is 00:19:36 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. I read it. And one of the things it talks about is how the innovation that ends up taking out or transforming an industry, 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.

Starting point is 00:20:16 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. They were more expensive and 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 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 when I think about that analogy in the context of all of this I go okay it's imperfect at the time at the moment

Starting point is 00:20:54 the economic models aren't perfectly ironed out they're still figuring out how to make it cheaper, the infrastructure, etc. But 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, and then you imagine a 5% reduction in cost, which is what we did with the internet, what we did with cars. Yeah, but that's law.

Starting point is 00:21:29 Moore's Law is Ethereum. More's Law is not with GPUs. So, let me actually explain. So, Invidio. Inventor, I think it was in the 2000s, they put out something called Kuda, which is the underlying software library and the way to run software on GPUs. Took them, solid decade or more, to make it something whether you could do data analytics, one of the early things, Mapper and such. And then when AI came along, they'd had lots of experience with it. But nevertheless, this company has got more money, more attention, more geniuses. is behind them. More people focused on making their things more efficient than anyone could ever

Starting point is 00:22:04 ask for. And the video for anyone that doesn't know makes the chips. So, and that Kuda thing I mentioned, they were the ones with Kuda and Kuda 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 their 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 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 who you'd measure that

Starting point is 00:22:44 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 are uses for LLMs in coding. I don't dispute that. But even saying 5% what does that mean? Is it better at Rust?

Starting point is 00:23:09 Is it better at C++? I'd say productivity. So just like, yeah, 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 because this newsletters really long. That's an insane way of valuing it. With coding, it would be, I mean, it's even difficult to evaluate because it's, is the software out there better?

Starting point is 00:23:29 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 metas and 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.

Starting point is 00:23:48 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, is 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,

Starting point is 00:24:12 which we have seen since Chachapiti came out. I remember when Chachapit came out and I was in Asia, and I was there showing it to my fiancé, I was like, look, you can do this. And it was hallucinating once in a while and getting things wrong. I actually don't have that experience anymore. I have moments where I believe its reasoning is weak, but I don't have outright hallucinations anymore. See, that I disagree. Give me an example of what you define as a hallucination.

Starting point is 00:24:36 Okay, great one. So I have a Bloomberg terminal. The very useful thing they have on there is AskB. So when you do a Bloomberg inquiry to look up what we think Nvidia's revenue is going to be next quarter. It runs something called BQO, which is its own programming language. Now, instead of having to learn that, you can just type into AskB and it will generate it. it and run it for you. And so you get pulled up and you know where the date is coming from. Deals with hallucinations real well. The other day, I was like, you know what? We get a little spicy.

Starting point is 00:25:01 I'm going to look up the growth rate of stocks of Microsoft Google, Meta and Amazon over the course of five years, I think it was. Yeah. And I was about to, I was copy pasted it over, something looked at in Excel. It was about to, it was right in the news letter. 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 can't. of core, 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, 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 six months.

Starting point is 00:25:52 you haven't really been coding with your own hands for a while. Maybe you've forgotten a few things. You had this slot to look for you. Fuck, you know, what I'm not doing it? And so the problem has become multiplicative. And I don't really know how you train them out of that, and they've certainly not succeeded. So, on one hand, they have got better. But one of the main ways they evaluate them getting better are benchmarks that are adjusted

Starting point is 00:26:15 specifically for large language models, because you can't just have them do tasks. They've got better at that. They've found some tasks. they can have them do on. But even them like meter, METR, 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 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 Vectaria hallucination leaderboard,

Starting point is 00:26:43 shows that hallucination rates on simple summarization tasks have plummeted from around 21%, 21.8% four 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'd say is when I think about other technologies. There's two more points. Other technologies at their inception when they first came to the world, like the internet, also had technical difficulties. I remember growing up

Starting point is 00:27:27 with dial-up modems and I couldn't go on the phone at the same time as going on the internet. I'd have to stop RuneScape upstairs to go on the phone. And you thought that this is crap, this is crap, technology's crap. I don't know, mate. I loved it. Yeah, I know. It felt like magic. And then, in hindsight, you go, wow, I now have Starlink and 5G internet from my phone. It's unbelievable. You couldn't leave the house with internet before. And that's what I mean by the rate of improvement thinking. I'd say the last point is we often compare AI to perfection. Whereas that's not actually the alternative.

Starting point is 00:28:00 In the working world, like if I wanted to do, let's say a simple writing task, I should compare AI to my 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. at all, if I'm hiring someone, an intern who might also be prone to hallucination or have gaps in their knowledge. So it's not actually like we're comparing, we should compare AI to perfection, it's AI to the other alternatives. And if someone hallucinates 0.7% at the time, but knows way more and is faster, maybe on a net basis, that's a good trade. Maybe I should use AI.

Starting point is 00:28:38 So let's start with an example. Someone I love dearly. Matt here is my editor. Yeah. There's a side of Liverpool. Wonderful guy. I don't pay Matt Hughes because he knows everything. I pay him because he has incredible context and a ton of knowledge and he's willing to expand it and work with me and moral sport.

Starting point is 00:28:56 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 an absolute fucking venom for the people he hates. 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 the thing is, are you really paying an intern for something basic?

Starting point is 00:29:28 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 and take them out of Pinocchio status. But it's also an intern learns. an intern gets context and an intern learns your habits and AI gets context and learns

Starting point is 00:29:47 No it doesn't It doesn't I mean it doesn't The way it learns is you create a giant clodda MD file That it sometimes doesn't read Sometimes does read You create a harness

Starting point is 00:29:57 It's like it's Peewee's breakfast machine From Peewee's playoffs You have to do all these contrivances To mitigate the hallucinations And even then at the end How much effort have you put in But so okay This is an extreme simplified example

Starting point is 00:30:10 If I went on my clod now And said what's my dog's name It would know my dog's name. Jesus Christ. This company raised $95 billion this year. I'm using an extreme simplified example to show that it can remember things from the past. Obviously, it knows much more complex things as well. But I just use that as an example.

Starting point is 00:30:27 So we accept the fact that it does have memory of the past. It has files it can access that have stuff on it. Yeah, 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?

Starting point is 00:30:46 Does it have good days and baddies? Is it there for you? Because it's just a fucking 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,

Starting point is 00:31:02 the way in which we accrue experiences is a milestone 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 were describing the process of how a human does memory. The way that an AI does memory is different, but the thing that people care about is their 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.

Starting point is 00:31:38 You could say, you teach them, you put lots of effort into them. You give them lots of context. 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.

Starting point is 00:31:55 And we get, you know, because I have 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? Because actually, no, who cares what the product? People care about, they pay for the outcome, the product. I actually disagree about the process because Matt Hughes, for example.

Starting point is 00:32:19 Your editor, yeah, yeah. Watching him go down a rabbit hole and being there with him, and actually vice versa, him doing the same thing. We wrote these, well, I mean, we were working on the research. I ended up sitting there for like the day long session of writing 11,000 words, and he had given me a bunch of notes. It was actually just, even describing that process, I feel so happy, because, it was like, us being like, I can't believe how fuck these, Jesus Christ, they can't, 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

Starting point is 00:32:54 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 not. novel outputs. If I needed a generic slop output, sure. But I've used some of the higher-end 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 notice 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, there's many more.

Starting point is 00:33:34 But you said, 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 just care about the output. I could use it to learn. I could say as far with me. What if the revenue number was wrong once? You should have defined deterministic ways of knowing those numbers.

Starting point is 00:34:02 You should not rely on them. Even with the terminal running BQL, which I trust, I will double triple 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 LLMs do that and I just don't see them getting better in a way that does that because it's just not what they do. And also there's the other problem of the more detailed the report and 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's got it right. I can trust.

Starting point is 00:34:39 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 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 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 leader board,

Starting point is 00:35:20 it continually delivers for people. And that's why we've seen the fastest... I mean, is that what that board says? Well, it's saying, like, is it getting it wrong? Is it hallucinated? On simple tasks. Simple tasks. How are those defined? I don't know. That's the thing, though. Because this is actually a very illustrative thing of the AI industry. They are the what about is, masters. They have like, well, look, we've got this, we've got this benchmark that says we're good at this. And look, the number's higher. What's the number mean? 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

Starting point is 00:35:58 iPhone came out, I got it was at Penn State at the time. Oh, I felt like the apes at the beginning of 2001. I was a fucking visual voice mail. It was immediate. And I showed it to text. friends. I showed it to the most normal people in the world. And everyone was like, holy shit, 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 have bias when I just love gadgets. But I remember the famous Steve Barmer, who was the CEO of Microsoft interview, where he was told about the iPhone. And he bursts out laughing.

Starting point is 00:36:39 $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 queue phone now for $99. It's a very capable machine. It'll do music.

Starting point is 00:37:00 It'll do internet. It'll do email. It'll do instant messaging. So I kind of look at that, and I say, well, I like our strategy. I like it a lot. He burst out laughing mocking here. 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.

Starting point is 00:37:22 You were still spending 500 bucks. But the thing I'm getting at is, you didn't have to explain to someone what, 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 shit, this could actually change things. Mobile web, even though it was a monstrosity, it was so bad at first. first. Even then you could get your emails and you could just look at them. But it's BlackBerrys 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 a cell phone designed even like a computer. It was obvious. It was obvious in the beginning.

Starting point is 00:37:57 I was dating to go in the center of Pennsylvania at the time and everyone I showed it to. It's 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 Chachiboutes growth, 100 million active users in just the first 60 days after launching? For comparison, TikTok took nine months. Instagram took 2.5 years. And the internet itself, the World Wide Web, took roughly seven years to reach that scale. Over 60% of the US adults are integrated into AI tools

Starting point is 00:38:37 in their daily and regular routines within three years of the launch, reaching 40% of the population. And that same milestone took the internet five years and personal computers nearly 12. Okay. So, like, this is the part that's giving me dissonance.

Starting point is 00:38:51 It's like, when I showed my fiancé chat TBT, 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 certain images

Starting point is 00:39:06 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 if she was actually, you know, she'd get transformative. Would she pay the per million token rate? Would she pay the actual rate? Because that's the thing.

Starting point is 00:39:26 If this was sold at its honest cost, I would actually, if, and people were reacting like that and they were paying two, three, four dollars every time they did something and they were genuinely happy, that might be an argument. What is the argument? What would be the honest cost if they weren't subsidizing? The actual per million token cost, the actual API cost. They should charge. Do you know how much that is relative to what they charge?

Starting point is 00:39:44 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 second modem. Even back then, I was like, fuck, if this was faster. And that was like immediate just like if this was faster because it was slow. You go on like happy puppy or something. Download take all bloody day. waiting for shareware to download. Immediately, like, if I could do this faster, it would be better.

Starting point is 00:40:10 And even back now, 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 Covello from Goldman Sachs in a report he did in 2024 that was Gen A.I. 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 roadmap 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.

Starting point is 00:40:52 We're not sure if we can fully not trust it, not in the all one 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. but we're not going to expose consumers to it and actually be like called them like, I don't know, language models and no

Starting point is 00:41:11 generative AI stuff. Just being, not even calling 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.

Starting point is 00:41:26 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 take to leave your wife. All of these crazy things. And what's funny is when the writer Kevin Roos, I think it was. He was speaking to Kevin Scott, the CTO of Microsoft about it,

Starting point is 00:41:40 and Kevin Scott goes, you know, I'm just glad we're having this conversation. Instead of being like, settle down, Beavis, it's a website, the website told you something, it's just LLMs. They talked it up.

Starting point is 00:41:50 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.

Starting point is 00:42:03 I think it's in Louisiana. It's one of mussels. 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 ramp. Do you know what's interesting? I almost feel like so much of what you're saying is true. And also, it can be true that this technology is going to profoundly change the world. And I think like, you know, I think back to the early days of, the internet is maybe the closest analogy we have of, you know, in the dot-com bubble. You know, you wrote this great essay.

Starting point is 00:42:36 Oh, yes, yes. Which I found really funny, especially the name. The rot economy, and you talked about the rotcom bubble. Yes. Talking about how AI is less value than people think. And in that, in the sort of dot-com bubble, what you saw is huge hype, people overselling the capabilities of their websites and what they were building. But in the wake of the dot-com bubble, yes, 90% of stuff went to zero. but you had generational companies born that changed the world.

Starting point is 00:43:07 Right. And so I do, I kind of reflect, and that's what bubbles do, right? But that was huge high overinvestment. Investors get crazy delusional. They think everything's going to change. At the same time, you do have skeptics in these moments. The dot-com 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

Starting point is 00:43:31 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 CD-ROM can take the place of a competent teacher. Commerce and businesses will shift from offices and malls to networks and modems? Bologna, so how come my local mall does a roaring business,

Starting point is 00:44:01 and the cyber mall gets zero business. And then I'll give you one more from Krugerman, 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 may actually be the worst one of those particular. Hang around any bar in Middle America. Honestly, the best conversation.

Starting point is 00:44:23 But it's just all the same thing. I actually, so Clifford Stoll actually, his piece was interesting because there were some bono points in it. But he made points about how, like, overwhelming amount of bad information out there is bad for society. It'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 dot com bubble was actually two bubbles. There was the website bubble, which was just

Starting point is 00:44:50 trash on trash and trash. It was just like, I think what was it, excite at home bought a eGy eating card company for like a billion dollars. It was insane crap happening. That was so small. the big thing that people are thinking about is the 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 six to 12 months, maybe longer. And just thus there was a massive overbuild of fiber optic cable and indeed the transmission stations and such to simplifying to bring that to people's houses. and there was the assumption that, well, that would all get lit up and people would want it immediately. It didn't really happen.

Starting point is 00:45:35 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 and yet are not paying the real cost. I agree.

Starting point is 00:45:55 On top of that, we already have all of the possible marketing in the law. 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 single digit billions from selling AI software. And Christ almighty, outside of OpenAI and Anthropic, we'd 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

Starting point is 00:46:40 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's seems to magically be able to make this profitable or indeed even less costly. Even in video with Vera Rubin, they're more expensive new GPU system. Even then they're like, yeah, 10x more efficient. It's more dollars per megawatt. They're all coy about it. They don't just say, yeah, we worked with open AI and Anthropic and we found it reduced their cost by 50%. Easiest thing in the world if it was true. And that's because it's not happening. And this isn't a

Starting point is 00:47:21 case where... So you're saying there's not going to be the demand for, let's say, you know, There's different types of AI, generative AI. 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 protein folding, nothing to do with LLMs. Robotics, not LLMs. 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.

Starting point is 00:47:49 They'll go, um, um, sir, you forgot to give this homework. And also, AI, it's working on curing cancer when it's just like, no, That's not LLMs. Stop giving them credit. The similarity, though, is they all need GPUs. No, 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 shit.

Starting point is 00:48:11 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 pregenerative. How would you run the type of AI that sits in a robot, let's say one of the optimist robots, if you didn't have a GPU. So, Matick, Matick has this cleaning robot, for example. That thing has not got a little GPU in it. What it has, and may indeed have used some GPUs, but no year or as many as they need for generative AI,

Starting point is 00:48:39 to run the data, feed-draining data into it so it's able to clean a house. But when the little bugger's going around cleaning my floor, turds leekled, if 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 sense, sightland climate set in February, there's 190 gigawatts of data centers under implanting, don't know about under construction.

Starting point is 00:49:00 That works out for about 12 million megawatts, what, like 1.6 trillion to $3 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 AI or Anthropic,

Starting point is 00:49:17 Microsoft feeding money to open AI and Anthropic, Google feeding money to open AI and Anthropic, well, Google hasn't fed it to open AI yet, but they're a pretty big customer, billions of dollars. The con side is that we are building these effigies to capitalism, these giant GPU data centers, and people are being told, well, it's for AI. You know, the thing that's done all this other stuff that's unrelated,

Starting point is 00:49:39 or the worst thing I've seen, it's like, oh, you like online banking, where you do like data centers. There's a big difference between a data center for regular non-GPU compute for standing up a server, a content delivery system like Akamai or something that brings the website to you, or how meta runs face. Facebook. That is not the same. It takes way less power, mostly CPU driven, compared to these giant GPU data centers that are for one thing. One thing on. But I was doing the research, 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.

Starting point is 00:50:16 Yeah. AI systems like DeepMind's Alpha Fold, the Protein Folding Company, used for genomic sequencing and climate forecasting, etc. Run on high-performance computing clusters. These require immense, precision, and continuous heavy compute in data centers. Yeah. Training the brains for self-driving cars requires billions of miles of simulated physics environments. The AI isn't generating text. It's learning to navigate 3D spaces and gravity and relies on data centers.

Starting point is 00:50:44 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, I believe they've had their own data centers when it comes to training the autopilot system for better or for worse. That's how we've done it before. Again, that is not why we're building these data centers. These data centers are being built to sell to generative AI companies to either train systems or run inference.

Starting point is 00:51:14 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. Because everyone saw that, they went, well, they wouldn't do that for no reason. They went, we've 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

Starting point is 00:51:52 is because it would become alarmingly obvious that this was the case. It turns out that the only real big customers, because 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

Starting point is 00:52:12 buy a bunch of GPS 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. That is a great productivity gain for those companies, right?

Starting point is 00:52:36 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 is Google worse? Well, I'll tell the story of a real asshole. Guy called Prabagovam. Previously, one of the heads of ads at Google, in 2019, Google calls something called a code yellow, which is when they said, we've got a problem.

Starting point is 00:53:00 And it was material weakness in query numbers, which means the amount of times that people were searching on Google search. A guy called Ben Goams, internal at Google, then the head of Google search, says, wait a minute, to increase this number of using Google more, we're going to have to, I mean, 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 Thaker was another engineer who was saying, yeah, can we please tell Sundar this? Because this doesn't seem good.

Starting point is 00:53:30 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 it would make them more money, you say? Yes. Because you'd show them more ads. So if you are spending more time on Google because Google's work... But is this linked to AI doing code? Oh, I'll get there.

Starting point is 00:53:46 So this is, the problem is, is that this guy called Prabagovang, he was 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. Nick Fox, who was there as well, I believe he was actually taken over Google search. They've got to make him go up. This is our new reality. Sometime in early 2020, Prabagar Ragavan takes over Google search. From then, and this is what I believe. can't prove it. If you go and look around the various SEO sites such a journal and the various forums,

Starting point is 00:54:14 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 Prabagar, 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?

Starting point is 00:54:44 Well, shit, 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 when they click away from Google, they'll just only use Google. Instead of generating answers, by which I mean giving you search results you click through, now Google is the answer. Is it right? God, no.

Starting point is 00:55:04 It might tell you to eat rocks, might eat poisonous mushrooms. Maybe I'll give you a few links you could click through. But the ideal situation was that AI was the ultimate form of Google's evil, which were... But I'm saying here, I'm saying here, but that's not the fact that coders could 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 levered that, a problem across the whole tech industry. Okay, so you're saying that you're saying Google is going down more.

Starting point is 00:55:32 Yes, Google is less stable. Google Docs is a bugfest right now and has been... a while, Google Sheets, same deal. And the thing is, you're right, I'm being in a little unfair. This is everyone. It's the same with Microsoft. It's the same with Amazon. It's the same across the...

Starting point is 00:55:45 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, I think, two or three times this year, because of AI tools. And honestly, you're right. It's kind of hard to quantify outside of anecdotes.

Starting point is 00:56:01 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 flippin' mess these days. Okay, so the research says, the short answer is yes.

Starting point is 00:56:20 Tech downtime and software outages have demonstrably increased over the last few years, and industry data points directly to the explosion of AI-assisted coding as a primary culprit. The problem is hitting the tech industry from two entirely different directions. The code itself is getting bugier and the sheer volume of AI activity is literally crashing the underlying infrastructure.

Starting point is 00:56:40 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 a real mess as well because open sources have this problem as well. Because it's well-meaning people. They're like, I'll learn a bit of code with an LLM. I'm going to go out and do some stuff.

Starting point is 00:56:58 I'm going to make this project better. And these people barely understand what they're shipping. Or maybe they understand a bit of code and they say, Oh, Dunning Kruger this motherfucker. 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.

Starting point is 00:57:16 It is. Because we're going, okay, look, I'll let it write 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. It's to get sort of to take shortcuts, to spend less energy on an activity if you can. Right.

Starting point is 00:57:33 But the AI's still making them. mistake and we're still making all the promises of AI. That's the thing. This thing is meant to be this autonomous perfect. You say it can't be perfect. I don't know. Based on what Sam Orkman has been saying for the last few years, Clammy Sammy's been promising the world saying this will replace software engineers. Dario Amadei, 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 them all. Again, if they were saying it would be smaller and they were like, yeah, it does have issues and we must. We must be, none of this, oh, what if it wakes up and it's super powerful? Just like, yeah,

Starting point is 00:58:10 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 consistent pattern with all of them is how quickly they move.

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Starting point is 01:00:36 and he was saying that, I think in a couple of years' time, we won't need drivers 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 the CEO saying that there will be job disruption, you say that they are not telling the truth.

Starting point is 01:01:02 Yes. Or they're guessing in a way that's very good for them. Think about it from perspective of Microsoft, Satchinadella. 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. It's going to be so powerful. And then he'll change his tune and say, actually, it's not going to replace workers.

Starting point is 01:01:20 It'll make them more powerful because the things aren't. up. Dora 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 Waymo's taken. I find Waymo fascinating. I think that shit's really cool. I think there are socio-economic problems that will come from it. I think there are actual real problems that will emerge. And also, what kind of problems? Well, I mean, socioeconomically, there are, like you said, one of the largest employment centres in the world. I mean, just the economics of cabs will fall apart. But again, we are nowhere, no, nowhere near that.

Starting point is 01:01:52 We're not even close. Waymo 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-vis thing. Does it even notice it's a child?

Starting point is 01:02:13 Again, this is a really interesting but very, very applicable example of 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 the 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 Zooks, cars just get fucking stuck.

Starting point is 01:02:50 They're autonomous cars. Yeah, yeah. 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 happened outside of a hotel when I got out of a Waymo in San Francisco. Just stopped at the same. And then a bunch of cars and another Waymo got stuck behind it.

Starting point is 01:03:05 And these are kind of... I've seen some human bad drivers as well. 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're. we should do. I'm not saying autonomous cars are bad. I'm saying we need to be so, so, so careful and treat them as guilty until proven innocent because we can prove. And also, they have people overlooking them. They actually have people monitoring the routes. It is something they cannot rush out.

Starting point is 01:03:35 It doesn't seem like they're rushing it, which is good. And they're not promising the world. I do agree. Listen, I'm a big fan of a big fan of taxi drivers generally, in part because I spend a lot of time in taxis. I'm not just getting in there because I want to get for it for me to be. 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 a 68% lower overall crash involvement rate when you're in an autonomous vehicle.

Starting point is 01:04:03 Autonomous vehicles experience roughly 2.1 police reported crashes per million miles compared to humans that are at roughly 4.68 per million miles. Right. So a 55% reduction when you get an autonomous vehicle. An autonomous vehicles show an 80 to 81% reduction in. crashes resulting in injuries versus human drivers. So you're 85% less likely to be involved in a single vehicle crash, like hitting a wall or a tree.

Starting point is 01:04:28 Right. If you're an autonomous vehicle versus being driven by human. I agree, but... So it's safer. 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 of AI?

Starting point is 01:04:48 If we were just talking about that, we'd be having a different conversation. I guess the question here was really around job disruption. Like, you know, we look across industries and we go, driving's a massive profession. Is there going to be job disruption because cars can now drive themselves? If we think about white-collar jobs, you know, lawyers and accountants, people sit here and they tell me that lawyers and accountants, the profession, right, I should say some of the skills within the profession will be relegated to AI's to do. Here's the thing. Lawyers, for example, great example. always hearing fucking legal partners talking about AI.

Starting point is 01:05:22 Never the associates. The associates are the ones that go out and find the precedent. They're the ones that go and do the grunt work. They're the ones who are pulling motions half the time. The partners, 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 fucking awesome.

Starting point is 01:05:39 I'm hearing a bunch of well-paid people that have sat on chat GPT and gone, yeah, I'm the greatest lawyer ever. They're not the ones that I want to hear from. actual workers. White-collar labor disruption is not happening. Open AI had a study that came out, I think like a week ago, that said there was no connection between spending on AI tokens and revenue per employee. Like, this is open. And that's the thing. What does that mean? Could you explain that to as in the more tokens you spend has no correlation at all with the amount of money you make. So the second report they've put out, the other one was like hallucinations are mathematically guaranteed.

Starting point is 01:06:13 Kind of almost, it's the one thing I respect about that company. Occasionally they just put out a study. you know shit it kind of sucks but the people that are having their lives disrupted workwise 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 art for they would have sold it to the global stuff 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 labour force might have some things that are slightly changing,

Starting point is 01:06:56 but there is no evidence of productivity gains. In fact, if there were, they would be screaming it from the rooftops. There was an Ox of an economic study last year where it's like, oh, young people are finding less jobs because of AI. We actually read the study, which multiple journalists did not. It was a single line that said, yeah, we saw some correlation. Didn't give a number. didn't actually say what the correlation was. We are so conditioned to believe that the rich and powerful know what they're doing that we internalize these narratives about, like, well, previous booms lost a lot of money. Well, technology takes time to do stuff.

Starting point is 01:07:31 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 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 the other one you said was they'll get better.

Starting point is 01:07:57 But that's the thing. Okay, because another thing got better, this will get better? No, but 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. Okay, I know, I get what you mean. Yeah. What they are fundamentally misleading people about is how possible it is,

Starting point is 01:08:13 how many actual signs they're. 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 Rube Goldberg machine. And even then, in a reliable way that was making the customer more money, being productive in a way you can say with your whole chest without a series of asterisks. And that's how it is across the board. The people that are most excited about this, psychopaths on Twitter in many cases, are people that I believe. Psychopaths on Twitter. They really are. I'm sorry. They're some people on Twitter, because the other thing about this is, this is really unique to the AI

Starting point is 01:08:49 industry. I've never seen 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. Months 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.

Starting point is 01:09:30 And Open AI, in particular, in particular, Mr. Altman, they have been fermenting this. They're Tebow as well, T-I-B-O, one of their guys are Open AI. They ferment this thing online. They build this kind of parosocial relationship with both. 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 fuck about, I don't know,

Starting point is 01:09:55 Medicare for all, or poverty or things, 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 Duma's that have come here and told, you know, some of the original founding fathers of AI like Jeffrey Hinton,

Starting point is 01:10:14 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 fucking dangerous. And there is a significant chance we could fuck up the planet. And what we've seen is this slow pivot away from it. Which is so strange. Because now they're getting booed and they're being attacked.

Starting point is 01:10:38 They've been this slow pivot away from it. And the pivot almost sounds a little bit like you're not. 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, it's just, nah, it's not dangerous at all. But that's the funny thing. And that's why I'm saying, like, you're, they, I actually think there might be a couple of PR people at these big AI companies thinking, thank God for Ed. Oh, I don't know about that. 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

Starting point is 01:11:08 no technology. And I think they don't think that. Here's the thing. I think Altman and Amade is some of the most deeply corrupt and cynical people in the world. Of course they were going to say, from the, it was early 2023. Ormond 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 fucking liars, these people. And you know why he wants to say that?

Starting point is 01:11:31 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 by. And by the way, every single scam and con starts with rushing you. Every single trick in history begins with saying, you must do this now. And best piece of advice I ever got was if anyone tries to rush you and it's not literally a mortal thing like you are bleeding or on fire or the house is on fire, slow down. And yet all of these companies say, it's so scary. And now they're talking about slowdowns. But you ever notice that Amaday and

Starting point is 01:12:08 Orm and Ormann they say, well, maybe we should slow down progress. And then they don't. Right now, Orkman's saying, oh, we slow down progress because we're so delayed. Now they're out of compute. Now they're doing it. I can guarantee you, by the way, they're PR people. Do not like me. I know for, I know, I don't think Open AIs PR people are super fond of me. But I bet there's elements of what you're saying, because you're calming people.

Starting point is 01:12:26 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. I don't, I just fundamentally disagree. I think it.

Starting point is 01:12:40 So the timelines did it. And I sit here and what I do is I log their quotes over time. Oh. 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.

Starting point is 01:12:58 Elon says a lot of things. And then you track it over time and it evolves to this age of abundance. We're all going to have unlimited stuff. And then the new slogan at chat TBT is intelligence for everyone. It's suddenly, and all the. And whenever Dario comes out and says, by the way, it's really rock and dangerous, they attack Dario. Yeah, they hate him. That man, Dario is.

Starting point is 01:13:19 They're like, Dario, shut the fuck up. Honestly, I've been saying Dario shut the fuck up for years. 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 shit. America doesn't regulate shit. We are in the, we are still trapped in the house. of Milton Friedman, Margaret Thatcher, and fucking Ronald Reagan,

Starting point is 01:13:45 were still stuck in the neoliberalistic hellscape, which is growth at all costs free market capitalism. So no, no one's regulating. The regulation of these companies should have been, I don't know, breaking up, put these bastards aside, break out these fuckers, for sure. We shouldn't have companies this big. It makes things worse. But these technologies are dangerous.

Starting point is 01:14:04 I mean, they're dangerous, but not in the ways they've been warning about. Less than if we think about cyber hacking. Right. And just be clear, those. Cyber hacking things that happened were not a result of, they were like, break out of the sandbox. And then they set the sandbox up wrong. They set up the server they were on wrong. But I mean, you know, advanced AI models could very easily, because 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.

Starting point is 01:14:32 Yeah. At scale and arguably at a higher intelligence and faster and wider than a human hacker could theoretically. 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 Chinese get a hold of these models. We couldn't possibly because what if these models fall into the wrong hands? They're

Starting point is 01:15:10 already in the wrong hands. Mark Zuckerberg, Sam Alman, Dario Amadee. 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 fuck 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 it, would you agree that it's an intelligence, and I'll call it that, you know, you might disagree with that terminology, but an intelligence that can go out onto the internet and click around and take actions is inherently there's risks associated with that. Well, the second part I agree with, the risks.

Starting point is 01:15:48 We've had people running automated scripts and 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 et al have been warning about. They've been saying, all these things could destroy society. They can manipulate people when you actually look at the underlying things, not so much.

Starting point is 01:16:09 Jeffrey Hinton as well, talking his book, still got his Google stock, I think. 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 now. But let's get back to the cybersecurity side. I agree, this is dangerous. These people should not have access to so much compute. They clearly don't know what to do with it.

Starting point is 01:16:29 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 feel like with this particular thing as well, we got to this point and let the genie out of the bottle

Starting point is 01:16:50 to use an annoying Sam Altman term. We let this happen because we let these companies be unregulated and use as much computers we want. We had these fucking enablers allow them to burn as much computers they want. And also, for all of these dire warnings about AI dangers, that one seems to have fucking done anything. Okay, we're going to play a game, Ed.

Starting point is 01:17:09 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, great. 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 belief. Okay, let's go. Let's do this.

Starting point is 01:17:36 What does it say? And what's your one sentence? It says the AI industry is creating enormous economic growth. No, it's not. It's nowhere in the data. Okay. It's like it's just, may I do a second sentence? Go ahead.

Starting point is 01:17:49 Pretty much all of the economics is either in video feeding money to its companies like Corwave or these three companies feeding money to these ones to spend it with 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,

Starting point is 01:18:06 so the speculative investment in GPUs and data center infrastructure, that's happening. But as far as like, spend on AI goes, barely cracking $100 billion, and most of that is just these two running their services and paying these three companies, Oracle, or even others. But $100 billion is a lot of money for a relatively new technology. Not when you've spent $300 billion in equity funding, and 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.

Starting point is 01:18:35 But the $100 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 are going to use it more. If this was all on a per million token basis, we'd be having a different conversation. Okay, fine. Cool. Next one. The United States need to spend trillions to beat China in the AI race.

Starting point is 01:18:58 Let's see. What AI race? That's actually my point. It's what AI race is there? Is it to make big scary LLMs? They did that already. Without the Nvidia GPUs, by the way, they've got Blackwell GPS. Kukashi and Justario, two amazing analysts, I love.

Starting point is 01:19:16 They've been on this for years. It's like, China's already had Nvidia GPUs that they're not meant to have for years. But also, to do what? They already got the LLMs? What's the race to do to make us spend more money than them for us to constantly piss our pants worrying about China? Because they won, if that's the case. Myth number three. AI will replace all human jobs.

Starting point is 01:19:36 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

Starting point is 01:19:56 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 one of my myth-busters piece. It was definitely about generative AI.

Starting point is 01:20:11 Okay, but what about robotics? The thing is, the optimist robot, that Elon's working on it, Tesla. The one where even in the demo of the hand, 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.

Starting point is 01:20:33 Have you seen them Chinese robots? I know you've seen them trying to. Well, they need, what's it called? The one that can dance and that, but they can't really do human things. Well, it's just, it is pretty mind-blowing. Robotics are fucking cool. Like, I'm not going to pretend I don't think robots are cool. I wish they were building robots and actually doing cool shit.

Starting point is 01:20:51 I wish the tech industry still made fun stuff and interesting stuff. Instead, we get these fucking large language models. But AI plus robotics is, you know, I was in San Francisco and I went to this massive incubator there. And when I'd gone there three years earlier, was all software start-ups. 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?

Starting point is 01:21:13 There was this one robot where it was just the arm and it had a frying pan on it. Yeah. And its whole thing is it cooks for you. Yeah. So it was showing me at cooking, whatever. And he goes, well, you know the arm. He goes, the hardware part, the physical parts, that's always been fairly cheap. Yeah.

Starting point is 01:21:28 He goes, the expensive part was the intelligence. Yeah. 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. Yeah.

Starting point is 01:21:40 And the data is very expensive. Yeah. The thing is, cybercabs 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 human jobs are multifaceted. Human jobs change with environments.

Starting point is 01:21:56 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 road. 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 just, I just, I ask these questions, not because I'm trying to be like, actually I'm trying to form my own opinion on these things. And I, I do think, you know,

Starting point is 01:22:24 as it's written that, it says AI will replace all human jobs. Obviously not. Obviously that's bullshit. Yeah. But 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. 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.

Starting point is 01:22:51 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. you're not talking about agentic AI, which... Agentic AI is LLMs. Agentic AI is just a fancy way of saying an LLM talking to an LLM with a harness on top. That is still LLAMs.

Starting point is 01:23:11 Agentic AI is one of the bigger lies they 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 LLMs talking to other LLMs to do LLMs. Taking screenshots and putting them in LLMs and stuff. Oh, God, yeah. Okay, but, but, you know, I could make the case that... I'm just thinking about my personal usage.

Starting point is 01:23:31 I definitely use agents to do things that I would have previously asked people to do. It's not to say that I still don't hire because 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 showing me a piece of paper back in the day, I guess. Now my chief of stuff is no longer doing that job. You still have a chief of staff.

Starting point is 01:23:55 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. Triage of emails. Didn't spend a trillion dollars on triaging email.

Starting point is 01:24:08 Look, that's the promise. If they'd spent $10 billion and this was much smaller and you're like, I go, cool, software, yay. A lot of the things that people are impressed with like script stuff as well, it's just LMs doing Python. You should be impressed by Python code. Python's incredible. You can scrape websites. You get downloaded shit. It's awesome.

Starting point is 01:24:24 But the point of 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

Starting point is 01:24:39 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 over-promised that. I mean, they over-promised on the businesses,

Starting point is 01:24:57 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, think a billion dollar market cap.

Starting point is 01:25:33 They were talking. Yeah, there was a massive hype in the dot-com era. I read a lot of those stories. The hype was nowhere near. You didn't have articles everywhere that were saying, if you don't get online, you'll be left behind. You didn't have professional consequences. Nick Serreschen mentioned his blog earlier. He described this thing, global, AI's of history, ain't global decision making, where he said that you have businesses you work at,

Starting point is 01:25:55 where if you don't say that you're more productive with AI, whether or all you're not, it's true as 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 shit, 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 decentralization of media in general, as course, this as well. And also the fact of day trading. There's so many different things that are different. It's crazy. I do think AI is different from the internet, in part if you just measured it on the speed of adoption, especially if we could just think about generative AI. I know AI's lots of things.

Starting point is 01:26:35 But the adoption of the internet required physical connections to your house. The adoption of generative AI involves having a web browser. It took a vast amount of effort to bring internet to people. Even with dial-up connections, it still required the distribution. And that's why it was so slow and there was less, you know, there was less hype than air. 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. It was generative AI. There's generative AI. There's like real world AI.

Starting point is 01:27:04 But 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 Unibeme robot. They're saying use LLMs so that I, and that's the thing, they have this theory, the era of the business idiot. 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 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

Starting point is 01:27:39 genius machine to do more work? And yeah, if you're a boss that goes to lunch, leaves lunch, and sometimes reads your emails, L-LMs are magic. I, do you know, one of the most compelling arguments I have for the over-hype of AI in a world where everybody has access to these tools, whatever the tools can do would largely be commoditized. And what the tools can't do, which one could say is the human taste judgment. You could say it's people's 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.

Starting point is 01:28:16 So the very nature that we're commoditizing the generation of content or whatever you want to call it code means that's actually not where the value will accrue 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 great thing is not of value. So I think a lot, I've been thinking a lot actually about how do you avoid the temptation of sloppification of the things you make, the value you put into the world. It's a very simple example that people will be able to relate to. If you use chat, GPT or Anthropic, you know, Claude, to make your LinkedIn post, let's say, they will be shit to LinkedIn posts because everybody else is using them.

Starting point is 01:28:59 And actually, a great LinkedIn post now is someone who doesn't use them and makes something that's like irreplaceably human and deeper and more personal, end of one, lived experience, 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 change? Like really, like what? The sloppification of stuff.

Starting point is 01:29:23 We've got a bunch of sorts. slob, but these people were half-assing their jobs before. It's just a half-arsery machine. And it's just, 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 shit before have now got the dog shit machine to pump out dog shit. It's, so there's a guy called Carl Brown, internet box. Awesome guy. Great software engineer. He said, I might said this earlier. So it makes the easier 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.

Starting point is 01:29:54 My kid loves Minecraft. I was trying to fix a fucking broken mod because he loves these Withers Storm. It's awesome. And it still took me half an hour and kept getting things wrong. What do you use AI for, generative AI? I really don't. You don't use it. With Bloomberg Terminal, I use AskB, which is just when it's like requesting the consensus

Starting point is 01:30:11 analyst estimates for InVVVVD. 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.

Starting point is 01:30:23 The one thing I will defend it on is it's really good for tech support. Like I had this thing called Synergy in my New York place I go to. I had this monitor wherever. MacBook and a PC laptop. And this thing's Synergy for using the same mouse and keyboard. Drop in a giant fucking troubleshooting log into this thing going what's wrong and it going, this is wrong? Yeah, it's super useful. Is that trillion dollars?

Starting point is 01:30:46 No. Is that a two trillion dollar company? No. Better than Google though, right? Better than Google Search. No, I mean, yeah, remember. Do you use Google Search still? I try. I have to fucking push the crap out of the way and scroll. I can't remember the last time I did a Google search.

Starting point is 01:31:00 Christ, I find myself using Bing sometimes. I know. I hate saying it too. But I have to scroll past the AI crap because I want the good stuff. 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 to... Oh, no. I must be clear. That may be the only LLM use case I defend. The troubleshooting thing is awesome for it. It's the one weakness I have.

Starting point is 01:31:33 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... Er 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.

Starting point is 01:31:52 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 and chat Djibati, who have sat here with, these are critics that are warning of the impacts that'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 Codex guy that was nothing more than

Starting point is 01:32:21 badly written science fiction that he's already had to walk back. But you know he could have made more money by staying at Chachapitu. Could he? I mean, it looks like it. If he had options early, sticking around when he made him. Did he lose the options? How much do he lose? You're not saying that they're being critical.

Starting point is 01:32:37 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 boogie man out in the future where it's like, 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.

Starting point is 01:33:00 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 shit wrong. In the tiniest words possible, they don't talk about the fact that these things are trained on stealing millions of people. 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. 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 test that are rigged for the models. It's got better

Starting point is 01:33:40 at tests where you can train for the test. Okay, so 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. Oh, that's, there we go. Yeah. Because it's not got new features.

Starting point is 01:34:00 You'll notice that outside of Open AI and Anthropic, the VAR, when you remove the coding startups, there's basically no successful AI start up company. So we agree that it's got better, it's got more capable at doing things. Yeah. Okay. So over time, AI has got more capable. If we imagine that trajectory will keep. continue. It will get more capable. Then at some point it does cross, you know, this is what

Starting point is 01:34:27 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 at admin. Video generation, photo generation, okay. Text generation, theoretically, coding. Right. And then I'd say agentic workflows. What is an agentic workflow?

Starting point is 01:34:55 So automated workflows where you're doing the same. I mean, a good example is looking at the back end data of the diary of a CEO. Summarizing. Looking at all of the data, adjusting all of it, going out onto the internet and searching who Ed is, looking at every interview you've ever done ever. Uh-huh. This is summarizing and generating. Making a little model on, you know, the things people want to know from Ed.

Starting point is 01:35:15 Uh-huh. Producing a report, sending that to my inbox. Uh-huh. Me getting a 2030, 40, 40, 50 page report on Ed before he arrives. This is all basically the same thing it's been doing for years, though. It's not really new capabilities. Research. It's still the same thing. They've had web search for years.

Starting point is 01:35:30 They've had report generation for years. Well, we couldn't generate high-quality videos that are indistinguishable from cameras. Seed dance and these ones that look like movies 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 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.

Starting point is 01:36:01 This is pretty much how machine learning goes when you feed it more data. Exactly. And you put more compute behind it. So if this continues, what does the future look like? So the rebuttal I was expecting to hear is that it won't continue. And I actually don't think it, I think that there are hard limits that we're going to hit. So you do believe that there's a hard limit somewhere. We've kind of already hit the diminishing returns level because, for example, video generation,

Starting point is 01:36:26 which is, by the way, far less an American concern anymore. Open AI shut down SORA. I think you can still use the API. But nevertheless, look around you with the amount of stuff and 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 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

Starting point is 01:36:55 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 Cannes. It didn't. It aired in the city of Cannes during the Cannesville 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.

Starting point is 01:37:16 the reason I keep coming back to the capabilities there, 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 linearly-ish better at. But again, there's a ceiling to that. Like, okay, so it gets really good at research. What does that actually mean?

Starting point is 01:37:39 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 neurosymbolic. You actually need to build a structure around the AI to make it work. And even then,

Starting point is 01:37:54 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. You know there's lots of people listening now that, like, they've had their workflows completely transformed

Starting point is 01:38:10 by these tools. They'll be... 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?

Starting point is 01:38:21 But putting all that aside, what workflows? Because of it's, yeah, did a bunch of web scraping or web searches? I'm just not impressed. Did you make an entire fucking movie?

Starting point is 01:38:29 No, you didn't. Is it speeding up your coding? Yeah, I believe that. I've heard that for multiple people. But again, how much can you trust this? I think what I was getting at is, you know,

Starting point is 01:38:39 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 compute and all that, with fast processors, those are hardware breakthroughs. The hardware breakthrough companies don't seem to be fixing the LLM problems. Despite the all the

Starting point is 01:39:08 Kings horses, all the Kingsmen, we what? Nine, ten generations of TPUs from Google now. Broadcoms building stuff with open AI, their jalapinians. 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, and the other fucking week was saying, it's going to be, in like six months, we'll be like a genie that you can ask wishes for from.

Starting point is 01:39:44 It's like, motherfucker's never watched Aladdin. What's he talking about? Also, the genius is 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

Starting point is 01:40:09 hardware engineers. And where are we? Trillion plus dollars in with the first. future great financial crisis and the world's greatest marketing sci-op. 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? That'll be powered by the underlying AI infrastructure.

Starting point is 01:40:30 It'll be the more data centers. It'll be energy coming down. How? How does a GPU full data center translate to a Nikon camera that can, I don't know even what you'd think. 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.

Starting point is 01:40:52 I could see it happening. It's not really kind of happening. What does that have to do with the data centers? Because these data centers, again, are not being built to make your consumer electronics smarter. They're not being built for anything other than speculating on the ability to capture demand for generative AI services. But it's not just generative AI.

Starting point is 01:41:08 We went through that. It is. No, but those data centers, they are being built for generative AI. They are not being built for anything else. Would 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 guys sat in front of me called Ed with Blusha and Coffee, we now can train the AI to serve whoever wants BlueShire, adding with coffee to the right user, which means people are retained longer because... Isn't 15 basis points like 0.15%.

Starting point is 01:41:51 Yeah, it's fuck all, but it makes a difference at scale. It makes a big difference at scale. Yeah, but $100-something billion in and the best you've got is 0.15%. If he could be fired, 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'd take your point about scale.

Starting point is 01:42:12 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 outside of us thinking about just generating a sentence. And that's generate the gem their generative model. Muse. Oh, Muse Spark is their LLM. Jim is their generative. It's Mews.

Starting point is 01:42:34 Well, Mews. 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 pop-up things. Meta as fuck. God damn that company sucks. Like every time I think about how they've ruined that product. But that's the thing though.

Starting point is 01:42:48 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.

Starting point is 01:43:03 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, we're like, well, if we move all the pieces around in three 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 two, three hours with a guest,

Starting point is 01:43:31 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 fiancé and my colleagues at work and other people in my life. Whenever we get a minute, we play the diary of a CEO conversation cards. And it is incredible what happens. These are great if you're in a romantic relationship and you want to connect your partner more. These are also great if you're in a team and you want to bond your team together. And I have to say they're also great for families that

Starting point is 01:44:11 want to learn more about each other and that need a good excuse to spend some time in a digital world in the analogue 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. 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 gasey? 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 fucked up. And now they're thinking, shit, like we've spent all this investor money.

Starting point is 01:44:44 Kind of like the MetaVus was a bit of a fucking. 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 one. I shouldn't say dream because it's not a dream that I've had. Dream that they had.

Starting point is 01:44:54 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 dot-com boom in this regard of the same. NFTs were the same. Right. So, one could argue. a lot of the crypto industry was the same. It's Wang that is inflated by the media. The difference is

Starting point is 01:45:12 the reason the met of us 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 record earnings in 2021. There's a bunch of money flowing in the system thanks to post-COVID, the PRDC, 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 hypergrowth ideas anymore. So suddenly they started buying GPUs. And when they bought GPUs, people went, they're doing AI.

Starting point is 01:45:48 Oh, we better buy the stock. And the stock's one on an incredible run. Mayer's like several hundred percent. 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 revenue is growing because of AI, right? Microsoft's revenue is growing because of AI, right? the Fagaze 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.

Starting point is 01:46:14 Did you just say, Amy Hood just going to take him out back, send him to the glue factory or something? I do think there's overspending. I want to concede that. Traumatic. 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 in terms of like, I think there is practical uses. for this technology. And I think when people realize that through history, they go crazy because

Starting point is 01:46:40 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. 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'm saying that I do think there is very, value in the underlying technology. Sure.

Starting point is 01:47:07 And so I think I'm not saying how much value. Right. Okay. I get you 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,

Starting point is 01:47:20 say that you're on a desert island and then someone says they found a banana tree. Right. And there's 10,000 people on the island. Okay. They are going to stam fucking peed towards where they think the banana tree is. Banana grass. They are going to fucking claw each other to pieces. And if your essay here is right that there was desperation because they hadn't found

Starting point is 01:47:40 an innovation in a while, maybe that explains it. Maybe there is a bit of value here. Right. And they're stam fucking 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 are matter. Their main business lines are running out of growth. There's only so much they can grow.

Starting point is 01:48:00 And indeed, in the next three and a half years, analysts think that these two bastards, These two, Open A&A&A&Athropic, 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. So your point about bananas? I actually agree. That is the Rockcom bubble.

Starting point is 01:48:19 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 rock hard, overnight. They loved it. There were stories about how they were sending armored. cars with the GPUs to Microsoft to make sure Microsoft got the GPUs. And so everyone saw all that money flowing in, even though they never disclosed AI revenues, they saw the expenditures and they

Starting point is 01:48:42 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 with out. Circular, you talked about

Starting point is 01:49:28 VC's there. Venture capitalists who are, by the way, the majority of the funding that Open AI got in the last six months came from SoftBank, Nvidia and Amazon. Okay. Yeah. So just the point is, you're talking about progress continuing. Progress in LLMs can only continue as long as the money keeps flowing. Once the money stops flowing, the progress stops. But isn't that most like like Spotify didn't make money for 20 years? Spotify didn't lose $20.9 billion in one year. They didn't need to raised $217 billion in the space of six months. Yeah, and Uber's 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. They'd spent.

Starting point is 01:50:12 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 TLD. Yes, but the amount of money they lost is completely, Just magnitudes different on a level where these three... Can I argue then that that's because the potential of intelligence permeates everything, whereas Amazon at the time was like selling books? No. That was bringing retail online.

Starting point is 01:50:41 When Amazon Web Services grew, it was... Oh, Amazon Web Services. So Amazon Cloud. With Amazon Web Services, the reason I bring that up, 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-facing. I may be wrong on the dates there, but 2015 was the year it became profitable.

Starting point is 01:51:03 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 a way, but their margins actually started improving because AWS was a very margin-heavy business. It was great. Yeah. 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.

Starting point is 01:51:48 They have gone from being these cash machines to these cash furnaces. You said a second ago, this can only continue if investors continue to invest. Yes. And I was saying, 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 reference like AWS? And one would say that the opportunity of intelligence permeates everything. So the TAM, the total adjustable market, is enormous.

Starting point is 01:52:25 Maybe the rebuttal back to me is about open source and all these kinds of things. No, no, no. I actually know what you're going at. So what you were describing there is the argument that Satchinadella or Sam would make. But the theoretical opportunity of large language models. And I could have bought that shit 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.

Starting point is 01:52:52 These two companies, open AI, just open AI. Clammy Sam Morton has said, Wall Street General and Issa Gardesi 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. It is crazy. And a large chunk of that is training.

Starting point is 01:53:15 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, well, I train with them, I'll live with them. I'll live 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. 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

Starting point is 01:53:49 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 to feed the data into the GPUs. Put aside all the thoughts I have. Just the raw capital to get them this far has cost increasingly larger amounts of money and increasingly larger amounts of training money for training runs that sometimes can fail. GPT5 was meant to be this panacea for the AI industry.

Starting point is 01:54:17 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 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 Open AI because

Starting point is 01:54:37 Oracle's future depends on Open AI spending $300 billion over five years. It's absolutely fascinating because I was just reading through a list of quotes from the big CEOs of the AI companies. to see what they would rebuttal you. Yeah. And they're all basically saying the same thing. They're all saying, this is an exact quote from Sundar, who is the CEO of Google.

Starting point is 01:54:57 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 Jassy CEO of Amazon, we're not investing approximately 200 billion in CAPEX in 2026

Starting point is 01:55:11 on a hunch. We're not going to be conservative in how we play this. We're investing. investing to be the meaningful leader in our future business operating income and free cash flow will be much larger because of this investment. Then Mark Zuckerberg, C of Meta says, will 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 a Shrek with law Fadquod. 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

Starting point is 01:55:43 money. You can't fire me because 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 way and hope he's right. And I don't know if the people are not matter. He's not right. 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 mean this. It's a good question. Because it's like, they don't. And the thing is, what would it take for me to be wrong? A bunch of hardware breakthroughs to make this profitable. A bunch of question. New mathematics. 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

Starting point is 01:56:16 trillion dollars, not the people promising the world. The asshole with the blog is the one who's like, no, no, no, no. If they came here, they'd be on the hot seat too. Oh, they won't talk to me. Don't know why, Steve. It's because I call him clammy-sami. I think it's because my guests are quite critical that I don't think so I'm open and wants to come here. Mr. Orman, go on, Steve, should I 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 rockcom 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

Starting point is 01:56:52 questions I was really excited to ask you, which is you have a different opinion. We said this at the top. Yeah. You have a very different opinion from a lot of people. Yeah. I would categorize the two most popular opinions as 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. It's 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 there's no real underlying value in the technology and it's overhyped. Yes. And there's way too much spending.

Starting point is 01:57:22 I mean, a few people agree on the spending part. Yeah. But the other part. So with you, it's 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 reduce the cost by like a thousand. It would have to be just a dramatic breakthrough that is not happening.

Starting point is 01:57:44 Just 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 Vineland, 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.

Starting point is 01:58:04 I think the use of gas turbines is fucking disgraceful. 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 beat of 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 to have a good idea. They go to a bank, bank of talent, they'll go fuck themselves. They'll say, I'm not going to, you're going to make a store that sells stuff?

Starting point is 01:58:35 Screw you. You want to build a data center? Jensen Huang will back you. Jensen Hong will give you 25% residual value. You want to build a regular business that's even profitable? fuck you. No, a venture capital list 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 GPS. He'll give

Starting point is 01:59:01 you a contract. Coreweaves, a great example. A neocloud, which is just a company that builds data centers and puts GPUs and rent people. InVIDIA, one of their first investors. In 2020, signed a $1.3 billion contract to rent back their GPUs from Corweaves so that Corweave go to a bank and go, I've got a customer. Yeah, it's the guy I'm buying the GPUs from, with that 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.

Starting point is 01:59:34 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 very, billions from Jensen. It doesn't matter. It's so interesting. It's interesting because you are the first person that I've spoken to that has that opinion. I am pro user. Let's take another myth.

Starting point is 01:59:52 AI will be conscious. So, super intelligence, artificial general intelligence. These are theories. Anyone saying this stuff will become this is just guessing and does not have proof. And that's really it.

Starting point is 02:00:08 Okay. Let's take another myth. AI systems are already blackmailing and escaping control. So this is a really specific one. Anthropic. There's actually two. OpenAI's GPT 3.5. I realize this is more than the sentence, I apologize. In their system card,

Starting point is 02:00:27 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 being... As in a person that you rent, not even to do a capture. It's something you rent to like nail that picture up in your apartment.

Starting point is 02:00:53 It's an insane example. This was covered as if these things blackmailed someone 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 Anthropics said, oh yeah, a model was blackmailing someone. one saying that if you don't do this, I'll email proof that you slept with someone else often 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

Starting point is 02:01:25 slop me up. 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 for and they can turn on the fucking news and there's some asshole being like yeah you should be terrified it blackmailed someone but this is this is so counterintuitive of their interest

Starting point is 02:01:51 to some degree and they've experienced it backfire well they have now like it's literally backfired it's backfired Eric Schmidt getting booed at the commencement speech by 8000 people every time he said the word AI but I mean this is this is I mean these series are being attacked at home yeah which fucking sucks

Starting point is 02:02:08 Yeah, which is terrible. I must be clear, like, he disliked you come. Don't fucking hurt people. Yeah, don't attack people at home. 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.

Starting point is 02:02:27 Travis Kallnick'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 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 or do whatever you want. They didn't expect, I think, what has. I agree, this has backfired on them because they were inarticular. They're disconnected from regular people. Sam Alton drives a $5 million car around San Francisco.

Starting point is 02:03:01 So that man's doing like nine 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. This was, all of this blackmail stuff, was an attempt to make it mystic. It was a mysticism attempt. It was to make it seem like this unknowable, impossible to control, just this powerful thing. But we're the only ones. We are the only us. Only these two angels could possibly control the beast we've created. 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 balance in his writing about the risk profile.

Starting point is 02:03:45 Whereas the others, they seem to kind of move with the wind. Do you know what you mean? The reason I don't like Dario is Dario was doing the scare tactics thing when he worked to Open AI. When GPT2 came out, it was 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.

Starting point is 02:04:13 Good. But the thing is... Sorry, I mean, clarify the word attack. Dario 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. But the point I want to make there as well is, again, wow, you're so scared of how powerful this is.

Starting point is 02:04:33 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? Why, we have an alignment team. So does every AI lab. Well, I guess open AI cycles through those really quickly.

Starting point is 02:04:44 Here's the thing. If I'm Dario-Amadam, I'm scared of all things changing. And I thought I had made a thing that would eliminate all jobs. I'd be fucking terrified. I'd be walking around with like a 10-ton weight on my back. The shot, the responsibility, the fact he doesn't, the fact he wants to be this weird elder statesman that's too scared to hold Sam Altman's hand at an event

Starting point is 02:05:04 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 AI and Anthropic are basically the same level of bad company. I think Anthropic is more cult-like. I think it's so weird. Like Jack Clark over there, one of the co-founders, that fella used to be at the register. He used to be one of the most critical journalist ever. Now it's like something took over him because they talk of these things in these high-fludent terms.

Starting point is 02:05:30 But then again, maybe the people are Anthropic buy their shit. Maybe some of the people open AI by this year. 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, 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 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.

Starting point is 02:05:58 It would have to be indistinguishable for magic. And the reason I have these high standards is they, they would have to continue. set them. It's interesting as well because all these myths and all these conversations, it's about technology but it's also an information war. It's literally narrative versus narrative

Starting point is 02:06:14 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've got to talk about today, mate. You get two weeks in the future max. Because if they

Starting point is 02:06:30 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 but no 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 much lower cost it wasn't like you paid uber two hundred or sorry 20 bucks a month and you could get 500 miles of uber and then one day you started paying by the mile because that's what's happening with this have they they've they've changed their business model for customers like me now so that I have to buy credits.

Starting point is 02:07:07 No. So you, well, kind of with FAPE. 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, So expensive. We've got to control these costs.

Starting point is 02:07:34 Uber's COO says Andrew McDonald, I think he said that it's getting hard to justify because it's hard to connect spending money on tokens to actual useful outcomes. He said the thing. Like he said the actual thing I'd be saying. And it's... So we're in an AI bubble. Yes.

Starting point is 02:07:50 And 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, I'll be in an AI bubble. And what happens when the bubble pops?

Starting point is 02:08:03 Yes, and it depends. So the big thing that people say is, oh, it will 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 rock-com 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 release their auditive financials.

Starting point is 02:08:31 that was. But they've delayed to next year. Sarah Fryer, the CFO, has now said, well, they'll do it earlier than 2027 or 2027. Great answer there. For anyone that doesn't understand what going public means, that means joining the stock market. And at such a time 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 oftentimes companies will flirt with the idea of, we'll go public someday soon, because investors will have a moment in their head well they'll get their money back at a return. So you kind of need to, if you're in these guys' shoes,

Starting point is 02:09:06 you kind of need to be flirting with going public or investors won't want to invest. Open AI up until this point has been a private company and their last funding round they will value at $865 billion. Now, when they tried to go public, New York Times Mike Isaac reported this. They tried to list that, well, they wanted to go at a $1 trillion valuation. Apparently, their advisor said, no, don't do that.

Starting point is 02:09:31 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 $100 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 even the same one they raise that. So they're probably going to have to take a flat, so the same amount or down that.

Starting point is 02:09:54 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, Open AI is the kind of catastrophe center here because Anthropic is likely going to be it to go public. And once Anthropic goes public, it'll be borderline impossible for Open AI to do so because Anthropic, an unprofitable, AI lab, but a better business that's growing faster than Open AIs. I believe they have a ceiling. They're eventually going to face perdition to. I think sometime in 2007 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,

Starting point is 02:10:33 tens of billions of dollars into them. So you think Open AI runs out of steam in 27? 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 go out and try and raise.

Starting point is 02:10:45 And they have trouble raising another round. I think maybe Nvidia props them up a little, maybe private credit, Black Star and BlackRock and the like, the ones, and the reason that private credit is getting involved, so asset managers is because they're investing in the data centers, and they know this company's most of the data center demand. Okay, so they run out of esteem 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.

Starting point is 02:11:07 They're going to get savage. We work was a great example. Another soft bank classic. Now, I think open AI collapses. There are many different ways it could happen. There are many different ways that could end. But the crucial thing is, is that there are multiple companies that are existentially tied. to Open AI. SoftBank, one of the largest companies in the Japanese stock market, a holding company

Starting point is 02:11:28 with lots of investments. They have, on paper, about $100 billion worth of Open AI stock. If they can't go public, they can't do Diddley squat with them. And so SoftBanks future, their ability to continue paying the people around them and existing as a business, relies on their ability to continually liquidate funds, 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 Open A1A. I 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,

Starting point is 02:12:08 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 hypergrowth 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. It allows them to say, we're still doing something, we're working on AI, don't think too hard, and also their current businesses are still growing. Their current businesses will eventually slow. There's only so many price increases, there's only so many tweaks to ads, only so many tweaks to Google search, only so many ways that Amazon can screw merchants. So in that tech depression, which you think you might be triggered in

Starting point is 02:12:50 27, 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 sort of downstream consequence, the sort of domino effect? There's so much to imagine that it's difficult to capture everything. But there are a few things that worry me. First of all, a ton of American money, just regular people's money, retail investors, are in these companies. And they bought into the Magnificent Seven, thinking the number go up forever. Nvidia is the largest company on the Fortune 500 NASDAQ as well and like 7 or 8% of the S&P 500

Starting point is 02:13:27 That company, when the bottom falls out from Nvidia And we haven't really got into it But Nvidia is doing the most circular refinancing Feeding companies money so that they can raise debt to buy more GPUs I think Nvidia's revenue could go 50 to 70% now I think that Nvidia back in 2022 was making single digit billion dollars And what happens though I'm thinking about like Jenny and Dave that are watching this right now

Starting point is 02:13:49 and they aren't 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

Starting point is 02:14:05 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 investment and AI going to zero. Because when it comes to building a company on top of an LLM, all of those

Starting point is 02:14:24 are unprofitable too. And the thing is, LLM companies have not really been acquired. The exception being cursor bought by Elon Musk for the coding side, but you have cognition, which is just another LLM company raising a $26 billion valuation. That means that company has to go public because who's buying a company at $26 billion other than Elon Musk? And there are rumors that Elon Musk was trying to buy them as well. Is Elon Musk just going to pick off every, like, LLM company? Like, on a fucking T.J. Max 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,

Starting point is 02:15:03 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 rental 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 they're not really being a home for that value or that money. So much is riding on these

Starting point is 02:15:40 companies, but you can't bail it out. You can theoretically bail out open air. I don't. I don't. 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 Open AI. Oracle is building 7.1 gigawatts of data centers, so over $400 billion worth. Just for Open AI. There is not a customer on Earth. And Oracle's revenue has been flat last 15 years when you adjust for inflation. Without Open AI, Oracle dies. So you think Open AI is going to crash and run out of money, and that's going to cause this domino effect across these other big tech companies,

Starting point is 02:16:18 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, a total value put in. So the amount of money you get back for your dollar is between 0.8 and 1.21. meaning for every dollar you invest you get 80 cents to a dollar 20.

Starting point is 02:16:46 Paper gains. Well, no, that's just actual gains. Paper gains, they'll give you, but even an 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.

Starting point is 02:17:05 And they're raising off paper gains. And paper gains, I mean just being able to say, oh, look, the valuation of Anthropic went up. But 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.

Starting point is 02:17:35 It's really that, it's like, when you're reaping, it's like, yeah, fuck yeah, this rog's sewing. Ah, shit, this sucks. because right now they're all like, yeah, all the speculative gains are awesome, the paper gains are awesome, the theoretical 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.

Starting point is 02:18:03 If that changes, to quote Ed Ellson from Prof G 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 shit. 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

Starting point is 02:18:35 conservative? Should they? Yes. 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? I'm thinking. Oh, God, no. I, honestly, I live in cash right now. I, I, I, I don't fucking 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 were 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.

Starting point is 02:19:12 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.

Starting point is 02:19:39 You go, I made $38. 37 billion dollars, right? Wow, that's so much. Run rate may be 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, this technology is going to be so great and so transformative that we are investing a ton of money 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 get that. I think that's

Starting point is 02:20:17 their sentiment. I'm not defending them or anything. I'm just, I'm trying to provide enough, like, balance to see if we can dance between these two perspectives. And a lot of people would say that there's going to be a bloodbath. Because they can't all win big in the way that they're kind of describing. So someone's going to have to lose. And when one of these players starts to lose big, I think it could, as you say, there could be some kind of domino effect or contraction. Yeah. And I think the thing that people want to believe is the dot-com bubble thing. It's like it worked out afterwards because Amazon, Oracle, they didn't die after the dot-com bubble.

Starting point is 02:20:52 They're actually fine. It isn't like that. They're bigger companies. They have bigger promises. And even, I'm not like Oracle I actually think could die. RIP, Larry. It couldn't happen to a nastier man. You don't like these people, do you? No. I actually... Again, I ask this question purely because I want an answer, not because I agree or disagree.

Starting point is 02:21:11 But why don't you like these people? I don't like being misled. And I don't think regular people are being misled either. And I really don't think that the average person can get away with bullshitting as much of 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.

Starting point is 02:21:34 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, though, the really great people, but it's like seeing these ultra rich, ultra wealthy, ultra powerful people, life of their fucking 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 it at such link 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 loadsome. I find these companies don't make good products anymore. They don't care about the customers. And they treat

Starting point is 02:22:18 their customers with contempt. If people want to go read more about your work, 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 off time. I'm going to link both of them below. So if anyone wants to read more, get more detail 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 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 the next person.

Starting point is 02:22:52 Yeah, yeah, yeah. That is just not the nature of information in the world and opinions and progress and discussion. 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 do your own research. Yeah. Whether it's on your health or whether it's on something like this is to watch and do your own research and to learn.

Starting point is 02:23:14 And I would say also never believe one person, never believe one particular perspective religiously. You know, collect a body of evidence and follow the evidence yourself. But I love watching your YouTube because it provides a different opinion. And that challenges me to think beyond my current. opinion about what might be possible. So when I've heard you talking about how this is an economic bubble, and I've heard you talk about the CAPEX spend with these big sort of frontier AI labs, it really did make me pause for a second. And it really did make me consider

Starting point is 02:23:48 that there could be a bit of Fagasy going on here. Yeah. And then it made me reflect on history and go, you know, through history, there's always a bit of Fagasy in these moments. And oh, that's an interesting take. And what's going to happen in 2027, 2028 when there's a bit of a market pullback and so I highly recommend people go watch because you do you challenge me to think differently yeah and we need some of those contrarian voices to to have honest discussions so thank you for doing what you do really appreciate it and I find you to be a very compelling captivating communicator and I 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

Starting point is 02:24:23 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'm a critic, I'm a skeptic, what caught, I have found that showing and appreciating and loving the people around you and uplifting them and raising them up as you succeed is the way we do that. Your success should be everyone around you. It's not economic. It's talking about Matt Hughes for a while, made me really happy. This whole thing has been at times quite grueling and quite negative and quite brutal. But the love I've found and the joy I've found from community and the people around,

Starting point is 02:25:08 because even in the small groups of haters, even like Gary Marcus and so, the people I talk to, Edward on Gweso 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 and how bad things are, finding the people who maybe find it repulsive to finding the people

Starting point is 02:25:32 finding your people who can be there and the people who would talk to you about it even like Troy and Jake my trainers who's so excited about this even talk to them about the shit as normal people

Starting point is 02:25:42 knowing that there are people there going through the narrow strokers 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 all over the place point

Starting point is 02:25:52 but it's just it's really easy to get hard locked and 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 I feel is they feel like they have a voice and they feel like someone is there for you. And I don't think it can be understated how much it means when you just reach out to someone you love and tell them you love them, tell them you love them their shit rocks. Say that they're shit bangs.

Starting point is 02:26:21 Tell everyone you, when you like an artist or a writer's thing or a podcast like this, tell them you fucking love. We don't do this enough and we need to do it more. Well, that's a good closing message. So if you do, if you have enjoyed the conversation today with Ed, please do let Ed know that you love it down below. But please do leave your opinions down below.

Starting point is 02:26:38 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

Starting point is 02:26:48 that are demystifying a lot of the Fagasy and the narrative in this moment in time and you're certainly one of them. I really enjoyed the conversation. Thank you. Thanks so much.

The Diary Of A CEO with Steven Bartlett - The Man Who Calls BS On AI: AI Is The World’s Greatest SCAM, And They All Know It! | Ed Zitron

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